title:
Integral representations for higher-order Fr\'echet derivatives of
  matrix functions: Quadrature algorithms and new results on the level-2
  condition number

abstract:
  We propose an integral representation for the higher-order Fr\'echet
derivative of analytic matrix functions $f(A)$ which unifies known results for
the first-order Fr\'echet derivative of general analytic matrix functions and
for higher-order Fr\'echet derivatives of $A^{-1}$. We highlight two
applications of this integral representation: On the one hand, it allows to
find the exact value of the level-2 condition number (i.e., the condition
number of the condition number) of $f(A)$ for a large class of functions $f$
when $A$ is Hermitian. On the other hand, it also allows to use numerical
quadrature methods to approximate higher-order Fr\'echet derivatives. We
demonstrate that in certain situations -- in particular when the derivative
order $k$ is moderate and the direction terms in the derivative have low-rank
structure -- the resulting algorithm can outperform established methods from
the literature by a large margin.


title: Part-Aware Self-Supervised Pre-Training for Person Re-Identification

abstract:
  In person re-identification (ReID), very recent researches have validated
pre-training the models on unlabelled person images is much better than on
ImageNet. However, these researches directly apply the existing self-supervised
learning (SSL) methods designed for image classification to ReID without any
adaption in the framework. These SSL methods match the outputs of local views
(e.g., red T-shirt, blue shorts) to those of the global views at the same time,
losing lots of details. In this paper, we propose a ReID-specific pre-training
method, Part-Aware Self-Supervised pre-training (PASS), which can generate
part-level features to offer fine-grained information and is more suitable for
ReID. PASS divides the images into several local areas, and the local views
randomly cropped from each area are assigned with a specific learnable [PART]
token. On the other hand, the [PART]s of all local areas are also appended to
the global views. PASS learns to match the output of the local views and global
views on the same [PART]. That is, the learned [PART] of the local views from a
local area is only matched with the corresponding [PART] learned from the
global views. As a result, each [PART] can focus on a specific local area of
the image and extracts fine-grained information of this area. Experiments show
PASS sets the new state-of-the-art performances on Market1501 and MSMT17 on
various ReID tasks, e.g., vanilla ViT-S/16 pre-trained by PASS achieves
92.2\%/90.2\%/88.5\% mAP accuracy on Market1501 for supervised/UDA/USL ReID.
Our codes are available at https://github.com/CASIA-IVA-Lab/PASS-reID.


title: Digital Speech Algorithms for Speaker De-Identification

abstract:
  The present work is based on the COST Action IC1206 for De-identification in
multimedia content. It was performed to test four algorithms of voice
modifications on a speech gender recognizer to find the degree of modification
of pitch when the speech recognizer have the probability of success equal to
the probability of failure. The purpose of this analysis is to assess the
intensity of the speech tone modification, the quality, the reversibility and
not-reversibility of the changes made.


title: A Preliminary Study on Aging Examining Online Handwriting

abstract:
  In order to develop infocommunications devices so that the capabilities of
the human brain may interact with the capabilities of any artificially
cognitive system a deeper knowledge of aging is necessary. Especially if
society does not want to exclude elder people and wants to develop automatic
systems able to help and improve the quality of life of this group of
population, healthy individuals as well as those with cognitive decline or
other pathologies. This paper tries to establish the variations in handwriting
tasks with the goal to obtain a better knowledge about aging. We present the
correlation results between several parameters extracted from online
handwriting and the age of the writers. It is based on BIOSECURID database,
which consists of 400 people that provided several biometric traits, including
online handwriting. The main idea is to identify those parameters that are more
stable and those more age dependent. One challenging topic for disease diagnose
is the differentiation between healthy and pathological aging. For this
purpose, it is necessary to be aware of handwriting parameters that are, in
general, not affected by aging and those who experiment changes, increase or
decrease their values, because of it. This paper contributes to this research
line analyzing a selected set of online handwriting parameters provided by a
healthy group of population aged from 18 to 70 years. Preliminary results show
that these parameters are not affected by aging and therefore, changes in their
values can only be attributed to motor or cognitive disorders.


title: Nonlinear Isometric Manifold Learning for Injective Normalizing Flows

abstract:
  To model manifold data using normalizing flows, we propose to employ the
isometric autoencoder to design nonlinear encodings with explicit inverses. The
isometry allows us to separate manifold learning and density estimation and
train both parts to high accuracy. Applied to the MNIST data set, the combined
approach generates high-quality images.


title: Strongly Rayleigh Processes are Tail Trivial

abstract:
  We prove that every strongly Rayleigh sequence of random variables has a
trivial tail sigma-field. This is a generalization of an earlier result of
Lyons which established tail triviality for discrete determinantal processes.
Unlike Lyons' proof, our approach is probabilistic in nature and relies on
negative dependence properties of strongly Rayleigh processes.


title:
Review of gravitational wave solutions in quadratic metric-affine
  gravity

abstract:
  In this review we consider the quadratic Metric-Affine Gauge gravity
Lagrangian, which contains all the algebraic invariants up to quadratic order
in torsion, nonmetricity and curvature. The goal will be to collect the known
exact solutions for this theory that describe the propagation of a
gravitational wave and some useful properties. We concentrate on solutions
whose connections are different from the Levi-Civita one and provide also the
extension of already known axial torsion solutions to more general Lagrangians.
At the end, we will briefly discuss colliding waves and Riemannian solutions.


title:
Dynamic Group Transformer: A General Vision Transformer Backbone with
  Dynamic Group Attention

abstract:
  Recently, Transformers have shown promising performance in various vision
tasks. To reduce the quadratic computation complexity caused by each query
attending to all keys/values, various methods have constrained the range of
attention within local regions, where each query only attends to keys/values
within a hand-crafted window. However, these hand-crafted window partition
mechanisms are data-agnostic and ignore their input content, so it is likely
that one query maybe attends to irrelevant keys/values. To address this issue,
we propose a Dynamic Group Attention (DG-Attention), which dynamically divides
all queries into multiple groups and selects the most relevant keys/values for
each group. Our DG-Attention can flexibly model more relevant dependencies
without any spatial constraint that is used in hand-crafted window based
attention. Built on the DG-Attention, we develop a general vision transformer
backbone named Dynamic Group Transformer (DGT). Extensive experiments show that
our models can outperform the state-of-the-art methods on multiple common
vision tasks, including image classification, semantic segmentation, object
detection, and instance segmentation.


title:
Drivers and challenges of internet of things diffusion in smart stores:
  A field exploration

abstract:
  The digitally disruptive environment has evolved rapidly due to the
introduction of new advancements within the field of smart applications.
Applications of one of the most prominent technologies, Internet of Things
(IoT), often appear in the retail sector, where smart services have transformed
the customer experience holistically. Presented in this paper are the findings
from an exploratory field study in the retail service sector, which drew on the
views of experienced practitioners about the smart store experience and the
associated changes. The paper presents an overview of the drivers of smart
retail service diffusion and the relevant challenges, such as the business
expectations and the heterogeneity of devices. The arising themes indicate that
IoT security is a major challenge for businesses installing IoT devices in
their journey towards smart store transformation. The paper highlights the
importance of a secure data-sharing IoT environment that respects customer
privacy as the smart experience in-store offers data-driven insights and
services. Implications for research and practice are discussed in terms of the
customer experience relevant to the identified challenges.


title:
The Excited State Dynamics of a Mutagenic Cytidine Etheno Adduct
  Investigated by Combining Time-Resolved Spectroscopy and Quantum Mechanical
  Calculations

abstract:
  Joint femtosecond fluorescence upconversion experiments and theoretical
calculations provide a hitherto unattained degree of characterization and
understanding of the mutagenic etheno adduct 3,N4-etheno-2'-deoxycytidine
($\epsilon$dC) excited state relaxation. This endogenously formed lesion is
attracting great interest because of its ubiquity in human tissues and its
highly mutagenic properties. The $\epsilon$dC fluorescence is modified with
respect to that of the canonical base dC, with a 3-fold increased lifetime and
quantum yield at neutral pH. This behavior is amplified upon protonation of the
etheno ring ($\epsilon$dCH+). Quantum mechanical calculations show that the
lowest energy state $\pi$$\pi$*1 is responsible for the fluorescence and that
the main nonradiative decay pathway to the ground state goes through an
ethene-like conical intersection, involving the out-of-plane motion of the C5
and C6 substituents. This conical intersection is lower in energy than the
$\pi$$\pi$* state ($\pi$$\pi$*1) minimum, but a sizable energy barrier explains
the increase of $\epsilon$dC and $\epsilon$dCH+ fluorescence lifetimes with
respect to that of dC.


title:
Preliminary Thesis on the First Part of the ALLEGRO CFD Benchmark
  Exercise

abstract:
  At BME (Budapest University of Technology and Economics) NTI (Institute of
Nuclear Technics), a 7 pin rod bundle test section has been built in order to
investigate the hydraulic behavior of the coolant in such design and to develop
CFD models that could properly simulate the flow conditions in the ALLEGRO
core. PIROUETTE (PIv ROd bUndlE Test faciliTy at bmE) is a test facility, which
was designed to investigate the emerging flow conditions in various nuclear
fuel assembly rod bundles. The measurement method is based on Particle Image
Velocimetry (PIV) with Matching of Index of Refractory (MIR) method. In the
test loop, it was necessary to install a flow straightener that was able to
condition the velocity field before the rod bundle. The results of CFD
simulations could be used to improve the understanding of the inlet conditions
in the rod bundle test section.


title: Improved resolvent bounds for radial potentials. II

abstract:
  We prove semiclassical resolvent estimates for the Schr{\"o}dinger operator
in R d , d $\ge$ 3, with real-valued radial potentials V $\in$ L $\infty$ (R
d). We show that if V (x) = O x --$\delta$ with $\delta$ > 4, then the
resolvent bound is of the form exp Ch -- $\delta$ $\delta$--1 log(h --1) 1
$\delta$--1 with some constant C > 0. If V (x) = O e -- C x $\alpha$ with C,
$\alpha$ > 0, we get better resolvent bounds of the form exp Ch --1 log(h --1


title:
On the Diophantine equation
  $\sigma_{2}(\overline{X}_{n})=\sigma_{n}(\overline{X}_{n})$

abstract:
  In this note we investigate the set $S(n)$ of positive integer solutions of
the title Diophantine equation. In particular, for a given $n$ we prove
boundedness of the number of solutions, give precise upper bound on the common
value of $\sigma_{2}(\overline{X}_{n})$ and $\sigma_{n}(\overline{X}_{n})$
together with the biggest value of the variable $x_{n}$ appearing in the
solution. Moreover, we enumerate all solutions for $n\leq 16$ and discuss the
set of values of $x_{n}/x_{n-1}$ over elements of $S(n)$.


