title: SOCIOFILLMORE: A Tool for Discovering Perspectives

abstract:
  SOCIOFILLMORE is a multilingual tool which helps to bring to the fore the
focus or the perspective that a text expresses in depicting an event. Our tool,
whose rationale we also support through a large collection of human judgements,
is theoretically grounded on frame semantics and cognitive linguistics, and
implemented using the LOME frame semantic parser. We describe SOCIOFILLMORE's
development and functionalities, show how non-NLP researchers can easily
interact with the tool, and present some example case studies which are already
incorporated in the system, together with the kind of analysis that can be
visualised.


title: Regularity of fully non-linear elliptic equations on Hermitian manifolds

abstract:
  In this paper we propose new insights and ideas to set up quantitative
boundary estimates for solutions to Dirichlet problem of a class of fully
non-linear elliptic equations on compact Hermitian manifolds with real analytic
Levi flat boundary. With the quantitative boundary estimates at hand, we can
establish the gradient estimate and give a unified approach to investigate the
existence and regularity of solutions of Dirichlet problem with sufficiently
smooth boundary data, which include the geodesic equation in the space of
K\"ahler metrics as a special case. Our method can also be applied to Dirichlet
problem for analogous fully non-linear elliptic equations on a compact
Riemannian manifold with concave boundary.


title: Bogoliubov Theory in the Gross-Pitaevskii Limit: a Simplified Approach

abstract:
  We show that Bogoliubov theory correctly predicts the low-energy spectral
properties of Bose gases in the Gross-Pitaevskii regime. We recover recent
results from \cite{BBCS,BBCS4}. While our main strategy is similar to the one
developed in \cite{BBCS,BBCS4}, we combine it with new ideas, taken in part
from \cite{Hainzl,NT}; this makes our proof substantially simpler and shorter.
As an important step towards the proof of Bogoliubov theory, we show that
low-energy states exhibit complete Bose-Einstein condensation with optimal
control on the number of orthogonal excitations.


title: Multi-Modal Attribute Extraction for E-Commerce

abstract:
  To improve users' experience as they navigate the myriad of options offered
by online marketplaces, it is essential to have well-organized product
catalogs. One key ingredient to that is the availability of product attributes
such as color or material. However, on some marketplaces such as
Rakuten-Ichiba, which we focus on, attribute information is often incomplete or
even missing. One promising solution to this problem is to rely on deep models
pre-trained on large corpora to predict attributes from unstructured data, such
as product descriptive texts and images (referred to as modalities in this
paper). However, we find that achieving satisfactory performance with this
approach is not straightforward but rather the result of several refinements,
which we discuss in this paper. We provide a detailed description of our
approach to attribute extraction, from investigating strong single-modality
methods, to building a solid multimodal model combining textual and visual
information. One key component of our multimodal architecture is a novel
approach to seamlessly combine modalities, which is inspired by our
single-modality investigations. In practice, we notice that this new
modality-merging method may suffer from a modality collapse issue, i.e., it
neglects one modality. Hence, we further propose a mitigation to this problem
based on a principled regularization scheme. Experiments on Rakuten-Ichiba data
provide empirical evidence for the benefits of our approach, which has been
also successfully deployed to Rakuten-Ichiba. We also report results on
publicly available datasets showing that our model is competitive compared to
several recent multimodal and unimodal baselines.


title: Towards Automated Real-time Evaluation in Text-based Counseling

abstract:
  Automated real-time evaluation of counselor-client interaction is important
for ensuring quality counseling but the rules are difficult to articulate.
Recent advancements in machine learning methods show the possibility of
learning such rules automatically. However, these methods often demand large
scale and high quality counseling data, which are difficult to collect. To
address this issue, we build an online counseling platform, which allows
professional psychotherapists to provide free counseling services to those are
in need. In exchange, we collect the counseling transcripts. Within a year of
its operation, we manage to get one of the largest set of (675) transcripts of
counseling sessions. To further leverage the valuable data we have, we label
our dataset using both coarse- and fine-grained labels and use a set of
pretraining techniques. In the end, we are able to achieve practically useful
accuracy in both labeling system.


title: Generalization Through The Lens Of Leave-One-Out Error

abstract:
  Despite the tremendous empirical success of deep learning models to solve
various learning tasks, our theoretical understanding of their generalization
ability is very limited. Classical generalization bounds based on tools such as
the VC dimension or Rademacher complexity, are so far unsuitable for deep
models and it is doubtful that these techniques can yield tight bounds even in
the most idealistic settings (Nagarajan & Kolter, 2019). In this work, we
instead revisit the concept of leave-one-out (LOO) error to measure the
generalization ability of deep models in the so-called kernel regime. While
popular in statistics, the LOO error has been largely overlooked in the context
of deep learning. By building upon the recently established connection between
neural networks and kernel learning, we leverage the closed-form expression for
the leave-one-out error, giving us access to an efficient proxy for the test
error. We show both theoretically and empirically that the leave-one-out error
is capable of capturing various phenomena in generalization theory, such as
double descent, random labels or transfer learning. Our work therefore
demonstrates that the leave-one-out error provides a tractable way to estimate
the generalization ability of deep neural networks in the kernel regime,
opening the door to potential, new research directions in the field of
generalization.


title:
Completed prismatic $F$-crystals and crystalline $\mathbf{Z}_p$-local
  systems

abstract:
  We introduce the notion of completed $F$-crystals on the absolute prismatic
site of a smooth $p$-adic formal scheme. We define a functor from the category
of completed prismatic $F$-crystals to that of crystalline \'etale
$\mathbf{Z}_p$-local systems on the generic fiber of the formal scheme and show
that it gives an equivalence of categories. This generalizes the work of Bhatt
and Scholze, which treats the case of a complete discrete valuation ring with
perfect residue field.


title:
S-Rocket: Selective Random Convolution Kernels for Time Series
  Classification

abstract:
  Random convolution kernel transform (Rocket) is a fast, efficient, and novel
approach for time series feature extraction, using a large number of randomly
initialized convolution kernels, and classification of the represented features
with a linear classifier, without training the kernels. Since these kernels are
generated randomly, a portion of these kernels may not positively contribute in
performance of the model. Hence, selection of the most important kernels and
pruning the redundant and less important ones is necessary to reduce
computational complexity and accelerate inference of Rocket. Selection of these
kernels is a combinatorial optimization problem. In this paper, the kernels
selection process is modeled as an optimization problem and a population-based
approach is proposed for selecting the most important kernels. This approach is
evaluated on the standard time series datasets and the results show that on
average it can achieve a similar performance to the original models by pruning
more than 60% of kernels. In some cases, it can achieve a similar performance
using only 1% of the kernels.


title:
Atomistic Simulation Framework for Molten Salt Vapor-Liquid Equilibrium
  Prediction and its Application to NaCl

abstract:
  Knowledge of the vapor-liquid equilibrium (VLE) properties of molten salts is
important in the design of thermal energy storage systems for solar power and
nuclear energy production applications. The high temperatures involved make
their experimental determination problematic, and the development of both
macroscopic thermodynamic correlations and predictive molecular-based
methodologies are complicated by the requirement to appropriately incorporate
the chemically reacting vapor-phase species. We derive a general
thermodynamic-based atomistic simulation framework for molten salt VLE
prediction and show its application to NaCl. Its input quantities are
temperature-dependent ideal-gas free energy data for the vapor phase reactions,
and density and residual chemical potential data for the liquid. If these are
not available experimentally, the former may be predicted using standard
electronic structure software, and the latter by means of classical atomistic
simulation methodology. The framework predicts the temperature dependence of
vapor pressure, coexisting phase densities, vapor phase composition, and
vaporization enthalpy. It also predicts the concentrations of vapor phase
species present in minor amounts (such as the free ions), quantities that are
extremely difficult to measure experimentally. We furthermore use the VLE
results to obtain approximations to the complete VLE binodal dome and the
critical properties. We verify the framework for molten NaCl, for which
experimentally based density and chemical potential data are available in the
literature. We then apply it to the analysis of NaCl simulation data for two
commonly used atomistic force fields. The framework can be readily extended to
molten salt mixtures and to ionic liquids.


title:
A solar-cycle study of coronal rotation: large variations, rapid
  changes, and implications for solar wind models

abstract:
  Information on the rotation rate of the corona, and its variation over
latitude and solar cycle, is valuable for making global connections between the
corona and the Sun, for global estimates of reconnection rates, and as a basic
parameter for solar wind modelling. Here, we use a time series of tomographical
maps gained from coronagraph observations between 2007 - 2020 to directly
measure the longitudinal drift of high-density streamers over time. The method
reveals abrupt changes in rotation rates, revealing a complex relationship
between the coronal rotation and the underlying photosphere. The majority of
rates are between -1.0 to +0.5$^\circ$/day relative to the standard Carrington
rate of 14.18$^\circ$/day, although rates are measured as low as
-2.2$^\circ$/day and as high as 1.6$^\circ$/day. Equatorial rotation rates
during the 2008 solar minimum are slightly faster than the Carrington rate,
with an abrupt switch to slow rotation in 2009, then a return to faster rates
in 2017. Abrupt changes and large variations in rates are seen at all
latitudes. Comparison with a magnetic model suggests that periods of equatorial
fast rotation are associated with times when a large proportion of the magnetic
footpoints of equatorial streamers are near the equator, and we interpret the
abrupt changes in terms of the latitudinal distribution of the streamer
photospheric footpoints. The coronal rotation rate is a key parameter for solar
wind models, and variations of up to a degree per day or more can lead to large
systematic errors over forecasting periods of longer than a few days. The
approach described in this paper gives corrected values that can form a part of
future forecasting efforts.


title: Fully heavy pentaquark states in constituent quark model

abstract:
  The LHCb collaboration reported a fully charmed tetraquark state X(6900) in
the invariant mass spectrum of $J/\psi$ pairs in 2020. This discovery inspires
us to further study the fully heavy pentaquark system. In this work, we
investigate systematically all possible configurations for ground fully heavy
pentaquark system via the variational method in the constituent quark model.
According to our calculations, we further analyze the relative lengths between
quarks and the contributions to the pentaquark masses from different terms of
the Hamiltonian. We think no stable states exist in fully heavy pentaquark
system. We hope that our study will be helpful to explore for fully heavy
pentaquark states.


