title: On a phase field model for RNA-Protein dynamics

abstract:
  A phase field model which describes the formation of protein-RNA complexes
subject to phase segregation is analyzed. A single protein, two RNA species,
and two complexes are considered. Protein and RNA species are governed by
coupled reaction-diffusion equations which also depend on the two complexes.
The latter ones are driven by two Cahn-Hilliard equations with Flory-Huggins
potential and reaction terms depending on the solution variables. The resulting
nonlinear coupled system is equipped with no-flux boundary conditions and
suitable initial conditions. The former ones entail some mass conservation
constraints which are also due to the nature of the reaction terms. The
existence of global weak solutions in a bounded (two- or) three-dimensional
domain is established. In dimension two, some weighted-in-time regularity
properties are shown. In particular, the complexes instantaneously get
uniformly separated from the pure phases. Taking advantage of this result, a
unique continuation property is proven. Among the many technical difficulties,
the most significant one arises from the fact that the two complexes are
initially nonexistent, so their initial conditions are zero i.e., they start
from a pure phase. Thus we must solve, in particular, a system of two coupled
Cahn-Hilliard equations with singular potential and nonlinear sources without
the usual assumption on the initial datum, i.e., the initial phase cannot be
pure. This novelty requires a new approach to estimate the chemical potential
in a suitable $L^p(L^2)$-space with $p\in(1,2)$. This technique can be extended
to other models like, for instance, the well-known Cahn-Hilliard-Oono equation.


title:
Predicting Bearings' Degradation Stages for Predictive Maintenance in
  the Pharmaceutical Industry

abstract:
  In the pharmaceutical industry, the maintenance of production machines must
be audited by the regulator. In this context, the problem of predictive
maintenance is not when to maintain a machine, but what parts to maintain at a
given point in time. The focus shifts from the entire machine to its component
parts and prediction becomes a classification problem. In this paper, we focus
on rolling-elements bearings and we propose a framework for predicting their
degradation stages automatically. Our main contribution is a k-means bearing
lifetime segmentation method based on high-frequency bearing vibration signal
embedded in a latent low-dimensional subspace using an AutoEncoder. Given
high-frequency vibration data, our framework generates a labeled dataset that
is used to train a supervised model for bearing degradation stage detection.
Our experimental results, based on the FEMTO Bearing dataset, show that our
framework is scalable and that it provides reliable and actionable predictions
for a range of different bearings.


title:
Separating the output ports of a Bragg interferometer via velocity
  selective transport

abstract:
  We report on the study of a detection scheme based on a Bloch separator in
two-photon Bragg interferometers. We increase the spatial separation between
the two output ports of the interferometer by selectively imparting 30 Bloch
oscillations to one of them before their detection via time of flight. This
method allows increasing the duration of the interferometer by reducing the
time for discriminating the ports of the interferometer at detection. We study
in detail the impact of this separator on the performance of a dual gravity
sensor, and in particular on its measurement sensitivities to gravity
acceleration and gravity gradients.


title:
Incidence geometry of the Fano plane and Freudenthal's ansatz for the
  construction of (split) octonions

abstract:
  In this article we consider structures on a Fano plane ${\cal F}$ which allow
a generalisation of Freudenthal's construction of a norm and a bilinear
multiplication law on an eight-dimensional vector space ${\mathbb O_{\cal F}}$
canonically associated to ${\cal F}$. We first determine necessary and
sufficient conditions in terms of the incidence geometry of ${\cal F}$ for
these structures to give rise to division composition algebras, and classify
the corresponding structures using a logarithmic version of the multiplication.
We then show how these results can be used to deduce analogous results in the
split composition algebra case.


title: On 1-semiregular and 2-semiregular rings

abstract:
  In this paper, we are mainly interested in the two questions "which are the
commutative rings on which every finitely presented modules is [Formula: see
text]-periodic (respectively, [Formula: see text]-periodic)?". It is proved
that these kinds of rings are particular cases of semiregular rings. So, we
call them [Formula: see text]-semiregular and [Formula: see text]-semiregular
rings, respectively. We establish characterizations of these rings in terms of
various classical notions and we provide several examples of such rings.


title: Maximal H\"ormander Functional Calculus on Lp Spaces and UMD Lattices

abstract:
  Let $A$ be a generator of an analytic semigroup having a H{\"o}rmander
functional calculus on $X = L^p(\Omega ,Y)$, where $Y$ is a UMD lattice. Using
methods from Banach space geometry in connection with functional calculus, we
show that for H{\"o}rmander spectral multipliers decaying sufficiently fast at
$\infty$, there holds a maximal estimate $\| \sup_{t \geq 0} |m(tA)f|\,
\|_{L^p(\Omega ,Y)} \lesssim \|f\|_{L^p(\Omega ,Y)}$. We also show square
function estimates $\left\| \left( \sum_k \sup _{t \geq 0} |m_k(tA)f_k|^2
\right)^{\frac12} \right\|_{L^p(\Omega ,Y)} \lesssim \left\| \left( \sum _k
|f_k|^2 \right)^{\frac12} \right\|_{L^p(\Omega ,Y)}$ for suitable families of
spectral multipliers $m_k$, which are even new for the euclidean Laplacian on
scalar valued $L^p(\mathbb{R}^d)$. As corollaries, we obtain maximal estimates
for wave propagators and Bochner--Riesz means. Finally, we illustrate the
results by giving several examples of operators $A$ that admit a H{\"o}rmander
functional calculus on some $L^p(\Omega ,Y)$ and discuss examples of lattices
$Y$ and non-self-adjoint operators $A$ fitting our context.


title: Boundedness and unboundedness in total variation regularization

abstract:
  We consider whether minimizers for total variation regularization of linear
inverse problems belong to $L^\infty$ even if the measured data does not. We
present a simple proof of boundedness of the minimizer for fixed regularization
parameter, and derive the existence of uniform bounds for sufficiently small
noise under a source condition and adequate a priori parameter choices. To show
that such a result cannot be expected for every fidelity term and dimension we
compute an explicit radial unbounded minimizer, which is accomplished by
proving the equivalence of weighted one-dimensional denoising with a
generalized taut string problem. Finally, we discuss the possibility of
extending such results to related higher-order regularization functionals,
obtaining a positive answer for the infimal convolution of first and second
order total variation.


title:
Efficient Cooperation Strategy Generation in Multi-Agent Video Games via
  Hypergraph Neural Network

abstract:
  The performance of deep reinforcement learning (DRL) in single-agent video
games is astounding due to its benefits in dealing with sequential
decision-making challenges. However, researchers have extra difficulties while
working with video games in multi-agent environments. One of the most pressing
issues presently being addressed is how to create sufficient collaboration
between different agents in a scenario with numerous agents. To address this
issue, we propose a novel algorithm based on the actor-critic method, which
adapts the hypergraph structure of agents and employs hypergraph convolution to
complete information feature extraction and representation between agents,
resulting in efficient collaboration. Based on distinct generating methods of
hypergraph structure, HGAC and ATT-HGAC algorithms are given. We demonstrate
the advantages of our approach over other existing methods. Ablation and
visualization studies also confirm the relevance of each component of the
algorithm.


title:
On uniform controllability of 1D transport equations in the vanishing
  viscosity limit

abstract:
  We consider a one dimensional transport equation with varying vector field
and a small viscosity coefficient, controlled by one endpoint of the interval.
We give upper and lower bounds on the minimal time needed to control to zero,
uniformly in the vanishing viscosity limit. We assume that the vector field
varies on the whole interval except at one point. The upper/lower estimates we
obtain depend on geometric quantities such as an Agmon distance and the
spectral gap of an associated semiclassical Schr{\"o}dinger operator. They
improve, in this particular situation, the results obtained in the companion
paper [LL21]. The proofs rely on a reformulation of the problem as a uniform
observability question for the semiclassical heat equation together with a fine
analysis of localization of eigenfunctions both in the semiclassically allowed
and forbidden regions [LL22], together with estimates on the spectral gap
[HS84, All98]. Along the proofs, we provide with a construction of biorthogonal
families with fine explicit bounds, which we believe is of independent
interest.


title:
On a question of B.J. Baker and M. Laidacker concerning disjoint
  compacta in $\mathbb R^N$

abstract:
  We describe wild embeddings of polyhedra into $\mathbb{R}^N$ which show that
the answer to the question of B.J. Baker--M. Laidacker (1989) concerning
uncountable families of pairwise disjoint compacta can be twofold. The central
idea of our construction is the use of specific wild Cantor sets, namely,
Antoine--Blankinship--Ivanov necklaces and Krushkal sticky sets. Our basic
tools are Antoine's methods and Shtan'ko demension theory.


title:
Influence of Charge on Extended Decoupled Anisotropic Solutions in
  $f(\mathcal{R},\mathcal{T},\mathcal{R}_{\lambda\xi}\mathcal{T}^{\lambda\xi})$
  Gravity

abstract:
  In this paper, we consider static self-gravitating spherical spacetime and
determine various anisotropic solutions through the extended gravitational
decoupling technique in
$f(\mathcal{R},\mathcal{T},\mathcal{R}_{\lambda\xi}\mathcal{T}^{\lambda\xi})$
gravity to analyze the influence of electromagnetic field on them. We construct
two different sets of modified field equations by employing the transformations
on both radial as well as temporal metric potentials. The first set symbolizes
the isotropic fluid distribution, thus we take Krori-Barua solution to deal
with it. The indefinite second sector comprises the influence of anisotropy. In
this regard, we apply some constraints to determine unknowns. Further, we
observe the impact of charge as well as decoupling parameter $\zeta$ on the
developed physical variables (such as energy density,~radial and tangential
pressures) and anisotropy. We also analyze other physical features of the
compact geometry like mass, compactness and redshift along with the energy
conditions. Eventually, we find that our both solutions show less stable
behavior for higher values of charge near the boundary in this gravity.


title:
A lattice model of ternary mixtures of lipids and cholesterol with
  tunable domain sizes

abstract:
  Much of our understanding of the physical mechanisms underlying the formation
of raft domains in biological membranes is based on atomistic simulations of
simple model systems, especially of ternary mixtures consisting of saturated
and unsaturated lipids, and cholesterol. To explore the properties of such
systems at macroscopically large scales, we here present a simple ternary
mixture lattice model, involving a small number of nearest neighbor interaction
terms. Monte Carlo simulations of mixtures with different compositions show an
excellent agreement with experimental and atomistic simulation observations
across multiple scale, ranging from the local distributions of lipids to the
macroscopic phase diagram of the system. The simplicity of the model allows us
to identify the roles played by the different interactions between components,
and the interplay between them. Importantly, by changing the value of one of
the model parameters, we can tune the size of the liquid ordered domains,
thereby to simulate both Type II mixtures exhibiting macroscopic phase
separation and Type I mixtures with nanoscopic domains which are considered as
better models for lipid rafts. Both types of mixtures seem to fit the same
generic low-temperature phase diagram, but in the latter case the
identification of phase coexistence requires deeper analysis of the internal
structure of the domains. Our model results suggest that short range packing is
likely to be a key regulator of the stability and size distribution of raft
domains in biological membranes that contain many lipid species with different
melting temperatures.


