title: Perverse schobers and 3d mirror symmetry

abstract:
  The proposed physical duality known as 3d mirror symmetry relates the
geometries of dual pairs of holomorphic symplectic stacks. It has served in
recent years as a guiding principle for developments in representation theory.
However, due to the lack of definitions, thus far only small pieces of the
subject have been mathematically accessible.
  In this paper, we formulate abelian 3d mirror symmetry as an equivalence
between a pair of 2-categories constructed from the algebraic and symplectic
geometry, respectively, of Gale dual toric cotangent stacks. In the simplest
case, our theorem provides a spectral description of the 2-category of
spherical functors - i.e., perverse schobers on the affine line with
singularities at the origin. We expect that our results can be extended from
toric cotangent stacks to hypertoric varieties, which would provide a
categorification of previous results on Koszul duality for hypertoric
categories $\mathcal{O}$. Our methods also suggest approaches to 2-categorical
3d mirror symmetry for more general classes of spaces of interest in geometric
representation theory.
  Along the way, we establish two results that may be of independent interest:
(1) a version of the theory of Smith ideals in the setting of stable
$\infty$-categories; and (2) an ambidexterity result for co/limits of
presentable enriched $\infty$-categories over $\infty$-groupoids.


title: Memory Efficient Tries for Sequential Pattern Mining

abstract:
  The rapid and continuous growth of data has increased the need for scalable
mining algorithms in unsupervised learning and knowledge discovery. In this
paper, we focus on Sequential Pattern Mining (SPM), a fundamental topic in
knowledge discovery that faces a well-known memory bottleneck. We examine
generic dataset modeling techniques and show how they can be used to improve
SPM algorithms in time and memory usage. In particular, we develop trie-based
dataset models and associated mining algorithms that can represent as well as
effectively mine orders of magnitude larger datasets compared to the state of
the art. Numerical results on real-life large-size test instances show that our
algorithms are also faster and more memory efficient in practice.


title:
Approximation of N-player stochastic games with singular controls by
  mean field games

abstract:
  This paper establishes that a class of $N$-player stochastic games with
singular controls, either of bounded velocity or of finite variation, can both
be approximated by mean field games (MFGs) with singular controls of bounded
velocity. More specifically, it shows (i) the optimal control to an MFG with
singular controls of a bounded velocity $\theta$ is shown to be an
$\epsilon_N$-NE to an $N$-player game with singular controls of the bounded
velocity, with $\epsilon_N = O(\frac{1}{\sqrt{N}})$, and (ii) the optimal
control to this MFG is an $(\epsilon_N + \epsilon_{\theta})$-NE to an
$N$-player game with singular controls of finite variation, where
$\epsilon_{\theta}$ is an error term that depends on $\theta$. This work
generalizes the classical result on approximation $N$-player games by MFGs, by
allowing for discontinuous controls.


title:
A Machine Learning Framework for Event Identification via Modal Analysis
  of PMU Data

abstract:
  Power systems are prone to a variety of events (e.g. line trips and
generation loss) and real-time identification of such events is crucial in
terms of situational awareness, reliability, and security. Using measurements
from multiple synchrophasors, i.e., phasor measurement units (PMUs), we propose
to identify events by extracting features based on modal dynamics. We combine
such traditional physics-based feature extraction methods with machine learning
to distinguish different event types. Including all measurement channels at
each PMU allows exploiting diverse features but also requires learning
classification models over a high-dimensional space. To address this issue,
various feature selection methods are implemented to choose the best subset of
features. Using the obtained subset of features, we investigate the performance
of two well-known classification models, namely, logistic regression (LR) and
support vector machines (SVM) to identify generation loss and line trip events
in two datasets. The first dataset is obtained from simulated generation loss
and line trip events in the Texas 2000-bus synthetic grid. The second is a
proprietary dataset with labeled events obtained from a large utility in the
USA involving measurements from nearly 500 PMUs. Our results indicate that the
proposed framework is promising for identifying the two types of events.


title: Chain-level equivariant string topology for simply connected manifolds

abstract:
  We prove that on 2-connected closed oriented manifolds, the analytic and
algebraic constructions of chain-level equivariant string topology coincide.
The corresponding structure is invariant under orientation preserving homotopy
equivalences and induces on homology the involutive Lie bialgebra structure of
Chas and Sullivan.


title: Problems hard for treewidth but easy for stable gonality

abstract:
  We show that some natural problems that are XNLP-hard (which implies
W[t]-hardness for all t) when parameterized by pathwidth or treewidth, become
FPT when parameterized by stable gonality, a novel graph parameter based on
optimal maps from graphs to trees. The problems we consider are classical flow
and orientation problems, such as Undirected Flow with Lower Bounds (which is
strongly NP-complete, as shown by Itai), Minimum Maximum Outdegree (for which
W[1]-hardness for treewidth was proven by Szeider), and capacitated
optimization problems such as Capacitated (Red-Blue) Dominating Set (for which
W[1]-hardness was proven by Dom, Lokshtanov, Saurabh and Villanger). Our
hardness proofs (that beat existing results) use reduction to a recent
XNLP-complete problem (Accepting Non-deterministic Checking Counter Machine).
The new easy parameterized algorithms use a novel notion of weighted tree
partition with an associated parameter that we call treebreadth, inspired by
Seese's notion of tree-partite graphs, as well as techniques from dynamical
programming and integer linear programming.


title:
Close-up and Whispering: An Understanding of Multimodal and Parasocial
  Interactions in YouTube ASMR videos

abstract:
  ASMR (Autonomous Sensory Meridian Response) has grown to immense popularity
on YouTube and drawn HCI designers' attention to its effects and applications
in design. YouTube ASMR creators incorporate visual elements, sounds, motifs of
touching and tasting, and other scenarios in multisensory video interactions to
deliver enjoyable and relaxing experiences to their viewers. ASMRtists engage
viewers by social, physical, and task attractions. Research has identified the
benefits of ASMR in mental wellbeing. However, ASMR remains an understudied
phenomenon in the HCI community, constraining designers' ability to incorporate
ASMR in video-based designs. This work annotates and analyzes the interaction
modalities and parasocial attractions of 2663 videos to identify unique
experiences. YouTube comment sections are also analyzed to compare viewers'
responses to different ASMR interactions. We find that ASMR videos are
experiences of multimodal social connection, relaxing physical intimacy, and
sensory-rich activity observation. Design implications are discussed to foster
future ASMR-augmented video interactions.


title:
What Do They Capture? -- A Structural Analysis of Pre-Trained Language
  Models for Source Code

abstract:
  Recently, many pre-trained language models for source code have been proposed
to model the context of code and serve as a basis for downstream code
intelligence tasks such as code completion, code search, and code
summarization. These models leverage masked pre-training and Transformer and
have achieved promising results. However, currently there is still little
progress regarding interpretability of existing pre-trained code models. It is
not clear why these models work and what feature correlations they can capture.
In this paper, we conduct a thorough structural analysis aiming to provide an
interpretation of pre-trained language models for source code (e.g., CodeBERT,
and GraphCodeBERT) from three distinctive perspectives: (1) attention analysis,
(2) probing on the word embedding, and (3) syntax tree induction. Through
comprehensive analysis, this paper reveals several insightful findings that may
inspire future studies: (1) Attention aligns strongly with the syntax structure
of code. (2) Pre-training language models of code can preserve the syntax
structure of code in the intermediate representations of each Transformer
layer. (3) The pre-trained models of code have the ability of inducing syntax
trees of code. Theses findings suggest that it may be helpful to incorporate
the syntax structure of code into the process of pre-training for better code
representations.


title:
Single Machine Weighted Number of Tardy Jobs Minimization With Small
  Weights

abstract:
  In this paper we prove new results concerning pseudo-polynomial time
algorithms for the classical scheduling problem of minimizing the weighted
number of jobs on a single machine, the so-called $1 \mid \mid \Sigma w_j U_j$
problem. The previously best known pseudo-polynomial algorithm for this
problem, due to Lawler and Moore [Management Science'69], dates back to the
late 60s and has running time $O(d_{\max}n)$ or $O(wn)$, where $d_{max}$ and
$w$ are the maximum due date and sum of weights of the job set respectively.
Using the recently introduced "prediction technique" by Bateni et al.
[STOC'19], we present an algorithm for the problem running in
$\widetilde{O}(d_{\#}(n +dw_{\max}))$ time, where $d_{\#}$ is the number of
different due dates in the instance, $d$ is the total sum of the $d_{\#}$
different due dates, and $w_{\max}$ is the maximum weight of any job. This
algorithm outperform the algorithm of Lawler and Moore for certain ranges of
the above parameters, and provides the first such improvement for over 50
years. We complement this result by showing that $1 \mid \mid \Sigma w_j U_j$
has no $\widetilde{O}(n +w^{1-\varepsilon}_{\max}n)$ nor $\widetilde{O}(n
+w_{\max}n^{1-\varepsilon})$ time algorithms assuming $\forall \exists$-SETH
conjecture, a recently introduced variant of the well known Strong Exponential
Time Hypothesis (SETH).


title:
Discovery of pulsating components in eclipsing binary systems through
  the TESS light curves: The cases of CPD-30 740, HD 97329, V1637 Ori and TYC
  683-640-1

abstract:
  We present the first evidence for the pulsations of primary components of
four eclipsing binary systems. The TESS light curves of the targets are
analyzed, the light and the absolute parameters are derived. Fourier analyses
are applied to the residual data subtracted from the binary models to reveal
the pulsational characteristics of the primaries. The components of the systems
are compared to well-known binary systems of the same morphological type. The
types of pulsational classes for the primaries are also discussed. Results show
that CPD-30 740 and V1637 Ori are oscillating eclipsing Algol type binary
stars, while HD 97329 and TYC 683-640-1 are found to be detached systems with
pulsating primary components. We conclude that the primary components of the
systems are pulsating stars.


title: Continual Learning from Demonstration of Robotic Skills

abstract:
  Methods for teaching motion skills to robots focus on training for a single
skill at a time. Robots capable of learning from demonstration can considerably
benefit from the added ability to learn new movements without forgetting past
knowledge. To this end, we propose an approach for continual learning from
demonstration using hypernetworks and neural ordinary differential equation
solvers. We empirically demonstrate the effectiveness of our approach in
remembering long sequences of trajectory learning tasks without the need to
store any data from past demonstrations. Our results show that hypernetworks
outperform other state-of-the-art regularization-based continual learning
approaches for learning from demonstration. In our experiments, we use the
popular LASA trajectory benchmark, and a new dataset of kinesthetic
demonstrations that we introduce in this paper called the HelloWorld dataset.
We evaluate our approach using both trajectory error metrics and continual
learning metrics, and we propose two new continual learning metrics. Our code,
along with the newly collected dataset, is available at
https://github.com/sayantanauddy/clfd.


