title: Infinite-Dimensional Sparse Learning in Linear System Identification

abstract:
  Regularized methods have been widely applied to system identification
problems without known model structures. This paper proposes an
infinite-dimensional sparse learning algorithm based on atomic norm
regularization. Atomic norm regularization decomposes the transfer function
into first-order atomic models and solves a group lasso problem that selects a
sparse set of poles and identifies the corresponding coefficients. The
difficulty in solving the problem lies in the fact that there are an infinite
number of possible atomic models. This work proposes a greedy algorithm that
generates new candidate atomic models maximizing the violation of the
optimality condition of the existing problem. This algorithm is able to solve
the infinite-dimensional group lasso problem with high precision. The algorithm
is further extended to reduce the bias and reject false positives in pole
location estimation by iteratively reweighted adaptive group lasso and
complementary pairs stability selection respectively. Numerical results
demonstrate that the proposed algorithm performs better than benchmark
parameterized and regularized methods in terms of both impulse response fitting
and pole location estimation.


title: SASV 2022: The First Spoofing-Aware Speaker Verification Challenge

abstract:
  The first spoofing-aware speaker verification (SASV) challenge aims to
integrate research efforts in speaker verification and anti-spoofing. We extend
the speaker verification scenario by introducing spoofed trials to the usual
set of target and impostor trials. In contrast to the established ASVspoof
challenge where the focus is upon separate, independently optimised spoofing
detection and speaker verification sub-systems, SASV targets the development of
integrated and jointly optimised solutions. Pre-trained spoofing detection and
speaker verification models are provided as open source and are used in two
baseline SASV solutions. Both models and baselines are freely available to
participants and can be used to develop back-end fusion approaches or
end-to-end solutions. Using the provided common evaluation protocol, 23 teams
submitted SASV solutions. When assessed with target, bona fide non-target and
spoofed non-target trials, the top-performing system reduces the equal error
rate of a conventional speaker verification system from 23.83% to 0.13%. SASV
challenge results are a testament to the reliability of today's
state-of-the-art approaches to spoofing detection and speaker verification.


title:
Open-VICO: An Open-Source Gazebo Toolkit for Multi-Camera-based Skeleton
  Tracking in Human-Robot Collaboration

abstract:
  Simulation tools are essential for robotics research, especially for those
domains in which safety is crucial, such as Human-Robot Collaboration (HRC).
However, it is challenging to simulate human behaviors, and existing robotics
simulators do not integrate functional human models. This work presents
Open-VICO~\footnote{\url{https://gitlab.iit.it/hrii-public/open-vico}}, an
open-source toolkit to integrate virtual human models in Gazebo focusing on
vision-based human tracking. In particular, Open-VICO allows to combine in the
same simulation environment realistic human kinematic models, multi-camera
vision setups, and human-tracking techniques along with numerous robot and
sensor models thanks to Gazebo. The possibility to incorporate pre-recorded
human skeleton motion with Motion Capture systems broadens the landscape of
human performance behavioral analysis within Human-Robot Interaction (HRI)
settings. To describe the functionalities and stress the potential of the
toolkit four specific examples, chosen among relevant literature challenges in
the field, are developed using our simulation utils: i) 3D multi-RGB-D camera
calibration in simulation, ii) creation of a synthetic human skeleton tracking
dataset based on OpenPose, iii) multi-camera scenario for human skeleton
tracking in simulation, and iv) a human-robot interaction example. The key of
this work is to create a straightforward pipeline which we hope will motivate
research on new vision-based algorithms and methodologies for lightweight
human-tracking and flexible human-robot applications.


title: On the biharmonic heat equation on complete Riemannian manifolds

abstract:
  We study entire solutions of the biharmonic heat equation on complete
Riemannian manifolds without boundary. We provide exponential decay estimates
for the biharmonic heat kernel under assumptions on the lower bound of Ricci
curvature and noncollapsing of unit balls. And we prove a uniqueness criteria
for the Cauchy problem. As corollaries we prove the conservation law for the
biharmonic heat kernel and a uniform L-infinite estimate for entire solutions
starting with bounded initial data.


title: Partially accreted crusts of neutron stars

abstract:
  Neutron stars in low-mass binary systems are subject to accretion. The common
assumption in studying the properties of the neutron star crust is the fully
accreted crust approximation. However, observations of some X-ray transient
sources indicate that the original crust has not been completely replaced by
accreted material, but is partly composed of the compressed original crust. A
two-part (or hybrid) crust made of the original crust that is compressed and of
the accreted material crashing onto it was reconstructed as a function of the
accretion stage. The differences in the composition and energy sources for the
fully accreted and hybrid crusts influence the cooling and transport
properties. A simple semi-empirical formula of a compressible liquid drop was
used. We compared the nuclear reactions triggered by compression in the
original crust and in the accreted matter part of the hybrid crust. We discuss
another crust compression astrophysical phenomenon related to spinning neutron
stars. The compression of the originally catalyzed outer crust triggers
exothermic reactions (electron captures and pycnonuclear fusions) that deposit
heat in the crust. The heat sources are cataloged as a function of the
compression until the fully accreted crust approximation is reached. The
pressure at which neutron drip occurs is a nonmonotonic function of the depth,
leading to a temporary neutron drip anomaly. The additional potential source of
energy for partially accreted crusts is the occurrence of a density inversion
phenomenon between some compressed layers. The original crust of a neutron star
cannot be neglected when the original crust is not fully replaced by the
accreted matter. The amount of heat associated with the compression of the
original crust is on the same order of magnitude as that from the sources
acting in the accreted part of the hybrid crust.


title:
Nonlinear polarization and transverse mode dynamics of a broad-area
  VCSEL in continuous wave operation

abstract:
  We study the nonlinear dynamics of a broad-area VCSEL in solitary
continuous-wave operation with a comprehensive investigation of its
polarization state, lasing spectra, near-field distributions and temporal
dynamics. Fluctuations at the frequency of the birefringence splitting and
other frequency components develop in a series of bifurcations. The
bifurcations coincide with changes of the transverse lasing modes and/or the
polarization state, demonstrating the importance of both the spatial and
polarization degrees of freedom for the mode competition. In consequence, the
inherent nonlinear dynamics of the broad-area VCSEL is significantly more
complex than the dynamics of narrow VCSELs.


title: Time spent in a ball by a critical branching random walk

abstract:
  We study a critical branching random walk on $\mathbb Z^d$. We focus on the
tail of the time spent in a ball, and our study, in dimension four and higher,
sheds new light on the recent result of Angel, Hutchcroft and Jarai, in
particular on the special features of the critical dimension four. Finally, we
analyse the number of walks transported by the branching random walk on the
boundary of a distant ball.


title: Using Domain Knowledge for Low Resource Named Entity Recognition

abstract:
  In recent years, named entity recognition has always been a popular research
in the field of natural language processing, while traditional deep learning
methods require a large amount of labeled data for model training, which makes
them not suitable for areas where labeling resources are scarce. In addition,
the existing cross-domain knowledge transfer methods need to adjust the entity
labels for different fields, so as to increase the training cost. To solve
these problems, enlightened by a processing method of Chinese named entity
recognition, we propose to use domain knowledge to improve the performance of
named entity recognition in areas with low resources. The domain knowledge
mainly applied by us is domain dictionary and domain labeled data. We use
dictionary information for each word to strengthen its word embedding and
domain labeled data to reinforce the recognition effect. The proposed model
avoids large-scale data adjustments in different domains while handling named
entities recognition with low resources. Experiments demonstrate the
effectiveness of our method, which has achieved impressive results on the data
set in the field of scientific and technological equipment, and the F1 score
has been significantly improved compared with many other baseline methods.


title:
Global Regular Solutions for the Multi-dimensional Kuramoto-Sivashinsky
  Equation posed on Smooth Domains

abstract:
  Initial-boundary value problems for the n-dimensional (n is a natural number
from the interval [2,7]) Kuramoto-Sivashinsky equation posed on smooth bounded
domains in R^n were considered. The existence and uniqueness of global regular
solutions as well as their exponential decay have been considered.


title:
An improved approximation algorithm for maximizing a DR-submodular
  function over a convex set

abstract:
  Maximizing a DR-submodular function subject to a general convex set is an
NP-hard problem arising from many applications in combinatorial optimization
and machine learning. While it is highly desirable to design efficient
approximation algorithms under this general setting where neither the objective
function is monotonic nor the feasible set is down-closed, our main
contribution is to present a 0.25-approximation Frank-Wolfe type of algorithm
with a sub-exponential time-complexity under the value oracle model.


title:
Learning Personalized Human-Aware Robot Navigation Using Virtual Reality
  Demonstrations from a User Study

abstract:
  For the most comfortable, human-aware robot navigation, subjective user
preferences need to be taken into account. This paper presents a novel
reinforcement learning framework to train a personalized navigation controller
along with an intuitive virtual reality demonstration interface. The conducted
user study provides evidence that our personalized approach significantly
outperforms classical approaches with more comfortable human-robot experiences.
We achieve these results using only a few demonstration trajectories from
non-expert users, who predominantly appreciate the intuitive demonstration
setup. As we show in the experiments, the learned controller generalizes well
to states not covered in the demonstration data, while still reflecting user
preferences during navigation. Finally, we transfer the navigation controller
without loss in performance to a real robot.


title:
A Bayesian Approach to Modelling Biological Pattern Formation with
  Limited Data

abstract:
  Pattern formation in biological tissues plays an important role in the
development of living organisms. Since the classical work of Alan Turing, a
pre-eminent way of modelling has been through reaction-diffusion mechanisms.
More recently, alternative models have been proposed, that link dynamics of
diffusing molecular signals with tissue mechanics. In order to distinguish
among different models, they should be compared to experimental observations.
However, in many experimental situations only the limiting, stationary regime
of the pattern formation process is observable, without knowledge of the
transient behaviour or the initial state. The unstable nature of the underlying
dynamics in all alternative models seriously complicates model and parameter
identification, since small changes in the initial condition lead to distinct
stationary patterns. To overcome this problem the initial state of the model
can be randomised. In the latter case, fixed values of the model parameters
correspond to a family of patterns rather than a fixed stationary solution, and
standard approaches to compare pattern data directly with model outputs, e.g.,
in the least squares sense, are not suitable. Instead, statistical
characteristics of the patterns should be compared, which is difficult given
the typically limited amount of available data in practical applications. To
deal with this problem, we extend a recently developed statistical approach for
parameter identification using pattern data, the so-called Correlation Integral
Likelihood (CIL) method. We suggest modifications that allow increasing the
accuracy of the identification process without resizing the data set. The
proposed approach is tested using different classes of pattern formation
models. For all considered equations, parallel GPU-based implementations of the
numerical solvers with efficient time stepping schemes are provided.


