Abstract:
  This paper develops a construction-based dialectometry capable of identifying
previously unknown constructions and measuring the degree to which a given
construction is subject to regional variation. The central idea is to learn a
grammar of constructions (a CxG) using construction grammar induction and then
to use these constructions as features for dialectometry. This offers a method
for measuring the aggregate similarity between regional CxGs without limiting
in advance the set of constructions subject to variation. The learned CxG is
evaluated on how well it describes held-out test corpora while dialectometry is
evaluated on how well it can model regional varieties of English. Themethod is
tested using two distinct datasets: First, the International Corpus of English
representing eight outer circle varieties; Second, a web-crawled corpus
representing five inner circle varieties. Results show that themethod (1)
produces a grammar with stable quality across sub-sets of a single corpus that
is (2) capable of distinguishing between regional varieties of Englishwith a
high degree of accuracy, thus (3) supporting dialectometricmethods formeasuring
the similarity between varieties of English and (4) measuring the degree to
which each construction is subject to regional variation. This is important for
cognitive sociolinguistics because it operationalizes the idea that competition
between constructions is organized at the functional level so that
dialectometry needs to represent as much of the available functional space as
possible.


Abstract:
  Most of our knowledge about the structure of the Milky Way has come from the
study of variable stars. Among the variables, mimicking the periodic variation
of pulsating stars, are the eclipsing binaries. These stars are important in
astrophysics because they allow us to directly measure radii and masses of the
components, as well as the distance to the system, thus being useful in studies
of Galactic structure alongside pulsating RR Lyrae and Cepheids. Using the
distinguishing features of their light curves, one can identify them using a
semi-automated process. In this work, we present a strategy to search for
eclipsing variables in the inner VVV bulge across an area of 13.4 sq. deg.
within $1.68^{\rm o}<l<7.53^{\rm o}$ and $-3.73^{\rm o}<b<-1.44^{\rm o}$,
corresponding to the VVV tiles b293 to b296 and b307 to b310. We accurately
classify 212 previously unknown eclipsing binaries, including six very reddened
sources. The preliminary analysis suggests these eclipsing binaries are located
in the most obscured regions of the foreground disk and bulge of the Galaxy.
This search is therefore complementary to other variable stars searches carried
out at optical wavelengths.


Abstract:
  In this article, we consider the Euclidean dispersion problems. Let
$P=\{p_{1}, p_{2}, \ldots, p_{n}\}$ be a set of $n$ points in $\mathbb{R}^2$.
For each point $p \in P$ and $S \subseteq P$, we define $cost_{\gamma}(p,S)$ as
the sum of Euclidean distance from $p$ to the nearest $\gamma $ point in $S
\setminus \{p\}$. We define $cost_{\gamma}(S)=\min_{p \in
S}\{cost_{\gamma}(p,S)\}$ for $S \subseteq P$. In the $\gamma$-dispersion
problem, a set $P$ of $n$ points in $\mathbb{R}^2$ and a positive integer $k
\in [\gamma+1,n]$ are given. The objective is to find a subset $S\subseteq P$
of size $k$ such that $cost_{\gamma}(S)$ is maximized. We consider both
$2$-dispersion and $1$-dispersion problem in $\mathbb{R}^2$. Along with these,
we also consider $2$-dispersion problem when points are placed on a line. In
this paper, we propose a simple polynomial time $(2\sqrt 3 + \epsilon )$-factor
approximation algorithm for the $2$-dispersion problem, for any $\epsilon > 0$,
which is an improvement over the best known approximation factor $4\sqrt3$
[Amano, K. and Nakano, S. I., An approximation algorithm for the $2$-dispersion
problem, IEICE Transactions on Information and Systems, Vol. 103(3), pp.
506-508, 2020]. Next, we develop a common framework for designing an
approximation algorithm for the Euclidean dispersion problem. With this common
framework, we improve the approximation factor to $2\sqrt 3$ for the
$2$-dispersion problem in $\mathbb{R}^2$. Using the same framework, we propose
a polynomial time algorithm, which returns an optimal solution for the
$2$-dispersion problem when points are placed on a line. Moreover, to show the
effectiveness of the framework, we also propose a $2$-factor approximation
algorithm for the $1$-dispersion problem in $\mathbb{R}^2$.


Abstract:
  We study the thermodynamics of Ayon Beato-Garcia black hole and the relation
between the photon orbits and the thermodynamic phase transition of the black
hole. Then we study the interaction between the microstructures of the black
hole using Ruppeiner geometry. The radius of photon orbit and minimum impact
parameter behave non-monotonously below the critical point, which mimics the
behaviour of Hawking temperature and pressure in extended thermodynamics. Their
change during the large black hole and small black hole phase transition serve
as the order parameter, having a critical exponent $1/2$. The results show that
gravity and thermodynamics of Ayon Beato-Garcia black hole are closely related.
Following this we study the thermodynamic geometry which gives insight of the
microstructure interaction of the black hole. We find that the large black hole
phase is analogous to bosonic gas with a dominant attractive interaction, and
the small black hole phase behaves like an anyonic gas with both attractive and
repulsive interactions.


Abstract:
  We investigate how physical quantities associated with relativistic stars in
the Jordan and Einstein frames are related by the generalized disformal
transformations constructed by the scalar and vector fields within the
slow-rotation approximation. We consider the most general scalar disformal
transformation constructed by the scalar field, and by the vector field without
and with the $U(1)$ gauge symmetry, respectively. At the zeroth order of the
slow-rotation approximation, by imposing that both the metrics of the Jordan
and Einstein frames are asymptotically flat, we show that the
Arnowitt-Deser-Misner mass is frame invariant. At the first order of the
slow-rotation approximation, we discuss the disformal transformations of the
frame-dragging function, angular velocity, angular momentum, and moment of
inertia of the star. We show that the angular velocity of the star is frame
invariant in all the cases. While the angular momentum and moment of inertia
are invariant under the scalar disformal transformation, they are not under the
vector disformal transformation without and with the $U(1)$ gauge symmetry.


Abstract:
  A type-II InAs/AlAs$_{0.16}$Sb$_{0.84}$ multiple-quantum well sample is
investigated for the photoexcited carrier dynamics as a function of excitation
photon energy and lattice temperature. Time-resolved measurements are performed
using a near-infrared pump pulse, with photon energies near to and above the
band gap, probed with a terahertz probe pulse. The transient terahertz
absorption is characterized by a multi-rise, multi-decay function that captures
long-lived decay times and a metastable state of for an excess-photon energy of
$>100$ meV. For sufficient excess-photon energy, excitation of the metastable
state is followed by a transition to the long-lived states. Excitation
dependence of the long-lived states map onto a near-direct band gap ($E{_g}$)
density of states with an Urbach tail below $E{_g}$. As temperature increases,
the long-lived decay times increase $<E{_g}$, due to the increased phonon
interaction of the unintentional defect states, and by phonon stabilization of
the hot carriers $>E{_g}$. Additionally, Auger (and/or trap-assisted Auger)
scattering above the onset of the plateau may also contribute to longer
hot-carrier lifetimes. Meanwhile, the initial decay component shows strong
dependence on excitation energy and temperature, reflecting the complicated
initial transfer of energy between valence-band and defect states, indicating
methods to further prolong hot carriers for technological applications.


Abstract:
  Case-based learning is a powerful pedagogical method of creating dialogue
between theory and practice. CBL is particularly suited to executive learning
as it instigates critical discussion and draws out relevant experiences. In
this paper we used a real-world case to teach Information Security Management
to students in Management Information Systems. The real-world case is described
in a legal indictment, T-mobile USA Inc v Huawei Device USA Inc. and Huawei
Technologies Co. LTD, alleging theft of intellectual property and breaches of
contract concerning confidentiality and disclosure of sensitive information.
The incident scenario is interesting as it relates to a business asset that has
both digital and physical components that has been compromised through an
unconventional cyber-physical attack facilitated by insiders. The scenario
sparked an interesting debate among students about the scope and definition of
security incidents, the role and structure of the security unit, the utility of
compliance-based approaches to security, and the inadequate use of threat
intelligence in modern security strategies.


Abstract:
  This work explores the novel idea of learning a submodular scoring function
to improve the specificity/selectivity of existing feature attribution methods.
Submodular scores are natural for attribution as they are known to accurately
model the principle of diminishing returns. A new formulation for learning a
deep submodular set function that is consistent with the real-valued
attribution maps obtained by existing attribution methods is proposed. This
formulation not only ensures that the scores for the heat maps that include the
highly attributed features across the existing methods are high, but also that
the score saturates even for the most specific heat map. The final attribution
value of a feature is then defined as the marginal gain in the induced
submodular score of the feature in the context of other highly attributed
features, thus decreasing the attribution of redundant yet discriminatory
features. Experiments on multiple datasets illustrate that the proposed
attribution method achieves higher specificity while not degrading the
discriminative power.


Abstract:
  In this paper, we theoretically propose a new hashing scheme to establish the
sparse Fourier transform in high-dimensional space. The estimation of the
algorithm complexity shows that this sparse Fourier transform can overcome the
curse of dimensionality. To the best of our knowledge, this is the first
polynomial-time algorithm to recover the high-dimensional continuous
frequencies.


Abstract:
  In [1] and [2] we developed a duality theory for multilinear norm
inequalities. This duality theory is underpinned by a disentanglement theorem,
which states that estimates on a weighted geometric mean over (convex) families
of functions can be disentangled into quantitatively linked estimates on each
family separately.
  The disentanglement theorem was proved in [1] via minimax theory together
with weak*-compactness arguments in the space of finitely additive measures,
and an application of the Yosida-Hewitt theory of such measures.
  In this paper we provide an alternate, rather elementary approach to the
disentanglement theorem: we first prove a finite-dimensional case via
perturbation and strong compactness, and then, building on that, the general
infinite-dimensional case via the finite intersection property and weak
compactness.