title:
mwp-Analysis Improvement and Implementation: Realizing Implicit
  Computational Complexity

abstract:
  Implicit Computational Complexity (ICC) drives better understanding of
complexity classes, but it also guides the development of resources-aware
languages and static source code analyzers. Among the methods developed, the
mwp-flow analysis certifies polynomial bounds on the size of the values
manipulated by an imperative program. This result is obtained by bounding the
transitions between states instead of focusing on states in isolation, as most
static analyzers do, and is not concerned with termination or tight bounds on
values. Those differences, along with its built-in compositionality, make the
mwp-flow analysis a good target for determining how ICC-inspired techniques
diverge compared with more traditional static analysis methods. This paper's
contributions are threefold: we fine-tune the internal machinery of the
original analysis to make it tractable in practice; we extend the analysis to
function calls and leverage its machinery to compute the result of the analysis
efficiently; and we implement the resulting analysis as a lightweight tool to
automatically perform data-size analysis of C programs. This documented effort
prepares and enables the development of certified complexity analysis, by
transforming a costly analysis into a tractable program, that furthermore
decorrelates the problem of deciding if a bound exist with the problem of
computing it.


title:
An Online Semantic Mapping System for Extending and Enhancing Visual
  SLAM

abstract:
  We present a real-time semantic mapping approach for mobile vision systems
with a 2D to 3D object detection pipeline and rapid data association for
generated landmarks. Besides the semantic map enrichment the associated
detections are further introduced as semantic constraints into a simultaneous
localization and mapping (SLAM) system for pose correction purposes. This way,
we are able generate additional meaningful information that allows to achieve
higher-level tasks, while simultaneously leveraging the view-invariance of
object detections to improve the accuracy and the robustness of the odometry
estimation. We propose tracklets of locally associated object observations to
handle ambiguous and false predictions and an uncertainty-based greedy
association scheme for an accelerated processing time. Our system reaches
real-time capabilities with an average iteration duration of 65~ms and is able
to improve the pose estimation of a state-of-the-art SLAM by up to 68% on a
public dataset. Additionally, we implemented our approach as a modular ROS
package that makes it straightforward for integration in arbitrary graph-based
SLAM methods.


title:
Colloidal Synthesis of Nanoparticles: from Bimetallic to High Entropy
  Alloys

abstract:
  At the nanometric scale, the synthesis of a random alloy (i.e. without phase
segregation, whatever the composition) by chemical synthesis remains a not easy
task, even for simple binary type systems. In this context, a unique approach
based on the colloidal route is proposed enabling the synthesis of face-centred
cubic and monodisperse bimetallic, trimetallic, tetrametallic and pentametallic
nanoparticles with diameters around 5 nm as solid solutions. The Fe-Co-Ni-Pt-Ru
alloy and its subsets are considered which is a challenging task as each
element has fairly different physico-chemical properties. Nanoparticles are
prepared by temperature-assisted co-reduction of metal acetylacetonate
precursors in the presence of surfactants. It is highlighted how the
correlation between precursors' degradation temperatures and reduction
potentials values of the metal cations is the driving force to achieve a
homogenous distribution of all elements within the nanoparticles.


title:
Cosmological simulations of self-interacting Bose-Einstein condensate
  dark matter

abstract:
  Fully 3D cosmological simulations of scalar field dark matter with
self-interactions, also known as Bose-Einstein condensate dark matter, are
performed using a set of effective hydrodynamic equations. These are derived
from the non-linear Schr\"odinger equation by performing a smoothing operation
over scales larger than the de Broglie wavelength, but smaller than the
self-interaction Jeans' length. The dynamics on the de Broglie scale become an
effective thermal energy in the hydrodynamic approximation, which is assumed to
be subdominant in the initial conditions, but become important as structures
collapse and the fluid is shock-heated. The halos that form have
Navarro-Frenk-White envelopes, while the centers become cored due to the fluid
pressures (thermal + self-interaction). The core radii are mostly determined by
the self-interaction Jeans' length, even though the effective thermal energy
eventually dominates over the self-interaction energy everywhere, a result that
is insensitive to the initial smallness of the thermal energy. Scaling
relations for the simulated population of halos are compared with Milky Way
dwarf spheroidals and nearby galaxies, assuming a Burkert halo profile, and are
found to not match, also for core radii $R_c\approx 1\text{kpc}$, which has
generally been the value expected to resolve the cusp-core issue. However, the
simulations have a limited volume, and therefore a limited halo mass range,
include no baryonic physics, and use fiducial cold dark matter initial
conditions with a cut-off near the Jeans' length at $z=50$, all of which can
affect the halo properties. [Abridged]


title: Combinatorial expressions of Hopf polynomial invariants

abstract:
  In 2017 Aguiar and Ardila provided a generic way to construct polynomial
invariants of combinatorial objects using the notions of Hopf monoids and
characters of Hopf monoids. They show that it is possible to find a
combinatorial interpretation of these polynomials over negative integers using
the antipode and give a cancellation-free grouping-free formula for the
antipode on generalized permutahedra. In this work, we give a combinatorial
interpretation of these polynomials over both positive integers and negative
integers for the Hopf monoids of generalized permutahedra and hypergraphs and
for every character on these two Hopf monoids. In the case of hypergraphs, we
present two different proofs for the interpretation on negative integers. We
then deduce similar results on other combinatorial objects including graphs,
simplicial complexes and building sets.


title:
Inductive Superconducting Quantum Interference Proximity Transistor: the
  L-SQUIPT

abstract:
  The design for an inductive superconducting quantum interference proximity
transistor with enhanced performance, the L-SQUIPT, is presented and analyzed.
The interferometer is based on a double-loop structure, where each ring
comprises a superconductor-normal metal-superconductor mesoscopic Josephson
weak-link and the read-out electrode is implemented in the form of a
superconducting tunnel probe. Our design allows both to improve the coupling of
the transistor to the external magnetic field and to increase the
characteristic magnetic flux transfer functions, thereby leading to an improved
ultrasensitive quantum limited magnetometer. The L-SQUIPT behavior is analyzed
in both the dissipative and the dissipationless Josephson-like operation modes,
in the dissipative or in the dissipationless Josephson-like operation mode in
the latter case by exploiting both an inductive and a dispersive readout
scheme. The improved performance makes the L-SQUIPT promising for magnetic
field detection as well as for specific applications in quantum technology,
where a responsive dispersive magnetometry at milliKelvin temperatures is
required.


title: RC-MVSNet: Unsupervised Multi-View Stereo with Neural Rendering

abstract:
  Finding accurate correspondences among different views is the Achilles' heel
of unsupervised Multi-View Stereo (MVS). Existing methods are built upon the
assumption that corresponding pixels share similar photometric features.
However, multi-view images in real scenarios observe non-Lambertian surfaces
and experience occlusions. In this work, we propose a novel approach with
neural rendering (RC-MVSNet) to solve such ambiguity issues of correspondences
among views. Specifically, we impose a depth rendering consistency loss to
constrain the geometry features close to the object surface to alleviate
occlusions. Concurrently, we introduce a reference view synthesis loss to
generate consistent supervision, even for non-Lambertian surfaces. Extensive
experiments on DTU and Tanks\&Temples benchmarks demonstrate that our RC-MVSNet
approach achieves state-of-the-art performance over unsupervised MVS frameworks
and competitive performance to many supervised methods.The trained models and
code will be released at https://github.com/Boese0601/RC-MVSNet.


title:
Tidal dissipation due to inertial waves can explain the circularization
  periods of solar-type binaries

abstract:
  Tidal dissipation is responsible for circularizing the orbits and
synchronizing the spins of solar-type close binary stars, but the mechanisms
responsible are not fully understood. Previous work has indicated that
significant enhancements to the theoretically-predicted tidal dissipation rates
are required to explain the observed circularization periods
($P_\mathrm{circ}$) in various stellar populations, and their evolution with
age. This was based partly on the common belief that the dominant mechanism of
tidal dissipation in solar-type stars is turbulent viscosity acting on
equilibrium tides in convective envelopes. In this paper we study tidal
dissipation in both convection and radiation zones of rotating solar-type stars
following their evolution. We study equilibrium tide dissipation, incorporating
a frequency-dependent effective viscosity motivated by the latest
hydrodynamical simulations, and inertial wave (dynamical tide) dissipation,
adopting a frequency-averaged formalism that accounts for the realistic
structure of the star. We demonstrate that the observed binary circularization
periods can be explained by inertial wave (dynamical tide) dissipation in
convective envelopes. This mechanism is particularly efficient during pre-main
sequence phases, but it also operates on the main sequence if the spin is close
to synchronism. The predicted $P_\mathrm{circ}$ due to this mechanism increases
with main-sequence age in accord with observations. We also demonstrate that
both equilibrium tide and internal gravity wave dissipation are unlikely to
explain the observed $P_\mathrm{circ}$, even during the pre-main sequence,
based on our best current understanding of these mechanisms. Finally, we
advocate more realistic dynamical studies of stellar populations that employ
tidal dissipation due to inertial waves.


title:
Efficient and Accurate Hyperspectral Pansharpening Using 3D VolumeNet
  and 2.5D Texture Transfer

abstract:
  Recently, convolutional neural networks (CNN) have obtained promising results
in single-image SR for hyperspectral pansharpening. However, enhancing CNNs'
representation ability with fewer parameters and a shorter prediction time is a
challenging and critical task. In this paper, we propose a novel multi-spectral
image fusion method using a combination of the previously proposed 3D CNN model
VolumeNet and 2.5D texture transfer method using other modality high resolution
(HR) images. Since a multi-spectral (MS) image consists of several bands and
each band is a 2D image slice, MS images can be seen as 3D data. Thus, we use
the previously proposed VolumeNet to fuse HR panchromatic (PAN) images and
bicubic interpolated MS images. Because the proposed 3D VolumeNet can
effectively improve the accuracy by expanding the receptive field of the model,
and due to its lightweight structure, we can achieve better performance against
the existing method without purchasing a large number of remote sensing images
for training. In addition, VolumeNet can restore the high-frequency information
lost in the HR MR image as much as possible, reducing the difficulty of feature
extraction in the following step: 2.5D texture transfer. As one of the latest
technologies, deep learning-based texture transfer has been demonstrated to
effectively and efficiently improve the visual performance and quality
evaluation indicators of image reconstruction. Different from the texture
transfer processing of RGB image, we use HR PAN images as the reference images
and perform texture transfer for each frequency band of MS images, which is
named 2.5D texture transfer. The experimental results show that the proposed
method outperforms the existing methods in terms of objective accuracy
assessment, method efficiency, and visual subjective evaluation.