title:
Neural Estimation and Optimization of Directed Information over
  Continuous Spaces

abstract:
  This work develops a new method for estimating and optimizing the directed
information rate between two jointly stationary and ergodic stochastic
processes. Building upon recent advances in machine learning, we propose a
recurrent neural network (RNN)-based estimator which is optimized via gradient
ascent over the RNN parameters. The estimator does not require prior knowledge
of the underlying joint and marginal distributions. The estimator is also
readily optimized over continuous input processes realized by a deep generative
model. We prove consistency of the proposed estimation and optimization methods
and combine them to obtain end-to-end performance guarantees. Applications for
channel capacity estimation of continuous channels with memory are explored,
and empirical results demonstrating the scalability and accuracy of our method
are provided. When the channel is memoryless, we investigate the mapping
learned by the optimized input generator.


title: Minimal free resolution of Crystal module

abstract:
  The join-meet ideal was introduced by Takayuki Hibi in 1987. It is binomial
ideals that are defined by finite lattices. We study the join-meet ideal of
non-distributive finite lattices that do not always satisfy modular. In
particular, we work on the case of Crystal lattice which is one of them. It was
introduced by Yohei Oshida in 2022. The Crystal module in the title is the
residue class ring which is in relation to the join-meet ideal of Crystal
lattice. In this paper, we give important inequalities about the minimal free
resolution of the Crystal module. The important point about this result is that
the projective dimension and Betti-number of the Crystal module can be
evaluated by using the Koszul complex.


title:
Modified AKNS model, Riccati-type pseudo-potential approach and infinite
  towers of quasi-conservation laws

abstract:
  A dual Riccati-type pseudo-potential formulation is introduced for a modified
AKNS system (MAKNS) and infinite towers of novel anomalous conservation laws
are uncovered. In addition, infinite towers of exact non-local conservation
laws are uncovered in a linear formulation of the system. It is shown that
certain modifications of the non-linear Schr\"odinger model (MNLS) can be
obtained through a reduction process starting from the MAKNS model. So, the
novel infinite sets of quasi-conservation laws and related anomalous charges
are constructed by an unified and rigorous approach based on the Riccati-type
pseudo-potential method, for the standard NLS and modified MNLS cases,
respectively. The non-local properties, the complete list of towers of infinite
number of anomalous charges and the (non-local) exact conservation laws of the
quasi-integrable systems, such as the deformed Bullough-Dodd, Toda, KdV and
SUSY sine-Gordon systems can be studied in the framework presented in this
paper. Our results may find many applications since the AKNS-type system arises
in several branches of non-linear physics, such as Bose-Einstein condensation,
superconductivity and soliton turbulence.


title: Sequential image recovery from noisy and under-sampled Fourier data

abstract:
  A new algorithm is developed to jointly recover a temporal sequence of images
from noisy and under-sampled Fourier data. Specifically, we consider the case
where each data set is missing vital information that prevents its (individual)
accurate recovery. Our new method is designed to restore the missing
information in each individual image by "borrowing" it from the other images in
the sequence. As a result, {\em all} of the individual reconstructions yield
improved accuracy. The use of high resolution Fourier edge detection methods is
essential to our algorithm. In particular, edge information is obtained
directly from the Fourier data which leads to an accurate coupling term between
data sets. Moreover, data loss is largely avoided as coarse reconstructions are
not required to process inter- and intra-image information. Numerical examples
are provided to demonstrate the accuracy, efficiency and robustness of our new
method.


title: Numerical boundary control for semilinear hyperbolic systems

abstract:
  This work is devoted to the design of boundary controls of physical systems
that are described by semilinear hyperbolic balance laws. A computational
framework is presented that yields sufficient conditions for a boundary control
to steer the system towards a desired state. The presented approach is based on
a Lyapunov stability analysis and a CWENO-type reconstruction.


title:
Universal Graph Filter Design based on Butterworth, Chebyshev and
  Elliptic Functions

abstract:
  Graph filters are crucial tools in processing the spectrum of graph signals.
In this paper, we propose to design universal IIR graph filters with low
computational complexity by using three kinds of functions, which are
Butterworth, Chebyshev, and Elliptic functions, respectively. Specifically,
inspired by the classical analog filter design method, we first derive the
zeros and poles of graph frequency responses. With these zeros and poles, we
construct the conjugate graph filters to design the Butterworth high pass graph
filter, Chebyshev high pass graph filter, and Elliptic high pass graph filter,
respectively. On this basis, we further propose to construct a desired graph
filter of low pass, band pass, and band stop by mapping the parameters of the
desired graph filter to those of the equivalent high pass graph filter.
Furthermore, we propose to set the graph filter order given the maximum
passband attenuation and the minimum stopband attenuation. Our numerical
results show that the proposed graph filter design methods realize the desired
frequency response more accurately than the autoregressive moving average
(ARMA) graph filter design method, the linear least-squares fitting (LLS) based
graph filter design method, and the Chebyshev FIR polynomial graph filter
design method.


title:
Adaptive Risk Tendency: Nano Drone Navigation in Cluttered Environments
  with Distributional Reinforcement Learning

abstract:
  Enabling robots with the capability of assessing risk and making risk-aware
decisions is widely considered a key step toward ensuring robustness for robots
operating under uncertainty. In this paper, we consider the specific case of a
nano drone robot learning to navigate an apriori unknown environment while
avoiding obstacles under partial observability. We present a distributional
reinforcement learning framework in order to learn adaptive risk tendency
policies. Specifically, we propose to use tail conditional variance of the
learnt action-value distribution as an uncertainty measurement, and use a
exponentially weighted average forecasting algorithm to automatically adapt the
risk-tendency at run-time based on the observed uncertainty in the environment.
We show our algorithm can adjust its risk-sensitivity on the fly both in
simulation and real-world experiments and achieving better performance than
risk-neutral policy or risk-averse policies. Code and real-world experiment
video can be found in this repository:
\url{https://github.com/tudelft/risk-sensitive-rl.git}


title:
Stationary Covariance Regime for Affine Stochastic Covariance Models in
  Hilbert Spaces

abstract:
  We study the long-time behavior of affine processes on positive self-adjoiont
Hilbert-Schmidt operators which are of pure-jump type, conservative and have
finite second moment. For subcritical processes we prove the existence of a
unique limit distribution and construct the corresponding stationary affine
process. Moreover, we obtain an explicit convergence rate of the underlying
transition kernels to the limit distribution in the Wasserstein distance of
order $p\in [1, 2]$ and provide explicit formulas for the first two moments of
the limit distribution. We apply our results to the study of
infinite-dimensional affine stochastic covariance models in the stationary
covariance regime, where the stationary affine process models the instantaneous
covariance process. In this context we investigate the behavior of the implied
forward volatility smile for large forward dates in a geometric affine forward
curve model used for the modeling of forward curve dynamics in fixed income or
commodity markets formulated in the Heath-Jarrow-Morton-Musiela framework.


title:
Measuring the Impact of Taxes and Public Services on Property Values: A
  Double Machine Learning Approach

abstract:
  How do property prices respond to changes in local taxes and local public
services? Attempts to measure this, starting with Oates (1969), have suffered
from a lack of local public service controls. Recent work attempts to overcome
such data limitations through the use of quasi-experimental methods. We revisit
this fundamental problem, but adopt a different empirical strategy that pairs
the double machine learning estimator of Chernozhukov et al. (2018) with a
novel dataset of 947 time-varying local characteristic and public service
controls for all municipalities in Sweden over the 2010-2016 period. We find
that properly controlling for local public service and characteristic controls
more than doubles the estimated impact of local income taxes on house prices.
We also exploit the unique features of our dataset to demonstrate that tax
capitalization is stronger in areas with greater municipal competition,
providing support for a core implication of the Tiebout hypothesis. Finally, we
measure the impact of public services, education, and crime on house prices and
the effect of local taxes on migration.


title: Non-Thermal Fixed Points in Bose Gas Experiments

abstract:
  One of the most challenging tasks in physics has been understanding the route
an out-of-equilibrium system takes to its thermalized state. This problem can
be particularly overwhelming when one considers a many-body quantum system.
However, several recent theoretical and experimental studies have indicated
that some far-from-equilibrium systems display universal dynamics when close to
a so-called non-thermal fixed point (NTFP), following a rescaling of both space
and time. This opens up the possibility of a general framework for studying and
categorizing out-of-equilibrium phenomena into well-defined universality
classes. This paper reviews the recent advances in observing NTFPs in
experiments involving Bose gases. We provide a brief introduction to the theory
behind this universal scaling, focusing on experimental observations of NTFPs.
We present the benefits of NTFP universality classes by analogy with
renormalization group theory in equilibrium critical phenomena.


title:
Knowledge-Guided Learning for Transceiver Design in Over-the-Air
  Federated Learning

abstract:
  In this paper, we consider communication-efficient over-the-air federated
learning (FL), where multiple edge devices with non-independent and identically
distributed datasets perform multiple local iterations in each communication
round and then concurrently transmit their updated gradients to an edge server
over the same radio channel for global model aggregation using over-the-air
computation (AirComp). We derive the upper bound of the time-average norm of
the gradients to characterize the convergence of AirComp-assisted FL, which
reveals the impact of the model aggregation errors accumulated over all
communication rounds on convergence. Based on the convergence analysis, we
formulate an optimization problem to minimize the upper bound to enhance the
learning performance, followed by proposing an alternating optimization
algorithm to facilitate the optimal transceiver design for AirComp-assisted FL.
As the alternating optimization algorithm suffers from high computation
complexity, we further develop a knowledge-guided learning algorithm that
exploits the structure of the analytic expression of the optimal transmit power
to achieve computation-efficient transceiver design. Simulation results
demonstrate that the proposed knowledge-guided learning algorithm achieves a
comparable performance as the alternating optimization algorithm, but with a
much lower computation complexity. Moreover, both proposed algorithms
outperform the baseline methods in terms of convergence speed and test
accuracy.