title:
Influence of Asymmetric Parameters in Higher-Order Coupling With Bimodal
  Frequency Distribution

abstract:
  We investigate the phase diagram of the Sakaguchi-Kuramoto model with a
higher order interaction along with the traditional pairwise interaction. We
also introduce asymmetry parameters in both the interaction terms and
investigate the collective dynamics and their transitions in the phase diagrams
under both unimodal and bimodal frequency distributions. We deduce the
evolution equations for the macroscopic order parameters and eventually derive
pitchfork and Hopf bifurcation curves. Transition from the incoherent state to
standing wave pattern is observed in the presence of the unimodal frequency
distribution. In contrast, a rich variety of dynamical states such as the
incoherent state, partially synchronized state-I, partially synchronized
state-II, and standing wave patterns and transitions among them are observed in
the phase diagram, via various bifurcation scenarios including saddle-node and
homoclinic bifurcations, in the presence of bimodal frequency distribution.
Higher order coupling enhances the spread of the bistable regions in the phase
diagrams and also leads to the manifestation of bistability between incoherent
and partially synchronized states even with unimodal frequency distribution,
which is otherwise not observed with the pairwise coupling. Further, the
asymmetry parameters facilitate the onset of several bistable and multistable
regions in the phase diagrams. Very large values of the asymmetry parameters
allow the phase diagrams to admit only the monostable dynamical states.


title:
Uniform observation of semiclassical Schr{\"o}dinger eigenfunctions on
  an interval

abstract:
  We consider eigenfunctions of a semiclassical Schr{\"o}dinger operator on an
interval, with a single-well type potential and Dirichlet boundary conditions.
We give upper/lower bounds on the L^2 density of the eigenfunctions that are
uniform in both semiclassical and high energy limits. These bounds are optimal
and are used in an essential way in a companion paper in application to a
controllability problem. The proofs rely on Agmon estimates and a Gronwall type
argument in the classically forbidden region, and on the description of
semiclassical measures for boundary value problems in the classically allowed
region. Limited regularity for the potential is assumed.


title:
Complex Structures, T-duality and Worldsheet Instantons in Born Sigma
  Models

abstract:
  We investigate doubled (generalized) complex structures in $2D$-dimensional
Born geometries where T-duality symmetry is manifestly realized. We show that
K\"{a}hler, hyperk\"{a}hler, bi-hermitian and bi-hypercomplex structures of
spacetime are implemented in Born geometries as doubled structures. We find
that the Born structures and the generalized K\"{a}hler (hyperk\"{a}hler)
structures appear as subalgebras of bi-quaternions and split-tetra-quaternions.
We find parts of these structures are classified by Clifford algebras. We then
study the T-duality nature of the worldsheet instantons in Born sigma models.
We show that the instantons in K\"{a}hler geometries are related to those in
bi-hermitian geometries in a non-trivial way.


title: Numerical And Koadaira Dimensions Of Cotangents Bundles

abstract:
  We conjecture the equality of the numerical and Kodaira dimensions
$\nu_1^*(X)$ and $\kappa_1^*(X)$ for the cotangent bundle of a compact K\"ahler
manifold $X$, similar to the classical case of the canonical bundle. We show it
or reduce it to the classical case of the canonical bundle in some peculiar
manifolds: among them, the rationally connected ones, or resolutions of
varieties with klt singularities and trivial first Chern class, in which case
we show that $\nu_1^*(X)=\kappa_1^*(X)=q'(X)-dim(X)$, where $q'(X)$ is the
maximal irregularity of a finite \'etale cover of $X$. The proof rests on the
Beauville-Bogomolov decomposition, and a direct computation for smooth models
of quotients $A/G$ of complex tori by finite groups. We conjecture that these
equalities hold true, much more generally, when $X$ is `special'. The invariant
$\kappa_1^*$ was already introduced and studied by Fumio Sakai in \cite{Sak},
the particular case of the preceding conjecture when $\kappa_1^*(X)=-dim(X)$
was introduced and studied in \cite{HP20}.


title: Mardesic factorization theorem for asymptotic dimension

abstract:
  The main goal of this note is to prove a coarse analogue of Factorization
Theorems in Dimension Theory: Let $f: X \rightarrow Y$ be a coarsely continuous
map. Then $f$ factors through coarsely continuous maps $g : X \rightarrow Z$
and $h : Z \rightarrow Y$ with asymptotic dimension of $Z$ at most asymptotic
dimension of $X$ and the weight of $Z$ at most the weight of $Y$.


title:
Arbitrarily high-order energy-conserving methods for Hamiltonian
  problems with quadratic holonomic constraints

abstract:
  In this paper, we define arbitrarily high-order energy-conserving methods for
Hamiltonian systems with quadratic holonomic constraints. The derivation of the
methods is made within the so-called line integral framework. Numerical tests
to illustrate the theoretical findings are presented.


title:
ALMA-IMF III -- Investigating the origin of stellar masses: Top-heavy
  core mass function in the W43-MM2&MM3 mini-starburst

abstract:
  The ALMA-IMF Large Program observed the W43-MM2-MM3 ridge, whose 1.3mm and
3mm ALMA 12m array continuum images reach a 2500au spatial resolution. We used
both the best-sensitivity and the line-free ALMA-IMF images, reduced the noise
with the multi-resolution segmentation technique MnGSeg, and derived the most
complete and most robust core catalog possible. Using two different extraction
software packages, getsf and GExt2D, we identified 200 compact sources, whose
100 common sources have on average fluxes consistent to within 30%. We filtered
sources with non-negligible free-free contamination and corrected fluxes from
line contamination, resulting in a W43-MM2-MM3 catalog of 205 getsf cores. With
a median deconvolved FWHM size of 3400au, core masses range from 0.1Msun to
70Msun and the getsf catalog is 90% complete down to 0.8Msun. The high-mass end
of the core mass function (CMF) of W43-MM2-MM3 is top-heavy compared to the
canonical IMF. Fitting the cumulative CMF with a single power law of the form
N(>logM)\propto M^a, we measured a=-0.95\pm0.04, compared to the canonical
a=-1.35 Salpeter IMF slope. The slope of the CMF is robust with respect to map
processing, extraction software package, and reasonable variations in the
assumptions taken to estimate core masses. We explore several assumptions on
how cores transfer their mass to stars and sub-fragment to predict the IMF
resulting from the W43-MM2-MM3 CMF. In stark contrast to the commonly accepted
paradigm, our result argues against the universality of the CMF shape. More
robust functions of the star-formation efficiency and core sub-fragmentation
are required to better predict the resulting IMF, here suggested to remain
top-heavy at the end of the star-formation phase. If confirmed, the IMFs
emerging from starburst events could inherit their top-heavy shape from their
parental CMFs, challenging the IMF universality.


title: Rectification of a deep water model for surface gravity waves

abstract:
  In this work we discuss an approximate model for the propagation of deep
irrotational water waves; specifically the model obtained when retaining only
quadratic nonlinearities in the water waves system under the
Zakharov-Craig-Sulem formulation. We argue that the initial-value problem
associated with this system is most likely ill-posed in finite-regularity
spaces, explaining spurious oscillations reported in numerical simulations for
instance in GN07, and thus agreeing with the conclusion of ABN14 although not
on the proposed instability mechanism. We show that the system can be
"rectified". Indeed, through the introduction of suitable regularizing
operators we can recover well-posedness properties without sacrificing other
desirable features such as a canonical Hamiltonian structure, cubic accuracy as
an asymptotic model, and efficient numerical integration. Our study is
supported with detailed and reproducible numerical simulations.


title: Gravitational Wave Electrodynamics

abstract:
  We derive Maxwell equations for electric and magnetic fields in curved
spacetime from first principles. We show that the latter system enjoys an SO(2)
duality symmetry, contrary to the results of previous works. We find that in a
detector with static background magnetic or electric field, the weak gravity
effects are analogous to the effects from a medium composed of both
electrically and magnetically charged particles. We show that generation of
electromagnetic radiation by gravitational waves in an external magnetic field
is four times more efficient than previously believed. We discuss differences
between axion and gravitational wave electrodynamics.


title:
Evaluating State of the Art, Forecasting Ensembles- and Meta-learning
  Strategies for Model Fusion

abstract:
  Techniques of hybridisation and ensemble learning are popular model fusion
techniques for improving the predictive power of forecasting methods. With
limited research that instigates combining these two promising approaches, this
paper focuses on the utility of the Exponential-Smoothing-Recurrent Neural
Network (ES-RNN) in the pool of base models for different ensembles. We compare
against some state of the art ensembling techniques and arithmetic model
averaging as a benchmark. We experiment with the M4 forecasting data set of
100,000 time-series, and the results show that the Feature-based Forecast Model
Averaging (FFORMA), on average, is the best technique for late data fusion with
the ES-RNN. However, considering the M4's Daily subset of data, stacking was
the only successful ensemble at dealing with the case where all base model
performances are similar. Our experimental results indicate that we attain
state of the art forecasting results compared to N-BEATS as a benchmark. We
conclude that model averaging is a more robust ensemble than model selection
and stacking strategies. Further, the results show that gradient boosting is
superior for implementing ensemble learning strategies.


title: Displaced Searches for Vector Bosons at Belle II

abstract:
  With a design luminosity of 50 ab$^{-1}$ and detectors with tracking
capabilities extending beyond 1 m, the Belle II experiment is the perfect
laboratory for the search of particles that couple weakly to the Standard Model
and have a characteristic decay length of a few centimetres and more. We show
that for models of dark photons and other light vector bosons, Belle II will be
successful in probing regions of parameter space which are as of now unexplored
by any experiment. In addition, for models where the vector boson couples
sub-dominantly to the electron and quarks as compared to muons, e.g. in the
$L_\mu-L_\tau$ model, Belle II will probe regions of mass and couplings
compatible with the anomalous magnetic moment of muon. We discuss these results
and derive the projected sensitivity of Belle II for a handful of other models.
Finally, even with the currently accumulated data, $\sim 200$ fb$^{-1}$, Belle
II should be able to cover regions of parameter space pertaining to the X(17)
boson postulated to solve the ATOMKI anomaly.