title:
Using machine learning to auto-tune chi-squared tests for gravitational
  wave searches

abstract:
  The sensitivity of gravitational wave searches is reduced by the presence of
non-Gaussian noise in the detector data. These non-Gaussianities often match
well with the template waveforms used in matched filter searches, and require
signal-consistency tests to distinguish them from astrophysical signals.
However, empirically tuning these tests for maximum efficacy is time consuming
and limits the complexity of these tests. In this work we demonstrate a
framework to use machine-learning techniques to automatically tune
signal-consistency tests. We implement a new $\chi^2$ signal-consistency test
targeting the large population of noise found in searches for intermediate mass
black hole binaries, training the new test using the framework set out in this
paper. We find that this method effectively trains a complex model to
down-weight the noise, while leaving the signal population relatively
unaffected. This improves the sensitivity of the search by $\sim 11\%$ for
signals with masses $> 300 M_\odot$. In the future this framework could be used
to implement new tests in any of the commonly used matched-filter search
algorithms, further improving the sensitivity of our searches.


title: Secure and Authorized Client-to-Client Communication for LwM2M

abstract:
  Constrained devices on the Internet of Things (IoT) continuously produce and
consume data. LwM2M manages millions of these devices in a server-centric
architecture, which challenges edge networks with expensive uplinks and
time-sensitive use cases. In this paper, we contribute two LwM2M extensions to
enable client-to-client (C2C) communication: (i) an authorization mechanism for
clients, and (ii) an extended management interface to allow secure C2C access
to resources. We analyse the security properties of the proposed extensions and
show that they are compliant with LwM2M security requirements. Our performance
evaluation on off-the-shelf IoT hardware shows that C2C communication
outperforms server-centric deployments. First, LwM2M deployments with edge C2C
communication yield a ~90% faster notification delivery and ~8x greater
throughput compared to common server-centric scenarios, while keeping a small
memory overhead of ~8%. Second, in server-centric communication, the delivery
rate degrades when resource update intervals drop below 100 ms.


title:
Safety Verification of Autonomous Systems: A Multi-Fidelity
  Reinforcement Learning Approach

abstract:
  As autonomous and semi-autonomous agents become more integrated with society,
validation of their safety is increasingly important. The scenarios under which
they are used, however, can be quite complicated; as such, formal verification
may be impossible. To this end, simulation-based safety verification is being
used more frequently to understand failure scenarios for the most complex
problems. Recent approaches, such as adaptive stress testing (AST), use
reinforcement learning, making them prone to excessive exploitation of known
failures, limiting coverage of the space of failures. To overcome this, the
work below defines a class of Markov decision processes, the knowledge MDP,
which captures information about the learned model to reason over. More
specifically, by leveraging, the "knows what it knows" (KWIK) framework, the
learner estimates its knowledge (model estimates and confidence, as well as
assumptions) about the underlying system. This formulation is vetted through
MF-KWIK-AST which extends bidirectional learning in multiple fidelities (MF) of
simulators to the safety verification problem. The knowledge MDP formulation is
applied to detect convergence of the model, penalizing this behavior to
encourage further exploration. Results are evaluated in a grid world, training
an adversary to intercept a system under test. Monte Carlo trials compare the
relative sample efficiency of MF-KWIK-AST to learning with a single-fidelity
simulator, as well as demonstrate the utility of incorporating knowledge about
learned models into the decision making process.


title:
Tuning a quantum-confined silver monolayer beneath epitaxial graphene
  via surface charge-transfer doping

abstract:
  Recently the graphene/SiC interface has emerged as a versatile platform for
the epitaxy of otherwise unstable, monoelemental, two-dimensional (2D) layers
via intercalation. Intrinsically capped into a van der Waals heterostructure
with overhead graphene, they compose a new class of quantum materials with
striking properties contrasting their parent bulk crystals. Intercalated silver
presents a prototypical example where 2D quantum confinement and inversion
symmetry breaking entail a metal-to-semiconductor transition. However, little
is known about the associated unoccupied states and coherent control of the
Fermi level position across the bandgap would be desirable. Here, we n-type
dope a graphene/2D-Ag/SiC heterostack via in situ potassium deposition and
probe its band structure by means of synchrotron-based angle-resolved
photoelectron spectroscopy. While the induced carrier densities on the order of
$10^{14}$ cm$^{-2}$ are not yet sufficient to reach the onset of the silver
conduction band, the band alignment of graphene can be tuned relative to the
rigidly shifting Ag valence band and substrate core levels. We further
demonstrate an ordered potassium adlayer ($2\times 2$ relative to graphene)
with free-electron-like dispersion, suppressing plasmaron quasiparticles in
graphene via enhanced metalization of the heterostack. Our results establish
surface charge-transfer doping as an efficient handle to tune band alignment
and electronic properties of a van der Waals heterostructure assembled from
graphene and a novel type of monolayered quantum material.


title:
Gain and loss induced topological insulating phase in a non Hermitian
  electrical circuit

abstract:
  There have been considerable efforts devoted to the study of topological
phases in certain non-Hermitian systems that possess real eigenfrequencies in
the presence of gain and loss. However, it is challenging to experimentally
realize such non-Hermitian topological insulators in either quantum or photonic
systems, due to the difficulties in introducing controlled gain and loss. On
the other hand, the wide choices of active circuit components provide us with
unprecedented convenience and flexibility in engineering non-Hermitian
topological insulators in electrical circuits. Here, we report experimental
realization of a one-dimensional (1D) non-Hermitian topological circuit which
exhibits topologically protected edge state purely induced by gain and loss. We
show that by tuning the value of the positive/negative resistors in the
circuit, our system can switch between different topological phase regions. The
topological edge states and interface states are observed at the circuit edge
and at the interface between a trivial and nontrivial circuit, which are
manifested by a prominent impedance peak at the mid-gap frequency topologically
robust to variations of circuit parameters. Our work opens a new gateway
towards actively controllable topological systems.


title:
Multi-Modal Lidar Dataset for Benchmarking General-Purpose Localization
  and Mapping Algorithms

abstract:
  Lidar technology has evolved significantly over the last decade, with higher
resolution, better accuracy, and lower cost devices available today. In
addition, new scanning modalities and novel sensor technologies have emerged in
recent years. Public datasets have enabled benchmarking of algorithms and have
set standards for the cutting edge technology. However, existing datasets are
not representative of the technological landscape, with only a reduced number
of lidars available. This inherently limits the development and comparison of
general-purpose algorithms in the evolving landscape. This paper presents a
novel multi-modal lidar dataset with sensors showcasing different scanning
modalities (spinning and solid-state), sensing technologies, and lidar cameras.
The focus of the dataset is on low-drift odometry, with ground truth data
available in both indoors and outdoors environment with sub-millimeter accuracy
from a motion capture (MOCAP) system. For comparison in longer distances, we
also include data recorded in larger spaces indoors and outdoors. The dataset
contains point cloud data from spinning lidars and solid-state lidars. Also, it
provides range images from high resolution spinning lidars, RGB and depth
images from a lidar camera, and inertial data from built-in IMUs. This is, to
the best of our knowledge, the lidar dataset with the most variety of sensors
and environments where ground truth data is available. This dataset can be
widely used in multiple research areas, such as 3D LiDAR simultaneous
localization and mapping (SLAM), performance comparison between multi-modal
lidars, appearance recognition and loop closure detection. The datasets are
available at: https://github.com/TIERS/tiers-lidars-dataset.


title:
Portable magnetic resonance imaging of patients indoors, outdoors and at
  home

abstract:
  Mobile medical imaging devices are invaluable for clinical diagnostic
purposes both in and outside healthcare institutions. Among the various imaging
modalities, only a few are readily portable. Magnetic resonance imaging (MRI),
the gold standard for numerous healthcare conditions, does not traditionally
belong to this group. Recently, low-field MRI start-up companies have
demonstrated the first decisive steps towards portability within medical
facilities, but these are so far incompatible with more demanding use cases
such as in remote and developing regions, sports facilities and events, medical
and military camps, or home healthcare. Here we present in vivo images taken
with a light, home-made, low-field extremity MRI scanner outside the controlled
environment provided by medical facilities. To demonstrate the true portability
of the system and benchmark its performance in various relevant scenarios, we
have acquired images of a volunteer's knee in: i) an MRI physics laboratory;
ii) an office room; iii) outside a campus building, connected to a nearby power
outlet; iv) in open air, powered from a small fuel-based generator; and v) at
the volunteer's home. All images have been acquired within clinically viable
times, and signal-to-noise ratios (SNR) and tissue contrast suffice for 2D and
3D reconstructions with diagnostic value, with comparable overall image quality
across all five situations. Furthermore, the volunteer carries a fixation
metallic implant screwed to the femur, which leads to strong artifacts in
standard clinical systems but appears sharp in our low-field acquisitions.
Altogether, this work opens a path towards highly accessible MRI under
circumstances previously unrealistic.


title: Negative-Weight Single-Source Shortest Paths in Near-linear Time

abstract:
  We present a randomized algorithm that computes single-source shortest paths
(SSSP) in $O(m\log^8(n)\log W)$ time when edge weights are integral and can be
negative. This essentially resolves the classic negative-weight SSSP problem.
The previous bounds are $\tilde O((m+n^{1.5})\log W)$ [BLNPSSSW FOCS'20] and
$m^{4/3+o(1)}\log W$ [AMV FOCS'20]. Near-linear time algorithms were known
previously only for the special case of planar directed graphs [Fakcharoenphol
and Rao FOCS'01].
  In contrast to all recent developments that rely on sophisticated continuous
optimization methods and dynamic algorithms, our algorithm is simple: it
requires only a simple graph decomposition and elementary combinatorial tools.
In fact, ours is the first combinatorial algorithm for negative-weight SSSP to
break through the classic $\tilde O(m\sqrt{n}\log W)$ bound from over three
decades ago [Gabow and Tarjan SICOMP'89].


title:
Graph Neural Networks for Image Classification and Reinforcement
  Learning using Graph representations

abstract:
  In this paper, we will evaluate the performance of graph neural networks in
two distinct domains: computer vision and reinforcement learning. In the
computer vision section, we seek to learn whether a novel non-redundant
representation for images as graphs can improve performance over trivial pixel
to node mapping on a graph-level prediction graph, specifically image
classification. For the reinforcement learning section, we seek to learn if
explicitly modeling solving a Rubik's cube as a graph problem can improve
performance over a standard model-free technique with no inductive bias.


