title:
The distance to cubic symmetry class as a polynomial optimization
  problem

abstract:
  Generically, a fully measured elasticity tensor has no material symmetry. For
single crystals with a cubic lattice, or for the aeronautics turbine blades
superalloys such as Nickelbased CMSX-4, cubic symmetry is nevertheless
expected. It is in practice necessary to compute the nearest cubic elasticity
tensor to a given raw one. Mathematically formulated, the problem consists in
finding the distance between a given tensor and the cubic symmetry stratum. It
is known that closed symmetry strata (for any tensorial representation of the
rotation group) are semialgebraic sets, defined by polynomial equations and
inequalities. It has been recently shown that the closed cubic elasticity
stratum is moreover algebraic, which means that it can be defined by polynomial
equations only (without requirement to polynomial inequalities). We propose to
make use of this mathematical property to formulate the distance to cubic
symmetry problem as a polynomial (in fact quadratic) optimization problem, and
to derive its quasi-analytical solution using the technique of Gr{\"o}bner
bases. The proposed methodology also applies to cubic Hill elasto-plasticity
(where two fourth-order constitutive tensors are involved).


title:
The Moral Debater: A Study on the Computational Generation of Morally
  Framed Arguments

abstract:
  An audience's prior beliefs and morals are strong indicators of how likely
they will be affected by a given argument. Utilizing such knowledge can help
focus on shared values to bring disagreeing parties towards agreement. In
argumentation technology, however, this is barely exploited so far. This paper
studies the feasibility of automatically generating morally framed arguments as
well as their effect on different audiences. Following the moral foundation
theory, we propose a system that effectively generates arguments focusing on
different morals. In an in-depth user study, we ask liberals and conservatives
to evaluate the impact of these arguments. Our results suggest that,
particularly when prior beliefs are challenged, an audience becomes more
affected by morally framed arguments.


title: HandOccNet: Occlusion-Robust 3D Hand Mesh Estimation Network

abstract:
  Hands are often severely occluded by objects, which makes 3D hand mesh
estimation challenging. Previous works often have disregarded information at
occluded regions. However, we argue that occluded regions have strong
correlations with hands so that they can provide highly beneficial information
for complete 3D hand mesh estimation. Thus, in this work, we propose a novel 3D
hand mesh estimation network HandOccNet, that can fully exploits the
information at occluded regions as a secondary means to enhance image features
and make it much richer. To this end, we design two successive
Transformer-based modules, called feature injecting transformer (FIT) and self-
enhancing transformer (SET). FIT injects hand information into occluded region
by considering their correlation. SET refines the output of FIT by using a
self-attention mechanism. By injecting the hand information to the occluded
region, our HandOccNet reaches the state-of-the-art performance on 3D hand mesh
benchmarks that contain challenging hand-object occlusions. The codes are
available in: https://github.com/namepllet/HandOccNet.


title: Style-Guided Domain Adaptation for Face Presentation Attack Detection

abstract:
  Domain adaptation (DA) or domain generalization (DG) for face presentation
attack detection (PAD) has attracted attention recently with its robustness
against unseen attack scenarios. Existing DA/DG-based PAD methods, however,
have not yet fully explored the domain-specific style information that can
provide knowledge regarding attack styles (e.g., materials, background,
illumination and resolution). In this paper, we introduce a novel Style-Guided
Domain Adaptation (SGDA) framework for inference-time adaptive PAD.
Specifically, Style-Selective Normalization (SSN) is proposed to explore the
domain-specific style information within the high-order feature statistics. The
proposed SSN enables the adaptation of the model to the target domain by
reducing the style difference between the target and the source domains.
Moreover, we carefully design Style-Aware Meta-Learning (SAML) to boost the
adaptation ability, which simulates the inference-time adaptation with style
selection process on virtual test domain. In contrast to previous domain
adaptation approaches, our method does not require either additional auxiliary
models (e.g., domain adaptors) or the unlabeled target domain during training,
which makes our method more practical to PAD task. To verify our experiments,
we utilize the public datasets: MSU-MFSD, CASIA-FASD, OULU-NPU and Idiap
REPLAYATTACK. In most assessments, the result demonstrates a notable gap of
performance compared to the conventional DA/DG-based PAD methods.


title: On spanning tree edge denpendences of graphs

abstract:
  Let $\tau(G)$ and $\tau_G(e)$ be the number of spanning trees of a connected
graph $G$ and the number of spanning trees of $G$ containing edge $e$. The
ratio $d_{G}(e)=\tau_{G}(e)/\tau(G)$ is called the spanning tree edge density
of $e$, or simply density of $e$. The maximum density
$\mbox{dep}(G)=\max\limits_{e\in E(G)}d_{G}(e)$ is called the spanning tree
edge dependence of $G$, or simply dependence of $G$. Given a rational number
$p/q\in (0,1)$, if there exists a graph $G$ and an edge $e\in E(G)$ such that
$d_{G}(e)=p/q$, then we say the density $p/q$ is constructible. More specially,
if there exists a graph $G$ such that $\mbox{dep}(G)=p/q$, then we say the
dependence $p/q$ is constructible. In 2002, Ferrara, Gould, and Suffel raised
the open problem of which rational densities and dependences are constructible.
In 2016, Kahl provided constructions that show all rational densities and
dependences are constructible. Moreover, He showed that all rational densities
are constructible even if $G$ is restricted to bipartite graphs or planar
graphs. He thus conjectured that all rational dependences are also
constructible even if $G$ is restricted to bipartite graphs (Conjecture 1), or
planar graphs (Conjecture 2). In this paper, by combinatorial and electric
network approach, firstly, we show that all rational dependences are
constructible via bipartite graphs, which confirms the first conjecture of
Kahl. Secondly, we show that all rational dependences are constructible for
planar multigraphs, which confirms Kahl's second conjecture for planar
multigraphs. However, for (simple) planar graphs, we disprove the second
conjecture of Kahl by showing that the dependence of any planar graph is larger
than $\frac{1}{3}$. On the other hand, we construct a family of planar graphs
that show all rational dependences $p/q>\frac{1}{2}$ are constructible via
planar graphs.


title: Becoming the World's Highest Rated Chess Player

abstract:
  The Elo rating system measures the approximate skill of each competitor in a
game or sport. As a competitor competes, their rating changes based on their
wins and losses. Increasing one's rating can be difficult work; one must hone
their skills and consistently beat the competition. Alternatively, with enough
money you can rig the outcome of games to boost your rating. Say you manage to
get together $n$ people (including yourself) and acquire enough money to rig
$k$ games. How high can you get your rating? The people you gathered aren't
very interested in the game, and will only play if you pay them to. We resolve
this question for $n=2$ and $n=\Omega(k^{1/3})$ (up to a log factor) for the
standard Elo system, and provide close upper and lower bounds for a wider class
of rating systems that resemble Elo.


title:
SPRINT: Semantics for PerfoRmant and scalable INteroperability of
  multimodal Transport

abstract:
  Interoperability is a long-standing challenge slowing down the digitalization
of mobility systems and the provision of full mobility-as-a-service offerings.
This paper presents early results achieved by the SPRINT project (Semantics for
PerfoRmant and scalable INteroperability of multimodal Transport), an integral
part of the Shift2Rail IP4 work programme, in meeting the challenge. It
introduces the conceptual architecture and describes the demonstrator
implementation of the Interoperability Framework (IF), a solution designed to
support servitization in the mobility domain in two directions: From the Travel
Application perspective, the IF provides semantically-consistent abstractions
of distributed ICT resources offered by Service Provider Companies, insulating
them from the "mechanics" of operating remotely over networks and across
multiple communication protocols and/or data formats; from the Service Provider
Companies perspective, the IF allows leveraging their native ICT computing
environment and resources as elements of an end-to-end integrated intermodal
mobility solution, insulating them from the specifics of the customer front-end
applications.


title:
Deep Learning Jet Image as a Probe of Light Higgsino Dark Matter at the
  LHC

abstract:
  Higgsino in supersymmetric standard models can play the role of dark matter
particle. In conjunction with the naturalness criterion, the higgsino mass
parameter is expected to be around the electroweak scale. In this work, we
explore the potential of probing the nearly degenerate light higgsinos with
machine learning at the LHC. By representing the jets as 2D images, we use the
convolutional neural network (CNN) to enhance the signal significance. We find
that our deep learning jet image method can improve the previous result based
on the conventional cut-flow by about a factor of two at the High-Luminosity
LHC.


title:
Two-Photon-Exchange effect in $ep\rightarrow en\pi^+$ at small $-t$ with
  the hadronic model and dispersion relation approach

abstract:
  In this work, the two-photon-exchange (TPE) effect in $ep\rightarrow en\pi^+$
at small $-t$ is discussed. In the previous work, the TPE contribution with one
$\pi$ intermediate state is estimated numerically within a hadronic model under
the pion-dominance approximation. Here we extend the discussion to include one
$\rho$ intermediate state. The TPE contribution can be described by one scalar
function in the limit $m_e\rightarrow 0$, the dispersion relation (DR)
satisfied by this scalar function is analysed. The analytic expressions for the
imaginary parts of the TPE contributions from one $\pi$ or one $\rho$
intermediate state are given within the hadronic model. Combining these
analytic expressions and the DR, the corresponding real parts of the TPE
contributions can be estimated easily at any available region. This can help
the further experimental analysis to include the TPE contributions in a
convenient way. The numeric results show that the TPE correction with one
$\rho$ intermediate state is much smaller than that with one $\pi$ intermediate
state in the current energy region. These results suggest that the TPE
contribution with an elastic state is the main TPE contribution in
$ep\rightarrow en\pi^+$ at small $-t$.


title: On the Christoffel function and classification in data analysis

abstract:
  We show that the empirical Christoffel function associated with a cloud of
finitely many points sampled from a distribution, can provide a simple tool for
supervised classification in data analysis, with good generalization
properties.


