title: Strong in-domatic number in digraphs

abstract:
  Let $D=(V,A)$ be a digraph and $\mathfrak{S}$ a partition of $V(D)$. We say
that $\mathfrak{S}$ is a strong in-domatic partition if every $S$ in
$\mathfrak{S}$ holds that every vertex not in $S$ has at least one out-neighbor
in $S$, that is $S$ is an in-dominating set, and $D\langle S \rangle$ is
strongly connected. The maximum number of elements in a strong in-domatic
partition is called the strong in-domatic number of $D$ and it is denoted by
$\mathsf{d}_{s}^{-}(D)$. In this paper we introduce those concepts and
determine the value of $\mathsf{d}_{s}^{-}$ for semicomplete digraphs and
planar digraphs. We show some structural properties of digraphs which have a
strong in-domatic partition and we see some bounds for $\mathsf{d}_{s}^{-}(D)$.
Then we study this concept in the Cartesian product, composition, line digraph
and other associated digraphs.
  In addition, we characterize strong in-domatic critical digraphs and we give
two families strong in-domatic critical digraphs which hold some properties,
where a strong in-domatic critical digraph $D$ holds that
$\mathsf{d}_{s}^{-}(D-e) = \mathsf{d}_{s}^{-}(D) -1 $ for every $e$ in $A(D)$.


title:
Sensitive vPSA -- Exploring Sensitivity in Visual Parameter Space
  Analysis

abstract:
  The sensitivity of parameters in computational science problems is difficult
to assess, especially for algorithms with multiple input parameters and diverse
outputs. This work seeks to explore sensitivity analysis in the visualization
domain, introducing novel techniques for respective visual analyses of
parameter sensitivity in multi-dimensional algorithms. First, the sensitivity
analysis background is revisited, highlighting the definition of sensitivity
analysis and approaches analyzing global and local sensitivity as well as the
differences of sensitivity analysis to the more common uncertainty analysis. We
introduce and explore parameter sensitivity using visualization techniques from
overviews to details on demand, covering the analysis of all aspects of
sensitivity in a prototypical implementation. The respective visualization
techniques outline the algorithmic in- and outputs including indications, on
how sensitive an input is with regard to the outputs. The detailed sensitivity
information is communicated through constellation plots for the exploration of
input and output spaces. A matrix view is discussed for localized information
on the sensitivity of specific outputs to specific inputs. A 3D view provides
the link of the parameter sensitivity to the spatial domain, in which the
results of the multi-dimensional algorithms are embedded. The proposed
sensitivity analysis techniques are implemented and evaluated in a prototype
called Sensitivity Explorer. We show that Sensitivity Explorer reliably
identifies the most influential parameters and provides insights into which of
the output characteristics these affect as well as to which extent.


title: The Young Binary DQ Tau Produces Another X-ray Flare Near Periastron

abstract:
  This work is part of a multi-wavelength program to study the effects of
X-ray/UV/optical stellar radiation on the chemistry of the circumbinary disk
around the young high-eccentricity binary DQ Tau. ALMA observations for
near/around December 5, 2021 periastron were postponed due to bad weather, but
supporting Swift-XRT-UVOT TOO observations were successful. These Swift
observations along with previous X-ray-optical-mm data show that DQ Tau keeps
exhibiting powerful flares near periastron, offering a unique laboratory for
studies of flare effects on the gas-phase ion chemistry in protoplanetary
disks.


title: Inverse Orbital Torque via Spin-Orbital Entangled States

abstract:
  While current-induced torque by orbital current has been experimentally found
in various structures, evidence for its reciprocity has been missing so far.
Here, we report experimental evidences of strong inverse orbital torque in
YIG/Pt/CuOx (YIG = Y3Fe5O12) mediated by spin-orbital entangled electronic
states in Pt. By injecting spin current from YIG to Pt by the spin pumping via
ferromagnetic resonance and by the spin Seebeck effect, we find a pronounced
inverse spin Hall effect-like signal. While a part of the signal is explained
as due to the inverse spin-orbital Hall effect in Pt, we also find substantial
increase of the signal in YIG/Pt/CuOx structures compared to the signal in
YIG/Pt. We attribute this to the inverse orbital Edelstein effect at Pt/CuOx
interface mediated by the spin-orbital entangled states in Pt. Our work paves
the way toward understanding of spin-orbital entangled physics in
nonequilibrium and provides a way for electrical detection of the orbital
current in orbitronic device applications.


title:
Revealing Cumulative Risks in Online Personal Information: A Data
  Narrative Study

abstract:
  When pieces from an individual's personal information available online are
connected over time and across multiple platforms, this more complete digital
trace can give unintended insights into their life and opinions. In a data
narrative interview study with 26 currently employed participants, we examined
risks and harms to individuals and employers when others joined the dots
between their online information. We discuss the themes of visibility and
self-disclosure, unintentional information leakage and digital privacy
literacies constructed from our analysis. We contribute insights not only into
people's difficulties in recalling and conceptualising their digital traces but
of subsequently envisioning how their online information may be combined, or
(re)identified across their traces and address a current gap in research by
showing that awareness is lacking around the potential for personal information
to be correlated by and made coherent to/by others, posing risks to
individuals, employers, and even the state. We touch on inequalities of
privacy, freedom and legitimacy that exist for different groups with regard to
what they make (or feel compelled to make) available online and we contribute
to current methodological work on the use of sketching to support visual sense
making in data narrative interviews. We conclude by discussing the need for
interventions that support personal reflection on the potential visibility of
combined digital traces to spotlight hidden vulnerabilities, and promote more
proactive action about what is shared and not shared online.


title:
Product Market Demand Analysis Using NLP in Banglish Text with Sentiment
  Analysis and Named Entity Recognition

abstract:
  Product market demand analysis plays a significant role for originating
business strategies due to its noticeable impact on the competitive business
field. Furthermore, there are roughly 228 million native Bengali speakers, the
majority of whom use Banglish text to interact with one another on social
media. Consumers are buying and evaluating items on social media with Banglish
text as social media emerges as an online marketplace for entrepreneurs. People
use social media to find preferred smartphone brands and models by sharing
their positive and bad experiences with them. For this reason, our goal is to
gather Banglish text data and use sentiment analysis and named entity
identification to assess Bangladeshi market demand for smartphones in order to
determine the most popular smartphones by gender. We scraped product related
data from social media with instant data scrapers and crawled data from
Wikipedia and other sites for product information with python web scrapers.
Using Python's Pandas and Seaborn libraries, the raw data is filtered using NLP
methods. To train our datasets for named entity recognition, we utilized
Spacey's custom NER model, Amazon Comprehend Custom NER. A tensorflow
sequential model was deployed with parameter tweaking for sentiment analysis.
Meanwhile, we used the Google Cloud Translation API to estimate the gender of
the reviewers using the BanglaLinga library. In this article, we use natural
language processing (NLP) approaches and several machine learning models to
identify the most in-demand items and services in the Bangladeshi market. Our
model has an accuracy of 87.99% in Spacy Custom Named Entity recognition,
95.51% in Amazon Comprehend Custom NER, and 87.02% in the Sequential model for
demand analysis. After Spacy's study, we were able to manage 80% of mistakes
related to misspelled words using a mix of Levenshtein distance and ratio
algorithms.


title:
Path and trajectory planning of a tethered UAV-UGV marsupial robotics
  system

abstract:
  This paper addresses the problem of trajectory planning in a marsupial
robotic system consisting of an unmanned aerial vehicle (UAV) linked to an
unmanned ground vehicle (UGV) through a non-taut tether that has a controllable
length. The objective is to determine a synchronized collision-free trajectory
for the three marsupial system agents: UAV, UGV, and tether. First, we present
a path planning solution based on optimal Rapidly exploring Random Trees (RRT*)
that takes into account constraints related to the positions of UAV, UGV,
tether and the 3D environment. The specialization of the main RRT* methods
allows us to obtain feasible solutions in short times. Then, the paper presents
a trajectory planner based on non-linear least squares. The optimizer takes
into account aspects not considered in the path planning, like temporal
constraints of the motion that impose limits on the velocities and
accelerations of the robots. Results from simulated scenarios demonstrate that
the approach is able to generate obstacle-free and smooth trajectories for the
UAV, UGV, and tether.


title: Two-loop supergravity on AdS$_5\times$S$^5$ from CFT

abstract:
  We describe a construction of the two-loop amplitude of four graviton
supermultiplets in AdS$_5\times$S$^5$. We start from an ansatz for a
preamplitude from which we generate the full amplitude under the action of a
specific Casimir operator. The ansatz captures a recent ansatz of Huang and
Yuan and we confirm their result through similar constraints. We identify a
class of four-dimensional `zigzag' integrals which are perfectly adapted to
describing the leading logarithmic discontinuity to all orders. We also observe
that a bonus crossing symmetry of the preamplitude follows from the
transformation properties of the Casimir operator. Combined with the zigzag
integrals this allows us to construct a crossing symmetric function with the
correct leading logarithmic discontinuities in all channels.
  From the two-loop result we extract an explicit expression for the two-loop
correction to the anomalous dimensions of twist-four operators of generic spin
which includes dependence on (alternating) nested harmonic sums up to weight
three. We also revisit the prescription of the bulk-point limit of AdS
amplitudes and show how it recovers the full flat-space amplitude, not just its
discontinuity. With this extended notion of the bulk-point limit we reproduce
the scale-dependent logarithmic threshold terms of type IIB string theory in
flat-space.


title: WiFiEye -- Seeing over WiFi Made Accessible

abstract:
  While commonly used for communication purposes, an increasing number of
recent studies consider WiFi for sensing. In particular, wireless signals are
altered (e.g., reflected and attenuated) by the human body and objects in the
environment. This can be perceived by an observer to infer information on human
activities or changes in the environment and, hence, to "see" over WiFi. Until
now, works on WiFi-based sensing have resulted in a set of custom software
tools - each designed for a specific purpose. Moreover, given how scattered the
literature is, it is difficult to even identify all steps/functions necessary
to build a basic system for WiFi-based sensing. This has led to a high entry
barrier, hindering further research in this area. There has been no effort to
integrate these tools or to build a general software framework that can serve
as the basis for further research, e.g., on using machine learning to interpret
the altered WiFi signals. To address this issue, in this paper, we propose
WiFiEye - a generic software framework that makes all necessary steps/functions
available "out of the box". This way, WiFiEye allows researchers to easily
bootstrap new WiFi-based sensing applications, thereby, focusing on research
rather than on implementation aspects. To illustrate WiFiEye's workflow, we
present a case study on WiFi-based human activity recognition.


