title: Traits for Correct-by-Construction Programming

abstract:
  We demonstrate that traits are a natural way to support
correctness-by-construction (CbC) in an existing programming language in the
presence of traditional post-hoc verification (PhV). With
Correctness-by-Construction, programs are constructed incrementally along with
a specification that is inherently guaranteed to be satisfied. CbC is complex
to use without specialized tool support, since it needs a set of refinement
rules of fixed granularity which are additional rules on top of the programming
language.
  In this work, we propose TraitCbC, an incremental program construction
procedure that implements correctness-by-construction on the basis of PhV by
using traits. TraitCbC enables program construction by trait composition
instead of refinement rules. It provides a programming guideline, which similar
to CbC should lead to well-structured programs, and allows flexible reuse of
verified program building blocks. We introduce TraitCbC formally and prove the
soundness of our verification strategy. Additionally, we implement TraitCbC as
a proof of concept.


title:
Open-Loop Polarization Mode Dispersion Mitigation for Fibre-Optic Time
  and Frequency Transfer

abstract:
  The non-reciprocal and dynamic nature of polarization mode dispersion (PMD)
in optical fibers can be a problem for accurate time and frequency transfer.
Here a simple, passive solution is put forward that is based on transmitting
optical pulses with alternating orthogonal polarization. The fast and
deterministic polarization modulation means that the PMD noise is pushed far
away from the frequencies of interest and upon reflection from a Faraday mirror
at the receiver, the pulses have a well defined polarization when they return
to the transmitter, which facilitates stable optical phase detection and fibre
phase compensation. In an open-loop test setup that uses a mode-locked laser
and a simple pulse interleaver, the polarization mode dispersion is shown to be
reduced by more than two orders of magnitude


title:
Principled inference of hyperedges and overlapping communities in
  hypergraphs

abstract:
  Hypergraphs, encoding structured interactions among any number of system
units, have recently proven a successful tool to describe many real-world
biological and social networks. Here we propose a framework based on
statistical inference to characterize the structural organization of
hypergraphs. The method allows to infer missing hyperedges of any size in a
principled way, and to jointly detect overlapping communities in presence of
higher-order interactions. Furthermore, our model has an efficient numerical
implementation, and it runs faster than dyadic algorithms on pairwise records
projected from higher-order data. We apply our method to a variety of
real-world systems, showing strong performance in hyperedge prediction tasks,
detecting communities well aligned with the information carried by
interactions, and robustness against addition of noisy hyperedges. Our approach
illustrates the fundamental advantages of a hypergraph probabilistic model when
modeling relational systems with higher-order interactions.

categories: cs.SI physics.data-an physics.soc-ph stat.ML


title: Combinatorial identities and hypergeometric series

abstract:
  This paper describes a method to find a connection between combinatorial
identities and hypergeometric series with a number of examples. Combinatorial
identities can often be written as hypergeometric series with unit argument. In
a number of cases these hypergeometric series are balanced and can be reduced
to a simpler form. In this paper some combinatorial identities are proved using
this method assuming that the results in the tables of Prudnikov et al. [12]
are proven without using hypergeometric functions.


title: Gravitational Waves and Electromagnetic Transients

abstract:
  The advanced gravitational wave (GW) detector network has started {routine
detection of } signals from merging compact binaries. Data indicate that in a
fair fraction of these sources, at least one component was a neutron star,
bringing with it the possibility of electromagnetic (EM) radiation. So far, a
confirmed link between EM and GW radiation has been established for only one
source, GW170817. Joint analysis of broadband multiwavelength data and the GW
signal have yielded rich information spanning fields as varied as jet physics,
cosmology, and nucleosynthesis. Here, we discuss the importance of such joint
observations, as well as current and near-future efforts to discover and study
more EM counterparts to GW sources.


title:
ADFF: Attention Based Deep Feature Fusion Approach for Music Emotion
  Recognition

abstract:
  Music emotion recognition (MER), a sub-task of music information retrieval
(MIR), has developed rapidly in recent years. However, the learning of
affect-salient features remains a challenge. In this paper, we propose an
end-to-end attention-based deep feature fusion (ADFF) approach for MER. Only
taking log Mel-spectrogram as input, this method uses adapted VGGNet as spatial
feature learning module (SFLM) to obtain spatial features across different
levels. Then, these features are fed into squeeze-and-excitation (SE)
attention-based temporal feature learning module (TFLM) to get multi-level
emotion-related spatial-temporal features (ESTFs), which can discriminate
emotions well in the final emotion space. In addition, a novel data processing
is devised to cut the single-channel input into multi-channel to improve
calculative efficiency while ensuring the quality of MER. Experiments show that
our proposed method achieves 10.43% and 4.82% relative improvement of valence
and arousal respectively on the R2 score compared to the state-of-the-art
model, meanwhile, performs better on datasets with distinct scales and in
multi-task learning.


title: On the Potential of Using Sub-THz Frequencies for Beyond 5G

abstract:
  This paper studies the potential of using above 71GHz frequencies for
5G-Advanced or later in 6G. More specifically, the focus is to analyze what
could be needed in terms of waveform and numerologies. The results suggest that
higher baseline subcarrier spacings (SCSs) may be needed when moving above
71GHz, to fulfill the need for higher required bandwidths and phase noise
robustness. The required SCS depends on carrier frequency and modulation order.
It is also illustrated that single-carrier waveforms, especially Known Tail
Discrete Fourier Transform Spread Orthogonal Frequency Division Multiplexing
(KT-DFT-s-OFDM) waveform is a potential candidate to be used in 5G-Advanced or
6G for sub-THz frequencies due to its robustness to phase noise, lower output
power back-off and flexible adaptation of head and tail lengths.


title:
Global gyrokinetic simulations of ASDEX Upgrade up to the transport
  time-scale with GENE-Tango

abstract:
  An accurate description of turbulence up to the transport time scale is
essential for predicting core plasma profiles and enabling reliable
calculations for designing advanced scenarios and future devices. Here, we
exploit the gap separation between turbulence and transport time scales and
couple the global gyrokinetic code GENE to the transport-solver Tango,
including kinetic electrons, collisions, realistic geometries, toroidal
rotation and electromagnetic effects for the first time. This approach
overcomes gyrokinetic codes' limitations and enables high-fidelity profile
calculations in experimentally relevant plasma conditions, significantly
reducing the computational cost.
  We present numerical results of GENE-Tango for two ASDEX Upgrade discharges,
one of which exhibits a pronounced peaking of the ion temperature profile not
reproduced by TGLF-ASTRA. We show that GENE-Tango can correctly capture the ion
temperature peaking observed in the experiment. By retaining different physical
effects in the GENE simulations, e.g., collisions, toroidal rotation and
electromagnetic effects, we demonstrate that the ion temperature profile's
peaking is due to electromagnetic effects of submarginal MHD instability. Based
on these results, the expected GENE-Tango speedup for the ITER standard
scenario is larger than two orders of magnitude compared to a single
gyrokinetic simulation up to the transport time scale, possibly making
first-principles ITER simulations feasible on current computing resources.


title:
The electron foreshock at high-Mach-number nonrelativistic oblique
  shocks

abstract:
  In the Universe matter outside of stars and compact objects is mostly
composed of collisionless plasma. The interaction of a supersonic plasma flow
with an obstacle results in collisionless shocks that are often associated with
intense nonthermal radiation and the production of cosmic ray particles.
Motivated by simulations of non-relativistic high-Mach-number shocks in
supernova remnants, we investigate the instabilities excited by relativistic
electron beams in the extended foreshock of oblique shocks. The phase-space
distributions in the inner and outer foreshock regions are derived with a
Particle-in-Cell simulation of the shock and used as initial conditions for
simulations with periodic boundary conditions to study their relaxation towards
equilibrium. We find that the observed electron-beam instabilities agree very
well with the predictions of a linear dispersion analysis: the electrostatic
electron-acoustic instability dominates in the outer region of the foreshock,
while the denser electron beams in the inner foreshock drive the gyroresonant
oblique-whistler instability.


title:
Functional Pearl: Dependent type inference via free higher-order
  unification

abstract:
  Many type theories rely significantly on dependent types. Implementing a
proof assistant or a programming language based on such a theory is often
challenging, as type inference is notoriously hard in the presence of dependent
types. The cornerstone of dependent type inference is higher-order unification.
Even though many algorithms exist, state-of-the-art is implementing it directly
for a particular proof assistant. We propose a novel approach for abstract
syntax representation, which we call free scoped monads. This approach
capitalizes on the ideas of free monads and Swierstra's data types \`a la
carte, combining them with intrinsically well-scoped term representation via
Bird and Patterson's de Bruijn notation as nested data types. These
constructions allow us to handle scopes in a language-agnostic way and provide
generic higher-order unification algorithms, which serve as a foundation for
dependent type inference. Using the Haskell programming language, we apply this
approach to simply typed lambda calculus and Martin L\"of Type Theory. We show
that our approach is transparent for the user, yet provides non-trivial type
inference almost for free.


title: Impurity scattering in superconductors revisited

abstract:
  The diagrammatic formalism and transport equation are conventionally
considered as separate but complementary techniques to tackle the impurity
scattering effect. To compare with the previous studies from the
gauge-invariant kinetic equation approach [F. Yang and M. W. Wu, Phys. Rev. B
98, 094507 (2018); 102, 144508 (2020)], we analytically perform a diagrammatic
formulation of the impurity scattering in superconductors, with both transport
and collective Higgs mode studied, in order to fill the long missing
calculation of the Kubo current-current correlation in superconductors with
impurity scattering and resolve the controversy (whether the impurity
scattering can lead to the damping of Higgs mode) between gauge-invariant
kinetic equation and Eilenberger equation. For transport behavior, through a
special unitary transformation that is equivalent to the Wilson-line technique
for the diamagnetic response, we derive the Meissner-supercurrent vertex. Then,
by formulating the supercurrent-supercurrent correlation with Born and vertex
corrections from impurity scattering, we recover the previously revealed
microscopic momentum-relaxation rate of superfluid by gauge-invariant kinetic
equation. This rate is finite only when the superconducting velocity is larger
than a threshold, at which the normal fluid emerges and causes the friction
with the superfluid current, similar to the Landau's superfluid theory of
liquid helium. This derivation also provides a physical understanding of the
relaxation-time approximation in the previous diagrammatic formulation in the
literature, which leads to the friction resistance of the Meissner
supercurrent. For the collective Higgs mode, we calculate the
amplitude-amplitude correlation with Born and vertex corrections from impurity
scattering. The vertex correction, which only emerges at nonequilibrium case,
leads to a Higgs-mode damping, ......