Abstract:
  Due to the vast compositional space of multi-principal element alloys
(MPEAs), the rational design of MPEAs for optimized microstructures is
difficult. Therefore, a high-throughput first-principles study of
Mo-V-Nb-Ti-Zr, a refractory MPEA, was conducted to gain insights into the
underlying microstructures. Using Monte-Carlo simulations powered by cluster
expansion, we uncover the principles governing the MPEA's microstructures
across a large compositional space that includes non-equiatomic compositions
and encompasses the constituent binaries, ternaries, and quaternaries. In the
spirit of Hume-Rothery rules for complete solid solubility, we present a
quantitative expression for predicting solid solution formation from the
composition. Within a consistent framework, our results reproduce the
microstructural observations (solid solution vs. segregation) from numerous
experiments and provide microstructural predictions for unexplored regions in
the compositional space. Our work illuminates the MPEA's microstructures in
terms of the separation and clustering tendencies of the elements, presenting a
set of simple but powerful design principles for future experiments to
rationally design MPEAs with the desired microstructures for superior
mechanical properties.


Abstract:
  Many-Worlds quantum mechanics differs from standard quantum mechanics in that
in Many-Worlds, the wave function is a relative density of universes in the
multiverse amplitude rather than a probability amplitude. This means that in
Many-Worlds, the Born frequencies are approached rather than given a priori.
Thus in Many-Worlds the rate of approach to the final frequencies can be
calculated and compared with observation. I use Many-Worlds to derive the rate
of approach in the double slit experiment, and show that it agrees with
observation. Standard quantum theory has never been used to derive an approach
formula because it cannot be so used, as has been tacitly acknowledged for 70
years.


Abstract:
  Lung ultrasound (LUS) imaging is used to assess lung abnormalities, including
the presence of B-line artefacts due to fluid leakage into the lungs caused by
a variety of diseases. However, manual detection of these artefacts is
challenging. In this paper, we propose a novel methodology to automatically
detect and localize B-lines in LUS videos using deep neural networks trained
with weak labels. To this end, we combine a convolutional neural network (CNN)
with a long short-term memory (LSTM) network and a temporal attention
mechanism. Four different models are compared using data from 60 patients.
Results show that our best model can determine whether one-second clips contain
B-lines or not with an F1 score of 0.81, and extracts a representative frame
with B-lines with an accuracy of 87.5%.


Abstract:
  In classification with a reject option, the classifier is allowed in
uncertain cases to abstain from prediction. The classical cost-based model of a
reject option classifier requires the cost of rejection to be defined
explicitly. An alternative bounded-improvement model, avoiding the notion of
the reject cost, seeks for a classifier with a guaranteed selective risk and
maximal cover. We coin a symmetric definition, the bounded-coverage model,
which seeks for a classifier with minimal selective risk and guaranteed
coverage. We prove that despite their different formulations the three
rejection models lead to the same prediction strategy: a Bayes classifier
endowed with a randomized Bayes selection function. We define a notion of a
proper uncertainty score as a scalar summary of prediction uncertainty
sufficient to construct the randomized Bayes selection function. We propose two
algorithms to learn the proper uncertainty score from examples for an arbitrary
black-box classifier. We prove that both algorithms provide Fisher consistent
estimates of the proper uncertainty score and we demonstrate their efficiency
on different prediction problems including classification, ordinal regression
and structured output classification.


Abstract:
  Graph neural networks (GNN) have been shown to provide substantial
performance improvements for representing and modeling atomistic materials
compared with descriptor-based machine-learning models. While most existing GNN
models for atomistic predictions are based on atomic distance information, they
do not explicitly incorporate bond angles, which are critical for
distinguishing many atomic structures. Furthermore, many material properties
are known to be sensitive to slight changes in bond angles. We present an
Atomistic Line Graph Neural Network (ALIGNN), a GNN architecture that performs
message passing on both the interatomic bond graph and its line graph
corresponding to bond angles. We demonstrate that angle information can be
explicitly and efficiently included, leading to improved performance on
multiple atomistic prediction tasks. We use ALIGNN models for predicting 52
solid-state and molecular properties available in the JARVIS-DFT, Materials
project, and QM9 databases. ALIGNN can outperform some previously reported GNN
models on atomistic prediction tasks by up to 85 % in accuracy with better or
comparable model training speed.


Abstract:
  Low congestion shortcuts, introduced by Ghaffari and Haeupler (SODA 2016),
provide a unified framework for global optimization problems in the congest
model of distributed computing. Roughly speaking, for a given graph $G$ and a
collection of vertex-disjoint connected subsets $S_1,\ldots, S_\ell \subseteq
V(G)$, $(c,d)$ low-congestion shortcuts augment each subgraph $G[S_i]$ with a
subgraph $H_i \subseteq G$ such that: (i) each edge appears on at most $c$
subgraphs (congestion bound), and (ii) the diameter of each subgraph $G[S_i]
\cup H_i$ is bounded by $d$ (dilation bound). It is desirable to compute
shortcuts of small congestion and dilation as these quantities capture the
round complexity of many global optimization problems in the congest model. For
$n$-vertex graphs with constant diameter $D=O(1)$, Elkin (STOC 2004) presented
an (implicit) shortcuts lower bound with
$c+d=\widetilde{\Omega}(n^{(D-2)/(2D-2)})$. A nearly matching upper bound,
however, was only recently obtained for $D \in \{3,4\}$ by Kitamura et al.
(DISC 2019).
  In this work, we resolve the long-standing complexity gap of shortcuts in
constant diameter graphs, originally posed by Lotker et al. (PODC 2001). We
present new shortcut constructions which match, up to poly-logarithmic terms,
the lower bounds of Das-Sarma et al. As a result, we provide improved and
existentially optimal algorithms for several network optimization tasks in
constant diameter graphs, including MST, $(1+\epsilon)$-approximate minimum
cuts and more.


Abstract:
  We analyze the Prandtl number (Pr) dependence of spectra and fluxes of
kinetic energy, as well as the energy injection rates and dissipation rates, of
turbulent thermal convection using numerical data. As expected, for a flow with
$ \mathrm{Pr} \lessapprox 1 $, the inertial-range kinetic energy flux is
constant, and the kinetic energy spectrum is Kolmogorov-like ($ k^{-5/3} $).
More importantly, we show that the amplitudes of the kinetic energy fluxes and
spectra and those of structure functions increase with the decrease of Pr, thus
exhibiting stronger nonlinearity for flows with small Prandtl numbers.
Consistent with these observations, the kinetic energy injection rates and the
dissipation rates too increase with the decrease of Pr. Our results are in
agreement with earlier studies that report the Reynolds number to be a
decreasing function of Prandtl number in turbulent convection. On the other
hand, the tail of the probability distributions of the local heat flux grows
with the increase of Pr, indicating increased fluctuations in the local heat
flux with Pr.


Abstract:
  We consider the problem of learning a tree-structured Ising model from data,
such that subsequent predictions computed using the model are accurate.
Concretely, we aim to learn a model such that posteriors $P(X_i|X_S)$ for small
sets of variables $S$ are accurate. Since its introduction more than 50 years
ago, the Chow-Liu algorithm, which efficiently computes the maximum likelihood
tree, has been the benchmark algorithm for learning tree-structured graphical
models. A bound on the sample complexity of the Chow-Liu algorithm with respect
to the prediction-centric local total variation loss was shown in [BK19]. While
those results demonstrated that it is possible to learn a useful model even
when recovering the true underlying graph is impossible, their bound depends on
the maximum strength of interactions and thus does not achieve the
information-theoretic optimum. In this paper, we introduce a new algorithm that
carefully combines elements of the Chow-Liu algorithm with tree metric
reconstruction methods to efficiently and optimally learn tree Ising models
under a prediction-centric loss. Our algorithm is robust to model
misspecification and adversarial corruptions. In contrast, we show that the
celebrated Chow-Liu algorithm can be arbitrarily suboptimal.


Abstract:
  In this paper, we present a complete proof of the so-called First On-Shell
Theorem that determines dynamical content of the unfolded equations for free
symmetric massless fields of arbitrary integer spin in any dimension and
arbitrary integer or half-integer spin in four dimensions. This is achieved by
calculation of the respective $\sigma_-$ cohomology both in the tensor language
in Minkowski space of any dimension and in terms of spinors in $AdS_4$. In the
$d$-dimensional case $H^p(\sigma_-)$ is computed for any $p$ and interpretation
of $H^p(\sigma_-)$ is given both for the original Fronsdal system and for the
associated systems of higher form fields.


Abstract:
  We present a drift kinetic model for the free expansion of a thermal plasma
out of a magnetic nozzle. This problem relates to plasma space propulsion
systems, natural environments such as the solar wind, and end losses from the
expander region of mirror magnetically confined fusion concepts such as the Gas
Dynamic Trap. The model incorporates trapped and passing orbit types
encountered in the mirror expander geometry and maps to an upstream thermal
distribution. This boundary condition and quasineutrality require the
generation of an ambipolar potential drop of $\sim5 T_e/e$, forming a thermal
barrier for the electrons. The model for the electron and ion velocity
distributions and fluid moments is confirmed with data from a fully kinetic
simulation. Finally, the model is extended to account for a population of fast
sloshing ions arising from neutral beam heating within a magnetic mirror, again
resulting in good agreement with a corresponding kinetic simulation.


Abstract:
  Let $\mathcal{F}$ be a foliation on a projective manifold $X$ with
$-K_{\mathcal{F}}$ nef. Assume that either $\mathcal{F}$ is regular, or it has
a compact leaf. We prove that there is a locally trivial fibration $f\colon
X\to Y$, and a foliation $\mathcal{G}$ on $Y$ with $K_{\mathcal{G}} \equiv 0$,
such that $\mathcal{F} = f^{-1}(\mathcal{G})$.


Abstract:
  We propose a roadmap for leveraging the tremendous opportunities the Internet
of Things (IoT) has to offer. We argue that the combination of the recent
advances in service computing and IoT technology provide a unique framework for
innovations not yet envisaged, as well as the emergence of yet-to-be-developed
IoT applications. This roadmap covers: emerging novel IoT services,
articulation of major research directions, and suggestion of a roadmap to guide
the IoT and service computing community to address key IoT service challenges.


Abstract:
  This paper is concerned with a diffuse interface model called as
Navier-Stokes/Cahn-Hilliard system. This model is usually used to describe the
motion of immiscible two-phase flow with diffusion interface. For the periodic
boundary value problem of this system in torus $\mathbb{T}^3$, we prove that
there exists a global unique strong solution near the phase separation state,
which means no vacuum, shock wave, mass concentration, interface collision and
rupture will be developed in finite time. Furthermore, we established the large
time behavior of these global strong solution of this system. In particular, we
find that the phase field decays algebraically to the phase separation state.


