title:
Relativistic propagation and frame dragging time delay in the timing of
  a pulsar orbiting the supermassive black hole SgrA*

abstract:
  Timing a pulsar in a close orbit around the supermassive black hole SgrA* at
the center of the Milky Way would open the window for an accurate determination
of the black hole parameters and for new tests of General Relativity and
alternative modified gravity theories. An important relativistic effect which
has to be taken into account in the timing model is the propagation delay of
the pulses in the gravitational field of the black hole. Due to the extreme
mass ratio of the pulsar and the supermassive back hole we use the test
particle limit to derive an exact analytical formula for the propagation delay
in a Kerr spacetime and deduce a relativistic formula for the frame dragging
effect on the arrival time. As an illustration, we treat an edge-on orbit in
which the frame dragging effect is expected to be maximal. We compare our
formula for the propagation time delay with Post-Newtonian approaches, and in
particular with the frame dragging terms derived in previous works by Wex &
Kopeikin and Rafikov & Lai. Our approach correctly identifies the asymmetry of
the frame dragging delay with respect to superior conjunction, avoids
singularities in the time delay, and indicates that in the Post-Newtonian
approach frame dragging effects are generally slightly overestimated.


title: Watkins' conjecture for elliptic curves over function fields

abstract:
  In 2002 Watkins conjectured that given an elliptic curve defined over
$\mathbb{Q}$, its Mordell-Weil rank is at most the $2$-adic valuation of its
modular degree. We consider the analogous problem over function fields of
positive characteristic, and we prove it in several cases. More precisely,
every modular semi-stable elliptic curve over $\mathbb{F}_q(T)$ after extending
constant scalars, and every quadratic twist of a modular elliptic curve over
$\mathbb{F}_q(T)$ by a polynomial with sufficiently many prime factors satisfy
the analogue of Watkins' conjecture. Furthermore, for a well-known family of
elliptic curves with unbounded rank due to Ulmer, we prove the analogue of
Watkins' conjecture.


title: Structure-preserving model reduction of multi-symplectic PDEs

abstract:
  Many Hamiltonian systems can be recast in multi-symplectic form. We develop a
structure-preserving reduced-order model (ROM) for multi-symplectic Hamiltonian
PDEs under periodic boundary conditions. The full-order solutions are obtained
by finite difference discretization in space and the energy preserving average
vector field (AVF) method. The ROM is constructed in the same way as the
full-order model (FOM) applying proper orthogonal decomposition (POD) with the
Galerkin projection. The reduced-order system has the same multi-symplectic
structure as the FOM, and preserves the discrete reduced global energy.
Applying the discrete empirical interpolation method (DEIM), the reduced-order
solutions are computed efficiently in the online stage. An a priori error bound
is derived for the DEIM approximation to the nonlinear Hamiltonian. The
accuracy and computational efficiency of the ROMs are demonstrated for one- and
two-dimensional multi-symplectic Hamiltonian partial differential equations
such as KdV equation, Zakharov-Kuznetzov equation, and NLS equation.
Preservation of the reduced energies shows that the reduced-order solutions
ensure the long-term stability of the solutions.


title:
Numerical analysis of effective refractive index ultrasonic sensor based
  on Cantilever arm structure slot-based dual-micro-ring resonator

abstract:
  We propose a novel ultrasonic sensor structure composed of Cantilever arm
structure slot dual-micro-ring resonators(DMRR).We present a theoretical
analysis of transmission by using the coupled mode theory.The mode field
distributions and sound pressure distributions of transmission spectrum are
obtained from 3D simulations based on Comsol Multi-physics(COMSOL)method.Our
ultrasonic sensor exhibit theoretical sensitivity as high as 1462.5mV/kPa,which
is 22 times higher than that of single slot-based micro-ring ultrasonic
sensor.Our ultrasonic sensor offers higher sensitivity and a larger detection
frequency range than conventional piezoelectric based ultrasound transducer.The
results show that the sensing characteristics of our system can be optimized
through changing the position and the angle of sound field.Our ultrasonic
sensor with an area of 25um*60um, the-factor can be approximately 1540 with
radius of 5um We detect an angular range of -90o to 90o and a minimum distance
of 0.01um. Finally,we calculate the Cantilever arm structure slot DMRR array
ultrasonic sensor's optical performance. Our proposed design provides a
promising candidate for a hydrophone.


title: Torus Lorenz Links obtained by Full Twists along Torus Links

abstract:
  All knots are known to be hyperbolic, satellite, or torus knots, and one
important family is Lorenz links, or T-links, which arise from dynamics.
However, it remains difficult to determine the geometric type of a Lorenz link
from a description via dynamics or as a T-link. In this paper, we consider
those T-links that are torus links. We show that T-links obtained by full
twists along torus links can never be torus links, aside from a family of
cases. This addresses a question of Birman and Kofman.


title: On approximation by inner functions

abstract:
  This note finds an approximation of every matrix-valued contractive
holomorphic function on the open unit disc by rational inner functions. This
theorem is then applied to obtain a uniform approximation result for
holomorphic functions which extend continuously to the unit circle. Also, as
applications, approximation results are obtained for holomorphic functions on
the unit disc with values in certain other domains of interest.


title:
Antiferromagnetic helix as an efficient spin polarizer: Interplay
  between electric field and higher ordered hopping

abstract:
  Considering an antiferromagnetic helix system, possessing zero net
magnetization, we report spin filtration operation. Common wisdom suggests that
for such a system, spin polarized current is no longer available from a beam of
unpolarized electrons. But, once we apply an electric field perpendicular to
the helix axis, a large separation between up and down spin energy channels
takes place which yields a high degree of spin polarization. Such a
prescription has not been reported so far to the best of our concern. Employing
a tight-binding framework to illustrate the antiferromagnetic helix, we compute
spin filtration efficiency by determining spin selective currents using
Landauer-B\"{u}ttiker formalism. Geometrical conformation plays an important
role on spin channel separation, and here we critically investigate the effects
of short-range and long-range hoppings of electrons in presence of the electric
field. We find that the filtration performance gets improved with increasing
the range hopping of electrons. Moreover, the phase of spin polarization can be
altered selectively by changing the strength and direction of the field, and
also by regulating the physical parameters that describe the helix. Finally, we
explore the specific role of dephasing, to make the system more realistic and
to make the discussion a self-contained one. Our analysis may provide a new
route of getting conformation dependent spin polarization possessing longer
range hopping of electrons, and can be generalized to different kinds of
fascinating anti-ferromagnetic systems.

categories: cond-mat.mes-hall cond-mat.dis-nn cond-mat.mtrl-sci cond-mat.other cond-mat.soft


title:
Geolocation estimation of target vehicles using image processing and
  geometric computation

abstract:
  Estimating vehicles' locations is one of the key components in intelligent
traffic management systems (ITMSs) for increasing traffic scene awareness.
Traditionally, stationary sensors have been employed in this regard. The
development of advanced sensing and communication technologies on modern
vehicles (MVs) makes it feasible to use such vehicles as mobile sensors to
estimate the traffic data of observed vehicles. This study aims to explore the
capabilities of a monocular camera mounted on an MV in order to estimate the
geolocation of the observed vehicle in a global positioning system (GPS)
coordinate system. We proposed a new methodology by integrating deep learning,
image processing, and geometric computation to address the observed-vehicle
localization problem. To evaluate our proposed methodology, we developed new
algorithms and tested them using real-world traffic data. The results indicated
that our proposed methodology and algorithms could effectively estimate the
observed vehicle's latitude and longitude dynamically.


title: Deep Learning Serves Traffic Safety Analysis: A Forward-looking Review

abstract:
  This paper explores Deep Learning (DL) methods that are used or have the
potential to be used for traffic video analysis, emphasizing driving safety for
both Autonomous Vehicles (AVs) and human-operated vehicles. We present a
typical processing pipeline, which can be used to understand and interpret
traffic videos by extracting operational safety metrics and providing general
hints and guidelines to improve traffic safety. This processing framework
includes several steps, including video enhancement, video stabilization,
semantic and incident segmentation, object detection and classification,
trajectory extraction, speed estimation, event analysis, modeling and anomaly
detection. Our main goal is to guide traffic analysts to develop their own
custom-built processing frameworks by selecting the best choices for each step
and offering new designs for the lacking modules by providing a comparative
analysis of the most successful conventional and DL-based algorithms proposed
for each step. We also review existing open-source tools and public datasets
that can help train DL models. To be more specific, we review exemplary traffic
problems and mentioned requires steps for each problem. Besides, we investigate
connections to the closely related research areas of drivers' cognition
evaluation, Crowd-sourcing-based monitoring systems, Edge Computing in roadside
infrastructures, ADS-equipped AVs, and highlight the missing gaps. Finally, we
review commercial implementations of traffic monitoring systems, their future
outlook, and open problems and remaining challenges for widespread use of such
systems.


title: Quality Controlled Paraphrase Generation

abstract:
  Paraphrase generation has been widely used in various downstream tasks. Most
tasks benefit mainly from high quality paraphrases, namely those that are
semantically similar to, yet linguistically diverse from, the original
sentence. Generating high-quality paraphrases is challenging as it becomes
increasingly hard to preserve meaning as linguistic diversity increases. Recent
works achieve nice results by controlling specific aspects of the paraphrase,
such as its syntactic tree. However, they do not allow to directly control the
quality of the generated paraphrase, and suffer from low flexibility and
scalability. Here we propose $QCPG$, a quality-guided controlled paraphrase
generation model, that allows directly controlling the quality dimensions.
Furthermore, we suggest a method that given a sentence, identifies points in
the quality control space that are expected to yield optimal generated
paraphrases. We show that our method is able to generate paraphrases which
maintain the original meaning while achieving higher diversity than the
uncontrolled baseline. The models, the code, and the data can be found in
https://github.com/IBM/quality-controlled-paraphrase-generation.


title: Transfer Dynamics in Emergent Evolutionary Curricula

abstract:
  PINSKY is a system for open-ended learning through neuroevolution in
game-based domains. It builds on the Paired Open-Ended Trailblazer (POET)
system, which originally explored learning and environment generation for
bipedal walkers, and adapts it to games in the General Video Game AI (GVGAI)
system. Previous work showed that by co-evolving levels and neural network
policies, levels could be found for which successful policies could not be
created via optimization alone. Studied in the realm of Artificial Life as a
potentially open-ended alternative to gradient-based fitness, minimal criteria
(MC)-based selection helps foster diversity in evolutionary populations. The
main question addressed by this paper is how the open-ended learning actually
works, focusing in particular on the role of transfer of policies from one
evolutionary branch ("species") to another. We analyze the dynamics of the
system through creating phylogenetic trees, analyzing evolutionary trajectories
of policies, and temporally breaking down transfers according to species type.
Furthermore, we analyze the impact of the minimal criterion on generated level
diversity and inter-species transfer. The most insightful finding is that
inter-species transfer, while rare, is crucial to the system's success.


