title:
Quantum order-by-disorder induced phase transition in Rydberg ladders
  with staggered detuning

abstract:
  $^{87}{\rm Rb}$ atoms are known to have long-lived Rydberg excited states
with controllable excitation amplitude (detuning) and strong repulsive van der
Waals interaction $V_{{\bf r} {\bf r'}}$ between excited atoms at sites ${\bf
r}$ and ${\bf r'}$. Here we study such atoms in a two-leg ladder geometry in
the presence of both staggered and uniform detuning with amplitudes $\Delta$
and $\lambda$ respectively. We show that when $V_{{\bf r r'}} \gg(\ll) \Delta,
\lambda$ for $|{\bf r}-{\bf r'}|=1(>1)$, these ladders host a plateau for a
wide range of $\lambda/\Delta$ where the ground states are selected by a
quantum order-by-disorder mechanism from a macroscopically degenerate manifold
of Fock states with fixed Rydberg excitation density $1/4$. Our study further
unravels the presence of an emergent Ising transition stabilized via the
order-by-disorder mechanism inside the plateau. We identify the competing terms
responsible for the transition and estimate a critical detuning
$\lambda_c/\Delta=1/3$ which agrees well with exact-diagonalization based
numerical studies. We also study the fate of this transition for a realistic
interaction potential $V_{{\bf r} {\bf r'}} = V_0 /|{\bf r}-{\bf r'}|^6$,
demonstrate that it survives for a wide range of $V_0$, and provide analytic
estimate of $\lambda_c$ as a function of $V_0$. This allows for the possibility
of a direct verification of this transition in standard experiments which we
discuss.


title: Coupled Iterative Refinement for 6D Multi-Object Pose Estimation

abstract:
  We address the task of 6D multi-object pose: given a set of known 3D objects
and an RGB or RGB-D input image, we detect and estimate the 6D pose of each
object. We propose a new approach to 6D object pose estimation which consists
of an end-to-end differentiable architecture that makes use of geometric
knowledge. Our approach iteratively refines both pose and correspondence in a
tightly coupled manner, allowing us to dynamically remove outliers to improve
accuracy. We use a novel differentiable layer to perform pose refinement by
solving an optimization problem we refer to as Bidirectional Depth-Augmented
Perspective-N-Point (BD-PnP). Our method achieves state-of-the-art accuracy on
standard 6D Object Pose benchmarks. Code is available at
https://github.com/princeton-vl/Coupled-Iterative-Refinement.


title:
Absolute neutrino mass scale and dark matter stability from flavour
  symmetry

abstract:
  We explore a simple but extremely predictive extension of the scotogenic
model. We promote the scotogenic symmetry $\mathbb{Z}_2$ to the flavour
non-Abelian symmetry $\Sigma(81)$, which can also automatically protect dark
matter stability. In addition, $\Sigma(81)$ leads to striking predictions in
the lepton sector: only Inverted Ordering is realised, the absolute neutrino
mass scale is predicted to be $m_\text{lightest} \approx 7.5 \times 10^{-4}$ eV
and the Majorana phases are correlated in such a way that $|m_{ee}| \approx
0.018$ eV. The model also leads to a strong correlation between the solar
mixing angle $\theta_{12}$ and $\delta_{CP}$, which may be falsified by the
next generation of neutrino oscillation experiments. The setup is minimal in
the sense that no additional symmetries or flavons are required.


title:
A spectroscopic multiplicity survey of Galactic Wolf-Rayet stars: II.
  The northern WNE sequence

abstract:
  Most massive stars reside in multiple systems that will interact over the
course of their lifetime. Classical Wolf-Rayet (WR) stars represent the final
end stages of stellar evolution at the upper-mass end. As part of a
homogeneous, magnitude-limited ($V\leq12$) spectroscopic survey of northern
Galactic WR stars, this paper aims to establish the observed and intrinsic
multiplicity properties of the early-type nitrogen-rich WR population (WNE). We
obtained high-resolution spectroscopic time series of the complete
magnitude-limited sample of 16 WNE stars observable with the 1.2 m Mercator
telescope at La Palma, typically providing a time base of about two to eight
years. We measured relative radial velocities (RVs) using cross-correlation and
used RV variations to flag binary candidates. Adopting a peak-to-peak RV
variability threshold of 50 km/s as a criterion found an observed multiplicity
fraction of 0.44$\pm$0.12. Using an updated Monte Carlo method with a Bayesian
framework, we calculated the three-dimensional likelihood for the intrinsic
binary fraction, the maximum period, and the power-law index for the period
distribution for the WNE population. We also re-derived multiplicity parameters
for the Galactic WC population. We found an intrinsic multiplicity fraction of
$0.56\substack{+0.20 \\ -0.15}$ for the parent WNE population. For the Galactic
WC population, we re-derive an intrinsic multiplicity fraction of
$0.96\substack{+0.04 \\ -0.22}$. The derived multiplicity parameters for the
WNE population are quite similar to those derived for main-sequence O binaries
but differ from those of the WC population. The significant shift in the WC
period distribution towards longer periods is too large to be explained via
expansion of the orbit due to stellar winds, and we discuss possible
implications of our results.


title:
Covariance-analytic performance criteria, Hardy-Schatten norms and
  Wick-like ordering of cascaded systems

abstract:
  This paper is concerned with linear stochastic systems whose output is a
stationary Gaussian random process related by an integral operator to a
standard Wiener process at the input. We consider a performance criterion which
involves the trace of an analytic function of the spectral density of the
output process. This class of "covariance-analytic" cost functionals includes
the usual mean square and risk-sensitive criteria as particular cases. Due to
the presence of the "cost-shaping" analytic function, the performance criterion
is related to higher-order Hardy-Schatten norms of the system transfer
function. These norms have links with the asymptotic properties of cumulants of
finite-horizon quadratic functionals of the system output and satisfy
variational inequalities pertaining to system robustness to statistically
uncertain inputs. In the case of strictly proper finite-dimensional systems,
governed in state space by linear stochastic differential equations, we develop
a method for recursively computing the Hardy-Schatten norms through a recently
proposed technique of rearranging cascaded linear systems, which resembles the
Wick ordering of annihilation and creation operators in quantum mechanics. The
resulting computational procedure involves a recurrence sequence of solutions
to algebraic Lyapunov equations and represents the covariance-analytic cost as
the squared $\mathcal{H}_2$-norm of an auxiliary cascaded system. These results
are also compared with an alternative approach which uses higher-order
derivatives of stabilising solutions of parameter-dependent algebraic Riccati
equations.


title: Neutron Stars

abstract:
  Neutron stars are highly compact astrophysical objects and therefore of
utmost relevance to learn about theories of gravity. Whereas the proper
equation of state of the nuclear matter inside neutron stars is not yet known,
and a wide range of equations of state is still compatible with observations,
this uncertainty can be overcome to a large extent, when dimensionless neutron
star properties are considered. In this case universal relations between
neutron star properties and for the gravitational radiation in the form of
quasi-normal modes arise. These universal relations can be rather distinct for
alternative theories of gravity as compared to General Relativity. Moreover,
the presence of new degrees of freedom in alternative theories of gravity leads
to further types of gravitational radiation, that may be revealed by pulsar
observations and gravitational wave detectors. Here an introduction to neutron
stars, their properties and universal relations is presented, followed by two
examples of alternative theories of gravity featuring interesting effects for
neutrons stars.


title:
Is Institutional Evolution in Online Communities Driven by Selection or
  Stochasticity?

abstract:
  Institutions and cultures sometimes change adaptively in response to changes
in the environment. But sometimes institutional change is due to stochastic
drives including drift, path dependency, and blind imitation. Disentangling the
selective and stochastic components of social system change enables us to
identify the key features to organizational development in the long run.
Evo-lutionary approaches provide organizational science abundant theories to
demonstrate organi-zational evolution through tracking particular beneficial or
harmful features. We applied two of the most applied evolutionary models, the
Price equation and the bet-hedging model, on online community data to quantify
empirically different drivers in institutional evolution among 20,000 Minecraft
communities. As a result, we find strong selection pressure on administrative
rules and information rules, suggesting that their positive payoff is the main
reason for their frequency change. We also find that stochastic drives decrease
the average frequency of administrative rules. The result makes sense when
explained in light of evolutionary bet-hedging. Additionally, we show through
the bet-hedging result that institutional diversity contributes to the growth
and stability of information, communication, and economic rules.


title: Leveraging Unlabeled Data for Sketch-based Understanding

abstract:
  Sketch-based understanding is a critical component of human cognitive
learning and is a primitive communication means between humans. This topic has
recently attracted the interest of the computer vision community as sketching
represents a powerful tool to express static objects and dynamic scenes.
Unfortunately, despite its broad application domains, the current sketch-based
models strongly rely on labels for supervised training, ignoring knowledge from
unlabeled data, thus limiting the underlying generalization and the
applicability. Therefore, we present a study about the use of unlabeled data to
improve a sketch-based model. To this end, we evaluate variations of VAE and
semi-supervised VAE, and present an extension of BYOL to deal with sketches.
Our results show the superiority of sketch-BYOL, which outperforms other
self-supervised approaches increasing the retrieval performance for known and
unknown categories. Furthermore, we show how other tasks can benefit from our
proposal.


title:
The transfer of polarized radiation in resonance lines with partial
  frequency redistribution, J-state interference, and arbitrary magnetic
  fields. A radiative transfer code and useful approximations

abstract:
  We present the theoretical framework and numerical methods we have
implemented to solve the problem of the generation and transfer of polarized
radiation in spectral lines out of local thermodynamical equilibrium, while
accounting for scattering polarization, partial frequency redistribution,
J-state interference, and hyperfine structure. The resulting radiative transfer
code allows modeling the impact of magnetic fields of arbitrary strength and
orientation through the Hanle, incomplete Paschen-Back, and magneto-optical
effects. We also evaluate the suitability of a series of approximations for
modeling the scattering polarization in the wings of strong resonance lines
that would be particularly useful for the numerically intensive case of
three-dimensional radiative transfer. We examine the suitability of the
considered approximation using our radiative transfer code to model the Stokes
profiles of the Mg II h & k lines and of the H I Lyman alpha line in magnetized
one-dimensional models of the solar atmosphere. Neglecting Doppler
redistribution in the scattering processes that are unperturbed by elastic
collisions produces a negligible error in the scattering polarization wings of
the Mg II resonance lines and a minor one in the Lyman alpha wings, although it
is unsuitable to model the cores of these lines. For both lines, the scattering
processes that are perturbed by elastic collisions give a significant
contribution only to the intensity component of the emissivity. Neglecting
collisional as well as Doppler redistribution represents a rough but suitable
approximation for the wings of the Mg II resonance lines, but a very poor one
for the Lyman alpha wings. The magnetic sensitivity in the scattering
polarization wings of the considered lines can be modeled by accounting for the
magnetic field only in the eta I and rho V coefficients of the Stokes-vector
transfer equation.