title:
Creativity in translation: machine translation as a constraint for
  literary texts

abstract:
  This article presents the results of a study involving the translation of a
short story by Kurt Vonnegut from English to Catalan and Dutch using three
modalities: machine-translation (MT), post-editing (PE) and translation without
aid (HT). Our aim is to explore creativity, understood to involve novelty and
acceptability, from a quantitative perspective. The results show that HT has
the highest creativity score, followed by PE, and lastly, MT, and this is
unanimous from all reviewers. A neural MT system trained on literary data does
not currently have the necessary capabilities for a creative translation; it
renders literal solutions to translation problems. More importantly, using MT
to post-edit raw output constrains the creativity of translators, resulting in
a poorer translation often not fit for publication, according to experts.


title: Interface collisions with diffusive mass transport

abstract:
  We report on a linear Langevin model that describes the evolution of the
roughness of two interfaces that move towards each other and are coupled by a
diffusion field. This model aims at describing the closing of the gap between
two two-dimensional material domains during growth, and the subsequent
formation of a rough grain boundary. We assume that deposition occurs in the
gap between the two domains and that the growth units diffuse and may attach to
the edges of the domains. These units can also detach from edges, diffuse, and
re-attach elsewhere. For slow growth, the edge roughness increases monotonously
and then saturates at some equilibrium value. For fast growth, the roughness
exhibits a maximum just before the collision between the two interfaces, which
is followed by a minimum. The peak of the roughness can be dominated by
statistical fluctuations or by edge instabilities. A phase diagram with three
regimes is obtained: slow growth without peak, peak dominated by statistical
fluctuations, and peak dominated by instabilities. These results reproduce the
main features observed in Kinetic Monte Carlo simulations.


title:
Emergent Parallel Transports and Curvatures in Non-Hermitian Quantum
  Mechanics

abstract:
  Studies have shown that the Hilbert spaces of non-Hermitian systems require
non-trivial metrics. Here, we demonstrate how evolution dimensions, in addition
to time, can emerge naturally from the geometric formalism. Specifically, the
Hamiltonian can be interpreted as a Christoffel symbol-like operator, and
Schroedinger's equation as a parallel transport. We then derive the evolution
equations for the states and the metric along the emergent dimensions and find
that the curvature of the Hilbert space bundle for any given closed system is
locally flat. Finally, we show that the fidelity susceptibilities and the Berry
curvatures of states are related to emergent parallel transports.


title:
Gradient corrections to the local density approximation in the
  one-dimensional Bose gas

abstract:
  The local density approximation (LDA) is the central technical tool in the
modeling of quantum gases in trapping potentials. It consists in treating the
gas as an assembly of independent mesoscopic fluid cells at equilibrium with a
local chemical potential, and it is justified when the correlation length is
larger than the size of the cells. The LDA is often regarded as a crude
approximation, particularly in the ground state of the one-dimensional (1D)
Bose gas, where the correlation length is infinite. Here we take another look
at the LDA. The local density $\rho(x)$ is viewed as a functional of the
trapping potential $V(x)$, to which one applies a gradient expansion. The
zeroth order in that expansion is the LDA. The first-order correction in the
gradient expansion vanishes due to reflection symmetry. At second order, there
are two corrections proportional to $d^2V/dx^2$ and $(dV/dx)^2$, and we propose
a method to determine the corresponding coefficients by a perturbative
calculation in the Lieb-Liniger model. This leads to an expression for the
coefficients in terms of matrix elements of the density operator, which can be
evaluated numerically for arbitrary repulsion strength. In the limits of
infinite repulsion (hard-core bosons) and small repulsion (quasi-condensate),
the coefficients are evaluated analytically. The corrected LDA density profiles
are compared to DMRG calculations, with significant improvement compared to
zeroth-order LDA.


title:
Hybrid Simulation-based Resource Planning and Constructability Analysis
  of RCC Pavement Projects

abstract:
  One of the critical challenges in infrastructural constructions is designing
and planning operations and their related resources. The complex interlinked
composition of different factors and variables affecting resource productivity
has made simulation a powerful approach for operational planning. The
construction sector has recently seen a notable surge in applying various
simulation tools to enhance further the quality of projects planning,
particularly in large-scale infrastructure developments (e.g., highway
construction). Due to possible cost overruns in improper resource allocation,
optimizing the design and construction planning stages of megaprojects such as
massive pavement projects is essential. Recent studies aimed to build a
simulation-based strategy in construction designing and planning by combining
various simulation approaches (e.g., discrete-event simulation, system
dynamics, agent-based simulation, and hybrid simulation) to enhance the
planning phase. This paper introduces an evolving real-time hybrid simulation
technique regarding the projects intrinsic time-varying inputs and factors to
optimize the planning of Roller Compacted Concrete (RCC) pavement projects.
Several scenarios are investigated using various resource combinations to
achieve the best execution method for delivering concrete to the project. An
actual highway project case study validates the proposed model and its
application for future projects. This studys findings exhibit the proficiencies
of the simulation-based approach in resource planning of RCC pavement projects
within the time and cost constraints and their related regulations.


title:
NumGLUE: A Suite of Fundamental yet Challenging Mathematical Reasoning
  Tasks

abstract:
  Given the ubiquitous nature of numbers in text, reasoning with numbers to
perform simple calculations is an important skill of AI systems. While many
datasets and models have been developed to this end, state-of-the-art AI
systems are brittle; failing to perform the underlying mathematical reasoning
when they appear in a slightly different scenario. Drawing inspiration from
GLUE that was proposed in the context of natural language understanding, we
propose NumGLUE, a multi-task benchmark that evaluates the performance of AI
systems on eight different tasks, that at their core require simple arithmetic
understanding. We show that this benchmark is far from being solved with neural
models including state-of-the-art large-scale language models performing
significantly worse than humans (lower by 46.4%). Further, NumGLUE promotes
sharing knowledge across tasks, especially those with limited training data as
evidenced by the superior performance (average gain of 3.4% on each task) when
a model is jointly trained on all the tasks as opposed to task-specific
modeling. Finally, we hope that NumGLUE will encourage systems that perform
robust and general arithmetic reasoning within language, a first step towards
being able to perform more complex mathematical reasoning.


title: Coverings and liftings of generalized crossed modules

abstract:
  In the theory of crossed modules, considering arbitrary self-actions instead
of conjugation allows for the extension of the concept of crossed modules and
thus the notion of generalized crossed module emerges. In this paper we give a
precise definition for generalized cat$^1$-groups and obtain a functor from the
category of generalized cat$^1$-groups to generalized crossed modules. Further,
we introduce the notions of coverings and liftings for generalized crossed
modules and investigate properties of these structures. Main objective of this
study is to obtain an equivalence between the category of coverings and the
category of liftings of a given generalized crossed module $(A,B,\alpha)$.


title:
Plane-Wave-Based Stochastic-Deterministic Density Functional Theory for
  Extended Systems

abstract:
  Traditional finite-temperature Kohn-Sham density functional theory (KSDFT)
has an unfavorable scaling with respect to the electron number or at high
temperatures. The evaluation of the ground-state density in KSDFT can be
replaced by the Chebyshev trace (CT) method. In addition, the use of stochastic
orbitals within the CT method leads to the stochastic density functional theory
[Phys. Rev. Lett. 111, 106402 (2013)] (SDFT) and its improved theory, mixed
stochastic-deterministic density functional theory [Phys. Rev. Lett. 125,
055002 (2020)] (MDFT). We have implemented the above four methods within the
first-principles package ABACUS. All of the four methods are based on the
plane-wave basis set with the use of norm-conserving pseudopotentials and the
periodic boundary conditions with the use of $k$-point sampling in the
Brillouin zone. By using the KSDFT calculation results as benchmarks, we
systematically evaluate the accuracy and efficiency of the CT, SDFT, and MDFT
methods via examining a series of physical properties, which include the
electron density, the free energy, the atomic forces, stress, and density of
states for a few condensed phase systems. The results suggest that our
implementations of CT, SDFT, and MDFT not only reproduce the KSDFT results with
a high accuracy, but also exhibit several advantages over the tradition KSDFT
method. We expect these methods can be of great help in studying
high-temperature and large-size extended systems such as warm dense matter and
dense plasma.


title: Robust online joint state/input/parameter estimation of linear systems

abstract:
  This paper presents a method for jointly estimating the state, input, and
parameters of linear systems in an online fashion. The method is specially
designed for measurements that are corrupted with non-Gaussian noise or
outliers, which are commonly found in engineering applications. In particular,
it combines recursive, alternating, and iteratively-reweighted least squares
into a single, one-step algorithm, which solves the estimation problem online
and benefits from the robustness of least-deviation regression methods. The
convergence of the iterative method is formally guaranteed. Numerical
experiments show the good performance of the estimation algorithm in presence
of outliers and in comparison to state-of-the-art methods.


title: Several Roman domination graph invariants on Kneser graphs

abstract:
  In this paper the following Roman domination graph invariants on Kneser
graphs are considered:
  Roman domination, total Roman domination and signed Roman domination. We
present the closed formulas for Roman domination and total Roman domination
proving that for $n\geqslant k(k+1)$, $\gamma_{R}(K_{n,k})
=\gamma_{tR}(K_{n,k}) = 2(k+1)$. For signed Roman domination, the new lower and
upper bounds for $K_{n,2}$ are provided: we prove that for $n\geqslant 9$ the
lower bound is equal to 2, while the upper bound depends on the parity of $n$
and it is equal to 3, if $n$ is odd and equal to $5$, if $n$ is even. For
smaller values of $n$ exact values are found by exact methods.


