title: What is the effect of EU's fuel-tax cuts on Russia's oil income?

abstract:
  Following the oil-price surge in the wake of Russia's invasion of Ukraine,
many countries in the EU are proposing to cut taxes on petrol and diesel. Using
standard theory and empirical estimates, we assess how such tax cuts will
influence the oil income in Russia. We find that a tax cut of 20 euro cents per
liter would increase Russia's oil profits by around 11-17 million Euros per day
in the short run and long run. This is equivalent to 4100-6300 million Euros in
a year, 0.3-0.5% of Russia's GDP or 7-11% of its military spending. We show
that a cash transfer to EU citizens, with an equivalent fiscal burden as the
tax cut, reduces these side effects to a fraction.


title: Swarm behavior tracking based on a deep vision algorithm

abstract:
  The intelligent swarm behavior of social insects (such as ants) springs up in
different environments, promising to provide insights for the study of embodied
intelligence. Researching swarm behavior requires that researchers could
accurately track each individual over time. Obviously, manually labeling
individual insects in a video is labor-intensive. Automatic tracking methods,
however, also poses serious challenges: (1) individuals are small and similar
in appearance; (2) frequent interactions with each other cause severe and
long-term occlusion. With the advances of artificial intelligence and computing
vision technologies, we are hopeful to provide a tool to automate monitor
multiple insects to address the above challenges. In this paper, we propose a
detection and tracking framework for multi-ant tracking in the videos by: (1)
adopting a two-stage object detection framework using ResNet-50 as backbone and
coding the position of regions of interest to locate ants accurately; (2) using
the ResNet model to develop the appearance descriptors of ants; (3)
constructing long-term appearance sequences and combining them with motion
information to achieve online tracking. To validate our method, we construct an
ant database including 10 videos of ants from different indoor and outdoor
scenes. We achieve a state-of-the-art performance of 95.7\% mMOTA and 81.1\%
mMOTP in indoor videos, 81.8\% mMOTA and 81.9\% mMOTP in outdoor videos.
Additionally, Our method runs 6-10 times faster than existing methods for
insect tracking. Experimental results demonstrate that our method provides a
powerful tool for accelerating the unraveling of the mechanisms underlying the
swarm behavior of social insects.


title: Tapered multi-core fiber for lensless endoscopes

abstract:
  We present a novel fiber-optic component, a "tapered multi-core fiber (MCF)",
designed for integration into ultra-miniaturized endoscopes for minimally
invasive two-photon point-scanning imaging and to address the power delivery
issue that has faced MCF based lensless endoscopes. With it we achieve
experimentally a factor 6.0 increase in two-photon signal yield while keeping
the ability to point-scan by the memory effect, and a factor 8.9 sacrificing
the memory effect. To reach this optimal design we first develop and validate a
fast numerical model capable of predicting the essential properties of an
arbitrarily tapered MCF from its structural parameters. We then use this model
to identify the tapered MCF design parameters that result in a chosen set of
target properties (point-spread function, delivered power, presence or absence
of memory effect). We fabricate the identified target designs by stack-and-draw
and post-processing on a CO$_{2}$ laser-based glass processing and splicing
system. Finally we demonstrate the performance gain of the fabricated tapered
MCFs in two-photon imaging when used in a lensless endoscope system. Our
results show that tailoring of the taper profile brings new degrees of freedom
that can be efficiently exploited for lensless endoscopes.


title:
Using Decision Tree as Local Interpretable Model in Autoencoder-based
  LIME

abstract:
  Nowadays, deep neural networks are being used in many domains because of
their high accuracy results. However, they are considered as "black box", means
that they are not explainable for humans. On the other hand, in some tasks such
as medical, economic, and self-driving cars, users want the model to be
interpretable to decide if they can trust these results or not. In this work,
we present a modified version of an autoencoder-based approach for local
interpretability called ALIME. The ALIME itself is inspired by a famous method
called Local Interpretable Model-agnostic Explanations (LIME). LIME generates a
single instance level explanation by generating new data around the instance
and training a local linear interpretable model. ALIME uses an autoencoder to
weigh the new data around the sample. Nevertheless, the ALIME uses a linear
model as the interpretable model to be trained locally, just like the LIME.
This work proposes a new approach, which uses a decision tree instead of the
linear model, as the interpretable model. We evaluate the proposed model in
case of stability, local fidelity, and interpretability on different datasets.
Compared to ALIME, the experiments show significant results on stability and
local fidelity and improved results on interpretability.


title:
Gravitational entanglement and the mass contribution of internal energy
  in nonrelativistic quantum systems

abstract:
  Recently, interest has increased in the entanglement of remote quantum
particles through the Newtonian gravitational interaction, both from a
fundamental perspective and as a test case for the quantization of gravity.
Likewise, post-Newtonian gravitational effects in composite nonrelativistic
quantum systems have been discussed, where the internal energy contributes to
the mass, promoting the mass to a Hilbert space operator. Employing a modified
version of a previously considered thought experiment, it can be shown that
both concepts, when combined, result in inconsistencies, reinforcing the
arguments for the necessity of a rigorous derivation of the nonrelativistic
limit of gravitating quantum matter from first principles.


title:
Multi-Sample $\zeta$-mixup: Richer, More Realistic Synthetic Samples
  from a $p$-Series Interpolant

abstract:
  Modern deep learning training procedures rely on model regularization
techniques such as data augmentation methods, which generate training samples
that increase the diversity of data and richness of label information. A
popular recent method, mixup, uses convex combinations of pairs of original
samples to generate new samples. However, as we show in our experiments, mixup
can produce undesirable synthetic samples, where the data is sampled off the
manifold and can contain incorrect labels. We propose $\zeta$-mixup, a
generalization of mixup with provably and demonstrably desirable properties
that allows convex combinations of $N \geq 2$ samples, leading to more
realistic and diverse outputs that incorporate information from $N$ original
samples by using a $p$-series interpolant. We show that, compared to mixup,
$\zeta$-mixup better preserves the intrinsic dimensionality of the original
datasets, which is a desirable property for training generalizable models.
Furthermore, we show that our implementation of $\zeta$-mixup is faster than
mixup, and extensive evaluation on controlled synthetic and 24 real-world
natural and medical image classification datasets shows that $\zeta$-mixup
outperforms mixup and traditional data augmentation techniques.


title:
Autoencoding Language Model Based Ensemble Learning for Commonsense
  Validation and Explanation

abstract:
  An ultimate goal of artificial intelligence is to build computer systems that
can understand human languages. Understanding commonsense knowledge about the
world expressed in text is one of the foundational and challenging problems to
create such intelligent systems. As a step towards this goal, we present in
this paper ALMEn, an Autoencoding Language Model based Ensemble learning method
for commonsense validation and explanation. By ensembling several advanced
pre-trained language models including RoBERTa, DeBERTa, and ELECTRA with
Siamese neural networks, our method can distinguish natural language statements
that are against commonsense (validation subtask) and correctly identify the
reason for making against commonsense (explanation selection subtask).
Experimental results on the benchmark dataset of SemEval-2020 Task 4 show that
our method outperforms state-of-the-art models, reaching 97.9% and 95.4%
accuracies on the validation and explanation selection subtasks, respectively.


title: HD 183986: a high-contrast SB2 system with a pulsating component

abstract:
  There is a small group of peculiar early-type stars on the main sequence that
show different rotation velocities from different spectral lines. This
inconsistency might be due to the binary nature of these objects. We aim to
verify this hypothesis by a more detailed spectroscopic and photometric
investigation of one such object: HD 183986. We obtained 151 high and medium
resolution spectra that covered an anticipated long orbital period. There is
clear evidence of theorbital motion of the primary component. We uncovered a
very faint and broad spectrum of the secondary component. The corresponding SB2
orbital parameters, and the component spectra, were obtained by Fourier
disentangling using the KOREL code. The component spectra were further modeled
by iSpec code to arrive at the atmospheric quantities and the projected
rotational velocities. We have proven that this object is a binary star with
the period $P$ = 1268.2(11) d, eccentricity $e$ = 0.5728(20), and mass ratio
$q$ = 0.655. The primary component is a slowly rotating star ($v \sin i = 27$
km.s$^{-1}$) while the cooler and less massive secondary rotates much faster
($v \sin i \sim 120$ km.s$^{-1}$). Photometric observations obtained by the
TESS satellite were also investigated to shed more light on this object. A
multi-period photometric variability was detected in the TESS data ranging from
hours (the $\delta$ Sct-type variability) to a few days (spots/rotational
variability). The physical parameters of the components and the origin of the
photometric variability are discussed in more detail.


title: Enabling Deep Learning for All-in EDGE paradigm

abstract:
  Deep Learning-based models have been widely investigated, and they have
demonstrated significant performance on non-trivial tasks such as speech
recognition, image processing, and natural language understanding. However,
this is at the cost of substantial data requirements. Considering the
widespread proliferation of edge devices (e.g. Internet of Things devices) over
the last decade, Deep Learning in the edge paradigm, such as device-cloud
integrated platforms, is required to leverage its superior performance.
Moreover, it is suitable from the data requirements perspective in the edge
paradigm because the proliferation of edge devices has resulted in an explosion
in the volume of generated and collected data. However, there are difficulties
due to other requirements such as high computation, high latency, and high
bandwidth caused by Deep Learning applications in real-world scenarios. In this
regard, this survey paper investigates Deep Learning at the edge, its
architecture, enabling technologies, and model adaption techniques, where edge
servers and edge devices participate in deep learning training and inference.
For simplicity, we call this paradigm the All-in EDGE paradigm. Besides, this
paper presents the key performance metrics for Deep Learning at the All-in EDGE
paradigm to evaluate various deep learning techniques and choose a suitable
design. Moreover, various open challenges arising from the deployment of Deep
Learning at the All-in EDGE paradigm are identified and discussed.


