title:
Democratic Governance and International Research Collaboration: A
  Longitudinal Analysis of the Global Science Network

abstract:
  The connection between democracy and science has traditionally been examined
through philosophical conjecture and single country case studies. As such,
there remains limited global scale empirical research on the topic. However,
there is a large body of evidence suggesting that research collaboration is
associated with scientific productivity and performance. Further, international
relations theory suggests democratic nations are more likely to cooperate
across a range of modalities. This study explores country level factors that
affect dynamics of the global scientific research collaboration network,
focusing on structural effects of democratic governance on the formation,
persistence, and strength of international research collaboration ties. This
study combines longitudinal data between 2008 and 2017 from the Varieties of
Democracy Project, World Bank Indicators, Scopus, and Web of Science
bibliometric data. Methods of analysis include a combination of temporal and
weighted exponential random graph models to test hypotheses regarding the
connection between democratic governance and global collaboration patterns. The
results suggest positive significant effects of both democratic governance on
international research collaboration and assortative mixing between countries
with similar levels of democratic governance. Finally, the results also show
support for drivers of collaboration including exogenous economic, population,
and geo-political factors, as well as endogenous network effects including
preferential attachment and transitivity.


title: Automatic Non-Invasive Isolation of Respiratory Cycles

abstract:
  In this paper, we introduce a novel algorithm designed to isolate individual
respiratory cycles on a thoracic respiratory inductance plethysmography signal.
The algorithm locates breaths using signal processing and statistical methods
and enables the analysis of sleep data on an individual breath level. The
algorithm was evaluated on 7.3 hours of hand-annotated data, or 8782 individual
breaths in total, and was estimated to correctly isolate 94% of respiratory
cycles while producing false positives that amount to only 5% of the total
number of detections. The algorithm was specifically evaluated on data
containing a great number of sleep-disordered breathing events. We found that
the algorithm did not suffer in terms of accuracy when detecting breaths in the
presence of sleep-disordered breathing. The algorithm was also evaluated across
a large set of participants, and we found that the accuracy of the algorithm
was consistent across participants. This algorithm is finally made public via
an open-source Python library.


title: A Brief Overview of Unsupervised Neural Speech Representation Learning

abstract:
  Unsupervised representation learning for speech processing has matured
greatly in the last few years. Work in computer vision and natural language
processing has paved the way, but speech data offers unique challenges. As a
result, methods from other domains rarely translate directly. We review the
development of unsupervised representation learning for speech over the last
decade. We identify two primary model categories: self-supervised methods and
probabilistic latent variable models. We describe the models and develop a
comprehensive taxonomy. Finally, we discuss and compare models from the two
categories.


title:
Understanding microbiome dynamics via interpretable graph representation
  learning

abstract:
  Large-scale perturbations in the microbiome constitution are strongly
correlated, whether as a driver or a consequence, with the health and
functioning of human physiology. However, understanding the difference in the
microbiome profiles of healthy and ill individuals can be complicated due to
the large number of complex interactions among microbes. We propose to model
these interactions as a time-evolving graph whose nodes are microbes and edges
are interactions among them. Motivated by the need to analyse such complex
interactions, we develop a method that learns a low-dimensional representation
of the time-evolving graph and maintains the dynamics occurring in the
high-dimensional space. Through our experiments, we show that we can extract
graph features such as clusters of nodes or edges that have the highest impact
on the model to learn the low-dimensional representation. This information can
be crucial to identify microbes and interactions among them that are strongly
correlated with clinical diseases. We conduct our experiments on both synthetic
and real-world microbiome datasets.


title: Quantum Image Processing

abstract:
  Image processing is popular in our daily life because of the need to extract
essential information from our 3D world, including a variety of applications in
widely separated fields like bio-medicine, economics, entertainment, and
industry. The nature of visual information, algorithm complexity, and the
representation of 3D scenes in 2D spaces are all popular research topics. In
particular, the rapidly increasing volume of image data as well as increasingly
challenging computational tasks have become important driving forces for
further improving the efficiency of image processing and analysis. Since the
concept of quantum computing was proposed by Feynman in 1982, many achievements
have shown that quantum computing has dramatically improved computational
efficiency [1]. Quantum information processing exploit quantum mechanical
properties, such as quantum superposition, entanglement and parallelism, and
effectively accelerate many classical problems like factoring large numbers,
searching an unsorted database, Boson sampling, quantum simulation, solving
linear systems of equations, and machine learning. These unique quantum
properties may also be used to speed up signal and data processing. In quantum
image processing, quantum image representation plays a key role, which
substantively determines the kinds of processing tasks and how well they can be
performed.


title: Note On The Catalan Constant And Prime Triples

abstract:
  Assuming the conjectured existence of infinitely many prime triples $p_n$, $
p_{n+1}$, and $p_{n+2}$ as $n \to \infty$, this note provides a conditional
proof that the Catalan constant $\beta(2)=0.9159655941\ldots $ is an irrational
number. Moreover, the numerical data suggests that the irrationality measure is
$\mu(\beta(2))=2$, the same as almost every irrational real numbers.


title:
Photothermally induced, reversible phase transition in methylammonium
  lead triiodide

abstract:
  Metal halide perovskites (MHPs) are known to undergo several structural phase
transitions, from lower to higher symmetry, upon heating. While structural
phase transitions have been investigated by a wide range of optical, thermal
and electrical methods, most measurements are quasi-static and hence do not
provide direct information regarding the fundamental timescale of phase
transitions in this emerging class of semiconductors. Here we investigate the
timescale of the orthorhombic-to-tetragonal phase transition in the
prototypical metal halide perovskite, methylammonium lead triiodide (CH3NH3PbI3
or MAPbI3) using cryogenic nanosecond transient absorption spectroscopy. By
using mid-infrared pump pulses to impulsively heat up the material at slightly
below the phase-transition temperature and probing the transient optical
response as a function of delay time, we observed a clean signature of a
transient, reversible orthorhombic-to-tetragonal phase transition. The forward
phase transition is found to proceed at tens of nanoseconds timescale, after
which a backward phase transition progresses at a timescale commensurate with
heat dissipation from the film to the underlying substrate. A high degree of
transient phase transition is observed accounting for one third of the
steady-state phase transition. In comparison to fully inorganic phase-change
materials such as VO2, the orders of magnitude slower phase transition in
MAPbI3 can be attributed to the large energy barrier associated with the strong
hydrogen bonding between the organic cation and the inorganic framework. Our
approach paves the way for unraveling phase transition dynamics in MHPs and
other hybrid semiconducting materials.


title:
General properties of fidelity in non-Hermitian quantum systems with PT
  symmetry

abstract:
  The fidelity susceptibility is a tool for studying quantum phase transitions
in the Hermitian condensed matter systems. Recently, it has been generalized
with the biorthogonal basis for the non-Hermitian quantum systems. From the
general perturbation description with the constrain of parity-time (PT)
symmetry, we show that the fidelity $\mathcal{F}$ is always real for the
PT-symmetric states. For the PT-broken states, the real part of the fidelity
susceptibility equals to one half of the sum of the fidelity susceptibility of
the PT-broken and the PT-partner states, $\mathrm{Re}[\mathcal{X}_F] =
\frac{1}{2}(\mathcal{X}_F +\bar{\mathcal{X}}_F)$. The negative infinity of the
fidelity susceptibility is explored by the perturbation theory when the
parameter approaches the exceptional point (EP). Moreover, at the second-order
EP where two eigenstates and eigenenergies coalesce, we prove that the real
part of the fidelity between PT-symmetric and PT-broken states is
$\mathrm{Re}\mathcal{F}=\frac{1}{2}$. We demonstrate these general properties
for non-interacting and interacting systems by two examples: the two-legged
non-Hermitian Su-Schrieffer-Heeger (SSH) model and the non-Hermitian XXZ spin
chain.

categories: quant-ph cond-mat.mes-hall cond-mat.other cond-mat.str-el physics.comp-ph


title: Implicit Polarized F: local type inference for impredicativity

abstract:
  System F, the polymorphic lambda calculus, features the principle of
impredicativity: polymorphic types may be (explicitly) instantiated at other
types, enabling many powerful idioms such as Church encoding and data
abstraction. Unfortunately, type applications need to be implicit for a
language to be human-usable, and the problem of inferring all type applications
in System F is undecidable. As a result, language designers have historically
avoided impredicative type inference.
  We reformulate System F in terms of call-by-push-value, and study type
inference for it. Surprisingly, this new perspective yields a novel type
inference algorithm which is extremely simple to implement (not even requiring
unification), infers many types, and has a simple declarative specification.
Furthermore, our approach offers type theoretic explanations of how many of the
heuristics used in existing algorithms for impredicative polymorphism arise.


title:
Shape analyticity and singular perturbations for layer potential
  operators

abstract:
  We study the effect of regular and singular domain perturbations on layer
potential operators for the Laplace equation. First, we consider layer
potentials supported on a diffeomorphic image $\phi(\partial\Omega)$ of a
reference set $\partial\Omega$ and we present some real analyticity results for
the dependence upon the map $\phi$. Then we introduce a perforated domain
$\Omega(\epsilon)$ with a small hole of size $\epsilon$ and we compute power
series expansions that describe the layer potentials on
$\partial\Omega(\epsilon)$ when the parameter $\epsilon$ approximates the
degenerate value $\epsilon=0$.


title: Quantum value for a family of $I_{3322}$-like Bell functionals

abstract:
  We introduce a three-parameter family of Bell functionals that extends those
studied in reference [Phys. Rev. Research 2, 033420 (2020)] by including a
marginal contribution. An analysis of their quantum value naturally splits the
family into two branches, and for the first of them we show that this value is
given by a simple function of the parameters defining the functionals. In this
case we completely characterise the realisations attaining the optimal value
and show that these functionals can be used to self-test any partially
entangled state of two qubits. The optimal measurements, however, are not
unique and form a one-parameter family of qubit measurements. The second
branch, which includes the well-known $I_{3322}$ functional, is studied
numerically. We identify the region in the parameter space where the quantum
value can be attained, with two-dimensional systems and characterise the state
and measurements attaining this value. Finally, we show that the set of
realisations introduced in reference [Phys. Rev. A 82, 022116 (2010)] to obtain
the maximal violation of the $I_{3322}$ inequality succeeds in approaching the
optimal value for a large subset of the functionals in this branch. In these
cases we analyse and discuss the main features of the optimal realisations.