Abstract:
  Recently, several works have addressed modeling of 3D shapes using deep
neural networks to learn implicit surface representations. Up to now, the
majority of works have concentrated on reconstruction quality, paying little or
no attention to model size or training time. This work proposes LightSAL, a
novel deep convolutional architecture for learning 3D shapes; the proposed work
concentrates on efficiency both in network training time and resulting model
size. We build on the recent concept of Sign Agnostic Learning for training the
proposed network, relying on signed distance fields, with unsigned distance as
ground truth. In the experimental section of the paper, we demonstrate that the
proposed architecture outperforms previous work in model size and number of
required training iterations, while achieving equivalent accuracy. Experiments
are based on the D-Faust dataset that contains 41k 3D scans of human shapes.
The proposed model has been implemented in PyTorch.


Abstract:
  We numerically study by lattice Boltzmann simulations the rheological
properties of an active emulsion made of a suspension of an active polar gel
embedded in an isotropic passive background. We find that the hexatic
equilibrium configuration of polar droplets is highly sensitive to both active
injection and external forcing and may either lead to asymmetric unidirectional
states which break top-bottom symmetry or symmetric ones. In this latter case,
for large enough activity, the system develops a shear-thickening regime at low
shear rates. Importantly, for larger external forcing a regime with stable
negative effective viscosity is found. Moreover, at intermediate activity a
region of multistability is encountered and we show that a maximum entropy
production principle holds in selecting the most favorable state.


Abstract:
  We show that minimal-surface non-Euclidean elastic plates share the same
low-energy effective theory as Haldane's dimerized quantum spin chain. As a
result, such elastic plates support fractional excitations, which take the form
of charge-$1/2$ solitons between degenerate states of the plates, in strong
analogy to their quantum counterpart. These fractional solitons exhibit
properties similar to fractional excitations in quantum fractional topological
states, including deconfinement and braiding, as well as unique new features
such as holographic properties and diode-like nonlinear response, demonstrating
great potentials for applications as mechanical metamaterials.


Abstract:
  In this note we make progress toward a conjecture of Durham--Fanoni--Vlamis,
showing that every infinite-type surface with finite-invariance index 1 and no
nondisplaceable compact subsurfaces fails to have a good curve graph, that is,
a connected graph where vertices represent homotopy classes of essential simple
closed curves and where the natural mapping class group action has infinite
diameter orbits. Our arguments use tools developed by Mann--Rafi in their study
of the coarse geometry of big mapping class groups.


Abstract:
  Thermal images reveal medically important physiological information about
human stress, signs of inflammation, and emotional mood that cannot be seen on
visible images. Providing a method to generate thermal faces from visible
images would be highly valuable for the telemedicine community in order to show
this medical information. To the best of our knowledge, there are limited works
on visible-to-thermal (VT) face translation, and many current works go the
opposite direction to generate visible faces from thermal surveillance images
(TV) for law enforcement applications. As a result, we introduce favtGAN, a VT
GAN which uses the pix2pix image translation model with an auxiliary sensor
label prediction network for generating thermal faces from visible images.
Since most TV methods are trained on only one data source drawn from one
thermal sensor, we combine datasets from faces and cityscapes. These combined
data are captured from similar sensors in order to bootstrap the training and
transfer learning task, especially valuable because visible-thermal face
datasets are limited. Experiments on these combined datasets show that favtGAN
demonstrates an increase in SSIM and PSNR scores of generated thermal faces,
compared to training on a single face dataset alone.


Abstract:
  Mn ions' doping site and valence were studied in PbTiO$_{3}$ (PT) with the
native vacancy defects by the first-principles calculations. Firstly, the
native vacancy defects of Pb, O and Ti in PT were investigated and it was found
that Pb vacancy is preferred to others. And then the growth of Mn doped PT
should be preferred to Mn ion substituting for an A-site Pb ion with +3 valence
when Pb is deficient under equilibrium conditions driven solely by minimization
of the formation energy, and this could result in a larger lattice distortion
of PT. In addition, when Mn enters the Pb site, the electronegativity of O
becomes weaker which makes the domain movement easier in PT to improve the
performance of PT, while Mn ion substitution for a B-site Ti ion is the
opposite.


Abstract:
  Single image super-resolution (SISR) aims to obtain a high-resolution output
from one low-resolution image. Currently, deep learning-based SISR approaches
have been widely discussed in medical image processing, because of their
potential to achieve high-quality, high spatial resolution images without the
cost of additional scans. However, most existing methods are designed for
scale-specific SR tasks and are unable to generalise over magnification scales.
In this paper, we propose an approach for medical image arbitrary-scale
super-resolution (MIASSR), in which we couple meta-learning with generative
adversarial networks (GANs) to super-resolve medical images at any scale of
magnification in (1, 4]. Compared to state-of-the-art SISR algorithms on
single-modal magnetic resonance (MR) brain images (OASIS-brains) and
multi-modal MR brain images (BraTS), MIASSR achieves comparable fidelity
performance and the best perceptual quality with the smallest model size. We
also employ transfer learning to enable MIASSR to tackle SR tasks of new
medical modalities, such as cardiac MR images (ACDC) and chest computed
tomography images (COVID-CT). The source code of our work is also public. Thus,
MIASSR has the potential to become a new foundational pre-/post-processing step
in clinical image analysis tasks such as reconstruction, image quality
enhancement, and segmentation.


Abstract:
  We propose a new perspective on video understanding by casting the video
recognition problem as an image recognition task. We show that an image
classifier alone can suffice for video understanding without temporal modeling.
Our approach is simple and universal. It composes input frames into a super
image to train an image classifier to fulfill the task of action recognition,
in exactly the same way as classifying an image. We prove the viability of such
an idea by demonstrating strong and promising performance on four public
datasets including Kinetics400, Something-to-something (V2), MiT and Jester,
using a recently developed vision transformer. We also experiment with the
prevalent ResNet image classifiers in computer vision to further validate our
idea. The results on Kinetics400 are comparable to some of the best-performed
CNN approaches based on spatio-temporal modeling. our code and models will be
made available at https://github.com/IBM/sifar-pytorch.


Abstract:
  Competing risks data are common in medical studies, and the sub-distribution
hazard (SDH) ratio is considered an appropriate measure. However, because the
limitations of hazard itself are not easy to interpret clinically and because
the SDH ratio is valid only under the proportional SDH assumption, this article
introduced an alternative index under competing risks, named restricted mean
time lost (RMTL). Several test procedures were also constructed based on RMTL.
First, we introduced the definition and estimation of RMTL based on
Aalen-Johansen cumulative incidence functions. Then, we considered several
combined tests based on the SDH and the RMTL difference (RMTLd). The
statistical properties of the methods are evaluated using simulations and are
applied to two examples. The type I errors of combined tests are close to the
nominal level. All combined tests show acceptable power in all situations. In
conclusion, RMTL can meaningfully summarize treatment effects for clinical
decision making, and three combined tests have robust power under various
conditions, which can be considered for statistical inference in real data
analysis.


Abstract:
  We consider the mean-field Zero-Range process in the regime where the
potential function $r$ is increasing to infinity at sublinear speed, and the
density of particles is bounded. We determine the mixing time of the system,
and establish cutoff. We also prove that the Poincar\'e constant is bounded
away from zero and infinity. This mean-field estimate extends to arbitrary
geometries via a comparison argument. Our proof uses the path-coupling method
of Bubley and Dyer and stochastic calculus.


Abstract:
  In this paper, we introduce the cohomology theory of $\mathcal{O}$-operators
on Hom-associative algebras. This cohomology can also be viewed as the
Hochschild cohomology of a certain Hom-associative algebra with coefficients in
a suitable bimodule. Next, we study infinitesimal and formal deformations of an
$\mathcal{O}$-operator and show that they are governed by the above-defined
cohomology. Furthermore, the notion of Nijenhuis elements associated with an
$\mathcal{O}$-operator is introduced to characterize trivial infinitesimal
deformations.


Abstract:
  Hyperspectral imaging (HSI) unlocks the huge potential to a wide variety of
applications relied on high-precision pathology image segmentation, such as
computational pathology and precision medicine. Since hyperspectral pathology
images benefit from the rich and detailed spectral information even beyond the
visible spectrum, the key to achieve high-precision hyperspectral pathology
image segmentation is to felicitously model the context along high-dimensional
spectral bands. Inspired by the strong context modeling ability of
transformers, we hereby, for the first time, formulate the contextual feature
learning across spectral bands for hyperspectral pathology image segmentation
as a sequence-to-sequence prediction procedure by transformers. To assist
spectral context learning procedure, we introduce two important strategies: (1)
a sparsity scheme enforces the learned contextual relationship to be sparse, so
as to eliminates the distraction from the redundant bands; (2) a spectral
normalization, a separate group normalization for each spectral band, mitigates
the nuisance caused by heterogeneous underlying distributions of bands. We name
our method Spectral Transformer (SpecTr), which enjoys two benefits: (1) it has
a strong ability to model long-range dependency among spectral bands, and (2)
it jointly explores the spatial-spectral features of HSI. Experiments show that
SpecTr outperforms other competing methods in a hyperspectral pathology image
segmentation benchmark without the need of pre-training. Code is available at
https://github.com/hfut-xc-yun/SpecTr.