title: Constraints on TESS albedos for five hot Jupiters

abstract:
  Photometric observations of occultations of transiting exoplanets can place
important constraints on the thermal emission and albedos of their atmospheres.
We analyse photometric measurements and derive geometric albedo
($A_\mathrm{g}$) constraints for five hot Jupiters observed with TESS in the
optical: WASP-18 b, WASP-36 b, WASP-43 b, WASP-50 b and WASP-51 b. For WASP-43
b, our results are complemented by a VLT/HAWK-I observation in the
near-infrared at $2.09~\mu$m. We derive the first geometric albedo constraints
for WASP-50 b and WASP-51 b: $A_\mathrm{g}<0.445$ and $A_\mathrm{g}<0.368$,
respectively. We find that WASP-43 b and WASP-18 b are both consistent with low
geometric albedos ($A_\mathrm{g}<0.16$) even though they lie at opposite ends
of the hot Jupiter temperature range with equilibrium temperatures of
$\sim1400$ K and $\sim2500$ K, respectively. We report self-consistent
atmospheric models which explain broadband observations for both planets from
TESS, \HST, \Spitzer and VLT/HAWK-I. We find that the data of both hot Jupiters
can be explained by thermal emission alone and inefficient day-night energy
redistribution. The data do not require optical scattering from clouds/hazes,
consistent with the low geometric albedos observed.


title:
A Comprehensive Review of Sign Language Recognition: Different Types,
  Modalities, and Datasets

abstract:
  A machine can understand human activities, and the meaning of signs can help
overcome the communication barriers between the inaudible and ordinary people.
Sign Language Recognition (SLR) is a fascinating research area and a crucial
task concerning computer vision and pattern recognition. Recently, SLR usage
has increased in many applications, but the environment, background image
resolution, modalities, and datasets affect the performance a lot. Many
researchers have been striving to carry out generic real-time SLR models. This
review paper facilitates a comprehensive overview of SLR and discusses the
needs, challenges, and problems associated with SLR. We study related works
about manual and non-manual, various modalities, and datasets. Research
progress and existing state-of-the-art SLR models over the past decade have
been reviewed. Finally, we find the research gap and limitations in this domain
and suggest future directions. This review paper will be helpful for readers
and researchers to get complete guidance about SLR and the progressive design
of the state-of-the-art SLR model


title: Information-driven Path Planning for Hybrid Aerial Underwater Vehicles

abstract:
  This paper presents a novel Rapidly-exploring Adaptive Sampling Tree (RAST)
algorithm for the adaptive sampling mission of a hybrid aerial underwater
vehicle (HAUV) in an air-sea 3D environment. This algorithm innovatively
combines the tournament-based point selection sampling strategy, the
information heuristic search process and the framework of Rapidly-exploring
Random Tree (RRT) algorithm. Hence can guide the vehicle to the region of
interest to scientists for sampling and generate a collision-free path for
maximizing information collection by the HAUV under the constraints of
environmental effects of currents or wind and limited budget. The simulation
results show that the fast search adaptive sampling tree algorithm has higher
optimization performance, faster solution speed and better stability than the
Rapidly-exploring Information Gathering Tree (RIGT) algorithm and the particle
swarm optimization (PSO) algorithm.


title: Coarse-to-Fine Feature Mining for Video Semantic Segmentation

abstract:
  The contextual information plays a core role in semantic segmentation. As for
video semantic segmentation, the contexts include static contexts and motional
contexts, corresponding to static content and moving content in a video clip,
respectively. The static contexts are well exploited in image semantic
segmentation by learning multi-scale and global/long-range features. The
motional contexts are studied in previous video semantic segmentation. However,
there is no research about how to simultaneously learn static and motional
contexts which are highly correlated and complementary to each other. To
address this problem, we propose a Coarse-to-Fine Feature Mining (CFFM)
technique to learn a unified presentation of static contexts and motional
contexts. This technique consists of two parts: coarse-to-fine feature
assembling and cross-frame feature mining. The former operation prepares data
for further processing, enabling the subsequent joint learning of static and
motional contexts. The latter operation mines useful information/contexts from
the sequential frames to enhance the video contexts of the features of the
target frame. The enhanced features can be directly applied for the final
prediction. Experimental results on popular benchmarks demonstrate that the
proposed CFFM performs favorably against state-of-the-art methods for video
semantic segmentation. Our implementation is available at
https://github.com/GuoleiSun/VSS-CFFM


title: Sparse Optical Flow-Based Line Feature Tracking

abstract:
  In this paper we propose a novel sparse optical flow (SOF)-based line feature
tracking method for the camera pose estimation problem. This method is inspired
by the point-based SOF algorithm and developed based on an observation that two
adjacent images in time-varying image sequences satisfy brightness invariant.
Based on this observation, we re-define the goal of line feature tracking:
track two endpoints of a line feature instead of the entire line based on gray
value matching instead of descriptor matching. To achieve this goal, an
efficient two endpoint tracking (TET) method is presented: first, describe a
given line feature with its two endpoints; next, track the two endpoints based
on SOF to obtain two new tracked endpoints by minimizing a pixel-level
grayscale residual function; finally, connect the two tracked endpoints to
generate a new line feature. The correspondence is established between the
given and the new line feature. Compared with current descriptor-based methods,
our TET method needs not to compute descriptors and detect line features
repeatedly. Naturally, it has an obvious advantage over computation.
Experiments in several public benchmark datasets show our method yields highly
competitive accuracy with an obvious advantage over speed.


title:
Predicting Performance of Heterogeneous AI Systems with Discrete-Event
  Simulations

abstract:
  In recent years, artificial intelligence (AI) technologies have found
industrial applications in various fields. AI systems typically possess complex
software and heterogeneous CPU/GPU hardware architecture, making it difficult
to answer basic questions considering performance evaluation and software
optimization. Where is the bottleneck impeding the system? How does the
performance scale with the workload? How the speed-up of a specific module
would contribute to the whole system? Finding the answers to these questions
through experiments on the real system could require a lot of computational,
human, financial, and time resources. A solution to cut these costs is to use a
fast and accurate simulation model preparatory to implementing anything in the
real system. In this paper, we propose a discrete-event simulation model of a
high-load heterogeneous AI system in the context of video analytics. Using the
proposed model, we estimate: 1) the performance scalability with the increasing
number of cameras; 2) the performance impact of integrating a new module; 3)
the performance gain from optimizing a single module. We show that the
performance estimation accuracy of the proposed model is higher than 90%. We
also demonstrate, that the considered system possesses a counter-intuitive
relationship between workload and performance, which nevertheless is correctly
inferred by the proposed simulation model.


title:
Learning to Sieve: Prediction of Grading Curves from Images of Concrete
  Aggregate

abstract:
  A large component of the building material concrete consists of aggregate
with varying particle sizes between 0.125 and 32 mm. Its actual size
distribution significantly affects the quality characteristics of the final
concrete in both, the fresh and hardened states. The usually unknown variations
in the size distribution of the aggregate particles, which can be large
especially when using recycled aggregate materials, are typically compensated
by an increased usage of cement which, however, has severe negative impacts on
economical and ecological aspects of the concrete production. In order to allow
a precise control of the target properties of the concrete, unknown variations
in the size distribution have to be quantified to enable a proper adaptation of
the concrete's mixture design in real time. To this end, this paper proposes a
deep learning based method for the determination of concrete aggregate grading
curves. In this context, we propose a network architecture applying multi-scale
feature extraction modules in order to handle the strongly diverse object sizes
of the particles. Furthermore, we propose and publish a novel dataset of
concrete aggregate used for the quantitative evaluation of our method.


title:
Estimation of the masses in the Local Group by Gradient Boosted Decision
  Trees

abstract:
  Our goal is to estimate the mass of the Local Group (LG) and the individual
masses of its primary galaxies, the M31 and the Milky Way (MW). We do this by
means of a supervised machine learning algorithm, the gradient boosted decision
trees (GBDT) and using the observed distance and relative velocity of the two
as input parameters. The GBDT is applied to a sample of 2148 mock LGs drawn
from a set of 5 dark matter (DM)-only simulations, ran within the standard
$\Lambda$CDM\ cosmological model. The selection of the mock LGs is guided by a
LG model, which defines such objects. The role of the observational
uncertainties of the input parameters is gauged by applying the model to an
ensemble of mock LGs pairs whose observables are these input parameters
perturbed by their corresponding observational errors. Finally the
observational data of the actual LG is used to infer its relevant masses. Our
main results are the sum and the individual masses of the MW and M31: $M_{tot}
= 3.31 ^{+0.79}_{-0.67} $, $M_{MW}=1.15^{+0.25}_{-0.22}$ and
$M_{M31}=2.01^{+0.65}_{-0.39} \ \ \times 10^{12}M_{\odot}$ (corresponding to
the median and the 1st and 3rd quartiles). The ratio of the masses is
$M_{M31}/M_{MW}=1.75^{+0.54}_{-0.28}$, where by convention the M31 is defined
here to be the more massive of the two halos.


title: Chemical Abundance of z~6 Quasar Broad-Line Regions in the XQR-30 Sample

abstract:
  The elemental abundances in the broad-line regions of high-redshift quasars
trace the chemical evolution in the nuclear regions of massive galaxies in the
early universe. In this work, we study metallicity-sensitive broad
emission-line flux ratios in rest-frame UV spectra of 25 high-redshift (5.8 < z
< 7.5) quasars observed with the VLT/X-shooter and Gemini/GNIRS instruments,
ranging over $\log(M_{\rm{BH}}/M_{\odot})= 8.4-9.8$ in black hole mass and
$\log(L_{\rm{bol}}/\rm{erg\, s}^{-1})= 46.7-47.7$ in bolometric luminosity. We
fit individual spectra and composites generated by binning across quasar
properties: bolometric luminosity, black hole mass, and blueshift of the \civ\,
line, finding no redshift evolution in the emission-line ratios by comparing
our high-redshift quasars to lower-redshift (2.0 < z < 5.0) results presented
in the literature. Using Cloudy-based locally optimally-emitting cloud
photoionisation model relations between metallicity and emission-line flux
ratios, we find the observable properties of the broad emission lines to be
consistent with emission from gas clouds with metallicity that are at least 2-4
times solar. Our high-redshift measurements also confirm that the blueshift of
the CIV emission line is correlated with its equivalent width, which influences
line ratios normalised against CIV. When accounting for the CIV blueshift, we
find that the rest-frame UV emission-line flux ratios do not correlate
appreciably with the black hole mass or bolometric luminosity.


