title:
Global well-posedness of the two-dimensional random viscous nonlinear
  wave equations

abstract:
  We study well-posedness of viscous nonlinear wave equations (vNLW) on the
two-dimensional torus with a stochastic forcing or randomized initial data. In
particular, we prove pathwise global well-posedness of the stochastic
defocusing vNLW with an additive stochastic forcing $D^\alpha \xi$, where
$\alpha < \frac 12$ and $\xi$ denotes the space-time white noise. We also study
the deterministic vNLW with randomized initial data and prove its almost sure
global well-posedness in the defocusing case.


title: Bending Teichm\"uller spaces and character varieties

abstract:
  We construct certain analogues of the Poincare holonomy varieties ($sl_2
C$-opers) for compact Riemann surfaces, from the view point of Thurston's
parametrization of $CP^1$-structures


title: Quantifying Societal Bias Amplification in Image Captioning

abstract:
  We study societal bias amplification in image captioning. Image captioning
models have been shown to perpetuate gender and racial biases, however, metrics
to measure, quantify, and evaluate the societal bias in captions are not yet
standardized. We provide a comprehensive study on the strengths and limitations
of each metric, and propose LIC, a metric to study captioning bias
amplification. We argue that, for image captioning, it is not enough to focus
on the correct prediction of the protected attribute, and the whole context
should be taken into account. We conduct extensive evaluation on traditional
and state-of-the-art image captioning models, and surprisingly find that, by
only focusing on the protected attribute prediction, bias mitigation models are
unexpectedly amplifying bias.


title:
Towards Maintainable Platform Software -- Delivery Cost Control in
  Continuous Software Development

abstract:
  Modern platform software delivery cost increases rapidly as it usually needs
to align with many hardware and silicon's TTMs, feature evolvement and involves
hundreds of engineers. In this paper, citing one ultra-large-scale software -
Intel Media Driver as an example, we analyze the hotspots leading to delivery
cost increase in continuous software development, the challenges on our
software design and our experiences on software delivery cost shrink against
the targeted design enhancements. We expect the identified hotspots can help
more researchers to form the corresponding research agendas and the experiences
shared can help following practitioners to apply similar enhancements.


title:
Inverse nodal problem for Dirac operator with integral type nonlocal
  boundary conditions

abstract:
  In this paper, Dirac operator with some integral type nonlocal boundary
conditions is studied. We show that the coefficients of the problem can be
uniquely determined by a dense set of nodal points. Moreover, we give an
algorithm for the reconstruction of some coefficients of the operator.


title:
Learning to act: a Reinforcement Learning approach to recommend the best
  next activities

abstract:
  The rise of process data availability has led in the last decade to the
development of several data-driven learning approaches. However, most of these
approaches limit themselves to use the learned model to predict the future of
ongoing process executions. The goal of this paper is moving a step forward and
leveraging data with the purpose of learning to act by supporting users with
recommendations for the best strategy to follow, in order to optimize a measure
of performance. In this paper, we take the (optimization) perspective of one
process actor and we recommend the best activities to execute next, in response
to what happens in a complex external environment, where there is no control on
exogenous factors. To this aim, we investigate an approach that learns, by
means of Reinforcement Learning, an optimal policy from the observation of past
executions and recommends the best activities to carry on for optimizing a Key
Performance Indicator of interest. The potentiality of the approach has been
demonstrated on two scenarios taken from real-life data.


title:
Time Reversal for Multiple Access and Mobility: Algorithmic Design and
  Experimental Results

abstract:
  Time Reversal (TR) has been proposed as a competitive precoding strategy for
low-complexity wireless devices relying on Ultra-WideBand (UWB) signal
waveforms. However, when TR is applied for multiple access, the signals
received by the multiple users suffer from significant levels of inter-symbol
and inter-user interference, which requires additional processing for
mitigation by each receiving user. In this paper, we present an iterative
Time-Reversal Division Multiple Access (TRDMA) approach that aims to dim the
latter interference levels. The performance of iterative TRDMA is evaluated
experimentally in a reverberation chamber that mimics a rich scattering indoor
wireless propagation environment. The improved efficiency, in terms of the
number of algorithmic iterations, of the proposed approach compared to
conventional TRDMA, is demonstrated. We also consider a mobile user
configuration, where the position of the receiver changes between the channel
estimation and data transmission steps. It is showcased, even for this
experimental setup, that the proposed iterative TRDMA approach is more
efficient than conventional precoding schemes.


title: (Nearly) All Cardinality Estimators Are Differentially Private

abstract:
  We consider privacy in the context of streaming algorithms for cardinality
estimation. We show that a large class of algorithms all satisfy
$\epsilon$-differential privacy, so long as (a) the algorithm is combined with
a simple down-sampling procedure, and (b) the cardinality of the input stream
is $\Omega(k/\epsilon)$. Here, $k$ is a certain parameter of the sketch that is
always at most the sketch size in bits, but is typically much smaller. We also
show that, even with no modification, algorithms in our class satisfy
$(\epsilon, \delta)$-differential privacy, where $\delta$ falls exponentially
with the stream cardinality.
  Our analysis applies to essentially all popular cardinality estimation
algorithms, and substantially generalizes and tightens privacy bounds from
earlier works.


title: Neural Face Video Compression using Multiple Views

abstract:
  Recent advances in deep generative models led to the development of neural
face video compression codecs that use an order of magnitude less bandwidth
than engineered codecs. These neural codecs reconstruct the current frame by
warping a source frame and using a generative model to compensate for
imperfections in the warped source frame. Thereby, the warp is encoded and
transmitted using a small number of keypoints rather than a dense flow field,
which leads to massive savings compared to traditional codecs. However, by
relying on a single source frame only, these methods lead to inaccurate
reconstructions (e.g. one side of the head becomes unoccluded when turning the
head and has to be synthesized). Here, we aim to tackle this issue by relying
on multiple source frames (views of the face) and present encouraging results.


title:
Physics-informed deep-learning applications to experimental fluid
  mechanics

abstract:
  High-resolution reconstruction of flow-field data from low-resolution and
noisy measurements is of interest due to the prevalence of such problems in
experimental fluid mechanics, where the measurement data are in general sparse,
incomplete and noisy. Deep-learning approaches have been shown suitable for
such super-resolution tasks. However, a high number of high-resolution examples
is needed, which may not be available for many cases. Moreover, the obtained
predictions may lack in complying with the physical principles, e.g. mass and
momentum conservation. Physics-informed deep learning provides frameworks for
integrating data and physical laws for learning. In this study, we apply
physics-informed neural networks (PINNs) for super-resolution of flow-field
data both in time and space from a limited set of noisy measurements without
having any high-resolution reference data. Our objective is to obtain a
continuous solution of the problem, providing a physically-consistent
prediction at any point in the solution domain. We demonstrate the
applicability of PINNs for the super-resolution of flow-field data in time and
space through three canonical cases: Burgers' equation, two-dimensional vortex
shedding behind a circular cylinder and the minimal turbulent channel flow. The
robustness of the models is also investigated by adding synthetic Gaussian
noise. Our results show excellent capabilities of PINNs in the context of data
augmentation for experiments in fluid mechanics.


title:
Model for Nitric oxide and its dayglow emission in the Martian upper
  atmosphere using NGIMS/MAVEN measured neutral and ion densities

abstract:
  A comprehensive study of Nitric oxide (NO) chemistry in the Martian upper
atmosphere is restricted due to the lack of requisite measurements. NO is an
abundant form of odd nitrogen species in the Martian lower atmosphere and its
density depends on several photochemical processes. We have developed a
photochemical model to study the NO density in the dayside of Martian upper
atmosphere by accounting for various production and loss mechanisms. By
utilizing the Neutral Gas and Ion Mass Spectrometer (NGIMS) on-board Mars
Atmosphere and Volatile Evolution (MAVEN) mission measured neutral and ion
densities during deep dip 8 and 9 campaigns, we modelled NO number density in
the Martian sunlit upper atmosphere for the altitudes between 120 and 200 km.
The modelled NO densities are employed to calculate NO (1,0) gamma band
emission intensity profiles in the dayside upper atmosphere of Mars. The
calculated NO density and its gamma band intensity profiles are found to be
consistent with Imaging Ultraviolet Spectrograph (IUVS) onboard MAVEN
observations and also with other modelling studies. We found that the local CO2
and N2 density variations can lead to a change in NO density and consequently
its dayglow intensity by a factor of 2 to 5. Since NO is a trace constituent
and also its dayglow emissions are strongly obscured by CO Cameron band
emissions, we suggest that the derivation of NO number density based on our
approach can constrain its abundance in the dayside upper atmosphere of Mars.
More observations of (1-0) gamma band emission along with modelling will help
to study the global distribution of NO in the Martian atmosphere.


title: Short-Term Word-Learning in a Dynamically Changing Environment

abstract:
  Neural sequence-to-sequence automatic speech recognition (ASR) systems are in
principle open vocabulary systems, when using appropriate modeling units. In
practice, however, they often fail to recognize words not seen during training,
e.g., named entities, numbers or technical terms. To alleviate this problem,
Huber et al. proposed to supplement an end-to-end ASR system with a word/phrase
memory and a mechanism to access this memory to recognize the words and phrases
correctly. In this paper we study, a) methods to acquire important words for
this memory dynamically and, b) the trade-off between improvement in
recognition accuracy of new words and the potential danger of false alarms for
those added words. We demonstrate significant improvements in the detection
rate of new words with only a minor increase in false alarms (F1 score 0.30
$\rightarrow$ 0.80), when using an appropriate number of new words. In
addition, we show that important keywords can be extracted from supporting
documents and used effectively.


