Abstract:
  We prove that the rank of the cohomology of a closed symplectic manifold with
coefficients in a field of characteristic $p$ is smaller than the number of
periodic orbits of any non-degenerate Hamiltonian flow. Following Floer, the
proof relies on constructing a homology group associated to each such flow, and
comparing it with the homology of the ambient symplectic manifold. The proof
does not proceed by constructing a version of Floer's complex with
characteristic $p$ coefficients, but uses instead the canonical (stable)
complex orientations of moduli spaces of Floer trajectories to construct a
version of Floer homology with coefficients in Morava's $K$-theories, and can
thus be seen as an implementation of Cohen, Jones, and Segal's vision for a
Floer homotopy theory. The key feature of Morava K-theory that allows the
construction to be carried out is the fact that the corresponding homology and
cohomology groups of classifying spaces of finite groups satisfy Poincar\'e
duality.


Abstract:
  In this work, in the context of modified gravity, a curved spacetime
analogous to the "canonical acoustic black hole" is constructed. The source is
a self-interacting scalar field which is non-minimally coupled to gravity
through the Gauss-Bonnet invariant. The scalar-Gauss-Bonnet coupling function
is characterized by three positive parameters: $\sigma$ with units of
$(length)$, $\mu$ with units of $(length)^{4}$, and a dimensionless parameter
$s$, thus defining a three-parameter model for which the line element of
canonical acoustic black hole is a solution. The spacetime is equipped with
spherical and static symmetry and has a single horizon determined in
Schwarzschild coordinates by the region $r=\mu^{1/4}$. The solution admits a
photon sphere at $r=(3\mu)^{1/4}$, and it is shown that in the region
$(3\mu)^{1/4}\leq r<\infty$ the scalar field satisfies the null, weak, and
strong energy conditions. Nonetheless, the model with $s=1$ has major physical
relevance since for this case the scalar field is well defined in the entire
region $r\geq\mu^{1/4}$, while for $s\neq1$ the scalar field blows up on the
horizon.


Abstract:
  The idea of nondeterministic typical hesitant fuzzy automata is a
generalization of the fuzzy automata presented by Costa and Bedregal. This
paper, presents the sufficient and necessary conditions for a typical hesitant
fuzzy language to be computed by nondeterministic typical hesitant fuzzy
automata. Besides, the paper introduces a new class of Typical Hesitant Fuzzy
Automata with crisp transitions, and we will show that this new class is
equivalent to the original class introduced by Costa and Bedregal


Abstract:
  The Hamiltonian Monte Carlo (HMC) method allows sampling from continuous
densities. Favorable scaling with dimension has led to wide adoption of HMC by
the statistics community. Modern auto-differentiating software should allow
more widespread usage in Bayesian inverse problems. This paper analyzes the two
major difficulties encountered using HMC for inverse problems: poor
conditioning and multi-modality. Novel results on preconditioning and replica
exchange Monte Carlo parameter selection are presented in the context of
spectroscopy. Recommendations are analyzed rigorously in the Gaussian case, and
shown to generalize in a fusion plasma reconstruction.


Abstract:
  A planner aims to target individuals who exceed a threshold in a
characteristic, such as wealth or ability. The individuals can rank their
friends according to the characteristic. We study a strategy-proof mechanism
for the planner to use the rankings for targeting. We discuss how the mechanism
works in practice, when the rankings may contain errors.


Abstract:
  The most common tools for word-alignment rely on a large amount of parallel
sentences, which are then usually processed according to one of the IBM model
algorithms. The training data is, however, the same as for machine translation
(MT) systems, especially for neural MT (NMT), which itself is able to produce
word-alignments using the trained attention heads. This is convenient because
word-alignment is theoretically a viable byproduct of any attention-based NMT,
which is also able to provide decoder scores for a translated sentence pair.
  We summarize different approaches on how word-alignment can be extracted from
alignment scores and then explore ways in which scores can be extracted from
NMT, focusing on inferring the word-alignment scores based on output sentence
and token probabilities. We compare this to the extraction of alignment scores
from attention. We conclude with aggregating all of the sources of alignment
scores into a simple feed-forward network which achieves the best results when
combined alignment extractors are used.


Abstract:
  We propose subsampling as a unified algorithmic technique for submodular
maximization in centralized and online settings. The idea is simple:
independently sample elements from the ground set, and use simple combinatorial
techniques (such as greedy or local search) on these sampled elements. We show
that this approach leads to optimal/state-of-the-art results despite being much
simpler than existing methods. In the usual offline setting, we present
SampleGreedy, which obtains a $(p + 2 + o(1))$-approximation for maximizing a
submodular function subject to a $p$-extendible system using $O(n + nk/p)$
evaluation and feasibility queries, where $k$ is the size of the largest
feasible set. The approximation ratio improves to $p+1$ and $p$ for monotone
submodular and linear objectives, respectively. In the streaming setting, we
present SampleStreaming, which obtains a $(4p +2 - o(1))$-approximation for
maximizing a submodular function subject to a $p$-matchoid using $O(k)$ memory
and $O(km/p)$ evaluation and feasibility queries per element, where $m$ is the
number of matroids defining the $p$-matchoid. The approximation ratio improves
to $4p$ for monotone submodular objectives. We empirically demonstrate the
effectiveness of our algorithms on video summarization, location summarization,
and movie recommendation tasks.


Abstract:
  We present GNNAutoScale (GAS), a framework for scaling arbitrary
message-passing GNNs to large graphs. GAS prunes entire sub-trees of the
computation graph by utilizing historical embeddings from prior training
iterations, leading to constant GPU memory consumption in respect to input node
size without dropping any data. While existing solutions weaken the expressive
power of message passing due to sub-sampling of edges or non-trainable
propagations, our approach is provably able to maintain the expressive power of
the original GNN. We achieve this by providing approximation error bounds of
historical embeddings and show how to tighten them in practice. Empirically, we
show that the practical realization of our framework, PyGAS, an easy-to-use
extension for PyTorch Geometric, is both fast and memory-efficient, learns
expressive node representations, closely resembles the performance of their
non-scaling counterparts, and reaches state-of-the-art performance on
large-scale graphs.


Abstract:
  Synthesizing graceful and life-like behaviors for physically simulated
characters has been a fundamental challenge in computer animation. Data-driven
methods that leverage motion tracking are a prominent class of techniques for
producing high fidelity motions for a wide range of behaviors. However, the
effectiveness of these tracking-based methods often hinges on carefully
designed objective functions, and when applied to large and diverse motion
datasets, these methods require significant additional machinery to select the
appropriate motion for the character to track in a given scenario. In this
work, we propose to obviate the need to manually design imitation objectives
and mechanisms for motion selection by utilizing a fully automated approach
based on adversarial imitation learning. High-level task objectives that the
character should perform can be specified by relatively simple reward
functions, while the low-level style of the character's behaviors can be
specified by a dataset of unstructured motion clips, without any explicit clip
selection or sequencing. These motion clips are used to train an adversarial
motion prior, which specifies style-rewards for training the character through
reinforcement learning (RL). The adversarial RL procedure automatically selects
which motion to perform, dynamically interpolating and generalizing from the
dataset. Our system produces high-quality motions that are comparable to those
achieved by state-of-the-art tracking-based techniques, while also being able
to easily accommodate large datasets of unstructured motion clips. Composition
of disparate skills emerges automatically from the motion prior, without
requiring a high-level motion planner or other task-specific annotations of the
motion clips. We demonstrate the effectiveness of our framework on a diverse
cast of complex simulated characters and a challenging suite of motor control
tasks.


Abstract:
  The classical Banach space $L_1(L_p)$ consists of measurable scalar functions
$f$ on the unit square for which $$\|f\| = \int_0^1\Big(\int_0^1 |f(x,y)|^p
dy\Big)^{1/p}dx < \infty.$$ We show that $L_1(L_p)$ $(1 < p < \infty)$ is
primary, meaning that, whenever $L_1(L_p) = E\oplus F$ then either $E$ or $F$
is isomorphic to $L_1(L_p)$. More generally we show that $L_1(X)$ is primary,
for a large class of rearrangement invariant Banach function spaces.


Abstract:
  Inspired by recent developments in the study of chaos in many-body systems,
we construct a measure of local information spreading for a stochastic Cellular
Automaton in the form of a spatiotemporally resolved Hamming distance. This
decorrelator is a classical version of an Out-of-Time-Order Correlator studied
in the context of quantum many-body systems. Focusing on the one-dimensional
Kauffman Cellular Automaton, we extract the scaling form of our decorrelator
with an associated butterfly velocity $v_b$ and a velocity-dependent Lyapunov
exponent $\lambda(v)$. The existence of the latter is not a given in a discrete
classical system. Second, we account for the behaviour of the decorrelator in a
framework based solely on the boundary of the information spreading, including
an effective boundary random walk model yielding the full functional form of
the decorrelator. In particular, we obtain analytic results for $v_b$ and the
exponent $\beta$ in the scaling ansatz $\lambda(v) \sim \mu (v - v_b)^\beta$,
which is usually only obtained numerically. Finally, a full scaling collapse
establishes the decorrelator as a unifying diagnostic of information spreading.


Abstract:
  The article presents and evaluates a scalable FRaGenLP algorithm for
generating random linear programming problems of large dimension $n$ on cluster
computing systems. To ensure the consistency of the problem and the boundedness
of the feasible region, the constraint system includes $2n+1$ standard
inequalities, called support inequalities. New random inequalities are
generated and added to the system in a manner that ensures the consistency of
the constraints. Furthermore, the algorithm uses two likeness metrics to
prevent the addition of a new random inequality that is similar to one already
present in the constraint system. The algorithm also rejects random
inequalities that cannot affect the solution of the linear programming problem
bounded by the support inequalities. The parallel implementation of the
FRaGenLP algorithm is performed in C++ through the parallel BSF-skeleton, which
encapsulates all aspects related to the MPI-based parallelization of the
program. We provide the results of large-scale computational experiments on a
cluster computing system to study the scalability of the FRaGenLP algorithm.


