  Video generation is important, especially in medicine, as much data is given
in this form. However, video generation of high-resolution data is a very
demanding task for generative models, due to the large need for memory. In this
paper, we propose Memory Efficient Video GAN (MeVGAN) - a Generative
Adversarial Network (GAN) which uses plugin-type architecture. We use a
pre-trained 2D-image GAN and only add a simple neural network to construct
respective trajectories in the noise space, so that the trajectory forwarded
through the GAN model constructs a real-life video. We apply MeVGAN in the task
of generating colonoscopy videos. Colonoscopy is an important medical
procedure, especially beneficial in screening and managing colorectal cancer.
However, because colonoscopy is difficult and time-consuming to learn,
colonoscopy simulators are widely used in educating young colonoscopists. We
show that MeVGAN can produce good quality synthetic colonoscopy videos, which
can be potentially used in virtual simulators.


  We present the Chandra Multiwavelength Project (ChaMP) X-ray point source
number counts and the cosmic X-ray background (CXRB) flux densities in multiple
energy bands. From the ChaMP X-ray point source catalog, ~5,500 sources are
selected covering 9.6 deg^2 in sky area. To quantitatively characterize the
sensitivity and completeness of the ChaMP sample, we perform extensive
simulations. We also include the ChaMP+CDFs (Chandra Deep Fields) number counts
to cover large flux ranges from 2x10^{-17} to 2.4x10^{-12} (0.5-2 keV) and from
2x10^{-16} to 7.1x10^{-12} (2-8 keV) erg/cm^2/sec. The ChaMP and the ChaMP+CDFs
differential number counts are well fitted with a broken power law. The best
fit faint and bright power indices are 1.49^{+0.02}_{-0.02} and
2.36^{+0.05}_{-0.05} (0.5-2 keV), and 1.58^{+0.01}_{-0.01} and
2.59^{+0.06}_{-0.05} (2-8 keV), respectively. We detect breaks in the
differential number counts and they appear at different fluxes in different
energy bands. Assuming a single power law model for a source spectrum, we find
that the same population(s) of soft X-ray sources causes the break in the
differential number counts for all energy bands. We measure the resolved CXRB
flux densities from the ChaMP and the ChaMP+CDFs number counts with and without
bright target sources. Adding the known unresolved CXRB to the ChaMP+CDF
resolved CXRB, we also estimate total CXRB flux densities. The fractions of the
resolved CXRB without target sources are 78^{+1}_{-1}% and 81^{+2}_{-2}% in the
0.5-2 keV and 2-8 keV bands, respectively, somewhat lower, though generally
consistent with earlier numbers since their large errors. These fractions
increase by ~1% when target sources are included.


  The main aim of this work is to give a general approach to the celebrated
Kahane-Salem-Zygmund inequalities. We prove estimates for exponential Orlicz
norms of averages $\sup_{1\le j \leq N} \big |\sum_{1 \leq i \leq
K}\gamma_i(\cdot) a_{i,j}\big|$ where $(a_{i,j})$ denotes a matrix of scalars
and the $(\gamma_i)$ a sequence of real or complex subgaussian random
variables. Lifting these inequalities to finite dimensional Banach spaces, we
get novel Kahane-Salem-Zygmund type inequalities -- in particular, for spaces
of subgaussian random polynomials and multilinear forms on finite dimensional
Banach spaces as well as subgaussian random Dirichlet polynomials. Finally, we
use abstract interpolation theory to widen our approach considerably.


  This paper studies the optimal output-feedback control of a linear
time-invariant system where a stochastic event-based scheduler triggers the
communication between the sensor and the controller. The primary goal of the
use of this type of scheduling strategy is to provide significant reductions in
the usage of the sensor-to-controller communication and, in turn, improve
energy expenditure in the network. In this paper, we aim to design an
admissible control policy, which is a function of the observed output, to
minimize a quadratic cost function while employing a stochastic event-triggered
scheduler that preserves the Gaussian property of the plant state and the
estimation error. For the infinite horizon case, we present analytical
expressions that quantify the trade-off between the communication cost and
control performance of such event-triggered control systems. This trade-off is
confirmed quantitatively via numerical examples.


  Models in which the Higgs boson is a composite pseudo-Nambu-Goldstone boson
offer attractive solutions to the Higgs mass naturalness problem. We consider
three such models based on the minimal $SO(5) \rightarrow SO(4)$ symmetry
breaking pattern, and perform convergent global fits on the models under a
Bayesian framework in order to find the regions of their parameter spaces that
best fit a wide range of constraints, including recent Higgs measurements. We
use a novel technique to analyse the fine-tuning of the models, quantifying the
tuning as the Kullback-Leibler divergence from the prior to the posterior
probability on the parameter space. Each model is found to be able to satisfy
all constraints at the $3\sigma$ level simultaneously. As a by-product of the
fits, we analyse the collider phenomenology of our models in these viable
regions. In two of the three models, we find that the $g g \rightarrow H
\rightarrow \gamma \gamma$ cross section is less than ${\sim}90$% that
predicted by the SM, which is already in slight tension with experiment and
could potentially be ruled out in the future high-luminosity run of the LHC. In
addition, the lightest fermions $F$ arising from the new strong dynamics in
these models are seen in general to lie above ${\sim}1.1$ TeV, with the $F
\rightarrow tW^{+}$ and $F \rightarrow \bar{b}W^{+}$ decays offering
particularly promising channels for probing these models in future collider
searches.


  We introduce a new type of models for two-component systems in one dimension
subject to exact solutions by Bethe ansatz, where the interspecies interactions
are tunable via Feshbach resonant interactions. The applicability of Bethe
ansatz is obtained by fine-tuning the resonant energies, and the resulting
systems can be described by introducing intraspecies repulsive and interspecies
attractive couplings $c_1$ and $c_2$. This kind of systems admits two types of
interesting solutions: In the regime with $c_1>c_2$, the ground state is a
Fermi sea of two-strings, where the Fermi momentum $Q$ is constrained to be
smaller than a certain value $Q^*$, and it provides an ideal scenario to
realize BCS-BEC crossover (from weakly attractive atoms to weakly repulsive
molecules) in one dimension; In the opposite regime with $c_1<c_2$, the ground
state is a single bright soliton even for fermionic atoms, which reveals itself
as an embedded string solution.


  We find a new finite algorithm for evaluation of Lipschitz-free $p$-space
norm in finite-dimensional Lipschitz-free $p$-spaces. We use this algorithm to
deal with the problem of whether given $p$-metric spaces $N\subset M$, the
canonical embedding of $\mathcal{F}_p(N)$ into $\mathcal{F}_p(M)$ is an
isomorphism. The most significant result in this direction is that the answer
is positive if $N\subset M$ are metric spaces.


  We investigated data embroidery with black-and-white textures, identifying
challenges in the use of textures for machine embroidery based on our own
experience. Data embroidery, as a method of physically representing data,
offers a unique way to integrate personal data into one's everyday fabric-based
objects. Owing to their monochromatic characteristics, black-and-white textures
promise to be easy to employ in machine embroidery. We experimented with
different textured visualizations designed by experts and, in this paper, we
detail our workflow and evaluate the performance and suitability of different
textures. We then conducted a survey on vegetable preferences within a family
and created a canvas bag as a case study, featuring the embroidered family data
to show how embroidered data can be used in practice.


  We develop a new location spoofing detection algorithm for geo-spatial
tagging and location-based services in the Internet of Things (IoT), called
Enhanced Location Spoofing Detection using Audibility (ELSA) which can be
implemented at the backend server without modifying existing legacy IoT
systems. ELSA is based on a statistical decision theory framework and uses
two-way time-of-arrival (TW-TOA) information between the user's device and the
anchors. In addition to the TW-TOA information, ELSA exploits the implicit
available audibility information to improve detection rates of location
spoofing attacks. Given TW-TOA and audibility information, we derive the
decision rule for the verification of the device's location, based on the
generalized likelihood ratio test. We develop a practical threat model for
delay measurements spoofing scenarios, and investigate in detail the
performance of ELSA in terms of detection and false alarm rates. Our extensive
simulation results on both synthetic and real-world datasets demonstrate the
superior performance of ELSA compared to conventional non-audibility-aware
approaches.


  We use the database leak of Mt. Gox exchange to analyze the dynamics of the
price of bitcoin from June 2011 to November 2013. This gives us a rare
opportunity to study an emerging retail-focused, highly speculative and
unregulated market with trader identifiers at a tick transaction level. Jumps
are frequent events and they cluster in time. The order flow imbalance and the
preponderance of aggressive traders, as well as a widening of the bid-ask
spread predict them. Jumps have short-term positive impact on market activity
and illiquidity and see a persistent change in the price.


  We analyse the flow of information in multiplex networks by means of the
communicability function. First, we generalize this measure from its definition
from simple graphs to multiplex networks. Then, we study its relevance for the
analysis of real-world systems by studying a social multiplex where information
flows using formal/informal channels and an air transportation system where the
layers represent different air companies. Accordingly, the communicability,
which is essential for the good performance of these complex systems, emerges
at a systemic operation point in the multiplex where the performance of the
layers operates in a coordinated way very differently from the state
represented by a collection of unconnected networks.


  Active matter systems operate far from equilibrium due to the continuous
energy injection at the scale of constituent particles. At larger scales,
described by coarse-grained models, the global entropy production rate S
quantifies the probability ratio of forward and reversed dynamics and hence the
importance of irreversibility at such scales: it vanishes whenever the
coarse-grained dynamics of the active system reduces to that of an effective
equilibrium model. We evaluate S for a class of scalar stochastic field
theories describing the coarse-grained density of self-propelled particles
without alignment interactions, capturing such key phenomena as
motility-induced phase separation. We show how the entropy production can be
decomposed locally (in real space) or spectrally (in Fourier space), allowing
detailed examination of the spatial structure and correlations that underly
departures from equilibrium. For phase-separated systems, the local entropy
production is concentrated mainly on interfaces with a bulk contribution that
tends to zero in the weak-noise limit. In homogeneous states, we find a
generalized Harada-Sasa relation that directly expresses the entropy production
in terms of the wavevector-dependent deviation from the fluctuation-dissipation
relation between response functions and correlators. We discuss extensions to
the case where the particle density is coupled to a momentum-conserving
solvent, and to situations where the particle current, rather than the density,
should be chosen as the dynamical field. We expect the new conceptual tools
developed here to be broadly useful in the context of active matter, allowing
one to distinguish when and where activity plays an essential role in the
dynamics.