title:
On Pitfalls of Identifiability in Unsupervised Learning. A Note on:
  "Desiderata for Representation Learning: A Causal Perspective"

abstract:
  Model identifiability is a desirable property in the context of unsupervised
representation learning. In absence thereof, different models may be
observationally indistinguishable while yielding representations that are
nontrivially related to one another, thus making the recovery of a ground truth
generative model fundamentally impossible, as often shown through suitably
constructed counterexamples. In this note, we discuss one such construction,
illustrating a potential failure case of an identifiability result presented in
"Desiderata for Representation Learning: A Causal Perspective" by Wang & Jordan
(2021). The construction is based on the theory of nonlinear independent
component analysis. We comment on implications of this and other
counterexamples for identifiable representation learning.


title: Physics in Ultra-Strong Magnetic Fields

abstract:
  Several populations of neutron stars have surface magnetic fields above the
critical strength of 4.4 x 10^{13} G where the electron cyclotron energy equals
its rest mass energy. These include high-field rotation-powered pulsars, X-ray
dim isolated neutron stars (XDIN), and magnetars. In such ultra-strong fields,
quantum effects in physical processes as well as additional exotic Quantum
Electrodynamic processes only occurring at these high field strengths have a
significant influence on the emitted radiation. Although very strong magnetic
fields play a critical role both inside and outside of neutron stars, I will
review primarily processes that operate in the neutron star magnetospheres and
how they influence the observed radiation.


title:
Polarisation signatures in radio for inclined cosmic-ray induced
  air-shower identification

abstract:
  Autonomous radio-detection, i.e., detection of air-showers with standalone
radio arrays, is one of the major technical challenges to overcome for the next
generation astroparticle detectors. In this context, we study polarisation
signatures of simulated radio signals to perform an identification of the
associated air-showers initiated by cosmic-rays and neutrinos. We compare the
two sources of radio emission (the charge excess and geomagnetic) and show that
the former is almost negligible for inclined (zenith angle $>65^{\circ}$)
cosmic-ray air-showers. This provides an efficient background rejection
criterion at the DAQ level, based on the projection of the total electric field
along the direction of the local magnetic field. This relevant quantity can be
easily computed -- even in an online treatment -- for antennas measuring three
orthogonal polarisations. Independently of the experimental antenna layout, we
estimate that assuming a random polarisation of noise events, a rejection from
$\approx 72\%$ (for a non favorable detector location) to $\approx 93\%$ (for a
favorable location) of the noise induced events and a trigger efficiency of 87%
(93%) with a $3\sigma$ ($5\sigma$) trigger threshold level should be
achievable. We also show that neutrino-induced showers present a charge excess
to geomagnetic signal ratio up to $\sim 10$ times higher than for cosmic ray
showers. Although this characteristic makes the identification of
neutrino-induced showers challenging via the method developed here, it provides
an efficient criterion to perform an offline discrimination between cosmic-ray
and neutrino primaries. The stronger charge excess emission will also help the
reconstruction of air-shower parameters, such as the core position.


title: Enrichment of Jupiter's atmosphere by late planetesimal bombardment

abstract:
  Jupiter's atmosphere is enriched with heavy elements by a factor of about 3
compared to proto-solar. The origin of this enrichment and whether it represent
the bulk composition of the planetary envelope remain unknown. Internal
structure models of Jupiter suggest that its envelope is separated from the
deep interior and that the planet is not fully mixed. This implies that
Jupiter's atmosphere was enriched with heavy elements just before the end of
its formation. Such enrichment can be a result of late planetesimal accretion.
However, in-situ Jupiter formation models suggest the decreasing accretion rate
with increasing planetary mass, which cannot explain Jupiter's atmospheric
enrichment. In this study, we model Jupiter's formation and show that an
migration of proto-Jupiter from $\sim$ 20 AU to its current location can lead
to a late planetesimal accretion and atmospheric enrichment. Late planetesimal
accretion does not occur if proto-Jupiter migrates only a few AU. We suggest
that if Jupiter's outermost layer is fully-mixed and is relatively thin (up to
$\sim$ 20\% of its mass), such late accretion can explain its measured
atmospheric composition. It is therefore possible that Jupiter underwent
significant orbital migration followed by late planetesimal accretion.


title: Some Algebraic Aspects of combined matrices

abstract:
  In this work, we study some algebraic aspects of combined matrices
$\mathcal{C}(A)$, where the entries of $A$ belongs to a number field $K$ and
$A$ is a non singular matrix. In other words, $A$ is a $n\times n$ belonging to
the General Linear Group over $K$ denoted by $\mathrm{GL}_n(K)$. Also we
analyze the case in which the matrix $A$ belongs to algebraic subgroups of
$\mathrm{GL}_n(K)$ such as unimodular group, that is $A^2$ is a $n\times n$
belonging to the Special Linear Group denoted by $\mathrm{SL}_n(K)$, Triangular
groups, Diagonal groups among others. In particular, we analyze entirely the
case $n=2$ giving explicitely the diagonalization of $\mathcal{C}(A)$, that
includes characteristic polynomials, eigenvalues and eigenfactors.


title:
Defect Modulated Band Modification in Ion Implanted MgO Crystal:
  Experimental and Ab Initio Calculations

abstract:
  Defects creation and annihilation is a fundamental concept in device
fabrication. This report studies the optical bandgap modification in MgO by MeV
Ni ion irradiation-induced defect states between valance and conduction band.
Ion implantation on MgO single crystal produces substitutional defect states
along with F (anionic vacancy center), $F_2$, other oxygen vacancy center and V
(cationic vacancy center) centers confirmed from absorption and
photoluminescence spectra that can be applied as filament in valance charge
memory based resistive random access memories. The variation of optical bandgap
with Ni ion fluences is ascertained by modifying the electronic band structure.
Density Functional Theory (DFT) calculation assists in understanding the
evolution of electronic band structure for vacancies and substitutional defects
consisting of MgO structures.


title: Multi-Task Deep Residual Echo Suppression with Echo-aware Loss

abstract:
  This paper introduces the NWPU Team's entry to the ICASSP 2022 AEC Challenge.
We take a hybrid approach that cascades a linear AEC with a neural post-filter.
The former is used to deal with the linear echo components while the latter
suppresses the residual non-linear echo components. We use gated convolutional
F-T-LSTM neural network (GFTNN) as the backbone and shape the post-filter by a
multi-task learning (MTL) framework, where a voice activity detection (VAD)
module is adopted as an auxiliary task along with echo suppression, with the
aim to avoid over suppression that may cause speech distortion. Moreover, we
adopt an echo-aware loss function, where the mean square error (MSE) loss can
be optimized particularly for every time-frequency bin (TF-bin) according to
the signal-to-echo ratio (SER), leading to further suppression on the echo.
Extensive ablation study shows that the time delay estimation (TDE) module in
neural post-filter leads to better perceptual quality, and an adaptive filter
with better convergence will bring consistent performance gain for the
post-filter. Besides, we find that using the linear echo as the input of our
neural post-filter is a better choice than using the reference signal directly.
In the ICASSP 2022 AEC-Challenge, our approach has ranked the 1st place on word
accuracy (WAcc) (0.817) and the 3rd place on both mean opinion score (MOS)
(4.502) and the final score (0.864).


title: HAKE: A Knowledge Engine Foundation for Human Activity Understanding

abstract:
  Human activity understanding is of widespread interest in artificial
intelligence and spans diverse applications like health care and behavior
analysis. Although there have been advances with deep learning, it remains
challenging. The object recognition-like solutions usually try to map pixels to
semantics directly, but activity patterns are much different from object
patterns, thus hindering another success. In this work, we propose a novel
paradigm to reformulate this task in two-stage: first mapping pixels to an
intermediate space spanned by atomic activity primitives, then programming
detected primitives with interpretable logic rules to infer semantics. To
afford a representative primitive space, we build a knowledge base including
26+ M primitive labels and logic rules from human priors or automatic
discovering. Our framework, Human Activity Knowledge Engine (HAKE), exhibits
superior generalization ability and performance upon canonical methods on
challenging benchmarks. Code and data are available at http://hake-mvig.cn/.


title: Evolution of the long-period pulsar GLEAM-X J162759.5-523504.3

abstract:
  The long-period ($P = 1091$ s) of the recently discovered pulsar GLEAM-X
J162759.5-523504.3 can be attained by neutron stars evolving with fallback
discs and magnetic dipole moments of a few $10^{30}$ G cm$^3$ at ages greater
than $\sim 2 \times 10^5$ yr consistently with the observational upper limits
to the period derivative, $\dot{P}$, and the X-ray luminosity, $L_X$, of the
source. The current upper limits for $\dot{P}$ allow two alternative present
states: (1) The disc is still active with ongoing accretion at a low rate such
that the accretion luminosity is much less than the neutron star's cooling
luminosity, which in turn is below the upper limit for $L_X$. In this scenario
the spin-down will continue at $\dot{P} \sim 10^{-10}$ s s$^{-1}$ until the
disc becomes inactive; the final period will be $P \sim$ a few $10^3$ s. (2)
The disc is already inactive, there is no accretion. In this case the period
evolution has leveled off to the observed value in the final period range. The
remaining, very weak, dipole torque sustaining asymptotic spin-down at $\dot{P}
\sim 4 \times 10^{-18}$ s s$^{-1}$. Long periods $P \sim$ a few $10^3$ s were
predicted for the final states of soft gamma repeaters and anomalous X-ray
pulsars with relatively strong dipole fields in earlier work with the fallback
disc model.


title:
Agent-Based Model Framework for the North Carolina Modeling Infectious
  Diseases Program (NC MInD ABM) Overview, Design Concepts, and Details
  Protocol

abstract:
  To help facilitate a variety of simulations related to healthcare facilities
in North Carolina, we have developed an agent-based model (ABM) to accurately
simulate patient (i.e., agent) movement to and from these facilities. This is
an Overview, Design Concepts, and Details (ODD) Protocol, a standardized method
for describing ABMs. This ODD provides detailed information on healthcare
facilities in North Carolina, the agent movement to and between them, and any
decisions that were made during the creation of this model. This ABM is
intended to be used alongside disease-specific submodels. It can be used for
purposes such as simulating the success of interventions on reducing disease
transmission, simulating strain on facility resources (including staff and
materials), and forecasting hospital capacity. Disease-specific ODDs should
accompany this document. No details related to any submodels that use this ABM
as a base model are included.