title: Generalised U-dual solutions in supergravity

abstract:
  We discuss the notion of generalised U-duality as a solution generating
technique in supergravity. We demonstrate a method to take solutions of type
IIA supergravity on a 3-sphere, with NSNS flux, to new solutions of
11-dimensional supergravity, using exceptional geometry techniques. These new
solutions are characterised by an underlying 3-algebra structure, and
generalise features of solutions obtained by non-abelian T-duality, which
involve an underlying ordinary Lie algebra. We focus on an example where we
start with the pp-F1-NS5 solution in type IIA supergravity. We discuss the
properties of our resulting new solution, including the possibility of viewing
it globally as a U-fold, and its M2 and M5 brane charges. In the extremal case,
the new solution admits an AdS${}_3$ limit, which falls into a recently
constructed class of M-theory AdS$_{3}$ backgrounds -- this provides a global
completion of our solution with a well-defined holographic dual, similar to the
global completions of non-abelian T-dual solutions. Our full solution is a
6-vector deformation of this AdS${}_3$ limit. We also explicitly solve the
Killing spinor equation in the AdS${}_3$ limit, finding a $\frac{1}{2}$-BPS
solution.


title:
Impact of Variable Photospheric Radius on Exoplanet Atmospheric
  Retrievals

abstract:
  Inverse techniques are used to extract information about an exoplanet's
atmosphere. These techniques are prone to biased results if the appropriate
forward model is not used. One assumption used in a forward model is to assume
that the radius of the planet is constant with wavelength, however a more
realistic assumption is that the photospheric radius varies with each
wavelength. We explore the bias induced when attempting to extract the
molecular abundance from an emission spectrum which was generated with a
variable radius. We find that for low gravity planets, the retrieval model is
not able to fit the data if a constant radius model is used. We find that
biased results are obtained when studying a typical hot Jupiter in the MIRI LRS
wavelength range. Finally, we show that high gravity planets do not suffer a
bias. We recommend that future spectral retrievals that interpret exoplanet
emission spectra should take into account a variable radius.


title: Low degree Hurwitz stacks in the Grothendieck ring

abstract:
  For $2 \leq d \leq 5$, we show that the class of the Hurwitz space of smooth
degree $d$, genus $g$ covers of $\mathbb P^1$ stabilizes in the Grothendieck
ring of stacks as $g \to \infty$, and we give a formula for the limit. We also
verify this stabilization when one imposes ramification conditions on the
covers, and obtain a particularly simple answer for this limit when one
restricts to simply branched covers.


title:
A second order upper bound on the ground state energy of a Bose gas
  beyond the Gross-Pitaevskii regime

abstract:
  We consider a system of $N$ bosons in a unitary box in the grand-canonical
setting interacting through a potential with scattering length scaling as
$N^{-1+\kappa},$ $\kappa\in (0,2/3).$ This regimes interpolate between the
Gross-Pitaevskii regime ($\kappa=0$) and the thermodynamic limit
($\kappa=2/3$). In Forum Math. Sigma 9 (2021), e74, as an intermediate step to
prove an upper bound in agreement with the Lee-Huang-Yang formula in the
thermodynamic limit, it is obtained a second order upper bound on the ground
state energy for $\kappa<5/9.$ In this paper, thanks to a more careful analysis
of the error terms, we extend the above mentioned result to $\kappa<7/12$.


title:
Nonlinear Reduced Order Modelling of Soil Structure Interaction Effects
  via LSTM and Autoencoder Neural Networks

abstract:
  In the field of structural health monitoring (SHM), inverse problems which
require repeated analyses are common. With the increase in the use of nonlinear
models, the development of nonlinear reduced order modelling techniques is of
paramount interest. Of considerable research interest, is the use of flexible
and scalable machine learning methods which can learn to approximate the
behaviour of nonlinear dynamic systems using input and output data. One such
nonlinear system of interest, in the context of wind turbine structures, is the
soil structure interaction (SSI) problem. Soil demonstrates strongly nonlinear
behaviour with regards to its restoring force and has been shown to
considerably influence the dynamic response of wind turbine structures. In this
work, we demonstrate the application of a recently developed nonlinear reduced
order modelling method, which leverages Autoencoder and LSTM neural networks,
to a nonlinear soil structure interaction problem of a wind turbine monopile
subject to realistic loading at the seabed level. The accuracy and efficiency
of the methodology is compared to full order simulations carried out using
Abaqus. The ROM was shown to have good fidelity and a considerable reduction in
computational time for the system considered.


title: Duality via convolution of W-algebras

abstract:
  Feigin-Frenkel duality is the isomorphism between the principal
$\mathcal{W}$-algebras of a simple Lie algebra $\mathfrak{g}$ and its Langlands
dual Lie algebra ${}^L\mathfrak{g}$. A generalization of this duality to a
larger family of $\mathcal{W}$-algebras called hook-type was recently
conjectured by Gaiotto and Rap\v{c}\'ak and proved by the first two authors. It
says that the affine cosets of two different hook-type $\mathcal{W}$-algebras
are isomorphic. A natural question is whether the duality between affine cosets
can be enhanced to a duality between the full $\mathcal{W}$-algebras. There is
a convolution operation that maps a hook-type $\mathcal{W}$-algebra
$\mathcal{W}$ to a certain relative semi-infinite cohomology of $\mathcal{W}$
tensored with a suitable kernel VOA. The first two authors conjectured
previously that this cohomology is isomorphic to the Feigin-Frenkel dual
hook-type $\mathcal{W}$-algebra. Our main result is a proof of this conjecture.


title: Stochastic Model Predictive Control using Initial State Optimization

abstract:
  We propose a stochastic MPC scheme using an optimization over the initial
state for the predicted trajectory. Considering linear discrete-time systems
under unbounded additive stochastic disturbances subject to chance constraints,
we use constraint tightening based on probabilistic reachable sets to design
the MPC. The scheme avoids the infeasibility issues arising from unbounded
disturbances by including the initial state as a decision variable. We show
that the stabilizing control scheme can guarantee constraint satisfaction in
closed loop, assuming unimodal disturbances. In addition to illustrating these
guarantees, the numerical example indicates further advantages of optimizing
over the initial state for the transient behavior.


title:
MooAFEM: An object oriented Matlab code for higher-order (nonlinear)
  adaptive FEM

abstract:
  We present an easily accessible, object oriented code (written exclusively in
Matlab) for finite element simulations in 2D. The object oriented programming
paradigm allows for fast implementation of higher-order FEM on triangular
meshes for problems with very general coefficients. In particular, our code can
handle problems typically arising from iterative linearization methods used to
solve nonlinear PDEs. We explain the basic principles of our code and give
numerical experiments that underline its flexibility as well as its efficiency.


title: The fate of the spin polaron in the 1D antiferromagnet

abstract:
  The stability of the spin polaron quasiparticle, well established in studies
of a single hole in the 2D antiferromagnet, is investigated in the 1D
antiferromagnet using a $t$-$J$ model. We perform an exact slave fermion
transformation to the holon-magnon basis, and diagonalize numerically the
resulting model in the presence of a single hole. We prove that the spin
polaron is stable for any strength of the magnon-magnon interactions except for
the unique value of the SU(2)-symmetric 1D $t$-$J$ model. In the latter case,
we verify the long known instability of the spin polaron to spin-charge
separation. Even here, however, we find strong remnants of the spin polaron
physics in the single-particle spectral function. Altogether, this indicates an
exceptional robustness of the spin polaron picture in the 1D antiferromagnet,
that has important consequences for the interpretation of the ARPES spectra of
quasi-1D doped antiferromagnets.


title: Understanding Vacuum Arcs and Gradient Limits

abstract:
  Although a general model of vacuum arcs and gradient limits would be widely
useful, roughly 120 years after the first good experimental data on these arcs,
this important field continues to be unsettled. This problem is a limitation in
a number of technologies and has applications in many fields. Large tokamaks
are sensitive to arcing on the plasma facing components, linac costs depend on
their maximum operating fields, power transmission efficiency depends on the
voltage that can be maintained, and the efficiency of Atom Probe Tomography
depends on avoiding sample failures. A multidisciplinary study of this field
could improve the precision and applicability of the theoretical models used.
We outline the basic mechanisms involved in arcing and the issues that
determine the physics of arcs. We divide the process into four stages; the
trigger, plasma formation, plasma evolution and surface damage, in order to
look at the physical principles involved. We try to identify the dominant
mechanisms, critical issues and desirable aspects of an R&D program to produce
a more precise and general model.


title: Local Constraint-Based Causal Discovery under Selection Bias

abstract:
  We consider the problem of discovering causal relations from independence
constraints selection bias in addition to confounding is present. While the
seminal FCI algorithm is sound and complete in this setup, no criterion for the
causal interpretation of its output under selection bias is presently known. We
focus instead on local patterns of independence relations, where we find no
sound method for only three variable that can include background knowledge.
Y-Structure patterns are shown to be sound in predicting causal relations from
data under selection bias, where cycles may be present. We introduce a
finite-sample scoring rule for Y-Structures that is shown to successfully
predict causal relations in simulation experiments that include selection
mechanisms. On real-world microarray data, we show that a Y-Structure variant
performs well across different datasets, potentially circumventing spurious
correlations due to selection bias.


title: Context Enhanced Short Text Matching using Clickthrough Data

abstract:
  The short text matching task employs a model to determine whether two short
texts have the same semantic meaning or intent. Existing short text matching
models usually rely on the content of short texts which are lack information or
missing some key clues. Therefore, the short texts need external knowledge to
complete their semantic meaning. To address this issue, we propose a new short
text matching framework for introducing external knowledge to enhance the short
text contextual representation. In detail, we apply a self-attention mechanism
to enrich short text representation with external contexts. Experiments on two
Chinese datasets and one English dataset demonstrate that our framework
outperforms the state-of-the-art short text matching models.