title:
Distributed Task Management in the Heterogeneous Fog: A Socially Concave
  Bandit Game

abstract:
  Fog computing has emerged as a potential solution to the explosive
computational demand of mobile users. This potential mainly stems from the
capacity of task offloading and allocation at the network edge, which reduces
the delay and improves the quality of service. Despite the significant
potential, optimizing the performance of a fog network is often challenging. In
the fog architecture, the computing nodes are heterogeneous smart devices with
distinct abilities and capacities, thereby, preferences. Besides, in an
ultra-dense fog network with random task arrival, centralized control results
in excessive overhead, and therefore, it is not feasible. We study a
distributed task allocation problem in a heterogeneous fog computing network
under uncertainty. We formulate the problem as a social-concave game, where the
players attempt to minimize their regret on the path to Nash equilibrium. To
solve the formulated problem, we develop two no-regret decision-making
strategies. One strategy, namely bandit gradient ascent with momentum, is an
online convex optimization algorithm with bandit feedback. The other strategy,
Lipschitz Bandit with Initialization, is an EXP3 multi-armed bandit algorithm.
We establish a regret bound for both strategies and analyze their convergence
characteristics. Moreover, we compare the proposed strategies with a
centralized allocation strategy named Learning with Linear Rewards. Theoretical
and numerical analysis shows the superior performance of the proposed
strategies for efficient task allocation compared to the state-of-the-art
methods.


title:
Detection threshold for correlated Erd\H{o}s-R\'enyi graphs via densest
  subgraphs

abstract:
  The problem of detecting edge correlation between two Erd\H{o}s-R\'enyi
random graphs on $n$ unlabeled nodes can be formulated as a hypothesis testing
problem: under the null hypothesis, the two graphs are sampled independently;
under the alternative, the two graphs are independently sub-sampled from a
parent graph which is Erd\H{o}s-R\'enyi $\mathbf{G}(n, p)$ (so that their
marginal distributions are the same as the null). We establish a sharp
information-theoretic threshold when $p = n^{-\alpha+o(1)}$ for $\alpha\in (0,
1]$ which sharpens a constant factor in a recent work by Wu, Xu and Yu. A key
novelty in our work is an interesting connection between the detection problem
and the densest subgraph of an Erd\H{o}s-R\'enyi graph.


title: The Digitalization of Bioassays in the Open Research Knowledge Graph

abstract:
  Background: Recent years are seeing a growing impetus in the semantification
of scholarly knowledge at the fine-grained level of scientific entities in
knowledge graphs. The Open Research Knowledge Graph (ORKG)
https://www.orkg.org/ represents an important step in this direction, with
thousands of scholarly contributions as structured, fine-grained,
machine-readable data. There is a need, however, to engender change in
traditional community practices of recording contributions as unstructured,
non-machine-readable text. For this in turn, there is a strong need for AI
tools designed for scientists that permit easy and accurate semantification of
their scholarly contributions. We present one such tool, ORKG-assays.
Implementation: ORKG-assays is a freely available AI micro-service in ORKG
written in Python designed to assist scientists obtain semantified bioassays as
a set of triples. It uses an AI-based clustering algorithm which on
gold-standard evaluations over 900 bioassays with 5,514 unique property-value
pairs for 103 predicates shows competitive performance. Results and Discussion:
As a result, semantified assay collections can be surveyed on the ORKG platform
via tabulation or chart-based visualizations of key property values of the
chemicals and compounds offering smart knowledge access to biochemists and
pharmaceutical researchers in the advancement of drug development.


title:
Impact of in-situ controlled disorder screening on fractional quantum
  Hall effects and composite-fermion transport

abstract:
  We examine the impact of random potential due to remote impurites (RIs) and
its in-situ controlled screening on fractional quantum Hall effects (FQHEs)
around Landau-level filling factor $\nu = 1/2$. The experiment is made possible
by using a dual-gate GaAs quantum well (QW) that allows for the independent
control of the density $n_{e}$ of the two-dimensional electron system in the QW
and that ($n_\text{SL}$) of excess electrons in the modulation-doping
superlattice. As the screening is reduced by decreasing $n_\text{SL}$ at a
fixed $n_{e}$, we observe a decrease in the apparent energy gap of the FQHEs
deduced from thermal activation, which signifies a corresponding increase in
the disorder broadening $\Gamma$ of composite fermions (CFs). Interestingly,
the increase in $\Gamma$ is accompanied by a noticeable increase in the
longitudinal resistivity at $\nu = 1/2$ ($\rho_{1/2}$), with a much stronger
correlation with $\Gamma$ than electron mobility $\mu$ has. The in-situ control
of RI screening enables us to disentangle the contributions of RIs and
background impurities (BIs) to $\rho_{1/2}$, with the latter in good agreement
with the CF theory. We construct a scaling plot that helps in estimating the BI
contribution to $\rho_{1/2}$ for a given set of $n_{e}$ and $\mu$.


title: Bulk-interface correspondence from quantum distance in flat band systems

abstract:
  The key notion of topological analysis is the bulk-boundary correspondence,
which provides us with direct access to the topological structure of a Bloch
wave function via the existence of boundary or interface modes. While only the
topology of the wave function has been considered relevant to boundary modes,
we first demonstrate that another geometric quantity, the so-called quantum
distance, can host a new kind of bulk-interface correspondence. We consider a
generic class of two-dimensional flat band systems, where the flat band has a
parabolic band-crossing with another dispersive band. While such flat bands are
known to be topologically trivial, we show that the nonzero maximum quantum
distance between the eigenstates of the flat band around the touching point
guarantees the existence of boundary modes at the interface between two domains
with different chemical potentials. Moreover, the maximum quantum distance can
predict even the explicit form of the dispersion relation and decay length of
the interface modes.


title:
Demystifying the Neural Tangent Kernel from a Practical Perspective: Can
  it be trusted for Neural Architecture Search without training?

abstract:
  In Neural Architecture Search (NAS), reducing the cost of architecture
evaluation remains one of the most crucial challenges. Among a plethora of
efforts to bypass training of each candidate architecture to convergence for
evaluation, the Neural Tangent Kernel (NTK) is emerging as a promising
theoretical framework that can be utilized to estimate the performance of a
neural architecture at initialization. In this work, we revisit several
at-initialization metrics that can be derived from the NTK and reveal their key
shortcomings. Then, through the empirical analysis of the time evolution of
NTK, we deduce that modern neural architectures exhibit highly non-linear
characteristics, making the NTK-based metrics incapable of reliably estimating
the performance of an architecture without some amount of training. To take
such non-linear characteristics into account, we introduce Label-Gradient
Alignment (LGA), a novel NTK-based metric whose inherent formulation allows it
to capture the large amount of non-linear advantage present in modern neural
architectures. With minimal amount of training, LGA obtains a meaningful level
of rank correlation with the post-training test accuracy of an architecture.
Lastly, we demonstrate that LGA, complemented with few epochs of training,
successfully guides existing search algorithms to achieve competitive search
performances with significantly less search cost. The code is available at:
https://github.com/nutellamok/DemystifyingNTK.


title:
CFT approach to constraint operators for ($\beta$-deformed) hermitian
  one-matrix models

abstract:
  Since the ($\beta$-deformed) hermitian one-matrix models can be represented
as the integrated conformal field theory (CFT) expectation values, we construct
the operators in terms of the generators of the Heisenberg algebra such that
the constraints can be derived by inserting the constructed operators into the
integrated expectation values. In terms of the derived constraint operators and
one of the Virasoro constraint operators, we may construct the $W_{1+\infty}$
constraints with higher algebraic structures for these two matrix models. We
also give the total derivative operators associating with the derived
constraint operators and analyze their remarkable properties. Furthermore, we
explore the intrinsic connection between these constraint operators and
$W$-representations of some matrix models. For the Gaussian hermitian
one-matrix model in the external field and $\beta$-deformed $N\times N$ complex
matrix model, we investigate the superintegrability and derive the
corresponding character expansions from their $W$-representations. Moreover a
conjectured formula for the averages of Jack polynomials in the literature is
proved.


title:
Computer Science Named Entity Recognition in the Open Research Knowledge
  Graph

abstract:
  Domain-specific named entity recognition (NER) on Computer Science (CS)
scholarly articles is an information extraction task that is arguably more
challenging for the various annotation aims that can beset the task and has
been less studied than NER in the general domain. Given that significant
progress has been made on NER, we believe that scholarly domain-specific NER
will receive increasing attention in the years to come. Currently, progress on
CS NER -- the focus of this work -- is hampered in part by its recency and the
lack of a standardized annotation aim for scientific entities/terms. This work
proposes a standardized task by defining a set of seven contribution-centric
scholarly entities for CS NER viz., research problem, solution, resource,
language, tool, method, and dataset. Following which, its main contributions
are: combines existing CS NER resources that maintain their annotation focus on
the set or subset of contribution-centric scholarly entities we consider;
further, noting the need for big data to train neural NER models, this work
additionally supplies thousands of contribution-centric entity annotations from
article titles and abstracts, thus releasing a cumulative large novel resource
for CS NER; and, finally, trains a sequence labeling CS NER model inspired
after state-of-the-art neural architectures from the general domain NER task.
Throughout the work, several practical considerations are made which can be
useful to information technology designers of the digital libraries.


title: Geometric phase for twisted photons

abstract:
  We use a purely geometric method to understand the rotation of the photonic
polarization vector in an optical fiber without birefringence. This rotation
was explained due to Berry's phase of spin-1 photons. Via our method, we
predict similar geometric rotations also exist for twisted photons carrying
orbital angular momentum (OAM). The corresponding geometric phase can be
applied in photonic OAM-state-based quantum computation and quantum sensing.


title:
S2-Net: Self-supervision Guided Feature Representation Learning for
  Cross-Modality Images

abstract:
  Combining the respective advantages of cross-modality images can compensate
for the lack of information in the single modality, which has attracted
increasing attention of researchers into multi-modal image matching tasks.
Meanwhile, due to the great appearance differences between cross-modality image
pairs, it often fails to make the feature representations of correspondences as
close as possible. In this letter, we design a cross-modality feature
representation learning network, S2-Net, which is based on the recently
successful detect-and-describe pipeline, originally proposed for visible images
but adapted to work with cross-modality image pairs. To solve the consequent
problem of optimization difficulties, we introduce self-supervised learning
with a well-designed loss function to guide the training without discarding the
original advantages. This novel strategy simulates image pairs in the same
modality, which is also a useful guide for the training of cross-modality
images. Notably, it does not require additional data but significantly improves
the performance and is even workable for all methods of the detect-and-describe
pipeline. Extensive experiments are conducted to evaluate the performance of
the strategy we proposed, compared to both handcrafted and deep learning-based
methods. Results show that our elegant formulation of combined optimization of
supervised and self-supervised learning outperforms state-of-the-arts on
RoadScene and RGB-NIR datasets.