title:
Quantum Mechanics: reality or an illusion of the detector? Paper I:
  Interference with non-interacting photons and a special type of detector

abstract:
  We present a statistical model of non-interacting individual classical
particles that may lead to a microscopic implementation of quantum mechanics.
The model requires the action of a special type of detector that detects and
records individual particles. According to our model, microscopic particles are
classical entities that follow classical trajectories without nonlocal
interactions between them. As long as they remain undetected, particles carry
the information of a phase equal to an action integral along their trajectory.
At the point of their detection, our special type of detector collects the
phases from all particles reaching it, adds them up over time as complex
numbers, and divides them by the square root of their number. The detector
records a detection each time the square of the magnitude of the ensuing vector
crosses an integer value. We test our model with numerical simulations of a
double-slit experiment, and obtain an interference pattern analogous to the one
observed in an actual physical experiment.


title: Quantum turbulence and Planckian dissipation

abstract:
  The notion of the Planckian dissipation is extended to the system of the
Caroli-de Gennes-Matricon discrete energy levels in the vortex core of
superconductors and fermionic superfluids. In this extension, the Planck
dissipation takes place when the relaxation time $\tau$ is comparable with the
quantum Heisenberg time $t_H=\hbar/\Delta E$, where $\Delta E$ is the
interlevel distance in the vortex core (the minigap). This type of Planck
dissipation has two important physical consequences. First, it determines the
regime, when the effect of the axial anomaly becomes important. The anomalous
spectral flow of the energy levels along the chiral branch of the Caroli-de
Gennes-Matricon states becomes important in the super-Planckian region, i.e.
when $\tau <\hbar/\Delta E$. Second, the Planck dissipation separates the
laminar flow of the superfluid liquid at $\tau<\hbar/\Delta E$ and the vortex
turbulence regime at $\tau>\hbar/\Delta E$.


title: Spreadsheet computing with Finite Domain Constraint Enhancements

abstract:
  Spreadsheet computing is one of the more popular computing methodologies in
today's modern society. The spreadsheet application's ease of use and
usefulness has enabled non-programmers to perform programming-like tasks in a
familiar setting modeled after the tabular "pen and paper" approach. However,
spreadsheet applications are limited to bookkeeping-like tasks due to their
single-direction data flow. This thesis demonstrates an extension of the
spreadsheet computing paradigm in overcoming this limitation to solve
constraint satisfaction problems. We present a framework seamlessly
incorporating a finite constraint solver with the spreadsheet computing
paradigm. This framework allows the individual cells in the spreadsheet to be
attached to either a finite domain or a constraint specifying the relationship
among the cells. The framework provides an interface for constraint solving and
further enhances the spreadsheet computing paradigm by providing a set of
spreadsheet-specific constraints that will aid in controlling the scalability
of large spreadsheet applications implementations. Finally, we provide examples
to demonstrate the usability and usefulness of the extended spreadsheet
paradigm.
  Keywords: Spreadsheet computing, Constraint Logic Programming, Constraint
satisfaction, Domain-Specific language, Excel, SWI Prolog, C#


title:
AraBART: a Pretrained Arabic Sequence-to-Sequence Model for Abstractive
  Summarization

abstract:
  Like most natural language understanding and generation tasks,
state-of-the-art models for summarization are transformer-based
sequence-to-sequence architectures that are pretrained on large corpora. While
most existing models focused on English, Arabic remained understudied. In this
paper we propose AraBART, the first Arabic model in which the encoder and the
decoder are pretrained end-to-end, based on BART. We show that AraBART achieves
the best performance on multiple abstractive summarization datasets,
outperforming strong baselines including a pretrained Arabic BERT-based model
and multilingual mBART and mT5 models.


title:
Transitivity of normal subgroups of the mapping class group on character
  varieties

abstract:
  We prove that the action of any non-trivial normal subgroup of the mapping
class group of a surface of genus $g\geqslant 2$ is almost minimal on the
character variety $X(\pi_1\Sigma_g,{\rm SU}_2)$: the orbit of almost every
point is dense.


title: Fast OGDA in continuous and discrete time

abstract:
  We study in a real Hilbert space continuous in time dynamics as well as
numerical algorithms for the problem of approaching the set of zeros of a
single-valued monotone and continuous operator $V$. The starting point is a
second order dynamical system that combines a vanishing damping term with the
time derivative of $V$ along the trajectory. Our method exhibits fast
convergence rates of order $o \left( \frac{1}{t\beta(t)} \right)$ for
$\|V(z(t))\|$, and $\beta(\cdot)$ is a nondecreasing function satisfiyng a
growth condition, and also for the restricted gap function, which is a measure
of optimality for variational inequalities. We also prove the weak convergence
of the trajectory to a zero of $V$. Temporal discretizations generate implicit
and explicit numerical algorithms, which can be both seen as accelerated
versions of the Optimistic Gradient Descent Ascent (OGDA) method, for which we
prove that the generated sequence of iterates shares the asymptotic features of
the continuous dynamics. In particular we show for the implicit numerical
algorithm convergence rates of order $o \left( \frac{1}{k\beta_k} \right)$ for
$\|V(z^k)\|$ and the restricted gap function, where $(\beta_k)_{k \geq 0}$ is a
nondecreasing sequence satisfying a growth condition. For the explicit
numerical algorithm we show by additionally assuming that the operator $V$ is
Lipschitz continuous convergence rates of order $o \left( \frac{1}{k} \right)$
for $\|V(z^k)\|$ and the restricted gap function. All convergence rate
statements are last iterate convergence results; in addition to these we prove
for both algorithms the convergence of the iterates to a zero of $V$. To our
knowledge, our study exhibits the best known convergence rate results for
monotone equations. Numerical experiments indicate the overwhelming superiority
of our explicit numerical algorithm over other methods for monotone equations.


title: YFS Resummation for Future Lepton-Lepton Colliders in SHERPA

abstract:
  We present an implementation of the Yennie--\-Frautschi--\-Suura (YFS) scheme
for the all-orders resummation of logarithms from the emission of soft real and
virtual photons in processes that are critical for future lepton colliders.
They include, in particular, $e^-e^+\to f\bar{f}$ and $e^-e^+\to W^-W^+$, where
we validate the results of our implementation, improved with fixed-order
corrections, with those obtained from the most precise calculations. We also
show, for the first time, results for the Higgs-Strahlungs process, $e^-e^+\to
ZH$, in YFS resummation including fixed-order improvements up to order
$\alpha^3L^3$.


title:
Optimizing Trajectories for Highway Driving with Offline Reinforcement
  Learning

abstract:
  Implementing an autonomous vehicle that is able to output feasible, smooth
and efficient trajectories is a long-standing challenge. Several approaches
have been considered, roughly falling under two categories: rule-based and
learning-based approaches. The rule-based approaches, while guaranteeing safety
and feasibility, fall short when it comes to long-term planning and
generalization. The learning-based approaches are able to account for long-term
planning and generalization to unseen situations, but may fail to achieve
smoothness, safety and the feasibility which rule-based approaches ensure.
Hence, combining the two approaches is an evident step towards yielding the
best compromise out of both. We propose a Reinforcement Learning-based
approach, which learns target trajectory parameters for fully autonomous
driving on highways. The trained agent outputs continuous trajectory parameters
based on which a feasible polynomial-based trajectory is generated and
executed. We compare the performance of our agent against four other highway
driving agents. The experiments are conducted in the Sumo simulator, taking
into consideration various realistic, dynamically changing highway scenarios,
including surrounding vehicles with different driver behaviors. We demonstrate
that our offline trained agent, with randomly collected data, learns to drive
smoothly, achieving velocities as close as possible to the desired velocity,
while outperforming the other agents. Code, training data and details available
at: https://nrgit.informatik.uni-freiburg. de/branka.mirchevska/offline-rl-tp.


title: Dynamic Certification for Autonomous Systems

abstract:
  Autonomous systems are often deployed in complex sociotechnical environments,
such as public roads, where they must behave safely and securely. Unlike many
traditionally engineered systems, autonomous systems are expected to behave
predictably in varying "open world" environmental contexts that cannot be fully
specified formally. As a result, assurance about autonomous systems requires us
to develop new certification methods and mathematical tools that can bound the
uncertainty engendered by these diverse deployment scenarios, rather than
relying on static tools.


title:
Domain-knowledge-aided machine learning method for properties prediction
  of soft magnetic metallic glasses

abstract:
  A machine learning (ML) method aided by domain knowledge was proposed to
predict saturated magnetization (Bs) and critical diameter (Dmax) of soft
magnetic metallic glass (MGs). Two datasets were established based on published
experimental works about soft magnetic MGs. A general feature space was
proposed and proved to be adaptive for ML model training for different
prediction tasks. It was found that the predictive performance of ML models was
better than traditional physical knowledge-based estimation methods. In
addition, domain knowledge aided feature selection can greatly reduce the
number of features without significantly reducing the prediction accuracy.
Finally, binary classification of the critical size of soft magnetic metallic
glass was studied.


title:
Large anomalous Hall effect and anisotropic magnetoresistance in
  intrinsic nanoscale spin-valve-type structure of an antiferromagnet

abstract:
  A spin valve is a prototype of spin-based electronic devices found on
ferromagnets, in which an antiferromagnet plays a supporting role. Recent
findings in antiferromagnetic spintronics show that an antiferromagnetic order
in single-phase materials solely governs dynamic transport, and
antiferromagnets are considered promising candidates for spintronic technology.
In this work, we demonstrated antiferromagnet-based spintronic functionality on
an itinerant Ising antiferromagnet of Ca0.9Sr0.1Co2As2 by integrating nanoscale
spin-valve-type structure and investigating anisotropic magnetic properties
driven by spin-flips. Multiple stacks of 1 nm thick spin-valve-like unit are
intrinsically embedded in the antiferromagnetic spin structure. In the presence
of a rotating magnetic field, a new type of the spin-valve-like operation was
observed for large anomalous Hall conductivity and anisotropic
magnetoresistance, whose effects are maximized above the spin-flip transition.
In addition, a joint experimental and theoretical study provides an efficient
tool to read out various spin states, which scheme can be useful for
implementing extensive spintronic applications.


