title: HLDC: Hindi Legal Documents Corpus

abstract:
  Many populous countries including India are burdened with a considerable
backlog of legal cases. Development of automated systems that could process
legal documents and augment legal practitioners can mitigate this. However,
there is a dearth of high-quality corpora that is needed to develop such
data-driven systems. The problem gets even more pronounced in the case of low
resource languages such as Hindi. In this resource paper, we introduce the
Hindi Legal Documents Corpus (HLDC), a corpus of more than 900K legal documents
in Hindi. Documents are cleaned and structured to enable the development of
downstream applications. Further, as a use-case for the corpus, we introduce
the task of bail prediction. We experiment with a battery of models and propose
a Multi-Task Learning (MTL) based model for the same. MTL models use
summarization as an auxiliary task along with bail prediction as the main task.
Experiments with different models are indicative of the need for further
research in this area. We release the corpus and model implementation code with
this paper: https://github.com/Exploration-Lab/HLDC


title:
Irreversible Efficiency and Generalized Carnot Theorem for Heat Engines
  Operating in Linear Response Regime

abstract:
  The Carnot theorem, one expression of the second law of thermodynamics,
places a fundamental upper bound on the efficiency of heat engines operating
between two heat baths. The Carnot theorem can be stated in a more generalized
form for heat engines operating with multiple heat baths, where the maximum
efficiency is achieved for reversible heat engines operating quasistatically
between two heat baths. In this study, we determine the irreversible efficiency
of general heat engines operating in a linear response regime by quantifying
the impact of the dissipation by irreversible operations. The generalized
Carnot theorem is derived as a natural consequence of it. Because the result
obtained is based on the linear response relation and fluctuation-dissipation
theorem in the universal framework of linear response theory, it has wide
applicability to heat engines operating in the linear response regime.


title: Quantum tunnelling driven H$_2$ formation on graphene

abstract:
  It is commonly believed that it is unfavourable for adsorbed H atoms on
carbonaceous surfaces to form H$_2$ without the help of incident H atoms. Using
ring-polymer instanton theory to describe multidimensional tunnelling effects,
combined with ab initio electronic structure calculations, we find that these
quantum-mechanical simulations reveal a qualitatively different picture.
Recombination of adsorbed H atoms, which was believed to be irrelevant at low
temperature due to high barriers, is enabled by deep tunnelling, with reaction
rates enhanced by tens of orders of magnitude. Furthermore, we identify a new
path for H recombination that proceeds via multidimensional tunnelling, but
would have been predicted to be unfeasible by a simple one-dimensional
description of the reaction. The results suggest that hydrogen molecule
formation at low temperatures are rather fast processes that should not be
ignored in experimental settings and natural environments with graphene,
graphite and other planar carbon segments.


title: Benjamin-Feir instability of Stokes waves in finite depth

abstract:
  Whitham and Benjamin predicted in 1967 that small-amplitude periodic
traveling Stokes waves of the 2d-gravity water waves equations are linearly
unstable with respect to long-wave perturbations, if the depth $ \mathtt h $ is
larger than a critical threshold $\mathtt{h}_{{WB}} \approx 1.363 $. In this
paper we completely describe, for any value of $ \mathtt h >0 $, the four
eigenvalues close to zero of the linearized equations at the Stokes wave, as
the Floquet exponent is turned on. We prove in particular the existence of a
unique $ \mathtt{h}_{WB} $, which coincides with the one in [9,38,2], such
that, for any $ 0 < \mathtt h < {\mathtt{h}_{{WB}}} $ the eigenvalues close to
zero remain purely imaginary and, for any $ \mathtt h > {\mathtt{h}_{{WB}}}$, a
pair of non-purely imaginary eigenvalues depicts a closed figure "8",
parameterized by the Floquet exponent. As $ {\mathtt h} \to \mathtt{h}_{WB}^+ $
this figure "8" collapses to the origin of the complex plane. Our proof is
based on a combination of Kato's similarity transformation theory and a "KAM"
inspired block-diagonalization procedure. We exploit in crucial way the
Hamiltonian and reversible structure of the water waves equations.


title: Inventory of high-quality flat-band van der Waals materials

abstract:
  More is left to do in the field of flat bands besides proposing theoretical
models. One unexplored area is the flat bands featured in the van der Waals
(vdW) materials. Exploring more flat-band material candidates and moving the
promising materials toward applications have been well recognized as the
cornerstones for the next-generation high-efficiency devices. Here, we utilize
a powerful high-throughput tool to screen desired vdW materials based on the
Inorganic Crystal Structure Database. Through layers of filtration, we obtained
861 potential monolayers from 4997 vdW materials. Significantly, it is the
first example to introduce flat-band electronic properties in the vdW materials
and propose three families of representative flat-band materials by mapping
two-dimensional (2D) flat-band lattice models. Unlike existing screening
schemes, a simple, universal rule, i.e., 2D flat-band score criterion, is first
proposed to efficiently identify 229 high-quality flat-band candidates, and
guidance is provided to diagnose the quality of 2D flat bands. All these
efforts to screen experimental available flat-band candidates will certainly
motivate continuing exploration towards the realization of this class of
special materials and their applications in material science.


title:
Constrained Sequence-to-Tree Generation for Hierarchical Text
  Classification

abstract:
  Hierarchical Text Classification (HTC) is a challenging task where a document
can be assigned to multiple hierarchically structured categories within a
taxonomy. The majority of prior studies consider HTC as a flat multi-label
classification problem, which inevitably leads to "label inconsistency"
problem. In this paper, we formulate HTC as a sequence generation task and
introduce a sequence-to-tree framework (Seq2Tree) for modeling the hierarchical
label structure. Moreover, we design a constrained decoding strategy with
dynamic vocabulary to secure the label consistency of the results. Compared
with previous works, the proposed approach achieves significant and consistent
improvements on three benchmark datasets.


title: Non-neglectable entropy effect on sintering of supported nanoparticles

abstract:
  Sintering refers to particle coalescence by heat, which has been known as a
thermal phenomenon involving all aspects of natural science for centuries. It
is particularly important in heterogeneous catalysis because normally sintering
results in deactivation of the catalysts. In previous studies, the enthalpy
contribution was considered to be dominant in sintering and the entropy effect
is generally considered neglectable. However, we unambiguously demonstrate in
this work that entropy could prevail over the enthalpy contribution to dominate
the sintering behavior of supported nanoparticles (NPs) by designed experiments
and improved theoretical framework. Using in situ Cs-corrected environmental
scanning transmission electron microscopy and synchrotron-based ambient
pressure X-ray photoelectron spectroscopy, we observe the unprecedent
entropy-driven phenomenon that supported NPs reversibly redisperse upon heating
and sinter upon cooling in three systems (Pd-CeO2, Cu-TiO2, Ag-TiO2). We
quantitatively show that the configurational entropy of highly dispersed
ad-atoms is large enough to reverse their sintering tendency at the elevated
temperature. This work reshapes the basic understanding of sintering at the
nanoscale and opens the door for various de-novo designs of thermodynamically
stable nanocatalysts.


title: Nonlinear transport equations and quasiconformal maps

abstract:
  We prove existence of solutions to a nonlinear transport equation in the
plane, for which the velocity field is obtained as the convolution of the
classical Cauchy Kernel with the unknown. Even though the initial datum is
bounded and compactly supported, the velocity field may have unbounded
divergence. The proof is based on the compactness property of quasiconformal
mappings.


title:
Amplitude and phase fluctuations of gravitational waves magnified by
  strong gravitational lensing

abstract:
  We discuss how small-scale density perturbations on the Fresnel scale affect
amplitudes and phases of gravitational waves that are magnified by
gravitational lensing in geometric optics. We derive equations that connect the
small-scale density perturbations with the amplitude and phase fluctuations to
show that such perturbative wave optics effects are significantly boosted in
the presence of macro model magnifications such that amplitude and phase
fluctuations can easily be observed for highly magnified gravitational waves.
We discuss expected signals due to microlensing by stars, dark matter
substructure, fuzzy dark matter, and primordial black holes.


title: Unbiased Top-k Learning to Rank with Causal Likelihood Decomposition

abstract:
  Unbiased learning to rank has been proposed to alleviate the biases in the
search ranking, making it possible to train ranking models with user
interaction data. In real applications, search engines are designed to display
only the most relevant k documents from the retrieved candidate set. The rest
candidates are discarded. As a consequence, position bias and sample selection
bias usually occur simultaneously. Existing unbiased learning to rank
approaches either focus on one type of bias (e.g., position bias) or mitigate
the position bias and sample selection bias with separate components,
overlooking their associations. In this study, we first analyze the mechanisms
and associations of position bias and sample selection bias from the viewpoint
of a causal graph. Based on the analysis, we propose Causal Likelihood
Decomposition (CLD), a unified approach to simultaneously mitigating these two
biases in top-k learning to rank. By decomposing the log-likelihood of the
biased data as an unbiased term that only related to relevance, plus other
terms related to biases, CLD successfully detaches the relevance from position
bias and sample selection bias. An unbiased ranking model can be obtained from
the unbiased term, via maximizing the whole likelihood. An extension to the
pairwise neural ranking is also developed. Advantages of CLD include
theoretical soundness and a unified framework for pointwise and pairwise
unbiased top-k learning to rank. Extensive experimental results verified that
CLD, including its pairwise neural extension, outperformed the baselines by
mitigating both the position bias and the sample selection bias. Empirical
studies also showed that CLD is robust to the variation of bias severity and
the click noise.


title:
How do topological entropy and factor complexity behave under monoid
  morphisms and free group basis changes ?

abstract:
  For any non-erasing free monoid morphism $\sigma: \cal A^* \to \cal B^*$, and
for any subshift $X \subset \cal A^\Z$ and its image subshift $Y = \sigma(X)
\subset \cal B^\Z$, the associated complexity functions $p_X$ and $p_Y$ are
shown to satisfy: there exist constants $c, d, C > 0$ such that $$c \cdot p_X(d
\cdot n) \,\, \leq \,\, p_Y(n) \,\, \leq \,\, C \cdot p_X(n)$$ holds for all
sufficiently large integers $n \in \N$, provided that $\sigma$ is recognizable
in $X$. If $\sigma$ is in addition letter-to-letter, then $p_Y$ belongs to
$\Theta(p_X)$ (and conversely). Otherwise, however, there are examples where
$p_X$ is not in $\cal O(p_Y)$.
  It follows that in general the value $h_X$ of the topological entropy of $X$
is not preserved when applying a morphism $\sigma$ to $X$, even if $\sigma$ is
recognizable in $X$.
  As a consequence, there is no meaningful way to define the topological
entropy of a current on a free group $F_N$; only the distinction of currents
$\mu$ with topological entropy $h_{\tiny\supp(\mu)} = 0$ and
$h_{\tiny\supp(\mu)} > 0$ is well defined.


