title:
Stellar graveyards: Clustering of compact objects in globular clusters
  NGC 3201 and NGC 6397

abstract:
  We analyse Gaia EDR3 and re-calibrated HST proper motion data from the
core-collapsed and non core-collapsed globular clusters NGC 6397 and NGC 3201,
respectively, with the Bayesian mass-orbit modelling code MAMPOSSt-PM. We use
Bayesian evidence and realistic mock data sets constructed with AGAMA to select
between different mass models. In both clusters, the velocities are consistent
with isotropy within the extent of our data. We robustly detect a dark central
mass (DCM) of roughly 1000 solar masses in both clusters. Our MAMPOSSt-PM fits
strongly prefer an extended DCM in NGC 6397, while only presenting a mild
preference for it in NGC 3201, with respective sizes of a roughly one and a few
per cent of the cluster effective radius. We explore the astrophysics behind
our results with the CMC Monte Carlo N-body code, whose snapshots best matching
the phase space observations lead to similar values for the mass and size of
the DCM. The internal kinematics are thus consistent with a population of
hundreds of massive white dwarfs in NGC 6397, and roughly 100 segregated
stellar-mass black holes in NGC 3201, as previously found with CMC. Such
analyses confirm the accuracy of both mass-orbit modelling and Monte Carlo
N-body techniques, which together provide more robust predictions on the DCM of
globular clusters (core-collapsed or not). This opens possibilities to
understand a vast range of interesting astrophysical phenomena in clusters,
such as fast radio bursts, compact object mergers, and gravitational waves.


title: Context-Based MEC Platform for Augmented-Reality Services in 5G Networks

abstract:
  Augmented reality (AR) has drawn great attention in recent years. However,
current AR devices have drawbacks, e.g., weak computation ability and large
power consumption. To solve the problem, mobile edge computing (MEC) can be
introduced as a key technology to offload data and computation from AR devices
to MEC servers via 5th Generation Mobile Communication Technology (5G)
networks. To this end, a context-based MEC platform for AR services in 5G
networks is proposed in this paper. On the platform, MEC is employed as a data
processing center while AR devices are simplified as universal input/output
devices, which overcomes their limitations and achieves better user experience.
Moreover, the proof-of-concept (PoC) hardware prototype of the platform, and
two typical use cases providing AR services of navigation and face recognition
respectively are implemented to demonstrate the feasibility and effectiveness
of the platform. Finally, the performance of the platform is also numerically
evaluated, and the results validate the system design and agree well with the
design expectations.


title: A time dependent singularly perturbed problem with shift in space

abstract:
  We consider a singularly perturbed time dependent problem with a shift term
in space. On appropriately defined layer adapted meshes of Dur\'an- and S-type
we derive a-priori error estimates for the stationary problem. Using a
discontinuous Galerkin method in time we obtain error estimates for the full
discretisation. Introduction of a weighted scalar products and norms allows us
to estimate the time-dependent problem in energy and balanced norm. So far it
was open to prove such a result. Some numerical results are given to confirm
the predicted theory and to show the effect of shifts on the solution.


title:
The Disagreement Problem in Explainable Machine Learning: A
  Practitioner's Perspective

abstract:
  As various post hoc explanation methods are increasingly being leveraged to
explain complex models in high-stakes settings, it becomes critical to develop
a deeper understanding of if and when the explanations output by these methods
disagree with each other, and how such disagreements are resolved in practice.
However, there is little to no research that provides answers to these critical
questions. In this work, we introduce and study the disagreement problem in
explainable machine learning. More specifically, we formalize the notion of
disagreement between explanations, analyze how often such disagreements occur
in practice, and how do practitioners resolve these disagreements. To this end,
we first conduct interviews with data scientists to understand what constitutes
disagreement between explanations generated by different methods for the same
model prediction, and introduce a novel quantitative framework to formalize
this understanding. We then leverage this framework to carry out a rigorous
empirical analysis with four real-world datasets, six state-of-the-art post hoc
explanation methods, and eight different predictive models, to measure the
extent of disagreement between the explanations generated by various popular
explanation methods. In addition, we carry out an online user study with data
scientists to understand how they resolve the aforementioned disagreements. Our
results indicate that state-of-the-art explanation methods often disagree in
terms of the explanations they output. Our findings also underscore the
importance of developing principled evaluation metrics that enable
practitioners to effectively compare explanations.


title:
Deep inelastic collision of two-dimensional anisotropic dipolar
  condensate solitons

abstract:
  The possibility of generating stable anisotropic solitons in dipolar
Bose-Einstein condensates (BECs) leads to a different scenario not possible in
a nondipolar BEC with contact interaction. We study the statics and dynamics of
anisotropic bright solitons in quasi-two-dimensional BECs consisting of
polarized dipolar atoms.We study the collision dynamics of two such solitons at
different velocities for different angles between the polarization and
collision directions. The collision is found to be quasi elastic at large
velocities. At small velocities the collision is inelastic leading to the
formation of a coalesced soliton in an excited scissors mode, monopole mode or
quadrupole mode. Also, at small velocities, after collision, a large change of
direction of motion of the solitons is possible. The investigation is performed
through a numerical solution of the underlying mean-field Gross-Pitaevskii
equation.


title:
A Taxonomy for Contrasting Industrial Control Systems Asset Discovery
  Tools

abstract:
  Asset scanning and discovery is the first and foremost step for organizations
to understand what assets they have and what to protect. There is currently a
plethora of free and commercial asset scanning tools specializing in
identifying assets in industrial control systems (ICS). However, there is
little information available on their comparative capabilities and how their
respective features contrast. Nor is it clear to what depth of scanning these
tools can reach and whether they are fit-for-purpose in a scaled industrial
network architecture. We provide the first systematic feature comparison of
free-to-use asset scanning tools on the basis of an ICS scanning taxonomy that
we propose. Based on the taxonomy, we investigate scanning depths reached by
the tools' features and validate our investigation through experimentation on
Siemens, Schneider Electric, and Allen Bradley devices in a testbed
environment.


title:
Brownian non-Gaussian polymer diffusion and queing theory in the
  mean-field limit

abstract:
  We link the Brownian non-Gaussian diffusion of a polymer center of mass to a
microscopic cause: the polymerization/depolymerization phenomenon occurring
when the polymer is in contact with a monomer chemostat. The anomalous behavior
is triggered by the polymer critical point, separating the dilute and the dense
phase in the grand canonical ensemble. In the mean-field limit we establish
contact with queuing theory and show that the kurtosis of the polymer center of
mass diverges alike a response function when the system becomes critical, a
result which holds for general polymer dynamics (Zimm, Rouse, reptation). Both
the equilibrium and nonequilibrium behaviors are solved exactly as a reference
study for novel stochastic modeling and experimental setup.


title: Graph Coloring with Physics-Inspired Graph Neural Networks

abstract:
  We show how graph neural networks can be used to solve the canonical graph
coloring problem. We frame graph coloring as a multi-class node classification
problem and utilize an unsupervised training strategy based on the statistical
physics Potts model. Generalizations to other multi-class problems such as
community detection, data clustering, and the minimum clique cover problem are
straightforward. We provide numerical benchmark results and illustrate our
approach with an end-to-end application for a real-world scheduling use case
within a comprehensive encode-process-decode framework. Our optimization
approach performs on par or outperforms existing solvers, with the ability to
scale to problems with millions of variables.

categories: cs.LG cond-mat.dis-nn cs.AI math.OC quant-ph


title: A new analytical scattering phase function for interstellar dust

abstract:
  Context: Properly modelling scattering by interstellar dust grains requires a
good characterisation of the scattering phase function. The Henyey-Greenstein
phase function has become the standard for describing anisotropic scattering by
dust grains, but it is a poor representation of the real scattering phase
function outside the optical range. Aims: We investigate alternatives for the
Henyey-Greenstein phase function that would allow the scattering properties of
dust grains to be described. Our goal is to find a balance between realism and
complexity: the scattering phase function should be flexible enough to provide
an accurate fit to the scattering properties of dust grains over a wide
wavelength range, and it should be simple enough to be easy to handle,
especially in the context of radiative transfer calculations. Methods: We fit
various analytical phase functions to the scattering phase function
corresponding to the BARE-GR-S model, one of the most popular and commonly
adopted models for interstellar dust. We weigh the accuracy of the fit against
the number of free parameters in the analytical phase functions. Results: We
confirm that the Henyey-Greenstein phase functions poorly describe scattering
by dust grains, particularly at ultraviolet (UV) wavelengths, with relative
differences of up to 50%. The Draine phase function alleviates this problem at
near-infrared (NIR) wavelengths, but not in the UV. The two-term
Reynolds-McCormick phase function, recently advocated in the context of light
scattering in nanoscale materials and aquatic media, describes the BARE-GR-S
data very well, but its five free parameters are degenerate. We propose a
simpler phase function, the two-term ultraspherical-2 (TTU2) phase function,
that also provides an excellent fit to the BARE-GR-S phase function over the
entire UV-NIR wavelength range. (Abridged)


title:
Deep sub-arcsecond widefield imaging of the Lockman Hole field at 144
  MHz

abstract:
  High quality low-frequency radio surveys have the promise of advancing our
understanding of many important topics in astrophysics, including the life
cycle of active galactic nuclei (AGN), particle acceleration processes in jets,
the history of star formation, and exoplanet magnetospheres. Currently leading
low-frequency surveys reach an angular resolution of a few arcseconds. However,
this resolution is not yet sufficient to study the more compact and distant
sources in detail. Sub-arcsecond resolution is therefore the next milestone in
advancing these fields. The biggest challenge at low radio frequencies is the
ionosphere. If not adequately corrected for, ionospheric seeing blurs the
images to arcsecond or even arcminute scales. Additionally, the required image
size to map the degree-scale field of view of low-frequency radio telescopes at
this resolution is far greater than what typical soft- and hardware is
currently capable of handling. Here we present for the first time (to the best
of our knowledge) widefield sub-arcsecond imaging at low radio frequencies. We
derive ionospheric corrections in a few dozen individual directions and apply
those during imaging efficiently using a recently developed imaging algorithm
(arXiv:1407.1943, arXiv:1909.07226). We demonstrate our method by applying it
to an eight hour observation of the International LOw Frequency ARray (LOFAR)
Telescope (ILT) (arXiv:1305.3550). Doing so we have made a sensitive $7.4\
\mathrm{deg}^2$ $144\ \mathrm{MHz}$ map at a resolution of $0.3''$ reaching
$25\ \mu\mathrm{Jy\ beam}^{-1}$ near the phase centre. The estimated $250,000$
core hours used to produce this image, fit comfortably in the budget of
available computing facilities. This result will enable future mapping of the
entire northern low-frequency sky at sub-arcsecond resolution.