Abstract:
  The ability to identify stock market trends has obvious advantages for
investors. Buying stock on an upward trend (as well as selling it in case of
downward movement) results in profit. Accordingly, the start and end-points of
the trend are the optimal points for entering and leaving the market. The
research concentrates on recognizing stock market long-term upward and downward
trends. The key results are obtained with the use of gradient boosting
algorithms, XGBoost in particular. The raw data is represented by time series
with basic stock market quotes with periods labelled by experts as Trend or
Flat. The features are then obtained via various data transformations, aiming
to catch implicit factors resulting in a change of stock direction. Modelling
is done in two stages: stage one aims to detect endpoints of tendencies (i.e.
sliding windows), stage two recognizes the tendency itself inside the window.
The research addresses such issues as imbalanced datasets and contradicting
labels, as well as the need for specific quality metrics to keep up with
practical applicability. The model can be used to design an investment strategy
though further research in feature engineering and fine calibration is
required.This paper is the full text of the research, presented at the 20th
International Conference on Artificial Intelligence and Soft Computing Web
System (ICAISC 2021)


Abstract:
  Humans perceive and construct the surrounding world as an arrangement of
simple parametric models. In particular, man-made environments commonly consist
of volumetric primitives such as cuboids or cylinders. Inferring these
primitives is an important step to attain high-level, abstract scene
descriptions. Previous approaches directly estimate shape parameters from a 2D
or 3D input, and are only able to reproduce simple objects, yet unable to
accurately parse more complex 3D scenes. In contrast, we propose a robust
estimator for primitive fitting, which can meaningfully abstract real-world
environments using cuboids. A RANSAC estimator guided by a neural network fits
these primitives to 3D features, such as a depth map. We condition the network
on previously detected parts of the scene, thus parsing it one-by-one. To
obtain 3D features from a single RGB image, we additionally optimise a feature
extraction CNN in an end-to-end manner. However, naively minimising
point-to-primitive distances leads to large or spurious cuboids occluding parts
of the scene behind. We thus propose an occlusion-aware distance metric
correctly handling opaque scenes. The proposed algorithm does not require
labour-intensive labels, such as cuboid annotations, for training. Results on
the challenging NYU Depth v2 dataset demonstrate that the proposed algorithm
successfully abstracts cluttered real-world 3D scene layouts.


Abstract:
  The novel COVID-19 is a global pandemic disease overgrowing worldwide.
Computer-aided screening tools with greater sensitivity is imperative for
disease diagnosis and prognosis as early as possible. It also can be a helpful
tool in triage for testing and clinical supervision of COVID-19 patients.
However, designing such an automated tool from non-invasive radiographic images
is challenging as many manually annotated datasets are not publicly available
yet, which is the essential core requirement of supervised learning schemes.
This article proposes a 3D Convolutional Neural Network (CNN)-based
classification approach considering both the inter- and intra-slice spatial
voxel information. The proposed system is trained in an end-to-end manner on
the 3D patches from the whole volumetric CT images to enlarge the number of
training samples, performing the ablation studies on patch size determination.
We integrate progressive resizing, segmentation, augmentations, and
class-rebalancing to our 3D network. The segmentation is a critical
prerequisite step for COVID-19 diagnosis enabling the classifier to learn
prominent lung features while excluding the outer lung regions of the CT scans.
We evaluate all the extensive experiments on a publicly available dataset,
named MosMed, having binary- and multi-class chest CT image partitions. Our
experimental results are very encouraging, yielding areas under the ROC curve
of 0.914 and 0.893 for the binary- and multi-class tasks, respectively,
applying 5-fold cross-validations. Our method's promising results delegate it
as a favorable aiding tool for clinical practitioners and radiologists to
assess COVID-19.


Abstract:
  The orthogonal derivative is defined as a limit of an integral whose kernel
contains an orthogonal polynomial with its measure. When in practice no limit
is taken, it means that the accuracy of the derivative depends on the second
derivative of the given function. Liptaj shows that it is possible to define a
kernel in such a way that the accuracy depends on a higher derivative at your
own choice. The accuracy is therefore much greater than with the orthogonal
derivative. However Diekema and Koornwinder find a similar extension starting
directly from of the orthogonal derivative. The new kernel is not orthogonal
for order greater then one. The transfer function for this new derivative is
given.


Abstract:
  We address the problem of constructing accurate mathematical models of the
dynamics of complex systems projected on a collective variable. To this aim we
introduce a conceptually simple yet effective algorithm for estimating the
parameters of Langevin and Fokker-Planck equations from a set of short,
possibly out-of-equilibrium molecular dynamics trajectories, obtained for
instance from transition path sampling or as relaxation from high free-energy
configurations. The approach maximizes the model likelihood based on any
explicit expression of the short-time propagator, hence it can be applied to
different evolution equations. We demonstrate the numerical efficiency and
robustness of the algorithm on model systems, and we apply it to reconstruct
the projected dynamics of pairs of C60 and C240 fullerene molecules in explicit
water. Our methodology allows reconstructing the accurate thermodynamics and
kinetics of activated processes, namely free energy landscapes, diffusion
coefficients and kinetic rates. Compared to existing enhanced sampling methods,
we directly exploit short unbiased trajectories, at a competitive computational
cost.


Abstract:
  In recent years, storing large volumes of data on distributed devices has
become commonplace. Applications involving sensors, for example, capture data
in different modalities including image, video, audio, GPS and others. Novel
algorithms are required to learn from this rich distributed data. In this
paper, we present consensus based multi-layer perceptrons for
resource-constrained devices. Assuming nodes (devices) in the distributed
system are arranged in a graph and contain vertically partitioned data, the
goal is to learn a global function that minimizes the loss. Each node learns a
feed-forward multi-layer perceptron and obtains a loss on data stored locally.
It then gossips with a neighbor, chosen uniformly at random, and exchanges
information about the loss. The updated loss is used to run a back propagation
algorithm and adjust weights appropriately. This method enables nodes to learn
the global function without exchange of data in the network. Empirical results
reveal that the consensus algorithm converges to the centralized model and has
performance comparable to centralized multi-layer perceptrons and tree-based
algorithms including random forests and gradient boosted decision trees.


Abstract:
  This paper investigates whether computer usage profiles comprised of
process-, network-, mouse-, and keystroke-related events are unique and
consistent over time in a naturalistic setting, discussing challenges and
opportunities of using such profiles in applications of continuous
authentication. We collected ecologically-valid computer usage profiles from 31
MS Windows 10 computer users over 8 weeks and submitted this data to
comprehensive machine learning analysis involving a diverse set of online and
offline classifiers. We found that: (i) profiles were mostly consistent over
the 8-week data collection period, with most (83.9%) repeating computer usage
habits on a daily basis; (ii) computer usage profiling has the potential to
uniquely characterize computer users (with a maximum F-score of 99.90%); (iii)
network-related events were the most relevant features to accurately recognize
profiles (95.69% of the top features distinguishing users were
network-related); and (iv) binary models were the most well-suited for profile
recognition, with better results achieved in the online setting compared to the
offline setting (maximum F-score of 99.90% vs. 95.50%).


Abstract:
  As the demand for vehicles continues to outpace construction of new roads, it
becomes imperative we implement strategies that improve utilization of existing
transport infrastructure. Traffic sensors form a crucial part of many such
strategies, giving us valuable insights into road utilization. However, due to
cost and lead time associated with installation and maintenance of traffic
sensors, municipalities and traffic authorities look toward cheaper and more
scalable alternatives. Due to their ubiquitous nature and wide global
deployment, cellular networks offer one such alternative. In this paper we
present a novel method for traffic flow estimation using standardized LTE/4G
radio frequency performance measurement counters. The problem is cast as a
supervised regression task using both classical and deep learning methods. We
further apply transfer learning to compensate that many locations lack traffic
sensor data that could be used for training. We show that our approach benefits
from applying transfer learning to generalize the solution not only in time but
also in space (i.e., various parts of the city). The results are very promising
and, unlike competing solutions, our approach utilizes aggregate LTE radio
frequency counter data that is inherently privacy-preserving, readily
available, and scales globally without any additional network impact.


Abstract:
  We present a new approach for learning unsupervised node representations in
community graphs. We significantly extend the Interferometric Graph Transform
(IGT) to community labeling: this non-linear operator iteratively extracts
features that take advantage of the graph topology through demodulation
operations. An unsupervised feature extraction step cascades modulus
non-linearity with linear operators that aim at building relevant invariants
for community labeling. Via a simplified model, we show that the IGT
concentrates around the E-IGT: those two representations are related through
some ergodicity properties. Experiments on community labeling tasks show that
this unsupervised representation achieves performances at the level of the
state of the art on the standard and challenging datasets Cora, Citeseer,
Pubmed and WikiCS.


Abstract:
  We quantify the information content of the non-linear matter power spectrum,
the halo mass function, and the void size function, using the Quijote $N$-body
simulations. We find that these three statistics exhibit very different
degeneracies amongst the cosmological parameters, and thus the combination of
all three probes enables the breaking of degeneracies, in turn yielding
remarkably tight constraints. We perform a Fisher analysis using the full
covariance matrix, including all auto- and cross-correlations, finding that
this increases the information content for neutrino mass compared to a
correlation-free analysis. The multiplicative improvement of the constraints on
the cosmological parameters obtained by combining all three probes compared to
using the power spectrum alone are: 137, 5, 8, 20, 10, and 43, for $\Omega_m$,
$\Omega_b$, $h$, $n_s$, $\sigma_8$, and $M_\nu$, respectively. The marginalized
error on the sum of the neutrino masses is $\sigma(M_\nu)=0.018\,{\rm eV}$ for
a cosmological volume of $1\,(h^{-1}{\rm Gpc})^3$, using $k_{\max}=0.5\,h{\rm
Mpc}^{-1}$, and without CMB priors. We note that this error is an underestimate
insomuch as we do not consider super-sample covariance, baryonic effects, and
realistic survey noises and systematics. On the other hand, it is an
overestimate insomuch as our cuts and binning are suboptimal due to
restrictions imposed by the simulation resolution. Given upcoming galaxy
surveys will observe volumes spanning $\sim 100\,(h^{-1}{\rm Gpc})^3$, this
presents a promising new avenue to measure neutrino mass without being
restricted by the need for accurate knowledge of the optical depth, which is
required for CMB-based measurements. Furthermore, the improved constraints on
other cosmological parameters, notably $\Omega_m$, may also be competitive with
CMB-based measurements.


Abstract:
  We study how an observationally-motivated, metallicity-dependent initial mass
function (IMF) affects the feedback budget and observables of an ultra-faint
dwarf galaxy. We model the evolution of a low-mass ($\approx 8 \, \times \,
10^{8} \, \rm M_{\odot}$) dark matter halo with cosmological, zoomed
hydrodynamical simulations capable of resolving individual supernovae
explosions. We complement the EDGE galaxy formation model from Agertz et al.
(2020) with a new prescription for IMF variations according to Geha et al.
(2013). At the low metallicities typical of faint dwarf galaxies, the IMF
becomes top-heavy, increasing the efficiency of supernova and photo-ionization
feedback in regulating star formation. This results in a 100-fold reduction of
the final stellar mass of the dwarf compared to a canonical IMF, at fixed
dynamical mass. The increase in the feedback budget is nonetheless met by
increased metal production from more numerous massive stars, leading to nearly
constant iron content at $z=0$. A metallicity-dependent IMF therefore provides
a mechanism to produce low-mass ($\rm M_{\star}\sim 10^3 \rm M_{\odot}$), yet
enriched ($\rm [Fe/H]\approx -2$) field dwarf galaxies, thus opening a
self-consistent avenue to populate the plateau in $\rm [Fe/H]$ at the faintest
end of the mass-metallicity relation.