title:
Automatic Detection of Speech Sound Disorder in Child Speech Using
  Posterior-based Speaker Representations

abstract:
  This paper presents a macroscopic approach to automatic detection of speech
sound disorder (SSD) in child speech. Typically, SSD is manifested by
persistent articulation and phonological errors on specific phonemes in the
language. The disorder can be detected by focally analyzing the phonemes or the
words elicited by the child subject. In the present study, instead of
attempting to detect individual phone- and word-level errors, we propose to
extract a subject-level representation from a long utterance that is
constructed by concatenating multiple test words. The speaker verification
approach, and posterior features generated by deep neural network models, are
applied to derive various types of holistic representations. A linear
classifier is trained to differentiate disordered speech in normal one. On the
task of detecting SSD in Cantonese-speaking children, experimental results show
that the proposed approach achieves improved detection performance over
previous method that requires fusing phone-level detection results. Using
articulatory posterior features to derive i-vectors from multiple-word
utterances achieves an unweighted average recall of 78.2% and a macro F1 score
of 78.0%.


title: TransductGAN: a Transductive Adversarial Model for Novelty Detection

abstract:
  Novelty detection, a widely studied problem in machine learning, is the
problem of detecting a novel class of data that has not been previously
observed. A common setting for novelty detection is inductive whereby only
examples of the negative class are available during training time. Transductive
novelty detection on the other hand has only witnessed a recent surge in
interest, it not only makes use of the negative class during training but also
incorporates the (unlabeled) test set to detect novel examples. Several studies
have emerged under the transductive setting umbrella that have demonstrated its
advantage over its inductive counterpart. Depending on the assumptions about
the data, these methods go by different names (e.g. transductive novelty
detection, semi-supervised novelty detection, positive-unlabeled learning,
out-of-distribution detection). With the use of generative adversarial networks
(GAN), a segment of those studies have adopted a transductive setup in order to
learn how to generate examples of the novel class. In this study, we propose
TransductGAN, a transductive generative adversarial network that attempts to
learn how to generate image examples from both the novel and negative classes
by using a mixture of two Gaussians in the latent space. It achieves that by
incorporating an adversarial autoencoder with a GAN network, the ability to
generate examples of novel data points offers not only a visual representation
of novelties, but also overcomes the hurdle faced by many inductive methods of
how to tune the model hyperparameters at the decision rule level. Our model has
shown superior performance over state-of-the-art inductive and transductive
methods. Our study is fully reproducible with the code available publicly.


title: Equivalences among Z_{p^s}-linear Generalized Hadamard Codes

abstract:
  The $\Z_{p^s}$-additive codes of length $n$ are subgroups of $\Z_{p^s}^n$,
and can be seen as a generalization of linear codes over $\Z_2$, $\Z_4$, or
$\Z_{2^s}$ in general. A $\Z_{p^s}$-linear generalized Hadamard (GH) code is a
GH code over $\Z_p$ which is the image of a $\Z_{p^s}$-additive code by a
generalized Gray map. A partial classification of these codes by using the
dimension of the kernel is known. In this paper, we establish that some
$\Z_{p^s}$-linear GH codes of length $p^t$ are equivalent, once $t$ is fixed.
This allows us to improve the known upper bounds for the number of such
nonequivalent codes. Moreover, up to $t=10$, this new upper bound coincides
with a known lower bound (based on the rank and dimension of the kernel).


title:
AutoCoMet: Smart Neural Architecture Search via Co-Regulated Shaping
  Reinforcement

abstract:
  Designing suitable deep model architectures, for AI-driven on-device apps and
features, at par with rapidly evolving mobile hardware and increasingly complex
target scenarios is a difficult task. Though Neural Architecture Search
(NAS/AutoML) has made this easier by shifting paradigm from extensive manual
effort to automated architecture learning from data, yet it has major
limitations, leading to critical bottlenecks in the context of mobile devices,
including model-hardware fidelity, prohibitive search times and deviation from
primary target objective(s). Thus, we propose AutoCoMet that can learn the most
suitable DNN architecture optimized for varied types of device hardware and
task contexts, ~ 3x faster. Our novel co-regulated shaping reinforcement
controller together with the high fidelity hardware meta-behavior predictor
produces a smart, fast NAS framework that adapts to context via a generalized
formalism for any kind of multi-criteria optimization.


title:
On minimal LPV state-space representations in innovation form: an
  algebraic characterization

abstract:
  In this paper we will propose a definition of the concept of minimal
state-space representations in innovation form for LPV. We also present
algebraic conditions for a stochastic LPV state-space representation to be
minimal in forward innovation form and discuss an algorithm for transforming
any stochastic LPV state-space representation to a minimal one in innovation
form.


title:
Proximal-like algorithms for equilibrium seeking in mixed-integer Nash
  equilibrium problems

abstract:
  We consider potential games with mixed-integer variables, for which we
propose two distributed, proximal-like equilibrium seeking algorithms.
Specifically, we focus on two scenarios: i) the underlying game is generalized
ordinal and the agents update through iterations by choosing an exact optimal
strategy; ii) the game admits an exact potential and the agents adopt
approximated optimal responses. By exploiting the properties of
integer-compatible regularization functions used as penalty terms affecting the
local cost function of each agent, we are able to show that both algorithms
converge to either an exact or an $\epsilon$-approximate equilibrium. We
corroborate our findings on a numerical instance of a Cournot oligopoly model.


title:
On the weight of single and recurrent scattering in the reflection
  matrix of complex media

abstract:
  In a heterogeneous medium, the wavefield can be decomposed as an infinite
series known as the Born expansion. Each term of the Born expansion corresponds
to a scattering order, it is thus theoretically possible to discriminate single
and multiple scattering field components. Experimentally, what is actually
measured is the total field in which all scattering orders interfere.
Conventional imaging methods usually rely on the assumption that the multiple
scattering contribution can be disregarded. In a back-scattering configuration,
this assumption is valid for small depths, and begins to fail for depths larger
than the scattering mean-free path $\ell_s$. It is therefore a key issue to
estimate the relative amount of single and multiple scattering in experimental
data. To this end, a single scattering estimator $\hat{\rho}$ computed from the
reflection matrix has been introduced in order to assess the weight of single
scattering in the backscattered wavefield. In this article, the meaning of this
estimator is investigated and a particular attention is given to recurrent
scattering. In a diffraction-limited experiment, a multiple scattering sequence
is said to be recurrent if the first and last scattering events occur in the
same resolution cell. Recurrent scattering is shown to be responsible for
correlations between single scattering and higher scattering orders of the Born
expansion, inducing a bias to the estimator $\hat{\rho}$ that should rather be
termed confocal scattering ratio. Interestingly, a more robust estimator is
built by projecting the reflection matrix in a focused basis. The argument is
sustained by numerical simulations as well as ultrasonic data obtained around
1.5~MHz in a model medium made of nylon rods immersed in water. From a more
general perspective, this work raises fundamental questions about the impact of
recurrent scattering on wave imaging.


title:
A stochastic generalized Nash equilibrium model for platforms
  competition in the ride-hail market

abstract:
  The presence of uncertainties in the ride-hailing market complicates the
pricing strategies of on-demand platforms that compete each other to offer a
mobility service while striving to maximize their profit. Looking at this
problem as a stochastic generalized Nash equilibrium problem (SGNEP), we design
a distributed, stochastic equilibrium seeking algorithm with Tikhonov
regularization to find an optimal pricing strategy. Remarkably, the proposed
iterative scheme does not require an increasing (possibly infinite) number of
samples of the random variable to perform the stochastic approximation, thus
making it appealing from a practical perspective. Moreover, we show that the
algorithm returns a Nash equilibrium under mere monotonicity assumption and a
careful choice of the step size sequence, obtained by exploiting the specific
structure of the SGNEP at hand. We finally corroborate our results on a
numerical instance of the on-demand ride-hailing market.


title:
Deep Reinforcement Learning for Data-Driven Adaptive Scanning in
  Ptychography

abstract:
  We present a method that lowers the dose required for a ptychographic
reconstruction by adaptively scanning the specimen, thereby providing the
required spatial information redundancy in the regions of highest importance.
The proposed method is built upon a deep learning model that is trained by
reinforcement learning (RL), using prior knowledge of the specimen structure
from training data sets. We show that equivalent low-dose experiments using
adaptive scanning outperform conventional ptychography experiments in terms of
reconstruction resolution.


title:
Quality Assurance of Generative Dialog Models in an Evolving
  Conversational Agent Used for Swedish Language Practice

abstract:
  Due to the migration megatrend, efficient and effective second-language
acquisition is vital. One proposed solution involves AI-enabled conversational
agents for person-centered interactive language practice. We present results
from ongoing action research targeting quality assurance of proprietary
generative dialog models trained for virtual job interviews. The action team
elicited a set of 38 requirements for which we designed corresponding automated
test cases for 15 of particular interest to the evolving solution. Our results
show that six of the test case designs can detect meaningful differences
between candidate models. While quality assurance of natural language
processing applications is complex, we provide initial steps toward an
automated framework for machine learning model selection in the context of an
evolving conversational agent. Future work will focus on model selection in an
MLOps setting.


title:
Spatiotemporal Patterns in Neurobiology: An Overview for Future
  Artificial Intelligence

abstract:
  In recent years, there has been increasing interest in developing models and
tools to address the complex patterns of connectivity found in brain tissue.
Specifically, this is due to a need to understand how emergent properties
emerge from these network structures at multiple spatiotemporal scales. We
argue that computational models are key tools for elucidating the possible
functionalities that can emerge from interactions of heterogeneous neurons
connected by complex networks on multi-scale temporal and spatial domains. Here
we review several classes of models including spiking neurons, integrate and
fire neurons with short term plasticity (STP), conductance based
integrate-and-fire models with STP, and population density neural field (PDNF)
models using simple examples with emphasis on neuroscience applications while
also providing some potential future research directions for AI. These
computational approaches allow us to explore the impact of changing underlying
mechanisms on resulting network function both experimentally as well as
theoretically. Thus we hope these studies will inform future developments in
artificial intelligence algorithms as well as help validate our understanding
of brain processes based on experiments in animals or humans.


