title:
Low temperature synthesis of BiFeO$_{3}$ nanoparticles with enhanced
  magnetization and promising photocatalytic performance in dye degradation and
  hydrogen evolution

abstract:
  In this investigation, we have synthesized BiFeO$_{3}$ nanoparticles by
varying hydrothermal reaction temperatures from 200 $^{\circ}$C to 120
$^{\circ}$C to assess their visible-light-driven photocatalytic activity along
with their applicability for hydrogen production via water splitting. The
rhombohedral perovskite structure of BiFeO$_{3}$ is formed for hydrothermal
reaction temperature up to 160 $^{\circ}$C, however, for a further decrement of
reaction temperature a mixed sillenite phase is observed. The XRD Rietveld
analysis, XPS analysis and FESEM imaging ensure the formation of single-phase
and well crystalline nanoparticles at 160 $^{\circ}$C reaction temperature with
20 nm of average size. The nanoparticles fabricated at this particular reaction
temperature also exhibit improved magnetization, reduced leakage current
density and excellent ferroelectric behavior. These nanoparticles demonstrate
considerably high absorbance in the visible range with a low bandgap (2.1 eV).
The experimentally observed bandgap is in excellent agreement with the
calculated bandgap using the first-principles calculations. The favorable
photocatalytic performance of these nanoparticles has been able to generate
more than two times of solar hydrogen compared to that produced by bulk
BiFeO$_{3}$ as well as commercially available Degussa P25 titania. Notably, the
experimentally observed bandgap is almost equal for both bulk material and
nanoparticles prepared at different reaction temperatures. Therefore, in solar
energy applications, the superiority of BFO nanoparticles prepared at 160
$^{\circ}$C reaction temperature may be attributed not only to solely their
bandgap but also to other factors, such as reduced particle size, excellent
morphology, well crystallinity, large surface to volume ratio, ferroelectricity
and so on.


title: Iota: A Framework for Analyzing System-Level Security of IoTs

abstract:
  Most IoT systems involve IoT devices, communication protocols, remote cloud,
IoT applications, mobile apps, and the physical environment. However, existing
IoT security analyses only focus on a subset of all the essential components,
such as device firmware, and ignore IoT systems' interactive nature, resulting
in limited attack detection capabilities. In this work, we propose Iota, a
logic programming-based framework to perform system-level security analysis for
IoT systems. Iota generates attack graphs for IoT systems, showing all of the
system resources that can be compromised and enumerating potential attack
traces. In building Iota, we design novel techniques to scan IoT systems for
individual vulnerabilities and further create generic exploit models for IoT
vulnerabilities. We also identify and model physical dependencies between
different devices as they are unique to IoT systems and are employed by
adversaries to launch complicated attacks. In addition, we utilize NLP
techniques to extract IoT app semantics based on app descriptions. To evaluate
vulnerabilities' system-wide impact, we propose two metrics based on the attack
graph, which provide guidance on fortifying IoT systems. Evaluation on 127 IoT
CVEs (Common Vulnerabilities and Exposures) shows that Iota's exploit modeling
module achieves over 80% accuracy in predicting vulnerabilities' preconditions
and effects. We apply Iota to 37 synthetic smart home IoT systems based on
real-world IoT apps and devices. Experimental results show that our framework
is effective and highly efficient. Among 27 shortest attack traces revealed by
the attack graphs, 62.8% are not anticipated by the system administrator. It
only takes 1.2 seconds to generate and analyze the attack graph for an IoT
system consisting of 50 devices.


title:
The vanishing discount problem for monotone systems of Hamilton-Jacobi
  equations: a counterexample to the full convergence

abstract:
  In recent years there has been intense interest in the vanishing discount
problem for Hamilton-Jacobi equations. In the case of the scalar equation, B.
Ziliotto has recently given an example of the Hamilton-Jacobi equation having
non-convex Hamiltonian in the gradient variable, for which the full convergence
of the solutions does not hold as the discount factor tends to zero. We give
here an explicit example of nonlinear monotone systems of Hamilton-Jacobi
equations having convex Hamiltonians in the gradient variable, for which the
full convergence of the solutions fails as the discount factor goes to zero.


title:
Enhanced photocatalytic dye degradation and hydrogen production ability
  of Bi$_{25}$FeO$_{40}$-rGO nanocomposite and mechanism insight

abstract:
  A comprehensive comparison between Bi$_{25}$FeO$_{40}$-reduced graphene
oxide(rGO) nanocomposite and BiFeO$_{3}$-rGO nanocomposite has been performed
to investigate their photocatalytic abilities in degradation of Rhodamine B dye
and generation of hydrogen by water-splitting. The hydrothermal technique
adapted for synthesis of the nanocomposites provides a versatile
temperature-controlled phase selection between perovskite BiFeO$_{3}$ and
sillenite Bi$_{25}$FeO$_{40}$. Both perovskite and sillenite structured
nanocomposites are stable and exhibit considerably higher photocatalytic
ability over pure BiFeO$_{3}$ nanoparticles and commercially available Degussa
P25 titania. Notably, Bi$_{25}$FeO$_{40}$- rGO nanocomposite has demonstrated
superior photocatalytic ability and stability under visible light irradiation
than that of BiFeO$_{3}$-rGO nanocomposite. The possible mechanism behind the
superior photocatalytic performance of Bi$_{25}$FeO$_{40}$-rGO nanocomposite
has been critically discussed.


title:
Asymptotic Critical Radii in Random Geometric Graphs over 3-Dimensional
  Convex regions

abstract:
  This article presents the precise asymptotical distribution of two types of
critical transmission radii, defined in terms of k-connectivity and the minimum
vertex degree, for a random geometry graph distributed over a 3-Dimensional
Convex region.


title: A Survey on Poisoning Attacks Against Supervised Machine Learning

abstract:
  With the rise of artificial intelligence and machine learning in modern
computing, one of the major concerns regarding such techniques is to provide
privacy and security against adversaries. We present this survey paper to cover
the most representative papers in poisoning attacks against supervised machine
learning models. We first provide a taxonomy to categorize existing studies and
then present detailed summaries for selected papers. We summarize and compare
the methodology and limitations of existing literature. We conclude this paper
with potential improvements and future directions to further exploit and
prevent poisoning attacks on supervised models. We propose several unanswered
research questions to encourage and inspire researchers for future work.


title:
A simple language-agnostic yet very strong baseline system for hate
  speech and offensive content identification

abstract:
  For automatically identifying hate speech and offensive content in tweets, a
system based on a classical supervised algorithm only fed with character
n-grams, and thus completely language-agnostic, is proposed by the SATLab team.
After its optimization in terms of the feature weighting and the classifier
parameters, it reached, in the multilingual HASOC 2021 challenge, a medium
performance level in English, the language for which it is easy to develop deep
learning approaches relying on many external linguistic resources, but a far
better level for the two less resourced language, Hindi and Marathi. It ends
even first when performances are averaged over the three tasks in these
languages, outperforming many deep learning approaches. These performances
suggest that it is an interesting reference level to evaluate the benefits of
using more complex approaches such as deep learning or taking into account
complementary resources.


title:
The Chiral Induced Spin Selectivity Effect -- What It Is, What It Is
  Not, And Why It Matters

abstract:
  The chiral induced spin selectivity effect is an excited states phenomenon,
which can be probed using photo-spectroscopy as well as transport measurements.
On the one hand such measurements represent averaged quantities, on the other
hand nearly all theoretical descriptions, with only a few exceptions, have been
concerned with energy dependent properties of the pertinent structures. While
those properties may or may not be relevant for the chiral induced spin
selectivity effect, many of those properties have been attributed as being the,
or part of the, origins of the effect. Here, it is demonstrated that, for
instance, the spin-resolved transmission provides little, if any, information
about the chiral induced spin selectivity effect. Moreover, although effective
single-electron theory can be used in this context, reasons are given for why
such descriptions are not viable.


title: Hadron structure at small-x via unintegrated gluon densities

abstract:
  Inclusive as well as exclusive emissions in forward and central directions of
rapidity are widely recognized as excellent channels to access the proton
structure at small-x. In this regime, to describe nucleons structure, it is
necessary to use kT-unintegrated distributions. In particular, at large
transverse momenta, the x-evolution of the so-called unintegrated gluon
distribution is driven by the Balitsky-Fadin-Kuraev-Lipatov equation, within
the framework of the high-energy factorization (HEF). Recent analyses on the
diffractive electroproduction of \rho mesons have corroborated the underlying
assumption that the small-size dipole scattering mechanism is at work, thus
validating the use of the HEF formalism. Nonetheless, a significant sensitivity
of polarized cross sections to intermediate values of the meson transverse
momenta, where, in the case of inclusive emissions, a description at the hand
of the transverse momentum dependent (TMD) factorization starts to be the most
appropriate framework, has been observed. In this work, we will review the
formal description of the UGD within the BFKL approach and present some UGD
models that have been proposed, then we will describe the state of the art of
some recent phenomenological analyses.


title:
Score-based Generative Modeling of Graphs via the System of Stochastic
  Differential Equations

abstract:
  Generating graph-structured data requires learning the underlying
distribution of graphs. Yet, this is a challenging problem, and the previous
graph generative methods either fail to capture the permutation-invariance
property of graphs or cannot sufficiently model the complex dependency between
nodes and edges, which is crucial for generating real-world graphs such as
molecules. To overcome such limitations, we propose a novel score-based
generative model for graphs with a continuous-time framework. Specifically, we
propose a new graph diffusion process that models the joint distribution of the
nodes and edges through a system of stochastic differential equations (SDEs).
Then, we derive novel score matching objectives tailored for the proposed
diffusion process to estimate the gradient of the joint log-density with
respect to each component, and introduce a new solver for the system of SDEs to
efficiently sample from the reverse diffusion process. We validate our graph
generation method on diverse datasets, on which it either achieves
significantly superior or competitive performance to the baselines. Further
analysis shows that our method is able to generate molecules that lie close to
the training distribution yet do not violate the chemical valency rule,
demonstrating the effectiveness of the system of SDEs in modeling the node-edge
relationships.