title: HyLa: Hyperbolic Laplacian Features For Graph Learning

abstract:
  Due to its geometric properties, hyperbolic space can support high-fidelity
embeddings of tree- and graph-structured data. For graph learning, points in
hyperbolic space have been used successfully as signals in deep neural
networks: e.g. hyperbolic graph convolutional networks (GCN) can outperform
vanilla GCN. However, existing hyperbolic networks are computationally
expensive and can be numerically unstable, and cannot scale to large graphs due
to these shortcomings. In this paper, we propose HyLa, a completely different
approach to using hyperbolic space in graph learning: HyLa maps once from a
learned hyperbolic-space embedding to Euclidean space via the eigenfunctions of
the Laplacian operator in the hyperbolic space. Our method is inspired by the
random Fourier feature methodology, which uses the eigenfunctions of the
Laplacian in Euclidean space. We evaluate HyLa on downstream tasks including
node classification and text classification, where HyLa shows significant
improvements over hyperbolic GCN and other baselines.


title:
Closed linear spaces consisting of strongly norm attaining Lipschitz
  mappings

abstract:
  Given a pointed metric space $M$, we study when there exist $n$-dimensional
linear subspaces of $\operatorname{Lip}_0(M)$ consisting of strongly
norm-attaining Lipschitz functionals, for $n\in\mathbb{N}$. We show that this
is always the case for infinite metric spaces, providing a definitive answer to
the question. We also study the possible sizes of such infinite-dimensional
closed linear subspaces $Y$, as well as the inverse question, that is, the
possible sizes of the metric space $M$ given that such a subspace $Y$ exists.
We also show that if the metric space $M$ is $\sigma$-precompact, then the
aforementioned subspaces $Y$ need to be always separable and isomorphically
polyhedral, and we show that for spaces containing $[0,1]$ isometrically, they
can be infinite-dimensional.


title:
Domain-Adjusted Regression or: ERM May Already Learn Features Sufficient
  for Out-of-Distribution Generalization

abstract:
  A common explanation for the failure of deep networks to generalize
out-of-distribution is that they fail to recover the "correct" features.
Focusing on the domain generalization setting, we challenge this notion with a
simple experiment which suggests that ERM already learns sufficient features
and that the current bottleneck is not feature learning, but robust regression.
We therefore argue that devising simpler methods for learning predictors on
existing features is a promising direction for future research. Towards this
end, we introduce Domain-Adjusted Regression (DARE), a convex objective for
learning a linear predictor that is provably robust under a new model of
distribution shift. Rather than learning one function, DARE performs a
domain-specific adjustment to unify the domains in a canonical latent space and
learns to predict in this space. Under a natural model, we prove that the DARE
solution is the minimax-optimal predictor for a constrained set of test
distributions. Further, we provide the first finite-environment convergence
guarantee to the minimax risk, improving over existing results which show a
"threshold effect". Evaluated on finetuned features, we find that DARE compares
favorably to prior methods, consistently achieving equal or better performance.


title:
A Graph-Matching Approach for Cross-view Registration of Over-view 2 and
  Street-view based Point Clouds

abstract:
  In this paper, based on the assumption that the object boundaries (e.g.,
buildings) from the over-view data should coincide with footprints of
fa\c{c}ade 3D points generated from street-view photogrammetric images, we aim
to address this problem by proposing a fully automated geo-registration method
for cross-view data, which utilizes semantically segmented object boundaries as
view-invariant features under a global optimization framework through
graph-matching: taking the over-view point clouds generated from
stereo/multi-stereo satellite images and the street-view point clouds generated
from monocular video images as the inputs, the proposed method models segments
of buildings as nodes of graphs, both detected from the satellite-based and
street-view based point clouds, thus to form the registration as a
graph-matching problem to allow non-rigid matches; to enable a robust solution
and fully utilize the topological relations between these segments, we propose
to address the graph-matching problem on its conjugate graph solved through a
belief-propagation algorithm. The matched nodes will be subject to a further
optimization to allow precise-registration, followed by a constrained bundle
adjustment on the street-view image to keep 2D29 3D consistencies, which yields
well-registered street-view images and point clouds to the satellite point
clouds.


title: An experimental study of the vision-bottleneck in VQA

abstract:
  As in many tasks combining vision and language, both modalities play a
crucial role in Visual Question Answering (VQA). To properly solve the task, a
given model should both understand the content of the proposed image and the
nature of the question. While the fusion between modalities, which is another
obviously important part of the problem, has been highly studied, the vision
part has received less attention in recent work. Current state-of-the-art
methods for VQA mainly rely on off-the-shelf object detectors delivering a set
of object bounding boxes and embeddings, which are then combined with question
word embeddings through a reasoning module. In this paper, we propose an
in-depth study of the vision-bottleneck in VQA, experimenting with both the
quantity and quality of visual objects extracted from images. We also study the
impact of two methods to incorporate the information about objects necessary
for answering a question, in the reasoning module directly, and earlier in the
object selection stage. This work highlights the importance of vision in the
context of VQA, and the interest of tailoring vision methods used in VQA to the
task at hand.


title: Lagrangian mean curvature flow in the complex projective plane

abstract:
  We prove a Thomas--Yau-type conjecture for monotone Lagrangian tori
satisfying a symmetry condition in the complex projective plane
$\mathbb{CP}^2$. We show that such tori exist for all time under Lagrangian
mean curvature flow with surgery, undergoing at most a finite number of
surgeries before flowing to a minimal Clifford torus in infinite time.
Furthermore, we show that we can construct a torus with any finite number of
surgeries before convergence. Along the way, we prove many interesting
subsidiary results and develop methods which should be useful in studying
Lagrangian mean curvature flow in non-Calabi--Yau manifolds, even in
non-symmetric cases.


title:
Physics-Informed Deep Monte Carlo Quantile Regression method for
  Interval Multilevel Bayesian Network-based Satellite Heat Reliability
  Analysis

abstract:
  Temperature field reconstruction is essential for analyzing satellite heat
reliability. As a representative machine learning model, the deep convolutional
neural network (DCNN) is a powerful tool for reconstructing the satellite
temperature field. However, DCNN needs a lot of labeled data to learn its
parameters, which is contrary to the fact that actual satellite engineering can
only acquire noisy unlabeled data. To solve the above problem, this paper
proposes an unsupervised method, i.e., the physics-informed deep Monte Carlo
quantile regression method, for reconstructing temperature field and
quantifying the aleatoric uncertainty caused by data noise. For one thing, the
proposed method combines a deep convolutional neural network with the known
physics knowledge to reconstruct an accurate temperature field using only
monitoring point temperatures. For another thing, the proposed method can
quantify the aleatoric uncertainty by the Monte Carlo quantile regression.
Based on the reconstructed temperature field and the quantified aleatoric
uncertainty, this paper models an interval multilevel Bayesian Network to
analyze satellite heat reliability. Two case studies are used to validate the
proposed method.


title:
Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural
  Network Explanations

abstract:
  The evaluation of explanation methods is a research topic that has not yet
been explored deeply, however, since explainability is supposed to strengthen
trust in artificial intelligence, it is necessary to systematically review and
compare explanation methods in order to confirm their correctness. Until now,
no tool exists that exhaustively and speedily allows researchers to
quantitatively evaluate explanations of neural network predictions. To increase
transparency and reproducibility in the field, we therefore built Quantus - a
comprehensive, open-source toolkit in Python that includes a growing,
well-organised collection of evaluation metrics and tutorials for evaluating
explainable methods. The toolkit has been thoroughly tested and is available
under open source license on PyPi (or on
https://github.com/understandable-machine-intelligence-lab/quantus/).


title: Threats to Pre-trained Language Models: Survey and Taxonomy

abstract:
  Pre-trained language models (PTLMs) have achieved great success and
remarkable performance over a wide range of natural language processing (NLP)
tasks. However, there are also growing concerns regarding the potential
security issues in the adoption of PTLMs. In this survey, we comprehensively
systematize recently discovered threats to PTLM systems and applications. We
perform our attack characterization from three interesting perspectives. (1) We
show threats can occur at different stages of the PTLM pipeline raised by
different malicious entities. (2) We identify two types of model
transferability (landscape, portrait) that facilitate attacks. (3) Based on the
attack goals, we summarize four categories of attacks (backdoor, evasion, data
privacy and model privacy). We also discuss some open problems and research
directions. We believe our survey and taxonomy will inspire future studies
towards secure and privacy-preserving PTLMs.


title: Elastic Optical Fibres for Wearable Devices

abstract:
  Wearable devices are becoming increasingly common, addressing needs in both
the fitness and the medical markets. This trend has accelerated with the growth
in telemedicine, particularly after COVID-19. Optical fibre-based sensors are
an ideal candidate technology to be included in such monitoring devices as they
can measure a large number of parameters and they are easily integrated in
wearables. However, their use has been limited due to their sensitivity being
too low compared to the one needed to obtain information from the external
perturbations of the human body. This requires highly deformable fibres. In
this paper, we describe the use of a hollow polyurethane optical fibre sensor,
operating through capillary guidance, to monitor breathing and cardiac
activity. These fibres can be conformal to the skin and could potentially be
incorporated into clothing. The extreme deformability of the material combined
with the hollow nature of the fibre provide the necessary sensitivity to
capture the pressure changes associated with breathing and arterial pulses.
They allow us to recover and identify features of the cardiac pulse wave with
high fidelity. In order to reduce sources of error and to increase the signal
to noise ratio, we use black fibres. We discuss some unusual features of these
fibre sensors, including the transfer function and how to configure for a
monotonic response. Using these unusual fibres, we demonstrate wearable sensors
for respiration and cardiac signals that produce physiologically rich data.


title: From $p$-Values to Posterior Probabilities of Hypothesis

abstract:
  Minimum Bayes factors are commonly used to transform two-sided p-values to
lower bounds on the posterior probability of the null hypothesis, as in
Pericchi et al. (2017). In this article, we show posterior probabilities of
hypothesis by transforming the commonly used -eplog(p), proposed by Vovk (1993)
and Sellke et al. (2001). This is achieved after adjusting this minimum Bayes
factor with the information to approximate it to an exact Bayes factor, not
only when p is a p-value but also when p is a pseudo p-value in the sense of
Casella and Berger (2001). Additionally, we show the fit to a refined version
to linear models.


