title:
Deformation and Correspondence Aware Unsupervised Synthetic-to-Real
  Scene Flow Estimation for Point Clouds

abstract:
  Point cloud scene flow estimation is of practical importance for dynamic
scene navigation in autonomous driving. Since scene flow labels are hard to
obtain, current methods train their models on synthetic data and transfer them
to real scenes. However, large disparities between existing synthetic datasets
and real scenes lead to poor model transfer. We make two major contributions to
address that. First, we develop a point cloud collector and scene flow
annotator for GTA-V engine to automatically obtain diverse realistic training
samples without human intervention. With that, we develop a large-scale
synthetic scene flow dataset GTA-SF. Second, we propose a mean-teacher-based
domain adaptation framework that leverages self-generated pseudo-labels of the
target domain. It also explicitly incorporates shape deformation regularization
and surface correspondence refinement to address distortions and misalignments
in domain transfer. Through extensive experiments, we show that our GTA-SF
dataset leads to a consistent boost in model generalization to three real
datasets (i.e., Waymo, Lyft and KITTI) as compared to the most widely used FT3D
dataset. Moreover, our framework achieves superior adaptation performance on
six source-target dataset pairs, remarkably closing the average domain gap by
60%. Data and codes are available at https://github.com/leolyj/DCA-SRSFE


title: CRAFT: Cross-Attentional Flow Transformer for Robust Optical Flow

abstract:
  Optical flow estimation aims to find the 2D motion field by identifying
corresponding pixels between two images. Despite the tremendous progress of
deep learning-based optical flow methods, it remains a challenge to accurately
estimate large displacements with motion blur. This is mainly because the
correlation volume, the basis of pixel matching, is computed as the dot product
of the convolutional features of the two images. The locality of convolutional
features makes the computed correlations susceptible to various noises. On
large displacements with motion blur, noisy correlations could cause severe
errors in the estimated flow. To overcome this challenge, we propose a new
architecture "CRoss-Attentional Flow Transformer" (CRAFT), aiming to revitalize
the correlation volume computation. In CRAFT, a Semantic Smoothing Transformer
layer transforms the features of one frame, making them more global and
semantically stable. In addition, the dot-product correlations are replaced
with transformer Cross-Frame Attention. This layer filters out feature noises
through the Query and Key projections, and computes more accurate correlations.
On Sintel (Final) and KITTI (foreground) benchmarks, CRAFT has achieved new
state-of-the-art performance. Moreover, to test the robustness of different
models on large motions, we designed an image shifting attack that shifts input
images to generate large artificial motions. Under this attack, CRAFT performs
much more robustly than two representative methods, RAFT and GMA. The code of
CRAFT is is available at https://github.com/askerlee/craft.


title: Multi-Granularity Alignment Domain Adaptation for Object Detection

abstract:
  Domain adaptive object detection is challenging due to distinctive data
distribution between source domain and target domain. In this paper, we propose
a unified multi-granularity alignment based object detection framework towards
domain-invariant feature learning. To this end, we encode the dependencies
across different granularity perspectives including pixel-, instance-, and
category-levels simultaneously to align two domains. Based on pixel-level
feature maps from the backbone network, we first develop the omni-scale gated
fusion module to aggregate discriminative representations of instances by
scale-aware convolutions, leading to robust multi-scale object detection.
Meanwhile, the multi-granularity discriminators are proposed to identify which
domain different granularities of samples(i.e., pixels, instances, and
categories) come from. Notably, we leverage not only the instance
discriminability in different categories but also the category consistency
between two domains. Extensive experiments are carried out on multiple domain
adaptation scenarios, demonstrating the effectiveness of our framework over
state-of-the-art algorithms on top of anchor-free FCOS and anchor-based Faster
RCNN detectors with different backbones.


title: Semantic-shape Adaptive Feature Modulation for Semantic Image Synthesis

abstract:
  Recent years have witnessed substantial progress in semantic image synthesis,
it is still challenging in synthesizing photo-realistic images with rich
details. Most previous methods focus on exploiting the given semantic map,
which just captures an object-level layout for an image. Obviously, a
fine-grained part-level semantic layout will benefit object details generation,
and it can be roughly inferred from an object's shape. In order to exploit the
part-level layouts, we propose a Shape-aware Position Descriptor (SPD) to
describe each pixel's positional feature, where object shape is explicitly
encoded into the SPD feature. Furthermore, a Semantic-shape Adaptive Feature
Modulation (SAFM) block is proposed to combine the given semantic map and our
positional features to produce adaptively modulated features. Extensive
experiments demonstrate that the proposed SPD and SAFM significantly improve
the generation of objects with rich details. Moreover, our method performs
favorably against the SOTA methods in terms of quantitative and qualitative
evaluation. The source code and model are available at
https://github.com/cszy98/SAFM.


title:
The bioacoustic proof of the effects of raising awareness of noise
  pollution among visitors to the Port Cros National Park using binding
  communication

abstract:
  Assuming that the anthropogenic impact of visitors to a natural park can be
reduced by communication actions, we have drawn up a bioacoustical protocol
combined with a protocol to measure the effectiveness of the communication
actions. A simple eco-awareness communication method will be compared to a
binding communication method. We address the questions: does visitors'
eco-awareness drive them to change their attitudes and behavior in order to
limit their own noise disturbance, and does commitment improve their awareness?
We detail here the main concepts considered and the methodology followed to
discuss the preliminary results.


title: Transducing paths in graph classes with unbounded shrubdepth

abstract:
  Transductions are a general formalism for expressing transformations of
graphs (and more generally, of relational structures) in logic. We prove that a
graph class $\mathscr{C}$ can be $\mathsf{FO}$-transduced from a class of
bounded-height trees (that is, has bounded shrubdepth) if, and only if, from
$\mathscr{C}$ one cannot $\mathsf{FO}$-transduce the class of all paths. This
establishes one of the three remaining open questions posed by Blumensath and
Courcelle about the $\mathsf{MSO}$-transduction quasi-order, even in the
stronger form that concerns $\mathsf{FO}$-transductions instead of
$\mathsf{MSO}$-transductions.
  The backbone of our proof is a graph-theoretic statement that says the
following: If a graph $G$ excludes a path, the bipartite complement of a path,
and a half-graph as semi-induced subgraphs, then the vertex set of $G$ can be
partitioned into a bounded number of parts so that every part induces a cograph
of bounded height, and every pair of parts semi-induce a bi-cograph of bounded
height. This statement may be of independent interest; for instance, it implies
that the graphs in question form a class that is linearly $\chi$-bounded.


title:
Improved Lower Bounds on the Domination Number of Hypercubes and Binary
  Codes with Covering Radius One

abstract:
  A dominating set on an $n $-dimensional hypercube is equivalent to a binary
covering code of length $n $ and covering radius 1. It is still an open problem
to determine the domination number $\gamma(Q_n)$ for $ n\geq9$ and $
n\ne2^{k},2^{k}-1 $ ($k\in\mathbb{N} $). When $n$ is a multiple of 6, the best
known lower bound is $\gamma(Q_n)\geq \frac{2^n}{n}$, given by Van Wee (1988).
In this article, we present a new method using congruence properties due to
Laurent Habsieger (1997) and obtained an improved lower bound $\gamma(Q_n)\geq
\frac{(n-2)2^n }{n^2-2n-2}$.


title: Fully non-positive-partial-transpose genuinely entangled subspaces

abstract:
  Genuinely entangled subspaces are a class of subspaces in the multipartite
Hilbert spaces that are composed of only genuinely entangled states. They are
thus an interesting object of study in the context of multipartite
entanglement. Here we provide a construction of multipartite subspaces that are
not only genuinely entangled but also fully non-positive-partial-transpose
(NPT) in the sense that any mixed state supported on them has non-positive
partial transpose across any bipartition. Our construction originates from the
stabilizer formalism known for its use in quantum error correction. To this
end, we first introduce a couple of criteria allowing to assess whether any
state from a given non-trivial stabilizer subspace is genuinely multipartite
entangled. We then use these criteria to construct genuinely entangled
stabilizer subspaces for any number of parties and arbitrary local dimension
and conjecture them to be of maximal dimension achievable within the stabilizer
formalism. At the same time, we prove that every genuinely entangled subspace
is fully NPT in the above sense, which implies a quite surprising fact that no
genuinely entangled stabilizer subspace can support PPT entangled states.


title:
Biomechanical Lower Limb Model to Predict Patellar Position Alteration
  after Medial Open Wedge High Tibial Osteotomy

abstract:
  Medial open-wedge high tibial osteotomy is a surgical treatment for patients
with a varus deformity and early-stage medial knee osteoarthritis. Observations
suggest that this surgery can negatively affect the patellofemoral joint and
change the patellofemoral kinematics. However, what causes these effects and
how the correction angle can change the surgery's impact on the patellofemoral
joint has not been investigated before. The objective of this study was to
develop a biomechanical model that can predict the surgery's impact on the
patellar position and find the correlation between the opening angles and the
patellar position after the surgery. A combined finite element and multibody
model of the lower limb was developed. The model's capabilities for predicting
the patellofemoral kinematics were evaluated by performing a passive deep
flexion simulation of the native knee and comparing the outcomes with magnetic
resonance images of the study subject at various flexion angles. The model at a
fixed knee flexion angle was then used to simulate the high tibial osteotomy
surgery virtually. The results showed a correlation between the wedge opening
angles and the patellar position in various degrees of freedom. These results
indicate that larger wedge openings result in increased values of patellar
distalization, lateral patellar shift, patellar rotation, and patellar internal
tilt. The developed model in this study can be used in future studies to
monitor the stress distribution on the patellar cartilage and connecting
tissues to investigate their relationship with observations of pain and
cartilage injury due to post-operative altered patellar kinematics.


title: On structural parameter estimation of the Markov Q-process

abstract:
  In the paper we consider a stochastic model which called Markov Q-processes
that forms a continuous-time Markov population system. Markov Q-processes are
defined as stochastic Markov branching processes with trajectories continuing
in the remote future. Estimation of the structural parameter of the Markov
Q-process is the main goal of this paper. To estimate this parameter, an
unbiased estimator of the Lotka-Nagaev type is proposed. An asymptotic
expansion of the variance of this estimator is found.


