title:
The Cosmic Mach Number as an Environment Measure for the Underlying Dark
  Matter Density Field

abstract:
  Using cosmological dark matter only simulations of a $(1.6$ Gpc$/h)^3$ volume
from the Legacy simulation project, we calculate Cosmic Mach Numbers (CMN) and
perform a theoretical investigation of their relation with halo properties and
features of the density field to gauge their use as an measure of the
environment.
  CMNs calculated on individual spheres show correlations with both the
overdensity in a region and the density gradient in the direction of the bulk
flow around that region. To reduce the scatter around the median of these
correlations, we introduce a new measure, the rank ordered Cosmic Mach number
($\hat{\mathcal{M}}_g$), which shows a tight correlations with the overdensity
$\delta=\frac{\rho-\bar{\rho}}{\bar{\rho}}$. Measures of the large scale
density gradient as well as other average properties of the halo population in
a region show tight correlations with $\hat{\mathcal{M}}_g$ as well. Our
results in this first empirical study suggest that $\hat{\mathcal{M}}_g$ is an
excellent proxy for the underlying density field and hence environment that can
circumvent reliance on number density counts in a region. For scales between
$10$ and $100 Mpc$/h, Mach numbers calculated using dark matter halos $(>
10^{12}$ M$_{\odot})$ that would typically host massive galaxies are consistent
with theoretical predictions of the linear matter power spectrum at a level of
$10\%$ due to non-linear effects of gravity. At redshifts $z\geq 3$, these
deviations disappear. We also quantify errors due to missing large scale modes
in simulations. Simulations of box size $\leq 1 $ Gpc/$h$ typically predict
CMNs 10-30\% too small on scales of$\sim 100$ Mpc$/h$.


title: 2D discrete Hodge-Dirac operator on the torus

abstract:
  We discuss a discretisation of the de Rham-Hodge theory in the
two-dimensional case based on a discrete exterior calculus framework. We
present discrete analogues of the Hodge-Dirac and Laplace operators in which
key geometric aspects of the continuum counterpart are captured. We provide and
prove a discrete version of the Hodge decomposition theorem. Special attention
has been paid to discrete models on a combinatorial torus. In this particular
case, we also define and calculate the cohomology groups.


title:
Constraints on the electron-hole pair creation energy and Fano factor
  below 150 eV from Compton scattering in a Skipper-CCD

abstract:
  Fully-depleted thick silicon Skipper-charge-coupled devices (Skipper-CCDs)
are an important technology to probe neutrino and light-dark-matter
interactions due to their sub-electron read-out noise. However, the successful
search for rare neutrino or dark-matter events requires the signal and all
backgrounds to be fully characterized. In particular, a measurement of the
electron-hole pair creation energy below 150 eV and the Fano factor are
necessary for characterizing the dark matter and neutrino signals. Moreover,
photons from background radiation may Compton scatter in the silicon bulk,
producing events that can mimic a dark matter or neutrino signal. We present a
measurement of the Compton spectrum using a Skipper-CCD and a $^{241}$Am
source. With these data, we estimate the electron-hole pair-creation energy to
be $\left(3.71 \pm 0.08\right)$ eV at 130 K in the energy range between 99.3 eV
and 150 eV. By measuring the widths of the steps at 99.3 eV and 150 eV in the
Compton spectrum, we introduce a novel technique to measure the Fano factor,
setting an upper limit of 0.31 at 90% C.L. These results prove the potential of
Skipper-CCDs to characterize the Compton spectrum and to measure precisely the
Fano factor and electron-hole pair creation energy below 150 eV.


title: Learnings from Federated Learning in the Real world

abstract:
  Federated Learning (FL) applied to real world data may suffer from several
idiosyncrasies. One such idiosyncrasy is the data distribution across devices.
Data across devices could be distributed such that there are some "heavy
devices" with large amounts of data while there are many "light users" with
only a handful of data points. There also exists heterogeneity of data across
devices. In this study, we evaluate the impact of such idiosyncrasies on
Natural Language Understanding (NLU) models trained using FL. We conduct
experiments on data obtained from a large scale NLU system serving thousands of
devices and show that simple non-uniform device selection based on the number
of interactions at each round of FL training boosts the performance of the
model. This benefit is further amplified in continual FL on consecutive time
periods, where non-uniform sampling manages to swiftly catch up with FL methods
using all data at once.


title: Distribution Regression with Sliced Wasserstein Kernels

abstract:
  The problem of learning functions over spaces of probabilities - or
distribution regression - is gaining significant interest in the machine
learning community. A key challenge behind this problem is to identify a
suitable representation capturing all relevant properties of the underlying
functional mapping. A principled approach to distribution regression is
provided by kernel mean embeddings, which lifts kernel-induced similarity on
the input domain at the probability level. This strategy effectively tackles
the two-stage sampling nature of the problem, enabling one to derive estimators
with strong statistical guarantees, such as universal consistency and excess
risk bounds. However, kernel mean embeddings implicitly hinge on the maximum
mean discrepancy (MMD), a metric on probabilities, which may fail to capture
key geometrical relations between distributions. In contrast, optimal transport
(OT) metrics, are potentially more appealing, as documented by the recent
literature on the topic. In this work, we propose the first OT-based estimator
for distribution regression. We build on the Sliced Wasserstein distance to
obtain an OT-based representation. We study the theoretical properties of a
kernel ridge regression estimator based on such representation, for which we
prove universal consistency and excess risk bounds. Preliminary experiments
complement our theoretical findings by showing the effectiveness of the
proposed approach and compare it with MMD-based estimators.


title:
Asymptotic non-equivalence of affirmative actions: The top trading
  cycles mechanism case

abstract:
  This note analyzes the asymptotic performance of two popular affirmative
action policies, majority quota and minority reserve, under the top trading
cycles mechanism (TTCM). These two affirmative actions will induce different
matching outcomes with non-negligible probability under the TTCM even if the
number of reserved seats for minorities grows relatively slow in large markets.


title:
Stein's method for steady-state diffusion approximation in Wasserstein
  distance

abstract:
  We provide a general steady-state diffusion approximation result which bounds
the Wasserstein distance between the reversible measure $\mu$ of a diffusion
process and the measure $\nu$ of an approximating Markov chain. Our result is
obtained thanks to a generalization of a new approach to Stein's method which
may be of independent interest. As an application, we study the invariant
measure of a random walk on a $k$-nearest neighbors graph, providing a
quantitative answer to a problem of interest to the machine learning community.


title: Relations among topological solitons

abstract:
  We clarify relations among topological solitons in various dimensions: a
domain wall, non-Abelian vortex, magnetic monopole and Yang-Mills instanton,
together with a (non-Abelian) sine-Gordon soliton, baby Skyrmion (lump) and
Skyrmion. We construct a composite configuration consisting of a domain wall,
vortex, magnetic monopole and Yang-Mills instanton
(wall-vortex-monopole-instanton) using the effective theory technique or moduli
approximation. Removing some solitons from such a composite, we obtain all
possible composite solitons in the form of solitons within a soliton, including
all the previously known configurations, yielding relations among topological
solitons.


title:
If a Human Can See It, So Should Your System: Reliability Requirements
  for Machine Vision Components

abstract:
  Machine Vision Components (MVC) are becoming safety-critical. Assuring their
quality, including safety, is essential for their successful deployment.
Assurance relies on the availability of precisely specified and, ideally,
machine-verifiable requirements. MVCs with state-of-the-art performance rely on
machine learning (ML) and training data but largely lack such requirements.
  In this paper, we address the need for defining machine-verifiable
reliability requirements for MVCs against transformations that simulate the
full range of realistic and safety-critical changes in the environment. Using
human performance as a baseline, we define reliability requirements as: 'if the
changes in an image do not affect a human's decision, neither should they
affect the MVC's.' To this end, we provide: (1) a class of safety-related image
transformations; (2) reliability requirement classes to specify
correctness-preservation and prediction-preservation for MVCs; (3) a method to
instantiate machine-verifiable requirements from these requirements classes
using human performance experiment data; (4) human performance experiment data
for image recognition involving eight commonly used transformations, from about
2000 human participants; and (5) a method for automatically checking whether an
MVC satisfies our requirements. Further, we show that our reliability
requirements are feasible and reusable by evaluating our methods on 13
state-of-the-art pre-trained image classification models. Finally, we
demonstrate that our approach detects reliability gaps in MVCs that other
existing methods are unable to detect.


title:
Ultrafast Emergence of Ferromagnetism in Antiferromagnetic FeRh in High
  Magnetic Fields

abstract:
  The nature of the counter-intuitive heat-induced ferromagnetism in FeRh has
been an ongoing debate for about 60 years, resembling the dispute about the
chicken-or-egg causality dilemma. FeRh is antiferromagnetic at low temperatures
and becomes ferromagnetic when heated above 370 K, accompanied by an expansion
of the unit cell. It is, however, still unknown whether the change in the
magnetic order drives the lattice expansion or vice versa. To resolve this
magnetism-or-lattice causality dilemma we accelerated the spin dynamics in high
magnetic fields up to 25 T, while observing both the time-resolved structural
and magnetic changes. We discovered a new regime of the fastest possible
emergence of ferromagnetism which is at the speed limit of the structural
change. As a result, both actors evolve simultaneously, which resolves the
magnetism-or-lattice causality dilemma.


title:
Robustness Verification for Attention Networks using Mixed Integer
  Programming

abstract:
  Attention networks such as transformers have been shown powerful in many
applications ranging from natural language processing to object recognition.
This paper further considers their robustness properties from both theoretical
and empirical perspectives. Theoretically, we formulate a variant of attention
networks containing linearized layer normalization and sparsemax activation,
and reduce its robustness verification to a Mixed Integer Programming problem.
Apart from a na\"ive encoding, we derive tight intervals from admissible
perturbation regions and examine several heuristics to speed up the
verification process. More specifically, we find a novel bounding technique for
sparsemax activation, which is also applicable to softmax activation in general
neural networks. Empirically, we evaluate our proposed techniques with a case
study on lane departure warning and demonstrate a performance gain of
approximately an order of magnitude. Furthermore, although attention networks
typically deliver higher accuracy than general neural networks, contrasting its
robustness against a similar-sized multi-layer perceptron surprisingly shows
that they are not necessarily more robust.