Abstract:
  Unsupervised domain adaptation (UDA) and semi-supervised learning (SSL) are
two typical strategies to reduce expensive manual annotations in machine
learning. In order to learn effective models for a target task, UDA utilizes
the available labeled source data, which may have different distributions from
unlabeled samples in the target domain, while SSL employs few manually
annotated target samples. Although UDA and SSL are seemingly very different
strategies, we find that they are closely related in terms of task objectives
and solutions, and SSL is a special case of UDA problems. Based on this
finding, we further investigate whether SSL methods work on UDA tasks. By
adapting eight representative SSL algorithms on UDA benchmarks, we show that
SSL methods are strong UDA learners. Especially, state-of-the-art SSL methods
significantly outperform existing UDA methods on the challenging UDA benchmark
of DomainNet, and state-of-the-art UDA methods could be further enhanced with
SSL techniques. We thus promote that SSL methods should be employed as
baselines in future UDA studies and expect that the revealed relationship
between UDA and SSL could shed light on future UDA development. Codes are
available at \url{https://github.com/YBZh}.


Abstract:
  We present BERTGEN, a novel generative, decoder-only model which extends BERT
by fusing multimodal and multilingual pretrained models VL-BERT and M-BERT,
respectively. BERTGEN is auto-regressively trained for language generation
tasks, namely image captioning, machine translation and multimodal machine
translation, under a multitask setting. With a comprehensive set of
evaluations, we show that BERTGEN outperforms many strong baselines across the
tasks explored. We also show BERTGEN's ability for zero-shot language
generation, where it exhibits competitive performance to supervised
counterparts. Finally, we conduct ablation studies which demonstrate that
BERTGEN substantially benefits from multi-tasking and effectively transfers
relevant inductive biases from the pre-trained models.


Abstract:
  Quantum Computing is a new paradigm that enables several advances which are
impossible using classical technology. With the rise of quantum computers, the
software is also invited to change so that it can better fit this new
computation way. However, although a lot of research is being conducted in the
quantum computing field, it is still scarce studies about the differences of
the software and software engineering in this new context. Therefore, this
article presents a systematic mapping study to present a wide review on the
particularities and characteristics of software that are developed for quantum
computers. A total of 24 papers were selected using digital libraries with the
objective of answering three research questions elaborated in the conduct of
this research.


Abstract:
  We introduce the task of historical text summarisation, where documents in
historical forms of a language are summarised in the corresponding modern
language. This is a fundamentally important routine to historians and digital
humanities researchers but has never been automated. We compile a high-quality
gold-standard text summarisation dataset, which consists of historical German
and Chinese news from hundreds of years ago summarised in modern German or
Chinese. Based on cross-lingual transfer learning techniques, we propose a
summarisation model that can be trained even with no cross-lingual (historical
to modern) parallel data, and further benchmark it against state-of-the-art
algorithms. We report automatic and human evaluations that distinguish the
historic to modern language summarisation task from standard cross-lingual
summarisation (i.e., modern to modern language), highlight the distinctness and
value of our dataset, and demonstrate that our transfer learning approach
outperforms standard cross-lingual benchmarks on this task.


Abstract:
  Analysing dependent risks is an important task for insurance companies. A
dependency is reflected in the fact that information about one random variable
provides information about the likely distribution of values of another random
variable. Insurance companies in particular must investigate such dependencies
between different lines of business and the effects that an extreme loss event,
such as an earthquake or hurricane, has across multiple lines of business
simultaneously. Copulas provide a popular model-based approach to analysing the
dependency between risks, and the coefficient of tail dependence is a measure
of dependence for extreme losses. Besides commonly used empirical estimators
for estimating the tail dependence coefficient, copula fitting can lead to
estimation of such coefficients directly or can verify their existence.
Generally, a range of copula models is available to fit a data set well,
leading to multiple different tail dependence results; a method based on
Bayesian model averaging is designed to obtain a unified estimate of tail
dependence. In this article, this model-based coefficient estimation method is
illustrated through a variety of copula fitting approaches and results are
presented for several simulated data sets and also a real general insurance
loss data set.


Abstract:
  Sesqui-pushout (SqPO) rewriting along non-linear rules and for monic matches
is well-known to permit the modeling of fusing and cloning of vertices and
edges, yet to date, no construction of a suitable concurrency theorem was
available. The lack of such a theorem, in turn, rendered compositional
reasoning for such rewriting systems largely infeasible. We develop in this
paper a suitable concurrency theorem for non-linear SqPO-rewriting in
categories that are quasi-topoi (subsuming the example of adhesive categories)
and with matches required to be regular monomorphisms of the given category.
Our construction reveals an interesting "backpropagation effect" in computing
rule compositions. We derive in addition a concurrency theorem for non-linear
double pushout (DPO) rewriting in rm-adhesive categories. Our results open
non-linear SqPO and DPO semantics to the rich static analysis techniques
available from concurrency, rule algebra and tracelet theory.


Abstract:
  In this paper, some enhanced error estimates are derived for the augmented
subspace methods which are designed for solving eigenvalue problems. We will
show that the augmented subspace methods have the second order convergence rate
which is better than the existing results. These sharper estimates provide a
new dependence of convergence rate on the coarse spaces in augmented subspace
methods. These new results are also validated by some numerical examples.


Abstract:
  In this paper we present a quantum algorithm which increases the amplitude of
the states corresponding to the solutions of the search problem by a factor of
almost two.


Abstract:
  In this article, high frequency stability estimates for the determination of
the potential in the Schr\"odinger equation are studied when the boundary
measurements are made on slightly more than half the boundary. The estimates
reflect the increasing stability property with growing frequency.


Abstract:
  The spread of an infection, a contagion, meme, emotion, message and various
other spreadable objects have been discussed in several works. Burning and
firefighting have been discussed in particular on static graphs. Graph burning
simulates the notion of the spread of "fire" throughout a graph (plus, one
unburned node burned at each time-step); graph firefighting simulates the
defending of nodes by placing firefighters on the nodes which have not been
already burned while the fire is being spread (started by only a single fire
source).
  This article studies a combination of firefighting and burning on a graph
class which is a variation (generalization) of temporal graphs. Nodes can be
infected from "outside" a network. We present a notion of both upgrading (of
unburned nodes, similar to firefighting) and repairing (of infected nodes). The
nodes which are burned, firefighted, or repaired are chosen probabilistically.
So a variable amount of nodes are allowed to be infected, upgraded and repaired
in each time step.
  In the model presented in this article, both burning and firefighting proceed
concurrently, we introduce such a system to enable the community to study the
notion of spread of an infection and the notion of upgrade/repair against each
other. The graph class that we study (on which, these processes are simulated)
is a variation of temporal graph class in which at each time-step,
probabilistically, a communication takes place (iff an edge exists in that time
step). In addition, a node can be "worn out" and thus can be removed from the
network, and a new healthy node can be added to the network as well. This class
of graphs enables systems with high complexity to be able to be simulated and
studied.


Abstract:
  We prove multi-parameter Leibniz rules corresponding to flag paraproducts of
arbitrary complexity in mixed-norm spaces, including endpoint estimates. The
proof relies on multi-linear harmonic analysis techniques and a quantitative
treatment of the commutators introduced by Bourgain and Li. The argument is
robust and applicable to a generic class of multipliers, including (symmetric)
Mikhlin multipliers of positive order and asymmetric variants of partial
differential operators and Mikhlin multipliers of positive order.


Abstract:
  The literature on precision differential abundances (PDAs) in stars is
extensive. Surveys include sun-like stars in the solar neighborhood, binary
systems, and Galactic clusters. Numerous references as well as a discussion of
relevant mechanisms may be found in papers by Ramirez, et al. (2019) and Nagar,
et al. (2020). A strong impetus for this work is the probability that the
abundances have been influenced by exoplanetary systems and their evolution.
  We calculate the resulting differential abundances ([El/H]) assuming a given
amount of material with the composition of the bulk earth (Wang, et al. 2018)
was added to the stellar convection zone of a dwarf G-type star. The mass of
the convection zone is uncertain and variable, depending on the spectral type.
Here, we assume a mass of $5\cdot 10^{31}$ gm for the stellar convection zone
(SCZ). This is 0.025 $M_\odot$ (Pinsonneault, et al. 2001, Chambers, 2010). For
other SCZ masses, the parameters must be adjusted accordingly. In general, the
sunlike star will not have exactly the solar composition. This contingency is
roughly taken into account in our model. An appendix discusses issues of
volatility and condensation temperature.


Abstract:
  Simultaneously with the transformation in the energy system, the spot and
ancillary service markets for electricity have become increasingly flexible
with shorter service periods and lower minimum powers. This flexibility has
made the fastest form of frequency regulation - the frequency containment
reserve (FCR) - particularly attractive for large-scale battery storage systems
(BSSs) and led to a market growth of these systems. However, this growth
resulted in high competition and consequently falling FCR prices, making the
FCR market increasingly unattractive to large-scale BSSs. In the context of
multi-use concepts, this market may be interesting especially for a pool of
electric vehicles (EVs), which can generate additional revenue during their
idle times. In this paper, multi-year measurement data of 22 commercial EVs are
used for the development of a simulation model for marketing FCR. In addition,
logbooks of more than 460 vehicles of different economic sectors are evaluated.
Based on the simulations, the effects of flexibilization on the marketing of a
pool of EVs are analyzed for the example of the German FCR market design, which
is valid for many countries in Europe. It is shown that depending on the
sector, especially the recently made changes of service periods from one week
to one day and from one day to four hours generate the largest increase in
available pool power. Further reductions in service periods, on the other hand,
offer only a small advantage, as the idle times are often longer than the short
service periods. In principle, increasing flexibility overcompensates for
falling FCR prices and leads to higher revenues, even if this does not apply
across all sectors examined. A pool of 1,000 EVs could theoretically generate
revenues of about 5,000 EUR - 8,000 EUR per week on the German FCR market in
2020.