title:
Non-perturbative renormalization of the QCD flavour-singlet local vector
  current

abstract:
  We compute non-perturbatively the renormalization constant of the
flavour-singlet local vector current $Z_V$ in lattice QCD with 3 massless
flavours. Gluons are discretized by the Wilson plaquette action while quarks by
the O($a$)-improved Wilson--Dirac operator. The constant $Z_V$ is fixed by
comparing the expectation values (1-point functions) of the conserved and local
vector currents at finite temperature in the presence of shifted boundary
conditions and at non-zero imaginary chemical potential. Monte Carlo
simulations with a moderate computational cost allow us to obtain $Z_V$ with an
accuracy of about 8\textperthousand\, for values of the inverse bare coupling
constant $\beta=6/g_0^2$ in the range $5.3 \leq \beta \leq 11.5$.


title:
Personalized Graph Summarization: Formulation, Scalable Algorithms, and
  Applications

abstract:
  Are users of an online social network interested equally in all connections
in the network? If not, how can we obtain a summary of the network personalized
to specific users? Can we use the summary for approximate query answering?
  As massive graphs (e.g., online social networks, hyperlink networks, and road
networks) have become pervasive, graph compression has gained importance for
the efficient processing of such graphs with limited resources. Graph
summarization is an extensively-studied lossy compression method. It provides a
summary graph where nodes with similar connectivity are merged into supernodes,
and a variety of graph queries can be answered approximately from the summary
graph.
  In this work, we introduce a new problem, namely personalized graph
summarization, where the objective is to obtain a summary graph where more
emphasis is put on connections closer to a given set of target nodes. Then, we
propose PeGaSus, a linear-time algorithm for the problem. Through experiments
on six real-world graphs, we demonstrate that PeGaSus is (a) Effective:
node-similarity queries for target nodes can be answered significantly more
accurately from personalized summary graphs than from non-personalized ones of
similar size, (b) Scalable: it summarizes graphs with up to one billion edges,
and (c) Applicable to distributed multi-query answering: it successfully
replaces graph partitioning for communication-free multi-query processing.


title:
Reference-State Error Mitigation: A Strategy for High Accuracy Quantum
  Computation of Chemistry

abstract:
  Decoherence and gate errors severely limit the capabilities of
state-of-the-art quantum computers. This work introduces a strategy for
reference-state error mitigation (REM) of quantum chemistry that can be
straightforwardly implemented on current and near-term devices. REM can be
applied alongside existing mitigation procedures, while requiring minimal
post-processing and only one or no additional measurements. The approach is
agnostic to the underlying quantum mechanical ansatz and is designed for the
variational quantum eigensolver (VQE). Two orders-of-magnitude improvement in
the computational accuracy of ground state energies of small molecules (H2,
HeH+ and LiH) is demonstrated on superconducting quantum hardware. Simulations
of noisy circuits with a depth exceeding 1000 two-qubit gates are used to argue
for scalability of the method.


title:
STUDIES: Corpus of Japanese Empathetic Dialogue Speech Towards Friendly
  Voice Agent

abstract:
  We present STUDIES, a new speech corpus for developing a voice agent that can
speak in a friendly manner. Humans naturally control their speech prosody to
empathize with each other. By incorporating this "empathetic dialogue" behavior
into a spoken dialogue system, we can develop a voice agent that can respond to
a user more naturally. We designed the STUDIES corpus to include a speaker who
speaks with empathy for the interlocutor's emotion explicitly. We describe our
methodology to construct an empathetic dialogue speech corpus and report the
analysis results of the STUDIES corpus. We conducted a text-to-speech
experiment to initially investigate how we can develop more natural voice agent
that can tune its speaking style corresponding to the interlocutor's emotion.
The results show that the use of interlocutor's emotion label and
conversational context embedding can produce speech with the same degree of
naturalness as that synthesized by using the agent's emotion label. Our project
page of the STUDIES corpus is http://sython.org/Corpus/STUDIES.


title:
Dissolved gas monitoring probe without liquid-gas separation under
  strong electromagnetic interference

abstract:
  Rapid and direct monitoring of dissolved gases in liquids under strong
electromagnetic interference is very important. Electronic gas sensors that can
be placed into liquids are difficult to work reliably under strong
electromagnetic fields. The existing optical monitoring techniques for
dissolved gases all require gas-liquid separation, and are conducted in gas
phase, which have poor timeliness. In this paper, a dissolved gas monitoring
probe without liquid-gas separation under strong electromagnetic interference
is proposed. We take transformer oil-dissolved acetylene monitoring as an
example, an oil-core photonic crystal fiber photothermal interferometry probe
is proposed and demonstrates the feasibility of trace oil-dissolved acetylene
directly monitoring without oil-gas separation. The minimum detection limit
reaches 1.4 ppm, and the response time is about 70 minutes. Due to the good
insulation performance and the compact size, the all-fiber probe provides
applicability to be placed into transformer oil and perform on-line monitoring
under strong electromagnetic interference.


title:
Computer modeling of $t\bar{t}h(H)$ Higgs boson production with MSSM
  model

abstract:
  We studied the properties of the MSSM Higgs bosons, h and H through the decay
into b-quarks in associated production with a top-quark pair. There was used
the tree-level Higgs sector described by two parameters M$_A$ and tan$\beta$
and found their optimal values according to experimental data of ATLAS
detector. Using the restricted parameter space we calculated cross sections of
associated $t\bar{t}h(H)$ production at 13 and 14 TeV, the corresponding
kinematical cuts, mass distributions and Branching Ratios of h and H decays
into $b\bar{b}$ quark pair.


title:
Functional principal component analysis for longitudinal observations
  with sampling at random

abstract:
  Functional principal component analysis has been shown to be invaluable for
revealing variation modes of longitudinal outcomes, which serves as important
building blocks for forecasting and model building. Decades of research have
advanced methods for functional principal component analysis often assuming
independence between the observation times and longitudinal outcomes. Yet such
assumptions are fragile in real-world settings where observation times may be
driven by outcome-related reasons. Rather than ignoring the informative
observation time process, we explicitly model the observational times by a
counting process dependent on time-varying prognostic factors. Identification
of the mean, covariance function, and functional principal components ensues
via inverse intensity weighting. We propose using weighted penalized splines
for estimation and establish consistency and convergence rates for the weighted
estimators. Simulation studies demonstrate that the proposed estimators are
substantially more accurate than the existing ones in the presence of a
correlation between the observation time process and the longitudinal outcome
process. We further examine the finite-sample performance of the proposed
method using the Acute Infection and Early Disease Research Program study.


title:
Extending inferences from a cluster randomized trial to a target
  population

abstract:
  We describe methods that extend (generalize or transport) causal inferences
from cluster randomized trials to a target population of clusters, under a
general nonparametric model that allows for arbitrary within-cluster
dependence. We propose doubly robust estimators of potential outcome means in
the target population that exploit individual-level data on covariates and
outcomes to improve efficiency and are appropriate for use with machine
learning methods. We illustrate the methods using a cluster randomized trial of
influenza vaccination strategies conducted in 818 nursing homes nested in a
cohort of 4,475 trial-eligible Medicare-certified nursing homes.


title:
Lifetime Prediction of 1550 nm DFB Laser using Machine learning
  Techniques

abstract:
  A novel approach based on an artificial neural network (ANN) for lifetime
prediction of 1.55 um InGaAsP MQW-DFB laser diodes is presented. It outperforms
the conventional lifetime projection using accelerated aging tests.


title:
Analysis and Performance Evaluation of Mobility for Multi-Panel User
  Equipment in 5G Networks

abstract:
  Frequency Range 2 (FR2) has become an integral part of 5G networks to fulfill
the ever increasing demand for user data throughput. However, radio signals in
FR2 experience high path and diffraction loss in mobile environments. To
address this issue, multi-panel user equipment (MPUE) is adopted for FR2
whereby multiple antenna panels are placed on the UE body to leverage gains
from antenna directivity. In contrast to traditional UEs with isotropic
radiation patterns, signal measurements of cells in the network may not be
available on all panels simultaneously for MPUE, which may result in outdated
signal measurements that affect the reliability of mobility decisions. In this
paper, we investigate the mobility performance of two different MPUE schemes
following different paradigms for signal measurement and compare their
performance with traditional UEs. This performance evaluation is based in
multi-beam 5G networks operating in FR2 where there are multiple simultaneously
active beams per cell to realize the high throughput requirements. Furthermore,
an in-depth analysis of the mobility performance is carried out to determine
the best mobility parameter combinations for the different MPUE schemes.
Results have shown that both MPUE schemes offer considerable mobility
performance gains as compared to traditional UEs. Moreover, it is seen that the
MPUE schemes require different mobility parameter settings for the best
mobility performance.


title:
Parking Behaviour Analysis of Shared E-Bike Users Based on a Real-World
  Dataset -- A Case Study in Dublin, Ireland

abstract:
  In recent years, an increasing number of shared E-bikes have been rolling out
rapidly in our cities. It therefore becomes important to understand new
behaviour patterns of the cyclists in using these E-bikes as a foundation for
the novel design of shared micromobility services as part of the realisation
for next generation intelligent transportation systems. In this paper, we
deeply investigate the users' behaviour of shared E-bikes in a case study by
using the real-world dataset collected from the shared E-bike company, MOBY,
which currently operates in Dublin, Ireland. More specifically, we look into
the parking behaviours of users as we know that inappropriate parking of these
bikes can not only increase the management costs of the company but also result
in other users' inconveniences, especially in situations of battery shortage,
which inevitably reduces the overall operational efficacy of these shared
E-bikes. Our work has conducted analysis at both bike station and individual
level in a fully anonymous and GDPR-Compliant manner, and our results have
shown that up to 12.9% of shared E-bike users did not park their bikes properly
at the designated stands. Different visualisation tools have been applied to
better illustrate our obtained results.


title: Enhanced versatility of table-top X-rays from van der Waals structures

abstract:
  Van der Waals (vdW) materials have attracted much interest for their myriad
unique electronic, mechanical and thermal properties. In particular, they are
promising candidates for monochromatic, table-top X-ray sources. This work
reveals that the versatility of the table-top vdW X-ray source goes beyond what
has been demonstrated so far. By introducing a tilt angle between the vdW
structure and the incident electron beam, it is theoretically and
experimentally shown that the accessible photon energy range is more than
doubled. This allows for greater versatility in real-time tuning of the vdW
X-ray source. Furthermore, this work shows that the accessible photon energy
range is maximized by simultaneously controlling both the electron energy and
the vdW structure tilt. These results should pave the way for highly tunable,
compact X-ray sources, with potential applications including hyperspectral
X-ray fluoroscopy and X-ray quantum optics.