title: Scalable computational kernels for mortar finite element methods

abstract:
  Targeting simulations on parallel hardware architectures, this paper presents
computational kernels for efficient computations in mortar finite element
methods. Mortar methods enable a variationally consistent imposition of
coupling conditions at high accuracy, but come with considerable numerical
effort and cost for the evaluation of the mortar integrals to compute the
coupling operators. In this paper, we identify bottlenecks in parallel data
layout and domain decomposition that hinder an efficient evaluation of the
mortar integrals. We then propose a set of computational strategies to restore
optimal parallel communication and scalability for the core kernels devoted to
the evaluation of mortar terms. We exemplarily study the proposed algorithmic
components in the context of three-dimensional large-deformation contact
mechanics, both for cases with fixed and dynamically varying interface
topology, yet these concepts can naturally and easily be transferred to other
mortar applications, e.g. classical meshtying problems. To restore parallel
scalability, we employ overlapping domain decompositions of the interface
discretization independent from the underlying volumes and then tackle parallel
communication for the mortar evaluation by a geometrically motivated reduction
of ghosting data. Using three-dimensional contact examples, we demonstrate
strong and weak scalability of the proposed algorithms up to 480 parallel
processes as well as study and discuss improvements in parallel communication
related to mortar finite element methods. For the first time, dynamic load
balancing is applied to mortar contact problems with evolving contact zones,
such that the computational work is well balanced among all parallel processors
independent of the current state of the simulation.


title:
Alexa, in you, I trust! Fairness and Interpretability Issues in
  E-commerce Search through Smart Speakers

abstract:
  In traditional (desktop) e-commerce search, a customer issues a specific
query and the system returns a ranked list of products in order of relevance to
the query. An increasingly popular alternative in e-commerce search is to issue
a voice-query to a smart speaker (e.g., Amazon Echo) powered by a voice
assistant (VA, e.g., Alexa). In this situation, the VA usually spells out the
details of only one product, an explanation citing the reason for its
selection, and a default action of adding the product to the customer's cart.
This reduced autonomy of the customer in the choice of a product during
voice-search makes it necessary for a VA to be far more responsible and
trustworthy in its explanation and default action.
  In this paper, we ask whether the explanation presented for a product
selection by the Alexa VA installed on an Amazon Echo device is consistent with
human understanding as well as with the observations on other traditional
mediums (e.g., desktop ecommerce search). Through a user survey, we find that
in 81% cases the interpretation of 'a top result' by the users is different
from that of Alexa. While investigating for the fairness of the default action,
we observe that over a set of as many as 1000 queries, in nearly 68% cases,
there exist one or more products which are more relevant (as per Amazon's own
desktop search results) than the product chosen by Alexa. Finally, we conducted
a survey over 30 queries for which the Alexa-selected product was different
from the top desktop search result, and observed that in nearly 73% cases, the
participants preferred the top desktop search result as opposed to the product
chosen by Alexa. Our results raise several concerns and necessitates more
discussions around the related fairness and interpretability issues of VAs for
e-commerce search.


title:
Direct counterfactual quantum communication protocol beyond single
  photon source

abstract:
  The direct counterfactual quantum communication protocol involving double
chained Mach-Zehnder interferometers requires a single photon input. Here, we
show that even with multiphoton light sources, including a strong coherent
light source as inputs, the counterfactual communication can be achieved with
success probability approaching unity in the ideal asymptotic limit. The path
evolution of multiple photons is non-locally controlled. Thus, information is
transmitted without any photons, or any other auxiliary information carriers,
appearing in the public transmission channel. The effect is quantum since
quantum measurements are essential requirement for this protocol. We also
propose a modified scheme for observing multiphoton input counterfactual
communication under currently available experimental research.


title:
Accelerometer-based Bed Occupancy Detection for Automatic, Non-invasive
  Long-term Cough Monitoring

abstract:
  We present a new machine learning based bed-occupancy detection system that
uses the accelerometer signal captured by a bed-attached consumer smartphone.
Automatic bed-occupancy detection is necessary for automatic long-term cough
monitoring, since the time which the monitored patient occupies the bed is
required to accurately calculate a cough rate. Accelerometer measurements are
more cost effective and less intrusive than alternatives such as video
monitoring or pressure sensors. A 249-hour dataset of manually-labelled
acceleration signals gathered from seven patients undergoing treatment for
tuberculosis (TB) was compiled for experimentation. These signals are
characterised by brief activity bursts interspersed with long periods of little
or no activity, even when the bed is occupied. To process them effectively, we
propose an architecture consisting of three interconnected components. An
occupancy-change detector locates instances at which bed occupancy is likely to
have changed, an occupancy-interval detector classifies periods between
detected occupancy changes and an occupancy-state detector corrects
falsely-identified occupancy changes. Using long short-term memory (LSTM)
networks, this architecture was demonstrated to achieve an AUC of 0.94. When
integrated into a complete cough monitoring system, the daily cough rate of a
patient undergoing TB treatment was determined over a period of 14 days. As the
colony forming unit (CFU) counts decreased and the time to positivity (TPP)
increased, the measured cough rate decreased, indicating effective TB
treatment. This provides a first indication that automatic cough monitoring
based on bed-mounted accelerometer measurements may present a non-invasive,
non-intrusive and cost-effective means of monitoring long-term recovery of TB
patients.


title:
Dynamics of finite-size spheroids in turbulent flow: the roles of flow
  structures and particle boundary layers

abstract:
  We study the translational and rotational dynamics of neutrally-buoyant
finite-size spheroids in hydrodynamic turbulence by means of fully resolved
numerical simulations. We examine axisymmetric shapes, from oblate to prolate,
and the particle volume dependences. We show that the accelerations and
rotations experienced by non-spherical inertial-scale particles result from
volume filtered fluid forces and torques, similar to spherical particles.
However, the particle orientations carry signatures of preferential alignments
with the surrounding flow structures, which is reflected in distinct axial and
lateral fluctuations for accelerations and rotation rates. The randomization of
orientations does not occur even for particles with volume equivalent diameter
size in the inertial range, here up to 60 $\eta$ at $Re_{\lambda}=120$.
Additionally, we demonstrate that the role of fluid boundary layers around the
particles cannot be neglected to reach a quantitative understanding of particle
statistical dynamics, as they affect the intensities of angular velocities, and
the relative importance of tumbling with respect to spinning rotations. This
study brings to the fore the importance of inertial-scale flow structures in
homogeneous and isotropic turbulence and their impacts on the transport of
neutrally-buoyant bodies with size in the inertial range.


title: A Unique Cardiac Electrophysiological 3D Model

abstract:
  Mathematical models of cardiac electrical activity are one of the most
important tools for elucidating information about the heart diagnostic. Even
though it is one of the major problems in biomedical research, an efficient
mathematical formulation for this modelling has still not been found.
  In this paper, we present an outstanding mathematical model. It relies on a
five dipole representation of the cardiac electric source, each one associated
with the well-known waves of the electrocardiogram signal. The mathematical
formulation is simple enough to be easily parametrized and rich enough to
provide realistic signals. Beyond the physical basis of the model, the
parameters are physiologically interpretable as they characterize the wave
shape, similar to what a physician would look for in signals, thus making them
very useful in diagnosis.
  The model accurately reproduces the electrocardiogram and vectocardiogram
signals of any diseased or healthy heart, bringing together different systems
in a single model.
  Furthermore, a novel algorithm accurately identifies the model parameters.
This new discovery represents a revolution in electrocardiography research,
solving one of the main problems in this field. It is especially useful for the
automatic diagnosis of cardiovascular diseases, patient follow-up or
decision-making on new therapies.


title:
Scaling, Mirror Symmetries and Musical Consonances Among the Distances
  of the Planets of the Solar System

abstract:
  Orbital systems are often self-organized and/or characterized by harmonic
relations. Inspired by music theory, we rewrite the Geddes and King-Hele
(QJRAS, 24, 10-13, 1983) equations for mirror symmetries among the distances of
the planets of the solar system in an elegant and compact form by using the
2/3rd power of the ratios of the semi-major axis lengths of two neighboring
planets (eight pairs, including the belt of the asteroids). This metric
suggests that the solar system could be characterized by a scaling and
mirror-like structure relative to the asteroid belt that relates together the
terrestrial and Jovian planets. These relations are based on a 9/8 ratio
multiplied by powers of 2, which correspond musically to the interval of the
Pythagorean epogdoon (a Major Second) and its addition with one or more
octaves. Extensions of the same model are discussed and found compatible also
with the still hypothetical vulcanoid asteroids versus the transneptunian
objects. The found relation also suggests that the planetary self-organization
of our system could be generated by the 3:1 and 7:3 resonances of Jupiter,
which are already known to have shaped the asteroid belt. The proposed model
predicts the main Kirkwood asteroid gaps and the ratio among the planetary
orbital parameters with a 99% accuracy.


title: Degree 2 Boolean Functions on Grassmann Graphs

abstract:
  We investigate the existence of Boolean degree $d$ functions on the Grassmann
graph of $k$-spaces in the vector space $\mathbb{F}_q^n$. For $d=1$ several
non-existence and classification results are known, and no non-trivial examples
are known for $n \geq 5$. This paper focusses on providing a list of examples
on the case $d=2$ in general dimension and in particular for $(n, k)=(6,3)$ and
$(n,k) = (8, 4)$.
  We also discuss connections to the analysis of Boolean functions, regular
sets/equitable bipartitions/perfect 2-colorings in graphs, $q$-analogs of
designs, and permutation groups. In particular, this represents a natural
generalization of Cameron-Liebler line classes.