title:
EdgeFormer: Improving Light-weight ConvNets by Learning from Vision
  Transformers

abstract:
  Recently, vision transformers started to show impressive results which
outperform large convolution based models significantly. However, in the area
of small models for mobile or resource constrained devices, ConvNet still has
its own advantages in both performance and model complexity. We propose
EdgeFormer, a pure ConvNet based backbone model that further strengthens these
advantages by fusing the merits of vision transformers into ConvNets.
Specifically, we propose global circular convolution (GCC) with position
embeddings, a light-weight convolution op which boasts a global receptive field
while producing location sensitive features as in local convolutions. We
combine the GCCs and squeeze-exictation ops to form a meta-former like model
block, which further has the attention mechanism like transformers. The
aforementioned block can be used in plug-and-play manner to replace relevant
blocks in ConvNets or transformers. Experiment results show that the proposed
EdgeFormer achieves better performance than popular light-weight ConvNets and
vision transformer based models in common vision tasks and datasets, while
having fewer parameters and faster inference speed. For classification on
ImageNet-1k, EdgeFormer achieves 78.6% top-1 accuracy with about 5.0 million
parameters, saving 11% parameters and 13% computational cost but gaining 0.2%
higher accuracy and 23% faster inference speed (on ARM based Rockchip RK3288)
compared with MobileViT, and uses only 0.5 times parameters but gaining 2.7%
accuracy compared with DeIT. On MS-COCO object detection and PASCAL VOC
segmentation tasks, EdgeFormer also shows better performance. Code is available
at https://github.com/hkzhang91/EdgeFormer


title: Can the "shadow" of graphene band clarify its flatness?

abstract:
  Graphene band renormalization at the proximity of the van Hove singularity
(VHS) has been investigated by angle-resolved photoemission spectroscopy
(ARPES) on the Li-doped quasi-freestanding graphene on the cobalt (0001)
surface. The absence of graphene band hybridization with the substrate, the
doping contribution well represented by a rigid energy shift and the excellent
electron-electron interaction screening ensured by the metallic substrate offer
a privileged point of view for such investigation. A clear ARPES signal is
detected along the M point of the graphene Brillouin zone, giving rise to an
apparent flattened band. By simulating the graphene spectral function from the
density functional theory calculated bands, we demonstrate that the
photoemission signal along the M point originates from the "shadow" of the
spectral function of the unoccupied band above the Fermi level. Such
interpretation put forward the absence of any additional strong correlation
effects at the VHS proximity, reconciling the mean field description of the
graphene band structure even in the highly doped scenario.


title:
Homologous compact major blowout-eruption solar flares and their
  production of broad CMEs

abstract:
  We analyze the formation mechanism of three homologous broad coronal mass
ejections (CMEs) resulting from a series of solar blowout-eruption flares with
successively increasing intensities (M2.0, M2.6, and X1.0). The flares
originated from active region NOAA 12017 during 2014 March 28-29 within an
interval of approximately 24 hr. Coronal magnetic field modeling based on
nonlinear-force-free-field extrapolation helps to identify low-lying closed
bipolar loops within the flaring region enclosing magnetic flux ropes. We
obtain a double flux rope system under closed bipolar fields for the all the
events. The sequential eruption of the flux ropes led to homologous flares,
each followed by a CME. Each of the three CMEs formed from the eruptions
gradually attain a large angular width, after expanding from the compact
eruption-source site. We find these eruptions and CMEs to be consistent with
the 'magnetic-arch-blowout' scenario: each compact-flare blowout eruption was
seated in one foot of a far-reaching magnetic arch, exploded up the encasing
leg of the arch, and blew out the arch to make a broad CME.


title:
Small-time local controllability of the bilinear Schr\"odinger equation,
  despite a quadratic obstruction, thanks to a cubic term

abstract:
  We consider a 1D linear Schr{\"o}dinger equation, on a bounded interval, with
Dirichlet boundary conditions and bilinear control. We study its
controllability around the ground state when the linearized system is not
controllable. More precisely, we study to what extent the nonlinear terms of
the expansion can recover the directions lost at the first order.In previous
works, for any positive integer $n$, assumptions have been formulated under
which the quadratic term induces a drift in the nonlinear dynamics, quantified
by the $H^{-n}$ norm of the control. This drift is an obstruction to the
small-time local controllability (STLC) under a smallness assumption on the
controls in regular spaces. In this paper, we prove that for controls small in
less regular spaces, the cubic term can recover the controllability lost at the
linear level, despite the quadratic drift. The proof is inspired by Sussman's
method to prove the sufficiency of the $\mathcal{S}(\theta)$ condition for STLC
of ODEs. However, it uses a different global strategy relying on a new concept
of tangent vector, better adapted to the infinite-dimensional setting of PDEs.
Given a target, we first realize the expected motion along the lost direction
by using control variations for which the cubic term dominates the quadratic
one. Then, we correct the other components exactly, by using a STLC in
projection result, with simultaneous estimates of weak norms of the control.
These estimates ensure that the new error along the lost direction is
negligible, and we conclude with the Brouwer fixed point theorem.


title: Probing the Anisotropic Universe with Gravitational Waves

abstract:
  Modern cosmology rests on the working assumption that the Universe is
isotropic and homogeneous at large scales. Here, we document a number of
anomalous observations pointing to an anisotropic Universe in a direction
consistent with the CMB dipole. If physical, this should be confirmed by any
cosmological probe with sufficient data quality. To that end, we perform the
first hemispherical decomposition of bright and dark standard siren $H_0$
determinations, finding that the maximum of the $H_0$ posterior is larger in
the CMB dipole direction for dark sirens, but including the bright siren
posterior reverses this conclusion. As may be expected, the limited number of
GW observations is consistent with an isotropic Universe, but this can change
going forward. It is imperative to repeat these tests as GW sources become more
numerous and better localised.


title:
The Internet of Things enabling communication technologies, applications
  and challenges: a survey

abstract:
  Recently, the IoT has gained great importance, which results in the evolution
of communication technologies to meet the needs of different IoT applications.
In addition, several domains integrate the IoT technologies in several
applications of our professional life and daily activities. However, the IoT
still presents many challenges and issues. This article describes in details
the emerging communication technologies used in the IoT networks and enumerates
the common domains of their application. It also describes the main challenges
of the IoT and its use in order to exploit its advantages at most.


title:
Betweenness Approximation for Hypernetwork Dismantling with Hypergraph
  Neural Network

abstract:
  The purpose of Network Dismantling (ND) is to find an optimal set of nodes
and removing these nodes can greatly decrease the network connectivity.
However, current dismantling methods are mainly focus on traditional simple
network which only consider the pairwise interaction between two nodes, while
the hypernetwork, which can model higher order groupwise relation among
arbitrary nodes, is more suitable for modeling real world. Due to the
structural difference between simple network and hypernetwork, current
dismantling methods cannot be directly applied to hypernetwork dismantling.
Although some centrality measures in hypernetwork can be used for hypernetwork
dismantling, they face the problem of banlancing effect and efficiency.
Therefore, in this paper, we propose a novel HyperNetwork Dismantling methods
based on hypergraph neural network, called HND. Specifically, our method first
generates plenty of node ranking samples with the help of synthetic
hypernetwork generator. Then, a node betweenness approximation model in
hypernetwork is built based on hypergraph neural network. And this model is
trained on those ranking samples generated in previous step until convergence.
Finally, the well-trained model is utilized to approximate the nodes'
betweenness in real world hypernetworks and further used for dismantling. To
confirm the effectiveness of our method, we conduct extensive experiments on
five real world hypernetworks. The experimental results demostrate that the HND
outperforms various baselines.


title:
Enhancing Door Detection for Autonomous Mobile Robots with
  Environment-Specific Data Collection

abstract:
  Door detection represents a fundamental capability for autonomous mobile
robots employed in tasks involving indoor navigation. Recognizing the presence
of a door and its status (open or closed) can induce a remarkable impact on the
navigation performance, especially for dynamic settings where doors can enable
or disable passages, hence changing the actual topology of the map. In this
work, we address the problem of building a door detector module for an
autonomous mobile robot deployed in a long-term scenario, namely operating in
the same environment for a long time, thus observing the same set of doors from
different points of view. First, we show how the mainstream approach for door
detection, based on object recognition, falls short in considering the
constrained perception setup typical of a mobile robot. Hence, we devise a
method to build a dataset of images taken from a robot's perspective and we
exploit it to obtain a door detector based on an established deep-learning
object-recognition method. We then exploit the long-term assumption of our
scenario to qualify the model on the robot working environment via fine-tuning
with additional images acquired in the deployment environment. Our experimental
analysis shows how this method can achieve good performance and highlights a
trade-off between costs and benefits of the fine-tuning approach.


title: Data fusion of distance sampling and capture-recapture data

abstract:
  Species distribution models (SDMs) are increasingly used in ecology,
biogeography, and wildlife management to learn about the species-habitat
relationships and abundance across space and time. Distance sampling (DS) and
capture-recapture (CR) are two widely collected data types to learn about
species-habitat relationships and abundance; still, they are seldomly used in
SDMs due to the lack of spatial coverage. However, data fusion of the two data
sources can increase spatial coverage, which can reduce parameter uncertainty
and make predictions more accurate, and therefore, can be used for species
distribution modeling. We developed a model-based approach for data fusion of
DS and CR data. Our modeling approach accounts for two common missing data
issues: 1) missing individuals that are missing not at random (MNAR) and 2)
partially missing location information. Using a simulation experiment, we
evaluated the performance of our modeling approach and compared it to existing
approaches that use ad-hoc methods to account for missing data issues. Our
results show that our approach provides unbiased parameter estimates with
increased efficiency compared to the existing approaches. We demonstrated our
approach using data collected for Grasshopper Sparrows (Ammodramus savannarum)
in north-eastern Kansas, USA.