title:
Polycrystalline exchange-biased bilayers: magnetically effective vs.
  structural antiferromagnetic grain volume distribution

abstract:
  The magnetic characteristics of polycrystalline exchange-biased
antiferromagnet/ferromagnet-bilayers are determined by a complex interplay of
parameters, describing structural and magnetic properties of the material
system, including in particular the grain volume distribution of the
antiferromagnet. An ideal characterization of such systems would be a
non-destructive determination of the relevant parameters for each individual
grain. This is in most cases not feasible, since typical characterization
methods do average over larger areas. Here, we show that it is however possible
to determine averaged microscopic parameters from averaged macroscopic magnetic
quantities measured by vectorial Kerr magnetometry in comparison to an
elaborate model. In particular, we estimate the magnetically effective
antiferromagnetic grain size distribution, being essential for the interface
exchange coupling to the ferromagnetic layer. We found that the distribution of
magnetically active grain sizes differs from the structural one, indicating
that the antiferromagnetic order, relevant for the exchange bias, extends only
over a part of the grains' structural volumes.


title:
The structure of the regular part of the free boundary close to
  singularities in the obstacle problem

abstract:
  We prove the -- to the best knowledge of the authors -- first result on the
fine asymptotic behavior of the regular part of the free boundary of the
obstacle problem close to singularities. The result is motivated by our recent
partial answer to a long standing conjecture and the first partial
classification of global solutions of the obstacle problem with unbounded
coincidence sets in higher dimensions.


title:
Online Adaptive Identification of Switched Affine Systems Using a
  Two-Tier Filter Architecture with Memory

abstract:
  This work proposes an online adaptive identification method for multi-input
multi-output (MIMO) switched affine systems with guaranteed parameter
convergence. A family of online parameter estimators is used that is equipped
with a dual-layer low pass filter architecture to facilitate parameter learning
and identification of each subsystem. The filters capture information about the
unknown parameters in the form of a prediction error which is used in the
parameter estimation algorithm. A salient feature of the proposed method that
distinguishes it from most previous results is the use of a memory bank that
stores filter values and promotes parameter learning during both active and
inactive phases of a subsystem. Specifically, the learnt experience from the
previous active phase of a subsystem is retained in the memory and leveraged
for parameter learning in its subsequent active and inactive phases. Further, a
new notion of intermittent initial excitation (IIE) is introduced that extends
the previously established initial excitation (IE) condition to the switched
system framework. IIE is shown to be sufficient to ensure exponential
convergence of the switched system parameters.


title: Boosting Self-Supervised Embeddings for Speech Enhancement

abstract:
  Self-supervised learning (SSL) representation for speech has achieved
state-of-the-art (SOTA) performance on several downstream tasks. However, there
remains room for improvement in speech enhancement (SE) tasks. In this study,
we used a cross-domain feature to solve the problem that SSL embeddings may
lack fine-grained information to regenerate speech signals. By integrating the
SSL representation and spectrogram, the result can be significantly boosted. We
further study the relationship between the noise robustness of SSL
representation via clean-noisy distance (CN distance) and the layer importance
for SE. Consequently, we found that SSL representations with lower noise
robustness are more important. Furthermore, our experiments on the VCTK-DEMAND
dataset demonstrated that fine-tuning an SSL representation with an SE model
can outperform the SOTA SSL-based SE methods in PESQ, CSIG and COVL without
invoking complicated network architectures. In later experiments, the CN
distance in SSL embeddings was observed to increase after fine-tuning. These
results verify our expectations and may help design SE-related SSL training in
the future.


title: PSTR: End-to-End One-Step Person Search With Transformers

abstract:
  We propose a novel one-step transformer-based person search framework, PSTR,
that jointly performs person detection and re-identification (re-id) in a
single architecture. PSTR comprises a person search-specialized (PSS) module
that contains a detection encoder-decoder for person detection along with a
discriminative re-id decoder for person re-id. The discriminative re-id decoder
utilizes a multi-level supervision scheme with a shared decoder for
discriminative re-id feature learning and also comprises a part attention block
to encode relationship between different parts of a person. We further
introduce a simple multi-scale scheme to support re-id across person instances
at different scales. PSTR jointly achieves the diverse objectives of
object-level recognition (detection) and instance-level matching (re-id). To
the best of our knowledge, we are the first to propose an end-to-end one-step
transformer-based person search framework. Experiments are performed on two
popular benchmarks: CUHK-SYSU and PRW. Our extensive ablations reveal the
merits of the proposed contributions. Further, the proposed PSTR sets a new
state-of-the-art on both benchmarks. On the challenging PRW benchmark, PSTR
achieves a mean average precision (mAP) score of 56.5%. The source code is
available at \url{https://github.com/JialeCao001/PSTR}.


title:
Robust and Explainable Autoencoders for Unsupervised Time Series Outlier
  Detection---Extended Version

abstract:
  Time series data occurs widely, and outlier detection is a fundamental
problem in data mining, which has numerous applications. Existing
autoencoder-based approaches deliver state-of-the-art performance on
challenging real-world data but are vulnerable to outliers and exhibit low
explainability. To address these two limitations, we propose robust and
explainable unsupervised autoencoder frameworks that decompose an input time
series into a clean time series and an outlier time series using autoencoders.
Improved explainability is achieved because clean time series are better
explained with easy-to-understand patterns such as trends and periodicities. We
provide insight into this by means of a post-hoc explainability analysis and
empirical studies. In addition, since outliers are separated from clean time
series iteratively, our approach offers improved robustness to outliers, which
in turn improves accuracy. We evaluate our approach on five real-world datasets
and report improvements over the state-of-the-art approaches in terms of
robustness and explainability.
  This is an extended version of "Robust and Explainable Autoencoders for
Unsupervised Time Series Outlier Detection", to appear in IEEE ICDE 2022.


title:
Domain Adaptation for Time-Series Classification to Mitigate Covariate
  Shift

abstract:
  The performance of a machine learning model degrades when it is applied to
data from a similar but different domain than the data it has initially been
trained on. To mitigate this domain shift problem, domain adaptation (DA)
techniques search for an optimal transformation that converts the (current)
input data from a source domain to a target domain to learn a domain-invariant
representations that reduces domain discrepancy.
  This paper proposes a novel supervised domain adaptation based on two steps.
First, we search for an optimal class-dependent transformation from the source
to the target domain from a few samples. We consider optimal transport methods
such as the earth mover distance with Laplacian regularization, Sinkhorn
transport and correlation alignment. Second, we use embedding similarity
techniques to select the corresponding transformation at inference. We use
correlation metrics and maximum mean discrepancy with higher-order moment
matching techniques. We conduct an extensive evaluation on time-series datasets
with domain shift including simulated and various online handwriting datasets
to demonstrate the performance.


title:
Binary Spatial Random Field Reconstruction from Non-Gaussian
  Inhomogeneous Time-series Observations

abstract:
  We develop a new model for binary spatial random field reconstruction of a
physical phenomenon which is partially observed via inhomogeneous time-series
data. We consider a sensor network deployed over a vast geographical region
where sensors observe temporal processes and transmit compressed observations
to the Fusion Center (FC). Two types of sensors are considered; one collects
point observations at specific time points while the other collects integral
observations over time intervals. Subsequently, the FC uses the compressed
observations to infer the spatial phenomenon modeled as a binary spatial random
field. We show that the resulting posterior predictive distribution is
intractable and develop a tractable two-step procedure to perform inference.
First, we develop procedures to approximately perform Likelihood Ratio Tests on
the time-series data, for both point sensors and integral sensors, in order to
compress the temporal observations to a single bit. Second, after the
compressed observations are transmitted to the FC, we develop a Spatial Best
Linear Unbiased Estimator (S-BLUE) in order for the FC to reconstruct the
binary spatial random field at an arbitrary spatial location. Finally, we
present a comprehensive study of the performance of the proposed approaches
using both synthetic and real-world experiments. A weather dataset from the
National Environment Agency (NEA) of Singapore with fields including
temperature and relative humidity is used in the real-world experiments to
validate the proposed approaches.


title:
Infinitely many non-conservative solutions for the three-dimensional
  Euler equations with arbitrary initial data in $C^{1/3-\epsilon}$

abstract:
  Let $0<\beta<\bar\beta<1/3$. We construct infinitely many distributional
solutions in $C^{\beta}_{x,t}$ to the three-dimensional Euler equations that do
not conserve the energy, for a given initial data in $C^{\bar\beta}$. We also
show that there is some limited control on the increase in the energy for
$t>1$.


title:
A tutorial for using propensity score weighting for moderation analysis:
  an application to smoking disparities among LGB adults

abstract:
  Objective. To provide step-by-step guidance and STATA and R code for using
propensity score (PS) weighted analyses when estimating moderation effects.
Data Sources and Study Setting. Using data on 41,832 adults from the 2019
National Survey on Drug Use and Health (NSDUH), we examine if gender moderates
the association between sexual minority status and adult smoking prevalence and
illustrate the application of the key steps outlined in this tutorial Study
Design. Tutorial illustrating the key steps for estimating and testing
moderation. Steps include (1) examining covariate overlap across
treatment/exposure groups within levels of the moderator, (2) estimating the PS
weights, (3) assessing the quality of the PS weights, (4) estimating the
moderated effects, and (5) assessing sensitivity of findings to unobserved
confounding. Principal Findings. All balance criteria suggesting successful
balance has been achieved within each level of the moderator when estimating
the needed PS weights. In our outcome models, the estimated risk difference of
current smoking for LGB vs heterosexual males is 0.05 (95% CI = 0.00, 0.10. In
contrast, the risk difference for female LGB individuals is larger, equaling
0.15 (95% CI = 0.08, 0.18) and supporting clear evidence of moderation in the
case study. Notably, the risk difference for females was highly robust to
unobserved confounders while the risk difference for males was highly sensitive
and easy to wipe away. Conclusions. Estimating robust moderation effects in
observational studies requires thoughtful consideration of imbalances between
the key groups being compared. Propensity score weighting within each level of
the moderator can improve the estimated moderation effects by minimizing bias
from imbalance within the moderator subgroups.