title:
Particle Physics Outreach to K-12 Schools and Opportunities in
  Undergraduate Education

abstract:
  To develop an increase in societal interest in the fundamental sciences of
particle physics and particularly for maintaining the support structures needed
to succeed in experiments that take several decades to develop and complete,
requires strong educational back-grounding at all levels of the instructional
system and notably at early stages in the process. While many (particularly
young) students might show an early interest and aptitude for science and
mathematics at the elementary level, the structures are not necessarily in
place to capture, nurture and develop such nascent interests. To encourage and
strengthen such interests, strong connections must be made at K-12 and
Undergraduate levels. The paper discusses the on-going efforts and makes
recommendations.


title: Anisotropic f(Q) gravity model with bulk viscosity

abstract:
  In this paper, we are going to examine the spatially homogeneous and
anisotropic LRS Bianchi type-I (B-I) Universe with viscous fluid in the
framework of $% f\left( Q\right) $ symmetric teleparallel gravity (Phys. Rev. D
98.4 (2018): 044048). To solve the field equations, the following two
hypotheses are proposed: (i) the relationship between the directional Hubble
parameters i.e. $H_{x}=nH_{y}$ (where $n\neq 0,1$ is an arbitrary real number)
which is obtained from the proportionality relation between the expansion
scalar $% \theta $ and shear scalar $\sigma ^{2}$ i.e.$\ \theta \propto \sigma
^{2}$, and (ii) the relationship between the average Hubble parameter $H$ and
bulk viscous coefficient i.e. $\zeta =\zeta_{0}+\zeta_{1}H+\zeta_{2}\left(
\frac{\overset{.}{H}}{H}+H\right) $, where $\zeta_{0}$, $\zeta_{1}$ and $%
\zeta_{2}$ are constants. Some cosmological parameters such as the deceleration
parameter, EoS parameter, skewness parameter, statefinder and $% Om(z)$
diagnostic parameters of the model are produced and evaluated through graphical
representation. It was also confirmed that our $f\left( Q\right) $ gravity
model with bulk viscosity is consistent with recent observational data.


title: Chebyshev polynomials in the 16th century

abstract:
  We give a few examples of Chebyshev polynomials that appeared in mathematical
problems from the 16th and 17th century. The main example is the famous
equation of Adrianus Romanus (Adriaan van Roomen) containing a polynomial of
degree $45$.


title: MOVES V. Modelling star-planet magnetic interactions of HD 189733

abstract:
  Magnetic interactions between stars and close-in planets may lead to a
detectable signal on the stellar disk. HD 189733 is one of the key exosystems
thought to harbor magnetic interactions, which may have been detected in August
2013. We present a set of twelve wind models at that period, covering the
possible coronal states and coronal topologies of HD 189733 at that time. We
assess the power available for the magnetic interaction and predict its
temporal modulation. By comparing the predicted signal with the observed
signal, we find that some models could be compatible with an interpretation
based on star-planet magnetic interactions. We also find that the observed
signal can be explained only with a stretch-and-break interaction mechanism,
while that the Alfv\'en wings scenario cannot deliver enough power. We finally
demonstrate that the past observational cadence of HD 189733 leads to a
detection rate of only between 12 to 23%, which could explain why star-planet
interactions have been hard to detect in past campaigns. We conclude that the
firm confirmation of their detection will require dedicated spectroscopic
observations covering densely the orbital and rotation period, combined with
scarcer spectropolarimetric observations to assess the concomitant large-scale
magnetic topology of the star.


title:
Theory and phenomenology of a four dimensional string-corrected black
  hole

abstract:
  We construct an effective four dimensional string-corrected black hole (4D
SCBH) by rescaling the string coupling parameter in a $D$-dimensional
Callan-Myers-Perry black hole. From the theoretical point of view, the most
interesting findings are that the string corrections coincide with the
so-called generalized uncertainty principle (GUP) corrections to black hole
solutions, Bekenstein-Hawking entropy acquires logarithmic corrections, and
that there exists a critical value of the coupling parameter for which the
black hole temperature vanishes. We also find that, due to the string
corrections the nature of the central singularity may be altered from spacelike
to timelike singularity. In addition, we study the possibility of testing such
a black hole with astrophysical observations. Since the dilaton field does not
decouple from the metric it is not a priori clear that the resulting 4D SCBH
offers only small corrections to the Schwarzschild black hole. We used motion
of the S2 star around the black hole at the center of our galaxy to constrain
the parameters (the string coupling parameter and ADM mass) of the 4D SCBH. To
test the weak gravity regime we calculate the deflection angle in this geometry
and apply it to gravitational lensing. To test the strong field regime, we
calculate the black hole shadow radius. While we find that the observables
change as we change the string coupling parameter, the magnitude of the change
is too small to distinguish it from the Schwarzschild black hole. With the
current precision, to the leading order terms, the 4D SCBH cannot be
distinguished from the Schwarzschild black hole.


title: On the Origin of Black Hole Paradoxes

abstract:
  Black hole firewall paradox is an inconsistency between four postulates in
black hole physics: (1) the unitary evolution in quantum systems, (2)
application of the semi-classical field theory in low curvature backgrounds,
(3) statistical mechanical origin of the black hole entropy, and (4) the
equivalence principle in the version of no drama for free-falling observers in
the vicinity of the horizon. Based on the existence of the Hawking radiation
for the static observers standing outside a Schwarzschild black hole, we show a
direct contradiction between the postulates (2) and (4). If there is not a way
out of this new problem, it implies the necessity of relaxing one of these two
assumptions for resolving the black hole firewall paradox.


title:
Novel Physics with International Pulsar Timing Array: Axionlike
  Particles, Domain Walls and Cosmic Strings

abstract:
  After NANOGrav, the IPTA collaboration also reports a strong evidence of a
stochastic gravitation wave background. This hint has very important
implications for fundamental physics. With the recent IPTA data release two, we
attempt to search signals of light new physics. and give new constraints on the
audible axion, domain walls and cosmic strings models. We find that the best
fit point corresponding to a decay constant $F\approx5\times10^{17}$ GeV and an
axion mass $m_a\approx2\times10^{-13}$ eV from NANOGrav data is ruled out by
IPTA at beyond $2\sigma$ confidence level. Fixing the coupling strength
$\lambda=1$, we obtain a $2\sigma$ lower bound on the breaking scale of $Z_2$
symmetry $\eta>135$ TeV. Interestingly, we give a very strong restriction on
the cosmic-string tension $\mathrm{log}_{10}\,G\mu=-8.93_{-0.06}^{+0.12}$ at
$1\sigma$ confidence level. Employing the rule of Bayes factor, we find that
IPTA data has a moderate, strong and inconclusive preference of an uncorrelated
common power-law (CPL) model over audible axion, domain walls and cosmic
strings, respectively. This means that it is hard to distinguish CPL from
cosmic strings with current observations and more pulsar timing data with high
precision are required to give new clues of underlying physics.

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


title: Active Meta-Learner for Log Analysis

abstract:
  The analysis of logs is a vital activity undertaken for cyber investigation,
digital forensics and fault detection to enhance system and cyber resilience.
However, performing log analysis is a complex task. It requires extensive
knowledge of how the logs are generated and the format of the log entries used.
Also, it requires extensive knowledge or expertise in the identifying anomalous
log entries from normal or benign log entries. This is especially complex when
the forms of anomalous entries are constrained by what are the known forms of
internal or external attacks techniques or the varied forms of disruptions that
may exists. New or evasive forms of such disruptions are difficult to define.
The challenge of log analysis is further complicated by the volume of log
entries. Even with the availability of such log data, labelling such log
entries would be a massive undertaking. Hence this research seeks to address
these challenges with its novel Deep Learning model that learns and improves
itself progressively with inputs or corrections provided when available. The
practical application of such model construct facilitates log analysis or
review with abilities to learn or incorporate new patterns to spot anomalies or
ignore false positives.


title:
Bike Sharing Demand Prediction based on Knowledge Sharing across Modes:
  A Graph-based Deep Learning Approach

abstract:
  Bike sharing is an increasingly popular part of urban transportation systems.
Accurate demand prediction is the key to support timely re-balancing and ensure
service efficiency. Most existing models of bike-sharing demand prediction are
solely based on its own historical demand variation, essentially regarding bike
sharing as a closed system and neglecting the interaction between different
transport modes. This is particularly important because bike sharing is often
used to complement travel through other modes (e.g., public transit). Despite
some recent efforts, there is no existing method capable of leveraging
spatiotemporal information from multiple modes with heterogeneous spatial
units. To address this research gap, this study proposes a graph-based deep
learning approach for bike sharing demand prediction (B-MRGNN) with multimodal
historical data as input. The spatial dependencies across modes are encoded
with multiple intra- and inter-modal graphs. A multi-relational graph neural
network (MRGNN) is introduced to capture correlations between spatial units
across modes, such as bike sharing stations, subway stations, or ride-hailing
zones. Extensive experiments are conducted using real-world bike sharing,
subway and ride-hailing data from New York City, and the results demonstrate
the superior performance of our proposed approach compared to existing methods.


title: The SVOM Mission

abstract:
  The Sino-French space mission SVOM is mainly designed to detect, localize and
follow-up Gamma-Ray Bursts and other high-energy transients. The satellite, to
be launched mid 2023, embarks two wide-field gamma-ray instruments and two
narrow-field telescopes operating at X-ray and optical wavelengths. It is
complemented by a dedicated ground segment encompassing a set of wide-field
optical cameras and two 1-meter class follow-up telescopes. In this
contribution, we describe the main characteristics of the mission and discuss
its scientific rationale and some original GRB studies that it will enable.


title: Mean-Field Continuum Model of the Twisted Kitaev Bilayer

abstract:
  Kitaev quantum spin liquids have been the focus of intense research effort
thanks to the discovery of various materials (e.g., RuCl$_3$) that approximate
their intriguing physics. In this paper we construct a mean-field approximation
for a moire superlattice emerging in twisted Kitaev bilayers in terms of
solutions of commensurate bilayers. We show that the band structure of
deconfined spinons, defined on the mini-Brillouin zone of the superlattice, is
greatly modified. Bands which are almost perfectly flat appear at energies
above the lowest gap. Including intralayer modulation, such bands become
isolated from other dispersive ones. Intriguingly, flat-band eigenstates
exhibit a localization akin to wavefunctions of Kagome lattices.