title:
Effect of Diffraction on Wigner Distributions of Optical Fields and how
  to Use It in Optical Resonator Theory. III -- Ray Tracing in Resonators

abstract:
  The third part of the paper is devoted to ray tracing in optical resonators.
The employed method for dealing with the issue uses the elliptical or
hyperbolic rotations that Wigner distributions associated with optical fields
undergo during propagation from one spherical mirror of a resonator to the
other. It is illustrated by various examples concerning meridional or skew
rays, reentrant in stable resonators or propagating in unstable resonators.
  A classification of optical resonators is eventually deduced.


title:
The harmonic maps compactification of Teichm\"uller spaces for punctured
  Riemann surfaces

abstract:
  Wolf gave a homeomorphism from the Teichm\"uller space to the space of
quadratic differentials on a closed Riemann surface by using harmonic maps.
Moreover, using harmonic maps rays, he gave a compactification of the
Teichm\"uller space and show that it coincides with the Thurston
compactification. In this paper, we extend the harmonic maps compactification
to the Teichm\"uller spaces of punctured Riemann surfaces, and show that it
still coincides with the Thurston compactification.


title:
Opportunities for Physical Layer Security in UAV Communication Enhanced
  with Intelligent Reflective Surfaces

abstract:
  Unmanned Aerial Vehicles (UAVs) are an important component of next-generation
wireless networks that can assist in high data rate communications and provide
enhanced coverage.Their high mobility and aerial nature offer deployment
flexibility and low-cost infrastructure support to existing cellular networks
and provide many applications that rely on mobile wireless communications.
However, security is a major challenge in UAV communications, and Physical
Layer Security (PLS) is an important technique to improve the reliability and
security of data shared with the assistance of UAVs. Recently, Intelligent
Reflecting Surfaces (IRS) have emerged as a novel technology to extend and/or
enhance wireless coverage by re-configuring the propagation environment of
communications. This paper provides an overview of how IRS can improve the PLS
of UAV networks. We discuss different use cases of PLS for IRS enhanced UAV
communications and briefly review the recent advances in this area. Then based
on the recent advances, we also present a case study that utilizes alternate
optimization to maximize the secrecy capacity for IRS enhanced UAV scenario in
the presence of multiple eavesdroppers. Finally, we highlight several open
issues and research challenges to realize PLS in IRS enhanced UAV
communications.


title:
Suffix tree-based linear algorithms for multiple prefixes, single suffix
  counting and listing problems

abstract:
  Given two strings $T$ and $S$ and a set of strings $P$, for each string $p
\in P$, consider the unique substrings of $T$ that have $p$ as their prefix and
$S$ as their suffix. Two problems then come to mind; the first problem being
the counting of such substrings, and the second problem being the problem of
listing all such substrings. In this paper, we describe linear-time,
linear-space suffix tree-based algorithms for both problems. More specifically,
we describe an $O(|T| + |P|)$ time algorithm for the counting problem, and an
$O(|T| + |P| + \#(ans))$ time algorithm for the listing problem, where
$\#(ans)$ refers to the number of strings being listed in total, and $|P|$
refers to the total length of the strings in $P$. We also consider the reversed
version of the problems, where one prefix condition string and multiple suffix
condition strings are given instead, and similarly describe linear-time,
linear-space algorithms to solve them.


title: A toy model for "elementariness"

abstract:
  Motivated by recent efforts to analyze corrections to Weinberg's relations
for the scattering length and effective range in the presence of a
near-threshold bound state, we play around with an instructive toy model for
non-relativistic scattering in a central potential. The model allows to
interpolate between bound-state configurations of high "compositeness", where
the wave function is spread over a wide region beyond the range of the
interaction, and compact configurations of high "elementariness", where the
wave function is confined to a small region around the center of the potential.


title:
End-to-End Trajectory Distribution Prediction Based on Occupancy Grid
  Maps

abstract:
  In this paper, we aim to forecast a future trajectory distribution of a
moving agent in the real world, given the social scene images and historical
trajectories. Yet, it is a challenging task because the ground-truth
distribution is unknown and unobservable, while only one of its samples can be
applied for supervising model learning, which is prone to bias. Most recent
works focus on predicting diverse trajectories in order to cover all modes of
the real distribution, but they may despise the precision and thus give too
much credit to unrealistic predictions. To address the issue, we learn the
distribution with symmetric cross-entropy using occupancy grid maps as an
explicit and scene-compliant approximation to the ground-truth distribution,
which can effectively penalize unlikely predictions. In specific, we present an
inverse reinforcement learning based multi-modal trajectory distribution
forecasting framework that learns to plan by an approximate value iteration
network in an end-to-end manner. Besides, based on the predicted distribution,
we generate a small set of representative trajectories through a differentiable
Transformer-based network, whose attention mechanism helps to model the
relations of trajectories. In experiments, our method achieves state-of-the-art
performance on the Stanford Drone Dataset and Intersection Drone Dataset.


title:
Exclusive production of light vector mesons at next-to-leading order in
  the dipole picture

abstract:
  Exclusive production of light vector mesons in deep inelastic scattering is
calculated at next-to-leading order in the dipole picture in the limit of high
photon virtuality. The resulting expression is free of any divergences and
suitable for numerical evaluations. The higher-order corrections are found to
be numerically important, but they can be mostly captured by the
nonperturbative fit parameters describing the initial condition for the
small-$x$ evolution of the dipole scattering amplitude. The vector meson
production cross section is shown to depend only weakly on the meson
distribution amplitude and the factorization scale. We also present
phenomenological comparisons of our result to the existing exclusive $\phi$ and
$\rho$ production data from HERA and find an excellent agreement at high
virtualities.


title: MBORE: Multi-objective Bayesian Optimisation by Density-Ratio Estimation

abstract:
  Optimisation problems often have multiple conflicting objectives that can be
computationally and/or financially expensive. Mono-surrogate Bayesian
optimisation (BO) is a popular model-based approach for optimising such
black-box functions. It combines objective values via scalarisation and builds
a Gaussian process (GP) surrogate of the scalarised values. The location which
maximises a cheap-to-query acquisition function is chosen as the next location
to expensively evaluate. While BO is an effective strategy, the use of GPs is
limiting. Their performance decreases as the problem input dimensionality
increases, and their computational complexity scales cubically with the amount
of data. To address these limitations, we extend previous work on BO by
density-ratio estimation (BORE) to the multi-objective setting. BORE links the
computation of the probability of improvement acquisition function to that of
probabilistic classification. This enables the use of state-of-the-art
classifiers in a BO-like framework. In this work we present MBORE:
multi-objective Bayesian optimisation by density-ratio estimation, and compare
it to BO across a range of synthetic and real-world benchmarks. We find that
MBORE performs as well as or better than BO on a wide variety of problems, and
that it outperforms BO on high-dimensional and real-world problems.


title: Classical and Quantum Gases on a Semiregular Mesh

abstract:
  The main objective of a statistical mechanical calculation is drawing the
phase diagram of a many-body system. In this respect, discrete systems offer
the clear advantage over continuum systems of an easier enumeration of
microstates, though at the cost of added abstraction. With this in mind, we
examine a system of particles living on the vertices of the (biscribed)
pentakis dodecahedron, using different couplings for first and second neighbor
particles to induce a competition between icosahedral and dodecahedral orders.
After working out the phases of the model at zero temperature, we carry out
Metropolis Monte Carlo simulations at finite temperature, highlighting the
existence of smooth transitions between distinct "phases", The sharpest of
these crossovers are characterized by hysteretic behavior near zero
temperature, which reveals a bottleneck issue for Metropolis dynamics in state
space. Next, we introduce the quantum (Bose-Hubbard) counterpart of the
previous model and calculate its phase diagram at zero and finite temperatures
using the decoupling approximation. We thus uncover, in addition to Mott
insulating "solids", also the existence of supersolid "phases" which
progressively shrink as the system is heated up. We argue that a quantum system
of the kind described here can be realized with programmable holographic
optical tweezers.


title:
Geometrical representation of the multi-dimensional consistency : 1-form
  case

abstract:
  A new notion of integrability called the multi-dimensional consistency for
the integrable systems with the 1-form structure is captured in the geometrical
language both in classical and quantum realms. A zero-curvature condition,
which implies the multi-dimensional consistency, will be a key relation in
various contexts, e.g. Hamiltonian vector fields, Lagrangian vector fields,
temporal Lax matrices and Hamiltonian operators. Therefore, the existence of
the zero-curvature condition directly leads to the path-independent feature in
various types of mappings (which will be expressed in terms of the Wilson
line), e.g. a family of maps in N-parameter groups on contangent and tangent
bundles, time evolution maps in the Lax pair level and unitary multi-time
evolution operators in the Schrodinger picture. The main highlights of this
work are the following. The new mathematical objects, called Hamilton vector
field and Lagrange vector field defined in the space of time variables, are
introduced to alternatively capture integrability of the systems. To ensure
integrability, these vector fields must be conservative and irrotational.
Another important result is the formulation of the continuous multi-time
propagator. This new type of the propagator exhibits the path-independent
feature on the space of time variables. Consequently, a new perspective on
summing all possible paths unavoidably arises as not only all possible paths on
the space of dependent variables but also on the space of independent variables
must be taken into account.


title:
A Dataset of Images of Public Streetlights with Operational Monitoring
  using Computer Vision Techniques

abstract:
  A dataset of street light images is presented. Our dataset consists of
$\sim350\textrm{k}$ images, taken from 140 UMBRELLA nodes installed in the
South Gloucestershire region in the UK. Each UMBRELLA node is installed on the
pole of a lamppost and is equipped with a Raspberry Pi Camera Module v1 facing
upwards towards the sky and lamppost light bulb. Each node collects an image at
hourly intervals for 24h every day. The data collection spans for a period of
six months.
  Each image taken is logged as a single entry in the dataset along with the
Global Positioning System (GPS) coordinates of the lamppost. All entries in the
dataset have been post-processed and labelled based on the operation of the
lamppost, i.e., whether the lamppost is switched ON or OFF. The dataset can be
used to train deep neural networks and generate pre-trained models providing
feature representations for smart city CCTV applications, smart weather
detection algorithms, or street infrastructure monitoring. The dataset can be
found at \url{https://doi.org/10.5281/zenodo.6046758}.