Abstract:
  Camouflaged object detection (COD) aims to segment camouflaged objects hiding
in the environment, which is challenging due to the similar appearance of
camouflaged objects and their surroundings. Research in biology suggests that
depth can provide useful object localization cues for camouflaged object
discovery, as all the animals have 3D perception ability. However, the depth
information has not been exploited for camouflaged object detection. To explore
the contribution of depth for camouflage detection, we present a depth-guided
camouflaged object detection network with pre-computed depth maps from existing
monocular depth estimation methods. Due to the domain gap between the depth
estimation dataset and our camouflaged object detection dataset, the generated
depth may not be accurate enough to be directly used in our framework. We then
introduce a depth quality assessment module to evaluate the quality of depth
based on the model prediction from both RGB COD branch and RGB-D COD branch.
During training, only high-quality depth is used to update the modal
interaction module for multi-modal learning. During testing, our depth quality
assessment module can effectively determine the contribution of depth and
select the RGB branch or RGB-D branch for camouflage prediction. Extensive
experiments on various camouflaged object detection datasets prove the
effectiveness of our solution in exploring the depth information for
camouflaged object detection. Our code and data is publicly available at:
\url{https://github.com/JingZhang617/RGBD-COD}.


Abstract:
  Due to the large volume of data and information generated by a multitude of
social data sources, it is a huge challenge to manage and extract useful
knowledge, especially given the different forms of data, streaming data and
uncertainty and ambiguity of data. Hence, there are still challenges in this
area of BD analytics research to capture, store, process, visualise, query, and
manipulate datasets to derive meaningful information that is specific to an
application's domain. This chapter attempts to address this problem by studying
Semantic Analytics and domain knowledge modelling, and to what extent these
technologies can be utilised toward better understanding to the social textual
contents. In particular, the chapter gives an overview of semantic analysis and
domain ontology followed by shedding light on domain knowledge modelling,
inference, semantic storage, and publicly available semantic tools and APIs.
Also, the theoretical notion of Knowledge Graphs is reported and their
interlinking with SBD is discussed. The utility of the semantic analytics is
demonstrated and evaluated through a case study on social data in the context
of politics domain.


Abstract:
  Temporal logic inference is the process of extracting formal descriptions of
system behaviors from data in the form of temporal logic formulas. The existing
temporal logic inference methods mostly neglect uncertainties in the data,
which results in limited applicability of such methods in real-world
deployments. In this paper, we first investigate the uncertainties associated
with trajectories of a system and represent such uncertainties in the form of
interval trajectories. We then propose two uncertainty-aware signal temporal
logic (STL) inference approaches to classify the undesired behaviors and
desired behaviors of a system. Instead of classifying finitely many
trajectories, we classify infinitely many trajectories within the interval
trajectories. In the first approach, we incorporate robust semantics of STL
formulas with respect to an interval trajectory to quantify the margin at which
an STL formula is satisfied or violated by the interval trajectory. The second
approach relies on the first learning algorithm and exploits the decision tree
to infer STL formulas to classify behaviors of a given system. The proposed
approaches also work for non-separable data by optimizing the worst-case
robustness in inferring an STL formula. Finally, we evaluate the performance of
the proposed algorithms in two case studies, where the proposed algorithms show
reductions in the computation time by up to four orders of magnitude in
comparison with the sampling-based baseline algorithms (for a dataset with 800
sampled trajectories in total).


Abstract:
  Formation of hazes at microbar pressures has been explored by theoretical
models of exoplanet atmospheres to explain Rayleigh scattering and/or
featureless transmission spectra, however observational evidence of aerosols in
the low pressure formation environments has proved elusive. Here, we show
direct evidence of aerosols existing at $\sim$1 microbar pressures in the
atmosphere of the warm sub-Saturn WASP-69b using observations taken with Space
Telescope Imaging Spectrograph (STIS) and Wide Field Camera 3 (WFC3)
instruments on the Hubble Space Telescope. The transmission spectrum shows a
wavelength-dependent slope induced by aerosol scattering that covers 11 scale
heights of spectral modulation. Drawing on the extensive studies of haze in our
Solar System, we model the transmission spectrum based on a scaled version of
Jupiter's haze density profile to show that WASP-69b transmission spectrum can
be produced by scattering from an approximately constant density of particles
extending throughout the atmospheric column from 40 millibar to microbar
pressures. These results are consistent with theoretical expectations based on
microphysics of the aerosol particles that have suggested haze can exist at
microbar pressures in exoplanet atmospheres.


Abstract:
  Motivated by the recent experimental demonstrations of quantum supremacy,
proving the hardness of the output of random quantum circuits is an imperative
near term goal. We prove under the complexity theoretical assumption of the
non-collapse of the polynomial hierarchy that approximating the output
probabilities of random quantum circuits to within $\exp(-\Omega(m\log m))$
additive error is hard for any classical computer, where $m$ is the number of
gates in the quantum computation. More precisely, we show that the above
problem is $\#\mathsf{P}$-hard under $\mathsf{BPP}^{\mathsf{NP}}$ reduction. In
the recent experiments, the quantum circuit has $n$-qubits and the architecture
is a two-dimensional grid of size $\sqrt{n}\times\sqrt{n}$. Indeed for constant
depth circuits approximating the output probabilities to within
$2^{-\Omega(n\log{n})}$ is hard. For circuits of depth $\log{n}$ or $\sqrt{n}$
for which the anti-concentration property holds, approximating the output
probabilities to within $2^{-\Omega(n\log^2{n})}$ and $2^{-\Omega(n^{3/2}\log
n)}$ is hard respectively. We then show that the hardness results extend to any
open neighborhood of an arbitrary (fixed) circuit including the trivial circuit
with identity gates. We made an effort to find the best proofs and proved these
results from first principles, which do not use the standard techniques such as
the Berlekamp--Welch algorithm, the usual Paturi's lemma, and Rakhmanov's
result.


Abstract:
  We demonstrate efficient generation of mid-infrared frequency combs based on
continuous-wave-seeded femtosecond optical parametric generation in nonlinear
waveguides. Conversion of the near-infrared pump to signal and idler light
takes place with very high efficiency (74 %) and the threshold (25 pJ) is over
300 times lower than in bulk analogs. Relative intensity noise of the
mid-infrared comb is exceptionally low, below 5*10-5 (detector-limited,
integrated from 10 Hz to 2 MHz). Furthermore, the mid-infrared bandwidth can be
increased by driving the process with broadband pump obtained via
supercontinuum generation.


Abstract:
  We inspect the exclusive hadronic decay modes $ B_d\to \phi (\eta^{(')}, \pi,
\omega)$, induced by quark level transition as $b\to d$ $(\Delta S=0)$, in
vector like down quark model. As these decay modes insist highly suppressed
followed by the predicted branching fraction $\mathcal{O}(10^{-9})$ which
reflects to scrutinize physics beyond the standard model. We constrain the new
parameter space inferred from experimental limits on leptonic $B_d\to \ell
\ell(\ell=e, \mu, \tau)$ and nonleptonic decay modes $B_d \to \eta' \pi^0$ and
$B_u\to \rho^- \eta'$. We then check the new physics contributions can have
significant impact on the prominent observable so called branching ratio of
$B_d\to \phi (\eta^{(')},\pi, \omega)$ processes.


Abstract:
  This paper deals with the numerical approximation of the biharmonic inverse
source problem in an abstract setting in which the measurement data is
finite-dimensional. This unified framework in particular covers the conforming
and nonconforming finite element methods (FEMs). The inverse problem is
analysed through the forward problem. Error estimate for the forward solution
is derived in an abstract set-up that applies to conforming and Morley
nonconforming FEMs. Since the inverse problem is ill-posed, Tikhonov
regularisation is considered to obtain a stable approximate solution. Error
estimate is established for the regularised solution for different
regularisation schemes. Numerical results that confirm the theoretical results
are also presented.


Abstract:
  Motivated by the free products of groups, the direct sums of modules, and
Shelah's $(\lambda,2)$-goodness, we study strong amalgamation properties in
Abstract Elementary Classes. Such a notion of amalgamation consists of a
selection of certain amalgams for every triple $M_0\leq M_1, M_2$, and we show
that if $K$ designates a unique strong amalgam to every triple $M_0\leq M_1,
M_2$, then $K$ satisfies categoricity transfer at cardinals
$\geq\theta(K)+2^{\text{LS}(K)}$, where $\theta(K)$ is a cardinal associated
with the notion of amalgamation. We also show that if such a unique choice does
not exist, then there is some model $M\in K$ having $2^{|M|}$ many extensions
which cannot be embedded in each other over $M$. Thus, for AECs which admit a
notion of amalgamation, the property of having unique amalgams is a dichotomy
property in the sense of Shelah's classification theory.


Abstract:
  We compute explicitly the least degree of generators of almost all symbolic
powers of the defining ideal of Fermat-like configuration of lines in
$\mathbb{P}^3_\mathbb{C}$ and provide efficient bounds for remaining cases. We
will also compute explicitly those numbers for ideal determining the singular
locus of the arrangement of lines given by the pseudoreflection group $A_3$. As
direct applications, we verify Chudnovsky's(-like) Conjecture,
Demailly's(-like) Conjecture and Harbourne-Huneke Containment problem as well
as calculate the Waldschmidt constant and (asymptotic) resurgence number.


Abstract:
  Instead of using expensive manual annotations, researchers have proposed to
train named entity recognition (NER) systems using heuristic labeling rules.
However, devising labeling rules is challenging because it often requires a
considerable amount of manual effort and domain expertise. To alleviate this
problem, we propose \textsc{GLaRA}, a graph-based labeling rule augmentation
framework, to learn new labeling rules from unlabeled data. We first create a
graph with nodes representing candidate rules extracted from unlabeled data.
Then, we design a new graph neural network to augment labeling rules by
exploring the semantic relations between rules. We finally apply the augmented
rules on unlabeled data to generate weak labels and train a NER model using the
weakly labeled data. We evaluate our method on three NER datasets and find that
we can achieve an average improvement of +20\% F1 score over the best baseline
when given a small set of seed rules.