title:
Electronic and optical properties of crystalline nitrogen versus black
  phosphorus: A comparative first-principles study

abstract:
  Crystalline nitrogen (bN) is an allotrope of nitrogen with the black
phosphorus (bP) structure recently synthesized at high pressure by two
independent research groups [Sci. Adv. 6, eaba9206 (2020), Phys. Rev. Lett.
124, 216001 (2020)]. Here, we present a systematic study of the electronic and
optical properties of bN focusing on its comparison with bP. To this end, we
use the state-of-the-art quasiparticle self-consistent $GW$ approach with
vertex corrections both in the electronic and optical channels. Despite many
similarities, the properties of bN are found to be considerably different.
Unlike bP, bN exhibits a larger optical gap (2.5 vs. 0.26 eV) making bN
transparent in the visible spectral region with highly anisotropic optical
response. This difference can be primarily attributed to a considerably reduced
dielectric screening in bN, leading to enhancement of the effective Coulomb
interaction. Despite relatively strong Coulomb interaction, exciton formation
is largely suppressed in both materials. Our analysis of the elastic properties
shows exceptionally high stiffness of bN, comparable to that of diamond.


title:
Correlations between turbulent velocity and density fields in the local
  interstellar medium

abstract:
  Kalberla et al. used HI4PI data to analyze velocity and density fluctuations
in the interstellar medium (ISM). They applied the Yuen et al. (2021) velocity
decomposition algorithm (VDA) for separating such fluctuations in the
position-position-velocity (PPV) space. In the first version of this manuscript
they came to the conclusion that velocity and density fields are statistically
correlated. Yuen et al. (2021) tried to reproduce these results and pointed to
a likely mistake in the VDA expression that was used. We confirm that there was
such a software problem. The statement that VDA derived density and velocity
fields from HI4PI are anti-correlated needs to be withdrawn. Correct is that
these density and velocity fields are uncorrelated. In turn major parts of the
conclusions in the first version of this manuscript, based on an erroneous
correlation, are invalid. The submission to A&A was withdrawn on 24 February
2022.


title:
On the Divergence of the Ferromagnetic Susceptibility in the SU(N)
  Nagaoka-Thouless Ferromagnet

abstract:
  Using finite temperature strong coupling expansions for the SU(N) Hubbard
Model, we calculate the thermodynamic properties of the model in the
infinite-$U$ limit for arbitrary density $0\leq \rho \leq 1$ and all $N$. We
express the ferromagnetic susceptibility of the model as a Curie term plus a
$\Delta \chi$, an excess susceptibility above the Curie-behavior. We show that,
on a bipartite lattice, graph by graph the contributions to $\Delta \chi$ are
non-negative in the limit that the hole density $\delta=1-\rho$ goes to zero.
By summing the contributions from all graphs consisting of closed loops we find
that the low hole-density ferromagnetic susceptibility diverges exponentially
as $\exp{\Delta /T}$ as $T \to 0$ in two and higher dimensions. This
demonstrates that Nagaoka-Thouless ferromagnetic state exists as a
thermodynamic state of matter at low enough density of holes and sufficiently
low temperatures. The constant $\Delta$ scales with the SU(N) parameter $N$ as
$1/N$ implying that ferromagnetism is gradually weakened with increasing $N$ as
the characteristic temperature scale for ferromagnetic order goes down.


title:
Security of Microservice Applications: A Practitioners' Perspective on
  Challenges and Best Practices

abstract:
  Cloud-based application deployment is becoming increasingly popular among
businesses, thanks to the emergence of microservices. However, securing such
architectures is a challenging task since traditional security concepts cannot
be directly applied to microservice architectures due to their distributed
nature. The situation is exacerbated by the scattered nature of guidelines and
best practices advocated by practitioners and organizations in this field. This
research paper we aim to shay light over the current microservice security
discussions hidden within Grey Literature (GL) sources. Particularly, we
identify the challenges that arise when securing microservice architectures, as
well as solutions recommended by practitioners to address these issues. For
this, we conducted a systematic GL study on the challenges and best practices
of microservice security present in the Internet with the goal of capturing
relevant discussions in blogs, white papers, and standards. We collected 312 GL
sources from which 57 were rigorously classified and analyzed. This analysis on
the one hand validated past academic literature studies in the area of
microservice security, but it also identified improvements to existing
methodologies pointing towards future research directions.


title: The neutron-proton mass difference

abstract:
  We present a lattice calculation of the mass difference between neutron and
proton, for which we find $ M_n - M_p = 1.73(69) \, \text{MeV}$. This is
obtained at 1st order in the $QED$ coupling $\alpha_{EM}$ and in the mass
difference between $u$ and $d$ quarks $\frac{m_d-m_u}{\Lambda_{QCD}}$. We adopt
a purely hadronic scheme to renormalize the theory and provide a prescription
to separate the $QED$ and strong $IB$ contributions. The simulation is carried
out using the ETMC gauge configurations with $N_f=2+1+1$ dynamical quarks. We
extrapolate among $3$ values of the lattice spacing and pion masses in the
range $M_\pi \simeq 200 - 450 $ MeV.


title: The RoyalFlush System of Speech Recognition for M2MeT Challenge

abstract:
  This paper describes our RoyalFlush system for the track of multi-speaker
automatic speech recognition (ASR) in the M2MeT challenge. We adopted the
serialized output training (SOT) based multi-speakers ASR system with
large-scale simulation data. Firstly, we investigated a set of front-end
methods, including multi-channel weighted predicted error (WPE), beamforming,
speech separation, speech enhancement and so on, to process training,
validation and test sets. But we only selected WPE and beamforming as our
frontend methods according to their experimental results. Secondly, we made
great efforts in the data augmentation for multi-speaker ASR, mainly including
adding noise and reverberation, overlapped speech simulation, multi-channel
speech simulation, speed perturbation, front-end processing, and so on, which
brought us a great performance improvement. Finally, in order to make full use
of the performance complementary of different model architecture, we trained
the standard conformer based joint CTC/Attention (Conformer) and U2++ ASR model
with a bidirectional attention decoder, a modification of Conformer, to fuse
their results. Comparing with the official baseline system, our system got a
12.22% absolute Character Error Rate (CER) reduction on the validation set and
12.11% on the test set.


title:
Measuring Disparate Outcomes of Content Recommendation Algorithms with
  Distributional Inequality Metrics

abstract:
  The harmful impacts of algorithmic decision systems have recently come into
focus, with many examples of systems such as machine learning (ML) models
amplifying existing societal biases. Most metrics attempting to quantify
disparities resulting from ML algorithms focus on differences between groups,
dividing users based on demographic identities and comparing model performance
or overall outcomes between these groups. However, in industry settings, such
information is often not available, and inferring these characteristics carries
its own risks and biases. Moreover, typical metrics that focus on a single
classifier's output ignore the complex network of systems that produce outcomes
in real-world settings. In this paper, we evaluate a set of metrics originating
from economics, distributional inequality metrics, and their ability to measure
disparities in content exposure in a production recommendation system, the
Twitter algorithmic timeline. We define desirable criteria for metrics to be
used in an operational setting, specifically by ML practitioners. We
characterize different types of engagement with content on Twitter using these
metrics, and use these results to evaluate the metrics with respect to the
desired criteria. We show that we can use these metrics to identify content
suggestion algorithms that contribute more strongly to skewed outcomes between
users. Overall, we conclude that these metrics can be useful tools for
understanding disparate outcomes in online social networks.


title:
Spatial Diversity in Radar Detection via Active Reconfigurable
  Intelligent Surfaces

abstract:
  Active reconfigurable intelligent surfaces (RISs) are a novel and promising
technology that allows to control the radio propagation environment, while
compensating for the product path loss along the RIS-assisted path. In this
letter, we consider the classical radar detection problem and propose to use an
active RIS to obtain a second independent look of a prospective target
illuminated by the radar transmitter. At the design stage, we select the power
emitted by the radar, the number of RIS elements, and their amplification
factor in order to maximize the detection probability, for a fixed probability
of false alarm and a common (among radar and RIS) power budget. An illustrative
example is provided to assess the achievable detection performance, also in
comparison with that of a radar operating alone or with the help of a passive
RIS.


title:
The main perturbing objects on the orbits of (616) Prometheus and (617)
  Pandora

abstract:
  The dynamical evolution of the Prometheus and Pandora pair of satellites is
chaotic, with a short 3.3 years Lyapunov time. It is known that the
anti-alignment of the apses line of Prometheus and Pandora, which occurs every
6.2 years, is a critical configuration that amplifies their chaotic dynamical
evolution. However, the mutual interaction between Prometheus and Pandora is
not enough to explain the longitudinal lags observed by the Hubble Space
Telescope. The main goal of the current work is to identify the main
contributors to the chaotic dynamical evolution of the Prometheus-Pandora pair
beyond themselves. Therefore, in this work, we first explore the sensibility of
this dynamical system to understand it numerically and then build numerical
experiments to reach our goals. We identified that almost all major satellites
of the Saturn system play a significant role in the evolution of Prometheus'
and Pandora's orbits.


title:
NCQ scaling of $f_{0}(980)$ elliptic flow in 200 GeV Au+Au collisions by
  STAR and its constituent quark content

abstract:
  Searching for exotic state particles and studying their properties have
furthered our understanding of quantum chromodynamics (QCD). The $f_{0}$(980)
resonance is an exotic state with relatively high production rate in
relativistic heavy-ion collisions, decaying primarily into $\pi\pi$. Currently,
the structure and quark content of the $f_{0}$(980) are unknown with several
predictions from theory being a $q\overline{q}$ state, a
$qq\overline{q}\overline{q}$ state, a $K\overline{K}$ molecule state, or a
gluonium state. We report the first $f_{0}$(980) elliptic flow ($v_{2}$)
measurement from 200 GeV Au+Au collisions at STAR. The transverse momentum
dependence of $v_{2}$ is examined and compared to those of other hadrons
(baryons and mesons). The empirical number of constituent quark (NCQ) scaling
is used to investigate the constituent quark content of $f_{0}$(980), which may
potentially address an important question in QCD.