title: Flexible fast-convolution processing for cellular radio evolution

abstract:
  Orthogonal frequency-division multiplexing (OFDM) has been selected as a
baseline waveform for long-term evolution (LTE) and fifth-generation new radio
(5G NR). Fast-convolution (FC)-based frequency-domain signal processing has
been considered recently as an effective tool for spectrum enhancement of
OFDM-based waveforms. FC-based filtering approximates linear convolution by
effective fast Fourier transform (FFT)-based circular convolutions using partly
overlapping processing blocks. In earlier work, we have shown that FC-based
filtering is a very flexible and efficient tool for filtered-OFDM signal
generation and receiver side subband filtering. In this paper, we present a
symbol-synchronous FC-processing scheme flexibly allowing filter
re-configuration time resolution equal to one OFDM symbol while supporting
tight carrier-wise filtering for 5G NR in mixed-numerology scenarios with
adjustable subcarrier spacings, center frequencies, and subband bandwidths as
well as providing co-exitence with LTE. The proposed scheme is demonstrated to
support envisioned use cases of 5G NR and provide flexible starting point for
sixth generation development.


title: A Survey on Automated Sarcasm Detection on Twitter

abstract:
  Automatic sarcasm detection is a growing field in computer science. Short
text messages are increasingly used for communication, especially over social
media platforms such as Twitter. Due to insufficient or missing context,
unidentified sarcasm in these messages can invert the meaning of a statement,
leading to confusion and communication failures. This paper covers a variety of
current methods used for sarcasm detection, including detection by context,
posting history and machine learning models. Additionally, a shift towards deep
learning methods is observable, likely due to the benefit of using a model with
induced instead of discrete features combined with the innovation of
transformers.


title:
Representations of a special Jordan triple system of rectangular
  matrices

abstract:
  Let $\mathcal{J}_{\real}$ be the special Jordan triple system of all $p
\times q$ ($p\neq q$; $p,q >1)$ rectangular matrices over $\real$ with the
triple product $\{x,y,z\}= x y^t z+ z y^t x $, where $y^t$ is the transpose of
$y$. We investigate the universal associative envelope
$\mathcal{U}(\mathcal{J}_{\real})$ of $\mathcal{J}_{\real}$. In particular, we
prove that $\mathcal{U}(\mathcal{J}_{\real})$ is isomorphic to $M_{p+q \times
p+q}(\real)$, where $M_{p+q\times p+q} (\real)$ is the ordinary associative
algebra of all $(p+q) \times (p+q)$ matrices over $\real$. It follows that
$\mathcal{U}(\mathcal{J}_{\real})$ is semisimple and $\mathcal{J}_{\real}$ has
only one nontrivial finite-dimensional irreducible representation. The center
of $\mathcal{U}(\mathcal{J}_{\real})$ is determined.


title:
Less is More: Reversible Steganography with Uncertainty-Aware Predictive
  Analytics

abstract:
  Artificial neural networks have advanced the frontiers of reversible
steganography. The core strength of neural networks is the ability to render
accurate predictions for a bewildering variety of data. Residual modulation is
recognised as the most advanced reversible steganographic algorithm for digital
images and the pivot of which is the predictive module. The function of this
module is to predict pixel intensity given some pixel-wise contextual
information. This task can be perceived as a low-level vision problem and hence
neural networks for addressing a similar class of problems can be deployed. On
top of the prior art, this paper analyses the predictive uncertainty and endows
the predictive module with the option to abstain when encountering a high level
of uncertainty. Uncertainty analysis can be formulated as a pixel-level binary
classification problem and tackled by both supervised and unsupervised
learning. In contrast to handcrafted statistical analytics, learning-based
analytics can learn to follow some general statistical principles and
simultaneously adapt to a specific predictor. Experimental results show that
steganographic performance can be remarkably improved by adaptively filtering
out the unpredictable regions with the learning-based uncertainty analysers.


title: Intent Contrastive Learning for Sequential Recommendation

abstract:
  Users' interactions with items are driven by various intents (e.g., preparing
for holiday gifts, shopping for fishing equipment, etc.).However, users'
underlying intents are often unobserved/latent, making it challenging to
leverage such latent intents forSequentialrecommendation(SR). To investigate
the benefits of latent intents and leverage them effectively for
recommendation, we proposeIntentContrastiveLearning(ICL), a general learning
paradigm that leverages a latent intent variable into SR. The core idea is to
learn users' intent distribution functions from unlabeled user behavior
sequences and optimize SR models with contrastive self-supervised learning
(SSL) by considering the learned intents to improve recommendation.
Specifically, we introduce a latent variable to represent users' intents and
learn the distribution function of the latent variable via clustering. We
propose to leverage the learned intents into SR models via contrastive SSL,
which maximizes the agreement between a view of sequence and its corresponding
intent. The training is alternated between intent representation learning and
the SR model optimization steps within the generalized expectation-maximization
(EM) framework. Fusing user intent information into SR also improves model
robustness. Experiments conducted on four real-world datasets demonstrate the
superiority of the proposed learning paradigm, which improves performance, and
robustness against data sparsity and noisy interaction issues.


title:
A rigorous stochastic theory for spike pattern formation in recurrent
  neural networks with arbitrary connection topologies

abstract:
  Cortical networks exhibit synchronized activity which often occurs in
spontaneous events in the form of spike avalanches. Since synchronization has
been causally linked to central aspects of brain function such as selective
signal processing and integration of stimulus information, participating in an
avalanche is a form of a transient synchrony which temporarily creates neural
assemblies and hence might especially be useful for implementing flexible
information processing. For understanding how assembly formation supports
neural computation, it is therefore essential to establish a comprehensive
theory of how network structure and dynamics interact to generate specific
avalanche patterns and sequences. Here we derive exact avalanche distributions
for a finite network of recurrently coupled spiking neurons with arbitrary
non-negative interaction weights, which is made possible by formally mapping
the model dynamics to a linear, random dynamical system on the $N$-torus and by
exploiting self-similarities inherent in the phase space. We introduce the
notion of relative unique ergodicity and show that this property is guaranteed
if the system is driven by a time-invariant Bernoulli process. This approach
allows us not only to provide closed-form analytical expressions for avalanche
size, but also to determine the detailed set(s) of units firing in an avalanche
(i.e., the avalanche assembly). The underlying dependence between network
structure and dynamics is made transparent by expressing the distribution of
avalanche assemblies in terms of the induced graph Laplacian. We explore
analytical consequences of this dependence and provide illustrating examples.


title:
Comparative study of 3D object detection frameworks based on LiDAR data
  and sensor fusion techniques

abstract:
  Estimating and understanding the surroundings of the vehicle precisely forms
the basic and crucial step for the autonomous vehicle. The perception system
plays a significant role in providing an accurate interpretation of a vehicle's
environment in real-time. Generally, the perception system involves various
subsystems such as localization, obstacle (static and dynamic) detection, and
avoidance, mapping systems, and others. For perceiving the environment, these
vehicles will be equipped with various exteroceptive (both passive and active)
sensors in particular cameras, Radars, LiDARs, and others. These systems are
equipped with deep learning techniques that transform the huge amount of data
from the sensors into semantic information on which the object detection and
localization tasks are performed. For numerous driving tasks, to provide
accurate results, the location and depth information of a particular object is
necessary. 3D object detection methods, by utilizing the additional pose data
from the sensors such as LiDARs, stereo cameras, provides information on the
size and location of the object. Based on recent research, 3D object detection
frameworks performing object detection and localization on LiDAR data and
sensor fusion techniques show significant improvement in their performance. In
this work, a comparative study of the effect of using LiDAR data for object
detection frameworks and the performance improvement seen by using sensor
fusion techniques are performed. Along with discussing various state-of-the-art
methods in both the cases, performing experimental analysis, and providing
future research directions.


title:
LEAPMood: Light and Efficient Architecture to Predict Mood with Genetic
  Algorithm driven Hyperparameter Tuning

abstract:
  Accurate and automatic detection of mood serves as a building block for use
cases like user profiling which in turn power applications such as advertising,
recommendation systems, and many more. One primary source indicative of an
individual's mood is textual data. While there has been extensive research on
emotion recognition, the field of mood prediction has been barely explored. In
addition, very little work is done in the area of on-device inferencing, which
is highly important from the user privacy point of view. In this paper, we
propose for the first time, an on-device deep learning approach for mood
prediction from textual data, LEAPMood. We use a novel on-device
deployment-focused objective function for hyperparameter tuning based on the
Genetic Algorithm (GA) and optimize the parameters concerning both performance
and size. LEAPMood consists of Emotion Recognition in Conversion (ERC) as the
first building block followed by mood prediction using K-means clustering. We
show that using a combination of character embedding, phonetic hashing, and
attention along with Conditional Random Fields (CRF), results in a performance
closely comparable to that of the current State-Of-the-Art with a significant
reduction in model size (> 90%) for the task of ERC. We achieve a Micro F1
score of 62.05% with a memory footprint of a mere 1.67MB on the DailyDialog
dataset. Furthermore, we curate a dataset for the task of mood prediction
achieving a Macro F1-score of 72.12% with LEAPMood.


title:
New jet feature in the parsec-scale jet of the blazar OJ287 connected to
  the 2017 teraelectronvolt flaring activity

abstract:
  In February 2017 the blazar OJ287, one of the best super-massive
binary-black-hole-system candidates, was detected for the first time at very
high energies (VHEs; E>100GeV) with the ground-based gamma-ray observatory
VERITAS. Very high energy gamma rays are thought to be produced in the near
vicinity of the central engine in active galactic nuclei. For this reason, and
with the main goal of providing useful information for the characterization of
the physical mechanisms connected with the observed teraelectronvolt flaring
event, we investigate the parsec-scale source properties by means of
high-resolution very long baseline interferometry observations. We use 86 GHz
Global Millimeter-VLBI Array (GMVA) observations from 2015 to 2017 and combine
them with additional multiwavelength radio observations at different
frequencies from other monitoring programs. We investigate the source structure
by modeling the brightness distribution with two-dimensional Gaussian
components in the visibility plane. In the GMVA epoch following the source VHE
activity, we find a new jet feature (labeled K) at about 0.2 mas from the core
region and located in between two quasi-stationary components (labeled S1 and
S2). Multiple periods of enhanced activity are detected at different radio
frequencies before and during the VHE flaring state. Based on the findings of
this work, we identify as a possible trigger for the VHE flaring emission
during the early months of 2017 the passage of a new jet feature through a
recollimation shock (represented by the model-fit component S1) in a region of
the jet located at a de-projected distance of about 10 pc from the radio core.