Abstract:
  We theoretically study the fundamental problem of learning a single neuron
with a bias term ($\mathbf{x} \mapsto \sigma(<\mathbf{w},\mathbf{x}> + b)$) in
the realizable setting with the ReLU activation, using gradient descent.
Perhaps surprisingly, we show that this is a significantly different and more
challenging problem than the bias-less case (which was the focus of previous
works on single neurons), both in terms of the optimization geometry as well as
the ability of gradient methods to succeed in some scenarios. We provide a
detailed study of this problem, characterizing the critical points of the
objective, demonstrating failure cases, and providing positive convergence
guarantees under different sets of assumptions. To prove our results, we
develop some tools which may be of independent interest, and improve previous
results on learning single neurons.


Abstract:
  In this pilot study, we investigate the use of a deep learning (DL) model to
temporally evolve the dynamics of gas accreting onto a black hole in the form
of a radiatively inefficient accretion flow (RIAF). We have trained a machine
to forecast such a spatiotemporally chaotic system -- i.e. black hole weather
forecasting -- using a convolutional neural network (CNN) and a training
dataset which consists of numerical solutions of the hydrodynamical equations,
for a range of initial conditions. We find that deep neural networks seem to
learn well black hole accretion physics and evolve the accretion flow orders of
magnitude faster than traditional numerical solvers, while maintaining a
reasonable accuracy for a long time. For instance, CNNs predict well the
temporal evolution of a RIAF over a long duration of $8\times 10^4 GM/c^3$,
which corresponds to 80 dynamical times at $r=100 GM/c^2$. The DL model is able
to evolve flows from initial conditions not present in the training dataset
with good accuracy. Our approach thus seems to generalize well. Once trained,
the DL model evolves a turbulent RIAF on a single GPU four orders of magnitude
faster than usual fluid dynamics integrators running in parallel on 200 CPU
cores. We speculate that a data-driven machine learning approach should be very
promising for accelerating not only fluid dynamics simulations, but also
general relativistic magnetohydrodynamic ones.


Abstract:
  We analyze the magnetic excitations and the spin-mediated superconductivity
in iron-based superconductors within a low-energy model that operates in the
band basis but fully incorporates the orbital character of the spin
excitations. We show how the orbital selectivity, encoded in our low-energy
description, simplifies substantially the analysis and allows for analytical
treatments, while retaining all the main features of both spin-excitations and
gap functions computed using multiorbital models. Importantly, our analysis
unveils the orbital matching between the hole and electron pockets as the key
parameter to determine the momentum-dependence and the hierarchy of the
superconducting gaps, instead of the Fermi surface matching as in the common
nesting scenario.


Abstract:
  Let $S$ be a symmetric linear relation in the Pontyagin space $\left(
\mathcal{K},\, \left[ .,. \right] \right)$ and let $\Pi=\left(
\mathcal{\mathcal{H}}, \Gamma_{0}, \Gamma_{1} \right)$ be the corresponding
boundary triple. We prove that the corresponding Weyl function $Q$ satisfies
$Q\in N_{\kappa }(\mathcal{H})$. Conversely, for regular $Q\in N_{\kappa
}(\mathcal{H})$, we find linear relation $S \subsetneq A$, where $A$ is
representing self-adjoint linear relation of $Q$, and we prove that $Q$ is the
Weyl function of the relation $S$. We also prove $\hat{A}=\ker \Gamma_{1}$,
where $\hat{A}$ is the representing relation of the $\hat{Q}:=-Q^{-1}$. In
addition, if we assume that the derivative at infinity $Q^{'}\left( \infty
\right):=\lim \limits_{z \to \infty}{zQ(z)}$ is a boundedly invertible operator
then we are able to decompose $A$, $\hat{A}$ and $S^{+}$ in terms of $S$, i.e.
we express relation matrices of $A$, $\hat{A}$ and $S^{+}$ in terms of $S$,
which is a bounded operator in this case.


Abstract:
  We consider the robust single-source capacitated facility location problem
with uncertainty in customer demands. A cardinality-constrained uncertainty set
is assumed for the robust problem. To solve it efficiently, we propose an
allocation-based formulation derived by Dantzig-Wolfe decomposition and a
branch-and-price algorithm. The computational experiments show that our
branch-and-price algorithm outperforms CPLEX in many cases, which solves the
ordinary robust reformulation. We also examine the trade-off relationship
between the empirical probability of infeasibility and the additional costs
incurred and observe that the robustness of solutions can be improved
significantly with small additional costs.


Abstract:
  We propose using deep reinforcement learning to solve dynamic stochastic
general equilibrium models. Agents are represented by deep artificial neural
networks and learn to solve their dynamic optimisation problem by interacting
with the model environment, of which they have no a priori knowledge. Deep
reinforcement learning offers a flexible yet principled way to model bounded
rationality within this general class of models. We apply our proposed approach
to a classical model from the adaptive learning literature in macroeconomics
which looks at the interaction of monetary and fiscal policy. We find that,
contrary to adaptive learning, the artificially intelligent household can solve
the model in all policy regimes.


Abstract:
  Given a hermitian line bundle $L\to M$ on a closed Riemannian manifold
$(M^n,g)$, the self-dual Yang-Mills-Higgs energies are a natural family of
functionals \begin{align*}
  &E_\epsilon(u,\nabla):=\int_M\Big(|\nabla
u|^2+\epsilon^2|F_\nabla|^2+\frac{(1-|u|^2)^2}{4\epsilon^2}\Big) \end{align*}
defined for couples $(u,\nabla)$ consisting of a section $u\in\Gamma(L)$ and a
hermitian connection $\nabla$ with curvature $F_\nabla$. While the critical
points of these functionals have been well-studied in dimension two by the
gauge theory community, it was shown in previous work of the second- and
third-named authors that critical points in higher dimension converge as
$\epsilon\to 0$ (in an appropriate sense) to minimal submanifolds of
codimension two, with strong parallels to the correspondence between the
Allen-Cahn equations and minimal hypersurfaces.
  In this paper, we complement this idea by showing the $\Gamma$-convergence of
$E_\epsilon$ to ($2\pi$ times) the codimension two area: more precisely, given
a family of couples $(u_\epsilon,\nabla_\epsilon)$ with $\sup_\epsilon
E_\epsilon(u_\epsilon,\nabla_\epsilon)<\infty$, we prove that a suitable gauge
invariant Jacobian $J(u_\epsilon,\nabla_\epsilon)$ converges to an integral
$(n-2)$-cycle $\Gamma$, in the homology class dual to the Euler class $c_1(L)$,
with mass $2 \pi \mathbb{M}(\Gamma)\le\liminf_{\epsilon \rightarrow
0}E_\epsilon(u_\epsilon,\nabla_\epsilon)$. We also obtain a recovery sequence
for any integral cycle in this homology class.
  Finally, we apply these techniques to compare min-max values for the
$(n-2)$-area from the Almgren-Pitts theory with those obtained from the
Yang-Mills-Higgs framework, showing that the former values always provide a
lower bound for the latter. As an ingredient, we also establish a Huisken-type
monotonicity result along the gradient flow of $E_{\epsilon}$.


Abstract:
  We study the problem of polygonal curve simplification under uncertainty,
where instead of a sequence of exact points, each uncertain point is
represented by a region, which contains the (unknown) true location of the
vertex. The regions we consider are disks, line segments, convex polygons, and
discrete sets of points. We are interested in finding the shortest subsequence
of uncertain points such that no matter what the true location of each
uncertain point is, the resulting polygonal curve is a valid simplification of
the original polygonal curve under the Hausdorff or the Fr\'echet distance. For
both these distance measures, we present polynomial-time algorithms for this
problem.


Abstract:
  We show for the first time that a multilayer perceptron (MLP) can serve as
the only scene representation in a real-time SLAM system for a handheld RGB-D
camera. Our network is trained in live operation without prior data, building a
dense, scene-specific implicit 3D model of occupancy and colour which is also
immediately used for tracking.
  Achieving real-time SLAM via continual training of a neural network against a
live image stream requires significant innovation. Our iMAP algorithm uses a
keyframe structure and multi-processing computation flow, with dynamic
information-guided pixel sampling for speed, with tracking at 10 Hz and global
map updating at 2 Hz. The advantages of an implicit MLP over standard dense
SLAM techniques include efficient geometry representation with automatic detail
control and smooth, plausible filling-in of unobserved regions such as the back
surfaces of objects.


Abstract:
  Context. Magnetic helicity is a physical quantity of great importance in the
study of astrophysical and natural plasmas. Although a density for helicity
cannot be defined, a good proxy for it is field line helicity. The appropriate
quantity for use in solar conditions is relative field line helicity (RFLH).
Aims. This work aims to study in detail the behaviour of RFLH, for the first
time, in a solar active region (AR). Methods. The target active region is the
large, eruptive AR 11158. In order to compute RFLH and all other quantities of
interest we use a non-linear force-free reconstruction of the AR coronal
magnetic field of excelent quality. Results. We find that the photospheric
morphology of RFLH is quite different than that of the magnetic field or of the
electrical current, and this is not sensitive to the chosen gauge in the
computation of RFLH. The value of helicity experiences a large decrease, 25% of
its pre-flare value, during an X-class flare of the AR, a change that is also
depicted in the photospheric morphology of RFLH. Moreover, the area of this
change coincides with the area that encompasses the flux rope, the magnetic
structure that later erupted. Conclusions. The use of RFLH can provide
important information about the value and location of the magnetic helicity
expelled from the solar atmosphere during eruptive events.


Abstract:
  We propose a new algorithm for Promise Constraint Satisfaction Problems
(PCSPs). It is a combination of the Constraint Basic LP relaxation and the
Affine IP relaxation (CLAP). We give a characterisation of the power of CLAP in
terms of a minion homomorphism. Using this characterisation, we identify a
certain weak notion of symmetry which, if satisfied by infinitely many
polymorphisms of PCSPs, guarantees tractability.
  We demonstrate that there are PCSPs solved by CLAP that are not solved by any
of the existing algorithms for PCSPs; in particular, not by the BLP+AIP
algorithm of Brakensiek and Guruswami [SODA'20] and not by a reduction to
tractable finite-domain CSPs.