title: Harmonic flow field representations of quantum bits and gates

abstract:
  We describe a general procedure for mapping arbitrary $n$-qubit states to
two-dimensional (2D) vector fields. The mappings use complex rational function
representations of individual qubits, producing classical vector field
configurations that can be interpreted in terms of 2D inviscid fluid flows or
electric fields. Elementary qubits are identified with localized defects in 2D
harmonic vector fields, and multi-qubit states find natural field
representations via complex superpositions of vector field products. In
particular, separable states appear as highly symmetric flow configurations,
making them both dynamically and visually distinct from entangled states. The
resulting real-space representations of entangled qubit states enable an
intuitive visualization of their transformations under quantum logic
operations. We demonstrate this for the quantum Fourier transform and the
period finding process underlying Shor's algorithm, along with other quantum
algorithms. Due to its generic construction, the mapping procedure suggests the
possibility of extending concepts such as entanglement or entanglement entropy
to classical continuum systems, and thus may help guide new experimental
approaches to information storage and non-standard computation.


title: Progress report on the online processing upgrade at the NA62 experiment

abstract:
  A new FPGA-based low-level trigger processor has been installed at the NA62
experiment. It is intended to extend the features of its predecessor due to a
faster interconnection technology and additional logic resources available on
the new platform. With the aim of improving trigger selectivity and exploring
new architectures for complex trigger computation, a GPU system has been
developed and a neural network on FPGA is in progress. They both process data
streams from the Ring Imaging Cherenkov detector of the experiment to extract
in real time high level features for the trigger logic. Description of the
systems, latest developments and design flows are reported in this paper.


title: SPIDER: Specification-based Integration Defect Revealer

abstract:
  Modern software design practice implies widespread use in the development of
ready-made components, usually designed as external libraries. The undoubted
advantages of reusing third-party code can be offset by integration errors that
appear in the developed software. The reason for the appearance of such errors
is mainly due to misunderstanding or incomplete understanding by the programmer
of the details of external libraries such as an internal structure and the
subtleties of functioning. The documentation provided with the libraries is
often very sparse and describes only the main intended scenarios for the
interaction of the program and the library. In this paper, we propose the
approach based on the use of formal library specifications, which allows
detecting integration errors using static analysis methods. To do this, the
external library is described using the LibSL specification language, the
resulting description is translated into the internal data structures of the
KEX analyzer. The execution of the incorrect scenarios of library usage, such
as the incorrect sequence of method calls or the violation of the API function
contract, is marked in the program model with special built-in functions of the
KEX analyzer. Later, when analyzing the program, KEX becomes able to detect
integration errors, since incorrect library usage scenarios are diagnosed as
calling marked functions. The proposed approach is implemented as SPIDER
(SPecification-based Integration Defect Revealer), which is an extension of the
Kex analyzer and has proven its efficiency by detecting integration errors of
different classes on several special-made projects, as well as on several
projects taken from open repositories.


title:
Detecting Anomalies within Time Series using Local Neural
  Transformations

abstract:
  We develop a new method to detect anomalies within time series, which is
essential in many application domains, reaching from self-driving cars,
finance, and marketing to medical diagnosis and epidemiology. The method is
based on self-supervised deep learning that has played a key role in
facilitating deep anomaly detection on images, where powerful image
transformations are available. However, such transformations are widely
unavailable for time series. Addressing this, we develop Local Neural
Transformations(LNT), a method learning local transformations of time series
from data. The method produces an anomaly score for each time step and thus can
be used to detect anomalies within time series. We prove in a theoretical
analysis that our novel training objective is more suitable for transformation
learning than previous deep Anomaly detection(AD) methods. Our experiments
demonstrate that LNT can find anomalies in speech segments from the LibriSpeech
data set and better detect interruptions to cyber-physical systems than
previous work. Visualization of the learned transformations gives insight into
the type of transformations that LNT learns.


title: Spectral embedding and the latent geometry of multipartite networks

abstract:
  Spectral embedding finds vector representations of the nodes of a network,
based on the eigenvectors of its adjacency or Laplacian matrix, and has found
applications throughout the sciences. Many such networks are multipartite,
meaning their nodes can be divided into partitions and nodes of the same
partition are never connected. When the network is multipartite, this paper
demonstrates that the node representations obtained via spectral embedding live
near partition-specific low-dimensional subspaces of a higher-dimensional
ambient space. For this reason we propose a follow-on step after spectral
embedding, to recover node representations in their intrinsic rather than
ambient dimension, proving uniform consistency under a low-rank, inhomogeneous
random graph model. Our method naturally generalizes bipartite spectral
embedding, in which node representations are obtained by singular value
decomposition of the biadjacency or bi-Laplacian matrix.


title:
Variance matrix priors for Dirichlet process mixture models with
  Gaussian kernels

abstract:
  The Dirichlet Process Mixture Model (DPMM) is a Bayesian non-parametric
approach widely used for density estimation and clustering. In this manuscript,
we study the choice of prior for the variance or precision matrix when Gaussian
kernels are adopted. Typically, in the relevant literature, the assessment of
mixture models is done by considering observations in a space of only a handful
of dimensions. Instead, we are concerned with more realistic problems of higher
dimensionality, in a space of up to 20 dimensions. We observe that the choice
of prior is increasingly important as the dimensionality of the problem
increases. After identifying certain undesirable properties of standard priors
in problems of higher dimensionality, we review and implement possible
alternative priors. The most promising priors are identified, as well as other
factors that affect the convergence of MCMC samplers. Our results show that the
choice of prior is critical for deriving reliable posterior inferences. This
manuscript offers a thorough overview and comparative investigation into
possible priors, with detailed guidelines for their implementation. Although
our work focuses on the use of the DPMM in clustering, it is also applicable to
density estimation.


title: Minimum-Time Quadrotor Waypoint Flight in Cluttered Environments

abstract:
  We tackle the problem of planning a minimum-time trajectory for a quadrotor
over a sequence of specified waypoints in the presence of obstacles while
exploiting the full quadrotor dynamics. This problem is crucial for autonomous
search and rescue and drone racing scenarios but was, so far, unaddressed by
the robotics community \emph{in its entirety} due to the challenges of
minimizing time in the presence of the non-convex constraints posed by
collision avoidance. Early works relied on simplified dynamics or polynomial
trajectory representations that did not exploit the full actuator potential of
a quadrotor and, thus, did not aim at minimizing time. We address this
challenging problem by using a hierarchical, sampling-based method with an
incrementally more complex quadrotor model. Our method first finds paths in
different topologies to guide subsequent trajectory search for a kinodynamic
point-mass model. Then, it uses an asymptotically-optimal, kinodynamic
sampling-based method based on a full quadrotor model on top of the point-mass
solution to find a feasible trajectory with a time-optimal objective. The
proposed method is shown to outperform all related baselines in cluttered
environments and is further validated in real-world flights at over 60km/h in
one of the world's largest motion capture systems. We release the code open
source.


title:
Adaptive two capacitor model to describe slide electrification in moving
  water drops

abstract:
  Slide electrification is a spontaneous charge separation between a surface
and a sliding drop. Here, we describe this effect in terms of a voltage
generated at the three-phase contact line. This voltage moves charges between
capacitors, one formed by the drop and one on the surface. By introducing an
adaptation of the voltage upon water contact, we can model drop charge
experiments on many surfaces, including more exotic ones with drop-rate
dependent charge polarity. Thus, the adaptive two capacitor model enables new
insights into the molecular details of the charge separation mechanism.


title:
Systematically improving existing k-means initialization algorithms at
  nearly no cost, by pairwise-nearest-neighbor smoothing

abstract:
  We present a meta-method for initializing (seeding) the $k$-means clustering
algorithm called PNN-smoothing. It consists in splitting a given dataset into
$J$ random subsets, clustering each of them individually, and merging the
resulting clusterings with the pairwise-nearest-neighbor (PNN) method. It is a
meta-method in the sense that when clustering the individual subsets any
seeding algorithm can be used. If the computational complexity of that seeding
algorithm is linear in the size of the data $N$ and the number of clusters $k$,
PNN-smoothing is also almost linear with an appropriate choice of $J$, and in
fact only at most a few percent slower in most cases in practice. We show
empirically, using several existing seeding methods and testing on several
synthetic and real datasets, that this procedure results in systematically
better costs. It can even be applied recursively, and easily parallelized. Our
implementation is publicly available at
https://github.com/carlobaldassi/KMeansPNNSmoothing.jl


title: PACSan: Enforcing Memory Safety Based on ARM PA

abstract:
  Memory safety is a key security property that stops memory corruption
vulnerabilities. Existing sanitizers enforce checks and catch such bugs during
development and testing. However, they either provide partial memory safety or
have overwhelmingly high performance overheads. Our novel sanitizer PACSan
enforces spatial and temporal memory safety with no false positives at low
performance overheads. PACSan removes the majority of the overheads involved in
pointer tracking by sealing metadata in pointers through ARM PA (Pointer
Authentication), and performing the memory safety checks when pointers are
dereferenced. We have developed a prototype of PACSan and systematically
evaluated its security and performance on the Magma, Juliet, Nginx, and SPEC
CPU2017 test suites, respectively. In our evaluation, PACSan shows no false
positives together with negligible false negatives, while introducing stronger
security guarantees and lower performance overheads than state-of-the-art
sanitizers, including HWASan, ASan, SoftBound+CETS, Memcheck, LowFat, and
PTAuth. Specifically, PACSan has 0.84x runtime overhead and 1.92x memory
overhead on average. Compared to the widely deployed ASan, PACSan has no false
positives and much fewer false negatives and reduces 7.172% runtime overheads
and 89.063%memory overheads.


title: On Sibson's $\alpha$-Mutual Information

abstract:
  We explore a family of information measures that stems from R\'enyi's
$\alpha$-Divergences with $\alpha<0$. In particular, we extend the definition
of Sibson's $\alpha$-Mutual Information to negative values of $\alpha$ and show
several properties of these objects. Moreover, we highlight how this family of
information measures is related to functional inequalities that can be employed
in a variety of fields, including lower-bounds on the Risk in Bayesian
Estimation Procedures.


title: Spin waves in spin hydrodynamics

abstract:
  The propagation properties of spin degrees of freedom are analyzed in the
framework of relativistic hydrodynamics with spin based on the de Groot--van
Leeuwen--van Weert definitions of the energy-momentum and spin tensors. We
derive the analytical expression for the spin wave velocity for arbitrary
statistics and show that it goes to half the speed of light in the
ultra-relativistic limit. We find that only the transverse degrees of freedom
propagate, analogously to electromagnetic waves. Finally, we consider the
effect of dissipative corrections and calculate the damping coefficients for
the case of Maxwell-J\"uttner statistics.