title:
Robust Modeling of Unknown Dynamical Systems via Ensemble Averaged
  Learning

abstract:
  Recent work has focused on data-driven learning of the evolution of unknown
systems via deep neural networks (DNNs), with the goal of conducting long time
prediction of the evolution of the unknown system. Training a DNN with low
generalization error is a particularly important task in this case as error is
accumulated over time. Because of the inherent randomness in DNN training,
chiefly in stochastic optimization, there is uncertainty in the resulting
prediction, and therefore in the generalization error. Hence, the
generalization error can be viewed as a random variable with some probability
distribution. Well-trained DNNs, particularly those with many hyperparameters,
typically result in probability distributions for generalization error with low
bias but high variance. High variance causes variability and unpredictably in
the results of a trained DNN. This paper presents a computational technique
which decreases the variance of the generalization error, thereby improving the
reliability of the DNN model to generalize consistently. In the proposed
ensemble averaging method, multiple models are independently trained and model
predictions are averaged at each time step. A mathematical foundation for the
method is presented, including results regarding the distribution of the local
truncation error. In addition, three time-dependent differential equation
problems are considered as numerical examples, demonstrating the effectiveness
of the method to decrease variance of DNN predictions generally.


title: Computation of the lateral shift due to atmospheric refraction

abstract:
  Atmospheric refraction modifies the apparent position of objects in the sky.
We computed the lateral translation that is to be considered for short-range
applications, such as wavefront sensing and meteor trajectories. We aim to
calculate the lateral shift at each altitude and study its variation according
to meteorological conditions and the location of the observation site. We also
pay special attention to the chromatism of this lateral shift. We extracted the
variation equations of refraction from the geometric tracing of a light ray
path. A numerical method and a dry atmosphere model allowed us to numerically
integrate the system of coupled equations. In addition to this, based on Taylor
expansions, we established three analytic approximations of the lateral shift,
one of which is the one already known in the literature. We compared the three
approximations to the numerical solution. All these estimators are included in
a Python 3.2 package, which is available online. Using the numerical
integration estimator, we calculated the lateral shift values for any zenith
angle including low elevations. The shift is typically around 3 m at a zenith
angle of 45{\deg}, 10 m at 65{\deg}, and even 300 m{\deg} at 85{\deg}. Next,
the study of the variability of the lateral shift as a function of wavelength
shows differences of up to 2% between the visible and near infrared. The
analysis of the errors of each approximation shows the ranges of validity of
the three estimators as a function of the zenith angle. The flat Earth
estimator achieves a relative error of less than 1% up to 55{\deg} while the
new extended second-order estimators improves this result up to 75{\deg}. The
flat Earth estimator is sufficient for applications where the zenith angle is
below 55{\deg} but a refined estimator is necessary to estimate meteor
trajectories at low elevations.


title: Stability of peakons of the Camassa-Holm equation beyond wave breaking

abstract:
  Using a generalized framework that consists of evolution of the solution to
the Camassa- Holm equation and its energy measure, we establish the
global-in-time orbital stability of peakons with respect to the perturbed
(energy) conservative solutions to the Camassa-Holm equation. Especially, we
extend the H1-stability result obtained by Constantin and Strauss (Comm. Pure
Appl. Math., 53(5), 603-610, 2000) globally-in-time, even after the perturbed
solutions experience wave breaking. In addition, our result also shows that the
singular part of the energy measure of the perturbed solutions will remain
stable for all times.


title:
Path Weight Sampling: Exact Monte Carlo Computation of the Mutual
  Information between Stochastic Trajectories

abstract:
  Most natural and engineered information-processing systems transmit
information via signals that vary in time. Computing the information
transmission rate or the information encoded in the temporal characteristics of
these signals, requires the mutual information between the input and output
signals as a function of time, i.e. between the input and output trajectories.
Yet, this is notoriously difficult because of the high-dimensional nature of
the trajectory space, and all existing techniques require approximations. We
present an exact Monte Carlo technique called Path Weight Sampling (PWS) that,
for the first time, makes it possible to compute the mutual information between
input and output trajectories for any stochastic system that is described by a
master equation. The principal idea is to use the master equation to evaluate
the exact conditional probability of an individual output trajectory for a
given input trajectory, and average this via Monte Carlo sampling in trajectory
space to obtain the mutual information. We present three variants of PWS, which
all generate the trajectories using the standard stochastic simulation
algorithm. While Direct PWS is a brute-force method, Rosenbluth-Rosenbluth PWS
exploits the analogy between signal trajectory sampling and polymer sampling,
and Thermodynamic Integration PWS is based on a reversible work calculation in
trajectory space. PWS also makes it possible to compute the mutual information
between input and output trajectories for systems with hidden internal states
as well as systems with feedback from output to input. Applying PWS to the
bacterial chemotaxis system, consisting of 173 coupled chemical reactions,
demonstrates not only that the scheme is highly efficient, but also that the
appropriate measure for quantifying information transmission via time-varying
signals is indeed the information transmission rate.

categories: q-bio.MN cond-mat.soft cs.IT math.IT physics.bio-ph


title: Steady-state microwave mode cooling with a diamond NV ensemble

abstract:
  A fundamental result of quantum mechanics is that the fluctuations of a
bosonic field are given by its temperature $T$. An electromagnetic mode with
frequency $\omega$ in the microwave band has a significant thermal photon
occupation at room temperature according to the Bose-Einstein distribution
$\bar{n} = k_BT / \hbar\omega$. The room temperature thermal state of a 3 GHz
mode, for example, is characterized by a mean photon number $\bar{n} \sim 2000$
and variance $\Delta n^2 \approx \bar{n}^2$. This thermal variance sets the
measurement noise floor in applications ranging from wireless communications to
positioning, navigation, and timing to magnetic resonance imaging. We overcome
this barrier in continuously cooling a ${\sim} 3$ GHz cavity mode by coupling
it to an ensemble of optically spin-polarized nitrogen-vacancy (NV) centers in
a room-temperature diamond. The NV spins are pumped into a low entropy state
via a green laser and act as a heat sink to the microwave mode through their
collective interaction with microwave photons. Using a simple detection circuit
we report a peak noise reduction of $-2.1 \pm 0.1 \, \textrm{dB}$ and minimum
cavity mode temperature of $178.3 \pm 5.4 \textrm{K}$. We present also a
linearized model to identify the important features of the cooling, and
demonstrate its validity through magnetically tuned, spectrally resolved
measurements. The realization of efficient mode cooling at ambient temperature
opens the door to applications in precision measurement and communication, with
the potential to scale towards fundamental quantum limits.


title: Hierarchical Sketch Induction for Paraphrase Generation

abstract:
  We propose a generative model of paraphrase generation, that encourages
syntactic diversity by conditioning on an explicit syntactic sketch. We
introduce Hierarchical Refinement Quantized Variational Autoencoders (HRQ-VAE),
a method for learning decompositions of dense encodings as a sequence of
discrete latent variables that make iterative refinements of increasing
granularity. This hierarchy of codes is learned through end-to-end training,
and represents fine-to-coarse grained information about the input. We use
HRQ-VAE to encode the syntactic form of an input sentence as a path through the
hierarchy, allowing us to more easily predict syntactic sketches at test time.
Extensive experiments, including a human evaluation, confirm that HRQ-VAE
learns a hierarchical representation of the input space, and generates
paraphrases of higher quality than previous systems.


title:
The evaporation and Greybody factor of the new EUP-corrected
  Schwarzschild black hole

abstract:
  Based on the minimum measurable momentum concepts associated with the quantum
gravity effects acting on the large-scale dynamics of the universe. We use the
new extended uncertainty principle (EUP) to study the Hawking temperature and
black hole evaporation. The results show the new EUP quantum correction may
shorten the lifetime of the massive black hole. To verify the new EUP on the
black hole stability, we further investigate the quasinormal frequencies in
terms of the 3rd order WKB approximation. The corrected Schwarzschild black
hole is still stable under the uncertainty parameter {\alpha} is considered to
be a small value. Furthermore, we analyze the transmission and reflection
coefficients and discuss the absorption cross section with graphical methods in
detail. It shows that the uncertainty parameter {\alpha} has a non-negligible
effect on the scattering amplitudes of the corrected black hole.


title: Self-consistent Density Functional Calculations without Diagonalization

abstract:
  We develop a fully self-consistent first-principle calculation method based
on the density functional theory. Comparing with the traditional Kohn-Sham
method, the real-space distribution of electron density is obtained from the
Hamiltonian without any diagonalization. In particular, a Fermi-Dirac filter
applied on a random state combined with a time-evolution of the state vector is
introduced to approximate the electron density. We show analytically that the
average of electron densities calculated using different random states
converges to the one obtained from diagonalization. Our approach is verified
numerically in different systems including single atoms, diatomic molecules,
clusters, and crystals. The computational costs (memory and time) scale
linearly with the system size. Further electronic, optical, and transport
calculations can be realized based on the ground state Hamiltonian using
linear-scaled time-evolution methods. Our new approach for obtaining electron
density without diagonalization provides a powerful strategy to solve problems
in complex quantum systems.


title:
Tensor Programs V: Tuning Large Neural Networks via Zero-Shot
  Hyperparameter Transfer

abstract:
  Hyperparameter (HP) tuning in deep learning is an expensive process,
prohibitively so for neural networks (NNs) with billions of parameters. We show
that, in the recently discovered Maximal Update Parametrization (muP), many
optimal HPs remain stable even as model size changes. This leads to a new HP
tuning paradigm we call muTransfer: parametrize the target model in muP, tune
the HP indirectly on a smaller model, and zero-shot transfer them to the
full-sized model, i.e., without directly tuning the latter at all. We verify
muTransfer on Transformer and ResNet. For example, 1) by transferring
pretraining HPs from a model of 13M parameters, we outperform published numbers
of BERT-large (350M parameters), with a total tuning cost equivalent to
pretraining BERT-large once; 2) by transferring from 40M parameters, we
outperform published numbers of the 6.7B GPT-3 model, with tuning cost only 7%
of total pretraining cost. A Pytorch implementation of our technique can be
found at github.com/microsoft/mup and installable via `pip install mup`.