title:
On Manifold Hypothesis: Hypersurface Submanifold Embedding Using
  Osculating Hyperspheres

abstract:
  Consider a set of $n$ data points in the Euclidean space $\mathbb{R}^d$. This
set is called dataset in machine learning and data science. Manifold hypothesis
states that the dataset lies on a low-dimensional submanifold with high
probability. All dimensionality reduction and manifold learning methods have
the assumption of manifold hypothesis. In this paper, we show that the dataset
lies on an embedded hypersurface submanifold which is locally
$(d-1)$-dimensional. Hence, we show that the manifold hypothesis holds at least
for the embedding dimensionality $d-1$. Using an induction in a pyramid
structure, we also extend the embedding dimensionality to lower embedding
dimensionalities to show the validity of manifold hypothesis for embedding
dimensionalities $\{1, 2, \dots, d-1\}$. For embedding the hypersurface, we
first construct the $d$ nearest neighbors graph for data. For every point, we
fit an osculating hypersphere $S^{d-1}$ using its neighbors where this
hypersphere is osculating to a hypothetical hypersurface. Then, using surgery
theory, we apply surgery on the osculating hyperspheres to obtain $n$
hyper-caps. We connect the hyper-caps to one another using partial
hyper-cylinders. By connecting all parts, the embedded hypersurface is obtained
as the disjoint union of these elements. We discuss the geometrical
characteristics of the embedded hypersurface, such as having boundary, its
topology, smoothness, boundedness, orientability, compactness, and injectivity.
Some discussion are also provided for the linearity and structure of data. This
paper is the intersection of several fields of science including machine
learning, differential geometry, and algebraic topology.


title: Some notes on Goodman's marginal-free correspondence analysis

abstract:
  In his seminal paper Goodman (1996) introduced marginal-free correspondence
analysis; where his principal aim was to reconcile Pearson correlation measure
with Yule's association measure in the analysis of contingency tables. We show
that marginal-free correspondence analysis is a particular case of
correspondence analysis with prespecified weights studied in the beginning of
the 1980s by Benz\'ecri and his students. Furthermore, we show that it is also
a particular first-order approximation of logratio analysis with uniform
weights. Key words: Marginal-free correspondence analysis; logratio analysis;
interactions; scale invariance; taxicab singular value decomposition.


title: Infinitely Divisible Distributions and Commutative Diagrams

abstract:
  We study infinitely divisible (ID) distributions on the nonnegative half-line
$\mathbb{R}_+$. The L\'{e}vy-Khintchine representation of such distributions is
well-known. Our primary contribution is to cast the probabilistic objects and
the relations amongst them in a unified visual form that we refer to as the
L\'{e}vy-Khintchine commutative diagram (LKCD). While it is introduced as a
representational tool, the LKCD facilitates the exploration of new ID
distributions and may thus also be looked upon, at least in part, as a
discovery tool. The basic object of the study is the gamma distribution.
Closely allied to this is the $\alpha$-stable distribution on $\mathbb{R}_+$
for $0<\alpha<1$, which we regard as arising from the gamma distribution rather
than as a separate object. It is characterised by its Laplace transform
$\exp(-s^\alpha)$ for $0<\alpha<1$. It is indeed often characterised as an
instance of a class of ID distributions known as generalised gamma convolutions
(GGCs). We make use of convolutions and mixtures of gamma and stable densities
to generate densities of other GGC distributions, with particular cases
involving Bessel, confluent hypergeometric, Mittag-Leffler and parabolic
cylinder functions. We present all instances as LKCD representations.


title: A Gaussian method for the operator square root

abstract:
  We consider the approximation of the inverse square root of regularly
accretive operators in Hilbert spaces. The approximation is of rational type
and comes from the use of the Gauss-Legendre rule applied to a special integral
formulation of the problem. We derive sharp error estimates, based on the use
of the numerical range, and provide some numerical experiments. For practical
purposes, the finite dimensional case is also considered. In this setting, the
convergence is shown to be of exponential type.


title: On the cofinality of the least $\lambda$-strongly compact cardinal

abstract:
  In this paper, we characterize the possible cofinalities of the least
$\lambda$-strongly compact cardinal. We show that, on the one hand, for any
regular cardinal, $\delta$, that carries a $\lambda$-complete uniform
ultrafilter, it is consistent, relative to the existence of a supercompact
cardinal above $\delta$, that the least $\lambda$-strongly compact cardinal has
cofinality $\delta$. On the other hand, provably the cofinality of the least
$\lambda$-strongly compact cardinal always carries a $\lambda$-complete uniform
ultrafilter.


title:
MFA: TDNN with Multi-scale Frequency-channel Attention for
  Text-independent Speaker Verification with Short Utterances

abstract:
  The time delay neural network (TDNN) represents one of the state-of-the-art
of neural solutions to text-independent speaker verification. However, they
require a large number of filters to capture the speaker characteristics at any
local frequency region. In addition, the performance of such systems may
degrade under short utterance scenarios. To address these issues, we propose a
multi-scale frequency-channel attention (MFA), where we characterize speakers
at different scales through a novel dual-path design which consists of a
convolutional neural network and TDNN. We evaluate the proposed MFA on the
VoxCeleb database and observe that the proposed framework with MFA can achieve
state-of-the-art performance while reducing parameters and computation
complexity. Further, the MFA mechanism is found to be effective for speaker
verification with short test utterances.


title:
Efficient learning of hidden state LTI state space models of unknown
  order

abstract:
  The aim of this paper is to address two related estimation problems arising
in the setup of hidden state linear time invariant (LTI) state space systems
when the dimension of the hidden state is unknown. Namely, the estimation of
any finite number of the system's Markov parameters and the estimation of a
minimal realization for the system, both from the partial observation of a
single trajectory. For both problems, we provide statistical guarantees in the
form of various estimation error upper bounds, $\rank$ recovery conditions, and
sample complexity estimates.
  Specifically, we first show that the low $\rank$ solution of the Hankel
penalized least square estimator satisfies an estimation error in $S_p$-norms
for $p \in [1,2]$ that captures the effect of the system order better than the
existing operator norm upper bound for the simple least square. We then provide
a stability analysis for an estimation procedure based on a variant of the
Ho-Kalman algorithm that improves both the dependence on the dimension and the
least singular value of the Hankel matrix of the Markov parameters. Finally, we
propose an estimation algorithm for the minimal realization that uses both the
Hankel penalized least square estimator and the Ho-Kalman based estimation
procedure and guarantees with high probability that we recover the correct
order of the system and satisfies a new fast rate in the $S_2$-norm with a
polynomial reduction in the dependence on the dimension and other parameters of
the problem.


title: Target Space Entanglement in Quantum Mechanics of Fermions and Matrices

abstract:
  Quantum entanglement is closely related to the structure of spacetime in
quantum gravity. For quantum field theories or statistical models, we usually
consider base space entanglement. However, target space instead of base space
sometimes directly connects to our spacetime. In these cases, it is natural to
consider a concept of target space entanglement. To define the target space
entanglement, we consider a generalized definition of entanglement entropy
based on an algebraic approach. This approach is reviewed and is applied to the
first quantized particles, in particular, fermions. This article is based on
the paper JHEP 08 (2021) 046 [arXiv:2105.13726].


title: Non-Vacuous Generalisation Bounds for Shallow Neural Networks

abstract:
  We focus on a specific class of shallow neural networks with a single hidden
layer, namely those with $L_2$-normalised data and either a sigmoid-shaped
Gaussian error function ("erf") activation or a Gaussian Error Linear Unit
(GELU) activation. For these networks, we derive new generalisation bounds
through the PAC-Bayesian theory; unlike most existing such bounds they apply to
neural networks with deterministic rather than randomised parameters. Our
bounds are empirically non-vacuous when the network is trained with vanilla
stochastic gradient descent on MNIST and Fashion-MNIST.


title: Feasible Wrench Set Computation for Legged Robots

abstract:
  During locomotion, legged robots interact with the ground by sequentially
establishing and breaking contact. The interaction wrenches that arise from
contact are used to steer the robot's Center of Mass (CoM) and reject
perturbations that make the system deviate from the desired trajectory and
often make them fall. The feasibility of a given control target (desired CoM
wrench or acceleration) is conditioned by the contact point distribution,
ground friction, and actuation limits. In this work, we develop an algorithm to
compute the set of feasible wrenches that a legged robot can exert on its CoM
through contact. The presented method can be used with any amount of
non-coplanar contacts and takes into account actuation limits and limitations
based on an inelastic contact model with Coulomb friction. This is exemplified
with a planar biped model standing with the feet at different heights.
Exploiting assumptions from the contact model, we explain how to compute the
set of wrenches that are feasible on the CoM when the contacts remain in
position as well as the ones that are feasible when some of the contacts are
broken. Therefore, this algorithm can be used to assess whether a switch in
contact configuration is feasible while achieving a given control task.
Furthermore, the method can be used to identify the directions in which the
system is not actuated (i.e. a wrench cannot be exerted in those directions).
We show how having a joint be actuated or passive can change the non-actuated
wrench directions of a robot at a given pose using a spatial model of a
lower-extremity exoskeleton. Therefore, this algorithm is also a useful tool
for the design phase of the system. This work presents a useful tool for the
control and design of legged systems that extends on the current state of the
art.


title: Use and abuse of instance parameters in the Lean mathematical library

abstract:
  The Lean mathematical library mathlib features extensive use of the typeclass
pattern for organising mathematical structures, based on Lean's mechanism of
instance parameters. Related mechanisms for typeclasses are available in other
provers including Agda, Coq and Isabelle with varying degrees of adoption. This
paper analyses representative examples of design patterns involving instance
parameters in the current Lean 3 version of mathlib, focussing on complications
arising at scale and how the mathlib community deals with them.


