abstract:
  Our book "The Reality of Multi-Lingual Machine Translation" discusses the
benefits and perils of using more than two languages in machine translation
systems. While focused on the particular task of sequence-to-sequence
processing and multi-task learning, the book targets somewhat beyond the area
of natural language processing. Machine translation is for us a prime example
of deep learning applications where human skills and learning capabilities are
taken as a benchmark that many try to match and surpass. We document that some
of the gains observed in multi-lingual translation may result from simpler
effects than the assumed cross-lingual transfer of knowledge.
  In the first, rather general part, the book will lead you through the
motivation for multi-linguality, the versatility of deep neural networks
especially in sequence-to-sequence tasks to complications of this learning. We
conclude the general part with warnings against too optimistic and unjustified
explanations of the gains that neural networks demonstrate.
  In the second part, we fully delve into multi-lingual models, with a
particularly careful examination of transfer learning as one of the more
straightforward approaches utilizing additional languages. The recent
multi-lingual techniques, including massive models, are surveyed and practical
aspects of deploying systems for many languages are discussed. The conclusion
highlights the open problem of machine understanding and reminds of two ethical
aspects of building large-scale models: the inclusivity of research and its
ecological trace.


title: Out-of-Time-Order correlators in driven conformal field theories

abstract:
  We compute Out-of-Time-Order correlators (OTOCs) for conformal field theories
(CFTs) subjected to either continuous or discrete periodic drive protocols.
This is achieved by an appropriate analytic continuation of the stroboscopic
time. After detailing the general structure, we perform explicit calculations
in large-$c$ CFTs where we find that OTOCs display an exponential, an
oscillatory and a power-law behaviour in the heating phase, the non-heating
phase and on the phase boundary, respectively. In contrast to this, for the
Ising CFT representing an integrable model, OTOCs never display such
exponential growth. This observation hints towards how OTOCs can demarcate
between integrable and chaotic CFT models subjected to a periodic drive. We
further explore properties of the light-cone which is characterized by the
corresponding butterfly velocity as well as the Lyapunov exponent.
Interestingly, as a consequence of the spatial inhomogeneity introduced by the
drive, the butterfly velocity, in these systems, has an explicit dependence on
the initial location of the operators. We chart out the dependence of the
Lyapunov exponent and the butterfly velocities on the frequency and amplitude
of the drive for both protocols and discuss the fixed point structure which
differentiates such driven CFTs from their un-driven counterparts.


title:
From Low to High Order Motion Planners: Safe Robot Navigation using
  Motion Prediction and Reference Governor

abstract:
  Safe navigation around obstacles is a fundamental challenge for highly
dynamic robots. The state-of-the-art approach for adapting simple reference
path planners to complex robot dynamics using trajectory optimization and
tracking control is brittle and requires significant replanning cycles. In this
paper, we introduce a novel feedback motion planning framework that extends the
applicability of low-order (e.g. position-/velocity-controlled) reference
motion planners to high-order (e.g., acceleration-/jerk-controlled) robot
models using motion prediction and reference governors. We use predicted robot
motion range for safety assessment and establish a bidirectional interface
between high-level planning and low-level control via a reference governor. We
describe the generic fundamental building blocks of our feedback motion
planning framework and give specific example constructions for motion control,
prediction, and reference planning. We prove the correctness of our planning
framework and demonstrate its performance in numerical simulations. We conclude
that accurate motion prediction is crucial for closing the gap between
high-level planning and low-level control.


title: A Novel Greedy Approach To Harmonic Summing Using GPUs

abstract:
  Incoherent harmonic summing is a technique which is used to improve the
sensitivity of Fourier domain search methods. A one dimensional harmonic sum is
used in time-domain radio astronomy as part of the Fourier domain periodicity
search, a type of search used to detect isolated single pulsars. The main
problem faced when implementing the harmonic sum on many-core architectures,
like GPUs, is the very unfavourable memory access pattern of the harmonic sum
algorithm. The memory access pattern gets worse as the dimensionality of the
harmonic sum increases. Here we present a set of algorithms for calculating the
harmonic sum that are suited to many-core architectures such as GPUs. We
present an evaluation of the sensitivity of these different approaches, and
their performance. This work forms part of the AstroAccelerate project which is
a GPU accelerated software package for processing time-domain radio astronomy
data.


title:
Improving generalization with synthetic training data for deep learning
  based quality inspection

abstract:
  Automating quality inspection with computer vision techniques is often a very
data-demanding task. Specifically, supervised deep learning requires a large
amount of annotated images for training. In practice, collecting and annotating
such data is not only costly and laborious, but also inefficient, given the
fact that only a few instances may be available for certain defect classes. If
working with video frames can increase the number of these instances, it has a
major disadvantage: the resulting images will be highly correlated with one
another. As a consequence, models trained under such constraints are expected
to be very sensitive to input distribution changes, which may be caused in
practice by changes in the acquisition system (cameras, lights), in the parts
or in the defects aspect. In this work, we demonstrate the use of randomly
generated synthetic training images can help tackle domain instability issues,
making the trained models more robust to contextual changes. We detail both our
synthetic data generation pipeline and our deep learning methodology for
answering these questions.


title:
Exploratory Hidden Markov Factor Models for Longitudinal Mobile Health
  Data: Application to Adverse Posttraumatic Neuropsychiatric Sequelae

abstract:
  Adverse posttraumatic neuropsychiatric sequelae (APNS) are common among
veterans and millions of Americans after traumatic events and cause tremendous
burdens for trauma survivors and society. Many studies have been conducted to
investigate the challenges in diagnosing and treating APNS symptoms. However,
progress has been limited by the subjective nature of traditional measures.
This study is motivated by the objective mobile device data collected from the
Advancing Understanding of RecOvery afteR traumA (AURORA) study. We develop
both discrete-time and continuous-time exploratory hidden Markov factor models
to model the dynamic psychological conditions of individuals with either
regular or irregular measurements. The proposed models extend the conventional
hidden Markov models to allow high-dimensional data and feature-based
nonhomogeneous transition probability between hidden psychological states. To
find the maximum likelihood estimates, we develop a Stabilized
Expectation-Maximization algorithm with Initialization Strategies (SEMIS).
Simulation studies with synthetic data are carried out to assess the
performance of parameter estimation and model selection. Finally, an
application to the AURORA data is conducted, which captures the relationships
between heart rate variability, activity, and APNS consistent with existing
literature.


title: In-beam $\gamma$-ray spectroscopy of $^{32}$Mg via direct reactions

abstract:
  Background: The nucleus $^{32}$Mg ($N=20$ and $Z=12$) plays a central role in
the so-called "island of inversion" where in the ground states $sd$-shell
neutrons are promoted to the $fp$-shell orbitals across the shell gap,
resulting in the disappearance of the canonical neutron magic number $N=20$.
Purpose: The primary goals of this work are to extend the level scheme of
$^{32}$Mg, provide spin-parity assignments to excited states, and discuss the
microscopic structure of each state through comparisons with theoretical
calculations. Method: In-beam $\gamma$-ray spectroscopy of $^{32}$Mg was
performed using two direct-reaction probes, one-neutron (two-proton) knockout
reactions on $^{33}$Mg ($^{34}$Si). Final-state exclusive cross sections and
parallel momentum distributions were extracted from the experimental data and
compared with eikonal-based reaction model calculations combined with
shell-model overlap functions. Results: Owing to the remarkable selectivity of
the one-neutron and two-proton knockout reactions, a significantly updated
level scheme for $^{32}$Mg, which exhibits negative-parity intruder and
positive-parity normal states, was constructed. The experimental results were
confronted with four different nuclear structure models. Conclusions: In some
of these models, different aspects of $^{32}$Mg and the transition into the
island of inversion are well described. However, unexplained discrepancies
remain, and even with the help of these state-of-the-art theoretical
approaches, the structure of this key nucleus is not yet fully captured.


title: Cavity-mediated electron-photon pairs

abstract:
  Advancing quantum information, communication and sensing relies on the
generation and control of quantum correlations in complementary degrees of
freedom. Here, we demonstrate the preparation of electron-photon pair states
using the phase-matched interaction of free electrons with the evanescent
vacuum field of a photonic-chip-based optical microresonator. Spontaneous
inelastic scattering produces intracavity photons coincident with
energy-shifted electrons. Harnessing these pairs for correlation-enhanced
imaging, we achieve a two-orders of magnitude contrast improvement in
cavity-mode mapping by coincidence-gated electron spectroscopy. This parametric
pair-state preparation will underpin the future development of free-electron
quantum optics, providing a pathway to quantum-enhanced imaging,
electron-photon entanglement, and heralded single-electron and Fock-state
photon sources.


title:
The albatross optimized flight is an extremum seeking phenomenon:
  autonomous and stable controller for dynamic soaring

abstract:
  The albatross optimized flight maneuver - known as Dynamic Soaring (DS) - is
nothing but a wonder of physics, biology, and engineering. In an ideal DS
cycle, this fascinating bird can travel in the desired flight direction, for
free, by harvesting energy from the wind, and thus, it achieves a neutral
energy cycle. This phenomenon has triggered a momentous interest among
aerospace, control and robotic engineering communities; if DS is mimicked, we
have arrived at a new class of Unmanned Aerial Vehicles (UAVs) which are very
energy-efficient during part, or the full, duration of their flight. However,
the DS problem is highly nonlinear, under-actuated, and dependent on the wind
profiles. This have resulted in decades of DS control literature that, while
making progress addressing the control problem, seem not to be aligned well
with the nature of the DS phenomenon itself. The control works associated with
DS in literature rely heavily on constrained optimal control algorithms,
control designs that require a mathematical expression of the objective
function, and pre-defined wind profile models. Clearly, a functioning
controller for DS that allows meaningful bio-mimicry of the albatross, needs to
be autonomous, real time, stable, and capable of tolerating absence of the
expression of the objective function. The qualifications of such controller are
the very same characteristics of Extremum Seeking Control (ESC) systems. In
this paper, we show that Extremum Seeking Control (ESC) systems existing in
control literature for decades are a natural characterization for the DS
problem. We provide the DS problem setup, design, stability, and simulation
results of the introduced ESC systems. The results, supported by comparison
with optimal control solvers, emphasize that the DS phenomenon is a natural
expression of ESC systems in nature and that DS can be performed autonomously
and in real time.