title: Hardy Sums and the Problem of Counting Lattice Points in Triangles

abstract:
  The problem of finding the number of lattice points in a triangle has a
classical solution if the lattice is $\mathbf{Z}^2$ and the vertices of the
triangle have integer-valued coordinates. In this note, we consider what
happens when we replace the lattice by $(2 \mathbf{Z})^2$ and give an explicit
formula for the number of lattice points of $(2 \mathbf{Z})^2$ inside a
triangle in terms of Hardy sums.


title:
Rings of Siegel-Jacobi forms of bounded relative index are not finitely
  generated

abstract:
  We show that the ring of Siegel-Jacobi forms of fixed degree and of fixed or
bounded ratio between weight and index is not finitely generated. Our main tool
is the theory of toroidal b-divisors and their relation to convex geometry. As
a byproduct of our methods we recover a formula due to Tai for the asymptotic
dimension of the space of Siegel-Jacobi forms of given ratio between weight and
index.


title:
Geometry of hyperbolic Cauchy-Riemann singularities and KAM-like theory
  for holomorphic involutions

abstract:
  This article is concerned with the geometry of germs of real analytic
surfaces in $(\mathbb{C}^2,0)$ having an isolated Cauchy-Riemann (CR)
singularity at the origin. These are perturbations of {\it Bishop quadrics}.
There are two kinds of CR singularities stable under perturbation~: {\it
elliptic} and {\it hyperbolic}. Elliptic case was studied by Moser-Webster
\cite{moser-webster} who showed that such a surface is locally, near the CR
singularity, holomorphically equivalent to {\it normal form} from which lots of
geometric features can be read off.
  In this article we focus on perturbations of {\it hyperbolic} quadrics. As
was shown by Moser-Webster \cite{moser-webster}, such a surface can be
transformed to a formal {\it normal form} by a formal change of coordinates
that may not be holomorphic in any neighborhood of the origin.
  Given a {\it non-degenerate} real analytic surface $M$ in $(\mathbb{C}^2,0)$
having a {\it hyperbolic} CR singularity at the origin, we prove the existence
of a non-constant Whitney smooth family of connected holomorphic curves
intersecting $M$ along holomorphic hyperbolas. This is the very first result
concerning hyperbolic CR singularity not equivalent to quadrics.
  This is a consequence of a non-standard KAM-like theorem for pair of germs of
holomorphic involutions $\{\tau_1,\tau_2\}$ at the origin, a common fixed
point. We show that such a pair has large amount of invariant analytic sets
biholomorphic to $\{z_1z_2=const\}$ (which is not a torus) in a neighborhood of
the origin, and that they are conjugate to restrictions of linear maps on such
invariant sets.


title:
Interoperability in the IoT -- An Evaluation of the Semantic-Based
  Approach

abstract:
  While the management of heterogeneous network devices is usually solved by
protocols like SNMP and NETCONF, there is still no such accepted solution for
the management of heterogeneous IoT devices. To avoid the vendor lock-in,
several organizations like the IETF, W3C and ETSI are working on standards with
a semantic-based approach. While the semantic approach seems to be appealing to
solve the interoperability problem, there is still the question whether this
approach is suited for constrained IoT devices. Herein, we present the
evaluation of the MYNO, a semantic-based framework. MYNO is based on standards
and open-source libraries and aims to support the management of constrained
devices in the Internet of Things (IoT). We demonstrate the benefits of the
semantic-based approach using a precision agriculture use case.


title: Nanocryotron-driven Charge Configuration Memory

abstract:
  Cryo-computing is presently severely limited by the absence of a suitable
fast and energy efficient cryo-memory. Ideally, such memory should be
compatible with single-flux quantum (SFQ) logic in terms of speed, switching
energy and matching impedance. Here we present an implementation of
non-volatile charge configuration memory (CCM) in a cryo-computing environment
by combining it in parallel with a pulse-triggered superconducting nanowire
cryotron (nTron). The combined device is modeled in terms of the dynamical
response of the SC order parameter in a current-controlled nanowire with a CCM
shunt. Analysis of timedynamics and current-voltage characteristics based on
measured device parameters show that single flux quantum (SFQ)-level pulses can
drive non-volatile CCM on the picosecond timescale, while allowing the nTron to
operate in non-latching mode. The inherent high energy efficiency and ultrahigh
speed makes this hybrid device an ideal memory for use in cryo-computing and
quantum computing peripheral devices.

categories: physics.comp-ph cond-mat.mes-hall cond-mat.supr-con physics.app-ph


title:
On virtual embeddings of braid groups into mapping class groups of
  surfaces

abstract:
  In this article, we give a necessary and sufficient condition for embedding a
finite index subgroup of Artin's braid group into the mapping class group of a
connected orientable surface.


title: Passive Motion Detection via mmWave Communication System

abstract:
  In this paper, an integrated passive sensing and communication system working
in 60 GHz band is elaborated, and the sensing performance is investigated in an
application of hand gesture recognition. Specifically, in this integrated
system, there are two radio frequency (RF) chains at the receiver and one at
the transmitter. Each RF chain is connected with one phased array for analog
beamforming. To facilitate simultaneous sensing and communication, the
transmitter delivers one stream of information-bearing signals via two beam
lobes, one is aligned with the main signal propagation path and the other is
directed to the sensing target. Signals from the two lobes are received by the
two RF chains at the receiver, respectively. By cross ambiguity coherent
processing, the time-Doppler spectrograms of hand gestures can be obtained.
Relying on the passive sensing system, a dataset of received signals, where
three types of hand gestures are sensed, is collected by using Line-of-Sight
(LoS) and Non-Line-of-Sight (NLoS) paths as the reference channel respectively.
Then a neural network is trained by the dataset for motion detection. It is
shown that the classification accuracy rate is high as long as sufficient
sensing time is assured. Finally, an empirical model characterizing the
relation between the classification accuracy and sensing duration is derived
analytically.


title:
Relaxation to a parity-time symmetric generalized Gibbs ensemble after a
  quantum quench in a driven-dissipative Kitaev chain

abstract:
  The construction of the generalized Gibbs ensemble, to which isolated
integrable quantum many-body systems relax after a quantum quench, is based
upon the principle of maximum entropy. In contrast, there are no universal and
model-independent laws that govern the relaxation dynamics and stationary
states of open quantum systems, which are subjected to Markovian drive and
dissipation. Yet, as we show, relaxation of driven-dissipative systems after a
quantum quench can, in fact, be determined by a maximum entropy ensemble, if
the Liouvillian that generates the dynamics of the system has parity-time
symmetry. Focusing on the specific example of a driven-dissipative Kitaev
chain, we show that, similarly to isolated integrable systems, the approach to
a parity-time symmetric generalized Gibbs ensemble becomes manifest in the
relaxation of local observables and the dynamics of entanglement. In contrast,
the directional pumping of fermion parity, which is induced by nontrivial
non-Hermitian topology of the Kitaev chain, represents a phenomenon that is
unique to relaxation dynamics in driven-dissipative systems. Upon increasing
the strength of dissipation, parity-time symmetry is broken at a finite
critical value, which thus constitutes a sharp dynamical transition that
delimits the applicability of the principle of maximum entropy. While we focus
here on the specific example of the Kitaev chain, our results generalize
readily to all parity-time symmetric and integrable driven-dissipative systems
that are described by or can be mapped to noninteracting fermions.


title:
Coexistence of Surface Lattice Resonances and Bound states In the
  Continuum in a Plasmonic Lattice

abstract:
  We present a numerical study on a 2D array of plasmonic structures covered by
a subwavelength film. We explain the origin of surface lattice resonances
(SLRs) using coupled dipole approximation and show that the
diffraction-assisted plasmonic resonances and formation of bound states in the
continuum (BICs) can be controlled by altering the optical environment. Our
study shows that when the refractive index contrast is small ( $\Delta$n <
-0.1), the SLR cannot be excited, while a significant contrast ($\Delta$n >
0.3) not only sustains plasmonic-induced resonances but also forms both
symmetry-protected and accidental BICs. The results can aid the streamlined
design of plasmonic lattices in studies on light-matter interaction and
applications in biosensors and optoelectronic devices.


title:
The preferential orientation of magnetic switchbacks, implications for
  solar magnetic flux transport

abstract:
  We investigate the orientation of the magnetic field deflections in
switchbacks (SB) to determine if they are characterised by a possible
preferential orientation. We compute the deflection angles of the magnetic
field relative to the Parker spiral direction for encounters 1 to 9 of the PSP
mission. We first characterize the distribution of these deflection angles for
calm solar wind intervals, and assess the precision of the Parker model as a
function of distance to the Sun. We then assume that the solar wind is composed
of two populations, the background calm solar wind and the population of SB,
characterized by larger fluctuations. We model the total distribution of
deflection angles we observe in the solar wind as a weighed sum of two distinct
normal distributions, each corresponding to one of the populations. We fit the
observed data with our model using a MCMC algorithm and retrieve the most
probable mean vector and covariance matrix coefficients of the two Gaussian
functions, as well as the population proportion. We first observe that the
accuracy of the spiral direction in the ecliptic is a function of radial
distance, in a manner that is consistent with PSP being near the solar wind
acceleration region. We then find that the fitted switchback population
presents a systematic bias in its deflections compared to the calm solar wind
population. This result holds for all encounters but E6, and regardless of the
magnetic field main polarity. This implies a marked preferential orientation of
SB in the clockwise direction in the ecliptic plane, and we discuss this result
and its implications in the context of the existing switchback formation
theories. Finally, we report the observation of a 12-hour patch of SB that
systematically deflect in the same direction, so that the magnetic field vector
tip within the patch deflects and returns to the Parker spiral within a given
plane.