title: PrivPAS: A real time Privacy-Preserving AI System and applied ethics

abstract:
  With 3.78 billion social media users worldwide in 2021 (48% of the human
population), almost 3 billion images are shared daily. At the same time, a
consistent evolution of smartphone cameras has led to a photography explosion
with 85% of all new pictures being captured using smartphones. However, lately,
there has been an increased discussion of privacy concerns when a person being
photographed is unaware of the picture being taken or has reservations about
the same being shared. These privacy violations are amplified for people with
disabilities, who may find it challenging to raise dissent even if they are
aware. Such unauthorized image captures may also be misused to gain sympathy by
third-party organizations, leading to a privacy breach. Privacy for people with
disabilities has so far received comparatively less attention from the AI
community. This motivates us to work towards a solution to generate
privacy-conscious cues for raising awareness in smartphone users of any
sensitivity in their viewfinder content. To this end, we introduce PrivPAS (A
real time Privacy-Preserving AI System) a novel framework to identify sensitive
content. Additionally, we curate and annotate a dataset to identify and
localize accessibility markers and classify whether an image is sensitive to a
featured subject with a disability. We demonstrate that the proposed
lightweight architecture, with a memory footprint of a mere 8.49MB, achieves a
high mAP of 89.52% on resource-constrained devices. Furthermore, our pipeline,
trained on face anonymized data, achieves an F1-score of 73.1%.


title:
Existence of solutions to a generalized self-dual Chern-Simons equation
  on finite graphs

abstract:
  Let $G=(V,E)$ be a connected finite graph. We study the existence of
solutions for the following generalized Chern-Simons equation on $G$
\begin{equation*}
  \Delta u=\lambda \mathrm{e}^{u}\left(\mathrm{e}^{u}-1\right)^{5}+4 \pi
\sum_{s=1}^{N} \delta_{p_{s}} \quad , \end{equation*} where $\lambda>0$,
$\delta_{p_{s}}$ is the Dirac mass at the vetex $p_s$, and $p_1, p_2,\dots,
p_N$ are arbitrarily chosen distinct vertices on the graph. We show that there
exists a critial value $\hat{\lambda}$ such that when $\lambda >
\hat{\lambda}$, the generalized Chern-Simons equation has at least two
solutions, when $\lambda = \hat{\lambda}$, the generalized Chern-Simons
equation has a solution, and when $\lambda < \hat\lambda$, the generalized
Chern-Simons equation has no solution.


title: LyaNet: A Lyapunov Framework for Training Neural ODEs

abstract:
  We propose a method for training ordinary differential equations by using a
control-theoretic Lyapunov condition for stability. Our approach, called
LyaNet, is based on a novel Lyapunov loss formulation that encourages the
inference dynamics to converge quickly to the correct prediction.
Theoretically, we show that minimizing Lyapunov loss guarantees exponential
convergence to the correct solution and enables a novel robustness guarantee.
We also provide practical algorithms, including one that avoids the cost of
backpropagating through a solver or using the adjoint method. Relative to
standard Neural ODE training, we empirically find that LyaNet can offer
improved prediction performance, faster convergence of inference dynamics, and
improved adversarial robustness. Our code available at
https://github.com/ivandariojr/LyapunovLearning .


title:
Complex-amplitude Fourier single-pixel imaging via coherent structured
  illumination

abstract:
  We propose a method of complex-amplitude Fourier single-pixel imaging (CFSI)
with coherent structured illumination to acquire both the amplitude and phase
of an object. In the proposed method, an object is illustrated by a series of
coherent structured light fields which are generated by a phase-only spatial
light modulator, the complex Fourier spectrum of the object can be acquired
sequentially by a single-pixel photodetector. Then the desired
complex-amplitude image can be retrieved directly by applying an inverse
Fourier transform. We experimentally implemented this CFSI with several
different types of objects. The experimental results show that the proposed
method provides a promising complex-amplitude imaging approach with high
quality and a stable configuration. Thus, it might find broad applications in
optical metrology and biomedical science.


title:
More methods for calculating the topological charge (density) of SU(N)
  lattice gauge fields in 3+1 dimensions

abstract:
  We revisit old ideas that smearing or blocking an SU(N) lattice gauge field,
or averaging over an ensemble of fields created in the neighbourhood of that
field, can reduce the high frequency fluctuations sufficiently that the naive
lattice operator for the topological charge density is able to provide a
reliable measure of the topological charge of the field. We show that these
three methods do indeed provide additional simple methods for calculating the
total topological charge, with smearing particularly economical at current
couplings. More interestingly, the ensemble average method can also be used to
expose the distribution in space-time of the topological charge and this
conceptually transparent, albeit computationally expensive, method provides a
useful benchmark against which to compare other methods. Using this benchmark
we find that a few smearing steps are also reliable in exposing the
distribution in space-time of the topological charge, thus providing a very
economical and simple method for doing so. We also use the same benchmark to
determine what is the number of `cooling' sweeps one needs to perform in order
to expose the charge density reliably.


title:
Graph Neural Network with Curriculum Learning for Imbalanced Node
  Classification

abstract:
  Graph Neural Network (GNN) is an emerging technique for graph-based learning
tasks such as node classification. In this work, we reveal the vulnerability of
GNN to the imbalance of node labels. Traditional solutions for imbalanced
classification (e.g. resampling) are ineffective in node classification without
considering the graph structure. Worse still, they may even bring overfitting
or underfitting results due to lack of sufficient prior knowledge. To solve
these problems, we propose a novel graph neural network framework with
curriculum learning (GNN-CL) consisting of two modules. For one thing, we hope
to acquire certain reliable interpolation nodes and edges through the novel
graph-based oversampling based on smoothness and homophily. For another, we
combine graph classification loss and metric learning loss which adjust the
distance between different nodes associated with minority class in feature
space. Inspired by curriculum learning, we dynamically adjust the weights of
different modules during training process to achieve better ability of
generalization and discrimination. The proposed framework is evaluated via
several widely used graph datasets, showing that our proposed model
consistently outperforms the existing state-of-the-art methods.


title:
Analyticity of Parametric and Stochastic Elliptic Eigenvalue Problems
  with an Application to Quasi-Monte Carlo Methods

abstract:
  In the present paper, we study the analyticity of the leftmost eigenvalue of
the linear elliptic partial differential operator with random coefficient and
analyze the convergence rate of the quasi-Monte Carlo method for approximation
of the expectation of this quantity. The random coefficient is assumed to be
represented by an affine expansion $a_0(\boldsymbol{x})+\sum_{j\in
\mathbb{N}}y_ja_j(\boldsymbol{x})$, where elements of the parameter vector
$\boldsymbol{y}=(y_j)_{j\in \mathbb{N}}\in U^\infty$ are independent and
identically uniformly distributed on $U:=[-\frac{1}{2},\frac{1}{2}]$. Under the
assumption $ \|\sum_{j\in \mathbb{N}}\rho_j|a_j|\|_{L_\infty(D)} <\infty$ with
some positive sequence $(\rho_j)_{j\in \mathbb{N}}\in \ell_p(\mathbb{N})$ for
$p\in (0,1]$ we show that for any $\boldsymbol{y}\in U^\infty$, the elliptic
partial differential operator has a countably infinite number of eigenvalues
$(\lambda_j(\boldsymbol{y}))_{j\in \mathbb{N}}$ which can be ordered
non-decreasingly. Moreover, the spectral gap
$\lambda_2(\boldsymbol{y})-\lambda_1(\boldsymbol{y})$ is uniformly positive in
$U^\infty$. From this, we prove the holomorphic extension property of
$\lambda_1(\boldsymbol{y})$ to a complex domain in $\mathbb{C}^\infty$ and
estimate mixed derivatives of $\lambda_1(\boldsymbol{y})$ with respect to the
parameters $\boldsymbol{y}$ by using Cauchy's formula for analytic functions.
Based on these bounds we prove the dimension-independent convergence rate of
the quasi-Monte Carlo method to approximate the expectation of
$\lambda_1(\boldsymbol{y})$. In this case, the computational cost of fast
component-by-component algorithm for generating quasi-Monte Carlo $N$-points
scales linearly in terms of integration dimension.


title: Presentations of Dehn quandles

abstract:
  The paper gives two approaches to write explicit presentations for the class
of Dehn quandles using presentations of their underlying groups. The first
approach gives finite presentations for Dehn quandles of a class of Garside
groups and Gaussian groups. The second approach is for general Dehn quandles
when the centralisers of generators of their underlying groups are known.
Several examples including Dehn quandles of spherical Artin groups, surface
groups and mapping class groups of orientable surfaces are given to illustrate
the results.


title:
Adaptive information-based methods for determining the co-integration
  rank in heteroskedastic VAR models

abstract:
  Standard methods, such as sequential procedures based on Johansen's
(pseudo-)likelihood ratio (PLR) test, for determining the co-integration rank
of a vector autoregressive (VAR) system of variables integrated of order one
can be significantly affected, even asymptotically, by unconditional
heteroskedasticity (non-stationary volatility) in the data. Known solutions to
this problem include wild bootstrap implementations of the PLR test or the use
of an information criterion, such as the BIC, to select the co-integration
rank. Although asymptotically valid in the presence of heteroskedasticity,
these methods can display very low finite sample power under some patterns of
non-stationary volatility. In particular, they do not exploit potential
efficiency gains that could be realised in the presence of non-stationary
volatility by using adaptive inference methods. Under the assumption of a known
autoregressive lag length, Boswijk and Zu (2022) develop adaptive PLR test
based methods using a non-parameteric estimate of the covariance matrix
process. It is well-known, however, that selecting an incorrect lag length can
significantly impact on the efficacy of both information criteria and bootstrap
PLR tests to determine co-integration rank in finite samples. We show that
adaptive information criteria-based approaches can be used to estimate the
autoregressive lag order to use in connection with bootstrap adaptive PLR
tests, or to jointly determine the co-integration rank and the VAR lag length
and that in both cases they are weakly consistent for these parameters in the
presence of non-stationary volatility provided standard conditions hold on the
penalty term. Monte Carlo simulations are used to demonstrate the potential
gains from using adaptive methods and an empirical application to the U.S. term
structure is provided.