title:
Computing roadmaps in unbounded smooth real algebraic sets I:
  connectivity results

abstract:
  Answering connectivity queries in real algebraic sets is a fundamental
problem in effective real algebraic geometry that finds many applications in
e.g. robotics where motion planning issues are topical. This computational
problem is tackled through the computation of so-called roadmaps which are real
algebraic subsets of the set V under study, of dimension at most one, and which
have a connected intersection with all semi-algebraically connected components
of V. Algorithms for computing roadmaps rely on statements establishing
connectivity properties of some well-chosen subsets of V , assuming that V is
bounded. In this paper, we extend such connectivity statements by dropping the
boundedness assumption on V. This exploits properties of so-called generalized
polar varieties, which are critical loci of V for some well-chosen polynomial
maps.


title: Generative Cooperative Learning for Unsupervised Video Anomaly Detection

abstract:
  Video anomaly detection is well investigated in weakly-supervised and
one-class classification (OCC) settings. However, unsupervised video anomaly
detection methods are quite sparse, likely because anomalies are less frequent
in occurrence and usually not well-defined, which when coupled with the absence
of ground truth supervision, could adversely affect the performance of the
learning algorithms. This problem is challenging yet rewarding as it can
completely eradicate the costs of obtaining laborious annotations and enable
such systems to be deployed without human intervention. To this end, we propose
a novel unsupervised Generative Cooperative Learning (GCL) approach for video
anomaly detection that exploits the low frequency of anomalies towards building
a cross-supervision between a generator and a discriminator. In essence, both
networks get trained in a cooperative fashion, thereby allowing unsupervised
learning. We conduct extensive experiments on two large-scale video anomaly
detection datasets, UCF crime, and ShanghaiTech. Consistent improvement over
the existing state-of-the-art unsupervised and OCC methods corroborate the
effectiveness of our approach.


title: Reaching Efficient Byzantine Agreements in Bipartite Networks

abstract:
  For reaching efficient deterministic synchronous Byzantine agreement upon
partially connected networks, the traditional broadcast primitive is extended
and integrated with a general framework. With this, the Byzantine agreement is
extended to fully connected bipartite networks and some bipartite
bounded-degree networks. The complexity of the Byzantine agreement is lowered
and optimized with the so-called Byzantine-levers under a general system
structure. Some bipartite simulation of the butterfly networks and some finer
properties of bipartite bounded-degree networks are also provided for building
efficient incomplete Byzantine agreement under the same system structure. It
shows that efficient real-world Byzantine agreement systems can be built with
the provided algorithms with sufficiently high system assumption coverage.
Meanwhile, the proposed bipartite solutions can improve the dependability of
the systems in some open, heterogeneous, and even antagonistic environments.


title: Dark Matter and Muon $g-2$ Anomaly via Scale Symmetry Breaking

abstract:
  The Standard Model (SM) without the Higgs mass term is scale invariant.
Gildener and Weinberg generalized the scale invariant standard model (SISM) by
including the multiplication of scalars in quartic forms. They pointed out that
along the flat direction only one scalar -- called the scalon -- is classically
massless and all other scalars are massive. Here we choose a SISM with one
scalon and one heavy scalar and extend that further respecting the scale
invariance by a vector-like lepton (VLL). By an appropriate choice of the flat
direction, the heavy scalar enjoys the $\mathbb{Z}_2$ symmetry and is assumed
as DM particle. The scalon connects the visible and dark sector via the
Higgs-portal and by interacting with both the muon lepton and the VLL. The VLL
is charged under $U(1)_Y$ and interacts with $\gamma/Z$ bosons. We show that
the model correctly accounts for the observed dark matter (DM) relic abundance
in the universe, while naturally evading the current and future bounds from
direct detection (DD) experiments. Moreover, the model is capable to explain
the $(g-2)_\mu$ anomaly observed in Fermilab. We also show a feature in SISM
scenarios which is not present in other Higgs-portal models; despite having the
Higgs-portal term $|H|^2 s^2$ ($s$ being the scalon) in SISM, the effective
potential after the electroweak symmetry breaking lacks an important expected
vertex $h s^2$. This property immediately forbids the tree-level invisible
Higgs decay $h\to ss$ and the one-loop Higgs decay $h\to \mu^+ \mu^-$.


title:
Few-Shot Traffic Prediction with Graph Networks using Locale as
  Relational Inductive Biases

abstract:
  Accurate short-term traffic prediction plays a pivotal role in various smart
mobility operation and management systems. Currently, most of the
state-of-the-art prediction models are based on graph neural networks (GNNs),
and the required training samples are proportional to the size of the traffic
network. In many cities, the available amount of traffic data is substantially
below the minimum requirement due to the data collection expense. It is still
an open question to develop traffic prediction models with a small size of
training data on large-scale networks. We notice that the traffic states of a
node for the near future only depend on the traffic states of its localized
neighborhoods, which can be represented using the graph relational inductive
biases. In view of this, this paper develops a graph network (GN)-based deep
learning model LocaleGn that depicts the traffic dynamics using localized data
aggregating and updating functions, as well as the node-wise recurrent neural
networks. LocaleGn is a light-weighted model designed for training on few
samples without over-fitting, and hence it can solve the problem of few-shot
traffic prediction. The proposed model is examined on predicting both traffic
speed and flow with six datasets, and the experimental results demonstrate that
LocaleGn outperforms existing state-of-the-art baseline models. It is also
demonstrated that the learned knowledge from LocaleGn can be transferred across
cities. The research outcomes can help to develop light-weighted traffic
prediction systems, especially for cities lacking in historically archived
traffic data.


title:
GaitStrip: Gait Recognition via Effective Strip-based Feature
  Representations and Multi-Level Framework

abstract:
  Many gait recognition methods first partition the human gait into N-parts and
then combine them to establish part-based feature representations. Their gait
recognition performance is often affected by partitioning strategies, which are
empirically chosen in different datasets. However, we observe that strips as
the basic component of parts are agnostic against different partitioning
strategies. Motivated by this observation, we present a strip-based multi-level
gait recognition network, named GaitStrip, to extract comprehensive gait
information at different levels. To be specific, our high-level branch explores
the context of gait sequences and our low-level one focuses on detailed posture
changes. We introduce a novel StriP-Based feature extractor (SPB) to learn the
strip-based feature representations by directly taking each strip of the human
body as the basic unit. Moreover, we propose a novel multi-branch structure,
called Enhanced Convolution Module (ECM), to extract different representations
of gaits. ECM consists of the Spatial-Temporal feature extractor (ST), the
Frame-Level feature extractor (FL) and SPB, and has two obvious advantages:
First, each branch focuses on a specific representation, which can be used to
improve the robustness of the network. Specifically, ST aims to extract
spatial-temporal features of gait sequences, while FL is used to generate the
feature representation of each frame. Second, the parameters of the ECM can be
reduced in test by introducing a structural re-parameterization technique.
Extensive experimental results demonstrate that our GaitStrip achieves
state-of-the-art performance in both normal walking and complex conditions.


title: Comparing lifetime learning methods for morphologically evolving robots

abstract:
  Evolving morphologies and controllers of robots simultaneously leads to a
problem: Even if the parents have well-matching bodies and brains, the
stochastic recombination can break this match and cause a body-brain mismatch
in their offspring. We argue that this can be mitigated by having newborn
robots perform a learning process that optimizes their inherited brain quickly
after birth. We compare three different algorithms for doing this. To this end,
we consider three algorithmic properties, efficiency, efficacy, and the
sensitivity to differences in the morphologies of the robots that run the
learning process.


title:
Complete affine manifolds with Anosov holonomy groups II: partially
  hyperbolic holonomy and cohomological dimensions

abstract:
  Let $N$ be a complete affine manifold $A^n/\Gamma$ of dimension $n$ where
$\Gamma$ is an affine transformation group and $K(\Gamma, 1)$ is realized as a
finite CW-complex. $N$ has a partially hyperbolic holonomy group if the tangent
bundle pulled over the unit tangent bundle over a sufficiently large compact
part splits into expanding, neutral, and contracting subbundles along the
geodesic flow. We show that if the holonomy group is partially hyperbolic of
index $k$, $k < n/2$, then $\mathrm{cd}(\Gamma) \leq n-k$. Moreover, if a
finitely-presented affine group $\Gamma$ acts on $A^n$ properly discontinuously
and freely with the $k$-Anosov linear group for $k \leq n/2$, then
$\mathrm{cd}(\Gamma) \leq n-k$. Also, there exists a compact collection of
$n-k$-dimensional affine subspaces where $\Gamma$ acts on. The techniques here
are mostly from coarse geometry.


title:
A Dynamic Hierarchical Framework for IoT-assisted Metaverse
  Synchronization

abstract:
  Metaverse has recently attracted much attention from both academia and
industry. Virtual services, ranging from virtual driver training to online
route optimization for smart goods delivery, are emerging in the Metaverse. To
make the human experience of virtual life more real, digital twins (DTs),
namely digital replicas of physical objects, are key enablers. However, DT
status may not always accurately reflect that of its real-world twin because
the latter may be subject to changes with time. As such, it is necessary to
synchronize a DT with its physical counterpart to ensure that its status is
accurate for virtual businesses in the Metaverse. In this paper, we propose a
dynamic hierarchical framework in which a group of IoT devices is incentivized
to sense and collect physical objects' status information collectively so as to
assists virtual service providers (VSPs) in synchronizing DTs. Based on the
collected sensing data and the value decay rate of the DTs, the VSPs can
determine synchronization intensities to maximize their payoffs. In our
proposed dynamic hierarchical framework, the lower-level evolutionary game
captures the VSPs selection by the IoT device population, and the upper-level
differential game captures the VSPs payoffs, which are affected by the
synchronization strategy, IoT devices selections, and the DTs value status,
given VSPs are simultaneous decision makers. We further consider the case in
which some VSPs are first movers and extend it as a Stackelberg differential
game. We theoretically and experimentally show that the equilibrium to the
lower-level game exists and is evolutionarily robust, and provide a sensitivity
analysis with respect to various system parameters. Experiments show that the
proposed dynamic hierarchical game outperform the baseline.


title: On Generalizing Beyond Domains in Cross-Domain Continual Learning

abstract:
  Humans have the ability to accumulate knowledge of new tasks in varying
conditions, but deep neural networks often suffer from catastrophic forgetting
of previously learned knowledge after learning a new task. Many recent methods
focus on preventing catastrophic forgetting under the assumption of train and
test data following similar distributions. In this work, we consider a more
realistic scenario of continual learning under domain shifts where the model
must generalize its inference to an unseen domain. To this end, we encourage
learning semantically meaningful features by equipping the classifier with
class similarity metrics as learning parameters which are obtained through
Mahalanobis similarity computations. Learning of the backbone representation
along with these extra parameters is done seamlessly in an end-to-end manner.
In addition, we propose an approach based on the exponential moving average of
the parameters for better knowledge distillation. We demonstrate that, to a
great extent, existing continual learning algorithms fail to handle the
forgetting issue under multiple distributions, while our proposed approach
learns new tasks under domain shift with accuracy boosts up to 10% on
challenging datasets such as DomainNet and OfficeHome.