title:
Splashing, Recoiling and Deposition: Simulating Droplet Impact Dynamics
  in Ultracold Bose Gases

abstract:
  Droplet impact on a surface is closely tied to a diverse field of nature and
practical applications, while a complete control of its outcomes remains
challenging due to various unmanageable factors related to droplet, surface and
surrounding environment. In this work, we propose the quantum simulation of
droplet impact dynamics in the platform of ultracold atoms. Specifically, we
study the quantum-fluctuation-driven dynamics(QFDD) of two-dimensional
Bose-Bose mixtures from an initial Townes soliton towards the formation of
quantum droplet. By tuning the fluctuation energy of initial Townes state
through its size and number, the subsequent QFDD can exhibit distinct outcomes
including splashing, recoiling and deposition, thereby well simulating the
droplet impact dynamics. We have utilized the Weber number to identify the
thresholds of splashing and recoiling, and further established a universal
scaling law between the maximum spreading factor and the Weber number in
recoiling regime. In addition, we show the residual QFDD in deposition regime
can be used to probe the collective breathing modes of quantum droplet. Our
results, which can be directly tested in cold atoms experiments, pave the way
for exploring the intriguing droplet dynamics in a clean and fully controlled
quantum setting.


title:
RFVTM: A Recovery and Filtering Vertex Trichotomy Matching for Remote
  Sensing Image Registration

abstract:
  Reliable feature point matching is a vital yet challenging process in
feature-based image registration. In this paper,a robust feature point matching
algorithm called Recovery and Filtering Vertex Trichotomy Matching (RFVTM) is
proposed to remove outliers and retain sufficient inliers for remote sensing
images. A novel affine invariant descriptor called vertex trichotomy descriptor
is proposed on the basis of that geometrical relations between any of vertices
and lines are preserved after affine transformations, which is constructed by
mapping each vertex into trichotomy sets. The outlier removals in Vertex
Trichotomy Matching (VTM) are implemented by iteratively comparing the
disparity of corresponding vertex trichotomy descriptors. Some inliers
mistakenly validated by a large amount of outliers are removed in VTM
iterations, and several residual outliers close to correct locations cannot be
excluded with the same graph structures. Therefore, a recovery and filtering
strategy is designed to recover some inliers based on identical vertex
trichotomy descriptors and restricted transformation errors. Assisted with the
additional recovered inliers, residual outliers can also be filtered out during
the process of reaching identical graph for the expanded vertex sets.
Experimental results demonstrate the superior performance on precision and
stability of this algorithm under various conditions, such as remote sensing
images with large transformations, duplicated patterns, or inconsistent
spectral content.


title:
Leveraging Phone Mask Training for Phonetic-Reduction-Robust E2E Uyghur
  Speech Recognition

abstract:
  In Uyghur speech, consonant and vowel reduction are often encountered,
especially in spontaneous speech with high speech rate, which will cause a
degradation of speech recognition performance. To solve this problem, we
propose an effective phone mask training method for Conformer-based Uyghur
end-to-end (E2E) speech recognition. The idea is to randomly mask off a certain
percentage features of phones during model training, which simulates the above
verbal phenomena and facilitates E2E model to learn more contextual
information. According to experiments, the above issues can be greatly
alleviated. In addition, deep investigations are carried out into different
units in masking, which shows the effectiveness of our proposed masking unit.
We also further study the masking method and optimize filling strategy of phone
mask. Finally, compared with Conformer-based E2E baseline without mask
training, our model demonstrates about 5.51% relative Word Error Rate (WER)
reduction on reading speech and 12.92% on spontaneous speech, respectively. The
above approach has also been verified on test-set of open-source data THUYG-20,
which shows 20% relative improvements.


title: Efficient comparison of sentence embeddings

abstract:
  The domain of natural language processing (NLP), which has greatly evolved
over the last years, has highly benefited from the recent developments in word
and sentence embeddings. Such embeddings enable the transformation of complex
NLP tasks, like semantic similarity or Question and Answering (Q&A), into much
simpler to perform vector comparisons. However, such a problem transformation
raises new challenges like the efficient comparison of embeddings and their
manipulation. In this work, we will discuss about various word and sentence
embeddings algorithms, we will select a sentence embedding algorithm, BERT, as
our algorithm of choice and we will evaluate the performance of two vector
comparison approaches, FAISS and Elasticsearch, in the specific problem of
sentence embeddings. According to the results, FAISS outperforms Elasticsearch
when used in a centralized environment with only one node, especially when big
datasets are included.


title: Improving Target Sound Extraction with Timestamp Information

abstract:
  Target sound extraction (TSE) aims to extract the sound part of a target
sound event class from a mixture audio with multiple sound events.
  The previous works mainly focus on the problems of weakly-labelled data,
jointly learning and new classes, however, no one cares about the onset and
offset times of the target sound event, which has been emphasized in the
auditory scene analysis. In this paper, we study to utilize such timestamp
information to help extract the target sound via a target sound detection
network and a target-weighted time-frequency loss function.
  More specifically, we use the detection result of a target sound detection
(TSD) network as the additional information to guide the learning of target
sound extraction network. We also find that the result of TSE can further
improve the performance of the TSD network, so that a mutual learning framework
of the target sound detection and extraction is proposed. In addition, a
target-weighted time-frequency loss function is designed to pay more attention
to the temporal regions of the target sound during training. Experimental
results on the synthesized data generated from the Freesound Datasets show that
our proposed method can significantly improve the performance of TSE.


title: Semantic-Aware Domain Generalized Segmentation

abstract:
  Deep models trained on source domain lack generalization when evaluated on
unseen target domains with different data distributions. The problem becomes
even more pronounced when we have no access to target domain samples for
adaptation. In this paper, we address domain generalized semantic segmentation,
where a segmentation model is trained to be domain-invariant without using any
target domain data. Existing approaches to tackle this problem standardize data
into a unified distribution. We argue that while such a standardization
promotes global normalization, the resulting features are not discriminative
enough to get clear segmentation boundaries. To enhance separation between
categories while simultaneously promoting domain invariance, we propose a
framework including two novel modules: Semantic-Aware Normalization (SAN) and
Semantic-Aware Whitening (SAW). Specifically, SAN focuses on category-level
center alignment between features from different image styles, while SAW
enforces distributed alignment for the already center-aligned features. With
the help of SAN and SAW, we encourage both intra-category compactness and
inter-category separability. We validate our approach through extensive
experiments on widely-used datasets (i.e. GTAV, SYNTHIA, Cityscapes, Mapillary
and BDDS). Our approach shows significant improvements over existing
state-of-the-art on various backbone networks. Code is available at
https://github.com/leolyj/SAN-SAW


title:
Recordism: A social-scientific prospect of blockchain from social,
  legal, financial, and technological perspectives

abstract:
  Blockchain has the potential to reform the architecture of cyberspace and
transform the storage, circulation and exchange of information through
decentralization, transparency and de-identification. Meaning that ordinary
participants can become traders, miners, retailers, and customers
simultaneously, breaking the barriers and reducing the information gap between
participants in the community, contributing to the futuristic metaverse with an
open progressive and equal ideology. Such information transformation empowered
by blockchain also profoundly impacts our methodological cognition, legal
governance on cyberspace and financial and technological development.
  This study explores the main question: what are the implications of the
blockchain-driven information revolution for society and social sciences? In
order to answer this main question, this paper chooses four perspectives, which
are methodological, legal, financial and technical. By analysis of these four
perspectives, this paper is expected to provide a more comprehensive analysis
of the blockchain-driven impact on society, social sciences, and technology to
contribute to current scholarships. Additionally, regarding blockchain as an
innovative methodological cognition, it grows on top of other technologies
while helping advance other technologies. This paper concludes that although
there are few frictions between blockchain and current social architecture,
blockchain is so much more than the technology itself, that can be a
representative of the community, acting as the source of trust, watcher of
governance, law enforcer for virtual activities, and an incubator for future
technologies.


title: Graph-based Approximate NN Search: A Revisit

abstract:
  Nearest neighbor search plays a fundamental role in many disciplines such as
multimedia information retrieval, data-mining, and machine learning. The
graph-based search approaches show superior performance over other types of
approaches in recent studies. In this paper, the graph-based NN search is
revisited. We optimize two key components in the approach, namely the search
procedure and the graph that supports the search. For the graph construction, a
two-stage graph diversification scheme is proposed, which makes a good
trade-off between the efficiency and reachability for the search procedure that
builds upon it. Moreover, the proposed diversification scheme allows the search
procedure to decide dynamically how many nodes should be visited in one node's
neighborhood. By this way, the computing power of the devices is fully utilized
when the search is carried out under different circumstances. Furthermore, two
NN search procedures are designed respectively for small and large batch
queries on the GPU. The optimized NN search, when being supported by the
two-stage diversified graph, outperforms all the state-of-the-art approaches on
both the CPU and the GPU across all the considered large-scale datasets.


title: AdaSmooth: An Adaptive Learning Rate Method based on Effective Ratio

abstract:
  It is well known that we need to choose the hyper-parameters in Momentum,
AdaGrad, AdaDelta, and other alternative stochastic optimizers. While in many
cases, the hyper-parameters are tuned tediously based on experience becoming
more of an art than science. We present a novel per-dimension learning rate
method for gradient descent called AdaSmooth. The method is insensitive to
hyper-parameters thus it requires no manual tuning of the hyper-parameters like
Momentum, AdaGrad, and AdaDelta methods. We show promising results compared to
other methods on different convolutional neural networks, multi-layer
perceptron, and alternative machine learning tasks. Empirical results
demonstrate that AdaSmooth works well in practice and compares favorably to
other stochastic optimization methods in neural networks.


title: Online Convolutional Re-parameterization

abstract:
  Structural re-parameterization has drawn increasing attention in various
computer vision tasks. It aims at improving the performance of deep models
without introducing any inference-time cost. Though efficient during inference,
such models rely heavily on the complicated training-time blocks to achieve
high accuracy, leading to large extra training cost. In this paper, we present
online convolutional re-parameterization (OREPA), a two-stage pipeline, aiming
to reduce the huge training overhead by squeezing the complex training-time
block into a single convolution. To achieve this goal, we introduce a linear
scaling layer for better optimizing the online blocks. Assisted with the
reduced training cost, we also explore some more effective re-param components.
Compared with the state-of-the-art re-param models, OREPA is able to save the
training-time memory cost by about 70% and accelerate the training speed by
around 2x. Meanwhile, equipped with OREPA, the models outperform previous
methods on ImageNet by up to +0.6%.We also conduct experiments on object
detection and semantic segmentation and show consistent improvements on the
downstream tasks. Codes are available at
https://github.com/JUGGHM/OREPA_CVPR2022 .