title:
Blue Data Computation Maximization in 6G Space-Air-Sea Non-Terrestrial
  Networks

abstract:
  Non-terrestrial networks (NTN), encompassing space and air platforms, are a
key component of the upcoming sixth-generation (6G) cellular network.
Meanwhile, maritime network traffic has grown significantly in recent years due
to sea transportation used for national defense, research, recreational
activities, domestic and international trade. In this paper, the seamless and
reliable demand for communication and computation in maritime wireless networks
is investigated. Two types of marine user equipment (UEs), i.e., low-antenna
gain and high-antenna gain UEs, are considered. A joint task computation and
time allocation problem for weighted sum-rate maximization is formulated as
mixed-integer linear programming (MILP). The goal is to design an algorithm
that enables the network to efficiently provide backhaul resources to an
unmanned aerial vehicle (UAV) and offload HUEs tasks to LEO satellite for blue
data (i.e., marine user's data). To solve this MILP, a solution based on the
Bender and primal decomposition is proposed. The Bender decomposes MILP into
the master problem for binary task decision and subproblem for continuous-time
resource allocation. Moreover, primal decomposition deals with a coupling
constraint in the subproblem. Finally, numerical results demonstrate that the
proposed algorithm provides the maritime UEs coverage demand in polynomial time
computational complexity and achieves a near-optimal solution.


title:
Deeply Learned Preselection of Higgs Dijet Decays at Future Lepton
  Colliders

abstract:
  Future electron-positron colliders will play a leading role in the precision
measurement of Higgs boson couplings which is one of the central interests in
particle physics. Aiming at maximizing the performance to measure the Higgs
couplings to the bottom, charm and strange quarks, we develop machine learning
methods to improve the selection of events with a Higgs decaying to dijets. Our
methods are based on the Boosted Decision Tree (BDT), Fully-Connected Neural
Network (FCNN) and Convolutional Neural Network (CNN). We find that the BDT and
FCNN-based algorithms outperform the conventional cut-based method. With our
improved selection of Higgs decaying to dijet events using the FCNN, the charm
quark signal strength is measured with a $16\%$ error, which is roughly a
factor of two better than the $34\%$ precision obtained by the cut-based
analysis. Also, the strange quark signal strength is constrained as $\mu_{ss}
\lesssim 35$ at the $95\%$ C.L. with the FCNN, which is to be compared with
$\mu_{ss} \lesssim 70$ obtained by the cut-based method.


title: Aspect-based Sentiment Analysis through EDU-level Attentions

abstract:
  A sentence may express sentiments on multiple aspects. When these aspects are
associated with different sentiment polarities, a model's accuracy is often
adversely affected. We observe that multiple aspects in such hard sentences are
mostly expressed through multiple clauses, or formally known as elementary
discourse units (EDUs), and one EDU tends to express a single aspect with
unitary sentiment towards that aspect. In this paper, we propose to consider
EDU boundaries in sentence modeling, with attentions at both word and EDU
levels. Specifically, we highlight sentiment-bearing words in EDU through
word-level sparse attention. Then at EDU level, we force the model to attend to
the right EDU for the right aspect, by using EDU-level sparse attention and
orthogonal regularization. Experiments on three benchmark datasets show that
our simple EDU-Attention model outperforms state-of-the-art baselines. Because
EDU can be automatically segmented with high accuracy, our model can be applied
to sentences directly without the need of manual EDU boundary annotation.


title: On order continuity of positive operators

abstract:
  We consider vector lattices endowed with locally solid convergence
structures, which are not necessarily topological. We characterize order
convergence as the strongest locally solid convergence on a vector lattice in
which increasing nets converge to their supremums. For a large class of
convergences we prove that a positive operator is order continuous if and only
if its restriction to a dense regular sublattice is order continuous, and that
the closure of a regular sublattice is regular with the original sublattice
being order dense in the closure. We also present an example of a regular
sublattice of a locally solid topological vector lattice whose closure is not
regular.


title: Multidimensional Cybersecurity Framework for Strategic Foresight

abstract:
  Cybersecurity is now at the forefront of most organisational digital
transformative agendas and National economic, social and political programmes.
Hence its impact to society can no longer be seen to be one dimensional. The
rise in National cybersecurity laws and regulations is a good indicator of its
perceived importance to nations. And the recent awakening for social and
ethical transparency in society and coupled with sustainability issues
demonstrate the need for a paradigm shift in how cybersecurity discourses can
now happen. In response to this shift, a multidimensional cybersecurity
framework for strategic foresight underpinned on situational awareness is
proposed. The conceptual cybersecurity framework comprising six domains such as
Physical, Cultural, Economic, Social, Political and Cyber, is discussed. The
guiding principles underpinning the framework are outlined, followed by
in-depth reflection on the Business, Operational, Technological and Human
(BOTH) factors and their implications for strategic foresight for
cybersecurity.


title: On the Fatou theorem for d_J-bar subsolutions in wedges

abstract:
  We prove a version of the Fatou theorem for bounded functions with bounded
d_J-bar diferential on wedge-type domains in an almost complex manifold.


title: Event Log Generation: An Industry Perspective

abstract:
  This paper presents the results of an industry expert survey about event log
generation in process mining. It takes academic assumptions as a starting point
and elicits practitioner's assessments of statements about process execution,
process scoping, process discovery, and process analysis. The results of the
survey shed some light on challenges and perspectives around event log
generation, as well as on the relationship between process models and process
execution, and derive challenges for event log generation from it. The
responses indicate that particularly relevant challenges exist around data
integration and quality, and that process mining can benefit from a systematic
integration with more traditional and wide-spread business intelligence
approaches.


title:
Science Facing Interoperability as a Necessary Condition of Success and
  Evil

abstract:
  Artificial intelligence (AI) systems, such as machine learning algorithms,
have allowed scientists, marketers and governments to shed light on
correlations that remained invisible until now. Beforehand, the dots that we
had to connect in order to imagine a new knowledge were either too numerous,
too sparse or not even detected. Sometimes, the information was not stored in
the same data lake or format and was not able to communicate. But in creating
new bridges with AI, many problems appeared such as bias reproduction, unfair
inferences or mass surveillance. Our aim is to show that, on one hand, the AI's
deep ethical problem lays essentially in these new connections made possible by
systems interoperability. In connecting the spheres of our life, these systems
undermine the notion of justice particular to each of them, because the new
interactions create dominances of social goods from a sphere to another. These
systems make therefore spheres permeable to one another and, in doing so, they
open to progress as well as to tyranny. On another hand, however, we would like
to emphasize that the act to connect what used to seem a priori disjoint is a
necessary move of knowledge and scientific progress.


title:
TorchMD-NET: Equivariant Transformers for Neural Network based Molecular
  Potentials

abstract:
  The prediction of quantum mechanical properties is historically plagued by a
trade-off between accuracy and speed. Machine learning potentials have
previously shown great success in this domain, reaching increasingly better
accuracy while maintaining computational efficiency comparable with classical
force fields. In this work we propose TorchMD-NET, a novel equivariant
transformer (ET) architecture, outperforming state-of-the-art on MD17, ANI-1,
and many QM9 targets in both accuracy and computational efficiency. Through an
extensive attention weight analysis, we gain valuable insights into the black
box predictor and show differences in the learned representation of conformers
versus conformations sampled from molecular dynamics or normal modes.
Furthermore, we highlight the importance of datasets including off-equilibrium
conformations for the evaluation of molecular potentials.


title: Some Properties of Coefficients Kolchin Dimension Polynomial

abstract:
  The article presents a formula expressing Macaulay constants of a numerical
polynomial through its minimizing coefficients. From this, we have that
Macaulay constants of Kolchin dimension polynomials do not decrease.
  For the minimal differential dimension polynomial (this concept was
introduced by W.Sitt in [5]) we will prove a criterion for Macaulay constants
to be equal. In this case, as the example (2) shows, there are no bounds from
above to the Macaulay constants of the dimension polynomial for starting
generator.


title: Unsupervised Learning on 3D Point Clouds by Clustering and Contrasting

abstract:
  Learning from unlabeled or partially labeled data to alleviate human labeling
remains a challenging research topic in 3D modeling. Along this line,
unsupervised representation learning is a promising direction to auto-extract
features without human intervention. This paper proposes a general unsupervised
approach, named \textbf{ConClu}, to perform the learning of point-wise and
global features by jointly leveraging point-level clustering and instance-level
contrasting. Specifically, for one thing, we design an Expectation-Maximization
(EM) like soft clustering algorithm that provides local supervision to extract
discriminating local features based on optimal transport. We show that this
criterion extends standard cross-entropy minimization to an optimal transport
problem, which we solve efficiently using a fast variant of the Sinkhorn-Knopp
algorithm. For another, we provide an instance-level contrasting method to
learn the global geometry, which is formulated by maximizing the similarity
between two augmentations of one point cloud. Experimental evaluations on
downstream applications such as 3D object classification and semantic
segmentation demonstrate the effectiveness of our framework and show that it
can outperform state-of-the-art techniques.


title: Rubio de Francia Extrapolation Theorems for Quasi-Monotone Functions

abstract:
  We prove Rubio de Francia extrapolation results in Lebesgue and grand
Lebesgue spaces for quasi monotone functions with $QB_{\beta,p}$ weights. The
extrapolation in Lebesgue spaces with the weight class $QB_{\beta,\infty}$ has
also been investigated. As an application, we characterize the boundedness of
the Hardy averaging operator for quasi monotone functions in the grand Lebesgue
spaces.


title:
Optimization of a Real-Time Wavelet-Based Algorithm for Improving Speech
  Intelligibility

abstract:
  The optimization of a wavelet-based algorithm to improve speech
intelligibility is reported. The discrete-time speech signal is split into
frequency sub-bands via a multi-level discrete wavelet transform. Various gains
are applied to the sub-band signals before they are recombined to form a
modified version of the speech. The sub-band gains are adjusted while keeping
the overall signal energy unchanged, and the speech intelligibility under
various background interference and simulated hearing loss conditions is
enhanced and evaluated objectively and quantitatively using Google
Speech-to-Text transcription. For English and Chinese noise-free speech,
overall intelligibility is improved, and the transcription accuracy can be
increased by as much as 80 percentage points by reallocating the spectral
energy toward the mid-frequency sub-bands, effectively increasing the
consonant-vowel intensity ratio. This is reasonable since the consonants are
relatively weak and of short duration, which are therefore the most likely to
become indistinguishable in the presence of background noise or high-frequency
hearing impairment. For speech already corrupted by noise, improving
intelligibility is challenging but still realizable. The proposed algorithm is
implementable for real-time signal processing and comparatively simpler than
previous algorithms. Potential applications include speech enhancement, hearing
aids, machine listening, and a better understanding of speech intelligibility.