title:
Universal Prototype Transport for Zero-Shot Action Recognition and
  Localization

abstract:
  This work addresses the problem of recognizing action categories in videos
for which no training examples are available. The current state-of-the-art
enables such a zero-shot recognition by learning universal mappings from videos
to a shared semantic space, either trained on large-scale seen actions or on
objects. While effective, we find that universal action and object mappings are
biased to their seen categories. Such biases are further amplified due to
biases between seen and unseen categories in the semantic space. The
compounding biases result in many unseen action categories simply never being
selected during inference, hampering zero-shot progress. We seek to address
this limitation and introduce universal prototype transport for zero-shot
action recognition. The main idea is to re-position the semantic prototypes of
unseen actions through transduction, i.e. by using the distribution of the
unlabelled test set. For universal action models, we first seek to find a
hyperspherical optimal transport mapping from unseen action prototypes to the
set of all projected test videos. We then define a target prototype for each
unseen action as the weighted Fr\'echet mean over the transport couplings.
Equipped with a target prototype, we propose to re-position unseen action
prototypes along the geodesic spanned by the original and target prototypes,
acting as a form of semantic regularization. For universal object models, we
outline a variant that defines target prototypes based on an optimal transport
between unseen action prototypes and semantic object prototypes. Empirically,
we show that universal prototype transport diminishes the biased selection of
unseen action prototypes and boosts both universal action and object models,
resulting in state-of-the-art performance for zero-shot classification and
spatio-temporal localization.


title:
GaitEdge: Beyond Plain End-to-end Gait Recognition for Better
  Practicality

abstract:
  Gait is one of the most promising biometrics to identify individuals at a
long distance. Although most previous methods have focused on recognizing the
silhouettes, several end-to-end methods that extract gait features directly
from RGB images perform better. However, we argue that these end-to-end methods
inevitably suffer from the gait-unrelated noises, i.e., low-level texture and
colorful information. Experimentally, we design both the cross-domain
evaluation and visualization to stand for this view. In this work, we propose a
novel end-to-end framework named GaitEdge which can effectively block
gait-unrelated information and release end-to-end training potential.
Specifically, GaitEdge synthesizes the output of the pedestrian segmentation
network and then feeds it to the subsequent recognition network, where the
synthetic silhouettes consist of trainable edges of bodies and fixed interiors
to limit the information that the recognition network receives. Besides,
GaitAlign for aligning silhouettes is embedded into the GaitEdge without loss
of differentiability. Experimental results on CASIA-B and our newly built
TTG-200 indicate that GaitEdge significantly outperforms the previous methods
and provides a more practical end-to-end paradigm for blocking RGB noises
effectively. All the source code will be released.


title:
Characterization of Low Surface Brightness structures in annotated deep
  images

abstract:
  The characterization of Low Surface Brightness (LSB) stellar structures
around galaxies such as tidal debris of on-going or past collisions is
essential to constrain models of galactic evolution. Our goal is to obtain
quantitative measurements of LSB structures identified in deep images of
samples consisting of hundreds of galaxies. We developed an online annotation
tool that enables contributors to delineate the shapes of diffuse extended
stellar structures, as well as artefacts or foreground structures. All
parameters are automatically stored in a database which may be queried to
retrieve quantitative measurements. We annotated LSB structures around 352
nearby massive galaxies with deep images obtained with the CFHT as part of two
large programs: MATLAS and UNIONS/CFIS. Each LSB structure was delineated and
labeled according to its likely nature: stellar shells, streams associated to a
disrupted satellite, tails formed in major mergers, ghost reflections or
cirrus. From our database containing 8441 annotations, the area, size, median
surface brightness and distance to the host of 228 structures were computed.
The results confirm the fact that tidal structures defined as streams are
thinner than tails, as expected by numerical simulations. In addition, tidal
tails appear to exhibit a higher surface brightness than streams (by about 1
mag), which may be related to different survival times for the two types of
collisional debris. We did not detect any tidal feature fainter than 27.5
mag.arcsec$^{-2}$, while the nominal surface brightness limits of our surveys
range between 28.3 and 29 mag.arcsec$^{-2}$, a difference that needs to be
taken into account when estimating the sensitivity of future surveys to
identify LSB structures. Our annotation database of observed LSB structures may
be used for quantitative analysis and as a training set for machine learning
algorithms (abbreviated).


title:
Recent Formation of a Spiral Disk Hosting Progenitor Globular Clusters
  at the center of the Perseus Brightest Cluster Galaxy: I. Spiral Disk

abstract:
  We address the nature and origin of a spiral disk at the center of NGC 1275,
the giant elliptical galaxy at the center of the Perseus cluster, that spans a
radius of $\sim$$5\,\rm kpc$. By comparing stellar absorption lines measured in
long-slit optical spectra with synthetic spectra for single stellar
populations, we find that fitting of these lines requires two stellar
populations: (i) a very young population that peaks in radial velocity at $\pm
250 {\rm \, km \, s^{-1}}$ of the systemic velocity within a radius of
$\sim$$720\,\rm pc$ of the nucleus, a $1\,\sigma$ velocity dispersion
significantly lower than $140 {\rm \, km \, s^{-1}}$, and an age of $0.15 \pm
0.05 \rm\,Gyr$; and (ii) a very old population having a constant radial
velocity with a radius corresponding to the systemic velocity, a much broader
velocity dispersion of $\sim$$250 {\rm \, km \, s^{-1}}$, and an age of around
$10\,\rm Gyr$. We attribute the former to a post-starburst population
associated with the spiral disk, and the latter to the main stellar body of NGC
1275 along the same sight line. If the spiral disk is the remnant of a
cannibalized galaxy, then its progenitor would have had to retain an enormous
amount of gas in the face of intensive ram-pressure stripping so as to form a
total initial mass in stars of $\sim 3 \times 10^9 \,M_\odot$. More likely, the
central spiral originally comprised a gaseous body accreted over the distant
past from a residual cooling flow, before experiencing a starburst
$\sim$$0.15\,\rm Gyr$ ago to form its stellar body.


title: Computational Benchmarks with Optimal Multilevel Argyris FEM

abstract:
  The main drawback for the application of the conforming Argyris FEM is the
labourious implementation on the one hand and the low convergence rates on the
other. If no appropriate adaptive meshes are utilised, only the convergence
rate caused by corner singularities [Blum and Rannacher, 1980], far below the
approximation order for smooth functions, can be achieved. The fine
approximation with the Argyris FEM produces high-dimensional linear systems and
for a long time an optimal preconditioned scheme was not available for
unstructured grids. This paper presents numerical benchmarks to confirm that
the adaptive multilevel solver for the hierarchical Argyris FEM from
[Carstensen and Hu, 2021] is in fact highly efficient and of linear time
complexity. Moreover, the very first display of optimal convergence rates in
practically relevant benchmarks with corner singularities and general boundary
conditions lead to the rehabilitation of the Argyris finite element from the
computational perspective.


title:
Adaptations to a geomagnetic field interpolation method in Southern
  Africa

abstract:
  Space weather and its impact on infrastructure presents a clear risk in the
modern era, as evidenced by the adverse effects of geomagnetically induced
currents (GICs) in power networks. To model GICs, ground-based geomagnetic
field (B-field) measurements are critical and need to be available in the
region of interest. A challenge globally lies in the sparse distribution of
magnetometer arrays, which are seldom located near critical power network
nodes. Interpolation of the geomagnetic field (B-field) is often needed, with
the spherical elementary current system (SECS) approach developed for
high-latitude regions favoured. We adapt this interpolation scheme to include
low-cost variometers to interpolate dB/dt directly and increase interpolation
accuracy. A further adaptation to the scheme is to physically represent the
mid-latitude context where most power networks and pipelines lie. The driving
current systems in these regions differ from their high-latitude counterparts.
Using a physics-consistent mid-latitude version of SECS, we show why previous
implementations in Southern Africa are incorrect but still result in useful
interpolation. The scope of these adaptations not only has direct application
to research in general, but also to utilities, where effective low-cost
instrumentation can be used to improve GIC modelling accuracy.


title: Universality of linear perturbations in SU($N$)-natural inflation

abstract:
  We prove the universality of predictions for linear perturbations from the
entire class of models of inflation driven by a pseudo-scalar field coupled to
an SU($N$) gauge boson, where SU($2$) subgroups in the SU($N$) crossed with the
background spatial SO($3$) spontaneously break into a single SO($3$). The
effect of which SU($2$) subgroup in SU($N$) acquires a VEV through spontaneous
symmetry breaking can be quantified by a single parameter $\lambda$, which
always appears in combination with the gauge coupling constant $g$. In the
linear perturbations, as well as the background system, the same dynamics and
predictions as in the chromo-natural inflation hold for its SU($N$) extension
by replacing $g \to g\lambda$. The latter models thereby draw the same
prediction curve on the $n_s$-$r$ plane as the former at the tree level as long
as $g \lambda$ stays constant during inflation. We briefly discuss possible
transitions from one value of $\lambda$ to another during inflation and the
observational prospects.


title: Contrastive Conditional Neural Processes

abstract:
  Conditional Neural Processes~(CNPs) bridge neural networks with probabilistic
inference to approximate functions of Stochastic Processes under meta-learning
settings. Given a batch of non-{\it i.i.d} function instantiations, CNPs are
jointly optimized for in-instantiation observation prediction and
cross-instantiation meta-representation adaptation within a generative
reconstruction pipeline. There can be a challenge in tying together such two
targets when the distribution of function observations scales to
high-dimensional and noisy spaces. Instead, noise contrastive estimation might
be able to provide more robust representations by learning distributional
matching objectives to combat such inherent limitation of generative models. In
light of this, we propose to equip CNPs by 1) aligning prediction with encoded
ground-truth observation, and 2) decoupling meta-representation adaptation from
generative reconstruction. Specifically, two auxiliary contrastive branches are
set up hierarchically, namely in-instantiation temporal contrastive
learning~({\tt TCL}) and cross-instantiation function contrastive
learning~({\tt FCL}), to facilitate local predictive alignment and global
function consistency, respectively. We empirically show that {\tt TCL} captures
high-level abstraction of observations, whereas {\tt FCL} helps identify
underlying functions, which in turn provides more efficient representations.
Our model outperforms other CNPs variants when evaluating function distribution
reconstruction and parameter identification across 1D, 2D and high-dimensional
time-series.