title:
Sign-tunable anisotropic magnetoresistance and electrically detectable
  dual magnetic phases in a helical antiferromagnet

abstract:
  The helimagnetic order describes a non-collinear spin texture of
antiferromagnets, arising from competing exchange interactions. Although
collinear antiferromagnets are elemental building blocks of antiferromagnetic
(AFM) spintronics, the potential of implementing spintronic functionality in
non-collinear antiferromagnets has not been clarified thus far. Here, we
propose an AFM helimagnet of EuCo2As2 as a novel single-phase spintronic
material that exhibits a remarkable sign reversal of anisotropic
magnetoresistance (AMR). The contrast in the AMR arises from two electrically
distinctive magnetic phases with spin reorientation driven by magnetic field
lying on the easy-plane, which switches the sign of the AMR from positive to
negative. Further, various AFM memory states associated with the evolution of
the spin structure under magnetic fields were identified theoretically, based
on an easy-plane anisotropic spin model. These results reveal that
non-collinear antiferromagnets hold potential for developing spintronic
devices.


title:
PTM4Tag: Sharpening Tag Recommendation of Stack Overflow Posts with
  Pre-trained Models

abstract:
  Stack Overflow is often viewed as the most influential Software Question
Answer (SQA) website with millions of programming-related questions and
answers. Tags play a critical role in efficiently structuring the contents in
Stack Overflow and are vital to support a range of site operations, e.g.,
querying relevant contents. Poorly selected tags often introduce extra noise
and redundancy, which leads to tag synonym and tag explosion problems. Thus, an
automated tag recommendation technique that can accurately recommend
high-quality tags is desired to alleviate the problems mentioned above.
Inspired by the recent success of pre-trained language models (PTMs) in natural
language processing (NLP), we present PTM4Tag, a tag recommendation framework
for Stack Overflow posts that utilize PTMs with a triplet architecture, which
models the components of a post, i.e., Title, Description, and Code with
independent language models. To the best of our knowledge, this is the first
work that leverages PTMs in the tag recommendation task of SQA sites. We
comparatively evaluate the performance of PTM4Tag based on five popular
pre-trained models: BERT, RoBERTa, ALBERT, CodeBERT, and BERTOverflow. Our
results show that leveraging the software engineering (SE) domain-specific PTM
CodeBERT in PTM4Tag achieves the best performance among the five considered
PTMs and outperforms the state-of-the-art deep learning (Convolutional Neural
Network-based) approach by a large margin in terms of average $Precision@k$,
$Recall@k$, and $F1$-$score@k$. We conduct an ablation study to quantify the
contribution of a post's constituent components (Title, Description, and Code
Snippets) to the performance of PTM4Tag. Our results show that Title is the
most important in predicting the most relevant tags, and utilizing all the
components achieves the best performance.


title: Controllable energy angular spectrum method

abstract:
  A controllable energy method, which considers the undersampling issue of the
transfer function and valid spectral energy of a source signal, is proposed to
implement angular spectrum diffraction calculation in near and far fields. The
proposed method provides an optimized frequency boundary $f_{CE}$ within which
it always keeps controllable energy to be diffracted. The controllable energy
angular spectrum method significantly reduces the number of samples while
having the same accuracy as previous angular spectrum methods, implying a
higher calculation efficiency. The new perspective of analyzing spectral energy
is shown to improve the performance of relevant diffraction calculations.


title: Proper spaces are spectral

abstract:
  Since Hochster's work, spectral spaces have attracted increasing interest.
Through this note we intend to show that the set of proper ideals of a ring
endowed with coarse lower topology is a spectral space.


title:
Screw motion surfaces of constant mean curvature in homogeneous
  3-manifolds

abstract:
  We study the geometric behavior of constant mean curvature surfaces invariant
under screw motion in the homogeneous 3-manifolds $\mathbb{E}(\kappa,\tau)$ as
well as the space-forms of non-negative curvature and give a complete
classification. We give a unified presentation of results that have appeared in
the literature in various forms.


title:
Prediction of novel two-dimensional rare-earth material with
  room-temperature ferromagnetism and large perpendicular magnetic anisotropy

abstract:
  Novel 2D ferromagnets with high Curie temperature and large perpendicular
magnetic anisotropy are especially attractive owing to the future promising
application in modern spintronics, but meanwhile the 2D ferromagnetic materials
with high Curie temperature and large perpendicular magnetic anisotropy are
rarely reported. Based on density functional theory (DFT) calculations, we
predict a new kind of 2D ferromagnetic materials - GdB2N2, which possesses
large magnetic moment, high Curie temperature (335 K) and large perpendicular
magnetic anisotropy (10.38 meV/f.u.). Biaxial strain ranging from -0.5% to 5%
and different concentrations of charge-carrier doping are applied to reveal the
influence on the Curie temperature and magnetic anisotropy energy (MAE).
Besides, magnetic coupling process within GdB2N2 is found to be via a
Ruderman-Kittel-Kasuya-Yosida (RKKY) mechanism. In summary, our work here
predicts a novel 2D rare-earth material GdB2N2, which not only enriches the
category of 2D room-temperature ferromagnets, but also proposes a new
possibility of combining traditional 2D materials and rare-earth materials to
achieve more intriguing magnetic properties, finally it carves out the path for
the next-generation spintronic devices and sensors.


title: SOLIS: Autonomous Solubility Screening using Deep Neural Networks

abstract:
  Accelerating material discovery has tremendous societal and industrial
impact, particularly for pharmaceuticals and clean energy production. Many
experimental instruments have some degree of automation, facilitating
continuous running and higher throughput. However, it is common that sample
preparation is still carried out manually. This can result in researchers
spending a significant amount of their time on repetitive tasks, which
introduces errors and can prohibit production of statistically relevant data.
Crystallisation experiments are common in many chemical fields, both for
purification and in polymorph screening experiments. The initial step often
involves a solubility screen of the molecule; that is, understanding whether
molecular compounds have dissolved in a particular solvent. This usually can be
time consuming and work intensive. Moreover, accurate knowledge of the precise
solubility limit of the molecule is often not required, and simply measuring a
threshold of solubility in each solvent would be sufficient. To address this,
we propose a novel cascaded deep model that is inspired by how a human chemist
would visually assess a sample to determine whether the solid has completely
dissolved in the solution. In this paper, we design, develop, and evaluate the
first fully autonomous solubility screening framework, which leverages
state-of-the-art methods for image segmentation and convolutional neural
networks for image classification. To realise that, we first create a dataset
comprising different molecules and solvents, which is collected in a real-world
chemistry laboratory. We then evaluated our method on the data recorded through
an eye-in-hand camera mounted on a seven degree-of-freedom robotic manipulator,
and show that our model can achieve 99.13% test accuracy across various setups.


title:
Gradient-based parameter calibration of an anisotropic interaction model
  for pedestrian dynamics

abstract:
  We propose an extension of the anisotropic interaction model which allows for
collision avoidance in pairwise interactions by a rotation of forces
\cite{arXiv:1912.04234} by including the agents' body size. The influence of
the body size on the self-organization of the agents in channel and crossing
scenarios as well as the fundamental diagram is studied. Since the model is
stated as a coupled system of ordinary differential equations, we are able to
give a rigorous well-posedness analysis. Then we state a parameter calibration
problem that involves data from real experiments. We prove the existence of a
minimizer and derive the corresponding first-order optimality conditions. With
the help of these conditions we propose a gradient descent algorithm based on
mini-batches of the data set. We employ the proposed algorithm to fit the
parameter of the collision avoidance and the strength parameters of the
interaction forces to given real data from experiments. The results underpin
the feasibility of the method.


title: Mathematical Representation of Multitarget Systems

abstract:
  This paper systematically compares two mathematical foundations for
multitarget tracking: labeled random finite sets (LRFS's) and trajectory random
finite sets (TRFS's).


title:
A Local Convergence Theory for the Stochastic Gradient Descent Method in
  Non-Convex Optimization With Non-isolated Local Minima

abstract:
  Non-convex loss functions arise frequently in modern machine learning, and
for the theoretical analysis of stochastic optimization methods, the presence
of non-isolated minima presents a unique challenge that has remained
under-explored. In this paper, we study the local convergence of the stochastic
gradient descent method to non-isolated global minima. Under mild assumptions,
we estimate the probability for the iterations to stay near the minima by
adopting the notion of stochastic stability. After establishing such stability,
we present the lower bound complexity in terms of various error criteria for a
given error tolerance $\epsilon$ and a failure probability $\gamma$.


title: Towards Self-Supervised Gaze Estimation

abstract:
  Recent joint embedding-based self-supervised methods have surpassed standard
supervised approaches on various image recognition tasks such as image
classification. These self-supervised methods aim at maximizing agreement
between features extracted from two differently transformed views of the same
image, which results in learning an invariant representation with respect to
appearance and geometric image transformations. However, the effectiveness of
these approaches remains unclear in the context of gaze estimation, a
structured regression task that requires equivariance under geometric
transformations (e.g., rotations, horizontal flip). In this work, we propose
SwAT, an equivariant version of the online clustering-based self-supervised
approach SwAV, to learn more informative representations for gaze estimation.
We identify the most effective image transformations for self-supervised
pretraining and demonstrate that SwAT, with ResNet-50 and supported with
uncurated unlabeled face images, outperforms state-of-the-art gaze estimation
methods and supervised baselines in various experiments. In particular, we
achieve up to 57% and 25% improvements in cross-dataset and within-dataset
evaluation tasks on existing benchmarks (ETH-XGaze, Gaze360, and MPIIFaceGaze).


title:
GCF: Generalized Causal Forest for Heterogeneous Treatment Effect
  Estimation in Online Marketplace

abstract:
  Uplift modeling is a rapidly growing approach that utilizes machine learning
and causal inference methods to estimate the heterogeneous treatment effects.
It has been widely adopted and applied to online marketplaces to assist
large-scale decision-making in recent years. The existing popular methods, like
forest-based modeling, either work only for discrete treatments or make
partially linear or parametric assumptions that may suffer from model
misspecification. To alleviate these problems, we extend causal forest (CF)
with non-parametric dose-response functions (DRFs) that can be estimated
locally using a kernel-based doubly robust estimator. Moreover, we propose a
distance-based splitting criterion in the functional space of conditional DRFs
to capture the heterogeneity for the continuous treatments. We call the
proposed algorithm generalized causal forest (GCF) as it generalizes the use
case of CF to a much broader setup. We show the effectiveness of GCF by
comparing it to popular uplift modeling models on both synthetic and real-world
datasets. We implement GCF in Spark and successfully deploy it into DiDi's
real-time pricing system. Online A/B testing results further validate the
superiority of GCF.