title:
On strong second-order optimality conditions under relaxed constant rank
  constraint qualification

abstract:
  We discuss the (first- and second-order) optimality conditions for nonlinear
programming under the relaxed constant rank constraint qualification. This
condition generalizes the so-called linear independence constraint
qualification. Although the optimality conditions are well established in the
literature, the proofs presented here are based solely on the well-known
inverse function theorem. This is the only prerequisite from real analysis used
to establish two auxiliary results needed to prove the optimality conditions,
thereby making this paper totally self-contained.


title:
Nonlinear stability of the totally geodesic wave maps in anisotropic
  manifolds

abstract:
  In this article we investigate a type of totally geodesic map which has its
image being a geodesic in an anisotropic Riemannian manifold. We consider its
nonlinear stability among the family of wave maps. We first establish the
factorization property and then formulate the stability problem into a PDE
system in a specially constructed chart of geodesic normal coordinates. With a
generalization of the hyperboloidal foliation, we establish the global
existence result associate to small initial data for this PDE system, which
leads to the geometric stability.


title:
Identification of feasible pathway information for c-di-GMP binding
  proteins in cellulose production

abstract:
  In this paper, we utilize a machine learning approach to identify the
significant pathways for c-di-GMP signaling proteins. The dataset involves gene
counts from 12 pathways and 5 essential c-di-GMP binding domains for 1024
bacterial genomes. Two novel approaches, Least absolute shrinkage and selection
operator (Lasso) and Random forests, have been applied for analyzing and
modeling the dataset. Both approaches show that bacterial chemotaxis is the
most essential pathway for c-di-GMP encoding domains. Though popular for
feature selection, the strong regularization of Lasso method fails to associate
any pathway to MshE domain. Results from the analysis may help to understand
and emphasize the supporting pathways involved in bacterial cellulose
production. These findings demonstrate the need for a chassis to restrict the
behavior or functionality by deactivating the selective pathways in cellulose
production.


title:
Application of WGAN-GP in recommendation and Questioning the relevance
  of GAN-based approaches

abstract:
  Many neural-based recommender systems were proposed in recent years and part
of them used Generative Adversarial Networks (GAN) to model user-item
interactions. However, the exploration of Wasserstein GAN with Gradient Penalty
(WGAN-GP) on recommendation has received relatively less scrutiny. In this
paper, we focus on two questions: 1- Can we successfully apply WGAN-GP on
recommendation and does this approach give an advantage compared to the best
GAN models? 2- Are GAN-based recommender systems relevant? To answer the first
question, we propose a recommender system based on WGAN-GP called CFWGAN-GP
which is founded on a previous model (CFGAN). We successfully applied our
method on real-world datasets on the top-k recommendation task and the
empirical results show that it is competitive with state-of-the-art GAN
approaches, but we found no evidence of significant advantage of using WGAN-GP
instead of the original GAN, at least from the accuracy point of view. As for
the second question, we conduct a simple experiment in which we show that a
well-tuned conceptually simpler method outperforms GAN-based models by a
considerable margin, questioning the use of such models.


title:
Phonon-phonon interactions in the polarizarion dependence of Raman
  scattering

abstract:
  We have found that the polarization dependence of the Raman signal in organic
crystals can only be described by a fourt-rank formalism formalism. The
generalization from the traditional second-rank Raman tensor $\mathcal{R}$ is
physically motivated by consideration of the light scattering mechanism of
anharmonic crystals at finite temperatures, and explained in terms of
off-diagonal components of the crystal self-energy. We thus establish a novel
manifestation of anharmonicity in inelastic light scattering, markedly separate
from the better known phonon lifetime.


title:
Infinite dimensional spaces in the set of strongly norm-attaining
  Lipschitz maps

abstract:
  We prove that if $M$ is an infinite complete metric space then the set of
strongly norm-attaining Lipschitz functions $\SA(M)$ contains a linear subspace
isomorphic to $c_0$. This solves an open question posed by V. Kadets and O.
Rold\'an.


title: Expanding the Latent Space of StyleGAN for Real Face Editing

abstract:
  Recently, a surge of face editing techniques have been proposed to employ the
pretrained StyleGAN for semantic manipulation. To successfully edit a real
image, one must first convert the input image into StyleGAN's latent variables.
However, it is still challenging to find latent variables, which have the
capacity for preserving the appearance of the input subject (e.g., identity,
lighting, hairstyles) as well as enabling meaningful manipulations. In this
paper, we present a method to expand the latent space of StyleGAN with
additional content features to break down the trade-off between low-distortion
and high-editability. Specifically, we proposed a two-branch model, where the
style branch first tackles the entanglement issue by the sparse manipulation of
latent codes, and the content branch then mitigates the distortion issue by
leveraging the content and appearance details from the input image. We confirm
the effectiveness of our method using extensive qualitative and quantitative
experiments on real face editing and reconstruction tasks.


title: Complete ZX-calculi for the stabiliser fragment in odd prime dimensions

abstract:
  We introduce a family of ZX-calculi which axiomatise the stabiliser fragment
of quantum theory in odd prime dimensions. These calculi recover many of the
nice features of the qubit ZX-calculus which were lost in previous proposals
for higher-dimensional systems. We then prove that these calculi are complete,
i.e. provide a set of rewrite rules which can be used to prove any equality of
stabiliser quantum operations. Adding a discard construction, we obtain a
calculus complete for mixed state stabiliser quantum mechanics in odd prime
dimensions, and this furthermore gives a complete axiomatisation for the
related diagrammatic language for affine co-isotropic relations.


title: The eclipsing binary systems with $\delta$ Scuti component -- II. AB Cas

abstract:
  We present a complex study of the eclipsing binary system, AB Cas. The
analysis of the whole TESS light curve, corrected for the binary effects,
reveals 112 significant frequency peaks with 17 independent signals. The
dominant frequency $f_1 = $17.1564 d$^{-1}$ is a radial fundamental mode. The
$O-C$ analysis of the times of light minima from over 92 years leads to a
conclusion that due to the ongoing mass transfer the system exhibits a change
of the orbital period at a rate of 0.03 s per year. In order to find
evolutionary models describing the current stage of AB Cas, we perform binary
evolution computations. Our results show the AB Cas system as a product of the
rapid non-conservative mass transfer with about 5-26 % of transferred mass lost
from the system. This process heavily affected the orbital characteristics of
this binary and its components in the past. In fact, this system closely
resembles the formation scenarios of EL CVn type binaries. For the first time,
we demonstrate the effect of binary evolution on radial pulsations and
determine the lines of constant frequency on the HR diagram. From the binary
and seismic modelling, we obtain constraints on various parameters. In
particular, we constrain the overshooting parameter, $f_{\rm ov}\in
[0.010,~0.018]$, the mixing-length parameter, $\alpha_{\rm MLT}\in[1.2,~1.5]$
and the age, $t \in [2.3,~3.4]$ Gyr.


title: A Gaussian Process Model for Opponent Prediction in Autonomous Racing

abstract:
  In head-to-head racing, performing tightly constrained, but highly rewarding
maneuvers, such as overtaking, require an accurate model of interactive
behavior of the opposing target vehicle (TV). However, such information is not
typically made available in competitive scenarios, we therefore propose to
construct a prediction and uncertainty model given data of the TV from previous
races. In particular, a one-step Gaussian Process (GP) model is trained on
closed-loop interaction data to learn the behavior of a TV driven by an unknown
policy. Predictions of the nominal trajectory and associated uncertainty are
rolled out via a sampling-based approach and are used in a model predictive
control (MPC) policy for the ego vehicle in order to intelligently trade-off
between safety and performance when attempting overtaking maneuvers against a
TV. We demonstrate the GP-based predictor in closed loop with the MPC policy in
simulation races and compare its performance against several predictors from
literature. In a Monte Carlo study, we observe that the GP-based predictor
achieves similar win rates while maintaining safety in up to 3x more races. We
finally demonstrate the prediction and control framework in real-time on
hardware experiments.


title: AccMPEG: Optimizing Video Encoding for Video Analytics

abstract:
  With more videos being recorded by edge sensors (cameras) and analyzed by
computer-vision deep neural nets (DNNs), a new breed of video streaming systems
has emerged, with the goal to compress and stream videos to remote servers in
real time while preserving enough information to allow highly accurate
inference by the server-side DNNs. An ideal design of the video streaming
system should simultaneously meet three key requirements: (1) low latency of
encoding and streaming, (2) high accuracy of server-side DNNs, and (3) low
compute overheads on the camera. Unfortunately, despite many recent efforts,
such video streaming system has hitherto been elusive, especially when serving
advanced vision tasks such as object detection or semantic segmentation. This
paper presents AccMPEG, a new video encoding and streaming system that meets
all the three requirements. The key is to learn how much the encoding quality
at each (16x16) macroblock can influence the server-side DNN accuracy, which we
call accuracy gradient. Our insight is that these macroblock-level accuracy
gradient can be inferred with sufficient precision by feeding the video frames
through a cheap model. AccMPEG provides a suite of techniques that, given a new
server-side DNN, can quickly create a cheap model to infer the accuracy
gradient on any new frame in near realtime. Our extensive evaluation of AccMPEG
on two types of edge devices (one Intel Xeon Silver 4100 CPU or NVIDIA Jetson
Nano) and three vision tasks (six recent pre-trained DNNs) shows that AccMPEG
(with the same camera-side compute resources) can reduce the end-to-end
inference delay by 10-43% without hurting accuracy compared to the
state-of-the-art baselines


title: Building Change Detection using Multi-Temporal Airborne LiDAR Data

abstract:
  Building change detection is essential for monitoring urbanization, disaster
assessment, urban planning and frequently updating the maps. 3D structure
information from airborne light detection and ranging (LiDAR) is very effective
for detecting urban changes. But the 3D point cloud from airborne LiDAR(ALS)
holds an enormous amount of unordered and irregularly sparse information.
Handling such data is tricky and consumes large memory for processing. Most of
this information is not necessary when we are looking for a particular type of
urban change. In this study, we propose an automatic method that reduces the 3D
point clouds into a much smaller representation without losing the necessary
information required for detecting Building changes. The method utilizes the
Deep Learning(DL) model U-Net for segmenting the buildings from the background.
Produced segmentation maps are then processed further for detecting changes and
the results are refined using morphological methods. For the change detection
task, we used multi-temporal airborne LiDAR data. The data is acquired over
Stockholm in the years 2017 and 2019. The changes in buildings are classified
into four types: 'newly built', 'demolished', 'taller' and 'shorter'. The
detected changes are visualized in one map for better interpretation.