title: Gen\'eLive! Generating Rhythm Actions in Love Live!

abstract:
  A rhythm action game is a music-based video game in which the player is
challenged to issue commands at the right timings during a music session. The
timings are rendered in the chart, which consists of visual symbols, called
notes, flying through the screen. KLab Inc., a Japan-based video game
developer, has operated rhythm action games including a title for the "Love
Live!" franchise, which became a hit across Asia and beyond. Before this work,
the company generated the charts manually, which resulted in a costly business
operation. This paper presents how KLab applied a deep generative model for
synthesizing charts, and shows how it has improved the chart production
process, reducing the business cost by half. Existing generative models
generated poor quality charts for easier difficulty modes. We report how we
overcame this challenge through a multi-scaling model dedicated to rhythm
actions, by considering beats among other things. Our model, named Gen\'eLive!,
is evaluated using production datasets at KLab as well as open datasets.


title: The k-outlier Fr\'echet distance

abstract:
  The Fr\'echet distance is a popular metric for curves; however, its
bottleneck character is a disadvantage in many applications. Here we introduce
two variants of the Fr\'echet distance to cope with this problem and expand the
work on shortcut Fr\'echet distances. We present an efficient algorithm for
computing the new distance measure.


title:
NeuralHOFusion: Neural Volumetric Rendering under Human-object
  Interactions

abstract:
  4D modeling of human-object interactions is critical for numerous
applications. However, efficient volumetric capture and rendering of complex
interaction scenarios, especially from sparse inputs, remain challenging. In
this paper, we propose NeuralHOFusion, a neural approach for volumetric
human-object capture and rendering using sparse consumer RGBD sensors. It
marries traditional non-rigid fusion with recent neural implicit modeling and
blending advances, where the captured humans and objects are layerwise
disentangled. For geometry modeling, we propose a neural implicit inference
scheme with non-rigid key-volume fusion, as well as a template-aid robust
object tracking pipeline. Our scheme enables detailed and complete geometry
generation under complex interactions and occlusions. Moreover, we introduce a
layer-wise human-object texture rendering scheme, which combines volumetric and
image-based rendering in both spatial and temporal domains to obtain
photo-realistic results. Extensive experiments demonstrate the effectiveness
and efficiency of our approach in synthesizing photo-realistic free-view
results under complex human-object interactions.


title:
A Systematic Literature Review about Idea Mining: The Use of
  Machine-driven Analytics to Generate Ideas

abstract:
  Idea generation is the core activity of innovation. Digital data sources,
which are sources of innovation, such as patents, publications, social media,
websites, etc., are increasingly growing at unprecedented volume. Manual idea
generation is time-consuming and is affected by the subjectivity of the
individuals involved. Therefore, the use machine-driven data analytics
techniques to analyze data to generate ideas and support idea generation by
serving users is useful. The objective of this study is to study state-of
the-art machine-driven analytics for idea generation and data sources, hence
the result of this study will generally server as a guideline for choosing
techniques and data sources. A systematic literature review is conducted to
identify relevant scholarly literature from IEEE, Scopus, Web of Science and
Google Scholar. We selected a total of 71 articles and analyzed them
thematically. The results of this study indicate that idea generation through
machine-driven analytics applies text mining, information retrieval (IR),
artificial intelligence (AI), deep learning, machine learning, statistical
techniques, natural language processing (NLP), NLP-based morphological
analysis, network analysis, and bibliometric to support idea generation. The
results include a list of techniques and procedures in idea generation through
machine-driven idea analytics. Additionally, characterization and heuristics
used in idea generation are summarized. For the future, tools designed to
generate ideas could be explored.


title:
On Digital Subcarrier Multiplexing under A Bandwidth Limitation and ASE
  Noise

abstract:
  We show that digital subcarrier multiplexing (DSM) systems require much
greater complexity for Nyquist pulse shaping than single-carrier (SC) systems,
and it is a misconception that both systems use the same bandwidth when using
the same pulse shaping. Through back-to-back (B2B) experiments with realistic
transmitter (TX) modules and amplified spontaneous emission (ASE) noise
loading, we show that even with optimized waterfilling and entropy loading, DSM
does not achieve a larger net data rate (NDR) compared to SC when only ASE
noise exists in the channel in long-haul transmission scenarios.


title:
Hidden symmetry of the static response of black holes: Applications to
  Love numbers

abstract:
  We show that any static linear perturbations around a spherically symmetric
black hole enjoys a set of conserved charges which forms a centrally extended
Schr\"{o}dinger algebra sh(1)=sl$(2,\mathbb{R})\ltimes \mathcal{H}$. The
central charge is given by the black hole mass, echoing results on black hole
entropy from near-horizon diffeomorphism symmetry. The finite symmetry
transformations generated by these conserved charges correspond to Galilean and
conformal transformations of the static field and of the coordinates. This new
structure allows one to discuss the static response of a Schwarzschild black
hole in the test field approximation from a \textit{ symmetry-based approach}.
First we show that the (horizontal) symmetry protecting the vanishing of the
Love numbers recently exhibited by Hui et al coincides with one of the
sl($2,\mathbb{R}$) generators of the Schr\"{o}dinger group. Then, it is
demonstrated that the HJPSS symmetry is selected thanks to the spontaneous
breaking of the full Schr\"{o}dinger symmetry at the horizon down to a simple
abelian sub-group. The latter can be understood as the symmetry protecting the
regularity of the test field at the horizon. In the 4-dimensional case, this
provides a symmetry protection for the vanishing of the Schwarzschild Love
numbers.


title: Convergence of a New Learning Algorithm

abstract:
  A new learning algorithm proposed by Brandt and Lin for neural network [1],
[2] has been shown to be mathematically equivalent to the conventional
back-propagation learning algorithm, but has several advantages over the
backpropagation algorithm, including feedback-network-free implementation and
biological plausibility. In this paper, we investigate the convergence of the
new algorithm. A necessary and sufficient condition for the algorithm to
converge is derived. A convergence measure is proposed to measure the
convergence rate of the new algorithm. Simulation studies are conducted to
investigate the convergence of the algorithm with respect to the number of
neurons, the connection distance, the connection density, the ratio of
excitatory/inhibitory synapses, the membrane potentials, and the synapse
strengths.


title:
A multi-domain VNE algorithm based on multi-objective optimization for
  IoD architecture in Industry 4.0

abstract:
  Unmanned aerial vehicle (UAV) has a broad application prospect in the future,
especially in the Industry 4.0. The development of Internet of Drones (IoD)
makes UAV operation more autonomous. Network virtualization technology is a
promising technology to support IoD, so the allocation of virtual resources
becomes a crucial issue in IoD. How to rationally allocate potential material
resources has become an urgent problem to be solved. The main work of this
paper is presented as follows: (1) In order to improve the optimization
performance and reduce the computation time, we propose a multi-domain virtual
network embedding algorithm (MP-VNE) adopting the centralized hierarchical
multi-domain architecture. The proposed algorithm can avoid the local optimum
through incorporating the genetic variation factor into the traditional
particle swarm optimization process. (2) In order to simplify the
multi-objective optimization problem, we transform the multi-objective problem
into a single-objective problem through weighted summation method. The results
prove that the proposed algorithm can rapidly converge to the optimal solution.
(3) In order to reduce the mapping cost, we propose an algorithm for selecting
candidate nodes based on the estimated mapping cost. Each physical domain
calculates the estimated mapping cost of all nodes according to the formula of
the estimated mapping cost, and chooses the node with the lowest estimated
mapping cost as the candidate node. The simulation results show that the
proposed MP-VNE algorithm has better performance than MC-VNM, LID-VNE and other
algorithms in terms of delay, cost and comprehensive indicators.


title: Benchmark Assessment for DeepSpeed Optimization Library

abstract:
  Deep Learning (DL) models are widely used in machine learning due to their
performance and ability to deal with large datasets while producing high
accuracy and performance metrics. The size of such datasets and the complexity
of DL models cause such models to be complex, consuming large amount of
resources and time to train. Many recent libraries and applications are
introduced to deal with DL complexity and efficiency issues. In this paper, we
evaluated one example, Microsoft DeepSpeed library through classification
tasks. DeepSpeed public sources reported classification performance metrics on
the LeNet architecture. We extended this through evaluating the library on
several modern neural network architectures, including convolutional neural
networks (CNNs) and Vision Transformer (ViT). Results indicated that DeepSpeed,
while can make improvements in some of those cases, it has no or negative
impact on others.


title: Morphology Without Borders: Clause-Level Morphological Annotation

abstract:
  Morphological tasks use large multi-lingual datasets that organize words into
inflection tables, which then serve as training and evaluation data for various
tasks. However, a closer inspection of these data reveals profound
cross-linguistic inconsistencies, that arise from the lack of a clear
linguistic and operational definition of what is a word, and that severely
impair the universality of the derived tasks. To overcome this deficiency, we
propose to view morphology as a clause-level phenomenon, rather than
word-level. It is anchored in a fixed yet inclusive set of features homogeneous
across languages, that encapsulates all functions realized in a saturated
clause. We deliver MightyMorph, a novel dataset for clause-level morphology
covering 4 typologically-different languages: English, German, Turkish and
Hebrew. We use this dataset to derive 3 clause-level morphological tasks:
inflection, reinflection and analysis. Our experiments show that the
clause-level tasks are substantially harder than the respective word-level
tasks, while having comparable complexity across languages. Furthermore,
redefining morphology to the clause-level provides a neat interface with
contextualized language models (LMs) and can be used to probe LMs capacity to
encode complex morphology. Taken together, this work opens up new horizons in
the study of computational morphology, leaving ample space for studying neural
morphological modeling cross-linguistically.