title: Intelligence at the Extreme Edge: A Survey on Reformable TinyML

abstract:
  The rapid miniaturization of Machine Learning (ML) for low powered processing
has opened gateways to provide cognition at the extreme edge (E.g., sensors and
actuators). Dubbed Tiny Machine Learning (TinyML), this upsurging research
field proposes to democratize the use of Machine Learning (ML) and Deep
Learning (DL) on frugal Microcontroller Units (MCUs). MCUs are highly
energy-efficient pervasive devices capable of operating with less than a few
Milliwatts of power. Nevertheless, many solutions assume that TinyML can only
run inference. Despite this, growing interest in TinyML has led to work that
makes them reformable, i.e., work that permits TinyML to improve once deployed.
In line with this, roadblocks in MCU based solutions in general, such as
reduced physical access and long deployment periods of MCUs, deem reformable
TinyML to play a significant part in more effective solutions. In this work, we
present a survey on reformable TinyML solutions with the proposal of a novel
taxonomy for ease of separation. Here, we also discuss the suitability of each
hierarchical layer in the taxonomy for allowing reformability. In addition to
these, we explore the workflow of TinyML and analyze the identified deployment
schemes and the scarcely available benchmarking tools. Furthermore, we discuss
how reformable TinyML can impact a few selected industrial areas and discuss
the challenges and future directions.


title: Fundamental scales in the kinematic phase of the turbulent dynamo

abstract:
  The turbulent dynamo is a powerful mechanism that converts turbulent kinetic
energy to magnetic energy. A key question regarding the magnetic field
amplification by turbulence, is, on what scale, $k_{\rm p}$, do magnetic fields
become most concentrated? There has been some disagreement about whether
$k_{\rm p}$ is controlled by the viscous scale, $k_\nu$ (where turbulent
kinetic energy dissipates), or the resistive scale, $k_\eta$ (where magnetic
fields dissipate). Here we use direct numerical simulations of
magnetohydrodynamic turbulence to measure characteristic scales in the
kinematic phase of the turbulent dynamo. We run $104$-simulations with
hydrodynamic Reynolds numbers of $10 \leq {\rm Re} \leq 3600$, and magnetic
Reynolds numbers of $270 \leq {\rm Rm} \leq 4000$, to explore the dependence of
$k_{\rm p}$ on $k_\nu$ and $k_\eta$. Using physically motivated models for the
kinetic and magnetic energy spectra, we measure $k_\nu$, $k_\eta$ and $k_{\rm
p}$, making sure that the obtained scales are numerically converged. We
determine the overall dissipation scale relations $k_\nu =
(0.025^{+0.005}_{-0.006})\, k_{\rm turb}\, {\rm Re}^{3/4}$ and $k_\eta =
(0.88^{+0.21}_{-0.23})\, k_\nu\, {\rm Pm}^{1/2}$, where $k_{\rm turb}$ is the
turbulence driving wavenumber and ${\rm Pm}={\rm Rm}/{\rm Re}$ is the magnetic
Prandtl number. We demonstrate that the principle dependence of $k_{\rm p}$ is
on $k_\eta$. For plasmas where ${\rm Re} \gtrsim 100$, we find that $k_{\rm p}
= (1.2_{-0.2}^{+0.2})\, k_\eta$, with the proportionality constant related to
the power-law `Kazantsev' exponent of the magnetic power spectrum. Throughout
this study, we find a dichotomy in the fundamental properties of the dynamo
where ${\rm Re} > 100$, compared to ${\rm Re} < 100$. We report a minimum
critical hydrodynamic Reynolds number, ${\rm Re}_{\rm crit} = 100$ for bonafide
turbulent dynamo action.

categories: astro-ph.SR astro-ph.GA astro-ph.HE physics.flu-dyn physics.plasm-ph


title:
Ramsey properties of products and pullbacks of categories and the
  Grothendieck construction

abstract:
  In this paper we provide purely categorical proofs of two important results
of structural Ramsey theory: the result of M.\ Soki\'c that the free product of
Ramsey classes is a Ramsey class and the result of M.\ Bodirsky that adding
constants to the language of a Ramsey class preserves the Ramsey property. The
proofs that we present here ignore the model-theoretic background of these
statements. Instead, they focus on categorical constructions by which the
classes can be constructed, generalizing the original statements along the way.
It turns out that the restriction to classes of relational structures, although
fundamental for the original proof strategies, is not relevant for the
statements themselves. The categorical proofs we present here remove all the
restrictions on the signature of first-order structures and provide the
information not only about the Ramsey property but also about the Ramsey
degrees.


title: Convective Line Shifts in the Spectra of Solar-Type Stars

abstract:
  The Doppler line shifts in the spectra of the Sun and stars with effective
temperatures from 4800 to 6200 K were measured and the average connective
(granulation) velocities were estimated. The absolute scale of the line shifts
for the stars was established on the basis of the derived dependence of the
shifts of solar lines on optical depth. For FGK solar-type stars, curves of
convection velocities as a function of the height in the atmosphere in a large
range of heights from 150 to 700 km were obtained for the first time. All these
curves indicate a decrease in blue shifts with height, which means that the
granulation velocities through the photosphere slow down to zero. In the lower
chromosphere, red shifts of strong Mg I lines are observed, which indicate a
change in the direction of granulation velocities to the opposite and confirm
the effects of reversal of granulation at heights above 600 km. In cooler K
stars, granulation shifts change with height on average from -50 to 100 m/s,
while they change more sharply in hotter FG stars from -700 to 300 m/s. The
gradient of the line shift curves increases with an increase in the effective
temperature and a decrease in gravity, metallicity, and age of the star. The
connective velocity of the star averaged over all analyzed heights increases
from -90 to -560 m/s from colder to hotter stars. It correlates with
macroturbulence, asymmetry of spectral lines, and the rotation velocity of the
star. We also obtained the radial velocities of the stars and compared them
with the SIMBAD data. Our analysis has shown that the individual granular
velocities of the stars must be taken into account when determining the radial
velocities.


title:
Estimation of the Star Formation Rate of Galaxies with Radio Continuum
  Obtained with Murchison Widefield Array

abstract:
  We investigate the correlation between the integrated low-frequency and
infrared (IR) emissions of star-forming galaxies extracted from the {\sl
Herschel} Reference Survey. By taking advantage of the GaLactic Extragalactic
All-sky MWA (GLEAM) survey operated by the Murchison Widefield Array (MWA) we
examine how this correlation varies at a function of frequency across the 20
GLEAM narrow bands at $72\mbox{--}231\; [\mbox{MHz}]$. These examinations are
important for ensuring the reliability of the radio luminosity as a SFR
indicator. In this study, we focus on 18 star-forming galaxies whose radio
emission is detected by the GLEAM survey. These galaxies show that a single
power-law is sufficient to characterise the far-infrared-to-radio correlation
across the GLEAM frequency bands and up to $1.5\; [\mbox{GHz}]$. Thus, the
radio continuum in this wavelength range can serve as a good dust
extinction-free SFR estimator. This is particularly important for future
investigation of the cosmic SFR independently from other estimators, since the
radio continuum can be detected from $z=0$ to high redshifts ($z \sim
5\mbox{--}10$) in a coherent manner.


title:
Automatic Registration of Images with Inconsistent Content Through
  Line-Support Region Segmentation and Geometrical Outlier Removal

abstract:
  The implementation of automatic image registration is still difficult in
various applications. In this paper, an automatic image registration approach
through line-support region segmentation and geometrical outlier removal
(ALRS-GOR) is proposed. This new approach is designed to address the problems
associated with the registration of images with affine deformations and
inconsistent content, such as remote sensing images with different spectral
content or noise interference, or map images with inconsistent annotations. To
begin with, line-support regions, namely a straight region whose points share
roughly the same image gradient angle, are extracted to address the issues of
inconsistent content existing in images. To alleviate the incompleteness of
line segments, an iterative strategy with multi-resolution is employed to
preserve global structures that are masked at full resolution by image details
or noise. Then, Geometrical Outlier Removal (GOR) is developed to provide
reliable feature point matching, which is based on affineinvariant geometrical
classifications for corresponding matches initialized by SIFT. The candidate
outliers are selected by comparing the disparity of accumulated classifications
among all matches, instead of conventional methods which only rely on local
geometrical relations. Various image sets have been considered in this paper
for the evaluation of the proposed approach, including aerial images with
simulated affine deformations, remote sensing optical and synthetic aperture
radar images taken at different situations (multispectral, multisensor, and
multitemporal), and map images with inconsistent annotations. Experimental
results demonstrate the superior performance of the proposed method over the
existing approaches for the whole data set.


title:
PixelFolder: An Efficient Progressive Pixel Synthesis Network for Image
  Generation

abstract:
  Pixel synthesis is a promising research paradigm for image generation, which
can well exploit pixel-wise prior knowledge for generation. However, existing
methods still suffer from excessive memory footprint and computation overhead.
In this paper, we propose a progressive pixel synthesis network towards
efficient image generation, coined as PixelFolder. Specifically, PixelFolder
formulates image generation as a progressive pixel regression problem and
synthesizes images by a multi-stage paradigm, which can greatly reduce the
overhead caused by large tensor transformations. In addition, we introduce
novel pixel folding operations to further improve model efficiency while
maintaining pixel-wise prior knowledge for end-to-end regression. With these
innovative designs, we greatly reduce the expenditure of pixel synthesis, e.g.,
reducing 90% computation and 57% parameters compared to the latest pixel
synthesis method called CIPS. To validate our approach, we conduct extensive
experiments on two benchmark datasets, namely FFHQ and LSUN Church. The
experimental results show that with much less expenditure, PixelFolder obtains
new state-of-the-art (SOTA) performance on two benchmark datasets, i.e., 3.77
FID and 2.45 FID on FFHQ and LSUN Church, respectively. Meanwhile, PixelFolder
is also more efficient than the SOTA methods like StyleGAN2, reducing about 74%
computation and 36% parameters, respectively. These results greatly validate
the effectiveness of the proposed PixelFolder.