title:
Measurement of forward photon production cross-section in pp collisions
  at $\sqrt{s}$ = 510 GeV with RHICf detector

abstract:
  This study reported the inclusive differential production cross-section of
photons in six pseudorapidity regions: 6.1 < $\eta$ < 6.5, 6.5 < $\eta$ < 7.0,
7.0 < $\eta$ <7.5, 7.5 < $\eta$ <8.0, 8.0 < $\eta$ < 8.5, and $\eta$ > 8.5,
measured through the RHICf experiment with pp collisions at $\sqrt{s}$ = 510
GeV conducted in June 2017. In addition, the cross-sections in the three
regions of the $x_F$-$p_T$ phase space coverage that are same as those of the
LHCf results at $\sqrt{s}$ = 7 and 13 TeV were obtained and compared.
Considering the uncertainties, the results were observed to be consistent with
both the Feynman scaling law and the model predictions of EPOS-LHC,
QGSJET-II-04, Sibyll 2.3d, and DPMjet-III 2019.1, although certain models
exhibited weak collision energy dependencies.


title: Black-box estimation of expanding parameter for de Sitter universe

abstract:
  We study the black-box parameter estimation of expanding parameters and the
dynamics of Gaussian interferometric power for the de Sitter space. We find
that the state between separated open charts can be employed as probe state for
the black-box quantum metrology. This is nontrivial because the open charts are
causally disconnected and classical information can not be exchanged between
them according to the general relativity. It is shown that the mass of the
scalar field remarkably affects the accuracy of the black-box parameter
estimation in the de Sitter space, which is quite different from the flat space
case where the mass parameter does not influence the precision of estimation.
Quantum discord is found to be a key resource for the estimation of the
expanding parameter when there is no entanglement between the initially
uncorrelated open charts.It is demonstrated that the role of the probe state
between different open charts is quite distinct because curvature effect of the
de sitter space damages quantum resources for the initially correlated probe
states, while it generates quantum resources for the initially uncorrelated
probe states.


title: Comparative Evaluations of Visualization Onboarding Methods

abstract:
  Comprehending and exploring large and complex data is becoming increasingly
important for users in a wide range of application domains. Still, non-experts
in visual data analysis often have problems with correctly reading and
interpreting information from visualizations that are new to them. To support
novices in learning how to use new digital technologies, the concept of
onboarding has been successfully applied in other fields and first approaches
also exist in the visualization domain. However, empirical evidence on the
effectiveness of such approaches is scarce. Therefore, we conducted 3 studies:
1) Firstly, we explored the effect of vis onboarding, using an interactive
step-by-step guide, on user performance for four increasingly complex
visualization techniques. We performed a between-subject experiment with 596
participants in total. The results showed that there are no significant
differences between the answer correctness of the questions with and without
onboarding. Furthermore, participants commented that for highly familiar
visualization types no onboarding is needed. 2) Second, we performed another
study with MTurk workers to assess if there is a difference in user
performances on different onboarding types: step-by-step, scrollytelling
tutorial, and video tutorial. The study revealed that the video tutorial was
ranked as the most positive on average, based on sentiment analysis, followed
by the scrollytelling tutorial and the interactive step-by-step guide. 3) For
our third study with students, we gathered data on users' experience in using
an in-situ scrollytelling for the VA tool. The results showed that they
preferred scrollytelling over the tutorial integrated into the landing page. In
summary, the in-situ scrollytelling approach works well for visualization
onboarding and a video tutorial can help to introduce interaction techniques.


title:
A Pressure Correction Projection Finite Element Method for The 2D/3D
  Time-Dependent Thermomicropolar Fluid Problem

abstract:
  In this paper, the pressure correctionfinite element method is proposed for
the 2D/3D time-dependent thermomicropolarfluid equations. Thefirst-order and
second-order backward difference formulas (BDF) are adopted to approximate the
time derivative term, stability analysis and error estimation of the
first-order semi-discrete scheme are proved. Finally, some numerical examples
are given to show the effectiveness and reliability of the proposed method,
which can be used to simulate the problem with high Rayleigh number.


title:
Simultaneous Communication and Tracking in Arbitrary Trajectories via
  Beam-Space Processing

abstract:
  In this paper, we develop a beam tracking scheme for an orthogonal frequency
division multiplexing (OFDM) Integrated Sensing and Communication (ISAC) system
with a hybrid digital analog (HDA) architecture operating in the millimeter
wave (mmWave) band. Our tracking method consists of an estimation step inspired
by radar signal processing techniques, and a prediction step based on simple
kinematic equations. The hybrid architecture exploits the predicted state
information to focus only on the directions of interest, trading off
beamforming gain, hardware complexity and multistream processing capabilities.
Our extensive simulations in arbitrary trajectories show that the proposed
method can outperform state of the art beam tracking methods in terms of
prediction accuracy and consequently achievable communication rate, and is
fully capable of dealing with highly non-linear dynamic motion patterns.


title:
Optimizing the Production of Test Vehicles using Hybrid Constrained
  Quantum Annealing

abstract:
  Optimization of pre-production vehicle configurations is one of the
challenges in the automotive industry. Given a list of tests requiring cars
with certain features, it is desirable to find the minimum number of cars that
cover the tests and obey the configuration rules. In this paper, we model the
problem in the framework of satisfiability and solve it by utilizing the newly
introduced hybrid constrained quadratic model (CQM) solver provided by D-Wave.
The problem definition is based on the "Optimizing the Production of Test
Vehicles" use case given in the BMW Quantum Computing Challenge. We formulate a
constrained quadratic model for the problem and use a greedy algorithm to
configure the cars. We benchmark the results obtained from the CQM solver with
the results from the classical solvers like CBC (Coin-or branch and cut) and
Gurobi. We conclude that the performance of the CQM solver is comparable to
classical solvers in optimizing the number of test vehicles. As an extension to
the problem, we describe how the scheduling of the tests can be incorporated
into the model.


title: CVD-based small-angle twisted bilayer graphene

abstract:
  To realize the applicative potential of 2D materials twistronics, scalable
synthesis and assembly techniques need to meet stringent requirements in terms
of interface cleanness and twist-angle homogeneity. Here, we show that
small-angle twisted bilayer graphene can be assembled from separated graphene
single-crystals grown by chemical vapour deposition, ensuring electronic
properties dominated by a device-scale-uniform moir\'e potential. Via
low-temperature dual-gated magnetotransport we demonstrate the hallmarks of a
2.4{\deg}-twisted superlattice, including tunable regimes of interlayer
coupling, reduced Fermi velocity, large interlayer capacitance, and
density-independent Brown-Zak oscillations. The observation of these features
in the electrical transport characteristics establishes CVD-based twisted
bilayer graphene as an alternative to tear-and-stack exfoliated flakes for
fundamental studies, while serving as a proof-of-concept for future large-scale
assembly.


title:
Development of a Scale to Measure Technology Acceptance in Smart
  Agriculture

abstract:
  This paper describes the development of a scale to measure technology
acceptance in smart agriculture. The scale is intended for use in diverse
situations, ranging for the evaluation of existing technologies already in
widespread use, to the evaluation of prototype systems. A systematic screening
of prior literature was conducted to identify initial scale items regarding how
technology acceptance is currently understood and measured. These items were
iteratively reviewed and systematically categorised to develop the final scale
proposed in this paper. In future work, this scale will be validated through
user studies. The purpose of this paper is to make the initial scale available
to the research community with a view to initial use and further evaluation.


title:
Semantic properties of English nominal pluralization: Insights from word
  embeddings

abstract:
  Semantic differentiation of nominal pluralization is grammaticalized in many
languages. For example, plural markers may only be relevant for human nouns.
English does not appear to make such distinctions. Using distributional
semantics, we show that English nominal pluralization exhibits semantic
clusters. For instance, pluralization of fruit words is more similar to one
another and less similar to pluralization of other semantic classes. Therefore,
reduction of the meaning shift in plural formation to the addition of an
abstract plural meaning is too simplistic. A semantically informed method,
called CosClassAvg, is introduced that outperforms pluralization methods in
distributional semantics which assume plural formation amounts to the addition
of a fixed plural vector. In comparison with our approach, a method from
compositional distributional semantics, called FRACSS, predicted plural vectors
that were more similar to the corpus-extracted plural vectors in terms of
direction but not vector length. A modeling study reveals that the observed
difference between the two predicted semantic spaces by CosClassAvg and FRACSS
carries over to how well a computational model of the listener can understand
previously unencountered plural forms. Mappings from word forms, represented
with triphone vectors, to predicted semantic vectors are more productive when
CosClassAvg-generated semantic vectors are employed as gold standard vectors
instead of FRACSS-generated vectors.


title: Process Mining Analysis of Puzzle-Based Cybersecurity Training

abstract:
  The hands-on cybersecurity training quality is crucial to mitigate cyber
threats and attacks effectively. However, practical cybersecurity training is
strongly process-oriented, making the post-training analysis very difficult.
This paper presents process-mining methods applied to the learning analytics
workflow. We introduce a unified approach to reconstruct behavioral graphs from
sparse event logs of cyber ranges. Furthermore, we discuss significant data
features that affect their practical usability for educational process mining.
Based on that, methods of dealing with the complexity of process graphs are
presented, taking advantage of the puzzle-based gamification of in-class
training sessions.


title: On Reinforcement Learning, Effect Handlers, and the State Monad

abstract:
  We study the algebraic effects and handlers as a way to support
decision-making abstractions in functional programs, whereas a user can ask a
learning algorithm to resolve choices without implementing the underlying
selection mechanism, and give a feedback by way of rewards. Differently from
some recently proposed approach to the problem based on the selection monad
[Abadi and Plotkin, LICS 2021], we express the underlying intelligence as a
reinforcement learning algorithm implemented as a set of handlers for some of
these algebraic operations, including those for choices and rewards. We show
how we can in practice use algebraic operations and handlers -- as available in
the programming language EFF -- to clearly separate the learning algorithm from
its environment, thus allowing for a good level of modularity. We then show how
the host language can be taken as a lambda-calculus with handlers, this way
showing what the essential linguistic features are. We conclude by hinting at
how type and effect systems could ensure safety properties, at the same time
pointing at some directions for further work.