  Using the recently commissioned multi-object spectrograph AAOmega on the 3.9m
AAT we have obtained medium-resolution near-infrared spectra for 10,500 stars
in and around five southern globular clusters. The targets were 47 Tuc, M12,
M30, M55 and NGC 288. We have measured radial velocities to +/- 1 km/s with the
cross correlation method and estimated metallicity, effective temperature,
surface gra vity and rotational velocity for each star by fitting synthetic
model spectra. An analysis of the velocity maps and velocity dispersion of
member stars revealed systemic rotation in four of the target clusters.


  We present here a detailed analysis of an unusual absorption line system in
the quasar SDSS J122826.79+100532.2. The absorption lines in the system have a
common redshifted velocity structure starting from $v\sim0$ and extending to
$\sim1,000\ \mathrm{km~s}^{-1}$, and are clearly detected in hydrogen Balmer
series up to H$\iota$, in metastable neutral helium triplet, and in optical
lines of excited states of single ionized iron. We estimated that the absorber
has a density $n_{\mathrm{H}}\approx10^{8.4}\ \mathrm{cm}^{-3}$ and an
ionization parameter $U\approx10^{-1.2}$, thereupon located it at
$r_{\mathrm{abs}}\approx1.5$ pc from the central supermassive black hole. The
inferred distance is remarkably similar to the evaporation radius for dust
grains $r_{\mathrm{evap}}\approx1$ pc in the quasar. Thus the absorber may be a
probe of an inflow starting from the dusty torus and feeding the accretion
disk. Both the featureless continuum and the broad emission lines are heavily
reddened with $E(B-V)\approx0.66$, in contrast to the narrow emission lines
whose reddening is negligible. The dusty medium could be located in between the
broad and narrow emission line regions, and possibly be associated with a
'cold' narrow absorption line system detected in \ion{Ca}{2} and \ion{Na}{1}
doublets nearly unshifted from the quasar systemic velocity. SDSS
J122826.79+100532.2 might represent such a rare case that both the inflow and
the torus could be tracked by absorption lines.


  We present an extended structural study of globular complexes made by mixing
a positively charge protein (lysozyme) and a negatively charged polyelectrolyte
(PSS). We study the influence of all the parameters that may act on the
structure of the complexes (charge densities and concentration of the species,
partial hydrophobicity of the polyion chain, ionic strength). The structures on
a 15 scale range lying from 10{\AA} to 1000{\AA} are measured by SANS. Whatever
the conditions, the same structure is found, based on the formation of dense
globules of ~ 100{\AA} with a neutral core and a volume fraction of organic
species (compacity) of ~ 0.3. At higher scale, the globules are arranged in
fractal aggregates. Zetametry measurements show that globular complexes have a
total positive charge when the charge ratio of species introduced in the
mixture [-]/[+]intro > 1 and a total 20 negative charge when [-]/[+]intro < 1.
This comes from the presence of charged species in slight excess in a layer at
the surface of the globules. The globule finite size is determined by the Debye
length k-1 whatever the way the physicochemical parameters are modified in the
system, as long as chain-protein interactions are of simple electrostatics
nature. The mean number of proteins per primary complex Nlyso_comp grows
exponentially on a master curve with k-1. This enables to picture 25 the
mechanisms of formation of the complexes. There is an initial stage of
formation where the growth of the complexes is only driven by attractions
between opposite species associated with counterion release. During the growth
of the complexes, the globules progressively repell themselves by electrostatic
repulsion because their charge increases. When this repulsion becomes dominent
in the system, globules stop growing and behave like charged colloids: they
aggregate 30 with a RLCA process, which leads to the formation of fractal
aggregates of dimension Df 2.1.


  The infinite projected entangled pair states (iPEPS) technique [J. Jordan
{\it et al.}, Phys. Rev. Lett. {\bf 101}, 250602 (2008)] has been widely used
in the recent years to assess the properties of two-dimensional quantum
systems, working directly in the thermodynamic limit. This formalism, which is
based upon a tensor-network representation of the ground-state wave function,
has several appealing features, e.g., encoding the so-called area law of
entanglement entropy by construction; still, the method presents critical
issues when dealing with the optimization of tensors, in order to find the best
possible approximation to the exact ground state of a given Hamiltonian. Here,
we discuss the obstacles that arise in the optimization by imaginary-time
evolution within the so-called simple and full updates and connect them to the
emergence of a sharp multiplet structure in the "virtual" indices of tensors.
In this case, a generic choice of the bond dimension $D$ is not compatible with
the multiplets and leads to a symmetry breaking (e.g., generating a finite
magnetic order). In addition, varying the initial guess, different final states
may be reached, with very large deviations in the magnetization value. In order
to exemplify this behavior, we show the results of the $S=1/2$ Heisenberg model
on an array of coupled ladders, for which a vanishing magnetization below the
critical interladder coupling is recovered only for selected values of $D$,
while a blind optimization with a generic $D$ gives rise to a finite
magnetization down to the limit of decoupled ladders.


  We present an approach for the data-driven modeling of nonlinear viscoelastic
materials at small strains which is based on physics-augmented neural networks
(NNs) and requires only stress and strain paths for training. The model is
built on the concept of generalized standard materials and is therefore
thermodynamically consistent by construction. It consists of a free energy and
a dissipation potential, which can be either expressed by the components of
their tensor arguments or by a suitable set of invariants. The two potentials
are described by fully/partially input convex neural networks. For training of
the NN model by paths of stress and strain, an efficient and flexible training
method based on a recurrent cell, particularly a long short-term memory cell,
is developed to automatically generate the internal variable(s) during the
training process. The proposed method is benchmarked and thoroughly compared
with existing approaches. These include a method that obtains the internal
variable by integrating the evolution equation over the entire sequence, while
the other method uses an an auxiliary feedforward neural network for the
internal variable(s). Databases for training are generated by using a
conventional nonlinear viscoelastic reference model, where 3D and 2D plane
strain data with either ideal or noisy stresses are generated. The
coordinate-based and the invariant-based formulation are compared and the
advantages of the latter are demonstrated. Afterwards, the invariant-based
model is calibrated by applying the three training methods using ideal or noisy
stress data. All methods yield good results, but differ in computation time and
usability for large data sets. The presented training method based on a
recurrent cell turns out to be particularly robust and widely applicable and
thus represents a promising approach for the calibration of other types of
models as well.


  An exotic anomalous plasmon mode is found in strained Weyl semimetals
utilizing the topological Landau Fermi liquid and chiral kinetic theories, in
which quasiparticle interactions are modeled by long-range Coulomb and residual
short-range interactions. The gapped collective mode is derived from the
dynamical charge pumping between the bulk and the surface and behaves like
$k_{\rm F}^{-1}$. The charge oscillations are accurately determined by the
coupling between the induced electric field and the background pseudofields.
This novel mode unidirectionally disperses along the pseudomagnetic field and
manifests itself in an unusual thermal conductivity in apparent violation of
the Wiedemann-Franz law. The excitation can be achieved experimentally by
mechanical vibrations of the crystal lattice in the THz regime.


  In this short note we wonder about the explicit expression of the expanding
of the $p$-th Lovelock product. We use the 1990's works of S. A. Fulling et al.
on the symmetries of the Riemann tensor, and we show that the number of
independent scalars appearing in this expanding is equal to the number of Young
diagrams with all row lengths even in the decomposition of the $p$-th plethysm
of the Young diagram representing the symmetries of the Riemann tensor.


  This article discusses the effects of the spiral-arm corotation on the
stellar dynamics in the Solar Neighborhood (SN). All our results presented here
rely on: 1) observational evidence that the Sun lies near the corotation
circle, where stars rotate with the same angular velocity as the spiral-arm
pattern; the corotation circle establishes domains of the corotation resonance
(CR) in the Galactic disk; 2) dynamical constraints that put the spiral-arm
potential as the dominant perturbation in the SN, comparing with the effects of
the central bar in the SN; 3) a long-lived nature of the spiral structure,
promoting a state of dynamical relaxing and phase-mixing of the stellar orbits
in response to the spiral perturbation. With an analytical model for the
Galactic potential, composed of an axisymmetric background deduced from the
observed rotation curve, and perturbed by a four-armed spiral pattern,
numerical simulations of stellar orbits are performed to delineate the domains
of regular and chaotic motions shaped by the resonances. Such studies show that
stars can be trapped inside the stable zones of the spiral CR, and this orbital
trapping mechanism could explain the dynamical origin of the Local arm of the
Milky Way (MW). The spiral CR and the near high-order epicyclic resonances
influence the velocity distribution in the SN, creating the observable
structures such as moving groups and their radially extended counterpart known
as diagonal ridges. The Sun and most of the SN stars evolve inside a stable
zone of the spiral CR, never crossing the main spiral-arm structure, but
oscillating in the region between the Sagittarius-Carina and Perseus arms. This
orbital behavior of the Sun brings insights to our understanding of questions
concerning the solar system evolution, the Earth environment changes, and the
preservation of life on Earth.


  We present a numerical study of the behaviour of two magnetic dipolar atoms
trapped in a harmonic potential and exhibiting the standard Einstein-de Haas
effect while subject to a time dependent homogeneous magnetic field. Using a
simplified description of the short range interaction and the full expression
for the dipole-dipole forces we show, that under experimentally realisable
conditions two dysprosium atoms may be pumped to a high ($l>20$) value of the
relative orbital angular momentum.


  Ewald summation is an efficient method for computing the periodic sums that
appear when considering the Green's functions of Stokes flow together with
periodic boundary conditions. We show how Ewald summation, and accompanying
truncation error estimates, can be easily derived for the rotlet, by
considering it as a superposition of electrostatic force calculations.