title:
Power Saving Techniques in 3GPP 5G New Radio: A Comprehensive Latency
  and Reliability Analysis

abstract:
  Energy efficiency is critical for future sustainable cellular systems. Power
saving optimization has been a key part of the fifth generation (5G) new radio
specifications. For 5Gadvanced and future 6G, with the anticipation of a
trillion internet of things (IoTs) devices with non-rechargeable or low-density
batteries, device power efficiency is rather essential. There are numerous
contributions from industry and academia which present the potential power
saving gains of the various 5G power saving techniques; however, there is a
lack of art on the performance cost paid to achieve such power saving gains.
Therefore, this paper presents a comprehensive evaluation of the radio latency
and reliability cost, which is lost due to a certain 5G new radio power saving
feature. A thorough review of the state of-the-art 5G power saving techniques
is introduced. Extensive system level simulations are performed to evaluate the
latency and reliability cost of the considered power saving features. The paper
offers valuable recommendations for supporting power efficient latency-critical
traffic for beyond 5G-advanced systems.


title: Simplicity conditions for binary orthogonal arrays

abstract:
  It is known that correlation-immune (CI) Boolean functions used in the
framework of side-channel attacks need to have low Hamming weights. The
supports of CI functions are (equivalently) simple orthogonal arrays when their
elements are written as rows of an array. The minimum Hamming weight of a CI
function is then the same as the minimum number of rows in a simple orthogonal
array. In this paper, we use Rao's Bound to give a sufficient condition on the
number of rows, for a binary orthogonal array (OA) to be simple. We apply this
result for determining the minimum number of rows in all simple binary
orthogonal arrays of strengths 2 and 3; we show that this minimum is the same
in such case as for all OA, and we extend this observation to some OA of
strengths $4$ and $5$. This allows us to reply positively, in the case of
strengths 2 and 3, to a question raised by the first author and X. Chen on the
monotonicity of the minimum Hamming weight of 2-CI Boolean functions, and to
partially reply positively to the same question in the case of strengths 4 and
5.


title:
Semiconductor Bloch equation analysis of optical Stark and Bloch-Siegert
  shifts in monolayers WSe$_2$ and MoS$_2$

abstract:
  We report on the theoretical and experimental investigation of
valley-selective optical Stark and Bloch-Siegert shifts of exciton resonances
in monolayers WSe$_2$ and MoS$_2$ induced by strong circularly polarized
nonresonant optical fields. We predict and observe transient shifts of both 1sA
and 1sB exciton transitions in the linear interaction regime. The theoretical
description is based on semiconductor Bloch equations. The solutions of the
equations are obtained with a modified perturbation technique, which takes into
account many-body Coulomb interaction effects. These solutions allow to explain
the polarization dependence of the shifts and calculate their values
analytically. We found experimentally the limits of the applicability of the
theoretical description by observing the transient exciton spectra change due
to many-body effects at high field amplitudes of the driving wave.


title:
Capacity Cost of Fulfilling the URLLC Performance in Industrial 5G New
  Radio Deployments

abstract:
  The development of the 5G new radio specifications has been derived by the
the deterministic low latency use cases such as the ultra-reliable and
low-latency communications (URLLC). A URLLC application requires a stringent
radio latency and reliability performance, e.g., one-way radio latency of 1 ms
with 99.999% success probability. Furthermore, there is a concurrent
progressive demand for broadband capacity cellular applications, e.g., enhanced
mobile broadband (eMBB) use-cases. The coexistence among the URLLC and eMBB
service classes over a single radio spectrum is a challenging task since
achieving the tight URLLC radio targets typically results in a capacity loss.
Hence, it is vital for telecom operators to understand the capacity cost of
fulling the various URLLC requirements in order to sufficiently plan the
corresponding pricing models. Hence, in this work, a comprehensive analysis of
the system capacity loss is presented to achieve the various requirements of
the different URLLC use-cases. An extensive set of realistic system level
simulations is performed and introduced where valuable insights and system
design recommendations on the URLLC-eMBB quality of service coexistence are
presented.


title: Countering Active Attacks on RAFT-based IoT Blockchain Networks

abstract:
  This paper considers an Internet of Thing (IoT) blockchain network consisting
of a leader node and various follower nodes which together implement the RAFT
consensus protocol to verify a blockchain transaction, as requested by a
blockchain client. Further, two kinds of active attacks, i.e., jamming and
impersonation, are considered on the IoT blockchain network due to the presence
of multiple {\it active} malicious nodes in the close vicinity. When the IoT
network is under the jamming attack, we utilize the stochastic geometry tool to
derive the closed-form expressions for the coverage probabilities for both
uplink and downlink IoT transmissions. On the other hand, when the IoT network
is under the impersonation attack, we propose a novel method that enables a
receive IoT node to exploit the pathloss of a transmit IoT node as its
fingerprint to implement a binary hypothesis test for transmit node
identification. To this end, we also provide the closed-form expressions for
the probabilities of false alarm, missed detection and miss-classification.
Finally, we present detailed simulation results that indicate the following: i)
the coverage probability improves as the jammers' locations move away from the
IoT network, ii) the three error probabilities decrease as a function of the
link quality.


title: Discovery of X-shaped Morphology of the Giant Radio Galaxy GRG 0503-286

abstract:
  The high surface-brightness sensitivity of the GLEAM survey image of the
giant radio galaxy GRG 0503-28 at 70-230 MHz has revealed an
inversion-symmetric bending of its two lobes, while maintaining between their
bent portions a ~200 kpc wide strip-like radio emission gap. This lends the
source the appearance of a mega-sized X-shaped radio galaxy. Identifying the
emission gap with the presence of a gaseous layer, probably a WHIM-filled sheet
in the cosmic web, we suggest that the layer is the most likely cause of the
inversion-symmetric bending of the two radio lobes. Multiple observational
manifestations of such gaseous layers are noted. The two lobes of this GRG,
known to extend very asymmetrically from the host galaxy, are remarkably
symmetric about the emission gap, confirming a curious trend noted earlier for
double radio sources of normal dimensions. The anomalous radio spectral
gradient reported for the northern lobe of this GRG is not substantiated.


title:
Rotated Object Detection via Scale-invariant Mahalanobis Distance in
  Aerial Images

abstract:
  Rotated object detection in aerial images is a meaningful yet challenging
task as objects are densely arranged and have arbitrary orientations. The
eight-parameter (coordinates of box vectors) methods in rotated object
detection usually use ln-norm losses (L1 loss, L2 loss, and smooth L1 loss) as
loss functions. As ln-norm losses are mainly based on non-scale-invariant
Minkowski distance, using ln-norm losses will lead to inconsistency with the
detection metric rotational Intersection-over-Union (IoU) and training
instability. To address the problems, we use Mahalanobis distance to calculate
loss between the predicted and the target box vertices' vectors, proposing a
new loss function called Mahalanobis Distance Loss (MDL) for eight-parameter
rotated object detection. As Mahalanobis distance is scale-invariant, MDL is
more consistent with detection metric and more stable during training than
ln-norm losses. To alleviate the problem of boundary discontinuity like all
other eight-parameter methods, we further take the minimum loss value to make
MDL continuous at boundary cases. We achieve state-of-art performance on
DOTA-v1.0 with the proposed method MDL. Furthermore, compared to the experiment
that uses smooth L1 loss, we find that MDL performs better in rotated object
detection.


title:
Positivity of the Invariant Kernel Underlying Quantum Theory of the
  Coulomb Field

abstract:
  We present a proof of positivity of an invariant kernel, which is of basic
importance for the Staruszkiewicz theory of the quantum Coulomb field.
Presented proof of positivity is independent of the Staruszkiewicz theory and
is based on the classical Schoenberg's theorem for conditionally negative
definite functions, as well as on the generalized Bochner's theorem.


title:
Giant valley-selective Stark and Bloch-Siegert shifts of exciton
  resonances in WSe$_2$ and MoS$_2$ monolayers

abstract:
  In this letter we demonstrate that the valley degeneracy of exciton states in
monolayers of WSe$_2$ and MoS$_2$ can be lifted by the interaction with strong
circularly-polarized infrared pulses with durations of only few periods of the
electric field whose photon energy is much lower than the energy of the
excitonic transition. The observed valley-sensitive blue shifts of excitonic
absorption lines are consequences of optical Stark and Bloch-Siegert shifts
acting exclusively on the opposite valleys of the monolayer. We measured the
transient valley-selective changes of sample reflectivity for 1sA as well as
for 1sB exciton transitions corresponding to the two most intensive resonances
in the studied materials. For the studied phenomena we developed a theoretical
description based on semiconductor Bloch equations, which goes beyond the
simple two-level model used in previous investigations. The theoretical
approach takes into account Coulomb many-body effects in the monolayer and
provides a unified description of both types of shifts. The detected
room-temperature excitonic energy shifts of up to 30\,meV pave the way for
practical applications of these effects.


title:
Privacy-preserving Anomaly Detection in Cloud Manufacturing via
  Federated Transformer

abstract:
  With the rapid development of cloud manufacturing, industrial production with
edge computing as the core architecture has been greatly developed. However,
edge devices often suffer from abnormalities and failures in industrial
production. Therefore, detecting these abnormal situations timely and
accurately is crucial for cloud manufacturing. As such, a straightforward
solution is that the edge device uploads the data to the cloud for anomaly
detection. However, Industry 4.0 puts forward higher requirements for data
privacy and security so that it is unrealistic to upload data from edge devices
directly to the cloud. Considering the above-mentioned severe challenges, this
paper customizes a weakly-supervised edge computing anomaly detection
framework, i.e., Federated Learning-based Transformer framework
(\textit{FedAnomaly}), to deal with the anomaly detection problem in cloud
manufacturing. Specifically, we introduce federated learning (FL) framework
that allows edge devices to train an anomaly detection model in collaboration
with the cloud without compromising privacy. To boost the privacy performance
of the framework, we add differential privacy noise to the uploaded features.
To further improve the ability of edge devices to extract abnormal features, we
use the Transformer to extract the feature representation of abnormal data. In
this context, we design a novel collaborative learning protocol to promote
efficient collaboration between FL and Transformer. Furthermore, extensive case
studies on four benchmark data sets verify the effectiveness of the proposed
framework. To the best of our knowledge, this is the first time integrating FL
and Transformer to deal with anomaly detection problems in cloud manufacturing.