title:
Molecular Orientation-Induced Second Harmonic Generation: deciphering
  different contributions apart

abstract:
  We demonstrate and explore an all-optical technique for direct monitoring the
orientation dynamics of gas phase molecular ensembles. The technique termed
'MOISH' utilizes the transiently lifted inversion symmetry of polar gas media
and provides a sensitive and spatially localized probing of second harmonic
generation signal that is directly correlated with the orientation of the gas.
Our experimental results reveal selective electronic and nuclear dynamical
contributions to the overall nonlinear optical signal and decipher them apart
using the "reporter gas" approach. 'MOISH' provides new, crucial means for
exploring controlled rotational dynamics via concerted terahertz and optical
field excitation.


title: Loss of material trainability through an unusual transition

abstract:
  Material training is a method to endow materials with specific responses
through a self-organization process guided by external driving. Here, we study
the complexity of attainable responses, as expressed in the number of sites
that are simultaneously controlled. We find that with increased complexity
convergence to the desired response becomes very slow. The training error
decays as power-law with an exponent that varies continuously and vanishes at a
critical threshold, marking the limit of responses that can be trained. Since
training aims at creating a low energy mode, we search for the a signature of
this transition in the density of states. As approaching the critical threshold
there is a proliferation of low frequency modes, that appear to reach arbitrary
low frequencies near criticality. This suggests that training fails due to
these spurious competing low frequency modes. Our work explains how the
presence of an exotic critical point affects the convergence of training, and
could be relevant for understanding learning in physical systems.


title: PhD thesis: SQL Comprehension and Synthesis

abstract:
  Structured Query Language (SQL) remains the standard language used in
Relational Database Management Systems (RDBMSs) and has found applications in
healthcare (patient registries), businesses (inventories, trend analysis),
military, education, etc. Although SQL statements are English-like, the process
of writing SQL queries is often problematic for nontechnical end-users in the
industry. Similarly, formulating and comprehending written queries can be
confusing, especially for undergraduate students. One of the pivotal reasons
given for these difficulties lies with the simple syntax of SQL, which is often
misleading and hard to understand. An ideal solution is to present these two
audiences: undergraduate students and nontechnical end-users with learning and
practice tools. These tools are mostly electronic and can be used to aid their
understanding, as well as enable them to write correct SQL queries. This work
proposes a new approach aimed at understanding and writing correct SQL queries
using principles from Formal Language and Automata Theory. We present
algorithms based on: regular expressions for the recognition of simple query
constructs, context-free grammars for the recognition of nested queries, and a
jumping finite automaton for the synthesis of SQL queries from natural language
descriptions. As proof of concept, these algorithms were further implemented
into interactive software tools aimed at improving SQL comprehension.
Evaluation of these tools showed that the majority of participants agreed that
the tools were intuitive and aided their understanding of SQL queries. These
tools should, therefore, find applications in aiding SQL comprehension at
higher learning institutions and assist in the writing of correct queries in
data-centered industries.


title: On Successive Approximations for Compact-Valued Nonexpansive Mappings

abstract:
  We show that for a given initial point the typical, in the sense of Baire
category, nonexpansive compact valued mapping $F$ has the following properties:
there is a unique sequence of successive approximations and this sequence
converges to a fixed point of $F$.


title:
Gravitomagnetic interaction of a Kerr black hole with a magnetic field
  as the source of the jetted GeV radiation of gamma-ray bursts

abstract:
  We show that the gravitomagnetic interaction of a Kerr black hole (BH) with a
surrounding magnetic field induces an electric field that accelerates charged
particles to ultra-relativistic energies in the vicinity of the BH. Along the
BH rotation axis, these electrons/protons can reach energies of even thousands
of PeV, so stellar-mass BHs in long gamma-ray bursts (GRBs) and supermassive
BHs in active galactic nuclei (AGN) can contribute to the ultrahigh-energy
cosmic rays (UHECRs) thorough this mechanism. At off-axis latitudes, the
particles accelerate to energies of hundreds of GeV and emit synchrotron
radiation at GeV energies. This process occurs within $60^\circ$ around the BH
rotation axis, and due to the equatorial-symmetry, it forms a double-cone
emission. We outline the theoretical framework describing these acceleration
and radiation processes, how they extract the rotational energy of the Kerr BH
and the consequences for the astrophysics of GRBs.


title: Pizzetti formulae and the Radon Transform on the Sphere

abstract:
  In this paper, we obtain Pizzetti-type formulae on regions of the the unit
sphere $\mathbb{S}^{m-1}$ of $\mathbb{R}^m$, and study their applications to
the problem of inverting the spherical Radon transform. In particular, we
approach integration over $(m-2)$-dimensional sub-spheres of
$\mathbb{S}^{m-1}$, $(m-1)$-dimensional sub-balls, and over $(m-1)$-dimensional
spherical caps as the action of suitable concentrated delta distributions. In
turn, this leads to Pizzetti formulae that express such integrals in terms of
the action of SO$(m-1)$-invariant differential operators. In the last section
of the paper, we use some of these expressions to derive the inversion formulae
for the Radon transform on $\mathbb{S}^{m-1}$ in a direct way.


title: Hamilton-Jacobi approach to thermodynamic transformations

abstract:
  In this note, we formulate and study a Hamilton-Jacobi approach for
describing thermodynamic transformations. The thermodynamic phase space assumes
the structure of a contact manifold with the points representing equilibrium
states being restricted to certain submanifolds of this phase space. We
demonstrate that Hamilton-Jacobi theory self consistently describes
thermodynamic transformations on the space of externally controllable
parameters or equivalently the space of equilibrium states. It turns out that
in the Hamilton-Jacobi description, the choice of the principal function is not
unique but, the resultant dynamical description for a given transformation
remains the same irrespective of this choice. Some examples involving
thermodynamic transformations of the ideal gas are discussed where the
characteristic curves on the space of equilibrium states completely describe
the dynamics.

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


title:
Bayesian Mendelian randomization testing of interval causal null
  hypotheses: ternary decision rules and loss function calibration

abstract:
  Our approach to Mendelian Randomization (MR) analysis is designed to increase
reproducibility of causal effect "discoveries" by: (i) using a Bayesian
approach to inference; (ii) replacing the point null hypothesis with a region
of practical equivalence consisting of values of negligible magnitude for the
effect of interest, while exploiting the ability of Bayesian analysis to
quantify the evidence of the effect falling inside/outside the region; (iii)
rejecting the usual binary decision logic in favour of a ternary logic where
the hypothesis test may result in either an acceptance or a rejection of the
null, while also accommodating an "uncertain" outcome. We present an approach
to calibration of the proposed method via loss function, which we use to
compare our approach with a frequentist one. We illustrate the method with the
aid of a study of the causal effect of obesity on risk of juvenile myocardial
infarction.


title:
State space partitioning based on constrained spectral clustering for
  block particle filtering

abstract:
  The particle filter (PF) is a powerful inference tool widely used to estimate
the filtering distribution in non-linear and/or non-Gaussian problems. To
overcome the curse of dimensionality of PF, the block PF (BPF) inserts a
blocking step to partition the state space into several subspaces or blocks of
smaller dimension so that the correction and resampling steps can be performed
independently on each subspace. Using blocks of small size reduces the variance
of the filtering distribution estimate, but in turn the correlation between
blocks is broken and a bias is introduced.
  When the dependence relationships between state variables are unknown, it is
not obvious to decide how to split the state space into blocks and a
significant error overhead may arise from a poor choice of partitioning. In
this paper, we formulate the partitioning problem in the BPF as a clustering
problem and we propose a state space partitioning method based on spectral
clustering (SC). We design a generalized BPF algorithm that contains two new
steps: (i) estimation of the state vector correlation matrix from predicted
particles, (ii) SC using this estimate as the similarity matrix to determine an
appropriate partition. In addition, a constraint is imposed on the maximal
cluster size to prevent SC from providing too large blocks. We show that the
proposed method can bring together in the same blocks the most correlated state
variables while successfully escaping the curse of dimensionality.


title: Monotone cohomologies and oriented matchings

abstract:
  In this paper, we extend the definition of cohomology associated to monotone
graph properties, to encompass twisted functor coefficients. We introduce
oriented matchings on graphs, and focus on their (twisted) cohomology groups.
We characterise oriented matchings in terms of induced free-flow pseudoforests,
and explicitly determine the homotopy type of the associated simplicial
complexes. Furthermore, we provide a connection between the cohomology of
oriented matchings with certain functor coefficients, and the recently defined
multipath cohomology. Finally, we define a further oriented homology for graphs
and interpret it as a count of free-flow orientations.


title: Universal planar graphs for the topological minor relation

abstract:
  Huynh et al. recently showed that a countable graph $G$ which contains every
countable planar graph as a subgraph must contain arbitrarily large finite
complete graphs as topological minors, and an infinite complete graph as a
minor. We strengthen this result by showing that the same conclusion holds, if
$G$ contains every countable planar graph as a topological minor. In
particular, there is no countable planar graph containing every countable
planar graph as a topological minor, answering a question by Diestel and
K\"uhn.
  Moreover, we construct a locally finite planar graph which contains every
locally finite planar graph as a topological minor. This shows that in the
above result it is not enough to require that $G$ contains every locally finite
planar graph as a topological minor.


title:
Understanding the role of intramolecular ion-pair interactions in
  conformational stability using an ab initio thermodynamic cycle

abstract:
  Intramolecular ion-pair interactions yield shape and functionality to many
important molecules. With proper orientation, these interactions can overcome
steric factors and are hence responsible for the compact structures noted for
several peptides. In this study, we present a thermodynamic cycle based on
isoelectronic, alchemical mutation to estimate intramolecular ion-pair
interaction energy. We determine these energies for 26 benchmark molecules with
well known ion-pair combinations and compare them with results obtained using
intramolecular symmetry-adapted perturbation theory. For systems with long
linkers, the ion-pair energies evaluated using both approaches deviate by less
than 2.5% in vacuum phase. The thermodynamic cycle, especially when based on
total energies from a density functional approximation, has a favorable
cost-to-accuracy trade-off facilitating calculations of model tripeptides with
continuum/micro-solvation modeling. Finally, we quantify salt-bridge
interactions in four large peptides: crambin (1EJG), bradykinin (1BDK), a
scorpion toxin (1SCO), and a mini-protein with a tryptophan cage (1L2Y).