title: T-Cal: An optimal test for the calibration of predictive models

abstract:
  The prediction accuracy of machine learning methods is steadily increasing,
but the calibration of their uncertainty predictions poses a significant
challenge. Numerous works focus on obtaining well-calibrated predictive models,
but less is known about reliably assessing model calibration. This limits our
ability to know when algorithms for improving calibration have a real effect,
and when their improvements are merely artifacts due to random noise in finite
datasets. In this work, we consider detecting mis-calibration of predictive
models using a finite validation dataset as a hypothesis testing problem. The
null hypothesis is that the predictive model is calibrated, while the
alternative hypothesis is that the deviation from calibration is sufficiently
large.
  We find that detecting mis-calibration is only possible when the conditional
probabilities of the classes are sufficiently smooth functions of the
predictions. When the conditional class probabilities are H\"older continuous,
we propose T-Cal, a minimax optimal test for calibration based on a debiased
plug-in estimator of the $\ell_2$-Expected Calibration Error (ECE). We further
propose Adaptive T-Cal, a version that is adaptive to unknown smoothness. We
verify our theoretical findings with a broad range of experiments, including
with several popular deep neural net architectures and several standard
post-hoc calibration methods. T-Cal is a practical general-purpose tool, which
-- combined with classical tests for discrete-valued predictors -- can be used
to test the calibration of virtually any probabilistic classification method.


title: STUN: Self-Teaching Uncertainty Estimation for Place Recognition

abstract:
  Place recognition is key to Simultaneous Localization and Mapping (SLAM) and
spatial perception. However, a place recognition in the wild often suffers from
erroneous predictions due to image variations, e.g., changing viewpoints and
street appearance. Integrating uncertainty estimation into the life cycle of
place recognition is a promising method to mitigate the impact of variations on
place recognition performance. However, existing uncertainty estimation
approaches in this vein are either computationally inefficient (e.g., Monte
Carlo dropout) or at the cost of dropped accuracy. This paper proposes STUN, a
self-teaching framework that learns to simultaneously predict the place and
estimate the prediction uncertainty given an input image. To this end, we first
train a teacher net using a standard metric learning pipeline to produce
embedding priors. Then, supervised by the pretrained teacher net, a student net
with an additional variance branch is trained to finetune the embedding priors
and estimate the uncertainty sample by sample. During the online inference
phase, we only use the student net to generate a place prediction in
conjunction with the uncertainty. When compared with place recognition systems
that are ignorant to the uncertainty, our framework features the uncertainty
estimation for free without sacrificing any prediction accuracy. Our
experimental results on the large-scale Pittsburgh30k dataset demonstrate that
STUN outperforms the state-of-the-art methods in both recognition accuracy and
the quality of uncertainty estimation.


title: Identification in Tree-shaped Linear Structural Causal Models

abstract:
  Linear structural equation models represent direct causal effects as directed
edges and confounding factors as bidirected edges. An open problem is to
identify the causal parameters from correlations between the nodes. We
investigate models, whose directed component forms a tree, and show that there,
besides classical instrumental variables, missing cycles of bidirected edges
can be used to identify the model. They can yield systems of quadratic
equations that we explicitly solve to obtain one or two solutions for the
causal parameters of adjacent directed edges. We show how multiple missing
cycles can be combined to obtain a unique solution. This results in an
algorithm that can identify instances that previously required approaches based
on Gr\"obner bases, which have doubly-exponential time complexity in the number
of structural parameters.


title: Efficient Video Instance Segmentation via Tracklet Query and Proposal

abstract:
  Video Instance Segmentation (VIS) aims to simultaneously classify, segment,
and track multiple object instances in videos. Recent clip-level VIS takes a
short video clip as input each time showing stronger performance than
frame-level VIS (tracking-by-segmentation), as more temporal context from
multiple frames is utilized. Yet, most clip-level methods are neither
end-to-end learnable nor real-time. These limitations are addressed by the
recent VIS transformer (VisTR) which performs VIS end-to-end within a clip.
However, VisTR suffers from long training time due to its frame-wise dense
attention. In addition, VisTR is not fully end-to-end learnable in multiple
video clips as it requires a hand-crafted data association to link instance
tracklets between successive clips. This paper proposes EfficientVIS, a fully
end-to-end framework with efficient training and inference. At the core are
tracklet query and tracklet proposal that associate and segment
regions-of-interest (RoIs) across space and time by an iterative query-video
interaction. We further propose a correspondence learning that makes tracklets
linking between clips end-to-end learnable. Compared to VisTR, EfficientVIS
requires 15x fewer training epochs while achieving state-of-the-art accuracy on
the YouTube-VIS benchmark. Meanwhile, our method enables whole video instance
segmentation in a single end-to-end pass without data association at all.


title:
A study on the distribution of social biases in self-supervised learning
  visual models

abstract:
  Deep neural networks are efficient at learning the data distribution if it is
sufficiently sampled. However, they can be strongly biased by non-relevant
factors implicitly incorporated in the training data. These include operational
biases, such as ineffective or uneven data sampling, but also ethical concerns,
as the social biases are implicitly present\textemdash even inadvertently, in
the training data or explicitly defined in unfair training schedules. In tasks
having impact on human processes, the learning of social biases may produce
discriminatory, unethical and untrustworthy consequences. It is often assumed
that social biases stem from supervised learning on labelled data, and thus,
Self-Supervised Learning (SSL) wrongly appears as an efficient and bias-free
solution, as it does not require labelled data. However, it was recently proven
that a popular SSL method also incorporates biases. In this paper, we study the
biases of a varied set of SSL visual models, trained using ImageNet data, using
a method and dataset designed by psychological experts to measure social
biases. We show that there is a correlation between the type of the SSL model
and the number of biases that it incorporates. Furthermore, the results also
suggest that this number does not strictly depend on the model's accuracy and
changes throughout the network. Finally, we conclude that a careful SSL model
selection process can reduce the number of social biases in the deployed model,
whilst keeping high performance.


title:
Reasoning about Counterfactuals to Improve Human Inverse Reinforcement
  Learning

abstract:
  To collaborate well with robots, we must be able to understand their decision
making. Humans naturally infer other agents' beliefs and desires by reasoning
about their observable behavior in a way that resembles inverse reinforcement
learning (IRL). Thus, robots can convey their beliefs and desires by providing
demonstrations that are informative for a human's IRL. An informative
demonstration is one that differs strongly from the learner's expectations of
what the robot will do given their current understanding of the robot's
decision making. However, standard IRL does not model the learner's existing
expectations, and thus cannot do this counterfactual reasoning. We propose to
incorporate the learner's current understanding of the robot's decision making
into our model of human IRL, so that our robot can select demonstrations that
maximize the human's understanding. We also propose a novel measure for
estimating the difficulty for a human to predict instances of a robot's
behavior in unseen environments. A user study finds that our test difficulty
measure correlates well with human performance and confidence. Interestingly,
considering human beliefs and counterfactuals when selecting demonstrations
decreases human performance on easy tests, but increases performance on
difficult tests, providing insight on how to best utilize such models.


title:
Gravitational wave birefringence in spatially curved teleparallel
  cosmology

abstract:
  We study tensor perturbations around the most general cosmologically
symmetric backgrounds in a class of teleparallel gravity theories known as New
General Relativity. These theories comprise a one-parameter class, which is
fully consistent with observations at the post-Newtonian level, and which
contains the teleparallel equivalent of General Relativity as a special case.
We find that for a particular class of cosmological background geometries these
theories exhibit gravitational wave birefringence and dispersion, i.e., the
dispersion relation for gravitational waves depends on their polarization and
wave number. The strength of this effect is directly related to the spatial
curvature of the cosmological background and the parameter describing the
deviation of the theory from General Relativity. We discuss the possibility of
observing this effect in gravitational wave experiments.


title:
Improved Approximation Algorithms and Lower Bounds for
  Search-Diversification Problems

abstract:
  We study several questions related to diversifying search results. We give
improved approximation algorithms in each of the following problems, together
with some lower bounds.
  - We give a polynomial-time approximation scheme (PTAS) for a diversified
search ranking problem [Bansal et al., ICALP 2010] whose objective is to
minimizes the discounted cumulative gain. Our PTAS runs in time
$n^{2^{O(\log(1/\epsilon)/\epsilon)}} \cdot m^{O(1)}$ where $n$ denotes the
number of elements in the databases. Complementing this, we show that no PTAS
can run in time $f(\epsilon) \cdot (nm)^{2^{o(1/\epsilon)}}$ assuming Gap-ETH;
therefore our running time is nearly tight. Both of our bounds answer open
questions of Bansal et al.
  - We next consider the Max-Sum Dispersion problem, whose objective is to
select $k$ out of $n$ elements that maximizes the dispersion, which is defined
as the sum of the pairwise distances under a given metric. We give a
quasipolynomial-time approximation scheme for the problem which runs in time
$n^{O_{\epsilon}(\log n)}$. This improves upon previously known polynomial-time
algorithms with approximate ratios 0.5 [Hassin et al., Oper. Res. Lett. 1997;
Borodin et al., ACM Trans. Algorithms 2017]. Furthermore, we observe that known
reductions rule out approximation schemes that run in
$n^{\tilde{o}_\epsilon(\log n)}$ time assuming ETH.
  - We consider a generalization of Max-Sum Dispersion called Max-Sum
Diversification. In addition to the sum of pairwise distance, the objective
includes another function $f$. For monotone submodular $f$, we give a
quasipolynomial-time algorithm with approximation ratio arbitrarily close to
$(1 - 1/e)$. This improves upon the best polynomial-time algorithm which has
approximation ratio $0.5$ by Borodin et al. Furthermore, the $(1 - 1/e)$ factor
is tight as achieving better-than-$(1 - 1/e)$ approximation is NP-hard [Feige,
J. ACM 1998].


title: Bloch oscillations in the magnetoconductance of twisted bilayer graphene

abstract:
  We identify a mapping between two-dimensional (2D) electron transport in a
minimally twisted graphene bilayer and a 1D quantum walk, where one spatial
dimension plays the role of time. In this mapping a magnetic field B
perpendicular to the bilayer maps onto an electric field. Bloch oscillations
due to the periodic motion in a 1D Bloch band can then be observed in purely DC
transport as magnetoconductance oscillations with periodicity set by the Bloch
frequency.


title:
Robust PAC$^m$: Training Ensemble Models Under Model Misspecification
  and Outliers

abstract:
  Standard Bayesian learning is known to have suboptimal generalization
capabilities under model misspecification and in the presence of outliers.
PAC-Bayes theory demonstrates that the free energy criterion minimized by
Bayesian learning is a bound on the generalization error for Gibbs predictors
(i.e., for single models drawn at random from the posterior) under the
assumption of sampling distributions uncontaminated by outliers. This viewpoint
provides a justification for the limitations of Bayesian learning when the
model is misspecified, requiring ensembling, and when data is affected by
outliers. In recent work, PAC-Bayes bounds - referred to as PAC$^m$ - were
derived to introduce free energy metrics that account for the performance of
ensemble predictors, obtaining enhanced performance under misspecification.
This work presents a novel robust free energy criterion that combines the
generalized logarithm score function with PAC$^m$ ensemble bounds. The proposed
free energy training criterion produces predictive distributions that are able
to concurrently counteract the detrimental effects of model misspecification
and outliers.