Abstract:
  We provide a systematic analysis of the multipolar gravitational waveform,
energy and angular momentum fluxes emitted by a nonspinning test particle
orbiting a Kerr black hole along equatorial, eccentric orbits. These quantities
are computed by numerically solving the Teukolsky equation in the time domain
and are then used to test and improve the radiation reaction (and waveform) of
an effective-one-body (EOB) model. Eccentricity is incorporated into EOB by
replacing the quasi-circular Newtonian (or leading-order) prefactors in the
EOB-factorized multipolar waveform (and fluxes) with their generic
counterparts. The comparison between numerical and analytical quantities is
carried out over a large portion of the parameter space, notably for orbits
close to the separatrix and with high eccentricities. The analytical model
agrees to $\sim 1\%$ with the numerical data for orbits with moderate
eccentricities ($e\lesssim 0.3$) and moderate spins ($\hat{a}\lesssim 0.5$),
although this increases up to $\sim 33\%$ for large, positive, black hole spins
($\sim 0.9$) and large eccentricities ($\sim 0.9$). For moderate
eccentricities, the new EOB fluxes can be used to drive the dynamics through
the nonadiabatic transition from eccentric inspiral to plunge, merger and
ringdown, thus providing accurate an description of the merger. We also show
that the same technique can be applied to hyperbolic encounters. In general,
our approach to radiation reaction for eccentric inspirals should be seen as a
first step toward EOB modelization of extreme-mass-ratio-inspirals waveforms
for LISA.


Abstract:
  There is an increasing scientific interest in automatically analysing and
understanding human behavior, with particular reference to the evolution of
facial expressions and the recognition of the corresponding emotions. In this
paper we propose a technique based on Functional ANOVA to extract significant
patterns of face muscles movements, in order to identify the emotions expressed
by actors in recorded videos. We determine if there are time-related
differences on expressions among emotional groups by using a functional F-test.
Such results are the first step towards the construction of a reliable
automatic emotion recognition system


Abstract:
  Multimedia content is of predominance in the modern Web era. Investigating
how users interact with multimodal items is a continuing concern within the
rapid development of recommender systems. The majority of previous work focuses
on modeling user-item interactions with multimodal features included as side
information. However, this scheme is not well-designed for multimedia
recommendation. Specifically, only collaborative item-item relationships are
implicitly modeled through high-order item-user-item relations. Considering
that items are associated with rich contents in multiple modalities, we argue
that the latent semantic item-item structures underlying these multimodal
contents could be beneficial for learning better item representations and
further boosting recommendation. To this end, we propose a LATent sTructure
mining method for multImodal reCommEndation, which we term LATTICE for brevity.
To be specific, in the proposed LATTICE model, we devise a novel modality-aware
structure learning layer, which learns item-item structures for each modality
and aggregates multiple modalities to obtain latent item graphs. Based on the
learned latent graphs, we perform graph convolutions to explicitly inject
high-order item affinities into item representations. These enriched item
representations can then be plugged into existing collaborative filtering
methods to make more accurate recommendations. Extensive experiments on three
real-world datasets demonstrate the superiority of our method over
state-of-the-art multimedia recommendation methods and validate the efficacy of
mining latent item-item relationships from multimodal features.


Abstract:
  Over the last years, machine learning techniques have been applied to more
and more application domains, including software engineering and, especially,
software quality assurance. Important application domains have been, e.g.,
software defect prediction or test case selection and prioritization. The
ability to predict which components in a large software system are most likely
to contain the largest numbers of faults in the next release helps to better
manage projects, including early estimation of possible release delays, and
affordably guide corrective actions to improve the quality of the software.
However, developing robust fault prediction models is a challenging task and
many techniques have been proposed in the literature. Closely related to
estimating defect-prone parts of a software system is the question of how to
select and prioritize test cases, and indeed test case prioritization has been
extensively researched as a means for reducing the time taken to discover
regressions in software. In this survey, we discuss various approaches in both
fault prediction and test case prioritization, also explaining how in recent
studies deep learning algorithms for fault prediction help to bridge the gap
between programs' semantics and fault prediction features. We also review
recently proposed machine learning methods for test case prioritization (TCP),
and their ability to reduce the cost of regression testing without negatively
affecting fault detection capabilities.


Abstract:
  We present our recent study of cross sections and angular distributions of
projectile fragments from heavy-ion reactions at beam energy of 15 MeV/nucleon.
We studied the production cross sections and the angular distributions of
neutron-rich nuclides from collisions of a 86 Kr (15 MeV/nucleon) beam with
heavy targets ( 64 Ni, 124 Sn and 238 U). Experimental data from our previous
work at Texas A & M were compared with model calculations. Our calculations
were based on a two-step approach: the dynamical stage of the collision was
described with, first, the phenomenological Deep-Inelastic Transfer model (DIT)
and, alternatively, with the microscopic Constrained Molecular Dynamics model
(CoMD). The de-excitation of the hot heavy projectile fragments was performed
with the Statistical Multifragmentation Model (SMM). An overall good
discription of the available data was obtained with the models employed.
Furthermore, we performed calculations with a radioactive beam of 92 Kr (15
MeV/nucleon) interacting with a target of 238 U. We observed that the
multinucleon transfer mechanism leads to extremely neutron-rich nuclides toward
and beyond the astrophysical r-process path.


Abstract:
  For scattering amplitudes in strong background fields, it is -- at least in
principle -- possible to perturbatively expand the background to obtain
higher-point vacuum amplitudes. In the case of self-dual plane wave backgrounds
we consider this expansion for two-point, one-loop amplitudes in pure
Yang-Mills, QED and QCD. This enables us to obtain multicollinear limits of
1-loop vacuum amplitudes; the resulting helicity configurations are
surprisingly restricted, with only the all-plus helicity amplitude surviving.
These results are shown to be consistent with well-known vacuum amplitudes. We
also show that for both abelian and non-abelian supersymmetric gauge theories,
there is no helicity flip (and hence no vacuum birefringence) on any plane wave
background, generalising a result previously known in the Euler-Heisenberg
limit of super-QED.


Abstract:
  Reinforcement learning is an active research area with a vast number of
applications in robotics, and the RoboCup competition is an interesting
environment for studying and evaluating reinforcement learning methods. A known
difficulty in applying reinforcement learning to robotics is the high number of
experience samples required, being the use of simulated environments for
training the agents followed by transfer learning to real-world (sim-to-real) a
viable path. This article introduces an open-source simulator for the IEEE Very
Small Size Soccer and the Small Size League optimized for reinforcement
learning experiments. We also propose a framework for creating OpenAI Gym
environments with a set of benchmarks tasks for evaluating single-agent and
multi-agent robot soccer skills. We then demonstrate the learning capabilities
of two state-of-the-art reinforcement learning methods as well as their
limitations in certain scenarios introduced in this framework. We believe this
will make it easier for more teams to compete in these categories using
end-to-end reinforcement learning approaches and further develop this research
area.


Abstract:
  We show that the polygamous nature of multi-party quantum correlations can be
characterized in a {\em stronger} form based on Tsallis $q$-entropy and
$q$-expectation value. By considering the amount of entanglement that can be
distributed in multi-party systems, we establish a class of strong polygamy
inequalities of multi-party entanglement in terms of Tsallis $q$-entropy and
$q$-expectation for $q \geq 1$. Our new class of inequalities is in fact
tighter than the usual polygamy inequalities of multi-party entanglement, and
the tightness is explicitly illustrated by an example. Moreover, our new class
of inequalities is concerned with the $q$-expected entanglement distributed
between a single party and any possible subsets of the rest parties whereas the
usual polygamy inequality only considers the entanglement between a single
party and another. We further establish the equivalence between strong polygamy
inequalities of quantum entanglement and quantum discord distributed in
multi-party quantum systems.


Abstract:
  Despite the great efforts to find an effective way for COVID-19 prediction,
the virus nature and mutation represent a critical challenge to diagnose the
covered cases. However, developing a model to predict COVID-19 via Chest X-Ray
(CXR) images with accurate performance is necessary to help in early diagnosis.
In this paper, a hybrid quantum-classical convolutional Neural Networks (HQCNN)
model used the random quantum circuits (RQCs) as a base to detect COVID-19
patients with CXR images. A collection of 6952 CXR images, including 1161
COVID-19, 1575 normal, and 5216 pneumonia images, were used as a dataset in
this work. The proposed HQCNN model achieved higher performance with an
accuracy of 98.4\% and a sensitivity of 99.3\% on the first dataset cases.
Besides, it obtained an accuracy of 99\% and a sensitivity of 99.7\% on the
second dataset cases. Also, it achieved accuracy, and sensitivity of 88.6\%,
and 88.7\%, respectively, on the third multi-class dataset cases. Furthermore,
the HQCNN model outperforms various models in balanced accuracy, precision,
F1-measure, and AUC-ROC score. The experimental results are achieved by the
proposed model prove its ability in predicting positive COVID-19 cases.


Abstract:
  In this paper, we consider decentralized federated learning (FL) over
wireless networks, where over-the-air computation (AirComp) is adopted to
facilitate the local model consensus in a device-to-device (D2D) communication
manner. However, the AirComp-based consensus phase brings the additive noise in
each algorithm iterate and the consensus needs to be robust to wireless network
topology changes, which introduce a coupled and novel challenge of establishing
the convergence for wireless decentralized FL algorithm. To facilitate
consensus phase, we propose an AirComp-based DSGD with gradient tracking and
variance reduction (DSGT-VR) algorithm, where both precoding and decoding
strategies are developed for D2D communication. Furthermore, we prove that the
proposed algorithm converges linearly and establish the optimality gap for
strongly convex and smooth loss functions, taking into account the channel
fading and noise. The theoretical result shows that the additional error bound
in the optimality gap depends on the number of devices. Extensive simulations
verify the theoretical results and show that the proposed algorithm outperforms
other benchmark decentralized FL algorithms over wireless networks.


Abstract:
  Fels-Kaup (Acta Mathematica 2008) classified homogeneous $\mathfrak{C}_{2,1}$
hypersurfaces $M^5 \subset \mathbb{C}^3$ and discovered that they are all
biholomorphic to tubes $S^2 \times i \mathbb{R}^3$ over some affinely
homogeneous surface $S^2 \subset \mathbb{R}^3$. The second and third authors in
2003.08166, by performing highly non-straightforward calculations, conducted
the Cartan method of equivalence to classify homogeneous models of PDE systems
related to such $\mathfrak{C}^{2,1}$ hypersurfaces $M^5 \subset \mathbb{C}^3$.
Kolar-Kossovskiy 1905.05629 and the authors 2003.01952 constructed a formal and
a convergent Poincar\'e-Moser normal form for $\mathfrak{C}_{2,1}$
hypersurfaces $M^5 \subset \mathbb{C}^3$. But this was only a first,
preliminary step. Indeed, the invariant branching tree underlying Fels-Kaup's
classification was still missing in the literature, due to computational
obstacles. The present work applies the power series method of equivalence,
confirms Fels-Kaup 2008, and finds a differential-invariant tree. To terminate
the middle (thickest) branch, it is necessary to compute up to order $10$ with
$5$ variables. Again, calculations, done by hand, are non-straightforward.