title:
Double Diffusion Maps and their Latent Harmonics for Scientific
  Computations in Latent Space

abstract:
  We introduce a data-driven approach to building reduced dynamical models
through manifold learning; the reduced latent space is discovered using
Diffusion Maps (a manifold learning technique) on time series data. A second
round of Diffusion Maps on those latent coordinates allows the approximation of
the reduced dynamical models. This second round enables mapping the latent
space coordinates back to the full ambient space (what is called lifting); it
also enables the approximation of full state functions of interest in terms of
the reduced coordinates. In our work, we develop and test three different
reduced numerical simulation methodologies, either through pre-tabulation in
the latent space and integration on the fly or by going back and forth between
the ambient space and the latent space. The data-driven latent space simulation
results, based on the three different approaches, are validated through (a) the
latent space observation of the full simulation through the Nystr\"om Extension
formula, or through (b) lifting the reduced trajectory back to the full ambient
space, via Latent Harmonics. Latent space modeling often involves additional
regularization to favor certain properties of the space over others, and the
mapping back to the ambient space is then constructed mostly independently from
these properties; here, we use the same data-driven approach to construct the
latent space and then map back to the ambient space.


title:
Silicon Photonic Single-Sideband Generation with Dual-Parallel
  Mach-Zehnder Modulators for Atom Interferometry Applications

abstract:
  We demonstrate a silicon photonic carrier-suppressed single-sideband (CS-SSB)
modulator with dual-parallel Mach-Zehnder modulators (DP-MZMs) operating near
1550 nm with a measured carrier suppression of 30 dB and an ultra-high sideband
suppression (SSR) of 47.8 dB at 1 GHz with peak conversion efficiency of -6.846
dB (20.7%). We extensively study the effects of imbalances in both the optical
and RF phases and amplitudes on the side-band performance. Furthermore, with
our silicon photonic modulator, we successfully demonstrate state-selective
detection for atoms with time-multiplexed frequency shifting and atom
interferometer fringes in a Rubidium ($^{87}$Rb) atom system to estimate
gravitational acceleration. These multidisciplinary efforts demonstrate
progress towards an integrated silicon photonic chip-scale laser system for
atom interferometry and quantum information science applications.


title:
The average genus of oriented rational links with a given crossing
  number

abstract:
  In this paper, we enumerate the number of oriented rational knots and the
number of oriented rational links with any given crossing number and minimum
genus. This allows us to obtain a precise formula for the average minimal genus
of oriented rational knots and links with any given crossing number.


title: Automation of Radiation Treatment Planning for Rectal Cancer

abstract:
  To develop an automated workflow for rectal cancer three-dimensional
conformal radiotherapy treatment planning that combines deep-learning(DL)
aperture predictions and forward-planning algorithms. We designed an algorithm
to automate the clinical workflow for planning with field-in-field. DL models
were trained, validated, and tested on 555 patients to automatically generate
aperture shapes for primary and boost fields. Network inputs were digitally
reconstructed radiography, gross tumor volume(GTV), and nodal GTV. A physician
scored each aperture for 20 patients on a 5-point scale(>3 acceptable). A
planning algorithm was then developed to create a homogeneous dose using a
combination of wedges and subfields. The algorithm iteratively identifies a
hotspot volume, creates a subfield, and optimizes beam weight all without user
intervention. The algorithm was tested on 20 patients using clinical apertures
with different settings, and the resulting plans(4 plans/patient) were scored
by a physician. The end-to-end workflow was tested and scored by a physician on
39 patients using DL-generated apertures and planning algorithms. The predicted
apertures had Dice scores of 0.95, 0.94, and 0.90 for posterior-anterior,
laterals, and boost fields, respectively. 100%, 95%, and 87.5% of the
posterior-anterior, laterals, and boost apertures were scored as clinically
acceptable, respectively. Wedged and non-wedged plans were clinically
acceptable for 85% and 50% of patients, respectively. The final plans hotspot
dose percentage was reduced from 121%($\pm$ 14%) to 109%($\pm$ 5%) of
prescription dose. The integrated end-to-end workflow of automatically
generated apertures and optimized field-in-field planning gave clinically
acceptable plans for 38/39(97%) of patients. We have successfully automated the
clinical workflow for generating radiotherapy plans for rectal cancer for our
institution.


title:
Personalized Driving Behaviors and Fuel Economy over Realistic Commute
  Traffic: Modeling, Correlation, and Prediction

abstract:
  Drivers have distinctively diverse behaviors when operating vehicles in
natural traffic flow, such as preferred pedal position, car-following distance,
preview time headway, etc. These highly personalized behavioral variations are
known to impact vehicle fuel economy qualitatively. Nevertheless, the
quantitative relationship between driving behaviors and vehicle fuel
consumption remains obscure. Addressing this critical missing link will
contribute to the improvement of transportation sustainability, as well as
understanding drivers' behavioral diversity. This study proposed an integrated
microscopic driver behavior and fuel consumption model to assess and predict
vehicle fuel economy with naturalistic highway and local commuting traffic
data. Through extensive Monte Carlo simulations, significant correlation
results are revealed between specific individual driving preferences and fuel
economy over drivers' frequent commuting routes. Correlation results indicate
that the differences in fuel consumption incurred by various driving behaviors,
even in the same traffic conditions, can be as much as 29% for a light-duty
truck and 15% for a passenger car. A Gaussian Process Regression model is
further trained, validated, and tested under different traffic and vehicle
conditions to predict fuel consumption based on drivers' personalized
behaviors. Such a quantitative and personalized model can be used to identify
and recommend fuel-friendly driving behaviors and routes, demonstrating a
strong incentive for relevant stakeholders.


title: Multi stain graph fusion for multimodal integration in pathology

abstract:
  In pathology, tissue samples are assessed using multiple staining techniques
to enhance contrast in unique histologic features. In this paper, we introduce
a multimodal CNN-GNN based graph fusion approach that leverages complementary
information from multiple non-registered histopathology images to predict
pathologic scores. We demonstrate this approach in nonalcoholic steatohepatitis
(NASH) by predicting CRN fibrosis stage and NAFLD Activity Score (NAS). Primary
assessment of NASH typically requires liver biopsy evaluation on two
histological stains: Trichrome (TC) and hematoxylin and eosin (H&E). Our
multimodal approach learns to extract complementary information from TC and H&E
graphs corresponding to each stain while simultaneously learning an optimal
policy to combine this information. We report up to 20% improvement in
predicting fibrosis stage and NAS component grades over single-stain modeling
approaches, measured by computing linearly weighted Cohen's kappa between
machine-derived vs. pathologist consensus scores. Broadly, this paper
demonstrates the value of leveraging diverse pathology images for improved
ML-powered histologic assessment.


title:
Phenomenology at the Large Hadron Collider with Deep Learning: the case
  of vector-like quarks decaying to light jets

abstract:
  In this work, we continue our exploration of TeV-scale vector-like fermion
signatures inspired by a Grand Unification scenario based on the trinification
gauge group. A particular focus is given to pair-production topologies of
vector-like quarks (VLQs) at the LHC, in a multi-jet plus a charged lepton and
a missing energy signature. We employ Deep Learning methods and techniques
based in evolutive algorithms that optimize hyper-parameters in the neural
network construction, whose objective is to maximise the Asimov estimate for
distinct VLQ masses. In this article, we consider the implications of an
innovative approach by simultaneously combining detector images (also known as
jet images) and tabular data containing kinematic information from the final
states. With this technique we are able to exclude VLQs, that are specific for
the considered model, to up a mass of 1.2 TeV in both the high-luminosity the
Run-III phases of the LHC programme.


title:
Data Bootstrapping Approaches to Improve Low Resource Abusive Language
  Detection for Indic Languages

abstract:
  Abusive language is a growing concern in many social media platforms.
Repeated exposure to abusive speech has created physiological effects on the
target users. Thus, the problem of abusive language should be addressed in all
forms for online peace and safety. While extensive research exists in abusive
speech detection, most studies focus on English. Recently, many smearing
incidents have occurred in India, which provoked diverse forms of abusive
speech in online space in various languages based on the geographic location.
Therefore it is essential to deal with such malicious content. In this paper,
to bridge the gap, we demonstrate a large-scale analysis of multilingual
abusive speech in Indic languages. We examine different interlingual transfer
mechanisms and observe the performance of various multilingual models for
abusive speech detection for eight different Indic languages. We also
experiment to show how robust these models are on adversarial attacks. Finally,
we conduct an in-depth error analysis by looking into the models' misclassified
posts across various settings. We have made our code and models public for
other researchers.


title: Aubry set for sub-Riemannian control systems

abstract:
  In the paper [P. Cannarsa, C. Mendico, Asymptotic analysis for
Hamilton-Jacobi- Bellman equations on Euclidean space, (2021) Arxiv], we proved
the existence of the limit as the time horizon goes to infinity of the averaged
value function of an optimal control problem. For the classical Tonelli case
such a limit is called the critical constant of the problem. In the special
case of sub-Riemannian control systems, we also proved the existence of a
critical solution, that is, a continuous solution to the Hamilton-Jacobi
equation associated with such a constant, which also coincides with its
Lax-Oleinik evolution. Here, we focus our attention on the sub- Riemannian case
providing a variational representation formula for the critical constant which
uses an adapted notion of closed measures. Having such a formula at our
disposal, we define and study the Aubry set. First, we investigate dynamical
and topological properties of such a set w.r.t. a suitable class of minimizing
trajectories of the Lagrangian action. Then, we show that critical solutions to
the Hamilton-Jacobi equation are horizontally differentiable and satisfy the
equation in classical sense on the Aubry set.