title:
How to Register a Live onto a Liver ? Partial Matching in the Space of
  Varifolds

abstract:
  Partial shapes correspondences is a problem that often occurs in computer
vision (occlusion, evolution in time...). In medical imaging, data may come
from different modalities and be acquired under different conditions which
leads to variations in shapes and topologies. In this paper we use an
asymmetric data dissimilarity term applicable to various geometric shapes like
sets of curves or surfaces, assessing the embedding of a shape into another one
without relying on correspondences. It is designed as a data attachment for the
Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework, allowing to
compute a meaningful deformation of one shape onto a subset of the other. We
refine it in order to control the resulting non-rigid deformations and provide
consistent deformations of the shapes along with their ambient space. We show
that partial matching can be used for robust multi-modal liver registration
between a Computed Tomography (CT) volume and a Cone Beam Computed Tomography
(CBCT) volume. The 3D imaging of the patient CBCT at point of care that we call
live is truncated while the CT pre-intervention provides a full visualization
of the liver. The proposed method allows the truncated surfaces from CBCT to be
aligned non-rigidly, yet realistically, with surfaces from CT with an average
distance of 2.6mm(+/- 2.2). The generated deformations extend consistently to
the liver volume, and are evaluated on points of interest for the physicians,
with an average distance of 5.8mm (+/- 2.7) for vessels bifurcations and 5.13mm
(+/- 2.5) for tumors landmarks. Such multi-modality volumes registrations would
help the physicians in the perspective of navigating their tools in the
patient's anatomy to locate structures that are hardly visible in the CBCT used
during their procedures. Our code is available at
https://github.com/plantonsanti/PartialMatchingVarifolds.


title: Three-Stream Joint Network for Zero-Shot Sketch-Based Image Retrieval

abstract:
  The Zero-Shot Sketch-based Image Retrieval (ZS-SBIR) is a challenging task
because of the large domain gap between sketches and natural images as well as
the semantic inconsistency between seen and unseen categories. Previous
literature bridges seen and unseen categories by semantic embedding, which
requires prior knowledge of the exact class names and additional extraction
efforts. And most works reduce domain gap by mapping sketches and natural
images into a common high-level space using constructed sketch-image pairs,
which ignore the unpaired information between images and sketches. To address
these issues, in this paper, we propose a novel Three-Stream Joint Training
Network (3JOIN) for the ZS-SBIR task. To narrow the domain differences between
sketches and images, we extract edge maps for natural images and treat them as
a bridge between images and sketches, which have similar content to images and
similar style to sketches. For exploiting a sufficient combination of sketches,
natural images, and edge maps, a novel three-stream joint training network is
proposed. In addition, we use a teacher network to extract the implicit
semantics of the samples without the aid of other semantics and transfer the
learned knowledge to unseen classes. Extensive experiments conducted on two
real-world datasets demonstrate the superiority of our proposed method.


title: Local Random Feature Approximations of the Gaussian Kernel

abstract:
  A fundamental drawback of kernel-based statistical models is their limited
scalability to large data sets, which requires resorting to approximations. In
this work, we focus on the popular Gaussian kernel and on techniques to
linearize kernel-based models by means of random feature approximations. In
particular, we do so by studying a less explored random feature approximation
based on Maclaurin expansions and polynomial sketches. We show that such
approaches yield poor results when modelling high-frequency data, and we
propose a novel localization scheme that improves kernel approximations and
downstream performance significantly in this regime. We demonstrate these gains
on a number of experiments involving the application of Gaussian process
regression to synthetic and real-world data of different data sizes and
dimensions.


title:
Positive Feature Values Prioritized Hierarchical Redundancy Eliminated
  Tree Augmented Naive Bayes Classifier for Hierarchical Feature Spaces

abstract:
  The Hierarchical Redundancy Eliminated Tree Augmented Naive Bayes (HRE-TAN)
classifier is a semi-naive Bayesian model that learns a type of hierarchical
redundancy-free tree-like feature representation to estimate the data
distribution. In this work, we propose two new types of positive feature values
prioritized hierarchical redundancy eliminated tree augmented naive Bayes
classifiers that focus on features bearing positive instance values. The two
newly proposed methods are applied to 28 real-world bioinformatics datasets
showing better predictive performance than the conventional HRE-TAN classifier.


title:
An Analysis of Discretization Methods for Communication Learning with
  Multi-Agent Reinforcement Learning

abstract:
  Communication is crucial in multi-agent reinforcement learning when agents
are not able to observe the full state of the environment. The most common
approach to allow learned communication between agents is the use of a
differentiable communication channel that allows gradients to flow between
agents as a form of feedback. However, this is challenging when we want to use
discrete messages to reduce the message size since gradients cannot flow
through a discrete communication channel. Previous work proposed methods to
deal with this problem. However, these methods are tested in different
communication learning architectures and environments, making it hard to
compare them. In this paper, we compare several state-of-the-art discretization
methods as well as two methods that have not been used for communication
learning before. We do this comparison in the context of communication learning
using gradients from other agents and perform tests on several environments.
Our results show that none of the methods is best in all environments. The best
choice in discretization method greatly depends on the environment. However,
the discretize regularize unit (DRU), straight through DRU and the straight
through gumbel softmax show the most consistent results across all the tested
environments. Therefore, these methods prove to be the best choice for general
use while the straight through estimator and the gumbel softmax may provide
better results in specific environments but fail completely in others.


title:
Theoretical wind clumping predictions from 2D LDI models of O-star winds
  at different metallicities

abstract:
  Hot, massive (OB) stars experience strong line-driven stellar winds and mass
loss. As the majority of efficient driving lines are metallic, the amount of
wind driving and mass loss is dependent on the stellar metallicity Z. In
addition, line-driven winds are intrinsically inhomogeneous and clumpy.
However, to date, neither theoretical nor empirical studies of line-driven
winds have investigated how such wind clumping may also depend on Z. We
theoretically investigated the degree of wind clumping due to the
line-deshadowing instability (LDI) as a function of Z. We performed
two-dimensional hydrodynamic simulations of the LDI with an assumed
one-dimensional radiation line force for a grid of O-star wind models with
fixed luminosity, but with different metal contents by varying the accumulative
line strength Qbar describing the total ensemble of driving lines. We find
that, for this fixed luminosity, the amount of wind clumping decreases with
metallicity. The decrease is clearly seen in the statistical properties of our
simulations, but is nonetheless rather weak; a simple power-law fit for the
dependence of the clumping factor f_cl = <rho^2>/<rho>^2 on metallicity yields
f_cl ~ Z^(0.15 +/- 0.01). This implies that empirically derived power-law
dependencies of mass-loss rate Mdot on metallicity -- which were previously
inferred from spectral diagnostics effectively depending on Mdot*sqrt(f_cl) but
without having any constraints on f_cl(Z) -- should be only modestly altered by
clumping. We expect that this prediction can be directly tested using new data
from the Hubble Space Telescope Ultraviolet Legacy Library of Young Stars as
Essential Standards (ULLYSES) project.


title:
Simulating dynamical phases of chiral $p+ i p$ superconductors with a
  trapped ion magnet

abstract:
  Two-dimensional $p+ i p$ superconductors and superfluids are systems that
feature chiral behavior emerging from the Cooper pairing of electrons or
neutral fermionic atoms with non-zero angular momentum. Their realization has
been a longstanding goal because they offer great potential utility for quantum
computation and memory. However, they have so far eluded experimental
observation both in solid state systems as well as in ultracold quantum gases.
Here, we propose to leverage the tremendous control offered by rotating
two-dimensional trapped-ion crystals in a Penning trap to simulate the
dynamical phases of two-dimensional $p+ip$ superfluids. This is accomplished by
mapping the presence or absence of a Cooper pair into an effective spin-1/2
system encoded in the ions' electronic levels. We show how to infer the
topological properties of the dynamical phases, and discuss the role of beyond
mean-field corrections. More broadly, our work opens the door to use trapped
ion systems to explore exotic models of topological superconductivity and also
paves the way to generate and manipulate skyrmionic spin textures in these
platforms.

categories: quant-ph cond-mat.quant-gas cond-mat.supr-con physics.atom-ph


title: Cosmological Dynamics of Relativistic MOND

abstract:
  In this paper, we investigate the recently proposed relativistic theory for
MOND using the phase space analysis. Unlike its precedent theory, namely TeVeS,
this theory is claimed to agree with the observed Cosmic Microwave Background
and matter power spectra. We convert the background cosmological equations of
the theory to a set of first-order autonomous equations. Then we explore the
corresponding critical points and their physical meaning. This method is
powerful in the sense that the cosmological behavior of the model is diagnosed
independently of the magnitude of the free parameters of the theory. We show
that the theory has a viable sequence of cosmological epochs. Although using
specific initial conditions, this theory mimics the standard cosmological
model; it provides a richer structure as far as the background cosmology is
concerned. This implies that further investigations are required to see if this
theory contributes to addressing the current cosmological tensions.


title:
What do Toothbrushes do in the Kitchen? How Transformers Think our World
  is Structured

abstract:
  Transformer-based models are now predominant in NLP. They outperform
approaches based on static models in many respects. This success has in turn
prompted research that reveals a number of biases in the language models
generated by transformers. In this paper we utilize this research on biases to
investigate to what extent transformer-based language models allow for
extracting knowledge about object relations (X occurs in Y; X consists of Z;
action A involves using X). To this end, we compare contextualized models with
their static counterparts. We make this comparison dependent on the application
of a number of similarity measures and classifiers. Our results are threefold:
Firstly, we show that the models combined with the different similarity
measures differ greatly in terms of the amount of knowledge they allow for
extracting. Secondly, our results suggest that similarity measures perform much
worse than classifier-based approaches. Thirdly, we show that, surprisingly,
static models perform almost as well as contextualized models -- in some cases
even better.


title: A Generative Approach for Financial Causality Extraction

abstract:
  Causality represents the foremost relation between events in financial
documents such as financial news articles, financial reports. Each financial
causality contains a cause span and an effect span. Previous works proposed
sequence labeling approaches to solve this task. But sequence labeling models
find it difficult to extract multiple causalities and overlapping causalities
from the text segments. In this paper, we explore a generative approach for
causality extraction using the encoder-decoder framework and pointer networks.
We use a causality dataset from the financial domain, \textit{FinCausal}, for
our experiments and our proposed framework achieves very competitive
performance on this dataset.


title: Highly-transmitting modes of light in dynamic atmospheric turbulence

abstract:
  We show that instantaneous spatial singular modes of light in a dynamically
evolving, turbulent atmosphere offer significantly improved high-fidelity
signal transmission as compared to standard encoding bases corrected by
adaptive optics. Their enhanced stability in stronger turbulence is associated
with a subdiffusive algebraic decay of the transmitted power with evolution
time.