title:
Inference over radiative transfer models using variational and
  expectation maximization methods

abstract:
  Earth observation from satellites offers the possibility to monitor our
planet with unprecedented accuracy. Radiative transfer models (RTMs) encode the
energy transfer through the atmosphere, and are used to model and understand
the Earth system, as well as to estimate the parameters that describe the
status of the Earth from satellite observations by inverse modeling. However,
performing inference over such simulators is a challenging problem. RTMs are
nonlinear, non-differentiable and computationally costly codes, which adds a
high level of difficulty in inference. In this paper, we introduce two
computational techniques to infer not only point estimates of biophysical
parameters but also their joint distribution. One of them is based on a
variational autoencoder approach and the second one is based on a Monte Carlo
Expectation Maximization (MCEM) scheme. We compare and discuss benefits and
drawbacks of each approach. We also provide numerical comparisons in synthetic
simulations and the real PROSAIL model, a popular RTM that combines land
vegetation leaf and canopy modeling. We analyze the performance of the two
approaches for modeling and inferring the distribution of three key biophysical
parameters for quantifying the terrestrial biosphere.


title:
Two-qubit gate using conditional driving for highly detuned
  Kerr-nonlinear parametric oscillators

abstract:
  A Kerr-nonlinear parametric oscillator (KPO) is one of the promising devices
to realize qubits for universal quantum computing. The KPO can stabilize two
coherent states with opposite phases, yielding a quantum superposition called a
Schr\"{o}dinger cat state. Universal quantum computing with KPOs requires three
kinds of quantum gates: $R_z, R_x$, and $R_{zz}$ gates. We theoretically
propose a two-qubit gate $R_{zz}$ for highly detuned KPOs. In the proposed
scheme, we add another two-photon drive for the first KPO. This leads to the
$R_{zz}$ gate based on the driving of the second KPO depending on the first-KPO
state, which we call "conditional driving." First, we perform simulations using
a conventional KPO Hamiltonian derived from a superconducting-circuit model
under some approximations and evaluate the gate fidelity. Next, we also perform
numerical simulations of the two-qubit gate using the superconducting-circuit
model without the approximations. The simulation results indicate that
two-qubit gates can be implemented with high fidelity ($>99.9\%$) for rotation
angles required for universality.


title: Barcodes distinguish morphology of neuronal tauopathy

abstract:
  The geometry of neurons is known to be important for their functions. Hence,
neurons are often classified by their morphology. Two recent methods,
persistent homology and the topological morphology descriptor, assign a
morphology descriptor called a barcode to a neuron equipped with a given
function, such as the Euclidean distance from the root of the neuron. These
barcodes can be converted into matrices called persistence images, which can
then be averaged across groups. We show that when the defining function is the
path length from the root, both the topological morphology descriptor and
persistent homology are equivalent. We further show that persistence images
arising from the path length procedure provide an interpretable summary of
neuronal morphology. We introduce {topological morphology functions}, a class
of functions similar to Sholl functions, that can be recovered from the
associated topological morphology descriptor. To demonstrate this topological
approach, we compare healthy cortical and hippocampal mouse neurons to those
affected by progressive tauopathy. We find a significant difference in the
morphology of healthy neurons and those with a tauopathy at a postsymptomatic
age. We use persistence images to conclude that the diseased group tends to
have neurons with shorter branches as well as fewer branches far from the soma.


title: A universal model for drag on a spherical bubble

abstract:
  A theoretical expression for the drag on a spherical bubble is derived for
the entire range from very viscous to inertial flow conditions. It is based on
a solution for only that part of the velocity profile that determines the drag.
It is assumed the surface of the bubble has a zero tangential stress condition.
Excellent agreement with a previously proposed empirical model by Mei et al. is
obtained. This shows that a theoretical framework with relatively simple
physics can still predict the terminal velocity of a spherical bubble
accurately. To the best of our knowledge, this is one of the few models in
fluid dynamics to predict drag on an object for a range of Reynolds numbers
that spans many orders of magnitude.


title:
Implementing a Real-Time, YOLOv5 based Social Distancing Measuring
  System for Covid-19

abstract:
  The purpose of this work is, to provide a YOLOv5 deep learning-based social
distance monitoring framework using an overhead view perspective. In addition,
we have developed a custom defined model YOLOv5 modified CSP (Cross Stage
Partial Network) and assessed the performance on COCO and Visdrone dataset with
and without transfer learning. Our findings show that the developed model
successfully identifies the individual who violates the social distances. The
accuracy of 81.7% for the modified bottleneck CSP without transfer learning is
observed on COCO dataset after training the model for 300 epochs whereas for
the same epochs, the default YOLOv5 model is attaining 80.1% accuracy with
transfer learning. This shows an improvement in accuracy by our modified
bottleneck CSP model. For the Visdrone dataset, we are able to achieve an
accuracy of upto 56.5% for certain classes and especially an accuracy of 40%
for people and pedestrians with transfer learning using the default YOLOv5s
model for 30 epochs. While the modified bottleneck CSP is able to perform
slightly better than the default model with an accuracy score of upto 58.1% for
certain classes and an accuracy of ~40.4% for people and pedestrians.


title: Memory effect on the bidirectional teleportation

abstract:
  In this contribution, we have investigated the bidirectional quantum
teleportation (BQT) of single-qubit states using a Bell state influenced by
decoherence channels with memory, dephasing and amplitude damping channels. The
expressions of the negativity, as a measure of the entanglement remaining in
the BQT quantum channel, the teleportation fidelities and the quantum Fisher
information are also evaluated. We find that both these last quantities depend
on the survival amount of entanglement in the BQT quantum channel, on the
decoherence factor and on the correlation degree of the decoherence channel. We
show that in the Markovian regime, the Negativity, the teleportation average
fidelities and the quantum Fisher information are slightly enhanced by
considering the classical channel correlations. Besides, in the non-Markovian
regime, these three quantities could be improved for a long period of time.


title:
Accurate near wall steady flow field prediction using Physics Informed
  Neural Network (PINN)

abstract:
  In this paper, Physics Informed Neural Network (PINN) is explored in order to
obtain flow predictions near the wall region accurately with measurements (or
sampling points) away from the wall. Often, in fluid mechanics experiments, it
is difficult to perform velocity measurements near the wall accurately.
Therefore, the present study reveals a new and elegant approach to recover the
flow solutions near the wall. Laminar boundary layer flow over a flat plate
case is considered for this study in order to explore the ability of PINN to
accurately predict the flow field. All the required sampling data for this
study is obtained from CFD simulations. A wide range of Reynolds number cases
from Re=500 to 100000 has been investigated. First, using PINN, the boundary
layer solution is obtained with three different types of boundary conditions.
Further, the influence of the location of the sampling points on the accuracy
is analysed. From the velocity profiles and the skin friction coefficient
distribution, it is clear that PINN results are reasonably accurate near the
wall with only a few sampling points away from the wall. This approach has
potential application in experiments to obtain the near wall solutions
accurately with measurements away from the wall.


title: Learning Online Multi-Sensor Depth Fusion

abstract:
  Many hand-held or mixed reality devices are used with a single sensor for 3D
reconstruction, although they often comprise multiple sensors. Multi-sensor
depth fusion is able to substantially improve the robustness and accuracy of 3D
reconstruction methods, but existing techniques are not robust enough to handle
sensors which operate with diverse value ranges as well as noise and outlier
statistics. To this end, we introduce SenFuNet, a depth fusion approach that
learns sensor-specific noise and outlier statistics and combines the data
streams of depth frames from different sensors in an online fashion. Our method
fuses multi-sensor depth streams regardless of time synchronization and
calibration and generalizes well with little training data. We conduct
experiments with various sensor combinations on the real-world CoRBS and
Scene3D datasets, as well as the Replica dataset. Experiments demonstrate that
our fusion strategy outperforms traditional and recent online depth fusion
approaches. In addition, the combination of multiple sensors yields more robust
outlier handling and precise surface reconstruction than the use of a single
sensor.


title:
Predictive Coding and Stochastic Resonance: Towards a Unified Theory of
  Auditory (Phantom) Perception

abstract:
  Cognitive computational neuroscience (CCN) suggests that to gain a
mechanistic understanding of brain function, hypothesis driven experiments
should be accompanied by biologically plausible computational models. This
novel research paradigm offers a way from alchemy to chemistry, in auditory
neuroscience. With a special focus on tinnitus - as the prime example of
auditory phantom perception - we review recent work at the intersection of
artificial intelligence, psychology, and neuroscience, foregrounding the idea
that experiments will yield mechanistic insight only when employed to test
formal or computational models. This view challenges the popular notion that
tinnitus research is primarily data limited, and that producing large,
multi-modal, and complex data-sets, analyzed with advanced data analysis
algorithms, will lead to fundamental insights into how tinnitus emerges. We
conclude that two fundamental processing principles - being ubiquitous in the
brain - best fit to a vast number of experimental results and therefore provide
the most explanatory power: predictive coding as a top-down, and stochastic
resonance as a complementary bottom-up mechanism. Furthermore, we argue that
even though contemporary artificial intelligence and machine learning
approaches largely lack biological plausibility, the models to be constructed
will have to draw on concepts from these fields; since they provide a formal
account of the requisite computations that underlie brain function.
Nevertheless, biological fidelity will have to be addressed, allowing for
testing possible treatment strategies in silico, before application in animal
or patient studies. This iteration of computational and empirical studies may
help to open the "black boxes" of both machine learning and the human brain.


title:
Event Transformer. A sparse-aware solution for efficient event data
  processing

abstract:
  Event cameras are sensors of great interest for many applications that run in
low-resource and challenging environments. They log sparse illumination changes
with high temporal resolution and high dynamic range, while they present
minimal power consumption. However, top-performing methods often ignore
specific event-data properties, leading to the development of generic but
computationally expensive algorithms. Efforts toward efficient solutions
usually do not achieve top-accuracy results for complex tasks. This work
proposes a novel framework, Event Transformer (EvT), that effectively takes
advantage of event-data properties to be highly efficient and accurate. We
introduce a new patch-based event representation and a compact transformer-like
architecture to process it. EvT is evaluated on different event-based
benchmarks for action and gesture recognition. Evaluation results show better
or comparable accuracy to the state-of-the-art while requiring significantly
less computation resources, which makes EvT able to work with minimal latency
both on GPU and CPU.