  Photon-jet correlations are a promising channel for the study of parton
energy loss in nuclear collisions. While existing measurements in pp and
nuclear collisions have used high energy photons and jets, we focus on an
unexplored kinematic range given by $12<p_{\mathrm{T}}<30$ GeV/$c$ photons and
the corresponding low jet $p_{\mathrm{T}}$. We present results obtained using
5.02 TeV pp and p-Pb collisions. A combination of isolation and electromagnetic
shower-shape variables is used to reduce the large background from meson decays
and fragmentation photons. We show how the access to this kinematic range of
hard probes was achieved with a novel combination of high rate and low-momentum
tracking using the electromagnetic calorimeters and the inner tracking system
of the ALICE experiment.


  I comment critically on the use and misuse of the theory of vacuum,
pseudoparticles and pseudotensors. The mathematical and phenomenological
arguments against the Higgs mechanism and the inflationary scenario are
presented. I conclude with a proposal on how to mathematically improve our
understanding of fundamental interactions, basing it on the theory of
noncontractible space. The nature of the force of gravity appears crucial in my
reasoning.


  Kernel methods are one of the mainstays of machine learning, but the problem
of kernel learning remains challenging, with only a few heuristics and very
little theory. This is of particular importance in methods based on estimation
of kernel mean embeddings of probability measures. For characteristic kernels,
which include most commonly used ones, the kernel mean embedding uniquely
determines its probability measure, so it can be used to design a powerful
statistical testing framework, which includes nonparametric two-sample and
independence tests. In practice, however, the performance of these tests can be
very sensitive to the choice of kernel and its lengthscale parameters. To
address this central issue, we propose a new probabilistic model for kernel
mean embeddings, the Bayesian Kernel Embedding model, combining a Gaussian
process prior over the Reproducing Kernel Hilbert Space containing the mean
embedding with a conjugate likelihood function, thus yielding a closed form
posterior over the mean embedding. The posterior mean of our model is closely
related to recently proposed shrinkage estimators for kernel mean embeddings,
while the posterior uncertainty is a new, interesting feature with various
possible applications. Critically for the purposes of kernel learning, our
model gives a simple, closed form marginal pseudolikelihood of the observed
data given the kernel hyperparameters. This marginal pseudolikelihood can
either be optimized to inform the hyperparameter choice or fully Bayesian
inference can be used.


  Relation between global transition function and local transition function of
a homogeneous one dimensional cellular automaton (CA) is investigated for some
standard transition functions. It could be shown that left shift and right
shift CA are invertible. The final result of this paper states that the set of
all left and right shift CA together with the identity CA on the same set forms
an abelian group.


  Molecular communication is a bio-inspired communication paradigm where
molecules are used as the information carrier. This paper considers a molecular
communication network where the transmitter uses concentration modulated
signals for communication. Our focus is to design receivers that can demodulate
these signals. We impose three features on our receivers. We want the receivers
to use enzymatic cycles as their building blocks, have high input impedance and
can work approximately as a maximum a posteriori (MAP) demodulator. No
receivers with all these three features exist in the current molecular
communication literature. We consider enzymatic cycles because they are a very
common class of chemical reactions that are found in living cells. Since a
receiver is to be placed in the communication environment, it should ideally
have a high input impedance so that it has minimal impact on the environment
and on other receivers. Lastly, a MAP receiver has good statistical
performance. In this paper, we show how we can use time-scale separation to
make an enzymatic cycle to have high input impedance and how the parameters of
the enzymatic cycles can be chosen so that the receiver can approximately
implement a MAP demodulator. We use simulation to study the performance of this
receiver. In particular, we consider an environment with multiple receivers and
show that a receiver has little impact on the bit error ratio of a nearby
receiver because they have high input impedance.


  We created a catalog of photometric redshift of ~3,000,000 SDSS galaxies
annotated by their broad morphology. The photometric redshift was optimized by
testing and comparing several pattern recognition algorithms and variable
selection strategies, trained and tested on a subset of the galaxies in the
catalog that had spectra. The galaxies in the catalog have i magnitude brighter
than 18 and Petrosian radius greater than 5.5''. The majority of these objects
are not included in previous SDSS photometric redshift catalogs such as the
photoz table of SDSS DR12. Analysis of the catalog shows that the number of
galaxies in the catalog that are visually spiral increases until redshift of
~0.085, where it peaks and starts to decrease. It also shows that the number of
spiral galaxies compared to elliptical galaxies drops as the redshift
increases. The catalog is publicly available at
https://figshare.com/articles/Morphology_and_photometric_redshift_catalog/4833593


  We extend the Standard Model by adding a second Higgs doublet and a
right-handed neutrino singlet with a heavy Majorana mass term. In this model,
there are one heavy and three light Majorana neutrinos with a mass hierarchy
m_3 >> m_2 >> m_1 such that that only m_3 is non-zero at the tree level and
light because of the seesaw mechanism, m_2 is generated at the one-loop and m_1
at the two-loop level. We show that the atmospheric neutrino oscillations and
large mixing MSW solar neutrino transitions with $\Delta m^2_\mathrm{atm}
\simeq m_3^2$ and $\Delta m^2_\mathrm{solar} \simeq m_2^2$, respectively, are
naturally accommodated in this model without employing any symmetry.


  We consider the most general renormalizable chiral Yukawa model with
$SU(3)_{\rm color}$ replaced by $SU(N_c)$, $SU(2)_{\rm L}$ replaced by $SU(N_w
)$ and $U(1)_{Y}$ replaced by $U(1)^{N_w -1}$ in the limit $N_c
\rightarrow\infty$, $N_w \rightarrow\infty$ with the ratio
$\rho=\sqrt{{N_w}\over{N_c}} \ne 0,\infty$ held fixed. Since for $N_w \ge 3$
only one renormalizable Yukawa coupling per family exists and there is no
mixing between families the limit is appropriate for the description of the
effects of a heavy top quark when all the other fermions are taken to be
massless. The large $N=\sqrt{N_{c} N_{w}}$ expansion is expected to be no worse
quantitatively in this model that in the purely scalar case and the $N=\infty$
limit is soluble even when the model is regularized non--perturbatively. A
rough estimate of the triviality bound on the Yukawa coupling is equivalent to
$m_t \le 1~TeV$.


  Natural language processing tools are used to automatically detect
disturbances in transcribed speech of schizophrenia inpatients who speak
Hebrew. We measure topic mutation over time and show that controls maintain
more cohesive speech than inpatients. We also examine differences in how
inpatients and controls use adjectives and adverbs to describe content words
and show that the ones used by controls are more common than the those of
inpatients. We provide experimental results and show their potential for
automatically detecting schizophrenia in patients by means only of their speech
patterns.


  Online platforms play a relevant role in the creation and diffusion of false
or misleading news. Concerningly, the COVID-19 pandemic is shaping a
communication network - barely considered in the literature - which reflects
the emergence of collective attention towards a topic that rapidly gained
universal interest. Here, we characterize the dynamics of this network on
Twitter, analyzing how unreliable content distributes among its users. We find
that a minority of accounts is responsible for the majority of the
misinformation circulating online, and identify two categories of users: a few
active ones, playing the role of "creators", and a majority playing the role of
"consumers". The relative proportion of these groups ($\approx$14% creators -
86% consumers) appears stable over time: Consumers are mostly exposed to the
opinions of a vocal minority of creators, that could be mistakenly understood
as of representative of the majority of users. The corresponding pressure from
a perceived majority is identified as a potential driver of the ongoing
COVID-19 infodemic.


  A key factor in the success of deep neural networks is the ability to scale
models to improve performance by varying the architecture depth and width. This
simple property of neural network design has resulted in highly effective
architectures for a variety of tasks. Nevertheless, there is limited
understanding of effects of depth and width on the learned representations. In
this paper, we study this fundamental question. We begin by investigating how
varying depth and width affects model hidden representations, finding a
characteristic block structure in the hidden representations of larger capacity
(wider or deeper) models. We demonstrate that this block structure arises when
model capacity is large relative to the size of the training set, and is
indicative of the underlying layers preserving and propagating the dominant
principal component of their representations. This discovery has important
ramifications for features learned by different models, namely, representations
outside the block structure are often similar across architectures with varying
widths and depths, but the block structure is unique to each model. We analyze
the output predictions of different model architectures, finding that even when
the overall accuracy is similar, wide and deep models exhibit distinctive error
patterns and variations across classes.


  Cable-Driven Parallel Robots (CDPRs) are a kind of parallel robots that have
cables instead of rigid links. Implementing vision-based control on CDPRs leads
to a good final accuracy despite modeling errors and other perturbations in the
system. However, unlike final accuracy, the trajectory to the goal can be
affected by the perturbations in the system. This paper proposes the use of
trajectory tracking to improve the robustness of 2 1/2 D visual servoing
control of CDPRs. Lyapunov stability analysis is performed and, as a result, a
novel workspace, named control stability workspace, is defined. This workspace
defines the set of moving-platform poses where the robot is able to execute its
task while being stable. The improvement of robustness is clearly shown in
experimental validation.


  In terms of spinless fermions and spin waves, we describe magnetic properties
of a spin-1/2 ferromagnetic-antiferromagnetic bond-alternating chain which
behaves as a Haldane-gap antiferromagnet. On one hand, we employ the
Jordan-Wigner transformation and treat the fermionic Hamiltonian within the
Hartree-Fock approximation. On the other hand, we employ the Holstein-Primakoff
transformation and modify the conventional spin-wave theory so as to restore
the sublattice symmetry. We calculate the excitation gap, the specific heat,
the magnetic susceptibility, magnetization curves, and the nuclear spin-lattice
relaxation rate with varying bond alternation. These schemes are further
applied to a bond-alternating tetramerized chain which behaves as a
ferrimagnet. The fermionic language is particularly stressed as a useful tool
to investigate one-dimensional spin-gapped antiferromagnets, while the bosonic
one works better for ferrimagnets.


  We present the stellar and planetary properties for 85 TESS Objects of
Interest (TOIs) hosting 108 planet candidates which comprise the TESS-Keck
Survey (TKS) sample. We combine photometry, high-resolution spectroscopy, and
Gaia parallaxes to measure precise and accurate stellar properties. We then use
these parameters as inputs to a lightcurve processing pipeline to recover
planetary signals and homogeneously fit their transit properties. Among these
transit fits, we detect significant transit-timing variations among at least
three multi-planet systems (TOI-1136, TOI-1246, TOI-1339) and at least one
single-planet system (TOI-1279). We also reduce the uncertainties on
planet-to-star radius ratios $R_p/R_\star$ across our sample, from a median
fractional uncertainty of 8.8$\%$ among the original TOI Catalog values to
3.0$\%$ among our updated results. With this improvement, we are able to
recover the Radius Gap among small TKS planets and find that the topology of
the Radius Gap among our sample is broadly consistent with that measured among
Kepler planets. The stellar and planetary properties presented here will
facilitate follow-up investigations of both individual TOIs and broader trends
in planet properties, system dynamics, and the evolution of planetary systems.