title:
A deep complex network with multi-frame filtering for stereophonic
  acoustic echo cancellation

abstract:
  In hands-free communication system, the coupling between the loudspeaker and
the microphone will generate echo signal, which can severely impair the quality
of communication. Meanwhile, various types of noise in the communication
environment further destroy the speech quality and intelligibility. It is hard
to extract the near-end signal from the microphone input signal within one
step, especially in low signal-to-noise ratios. In this paper, we propose a
multi-stage approach to address this issue. On the one hand, we decompose the
echo cancellation into two stages, including linear echo cancellation module
and residual echo suppression module. A multi-frame filtering strategy is
introduced to benefit estimating linear echo by utilizing more inter-frame
information. On the other hand, we decouple the complex spectral mapping into
magnitude estimation and complex spectra refine. Experimental results
demonstrate that our proposed approach achieves stage-of-the-art performance
over previous advanced algorithms under various conditions.


title:
A lattice Boltzmann model for self-diffusiophoretic particles near and
  at liquid-liquid interfaces

abstract:
  We introduce a novel mesoscopic computational model based on a
multiphase-multicomponent lattice Boltzmann method for the simulation of
self-phoretic particles in the presence of liquid-liquid interfaces. Our model
features fully resolved solvent hydrodynamics and, thanks to its versatility,
it can handle important aspects of the multiphysics of the problem, including
particle wettability and differential solubility of the product in the two
liquid phases. The method is extensively validated in simple numerical
experiments, whose outcome is theoretically predictable, and then applied to
the study of the behaviour of active particles next to and trapped at
interfaces. We show that their motion can be variously steered by tuning
relevant control parameters, such as the phoretic mobilities, the contact angle
and the product solubility.


title: Poisson generic sequences

abstract:
  Years ago, Zeev Rudnick defined Poisson generic sequences as those where the
number of occurrences of the long strings in the initial segments of the
sequences follow the Poisson distribution. Peres and Weiss proved that almost
all sequences, with respect to Lebesgue measure, are Poisson generic, but they
did not publish their proof. In this note first we transcribe Peres and Weiss'
proof and then we show that there are computable Poisson generic instances and
that all Martin-L\"of random sequences are Poisson generic.


title: Universal Dynamics of Heavy Operators in Boundary CFT$_2$

abstract:
  We derive a universal asymptotic formula for generic boundary conditions for
the average value of the bulk-to-boundary and boundary Operator Product
Expansion coefficients of any unitary, compact two-dimensional Boundary CFT
(BCFT) with $c>1$. The asymptotic limit consists of taking one or more boundary
primary operators -- which transform under a single copy of the Virasoro
algebra -- to have parametrically large conformal dimension for fixed central
charge. In particular, we find a \textit{single} universal expression that
interpolates between distinct heavy regimes, exactly as in the case of bulk OPE
asymptotics\cite{Collier:2019weq}. The expression depends universally on the
boundary entropy and the central charge, and not on any other details of the
theory. We derive these asymptotics by studying crossing symmetry of various
correlation functions on higher genus Riemann surfaces with open boundaries.
Essential in the derivation is the use of the irrational versions of the
crossing kernels that relate holomorphic Virasoro blocks in different channels.
Our results strongly suggest an extended version of the Eigenstate
Thermalization Hypothesis for boundary OPE coefficients, where the hierarchy
between the diagonal and non-diagonal term in the ansatz is further controlled
by the boundary entropy. We finally comment on the applications of our results
in the context of $\text{AdS}_3/\text{BCFT}_2$, as well as on the recent
relation of BCFTs with lower dimensional models of evaporating black holes.


title: An architecture for quantum networking of neutral atom processors

abstract:
  Development of a network for remote entanglement of quantum processors is an
outstanding challenge in quantum information science. We propose and analyze a
two-species architecture for remote entanglement of neutral atom quantum
computers based on integration of optically trapped atomic qubit arrays with
fast optics for photon collection. One of the atomic species is used for
atom-photon entanglement, and the other species provides local processing. We
compare the achievable rates of remote entanglement generation for two optical
approaches: free space photon collection with a lens and a near-concentric,
long working distance resonant cavity. Laser cooling and trapping within the
cavity removes the need for mechanical transport of atoms from a source region,
which allows for a fast repetition rate. Using optimized values of the cavity
finesse, remote entanglement generation rates $> 10^3~\rm s^{-1}$ are predicted
for experimentally feasible parameters.


title:
SN 2018bsz: a Type I superluminous supernova with aspherical
  circumstellar material

abstract:
  We present a spectroscopic analysis of Type I superluminous supernova
(SLSN-I), SN 2018bsz. While it closely resembles SLSNe-I, the multi-component
H$\alpha$ line appearing at $\sim30$ d post-maximum is the most atypical. The
H$\alpha$ is characterised by two emission components, one at $+3000$ km/s and
a second at $-7500$ km/s, with a third, near-zero velocity component appearing
after a delay. The blue and central components can be described by Gaussian
profiles of intermediate width, but the red component is significantly broader
and Lorentzian. The blue component evolves towards lower velocity before fading
at $100$ d post-peak, concurrently with a light curve break. Multi-component
profiles are observed in other hydrogen lines including Pa$\beta$, and in lines
of Ca II and He I. Spectropolarimetry obtained before (10.2 d) and after (38.4
d) the appearance of the H lines show a large shift on the Stokes $Q$ -- $U$
plane consistent with SN 2018bsz undergoing radical changes in its geometry.
Assuming the SN is almost unpolarised at 10.2 d, the continuum polarisation at
38.4 d reaches $P \sim1.8\%$ implying a highly asymmetric configuration. We
propose that the observed evolution of SN 2018bsz can be explained by highly
aspherical CSM. After the SN explosion, the CSM is quickly overtaken by the
ejecta, but as the photosphere starts to recede, the different CSM regions
re-emerge producing the peculiar line profiles. Based on the first appearance
of H$\alpha$, we can constrain the distance of the CSM to be less than $430$
AU, or even lower ($<87$ AU) if the pre-peak plateau is related to an eruption
that created the CSM. The presence of CSM has been inferred for other SLSNe-I.
However, it is not clear whether the rare properties of SN 2018bsz can be
generalised for SLSNe-I or whether they are the result of an uncommon
evolutionary path, possibly involving a binary companion.


title:
On constant-time quantum annealing and guaranteed approximations for
  graph optimization problems

abstract:
  Quantum Annealing (QA) is a computational framework where a quantum system's
continuous evolution is used to find the global minimum of an objective
function over an unstructured search space. It can be seen as a general
metaheuristic for optimization problems, including NP-hard ones if we allow an
exponentially large running time. While QA is widely studied from a heuristic
point of view, little is known about theoretical guarantees on the quality of
the solutions obtained in polynomial time.
  In this paper we use a technique borrowed from theoretical physics, the
Lieb-Robinson (LR) bound, and develop new tools proving that short, constant
time quantum annealing guarantees constant factor approximations ratios for
some optimization problems when restricted to bounded degree graphs.
Informally, on bounded degree graphs the LR bound allows us to retrieve a
(relaxed) locality argument, through which the approximation ratio can be
deduced by studying subgraphs of bounded radius.
  We illustrate our tools on problems MaxCut and Maximum Independent Set for
cubic graphs, providing explicit approximation ratios and the runtimes needed
to obtain them. Our results are of similar flavor to the well-known ones
obtained in the different but related QAOA (quantum optimization algorithms)
framework.
  Eventually, we discuss theoretical and experimental arguments for further
improvements.


title: Parity-violation studies with partially stripped ions

abstract:
  We present a theoretical study of photoexcitation of highly charged ions from
their ground states, a process which can be realized at the Gamma Factory at
CERN. Special attention is paid to the question of how the excitation rates are
affected by the mixing of opposite-parity ionic levels, which is induced both
by an external electric field and the weak interaction between electrons and
the nucleus. In order to reinvestigate this "Stark-plus-weak-interaction"
mixing, well-known in neutral atomic systems, we employ relativistic Dirac
theory. Based on the developed approach, detailed calculations are performed
for the ${\rm 1s}_{1/2} \to {\rm 2s}_{1/2}$ and ${\rm 1s}^2 \; {\rm 2s}_{1/2}
\to {\rm 1s}^2 \; {\rm 3s}_{1/2}$ (M1 + parity-violating-E1) transitions in
hydrogen- and lithium-like ions, respectively. In particular, we focus on the
difference between the excitation rates obtained for the right- and
left-circularly polarized incident light. This difference arises due to the
parity violating mixing of ionic levels and is usually characterized in terms
of the circular-dichroism parameter. We argue that future measurements of
circular dichroism, performed with highly charged ions in the SPS or LHC rings,
may provide valuable information on the electron-nucleus weak-interaction
coupling.


title:
Limits of Bergman kernels on a tower of coverings of compact K\"ahler
  manifolds

abstract:
  We prove the convergence of the Bergman kernels and the $L^2$-Hodge numbers
on a tower of Galois coverings $\{X_j\}$ of a compact K\"ahler manifold $X$
converging to an infinite Galois (not necessarily universal) covering
$\widetilde{X}$. We also show that, as an application, there exists a
holomorphic immersion $X_j \rightarrow \mathbb{P}(H^0(X_j, K_{X_j})^*)$ for
sufficiently large $j$ if $\widetilde{X}$ can be immersed into the
projectivization $\mathbb{P}(H^0_{(2)}(\widetilde{X}, K_{\widetilde{X}})^*)$ of
the dual Hilbert space of $L^2$-holomorphic sections to the canonical line
bundle.


title:
Mathematical Content Browsing for Print-Disabled Readers Based on
  Virtual-World Exploration and Audio-Visual Sensory substitution

abstract:
  Documents containing mathematical content remain largely inaccessible to
blind and visually impaired readers because they are predominantly published as
untagged PDF which does not include the semantic data necessary for effective
accessibility. We present a browsing approach for print-disabled readers
specifically aimed at such mathematical content. This approach draws on the
navigational mechanisms often used to explore the virtual worlds of text
adventure games with audio-visual sensory substitution for graphical content.
The relative spatial placement of the elements of an equation are represented
as a virtual world, so that the reader can navigate from element to element.
Text elements are announced conventionally using synthesised speech while
graphical elements, such as roots and fraction lines, are rendered using a
modification of the vOICe algorithm. The virtual world allows the reader to
interactively discover the spatial structure of the equation, while the
rendition of graphical elements as sound allows the shape and identity of
elements that cannot be synthesised as speech to be discovered and recognised.
The browsing approach was evaluated by eleven blind and fourteen sighted
participants in a user trial that included the identification of twelve
equations extracted from PDF documents. Overall, equations were identified
completely correctly in 78% of cases (74% and 83% respectively for blind and
sighted subjects). If partial correctness is considered, the performance is
substantially higher. We conclude that the integration of a spatial model
represented as a virtual world in conjunction with audio-visual sensory
substitution for non-textual elements can be an effective way for blind and
visually impaired readers to read currently inaccessible mathematical content
in PDF documents.