title: The space of metric structures on hyperbolic groups

abstract:
  We study the metric and topological properties of the space $\mathscr{D}(G)$
of left-invariant hyperbolic metrics on the non-elementary hyperbolic group $G$
that are quasi-isometric to a word metric, up to rough similarity. This space
naturally contains the Teichm\"uller space in case $G$ is a surface group and
the Culler-Vogtmann outer space when $G$ is a free group. Endowed with a
natural metric reminiscent of the (symmetrized) Thurston's metric on
Teichm\"uller space, we prove that $\mathscr{D}(G)$ is an unbounded
contractible metric space and that $\mathrm{Out}(G)$ acts metrically properly
by isometries on it. If we restrict ourselves to the subspace
$\mathscr{D}_{\delta}(G)$ of the points represented by $\delta$-hyperbolic
metrics with critical exponent 1, we prove that its closure
$\bar{\mathscr{D}_{\delta}(G)}$ is either empty or proper, and that
$\mathscr{D}_{\delta}(G)$ is closed if $G$ is torsion-free. We also prove
continuity of the Bowen-Margulis map from $\mathscr{D}_{\delta}(G)$ into the
space $\mathbb{P}\mathcal{C}urr(G)$ of projective geodesic currents on $G$,
extending similar results for surface and free groups, and the continuity of
the (normalized) mean distortion as a function on $\mathscr{D}(G)\times
\mathscr{D}(G)$.


title: Probing details of spin--orbit coupling through Pauli spin blockade

abstract:
  Spin--orbit interaction (SOI) plays a fundamental role in many
low-dimensional semiconductor and hybrid quantum devices. In the rapidly
evolving field of semiconductor spin qubits, SOI is an essential ingredient
that can allow for ultrafast qubit control. The exact manifestation of SOI in a
given device is, however, often both hard to predict theoretically and probe
experimentally. Here, we develop a detailed theoretical connection between the
leakage current through a double quantum dot in Pauli spin blockade and the
underlying SOI in the system. We present a general analytic expression for the
leakage current, which allows to connect experimentally observable features to
both the magnitude and orientation of the effective spin--orbit field acting on
the moving carriers. Motivated by the large recent interest in hole-based
quantum devices, we further zoom in on the case of Pauli blockade of hole
spins, assuming a strong transverse confinement potential. In this limit we
also find an analytic expression for the current at low external magnetic
field, that includes the effect of hyperfine coupling of the hole spins to
randomly fluctuating nuclear spin baths. This result can be used to extract
detailed information about both hyperfine and spin--orbit coupling parameters
for hole spins in devices with a significant fraction of non-zero nuclear
spins.


title:
Design, Implementation, and Evaluation of Blockchain-Based Trusted
  Achievement Record System for Students in Higher Education

abstract:
  With a growing number of institutions involved in the global education
market, it has become increasingly challenging to verify the authenticity of
academic achievements such as CVs and diplomas. Blockchain is an enabling
technology that can play a key role in solving this problem. This study
introduces a blockchain-based achievement record system that produces a
verifiable record of achievements. The proposed system aims to facilitate the
process of authentication and validation of certificates reliably, easily, and
quickly, leveraging the unique capabilities offered through Blockchain
technology (public Ethereum Blockchain) and smart contracts. We present the
design and implementation of the system and its components and tools. We then
evaluate the system through a number of studies to measure the system's
usability, effectiveness, performance, and cost. A System Usability Scale (SUS)
test gave a scale of 77.1. Through a literature survey, we demonstrate that
this system is a significant improvement on legacy systems, being both more
user-friendly and more efficient. We also conduct a detailed cost analysis and
discuss the positives and limitations of alternative blockchain solutions.


title:
Global well-posedness for the derivative nonlinear Schr\"odinger
  equation in $L^2(\mathbb{R})$

abstract:
  We prove that the derivative nonlinear Schr\"odinger equation in one space
dimension is globally well-posed on the line in $L^2(\mathbb{R})$, which is the
scaling-critical space for this equation.


title:
On $L^\infty$ estimates for fully nonlinear partial differential
  equations on Hermitian manifolds

abstract:
  Sharp $L^\infty$ estimates are obtained for general classes of fully
non-linear PDE's on Hermitian manifolds. The method builds on the method of
comparison with auxiliary Monge-Amp\`ere equations introduced earlier in the
K\"ahler case by the authors in joint work with F. Tong, but uses this time
auxiliary equations on open balls with a Dirichlet condition. The role of Yau's
theorem in the compact K\"ahler case is now played by the theorem of
Caffarelli-Kohn-Nirenberg-Spruck for the Dirichlet problem. Global estimates
are then deduced by combining arguments of Blocki with exponential estimates
due to Kolodziej generalizing the classic inequality of Brezis-Merle. The
method applies not just to compact Hermitian manifolds, but also to the
Dirichlet problem, to open manifolds with a lower bound on their injectivity
radii, and to $(n-1)$-form Monge-Amp\`ere equations.


title:
Systematic description of COVID-19 pandemic using exact SIR solutions
  and Gumbel distributions

abstract:
  An epidemiological study of deaths is carried out in a dozen countries by
analyzing the first wave of the COVID-19 pandemic. These countries are among
those most affected by the first wave, i.e. where daily-death data series may
closely resemble a solution of the basic SIR equations. The SIR equations are
solved parametrically using the proper time as parameter. Some general
properties of the SIR solutions are studied such as time-scaling and asymmetry.
Additionally, we use approximations to the SIR solutions through Gumbel
functions, which present a very similar behavior. The parameters of the SIR
model and the Gumbel function are extracted from the data and compared for the
different countries. It is found that ten of the selected countries are very
well described by the solutions of the SIR model, with a basic reproduction
number between 3 and 8.


title:
A direct measurement of the distance to the Galactic center using the
  kinematics of bar stars

abstract:
  The distance to the Galactic center $R_0$ is a fundamental parameter for
understanding the Milky Way, because all observations of our Galaxy are made
from our heliocentric reference point. The uncertainty in $R_0$ limits our
knowledge of many aspects of the Milky Way, including its total mass and the
relative mass of its major components, and any orbital parameters of stars
employed in chemo-dynamical analyses. While measurements of $R_0$ have been
improving over a century, measurements in the past few years from a variety of
methods still find a wide range of $R_0$ being somewhere within $8.0$ to
$8.5\,\mathrm{kpc}$. The most precise measurements to date have to assume that
Sgr A$^*$ is at rest at the Galactic center, which may not be the case. In this
paper, we use maps of the kinematics of stars in the Galactic bar derived from
APOGEE DR17 and Gaia EDR3 data augmented with spectro-photometric distances
from the \texttt{astroNN} neural-network method. These maps clearly display the
minimum in the rotational velocity $v_T$ and the quadrupolar signature in
radial velocity $v_R$ expected for stars orbiting in a bar. From the minimum in
$v_T$, we measure $R_0 = 8.23 \pm 0.12\,\mathrm{kpc}$. We validate our
measurement using realistic $N$-body simulations of the Milky Way. We further
measure the pattern speed of the bar to be $\Omega_\mathrm{bar} =
40.08\pm1.78\,\mathrm{km\,s}^{-1}\mathrm{kpc}^{-1}$. Because the bar forms out
of the disk, its center is manifestly the barycenter of the bar+disc system and
our measurement is therefore the most robust and accurate measurement of $R_0$
to date.


title:
These Deals Won't Last! Longevity, Uniformity and Bias in Product Badge
  Assignment in E-Commerce Platforms

abstract:
  Product badges are ubiquitous in e-commerce platforms, acting as effective
psychological triggers to nudge customers to buy specific products, boosting
revenues. However, to the best of our knowledge, there has been no attempt to
systematically study these badges and their several idiosyncrasies - we intend
to close this gap in our current work. Specifically, we try to answer questions
such as: How long does a product retain a badge on a given platform? If a
product is sold on different platforms, then does it receive similar badges?
How do the products that receive badges differ from those which do not, in
terms of price, customer rating, etc. We collect longitudinal data from several
e-commerce platforms over 45 days, and find that although most of the badges
are short-lived, there are several permanent badge assignments and that too for
badges meant to denote urgency or scarcity. Furthermore, it is unclear how the
badge assignments are done, and we find evidence that highly-rated products are
missing out on badges compared to lower quality ones. Our work calls for
greater transparency in the badge assignment process to inform customers, as
well as to reduce dissatisfaction among the sellers dependent on the platforms
for their revenues.


title:
Generative 3D Animation Pipelines: Automating Facial Retargeting
  Workflows

abstract:
  Design tools in the 3D industry, while powerful, are still tedious and
labor-intensive when it comes to bringing a creative idea for a visual effect
to life. In this position paper, we discussed how an infamous generative
synthetic media, deepfakes, could be of use and embedded into common
sophisticated 3D workflows to reduce user workloads in areas such as 3D model
editing, material design, and character animation. As a case discussion, we
also prototyped a tool to address the retargeting problem in character
animation. Although deepfakes themselves have received a negative public image,
the results of our interviews with field experts are unexpectedly positive in
regard to our tool that utilizes deepfake algorithms. Lastly, we also discussed
our experience and observed design practices to put deepfakes to good use,
including how we could avoid potential misuses directly by design, how this
design changes user interactions, and subsequent open issues.


title:
An Empirical Study of the Occurrence of Heavy-Tails in Training a ReLU
  Gate

abstract:
  A particular direction of recent advance about stochastic deep-learning
algorithms has been about uncovering a rather mysterious heavy-tailed nature of
the stationary distribution of these algorithms, even when the data
distribution is not so. Moreover, the heavy-tail index is known to show
interesting dependence on the input dimension of the net, the mini-batch size
and the step size of the algorithm. In this short note, we undertake an
experimental study of this index for S.G.D. while training a $\relu$ gate (in
the realizable and in the binary classification setup) and for a variant of
S.G.D. that was proven in Karmakar and Mukherjee (2022) for ReLU realizable
data. From our experiments we conjecture that these two algorithms have similar
heavy-tail behaviour on any data where the latter can be proven to converge.
Secondly, we demonstrate that the heavy-tail index of the late time iterates in
this model scenario has strikingly different properties than either what has
been proven for linear hypothesis classes or what has been previously
demonstrated for large nets.