title:
MI-BMInet: An Efficient Convolutional Neural Network for Motor Imagery
  Brain--Machine Interfaces with EEG Channel Selection

abstract:
  A brain--machine interface (BMI) based on motor imagery (MI) enables the
control of devices using brain signals while the subject imagines performing a
movement. It plays an important role in prosthesis control and motor
rehabilitation and is a crucial element towards the future Internet of Minds
(IoM). To improve user comfort, preserve data privacy, and reduce the system's
latency, a new trend in wearable BMIs is to embed algorithms on low-power
microcontroller units (MCUs) to process the electroencephalographic (EEG) data
in real-time close to the sensors into the wearable device. However, most of
the classification models present in the literature are too resource-demanding,
making them unfit for low-power MCUs. This paper proposes an efficient
convolutional neural network (CNN) for EEG-based MI classification that
achieves comparable accuracy while being orders of magnitude less
resource-demanding and significantly more energy-efficient than
state-of-the-art (SoA) models for a long-lifetime battery operation. We propose
an automatic channel selection method based on spatial filters and quantize
both weights and activations to 8-bit precision to further reduce the model
complexity with negligible accuracy loss. Finally, we efficiently implement and
evaluate the proposed models on a parallel ultra-low power (PULP) MCU. The most
energy-efficient solution consumes only 50.10 uJ with an inference runtime of
5.53 ms and an accuracy of 82.51% while using 6.4x fewer EEG channels, becoming
the new SoA for embedded MI-BMI and defining a new Pareto frontier in the
three-way trade-off among accuracy, resource cost, and power usage.


title:
On-the-fly Feature Based Speaker Adaptation for Dysarthric and Elderly
  Speech Recognition

abstract:
  Automatic recognition of dysarthric and elderly speech highly challenging
tasks to date. Speaker-level heterogeneity attributed to accent or gender
commonly found in normal speech, when aggregated with age and speech impairment
severity, create large diversity among speakers. Speaker adaptation techniques
play a crucial role in personalization of ASR systems for such users. Their
mobility issues limit the amount of speaker-level data available for model
based adaptation. To this end, this paper investigates two novel forms of
feature based on-the-fly rapid speaker adaptation approaches. The first is
based on speaker-level variance regularized spectral basis embedding (SBEVR)
features, while the other uses on-the-fly learning hidden unit contributions
(LHUC) transforms conditioned on speaker-level spectral features. Experiments
conducted on the UASpeech dysarthric and DimentiaBank Pitt elderly speech
datasets suggest the proposed SBEVR features based adaptation statistically
significantly outperform both the baseline on-the-fly i-Vector adapted hybrid
TDNN/DNN systems by up to 2.48% absolute (7.92% relative) reduction in word
error rate (WER), and offline batch mode model based LHUC adaptation using all
speaker-level data by 0.78% absolute (2.41% relative) in WER reduction.


title:
A flow approach to the prescribed Gaussian curvature problem in
  $\mathbb{H}^{n+1}$

abstract:
  In this paper, we study the following prescribed Gaussian curvature problem
$$K=\frac{\tilde{f}(\theta)}{\phi(\rho)^{\alpha-2}\sqrt{\phi(\rho)^2+|\bar{\nabla}\rho|^2}},$$
a generalization of the Alexandrov problem ($\alpha=n+1$) in hyperbolic space,
where $\tilde{f}$ is a smooth positive function on $\mathbb{S}^{n}$, $\rho$ is
the radial function of the hypersurface, $\phi(\rho)=\sinh\rho$ and $K$ is the
Gauss curvature. By a flow approach, we obtain the existence and uniqueness of
solutions to the above equations when $\alpha\geq n+1$. Our argument provides a
parabolic proof in smooth category for the Alexandrov problem in
$\mathbb{H}^{n+1}$. We also consider the cases $2<\alpha\leq n+1$ under the
evenness assumption of $\tilde{f}$ and prove the existence of solutions to the
above equations.


title: Compact objects of spherical symmetry in beyond Horndeski theories

abstract:
  We analyse in all generality beyond Horndeski theories of shift symmetry in a
static and spherically symmetric spacetime. By introducing four auxiliary
functions, we write the field equations in a particularly compact form. We show
that assuming additionally parity symmetry renders the system directly
integrable giving multiple families of black-hole solutions. These have
typically an asymptotically-flat Reissner-Nordstrom behaviour, and emerge in
the presence of a canonical kinetic term for the scalar field. In the absence
of parity symmetry, we present a general method which allows us to integrate
the field equations by choosing the form of only one coupling function and an
auxiliary quantity. This method leads to asymptotically flat and AdS black hole
solutions with differing properties. We finally discuss disformal
transformations within this context as a means of obtaining wormhole and black
hole solutions in different theories.


title:
An all-regime, well-balanced, positive and entropy satisfying one-step
  finite volume scheme for the Euler's equations of gas dynamics with gravity

abstract:
  In this paper, we propose a flux splitting finite volume method for the
approximation of the Euler equations with source terms derived from a
potential. The flux splitting strategy that we adopt here relies on a separate
treatment of the terms related to pressure effects from the terms related to
transport. We show that this approach can be recast into a relaxation
approximation that shares similarities with the Lagrange-Projection method, so
that the present flux splitting method can be viewed as an "unsplit
Lagrange-Projection" algorithm. We perform a truncation analysis error in the
low Mach regime that suggests a flux modification in order to preserve the
accuracy of the method in this regime. We show that the resulting method is
well-balanced in the sense that it preserves hydrostatic equilibrium profiles.
Under a CFL condition, the numerical scheme is also positivity preserving for
the mass and the internal energy and it also verifies a discrete entropy
inequality. One-dimensional and two-dimensional numerical experiments show the
ability of the method to deal with a wide range of Mach regime, shocks,
rarefactions and to preserves hydrostatic equilibrium states.


title: New insights into four-boson renormalization group limit cycles

abstract:
  Using machine learning techniques, we verify that the emergence of
renormalization group limit cycles beyond the unitary limit is transferred from
the three-boson subsystems to the whole four-boson system. Focussing on four
identical bosons, we first generate populations of synthetic singular
potentials within the latent space of a boosted ensemble of variational
autoencoders. After introducing the limit cycle loss for measuring the
deviation of a given renormalization group flow from limit cycle behavior, we
minimize it by applying an elitist genetic algorithm to the generated
populations. The fittest potentials are observed to accumulate around the
inverse-square potential, which we prove to generate limit cycles for four
bosons and which is already known to produce limit cycles in the three-boson
system. This also indicates that a four-body term does not enter low-energy
observables at leading order, since we do not observe any additional scale to
emerge.

categories: nucl-th cs.LG hep-th physics.comp-ph quant-ph


title: Isometric orbit equivalence for probability-measure preserving actions

abstract:
  We study probability-measure preserving (p.m.p.) actions of finitely
generated groups via the graphings they define. We introduce and study the
notion of isometric orbit equivalence for p.m.p. actions: two p.m.p. actions
are isometric orbit equivalent if the graphings defined by some fixed
generating systems of the groups are measurably isometric. We highlight two
kind of phenomena. First, we prove that the notion of isometric orbit
equivalence is rigid for groups whose Cayley graph, with respect to a fixed
generating system, has a countable group of automorphism. On the other hand, we
introduce a general construction of isometric orbit equivalent p.m.p. actions,
which leads to interesting nontrivial examples of isometric orbit equivalent
p.m.p. actions for the free group. In particular, our examples show that mixing
is not invariant under isometric orbit equivalence.


title: Ruling out an inflation driven by kinetic coupling

abstract:
  We demonstrate that the latest constraints on inflationary observables,
namely the tensor-to-scalar ratio $r$ and the scalar spectral index $n_{_S}$,
from the Cosmic Background Radiation (CMB) observations are already strong
enough to rule out the model of a scalar field kinetically coupled to gravity
with a positive coupling constant and a power law potential. The case for a
negative coupling constant needs special treatment.


title: Observation of quadruple Weyl point in hybrid-Weyl phononic crystals

abstract:
  The discovery of Weyl semimetals opens the door for searching topological
semimetals in physical science. The Weyl points are generally recognized as
conventional, quadratic, spin-1, and those of high topological charges. Here we
report the observation of the quadruple Weyl point of charge 4, the highest
topological charge a twofold degenerate node can carry. Besides the quadruple
Weyl point, the phononic semimetal also hosts conventional, quadratic, and
spin-1 Weyl points, which stands as a system with yet the richest types of Weyl
points. The quadruple-helicoid surface states, specific to the quadruple Weyl
point, are demonstrated. The finding of the high-charge Weyl point enriches the
knowledge of Weyl semimetals and may stimulate related researches in other
systems, such as photonic, mechanical and cold atom systems.


title: Bribes to Miners: Evidence from Ethereum

abstract:
  Though blockchain aims to alleviate bribing attacks, users can collude with
miners by directly sending bribes. This paper focuses on empirical evidence of
bribes to miners, and the detected behaviour implies that mining power could be
exploited. By scanning transactions on Ethereum, transactions for potential
direct bribes are filtered, and we find that the potential bribers and bribees
are centralized in a small group. After constructing proxies of active level of
potential bribing, we find that potential bribes can affect the status of
Ethereum and other mainstream blockchains, and network adoption of blockchain
can be influenced as well. Besides, direct bribes can be related to stock
markets, e.g., S&P 500 and Nasdaq.


title:
Analysis of compressible bubbly flows. Part II: Derivation of a
  macroscopic model

abstract:
  This paper is the second of the series of two papers, which focuses on the
derivation of an averaged 1D model for compressible bubbly flows. For this, we
start from a microscopic description of the interactions between a large but
finite number of small bubbles with a surrounding compressible fluid. This
microscopic model has been derived and analysed in the first paper. In the
present one, provided physical parameters scale according to the number of
bubbles, we prove that solutions to the microscopic model exist on a timespan
independent of the number of bubbles. Considering then that we have a large
number of bubbles, we propose a construction of the macroscopic variables and
derive the averaged system satisfied by these quantities. Our method is based
on a compactness approach in a strong-solution setting. In the last section, we
propose the derivation of the Williams-Boltzmann equation corresponding to our
setting.


title: The SAME score: Improved cosine based bias score for word embeddings

abstract:
  Over the last years, word and sentence embeddings have established as text
preprocessing for all kinds of NLP tasks and improved performances in these
tasks significantly. Unfortunately, it has also been shown that these
embeddings inherit various kinds of biases from the training data and thereby
pass on biases present in society to NLP solutions. Many papers attempted to
quantify bias in word or sentence embeddings to evaluate debiasing methods or
compare different embedding models, often with cosine-based scores. However,
some works have raised doubts about these scores showing that even though they
report low biases, biases persist and can be shown with other tests. In fact,
there is a great variety of bias scores or tests proposed in the literature
without any consensus on the optimal solutions. We lack works that study the
behavior of bias scores and elaborate their advantages and disadvantages. In
this work, we will explore different cosine-based bias scores. We provide a
bias definition based on the ideas from the literature and derive novel
requirements for bias scores. Furthermore, we thoroughly investigate the
existing cosine-based scores and their limitations in order to show why these
scores fail to report biases in some situations. Finally, we propose a new bias
score, SAME, to address the shortcomings of existing bias scores and show
empirically that SAME is better suited to quantify biases in word embeddings.