title:
A note on the global regularity results for strongly nonhomogeneous
  $p,q$-fractional problems and applications

abstract:
  In this article, we communicate with the glimpse of the proofs of global
regularity results for weak solutions to a class of problems involving
fractional $(p,q)$-Laplacian, denoted by
$(-\Delta)^{s_1}_{p}+(-\Delta)^{s_2}_{q}$, for $s_2, s_1\in (0,1)$ and
$1<p,q<\infty$. We also obtain the boundary H\"older continuity results for the
weak solutions to the corresponding problems involving at most critical growth
nonlinearities. These results are almost optimal. Moreover, we establish Hopf
type maximum principle and strong comparison principle. As an application to
these new results, we prove the Sobolev versus H\"older minimizer type result,
which provides the multiplicity of solutions in the spirit of seminal work
\cite{Brezis-Nirenberg}.


title:
Event-Triggered Optimal Attitude Consensus of Multiple Rigid Body
  Networks with Unknown Dynamics

abstract:
  In this paper, an event-triggered Reinforcement Learning (RL) method is
proposed for the optimal attitude consensus of multiple rigid body networks
with unknown dynamics. Firstly, the consensus error is constructed through the
attitude dynamics. According to the Bellman optimality principle, the implicit
form of the optimal controller and the corresponding Hamilton-Jacobi-Bellman
(HJB) equations are obtained. Because of the augmented system, the optimal
controller can be obtained directly without relying on the system dynamics.
Secondly, the self-triggered mechanism is applied to reduce the computing and
communication burden when updating the controller. In order to address the
problem that the HJB equations are difficult to solve analytically, a RL method
which only requires measurement data at the event-triggered instants is
proposed. For each agent, only one neural network is designed to approximate
the optimal value function. Each neural network is updated only at the event
triggered instants. Meanwhile, the Uniformly Ultimately Bounded (UUB) of the
closed-loop system is obtained, and the Zeno behavior is also avoided. Finally,
the simulation results on a multiple rigid body network demonstrate the
validity of the proposed method.


title: Many-body topology of non-Hermitian systems

abstract:
  Non-Hermiticity gives rise to unique topological phases that have no
counterparts in Hermitian systems. Such intrinsic non-Hermitian topological
phases appear even in one dimension while no topological phases appear in
one-dimensional Hermitian systems. Despite the recent considerable interest,
the intrinsic non-Hermitian topological phases have been mainly investigated in
noninteracting systems described by band theory. It has been unclear whether
they survive or reduce in the presence of many-body interactions. Here, we
demonstrate that the intrinsic non-Hermitian topological phases in one
dimension survive even in the presence of many-body interactions. We formulate
a many-body topological invariant by the winding of the complex-valued
many-body spectrum in terms of a U (1) gauge field (magnetic flux). As an
illustrative example, we investigate the interacting Hatano-Nelson model and
find a unique topological phase and skin effect induced by many-body
interactions.


title:
A Practical Vehicle Routing Problem for Monitoring Water Distribution
  Networks

abstract:
  In this work, we introduce a generalization of the well-known Vehicle Routing
Problem (VRP) for a specific application in the monitoring of a Water
Distribution Network (WDN). In this problem, for each day over a planning
period, multiple technicians must visit a sequence of nodes in the WDN and
perform a series of tests to check the quality of water. Some special nodes
(i.e., wells) require technicians to first collect a key from a key center. The
key must then be returned to the same key center after the test has been
performed, thus introducing precedence constraints and multiple visits in the
routes. To solve the problem, three mathematical models and an Iterated Local
Search have been implemented. The efficiency of the proposed methods is
demonstrated by means of extensive computational tests on randomly created
instances, as well as on instances derived from a real-world case study.


title:
Intelligent Reflecting Surface-Aided Spectrum Sensing for Cognitive
  Radio

abstract:
  Spectrum sensing is a key enabling technique for cognitive radio (CR), which
provides essential information on the spectrum availability. However, due to
severe wireless channel fading and path loss, the primary user (PU) signals
received at the CR or secondary user (SU) can be practically too weak for
reliable detection. To tackle this issue, we consider in this letter a new
intelligent reflecting surface (IRS)-aided spectrum sensing scheme for CR, by
exploiting the large aperture and passive beamforming gains of IRS to boost the
PU signal strength received at the SU to facilitate its spectrum sensing.
Specifically, by dynamically changing the IRS reflection over time according to
a given codebook, its reflected signal power varies substantially at the SU,
which is utilized for opportunistic signal detection. Furthermore, we propose a
weighted energy detection method by combining the received signal power values
over different IRS reflections, which significantly improves the detection
performance. Simulation results validate the performance gain of the proposed
IRS-aided spectrum sensing scheme, as compared to different benchmark schemes.


title: Exploring the Dynamics of the Circumcenter Map

abstract:
  Using experimental techniques, we study properties of the "circumcenter map",
which sends a polygon to a scaled and rotated copy of itself. We also explore
the topology of area-expanding and area-contracting regions induced by this
map.


title: Multiscale Modeling of Sorption Kinetics

abstract:
  In this paper we propose and validate a multiscale model for the description
of particle diffusion in presence of trapping boundaries. We start from a
drift-diffusion equation in which the drift term describes the effect of bubble
traps, and is modeled by a short range potential with an attractive term and a
repulsive core. The interaction of the particles attracted by the bubble
surface is simulated by the Lennard-Jones potential that simplifies the capture
due to the hydrophobic properties of the ions. In our model the effect of the
potential is replaced by a suitable boundary condition derived by mass
conservation and asymptotic analysis. The potential is assumed to have a range
of small size $\varepsilon$. An asymptotic expansion in the $\varepsilon$ is
considered, and the boundary conditions are obtained by retaining the lowest
order terms in the expansion.
  Another aspect we investigate is saturation effect coming from high
concentrations in the proximity of the bubble surface. Various studies show
that these reactions lead to a modification of the model, including also non
linear terms. The validity of the model is carefully checked with several tests
in 1D, 2D and different geometries.


title: Charge Transport in Mixed Metal Halide Perovskite Semiconductors

abstract:
  Investigation of the inherent field-driven charge transport behaviour of 3D
lead halide perovskites has largely remained a challenging task, owing
primarily to undesirable ionic migration effects near room temperature. In
addition, the presence of methylammonium in many high performing 3D perovskite
compositions introduces additional instabilities, which limit reliable room
temperature optoelectronic device operation. Here, we address both these
challenges and demonstrate that field-effect transistors (FETs) based on
methylammonium-free, mixed-metal (Pb/Sn) perovskite compositions, that are
widely studied for solar cell and light-emitting diode applications, do not
suffer from ion migration effects as their pure Pb counterparts and reliably
exhibit hysteresis free p-type transport with high mobility reaching 5.4
$cm^2/Vs$, ON/OFF ratio approaching $10^6$, and normalized channel conductance
of 3 S/m. The reduced ion migration is also manifested in an activated
temperature dependence of the field-effect mobility with low activation energy,
which reflects a significant density of shallow electronic defects. We
visualize the suppressed in-plane ionic migration in Sn-containing perovskites
compared to their pure-Pb counterparts using photoluminescence microscopy under
bias and demonstrate promising voltage and current-stress device operational
stabilities. Our work establishes FETs as an excellent platform for providing
fundamental insights into the doping, defect and charge transport physics of
mixed-metal halide perovskite semiconductors to advance their applications in
optoelectronic devices.


title:
Confluences of exceptional points and a systematic classification of
  quantum catastrophes

abstract:
  Specific quantum phase transitions of our interest are assumed associated
with the fall of a closed, unitary quantum system into its exceptional-point
(EP) singularity. The physical realization of such a "quantum catastrophe"
(connected, typically, with an instantaneous loss of the diagonalizability of
the corresponding parameter-dependent Hamiltonian $H(g)$) depends, naturally,
on the formal mathematical characteristics of the EP, i.e., in essence, on its
so called algebraic multiplicity $N$ and geometric multiplicity $K$. In our
paper we assume that both of them are finite, and we illustrate and discuss,
using several solvable toy models, some of the most elementary mechanisms of
the EP-merger realization of the process of the transition $g \to g^{(EP)}$.


title: Bogoyavlensky lattices and generalized Catalan numbers

abstract:
  We study the problem of the decay of initial data in the form of a unit step
for the Bogoyavlensky lattices. In contrast to the Gurevich--Pitaevskii problem
of the decay of initial discontinuity for the KdV equation, it turns out to be
exactly solvable, since the dynamics is linearizable due to termination on the
half-line. The answer is written in terms of generalized hypergeometric
functions, which serve as exponential generating functions for generalized
Catalan numbers. This can be proved by the fact that the generalized Hankel
determinants for these numbers are equal to 1, which is a well-known result in
combinatorics. Another method is based on a non-autonomous symmetry reduction
consistent with the dynamics. It reduces the lattice equation to a
finite-dimensional system and makes it possible to solve the problem for a more
general finite-parameter family of initial data.


title: DEVO: Depth-Event Camera Visual Odometry in Challenging Conditions

abstract:
  We present a novel real-time visual odometry framework for a stereo setup of
a depth and high-resolution event camera. Our framework balances accuracy and
robustness against computational efficiency towards strong performance in
challenging scenarios. We extend conventional edge-based semi-dense visual
odometry towards time-surface maps obtained from event streams. Semi-dense
depth maps are generated by warping the corresponding depth values of the
extrinsically calibrated depth camera. The tracking module updates the camera
pose through efficient, geometric semi-dense 3D-2D edge alignment. Our approach
is validated on both public and self-collected datasets captured under various
conditions. We show that the proposed method performs comparable to
state-of-the-art RGB-D camera-based alternatives in regular conditions, and
eventually outperforms in challenging conditions such as high dynamics or low
illumination.


title:
Lower-bounds on the Bayesian Risk in Estimation Procedures via
  $f$-Divergences

abstract:
  We consider the problem of parameter estimation in a Bayesian setting and
propose a general lower-bound that includes part of the family of
$f$-Divergences. The results are then applied to specific settings of interest
and compared to other notable results in the literature. In particular, we show
that the known bounds using Mutual Information can be improved by using, for
example, Maximal Leakage, Hellinger divergence, or generalizations of the
Hockey-Stick divergence.