title: Online Weak-form Sparse Identification of Partial Differential Equations

abstract:
  This paper presents an online algorithm for identification of partial
differential equations (PDEs) based on the weak-form sparse identification of
nonlinear dynamics algorithm (WSINDy). The algorithm is online in a sense that
if performs the identification task by processing solution snapshots that
arrive sequentially. The core of the method combines a weak-form discretization
of candidate PDEs with an online proximal gradient descent approach to the
sparse regression problem. In particular, we do not regularize the
$\ell_0$-pseudo-norm, instead finding that directly applying its proximal
operator (which corresponds to a hard thresholding) leads to efficient online
system identification from noisy data. We demonstrate the success of the method
on the Kuramoto-Sivashinsky equation, the nonlinear wave equation with
time-varying wavespeed, and the linear wave equation, in one, two, and three
spatial dimensions, respectively. In particular, our examples show that the
method is capable of identifying and tracking systems with coefficients that
vary abruptly in time, and offers a streaming alternative to problems in higher
dimensions.


title:
Human Biometric Signals Monitoring based on WiFi Channel State
  Information using Deep Learning

abstract:
  In this paper, we first present a single-input, multiple-output convolutional
neural network that can estimate both heart rate and respiration rate
simultaneously by exploiting the underlying link between heart rate and
respiration rate. The inputs to the neural network are the amplitude and phase
of channel state information collected by a pair of WiFi devices. Our
WiFi-based technique addresses privacy concerns and is adaptable to a variety
of settings. This system overall accuracy for the heart and respiration rate
estimation can reach 99.109% and 98.581%, respectively. Furthermore, we
developed and analyzed two deep learning-based neural network classification
algorithms for categorizing four types of sleep stages: wake, rapid eye
movement (REM) sleep, non-rapid eye movement (NREM) light sleep, and NREM deep
sleep. This system overall classification accuracy can reach 95.925%


title: End-to-end Multiple Instance Learning with Gradient Accumulation

abstract:
  Being able to learn on weakly labeled data, and provide interpretability, are
two of the main reasons why attention-based deep multiple instance learning
(ABMIL) methods have become particularly popular for classification of
histopathological images. Such image data usually come in the form of
gigapixel-sized whole-slide-images (WSI) that are cropped into smaller patches
(instances). However, the sheer size of the data makes training of ABMIL models
challenging. All the instances from one WSI cannot be processed at once by
conventional GPUs. Existing solutions compromise training by relying on
pre-trained models, strategic sampling or selection of instances, or
self-supervised learning. We propose a training strategy based on gradient
accumulation that enables direct end-to-end training of ABMIL models without
being limited by GPU memory. We conduct experiments on both QMNIST and
Imagenette to investigate the performance and training time, and compare with
the conventional memory-expensive baseline and a recent sampled-based approach.
This memory-efficient approach, although slower, reaches performance
indistinguishable from the memory-expensive baseline.


title: Predictive and Contrastive: Dual-Auxiliary Learning for Recommendation

abstract:
  Self-supervised learning (SSL) recently has achieved outstanding success on
recommendation. By setting up an auxiliary task (either predictive or
contrastive), SSL can discover supervisory signals from the raw data without
human annotation, which greatly mitigates the problem of sparse user-item
interactions. However, most SSL-based recommendation models rely on
general-purpose auxiliary tasks, e.g., maximizing correspondence between node
representations learned from the original and perturbed interaction graphs,
which are explicitly irrelevant to the recommendation task. Accordingly, the
rich semantics reflected by social relationships and item categories, which lie
in the recommendation data-based heterogeneous graphs, are not fully exploited.
To explore recommendation-specific auxiliary tasks, we first quantitatively
analyze the heterogeneous interaction data and find a strong positive
correlation between the interactions and the number of user-item paths induced
by meta-paths. Based on the finding, we design two auxiliary tasks that are
tightly coupled with the target task (one is predictive and the other one is
contrastive) towards connecting recommendation with the self-supervision
signals hiding in the positive correlation. Finally, a model-agnostic
DUal-Auxiliary Learning (DUAL) framework which unifies the SSL and
recommendation tasks is developed. The extensive experiments conducted on three
real-world datasets demonstrate that DUAL can significantly improve
recommendation, reaching the state-of-the-art performance.


title:
Can Chameleon fields be the source of both the dark energy dipole and
  the CMB dipole?

abstract:
  Recent research shows that the local group is moving toward (l,b)=(276,30)
relative to the cosmic background radiation at a speed of about $600 kms^{-1}$
which is known as the cosmic background radiation dipole. The exact cause of
this movement is still unknown. Areas with high mass densities such as galactic
superclusters seem to be one of the causes of this flow. There are several
methods for simulating the motion of local clusters, one of which is the bulk
flow. The bulk current can be seen as a mass movement of a large part of the
universe. This anisotropy at the local scale seems to have the same origin as
the anisotropy at the larger scale. In this paper, anisotropies on both small
and large scales were investigated using chameleon fields. The data used are
Type Ia supernovae (Pantheon catalog for a total of 1,048 supernovae in
redshift (0.15<z<2.3)). The results showed that on a smaller scale (less than
150 MPa) the direction of motion of the local group galaxies is the same as the
direction of the bulk, and on larger scales, the direction of the bulk current
is the same as the direction of the current of dark energy dipole.


title:
Attention-Based Lip Audio-Visual Synthesis for Talking Face Generation
  in the Wild

abstract:
  Talking face generation with great practical significance has attracted more
attention in recent audio-visual studies. How to achieve accurate lip
synchronization is a long-standing challenge to be further investigated.
Motivated by xxx, in this paper, an AttnWav2Lip model is proposed by
incorporating spatial attention module and channel attention module into
lip-syncing strategy. Rather than focusing on the unimportant regions of the
face image, the proposed AttnWav2Lip model is able to pay more attention on the
lip region reconstruction. To our limited knowledge, this is the first attempt
to introduce attention mechanism to the scheme of talking face generation. An
extensive experiments have been conducted to evaluate the effectiveness of the
proposed model. Compared to the baseline measured by LSE-D and LSE-C metrics, a
superior performance has been demonstrated on the benchmark lip synthesis
datasets, including LRW, LRS2 and LRS3.


title:
SimpleTrack: Rethinking and Improving the JDE Approach for Multi-Object
  Tracking

abstract:
  Joint detection and embedding (JDE) based methods usually estimate bounding
boxes and embedding features of objects with a single network in Multi-Object
Tracking (MOT). In the tracking stage, JDE-based methods fuse the target motion
information and appearance information by applying the same rule, which could
fail when the target is briefly lost or blocked. To overcome this problem, we
propose a new association matrix, the Embedding and Giou matrix, which combines
embedding cosine distance and Giou distance of objects. To further improve the
performance of data association, we develop a simple, effective tracker named
SimpleTrack, which designs a bottom-up fusion method for Re-identity and
proposes a new tracking strategy based on our EG matrix. The experimental
results indicate that SimpleTrack has powerful data association capability,
e.g., 61.6 HOTA and 76.3 IDF1 on MOT17. In addition, we apply the EG matrix to
5 different state-of-the-art JDE-based methods and achieve significant
improvements in IDF1, HOTA and IDsw metrics, and increase the tracking speed of
these methods by about 20%.


title:
Leveraging Randomized Smoothing for Optimal Control of Nonsmooth
  Dynamical Systems

abstract:
  Optimal control (OC) algorithms such as Differential Dynamic Programming
(DDP) take advantage of the derivatives of the dynamics to efficiently control
physical systems. Yet, in the presence of nonsmooth dynamical systems, such
class of algorithms are likely to fail due, for instance, to the presence of
discontinuities in the dynamics derivatives or because of non-informative
gradient. On the contrary, reinforcement learning (RL) algorithms have shown
better empirical results in scenarios exhibiting non-smooth effects (contacts,
frictions, etc). Our approach leverages recent works on randomized smoothing
(RS) to tackle non-smoothness issues commonly encountered in optimal control,
and provides key insights on the interplay between RL and OC through the prism
of RS methods. This naturally leads us to introduce the randomized Differential
Dynamic Programming (R-DDP) algorithm accounting for deterministic but
non-smooth dynamics in a very sample-efficient way. The experiments demonstrate
that our method is able to solve classic robotic problems with dry friction and
frictional contacts, where classical OC algorithms are likely to fail and RL
algorithms require in practice a prohibitive number of samples to find an
optimal solution.


title: Modular sheaves on hyperk\"ahler fourfolds of Kummer type

abstract:
  We partially extend to hyperk\"ahler fourfolds of Kummer type the results on
hyperk\"ahler varieties of type $K3^{[2]}$ proved in a previous paper. We show
that if $(M,h)$ is a general polarized HK fourfold of Kummer type such that
$q_M(h)\equiv -6\pmod{16}$ and the divisibility of $h$ is $2$, then there
exists a slope stable rank $4$ vector bundle ${\mathscr F}$ on $M$ such that $
c_1({\mathscr F})= h$ and $\Delta({\mathscr F})=c_2(M)$. Moreover if ${\mathscr
F}$ is such a vector bundle and $\varphi$ is an automorphism of $M$ acting
trivially on $H^2(M)$, then $\varphi^{*}({\mathscr F})$ is isomorphic to
${\mathscr F}$. Lastly, the general such ${\mathscr F}$ is rigid. (There is a
similar result for polarizations $h$ such that $q_M(h)\equiv -6\pmod{144}$ and
the divisibility of $h$ is $6$.) We are motivated by the desire to describe
explicitly a locally complete family of polarized HK fourfolds of Kummer type.