title: Creating bulk ultrastable glasses by random particle bonding

abstract:
  A recent breakthrough in glass science has been the synthesis of ultrastable
glasses via physical vapor deposition techniques. These samples display
enhanced thermodynamic, kinetic and mechanical stability, with important
implications for fundamental science and technological applications. However,
the vapor deposition technique is limited to atomic, polymer and organic
glass-formers and is only able to produce thin film samples. Here, we propose a
novel approach to generate ultrastable glassy configurations in the bulk, via
random particle bonding, and using computer simulations we show that this
method does indeed allow for the production of ultrastable glasses. Our
technique is in principle applicable to any molecular or soft matter system,
such as colloidal particles, with tunable bonding interactions, thus opening
the way to the design of ultrastable colloidal glasses.


title:
New orthogonality relations for super-Jack polynomials and an associated
  Lassalle--Nekrasov correspondence

abstract:
  The super-Jack polynomials, introduced by Kerov, Okounkov and Olshanski, are
polynomials in $n+m$ variables, which reduce to the Jack polynomials when $n=0$
or $m=0$ and provide joint eigenfunctions of the quantum integrals of the
trigonometric deformed Calogero-Moser-Sutherland system. We prove that the
super-Jack polynomials are orthogonal with respect to a bilinear form of the
form $(p,q)\mapsto (L_pq)(0)$, with $L_p$ quantum integrals of the rational
deformed Calogero-Moser-Sutherland system. In addition, we provide a new proof
of the Lassalle-Nekrasov correspondence between trigonometric and rational
harmonic deformed Calogero-Moser-Sutherland systems and infer orthogonality of
super-Hermite polynomials, which provide joint eigenfunctions of the latter
system.


title: Some Notes on Polyadic Concept Analysis

abstract:
  Despite the popularity of Formal Concept Analysis (FCA) as a mathematical
framework for data analysis, some of its extensions are still considered
arcane. Polyadic Concept Analysis (PCA) is one of the most promising yet
understudied of these extensions. This formalism offers many interesting open
questions but is hindered in its dissemination by complex notations and a lack
of agreed-upon basic definitions. In this paper, we discuss in a mostly
informal way the fundamental differences between FCA and PCA in the relation
between contexts, conceptual structures, and rules. We identify open questions,
present partial results on the maximal size of concept n-lattices and suggest
new research directions.


title: Boosting Black-Box Adversarial Attacks with Meta Learning

abstract:
  Deep neural networks (DNNs) have achieved remarkable success in diverse
fields. However, it has been demonstrated that DNNs are very vulnerable to
adversarial examples even in black-box settings. A large number of black-box
attack methods have been proposed to in the literature. However, those methods
usually suffer from low success rates and large query counts, which cannot
fully satisfy practical purposes. In this paper, we propose a hybrid attack
method which trains meta adversarial perturbations (MAPs) on surrogate models
and performs black-box attacks by estimating gradients of the models. Our
method uses the meta adversarial perturbation as an initialization and
subsequently trains any black-box attack method for several epochs.
Furthermore, the MAPs enjoy favorable transferability and universality, in the
sense that they can be employed to boost performance of other black-box
adversarial attack methods. Extensive experiments demonstrate that our method
can not only improve the attack success rates, but also reduces the number of
queries compared to other methods.


title: Cosmology of a new class of massive vector fields

abstract:
  I introduce (Extended) Proca-Nuevo, a non-linear theory of a massive spin-1
field enjoying a non-linearly realized constraint. I will provide a
covariantization scheme that allows for consistent, ghost-free cosmological
solutions, describing the correct number of dynamical variables in the presence
of perfect fluid matter. I will finally exhibit explicit hot Big Bang solutions
featuring a late-time self-accelerating epoch, where all the stability and
subluminality conditions are satisfied and where gravitational waves behave
precisely as in General Relativity.


title:
Self-calibration of photometric redshift scatter from DECaLS DR8 power
  spectrum and validation with simulated catalogues

abstract:
  The uncertainty in the photometric redshift estimation is one of the major
systematics in weak lensing cosmology. The self-calibration method is able to
reduce this systematics without assuming strong priors. We improve the recently
proposed self-calibration algorithm to enhance the stability and robustness
with the noisy measurement. The improved algorithm is tested on the power
spectrum measured from the simulated catalogues constructed according to DECaLS
DR8 photometric catalogue. For the fiducial analysis with 5 equal-width
redshift bins over $0<z<1$ and 6 bands over scales $100 \leqslant \ell<1000$,
we find that the improved algorithm successfully reconstructs the scatter rates
and the auto power spectrum in true redshift bins at the level of $\sim0.015$
and $\sim4.4\%$, respectively. The bias of the mean redshift is reduced by more
than 50 per cent compared to the photo-$z$ without self-calibration. The
reconstructed results of DECaLS DR8 galaxy sample are in line with the
expectations from the simulation validation, and are consistent with the
analysis on correlation functions in a companion paper. The self-calibration
code is publicly available at https://github.com/PengHui-hub/FP-NMF.


title: Hyperbolic inflation in the Jordan frame

abstract:
  We consider a multi-scalar field model in the Jordan frame which can be seen
as a two-scalar field model where the Brans-Dicke field interacts in the
kinetic part with the second scalar field. This theory under a conformal
transformation reduces to the hyperbolic inflation. We show that scaling
solutions and the de Sitter universe are provided by theory. In the study of
asymptotic dynamics, we determine an attractor where all the fluid sources
contribute in the cosmological fluid. This attractor is always a spiral and it
can be seen as the analogue of the hyperbolic inflation in the Jordan frame.


title:
Idea engines: A unified theory of innovation and obsolescence from
  markets and genetic evolution to science

abstract:
  Innovation and obsolescence describes dynamics of ever-churning and adapting
social and biological systems from the development of economic markets and
scientific progress to biological evolution. The shared aspect of this picture
is that agents destroy and extend the "idea lattice" in which they live,
finding new possibilities and rendering old solutions irrelevant. We focus on
this aspect with a simple model to study the central relationship between the
rates at which replicating agents discover new ideas and at which old ideas are
rendered obsolete. When these rates are equal, the space of the possible (e.g.
ideas, markets, technologies, mutations) remains finite. A positive or negative
difference distinguishes flourishing, ever-expanding idea lattices from
Schumpeterian dystopias in which obsolescence causes the system to collapse. We
map the phase space in terms of the rates at which new agents enter, replicate,
and die. When we extend our model to higher dimensional graphs, cooperative
agents, or inverted, obsolescence-driven innovation, we find that the essential
features of the model are preserved. In all cases, we predict variation in the
density profile of agents along the spectrum of new to old such as a drop in
density close to both frontiers. When comparing our model to data, we discover
that the density reveals a follow-the-leader dynamic in firm cost efficiency
and biological evolution, whereas scientific progress reflects consensus that
waits on old ideas to go obsolete. We show how the fundamental forces of
innovation and obsolescence provide a unifying perspective on complex systems
that may help us understand, harness, and shape their collective outcomes.


title:
Myoelectric Pattern Recognition Performance Enhancement Using Nonlinear
  Features

abstract:
  The multichannel electrode array used for electromyogram (EMG) pattern
recognition provides good performance, but it has a high cost, is
computationally expensive, and is inconvenient to wear. Therefore, researchers
try to use as few channels as possible while maintaining improved pattern
recognition performance. However, minimizing the number of channels affects the
performance due to the least separable margin among the movements possessing
weak signal strengths. To meet these challenges, two time-domain features based
on nonlinear scaling, the log of the mean absolute value (LMAV) and the
nonlinear scaled value (NSV), are proposed. In this study, we validate the
proposed features on two datasets, existing four feature extraction methods,
variable window size and various signal to noise ratios (SNR). In addition, we
also propose a feature extraction method where the LMAV and NSV are grouped
with the existing 11 time-domain features. The proposed feature extraction
method enhances accuracy, sensitivity, specificity, precision, and F1 score by
1.00%, 5.01%, 0.55%, 4.71%, and 5.06% for dataset 1, and 1.18%, 5.90%, 0.66%,
5.63%, and 6.04% for dataset 2, respectively. Therefore, the experimental
results strongly suggest the proposed feature extraction method, for taking a
step forward with regard to improved myoelectric pattern recognition
performance.


title:
A Hybrid Learning and Optimization Framework to Achieve Physically
  Interactive Tasks with Mobile Manipulators

abstract:
  This paper proposes a hybrid learning and optimization framework for mobile
manipulators for complex and physically interactive tasks. The framework
exploits the MOCA-MAN interface to obtain intuitive and simplified human
demonstrations and Gaussian Mixture Model/Gaussian Mixture Regression to encode
and generate the learned task requirements in terms of position, velocity, and
force profiles. Next, using the desired trajectories and force profiles
generated by GMM/GMR, the impedance parameters of a Cartesian impedance
controller are optimized online through a Quadratic Program augmented with an
energy tank to ensure the passivity of the controlled system. Two experiments
are conducted to validate the framework, comparing our method with two
approaches with constant stiffness (high and low). The results showed that the
proposed method outperforms the other two cases in terms of trajectory tracking
and generated interaction forces, even in the presence of disturbances such as
unexpected end-effector collisions.


title: Sublinear-Time Probabilistic Cellular Automata

abstract:
  We propose and investigate a probabilistic model of sublinear-time
one-dimensional cellular automata. In particular, we modify the model of ACA
(which are cellular automata that accept if and only if all cells
simultaneously accept) so that every cell changes its state not only dependent
on the states it sees in its neighborhood but also on an unbiased coin toss of
its own. The resulting model is dubbed \emph{probabilistic ACA} (PACA),
accordingly. We consider both one- and two-sided error versions of the model
(in the same spirit as the classical Turing machine classes $\mathsf{RP}$ and
$\mathsf{BPP}$) and establish a separation between the classes of languages
they can recognize all the way up to $o(\sqrt{n})$ time. We also prove that the
derandomization of $T(n)$-time PACA (to polynomial-time deterministic cellular
automata) for various regimes of $T(n) = \omega(\log n)$ implies non-trivial
derandomization results for the class $\mathsf{RP}$ (e.g., $\mathsf{P} =
\mathsf{RP}$). Last but not least, as our main contribution we give a full
characterization of the constant-time PACA classes: For one-sided error, the
class is equal to that of the deterministic model; that is, we prove that
constant-time one-sided error PACA can be fully derandomized with only a
constant multiplicative overhead in time complexity. As for two-sided error, we
characterize the respective class in terms of a linear threshold condition and
prove that it lies in-between the class of strictly locally testable languages
($\mathsf{SLT}$) and that of locally threshold testable languages
($\mathsf{LTT}$) while being incomparable to the locally testable languages
($\mathsf{LT}$).