title:
Synthesis, structure and electric conductivity of higher hydrides of
  ytterbium at high pressure

abstract:
  While most of the rare earth metals readily form trihydrides, due to
increased stability of the filled 4f electronic shell for Yb(II), only
YbH$_{2.67}$, formally corresponding to Yb$^{\rm II}$(Yb$^{\rm III}$H$_4$)$_2$
or Yb$_3$H$_8$, remains the highest hydride of ytterbium. Utilizing diamond
anvil cell methodology and synchrotron powder x-ray diffraction we have
attempted to push this limit further via hydrogenation of metallic Yb (at room
temperature and heated in situ) and of Yb$_3$H$_8$. Compression of the latter
has also been investigated in a neutral pressure transmitting medium, PTM.
While the in situ heating of Yb facilitates the formation of YbH$_2$ plus x
hydride, we have not observed the clear qualitative differences between the
systems compressed in H$_2$ and He or Ne PTM. In all these cases a sequence of
phase transitions from the unit cells of P-31m symmetry to the I4/m and I4/mmm
systems occurred within ca. 13 to 18 GPa and around 27 GPa, respectively. At
the same time, the molecular volume of the systems compressed in H$_2$ PTM is
ca. 1.5% larger than of those compressed in inert gases, suggesting a small
hydrogen uptake. Nevertheless, hydrogenation towards YbH$_3$ is incomplete, and
polyhydrides do not form up to the highest pressure studied here of ca. 75 GPa.
As pointed out by our electronic transport measurements under compression in
NaCl PTM, the mixed-valence Yb$_3$H$_8$ retains its semiconducting character up
to at least 50 GPa, although the very low remnant activation energy of
conduction, smaller than 5 meV, suggests that the metallization under further
compression should be achievable. Finally, we provide a theoretical description
of a hypothetical stoichiometric YbH$_3$.


title: Coupled Splines for Sparse Curve Fitting

abstract:
  We formulate as an inverse problem the construction of sparse parametric
continuous curve models that fit a sequence of contour points. Our prior is
incorporated as a regularization term that encourages rotation invariance and
sparsity. We prove that an optimal solution to the inverse problem is a closed
curve with spline components. We then show how to efficiently solve the task
using B-splines as basis functions. We extend our problem formulation to curves
made of two distinct components with complementary smoothness properties and
solve it using hybrid splines. We illustrate the performance of our model on
contours of different smoothness. Our experimental results show that we can
faithfully reconstruct any general contour using few parameters, even in the
presence of imprecisions in the measurements.


title:
Comment on "Coulomb Instabilities of a Three-Dimensional Higher-Order
  Topological Insulator"

abstract:
  Based on renormalization-group (RG) calculations, a recent Letter by Zhao et
al [Phys. Rev. Lett. 127, 176601 (2021)] claimed that three-dimensional
second-order topological insulators (SOTIs) are always unstable to the Coulomb
interaction and they will thus undergo topological phase transitions to either
topological insulators (TIs) or normal insulators. While the calculations in
this paper are correct, the conclusion about the topological phase transition
from SOTI to TI is not true. The reason behind this false conclusion lies in
that the authors use a wrong criterion to identify the phase transition. In
this Comment we would like to remind that, to locate the possible transitions
from SOTI to TI, an appropriate quantity is the sign-changing mass gap
$m_\textrm{surf}$ for the surface states. By examining its behavior within RG
approach, the stability of SOTI against weak Coulomb interactions is
demonstrated.


title: Cassiopeia A reveals past interaction with circumstellar shell

abstract:
  Observations of the SNR Cassiopeia A (Cas A) show asymmetries in the reverse
shock that cannot be explained by models describing a remnant expanding through
a spherically symmetric wind of the progenitor star. We investigate whether a
past interaction of Cas A with a massive asymmetric shell of the circumstellar
medium can account for the observed asymmetries. We performed 3D MHD
simulations that describe the remnant evolution from the SN to its interaction
with a circumstellar shell. The initial conditions are provided by a 3D
neutrino-driven SN model whose morphology resembles Cas A. We explored the
parameter space of the shell, searching for a set of parameters able to produce
reverse shock asymmetries at the age of 350 years analogous to those observed
in Cas A. The interaction of the remnant with the shell can match the observed
reverse shock asymmetries if the shell was asymmetric with the densest portion
in the nearside to the northwest (NW). According to our models, the shell was
thin with radius 1.5 pc. The reverse shock shows the following asymmetries at
the age of Cas A: i) it moves inward in the observer frame in the NW region,
while it moves outward in other regions; ii) the geometric center of the
reverse shock is offset to the NW by 0.1 pc from the geometric center of the
forward shock; iii) the reverse shock in the NW region has enhanced nonthermal
emission because, there, the ejecta enter the reverse shock with a higher
relative velocity (between 4000 and 7000 km/s) than in other regions (below
2000 km/s). Our findings suggest the interaction of Cas A with an asymmetric
circumstellar shell between 180 and 240 years after the SN event. We suggest
that the shell was, most likely, the result of a massive eruption from the
progenitor star that occurred about 10^5 years prior to core-collapse. We
estimate a total mass of the shell of approximately 2.6 Msun.


title:
Entanglement: Cybercrime Connections of an Internet Marketing Forum
  Population

abstract:
  Many activities related to cybercrime operations do not require much secrecy,
such as developing websites or translating texts. This research provides
indications that many users of a popular public internet marketing forum have
connections to cybercrime. It does so by investigating the involvement in
cybercrime of a population of users interested in internet marketing, both at a
micro and macro scale. The research starts with a case study of three users
confirmed to be involved in cybercrime and their use of the public forum where
users share information about online advertising. It provides a first glimpse
that some business with cybercrime connection is being conducted in the clear.
The study then pans out to investigate the forum population's ties with
cybercrime by finding crossover users, who are users from the public forum who
also comment on cybercrime forums. The cybercrime forums on which they discuss
are analyzed and crossover users' strength of participation is reported. Also,
to assess if they represent a sub-group of the forum population, their posting
behavior on the public forum is compared with that of non-crossover users. This
blend of analyses shows that (i) a minimum of 7.2% of the public forum
population are crossover users that have ties with cybercrime forums; (ii)
their participation in cybercrime forums is limited; and (iii) their posting
behavior is relatively indistinguishable from that of non-crossover users. This
is the first study to formally quantify how users of an internet marketing
public forum, a space for informal exchanges, have ties to cybercrime
activities. We conclude that crossover users are a substantial part of the
population in the public forum, and, even though they have thus far been
overlooked, their aggregated effect in the ecosystem must be considered.


title:
AI-as-a-Service Toolkit for Human-Centered Intelligence in Autonomous
  Driving

abstract:
  This paper presents a proof-of-concept implementation of the AI-as-a-Service
toolkit developed within the H2020 TEACHING project and designed to implement
an autonomous driving personalization system according to the output of an
automatic driver's stress recognition algorithm, both of them realizing a
Cyber-Physical System of Systems. In addition, we implemented a data-gathering
subsystem to collect data from different sensors, i.e., wearables and cameras,
to automatize stress recognition. The system was attached for testing to a
driving simulation software, CARLA, which allows testing the approach's
feasibility with minimum cost and without putting at risk drivers and
passengers. At the core of the relative subsystems, different learning
algorithms were implemented using Deep Neural Networks, Recurrent Neural
Networks, and Reinforcement Learning.


title: Improving Lyrics Alignment through Joint Pitch Detection

abstract:
  In recent years, the accuracy of automatic lyrics alignment methods has
increased considerably. Yet, many current approaches employ frameworks designed
for automatic speech recognition (ASR) and do not exploit properties specific
to music. Pitch is one important musical attribute of singing voice but it is
often ignored by current systems as the lyrics content is considered
independent of the pitch. In practice, however, there is a temporal correlation
between the two as note starts often correlate with phoneme starts. At the same
time the pitch is usually annotated with high temporal accuracy in ground truth
data while the timing of lyrics is often only available at the line (or word)
level. In this paper, we propose a multi-task learning approach for lyrics
alignment that incorporates pitch and thus can make use of a new source of
highly accurate temporal information. Our results show that the accuracy of the
alignment result is indeed improved by our approach. As an additional
contribution, we show that integrating boundary detection in the
forced-alignment algorithm reduces cross-line errors, which improves the
accuracy even further.


title:
Understanding the effect of mechanical strains on the catalytic activity
  of transition metals

abstract:
  The effect of elastic strains on the catalytic activity for the hydrogen
evolution reaction (HER) and the oxygen reduction reaction (ORR) was analyzed
on thirteen late transition metals: eight (111) surfaces of fcc metals (Ni, Cu,
Pd, Ag, Pt, Au, Rh, Ir) and five (0001) surfaces of hcp metals (Co, Zn, Cd, Ru,
and Os). The corresponding adsorption energies for the different intermediate
reactions up to strains dictated by the mechanical stability limits were
previously obtained by means of density functional theory calculations. It was
found that the elastic strains can be used to tune the catalytic activity of
different metals by reducing the energy barrier of the rate limiting step and
even to reach the cusp of the volcano plot. The largest changes in catalytic
activity with strain for the HER were found in Pt, Au, and Ir while Co and Ni
were very insensitive to this strategy. In the case of the ORR, the catalytic
activity of Au could be enhanced by the application of tensile strains while
that of Cu, Ni, Pt, Pd, Rh, Co, Ru, and Os was improved by the application of
compressive strains. However, the catalytic activity of Ir was rather
insensitive to mechanical deformations. Elastic strains were able to modify the
rate limiting reaction in Au, Pt, Ag, and Os and it was possible to achieve the
cusp of the volcano plot in these metals. Final, mechanical instabilities were
attained at small strains in Zn and Cd, which did not lead to significant
changes in the catalytic activity for HER and ORR. These results provide a
framework to systematically investigate the application of elastic strains in
the design of new catalysts.