title:
Interactivity: the missing link between virtual reality technology and
  drug discovery pipelines

abstract:
  The potential of virtual reality (VR) to contribute to drug design and
development has been recognised for many years. Hardware and software
developments now mean that this potential is beginning to be realised, and VR
methods are being actively used in this sphere. A recent advance is to use VR
not only to visualise and interact with molecular structures, but also to
interact with molecular dynamics simulations of 'on the fly' (interactive
molecular dynamics in VR, IMD-VR), which is useful not only for flexible
docking but also to examine binding processes and conformational changes.
iMD-VR has been shown to be useful for creating complexes of ligands bound to
target proteins, e.g., recently applied to peptide inhibitors of the SARS-CoV-2
main protease. In this review, we use the term 'interactive VR' to refer to
software where interactivity is an inherent part of the user VR experience
e.g., in making structural modifications or interacting with a physically
rigorous molecular dynamics (MD) simulation, as opposed to simply using VR
controllers to rotate and translate the molecule for enhanced visualisation.
Here, we describe these methods and their application to problems relevant to
drug discovery, highlighting the possibilities that they offer in this arena.
We suggest that the ease of viewing and manipulating molecular structures and
dynamics, and the ability to modify structures on the fly (e.g., adding or
deleting atoms) makes modern interactive VR a valuable tool to add to the
armoury of drug development methods.


title:
Social-DualCVAE: Multimodal Trajectory Forecasting Based on Social
  Interactions Pattern Aware and Dual Conditional Variational Auto-Encoder

abstract:
  Pedestrian trajectory forecasting is a fundamental task in multiple utility
areas, such as self-driving, autonomous robots, and surveillance systems. The
future trajectory forecasting is multi-modal, influenced by physical
interaction with scene contexts and intricate social interactions among
pedestrians. The mainly existing literature learns representations of social
interactions by deep learning networks, while the explicit interaction patterns
are not utilized. Different interaction patterns, such as following or
collision avoiding, will generate different trends of next movement, thus, the
awareness of social interaction patterns is important for trajectory
forecasting. Moreover, the social interaction patterns are privacy concerned or
lack of labels. To jointly address the above issues, we present a social-dual
conditional variational auto-encoder (Social-DualCVAE) for multi-modal
trajectory forecasting, which is based on a generative model conditioned not
only on the past trajectories but also the unsupervised classification of
interaction patterns. After generating the category distribution of the
unlabeled social interaction patterns, DualCVAE, conditioned on the past
trajectories and social interaction pattern, is proposed for multi-modal
trajectory prediction by latent variables estimating. A variational bound is
derived as the minimization objective during training. The proposed model is
evaluated on widely used trajectory benchmarks and outperforms the prior
state-of-the-art methods.


title: Heating of the solar chromosphere through current dissipation

abstract:
  The solar chromosphere is heated to temperatures higher than predicted by
radiative equilibrium. This excess heating is greater in active regions where
the magnetic field is stronger. We aim to investigate the magnetic topology
associated with an area of enhanced millimeter (mm) brightness temperatures in
a solar active region mapped by the Atacama Large Millimeter/submillimeter
Array (ALMA) using spectropolarimetric co-observations with the 1-m Swedish
Solar Telescope (SST). We used Milne-Eddington inversions, nonlocal
thermodynamic equilibrium (non-LTE) inversions, and a magnetohydrostatic
extrapolation to obtain constraints on the three-dimensional stratification of
temperature, magnetic field, and radiative energy losses. We compared the
observations to a snapshot of a magnetohydrodynamics simulation and investigate
the formation of the thermal continuum at 3 mm using contribution functions. We
find enhanced heating rates in the upper chromosphere of up to $\sim
5\rm\,kW\,m^{-2}$, where small-scale emerging loops interact with the overlying
magnetic canopy leading to current sheets as shown by the magnetic field
extrapolation. Our estimates are about a factor of two higher than canonical
values, but they are limited by the ALMA spatial resolution ($\sim
1.2^{\prime\prime}$). Band 3 brightness temperatures reach about
$\sim10^{4}\,$K in the region, and the transverse magnetic field strength
inferred from the non-LTE inversions is on the order of $\sim 500\,$G in the
chromosphere. We are able to quantitatively reproduce many of the observed
features, including the integrated radiative losses in our numerical
simulation. We conclude that the heating is caused by dissipation in current
sheets. However, the simulation shows a complex stratification in the flux
emergence region where distinct layers may contribute significantly to the
emission in the mm continuum.


title: From Generalisation Error to Transportation-cost Inequalities and Back

abstract:
  In this work, we connect the problem of bounding the expected generalisation
error with transportation-cost inequalities. Exposing the underlying pattern
behind both approaches we are able to generalise them and go beyond
Kullback-Leibler Divergences/Mutual Information and sub-Gaussian measures. In
particular, we are able to provide a result showing the equivalence between two
families of inequalities: one involving functionals and one involving measures.
This result generalises the one proposed by Bobkov and G\"otze that connects
transportation-cost inequalities with concentration of measure. Moreover, it
allows us to recover all standard generalisation error bounds involving mutual
information and to introduce new, more general bounds, that involve arbitrary
divergence measures.


title:
Bingham Policy Parameterization for 3D Rotations in Reinforcement
  Learning

abstract:
  We propose a new policy parameterization for representing 3D rotations during
reinforcement learning. Today in the continuous control reinforcement learning
literature, many stochastic policy parameterizations are Gaussian. We argue
that universally applying a Gaussian policy parameterization is not always
desirable for all environments. One such case in particular where this is true
are tasks that involve predicting a 3D rotation output, either in isolation, or
coupled with translation as part of a full 6D pose output. Our proposed Bingham
Policy Parameterization (BPP) models the Bingham distribution and allows for
better rotation (quaternion) prediction over a Gaussian policy parameterization
in a range of reinforcement learning tasks. We evaluate BPP on the rotation
Wahba problem task, as well as a set of vision-based next-best pose robot
manipulation tasks from RLBench. We hope that this paper encourages more
research into developing other policy parameterization that are more suited for
particular environments, rather than always assuming Gaussian.


title: Uncertainty Modeling for Out-of-Distribution Generalization

abstract:
  Though remarkable progress has been achieved in various vision tasks, deep
neural networks still suffer obvious performance degradation when tested in
out-of-distribution scenarios. We argue that the feature statistics (mean and
standard deviation), which carry the domain characteristics of the training
data, can be properly manipulated to improve the generalization ability of deep
learning models. Common methods often consider the feature statistics as
deterministic values measured from the learned features and do not explicitly
consider the uncertain statistics discrepancy caused by potential domain shifts
during testing. In this paper, we improve the network generalization ability by
modeling the uncertainty of domain shifts with synthesized feature statistics
during training. Specifically, we hypothesize that the feature statistic, after
considering the potential uncertainties, follows a multivariate Gaussian
distribution. Hence, each feature statistic is no longer a deterministic value,
but a probabilistic point with diverse distribution possibilities. With the
uncertain feature statistics, the models can be trained to alleviate the domain
perturbations and achieve better robustness against potential domain shifts.
Our method can be readily integrated into networks without additional
parameters. Extensive experiments demonstrate that our proposed method
consistently improves the network generalization ability on multiple vision
tasks, including image classification, semantic segmentation, and instance
retrieval. The code can be available at https://github.com/lixiaotong97/DSU.


title:
Bayesian Inverse Uncertainty Quantification of the Physical Model
  Parameters for the Spallation Neutron Source First Target Station

abstract:
  The reliability of the mercury spallation target is mission-critical for the
neutron science program of the spallation neutron source at the Oak Ridge
National Laboratory. We present an inverse uncertainty quantification (UQ)
study using the Bayesian framework for the mercury equation of state model
parameters, with the assistance of polynomial chaos expansion surrogate models.
By leveraging high-fidelity structural mechanics simulations and real measured
strain data, the inverse UQ results reveal a maximum-a-posteriori estimate,
mean, and standard deviation of $6.5\times 10^4$ ($6.49\times 10^4 \pm
2.39\times 10^3$) Pa for the tensile cutoff threshold, $12112.1$ ($12111.8 \pm
14.9$) kg/m$^3$ for the mercury density, and $1850.4$ ($1849.7 \pm 5.3$) m/s
for the mercury speed of sound. These values do not necessarily represent the
nominal mercury physical properties, but the ones that fit the strain data and
the solid mechanics model we have used, and can be explained by three reasons:
The limitations of the computer model or what is known as the model-form
uncertainty, the biases and errors in the experimental data, and the mercury
cavitation damage that also contributes to the change in mercury behavior.
Consequently, the equation of state model parameters try to compensate for
these effects to improve fitness to the data. The mercury target simulations
using the updated parametric values result in an excellent agreement with 88%
average accuracy compared to experimental data, 6% average increase compared to
reference parameters, with some sensors experiencing an increase of more than
25%. With a more accurate simulated strain response, the component fatigue
analysis can utilize the comprehensive strain history data to evaluate the
target vessel's lifetime closer to its real limit, saving tremendous target
costs.


title:
Continuous permanent unobserved heterogeneity in dynamic discrete choice
  models

abstract:
  In dynamic discrete choice (DDC) analysis, it is common to use finite mixture
models to control for unobserved heterogeneity -- that is, by assuming there is
a finite number of agent `types'. However, consistent estimation typically
requires both a priori knowledge of the number of agent types and a high-level
injectivity condition that is difficult to verify. This paper provides
low-level conditions for identification of continuous permanent unobserved
heterogeneity in dynamic discrete choice (DDC) models. The results apply to
both finite- and infinite-horizon DDC models, do not require a full support
assumption, nor a large panel, and place no parametric restriction on the
distribution of unobserved heterogeneity. Furthermore, I present a
seminonparametric estimator that is computationally attractive and can be
implemented using familiar parametric methods. Finally, in an empirical
application, I apply this estimator to the labor force participation model of
Altug and Miller (1998). In this model, permanent unobserved heterogeneity may
be interpreted as individual-specific labor productivity, and my results imply
that the distribution of labor productivity can be estimated from the
participation model.