title: Long-term Video Frame Interpolation via Feature Propagation

abstract:
  Video frame interpolation (VFI) works generally predict intermediate frame(s)
by first estimating the motion between inputs and then warping the inputs to
the target time with the estimated motion. This approach, however, is not
optimal when the temporal distance between the input sequence increases as
existing motion estimation modules cannot effectively handle large motions.
Hence, VFI works perform well for small frame gaps and perform poorly as the
frame gap increases. In this work, we propose a novel framework to address this
problem. We argue that when there is a large gap between inputs, instead of
estimating imprecise motion that will eventually lead to inaccurate
interpolation, we can safely propagate from one side of the input up to a
reliable time frame using the other input as a reference. Then, the rest of the
intermediate frames can be interpolated using standard approaches as the
temporal gap is now narrowed. To this end, we propose a propagation network
(PNet) by extending the classic feature-level forecasting with a novel
motion-to-feature approach. To be thorough, we adopt a simple interpolation
model along with PNet as our full model and design a simple procedure to train
the full model in an end-to-end manner. Experimental results on several
benchmark datasets confirm the effectiveness of our method for long-term VFI
compared to state-of-the-art approaches.


title:
Electric-field-induced oscillations in ionic fluids: a unified
  formulation of modified Poisson-Nernst-Planck models and its relevance to
  correlation function analysis

abstract:
  We theoretically investigate an electric-field-driven system of charged
spheres as a primitive model of concentrated electrolytes under an applied
electric field. First, we provide a unified formulation for the stochastic
charge and density dynamics of the electric-field-driven primitive model using
the stochastic density functional theory (DFT). The stochastic DFT integrates
various frameworks of the equilibrium and dynamic DFTs, the liquid state
theory, and the field-theoretic approach, which allows us to justify in a
unified manner various modifications previously made for the
Poisson-Nernst-Planck model. Next, we consider stationary density-density and
charge-charge correlation functions of the primitive model with a static
electric field. We focus on an electric-field-induced synchronization between
the emergence of density and charge oscillations, or the crossover from
monotonic to oscillatory decay of density-density and charge-charge
correlations. The correlation function analysis demonstrates the appearance of
stripe states formed by segregation bands perpendicular to the external field.
We also predict the following: (i) the electric-field-induced crossover occurs
prior to the conventional Kirkwood crossover without an applied electric field,
and (ii) the ion concentration dependence of the decay lengths at the
electric-field-induced crossovers bears a similarity to the underscreening
behavior found by simulation and theoretical studies on the oscillatory decay
length in equilibrium.

categories: cond-mat.soft cond-mat.mtrl-sci cond-mat.stat-mech physics.chem-ph


title: Heterogeneous Differential Privacy via Graphs

abstract:
  We generalize a previous framework for designing utility-optimal
differentially private (DP) mechanisms via graphs, where datasets are vertices
in the graph and edges represent dataset neighborhood. The boundary set
contains datasets where an individual's response changes the binary-valued
query compared to its neighbors. Previous work was limited to the homogeneous
case where the privacy parameter $\varepsilon$ across all datasets was the same
and the mechanism at boundary datasets was identical. In our work, the
mechanism can take different distributions at the boundary and the privacy
parameter $\varepsilon$ is a function of neighboring datasets, which recovers
an earlier definition of personalized DP as special case. The problem is how to
extend the mechanism, which is only defined at the boundary set, to other
datasets in the graph in a computationally efficient and utility optimal
manner. Using the concept of strongest induced DP condition we solve this
problem efficiently in polynomial time (in the size of the graph).


title:
Light driven magnetic transitions in transition metal dichalcogenide
  heterobilayers

abstract:
  Motivated by the recent excitement around the physics of twisted transition
metal dichalcogenide (TMD) multilayer systems, we study strongly correlated
phases of TMD heterobilayers under the influence of waveguide light. The
waveguide allows longitudinally polarized light, which in the high frequency
limit can be used to selectively modify interlayer hoppings in a tight binding
model. We argue based on quasi-degenerate perturbation theory that changes to
the interlayer hoppings can be captured as a modulation to the strength of the
moir\'e potential in a continuum model. As a consequence, waveguide light can
be used to drive transitions between a myriad of different magnetic phases,
including a transition from a $120^\circ$ Neel phase to a stripe ordered
magnetic phase, or from a spin density wave phase to a paramagnetic phase,
among others. We comment on the experimental feasibility of Floquet state
preparation and argue that it is within reach of available techniques when the
system is coupled to a judiciously chosen bath.


title:
Investigating Self-supervised Pretraining Frameworks for Pathological
  Speech Recognition

abstract:
  We investigate the performance of self-supervised pretraining frameworks on
pathological speech datasets used for automatic speech recognition (ASR).
Modern end-to-end models require thousands of hours of data to train well, but
only a small number of pathological speech datasets are publicly available. A
proven solution to this problem is by first pretraining the model on a huge
number of healthy speech datasets and then fine-tuning it on the pathological
speech datasets. One new pretraining framework called self-supervised learning
(SSL) trains a network using only speech data, providing more flexibility in
training data requirements and allowing more speech data to be used in
pretraining. We investigate SSL frameworks such as the wav2vec 2.0 and WavLM
models using different setups and compare their performance with different
supervised pretraining setups, using two types of pathological speech, namely,
Japanese electrolaryngeal and English dysarthric. Although the SSL setup is
promising against Transformer-based supervised setups, other supervised setups
such as the Conformer still outperform SSL pretraining. Our results show that
the best supervised setup outperforms the best SSL setup by 13.9% character
error rate in electrolaryngeal speech and 16.8% word error rate in dysarthric
speech.


title:
Complex structure due to As bonding and interplay with electronic
  structure in superconducting BaNi2As2

abstract:
  BaNi2As2 is a superconductor chemically related to the Fe-based
superconductors, with a complex and poorly understood structural phase
transition. We show based on first principles calculations that in fact there
are two distinct competing structures. These structures are very different from
electronic, transport and bonding points of view but are close in energy. These
arise due to complex As bonding patterns and drive distortions of the Ni
layers. This is supported by photoemission experiments. This leads to an
interplay of electronic and structural behavior including induced anisotropic
of the electronic transport. The competition between these distortions is
associated with the complex behavior observed in BaNi2As2 samples.


title:
Machine Learning model driven prediction of the initial geometry in
  Heavy-Ion Collision experiments

abstract:
  We demonstrate high prediction accuracy of three important properties that
determine the initial geometry of the heavy-ion collision (HIC) experiments by
using supervised Machine Learning (ML) methods. These properties are the impact
parameter, the eccentricity and the participant eccentricity. Though ML
techniques have been used previously to determine the impact parameter of these
collisions, we study multiple ML algorithms, their error spectrum, and sampling
methods using exhaustive parameter scans and ablation studies to determine a
combination of efficient algorithm and tuned training set that gives multi-fold
improvement in accuracy for all three different heavy-ion collision models. The
three models chosen are a transport model, a hydrodynamic model and a hybrid
model. The motivation of using three different heavy-ion collision models was
to show that even if the model is trained using a transport model, it gives
accurate results for a hydrodynamic model as well as a hybrid model. We show
that the accuracy of the impact parameter prediction depends on the centrality
of the collision. With the standard application of ML training methods,
prediction accuracy is considerable low for central collisions. Our method
increases this accuracy by multiple folds. We also show that the eccentricity
prediction accuracy can be improved by inclusion of the impact parameter as a
feature in all these algorithms. We discuss how the errors can be minimized and
the accuracy can be improved to a great extent in all the ranges of impact
parameter and eccentricity predictions.


title: Clean Implicit 3D Structure from Noisy 2D STEM Images

abstract:
  Scanning Transmission Electron Microscopes (STEMs) acquire 2D images of a 3D
sample on the scale of individual cell components. Unfortunately, these 2D
images can be too noisy to be fused into a useful 3D structure and facilitating
good denoisers is challenging due to the lack of clean-noisy pairs.
Additionally, representing a detailed 3D structure can be difficult even for
clean data when using regular 3D grids. Addressing these two limitations, we
suggest a differentiable image formation model for STEM, allowing to learn a
joint model of 2D sensor noise in STEM together with an implicit 3D model. We
show, that the combination of these models are able to successfully disentangle
3D signal and noise without supervision and outperform at the same time several
baselines on synthetic and real data.


title:
Impact of Network Densification on the Performance of a Non-Public URLLC
  Factory Network

abstract:
  Densification of the network deployment, for example by adding new sectors or
sites within an existing mobile communication network, has traditionally been
an efficient way to improve the system coverage and capacity. That will be the
case even for the ultra-reliable low-latency communication (URLLC) services,
but the overall situation and the feasibility of the different deployment
options will depend on the characteristics of the URLLC service in question.
URLLC services with relaxed latency requirements, allowing multiple
transmission attempts, can tolerate a decent level of inter-cell interference
while still being able to guarantee the desired quality-of-service for all
users, which makes it possible to improve the URLLC service capacity by adding
new gNodeBs with omnidirectional or directional antennas. However, when the
URLLC services require extremely low latency that do not allow for
retransmissions, the system performance becomes quite sensitive to the
inter-cell interference, which means that in practice the gNodeBs should be
equipped with beamformed antennas to reach high levels of URLLC service
capacity. Finally, an active distributed antenna system could be an efficient
way to secure good coverage throughout the desired service area.


title:
Training Speaker Embedding Extractors Using Multi-Speaker Audio with
  Unknown Speaker Boundaries

abstract:
  In this paper, we demonstrate a method for training speaker embedding
extractors using weak annotation. More specifically, we are using the full
VoxCeleb recordings and the name of the celebrities appearing on each video
without knowledge of the time intervals the celebrities appear in the video. We
show that by combining a baseline speaker diarization algorithm that requires
no training or parameter tuning, a modified loss with aggregation over
segments, and a two-stage training approach, we are able to train a competitive
ResNet-based embedding extractor. Finally, we experiment with two different
aggregation functions and analyze their behaviour in terms of their gradients.