title: The Potential to Probe Solar Neutrino Physics with LiCl Water Solution

abstract:
  Lithium chloride water solution is a good option for solar neutrino
detection. The $\nu_e$ charged-current (CC) interaction cross-section on
$\rm{{}^{7}Li}$ is evaluated with new B(GT) experimental measurements. The
total CC interaction cross-section weighted by the solar $^8$B electron
neutrino spectrum is $3.759\times10^{-42} \rm{cm}^2$, which is about 60 times
that of the neutrino-electron elastic scattering process. The final state
effective kinetic energy after the CC interaction on $\rm{{}^{7}Li}$ directly
reflects the neutrino energy, which stands in sharp contrast to the plateau
structure of recoil electrons of the elastic scattering. With the high
solubility of LiCl of 74.5 g/100 g water at 10$^\circ$C and the high natural
abundance of 92.41%, the molarity of $\rm{{}^{7}Li}$ in water can reach 11
mol/L for safe operation at room temperature. The CC event rate of $\nu_e$ on
$\rm{{}^{7}Li}$ in the LiCl water solution is comparable to that of
neutrino-electron elastic scattering. In addition, the $\nu_e$ CC interaction
with the contained $\rm{{}^{37}Cl}$ also contributes a few percent of the total
CC event rate. The contained $\rm{{}^{35}Cl}$ and $\rm{{}^{6}Li}$ also make a
delay-coincidence detection for electron antineutrinos possible. The
recrystallization method is found to be applicable for LiCl sample
purification. The measured attenuation length of $11\pm1$ m at 430 nm shows
that the LiCl solution is practicable for a 10-m diameter detector for solar
neutrino detection. Clear advantages are found in studying the upturn effect of
solar neutrino oscillation, light sterile neutrinos, and Earth matter effect.
The sensitivities in discovering solar neutrino upturn and light sterile
neutrinos are shown.


title: Rank-uniform local law for Wigner matrices

abstract:
  We prove a general local law for Wigner matrices which optimally handles
observables of arbitrary rank and thus it unifies the well-known averaged and
isotropic local laws. As an application, we prove that the quadratic forms of a
general deterministic matrix $A$ on the bulk eigenvectors of a Wigner matrix
has approximately Gaussian fluctuation. For the bulk spectrum, we thus
generalize our previous result \cite{2103.06730} valid for test matrices $A$ of
large rank as well as the result of Benigni and Lopatto \cite{2103.12013} valid
for specific small rank observables.


title:
The world seems different in a social context: a neural network analysis
  of human experimental data

abstract:
  Human perception and behavior are affected by the situational context, in
particular during social interactions. A recent study demonstrated that humans
perceive visual stimuli differently depending on whether they do the task by
themselves or together with a robot. Specifically, it was found that the
central tendency effect is stronger in social than in non-social task settings.
The particular nature of such behavioral changes induced by social interaction,
and their underlying cognitive processes in the human brain are, however, still
not well understood. In this paper, we address this question by training an
artificial neural network inspired by the predictive coding theory on the above
behavioral data set. Using this computational model, we investigate whether the
change in behavior that was caused by the situational context in the human
experiment could be explained by continuous modifications of a parameter
expressing how strongly sensory and prior information affect perception. We
demonstrate that it is possible to replicate human behavioral data in both
individual and social task settings by modifying the precision of prior and
sensory signals, indicating that social and non-social task settings might in
fact exist on a continuum. At the same time an analysis of the neural
activation traces of the trained networks provides evidence that information is
coded in fundamentally different ways in the network in the individual and in
the social conditions. Our results emphasize the importance of computational
replications of behavioral data for generating hypotheses on the underlying
cognitive mechanisms of shared perception and may provide inspiration for
follow-up studies in the field of neuroscience.


title: NuSTAR discovery of a cyclotron line in GRO J1750-27

abstract:
  GRO J1750-27, discovered during an outburst in 1995 with CGRO-BATSE, is one
of the farthest known Be-X-ray binary systems. This relatively poorly studied
system recently went into an outburst. From the measurements of the spin-up
rate of this pulsar, the X-ray fluxes during the two previous outbursts in 2008
and 2014-2015, and using the standard theory of accretion torque on magnetized
neutron stars, the magnetic field strength was estimated to be $2 \times
10^{12}$ G and $3.5-4.5 \times 10^{12}$ G respectively. The uncertainty in the
distance causes large uncertainty in the estimated magnetic field. The source
was observed during the latest outburst using the NuSTAR telescope during the
rising phase of the outburst. We estimate the spin period of the source to be ~
4.45s using which we produced energy-resolved pulse profiles between 3 and 79
keV. We find that the profiles appear to be double-peaked at low energies (<20
keV) while evolving into a single peak at higher energies (>20 keV). The
broadband spectrum of this source was described by a power-law modified by an
exponential cut off and we report the discovery of a cyclotron resonant
scattering feature (CRSF) in this hard X-ray spectrum of this source at ~43 keV
indicating a magnetic field strength of 3.7 $\times 10^{12}$ G. Our estimate of
the magnetic field strength using the cyclotron line is consistent with the
estimate made using the accretion torque model.


title: Robustness and Adaptation to Hidden Factors of Variation

abstract:
  We tackle here a specific, still not widely addressed aspect, of AI
robustness, which consists of seeking invariance / insensitivity of model
performance to hidden factors of variations in the data. Towards this end, we
employ a two step strategy that a) does unsupervised discovery, via generative
models, of sensitive factors that cause models to under-perform, and b)
intervenes models to make their performance invariant to these sensitive
factors' influence. We consider 3 separate interventions for robustness,
including: data augmentation, semantic consistency, and adversarial alignment.
We evaluate our method using metrics that measure trade offs between invariance
(insensitivity) and overall performance (utility) and show the benefits of our
method for 3 settings (unsupervised, semi-supervised and generalization).


title: Real eigenstructure of regular simplex tensors

abstract:
  We are concerned with the eigenstructure of supersymmetric tensors. Like in
the matrix case, normalized tensor eigenvectors are fixed points of the tensor
power iteration map. However, unless the given tensor is orthogonally
decomposable, some of these fixed points may be repelling and therefore be
undetectable by any numerical scheme. In this paper, we consider the case of
regular simplex tensors whose symmetric decomposition is induced by an
overcomplete, equiangular set of $n+1$ vectors from $\mathbb R^n$. We discuss
the full real eigenstructure of such tensors, including the robustness analysis
of all normalized eigenvectors. As it turns out, regular simplex tensors
exhibit robust as well as non-robust eigenvectors which, moreover, only partly
coincide with the generators from the symmetric tensor decomposition.


title: Jupiter's inhomogeneous envelope

abstract:
  While Jupiter's massive gas envelope consists mainly of hydrogen and helium,
the key to understanding Jupiter's formation and evolution lies in the
distribution of the remaining (heavy) elements. Before the Juno mission, the
lack of high-precision gravity harmonics precluded the use of statistical
analyses in a robust determination of the heavy-elements distribution in
Jupiter's envelope. In this paper, we assemble the most comprehensive and
diverse collection of Jupiter interior models to date and use it to study the
distribution of heavy elements in the planet's envelope. We apply a Bayesian
statistical approach to our interior model calculations, reproducing the Juno
gravitational and atmospheric measurements and constraints from the deep zonal
flows. Our results show that the gravity constraints lead to a deep entropy of
Jupiter corresponding to a 1 bar temperature 5-15 K higher than traditionally
assumed. We also find that uncertainties in the equation of state are crucial
when determining the amount of heavy elements in Jupiter's interior. Our models
put an upper limit to the inner compact core of Jupiter of 7 Earth masses,
independently on the structure model (with or without dilute core) and the
equation of state considered. Furthermore, we robustly demonstrate that
Jupiter's envelope is inhomogenous, with a heavy-element enrichment in the
interior relative to the outer envelope. This implies that heavy element
enrichment continued through the gas accretion phase, with important
implications for the formation of giant planets in our solar system and beyond.


title:
Axial and Vector Structure Functions for Lepton-Nucleon Scattering,
  NuFact 2021 Update

abstract:
  We report on an update (2021) of a phenomenological model for inelastic
neutrino- and electron-nucleon scattering cross sections using effective
leading order parton distribution functions with a new scaling variable
$\xi_w$. Non-perturbative effects are well described using the $\xi_w$ scaling
variable in combination with multiplicative $K$ factors at low $Q^2$. The model
describes all inelastic charged-lepton-nucleon scattering data
(HERA/NMC/BCDMS/SLAC/JLab) ranging from very high $Q^2$ to very low $Q^2$ and
down to the $Q^2=0$ photo-production region. The model has been developed to be
used in analyses of neutrino oscillation experiments in the few GeV region. The
2021 update accounts for the difference between axial and vector structure
functions which brings it into much better agreement with neutrino-nucleon
total cross section measurements. The model has been developed primarily for
hadronic final state masses $W$ above 1.8 GeV. However with additional
parameters the model also describes the $average$ neutrino cross sections in
the resonance region down to $W$=1.4 GeV.


title: Optimal Transport Reconstruction of Baryon Acoustic Oscillations

abstract:
  A model-independent, weighted semi-discrete, fast optimal transport algorithm
to reconstruct the Lagrangian positions of proto-halos from their evolved
Eulerian positions is presented. Tests with state-of-art cosmological
simulations show that the positions of proto-halos are reconstructed
accurately, without having to assume a background cosmology. The algorithm,
which makes use of a mass estimate of the biased tracers, but is robust to
errors in this estimation, recovers the shape and amplitude of the initial pair
correlation function of the tracers, enabling sub-percent precision in the BAO
distance scale that is not tied to a cosmological model. In principle, our
algorithm also allows direct and independent determinations of the bias factor
and the smearing scale, potentially providing new methods for breaking the
degeneracy between the bias factor $b$ and $\sigma_8$.