title:
Predicting Voting Outcomes in the Presence of Communities, Echo Chambers
  and Multiple Parties

abstract:
  A recently proposed graph-theoretic metric, the influence gap, has shown to
be a reliable predictor of the effect of social influence in two-party
elections, albeit only tested on regular and scale-free graphs. Here, we
investigate whether the influence gap is able to predict the outcome of
multi-party elections on networks exhibiting community structure, i.e., made of
highly interconnected components, and therefore more resembling of real-world
interaction. To encode communities we build on the classical model of caveman
graphs, which we extend to a richer graph family that displays different levels
of homophily, i.e., how much connections and opinions are intertwined. First,
we study the predictive power of the influence gap in the presence of
communities. We show that when there is no clear initial majority the influence
gap is not a good predictor of the election outcome. When we instead allow for
varying majorities, although the influence gap improves as a predictor,
counting the initial partisan majority does consistently better, across all
levels of homophily. Second, we study the combined effect of the more
predictive metrics, as function of the homophily levels. Using regression
models, we demonstrate that the influence gap combined with the initial votes
count does increase the overall predictive power for some levels of homophily.
Third, we study elections with more than two parties. Specifically, we extend
the definition of the influence gap to any number of parties, considering
various generalisations, and show that the initial votes count has an even
higher predictive power when compared to influence gap than it did in the
two-party case.


title:
A "Hyperburst" in the MAXI J0556-332 Neutron Star: Evidence for a New
  Type of Thermonuclear Explosion

abstract:
  The study of transiently accreting neutron stars provides a powerful means to
elucidate the properties of neutron star crusts. We present extensive numerical
simulations of the evolution of the neutron star in the transient low-mass
X-ray binary MAXI J0556--332. We model nearly twenty observations obtained
during the quiescence phases after four different outbursts of the source in
the past decade, considering the heating of the star during accretion by the
deep crustal heating mechanism complemented by some shallow heating source. We
show that cooling data are consistent with a single source of shallow heating
acting during the last three outbursts, while a very different and powerful
energy source is required to explain the extremely high effective temperature
of the neutron star, ~350 eV, when it exited the first observed outburst. We
propose that a gigantic thermonuclear explosion, a "hyperburst" from unstable
burning of neutron rich isotopes of oxygen or neon, occurred a few weeks before
the end of the first outburst, releasing 10^44 ergs at densities of the order
of 10^11 g/cm^3. This would be the first observation of a hyperburst and these
would be extremely rare events as the build up of the exploding layer requires
about a millennium of accretion history. Despite its large energy output, the
hyperburst did not produce, due to its depth, any noticeable increase in
luminosity during the accretion phase and is only identifiable by its imprint
on the later cooling of the neutron star.


title: Topological bands in monolayer PdSe$_2$

abstract:
  The electronic structure of palladium diselenide (PdSe$_2$) in its monolayer
form is analyzed from the topological band theory perspective. Employing
first-principles calculations, effective models and symmetry indicators we
found that the low-lying conduction bands are topologically nontrivial,
protected by time reversal and crystalline symmetries. Numerical evidence
supporting the nontrivial character of the bands is presented and examples of
physical responses are discussed.


title: RF gymnastics with transfer matrices

abstract:
  We introduce transfer matrices to describe the motion of particles in the
vicinity of the stable and unstable fixed points of longitudinal phase space
and use them to analyze the transfer of bunches between radio-frequency systems
operating at different harmonics and voltages.


title:
Classifying the globally rigid edge-transitive graphs and
  distance-regular graphs

abstract:
  A graph is said to be globally rigid if almost all embeddings of the graph's
vertices in the Euclidean plane will define a system of edge-length equations
with a unique (up to isometry) solution. In 2007, Jackson, Servatius and
Servatius characterised exactly which vertex-transitive graphs are globally
rigid solely by their degree and maximal clique number, two easily computable
parameters for vertex-transitive graphs. In this short note we will extend this
characterisation to all graphs that are determined by their automorphism group.
We do this by characterising exactly which edge-transitive graphs and
distance-regular graphs are globally rigid by their minimal and maximal
degrees.


title:
Blockchain-based Digital Twin for Supply Chain Management:
  State-of-the-Art Review and Future Research Directions

abstract:
  Supply chain management (SCM) plays a vital role in the global economy, as
evidenced by recent COVID-19 supply chain challenges. Traditional SCM faces
security and efficiency issues, but they can be addressed by leveraging digital
twins (DTs) and blockchain technology. T he combination of blockchain and DTs
can refine the concepts of both technologies and reform SCM to advance into
Industry 4.0. In this paper, we provide a comprehensive literature review of
the blockchain-based digital twin (DT) solutions to optimise the processes of
data management, data storage, and data sharing in SCM. We also investigate the
key benefits of the integration of blockchain and DTs and examine their
potential implementation in various SCM areas, including smart manufacturing,
intelligent maintenance, and blockchain-based DT shop floor, warehouse, and
logistics. Finally, we put forward recommendations for future research
directions.


title:
Improving the Sample-Complexity of Deep Classification Networks with
  Invariant Integration

abstract:
  Leveraging prior knowledge on intraclass variance due to transformations is a
powerful method to improve the sample complexity of deep neural networks. This
makes them applicable to practically important use-cases where training data is
scarce. Rather than being learned, this knowledge can be embedded by enforcing
invariance to those transformations. Invariance can be imposed using
group-equivariant convolutions followed by a pooling operation.
  For rotation-invariance, previous work investigated replacing the spatial
pooling operation with invariant integration which explicitly constructs
invariant representations. Invariant integration uses monomials which are
selected using an iterative approach requiring expensive pre-training. We
propose a novel monomial selection algorithm based on pruning methods to allow
an application to more complex problems. Additionally, we replace monomials
with different functions such as weighted sums, multi-layer perceptrons and
self-attention, thereby streamlining the training of
invariant-integration-based architectures.
  We demonstrate the improved sample complexity on the Rotated-MNIST, SVHN and
CIFAR-10 datasets where rotation-invariant-integration-based Wide-ResNet
architectures using monomials and weighted sums outperform the respective
baselines in the limited sample regime. We achieve state-of-the-art results
using full data on Rotated-MNIST and SVHN where rotation is a main source of
intraclass variation. On STL-10 we outperform a standard and a
rotation-equivariant convolutional neural network using pooling.


title:
Self-supervised Contrastive Learning for Cross-domain Hyperspectral
  Image Representation

abstract:
  Recently, self-supervised learning has attracted attention due to its
remarkable ability to acquire meaningful representations for classification
tasks without using semantic labels. This paper introduces a self-supervised
learning framework suitable for hyperspectral images that are inherently
challenging to annotate. The proposed framework architecture leverages
cross-domain CNN, allowing for learning representations from different
hyperspectral images with varying spectral characteristics and no pixel-level
annotation. In the framework, cross-domain representations are learned via
contrastive learning where neighboring spectral vectors in the same image are
clustered together in a common representation space encompassing multiple
hyperspectral images. In contrast, spectral vectors in different hyperspectral
images are separated into distinct clusters in the space. To verify that the
learned representation through contrastive learning is effectively transferred
into a downstream task, we perform a classification task on hyperspectral
images. The experimental results demonstrate the advantage of the proposed
self-supervised representation over models trained from scratch or other
transfer learning methods.


title: Single Nitrogen-Vacancy-NMR of Amine-Functionalized Diamond Surfaces

abstract:
  Nuclear magnetic resonance (NMR) imaging with shallow nitrogen-vacancy (NV)
centers in diamond offers an exciting route toward sensitive and localized
chemical characterization at the nanoscale. Remarkable progress has been made
to combat the degradation in coherence time and stability suffered by
near-surface NV centers using suitable chemical surface termination. However,
approaches that also enable robust control over adsorbed molecule density,
orientation, and binding configuration are needed. We demonstrate a diamond
surface preparation for mixed nitrogen- and oxygen-termination that
simultaneously improves NV center coherence times for emitters <10-nm-deep and
enables direct and recyclable chemical functionalization via amine-reactive
crosslinking. Using this approach, we probe single NV centers embedded in
nanopillar waveguides to perform $^{19}\mathrm{F}$ NMR sensing of covalently
bound trifluoromethyl tags in the ca. 50-100 molecule regime. This work
signifies an important step toward nuclear spin localization and structure
interrogation at the single-molecule level.


title:
Renormalization of the $3D$ $SU(N)$ scalar energy-momentum tensor using
  the Wilson flow

abstract:
  In the holographic approach to cosmology, cosmological observables are
described in terms of correlators of a three-dimensional boundary quantum field
theory. As a concrete model, we study the $3D$ massless $SU(N)$ scalar matrix
field theory with a $\phi^4$ interaction. On the lattice, the energy-momentum
tensor (EMT) in this theory can mix with the operator $\phi^2$. We utilize the
Wilson Flow to renormalize the EMT on the lattice, and present numerical
results for the mixing coefficient for $N = 2$. Obtaining the renormalized EMT
will allow us to make predictions for the CMB power spectra in the regime where
the dual QFT is non-perturbative.


title:
Computing Rule-Based Explanations of Machine Learning Classifiers using
  Knowledge Graphs

abstract:
  The use of symbolic knowledge representation and reasoning as a way to
resolve the lack of transparency of machine learning classifiers is a research
area that lately attracts many researchers. In this work, we use knowledge
graphs as the underlying framework providing the terminology for representing
explanations for the operation of a machine learning classifier. In particular,
given a description of the application domain of the classifier in the form of
a knowledge graph, we introduce a novel method for extracting and representing
black-box explanations of its operation, in the form of first-order logic rules
expressed in the terminology of the knowledge graph.