title: An adaptive model checking test for functional linear model

abstract:
  Numerous studies have been devoted to the estimation and inference problems
for functional linear models (FLM). However, few works focus on model checking
problem that ensures the reliability of results. Limited tests in this area do
not have tractable null distributions or asymptotic analysis under
alternatives. Also, the functional predictor is usually assumed to be fully
observed, which is impractical. To address these problems, we propose an
adaptive model checking test for FLM. It combines regular moment-based and
conditional moment-based tests, and achieves model adaptivity via the dimension
of a residual-based subspace. The advantages of our test are manifold. First,
it has a tractable chi-squared null distribution and higher powers under the
alternatives than its components. Second, asymptotic properties under different
underlying models are developed, including the unvisited local alternatives.
Third, the test statistic is constructed upon finite grid points, which
incorporates the discrete nature of collected data. We develop the desirable
relationship between sample size and number of grid points to maintain the
asymptotic properties. Besides, we provide a data-driven approach to estimate
the dimension leading to model adaptivity, which is promising in sufficient
dimension reduction. We conduct comprehensive numerical experiments to
demonstrate the advantages the test inherits from its two simple components.


title: Quantum materials for energy-efficient neuromorphic computing

abstract:
  Neuromorphic computing approaches become increasingly important as we address
future needs for efficiently processing massive amounts of data. The unique
attributes of quantum materials can help address these needs by enabling new
energy-efficient device concepts that implement neuromorphic ideas at the
hardware level. In particular, strong correlations give rise to highly
non-linear responses, such as conductive phase transitions that can be
harnessed for short and long-term plasticity. Similarly, magnetization dynamics
are strongly non-linear and can be utilized for data classification. This paper
discusses select examples of these approaches, and provides a perspective for
the current opportunities and challenges for assembling quantum-material-based
devices for neuromorphic functionalities into larger emergent complex network
systems.

categories: cs.ET cond-mat.mtrl-sci cs.NE physics.app-ph


title: Topological circuit of a versatile non-Hermitian quantum system

abstract:
  We propose an resistors, inductors and capacitors (RLC) electrical circuit to
theoretically analyze and fully simulate a new type of non-Hermitian
Su-Schrieffer-Heeger (SSH) model with complex hoppings. We formulate its
construction and investigate its properties by taking advantage of the
circuit's versatility. Rich physical properties can be identified in this
system from the normal modes of oscillation of the RLC circuit, including the
highly tunable bulk-edge correspondence between topological winding numbers and
edge states and the non-Hermitian skin phenomenon originating from a novel
complex energy plane topology. The present study is able to show the wide and
appealing topological physics inherent to electric circuits, which is readily
generalizable to a plenty of both Hermitian and non-Hermitian nontrivial
systems.


title:
Lifelong Self-Adaptation: Self-Adaptation Meets Lifelong Machine
  Learning

abstract:
  In the past years, machine learning (ML) has become a popular approach to
support self-adaptation. While ML techniques enable dealing with several
problems in self-adaptation, such as scalable decision-making, they are also
subject to inherent challenges. In this paper, we focus on one such challenge
that is particularly important for self-adaptation: ML techniques are designed
to deal with a set of predefined tasks associated with an operational domain;
they have problems to deal with new emerging tasks, such as concept shift in
input data that is used for learning. To tackle this challenge, we present
\textit{lifelong self-adaptation}: a novel approach to self-adaptation that
enhances self-adaptive systems that use ML techniques with a lifelong ML layer.
The lifelong ML layer tracks the running system and its environment, associates
this knowledge with the current tasks, identifies new tasks based on
differentiations, and updates the learning models of the self-adaptive system
accordingly. We present a reusable architecture for lifelong self-adaptation
and apply it to the case of concept drift caused by unforeseen changes of the
input data of a learning model that is used for decision-making in
self-adaptation. We validate lifelong self-adaptation for two types of concept
drift using two cases.


title:
Multi-messenger observations of thunderstorm-related bursts of cosmic
  rays

abstract:
  We present the facilities of the Aragats Space Environmental Center in
Armenia used during multi-year observations of the thunderstorm ground
enhancements (TGEs) and corresponding environmental parameters. We analyze the
characteristics of the scintillation detectors, operated on Aragats, and
describe the coordinated detection of TGEs by the network of scintillation
detectors, field meters, and environmental parameters. By using a fast
synchronized data acquisition system we reveal correlations of the multivariate
data on time scales from second to nanosecond which allow us to gain insight
into the TGE and lightning origin and their interrelations.


title:
Decoherence and classicalization of continuous-time quantum walks on
  graphs

abstract:
  We address decoherence and classicalization of continuous-time quantum walks
(CTQWs) on graphs. In particular, we investigate three different models of
decoherence, and employ the quantum-classical (QC) dynamical distance as a
figure of merit to assess whether, and to which extent, decoherence
classicalizes the CTQW, i.e. turns it into the analogue classical process. We
show that the dynamics arising from intrinsic decoherence, i.e. dephasing in
the energy basis, do not fully classicalize the walker and partially preserves
quantum features. On the other hand, dephasing in the position basis, as
described by the Haken-Strobl master equation or by the quantum stochastic walk
(QSW) model, asymptotically destroys the quantumness of the walker, making it
equivalent to a classical random walk. We also investigate the speed of the
classicalization process, and observe a faster convergence of the QC-distance
to its asymptotic value for intrinsic decoherence and the QSW models, whereas
in the Haken-Strobl scenario, larger values of the decoherence rate induce
localization of the walker.


title: Parallel Power System Restoration

abstract:
  Power system restoration is an essential activity for grid resilience, where
grid operators restart generators, re-establish transmission paths, and restore
loads after a blackout event. The core decisions in restoration planning are to
partition the grid into a few sub-networks, each of which has an initial power
source for black-start (called sectionalization problem), and then restart the
generators and pick up the loads in each network (called generator startup
sequencing problem or GSS) in a parallel fashion, aiming to restore electric
service in shortest time. Due to the significant complexity of each problem,
the sectionalization and GSS problems are usually solved separately, resulting
in a sub-optimal solution. In this work, we develop models and computational
methods to solve the two problems simultaneously. We first study the
computational complexity of the GSS problem and develop an efficient integer
linear programming formulation. We then integrate the GSS problem with the
sectionalization problem and develop an integer linear programming formulation
for PPSR to find exact optimal solutions. To scale up to larger systems, we
then develop bounding approaches that find an upper bound and a lower bound
efficiently. At last, to address challenges in extreme cases where a
ultra-large power grid needs to be restored when the solution time is strictly
limited, we develop a randomized rounding method to find a feasible solution
with satisfied quality in a very short time. The computational experiments
demonstrate that the set of proposed approaches are able to find a good
solution for PPSR in up to 2000-bus systems.


title:
Fast full patient and radioisotope Monte Carlo simulations of targeted
  radionuclide therapy: introducing egs_mird

abstract:
  BACKGROUND: Targeted Radionuclide Therapy (TRT) is a fast-growing field
garnering much interest, however, dosimetry calculation techniques remain
relatively simple. PURPOSE: To introduce egs_mird, a new Monte Carlo
application built in EGSnrc which allows users to model full patient tissue and
density (using clinical CT images) and radionuclide distribution (using
clinical PET images) for fast and detailed TRT dose calculation. METHODS: The
novel application egs_mird is introduced, including its structure and variation
reduction approaches. A new egs++ source class egs_internal_source and a
modified version of egs_radionuclide_source are described. The new code is
compared to other MC calculations of S-value kernels, along with
self-validation using the electron Fano test. Full patient prostate 177Lu TRT
cancer treatment simulations are performed using a single set of patient DICOM
CT and [18F]-DCFPyL PET data. RESULTS: Good agreement is found between S-value
kernels calculated using egs_mird and those found in the literature. The Fano
test is satisfied to 0.1%. Patient prostate, rectum, bone marrow, and bladder
dose volume histogram results did not vary significantly when using the
track-length estimator and not modelling electron transport, modelling bone
marrow explicitly (rather than using generic tissue compositions), and reducing
activity to voxels containing partial or full calcifications to half or none,
respectively. Simulations using the track-length estimator can be completed in
under 15 minutes. CONCLUSION: This work shows egs mird to be a reliable MC code
for computing TRT doses as realistically as the patient CT and PET data allow,
supporting the use of egs mird for dose calculations in TRT.


title: Coarse-to-Fine Sparse Sequential Recommendation

abstract:
  Sequential recommendation aims to model dynamic user behavior from historical
interactions. Self-attentive methods have proven effective at capturing
short-term dynamics and long-term preferences. Despite their success, these
approaches still struggle to model sparse data, on which they struggle to learn
high-quality item representations. We propose to model user dynamics from
shopping intents and interacted items simultaneously. The learned intents are
coarse-grained and work as prior knowledge for item recommendation. To this
end, we present a coarse-to-fine self-attention framework, namely CaFe, which
explicitly learns coarse-grained and fine-grained sequential dynamics.
Specifically, CaFe first learns intents from coarse-grained sequences which are
dense and hence provide high-quality user intent representations. Then, CaFe
fuses intent representations into item encoder outputs to obtain improved item
representations. Finally, we infer recommended items based on representations
of items and corresponding intents. Experiments on sparse datasets show that
CaFe outperforms state-of-the-art self-attentive recommenders by 44.03% NDCG@5
on average.


title:
Renormalized energy between fractional vortices with topologically
  induced free discontinuities on 2-dimensional Riemannian manifolds

abstract:
  On a two-dimensional Riemannian manifold without boundary we consider the
variational limit of a family of functionals given by the sum of two terms: a
Ginzburg-Landau and a perimeter term. Our scaling allows low-energy states to
be described by an order parameter which can have finitely many point
singularities (vortex-like defects) of (possibly) fractional-degree connected
by line discontinuities (string defects) of finite length. Our main result is a
compactness and $\Gamma$-convergence theorem which shows how the coarse grained
limit energy depends on the geometry of the manifold in driving the interaction
between vortices and string defects.