title:
Alternating Direction Based Sequential Boolean Quadratic Programming
  Method for Transmit Antenna Selection

abstract:
  The wireless mobile communication system is updated and iterated on the whole
almost every decade. It is now in the development period of the application
scenarios of the fifth generation mobile communication system (5G).
Unfortunately, 5G relies on plenty of small base stations with a large number
of antennas that consume a lot of energy. In this paper, a novel Boolean
variable quadratic programming algorithm is designed for the antenna selection
optimization problem to reduce power consumption. Experiments show that the
proposed algorithm achieves high complementarity satisfaction accuracy with
only a few steps.


title: Parameter-Efficient Abstractive Question Answering over Tables or Text

abstract:
  A long-term ambition of information seeking QA systems is to reason over
multi-modal contexts and generate natural answers to user queries. Today,
memory intensive pre-trained language models are adapted to downstream tasks
such as QA by fine-tuning the model on QA data in a specific modality like
unstructured text or structured tables. To avoid training such memory-hungry
models while utilizing a uniform architecture for each modality,
parameter-efficient adapters add and train small task-specific bottle-neck
layers between transformer layers. In this work, we study parameter-efficient
abstractive QA in encoder-decoder models over structured tabular data and
unstructured textual data using only 1.5% additional parameters for each
modality. We also ablate over adapter layers in both encoder and decoder
modules to study the efficiency-performance trade-off and demonstrate that
reducing additional trainable parameters down to 0.7%-1.0% leads to comparable
results. Our models out-perform current state-of-the-art models on tabular QA
datasets such as Tablesum and FeTaQA, and achieve comparable performance on a
textual QA dataset such as NarrativeQA using significantly less trainable
parameters than fine-tuning.


title: Ultracool dwarfs identified using spectra in LAMOST DR7

abstract:
  In this work, we identify 734 ultracool dwarfs with a spectral type of M6 or
later, including one L0. Of this sample, 625 were studied spectroscopically for
the first time. All of these ultracool dwarfs are within 360~pc, with a
\textit{Gaia} G magnitude brighter than ~19.2 mag. By studying the spectra and
checking their stellar parameters (Teff, logg, and [FeH] derived with the
LAMOST pipeline, we found their cool red nature and their metallicity to be
consistent with the nature of Galactic thin-disk objects. Furthermore, 77 of
them show lithium absorption lines at 6708A, further indicating their young
ages and substellar nature. Kinematics obtained through LAMOST radial
velocities, along with the proper motion and parallax data from Gaia EDR3, also
suggest that the majority of our targets are thin-disk objects. Kinematic ages
were estimated through the relationship between the velocity dispersion and the
average age for a certain population. Moreover, we identified 35 binaries, with
6 of them reported as binaries for the first time.


title:
ECCV Caption: Correcting False Negatives by Collecting
  Machine-and-Human-verified Image-Caption Associations for MS-COCO

abstract:
  Image-Text matching (ITM) is a common task for evaluating the quality of
Vision and Language (VL) models. However, existing ITM benchmarks have a
significant limitation. They have many missing correspondences, originating
from the data construction process itself. For example, a caption is only
matched with one image although the caption can be matched with other similar
images, and vice versa. To correct the massive false negatives, we construct
the Extended COCO Validation (ECCV) Caption dataset by supplying the missing
associations with machine and human annotators. We employ five state-of-the-art
ITM models with diverse properties for our annotation process. Our dataset
provides x3.6 positive image-to-caption associations and x8.5 caption-to-image
associations compared to the original MS-COCO. We also propose to use an
informative ranking-based metric, rather than the popular Recall@K(R@K). We
re-evaluate the existing 25 VL models on existing and proposed benchmarks. Our
findings are that the existing benchmarks, such as COCO 1K R@K, COCO 5K R@K,
CxC R@1 are highly correlated with each other, while the rankings change when
we shift to the ECCV mAP. Lastly, we delve into the effect of the bias
introduced by the choice of machine annotator. Source code and dataset are
available at https://github.com/naver-ai/eccv-caption


title:
Topological multiferroic order in twisted transition metal
  dichalcogenide bilayers

abstract:
  Layered van der Waals materials have risen as powerful platforms to
artificially engineer correlated states of matter. Here we show the emergence
of a multiferroic order in a twisted dichalcogenide bilayer superlattice at
quarter-filling. We show that the competition between Coulomb interactions
leads to the simultaneous emergence of ferrimagnetic and ferroelectric orders.
We derive the magnetoelectric coupling for this system, which leads to a direct
strong coupling between the charge and spin orders. We show that, due to
intrinsic spin-orbit coupling effects, the electronic structure shows a
non-zero Chern number, thus displaying a topological multiferroic order. We
show that this topological state gives rise to interface modes at the different
magnetic and ferroelectric domains of the multiferroic. We demonstrate that
these topological modes can be tuned with external electric fields as well as
triggered by supermoire effects generated by a substrate. Our results put
forward twisted van der Waals materials as a potential platform to explore
multiferroic symmetry breaking orders and, ultimately, controllable topological
excitations in magnetoelectric domains.


title: Robust Event-Driven Interactions in Cooperative Multi-Agent Learning

abstract:
  We present an approach to reduce the communication required between agents in
a Multi-Agent learning system by exploiting the inherent robustness of the
underlying Markov Decision Process. We compute so-called robustness surrogate
functions (off-line), that give agents a conservative indication of how far
their state measurements can deviate before they need to update other agents in
the system. This results in fully distributed decision functions, enabling
agents to decide when it is necessary to update others. We derive bounds on the
optimality of the resulting systems in terms of the discounted sum of rewards
obtained, and show these bounds are a function of the design parameters.
Additionally, we extend the results for the case where the robustness surrogate
functions are learned from data, and present experimental results demonstrating
a significant reduction in communication events between agents.


title: The seriation problem in the presence of a double Fiedler value

abstract:
  Seriation is a problem consisting of seeking the best enumeration order of a
set of units whose interrelationship is described by a bipartite graph, that
is, a graph whose nodes are partitioned in two sets and arcs only connect nodes
in different groups. An algorithm for spectral seriation based on the use of
the Fiedler vector of the Laplacian matrix associated to the problem was
developed by Atkins et al., under the assumption that the Fiedler value is
simple. In this paper, we analyze the case in which the Fiedler value of the
Laplacian is not simple, discuss its effect on the set of the admissible
solutions, and study possible approaches to actually perform the computation.
Examples and numerical experiments illustrate the effectiveness of the proposed
methods.


title: Characterization of the GRB Fundamental Plane using Fermi-GBM data

abstract:
  A recent work has found a tight global relation between the GRB fluence, peak
flux ( based on the optimum time scale determined from the Bayesian
Blocks-based analysis) and duration using data from the Fermi Gamma-Ray burst
monitor, which they have dubbed as "Fundamental Plane". We quantitatively
characterize the tightness of this Fundamental Plane relation using by
calculating the scatter in dex. We also check for a fundamental plane using the
peak flux over time scales of 64 ms, 256 ms and 1024 ms. For our analysis, we
incorporate the uncertainties in the above observables and carried out both a
PCA as well as regression-based analysis. We find that the scatter in the
fundamental plane is 0.16-0.17 dex. This is not as as tight as some of the
other well known scaling relations in Astrophysics.


title:
A Framework for Distributed Estimation with Reduced Communication via
  Event-Based Strategies

abstract:
  This paper considers the problem of distributed estimation in a sensor
network, where multiple sensors are deployed to infer the state of a linear
time-invariant (LTI) Gaussian system. By proposing a lossless decomposition of
Kalman filter, a framework of event-based distributed estimation is developed,
where each sensor node runs a local filter using solely its own measurement,
alongside with an event-based synchronization algorithm to fuse the neighboring
information. One novelty of the proposed framework is that it decouples the
local filter from synchronization process. By doing so, we prove that a general
class of triggering strategies can be applied in our framework, which yields
stable distributed estimators under the minimal requirements of network
connectivity and collective system observability. As compared with existing
works, the proposed algorithm enjoys lower data size for each transmission.
Moreover, the developed results can be generalized to achieve a distributed
implementation of any Luenberger observer. By solving a semi-definite
programming (SDP), we further present a low-rank estimator design to obtain the
optimal gain of Luenberger observer such that the distributed estimation is
realized under the constraint of message complexity. Numerical examples are
finally provided to demonstrate the proposed methods.


title: Valuations with infinite limit-depth

abstract:
  For a certain field $K$, we construct a valuation-algebraic valuation on the
polynomial ring $K[x]$, whose Maclane--Vaqui\'e chain consists of an infinite
(countable) number of limit augmentations


title: Impact of Software Engineering Research in Practice

abstract:
  Existing work on the practical impact of software engineering (SE) research
examines industrial relevance rather than adoption of study results, hence the
question of how results have been practically applied remains open. To answer
this and investigate the outcomes of impactful research, we performed a
quantitative and qualitative analysis of 4,335 SE patents citing 1,668 SE
papers published between 1975-2017. Moreover, we conducted a survey study on
413 authors of 501 top-cited and awarded publications, achieving 25% response
rate. Overall, researchers have equipped practitioners with various tools,
processes, and methods, and improved many existing products. SE practice seems
to value knowledge-seeking research and is impacted by diverse
cross-disciplinary SE areas. Practitioner-oriented publication venues appear
more impactful than researcher-, while industry-related tracks in conferences
could enhance their impact. Some research works did not reach a wide footprint
due to limited funding resources or unfavorable cost-benefit tradeoff of the
proposed solutions. The need for higher funding in SE research could be
corroborated through a dedicated empirical study. In general, the assessment of
impact is subject to its definition. Therefore, academia and industry could
jointly agree on a formal description to set a common ground for subsequent
research on the topic.