title:
Solving the Initial Value Problem of Ordinary Differential Equations by
  Lie Group based Neural Network Method

abstract:
  To combine a feedforward neural network (FNN) and Lie group (symmetry) theory
of differential equations (DEs), an alternative artificial NN approach is
proposed to solve the initial value problems (IVPs) of ordinary DEs (ODEs).
Introducing the Lie group expressions of the solution, the trial solution of
ODEs is split into two parts. The first part is a solution of other ODEs with
initial values of original IVP. This is easily solved using the Lie group and
known symbolic or numerical methods without any network parameters (weights and
biases). The second part consists of an FNN with adjustable parameters. This is
trained using the error back propagation method by minimizing an error (loss)
function and updating the parameters. The method significantly reduces the
number of the trainable parameters and can more quickly and accurately learn
the real solution, compared to the existing similar methods. The numerical
method is applied to several cases, including physical oscillation problems.
The results have been graphically represented, and some conclusions have been
made.


title: Reinforcement Learning for Location-Aware Scheduling

abstract:
  Recent techniques in dynamical scheduling and resource management have found
applications in warehouse environments due to their ability to organize and
prioritize tasks in a higher temporal resolution. The rise of deep
reinforcement learning, as a learning paradigm, has enabled decentralized agent
populations to discover complex coordination strategies. However, training
multiple agents simultaneously introduce many obstacles in training as
observation and action spaces become exponentially large. In our work, we
experimentally quantify how various aspects of the warehouse environment (e.g.,
floor plan complexity, information about agents' live location, level of task
parallelizability) affect performance and execution priority. To achieve
efficiency, we propose a compact representation of the state and action space
for location-aware multi-agent systems, wherein each agent has knowledge of
only self and task coordinates, hence only partial observability of the
underlying Markov Decision Process. Finally, we show how agents trained in
certain environments maintain performance in completely unseen settings and
also correlate performance degradation with floor plan geometry.


title:
EEG to fMRI Synthesis Benefits from Attentional Graphs of Electrode
  Relationships

abstract:
  Topographical structures represent connections between entities and provide a
comprehensive design of complex systems. Currently these structures are used to
discover correlates of neuronal and haemodynamical activity. In this work, we
incorporate them with neural processing techniques to perform regression, using
electrophysiological activity to retrieve haemodynamics. To this end, we use
Fourier features, attention mechanisms, shared space between modalities and
incorporation of style in the latent representation. By combining these
techniques, we propose several models that significantly outperform current
state-of-the-art of this task in resting state and task-based recording
settings. We report which EEG electrodes are the most relevant for the
regression task and which relations impacted it the most. In addition, we
observe that haemodynamic activity at the scalp, in contrast with sub-cortical
regions, is relevant to the learned shared space. Overall, these results
suggest that EEG electrode relationships are pivotal to retain information
necessary for haemodynamical activity retrieval.


title:
Borderline Lipschitz regularity for bounded minimizers of functionals
  with (p,q)-growth

abstract:
  We prove local Lipschitz regularity for bounded minimizers of functionals
with nonstandard $p,q$-growth with the source term in the Lorentz space
$L(N,1)$ under the restriction $q<p+1+p\,\min\left\{\frac
1N,\frac{2(p-1)}{Np-2p+2}\right\}$. This extends the recent work by
Beck-Mingione to bounded minimizers under weaker hypothesis and is sharp for
some special ranges of $p$, $q$ and $N$.


title:
Towards Unbiased Multi-label Zero-Shot Learning with Pyramid and
  Semantic Attention

abstract:
  Multi-label zero-shot learning extends conventional single-label zero-shot
learning to a more realistic scenario that aims at recognizing multiple unseen
labels of classes for each input sample. Existing works usually exploit
attention mechanism to generate the correlation among different labels.
However, most of them are usually biased on several major classes while neglect
most of the minor classes with the same importance in input samples, and may
thus result in overly diffused attention maps that cannot sufficiently cover
minor classes. We argue that disregarding the connection between major and
minor classes, i.e., correspond to the global and local information,
respectively, is the cause of the problem. In this paper, we propose a novel
framework of unbiased multi-label zero-shot learning, by considering various
class-specific regions to calibrate the training process of the classifier.
Specifically, Pyramid Feature Attention (PFA) is proposed to build the
correlation between global and local information of samples to balance the
presence of each class. Meanwhile, for the generated semantic representations
of input samples, we propose Semantic Attention (SA) to strengthen the
element-wise correlation among these vectors, which can encourage the
coordinated representation of them. Extensive experiments on the large-scale
multi-label zero-shot benchmarks NUS-WIDE and Open-Image demonstrate that the
proposed method surpasses other representative methods by significant margins.


title:
Dissociative recombination of CH$^+$ molecular ion induced by very low
  energy electrons

abstract:
  We used the multichannel quantum defect theory to compute cross sections and
rate coefficients for the dissociative recombination of CH$^+$ initially in its
lowest vibrational level $v_i^+ = 0$ with electrons of incident energy bellow
$0.2$ eV. We have focused on the contribution of the $2$ $^2\Pi$ state which is
the main dissociative recombination route at low collision energies. The final
cross section is obtained by averaging the relevant initial rotational states
$(N_i^+ = 0,\dots,10)$ with a $300$ K Boltzmann distribution.The Maxwell
isotropic rate coefficients for dissociative recombination are also calculated
for different initial rotational states and for electronic temperatures up to a
few hundred Kelvins. Our results are compared to storage-ring measurements.


title: Self-directed Learning of Action Models using Exploratory Planning

abstract:
  Complex, real-world domains may not be fully modeled for an agent, especially
if the agent has never operated in the domain before. The agent's ability to
effectively plan and act in such a domain is influenced by its knowledge of
when it can perform specific actions and the effects of those actions. We
describe a novel exploratory planning agent that is capable of learning action
preconditions and effects without expert traces or a given goal. The agent's
architecture allows it to perform both exploratory actions as well as
goal-directed actions, which opens up important considerations for how
exploratory planning and goal planning should be controlled, as well as how the
agent's behavior should be explained to any teammates it may have. The
contributions of this work include a new representation for contexts called
Lifted Linked Clauses, a novel exploration action selection approach using
these clauses, an exploration planner that uses lifted linked clauses as goals
in order to reach new states, and an empirical evaluation in a scenario from an
exploration-focused video game demonstrating that lifted linked clauses improve
exploration and action model learning against non-planning baseline agents.


title: On the unramified Eisenstein spectrum

abstract:
  For a split reductive group ${}^L G$ over a global field, we determine the
spectrum of the spherical Hecke algebra coming from the unramified Eisenstein
series for the minimal parabolic ${}^L B$. This is done using a certain
decomposition of the Springer stack $T^*(B \backslash G/ B)$ for the Langlands
dual group in the additive group of cobordisms of cohomologically proper
derived quotient stacks.


title: The Charney-Davis conjecture for simple thin polyominoes

abstract:
  Let $\mathcal{P}$ be a simple thin polyomino and $\Bbbk$ a field. Let $R$ be
the toric $\Bbbk$-algebra associated to $\mathcal{P}$. Write the Hilbert series
of $R$ as $h_{R}(t)/(1-t)^{\dim(R)}$. We show that
$$(-1)^{\left\lfloor{\frac{\mathrm{deg} h_R(t)}{2}}\right\rfloor}h_{R}(-1) \geq
0$$ if $R$ is Gorenstein. This shows that the Gorenstein rings associated to
simple thin polyominoes satisfy the Charney-Davis conjecture.


title: Optimal Lockdown Management using Short Term COVID-19 Prediction Model

abstract:
  This paper proposes optimal lockdown management policies based on short-term
prediction of active COVID-19 confirmed cases to ensure the availability of
critical medical resources. The optimal time to start the lockdown from the
current time is obtained after maximizing a cost function considering economic
value subject to constraints of availability of medical resources, and maximum
allowable value of daily growth rate and Test Positive Ratio. The estimated
value of required medical resources is calculated as a function of total active
cases. The predicted value of active cases is calculated using an adaptive
short-term prediction model. The proposed approach can be easily implementable
by a local authority. An optimal lockdown case study for Delhi during the
second wave in the month of April 2021 is presented using the proposed
formulation.


title:
DATGAN: Integrating expert knowledge into deep learning for synthetic
  tabular data

abstract:
  Synthetic data can be used in various applications, such as correcting bias
datasets or replacing scarce original data for simulation purposes. Generative
Adversarial Networks (GANs) are considered state-of-the-art for developing
generative models. However, these deep learning models are data-driven, and it
is, thus, difficult to control the generation process. It can, therefore, lead
to the following issues: lack of representativity in the generated data, the
introduction of bias, and the possibility of overfitting the sample's noise.
This article presents the Directed Acyclic Tabular GAN (DATGAN) to address
these limitations by integrating expert knowledge in deep learning models for
synthetic tabular data generation. This approach allows the interactions
between variables to be specified explicitly using a Directed Acyclic Graph
(DAG). The DAG is then converted to a network of modified Long Short-Term
Memory (LSTM) cells to accept multiple inputs. Multiple DATGAN versions are
systematically tested on multiple assessment metrics. We show that the best
versions of the DATGAN outperform state-of-the-art generative models on
multiple case studies. Finally, we show how the DAG can create hypothetical
synthetic datasets.


title:
On the connection between the Fueter-Sce-Qian theorem and the
  generalized CK-extension

abstract:
  The Fueter-Sce-Qian theorem provides a way of inducing axial monogenic
functions in $\mathbb{R}^{m+1}$ from holomorphic intrinsic functions of one
complex variable. This result was initially proved by Fueter and Sce for the
cases where the dimension $m$ is odd using pointwise differentiation, while the
extension to the cases where $m$ is even was proved by Qian using the
corresponding Fourier multipliers.
  In this paper, we provide an alternative description of the Fueter-Sce-Qian
theorem in terms of the generalized CK-extension. The latter characterizes
axial null solutions of the Cauchy-Riemann operator in $\mathbb{R}^{m+1}$ in
terms of their restrictions to the real line. This leads to a one-to-one
correspondence between the space of axially monogenic functions in
$\mathbb{R}^{m+1}$ and the space of analytic functions of one real variable.
  We provide explicit expressions for the Fueter-Sce-Qian map in terms of the
generalized CK-extension for both cases, $m$ even and $m$ odd. These
expressions allow for a plane wave decomposition of the Fueter-Sce-Qian map or,
more in particular, a factorization of this mapping in terms of the dual Radon
transform. In turn, this decomposition provides a new possibility for extending
the Coherent State Transform (CST) to Clifford Analysis. In particular, we
construct an axial CST defined through the Fueter-Sce-Qian mapping, and show
how it is related to the axial and slice CSTs already studied in the
literature.