  Experiments on vertically oscillated granular layers in an evacuated
container reveal a sequence of well-defined pattern bifurcations as the
container acceleration is increased. Period doublings of the layer center of
mass motion and a parametric wave instability interact to produce hexagons and
more complicated patterns composed of distinct spatial domains of different
relative phase separated by kinks (phase discontinuities). Above a critical
acceleration, the layer becomes disordered in both space and time.


  I derive the correlation function for a general theory of two-valued spin
variables that satisfy the fundamental conservation law of angular momentum.
The unique theory-independent correlation function is identical to the quantum
mechanical correlation function. I prove that any theory of correlations of
such discrete variables satisfying the fundamental conservation law of angular
momentum violates the Bell's inequalities. Taken together with the Bell's
theorem, this result has far reaching implications. No theory satisfying
Einstein locality, reality in the EPR-Bell sense, and the validity of the
conservation law can be constructed. Therefore, all local hidden variable
theories are incompatible with fundamental symmetries and conservation laws.
Bell's inequalities can be obeyed only by violating a conservation law. The
implications for experiments on Bell's inequalities are obvious. The result
provides new insight regarding entanglement, and its measures.


  Modelling disease progression of iron deficiency anaemia (IDA) following oral
iron supplement prescriptions is a prerequisite for evaluating the
cost-effectiveness of oral iron supplements. Electronic health records (EHRs)
from the Clinical Practice Research Datalink (CPRD) provide rich longitudinal
data on IDA disease progression in patients registered with 663 General
Practitioner (GP) practices in the UK, but they also create challenges in
statistical analyses. First, the CPRD data are clustered at multi-levels (i.e.,
GP practices and patients), but their large volume makes it computationally
difficult to implement estimation of standard random effects models for
multi-level data. Second, observation times in the CPRD data are irregular and
could be informative about the disease progression. For example, shorter/longer
gap times between GP visits could be associated with deteriorating/improving
IDA. Existing methods to address informative observation times are mostly based
on complex joint models, which adds more computational burden. To tackle these
challenges, we develop a computationally efficient approach to modelling
disease progression with EHRs data while accounting for variability at
multi-level clusters and informative observation times. We apply the proposed
method to the CPRD data to investigate IDA improvement and treatment
intolerance following oral iron prescriptions in primary care of the UK.


  The Taylor-Aris theory of shear diffusion predicts that the effective
diffusivity of a tracer molecule in a sheared liquid is enhanced by a term
quadratic in the shear rate. In sheared supercooled liquids, instead, the
observed enhancement is linear in the shear rate. This is a fundamental
observation for the physics of nonequilibrium liquids for which no theory or
fundamental understanding is available. We derive a formula for the effective
molecular diffusivity in supercooled liquids under shear flow based on the
underlying Smoluchowski equation with shear (Smoluchowski diffusion-convection
equation) with an energy barrier representing a crowded energy landscape. The
obtained formula correctly recovers the effective diffusivity with a correction
term linear in the shear rate, in agreement with results from numerical
simulations of different liquids as well as with earlier experimental results
on shear melting of colloidal glass. The theory predictions are supported by
comparisons with molecular simulations of supercooled water and supercooled
Lennard-Jones liquids, which confirm that the predicted enhancement of
diffusivity is inversely proportional to temperature and directly proportional
to the zero shear viscosity.


  In Phys. Rev. Lett. 67, 937 (1991) [1], Haldane introduced the fruitful
concept of fractional exclusion statistics (FES). One of the most influential
papers in which the thermodynamics of FES systems was deduced is Y.-S. Wu,
Phys. Rev. Lett. 73, 922 (1994). Unfortunately, some important, but eventually
subtle, properties of the exclusion statistics parameters were overlooked in
the original paper [1] and in all the papers after that, including [2]. This
omission makes the thermodynamics of FES systems inconsistent when mutual
exclusion statistics is manifesting in the system. By this Comment I want to
point-out this error--an error that persisted for such a long time--and to give
the correct statistical mechanics interpretation of FES.


  The central idea of this paper is to demonstrate the strength of lyrics for
music mining and natural language processing (NLP) tasks using the distributed
representation paradigm. For music mining, we address two prediction tasks for
songs: genre and popularity. Existing works for both these problems have two
major bottlenecks. First, they represent lyrics using handcrafted features that
require intricate knowledge of language and music. Second, they consider lyrics
as a weak indicator of genre and popularity. We overcome both the bottlenecks
by representing lyrics using distributed representation. In our work, genre
identification is a multi-class classification task whereas popularity
prediction is a binary classification task. We achieve an F1 score of around
0.6 for both the tasks using only lyrics. Distributed representation of words
is now heavily used for various NLP algorithms. We show that lyrics can be used
to improve the quality of this representation.


  The simulation of the expectation of a stochastic quantity E[Y] by Monte
Carlo methods is known to be computationally expensive especially if the
stochastic quantity or its approximation Y_n is expensive to simulate, e.g.,
the solution of a stochastic partial differential equation. If the convergence
of Y_n to Y in terms of the error |E[Y - Y_n]| is to be simulated, this will
typically be done by a Monte Carlo method, i.e., |E[Y] - E_N[Y_n]| is computed.
In this article upper and lower bounds for the additional error caused by this
are determined and compared to those of |E_N[Y - Y_n]|, which are found to be
smaller. Furthermore, the corresponding results for multilevel Monte Carlo
estimators, for which the additional sampling error converges with the same
rate as |E[Y - Y_n]|, are presented. Simulations of a stochastic heat equation
driven by multiplicative Wiener noise and a geometric Brownian motion are
performed which confirm the theoretical results and show the consequences of
the presented theory for weak error simulations.


  A brief account of the present status of the recent nonlocal generalization
of Einstein's theory of gravitation is presented. The main physical assumptions
that underlie this theory are described. We clarify the physical meaning and
significance of Weitzenb\"ock's torsion, and emphasize its intimate
relationship with the gravitational field, characterized by the Riemannian
curvature of spacetime. In this theory, nonlocality can simulate dark matter;
in fact, in the Newtonian regime, we recover the phenomenological Tohline-Kuhn
approach to modified gravity. To account for the observational data regarding
dark matter, nonlocality is associated with a characteristic length scale of
order 1 kpc. The confrontation of nonlocal gravity with observation is briefly
discussed.


  We present a detailed error analysis of Ramanujan's most accurate
approximation to the perimeter of an ellipse.


  Nonlocal quantum correlation has been the main issue of quantum mechanics
over the last century. The Hong-Ou-Mandel (HOM) effect relates to the
two-photon intensity correlation on a beam splitter, resulting in a
nonclassical photon-bunching phenomenon. The HOM effect has been used to verify
the quantum feature via Bell measurements for quantum technologies such as
quantum repeaters and photonics quantum computing. Here, a coherence version of
the HOM effect is proposed and analyzed to understand the fundamental physics
of the anticorrelation and entanglement. For this, frequency-correlated
coherent photon pairs are prepared in an independent set of Mach-Zhender
interferometers (MZI) using a synchronized pair of modulators from an
attenuated laser. For the HOM effect, the phase relation between
frequency-correlated photons plays an essential role. For the product-basis
randomness, the symmetrically modulated two independent MZIs are combined
together incoherently. A classical intensity product between two independent
photodetectors is also discussed for the same HOM effect in a selective
macroscopic measurement scheme.


  We show that a sequence of smooth analytic curves of the unit ball of the
complex plane, for which the genus is bounded by the area, converges to a
lamination in a weak sense.


  Understanding the interactions of agents trained with deep reinforcement
learning is crucial for deploying agents in games or the real world. In the
former, unreasonable actions confuse players. In the latter, that effect is
even more significant, as unexpected behavior cause accidents with potentially
grave and long-lasting consequences for the involved individuals. In this work,
we propose using program synthesis to imitate reinforcement learning policies
after seeing a trajectory of the action sequence. Programs have the advantage
that they are inherently interpretable and verifiable for correctness. We adapt
the state-of-the-art program synthesis system DreamCoder for learning concepts
in grid-based environments, specifically, a navigation task and two miniature
versions of Atari games, Space Invaders and Asterix. By inspecting the
generated libraries, we can make inferences about the concepts the black-box
agent has learned and better understand the agent's behavior. We achieve the
same by visualizing the agent's decision-making process for the imitated
sequences. We evaluate our approach with different types of program
synthesizers based on a search-only method, a neural-guided search, and a
language model fine-tuned on code.


  We obtain bounds on the complex eigenvalues of non-self-adjoint Schr\"odinger
operators with complex potentials, and also on the complex resonances of
self-adjoint Schr\"odinger operators. Our bounds are compared with numerical
results, and are seen to provide useful information.


  It has been suggested that accretion-induced collapse (AIC) is a
non-negligible path for the formation of the observed neutron stars (NSs). An
ONe white dwarf (WD) that accretes material from a He star may experience AIC
process and eventually produce intermediate-mass binary pulsars (IMBPs), named
as the ONe WD+He star scenario. Note that previous studies can only account for
part of the observed IMBPs with short orbital periods. In this work, we
investigate the evolution of about 900 ONe WD+He star binaries to explore the
distribution of IMBPs. We found that the ONe WD+He star scenario could form
IMBPs including pulsars with 5-340 ms spin periods and 0.75-1.38 Msun WD
companions, in which the orbital periods range from 0.04 to 900 d. Compared
with the 20 observed IMBPs, this scenario can cover the parameters of 13
sources in the final orbital period-WD mass plane and the Corbet diagram, most
of which has short orbital periods. We found that the ONe WD+He star scenario
can explain almost all the observed IMBPs with short orbital periods. This work
can well match the observed parameters of PSR J1802-2124 (one of the two
precisely observed IMBPs), providing a possible evolutional path for its
formation. We also speculate that the compact companion of HD 49798 (a hydrogen
depleted sdO6 star) may be not a NS based on the present work.