title: Gaussian Process-based Spatial Reconstruction of Electromagnetic fields

abstract:
  These days we live in a world with a permanent electromagnetic field. This
raises many questions about our health and the deployment of new equipment. The
problem is that these fields remain difficult to visualize easily, which only
some experts can understand. To tackle this problem, we propose to spatially
estimate the level of the field based on a few observations at all positions of
the considered space. This work presents an algorithm for spatial
reconstruction of electromagnetic fields using the Gaussian Process. We
consider a spatial, physical phenomenon observed by a sensor network. A
Gaussian Process regression model with selected mean and covariance function is
implemented to develop a 9 sensors-based estimation algorithm. A Bayesian
inference approach is used to perform the model selection of the covariance
function and to learn the hyperparameters from our data set. We present the
prediction performance of the proposed model and compare it with the case where
the mean is zero. The results show that the proposed Gaussian Process-based
prediction model reconstructs the EM fields in all positions only using 9
sensors.


title:
KamNet: An Integrated Spatiotemporal Deep Neural Network for Rare Event
  Search in KamLAND-Zen

abstract:
  Rare event searches allow us to search for new physics at energy scales
inaccessible with other means by leveraging specialized large-mass detectors.
Machine learning provides a new tool to maximize the information provided by
these detectors. The information is sparse, which forces these algorithms to
start from the lowest level data and exploit all symmetries in the detector to
produce results. In this work we present KamNet which harnesses breakthroughs
in geometric deep learning and spatiotemporal data analysis to maximize the
physics reach of KamLAND-Zen, a kiloton scale spherical liquid scintillator
detector searching for neutrinoless double beta decay ($0\nu\beta\beta$). Using
a simplified background model for KamLAND we show that KamNet outperforms a
conventional CNN on benchmarking MC simulations with an increasing level of
robustness. Using simulated data, we then demonstrate KamNet's ability to
increase KamLAND-Zen's sensitivity to $0\nu\beta\beta$ and $0\nu\beta\beta$ to
excited states. A key component of this work is the addition of an attention
mechanism to elucidate the underlying physics KamNet is using for the
background rejection.


title: Euclidean de Sitter Black Holes and Microcanonical Equilibrium

abstract:
  Schwarzschild-de Sitter (SdS) black holes do not admit a completely smooth
Euclidean continuation. We discuss some modifications of the gravitational path
integral that give Euclidean SdS a semiclassical equilibrium interpretation.
First we consider "gravity in a cavity," defining the canonical ensemble in a
box that excises one horizon. However, this standard approach does not work for
positive cosmological constant: the solution of lowest free energy has a
negative heat capacity, which is inconsistent if it is to provide the leading
semiclassical contribution to a canonical partition function. Instead we modify
the boundary conditions in the path integral to construct the microcanonical
partition function, which appears to be well-defined. We then bring two
ensembles into contact and remove the boundary, producing states of a larger
microcanonical ensemble that contain, for example, both a black hole and a
cosmological horizon at once. These systems are closed and have no boundary,
but they must possess some form of mild metric discontinuity. We discuss the
case where the discontinuity is equivalent to the insertion of a thin, rigid
membrane, separating two systems that can exchange energy and are at local
equilibrium. Equilibrium configurations obtained in this way are found to be
thermodynamically unstable if they contain a black hole.


title:
Don't Roll the Dice, Ask Twice: The Two-Query Distortion of Matching
  Problems and Beyond

abstract:
  In most social choice settings, the participating agents are typically
required to express their preferences over the different alternatives in the
form of linear orderings. While this simplifies preference elicitation, it
inevitably leads to high distortion when aiming to optimize a cardinal
objective such as the social welfare, since the values of the agents remain
virtually unknown. A recent array of works put forward the agenda of designing
mechanisms that can learn the values of the agents for a small number of
alternatives via queries, and use this extra information to make a
better-informed decision, thus improving distortion. Following this agenda, in
this work we focus on a class of combinatorial problems that includes most
well-known matching problems and several of their generalizations, such as
One-Sided Matching, Two-Sided Matching, General Graph Matching, and
$k$-Constrained Resource Allocation. We design two-query mechanisms that
achieve the best-possible worst-case distortion in terms of social welfare, and
outperform the best-possible expected distortion that can be achieved by
randomized ordinal mechanisms.


title:
Kinetic modeling of three-dimensional electrostatic-solitary and surface
  waves in beam neutralization

abstract:
  This work studies the fundamental plasma processes involved in the
neutralization of an ion beam's space-charge by electrons emitted by a filament
using Particle-in-Cell simulations. While filament neutralization is
economical, previous experiments have shown that a variety of waves become
excited in this process that limit the space-charge neutralization. In this
work, the formation and movement of electrostatic solitary waves(ESWs), which
have low dissipation rates, are characterized for 2D planar and 3D cylindrical
beams and are observed to generate waves that survive for a long time and slow
the process of beam neutralization. Further, through a 1D
Bernstein-Greene-Kruskal (BGK) analysis, we find that the non-Maxwellian nature
of the beam electrons gives rise to large-sized ESWs that are not predicted by
theory which assumes that the electrons may be described by a Maxwellian
distribution. Our PIC simulations are sufficiently sensitive to be able to
resolve important three-dimensional effects in a 3D cylindrical geometry that
lead to the excitation of Trivelpiece-Gould surface waves due to high-energy
electrons present at the beginning of neutralization.


title: Thermodynamics-informed graph neural networks

abstract:
  In this paper we present a deep learning method to predict the temporal
evolution of dissipative dynamic systems. We propose using both geometric and
thermodynamic inductive biases to improve accuracy and generalization of the
resulting integration scheme. The first is achieved with Graph Neural Networks,
which induces a non-Euclidean geometrical prior with permutation invariant node
and edge update functions. The second bias is forced by learning the GENERIC
structure of the problem, an extension of the Hamiltonian formalism, to model
more general non-conservative dynamics. Several examples are provided in both
Eulerian and Lagrangian description in the context of fluid and solid mechanics
respectively, achieving relative mean errors of less than 3% in all the tested
examples. Two ablation studies are provided based on recent works in both
physics-informed and geometric deep learning.


title: Low complexity equalization for AFDM in doubly dispersive channels

abstract:
  Affine Frequency Division Multiplexing (AFDM), which is based on discrete
affine Fourier transform (DAFT), has recently been proposed for reliable
communication in high-mobility scenarios. Two low complexity detectors for AFDM
are introduced here. Approximating the channel matrix as a band matrix via
placing null symbols in the AFDM frame in the DAFT domain, a low complexity
MMSE detection is proposed by means of the $\rm{LDL}$ factorization.
Furthermore, exploiting the sparsity of the channel matrix, we propose a low
complexity iterative decision feedback equalizer (DFE) based on weighted
maximal ratio combining (MRC), which extracts and combines the received
multipath components of the transmitted symbols in the DAFT domain. Simulation
results show that the proposed detectors have similar performance, while
weighted MRC-based DFE has lower complexity than band-matrix-approximation
LMMSE when the channel impulse response has gaps.


title: Cohomology of finite subgroups of the plane Cremona group

abstract:
  An equivariant stable birational invariant of an action of a finite group on
a smooth projective variety is the first cohomology group of the Picard module.
Bogomolov-Prokhorov and Shinder computed this for actions of cyclic groups on
rational surfaces, with maximal stabilizers, in terms of the geometry of the
fixed point locus. Using the Brauer group of the quotient stack, we extend the
computation to more general actions and relate it to the equivariant Burnside
group formalism.


title: Query Processing on Tensor Computation Runtimes

abstract:
  The huge demand for computation in artificial intelligence (AI) is driving
unparalleled investments in new hardware and software systems for AI. This
leads to an explosion in the number of specialized hardware devices, which are
now part of the offerings of major cloud providers. Meanwhile, by hiding the
low-level complexity through a tensor-based interface, tensor computation
runtimes (TCRs) such as PyTorch allow data scientists to efficiently exploit
the exciting capabilities offered by the new hardware. In this paper, we
explore how databases can ride the wave of innovation happening in the AI
space. Specifically, we present Tensor Query Processor (TQP): a SQL query
processor leveraging the tensor interface of TCRs. TQP is able to efficiently
run the full TPC-H benchmark by implementing novel algorithms for executing
relational operators on the specialized tensor routines provided by TCRs.
Meanwhile, TQP can target various hardware while only requiring a fraction of
the usual development effort. Experiments show that TQP can improve query
execution time by up to 20x over CPU-only systems, and up to 5x over
specialized GPU solutions. Finally, TQP can accelerate queries mixing ML
predictions and SQL end-to-end, and deliver up to 5x speedup over CPU
baselines.


title:
Highly efficient UV detection in a metal-semiconductor-metal detector
  with epigraphene

abstract:
  We show that epitaxial graphene on silicon carbide (epigraphene) grown at
high temperatures (T > 1850 {\deg}C) readily acts as material for implementing
solar-blind ultraviolet (UV) detectors with outstanding performance. We present
centimeter-sized epigraphene metal-semiconductor-metal (MSM) detectors with
peak external quantum efficiency of ~ 85% for wavelengths 250-280 nm,
corresponding to nearly 100% internal quantum efficiency when accounting for
reflection losses. Zero bias operation is possible in asymmetric devices, with
the responsivity to UV remaining as high as R = 134 mA/W, making this a
self-powered detector. The low dark currents Io ~50 fA translate into an
estimated record high specific detectivity D = 3.5 x 10^15 Jones. The
performance that we demonstrate, together with material reproducibility,
renders epigraphene technologically attractive to implement high-performance
planar MSM devices with a low processing effort, including multi-pixel UV
sensor arrays, suitable for a number of practical applications.


title: An observer cascade for velocity and multiple line estimation

abstract:
  Previous incremental estimation methods consider estimating a single line,
requiring as many observers as the number of lines to be mapped. This leads to
the need for having at least $4N$ state variables, with $N$ being the number of
lines. This paper presents the first approach for multi-line incremental
estimation. Since lines are common in structured environments, we aim to
exploit that structure to reduce the state space. The modeling of structured
environments proposed in this paper reduces the state space to $3N + 3$ and is
also less susceptible to singular configurations. An assumption the previous
methods make is that the camera velocity is available at all times. However,
the velocity is usually retrieved from odometry, which is noisy. With this in
mind, we propose coupling the camera with an Inertial Measurement Unit (IMU)
and an observer cascade. A first observer retrieves the scale of the linear
velocity and a second observer for the lines mapping. The stability of the
entire system is analyzed. The cascade is shown to be asymptotically stable and
shown to converge in experiments with simulated data.