title: Modeling observed gender imbalances in academic citation practices

abstract:
  In multiple academic disciplines, having a perceived gender of `woman' is
associated with a lower than expected rate of citations. In some fields, that
disparity is driven primarily by the citations of men and is increasing over
time despite increasing diversification of the profession. It is likely that
complex social interactions and individual ideologies shape these disparities.
Computational models of select factors that reproduce empirical observations
can help us understand some of the minimal driving forces behind these complex
phenomena and therefore aid in their mitigation. Here, we present a simple
agent-based model of citation practices within academia, in which academics
generate citations based on three factors: their estimate of the collaborative
network of the field, how they sample that estimate, and how open they are to
learning about their field from other academics. We show that increasing
homophily -- or the tendency of people to interact with others more like
themselves -- in these three domains is sufficient to reproduce observed biases
in citation practices. We find that homophily in sampling an estimate of the
field influences total citation rates, and openness to learning from new and
unfamiliar authors influences the change in those citations over time. We next
model a real-world intervention -- the citation diversity statement -- which
has the potential to influence both of these parameters. We determine a
parameterization of our model that matches the citation practices of academics
who use the citation diversity statement. This parameterization paired with an
openness to learning from many new authors can result in citation practices
that are equitable and stable over time. Ultimately, our work underscores the
importance of homophily in shaping citation practices and provides evidence
that specific actions may mitigate biased citation practices in academia.


title: SoFaiR: Single Shot Fair Representation Learning

abstract:
  To avoid discriminatory uses of their data, organizations can learn to map
them into a representation that filters out information related to sensitive
attributes. However, all existing methods in fair representation learning
generate a fairness-information trade-off. To achieve different points on the
fairness-information plane, one must train different models. In this paper, we
first demonstrate that fairness-information trade-offs are fully characterized
by rate-distortion trade-offs. Then, we use this key result and propose SoFaiR,
a single shot fair representation learning method that generates with one
trained model many points on the fairness-information plane. Besides its
computational saving, our single-shot approach is, to the extent of our
knowledge, the first fair representation learning method that explains what
information is affected by changes in the fairness / distortion properties of
the representation. Empirically, we find on three datasets that SoFaiR achieves
similar fairness-information trade-offs as its multi-shot counterparts.


title: MemFHE: End-to-End Computing with Fully Homomorphic Encryption in Memory

abstract:
  The increasing amount of data and the growing complexity of problems has
resulted in an ever-growing reliance on cloud computing. However, many
applications, most notably in healthcare, finance or defense, demand security
and privacy which today's solutions cannot fully address. Fully homomorphic
encryption (FHE) elevates the bar of today's solutions by adding
confidentiality of data during processing. It allows computation on fully
encrypted data without the need for decryption, thus fully preserving privacy.
To enable processing encrypted data at usable levels of classic security, e.g.,
128-bit, the encryption procedure introduces noticeable data size expansion -
the ciphertext is much bigger than the native aggregate of native data types.
In this paper, we present MemFHE which is the first accelerator of both client
and server for the latest Ring-GSW (Gentry, Sahai, and Waters) based
homomorphic encryption schemes using Processing In Memory (PIM). PIM alleviates
the data movement issues with large FHE encrypted data, while providing in-situ
execution and extensive parallelism needed for FHE's polynomial operations.
While the client-PIM can homomorphically encrypt and decrypt data, the
server-PIM can process homomorphically encrypted data without decryption.
MemFHE's server-PIM is pipelined and is designed to provide flexible
bootstrapping, allowing two encryption techniques and various FHE
security-levels based on the application requirements. We evaluate MemFHE for
various security-levels and compare it with state-of-the-art CPU
implementations for Ring-GSW based FHE. MemFHE is up to 20kx (265x) faster than
CPU (GPU) for FHE arithmetic operations and provides on average 2007x higher
throughput than the state-of-the-art while implementing neural networks with
FHE.


title:
The young Galactic cluster NGC 225: binary stars content and total mass
  estimate

abstract:
  Galactic star clusters are known to harbour a significant amount of binary
stars, yet their role in the dynamical evolution of the cluster as a whole is
not comprehensively understood. We investigated the influence of binary stars
on the total mass estimate for the case of the moderately populated Galactic
star cluster NGC 225. The analysis of multi-epoch radial velocities of the 29
brightest cluster members, obtained over two observational campaigns, in
1990-1991 and in 2019-2020, yields a value of binary fraction of $\alpha =0.52$
(15 stars out of 29). Using theoretical isochrones and Monte Carlo simulations
we found that the cluster mass increases at least 1.23 times when binaries are
properly taken into account. By combining Gaia EDR3 photometric data with our
spectroscopic observations, we derived estimates of NGC 225 fundamental
parameters as follows: mean radial velocity $<V_r> = -9.8 \pm 0.7$ km s$^{-1}$,
$\log(\tau)$=8.0-8.2 dex, distance $ D = 676 \pm 22$ pc, and colour excess
$E(B-V) = 0.29 \pm 0.01$ mag.


title:
Parkinson's disease diagnostics using AI and natural language knowledge
  transfer

abstract:
  In this work, the issue of Parkinson's disease (PD) diagnostics using
non-invasive antemortem techniques was tackled. A deep learning approach for
classification of raw speech recordings in patients with diagnosed PD was
proposed. The core of proposed method is an audio classifier using knowledge
transfer from a pretrained natural language model, namely \textit{wav2vec 2.0}.
Method was tested on a group of 38 PD patients and 10 healthy persons above the
age of 50. A dataset of speech recordings acquired using a smartphone recorder
was constructed and the recordings were label as PD/non-PD with severity of the
disease additionally rated using Hoehn-Yahr scale. The audio recordings were
cut into 2141 samples that include sentences, syllables, vowels and sustained
phonation. The classifier scores up to 97.92\% of cross-validated accuracy.
Additionally, paper presents results of a human-level performance assessment
questionnaire, which was consulted with the neurology professionals


title:
Process Knowledge-infused Learning for Suicidality Assessment on Social
  Media

abstract:
  Improving the performance and natural language explanations of deep learning
algorithms is a priority for adoption by humans in the real world. In several
domains, such as healthcare, such technology has significant potential to
reduce the burden on humans by providing quality assistance at scale. However,
current methods rely on the traditional pipeline of predicting labels from
data, thus completely ignoring the process and guidelines used to obtain the
labels. Furthermore, post hoc explanations on the data to label prediction
using explainable AI (XAI) models, while satisfactory to computer scientists,
leave much to be desired to the end-users due to lacking explanations of the
process in terms of human-understandable concepts. We \textit{introduce},
\textit{formalize}, and \textit{develop} a novel Artificial Intelligence (A)
paradigm -- Process Knowledge-infused Learning (PK-iL). PK-iL utilizes a
structured process knowledge that explicitly explains the underlying prediction
process that makes sense to end-users. The qualitative human evaluation
confirms through a annotator agreement of 0.72, that humans are understand
explanations for the predictions. PK-iL also performs competitively with the
state-of-the-art (SOTA) baselines.


title: Learning Eco-Driving Strategies at Signalized Intersections

abstract:
  Signalized intersections in arterial roads result in persistent vehicle
idling and excess accelerations, contributing to fuel consumption and CO2
emissions. There has thus been a line of work studying eco-driving control
strategies to reduce fuel consumption and emission levels at intersections.
However, methods to devise effective control strategies across a variety of
traffic settings remain elusive. In this paper, we propose a reinforcement
learning (RL) approach to learn effective eco-driving control strategies. We
analyze the potential impact of a learned strategy on fuel consumption, CO2
emission, and travel time and compare with naturalistic driving and model-based
baselines. We further demonstrate the generalizability of the learned policies
under mixed traffic scenarios. Simulation results indicate that scenarios with
100% penetration of connected autonomous vehicles (CAV) may yield as high as
18% reduction in fuel consumption and 25% reduction in CO2 emission levels
while even improving travel speed by 20%. Furthermore, results indicate that
even 25% CAV penetration can bring at least 50% of the total fuel and emission
reduction benefits.


title: On the Verification of Belief Programs

abstract:
  In a recent paper, Belle and Levesque proposed a framework for a type of
program called belief programs, a probabilistic extension of GOLOG programs
where every action and sensing result could be noisy and every test condition
refers to the agent's subjective beliefs. Inherited from GOLOG programs, the
action-centered feature makes belief programs fairly suitable for high-level
robot control under uncertainty. An important step before deploying such a
program is to verify whether it satisfies properties as desired. At least two
problems exist in doing verification: how to formally specify properties of a
program and what is the complexity of verification. In this paper, we propose a
formalism for belief programs based on a modal logic of actions and beliefs.
Among other things, this allows us to express PCTL-like temporal properties
smoothly. Besides, we investigate the decidability and undecidability for the
verification problem of belief programs.


title:
Nonlinear eigenvalue methods for linear pointwise stability of nonlinear
  waves

abstract:
  We propose an iterative method to find pointwise growth exponential growth
rates in linear problems posed on essentially one-dimensional domains. Such
pointwise growth rates capture pointwise stability and instability in extended
systems and arise as spectral values of a family of matrices that depends
analytically on a spectral parameter, obtained via a scattering-type problem.
Different from methods in the literature that rely on computing determinants of
this nonlinear matrix pencil, we propose and analyze an inverse power method
that allows one to locate robustly the closest spectral value to a given
reference point in the complex plane. The method finds branch points,
eigenvalues, and resonance poles without a priori knowledge.