title: Nondiffracting Supertoroidal Pulses: Optical "K\'arm\'an vortex streets"

abstract:
  Recently introduced supertoroidal light pulses [Nat. Commun. 15, 5891 (2021)]
are a family of space-time nonseparable freespace electromagnetic excitations
with unique topological properties including skyrmionic field configurations,
fractal-like patterns of singularities, and extended areas of energy backflow.
Here we report nondiffracting supertoroidal pulses (ND-STPs),
propagation-robust skyrmionic and vortex-ring field that endure the singular
configurations over arbitrary propagation distances. Intriguingly, the field
structure in of ND-STPs has a strong similarity with a von K\'arm\'an vortex
street, a pattern of swirling vortices observed in fluid and gas dynamics that
is responsible for the "singing" of suspended telephone lines in wind. We argue
that ND-STPs are of interest as directed energy channels for telecom
applications.


title: Computation over Tensor Stiefel Manifold

abstract:
  Let $*$ denote the t-product between two third-order tensors. The purpose of
this work is to study fundamental computation over the set $St(n,p,l):=
\{\mathcal Q\in \mathbb R^{n\times p\times l} \mid \mathcal Q^{\top}* \mathcal
Q = \mathcal I \}$, where $\mathcal Q$ is a third-order tensor of size $n\times
p \times l$ and $\mathcal I$ ($n\geq p$) is the identity tensor. It is first
verified that $St(n,p,l)$ endowed with the usual Frobenius norm forms a
Riemannian manifold, which is termed as the (third-order) \emph{tensor Stiefel
manifold} in this work. We then derive the tangent space, Riemannian gradient,
and Riemannian Hessian on $St(n,p,l)$. In addition, formulas of various
retractions based on t-QR, t-polar decomposition, Cayley transform, and
t-exponential, as well as vector transports, are presented. It is expected that
analogous to their matrix counterparts, the formulas derived in this study may
provide basic building blocks for designing and analyzing Riemannian
algorithms, such as Riemannian gradient/conjugate gradient/(quasi-)Newton
methods for optimization over the tensor Stiefel manifold.


title: First integrals for Finsler metrics with vanishing $\chi$-curvature

abstract:
  We prove that in a Finsler manifold with vanishing $\chi$-curvature (in
particular with constant flag curvature) some non-Riemannian geometric
structures are geodesically invariant and hence they induce a set of
non-Riemannian first integrals. Two alternative expressions of these first
integrals can be obtained either in terms of the mean Berwald curvature, or as
functions of the mean Cartan torsion and the mean Landsberg curvature.


title:
Classical space-time geometry and the weak gravity regime in the IKKT
  matrix model

abstract:
  We discuss the reconstruction of generic 3+1-dimensional space-time
geometries from covariant quantum spaces as backgrounds in the IKKT matrix
model. Generic classical geometries can be realized within the weak gravity
regime, without inducing significant higher-spin contributions. In the strong
gravity regime i.e. for strong curvature, the background acquires significant
higher spin contributions, so that the classical geometry is no longer
adequate. Assuming that the scale of noncommutativity is given by the Planck
scale, the weak gravity regime is bounded by a curvature scale of the order
$10^{-4} m$, and easily compatible with known gravitational physics. This
justifies the framework for emergent gravity given by the semi-classical matrix
model, supplemented by an induced Einstein-Hilbert action which arises in the
presence of fuzzy extra dimensions.


title:
Anytime-valid sequential testing for elicitable functionals via
  supermartingales

abstract:
  We design sequential tests for a large class of nonparametric null hypotheses
based on elicitable and identifiable functionals. Such functionals are defined
in terms of scoring functions and identification functions, which are ideal
building blocks for constructing nonnegative supermartingales under the null.
This in turn yields anytime valid tests via Ville's inequality. Using regret
bounds from Online Convex Optimization, we obtain rigorous guarantees on the
asymptotic power of the tests for a wide range of alternative hypotheses. Our
results allow for bounded and unbounded data distributions, assuming that a
sub-$\psi$ tail bound is satisfied.


title: Learning Stable Dynamical Systems for Visual Servoing

abstract:
  This work presents the dual benefit of integrating imitation learning
techniques, based on the dynamical systems formalism, with the visual servoing
paradigm. On the one hand, dynamical systems allow to program additional skills
without explicitly coding them in the visual servoing law, but leveraging few
demonstrations of the full desired behavior. On the other, visual servoing
allows to consider exteroception into the dynamical system architecture and be
able to adapt to unexpected environment changes. The beneficial combination of
the two concepts is proven by applying three existing dynamical systems methods
to the visual servoing case. Simulations validate and compare the methods;
experiments with a robot manipulator show the validity of the approach in a
real-world scenario.


title:
A Robust Learning Rule for Soft-Bounded Memristive Synapses Competitive
  with Supervised Learning in Standard Spiking Neural Networks

abstract:
  Memristive devices are a class of circuit elements that shows great promise
as future building block for brain-inspired computing. One influential view in
theoretical neuroscience sees the brain as a function-computing device: given
input signals, the brain applies a function in order to generate new internal
states and motor outputs. Therefore, being able to approximate functions is a
fundamental axiom to build upon for future brain research and to derive more
efficient computational machines. In this work we apply a novel supervised
learning algorithm - based on controlling niobium-doped strontium titanate
memristive synapses - to learning non-trivial multidimensional functions. By
implementing our method into the spiking neural network simulator Nengo, we
show that we are able to at least match the performance obtained when using
ideal, linear synapses and - in doing so - that this kind of memristive device
can be harnessed as computational substrate to move towards more efficient,
brain-inspired computing.


title:
Brittle fracture studied by ultra-high speed synchrotron X-ray
  diffraction imaging

abstract:
  Ever since the very first human-made knapped tools, the control of fracture
propagation in brittle materials has been a vector of technological
development. Nowadays, a broad range of applications relies on crack
propagation control, from the mitigation of damages, e.g., from impacts in
glass screens or windshields, to industrial processes harnessing fracture to
achieve clean cuts over large distances. Yet, studying the fracture in real
time is a challenging task, since cracks can propagate up to a few km/s in
materials that are often opaque. Here, we report on the in situ investigation
of cracks propagating at up to 2.5 km/s along a (001) plane of a silicon single
crystal, using X-ray diffraction megahertz imaging with intense and
time-structured synchrotron radiation. The studied system is based on the Smart
Cut process, where a buried layer in a material (typically Si) is weakened by
micro-cracks and then used to drive a macroscopic crack (0.1 m) in a plane
parallel to the surface with minimal deviation (1 nm). The results we report
here provide the first direct confirmation that the shape of the crack front is
not affected by the distribution of the micro-cracks, which had been a
postulate for previous studies based on post-fracture results. We further
measured instantaneous crack velocities over the centimeter-wide field-of-view,
which had only been previously inferred from sparse point measurements, and
evidence the effect of local heating by the X-ray beam. Finally, we also
observed the post-crack movements of the separated wafer parts, which can be
explained using pneumatics and elasticity. Thus, this study provides a
comprehensive view of controlled fracture propagation in a crystalline
material, paving the way for the in situ measurement of ultra-fast strain field
propagation.


title:
Dark matter in a CP-violating three-Higgs-doublet model with $S_3$
  symmetry

abstract:
  In spite of the success of the Standard Model of Particle Physics, there are
some theoretical predictions which are not yet fully established experimentally
as well as some experimental observations which cannot be fitted within its
theoretical framework, thus requiring physics beyond the Standard Model. One of
these is a hypothetical non-luminous form of matter - dark matter. Models with
an extended scalar electroweak sector yield plausible dark matter candidates.
In this paper we study a specific model, C-III-a, from a family of
$S_3$-symmetric three-Higgs-doublet models. The model consists of two active
SU(2) doublets and an inert one. The latter is inert due to a $\mathbb{Z}_2$
symmetry that survives the breaking of $S_3$, and would accommodate a dark
matter particle. We explore the model numerically, based on theoretical and
experimental constraints. After applying a number of successive checks over the
parameter space we found a viable dark matter mass region in the range
$[6.5;\,44.5]~\text{GeV}$. This region is drastically different from the
Higgs-like dark matter states that have been proposed: the well-known Inert
Doublet Model and models with three scalar doublets, with one or two inert
doublets. Furthermore, the C-III-a model allows for spontaneous CP violation.


title: Paraxial Sharp-Edge Diffraction: A General Computational Approach

abstract:
  A general reformulation of classical sharp-edge diffraction theory is
proposed within paraxial approximation. The, not so much known, Poincar\'e
vector potential construction is employed directly inside Fresnel's 2D integral
in order for it to be converted into a single 1D contour integral over the
aperture boundary. Differently from the recently developed paraxial
revisitation of BDW's theory, such approach can be applied to arbitrary
wavefield distributions impinging onto arbitrarily shaped sharp-edge planar
apertures. A couple of interesting examples of application of the proposed
method is presented.


title:
Enhanced superconductivity and moderate spin fluctuations suppressed at
  low energies in heavily electron-doped La1111-based superconductor

abstract:
  To elucidate the origin of re-enhanced high-$T_c$ phase in the heavily
electron-doped Fe-pnictides, systematic $^{75}$As NMR studies are performed on
heavily electron-doped LaFe$Pn$O$_{0.75}$H$_{0.25}$ by controlling the
pnictogen height ($h_{Pn}$) from the Fe plane through the substitution at
$Pn$(=As) site with Sb or P. The measurements of nuclear spin relaxation rate
(1/$T_1$) and Knight shift ($K$) reveal that the moderate spin fluctuations at
high temperatures are suppressed toward low temperatures. Such characteristic
spin fluctuations with gap like feature at low energies are more enlarged in
higher $T_c$ compounds with higher $h_{Pn}$, while those are totally suppressed
in non-superconducting compounds with lower $h_{Pn}$. This implies that the
contribution of the finite energy part in the spin fluctuation spectrum is
crucial for enhancing $T_c$ in the heavily electron-doped regime. This is in
contrast to many cases of typical Fe-based compounds with hole and electron
Fermi surfaces of similar sizes, where the spin fluctuations at low energies
develop significantly at low temperatures. The features in the heavily
electron-doped states are discussed in relation with the characteristics of the
faint hole Fermi surface derived from $d_{xy}$ orbital that rises when $h_{Pn}$
is high, together with the enhanced electron correlation effects.