title:
Determination of Physical and Mechanical properties of Sugarcane
  Single-Bud Billet

abstract:
  Determining the physical and mechanical properties of sugarcane single-bud
billets is a critical step in the mechanical structure design of a sugarcane
planter. In this study, the TaiTang F66 cultivar sugarcane samples are
analyzed. The moisture content of the billets is found to range from 63.78% to
77.72%, and the average density is 244.67 kg/m3. The coefficient of restitution
(CoR) of the samples is determined by conducting a drop test wherein the
samples are dropped onto a steel plate from different heights. The static
friction coefficient (SFC) of four types of samples is determined by the
inclined plate method at two orientations. In addition, the rolling friction
coefficient (RFC) is determined at three plate inclination angles and sample
displacement. The experiment results show that with increasing drop height and
moisture content, the billet steel CoR decreases from 0.625 to 0.458, while the
billet billet CoR increases from 0.603 to 0.698. With an increase in contact
area, the billet steel SFC decreases from 0.515 to 0.377 and the billet billet
SFC decreases from 0.498 to 0.323. With increasing angle and sample
displacement, the billet steel RFC increases from 0.0315 to 0.2175 and the
billet billet RFC increases from 0.0203 to 0.1007. These parameters are useful
in the design and optimization of sugarcane single-bud billet planters using
EDEM simulation.


title:
Condensation and crystal nucleation in a lattice gas with a realistic
  phase diagram

abstract:
  We reconsider model II of [J. Chem. Phys. 1968, 49, 1778--1783], a
two-dimensional lattice-gas system featuring a crystalline phase and two
distinct fluid phases (liquid and vapor). In this system, a particle prevents
other particles from occupying sites up to third neighbors on the square
lattice, while attracting (with decreasing strength) particles sitting at
fourth- or fifth-neighbor sites. To make the model more realistic, we assume a
finite repulsion at third-neighbor distance, with the result that a second
crystalline phase appears at higher pressures. However, the similarity with
real-world substances is only partial: on closer inspection the alleged
liquid-vapor transition turns out to be a continuous (albeit sharp) crossover,
even near the putative triple point. Closer to the standard picture is instead
the freezing transition, as we show by computing the free-energy barrier to
crystal nucleation from the "liquid".


title:
Search and identification of transient and variable radio sources using
  MeerKAT observations: a case study on the MAXI J1820+070 field

abstract:
  Many transient and variable sources detected at multiple wavelengths are also
observed to vary at radio frequencies. However, these samples are typically
biased towards sources that are initially detected in wide-field optical, X-ray
or gamma-ray surveys. Many sources that are insufficiently bright at higher
frequencies are therefore missed, leading to potential gaps in our knowledge of
these sources and missing populations that are not detectable in optical,
X-rays or gamma-rays. Taking advantage of new state-of-the-art radio facilities
that provide high quality wide-field images with fast survey speeds, we can now
conduct unbiased surveys for transient and variable sources at radio
frequencies. In this paper, we present an unbiased survey using observations
obtained by MeerKAT, a mid-frequency ($\sim$1.4 GHz) radio array in South
Africa's Karoo Desert. The observations used were obtained as part of a weekly
monitoring campaign for X-ray binaries (XRBs) and we focus on the field of MAXI
J1820+070. We develop methods to optimally filter transient and variable
candidates that can be directly applied to other datasets. In addition to MAXI
J1820+070, we identify four likely active galactic nuclei, one source that
could be a Galactic source (pulsar or quiescent X-ray binary) or an AGN, and
one variable pulsar. No transient sources, defined as being undetected in deep
images, were identified leading to a transient surface density of
$<3.7\times10^{-2}$ deg$^{-2}$ at a sensitivity of 1 mJy on timescales of one
week at 1.4 GHz.


title:
Spatial decay of the multi-solitons of the generalized Korteweg-de Vries
  equation

abstract:
  We study pointwise spatial decay of multi-solitons of the generalized
Korteweg-de Vries equations. We obtain that, uniformly in time, these solutions
and their derivatives decay exponentially in space on the left of and in the
solitons region, and prove rapid decay on the right of the solitons.


title:
Desenvolvimento de ferramenta de simula\c{c}\~ao para aux\'ilio no
  ensino da disciplina de rob\'otica industrial

abstract:
  Currently, robotics is one of the fastest growing areas not only in the
industrial sector but also in the consumer and service sectors. Several areas
benefit from the technological advancement of robotics, especially the
industrial area those benefits from gains in productivity and quality. However,
to supply this growing demand it is necessary for the newly graduated
professionals to have a deeper understanding of how to design and control a
robotic manipulator. It is logical that in order to obtain this more in-depth
knowledge of robotics, it is necessary to have an experience with a real
robotic manipulator, since the practice is a much more efficient way of
learning than theory. However, it is known that a robotic arm is not a cheap
investment, and its maintenance is not cheap either. Therefore, many
educational institutions are not able to provide this type of experience to
their students. With this in mind, and through the use of Unity 3D, which is a
game development software, a robotic arm simulator has been developed to
correlate classroom theory with what actually happens in practice. The robotic
manipulators implemented on this simulator can be controlled by both inverse
kinematics (which is the industry standard) and direct kinematics.


title:
Assessing the risk of re-identification arising from an attack on
  anonymised data

abstract:
  Objective: The use of routinely-acquired medical data for research purposes
requires the protection of patient confidentiality via data anonymisation. The
objective of this work is to calculate the risk of re-identification arising
from a malicious attack to an anonymised dataset, as described below. Methods:
We first present an analytical means of estimating the probability of
re-identification of a single patient in a k-anonymised dataset of Electronic
Health Record (EHR) data. Second, we generalize this solution to obtain the
probability of multiple patients being re-identified. We provide synthetic
validation via Monte Carlo simulations to illustrate the accuracy of the
estimates obtained. Results: The proposed analytical framework for risk
estimation provides re-identification probabilities that are in agreement with
those provided by simulation in a number of scenarios. Our work is limited by
conservative assumptions which inflate the re-identification probability.
Discussion: Our estimates show that the re-identification probability increases
with the proportion of the dataset maliciously obtained and that it has an
inverse relationship with the equivalence class size. Our recursive approach
extends the applicability domain to the general case of a multi-patient
re-identification attack in an arbitrary k-anonymisation scheme. Conclusion: We
prescribe a systematic way to parametrize the k-anonymisation process based on
a pre-determined re-identification probability. We observed that the benefits
of a reduced re-identification risk that come with increasing k-size may not be
worth the reduction in data granularity when one is considering benchmarking
the re-identification probability on the size of the portion of the dataset
maliciously obtained by the adversary.


title:
A Character-level Span-based Model for Mandarin Prosodic Structure
  Prediction

abstract:
  The accuracy of prosodic structure prediction is crucial to the naturalness
of synthesized speech in Mandarin text-to-speech system, but now is limited by
widely-used sequence-to-sequence framework and error accumulation from previous
word segmentation results. In this paper, we propose a span-based Mandarin
prosodic structure prediction model to obtain an optimal prosodic structure
tree, which can be converted to corresponding prosodic label sequence. Instead
of the prerequisite for word segmentation, rich linguistic features are
provided by Chinese character-level BERT and sent to encoder with
self-attention architecture. On top of this, span representation and label
scoring are used to describe all possible prosodic structure trees, of which
each tree has its corresponding score. To find the optimal tree with the
highest score for a given sentence, a bottom-up CKY-style algorithm is further
used. The proposed method can predict prosodic labels of different levels at
the same time and accomplish the process directly from Chinese characters in an
end-to-end manner. Experiment results on two real-world datasets demonstrate
the excellent performance of our span-based method over all
sequence-to-sequence baseline approaches.


title:
Aplica\c{c}\~ao de ros como ferramenta de ensino a rob\'otica / using
  ros as a robotics teaching tool

abstract:
  The study of robotic manipulators is the main goal of Industrial Robotics
Class, part of Control Engineers training course. There is a difficulty in
preparing academic practices and projects in the area of robotics due to the
high cost of specific educational equipment. The practical classes and the
development of projects are very important for engineers training, it is
proposed to use simulation software in order to provide practical experience
for the students of the discipline. In this context, the present article aims
to expose the use of the Robot Operation System (ROS) as a tool to develop a
robotic arm and implement the functionality of forward and inverse kinematics.
Such development could be used as an educational tool to increase the interest
and learning of students in the robotics discipline and to expand research
areas for the discipline.


title:
Development of a robotic manipulator: Applying interdisciplinarity in
  Computer Assister Project, Microcontrollers and Industrial Robotics

abstract:
  This work was conceived based on Project-Based Learning (ABP) and presents
the design, development and mathematical modeling steps of a low-cost robotic
manipulator with five degrees of freedom through an interdisciplinary project
linking two very important disciplines of the course of Control Engineering and
Automation of the University of Fortaleza: Computer Aided Design,
Microcontrollers and Industrial Robotics. At the end are presented the results
that the project has brought to the best learning of the discipline on the
optics of the tutor and students.


title:
The Apertif science verification campaign - Characteristics of polarised
  radio sources

abstract:
  We analyse five early science datasets from the APERture Tile in Focus
(Apertif) phased array feed system to verify the polarisation capabilities of
Apertif in view of future larger data releases. We aim to characterise the
source population of the polarised sky in the L-Band using polarised source
information in combination with IR and optical data. We use automatic routines
to generate full field-of-view Q- and U-cubes and perform RM-Synthesis, source
finding, and cross-matching with published radio, optical, and IR data to
generate polarised source catalogues. SED-fitting routines were used to
determine photometric redshifts, star-formation rates, and galaxy masses. IR
colour information was used to classify sources as AGN or
star-forming-dominated and early- or late-type. We surveyed an area of
56deg$^2$ and detected 1357 polarised source components in 1170 sources. The
fraction of polarised sources is 10.57% with a median fractional polarisation
of 4.70$\pm$0.14%. We confirmed the reliability of the Apertif measurements by
comparing them with polarised cross-identified NVSS sources. Average RMs of the
individual fields lie within the error of the best Milky Way foreground
measurements. All of our polarised sources were found to be dominated by AGN
activity in the radio regime with most of them being radio-loud (79%) and of
the FRII class (87%). The host galaxies of our polarised source sample are
dominated by intermediate disc and star-forming disc galaxies. The contribution
of star formation to the radio emission is on the order of a few percent for
$\approx$10% of the polarised sources while for $\approx$90% it is completely
dominated by the AGN. We do not see any change in fractional polarisation for
different star-formation rates of the AGN host galaxies.