title:
Inter-Cell Slicing Resource Partitioning via Coordinated Multi-Agent
  Deep Reinforcement Learning

abstract:
  Network slicing enables the operator to configure virtual network instances
for diverse services with specific requirements. To achieve the slice-aware
radio resource scheduling, dynamic slicing resource partitioning is needed to
orchestrate multi-cell slice resources and mitigate inter-cell interference. It
is, however, challenging to derive the analytical solutions due to the complex
inter-cell interdependencies, interslice resource constraints, and
service-specific requirements. In this paper, we propose a multi-agent deep
reinforcement learning (DRL) approach that improves the max-min slice
performance while maintaining the constraints of resource capacity. We design
two coordination schemes to allow distributed agents to coordinate and mitigate
inter-cell interference. The proposed approach is extensively evaluated in a
system-level simulator. The numerical results show that the proposed approach
with inter-agent coordination outperforms the centralized approach in terms of
delay and convergence. The proposed approach improves more than two-fold
increase in resource efficiency as compared to the baseline approach.


title:
An ultraweak variational method for parameterized linear
  differential-algebraic equations

abstract:
  We investigate an ultraweak variational formulation for (parameterized)
linear differential-algebraic equations (DAEs) w.r.t. the time variable which
yields an optimally stable system. This is used within a Petrov-Galerkin method
to derive a certified detailed discretization which provides an approximate
solution in an ultraweak setting as well as for model reduction w.r.t. time in
the spirit of the Reduced Basis Method (RBM). A computable sharp error bound is
derived. Numerical experiments are presented that show that this method yields
a significant reduction and can be combined with well-known system theoretic
methods such as Balanced Truncation to reduce the size of the DAE.


title: New low mass ratio contact binaries in the Catalina Sky Survey

abstract:
  We present the identification and photometric analysis of 30 new low mass
ratio (LMR) totally eclipsing contact binaries found in Catalina Sky Survey
data. The LMR candidates are identified using Fourier coefficients and visual
inspection. We perform a detailed scan in the parameter plane of mass-ratio (q)
versus inclination (i) using Phoebe-0.31 scripter to derive the best (q,i) pair
for the initial models. The relative physical parameters are determined from
the final model of each system. A Monte-Carlo approach was adopted to derive
the parameter errors. The resulting parameters confirm the identification. The
approximate absolute physical parameters of the systems are estimated based on
the light curve solutions and Gaia early Data Release 3 distances. Twelve out
of 30 new systems have fill-out factors $f\gt 50 \%$ and $q \leq 0.25$ (deep
contact LMR systems), and 8 of them, to within errors, are extreme LMR deep
systems with $q\leq0.1$. We discuss the evolutionary status of the 30 LMR
systems in comparison with the most updated catalog of LMR systems from the
literature. The scenario of the LMR systems as pre-merger candidates forming
fast rotating stars is investigated for all systems, new and old, based both on
Hut's stability criteria and critical instability mass ratio ($q_{inst}$)
relation. CSS$\_$J075848.2+125656, with $\frac{q}{q_{inst}}=1.23\pm 0.23$, and
CSS$\_$J093010.1-021624, with $\frac{q}{q_{inst}}=1.25\pm 0.23$, can be
considered as merger candidates.


title:
Compositional Trends in Surface Enhanced Diffusion in Lead Silicate
  Glasses

abstract:
  In this work, we use molecular dynamics simulations to study the enhancement
of surface over bulk diffusion (surface enhanced diffusion) in (PbO)x(SiO2)1-x
glasses. This work is motivated to better understand surface diffusion in
glasses and its connection to fragility, and to enhance surface diffusion in
silica and related glasses for greater thermodynamic stability during
vapor-deposition. By adding PbO to silica, the fragility of glass increases
continuously for 10% <= x <= 70% during experiments. The increase in fragility
may correspond to an increase in surface enhanced diffusion, as fragility and
surface diffusion are correlated. We observe that for the silicates
investigated, while surface enhanced diffusion increases with fragility, the
enhancement is quite small. The slower diffusing Si and O atoms have higher
enhancements, which could allow for some surface stabilization effects. We
demonstrate that there are only small changes in atomic arrangements,
consistent with the similar diffusion rates, at the surface as compared to
bulk. Finally, we examine the trend of bulk versus surface diffusion in view of
previous observations in organic and metallic glasses and found that in oxides,
fragility increase may not be strongly linked to enhanced surface diffusion.


title:
Rethinking the Role of Demonstrations: What Makes In-Context Learning
  Work?

abstract:
  Large language models (LMs) are able to in-context learn -- perform a new
task via inference alone by conditioning on a few input-label pairs
(demonstrations) and making predictions for new inputs. However, there has been
little understanding of how the model learns and which aspects of the
demonstrations contribute to end task performance. In this paper, we show that
ground truth demonstrations are in fact not required -- randomly replacing
labels in the demonstrations barely hurts performance, consistently over 12
different models including GPT-3. Instead, we find that other aspects of the
demonstrations are the key drivers of end task performance, including the fact
that they provide a few examples of (1) the label space, (2) the distribution
of the input text, and (3) the overall format of the sequence. Together, our
analysis provides a new way of understanding how and why in-context learning
works, while opening up new questions about how much can be learned from large
language models through inference alone.


title:
RELMOBNET: A Robust Two-Stage End-To-End Training Approach For
  MOBILENETV3 Based Relative Camera Pose Estimation

abstract:
  Relative camera pose estimation plays a pivotal role in dealing with 3D
reconstruction and visual localization. To address this, we propose a Siamese
network based on MobileNetV3-Large for an end-to-end relative camera pose
regression independent of camera parameters. The proposed network uses pair of
images taken at different locations in the same scene to estimate the 3D
translation vector and rotation vector in unit quaternion. To increase the
generality of the model, rather than training it for a single scene, data for
four scenes are combined to train a single universal model to estimate the
relative pose. Further for independency of hyperparameter weighing between
translation and rotation loss is not used. Instead we use the novel two-stage
training procedure to learn the balance implicitly with faster convergence. We
compare the results obtained with the Cambridge Landmarks dataset, comprising
of different scenes, with existing CNN-based regression methods as baselines,
e.g., RPNet and RCPNet. The findings indicate that, when compared to RCPNet,
proposed model improves the estimation of the translation vector by a
percentage change of 16.11%, 28.88%, 52.27% on the Kings College, Old Hospital,
St Marys Church scenes from Cambridge Landmarks dataset, respectively.


title: Theia: Summary of physics program. Snowmass White Paper Submission

abstract:
  Theia would be a novel, "hybrid" optical neutrino detector, with a rich
physics program. This paper is intended to provide a brief overview of the
concepts and physics reach of Theia. Full details can be found in the Theia
white paper [1].


title: On $T$-dual pairs of stable AdS/dS vacua ?

abstract:
  We present $T$-dual pairs of tachyon free (stable) AdS as well as dS
solutions in the context of type II orientifold compactifications. In our
construction, the type IIA setting is a purely geometric flux model which, in
addition, includes the usual NS-NS three-form flux ${\rm H}_3$ and the RR
$p$-form fluxes ${\rm F}_p$ for $p \in \{0, 2, 4, 6\}$, while the $T$-dual type
IIB model includes (non-)geometric fluxes along with the usual ${\cal S}$-dual
pair of the three-form $(F_3, H_3)$ fluxes. In both the models there is one
combination of the RR axions which remains flat, and the dS solution is
realized through the $D$-term effects induced by (non-)geometric fluxes,
without which one can only have AdS solutions. Using flux scaling arguments we
also discuss how to engineer a parametric control in these models, in the sense
of realizing the respective AdS/dS solutions in large volume, weak
string-coupling as well as large complex-structure regime. We also discuss open
possibilities (e.g. about the unknown status on having the complete set of
Bianchi identities) under which such tachyon free dS solutions may or may not
be viable.


title: A lower limit for Newtonian-noise models of the Einstein Telescope

abstract:
  The Einstein Telescope (ET) is a proposed third-generation gravitational-wave
(GW) underground observatory. It will have greatly increased sensitivity
compared to current GW detectors, and it is designed to extend the observation
band down to a few Hz. At these frequencies, a major limitation of the ET
sensitivity is predicted to be due to gravitational fluctuations produced by
the environment, most importantly by the seismic field, which give rise to the
so-called Newtonian noise (NN). Accurate models of ET NN are crucial to assess
the compatibility of an ET candidate site with the ET sensitivity target also
considering a possible reduction of NN by noise cancellation. With NN models
becoming increasingly complex as they include details of geology and
topography, it is crucial to have tools to make robust assessments of their
accuracy. For this purpose, we derive a lower bound on seismic NN spectra,
which is weakly dependent on geology and properties of the seismic field. As a
first application, we use the lower limit to compare it with NN estimates
recently calculated for the Sardinia and Euregio Meuse-Rhine (EMR) candidate
sites. We find the utility of the method, which shows an inconsistency with the
predictions for the EMR site, which indicates that ET NN models require further
improvement.


title:
Perturbative renormalization and thermodynamics of quantum crystalline
  membranes

abstract:
  We analyze the statistical mechanics of a free-standing quantum crystalline
membrane within the framework of a systematic perturbative renormalization
group (RG). A power-counting analysis shows that the leading singularities of
correlation functions can be analyzed within an effective renormalizable model
in which the kinetic energy of in-plane phonons and subleading geometrical
nonlinearities in the expansion of the strain tensor are neglected. For
membranes at zero temperature, governed by zero-point motion, the RG equations
of the effective model provide a systematic derivation of logarithmic
corrections to the bending rigidity and to the elastic Young modulus derived in
earlier investigations. In the limit of a weakly applied external tension, the
stress-strain relation at $T = 0$ is anomalous: the linear Hooke's law is
replaced with a singular law exhibiting logarithmic corrections. For small, but
finite temperatures, we use techniques of finite-size scaling to derive general
relations between the zero-temperature RG flow and scaling laws of
thermodynamic quantities such as the thermal expansion coefficient $\alpha$,
the entropy S, and the specific heat C. A combination of the scaling relations
with an analysis of thermal fluctuations shows that, for small temperatures,
the thermal expansion coefficient $\alpha$ is negative and logarithmically
dependent on $T$, as predicted in an earlier work. Although the requirement
$\lim_{T \to 0} \alpha = 0$, expected from the third law of thermodynamics is
formally satisfied, $\alpha$ is predicted to exhibit such a slow variation to
remain practically constant down to unaccessibly small temperatures.