  Radiatively inefficient accretion flow models have been shown to accurately
account for the spectrum and luminosity observed from Sgr A* in the X-ray
regime down to mm wavelengths. However, observations at a few GHz cannot be
explained by thermal electrons alone but require the presence of an additional
non-thermal particle population. Here, we propose a model for the origin of
such a population in the accretion flow via means of a pulsar orbiting the
supermassive black hole in our Galaxy. Interactions between the relativistic
pulsar wind with the disc lead to the formation of a bow shock in the wind.
During the pulsar's transit through the accretion disc, relativistic pairs,
accelerated at the shock front, are injected into the disc. The radio-emitting
particles are long-lived and remain within the disc long after the pulsar's
transit. Periodic pulsar transits through the disc result in regular injection
episodes of non-thermal particles. We show that for a pulsar with spin-down
luminosity $L_{\rm sd} \sim 3\times 10^{35}$ erg s$^{-1}$ and a wind Lorentz
factor of $\gamma_{\rm w} \sim 10^4$ a quasi-steady synchrotron emission is
established with luminosities in the $1-10$ GHz range comparable to the
observed one.


  This paper proposes a method to detect bank frauds using a mixed approach
combining a stochastic intensity model with the probability of fraud observed
on transactions. It is a dynamic unsupervised approach which is able to predict
financial frauds. The fraud prediction probability on the financial transaction
is derived as a function of the dynamic intensities. In this context, the
Kalman filter method is proposed to estimate the dynamic intensities. The
application of our methodology to financial datasets shows a better predictive
power in higher imbalanced data compared to other intensity-based models.


  For civil structures, structural damage due to severe loading events such as
earthquakes, or due to long-term environmental degradation, usually occurs in
localized areas of a structure. A new sparse Bayesian probabilistic framework
for computing the probability of localized stiffness reductions induced by
damage is presented that uses noisy incomplete modal data from before and after
possible damage. This new approach employs system modal parameters of the
structure as extra variables for Bayesian model updating with incomplete modal
data. A specific hierarchical Bayesian model is constructed that promotes
spatial sparseness in the inferred stiffness reductions in a way that is
consistent with the Bayesian Ockham razor. To obtain the most plausible model
of sparse stiffness reductions together with its uncertainty within a specified
class of models, the method employs an optimization scheme that iterates among
all uncertain parameters, including the hierarchical hyper-parameters. The
approach has four important benefits: (1) it infers spatially-sparse stiffness
changes based on the identified modal parameters; (2) the uncertainty in the
inferred stiffness reductions is quantified; (3) no matching of model and
experimental modes is needed, and (4) solving the nonlinear eigenvalue problem
of a structural model is not required. The proposed method is applied to two
previously-studied examples using simulated data: a ten-story shear-building
and the three-dimensional braced-frame model from the Phase II Simulated
Benchmark problem sponsored by the IASC-ASCE Task Group on Structural Health
Monitoring. The results show that the occurrence of false-positive and
false-negative damage detection is clearly reduced in the presence of modeling
error. Furthermore, the identified most probable stiffness loss ratios are
close to their actual values.


  Open set recognition (OSR), aiming to simultaneously classify the seen
classes and identify the unseen classes as 'unknown', is essential for reliable
machine learning.The key challenge of OSR is how to reduce the empirical
classification risk on the labeled known data and the open space risk on the
potential unknown data simultaneously. To handle the challenge, we formulate
the open space risk problem from the perspective of multi-class integration,
and model the unexploited extra-class space with a novel concept Reciprocal
Point. Follow this, a novel learning framework, termed Adversarial Reciprocal
Point Learning (ARPL), is proposed to minimize the overlap of known
distribution and unknown distributions without loss of known classification
accuracy. Specifically, each reciprocal point is learned by the extra-class
space with the corresponding known category, and the confrontation among
multiple known categories are employed to reduce the empirical classification
risk. Then, an adversarial margin constraint is proposed to reduce the open
space risk by limiting the latent open space constructed by reciprocal points.
To further estimate the unknown distribution from open space, an instantiated
adversarial enhancement method is designed to generate diverse and confusing
training samples, based on the adversarial mechanism between the reciprocal
points and known classes. This can effectively enhance the model
distinguishability to the unknown classes. Extensive experimental results on
various benchmark datasets indicate that the proposed method is significantly
superior to other existing approaches and achieves state-of-the-art
performance.


  We discuss a substantial update to the Grid software library for Lattice QCD,
enabling it to port to multiple GPU architectures while retaining CPU
vectorisation and SIMD execution within OpenMP threads. The GPU environments
supported include vendor specific Nvidia CUDA and AMD HIP environments and a
(mostly) standards based SYCL implementation. This is performed by an internal
abstraction interface giving single source cross-platform performance
portability across all number of planned Exascale architectures, and all those
planned by the US Department of Energy.


  Quantum coherence can significantly increase the strength of the forward
scattering of neutrinos propagating through the Earth and interacting with
matter. The index of refraction of the neutrinos propagating in a medium and
hence their phase velocity is determined by the forward scattering. So,
depending on the nature of the interaction of neutrinos with matter, their
phase velocity can be larger than the speed of light in vacuum. We show that
such effects can explain the apparent superluminal propagation of muon
neutrinos found recently by the OPERA experiment. Our proposal explains why the
neutrino oscillations and the propagation of neutrinos from supernova 1987A are
unaffected. It can be verified by changing the amount of neutrino coherence or
by changing the composition of matter in which they propagate.


  Let $G$ be a directed graph with $n$ vertices and $m$ edges, embedded on a
surface $S$, possibly with boundary, with first Betti number $\beta$. We
consider the complexity of finding closed directed walks in $G$ that are either
contractible (trivial in homotopy) or bounding (trivial in integer homology) in
$S$. Specifically, we describe algorithms to determine whether $G$ contains a
simple contractible cycle in $O(n+m)$ time, or a contractible closed walk in
$O(n+m)$ time, or a bounding closed walk in $O(\beta (n+m))$ time. Our
algorithms rely on subtle relationships between strong connectivity in $G$ and
in the dual graph $G^*$; our contractible-closed-walk algorithm also relies on
a seminal topological result of Hass and Scott. We also prove that detecting
simple bounding cycles is NP-hard.
  We also describe three polynomial-time algorithms to compute shortest
contractible closed walks, depending on whether the fundamental group of the
surface is free, abelian, or hyperbolic. A key step in our algorithm for
hyperbolic surfaces is the construction of a context-free grammar with
$O(g^2L^2)$ non-terminals that generates all contractible closed walks of
length at most L, and only contractible closed walks, in a system of quads of
genus $g\ge2$. Finally, we show that computing shortest simple contractible
cycles, shortest simple bounding cycles, and shortest bounding closed walks are
all NP-hard.


  More and more works are done on the design of the Unified Modeling Language
(UML) which is designed to help us for modeling effective object oriented
software, Existing Object-Oriented design methods are not mature enough to
capture non-functional requirement such as concurrency, fault tolerance,
distribution and persistence of a software approach. Our approach proposed to
use aspect-oriented software development (AOSD) mechanisms to solve the issues
for interactions of the communication diagram in UML that support only the
Object-Oriented mechanisms,thus AOSD allow to design programs that are out of
reach of strict Object-Orientation and could possibly improve the structures
and implementations.


  Recent HERA data on structure functions and reduced cross-sections are
presented and their significance for our understanding of the low-x region is
discussed.


  This paper presents a hybrid Mesh of Things (MoT) network performance model
to evaluate the end-to-end Packet Delivery Ratio (PDR) and latency. These PDR
and latency measures are used to identify both a de-tangled mesh as well as to
track the mesh successfully. A de-tangled mesh is a mesh with an anomaly where
one or more nodes are separated from the rest of the mesh network. We
demonstrate the performance model of a hybrid BLE mesh-PLC network by
considering an air cargo monitoring application and validate with experimental
PDR and latency data. The link uncertainty in Bluetooth Low Energy (BLE) mesh
may be attributed to (a) RF interference,(b)~Transmitter's vicinity range, and
(c) Receiver sensitivity. In contrast, the link uncertainty in Power Line
Communication (PLC) may be attributed to: (a) Colored background noise,
(b)~Channel frequency response, and (c) Impulse noise appearing due to load
state as well as variations in the powerline. In our work, we construct an
equivalent Bayesian network for the mesh to be tracked, capture the uncertainty
within the mesh links using the Noisy-OR and the Noisy-Integer addition model
and perform belief propagation to detect and localize a network anomaly.


  Various convolutional neural network (CNN) based concepts have been
introduced for the prostate's automatic segmentation and its coarse subdivision
into transition zone (TZ) and peripheral zone (PZ). However, when targeting a
fine-grained segmentation of TZ, PZ, distal prostatic urethra (DPU) and the
anterior fibromuscular stroma (AFS), the task becomes more challenging and has
not yet been solved at the level of human performance. One reason might be the
insufficient amount of labeled data for supervised training. Therefore, we
propose to apply a semi-supervised learning (SSL) technique named
uncertainty-aware temporal self-learning (UATS) to overcome the expensive and
time-consuming manual ground truth labeling. We combine the SSL techniques
temporal ensembling and uncertainty-guided self-learning to benefit from
unlabeled images, which are often readily available. Our method significantly
outperforms the supervised baseline and obtained a Dice coefficient (DC) of up
to 78.9% , 87.3%, 75.3%, 50.6% for TZ, PZ, DPU and AFS, respectively. The
obtained results are in the range of human inter-rater performance for all
structures. Moreover, we investigate the method's robustness against noise and
demonstrate the generalization capability for varying ratios of labeled data
and on other challenging tasks, namely the hippocampus and skin lesion
segmentation. UATS achieved superiority segmentation quality compared to the
supervised baseline, particularly for minimal amounts of labeled data.