title:
Domain Adaptation for Sparse-Data Settings: What Do We Gain by Not Using
  Bert?

abstract:
  The practical success of much of NLP depends on the availability of training
data. However, in real-world scenarios, training data is often scarce, not
least because many application domains are restricted and specific. In this
work, we compare different methods to handle this problem and provide
guidelines for building NLP applications when there is only a small amount of
labeled training data available for a specific domain. While transfer learning
with pre-trained language models outperforms other methods across tasks,
alternatives do not perform much worse while requiring much less computational
effort, thus significantly reducing monetary and environmental cost. We examine
the performance tradeoffs of several such alternatives, including models that
can be trained up to 175K times faster and do not require a single GPU.


title:
Applying PBL in the Development and Modeling of kinematics for Robotic
  Manipulators with Interdisciplinarity between Computer-Assisted Project,
  Robotics, and Microcontrollers

abstract:
  Considering the difficulty of students in calculating the direct and inverse
kinematics of a robotic manipulator using only conventional tools of a
classroom, this article proposes the application of Project Based Learning
(ABP) through the design, development, mathematical modeling of a robotic
manipulator as an integrative project of the disciplines of Industrial
Robotics, Microcontrollers and Computer Assisted Design with students of the
Control and Automation Engineering of the University of Fortaleza. Once
designed and machined, the manipulator arm was assembled using servo motors
connected to a microcontroled prototyping board, to then have its kinematics
calculated. At the end are presented the results that the project has brought
to the learning of the disciplines on the optics of the tutor and students.


title: Neural Architecture Search for Speech Emotion Recognition

abstract:
  Deep neural networks have brought significant advancements to speech emotion
recognition (SER). However, the architecture design in SER is mainly based on
expert knowledge and empirical (trial-and-error) evaluations, which is
time-consuming and resource intensive. In this paper, we propose to apply
neural architecture search (NAS) techniques to automatically configure the SER
models. To accelerate the candidate architecture optimization, we propose a
uniform path dropout strategy to encourage all candidate architecture
operations to be equally optimized. Experimental results of two different
neural structures on IEMOCAP show that NAS can improve SER performance (54.89\%
to 56.28\%) while maintaining model parameter sizes. The proposed dropout
strategy also shows superiority over the previous approaches.


title: Deterministic Terahertz wave control in scattering media

abstract:
  Scattering-assisted synthesis of broadband optical pulses is recognized to
have a cross-disciplinary funda-mental and application importance. Achieving
full-waveform synthesis generally requires means for assessing the
instantaneous electric field, i.e. the absolute electromagnetic phase. These
are generally not accessible to established methodologies for
scattering-assisted pulse envelope and phase shaping. The lack of field
sensitivity also results in complex indirect approaches to evaluate the
scattering space-time properties. The terahertz frequency domain potentially
offers some distinctive new possibilities thanks to the availability of methods
to perform absolute measurements of the scattered electric field, as opposed to
optical intensity-based diagnostics. An interesting conceptual question is
whether this additional degree of freedom can lead to different types of
methodologies toward wave shaping and to a direct field-waveform control. In
this work, we theoretically investigate a deterministic scheme to achieve
broadband, spatio-temporal waveform control of terahertz fields mediated by a
scattering medium. The direct field access via Time-Domain Spectroscopy enables
a process in which the field and scattering matrix of the medium are assessed
with minimal experimental efforts. Then, the illumination conditions for an
arbitrary targeted output field waveform are deterministically determined. In
addition, the complete field knowledge enables reconstructing field
distributions with complex phase profiles, as in the case of phase-only masks
and optical vortices, a significantly challenging task for traditional
implementations at optical frequencies based on intensity measurements aided
with interferometric techniques.


title:
WavThruVec: Latent speech representation as intermediate features for
  neural speech synthesis

abstract:
  Recent advances in neural text-to-speech research have been dominated by
two-stage pipelines utilizing low-level intermediate speech representation such
as mel-spectrograms. However, such predetermined features are fundamentally
limited, because they do not allow to exploit the full potential of a
data-driven approach through learning hidden representations. For this reason,
several end-to-end methods have been proposed. However, such models are harder
to train and require a large number of high-quality recordings with
transcriptions. Here, we propose WavThruVec - a two-stage architecture that
resolves the bottleneck by using high-dimensional Wav2Vec 2.0 embeddings as
intermediate speech representation. Since these hidden activations provide
high-level linguistic features, they are more robust to noise. That allows us
to utilize annotated speech datasets of a lower quality to train the
first-stage module. At the same time, the second-stage component can be trained
on large-scale untranscribed audio corpora, as Wav2Vec 2.0 embeddings are
time-aligned and speaker-independent. This results in an increased
generalization capability to out-of-vocabulary words, as well as to a better
generalization to unseen speakers. We show that the proposed model not only
matches the quality of state-of-the-art neural models, but also presents useful
properties enabling tasks like voice conversion or zero-shot synthesis.


title:
Towards Robust Rain Removal Against Adversarial Attacks: A Comprehensive
  Benchmark Analysis and Beyond

abstract:
  Rain removal aims to remove rain streaks from images/videos and reduce the
disruptive effects caused by rain. It not only enhances image/video visibility
but also allows many computer vision algorithms to function properly. This
paper makes the first attempt to conduct a comprehensive study on the
robustness of deep learning-based rain removal methods against adversarial
attacks. Our study shows that, when the image/video is highly degraded, rain
removal methods are more vulnerable to the adversarial attacks as small
distortions/perturbations become less noticeable or detectable. In this paper,
we first present a comprehensive empirical evaluation of various methods at
different levels of attacks and with various losses/targets to generate the
perturbations from the perspective of human perception and machine analysis
tasks. A systematic evaluation of key modules in existing methods is performed
in terms of their robustness against adversarial attacks. From the insights of
our analysis, we construct a more robust deraining method by integrating these
effective modules. Finally, we examine various types of adversarial attacks
that are specific to deraining problems and their effects on both human and
machine vision tasks, including 1) rain region attacks, adding perturbations
only in the rain regions to make the perturbations in the attacked rain images
less visible; 2) object-sensitive attacks, adding perturbations only in regions
near the given objects. Code is available at
https://github.com/yuyi-sd/Robust_Rain_Removal.


title: Probabilistic Map Matching for Robust Inertial Navigation Aiding

abstract:
  Robust aiding of inertial navigation systems in GNSS-denied environments is
critical for the removal of accumulated navigation error caused by the drift
and bias inherent in inertial sensors. One way to perform such an aiding uses
matching of geophysical measurements, such as gravimetry, gravity gradiometry
or magnetometry, with a known geo-referenced map. Although simple in concept,
this map matching procedure is challenging: the measurements themselves are
noisy; their associated spatial location is uncertain; and the measurements may
match multiple points within the map (i.e. non-unique solution). In this paper,
we propose a probabilistic multiple hypotheses tracker to solve the map
matching problem and allow robust inertial navigation aiding. Our approach
addresses the problem both locally, via probabilistic data association, and
temporally by incorporating the underlying platform kinematic constraints into
the tracker. The map matching output is then integrated into the navigation
system using an unscented Kalman filter. Additionally, we present a statistical
measure of local map information density -- the map feature variability -- and
use it to weight the output covariance of the proposed algorithm. The
effectiveness and robustness of the proposed algorithm are demonstrated using a
navigation scenario involving gravitational map matching.


title:
Formation of Two-Ion Crystals by Injection from a Paul-Trap Source into
  a High-Magnetic-Field Penning Trap

abstract:
  Two-ion crystals constitute a platform for investigations of quantum nature
that can be extended to any ion species or charged particle provided one of the
ions in the crystal can be directly laser-cooled and manipulated with laser
radiation. This paper presents the formation of two-ion crystals for quantum
metrology in a 7-tesla open-ring Penning trap. $^{40}$Ca$^+$ ions are produced
either internally by photoionization or externally in a (Paul-trap) source,
transported through the strong magnetic field gradient of the superconducting
solenoid, and captured in-flight with a mean kinetic energy of a few
electronvolts with respect to the minimum of the Penning-trap potential well.
Laser cooling of the two-ion crystal in a strong magnetic field towards
reaching the quantum regime is also presented with particular emphasis on the
cooling of the radial modes.


title: Contributions to interframe coding

abstract:
  Advanced motion models (4 or 6 parameters) are needed for a good
representation of the motion experimented by the different objects contained in
a sequence of images. If the image is split in very small blocks, then an
accurate description of complex movements can be achieved with only 2
parameters. This alternative implies a large set of vectors per image. We
propose a new approach to reduce the number of vectors, using different block
sizes as a function of the local characteristics of the image, without
increasing the error accepted with the smallest blocks. A second algorithm is
proposed for an inter/intraframe coder.


title: Learning from few examples with nonlinear feature maps

abstract:
  In this work we consider the problem of data classification in post-classical
settings were the number of training examples consists of mere few data points.
We explore the phenomenon and reveal key relationships between dimensionality
of AI model's feature space, non-degeneracy of data distributions, and the
model's generalisation capabilities. The main thrust of our present analysis is
on the influence of nonlinear feature transformations mapping original data
into higher- and possibly infinite-dimensional spaces on the resulting model's
generalisation capabilities. Subject to appropriate assumptions, we establish
new relationships between intrinsic dimensions of the transformed data and the
probabilities to learn successfully from few presentations.


title:
Influence of the recoil-order and radiative correction on the beta decay
  correlation coefficients in mirror decays

abstract:
  Measurements of the beta decay correlation coefficients in nuclear decay aim
for a precision below $1\%$ and theoretical predictions should follow this
trend. In this work, the influence of the two dominant Standard Model
correction terms, i.e. the recoil-order and the radiative correction, are
studied for the most commonly measured beta correlations, i.e. the
$\beta$-asymmetry parameter ($A_{\beta}$) and the $\beta-\nu$ angular
correlation ($a_{\beta \nu}$). The recoil-order correction is calculated with
the well-known Holstein formalism using the impulse approximation to evaluate
experimentally inaccessible form factors. For the $\beta-\nu$ angular
correlation previously unpublished, semi-analytical radiative correction values
are tabulated. Results are presented for the mirror beta decays up to $A=45$.
We examine the effect of both corrections and provide a comparison between
different isotopes. This comparison will help planning, analysing, and
comparing future experimental efforts.