title: Edge Contraction and Forbidden Induced Graphs

abstract:
  A graph $G$ is $H$-free if any subset of $V(G)$ does not induce a subgraph of
$G$ that is isomorphic to $H$. Given a graph $H$, we present sufficient and
necessary conditions for a graph $G$ such that $G/e$ is $H$-free for any edge
$e$ in $E(G)$. Thereafter, we use these conditions to characterize forests,
claw-free, $2K_{2}$-free, $C_{4}$-free, $C_{5}$-free, split, and pseudo-split
graphs.


title:
Invariant Family of Leaf measures and The Ledrappier-Young Property for
  Hyperbolic Equilibrium States

abstract:
  $M$ is a Riemannian, boundaryless, and compact manifold with $\dim M\geq 2$,
and $f$ is a $C^{1+\beta}$ ($\beta>0$) diffeomorphism of $M$. $\varphi$ is a
H\"older continuous potential on $M$. We construct an invariant and absolutely
continuous family of measures (with transformation relations defined by
$\varphi$), which sit on local unstable leaves. We present two main
applications. First, given an ergodic homoclinic class $H_\chi(p)$, we prove
that $\varphi$ admits a local equilibrium state on $H_\chi(p)$ if and only if
$\varphi$ is "recurrent on $H_\chi(p)$" (a condition tested by counting
periodic points), and one of the leaf measures gives a positive measure to a
set of positively recurrent hyperbolic points; and if an equilibrium measure
exists, the said invariant and absolutely continuous family of measures
constitutes as its conditional measures. An immediate corollary is the local
product structure of hyperbolic equilibrium states.
  Second, we prove a Ledrappier-Young property for hyperbolic equilibrium
states -- if $\varphi$ admits a conformal family of leaf measures, and a
hyperbolic local equilibrium state, then the leaf measures of the invariant
family (respective to $\varphi$) are equivalent to the conformal measures (on a
full measure set). This extends the celebrated result by Ledrappier and Young
for hyperbolic SRB measures, which states that a hyperbolic equilibrium state
of the geometric potential (with pressure 0) has conditional measures on local
unstable leaves which are absolutely continuous w.r.t the Riemannian volume of
these leaves.


title: Charge radii of Ca isotopes and correlations

abstract:
  We study the effects of short- and long-range correlations on the charge
radii of Ca isotopes. We start our investigation with an independent particle
model consisting in Hartree-Fock plus Bardeen-Cooper-Schrieffer calculations
with finite-range effective nucleon-nucleon interactions of Gogny type. The
short-range correlations effects are evaluated by considering all the terms of
a cluster expansion containing a single correlation line. The long-range
correlations are taken into account by including the coupling with the
quasi-particle random phase approximation phonons. While the effects of the
short-range correlations are negligible, those of the long-range correlations
largely modify the independent particle model results and improve the agreement
with the experimental data.


title: Constructing Group-Invariant CR Mappings

abstract:
  We construct CR mappings between spheres that are invariant under actions of
finite unitary groups. In particular, we combine a tensoring procedure with
D'Angelo's construction of a canonical group-invariant CR mapping to obtain new
invariant mappings. We also explore possible gap phenomena in this setting.


title:
Detection of Travel Time Anisotropy from Subsurface Horizontal Magnetic
  Fields

abstract:
  A time-distance measurement technique is derived to isolate phase travel time
anisotropy caused by subsurface horizontal magnetic fields, and a method which
uses the measured anisotropy to estimate the field's orientation is also
derived. A simulation of acoustic waves propagating in a uniform, inclined
magnetic field with solar background structure is used to verify the derived
technique. Then, the procedure is applied to a numerical simulation of a
sunspot, for which the subsurface state is known, to provide context for the
results obtained from the study of several sunspots observed by the
Helioseismic and Magnetic Imager. Significant anisotropies are detected, on the
order of one minute, and the subsurface field's azimuth is estimated and
compared with the azimuth of the surface magnetic field. In all cases, the
subsurface azimuth is found to be well-aligned with that of the surface, and
the results from the numerical simulation are used to interpret features in the
detected travel time anisotropy.


title:
Understanding the growth of high-aspect-ratio grains in granular
  L10-FePt thin-film magnetic media

abstract:
  A systematic investigation has been performed to optimize the microstructure
of $\mathrm{L1_0-FePt-SiO_x}$ granular thin film as recording media for
heat-assisted magnetic recording. The FePt-BN nucleation layer, which is stable
even at $700^\circ \text{C}$, is used to control the grain sizes and
microstructure during high-temperature processing. The study finds that films
of high-aspect-ratio FePt grains with well-formed silicon oxide (SiOx) grain
boundaries require the grading of the deposition temperature during film growth
as well as the grading of the silicon oxide concentration. Well-isolated
columnar grains of $\mathrm{L1_0-FePt}$ with an average height greater than 11
nm and diameters less than 7 nm have been achieved. Transmission electron
microscopy (TEM) analysis of the microstructures of samples produced under a
variety of non-optimal conditions is presented to show how the microstructure
of the films depends on each of the sputtering parameters.


title: Inference in Linear Dyadic Data Models with Network Spillovers

abstract:
  When using dyadic data (i.e., data indexed by pairs of units, such as trade
flow data between two countries), researchers typically assume a linear model,
estimate it using Ordinary Least Squares and conduct inference using
"dyadic-robust" variance estimators. The latter assumes that dyads are
uncorrelated if they do not share a common unit (e.g., if one country does not
appear in both pairs of trade flow data). We show that this assumption does not
hold in many empirical applications because indirect links may exist due to
network connections, e.g., different country-pairs may have correlated trade
outcomes due to sharing common trading partner links. Hence, as we prove, then
show in Monte Carlo simulations, "dyadic-robust" estimators can be severely
biased. We develop a consistent variance estimator appropriate for such
contexts by leveraging results in network econometrics. Our estimator has good
finite sample properties in numerical simulations. We then illustrate our
message with an application to voting behavior by seating neighbors in the
European Parliament.


title: Weakly Supervised Learning of Keypoints for 6D Object Pose Estimation

abstract:
  State-of-the-art approaches for 6D object pose estimation require large
amounts of labeled data to train the deep networks. However, the acquisition of
6D object pose annotations is tedious and labor-intensive in large quantity. To
alleviate this problem, we propose a weakly supervised 6D object pose
estimation approach based on 2D keypoint detection. Our method trains only on
image pairs with known relative transformations between their viewpoints.
Specifically, we assign a set of arbitrarily chosen 3D keypoints to represent
each unknown target 3D object and learn a network to detect their 2D
projections that comply with the relative camera viewpoints. During inference,
our network first infers the 2D keypoints from the query image and a given
labeled reference image. We then use these 2D keypoints and the arbitrarily
chosen 3D keypoints retained from training to infer the 6D object pose.
Extensive experiments demonstrate that our approach achieves comparable
performance with state-of-the-art fully supervised approaches.


title: Theory of Superconductivity Mediated by Topological Phonons

abstract:
  Topological phononic insulators are the counterpart of three-dimensional
quantum spin Hall insulators in phononic systems and, as such, their
topological surfaces are characterized by Dirac cone-shaped gapless edge states
arising as a consequence of a bulk-boundary correspondence. We propose a
theoretical framework for the possible superconducting phase in these
materials, where the attractive interaction between electrons is mediated by
topological phonons in nontrivial boundary modes. Within the BCS limit, we
develop a self-consistent two-band gap equation, whose solutions show that the
superconducting critical temperature has a non-monotonic behaviour with respect
to the phononic frequency in the Kramers-like point. This remarkable behaviour
is produced by a resonance, that occurs when electrons and phonons on the
topological surfaces have the same energy: this effectively increases the
electron-phonon interaction and hence the Cooper pair binding energy, thus
establishing an optimal condition for the superconducting phase. With this
mechanism, the $T_{c}$ can be increased by well over a factor two, and the
maximum enhancement occurs in the degenerate phononic flat-band limit.

categories: cond-mat.supr-con cond-mat.mes-hall cond-mat.mtrl-sci cond-mat.soft quant-ph


title:
Almost sure local well-posedness for cubic nonlinear Schrodinger
  equation with higher order operators

abstract:
  In this paper, we study the local well-posedness of the cubic Schr\" odinger
equation: $$ (i\partial_t - P) u = \pm |u|^2 u\quad \textrm{ on }\ I\times \R^d
,$$ with randomized initial data, and $P$ being an operator of degree $s \geq
2$. Using careful estimates in anisotropic spaces, we improve and extend known
results for the standard Schr\"odinger equation (that is, $P$ being Laplacian)
to any dimension under natural assumptions on $P$, whose Fourier symbol might
be sign changing. Quite interestingly, we also exhibit the existence of a new
regime depending on $s$ and $d$, which was not present for the Laplacian.


title:
To Migrate or not to Migrate: An Analysis of Operator Migration in
  Distributed Stream Processing

abstract:
  One of the most important issues in data stream processing systems is to use
operator migration to handle highly variable workloads in a cost-efficient
manner and adapt to the needs at any given time on demand. Operator migration
is a complex process that involves changes in the state and stream management
of a running query, typically without any loss of data, and with as little
disruption to the execution as possible. This survey provides an overview of
solutions for operator migration from a historical perspective as well as the
perspective of the goal of migration. It introduces a conceptual model of
operator migration to establish a unified terminology and classify existing
solutions. Existing work in the area is analyzed to separate the mechanism of
migration from the decision to migrate the data. In case of the latter, a
cost-benefit analysis is emphasized that is important for operator migration
but is often only implicitly addressed, or is neglected altogether. A
description of the available solutions provides the reader with a good
understanding of the design alternatives from an algorithmic viewpoint. We
complement this with an empirical study to provide quantitative insights on the
impact of different design alternatives on the mechanisms of migration.