title: Electron Temperature Relaxation in the Clusterized Ultracold Plasmas

abstract:
  Ultracold plasmas are a promising candidate for the creation of
strongly-coupled Coulomb systems. Unfortunately, the values of the coupling
parameter Gamma_e actually achieved after photoionization of the neutral atoms
remain relatively small because of the large initial kinetic energy of the
electrons. A conceivable way to get around this obstacle might be to utilize a
spontaneous ionization of the ultracold Rydberg gas, where the initial kinetic
energies could be much less. However, the spontaneous avalanche ionization will
result in a very inhomogeneous distribution (clusterization) of the ions, which
can change the efficiency of the electron relaxation in the vicinity of such
clusters substantially. In the present work, this hypothesis is tested by an
extensive set of numerical simulations. As a result, it is found that despite
less initial kinetic energy, the subsequent relaxation of the electron
velocities in the clusterized plasmas proceeds much more violently than in the
case of the statistically-uniform ionic distribution. The electron temperature,
firstly, experiences a sharp initial jump (presumably, caused by the
"virialization" of energies of the charged particles) and, secondly, exhibits a
gradual subsequent increase (presumably, associated with a multi-particle
recombination of the electrons at the ionic clusters). As a possible tool to
reduce the anomalous temperature increase, we considered also a two-step plasma
formation, involving the blockaded Rydberg states. This leads to a suppression
of the clusterization due to a quasi-regular distribution of ions. In such a
case, according to the numerical simulations, the subsequent evolution of the
electron temperature proceeds more gently, approximately with the same rate as
in the statistically-uniform ionic distribution.


title:
Direct Measurement of the Nickel Spectrum in Cosmic Rays in the Energy
  Range from 8.8 GeV/n to 240 GeV/n with CALET on the International Space
  Station

abstract:
  The relative abundance of cosmic ray nickel nuclei with respect to iron is by
far larger than for all other trans-iron elements, therefore it provides a
favorable opportunity for a low background measurement of its spectrum. Since
nickel, as well as iron, is one of the most stable nuclei, the nickel energy
spectrum and its relative abundance with respect to iron provide important
information to estimate the abundances at the cosmic ray source and to model
the Galactic propagation of heavy nuclei. However, only a few direct
measurements of cosmic-ray nickel at energy larger than $ \sim$ 3 GeV/n are
available at present in the literature and they are affected by strong
limitations in both energy reach and statistics. In this paper we present a
measurement of the differential energy spectrum of nickel in the energy range
from 8.8 to 240 GeV/n, carried out with unprecedented precision by the
Calorimetric Electron Telescope (CALET) in operation on the International Space
Station since 2015. The CALET instrument can identify individual nuclear
species via a measurement of their electric charge with a dynamic range
extending far beyond iron (up to atomic number $ Z $ = 40). The particle's
energy is measured by a homogeneous calorimeter (1.2 proton interaction
lengths, 27 radiation lengths) preceded by a thin imaging section (3 radiation
lengths) providing tracking and energy sampling. This paper follows our
previous measurement of the iron spectrum [O. Adriani et al., Phys. Rev. Lett.
126, 241101 (2021).], and it extends our investigation on the energy dependence
of the spectral index of heavy elements. It reports the analysis of nickel data
collected from November 2015 to May 2021 and a detailed assessment of the
systematic uncertainties. In the region from 20 to 240 GeV$ /n $ our present
data are compatible within the errors with a single power law with spectral
index $ -2.51 \pm 0.07 $.


title:
Chordal Sparsity for Lipschitz Constant Estimation of Deep Neural
  Networks

abstract:
  Lipschitz constants of neural networks allow for guarantees of robustness in
image classification, safety in controller design, and generalizability beyond
the training data. As calculating Lipschitz constants is NP-hard, techniques
for estimating Lipschitz constants must navigate the trade-off between
scalability and accuracy. In this work, we significantly push the scalability
frontier of a semidefinite programming technique known as LipSDP while
achieving zero accuracy loss. We first show that LipSDP has chordal sparsity,
which allows us to derive a chordally sparse formulation that we call
Chordal-LipSDP. The key benefit is that the main computational bottleneck of
LipSDP, a large semidefinite constraint, is now decomposed into an equivalent
collection of smaller ones: allowing Chordal-LipSDP to outperform LipSDP
particularly as the network depth grows. Moreover, our formulation uses a
tunable sparsity parameter that enables one to gain tighter estimates without
incurring a significant computational cost. We illustrate the scalability of
our approach through extensive numerical experiments.


title: On rapid binary mass transfer -- I. Physical model

abstract:
  In some semi-detached binary systems, the donor star may transfer mass to the
companion at a very high rate. We propose that, at sufficiently high mass
transfer rates such that the accretion disk around the companion becomes
geometrically thick (or advection-dominated) near the disk outer radius, a
large fraction of the transferred mass will be lost through the outer
Lagrangian (L2) point. A physical model is constructed where the L2 mass loss
fraction is given by the requirement that the remaining material in the disk
has Bernoulli number equal to the L2 potential energy. Our model predicts
significant L2 mass loss at mass transfer rates exceeding a few times 10^{-4}
Msun/yr -- an equatorial circum-binary outflow (CBO) is formed in these
systems. Implications for the orbital evolution and the observational
appearance are discussed. In particular, (1) rapid angular momentum loss from
the system tends to shrink the orbital separation and hence may increase the
formation rate of mergers and gravitational-wave sources; (2) photons from the
hot disk wind are reprocessed by the CBO into longer wavelength emission in the
infrared bands, consistent with Spitzer observations of some ultra-luminous
X-ray sources.


title: Stability of heteroclinic cycles: a new approach

abstract:
  This paper analyses the stability of cycles within a heteroclinic network
lying in a three-dimensional manifold formed by six cycles, for a one-parameter
model developed in the context of game theory. We show the asymptotic stability
of the network for a range of parameter values compatible with the existence of
an interior equilibrium and we describe an asymptotic technique to decide which
cycle (within the network) is visible in numerics. The technique consists of
reducing the relevant dynamics to a suitable one-dimensional map, the so called
\emph{projective map}. Stability of the fixed points of the projective map
determines the stability of the associated cycles. The description of this new
asymptotic approach is applicable to more general types of networks and is
potentially useful in computational dynamics.


title: Transformer-Empowered Content-Aware Collaborative Filtering

abstract:
  Knowledge graph (KG) based Collaborative Filtering is an effective approach
to personalizing recommendation systems for relatively static domains such as
movies and books, by leveraging structured information from KG to enrich both
item and user representations. Motivated by the use of Transformers for
understanding rich text in content-based filtering recommender systems, we
propose Content-aware KG-enhanced Meta-preference Networks as a way to enhance
collaborative filtering recommendation based on both structured information
from KG as well as unstructured content features based on Transformer-empowered
content-based filtering. To achieve this, we employ a novel training scheme,
Cross-System Contrastive Learning, to address the inconsistency of the two very
different systems and propose a powerful collaborative filtering model and a
variant of the well-known NRMS system within this modeling framework. We also
contribute to public domain resources through the creation of a large-scale
movie-knowledge-graph dataset and an extension of the already public
Amazon-Book dataset through incorporation of text descriptions crawled from
external sources. We present experimental results showing that enhancing
collaborative filtering with Transformer-based features derived from
content-based filtering outperforms strong baseline systems, improving the
ability of knowledge-graph-based collaborative filtering systems to exploit
item content information.


title:
Production of Categorical Data Verifying Differential Privacy:
  Conception and Applications to Machine Learning

abstract:
  Private and public organizations regularly collect and analyze digitalized
data about their associates, volunteers, clients, etc. However, because most
personal data are sensitive, there is a key challenge in designing
privacy-preserving systems. To tackle privacy concerns, research communities
have proposed different methods to preserve privacy, with Differential privacy
(DP) standing out as a formal definition that allows quantifying the
privacy-utility trade-off. Besides, with the local DP (LDP) model, users can
sanitize their data locally before transmitting it to the server. The objective
of this thesis is thus two-fold: O$_1$) To improve the utility and privacy in
multiple frequency estimates under LDP guarantees, which is fundamental to
statistical learning. And O$_2$) To assess the privacy-utility trade-off of
machine learning (ML) models trained over differentially private data. For
O$_1$, we first tackled the problem from two "multiple" perspectives, i.e.,
multiple attributes and multiple collections throughout time, while focusing on
utility. Secondly, we focused our attention on the multiple attributes aspect
only, in which we proposed a solution focusing on privacy while preserving
utility. In both cases, we demonstrate through analytical and experimental
validations the advantages of our proposed solutions over state-of-the-art LDP
protocols. For O$_2$, we empirically evaluated ML-based solutions designed to
solve real-world problems while ensuring DP guarantees. Indeed, we mainly used
the input data perturbation setting from the privacy-preserving ML literature.
This is the situation in which the whole dataset is sanitized independently
and, thus, we implemented LDP algorithms from the perspective of the
centralized data owner. In all cases, we concluded that differentially private
ML models achieve nearly the same utility metrics as non-private ones.


title: General Boosted Kerr Black Holes and Papapetrou Electrodynamics

abstract:
  In this paper the spacetime of a general boosted Kerr black hole relative to
a Lorentz frame at future null infinity is regarded as a background to examine
its respective Papapetrou fields. Taking into account its sole Killing vector
we evaluate the electric and magnetic components -- in Bondi-Sachs (BS) and
Kerr-Schild (KS) coordinates -- of the Maxwell field which comes from spacetime
isometries. To this end we consider a general timelike observer in KS
coordinates. Satisfying sufficient conditions for horizon and ergosphere
formation it is shown that nonsingular magnetic field configurations are formed
around the boosted Kerr black hole while the electric counterpart vanishes.
Different magnetic field patterns are discussed in the case of variations of
the boost parameter $\gamma$ and direction $\boldsymbol{\hat{n}}$.


title: An astronomical analysis of Hipparchus' Commentaries

abstract:
  Hipparchus is considered the greatest astronomer of antiquity. However, his
fame is due, more than to what he has done, or said, or written, to what the
scientists of some generation or of a few cen-turies later have written of him:
above all Ptolemy. The obvious discrepancy between the declared period and the
real one of the stellar catalogue of the Almagest has led to the belief that
the cata-logue was in reality made by Hipparchus. This factor has contributed
greatly to increase his fame, to the detriment of the one of Ptolemy. However,
the only work of Hipparchus that we have re-ceived, Commentaries on Aratus and
Eudoxus phenomena, has never been analyzed with the nec-essary accuracy so far.
Such an examination, which is carried out here for the first time, reveals a
very different scientist from the one represented by orthodox historiography
and is intended as an attempt to avoid the continuation of a singular error,
that of judging the value of a scholar not by the existing parts of his work,
but only by those that are missing.