title:
Dwell fatigue in titanium and the connection between slip intermittency
  and alloying

abstract:
  Dwell fatigue, the reduction in fatigue life experienced by titanium alloys
due to holds at stresses as low as 60% of yield, has been implicated in several
uncontained jet engine failures. Dislocation slip has long been observed to be
an intermittent, scale-bridging phenomenon, similar to that seen in earthquakes
but at the nanoscale, leading to the speculation that large stress bursts might
promote the initial opening of a crack. Here we observe such stress bursts at
the scale of individual grains in situ, using high energy X-ray diffraction
microscopy in Ti-7Al-O alloys. This shows that the detrimental effect of
precipitation of ordered Ti_3Al is to increase the magnitude of rare pri<a> and
bas<a> slip bursts associated with slip localisation. In contrast, the addition
of trace O interstitials is beneficial, reducing the magnitude of bas<a> slip
bursts and increasing the homogeneity between basal and prismatic <a> slip.
This is further evidence that the formation of long paths for easy basal plane
slip localisation should be avoided when engineering titanium alloys against
dwell fatigue.


title:
Adaptation to CT Reconstruction Kernels by Enforcing Cross-domain
  Feature Maps Consistency

abstract:
  Deep learning methods provide significant assistance in analyzing coronavirus
disease (COVID-19) in chest computed tomography (CT) images, including
identification, severity assessment, and segmentation. Although the earlier
developed methods address the lack of data and specific annotations, the
current goal is to build a robust algorithm for clinical use, having a larger
pool of available data. With the larger datasets, the domain shift problem
arises, affecting the performance of methods on the unseen data. One of the
critical sources of domain shift in CT images is the difference in
reconstruction kernels used to generate images from the raw data (sinograms).
In this paper, we show a decrease in the COVID-19 segmentation quality of the
model trained on the smooth and tested on the sharp reconstruction kernels.
Furthermore, we compare several domain adaptation approaches to tackle the
problem, such as task-specific augmentation and unsupervised adversarial
learning. Finally, we propose the unsupervised adaptation method, called
F-Consistency, that outperforms the previous approaches. Our method exploits a
set of unlabeled CT image pairs which differ only in reconstruction kernels
within every pair. It enforces the similarity of the network hidden
representations (feature maps) by minimizing mean squared error (MSE) between
paired feature maps. We show our method achieving 0.64 Dice Score on the test
dataset with unseen sharp kernels, compared to the 0.56 Dice Score of the
baseline model. Moreover, F-Consistency scores 0.80 Dice Score between
predictions on the paired images, which almost doubles the baseline score of
0.46 and surpasses the other methods. We also show F-Consistency to better
generalize on the unseen kernels and without the specific semantic content,
e.g., presence of the COVID-19 lesions.


title: Enriching Scholarly Knowledge with Context

abstract:
  Leveraging a GraphQL-based federated query service that integrates multiple
scholarly communication infrastructures (specifically, DataCite, ORCID, ROR,
OpenAIRE, Semantic Scholar, Wikidata and Altmetric), we develop a novel web
widget based approach for the presentation of scholarly knowledge with rich
contextual information. We implement the proposed approach in the Open Research
Knowledge Graph (ORKG) and showcase it on three kinds of widgets. First, we
devise a widget for the ORKG paper view that presents contextual information
about related datasets, software, project information, topics, and metrics.
Second, we extend the ORKG contributor profile view with contextual information
including authored articles, developed software, linked projects, and research
interests. Third, we advance ORKG comparison faceted search by introducing
contextual facets (e.g. citations). As a result, the devised approach enables
presenting ORKG scholarly knowledge flexibly enriched with contextual
information sourced in a federated manner from numerous technologically
heterogeneous scholarly communication infrastructures.


title:
Nonlinear geometrically exact dynamics of fluid-conveying cantilevered
  hard magnetic soft pipe with uniform and nonuniform magnetizations

abstract:
  It is generally acknowledged that a hanging cantilevered pipe conveying fluid
becomes unstable by flutter-type instability at a critical flow velocity;
moreover, the pipe undergoes periodic self-excited oscillations in the
post-flutter region. Additionally, the critical flow velocity increases when
the magnetized pipe is exposed to an actuating parallel magnetic field. The
question arises as to whether the actuating magnetic field leads to lessening
the oscillation amplitude of the system in the post-flutter region. To answer
the question, the nonlinear responses of a fluid-conveying cantilevered hard
magnetic soft pipe with uniform and nonuniform magnetizations under an
actuating parallel magnetic field are examined. In the case of the nonuniform
magnetization, the mass density and elastic modulus of the pipe in addition to
its residual magnetic flux density vary along its length. The mathematical
formulation is constructed via a nonlinear geometrically exact model and is
solved by employing the Galerkin technique in conjunction with the Runge-Kutta
finite difference scheme. The numerical results are then analyzed to reveal the
role of magnetization in the magneto-hydro-elastic responses of the system in
the absence and presence of magnetic field.


title: Data-driven bicycle network planning for demand and safety

abstract:
  Developing safe infrastructure for cycling and micromobility is an efficient
pathway towards climate-friendly, sustainable, and livable cities. However,
urban cycling infrastructure is typically planned ad-hoc and at best informed
by survey data. For a systematic, data-driven planning process here we develop
an automated planning framework using data of the existing network and of
empirical e-scooter trips and bicycle crashes as proxies for demand and safety,
to generate a cohesive network optimizing public health benefits while
minimizing investments. We introduce a parameter that tunes the focus between
demand-based and safety-based development, and investigate systematically this
tradeoff for the city of Turin. We find that a full focus on demand or safety
generates different network extensions in the short term, with an optimal
tradeoff in-between. In the long term our framework improves overall network
quality independent of short-term focus. Thus, this data-driven process can
provide urban planners with automated assistance for variable short-term
scenario planning while maintaining the long-term goal of a sustainable,
city-spanning micromobility network.


title: Low energy cosmic rays

abstract:
  Low energy cosmic rays (up to the GeV energy domain) play a crucial role in
the physics and chemistry of the densest phase of the interstellar medium.
Unlike interstellar ionising radiation, they can penetrate large column
densities of gas, and reach molecular cloud cores. By maintaining there a small
but not negligible gas ionisation fraction, they dictate the coupling between
the plasma and the magnetic field, which in turn affects the dynamical
evolution of clouds and impacts on the process of star and planet formation.
The cosmic-ray ionisation of molecular hydrogen in interstellar clouds also
drives the rich interstellar chemistry revealed by observations of spectral
lines in a broad region of the electromagnetic spectrum, spanning from the
submillimetre to the visual band. Some recent developments in various branches
of astrophysics provide us with an unprecedented view on low energy cosmic
rays. Accurate measurements and constraints on the intensity of such particles
are now available both for the very local interstellar medium and for distant
interstellar clouds. The interpretation of these recent data is currently
debated, and the emerging picture calls for a reassessment of the scenario
invoked to describe the origin and/or the transport of low energy cosmic rays
in the Galaxy.


title:
DV-QKD Coexistence With 1.6 Tbps Classical Channels Over Hollow Core
  Fibre

abstract:
  The feasibility of coexisting a quantum channel with carrier-grade classical
optical channels over Hollow Core Nested Antiresonant Nodeless Fibre (HC-NANF)
is experimentally explored for the first time in terms of achievable quantum
bit error rate (QBER), secret key rate (SKR) as well as classical signal bit
error rates (BER). A coexistence transmission of 1.6 Tbps is achieved for the
classical channels simultaneously with a quantum channel over a 2 km-long
HC-NANF with a total coexistence power of 0 dBm. To find the best and worst
wavelength position for the classical channels, we simulated different
classical channels bands with different spacing between the quantum and
classical channels considering the crosstalk generated from both Raman
scattering and four-wave-mixing (FWM) on the quantum channel. Following our
simulation, we numerically estimate the best (Raman spectrum dip) and worst
locations (Raman spectrum peak) of the classical channel with respect to its
impact on the performance on the quantum channel in terms of SKR and QBER. We
further implemented a testbed to experimentally test both single mode fibre
(SMF) and HC-NANF in the best and worst-case scenarios. In the best-case
scenario, the spacing between quantum and classical is 200 GHz (1.6 nm) with 50
GHz (0.4 nm) spacing between each classical channel. The SKR was preserved
without any noticeable changes when coexisting the quantum channel with eight
classical channels at 0 dBm total coexistence power in HC-NANF compared to a
significant drop of 73% when using SMF at -24 dBm total coexistence power which
is 250 times lower than the power used in HC-NANF. In the worst-case scenario
using the same powers, and with 1 THz (8 nm) spacing between quantum and
classical channels, the SKR dropped 10% using the HC-NANF, whereas in the SMF
the SKR plummeted to zero.


title: Towards Implicit Text-Guided 3D Shape Generation

abstract:
  In this work, we explore the challenging task of generating 3D shapes from
text. Beyond the existing works, we propose a new approach for text-guided 3D
shape generation, capable of producing high-fidelity shapes with colors that
match the given text description. This work has several technical
contributions. First, we decouple the shape and color predictions for learning
features in both texts and shapes, and propose the word-level spatial
transformer to correlate word features from text with spatial features from
shape. Also, we design a cyclic loss to encourage consistency between text and
shape, and introduce the shape IMLE to diversify the generated shapes. Further,
we extend the framework to enable text-guided shape manipulation. Extensive
experiments on the largest existing text-shape benchmark manifest the
superiority of this work. The code and the models are available at
https://github.com/liuzhengzhe/Towards-Implicit Text-Guided-Shape-Generation.