  The classical DICE model is a widely accepted integrated assessment model for
the joint modeling of economic and climate systems, where all model state
variables evolve over time deterministically. We reformulate and solve the DICE
model as an optimal control dynamic programming problem with six state
variables (related to the carbon concentration, temperature, and economic
capital) evolving over time deterministically and affected by two controls
(carbon emission mitigation rate and consumption). We then extend the model by
adding a discrete stochastic shock variable to model the economy in the
stressed and normal regimes as a jump process caused by events such as the
COVID-19 pandemic. These shocks reduce the world gross output leading to a
reduction in both the world net output and carbon emission. The extended model
is solved under several scenarios as an optimal stochastic control problem,
assuming that the shock events occur randomly on average once every 100 years
and last for 5 years. The results show that, if the world gross output recovers
in full after each event, the impact of the COVID-19 events on the temperature
and carbon concentration will be immaterial even in the case of a conservative
10\% drop in the annual gross output over a 5-year period. The impact becomes
noticeable, although still extremely small (long-term temperature drops by
$0.1^\circ \mathrm{C}$), in a presence of persistent shocks of a 5\% output
drop propagating to the subsequent time periods through the recursively reduced
productivity. If the deterministic DICE model policy is applied in a presence
of stochastic shocks (i.e. when this policy is suboptimal), then the drop in
temperature is larger (approximately $0.25^\circ \mathrm{C}$), that is, the
lower economic activities owing to shocks imply that more ambitious mitigation
targets are now feasible at lower costs.


  We determine the characters of SL(2) representations of groups and surface
groups.


  Experiments, analyses, and simulations have shown that the engine exhaust
plume of a Mars lander large enough for human spaceflight will create a deep
crater in the martian soil, blowing ejecta to approximately 1 km distance,
damaging the bottom of the lander with high-momentum rock impacts, and possibly
tilting the lander as the excavated hole collapses to become a broad residual
crater upon engine cutoff. Because of this, we deem that we will not have
adequate safety margins to land humans on Mars unless we robotically stabilize
the soil to form in situ landing pads prior to the mission. It will take a
significant amount of time working in a harsh off-planet environment to develop
and certify the new technologies and procedures for in situ landing pad
construction. The only place to reasonably accomplish this is on the Moon.


  Precession has been proposed as an alternative power source for planetary
dynamos. Previous hydrodynamic simulations suggested that precession can
generate very complex flows in planetary liquid cores [Y. Lin, P. Marti, and J.
Noir, "Shear-driven parametric instability in a precessing sphere," Physics of
Fluids 27, 046601 (2015)]. In the present study, we numerically investigate the
magnetohydrodynamics of a precessing sphere. We demonstrate precession driven
dynamos in different flow regimes, from laminar to turbulent flows. In
particular, we highlight the magnetic field generation by large scale cyclonic
vortices, which has not been explored previously. In this regime, dynamos can
be sustained at relatively low Ekman numbers and magnetic Prandtl numbers,
which paves the way for planetary applications.


  Below a temperature of approximately 29 K the manganese magnetic moments of
the cubic binary compound MnSi order to a long-range incommensurate helical
magnetic structure. Here, we quantitatively analyze a high-statistic zero-field
muon spin rotation spectrum recorded in the magnetically ordered phase of MnSi
by exploiting the result of representation theory as applied to the
determination of magnetic structures. Instead of a gradual rotation of the
magnetic moments when moving along a <111> axis, we find that the angle of
rotation between the moments of certain subsequent planes is essentially
quenched. It is the magnetization of pairs of planes which rotates when moving
along a <111> axis, thus preserving the overall helical structure.


  In this lecture series I will give a fundamental insight into configuration
space techniques which are of help to calculate a broad class of Feynman
diagrams, the sunrise-type diagrams. Applications are shown along with basic
concepts and techniques.


  This work derives an analytical formula for the asymptotic state---the
quantum state resulting from an infinite number of applications of a general
quantum channel on some initial state. For channels admitting multiple fixed or
rotating points, conserved quantities---the left fixed/rotating points of the
channel---determine the dependence of the asymptotic state on the initial
state. The formula stems from a Noether-like theorem stating that, for any
channel admitting a full-rank fixed point, conserved quantities commute with
that channel's Kraus operators up to a phase. The formula is applied to
adiabatic transport of the fixed-point space of channels, revealing cases where
the dissipative/spectral gap can close during any segment of the adiabatic
path. The formula is also applied to calculate expectation values of
noninjective matrix product states (MPS) in the thermodynamic limit, revealing
that those expectation values can also be calculated using an MPS with reduced
bond dimension and a modified boundary.


  Following the well-extablished mathematical approach to persistence and its
recent developments we give a rigorous theoretical explanation to the numerical
results obtained for a certain prey-predator model with functional response of
Holling type II equipped with an infectious disease in the predator population.
  The proof relies on some repelling conditions that can be applied in an
iterative way on a suitable decomposition of the boundary. A full stability
analysis is developed, showing how the "invasion condition" for the disease is
derived. Some counterexamples and possible further investigations are
discussed.


  The paper describes a novel method for studying the stability of
nonautonomous dynamical systems. This method based on the flow and divergence
of the vector field with coupling to the method of Lyapunov functions. The
necessary and sufficient stability conditions are formulated. It is shown that
the necessary stability conditions are related to the integral and differential
forms of continuity equations with the sources (the flux is directed inward)
located in the equilibrium points of the dynamical system. The sufficient
stability conditions are applied to design the state feedback control laws. The
proposed control law is found as a solution of partial differential inequality,
whereas the control law based on Lyapunov technique is found from the solution
of algebraic inequality. The examples illustrate the effectiveness of the
proposed method compared with some existing ones.


  Phonon densities of states (DOS) of bcc $\alpha$-$^{57}$Fe were measured from
room temperature through the 1044K Curie transition and the 1185K fcc
$\gamma$-Fe phase transition using nuclear resonant inelastic x-ray scattering.
At higher temperatures all phonons shift to lower energies (soften) with
thermal expansion, but the low transverse modes soften especially rapidly above
700K, showing strongly nonharmonic behavior that persists through the magnetic
transition. Interatomic force constants for the bcc phase were obtained by
iteratively fitting a Born-von K\'{a}rm\'{a}n model to the experimental phonon
spectra using a genetic algorithm optimization. The second-nearest-neighbor
fitted axial force constants weakened significantly at elevated temperatures.
An unusually large nonharmonic behavior is reported, which increases the
vibrational entropy and accounts for a contribution of 35 meV/atom in the free
energy at high temperatures. The nonharmonic contribution to the vibrational
entropy follows the thermal trend of the magnetic entropy, and may be coupled
to magnetic excitations. A small change in vibrational entropy across the
$\alpha$-$\gamma$ structural phase transformation is also reported.


  The inner regions of the envelopes surrounding young protostars are
characterised by a complex chemistry, with prebiotic molecules present on the
scales where protoplanetary disks eventually may form. This paper introduces a
systematic survey, "Protostellar Interferometric Line Survey (PILS)" of the
Class 0 protostellar binary IRAS 16293-2422 using the Atacama Large
Millimeter/submillimeter Array (ALMA). The survey covers the full frequency
range from 329 to 363 GHz (0.8 mm) with additional targeted observations at 3.0
and 1.3 mm. More than 10,000 features are detected toward one component in the
protostellar binary. Glycolaldehyde, its isomers, methyl formate and acetic
acid, and its reduced alcohol, ethylene glycol, are clearly detected. For
ethylene glycol both lowest state conformers, aGg' and gGg', are detected, the
latter for the first time in the ISM. The abundance of glycolaldehyde is
comparable to or slightly larger than that of ethylene glycol. In comparison to
the Galactic Center, these two species are over-abundant relative to methanol,
possibly an indication of formation at low temperatures in CO-rich ices. Both
13C and deuterated isotopologues of glycolaldehyde are detected, also for the
first time ever in the ISM. For the deuterated species, a D/H ratio of
approximately 5% is found with no differences between the deuteration in the
different functional groups of glycolaldehyde. Measurements of the 13C-species
lead to a 12C:13C ratio of approximately 30, lower than the typical ISM value.
This low ratio may reflect an enhancement of 13CO in the ice due to either
ion-molecule reactions in the gas before freeze-out or differences in the
temperatures where 12CO and 13CO ices sublimate. The results reinforce the
importance of low-temperature grain surface chemistry for the formation of
prebiotic molecules seen here in the gas after sublimation of the entire ice
mantle.


  Marginally outer trapped surfaces are widely considered as the best
quasi-local replacements for event horizons of black holes in General
Relativity. However, this equivalence is far from being proved, even in
stationary and static situations. In this paper we study an important aspect of
this equivalence, namely whether classic uniqueness theorems of static black
holes can be extended to static spacetimes containing weakly outer trapped
surfaces or not. Our main theorem states that, under reasonable hypotheses, a
static spacetime satisfying the null energy condition and containing an
asymptotically flat initial data set, possibly with boundary, which possesses a
bounding weakly outer trapped surface is a unique spacetime. A related result
to this theorem was given in arXiv:0711.1299, where we proved that no bounding
weakly outer trapped surface can penetrate into the exterior region of the
initial data where the static Killing vector is timelike. In this paper, we
also fill some gaps in arXiv:0711.1299 and extend this confinement result to
initial data sets with boundary.


  We derive a Bernstein type result for the special Lagrangian equation,
namely, any global convex solution must be quadratic. In terms of minimal
surfaces, the result says that any global minimal Lagrangian graph with convex
potential must be a hyper-plane.


  The one loop polarization insertions for the kaon and the $\phi$ mesons are
evaluated in a hadronic medium as functions of the field intensity and the
baryonic density. For this purpose an effective chiral model of the hadronic
interaction is used, supplemented with a phenomenological $K-\phi$ interaction.
The propagators of the charged particles include the full effect of the
coupling to the magnetic field which induces the quantum Landau levels.
Consequently, the vacuum diagrams are defined within a zeta function
regularization scheme. The effective masses as well as the decay widths are
introduced and examined as functions of matter density and the magnetic
intensity.


  By relaxing the regularity conditions imposed in arXiv:1107.1722 on half-BPS
solutions to six-dimensional Type~4b supergravity, we enlarge the space of
solutions to include two new half-BPS configurations, which we refer to as the
\kap\ and the \funnel. We give evidence that the \kap\ and \funnel\ can be
interpreted as fully back-reacted brane solutions with respectively $AdS_2$ and
$AdS_2\times S^2$ world volumes. \kap\ and \funnel\ solutions with a single
asymptotic $AdS_3 \times S^3$ region are constructed analytically. We argue
that \kap\ solutions provide simple examples of holographic duals to boundary
CFTs in two dimensions and present calculations of their holographic boundary
entropy to support the BCFT dual picture.