title:
Dynamic Regret of Online Mirror Descent for Relatively Smooth Convex
  Cost Functions

abstract:
  The performance of online convex optimization algorithms in a dynamic
environment is often expressed in terms of the dynamic regret, which measures
the decision maker's performance against a sequence of time-varying
comparators. In the analysis of the dynamic regret, prior works often assume
Lipschitz continuity or uniform smoothness of the cost functions. However,
there are many important cost functions in practice that do not satisfy these
conditions. In such cases, prior analyses are not applicable and fail to
guarantee the optimization performance. In this letter, we show that it is
possible to bound the dynamic regret, even when neither Lipschitz continuity
nor uniform smoothness is present. We adopt the notion of relative smoothness
with respect to some user-defined regularization function, which is a much
milder requirement on the cost functions. We first show that under relative
smoothness, the dynamic regret has an upper bound based on the path length and
functional variation. We then show that with an additional condition of
relatively strong convexity, the dynamic regret can be bounded by the path
length and gradient variation. These regret bounds provide performance
guarantees to a wide variety of online optimization problems that arise in
different application domains. Finally, we present numerical experiments that
demonstrate the advantage of adopting a regularization function under which the
cost functions are relatively smooth.


title: Individual Fairness in Feature-Based Pricing for Monopoly Markets

abstract:
  We study fairness in the context of feature-based price discrimination in
monopoly markets. We propose a new notion of individual fairness, namely,
\alpha-fairness, which guarantees that individuals with similar features face
similar prices. First, we study discrete valuation space and give an analytical
solution for optimal fair feature-based pricing. We show that the cost of fair
pricing is defined as the ratio of expected revenue in an optimal feature-based
pricing to the expected revenue in an optimal fair feature-based pricing (CoF)
can be arbitrarily large in general. When the revenue function is continuous
and concave with respect to the prices, we show that one can achieve CoF
strictly less than 2, irrespective of the model parameters. Finally, we provide
an algorithm to compute fair feature-based pricing strategy that achieves this
CoF.


title: Neutron stars and phase diagram in a hard-wall AdS/QCD model

abstract:
  We study the phase diagram of (large-$N_c$) QCD using a simplistic
holographic hard-wall model with a dynamical scalar field and a homogeneous
Ansatz representing a smeared instanton/baryon density. The resulting phase
diagram is qualitatively consistent with expectations, including a mesonic,
baryonic, quarkyonic, and quark-gluon plasma phase. As in other holographic
models, we also find a baryonic popcorn transition, which appears at large
chemical potential as a crossover. We then evaluate the nuclear matter equation
of state, which turns out to be rather stiff with a large peaked sound velocity
above the conformal limit, construct corresponding neutron stars using the TOV
equations, and finally use a full numerical gravity/hydrodynamics computation
to extract the gravitational wave signal of neutron star mergers.


title:
An initial alignment between neural network and target is needed for
  gradient descent to learn

abstract:
  This paper introduces the notion of "Initial Alignment" (INAL) between a
neural network at initialization and a target function. It is proved that if a
network and target function do not have a noticeable INAL, then noisy gradient
descent on a fully connected network with normalized i.i.d. initialization will
not learn in polynomial time. Thus a certain amount of knowledge about the
target (measured by the INAL) is needed in the architecture design. This also
provides an answer to an open problem posed in [AS20]. The results are based on
deriving lower-bounds for descent algorithms on symmetric neural networks
without explicit knowledge of the target function beyond its INAL.


title: Building a 3-Player Mahjong AI using Deep Reinforcement Learning

abstract:
  Mahjong is a popular multi-player imperfect-information game developed in
China in the late 19th-century, with some very challenging features for AI
research. Sanma, being a 3-player variant of the Japanese Riichi Mahjong,
possesses unique characteristics including fewer tiles and, consequently, a
more aggressive playing style. It is thus challenging and of great research
interest in its own right, but has not yet been explored. In this paper, we
present Meowjong, an AI for Sanma using deep reinforcement learning. We define
an informative and compact 2-dimensional data structure for encoding the
observable information in a Sanma game. We pre-train 5 convolutional neural
networks (CNNs) for Sanma's 5 actions -- discard, Pon, Kan, Kita and Riichi,
and enhance the major action's model, namely the discard model, via self-play
reinforcement learning using the Monte Carlo policy gradient method. Meowjong's
models achieve test accuracies comparable with AIs for 4-player Mahjong through
supervised learning, and gain a significant further enhancement from
reinforcement learning. Being the first ever AI in Sanma, we claim that
Meowjong stands as a state-of-the-art in this game.


title:
A Robust Multi-Objective Bayesian Optimization Framework Considering
  Input Uncertainty

abstract:
  Bayesian optimization is a popular tool for data-efficient optimization of
expensive objective functions. In real-life applications like engineering
design, the designer often wants to take multiple objectives as well as input
uncertainty into account to find a set of robust solutions. While this is an
active topic in single-objective Bayesian optimization, it is less investigated
in the multi-objective case. We introduce a novel Bayesian optimization
framework to efficiently perform multi-objective optimization considering input
uncertainty. We propose a robust Gaussian Process model to infer the Bayes risk
criterion to quantify robustness, and we develop a two-stage Bayesian
optimization process to search for a robust Pareto frontier. The complete
framework supports various distributions of the input uncertainty and takes
full advantage of parallel computing. We demonstrate the effectiveness of the
framework through numerical benchmarks.


title: Witness Generation for JSON Schema

abstract:
  JSON Schema is an important, evolving standard schema language for families
of JSON documents. It is based on a complex combination of structural and
Boolean assertions, and features negation and recursion. The static analysis of
JSON Schema documents comprises practically relevant problems, including schema
satisfiability, inclusion, and equivalence. These three problems can be reduced
to witness generation: given a schema, generate an element of the schema, if it
exists, and report failure otherwise. Schema satisfiability, inclusion, and
equivalence have been shown to be decidable, by reduction to reachability in
alternating tree automata. However, no witness generation algorithm has yet
been formally described. We contribute a first, direct algorithm for JSON
Schema witness generation. We study its effectiveness and efficiency, in
experiments over several schema collections, including thousands of real-world
schemas. Our focus is on the completeness of the language, where we only
exclude the uniqueItems operator, and on the ability of the algorithm to run in
a reasonable time on a large set of real-world examples, despite the
exponential complexity of the underlying problem.


title: Crystal Melting, BPS Quivers and Plethystics

abstract:
  We study the refined and unrefined crystal/BPS partition functions of
D6-D2-D0 brane bound states for all toric Calabi-Yau threefolds without compact
4-cycles and some non-toric examples. They can be written as products of
(generalized) MacMahon functions. We check our expressions and use them as
vacuum characters to study the gluings. We then consider the wall crossings and
discuss possible crystal descriptions for different chambers. We also express
the partition functions in terms of plethystic exponentials. For $\mathbb{C}^3$
and tripled affine quivers, we find their connections to nilpotent Kac
polynomials. Similarly, the partition functions of D4-D2-D0 brane bound states
can be obtained by replacing the (generalized) MacMahon functions with the
inverse of (generalized) Euler functions.


title: Boosting Distributional Copula Regression

abstract:
  Capturing complex dependence structures between outcome variables (e.g.,
study endpoints) is of high relevance in contemporary biomedical data problems
and medical research. Distributional copula regression provides a flexible tool
to model the joint distribution of multiple outcome variables by disentangling
the marginal response distributions and their dependence structure. In a
regression setup each parameter of the copula model, i.e. the marginal
distribution parameters and the copula dependence parameters, can be related to
covariates via structured additive predictors. We propose a framework to fit
distributional copula regression models via a model-based boosting algorithm.
Model-based boosting is a modern estimation technique that incorporates useful
features like an intrinsic variable selection mechanism, parameter shrinkage
and the capability to fit regression models in high dimensional data setting,
i.e. situations with more covariates than observations. Thus, model-based
boosting does not only complement existing Bayesian and maximum-likelihood
based estimation frameworks for this model class but rather enables unique
intrinsic mechanisms that can be helpful in many applied problems. The
performance of our boosting algorithm in the context of copula regression
models with continuous margins is evaluated in simulation studies that cover
low- and high-dimensional data settings and situations with and without
dependence between the responses. Moreover, distributional copula boosting is
used to jointly analyze and predict the length and the weight of newborns
conditional on sonographic measurements of the fetus before delivery together
with other clinical variables.


title:
A CNN-based Post-Processor for Perceptually-Optimized Immersive Media
  Compression

abstract:
  In recent years, resolution adaptation based on deep neural networks has
enabled significant performance gains for conventional (2D) video codecs. This
paper investigates the effectiveness of spatial resolution resampling in the
context of immersive content. The proposed approach reduces the spatial
resolution of input multi-view videos before encoding, and reconstructs their
original resolution after decoding. During the up-sampling process, an advanced
CNN model is used to reduce potential re-sampling, compression, and synthesis
artifacts. This work has been fully tested with the TMIV coding standard using
a Versatile Video Coding (VVC) codec. The results demonstrate that the proposed
method achieves a significant rate-quality performance improvement for the
majority of the test sequences, with an average BD-VMAF improvement of 3.07
overall sequences.


title: Physically-informed data-driven modeling of active nematics

abstract:
  A continuum description is essential for understanding variety of collective
phenomena in active matter. However, building quantitative continuum models of
active matter from first principles can be extremely challenging due to both
the gaps in our knowledge and the complicated structure of nonlinear
interactions. Here we use a novel physically-informed data-driven approach to
construct a complete mathematical model of an active nematic from experimental
data describing kinesin-drive microtubule bundles confined to an oil-water
interface. We find that the structure of the model is similar to the
Leslie-Erisken and Beris-Edwards models, but there are notable differences.
Rather unexpectedly, elastic effects are found to play no role, with the
dynamics controlled entirely by the balance between active stresses and highly
anisotropic viscous stresses.


title: Data-driven discovery of active nematic hydrodynamics

abstract:
  Two-dimensional active nematics are often modeled using phenomenological
continuum theories that describe the dynamics of the nematic director and fluid
velocity through partial differential equations (PDEs). While these models
provide a statistically accurate description of the experiments, the
identification of the relevant terms in the PDEs and their parameters is
usually indirect. Here, we adapt a recently developed method to automatically
identify optimal continuum models for active nematics directly from the
spatio-temporal director and velocity data, via sparse fitting of the
coarse-grained fields onto generic low order PDEs. We test the method
extensively on computational models, and then apply it to data from experiments
on microtubule-based active nematics. Thereby, we identify the optimal models
for microtubule-based active nematics, along with the relevant phenomenological
parameters. We find that the dynamics of the orientation field are largely
governed by its coupling to the underlying flow, with free-energy gradients
playing a negligible role. Furthermore, by fitting the flow equation to
experimental data, we estimate a key parameter quantifying the `activity' of
the nematic.


