title: On two formulations of polar motion and identification of its sources

abstract:
  Differences in interpretation may arise from differences in formulation of
the equations of celestial mechanics. This paper focuses on the Liouville
system of differential equations. In a "modern" presentation of the equations,
variations in polar motion (PM) and variations in length of day (lod) are
decoupled. Their source terms result from redistribution of masses and torques.
Elasticity of the Earth, large earthquakes, or external forcing by the fluid
envelopes have been successively invoked in the "modern" approach. In the
"classical" presentation, PM is governed by the inclination of Earth's rotation
pole and the derivative of its declination (close to the derivative of lod).
These are coupled by the Liouville system and all "source" terms are
astronomical. The "classical" approach also implies that there should be a link
between the rotations and the torques exerted by the planets of the solar
system. A sufficiently strong source of energy can be found. There is
remarkable agreement between the sum of forces exerted by the four Jovian
planets and components of Earth's polar motion. Since 1970, lod tends to
decrease, but a recent acceleration of rotation velocity, that contradicts
previous models, finds a simple explanation with the "classical" formalism.
Analysis of lod finds nine components, all with physical sense: first comes a
"trend", then pseudo-oscillations with periods 80 yr , 18.6 yr, 11 yr , 1 year
and 0.5 yr, 27.54 days, 13.66 days, 13.63 days and 9.13 days. The longer
periods, 1 yr, 11 yr, 18.6 yr and 80 yr are common to lod and PM. The Liouville
system of equations in the "classical" analysis implies a strong link between
the two: the straightening of the inclination of the axis of rotation should
accompany a decrease in lod.Also, if this link is valid, all the components
with extraterrestrial periods should be present in the series of sunspots and
indeed they are.


title: Characterizations of variable fractional Haj{\l}asz-Sobolev spaces

abstract:
  Let (X, \r{ho},\mu) be a space of homogeneous type, a variable exponent
satisfying the globally log-Holder continuous condition. In this article, the
author introduce the variable fractional Sobolev spaces on X via Haj{\l}asz
gradient. Using various maximal functions, several characterizations of this
space are established.


title: Machine learning of the well known things

abstract:
  Machine learning (ML) in its current form implies that an answer to any
problem can be well approximated by a function of a very peculiar form: a
specially adjusted iteration of Heavyside theta-functions. It is natural to ask
if the answers to the questions, which we already know, can be naturally
represented in this form. We provide elementary, still non-evident examples
that this is indeed possible, and suggest to look for a systematic
reformulation of existing knowledge in a ML-consistent way. Success or a
failure of these attempts can shed light on a variety of problems, both
scientific and epistemological.


title: A target list for searching for habitable exomoons

abstract:
  We investigate the habitability of hypothethical moons orbiting known
exoplanets. This study focuses on big, rocky exomoons that are capable of
maintaining a significant atmosphere. To determine their habitability, we
calculate the incident stellar radiation and the tidal heating flux arising in
the moons as the two main contributors to the energy budget. We use the runaway
greenhouse and the maximum greenhouse flux limits as a definition of
habitability. For each exoplanet we run our calculations for plausible ranges
of physical and orbital parameters for the moons and the planet using a Monte
Carlo approach. We calculate the moon habitability probability for each planet
which is the fraction of the investigated cases that lead to habitable
conditions. Based on our results, we provide a target list for observations of
known exoplanets of which the top 10 planets have more than 50\% chance for
hosting habitable moons on stable orbits. Two especially promising candidates
are Kepler-62~f and Kepler-16~b, both of them with known masses and radii. Our
target list can help to detect the first habitable exomoon.


title: Gerrymandering Individual Fairness

abstract:
  Individual fairness, proposed by Dwork et al., is a fairness measure that is
supposed to prevent the unfair treatment of individuals on the subgroup level,
and to overcome the problem that group fairness measures are susceptible to
manipulation, or gerrymandering. The goal of the present paper is to explore
the extent to which it is possible to gerrymander individual fairness itself.
It will be proved that gerrymandering individual fairness in the context of
predicting scores is possible. It will also be argued that individual fairness
provides a very weak notion of fairness for some choices of feature space and
metric. Finally, it will be discussed how the general idea of individual
fairness may be preserved by formulating a notion of fairness that allows us to
overcome some of the problems with individual fairness identified here and
elsewhere.