  We discuss a thinning and an embedding procedure to construct finite Gibbs
processes with a given Papangelou intensity. Extending the approach in
Hofer-Temmel (2019) and Hofer-Temmel and Houdebert (2019) we will use this to
couple two finite Gibbs processes with different boundary conditions. As one
application we will establish Poisson approximation of point processes derived
from certain infinite volume Gibbs processes via dependent thinning. As another
application we shall discuss empty space probabilities of certain Gibbs
processes.


  The degree of polarimetric purity is an invariant dimensionless quantity that
characterizes the closeness of a polarization state of a wave to a pure state
and is related to the Von Neumann entropy. The polarimetric purity of a plane
wave characterized by the second-order statistics (i.e., the covariance matrix)
is uniquely described by the degree of polarization. However, the 2D formalism
is only applicable when the wave propagation direction is fixed. This
assumption is typical in optical and radar polarimetric measurements.
Therefore, one must consider all the components to describe the general state
of wave polarization. Starting from Samson and Barakat, several different
concepts have been proposed in the literature to describe the 3D degree of
polarization. We discuss two new ways of achieving such description: by the
Coefficient of Variation and by a Direct Sum Decomposition.


  Fluorescence telescopes are important instruments widely used in modern
experiments for registering ultraviolet radiation from extensive air showers
(EASs) generated by cosmic rays of ultra-high energies. We present a
proof-of-concept convolutional neural network aimed at reconstruction of energy
and arrival directions of primary particles using model data for two telescopes
developed by the international JEM-EUSO collaboration. We also demonstrate how
a simple convolutional encoder-decoder can be used for EAS track recognition.
The approach is generic and can be adopted for other fluorescence telescopes.


  In the Standard Model, the renormalization of the QCD vacuum angle $\theta$
is extremely tiny, and small $\theta$ is technically natural. In the general
Standard Model effective field theory (SMEFT), however, $\Delta\theta$ is
quadratically divergent, reflecting the fact that new sources of hadronic
CP-violation typically produce $\mathcal O(1)$ threshold corrections to
$\theta$. The observation of such CP-violating interactions would therefore be
in tension with solutions to the strong CP problem in which $\theta=0$ is an
ultraviolet boundary condition, pointing to the Peccei-Quinn mechanism as the
explanation for why $\theta$ is small in the infrared. We study the quadratic
divergences in $\theta$ arising from dimension-6 SMEFT operators and discuss
the discovery prospects for these operators at electric dipole moment
experiments, the LHC, and future proton-proton colliders.


  Co-clustering, that is, partitioning a numerical matrix into homogeneous
submatrices, has many applications ranging from bioinformatics to election
analysis. Many interesting variants of co-clustering are NP-hard. We focus on
the basic variant of co-clustering where the homogeneity of a submatrix is
defined in terms of minimizing the maximum distance between two entries. In
this context, we spot several NP-hard as well as a number of relevant
polynomial-time solvable special cases, thus charting the border of
tractability for this challenging data clustering problem. For instance, we
provide polynomial-time solvability when having to partition the rows and
columns into two subsets each (meaning that one obtains four submatrices). When
partitioning rows and columns into three subsets each, however, we encounter
NP-hardness even for input matrices containing only values from {0, 1, 2}.


  In this report, we describe the work done in a project that explored the
human information processing aspects of a personal memex (a memex to organize
personal information). In the project, we considered the use of the personal
memex, focusing on information recall, by three populations: people with Mild
Cognitive Impairment, those diagnosed with Macular Degeneration, and a
high-functioning population. The outcomes of the project included human
information processing-centered design guidelines for the memex interface, a
low-fidelity prototype, and an annotated bibliography for human information
processing, usability and design literature relating to the memex and the
populations we explored.


  Several physics aspects of the Seiberg-Witten solution of N=2 supersymmetric
Yang-Mills theory with SU(2) gauge group, supplemented with a small mass term
for the "matter" fields which leads to an $N=1$ theory with confinement, are
discussed. The light spectrum of the theory is understood on the basis of
current algebra relations, and CP invariance of the massless and massive
theories is studied. We find that in the massive (confining) theory the low
energy physics has an exact CP symmetry, while in a generic vacuum in the
massless theory CP invarince is spontaneously broken.


  We generalize the classical approach of describing the infinitesimal Torelli
map in terms of multiplication in a Jacobi ring to the case of quasi-smooth
complete intersections in weighted projective space. As an application, we
prove the infinitesimal Torelli theorem for hyperelliptic Fano threefolds of
Picard rank 1, index 1, degree 4 and study the action of the automorphism group
on cohomology. The results of this paper are used to prove Lang-Vojta's
conjecture for the moduli of such Fano threefolds in a follow-up paper.


  We study a general contracting problem between the principal and a finite set
of competitive agents, who perform equivalent changes of measure by controlling
the drift of the output process and the compensator of its associated jump
measure. In this setting, we generalize the dynamic programming approach
developed by Cvitani\'c, Possama\"i, and Touzi [12] and we also relax their
assumptions. We prove that the problem of the principal can be reformulated as
a standard stochastic control problem in which she controls the continuation
utility (or certainty equivalent) processes of the agents. Our assumptions and
conditions on the admissible contracts are minimal to make our approach work.
We review part of the literature and give examples on how they are usually
satisfied. We also present a smoothness result for the value function of a
risk-neutral principal when the agents have exponential utility functions. This
leads, under some additional assumptions, to the existence of an optimal
contract.


  Angular power spectra computed from Planck HFI 353 GHz intensity and
polarization maps produce a TB correlation that can be approximated by a power
law. Whether the observed TB correlation is an induced systematic feature or a
physical property of Galactic dust emission is of interest both for
cosmological and Galactic studies. We investigate the large angular scale E-
and B-mode morphology of microwave polarized thermal dust emission, and relate
it to physical quantities of polarization angle and polarization fraction. We
use empirical models of polarized dust to show that dust polarization angle is
a key factor in producing the TB correlation. A small sample of both simulated
and observed polarization angle maps are combined with 353 GHz intensity and
dust polarization fraction to produce a suite of maps from which we compute TB
and EB. Model realizations that produce a positive TB correlation are common
and can result from large-scale (>5 degree) structure in the polarization
angle. The TB correlation appears robust to introduction of individual
intensity, polarization angle and polarization fraction model components that
are independent of the 353 GHz observations. We conclude that the observed TB
correlation is likely the result of large-scale Galactic dust polarization
properties.


  We investigate the effect of electrostatic gating on the thermoelectric power
factor of p-type Si nanowires (NWs) of up to 20nm in diameter in the [100],
[110] and [111] crystallographic transport orientations. We use atomistic
tight-binding simulations for the calculation of the NW electronic structure,
coupled to linearized Boltzmann transport equation for the calculation of the
thermoelectric coefficients. We show that gated NW structures can provide ~5x
larger thermoelectric power factor compared to doped channels, attributed to
their high hole phonon-limited mobility, as well as gating induced
bandstructure modifications which further improve mobility. Despite the fact
that gating shifts the charge carriers near the NW surface, surface roughness
scattering is not strong enough to degrade the transport properties of the
accumulated hole layer. The highest power factor is achieved for the [111] NW,
followed by the [110], and finally by the [100] NW. As the NW diameter
increases, the advantage of the gated channel is reduced. We show, however,
that even at 20nm diameters, (the largest ones that we were able to simulate),
a ~3x higher power factor for gated channels is observed. Our simulations
suggest that the advantage of gating could still be present in NWs with
diameters of up to ~40nm.


  This paper addresses the modelling of requirements for a content
Recommendation System (RS) for Online Social Networks (OSNs). On OSNs, a user
switches roles constantly between content generator and content receiver. The
goals and softgoals are different when the user is generating a post, as
opposed as replying to a post. In other words, the user is generating instances
of different entities, depending on the role she has: a generator generates
instances of a "post", while the receiver generates instances of a "reply".
Therefore, we believe that when addressing Requirements Engineering (RE) for
RS, it is necessary to distinguish these roles clearly.
  We aim to model an essential dynamic on OSN, namely that when a user creates
(posts) content, other users can ignore that content, or themselves start
generating new content in reply, or react to the initial posting. This dynamic
is key to designing OSNs, because it influences how active users are, and how
attractive the OSN is for existing, and to new users. We apply a well-known
Goal Oriented RE (GORE) technique, namely i-star, and show that this language
fails to capture this dynamic, and thus cannot be used alone to model the
problem domain. Hence, in order to represent this dynamic, its relationships to
other OSNs' requirements, and to capture all relevant information, we suggest
using another modelling language, namely Petri Nets, on top of i-star for the
modelling of the problem domain. We use Petri Nets because it is a tool that is
used to simulate the dynamic and concurrent activities of a system and can be
used by both practitioners and theoreticians.


  We report interferometric radio CO2-1 observations at a resolution of 0.7",
and 0.085" resolution adaptive optics K-band spectroscopy of the inner few
arcseconds of NGC7469. The CO2-1 map shows a ring of molecular clouds and a
bright extended nucleus, with a bar or pair of spiral arms between them. The
dynamical structure of both the radio CO2-1 and the K-band H2 1-0S(1) lines can
be reproduced using an axisymmetric mass model comprising 3 components: a broad
disk, a ring 4-5" across, and an inner nuclear ring about 0.5" across. No
non-circular motions faster than the velocity residuals of 25 km/s are seen,
although this may be because in some cases a secondary bar is not expected to
cause gas inflow. From the dynamical mass and estimates of the stellar mass we
find that the CO-to-H_2 conversion is 0.4-0.8 times that for the Milky Way. The
H2 1-0S(1) morphology peaks at the nucleus, although there is no strong nuclear
mass concentration. The 1-0S(1) is likely to originate in X-ray of gas by the
AGN rather than via star formation. Using the 2.3um stellar CO2-0 absorption
and the continuum slope we have directly resolved the nuclear star cluster to
be 0.15-0.20" across, and find that it is asymmetric. This cluster has an age
of less than about 60Myr and contributes 20-30% of the nuclear K-band light,
and about 10% of the nuclear bolometric luminosity. Within a radius of ~4" gas
contributes more than half the total mass; but in the nucleus, within a radius
of 0.1", it is likely that most of the mass is due to stars


  Superconducting linacs are capable of producing intense, stable, high-quality
electron beams that have found widespread applications in science and industry.
The 9-cell 1.3-GHz superconducting standing-wave accelerating RF cavity
originally developed for $e^+/e^-$ linear-collider applications [B. Aunes, {\em
et al.} Phys. Rev. ST Accel. Beams {\bf 3}, 092001 (2000)] has been broadly
employed in various superconducting-linac designs. In this paper we discuss the
transfer matrix of such a cavity and present its measurement performed at the
Fermilab Accelerator Science and Technology (FAST) facility. The experimental
results are found to be in agreement with analytical calculations and numerical
simulations.