title: Applying Automatic Text Summarization for Fake News Detection

abstract:
  The distribution of fake news is not a new but a rapidly growing problem. The
shift to news consumption via social media has been one of the drivers for the
spread of misleading and deliberately wrong information, as in addition to it
of easy use there is rarely any veracity monitoring. Due to the harmful effects
of such fake news on society, the detection of these has become increasingly
important. We present an approach to the problem that combines the power of
transformer-based language models while simultaneously addressing one of their
inherent problems. Our framework, CMTR-BERT, combines multiple text
representations, with the goal of circumventing sequential limits and related
loss of information the underlying transformer architecture typically suffers
from. Additionally, it enables the incorporation of contextual information.
Extensive experiments on two very different, publicly available datasets
demonstrates that our approach is able to set new state-of-the-art performance
benchmarks. Apart from the benefit of using automatic text summarization
techniques we also find that the incorporation of contextual information
contributes to performance gains.


title:
Surface microlenses for much more efficient photodegradation in water
  treatment

abstract:
  The global need for clean water requires sustainable technology for purifying
contaminated water. Highly efficient solar-driven photodegradation is a
sustainable strategy for wastewater treatment. In this work, we demonstrate
that the photodegradation efficiency of micropollutants in water can be
improved by ~2-24 times by leveraging polymeric microlenses (MLs). These
microlenses (MLs) are fabricated from the in-situ polymerization of surface
nanodroplets. We found that photodegradation efficiency ({\eta}) in water
correlates approximately linearly with the sum of the intensity from all focal
points of MLs, although no difference in the photodegradation pathway is
detected from the chemical analysis of the byproducts. With the same overall
power over a given surface area, {\eta} is doubled by using ordered arrays,
compared to heterogeneous MLs on an unpatterned substrate. Higher {\eta} from
ML arrays may be attributed to a coupled effect from the focal points on the
same plane that creates high local concentrations of active species to further
speed up the rate of photodegradation. As a proof-of-concept for ML-enhanced
water treatment, MLs were formed on the inner wall of glass bottles that were
used as containers for water to be treated. Three representative
micropollutants (norfloxacin, sulfadiazine, and sulfamethoxazole) in the
bottles functionalized by MLs were photodegraded by 30% to 170% faster than in
normal bottles. Our findings suggest that the ML-enhanced photodegradation may
lead to a highly efficient solar water purification approach without a large
solar collector size. Such an approach may be particularly suitable for
portable transparent bottles in remote regions.


title: A diagrammatic view of differential equations in physics

abstract:
  Presenting systems of differential equations in the form of diagrams has
become common in certain parts of physics, especially electromagnetism and
computational physics. In this work, we aim to put such use of diagrams on a
firm mathematical footing, while also systematizing a broadly applicable
framework to reason formally about systems of equations and their solutions.
Our main mathematical tools are category-theoretic diagrams, which are well
known, and morphisms between diagrams, which have been less appreciated. As an
application of the diagrammatic framework, we show how complex, multiphysical
systems can be modularly constructed from basic physical principles. A wealth
of examples, drawn from electromagnetism, transport phenomena, fluid mechanics,
and other fields, is included.


title:
Deep Q-learning of global optimizer of multiply model parameters for
  viscoelastic imaging

abstract:
  Objective: Estimation of the global optima of multiple model parameters is
valuable in imaging to form a reliable diagnostic image. Given non convexity of
the objective function, it is challenging to avoid from different local minima.
Methods: We first formulate the global searching of multiply parameters to be a
k-D move in the parametric space, and convert parameters updating to be
state-action decision-making problem. We proposed a novel Deep Q-learning of
Model Parameters (DQMP) method for global optimization of model parameters by
updating the parameter configurations through actions that maximize a Q-value,
which employs a Deep Reward Network designed to learn global reward values from
both visible curve fitting errors and hidden parameter errors. Results: The
DQMP method was evaluated by viscoelastic imaging on soft matter by
Kelvin-Voigt fractional derivative (KVFD) modeling. In comparison to other
methods, imaging of parameters by DQMP yielded the smallest errors (< 2%) to
the ground truth images. DQMP was applied to viscoelastic imaging on biological
tissues, which indicated a great potential of imaging on physical parameters in
diagnostic applications. Conclusions: DQMP method is able to achieve global
optima, yielding accurate model parameter estimates in viscoelastic imaging.
Assessment of DQMP by simulation imaging and ultrasound breast imaging
demonstrated the consistency, reliability of the imaged parameters, and
powerful global searching ability of DQMP. Significance: DQMP method is
promising for imaging of multiple parameters, and can be generalized to global
optimization for many other complex nonconvex functions and imaging of physical
parameters.


title: Compliance Checking with NLI: Privacy Policies vs. Regulations

abstract:
  A privacy policy is a document that states how a company intends to handle
and manage their customers' personal data. One of the problems that arises with
these privacy policies is that their content might violate data privacy
regulations. Because of the enormous number of privacy policies that exist, the
only realistic way to check for legal inconsistencies in all of them is through
an automated method. In this work, we use Natural Language Inference (NLI)
techniques to compare privacy regulations against sections of privacy policies
from a selection of large companies. Our NLI model uses pre-trained embeddings,
along with BiLSTM in its attention mechanism. We tried two versions of our
model: one that was trained on the Stanford Natural Language Inference (SNLI)
and the second on the Multi-Genre Natural Language Inference (MNLI) dataset. We
found that our test accuracy was higher on our model trained on the SNLI, but
when actually doing NLI tasks on real world privacy policies, the model trained
on MNLI generalized and performed much better.


title: Probabilistic Embeddings with Laplacian Graph Priors

abstract:
  We introduce probabilistic embeddings using Laplacian priors (PELP). The
proposed model enables incorporating graph side-information into static word
embeddings. We theoretically show that the model unifies several previously
proposed embedding methods under one umbrella. PELP generalises graph-enhanced,
group, dynamic, and cross-lingual static word embeddings. PELP also enables any
combination of these previous models in a straightforward fashion. Furthermore,
we empirically show that our model matches the performance of previous models
as special cases. In addition, we demonstrate its flexibility by applying it to
the comparison of political sociolects over time. Finally, we provide code as a
TensorFlow implementation enabling flexible estimation in different settings.


title:
Bayesian Sequential Stacking Algorithm for Concurrently Designing
  Molecules and Synthetic Reaction Networks

abstract:
  In the last few years, de novo molecular design using machine learning has
made great technical progress but its practical deployment has not been as
successful. This is mostly owing to the cost and technical difficulty of
synthesizing such computationally designed molecules. To overcome such
barriers, various methods for synthetic route design using deep neural networks
have been studied intensively in recent years. However, little progress has
been made in designing molecules and their synthetic routes simultaneously.
Here, we formulate the problem of simultaneously designing molecules with the
desired set of properties and their synthetic routes within the framework of
Bayesian inference. The design variables consist of a set of reactants in a
reaction network and its network topology. The design space is extremely large
because it consists of all combinations of purchasable reactants, often in the
order of millions or more. In addition, the designed reaction networks can
adopt any topology beyond simple multistep linear reaction routes. To solve
this hard combinatorial problem, we present a powerful sequential Monte Carlo
algorithm that recursively designs a synthetic reaction network by sequentially
building up single-step reactions. In a case study of designing drug-like
molecules based on commercially available compounds, compared with heuristic
combinatorial search methods, the proposed method shows overwhelming
performance in terms of computational efficiency and coverage and novelty with
respect to existing compounds.


title: Multilingual Abusiveness Identification on Code-Mixed Social Media Text

abstract:
  Social Media platforms have been seeing adoption and growth in their usage
over time. This growth has been further accelerated with the lockdown in the
past year when people's interaction, conversation, and expression were limited
physically. It is becoming increasingly important to keep the platform safe
from abusive content for better user experience. Much work has been done on
English social media content but text analysis on non-English social media is
relatively underexplored. Non-English social media content have the additional
challenges of code-mixing, transliteration and using different scripture in
same sentence. In this work, we propose an approach for abusiveness
identification on the multilingual Moj dataset which comprises of Indic
languages. Our approach tackles the common challenges of non-English social
media content and can be extended to other languages as well.


title: Automatic Text Summarization Methods: A Comprehensive Review

abstract:
  One of the most pressing issues that have arisen due to the rapid growth of
the Internet is known as information overloading. Simplifying the relevant
information in the form of a summary will assist many people because the
material on any topic is plentiful on the Internet. Manually summarising
massive amounts of text is quite challenging for humans. So, it has increased
the need for more complex and powerful summarizers. Researchers have been
trying to improve approaches for creating summaries since the 1950s, such that
the machine-generated summary matches the human-created summary. This study
provides a detailed state-of-the-art analysis of text summarization concepts
such as summarization approaches, techniques used, standard datasets,
evaluation metrics and future scopes for research. The most commonly accepted
approaches are extractive and abstractive, studied in detail in this work.
Evaluating the summary and increasing the development of reusable resources and
infrastructure aids in comparing and replicating findings, adding competition
to improve the outcomes. Different evaluation methods of generated summaries
are also discussed in this study. Finally, at the end of this study, several
challenges and research opportunities related to text summarization research
are mentioned that may be useful for potential researchers working in this
area.


title:
Robust Portfolio Design and Stock Price Prediction Using an Optimized
  LSTM Model

abstract:
  Accurate prediction of future prices of stocks is a difficult task to
perform. Even more challenging is to design an optimized portfolio with weights
allocated to the stocks in a way that optimizes its return and the risk. This
paper presents a systematic approach towards building two types of portfolios,
optimum risk, and eigen, for four critical economic sectors of India. The
prices of the stocks are extracted from the web from Jan 1, 2016, to Dec 31,
2020. Sector-wise portfolios are built based on their ten most significant
stocks. An LSTM model is also designed for predicting future stock prices. Six
months after the construction of the portfolios, i.e., on Jul 1, 2021, the
actual returns and the LSTM-predicted returns for the portfolios are computed.
A comparison of the predicted and the actual returns indicate a high accuracy
level of the LSTM model.