title:
Generalized HTLC for Cross-Chain Swapping of Multiple Assets with
  Co-Ownerships

abstract:
  A core enabler for blockchain or DLT interoperability is the ability to
atomically exchange assets held by mutually untrusting owners on different
ledgers. This atomic swap problem has been well-studied for several years, with
the Hash Time Locked Contract (HTLC) emerging as a canonical solution. HTLC
ensures atomicity of exchange, albeit with caveats for node failure and
timeliness of claims. But a bigger limitation of HTLC is that it only applies
to a model consisting of two adversarial parties having sole ownership of an
asset each. Realistic extensions of the model in which assets may be jointly
owned by multiple parties, all of whose consents are required for exchanges, or
where multiple assets must be exchanged for one, cannot be handled by HTLC
because these scenarios present distinctly new threats of collusions among
subsets of parties. In this paper, we generalize the model of asset exchanges
across DLT networks, present the consequent threat model, and propose and
analyze an augmented HTLC protocol for atomic multi-party-and-asset exchanges.
We also show how our protocol can be implemented easily by augmenting existing
HTLC capabilities in the Weaver interoperability framework to enable asset
exchanges between networks built on Hyperledger Fabric or Corda.


title: The Dynamic Resilience of Urban Labour Networks

abstract:
  Understanding and potentially predicting or even controlling urban labour
markets represents a great challenge for workers and policy makers alike.
Cities are effective engines of economic growth and prosperity and incubate
complex dynamics within their labour market, and the labour markets they
support demonstrate considerable diversity. This presents a challenge to policy
makers who would like to optimise labour markets to benefit workers, promote
economic growth and manage the impact of technological change. While much
previous work has studied the economic characteristics of cities as a function
of size and examined the exposure of urban economies to automation, this has
often been from a static perspective. In this work we examine the structure of
city job networks to uncover the diffusive properties. More specifically, we
identify the occupations which are most important in promoting the diffusion of
beneficial or deleterious properties. We find that these properties vary
considerably with city size.


title:
Asymptotic expansions of Kummer hypergeometric functions with three
  asymptotic parameters $a$, $b$ and $z$

abstract:
  In a recent paper \cite{Temme:2021:AKH} new asymptotic expansions are given
for the Kummer functions $M(a,b,z)$ and $U(a,b+1,z)$ for large positive values
of $a$ and $b$, with $z$ fixed and special attention for the case $a\sim b$. In
this paper we extend the approach and also accept large values of $z$. The new
expansions are valid when at least one of the parameters $a$, $b$, or $z$ is
large. We provide numerical tables to show the performance of the expansions.


title:
Test of the Statistical Isotropy of the Universe using Gravitational
  Waves

abstract:
  Since WMAP and Planck some anomalous features appeared in the Cosmic
Microwave Background (CMB) large-angle anisotropy, the so-called anomalies. One
of these is the hemispherical power asymmetry, i.e. a difference in the average
power on the two hemispheres centered around (l, b) = (221, -20), which shows a
relatively high level of significance. Such an anomaly could be the signature
of a departure from statistical isotropy on large scales. Here we investigate
the physical origin of this anomaly using the Cosmological Gravitational Wave
Background (CGWB) detectable by future GW detectors. Indeed, the CGWB offers a
unique window to explore the early universe and we show that it can be used in
combination with CMB data to shed light on the statistical isotropy of our
universe. Specifically, we study the evolution of gravitons in the presence of
a modulating field in the scalar gravitational potentials accounting for the
hemispherical power asymmetry and we infer the amplitude of this modulating
field through a minimal variance estimator exploiting both constrained and
unconstrained realizations of the CGWB. We show that the addition of the CGWB
will allow an improvement in the assessment of the physical origin of the CMB
power asymmetry. Accounting for the expected performances of LISA and BBO, we
also show that the latter is expected to be signal-dominated on large-scales,
proving that the CGWB could be the keystone to assess the significance of this
anomaly.


title:
Venture capital investments through the lens of network and functional
  data analysis

abstract:
  In this paper we characterize the performance of venture capital-backed firms
based on their ability to attract investment. The aim of the study is to
identify relevant predictors of success built from the network structure of
firms' and investors' relations. Focusing on deal-level data for the health
sector, we first create a bipartite network among firms and investors, and then
apply functional data analysis (FDA) to derive progressively more refined
indicators of success captured by a binary, a scalar and a functional outcome.
More specifically, we use different network centrality measures to capture the
role of early investments for the success of the firm. Our results, which are
robust to different specifications, suggest that success has a strong positive
association with centrality measures of the firm and of its large investors,
and a weaker but still detectable association with centrality measures of small
investors and features describing firms as knowledge bridges. Finally, based on
our analyses, success is not associated with firms' and investors' spreading
power (harmonic centrality), nor with the tightness of investors' community
(clustering coefficient) and spreading ability (VoteRank).


title: ARIA: Adversarially Robust Image Attribution for Content Provenance

abstract:
  Image attribution -- matching an image back to a trusted source -- is an
emerging tool in the fight against online misinformation. Deep visual
fingerprinting models have recently been explored for this purpose. However,
they are not robust to tiny input perturbations known as adversarial examples.
First we illustrate how to generate valid adversarial images that can easily
cause incorrect image attribution. Then we describe an approach to prevent
imperceptible adversarial attacks on deep visual fingerprinting models, via
robust contrastive learning. The proposed training procedure leverages training
on $\ell_\infty$-bounded adversarial examples, it is conceptually simple and
incurs only a small computational overhead. The resulting models are
substantially more robust, are accurate even on unperturbed images, and perform
well even over a database with millions of images. In particular, we achieve
91.6% standard and 85.1% adversarial recall under $\ell_\infty$-bounded
perturbations on manipulated images compared to 80.1% and 0.0% from prior work.
We also show that robustness generalizes to other types of imperceptible
perturbations unseen during training. Finally, we show how to train an
adversarially robust image comparator model for detecting editorial changes in
matched images.


title: Hierarchical Control for Multi-Agent Autonomous Racing

abstract:
  We develop a hierarchical controller for multi-agent autonomous racing. A
high-level planner approximates the race as a discrete game with simplified
dynamics that encodes the complex safety and fairness rules seen in real-life
racing and calculates a series of target waypoints. The low-level controller
takes the resulting waypoints as a reference trajectory and computes
high-resolution control inputs by solving a simplified formulation of a
multi-agent racing game. We consider two approaches for the low-level planner
to construct two hierarchical controllers. One approach uses multi-agent
reinforcement learning (MARL), and the other solves a linear-quadratic Nash
game (LQNG) to produce control inputs. We test the controllers against three
baselines: an end-to-end MARL controller, a MARL controller tracking a fixed
racing line, and an LQNG controller tracking a fixed racing line. Quantitative
results show that the proposed hierarchical methods outperform their respective
baseline methods in terms of head-to-head race wins and abiding by the rules.
The hierarchical controller using MARL for low-level control consistently
outperformed all other methods by winning over 88% of head-to-head races and
more consistently adhered to the complex racing rules. Qualitatively, we
observe the proposed controllers mimicking actions performed by expert human
drivers such as shielding/blocking, overtaking, and long-term planning for
delayed advantages. We show that hierarchical planning for game-theoretic
reasoning produces competitive behavior even when challenged with complex rules
and constraints.


title:
Stochastic differential equations with respect to optional
  semimartingales and two reflecting regulated barriers

abstract:
  In this work, we introduce a new Skorokhod problem with two reflecting
barriers when the trajectories of the driven process and the barriers are right
and left limited. We show that this problem has an explicit unique solution in
a deterministic case. Then, we apply our result to study the existence and
uniqueness of solutions of reflected stochastic differential equations with
respect to optional semimartingales. The study is carried out on a probability
space that does not necessarily satisfy the usual conditions.


title:
Multiple quasi-perfect vector vortex beams with arbitrary 3D position on
  focus

abstract:
  In this paper we show a method for creating multiple independent
quasi-perfect vector vortex beams with real-time programmable radii,
topological charges, polarization orders and position in 3 dimensions, using a
device based on a phase-only liquid crystal on silicon display. We achieved the
simultaneous generation of up to 7 independent beams, with topological charges
from -3 to 3, and found great agreement between the simulated and the measured
phases and polarization structures. Additionally, we used the same scheme for
enhancing the depth of focus of a single beam, resulting in a "tube" beam that
preserves its properties during propagation.


title: Dynamic size counting in population protocols

abstract:
  The population protocol model describes a network of anonymous agents that
interact asynchronously in pairs chosen at random. Each agent starts in the
same initial state $s$. We introduce the *dynamic size counting* problem:
approximately counting the number of agents in the presence of an adversary who
at any time can remove any number of agents or add any number of new agents in
state $s$. A valid solution requires that after each addition/removal event,
resulting in population size $n$, with high probability each agent "quickly"
computes the same constant-factor estimate of the value $\log_2 n$ (how quickly
is called the *convergence* time), which remains the output of every agent for
as long as possible (the *holding* time). Since the adversary can remove
agents, the holding time is necessarily finite: even after the adversary stops
altering the population, it is impossible to *stabilize* to an output that
never again changes.
  We first show that a protocol solves the dynamic size counting problem if and
only if it solves the *loosely-stabilizing counting* problem: that of
estimating $\log n$ in a *fixed-size* population, but where the adversary can
initialize each agent in an arbitrary state, with the same convergence time and
holding time. We then show a protocol solving the loosely-stabilizing counting
problem with the following guarantees: if the population size is $n$, $M$ is
the largest initial estimate of $\log n$, and s is the maximum integer
initially stored in any field of the agents' memory, we have expected
convergence time $O(\log n + \log M)$, expected polynomial holding time, and
expected memory usage of $O(\log^2 (s) + (\log \log n)^2)$ bits. Interpreted as
a dynamic size counting protocol, when changing from population size $n_{prev}$
to $n_{next}$, the convergence time is $O(\log n_{next} + \log \log n_{prev})$.