title: A matrix for counting paths in acyclic colored digraphs

abstract:
  In this paper, we introduce a matrix $A_{\Gamma_{k, R}}$ associate with a
$k$-colored acyclic digraph $\Gamma_{k, R}$ such that $\text{det}(A_{\Gamma_{k,
R}})$ enumerates the paths in the digraph $\Gamma_{k, R}.$


title: On recognition of $A_6\times A_6$ by the set of conjugacy class sizes

abstract:
  For a finite group $G$ denote by $N(G)$ the set of conjugacy class sizes of
$G$. Recently the following question has been asked: Is it true that for each
nonabelian finite simple group $S$ and each $n\in\mathbb{N}$, if the set of
class sizes of a finite group $G$ with trivial center is the same as the set of
class sizes of the direct power $S^n$, then $G\simeq S^n$? In this paper we
approach an answer to this question by proving that $A_6\times A_6$ is uniquely
determined by $N(A_6\times A_6)$ among finite groups with trivial center.


title:
Fault-Tolerant Directional Couplers for State Manipulation in Silicon
  Photonic-Integrated Circuits

abstract:
  Photonic integrated circuits play a central role in current and future
applications such as communications, sensing, ranging, and information
processing. Photonic quantum computing will also likely require an integrated
optics architecture for improved stability, scalability, and performance.
Fault-tolerant quantum computing mandates very accurate and robust quantum
gates. In this work, we demonstrate high-fidelity directional couplers for
single-qubit gates in photonic integrated waveguides, utilizing a novel scheme
of detuning-modulated composite segments. Specific designs for reduced
sensitivity to wavelength variations and real-world geometrical fabrication
errors in waveguides width and depth are presented. Enhanced wavelength
tolerance is demonstrated experimentally. The concept shows great promise for
scaling high fidelity gates as part of integrated quantum optics architectures.


title:
Active-Sterile neutrino masses and mixings in $A_4$ minimal extended
  seesaw mechanism

abstract:
  Assuming the existence of an eV or KeV scale sterile neutrino, we develop a
3+1 neutrino mass model using $A_4\times Z_4 \times Z_2$ symmetry group. Three
Higgs $H, H^{\prime}$ and $H^{\prime\prime}$ are considered to give a desired
neutrino mass matrix which generates non-zero $\theta_{13}$. The model can give
neutrino mixing parameters that are well within the experimental 3$\sigma$
bounds. We also calculate the $4\times 4$ active-sterile neutrino mixing
matrix, and it is found to be consistent with experimental bounds.


title: Detection of Distracted Driver using Convolution Neural Network

abstract:
  With over 50 million car sales annually and over 1.3 million deaths every
year due to motor accidents we have chosen this space. India accounts for 11
per cent of global death in road accidents. Drivers are held responsible for
78% of accidents. Road safety problems in developing countries is a major
concern and human behavior is ascribed as one of the main causes and
accelerators of road safety problems. Driver distraction has been identified as
the main reason for accidents. Distractions can be caused due to reasons such
as mobile usage, drinking, operating instruments, facial makeup, social
interaction. For the scope of this project, we will focus on building a highly
efficient ML model to classify different driver distractions at runtime using
computer vision. We would also analyze the overall speed and scalability of the
model in order to be able to set it up on an edge device. We use CNN, VGG-16,
RestNet50 and ensemble of CNN to predict the classes.


title: Human-AI ecosystem with abrupt changes as a function of the composition

abstract:
  The progressive advent of artificial intelligence machines may represent both
an opportunity or a threat. In order to have an idea of what is coming we
propose a model that simulate a Human-AI ecosystem. In particular we consider
systems where agents present biases, peer-to-peer interactions and also three
body interactions that are crucial and describe two humans interacting with an
artificial agent and two artificial intelligence agents interacting with a
human. We focus our analysis by exploring how the relative fraction of
artificial intelligence agents affect that ecosystem. We find evidence that for
suitable values of the interaction parameters, arbitrarily small changes in
such percentage may trigger dramatic changes for the system that can be either
in one of the two polarised states or in an undecided state.


title:
Deterministic Gaussian conversion protocols for non-Gaussian single-mode
  resources

abstract:
  In the context of quantum technologies over continuous variables, Gaussian
states and operations are typically regarded as freely available, as they are
relatively easily accessible experimentally. In contrast, the generation of
non-Gaussian states, as well as the implementation of non-Gaussian operations,
pose significant challenges. This divide has motivated the introduction of
resource theories of non-Gaussianity. As for any resource theory, it is of
practical relevance to identify free conversion protocols between resources,
namely Gaussian conversion protocols between non-Gaussian states. Via
systematic numerical investigations, we address the approximate conversion
between experimentally relevant single-mode non-Gaussian states via arbitrary
deterministic one-to-one mode Gaussian maps. First, we show that cat and
binomial states are approximately equivalent for finite energy, while this
equivalence was previously known only in the infinite-energy limit. Then we
consider the generation of cat states from photon-added and photon-subtracted
squeezed states, improving over known schemes by introducing additional
squeezing operations. The numerical tools that we develop also allow to devise
conversions of trisqueezed into cubic-phase states beyond previously reported
performances. Finally, we identify various other conversions which instead are
not viable.


title: Is the dynamical quantum Cheshire cat detectable?

abstract:
  We explore how one might detect the dynamical quantum Cheshire cat proposed
by Aharonov et al. We show that, in practice, we need to bias the initial state
by adding/subtracting a small probability amplitude (`field') of the orthogonal
state, which travels with the disembodied property, to make the effect
detectable (i.e. if our initial state is $|\uparrow_z\rangle$, we need to bias
this with some small amount $\delta$ of state $|\downarrow_z\rangle$). This
biasing, which can be done either directly or via weakly entangling the state
with a pointer, effectively provides a phase reference with which we can
measure the evolution of the state. The outcome can then be measured as a small
probability difference in detections in a mutually unbiased basis, proportional
to this biasing $\delta$. We show this is different from counterfactual
communication, which provably does not require any probe field to travel
between sender Bob and receiver Alice for communication. We further suggest an
optical polarisation experiment where these phenomena might be demonstrated in
a laboratory.


title:
Towards Fair Evaluation of Dialogue State Tracking by Flexible
  Incorporation of Turn-level Performances

abstract:
  Dialogue State Tracking (DST) is primarily evaluated using Joint Goal
Accuracy (JGA) defined as the fraction of turns where the ground-truth dialogue
state exactly matches the prediction. Generally in DST, the dialogue state or
belief state for a given turn contains all the intents shown by the user till
that turn. Due to this cumulative nature of the belief state, it is difficult
to get a correct prediction once a misprediction has occurred. Thus, although
being a useful metric, it can be harsh at times and underestimate the true
potential of a DST model. Moreover, an improvement in JGA can sometimes
decrease the performance of turn-level or non-cumulative belief state
prediction due to inconsistency in annotations. So, using JGA as the only
metric for model selection may not be ideal for all scenarios. In this work, we
discuss various evaluation metrics used for DST along with their shortcomings.
To address the existing issues, we propose a new evaluation metric named
Flexible Goal Accuracy (FGA). FGA is a generalized version of JGA. But unlike
JGA, it tries to give penalized rewards to mispredictions that are locally
correct i.e. the root cause of the error is an earlier turn. By doing so, FGA
considers the performance of both cumulative and turn-level prediction flexibly
and provides a better insight than the existing metrics. We also show that FGA
is a better discriminator of DST model performance.


title:
Offline Reinforcement Learning for Safer Blood Glucose Control in People
  with Type 1 Diabetes

abstract:
  Hybrid closed loop systems represent the future of care for people with type
1 diabetes (T1D). These devices usually utilise simple control algorithms to
select the optimal insulin dose for maintaining blood glucose levels within a
healthy range. Online reinforcement learning (RL) has been utilised as a method
for further enhancing glucose control in these devices. Previous approaches
have been shown to reduce patient risk and improve time spent in the target
range when compared to classical control algorithms, but are prone to
instability in the learning process, often resulting in the selection of unsafe
actions. This work presents an evaluation of offline RL as a means for
developing clinically effective dosing policies without the need for patient
interaction. This paper examines the utility of BCQ, CQL and TD3-BC in managing
the blood glucose of nine virtual patients within the UVA/Padova glucose
dynamics simulator. When trained on less than a tenth of the data required by
online RL approaches, this work shows that offline RL can significantly
increase time in the healthy blood glucose range when compared to the strongest
state-of-art baseline. This is achieved without any associated increase in low
blood glucose events. Offline RL is also shown to be able to correct for common
and challenging scenarios such as incorrect bolus dosing, irregular meal
timings and sub-optimal training data.


title: The $\mathcal{R}$-height of semigroups and their bi-ideals

abstract:
  The $\mathcal{R}$-height of a semigroup $S$ is the height of the poset of
$\mathcal{R}$-classes of $S,$ i.e. the supremum of the lengths of chains of
$\mathcal{R}$-classes. Given a semigroup $S$ with finite $\mathcal{R}$-height,
we establish bounds on the $\mathcal{R}$-height of bi-ideals, one-sided ideals
and two-sided ideals; in particular, these substructures inherit the property
of having finite $\mathcal{R}$-height. We then investigate whether these bounds
can be attained.


title:
Towards Bright Gamma-Ray Flash Generation From Tailored Target
  Irradiated by Multi-Petawatt Laser

abstract:
  One of the remarkable phenomena in the laser-matter interaction is the
extremely efficient energy transfer to $\gamma$-photons, that appears as a
collimated $\gamma$-ray beam. For interactions of realistic laser pulses with
matter, existence of a background field plays a crucial role, since it hits the
target prior to the main pulse arrival, leading to a cloud of preplasma and
drilling a narrow channel inside the target. These effects significantly alter
the process of $\gamma$-photon generation. Here, we study this process by
importing the outcome of magnetohydrodynamic simulations of the target
interaction into particle-in-cell simulations for describing the
$\gamma$-photon generation. It is seen that the background field effect plays
an important positive role, enhancing the efficiency of laser pulse coupling
with the target, and generating high energy electron-positron pairs. It is
expected that such a $\gamma$-photon source will be actively used in various
applications in nuclear photonics, material science and astrophysical processes
modeling.