title: HiFi-VC: High Quality ASR-Based Voice Conversion

abstract:
  The goal of voice conversion (VC) is to convert input voice to match the
target speaker's voice while keeping text and prosody intact. VC is usually
used in entertainment and speaking-aid systems, as well as applied for speech
data generation and augmentation. The development of any-to-any VC systems,
which are capable of generating voices unseen during model training, is of
particular interest to both researchers and the industry. Despite recent
progress, any-to-any conversion quality is still inferior to natural speech.
  In this work, we propose a new any-to-any voice conversion pipeline. Our
approach uses automated speech recognition (ASR) features, pitch tracking, and
a state-of-the-art waveform prediction model. According to multiple subjective
and objective evaluations, our method outperforms modern baselines in terms of
voice quality, similarity and consistency.


title:
Conditional large-data global well-posedness of Dirac equation with
  Hartree-type nonlinearity

abstract:
  We study the Cauchy problems for the Hartree-type nonlinear Dirac equations
with Yukawa-type potential in two and three spatial dimensions. This paper
improves our previous results \cite{chohlee,cholee}; we establish global
well-posedness and scattering for large data with a certain condition. Firstly
we investigate the long-time behavior of solutions to the Dirac equation
satisfies good control provided that a particular dispersive norm of solutions
is bounded. The key of our proof relies on modifying multilinear estimates
obtained in our previous papers. Secondly, we obtain large data scattering by
exploiting the Majorana condition.


title:
Hypergraph Convolutional Networks via Equivalency between Hypergraphs
  and Undirected Graphs

abstract:
  As a powerful tool for modeling complex relationships, hypergraphs are
gaining popularity from the graph learning community. However, commonly used
frameworks in deep hypergraph learning focus on hypergraphs with
\textit{edge-independent vertex weights}(EIVWs), without considering
hypergraphs with \textit{edge-dependent vertex weights} (EDVWs) that have more
modeling power. To compensate for this, in this paper, we present General
Hypergraph Spectral Convolution(GHSC), a general learning framework that not
only can handle EDVW and EIVW hypergraphs, but more importantly, enables
theoretically explicitly utilizing the existing powerful Graph Convolutional
Neural Networks (GCNNs) such that largely ease the design of Hypergraph Neural
Networks. In this framework, the graph Laplacian of the given undirected GCNNs
is replaced with a unified hypergraph Laplacian that incorporates vertex weight
information from a random walk perspective by equating our defined generalized
hypergraphs with simple undirected graphs. Extensive experiments from various
domains including social network analysis, visual objective classification,
protein learning demonstrate that the proposed framework can achieve
state-of-the-art performance.


title: Direction of Arrival Estimation of Sound Sources Using Icosahedral CNNs

abstract:
  In this paper, we present a new model for Direction of Arrival (DOA)
estimation of sound sources based on an Icosahedral Convolutional Neural
Network (CNN) applied over SRP-PHAT power maps computed from the signals
received by a microphone array. This icosahedral CNN is equivariant to the 60
rotational symmetries of the icosahedron, which represent a good approximation
of the continuous space of spherical rotations, and can be implemented using
standard 2D convolutional layers, having a lower computational cost than most
of the spherical CNNs. In addition, instead of using fully connected layers
after the icosahedral convolutions, we propose a new soft-argmax function that
can be seen as a differentiable version of the argmax function and allows us to
solve the DOA estimation as a regression problem interpreting the output of the
convolutional layers as a probability distribution. We prove that using models
that fit the equivariances of the problem allows us to outperform other
state-of-the-art models with a lower computational cost and more robustness,
obtaining root mean square localization errors lower than 10{\deg} even in
scenarios with a reverberation time $T_{60}$ of 1.5 s.


title: A unified theory of learning

abstract:
  Recently machine learning using neural networks (NN) has been developed, and
many new methods have been suggested. These methods are optimized for the type
of input data and work very effectively, but they cannot be used with any kind
of input data universally. On the other hand, the human brain is universal for
any kind of problem, and we will be able to construct artificial general
intelligence if we can mimic the system of how the human brain works. We
consider how the human brain learns things uniformly, and find that the essence
of learning is the compression of information. We suggest a toy NN model which
mimics the system of the human brain, and we show that the NN can compress the
input information without ad hoc treatment, only by setting the loss function
properly. The loss function is expressed as the sum of the self-information to
remember and the loss of the information along with the compression, and its
minimum corresponds to the self-information of the original data. To evaluate
the self-information to remember, we provided the concept of memory. The memory
expresses the compressed information, and the learning proceeds by referring to
previous memories. There are many similarities between this NN and the human
brain, and this NN is a realization of the free-energy principle which is
considered to be a unified theory of the human brain. This work can be applied
to any kind of data analysis and cognitive science.


title: Sequential Recommendation with User Evolving Preference Decomposition

abstract:
  Modeling user sequential behaviors has recently attracted increasing
attention in the recommendation domain. Existing methods mostly assume coherent
preference in the same sequence. However, user personalities are volatile and
easily changed, and there can be multiple mixed preferences underlying user
behaviors. To solve this problem, in this paper, we propose a novel sequential
recommender model via decomposing and modeling user independent preferences. To
achieve this goal, we highlight three practical challenges considering the
inconsistent, evolving and uneven nature of the user behavior, which are seldom
noticed by the previous work. For overcoming these challenges in a unified
framework, we introduce a reinforcement learning module to simulate the
evolution of user preference. More specifically, the action aims to allocate
each item into a sub-sequence or create a new one according to how the previous
items are decomposed as well as the time interval between successive behaviors.
The reward is associated with the final loss of the learning objective, aiming
to generate sub-sequences which can better fit the training data. We conduct
extensive experiments based on six real-world datasets across different
domains. Compared with the state-of-the-art methods, empirical studies manifest
that our model can on average improve the performance by about 8.21%, 10.08%,
10.32%, and 9.82% on the metrics of Precision, Recall, NDCG and MRR,
respectively.


title: Pressure-induced superconductivity in kagome single crystal Pd3P2S8

abstract:
  Kagome lattice offers unique opportunities for the exploration of unusual
quantum states of correlated electrons. Here, we report on the observation of
superconductivity in a kagome single crystal Pd3P2S8 when a semiconducting to
metallic transition is driven by pressure. High-pressure resistance
measurements show that the metallization and superconductivity are
simultaneously observed at about 11 GPa. With increasing pressure, the
superconducting critical temperature Tc is monotonously enhanced from 2.6 K to
a maximum 7.7 K at ~52 GPa. Interestingly, superconductivity retains when the
pressure is fully released. Synchrotron XRD and Raman experiments consistently
evidence that the emergence of superconductivity is accompanied with an
amorphization and the retainability of superconductivity upon decompression can
be attributed to the irreversibility of the amorphization.


title:
A data-driven approach for the closure of RANS models by the divergence
  of the Reynolds Stress Tensor

abstract:
  In the present paper a new data-driven model to close and increase accuracy
of RANS equations is proposed. It is based on the direct approximation of the
divergence of the Reynolds Stress Tensor (RST) through a Neural Network (NN).
This choice is driven by the presence of the divergence of RST in the RANS
equations. Furthermore, once this data-driven approach is trained, there is no
need to run any turbulence model to close the equations. Finally, it is well
known that a good approximation of a function it is not necessarily a good
approximation of its derivative. The architecture and inputs choices of the
proposed network guarantee both Galilean and coordinates-frame rotation
invariances by looking to a vector basis expansion of the divergence of the
RST. Two well-known test cases are used to show advantages of the proposed
method compared to classic turbulence models.


title:
Semantic Pose Verification for Outdoor Visual Localization with
  Self-supervised Contrastive Learning

abstract:
  Any city-scale visual localization system has to overcome long-term
appearance changes, such as varying illumination conditions or seasonal changes
between query and database images. Since semantic content is more robust to
such changes, we exploit semantic information to improve visual localization.
In our scenario, the database consists of gnomonic views generated from
panoramic images (e.g. Google Street View) and query images are collected with
a standard field-of-view camera at a different time. To improve localization,
we check the semantic similarity between query and database images, which is
not trivial since the position and viewpoint of the cameras do not exactly
match. To learn similarity, we propose training a CNN in a self-supervised
fashion with contrastive learning on a dataset of semantically segmented
images. With experiments we showed that this semantic similarity estimation
approach works better than measuring the similarity at pixel-level. Finally, we
used the semantic similarity scores to verify the retrievals obtained by a
state-of-the-art visual localization method and observed that contrastive
learning-based pose verification increases top-1 recall value to 0.90 which
corresponds to a 2% improvement.


title: Quantum coherent control in pulsed waveguide optomechanics

abstract:
  Coherent control of traveling acoustic excitations in a waveguide system is
an interesting way to manipulate and transduce classical and quantum
information. So far, these interactions, often based on optomechanical
resonators or Brillouin scattering, have been studied in the steady-state
regime using continuous waves. However, waveguide experiments are often based
on optical pump pulses which require treatment in a dynamic framework. In this
paper, we present an effective Hamiltonian formalism in the dynamic regime
using optical pulses that links waveguide optomechanics and cavity
optomechanics, which can be used in the classical and quantum regime including
quantum noise. Based on our formalism, a closed solution for coupled-mode
equation under the undepleted assumption is provided and we found that the
strong coupling regime is already accessible in current Brillouin waveguides by
using pulses. We further investigate several possible experiments within
waveguide optomechanics, including Brillouin-based coherent transfer, Brillouin
cooling, and optoacoustic entanglement.


title: Accessing pion GPDs through the Sullivan process: is it feasible?

abstract:
  Describing hadronic structure is one of the most intriguing problems in
physics. In this respect, generalized parton distributions (GPDs) constitute an
outstanding tool, allowing to draw "three dimensional pictures" of hadron's
inside. Starting from contemporary models for pion's GPDs fulfilling all
constraints imposed by QCD, we compute Compton form factors (CFFs) of pions
subjected to deeply virtual Compton scattering. We show the behaviour of CFFs
to be gluon-dominated at EIC's kinematics. Finally we evaluate lepton-beam-spin
asymmetries in the Sullivan process, demonstrating the existence of such and
thus triggering optimism about the possibility of probing pion's 3D structure
at electron-ion colliders.