title:
Dual Quaternion Ambisonics Array for Six-Degree-of-Freedom Acoustic
  Representation

abstract:
  Spatial audio methods are gaining a growing interest due to the spread of
immersive audio experiences and applications, such as virtual and augmented
reality. For these purposes, 3D audio signals are often acquired through arrays
of Ambisonics microphones, each comprising four capsules that decompose the
sound field in spherical harmonics. In this paper, we propose a dual quaternion
representation of the spatial sound field acquired through an array of two
First Order Ambisonics (FOA) microphones. The audio signals are encapsulated in
a dual quaternion that leverages quaternion algebra properties to exploit
correlations among them. This augmented representation with 6 degrees of
freedom (6DOF) involves a more accurate coverage of the sound field, resulting
in a more precise sound localization and a more immersive audio experience. We
evaluate our approach on a sound event localization and detection (SELD)
benchmark. We show that our dual quaternion SELD model with temporal
convolution blocks (DualQSELD-TCN) achieves better results with respect to real
and quaternion-valued baselines thanks to our augmented representation of the
sound field. Full code is available at:
https://github.com/ispamm/DualQSELD-TCN.


title:
A Data-Driven Framework for Identifying Investment Opportunities in
  Private Equity

abstract:
  The core activity of a Private Equity (PE) firm is to invest into companies
in order to provide the investors with profit, usually within 4-7 years. To
invest into a company or not is typically done manually by looking at various
performance indicators of the company and then making a decision often based on
instinct. This process is rather unmanageable given the large number of
companies to potentially invest. Moreover, as more data about company
performance indicators becomes available and the number of different indicators
one may want to consider increases, manual crawling and assessment of
investment opportunities becomes inefficient and ultimately impossible. To
address these issues, this paper proposes a framework for automated data-driven
screening of investment opportunities and thus the recommendation of businesses
to invest in. The framework draws on data from several sources to assess the
financial and managerial position of a company, and then uses an explainable
artificial intelligence (XAI) engine to suggest investment recommendations. The
robustness of the model is validated using different AI algorithms, class
imbalance-handling methods, and features extracted from the available data
sources.


title: O-operators on Lie triple systems

abstract:
  The purpose of this paper is to study cohomology and deformations of
$\mathcal{O}$-operators on Lie triple systems. We define a cohomology of an
$\mathcal{O}$-operator $T$ as the Lie-Yamaguti cohomology of a certain Lie
triple system induced by $T$ with coefficients in a suitable representation.
Then we consider infinitesimal and formal deformations of
$\mathcal{O}$-operators from cohomological viewpoint. Moreover we provide
relationships between $\mathcal{O}$-operators on Lie algebras and associated
Lie triple systems.


title:
Chaos over Order: Mapping 3D Rotation of Triaxial Asteroids and Minor
  Planets

abstract:
  Celestial bodies approximated with rigid triaxial ellipsoids in a two-body
system can rotate chaotically due to the time-varying gravitational torque from
the central mass. At small orbital eccentricity values, rotation is short-term
orderly and predictable within the commensurate spin-orbit resonances, while at
eccentricity approaching unity, chaos completely takes over. Here, we present
the full 3D rotational equations of motion around all three principle axes for
triaxial minor planets and two independent methods of numerical solution based
on Euler rotations and quaternion algebra. The domains of chaotic rotation are
numerically investigated over the entire range of eccentricity with a
combination of trial integrations of Euler's equations of motion and the
GALI($k$) method. We quantify the dependence of the order--chaos boundaries on
shape by changing a prolateness parameter, and find that the main 1:1
spin-orbit resonance disappears for specific moderately prolate shapes already
at eccentricities as low as 0.3. The island of short-term stability around the
main 1:1 resonance shrinks with increasing eccentricity at a fixed low degree
of prolateness and completely vanishes at approximately 0.8. This island is
also encroached by chaos on longer time scales indicating longer Lyapunov
exponents. Trajectories in the close vicinity of the 3:2 spin-orbit resonance
become chaotic at smaller eccentricities, but separated enclaves of orderly
rotation emerge at eccentricities as high as 0.8. Initial perturbations of
rotational velocity in latitude away from the exact equilibrium result in a
spectrum of free libration, nutation, and polar wander, which is not well
matched by the linearized analysis omitting the inertial terms.


title: A Survey on Graph Representation Learning Methods

abstract:
  Graphs representation learning has been a very active research area in recent
years. The goal of graph representation learning is to generate graph
representation vectors that capture the structure and features of large graphs
accurately. This is especially important because the quality of the graph
representation vectors will affect the performance of these vectors in
downstream tasks such as node classification, link prediction and anomaly
detection. Many techniques are proposed for generating effective graph
representation vectors. Two of the most prevalent categories of graph
representation learning are graph embedding methods without using graph neural
nets (GNN), which we denote as non-GNN based graph embedding methods, and graph
neural nets (GNN) based methods. Non-GNN graph embedding methods are based on
techniques such as random walks, temporal point processes and neural network
learning methods. GNN-based methods, on the other hand, are the application of
deep learning on graph data. In this survey, we provide an overview of these
two categories and cover the current state-of-the-art methods for both static
and dynamic graphs. Finally, we explore some open and ongoing research
directions for future work.


title:
Evolution of Quantum Computing: A Systematic Survey on the Use of
  Quantum Computing Tools

abstract:
  Quantum Computing (QC) refers to an emerging paradigm that inherits and
builds with the concepts and phenomena of Quantum Mechanic (QM) with the
significant potential to unlock a remarkable opportunity to solve complex and
computationally intractable problems that scientists could not tackle
previously. In recent years, tremendous efforts and progress in QC mark a
significant milestone in solving real-world problems much more efficiently than
classical computing technology. While considerable progress is being made to
move quantum computing in recent years, significant research efforts need to be
devoted to move this domain from an idea to a working paradigm. In this paper,
we conduct a systematic survey and categorize papers, tools, frameworks,
platforms that facilitate quantum computing and analyze them from an
application and Quantum Computing perspective. We present quantum Computing
Layers, Characteristics of Quantum Computer platforms, Circuit Simulator,
Open-source Tools Cirq, TensorFlow Quantum, ProjectQ that allow implementing
quantum programs in Python using a powerful and intuitive syntax. Following
that, we discuss the current essence, identify open challenges and provide
future research direction. We conclude that scores of frameworks, tools and
platforms are emerged in the past few years, improvement of currently available
facilities would exploit the research activities in the quantum research
community.


title: Investigation of $K^+K^-$ pairs in the effective mass region near $2m_K$

abstract:
  The DIRAC experiment at CERN investigated in the reaction
$\rm{p}(24~\rm{GeV}/c) + Ni$ the particle pairs $K^+K^-, \pi^+ \pi^-$ and $p
\bar{p}$ with relative momentum $Q$ in the pair system less than 100 MeV/c.
Because of background influence studies, DIRAC explored three subsamples of
$K^+K^-$ pairs, obtained by subtracting -- using time-of-flight (TOF) technique
-- background from initial $Q$ distributions with $K^+K^-$ sample fractions
more than 70\%, 50\% and 30\%. The corresponding pair distributions in $Q$ and
in its longitudinal projection $Q_L$ were analyzed first in a Coulomb model,
which takes into account only Coulomb final state interaction (FSI) and
assuming point-like pair production. This Coulomb model analysis leads to a
$K^+K^-$ yield increase of about four at $Q_L=0.5$ MeV/c compared to 100 MeV/c.
In order to study contributions from strong interaction, a second more
sophisticated model was applied, considering besides Coulomb FSI also strong
FSI via the resonances $f_0(980)$ and $a_0(980)$ and a variable distance $r^*$
between the produced $K$ mesons. This analysis was based on three different
parameter sets for the pair production. For the 70\% subsample and with best
parameters, $3680\pm 370$ $K^+K^-$ pairs was found to be compared to $3900\pm
410$ $K^+K^-$ extracted by means of the Coulomb model. Knowing the efficiency
of the TOF cut for background suppression, the total number of detected
$K^+K^-$ pairs was evaluated to be around $40000\pm 10\%$, which agrees with
the result from the 30\% subsample. The $K^+K^-$ pair number in the 50\%
subsample differs from the two other values by about three standard deviations,
confirming -- as discussed in the paper -- that experimental data in this
subsample is less reliable.


title: Stewart's Theorem revisited: suppressing the norm $\pm 1$ hypothesis

abstract:
  Let $\gamma$ be an algebraic number of degree $2$ and not a root of unity. In
this note we show that there exists a prime ideal $\mathfrak{p}$ of
$\mathbb{Q}(\gamma)$ satisfying $\nu_\mathfrak{p}(\gamma^n-1)\ge 1$, such that
the rational prime $p$ underlying $\mathfrak{p}$ grows quicker than $n$.


title: Multitasking Scheduling with Shared Processing

abstract:
  Recently, the problem of multitasking scheduling has attracted a lot of
attention in the service industries where workers frequently perform multiple
tasks by switching from one task to another. Hall, Leung and Li (Discrete
Applied Mathematics 2016) proposed a shared processing multitasking scheduling
model which allows a team to continue to work on the primary tasks while
processing the routinely scheduled activities as they occur. The processing
sharing is achieved by allocating a fraction of the processing capacity to
routine jobs and the remaining fraction, which we denote as sharing ratio, to
the primary jobs.
  In this paper, we generalize this model to parallel machines and allow the
fraction of the processing capacity assigned to routine jobs to vary from one
to another. The objectives are minimizing makespan and minimizing the total
completion time. We show that for both objectives, there is no polynomial time
approximation algorithm unless P=NP if the sharing ratios are arbitrary for all
machines. Then we consider the problems where the sharing ratios on some
machines have a constant lower bound. For each objective, we analyze the
performance of the classical scheduling algorithms and their variations and
then develop a polynomial time approximation scheme when the number of machines
is a constant.