title: Valiron-Titchmarsh Theorem for Positive Temperatures

abstract:
  In this note, we prove an analog of the Valiron-Titchmarsch Theorem for
Positive Temperatures


title: Delaunay Component Analysis for Evaluation of Data Representations

abstract:
  Advanced representation learning techniques require reliable and general
evaluation methods. Recently, several algorithms based on the common idea of
geometric and topological analysis of a manifold approximated from the learned
data representations have been proposed. In this work, we introduce Delaunay
Component Analysis (DCA) - an evaluation algorithm which approximates the data
manifold using a more suitable neighbourhood graph called Delaunay graph. This
provides a reliable manifold estimation even for challenging geometric
arrangements of representations such as clusters with varying shape and density
as well as outliers, which is where existing methods often fail. Furthermore,
we exploit the nature of Delaunay graphs and introduce a framework for
assessing the quality of individual novel data representations. We
experimentally validate the proposed DCA method on representations obtained
from neural networks trained with contrastive objective, supervised and
generative models, and demonstrate various use cases of our extended single
point evaluation framework.


title: Q-Turn: Changing Paradigms In Quantum Science

abstract:
  Quantum information is a rapidly-growing interdisciplinary field at the
intersection of information science, computer science, mathematics, philosophy,
and quantum science, working at the core of our quantum technologies.
Regardless of its scientific success, quantum information is not exempt from
the intrinsic features that come from the fact that scientists are humans and
members of society: both the good and the bad of our social practices leak into
the scientific activity. In our scientific community, diversity or equal
opportunity problems are particularly difficult to observe due to social,
economic, or cultural barriers, often remaining invisible. How can our lack of
awareness negatively influence the progress of science in the long term? How
can our community grow into a better version of itself?
  This article reflects on how research events - such as conferences - can
contribute to a shift in our culture. This reflection draws on what we learn
from Q-Turn: an initiative triggered by postdoctoral researchers to discuss
these questions. One of Q-turn's main missions is to foster an inclusive
community and highlight outstanding research that may be under-appreciated in
other high-impact venues due to systemic biases. As well as a scientific
program, Q-turn features talks and discussions on issues that affect the
quantum information community, ranging from diversity and inclusion, health and
mental health, to workers' rights.
  In this perspective article, we will consider Q-Turn as an example of how a
research community can work to tackle systematic biases, review the successes,
and identify further points for development.


title: Enhancing expressivity of checked corecursive streams (extended version)

abstract:
  We propose a novel approach to stream definition and manipulation. Our
solution is based on two key ideas. Regular corecursion, which avoids non
termination by detecting cyclic calls, is enhanced, by allowing in equations
defining streams other operators besides the stream constructor. In this way,
some non-regular streams are definable. Furthermore, execution includes a
runtime check to ensure that the stream generated by a function call is
well-defined, in the sense that access to an arbitrary index always succeeds.
We extend the technique beyond the simple stream operators considered in
previous work, notably by adding an interleaving combinator which has a
non-trivial recursion scheme.


title: CLAS12 Track Reconstruction with Artificial Intelligence

abstract:
  In this article we describe the implementation of Artificial Intelligence
models in track reconstruction software for the CLAS12 detector at Jefferson
Lab. The Artificial Intelligence based approach resulted in improved track
reconstruction efficiency in high luminosity experimental conditions. The track
reconstruction efficiency increased by $10-12\%$ for single particle, and
statistics in multi-particle physics reactions increased by $15\%-35\%$
depending on the number of particles in the reaction. The implementation of
artificial intelligence in the workflow also resulted in a speedup of the
tracking by $35\%$.


title:
AnoMili: Spoofing Prevention and Explainable Anomaly Detection for the
  1553 Military Avionic Bus

abstract:
  MIL-STD-1553, a standard that defines a communication bus for interconnected
devices, is widely used in military and aerospace avionic platforms. Due to its
lack of security mechanisms, MIL-STD-1553 is exposed to cyber threats. The
methods previously proposed to address these threats are very limited,
resulting in the need for more advanced techniques. Inspired by the defense in
depth principle, we propose AnoMili, a novel protection system for the
MIL-STD-1553 bus, which consists of: (i) a physical intrusion detection
mechanism that detects unauthorized devices connected to the 1553 bus, even if
they are passive (sniffing), (ii) a device fingerprinting mechanism that
protects against spoofing attacks (two approaches are proposed: prevention and
detection), (iii) a context-based anomaly detection mechanism, and (iv) an
anomaly explanation engine responsible for explaining the detected anomalies in
real time. We evaluate AnoMili's effectiveness and practicability in two real
1553 hardware-based testbeds. The effectiveness of the anomaly explanation
engine is also demonstrated. All of the detection and prevention mechanisms
employed had high detection rates (over 99.45%) with low false positive rates.
The context-based anomaly detection mechanism obtained perfect results when
evaluated on a dataset used in prior work.


title: Sequence-to-Sequence Resources for Catalan

abstract:
  In this work, we introduce sequence-to-sequence language resources for
Catalan, a moderately under-resourced language, towards two tasks, namely:
Summarization and Machine Translation (MT). We present two new abstractive
summarization datasets in the domain of newswire. We also introduce a parallel
Catalan-English corpus, paired with three different brand new test sets.
Finally, we evaluate the data presented with competing state of the art models,
and we develop baselines for these tasks using a newly created Catalan BART. We
release the resulting resources of this work under open license to encourage
the development of language technology in Catalan.


title: Non-BPS Black Branes in M-theory over Calabi-Yau Threefolds

abstract:
  [abridged version] We study extremal solutions arising in M-theory
compactifications on Calabi-Yau threefolds, focussing on non-BPS attractors for
their importance in relation to the Weak Gravity Conjecture. In the
low-energy/field theory limit one obtains minimal N=2, D=5 supergravity coupled
to Abelian vector multiplets. By making use of the effective black hole
potential formalism with Lagrange multipliers and of the Attractor Mechanism,
and focussing on two-moduli complete intersection (CICY) or toric hypersurface
(THCY) Calabi-Yau threefolds, we investigate the possible non-uniqueness of the
attractor solutions, as well as the stability of non-BPS black holes and black
strings. In all models taken into consideration, we find that both BPS and
non-BPS extremal black hole attractors are always unique for a given,
supporting electric charge configuration; moreover, non-BPS black holes are
always unstable, and thus they decay into constituent BPS/anti-BPS pairs. For
what concerns extremal black strings, we confirm uniqueness also for non-BPS
strings in two-moduli CICY models. On the other hand, we discover multiple
non-BPS extremal black string attractors (with different tensions) in most of
the two-moduli THCY models, and we determine the corresponding magnetic
configurations supporting them; this indicates the existence of
volume-minimizing representatives in the same homology class having different
values of their local minimal volume. Moreover, we find that non-BPS black
strings, both for single and multiple solutions, are stable and thus enjoy
recombination of their constituent BPS/anti-BPS pairs.


title: Floquet engineering a bosonic Josephson junction

abstract:
  We study Floquet engineering of the tunnel coupling between a pair of
one-dimensional bosonic quasi-condensates in a tilted double-well potential. By
modulating the energy difference between the two wells, we re-establish tunnel
coupling and precisely control its amplitude and phase. This allows us to
initiate coherence between two initially uncorrelated Bose gases and prepare
different initial states in the emerging sine-Gordon Hamiltonian. We fully
characterize the Floquet system and study the dependence of both equilibrium
properties and relaxation on the modulation.


title:
Studying quantum algorithms for particle track reconstruction in the
  LUXE experiment

abstract:
  The LUXE experiment (LASER Und XFEL Experiment) is a new experiment in
planning at DESY Hamburg, which will study Quantum Electrodynamics (QED) at the
strong-field frontier. In this regime, QED is non-perturbative. This manifests
itself in the creation of physical electron-positron pairs from the QED vacuum.
LUXE intends to measure the positron production rate in this unprecedented
regime by using, among others, a silicon tracking detector. The large number of
expected positrons traversing the sensitive detector layers results in an
extremely challenging combinatorial problem, which can become computationally
very hard for classical computers. This paper presents a preliminary study to
explore the potential of quantum computers to solve this problem and to
reconstruct the positron trajectories from the detector energy deposits. The
reconstruction problem is formulated in terms of a quadratic unconstrained
binary optimisation. Finally, the results from the quantum simulations are
discussed and compared with traditional classical track reconstruction
algorithms.


title: Visual Acoustic Matching

abstract:
  We introduce the visual acoustic matching task, in which an audio clip is
transformed to sound like it was recorded in a target environment. Given an
image of the target environment and a waveform for the source audio, the goal
is to re-synthesize the audio to match the target room acoustics as suggested
by its visible geometry and materials. To address this novel task, we propose a
cross-modal transformer model that uses audio-visual attention to inject visual
properties into the audio and generate realistic audio output. In addition, we
devise a self-supervised training objective that can learn acoustic matching
from in-the-wild Web videos, despite their lack of acoustically mismatched
audio. We demonstrate that our approach successfully translates human speech to
a variety of real-world environments depicted in images, outperforming both
traditional acoustic matching and more heavily supervised baselines.


title: A Graphical Approach For Brain Haemorrhage Segmentation

abstract:
  Haemorrhaging of the brain is the leading cause of death in people between
the ages of 15 and 24 and the third leading cause of death in people older than
that. Computed tomography (CT) is an imaging modality used to diagnose
neurological emergencies, including stroke and traumatic brain injury. Recent
advances in Deep Learning and Image Processing have utilised different
modalities like CT scans to help automate the detection and segmentation of
brain haemorrhage occurrences. In this paper, we propose a novel implementation
of an architecture consisting of traditional Convolutional Neural Networks(CNN)
along with Graph Neural Networks(GNN) to produce a holistic model for the task
of brain haemorrhage segmentation.GNNs work on the principle of neighbourhood
aggregation thus providing a reliable estimate of global structures present in
images. GNNs work with few layers thus in turn requiring fewer parameters to
work with. We were able to achieve a dice coefficient score of around 0.81 with
limited data with our implementation.


title: A Review of zk-SNARKs

abstract:
  A zk-SNARK is a protocol that lets one party, the prover, prove to another
party, the verifier, that a statement about some privately-held information is
true without revealing the information itself. This paper describes technical
foundations, current applications, and some novel applications of zk-SNARKs.
Regarding technical foundations, we go over the Quadratic Arithmetic Program
reduction and the Pinocchio protocol. We then go over financial security
applications like Zcash and Tornado Cash, and zk-Rollup applications like zkEVM
and Darkforest. We propose novel zk-SNARK protocols for private auctions and
decentralized card games on the blockchain, providing code for the proposed
applications. We conclude by touching on promising zk-SNARK innovations, such
as zk-STARKs.