title: Upper bound for quantum entropy production from entropy flux

abstract:
  Entropy production is a key quantity characterizing nonequilibrium systems.
However, it can often be difficult to compute in practice, as it requires
detailed information about the system and the dynamics it undergoes. This
becomes even more difficult in the quantum domain, and if one is interested in
generic nonequilibrium reservoirs, for which the standard thermal recipes no
longer apply. In this paper, we derive an upper bound for the entropy
production in terms of the entropy flux for a class of systems for which the
flux is given in terms of a system's observable. Since currents are often
easily accessible in this class of systems, this bound should prove useful for
estimating the entropy production in a broad variety of processes. We
illustrate the applicability of the bound by considering a three-level maser
engine and a system interacting with a squeezed bath.


title:
Better magneto-optical filters with cascaded vapor cells in the
  Faraday-Faraday and Faraday-Voigt geometries

abstract:
  Single-cell magneto-optical Faraday filters find great utility and are
realized with either 'wing' or 'line center' spectral profiles. We show that
cascading a second cell with independent axial (Faraday) or transverse (Voigt)
magnetic field leads to improved performance in terms of figure of merit (FOM)
and spectral profile. The first cell optically rotates the plane of
polarization of light creating the high transmission window; the second cell
selectively absorbs the light eliminating unwanted transmission. Using
naturally-abundant Rb vapor cells, we realize a Faraday-Faraday wing filter and
the first recorded Faraday-Voigt line center filter which show excellent
agreement with theory. The two filters have FOM values of 0.86 and 1.63
GHz$^{-1}$ respectively, the latter of which is the largest FOM atomic line
filter recorded.


title:
Pruning In Time (PIT): A Lightweight Network Architecture Optimizer for
  Temporal Convolutional Networks

abstract:
  Temporal Convolutional Networks (TCNs) are promising Deep Learning models for
time-series processing tasks. One key feature of TCNs is time-dilated
convolution, whose optimization requires extensive experimentation. We propose
an automatic dilation optimizer, which tackles the problem as a weight pruning
on the time-axis, and learns dilation factors together with weights, in a
single training. Our method reduces the model size and inference latency on a
real SoC hardware target by up to 7.4x and 3x, respectively with no accuracy
drop compared to a network without dilation. It also yields a rich set of
Pareto-optimal TCNs starting from a single model, outperforming hand-designed
solutions in both size and accuracy.


title:
A Long Short-term Memory Based Recurrent Neural Network for
  Interventional MRI Reconstruction

abstract:
  Interventional magnetic resonance imaging (i-MRI) for surgical guidance could
help visualize the interventional process such as deep brain stimulation (DBS),
improving the surgery performance and patient outcome. Different from
retrospective reconstruction in conventional dynamic imaging, i-MRI for DBS has
to acquire and reconstruct the interventional images sequentially online. Here
we proposed a convolutional long short-term memory (Conv-LSTM) based recurrent
neural network (RNN), or ConvLR, to reconstruct interventional images with
golden-angle radial sampling. By using an initializer and Conv-LSTM blocks, the
priors from the pre-operative reference image and intra-operative frames were
exploited for reconstructing the current frame. Data consistency for radial
sampling was implemented by a soft-projection method. To improve the
reconstruction accuracy, an adversarial learning strategy was adopted. A set of
interventional images based on the pre-operative and post-operative MR images
were simulated for algorithm validation. Results showed with only 10 radial
spokes, ConvLR provided the best performance compared with state-of-the-art
methods, giving an acceleration up to 40 folds. The proposed algorithm has the
potential to achieve real-time i-MRI for DBS and can be used for general
purpose MR-guided intervention.


title: Localization of Forelli's theorem

abstract:
  The main purpose of this article is to present a localization of Forelli's
theorem for the functions holomorphic along a standard suspension of linear
discs. This generalizes one of the main results of \cite{CK21} and the original
Forelli's theorem.


title: Bayesian inverse problems using homotopy

abstract:
  In solving Bayesian inverse problems, it is often desirable to use a common
density parameterization to denote the prior and posterior. Typically we seek a
density from the same family as the prior which closely approximates the true
posterior. As one of the most important classes of distributions in statistics,
the exponential family is considered as the parameterization. The optimal
parameter values for representing the approximated posterior are achieved by
minimizing the deviation between the parameterized density and a homotopy that
deforms the prior density into the posterior density. Rather than trying to
solve the original problem, it is exactly converted into a corresponding system
of explicit ordinary first-order differential equations. Solving this system
over a finite 'time' interval yields the desired optimal density parameters.
This method is proven to be effective by some numerical examples.


title: Persistence of autoregressive sequences with logarithmic tails

abstract:
  We consider autoregressive sequences $X_n=aX_{n-1}+\xi_n$ and
$M_n=\max\{aM_{n-1},\xi_n\}$ with a constant $a\in(0,1)$ and with positive,
independent and identically distributed innovations $\{\xi_k\}$. It is known
that if $\mathbf P(\xi_1>x)\sim\frac{d}{\log x}$ with some $d\in(0,-\log a)$
then the chains $\{X_n\}$ and $\{M_n\}$ are null recurrent. We investigate the
tail behaviour of recurrence times in this case of logarithmically decaying
tails. More precisely, we show that the tails of recurrence times are regularly
varying of index $-1-d/\log a$. We also prove limit theorems for $\{X_n\}$ and
$\{M_n\}$ conditioned to stay over a fixed level $x_0$. Furthermore, we study
tail asymptotics for recurrence times of $\{X_n\}$ and $\{M_n\}$ in the case
when these chains are positive recurrent and the tail of $\log\xi_1$ is
subexponential.


title:
The number of distinct adjacent pairs in geometrically distributed
  words: a probabilistic and combinatorial analysis

abstract:
  The analysis of strings of $n$ random variables with geometric distribution
has recently attracted renewed interest: Archibald et al. consider the number
of distinct adjacent pairs in geometrically distributed words. They obtain the
asymptotic ($n\rightarrow\infty$) mean of this number in the cases of different
and identical pairs. In this paper we are interested in all asymptotic moments
in the identical case, in the asymptotic variance in the different case and in
the asymptotic distribution in both cases. We use two approaches: the first
one, the probabilistic approach, leads to variances in both cases and to some
conjectures on all moments in the identical case and on the distribution in
both cases. The second approach, the combinatorial one, relies on multivariate
pattern matching techniques, yielding exact formulas for first and second
moments. We use such tools as Mellin transforms, Analytic Combinatorics, Markov
Chains.


title: Phase structure of self-dual lattice gauge theories in 4d

abstract:
  We discuss U(1) lattice gauge theory models based on a modified Villain
formulation of the gauge action, which allows coupling to bosonic electric and
magnetic matter. The formulation enjoys a duality which maps electric and
magnetic sectors into each other. We propose several generalizations of the
model and discuss their 't~Hooft anomalies. A particularly interesting class of
theories is the one where electric and magnetic matter fields are coupled with
identical actions, such that for a particular value of the gauge coupling the
theory has a self-dual symmetry. The self-dual symmetry turns out to be a
generator of a group which is a central extension of $\mathbb Z_4$ by the
lattice translation symmetry group. The simplest case amenable to numerical
simulations is the case when there is exactly one electrically and one
magnetically charged boson. We discuss the phase structure of this theory and
the nature of the self-dual symmetry in detail. Using a suitable worldline
representation of the system we present the results of numerical simulations
that support the conjectured phase diagram.


title:
Gaussian Process filtering for calibration of low-cost air-pollution
  sensor network data

abstract:
  Low-cost air pollution sensors, offering hyper-local characterization of
pollutant concentrations, are becoming increasingly prevalent in environmental
and public health research. However, low-cost air pollution data can be noisy,
biased by environmental conditions, and usually need to be field-calibrated by
co-locating low-cost sensors with reference-grade instruments. We show,
theoretically and empirically, that the common of procedure of regression-based
calibration using co-located data systematically underestimates high
air-pollution concentrations, which are critical to diagnose from a health
perspective. Current calibration practices also often fail to utilize the
spatial correlation in pollutant concentrations. We propose a novel spatial
filtering approach to co-location-based calibration of low-cost networks that
mitigates the underestimation issue by using an inverse regression and
incorporates spatial correlation by second-stage modeling of the true pollutant
concentrations using a conditional Gaussian Process. Our approach works with
one or more co-located sites in the network and is dynamic, leveraging spatial
correlation with the latest available reference data. Through extensive
simulations, we demonstrate how the spatial filtering substantially improves
estimation of pollutant concentrations, and measures peak concentrations with
greater accuracy. We apply the methodology for calibration of a low-cost PM2.5
network in Baltimore, Maryland, and diagnose air-pollution peaks that are
missed by the regression-calibration.


title:
Non-linear Breit-Wheeler pair production in collisions of bremsstrahlung
  $\gamma-$quanta and a tightly focussed laser pulse

abstract:
  Experimental efforts toward the detection of the nonperturbative strong-field
regime of the Breit-Wheeler pair creation process plan to combine incoherent
sources of GeV $\gamma$ quanta and the coherent fields of tightly focussed
optical laser pulses. This endeavour calls for a theoretical understanding of
how the pair yields depend on the applied laser field profile. We provide
estimates for the number of produced pairs in a setup where the high-energy
radiation is generated via bremsstrahlung. Attention is paid to the role of the
transversal and longitudinal focussing of the laser field, along with the
incorporation of a Gaussian pulse envelope. We compare our corresponding
results with predictions from plane-wave models and determine the parameters of
focused laser pulses which maximize the pair yield at fixed pulse energy.
Besides, the impact of various super-Gaussian profiles for the laser pulse
envelope and its transverse shape is discussed.