title:
LANSCE-PSR Short-Pulse Upgrade for Improved Dark Sector Particle
  Searches with the Coherent Captain Mills Experiment

abstract:
  Proton beam dumps are prolific sources of charged and neutral pions, enabling
a powerful technique to search for dark matter, axions, sterile neutrinos,
tests of short baseline anomalies, and precision measurements of coherent
nucleus scattering neutrinos (CEvNS). The Lujan neutron elastic scattering
center at the Los Alamos Neutron Science Center (LANSCE) consists of an
800-MeV, short-pulse, 100-kW proton and spallation neutron source where such
searches are ongoing with the Coherent CAPTAIN Mills (CCM) 10-ton, liquid argon
detector. The employment of fast timing coincidence of the beam with the
detector is used to identify signals and reject background. The current beam
time width is 300 ns with an intensity of $3.1 \times 10^{13}$ protons per
pulse at 20 Hz. With upgrades to the Proton Storage Ring (PSR), the beam time
width may be compressed to 30 ns with minimal intensity loss, allowing an
increase in the signal to background (S/B) of more than 100 and an increase in
the sensitivity for dark matter and sterile neutrino searches of an order of
magnitude. This can be achieved with PSR accelerator upgrades on a time scale
of a few years and at a modest cost.


title: Four-dimensional Gait Surfaces for A Tilt-rotor -- Two Color Map Theorem

abstract:
  This article presents the four-dimensional surfaces which instruct the gait
plan for a tilt-rotor. The previous gaits analyzed in the tilt-rotor research
are inspired by animals; no theoretical base backs the robustness of these
gaits. This research deduces the gaits by diminishing the effect of the
attitude of the tilt-rotor for the first time. Four-dimensional gait surfaces
are subsequently found, on which the gaits are expected to be robust to the
attitude. These surfaces provide the region where the gait is suggested to be
planned. However, a discontinuous region may hinder the gait plan process while
utilizing the proposal gait surfaces. A Two Color Map Theorem is then
established to guarantee the continuity of each gait designed. The robustness
of the typical gaits obeying the Two Color Map Theorem and on the gait surface
is demonstrated by comparing the singular curve in attitude with the gaits not
on the gait surface. The result shows that the acceptable attitudes enlarge for
the gaits on the gait surface.


title: High Efficiency Pedestrian Crossing Prediction

abstract:
  Predicting pedestrian crossing intention is an indispensable aspect of
deploying advanced driving systems (ADS) or advanced driver-assistance systems
(ADAS) to real life. State-of-the-art methods in predicting pedestrian crossing
intention often rely on multiple streams of information as inputs, each of
which requires massive computational resources and heavy network architectures
to generate. However, such reliance limits the practical application of the
systems. In this paper, driven the the real-world demands of pedestrian
crossing intention prediction models with both high efficiency and accuracy, we
introduce a network with only frames of pedestrians as the input. Every
component in the introduced network is driven by the goal of light weight.
Specifically, we reduce the multi-source input dependency and employ light
neural networks that are tailored for mobile devices. These smaller neural
networks can fit into computer memory and can be transmitted over a computer
network more easily, thus making them more suitable for real-life deployment
and real-time prediction. To compensate the removal of the multi-source input,
we enhance the network effectiveness by adopting a multi-task learning
training, named "side task learning", to include multiple auxiliary tasks to
jointly learn the feature extractor for improved robustness. Each head handles
a specific task that potentially shares knowledge with other heads. In the
meantime, the feature extractor is shared across all tasks to ensure the
sharing of basic knowledge across all layers. The light weight but high
efficiency characteristics of our model endow it the potential of being
deployed on vehicle-based systems. Experiments validate that our model
consistently delivers outstanding performances.


title:
Dynamical templates for comparison to Lambda CDN: Static or dynamical
  dark energy?

abstract:
  This study forges new tools to discriminate between dynamical and static dark
energy. It provides accurate evolutionary templates of dynamical cosmological
parameters and fundamental constants as analytic functions of the scale factor.
They are designed to replace the commonly used parameterizations in likelihood
calculations with evolutionary templates based on the physics of specific
dynamical cosmologies and dark energy potentials. Thus they are termed
Cosmology and Potential Specific, CPS, templates. A suite of CPS templates are
calculated for a flat quintessence cosmology with a dark energy potential of
the same mathematical form as the Higgs potential. This Higgs inspired, HI,
polynomial potential produces a rich set of evolutions unlike most monomial
potentials. The study produces CPS templates that are analytic functions of the
scale factor. It uses a recently developed beta function formalism that
provides a differential function for the scalar in terms of the scale factor.
This establishes a methodology for easily producing CPS templates for other
dark energy potentials and cosmologies to determine the likelihoods of
dynamical cosmologies relative to Lambda CDM. The study also examines the
evolution of fundamental constants such as the proton to electron mass ratio
and the fine structure constant involving an intersection between particle
physics and cosmology. Appendix A displays an abridged suite of CPS templates
for flat quintessence and the HI dark energy potential.


title:
Testing Station for Fast Screening of Through Silicon Via (TSV)-enabled
  Application Specific Integrated Circuits (ASICs) for Hard X-ray Imaging
  Detectors

abstract:
  Application Specific Integrated Circuits (ASICs) are used in space-borne
instruments for signal processing and detector readout. The electrical
interface of these ASICs to frontend printed circuit boards (PCBs) is commonly
accomplished with wire bonds. Through Silicon Via (TSV) technology has been
proposed as an alternative interconnect technique that will reduce assembly
complexity of ASIC packaging by replacing wire bonding with flip-chip bonding.
TSV technology is advantageous in large detector arrays where TSVs enable close
detector tiling on all sides. Wafer-level probe card testing of TSV ASICs is
frustrated by solder balls introduced onto the ASIC surface for flip-chip
bonding that hamper alignment. Therefore, we developed the ASIC Test Stand
(ATS) to enable rapid screening and characterization of individual ASIC die. We
successfully demonstrated ATS operation on ASICs originally developed for
CdZnTe detectors on the Nuclear Spectroscopic and Telescope Array (NuSTAR)
mission that were later modified with TSVs in a via-last process. We tested
both back-side blind-TSVs and front-side through-TSVs, with results from
internal test pulser measurements that demonstrate performance equal to or
exceeding the probe card wafer-level testing data. The ATS can easily be
expanded or duplicated in order to parallelize ASIC screening for large area
imaging detectors of future space programs.


title:
Calculations of $p(n,\gamma )d$ reaction in chiral effective field
  theory

abstract:
  We present a calculation of the radiative capture cross section $p(n,\gamma
)d$ in the low-energy range, where the $M1$ reaction channel dominates.
Employing the LENPIC nucleon-nucleon interaction up to the fifth order (N4LO)
that is regularized by the semi-local coordinate space regulators, we obtain
the initial and final state wave functions, and evaluate the phase shifts of
the scattering state and deuteron properties. We derive the transition operator
from the chiral effective field theory up to the next-to-next-to leading order
(N2LO), where we also regularize the transition operator using regulators
consistent with those of the interactions. We compute the capture cross
sections and the results show a converging pattern with the chiral-order
expansion of the nucleon-nucleon interaction, where the regulator dependence of
the results is weak when higher-order nucleon-nucleon interactions are
employed. We quantify the uncertainties of the cross-section results due to the
chiral-order truncation. The chirally complete and consistent cross-section
results are performed up to N2LO and they compare well with the experiments and
other theoretical predictions.


title: MCMC for GLMMs

abstract:
  Generalized linear mixed models (GLMMs) are often used for analyzing
correlated non-Gaussian data. The likelihood function in a GLMM is available
only as a high dimensional integral, and thus closed-form inference and
prediction are not possible for GLMMs. Since the likelihood is not available in
a closed-form, the associated posterior densities in Bayesian GLMMs are also
intractable. Generally, Markov chain Monte Carlo (MCMC) algorithms are used for
conditional simulation in GLMMs and exploring these posterior densities. In
this article, we present different state of the art MCMC algorithms for fitting
GLMMs. These MCMC algorithms include efficient data augmentation strategies, as
well as diffusions based and Hamiltonian dynamics based methods. The Langevin
and Hamiltonian Monte Carlo methods presented here are applicable to any GLMMs,
and are illustrated using three most popular GLMMs, namely, the logistic and
probit GLMMs for binomial data and the Poisson-log GLMM for count data. We also
present efficient data augmentation algorithms for probit and logistic GLMMs.


title: Propagation-invariant space-time supermodes in a multimode waveguide

abstract:
  When an optical pulse is spatially localized in a highly multimoded
waveguide, its energy is typically distributed among a multiplicity of modes,
thus giving rise to a speckled transverse spatial profile that undergoes
erratic changes with propagation. It has been suggested theoretically that
pulsed multimode fields in which each wavelength is locked to an individual
mode at a prescribed axial wave number will propagate invariantly along the
waveguide at a tunable group velocity. In this conception, an initially
localized field remains localized along the waveguide. Here, we provide
proof-of-principle experimental confirmation for the existence of this new
class of pulsed guided fields, which we denote space-time supermodes, and
verify their propagation invariance in a planar waveguide. By superposing up to
21 modes, each assigned to a prescribed wavelength, we construct space-time
supermodes in a 170-micron-thick planar glass waveguide with group indices
extending from 1 to 2. The initial transverse width of the field is 6 microns,
and the waveguide length is 9.1 mm, which is 257x the associated Rayleigh
range. A variety of axially invariant transverse spatial profiles are produced
by judicious selection of the modes contributing to the ST supermode, including
single-peak and multi-peak fields, dark fields (containing a spatial dip), and
even flat uniform intensity profiles.


title: Truck Axle Detection with Convolutional Neural Networks

abstract:
  Axle count in trucks is important to the classification of vehicles and to
the operation of road systems, and is used in the determination of service fees
and the impact on the pavement. Although axle count can be achieved with
traditional methods, such as manual labor, it is increasingly possible to count
axles using deep learning and computer vision methods. This paper aims to
compare three deep learning object detection algorithms, YOLO, Faster R-CNN and
SSD, for the detection of truck axles. A dataset was built to provide training
and testing examples for the neural networks. Training was done on different
base models, to increase training time efficiency and to compare results. We
evaluated results based on three metrics: mAP, F1-score, and FPS count. Results
indicate that YOLO and SSD have similar accuracy and performance, with more
than 96% mAP for both models. Dataset and codes are publicly available for
download.