title: Black hole thermodynamics in asymptotically safe gravity

abstract:
  We have investigated the black hole thermodynamics and the phase transition
for renormalized group improved asymptotically safe Schwarzschild black hole.
This geometry takes into account the quantum gravitational correction in the
running gravitational constant identifying $G(r) \equiv G(k=k(r))$. We studied
various thermodynamic quantity like the Hawking temperature, specific heat and
entropy for the general parameter $\gamma$ for quantum corrected Schwarzschild
metric. We have noticed that the coefficient of the leading quantum correction,
that is, the logarithmic correction gets affected by the presence of $\gamma$.
We further study the local temperature, thermal stability of the black hole and
the free energy considering a cavity enclosing the black hole. According to the
local specific heat, there exists three black hole states, among them the large
and tiny black hole are thermally stable states. We further investigate the
on-shell free energy and find that no Hawking-Page phase transition occurs here
unlike the ordinary Schwarzschild black hole. The black hole state always
prevails for all temperatures. Also, we have found two critical points,
$T_{c1}$ and $T_{c2}$, corresponding to the phase transition from one black
hole state to another.


title: Spherical doubly warped spacetimes for radiating stars and cosmology

abstract:
  Spherically symmetric spacetimes are ambient spaces for models of stellar
collapse and inhomogeneous cosmology. We obtain results for the Weyl tensor and
the covariant form of the Ricci tensor on general doubly warped (DW)
spacetimes. In a spherically symmetric metric, the Ricci and electric tensors
become rank-2, built with a velocity vector field and its acceleration. Their
structure dictates the general form of the energy-momentum tensor in the
Einstein equations in DW spherical metrics. The anisotropic pressure and the
heat current of an imperfect fluid descend from the gradient of the
acceleration and the electric part of the Weyl tensor. For radiating stellar
collapse with heat flow, the junction conditions of the doubly warped metric
with the Vaidya metric are reviewed, with the boundary condition for the radial
pressure. The conditions for isotropy simply accomodate various models in the
literature. The anisotropy of the Ricci tensor in the special case of spherical
GRW space-times (geodesic velocity), gives Friedmann equations deviating from
standard FRW cosmology by terms due to the electric tensor. We introduce
"perfect 2-scalars" to discuss f(R) gravity with anisotropic fluid source in a
doubly warped spacetime, and show that the new geometric terms in the field
equations do not change the tensor structure of the fluid energy-momentum
tensor.


title:
Translating Clinical Delineation of Diabetic Foot Ulcers into Machine
  Interpretable Segmentation

abstract:
  Diabetic foot ulcer is a severe condition that requires close monitoring and
management. For training machine learning methods to auto-delineate the ulcer,
clinical staff must provide ground truth annotations. In this paper, we propose
a new diabetic foot ulcers dataset, namely DFUC2022, the largest segmentation
dataset where ulcer regions were manually delineated by clinicians. We assess
whether the clinical delineations are machine interpretable by deep learning
networks or if image processing refined contour should be used. By providing
benchmark results using a selection of popular deep learning algorithms, we
draw new insights into the limitations of DFU wound delineation and report on
the associated issues. This paper provides some observations on baseline models
to facilitate DFUC2022 Challenge in conjunction with MICCAI 2022. The
leaderboard will be ranked by Dice score, where the best FCN-based method is
0.5708 and DeepLabv3+ achieved the best score of 0.6277. This paper
demonstrates that image processing using refined contour as ground truth can
provide better agreement with machine predicted results. DFUC2022 will be
released on the 27th April 2022.


title: Should we trust climate change predictions?