title: 3DeformRS: Certifying Spatial Deformations on Point Clouds

abstract:
  3D computer vision models are commonly used in security-critical applications
such as autonomous driving and surgical robotics. Emerging concerns over the
robustness of these models against real-world deformations must be addressed
practically and reliably. In this work, we propose 3DeformRS, a method to
certify the robustness of point cloud Deep Neural Networks (DNNs) against
real-world deformations. We developed 3DeformRS by building upon recent work
that generalized Randomized Smoothing (RS) from pixel-intensity perturbations
to vector-field deformations. In particular, we specialized RS to certify DNNs
against parameterized deformations (e.g. rotation, twisting), while enjoying
practical computational costs. We leverage the virtues of 3DeformRS to conduct
a comprehensive empirical study on the certified robustness of four
representative point cloud DNNs on two datasets and against seven different
deformations. Compared to previous approaches for certifying point cloud DNNs,
3DeformRS is fast, scales well with point cloud size, and provides
comparable-to-better certificates. For instance, when certifying a plain
PointNet against a 3{\deg} z-rotation on 1024-point clouds, 3DeformRS grants a
certificate 3x larger and 20x faster than previous work.


title:
Super-Resolution for Selfie Biometrics: Introduction and Application to
  Face and Iris

abstract:
  The lack of resolution has a negative impact on the performance of
image-based biometrics. Many applications which are becoming ubiquitous in
mobile devices do not operate in a controlled environment, and their
performance significantly drops due to the lack of pixel resolution. While many
generic super-resolution techniques have been studied to restore low-resolution
images for biometrics, the results obtained are not always as desired. Those
generic methods are usually aimed to enhance the visual appearance of the
scene. However, producing an overall visual enhancement of biometric images
does not necessarily correlate with a better recognition performance. Such
techniques are designed to restore generic images and therefore do not exploit
the specific structure found in biometric images (e.g. iris or faces), which
causes the solution to be sub-optimal. For this reason, super-resolution
techniques have to be adapted for the particularities of images from a specific
biometric modality. In recent years, there has been an increased interest in
the application of super-resolution to different biometric modalities, such as
face iris, gait or fingerprint. This chapter presents an overview of recent
advances in super-resolution reconstruction of face and iris images, which are
the two prevalent modalities in selfie biometrics. We also provide experimental
results using several state-of-the-art reconstruction algorithms, demonstrating
the benefits of using super-resolution to improve the quality of face and iris
images prior to classification. In the reported experiments, we study the
application of super-resolution to face and iris images captured in the visible
range, using experimental setups that represent well the selfie biometrics
scenario.


title:
The consensus problem for opinion dynamics with local average random
  interactions

abstract:
  We study the consensus formation for an agents based model, generalizing that
originally proposed by Krause \cite{Kr}, by allowing the communication channels
between any couple of agents to be switched on or off randomly, at each time
step, with a probability law depending on the proximity of the agents'
opinions. Namely, we consider a system of agents sharing their opinions
according to the following updating protocol. At time $t+1$ the opinion
$X_{i}\left( t+1\right) \in\left[ 0,1\right] $ of agent $i$ is updated at the
weighted average of the opinions of the agents communicating with it at time
$t.$ The weights model the confidence level an agent assign to the opinions of
the other agents and are kept fixed by the system dynamics, but the set of
agents communicating with any agent $i$ at time $t+1$ is randomly updated in
such a way that the agent $j$ can be chosen to belong to this set independently
of the other agents with a probability that is a non increasing function of
$\left\vert X_{i}\left( t\right) -X_{j}\left( t\right) \right\vert .$ This
condition models the fact that a communication among the agents is more likely
to happen if their opinions are close. We prove that the system reaches
consensus, i.e. as the time tends to infinity the agents' opinions will reach
the same value exponentially fast.


title:
Fault Detection and Localization in Active Distribution Networks using
  Optimally Placed Phasor Measurements Units

abstract:
  This paper introduces an algorithm able to detect and localize the occurrance
of a fault in an Active Distribution Network, using the measurements collected
by Phasor Measurement Units (PMUs). First, a basic algorithm that works under
the assumption that all grid buses are equipped with a PMU is designed. Then,
formal observability conditions that allow detection and localization with a
reduced number of PMUs are provided. Based on these conditions, the algorithm
is extended to perform correctly when not all network buses are monitored.
Moreover, an Optimal Positioning Algorithm, always based on the observability
conditions, is designed. This algorithm allows the user to customize the fault
localization resolution. The approach is validated through simulations carried
out on a benchmark active distribution network.


title: Newton-Puiseux algorithm and triple points for planes curves

abstract:
  We study triple points on reduced plane curves. In order to do that, we
rewrite an algorithmic version of the Newton-Puiseux procedure so to get
polynomial approximations of parameterizations of branches of a curve at a
singular point.


title:
Instability of the Luttinger liquids towards an exotic quantum state of
  matter with highly degenerate ground states: an anisotropic extension of the
  ferromagnetic spin-1 biquadratic model

abstract:
  An extensive investigation, both numerical and analytical, is performed for
an anisotropic extension of the ferromagnetic spin-1 biquadratic model. The
ground state phase diagram accommodates three symmetry-protected trivial
phases, three coexisting fractal phases and six Luttinger liquid phases. A
novel universality class arises from an instability of a Luttinger liquid
towards an exotic quantum state of matter with highly degenerate ground states.
The latter in turn is a coexisting fractal phase, which may be attributed to
the coexistence of ${\rm SU}(2)$ symmetry spontaneous breaking with one type-B
Goldstone mode on the characteristic line: $J_y=J_z$ and its cyclic
permutations with respect to $x$, $y$ and $z$, and ${\rm U}(1)$ spontaneous
symmetry breaking without any gapless Goldstone mode away from the
characteristic lines.


title: Density Matrix Renormalization Group with Tensor Processing Units

abstract:
  Google's Tensor Processing Units (TPUs) are integrated circuits specifically
built to accelerate and scale up machine learning workloads. They can perform
fast distributed matrix multiplications and therefore be repurposed for other
computationally intensive tasks. In this work we demonstrate the use of TPUs
for accelerating and scaling up the density matrix renormalization group
(DMRG), a powerful numerical approach to compute the ground state of a local
quantum many-body Hamiltonian. The cost of DMRG scales with system size $N$ as
$O(ND^3)$, where the so-called bond dimension $D$ regulates how expressive the
underlying matrix product state (MPS) variational ansatz is. We consider
lattice models in two spatial dimensions, with square lattices of size
$10\times 10$ (free fermions) and $20\times 20$ (transverse field Ising model),
for which the required MPS bond dimension is known to scale at least as
$\exp(\sqrt{N})$. Using half of a TPU v3 pod (namely $1,\!024$ TPU v3 cores) we
reached an unprecedentedly large bond dimension $D = 2^{16} = 65,\!536$, for
which optimizing a single MPS tensor took about 2 minutes.


title:
Picosecond Pulsed Squeezing in Thin-Film Lithium Niobate Strip-Loaded
  Waveguides at Telecommunication Wavelengths

abstract:
  Achieving high level of pulsed squeezing, in a platform which offers
integration and stability, is a key requirement for continuous-variable quantum
information processing. Typically highly squeezed states are achieved with
narrow band optical cavities and bulk crystals, limiting scalability. Using
single-pass parametric down conversion in an integrated optical device, we
demonstrate quadrature squeezing of picosecond pulses in a thin-film lithium
niobate strip-loaded waveguide. For on-chip peak powers of less than 0.3 W, we
measure up to -0.33$\pm$0.07 dB of squeezing with an inferred on-chip value of
-1.7$\pm$0.4 dB. This work highlights the potential of the strip-loaded
waveguide platform for broadband squeezing applications and the development of
photonic quantum technologies.


title: Self-Supervised Losses for One-Class Textual Anomaly Detection

abstract:
  Current deep learning methods for anomaly detection in text rely on
supervisory signals in inliers that may be unobtainable or bespoke
architectures that are difficult to tune. We study a simpler alternative:
fine-tuning Transformers on the inlier data with self-supervised objectives and
using the losses as an anomaly score. Overall, the self-supervision approach
outperforms other methods under various anomaly detection scenarios, improving
the AUROC score on semantic anomalies by 11.6% and on syntactic anomalies by
22.8% on average. Additionally, the optimal objective and resultant learnt
representation depend on the type of downstream anomaly. The separability of
anomalies and inliers signals that a representation is more effective for
detecting semantic anomalies, whilst the presence of narrow feature directions
signals a representation that is effective for detecting syntactic anomalies.


title: Positive definite functions on a regular domain

abstract:
  We define positive and strictly positive definite functions on a domain and
study these functions on a list of regular domains. The list includes the unit
ball, conic surface, hyperbolic surface, solid hyperboloid, and simplex. Each
of these domains is embedded in a quadrant or a union of quadrants of the unit
sphere by a distance preserving map, from which characterizations of positive
definite and strictly positive definite functions are derived for these regular
domains.


title:
Mass estimation of the very massive galaxy cluster SRGe CL2305.2$-$2248
  from strong lensing

abstract:
  The galaxy cluster SRGe CL2305.2$-$2248 (SPT-CL J2305$-$2248, ACT-CL
J2305.1$-$2248) is one of the most massive clusters at high redshifts ($z
\simeq 0.76$) and is of great interest for cosmology. For an optical
identification of this cluster, deep images were obtained with the 1.5-m
Russian-Turkish telescope RTT-150. Together with the open archival data of the
Hubble Space Telescope, it became possible to identify candidates for
gravitationally lensed images of distant blue galaxies in the form of arcs and
arclets. The observed giant arc near the brightest cluster galaxies allowed us
to estimate the radius of the Einstein ring, which is $ 9.8 \pm 1.3 $
arcseconds. The photometric redshift of the lensed source was obtained ($ z_s =
2.44 \pm 0.07 $). Its use in combination with the Einstein radius estimate made
it possible to independently estimate the \cl2305 mass. It was done by
extrapolating the strong lensing results to large radii and using the model
density distribution profiles in relaxed clusters. This extrapolation leads to
mass estimates $ \sim 1.5-3 $ times smaller than those obtained from X-ray and
microwave observations. A probable cause for this discrepancy may be the
process of cluster merging, which is also confirmed by SRGe CL2305.2-2248
morphology in the optical range.