title:
A compact instrument for gamma-ray burst detection on a CubeSat platform
  II: Detailed design, assembly and validation

abstract:
  The Gamma-ray Module, GMOD, is a miniaturised novel gamma-ray detector which
will be the primary scientific payload on the Educational Irish Research
Satellite (EIRSAT-1) 2U CubeSat mission. GMOD comprises a compact (25mm
$\times$ 25mm $\times$ 40mm) cerium bromide scintillator coupled to a tiled
array of 4$\times$4 silicon photomultipliers, with front-end readout provided
by the IDE3380 SIPHRA. This paper presents the detailed GMOD design and the
accommodation of the instrument within the restrictive CubeSat form factor. The
electronic and mechanical interfaces are compatible with many off-the-shelf
CubeSat systems and structures. The energy response of the GMOD engineering
qualification model has been determined using radioactive sources, and an
energy resolution of 5.4% at 662keV has been measured.
  EIRSAT-1 will perform on-board processing of GMOD data. Trigger results,
including light-curves and spectra, will be incorporated into the spacecraft
beacon and transmitted continuously. Inexpensive hardware can be used to decode
the beacon signal, making the data accessible to a wide community.
  GMOD will have scientific capability for the detection of gamma-ray bursts,
in addition to the educational and technology demonstration goals of the
EIRSAT-1 mission. The detailed design and measurements to date demonstrate the
capability of GMOD in low Earth orbit, the scalability of the design for larger
CubeSats and as an element of future large gamma-ray missions.


title:
Post-Fabrication Trimming of Silicon Photonic Ring Resonators at
  Wafer-Scale

abstract:
  Silicon ring resonator-based devices, such as modulators, detectors, filters,
and switches, play important roles in integrated photonic circuits for optical
communication, high-performance computing, and sensing applications. However,
the high sensitivity to fabrication variations has limited their volume
manufacturability and commercial adoption. Here, we report a low-cost
post-fabrication trimming method to tune the resonance wavelength of a silicon
ring resonator and correct for fabrication variations at wafer-scale. We use a
Ge implant to create an index trimmable section in the ring resonator and an
on-chip heater to apply a precise and localized thermal annealing to tune and
set its resonance to a desired wavelength. We demonstrate resonance wavelength
trimming of ring resonators fabricated across a 300 mm silicon-on-insulator
(SOI) wafer to within +/-32 pm of a target wavelength of 1310 nm, providing a
viable path to high-volume manufacturing and opening up new practical
applications for these devices.


title: S{\l}oci\'nski-Wold decompositions for row-isometries

abstract:
  S{\l}oci\'nski gave sufficient conditions for commuting isometries to have a
nice Wold-like decomposition. In this note we provide analogous results for
row-isometries satisfying certain commutation relations. Other than known
results for doubly-commuting row-isometries, we provide sufficient condtions
for a Wold decomposition based on the Lebesgue decomposition of the
row-isometries.


title: Real-space Bell inequalities in de Sitter

abstract:
  Bell-inequality violations reveal the presence of quantum correlations
between two particles that have interacted and then separated. Their
generalisation to quantum fields is necessary to study a number of
field-theoretic setups, such as cosmological density fluctuations. In this
work, we show how Bell operators can be constructed for quantum fields in real
space, and for Gaussian states we compute their expectation value in terms of
the field power spectra. We then apply our formalism to a scalar field in
de-Sitter space-time. We find that, in spite of the tremendous production of
entangled particles with opposite wave momenta on large scales, Bell
inequalities are not violated in real space. The reason is that, when
considering measurements of a field at two distinct locations in real space,
one implicitly traces over the configuration of the field at every other
location, leading to a mixed bipartite system. This "effective decoherence"
effect is responsible for the erasure of quantum features, and casts some
doubts on our ability to reveal the quantum origin of cosmological structures.
We finally discuss these results in the light of quantum discord.


title:
X-ray Emission and Radio Emission from the Jets and Lobes of the
  Spiderweb Radio Galaxy

abstract:
  Deep Chandra and VLA imaging reveals a clear correlation between X-ray and
radio emission on scales $\sim 100$~kpc in the Spiderweb radio galaxy at
z=2.16. The X-ray emission associated with the extended radio source is likely
dominated by inverse Compton up-scattering of cosmic microwave background
photons by the radio emitting relativistic electrons. For regions dominated by
high surface brightness emission, such as hot spots and jet knots, the implied
magnetic fields are $\sim 50~\mu$G to $70~\mu$G. The non-thermal pressure is
these brighter regions is then $\sim 9\times 10^{-10}$ dyne cm$^{-2}$, or three
times larger than the non-thermal pressure derived assuming minimum energy
conditions, and an order of magnitude larger than the thermal pressure in the
ambient cluster medium. Assuming ram pressure confinement implies an average
advance speed for the radio source of $\sim 2400$ km s$^{-1}$, and a source age
of $\sim 3\times 10^7$ years. Considering the lower surface brightness, diffuse
radio emitting regions, we identify an evacuated cavity in the Ly$\alpha$
emission coincident with the tail of the eastern radio lobe. Making reasonable
assumptions for the radio spectrum, we find that the relativistic electrons and
fields in the lobe are plausibly in pressure equilibrium with the thermal gas,
and close to a minimum energy configuration. The radio morphology suggests that
the Spiderweb is a high-$z$ example of the rare class of hybrid morphology
radio sources (or HyMoRS), which we attribute to interaction with the
asymmetric gaseous environment indicated by the Ly$\alpha$ emission.


title: Summable Orbits

abstract:
  We introduce a class of orbits which may have $0$ Lyapunov exponents, but
still demonstrate some sensitivity to initial conditions. We construct a
countable Markov partition with a finite-to-one almost everywhere induced
coding, and which lifts the geometric potential with summable variations (for a
$C^{1+}$ diffeomorphism of a closed manifold of dimension $\geq2$). An
important tool we use is a shadowing theory for orbits which may have $0$
Lyapunov exponents.
  We construct (weak) stable and unstable leaves for such orbits using a Graph
Transform method, and prove the absolute continuity of these foliations w.r.t
holonomies. In particular, we discuss setups where these foliations exist, and
are strictly weak -- i.e., do not demonstrate exponential contraction. One
example is a family of non-uniformly hyperbolic diffeomorhpims where we are
able to simultaneously code all invariant measures in a finite-to-one almost
everyhwere fashion.


title:
Prior-informed Uncertainty Modelling with Bayesian Polynomial
  Approximations

abstract:
  Orthogonal polynomial approximations form the foundation to a set of
well-established methods for uncertainty quantification known as polynomial
chaos. These approximations deliver models for emulating physical systems in a
variety of computational engineering applications. In this paper, we describe a
Bayesian formulation of polynomial approximations capable of incorporating
uncertainties in input data. Through different priors in a hierarchical
structure, this permits us to incorporate expert knowledge on the inference
task via different approaches. These include beliefs of sparsity in the model;
approximate knowledge of the polynomial coefficients (e.g. through low-fidelity
estimates) or output mean, and correlated models that share similar functional
and/or physical behaviours. We show that through a Bayesian framework, such
prior knowledge can be leveraged to produce orthogonal polynomial
approximations with enhanced predictive accuracy.


title: Light-field control of real and virtual charge carriers

abstract:
  Light-driven electronic excitation is a cornerstone for energy and
information transfer. In the interaction of intense and ultrafast light fields
with solids, electrons may be excited irreversibly, or transiently during
illumination only. As the transient electron population cannot be observed
after the light pulse is gone it is referred to as virtual, while the
population remaining excited is called real. Virtual charge carriers have
recently been associated with high-harmonic generation and transient
absorption, while photocurrent generation may stem from real as well as virtual
charge carriers. Yet, a link between the carrier types in their generation and
importance for observables up to technological relevance is missing. Here we
show that real and virtual carriers can be excited and disentangled in the
optical generation of currents in a gold-graphene-gold heterostructure using
few-cycle laser pulses. Depending on the waveform used for photoexcitation,
real carriers receive net momentum and propagate to the gold electrodes, while
virtual carriers generate a polarization response read out at the gold-graphene
interfaces. Based on these insights, we further demonstrate a proof of concept
of a logic gate for future lightwave electronics. Our results offer a direct
means to monitor and excite real and virtual charge carriers. Individual
control over each type will dramatically increase the integrated circuit design
space and bring closer to reality petahertz signal processing.


title:
Magnetic response of nematic superconductors: skyrmion stripes and their
  signatures in muon spin relaxation experiments

abstract:
  We investigate the magnetic response of nematic superconductors, presenting a
new approach to find vortex and skyrmion structures beyond
symmetry-constraining anzatses. Using this approach we show that nematic
superconductors form distinctive skyrmion stripes. Our approach lends itself to
accurately determining the field distribution for muon spin rotation probes. We
use this to show that the skyrmion structure manifests as a double peak in the
field distribution, markedly different from the signal of standard vortex
lattices.


title:
Representations of the Lie superalgebra of superderivations of the
  Grassmann algebra at infinity

abstract:
  The Lie superalgebra $W(\infty)$ is defined to be the direct limit of the
simple finite-dimensional Cartan type Lie superalgebras $W(n)$ as $n$ goes to
infinity, where $W(n)$ denotes the Lie superalgebra of superderivations of the
Grassmann algebra $\Lambda(n)$. The zeroth component of $W(\infty)$ in its
natural $\mathbb{Z}$-grading is isomorphic to $\mathfrak{gl}(\infty)$.
  In this paper, we study $\mathbb{Z}$-graded modules over $W(\infty)$, and
introduce a category $\mathbb{T}_W$ of $W(\infty)$-modules that is closely
related to the Koszul category $\mathbb{T}_{\mathfrak{sl}(\infty)}$ of tensor
$\mathfrak{sl}(\infty)$-modules introduced and studied by Dan-Cohen, Serganova
and Penkov. We classify the simple objects of $\mathbb{T}_W$ (up to
isomorphism). We prove that each simple module in $\mathbb{T}_W$ is isomorphic
to the unique simple quotient of a module induced from a simple module in
$\mathbb{T}_{\mathfrak{gl}(\infty)}$, and vice versa, which is analogous to the
case for $W(n)$ studied by Serganova. We realize each simple module from
$\mathbb{T}_W$ as a module of tensor fields, generalizing work of Bernstein and
Leites for $W(n)$. We prove that the category $\mathbb{T}_W$ has enough
injective objects, and for each simple module, we provide an explicit injective
module in $\mathbb{T}_W$ that contains it.