title: Correcting Misproducted Speech using Spectrogram Inpainting

abstract:
  Learning a new language involves constantly comparing speech productions with
reference productions from the environment. Early in speech acquisition,
children make articulatory adjustments to match their caregivers' speech.
Grownup learners of a language tweak their speech to match the tutor reference.
This paper proposes a method to synthetically generate correct pronunciation
feedback given incorrect production. Furthermore, our aim is to generate the
corrected production while maintaining the speaker's original voice.
  The system prompts the user to pronounce a phrase. The speech is recorded,
and the samples associated with the inaccurate phoneme are masked with zeros.
This waveform serves as an input to a speech generator, implemented as a deep
learning inpainting system with a U-net architecture, and trained to output a
reconstructed speech. The training set is composed of unimpaired proper speech
examples, and the generator is trained to reconstruct the original proper
speech. We evaluated the performance of our system on phoneme replacement of
minimal pair words of English as well as on children with pronunciation
disorders. Results suggest that human listeners slightly prefer our generated
speech over a smoothed replacement of the inaccurate phoneme with a production
of a different speaker.


title:
Seismic Modeling and Migration with Random Boundaries on the NEC
  SX-Aurora TSUBASA

abstract:
  Seismic imaging is a computationally demanding and data-intensive activity in
the oil and gas industry. Reverse Time Migration (RTM) used in seismic
applications needs to store the forward-propagated wavefield (or source
wavefield) on disk. Aiming to mitigate the storage demand, we develop an RTM
that implements the source wavefield reconstruction by introducing a new wave
equation to the problem. We adjust the initial and boundary conditions to take
advantage of the properties of random boundary conditions (RBC). The RBC does
not suppress unwanted waves coming from the artificial boundary enabling the
full wavefield recovery. Besides, it explores low correlations with
non-coherent signals due to the random velocities in the boundary. We also
develop compiler-guided implementations on a vector processor for seismic
modeling and RTM, essential for Least-square Migration, Full Waveform
Inversion, and Uncertainty Quantification applications. We test the seismic
modeling and RTM on the 2-D Marmousi benchmark and 3-D HPC4E Seismic Test
Suite. The numerical experiments show that the RTM which implements the
wavefield reconstruction presents the best results in terms of execution time
and hard disk demand. Lastly, the vector processor implementation is the one
that requires fewer code modifications compared to the optimized baseline
versions of the seismic modeling and RTM and GPU implementations, particularly
for large 3D grids.


abstract:
  It is for the first time that Quantum Simulation for High Energy Physics
(HEP) is studied in the U.S. decadal particle-physics community planning, and
in fact until recently, this was not considered a mainstream topic in the
community. This fact speaks of a remarkable rate of growth of this subfield
over the past few years, stimulated by the impressive advancements in Quantum
Information Sciences (QIS) and associated technologies over the past decade,
and the significant investment in this area by the government and private
sectors in the U.S. and other countries. High-energy physicists have quickly
identified problems of importance to our understanding of nature at the most
fundamental level, from tiniest distances to cosmological extents, that are
intractable with classical computers but may benefit from quantum advantage.
They have initiated, and continue to carry out, a vigorous program in theory,
algorithm, and hardware co-design for simulations of relevance to the HEP
mission. This community whitepaper is an attempt to bring this exciting and yet
challenging area of research to the spotlight, and to elaborate on what the
promises, requirements, challenges, and potential solutions are over the next
decade and beyond.


title: HunYuan_tvr for Text-Video Retrieval

abstract:
  Text-Video Retrieval plays an important role in multi-modal understanding and
has attracted increasing attention in recent years. Most existing methods focus
on constructing contrastive pairs between whole videos and complete caption
sentences, while ignoring fine-grained cross-modal relationships, e.g., short
clips and phrases or single frame and word. In this paper, we propose a novel
method, named HunYuan\_tvr, to explore hierarchical cross-modal interactions by
simultaneously exploring video-sentence, clip-phrase, and frame-word
relationships. Considering intrinsic semantic relations between frames,
HunYuan\_tvr first performs self-attention to explore frame-wise correlations
and adaptively clusters correlated frames into clip-level representations.
Then, the clip-wise correlation is explored to aggregate clip representations
into a compact one to describe the video globally. In this way, we can
construct hierarchical video representations for frame-clip-video
granularities, and also explore word-wise correlations to form
word-phrase-sentence embeddings for the text modality. Finally, hierarchical
contrastive learning is designed to explore cross-modal
relationships,~\emph{i.e.,} frame-word, clip-phrase, and video-sentence, which
enables HunYuan\_tvr to achieve a comprehensive multi-modal understanding.
Further boosted by adaptive label denoising and marginal sample enhancement,
HunYuan\_tvr obtains new state-of-the-art results on various benchmarks, e.g.,
Rank@1 of 55.0%, 57.8%, 29.7%, 52.1%, and 57.3% on MSR-VTT, MSVD, LSMDC,
DiDemo, and ActivityNet respectively.


title: Overlay journals: a study of the current landscape

abstract:
  Overlay journals are characterised by their articles being archived on public
open access repositories, often already starting in their initial preprint form
as a prerequisite for submission to the journal prior to initiating the
peer-review process. In this study we aimed to identify currently active
overlay journals and examine their characteristics. We utilised an explorative
web search and contacted key service providers for additional information. The
final sample consisted of 35 active overlay journals. While the results show an
increase in the number of overlay journals in recent years, the current
presence of overlay journals is diminutive compared to the overall number of
open access journals. The majority of overlay journals publish articles in the
natural sciences, mathematics or computer sciences. Overlay journals are
commonly published by groups of scientists rather than formal organisations and
overlay journals may also rank highly within the traditional journal citation
metrics. Nearly none of the investigated journals charge fees from authors,
which is likely related to the cost-effectiveness of the overlay publishing
model. Both the growth in adoption of open access preprint repositories, and
researchers willingness to publish in overlay journals will determine the
models wider impact on scholarly publishing.


title: 110 MW Thin-Disk Oscillator

abstract:
  A compact Kerr-lens mode-locked thin-disk oscillator delivering 110 MW output
peak power, the highest among all oscillators, is reported. A pulse train with
a repetition rate of 14 MHz carries 115 fs long, 14.4 uJ pulses resulting in
202 W of average power. This compact, simple, and stable oscillator is a
suitable driver and an important milestone for further high harmonics
generation and the development of extreme ultraviolet transportable frequency
comb sources.


title:
Renormalisation in $\text{T}\overline{\text{T}}$-Deformed
  (Non-)Integrable Theories

abstract:
  That the exact quantum S-matrix of $\text{T}\overline{\text{T}}$-deformed
field theories is known has interesting consequences for their perturbative
renormalisation. Recent investigations into the interplay between
renormalisation and integrable deformations have focused on those cases where
the (undeformed) seed theories are integrable. In this paper, we study the
perturbative renormalisation of non-integrable
$\text{T}\overline{\text{T}}$-deformed field theories, focusing on a theory of
scalars with a quartic interaction. We show that in this theory, the 4-point
1-loop S-matrix exhibits features similar to its integrable cousins. We also
carry out a similar analysis for the case of an integrable quantum field theory
comprising a pair of chiral bosons. Our discussion of this latter theory
supplies the first explicit example of a perturbative field theory computation
in the formalism due to Sen.


title:
Generalizations of the flag variety tied to the Macdonald-theoretic
  delta operators

abstract:
  This is a chapter for the upcoming book "Handbook of Combinatorial Algebraic
Geometry". Comments are welcome! We cover symmetric function identities tied to
delta operators, as well as representation-theoretic and geometric avatars of
these identities.


title:
Blockchain Application Development Using Model-Driven Engineering and
  Low-Code Platforms: A Survey

abstract:
  The creation of blockchain-based software applications requires today
considerable technical knowledge, particularly in software design and
programming. This is regarded as a major barrier in adopting this technology in
business and making it accessible to a wider audience. As a solution, no-code
and low-code approaches have been proposed that require only little or no
programming knowledge for creating full-fledged software applications. In this
paper we review academic approaches from the discipline of model-driven
engineering as well as industrial no-code and low-code development platforms
for blockchains. We further present a case study for an integrated no-code
blockchain environment for demonstrating the state-of-the-art in this area.
Based on the gained insights we derive requirements for the future development
of no-code and low-code approaches that are dedicated to the field of
blockchains.


title:
Strichartz estimates and Blowup stability for energy critical nonlinear
  wave equations

abstract:
  We prove Strichartz estimates in similarity coordinates for the radial wave
equation with a self similar potential in dimensions $d\geq 3$. As an
application of these, we establish the asymptotic stability of the ODE blowup
profile of the energy critical radial nonlinear wave equation for $3\leq d\leq
6$.


title: Koszul duality for categories with a fixed object set

abstract:
  We define a notion of Koszul dual of a monoid object in a monoidal biclosed
model category. Our construction generalizes the classic Yoneda algebra
$Ext_A(k,k)$. We apply this general construction to define the Koszul dual of a
category enriched over spectra or chain complexes. This example relies on the
classical observation that enriched categories are monoid objects in a category
of enriched graphs. We observe that the category of enriched graphs is
biclosed, meaning that it comes with both left and right internal hom objects.
Given a category $R$ (which plays the role of the ground field $k$ in classical
algebra), and an augmented $R$-algebra $C$, we define the Koszul dual of $C$,
denoted $K(C)$, as the $R$-algebra of derived endomorphisms of $R$ in the
category of right $C$-modules.
  We establish the expected adjunctions between the categories of modules over
$C$ and modules over $K(C)$. We investigate the question of when the map from
$C$ to its double dual $K(K(C))$ is an equivalence. We also show that Koszul
duality of operads can be understood as a special case of Koszul duality of
categories. In this way we incorporate Koszul duality of operads in a wider
context, and possibly clarify some aspects of it.