title:
On the Suitability of Neural Networks as Building Blocks for The Design
  of Efficient Learned Indexes

abstract:
  With the aim of obtaining time/space improvements in classic Data Structures,
an emerging trend is to combine Machine Learning techniques with the ones
proper of Data Structures. This new area goes under the name of Learned Data
Structures. The motivation for its study is a perceived change of paradigm in
Computer Architectures that would favour the use of Graphics Processing Units
and Tensor Processing Units over conventional Central Processing Units. In
turn, that would favour the use of Neural Networks as building blocks of
Classic Data Structures. Indeed, Learned Bloom Filters, which are one of the
main pillars of Learned Data Structures, make extensive use of Neural Networks
to improve the performance of classic Filters. However, no use of Neural
Networks is reported in the realm of Learned Indexes, which is another main
pillar of that new area. In this contribution, we provide the first, and much
needed, comparative experimental analysis regarding the use of Neural Networks
as building blocks of Learned Indexes. The results reported here highlight the
need for the design of very specialized Neural Networks tailored to Learned
Indexes and it establishes a solid ground for those developments. Our findings,
methodologically important, are of interest to both Scientists and Engineers
working in Neural Networks Design and Implementation, in view also of the
importance of the application areas involved, e.g., Computer Networks and Data
Bases.


title:
Spatial pointwise behavior of time-periodic Navier-Stokes flow induced
  by oscillation of a moving obstacle

abstract:
  We study the spatial decay of time-periodic Navier-Stokes flow at the rate
$|x|^{-1}$ with/without wake structure in 3D exterior domains when a rigid body
moves periodically in time. In this regime the existence of time-periodic
solutions was established first in the 2006 paper by Galdi and Silvestre,
however, with little information about spatial behavior at infinity so that
uniqueness of solutions was not available. This latter issue has been addressed
by Galdi, who has recently succeeded in construction of a unique time-periodic
solution with spatial behavior mentioned above if translational and angular
velocities of the body fulfill, besides smallness and regularity, either of the
following assumptions: (i) translation or rotation is absent; (ii) both
velocities are parallel to the same constant vector. This paper shows the
existence of a unique time-periodic Navier-stokes flow in the small with values
in the weak-$L^3$ space and then deduces the desired pointwise decay of the
solution under some condition on the rigid motion of the body, that covers the
cases (i), (ii) mentioned above.


title:
A Joint Cross-Attention Model for Audio-Visual Fusion in Dimensional
  Emotion Recognition

abstract:
  Multimodal emotion recognition has recently gained much attention since it
can leverage diverse and complementary relationships over multiple modalities
(e.g., audio, visual, biosignals, etc.), and can provide some robustness to
noisy modalities. Most state-of-the-art methods for audio-visual (A-V) fusion
rely on recurrent networks or conventional attention mechanisms that do not
effectively leverage the complementary nature of A-V modalities. In this paper,
we focus on dimensional emotion recognition based on the fusion of facial and
vocal modalities extracted from videos. Specifically, we propose a joint
cross-attention model that relies on the complementary relationships to extract
the salient features across A-V modalities, allowing for accurate prediction of
continuous values of valence and arousal. The proposed fusion model efficiently
leverages the inter-modal relationships, while reducing the heterogeneity
between the features. In particular, it computes the cross-attention weights
based on correlation between the combined feature representation and individual
modalities. By deploying the combined A-V feature representation into the
cross-attention module, the performance of our fusion module improves
significantly over the vanilla cross-attention module. Experimental results on
validation-set videos from the AffWild2 dataset indicate that our proposed A-V
fusion model provides a cost-effective solution that can outperform
state-of-the-art approaches. The code is available on GitHub:
https://github.com/praveena2j/JointCrossAttentional-AV-Fusion.


title: Multiwavelength search for the origin of IceCube's neutrinos

abstract:
  The origin of astrophysical high-energy neutrinos detected by the IceCube
Neutrino Observatory remains a mystery to be solved. In this paper we search
for neutrino source candidates within the $90$\% containment area of $70$
track-like neutrino events recorded by the IceCube Neutrino Observatory. By
employing the Fermi/LAT 4FGL-DR2, the Swift-XRT 2SXPS and the CRATES catalogs,
we identify possible gamma, X-ray and flat-spectrum radio candidate sources of
track-type neutrinos. We find that based on the brightness of sources and their
spatial correlation with the track-type IceCube neutrinos, the constructed
neutrino samples represent special populations of sources taken from the full
Fermi-LAT 4FGL-DR2/Swift-XRT 2SXPS/CRATES catalogs with similar $p$-values of
$0.022$/$0.032$/$0.029$ (4.8 GHz)/$0.021$ (8.4 GHz), respectively, when we
assume 50\% astrophysical signalness (IceCube GOLD-channel) of the neutrinos.
After collecting redshifts and deriving sub-samples of the CRATES catalog
complete in luminosities, we find that the 4.8 GHz ($8.4$~GHz) sub-sample can
explain $6.6^{+11.9}_{-5.2}$ ($4.4^{+10.3}_{-3.5}$) neutrinos (90\% C.L.)
assuming they have 50\% astrophysical signalness, and assuming that the
probability to detect a neutrino is proportional to the ($k$-corrected) radio
flux. The overfluctuations indicate that a part of the sample is likely to
contribute and that more sophisticated schemes in the source catalog selection
are necessary to identify the neutrino sources at the $5\sigma$ level. Our
selection serves as a starting point to further select the correct sources.


title: From snapshots to manifolds - A tale of shear flows

abstract:
  We propose a novel non-linear manifold learning from snapshot data and
demonstrate its superiority over Proper Orthogonal Decomposition (POD) for
shedding-dominated shear flows. Key enablers are isometric feature mapping,
Isomap (Tenenbaum et al., 2000), as encoder and K-nearest neighbours (KNN)
algorithm as decoder. The proposed technique is applied to numerical and
experimental datasets including the fluidic pinball, a swirling jet, and the
wake behind a couple of tandem cylinders. Analyzing the fluidic pinball, the
manifold is able to describe the pitchfork bifurcation and the chaotic regime
with only three feature coordinates. These coordinates are linked to
vortex-shedding phases and the force coefficients. The manifold coordinates of
the swirling jet are comparable to the POD mode amplitudes, yet allow for a
more distinct manifold identification which is less sensitive to measurement
noise. As similar observation is made for the wake of two tandem cylinders
(Raiola et al., 2016). The tandem cylinders are aligned in streamwise distance
which corresponds to the transition between the single bluff body and the
reattachment regimes of vortex shedding. Isomap unveils these two shedding
regimes while the Lissajous plots of first two POD mode amplitudes feature a
single circle. The reconstruction error of the manifold model is small compared
to the fluctuation level, indicating that the low embedding dimensions contains
the coherent structure dynamics. The proposed Isomap-KNN manifold learner is
expected to be of large importance in estimation, dynamic modeling and control
for large range of configurations with dominant coherent structures.


title: On the Handwriting Tasks' Analysis to Detect Fatigue

abstract:
  Practical determination of physical recovery after intense exercise is a
challenging topic that must include mechanical aspects as well as cognitive
ones because most of physical sport activities, as well as professional
activities (including brain computer interface-operated systems), require good
shape in both of them. This paper presents a new online handwritten database of
20 healthy subjects. The main goal was to study the influence of several
physical exercise stimuli in different handwritten tasks and to evaluate the
recovery after strenuous exercise. To this aim, they performed different
handwritten tasks before and after physical exercise as well as other
measurements such as metabolic and mechanical fatigue assessment. Experimental
results showed that although a fast mechanical recovery happens and can be
measured by lactate concentrations and mechanical fatigue, this is not the case
when cognitive effort is required. Handwriting analysis revealed that
statistical differences exist on handwriting performance even after lactate
concentration and mechanical assessment recovery. Conclusions: This points out
a necessity of more recovering time in sport and professional activities than
those measured in classic ways.


title:
Optimized design of the lithium niobate for spectrally-pure-state
  generation at MIR wavelengths using metaheuristic algorithm

abstract:
  Quantum light sources in the mid-infrared (MIR) band play an important role
in many applications, such as quantum sensing, quantum imaging, and quantum
communication. However, there is still a lack of high-quality quantum light
sources in the MIR band, such as the spectrally pure single-photon source. In
this work, we present the generation of spectrally-pure state in an optimized
poled lithium niobate crystal using a metaheuristic algorithm. In particular,
we adopt the particle swarm optimization algorithm to optimize the duty cycle
of the poling period of the lithium niobate crystal. With our approach, the
spectral purity can be improved from 0.820 to 0.998 under the third
group-velocity-matched condition, and the wavelength-tunable range is from 3.0
$\mu$m to 4.0 $\mu$m for the degenerate case and 3.0 $\mu$m to 3.7 $\mu$m for
the nondegenerate case. Our work paves the way for developing quantum photonic
technologies at the MIR wavelength band.


title:
The holomorphic discrete series contribution to the generalized
  Whittaker Plancherel formula

abstract:
  For a Hermitian Lie group $G$ of tube type we find the contribution of the
holomorphic discrete series to the Plancherel decomposition of the Whittaker
space $L^2(G/N,\psi)$, where $N$ is the unipotent radical of the Siegel
parabolic subgroup and $\psi$ is a certain non-degenerate unitary character on
$N$. The holomorphic discrete series embeddings are constructed in terms of
generalized Whittaker vectors for which we find explicit formulas in the
bounded domain realization, the tube domain realization and the $L^2$-model of
the holomorphic discrete series. Although $L^2(G/N,\psi)$ does not have finite
multiplicities in general, the holomorphic discrete series contribution does.
  Moreover, we obtain an explicit formula for the formal dimensions of the
holomorphic discrete series embeddings, and we interpret the holomorphic
discrete series contribution to $L^2(G/N,\psi)$ as boundary values of
holomorphic functions on a domain $\Xi$ in a complexification $G_{\mathbb{C}}$
of $G$ forming a Hardy type space $\mathcal{H}_2(\Xi,\psi)$.


title: Defects and Frustration in the Packing of Soft Balls

abstract:
  Motivated by the recent studies of quasi-periodic equilibria of dense packing
of diblock and star polymers, this work introduces the Hookean-Voronoi energy,
a minimal model for the packing of soft, deformable balls. Considering the
planar case, we investigate the equilibrium packings of $N$ deformable circles
in a periodic square, deriving a reduced formulation of the system and
establish that it has a large, $O(N^2)$, family of ordered "single-string"
minimizers. Through numerical investigation of gradient flows from random
initial data, we show that for modest values of $N$ the system frequently
equilibrates to quasi-ordered states with low energy and large basins of
attraction. For larger $N$ the energy of equilibria approach a limiting
distribution, shaped by two mechanisms: a proliferation of moderate-energy
disordered equilibria that block access of the gradient flow to lower energy
quasi-ordered states and a rigid threshold on the maximum energy of stable
states that precludes high-energy equilibria. The system frustration, measuring
gap between the average equilibrium energy of the gradient flow and the system
ground state, increases with $N$ but saturates.