title:
Examples of compact quantum groups with
  $\operatorname{\mathsf{L}^{\!\infty}}(\mathbb{G})$ a factor

abstract:
  For each $\lambda\in\left]0,1\right]$ we exhibit an uncountable family of
compact quantum groups $\mathbb{G}$ such that the von Neumann algebra
$\mathsf{L}^{\!\infty}(\mathbb{G})$ is the injective factor of type
$\mathrm{III}_\lambda$ with separable predual. We also show that uncountably
many injective factors of type $\mathrm{III}_0$ arise as
$\mathsf{L}^{\!\infty}(\mathbb{G})$ for some compact quantum group
$\mathbb{G}$. To distinguish between our examples we introduce invariants
related to the scaling group modeled on the Connes invariant $T$ for von
Neumann algebras and investigate the connection between so obtained invariants
of $\mathbb{G}$ and the Connes invariants
$T(\mathsf{L}^{\!\infty}(\mathbb{G}))$, $S(\mathsf{L}^{\!\infty}(\mathbb{G}))$.
In the final section we show that factors of type $\mathrm{I}$ cannot be
obtained as $\mathsf{L}^{\!\infty}(\mathbb{G})$ for a non-trivial compact
quantum group $\mathbb{G}$.


title:
Improving anatomical plausibility in medical image segmentation via
  hybrid graph neural networks: applications to chest x-ray analysis

abstract:
  Anatomical segmentation is a fundamental task in medical image computing,
generally tackled with fully convolutional neural networks which produce dense
segmentation masks. These models are often trained with loss functions such as
cross-entropy or Dice, which assume pixels to be independent of each other,
thus ignoring topological errors and anatomical inconsistencies. We address
this limitation by moving from pixel-level to graph representations, which
allow to naturally incorporate anatomical constraints by construction. To this
end, we introduce HybridGNet, an encoder-decoder neural architecture that
leverages standard convolutions for image feature encoding and graph
convolutional neural networks (GCNNs) to decode plausible representations of
anatomical structures. We also propose a novel image-to-graph skip connection
layer which allows localized features to flow from standard convolutional
blocks to GCNN blocks, and show that it improves segmentation accuracy. The
proposed architecture is extensively evaluated in a variety of domain shift and
image occlusion scenarios, and audited considering different types of
demographic domain shift. Our comprehensive experimental setup compares
HybridGNet with other landmark and pixel-based models for anatomical
segmentation in chest x-ray images, and shows that it produces anatomically
plausible results in challenging scenarios where other models tend to fail.


title:
Reconfigurable Optical Networks with Self-Tunable Transceivers:
  Implementation Options and Control

abstract:
  This paper reviews methods for autonomous tuning of optical transceivers,
based on an overhead management channel between the modules on both sides of
the link. Different implementation options for the tuning principle, as well as
for the tunable laser are introduced.


title: Solving for the low-rank tensor components of a scattering wave function

abstract:
  Atomic and molecular breakup reactions, such as multiple-ionisation, are
described by a driven Schr\"odinger equation. This equation is equivalent to a
high-dimensional Helmholtz equation and it has solutions that are outgoing
waves, emerging from the target. We show that these waves can be described by a
low-rank approximation. For 2D problems this it a matrix product of two
low-rank matrices, for 3D problems it is a low-rank tensor decomposition. We
propose an iterative method that solves, in an alternating way, for these
low-rank components of the scattered wave. We illustrate the method with
examples in 2D and 3D.


title: What is a randomization test?

abstract:
  The meaning of randomization tests has become obscure in statistics education
and practice over the last century. This article makes a fresh attempt at
rectifying this core concept of statistics. A new term -- "quasi-randomization
test" -- is introduced to define significance tests based on theoretical models
and distinguish these tests from the "randomization tests" based on the
physical act of randomization. The practical importance of this distinction is
illustrated through a real stepped-wedge cluster-randomized trial. Building on
the recent literature of randomization inference, a general framework of
conditional randomization tests is developed and some practical methods to
construct conditioning events are given. The proposed terminology and framework
are then applied to understand several widely used (quasi-)randomization tests,
including Fisher's exact test, permutation tests for treatment effect,
quasi-randomization tests for independence and conditional independence,
adaptive randomization, and conformal prediction.


title: MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer

abstract:
  Monocular 3D object detection is an important yet challenging task in
autonomous driving. Some existing methods leverage depth information from an
off-the-shelf depth estimator to assist 3D detection, but suffer from the
additional computational burden and achieve limited performance caused by
inaccurate depth priors. To alleviate this, we propose MonoDTR, a novel
end-to-end depth-aware transformer network for monocular 3D object detection.
It mainly consists of two components: (1) the Depth-Aware Feature Enhancement
(DFE) module that implicitly learns depth-aware features with auxiliary
supervision without requiring extra computation, and (2) the Depth-Aware
Transformer (DTR) module that globally integrates context- and depth-aware
features. Moreover, different from conventional pixel-wise positional
encodings, we introduce a novel depth positional encoding (DPE) to inject depth
positional hints into transformers. Our proposed depth-aware modules can be
easily plugged into existing image-only monocular 3D object detectors to
improve the performance. Extensive experiments on the KITTI dataset demonstrate
that our approach outperforms previous state-of-the-art monocular-based methods
and achieves real-time detection. Code is available at
https://github.com/kuanchihhuang/MonoDTR


title:
Sequential time-window learning with approximate Bayesian computation:
  an application to epidemic forecasting

abstract:
  The long duration of the COVID-19 pandemic allowed for multiple bursts in the
infection and death rates, the so-called epidemic waves. This complex behavior
is no longer tractable by simple compartmental model and requires more
sophisticated mathematical techniques for analyzing epidemic data and
generating reliable forecasts. In this work, we propose a framework for
analyzing complex dynamical systems by dividing the data in consecutive
time-windows to be separately analyzed. We fit parameters for each time-window
through an Approximate Bayesian Computation (ABC) algorithm, and the posterior
distribution of parameters obtained for one window is used as the prior
distribution for the next window. This Bayesian learning approach is tested
with data on COVID-19 cases in multiple countries and is shown to improve ABC
performance and to produce good short-term forecasting.


title:
BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks
  with Partition-Parallelism and Random Boundary Node Sampling

abstract:
  Graph Convolutional Networks (GCNs) have emerged as the state-of-the-art
method for graph-based learning tasks. However, training GCNs at scale is still
challenging, hindering both the exploration of more sophisticated GCN
architectures and their applications to real-world large graphs. While it might
be natural to consider graph partition and distributed training for tackling
this challenge, this direction has only been slightly scratched the surface in
the previous works due to the limitations of existing designs. In this work, we
first analyze why distributed GCN training is ineffective and identify the
underlying cause to be the excessive number of boundary nodes of each
partitioned subgraph, which easily explodes the memory and communication costs
for GCN training. Furthermore, we propose a simple yet effective method dubbed
BNS-GCN that adopts random Boundary-Node-Sampling to enable efficient and
scalable distributed GCN training. Experiments and ablation studies
consistently validate the effectiveness of BNS-GCN, e.g., boosting the
throughput by up to 16.2x and reducing the memory usage by up to 58%, while
maintaining a full-graph accuracy. Furthermore, both theoretical and empirical
analysis show that BNS-GCN enjoys a better convergence than existing
sampling-based methods. We believe that our BNS-GCN has opened up a new
paradigm for enabling GCN training at scale. The code is available at
https://github.com/RICE-EIC/BNS-GCN.


title:
Brooks' Theorem in Graph Streams: A Single-Pass Semi-Streaming Algorithm
  for $\Delta$-Coloring

abstract:
  Every graph with maximum degree $\Delta$ can be colored with $(\Delta+1)$
colors using a simple greedy algorithm. Remarkably, recent work has shown that
one can find such a coloring even in the semi-streaming model. But, in reality,
one almost never needs $(\Delta+1)$ colors to properly color a graph. Indeed,
the celebrated \Brooks' theorem states that every (connected) graph beside
cliques and odd cycles can be colored with $\Delta$ colors. Can we find a
$\Delta$-coloring in the semi-streaming model as well?
  We settle this key question in the affirmative by designing a randomized
semi-streaming algorithm that given any graph, with high probability, either
correctly declares that the graph is not $\Delta$-colorable or outputs a
$\Delta$-coloring of the graph.
  The proof of this result starts with a detour. We first (provably) identify
the extent to which the previous approaches for streaming coloring fail for
$\Delta$-coloring: for instance, all these approaches can handle streams with
repeated edges and they can run in $o(n^2)$ time -- we prove that neither of
these tasks is possible for $\Delta$-coloring. These impossibility results
however pinpoint exactly what is missing from prior approaches when it comes to
$\Delta$-coloring.
  We then build on these insights to design a semi-streaming algorithm that
uses $(i)$ a novel sparse-recovery approach based on sparse-dense
decompositions to (partially) recover the "problematic" subgraphs of the input
-- the ones that form the basis of our impossibility results -- and $(ii)$ a
new coloring approach for these subgraphs that allows for recoloring of other
vertices in a controlled way without relying on local explorations or finding
"augmenting paths" that are generally impossible for semi-streaming algorithms.
We believe both these techniques can be of independent interest.


title: On Griffiths conjecture

abstract:
  By using techniques of holomorphic jets and Jacobian fields, we devise a
non-equidistribution theory of holomorphic curves into complex projective
varieties intersecting normal crossing divisors. Based on this theory
established, we prove the Griffiths conjecture and the Green-Griffiths
conjecture in Nevanlinna theory and algebraic geometry.


title:
Investigating the QCD dynamical entropy in high-energy hadronic
  collisions

abstract:
  The dynamical entropy of dense gluonic states in proton-proton collisions at
high energies is studied by using phenomenological models for the unintegrated
gluon distribution. The corresponding transverse momentum probability
distributions are evaluated in terms of rapidity. The dynamical entropy density
is obtained in the rapidity range relevant for the collisions at the Large
Hadron Collider. The total entropy density for the dense system is computed as
a function of the rapidity evolution $\Delta Y = Y-Y_0$ given an initial
rapidity $Y_0$. The theoretical uncertainties are investigated and comparison
with related approaches in literature is done.