title:
Contextual Information Based Anomaly Detection for a Multi-Scene UAV
  Aerial Videos

abstract:
  UAV based surveillance is gaining much interest worldwide due to its
extensive applications in monitoring wildlife, urban planning, disaster
management, campus security, etc. These videos are analyzed for
strange/odd/anomalous patterns which are essential aspects of surveillance. But
manual analysis of these videos is tedious and laborious. Hence, the
development of computer-aided systems for the analysis of UAV based
surveillance videos is crucial. Despite this interest, in literature, several
computer aided systems are developed focusing only on CCTV based surveillance
videos. These methods are designed for single scene scenarios and lack
contextual knowledge which is required for multi-scene scenarios. Furthermore,
the lack of standard UAV based anomaly detection datasets limits the
development of these systems. In this regard, the present work aims at the
development of a Computer Aided Decision support system to analyse UAV based
surveillance videos. A new UAV based multi-scene anomaly detection dataset is
developed with frame-level annotations for the development of computer aided
systems. It holistically uses contextual, temporal and appearance features for
accurate detection of anomalies. Furthermore, a new inference strategy is
proposed that utilizes few anomalous samples along with normal samples to
identify better decision boundaries. The proposed method is extensively
evaluated on the UAV based anomaly detection dataset and performed
competitively with respect to state-of-the-art methods.


title: Anomaly Detection for Compositional Data using VSI MEWMA control chart

abstract:
  In recent years, the monitoring of compositional data using control charts
has been investigated in the Statistical Process Control field. In this study,
we will design a Phase II Multivariate Exponentially Weighted Moving Average
(MEWMA) control chart with variable sampling intervals to monitor compositional
data based on isometric log-ratio transformation. The Average Time to Signal
will be computed based on the Markov chain approach to investigate the
performance of proposed chart. We also propose an optimal procedure to obtain
the optimal control limit, smoothing constant, and out-of-control Average Time
to Signal for different shift sizes and short sampling intervals. The
performance of proposed chart in comparison with the standard MEWMA chart for
monitoring compositional data is also provided. Finally, we end the paper with
a conclusion and some recommendations for future research.


title:
Eventor: An Efficient Event-Based Monocular Multi-View Stereo
  Accelerator on FPGA Platform

abstract:
  Event cameras are bio-inspired vision sensors that asynchronously represent
pixel-level brightness changes as event streams. Event-based monocular
multi-view stereo (EMVS) is a technique that exploits the event streams to
estimate semi-dense 3D structure with known trajectory. It is a critical task
for event-based monocular SLAM. However, the required intensive computation
workloads make it challenging for real-time deployment on embedded platforms.
In this paper, Eventor is proposed as a fast and efficient EMVS accelerator by
realizing the most critical and time-consuming stages including event
back-projection and volumetric ray-counting on FPGA. Highly paralleled and
fully pipelined processing elements are specially designed via FPGA and
integrated with the embedded ARM as a heterogeneous system to improve the
throughput and reduce the memory footprint. Meanwhile, the EMVS algorithm is
reformulated to a more hardware-friendly manner by rescheduling, approximate
computing and hybrid data quantization. Evaluation results on DAVIS dataset
show that Eventor achieves up to $24\times$ improvement in energy efficiency
compared with Intel i5 CPU platform.


title:
Gravitational wave non-linearities and pulsar-timing array angular
  correlations

abstract:
  Several pulsar-timing array (PTA) collaborations are finding tantalising
hints for a stochastic gravitational wave background signal in the nano-Hertz
regime. So far, though, no convincing evidence for the expected Hellings-Downs
quadrupolar correlations has been found. While this issue might get fixed at
the light of more accurate, forthcoming data, it is important to keep an eye
open on different possibilities, and explore scenarios able to produce
different types of PTA angular correlations. We point out that a stationary
non-Gaussian component to the gravitational wave background can modulate the
2-point PTA overlap reduction function, adding contributions that can help in
fitting the angular distribution of PTA data. We discuss possible sources for
such non-Gaussian signal in terms of cosmological processes occurring after
inflation ends, and we investigate further tests for this idea.


title:
UnShadowNet: Illumination Critic Guided Contrastive Learning For Shadow
  Removal

abstract:
  Shadows are frequently encountered natural phenomena that significantly
hinder the performance of computer vision perception systems in practical
settings, e.g., autonomous driving. A solution to this would be to eliminate
shadow regions from the images before the processing of the perception system.
Yet, training such a solution requires pairs of aligned shadowed and
non-shadowed images which are difficult to obtain. We introduce a novel weakly
supervised shadow removal framework UnShadowNet trained using contrastive
learning. It comprises of a DeShadower network responsible for removal of the
extracted shadow under the guidance of an Illumination network which is trained
adversarially by the illumination critic and a Refinement network to further
remove artifacts. We show that UnShadowNet can also be easily extended to a
fully-supervised setup to exploit the ground-truth when available. UnShadowNet
outperforms existing state-of-the-art approaches on three publicly available
shadow datasets (ISTD, adjusted ISTD, SRD) in both the weakly and fully
supervised setups.


title:
Shifting More Attention to Visual Backbone: Query-modulated Refinement
  Networks for End-to-End Visual Grounding

abstract:
  Visual grounding focuses on establishing fine-grained alignment between
vision and natural language, which has essential applications in multimodal
reasoning systems. Existing methods use pre-trained query-agnostic visual
backbones to extract visual feature maps independently without considering the
query information. We argue that the visual features extracted from the visual
backbones and the features really needed for multimodal reasoning are
inconsistent. One reason is that there are differences between pre-training
tasks and visual grounding. Moreover, since the backbones are query-agnostic,
it is difficult to completely avoid the inconsistency issue by training the
visual backbone end-to-end in the visual grounding framework. In this paper, we
propose a Query-modulated Refinement Network (QRNet) to address the
inconsistent issue by adjusting intermediate features in the visual backbone
with a novel Query-aware Dynamic Attention (QD-ATT) mechanism and query-aware
multiscale fusion. The QD-ATT can dynamically compute query-dependent visual
attention at the spatial and channel levels of the feature maps produced by the
visual backbone. We apply the QRNet to an end-to-end visual grounding
framework. Extensive experiments show that the proposed method outperforms
state-of-the-art methods on five widely used datasets.


title:
A Naturalistic Database of Thermal Emotional Facial Expressions and
  Effects of Induced Emotions on Memory

abstract:
  This work defines a procedure for collecting naturally induced emotional
facial expressions through the vision of movie excerpts with high emotional
contents and reports experimental data ascertaining the effects of emotions on
memory word recognition tasks. The induced emotional states include the four
basic emotions of sadness, disgust, happiness, and surprise, as well as the
neutral emotional state. The resulting database contains both thermal and
visible emotional facial expressions, portrayed by forty Italian subjects and
simultaneously acquired by appropriately synchronizing a thermal and a standard
visible camera. Each subject's recording session lasted 45 minutes, allowing
for each mode (thermal or visible) to collect a minimum of 2000 facial
expressions from which a minimum of 400 were selected as highly expressive of
each emotion category. The database is available to the scientific community
and can be obtained contacting one of the authors. For this pilot study, it was
found that emotions and/or emotion categories do not affect individual
performance on memory word recognition tasks and temperature changes in the
face or in some regions of it do not discriminate among emotional states.


title: On structure of $L^2$-harmonic functions for one-dimensional diffusions

abstract:
  In this note we analyse the harmonic functions in $L^2$-sense for an
irreducible diffusion on an interval.


title: Kink Form Factors

abstract:
  We use a recently constructed linearized soliton sector perturbation theory
to calculate the form factors relevant to the elastic scattering of
ultrarelativistic mesons off of nonrelativistic kinks. Both localized kink wave
packets and also delocalized momentum eigenstate kinks are considered. In the
delocalized case, the leading term is just the classical kink solution, as was
found by Goldstone and Jackiw. The leading delocalized quantum correction
agrees with that found by Gervais, Jevicki and Sakita in the $\phi^4$ model and
Weisz in the Sine-Gordon model. In the case of localized kink wave packets,
some corrections are found which scale with the wave packet width, and so will
be relevant for the coherent scattering of mesons off of kink wave packets.


title:
A framework for minimal hereditary classes of graphs of unbounded
  clique-width

abstract:
  We create a framework for hereditary graph classes $\mathcal{G}^\delta$ built
on a two-dimensional grid of vertices and edge sets defined by a triple
$\delta=\{\alpha,\beta,\gamma\}$ of objects that define edges between
consecutive columns, edges between non-consecutive columns (called bonds), and
edges within columns. This framework captures all previously proven minimal
hereditary classes of graph of unbounded clique-width, and many new ones,
although we do not claim this includes all such classes.
  We show that a graph class $\mathcal{G}^\delta$ has unbounded clique-width if
and only if a certain parameter $\mathcal{N}^\delta$ is unbounded. We further
show that $\mathcal{G}^\delta$ is minimal of unbounded clique-width (and,
indeed, minimal of unbounded linear clique-width) if another parameter
$\mathcal{M}^\beta$ is bounded, and also $\delta$ has defined recurrence
characteristics. Both the parameters $\mathcal{N}^\delta$ and
$\mathcal{M}^\beta$ are properties of a triple $\delta=(\alpha,\beta,\gamma)$,
and measure the number of distinct neighbourhoods in certain auxiliary graphs.
  Throughout our work, we introduce new methods to the study of clique-width,
including the use of Ramsey theory in arguments related to unboundedness, and
explicit (linear) clique-width expressions for subclasses of minimal classes of
unbounded clique-width.


title:
Transfer Learning Framework for Low-Resource Text-to-Speech using a
  Large-Scale Unlabeled Speech Corpus

abstract:
  Training a text-to-speech (TTS) model requires a large scale text labeled
speech corpus, which is troublesome to collect. In this paper, we propose a
transfer learning framework for TTS that utilizes a large amount of unlabeled
speech dataset for pre-training. By leveraging wav2vec2.0 representation,
unlabeled speech can highly improve performance, especially in the lack of
labeled speech. We also extend the proposed method to zero-shot multi-speaker
TTS (ZS-TTS). The experimental results verify the effectiveness of the proposed
method in terms of naturalness, intelligibility, and speaker generalization. We
highlight that the single speaker TTS model fine-tuned on the only 10 minutes
of labeled dataset outperforms the other baselines, and the ZS-TTS model
fine-tuned on the only 30 minutes of single speaker dataset can generate the
voice of the arbitrary speaker, by pre-training on unlabeled multi-speaker
speech corpus.