title:
The importance of being constrained: dealing with infeasible solutions
  in Differential Evolution and beyond

abstract:
  We argue that results produced by a heuristic optimisation algorithm cannot
be considered reproducible unless the algorithm fully specifies what should be
done with solutions generated outside the domain, even in the case of simple
box constraints. Currently, in the field of heuristic optimisation, such
specification is rarely mentioned or investigated due to the assumed triviality
or insignificance of this question. Here, we demonstrate that, at least in
algorithms based on Differential Evolution, this choice induces notably
different behaviours - in terms of performance, disruptiveness and population
diversity. This is shown theoretically (where possible) for standard
Differential Evolution in the absence of selection pressure and experimentally
for the standard and state-of-the-art Differential Evolution variants on
special test function $f_0$ and BBOB benchmarking suite, respectively.
Moreover, we demonstrate that the importance of this choice quickly grows with
problem's dimensionality. Different Evolution is not at all special in this
regard - there is no reason to presume that other heuristic optimisers are not
equally affected by the aforementioned algorithmic choice. Thus, we urge the
field of heuristic optimisation to formalise and adopt the idea of a new
algorithmic component in heuristic optimisers, which we call here a strategy of
dealing with infeasible solutions. This component needs to be consistently (a)
specified in algorithmic descriptions to guarantee reproducibility of results,
(b) studied to better understand its impact on algorithm's performance in a
wider sense and (c) included in the (automatic) algorithmic design. All of
these should be done even for problems with box constraints.


title: Cartoon-texture evolution for two-region image segmentation

abstract:
  Two-region image segmentation is the process of dividing an image into two
regions of interest, i.e., the foreground and the background. To this aim, Chan
et al. [Chan, Esedo\=glu, Nikolova, SIAM Journal on Applied Mathematics 66(5),
1632-1648, 2006] designed a model well suited for smooth images. One drawback
of this model is that it may produce a bad segmentation when the image contains
oscillatory components. Based on a cartoon-texture decomposition of the image
to be segmented, we propose a new model that is able to produce an accurate
segmentation of images also containing noise or oscillatory information like
texture. The novel model leads to a non-smooth constrained optimization problem
which we solve by means of the ADMM method. The convergence of the numerical
scheme is also proved. Several experiments on smooth, noisy, and textural
images show the effectiveness of the proposed model.


title: Lepton masses and mixing in a three-Higgs doublet model

abstract:
  In the three-Higgs doublet model (3HDM) frame, we search for discrete flavour
symmetries that give relations among the lepton masses and their mixing angles.
We explore discreet non-Abelian groups of order less than 1035, treating
neutrinos as Majorana or Dirac particles. Unlike the Standard Model, the 3HDM
does not require the flavor symmetry to be broken. Despite the four free
dynamic parameters available in the model, none of the groups fully predicts
the lepton data. However, some of the scanned groups provide either the correct
neutrino masses and mixing angles or the correct masses of the charged leptons.
$\Delta(96)$ is the smallest group compatible with the experimental data of
neutrino masses and PMNS mixing. $S_4$ is an approximate symmetry of Dirac
neutrino mixing, with parameters staying about $3\sigma$ apart from the
measured $\theta_{12}$, $\theta_{23}$, $\theta_{13}$ and $\delta_{CP}$.


title:
Identifying Scenarios in Field Data to Enable Validation of Highly
  Automated Driving Systems

abstract:
  Scenario-based approaches for the validation of highly automated driving
functions are based on the search for safety-critical characteristics of
driving scenarios using software-in-the-loop simulations. This search requires
information about the shape and probability of scenarios in real-world traffic.
The scope of this work is to develop a method that identifies redefined logical
driving scenarios in field data, so that this information can be derived
subsequently. More precisely, a suitable approach is developed, implemented and
validated using a traffic scenario as an example. The presented methodology is
based on qualitative modelling of scenarios, which can be detected in
abstracted field data. The abstraction is achieved by using universal elements
of an ontology represented by a domain model. Already published approaches for
such an abstraction are discussed and concretised with regard to the given
application. By examining a first set of test data, it is shown that the
developed method is a suitable approach for the identification of further
driving scenarios.


title: A Large Force Haptic Interface with Modular Linear Actuators

abstract:
  This paper presents a haptic interface with modular linear actuators which
can address limitations of conventional devices based on rotatory joints. The
proposed haptic interface is composed of parallel linear actuators that provide
high backdrivability and small inertia. The performance of the haptic interface
is compared with the conventional mechanisms in terms of force capability,
reflected inertia, and structural stiffness. High stiffness, large range of
motion with high force capability are achieved with the proposed mechanism,
which are in trade-off relationships in traditional haptic interfaces. The
device can apply up to 83 N continuously, which is three times larger than most
haptic devices. The theoretical minimum haptic force density and the stiffness
of the proposed mechanism were 1.3 to 1.9 times and 37 times of conventional
mechanisms in a similar condition, respectively. The system is also scalable
because its structural stiffness only depends on the timing belt stiffness,
while that of conventional haptic interfaces is inversely proportional to the
cube of structural lengths. The modular actuator design enables change of
degrees freedom (DOFs) for different applications. The proposed haptic
interface was tested by the interaction experiment with a virtual environment
with rigid walls.


title: Metric density results for the value distribution of Sudler products

abstract:
  We study the value distribution of the Sudler product $P_N(\alpha) :=
\prod_{n=1}^{N}\lvert2\sin(\pi n \alpha)\rvert$ for Lebesgue-almost every
irrational $\alpha$. We show that for every non-decreasing function $\psi:
(0,\infty) \to (0,\infty)$ with $\sum_{k=1}^{\infty} \frac{1}{\psi(k)} =
\infty$, the set $\{N \in \mathbb{N}: \log P_N(\alpha) \leq -\psi(\log N)\}$
has upper density $1$, which answers a question of Bence Borda. On the other
hand, we prove that $\{N \in \mathbb{N}: \log P_N(\alpha) \geq \psi(\log N)\}$
has upper density at least $\frac{1}{2}$, with remarkable equality if
$\liminf_{k \to \infty} \psi(k)/(k \log k) \geq C$ for some sufficiently large
$C > 0$.


title:
Modification of coupled integral equations method for calculation the
  accelerating structure characteristics

abstract:
  Modification of coupled integral equations method (CIEM) for calculating the
characteristics of the accelerating structures is presented in this paper. In
earlier developed CIEM schemes the coupled integral equations are derived for
the unknown electrical fields at interfaces that divide the adjacent volumes.
In addition to the standard division of the structured waveguide by interfaces
between the adjacent cells, we propose to introduce new interfaces in places
where electric field has the simplest transverse structure. Moreover, the
system of coupled integral equations is formulated for longitudinal electrical
fields in contrast to the standard approach where the transverse electrical
fields are unknowns. The final matrix equations contain expansion coefficients
of the longitudinal electric field at these additional interfaces. This
modification makes it possible to deal with a physical quantity that plays an
important role in the acceleration of particles (a longitudinal electric
field), and to obtain approximate equations for the case of a slow change in
the waveguide parameters.


title:
Robust Structure Identification and Room Segmentation of Cluttered
  Indoor Environments from Occupancy Grid Maps

abstract:
  Identifying the environment's structure, i.e., to detect core components as
rooms and walls, can facilitate several tasks fundamental for the successful
operation of indoor autonomous mobile robots, including semantic environment
understanding. These robots often rely on 2D occupancy maps for core tasks such
as localisation and motion and task planning. However, reliable identification
of structure and room segmentation from 2D occupancy maps is still an open
problem due to clutter (e.g., furniture and movable object), occlusions, and
partial coverage. We propose a method for the RObust StructurE identification
and ROom SEgmentation (ROSE^2 ) of 2D occupancy maps, which may be cluttered
and incomplete. ROSE^2 identifies the main directions of walls and is resilient
to clutter and partial observations, allowing to extract a clean, abstract
geometrical floor-plan-like description of the environment, which is used to
segment, i.e., to identify rooms in, the original occupancy grid map. ROSE^2 is
tested in several real-world publicly-available cluttered maps obtained in
different conditions. The results show how it can robustly identify the
environment structure in 2D occupancy maps suffering from clutter and partial
observations, while significantly improving room segmentation accuracy. Thanks
to the combination of clutter removal and robust room segmentation ROSE^2
consistently achieves higher performance than the state-of-the-art methods,
against which it is compared.


title: A dynamo simulation generating Saturn-like small magnetic dipole tilts

abstract:
  Among planetary dynamos, the magnetic field of Saturn stands out in its
exceptional level of axisymmetry. One of its peculiar features is that the
magnetic dipole mode is tilted with respect to the planetary rotation axis by
only $\approx 0.007^{\circ}$ or less. Numerical dynamo simulations performed in
this context have had great difficulty in producing such small dipole tilt
angles without introducing ad hoc ingredients such as a latitudinally varying
heat flux pattern in the outer layers or stably stratified layers (SSL). Here
we present a numerical dynamo simulation that generates a highly axisymmetric
dynamo with a dipole tilt of about $\approx 0.0008^{\circ}$ on average. The
model consists of a deep dynamo layer and an overlying low-conductivity layer
but without any SSL. We highlight a novel mechanism where strong differential
rotation generated in the atmospheric layer penetrates into the dynamo region,
helping to maintain a very small magnetic dipole tilt.


title:
On observability and optimal gain design for distributed linear
  filtering and prediction

abstract:
  This paper presents a new approach to distributed linear filtering and
prediction. The problem under consideration consists of a random dynamical
system observed by a multi-agent network of sensors where the network is
sparse. Inspired by the consensus+innovations type of distributed estimation
approaches, this paper proposes a novel algorithm that fuses the concepts of
consensus and innovations. The paper introduces a definition of distributed
observability, required by the proposed algorithm, which is a weaker assumption
than that of global observability and connected network assumptions combined
together. Following first principles, the optimal gain matrices are designed
such that the mean-squared error of estimation is minimized at each agent and
the distributed version of the algebraic Riccati equation is derived for
computing the gains.


title:
Melting of Polarization Vortices Crystals and Chiral Phase Transitions
  in Oxide Superlattices

abstract:
  We study the equilibrium arrangements of polarization vortices in
(PbTiO$_3$)$_n$/(SrTiO$_3$)$_n$ superlattices by means of second-principles
simulations. We find that, at low temperatures, polarization vortices organize
in a regular arrangement in which clockwise and counter-clockwise vortices
alternate positions, leading to a crystal-like structure with well defined
handedness. This chiral crystal melts at a critical temperature $T_\mathrm{M}$
into a chiral liquid, where long-range order is lost but handedness is
preserved. At even higher temperatures, $T_\mathrm{C}$, a second phase
transition occurs, at which the chiral liquid of polarization vortices loses
its handedness. Both phase transitions can be readily identified by the
adequate choices of order parameters.