title:
LatentFormer: Multi-Agent Transformer-Based Interaction Modeling and
  Trajectory Prediction

abstract:
  Multi-agent trajectory prediction is a fundamental problem in autonomous
driving. The key challenges in prediction are accurately anticipating the
behavior of surrounding agents and understanding the scene context. To address
these problems, we propose LatentFormer, a transformer-based model for
predicting future vehicle trajectories. The proposed method leverages a novel
technique for modeling interactions among dynamic objects in the scene.
Contrary to many existing approaches which model cross-agent interactions
during the observation time, our method additionally exploits the future states
of the agents. This is accomplished using a hierarchical attention mechanism
where the evolving states of the agents autoregressively control the
contributions of past trajectories and scene encodings in the final prediction.
Furthermore, we propose a multi-resolution map encoding scheme that relies on a
vision transformer module to effectively capture both local and global scene
context to guide the generation of more admissible future trajectories. We
evaluate the proposed method on the nuScenes benchmark dataset and show that
our approach achieves state-of-the-art performance and improves upon trajectory
metrics by up to 40%. We further investigate the contributions of various
components of the proposed technique via extensive ablation studies.


title: Understanding Failure Modes of Self-Supervised Learning

abstract:
  Self-supervised learning methods have shown impressive results in downstream
classification tasks. However, there is limited work in understanding their
failure models and interpreting the learned representations of these models. In
this paper, we tackle these issues and study the representation space of
self-supervised models by understanding the underlying reasons for
misclassifications in a downstream task. Over several state-of-the-art
self-supervised models including SimCLR, SwaV, MoCo V2 and BYOL, we observe
that representations of correctly classified samples have few discriminative
features with highly deviated values compared to other features. This is in a
clear contrast with representations of misclassified samples. We also observe
that noisy features in the representation space often correspond to spurious
attributes in images making the models less interpretable. Building on these
observations, we propose a sample-wise Self-Supervised Representation Quality
Score (or, Q-Score) that, without access to any label information, is able to
predict if a given sample is likely to be misclassified in the downstream task,
achieving an AUPRC of up to 0.90. Q-Score can also be used as a regularization
to remedy low-quality representations leading to 3.26% relative improvement in
accuracy of SimCLR on ImageNet-100. Moreover, we show that Q-Score
regularization increases representation sparsity, thus reducing noise and
improving interpretability through gradient heatmaps.


title:
DenseUNets with feedback non-local attention for the segmentation of
  specular microscopy images of the corneal endothelium with guttae

abstract:
  To estimate the corneal endothelial parameters from specular microscopy
images depicting cornea guttata (Fuchs dystrophy), we propose a new deep
learning methodology that includes a novel attention mechanism named feedback
non-local attention (fNLA). Our approach first infers the cell edges, then
selects the cells that are well detected, and finally applies a postprocessing
method to correct mistakes and provide the binary segmentation from which the
corneal parameters are estimated (cell density [ECD], coefficient of variation
[CV], and hexagonality [HEX]). In this study, we analyzed 1203 images acquired
with a Topcon SP-1P microscope, 500 of which contained guttae. Manual
segmentation was performed in all images. We compared the results of different
networks (UNet, ResUNeXt, DenseUNets, UNet++) and found that DenseUNets with
fNLA provided the best performance, with a mean absolute error of 23.16
[cells/mm$^{2}$] in ECD, 1.28 [%] in CV, and 3.13 [%] in HEX, which was 3-6
times smaller than the error obtained by Topcon's built-in software. Our
approach handled the cells affected by guttae remarkably well, detecting cell
edges occluded by small guttae while discarding areas covered by large guttae.
Overall, the proposed method obtained accurate estimations in extremely
challenging specular images.


title:
ROCT-Net: A new ensemble deep convolutional model with improved spatial
  resolution learning for detecting common diseases from retinal OCT images

abstract:
  Optical coherence tomography (OCT) imaging is a well-known technology for
visualizing retinal layers and helps ophthalmologists to detect possible
diseases. Accurate and early diagnosis of common retinal diseases can prevent
the patients from suffering critical damages to their vision. Computer-aided
diagnosis (CAD) systems can significantly assist ophthalmologists in improving
their examinations. This paper presents a new enhanced deep ensemble
convolutional neural network for detecting retinal diseases from OCT images.
Our model generates rich and multi-resolution features by employing the
learning architectures of two robust convolutional models. Spatial resolution
is a critical factor in medical images, especially the OCT images that contain
tiny essential points. To empower our model, we apply a new post-architecture
model to our ensemble model for enhancing spatial resolution learning without
increasing computational costs. The introduced post-architecture model can be
deployed to any feature extraction model to improve the utilization of the
feature map's spatial values. We have collected two open-source datasets for
our experiments to make our models capable of detecting six crucial retinal
diseases: Age-related Macular Degeneration (AMD), Central Serous Retinopathy
(CSR), Diabetic Retinopathy (DR), Choroidal Neovascularization (CNV), Diabetic
Macular Edema (DME), and Drusen alongside the normal cases. Our experiments on
two datasets and comparing our model with some other well-known deep
convolutional neural networks have proven that our architecture can increase
the classification accuracy up to 5%. We hope that our proposed methods create
the next step of CAD systems development and help future researches. The code
of this paper is shared at https://github.com/mr7495/OCT-classification.


title: Graph Neural Networks for Multimodal Single-Cell Data Integration

abstract:
  Recent advances in multimodal single-cell technologies have enabled
simultaneous acquisitions of multiple omics data from the same cell, providing
deeper insights into cellular states and dynamics. However, it is challenging
to learn the joint representations from the multimodal data, model the
relationship between modalities, and, more importantly, incorporate the vast
amount of single-modality datasets into the downstream analyses. To address
these challenges and correspondingly facilitate multimodal single-cell data
analyses, three key tasks have been introduced: $\textit{modality prediction}$,
$\textit{modality matching}$ and $\textit{joint embedding}$. In this work, we
present a general Graph Neural Network framework $\textit{scMoGNN}$ to tackle
these three tasks and show that $\textit{scMoGNN}$ demonstrates superior
results in all three tasks compared with the state-of-the-art and conventional
approaches. Our method is an official winner in the overall ranking of
$\textit{modality prediction}$ from
$\href{https://openproblems.bio/neurips_2021/}{\textit{NeurIPS 2021
Competition}}$.


title: TCTrack: Temporal Contexts for Aerial Tracking

abstract:
  Temporal contexts among consecutive frames are far from being fully utilized
in existing visual trackers. In this work, we present TCTrack, a comprehensive
framework to fully exploit temporal contexts for aerial tracking. The temporal
contexts are incorporated at \textbf{two levels}: the extraction of
\textbf{features} and the refinement of \textbf{similarity maps}. Specifically,
for feature extraction, an online temporally adaptive convolution is proposed
to enhance the spatial features using temporal information, which is achieved
by dynamically calibrating the convolution weights according to the previous
frames. For similarity map refinement, we propose an adaptive temporal
transformer, which first effectively encodes temporal knowledge in a
memory-efficient way, before the temporal knowledge is decoded for accurate
adjustment of the similarity map. TCTrack is effective and efficient:
evaluation on four aerial tracking benchmarks shows its impressive performance;
real-world UAV tests show its high speed of over 27 FPS on NVIDIA Jetson AGX
Xavier.


title:
Excitation of electrostatic solitary waves and surface waves in ion beam
  neutralization process

abstract:
  Unusually long electrostatic solitary waves (ESWs) are discovered in 2D and
3D Particle-in-Cell studies of the process of ion beam neutralization by
electron emission from filaments. These ESWs are long because trapped and
untrapped electron density perturbations nearly compensate each other. Surface
waves were discovered in the process of neutralization but were only observed
in 3D simulations. This is because the phase velocity of surface waves in a 2D
geometry is higher than in 3D giving the high-energy electrons generated
upstream near the electron source enough energy to excite such waves only in 3D
cylindrical beams.


title:
Natural Almost Hermitian Structures on Conformally Foliated
  4-Dimensional Lie Groups with Minimal Leaves

abstract:
  Let $(G,g)$ be a 4-dimensional Riemannian Lie group with a 2-dimensional
left-invariant, conformal foliation $\mathcal{F}$ with minimal leaves. Let $J$
be an almost Hermitian structure on $G$ adapted to the foliation $\mathcal{F}$.
The corresponding Lie algebra $\mathfrak{g}$ must then belong to one of 20
families $\mathfrak{g}_1,\dots,\mathfrak{g}_{20}$ according to S. Gudmundsson
and M. Svensson. We classify such structures $J$ which are almost K\"{a}hler
$(\mathcal{A}\mathcal{K})$, integrable $(\mathcal{I})$ or K\"{a}hler
$(\mathcal{K})$. Hereby, we construct 16 multi-dimensional almost K\"{a}hler
families, 18 integrable families and 11 K\"{a}hler families.


title:
Discovery of charge order and corresponding edge state in kagome magnet
  FeGe

abstract:
  Kagome materials often host exotic quantum phases, including spin liquids,
Chern gap, charge order, and superconductivity. Existing scanning microscopy
studies of the kagome charge order have been limited to non-kagome surface
layers. Here we tunnel into the kagome lattice of FeGe to uncover features of
the charge order. Our spectroscopic imaging identifes a 2x2 charge order in the
magnetic kagome lattice, resembling that discovered in kagome superconductors.
Spin-mapping across steps of unit-cell-height demonstrates that this charge
order emerges from spin-polarized electrons with an antiferromagnetic stacking
order. We further uncover the correlation between antiferromagnetism and charge
order anisotropy, highlighting the unusual magnetic coupling of the charge
order. Finally, we detect a pronounced edge state within the charge order
energy gap, which is robust against the irregular shape of the kagome lattice
edges. We discuss our results with the theoretically considered topological
features of the kagome charge order including orbital magnetism and
bulk-boundary correspondence.


title: Quantum Reinforcement Learning via Policy Iteration

abstract:
  Quantum computing has shown the potential to substantially speed up machine
learning applications, in particular for supervised and unsupervised learning.
Reinforcement learning, on the other hand, has become essential for solving
many decision making problems and policy iteration methods remain the
foundation of such approaches. In this paper, we provide a general framework
for performing quantum reinforcement learning via policy iteration. We validate
our framework by designing and analyzing: \emph{quantum policy evaluation}
methods for infinite horizon discounted problems by building quantum states
that approximately encode the value function of a policy $\pi$; and
\emph{quantum policy improvement} methods by post-processing measurement
outcomes on these quantum states. Last, we study the theoretical and
experimental performance of our quantum algorithms on two environments from
OpenAI's Gym.