title: Enabling Imitation-Based Cooperation in Dynamic Social Networks

abstract:
  The emergence of cooperation among self-interested agents has been a key
concern of the multi-agent systems community for decades. With the increased
importance of network-mediated interaction, researchers have shifted the
attention on the impact of social networks and their dynamics in promoting or
hindering cooperation, drawing various context-dependent conclusions. For
example, some lines of research, theoretical and experimental, suggest the
existence of a threshold effect in the ratio of timescales of network
evolution, after which cooperation will emerge, whereas other lines dispute
this, suggesting instead a Goldilocks zone. In this paper we provide an
evolutionary game theory framework to understand coevolutionary processes from
a bottom up perspective - in particular the emergence of a cooperator-core and
defector-periphery - clarifying the impact of partner selection and imitation
strategies in promoting cooperative behaviour, without assuming underlying
communication or reputation mechanisms. In doing so we provide a unifying
framework to study imitation-based cooperation in dynamic social networks and
show that disputes in the literature can in fact coexist in so far as the
results stem from different equally valid assumptions.


title:
Hearing Loss, Cognitive Load and Dementia: An Overview of Interrelation,
  Detection and Monitoring Challenges with Wearable Non-invasive Microwave
  Sensors

abstract:
  This paper provides an overview of hearing loss effects on neurological
function and progressive diseases; and explores the role of cognitive load
monitoring to detect dementia. It also investigates the prospects of utilizing
hearing aid technology to reverse cognitive decline and delay the onset of
dementia, for the old age population. The interrelation between hearing loss,
cognitive load and dementia is discussed. Future considerations for improvement
with respect to robust diagnosis, user centricity, device accuracy and privacy
for wider clinical practice is also explored. The review concludes by
discussing the future scope and potential of designing practical wearable
microwave technologies and evaluating their use in smart care homes setting.


title: Rainbow Differential Privacy

abstract:
  We extend a previous framework for designing differentially private (DP)
mechanisms via randomized graph colorings that was restricted to binary
functions, corresponding to colorings in a graph, to multi-valued functions. As
before, datasets are nodes in the graph and any two neighboring datasets are
connected by an edge. In our setting, we assume each dataset has a preferential
ordering for the possible outputs of the mechanism, which we refer to as a
rainbow. Different rainbows partition the graph of datasets into different
regions. We show that when the DP mechanism is pre-specified at the boundary of
such regions, at most one optimal mechanism can exist. Moreover, if the
mechanism is to behave identically for all same-rainbow boundary datasets, the
problem can be greatly simplified and solved by means of a morphism to a line
graph. We then show closed form expressions for the line graph in the case of
ternary functions. Treatment of ternary queries in this paper displays enough
richness to be extended to higher-dimensional query spaces with preferential
query ordering, but the optimality proof does not seem to follow directly from
the ternary proof.


title:
Stellar mass as the "glocal" driver of galaxies' stellar population
  properties

abstract:
  The properties of the stellar populations in a galaxy are known to correlate
with the amount and the distribution of stellar mass. We take advantage of the
maps of light-weighted mean stellar age Agewr and metallicity Z*wr for a sample
of 362 galaxies from the integral-field spectroscopic survey CALIFA (summing up
to >600,000 individual regions of approximately 1 kpc linear size), produced in
our previous works, to investigate how these local properties react to the
local stellar-mass surface density mu* and to the global total stellar mass M*
and mean stellar-mass surface density <mu>e. We establish the existence of i) a
dual mu*-Agewr relation, resulting in a young sequence and an old ridge, and
ii) a mu*-Z*wr relation, overall independent of the age of the regions. The
global mass parameters (M* and, possibly secondarily, <mu>e) determine the
distribution of mu* in a galaxy and set the maximum attainable mu*, which
increases with M*. M* affects the shape and normalization of the local
relations up to a threshold mass of $\sim 10^{10.3}$ MSun, above which they
remain unchanged. We conclude that stellar mass is a "glocal" (i.e.
simultaneously global and local) driver of the stellar population properties.
We consider how the local and global mass-age and mass-metallicity relations
are connected, and in particular discuss how it is possible, from a single
local relation, to produce two different global mass-metallicity relations for
quiescent and star-forming galaxies respectively, as reported in the
literature. Structural differences in these two classes of galaxies are key to
explain the duality in global scaling relations and appear as essential in
modelling the baryonic cycle of galaxies.


title: Communication-Control Co-design in Wireless Edge Industrial Systems

abstract:
  We consider the problem of controlling a series of industrial systems, such
as industrial robotics, in a factory environment over a shared wireless channel
leveraging edge computing capabilities. The wireless control system model
supports the offloading of computational intensive functions, such as
perception workloads, to an edge server. However, wireless communications is
prone to packet loss and latency and can lead to instability or task failure if
the link is not kept sufficiently reliable. Because maintaining high
reliability and low latency at all times prohibits scalability due to resource
limitations, we propose a communication-control co-design paradigm that varies
the network quality of service (QoS) and resulting control actions to the
dynamic needs of each plant. We further propose a modular learning framework to
solve the complex learning task without knowledge of plant or communication
models in a series of learning steps and demonstrate its effectiveness in
learning resource-efficient co-design policies in a robotic conveyor belt task.


title: List Decoding of Quaternary Codes in the Lee Metric

abstract:
  We present a list decoding algorithm for quaternary negacyclic codes over the
Lee metric. To achieve this result, we use a Sudan-Guruswami type list decoding
algorithm for Reed-Solomon codes over certain ring alphabets. Our decoding
strategy for negacyclic codes over the ring $\mathbb Z_4$ combines the list
decoding algorithm by Wu with the Gr\"obner basis approach for solving a key
equation due to Byrne and Fitzpatrick.


title:
Segmentation by Test-Time Optimization (TTO) for CBCT-based Adaptive
  Radiation Therapy

abstract:
  Online adaptive radiotherapy (ART) requires accurate and efficient
auto-segmentation of target volumes and organs-at-risk (OARs) in mostly
cone-beam computed tomography (CBCT) images. Propagating expert-drawn contours
from the pre-treatment planning CT (pCT) through traditional or deep learning
(DL) based deformable image registration (DIR) can achieve improved results in
many situations. Typical DL-based DIR models are population based, that is,
trained with a dataset for a population of patients, so they may be affected by
the generalizability problem. In this paper, we propose a method called
test-time optimization (TTO) to refine a pre-trained DL-based DIR population
model, first for each individual test patient, and then progressively for each
fraction of online ART treatment. Our proposed method is less susceptible to
the generalizability problem, and thus can improve overall performance of
different DL-based DIR models by improving model accuracy, especially for
outliers. Our experiments used data from 239 patients with head and neck
squamous cell carcinoma to test the proposed method. Firstly, we trained a
population model with 200 patients, and then applied TTO to the remaining 39
test patients by refining the trained population model to obtain 39
individualized models. We compared each of the individualized models with the
population model in terms of segmentation accuracy. The number of patients with
at least 0.05 DSC improvement or 2 mm HD95 improvement by TTO averaged over the
17 selected structures for the state-of-the-art architecture Voxelmorph is 10
out of 39 test patients. The average time for deriving the individualized model
using TTO from the pre-trained population model is approximately four minutes.
When adapting the individualized model to a later fraction of the same patient,
the average time is reduced to about one minute and the accuracy is slightly
improved.


title:
Adaptive Bayesian Variable Clustering via Structural Learning of Breast
  Cancer Data

abstract:
  Clustering of proteins is of interest in cancer cell biology. This article
proposes a hierarchical Bayesian model for protein (variable) clustering
hinging on correlation structure. Starting from a multivariate normal
likelihood, we enforce the clustering through prior modeling using angle based
unconstrained reparameterization of correlations and assume a truncated Poisson
distribution (to penalize the large number of clusters) as prior on the number
of clusters. The posterior distributions of the parameters are not in explicit
form and we use a reversible jump Markov chain Monte Carlo (RJMCMC) based
technique is used to simulate the parameters from the posteriors. The end
products of the proposed method are estimated cluster configuration of the
proteins (variables) along with the number of clusters. The Bayesian method is
flexible enough to cluster the proteins as well as the estimate the number of
clusters. The performance of the proposed method has been substantiated with
extensive simulation studies and one protein expression data with a hereditary
disposition in breast cancer where the proteins are coming from different
pathways.


title:
Transferable Student Performance Modeling for Intelligent Tutoring
  Systems

abstract:
  Millions of learners worldwide are now using intelligent tutoring systems
(ITSs). At their core, ITSs rely on machine learning algorithms to track each
user's changing performance level over time to provide personalized
instruction. Crucially, student performance models are trained using
interaction sequence data of previous learners to analyse data generated by
future learners. This induces a cold-start problem when a new course is
introduced for which no training data is available. Here, we consider transfer
learning techniques as a way to provide accurate performance predictions for
new courses by leveraging log data from existing courses. We study two
settings: (i) In the naive transfer setting, we propose course-agnostic
performance models that can be applied to any course. (ii) In the inductive
transfer setting, we tune pre-trained course-agnostic performance models to new
courses using small-scale target course data (e.g., collected during a pilot
study). We evaluate the proposed techniques using student interaction sequence
data from 5 different mathematics courses containing data from over 47,000
students in a real world large-scale ITS. The course-agnostic models that use
additional features provided by human domain experts (e.g, difficulty ratings
for questions in the new course) but no student interaction training data for
the new course, achieve prediction accuracy on par with standard BKT and PFA
models that use training data from thousands of students in the new course. In
the inductive setting our transfer learning approach yields more accurate
predictions than conventional performance models when only limited student
interaction training data (<100 students) is available to both.


title:
Study of UV bright sources in globular cluster NGC 4590 using
  Ultraviolet Imaging Telescope (UVIT) observations

abstract:
  We have studied ultraviolet (UV) bright sources in the Galactic globular
cluster (GGC) NGC 4590 using Ultraviolet Imaging Telescope (UVIT) on-board the
\mbox{{\em AstroSat}} satellite. Using UV-optical color-magnitude diagrams
(CMDs), we have identified and characterized the sources of different
evolutionary stages i.e., blue horizontal branch stars (BHBs), extremely blue
horizontal branch stars (EHBs), blue straggler stars (BSs), variable stars,
etc. We estimated effective temperature (T$_{\mathrm{eff}}$), gravity
($\log$(g)), luminosity (L$_{bol}$), and hence the radius (R) of these hot
stars by fitting spectral energy distribution (SED) with the help of stellar
atmosphere models. Two new far-UV (FUV) bright cluster member stars situated
near the core of the cluster have been detected; one of them is an EHB star and
the other one is either in its post-blue hook evolutionary phase or in white
dwarf phase. The evolutionary status of all the hot stars, identified in the
cluster, has been investigated by using various evolutionary models. We find
the massive and younger BSs are concentrated at the center of the cluster
whereas the older and less massive BSs are distributed though out the cluster.
The BSs normalized radial distribution seems to be bi-modal with a minimum
located at r$_{\mathrm{min}}$ = 4.3 r$_c$. We calculated A$^+$ parameter of the
cluster which is obtained using cumulative normalized radial distribution of
horizontal branch stars (HBs) and BSs. We measured this value up to half-mass
radius of the cluster to be $+ 0.13$, which indicates that NGC 4590 is one of
the youngest clusters among dynamically intermediate age GGCs with a dynamical
age of $0.423\pm0.096$ Gyr.