title: Defining the role of open source software in research reproducibility

abstract:
  Reproducibility is inseparable from transparency, as sharing data, code and
computational environment is a pre-requisite for being able to retrace the
steps of producing the research results. Others have made the case that this
artifact sharing should adopt appropriate licensing schemes that permit reuse,
modification and redistribution. I make a new proposal for the role of open
source software, stemming from the lessons it teaches about distributed
collaboration and a commitment-based culture. Reviewing the defining features
of open source software (licensing, development, communities), I look for
explanation of its success from the perspectives of connectivism -- a learning
theory for the digital age -- and the language-action framework of Winograd and
Flores. I contend that reproducibility is about trust, which we build in
community via conversations, and open source software is a route to learn how
to be more effective learning (discovering) together.


title:
Band Flattening and Landau Level Merging in Strongly-Correlated
  Two-Dimensional Electron Systems

abstract:
  We review recent experimental results indicating the band flattening and
Landau level merging at the chemical potential in strongly-correlated
two-dimensional (2D) electron systems. In ultra-clean, strongly interacting 2D
electron system in SiGe/Si/SiGe quantum wells, the effective electron mass at
the Fermi level monotonically increases in the entire range of electron
densities, while the energy-averaged mass saturates at low densities. The
qualitatively different behavior of the two masses reveals a precursor to the
interaction-induced single-particle spectrum flattening at the chemical
potential in this electron system, in which case the fermion "condensation" at
the Fermi level occurs in a range of momenta, unlike the condensation of
bosons. In strong magnetic fields, perpendicular to the 2D electron layer, a
similar effect of different fillings of quantum levels at the chemical
potential -- the merging of the spin- and valley-split Landau levels at the
chemical potential -- is observed in Si inversion layers and bilayer 2D
electron system in GaAs. Indication of merging of the quantum levels of
composite fermions with different valley indices is also reported in
ultra-clean SiGe/Si/SiGe quantum wells.


title: Online multi-resolution fusion of space-borne multispectral images

abstract:
  Satellite imaging has a central role in monitoring, detecting and estimating
the intensity of key natural phenomena. One important feature of satellite
images is the trade-off between spatial/spectral resolution and their
revisiting time, a consequence of design and physical constraints imposed by
satellite orbit among other technical limitations. In this paper, we focus on
fusing multi-temporal, multi-spectral images where data acquired from different
instruments with different spatial resolutions is used. We leverage the spatial
relationship between images at multiple modalities to generate high-resolution
image sequences at higher revisiting rates. To achieve this goal, we formulate
the fusion method as a recursive state estimation problem and study its
performance in filtering and smoothing contexts. The proposed strategy clearly
outperforms competing methodologies, which is shown in the paper for real data
acquired by the Landsat and MODIS instruments.


title:
A search for distinctive footprints of compact binary coalescence within
  alternatives theories of gravity

abstract:
  We implement a theoretical analysis of the amplitude intensity associated to
tensorial and non-tensorial polarization modes generated by binary systems at
their inspiral stage, within the alternative theories of gravity of Brans
Dicke, Rosen, and Lightman Lee. This study is performed without making an
explicit use of the Transverse Traceless gauge of the General Relativity
approach, and at the Newtonian limit. Consequently such supplementary sectors
(non-tensorial) appear due to additional degrees of freedom in modified
theories of gravitation. We model and compare the different polarization modes
and strain signals for each scheme varying the sky location. Thus offering a
framework to confront the physical behavior of the signal, which in turn allows
us to identify the locations where these modes are more intense and therefore
susceptible to being detected.


title: Toward Policy Explanations for Multi-Agent Reinforcement Learning

abstract:
  Advances in multi-agent reinforcement learning (MARL) enable sequential
decision making for a range of exciting multi-agent applications such as
cooperative AI and autonomous driving. Explaining agent decisions is crucial
for improving system transparency, increasing user satisfaction, and
facilitating human-agent collaboration. However, existing works on explainable
reinforcement learning mostly focus on the single-agent setting and are not
suitable for addressing challenges posed by multi-agent environments. We
present novel methods to generate two types of policy explanations for MARL:
(i) policy summarization about the agent cooperation and task sequence, and
(ii) language explanations to answer queries about agent behavior. Experimental
results on three MARL domains demonstrate the scalability of our methods. A
user study shows that the generated explanations significantly improve user
performance and increase subjective ratings on metrics such as user
satisfaction.


title: A New Spin on Color Quantization

abstract:
  We address the problem of image color quantization using a Maximum Entropy
based approach. We argue that adding thermal noise to the system yields better
visual impressions than that obtained from a simple energy minimization. To
quantify this observation, we introduce the coarse-grained quantization error,
and seek the optimal temperature which minimizes this new observable. By
comparing images with different structural properties, we show that the optimal
temperature is a good proxy for complexity at different scales. Finally, having
shown that the convoluted error is a key observable, we directly minimize it
using a Monte Carlo algorithm to generate a new series of quantized images.
Adopting an original approach based on the informativity of finite size
samples, we are able to determine the optimal convolution parameter leading to
the best visuals.

categories: cond-mat.stat-mech cs.IT math.IT physics.data-an


title:
On mixed partial derivatives of modified Bernstein-Stancu polynomials
  for functions of several variables

abstract:
  The goal of the paper is establishing the approximation of mixed partial
derivatives of the second order of a function of several variables via modified
Bernstein polynomials in the $L_1$ norm under the minimal regularity.


title: Multiplication of quandle structures

abstract:
  We generalise the construction of $Q$-family of quandles and $G$-family of
quandles which were introduced in the paper of A. Ishii, M. Iwakiri, Y. Jang,
K. Oshiro, and find connection with other constructions of quandles. We define
a composition of quandl's structures, which are defined on the same set and
find conditions under which this composition gives a quandle. Further we prove
that under this multiplication we get a group and show that this group is
abelian.


title: A note on the relativistic temperature

abstract:
  We show that the largely debated Planck-Einstein and Ott-Arzelies
relativistic transformations of temperature do not satisfy the closure group
property that two successive temperature transformations must be equivalent to
a single temperature transformation of the same form with the involved
reference frame velocities satisfying the velocity addition law of special
relativity. We then suggest relativistic transformations of temperature that do
satisfy this closure requirement and argue that they may be interpreted as
particular cases of the so-called directional temperature.


title:
Micron-scale heterogeneous catalysis with Bayesian force fields from
  first principles and active learning

abstract:
  Quantum-mechanically accurate reactive molecular dynamics (MD) at the scale
of billions of atoms has been achieved for the heterogeneous catalytic system
of H$_2$/Pt(111) using the FLARE Bayesian force field. This achievement
provides accelerated time-to-solution from first principles, with Bayesian
active learning enabling efficient and autonomous training of the machine
learning model. The resulting model is then deployed in LAMMPS on GPUs using
the Kokkos performance portability library. The Bayesian force field provides
quantitative uncertainty of predictions on every atomic environment, critical
for detecting configurations in large reactive simulations that are outside of
the training set. Scaling benchmarks were performed using real-application MD
of the H$_2$/Pt(111) heterogeneous catalysis on the Summit supercomputer, with
simulations reaching 0.5 trillion atoms on 4556 GPU nodes.


title: Gaia Early Data Release 3: The celestial reference frame (Gaia-CRF3)

abstract:
  Gaia-CRF3 is the celestial reference frame for positions and proper motions
in the third release of data from the Gaia mission, Gaia DR3 (and for the early
third release, Gaia EDR3, which contains identical astrometric results). The
reference frame is defined by the positions and proper motions at epoch 2016.0
for a specific set of extragalactic sources in the (E)DR3 catalogue.
  We describe the construction of Gaia-CRF3, and its properties in terms of the
distributions in magnitude, colour, and astrometric quality.
  Compact extragalactic sources in Gaia DR3 were identified by positional
cross-matching with 17 external catalogues of quasars (QSO) and active galactic
nuclei (AGN), followed by astrometric filtering designed to remove stellar
contaminants. Selecting a clean sample was favoured over including a higher
number of extragalactic sources. For the final sample, the random and
systematic errors in the proper motions are analysed, as well as the
radio-optical offsets in position for sources in the third realisation of the
International Celestial Reference Frame (ICRF3).
  The Gaia-CRF3 comprises about 1.6 million QSO-like sources, of which 1.2
million have five-parameter astrometric solutions in Gaia DR3 and 0.4 million
have six-parameter solutions. The sources span the magnitude range G = 13 to 21
with a peak density at 20.6 mag, at which the typical positional uncertainty is
about 1 mas. The proper motions show systematic errors on the level of 12
${\mu}$as yr${}^{-1}$ on angular scales greater than 15 deg. For the 3142
optical counterparts of ICRF3 sources in the S/X frequency bands, the median
offset from the radio positions is about 0.5 mas, but exceeds 4 mas in either
coordinate for 127 sources. We outline the future of the Gaia-CRF in the next
Gaia data releases.


title: Wasmati: An Efficient Static Vulnerability Scanner for WebAssembly

abstract:
  WebAssembly is a new binary instruction format that allows targeted compiled
code written in high-level languages to be executed with near-native speed by
the browser's JavaScript engine. However, given that WebAssembly binaries can
be compiled from unsafe languages like C/C++, classical code vulnerabilities
such as buffer overflows or format strings can be transferred over from the
original programs down to the cross-compiled binaries. As a result, this
possibility of incorporating vulnerabilities in WebAssembly modules has widened
the attack surface of modern web applications. This paper presents Wasmati, a
static analysis tool for finding security vulnerabilities in WebAssembly
binaries. It is based on the generation of a code property graph (CPG), a
program representation previously adopted for detecting vulnerabilities in
various languages but hitherto unapplied to WebAssembly. We formalize the
definition of CPG for WebAssembly, introduce techniques to generate CPG for
complex WebAssembly, and present four different query specification languages
for finding vulnerabilities by traversing a program's CPG. We implemented ten
queries capturing different vulnerability types and extensively tested Wasmati
on four heterogeneous datasets. We show that Wasmati can scale the generation
of CPGs for large real-world applications and can efficiently find
vulnerabilities for all our query types. We have also tested our tool on
WebAssembly binaries collected in the wild and identified several potential
vulnerabilities, some of which we have manually confirmed to exist unless the
enclosing application properly sanitizes the interaction with such affected
binaries.