title: Integrated correlators in $\mathcal{N}=4$ super Yang-Mills and periods

abstract:
  We study perturbative aspects of recently proposed integrated four-point
correlators in $\mathcal{N}=4$ supersymmetric Yang-Mills with all classical
gauge groups using standard Feynman diagram computations. We argue that
perturbative contributions of the integrated correlators are given by linear
combinations of periods of certain conformal Feynman graphs, which were
originally introduced for the construction of perturbative loop integrands of
the un-integrated correlator. This observation allows us to evaluate the
integrated correlators to high loop orders. We explicitly compute one of the
integrated correlators up to four loops in the planar limit, and up to three
loops for the other integrated correlator, and find agreement with the results
obtained from supersymmetric localisation. The identification between the
integrated correlators and certain periods also implies non-trivial relations
among these periods, given that one may compute the integrated correlators
using localisation. We illustrate this idea by considering one of the
integrated correlators at five loops in the planar limit, where the
localisation result leads to a prediction for the period of a certain six-loop
integral.


title:
Feasibility of detecting and characterizing embedded low-mass giant
  planets in gaps in the VIS/NIR wavelength range

abstract:
  High-contrast imaging in the visible and near-infrared (VIS/NIR) has revealed
the presence of a plethora of substructures in circumstellar disks (CSDs). One
of the most commonly observed substructures are concentric gaps that may hint
at the presence of embedded forming planets. However, direct detections of them
are rare, and thus ambiguity regarding the origin of most gap features remains.
The aim of this study is to investigate the capabilities of high-contrast
VIS/NIR imaging of directly detecting and characterizing low-mass giant planets
in gaps. To this end, a grid of models of protoplanetary disks was generated.
The models include a central T Tauri star surrounded by a face-on CSD harboring
an accreting planet, which itself is surrounded by a circumplanetary disk (CPD)
and carves a gap. We use the Monte Carlo radiative transfer code Mol3D to
generate temperature distributions and synthetic observations. Based on these
simulations, we measured the impact the planet and its CPD have on contrast
curves and quantified the impact of the observing wavelength and of five key
parameters on the determined signal strength. Then, we applied a detection
criterion on our results and assess the capabilities of SPHERE/VLT of detecting
the embedded planets. We find that a part of the investigated parameter space
includes detectable planets, and we elaborate on the implications of a
non-detection. Furthermore, we analyze the potential loss of valuable
information by the use of a too small coronagraphic mask. However, we find this
outcome to be very unlikely. Finally, within the VIS/NIR we identify for each
of the investigated basic properties of the planets and the disks the most
promising observing wavelengths that enable us to distinguish between different
underlying parameter values. We find, that the detectability and the
characterization often benefit from different observing wavelengths.


title:
Incompressible polar active fluids with quenched disorder in dimensions
  $d> 2$

abstract:
  We present a hydrodynamic theory of incompressible polar active fluids with
quenched disorder. This theory shows that such fluids can overcome the
disruption caused by the quenched disorder and move coherently, in the sense of
having a non-zero mean velocity in the hydrodynamic limit. However, the scaling
behavior of this class of active systems cannot be described by linearized
hydrodynamics in spatial dimensions between 2 and 5. Nonetheless, we obtain the
exact dimension-dependent scaling exponents in these dimensions.


title:
Generating Synthetic but Realistic Human Trafficking Networks for
  Modeling Disruptions through Transdisciplinary and Community-Based Action
  Research

abstract:
  One of the major challenges associated with applying operations research (OR)
models to disrupting human trafficking networks is a limited amount of reliable
data sources readily available for public use, since operations are
intentionally hidden to prevent detection, and data from known operations are
often incomplete. To help address this data gap, we propose a network generator
for domestic sex trafficking networks by integrating OR concepts and
qualitative research. Multiple sources have been triangulated to ensure that
networks produced by the generator are realistic, including law enforcement
case file analysis, interviews with domain experts, and a survivor-centered
advisory group with first-hand knowledge of sex trafficking. The output models
the relationships between traffickers, so-called "bottoms", and victims. This
generator allows operations researchers to access realistic sex trafficking
network structures in a responsible manner that does not disclose identifiable
details of the people involved. We demonstrate the use of output networks in
exploring policy recommendations from max flow network interdiction with
restructuring. To do so, we propose a novel conceptualization of flow as the
ability of a trafficker to control their victims. Our results show the
importance of understanding how sex traffickers react to disruptions,
especially in terms of recruiting new victims.


title:
Reconstruction of univariate functions from directional persistence
  diagrams

abstract:
  We describe a method for approximating a single-variable function $f$ using
persistence diagrams of sublevel sets of $f$ from height functions in different
directions. We provide algorithms for the piecewise linear case and for the
smooth case. Three directions suffice to locate all local maxima and minima of
a piecewise linear continuous function from its collection of directional
persistence diagrams, while five directions are needed in the case of smooth
functions with non-degenerate critical points.
  Our approximation of functions by means of persistence diagrams is motivated
by a study of importance attribution in machine learning, where one seeks to
reduce the number of critical points of signal functions without a significant
loss of information for a neural network classifier.


title:
Improving Health Mentioning Classification of Tweets using Contrastive
  Adversarial Training

abstract:
  Health mentioning classification (HMC) classifies an input text as health
mention or not. Figurative and non-health mention of disease words makes the
classification task challenging. Learning the context of the input text is the
key to this problem. The idea is to learn word representation by its
surrounding words and utilize emojis in the text to help improve the
classification results. In this paper, we improve the word representation of
the input text using adversarial training that acts as a regularizer during
fine-tuning of the model. We generate adversarial examples by perturbing the
embeddings of the model and then train the model on a pair of clean and
adversarial examples. Additionally, we utilize contrastive loss that pushes a
pair of clean and perturbed examples close to each other and other examples
away in the representation space. We train and evaluate the method on an
extended version of the publicly available PHM2017 dataset. Experiments show an
improvement of 1.0% over BERT-Large baseline and 0.6% over RoBERTa-Large
baseline, whereas 5.8% over the state-of-the-art in terms of F1 score.
Furthermore, we provide a brief analysis of the results by utilizing the power
of explainable AI.


title: Triangulations of Flow Polytopes, Ample Framings, and Gentle Algebras

abstract:
  The cone of nonnegative flows for a directed acyclic graph (DAG) is known to
admit regular unimodular triangulations induced by framings of the DAG. These
triangulations restrict to triangulations of the flow polytope for strength one
flows, which are called DKK triangulations. For a special class of framings
called ample framings, these triangulations of the flow cone project to a
complete fan. We characterize the DAGs that admit ample framings, and we
enumerate the number of ample framings for a fixed DAG. We establish a
connection between maximal simplices in DKK triangulations and $\tau$-tilting
posets for certain gentle algebras, which allows us to impose a poset structure
on the dual graph of any DKK triangulation for an amply framed DAG. Using this
connection, we are able to prove that for full DAGs, i.e., those DAGs with
inner vertices having in-degree and out-degree equal to two, the flow polytopes
are Gorenstein and have unimodal Ehrhart $h^\ast$-polynomials.


title: A general adaptive framework for multivariate point null testing

abstract:
  As a common step in refining their scientific inquiry, investigators are
often interested in performing some screening of a collection of given
statistical hypotheses. For example, they may wish to determine whether any one
of several patient characteristics are associated with a health outcome of
interest. Existing generic methods for testing a multivariate hypothesis --
such as multiplicity corrections applied to individual hypothesis tests -- can
easily be applied across a variety of problems but can suffer from low power in
some settings. Tailor-made procedures can attain higher power by building
around problem-specific information but typically cannot be easily adapted to
novel settings. In this work, we propose a general framework for testing a
multivariate point null hypothesis in which the test statistic is adaptively
selected to provide increased power. We present theoretical large-sample
guarantees for our test under both fixed and local alternatives. In simulation
studies, we show that tests created using our framework can perform as well as
tailor-made methods when the latter are available, and we illustrate how our
procedure can be used to create tests in two settings in which tailor-made
methods are not currently available.


title:
Determination of the Solar Rotation Elements and Period from Ru{\dj}er
  Bo\v{s}kovi\'c's Sunspot Observations in 1777

abstract:
  This paper focuses on the observations of sunspots made by Ru{\dj}er
Bo\v{s}kovi\'c in 1777. We derived the expressions needed to calculate the
elements of the Sun's rotation and period from observations. We used modern
ephemeris data in the processing of the observation results. Obtained results
are very similar to Bo\v{s}kovi\'c's original calculations. In addition to
historical significance, they also provide scientifically valuable data on the
Sun's differential rotation, which plays a significant role in generating and
maintaining solar magnetic activity.


title:
Field theory for zero temperature soft anharmonic spin glasses in a
  field

abstract:
  We introduce a finite dimensional anharmonic soft spin glass in a field and
show how it allows the construction a field theory at zero temperature and the
corresponding loop expansion. The mean field level of the model coincides with
a recently introduced fully connected model, the KHGPS model, and it has a spin
glass transition in a field at zero temperature driven by the appearance of
pseudogapped non-linear excitations. We analyze the zero temperature limit of
the theory and the behavior of the bare masses and couplings on approaching the
mean field zero temperature critical point. Focusing on the so called replicon
sector of the field theory, we show that the bare mass corresponding to
fluctuations in this sector is strictly positive at the transition in a certain
region of control parameter space. At the same time the two relevant cubic
coupling constants $g_1$ and $g_2$ show a non-analytic behavior in their bare
values: approaching the critical point at zero temperature, $g_1\to \infty$
while $g_2\propto T$ with a prefactor diverging at the transition. Along the
same lines we also develop the field theory to study the density of states of
the model in finite dimension. We show that in the mean field limit the density
of states converges to the one of the KHGPS model. However the construction
allows a treatment of finite dimensional effects in perturbation theory.


title: Sparse Bayesian Optimization

abstract:
  Bayesian optimization (BO) is a powerful approach to sample-efficient
optimization of black-box objective functions. However, the application of BO
to areas such as recommendation systems often requires taking the
interpretability and simplicity of the configurations into consideration, a
setting that has not been previously studied in the BO literature. To make BO
applicable in this setting, we present several regularization-based approaches
that allow us to discover sparse and more interpretable configurations. We
propose a novel differentiable relaxation based on homotopy continuation that
makes it possible to target sparsity by working directly with $L_0$
regularization. We identify failure modes for regularized BO and develop a
hyperparameter-free method, sparsity exploring Bayesian optimization (SEBO)
that seeks to simultaneously maximize a target objective and sparsity. SEBO and
methods based on fixed regularization are evaluated on synthetic and real-world
problems, and we show that we are able to efficiently optimize for sparsity.