title: Harmonic Hierarchies for Polynomial Optimization

abstract:
  We introduce novel polyhedral approximation hierarchies for the cone of
nonnegative forms on the unit sphere in $\mathbb{R}^n$ and for its (dual) cone
of moments. We prove computable quantitative bounds on the speed of convergence
of such hierarchies. We also introduce a novel optimization-free algorithm for
building converging sequences of lower bounds for polynomial minimization
problems on spheres. Finally some computational results are discussed,
showcasing our implementation of these hierarchies in the programming language
Julia.


title:
Learning to Schedule Heuristics for the Simultaneous Stochastic
  Optimization of Mining Complexes

abstract:
  The simultaneous stochastic optimization of mining complexes (SSOMC) is a
large-scale stochastic combinatorial optimization problem that simultaneously
manages the extraction of materials from multiple mines and their processing
using interconnected facilities to generate a set of final products, while
taking into account material supply (geological) uncertainty to manage the
associated risk. Although simulated annealing has been shown to outperform
comparing methods for solving the SSOMC, early performance might dominate
recent performance in that a combination of the heuristics' performance is used
to determine which perturbations to apply. This work proposes a data-driven
framework for heuristic scheduling in a fully self-managed hyper-heuristic to
solve the SSOMC. The proposed learn-to-perturb (L2P) hyper-heuristic is a
multi-neighborhood simulated annealing algorithm. The L2P selects the heuristic
(perturbation) to be applied in a self-adaptive manner using reinforcement
learning to efficiently explore which local search is best suited for a
particular search point. Several state-of-the-art agents have been incorporated
into L2P to better adapt the search and guide it towards better solutions. By
learning from data describing the performance of the heuristics, a
problem-specific ordering of heuristics that collectively finds better
solutions faster is obtained. L2P is tested on several real-world mining
complexes, with an emphasis on efficiency, robustness, and generalization
capacity. Results show a reduction in the number of iterations by 30-50% and in
the computational time by 30-45%.


title:
Inter-quintuple layer coupling and topological phase transitions in the
  chalcogenide topological insulators

abstract:
  Driving quantum phase transitions in the 3D topological insulators offers
pathways to tuning the topological states and their properties. We use
DFT-based calculations to systematically investigate topological phase
transitions in Bi$_2$Se$_3$, Sb$_2$Se$_3$, Bi$_2$Te$_3$ and Sb$_2$Te$_3$ by
varying the $c/a$ ratio of lattice constants. This ensures no net hydrostatic
pressure under anisotropic stress and strain and allows a clear identification
of the physics leading to the transition. As a function of $c/a$, all of these
materials exhibit structural and electronic stability of the quintuple layers
(QLs), and quasi-linear behavior of both the inter-quintuple layer distance and
the energy gap near the topological transition. Our results show that the
transition is predominantly controlled by the inter-QL physics, namely by
competing Coulomb and van der Waals interactions between the outer atomic
sheets in neighboring quintuple layers. We discuss the implications of our
results for topological tuning by alloying.


title: Introducing General Relativity in high school: a guide for teachers

abstract:
  Introducing Modern Physics represents an increasingly urgent need, towards
which physics education concentrates many efforts. In order to contribute to
this attempt, at the Department of Mathematics and Physics of Roma Tre
University in Rome we focused on the possibility of treating General Relativity
(GR) at high school level. We started with an interactive activity addressed to
students that exploits the rubber sheet analogy (RSA) to show various phenomena
related to gravity using the concept of space-time. Then, having verified its
effectiveness, we began to include it among the initiatives the Department
carry for high school teacher professional development, with the explicit aim
of making them capable of carrying on the activity autonomously in the
classrooms. In this paper, we analyse the teacher training approach we
realized, and all the materials developed.


title:
Normal forms, moving frames, and differential invariants for
  nondegenerate hypersurfaces in C^2

abstract:
  We use the method of equivariant moving frames to revisit the problem of
normal forms and equivalence of nondegenerate real hypersurfaces M \subset C^2
under the pseudo-group action of holomorphic transformations. The moving frame
recurrence formulae allow us to systematically and algorithmically recover the
results of Chern and Moser for hypersurfaces that are either non-umbilic at a
point p \in M or umbilic in an open neighborhood of it. In the former case, the
coefficients of the normal form expansion, when expressed as functions of the
jet of the hypersurface at the point, provide a complete system of functionally
independent differential invariants that can be used to solve the equivalence
problem. We prove that under a suitable genericity condition, the entire
algebra of differential invariants for such hypersurfaces can be generated,
through the operators of invariant differentiation, by a single real
differential invariant of order 7. We then apply moving frames to construct new
convergent normal forms for nondegenerate real hypersurfaces at singularly
umbilic points, namely those umbilic points where the hypersurface is not
identically umbilic around them.


title:
Correlation functions for open XXZ spin 1/2 quantum chains with
  unparallel boundary magnetic fields

abstract:
  In this paper we continue our derivation of the correlation functions of open
quantum spin 1/2 chains with unparallel magnetic fields on the edges; this time
for the more involved case of the XXZ spin 1/2 chains. We develop our study in
the framework of the quantum Separation of Variables (SoV), which gives us both
the complete spectrum characterization and simple scalar product formulae for
separate states, including transfer matrix eigenstates. Here, we leave the
boundary magnetic field in the first site of the chain completely arbitrary,
and we fix the boundary field in the last site $N$ of the chain to be a
specific value along the $z$-direction. This is a natural first choice for the
unparallel boundary magnetic fields. We prove that under these special boundary
conditions, on the one side, we have a simple enough complete spectrum
description in terms of homogeneous Baxter like $TQ$-equation. On the other
side, we prove a simple enough description of the action of a basis of local
operators on transfer matrix eigenstates as linear combinations of separate
states. Thanks to these results, we achieve our main goal to derive correlation
functions for a set of local operators both for the finite and half-infinite
chains, with multiple integral formulae in this last case.

categories: math-ph cond-mat.other hep-th math.MP nlin.SI


title: Fast Consensus and Metastability in a Highly Polarized Social Network

abstract:
  We introduce a new model for a highly polarized social network. This model is
a system of interacting marked point processes with memory of variable length.
Each point process indicates the successive times in which a social actor
express a "favorable" or "contrary" opinion on a certain subject. The
orientation and the rate at which an actor express an opinion is influenced by
the social pressure exerted on them, modulated by a polarization coefficient.
The social pressure on an actor is reset to 0, when they express an opinion,
and simultaneously the social pressures on all the other actors change by one
unit in the direction of the opinion that was just expressed. We prove that
when the polarization coefficient diverges, this social network reaches
instantaneous consensus. Here by consensus we mean the set of lists in which
all the social pressures push in the same direction. This consensus has a
metastable behavior. This means that the direction of the social pressures on
the actors globally changes after a long and unpredictable random time.


title: AutoFR: Automated Filter Rule Generation for Adblocking

abstract:
  Adblocking relies on filter lists, which are manually curated and maintained
by a small community of filter list authors. This manual process is laborious
and does not scale well to a large number of sites and over time. We introduce
AutoFR, a reinforcement learning framework to fully automate the process of
filter rule creation and evaluation. We design an algorithm based on multi-arm
bandits to generate filter rules while controlling the trade-off between
blocking ads and avoiding breakage. We test our implementation of AutoFR on
thousands of sites in terms of efficiency and effectiveness. AutoFR is
efficient: it takes only a few minutes to generate filter rules for a site.
AutoFR is also effective: it generates filter rules that can block 86% of the
ads, as compared to 87% by EasyList while achieving comparable visual breakage.
The filter rules generated by AutoFR generalize well to new and unseen sites.
We envision AutoFR to assist the adblocking community in automated filter rule
generation at scale.


title:
TerraPN: Unstructured Terrain Navigation using Online Self-Supervised
  Learning

abstract:
  We present TerraPN, a novel method that learns the surface properties
(traction, bumpiness, deformability, etc.) of complex outdoor terrains directly
from robot-terrain interactions through self-supervised learning, and uses it
for autonomous robot navigation. Our method uses RGB images of terrain surfaces
and the robot's velocities as inputs, and the IMU vibrations and odometry
errors experienced by the robot as labels for self-supervision. Our method
computes a surface cost map that differentiates smooth, high-traction surfaces
(low navigation costs) from bumpy, slippery, deformable surfaces (high
navigation costs). We compute the cost map by non-uniformly sampling patches
from the input RGB image by detecting boundaries between surfaces resulting in
low inference times (47.27% lower) compared to uniform sampling and existing
segmentation methods. We present a novel navigation algorithm that accounts for
a surface's cost, computes cost-based acceleration limits for the robot, and
dynamically feasible, collision-free trajectories. TerraPN's surface cost
prediction can be trained in ~25 minutes for five different surfaces, compared
to several hours for previous learning-based segmentation methods. In terms of
navigation, our method outperforms previous works in terms of success rates (up
to 35.84% higher), vibration cost of the trajectories (up to 21.52% lower), and
slowing the robot on bumpy, deformable surfaces (up to 46.76% slower) in
different scenarios.