title: Medusa: Universal Feature Learning via Attentional Multitasking

abstract:
  Recent approaches to multi-task learning (MTL) have focused on modelling
connections between tasks at the decoder level. This leads to a tight coupling
between tasks, which need retraining if a new task is inserted or removed. We
argue that MTL is a stepping stone towards universal feature learning (UFL),
which is the ability to learn generic features that can be applied to new tasks
without retraining.
  We propose Medusa to realize this goal, designing task heads with dual
attention mechanisms. The shared feature attention masks relevant backbone
features for each task, allowing it to learn a generic representation.
Meanwhile, a novel Multi-Scale Attention head allows the network to better
combine per-task features from different scales when making the final
prediction. We show the effectiveness of Medusa in UFL (+13.18% improvement),
while maintaining MTL performance and being 25% more efficient than previous
approaches.


title:
Unsupervised Anomaly and Change Detection with Multivariate
  Gaussianization

abstract:
  Anomaly detection is a field of intense research. Identifying low probability
events in data/images is a challenging problem given the high-dimensionality of
the data, especially when no (or little) information about the anomaly is
available a priori. While plenty of methods are available, the vast majority of
them do not scale well to large datasets and require the choice of some (very
often critical) hyperparameters. Therefore, unsupervised and computationally
efficient detection methods become strictly necessary. We propose an
unsupervised method for detecting anomalies and changes in remote sensing
images by means of a multivariate Gaussianization methodology that allows to
estimate multivariate densities accurately, a long-standing problem in
statistics and machine learning. The methodology transforms arbitrarily complex
multivariate data into a multivariate Gaussian distribution. Since the
transformation is differentiable, by applying the change of variables formula
one can estimate the probability at any point of the original domain. The
assumption is straightforward: pixels with low estimated probability are
considered anomalies. Our method can describe any multivariate distribution,
makes an efficient use of memory and computational resources, and is
parameter-free. We show the efficiency of the method in experiments involving
both anomaly detection and change detection in different remote sensing image
sets. Results show that our approach outperforms other linear and nonlinear
methods in terms of detection power in both anomaly and change detection
scenarios, showing robustness and scalability to dimensionality and sample
sizes.


title: Dedekind-finite cardinals having countable partitions

abstract:
  We study the possible structures which can be carried by sets which have no
countable subset, but which fail to be `surjectively Dedekind finite', in two
possible senses, that there is a surjection to $\omega$, or alternatively, that
there is no surjection to a proper superset.


title: Analogue quantum gravity in hyperbolic metamaterials

abstract:
  It is well known that extraordinary photons in hyperbolic metamaterials may
be described as living in an effective Minkowski spacetime, which is defined by
the peculiar form of the strongly anisotropic dielectric tensor in these
metamaterials. Here we demonstrate that within the scope of this approximation
the sound waves in hyperbolic metamaterials look similar to gravitational
waves, and therefore the quantized sound waves (phonons) look similar to
gravitons. Such an analogue model of quantum gravity looks especially
interesting near the phase transitions in hyperbolic metamaterials where it
becomes possible to switch quantum gravity effects on and off as a function of
metamaterial temperature. We also predict strong enhancement of
sonoluminescence in ferrofluid based hyperbolic metamaterials, which looks
analogous to particle creation in strong gravitational fields.


title:
High-precision solution of the Dirac Equation for the hydrogen molecular
  ion by an iterative method

abstract:
  The Dirac equation for H$_2^+$ is solved numerically using an iterative
method proposed by Kutzelnigg [Z. Phys. 11, 15 (1989]. The four-component
wavefunction is expanded in a newly introduced kinetically balanced exponential
basis set. The ground-state relativistic energy is obtained with an accuracy of
$10^{-20}$, which represents an improvement by several orders of magnitude, and
is shown to be in good agreement with results obtained from perturbation
theory.Highly accurate relativistic wavefunctions are obtained, which is a
first step towards nonperturbative calculations of the one-loop self-energy
correction in hydrogen molecular ions.


title:
Back to the Roots: Reconstructing Large and Complex Cranial Defects
  using an Image-based Statistical Shape Model

abstract:
  Designing implants for large and complex cranial defects is a challenging
task, even for professional designers. Current efforts on automating the design
process focused mainly on convolutional neural networks (CNN), which have
produced state-of-the-art results on reconstructing synthetic defects. However,
existing CNN-based methods have been difficult to translate to clinical
practice in cranioplasty, as their performance on complex and irregular cranial
defects remains unsatisfactory. In this paper, a statistical shape model (SSM)
built directly on the segmentation masks of the skulls is presented. We
evaluate the SSM on several cranial implant design tasks, and the results show
that, while the SSM performs suboptimally on synthetic defects compared to
CNN-based approaches, it is capable of reconstructing large and complex defects
with only minor manual corrections. The quality of the resulting implants is
examined and assured by experienced neurosurgeons. In contrast, CNN-based
approaches, even with massive data augmentation, fail or produce
less-than-satisfactory implants for these cases. Codes are publicly available
at https://github.com/Jianningli/ssm


title:
Optimal droop control placement in distribution network via an exact OPF
  relaxation method

abstract:
  In the last decade, the integration of Renewable Energy Sources (RES) in
distribution networks has been constantly increasing due to their many
technical, economical, and environmental benefits. However, the large-scale
penetration of RESs is limited by the grid security constraints, e.g., voltage
and current limits. The control of inverter-interfaced RESs can guarantee to
comply with those constraints while preserving the RESs performance. However,
the installation of additional controllable converter units introduces
supplementary investment costs and has therefore to be limited. In this paper,
a Mixed-Integer Second-Order Cone (MISOCP) optimization problem is developed to
optimally place the Q-V and P-V droop controllers for the RES converters. The
objectives of the optimization problem are to minimize investment costs,
maintenance costs, and the cost of energy purchase from the grid subject to the
system constraints. To provide an accurate and convex formulation of these
constraints, we adapt an augmented relaxation method, recently proposed to
address the optimal power flow problem in radial distribution networks. Our
method is tested on a standard IEEE 34-bus network and the results are compared
to those provided by alternative approaches.


title:
Force-Dependent Folding Kinetics of Single Molecules with Multiple
  Intermediates and Pathways

abstract:
  Most single-molecule studies derive the kinetic rates of native,
intermediate, and unfolded states from equilibrium hopping experiments. Here,
we apply Kramers kinetic diffusive model to derive the force-dependent kinetic
rates of intermediate states from non-equilibrium pulling experiments. From the
kinetic rates, we also extract the force-dependent kinetic barriers and the
equilibrium folding energies. We apply our method to DNA hairpins with multiple
folding pathways and intermediates. The experimental results agree with
theoretical predictions. Furthermore, the proposed non-equilibrium
single-molecule approach permits us to characterize kinetic and thermodynamic
properties of native, unfolded, and intermediate states that cannot be derived
from equilibrium hopping experiments.


title: Pronilpotent quotients associated with primitive substitutions

abstract:
  We describe the pronilpotent quotients of a class of projective profinite
groups, that we call $\omega$-presented groups, defined using a special type of
presentations. The pronilpotent quotients of an $\omega$-presented group are
completely determined by a single polynomial, closely related with the
characteristic polynomial of a matrix. We deduce that $\omega$-presented groups
are either perfect or admit the $p$-adic integers as quotients for cofinitely
many primes. We also find necessary conditions for absolute and relative
freeness of $\omega$-presented groups. Our main motivation comes from semigroup
theory: the maximal subgroups of free profinite monoids corresponding to
primitive substitutions are $\omega$-presented (a theorem due to Almeida and
Costa). We are able to show that the incidence matrix of a primitive
substitution carries partial information on the pronilpotent quotients of the
corresponding maximal subgroup. We apply this to deduce that the maximal
subgroups corresponding to primitive aperiodic substitutions of constant length
are not absolutely free.


title: A Neural Network for Solving Inverse Quasi-Variational Inequalities

abstract:
  We study the existence and uniqueness of solutions to the inverse
quasi-variational inequality problem. Motivated by the neural network approach
to solving optimization problems such as variational inequality, monotone
inclusion, and inverse variational problems, we consider a neural network
associated with the inverse quasi-variational inequality problem, and establish
the existence and uniqueness of a solution to the proposed network. We prove
that every trajectory of the proposed neural network converges to the unique
solution of the inverse quasi-variational inequality problem and that the
network is globally asymptotically stable at its equilibrium point. We also
prove that if the function which governs the inverse quasi-variational
inequality problem is strongly monotone and Lipschitz continuous, then the
network is globally exponentially stable at its equilibrium point. We
discretize the network and show that the sequence generated by the
discretization of the network converges strongly to a solution of the inverse
quasi-variational inequality problem under certain assumptions on the
parameters involved. Finally, we provide numerical examples to support and
illustrate our theoretical results.


title:
Dynamical stability and superconductivity of (Ce,La)H9 under high
  pressure

abstract:
  Recent experiments have shown that CeH9 and (Ce,La)H9 can be synthesized
under high pressure between 90-170GPa and become a superconductor with a high
value of superconducting critical temperature (Tc) between 100-200K. In this
work, we performed a theoretical study of a (Ce,La)H9 compound where the Ce:La
ratio is equal to 1. We used the density functional theory and the {\it ab
initio} molecular dynamics (AIMD) method. From the phonon dispersion, there
exist some unstable modes around the K-point phonons. Then, we performed AIMD
simulation at around 203K and found that the compound becomes stable. The
superconducting spectral function can be calculated. We found that $\lambda$ is
as high as 3.0 at 200GPa. By using Allen-Dynes-modified McMillan equation in
the strong coupling regime, we found that Tc = 87K at 200GPa.


title: Solving McKean-Vlasov SDEs via relative entropy

abstract:
  In this paper we explore the merit of relative entropy in proving weak
well-posedness of McKean-Vlasov SDEs, extending the technique introduced in
Lacker arXiv:2105.02983. We prove weak existence and uniqueness when the
interaction is only assumed to have linear growth. Meanwhile, we recover and
extend the current results when the interaction has Krylov's $L_t^q-L_x^p$ type
singularity for $\frac{d}{p}+\frac{2}{q}<1$, where $d$ is the dimension of
space. We connect the aforementioned two cases which are traditionally
disparate, and form a solution theory that is sufficiently robust to allow
perturbations of sublinear growth at the presence of singularity, giving rise
to the well-posedness of a new family of McKean-Vlasov SDEs. Our strategy
naturally extends to the cases of a fractional Brownian driving noise $B^H$ for
all $H\in\left(0,1\right)$, obtaining new results in each separate case
$H\in\left(0,\frac{1}{2}\right)$ and $H\in\left(\frac{1}{2},1\right)$.