Abstract:
  Pulsar searches are computationally demanding efforts to discover dispersed
periodic signals in time- and frequency-resolved data from radio telescopes.
The complexity and computational expense of simultaneously determining the
frequency-dependent delay (dispersion) and the periodicity of the signal is
further exacerbated by the presence of various types of radio-frequency
interference (RFI) and observing-system effects. New observing systems with
wider bandwidths, higher bit rates and greater overall sensitivity (also to
RFI) further enhance these challenges. We present a novel approach to the
analysis of pulsar search data. Specifically, we present a neural-network-based
pipeline that efficiently suppresses a wide range of RFI signals and
instrumental instabilities and furthermore corrects for (a priori unknown)
interstellar dispersion. After initial training of the network, our analysis
can be run in real time on a standard desktop computer with a commonly
available, consumer-grade GPU. We complement our neural network with standard
algorithms for periodicity searches. In particular with the Fast Fourier
Transform (FFT) and the Fast Folding Algorithm (FFA) and demonstrate that with
these straightforward extensions, our method is capable of identifying even
faint pulsars, while maintaining an extremely low number of false positives. We
furthermore apply our analysis to a subset of the PALFA survey and demonstrate
that in most cases the automated dispersion removal of our network produces a
time series of similar quality as dedispersing using the actual dispersion
measure of the pulsar in question. On our test data we are able to make
predictions whether a pulsar is present in the data or not 200 times faster
than real time.


Abstract:
  Continuum mechanics simulators, numerically solving one or more partial
differential equations, are essential tools in many areas of science and
engineering, but their performance often limits application in practice. Recent
modern machine learning approaches have demonstrated their ability to
accelerate spatio-temporal predictions, although, with only moderate accuracy
in comparison. Here we introduce MultiScaleGNN, a novel multi-scale graph
neural network model for learning to infer unsteady continuum mechanics.
MultiScaleGNN represents the physical domain as an unstructured set of nodes,
and it constructs one or more graphs, each of them encoding different scales of
spatial resolution. Successive learnt message passing between these graphs
improves the ability of GNNs to capture and forecast the system state in
problems encompassing a range of length scales. Using graph representations,
MultiScaleGNN can impose periodic boundary conditions as an inductive bias on
the edges in the graphs, and achieve independence to the nodes' positions. We
demonstrate this method on advection problems and incompressible fluid
dynamics. Our results show that the proposed model can generalise from uniform
advection fields to high-gradient fields on complex domains at test time and
infer long-term Navier-Stokes solutions within a range of Reynolds numbers.
Simulations obtained with MultiScaleGNN are between two and four orders of
magnitude faster than the ones on which it was trained.


Abstract:
  In robot sensing scenarios, instead of passively utilizing human captured
views, an agent should be able to actively choose informative viewpoints of a
3D object as discriminative evidence to boost the recognition accuracy. This
task is referred to as active object recognition. Recent works on this task
rely on a massive amount of training examples to train an optimal view
selection policy. But in realistic robot sensing scenarios, the large-scale
training data may not exist and whether the intelligent view selection policy
can be still learned from few object samples remains unclear. In this paper, we
study this new problem which is extremely challenging but very meaningful in
robot sensing -- Few-shot Active Object Recognition, i.e., to learn view
selection policies from few object samples, which has not been considered and
addressed before. We solve the proposed problem by adopting the framework of
meta learning and name our method "MetaView". Extensive experiments on both
category-level and instance-level classification tasks demonstrate that the
proposed method can efficiently resolve issues that are hard for
state-of-the-art active object recognition methods to handle, and outperform
several baselines by large margins.


Abstract:
  Bitcoin has become the leading cryptocurrency system, but the limit on its
transaction processing capacity has resulted in increased transaction fees and
delayed transaction confirmation. As such, it is pertinent to understand and
probably predict how transactions are handled by Bitcoin such that a user may
adapt the transaction requests and a miner may adjust the block generation
strategy and/or the mining pool to join. To this aim, the present paper
introduces results from an analysis of transaction handling in Bitcoin.
  Specifically, the analysis consists of two-part. The first part is an
exploratory data analysis revealing key characteristics in Bitcoin transaction
handling. The second part is a predictability analysis intended to provide
insights on transaction handling such as (i) transaction confirmation time,
(ii) block attributes, and (iii) who has created the block. The result shows
that some models do reasonably well for (ii), but surprisingly not for (i) or
(iii).


Abstract:
  As new classes of transients and variable stars are discovered, and
theoretical models are established to work or not to work for a few members of
the class, it is often the case that some researchers will make arguments on
the basis of Occam's razor that all members of the class must be produced by
whichever mechanism first successfully explained one of the objects. It is also
frequent that this assumption will be more more implicitly. Retrospective
analysis shows rather clearly that this argument fails a large fraction of the
time, and in many cases, this search for false consistency has led to more
fundamental astrophysical errors, a few of which are quite prominent in the
history of astronomy. A corollary of this is that on numerous occasions,
theoretical models to explain transients have turned out to be models that
describe real (but often not yet discovered) phenomenona other than the ones to
which they have first been applied, albeit with minor errors that caused the
model to appear to fit to a known phenomenon it did not describe. A set of
examples of such events is presented here (some of which will be quite familiar
to most astronomers), along with a discussion of why this phenomenon occurs,
how it may be manifesting itself at the present time. Some discussion will also
be made of why and when survey designs have led to immediate separation of
various transient mechanisms, generally by being overpowered in some way
relative to what is needed to {\it detect} a new class of objects.


Abstract:
  A novel multifunctional nanodevice based in doxorubicin (DOX)- loaded
mesoporous silica nanoparticles (MSNs) as nanoplatforms for the assembly of
different building blocks has been developed for bone cancer treatment. These
building blocks consists of: i) a polyacrylic acid (PAA) capping layer grafted
to MSNs via an acid-cleavable acetal linker, to minimize premature cargo
release and provide the nanosystem of pHresponsive drug delivery ability; and
ii) a targeting ligand, the plant lectin concanavalin A (ConA), able to
selectively recognize, bind and internalize owing to certain cell-surface
glycans, such as sialic acids (SA), overexpressed in given tumor cells. This
multifunctional nanosystem exhibits a noticeable higher internalization degree
into human osteosarcoma cells (HOS), overexpressing SA, compared to healthy
preosteoblast cells (MC3T3-E1). Moreover, the results indicate that small DOX
loading leads to almost 100% of osteosarcoma cell death in comparison with
healthy bone cells, which significantly preserve their viability. Besides, this
nanodevice has a cytotoxicity on tumor cells 8- fold higher than that caused by
the free drug. These findings demonstrate that the synergistic combination of
different building blocks into a unique nanoplatform increases antitumor
effectiveness and decreases toxicity towards normal cells. This line of attack
opens up new insights in targeted bone cancer therapy.


Abstract:
  We generalise the correspondence between $\aleph 0$-categorical theories and
their automorphism groups to arbitrary complete theories in classical logic,
and to some theories (including, in particular, all $\aleph 0$-categorical
ones) in continuous logic.


Abstract:
  In this paper, we consider the explicit bound for the second-order
approximation of the quadratic variation of a general fractional Gaussian
process $(G_t)_{t\ge 0}$. The second order mixed partial derivative of the
covariance function $ R(t,\, s)=\mathbb{E}[G_t G_s]$ can be decomposed into two
parts, one of which coincides with that of fractional Brownian motion and the
other of which is bounded by $(ts)^{H-1}$ up to a constant factor. This
condition is valid for a class of continuous Gaussian processes that fails to
be self-similar or have stationary increments. %Some examples include the
subfractional Brownian motion and the bi-fractional Brownian motion.
  Under this assumption, we obtain the optimal Berry-Ess\'{e}en bounds when
$H\in (0,\,\frac23]$ and the upper Berry-Ess\'{e}en bounds when $H\in
(\frac23,\,\frac34]$. As a by-product, we also show the almost sure central
limit theorem (ASCLT) for the quadratic variation when $H\in (0,\,\frac34]$.
The results extend that of \cite{NP 09} to the case of general Gaussian
processes, unify and improve the Berry-Ess\'{e}en bounds in \cite{Tu 11},
\cite{AE 12} and \cite{KL 21} for respectively the sub-fractional Brownian
motion, the bi-fractional Brownian motion and the sub-bifractional Brownian
motion.


Abstract:
  We show that if one starts with a Universe with some matter and a
cosmological constant, then quantum mechanics naturally induces an attractive
gravitational potential and an effective Newton's coupling. Thus gravity is an
emergent phenomenon and what should be quantized are the fundamental degrees of
freedom from which it emerges.


Abstract:
  R 144 is the visually brightest WR star in the Large Magellanic Cloud (LMC).
R 144 was reported to be a binary, making it potentially the most massive
binary thus observed. We perform a comprehensive spectral, photometric,
orbital, and polarimetric analysis of R 144.
  R 144 is an eccentric (e=0.51) 74.2-d binary comprising two relatively
evolved (age~2 Myr), H-rich WR stars. The hotter primary (WN5/6h, T=50 kK) and
the cooler secondary (WN6/7h,T=45kK) have nearly equal masses. The combination
of low rotation and H-depletion observed in the system is well reproduced by
contemporary evolution models that include boosted mass-loss at the upper-mass
end. The systemic velocity of R 144 and its relative isolation suggest that it
was ejected as a runaway from the neighbouring R 136 cluster. The optical
light-curve shows a clear orbital modulation that can be well explained as a
combination of two processes: excess emission stemming from wind-wind
collisions and double wind eclipses. Our light-curve model implies an orbital
inclination of i=60.4+-1.5deg, resulting in accurately constrained dynamical
masses of 74+-4 and 69+-4 Msun. Assuming that both binary components are core
H-burning, these masses are difficult to reconcile with the derived
luminosities (logL1,2 = 6.44, 6.39 [Lsun]), which correspond to evolutionary
masses of the order of 110 and 100Msun, respectively. Taken at face value, our
results imply that both stars have high classical Eddington factors of Gamma_e
= 0.78+-0.1. If the stars are on the main sequence, their derived radii
(~25Rsun) suggest that they are only slightly inflated, even at this high
Eddington factor. Alternatively, the stars could be core-He burning, strongly
inflated from the regular size of classical Wolf-Rayet stars (~1Rsun), a
scenario that could help resolve the observed mass discrepancy.