title:
Low luminosity Type II supernovae -- IV. SN 2020cxd and SN 2021aai, at
  the edges of the sub-luminous supernovae class

abstract:
  Photometric and spectroscopic data for two Low Luminosity Type IIP Supernovae
(LL SNe IIP) are presented. SN 2020cxd reaches a peak absolute magnitude
$M_{r}$ = -13.90 $\pm$ 0.05 mag two days after explosion, subsequently settling
on a plateau for $\sim$120 days. Through the luminosity of the late light curve
tail, we infer a synthesized $^{56}$Ni mass of (1.8$\pm$0.5) $\times$ 10$^{-3}$
M$_{\odot}$. During the early evolutionary phases, optical spectra show a blue
continuum ($T$ $>$ 8000 K) with broad Balmer lines displaying a P Cygni
profile, while at later phases Ca II, Fe II, Sc II and Ba II lines dominate the
spectra. Hydrodynamical modelling of the observables yields $R$ $\simeq$ 575
$R_{\odot}$ for the progenitor star, with $M_{ej}$ = 7.5 M$_{\odot}$ and $E$
$\simeq$ 0.097 foe emitted during the explosion. This low-energy event
originating from a low-mass progenitor star is compatible with both the
explosion of a red supergiant (RSG) star and with an Electron Capture Supernova
arising from a super asymptotic giant branch star. SN 2021aai reaches a maximum
luminosity of $M_{r}$ = -16.4 mag (correcting for $A_{V}$=1.9 mag), and
displays a remarkably long plateau ($\sim$140 days). The estimated $^{56}$Ni
mass is (1.4$\pm$0.5) $\times$ 10$^{-2}$ M$_{\odot}$. The expansion velocities
are compatible with those of other LL SNe IIP (few 10$^{3}$ km s$^{-1}$). The
physical parameters obtained through hydrodynamical modelling are $R$ $\simeq$
575 R$_{\odot}$, $M_{ej}$ = 15.5 M$_{\odot}$ and $E$ = 0.4 foe. SN 2021aai is
therefore interpreted as the explosion of a RSG, with properties that bridge
the class of LL SNe IIP with standard SN IIP events.


title:
Adapt$\mathcal{O}$r: Objective-Centric Adaptation Framework for Language
  Models

abstract:
  Progress in natural language processing research is catalyzed by the
possibilities given by the widespread software frameworks. This paper
introduces Adaptor library that transposes the traditional model-centric
approach composed of pre-training + fine-tuning steps to objective-centric
approach, composing the training process by applications of selected
objectives. We survey research directions that can benefit from enhanced
objective-centric experimentation in multitask training, custom objectives
development, dynamic training curricula, or domain adaptation. Adaptor aims to
ease reproducibility of these research directions in practice. Finally, we
demonstrate the practical applicability of Adaptor in selected unsupervised
domain adaptation scenarios.


title: Audio-Visual MLP for Scoring Sport

abstract:
  Figure skating scoring is a challenging task because it requires judging
players' technical moves as well as coordination with the background music.
Prior learning-based work cannot solve it well for two reasons: 1) each move in
figure skating changes quickly, hence simply applying traditional frame
sampling will lose a lot of valuable information, especially in a 3-5 minutes
lasting video, so an extremely long-range representation learning is necessary;
2) prior methods rarely considered the critical audio-visual relationship in
their models. Thus, we introduce a multimodal MLP architecture, named
Skating-Mixer. It extends the MLP-Mixer-based framework into a multimodal
fashion and effectively learns long-term representations through our designed
memory recurrent unit (MRU). Aside from the model, we also collected a
high-quality audio-visual FS1000 dataset, which contains over 1000 videos on 8
types of programs with 7 different rating metrics, overtaking other datasets in
both quantity and diversity. Experiments show the proposed method outperforms
SOTAs over all major metrics on the public Fis-V and our FS1000 dataset. In
addition, we include an analysis applying our method to recent competitions
that occurred in Beijing 2022 Winter Olympic Games, proving our method has
strong robustness.


title:
Sparsification and Filtering for Spatial-temporal GNN in Multivariate
  Time-series

abstract:
  We propose an end-to-end architecture for multivariate time-series prediction
that integrates a spatial-temporal graph neural network with a matrix filtering
module. This module generates filtered (inverse) correlation graphs from
multivariate time series before inputting them into a GNN. In contrast with
existing sparsification methods adopted in graph neural network, our model
explicitly leverage time-series filtering to overcome the low signal-to-noise
ratio typical of complex systems data. We present a set of experiments, where
we predict future sales from a synthetic time-series sales dataset. The
proposed spatial-temporal graph neural network displays superior performances
with respect to baseline approaches, with no graphical information, and with
fully connected, disconnected graphs and unfiltered graphs.


title:
Semi-Integrated-Sensing-and-Communication (Semi-ISaC) Networks Assisted
  by NOMA

abstract:
  This paper investigates non-orthogonal multiple access (NOMA) assisted
integrated sensing and communication (ISaC) networks. Compared to the
conventional ISaC networks, where the total bandwidth is used for both the
radar detection and wireless communications, the proposed Semi-ISaC networks
allow that a portion of bandwidth is used for ISaC and the rest of the
bandwidth is only utilized for wireless communications. We first derive the
analytical expressions of the outage probability for the communication signals,
including the signals for the radar target and the communication transmitter.
Additionally, we derive the analytical expressions of the ergodic radar
estimation information rate (REIR) for the radar echoes. The simulation results
show that 1) NOMA ISaC has better spectrum efficiency than the conventional
ISaC; and 2) The REIR is enhanced when we enlarge the density of pulses.


title:
Search for sub-GeV Dark Matter via Migdal effect with an EDELWEISS
  germanium detector with NbSi TES sensors

abstract:
  The EDELWEISS collaboration reports on the search for Dark Matter (DM)
particle interactions via Migdal effect with masses between $32$
MeV$\cdot$c$^{-2}$ to $2$ GeV$\cdot$c$^{-2}$ using a $200$ g cryogenic Ge
detector sensitive to simultaneously heat and ionization signals and operated
underground at the Laboratoire Souterrain de Modane in France. The phonon
signal was read out using a Transition Edge Sensor made of a NbSi thin film.
The detector was biased at $66$ V in order to benefit from the
Neganov-Trofimov-Luke amplification and resulting in a resolution on the energy
of electron recoils of $4.46$ eV$_{ee}$ (RMS) and an analysis threshold of $30$
eV$_{ee}$. The sensitivity is limited by a dominant background not associated
to charge creation in the detector. The search constrains a new region of
parameter space for cross-sections down to $10^{-29}$ cm$^2$ and masses between
$32$ and $100$ MeV$\cdot$c$^{-2}$. The achieved low threshold with the NbSi
sensor shows the relevance of its use for athermal-phonon sensitive devices for
low-mass DM searches.


title: Synthesizing Gauge fields via Multicolor Dressing of Rydberg-Atom Arrays

abstract:
  Arrays of highly excited Rydberg atoms can be used as powerful quantum
simulation platforms. Here, we introduce an approach that makes it possible to
implement fully controllable effective spin interactions in such systems. We
show that optical Rydberg-dressing with multicolor laser fields opens up
distinct interaction channels that enable complete site-selective control of
the induced interactions and favorable scaling with respect to decoherence. We
apply this method to generate synthetic gauge fields for Rydberg excitations,
where the effective magnetic flux can be individually tuned to an arbitrary
degree by simply changing the phases of the laser fields. The system can be
mapped to a highly anisotropic Heisenberg model, and the resulting spin
interaction opens the door for explorations of topological phenomena with
nonlocal density interactions. A remarkable consequence of the interaction is
the emergence of topologically protected long-range doublons, which exhibit
strongly correlated motion in a chiral and robust manner.


title:
Driving-induced multiple ${\cal PT}$-symmetry breaking and reentrant
  localization transitions in non-Hermitian Floquet quasicrystals

abstract:
  The cooperation between time-periodic driving fields and non-Hermitian
effects could endow systems with distinctive spectral and transport properties.
In this work, we uncover an intriguing class of non-Hermitian Floquet matter in
one-dimensional quasicrystals, which is characterized by the emergence of
multiple driving-induced ${\cal PT}$-symmetry breaking/restoration, mobility
edges, and reentrant localization transitions. These findings are demonstrated
by investigating the spectra, level statistics, inverse participation ratios
and wavepacket dynamics of a periodically quenched nonreciprocal Harper model.
Our results not only unveil the richness of localization phenomena in driven
non-Hermitian quasicrystals, but also highlight the advantage of Floquet
approach in generating unique types of nonequilibrium phases in open systems.

categories: quant-ph cond-mat.dis-nn cond-mat.mes-hall cond-mat.stat-mech


title: DeltaCNN: End-to-End CNN Inference of Sparse Frame Differences in Videos

abstract:
  Convolutional neural network inference on video data requires powerful
hardware for real-time processing. Given the inherent coherence across
consecutive frames, large parts of a video typically change little. By skipping
identical image regions and truncating insignificant pixel updates,
computational redundancy can in theory be reduced significantly. However, these
theoretical savings have been difficult to translate into practice, as sparse
updates hamper computational consistency and memory access coherence; which are
key for efficiency on real hardware. With DeltaCNN, we present a sparse
convolutional neural network framework that enables sparse frame-by-frame
updates to accelerate video inference in practice. We provide sparse
implementations for all typical CNN layers and propagate sparse feature updates
end-to-end - without accumulating errors over time. DeltaCNN is applicable to
all convolutional neural networks without retraining. To the best of our
knowledge, we are the first to significantly outperform the dense reference,
cuDNN, in practical settings, achieving speedups of up to 7x with only marginal
differences in accuracy.


title: Non-degenerate multi-rogue waves and easy ways of their excitation

abstract:
  In multi-component systems, several rogue waves can be simultaneously excited
using simple initial conditions in the form of a plane wave with a small
amplitude single-peak perturbation. This is in drastic contrast with the case
of multi-rogue waves of a single nonlinear Schr\"{o}dinger equation (or other
evolution equations) that require highly specific initial conditions to be
used. This possibility arises due to the higher variety of rogue waves in
multi-components systems each with individual eigenvalue of the inverse
scattering technique. In theory, we expand the limited class of Peregrine-type
solutions to a much larger family of non-degenerate rogue waves. The results of
our work may explain the increased chances of appearance of rogue waves in
crossing sea states (wind generated ocean gravity waves that form nonparallel
wave systems along the water surface) as well as provide new possibilities of
rogue wave observation in a wide range of multi-component physical systems such
as multi-component Bose-Einstein condensates, multi-component plasmas and in
birefringent optical fibres.


title:
Modulation instability and non-degenerate Akhmediev breathers of Manakov
  equations

abstract:
  We reveal a new class of \textit{non-degenerate} Akhmediev breather (AB)
solutions of Manakov equations that only exist in the focusing case. Based on
exact solutions, we present the existence diagram of such excitations on the
frequency-wavenumber plane. Conventional single-frequency modulation
instability leads to simultaneous excitation of three ABs with two of them
being non-degenerate.