title:
Revealing the microstructure of sodium-montmorillonite aqueous
  suspensions

abstract:
  Aqueous suspensions of geometrically anisometric (2D) sodium-montmorillonite
(Na-Mt) particles display a sol-gel transition at very low solids
concentrations. The underlying microstructure of the gel has remained a point
of contention since the time of Irving Langmuir. An in-situ investigation
encompassing length scales much larger than the individual particles is
required to provide support for one of the two models proposed in the
literature: 1) a percolated network governed by electrostatic attraction
between platelets; and 2) a jammed suspension stabilized by repulsive
electrostatic forces between particles. We settle this debate by
comprehensively probing the microstructure of Na-Mt suspensions using
ultra-small angle neutron/X-ray scattering and found that it is ordered and
contains entities that are at least an order of magnitude larger than the
individual particles. Complementary cryo-electron microscopy showed both the
presence of domains having strong particle-particle ordering and regions of
particle-particle aggregation. These data indicate 1) the presence of nematic
domains, which refutes a purely attractive nature, and 2) assembly of
particles, which refutes a purely repulsive nature. Na-Mt gels appear to have a
hybrid microstructure with both attractive and repulsive domains.


title: DNA Storage: A Promising Large Scale Archival Storage?

abstract:
  Deoxyribonucleic Acid (DNA), with its high density and long durability, is a
promising storage medium for long-term archival storage and has attracted much
attention. Several studies have verified the feasibility of using DNA for
archival storage with a small amount of data. However, the achievable storage
capacity of DNA as archival storage has not been comprehensively investigated
yet. Theoretically, the DNA storage density is about 1 exabyte/mm3 (109
GB/mm3). However, according to our investigation, DNA storage tube capacity
based on the current synthesizing and sequencing technologies is only at
hundreds of Gigabytes due to the limitation of multiple bio and technology
constraints. This paper identifies and investigates the critical factors
affecting the single DNA tube capacity for archival storage. Finally, we
suggest several promising directions to overcome the limitations and enhance
DNA storage capacity.


title: Testing multiflavored ULDM models with SPARC

abstract:
  We perform maximum likelihood estimates (MLEs) for single and double flavor
ultralight dark matter (ULDM) models using the Spitzer Photometry and Accurate
Rotation Curves (SPARC) database. These estimates are compared to MLEs for
several commonly used cold dark matter (CDM) models. By comparing various CDM
models we find, in agreement with previous studies, that the Burkert and
Einasto models tend to perform better than other commonly used CDM models. We
focus on comparisons between the Einasto and ULDM models and analyze cases for
which the ULDM particle masses are: free to vary; and fixed. For each of these
analyses, we perform fits assuming the soliton and halo profiles are: summed
together; and matched at a given radius. When we let the particle masses vary,
we find a negligible preference for any particular range of particle masses,
within $10^{-25}\,\text{eV}\leq m\leq10^{-19}\,\text{eV}$, when assuming the
summed models. For the matched models, however, we find that almost all
galaxies prefer particles masses in the range $10^{-23}\,\text{eV}\lesssim
m\lesssim10^{-20}\,\text{eV}$. For both double flavor models we find that most
galaxies prefer approximately equal particle masses. We find that the summed
models give much larger variances with respect to the soliton-halo (SH)
relation than the matched models. When the particle masses are fixed, the
matched models give median and mean soliton and halo values that fall within
the SH relation bounds, for most masses scanned. When the particle masses are
fixed in the fitting procedure, we find the best fit results for the particle
mass $m=10^{-20.5}\,\text{eV}$ (for the single flavor models) and
$m_1=10^{-20.5}\,\text{eV}$, $m_2=10^{-20.2}\,\text{eV}$ for the double flavor,
matched model. We discuss how our study will be furthered using a reinforcement
learning algorithm.


title: Internet of Things: System Reference Architecture

abstract:
  This document describes an IoT system reference architecture. It is a general
system architecture in the sense that it embodies key system components,
functions, and flows that are commonly encountered in IoT systems. Much of the
existing literature on IoT system architecture covers either specific
commercial solutions or abstract architecture descriptions that are conceptual
but not readily reducible to practice. This paper describes a general,
vendor-neutral architecture of the IoT system derived by abstracting the common
requirements and features of a variety of system specifications and
implementations. It covers architectural and design principles for constructing
the core IoT system overlay, specifically functions and components involved in
acquiring data, real-time processing, storage and delivery to applications and
services, such as visualization and AI. The appendix outlines the principles of
IoT information modeling and data and metadata handling. As a reference
architecture, this document is meant to serve as a template and a starting
blueprint for constructing specific IoT system solutions. The intent is to
maintain this material as a living document and evolve it over time into a
broader technical community consensus by incorporating and acknowledging the
accumulated feedback and contributions. Direct comments and suggestions are
invited now. More structured mechanisms of managing crowdsourcing and community
collaboration are planned for implementation if warranted.


title: Graphical Designs and Gale Duality

abstract:
  A graphical design is a subset of graph vertices such that the weighted
averages of certain graph eigenvectors over the design agree with their global
averages. We use Gale duality to show that positively weighted graphical
designs in regular graphs are in bijection with the faces of a generalized
eigenpolytope of the graph. This connection can be used to organize, compute
and optimize designs. We illustrate the power of this tool on three families of
Cayley graphs -- cocktail party graphs, cycles, and graphs of hypercubes -- by
computing or bounding the smallest designs that average all but the last
eigenspace in frequency order. We also prove that unless NP = coNP, there
cannot be an efficient description of all minimal designs that average a fixed
number of eigenspaces in a graph.


title: Function field genus theory for non-Kummer extensions

abstract:
  In this paper we first obtain the genus field of a finite abelian non-Kummer
$l$--extension of a global rational function field. Then, using that the genus
field of a composite of two abelian extensions of a global rational function
field with relatively prime degrees is equal to the composite of their
respective genus fields and our previous results, we deduce the general
expression of the genus field of a finite abelian extension of a global
rational function field.


title:
The Deformed Hermitian-Yang-Mills Equation and Level Sets of Harmonic
  Polynomials

abstract:
  Suppose $v(x,y):\mathbb C\rightarrow \mathbb R$ is an entire harmonic
polynomial with no critical points in the right half plane. Let $z_1,
z_2\in\mathbb C$ lie on a level set of $v$ , and assume ${\rm Re}(z_2)>{\rm
Re}(z_1)\geq0$. We give a necessary and sufficient condition, depending only on
algebraic properties of the polynomial $v$, for when there exists a smooth real
function $f$ whose graph $x+if(x)$ lies on a level curve of $v$ connecting
$z_1$ to $z_2$. Inspired by GIT, we construct a Kempf-Ness functional on an
appropriate function space, and prove the functional is bounded from below and
proper if and only if a such a graph exists. As an application, we find a
stability condition equivalent to the existence of a solution to the deformed
Hermitian-Yang-Mills equation on the family of projective bundles $
X_{r,m}:=\mathbb P(\mathcal O_{\mathbb P^m}\oplus \mathcal O_{\mathbb
P^m}(-1)^{\oplus (r+1)})$ with Calabi Symmetry.


title: Dealing with complex routing requirements using an MCDM based approach

abstract:
  The last decade has witnessed an ever-growing user demand for a better QoS
(Quality Of Service) and the fast growth of connected devices still put high
pressure on the legacy network infrastructures. To improve network
performances, better manage the resources and have a greater control over
traffic transmission, intelligent routing procedures are increasingly demanded.
Modern applications in the dynamic context of new emerging networks have their
own routing requirements, in terms of set of metrics to consider, their
importance and thresholds to respect. The objective of this work is to design
an approach based on MCDM (Multi-Criteria Decision Making) to decide complex
routing problems when assuming threshold constraints on metrics. We give the
mathematical framework to capture such requirements and to decide the routing.
A case study is presented to advocate the benefit of using our approach.


title:
Non-Euclidean Monotone Operator Theory with Applications to Recurrent
  Neural Networks

abstract:
  We provide a novel transcription of monotone operator theory to the
non-Euclidean finite-dimensional spaces $\ell_1$ and $\ell_{\infty}$. We first
establish properties of mappings which are monotone with respect to the
non-Euclidean norms $\ell_1$ or $\ell_{\infty}$. In analogy with their
Euclidean counterparts, mappings which are monotone with respect to a
non-Euclidean norm are amenable to numerous algorithms for computing their
zeros. We demonstrate that several classic iterative methods for computing
zeros of monotone operators are directly applicable in the non-Euclidean
framework. We present a case-study in the equilibrium computation of recurrent
neural networks and demonstrate that casting the computation as a suitable
operator splitting problem improves convergence rates.


title:
Core structure of static ferrodark solitons in a spin-1 Bose-Einstein
  condensate

abstract:
  We develop an analytical description of static ferrodark solitons in the
easy-plane phase of ferromagnetic spin-1 Bose-Einstein condensates. We find
that the type-I ferrodark soliton has a single width while the type-II
ferrodark soliton exhibits two characteristic length scales. The proposed
ansatzes show excellent agreement with numerical results. We demonstrate that
the ferrodark solitons are the lowest energy transverse magnetic kinks that
connect the oppositely magnetized magnetic domains. Spin-singlet amplitudes,
nematic tensor densities and nematic currents of ferrodark solitons are also
discussed.


title: Distortions of images of Schwarzschild lensing

abstract:
  We model the supermassive dark object $M87^*$ as a Schwarzschild lens and
study the variations in tangential, radial, and total (the product of
tangential and radial) magnifications of images (primary, secondary, and
relativistic) against the changes in angular source position and the ratio of
lens-source to the observer-source distance. Further, we study the behavior of
partial derivatives (with respect to the angular source position) of total
magnifications of images against the angular source position. Finally, we model
supermassive dark objects at centers of forty galaxies as Schwarzschild lenses
and study the variations in tangential, radial, and total magnifications of
images against the change in the ratio of mass of the lens to its distance.
These studies yield many non-intuitive results which are likely to be
significant for ngEHT observations. We {\em hypothesize} that there exists a
distortion parameter such that their signed sum of all images of singular
gravitational lensing of a source identically vanishes. We test this with
images of Schwarzschild lensing in weak and strong gravitational fields and
find that this aesthetically appealing hypothesis succeeds with flying colors.