title: Binomial coefficients, roots of unity and powers of prime numbers

abstract:
  Let $t\in\mathbb{N}_+$ be given. In this article we are interested in
characterizing those $d\in\mathbb{N}_+$ such that the congruence
$$\frac{1}{t}\sum_{s=0}^{t-1}{n+d\zeta_t^s\choose d-1}\equiv {n\choose
d-1}\pmod{d}$$ is true for each $n\in\mathbb{Z}$. In particular, assuming that
$d$ has a prime divisor greater than $t$, we show that the above congruence
holds for each $n\in\mathbb{Z}$ if and only if $d=p^r$, where $p$ is a prime
number greater than $t$ and $r\in\{1,\ldots ,t\}$.


title:
Interpretable part-whole hierarchies and conceptual-semantic
  relationships in neural networks

abstract:
  Deep neural networks achieve outstanding results in a large variety of tasks,
often outperforming human experts. However, a known limitation of current
neural architectures is the poor accessibility to understand and interpret the
network response to a given input. This is directly related to the huge number
of variables and the associated non-linearities of neural models, which are
often used as black boxes. When it comes to critical applications as autonomous
driving, security and safety, medicine and health, the lack of interpretability
of the network behavior tends to induce skepticism and limited trustworthiness,
despite the accurate performance of such systems in the given task.
Furthermore, a single metric, such as the classification accuracy, provides a
non-exhaustive evaluation of most real-world scenarios. In this paper, we want
to make a step forward towards interpretability in neural networks, providing
new tools to interpret their behavior. We present Agglomerator, a framework
capable of providing a representation of part-whole hierarchies from visual
cues and organizing the input distribution matching the conceptual-semantic
hierarchical structure between classes. We evaluate our method on common
datasets, such as SmallNORB, MNIST, FashionMNIST, CIFAR-10, and CIFAR-100,
providing a more interpretable model than other state-of-the-art approaches.


title: Nanophotonic resonance modes with the nanobem toolbox

abstract:
  Nanobem is a Matlab toolbox for the solution of Maxwell's equations for
nanophotonic systems and the computation of resonance modes, sometimes also
referred to as quasinormal modes or resonance states. It is based on a Galerkin
scheme for the boundary element method, using Raviart-Thomas shape elements for
the representation of the tangential electromagnetic fields at the particle
boundary. The toolbox is written in an object-oriented manner with the focus on
clarity rather than speed, and has been developed and tested for small to
intermediate problems with a few thousand boundary elements. The computation of
the resonance modes uses the contour integral method of Beyn.


title: Hybrid skin-topological modes without asymmetric couplings

abstract:
  Non-Hermitian skin effect (NHSE) in non-Hermitian lattice systems, associated
with a point gap on the complex energy plane, has attracted great theoretical
and experimental interest. Much less is studied on the so-called second-order
non-Hermitian skin effect, where the bulk does not support a point gap but
localization at the corner still occurs. This work discovers a class of hybrid
skin-topological modes as the second-order non-Hermitian skin effect without
asymmetric couplings. Specifically, by only adding gain/loss to two-dimensional
Chern insulators and so long as the gain/loss strength does not close the line
gap, all the topological edge states are localized at one corner under the open
boundary condition, with the bulk states extended. The resultant non-Hermitian
Chern bands can be still topologically characterized by Chern numbers, whereas
the hybrid skin-topological modes are understood via some auxiliary Hermitian
systems that belong to either intrinsic or extrinsic second-order topological
insulator phases. By proposing an innovative construction of auxiliary
Hamiltonian, our generic route to hybrid skin-topological modes is further
successfully extended to nonequilibrium topological systems with gain and loss,
where the anomalous Floquet band topology is no longer captured by band Chern
numbers. The extension thus leads to the intriguing finding of nonequilibrium
hybrid skin-topological modes. In addition to offering a straightforward route
to experimental realization of hybrid topological-skin effects, this study also
opens up a promising perspective for the understanding of corner localization
by revealing the synergy of three important concepts, namely, non-Hermitian
topological insulator, second-order non-Hermitian skin effect, and second-order
topological insulator.


title: Poncelet and the arquimedean twins

abstract:
  We give a sharp construction for twins in arbelos, based on polar
reciprocity. In the process, new circles displaying arquimedean afinities came
into scene.


title: Topological duality for distributive lattices, and applications

abstract:
  This book is a course in Stone-Priestley duality theory, with applications to
logic and theoretical computer science. Our target audience are graduate
students and researchers in mathematics and computer science. Our aim is to get
in a fairly full palette of duality tools as directly and quickly as possible,
then to illustrate and further elaborate these tools within the setting of
three emblematic applications: semantics of propositional logics, domain theory
in logical form, and the theory of profinite monoids for the study of regular
languages and automata. This preprint version contains the first part of the
book, a graduate level `crash course' in duality theory as it is practiced now.


title: Bifurcation structure of traveling pulses in Type-I excitable media

abstract:
  We have studied the existence of traveling pulses in a general Type-I
excitable 1-dimensional medium. We have obtained the stability region and
characterized the different bifurcations behind either the destruction or loss
of stability of the pulses. In particular, some of the bifurcations delimiting
the stability region have been connected, using singular limits, with the two
different scenarios that mediated the Type-I local excitability, i.e.
homoclinic (saddle-loop) and Saddle-Node on the Invariant Circle bifurcations.
The existence of the traveling pulses has been linked, outside the stability
region, to a drift pitchfork instability of localized steady structures.


title: Handling Higher-Order Effects

abstract:
  Algebraic effect handlers is a programming paradigm where programmers can
declare their own syntactic operations, and modularly define the semantics of
these using effect handlers.
  However, we cannot directly define algebraic effect handlers for many
higher-order operations (or higher-order effects) -- i.e., operations that have
computations as parameters.
  Examples of such higher-order effects include common programming features,
such as try-catch exception handlers, function abstraction, and more.
  In this paper we present a new kind of effect handler that addresses this
shortcoming.
  Our effect handler approach is closely related to previous work on scoped
effect handlers, which also supports higher-order effects.
  A key difference is that our effect handlers make it easy to understand
separate (higher-order) effects as separate concerns, since effects do not
interact.
  In contrast, effect interaction is the default with scoped effect handlers.
  While separate concerns is the default with our handlers, it is also possible
to define handlers where effects interact.


title: $\mu$BERT: Mutation Testing using Pre-Trained Language Models

abstract:
  We introduce $\mu$BERT, a mutation testing tool that uses a pre-trained
language model (CodeBERT) to generate mutants. This is done by masking a token
from the expression given as input and using CodeBERT to predict it. Thus, the
mutants are generated by replacing the masked tokens with the predicted ones.
We evaluate $\mu$BERT on 40 real faults from Defects4J and show that it can
detect 27 out of the 40 faults, while the baseline (PiTest) detects 26 of them.
We also show that $\mu$BERT can be 2 times more cost-effective than PiTest,
when the same number of mutants are analysed. Additionally, we evaluate the
impact of $\mu$BERT's mutants when used by program assertion inference
techniques, and show that they can help in producing better specifications.
Finally, we discuss about the quality and naturalness of some interesting
mutants produced by $\mu$BERT during our experimental evaluation.


title:
Realization of topological superlattices and the associated interface
  states in one-dimensional plasmonic crystals

abstract:
  In analogous to the Su-Schrieffer-Heeger (SSH) model, one-dimensional (1D)
electromagnetic (EM) crystals can exhibit nontrivial topological properties. In
particular, when a nontrivial EM crystal is in contact with its trivial
counterpart, a topologically protected interface state is formed. While much
attention has been focused on single interface state, multiple interface states
can interact collectively when under suitable conditions, giving rise to
trivial or nontrivial band topologies resembling to the standard SSH model.
Here, we study the topological properties of 1D metallic superlattices that
support multiple interface states. We first demonstrate single interface state
exists at the boundary between two topologically distinct metallic arrays that
carry Bloch-like surface plasmon polaritons. Such interface state is then used
as the building block for further constructing the superlattices. By exploiting
the separation between two dimerized interface states and the distinct inter-
and intra-interface configurations to facilitate different intracell and
intercell interactions, we vary the band topology of the superlattices. More
importantly, new superlattice interface state is formed when trivial and
nontrivial superlattices are brought together. The superlattice interface state
is found to have smaller angular divergence and longer localization length than
its single counterpart, thus is desired for robust signal transmission and high
finesse cavity.


title:
Visually Supervised Speaker Detection and Localization via Microphone
  Array

abstract:
  Active speaker detection (ASD) is a multi-modal task that aims to identify
who, if anyone, is speaking from a set of candidates. Current audio-visual
approaches for ASD typically rely on visually pre-extracted face tracks
(sequences of consecutive face crops) and the respective monaural audio.
However, their recall rate is often low as only the visible faces are included
in the set of candidates. Monaural audio may successfully detect the presence
of speech activity but fails in localizing the speaker due to the lack of
spatial cues. Our solution extends the audio front-end using a microphone
array. We train an audio convolutional neural network (CNN) in combination with
beamforming techniques to regress the speaker's horizontal position directly in
the video frames. We propose to generate weak labels using a pre-trained active
speaker detector on pre-extracted face tracks. Our pipeline embraces the
"student-teacher" paradigm, where a trained "teacher" network is used to
produce pseudo-labels visually. The "student" network is an audio network
trained to generate the same results. At inference, the student network can
independently localize the speaker in the visual frames directly from the audio
input. Experimental results on newly collected data prove that our approach
significantly outperforms a variety of other baselines as well as the teacher
network itself. It results in an excellent speech activity detector too.


title: On Credit Assignment in Hierarchical Reinforcement Learning

abstract:
  Hierarchical Reinforcement Learning (HRL) has held longstanding promise to
advance reinforcement learning. Yet, it has remained a considerable challenge
to develop practical algorithms that exhibit some of these promises. To improve
our fundamental understanding of HRL, we investigate hierarchical credit
assignment from the perspective of conventional multistep reinforcement
learning. We show how e.g., a 1-step `hierarchical backup' can be seen as a
conventional multistep backup with $n$ skip connections over time connecting
each subsequent state to the first independent of actions inbetween.
Furthermore, we find that generalizing hierarchy to multistep return estimation
methods requires us to consider how to partition the environment trace, in
order to construct backup paths. We leverage these insight to develop a new
hierarchical algorithm Hier$Q_k(\lambda)$, for which we demonstrate that
hierarchical credit assignment alone can already boost agent performance (i.e.,
when eliminating generalization or exploration). Altogether, our work yields
fundamental insight into the nature of hierarchical backups and distinguishes
this as an additional basis for reinforcement learning research.