title: Adversarial Neon Beam: Robust Physical-World Adversarial Attack to DNNs

abstract:
  In the physical world, light affects the performance of deep neural networks.
Nowadays, many products based on deep neural network have been put into daily
life. There are few researches on the effect of light on the performance of
deep neural network models. However, the adversarial perturbations generated by
light may have extremely dangerous effects on these systems. In this work, we
propose an attack method called adversarial neon beam (AdvNB), which can
execute the physical attack by obtaining the physical parameters of adversarial
neon beams with very few queries. Experiments show that our algorithm can
achieve advanced attack effect in both digital test and physical test. In the
digital environment, 99.3% attack success rate was achieved, and in the
physical environment, 100% attack success rate was achieved. Compared with the
most advanced physical attack methods, our method can achieve better physical
perturbation concealment. In addition, by analyzing the experimental data, we
reveal some new phenomena brought about by the adversarial neon beam attack.


title: On properties of the generalized Davenport Expansion

abstract:
  We study the continuity properties of a generalized Davenport Fourier
expansion we recently discovered, by imposing conditions on the coefficients.
We also put our expansion into perspective from the position of Appell
sequences.


title: The First Photometric Study of AH Mic Contact Binary System

abstract:
  The first multi-color light curve analysis of the AH Mic binary system is
presented. This system has very few past observations from the southern
hemisphere. We extracted the minima times from the light curves based on the
Markov Chain Monte Carlo (MCMC) approach and obtained a new ephemeris. To
provide modern photometric light curve solutions, we used the Physics of
Eclipsing Binaries (Phoebe) software package and the MCMC approach. Light curve
solutions yielded a system temperature ratio of 0.950, and we assumed a cold
star-spot for the hotter star based on the O'Connell effect. This analysis
reveals that AH Mic is a W-subtype W UMa contact system with a fill-out factor
of 21.3% and a mass ratio of 2.32. The absolute physical parameters of the
components are estimated by using the Gaia Early Data Release 3 (EDR3) parallax
method to be M_h(M_Sun)=0.702(26), M_c(M_Sun)=1.629(104), R_h(R_Sun)=0.852(21),
R_c(R_Sun)=1.240(28), L_h(L_Sun)=0.618(3) and L_c(L_Sun)=1.067(7). The orbital
angular momentum of the AH Mic binary system was found to be 51.866(35). The
components' positions of this system are plotted in the Hertzsprung-Russell
(H-R) diagram.


title:
ComputableViz: Mathematical Operators as a Formalism for Visualization
  Processing and Analysis

abstract:
  Data visualizations are created and shared on the web at an unprecedented
speed, raising new needs and questions for processing and analyzing
visualizations after they have been generated and digitized. However, existing
formalisms focus on operating on a single visualization instead of multiple
visualizations, making it challenging to perform analysis tasks such as sorting
and clustering visualizations. Through a systematic analysis of previous work,
we abstract visualization-related tasks into mathematical operators such as
union and propose a design space of visualization operations. We realize the
design by developing ComputableViz, a library that supports operations on
multiple visualization specifications. To demonstrate its usefulness and
extensibility, we present multiple usage scenarios concerning processing and
analyzing visualization, such as generating visualization embeddings and
automatically making visualizations accessible. We conclude by discussing
research opportunities and challenges for managing and exploiting the massive
visualizations on the web.


title:
Collaborative causal inference with a distributed data-sharing
  management

abstract:
  Data sharing barriers are paramount challenges arising from multicenter
clinical trials where multiple data sources are stored in a distributed fashion
at different local study sites. Merging such data sources into a common data
storage for a centralized statistical analysis requires a data use agreement,
which is often time-consuming. Data merging may become more burdensome when
causal inference is of primary interest because propensity score modeling
involves combining many confounding variables, and systematic incorporation of
this additional modeling in meta-analysis has not been thoroughly investigated
in the literature. We propose a new causal inference framework that avoids the
merging of subject-level raw data from multiple sites but needs only the
sharing of summary statistics. The proposed collaborative inference enjoys
maximal protection of data privacy and minimal sensitivity to unbalanced data
distributions across data sources. We show theoretically and numerically that
the new distributed causal inference approach has little loss of statistical
power compared to the centralized method that requires merging the entire data.
We present large-sample properties and algorithms for the proposed method. We
illustrate its performance by simulation experiments and a real-world data
example on a multicenter clinical trial of basal insulin treatment for reducing
the risk of post-transplantation diabetes among kidney-transplant patients.


title:
Polynomial Bounds On Parallel Repetition For All 3-Player Games With
  Binary Inputs

abstract:
  We prove that for every 3-player (3-prover) game $\mathcal G$ with value less
than one, whose query distribution has the support $\mathcal S = \{(1,0,0),
(0,1,0), (0,0,1)\}$ of hamming weight one vectors, the value of the $n$-fold
parallel repetition $\mathcal G^{\otimes n}$ decays polynomially fast to zero;
that is, there is a constant $c = c(\mathcal G)>0$ such that the value of the
game $\mathcal G^{\otimes n}$ is at most $n^{-c}$.
  Following the recent work of Girish, Holmgren, Mittal, Raz and Zhan (STOC
2022), our result is the missing piece that implies a similar bound for a much
more general class of multiplayer games: For $\textbf{every}$ 3-player game
$\mathcal G$ over $\textit{binary questions}$ and $\textit{arbitrary answer
lengths}$, with value less than 1, there is a constant $c = c(\mathcal G)>0$
such that the value of the game $\mathcal G^{\otimes n}$ is at most $n^{-c}$.
  Our proof technique is new and requires many new ideas. For example, we make
use of the Level-$k$ inequalities from Boolean Fourier Analysis, which, to the
best of our knowledge, have not been explored in this context prior to our
work.


title: Elementary Quantum Geometry

abstract:
  These Lecture Notes provide an elementary introduction to the quantization of
two-dimensional quantum gravity. Nothing beyond undergratuate physics and
mathematic is required. Explicit formulas for the partition functions for
universes with n boundaries are derived, as well as for the so-called two-point
function. The latter shows explicitly that the fractal dimension of a typical
two-dimensional quantum universe is four.


title:
On the $L_p$ Brunn-Minkowski theory and the $L_p$ Minkowski problem for
  $C$-coconvex sets

abstract:
  Let $C$ be a pointed closed convex cone in $\mathbb{R}^n$ with vertex at the
origin $o$ and having nonempty interior. The set $A\subset C$ is $C$-coconvex
if the volume of $A$ is finite and $A^{\bullet}=C\setminus A$ is a closed
convex set. For $0<p<1$, the $p$-co-sum of $C$-coconvex sets is introduced, and
the corresponding $L_p$ Brunn-Minkowski inequality for $C$-coconvex sets is
established. We also define the $L_p$ surface area measures, for $0\neq p\in
\mathbb{R}$, of certain $C$-coconvex sets, which are critical in deriving a
variational formula of the volume of the Wulff shape associated with a family
of functions obtained from the $p$-co-sum. This motivates the $L_p$ Minkowski
problem aiming to characterize the $L_p$ surface area measures of $C$-coconvex
sets. The existence of solutions to the $L_p$ Minkowski problem for all $0\neq
p\in \mathbb{R}$ is established. The $L_p$ Minkowski inequality for $0<p<1$ is
proved and is used to obtain the uniqueness of the solutions to the $L_p$
Minkowski problem for $0<p<1$.
  For $p=0$, we introduce $(1-\tau)\diamond A_1\oplus_0\tau\diamond A_2$, the
log-co-sum of two $C$-coconvex sets $A_{1}$ and $A_{2}$ with respect to $\tau
\in(0, 1)$, and prove the log-Brunn-Minkowski inequality of $C$-coconvex sets.
The log-Minkowski inequality is also obtained and is applied to prove the
uniqueness of the solutions to the log-Minkowski problem that characterizes the
cone-volume measures of $C$-coconvex sets. Our result solves an open problem
raised by Schneider in [Schneider, Adv. Math., 332 (2018), pp. 199-219].


title:
A Differential Evolution-Enhanced Latent Factor Analysis Model for
  High-dimensional and Sparse Data

abstract:
  High-dimensional and sparse (HiDS) matrices are frequently adopted to
describe the complex relationships in various big data-related systems and
applications. A Position-transitional Latent Factor Analysis (PLFA) model can
accurately and efficiently represent an HiDS matrix. However, its involved
latent factors are optimized by stochastic gradient descent with the specific
gradient direction step-by-step, which may cause a suboptimal solution. To
address this issue, this paper proposes a Sequential-Group-Differential-
Evolution (SGDE) algorithm to refine the latent factors optimized by a PLFA
model, thereby achieving a highly-accurate SGDE-PLFA model to HiDS matrices. As
demonstrated by the experiments on four HiDS matrices, a SGDE-PLFA model
outperforms the state-of-the-art models.


title:
CTRLEval: An Unsupervised Reference-Free Metric for Evaluating
  Controlled Text Generation

abstract:
  Existing reference-free metrics have obvious limitations for evaluating
controlled text generation models. Unsupervised metrics can only provide a
task-agnostic evaluation result which correlates weakly with human judgments,
whereas supervised ones may overfit task-specific data with poor generalization
ability to other datasets. In this paper, we propose an unsupervised
reference-free metric called CTRLEval, which evaluates controlled text
generation from different aspects by formulating each aspect into multiple text
infilling tasks. On top of these tasks, the metric assembles the generation
probabilities from a pre-trained language model without any model training.
Experimental results show that our metric has higher correlations with human
judgments than other baselines, while obtaining better generalization of
evaluating generated texts from different models and with different qualities.


title: Nearby Young Stars and Young Moving Groups

abstract:
  The past two decades have seen dramatic progress in our knowledge of the
population of stars of age $\lesssim$150 Myr that lie within $\sim$100 pc of
the Sun. Most such stars are found in loose kinematic groups ("nearby young
moving groups"; NYMGs). The proximity of NYMGs and their members facilitates
studies of the X-ray properties of coeval groups of pre-main sequence (pre-MS)
stars as well as of individual pre-MS systems. In this review, we focus on how
NYMG X-ray studies provide unique insight into the early evolution of stellar
magnetic activity, the X-ray signatures of accretion, and the irradiation and
dissipation of protoplanetary disks by high-energy photons originating with
their host pre-MS stars. We discuss the likely impacts of the next generation
of X-ray observing facilities on these aspects of the study of NYMGs and their
members.