title:
Dynamic state and parameter estimation in multi-machine power systems -
  Experimental demonstration using real-world PMU-measurements

abstract:
  Dynamic state and parameter estimation (DSE) plays a key role for reliably
monitoring and operating future, power-electronics-dominated power systems.
While DSE is a very active research field, experimental applications of
proposed algorithms to real-world systems remain scarce. This motivates the
present paper, in which we demonstrate the effectiveness of a DSE algorithm
previously presented by parts of the authors with real-world data collected by
a Phasor Measurement Unit (PMU) at a substation close to a power plant within
the extra-high voltage grid of Germany. To this end, at first we derive a
suitable mapping of the real-world PMU-measurements recorded at a substation
close to the power plant to the terminal bus of the power plants' synchronous
generator (SG). This mapping considers the high-voltage (HV) transmission line,
the tap-changing transformer and the auxiliary system of the power plant. Next,
we introduce several practically motivated extensions to the estimation
algorithm, which significantly improve its practical performance with
real-world measurements. Finally, we successfully validate the algorithm
experimentally in an auto- as well as a cross-validation.


title:
Sobolev Inequalities In Manifolds With Asymptotically Nonnegative
  Curvature

abstract:
  Using the ABP-method as in a recent work by Brendle, we establish some sharp
Sobolev and isoperimetric inequalities for compact domains and submanifolds in
a complete Riemannian manifold with asymptotically nonnegative curvature. These
inequalities generalize those given by Brendle in the case of complete
Riemannian manifolds with nonnegative curvature.


title: Chaos onset in large rings of Bose-Einstein condensates

abstract:
  We consider large rings of weakly-coupled Bose-Einstein condensates,
analyzing their transition to chaotic dynamics and loss of coherence.
Initially, a ring is considered to be in an eigenstate, i.e. in a commensurate
configuration with equal site fillings and equal phase differences between
neighboring sites. Such a ring should exhibit a circulating current whose value
will depend on the initial, non-zero phase difference. The appearance of such
currents is a signature of an established coherence along the ring. If phase
difference falls between $\pi/2$ and $3\pi/2$ and interparticle interaction in
condensates exceeds a critical interaction value $u_c$, the coherence is
supposed to be quickly destroyed because the system enters a chaotic regime due
to inherent instabilities. This is, however, only a part of the story. It turns
out that chaotic dynamics and resulting averaging of circular current to zero
is generally offset by a critical time-scale $t_c$, which is almost two orders
of magnitude larger than the one expected from the linear stability analysis.
We study the critical time-scale in detail in a broad parameter range.


title: A proof of Toponogov's theorem in Alexandrov geometry

abstract:
  This paper aims to give an elementary proof for Toponogov's theorem in
Alexandrov geometry with lower curvature bound. The idea of the proof comes
from the fact that, in Riemannian geometry, sectional curvature can be embodied
in the second variation formula.


title:
Composite Anderson acceleration method with dynamic window-sizes and
  optimized damping

abstract:
  In this paper, we propose and analyze a set of fully non-stationary Anderson
acceleration algorithms with dynamic window sizes and optimized damping.
Although Anderson acceleration (AA) has been used for decades to speed up
nonlinear solvers in many applications, most authors are simply using and
analyzing the stationary version of Anderson acceleration (sAA) with fixed
window size and a constant damping factor. The behavior and potential of the
non-stationary version of Anderson acceleration methods remain an open
question. Since most efficient linear solvers use composable algorithmic
components. Similar ideas can be used for AA to solve nonlinear systems. Thus
in the present work, to develop non-stationary Anderson acceleration
algorithms, we first propose two systematic ways to dynamically alternate the
window size $m$ by composition. One simple way to package sAA(m) with sAA(n) in
each iteration is applying sAA(m) and sAA(n) separately and then average their
results. It is an additive composite combination. The other more important way
is the multiplicative composite combination, which means we apply sAA(m) in the
outer loop and apply sAA(n) in the inner loop. By doing this, significant gains
can be achieved. Secondly, to make AA to be a fully non-stationary algorithm,
we need to combine these strategies with our recent work on the non-stationary
Anderson acceleration algorithm with optimized damping (AAoptD), which is
another important direction of producing non-stationary AA and nice performance
gains have been observed. Moreover, we also investigate the rate of convergence
of these non-stationary AA methods under suitable assumptions. Finally, our
numerical results show that some of these proposed non-stationary Anderson
acceleration algorithms converge faster than the stationary sAA method and they
may significantly reduce the storage and time to find the solution in many
cases.


title: FS6D: Few-Shot 6D Pose Estimation of Novel Objects

abstract:
  6D object pose estimation networks are limited in their capability to scale
to large numbers of object instances due to the close-set assumption and their
reliance on high-fidelity object CAD models. In this work, we study a new open
set problem; the few-shot 6D object poses estimation: estimating the 6D pose of
an unknown object by a few support views without extra training. To tackle the
problem, we point out the importance of fully exploring the appearance and
geometric relationship between the given support views and query scene patches
and propose a dense prototypes matching framework by extracting and matching
dense RGBD prototypes with transformers. Moreover, we show that the priors from
diverse appearances and shapes are crucial to the generalization capability
under the problem setting and thus propose a large-scale RGBD photorealistic
dataset (ShapeNet6D) for network pre-training. A simple and effective online
texture blending approach is also introduced to eliminate the domain gap from
the synthesis dataset, which enriches appearance diversity at a low cost.
Finally, we discuss possible solutions to this problem and establish benchmarks
on popular datasets to facilitate future research. The project page is at
\url{https://fs6d.github.io/}.


title:
A quantitative comparison of plantar soft tissue strainability
  distribution and homogeneity between ulcerated and non-ulcerated patients
  using strain elastography

abstract:
  The primary objective of this study was to develop a method that allows
accurate quantification of plantar soft tissue stiffness distribution and
homogeneity. The secondary aim of this study is to investigate if the
differences in soft tissue stiffness distribution and homogeneity can be
detected between ulcerated and non-ulcerated foot. Novel measures of individual
pixel stiffness, named as quantitative strainability (QS) and relative
strainability (RS) were developed. SE data obtained from 39 (9 with active
diabetic foot ulcers) patients with diabetic neuropathy. The patients with
active diabetic foot ulcer had wound in parts of the foot other than the first
metatarsal head and the heel where the elastography measures were conducted. RS
was used to measure changes and gradients in the stiffness distribution of
plantar soft tissues in participants with and without active diabetic foot
ulcer. The plantar soft tissue homogeneity in superior-inferior direction in
the left forefoot was significantly (p<0.05) higher in ulcerated group compared
to non-ulcerated group. The assessment of homogeneity showed potentials to
further explain the nature of the change in tissue that can increase internal
stress . This can have implications in assessing the vulnerability to soft
tissue damage and ulceration in diabetes.


title: Surface wave dispersion inversion using an energy likelihood function

abstract:
  Seismic surface wave dispersion inversion is used widely to study the
subsurface structure of the Earth. The dispersion property is usually measured
by using frequency-phase velocity (f-c) analysis and by picking phase
velocities from the obtained f-c spectrum. However, because of potential
contamination the f-c spectrum often has multimodalities at each frequency for
each mode. These introduce uncertainty and errors in the picked phase
velocities, and consequently the obtained shear velocity structure is biased.
To overcome this issue, in this study we introduce a new method which directly
uses the spectrum as data. We achieve this by solving the inverse problem in a
Bayesian framework and define a new likelihood function, the energy likelihood
function, which uses the spectrum energy to define data fit. We apply the new
method to a land dataset recorded by a dense receiver array, and compare the
results to those obtained using the traditional method. The results show that
the new method produces more accurate results since they better match
independent data from refraction tomography. This real-data application also
shows that it can be applied efficiently since it removes the need to pick
phase velocities, and with relatively little adjustment to current practice
since it uses standard f-c panels to define the likelihood. We therefore
recommend using the energy likelihood function rather than explicitly picking
phase velocities in surface wave dispersion inversion.


title: Wormholes with a warped extra dimension?

abstract:
  We investigate the role of a specifically warped extra dimension in
constructing examples of higher dimensional spacetimes representing Lorentzian
wormholes. The warping chosen is largely inspired by the well-known non-static
Witten bubble of nothing, though our spacetimes are static and geometrically
different. Vacuum solutions in $D\geq 5$ dimensions and others
(non-asymptotically flat) with `perfectly normal' matter stress energy are
interpreted as possible Lorentzian wormholes. Asymptotically flat wormholes in
$D\geq 5$ with `exotic matter' and within this class of spacetimes also appear
to exist in all dimensions. A wormhole-black hole correspondence via double
Wick rotation is revisited and discussed. Finally, geodesic motion as well as
the behaviour of geodesic congruences, in the sub-class of five dimensional,
warped, vacuum wormhole spacetimes is also briefly analysed, with the aim of
obtaining characteristic properties and specific signatures which may help
improve our understanding of these geometries.


title: Isomorphic Cross-lingual Embeddings for Low-Resource Languages

abstract:
  Cross-Lingual Word Embeddings (CLWEs) are a key component to transfer
linguistic information learnt from higher-resource settings into lower-resource
ones. Recent research in cross-lingual representation learning has focused on
offline mapping approaches due to their simplicity, computational efficacy, and
ability to work with minimal parallel resources. However, they crucially depend
on the assumption of embedding spaces being approximately isomorphic i.e.
sharing similar geometric structure, which does not hold in practice, leading
to poorer performance on low-resource and distant language pairs. In this
paper, we introduce a framework to learn CLWEs, without assuming isometry, for
low-resource pairs via joint exploitation of a related higher-resource
language. In our work, we first pre-align the low-resource and related language
embedding spaces using offline methods to mitigate the assumption of isometry.
Following this, we use joint training methods to develops CLWEs for the related
language and the target embed-ding space. Finally, we remap the pre-aligned
low-resource space and the target space to generate the final CLWEs. We show
consistent gains over current methods in both quality and degree of
isomorphism, as measured by bilingual lexicon induction (BLI) and eigenvalue
similarity respectively, across several language pairs: {Nepali, Finnish,
Romanian, Gujarati, Hungarian}-English. Lastly, our analysis also points to the
relatedness as well as the amount of related language data available as being
key factors in determining the quality of embeddings achieved.