title: Cubic sublattices

abstract:
  A sublattice of the three-dimensional integer lattice $\mathbb Z^3$ is called
cubic sublattice if there exists a basis of the sublattice whose elements are
pairwise orthogonal and of equal lengths. We show that for an integer vector
$\mathbf v\in\mathbb Z^3$ whose squared length is divisible by $d^2$, there
exists a cubic sublattice containing $\mathbf v$ with edge length $d$. This
improves one of the main result of a paper [arxiv:0806.3943] of Goswick et al.,
where similar theorem was proved by using the decomposition theory of Hurwitz
integral quaternions. We give an elementary proof heavily using cross product.
This method allows us to characterize the cubic sublattices.


title: Instance Segmentation for Autonomous Log Grasping in Forestry Operations

abstract:
  Wood logs picking is a challenging task to automate. Indeed, logs usually
come in cluttered configurations, randomly orientated and overlapping. Recent
work on log picking automation usually assume that the logs' pose is known,
with little consideration given to the actual perception problem. In this
paper, we squarely address the latter, using a data-driven approach. First, we
introduce a novel dataset, named TimberSeg 1.0, that is densely annotated,
i.e., that includes both bounding boxes and pixel-level mask annotations for
logs. This dataset comprises 220 images with 2500 individually segmented logs.
Using our dataset, we then compare three neural network architectures on the
task of individual logs detection and segmentation; two region-based methods
and one attention-based method. Unsurprisingly, our results show that
axis-aligned proposals, failing to take into account the directional nature of
logs, underperform with 19.03 mAP. A rotation-aware proposal method
significantly improve results to 31.83 mAP. More interestingly, a
Transformer-based approach, without any inductive bias on rotations,
outperformed the two others, achieving a mAP of 57.53 on our dataset. Our use
case demonstrates the limitations of region-based approaches for cluttered,
elongated objects. It also highlights the potential of attention-based methods
on this specific task, as they work directly at the pixel-level. These
encouraging results indicate that such a perception system could be used to
assist the operators on the short-term, or to fully automate log picking
operations in the future.


title:
Pay Attention to Relations: Multi-embeddings for Attributed Multiplex
  Networks

abstract:
  Graph Convolutional Neural Networks (GCNs) have become effective machine
learning algorithms for many downstream network mining tasks such as node
classification, link prediction, and community detection. However, most GCN
methods have been developed for homogenous networks and are limited to a single
embedding for each node. Complex systems, often represented by heterogeneous,
multiplex networks present a more difficult challenge for GCN models and
require that such techniques capture the diverse contexts and assorted
interactions that occur between nodes. In this work, we propose RAHMeN, a novel
unified relation-aware embedding framework for attributed heterogeneous
multiplex networks. Our model incorporates node attributes, motif-based
features, relation-based GCN approaches, and relational self-attention to learn
embeddings of nodes with respect to the various relations in a heterogeneous,
multiplex network. In contrast to prior work, RAHMeN is a more expressive
embedding framework that embraces the multi-faceted nature of nodes in such
networks, producing a set of multi-embeddings that capture the varied and
diverse contexts of nodes.
  We evaluate our model on four real-world datasets from Amazon, Twitter,
YouTube, and Tissue PPIs in both transductive and inductive settings. Our
results show that RAHMeN consistently outperforms comparable state-of-the-art
network embedding models, and an analysis of RAHMeN's relational self-attention
demonstrates that our model discovers interpretable connections between
relations present in heterogeneous, multiplex networks.


title:
Detection of the S(1) Rotational Line of H2 toward IRC+10216: A
  Simultaneous Measurement of Mass-Loss Rate and CO Abundance

abstract:
  We report the first detection of the S(1) pure rotational line of ortho-H2 at
17.04 um in an asymptotic giant branch star, using observations of IRC+10216
with the Echelon-cross-echelle Spectrograph (EXES) mounted on the Stratospheric
Observatory for Infrared Astronomy (SOFIA). This line, which was observed in a
very high sensitivity spectrum (RMS noise ~0.04% of the continuum), was
detected in the wing of a strong telluric line and displayed a P Cygni profile.
The spectral ranges around the frequencies of the S(5) and S(7) ortho-H2
transitions were observed as well but no feature was detected in spectra with
sensitivities of 0.12% and 0.09% regarding the continuum emission,
respectively. We used a radiation transfer code to model these three lines and
derived a mass-loss rate of 2.43(0.21)E-05 M_sun/yr without using the CO
abundance. The comparison of this rate with previous estimates derived from CO
observations suggests that the CO abundance relative to H2 is 6.7(1.4)E-04.
From this quantity and previously reported molecular abundances, we estimate
the O/H and C/H ratios to be 3.3(0.7)E-04 and >5.2(0.9)E-04, respectively. The
C/O ratio is >1.5(0.4). The absence of the S(5) and S(7) lines of ortho-H2 in
our observations can be explained by the opacity of hot dust within 5R* from
the center of the star. We estimate the intensity of the S(0) and S(2) lines of
para-H2 to be ~0.1% and 0.2% of the continuum, respectively, which are below
the detection limit of EXES.


title:
Time-resolved Ultrafast Interatomic Coulombic Decay in Superexcited
  Sodium-doped Helium Nanodroplets

abstract:
  The autoionization dynamics of superexcited superfluid He nanodroplets doped
with Na atoms is studied by extreme-ultraviolet (XUV) time-resolved electron
spectroscopy. Following excitation into the higher-lying droplet absorption
band, the droplet relaxes into the lowest metastable atomic $1s2s$ $^{1,\,3}$S
states from which Interatomic Coulombic Decay (ICD) takes places either between
two excited He atoms or between an excited He atom and a Na atom attached to
the droplet surface. Four main ICD channels are identified and their time
constants are determined by varying the delay between the XUV pulse and a UV
pulse that ionizes the initial excited state and thereby quenches ICD. The time
constants for the different channels all fall in the range $\sim$1~ps
indicating that the ICD dynamics are mainly determined by the droplet
environment. A periodic modulation of the transient ICD signals is tentatively
attributed to the oscillation of the bubble forming around the localized He
excitation. The ICD efficiency depends on the total number of excited states in
a droplet rather than the density of excited states pointing to a collective
enhancement of ICD.


title: The Kawahara Equation: Traveling Wave Solutions Joining Periodic Waves

abstract:
  The Kawahara equation is a weakly nonlinear long-wave model of dispersive
waves that emerges when leading order dispersive effects are in balance with
the next order correction. Traveling wave solutions of the Kawahara equation
satisfy a fourth-order ordinary differential equation in which the traveling
wave speed is a parameter. The fourth order equation has Hamiltonian structure
and admits a two-parameter family of single-phase periodic solutions with
varying speed and Hamiltonian. A set of jump conditions is derived for pairs of
periodic solutions with equal speed and Hamiltonian. These are necessary
conditions for the existence of traveling waves that asymptote to the periodic
orbits at $\pm \infty$. Bifurcation theory and parameter continuation are used
to construct multiple solution branches of the jump conditions. For each pair
of compatible periodic solutions, the heteroclinic orbit representing the
traveling wave is constructed from the intersection of stable and unstable
manifolds of the periodic orbits. Each branch terminates at an
equilibrium-to-periodic solution in which the equilibrium is the background for
a solitary wave that connects to the associated periodic solution.


title:
Computer Vision Aided Blockage Prediction in Real-World Millimeter Wave
  Deployments

abstract:
  This paper provides the first real-world evaluation of using visual (RGB
camera) data and machine learning for proactively predicting millimeter wave
(mmWave) dynamic link blockages before they happen. Proactively predicting
line-of-sight (LOS) link blockages enables mmWave/sub-THz networks to make
proactive network management decisions, such as proactive beam switching and
hand-off) before a link failure happens. This can significantly enhance the
network reliability and latency while efficiently utilizing the wireless
resources. To evaluate this gain in reality, this paper (i) develops a computer
vision based solution that processes the visual data captured by a camera
installed at the infrastructure node and (ii) studies the feasibility of the
proposed solution based on the large-scale real-world dataset, DeepSense 6G,
that comprises multi-modal sensing and communication data. Based on the adopted
real-world dataset, the developed solution achieves $\approx 90\%$ accuracy in
predicting blockages happening within the future $0.1$s and $\approx 80\%$ for
blockages happening within $1$s, which highlights a promising solution for
mmWave/sub-THz communication networks.


title: On the non-integrality of certain generalized binomial sums

abstract:
  We consider certain generalized binomial sums $\mathcal{S}_{(r,n)}(\ell)$ and
discuss the non-integrality of their values for integral parameters $n,r \geq
1$ and $\ell \in \mathbb{Z}$ in several cases using $p$-adic methods. In
particular, we show some properties of the denominator of
$\mathcal{S}_{(r,n)}(\ell)$. Viewed as polynomials, the sequence
$(\mathcal{S}_{(r,n)}(x))_{n \geq 0}$ forms an Appell sequence. The special
case $\mathcal{S}_{(r,n)}(2)$ reduces to the sum \[
  \sum_{k=0}^{n} \binom{n}{k} \frac{r}{r+k}, \] which has received some
attention in recent times from several authors regarding the conjectured
non-integrality of its values. Only specific cases have been proved yet. The
generalized results imply, among other things, for even $|\ell| \geq 2$ that
$\mathcal{S}_{(r,n)}(\ell) \notin \mathbb{Z}$ when $\binom{r+n}{r}$ is even,
e.g., $r$ and $n$ are odd. Although there exist exceptions where
$\mathcal{S}_{(r,n)}(\ell) \in \mathbb{Z}$, "almost all" values of
$\mathcal{S}_{(r,n)}(\ell)$ for $n,r \geq 1$ are non-integral for any fixed
$|\ell| \geq 2$. Subsequently, we also derive explicit inequalities between the
parameters for which $\mathcal{S}_{(r,n)}(\ell) \notin \mathbb{Z}$. Especially,
this is shown for certain small values of $\ell$ for $r \geq n$ and $n > r \geq
\frac{1}{5} n$. As a supplement, we finally discuss exceptional cases where
$\mathcal{S}_{(r,n)}(\ell) \in \mathbb{Z}$.