title: Noncommutative Geometry of the Quantum Disk

abstract:
  We discuss various aspects of noncommutative geometry of a smooth subalgebra
of the Toeplitz algebra. In particular, we study the structure of derivations
on this subalgebra.


title:
UrbanFly: Uncertainty-Aware Planning for Navigation Amongst High-Rises
  with Monocular Visual-Inertial SLAM Maps

abstract:
  We present UrbanFly: an uncertainty-aware real-time planning framework for
quadrotor navigation in urban high-rise environments. A core aspect of UrbanFly
is its ability to robustly plan directly on the sparse point clouds generated
by a Monocular Visual Inertial SLAM (VINS) backend. It achieves this by using
the sparse point clouds to build an uncertainty-integrated cuboid
representation of the environment through a data-driven monocular plane
segmentation network. Our chosen world model provides faster distance queries
than the more common voxel-grid representation, and UrbanFly leverages this
capability in two different ways leading to as many trajectory optimizers. The
first optimizer uses a gradient-free cross-entropy method to compute
trajectories that minimize collision probability and smoothness cost. Our
second optimizer is a simplified version of the first and uses a sequential
convex programming optimizer initialized based on probabilistic safety
estimates on a set of randomly drawn trajectories. Both our trajectory
optimizers are made computationally tractable and independent of the nature of
underlying uncertainty by embedding the distribution of collision violations in
Reproducing Kernel Hilbert Space. Empowered by the algorithmic innovation,
UrbanFly outperforms competing baselines in metrics such as collision rate,
trajectory length, etc., on a high fidelity AirSim simulator augmented with
synthetic and real-world dataset scenes.


title: Naturally small Yukawa couplings from trans-Planckian asymptotic safety

abstract:
  In gauge-Yukawa systems embedded in the framework of trans-Planckian
asymptotic safety we discuss the dynamical generation of arbitrarily small
Yukawa couplings driven by the presence of a non-interactive
infrared-attractive fixed point in the renormalization group flow. Additional
ultraviolet-attractive fixed points guarantee that the theory remains well
defined up to an infinitely high scale. We apply this mechanism to the Yukawa
couplings of the Standard Model extended with right-handed neutrinos, finding
that asymptotically safe solutions in agreement with the current experimental
determination of the masses and mixing angles exist for Dirac neutrinos with
normal mass ordering. We generalize the discussion by applying the same
mechanism to a new-physics model with sterile-neutrino dark matter, where we
generate naturally the feeble Yukawa interaction required to reproduce via
freeze-in the correct relic abundance.


title:
On characterization of the exponential distribution via hypoexponential
  distributions

abstract:
  The sum of independent, but not necessary identically distributed,
exponential random variables follows hypoexponential distribution. We study a
situation when the rate parameters of the exponential variables are not all
different from each other. We obtain a representation for the Laplace transform
of the hypoexponential distribution in the case of two repeated parameter
values. Applying this decomposition, we prove a characterization of the
exponential distribution.


title:
Structural and magnetic properties of the quasicrystal approximant
  Au$_{65}$Ga$_{21}$Tb$_{14}$

abstract:
  Magnetic properties of the quasicrystal approximant
Au$_{65}$Ga$_{21}$Tb$_{14}$ were investigated by the magnetization and the
neutron diffraction experiments. The temperature dependences of the magnetic
susceptibility and the magnetization curve indicate dominant ferromagnetic
interactions, while the whirling antiferromagnetic order was found from the
neutron diffraction experiments. In the antiferromagnetic phase, the magnetic
moments are aligned almost perpendicular to a pseudo five-fold symmetry axis,
which should correspond to the easy-axis direction of a Tb atom. The magnetic
properties similar to those of Au$_{72}$Al$_{14}$Tb$_{14}$ in spite of the
difference in the electron-per-atom ratio suggest the robustness of the
easy-axis anisotropy against the chemical substitution of the nonmagnetic
atoms.


title: Entropic measure of directional emissions in microcavity lasers

abstract:
  We propose a noble notion of the directional emission in microcavity lasers.
First, Shannon entropy of the far-field profiles in the polar coordinate can
quantify the degree of unidirectionality of the emission, while previous
notions about the unidirectionality can not efficiently measure in the robust
range against a variation of the deformation parameter. Second, a divergence
angle of the directional emission is defined phenomenologically in terms of
full width at half maximum, and it is barely applicable to a complicated peak
structure. However, Shannon entropy of semi-marginal probability of the
far-field profiles in the cartesian coordinate can present equivalent results,
and moreover it is applicable to even the cases with a complicated peak
structure of the emission.


title:
Differential Cost Analysis with Simultaneous Potentials and
  Anti-potentials

abstract:
  We present a novel approach to differential cost analysis that, given a
program revision, attempts to statically bound the difference in resource
usage, or cost, between the two program versions. Differential cost analysis is
particularly interesting because of the many compelling applications for it,
such as detecting resource-use regressions at code-review time or proving the
absence of certain side-channel vulnerabilities. One prior approach to
differential cost analysis is to apply relational reasoning that conceptually
constructs a product program on which one can over-approximate the difference
in costs between the two program versions. However, a significant challenge in
any relational approach is effectively aligning the program versions to get
precise results. In this paper, our key insight is that we can avoid the need
for and the limitations of program alignment if, instead, we bound the
difference of two cost-bound summaries rather than directly bounding the
concrete cost difference. In particular, our method computes a threshold value
for the maximal difference in cost between two program versions simultaneously
using two kinds of cost-bound summaries -- a potential function that evaluates
to an upper bound for the cost incurred in the first program and an
anti-potential function that evaluates to a lower bound for the cost incurred
in the second. Our method has a number of desirable properties: it can be fully
automated, it allows optimizing the threshold value on relative cost, it is
suitable for programs that are not syntactically similar, and it supports
non-determinism. We have evaluated an implementation of our approach on a
number of program pairs collected from the literature, and we find that our
method computes tight threshold values on relative cost in most examples.


title:
Accurate Online Posterior Alignments for Principled
  Lexically-Constrained Decoding

abstract:
  Online alignment in machine translation refers to the task of aligning a
target word to a source word when the target sequence has only been partially
decoded. Good online alignments facilitate important applications such as
lexically constrained translation where user-defined dictionaries are used to
inject lexical constraints into the translation model. We propose a novel
posterior alignment technique that is truly online in its execution and
superior in terms of alignment error rates compared to existing methods. Our
proposed inference technique jointly considers alignment and token
probabilities in a principled manner and can be seamlessly integrated within
existing constrained beam-search decoding algorithms. On five language pairs,
including two distant language pairs, we achieve consistent drop in alignment
error rates. When deployed on seven lexically constrained translation tasks, we
achieve significant improvements in BLEU specifically around the constrained
positions.


title:
Calibration window selection based on change-point detection for
  forecasting electricity prices

abstract:
  We employ a recently proposed change-point detection algorithm, the
Narrowest-Over-Threshold (NOT) method, to select subperiods of past
observations that are similar to the currently recorded values. Then,
contrarily to the traditional time series approach in which the most recent
$\tau$ observations are taken as the calibration sample, we estimate
autoregressive models only for data in these subperiods. We illustrate our
approach using a challenging dataset - day-ahead electricity prices in the
German EPEX SPOT market - and observe a significant improvement in forecasting
accuracy compared to commonly used approaches, including the Autoregressive
Hybrid Nearest Neighbors (ARHNN) method.


title:
Acoustic-to-articulatory Inversion based on Speech Decomposition and
  Auxiliary Feature

abstract:
  Acoustic-to-articulatory inversion (AAI) is to obtain the movement of
articulators from speech signals. Until now, achieving a speaker-independent
AAI remains a challenge given the limited data. Besides, most current works
only use audio speech as input, causing an inevitable performance bottleneck.
To solve these problems, firstly, we pre-train a speech decomposition network
to decompose audio speech into speaker embedding and content embedding as the
new personalized speech features to adapt to the speaker-independent case.
Secondly, to further improve the AAI, we propose a novel auxiliary feature
network to estimate the lip auxiliary features from the above personalized
speech features. Experimental results on three public datasets show that,
compared with the state-of-the-art only using the audio speech feature, the
proposed method reduces the average RMSE by 0.25 and increases the average
correlation coefficient by 2.0% in the speaker-dependent case. More
importantly, the average RMSE decreases by 0.29 and the average correlation
coefficient increases by 5.0% in the speaker-independent case.


title:
Compressible Velocity Transformations for Various Noncanonical
  Wall-Bounded Turbulent Flows

abstract:
  This work assesses several popular transformations for the velocity profile
through their application to several types of non-canonical compressible
wall-bounded turbulent flows. Specifically, this work explores DNS databases of
high-enthalpy boundary layers with dissociation and vibrational excitation,
supercritical channel and boundary-layer flows, and adiabatic boundary layers
with pressure gradients. The transformations considered include the van Driest
[Van Driest, J. Aeronaut. Sci., 18(1951):145-216], Zhang et al. [Zhang et al.,
Phys. Rev. Lett., 109(2012):054502], Trettel-Larsson [Trettel and Larsson,
Phys. Fluids, 28(2016):026102], data-driven [Volpiani et al., Phys. Rev.
Fluids, 5(2020):052602], and total-stress-based [Griffin et al., Proc. Natl.
Acad. Sci. U.S.A., 118(2021):e2111144118] transformations. The Trettel-Larsson
transformation collapses velocity profiles of high-enthalpy temporal boundary
layers but not the spatial boundary layers considered. For supercritical
channel flows, the Trettel-Larsson transformation also performs well over the
entire inner layer. None of the transformations above works for supercritical
boundary layers. For all the considered methods, the transformed velocity
profiles of boundary layers with weak pressure gradients coincide well with the
universal incompressible law of the wall. In summary, all these popular methods
fail to deliver uniform performance for non-canonical compressible wall-bounded
flows in the logarithmic region, and a more sophisticated version, which
accounts for these different physics, is needed. The data-driven and
total-stress-based transformations perform well in the viscous sublayer for all
the considered flows.


title:
Lower bound for entropy production rate in stochastic systems far from
  equilibrium

abstract:
  We show that the Schnakenberg's entropy production rate in a master equation
is lower bounded by a function of the weight of the Markov graph, here defined
as the sum of the absolute values of probability currents over the edges. The
result is valid for time-dependent nonequilibrium entropy production rates.
Moreover, in a general framework, we prove a theorem showing that the
Kullback-Leibler divergence between distributions $P(s)$ and $P'(s):=P(m(s))$,
where $m$ is an involution, $m(m(s))=s$, is lower bounded by a function of the
total variation of $P$ and $P'$, for any $m$. The bound is tight and it
improves on Pinsker's inequality for this setup. This result illustrates a
connection between nonequilibrium thermodynamics and graph theory with
interesting applications.