Abstract:
  The Eco-Driving control problem seeks to perform fuel efficient speed
planning for a Connected and Autonomous Vehicle (CAV) that can exploit
information available from advanced mapping, and from Vehicle-to-Everything
(V2X) communication. The ability of an Eco-Driving strategy to adapt in real
time to variable traffic scenarios where surrounding vehicles can be either
connected or unconnected is critical for further development and deployment of
this technology in the transportation sector. In this work, the Eco-Driving
strategy, formulated as a receding-horizon optimal control problem, is
integrated with a target vehicle speed prediction model and solved via Dynamic
Programming (DP) to determine the optimal speed trajectory in the presence of a
human-driven target vehicle. An encoder-decoder architecture analyzes the
patterns in the target vehicle velocity recorded over a historic window using a
Gated-Recurrent-Unit (GRU) based encoder and generates an estimate of the
future velocity trajectory using the GRU based decoder. A sensitivity study is
done to analyze the effect of the historical and prediction windows on the
accuracy of the velocity predictor. The proposed Eco-Driving controller is
evaluated through microscopic simulations using a traffic simulator.


Abstract:
  Observing the significance of spectrally-efficient secure non-orthogonal
multiple access (NOMA), this paper proposes a novel quality of service (QoS)
aware secure NOMA protocol that maximizes secrecy fairness among untrusted
users. Considering a base station (BS) and two users, a novel decoding order is
designed that provides security to both users. With the objective of ensuring
secrecy fairness between users, while satisfying their QoS requirements under
BS transmit power budget constraint, we explore the problem of minimizing the
maximum secrecy outage probability (SOP). Closed-form expression of pair outage
probability (POP) and optimal power allocation (PA) minimizing POP are
obtained. To analyze secrecy performance, analytical expressions of SOP for
both users are derived, and individual SOP minimization problems are solved
using concept of generalized-convexity. High signal-to-noise ratio
approximation of SOP and asymptotically optimized solution minimizing this
approximation is also found. Furthermore, a global-optimal solution from
secrecy fairness standpoint is obtained at low computational complexity, and
tight approximation is derived to get analytical insights. Numerical results
present useful insights on globally optimized PA which ensure secrecy fairness
and provide performance gain of about 55.12%, 69.30%, and 19.11%, respectively,
compared to fixed PA and individual users' optimal PAs. Finally, a tradeoff
between secrecy fairness performance and QoS demands is presented.


Abstract:
  This work utilizes the plethora of work on verification of sequential
programs for the purpose of verifying concurrent programs. We reduce the
verification of a concurrent program to a series of verification tasks of
sequential programs. Our approach is modular in the sense that each sequential
verification task roughly corresponds to the verification of a single thread,
with some additional information about the environment in which it operates.
Information regarding the environment is gathered during the run of the
algorithm, by need.
  While our approach is general, it specializes on concurrent programs where
the threads are structured hierarchically. The idea is to exploit the hierarchy
in order to minimize the amount of information that needs to be transferred
between threads. To that end, we verify one of the threads, considered "main",
as a sequential program. Its verification process initiates queries to its
"environment" (which may contain multiple threads). Those queries are answered
by sequential verification, if the environment consists of a single thread, or,
otherwise, by applying the same hierarchical algorithm on the environment.
  Our technique is fully automatic, and allows us to use any off-the-shelf
sequential model checker. We implemented our technique in a tool called CoMuS
and evaluated it against established tools for concurrent verification. Our
experiments show that it works particularly well on hierarchically structured
programs.


Abstract:
  Autoencoders are a widespread tool in machine learning to transform
high-dimensional data into a lowerdimensional representation which still
exhibits the essential characteristics of the input. The encoder provides an
embedding from the input data manifold into a latent space which may then be
used for further processing. For instance, learning interpolation on the
manifold may be simplified via the new manifold representation in latent space.
The efficiency of such further processing heavily depends on the regularity and
structure of the embedding. In this article, the embedding into latent space is
regularized via a loss function that promotes an as isometric and as flat
embedding as possible. The required training data comprises pairs of nearby
points on the input manifold together with their local distance and their local
Frechet average. This regularity loss functional even allows to train the
encoder on its own. The loss functional is computed via a Monte Carlo
integration which is shown to be consistent with a geometric loss functional
defined directly on the embedding map. Numerical tests are performed using
image data that encodes different data manifolds. The results show that smooth
manifold embeddings in latent space are obtained. These embeddings are regular
enough such that interpolation between not too distant points on the manifold
is well approximated by linear interpolation in latent space.


Abstract:
  This paper studies the fish growth trajectory tracking via reinforcement
learning under a representative bioenergetic growth model. Due to the complex
aquaculture condition and uncertain environmental factors such as temperature,
dissolved oxygen, un-ionized ammonia, and strong nonlinear couplings, including
multi-inputs of the fish growth model, the growth trajectory tracking problem
can not be efficiently solved by the model-based control approaches in
precision aquaculture. To this purpose, we formulate the growth trajectory
tracking problem as sampled-data optimal control using discrete state-action
pairs Markov decision process. We propose two Q-learning algorithms that learn
the optimal control policy from the sampled data of the fish growth
trajectories at every stage of the fish life cycle from juveniles to the
desired market weight in the aquaculture environment. The Q-learning scheme
learns the optimal feeding control policy to fish growth rate cultured in cages
and the optimal feeding rate control policy with an optimal temperature profile
for the aquaculture fish growth rate in tanks. The simulation results
demonstrate that both Q-learning strategies achieve high trajectory tracking
performance with less amount feeding rates.


Abstract:
  Heterogeneous catalysis is an example of a complex materials function,
governed by an intricate interplay of several processes, e.g., the different
surface chemical reactions, and the dynamic re-structuring of the catalyst
material at reaction conditions. Modelling the full catalytic progression via
first-principles statistical mechanics is impractical, if not impossible.
Instead, we show here how a tailored artificial-intelligence approach can be
applied, even to a small number of materials, to model catalysis and determine
the key descriptive parameters ("materials genes") reflecting the processes
that trigger, facilitate, or hinder catalyst performance. We start from a
consistent experimental set of "clean data", containing nine vanadium-based
oxidation catalysts. These materials were synthesized, fully characterized, and
tested according to standardized protocols. By applying the symbolic-regression
SISSO approach, we identify correlations between the few most relevant
materials properties and their reactivity. This approach highlights the
underlying physicochemical processes, and accelerates catalyst design.


Abstract:
  Software development methods are usually not applied by the book. Companies
are under pressure to continuously deploy software products that meet market
needs and stakeholders' requests. To implement efficient and effective
development processes, companies utilize multiple frameworks, methods and
practices, and combine these into hybrid methods. A common combination contains
a rich management framework to organize and steer projects complemented with a
number of smaller practices providing the development teams with tools to
complete their tasks. In this paper, based on 732 data points collected through
an international survey, we study the software development process use in
practice. Our results show that 76.8% of the companies implement hybrid
methods. Company size as well as the strategy in devising and evolving hybrid
methods affect the suitability of the chosen process to reach company or
project goals. Our findings show that companies that combine planned
improvement programs with process evolution can increase their process'
suitability by up to 5%.


Abstract:
  Blind face restoration usually relies on facial priors, such as facial
geometry prior or reference prior, to restore realistic and faithful details.
However, very low-quality inputs cannot offer accurate geometric prior while
high-quality references are inaccessible, limiting the applicability in
real-world scenarios. In this work, we propose GFP-GAN that leverages rich and
diverse priors encapsulated in a pretrained face GAN for blind face
restoration. This Generative Facial Prior (GFP) is incorporated into the face
restoration process via novel channel-split spatial feature transform layers,
which allow our method to achieve a good balance of realness and fidelity.
Thanks to the powerful generative facial prior and delicate designs, our
GFP-GAN could jointly restore facial details and enhance colors with just a
single forward pass, while GAN inversion methods require expensive
image-specific optimization at inference. Extensive experiments show that our
method achieves superior performance to prior art on both synthetic and
real-world datasets.


Abstract:
  We present RePOSE, a fast iterative refinement method for 6D object pose
estimation. Prior methods perform refinement by feeding zoomed-in input and
rendered RGB images into a CNN and directly regressing an update of a refined
pose. Their runtime is slow due to the computational cost of CNN, which is
especially prominent in multiple-object pose refinement. To overcome this
problem, RePOSE leverages image rendering for fast feature extraction using a
3D model with a learnable texture. We call this deep texture rendering, which
uses a shallow multi-layer perceptron to directly regress a view-invariant
image representation of an object. Furthermore, we utilize differentiable
Levenberg-Marquardt (LM) optimization to refine a pose fast and accurately by
minimizing the feature-metric error between the input and rendered image
representations without the need of zooming in. These image representations are
trained such that differentiable LM optimization converges within few
iterations. Consequently, RePOSE runs at 92 FPS and achieves state-of-the-art
accuracy of 51.6% on the Occlusion LineMOD dataset - a 4.1% absolute
improvement over the prior art, and comparable result on the YCB-Video dataset
with a much faster runtime. The code is available at
https://github.com/sh8/repose.


Abstract:
  Logical relations widely exist in human activities. Human use them for making
judgement and decision according to various conditions, which are embodied in
the form of \emph{if-then} rules. As an important kind of cognitive
intelligence, it is prerequisite of representing and storing logical relations
rightly into computer systems so as to make automatic judgement and decision,
especially for high-risk domains like medical diagnosis. However, current
numeric ANN (Artificial Neural Network) models are good at perceptual
intelligence such as image recognition while they are not good at cognitive
intelligence such as logical representation, blocking the further application
of ANN. To solve it, researchers have tried to design logical ANN models to
represent and store logical relations. Although there are some advances in this
research area, recent works still have disadvantages because the structures of
these logical ANN models still don't map more directly with logical relations
which will cause the corresponding logical relations cannot be read out from
their network structures. Therefore, in order to represent logical relations
more clearly by the neural network structure and to read out logical relations
from it, this paper proposes a novel logical ANN model by designing the new
logical neurons and links in demand of logical representation. Compared with
the recent works on logical ANN models, this logical ANN model has more clear
corresponding with logical relations using the more direct mapping method
herein, thus logical relations can be read out following the connection
patterns of the network structure. Additionally, less neurons are used.