Abstract:
  The reaction between atomic oxygen and molecular hydrogen is an important one
in astrochemistry as it regulates the abundance of the hydroxyl radical and
serves to open the chemistry of oxygen in diverse astronomical environments.
However, the existence of a high activation barrier in the reaction with ground
state oxygen atoms limits its efficiency in cold gas. In this study we
calculate the dependence of the reaction rate coefficient on the rotational and
vibrational state of H$_2$ and evaluate the impact on the abundance of OH in
interstellar regions strongly irradiated by far-UV photons, where H2 can be
efficiently pumped to excited vibrational states. We use a recently calculated
potential energy surface and carry out time-independent quantum mechanical
scattering calculations to compute rate coefficients for the reaction O(3P) +
H2(v,j) -> OH + H, with H2 in vibrational states v = 0-7 and rotational states
j = 0-10. We find that the reaction becomes significantly faster with
increasing vibrational quantum number of H2, although even for high vibrational
states of H2 (v = 4-5) for which the reaction is barrierless, the rate
coefficient does not strictly attain the collision limit and still maintains a
positive dependence with temperature. We implemented the calculated
state-specific rate coefficients in the Meudon PDR code to model the Orion Bar
PDR and evaluate the impact on the abundance of the OH radical. We find the
fractional abundance of OH is enhanced by up to one order of magnitude in
regions of the cloud corresponding to Av = 1.3-2.3, compared to the use of a
thermal rate coefficient for O + H2, although the impact on the column density
of OH is modest, of about 60 %. The calculated rate coefficients will be useful
to model and interpret JWST observations of OH in strongly UV-illuminated
environments.


Abstract:
  The decrease in the volatility and persistence of US inflation was
accompanied by a decline in the public's attention to inflation. This decline
in attention weakens the effectiveness of make-up policies and negative
interest rates and can lead to inflation-attention traps: prolonged periods of
a binding lower bound and low inflation due to slowly-adjusting inflation
expectations. To mitigate the drawbacks of lower attention, it is optimal to
increase the inflation target as attention declines. Accounting for the lower
bound fundamentally alters the normative implications of declining attention.
While lower attention raises welfare absent the lower-bound constraint, it
decreases welfare when accounting for the lower bound.


Abstract:
  Ultra-wideband (UWB) communications have gained popularity in recent years
for being able to provide distance measurements and localization with high
accuracy, which can enhance the capabilities of devices in the Internet of
Things (IoT). Since energy efficiency is of utmost concern in such
applications, in this work we evaluate the power and energy consumption,
distance measurements, and localization performance of two types of UWB
physical interfaces (PHYs), which use either a low- or high-rate pulse
repetition (LRP and HRP, respectively). The evaluation is done through
measurements acquired in identical conditions, which is crucial in order to
have a fair comparison between the devices. The LRP devices that we tested have
the same ranging and localization performance, but ten times (10x) lower power
consumption, 6x lower energy consumption per distance measurement, and at least
8x higher coverage than the HRP devices. Therefore, UWB LRP devices can offer
high-accuracy ranging and localization even to ultra-low-power devices in the
IoT. We performed measurements in typical LOS and NLOS scenarios and propose
theoretical models for the distance errors obtained in these situations. The
models can be used to simulate realistic building deployments and we illustrate
such an example. This paper, therefore, provides a comprehensive overview of
the energy demands, ranging characteristics, and localization performance of
state-of-the-art UWB devices.


Abstract:
  We present ALADIN (All Layer AdaIN); a novel architecture for searching
images based on the similarity of their artistic style. Representation learning
is critical to visual search, where distance in the learned search embedding
reflects image similarity. Learning an embedding that discriminates
fine-grained variations in style is hard, due to the difficulty of defining and
labelling style. ALADIN takes a weakly supervised approach to learning a
representation for fine-grained style similarity of digital artworks,
leveraging BAM-FG, a novel large-scale dataset of user generated content
groupings gathered from the web. ALADIN sets a new state of the art accuracy
for style-based visual search over both coarse labelled style data (BAM) and
BAM-FG; a new 2.62 million image dataset of 310,000 fine-grained style
groupings also contributed by this work.


Abstract:
  In recent years, artificial neural networks have developed into a powerful
tool for dealing with a multitude of problems for which classical solution
approaches reach their limits. However, it is still unclear why randomly
initialized gradient descent optimization algorithms, such as the well-known
batch gradient descent, are able to achieve zero training loss in many
situations even though the objective function is non-convex and non-smooth. One
of the most promising approaches to solving this problem in the field of
supervised learning is the analysis of gradient descent optimization in the
so-called overparameterized regime. In this article we provide a further
contribution to this area of research by considering overparameterized
fully-connected rectified artificial neural networks with biases. Specifically,
we show that for a fixed number of training data the mean squared error using
batch gradient descent optimization applied to such a randomly initialized
artificial neural network converges to zero at a linear convergence rate as
long as the width of the artificial neural network is large enough, the
learning rate is small enough, and the training input data are pairwise
linearly independent.


Abstract:
  This work presents our advancements in controlling an articulatory speech
synthesis engine, \textit{viz.}, Pink Trombone, with hand gestures. Our
interface translates continuous finger movements and wrist flexion into
continuous speech using vocal tract area-function based articulatory speech
synthesis. We use Cyberglove II with 18 sensors to capture the kinematic
information of the wrist and the individual fingers, in order to control a
virtual tongue. The coordinates and the bending values of the sensors are then
utilized to fit a spline tongue model that smoothens out the noisy values and
outliers. Considering the upper palate as fixed and the spline model as the
dynamically moving lower surface (tongue) of the vocal tract, we compute 1D
area functional values that are fed to the Pink Trombone, generating continuous
speech sounds. Therefore, by learning to manipulate one's wrist and fingers,
one can learn to produce speech sounds just through one's hands, without the
need for using the vocal tract.


Abstract:
  In this paper, we propose a blockchain-based cold chain technology for
vaccine cooling track. The COVID-19 pandemic has caused the death of millions
of people. An important step towards ending the pandemic is vaccination.
Vaccines must be kept under control temperature during the whole process, from
fabrication to the hands of the health professionals who will immunize the
population. However, there are numerous reports of vaccine loss due to
temperature variations, and, currently, people getting vaccinated have no
control if their vaccine was kept safe. Blockchain is a technology solution
that can provide public and verifiable records. We review the World Health
Organization (WHO) cool chain and Blockchain technology. Moreover, we describe
current IoT temperature monitoring devices and propose Blockcoldchain to track
vaccine cold chain using blockchain, thus proving an unalterable vaccine
temperature history. Our experimental results using smart contracts demonstrate
the system's feasibility.


Abstract:
  There is an increasing realization that algorithmic inductive biases are
central in preventing overfitting; empirically, we often see a benign
overfitting phenomenon in overparameterized settings for natural learning
algorithms, such as stochastic gradient descent (SGD), where little to no
explicit regularization has been employed. This work considers this issue in
arguably the most basic setting: constant-stepsize SGD (with iterate averaging
or tail averaging) for linear regression in the overparameterized regime. Our
main result provides a sharp excess risk bound, stated in terms of the full
eigenspectrum of the data covariance matrix, that reveals a bias-variance
decomposition characterizing when generalization is possible: (i) the variance
bound is characterized in terms of an effective dimension (specific for SGD)
and (ii) the bias bound provides a sharp geometric characterization in terms of
the location of the initial iterate (and how it aligns with the data covariance
matrix). More specifically, for SGD with iterate averaging, we demonstrate the
sharpness of the established excess risk bound by proving a matching lower
bound (up to constant factors). For SGD with tail averaging, we show its
advantage over SGD with iterate averaging by proving a better excess risk bound
together with a nearly matching lower bound. Moreover, we reflect on a number
of notable differences between the algorithmic regularization afforded by
(unregularized) SGD in comparison to ordinary least squares (minimum-norm
interpolation) and ridge regression. Experimental results on synthetic data
corroborate our theoretical findings.


Abstract:
  While GANs have shown success in realistic image generation, the idea of
using GANs for other tasks unrelated to synthesis is underexplored. Do GANs
learn meaningful structural parts of objects during their attempt to reproduce
those objects? In this work, we test this hypothesis and propose a simple and
effective approach based on GANs for semantic part segmentation that requires
as few as one label example along with an unlabeled dataset. Our key idea is to
leverage a trained GAN to extract pixel-wise representation from the input
image and use it as feature vectors for a segmentation network. Our experiments
demonstrate that GANs representation is "readily discriminative" and produces
surprisingly good results that are comparable to those from supervised
baselines trained with significantly more labels. We believe this novel
repurposing of GANs underlies a new class of unsupervised representation
learning that is applicable to many other tasks. More results are available at
https://repurposegans.github.io/.


Abstract:
  Sarcasm is a linguistic expression often used to communicate the opposite of
what is said, usually something that is very unpleasant with an intention to
insult or ridicule. Inherent ambiguity in sarcastic expressions, make sarcasm
detection very difficult. In this work, we focus on detecting sarcasm in
textual conversations from various social networking platforms and online
media. To this end, we develop an interpretable deep learning model using
multi-head self-attention and gated recurrent units. Multi-head self-attention
module aids in identifying crucial sarcastic cue-words from the input, and the
recurrent units learn long-range dependencies between these cue-words to better
classify the input text. We show the effectiveness of our approach by achieving
state-of-the-art results on multiple datasets from social networking platforms
and online media. Models trained using our proposed approach are easily
interpretable and enable identifying sarcastic cues in the input text which
contribute to the final classification score. We visualize the learned
attention weights on few sample input texts to showcase the effectiveness and
interpretability of our model.


Abstract:
  We study the problem of treasure hunt in a graph by a mobile agent. The nodes
in the graph are anonymous and the edges at any node $v$ of degree $deg(v)$ are
labeled arbitrarily as $0,1,\ldots, deg(v)-1$. A mobile agent, starting from a
node, must find a stationary object, called {\it treasure} that is located on
an unknown node at a distance $D$ from its initial position. The agent finds
the treasure when it reaches the node where the treasure is present. The {\it
time} of treasure hunt is defined as the number of edges the agent visits
before it finds the treasure. The agent does not have any prior knowledge about
the graph or the position of the treasure. An Oracle, that knows the graph, the
initial position of the agent, and the position of the treasure, places some
pebbles on the nodes, at most one per node, of the graph to guide the agent
towards the treasure.
  We target to answer the question: what is the fastest possible treasure hunt
algorithm regardless of the number of pebbles are placed?
  We show an algorithm that uses $O(D \log \Delta)$ pebbles to find the
treasure in a graph $G$ in time $O(D \log \Delta + \log^3 \Delta)$, where
$\Delta$ is the maximum degree of a node in $G$ and $D$ is the distance from
the initial position of the agent to the treasure. We show an almost matching
lower bound of $\Omega(D \log \Delta)$ on time of the treasure hunt using any
number of pebbles.