title: ZK-SecreC: a Domain-Specific Language for Zero Knowledge Proofs

abstract:
  We present ZK-SecreC, a domain-specific language for zero-knowledge proofs.
We present the rationale for its design, its syntax and semantics, and
demonstrate its usefulness on the basis of a number of non-trivial examples.
The design features a type system, where each piece of data is assigned both a
confidentiality and an integrity type, which are not orthogonal to each other.
We perform an empiric evaluation of the statements produced by its compiler in
terms of their size. We also show the integration of the compiler with the
implementation of a zero-knowledge proof technique, and evaluate the running
time of both Prover and Verifier.


title: De Sitter, gravitino mass and the swampland

abstract:
  In this contribution, we review a general argument showing that de Sitter
critical points of extended supergravity are in tension with the magnetic weak
gravity conjecture if the gravitino mass is vanishing. Motivated by this
assumption, we review then the gravitino conjecture, which states that the
limit of vanishing gravitino mass is pathological for the effective field
theory description. Finally, we discuss more in general the fate of de Sitter
critical points (with massless gravitini) in supergravity and comment on
extensions of these works along various directions. Part of the material here
presented is unpublished.


title:
High fidelity fluid-structure interaction by radial basis functions mesh
  adaption of moving walls: a workflow applied to an aortic valve

abstract:
  Fluid-Structure Interaction (FSI) can be investigated by means of non-linear
Finite Element Models (FEM), suitable to capture large deflections of
structural parts interacting with fluids, and Computational Fluid Dynamics
(CFD). High fidelity simulations are obtained using the fine spatial resolution
of both the structural and fluid computational grids. A key enabler to have a
proper exchange of information between the structural solver and the fluid one
is the management of the interface at wetted surfaces where the grids are
usually non matching. A class of applications, known also as one-way FSI
problems, involves a complex movement of the walls that is known in advance as
measured or as computed by FEM, and that has to be imposed at the boundaries
during a transient CFD solution. Effective methods for the time marching
adaption of the whole computational grid of the CFD model according to the
evolving shape of its boundaries are required. A very well established approach
consists of a continuum update of the mesh that is regenerated by adding and
removing cells to fit the evolution of the moving walls. In this paper, an
innovative method based on Radial Basis Functions (RBF) mesh morphing is
proposed, allowing the retention of the same mesh topology suitable for a
continuum update of the shape. The proposed method is exact at a set of given
key configurations and relies on shape blending time interpolation between key
frames. The study of the complex motion of a Polymeric-Prosthetic Heart Valve
(P-PHV) is presented using the new framework and considering as a reference the
established approach based on remeshing.


title: A Framework for Quantum Ray Tracing

abstract:
  Ray tracing algorithm simulates the physical movements of a huge amount of
rays to render a high quality image, in which the tracing procedure for each
ray can be implemented in parallel. By leveraging the inherent parallelism of
quantum computing, we propose a quantum ray tracing algorithm, which is proved
to have a quadratic speedup over the classical path tracing.


title: Dark Matter Production in Weyl $R^2$ Inflation

abstract:
  Dark matter and inflation are two key elements to understand the origin of
cosmic structures in modern cosmology, and yet their exact physical models
remain largely uncertain. The Weyl scaling invariant theory of gravity may
provide a feasible scheme to solve these two puzzles jointly, which contains a
massive gauge boson playing the role of dark matter candidate, and allows the
quadratic scalar curvature term, namely $R^2$, to realize a viable inflationary
mechanism in agreement with current observations. We ponder on the production
of dark matters in the Weyl $R^2$ model, including the contribution from the
non-perturbative production due to the quantum fluctuations from inflationary
vacuum and perturbative ones from scattering. We demonstrate that there are
generally three parameter ranges for viable dark matter production: (1) If the
reheating temperature is larger than $10^4~\mathrm{GeV}$, the Weyl gauge boson
as dark matter can be produced abundantly with mass larger than the inflation
scale $\sim 10^{13}~\mathrm{GeV}$. (2) Small mass region with
$2\times10^{-11}~\mathrm{GeV}$ for a higher reheating temperature. (3)
Annihilation channel becomes important in the case of higher reheating
temperature, which enables the Weyl gauge boson with mass up to
$4\times10^{16}~\mathrm{GeV}$ to be produced through freeze-in.


title:
Smallest and largest generalized eigenvalues of large moment matrices
  and some applications

abstract:
  The main aim of this work is to compare two Borel measures thorough their
moment matrices using a new notion of smallest and largest generalized
eigenvalues. With this approach we provide information in problems as the
localization of the support of a measure. In particular, we prove that if a
measure is comparable in an algebraic way with a measure in a Jordan curve then
the curve is contained in its support. We obtain a description of the convex
envelope of the support of a measure via certain Rayleigh quotients of certain
infinite matrices. Finally some applications concerning polynomial
approximation in mean square are given, generalizing the results in [9].


title:
Water megamaser and central black hole masses in a large sample of
  galaxies

abstract:
  Extragalactic water megamaser emissions at 22 GHz have been playing vital
roles in astrophysics. The limited detection rate of these masers has been
motivating researchers to find clues that can help characterize them. The
physical environments masers formed in is still ambiguous, accordingly,
statistical studies have been thoroughly used to resolve these favorable
environments. In this work, we go through the most essential parameter of
Active Galactic Nuclei (AGN), namely, the mass of the central supermassive
black hole (MBH) of the maser host galaxy. We study the correlation between
maser luminosity (LH2O) and MBH. The regression line of the relation is also
calculated. Additionally, sub-samples of megamasers (MMs), kilomasers (KMs),
and disc masers are studied. Our results show a very significant LH2O-MBH
correlation for our 68 galaxy sample. Unlike the results of previous works,
dividing the sample into MMs and KMs gives no privilege to MM galaxies. In
opposite to expectation, KMs have weak and low significant LH2O - MBH
correlation, while MMs show no correlation. The positive correlation in KMs can
be explained by the role of AGN therein, while the diversity of MMs types, with
some of which are not strongly related to AGN, may explain the correlation
missing. The 28 disc maser sample, where tight correlation is expected,
surprisingly shows a very weak and low significant LH2O - MBH correlation.
Future VLBI studies will eventually lead to a certain classification of a good
number of maser galaxies, which is essential to clearly establish the LH2O-MBH
relation.


title: WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit

abstract:
  Recently, we made available WeNet, a production-oriented end-to-end speech
recognition toolkit, which introduces a unified two-pass (U2) framework and a
built-in runtime to address the streaming and non-streaming decoding modes in a
single model. To further improve ASR performance and facilitate various
production requirements, in this paper, we present WeNet 2.0 with four
important updates. (1) We propose U2++, a unified two-pass framework with
bidirectional attention decoders, which includes the future contextual
information by a right-to-left attention decoder to improve the representative
ability of the shared encoder and the performance during the rescoring stage.
(2) We introduce an n-gram based language model and a WFST-based decoder into
WeNet 2.0, promoting the use of rich text data in production scenarios. (3) We
design a unified contextual biasing framework, which leverages user-specific
context (e.g., contact lists) to provide rapid adaptation ability for
production and improves ASR accuracy in both with-LM and without-LM scenarios.
(4) We design a unified IO to support large-scale data for effective model
training. In summary, the brand-new WeNet 2.0 achieves up to 10\% relative
recognition performance improvement over the original WeNet on various corpora
and makes available several important production-oriented features.


title: On Busy Periods of the Critical GI/G/1 Queue and BRAVO

abstract:
  We study critical GI/G/1 queues under finite second moment assumptions. We
show that the busy period distribution is regularly varying with index half. We
also review previously known M/G/1/ and M/M/1 derivations, yielding exact
asymptotics as well as a similar derivation for GI/M/1. The busy period
asymptotics determine the growth rate of moments of the renewal process
counting busy cycles. We further use this to demonstrate a BRAVO phenomenon
(Balancing Reduces Asymptotic Variance of Outputs) for the work-output process
(namely the busy-time). This yields new insight on the BRAVO effect.
  A second contribution of the paper is in settling previous conjectured
results about GI/G/1 and GI/G/s BRAVO. Previously, infinite buffer BRAVO was
generally only settled under fourth-moment assumptions together with an
assumption about the tail of the busy-period. In the current paper we
strengthen the previous results by reducing to assumptions to existence of
$2+\epsilon$ moments.


title:
Uniqueness of the Gibbs measure for the anti-ferromagnetic Potts model
  on the infinite $\Delta$-regular tree for large $\Delta$

abstract:
  In this paper we prove that for any integer $q\geq 5$, the anti-ferromagnetic
$q$-state Potts model on the infinite $\Delta$-regular tree has a unique Gibbs
measure for all edge interaction parameters $w\in [1-q/\Delta,1)$, provided
$\Delta$ is large enough. This confirms a longstanding folklore conjecture.


title: Efficient Virtual View Selection for 3D Hand Pose Estimation

abstract:
  3D hand pose estimation from single depth is a fundamental problem in
computer vision, and has wide applications.However, the existing methods still
can not achieve satisfactory hand pose estimation results due to view variation
and occlusion of human hand. In this paper, we propose a new virtual view
selection and fusion module for 3D hand pose estimation from single depth.We
propose to automatically select multiple virtual viewpoints for pose estimation
and fuse the results of all and find this empirically delivers accurate and
robust pose estimation. In order to select most effective virtual views for
pose fusion, we evaluate the virtual views based on the confidence of virtual
views using a light-weight network via network distillation. Experiments on
three main benchmark datasets including NYU, ICVL and Hands2019 demonstrate
that our method outperforms the state-of-the-arts on NYU and ICVL, and achieves
very competitive performance on Hands2019-Task1, and our proposed virtual view
selection and fusion module is both effective for 3D hand pose estimation.