categories: cond-mat.stat-mech math-ph math.MP physics.data-an


title:
Spectrum of Primordial Gravitational Waves in Modified Gravities: A
  Short Overview

abstract:
  In this work we shall exhaustively study the effects of modified gravity on
the energy spectrum of the primordial gravitational waves background. S.
Weinberg has also produced significant works related to the primordial
gravitational waves with the most important one being the effects of neutrinos
on primordial gravitational waves. With this sort review, our main aim is to
gather all the necessary information for studying the effects of modified
gravity on primordial gravitational waves in a concrete and quantitative way
and in a single paper. After reviewing all the necessary techniques for
extracting the general relativistic energy spectrum, and how to obtain in a WKB
way the modified gravity damping or amplifying factor, we concentrate on
specific forms of modified gravity of interest. The most important parameter
involved for the calculation of the effects of modified gravity on the energy
spectrum is the parameter $a_M$ which we calculate for the cases of $f(R,\phi)$
gravity, Chern-Simons-corrected $f(R,\phi)$ gravity,
Einstein-Gauss-Bonnet-corrected $f(R,\phi)$ gravity, and higher derivative
extended Einstein-Gauss-Bonnet-corrected $f(R,\phi)$ gravity. The exact forms
of $a_M$ is presented explicitly for the first time in the literature. With
regard to Einstein-Gauss-Bonnet-corrected $f(R,\phi)$ gravity, and higher
derivative extended Einstein-Gauss-Bonnet-corrected $f(R,\phi)$ gravity
theories, we focus on the case that the gravitational wave propagating speed is
equal to that of light's in vacuum. We provide expressions for $a_M$ expressed
in terms of the cosmic time and in terms of the redshift, which can be used
directly for the numerical calculation of the effect of modified gravity on the
primordial gravitational wave energy spectrum.


title: An improved one-dimensional Hardy inequality

abstract:
  We prove a one-dimensional Hardy inequality on the halfline with sharp
constant, which improves the classical form of this inequality. As a
consequence of this new inequality we can rederive known doubly weighted Hardy
inequalities. Our motivation comes from the theory of Schr\"odinger operators
and we explain the use of Hardy inequalities in that context.


title:
Find Another Me Across the World -- Large-scale Semantic Trajectory
  Analysis Using Spark

abstract:
  In today's society, location-based services are widely used which collect a
huge amount of human trajectories. Analyzing semantic meanings of these
trajectories can benefit numerous real-world applications, such as product
advertisement, friend recommendation, and social behavior analysis. However,
existing works on semantic trajectories are mostly centralized approaches that
are not able to keep up with the rapidly growing trajectory collections. In
this paper, we propose a novel large-scale semantic trajectory analysis
algorithm in Apache Spark. We design a new hash function along with efficient
distributed algorithms that can quickly compute semantic trajectory
similarities and identify communities of people with similar behavior across
the world. The experimental results show that our approach is more than 30
times faster than centralized approaches without sacrificing any accuracy like
other parallel approaches.


title: Co-VQA : Answering by Interactive Sub Question Sequence

abstract:
  Most existing approaches to Visual Question Answering (VQA) answer questions
directly, however, people usually decompose a complex question into a sequence
of simple sub questions and finally obtain the answer to the original question
after answering the sub question sequence(SQS). By simulating the process, this
paper proposes a conversation-based VQA (Co-VQA) framework, which consists of
three components: Questioner, Oracle, and Answerer. Questioner raises the sub
questions using an extending HRED model, and Oracle answers them one-by-one. An
Adaptive Chain Visual Reasoning Model (ACVRM) for Answerer is also proposed,
where the question-answer pair is used to update the visual representation
sequentially. To perform supervised learning for each model, we introduce a
well-designed method to build a SQS for each question on VQA 2.0 and VQA-CP v2
datasets. Experimental results show that our method achieves state-of-the-art
on VQA-CP v2. Further analyses show that SQSs help build direct semantic
connections between questions and images, provide question-adaptive
variable-length reasoning chains, and with explicit interpretability as well as
error traceability.


title:
SciNoBo : A Hierarchical Multi-Label Classifier of Scientific
  Publications

abstract:
  Classifying scientific publications according to Field-of-Science (FoS)
taxonomies is of crucial importance, allowing funders, publishers, scholars,
companies and other stakeholders to organize scientific literature more
effectively. Most existing works address classification either at venue level
or solely based on the textual content of a research publication. We present
SciNoBo, a novel classification system of publications to predefined FoS
taxonomies, leveraging the structural properties of a publication and its
citations and references organised in a multilayer network. In contrast to
other works, our system supports assignments of publications to multiple fields
by considering their multidisciplinarity potential. By unifying publications
and venues under a common multilayer network structure made up of citing and
publishing relationships, classifications at the venue-level can be augmented
with publication-level classifications. We evaluate SciNoBo on a publications'
dataset extracted from Microsoft Academic Graph and we perform a comparative
analysis against a state-of-the-art neural-network baseline. The results reveal
that our proposed system is capable of producing high-quality classifications
of publications.


title:
Simultaneous evidence of edge collapse and hub-filament configurations:
  A rare case study of a Giant Molecular Filament G45.3+0.1

abstract:
  We study multiwavelength and multiscale data to investigate the kinematics of
molecular gas associated with the star-forming complexes G045.49+00.04 (G45E)
and G045.14+00.14 (G45W) in the Aquila constellation. An analysis of the FUGIN
$^{13}$CO(1-0) line data unveils the presence of a giant molecular filament
(GMF G45.3+0.1; length $\sim$75 pc, mass $\sim$1.1$\times$10$^{6}$ M$_{\odot}$)
having a coherent velocity structure at [53, 63] km s$^{-1}$. The GMF G45.3+0.1
hosts G45E and G45W complexes at its opposite ends. We find large scale
velocity oscillations along GMF G45.3+0.1, which also reveals the linear
velocity gradients of $-$0.064 and $+$0.032 km s$^{-1}$ pc$^{-1}$ at its edges.
The photometric analysis of point-like sources shows the clustering of young
stellar object (YSO) candidate sources at the filament's edges where the
presence of dense gas and HII regions are also spatially observed. The Herschel
continuum maps along with the CHIMPS $^{13}$CO(3-2) line data unravel the
presence of parsec scale hub-filament systems (HFSs) in both the sites, G45E
and G45W. Our study suggests that the global collapse of GMF G45.3+0.1 is
end-dominated, with addition to the signature of global nonisotropic collapse
(GNIC) at the edges. Overall, GMF G45.3+0.1 is the first observational sample
of filament where the edge collapse and the hub-filament configurations are
simultaneously investigated. These observations open up the new possibility of
massive star formation, including the formation of HFSs.


title:
Chiral-anomaly-driven magnetotransport in the correlated Weyl magnet
  Mn$_3$Sn

abstract:
  The quest for topological states in strongly correlated materials is
challenging but essential from fundamental research and application
perspectives. The magnetic Weyl semimetal (WSM) state in the chiral
antiferromagnet Mn$_3$Sn emerges with strong electronic correlations, offering
an intriguing arena for exploring the interplay between Weyl fermions and
correlation physics. One prominent characteristic of the WSM state is the
chiral anomaly, yet the potential effects of electronic correlations on the
chiral anomaly remain unexplored. Here, we report a comprehensive study of the
in-plane magnetotransport properties of single-crystal Mn$_{3+x}$Sn$_{1-x}$
with three different Mn doping levels ($x=0.053$, 0.070, and 0.090). The excess
Mn leads to glassy ferromagnetic behavior and the Kondo effect, aside from
shifting the chemical potential relative to the Weyl nodes. Thus, systematic
tuning of the Mn doping level enables us to study the interplay between the
spin-fluctuation scatterings, the correlation effect, and the chiral anomaly.
We identify negative longitudinal magnetoresistance and planar Hall effect
specific to the chiral anomaly for all three doping levels, indicating that the
chiral anomaly persists in the presence of strong correlations.


title: Electricity Price Forecasting: The Dawn of Machine Learning

abstract:
  Electricity price forecasting (EPF) is a branch of forecasting on the
interface of electrical engineering, statistics, computer science, and finance,
which focuses on predicting prices in wholesale electricity markets for a whole
spectrum of horizons. These range from a few minutes (real-time/intraday
auctions and continuous trading), through days (day-ahead auctions), to weeks,
months or even years (exchange and over-the-counter traded futures and forward
contracts). Over the last 25 years, various methods and computational tools
have been applied to intraday and day-ahead EPF. Until the early 2010s, the
field was dominated by relatively small linear regression models and
(artificial) neural networks, typically with no more than two dozen inputs. As
time passed, more data and more computational power became available. The
models grew larger to the extent where expert knowledge was no longer enough to
manage the complex structures. This, in turn, led to the introduction of
machine learning (ML) techniques in this rapidly developing and fascinating
area. Here, we provide an overview of the main trends and EPF models as of
2022.


title:
Optical modelling of accommodative light field display system and
  prediction of human eye responses

abstract:
  The spatio-angular resolution of a light field (LF) display is a crucial
factor for delivering adequate spatial image quality and eliciting an
accommodation response. Previous studies have modelled retinal image formation
with an LF display and evaluated whether accommodation would be evoked
correctly. The models were mostly based on ray-tracing and a schematic eye
model, which pose computational complexity and inaccurately represent the human
eye population's behaviour. We propose an efficient wave-optics-based framework
to model the human eye and a general LF display. With the model, we simulated
the retinal point spread function (PSF) of a point rendered by an LF display at
various depths to characterise the retinal image quality. Additionally,
accommodation responses to rendered LF images were estimated by computing the
visual Strehl ratio based on the optical transfer function (VSOTF) from the
PSFs. We assumed an ideal LF display that had an infinite spatial resolution
and was free from optical aberrations in the simulation. We tested images
rendered at 0--4 dioptres of depths having angular resolutions of up to 4x4
viewpoints within a pupil. The simulation predicted small and constant
accommodation errors, which contradict the findings of previous studies. An
evaluation of the optical resolution of the rendered retinal image suggested a
trade-off between the maximum resolution achievable and the depth range of a
rendered image where in-focus resolution is kept high. The proposed framework
can be used to evaluate the upper bound of the optical performance of an LF
display for realistically aberrated eyes, which may help to find an optimal
spatio-angular resolution required to render a high quality 3D scene.