title: Circumbinary disk evolution in the presence of an outer companion star

abstract:
  We consider a hierarchical triple system consisting of an inner eccentric
binary with an outer companion. A highly misaligned circumbinary disk around
the inner binary is subject to two competing effects: (i) nodal precession
about the inner binary eccentricity vector that leads to an increase in
misalignment (polar alignment) and (ii) Kozai-Lidov (KL) oscillations of
eccentricity and inclination driven by the outer companion that leads to a
reduction in the misalignment. The outcome depends upon the ratio of the
timescales of these effects. If the inner binary torque dominates, then the
disk aligns to a polar orientation. If the outer companion torque dominates,
then the disk undergoes KL oscillations. In that case, the highly eccentric and
misaligned disk is disrupted and accreted by the inner binary, while some mass
is transferred to the outer companion. However, when the torques are similar,
the outer parts of the circumbinary disk can undergo large eccentricity
oscillations while the inclination remains close to the polar orientation. The
range of initial disk inclinations that evolve to a polar orientation is
smaller in the presence of the outer companion. Disk breaking is also more
likely, at least temporarily, during the polar alignment process. The stellar
orbits in HD 98800 have parameters such that polar alignment of the
circumbinary disk is expected. In the absence of the gas, solid particles are
unstable at much smaller radii than the gas disk inner tidal truncation radius
because KL driven eccentricity leads to close encounters with the binary.


title:
Bayesian Space-time SIR modeling of Covid-19 in two US states during the
  2020-2021 pandemic

abstract:
  This paper describes the Bayesian SIR modeling of the 3 waves of Covid-19 in
two contrasting US states during 2020-2021. A variety of models are evaluated
at the county level for goodness-of-fit and an assessment of confounding
predictors is also made. It is found that models with three deprivation
predictors and neighborhood effects are important. In addition the work index
from Google mobility was also found to provide increased explanation of the
transmission dynamic.


title: Black-Box Generalization

abstract:
  We provide the first generalization error analysis for black-box learning
through derivative-free optimization. Under the assumption of a Lipschitz and
smooth unknown loss, we consider the Zeroth-order Stochastic Search (ZoSS)
algorithm, that updates a $d$-dimensional model by replacing stochastic
gradient directions with stochastic differences of $K+1$ perturbed loss
evaluations per dataset (example) query. For both unbounded and bounded
possibly nonconvex losses, we present the first generalization bounds for the
ZoSS algorithm. These bounds coincide with those for SGD, and rather
surprisingly are independent of $d$, $K$ and the batch size $m$, under
appropriate choices of a slightly decreased learning rate. For bounded
nonconvex losses and a batch size $m=1$, we additionally show that both
generalization error and learning rate are independent of $d$ and $K$, and
remain essentially the same as for the SGD, even for two function evaluations.
Our results extensively extend and consistently recover established results for
SGD in prior work, on both generalization bounds and corresponding learning
rates. If additionally $m=n$, where $n$ is the dataset size, we derive
generalization guarantees for full-batch GD as well.


title:
Active Surrogate Estimators: An Active Learning Approach to
  Label-Efficient Model Evaluation

abstract:
  We propose Active Surrogate Estimators (ASEs), a new method for
label-efficient model evaluation. Evaluating model performance is a challenging
and important problem when labels are expensive. ASEs address this active
testing problem using a surrogate-based estimation approach, whereas previous
methods have focused on Monte Carlo estimates. ASEs actively learn the
underlying surrogate, and we propose a novel acquisition strategy, XWING, that
tailors this learning to the final estimation task. We find that ASEs offer
greater label-efficiency than the current state-of-the-art when applied to
challenging model evaluation problems for deep neural networks. We further
theoretically analyze ASEs' errors.


title:
Tuning the Hall response of a non-collinear antiferromagnet with
  spin-transfer torques

abstract:
  The kagome lattice antiferromagnets Mn$_3$X(= Sn, Ge) have a non-collinear
120$^\circ$ ordered ground state, which engenders a strong anomalous Hall
response. It has been shown that this response is linked to the magnetic order
and can be manipulated through it. In particular, previous works have shown
that uniaxial strain can be effectively used to manipulate the magnetic order
and hence the anomalous Hall response by changing the local energy landscape.
Here we use a combination of strain and spin-transfer torques to
deterministically switch between states of different chirality and Hall
response. Furthermore, we show that a similar manipulation of the strained
sample can be obtained by an oscillating magnetic field, which is of potential
interest in optical pulses.


title: Subsurface distances for hyperbolic 3-manifolds fibering over the circle

abstract:
  For a hyperbolic fibered 3-manifold M, we prove results that uniformly relate
the structure of surface projections as one varies the fibrations of M. This
extends our previous work from the fully-punctured to the general case.


title:
COLA: COarse LAbel pre-training for 3D semantic segmentation of sparse
  LiDAR datasets

abstract:
  Transfer learning is a proven technique in 2D computer vision to leverage the
large amount of data available and achieve high performance with datasets
limited in size due to the cost of acquisition or annotation. In 3D, annotation
is known to be a costly task; nevertheless, transfer learning methods have only
recently been investigated. Unsupervised pre-training has been heavily favored
as no very large annotated dataset are available. In this work, we tackle the
case of real-time 3D semantic segmentation of sparse outdoor LiDAR scans. Such
datasets have been on the rise, but with different label sets even for the same
task. In this work, we propose here an intermediate-level label set called the
coarse labels, which allows all the data available to be leveraged without any
manual labelization. This way, we have access to a larger dataset, alongside a
simpler task of semantic segmentation. With it, we introduce a new pre-training
task: the coarse label pre-training, also called COLA. We thoroughly analyze
the impact of COLA on various datasets and architectures and show that it
yields a noticeable performance improvement, especially when the finetuning
task has access only to a small dataset.


title:
3d $\mathcal{N}=2$ SO/USp adjoint SQCD: s-confinement and exact
  identites

abstract:
  We study 3d $\mathcal{N}=2$ SQCD with symplectic and orthogonal gauge groups
and adjoint matter. For $USp(2n)$ with two fundamentals and $SO(N)$ with one
vector these models have been recently shown to s-confine. Here we corroborate
the validity of this proposal by relating it to the confinement of $USp(2n)$
with four fundamentals and an antisymmetric tensor, using exact mathematical
results coming from the analysis of the partition function on the squashed
three-sphere. Our analysis allows us to conjecture new s-confining theories for
a higher number of fundamentals and vectors, in presence of linear monopole
superpotentials. We then prove the new dualities through a chain of adjoint
deconfinements and s-confining dualities.


title:
Stimulated vacuum emission and photon absorption in strong
  electromagnetic fields

abstract:
  According to quantum electrodynamics (QED), a strong external field can make
the vacuum state decay producing electron-positron pairs. Here we investigate
emission of soft photons which accompanies a nonperturbative process of pair
production. Our analysis is carried out within the Furry picture to first order
in the fine-structure constant. It is shown that the presence of photons in the
initial state gives rise to an additional (stimulated) channel of photon
emission besides the pure vacuum one. On the other hand, the number of final
(signal) photons includes also a negative contribution due to photon absorption
within the pair production process. These contributions are evaluated and
compared. To obtain quantitative predictions in the domain of realistic field
parameters, we employ the WKB approach. We propose to use an optical probe
photon beam, whose intensity changes as it traverses a spatial region where a
strong electric component of a background laser field is present. It is
demonstrated that relative intensity changes on the level of $1 \%$ can be
experimentally observed once the intensity of the strong background field
exceeds $10^{27}~\text{W/cm}^2$ within a large laser wavelength interval. This
finding is expected to significantly support possible experimental
investigations of nonlinear QED phenomena in the nonperturbative regime.


title: Kinematics of Luminous Blue Variables in the Large Magellanic Cloud

abstract:
  We study the kinematics of luminous blue variables (LBVs) in the Large
Magellanic Cloud (LMC). Using high-resolution spectra, we measure the systemic
radial velocities for a sample of 16 LBVs and LBV candidates. In order to
measure the net motion of LBVs compared to their local environments, we
subtract the projected line-of-sight velocity at the same location derived from
the rotation curve model of the LMC \citep{C22}. Using Bayesian inference, we
infer a velocity dispersion for LBVs of $40.96^{+9.61}_{-7.24}$ km s$^{-1}$. To
put LBVs in context with other evolved massive stars, we compare this to red
supergiants (RSGs) in the LMC, which have a significantly smaller velocity
dispersion of $16.48^{+0.41}_{-0.61}$ km s$^{-1}$. Moreover, 6 out of 16 LBVs
($\sim$38%) have radial velocities of more than 25 km s$^{-1}$, while only 9%
of RSG have such high velocities. This suggests that LBVs include more runaways
than the population of stars that evolves to become RSGs, indicating that LBVs
are preferentially kicked by a companion's supernova explosion as compared to
other evolved massive stars. Our investigation reveals other interesting clues
about LBVs in the LMC as well. We find that radial velocities and widths of
emission lines for each target remain constant over several epochs, whereas
measured absorption lines exhibit highly variable radial velocities for R110,
R81, S Dor, Sk-69$^\circ$142a, and Sk-69$^\circ$279. These five LBVs probably
have a binary companion. Additionally, we find that Sk-69$^\circ$142a
experienced its second outburst in 2019 September, shifting its status from
candidate to confirmed LBV.


title:
The Puzzling Story of Flare Inactive Ultra Fast Rotating M dwarfs. II.
  Searching for radial velocity variations

abstract:
  Observations made using TESS revealed a sample of low mass stars which show a
periodic modulation on a period $<0.2$~d. Surprisingly many of these Ultra Fast
Rotating (UFR) stars showed no evidence of flare activity which would be
expected from such rapidly rotating stars. We present results from a
spectroscopic survey of UFRs using the Nordic Optical Telescope to search for
radial velocity variations which could reveal evidence for binarity. Our sample
of 29 sources have a photometric period between 0.1-0.2d, cover spectral
classes of M0-4V, and show no evidence for flares. We detect only one source
with clear radial velocity shifts, with another two having Gaia RUWE values
which suggests they are binaries. Further observations reveal the former star
possibly contains a brown dwarf companion with a mass of M$_2$>58 M$_{\rm Jup}$
and probability P(M$_2$<90 M$_{\rm Jup}$) = 50\%. There is no evidence for the
companion in our spectra, strengthening the case for a brown dwarf companion.
We also examine the folded TESS light curves of all our targets, finding at
least two are eclipsing binaries and one which has been contaminated by a
spatially nearby $\delta$ Sct star. We estimate that around 1/4 of our targets
may have been contaminated by short period variable stars. However, the
majority of our targets are consistent with being single, low mass stars whose
variability is due to starspots. We outline the possible reasons why they are
not flare active despite being such rapid rotators.