title:
Nonequilibrium dynamics of $\alpha$-RuCl$_{3}$ -- a time-resolved
  magneto-optical spectroscopy study

abstract:
  We present time-resolved magneto-optical spectroscopy on the magnetic
Mott-Hubbard-insulating Kitaev spin liquid candidate $\alpha$-RuCl$_3$ to
investigate the nonequilibrium dynamics of its antiferromagnetically ordered
zigzag groundstate after photoexcitation. A systematic study of the transient
magnetic linear dichroism under different experimental conditions (temperature,
external magnetic field, photoexcitation density) gives direct access to the
dynamical interplay of charge excitations with the zigzag ordered state on
ultrashort time scales. We observe a rather slow initial demagnetization (few
to 10s of ps) followed by a long-lived non-thermal antiferromagnetic
spin-disordered state (100$-$1000s of ps), which can be understood in terms of
holons and doublons disordering the antiferromagnetic background after
photoexcitation. Varying temperature and fluence in the presence of an external
magnetic field reveals two distinct photoinduced dynamics associated with the
zigzag and quantum paramagnetic disordered phases. The photo-induced
non-thermal spin-disordered state shows universal compressed-exponential
recovery dynamics related to the growth and propagation of zigzag domains on
nanosecond time scales, which is interpreted within the framework of the
Fatuzzo-Labrune model for magnetization reversal. The study of nonequilibrium
states in strongly correlated materials is a relatively unexplored topic, but
our results are expected to be extendable to a large class of Mott-Hubbard
insulator materials with strong spin-orbit coupling.


title:
HECO: Automatic Code Optimizations for Efficient Fully Homomorphic
  Encryption

abstract:
  In recent years, Fully Homomorphic Encryption (FHE) has undergone several
breakthroughs and advancements leading to a leap in performance. Today,
performance is no longer a major barrier to adoption. Instead, it is the
complexity of developing an efficient FHE application that currently limits
deploying FHE in practice and at scale. Several FHE compilers have emerged
recently to ease FHE development. However, none of these answer how to
automatically transform imperative programs to secure and efficient FHE
implementations. This is a fundamental issue that needs to be addressed before
we can realistically expect broader use of FHE. Automating these
transformations is challenging because the restrictive set of operations in FHE
and their non-intuitive performance characteristics require programs to be
drastically transformed to achieve efficiency. In addition, existing tools are
monolithic and focus on individual optimizations. Therefore, they fail to fully
address the needs of end-to-end FHE development. In this paper, we present
HECO, a new end-to-end design for FHE compilers that takes high-level
imperative programs and emits efficient and secure FHE implementations. In our
design, we take a broader view of FHE development, extending the scope of
optimizations beyond the cryptographic challenges existing tools focus on.


title:
Estimating Point Exposure Effects on Count Outcomes with Observational
  Data

abstract:
  Causal inference methods can be applied to estimate the effect of a point
exposure or treatment on an outcome of interest using data from observational
studies. When the outcome of interest is a count, the estimand is often the
causal mean ratio, i.e., the ratio of the counterfactual mean count under
exposure to the counterfactual mean count under no exposure. This paper
considers estimators of the causal mean ratio based on inverse probability of
treatment weights, the parametric g-formula, and doubly robust estimation, each
of which can account for overdispersion, zero-inflation, and heaping in the
measured outcome. Methods are compared in simulations and are applied to data
from the Women's Interagency HIV Study to estimate the effect of incarceration
in the past six months on two count outcomes in the subsequent six months: the
number of sexual partners and the number of cigarettes smoked per day.


title: A Survey of Methods for Automated Algorithm Configuration

abstract:
  Algorithm configuration (AC) is concerned with the automated search of the
most suitable parameter configuration of a parametrized algorithm. There is
currently a wide variety of AC problem variants and methods proposed in the
literature. Existing reviews do not take into account all derivatives of the AC
problem, nor do they offer a complete classification scheme. To this end, we
introduce taxonomies to describe the AC problem and features of configuration
methods, respectively. We review existing AC literature within the lens of our
taxonomies, outline relevant design choices of configuration approaches,
contrast methods and problem variants against each other, and describe the
state of AC in industry. Finally, our review provides researchers and
practitioners with a look at future research directions in the field of AC.


title:
Thickness and temperature dependence of the atomic-scale structure of
  SrRuO$_3$ thin films

abstract:
  Due to the strong lattice-property relationships which exist in complex oxide
epitaxial layers, their electronic and magnetic properties can be modulated by
structural distortions induced at the atomic scale. The modification and
control can be affected at coherent heterointerfaces by epitaxial strain
imposed by the substrate or by structural modifications to accommodate the
film-substrate symmetry mismatch. Often these act in conjunction with a strong
dependence on the layer thickness, especially for ultrathin layers. Moreover,
as a result of these effects, the temperature dependence of the structure may
deviate largely from that of the bulk. The temperature-dependent structure of 3
to 44 unit cell thick ferromagnetic SrRuO$_3$ films grown on Nb-doped SrTiO$_3$
substrates are investigated using a combination of high-resolution synchrotron
X-ray diffraction and high-resolution electron microscopy. This aims to shed
light on the intriguing magnetic and magnetotransport properties of epitaxial
SRO layers, subjected to extensive investigations lately. The oxygen octahedral
tilts and rotations are found to be strongly dependent on the temperature, the
film thickness, and the distance away from the film-substrate interface. As a
striking manifestation of the coupling between magnetic order and lattice
structure, the Invar effect is observed below the ferromagnetic transition
temperature in epitaxial layers as thin as 8 unit cells, similar to bulk
ferromagnetic SrRuO$_3$.


title: Learning strides in convolutional neural networks

abstract:
  Convolutional neural networks typically contain several downsampling
operators, such as strided convolutions or pooling layers, that progressively
reduce the resolution of intermediate representations. This provides some
shift-invariance while reducing the computational complexity of the whole
architecture. A critical hyperparameter of such layers is their stride: the
integer factor of downsampling. As strides are not differentiable, finding the
best configuration either requires cross-validation or discrete optimization
(e.g. architecture search), which rapidly become prohibitive as the search
space grows exponentially with the number of downsampling layers. Hence,
exploring this search space by gradient descent would allow finding better
configurations at a lower computational cost. This work introduces DiffStride,
the first downsampling layer with learnable strides. Our layer learns the size
of a cropping mask in the Fourier domain, that effectively performs resizing in
a differentiable way. Experiments on audio and image classification show the
generality and effectiveness of our solution: we use DiffStride as a drop-in
replacement to standard downsampling layers and outperform them. In particular,
we show that introducing our layer into a ResNet-18 architecture allows keeping
consistent high performance on CIFAR10, CIFAR100 and ImageNet even when
training starts from poor random stride configurations. Moreover, formulating
strides as learnable variables allows us to introduce a regularization term
that controls the computational complexity of the architecture. We show how
this regularization allows trading off accuracy for efficiency on ImageNet.


title: On the size-Ramsey number of grids

abstract:
  We show that the size-Ramsey number of the $\sqrt{n} \times \sqrt{n}$ grid
graph is $O(n^{5/4})$, improving a previous bound of $n^{3/2 + o(1)}$ by
Clemens, Miralaei, Reding, Schacht, and Taraz.


title:
Subordination principle and Feynman-Kac formulae for generalized
  time-fractional evolution equations

abstract:
  We consider generalized time-fractional evolution equations of the form
$$u(t)=u_0+\int_0^tk(t,s)Lu(s)ds$$ with a fairly general memory kernel $k$ and
an operator $L$ being the generator of a strongly continuous semigroup. In
particular, $L$ may be the generator $L_0$ of a Markov process $\xi$ on some
state space $Q$, or $L:=L_0+b\nabla+V$ for a suitable potential $V$ and drift
$b$, or $L$ generating subordinate semigroups or Schr\"{o}dinger type groups.
This class of evolution equations includes in particular time- and space-
fractional heat and Schr\"odinger type equations. We show that a subordination
principle holds for such evolution equations and obtain Feynman-Kac formulae
for solutions of these equations with the use of different stochastic
processes, such as subordinate Markov processes and randomly scaled Gaussian
processes. In particular, we obtain some Feynman-Kac formulae with generalized
grey Brownian motion and other related self-similar processes with stationary
increments.

categories: math.PR math-ph math.AP math.FA math.MP quant-ph


title:
Constraints on the merging binary neutron star mass distribution and
  equation of state based on the fraction of jets

abstract:
  A relativistic jet has been produced in the single well-localised binary
neutron star (BNS) merger detected to date in gravitational waves (GWs), and
the local rates of BNS mergers and short gamma-ray bursts are of the same order
of magnitude. This suggests that jet formation is not a rare outcome for BNS
mergers, and we show that this intuition can be turned into a quantitative
constraint: at least about $1/3$ of GW-detected BNS mergers, and at least about
$1/5$ of all BNS mergers, should produce a successful jet (90\% credible
level). Whether a jet is launched depends on the properties of the merger
remnant and of the surrounding accretion disc, which in turn are a function of
the progenitor binary masses and equation of state (EoS). The fraction of jets
in the population therefore carries information about the binary component mass
distribution and EoS. Under the assumption that a jet can only be produced by a
black hole remnant surrounded by a non-negligible accretion disc, we show how
the jet fraction can be used to place a joint constraint on the space of BNS
component mass distributions and EoS. The result points to a broad mass
distribution, with particularly strong support for masses in the
$1.3-1.6\,\mathrm{M_\odot}$ range. The constraints on the EoS are shallow, but
we show how they will tighten as the knowledge on the jet fraction and mass
distribution improve. We also discuss how to extend the method to include
future BNS events, with possibly uncertain jet associations.


title:
Deselection of Base-Learners for Statistical Boosting -- with an
  Application to Distributional Regression

abstract:
  We present a new procedure for enhanced variable selection for component-wise
gradient boosting. Statistical boosting is a computational approach that
emerged from machine learning, which allows to fit regression models in the
presence of high-dimensional data. Furthermore, the algorithm can lead to
data-driven variable selection. In practice, however, the final models
typically tend to include too many variables in some situations. This occurs
particularly for low-dimensional data (p<n), where we observe a slow
overfitting behavior of boosting. As a result, more variables get included into
the final model without altering the prediction accuracy. Many of these false
positives are incorporated with a small coefficient and therefore have a small
impact, but lead to a larger model. We try to overcome this issue by giving the
algorithm the chance to deselect base-learners with minor importance. We
analyze the impact of the new approach on variable selection and prediction
performance in comparison to alternative methods including boosting with
earlier stopping as well as twin boosting. We illustrate our approach with data
of an ongoing cohort study for chronic kidney disease patients, where the most
influential predictors for the health-related quality of life measure are
selected in a distributional regression approach based on beta regression.