title: Computing Without Borders: The Way Towards Liquid Computing

abstract:
  Despite the de-facto technological uniformity fostered by the cloud and edge
computing paradigms, resource fragmentation across isolated clusters hinders
the dynamism in application placement, leading to suboptimal performance and
operational complexity. Building upon and extending these paradigms, we propose
a novel approach envisioning a transparent continuum of resources and services
on top of the underlying fragmented infrastructure, called liquid computing.
Fully decentralized, multi-ownership-oriented and intent-driven, it enables an
overarching abstraction for improved applications execution, while at the same
time opening up for new scenarios, including resource sharing and brokering.
Following the above vision, we present liqo, an open-source project that
materializes this approach through the creation of dynamic and seamless
Kubernetes multi-cluster topologies. Extensive experimental evaluations have
shown its effectiveness in different contexts, both in terms of Kubernetes
overhead and compared to other open-source alternatives.


title: HD 83443c: A highly eccentric giant planet on a 22-year orbit

abstract:
  We report the discovery of a highly eccentric long-period Jovian planet
orbiting the hot-Jupiter host HD\,83443. By combining radial velocity data from
four instruments (AAT/UCLES, Keck/HIRES, HARPS, Minerva-Australis) spanning
more than two decades, we find evidence for a planet with
m~sin~$i=1.35^{+0.07}_{-0.06}$\,\mj, moving on an orbit with $a=8.0\pm$0.8\,au
and eccentricity $e=0.76\pm$0.05. We combine our radial velocity analysis with
\textit{Gaia} eDR3 /\textit{Hipparcos} proper motion anomalies and derive a
dynamical mass of $1.5^{+0.5}_{-0.2} M_{\rm Jup}$. We perform a detailed
dynamical simulation that reveals locations of stability within the system that
may harbor additional planets, including stable regions within the habitable
zone of the host star. HD\,83443 is a rare example of a system hosting a hot
Jupiter and an exterior planetary companion. The high eccentricity of
HD\,83443c suggests that a scattering event may have sent the hot Jupiter to
its close orbit while leaving the outer planet on a wide and eccentric path.


title: Does fractal Universe favour warm inflation: Observational support?

abstract:
  The present work examines the possibility of warm inflationary paradigm in
the modified gravity theory with fractal geometry. By choosing the normal fluid
as radiation fluid and the effective fluid (with the extra term in modified
field equation) as the inflaton field both strong and weak dissipative regimes
have been studied using slow roll approximation with quasi-stable criteria for
radiation. Finally, using the Planck data set, the present model has been
analyzed for various choices of the fractal function and the dissipation
parameter.


title: Chromatic Drift of the Espresso Fabry-P\'erot Etalon

abstract:
  In the last decade, white-light illuminated Fabry-P\'erot interferometers
wave been established as a widely used, relatively simple, reliable, and
cost-effective way to precisely calibrate high-resolution echelle
spectrographs. However, Terrien et al. (2021) recently reported a chromatic
drift of the Fabry-P\'erot interferometer installed at the Habitable-zone
Planet Finder spectrograph. In particular, they found that the variation of the
etalon effective gap size is not achromatic as usually assumed but in fact
depends on wavelength. Here, we present a similar study of the Espresso
Fabry-P\'erot interferometer. Using daily calibrations spanning a period of
over 2.5 years, we also find clear evidence for a chromatic drift with an
amplitude of a few cm/s per day that has a characteristic, quasi-oscillatory
dependence on wavelength. We conclude that this effect is probably caused by an
aging of the dielectric mirror coatings and expect that similar chromatic
drifts might affect all Fabry-P\'erot interferometers used for calibration of
astronomical spectrographs. However, we also demonstrate that the chromatic
drift can be measured and in principle corrected using only standard
calibrations based on hollow cathode lamp spectra.


title: Nth power root topological phases in Hermitian and non-Hermitian systems

abstract:
  Constructing new topological phases is very important in both Hermitian and
non-Hermitian systems because of their potential applications. Here we propose
theoretically and demonstrate a general scheme experimentally to construct Nth
power root (NPR) topological phases. Such a scheme is not only suitable for
Hermitian systems, but also non-Hermitian systems. It is found that the robust
degree of edge state in the Hermitian system becomes stronger and stronger with
the increase of N. It tends to be a strongly surface localized form when N is
large enough. In the non-Hermitian system, the skin effect becomes more
apparent, and it approaches the ideal situation with the increase of N. This
means that edge states and skin effects can be observed by taking different N.
This scheme has been proved experimentally by designing circuits. Our work
opens up a new way to engineer topological states according to the
requirements, which is very important for developing topologically protected
devices, such as topology sensing, switches, and so on.


title:
Multiplexable frequency retuning of MKID arrays using their non-linear
  kinetic inductance

abstract:
  Microwave Kinetic Inductance Detector (MKID) arrays are currently being
developed and deployed for astronomical applications in the visible and near
infrared and for sub-millimetre astronomy. One of the main drawbacks of MKIDs
is that large arrays would exhibit a pixel yield, the percentage of
individually distinguishable pixels to the total number of pixels, of 75 - 80
%. Imperfections arising during the fabrication can induce an uncontrolled
shift in the resonance frequency of individual resonators which can end up
resonating at the same frequency of a different resonator. This makes a number
of resonators indistinguishable and therefore unusable for imaging. This paper
proposes an approach to individually re-tune the colliding resonators in order
to remove the degeneracy and increase the number of MKIDs with unique resonant
frequencies. The frequency re-tuning is achieved through a DC bias of the
resonator, the kinetic inductance of a superconducting thin film is current
dependent and its dependence is non linear. Even though this approach has been
already proposed, an innovative pixel design, described in this paper, may
solve two issues previously described in literature such as increased
electromagnetic losses to the DC-bias line, and the multiplexibility of
multiple resonators on a single feedline.


title:
Modeling and computation of an integral operator Riccati equation for an
  infinite-dimensional stochastic differential equation governing streamflow
  discharge

abstract:
  We propose a linear-quadratic (LQ) control problem of streamflow discharge by
optimizing an infinite-dimensional jump-driven stochastic differential equation
(SDE). Our SDE is a superposition of Ornstein-Uhlenbeck processes (supOU
process), generating a sub-exponential autocorrelation function observed in
actual data. The integral operator Riccati equation is heuristically derived to
determine the optimal control of the infinite-dimensional system. In addition,
its finite-dimensional version is derived with a discretized distribution of
the reversion speed and computed by a finite difference scheme. The optimality
of the Riccati equation is analyzed by a verification argument. The supOU
process is parameterized based on the actual data of a perennial river. The
convergence of the numerical scheme is analyzed through computational
experiments. Finally, we demonstrate the application of the proposed model to
realistic problems along with the Kolmogorov backward equation for the
performance evaluation of controls.


title:
Do Not Fire the Linguist: Grammatical Profiles Help Language Models
  Detect Semantic Change

abstract:
  Morphological and syntactic changes in word usage (as captured, e.g., by
grammatical profiles) have been shown to be good predictors of a word's meaning
change. In this work, we explore whether large pre-trained contextualised
language models, a common tool for lexical semantic change detection, are
sensitive to such morphosyntactic changes. To this end, we first compare the
performance of grammatical profiles against that of a multilingual neural
language model (XLM-R) on 10 datasets, covering 7 languages, and then combine
the two approaches in ensembles to assess their complementarity. Our results
show that ensembling grammatical profiles with XLM-R improves semantic change
detection performance for most datasets and languages. This indicates that
language models do not fully cover the fine-grained morphological and syntactic
signals that are explicitly represented in grammatical profiles.
  An interesting exception are the test sets where the time spans under
analysis are much longer than the time gap between them (for example,
century-long spans with a one-year gap between them). Morphosyntactic change is
slow so grammatical profiles do not detect in such cases. In contrast, language
models, thanks to their access to lexical information, are able to detect fast
topical changes.


title:
Root systems & Clifford algebras: from symmetries of viruses to $E_8$ &
  ADE correspondences

abstract:
  In this paper we discuss reflection groups and root systems, in particular
non-crystallographic ones, and a Clifford algebra framework for both these
concepts. A review of historical as well as more recent work on viral capsid
symmetries motivates the focus on the icosahedral root system $H_3$. We discuss
a notion of affine extension for non-crystallographic groups with applications
to fullerenes and viruses. The icosahedrally ordered component of the nucleic
acid within the virus capsid and the interaction between the two have shed
light on the viral assembly process with interesting applications to antiviral
therapies, drug delivery and nanotechnology. The Clifford algebra framework is
very natural, as it uses precisely the structure that is already implicit in
this root system and reflection group context, i.e. a vector space with an
inner product. In addition, it affords a uniquely simple reflection formula, a
double cover of group transformations, and more insight into the geometry, e.g.
the geometry of the Coxeter plane. This approach made possible a range of root
system induction proofs, such as the constructions of $E_8$ from $H_3$ and the
exceptional 4D root systems from 3D root systems. This makes explicit various
connections between what Arnold called Trinities (sets of three exceptional
cases). In fact this generalises further since the induction construction
contains additional cases, namely two infinite families of cases. It therefore
actually yields $ADE$ correspondences between three sets of different
mathematical concepts that are usually thought of separately as polytopes,
subgroups of $SU(2)$ and $ADE$ Lie algebras. Here we connect them explicitly
and in a unified way by thinking of them as three different sets of root
systems.

categories: math-ph math.GR math.MP math.QA q-bio.BM


title:
First-principles electronic structure investigation of
  HgBa$_{2}$Ca$_{n-1}$Cu$_{n}$O$_{2n+2+x}$ with the SCAN density functional

abstract:
  We perform first-principles calculation to study the electronic structure of
HgBa$_{2}$Ca$_{n-1}$Cu$_{n}$O$_{2n+2+x}$ copper oxides up to $n = 6$ for the
undoped parent compound $(x = 0)$ and up to $n = 3$ for the doped compound $(x
> 0)$ by means of the SCAN meta-GGA density functional. Our calculations
predict an antiferromagnetic insulator ground state for the parent compounds
with an energy gap that decreases with the number of CuO$_{2}$ planes. We
report structural, electronic and magnetic order evolution with $x$ which agree
with experiments. We find an enhanced density of states at Fermi level at $x
\approx 0.25$ for the single-layered compound manifesting in a peak of the
Sommerfeld parameter, which recently has been discussed as a possible signature
of quantum criticality generic to all cuprates.