title:
Superstatistical wind fields from point-wise atmospheric turbulence
  measurements

abstract:
  Accurate models of turbulent wind fields have become increasingly important
in the atmospheric sciences, e.g., for the determination of spatiotemporal
correlations in wind parks, the estimation of individual loads on turbine rotor
and blades, or for the modeling of particle-turbulence interaction in
atmospheric clouds or pollutant distributions in urban settings. Due to the
prohibitive task of resolving the fields across a broad range of scales, one
oftentimes has to resort to stochastic wind field models that fulfill specific,
empirically observed, properties. Here, we present a new model for the
generation of synthetic wind fields that can be apprehended as an extension of
the well-known Mann model for inflow turbulence in the wind energy sciences.
Whereas such Gaussian random field models solely control second-order
statistics (i.e., velocity correlation tensors or kinetic energy spectra), we
explicitly show that our extended model emulates the effects of higher-order
statistics as well. Most importantly, the empirically observed phenomenon of
small-scale intermittency, which can be regarded as one of the key features of
atmospheric turbulent flows, is reproduced with high accuracy and at
considerably low computational cost. Our method is based on a recently
developed multipoint statistical description of turbulent velocity fields [J.
Friedrich et al., J. Phys. Complex. 2 045006 (2021)] and consists of a
superposition of multivariate Gaussian statistics with fluctuating covariances.
We demonstrate exemplarily how such "superstatistical" wind fields can be
constrained on a certain number of point-wise measurement data from a
meteorological mast array.


title: Self-adapting infectious dynamics on random networks

abstract:
  Self-adaptive dynamics occurs in many physical systems such as
socio-economics, neuroscience, or biophysics. We formalize a self-adaptive
modeling approach, where adaptation takes place within a set of strategies
based on the history of the state of the system. This leads to piecewise
deterministic Markovian dynamics coupled to a non-Markovian adaptive mechanism.
We apply this framework to basic epidemic models (SIS, SIR) on random networks.
We consider a co-evolutionary dynamical network where node-states change
through the epidemics and network topology changes through creation and
deletion of edges. For a simple threshold base application of lockdown measures
we observe large regions in parameter space with oscillatory behavior. For the
SIS epidemic model, we derive analytic expressions for the oscillation period
from a pairwise closed model. Furthermore, we show that there is a link to
self-organized criticality as the basic reproduction number fluctuates around
one. We also study the dependence of our results on the underlying network
structure.


title:
Long-term scintillation studies of EPTA pulsars. I. Observations and
  basic results

abstract:
  Interstellar scintillation analysis of pulsars allows us to probe the
small-scale distribution and inhomogeneities of the ionized interstellar
medium.
  Our priority is to present the data set and the basic measurements of
scintillation parameters of pulsars employing long-term scintillation
observations carried out from 2011 January to 2020 August by the European
Pulsar Timing Array radio telescopes in the 21-cm and 11-cm bands.
Additionally, we aim to identify future possible lines of study using this
long-term scintillation dataset.
  We present the long-term time series of $\nu_{\rm d}$ and $\tau_{\rm d}$ for
13 pulsars. Sanity-checks and comparisons indicate that the scintillation
parameters of our work and previously published works are mostly consistent.
For two pulsars, PSRs~J1857+0943 and J1939+2134, we were able to obtain
measurements of the $\nu_{\rm d}$ at both bands, which allows us to derive the
time series of frequency scaling indices with a mean and a standard deviation
of 2.82$\pm$1.95 and 3.18$\pm$0.60, respectively. We found some interesting
features which will be studied in more detail in subsequent papers in this
series: (i) in the time series of PSR~J1939+2134, where the scintillation
bandwidth sharply increases or decreases associated with a sharp change of
dispersion measure; (ii) PSR~J0613$-$0200 and PSR~J0636+5126 show a strong
annual variation in the time series of the $\tau_{\rm d}$; (iii) PSR~J1939+2134
shows a weak anti-correlation between scintillation timescale and dispersion in
WSRT data.


title: Global and Asymptotically Efficient Localization from Range Measurements

abstract:
  We consider the range-based localization problem, which involves estimating
an object's position by using $m$ sensors, hoping that as the number $m$ of
sensors increases, the estimate converges to the true position with the minimum
variance. We show that under some conditions on the sensor deployment and
measurement noises, the LS estimator is strongly consistent and asymptotically
normal. However, the LS problem is nonsmooth and nonconvex, and therefore hard
to solve. We then devise realizable estimators that possess the same asymptotic
properties as the LS one. These estimators are based on a two-step estimation
architecture, which says that any $\sqrt{m}$-consistent estimate followed by a
one-step Gauss-Newton iteration can yield a solution that possesses the same
asymptotic property as the LS one. The keypoint of the two-step scheme is to
construct a $\sqrt{m}$-consistent estimate in the first step. In terms of
whether the variance of measurement noises is known or not, we propose the
Bias-Eli estimator (which involves solving a generalized trust region
subproblem) and the Noise-Est estimator (which is obtained by solving a convex
problem), respectively. Both of them are proved to be $\sqrt{m}$-consistent.
Moreover, we show that by discarding the constraints in the above two
optimization problems, the resulting closed-form estimators (called
Bias-Eli-Lin and Noise-Est-Lin) are also $\sqrt{m}$-consistent. Plenty of
simulations verify the correctness of our theoretical claims, showing that the
proposed two-step estimators can asymptotically achieve the Cramer-Rao lower
bound.


title: Multimodal Fusion Transformer for Remote Sensing Image Classification

abstract:
  Vision transformer (ViT) has been trending in image classification tasks due
to its promising performance when compared to convolutional neural networks
(CNNs). As a result, many researchers have tried to incorporate ViT models in
hyperspectral image (HSI) classification tasks, but without achieving
satisfactory performance. To this paper, we introduce a new multimodal fusion
transformer (MFT) network for HSI land-cover classification, which utilizes
other sources of multimodal data in addition to HSI. Instead of using
conventional feature fusion techniques, other multimodal data are used as an
external classification (CLS) token in the transformer encoder, which helps
achieving better generalization. ViT and other similar transformer models use a
randomly initialized external classification token {and fail to generalize
well}. However, the use of a feature embedding derived from other sources of
multimodal data, such as light detection and ranging (LiDAR), offers the
potential to improve those models by means of a CLS. The concept of
tokenization is used in our work to generate CLS and HSI patch tokens, helping
to learn key features in a reduced feature space. We also introduce a new
attention mechanism for improving the exchange of information between HSI
tokens and the CLS (e.g., LiDAR) token. Extensive experiments are carried out
on widely used and benchmark datasets i.e., the University of Houston, Trento,
University of Southern Mississippi Gulfpark (MUUFL), and Augsburg. In the
results section, we compare the proposed MFT model with other state-of-the-art
transformer models, classical CNN models, as well as conventional classifiers.
The superior performance achieved by the proposed model is due to the use of
multimodal information as external classification tokens.


title: Two conjectures on coarse conjugacy by Geller and Misiurewicz

abstract:
  In their study of coarse entropy, W. Geller and M. Misiurewicz introduced the
notion of coarse conjugacy: a version of conjugacy appropriate for dynamics on
metric spaces observed from afar. They made two conjectures on coarse conjugacy
generalising their results. We disprove both of these conjectures. We
investigate the impact of extra assumptions on the validity of the conjectures:
We show that the result of Geller and Misiurewicz towards one of the
conjectures can be considered optimal, and we prove the other conjecture under
an assumption complementary to that from the referenced work.


title:
An End-to-end Chinese Text Normalization Model based on Rule-guided
  Flat-Lattice Transformer

abstract:
  Text normalization, defined as a procedure transforming non standard words to
spoken-form words, is crucial to the intelligibility of synthesized speech in
text-to-speech system. Rule-based methods without considering context can not
eliminate ambiguation, whereas sequence-to-sequence neural network based
methods suffer from the unexpected and uninterpretable errors problem. Recently
proposed hybrid system treats rule-based model and neural model as two cascaded
sub-modules, where limited interaction capability makes neural network model
cannot fully utilize expert knowledge contained in the rules. Inspired by
Flat-LAttice Transformer (FLAT), we propose an end-to-end Chinese text
normalization model, which accepts Chinese characters as direct input and
integrates expert knowledge contained in rules into the neural network, both
contribute to the superior performance of proposed model for the text
normalization task. We also release a first publicly accessible largescale
dataset for Chinese text normalization. Our proposed model has achieved
excellent results on this dataset.


title: Quantum Mechanical Description of Thermo-Optic Interaction

abstract:
  Thermo-optic interaction significantly differs from the usual
particle-particle interactions in physics, as it is retarded in time. A
prominent platform for realising this kind of interaction are photon
Bose-Einstein condensates, which are created in dye-filled microcavities. The
dye solution continually absorbs and re-emits these photons, causing the photon
gas to thermalise and to form a Bose-Einstein condensate. Because of a
non-ideal quantum efficiency, these cycles heat the dye solution, creating a
medium that provides an effective thermo-optic photon-photon interaction. So
far, only a mean-field description of this process exists. This paper goes
beyond by working out a quantum mechanical description of the effective
thermo-optic photon-photon interaction. To this end, the self-consistent
modelling of the temperature diffusion builds the backbone of the modelling.
Furthermore, the manyfold experimental timescales allow for deriving an
approximate Hamiltonian. The resulting quantum theory is applied in the
perturbative regime to both a harmonic and a box potential for investigating
its prospect for precise measurements of the effective photon-photon
interaction strength.


title:
Scalable switched slab coupler based optical phased array on silicon
  nitride

abstract:
  A two-dimensional optical-phased array is demonstrated by using a
multiple-input star coupler, compatible with FMCW LiDAR. Previous approach
using a single-input design achieves two-dimensional beam-steering by relying
on a tunable laser source, taking advantage of grating coupler radiation angle
wavelength dependance and variation of the waveguide refractive index. While
implementing a convenient way to distribute power in a single step, star
coupler architecture is inefficient in terms of employed waveguide length and
thus, optical loss and footprint. Multi-input approach partially alleviates
this by condensing several single-input devices into one, permitting to reduce
the footprint proportionally to the employed number of inputs. We fabricated in
silicon nitride technology a proof of concept steerer with beam waist 0.36 deg
x 0.175 deg addressing a field of view of 15 deg x 2.8 deg. A new design
iteration is also reported with and 0.24 deg x 0.16 deg beam waist and 15 deg x
11.2 deg field of view. Implications of this optical-phased array chips from a
LiDAR system perspective are also presented.