title:
Precise Q value measurements of $^{112,113}$Ag and $^{115}$Cd with the
  Canadian Penning trap for evaluation of potential ultra-low Q value
  $\beta$-decays

abstract:
  An ultra-low Q value $\beta$-decay can occur from a parent nuclide to an
excited state in the daughter with $Q_{UL}$ <1 keV. These decays are of
interest for nuclear $\beta$-decay theory and as potential candidates in
neutrino mass determination experiments. To date, only one ultra-low Q value
$\beta$-decay has been observed -- that of $^{115}$In with $Q_\beta$ = 147(10)
eV. A number of other potential candidates exist, but improved mass
measurements are necessary to determine if the decays are energetically allowed
and, in fact, ultra-low. We performed precise $\beta$-decay Q value
measurements of $^{112,113}$Ag and $^{115}$Cd and combined them with nuclear
energy level data for the daughter isotopes to determine if the potential UL Q
value $\beta$-decay branches of $^{112,113}$Ag and $^{115}$Cd are energetically
allowed and <1 keV. The Canadian Penning Trap at ANL was used to measure the
cyclotron frequency ratios of singly-charged $^{112,113}$Ag and $^{115}$Cd ions
with respect to their daughters. From these measurements, the ground-state
$\beta$-decay Q values were obtained. The $^{112}$Ag, $^{113}$Ag, and
$^{115}$Cd $\beta$-decay Q values were measured to be 3990.16(22) keV,
2085.7(4.6) keV, and 1451.36(34) keV, respectively. These results were compared
to energies of excited states in $^{112}$Cd at 3997.75(14) keV, $^{113}$Cd at
2015.6(2.5) and 2080(10) keV, and $^{115}$In at 1448.787(9) keV, resulting in
$Q_{\textrm{UL}}$ values of --7.59(26) keV, 6(11) keV, and 2.57(34) keV,
respectively. The potential UL Q value decays of $^{112}$Ag and $^{115}$Cd have
been ruled out. $^{113}$Ag is still a possible candidate until a more precise
measurement of the 2080(10) keV, 1/2$^{+}$ state of $^{113}$Cd is available. In
the course of this work we have found the ground state mass of $^{113}$Ag
reported in the 2020AME to be lower than our measurement by 69(17) keV (a
4$\sigma$ discrepancy).


title: DataLab: A Platform for Data Analysis and Intervention

abstract:
  Despite data's crucial role in machine learning, most existing tools and
research tend to focus on systems on top of existing data rather than how to
interpret and manipulate data. In this paper, we propose DataLab, a unified
data-oriented platform that not only allows users to interactively analyze the
characteristics of data, but also provides a standardized interface for
different data processing operations. Additionally, in view of the ongoing
proliferation of datasets, \toolname has features for dataset recommendation
and global vision analysis that help researchers form a better view of the data
ecosystem. So far, DataLab covers 1,715 datasets and 3,583 of its transformed
version (e.g., hyponyms replacement), where 728 datasets support various
analyses (e.g., with respect to gender bias) with the help of 140M samples
annotated by 318 feature functions. DataLab is under active development and
will be supported going forward. We have released a web platform, web API,
Python SDK, PyPI published package and online documentation, which hopefully,
can meet the diverse needs of researchers.


title:
Full exceptional collections of vector bundles on rank-two linear GIT
  quotients

abstract:
  We produce full strong exceptional collections consisting of vector bundles
on the geometric invariant theory quotient of certain linear actions of a split
reductive group $G$ of rank two. The vector bundles correspond to irreducible
$G$-representations whose weights lie in an explicit bounded region in the
weight space of $G$. We also describe a method for constructing more examples
of linear GIT quotients with full strong exceptional collections of this kind
as "decorated" quiver varieties.


title: Electron scattering of mass-inverted in graphene quantum dots

abstract:
  We study the scattering of Dirac electrons of circular graphene quantum dot
with mass-inverted subject to electrostatic potential. The obtained solutions
of the energy spectrum are used to determine the scattering coefficients at the
interface of the two regions. Using the asymptotic solutions at large
arguments, we explicitly determine the radial component of reflected current
density and the scattering efficiency. It is found that the presence of a mass
term outside in addition to another one inside the quantum dot strongly affects
the scattering of electrons. In particular, a non-null square modulus of the
scattering coefficient is found at zero energy.


title:
Presheaves on $\mathcal{VI}$, $nil$-closed unstable algebras and their
  centres

abstract:
  A $nil$-closed, noetherian, unstable algebra $K$ over the Steenrod Algebra is
determined, up to isomorphism, by the functor
$\text{Hom}_{\mathcal{K}\text{f.g.}}(K,H^*(\_))$, which is a presheaf on the
category $\mathcal{VI}$ of finite dimensional vector spaces and injections, by
the theory of Henn-Lannes-Schwartz. In this article, we use this theory to
study the centre, in the sense of Heard, of a $nil$-closed noetherian unstable
algebra. For $F$ a presheaf on $\mathcal{VI}$, we construct a groupoid
$\mathcal{G}_F$ which encodes $F$. Then, taking
$F:=\text{Hom}_{\mathcal{K}\text{f.g.}}(K,H^*(\_))$, we show how the centre of
$K$ is determined by the associated groupoid. We also give a generalisation of
the second theorem of Adams-Wilkerson, defining sub-algebras
$H^*(W)^\mathcal{G}$ of $H^*(W)$ for appropriate groupoids $\mathcal{G}$. There
is a $H^*(C)$-comodule structure on $K$ that is associated with the centre. For
$K$ integral, we explain how the algebra of primitive elements of this
$H^*(C)$-comodule structure is also determined by the groupoid associated with
$\text{Hom}_{\mathcal{K}\text{f.g.}}(K,H^*(\_))$. Along the way, we prove that
this algebra of primitive elements is also noetherian.


title: Model predictive control for retinal laser treatment at 1 kHz

abstract:
  Laser photocoagulation is a technique applied in the treatment of retinal
diseases. While this is often done manually or using simple control schemes, we
pursue an optimization-based approach, namely Model Predictive Control (MPC),
to enforce bounds on the peak temperature and, thus, safety during the medical
treatment procedure - despite the spot-dependent absorption of the tissue. To
this end, a repetition rate of 1 kHz is desirable rendering the real-time
requirements a major challenge. We present a tailored MPC scheme using
parametric model reduction, an extended Kalman filter for the parameter and
state estimation, and suitably constructed stage costs and verify its
applicability both in simulation and experiments with porcine eyes. Moreover,
we give some insight on the implementation specifically tailored for fast
numerical computations.


title: One-dimensional SPT phases protected by frieze symmetries

abstract:
  We undertake a systematic study of symmetry-protected topological gapped
phases of quantum spin chains in the presence of the quasi-one-dimensional
frieze space groups. Here, the spatial symmetries of the one-dimensional
lattice are considered together with an additional 'vertical reflection', which
we take to be an on-site $\mathbb{Z}_2$ symmetry. We identify seventeen
distinct non-trivial phases and define canonical forms. If time-reversal is
combined with a shift over one site, then no new topological phases arise. We
furthermore construct explicit renormalization-group fixed point wavefunctions
for symmetry-protected topological phases with global on-site symmetries, and
demonstrate how group cohomology can be computed using the Smith normal form


title:
Bayesian Inference of Fiber Orientation and Polymer Properties in Short
  Fiber-Reinforced Polymer Composites

abstract:
  We present a Bayesian methodology to infer the elastic modulus of the
constituent polymer and the fiber orientation state in a short-fiber reinforced
polymer composite (SFRP). The properties are inversely determined using only a
few experimental tests. Developing composite manufacturing digital twins for
SFRP composite processes, including injection molding and extrusion deposition
additive manufacturing (EDAM) requires extensive experimental material
characterization. In particular, characterizing the composite mechanical
properties is time consuming and therefore, micromechanics models are used to
fully identify the elasticity tensor. Hence, the objective of this paper is to
infer the fiber orientation and the effective polymer modulus and therefore,
identify the elasticity tensor of the composite with minimal experimental
tests. To that end, we develop a hierarchical Bayesian model coupled with a
micromechanics model to infer the fiber orientation and the polymer elastic
modulus simultaneously which we then use to estimate the composite elasticity
tensor. We motivate and demonstrate the methodology for the EDAM process but
the development is such that it is applicable to other SFRP composites
processed via other methods. Our results demonstrate that the approach provides
a reliable framework for the inference, with as few as three tensile tests,
while accounting for epistemic and aleatory uncertainty. Posterior predictive
checks show that the model is able to recreate the experimental data well. The
ability of the Bayesian approach to calibrate the material properties and its
associated uncertainties, make it a promising tool for enabling a probabilistic
predictive framework for composites manufacturing digital twins.


title: Odd Colourings of Graph Products

abstract:
  The odd colouring number is a new graph parameter introduced by
Petru\v{s}evski and \v{S}krekovski. In this note, we show that graphs with so
called product structure have bounded odd-colouring number. By known results on
the product structure of $k$-planar graphs, this implies that $k$-planar graphs
have bounded odd-colouring number, which answers a question of Cranston,
Lafferty, and Song.


title:
Human Detection of Political Deepfakes across Transcripts, Audio, and
  Video

abstract:
  Recent advances in technology for hyper-realistic visual effects provoke the
concern that deepfake videos of political speeches will soon be visually
indistinguishable from authentic video recordings. Yet there exists little
empirical research on how audio-visual information influences people's
susceptibility to fall for political misinformation. The conventional wisdom in
the field of communication research predicts that people will fall for fake
news more often when the same version of a story is presented as a video as
opposed to text. However, audio-visual manipulations often leave distortions
that some but not all people may pick up on. Here, we evaluate how
communication modalities influence people's ability to discern real political
speeches from fabrications based on a randomized experiment with 5,727
participants who provide 61,792 truth discernment judgments. We show
participants soundbites from political speeches that are randomly assigned to
appear using permutations of text, audio, and video modalities. We find that
communication modalities mediate discernment accuracy: participants are more
accurate on video with audio than silent video, and more accurate on silent
video than text transcripts. Likewise, we find participants rely more on how
something is said (the audio-visual cues) rather than what is said (the speech
content itself). However, political speeches that do not match public
perceptions of politicians' beliefs reduce participants' reliance on visual
cues. In particular, we find that reflective reasoning moderates the degree to
which participants consider visual information: low performance on the
Cognitive Reflection Test is associated with an underreliance on visual cues
and an overreliance on what is said.


title: Learning to Identify Perceptual Bugs in 3D Video Games

abstract:
  Automated Bug Detection (ABD) in video games is composed of two distinct but
complementary problems: automated game exploration and bug identification.
Automated game exploration has received much recent attention, spurred on by
developments in fields such as reinforcement learning. The complementary
problem of identifying the bugs present in a player's experience has for the
most part relied on the manual specification of rules. Although it is widely
recognised that many bugs of interest cannot be identified with such methods,
little progress has been made in this direction. In this work we show that it
is possible to identify a range of perceptual bugs using learning-based methods
by making use of only the rendered game screen as seen by the player. To
support our work, we have developed World of Bugs (WOB) an open platform for
testing ABD methods in 3D game environments.