title:
Neural network approach to reconstructing spectral functions and complex
  poles of confined particles

abstract:
  Reconstructing spectral functions from propagator data is difficult as
solving the analytic continuation problem or applying an inverse integral
transformation are ill-conditioned problems. Recent work has proposed using
neural networks to solve this problem and has shown promising results, either
matching or improving upon the performance of other methods. We generalize this
approach by not only reconstructing spectral functions, but also (possible)
pairs of complex poles or an infrared (IR) cutoff. We train our network on
physically motivated toy functions, examine the reconstruction accuracy and
check its robustness to noise. Encouraging results are found on both toy
functions and genuine lattice QCD data for the gluon propagator, suggesting
that this approach may lead to significant improvements over current
state-of-the-art methods.


title: Finiteness and purity of subcategories of the module categories

abstract:
  In this paper, by using functor rings and functor categories, we study
finiteness and purity of subcategories of the module categories. We give a
characterisation of contravariantly finite resolving subcategories of the
module category of finite representation type in terms of their functor rings.
We also characterize contravariantly finite resolving subcategories of the
module category $\Lambda${\rm -mod} that contain the Jacobson radical of
$\Lambda$ of finite type, by their functor categories. We study the pure
semisimplicity conjecture for a locally finitely presented category
${\underrightarrow{\lim}}\mathscr{X}$ when $\mathscr{X}$ is a covariantly
finite subcategory of $\Lambda$-mod and every simple object in
Mod$(\mathscr{X}^{\rm op})$ is finitely presented and give a characterization
of covariantly finite subcategories of finite representation type in terms of
decomposition properties of their closure under filtered colimits. As a
consequence we study finiteness and purity of $n$-cluster tilting subcategories
and the subcategory of the Gorenstein projective modules of the module
categories. These results extend and unify some known results.


title:
Curvature-matter couplings in modified gravity: from linear models to
  conformally invariant theories

abstract:
  In this proceeding, we review modified theories of gravity with a
curvature-matter coupling between an arbitrary function of the scalar curvature
and the Lagrangian density of matter. This explicit nonminimal coupling induces
a non-vanishing covariant derivative of the energy-momentum tensor, that
implies non-geodesic motion and consequently leads to the appearance of an
extra force. Here, we explore the physical and cosmological implications of the
nonconservation of the energy-momentum tensor by using the formalism of
irreversible thermodynamics of open systems in the presence of matter
creation/annihilation. The particle creation rates, pressure, and the
expression of the comoving entropy are obtained in a covariant formulation and
discussed in detail. Applied together with the gravitational field equations,
the thermodynamics of open systems lead to a generalization of the standard
$\Lambda$CDM cosmological paradigm, in which the particle creation rates and
pressures are effectively considered as components of the cosmological fluid
energy-momentum tensor. Furthermore, we also briefly present the coupling of
curvature to geometry in conformal quadratic Weyl gravity, by assuming a
coupling term of the form $L_m\tilde{R}^2$, where $L_m$ is the ordinary matter
Lagrangian, and $\tilde{R}$ is the Weyl scalar. The coupling explicitly
satisfies the requirement of the conformal invariance of the theory. Expressing
$\tilde{R}^2$ with the use of an auxiliary scalar field and of the Weyl scalar,
the gravitational action can be linearized in the Ricci scalar, leading in the
Riemann space to a conformally invariant $f\left(R,L_m\right)$ type theory,
with the matter Lagrangian nonminimally coupled to geometry.


title:
Whole-Body Control for Velocity-Controlled Mobile Collaborative Robots
  Using Coupling Dynamic Movement Primitives

abstract:
  In this paper, we propose a unified whole-body control framework for
velocity-controlled mobile collaborative robots which can distribute task
motion into the base and arm according to specific task requirements by
adjusting weighting factors. Our framework focuses on addressing two
challenging issues in whole-body coordination: 1) different dynamic
characteristics of base and arm; 2) avoidance of violating constraints of both
safety and configuration. In addition, our controller involves Coupling Dynamic
Movement Primitive to enable the essential compliance capabilities for
collaboration and interaction applications, such as obstacle avoidance, hand
guiding, and force control. Based on these, we design a motion mode of
intuitive physical human-robot interaction through an adjusting strategy of the
weighting factors. The proposed controller is in closed-form and thus quite
computationally efficient against the-state-of-art optimization-based methods.
Experimental results of a real mobile collaborative robot validate the
effectiveness of the proposed controller. The documented video of the
experiments is available at https://youtu.be/x2Z593dV9C8.


title:
Self-Stabilizing Periodic Mutual-exclusive Propagation in Sparse
  Networks

abstract:
  Message propagation is fundamental in constructing distributed systems upon
sparsely connected communication networks. For providing easy message
propagation primitives, the mutual-exclusive propagation (MEP) of one-bit
messages is investigated with a simplified discrete system model. Inspired by
natural propagation systems, efficient self-stabilizing periodic MEP processes
are proposed with the MEP systems. For handling the worst-case scenarios, the
MEP systems are generally analyzed with formal proofs upon arbitrarily
connected networks. It shows that the MEP systems can be stabilized with
arbitrary initial system states within a short bounded time. Meanwhile, the
numeric simulation shows that the propagation errors can be significantly
reduced if the message delays are randomly distributed. Propagation patterns
are also discussed in further deriving finer propagation results in the MEP
systems. It shows that bounded clock drifts and some benign faults can be well
handled in the MEP systems. Featured by its very simple mechanism, the proposed
MEP primitive can be employed as a practical building block of upper-layer
self-stabilizing synchronous systems.


title:
Effective and holographic theories of strongly-correlated phases of
  matter with broken translations

abstract:
  We review recent progress in the effective description of strongly-correlated
phases of matter with spontaneously broken translations, such as charge density
waves or Wigner crystals. In real materials, disorder is inevitable and pins
the Goldstones of broken translations. We describe how pinning can be
incorporated in the effective field theory at low energies, without making any
assumption on the presence of boost symmetry. We review the essential role
played by gauge-gravity duality models in establishing these effective field
theories with only approximate symmetries. We close with a discussion on the
relevance of these models for the phenomenology of dc and ac transport in
strongly-correlated strange and bad metals, such as high temperature
superconductors.


title: Gluon TMDs and inelastic $J/\psi$ leptoproduction at the EIC

abstract:
  We study the Sivers azimuthal asymmetry for $J/\psi$ production in
semi-inclusive deep inelastic scattering, and the still poorly known gluon
Sivers function. We concentrate on the $J/\psi$ low transverse momentum region,
employing the transverse-momentum dependent generalised parton model (GPM), and
its colour gauge invariant extension (CGI-GPM), which includes final-state
interactions at leading order. We adopt the nonrelativistic QCD (NRQCD)
framework for the quarkonium formation mechanism, and compare our results for
the unpolarised cross section and the Sivers asymmetry with available data,
respectively from HERA and COMPASS. Finally, we give estimates for the
asymmetry in the kinematical regime of the future Electron-Ion Collider (EIC).


title:
Machine Learning based Anomaly Detection for Smart Shirt: A Systematic
  Review

abstract:
  In recent years, the popularity and use of Artificial Intelligence (AI) and
large investments on theInternet of Medical Things (IoMT) will be common to use
products such as smart socks, smartpants, and smart shirts. These products are
known as Smart Textile or E-textile, which has theability to monitor and
collect signals that our body emits. These signals make it possible to
extractanomalous components using Machine Learning (ML) techniques that play an
essential role in thisarea. This study presents a Systematic Review of the
Literature (SLR) on Anomaly Detection usingML techniques in Smart Shirt. The
objectives of the SLR are: (i) to identify what type of anomalythe smart shirt;
(ii) what ML techniques are being used; (iii) which datasets are being used;
(iv)identify smart shirt or signal acquisition devices; (v) list the
performance metrics used to evaluatethe ML model; (vi) the results of the
techniques in general; (vii) types of ML algorithms are beingapplied.The SLR
selected 11 primary studies published between 2017-2021. The results showed
that6 types of anomalies were identified, with the Fall anomaly being the most
cited. The Support VectorMachines (SVM) algorithm is most used. Most of the
primary studies used public or private datasets.The Hexoskin smart shirt was
most cited. The most used metric performance was Accuracy. Onaverage, almost
all primary studies presented a result above 90%, and all primary studies used
theSupervisioned type of ML.


title:
Switchable selective interactions in a Dicke Model with Driven Biased
  term

abstract:
  In this work, we propose a method to investigate controllable qubit-resonator
interactions in a Dicke model with driven biased term. The nonlinearity of
spectrum, which can be induced by qubit-resonator interactions, plays an
important role in such controllable interactions. To gain insight into
mechanism of the nonlinearity, we perform a unitary transformation to the
Hamiltonian. The results show that the nonlinearity of the transformed
Hamiltonian depends on the qubit-resonator coupling strength. The general forms
of the effective Hamiltonians are discussed in detail based on the frequency
modulation approach. The dynamical evolution can be switched on and off by
adjusting the modulation parameters. By utilizing such controllable
interactions, we discuss the creation of Dicke states and arbitrary
superposition of Dicke states. We also consider the nonlinearity of energy
level for the limit of large qubit numbers. In the thermodynamics limit, the
kerr type nonlinearity is induced from "magnon"-resonator coupling, and the
selective preparation of "magnon" Fock states can be studied under "magnon"
scenario.


title:
Lindblad master equations for quantum systems coupled to dissipative
  bosonic modes

abstract:
  We present a general approach to derive Lindblad master equations for a
subsystem whose dynamics is coupled to dissipative bosonic modes. The
derivation relies on a Schrieffer-Wolff transformation which allows to
eliminate the bosonic degrees of freedom after self-consistently determining
their state as a function of the coupled quantum system. We apply this
formalism to the dissipative Dicke model and derive a Lindblad master equation
for the atomic spins, which includes the coherent and dissipative interactions
mediated by the bosonic mode. This master equation accurately predicts the
Dicke phase transition and gives the correct steady state. In addition, we
compare the dynamics using exact diagonalization and numerical integration of
the master equation with the predictions of semiclassical trajectories. We
finally test the performance of our formalism by studying the relaxation of a
NOON state and show that the dynamics captures quantum metastability beyond the
mean-field approximation.