title:
Full-zone Persistent Spin Textures with Giant Spin Splitting in
  Two-dimensional Group IV-V Compounds

abstract:
  Persistent spin texture (PST), a property of solid-state materials preserving
a uniform spin configuration in the k-space, offers a route to deliver the
necessary long carrier spin lifetimes through persistent spin helix (PSH)
mechanism. However, most of the discovered PST are locally observed in the
small part around certain high symmetry k-points or lines in the first Brilouin
zone (FBZ), thus limiting the stability of the PSH state. Herein, by using
first-principles calculations supplemented with k.p analysis, we report the
emergence of full-zone PST in the monolayers of group IV-V A2B2 (A = Si, Sn,
Ge; B = Bi, Sb) compounds. Due to in-plane mirror symmetry operation of the
crystal and reduced symmetry of the arbitrary k-point in the FBZ, a fully
out-of plane spin texture preserves in the whole region of the FBZ.
Importantly, we observed giant spin splitting in which the PST sustains,
supporting large SOC parameters and small wavelengths of the PSH states. Our
k.p analysis also demonstrated that the PST is robust for the k point along the
specific high symmetry line under the external electric field, thus offering a
promising platform for future spintronic applications.


title:
Digital Twin of Wireless Systems: Overview, Taxonomy, Challenges, and
  Opportunities

abstract:
  Future wireless services must be focused on improving the quality of life by
enabling various applications, such as extended reality, brain-computer
interaction, and healthcare. These applications have diverse performance
requirements (e.g., user-defined quality of experience metrics, latency, and
reliability) that are challenging to be fulfilled by existing wireless systems.
To meet the diverse requirements of the emerging applications, the concept of a
digital twin has been recently proposed. A digital twin uses a virtual
representation along with security-related technologies (e.g., blockchain),
communication technologies (e.g., 6G), computing technologies (e.g., edge
computing), and machine learning, so as to enable the smart applications. In
this tutorial, we present a comprehensive overview on digital twins for
wireless systems. First, we present an overview of fundamental concepts (i.e.,
design aspects, high-level architecture, and frameworks) of digital twin of
wireless systems. Second, a comprehensive taxonomy is devised for both
different aspects. These aspects are twins for wireless and wireless for twins.
For the twins for wireless aspect, we consider parameters, such as twin objects
design, prototyping, deployment trends, physical devices design, interface
design, incentive mechanism, twins isolation, and decoupling. On the other
hand, for wireless for twins, parameters such as, twin objects access aspects,
security and privacy, and air interface design are considered. Finally, open
research challenges and opportunities are presented along with causes and
possible solutions.


title: A generalized Bohr-Rogosinski phenomenon

abstract:
  In this paper, we generalize the Bohr-Rogosinski sum for the Ma-Minda classes
of starlike and convex functions. Also the phenomenon is studied for the
classes of starlike functions with respect to symmetric points and conjugate
points along with their convex cases. Further, the connection between the
derived results and the known ones are established with the suitable examples.


title:
An Eikonal equation with vanishing Lagrangian arising in Global
  Optimization

abstract:
  We show a connection between global unconstrained optimization of a
continuous function $f$ and weak KAM theory for an eikonal-type equation
arising also in ergodic control. A solution $v$ of the critical Hamilton-Jacobi
equation is built by a small discount approximation as well as the long time
limit of an associated evolutive equation. Then $v$ is represented as the value
function of a control problem with target, whose optimal trajectories are
driven by a differential inclusion describing the gradient descent of $v$. Such
trajectories are proved to converge to the set of minima of $f$, using tools in
control theory and occupational measures.


title: The CMS Magnetic Field Measuring and Monitoring Systems

abstract:
  This review article describes the performance of the magnetic field measuring
and monitoring systems for the Compact Muon Solenoid (CMS) detector. To
cross-check the magnetic flux distribution obtained with the CMS magnet model,
four systems for measuring the magnetic flux density in the detector volume
were used. The magnetic induction inside the 6 m diameter superconducting
solenoid was measured and is currently monitored by four nuclear magnetic
resonance (NMR) probes installed using special tubes outside the barrel hadron
calorimeter. Two more NRM probes were installed at the faces of the tracking
system. The field inside the superconducting solenoid was precisely measured in
2006 in a cylindrical volume of 3.448 m in diameter and 7 m in length using 10
three-dimensional (3D) B-sensors based on the Hall effect. These B-sensors were
installed on each of the two propeller arms of an automated field-mapping
machine. In addition to these measurement systems, a system for monitoring the
magnetic field during the CMS detector operation has been developed. Inside the
solenoid in the horizontal plane, 4 3D B-sensors were installed at the faces of
the tracking detector. Twelve 3D B-sensors were installed on the surfaces of
the flux-return yoke nose disks. Seventy 3D B-sensors were installed in the air
gaps of the CMS magnet yoke. A specially developed flux loop technique was used
for the most complex measurements of the magnetic flux density inside the steel
blocks of the CMS magnet yoke. The flux loops are installed in 22 sections of
the flux-return yoke blocks. The areas enclosed by these coils varied from 0.3
to 1.59 m$^{2}$ in the blocks of the barrel wheels and from 0.5 to 1.12 m$^{2}$
in the blocks of the yoke endcap disks. The development of these systems and
the results of the magnetic flux density measurements across the CMS magnet are
presented and discussed in this review article.


title: Turbulence in the Sub-Alfv\'enic Solar Wind

abstract:
  Parker Solar Probe (PSP) entered a region of the sub-Alfvenic solar wind
during encounter 8 and we present the first detailed analysis of low-frequency
turbulence properties in this novel region. The magnetic field and flow
velocity vectors were highly aligned during this interval. By constructing
spectrograms of the normalized magnetic helicity, cross helicity, and residual
energy, we find that PSP observed primarily Alfvenic fluctuations, a
consequence of the highly field-aligned flow that renders quasi-2D fluctuations
unobservable to PSP. We extend Taylor hypothesis to sub and super Alfvenic
flows. Spectra for the fluctuating forward and backward Elsasser variables are
presented. The observed spectra are well fitted using a spectral theory for
nearly incompressible magnetohydrodynamics assuming a wave number anisotropy.
The density spectrum is a power law that resembles neither the Elsasser spectra
nor the compressible magnetic field spectrum, suggesting that these are
advected entropic rather than magnetosonic modes and not due to the parametric
decay instability. Spectra in the neighboring modestly super Alfvenic intervals
are similar.


title:
Multiplication formulas and isomorphism theorem of $\imath$Schur
  superalgebras

abstract:
  We introduce the notion of $\imath$Schur superalgebra, which can be regarded
as a type B/C counterpart of the $q$-Schur superalgebra (of type A) formulated
as centralizer algebras of certain signed $q$-permutation modules over Hecke
algebras. Some multiplication formulas for $\imath$Schur superalgebra are
obtained to construct their canonical bases. Furthermore, we established an
isomorphism theorem between the $\imath$Scuhr superalgebras and the $q$-Schur
superalgebras of type A, which helps us derive a semisimplicity criteria of the
$\imath$Schur superalgebras.


title: Using SEQUAL for Identifying Requirements to Ecore Editors

abstract:
  Software engineers who use Model-Driven Development may be using Ecore for
their work. Ecore is traditionally edited in Eclipse IDE, but a recent
transition to Web tools allows for development of new Ecore editors. To
investigate the needed functionality of such modeling tools, the model quality
framework SEQUAL has been applied. The paper presents the current results of
this task, producing requirements for tool functionality as quality improving
means for the following quality aspects: physical, empirical, syntactic,
semantic, pragmatic, social and deontic. The result is an extensive list of
tool functionality that could be implemented by the Ecore editor developers.
Although many requirements are identified, the framework should also help in
making trade-offs in case not all requirements can be implemented. In this way
the paper both contribute to identifying modeling tool functionality, and to
have input to improve SEQUAL as a general model quality framework. Further work
will need to be done on the implementation of the tools for properly evaluating
this work.


title: LST: Lexicon-Guided Self-Training for Few-Shot Text Classification

abstract:
  Self-training provides an effective means of using an extremely small amount
of labeled data to create pseudo-labels for unlabeled data. Many
state-of-the-art self-training approaches hinge on different regularization
methods to prevent overfitting and improve generalization. Yet they still rely
heavily on predictions initially trained with the limited labeled data as
pseudo-labels and are likely to put overconfident label belief on erroneous
classes depending on the first prediction. To tackle this issue in text
classification, we introduce LST, a simple self-training method that uses a
lexicon to guide the pseudo-labeling mechanism in a linguistically-enriched
manner. We consistently refine the lexicon by predicting confidence of the
unseen data to teach pseudo-labels better in the training iterations. We
demonstrate that this simple yet well-crafted lexical knowledge achieves
1.0-2.0% better performance on 30 labeled samples per class for five benchmark
datasets than the current state-of-the-art approaches.


title:
Catch Me if You Can: A Novel Task for Detection of Covert Geo-Locations
  (CGL)

abstract:
  Most visual scene understanding tasks in the field of computer vision involve
identification of the objects present in the scene. Image regions like
hideouts, turns, & other obscured regions of the scene also contain crucial
information, for specific surveillance tasks. Task proposed in this paper
involves the design of an intelligent visual aid for identification of such
locations in an image, which has either the potential to create an imminent
threat from an adversary or appear as the target zones needing further
investigation. Covert places (CGL) for hiding behind an occluding object are
concealed 3D locations, not detectable from the viewpoint (camera). Hence this
involves delineating specific image regions around the projections of outer
boundary of the occluding objects, as places to be accessed around the
potential hideouts. CGL detection finds applications in military
counter-insurgency operations, surveillance with path planning for an
exploratory robot. Given an RGB image, the goal is to identify all CGLs in the
2D scene. Identification of such regions would require knowledge about the 3D
boundaries of obscuring items (pillars, furniture), their spatial location with
respect to the neighboring regions of the scene. We propose this as a novel
task, termed Covert Geo-Location (CGL) Detection. Classification of any region
of an image as a CGL (as boundary sub-segments of an occluding object that
conceals the hideout) requires examining the 3D relation between boundaries of
occluding objects and their neighborhoods & surroundings. Our method
successfully extracts relevant depth features from a single RGB image and
quantitatively yields significant improvement over existing object detection
and segmentation models adapted and trained for CGL detection. We also
introduce a novel hand-annotated CGL detection dataset containing 1.5K
real-world images for experimentation.