categories: gr-qc astro-ph.CO astro-ph.GA astro-ph.HE hep-th


title: Models and Mechanisms for Fairness in Location Data Processing

abstract:
  Location data use has become pervasive in the last decade due to the advent
of mobile apps, as well as novel areas such as smart health, smart cities, etc.
At the same time, significant concerns have surfaced with respect to fairness
in data processing. Individuals from certain population segments may be
unfairly treated when being considered for loan or job applications, access to
public resources, or other types of services. In the case of location data,
fairness is an important concern, given that an individual's whereabouts are
often correlated with sensitive attributes, e.g., race, income, education.
While fairness has received significant attention recently, e.g., in the case
of machine learning, there is little focus on the challenges of achieving
fairness when dealing with location data. Due to their characteristics and
specific type of processing algorithms, location data pose important fairness
challenges that must be addressed in a comprehensive and effective manner. In
this paper, we adapt existing fairness models to suit the specific properties
of location data and spatial processing. We focus on individual fairness, which
is more difficult to achieve, and more relevant for most location data
processing scenarios. First, we devise a novel building block to achieve
fairness in the form of fair polynomials. Then, we propose two mechanisms based
on fair polynomials that achieve individual fairness, corresponding to two
common interaction types based on location data. Extensive experimental results
on real data show that the proposed mechanisms achieve individual location
fairness without sacrificing utility.


title:
On the growth of generalized Fourier coefficients of restricted
  eigenfunctions

abstract:
  Let $(M,g)$ be a smooth, compact, Riemannian manifold and $\{\phi_h\}$ a
sequence of $L^2$-normalized Laplace eigenfunctions on $M$. For a smooth
submanifold $H\subset M$, we consider the growth of the restricted
eigenfunctions $\phi_h|_H$ by testing them against a sequence of functions
$\{\psi_h\}$ on $H$ whose wavefront set avoids $S^*H$. That is, we study what
we call the generalized Fourier coefficients: $\langle
\phi_h,\psi_h\rangle_{L^2(H)}$. We give an explicit bound on these coefficients
depending on how the defect measures for the two collections of functions
$\phi_h$ and $\psi_h$ relate. This allows us to get a little$-o$ improvement
whenever the collection of recurrent directions over the wavefront set of
$\psi_h$ is small. To obtain our estimates, we utilize geodesic beam
techniques.


title:
Activity Report of the Second African Conference on Fundamental and
  Applied Physics, ACP2021

abstract:
  The African School of Fundamental Physics and Applications, also known as the
African School of Physics (ASP), was initiated in 2010, as a three-week
biennial event, to offer additional training in fundamental and applied physics
to African students with a minimum of three-year university education. Since
its inception, ASP has grown to be much more than a school. ASP has become a
series of activities and events with directed ethos towards physics as an
engine for development in Africa. One such activity of ASP is the African
Conference on Fundamental and Applied Physics (ACP). The first edition of ACP
took place during the 2018 edition of ASP at the University of Namibia in
Windhoek. In this paper, we report on the second edition of ACP, organized on
March 7--11, 2022, as a virtual event.


title: Spatially resolved mass flux measurements with dual comb spectroscopy

abstract:
  Providing an accurate, representative sample of mass flux across large open
areas for atmospheric studies or the extreme conditions of a hypersonic engine
is challenging for traditional intrusive or point-based sensors. Here, we
demonstrate that laser absorption spectroscopy with frequency combs can
simultaneously measure all of the components of mass flux (velocity,
temperature, pressure, and species concentration) with low uncertainty, spatial
resolution corresponding to the span of the laser line of sight, and no
supplemental sensor readings. The low uncertainty is provided by the broad
spectral bandwidth, high resolution, and extremely well-known and controlled
frequency axis of stabilized, mode-locked frequency combs. We demonstrate these
capabilities in the isolator of a ground-test supersonic propulsion engine at
Wright-Patterson Air Force Base. The mass flux measurements are consistent
within 3.6% of the facility-level engine air supply values. A vertical scan of
the laser beams in the isolator measures the spatially resolved mass flux,
which is compared with computational fluid dynamics simulations. A rigorous
uncertainty analysis demonstrates a DCS instrument uncertainty of ~0.4%, and
total uncertainty (including non-instrument sources) of ~7% for mass flux
measurements. These measurements demonstrate DCS as a low-uncertainty mass flux
sensor for a variety of applications.


title: Policy Learning with Competing Agents

abstract:
  Decision makers often aim to learn a treatment assignment policy under a
capacity constraint on the number of agents that they can treat. When agents
can respond strategically to such policies, competition arises, complicating
the estimation of the effect of the policy. In this paper, we study
capacity-constrained treatment assignment in the presence of such interference.
We consider a dynamic model where heterogeneous agents myopically best respond
to the previous treatment assignment policy. When the number of agents is large
but finite, we show that the threshold for receiving treatment under a given
policy converges to the policy's mean-field equilibrium threshold. Based on
this result, we develop a consistent estimator for the policy effect and
demonstrate in simulations that it can be used for learning optimal
capacity-constrained policies in the presence of strategic behavior.


title:
Assessing consistency between CMB temperature and polarization
  measurements, with application to Planck, ACT and SPT

abstract:
  Planck's Cosmic Microwave Background temperature and polarization
observations are the premier dataset for constraining cosmological models.
Cosmic variance limited temperature at large and intermediate scales today
dominates the constraints; polarization provides additional constraining power
and further scrutiny of the models. To complete this picture from Planck,
ground-based experiments, such as the Atacama Cosmology Telescope (ACT) and the
South Pole Telescope (SPT) continue to add temperature and polarization
measurements at small scales, allowing for the extraction of competitive
cosmological constraints from the $TE$ and $EE$ power spectra. Matching at the
same time all these stringent probes is a key challenge and validation step for
any cosmological model. In particular, $\Lambda$CDM requires a tight
consistency between the temperature and polarization measurements. In this
paper, we present a number of methods to identify and quantify possible
inconsistencies between temperature and polarization, we apply them to the
latest Planck, ACT and SPT data and find no evidence for a deviation from
$\Lambda$CDM. Application of these methods will have increased importance for
future, more constraining CMB data.


title:
Singular integral operators, $T1$ theorem, Littlewood-Paley theory and
  Hardy spaces in Dunkl Setting

abstract:
  The purpose of this paper is to introduce a new class of singular integral
operators in the Dunkl setting involving both the Euclidean metric and the
Dunkl metric. Then we provide the $T1$ theorem, the criterion for the
boundedness on $L^2$ for these operators. Applying this singular integral
operator theory, we establish the Littlewood-Paley theory and the Dunkl-Hardy
spaces. As applications, the boundedness of singular integral operators,
particularly, the Dunkl-Rieze transforms, on the Dunkl-Hardy spaces is given.
The $L^2$ theory and the singular integral operator theory play crucial roles.
New tools developed in this paper include the weak-type discrete Calder\'on
reproducing formulae, new test functions, and distributions, the
Littlewood-Paley, the wavelet-type decomposition, and molecule
characterizations of the Dunkl-Hardy space, Coifman's approximation to the
identity and the decomposition of the identity operator on $L^2$, Meyer's
commutation Lemma, and new almost orthogonal estimates in the Dunkl setting.


title: Hybrid Probabilistic-Snowball Sampling

abstract:
  Snowball sampling is the common name for sampling designs on human
populations where respondents are requested to share the questionnaire among
their social ties. With some exceptions, estimates from snowball samplings are
considered biased. However, the magnitude of the bias is influenced by a
combination of elements of the sampling design and features of the target
population. Hybrid Probabilistic-Snowball Sampling Designs (HPSSD) aims to
reduce the main source of bias in the snowball sample through randomly
oversampling the first stage 0 of the snowball.
  To check the behaviour of HPSSD for applications, we developed an algorithm
that, by grafting the edges of a stochastic blockmodel into a graph of cliques,
simulates an assortative network of tobacco smokers. Different outcomes of the
HPSSD operations are simulated, too.
  Inference on 8,000 runs of the simulation leads to think that HPSSD does not
improve reliability of samples that are already representative. But if
homophily in the population is sufficiently low, even the unadjusted sample
mean of HPSSD has a slightly better performance than a random, but undersized,
sampling.
  De-biasing the estimates of HPSSD shows improvement in the performance, so an
adjusted HPSSD estimator is a desirable development.


title:
ConceptExplainer: Understanding the Mental Model of Deep Learning
  Algorithms via Interactive Concept-based Explanations

abstract:
  Traditional deep learning interpretability methods which are suitable for
non-expert users cannot explain network behaviors at the global level and are
inflexible at providing fine-grained explanations. As a solution, concept-based
explanations are gaining attention due to their human intuitiveness and their
flexibility to describe both global and local model behaviors. Concepts are
groups of similarly meaningful pixels that express a notion, embedded within
the network's latent space and have primarily been hand-generated, but have
recently been discovered by automated approaches. Unfortunately, the magnitude
and diversity of discovered concepts makes it difficult for non-experts to
navigate and make sense of the concept space, and lack of easy-to-use software
also makes concept explanations inaccessible to many non-expert users. Visual
analytics can serve a valuable role in bridging these gaps by enabling
structured navigation and exploration of the concept space to provide
concept-based insights of model behavior to users. To this end, we design,
develop, and validate ConceptExplainer, a visual analytics system that enables
non-expert users to interactively probe and explore the concept space to
explain model behavior at the instance/class/global level. The system was
developed via iterative prototyping to address a number of design challenges
that non-experts face in interpreting the behavior of deep learning models. Via
a rigorous user study, we validate how ConceptExplainer supports these
challenges. Likewise, we conduct a series of usage scenarios to demonstrate how
the system supports the interactive analysis of model behavior across a variety
of tasks and explanation granularities, such as identifying concepts that are
important to classification, identifying bias in training data, and
understanding how concepts can be shared across diverse and seemingly
dissimilar classes.


title:
Some necessary and sufficient condition for finite generation of
  symbolic Rees rings

abstract:
  Consider the blow-up Y of a weighted projective plane at a point in the open
orbit over a field of characteristic 0. We assume that there exists a curve C
on Y such that C^2<0 and C.E=1, where E is the exceptional curve. In this paper
we give a (very simple) necessary and sufficient condition for finite
generation of the Cox ring of Y (Theorem~1.2). It is an affirmative answer to a
conjecture due to He and Kurano-Nishida.