title:
Non-Debye relaxations: The ups and downs of the stretched exponential vs
  Mittag-Leffler's matchings

abstract:
  Experimental data collected to provide us with information on the course of
dielectric relaxation phenomena are got according to two distinct schemes: one
can measure either the time decay of depolarization current or use methods of
the broadband dielectric spectroscopy. Both sets of data are usually fitted by
time or frequency dependent elementary functions which in turn may be
analytically transformed among themselves using the Laplace transform and
compared each other. This leads to the question on comparability of results got
using just mentioned experimental procedures. If we would like to do that in
the time domain we have to go beyond widely accepted Kohlrausch-Williams-Watts
approximation and get acquainted with description using the Mittag-Leffler
functions. To convince the reader that the latter is not difficult to
understand we propose to look at the problem from the point of view of objects
sitting in the heart of stochastic processes approach to relaxation. These are
the characteristic exponents which are read out from the standard non-Debye
frequency dependent patterns. Characteristic functions appear to be expressed
in terms of elementary functions which asymptotic analysis is simple. This
opens new possibility to compare behavior of functions used to describe
non-Debye relaxations. Results of such done comparison are fully confirmed by
calculations which use the powerful apparatus of the Mittag-Leffler functions.


title: Multiplicative structures on Moore spectra

abstract:
  In this article we show that $\mathbb{S}/8$ is an $\mathbb{E}_1$-algebra,
$\mathbb{S}/32$ is an $\mathbb{E}_2$-algebra, $\mathbb{S}/p^{n+1}$ is an
$\mathbb{E}_n$-algebra at odd primes and, more generally, for every $h$ and $n$
there exist generalized Moore spectra of type $h$ which admit an
$\mathbb{E}_n$-algebra structure.


title:
Modulo $\ell$ distinction problems for $\mathrm{GL}_2$ and
  $\mathrm{SL}_2$

abstract:
  This paper concerns the $\ell$-modular representations of $\mathrm{GL}_2(E)$
and $\mathrm{SL}_2(E)$ distinguished by a Galois involution, with $\ell$ an odd
prime different from $p$. We start by proving a general theorem allowing to
lift supercuspidal $\bar{\mathbf{F}}_{\ell}$-representations of
$\mathrm{GL}_n(F)$ distinguished by an arbitrary closed subgroup $H$ to a
distinguished supercuspidal $\bar{\mathbf{Q}}_{\ell}$-representation. Then we
give a complete classification of the $\mathrm{GL}_2(F)$-distinguished
representations of $\mathrm{GL}_2(E)$, where $E$ is a quadratic extension of
$F$. For supercuspidal representations, this extends the results of S\'echerre
to the case $p=2$. Using this classification we discuss a modular version of
the Prasad conjecture for $\mathrm{PGL}_2$. We show that the "classic" Prasad
conjecture fails in the modular setting. We propose a solution using
non-nilpotent Weil-Deligne representations. Finally, we apply the restriction
method of Anandavardhanan and Prasad to classify the
$\mathrm{SL}_2(F)$-distinguished modular representations of $\mathrm{SL}_2(E)$.


title: The X-ray Disk/Wind Degeneracy in AGN

abstract:
  Relativistic Fe K emission lines from accretion disks and from disk winds
encode key information about black holes, and their accretion and feedback
mechanisms. We show that these two processes can in principle produce
indistinguishable line profiles, such that they cannot be disentangled
spectrally. We argue that it is likely that in many cases both processes
contribute to the net line profile, and their relative contributions cannot be
constrained purely by Fe K spectroscopy. In almost all studies of Fe K emission
to date, a single process (either disk reflection or wind Compton scattering)
is assumed to dominate the total line profile. We demonstrate that fitting a
single process emission model (pure reflection or pure wind) to a hybrid line
profile results in large systematic biases in the estimates of key parameters,
such as mass outflow rate and spin. We discuss various strategies to mitigate
this effect, such as including high energy data covering the Compton hump, and
the implications for future X-ray missions.


title: 5G Routing Interfered Environment

abstract:
  5G is the next-generation cellular network technology, with the goal of
meeting the critical demand for bandwidth required to accommodate a high
density of users. It employs flexible architectures to accommodate the high
density. 5G is enabled by mmWave communication, which operates at frequencies
ranging from 30 to 300 GHz. This paper describes the design of the 5G Routing
Interfered Environment (5GRIE), a python-based environment based on Gym's
methods. The environment can run different algorithms to route packets with
source and destination pairs using a formulated interference model. Deep
Reinforcement Learning algorithms that use Stable-Baselines 3, as well as
heuristic-based algorithms like random or greedy, can be run on it. Profitable
is an algorithm that is provided.


title: Optimal Coupling of Jumpy Brownian Motion on the Circle

abstract:
  Consider a Brownian motion on the circumference of the unit circle, which
jumps to the opposite point of the circumference at incident times of an
independent Poisson process of rate $\lambda$. We examine the problem of
coupling two copies of this `jumpy Brownian motion' started from different
locations, so as to optimise certain functions of the coupling time. We
describe two intuitive co-adapted couplings (`Mirror' and `Synchronous') which
differ only when the two processes are directly opposite one another, and show
that the question of which strategy is best depends upon the jump rate
$\lambda$ in a non-trivial way. More precisely, we show that there exists a
critical value $\lambda^\star = 0.083\dots$ such that the Mirror coupling
minimises the mean coupling time within the class of all co-adapted couplings
when $\lambda<\lambda^\star$, but for $\lambda\ge \lambda^\star$ the
Synchronous coupling uniquely maximises the Laplace transform $E[e^{-\gamma
T}]$ of all coupling times $T$ within this class. Our proofs use a combination
of excursion theory and Bellman's principle. We also provide an explicit
description of a (non co-adapted) maximal coupling for any jump rate in the
case that the two jumpy Brownian motions begin at antipodal points of the
circle.


title: Multi-modal transducer-waveguide construct coupled to a medical needle

abstract:
  Annually, more than 16 billion medical needles are consumed worldwide.
However, the functions of the medical needle are still limited to cutting and
delivering or drawing material through the needle to a target site. Ultrasound
combined with hypodermic needle could potentially add value to many medical
applications such as pain reduction, adding precision, deflection reduction in
tissues and even improve tissue collection. In this study we introduce a
waveguide construct enabling an efficient way to convert a longitudinal wave
mode to flexural mode and to couple the converted wave mode to a conventional
medial needle, while maintaining high electric-to-acoustic power efficiency.
The structural optimization of the waveguide was realized in silico using the
finite element method followed by prototyping the construct and experimental
characterization of the prototypes. The experiments at 30 kHz demonstrate
flexural tip displacement up to 200 {\mu}m, at low electrical power consumption
(under 5 W), with up to 69% of efficiency. The high electric-to-acoustic
efficiency and small size of the transducer would facilitate design of medical
needle and biopsy devices, potentially enabling portability, batterization and
high patient safety with low electric powers.


title:
Residual finiteness of extensions of arithmetic subgroups of SU(d,1)
  with cusps

abstract:
  Let $\Gamma$ be an arithmetic subgroup of $SU(d,1)$ with cusps, and let
$X_\Gamma$ be the associated locally symmetric space. We prove that if the
first inner cohomology group $H^1_!(X_\Gamma,\mathbb{C})$ is non-zero then the
pre-image of $\Gamma$ in each connected cover of $SU(d,1)$ is residually
finite. We also give an example of a such a group $\Gamma$ for which
$H^1_!(X_\Gamma,\mathbb{C})$ is non-zero.


title:
Limited Parameter Denoising for Low-dose X-ray Computed Tomography Using
  Deep Reinforcement Learning

abstract:
  The use of deep learning has successfully solved several problems in the
field of medical imaging. Deep learning has been applied to the CT denoising
problem successfully. However, the use of deep learning requires large amounts
of data to train deep convolutional networks (CNNs). Moreover, due to large
parameter count, such deep CNNs may cause unexpected results. In this study, we
introduce a novel CT denoising framework, which has interpretable behaviour,
and provides useful results with limited data. We employ bilateral filtering in
both the projection and volume domains to remove noise. To account for
non-stationary noise, we tune the $\sigma$ parameters of the volume for every
projection view, and for every volume pixel. The tuning is carried out by two
deep CNNs. Due to impracticality of labelling, the two deep CNNs are trained
via a Deep-Q reinforcement learning task. The reward for the task is generated
by using a custom reward function represented by a neural network. Our
experiments were carried out on abdominal scans for the Mayo Clinic TCIA
dataset, and the AAPM Low Dose CT Grand Challenge. Our denoising framework has
excellent denoising performance increasing the PSNR from 28.53 to 28.93, and
increasing the SSIM from 0.8952 to 0.9204. We outperform several
state-of-the-art deep CNNs, which have several orders of magnitude higher
number of parameters (p-value (PSNR) = 0.000, p-value (SSIM) = 0.000). Our
method does not introduce any blurring, which is introduced by MSE loss based
methods, or any deep learning artifacts, which are introduced by WGAN based
models. Our ablation studies show that parameter tuning and using our reward
network results in the best possible results.