title:
Topological obstructions to the diagonalisation of pseudodifferential
  systems

abstract:
  Given a matrix pseudodifferential operator on a smooth manifold, one may be
interested in diagonalising it by choosing eigenvectors of its principal symbol
in a smooth manner. We show that diagonalisation is not always possible, on the
whole cotangent bundle or even in a single fibre. We identify global and local
topological obstructions to diagonalisation and examine physically meaningful
examples demonstrating that all possible scenarios can occur.


title:
Broad Emission and Absorption Line Outflows in the Quasar SDSS
  J163345.22+512748.4

abstract:
  We present a detailed study of the optical and NIR emission and absorption
line spectrum of the quasar SDSS J163345.22+512748.4. We discovered on the
newly acquired NIR spectrum a highly meta-stable neutral helium broad
absorption line (BAL) \heiozetz\ with a width of $\sim$ 2000 \kmps\ and a
blueshift of $\sim$ 7000 \kmps\ in the velocity space. The BAL system is also
significantly detected in \mgii\ and \heiteen. We estimate a column density of
$(5.0 \pm 1.7) \times 10^{14}$ cm$^{-2}$ for the HeI*(2~$^3$S) level, and infer
an ionization parameter of $U_{A} = 10^{-1.9\pm 0.2}$ for the BAL outflow
assuming that the BAL region is thick enough for a full development of an
ionization front. The total column density of the BAL outflow is constrained in
the range N$\rm _{H}$ $\sim$ 10$^{21}$-10$^{21.4}$ cm$^{-2}$. We also found
that the bulk of both MgII and UV FeII, as well as H$\alpha$ broad emission
lines (BELs) are blueshifted with a velocity of $\sim$ 2200 \kmps\ with respect
to the quasar systemic redshift. We constrain that the blueshifted BEL region
has a covering factor $C_{f}\approx 16\%$, a density n$\rm _{H}$ $\sim $
10$^{10.6}$-10$^{11.3}$ cm$^{-3}$, a column density N$\rm _{H}\gtrsim 10^{23}$
cm$^{-2}$, and an ionization parameter $U_{E}\sim 10^{-2.1}-10^{-1.5}$. The
outflow gas is located at $\sim$0.1 pc away from the central ionization source,
at a scale comparable to the BLR. A toy kinetic model has been proposed to
reproduce the profile of MgII BEL well if assuming a partial obscured
axisymmetric geometry of the outflow with a radial velocity as observed by the
BALs.


title:
Kepler K2 Campaign 9: II. First space-based discovery of an exoplanet
  using microlensing

abstract:
  We report on the discovery of a bound exoplanetary microlensing event from a
blind search of data gathered from Campaign 9 of the Kepler K2 mission (K2C9).
K2-2016-BLG-0005Lb is a densely sampled, binary caustic-crossing microlensing
event with caustic entry and exit points that are resolved in the K2C9 data,
enabling the lens-source relative proper motion to be measured. We have fitted
a binary microlens model to the K2 dataset, and to simultaneous observations
from the Optical Gravitational Lensing Experiment (OGLE-IV),
Canada-France-Hawaii Telescope (CFHT), Microlensing Observations in
Astrophysics (MOA-2), the Korean Microlensing Telescope Network (KMTNet), and
the United Kingdom InfraRed Telescope (UKIRT). Whilst the ground-based data
only sparsely sample the binary caustic, they provide a clear detection of
parallax that allows us to break completely the microlensing
mass-position-velocity degeneracy and measure the planet's mass directly. We
find a host mass of $0.58\pm0.03 ~{\rm M}_\odot$ and a planetary mass of $1.1
\pm 0.1 ~{\rm M_J}$. The system lies at a distance of $5.2 \pm 0.2~$kpc from
Earth towards the Galactic bulge. The projected physical separation of the
planet from its host is found to be $4.2 \pm 0.3~$au which, for circular
orbits, corresponds to $a = 4.4^{+1.9}_{-0.4}~$au and period $P =
13^{+9}_{-2}~$yr, making K2-2016-BLG-0005Lb a close Jupiter analogue. Though
previous exoplanet microlensing events have included space-based data, this
event is the first bound microlensing exoplanet to be discovered from
space-based data. Even through a space telescope not designed for microlensing
studies, this result highlights the advantages for exoplanet microlensing
discovery that come from continuous, high-cadence temporal sampling that is
possible from space. (Abridged).


title:
Multi-Agent Spatial Predictive Control with Application to Drone
  Flocking (Extended Version)

abstract:
  We introduce the novel concept of Spatial Predictive Control (SPC) to solve
the following problem: given a collection of agents (e.g., drones) with
positional low-level controllers (LLCs) and a mission-specific distributed cost
function, how can a distributed controller achieve and maintain cost-function
minimization without a plant model and only positional observations of the
environment? Our fully distributed SPC controller is based strictly on the
position of the agent itself and on those of its neighboring agents. This
information is used in every time-step to compute the gradient of the cost
function and to perform a spatial look-ahead to predict the best next target
position for the LLC. Using a high-fidelity simulation environment, we show
that SPC outperforms the most closely related class of controllers, Potential
Field Controllers, on the drone flocking problem. We also show that SPC is able
to cope with a potential sim-to-real transfer gap by demonstrating its
performance on real hardware, namely our implementation of flocking using nine
Crazyflie 2.1 drones.


title:
Sub-additivity of measure-theoretic entropies of commuting
  transformations on Banach space

abstract:
  We consider two commuting smooth transformations on Banach spaces, prove the
sub-additivity of measure-theoretic entropies and give the condition for
equality. This is an extension of Hu's work on commuting diffeomorphisms in a
finite-dimensional space (HuHu Yi 1993 Ergod. Th. Dynam. Sys. 13 73-100) to the
case of systems on a Banach space.


title: A comparative study between linear and nonlinear speech prediction

abstract:
  This paper is focused on nonlinear prediction coding, which consists on the
prediction of a speech sample based on a nonlinear combination of previous
samples. It is known that in the generation of the glottal pulse, the wave
equation does not behave linearly [2], [10], and we model these effects by
means of a nonlinear prediction of speech based on a parametric neural network
model. This work is centred on the neural net weight's quantization and on the
compression gain.


title: Qualification study of SiPMs on a large scale for the CMVD Experiment

abstract:
  A Cosmic Muon Veto (CMV) detector using extruded plastic scintillators is
being designed around the mini-Iron Calorimeter (mini-ICAL) detector at the
transit campus of the India based Neutrino Observatory, Madurai for the
feasibility study of shallow depth underground experiments. The scintillation
signals that are produced in the plastic due to muon trajectories are absorbed
by wavelength shifting (WLS) fibres. The WLS fibres re-emit photons of longer
wavelengths and propagate those to silicon photo-multipliers (SiPMs). The SiPMs
detect these photons, producing electronic signals. The CMV detector will use
more than 700 scintillators to cover the mini-ICAL detector and will require
around 3000 SiPMs. The design goal for the cosmic muon veto efficiency of the
CMV is >99.99%. Hence, every SiPM used in the detector needs to be tested and
characterised to satisfy the design goal of CMV. A mass testing system was
developed for the measurement of gain and choice of the overvoltage ($V_{ov}$)
of each SiPMs using an LED driver. The $V_{ov}$ is obtained by studying the
noise rate, the gain of the SiPM. This paper describes the experimental setup
used to test the SiPMs characteristics along with detailed studies of those
characteristics as a function of temperature.


title:
A Novel Probabilistic V2X Data Fusion Framework for Cooperative
  Perception

abstract:
  The paper addresses the vehicle-to-X (V2X) data fusion for cooperative or
collective perception (CP). This emerging and promising intelligent
transportation systems (ITS) technology has enormous potential for improving
efficiency and safety of road transportation. Recent advances in V2X
communication primarily address the definition of V2X messages and data
dissemination amongst ITS stations (ITS-Ss) in a traffic environment. Yet, a
largely unsolved problem is how a connected vehicle (CV) can efficiently and
consistently fuse its local perception information with the data received from
other ITS-Ss. In this paper, we present a novel data fusion framework to fuse
the local and V2X perception data for CP that considers the presence of
cross-correlation. The proposed approach is validated through comprehensive
results obtained from numerical simulation, CARLA simulation, and real-world
experimentation that incorporates V2X-enabled intelligent platforms. The
real-world experiment includes a CV, a connected and automated vehicle (CAV),
and an intelligent roadside unit (IRSU) retrofitted with vision and lidar
sensors. We also demonstrate how the fused CP information can improve the
awareness of vulnerable road users (VRU) for CV/CAV, and how this information
can be considered in path planning/decision making within the CAV to facilitate
safe interactions.


title:
PADA: Pruning Assisted Domain Adaptation for Self-Supervised Speech
  Representations

abstract:
  While self-supervised speech representation learning (SSL) models serve a
variety of downstream tasks, these models have been observed to overfit to the
domain from which the unlabelled data originates. To alleviate this issue, we
propose PADA (Pruning Assisted Domain Adaptation) and zero out redundant
weights from models pre-trained on large amounts of out-of-domain (OOD) data.
Intuitively, this helps to make space for the target-domain ASR finetuning. The
redundant weights can be identified through various pruning strategies which
have been discussed in detail as a part of this work. Specifically, we
investigate the effect of the recently discovered Task-Agnostic and Task-Aware
pruning on PADA and propose a new pruning paradigm based on the latter, which
we call Cross-Domain Task-Aware Pruning (CD-TAW). CD-TAW obtains the initial
pruning mask from a well fine-tuned OOD model, which makes it starkly different
from the rest of the pruning strategies discussed in the paper. Our proposed
CD-TAW methodology achieves up to 20.6% relative WER improvement over our
baseline when fine-tuned on a 2-hour subset of Switchboard data without
language model (LM) decoding. Furthermore, we conduct a detailed analysis to
highlight the key design choices of our proposed method.