title:
Spatially entangled photon-pairs from lithium niobate nonlocal
  metasurfaces

abstract:
  Metasurfaces consisting of nano-scale structures are underpinning new
physical principles for the creation and shaping of quantum states of light.
Multi-photon states that are entangled in spatial or angular domains are an
essential resource for quantum imaging and sensing applications, however their
production traditionally relies on bulky nonlinear crystals. We predict and
demonstrate experimentally the generation of spatially entangled photon pairs
through spontaneous parametric down-conversion from a metasurface incorporating
a nonlinear thin film of lithium niobate. This is achieved through nonlocal
resonances with tailored angular dispersion mediated by an integrated silica
meta-grating, enabling control of the emission pattern and associated quantum
states of photon pairs by designing the grating profile and tuning the pump
frequency. We measure the correlations of photon positions and identify their
spatial anti-bunching through violation of the classical Cauchy-Schwartz
inequality, witnessing the presence of multi-mode entanglement. Simultaneously,
the photon-pair rate is strongly enhanced by 450 times as compared to
unpatterned films due to high-quality-factor metasurface resonances, and the
coincidence to accidental ratio reaches 5000. These results pave the way to
miniaturization of various quantum devices by incorporating ultra-thin
metasurfaces functioning as room-temperature sources of quantum-entangled
photons.


title: Model predictions of wave overwash extent into the marginal ice zone

abstract:
  A model of the extent of wave driven overwash into fields of sea ice floes is
proposed. The extent model builds on previous work modelling wave overwash of a
single floe by regular waves by including irregular incoming waves and random
floe fields. The model is validated against a laboratory experiment. It is then
used to study the extent of wave overwash into marginal ice zones consisting of
pancake and fragmented floe fields. The effects of wave conditions and floe
geometry on predicted extents are investigated. Finally, the model is used to
predict the wave overwash extent for the conditions observed during a winter
(July) 2017 Antarctic voyage in which the sea surface was monitored by a
stereo-camera system.


title:
On the Reduction in Accuracy of Finite Difference Schemes on Manifolds
  without Boundary

abstract:
  We investigate error bounds for numerical solutions of divergence structure
linear elliptic PDEs on compact manifolds without boundary. Our focus is on a
class of monotone finite difference approximations, which provide a strong form
of stability that guarantees the existence of a bounded solution. In many
settings including the Dirichlet problem, it is easy to show that the resulting
solution error is proportional to the formal consistency error of the scheme.
We make the surprising observation that this need not be true for PDEs posed on
compact manifolds without boundary. By carefully constructing barrier
functions, we prove that the solution error achieved by a scheme with
consistency error $\mathcal{O}(h^\alpha)$ is bounded by
$\mathcal{O}(h^{\alpha/(d+1)})$ in dimension $d$. We also provide a specific
example where this predicted convergence rate is observed numerically. Using
these error bounds, we further design a family of provably convergent
approximations to the solution gradient.


title: Deliberation Model for On-Device Spoken Language Understanding

abstract:
  We propose a novel deliberation-based approach to end-to-end (E2E) spoken
language understanding (SLU), where a streaming automatic speech recognition
(ASR) model produces the first-pass hypothesis and a second-pass natural
language understanding (NLU) component generates the semantic parse by
conditioning on both ASR's text and audio embeddings. By formulating E2E SLU as
a generalized decoder, our system is able to support complex compositional
semantic structures. Furthermore, the sharing of parameters between ASR and NLU
makes the system especially suitable for resource-constrained (on-device)
environments; our proposed approach consistently outperforms strong pipeline
NLU baselines by 0.82% to 1.34% across various operating points on the spoken
version of the TOPv2 dataset. We demonstrate that the fusion of text and audio
features, coupled with the system's ability to rewrite the first-pass
hypothesis, makes our approach more robust to ASR errors. Finally, we show that
our approach can significantly reduce the degradation when moving from natural
speech to synthetic speech training, but more work is required to make
text-to-speech (TTS) a viable solution for scaling up E2E SLU.


title:
Growth at high substrate coverage can decrease the grain boundary
  roughness of 2D materials

abstract:
  Grain boundary roughness can affect electronic and mechanical properties of
two-dimensional materials. This roughness depends crucially on the growth
process by which the two-dimensional material is formed. To investigate the key
mechanisms that govern the roughness, we have performed kinetic Monte Carlo
simulations of a simple model that includes particle attachment, detachment,
and diffusion. We have studied the closure of the gap between two flakes during
growth, and the subsequent formation of the grain boundary (GB) for a broad
range of model parameters. The well known near-equilibrium (attachment-limited)
and unstable (diffusion-limited) growth regimes are identified, but we also
observe a third regime when the precursor flux is sufficiently high to fully
cover the gap between the edges. This high coverage regime forms GBs with
spatially uncorrelated roughness, which quickly relax to smoother
configurations. Extrapolating the numerical results (with support from a
theoretical approach) to edge lengths and gap widths of some micrometers, we
confirm the advantage of this regime to produce GBs with minimal roughness
faster than near-equilibrium conditions. This suggests an unexpected route
towards efficient growth of two-dimensional materials with smooth GBs.

categories: cond-mat.mtrl-sci cond-mat.mes-hall cond-mat.stat-mech physics.app-ph


title: Pregeometry over locally o-minimal structure and dimension

abstract:
  We define a discrete closure operation for definably complete locally
o-minimal structures $\mathcal M$. The pair of the underlying set of $\mathcal
M$ and the discrete closure operation forms a pregeometry. We define the rank
of a definable set over a set of parameters using this fact. A definable set
$X$ is of dimension equal to the rank of $X$ over the set of parameters of a
formula defining the set $X$. The structure $\mathcal M$ is simultaneously a
first-order topological structure. The dimension rank of a set definable in the
first-order topological structure $\mathcal M$ also coincides with its
dimension.


title: Brown measure of $R$-diagonal operators, revisited

abstract:
  We observe that subordination functions were used implicitly in
Haagerup-Schultz's approach for the Brown measure of $R$-diagonal operators.
This allows us to simplify the original argument and find a connection with the
other approach due to Belinschi-\'{S}niady-Speicher. The Brown measure formula
can be rewritten in terms of subordination functions.


title: A classically excited Hong-Ou-Mandel effect

abstract:
  Nonlocal quantum correlation has been the main subject of quantum mechanics
over the last century. Over the last few decades, various quantum technologies
have been developed and applied for real matters of quantum information
science. The Hong-Ou-Mandel (HOM) effect relates to the two-photon intensity
correlation on a beam splitter, resulting in a quantum bunching phenomenon. In
quantum information science, the HOM effect is a major test tool for the
quantum features of Bell states. Here, a classically excited HOM effect is
presented in a Mach-Zehnder interferometers using coherent photon pairs. In
this approach, basis-product indistinguishability between paired photons on a
beam splitter is achieved via fast sweeping acousto-optic modulators, whose
frequency sweeping directions are opposite to each other. The resulting HOM
effect is also verified via numerical calculations of the analytically driven
solutions.


title:
Notes on definably complete locally o-minimal expansions of ordered
  groups

abstract:
  We study definably complete locally o-minimal expansions of ordered groups in
this paper. A definable continuous function defined on a closed, bounded and
definable set behave like a continuous function on a compact set. We
demonstrate uniform continuity of a definable continuous function on a closed,
bounded and definable set and Arzela-Ascoli-type theorem. We propose a notion
of special submanifolds with tubular neighborhoods and show that any definable
set is decomposed into finitely many special submanifolds with tubular
neighborhoods.


title:
MonoTrack: Shuttle trajectory reconstruction from monocular badminton
  video

abstract:
  Trajectory estimation is a fundamental component of racket sport analytics,
as the trajectory contains information not only about the winning and losing of
each point, but also how it was won or lost. In sports such as badminton,
players benefit from knowing the full 3D trajectory, as the height of
shuttlecock or ball provides valuable tactical information. Unfortunately, 3D
reconstruction is a notoriously hard problem, and standard trajectory
estimators can only track 2D pixel coordinates. In this work, we present the
first complete end-to-end system for the extraction and segmentation of 3D
shuttle trajectories from monocular badminton videos. Our system integrates
badminton domain knowledge such as court dimension, shot placement, physical
laws of motion, along with vision-based features such as player poses and
shuttle tracking. We find that significant engineering efforts and model
improvements are needed to make the overall system robust, and as a by-product
of our work, improve state-of-the-art results on court recognition, 2D
trajectory estimation, and hit recognition.


title:
Outage Performance of RIS-aided Cooperative FD-SWIPT-NOMA in Nakagami-m
  Channels

abstract:
  In this work we {derive new} analytical expressions for the outage
probability (OP) of the downlink (DL) cooperative full-duplex (FD) simultaneous
wireless information power transfer (SWIPT) non-orthogonal multiple access
(NOMA) system aided by reconfigurable intelligent surfaces (RIS). The
expressions for both the strongest and weakest NOMA users are devised assuming
Nakagami-$m$ channel fading. The derived analytical OP expressions are simple
to compute yet accurate for a wide range of RIS passive elements
configurations, energy harvesting (EH) coefficient, and residual
self-interference (SI) levels, being extensively validated by numerical
simulations, demonstrating the correctness and accuracy of the proposed
analytical method. The OP expressions reveal how paramount is to mitigate the
SI in the FD relay mode, since for reasonable values of residual SI coefficient
($\bar{\omega}\geq -13$dB), it is notable its detrimental effect over the
system performance; hence, new SI reduction methods for FD relays are useful
for low number of passive elements. Also, applying the proposed OP expressions
to predict the behaviour of the RIS-NOMA system equipped with a higher number
of passive elements ($N\geq 30$) reveals a substantial reduction of the SI
effect, motivating the implementation of the cooperative FD communications.
Furthermore, we found the asymptotic behavior of outage probability of both
clustered users, as well as the equal diversity order for both users, given by
$\frac{N\mu^2}{2-2\mu^2}$ if the fraction of the harvest energy $\rho =0$ or
$0$ if $\rho\neq0$, indicating the influence of channel parameters and number
of RIS elements in the performance.