Abstract:
  The analyticity properties of the scattering amplitude for a massive scalar
field is reviewed in this article where the spacetime geometry is
$R^{3,1}\otimes S^1$ i.e. one spatial dimension is compact. Khuri investigated
the analyticity of scattering amplitude in a nonrelativitstic potential model
in three dimensions with an additional compact dimension. He showed that, under
certain circumstances, the forward amplitude is nonanalytic. He argued that in
high energy scattering if such a behaviour persists it would be in conflicts
with the established results of quantum field theory and LHC might observe such
behaviors. We envisage a real scalar massive field in flat Minkowski spacetime
in five dimensions. The Kaluza-Klein (KK) compactification is implemented on a
circle. The resulting four dimensional manifold is $R^{3,1}\otimes S^1$. The
LSZ formalism is adopted to study the analyticity of the scattering amplitude.
The nonforward dispersion relation is proved. In addition the Jin-Martin bound
and an analog of the Froissart-Martin bound are proved. A novel proposal is
presented to look for evidence of the large-radius-compactification scenario. A
seemingly violation of Froissart-Martin bound at LHC energy might hint that an
extra dimension might be decompactified. However, we find no evidence for
violation of the bound in our analysis.


Abstract:
  Graph convolutional networks (GCNs) have received considerable research
attention recently. Most GCNs learn the node representations in Euclidean
geometry, but that could have a high distortion in the case of embedding graphs
with scale-free or hierarchical structure. Recently, some GCNs are proposed to
deal with this problem in non-Euclidean geometry, e.g., hyperbolic geometry.
Although hyperbolic GCNs achieve promising performance, existing hyperbolic
graph operations actually cannot rigorously follow the hyperbolic geometry,
which may limit the ability of hyperbolic geometry and thus hurt the
performance of hyperbolic GCNs. In this paper, we propose a novel hyperbolic
GCN named Lorentzian graph convolutional network (LGCN), which rigorously
guarantees the learned node features follow the hyperbolic geometry.
Specifically, we rebuild the graph operations of hyperbolic GCNs with
Lorentzian version, e.g., the feature transformation and non-linear activation.
Also, an elegant neighborhood aggregation method is designed based on the
centroid of Lorentzian distance. Moreover, we prove some proposed graph
operations are equivalent in different types of hyperbolic geometry, which
fundamentally indicates their correctness. Experiments on six datasets show
that LGCN performs better than the state-of-the-art methods. LGCN has lower
distortion to learn the representation of tree-likeness graphs compared with
existing hyperbolic GCNs. We also find that the performance of some hyperbolic
GCNs can be improved by simply replacing the graph operations with those we
defined in this paper.


Abstract:
  We study the potential of future Parity-Violating Electron Scattering (PVES)
data to probe the parameter space of the Standard Model Effective Field Theory
(SMEFT). We contrast the constraints derived from Drell-Yan data taken at the
Large Hadron Collider (LHC) with projections of the planned PVES experiments
SoLID and P2. We show that the PVES data can complement the bounds set by the
LHC data in the dimension-6 operator space since it probes different
combinations of operators than Drell-Yan. The lower characteristic energy of P2
and SoLID also helps disentangle effects of dimension-6 and dimension-8
operators that are difficult to resolve with LHC Drell-Yan data alone.


Abstract:
  The Egalitarian Allocation (EA) is a well-known profit sharing method for
cooperative games which attempts to distribute profit among participants in a
most equal way while respecting the individual contributions to the obtained
profit. Despite having desirable properties from the viewpoint of game theory
like being contained in the core, the EA is in general hard to compute. Another
well-known method is given by Vickrey Payments (VP). Again, the VP have
desirable properties like coalitional rationality, the VP do not fulfill budget
balance in general and, thus, are not contained in the core in general. One
attempt to overcome this shortcoming is to scale down the VP. This can be done
by a unique scaling factor, or, by individual scaling factors. Now, the
individual scaled Vickrey Payments (ISV) are computed by maximizing the scaling
factors lexicographically. In this paper we show that the ISV payments are in
fact identical to a weighted EA, thus exhibiting an interesting connection
between EA and VP. With this, we conclude the uniqueness of the ISV payments
and provide a polynomial time algorithm for computing a special weighted EA.


Abstract:
  A novel theoretical convergence rate estimate for a Balancing Domain
Decomposition by Constraints algorithm is proven for the solution of the
cardiac Bidomain model, describing the propagation of the electric impulse in
the cardiac tissue. The non-linear system arises from a fully implicit time
discretization and a monolithic solution approach. The preconditioned
non-symmetric operator is constructed from the linearized system arising within
the Newton-Krylov approach for the solution of the non-linear problem; we
theoretically analyze and prove a convergence rate bound for the Generalised
Minimal Residual iterations' residual. The theory is confirmed by extensive
parallel numerical tests, widening the class of robust and efficient solvers
for implicit time discretizations of the Bidomain model.


Abstract:
  We use the superspace formulation of supergravity in eleven and ten
dimensions to compute fermion couplings on the M2-brane and on D$p$-branes. In
this formulation fermionic couplings arise naturally from the
$\theta$-expansion of the superfields from which the brane actions are
constructed. The techniques we use and develop can in principle be applied to
determine the fermionic couplings to general background fields up to arbitrary
order. Starting with the superspace formulation of 11-dimensional supergravity,
we use a geometric technique known as the `normal coordinate' method to obtain
the $\theta$-expansion of the M2-brane action. We then present a method which
allows us to translate the knowledge of fermionic couplings on the M2-brane to
knowledge of such couplings on the D2-brane, and then to any D$p$-brane. This
method is based on superspace generalizations of both the compactification
taking 11-dimensional supergravity to type IIA supergravity and the T-duality
rules connecting the type IIA and type IIB supergravities.


Abstract:
  We propose a cascade of neural models that performs sentence classification,
phrase recognition, and triple extraction to automatically structure the
scholarly contributions of NLP publications. To identify the most important
contribution sentences in a paper, we used a BERT-based classifier with
positional features (Subtask 1). A BERT-CRF model was used to recognize and
characterize relevant phrases in contribution sentences (Subtask 2). We
categorized the triples into several types based on whether and how their
elements were expressed in text, and addressed each type using separate
BERT-based classifiers as well as rules (Subtask 3). Our system was officially
ranked second in Phase 1 evaluation and first in both parts of Phase 2
evaluation. After fixing a submission error in Pharse 1, our approach yields
the best results overall. In this paper, in addition to a system description,
we also provide further analysis of our results, highlighting its strengths and
limitations. We make our code publicly available at
https://github.com/Liu-Hy/nlp-contrib-graph.


Abstract:
  Networks of genetic expression can be modelled by hypergraphs with the
additional structure that real coefficients are given to each vertex-edge
incidence. The spectra, i.e. the multiset of the eigenvalues, of such
hypergraphs, are known to encode structural information of the data. We show
how these spectra can be used, in particular, in order to give an estimation of
cellular redundancy, a novel measure of gene expression heterogeneity, of the
network. We analyze some simulated and real data sets of gene expression for
illustrating the new method proposed here.


Abstract:
  This PhD thesis is the conclusion of some of my works carried out at the
Centre de Physique Th\'eorique, under the supervision of Serge Lazzarini. Two
subjects are presented in the manuscript, both aiming at studying the effect of
the spin of elementary particles on otherwise known theories.
  First is a study of the L\'evy-Leblond-Newton (LLN) equation, which is used
to describe the evolution of a quantum system with spin 1/2 that is coupled to
its own gravitational potential. After reviewing some symmetries of non
relativistic Quantum Mechanics, and how to geometrize them with the help of
Bargmann structures, we recall what is the L\'evy-Leblond equation. In this
chapter, the LLN equation is described in a fully covariant way on Bargmann
structures. This covariant formulation then helps to derive the dynamical
symmetries of the equation, and conserved quantities. The symmetry group of
this equation turns out to be the already known Schr\"odinger-Newton group.
  The second part deals with the trajectory of particles with spin in General
Relativity. We first highlight Souriau's geometric method to derive the
Mathisson-Papapetrou-Dixon (MPD) equations, and then discuss the different
possible Spin Supplementary Conditions (SSC) that exist to close the system of
MPD equations. In this chapter, we assume the Tulczyjew SSC to describe the
trajectory of photons, leading to the Souriau-Saturnini equations. We then
present some works where we have applied these equations, in a Schwarzschild
spacetime, and in a spacetime deformed by a gravitational wave. In the first
work, we found gravitational birefringence: the trajectory deviates from the
geodesic plane, depending on the helicity of the photon and its wavelength. The
second work presented also found birefringence in gravitational interferometry
experiments, however the effect is many orders of magnitude lower than what we
can detect.


Abstract:
  Automation of excavation tasks requires real-time trajectory planning
satisfying various constraints. To guarantee both constraint feasibility and
real-time trajectory re-plannability, we present an integrated framework for
real-time optimization-based trajectory planning of a hydraulic excavator. The
proposed framework is composed of two main modules: a global planner and a
real-time local planner. The global planner computes the entire global
trajectory considering excavation volume and energy minimization while the
local counterpart tracks the global trajectory in a receding horizon manner,
satisfying dynamic feasibility, physical constraints, and
disturbance-awareness. We validate the proposed planning algorithm in a
simulation environment where two types of operations are conducted in the
presence of emulated disturbance from hydraulic friction and soil-bucket
interaction: shallow and deep excavation. The optimized global trajectories are
obtained in an order of a second, which is tracked by the local planner at
faster than 30 Hz. To the best of our knowledge, this work presents the first
real-time motion planning framework that satisfies constraints of a hydraulic
excavator, such as force/torque, power, cylinder displacement, and flow rate
limits.