title:
Higher rank 1+1 integrable Landau-Lifshitz field theories from
  associative Yang-Baxter equation

abstract:
  We propose a construction of 1+1 integrable Heisenberg-Landau-Lifshitz type
equations in the ${\rm gl}_N$ case. The dynamical variables are matrix elements
of $N\times N$ matrix $S$ with the property $S^2={\rm const}\cdot S$. The Lax
pair with spectral parameter is constructed by means of a quantum $R$-matrix
satisfying the associative Yang-Baxter equation. Equations of motion for ${\rm
gl}_N$ Landau-Lifshitz model are derived from the Zakharov-Shabat equations.
The model is simplified when ${\rm rank}(S)=1$. In this case the Hamiltonian
description is suggested. The described family of models includes the elliptic
model coming from ${\rm GL}_N$ Baxter-Belavin elliptic $R$-matrix. In $N=2$
case the widely known Sklyanin's elliptic Lax pair for XYZ Landau-Lifshitz
equation is reproduced. Our construction is also valid for trigonometric and
rational degenerations of the elliptic $R$-matrix.


title: Novel Applications for VAE-based Anomaly Detection Systems

abstract:
  The recent rise in deep learning technologies fueled innovation and boosted
scientific research. Their achievements enabled new research directions for
deep generative modeling (DGM), an increasingly popular approach that can
create novel and unseen data, starting from a given data set. As the technology
shows promising applications, many ethical issues also arise. For example,
their misuse can enable disinformation campaigns and powerful phishing
attempts. Research also indicates different biases affect deep learning models,
leading to social issues such as misrepresentation. In this work, we formulate
a novel setting to deal with similar problems, showing that a repurposed
anomaly detection system effectively generates novel data, avoiding generating
specified unwanted data. We propose Variational Auto-encoding Binary
Classifiers (V-ABC): a novel model that repurposes and extends the
Auto-encoding Binary Classifier (ABC) anomaly detector, using the Variational
Auto-encoder (VAE). We survey the limitations of existing approaches and
explore many tools to show the model's inner workings in an interpretable way.
This proposal has excellent potential for generative applications: models that
rely on user-generated data could automatically filter out unwanted content,
such as offensive language, obscene images, and misleading information.


title:
Skyline Operators and Regret Minimization Techniques for Managing User
  Preferences in the Query Process

abstract:
  The problem of selecting the most representative tuples from a dataset has
led to the development of powerful tools, among which Skyline and Ranking (or
Top-k) queries stand out for their ability to support the optimization of
multiple criteria in the query process. This paper surveys the remarkable
efforts made towards the extension of the aforementioned tools to overcome
their limitations, respectively the explosion of the output result and the
difficulty of query formulation. Moreover, we explore the application of these
state-of-the-art techniques as preference-based query frameworks, proposing a
comparison of their query personalization capabilities, the ability to control
the output size and their flexibility with respect to the user input
preferences.


title:
Pairing symmetry of twisted bilayer graphene: a phenomenological
  synthesis

abstract:
  One of the outstanding questions in the study of twisted bilayer graphene --
from both experimental and theoretical points of view -- is the nature of its
superconducting phase. In this work we perform a comprehensive synthesis of
existing experiments, and argue that experimental constraints are strong enough
to allow the structure of the superconducting order parameter to be nearly
uniquely determined. In particular, we argue that the order parameter is nodal,
and is formed from an admixture of spin-singlet and spin-triplet Cooper pairs.
This argument is made on phenomenological grounds, without committing to any
particular microscopic model of the superconductor. Existing data is
insufficient to determine the orbital parity of the order parameter, which
could be either p-wave or d-wave. We propose a way in which the measurement of
Andreev edge states can be used to distinguish between the two.


title:
Scheduling IoT Applications in Edge and Fog Computing Environments: A
  Taxonomy and Future Directions

abstract:
  Fog computing, as a distributed paradigm, offers cloud-like services at the
edge of the network with low latency and high-access bandwidth to support a
diverse range of IoT application scenarios. To fully utilize the potential of
this computing paradigm, scalable, adaptive, and accurate scheduling mechanisms
and algorithms are required to efficiently capture the dynamics and
requirements of users, IoT applications, environmental properties, and
optimization targets. This paper presents a taxonomy of recent literature on
scheduling IoT applications in Fog computing. Based on our new classification
schemes, current works in the literature are analyzed, research gaps of each
category are identified, and respective future directions are described.


title: RAMBO-RL: Robust Adversarial Model-Based Offline Reinforcement Learning

abstract:
  Offline reinforcement learning (RL) aims to find near-optimal policies from
logged data without further environment interaction. Model-based algorithms,
which learn a model of the environment from the dataset and perform
conservative policy optimisation within that model, have emerged as a promising
approach to this problem. In this work, we present Robust Adversarial
Model-Based Offline RL (RAMBO), a novel approach to model-based offline RL. To
achieve conservatism, we formulate the problem as a two-player zero sum game
against an adversarial environment model. The model is trained minimise the
value function while still accurately predicting the transitions in the
dataset, forcing the policy to act conservatively in areas not covered by the
dataset. To approximately solve the two-player game, we alternate between
optimising the policy and optimising the model adversarially. The problem
formulation that we address is theoretically grounded, resulting in a PAC
performance guarantee and a pessimistic value function which lower bounds the
value function in the true environment. We evaluate our approach on widely
studied offline RL benchmarks, and demonstrate that our approach achieves state
of the art performance.


title: Topology, nonlocality and duality in classical electrodynamics

abstract:
  We have recently (Heras et al. in Eur. Phys. J. Plus 136:847, 2021) argued
that classical electrodynamics can predict nonlocal effects by showing an
example of a topological and nonlocal electromagnetic angular momentum. In this
paper we discuss the dual of this angular momentum which is also topological
and nonlocal. We then unify both angular momenta by means of the
electromagnetic angular momentum arising in the configuration formed by a dyon
encircling an infinitely-long dual solenoid enclosing uniform electric and
magnetic fluxes and show that this electromagnetic angular momentum is
topological because it depends on a winding number, is nonlocal because the
electric and magnetic fields of this dual solenoid act on the dyon in regions
for which these fields are excluded and is invariant under electromagnetic
duality transformations. We explicitly verify that this duality-invariant
electromagnetic angular momentum is insensitive to the radiative effects of the
Li\'enard-Wiechert fields of the encircling dyon. We also show how duality
symmetry of this angular momentum suggests different physical interpretations
for the corresponding angular momenta that it unifies.


title: Schauder's Theorem and s-Numbers

abstract:
  Motivated by the well known theorem of Schauder, we study the relationship
between various s-numbers of an operator T and its adjoint T* between Banach
spaces.


title:
Fast Aquatic Swimmer Optimization with Differentiable Projective
  Dynamics and Neural Network Hydrodynamic Models

abstract:
  Aquatic locomotion is a classic fluid-structure interaction (FSI) problem of
interest to biologists and engineers. Solving the fully coupled FSI equations
for incompressible Navier-Stokes and finite elasticity is computationally
expensive. Optimizing robotic swimmer design within such a system generally
involves cumbersome, gradient-free procedures on top of the already costly
simulation. To address this challenge we present a novel, fully differentiable
hybrid approach to FSI that combines a 2D direct numerical simulation for the
deformable solid structure of the swimmer and a physics-constrained neural
network surrogate to capture hydrodynamic effects of the fluid. For the
deformable simulation of the swimmer's body, we use state-of-the-art techniques
from the field of computer graphics to speed up the finite-element method
(FEM). For the fluid simulation, we use a U-Net architecture trained with a
physics-based loss function to predict the flow field at each time step. The
pressure and velocity field outputs from the neural network are sampled around
the boundary of our swimmer using an immersed boundary method (IBM) to compute
its swimming motion accurately and efficiently. We demonstrate the
computational efficiency and differentiability of our hybrid simulator on a 2D
carangiform swimmer. Since both the solid simulator and the hydrodynamics model
are automatically differentiable, we obtain a fully differentiable FSI
simulator that can be used for computational co-design of geometry and controls
for rigid and soft bodies immersed in fluids, such as minimizing drag,
maximizing speed, or maximizing efficiency via direct gradient-based
optimization.


title:
Surrogate Assisted Evolutionary Multi-objective Optimisation applied to
  a Pressure Swing Adsorption system

abstract:
  Chemical plant design and optimisation have proven challenging due to the
complexity of these real-world systems. The resulting complexity translates
into high computational costs for these systems' mathematical formulations and
simulation models. Research has illustrated the benefits of using machine
learning surrogate models as substitutes for computationally expensive models
during optimisation. This paper extends recent research into optimising
chemical plant design and operation. The study further explores Surrogate
Assisted Genetic Algorithms (SA-GA) in more complex variants of the original
plant design and optimisation problems, such as the inclusion of parallel and
feedback components. The novel extension to the original algorithm proposed in
this study, Surrogate Assisted NSGA-\Romannum{2} (SA-NSGA), was tested on a
popular literature case, the Pressure Swing Adsorption (PSA) system. We further
provide extensive experimentation, comparing various meta-heuristic
optimisation techniques and numerous machine learning models as surrogates. The
results for both sets of systems illustrate the benefits of using Genetic
Algorithms as an optimisation framework for complex chemical plant system
design and optimisation for both single and multi-objective scenarios. We
confirm that Random Forest surrogate assisted Evolutionary Algorithms can be
scaled to increasingly complex chemical systems with parallel and feedback
components. We further find that combining a Genetic Algorithm framework with
Machine Learning Surrogate models as a substitute for long-running simulation
models yields significant computational efficiency improvements, 1.7 - 1.84
times speedup for the increased complexity examples and a 2.7 times speedup for
the Pressure Swing Adsorption system.


title:
Protein 3D structure-based neural networks highly improve the accuracy
  in compound-protein binding affinity prediction

abstract:
  Theoretically, the accuracy of computational models in predicting
compound-protein binding affinities (CPAs) could be improved by the
introduction of protein 3D structure information. However, most of these models
still suffer from a low accuracy due to the lack of an efficient approach to
encode informative protein features. The major challenge is how to combine the
multi-modal information such as the residue sequence of the protein, residue
atom coordinates and the torsion angles. To tackle this problem, we develop
Fast Evolutional Attention and Thoroughgoing-graph Neural Networks (FeatNN) to
facilitate the application of protein 3D structure information for predicting
CPAs. Specifically, we established a novel end-to-end architecture to jointly
embed torsion matrix, discrete distance matrix, and sequence information of
protein and extract compound features with deep graph convolution layers. In
addition, a new pairwise mapping attention mechanism is introduced to
comprehensively learn potential interaction information between proteins and
compounds. FeatNN considerably outperforms various state-of-the-art baselines
in CPA prediction with the Pearson value elevated by about 35.7%. Thus, FeatNN
provides an outstanding method for highly accurate CPA prediction and
facilitates high-throughput virtual screening of drug candidates.