title:
Influence of Communication Among Shared Developers on the Productivity
  of Open Source Software Projects

abstract:
  Many software developers rely on open source software for developing their
applications and writing their source codes. Measuring an independent project's
overall productivity is still an open problem for many technology companies. In
this project, we address to bridge the gap of analyzing which are the most
important features for prediction of a productivity based system. We have
chosen to collect data from GitHub via their application programming interfaces
(API) and analyze the data we gathered to understand the relation between the
average time to close an issue and the features that we collected. Since most
of the data we gathered were not Gaussian, we had to preprocess the data using
outlier detection and applying transformations before statistical modeling. The
best model we observed was polynomial regression with degree 5. Overall, we
noticed that there are many aspects of software development that make
developers increase their productivity.


title:
Realistic micromagnetic description of all-optical ultrafast switching
  processes in ferrimagnetic alloys

abstract:
  Both helicity-independent and helicity-dependent all-optical switching
processes driven by single ultrashort laser pulse have been experimentally
demonstrated in ferrimagnetic alloys as GdFeCo. Although the switching has been
previously reproduced by atomistic simulations, the lack of a robust
micromagnetic framework for ferrimagnets limits the predictions to small
nano-systems, whereas the experiments are usually performed with lasers and
samples of tens of micrometers. Here we develop a micromagnetic model based on
the extended Landau-Lifshitz-Bloch equation, which is firstly validated by
directly reproducing atomistic results for small samples and uniform laser
heating. After that, the model is used to study ultrafast single shot
all-optical switching in ferrimagnetic alloys under realistic conditions. We
find that the helicity-independent switching under a linearly polarized laser
pulse is a pure thermal phenomenon, in which the size of inverted area directly
correlates with the maximum electron temperature in the sample. On the other
hand, the analysis of the helicity-dependent processes under circular polarized
pulses in ferrimagnetic alloys with different composition indicates qualitative
differences between the results predicted by the magnetic circular dichroism
and the ones from inverse Faraday effect. Based on these predictions, we
propose experiments that would allow to resolve the controversy over the
physical phenomenon that underlies these helicity-dependent all optical
processes.


title: ELECTRODE: An electrochemistry package for atomistic simulations

abstract:
  Constant potential methods (CPM) enable computationally efficient simulations
of the solid-liquid interface at conducting electrodes in molecular dynamics
(MD). They have been successfully used, for example, to realistically model the
behavior of ionic liquids or water in salt electrolytes in supercapacitors and
batteries. The CPM models conductive electrodes by updating charges of
individual electrode atoms according to the applied electric potential and the
(time-dependent) local electrolyte structure. Here we present a feature-rich
CPM implementation, denoted ELECTRODE, for the Large-scale Atomic/Molecular
Massively Parallel Simulator (LAMMPS), which additionally includes a
constrained charge method and a thermo-potentiostat. The ELECTRODE package also
contains a finite-field approach, multiple corrections for non-periodic
boundary conditions of the particle-particle particle-mesh solver, and a
Thomas-Fermi model for using non-ideal metals as electrodes. We demonstrate the
capabilities of this implementation for a parallel-plate electrical
double-layer capacitor, for which we have studied charging times using the
different implemented methods. To prove the validity of the one-dimensional
correction for the long-range electrostatics, we estimated the vacuum
capacitance of a capacitor of two co-axial cylinders and compared it to the
analytical result. In summary, the ELECTRODE package enables efficient
electrochemical simulations using state-of-the-art methods, allowing one to
simulate heterogeneous electrodes. Moreover, it allows one to unveil how
curvature affects the capacitance of isolated carbon nanotubes.


title: Eichler integrals and generalized second order Eisenstein series

abstract:
  We show that all Eichler integrals, and more generally all "generalized
second order modular forms" can be expressed as linear combinations of
corresponding generalized second order Eisenstein series with coefficients in
classical modular forms. We determine the Fourier series expansions of
generalized second order Eisenstein series in level one, and provide tail
estimates via convexity bounds for additively twisted $\mathrm{L}$-functions.
As an application, we illustrate a bootstrapping procedure that yields
numerical evaluations of, for instance, Eichler integrals from merely the
associated cocycle. The proof of our main results rests on a filtration
argument that is largely rooted in previous work on vector-valued modular
forms, which we here formulate in classical terms.


title:
Introducing and solving generalized Black-Scholes PDEs through the use
  of functional calculus

abstract:
  We introduce some families of generalized Black--Scholes equations which
involve the Riemann-Liouville and Weyl space-fractional derivatives. We prove
that these generalized Black--Scholes equations are well-posed in
$(L^1-L^\infty)$-interpolation spaces. More precisely, we show that the
elliptic type operators involved in these equations generate holomorphic
semigroups. Then, we give explicit integral expressions for the associated
solutions. In the way to obtain well-posedness, we prove a new connection
between bisectorial operators and sectorial operators in an abstract setting.
Such a connection extends some known results in the topic to a wider family of
both operators and the functions involved.


title: Correlations and Work Statistics in Critical Quantum Systems

abstract:
  In this dissertation, we analyze equilibrium and out-of-equilibrium quantum
phase transitions present in quantum spin-$\frac{1}{2}$ models, using a quantum
information approach through quantum correlations and phase space formalism,
and a quantum thermodynamic approach through the work statistics formalism.


title:
Protein language models trained on multiple sequence alignments learn
  phylogenetic relationships

abstract:
  Self-supervised neural language models with attention have recently been
applied to biological sequence data, advancing structure, function and
mutational effect prediction. Some protein language models, including MSA
Transformer and AlphaFold's EvoFormer, take multiple sequence alignments (MSAs)
of evolutionarily related proteins as inputs. Simple combinations of MSA
Transformer's row attentions have led to state-of-the-art unsupervised
structural contact prediction. We demonstrate that similarly simple, and
universal, combinations of MSA Transformer's column attentions strongly
correlate with Hamming distances between sequences in MSAs. Therefore,
MSA-based language models encode detailed phylogenetic relationships. This
could aid them to separate coevolutionary signals encoding functional and
structural constraints from phylogenetic correlations arising from historical
contingency. To test this hypothesis, we generate synthetic MSAs, either
without or with phylogeny, from Potts models trained on natural MSAs. We
demonstrate that unsupervised contact prediction is indeed substantially more
resilient to phylogenetic noise when using MSA Transformer versus inferred
Potts models.


title:
Is there any upper bound on current fluctuation in cross-transport
  beyond linear response regime?

abstract:
  Input and output current fluctuations in stationary cross-transport systems
with two kinds of currents are investigated. In Saryal {\it et al.} [Phys. Rev.
Lett. 127, 190603 (2021)], upper bounds on input and output current
fluctuations are proven in the linear response regime and suggested the
validity beyond the linear response regime. We examine these bounds beyond the
linear response regime and find that these bounds are violated in general
nonequilibrium stationary conditions. We show two examples to demonstrate the
violation. Through these examples, we argue that no upper bound on input and
output current fluctuations exist beyond the linear response regime.


title: Graded Monads and Behavioural Equivalence Games

abstract:
  The framework of graded semantics uses graded monads to capture behavioural
equivalences of varying granularity, for example as found on the
linear-time/branching-time spectrum, over general system types. We describe a
generic Spoiler-Duplicator game for graded semantics that is extracted from the
given graded monad, and may be seen as playing out an equational proof;
instances include standard pebble games for simulation and bisimulation as well
as games for trace-like equivalences and coalgebraic behavioural equivalence.
Considerations on an infinite variant of such games lead to a novel notion of
infinite-depth graded semantics. Under reasonable restrictions, the
infinite-depth graded semantics associated to a given graded equivalence can be
characterized in terms of a determinization construction for coalgebras under
the equivalence at hand.


title:
Machine Composition of Korean Music via Topological Data Analysis and
  Artificial Neural Network

abstract:
  Common AI music composition algorithms based on artificial neural networks
are to train a machine by feeding a large number of music pieces and create
artificial neural networks that can produce music similar to the input music
data. This approach is a blackbox optimization, that is, the underlying
composition algorithm is, in general, not known to users.
  In this paper, we present a way of machine composition that trains a machine
the composition principle embedded in the given music data instead of directly
feeding music pieces. We propose this approach by using the concept of
{\color{black}{Overlap}} matrix proposed in \cite{TPJ}. In \cite{TPJ}, a type
of Korean music, so-called the {\it Dodeuri} music such as Suyeonjangjigok has
been analyzed using topological data analysis (TDA), particularly using
persistent homology. As the raw music data is not suitable for TDA analysis,
the music data is first reconstructed as a graph. The node of the graph is
defined as a two-dimensional vector composed of the pitch and duration of each
music note. The edge between two nodes is created when those nodes appear
consecutively in the music flow. Distance is defined based on the frequency of
such appearances. Through TDA on the constructed graph, a unique set of cycles
is found for the given music. In \cite{TPJ}, the new concept of the {\it
{\color{black}{Overlap}} matrix} has been proposed, which visualizes how those
cycles are interconnected over the music flow, in a matrix form.
  In this paper, we explain how we use the {\color{black}{Overlap}} matrix for
machine composition. The {\color{black}{Overlap}} matrix makes it possible to
compose a new music piece algorithmically and also provide a seed music towards
the desired artificial neural network. In this paper, we use the {\it Dodeuri}
music and explain detailed steps.


title:
Abstract Flow for Temporal Semantic Segmentation on the Permutohedral
  Lattice

abstract:
  Semantic segmentation is a core ability required by autonomous agents, as
being able to distinguish which parts of the scene belong to which object class
is crucial for navigation and interaction with the environment. Approaches
which use only one time-step of data cannot distinguish between moving objects
nor can they benefit from temporal integration. In this work, we extend a
backbone LatticeNet to process temporal point cloud data. Additionally, we take
inspiration from optical flow methods and propose a new module called Abstract
Flow which allows the network to match parts of the scene with similar abstract
features and gather the information temporally. We obtain state-of-the-art
results on the SemanticKITTI dataset that contains LiDAR scans from real urban
environments. We share the PyTorch implementation of TemporalLatticeNet at
https://github.com/AIS-Bonn/temporal_latticenet .