Abstract:
  This paper uses Covasim, an agent-based model (ABM) of COVID-19, to evaluate
and scenarios of epidemic spread in New York State (USA), the UK, and the
Novosibirsk region (Russia). Epidemiological parameters such as contagiousness
(virus transmission rate), initial number of infected people, and probability
of being tested depend on the region's demographic and geographical features,
the containment measures introduced; they are calibrated to data about COVID-19
spread in the region of interest. At the first stage of our study,
epidemiological data (numbers of people tested, diagnoses, critical cases,
hospitalizations, and deaths) for each of the mentioned regions were analyzed.
The data were characterized in terms of seasonality, stationarity, and
dependency spaces, and were extrapolated using machine learning techniques to
specify unknown epidemiological parameters of the model. At the second stage,
the Optuna optimizer based on the tree Parzen estimation method for objective
function minimization was applied to determine the model's unknown parameters.
The model was validated with the historical data of 2020. The modeled results
of COVID-19 spread in New York State, the UK and the Novosibirsk region have
demonstrated that if the level of testing and containment measures is
preserved, the number of positive cases in New York State and the Novosibirsk
region will remain the same during March of 2021, while in the UK it will
reduce. Due to the features of the data for the Novosibirsk region (two
datasets are characterized as stationary series with probability of 1), the
forecast precision is relatively high for the number of hospitalizations, but
is lower for new cases of COVID-19.


Abstract:
  Movement tracks of wild animals frequently fit models of anomalous rather
than simple diffusion, mostly reported as ergodic superdiffusive motion
combining area-restricted search within a local patch and larger-scale
commuting between patches, as highlighted by the L\'evy walk paradigm. Since
L\'evy walks are scale invariant, superdiffusive motion is also expected within
patches, yet investigation of such local movements has been precluded by the
lack of accurate high-resolution data at this scale. Here, using rich
high-resolution movement datasets ($>\! 7 \times 10^7$ localizations) from 70
individuals and continuous-time random walk modeling, we found subdiffusive
behavior and ergodicity breaking in the localized movement of three species of
avian predators. Small-scale, within-patch movement was qualitatively
different, not inferrable and separated from large-scale inter-patch movement
via a clear phase transition. Local search is characterized by long
power-law-distributed waiting times with diverging mean, giving rise to
ergodicity breaking in the form of considerable variability uniquely observed
at this scale. This implies that wild animal movement is scale specific rather
than scale free, with no typical waiting time at the local scale. Placing these
findings in the context of the static-ambush to mobile-cruise foraging
continuum, we verify predictions based on the hunting behavior of the study
species and the constraints imposed by their prey.


Abstract:
  Data aggregation is an efficient approach to handle the congestion introduced
by a massive number of machine type devices (MTDs). The aggregators not only
collect data but also implement scheduling mechanisms to cope with scarce
network resources. We use the concept of meta distribution (MD) of the
signal-to-interference ratio (SIR) to gain a complete understanding of the
per-link reliability and describe the performance of two scheduling methods for
data aggregation of machine type communication (MTC): random resource
scheduling (RRS) and channel-aware resource scheduling (CRS). The results show
the fraction of users in the network that achieves a target reliability, which
is an important aspect to consider when designing wireless systems with
stringent service requirements.


Abstract:
  We study three exceptional theta correspondences for p-adic groups, where one
member of the dual pair is the exceptional group G2. We prove the Howe duality
conjecture for these dual pairs and a dichotomy theorem, and determine
explicitly the theta lifts of all non-cuspidal representations.


Abstract:
  Several attacks have been proposed against Proof-of-Work blockchains, which
may increase the attacker's share of mining rewards (e.g., selfish mining,
block withholding). A further impact of such attacks, which has not been
considered in prior work, is that decreasing the profitability of mining for
honest nodes incentivizes them to stop mining or to leave the attacked chain
for a more profitable one. The departure of honest nodes exacerbates the attack
and may further decrease profitability and incentivize more honest nodes to
leave. In this paper, we first present an empirical analysis showing that there
is a statistically significant correlation between the profitability of mining
and the total hash rate, confirming that miners indeed respond to changing
profitability. Second, we present a theoretical analysis showing that selfish
mining under such elastic hash supply leads either to the collapse of a chain,
i.e., all honest nodes leaving, or to a stable equilibrium depending on the
attacker's initial share.


Abstract:
  When we use End-to-end automatic speech recognition (E2E-ASR) system for
real-world applications, a voice activity detection (VAD) system is usually
needed to improve the performance and to reduce the computational cost by
discarding non-speech parts in the audio. This paper presents a novel
end-to-end (E2E), multi-task learning (MTL) framework that integrates ASR and
VAD into one model. The proposed system, which we refer to as Long-Running
Speech Recognizer (LR-SR), learns ASR and VAD jointly from two seperate
task-specific datasets in the training stage. With the assistance of VAD, the
ASR performance improves as its connectionist temporal classification (CTC)
loss function can leverage the VAD alignment information. In the inference
stage, the LR-SR system removes non-speech parts at low computational cost and
recognizes speech parts with high robustness. Experimental results on segmented
speech data show that the proposed MTL framework outperforms the baseline
single-task learning (STL) framework in ASR task. On unsegmented speech data,
we find that the LR-SR system outperforms the baseline ASR systems that build
an extra GMM-based or DNN-based voice activity detector.


Abstract:
  We tackle a 3D scene stylization problem - generating stylized images of a
scene from arbitrary novel views given a set of images of the same scene and a
reference image of the desired style as inputs. Direct solution of combining
novel view synthesis and stylization approaches lead to results that are blurry
or not consistent across different views. We propose a point cloud-based method
for consistent 3D scene stylization. First, we construct the point cloud by
back-projecting the image features to the 3D space. Second, we develop point
cloud aggregation modules to gather the style information of the 3D scene, and
then modulate the features in the point cloud with a linear transformation
matrix. Finally, we project the transformed features to 2D space to obtain the
novel views. Experimental results on two diverse datasets of real-world scenes
validate that our method generates consistent stylized novel view synthesis
results against other alternative approaches.


Abstract:
  Pure Yang-Mills theory in 2 spacetime dimensions shows exact Casimir scaling.
Thus there are infinitely many string tensions, and this has been understood as
a result of non-propagating gluons in 2 dimensions. From ordinary symmetry
considerations, however, this richness in the spectrum of string tensions seems
mysterious. Conventional wisdom has it that it is the center symmetry that
classifies string tensions, but being finite it cannot explain infinitely many
confining strings. In this note, we resolve this discrepancy between dynamics
and kinematics by pointing out the existence of a non-invertible 1-form
symmetry, which is able to distinguish Wilson loops in different
representations. We speculate on possible implications for Yang-Mills theories
in 3 and 4 dimensions.


Abstract:
  Field Programmable Gate Arrays (FPGAs) have the potential to accelerate
specific HPC codes. However even with the advent of High Level Synthesis (HLS),
which enables FPGA programmers to write code in C or C++, programming such
devices still requires considerable expertise. Much of this is due to the fact
that these architectures are founded on dataflow rather than the Von Neumann
abstraction of CPUs or GPUs. Thus programming FPGAs via imperative languages is
not optimal and can result in very significant performance differences between
the first and final versions of algorithms on dataflow architectures with the
steps in between often not obvious and requiring considerable expertise.
  In this position paper we argue that languages built upon dataflow principals
should be exploited to enable fast by construction codes for FPGAs, and this is
akin to the programmer adopting the abstraction of developing a bespoke
dataflow machine specialised for their application. It is our belief that much
can be learnt from the generation of dataflow languages that gained popularity
in the 1970s and 1980s around programming general purpose dataflow machines,
and we introduce Lucent which is a modern derivative of Lucid, and used as a
vehicle to explore this hypothesis. The idea behind Lucent is to provide high
programmer productivity and performance for FPGAs by giving developers the most
suitable language level abstractions. The focus of Lucent is very much to
support the acceleration of HPC kernels, rather than the embedded electronics
and circuit level, and we provide a brief overview of the language driven by
examples.


Abstract:
  Machine learning is playing an increasingly important role in medical image
analysis, spawning new advances in the clinical application of neuroimaging.
There have been some reviews on machine learning and epilepsy before, and they
mainly focused on electrophysiological signals such as electroencephalography
(EEG) and stereo electroencephalography (SEEG), while neglecting the potential
of neuroimaging in epilepsy research. Neuroimaging has its important advantages
in confirming the range of the epileptic region, which is essential in
presurgical evaluation and assessment after surgery. However, it is difficult
for EEG to locate the accurate epilepsy lesion region in the brain. In this
review, we emphasize the interaction between neuroimaging and machine learning
in the context of epilepsy diagnosis and prognosis. We start with an overview
of epilepsy and typical neuroimaging modalities used in epilepsy clinics, MRI,
DWI, fMRI, and PET. Then, we elaborate two approaches in applying machine
learning methods to neuroimaging data: i) the conventional machine learning
approach combining manual feature engineering and classifiers, ii) the deep
learning approach, such as the convolutional neural networks and autoencoders.
Subsequently, the application of machine learning on epilepsy neuroimaging,
such as segmentation, localization, and lateralization tasks, as well as tasks
directly related to diagnosis and prognosis are looked into in detail. Finally,
we discuss the current achievements, challenges, and potential future
directions in this field, hoping to pave the way for computer-aided diagnosis
and prognosis of epilepsy.


Abstract:
  In this research-expository paper we recall the basic results of reduction
theory of positive definite quadratic forms. Using the result of Ryskov on
admissible centerings and the result of Tammela about the determination of a
Minkowski-reduced form, we prove that the absolute values of coordinates of a
minimum vector in a six-dimensional Minkowski-reduced basis are less or equal
to three. To get this little sharpening of the result which can be deduced
automatically from Tammela's works we combine some elementary geometric
reasonings with the mentioned theoretical results.


Abstract:
  Event detection (ED) aims at detecting event trigger words in sentences and
classifying them into specific event types. In real-world applications, ED
typically does not have sufficient labelled data, thus can be formulated as a
few-shot learning problem. To tackle the issue of low sample diversity in
few-shot ED, we propose a novel knowledge-based few-shot event detection method
which uses a definition-based encoder to introduce external event knowledge as
the knowledge prior of event types. Furthermore, as external knowledge
typically provides limited and imperfect coverage of event types, we introduce
an adaptive knowledge-enhanced Bayesian meta-learning method to dynamically
adjust the knowledge prior of event types. Experiments show our method
consistently and substantially outperforms a number of baselines by at least 15
absolute F1 points under the same few-shot settings.