title: Electricity generation from renewable energy based on abandoned wind fan

abstract:
  In the 21st century, our world is facing difficult conditions for serious
environmental pollution and the problem of energy shortage. An innovative idea
has emerged to recycle wind energy from air conditioning condenser fans in
outdoor buildings. Therefore, the main goal of this research is to develop
renewable wind energy from the condenser fan of an air conditioner using
Arduino as a microcontroller. This research moves towards a portable, low cost,
environmentally friendly mini device that harnesses renewable energies with
endless resources for future alternative power generation and reduces the
burden of consumers' electricity bills


title: Subordinate quadratic forms and isometric maps over semirings

abstract:
  The paper expands the theory of quadratic forms on modules over a semiring R,
introduced in [12]-[14], especially in the setup of tropical and supertropical
algebra. Isometric linear maps induce subordination on quadratic forms, and
provide a main tool in our current study. These maps allow lifts and pushdowns
of quadratic forms on different modules, preserving basic characteristic
properties.


title:
Tailoring 2D Fast Iterative Filtering algorithm for low-contrast optical
  fringe pattern preprocessing

abstract:
  Retrieving object phase from the optical fringe pattern is a critical task in
quantitative phase imaging and often requires appropriate image preprocessing
(background and noise minimization), especially when retrieving phase from the
single-shot fringe pattern image. In this article, for the first time, we
propose to adapt the 2D Fast Iterative Filtering (FIF) method for fringe
pattern decomposition and develop a novel version of FIF called the 2D fringe
pattern Fast Iterative Filtering (fpFIF2), that is tailored for fringe pattern
preprocessing. We show the positive influence of fpFIF2 onto fringe pattern
filtering comparing to the previous 2D FIF implementation regarding processing
speed, quality, and usage comfortability. We also compare the fpFIF2 with other
state-of-the-art fringe pattern filtering methods in terms of aiding the
Hilbert spiral transform method in phase retrieval. Employing numerical
simulations and experimental fringe analysis, we prove that fpFIF2 outperforms
reference methods, especially in terms of low-fringe-contrast phase
reconstruction quality and decomposition time.


title:
TOSE: A Fast Capacity Determination Algorithm Based on Random Matrix
  Theory

abstract:
  Wireless network capacity is one of the most important performance metrics
for wireless communication networks. Future wireless networks will be composed
of extremely large number of base stations (BSs) and users, and organized in
the form of multiple clusters. Unfortunately, the determination of average
cluster capacity for such future wireless networks is difficult, and lacks of
both analytical expressions and fast algorithms. In this paper, we propose a
fast algorithm TOSE to estimate the average cluster capacity based on the
random matrix theory (RMT). It can avoid the exact eigenvalue derivations of
large dimensional matrices, which are complicated and inevitable in
conventional capacity determination methods. Instead, fast eigenvalue
estimations can be realized based on RMT in our TOSE algorithm. In addition, we
derive the analytical upper and lower bounds of the average cluster capacity.
Our numerical experiments show that TOSE is faster than the conventional
Cholesky decomposition method, by at least three orders of magnitude. Besides,
TOSE has superior generality, since it is independent of the distributions of
BSs and users, and the shape of network areas.


title: Towards Comparing Performance of Algorithms in Hardware and Software

abstract:
  In this paper, we report on a preliminary investigation of the potential
performance gain of programs implemented in field-programmable gate array's
(FPGAs) using a high-level language Chisel compared to ordinary high-level
software implementations executed on general-purpose computers and embedded
systems. FPGAs inherently supports parallel evaluations while the sequential
computers do not, and for this preliminary investigation we have chosen a
highly parallelizable program as case study to show an upper bound of
performance gain. The purpose is to demonstrate whether or not programming
FPGAs has a potential for performance optimizations of ordinary programs.
  We have developed and evaluated Conway's Game of Life for an FPGA, an
embedded system Raspberry Pi 4, and a MacBook Pro Laptop. We have compared the
performance of programs over different input sizes to decide the relative
increase in runtime.


title: A Two Species Thomas-Fermi Model for Stellar Ground States

abstract:
  We present an energy functional for a Thomas-Fermi type two-fluid model of a
self-gravitating non-rotating charged body, with a non-relativistic kinetic
energy. We prove that, under certain conditions on the total number of
positively charged and negatively charged particles, a minimizer exists and
both fluids have compact support. We prove the same result for special
relativistic kinetic energy, assuming further conditions on the total number of
particles. In the non-relativistic kinetic energy case, we further prove the
uniqueness of the minimizer, as well as present results relating the general
shape of the minimizer to the total number of particles.


title: Droplet collapse during strongly supercooled transitions

abstract:
  We simulate the decay of isolated, spherically symmetric droplets in a
cosmological phase transition. It has long been posited that such heated
droplets of the metastable state could form, and they have recently been
observed in 3D multi-bubble simulations. In those simulations, the droplets
were associated with a reduction in the wall velocity and a decrease in the
kinetic energy of the fluid, with a consequent suppression in the gravitational
wave power spectrum. In the present work, we track the wall speed and kinetic
energy production in isolated droplets and compare them to those found in
multi-bubble collisions. The late-time wall velocities that we observe match
those of the 3D simulations, though we find that the spherical simulations are
a poor predictor of the kinetic energy production. This implies that
spherically symmetric simulations could be used to refine baryogenesis
predictions due to the formation of droplets, but not to estimate any
accompanying suppression of the gravitational wave signal.


title:
Defending Active Directory by Combining Neural Network based Dynamic
  Program and Evolutionary Diversity Optimisation

abstract:
  Active Directory (AD) is the default security management system for Windows
domain networks. We study a Stackelberg game model between one attacker and one
defender on an AD attack graph. The attacker initially has access to a set of
entry nodes. The attacker can expand this set by strategically exploring edges.
Every edge has a detection rate and a failure rate. The attacker aims to
maximize their chance of successfully reaching the destination before getting
detected. The defender's task is to block a constant number of edges to
decrease the attacker's chance of success. We show that the problem is #P-hard
and, therefore, intractable to solve exactly. We convert the attacker's problem
to an exponential sized Dynamic Program that is approximated by a Neural
Network (NN). Once trained, the NN provides an efficient fitness function for
the defender's Evolutionary Diversity Optimisation (EDO). The diversity
emphasis on the defender's solution provides a diverse set of training samples,
which improves the training accuracy of our NN for modelling the attacker. We
go back and forth between NN training and EDO. Experimental results show that
for R500 graph, our proposed EDO based defense is less than 1% away from the
optimal defense.


title: Linguistic-Acoustic Similarity Based Accent Shift for Accent Recognition

abstract:
  General accent recognition (AR) models tend to directly extract low-level
information from spectrums, which always significantly overfit on speakers or
channels. Considering accent can be regarded as a series of shifts relative to
native pronunciation, distinguishing accents will be an easier task with accent
shift as input. But due to the lack of native utterance as an anchor,
estimating the accent shift is difficult. In this paper, we propose
linguistic-acoustic similarity based accent shift (LASAS) for AR tasks. For an
accent speech utterance, after mapping the corresponding text vector to
multiple accent-associated spaces as anchors, its accent shift could be
estimated by the similarities between the acoustic embedding and those anchors.
Then, we concatenate the accent shift with a dimension-reduced text vector to
obtain a linguistic-acoustic bimodal representation. Compared with pure
acoustic embedding, the bimodal representation is richer and more clear by
taking full advantage of both linguistic and acoustic information, which can
effectively improve AR performance. Experiments on Accented English Speech
Recognition Challenge (AESRC) dataset show that our method achieves 77.42%
accuracy on Test set, obtaining a 6.94% relative improvement over a competitive
system in the challenge.


title: The saturation problem for refined Littlewood-Richardson coefficients

abstract:
  Given partitions $\lambda,\mu,\nu$ with at most $n$ nonzero parts and a
permutation $w \in S_n$, the refined Littlewood-Richardson coefficient
$c_{\lambda \mu}^\nu(w)$ is the multiplicity of the irreducible $GL_n
\mathbb(C)$ module $V(\nu)$ in the so-called Kostant-Kumar submodule
$K(\lambda, w, \mu)$ of the tensor product $V(\lambda) \otimes V(\mu)$. We
derive a hive model for these coefficients and prove that the saturation
property holds if $w$ is $312$-avoiding, $231$-avoiding or a commuting product
of such elements. This generalizes the classical Knutson-Tao saturation
theorem.


title:
Surrogate-Assisted Evolutionary Generative Design Of Breakwaters Using
  Deep Convolutional Networks

abstract:
  In the paper, a multi-objective evolutionary surrogate-assisted approach for
the fast and effective generative design of coastal breakwaters is proposed. To
approximate the computationally expensive objective functions, the deep
convolutional neural network is used as a surrogate model. This model allows
optimizing a configuration of breakwaters with a different number of structures
and segments. In addition to the surrogate, an assistant model was developed to
estimate the confidence of predictions. The proposed approach was tested on the
synthetic water area, the SWAN model was used to calculate the wave heights.
The experimental results confirm that the proposed approach allows obtaining
more effective (less expensive with better protective properties) solutions
than non-surrogate approaches for the same time.


title: Energy Consumption and Performance of Heapsort in Hardware and Software

abstract:
  In this poster abstract we will report on a case study on implementing the
Heapsort algorithm in hardware and software and comparing their time and energy
consumption. Our experiment shows that the Hardware implementation is more
energy efficient, but slower than the Software implementation due to a low
clock frequency. It also indicate that the optimal degree of parallelization
differs when optimizing for time compared to optimizing for time.


title:
Curvature-driven growth and interfacial noise in the voter model with
  self-induced zealots

abstract:
  We introduce a variant of the voter model in which agents may have different
degrees of confidence on their opinions. Those with low confidence are normal
voters whose state can change upon a single contact with a different
neighboring opinion. However, confidence increases with opinion reinforcement
and, above a certain threshold, these agents become zealots that do not change
opinion. We show that both strategies, normal voters and zealots, may coexist,
leading to a competition between two different kinetic mechanisms:
curvature-driven growth and interfacial noise. The kinetically constrained
zealots are formed well inside the clusters, away from the different opinions
at the surfaces that help keep the confidence not so high. Normal voters
concentrate in a region around the interfaces and their number, that is related
with the distance between the surface and the zealotry bulk, depends on the
rate the confidence changes. Despite this interface being rough and fragmented,
typical of the voter model, the presence of zealots in the bulk of these
domains, induces a curvature-driven dynamics, similar to the low temperature
coarsening behavior of the non-conserved Ising model after a temperature
quench.