title: Symmetric Volume Maps

abstract:
  Although shape correspondence is a central problem in geometry processing,
most methods for this task apply only to two-dimensional surfaces. The
neglected task of volumetric correspondence--a natural extension relevant to
shapes extracted from simulation, medical imaging, volume rendering, and even
improving surface maps of boundary representations--presents unique challenges
that do not appear in the two-dimensional case. In this work, we propose a
method for mapping between volumes represented as tetrahedral meshes. Our
formulation minimizes a distortion energy designed to extract maps
symmetrically, i.e., without dependence on the ordering of the source and
target domains. We accompany our method with theoretical discussion describing
the consequences of this symmetry assumption, leading us to select a
symmetrized ARAP energy that favors isometric correspondences. Our final
formulation optimizes for near-isometry while matching the boundary. We
demonstrate our method on a diverse geometric dataset, producing low-distortion
matchings that align to the boundary.


title:
AssistMe: Policy iteration for the longitudinal control of a
  non-holonomic vehicle

abstract:
  In this article we design a physically-inspired model-based assist-as-needed
semi-autonomous control (ASC) algorithm to address the problem of safely
driving a vehicle (a power wheelchair) in an environment with static obstacles.
Once implemented online, the proposed algorithm requires limited computing
power and relies on pre-computed (offline) maps (look-up tables). These are
readily available by implementing policy iteration that minimizes the expected
time to termination (safely stopping near an obstacle), by taking into account:
(i) the vehicle dynamics; (ii) the drivers' intention modeled as three separate
stochastic processes. We call them the expert driver, the naughty child and the
blind driver models. A study with healthy participants confirmed that ASC
outperforms a baseline rule-based control (a statistically significant result).


title:
Proper conflict-free and unique-maximum colorings of planar graphs with
  respect to neighborhoods

abstract:
  A {\em conflict-free coloring} of a graph {\em with respect to open} (resp.,
{\em closed}) {\em neighborhood} is a coloring of vertices such that for every
vertex there is a color appearing exactly once in its open (resp., closed)
neighborhood. Similarly, a {\em unique-maximum coloring} of a graph {\em with
respect to open} (resp., {\em closed}) {\em neighborhood} is a coloring of
vertices such that for every vertex the maximum color appearing in its open
(resp., closed) neighborhood appears exactly once. There is a vast amount of
literature on both notions where the colorings need not be proper, i.e.,
adjacent vertices are allowed to have the same color.
  In this paper, we initiate a study of both colorings in the proper settings
with the focus given mainly to planar graphs. We establish upper bounds for the
number of colors in the class of planar graphs for all considered colorings and
provide constructions of planar graphs attaining relatively high values of the
corresponding chromatic numbers. As a main result, we prove that every planar
graph admits a proper unique-maximum coloring with respect to open neighborhood
with at most 10 colors, and give examples of planar graphs needing at least $6$
colors for such a coloring. We also establish tight upper bounds for
outerplanar graphs. Finally, we provide several new bounds also for the
improper setting of considered colorings.


title: Minimum Ignition Energy of Methanol-Air Mixtures

abstract:
  A method for computing minimum ignition energies for gaseous fuel mixtures
with detailed and reduced chemistry, by numerical integration of time-dependent
conservation equations in a spherically symmetrical configuration, is presented
and discussed, testing its general characteristics and accuracy. The method is
applied to methanol-air mixtures described by a 38-step Arrhenius chemistry
description and by an 8-step chemistry description based on steady-state
approximations for reaction intermediaries. Comparisons of predictions with
results of available experimental measurements produced reasonable agreements
and supported both the robustness of the computational method and the
usefulness of the 8-step reduction in achieving accurate predictions.


title: A practical algorithm to minimize the overall error in FEM computations

abstract:
  Using the standard finite element method (FEM) to solve general partial
differential equations, the round-off error is found to be proportional to
$N^{\beta_{\rm R}}$, with $N$ the number of degrees of freedom (DoFs) and
$\beta_{\rm R}$ a coefficient. A method which uses a few cheap numerical
experiments is proposed to determine the coefficient of proportionality and
$\beta_{\rm R}$ in various space dimensions and FEM packages. Using the
coefficients obtained above, the strategy put forward in \cite{liu386balancing}
for predicting the highest achievable accuracy $E_{\rm min}$ and the associated
optimal number of DoFs $N_{\rm opt}$ for specific problems is extended to
general problems. This strategy allows predicting $E_{\rm min}$ accurately for
general problems, with the CPU time for obtaining the solution with the highest
accuracy $E_{\rm min}$ typically reduced by 60\%--90\%.


title: On cohomology and deformations of Jacobi-Jordan algebras

abstract:
  In this paper we study cohomology and deformations of Jacobi-Jordan algebras.
We develop their formal deformation theory. In particular, we introduce a
method to construct a versal deformation for a given Jacobi-Jordan algebra,
which can induce all deformations and is unique on the infinitesimal level. We
construct formal 1-parameter deformations of Jacobi-Jordan algebras up to
dimension 5 and versal deformations for 3-dimensional Jacobi-Jordan algebras.


title: Governance of Autonomous Agents on the Web: Challenges and Opportunities

abstract:
  The study of autonomous agents has a long tradition in the Multiagent Systems
and the Semantic Web communities, with applications ranging from automating
business processes to personal assistants. More recently, the Web of Things
(WoT), which is an extension of the Internet of Things (IoT) with metadata
expressed in Web standards, and its community provide further motivation for
pushing the autonomous agents research agenda forward. Although representing
and reasoning about norms, policies and preferences is crucial to ensuring that
autonomous agents act in a manner that satisfies stakeholder requirements,
normative concepts, policies and preferences have yet to be considered as
first-class abstractions in Web-based multiagent systems. Towards this end,
this paper motivates the need for alignment and joint research across the
Multiagent Systems, Semantic Web, and WoT communities, introduces a conceptual
framework for governance of autonomous agents on the Web, and identifies
several research challenges and opportunities.


title: Differentially Private Graph Classification with GNNs

abstract:
  Graph Neural Networks (GNNs) have established themselves as the
state-of-the-art models for many machine learning applications such as the
analysis of social networks, protein interactions and molecules. Several among
these datasets contain privacy-sensitive data. Machine learning with
differential privacy is a promising technique to allow deriving insight from
sensitive data while offering formal guarantees of privacy protection. However,
the differentially private training of GNNs has so far remained under-explored
due to the challenges presented by the intrinsic structural connectivity of
graphs. In this work, we introduce differential privacy for graph-level
classification, one of the key applications of machine learning on graphs. Our
method is applicable to deep learning on multi-graph datasets and relies on
differentially private stochastic gradient descent (DP-SGD). We show results on
a variety of synthetic and public datasets and evaluate the impact of different
GNN architectures and training hyperparameters on model performance for
differentially private graph classification. Finally, we apply explainability
techniques to assess whether similar representations are learned in the private
and non-private settings and establish robust baselines for future work in this
area.


title:
Causal Disentanglement for Semantics-Aware Intent Learning in
  Recommendation

abstract:
  Traditional recommendation models trained on observational interaction data
have generated large impacts in a wide range of applications, it faces bias
problems that cover users' true intent and thus deteriorate the recommendation
effectiveness. Existing methods tracks this problem as eliminating bias for the
robust recommendation, e.g., by re-weighting training samples or learning
disentangled representation. The disentangled representation methods as the
state-of-the-art eliminate bias through revealing cause-effect of the bias
generation. However, how to design the semantics-aware and unbiased
representation for users true intents is largely unexplored. To bridge the gap,
we are the first to propose an unbiased and semantics-aware disentanglement
learning called CaDSI (Causal Disentanglement for Semantics-Aware Intent
Learning) from a causal perspective. Particularly, CaDSI explicitly models the
causal relations underlying recommendation task, and thus produces
semantics-aware representations via disentangling users true intents aware of
specific item context. Moreover, the causal intervention mechanism is designed
to eliminate confounding bias stemmed from context information, which further
to align the semantics-aware representation with users true intent. Extensive
experiments and case studies both validate the robustness and interpretability
of our proposed model.


title:
Long-wavelength phonon dynamics in incommensurate Bi2Sr2CaCu2O(8+delta)
  crystals by Brillouin light scattering spectroscopy

abstract:
  Room temperature phonon dynamics in crystals of the high-Tc superconductor
Bi2Sr2CaCu2O(8+delta) were probed using Brillouin light scattering
spectroscopy. Eight distinct acoustic modes were observed and identified,
including two quasi-longitudinal bulk modes and four quasi-transverse bulk
modes. A peak at a frequency shift of ~95 GHz with behaviour reminiscent of an
optic phonon was also observed in the spectra. The existence and nature of
these modes is a manifestation of the incommensurate structure of
Bi2Sr2CaCu2O(8+delta) and suggests that it may be categorized as a so-called
composite incommensurate crystal comprised of two weakly interacting
sublattices. A mass ratio of m1/m2=0.36 obtained from the two measured
quasi-longitudinal acoustic velocities led to sublattice assignments of
Bi2Sr2O4 and CaCu2O4. The Brillouin data also places an upper limit of ~10 GHz
on the crossover frequency between commensurate and incommensurate phonon
dynamics.


title:
Differences in cell death and division rules can alter tissue rigidity
  and fluidization

abstract:
  Tissue mechanical properties such as rigidity and fluidity, and changes in
these properties driven by jamming-unjamming transitions (UJT), have come under
recent highlight as mechanical markers of health and disease in various
biological processes including cancer. However, most analysis of these
mechanical properties and UJT have sidestepped the effect of cellular death and
division in these systems. Cellular apoptosis (programmed cell death) and
mitosis (cell division) can drive significant changes in tissue properties. The
balance between the two is crucial in maintaining tissue function, and an
imbalance between the two is seen in situations such as cancer progression,
wound healing and necrosis. In this work we investigate the impact of cell
death and division on tissue mechanical properties, by incorporating specific
mechanosensitive triggers of cell death and division based on the size and
geometry of the cell within in silico models of tissue dynamics. Specifically,
we look at cell migration, tissue response to external stress, tissue extrusion
propensity and self-organization of different cell types within the tissue, as
a function of cell death and division and the rules that trigger these events.
We find that not only do cell death and division events significantly alter
tissue mechanics when compared to systems without these events, but that the
choice of triggers driving these cell death and division events also alter the
predicted tissue mechanics and overall system behavior.