  The usual Einstein's equations is modified as a one parameter family of
equations in the framework of rainbow gravity. In this paper we derive the
modified Friedmann-Robertson-Walker (FRW) equations when the cosmological
evolution of radiation particles is taken into account. In particular, given
some specific dispersion relations, the big bounce solutions to the modified
FRW equations can be derived. Notably, to obtain a well defined rainbow metric
at the moment of the big bounce, we find it seems necessary to introduce a
cosmological constant which depends on the energy of probes as well, implying
that a universe with a positive cosmological constant more likely undergoes a
big bounce at least at this phenomenological level.


  This is the final draft, containing very minor proof-reading corrections. Let
G in SL(n,\C) be a finite subgroup and \fie: Y -> X = \C^n/G any resolution of
singularities of the quotient space. We prove that crepant exceptional prime
divisors of Y correspond one-to-one with ``junior'' conjugacy classes of G.
When n = 2 this is a version of the McKay correspondence (with irreducible
representations of G replaced by conjugacy classes). In the case n = 3, a
resolution with K_Y = 0 is known to exist by work of Roan and others; we prove
the existence of a basis of H^*(Y, \Q) by algebraic cycles in one-to-one
correspondence with conjugacy classes of G. Our treatment leaves lots of open
problems.


  Correspondence search is an essential step in rigid point cloud registration
algorithms. Most methods maintain a single correspondence at each step and
gradually remove wrong correspondances. However, building one-to-one
correspondence with hard assignments is extremely difficult, especially when
matching two point clouds with many locally similar features. This paper
proposes an optimization method that retains all possible correspondences for
each keypoint when matching a partial point cloud to a complete point cloud.
These uncertain correspondences are then gradually updated with the estimated
rigid transformation by considering the matching cost. Moreover, we propose a
new point feature descriptor that measures the similarity between local point
cloud regions. Extensive experiments show that our method outperforms the
state-of-the-art (SoTA) methods even when matching different objects within the
same category. Notably, our method outperforms the SoTA methods when
registering real-world noisy depth images to a template shape by up to 20%
performance.


  We develop a framework for rendering photographic images, taking into account
display limitations, so as to optimize perceptual similarity between the
rendered image and the original scene. We formulate this as a constrained
optimization problem, in which we minimize a measure of perceptual
dissimilarity, the Normalized Laplacian Pyramid Distance (NLPD), which mimics
the early stage transformations of the human visual system. When rendering
images acquired with higher dynamic range than that of the display, we find
that the optimized solution boosts the contrast of low-contrast features
without introducing significant artifacts, yielding results of comparable
visual quality to current state-of-the art methods with no manual intervention
or parameter settings. We also examine a variety of other display constraints,
including limitations on minimum luminance (black point), mean luminance (as a
proxy for energy consumption), and quantized luminance levels (halftoning).
Finally, we show that the method may be used to enhance details and contrast of
images degraded by optical scattering (e.g. fog).


  We develop a transductive meta-learning method that uses unlabelled instances
to improve few-shot image classification performance. Our approach combines a
regularized Mahalanobis-distance-based soft k-means clustering procedure with a
modified state of the art neural adaptive feature extractor to achieve improved
test-time classification accuracy using unlabelled data. We evaluate our method
on transductive few-shot learning tasks, in which the goal is to jointly
predict labels for query (test) examples given a set of support (training)
examples. We achieve state of the art performance on the Meta-Dataset,
mini-ImageNet and tiered-ImageNet benchmarks. All trained models and code have
been made publicly available at github.com/plai-group/simple-cnaps.


  Self-supervised learning has attracted plenty of recent research interest.
However, most works for self-supervision in speech are typically unimodal and
there has been limited work that studies the interaction between audio and
visual modalities for cross-modal self-supervision. This work (1) investigates
visual self-supervision via face reconstruction to guide the learning of audio
representations; (2) proposes an audio-only self-supervision approach for
speech representation learning; (3) shows that a multi-task combination of the
proposed visual and audio self-supervision is beneficial for learning richer
features that are more robust in noisy conditions; (4) shows that
self-supervised pretraining can outperform fully supervised training and is
especially useful to prevent overfitting on smaller sized datasets. We evaluate
our learned audio representations for discrete emotion recognition, continuous
affect recognition and automatic speech recognition. We outperform existing
self-supervised methods for all tested downstream tasks. Our results
demonstrate the potential of visual self-supervision for audio feature learning
and suggest that joint visual and audio self-supervision leads to more
informative audio representations for speech and emotion recognition.


  We calculate the Mellin moments of the next-to-next-to leading order
coefficient functions for the Drell--Yan and Higgs production cross sections.
The results can be expressed in terms of multiple finite harmonic sums of
maximal weight w = 4. Using algebraic and structural relations between harmonic
sums one finds that besides the single harmonic sums only five basic sums and
their derivatives w.r.t. the summation index contribute. This representation
reduces the large complexity being present in x-space calculations and is well
suited for fast numerical implementations.


  Unconventional orbital paramagnetism without enhancement of the density of
states was recently discovered in the nodal-line semimetal ZrSiS. Here, we
propose a novel interband mechanism of orbital paramagnetism associated with
the negative curvature of energy dispersions, which successfully explains the
observed anomalous orbital paramagnetism. This negative curvature arises from
energy fluctuations along the nodal line, inherent in realistic nodal-line
materials. Our new mechanism indicates that such orbital paramagnetism serves
as strong evidence for the existence of nodal lines not only in ZrSiS but
potentially in various other nodal-line materials as well.


  A number of numeric measures like rough inclusion functions (RIFs) are used
in general rough sets and soft computing. But these are often intrusive by
definition, and amount to making unjustified assumptions about the data. The
contamination problem is also about recognizing the domains of discourses
involved in this, specifying errors and reducing data intrusion relative to
them. In this research, weak quasi rough inclusion functions (wqRIFs) are
generalized to general granular operator spaces with scope for limiting
contamination. New algebraic operations are defined over collections of such
functions, and are studied by the present author. It is shown by her that the
algebras formed by the generalized wqRIFs are ordered hemirings with additional
operators. By contrast, the generalized rough inclusion functions lack similar
structure. This potentially contributes to improving the selection (possibly
automatic) of such functions, training methods, and reducing contamination (and
data intrusion) in applications. The underlying framework and associated
concepts are explained in some detail, as they are relatively new.


  We decipher the microscopic mechanism of the formation of tilt in the
two-dimensional Dirac cone of $8Pmmn$ borphene. In our ab-initio calculations,
we identify relevant low-energy degrees of freedom on the $8Pmmn$ lattice and
find that these atomic orbitals reside on an effective honeycomb lattice (inner
sites), while the high-energy degrees of freedom reside on the rest of the
$8Pmmn$ lattice (ridge sites). Integrating out the high-energy atomic orbitals,
gives rise to remarkably large effective further neighbor hoppings on the
coarse grained "honeycomb graph" of inner sites that determine the location and
tilt of the Dirac cone. Molecular orbitals -- that can be modified by atomic
manipulations -- are responsible for the creation of further neighbor edges on
the honeycomb graph that controls the tilt of the resulting Dirac cone. This
leads to an effective tight-binding model on a parent honeycomb graph that
facilitates numerical modeling of various effects such as
disorder/interactions/symmetry-breaking for tilted Dirac cone fermions. Since
the tilt is a proxy to spacetime metric, our result offers a robust perspective
on the fabrication of desired emergent spacetime structure and synthesis of
geometric forces at ambient conditions that are likely to enhance our control
on the movement of electrons in electric/optical devices.


  A hybrid (i.e., physics-guided data-driven) feedforward tracking controller
is proposed for systems with unmodeled linear or nonlinear dynamics. The
controller is based on the filtered basis function (FBF) approach, hence it is
called a hybrid FBF controller. It formulates the feedforward control input to
a system as a linear combination of a set of basis functions whose coefficients
are selected to minimize tracking errors. The basis functions are filtered
using a combination of two linear models to predict and minimize the tracking
errors. The first model is physics-based and remains unaltered during the
execution of the controller, while the second is data-driven and is
continuously updated during the execution of the controller. To ensure its
practicality and safe learning, the proposed hybrid FBF controller is equipped
with the ability to handle delays in data acquisition and to detect impending
instability due to its inherent data-driven feedback loop. Its effectiveness is
demonstrated via application to vibration compensation of a 3D printer with
unmodeled linear and nonlinear dynamics. Thanks to the proposed hybrid FBF
controller, the tracking accuracy of the 3D printer is significantly improved
in experiments involving high-speed printing, compared to a standard FBF
controller that does not incorporate a data-driven model. Furthermore, the
ability of the hybrid FBF controller to detect and, hence, potentially avoid
impending instability is demonstrated offline using data collected online from
experiments.


  Pen computing tools such as Tablet PC, Tablet Monitors and its various
supporting software tool offer another dimension to enhance our today's
digitally integrated and connected classroom learning environment. This paper
first reviews the various state-of-the-art pen-computing hardware and software
that have been applied in the classroom setting to introduce student-centric
learning, collaboration and making annotations and designing classroom
activities easier. We then propose a new classroom environment which is fully
equipped with Tablet devices and the supporting software tools for the goals of
1) easy electronic ink annotations with least constraints; 2) enhanced active
learning with timely feedback; 3) enhanced student collaborations and 4)
lecture recording. The classroom has been put into practical teaching and
learning environment as a pilot project in our higher learning environment.
After overcoming the initial learning curves, the environment received positive
feedbacks from the teaching faculties as well as from the students.