title: Annihilator ideals of graph algebras

abstract:
  If $I$ is a (two-sided) ideal of a ring $R$, we let
$\operatorname{ann}_l(I)=\{r\in R\mid rI=0\},$ $\operatorname{ann}_r(I)=\{r\in
R\mid Ir=0\},$ and $\operatorname{ann}(I)=\operatorname{ann}_l(I)\cap
\operatorname{ann}_r(I)$ be the left, the right and the double annihilators. An
ideal $I$ is said to be an annihilator ideal if $I=\operatorname{ann}(J)$ for
some ideal $J$ (equivalently, $\operatorname{ann}(\operatorname{ann}(I))=I$).
We study annihilator ideals of Leavitt path algebras and graph $C^*$-algebras.
  Let $L_K(E)$ be the Leavitt path algebra of a graph $E$ over a field $K.$ If
$I$ is an ideal of $L_K(E),$ it has recently been shown that
$\operatorname{ann}(I)$ is a graded ideal (with respect to the natural grading
of $L_K(E)$ by $\mathbb Z$). We note that $\operatorname{ann}_l(I)$ and
$\operatorname{ann}_r(I)$ are also graded. For a graded ideal $I,$ we describe
$\operatorname{ann}(I)$ in terms of the properties of a pair of sets of
vertices of $E,$ known as an admissible pair, which naturally corresponds to
$I.$ Using such a description, we present properties of $E$ which are
equivalent with the requirement that each graded ideal of $L_K(E)$ is an
annihilator ideal. We show that the same properties of $E$ are also equivalent
with each of the following conditions: (1) The lattice of graded ideals of
$L_K(E)$ is a Boolean algebra; (2) Each closed gauge-invariant ideal of
$C^*(E)$ is an annihilator ideal; (3) The lattice of closed gauge-invariant
ideals of $C^*(E)$ is a Boolean algebra. In addition, we present properties of
$E$ which are equivalent with each of the following conditions: (1) Each ideal
of $L_K(E)$ is an annihilator ideal; (2) The lattice of ideals of $L_K(E)$ is a
Boolean algebra; (3) Each closed ideal of $C^*(E)$ is an annihilator ideal; (4)
The lattice of closed ideals of $C^*(E)$ is a Boolean algebra.


title:
Less Is More: Efficient Networked VR Transformation Handling Using
  Geometric Algebra

abstract:
  As shared, collaborative, networked, virtual environments become increasingly
popular, various challenges arise regarding the efficient transmission of model
and scene transformation data over the network. As user immersion and real-time
interactions heavily depend on VR stream synchronization, transmitting the
entire data sat does not seem a suitable approach, especially for sessions
involving a large number of users. Session recording is another
momentum-gaining feature of VR applications that also faces the same challenge.
The selection of a suitable data format can reduce the occupied volume, while
it may also allow effective replication of the VR session and optimized
post-processing for analytics and deep-learning algorithms. In this work, we
propose two algorithms that can be applied in the context of a networked
multiplayer VR session, to efficiently transmit the displacement and
orientation data from the users' hand-based VR HMDs. Moreover, we present a
novel method describing effective VR recording of the data exchanged in such a
session. Our algorithms, based on the use of dual-quaternions and multivectors,
impact the network consumption rate and are highly effective in scenarios
involving multiple users. By sending less data over the network and
interpolating the in-between frames locally, we manage to obtain better visual
results than current state-of-the-art methods. Lastly, we prove that, for
recording purposes, storing less data and interpolating them on-demand yields a
data set quantitatively close to the original one.


title: Hierarchical autoregressive neural networks for statistical systems

abstract:
  It was recently proposed that neural networks could be used to approximate
many-dimensional probability distributions that appear e.g. in lattice field
theories or statistical mechanics. Subsequently they can be used as variational
approximators to asses extensive properties of statistical systems, like free
energy, and also as neural samplers used in Monte Carlo simulations. The
practical application of this approach is unfortunately limited by its
unfavorable scaling both of the numerical cost required for training, and the
memory requirements with the system size. This is due to the fact that the
original proposition involved a neural network of width which scaled with the
total number of degrees of freedom, e.g. $L^2$ in case of a two dimensional
$L\times L$ lattice. In this work we propose a hierarchical association of
physical degrees of freedom, for instance spins, to neurons which replaces it
with the scaling with the linear extent $L$ of the system. We demonstrate our
approach on the two-dimensional Ising model by simulating lattices of various
sizes up to $128 \times 128$ spins, with time benchmarks reaching lattices of
size $512 \times 512$. We observe that our proposal improves the quality of
neural network training, i.e. the approximated probability distribution is
closer to the target that could be previously achieved. As a consequence, the
variational free energy reaches a value closer to its theoretical expectation
and, if applied in a Markov Chain Monte Carlo algorithm, the resulting
autocorrelation time is smaller. Finally, the replacement of a single neural
network by a hierarchy of smaller networks considerably reduces the memory
requirements.

categories: cond-mat.stat-mech cs.LG hep-lat stat.ML


title:
Segregated solutions for a critical elliptic system with a small
  interspecies repulsive force

abstract:
  We consider the elliptic system $$-\Delta u_i = u_i^3+\sum\limits_{j=1\atop
j\not=i}^{q+1}{ \beta_{ij}}u_i u_j^2\ \hbox{in}\ \mathbb R^4, \
i=1,\dots,q+1.$$ when $\alpha:=\beta_{ij}$ and
$\beta:=\beta_{i(q+1)}=\beta_{(q+1)j}$ for any $i,j=1,\dots,q.$ If $\beta<0$
and $|\beta|$ is small enough we build solutions such that each component
$u_{1},\dots,u_q$ blows-up at the vertices of $q$ polygons placed in different
great circles which are linked to each other, and the last component $u_{q+1}$
looks like the radial positive solution of the single equation.


title: Optimal Fine-Grained N:M sparsity for Activations and Neural Gradients

abstract:
  In deep learning, fine-grained N:M sparsity reduces the data footprint and
bandwidth of a General Matrix multiply (GEMM) by x2, and doubles throughput by
skipping computation of zero values. So far, it was only used to prune weights.
We examine how this method can be used also for activations and their gradients
(i.e., "neural gradients"). To this end, we first establish tensor-level
optimality criteria. Previous works aimed to minimize the mean-square-error
(MSE) of each pruned block. We show that while minimization of the MSE works
fine for pruning the activations, it catastrophically fails for the neural
gradients. Instead, we show that optimal pruning of the neural gradients
requires an unbiased minimum-variance pruning mask. We design such specialized
masks, and find that in most cases, 1:2 sparsity is sufficient for training,
and 2:4 sparsity is usually enough when this is not the case. Further, we
suggest combining several such methods together in order to speed up training
even more. A reference implementation is supplied in
https://github.com/brianchmiel/Act-and-Grad-structured-sparsity.


title: Spoofing-Aware Speaker Verification with Unsupervised Domain Adaptation

abstract:
  In this paper, we initiate the concern of enhancing the spoofing robustness
of the automatic speaker verification (ASV) system, without the primary
presence of a separate countermeasure module. We start from the standard ASV
framework of the ASVspoof 2019 baseline and approach the problem from the
back-end classifier based on probabilistic linear discriminant analysis. We
employ three unsupervised domain adaptation techniques to optimize the back-end
using the audio data in the training partition of the ASVspoof 2019 dataset. We
demonstrate notable improvements on both logical and physical access scenarios,
especially on the latter where the system is attacked by replayed audios, with
a maximum of 36.1% and 5.3% relative improvement on bonafide and spoofed cases,
respectively. We perform additional studies such as per-attack breakdown
analysis, data composition, and integration with a countermeasure system at
score-level with Gaussian back-end.


title:
Improving the operational stability of thermoelectric Zn$_4$Sb$_3$ by
  segmentation

abstract:
  The mixed ionic-electronic conductor $\beta$-Zn$_4$Sb$_3$ is a cheap and high
performing thermoelectric material, but under operating conditions with a
temperature gradient and a running current, the material decomposes as Zn
readily migrates in the structure. Here, we report an improved stability of
$\beta$-Zn$_4$Sb$_3$ by introducing ion-blocking interfaces of stainless steel
to segment the sample, produced by a rapid one-step Spark Plasma Sintering
synthesis. The stability of the samples is tested under temperature gradients
and electric currents, which reveals significantly improved stability of the
segmented samples compared to unsegmented samples. The segmented samples are
stable under temperature gradient from 250{\deg}C to room temperature with no
external current, whereas the unsegmented sample decomposes into ZnSb and Zn
under the same conditions. The thermoelectric figure of merit, zT, of the
segmented sample is slightly reduced, mainly due to the increased thermal
conductivity. In conclusion, a rapid one-step synthesis of segmented
$\beta$-Zn$_4$Sb$_3$ is developed, which successfully improves the long-term
operational stability by blocking the Zn ion migration.


title: Impact of Spin Wave Dispersion on Surface Acoustic Wave Velocity

abstract:
  The dependence of the velocity of surface acoustic wave (SAW) as a function
of an external applied magnetic field is investigated in a Fe thin film
epitaxially grown on a piezoelectric GaAs substrate. The SAW velocity is
observed to strongly depend on both the amplitude and direction of the magnetic
field. To interpret the experimental data a phenomenological approach to the
relative change in SAW velocity is implemented. We find that the experimental
velocity variation can be well reproduced provided that the spin wave
dispersion is taken into account. The validity of this phenomenological model
is attested by the comparison with a quasi-exact magnetoelastic one.


title: Word Order Does Matter (And Shuffled Language Models Know It)

abstract:
  Recent studies have shown that language models pretrained and/or fine-tuned
on randomly permuted sentences exhibit competitive performance on GLUE, putting
into question the importance of word order information. Somewhat
counter-intuitively, some of these studies also report that position embeddings
appear to be crucial for models' good performance with shuffled text. We probe
these language models for word order information and investigate what position
embeddings learned from shuffled text encode, showing that these models retain
information pertaining to the original, naturalistic word order. We show this
is in part due to a subtlety in how shuffling is implemented in previous work
-- before rather than after subword segmentation. Surprisingly, we find even
Language models trained on text shuffled after subword segmentation retain some
semblance of information about word order because of the statistical
dependencies between sentence length and unigram probabilities. Finally, we
show that beyond GLUE, a variety of language understanding tasks do require
word order information, often to an extent that cannot be learned through
fine-tuning.