title: Curvature on Graphs via Equilibrium Measures

abstract:
  We introduce a notion of curvature on finite, combinatorial graphs. It can be
easily computed by solving a linear system of equations. We show that graphs
with curvature bounded below by $K>0$ have diameter bounded by $\mbox{diam}(G)
\leq 2/K$ (a Bonnet-Myers theorem), that $\mbox{diam}(G) = 2/K$ implies that
$G$ has constant curvature (a Cheng theorem) and that there is a spectral gap
$\lambda_1 \geq 2K/n$ (a Lichnerowicz theorem). The curvature is computed for
several families of graphs and frequently coincides with Ollivier curvature or
Lin-Lu-Yau curvature. It is motivated by equilibrium measures in potential
theory, the von Neumann minimax theorem features prominently in the proofs.


title: Enhancement of power grid monitoring based on data weighting

abstract:
  With their expansion, national power grid have had to work with huge sets of
data received from a vast number of substations and power plants. Given their
large volume and variety, these data can be classified as big data. Managing
this massive amount of data is certainly challenging. Depending on the
application, parts of these data are more important for real-time network
operation. Computing a network's observability score without assigning weights
to different signals may not provide a complete picture of the received data's
validity and thus lead to incorrect assessments of the network status.
Consequently, signals critical to the network operation and functions of an
energy management system (EMS) should be assigned higher weights in
observability calculations. The weighted observability alongside the classic
non-weighted observability can serve as an indicator of each area's condition
in comparison to that of other areas and so greatly facilitate the monitoring
and verification of transmitted data. For calculating a weighted observability,
the current paper presents a method based on the Analytic Hierarchy Process
(AHP), in which higher weights are assigned to data that are more valuable for
and widely used by operators. The national electricity network of Iran was
chosen for the evaluation of the proposed method's effect on data quality and
operational risk. The results indicated that the introduced method positively
affects the quality of received data and also corrects erroneous data in the
network.


title:
Computational Aspects of Conditional Minisum Approval Voting in
  Elections with Interdependent Issues

abstract:
  Approval voting provides a simple, practical framework for multi-issue
elections, and the most representative example among such election rules is the
classic Minisum approval voting rule. We consider a generalization of Minisum,
introduced by the work of Barrot and Lang [2016], referred to as Conditional
Minisum, where voters are also allowed to express dependencies between issues.
The price we have to pay when we move to this higher level of expressiveness is
that we end up with a computationally hard rule. Motivated by this, we focus on
the computational aspects of Conditional Minisum, where progress has been
rather scarce so far. We identify restrictions that concern the voters'
dependencies and the value of an optimal solution, under which we provide the
first multiplicative approximation algorithms for the problem. At the same
time, by additionally requiring certain structural properties for the union of
dependencies cast by the whole electorate, we obtain optimal efficient
algorithms for well-motivated special cases. Overall, our work provides a
better understanding on the complexity implications introduced by conditional
voting.


title:
Selection in the Presence of Implicit Bias: The Advantage of
  Intersectional Constraints

abstract:
  In selection processes such as hiring, promotion, and college admissions,
implicit bias toward socially-salient attributes such as race, gender, or
sexual orientation of candidates is known to produce persistent inequality and
reduce aggregate utility for the decision maker. Interventions such as the
Rooney Rule and its generalizations, which require the decision maker to select
at least a specified number of individuals from each affected group, have been
proposed to mitigate the adverse effects of implicit bias in selection. Recent
works have established that such lower-bound constraints can be very effective
in improving aggregate utility in the case when each individual belongs to at
most one affected group. However, in several settings, individuals may belong
to multiple affected groups and, consequently, face more extreme implicit bias
due to this intersectionality. We consider independently drawn utilities and
show that, in the intersectional case, the aforementioned non-intersectional
constraints can only recover part of the total utility achievable in the
absence of implicit bias. On the other hand, we show that if one includes
appropriate lower-bound constraints on the intersections, almost all the
utility achievable in the absence of implicit bias can be recovered. Thus,
intersectional constraints can offer a significant advantage over a
reductionist dimension-by-dimension non-intersectional approach to reducing
inequality.


title: The hyperbolic umbilic singularity in fast-slow systems

abstract:
  Fast-slow systems with three slow variables and gradient structure in the
fast variables have, generically, hyperbolic umbilic, elliptic umbilic or
swallowtail singularities. In this article we provide a detailed local analysis
of a fast-slow system near a hyperbolic umbilic singularity. In particular, we
show that under some appropriate non-degeneracy conditions on the slow flow,
the attracting slow manifolds jump onto the fast regime and fan out as they
cross the hyperbolic umbilic singularity. The analysis is based on the blow-up
technique, in which the hyperbolic umbilic point is blown up to a 5-dimensional
sphere. Moreover, the reduced slow flow is also blown up and embedded into the
blown-up fast formulation. Further, we describe how our analysis is related to
classical theories such as catastrophe theory and constrained differential
equations.


title:
Single photon emission and recombination dynamics in self-assembled
  GaN/AlN quantum dots

abstract:
  III-nitride quantum dots (QDs) are a promising system actively studied for
their ability to maintain single photon emission up to room temperature. Here,
we report on the evolution of the emission properties of self-assembled GaN/AlN
QDs for temperatures ranging from 5 to 300K. We carefully track the
photoluminescence of a single QD and measure an optimum single photon purity of
g(2)(0) = 0.05+-0.02 at 5 K and 0.17+-0.8 at 300 K. We complement this study
with temperaturedependent time-resolved photoluminescence measurements (TRPL)
performed on a QD ensemble to further investigate the exciton recombination
dynamics of such polar zero-dimensional nanostructures. By comparing our
results to past reports, we emphasize the complexity of recombination processes
in this system. Instead of the more conventional mono-exponential decay typical
of exciton recombination, TRPL transients display a bi-exponential feature with
short- and long-lived components that persist in the low excitation regime.
From the temperature insensitivity of the long-lived excitonic component, we
first discard the interplay of dark-to-bright state refilling in the exciton
recombination process. Besides, this temperature-invariance also highlights the
absence of nonradiative exciton recombinations, a likely direct consequence of
the strong carrier confinement observed in GaN/AlN QDs up to 300K. Overall, our
results support the viability of these dots as a potential single-photon source
for quantum applications at room temperature.


title: Removing Distortion Effects in Music Using Deep Neural Networks

abstract:
  Audio effects are an essential element in the context of music production,
and therefore, modeling analog audio effects has been extensively researched
for decades using system-identification methods, circuit simulation, and
recently, deep learning. However, only few works tackled the reconstruction of
signals that were processed using an audio effect unit. Given the recent
advances in music source separation and automatic mixing, the removal of audio
effects could facilitate an automatic remixing system. This paper focuses on
removing distortion and clipping applied to guitar tracks for music production
while presenting a comparative investigation of different deep neural network
(DNN) architectures on this task. We achieve exceptionally good results in
distortion removal using DNNs for effects that superimpose the clean signal to
the distorted signal, while the task is more challenging if the clean signal is
not superimposed. Nevertheless, in the latter case, the neural models under
evaluation surpass one state-of-the-art declipping system in terms of
source-to-distortion ratio, leading to better quality and faster inference.


title: On Monte Carlo Tree Search for Weighted Vertex Coloring

abstract:
  This work presents the first study of using the popular Monte Carlo Tree
Search (MCTS) method combined with dedicated heuristics for solving the
Weighted Vertex Coloring Problem. Starting with the basic MCTS algorithm, we
gradually introduce a number of algorithmic variants where MCTS is extended by
various simulation strategies including greedy and local search heuristics. We
conduct experiments on well-known benchmark instances to assess the value of
each studied combination. We also provide empirical evidence to shed light on
the advantages and limits of each strategy.


title: Equality Is Not Equity: Proportional Fairness in Federated Learning

abstract:
  Ensuring fairness of machine learning (ML) algorithms is becoming an
increasingly important mission for ML service providers. This is even more
critical and challenging in the federated learning (FL) scenario, given a large
number of diverse participating clients. Simply mandating equality across
clients could lead to many undesirable consequences, potentially discouraging
high-performing clients and resulting in sub-optimal overall performance. In
order to achieve better equity rather than equality, in this work, we introduce
and study proportional fairness (PF) in FL, which has a deep connection with
game theory. By viewing FL from a cooperative game perspective, where the
players (clients) collaboratively learn a good model, we formulate PF as Nash
bargaining solutions. Based on this concept, we propose PropFair, a novel and
easy-to-implement algorithm for effectively finding PF solutions, and we prove
its convergence properties. We illustrate through experiments that PropFair
consistently improves the worst-case and the overall performances
simultaneously over state-of-the-art fair FL algorithms for a wide array of
vision and language datasets, thus achieving better equity.


title: Data-driven assessment of arch vortices in urban flows

abstract:
  Understanding flow structures in urban areas is being widely recognized as a
challenging concern due to its effect on urban development, air quality, and
pollutant dispersion. In this study, state-of-the-art data-driven methods for
modal analysis of urban flows are used to understand better the dominant flow
processes that occur in this phenomenon. Higher-order dynamic mode
decomposition (HODMD), a highly-efficient method to analyze turbulent flows, is
used with traditional techniques such as proper orthogonal decomposition (POD)
to analyze high-fidelity simulation data of a simplified urban environment.
Furthermore, the spatio-temporal Koopman decomposition (STKD) will be applied
to the temporal modes obtained with HODMD to perform spatial analysis. The flow
interaction within the canopy influences the flow structures, particularly the
arch-vortex. The latter is a vortical structure generally found downstream
wall-mounted obstacles that appears due to flow separation. Therefore, the main
objective of the present study is to characterize the mechanisms that promote
these dynamics in urban areas with different geometries. Remarkably, among all
the vortical structures identified by the HODMD algorithm, low- and
high-frequency modes are classified according to their relation with the arch
vortex. They are referred to as vortex-generators and vortex-breakers,
respectively. This classification implies that one of the processes driving the
formation and destruction of major vortical structures in between the buildings
is the interaction between low- and high-frequency structures. The high energy
revealed by the POD-decomposition for the vortex-breaker modes points to this
destruction process as the mechanism driving the flow dynamics.