title:
Approximate Weight Distribution of Polarization-Adjusted Convolutional
  (PAC) Codes

abstract:
  Polarization-adjusted convolutional (PAC) codes combine the polar and
convolutional transformations to enhance the distance properties of polar
codes. They offer a performance very close to the finite length
information-theoretic bounds for short block lengths. In this paper, we develop
a method of computing the weight distribution of PAC codes in an approximate
form by employing a probabilistic technique. We demonstrate that the results
well match the exact weight distributions for small codes that can be computed
using a brute-force algorithm. We also present a way employing the results
(along with the union bound on the code performance) to design specific PAC
codes, more precisely, to determine suitable rate profiles via simulated
annealing. Numerical examples illustrate that the PAC codes with the designed
rate profiles offer superior performance.


title:
Indefinite causal order with fixed temporal order for electrons and
  positrons

abstract:
  We provide an analysis of indefinite causal orders in relativistic quantum
mechanics based on the electron-positron picture of Feynman involving negative
energy electrons moving backward in time. We show that genuine implementations
of the paradigmatic quantum switch, but here violating some causal inequalities
and with a fixed temporal order, become possible in extreme external
electromagnetic field conditions allowing the presence of closed time-like
curves.


title:
Biological error correction codes generate fault-tolerant neural
  networks

abstract:
  It has been an open question in deep learning if fault-tolerant computation
is possible: can arbitrarily reliable computation be achieved using only
unreliable neurons? In the mammalian cortex, analog error correction codes
known as grid codes have been observed to protect states against neural spiking
noise, but their role in information processing is unclear. Here, we use these
biological codes to show that a universal fault-tolerant neural network can be
achieved if the faultiness of each neuron lies below a sharp threshold, which
we find coincides in order of magnitude with noise observed in biological
neurons. The discovery of a sharp phase transition from faulty to
fault-tolerant neural computation opens a path towards understanding noisy
analog systems in artificial intelligence and neuroscience.


title: Meta-Learning for Simple Regret Minimization

abstract:
  We develop a meta-learning framework for simple regret minimization in
bandits. In this framework, a learning agent interacts with a sequence of
bandit tasks, which are sampled i.i.d.\ from an unknown prior distribution, and
learns its meta-parameters to perform better on future tasks. We propose the
first Bayesian and frequentist algorithms for this meta-learning problem. The
Bayesian algorithm has access to a prior distribution over the meta-parameters
and its meta simple regret over $m$ bandit tasks with horizon $n$ is mere
$\tilde{O}(m / \sqrt{n})$. This is while we show that the meta simple regret of
the frequentist algorithm is $\tilde{O}(\sqrt{m} n + m/ \sqrt{n})$, and thus,
worse. However, the algorithm is more general, because it does not need a prior
distribution over the meta-parameters, and is easier to implement for various
distributions. We instantiate our algorithms for several classes of bandit
problems. Our algorithms are general and we complement our theory by evaluating
them empirically in several environments.


title:
Structure, friction, and Raman spectroscopy of Re-doped bulk MoS$_2$
  from first principles

abstract:
  Doping MoS$_2$ with Re is known to alter the electronic, structural, and
tribological properties. Re-doped MoS$_2$ has been mainly studied in monolayer
or few-layer form, but can also be relevant for applications in many-layer or
bulk form. In this work, we use density functional theory to explore the phase
stability, Raman spectra, and atomic force microscopy (AFM) sliding
interactions of bulk Re-doped MoS$_2$. We consider the possibility of the Re
dopant existing at different locations and provide experimentally
distinguishable characteristics of the most likely sites -- Mo-substitution and
tetrahedral (t-) intercalation. We demonstrate and benchmark an approach to
calculate Raman spectra of doped materials with metallic densities of states by
using atomic Raman tensors from the pristine material. Applying this method to
the metallic Re-doped structures, we find consistent shifts in the Raman-active
peaks depending on Re dopant position: redshifts in both $A_{\rm 1g}$ and
$E_{2g}^1$ peaks in the t-intercalated case versus a redshift for $A_{\rm 1g}$
and blueshift for $E_{\rm 2g}^1$ peaks in the Mo-substituted case. AFM friction
forces, if the MoS$_2$ surface remains flat and unpuckered, are increased by Re
dopants, similar for Mo-substitution and t-intercalation, and would decrease
with the number of layers.


title:
Collective plasmonic modes in the chiral multifold fermionic material
  CoSi

abstract:
  Plasmonics in topological semimetals offers exciting opportunities for
fundamental physics exploration as well as for technological applications.
Here, we investigate plasmons in the exemplar chiral crystal CoSi, which hosts
a variety of multifold fermionic excitations. We show that CoSi hosts two
distinct plasmon modes in the infrared regime at 0.1 eV and 1.1 eV in the
long-wavelength limit. The 0.1 eV plasmon is found to be highly dispersive, and
originates from intraband collective oscillations associated with a double
spin-1 excitation, while the 1.1 eV plasmon is dispersionless and it involves
interband correlations. Both plasmon modes lie outside the particle-hole
continuum and possess long lifetime. Our study indicates that the CoSi class of
materials will provide an interesting materials platform for exploring
fundamental and technological aspects of topological plasmonics.


title:
Combining Observational and Randomized Data for Estimating Heterogeneous
  Treatment Effects

abstract:
  Estimating heterogeneous treatment effects is an important problem across
many domains. In order to accurately estimate such treatment effects, one
typically relies on data from observational studies or randomized experiments.
Currently, most existing works rely exclusively on observational data, which is
often confounded and, hence, yields biased estimates. While observational data
is confounded, randomized data is unconfounded, but its sample size is usually
too small to learn heterogeneous treatment effects. In this paper, we propose
to estimate heterogeneous treatment effects by combining large amounts of
observational data and small amounts of randomized data via representation
learning. In particular, we introduce a two-step framework: first, we use
observational data to learn a shared structure (in form of a representation);
and then, we use randomized data to learn the data-specific structures. We
analyze the finite sample properties of our framework and compare them to
several natural baselines. As such, we derive conditions for when combining
observational and randomized data is beneficial, and for when it is not. Based
on this, we introduce a sample-efficient algorithm, called CorNet. We use
extensive simulation studies to verify the theoretical properties of CorNet and
multiple real-world datasets to demonstrate our method's superiority compared
to existing methods.


title:
AbGradCon 2021: Lessons in Digital Meetings, International
  Collaboration, and Interdisciplinarity in Astrobiology

abstract:
  The Astrobiology Graduate Conference (AbGradCon) is an annual conference both
organized for and by early career researchers, postdoctoral fellows, and
students as a way to train the next generation of astrobiologists and develop a
robust network of cohorts moving forward. AbGradCon 2021 was held virtually on
September 14-17, 2021, hosted by the Earth-Life Science Institute (ELSI) of
Tokyo Institute of Technology after postponement of the in-person event in 2020
due to the COVID-19 pandemic. The meeting consisted of presentations by 120
participants from a variety of fields, two keynote speakers, and other career
building events and workshops. Here, we report on the organizational and
executional aspects of AbGradCon 2021, including the meeting participant
demographics, various digital aspects introduced specifically for a virtual
edition of the meeting, and the abstract submission and evaluation process. The
abstract evaluation process of AbGradCon 2021 is unique in that all evaluations
are done by the peers of the applicants, and as astrobiology is inherently a
broad discipline, the abstract evaluation process revealed a number of trends
related to multidisciplinarity of the astrobiology field. We believe that
meetings like AbGradCon can provide a unique opportunity for students and early
career researchers in astrobiology to experience community building, inter- and
multidisciplinary collaboration, and career training and would be a welcome
sight in other fields as well. We hope that this report provides inspiration
and a basic roadmap for organizing future conferences in any field with similar
goals.

categories: astro-ph.IM astro-ph.EP astro-ph.GA astro-ph.SR


title: A stochastic household model for vector-borne diseases

abstract:
  We introduce a stochastic household model for vector-borne diseases, in
particular as relevant to prominent vectors belonging to the Aedes genus and
hence the Zika, chikungunya, and dengue viruses. In this model, vectors remain
local to each household, while hosts mix for a proportion of their time in
their household and the remaining proportion in the population at random. This
is approximated with a two-type branching process, allowing us to efficiently
calculate a number of useful epidemiological characteristics, such as
reproductive numbers, early growth rates and household-type proportions,
offspring distributions, probabilities of a major outbreak, and
within-household final size distributions. We compare control interventions of
spraying -- reducing the number of vectors in each household -- and
social-distancing -- having individuals spend more time at home -- in terms of
these characteristics.


title:
Annular structures in perturbed low mass disc-shaped gaseous nebulae II
  : general and polytropic models

abstract:
  This is the second of two papers where we study additional analytical
solutions of a bidimensional low mass gaseous disc rotating around a central
mass and submitted to small radial perturbations. In a first Paper,
hydrodynamics equations were solved for the equilibrium and perturbed
configurations and a wave-like equation for the gas perturbed specific mass was
deduced and solved analytically for several cases of exponents of the power law
distributions of the unperturbed specific mass and sound speed. In this paper,
two other general cases of exponents, including a polytropic case, are solved
analytically for small frequencies of the perturbations. Similar conclusions to
the ones of Paper I are found, namely that the maxima of the gas perturbed
specific mass are exponentially spaced and that their distance ratio is a
constant, function of disc characteristics and of the perturbations frequency.
Gaseous annular structures would eventually be formed in the disc by inward and
outward gas flows from zones of minima toward zones of maxima of perturbed
specific mass.


title:
Application of Tikhonov Regularization in Generalized Inverse of
  Adjacency Matrix of Undirected Graph

abstract:
  In this paper, we found the Moore-Penrose generalized inverse of adjacency
matrix of an undirected graph, explicitly. We proved that the matrix
$R_\lambda= [r_{ij}]$ is nonsingular where $r_{ii}=\frac{1}{\lambda}+ \deg v_i$
and $r_{ij}=\mid N_G(V_i)\cap N_G(V_j)\mid$ for $i\neq j$, and we proved that
$A^{\dagger}_G=[s_{ij}]_{1\leq i, j \leq n}$ where
$\displaystyle{s_{ij}=s_{ji}=\lim_{\lambda \rightarrow +\infty} \langle
R^{-1}_{\lambda}e_j, f_i \rangle }$. The proof of the main result was based on
the Tikhonov regularization.