title: A technique to select the most obscured galaxy nuclei

abstract:
  Compact obscured nuclei (CONs) are mainly found in local U/LIRGs. In the
local Universe, these sources are generally selected through the detection of
the HCN-vib (3-2) emission line at submillimetre wavelengths. In this work, we
present a diagnostic method to select deeply buried nuclei based on
mid-infrared (mid-IR) polycyclic aromatic hydrocarbons (PAHs) and continuum
ratios. Using Spitzer/IRS spectra of a representative sample of local ULIRGs
(z<0.27), we examine their PAH and underlying continuum emission ratios. For
deeply embedded sources, we find that the 9.7 micron silicate absorption band
has a particularly pronounced effect on the 11.3 micron PAH feature. The low
flux level in the nuclear silicate absorption band enhances the 11.3 micron PAH
feature contrast (high PAH equivalent width) compared to that of the other PAH
features. The technique has been extended to include the use of the continuum
ratios. However, the latter are affected both by the extinction coming from the
host galaxy as well as the nuclear region, whereas the foreground extinction is
cancelled out when using the PAH equivalent width ratios. We apply our method
to the HERUS and GOALS samples and classify as CON candidates 14 ULIRGs and 10
LIRGs, corresponding to 30% of ULIRGs and 7% of LIRGs from these samples. We
find that the observed continuum ratios of CON-dominated sources can be
explained by assuming torus models with a tapered disk geometry and a smooth
dust distribution. This suggests that the nuclear dusty structure of CONs has
an extremely high dust coverage. We also demonstrate that the use of mid-IR
color-color diagrams is an effective way to select CON-dominated sources at
different redshifts. In particular, the combination of filters of the JWST/MIRI
will enable the selection of CONs out to z~1.5. This will allow extending the
selection of CONs to high redshifts where U/LIRGs are more numerous.


title: On quasi-polynomials counting planar tight maps

abstract:
  A tight map is a map with some of its vertices marked, such that every vertex
of degree $1$ is marked. We give an explicit formula for the number
$N_{0,n}(d_1,\ldots,d_n)$ of planar tight maps with $n$ labeled faces of
prescribed degrees $d_1,\ldots,d_n$, where a marked vertex is seen as a face of
degree $0$. It is a quasi-polynomial in $(d_1,\ldots,d_n)$, as shown previously
by Norbury. Our derivation is bijective and based on the slice decomposition of
planar maps. In the non-bipartite case, we also rely on enumeration results for
two-type forests. We discuss the connection with the enumeration of non
necessarily tight maps. In particular, we provide a generalization of Tutte's
classical slicings formula to all non-bipartite maps.


title:
Switchable Wetting of Stimulus Responsive Polymer Brushes by Lipid
  Vesicles: Experiments and Simulations

abstract:
  We grafted polyacrylic acid brushes containing cysteine side chains at a
defined surface density on planar lipid membranes. Specular X-ray reflectivity
data indicated that the addition of Cd2+ ions induces the compaction of the
polymer brush layer and modulates the adhesion of lipid vesicles. The critical
threshold level inducing the switch from non-wetting to partial wetting state,
[Cd2+] = 0.25 mM, was determined by microinterferometry. The interactions
between vesicles and brushes were evaluated by height fluctuations of the
membrane in contact with brushes and the shape of vesicles near the surface. To
analyze these experiments, we have studied adhesion of axially symmetric
vesicles for finite-range membrane-substrate interaction and buoyancy through
simulations. We found that the local transversality condition that relates the
maximal curvature at the edge of the adhesion zone to the adhesion strength
remains rather accurate. Thus, although it is not experimentally possible to
prepare vesicles at zero buoyancy, we can use the transversality condition to
estimate the adhesion strength. However, the adhesion diagram is significantly
modified by a finite range of membrane-substrate interaction and buoyancy. For
downward buoyancy, vesicles merely sediment onto the substrate and there is no
mean-field adhesion transition. For upward buoyancy, adhered vesicles are
metastable at best. Thus, a mean-field adhesion transition can only occur at
vanishing buoyancy. Only for zero-range membrane-substrate interaction does a
second-order adhesion transition occur at finite interaction strength. For any
finite-range interaction, the transition occurs when the membrane-substrate
interaction changes from repulsive to attractive. We present a adhesion diagram
as a function of adhesion strength and buoyancy and compare the adhesion
behavior of vesicles to the wetting behavior of droplets of liquids.


title:
Sublinear Algorithms and Lower Bounds for Estimating MST and TSP Cost in
  General Metrics

abstract:
  We consider the design of sublinear space and query complexity algorithms for
estimating the cost of a minimum spanning tree (MST) and the cost of a minimum
traveling salesman (TSP) tour in a metric on $n$ points. We first consider the
$o(n)$-space regime and show that, when the input is a stream of all
$\binom{n}{2}$ entries of the metric, for any $\alpha \ge 2$, both MST and TSP
cost can be $\alpha$-approximated using $\tilde{O}(n/\alpha)$ space, and that
$\Omega(n/\alpha^2)$ space is necessary for this task. Moreover, we show that
even if the streaming algorithm is allowed $p$ passes over a metric stream, it
still requires $\tilde{\Omega}(\sqrt{n/\alpha p^2})$ space.
  We next consider the semi-streaming regime, where computing even the exact
MST cost is easy and the main challenge is to estimate TSP cost to within a
factor that is strictly better than $2$. We show that, if the input is a stream
of all edges of the weighted graph that induces the underlying metric, for any
$\varepsilon > 0$, any one-pass $(2-\varepsilon)$-approximation of TSP cost
requires $\Omega(\varepsilon^2 n^2)$ space; on the other hand, there is an
$\tilde{O}(n)$ space two-pass algorithm that approximates the TSP cost to
within a factor of 1.96.
  Finally, we consider the query complexity of estimating metric TSP cost to
within a factor that is strictly better than $2$, when the algorithm is given
access to a matrix that specifies pairwise distances between all points. For
MST estimation in this model, it is known that a
$(1+\varepsilon)$-approximation is achievable with
$\tilde{O}(n/\varepsilon^{O(1)})$ queries. We design an algorithm that performs
$\tilde{O}(n^{1.5})$ distance queries and achieves a strictly better than
$2$-approximation when either the metric is known to contain a spanning tree
supported on weight-$1$ edges or the algorithm is given access to a minimum
spanning tree of the graph.


title:
Postselection-free controlled generation of high-dimensional OAM
  entangled state

abstract:
  High-dimensional entangled states in orbital angular momentum (OAM) basis
offer unique advantages for several quantum information applications. However,
a given quantum information application for its optimal performance requires
generation of a specific OAM entangled state with full control over the shape
of the OAM Schmidt spectrum. Spontaneous parametric downconversion (SPDC) is
the most widely used method for generating OAM entangled states. Most of the
existing methods for controlling the generation of the state employ
postselection, which invariably compromises the security benefits of quantum
information applications. The postselection-free generation of OAM entangled
states can be achieved either by adjusting the SPDC phase matching condition or
by shaping the profile of the pump field used for down-conversion. While it is
known that the phase matching adjustments can only change the width of the OAM
Schmidt spectrum, even the most recent works based on pump shaping have
demonstrated generation of only up to 24-dimensional states with very limited
control over the OAM Schmidt spectrum. Here, we propose and experimentally
demonstrate a technique that employs both the phase matching adjustments and
pump-shaping for generating high-dimensional OAM entangled states with complete
control over the shape of the OAM Schmidt spectrum. We report generation of up
to 200-dimensional OAM entangled states with three different spectra, namely,
Gaussian, rectangular and triangular, with up to 99% generation accuracy.


title:
The effect of varying degrees of stenosis on transition to turbulence in
  oscillatory flows

abstract:
  Many complications in physiology are associated with a deviation in flow in
arteries due to a stenosis. The presence of stenosis may transition the flow to
weak turbulence. The degree of stenosis as well as its configuration whether
symmetric or non-symmetric to the parent artery influences whether the flow
would stay laminar or transition to turbulence. Plenty of research efforts
focus on investigating the role of varying degrees of stenosis in the onset of
turbulence under steady and pulsatile flow conditions. None of the studies,
however, have focused on investigating this under oscillatory flow conditions
as flow reversal is a major occurrence in a number of physiologic flows, and is
of particular relevance in cerebrospinal fluid (CSF) flow research. Following
up on the previous work in which a 75% stenosis was studied, this contribution
is a detailed investigation of the role of degrees of stenosis on transition in
an oscillatory flow. A cylindrical pipe with 25%, 50% and 60% reductions in
area in axisymmetric and eccentric configurations is studied for transition
with 3 different pulsation frequencies of a purely oscillatory flow. Cycle
averaged Reynolds numbers between 1800 and 2100 in steps of 100 are studied for
each configuration resulting in 72 simulations each conducted on 76800 CPU
cores of a modern supercomputer. It is found that a higher degree of stenosis
and eccentricity causes earlier transition to turbulence in oscillatory flow.
The results further demonstrate that a higher frequency of oscillation results
in larger hydrodynamic instability in the flow, which is more prominent in
smaller degrees of stenosis.


title:
SyncMesh: Improving Data Locality for Function-as-a-Service in Meshed
  Edge Networks

abstract:
  The increasing use of Internet of Things devices coincides with more
communication and data movement in networks, which can exceed existing network
capabilities. These devices often process sensor or user information, where
data privacy and latency are a major concern. Therefore, traditional approaches
like cloud computing do not fit well, yet new architectures such as edge
computing address this gap. In addition, the Function-as-a-Service (FaaS)
paradigm gains in prevalence as a workload execution platform, however the
decoupling of storage results in further challenges for highly distributed edge
environments.
  To address this, we propose SyncMesh, a system to manage, query, and
transform data in a scalable and stateless manner by leveraging the
capabilities of Function-as-a-Service and at the same time enabling data
locality. Furthermore, we provide a prototypical implementation and evaluate it
against established centralized and decentralized systems in regard to traffic
usage and request times.
  The preliminary results indicate that SyncMesh is able to exonerate the
network layer and accelerate the transmission of data to clients, while
simultaneously improving local data processing.


title:
"Born in Rome" or "Sleeping Beauty": Emergence of hashtag popularity on
  a microblogging site

abstract:
  To understand the emergence of hashtag popularity in online social networking
complex systems, we study the largest Chinese microblogging site Sina Weibo,
which has a Hot Search List (HSL) showing in real time the ranking of the 50
most popular hashtags based on search activity. We investigate the prehistory
of successful hashtags from 17 July 2020 to 17 September 2020 by mapping out
the related interaction network preceding the selection to HSL. We have found
that the circadian activity pattern has an impact on the time needed to get to
the HSL. When analyzing this time we distinguish two extreme categories: a)
"Born in Rome", which means hashtags are mostly first created by super-hubs or
reach super-hubs at an early stage during their propagation and thus gain
immediate wide attention from the broad public, and b) "Sleeping Beauty",
meaning the hashtags gain little attention at the beginning and reach
system-wide popularity after a considerable time lag. The evolution of the
repost networks of successful hashtags before getting to the HSL show two types
of growth patterns: "smooth" and "stepwise". The former is usually dominated by
a super-hub and the latter results from consecutive waves of contributions of
smaller hubs. The repost networks of unsuccessful hashtags exhibit a simple
evolution pattern.