title:
Technologies for AI-Driven Fashion Social Networking Service with
  E-Commerce

abstract:
  The rapid growth of the online fashion market brought demands for innovative
fashion services and commerce platforms. With the recent success of deep
learning, many applications employ AI technologies such as visual search and
recommender systems to provide novel and beneficial services. In this paper, we
describe applied technologies for AI-driven fashion social networking service
that incorporate fashion e-commerce. In the application, people can share and
browse their outfit-of-the-day (OOTD) photos, while AI analyzes them and
suggests similar style OOTDs and related products. To this end, we trained deep
learning based AI models for fashion and integrated them to build a fashion
visual search system and a recommender system for OOTD. With aforementioned
technologies, the AI-driven fashion SNS platform, iTOO, has been successfully
launched.


title:
Tailoring radiation pressure on infinite slab using pair of
  non-collinear plane waves

abstract:
  The electromagnetic field exerts radiation pressure on the matter and tends
to move it either in the backward or forward direction due to net optical
pulling or pushing force, respectively. In this work, we reveal an interesting
phenomenon of a local positive and negative radiation pressure on a dielectric
(chiral) slab by using two linearly (circularly) polarized plane waves. In this
regard, we develop for the first time, a theory to describe the local radiation
pressure appearing due to the interference between the two obliquely impinging
(non-collinear) light sources. Under this {situation}, the radiation pressure
strongly depends on the angle of incidence, the polarization of the
electromagnetic field and the chirality parameters of the slab (in the case of
chiral medium). Our numerical analysis shows that the radiation pressure,
exerted on a dielectric or a chiral slab due to the two incident plane waves,
is constant over the slab for normal incidence, and it varies locally for an
oblique incidence, which indeed follows the conservation laws at all incident
angles. It is expected that the results may find fruitful applications in
optical manipulation of soft matters, for instance, cell membranes, chiral
surfaces and other soft materials


title: Discovering new physics in rare kaon decays

abstract:
  The decays and mixing of $K$ mesons are remarkably sensitive to the weak
interactions of quarks and leptons at high energies. They provide important
tests of the standard model at both first and second order in the Fermi
constant $G_F$ and offer a window into possible new phenomena at energies as
high as 1,000 TeV. These possibilities become even more compelling as the
growing capabilities of lattice QCD make high-precision standard model
predictions possible. Here we discuss and attempt to forecast some of these
capabilities.


title: Computing the trace of an algebraic point on an elliptic curve

abstract:
  We present a simple and efficient algorithm to compute the sum of the
algebraic conjugates of a point on an elliptic curve.


title: Self-Supervised Road Layout Parsing with Graph Auto-Encoding

abstract:
  Aiming for higher-level scene understanding, this work presents a neural
network approach that takes a road-layout map in bird's-eye-view as input, and
predicts a human-interpretable graph that represents the road's topological
layout. Our approach elevates the understanding of road layouts from pixel
level to the level of graphs. To achieve this goal, an image-graph-image
auto-encoder is utilized. The network is designed to learn to regress the graph
representation at its auto-encoder bottleneck. This learning is self-supervised
by an image reconstruction loss, without needing any external manual
annotations. We create a synthetic dataset containing common road layout
patterns and use it for training of the auto-encoder in addition to the
real-world Argoverse dataset. By using this additional synthetic dataset, which
conceptually captures human knowledge of road layouts and makes this available
to the network for training, we are able to stabilize and further improve the
performance of topological road layout understanding on the real-world
Argoverse dataset. The evaluation shows that our approach exhibits comparable
performance to a strong fully-supervised baseline.


title:
Probabilistic prediction of Dst storms one-day-ahead using Full-Disk
  SoHO Images

abstract:
  We present a new model for the probability that the Disturbance storm time
(Dst) index exceeds -100 nT, with a lead time between 1 and 3 days. $Dst$
provides essential information about the strength of the ring current around
the Earth caused by the protons and electrons from the solar wind, and it is
routinely used as a proxy for geomagnetic storms. The model is developed using
an ensemble of Convolutional Neural Networks (CNNs) that are trained using SoHO
images (MDI, EIT and LASCO). The relationship between the SoHO images and the
solar wind has been investigated by many researchers, but these studies have
not explicitly considered using SoHO images to predict the $Dst$ index.
  This work presents a novel methodology to train the individual models and to
learn the optimal ensemble weights iteratively, by using a customized
class-balanced mean square error (CB-MSE) loss function tied to a least-squares
(LS) based ensemble.
  The proposed model can predict the probability that Dst<-100 nT 24 hours
ahead with a True Skill Statistic (TSS) of 0.62 and Matthews Correlation
Coefficient (MCC) of 0.37. The weighted TSS and MCC from Guastavino et al.
(2021) is 0.68 and 0.47, respectively. An additional validation during
non-Earth-directed CME periods is also conducted which yields a good TSS and
MCC score.

categories: physics.space-ph astro-ph.EP astro-ph.IM astro-ph.SR


title: Plethysm and a character embedding problem of Miller

abstract:
  We use a plethystic formula of Littlewood to answer a question of Miller on
embeddings of symmetric group characters. We also reprove a result of Miller on
character congruences.


title: On modified Halpern and Tikhonov-Mann iterations

abstract:
  We show that the asymptotic regularity and the strong convergence of the
modified Halpern iteration due to T.-H. Kim and H.-K. Xu and studied further by
A. Cuntavenapit and B. Panyanak and the Tikhonov-Mann iteration introduced by
H. Cheval and L. Leu\c{s}tean as a generalization of an iteration due to Y. Yao
et al. that has recently been studied by Bo\c{t} et al. can be reduced to each
other in general geodesic settings. This, in particular, gives a new proof of
the convergence result in Bo\c{t} et al. together with a generalization from
Hilbert to CAT(0) spaces. Moreover, quantitative rates of asymptotic regularity
and metastability due to K. Schade and U. Kohlenbach can be adapted and
transformed into rates for the Tikhonov-Mann iteration corresponding to recent
quantitative results on the latter of H. Cheval, L. Leu\c{s}tean and B. Dinis,
P. Pinto respectively. A transformation in the converse direction is also
possible. We also obtain rates of asymptotic regularity of order $O(1/n)$ for
both the modified Halpern (and so in particular for the Halpern iteration) and
the Tikhonov-Mann iteration in a general geodesic setting for a special choice
of scalars.


title:
Multi-Agent Relative Pose Estimation with UWB and Constrained
  Communications

abstract:
  Inter-agent relative localization is critical for any multi-robot system
operating in the absence of external positioning infrastructure or prior
environmental knowledge. We propose a novel inter-agent relative 2D pose
estimation system where each participating agent is equipped with several
ultra-wideband (UWB) ranging tags. Prior work typically supplements noisy UWB
range measurements with additional continuously transmitted data, such as
odometry, making these approaches scale poorly with increased swarm size or
decreased communication throughput. This approach addresses these concerns by
using only locally collected UWB measurements with no additionally transmitted
data. By modeling observed ranging biases and systematic antenna obstructions
in our proposed optimization solution, our experimental results demonstrate an
improved mean position error (while remaining competitive in other metrics)
over a similar state-of-the-art approach that additionally relies on
continuously transmitted odometry.


title: Machine Learning Algorithms in Design Optimization

abstract:
  Numerical optimization of complex systems benefits from the technological
development of computing platforms in the last twenty years. Unfortunately,
this is still not enough, and a large computational time is still necessary
when mathematical models that include richer (and therefore more realistic)
physical models are adopted.
  In this paper we show how the combination of optimization and Artificial
Intelligence (AI), in particular the Machine Learning algorithms, can help in
strongly reducing the overall computational times, making possible the use of
complex simulation systems within the optimization cycle. Original approaches
are also proposed.


title:
Underwater Light Field Retention : Neural Rendering for Underwater
  Imaging

abstract:
  Underwater Image Rendering aims to generate a true-tolife underwater image
from a given clean one, which could be applied to various practical
applications such as underwater image enhancement, camera filter, and virtual
gaming. We explore two less-touched but challenging problems in underwater
image rendering, namely, i) how to render diverse underwater scenes by a single
neural network? ii) how to adaptively learn the underwater light fields from
natural exemplars, i,e., realistic underwater images? To this end, we propose a
neural rendering method for underwater imaging, dubbed UWNR (Underwater Neural
Rendering). Specifically, UWNR is a data-driven neural network that implicitly
learns the natural degenerated model from authentic underwater images, avoiding
introducing erroneous biases by hand-craft imaging models. Compared with
existing underwater image generation methods, UWNR utilizes the natural light
field to simulate the main characteristics ofthe underwater scene. Thus, it is
able to synthesize a wide variety ofunderwater images from one clean image with
various realistic underwater images. Extensive experiments demonstrate that our
approach achieves better visual effects and quantitative metrics over previous
methods. Moreover, we adopt UWNR to build an open Large Neural Rendering
Underwater Dataset containing various types of water quality, dubbed LNRUD. The
source code and LNRUD are available at https: //github.com/Ephemeral182/UWNR.


title:
Computational ergonomics for task delegation in Human-Robot
  Collaboration: spatiotemporal adaptation of the robot to the human through
  contactless gesture recognition

abstract:
  The high prevalence of work-related musculoskeletal disorders (WMSDs) could
be addressed by optimizing Human-Robot Collaboration (HRC) frameworks for
manufacturing applications. In this context, this paper proposes two hypotheses
for ergonomically effective task delegation and HRC. The first hypothesis
states that it is possible to quantify ergonomically professional tasks using
motion data from a reduced set of sensors. Then, the most dangerous tasks can
be delegated to a collaborative robot. The second hypothesis is that by
including gesture recognition and spatial adaptation, the ergonomics of an HRC
scenario can be improved by avoiding needless motions that could expose
operators to ergonomic risks and by lowering the physical effort required of
operators. An HRC scenario for a television manufacturing process is optimized
to test both hypotheses. For the ergonomic evaluation, motion primitives with
known ergonomic risks were modeled for their detection in professional tasks
and to estimate a risk score based on the European Assembly Worksheet (EAWS). A
Deep Learning gesture recognition module trained with egocentric television
assembly data was used to complement the collaboration between the human
operator and the robot. Additionally, a skeleton-tracking algorithm provided
the robot with information about the operator's pose, allowing it to spatially
adapt its motion to the operator's anthropometrics. Three experiments were
conducted to determine the effect of gesture recognition and spatial adaptation
on the operator's range of motion. The rate of spatial adaptation was used as a
key performance indicator (KPI), and a new KPI for measuring the reduction in
the operator's motion is presented in this paper.