title: PAC-Bayesian Lifelong Learning For Multi-Armed Bandits

abstract:
  We present a PAC-Bayesian analysis of lifelong learning. In the lifelong
learning problem, a sequence of learning tasks is observed one-at-a-time, and
the goal is to transfer information acquired from previous tasks to new
learning tasks. We consider the case when each learning task is a multi-armed
bandit problem. We derive lower bounds on the expected average reward that
would be obtained if a given multi-armed bandit algorithm was run in a new task
with a particular prior and for a set number of steps. We propose lifelong
learning algorithms that use our new bounds as learning objectives. Our
proposed algorithms are evaluated in several lifelong multi-armed bandit
problems and are found to perform better than a baseline method that does not
use generalisation bounds.


title:
Regularising for invariance to data augmentation improves supervised
  learning

abstract:
  Data augmentation is used in machine learning to make the classifier
invariant to label-preserving transformations. Usually this invariance is only
encouraged implicitly by including a single augmented input during training.
However, several works have recently shown that using multiple augmentations
per input can improve generalisation or can be used to incorporate invariances
more explicitly. In this work, we first empirically compare these recently
proposed objectives that differ in whether they rely on explicit or implicit
regularisation and at what level of the predictor they encode the invariances.
We show that the predictions of the best performing method are also the most
similar when compared on different augmentations of the same input. Inspired by
this observation, we propose an explicit regulariser that encourages this
invariance on the level of individual model predictions. Through extensive
experiments on CIFAR-100 and ImageNet we show that this explicit regulariser
(i) improves generalisation and (ii) equalises performance differences between
all considered objectives. Our results suggest that objectives that encourage
invariance on the level of the neural network itself generalise better than
those that achieve invariance by averaging predictions of non-invariant models.


title:
$D$-semifaithful codes that are universal over both memoryless sources
  and distortion measures

abstract:
  We prove the existence of codebooks for d-semifaithful lossy compression that
are simultaneously universal with respect to both the class of finite-alphabet
memoryless sources and the class of all bounded additive distortion measures.
By applying independent random selection of the codewords according to a
mixture of all memoryless sources, we achieve redundancy rates that are within
O(log n/n) close to the empirical rate-distortion function of every given
source vector with respect to every bounded distortion measure. As outlined in
the last section, the principal ideas can also be extended significantly beyond
the class of memoryless sources, namely, to the setting of individual sequences
encoded by finite-state machines.


title:
Optimal $C^\infty$-approximation of functions with exponentially or
  sub-exponentially integrable derivative

abstract:
  We discuss Meyers-Serrin's type results for smooth approximations of
functions $b=b(t,x):\mathbb{R}\times\mathbb{R}^n\to\mathbb{R}^m$, with
convergence of an energy of the form \[ \int_{\mathbb{R}}\int_{\mathbb{R}^n}
w(t,x) \varphi\left(|Db(t,x)|\right)\mathrm{d} x \mathrm{d} t\,, \] where $w>0$
is a suitable weight function, and $\varphi:[0,\infty)\to [0,\infty)$ is a
convex function with $\varphi(0)=0$ having exponential or sub-exponential
growth.


title:
Tachyons and (non)vanishing scalar masses in six-dimensional gauge
  theories with flux compactification

abstract:
  In this paper, we study the possibility to obtain a massless scalar boson for
which quantum corrections to the mass vanish at all loop-order, which has been
recently understood to be due to a shift symmetry making the scalar a Goldstone
boson. We present the effective four-dimensional Lagrangian of a
six-dimensional gauge theory compactified on a torus with magnetic flux.
Because of this magnetic field, a symmetry of translation in the extra
dimensions is broken which implies the existence of a massless scalar boson. We
then explicitly check that a model with two U(1) gauge symmetries contains a
scalar boson with finite mass but protected from large quantum corrections.
Finally, we study the presence of tachyons in the model with non-abelian gauge
symmetry. In particular, we propose a way to eliminate these tachyons and we
compute the full mass spectrum of the scalars in this theory. Finally, we show
that our method preserve the chirality of fermions in the model.


title:
Metallicity Estimation of Core-Collapse Supernova HII Regions in
  Galaxies within 30 Mpc

abstract:
  This work presents measurements of the local HII environment metallicities of
core-collapse supernovae (SNe) within a luminosity distance of 30 Mpc. 76
targets were observed at the Isaac Newton Telescope and environment
metallicities could be measured for 65 targets using the N2 and O3N2 strong
emission line method. The cumulative distribution functions (CDFs) of the
environment metallicities of Type Ib and Ic SNe tend to higher metallicity than
Type IIP, however Type Ic are also present at lower metallicities whereas Type
Ib are not. The Type Ib frequency distribution is narrower (standard deviation
$\sim$0.06 dex) than the Ic and IIP distributions ($\sim$0.15 dex) giving some
evidence for a significant fraction of single massive progenitor stars; the low
metallicity of Type Ic suggests a significant fraction of compact binary
progenitors. However, both the Kolmogorov-Smirnov test and the Anderson-Darling
test indicate no statistical significance for a difference in the local
metallicities of the three SN types. Monte-Carlo simulations reveal a strong
sensitivity of these tests to the uncertainties of the derived metallicities.
Given the uncertainties of the strong emission methods, the applicability of
the tests seems limited. We extended our analysis with the data of the Type
Ib/Ic/IIP SN sample from Galbany et al. (2018). The CDFs created with their
sample confirm our CDFs very well. The statistical tests, combining our sample
and the Galbany et al. (2018) sample, indicate a significant difference between
Type Ib and Type IIP with <5% probability that they are drawn from the same
parent population.


title:
$Herschel$ investigation of cores and filamentary structures in the
  Perseus molecular cloud

abstract:
  Cores and filamentary structures are the prime birthplaces of stars, and play
key roles in the process of star formation. Latest advances in the methods of
multi-scale source and filament extraction, and in making high-resolution
column density map from $Herschel$ multi-wavelength observations enable us to
detect the filamentary network structures in highly complex molecular cloud
environments. The statistics for physical parameters shows that core mass
strongly correlates with core dust temperature, and $M/L$ strongly correlates
with $M/T$, which is in line with the prediction of the blackbody radiation,
and can be used to trace evolutionary sequence from unbound starless cores to
robust prestellar cores. Crest column densities of the filamentary structures
are clearly related with mass per unit length ($M_{\rm line}$), but are
uncorrelated by three orders ranging from $\sim 10^{20}$ to $\sim 10^{22}$ $
\rm cm^{-2}$ with widths. Full width at half maximum (FWHM) have a median value
of 0.15 pc, which is consistent with the 0.1 pc typical inner width of the
filamentary structures reported by previous research. We find $\sim $70\% of
robust prestellar cores (135/199) embedded in supercritical filaments with
$M_{\rm line}>16~M_{\odot}/{\rm pc}$, which implies that the gravitationally
bound cores come from fragmentation of supercritical filaments. And on the
basis of observational evidences that probability distribution function (PDF)
with power-law distribution in the Perseus south is flatter than north, YSO
number is significantly less than that in the north, and dust temperature
difference. We infer that south region is more gravitationally bound than north
region.


title: A survey on recursive towers and Ihara's constant

abstract:
  Since Serre gave his famous Harvard lectures in 1985 on various aspects of
the theory of algebraic curves defined over a finite field, there have been
many developments. In this survey article, an overview will be given on the
developments concerning the quantity $A(q)$, known as Ihara's constant. The
main focus will be on explicit techniques and in particular recursively defined
towers of function fields over a finite field, which have given good lower
bounds for Ihara's constant in the past.


title:
Comprehensive Review of Deep Learning-Based 3D Point Cloud Completion
  Processing and Analysis

abstract:
  Point cloud completion is a generation and estimation issue derived from the
partial point clouds, which plays a vital role in the applications in 3D
computer vision. The progress of deep learning (DL) has impressively improved
the capability and robustness of point cloud completion. However, the quality
of completed point clouds is still needed to be further enhanced to meet the
practical utilization. Therefore, this work aims to conduct a comprehensive
survey on various methods, including point-based, convolution-based,
graph-based, and generative model-based approaches, etc. And this survey
summarizes the comparisons among these methods to provoke further research
insights. Besides, this review sums up the commonly used datasets and
illustrates the applications of point cloud completion. Eventually, we also
discussed possible research trends in this promptly expanding field.


title:
SkillNet: A Sparsely Activated Model for General-Purpose Natural
  Language Understanding

abstract:
  Prevailing deep models are single-purpose and overspecialize at individual
tasks. However, when being extended to new tasks, they typically forget
previously learned skills and learn from scratch. We address this issue by
introducing SkillNet, a general-purpose model that stitches together existing
skills to learn new tasks more effectively. The key feature of our approach is
that it is sparsely activated guided by predefined skills. Different from
traditional dense models that always activate all the model parameters,
SkillNet only activates parts of the model parameters whose skills are relevant
to the target task. When learning for a new task, our approach precisely
activates required skills and also provides an option to add new skills. We
evaluate on natural language understandings tasks and have the following
findings. First, with only one model checkpoint, SkillNet performs better than
task-specific fine-tuning and two multi-task learning baselines (i.e., dense
model and Mixture-of-Experts model) on six tasks. Second, sparsely activated
pre-training further improves the overall performance. Third, SkillNet
significantly outperforms baseline systems when being extended to new tasks.


title:
EXORCISM: a spectroscopic survey of young eruptive variables (EXor and
  candidates)

abstract:
  We present an optical/near-IR survey of 11 variable young stars (EXors and
EXor candidates) aimed at deriving and monitoring their accretion properties.
About 30 optical and near-infrared spectra ($\Re$ $\sim$ 1500-2000) were
collected between 2014-2019 with the Large Binocular Telescope (LBT). From the
spectral analysis we have derived the accretion luminosity and mass accretion
rate, the visual extinction, the temperature and density of the permitted line
formation region, and the signature of the outflowing matter.
  Two sources (ASASSN-13db and iPTF15afq) have been observed in outburst and
quiescence, three during a high-level of brightness (XZ Tau, PV Cep, and NY
Ori), and the others in quiescence. These latter have accretion luminosity and
mass accretion rates in line with the values measured in classical T Tauri
stars of similar mass. All sources observed more than once present accretion
variability. The most extreme case is ASASSN-13db, for which the mass accretion
rate decreases by two orders of magnitude from the outburst peak in 2015 to
quiescence in 2017. Also, in NY Ori the accretion luminosity decreases by a
factor 25 in one year. In 80% of the sample we detect the [OI]630 nm line, a
tracer of mass loss. From the variability of the H$\alpha$/[OI]630, ratio, we
conclude that mass accretion variations are larger than mass loss variations.
From the analysis of the HI recombination lines a correlation is suggested
between the density of the line formation region and the level of accretion
activity of the source.