title: Depth zero representations over $\overline{\mathbb{Z}}[\frac{1}{p}]$

abstract:
  We consider the category of depth $0$ representations of a $p$-adic
quasi-split reductive group with coefficients in
$\overline{\mathbb{Z}}[\frac{1}{p}]$. We prove that the blocks of this category
are in natural bijection with the connected components of the space of tamely
ramified Langlands parameters for $G$ over
$\overline{\mathbb{Z}}[\frac{1}{p}]$. As a particular case, this depth $0$
category is thus indecomposable when the group is tamely ramified. Along the
way we prove a similar result for finite reductive groups. We then outline a
potential application to the Fargues-Scholze and Genestier-Lafforgue semisimple
local Langlands correspondences. Namely, contingent on a certain "independence
of $\ell$" property, our results imply that these correspondences take depth
$0$ representations to tamely ramified parameters.


title: Provable Reinforcement Learning with a Short-Term Memory

abstract:
  Real-world sequential decision making problems commonly involve partial
observability, which requires the agent to maintain a memory of history in
order to infer the latent states, plan and make good decisions. Coping with
partial observability in general is extremely challenging, as a number of
worst-case statistical and computational barriers are known in learning
Partially Observable Markov Decision Processes (POMDPs). Motivated by the
problem structure in several physical applications, as well as a commonly used
technique known as "frame stacking", this paper proposes to study a new
subclass of POMDPs, whose latent states can be decoded by the most recent
history of a short length $m$. We establish a set of upper and lower bounds on
the sample complexity for learning near-optimal policies for this class of
problems in both tabular and rich-observation settings (where the number of
observations is enormous). In particular, in the rich-observation setting, we
develop new algorithms using a novel "moment matching" approach with a sample
complexity that scales exponentially with the short length $m$ rather than the
problem horizon, and is independent of the number of observations. Our results
show that a short-term memory suffices for reinforcement learning in these
environments.


title:
CausPref: Causal Preference Learning for Out-of-Distribution
  Recommendation

abstract:
  In spite of the tremendous development of recommender system owing to the
progressive capability of machine learning recently, the current recommender
system is still vulnerable to the distribution shift of users and items in
realistic scenarios, leading to the sharp decline of performance in testing
environments. It is even more severe in many common applications where only the
implicit feedback from sparse data is available. Hence, it is crucial to
promote the performance stability of recommendation method in different
environments. In this work, we first make a thorough analysis of implicit
recommendation problem from the viewpoint of out-of-distribution (OOD)
generalization. Then under the guidance of our theoretical analysis, we propose
to incorporate the recommendation-specific DAG learner into a novel causal
preference-based recommendation framework named CausPref, mainly consisting of
causal learning of invariant user preference and anti-preference negative
sampling to deal with implicit feedback. Extensive experimental results from
real-world datasets clearly demonstrate that our approach surpasses the
benchmark models significantly under types of out-of-distribution settings, and
show its impressive interpretability.


title:
A model of virus infection with immune responses supports boosting CTL
  response to balance antibody response

abstract:
  We analyze a within-host model of virus infection with antibody and CD8+
cytotoxic T lymphocyte (CTL) responses proposed by Schwartz et al. (2013). The
goal of this work is to gain an overview of the stability of the
biologically-relevant equilibria as a function of the model's immune response
parameters. We show that the equilibria undergo at most two forward
transcritical bifurcations. The model is also explored numerically and results
are applied to equine infectious anemia virus infection. In order to arrive at
stability of the biologically-relevant endemic equilibrium characterized by
coexistence of antibody and CTL responses, the parameters promoting CTL
responses need to be boosted over parameters promoting antibody production.
This result may seem counter-intuitive (in that a weaker antibody response is
better) but can be understood in terms of a balance between CTL and antibody
responses that is needed to permit existence of CTLs. In conclusion, an
intervention such as a vaccine that is intended to control a persistent viral
infection with both immune responses should moderate the antibody response to
allow for stimulation of the CTL response.


title:
Analysis of Voltage Stability in Terms of Interactions of
  Q(U)-Characteristic Control in Distribution Grids

abstract:
  As the amount of volatile, renewable energy sources in power distribution
grids is increasing, the stability of the latter is a vital aspect for grid
operators. Within the STABEEL project, the authors develop rules on how to
parametrize the reactive power control of distributed energy resources to
increase the performance while guaranteeing stability. The work focuses on
distribution grids with a high penetration of distributed energy resources
equipped with Q(U)-characteristic. This contribution is based on the stability
assessment of previous work and introduces a new approach utilizing the circle
criterion. With the aim of extending existing technical guidelines, stability
assessment methods are applied to various distribution grids - including those
from the SimBench project. Herein, distributed energy resources can be modelled
as detailed control loops or as approximations, derived from technical
guidelines.


title: Data Consistency for Weakly Supervised Learning

abstract:
  In many applications, training machine learning models involves using large
amounts of human-annotated data. Obtaining precise labels for the data is
expensive. Instead, training with weak supervision provides a low-cost
alternative. We propose a novel weak supervision algorithm that processes noisy
labels, i.e., weak signals, while also considering features of the training
data to produce accurate labels for training. Our method searches over
classifiers of the data representation to find plausible labelings. We call
this paradigm data consistent weak supervision. A key facet of our framework is
that we are able to estimate labels for data examples low or no coverage from
the weak supervision. In addition, we make no assumptions about the joint
distribution of the weak signals and true labels of the data. Instead, we use
weak signals and the data features to solve a constrained optimization that
enforces data consistency among the labels we generate. Empirical evaluation of
our method on different datasets shows that it significantly outperforms
state-of-the-art weak supervision methods on both text and image classification
tasks.


title: BPS equations and solutions for Maxwell-scalar theory

abstract:
  Energy minimizing BPS equations and solutions are obtained for a class of
models in Maxwell-scalar theory, where an abelian electric charge is immersed
in an effective dielectric of a real scalar field. The first order BPS
equations are developed using the straightforward on-shell method introduced by
Atmaja and Ramadhan. Employment of an auxiliary function of the scalar field
allows a scalar potential that displays a tachyonic instability. Consequently,
a nontopological scalar soliton is found to form around the charge. Examples
and solutions are provided for (1) a point charge or sphere in a flat Minkowski
background, and (2) an overcharged compact object in a Reissner-Nordstrom
background. The solutions presented here for the former (Minkowski) case
recover those that have been previously obtained, while the latter solutions
are new BPS solutions.


title:
The amazing mixed polynomial closure and its applications to
  two-variable first-order logic

abstract:
  Polynomial closure is a standard operator which is applied to a class of
regular languages. In the paper, we investigate three restrictions called left
(LPol), right (RPol) and mixed polynomial closure (MPol). The first two were
known while MPol is new. We look at two decision problems that are defined for
every class C. Membership takes a regular language as input and asks if it
belongs to C. Separation takes two regular languages as input and asks if there
exists a third language in C including the first one and disjoint from the
second. We prove that LPol, RPol and MPol preserve the decidability of
membership under mild hypotheses on the input class, and the decidability of
separation under much stronger hypotheses. We apply these results to natural
hierarchies.
  First, we look at several language theoretic hierarchies that are built by
applying LPol, RPol and MPol recursively to a single input class. We prove that
these hierarchies can actually be defined using almost exclusively MPol. We
also consider quantifier alternation hierarchies for two-variable first-order
logic and prove that one can climb them using MPol. The result is generic in
the sense that it holds for most standard choices of signatures. We use it to
prove that for most of these choices, membership is decidable for all levels in
the hierarchy. Finally, we prove that separation is decidable for the hierarchy
of two-variable first-order logic equipped with only the linear order.


title: Equivariance versus Augmentation for Spherical Images

abstract:
  We analyze the role of rotational equivariance in convolutional neural
networks (CNNs) applied to spherical images. We compare the performance of the
group equivariant networks known as S2CNNs and standard non-equivariant CNNs
trained with an increasing amount of data augmentation. The chosen
architectures can be considered baseline references for the respective design
paradigms. Our models are trained and evaluated on single or multiple items
from the MNIST or FashionMNIST dataset projected onto the sphere. For the task
of image classification, which is inherently rotationally invariant, we find
that by considerably increasing the amount of data augmentation and the size of
the networks, it is possible for the standard CNNs to reach at least the same
performance as the equivariant network. In contrast, for the inherently
equivariant task of semantic segmentation, the non-equivariant networks are
consistently outperformed by the equivariant networks with significantly fewer
parameters. We also analyze and compare the inference latency and training
times of the different networks, enabling detailed tradeoff considerations
between equivariant architectures and data augmentation for practical problems.
The equivariant spherical networks used in the experiments will be made
available at https://github.com/JanEGerken/sem_seg_s2cnn .


title: Development of a compact muon veto for the NUCLEUS experiment

abstract:
  The NUCLEUS experiment aims to measure coherent elastic neutrino nucleus
scattering of reactor anti-neutrinos using cryogenic calorimeters. Operating at
an overburden of 3 m.w.e., muon-induced backgrounds are expected to be one of
the dominant background contributions. Besides a high efficiency to identify
muon events passing the experimental setup, the NUCLEUS muon veto has to
fulfill tight spatial requirements to fit the constraints given by the
experimental site and to minimize the induced detector dead-time. We developed
highly efficient and compact muon veto modules based on plastic scintillators
equipped with wavelength shifting fibers and silicon photo multipliers to
collect and detect the scintillation light. In this paper, we present the full
characterization of a prototype module with different light read-out
configurations. We conclude that an efficient and compact muon veto system can
be built for the NUCLEUS experiment from a cube assembly of the developed
modules. Simulations show that an efficiency for muon identification of >99 %
and an associated rate of 325 Hz is achievable, matching the requirements of
the NUCLEUS experiment.