title:
Human Instance Segmentation and Tracking via Data Association and
  Single-stage Detector

abstract:
  Human video instance segmentation plays an important role in computer
understanding of human activities and is widely used in video processing, video
surveillance, and human modeling in virtual reality. Most current VIS methods
are based on Mask-RCNN framework, where the target appearance and motion
information for data matching will increase computational cost and have an
impact on segmentation real-time performance; on the other hand, the existing
datasets for VIS focus less on all the people appearing in the video. In this
paper, to solve the problems, we develop a new method for human video instance
segmentation based on single-stage detector. To tracking the instance across
the video, we have adopted data association strategy for matching the same
instance in the video sequence, where we jointly learn target instance
appearances and their affinities in a pair of video frames in an end-to-end
fashion. We have also adopted the centroid sampling strategy for enhancing the
embedding extraction ability of instance, which is to bias the instance
position to the inside of each instance mask with heavy overlap condition. As a
result, even there exists a sudden change in the character activity, the
instance position will not move out of the mask, so that the problem that the
same instance is represented by two different instances can be alleviated.
Finally, we collect PVIS dataset by assembling several video instance
segmentation datasets to fill the gap of the current lack of datasets dedicated
to human video segmentation. Extensive simulations based on such dataset has
been conduct. Simulation results verify the effectiveness and efficiency of the
proposed work.


title:
Leibniz algebras whose solvable ideal is the maximal extension of the
  nilradical

abstract:
  The paper is devoted to the so-called complete Leibniz algebras. It is known
that a Lie algebra with a complete ideal is split. We will prove that this
result is valid for Leibniz algebras whose complete ideal is a solvable algebra
such that the codimension of nilradical is equal to the number of generators of
the nilradical.


title:
Dispersion estimates for the wave equation outside of a
  cylinder]{Dispersive estimates for the wave equation outside a cylinder in
  $\mathbb{R}^3$

abstract:
  We consider the wave equation with Dirichlet boundary conditions in the
exterior of a cylinder in R 3 and we construct a global in time parametrix to
derive sharp dispersion estimates for all frequencies (low and high) and, as a
corollary, Strichartz estimates, all matching the R 3 case.


title: Software Engineering for Quantum Programming: How Far Are We?

abstract:
  Quantum computing is no longer only a scientific interest but is rapidly
becoming an industrially available technology that can potentially overcome the
limits of classical computation. Over the last years, all major companies have
provided frameworks and programming languages that allow developers to create
their quantum applications. This shift has led to the definition of a new
discipline called quantum software engineering, which is demanded to define
novel methods for engineering large-scale quantum applications. While the
research community is successfully embracing this call, we notice a lack of
systematic investigations into the state of the practice of quantum
programming. Understanding the challenges that quantum developers face is vital
to precisely define the aims of quantum software engineering. Hence, in this
paper, we first mine all the GitHub repositories that make use of the most used
quantum programming frameworks currently on the market and then conduct coding
analysis sessions to produce a taxonomy of the purposes for which quantum
technologies are used. In the second place, we conduct a survey study that
involves the contributors of the considered repositories, which aims to elicit
the developers' opinions on the current adoption and challenges of quantum
programming. On the one hand, the results highlight that the current adoption
of quantum programming is still limited. On the other hand, there are many
challenges that the software engineering community should carefully consider:
these do not strictly pertain to technical concerns but also socio-technical
matters.


title: A Comparative Study of Fusion Methods for SASV Challenge 2022

abstract:
  Automatic Speaker Verification (ASV) system is a type of bio-metric
authentication. It can be attacked by an intruder, who falsifies data in order
to get access to protected information. Countermeasures (CM) are special
algorithms that detect these spoofing-attacks. While the ASVspoof Challenge
series were focused on the development of CM for fixed ASV system, the new
Spoofing Aware Speaker Verification (SASV) Challenge organizers believe that
best results can be achieved if CM and ASV systems are optimized jointly. One
of the approaches for cooperative optimization is a fusion over embeddings or
scores obtained from ASV and CM models. The baselines of SASV Challenge 2022
present two types of fusion: score-sum and back-end ensemble with a 3-layer
MLP. This paper describes our research of other fusion methods, including
boosting over embeddings, which has not been used in anti-spoofing studies
before.


title:
Dual metrics on the boundary of strictly polyhedral hyperbolic
  3-manifolds

abstract:
  Let $M$ be a compact oriented 3-manifold with non-empty boundary consisting
of surfaces of genii $>1$ such that the interior of $M$ is hyperbolizable. We
show that for each spherical cone-metric $d^*$ on $\partial M$ such that all
cone-angles are greater than $2\pi$ and the lengths of all closed geodesics
that are contractible in $M$ are greater than $2\pi$ there exists a unique
strictly polyhedral hyperbolic metric on $M$ such that $d^*$ is the induced
dual metric on $\partial M$.


title: Improving Language Identification of Accented Speech

abstract:
  Language identification from speech is a common preprocessing step in many
spoken language processing systems. In recent years, this field has seen a fast
progress, mostly due to the use of self-supervised models pretrained on
multilingual data and the use of large training corpora. This paper shows that
for speech with a non-native or regional accent, the accuracy of spoken
language identification systems drops dramatically, and that the accuracy of
identifying the language is inversely correlated with the strength of the
accent. We also show that using the output of a lexicon-free speech recognition
system of the particular language helps to improve language identification
performance on accented speech by a large margin, without sacrificing accuracy
on native speech. We obtain relative error rate reductions ranging from to 35
to 63% over the state-of-the-art model across several non-native speech
datasets.


title:
Analyzing the factors affecting usefulness of Self-Supervised
  Pre-trained Representations for Speech Recognition

abstract:
  Self-supervised learning (SSL) to learn high-level speech representations has
been a popular approach to building Automatic Speech Recognition (ASR) systems
in low-resource settings. However, the common assumption made in literature is
that a considerable amount of unlabeled data is available for the same domain
or language that can be leveraged for SSL pre-training, which we acknowledge is
not feasible in a real-world setting. In this paper, as part of the Interspeech
Gram Vaani ASR challenge, we try to study the effect of domain, language,
dataset size, and other aspects of our upstream pre-training SSL data on the
final performance low-resource downstream ASR task. We also build on the
continued pre-training paradigm to study the effect of prior knowledge
possessed by models trained using SSL. Extensive experiments and studies reveal
that the performance of ASR systems is susceptible to the data used for SSL
pre-training. Their performance improves with an increase in similarity and
volume of pre-training data. We believe our work will be helpful to the speech
community in building better ASR systems in low-resource settings and steer
research towards improving generalization in SSL-based pre-training for speech
systems.


title: Sensitivity and spectral control of network lasers

abstract:
  Recently, random lasing in complex networks has shown efficient lasing over
more than 50 localised modes, promoted by multiple scattering over the
underlying graph. If controlled, these network lasers can lead to
fast-switching multifunctional light sources with synthesised spectrum. Here,
we observe both in experiment and theory high sensitivity of the network laser
to the spatial shape of the pump profile, with mode intensity variation of up
to 280% for a non-homogeneous 7% pump decrease. We solve the nonlinear
equations within the steady state ab-initio laser theory (SALT) approximation
over a graph and we show selective lasing of around 90% of the top modes,
effectively programming the spectrum of the lasing networks. In our experiments
with polymer networks, this high sensitivity enables control of the lasing
spectrum through non-uniform pump patterns. We propose the underlying
complexity of the network modes as the key element behind efficient spectral
control opening the way for the development of optical devices with wide impact
for on-chip photonics for communication, sensing and computation.


title:
A readout-integrated time-bin qutrit analyzer for echo-based quantum
  memories

abstract:
  We present a method to project time-bin qutrits stored in an echo-based
quantum memory using several successive partial readouts of the memory. We
demonstrate how this scheme can be used to implement projections onto a full
set of mutually unbiased bases and, therefore, enables the characterization of
arbitrary quantum states. Further, we study the integration of this protocol
for the case of atomic frequency comb spin-wave storage by simulating the full
storage process and performing a storage experiment with bright time-bin pulses
in $^{151}$Eu$^{3+}$:Y$_2$SiO$_5$. In this context, a compound pulse for
implementing partial readouts in quick succession is introduced and
characterized.


title: Large maximal subgroups of finite almost simple groups

abstract:
  We prove in this paper that a finite almost simple group $R$ with socle the
non-abelian simple group $S$ possesses a conjugacy class of maximal subgroups
whose index coincides with the smallest index $\operatorname{l}(S)$ of a
maximal group of $S$ or there exists a natural number $v_S \leq
{\operatorname{l}(S)^2}$, depending only on $S$, such that $R$ has a conjugacy
class of core-free maximal subgroups with index $v_S$. We show that the number
of subgroups of the outer automorphism group of $S$ is bounded by $\log^3
{\operatorname{l}(S)}$ and $\operatorname{l}(S)^2 < |S|$.


title: Test comparison for Sobol Indices over nested sets of variables

abstract:
  Sensitivity indices are commonly used to quantify the relative influence of
any specific group of input variables on the output of a computer code. One
crucial question is then to decide whether a given set of variables has a
significant impact on the output. Sobol indices are often used to measure this
impact but their estimation can be difficult as they usually require a
particular design of experiment. In this work, we take advantage of the
monotonicity of Sobol indices with respect to set inclusion to test the
influence of some of the input variables. The method does not rely on a direct
estimation of the Sobol indices and can be performed under classical iid
sampling designs.


title: Matrix Polynomial Factorization via Higman Linearization

abstract:
  In continuation to our recent work on noncommutative polynomial
factorization, we consider the factorization problem for matrices of
polynomials and show the following results.
  (1) Given as input a full rank $d\times d$ matrix $M$ whose entries $M_{ij}$
are polynomials in the free noncommutative ring $\mathbb{F}_q\langle
x_1,x_2,\ldots,x_n \rangle$, where each $M_{ij}$ is given by a noncommutative
arithmetic formula of size at most $s$, we give a randomized algorithm that
runs in time polynomial in $d,s, n$ and $\log_2q$ that computes a factorization
of $M$ as a matrix product $M=M_1M_2\cdots M_r$, where each $d\times d$ matrix
factor $M_i$ is irreducible (in a well-defined sense) and the entries of each
$M_i$ are polynomials in $\mathbb{F}_q \langle x_1,x_2,\ldots,x_n \rangle$ that
are output as algebraic branching programs. We also obtain a deterministic
algorithm for the problem that runs in $poly(d,n,s,q)$.
  (2)A special case is the efficient factorization of matrices whose entries
are univariate polynomials in $\mathbb{F}[x]$. When $\mathbb{F}$ is a finite
field the above result applies. When $\mathbb{F}$ is the field of rationals we
obtain a deterministic polynomial-time algorithm for the problem.