title: Simulating Authenticated Broadcast in Networks of Bounded Degree

abstract:
  The authenticated broadcast is simulated in the bounded-degree networks to
provide efficient broadcast primitives for building efficient higher-layer
Byzantine protocols. A general abstraction of the relay-based broadcast system
is introduced, in which the properties of the relay-based broadcast primitives
are generalized. With this, fault-tolerant propagation is proposed as a
building block of the broadcast primitives. Meanwhile, complementary systems
are proposed in complementing fault-tolerant propagation and localized
communication. Analysis shows that efficient fault-tolerant propagation can be
built with sufficient initiation areas. Meanwhile, by integrating
fault-tolerant propagation and localized communication, efficient broadcast
primitives can be built in bounded-degree networks.


title: Deep Reinforcement Learning for Entity Alignment

abstract:
  Embedding-based methods have attracted increasing attention in recent entity
alignment (EA) studies. Although great promise they can offer, there are still
several limitations. The most notable is that they identify the aligned
entities based on cosine similarity, ignoring the semantics underlying the
embeddings themselves. Furthermore, these methods are shortsighted,
heuristically selecting the closest entity as the target and allowing multiple
entities to match the same candidate. To address these limitations, we model
entity alignment as a sequential decision-making task, in which an agent
sequentially decides whether two entities are matched or mismatched based on
their representation vectors. The proposed reinforcement learning (RL)-based
entity alignment framework can be flexibly adapted to most embedding-based EA
methods. The experimental results demonstrate that it consistently advances the
performance of several state-of-the-art methods, with a maximum improvement of
31.1% on Hits@1.


title:
Trajectory convergence from coordinate-wise decrease of general convex
  energy functions

abstract:
  We consider arbitrary trajectories subject to a coordinate-wise energy
decrease: the sign of the derivative of each entry is never the same as that of
the corresponding entry of the gradient of some convex energy function. We show
that this simple condition guarantees convergence to a point, to the minimum of
the energy functions, or to a set where its Hessian has very specific
properties. This extends and strengthens recent results that were restricted to
quadratic energy functions.


title: Least Square Estimation Network for Depth Completion

abstract:
  Depth completion is a fundamental task in computer vision and robotics
research. Many previous works complete the dense depth map with neural networks
directly but most of them are non-interpretable and can not generalize to
different situations well. In this paper, we propose an effective image
representation method for depth completion tasks. The input of our system is a
monocular camera frame and the synchronous sparse depth map. The output of our
system is a dense per-pixel depth map of the frame. First we use a neural
network to transform each pixel into a feature vector, which we call base
functions. Then we pick out the known pixels' base functions and their depth
values. We use a linear least square algorithm to fit the base functions and
the depth values. Then we get the weights estimated from the least square
algorithm. Finally, we apply the weights to the whole image and predict the
final depth map. Our method is interpretable so it can generalize well.
Experiments show that our results beat the state-of-the-art on NYU-Depth-V2
dataset both in accuracy and runtime. Moreover, experiments show that our
method can generalize well on different numbers of sparse points and different
datasets.


title:
Higher-order recurrence relations, Sobolev-type inner products and
  matrix factorizations

abstract:
  It is well known that Sobolev-type orthogonal polynomials with respect to
measures supported on the real line satisfy higher-order recurrence relations
and these can be expressed as a (2N+1)-banded symmetric semi-infinite matrix.
In this paper we state the connection between these (2N+1)-banded matrices and
the Jacobi matrices associated with the three-term recurrence relation
satisfied by the standard sequence of orthonormal polynomials with respect to
the 2-iterated Christoffel transformation of the measure.


title: Few Induced Disjoint Paths for $H$-Free Graphs

abstract:
  Paths $P^1,\ldots,P^k$ in a graph $G=(V,E)$ are mutually induced if any two
distinct $P^i$ and $P^j$ have neither common vertices nor adjacent vertices.
For a fixed integer $k$, the $k$-Induced Disjoint Paths problem is to decide if
a graph $G$ with $k$ pairs of specified vertices $(s_i,t_i)$ contains $k$
mutually induced paths $P^i$ such that each $P^i$ starts from $s_i$ and ends at
$t_i$. Whereas the non-induced version is well-known to be polynomial-time
solvable for every fixed integer $k$, a classical result from the literature
states that even $2$-Induced Disjoint Paths is NP-complete. We prove new
complexity results for $k$-Induced Disjoint Paths if the input is restricted to
$H$-free graphs, that is, graphs without a fixed graph $H$ as an induced
subgraph. We compare our results with a complexity dichotomy for Induced
Disjoint Paths, the variant where $k$ is part of the input.


title:
Boosting Byzantine Protocols in Large Sparse Networks with High System
  Assumption Coverage

abstract:
  To improve the overall efficiency and reliability of Byzantine protocols in
large sparse networks, we propose a new system assumption for developing
multi-scale fault-tolerant systems, with which several kinds of multi-scale
Byzantine protocols are developed in large sparse networks with high system
assumption coverage. By extending the traditional Byzantine adversary to the
multi-scale adversaries, it is shown that efficient deterministic Byzantine
broadcast and Byzantine agreement can be built in logarithmic-degree networks.
Meanwhile, it is shown that the multi-scale adversary can make a finer
trade-off between the system assumption coverage and the overall efficiency of
the Byzantine protocols, especially when a small portion of the low-layer
small-scale protocols are allowed to fail arbitrarily. With this, efficient
Byzantine protocols can be built in large sparse networks with high system
reliability.


title:
Mixed boundary value problems for parabolic equations in Sobolev spaces
  with mixed-norms

abstract:
  We establish $L_{q,p}$-estimates and solvability for mixed Dirichlet-conormal
problems for parabolic equations in a cylindrical Reifenberg-flat domain with a
rough time-dependent separation.


title:
Dominant-feature identification in data from Gaussian processes applied
  to Finnish forest inventory records

abstract:
  In spatial data, location-dependent variation leads to connected structures
known as features. Variations occur at different spatial scales and possibly
originate from distinct underlying processes. Each of these scales is
characterized by its own dominant features. Here we introduce a statistical
method for identifying these scales and their dominant features in data from
Gaussian processes. This identification involves credibly recognizing the
dominant features by scale-space decomposition and assessing feature attributes
by estimating covariance function parameters of the underlying processes and
their associations to potential drivers. We analyze Finnish forest inventory
data from the 1920s using this dominant-feature identification method and
identify the scales of variation in basal area estimates of most common Finnish
trees, including Scots pine, Norway spruce, birch, and other native deciduous
trees. Comparing the resulting scale-dependent features and their attributes in
these tree species, we identify the different effects of edaphic and
anthropogenic drivers on the spatial distribution of their basal areas. These
data are analyzed for the first time in terms of their scale of variation, and
the resulting scale-dependent maps and estimates are an essential contribution
to the historical forest ecology of Fennoscandia. Until now, this analysis was
not possible with conventional methods.


title:
On the diameter of Cayley graphs of classical groups with generating
  sets containing a transvection

abstract:
  A well-known conjecture of Babai states that if $G$ is any finite simple
group and $X$ is a generating set for $G$, then the diameter of the Cayley
graph $Cay(G,X)$ is bounded by $\log|G|^c$ for some universal constant $c$. In
this paper, we prove such a bound for $Cay(G,X)$ for $G=PSL(n,q),PSp(n,q)$ or
$PSU(n,q)$ where $q$ is odd, under the assumptions that $X$ contains a
transvection and $q\neq 9$ or $81$.


title: Dynamic ConvNets on Tiny Devices via Nested Sparsity

abstract:
  This work introduces a new training and compression pipeline to build Nested
Sparse ConvNets, a class of dynamic Convolutional Neural Networks (ConvNets)
suited for inference tasks deployed on resource-constrained devices at the edge
of the Internet-of-Things. A Nested Sparse ConvNet consists of a single ConvNet
architecture containing N sparse sub-networks with nested weights subsets, like
a Matryoshka doll, and can trade accuracy for latency at run time, using the
model sparsity as a dynamic knob. To attain high accuracy at training time, we
propose a gradient masking technique that optimally routes the learning signals
across the nested weights subsets. To minimize the storage footprint and
efficiently process the obtained models at inference time, we introduce a new
sparse matrix compression format with dedicated compute kernels that fruitfully
exploit the characteristic of the nested weights subsets. Tested on image
classification and object detection tasks on an off-the-shelf ARM-M7 Micro
Controller Unit (MCU), Nested Sparse ConvNets outperform variable-latency
solutions naively built assembling single sparse models trained as stand-alone
instances, achieving (i) comparable accuracy, (ii) remarkable storage savings,
and (iii) high performance. Moreover, when compared to state-of-the-art dynamic
strategies, like dynamic pruning and layer width scaling, Nested Sparse
ConvNets turn out to be Pareto optimal in the accuracy vs. latency space.


title: Semiparametric Modeling for Multivariate Survival Data via Copulas

abstract:
  We propose a new class of multivariate survival models based on archimedean
copulas with margins modeled by the Yang and Prentice (YP) model. The
Ali-Mikhail-Haq (AMH), Clayton, Frank, Gumbel-Hougaard (GH), and Joe copulas
are employed to accommodate the dependency among marginal distributions.
Baseline distributions are modeled semiparametrically by the piecewise
exponential (PE) distribution and the Bernstein polynomials. The new class of
models possesses some attractive features: i) the ability to take into account
survival data with crossing survival curves; ii) the inclusion of the
well-known proportional hazards (PH) and proportional odds (PO) models as
particular cases; iii) greater flexibility provided by the semiparametric
modeling of the marginal baseline distributions; iv) the availability of
closed-form expressions for the likelihood functions, leading to more
straightforward inferential procedures. We conducted an extensive Monte Carlo
simulation study to evaluate the performance of the proposed model. Finally, we
demonstrate the versatility of our new class of models through the analysis of
survival data involving patients diagnosed with ovarian cancer.