title:
hate-alert@DravidianLangTech-ACL2022: Ensembling Multi-Modalities for
  Tamil TrollMeme Classification

abstract:
  Social media platforms often act as breeding grounds for various forms of
trolling or malicious content targeting users or communities. One way of
trolling users is by creating memes, which in most cases unites an image with a
short piece of text embedded on top of it. The situation is more complex for
multilingual(e.g., Tamil) memes due to the lack of benchmark datasets and
models. We explore several models to detect Troll memes in Tamil based on the
shared task, "Troll Meme Classification in DravidianLangTech2022" at ACL-2022.
We observe while the text-based model MURIL performs better for Non-troll meme
classification, the image-based model VGG16 performs better for Troll-meme
classification. Further fusing these two modalities help us achieve stable
outcomes in both classes. Our fusion model achieved a 0.561 weighted average F1
score and ranked second in this task.


title: Bandwidth Allocation Games

abstract:
  Internet providers often offer data plans that, for each user's monthly
billing cycle, guarantee a fixed amount of data at high rates until a byte
threshold is reached, at which point the user's data rate is throttled to a
lower rate for the remainder of the cycle. In practice, the thresholds and
rates of throttling can appear and may be somewhat arbitrary. In this paper, we
evaluate the choice of threshold and rate as an optimization problem (regret
minimization) and demonstrate that intuitive formulations of client regret,
which preserve desirable fairness properties, lead to optimization problems
that have tractably computable solutions.
  We begin by exploring the effectiveness of using thresholding mechanisms to
modulate overall bandwidth consumption. Next, we separately consider the regret
of heterogeneous users who are {\em streamers}, wishing to view content over a
finite period of fixed rates, and users who are {\em file downloaders},
desiring a fixed amount of bandwidth per month at their highest obtainable
rate. We extend our analysis to a game-theoretic setting where users can choose
from a variety of plans that vary the cap on the unbounded-rate data, and
demonstrate the convergence of the game. Our model provides a fresh perspective
on a fair allocation of resources where the demand is higher than capacity,
while focusing on the real-world phenomena of bandwidth throttling practiced by
ISPs. We show how the solution to the optimization problem results in
allocations that exhibit several desirable fairness properties among the users
between whom the capacity must be partitioned.


title:
Self-scalable Tanh (Stan): Faster Convergence and Better Generalization
  in Physics-informed Neural Networks

abstract:
  Physics-informed Neural Networks (PINNs) are gaining attention in the
engineering and scientific literature for solving a range of differential
equations with applications in weather modeling, healthcare, manufacturing,
etc. Poor scalability is one of the barriers to utilizing PINNs for many
real-world problems. To address this, a Self-scalable tanh (Stan) activation
function is proposed for the PINNs. The proposed Stan function is smooth,
non-saturating, and has a trainable parameter. During training, it can allow
easy flow of gradients to compute the required derivatives and also enable
systematic scaling of the input-output mapping. It is shown theoretically that
the PINNs with the proposed Stan function have no spurious stationary points
when using gradient descent algorithms. The proposed Stan is tested on a number
of numerical studies involving general regression problems. It is subsequently
used for solving multiple forward problems, which involve second-order
derivatives and multiple dimensions, and an inverse problem where the thermal
diffusivity of a rod is predicted with heat conduction data. These case studies
establish empirically that the Stan activation function can achieve better
training and more accurate predictions than the existing activation functions
in the literature.


title:
The Security War in File Systems: An Empirical Study from A
  Vulnerability-Centric Perspective

abstract:
  This paper presents a systematic study on the security of modern file
systems, following a vulnerability-centric perspective. Specifically, we
collected 377 file system vulnerabilities committed to the CVE database in the
past 20 years. We characterize them from four dimensions that include why the
vulnerabilities appear, how the vulnerabilities can be exploited, what
consequences can arise, and how the vulnerabilities are fixed. This way, we
build a deep understanding of the attack surfaces faced by file systems, the
threats imposed by the attack surfaces, and the good and bad practices in
mitigating the attacks in file systems. We envision that our study will bring
insights towards the future development of file systems, the enhancement of
file system security, and the relevant vulnerability mitigating solutions.


title:
The Influence of the Other-Race Effect on Susceptibility to Face
  Morphing Attacks

abstract:
  Facial morphs created between two identities resemble both of the faces used
to create the morph. Consequently, humans and machines are prone to mistake
morphs made from two identities for either of the faces used to create the
morph. This vulnerability has been exploited in "morph attacks" in security
scenarios. Here, we asked whether the "other-race effect" (ORE) -- the human
advantage for identifying own- vs. other-race faces -- exacerbates morph attack
susceptibility for humans. We also asked whether face-identification
performance in a deep convolutional neural network (DCNN) is affected by the
race of morphed faces. Caucasian (CA) and East-Asian (EA) participants
performed a face-identity matching task on pairs of CA and EA face images in
two conditions. In the morph condition, different-identity pairs consisted of
an image of identity "A" and a 50/50 morph between images of identity "A" and
"B". In the baseline condition, morphs of different identities never appeared.
As expected, morphs were identified mistakenly more often than original face
images. Moreover, CA participants showed an advantage for CA faces in
comparison to EA faces (a partial ORE). Of primary interest, morph
identification was substantially worse for cross-race faces than for own-race
faces. Similar to humans, the DCNN performed more accurately for original face
images than for morphed image pairs. Notably, the deep network proved
substantially more accurate than humans in both cases. The results point to the
possibility that DCNNs might be useful for improving face identification
accuracy when morphed faces are presented. They also indicate the significance
of the ORE in morph attack susceptibility in applied settings.


title:
Finding a Landing Site on an Urban Area: A Multi-Resolution
  Probabilistic Approach

abstract:
  This paper considers the problem of finding a landing spot for a drone in a
dense urban environment. The conflicting requirement of fast exploration and
high resolution is solved using a multi-resolution approach, by which visual
information is collected by the drone at decreasing altitudes so that spatial
resolution of the acquired images increases monotonically. A probability
distribution is used to capture the uncertainty of the decision process for
each terrain patch. The distributions are updated as information from different
altitudes is collected. When the confidence level for one of the patches
becomes larger than a pre-specified threshold, suitability for landing is
declared. One of the main building blocks of the approach is a semantic
segmentation algorithm that attaches probabilities to each pixel of a single
view. The decision algorithm combines these probabilities with a priori data
and previous measurements to obtain the best estimates. Feasibility is
illustrated by presenting a number of examples generated by a realistic
closed-loop simulator.


title: Random site percolation on honeycomb lattices with complex neighborhoods

abstract:
  We present a rough estimation -- up to four significant digits, based on the
scaling hypothesis and the probability of belonging to the largest cluster vs.
the occupation probability -- of the critical occupation probabilities for the
random site percolation problem on a honeycomb lattice with complex
neighborhoods containing sites up to the fifth coordination zone. There are 31
such neighborhoods with their radius ranging from one to three and containing
from three to 24 sites. For two-dimensional regular lattices with compact
extended-range neighborhoods, in the limit of the large number $z$ of sites in
the neighborhoods, the site percolation thresholds $p_c$ follow the dependency
$p_c\propto 1/z$, as recently shown by Xun, Hao and Ziff [Physical Review E
105, 024105 (2022)]. On the contrary, noncompact neighborhoods (with holes)
destroy this dependence due to the degeneracy of the percolation threshold
(several values of $p_c$ corresponding to the same number $z$ of sites in the
neighborhoods). An example of a single-value index $\zeta=\sum_i z_i r_i$ --
where $z_i$ and $r_i$ are the number of sites and radius of the $i$-th
coordination zone, respectively -- characterizing the neighborhood and allowing
avoiding the above-mentioned degeneracy is presented. The percolation threshold
obtained follows the inverse square root dependence $p_c\propto 1/\sqrt\zeta$.
The functions boundaries() (written in C) for basic neighborhoods (for the
unique coordination zone) for the Newman and Ziff algorithm [Physical Review E
64, 016706 (2001)] are also presented.


title: On the fields of definition of genus-one covers of $\mathbb{P}^1$

abstract:
  There exist Belyi pairs which are not defined over their field of moduli. We
construct a series of such examples where the degree of a minimal field of
definition over the field of moduli is unbounded. Our approach also provides
counterexamples to the local-global principle for Belyi pairs.


title:
Correcting motion induced fluorescence artifacts in two-channel neural
  imaging

abstract:
  Imaging neural activity in a behaving animal presents unique challenges in
part because motion from an animal's movement creates artifacts in fluorescence
intensity time-series that are difficult to distinguish from neural signals of
interest. One approach to mitigating these artifacts is to image two channels;
one that captures an activity-dependent fluorophore, such as GCaMP, and another
that captures an activity-independent fluorophore such as RFP. Because the
activity-independent channel contains the same motion artifacts as the
activity-dependent channel, but no neural signals, the two together can be used
to remove the artifacts. Existing approaches for this correction, such as
taking the ratio of the two channels, do not account for channel independent
noise in the measured fluorescence. Moreover, no systematic comparison has been
made of existing approaches that use two-channel signals. Here, we present
Two-channel Motion Artifact Correction (TMAC), a method which seeks to remove
artifacts by specifying a generative model of the fluorescence of the two
channels as a function of motion artifact, neural activity, and noise. We
further present a novel method for evaluating ground-truth performance of
motion correction algorithms by comparing the decodability of behavior from two
types of neural recordings; a recording that had both an activity-dependent
fluorophore (GCaMP and RFP) and a recording where both fluorophores were
activity-independent (GFP and RFP). A successful motion-correction method
should decode behavior from the first type of recording, but not the second. We
use this metric to systematically compare five methods for removing motion
artifacts from fluorescent time traces. We decode locomotion from a GCaMP
expressing animal 15x more accurately on average than from control when using
TMAC inferred activity and outperform all other methods of motion correction
tested.