title:
An Effective Framework of Private Ethereum Blockchain Networks for Smart
  Grid

abstract:
  A smart grid is an important application in Industry 4.0 with a lot of new
technologies and equipment working together. Hence, sensitive data stored in
the smart grid is vulnerable to malicious modification and theft. This paper
proposes a framework to build a smart grid based on a highly effective private
Ethereum network. Our framework provides a real smart grid that includes modern
hardware and a smart contract to secure data in the blockchain network. To
obtain high throughput but a low uncle rate, the difficulty calculation method
used in the mining process of the Ethereum consensus mechanism is modified to
adapt to the practical smart grid setup. The performance in terms of throughput
and latency are evaluated by simulation and verified by the real smart grid
setup. The enhanced private Ethereum-based smart grid has significantly better
performance than the public one. Moreover, this framework can be applied to any
system used to store data in the Ethereum network.


title: Lindblad evolutions, Born rule, Heralding and cloning

abstract:
  We discuss an apparent difficulty in computing the radiation emitted by a
system undergoing Lindblad evolution. The difficulty is resolved by viewing the
problem as Born rule for conserved currents defined by the appropriate terms in
the adjoint Lindbladian.
  In Heralding Alice prepares Bob's system in a state that mirrors her test. We
show that heralding is consistent with no-cloning. This follows from the
observation that heralding is not a completely positive map.


title:
Embedding short-range correlations in relativistic density functionals
  through quasi-deuterons

abstract:
  The formation of clusters at sub-saturation densities constitutes an
essential feature for a reliable modelization of the nuclear matter equation of
state (EoS). Phenomenological models that make use of energy density
functionals (EDFs) offer a convenient approach to account for the presence of
these bound states of nucleons when introduced as additional degrees of
freedom. However, in these models clusters dissolve, by construction, when the
nuclear saturation density is approached from below, revealing inconsistencies
with recent findings that evidence the existence of short-range correlations
(SRCs) even at larger densities.
  In this work, within the EDF framework, a novel approach is proposed to embed
SRCs within a relativistic mean-field model with density dependent couplings.
This is realized through the introduction of suitable in-medium modifications
of the cluster binding energy shifts, which are responsible for describing the
cluster dissolution. As a first exploratory step, the example of a
quasi-deuteron within the generalized relativistic density functional approach
is investigated.
  For the first time, suitable parameterizations of the cluster mass shift at
zero temperature are derived for all baryon densities. They are constrained by
experimental results for the effective deuteron fraction in nuclear matter near
saturation and by microscopic many-body calculations in the low-density limit.
The strength of the deuteron-meson couplings is assessed to be of crucial
importance. The findings of the present study represent a first step to improve
the description of nuclear matter and its EoS at supra-saturation densities in
EDFs by considering correlations in an effective way. Novel effects on some
thermodynamic quantities, such as the matter incompressibility, the symmetry
energy and its slope, are finally discerned and discussed.


title:
A 3D Positioning-based Channel Estimation Method for RIS-aided mmWave
  Communications

abstract:
  A fundamental challenge in millimeter-wave (mmWave) communication is the
susceptibility to blocking objects. One way to alleviate this problem is the
use of reconfigurable intelligent surfaces (RIS). Nevertheless, due to the
large number of passive reflecting elements on RIS, channel estimation turns
out to be a challenging task. In this paper, we address the channel estimation
for RIS-aided mmWave communication systems based on a localization method. The
proposed idea consists of exploiting the sparsity of the mmWave channel and the
topology of the RIS. In particular, we first propose the concept of reflecting
unit set (RUS) to improve the flexibility of RIS. We then propose a novel
coplanar maximum likelihood-based (CML) 3D positioning method based on the RUS,
and derive the Cramer-Rao lower bound (CRLB) for the positioning method.
Furthermore, we develop an efficient positioning-based channel estimation
scheme with low computational complexity. Compared to state-of-the-art methods,
our proposed method requires less time-frequency resources in channel
acquisition as the complexity is independent to the total size of the RIS but
depends on the size of the RUSs, which is only a small portion of the RIS.
Large performance gains are confirmed in simulations, which proves the
effectiveness of the proposed method.


title: On the helicity of radiative electromagnetic fields

abstract:
  A relativistically invariant expression for the magnetic and electric
helicities of free electromagnetic field through the currents, which created
those field, are derived. It is demonstrated that the both helicities are
conserved quantities of radiative fields and are equal to each other. Moreover
the conservation of the helicity in radiative fields means that in such fields
electric and magnetic fields are always orthogonal to each other. Proceeding to
the quantum theory, it is shown that the corresponding quantum helicity field
operator is equal to the sum of projections of spin moments of all photons onto
their momentums. Finally, as an example, the expressions for the total
helicity, number of photons and the total spin momentum of the field of a
dipole electromagnetic pulse are obtained and discussed.


title:
Tomography-based observational measurements of the halo mass function
  via the submillimeter magnification bias

abstract:
  Aims. The main goal of this paper is to derive observational constraints on
the halo mass fuction (HMF) by performing a tomographic analysis of the
magnification bias signal on a sample of background submillimeter galaxies. The
results can then be compared with those from a non-tomographic study. Methods.
We measure the cross-correlation function between a sample of foreground GAMA
galaxies with spectroscopic redshifts in the range $0.1 < z < 0.8$ (and divided
up into four bins) and a sample of background submillimeter galaxies from
H-ATLAS with photometric redshifts in the range $1.2 < z < 4.0$. We model the
weak lensing signal within the halo model formalism and carry out a Markov
chain Monte Carlo algorithm to obtain the posterior distribution of all HMF
parameters, which we assume to follow the Sheth and Tormen (ST) three-parameter
and two-parameter fits. Results. While the observational constraints on the HMF
from the non-tomographic analysis are not stringent, there is a remarkable
improvement in terms of uncertainty reduction when tomography is adopted.
Moreover, with respect to the traditional ST triple of values from numerical
simulations, the results from the three-parameter fit predict a higher number
density of halos at masses below $10^{12}M_{\odot}/h$ at 95% credibility. The
two-parameter fit yields even more restricting results, with a larger number
density of halos below $10^{13}M_{\odot}/h$ and a lower one above
$10^{14}M_{\odot}/h$, this time at more than 3$\sigma$ credibility. Our results
are therefore in disagreement with the standard N-body values for the ST fit at
2$\sigma$ and 3$\sigma$, respectively.


title:
SyncNet: Using Causal Convolutions and Correlating Objective for Time
  Delay Estimation in Audio Signals

abstract:
  This paper addresses the task of performing robust and reliable time-delay
estimation in audio-signals in noisy and reverberating environments. In
contrast to the popular signal processing based methods, this paper proposes
machine learning based method, i.e., a semi-causal convolutional neural network
consisting of a set of causal and anti-causal layers with a novel
correlation-based objective function. The causality in the network ensures
non-leakage of representations from future time-intervals and the proposed loss
function makes the network generate sequences with high correlation at the
actual time delay. The proposed approach is also intrinsically interpretable as
it does not lose time information. Even a shallow convolution network is able
to capture local patterns in sequences, while also correlating them globally.
SyncNet outperforms other classical approaches in estimating mutual time delays
for different types of audio signals including pulse, speech and musical beats.


title: Analysis of Voice Conversion and Code-Switching Synthesis Using VQ-VAE

abstract:
  This paper presents an analysis of speech synthesis quality achieved by
simultaneously performing voice conversion and language code-switching using
multilingual VQ-VAE speech synthesis in German, French, English and Italian. In
this paper, we utilize VQ code indices representing phone information from
VQ-VAE to perform code-switching and a VQ speaker code to perform voice
conversion in a single system with a neural vocoder. Our analysis examines
several aspects of code-switching including the number of language switches and
the number of words involved in each switch. We found that speech synthesis
quality degrades after increasing the number of language switches within an
utterance and decreasing the number of words. We also found some evidence of
accent transfer when performing voice conversion across languages as observed
when a speaker's original language differs from the language of a synthetic
target utterance. We present results from our listening tests and discuss the
inherent difficulties of assessing accent transfer in speech synthesis. Our
work highlights some of the limitations and strengths of using a
semi-supervised end-to-end system like VQ-VAE for handling multilingual
synthesis. Our work provides insight into why multilingual speech synthesis is
challenging and we suggest some directions for expanding work in this area.


title:
Subjective Evaluation of Deep Learning Models for Symbolic Music
  Composition

abstract:
  Deep learning models are typically evaluated to measure and compare their
performance on a given task. The metrics that are commonly used to evaluate
these models are standard metrics that are used for different tasks. In the
field of music composition or generation, the standard metrics used in other
fields have no clear meaning in terms of music theory. In this paper, we
propose a subjective method to evaluate AI-based music composition systems by
asking questions related to basic music principles to different levels of users
based on their musical experience and knowledge. We use this method to compare
state-of-the-art models for music composition with deep learning. We give the
results of this evaluation method and we compare the responses of each user
level for each evaluated model.


title: SPIQ: Data-Free Per-Channel Static Input Quantization

abstract:
  Computationally expensive neural networks are ubiquitous in computer vision
and solutions for efficient inference have drawn a growing attention in the
machine learning community. Examples of such solutions comprise quantization,
i.e. converting the processing values (weights and inputs) from floating point
into integers e.g. int8 or int4. Concurrently, the rise of privacy concerns
motivated the study of less invasive acceleration methods, such as data-free
quantization of pre-trained models weights and activations. Previous approaches
either exploit statistical information to deduce scalar ranges and scaling
factors for the activations in a static manner, or dynamically adapt this range
on-the-fly for each input of each layers (also referred to as activations): the
latter generally being more accurate at the expanse of significantly slower
inference. In this work, we argue that static input quantization can reach the
accuracy levels of dynamic methods by means of a per-channel input quantization
scheme that allows one to more finely preserve cross-channel dynamics. We show
through a thorough empirical evaluation on multiple computer vision problems
(e.g. ImageNet classification, Pascal VOC object detection as well as
CityScapes semantic segmentation) that the proposed method, dubbed SPIQ,
achieves accuracies rivalling dynamic approaches with static-level inference
speed, significantly outperforming state-of-the-art quantization methods on
every benchmark.


title:
Computational performance studies for space-time phase-field fracture
  optimal control problems

abstract:
  The purpose of this work are computational demonstations for a newly
developed space-time phase-field fracture optimal control framework. The
optimal control solution algorithm is a Newton algorithm, which is obtained
with the reduced approach by eliminating the state constraint. Due to the crack
irreversibility constraint, a rate-independent problem arises, which is treated
by penalization and for which we utilize a space-time approach. Therein, we
deal with the state, adjoint, tangent, and adjoint Hessian equations. Our fully
discretized space-time optimization algorithm is presented and extensively
tested with various numerical experiments.