title:
Conditioning two diffusion processes with respect to their
  first-encounter properties

abstract:
  We consider two independent identical diffusion processes that annihilate
upon meeting in order to study their conditioning with respect to their
first-encounter properties. For the case of finite horizon $T<+\infty$, the
maximum conditioning consists in imposing the probability $P^*(x,y,T ) $ that
the two particles are surviving at positions $x$ and $y$ at time $T$, as well
as the probability $\gamma^*(z,t) $ of annihilation at position $z$ at the
intermediate times $t \in [0,T]$. The adaptation to various conditioning
constraints that are less-detailed than these full distributions is analyzed
via the optimization of the appropriate relative entropy with respect to the
unconditioned processes. For the case of infinite horizon $T =+\infty$, the
maximum conditioning consists in imposing the first-encounter probability
$\gamma^*(z,t) $ at position $z$ at all finite times $t \in [0,+\infty[$, whose
normalization $[1- S^*(\infty )]$ determines the conditioned probability
$S^*(\infty ) \in [0,1]$ of forever-survival. This general framework is then
applied to the explicit cases where the unconditioned processes are
respectively two Brownian motions, two Ornstein-Uhlenbeck processes, or two
tanh-drift processes, in order to generate stochastic trajectories satisfying
various types of conditioning constraints. Finally, the link with the
stochastic control theory is described via the optimization of the dynamical
large deviations at Level 2.5 in the presence of the conditioning constraints
that one wishes to impose.


title:
Efficient Two-Dimensional Self-Stabilizing Byzantine Clock
  Synchronization in WALDEN

abstract:
  For tolerating Byzantine faults of both the terminal and communication
components in self-stabilizing clock synchronization, the two-dimensional
self-stabilizing Byzantine-fault-tolerant clock synchronization problem is
investigated and solved. By utilizing the time-triggered (TT) stage provided in
the underlying networks as TT communication windows, the approximate agreement,
hopping procedure, and randomized grandmasters are integrated into the overall
solution. It is shown that with partitioning the communication components into
$3$ arbitrarily connected subnetworks, efficient synchronization can be
achieved with one such subnetwork and less than $1/3$ terminal components being
Byzantine. Meanwhile, the desired stabilization can be reached for the specific
networks in one or several seconds with high probabilities. This helps in
developing various distributed hard-real-time systems with stringent time,
resources, and safety requirements.


title: Automated Few-Shot Time Series Forecasting based on Bi-level Programming

abstract:
  New micro-grid design with renewable energy sources and battery storage
systems can help improve greenhouse gas emissions and reduce the operational
cost. To provide an effective short-/long-term forecasting of both energy
generation and load demand, time series predictive modeling has been one of the
key tools to guide the optimal decision-making for planning and operation. One
of the critical challenges of time series renewable energy forecasting is the
lack of historical data to train an adequate predictive model. Moreover, the
performance of a machine learning model is sensitive to the choice of its
corresponding hyperparameters. Bearing these considerations in mind, this paper
develops a BiLO-Auto-TSF/ML framework that automates the optimal design of a
few-shot learning pipeline from a bi-level programming perspective.
Specifically, the lower-level meta-learning helps boost the base-learner to
mitigate the small data challenge while the hyperparameter optimization at the
upper level proactively searches for the optimal hyperparameter configurations
for both base- and meta-learners. Note that the proposed framework is so
general that any off-the-shelf machine learning method can be used in a plug-in
manner. Comprehensive experiments fully demonstrate the effectiveness of our
proposed BiLO-Auto-TSF/ML framework to search for a high-performance few-shot
learning pipeline for various energy sources.


title: Open Set Domain Adaptation By Novel Class Discovery

abstract:
  In Open Set Domain Adaptation (OSDA), large amounts of target samples are
drawn from the implicit categories that never appear in the source domain. Due
to the lack of their specific belonging, existing methods indiscriminately
regard them as a single class unknown. We challenge this broadly-adopted
practice that may arouse unexpected detrimental effects because the decision
boundaries between the implicit categories have been fully ignored. Instead, we
propose Self-supervised Class-Discovering Adapter (SCDA) that attempts to
achieve OSDA by gradually discovering those implicit classes, then
incorporating them to restructure the classifier and update the domain-adaptive
features iteratively. SCDA performs two alternate steps to achieve implicit
class discovery and self-supervised OSDA, respectively. By jointly optimizing
for two tasks, SCDA achieves the state-of-the-art in OSDA and shows a
competitive performance to unearth the implicit target classes.


title:
Competing emergent Potts orders and possible nematic spin liquids in the
  Kagome $J_{1}-J_{3}$ Heisenberg model

abstract:
  The extreme frustration in the Kagome antiferromagnet makes it possible to
realize a large number of closely competing magnetic phases either at the
classical or the quantum level. Motivated by recent neutron scattering study on
the Kagome antiferromagnet vesignieite BaCu$_{3}$V$_{2}$O$_{8}$(OH)$_{2}$, we
have made systematic investigation of the phase diagram of the classical and
quantum $J_{1}-J_{3}$ antiferromagnetic Heisenberg model defined on the Kagome
lattice(KAFHM). While it is shown previously that a large antiferromagnetic
exchange between the third-neighboring spins can drive an emergent $q=4$ Potts
order through the order-by-disorder mechanism, it is elusive what will happen
in the so called "grey region" where the Luttinger-Tisza criteria fails to
predict the classical ordering pattern. Through extensive Monte Carlo
simulation, we find that the "grey region" is characterized by competing
emergent $q=3$ and $q=4$ Potts order, whose emergence are beyond the
description of the conventional order-by-disorder mechanism. Our Schwinger
Boson mean field calculation in the "grey region" confirms the existence of the
Potts-3 order in the ground state of quantum $J_{1}-J_{3}$ KAFHM. The predicted
Potts-3 state is found to take two different forms distinguished by their
projective symmetry group character(PSG or quantum order), although they have
exactly the same symmetry. A comparison with the exact diagonalization result
on a 36-site cluster shows that the "grey region" may indeed host a nematic
spin liquid ground state featuring an anisotropic ring structure around the
$\mathbf{q}=0$ point in its spinon dispersion relation. We find that such "grey
region" of extremely frustrated magnets should better be taken as the
playground to study the rich competition between exotic emergent phases, rather
than a burden on their theoretical analysis.


title:
Perspectives on the importance of complex systems in understanding
  ourclimate and climate change -- The Nobel Prize in Physics 2021

abstract:
  The Nobel Prize in Physics 2021 was awarded to Syukuro Manabe, Klaus
Hasselmann, and Giorgio Parisi for their 'groundbreaking contributions to our
understanding of complex systems' including major advances in the understanding
of our climate and climate change. In this perspective article, we review their
key contributions and discuss their relevance in relation to the present
understanding of our climate. We conclude by outlining some promising research
directions and open questions in climate science.


title:
Search for noncommutative interactions in
  $\gamma\gamma\rightarrow\gamma\gamma$ process at the LHC

abstract:
  The noncommutative QED (NCQED) has a non-Abelian nature due to the presence
of 3- and 4-photon vertices in the lagrangian. Thus, NCQED predicts a new
physics contribution to the $\gamma\gamma\rightarrow\gamma\gamma$ scattering
already at the tree level. We have examined NCQED by studying the
light-by-light process at the 14 TeV LHC with intact protons. Our results show
that the NC scale up to $\Lambda_{\mathrm{NC}} = 1.65(1.35)$ TeV can be probed
in the $pp\rightarrow p(\gamma\gamma)p \rightarrow p'(\gamma\gamma)p'$
collision for the time-space (space-space) NC parameters. These bounds are
stronger than the limits that can be obtained in the light-by-light scattering
at high energy linear colliders.


title: Neural Enhancement of Factor Graph-based Symbol Detection

abstract:
  We study the application of the factor graph framework for symbol detection
on linear inter-symbol interference channels. Cyclic factor graphs have the
potential to yield low-complexity symbol detectors, but are suboptimal if the
ubiquitous sum-product algorithm is applied. In this paper, we present and
evaluate strategies to improve the performance of cyclic factor graph-based
symbol detection algorithms by means of neural enhancement. In particular, we
apply neural belief propagation as an effective way to counteract the effect of
cycles within the factor graph. We further propose the application and
optimization of a linear preprocessor of the channel output. By modifying the
observation model, the preprocessing can effectively change the underlying
factor graph, thereby significantly improving the detection performance as well
as reducing the complexity.


title:
Continuous Self-Localization on Aerial Images Using Visual and Lidar
  Sensors

abstract:
  This paper proposes a novel method for geo-tracking, i.e. continuous metric
self-localization in outdoor environments by registering a vehicle's sensor
information with aerial imagery of an unseen target region. Geo-tracking
methods offer the potential to supplant noisy signals from global navigation
satellite systems (GNSS) and expensive and hard to maintain prior maps that are
typically used for this purpose. The proposed geo-tracking method aligns data
from on-board cameras and lidar sensors with geo-registered orthophotos to
continuously localize a vehicle. We train a model in a metric learning setting
to extract visual features from ground and aerial images. The ground features
are projected into a top-down perspective via the lidar points and are matched
with the aerial features to determine the relative pose between vehicle and
orthophoto.
  Our method is the first to utilize on-board cameras in an end-to-end
differentiable model for metric self-localization on unseen orthophotos. It
exhibits strong generalization, is robust to changes in the environment and
requires only geo-poses as ground truth. We evaluate our approach on the
KITTI-360 dataset and achieve a mean absolute position error (APE) of 0.94m. We
further compare with previous approaches on the KITTI odometry dataset and
achieve state-of-the-art results on the geo-tracking task.


title: Online Matching with Convex Delay Costs

abstract:
  We investigate the problem of Min-cost Perfect Matching with Delays (MPMD) in
which requests are pairwise matched in an online fashion with the objective to
minimize the sum of space cost and time cost. Though linear-MPMD (i.e., time
cost is linear in delay) has been thoroughly studied in the literature, it does
not well model impatient requests that are common in practice. Thus, we propose
convex-MPMD where time cost functions are convex, capturing the situation where
time cost increases faster and faster. Since the existing algorithms for
linear-MPMD are not competitive any more, we devise a new deterministic
algorithm for convex-MPMD problems. For a large class of convex time cost
functions, our algorithm achieves a competitive ratio of $O(k)$ on any
$k$-point uniform metric space, or $O(k\Delta)$ when the metric space has
aspect ratio $\Delta$. Moreover, we prove lower bounds for the competitive
ratio of deterministic and randomized algorithms, indicating that our
deterministic algorithm is optimal. This optimality uncover a substantial
difference between convex-MPMD and linear-MPMD, since linear-MPMD allows a
deterministic algorithm with constant competitive ratio on any uniform metric
space.