categories: cond-mat.str-el cond-mat.supr-con physics.chem-ph physics.comp-ph


title: Higher braidings of diagonal type

abstract:
  Heckenberger introduced the Weyl groupoid of a finite dimensional Nichols
algebra of diagonal type. We replace the matrix of its braiding by a higher
tensor and present a construction which yields further Weyl groupoids. Abelian
cohomology theory gives evidence for the existence of a higher braiding
associated to such a tensor.


title: Macroscopic analysis of time dependent plasticity in Ti alloys

abstract:
  Component failure due to cold dwell fatigue of titanium and its alloys is a
long-standing problem which has significant safety and economic implications to
the aviation industry. This can be addressed by understanding the governing
mechanisms of time dependent plasticity behaviour of Ti at low temperatures.
Here, stress relaxation tests were performed at four different temperatures on
three major alloy systems: commercially pure titanium (two alloys with
different oxygen content), Ti-6Al-4V (two microstructures with differing beta
phase fractions) and Ti-6Al-2Sn-4Zr-xMo (two alloys with different Mo content
x= 2 or 6, and portion of beta phase). Key parameters controlling the time
dependent plasticity were determined as a function of temperature. Both
activation volume and energy were found to increase with temperature in all six
alloys. It was found that the dwell fatigue effect is more significant by
oxygen alloying but is suppressed by the addition of Mo. The presence of the
beta phase did not strongly affect the dwell fatigue, however, it was
suppressed at high temperature due to the low strain rate and strain rate
sensitivity.


title: A simplified algorithm for the topological entropy of multimodal maps

abstract:
  A numerical algorithm to compute the topological entropy of multimodal maps
is proposed. This algorithm results from a closed formula containing the
so-called min-max symbols, which are closely related to the kneading symbols.
Furthermore, it simplifies a previous algorithm, also based on min-max symbols,
which was originally proposed for twice differentiable multimodal maps. The new
algorithm has been benchmarked against the old one with a number of multimodal
maps, the results being reported in the paper. In particular, the comparison is
favorable to the new algorithm, except in the unimodal case.


title:
Distributed Transmission and Spatially Coupled Forward Error Correction
  in Regenerative Multipoint-to-Point Networks

abstract:
  We investigate the performance of coded modulation for multi-hop regenerative
optical networks. We analyze options for computing decoder input LLRs, show
reach increases by optimized regenerator placement and experimentally compare
strategies and guidelines for distributed FEC.


title: Balls-in-bins models with asymmetric feedback and reflection

abstract:
  Balls-in-bins models describe a random sequential allocation of infinitely
many balls into a finite number of bins. In these models a ball is placed into
a bin with probability proportional to a given function (feedback function),
which depends on the number of existing balls in the bin. Typically, the
feedback function is the same for all bins (symmetric feedback), and there are
no constraints on the number of balls in the bins. In this paper we study
versions of BB models with two bins, in which the above assumptions are
violated. In the first model of interest the feedback functions can depend on a
bin (BB model with asymmetric feedback). In the case when both feedback
functions are power law and superlinear, a single bin receives all but finitely
many balls almost surely, and we study the probability that this happens for a
given bin. In particular, under certain initial conditions we derive the normal
approximation for this probability, which generalizes the result in [5]
obtained in the case of the symmetric feedback. The main part of the paper
concerns the BB model with asymmetric feedback evolving subject to certain
constraints on the numbers of allocated balls. The model can be interpreted as
a transient reflecting random walk in a curvilinear wedge, and we obtain a
complete classification of its long term behavior.


title:
The Strong Slope Conjecture and crossing numbers for Mazur doubles of
  knots

abstract:
  The Slope Conjecture proposed by Garoufalidis asserts that the degree of the
colored Jones polynomial determines a boundary slope, and its refinement, the
Strong Slope Conjecture proposed by Kalfagianni and Tran asserts that the
linear term in the degree determines the topology of an essential surface that
satisfies the Slope Conjecture. Under certain hypotheses, we show that Mazur
doubles of knots satisfy the Strong Slope Conjecture if the original knot does.
Consequently, any knot obtained by a finite sequence of cabling, untwisted
w--generalized Whitehead doublings with w > 0, connected sums and Mazur
doublings of B--adequate knots or torus knots satisfies the Strong Slope
Conjecture. On the other hand, it may be worth mentioning that under these
hypotheses, if there exists a knot with a Jones slope less than -1/4, then its
Mazur double would either provide a counterexample to the Strong Slope
Conjecture or have a Jones surface that is unrelated to any Jones surface of
the knot.
  Following work of Kalfagianni and Lee, we also use our results to show that
the Mazur double of an adequate knot K with trivial writhe has crossing number
either 9c(K)+2 or 9c(K)+3.


title: Hierarchical Quality-Diversity for Online Damage Recovery

abstract:
  Adaptation capabilities, like damage recovery, are crucial for the deployment
of robots in complex environments. Several works have demonstrated that using
repertoires of pre-trained skills can enable robots to adapt to unforeseen
mechanical damages in a few minutes. These adaptation capabilities are directly
linked to the behavioural diversity in the repertoire. The more alternatives
the robot has to execute a skill, the better are the chances that it can adapt
to a new situation. However, solving complex tasks, like maze navigation,
usually requires multiple different skills. Finding a large behavioural
diversity for these multiple skills often leads to an intractable exponential
growth of the number of required solutions. In this paper, we introduce the
Hierarchical Trial and Error algorithm, which uses a hierarchical behavioural
repertoire to learn diverse skills and leverages them to make the robot more
adaptive to different situations. We show that the hierarchical decomposition
of skills enables the robot to learn more complex behaviours while keeping the
learning of the repertoire tractable. The experiments with a hexapod robot show
that our method solves maze navigation tasks with 20% less actions in the most
challenging scenarios than the best baseline while having 57% less complete
failures.


title:
LiDAR Road-Atlas: An Efficient Map Representation for General 3D Urban
  Environment

abstract:
  In this work, we propose the LiDAR Road-Atlas, a compactable and efficient 3D
map representation, for autonomous robot or vehicle navigation in general urban
environment. The LiDAR Road-Atlas can be generated by an online mapping
framework based on incrementally merging local 2D occupancy grid maps (2D-OGM).
Specifically, the contributions of our LiDAR Road-Atlas representation are
threefold. First, we solve the challenging problem of creating local 2D-OGM in
non-structured urban scenes based on a real-time delimitation of traversable
and curb regions in LiDAR point cloud. Second, we achieve accurate 3D mapping
in multiple-layer urban road scenarios by a probabilistic fusion scheme. Third,
we achieve very efficient 3D map representation of general environment thanks
to the automatic local-OGM induced traversable-region labeling and a sparse
probabilistic local point-cloud encoding. Given the LiDAR Road-Atlas, one can
achieve accurate vehicle localization, path planning and some other tasks. Our
map representation is insensitive to dynamic objects which can be filtered out
in the resulting map based on a probabilistic fusion. Empirically, we compare
our map representation with a couple of popular map representation methods in
robotics and autonomous driving societies, and our map representation is more
favorable in terms of efficiency, scalability and compactness. In addition, we
also evaluate localization accuracy extensively given the created LiDAR
Road-Atlas representations on several public benchmark datasets. With a
16-channel LiDAR sensor, our method achieves an average global localization
errors of 0.26m (translation) and 1.07 degrees (rotation) on the Apollo
dataset, and 0.89m (translation) and 1.29 degrees (rotation) on the MulRan
dataset, respectively, at 10Hz, which validates the promising performance of
our map representation for autonomous driving.


title: Impact of the CDF $W$-mass anomaly on two Higgs doublet model

abstract:
  We consider the implication of the recent CDF $W$-mass anomaly in the general
framework of two Higgs doublet model. We find that the large deviation of the
$S$ and $T$ parameters from their SM values of zero leads to the upper limit of
about $1$ TeV on the heavy charged and neutral Higgs bosons when it is combined
with the theoretical constraints from the perturbative unitarity and for the
Higgs potential to be bounded from below.


title:
Single line Apollonian gaskets: is the limit a space filling fractal
  curve?

abstract:
  In this manuscript we study single-line approximations and fractals based on
the Apollonian gasket. The well-known Apollonian gasket is the limit case of
configurations of kissing circles. Rather than plotting the circles as discs on
a differently colored background (the traditional representation), we draw all
circles as one line without lifting the pen and without crossing itself.
Moreover, the configurations are nested. In this manuscript we explore whether
the limit of the line drawings gives rise to a space filling fractal curve.


title: Effective Temperature Pulses in Open Quantum Systems

abstract:
  Controlling the temperature of nano-scale quantum systems is becoming
increasingly important in the efforts to develop thermal devices such as
quantum heat valves, heat engines, and refrigerators, and to explore
fundamental concepts in quantum thermodynamics. In practice, however, it is
challenging to generate arbitrary time-dependent temperatures, similarly to
what has been achieved for electronic voltage pulses. To overcome this problem,
we here propose a fully quantum mechanical scheme to control the time-dependent
environment temperature of an open quantum system. To this end, we consider a
collection of quantum harmonic oscillators that mediate the interactions
between the quantum system and a thermal reservoir, and we show how an
effective time-dependent temperature can be realized by modulating the
oscillator frequencies in time. By doing so, we can apply effective temperature
pulses to the quantum system, and it can be cooled below the temperature of the
environment. Surprisingly, the scheme can be realized using only a few
oscillators, and our proposal thereby paves the way for controlling the
temperature of open quantum systems.


title:
PyDTS: A Python Package for Discrete Time Survival Analysis with
  Competing Risks

abstract:
  Time-to-event analysis (survival analysis) is used when the outcome or the
response of interest is the time until a pre-specified event occurs.
Time-to-event data are sometimes discrete either because time itself is
discrete or due to grouping of failure times into intervals or rounding off
measurements. In addition, the failure of an individual could be one of several
distinct failure types; known as competing risks (events) data. This work
focuses on discrete-time regression with competing events. We emphasize the
main difference between the continuous and discrete settings with competing
events, develop a new estimation procedure, and present PyDTS, an open source
Python package which implements our estimation procedure and other tools for
discrete-time-survival analysis with competing risks.