Abstract:
  We present an auto-differentiable spectral modeling of exoplanets and brown
dwarfs. This model enables a fully Bayesian inference of the high--dispersion
data to fit the ab initio line-by-line spectral computation to the observed
spectrum by combining it with the Hamiltonian Monte Carlo in recent
probabilistic programming languages. An open source code, exojax, developed in
this study, was written in Python using the GPU/TPU compatible package for
automatic differentiation and accelerated linear algebra, JAX (Bradbury et al.
2018). We validated the model by comparing it with existing opacity calculators
and a radiative transfer code and found reasonable agreements of the output. As
a demonstration, we analyzed the high-dispersion spectrum of a nearby brown
dwarf, Luhman 16 A and found that a model including water, carbon monoxide, and
$\mathrm{H_2/He}$ collision induced absorption was well fitted to the observed
spectrum ($R=10^5$ and 2.28-2.30 $\mu$m). As a result, we found that
$T_0=1295_{-32}^{+35}$ K at 1 bar and C/O $=0.62 \pm 0.03$, which is slightly
higher than the solar value. This work demonstrates the potential of full
Bayesian analysis of brown dwarfs and exoplanets as observed by high-dispersion
spectrographs and also directly-imaged exoplanets as observed by
high-dispersion coronagraphy.


Abstract:
  Large-scale intermittency is a widely observed phenomenon in convective
surface layer turbulence that induces non-Gaussian temperature statistics,
while such signature is not observed for velocity signals. Although approaches
based on probability density functions have been used so far, those are not
able to explain to what extent the signals' temporal structure impacts the
statistical characteristics of the velocity and temperature fluctuations. To
tackle this issue, a visibility network analysis is carried out on a
field-experimental dataset from a convective atmospheric surface layer flow.
Through surrogate data and network-based measures, we demonstrate that the
temperature intermittency is related to strong non-linear dependencies in the
temperature signals. Conversely, a competition between linear and non-linear
effects tends to inhibit the temperature-like intermittency behaviour in
streamwise and vertical velocities. Based on present findings, new research
avenues are likely to be opened up in studying large-scale intermittency in
convective turbulence.


Abstract:
  Rehabilitation is important to improve quality of life for mobility-impaired
patients. Smart walkers are a commonly used solution that should embed
automatic and objective tools for data-driven human-in-the-loop control and
monitoring. However, present solutions focus on extracting few specific metrics
from dedicated sensors with no unified full-body approach. We investigate a
general, real-time, full-body pose estimation framework based on two RGB+D
camera streams with non-overlapping views mounted on a smart walker equipment
used in rehabilitation. Human keypoint estimation is performed using a
two-stage neural network framework. The 2D-Stage implements a detection module
that locates body keypoints in the 2D image frames. The 3D-Stage implements a
regression module that lifts and relates the detected keypoints in both cameras
to the 3D space relative to the walker. Model predictions are low-pass filtered
to improve temporal consistency. A custom acquisition method was used to obtain
a dataset, with 14 healthy subjects, used for training and evaluating the
proposed framework offline, which was then deployed on the real walker
equipment. An overall keypoint detection error of 3.73 pixels for the 2D-Stage
and 44.05mm for the 3D-Stage were reported, with an inference time of 26.6ms
when deployed on the constrained hardware of the walker. We present a novel
approach to patient monitoring and data-driven human-in-the-loop control in the
context of smart walkers. It is able to extract a complete and compact body
representation in real-time and from inexpensive sensors, serving as a common
base for downstream metrics extraction solutions, and Human-Robot interaction
applications. Despite promising results, more data should be collected on users
with impairments, to assess its performance as a rehabilitation tool in
real-world scenarios.


Abstract:
  The population of artificial satellites and space debris orbiting the Earth
imposes non-negligible constraints on both space operations and ground-based
optical and radio astronomy. The ongoing deployment of several satellite
`mega-constellations' in the 2020s represents an additional threat that raises
significant concerns. The expected severity of its unwanted consequences is
still under study, including radio interference and information loss by
satellite streaks appearing in science images. In this Letter, we report a new
skyglow effect produced by space objects: increased night sky brightness caused
by sunlight reflected and scattered by that large set of orbiting bodies whose
direct radiance is a diffuse component when observed with the naked eye or with
low angular resolution photometric instruments. According to our preliminary
estimates, the zenith luminance of this additional light pollution source may
have already reached $\sim$20 $\mu$cd m$^{-2}$, which amounts to an
approximately 10 percent increase over the brightness of the night sky
determined by natural sources of light. This is the critical limit adopted in
1979 by the International Astronomical Union for the light pollution level not
to be exceeded at the sites of astronomical observatories.


Abstract:
  We evaluate some new three parameter families of finite reciprocal sums
involving Horadam numbers. We will also be able to state the results for the
infinite sums. Some Fibonacci and Lucas sums will be presented as examples.


Abstract:
  Let the finite distributive lattice $D$ be isomorphic to the congruence
lattice of a finite lattice $L$. Let $Q$ denote those elements of $D$ that
correspond to principal congruences under this isomorphism. Then $Q$ contains
$0,1 \in D$ and all the join-irreducible elements of $D$. If $Q$ contains
exactly these elements, we say that $L$ is a minimal representations of $D$ by
principal congruences of the lattice $L$.
  We characterize finite distributive lattices $D$ with a minimal
representation by principal congruences with the property that $D$ has at most
two dual atoms.


Abstract:
  Intelligent agents must pursue their goals in complex environments with
partial information and often limited computational capacity. Reinforcement
learning methods have achieved great success by creating agents that optimize
engineered reward functions, but which often struggle to learn in sparse-reward
environments, generally require many environmental interactions to perform
well, and are typically computationally very expensive. Active inference is a
model-based approach that directs agents to explore uncertain states while
adhering to a prior model of their goal behaviour. This paper introduces an
active inference agent which minimizes the novel free energy of the expected
future. Our model is capable of solving sparse-reward problems with a very high
sample efficiency due to its objective function, which encourages directed
exploration of uncertain states. Moreover, our model is computationally very
light and can operate in a fully online manner while achieving comparable
performance to offline RL methods. We showcase the capabilities of our model by
solving the mountain car problem, where we demonstrate its superior exploration
properties and its robustness to observation noise, which in fact improves
performance. We also introduce a novel method for approximating the prior model
from the reward function, which simplifies the expression of complex objectives
and improves performance over previous active inference approaches.


Abstract:
  We present the discovery in TMC-1 of vinyl acetylene, CH2CHCCH, and the
detection, for the first time in a cold dark cloud, of HCCN, HC4N, and
CH3CH2CN. A tentative detection of CH3CH2CCH is also reported. The column
density of vinyl acetylene is (1.2 +/- 0.2)e13 cm-2, which makes it one of the
most abundant closed-shell hydrocarbons detected in TMC-1. Its abundance is
only three times lower than that of propylene, CH3CHCH2. The column densities
derived for HCCN and HC4N are (4.4 +/- 0.4)e11 cm-2 and (3.7 +/- 0.4)e11 cm-2,
respectively. Hence, the HCCN/HC4N abundance ratio is 1.2 +/- 0.3. For ethyl
cyanide we derive a column density of (1.1 +/- 0.3)e11 cm-2. These results are
compared with a state-of-the-art chemical model of TMC-1, which is able to
account for the observed abundances of these molecules through gas-phase
chemical routes.


Abstract:
  We propose a new method to explore a possible departure from the standard
time evolution law for the dark matter density. We looked for a violation of
this law by using a deformed evolution law, given by $\rho_c(z) \propto
(1+z)^{3+\epsilon}$, and then constrain $\epsilon$. The dataset used for this
purpose consists of Strong Gravitational Lensing data obtained from SLOAN Lens
ACS, BOSS Emission-line Lens Survey, Strong Legacy Survey SL2S, and SLACS;
along with galaxy cluster X-ray gas mass fraction measurements obtained using
the Chandra Telescope. Our analyses show that $\epsilon$ is consistent with
zero within 1 $\sigma$ c.l., but the current dataset cannot rule out with high
confidence level interacting models of dark matter and dark energy.


Abstract:
  We investigate the physics of photonic band structures of the moir\'e
patterns that emerged when overlapping two uni-dimensional (1D) photonic
crystal slabs with mismatched periods. The band structure of our system is a
result of the interplay between intra-layer and inter-layer coupling
mechanisms, which can be fine-tuned via the distance separating the two layers.
We derive an effective Hamiltonian that captures the essential physics of the
system and reproduces all numerical simulations of electromagnetic solutions
with high accuracy. Most interestingly, \textit{magic distances} corresponding
to the emergence of photonic flatbands within the whole Brillouin zone of the
moir\'e superlattice are observed. We demonstrate that these flatband modes are
tightly localized within a moir\'e period. Moreover, we suggest a single-band
tight-binding model that describes the moir\'e minibands, of which the
tunnelling rate can be continuously tuned via the inter-layer strength. Our
results show that the band structure of bilayer photonic moir\'e can be
engineered in the same fashion as the electronic/excitonic counterparts. It
would pave the way to study many-body physics at photonic moir\'e flatbands and
novel optoelectronic devices.


Abstract:
  We provide a boundedness criterion for the integral operator $S_{\varphi}$ on
the fractional Fock-Sobolev space $F^{s,2}(\mathbb C^n)$, $s\geq 0$, where
$S_{\varphi}$ (introduced by Kehe Zhu) is given by \begin{eqnarray*}
S_{\varphi}F(z):= \int_{\mathbb{C}^n} F(w) e^{z \cdot\bar{w}} \varphi(z-
\bar{w}) d\lambda(w) \end{eqnarray*} with $\varphi$ in the Fock space
$F^2(\mathbb C^n)$ and $d\lambda(w): = \pi^{-n} e^{-|w|^2} dw$ the Gaussian
measure on the complex space $\mathbb{C}^{n}$. This extends the recent result
in Cao--Li--Shen--Wick--Yan. The main approach is to develop multipliers on the
fractional Hermite-Sobolev space $W_H^{s,2}(\mathbb R^n)$.