title:
A novel shape-based loss function for machine learning-based seminal
  organ segmentation in medical imaging

abstract:
  Automated medical image segmentation is an essential task to aid/speed up
diagnosis and treatment procedures in clinical practices. Deep convolutional
neural networks have exhibited promising performance in accurate and automatic
seminal segmentation. For segmentation tasks, these methods normally rely on
minimizing a cost/loss function that is designed to maximize the overlap
between the estimated target and the ground-truth mask delineated by the
experts. A simple loss function based on the degrees of overlap (i.e., Dice
metric) would not take into account the underlying shape and morphology of the
target subject, as well as its realistic/natural variations; therefore,
suboptimal segmentation results would be observed in the form of islands of
voxels, holes, and unrealistic shapes or deformations. In this light, many
studies have been conducted to refine/post-process the segmentation outcome and
consider an initial guess as prior knowledge to avoid outliers and/or
unrealistic estimations. In this study, a novel shape-based cost function is
proposed which encourages/constrains the network to learn/capture the
underlying shape features in order to generate a valid/realistic estimation of
the target structure. To this end, the Principal Component Analysis (PCA) was
performed on a vectorized training dataset to extract eigenvalues and
eigenvectors of the target subjects. The key idea was to use the reconstruction
weights to discriminate valid outcomes from outliers/erroneous estimations.


title:
Dynamical properties of magnetic topological insulator
  $T$Bi$_{2}$Te$_{4}$ ($T=$Mn, Fe): phonons dispersion, Raman active modes, and
  chiral phonons study

abstract:
  Recently discovered magnetic topological insulators $T$Bi$_{2}$Te$_{4}$
($T=$Mn, Fe) crystallize into the $R\bar{3}m$ rhombohedral structure and
exhibit the antiferromagnetic order. Here, we discuss the lattice dynamics of
these compounds to confirm the stability of these systems. We show that the
phonon dispersion does not contain soft modes, so both compounds are
dynamically stable in the $R\bar{3}m$ phase. We perform theoretical analyses of
the mode activity at $\Gamma$ point for the discussed compounds. In the case of
the Raman active modes, our results are in agreement with the experimentally
observed frequencies. Finally, we also discuss the possibility of realization
of chiral phonons.


title:
Joint brain tumor segmentation from multi MR sequences through a deep
  convolutional neural network

abstract:
  Brain tumor segmentation is highly contributive in diagnosing and treatment
planning. The manual brain tumor delineation is a time-consuming and tedious
task and varies depending on the radiologists skill. Automated brain tumor
segmentation is of high importance, and does not depend on either inter or
intra-observation. The objective of this study is to automate the delineation
of brain tumors from the FLAIR, T1 weighted, T2 weighted, and T1 weighted
contrast-enhanced MR sequences through a deep learning approach, with a focus
on determining which MR sequence alone or which combination thereof would lead
to the highest accuracy therein.


title: L2CS-Net: Fine-Grained Gaze Estimation in Unconstrained Environments

abstract:
  Human gaze is a crucial cue used in various applications such as human-robot
interaction and virtual reality. Recently, convolution neural network (CNN)
approaches have made notable progress in predicting gaze direction. However,
estimating gaze in-the-wild is still a challenging problem due to the
uniqueness of eye appearance, lightning conditions, and the diversity of head
pose and gaze directions. In this paper, we propose a robust CNN-based model
for predicting gaze in unconstrained settings. We propose to regress each gaze
angle separately to improve the per-angel prediction accuracy, which will
enhance the overall gaze performance. In addition, we use two identical losses,
one for each angle, to improve network learning and increase its
generalization. We evaluate our model with two popular datasets collected with
unconstrained settings. Our proposed model achieves state-of-the-art accuracy
of 3.92{\deg} and 10.41{\deg} on MPIIGaze and Gaze360 datasets, respectively.
We make our code open source at https://github.com/Ahmednull/L2CS-Net.


title: Bohr-Sommerfeld quantization of $b$-symplectic toric manifolds

abstract:
  We define the Bohr-Sommerfeld quantization via $T$-modules for a
$b$-symplectic toric manifold and show that it coincides with the formal
geometric quantization of [GMW18b]. In particular, we prove that its dimension
is given by a signed count of the integral points in the moment polytope of the
toric action on the manifold.


title:
Recovering single precision accuracy from Tensor Cores while surpassing
  the FP32 theoretical peak performance

abstract:
  Tensor Core is a mixed-precision matrix-matrix multiplication unit on NVIDIA
GPUs with a theoretical peak performance of more than 300 TFlop/s on Ampere
architectures. Tensor Cores were developed in response to the high demand of
dense matrix multiplication from machine learning. However, many applications
in scientific computing such as preconditioners for iterative solvers and
low-precision Fourier transforms can exploit these Tensor Cores. To compute a
matrix multiplication on Tensor Cores, we need to convert input matrices to
half-precision, which results in loss of accuracy. To avoid this, we can keep
the mantissa loss in the conversion using additional half-precision variables
and use them for correcting the accuracy of matrix-matrix multiplication. Even
with this correction, the use of Tensor Cores yields higher throughput compared
to FP32 SIMT Cores. Nevertheless, the correcting capability of this method
alone is limited, and the resulting accuracy cannot match that of a matrix
multiplication on FP32 SIMT Cores. We address this problem and develop a high
accuracy, high performance, and low power consumption matrix-matrix
multiplication implementation using Tensor Cores, which exactly matches the
accuracy of FP32 SIMT Cores while achieving superior throughput. The
implementation is based on NVIDIA's CUTLASS. We found that the key to achieving
this accuracy is how to deal with the rounding inside Tensor Cores and
underflow probability during the correction computation. Our implementation
achieves 51TFlop/s for a limited exponent range using FP16 Tensor Cores and
33TFlop/s for full exponent range of FP32 using TF32 Tensor Cores on NVIDIA
A100 GPUs, which outperforms the theoretical FP32 SIMT Core peak performance of
19.5TFlop/s.


title:
High-Resolution Peak Demand Estimation Using Generalized Additive Models
  and Deep Neural Networks

abstract:
  This paper presents a method for estimating high-resolution electricity peak
demand given lower resolution data. The technique won a data competition
organized by the British distribution network operator Western Power
Distribution. The exercise was to estimate the minimum and maximum load values
in a single substation in a one-minute resolution as precisely as possible. In
contrast, the data was given in half-hourly and hourly resolutions. The winning
method combines generalized additive models (GAM) and deep artificial neural
networks (DNN) which are popular in load forecasting. We provide an extensive
analysis of the prediction models, including the importance of input parameters
with a focus on load, weather, and seasonal effects. In addition, we provide a
rigorous evaluation study that goes beyond the competition frame to analyze the
robustness. The results show that the proposed methods are superior, not only
in the single competition month but also in the meaningful evaluation study.

categories: cs.LG cs.SY econ.EM eess.SY stat.AP stat.ML


title:
Combinatorial nanoparticle patterns assembled by photovoltaic
  optoelectronic tweezers

abstract:
  Photovoltaic optoelectronic tweezers (PVOT) have been proven to be an
efficient tool for the manipulation and massive assembly of micro/nano-objects.
The technique relies on the strong electric fields produced by certain
ferroelectric materials upon illumination due to the bulk photovoltaic effect
(customarily LiNbO3:Fe). Despite the rapid development of PVOT and the
achievement of high-quality 1D and 2D particle patterning, research efforts
aimed at the fabrication of combinatorial structures made up of multiple types
of particles have been scarce. Here, we have established the working principles
of three different methods to tackle this pending challenge. To that end,
dielectrophoresis and/or electrophoresis acting on neutral and charged
particles respectively, have been suitably exploited. Simple mixed structures
combining metallic and dielectric nanoparticles of different sizes have been
successfully obtained. The results lay the groundwork for future fabrication of
more complex combinatorial structures by PVOT, where micro/nanoparticles are
the basic building blocks of miniaturized functional devices.


title:
Learning to Ground Decentralized Multi-Agent Communication with
  Contrastive Learning

abstract:
  For communication to happen successfully, a common language is required
between agents to understand information communicated by one another. Inducing
the emergence of a common language has been a difficult challenge to
multi-agent learning systems. In this work, we introduce an alternative
perspective to the communicative messages sent between agents, considering them
as different incomplete views of the environment state. Based on this
perspective, we propose a simple approach to induce the emergence of a common
language by maximizing the mutual information between messages of a given
trajectory in a self-supervised manner. By evaluating our method in
communication-essential environments, we empirically show how our method leads
to better learning performance and speed, and learns a more consistent common
language than existing methods, without introducing additional learning
parameters.


title:
Semilinear elliptic equations on manifolds with nonnegative Ricci
  curvature

abstract:
  In this paper we prove classification results for solutions to subcritical
and critical semilinear elliptic equations with a nonnegative potential on
noncompact manifolds with nonnegative Ricci curvature. We show in the
subcritical case that all nonnegative solutions vanish identically. Moreover,
under some natural assumptions, in the critical case we prove a strong rigidity
result, namely we classify all nontrivial solutions showing that they exist
only if the potential is constant and the manifold is isometric to the
Euclidean space.


title: Behaviour of the lateral shower age of cosmic ray extensive air showers

abstract:
  Some simple arguments are introduced for a possible explanation of the
behavior of the lateral shower age of proton-initiated showers. The
corresponding analytical treatment based on the proposed argument is then
illustrated. Using the Monte Carlo simulation (MC) code CORSIKA, we have
validated how the different characteristics associated with the lateral shower
age predicted in the present analytical parametrization, can be understood. The
lateral shower age of a proton-initiated shower and its correlations with the
lateral shower ages of electron- and neutral pion-initiated showers supports
the idea that the result of superposition of several electromagnetic
sub-showers initiated by neutral pions might produce the lateral density
distribution of electrons of a proton initiated shower. It is also noticed with
the simulated data that the stated feature still persists even in the local
shower age representation.


title:
Quantum state transfer between a frequency-encoded photonic qubit and a
  quantum dot spin in a nanophotonic waveguide

abstract:
  We propose a deterministic yet fully passive scheme to transfer the quantum
state from a frequency-encoded photon to the spin of a quantum-dot mediated by
a nanophotonic waveguide. We assess the quality of the state transfer by
studying the effects of all relevant experimental imperfections on the
state-transfer fidelity. We show that a transfer fidelity exceeding 95% is
achievable for experimentally realistic parameters. Our work sets the stage for
deterministic solid-state quantum networks tailored to frequency-encoded
photonic qubits.