title: Graph similarity learning for change-point detection in dynamic networks

abstract:
  Dynamic networks are ubiquitous for modelling sequential graph-structured
data, e.g., brain connectome, population flows and messages exchanges. In this
work, we consider dynamic networks that are temporal sequences of graph
snapshots, and aim at detecting abrupt changes in their structure. This task is
often termed network change-point detection and has numerous applications, such
as fraud detection or physical motion monitoring. Leveraging a graph neural
network model, we design a method to perform online network change-point
detection that can adapt to the specific network domain and localise changes
with no delay. The main novelty of our method is to use a siamese graph neural
network architecture for learning a data-driven graph similarity function,
which allows to effectively compare the current graph and its recent history.
Importantly, our method does not require prior knowledge on the network
generative distribution and is agnostic to the type of change-points; moreover,
it can be applied to a large variety of networks, that include for instance
edge weights and node attributes. We show on synthetic and real data that our
method enjoys a number of benefits: it is able to learn an adequate graph
similarity function for performing online network change-point detection in
diverse types of change-point settings, and requires a shorter data history to
detect changes than most existing state-of-the-art baselines.


title:
State space models vs. multi-step predictors in predictive control: Are
  state space models complicating safe data-driven designs?

abstract:
  This paper contrasts recursive state space models and direct multi-step
predictors for linear predictive control. We provide a tutorial exposition for
both model structures to solve the following problems: 1. stochastic optimal
control; 2. system identification; 3. stochastic optimal control based on the
estimated model. Throughout the paper, we provide detailed discussions of the
benefits and limitations of these two model parametrizations for predictive
control and highlight the relation to existing works. Additionally, we derive a
novel (partially tight) constraint tightening for stochastic predictive control
with parametric uncertainty in the multi-step predictor.


title: Ab initio calculation of real solids via neural network ansatz

abstract:
  Neural networks have been applied to tackle many-body electron correlations
for small molecules and physical models in recent years. Here we propose a new
architecture that extends molecular neural networks with the inclusion of
periodic boundary conditions to enable ab initio calculation of real solids.
The accuracy of our approach is demonstrated in four different types of
systems, namely the one-dimensional periodic hydrogen chain, the
two-dimensional graphene, the three-dimensional lithium hydride crystal, and
the homogeneous electron gas, where the obtained results, e.g. total energies,
dissociation curves, and cohesive energies, outperform many traditional ab
initio methods and reach the level of the most accurate approaches. Our method
of extending a molecular neural network to periodic systems can be easily
integrated into other neural network structures, highlighting a promising
future of ab initio solution of more complex solid systems using neural network
ansatz, and more generally endorsing the application of machine learning in
materials simulation and condensed matter physics.


title:
Frequency-Directional Attention Model for Multilingual Automatic Speech
  Recognition

abstract:
  This paper proposes a model for transforming speech features using the
frequency-directional attention model for End-to-End (E2E) automatic speech
recognition. The idea is based on the hypothesis that in the phoneme system of
each language, the characteristics of the frequency bands of speech when
uttering them are different. By transforming the input Mel filter bank features
with an attention model that characterizes the frequency direction, a feature
transformation suitable for ASR in each language can be expected. This paper
introduces a Transformer-encoder as a frequency-directional attention model. We
evaluated the proposed method on a multilingual E2E ASR system for six
different languages and found that the proposed method could achieve, on
average, 5.3 points higher accuracy than the ASR model for each language by
introducing the frequency-directional attention mechanism. Furthermore,
visualization of the attention weights based on the proposed method suggested
that it is possible to transform acoustic features considering the frequency
characteristics of each language.


title: Gaussian Control Barrier Functions : A Non-Parametric Paradigm to Safety

abstract:
  Inspired by the success of control barrier functions (CBFs) in addressing
safety, and the rise of data-driven techniques for modeling functions, we
propose a non-parametric approach for online synthesis of CBFs using Gaussian
Processes (GPs). Mathematical constructs such as CBFs have achieved safety by
designing a candidate function a priori. However, designing such a candidate
function can be challenging. A practical example of such a setting would be to
design a CBF in a disaster recovery scenario where safe and navigable regions
need to be determined. The decision boundary for safety in such an example is
unknown and cannot be designed a priori. In our approach, we work with safety
samples or observations to construct the CBF online by assuming a flexible GP
prior on these samples, and term our formulation as a Gaussian CBF. GPs have
favorable properties, in addition to being non-parametric, such as analytical
tractability and robust uncertainty estimation. This allows realizing the
posterior components with high safety guarantees by incorporating variance
estimation, while also computing associated partial derivatives in closed-form
to achieve safe control. Moreover, the synthesized safety function from our
approach allows changing the corresponding safe set arbitrarily based on the
data, thus allowing non-convex safe sets. We validate our approach
experimentally on a quadrotor by demonstrating safe control for fixed but
arbitrary safe sets and collision avoidance where the safe set is constructed
online. Finally, we juxtapose Gaussian CBFs with regular CBFs in the presence
of noisy states to highlight its flexibility and robustness to noise. The
experiment video can be seen at: https://youtu.be/HX6uokvCiGk


title:
Continuous Ferromagnetic Quantum Phase Transition on Anisotropic Kondo
  Lattice

abstract:
  Motivated by the recent discovery of ferromagnetic quantum criticality in the
heavy fermion compound CeRh$_6$Ge$_4$, we develop a numerical algorithm of
infinite projected entangled pair states for the anisotropic ferromagnetic
Kondo-Heisenberg model in two dimension and study the ferromagnetic quantum
phase transitions with varying magnetic and hopping anisotropy. Our
calculations reveal a continuous ferromagnetic quantum phase transition in the
large anisotropic region and first-order quantum phase transitions for smaller
anisotropy. Our results highlight the importance of magnetic anisotropy on
ferromagnetic quantum criticality in Kondo lattice systems and provide an
explanation for the experimental observation in CeRh$_6$Ge$_4$ with a
quasi-one-dimensional magnetic structure. Our work opens the avenue for future
studies of the rich Kondo lattice physics using state-of-the-art tensor network
approaches.


title: Excitation and detection of acoustic phonons in nanoscale systems

abstract:
  Phonons play a key role in the physical properties of materials, and have
long been a topic of study in physics. While the effects of phonons had
historically been considered to be a hindrance, modern research has shown that
phonons can be exploited due to their ability to couple to other excitations
and consequently affect the thermal, dielectric, and electronic properties of
solid state systems, greatly motivating the engineering of phononic structures.
Advances in nanofabrication have allowed for structuring and phonon confinement
even down to the nanoscale, drastically changing material properties. Despite
developments in fabricating such nanoscale devices, the proper manipulation and
characterization of phonons continues to be challenging. However, a fundamental
understanding of these processes could enable the realization of key
applications in diverse fields such as topological phononics, information
technologies, sensing, and quantum electrodynamics, especially when integrated
with existing electronic and photonic devices. Here, we highlight seven of the
available methods for the excitation and detection of acoustic phonons and
vibrations in solid materials, as well as advantages, disadvantages, and
additional considerations related to their application. We then provide
perspectives towards open challenges in nanophononics and how the additional
understanding granted by these techniques could serve to enable the next
generation of phononic technological applications.


title:
The CUBES Instrument Model and Simulation Tools. Their role in the
  project Phase A study

abstract:
  We present the simulation tools developed to aid the design phase of the
Cassegrain U-Band Efficient Spectrograph (CUBES) for the Very Large Telescope
(VLT), exploring aspects of the system design and evaluating the performance
for different design configurations. CUBES aims to be the 'ultimate'
ultraviolet (UV) instrument at the European Southern Observatory (ESO) in terms
of throughput, with the goal to cover the bluest part of the spectrum
accessible from the ground (300 nm to 400 nm) with the highest possible
efficiency. Here we introduce the End-to-End (E2E) and the Exposure Time
Calculator (ETC) tools. The E2E simulator has been developed with different
versions to meet the needs of different users, including a version that can be
accessed for use by the broader scientific community using a Jupyter notebook.
The E2E tool was used by the system team to help define the Phase A baseline
design of the instrument, as well as in scientific evaluation of a possible
low-resolution mode. The ETC is a web-based tool through which the science
community are able to test a range of science cases for CUBES, demonstrating
its potential to push the limiting magnitude for the detection of specific
UV-features, such as abundance estimates of beryllium in main sequence stars.


title:
Multielectron effect in the strong-field ionization of aligned nonpolar
  molecules

abstract:
  We revisit strong-field ionization of aligned O$_2$, CO$_2$, and CS$_2$
molecules in light of recent advances in the field of strong-field physics, in
particular the inclusion of multielectron polarization in the numerical
solution of the time-dependent Schr{\"o}dinger equation (TDSE) within the
singe-active-electron approximation. Upon inclusion of multielectron
polarization and the associated induced dipole potential in the TDSE model, we
obtain angular distributions of total ionization yields which are in better
agreement with the available experimental results for CO$_2$ and CS$_2$. For
the probed molecules, the main effect of the multielectron polarization on the
photoelectron momentum distributions (PMDs) is captured by the introduction of
a field at short distances that counteracts the applied external field and
leads to a vanishing time-dependent interaction within a certain cut-off
radius. Hence, for the PMDs we find no imprints of the long-range part of the
laser-induced dipole potential.


title:
Speech Segmentation Optimization using Segmented Bilingual Speech Corpus
  for End-to-end Speech Translation

abstract:
  Speech segmentation, which splits long speech into short segments, is
essential for speech translation (ST). Popular VAD tools like WebRTC VAD have
generally relied on pause-based segmentation. Unfortunately, pauses in speech
do not necessarily match sentence boundaries, and sentences can be connected by
a very short pause that is difficult to detect by VAD. In this study, we
propose a speech segmentation method using a binary classification model
trained using a segmented bilingual speech corpus. We also propose a hybrid
method that combines VAD and the above speech segmentation method. Experimental
results revealed that the proposed method is more suitable for cascade and
end-to-end ST systems than conventional segmentation methods. The hybrid
approach further improved the translation performance.