title: MixNN: A design for protecting deep learning models

abstract:
  In this paper, we propose a novel design, called MixNN, for protecting deep
learning model structure and parameters. The layers in a deep learning model of
MixNN are fully decentralized. It hides communication address, layer parameters
and operations, and forward as well as backward message flows among
non-adjacent layers using the ideas from mix networks. MixNN has following
advantages: 1) an adversary cannot fully control all layers of a model
including the structure and parameters, 2) even some layers may collude but
they cannot tamper with other honest layers, 3) model privacy is preserved in
the training phase. We provide detailed descriptions for deployment. In one
classification experiment, we compared a neural network deployed in a virtual
machine with the same one using the MixNN design on the AWS EC2. The result
shows that our MixNN retains less than 0.001 difference in terms of
classification accuracy, while the whole running time of MixNN is about 7.5
times slower than the one running on a single virtual machine.


title: Partially Does It: Towards Scene-Level FG-SBIR with Partial Input

abstract:
  We scrutinise an important observation plaguing scene-level sketch research
-- that a significant portion of scene sketches are "partial". A quick pilot
study reveals: (i) a scene sketch does not necessarily contain all objects in
the corresponding photo, due to the subjective holistic interpretation of
scenes, (ii) there exists significant empty (white) regions as a result of
object-level abstraction, and as a result, (iii) existing scene-level
fine-grained sketch-based image retrieval methods collapse as scene sketches
become more partial. To solve this "partial" problem, we advocate for a simple
set-based approach using optimal transport (OT) to model cross-modal region
associativity in a partially-aware fashion. Importantly, we improve upon OT to
further account for holistic partialness by comparing intra-modal adjacency
matrices. Our proposed method is not only robust to partial scene-sketches but
also yields state-of-the-art performance on existing datasets.


title: Ulrich bundles on a general blow--up of the plane

abstract:
  We prove that on $X_n$, the plane blown--up at $n$ general points, there are
Ulrich line bundles with respect to a line bundle corresponding to curves of
degree $m$ passing simply through the $n$ blown--up points, with $m\leq
2\sqrt{n}$ and such that the line bundle in question is very ample on $X_n$. We
prove that the number of these Ulrich line bundles tends to infinity with $n$.
  We also prove the existence of slope--stable rank--$r$ Ulrich vector bundles
on $X_n$, for $n\geq 2$ and any $r \geq 1$ and we compute the dimensions of
their moduli spaces. These computations imply that $X_n$ is {Ulrich wild}.


title: Extracting Image Characteristics to Predict Crowdfunding Success

abstract:
  Despite an increase in the empirical study of crowdfunding platforms and the
prevalence of visual information, operations management and marketing
literature has yet to explore the role that image characteristics play in
crowdfunding success. The authors of this manuscript begin by synthesizing
literature on visual processing to identify several image characteristics that
are likely to shape crowdfunding success. After detailing measures for each
image characteristic, they use them as part of a machine-learning algorithm
(Bayesian additive trees), along with project characteristics and textual
information, to predict crowdfunding success. Results show that the inclusion
of these image characteristics substantially improves prediction over baseline
project variables, as well as textual features. Furthermore, image
characteristic variables exhibit high importance, similar to variables linked
to the number of pictures and number of videos. This research therefore offers
valuable resources to researchers and managers who are interested in the role
of visual information in ensuring new product success.


title:
Who is next: rising star prediction via diffusion of user interest in
  social networks

abstract:
  Finding items with potential to increase sales is of great importance in
online market. In this paper, we propose to study this novel and practical
problem: rising star prediction. We call these potential items Rising Star,
which implies their ability to rise from low-turnover items to best-sellers in
the future. Rising stars can be used to help with unfair recommendation in
e-commerce platform, balance supply and demand to benefit the retailers and
allocate marketing resources rationally. Although the study of rising star can
bring great benefits, it also poses challenges to us. The sales trend of rising
star fluctuates sharply in the short-term and exhibits more contingency caused
by some external events (e.g., COVID-19 caused increasing purchase of the face
mask) than other items, which cannot be solved by existing sales prediction
methods. To address above challenges, in this paper, we observe that the
presence of rising stars is closely correlated with the early diffusion of user
interest in social networks, which is validated in the case of Taocode (an
intermediary that diffuses user interest in Taobao). Thus, we propose a novel
framework, RiseNet, to incorporate the user interest diffusion process with the
item dynamic features to effectively predict rising stars. Specifically, we
adopt a coupled mechanism to capture the dynamic interplay between items and
user interest, and a special designed GNN based framework to quantify user
interest. Our experimental results on large-scale real-world datasets provided
by Taobao demonstrate the effectiveness of our proposed framework.


title: On Anomalous Diffusion of Devices in Molecular Communication Network

abstract:
  A one-dimensional (1-D) anomalous-diffusive molecular communication channel
is considered, wherein the devices (transmitter (TX) and receiver (RX)) can
move in either direction along the axis. For modeling the anomalous diffusion
of information carrying molecules (ICM) as well as that of the TX and RX, the
concept of time-scaled Brownian motion is explored. In this context, a novel
closed-form expression for the first hitting time density (FHTD) is derived.
Further, the derived FHTD is validated through particle-based simulation. For
the transmission of binary information, the timing modulation is exploited.
Furthermore, the channel is assumed as a binary erasure channel (BEC) and
analyzed in terms of achievable information rate (AIR).


title: Soundness of Data-Aware Processes with Arithmetic Conditions

abstract:
  Data-aware processes represent and integrate structural and behavioural
constraints in a single model, and are thus increasingly investigated in
business process management and information systems engineering. In this
spectrum, Data Petri nets (DPNs) have gained increasing popularity thanks to
their ability to balance simplicity with expressiveness. The interplay of data
and control-flow makes checking the correctness of such models, specifically
the well-known property of soundness, crucial and challenging. A major
shortcoming of previous approaches for checking soundness of DPNs is that they
consider data conditions without arithmetic, an essential feature when dealing
with real-world, concrete applications. In this paper, we attack this open
problem by providing a foundational and operational framework for assessing
soundness of DPNs enriched with arithmetic data conditions. The framework comes
with a proof-of-concept implementation that, instead of relying on ad-hoc
techniques, employs off-the-shelf established SMT technologies. The
implementation is validated on a collection of examples from the literature,
and on synthetic variants constructed from such examples.


title:
Data-Driven, Soft Alignment of Functional Data Using Shapes and
  Landmarks

abstract:
  Alignment or registration of functions is a fundamental problem in
statistical analysis of functions and shapes. While there are several
approaches available, a more recent approach based on Fisher-Rao metric and
square-root velocity functions (SRVFs) has been shown to have good performance.
However, this SRVF method has two limitations: (1) it is susceptible to over
alignment, i.e., alignment of noise as well as the signal, and (2) in case
there is additional information in form of landmarks, the original formulation
does not prescribe a way to incorporate that information. In this paper we
propose an extension that allows for incorporation of landmark information to
seek a compromise between matching curves and landmarks. This results in a soft
landmark alignment that pushes landmarks closer, without requiring their exact
overlays to finds a compromise between contributions from functions and
landmarks. The proposed method is demonstrated to be superior in certain
practical scenarios.


title: Performance of the collective three-level quantum thermal engine

abstract:
  We investigate the performance of a microscopic quantum heat engine
consisting of V- or Lambda-type emitters interacting collectively or
independently when being in contact with environmental thermal reservoirs.
Though the efficiency of a Carnot's cycle is always higher than those
associated with these setups, we have found that the performance of the
cooperative Lambda-type heat engine may be larger than that of the V-type under
similar conditions. Cooperativity among the emitters plays an important role
for the Lambda-type setup, significantly improving its performance, while is
less relevant for a V-type thermal engine. This is because the population
inversion on the working atomic transition as well as its off-diagonal elements
behave differently for these two atomic ensembles.


title:
An attention mechanism based convolutional network for satellite
  precipitation downscaling over China

abstract:
  Precipitation is a key part of hydrological circulation and is a sensitive
indicator of climate change. The Integrated Multi-satellitE Retrievals for the
Global Precipitation Measurement (GPM) mission (IMERG) datasets are widely used
for global and regional precipitation investigations. However, their local
application is limited by the relatively coarse spatial resolution. Therefore,
in this paper, an attention mechanism based convolutional network (AMCN) is
proposed to downscale GPM IMERG monthly precipitation data. The proposed method
is an end-to-end network, which consists of a global cross-attention module, a
multi-factor cross-attention module, and a residual convolutional module,
comprehensively considering the potential relationships between precipitation
and complicated surface characteristics. In addition, a degradation loss
function based on low-resolution precipitation is designed to physically
constrain the network training, to improve the robustness of the proposed
network under different time and scale variations. The experiments demonstrate
that the proposed network significantly outperforms three baseline methods.
Finally, a geographic difference analysis method is introduced to further
improve the downscaled results by incorporating in-situ measurements for
high-quality and fine-scale precipitation estimation.


title: Cross-Technology Communication for the Internet of Things: A Survey

abstract:
  The ever-developing Internet of Things (IoT) brings the prosperity of
wireless sensing and control applications. In many scenarios, different
wireless technologies coexist in the shared frequency medium as well as the
physical space. Such wireless coexistence may lead to serious cross-technology
interference (CTI) problems, e.g. channel competition, signal collision,
throughput degradation. Compared with traditional methods like interference
avoidance, tolerance, and concurrency mechanism, direct and timely information
exchange among heterogeneous devices is therefore a fundamental requirement to
ensure the usability, inter-operability, and reliability of the IoT. Under this
circumstance, Cross-Technology Communication (CTC) technique thus becomes a hot
topic in both academic and industrial fields, which aims at directly exchanging
data among heterogeneous devices that follow different standards. This paper
comprehensively summarizes the CTC techniques and reveals that the key
challenge for CTC lies in the heterogeneity of IoT devices, including the
incompatibility of technical standards and the asymmetry of connection
capability. Based on the above finding, we present a taxonomy of the existing
CTC works (packet-level CTCs and physical-level CTCs) and compare the existing
CTC techniques in terms of throughput, reliability, hardware modification, and
concurrency.