title:
Non-degenerate Kuznetsov-Ma solitons of Manakov equations and their
  physical spectra

abstract:
  We study the dynamics of Kuznetsov-Ma solitons (KMS) in the framework of
vector nonlinear Schr\"odinger (Manakov) equations. Exact multi-parameter
family of solutions for such KMSs is derived. This family of solutions includes
the known results as well as the previously unknown solutions in the form of
the non-degenerate KMSs. We present the existence diagram of such KMSs that
follows from the exact solutions. These non-degenerate KMSs are formed by
nonlinear superposition of two fundamental KMSs that have the same propagation
period but different eigenvalues. We present the amplitude profiles of new
solutions, their exact physical spectra, their link to ordinary vector solitons
and offer easy ways of their excitation using numerical simulations.


title:
Quasinormal modes of Schwarzschild black holes in projective invariant
  Chern-Simons modified gravity

abstract:
  We generalize the Chern-Simons modified gravity to the metric-affine case and
impose projective invariance by supplementing the Pontryagin density with
homothetic curvature terms which do not spoil topologicity. The latter is then
broken by promoting the coupling of the Chern-Simons term to a (pseudo)-scalar
field. The solutions for torsion and nonmetricity are derived perturbatively,
showing that they can be iteratively obtained from the background fields. This
allows us to describe the dynamics for the metric and the scalar field
perturbations in a self-consistent way, and we apply the formalism to the study
of quasinormal modes in a Schwarzschild black hole background. Unlike in the
metric formulation of this theory, we show that the scalar field is endowed
with dynamics even in the absence of its kinetic term in the action. Finally,
using numerical methods we compute the quasinormal frequencies and characterize
the late-time power law tails for scalar and metric perturbations, comparing
the results with the outcomes of the purely metric approach.


title: Cooperation and punishment mechanisms in uncertain and dynamic networks

abstract:
  This paper examines experimentally how reputational uncertainty and the rate
of change of the social environment determine cooperation. Reputational
uncertainty significantly decreases cooperation, while a fast-changing social
environment only causes a second-order qualitative increase in cooperation. At
the individual level, reputational uncertainty induces more leniency and
forgiveness in imposing network punishment through the link proposal and
removal processes, inhibiting the formation of cooperative clusters. However,
this effect is significant only in the fast-changing environment and not in the
slow-changing environment. A substitution pattern between network punishment
and action punishment (retaliatory defection) explains this discrepancy across
the two social environments.


title: Semi-Random Sparse Recovery in Nearly-Linear Time

abstract:
  Sparse recovery is one of the most fundamental and well-studied inverse
problems. Standard statistical formulations of the problem are provably solved
by general convex programming techniques and more practical, fast
(nearly-linear time) iterative methods. However, these latter "fast algorithms"
have previously been observed to be brittle in various real-world settings.
  We investigate the brittleness of fast sparse recovery algorithms to
generative model changes through the lens of studying their robustness to a
"helpful" semi-random adversary, a framework which tests whether an algorithm
overfits to input assumptions. We consider the following basic model: let
$\mathbf{A} \in \mathbb{R}^{n \times d}$ be a measurement matrix which contains
an unknown subset of rows $\mathbf{G} \in \mathbb{R}^{m \times d}$ which are
bounded and satisfy the restricted isometry property (RIP), but is otherwise
arbitrary. Letting $x^\star \in \mathbb{R}^d$ be $s$-sparse, and given either
exact measurements $b = \mathbf{A} x^\star$ or noisy measurements $b =
\mathbf{A} x^\star + \xi$, we design algorithms recovering $x^\star$
information-theoretically optimally in nearly-linear time. We extend our
algorithm to hold for weaker generative models relaxing our planted RIP
assumption to a natural weighted variant, and show that our method's guarantees
naturally interpolate the quality of the measurement matrix to, in some
parameter regimes, run in sublinear time.
  Our approach differs from prior fast iterative methods with provable
guarantees under semi-random generative models: natural conditions on a
submatrix which make sparse recovery tractable are NP-hard to verify. We design
a new iterative method tailored to the geometry of sparse recovery which is
provably robust to our semi-random model. We hope our approach opens the door
to new robust, efficient algorithms for natural statistical inverse problems.


title: Numerical prediction of erosion due to a cavitating jet

abstract:
  We numerically investigate the erosion potential of a cavitating liquid jet
by means of high-resolution finite volume simulations. As thermodynamic model,
we employ a barotropic equilibrium cavitation approach, embedded into a
homogeneous mixture model. To resolve the effects of collapsing vapor
structures and to estimate the erosion potential, full compressibility is
considered. Two different operating points featuring different cavitation
intensities are investigated and their erosion potential is estimated and
compared. Different methods are used for this purpose, including collapse
detection (Mihatsch et al., 2015), maximum pressure distribution on the wall,
and a new method of generating numerical pit equivalents. The data of numerical
pit equivalents is analyzed in detail and compared with experimental data from
the literature. Furthermore, a comprehensive grid study for both operating
points is presented.


title:
Mosco convergence of Sobolev spaces and Sobolev inequalities for
  nonsmooth domains

abstract:
  We find extremely general classes of nonsmooth open sets which guarantee
Mosco convergence for corresponding Sobolev spaces and the validity of Sobolev
inequalities with a uniform constant. An important feature of our results is
that the conditions we impose on the open sets for Mosco convergence and for
the Sobolev inequalities are of the same nature, therefore it is easy to check
when both are satisfied.
  Our analysis is motivated, in particular, by the study of the stability of
the direct acoustic scattering problem with respect to the scatterer, which we
also discuss.
  Concerning Mosco convergence in dimension 3 or higher, our result extends all
those previously known in the literature. Concerning Sobolev inequalities, our
approach seems to be new and considerably simplifies the conditions previously
required for the stability of acoustic direct scattering problems.


title:
Density distribution function of a self-gravitating isothermal
  compressible turbulent fluid in the context of molecular clouds ensembles --
  III. Virial analysis

abstract:
  In the present work we apply virial analysis to the model of self-gravitating
turbulent cloud ensembles introduced by Donkov & Stefanov (2018) and extended
by Donkov & Stefanov (2019). Using the Eulerian virial theorem at an arbitrary
scale, far from the cloud core, we derive an equation for the density profile
and solve it in approximate way. The result confirms the solution
$\varrho(\ell)=\ell^{-2}$ found in the previous papers. At scales far from the
core, we obtain virial equilibrium between i) gravitational energy and
accretion kinetic energy, or ii) between gravitational energy and accretion
plus turbulent kinetic energy per unit mass -- depending on the adopted scaling
relation for the turbulent velocity. Thus the obtained solution for density
profile is dynamically stable and shall be observable. At scales near the core,
one cannot neglect the second derivative of the moment of inertia of the gas,
which prevents derivation of an analytic equation for the density profile.
However, we obtain the qualitative result that gas near the core is not
virialized and its state can be characterized as marginally bound since the
total energy of the fluid element is close to zero.


title:
Visual-Language Navigation Pretraining via Prompt-based Environmental
  Self-exploration

abstract:
  Vision-language navigation (VLN) is a challenging task due to its large
searching space in the environment. To address this problem, previous works
have proposed some methods of fine-tuning a large model that pretrained on
large-scale datasets. However, the conventional fine-tuning methods require
extra human-labeled navigation data and lack self-exploration capabilities in
environments, which hinders their generalization of unseen scenes. To improve
the ability of fast cross-domain adaptation, we propose Prompt-based
Environmental Self-exploration (ProbES), which can self-explore the
environments by sampling trajectories and automatically generates structured
instructions via a large-scale cross-modal pretrained model (CLIP). Our method
fully utilizes the knowledge learned from CLIP to build an in-domain dataset by
self-exploration without human labeling. Unlike the conventional approach of
fine-tuning, we introduce prompt-based learning to achieve fast adaptation for
language embeddings, which substantially improves the learning efficiency by
leveraging prior knowledge. By automatically synthesizing
trajectory-instruction pairs in any environment without human supervision and
efficient prompt-based learning, our model can adapt to diverse vision-language
navigation tasks, including VLN and REVERIE. Both qualitative and quantitative
results show that our ProbES significantly improves the generalization ability
of the navigation model.


title:
DuMLP-Pin: A Dual-MLP-dot-product Permutation-invariant Network for Set
  Feature Extraction

abstract:
  Existing permutation-invariant methods can be divided into two categories
according to the aggregation scope, i.e. global aggregation and local one.
Although the global aggregation methods, e. g., PointNet and Deep Sets, get
involved in simpler structures, their performance is poorer than the local
aggregation ones like PointNet++ and Point Transformer. It remains an open
problem whether there exists a global aggregation method with a simple
structure, competitive performance, and even much fewer parameters. In this
paper, we propose a novel global aggregation permutation-invariant network
based on dual MLP dot-product, called DuMLP-Pin, which is capable of being
employed to extract features for set inputs, including unordered or
unstructured pixel, attribute, and point cloud data sets. We strictly prove
that any permutation-invariant function implemented by DuMLP-Pin can be
decomposed into two or more permutation-equivariant ones in a dot-product way
as the cardinality of the given input set is greater than a threshold. We also
show that the DuMLP-Pin can be viewed as Deep Sets with strong constraints
under certain conditions. The performance of DuMLP-Pin is evaluated on several
different tasks with diverse data sets. The experimental results demonstrate
that our DuMLP-Pin achieves the best results on the two classification problems
for pixel sets and attribute sets. On both the point cloud classification and
the part segmentation, the accuracy of DuMLP-Pin is very close to the so-far
best-performing local aggregation method with only a 1-2% difference, while the
number of required parameters is significantly reduced by more than 85% in
classification and 69% in segmentation, respectively. The code is publicly
available on https://github.com/JaronTHU/DuMLP-Pin.


title:
Hydrodynamic limit and cutoff for the biased adjacent walk on the
  simplex

abstract:
  We investigate the asymptotic in $N$ of the mixing times of a Markov dynamics
on $N-1$ ordered particles in an interval. This dynamics consists in resampling
at independent Poisson times each particle according to a probability measure
on the segment formed by its nearest neighbours. In the setting where the
resampling probability measures are symmetric, the asymptotic of the mixing
times were obtained and a cutoff phenomenon holds. In the present work, we
focus on an asymmetric version of the model and we establish a cutoff
phenomenon. An important part of our analysis consists in the derivation of a
hydrodynamic limit, which is given by a non-linear Hamilton-Jacobi equation
with degenerate boundary conditions.