Abstract:
  Modern data lakes are deeply heterogeneous in the vocabulary that is used to
describe data. We study a problem of disambiguation in data lakes: how can we
determine if a data value occurring more than once in the lake has different
meanings and is therefore a homograph? While word and entity disambiguation
have been well studied in computational linguistics, data management and data
science, we show that data lakes provide a new opportunity for disambiguation
of data values since they represent a massive network of interconnected values.
We investigate to what extent this network can be used to disambiguate values.
DomainNet uses network-centrality measures on a bipartite graph whose nodes
represent values and attributes to determine, without supervision, if a value
is a homograph. A thorough experimental evaluation demonstrates that
state-of-the-art techniques in domain discovery cannot be re-purposed to
compete with our method. Specifically, using a domain discovery method to
identify homographs has a precision and a recall of 38% versus 69% with our
method on a synthetic benchmark. By applying a network-centrality measure to
our graph representation, DomainNet achieves a good separation between
homographs and data values with a unique meaning. On a real data lake our
top-200 precision is 89%.


Abstract:
  For a subset $S$ of nonnegative integers and a vector
$\mathbf{a}=(a_1,\dots,a_k)$ of positive integers, let $V'_S(\mathbf{a})=\{
a_1s_1+\cdots+a_ks_k : s_i\in S\} \setminus \{0\}$. For a positive integer $n$,
let $\mathcal T(n)$ be the set of integers greater than or equal to $n$. In
this paper, we consider the problem of finding all vectors $\mathbf{a}$
satisfying $V'_S(\mathbf{a})=\mathcal T(n)$, when $S$ is the set of
(generalized) $m$-gonal numbers and $n$ is a positive integer. In particular,
we completely resolve the case when $S$ is the set of triangular numbers.


Abstract:
  In this paper we prove that the only blowup solutions to the focusing,
quintic nonlinear Schr{\"o}dinger equation with mass equal to the mass of the
soliton are rescaled solitons or the pseudoconformal transformation of those
solitons.


Abstract:
  Smart healthcare systems (SHSs) are providing fast and efficient disease
treatment leveraging wireless body sensor networks (WBSNs) and implantable
medical devices (IMDs)-based internet of medical things (IoMT). In addition,
IoMT-based SHSs are enabling automated medication, allowing communication among
myriad healthcare sensor devices. However, adversaries can launch various
attacks on the communication network and the hardware/firmware to introduce
false data or cause data unavailability to the automatic medication system
endangering the patient's life. In this paper, we propose SHChecker, a novel
threat analysis framework that integrates machine learning and formal analysis
capabilities to identify potential attacks and corresponding effects on an
IoMT-based SHS. Our framework can provide us with all potential attack vectors,
each representing a set of sensor measurements to be altered, for an SHS given
a specific set of attack attributes, allowing us to realize the system's
resiliency, thus the insight to enhance the robustness of the model. We
implement SHChecker on a synthetic and a real dataset, which affirms that our
framework can reveal potential attack vectors in an IoMT system. This is a
novel effort to formally analyze supervised and unsupervised machine learning
models for black-box SHS threat analysis.


Abstract:
  This report aims to study adaptive synchronization between a general class of
hyperchaotic complex-valued systems with unknown parameters, which is motivated
by extensive application areas of this topic in nonlinear sciences (e.g.,
secure communications, encryption techniques, etc.). Based on the complexity of
hyperchaotic dynamical systems, which may be beneficial in secure
communications, a scheme to achieve adaptive synchronization of a general class
of hyperchaotic complex-valued systems with unknown parameters is proposed. To
verify scheme's consistency, the control functions based on adaptive laws of
parameters are derived analytically, and the related numerical simulations are
performed. The complex-valued Rabinovich system describing the parametric
excitation of waves in a magneto-active plasma is considered as an interesting
example to study this kind of synchronization. A scheme for secure
communication and improving the cryptosystem is proposed; the sketch is
constructed to split the message and inject some bit of information signal into
parameters modulation and the other bit into the transmitter system's states,
which, in turn, complicates decryption task by intruders. Meanwhile, the
information signal can be accurately retrieved at the receiver side by adaptive
techniques and decryption function. Different types of encrypted messages are
considered for testing the robustness of the proposed scheme (e.g., plain text
and gray images with diverse scales of white Gaussian noise).


Abstract:
  Inspection and maintenance are two crucial aspects of industrial pipeline
plants. While robotics has made tremendous progress in the mechanic design of
in-pipe inspection robots, the autonomous control of such robots is still a big
open challenge due to the high number of actuators and the complex manoeuvres
required. To address this problem, we investigate the usage of Deep
Reinforcement Learning for achieving autonomous navigation of in-pipe robots in
pipeline networks with complex topologies. Moreover, we introduce a
hierarchical policy decomposition based on Hierarchical Reinforcement Learning
to learn robust high-level navigation skills. We show that the hierarchical
structure introduced in the policy is fundamental for solving the navigation
task through pipes and necessary for achieving navigation performances superior
to human-level control.


Abstract:
  In this paper, disturbance reconstruction and robust trajectory tracking
control of biped robots with hybrid dynamics in the port-Hamiltonian form is
investigated. A new type of Hamiltonian function is introduced, which ensures
the finite-time stability of the closed-loop system. The proposed control
system consists of two loops: an inner and an outer loop. A fractional
proportional-integral-derivative filter is used to achieve finite-time
convergence for position tracking errors at the outer loop. A fractional-order
sliding mode controller acts as a centralized controller at the inner-loop,
ensuring the finite-time stability of the velocity tracking error. In this
loop, the undesired effects of unknown external disturbance and parameter
uncertainties are compensated using estimators. Two disturbance estimators are
envisioned. The former is designed using fractional calculus. The latter is an
adaptive estimator, and it is constructed using the general dynamic of biped
robots. Stability analysis shows that the closed-loop system is finite-time
stable in both contact-less and impact phases. Simulation studies on two types
of biped robots (i.e., two-link walker and RABBIT biped robot) demonstrate the
proposed controller's tracking performance and disturbance rejection
capability.


Abstract:
  Aim The pharmacokinetic (PK) properties of cyclosporine (CsA) in renal
transplant recipients are patient- and time-dependent. Knowledge of this
time-related variability is necessary to maintain or achieve CsA target
exposure. Here, we aimed to identify factors explaining variabilities in CsA PK
properties and characterise time-dependent clearance (CL/F) by performing a
comprehensive analysis of CsA PK factors using population PK (popPK) modelling
of long-term follow-up data from our institution. Methods In total, 3,674
whole-blood CsA concentrations from 183 patients who underwent initial renal
transplantation were analysed using nonlinear mixed-effects modelling. The
effects of potential covariates were selected according to a previous report
and well-accepted theoretical mechanisms. Model-informed individualised
therapeutic regimens were also conducted. Results A two-compartment model
adequately described the data and the estimated mean CsA CL/F was 32.6 L h-1
(5%). Allometrically scaled body size, haematocrit (HCT) level, CGC haplotype
carrier status, and postoperative time may contribute to CsA PK variability.
The CsA bioavailability in patients receiving a prednisolone dose (PD) of 80 mg
was 20.6% lower than that in patients receiving 20 mg. A significant decrease
(52.6%) in CL/F was observed as the HCT increased from 10.5% to 60.5%. The CL/F
of the non-CGC haplotype carrier was 14.4% lower than that of the CGC haplotype
carrier at 3 months post operation. CsA dose adjustments should be considered
in different postoperative periods. Conclusions By monitoring body size, HCT,
PD, and CGC haplotype, changes in CsA CL/F over time could be predicted. Such
information could be used to optimise CsA therapy.


Abstract:
  We develop a "hybrid" model of the proton inelastic structure functions
applicable in a wide region of invariant mass of produced states $W$ and
momentum transfer $Q$ including deep inelastic scattering (DIS), nucleon
resonance production as well as the region close to inelastic threshold. DIS is
described in terms of the parton distributions together with higher-twist
corrections from available global QCD fit. The resonant part is addressed in
terms of the Breit-Wiegner contributions from five states: $\Delta(1232)$
resonance, $N(1440)$ Roper resonance, and three effective resonances describing
the second and third resonance regions. The couplings of the nucleon resonances
to photon are described in terms of helicity amplitudes. The nonresonant
background is addressed in terms of a smooth extrapolation of DIS structure
functions to low-$W$ and low-$Q$ values with the proper behavior at the real
photon limit $Q^2=0$ as well as near inelastic threshold. We independently
treat the transverse, $F_T$, and the longitudinal, $F_L$, structure function
and fix the model parameters from a global analysis of the world hydrogen
electroproduction and photoproduction inclusive cross-section data. We
demonstrate a very good performance of the model by comparing our predictions
with data on differential cross sections and the structure functions $F_2$ and
$R=F_L/F_T$.


Abstract:
  Recently, end-to-end Korean singing voice systems have been designed to
generate realistic singing voices. However, these systems still suffer from a
lack of robustness in terms of pronunciation accuracy. In this paper, we
propose N-Singer, a non-autoregressive Korean singing voice system, to
synthesize accurate and pronounced Korean singing voices in parallel. N-Singer
consists of a Transformer-based mel-generator, a convolutional network-based
postnet, and voicing-aware discriminators. It can contribute in the following
ways. First, for accurate pronunciation, N-Singer separately models linguistic
and pitch information without other acoustic features. Second, to achieve
improved mel-spectrograms, N-Singer uses a combination of Transformer-based
modules and convolutional network-based modules. Third, in adversarial
training, voicing-aware conditional discriminators are used to capture the
harmonic features of voiced segments and noise components of unvoiced segments.
The experimental results prove that N-Singer can synthesize a natural singing
voice in parallel with a more accurate pronunciation than the baseline model.


Abstract:
  We consider the quantum dynamics of $N$ interacting fermions in the large $N$
limit. The particles in the system interact with each other via repulsive
interaction that is regularized Coulomb potential with a polynomial cutoff with
respect to $N$.From the quantum system, we derive the Vlasov-Poisson system by
simultaneously estimating the semiclassical and mean-field residues in terms of
the Husimi measure.