title: Tests of physics beyond the Standard Model with single-electron ions

abstract:
  A highly effective approach to the search for hypothetical new interactions
through isotope shift spectroscopy of hydrogen-like ions is presented. A
weighted difference of the g factor and ground-state energy is shown to assist
in the suppression of detrimental uncertainties from nuclear structure, while
preserving the hypothetical contributions from new interactions. Experimental
data from only a single isotope pair is required. Account is taken of the
small, subleading nuclear corrections, allowing to show that, provided feasible
experimental progress is achieved in UV/X-ray spectroscopy, the presented
approach can yield competitive bounds on New Physics electron coupling
parameters improved by more than an order of magnitude compared to leading
bounds from atomic physics.


title:
High-temperature thermalization implies the emergence of quantum state
  designs

abstract:
  It was recently observed that in certain thermalizing many-body systems,
measuring the complement of a subsystem that thermalized to infinite
temperature in a suitable orthonormal basis gives rise to approximate quantum
state k-designs as post-measurement states on the subsystem. We prove that this
emergence of approximate k-designs holds true in every large system where some
small subsystem is close to being maximally mixed. On a technical level we show
that any high-dimensional purification of an approximately maximally mixed
state induces an approximate quantum state k-design when measured in a suitable
orthonormal basis. Moreover, we show that this is true with overwhelming
probability for measurement bases chosen uniformly at random from the Haar
measure.

categories: quant-ph cond-mat.stat-mech math-ph math.MP


title: Ranking with Confidence for Large Scale Comparison Data

abstract:
  In this work, we leverage a generative data model considering comparison
noise to develop a fast, precise, and informative ranking algorithm from
pairwise comparisons that produces a measure of confidence on each comparison.
The problem of ranking a large number of items from noisy and sparse pairwise
comparison data arises in diverse applications, like ranking players in online
games, document retrieval or ranking human perceptions. Although different
algorithms are available, we need fast, large-scale algorithms whose accuracy
degrades gracefully when the number of comparisons is too small. Fitting our
proposed model entails solving a non-convex optimization problem, which we
tightly approximate by a sum of quasi-convex functions and a regularization
term. Resorting to an iterative reweighted minimization and the Primal-Dual
Hybrid Gradient method, we obtain PD-Rank, achieving a Kendall tau 0.1 higher
than all comparing methods, even for 10\% of wrong comparisons in simulated
data matching our data model, and leading in accuracy if data is generated
according to the Bradley-Terry model, in both cases faster by one order of
magnitude, in seconds. In real data, PD-Rank requires less computational time
to achieve the same Kendall tau than active learning methods.


title:
Log-Euclidean Signatures for Intrinsic Distances Between Unaligned
  Datasets

abstract:
  The need for efficiently comparing and representing datasets with unknown
alignment spans various fields, from model analysis and comparison in machine
learning to trend discovery in collections of medical datasets. We use manifold
learning to compare the intrinsic geometric structures of different datasets by
comparing their diffusion operators, symmetric positive-definite (SPD) matrices
that relate to approximations of the continuous Laplace-Beltrami operator from
discrete samples. Existing methods typically compare such operators in a
pointwise manner or assume known data alignment. Instead, we exploit the
Riemannian geometry of SPD matrices to compare these operators and define a new
theoretically-motivated distance based on a lower bound of the log-Euclidean
metric. Our framework facilitates comparison of data manifolds expressed in
datasets with different sizes, numbers of features, and measurement modalities.
Our log-Euclidean signature (LES) distance recovers meaningful structural
differences, outperforming competing methods in various application domains.


title:
SubOmiEmbed: Self-supervised Representation Learning of Multi-omics Data
  for Cancer Type Classification

abstract:
  For personalized medicines, very crucial intrinsic information is present in
high dimensional omics data which is difficult to capture due to the large
number of molecular features and small number of available samples. Different
types of omics data show various aspects of samples. Integration and analysis
of multi-omics data give us a broad view of tumours, which can improve clinical
decision making. Omics data, mainly DNA methylation and gene expression
profiles are usually high dimensional data with a lot of molecular features. In
recent years, variational autoencoders (VAE) have been extensively used in
embedding image and text data into lower dimensional latent spaces. In our
project, we extend the idea of using a VAE model for low dimensional latent
space extraction with the self-supervised learning technique of feature
subsetting. With VAEs, the key idea is to make the model learn meaningful
representations from different types of omics data, which could then be used
for downstream tasks such as cancer type classification. The main goals are to
overcome the curse of dimensionality and integrate methylation and expression
data to combine information about different aspects of same tissue samples, and
hopefully extract biologically relevant features. Our extension involves
training encoder and decoder to reconstruct the data from just a subset of it.
By doing this, we force the model to encode most important information in the
latent representation. We also added an identity to the subsets so that the
model knows which subset is being fed into it during training and testing. We
experimented with our approach and found that SubOmiEmbed produces comparable
results to the baseline OmiEmbed with a much smaller network and by using just
a subset of the data. This work can be improved to integrate mutation-based
genomic data as well.


title: $p$-Adic interpolation of orbits under rational maps

abstract:
  Let $L$ be a field of characteristic zero, let $h:\mathbb{P}^1\to
\mathbb{P}^1$ be a rational map defined over $L$, and let $c\in
\mathbb{P}^1(L)$. We show that there exists a finitely generated subfield $K$
of $L$ over which both $c$ and $h$ are defined along with an infinite set of
inequivalent non-archimedean completions $K_{\mathfrak{p}}$ for which there
exists a positive integer $a=a(\mathfrak{p})$ with the property that for $i\in
\{0,\ldots ,a-1\}$ there exists a power series $g_i(t)\in
K_{\mathfrak{p}}[[t]]$ that converges on the closed unit disc of
$K_{\mathfrak{p}}$ such that $h^{an+i}(c)=g_i(n)$ for all sufficiently large
$n$. As a consequence we show that the dynamical Mordell-Lang conjecture holds
for split self-maps $(h,g)$ of $\mathbb{P}^1 \times X$ with $g$ \'etale.


title: Fair partitions of the plane into incongruent pentagons

abstract:
  Motivated by a question of R.\ Nandakumar, we show that the Euclidean plane
can be dissected into mutually incongruent convex pentagons of the same area
and the same perimeter.


title:
Environmental and Safety Impacts of Vehicle-to-Everything Enabled
  Applications: A Review of State-of-the-Art Studies

abstract:
  With the rapid development of communication technology, connected vehicles
(CV) have the potential, through the sharing of data, to enhance vehicle safety
and reduce vehicle energy consumption and emissions. Numerous research efforts
are quantifying the impacts of CV applications, assuming instant and accurate
communication among vehicles, devices, pedestrians, infrastructure, the
network, the cloud, and the grid, collectively known as V2X
(vehicle-to-everything). The use of cellular vehicle-to-everything (C-V2X), to
share data is emerging as an efficient means to achieve this objective. C-V2X
releases 14 and 15 utilize the 4G LTE technology and release 16 utilizes the
new 5G new radio (NR) technology. C-V2X can function without network
infrastructure coverage and has a better communication range, improved latency,
and greater data rates compared to older technologies. Such highly efficient
interchange of information among all participating parts in a CV environment
will not only provide timely data to enhance the capacity of the transportation
system but can also be used to develop applications that enhance vehicle safety
and minimize negative environmental impacts. However, before the full benefits
of CV can be achieved, there is a need to thoroughly investigate the
effectiveness, strengths, and weaknesses of different CV applications, the
communication protocols, the varied results with different CV market
penetration rates (MPRs), the interaction of CVs and human driven vehicles, the
integration of multiple applications, and the errors and latencies associated
with data communication. This paper reviews existing literature on the
environmental, mobility and safety impacts of CV applications, identifies the
gaps in our current research of CVs and recommends future research directions.


title: Neutrino Astronomy with IMB, Kamiokande and Super Kamiokande

abstract:
  Some of the earliest work on neutrino astronomy was accomplished by a class
of underground detectors primarily designed for particle physics goals . These
detectors used inexpensive water to obtain the large masses needed to observe
the very low interaction rates expected from neutrinos. They exploited the
relatively large light attenuation length and the index of refraction of the
water to get a very inexpensive cost per thousand tons of detector.
  The results obtained from these pioneering neutrino detectors have included
real time observation of solar neutrinos, supernova neutrinos, and atmospheric
neutrinos. Searches for neutrino point sources, dark matter and primordial
magnetic monopoles were also made using them.

categories: astro-ph.HE hep-ex hep-th nucl-ex nucl-th


title:
Separating Rule Discovery and Global Solution Composition in a Learning
  Classifier System

abstract:
  While utilization of digital agents to support crucial decision making is
increasing, trust in suggestions made by these agents is hard to achieve.
However, it is essential to profit from their application, resulting in a need
for explanations for both the decision making process and the model. For many
systems, such as common black-box models, achieving at least some
explainability requires complex post-processing, while other systems profit
from being, to a reasonable extent, inherently interpretable. We propose a
rule-based learning system specifically conceptualised and, thus, especially
suited for these scenarios. Its models are inherently transparent and easily
interpretable by design. One key innovation of our system is that the rules'
conditions and which rules compose a problem's solution are evolved separately.
We utilise independent rule fitnesses which allows users to specifically tailor
their model structure to fit the given requirements for explainability.


title: Recognising the overlap graphs of subtrees of restricted trees is hard

abstract:
  The overlap graphs of subtrees in a tree (SOGs) generalise many other graphs
classes with set representation characterisations. The complexity of
recognising SOGs in open. The complexities of recognising many subclasses of
SOGs are known. We consider several subclasses of SOGs by restricting the
underlying tree. For a fixed integer $k \geq 3$, we consider:
\begin{my_itemize}
  \item The overlap graphs of subtrees in a tree where that tree has $k$ leaves
  \item The overlap graphs of subtrees in trees that can be derived from a
given input tree by subdivision and have at least 3 leaves
  \item The overlap and intersection graphs of paths in a tree where that tree
has maximum degree $k$ \end{my_itemize}
  We show that the recognition problems of these classes are NP-complete. For
all other parameters we get circle graphs, well known to be polynomially
recognizable.