categories: cond-mat.soft physics.bio-ph q-bio.CB q-bio.TO


title:
The economics of malnutrition: Dietary transition and food system
  transformation

abstract:
  Rapid increases in food supplies have reduced global hunger, while rising
burdens of diet-related disease have made poor diet quality the leading cause
of death and disability around the world. Today's "double burden" of
undernourishment in utero and early childhood then undesired weight gain and
obesity later in life is accompanied by a third less visible burden of
micronutrient imbalances. The triple burden of undernutrition, obesity, and
unbalanced micronutrients that underlies many diet-related diseases such as
diabetes, hypertension and other cardiometabolic disorders often coexist in the
same person, household and community. All kinds of deprivation are closely
linked to food insecurity and poverty, but income growth does not always
improve diet quality in part because consumers cannot directly or immediately
observe the health consequences of their food options, especially for newly
introduced or reformulated items. Even after direct experience and
epidemiological evidence reveals relative risks of dietary patterns and
nutritional exposures, many consumers may not consume a healthy diet because
food choice is driven by other factors. This chapter reviews the evidence on
dietary transition and food system transformation during economic development,
drawing implications for how research and practice in agricultural economics
can improve nutritional outcomes.


title:
Communication Efficient Federated Learning via Ordered ADMM in a Fully
  Decentralized Setting

abstract:
  The challenge of communication-efficient distributed optimization has
attracted attention in recent years. In this paper, a communication efficient
algorithm, called ordering-based alternating direction method of multipliers
(OADMM) is devised in a general fully decentralized network setting where a
worker can only exchange messages with neighbors. Compared to the classical
ADMM, a key feature of OADMM is that transmissions are ordered among workers at
each iteration such that a worker with the most informative data broadcasts its
local variable to neighbors first, and neighbors who have not transmitted yet
can update their local variables based on that received transmission. In OADMM,
we prohibit workers from transmitting if their current local variables are not
sufficiently different from their previously transmitted value. A variant of
OADMM, called SOADMM, is proposed where transmissions are ordered but
transmissions are never stopped for each node at each iteration. Numerical
results demonstrate that given a targeted accuracy, OADMM can significantly
reduce the number of communications compared to existing algorithms including
ADMM. We also show numerically that SOADMM can accelerate convergence,
resulting in communication savings compared to the classical ADMM.


title:
Experimental Analysis of Oscillatory Premixed Flames in a Hele-Shaw Cell
  Propagating Towards a Closed End

abstract:
  An experimental study of methane, propane and dimethyl ether (DME) pre-mixed
flames propagating in a quasi-two-dimensional Hele-Shaw cell placed
horizontally is presented in this paper. The flames are ignited at the open end
of the combustion chamber and propagate towards the closed end. Our experiments
revealed two distinct propagation regimes depending on the equivalence ratio of
the mixture as a consequence of the coupling between the heat-release rate and
the acoustic waves. The primary acoustic instability induces a small-amplitude,
of around 8 mm, oscillatory motion across the chamber that is observed for lean
propane, lean DME, and rich methane flames. Eventually, a secondary acoustic
instability emerges for sufficiently rich (lean) propane and DME (methane)
flames, inducing large-amplitude oscillations in the direction of propagation
of the flame. The amplitude of these oscillations can be as large as 30 mm and
drastically changes the outline of the flame. The front then forms pulsating
finger-shaped structures that characterize the flame propagation under the
secondary acoustic instability. The experimental setup allows the recording of
the flame propagation from two different points of view. The top view is used
to obtain accurate quantitative information about the flame propagation, while
the lateral view offered a novel three dimensional perspective of the flame
that gives relevant information on the transition between the two oscillatory
regimes. The influence of the geometry of the Hele-Shaw cell and of the
equivalence ratio on the transition between the two acoustic-instability
regimes is analyzed.


title:
Multilevel Picard approximations of high-dimensional semilinear partial
  differential equations with locally monotone coefficient functions

abstract:
  The full history recursive multilevel Picard approximation method for
semilinear parabolic partial differential equations (PDEs) is the only method
which provably overcomes the curse of dimensionality for general time horizons
if the coefficient functions and the nonlinearity are globally Lipschitz
continuous and the nonlinearity is gradient-independent. In this article we
extend this result to locally monotone coefficient functions. Our results cover
a range of semilinear PDEs with polynomial coefficient functions.


title: Temporal Robustness of Stochastic Signals

abstract:
  We study the temporal robustness of stochastic signals. This topic is of
particular interest in interleaving processes such as multi-agent systems where
communication and individual agents induce timing uncertainty. For a
deterministic signal and a given specification, we first introduce the
synchronous and the asynchronous temporal robustness to quantify the signal's
robustness with respect to synchronous and asynchronous time shifts in its
sub-signals. We then define the temporal robustness risk by investigating the
temporal robustness of the realizations of a stochastic signal. This definition
can be interpreted as the risk associated with a stochastic signal to not
satisfy a specification robustly in time. In this definition, general forms of
specifications such as signal temporal logic specifications are permitted. We
show how the temporal robustness risk is estimated from data for the
value-at-risk. The usefulness of the temporal robustness risk is underlined by
both theoretical and empirical evidence. In particular, we provide various
numerical case studies including a T-intersection scenario in autonomous
driving.


title: Black Holes in Quantum Gravity

abstract:
  We review some recent results obtained for black holes using effective field
theory methods applied to quantum gravity, in particular the unique effective
action. Black holes are complex thermodynamical objects that not only have a
temperature but also have a pressure. Furthermore, they have quantum hair which
provides a solution to the black hole information paradox.


title:
GhostTalk: Interactive Attack on Smartphone Voice System Through Power
  Line

abstract:
  Inaudible voice command injection is one of the most threatening attacks
towards voice assistants. Existing attacks aim at injecting the attack signals
over the air, but they require the access to the authorized user's voice for
activating the voice assistants. Moreover, the effectiveness of the attacks can
be greatly deteriorated in a noisy environment. In this paper, we explore a new
type of channel, the power line side-channel, to launch the inaudible voice
command injection. By injecting the audio signals over the power line through a
modified charging cable, the attack becomes more resilient against various
environmental factors and liveness detection models. Meanwhile, the smartphone
audio output can be eavesdropped through the modified cable, enabling a
highly-interactive attack.
  To exploit the power line side-channel, we present GhostTalk, a new hidden
voice attack that is capable of injecting and eavesdropping simultaneously. Via
a quick modification of the power bank cables, the attackers could launch
interactive attacks by remotely making a phone call or capturing private
information from the voice assistants. GhostTalk overcomes the challenge of
bypassing the speaker verification system by stealthily triggering a switch
component to simulate the press button on the headphone. In case when the
smartphones are charged by an unaltered standard cable, we discover that it is
possible to recover the audio signal from smartphone loudspeakers by monitoring
the charging current on the power line. To demonstrate the feasibility, we
design GhostTalk-SC, an adaptive eavesdropper system targeting smartphones
charged in the public USB ports. To correctly recognize the private information
in the audio, GhostTalk-SC carefully extracts audio spectra and integrates a
neural network model to classify spoken digits in the speech.


title: A Note on Odd Colorings of 1-Planar Graphs

abstract:
  A proper coloring of a graph is odd if every non-isolated vertex has some
color that appears an odd number of times on its neighborhood. This notion was
recently introduced by Petru\v{s}evski and \v{S}krekovski, who proved that
every planar graph admits an odd $9$-coloring; they also conjectured that every
planar graph admits an odd $5$-coloring. Shortly after, this conjecture was
confirmed for planar graphs of girth at least seven by Cranston; outerplanar
graphs by Caro, Petru\v{s}evski, and \v{S}krekovski. Building on the work of
Caro, Petru\v{s}evski, and \v{S}krekovski, Petr and Portier then further proved
that every planar graph admits an odd $8$-coloring. In this note we prove that
every 1-planar graph admits an odd $23$-coloring, where a graph is 1-planar if
it can be drawn in the plane so that each edge is crossed by at most one other
edge.


title: VIS-iTrack: Visual Intention through Gaze Tracking using Low-Cost Webcam

abstract:
  Human intention is an internal, mental characterization for acquiring desired
information. From interactive interfaces containing either textual or graphical
information, intention to perceive desired information is subjective and
strongly connected with eye gaze. In this work, we determine such intention by
analyzing real-time eye gaze data with a low-cost regular webcam. We extracted
unique features (e.g., Fixation Count, Eye Movement Ratio) from the eye gaze
data of 31 participants to generate a dataset containing 124 samples of visual
intention for perceiving textual or graphical information, labeled as either
TEXT or IMAGE, having 48.39% and 51.61% distribution, respectively. Using this
dataset, we analyzed 5 classifiers, including Support Vector Machine (SVM)
(Accuracy: 92.19%). Using the trained SVM, we investigated the variation of
visual intention among 30 participants, distributed in 3 age groups, and found
out that young users were more leaned towards graphical contents whereas older
adults felt more interested in textual ones. This finding suggests that
real-time eye gaze data can be a potential source of identifying visual
intention, analyzing which intention aware interactive interfaces can be
designed and developed to facilitate human cognition.


title:
The modified quasichemical model in the Distinguishable-Pair
  Approximation for multicomponent solutions

abstract:
  The Modified Quasichemcial Model in the Distinguishable-Pair Approximation
(MQMDPA) for manifold short-range orders in liquids has been successfully
extended to multicomponent solutions. The extension is conducted by means of
the geometrical interpolation method. Three types of interpolation models,
namely Kohler, Toop and Chou, are introduced to initially formulate the pair
interaction energies in ternary solutions by employing those in their
constituent binary solutions. The pair energies can be expanded in terms of the
pair fractions (configuration-dependent) or in terms of the
coordination-equivalent fractions (composition-dependent). These methods are
subsequently extended for use in multicomponent solutions. A general formalism
for the combined Kohler-Toop model is employed to permit complete freedom of
choice to treat any ternary subsystems with a symmetric or asymmetric model.
Meanwhile, a general Chou model is also used to treat all ternary subsystems
without any human interference to select symmetric or asymmetric components but
only dependent upon the similarity and difference in properties from each two
binary solutions with a co-member. Advantages and shortcomings are critically
discussed regarding the utilization of different interpolation models.