title:
The Puzzling Story of Flare Inactive Ultra Fast Rotating M dwarfs. I.
  Exploring their Magnetic Fields

abstract:
  Stars which are rapidly rotating are expected to show high levels of activity
according to the activity-rotation relation. However, previous TESS studies
have found Ultra Fast Rotating (UFR) M dwarfs with periods less than one day
displaying low levels of flaring activity. As a result, in this study, we
utilise VLT/FORS2 spectropolarimetric data of ten M dwarf UFR stars between
spectral types $\sim$M2 - M6 all with $P_{\rm rot} <$ 1, to detect the presence
of a magnetic field. We divide our sample into rotation period bins of equal
size, with one star having many more flares in the TESS lightcurve than the
other. We also provide an analysis of the long-term variability within our
sample using TESS lightcurves taken during Cycles 1 and 3 (up to three years
apart). We identify 605 flares from our sample which have energies between
2.0$\times$10$^{31}$ and 5.4$\times$10$^{34}$ erg. Although we find no
significance difference in the flare rate between the Cycles, two of our
targets display changes in their lightcurve morphology, potentially caused by a
difference in the spot distribution. Overall, we find five stars (50%) in our
sample have a detectable magnetic field with strengths $\sim$1 - 2 kG. Of these
five, four were the more flare active stars within the period bins with one
being the less flare active star. It would appear the magnetic field strength
may not be the answer to the lack of flaring activity and supersaturation or
magnetic field configuration may play a role. However, it is clear the
relationship between rotation and activity is more complex than a steady
decrease over time.


title:
Infrared Properties of Quantum Gravity: UV/IR Mixing, Gravitizing the
  Quantum -- Theory and Observation

abstract:
  We discuss the possible appearance of several rather exotic phenomena in
quantum gravity, including UV/IR mixing, novel modifications of infrared
phenomenology that extend effective field theory approaches, and the relaxation
of the usual notions of locality. We discuss the relevance of such concepts in
quantum gravity for quantum information science, cosmology and general quantum
gravity phenomenology.


title: Counterfactual inference for sequential experimental design

abstract:
  We consider the problem of counterfactual inference in sequentially designed
experiments wherein a collection of $\mathbf{N}$ units each undergo a sequence
of interventions for $\mathbf{T}$ time periods, based on policies that
sequentially adapt over time. Our goal is counterfactual inference, i.e.,
estimate what would have happened if alternate policies were used, a problem
that is inherently challenging due to the heterogeneity in the outcomes across
units and time. To tackle this task, we introduce a suitable latent factor
model where the potential outcomes are determined by exogenous unit and time
level latent factors. Under suitable conditions, we show that it is possible to
estimate the missing (potential) outcomes using a simple variant of nearest
neighbors. First, assuming a bilinear latent factor model and allowing for an
arbitrary adaptive sampling policy, we establish a distribution-free
non-asymptotic guarantee for estimating the missing outcome of any unit at any
time; under suitable regularity condition, this guarantee implies that our
estimator is consistent. Second, for a generic non-parametric latent factor
model, we establish that the estimate for the missing outcome of any unit at
time $\mathbf{T}$ satisfies a central limit theorem as $\mathbf{T} \to \infty$,
under suitable regularity conditions. Finally, en route to establishing this
central limit theorem, we establish a non-asymptotic mean-squared-error bound
for the estimate of the missing outcome of any unit at time $\mathbf{T}$. Our
work extends the recently growing literature on inference with adaptively
collected data by allowing for policies that pool across units, and also
compliments the matrix completion literature when the entries are revealed
sequentially in an arbitrarily dependent manner based on prior observed data.


title: DeCorus: Hierarchical Multivariate Anomaly Detection at Cloud-Scale

abstract:
  Multivariate anomaly detection can be used to identify outages within large
volumes of telemetry data for computing systems. However, developing an
efficient anomaly detector that can provide users with relevant information is
a challenging problem. We introduce our approach to hierarchical multivariate
anomaly detection called DeCorus, a statistical multivariate anomaly detector
which achieves linear complexity. It extends standard statistical techniques to
improve their ability to find relevant anomalies within noisy signals and makes
use of types of domain knowledge that system operators commonly possess to
compute system-level anomaly scores. We describe the implementation of DeCorus
an online log anomaly detection tool for network device syslog messages
deployed at a cloud service provider. We use real-world data sets that consist
of $1.5$ billion network device syslog messages and hundreds of incident
tickets to characterize the performance of DeCorus and compare its ability to
detect incidents with five alternative anomaly detectors. While DeCorus
outperforms the other anomaly detectors, all of them are challenged by our data
set. We share how DeCorus provides value in the field and how we plan to
improve its incident detection accuracy.


title: Enumeration of Dyck paths with air pockets

abstract:
  We introduce and study the new combinatorial class of Dyck paths with air
pockets. We exhibit a bijection with the peakless Motzkin paths which
transports several pattern statistics and give bivariate generating functions
for the distribution of patterns as peaks, returns and pyramids. Then, we
deduce the popularities of these patterns and point out a link between the
popularity of pyramids and a special kind of closed smooth self-overlapping
curves, a subset of Fibonacci meanders. A similar study is conducted for the
subclass of non-decreasing Dyck paths with air pockets.


title: The Effect of Enriched Accreted Matter on the Development of Novae

abstract:
  The development of a nova eruption is well known to be determined by the
white dwarf (WD) mass and the rate at which it accretes mass from its donor.
One of the advancements in this field is the understanding that the occurrence
of a nova eruption depends on the presence of heavy elements in the envelope,
and that the concentration of these elements is highly dependent on the time
allotted for accretion. This results in many features of the eruption being
correlated with the mass fractions of heavy elements in the ejected material,
however, the accreted material is always assumed to be of solar metallicity.
Here we explored the entire range of accretion rates onto a 1.25$M_\odot$ WD
for two cases of highly enriched accreted material and find enrichment to have
an influence on certain features for high accretion rates, while the effect of
enrichment on low accretion rates is negligible. We further find that the
ignition of the thermonuclear runaway which is known to be dependent on the
accumulation of a critical mass, is actually dependent on the accumulation of a
critical amount of heavy elements.


title:
Optical Distinguishability of Mott Insulators in Time vs Frequency
  Domain

abstract:
  High Harmonic Generation (HHG) promises to provide insight into ultrafast
dynamics and has been at the forefront of attosecond physics since its
discovery. One class of materials that demonstrate HHG are Mott insulators
whose electronic properties are of great interest given their
strongly-correlated nature. Here, we use the paradigmatic representation of
Mott insulators, the half-filled Fermi-Hubbard model, to investigate the
potential of using HHG response to distinguish these materials. We develop an
analytical argument based on the Magnus expansion approximation to evolution by
the Schrodinger equation that indicates decreased distinguishability of Mott
insulators as lattice spacing, $a$, and the strength of the driving field,
$F_0$, increase relative to the frequency, $\omega_0$. This argument is then
bolstered through numerical simulations of different systems and subsequent
comparison of their responses in both the time and frequency domain.
Ultimately, we demonstrate reduced resolution of Mott insulators in both
domains when the dimensionless parameter $g \equiv aF_0 / \omega_0$ is large,
though the time domain provides higher distinguishability. Conductors are
exempted from these trends, becoming much more distinguishable in the frequency
domain at high $g$.


title:
Reviewing the Geometric Hamilton-Jacobi Theory concerning Jacobi and
  Leibniz identities

abstract:
  In this survey, we review the classical Hamilton Jacobi theory from a
geometric point of view in different geometric backgrounds. We propose a
Hamilton Jacobi equation for different geometric structures attending to one
particular characterization: whether they fulfill the Jacobi and Leibniz
identities simultaneously, or if at least they satisfy one of them.
  In this regard, we review the case of time dependent and dissipative physical
systems as systems that fulfill the Jacobi identity but not the Leibnitz
identity. Furthermore, we review the contact evolution Hamilton Jacobi theory
as a split off the regular contact geometry, and that actually satisfies the
Leibniz rule instead of Jacobi.
  Furthermore, we include a novel result, which is the Hamilton-Jacobi equation
for conformal Hamiltonian vector fields as a generalization of the well known
Hamilton Jacobi on a symplectic manifold, that is retrieved in the case of a
zero conformal factor. The interest of a geometric Hamilton Jacobi equation is
the primordial observation that if a Hamiltonian vector field can be projected
into a configuration manifold by means of a 1-form dW, then the integral curves
of the projected vector field can be transformed into integral curves of the
Hamiltonian vector field provided that W is a solution of the Hamilton-Jacobi
equation. Geometrically, the solution of the Hamilton Jacobi equation plays the
role of a Lagrangian submanifold of a certain bundle. Exploiting these features
in different geometric scenarios we propose a geometric theory for multiple
physical systems depending on the fundamental identities that their dynamic
satisfies. Different examples are pictured to reflect the results provided,
being all of them new, except for one that is reassessment of a previously
considered example.


title:
Unitary matrix models, free fermion ensembles, and the giant graviton
  expansion

abstract:
  We consider a class of matrix integrals over the unitary group $U(N)$ with an
infinite set of couplings characterized by a series $f(q)=\sum_{n \ge 1} a_n \,
q^n$, with $a_n \in \mathbb{Z}$. Such integrals arise in physics as the
partition functions of free four-dimensional gauge theories on $S^3$ and, in
particular, as the superconformal index of super Yang-Mills theory. We show
that any such model can be expressed in terms of a system of free fermions in
an ensemble parameterized by the infinite set of couplings. Integrating out the
fermions in a given quantum state leads to a convergent expansion as a series
of determinants, as shown by Borodin-Okounkov many years ago. By further
averaging over the ensemble, we obtain a formula for the matrix integral as a
$q$-series with successive terms suppressed by $q^{\alpha N + \beta}$ where
$\alpha$, $\beta$ do not depend on $N$. This provides a matrix-model
explanation of the giant graviton expansion that has been observed recently in
the literature.