title: Transformer-based HTR for Historical Documents

abstract:
  We apply the TrOCR framework to real-world, historical manuscripts and show
that TrOCR per se is a strong model, ideal for transfer learning. TrOCR has
been trained on English only, but it can adapt to other languages that use the
Latin alphabet fairly easily and with little training material. We compare
TrOCR against a SOTA HTR framework (Transkribus) and show that it can beat such
systems. This finding is essential since Transkribus performs best when it has
access to baseline information, which is not needed at all to fine-tune TrOCR.


title:
Online Skeleton-based Action Recognition with Continual Spatio-Temporal
  Graph Convolutional Networks

abstract:
  Graph-based reasoning over skeleton data has emerged as a promising approach
for human action recognition. However, the application of prior graph-based
methods, which predominantly employ whole temporal sequences as their input, to
the setting of online inference entails considerable computational redundancy.
In this paper, we tackle this issue by reformulating the Spatio-Temporal Graph
Convolutional Neural Network as a Continual Inference Network, which can
perform step-by-step predictions in time without repeat frame processing. To
evaluate our method, we create a continual version of ST-GCN, CoST-GCN,
alongside two derived methods with different self-attention mechanisms, CoAGCN
and CoS-TR. We investigate weight transfer strategies and architectural
modifications for inference acceleration, and perform experiments on the NTU
RGB+D 60, NTU RGB+D 120, and Kinetics Skeleton 400 datasets. Retaining similar
predictive accuracy, we observe up to 109x reduction in time complexity,
on-hardware accelerations of 26x, and reductions in maximum allocated memory of
52% during online inference.


title:
Sparse inference and active learning of stochastic differential
  equations from data

abstract:
  Automatic machine learning of empirical models from experimental data has
recently become possible as a result of increased availability of computational
power and dedicated algorithms. Despite the successes of non-parametric
inference and neural-network-based inference for empirical modelling, a
physical interpretation of the results often remains challenging. Here, we
focus on direct inference of governing differential equations from data, which
can be formulated as a linear inverse problem. A Bayesian framework with a
Laplacian prior distribution is employed for finding sparse solutions
efficiently. The superior accuracy and robustness of the method is demonstrated
for various cases, including ordinary, partial, and stochastic differential
equations. Furthermore, we develop an active learning procedure for the
automated discovery of stochastic differential equations. In this procedure,
learning of the unknown dynamical equations is coupled to the application of
perturbations to the measured system in a feedback loop. We demonstrate with
simulations that the active learning procedure improves the inference of
empirical, Langevin-type descriptions of stochastic processes.


title:
Reinforced MOOCs Concept Recommendation in Heterogeneous Information
  Networks

abstract:
  Massive open online courses (MOOCs), which provide a large-scale interactive
participation and open access via the web, are becoming a modish way for online
and distance education. To help users have a better study experience, many MOOC
platforms have provided the services of recommending courses to users. However,
we argue that directly recommending a course to users will ignore the expertise
levels of different users. To fill this gap, this paper studies the problem of
concept recommendation in a more fine-grained view. We propose a novel
Heterogeneous Information Networks based Concept Recommender with Reinforcement
Learning (HinCRec-RL) incorporated for concept recommendation in MOOCs.
Specifically, we first formulate the concept recommendation in MOOCs as a
reinforcement learning problem to better model the dynamic interaction among
users and knowledge concepts. In addition, to mitigate the data sparsity issue
which also exists in many other recommendation tasks, we consider a
heterogeneous information network (HIN) among users, courses, videos and
concepts, to better learn the semantic representation of users. In particular,
we use the meta-paths on HIN to guide the propagation of users' preferences and
propose a heterogeneous graph attention network to represent the meta-paths. To
validate the effectiveness of our proposed approach, we conduct comprehensive
experiments on a real-world dataset from XuetangX, a popular MOOC platform from
China. The promising results show that our proposed approach can outperform
other baselines.


title:
Learning Resilient Radio Resource Management Policies with Graph Neural
  Networks

abstract:
  We consider the problems of downlink user selection and power control in
wireless networks, comprising multiple transmitters and receivers communicating
with each other over a shared wireless medium. To achieve a high aggregate
rate, while ensuring fairness across all the receivers, we formulate a
resilient radio resource management (RRM) policy optimization problem with
per-user minimum-capacity constraints that adapt to the underlying network
conditions via learnable slack variables. We reformulate the problem in the
Lagrangian dual domain, and show that we can parameterize the user selection
and power control policies using a finite set of parameters, which can be
trained alongside the slack and dual variables via an unsupervised primal-dual
approach thanks to a provably small duality gap. We use a scalable and
permutation-equivariant graph neural network (GNN) architecture to parameterize
the RRM policies based on a graph topology derived from the instantaneous
channel conditions. Through experimental results, we verify that the
minimum-capacity constraints adapt to the underlying network configurations and
channel conditions. We further demonstrate that, thanks to such adaptation, our
proposed method achieves a superior tradeoff between the average rate and the
5th percentile rate -- a metric that quantifies the level of fairness in the
resource allocation decisions -- as compared to baseline algorithms.


title:
Thermodynamic descriptors to predict oxide formation in aqueous
  solutions

abstract:
  We formulate the maximum driving force (MDF) parameter as a descriptor to
capture the thermodynamic stability of aqueous surface scale creation over a
range of environmental conditions. We use formation free energies, $\Delta_f
G$s, sourced from high-throughput density functional theory (DFT) calculations
and experimental databases to compute the maximum driving force for a wide
variety of materials, including simple oxides, intermetallics, and alloys of
varying compositions. We show how to use the MDF to describe trends in aqueous
corrosion of nickel thin films determined from experimental
linear-sweep-voltometry data. We also show how to account for subsurface
oxidation behavior using depth-dependent effective chemical potentials. We
anticipate this approach will increase overall understanding of oxide formation
on chemically complex multielement alloys, where competing oxide phases can
form during transient aqueous corrosion.


title:
DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale
  Click-Through Rate Prediction

abstract:
  Learning feature interactions is important to the model performance of online
advertising services. As a result, extensive efforts have been devoted to
designing effective architectures to learn feature interactions. However, we
observe that the practical performance of those designs can vary from dataset
to dataset, even when the order of interactions claimed to be captured is the
same. That indicates different designs may have different advantages and the
interactions captured by them have non-overlapping information. Motivated by
this observation, we propose DHEN - a deep and hierarchical ensemble
architecture that can leverage strengths of heterogeneous interaction modules
and learn a hierarchy of the interactions under different orders. To overcome
the challenge brought by DHEN's deeper and multi-layer structure in training,
we propose a novel co-designed training system that can further improve the
training efficiency of DHEN. Experiments of DHEN on large-scale dataset from
CTR prediction tasks attained 0.27\% improvement on the Normalized Entropy (NE)
of prediction and 1.2x better training throughput than state-of-the-art
baseline, demonstrating their effectiveness in practice.


title:
Filter Drug-induced Liver Injury Literature with Natural Language
  Processing and Ensemble Learning

abstract:
  Drug-induced liver injury (DILI) describes the adverse effects of drugs that
damage liver. Life-threatening results including liver failure or death were
also reported in severe DILI cases. Therefore, DILI-related events are strictly
monitored for all approved drugs and the liver toxicity became important
assessments for new drug candidates. These DILI-related reports are documented
in hospital records, in clinical trial results, and also in research papers
that contain preliminary in vitro and in vivo experiments. Conventionally, data
extraction from previous publications relies heavily on resource-demanding
manual labelling, which considerably decreased the efficiency of the
information extraction process. The recent development of artificial
intelligence, particularly, the rise of natural language processing (NLP)
techniques, enabled the automatic processing of biomedical texts. In this
study, based on around 28,000 papers (titles and abstracts) provided by the
Critical Assessment of Massive Data Analysis (CAMDA) challenge, we benchmarked
model performances on filtering out DILI literature. Among four word
vectorization techniques, the model using term frequency-inverse document
frequency (TF-IDF) and logistic regression outperformed others with an accuracy
of 0.957 with our in-house test set. Furthermore, an ensemble model with
similar overall performances was implemented and was fine-tuned to lower the
false-negative cases to avoid neglecting potential DILI reports. The ensemble
model achieved a high accuracy of 0.954 and an F1 score of 0.955 in the
hold-out validation data provided by the CAMDA committee. Moreover, important
words in positive/negative predictions were identified via model
interpretation. Overall, the ensemble model reached satisfactory classification
results, which can be further used by researchers to rapidly filter
DILI-related literature.


title:
Linking Theories and Methods in Cognitive Sciences via Joint Embedding
  of the Scientific Literature: The Example of Cognitive Control

abstract:
  Traditionally, theory and practice of Cognitive Control are linked via
literature reviews by human domain experts. This approach, however, is
inadequate to track the ever-growing literature. It may also be biased, and
yield redundancies and confusion. Here we present an alternative approach. We
performed automated text analyses on a large body of scientific texts to create
a joint representation of tasks and constructs. More specifically, 531,748
scientific abstracts were first mapped into an embedding space using a
transformers-based language model. Document embeddings were then used to
identify a task-construct graph embedding that grounds constructs on tasks and
supports nuanced meaning of the constructs by taking advantage of constrained
random walks in the graph. This joint task-construct graph embedding, can be
queried to generate task batteries targeting specific constructs, may reveal
knowledge gaps in the literature, and inspire new tasks and novel hypotheses.


title: Arc-disjoint hamiltonian paths in Cartesian products of directed cycles

abstract:
  We show that if $C_1$ and $C_2$ are directed cycles (of length at least two),
then the Cartesian product $C_1 \Box C_2$ has two arc-disjoint hamiltonian
paths. (This answers a question asked by J. A. Gallian in 1985.) The same
conclusion also holds for the Cartesian product of any four or more directed
cycles (of length at least two), but some cases remain open for the Cartesian
product of three directed cycles.
  We also discuss the existence of arc-disjoint hamiltonian paths in
$2$-generated Cayley digraphs on (finite or infinite) abelian groups.