title: Coprimality of Fourier coefficients of eigenforms

abstract:
  Given a pair of distinct non-CM normalized eigenforms having integer Fourier
coefficients $a_1 (n)$ and $a_2(n)$, we count positive integers $n$ with
$(a_1(n), a_2(n))=1$ and make a conjecture about the density of the set of
primes $p$ for which $(a_1(p), a_2(p))=1$. We also study the average order of
the number of prime divisors of $(a_1(p), a_2(p))$.


title:
Topological Authentication Technique In Topologically Asymmetric
  Cryptosystem

abstract:
  Making topological authentication from theory to practical application is an
important and challenging task. More and more researchers pay attention on
coming quantum computation, privacy data protection, lattices and cryptography.
Research show the advantages of topological authentications through graph
operations, various matrices, graph colorings and graph labelings are: related
with two or more different mathematical areas, be not pictures, there are huge
number of colorings and labelings, rooted on modern mathematics, diversity of
asymmetric ciphers, simplicity and convenience, easily created,
irreversibility, computational security, provable security, and so on.
Topological authentications based on various graph homomorphisms,
degree-sequence homomorphisms, graph-set homomorphisms. Randomly topological
coding and topological authentications are based on Hanzi authentication,
randomly adding-edge-removing operation, randomly leaf-adding algorithms, graph
random increasing techniques, operation graphic lattice and dynamic networked
models and their spanning trees and maximum leaf spanning trees. Realization of
topological authentication is an important topic, we study: number-based
strings generated from colored graphs, particular graphs (complete graphs,
trees, planar graphs), some methods of generating public-keys. some techniques
of topologically asymmetric cryptosystem are: W-type matching labelings,
dual-type labelings, reciprocal-type labelings, topological homomorphisms,
indexed colorings, graphic lattices, degree-sequence lattices, every-zero
Cds-matrix groups of degree-sequences, every-zero graphic groups, graphic
lattices having coloring closure property, self-similar networked lattices.


title:
On uniqueness of symplectic fillings of links of some surface
  singularities

abstract:
  We consider the canonical contact structures on links of rational surface
singularities with reduced fundamental cycle. These singularities can be
characterized by their dual resolution graphs: the graph is a tree, and the
weight of each vertex is no greater than its negative valency. In a joint work
with Starkston, we previously showed that if the weight of each vertex in the
graph is at most -5, the contact structure has a unique symplectic filling (up
to symplectic deformation and blow-up). The proof was based on a symplectic
analog of de Jong-van Straten's description of smoothings of these
singularities. In this paper, we give a short self-contained proof of
uniqueness of fillings, via analysis of positive monodromy factorizations for
planar open books supporting these contact structures.


title:
Castelnuovo-Mumford regularity of ladder determinantal varieties and
  patches of Grassmannian Schubert varieties

abstract:
  We give degree formulas for Grothendieck polynomials indexed by vexillary
permutations and $1432$-avoiding permutations via tableau combinatorics. These
formulas generalize a formula for degrees of symmetric Grothendieck polynomials
which appeared in previous joint work of the authors with Y. Ren and A. St.
Dizier.
  We apply our formulas to compute Castelnuovo-Mumford regularity of classes of
generalized determinantal ideals. In particular, we give combinatorial formulas
for the regularities of all one-sided mixed ladder determinantal ideals. We
also derive formulas for the regularities of certain Kazhdan-Lusztig ideals,
including those coming from open patches of Schubert varieties in
Grassmannians. This provides a correction to a conjecture of
Kummini-Lakshmibai-Sastry-Seshadri (2015).


title:
Charge-dipole and dipole-dipole interactions in two-dimensional
  materials

abstract:
  We derive the explicit analytical form for the charge-dipole and
dipole-dipole interactions in 2D configuration space. We demonstrate that the
reduction of dimensionality can alter the charge-dipole and dipole-dipole
interactions in 2D case. The asymptotics of these interactions at large
distances coincide to the charge-dipole and dipole-dipole interactions in 3D
configuration space.


title:
Wi-Fi Rate Adaptation using a Simple Deep Reinforcement Learning
  Approach

abstract:
  The increasing complexity of recent Wi-Fi amendments is making optimal Rate
Adaptation (RA) a challenge. The use of classic algorithms or heuristic models
to address RA is becoming unfeasible due to the large combination of
configuration parameters along with the variability of the wireless channel.
Machine Learning-based solutions have been proposed in the state of art, to
deal with this complexity. However, they typically use complex models and their
implementation in real scenarios is difficult. We propose a simple Deep
Reinforcement Learning approach for the automatic RA in Wi-Fi networks, named
Data-driven Algorithm for Rate Adaptation (DARA). DARA is standard-compliant.
It dynamically adjusts the Wi-Fi Modulation and Coding Scheme (MCS) solely
based on the observation of the Signal-to-Noise Ratio (SNR) of the received
frames at the transmitter. Our simulation results show that DARA achieves up to
15\% higher throughput when compared with Minstrel High Throughput (HT) and
equals the performance of the Ideal Wi-Fi RA algorithm.


title:
Design of optimal low-thrust manoeuvres for remote sensing
  multi-satellite formation flying in low Earth orbit

abstract:
  This paper presents a strategy for optimal manoeuvre design of
multi-satellite formation flying in low Earth orbit environment, with the aim
of providing a tool for mission operation design. The proposed methodology for
formation flying manoeuvres foresees a continuous low-thrust control profile,
to enable the operational phases. The design is performed starting from the
dynamic representation described in the relative orbital elements, including
the main orbital perturbations effects. It also exploits an interface with the
classical radial-transversal-normal description to include the maximum delta-v
limitation and the safety condition requirements. The methodology is applied to
a remote sensing mission study, Formation Flying L-band Aperture Synthesis, for
land and ocean application, such as a potential high-resolution Soil Moisture
and Ocean Salinity (SMOS) follow-on mission, as part of a European Space Agency
mission concept study. Moreover, the results are applicable to a wide range of
low Earth orbit missions, exploiting a distributed system, and in particular to
Formation Flying L-band Aperture Synthesis (FFLAS) as a follow-on concept to
SMOS.


title:
A general framework for quantifying uncertainty at scale and its
  application to fusion research

abstract:
  In many fields of science, remarkably comprehensive and realistic
computational models are available nowadays. Often, the respective numerical
calculations call for the use of powerful supercomputers, and therefore only a
limited number of cases can be investigated explicitly. This prevents
straightforward approaches to important tasks like uncertainty quantification
and sensitivity analysis. As it turns out, this challenge can be overcome via
our recently developed sensitivity-driven dimension-adaptive sparse grid
interpolation strategy. The method exploits, via adaptivity, the structure of
the underlying model (such as lower intrinsic dimensionality and anisotropic
coupling of the uncertain inputs) to enable efficient and accurate uncertainty
quantification and sensitivity analysis at scale. We demonstrate the efficiency
of our approach in the context of fusion research, in a realistic,
computationally expensive scenario of turbulent transport in a magnetic
confinement device with eight uncertain parameters, reducing the effort by at
least two orders of magnitude. In addition, we show that our method
intrinsically provides an accurate reduced model that is nine orders of
magnitude cheaper than the high-fidelity model. Our approach enables studies
previously considered infeasible.

categories: physics.comp-ph cs.CE physics.plasm-ph stat.CO


title: Learning Sinkhorn divergences for supervised change point detection

abstract:
  Many modern applications require detecting change points in complex
sequential data. Most existing methods for change point detection are
unsupervised and, as a consequence, lack any information regarding what kind of
changes we want to detect or if some kinds of changes are safe to ignore. This
often results in poor change detection performance. We present a novel change
point detection framework that uses true change point instances as supervision
for learning a ground metric such that Sinkhorn divergences can be then used in
two-sample tests on sliding windows to detect change points in an online
manner. Our method can be used to learn a sparse metric which can be useful for
both feature selection and interpretation in high-dimensional change point
detection settings. Experiments on simulated as well as real world sequences
show that our proposed method can substantially improve change point detection
performance over existing unsupervised change point detection methods using
only few labeled change point instances.


title: Improved bounds for the dimension of divisibility

abstract:
  The dimension of a partially-ordered set $P$ is the smallest integer $d$ such
that one can embed $P$ into a product of $d$ linear orders. We prove that the
dimension of the divisibility order on the interval $\{1, \dotsc, n\}$ is
bounded above by $C(\log n)^2 (\log \log n)^{-2} \log \log \log n$ as $n$ goes
to infinity. This improves a recent result by Lewis and the first author, who
showed an upper bound of $C(\log n)^2 (\log \log n)^{-1}$ and a lower bound of
$c(\log n)^2 (\log \log n)^{-2}$, asymptotically. Our method exploits a
connection between the dimension of the divisibility order and the maximum size
of $r$-cover-free families.


title:
Liouville type theorems for solutions of the weighted fractional
  Lane-Emden system

abstract:
  In this paper, we prove Liouville type theorems for stable solutions to the
weighted fractional Lane-Emden system \begin{align*}
  (-\Delta)^s u = h(x)v^p,\quad (-\Delta)^s v= h(x)u^q, \quad u,v>0\quad
\mbox{in }\;\mathbb{R}^N, \end{align*} where $1<q\leq p$ and $h$ is a positive
continuous function in $\mathbb{R}^N$ satisfying $\displaystyle{\liminf_{|x|\to
\infty}}\frac{h(x)}{|x|^\ell} > 0$ with $\ell > 0.$ Our results generalize the
results established in \cite{HHM16} for the Laplacian case (correspond to
$s=1$) and improve the previous work \cite{TuanHoang21}. As a consequence, we
prove classification result for stable solutions to the weighted fractional
Lane-Emden equation $(-\Delta)^s u = h(x)u^p$ in $\mathbb{R}^N$.