title: The Complexity of Matching Games: A Survey

abstract:
  Matching games naturally generalize assignment games, a well-known class of
cooperative games. Over the years, matching games received a growing amount of
attention, in particular due to some recent breakthrough results and new
applications. This state-of-the-art survey provides an overview of matching
games and recent extensions, such as $b$-matching games and generalized
matching games. We focus on the computational complexity of various relevant
game-theoretical solution concepts, such as the core, nucleolus and Shapley
value.


title:
Gamma-ray halos around pulsars as the key to understanding cosmic ray
  transport in the Galaxy

abstract:
  Pulsars are factories of relativistic electrons and positrons that propagate
away from the pulsar, permeating later our Galaxy. The acceleration and
propagation of these particles are a matter of intense debate. In the last few
years, we had the opportunity to directly observing the injection of these
particles into the interstellar medium through the discovery of gamma-ray halos
around pulsars. This new type of gamma-ray source is produced by electrons and
positrons diffusing out of the pulsar wind nebula and scattering ambient photon
fields to produce gamma rays. This new field of study comes with a number of
observations and constraints at different wavelengths and a variety of
theoretical models explaining the characteristics of these halos. We examine
the characteristics of the propagation of cosmic rays inferred from the
observations of halos and their local and global implications on particle
transport in the Galaxy. We also discuss the prospects for observations of
these sources with facilities such as LHAASO, or CTA or SWGO in the near
future.


title:
Hydrothermal synthesis, structure, and antibacterial studies of a
  nanosized iron zeolite

abstract:
  The present research explores the effects of Percentage Fe incorporation on
the structure and antibacterial activity of Fe ZSM 5. Silica extracted from
local rice husk straw (white particles) by applying NaOH and HCl solutions for
consecutive chemical treatment was used for hydrothermal synthesis of FeZSM 5
with constant Si(Fe plus Al) ratios. The chemical and physical changes of ZSM 5
and Fe ZSM 5 surfaces were investigated by X ray diffraction analysis (XRD),
scanning electron microscopy (SEM), Fourier transform infrared spectroscopy
(FTIR), UV Vis spectroscopy, differential scanning calorimetry (DSC), and pore
structure analysis by N2 adsorption at 196 . XRD analysis revealed the typical
ZSM 5 structure with new diffraction lines attributed to the iron silicate
phase. FTIR spectral analysis of ZSM 5 samples containing iron display a new
band at 656 cm that is ascribed to the SiOFe group. The antibacterial activity
of such coatings towards different kinds of bacteria, such as S. pneumonia, B.
subtilis, E. coli and P. aeruginosa, and fungi, such as A. fumigatus and C.
albicans, for investigated ZSM 5 and Fe (20 and 100 percentages) samples showed
selective antibacterial actions.


title: Processes Parametrised by an Algebraic Theory

abstract:
  We develop a (co)algebraic framework to study a family of process calculi
with monadic branching structures and recursion operators. Our framework
features a uniform semantics of process terms and a complete axiomatisation of
semantic equivalence. We show that there are uniformly defined fragments of our
calculi that capture well-known examples from the literature like regular
expressions modulo bisimilarity and guarded Kleene algebra with tests. We also
derive new calculi for probabilistic and convex processes with an analogue of
Kleene star.


title:
A Multi-Fidelity Active Learning Method for Global Design Optimization
  Problems with Noisy Evaluations

abstract:
  A multi-fidelity (MF) active learning method is presented for design
optimization problems characterized by noisy evaluations of the performance
metrics. Namely, a generalized MF surrogate model is used for design-space
exploration, exploiting an arbitrary number of hierarchical fidelity levels,
i.e., performance evaluations coming from different models, solvers, or
discretizations, characterized by different accuracy. The method is intended to
accurately predict the design performance while reducing the computational
effort required by simulation-driven design (SDD) to achieve the global
optimum. The overall MF prediction is evaluated as a low-fidelity trained
surrogate corrected with the surrogates of the errors between consecutive
fidelity levels. Surrogates are based on stochastic radial basis functions
(SRBF) with least squares regression and in-the-loop optimization of
hyperparameters to deal with noisy training data. The method adaptively queries
new training data, selecting both the design points and the required fidelity
level via an active learning approach. This is based on the lower confidence
bounding method, which combines performance prediction and associated
uncertainty to select the most promising design regions. The fidelity levels
are selected considering the benefit-cost ratio associated with their use in
the training. The method's performance is assessed and discussed using four
analytical tests and three SDD problems based on computational fluid dynamics
simulations, namely the shape optimization of a NACA hydrofoil, the DTMB 5415
destroyer, and a roll-on/roll-off passenger ferry. Fidelity levels are provided
by both adaptive grid refinement and multi-grid resolution approaches. Under
the assumption of a limited budget of function evaluations, the proposed MF
method shows better performance in comparison with the model trained by
high-fidelity evaluations only.


title: Quadratic Forms in Prime Variables with small off-diagonal ranks

abstract:
  The main goal of this note is to establish the limits of L. Zhao's techniques
for counting solutions to quadratic forms in prime variables. Zhao considered
forms with rank at least 9, and showed that these equations have solutions in
primes provided there are no local obstructions. We consider in detail the
degenerate cases of off-diagonal rank 1 and 2, and improve the rank lower
bounds to at least 6 and at least 8 respectively. These results complement a
recent breakthrough of Green on the non-degenerate rank 8 case.


title: On the Behrend function and the blowup of some fat points

abstract:
  The Behrend function of a $\mathbb C$-scheme $X$ is a constructible function
$\nu_X\colon X(\mathbb C) \to \mathbb Z$ introduced by Behrend, intrinsic to
the scheme structure of $X$. It is a (subtle) invariant of singularities of
$X$, playing a prominent role in enumerative geometry. To date, only a handful
of general properties of the Behrend function are known. In this paper, we
compute it for a large class of fat points (schemes supported at a single
point). We first observe that, if $X \hookrightarrow \mathbb A^N$ is a fat
point, $\nu_X$ is the sum of the multiplicities of the irreducible components
of the exceptional divisor $E_{X}\mathbb A^N$ in the blowup
$\textrm{Bl}_{X}\mathbb A^N$. Moreover, we prove that $\nu_X$ can be computed
explicitly through the normalisation of $\textrm{Bl}_{X}\mathbb A^N$.
  The proofs of our explicit formulas for the Behrend function of a fat point
in $\mathbb A^2$ rely heavily on toric geometry techniques. Along the way, we
find a formula for the number of irreducible components of $E_{X}\mathbb A^2$,
where $X \hookrightarrow \mathbb A^2$ is a fat point such that
$\textrm{Bl}_{X}\mathbb A^2$ is normal.


title:
A topological Paley-Wiener-Schwartz Theorem for sections of homogeneous
  vector bundles on $G/K$

abstract:
  We study the Fourier transform for compactly supported distributional
sections of complex homogeneous vector bundles on symmetric spaces of
non-compact type $X = G/K$. We prove a characterisation of their range. In
fact, from Delorme's Paley-Wiener theorem for compactly supported smooth
functions on a real reductive group of Harish-Chandra class, we deduce
topological Paley-Wiener and Paley-Wiener-Schwartz theorems for sections.


title: Exel-Pardo algebras of self-similar $k$-graphs

abstract:
  We introduce the Exel-Pardo $*$-algebra $\mathrm{EP}_R(G,\Lambda)$ associated
to a self-similar $k$-graph $(G,\Lambda,\varphi)$. We prove the
$\mathbb{Z}^k$-graded and Cuntz-Krieger uniqueness theorems for such algebras
and investigate their ideal structure. In particular, we modify the graded
uniqueness theorem for self-similar 1-graphs, and then apply it to present
$\mathrm{EP}_R(G,\Lambda)$ as a Steinberg algebra and to study the ideal
structure.


title: A New Parameterized Interacting Holographic Dark Energy

abstract:
  We study a cosmological model based on the holographic principle that allows
an interaction between dark energy and dark matter with a Hubble infrared
cutoff. We adopt an agnostic point of view with respect to the form of the
interaction between these dark components by using redshift parameterizations
of the dark energy equation of state (DE EoS). Following this approach, we
obtain an analytical expression of the Hubble parameter and we test it with
four different parameterizations of the DE EoS using currently Pantheon
supernovae, Cosmic Chronometers and Galaxy Clustering data sets. Our analyses
show that the considered parameterizations on this agnostic proposal allow a
late accelerated cosmic expansion in comparison to the results from previous
works where specific forms of the interaction are employed but fail to explain
this behaviour. Furthermore, we find that our proposal solves the so-called
coincidence problem in Cosmology and in this direction shed some light on the
problem of the Hubble tension.


title: An elegant proof of self-testing for multipartite Bell inequalities

abstract:
  The predictions of quantum theory are incompatible with local-causal
explanations. This phenomenon is called Bell non-locality and is witnessed by
violation of Bell-inequalities. The maximal violation of certain
Bell-inequalities can only be attained in an essentially unique manner. This
feature is referred to as self-testing and constitutes the most accurate form
of certification of quantum devices. While self-testing in bipartite Bell
scenarios has been thoroughly studied, self-testing in the more complex
multipartite Bell scenarios remains largely unexplored. This work presents a
simple and broadly applicable self-testing argument for N-partite correlation
Bell inequalities with two binary outcome observables per party. Our proof
technique forms a generalization of the Mayer-Yao formulation and is not
restricted to linear Bell-inequalities, unlike the usual sum of squares method.
To showcase the versatility of our proof technique, we obtain self-testing
statements for N party Mermin-Ardehali-Belinskii-Klyshko (MABK) and
Werner-Wolf-Weinfurter-\.Zukowski-Brukner (WWW\.ZB) family of linear Bell
inequalities, and Uffink's family of N party quadratic Bell-inequalities.


title:
Optimal step length for the maximal decrease of a self-concordant
  function by the Newton method

abstract:
  In this paper we consider the problem of finding the optimal step length for
the Newton method on the class of self-concordant functions, with the decrease
in function value as criterion. We formulate this problem as an optimal control
problem and use optimal control theory to solve it.


title: Equidistribution for matings of quadratic maps with the Modular group

abstract:
  We study the asymptotic behavior of the family of holomorphic correspondences
$\lbrace\mathcal{F}_a\rbrace_{a\in\mathcal{K}}$, given by
$$\left(\frac{az+1}{z+1}\right)^2+\left(\frac{az+1}{z+1}\right)\left(\frac{aw-1}{w-1}\right)+\left(\frac{aw-1}{w-1}\right)^2=3.$$
It was proven by Bullet and Lomonaco that $\mathcal{F}_a$ is a mating between
the modular group $\operatorname{PSL}_2(\mathbb{Z})$ and a quadratic rational
map. We show for every $a\in\mathcal{K}$, the iterated images and preimages
under $\mathcal{F}_a$ of nonexceptional points equidistribute, in spite of the
fact that $\mathcal{F}_a$ is weakly-modular in the sense of Dinh, Kaufmann and
Wu but it is not modular. Furthermore, we prove that periodic points
equidistribute as well.


title:
Relation between dynamic heterogeneities observed in scattering
  experiments and four-body correlations

abstract:
  Dynamic heterogeneity is expected to be a key concept for understanding the
origin of slow dynamics near the glass transition. In previous studies,
quantitative evaluations of dynamic heterogeneity have been attempted using two
different routes, i.e., the speckle patterns in scattering experiments or the
four-body correlation functions of microscopic configuration data obtained from
molecular dynamics simulations or real-space observations using confocal
microscopy. However, the physical relationship between these dynamic
heterogeneities obtained using different methods has not been clarified. This
study proposes a connection between dynamic heterogeneities characterized based
on speckle patterns and those obtained from four-body correlations. The
validity of the relationship is also clarified.


title: Stochastic Thermodynamics in a Non-Markovian Dynamical System

abstract:
  The developing field of stochastic thermodynamics extends concepts of
macroscopic thermodynamics such as entropy production and work to the
microscopic level of individual trajectories taken by a system through phase
space. The scheme involves coupling the system to an environment - typically a
source of Markovian noise that affects the dynamics of the system. Here we
extend this framework to consider a non-Markovian environment, one whose
dynamics have memory and which create additional correlations with the system
variables, and illustrate this with a selection of simple examples. Such an
environment produces a rich variety of behaviours. In particular, for a case of
thermal relaxation, the distributions of entropy produced under the
non-Markovian dynamics differ from the equivalent case of Markovian dynamics
only by a delay time. When a time-dependent external work protocol is turned
on, the system's correlations with the environment can either assist or hinder
its approach to equilibrium, and affect its production of entropy, depending on
the coupling strength between the system and environment.


title:
FOLD-RM: A Scalable and Efficient Inductive Learning Algorithm for
  Multi-Category Classification of Mixed Data

abstract:
  FOLD-RM is an automated inductive learning algorithm for learning default
rules for mixed (numerical and categorical) data. It generates an (explainable)
answer set programming (ASP) rule set for multi-category classification tasks
while maintaining efficiency and scalability. The FOLD-RM algorithm is
competitive in performance with the widely-used XGBoost algorithm, however,
unlike XGBoost, the FOLD-RM algorithm produces an explainable model. FOLD-RM
outperforms XGBoost on some datasets, particularly large ones. FOLD-RM also
provides human-friendly explanations for predictions.


title:
A Generic Self-Supervised Framework of Learning Invariant Discriminative
  Features

abstract:
  Self-supervised learning (SSL) has become a popular method for generating
invariant representations without the need for human annotations. Nonetheless,
the desired invariant representation is achieved by utilising prior online
transformation functions on the input data. As a result, each SSL framework is
customised for a particular data type, e.g., visual data, and further
modifications are required if it is used for other dataset types. On the other
hand, autoencoder (AE), which is a generic and widely applicable framework,
mainly focuses on dimension reduction and is not suited for learning invariant
representation. This paper proposes a generic SSL framework based on a
constrained self-labelling assignment process that prevents degenerate
solutions. Specifically, the prior transformation functions are replaced with a
self-transformation mechanism, derived through an unsupervised training process
of adversarial training, for imposing invariant representations. Via the
self-transformation mechanism, pairs of augmented instances can be generated
from the same input data. Finally, a training objective based on contrastive
learning is designed by leveraging both the self-labelling assignment and the
self-transformation mechanism. Despite the fact that the self-transformation
process is very generic, the proposed training strategy outperforms a majority
of state-of-the-art representation learning methods based on AE structures. To
validate the performance of our method, we conduct experiments on four types of
data, namely visual, audio, text, and mass spectrometry data, and compare them
in terms of four quantitative metrics. Our comparison results indicate that the
proposed method demonstrate robustness and successfully identify patterns
within the datasets.


title:
Stochastic linear optimization never overfits with quadratically-bounded
  losses on general data

abstract:
  This work shows that a diverse collection of linear optimization methods,
when run on general data, fail to overfit, despite lacking any explicit
constraints or regularization: with high probability, their trajectories stay
near the curve of optimal constrained solutions over the population
distribution. This analysis is powered by an elementary but flexible proof
scheme which can handle many settings, summarized as follows. Firstly, the data
can be general: unlike other implicit bias works, it need not satisfy large
margin or other structural conditions, and moreover can arrive sequentially
IID, sequentially following a Markov chain, as a batch, and lastly it can have
heavy tails. Secondly, while the main analysis is for mirror descent, rates are
also provided for the Temporal-Difference fixed-point method from reinforcement
learning; all prior high probability analyses in these settings required
bounded iterates, bounded updates, bounded noise, or some equivalent. Thirdly,
the losses are general, and for instance the logistic and squared losses can be
handled simultaneously, unlike other implicit bias works. In all of these
settings, not only is low population error guaranteed with high probability,
but moreover low sample complexity is guaranteed so long as there exists any
low-complexity near-optimal solution, even if the global problem structure and
in particular global optima have high complexity.


title:
Upper tail behavior of the number of triangles in random graphs with
  constant average degree

abstract:
  Let $N$ be the number of triangles in an Erd\H{o}s-R\'enyi graph
$\mathcal{G}(n,p)$ on $n$ vertices with edge density $p=d/n,$ where $d>0$ is a
fixed constant. It is well known that $N$ weakly converges to the Poisson
distribution with mean ${d^3}/{6}$ as $n\rightarrow \infty$. We address the
upper tail problem for $N,$ namely, we investigate how fast $k$ must grow, so
that the probability of $\{N\ge k\}$ is not well approximated anymore by the
tail of the corresponding Poisson variable. Proving that the tail exhibits a
sharp phase transition, we essentially show that the upper tail is governed by
Poisson behavior only when $k^{1/3} \log k< (\frac{3}{\sqrt{2}})^{2/3} \log n$
(sub-critical regime) as well as pin down the tail behavior when $k^{1/3} \log
k> (\frac{3}{\sqrt{2}})^{2/3} \log n$ (super-critical regime). We further prove
a structure theorem, showing that the sub-critical upper tail behavior is
dictated by the appearance of almost $k$ vertex-disjoint triangles whereas in
the supercritical regime, the excess triangles arise from a clique like
structure of size approximately $(6k)^{1/3}$. This settles the long-standing
upper-tail problem in this case, answering a question of Aldous, complementing
a long sequence of works, spanning multiple decades, culminating in (Harel,
Moussat, Samotij,'19) which analyzed the problem only in the regime $p\gg
\frac{1}{n}.$ The proofs rely on several novel graph theoretical results which
could have other applications.


title: A single-point Reshetnyak's theorem

abstract:
  We prove a single-value version of Reshetnyak's theorem. Namely, if a
non-constant map $f \in W^{1,n}_{\text{loc}}(\Omega, \mathbb{R}^n)$ from a
domain $\Omega \subset \mathbb{R}^n$ satisfies the estimate $\lvert Df(x)
\rvert^n \leq K J_f(x) + \Sigma(x) \lvert f(x) - y_0 \rvert^n $ for some $K
\geq 1$, $y_0\in \mathbb{R}^n$ and $\Sigma \in
L^{1+\varepsilon}_{\text{loc}}(\Omega)$, then $f^{-1}\{y_0\}$ is discrete, the
local index $i(x, f)$ is positive in $f^{-1}\{y_0\}$, and every neighborhood of
a point of $f^{-1}\{y_0\}$ is mapped to a neighborhood of $y_0$. Assuming this
estimate for a fixed $K$ at every $y_0 \in \mathbb{R}^n$ is equivalent to
assuming that the map $f$ is $K$-quasiregular, even if the choice of $\Sigma$
is different for each $y_0$. Since the estimate also yields a single-value
Liouville theorem, it hence appears to be a good pointwise definition of
$K$-quasiregularity. As a corollary of our single-value Reshetnyak's theorem,
we obtain a higher-dimensional version of the argument principle that played a
key part in the solution to the Calder\'on problem.


title: The Habitable Zones of Rapidly Rotating Main Sequence A/F Stars

abstract:
  We investigate how rapid stellar rotation commonly seen in A/F stars can
influence planet habitability. Specifically, we model how rapid rotation
influences a planet's irradiation and determine the location of the habitable
zone for stars in the mass range $1.3M_\odot\leq M_\star\leq 2.2M_\odot$. Rapid
stellar rotation can dramatically change a star's luminosity and spectral
energy distribution and, therefore, can impact the habitability of any
surrounding planets. Stars of mass $M_\star\gtrsim1.3M_\odot$ commonly rotate
near their breakup speeds, which causes two effects relevant to planet
habitability. First, these stars flatten into oblate spheroids with shorter
polar radii and elongated equatorial radii. Second, rapid rotation induces a
pole-to-equator temperature gradient on the surface of these stars. Using a 1D
climate model, we calculate the inner and outer edges of the habitable zone of
well-known rapid rotators and average theoretical stars in our stellar mass
range. We find that, in general, rapid rotation causes the habitable zone to
reside closer in than for a non-rotating equivalent star. We also find that
gravity darkening dramatically reduces stellar UV emission, which combats the
common assumption that high-mass stars emit too much UV light for habitable
worlds. Overall, we determine that rapid stellar rotation has important
consequences for the overall habitability of a system and must be accounted for
both when modeling exoplanet environments and in observation of planets around
high-mass stars.


title:
The Higgs Boson Mass as Fundamental Parameter of the Minimal
  Supersymmetric Standard Model

abstract:
  In the Minimal Supersymmetric Standard Model (MSSM) the mass of the lightest
neutral Higgs boson is determined by the supersymmetric parameters. In the
$m_h$MSSM the precisely measured Higgs boson replaces the trilinear coupling
$A_t$ as input parameter. Expressions are derived to extract $A_t$ in a
semi-analytical form as a function of the light Higgs boson (pole) mass. An
algorithm is developed and implemented at two--loop precision, generalizable to
higher orders, to perform this inversion consistently. The result of the
algorithm, implemented in the SuSpect spectrum calculator, is illustrated on a
parameter set compatible with LHC measurements.