abstract:
  Current techniques for predicting climate change are mainly based on
"massive" deterministic numerical modeling. However, the ocean-atmosphere
system is a so-called "complex system", made up of a large number of
interacting elements. We show that, in such systems, owing to the particularly
large sensitivity to initial conditions, the approach of a possible tipping
over a critical point cannot be evidenced "by construction" using numerical
modeling, due to the divergence of computation time in the vicinity of the
tipping point. On the other hand, the increasing amplitudes of observed
climatic instabilities seem to be an obvious sign of the approach of such a
tipping point, easily interpreted as a "critical softening", well known in the
theory of dynamical systems, that would bring us irreversibly into a new and
totally unexplored equilibrium state, except for a significantly higher
temperature and in a much closer time than expected from numerical modeling
extrapolations. Thus, maintaining climate warming around 1.5$^o$C or 2$^o$C by
2030 or 2050 appears fairly unrealistic unless worldwide drastic green house
gases reduction measures are immediately taken and applied.


title: Multi-Layer Modeling of Dense Vegetation from Aerial LiDAR Scans

abstract:
  The analysis of the multi-layer structure of wild forests is an important
challenge of automated large-scale forestry. While modern aerial LiDARs offer
geometric information across all vegetation layers, most datasets and methods
focus only on the segmentation and reconstruction of the top of canopy. We
release WildForest3D, which consists of 29 study plots and over 2000 individual
trees across 47 000m2 with dense 3D annotation, along with occupancy and height
maps for 3 vegetation layers: ground vegetation, understory, and overstory. We
propose a 3D deep network architecture predicting for the first time both 3D
point-wise labels and high-resolution layer occupancy rasters simultaneously.
This allows us to produce a precise estimation of the thickness of each
vegetation layer as well as the corresponding watertight meshes, therefore
meeting most forestry purposes. Both the dataset and the model are released in
open access: https://github.com/ekalinicheva/multi_layer_vegetation.


title: MLO: Multi-Object Tracking and Lidar Odometry in Dynamic Environment

abstract:
  The SLAM system built on the static scene assumption will introduce
significant estimation errors when a large number of moving objects appear in
the field of view. Tracking and maintaining semantic objects is beneficial to
understand the scene and provide rich decision information for planning and
control modules. This paper introduces MLO , a multi-object Lidar odometry
which tracks ego-motion and movable objects with only the lidar sensor. First,
it achieves information extraction of foreground movable objects, surface road,
and static background features based on geometry and object fusion perception
module. While robustly estimating ego-motion, Multi-object tracking is
accomplished through the least-squares method fused by 3D bounding boxes and
geometric point clouds. Then, a continuous 4D semantic object map on the
timeline can be created. Our approach is evaluated qualitatively and
quantitatively under different scenarios on the public KITTI dataset. The
experiment results show that the ego localization accuracy of MLO is better
than A-LOAM system in highly dynamic, unstructured, and unknown semantic
scenes. Meanwhile, the multi-object tracking method with semantic-geometry
fusion also has apparent advantages in accuracy and tracking robustness
compared with the single method.


title: A Variant of the Kaplansky Problem for Maps on Positive Matrices

abstract:
  We prove that all injective maps on positive complex matrices which preserve
order and shrink spectrum are implemented by unitary or antiunitary
conjugations. We show by counterexamples that all assumptions are
indispensable. The result easily generalizes to maps on hermitian matrices.


title:
A Simulation-Optimization Framework To Improve The Organ Transplantation
  Offering System

abstract:
  We propose a simulation-optimization-based methodology to improve the way
that organ transplant offers are made to potential recipients. Our policy can
be applied to all types of organs, is implemented starting at the local level,
is flexible with respect to simultaneous offers of an organ to multiple
patients, and takes into account the quality of the organs under consideration.
We describe in detail our simulation-optimization procedure and how it uses
underlying real-world transplant data to inform the decision-making process. We
present results using our liver and kidney models, where we show that, under
our policy recommendations, more organs are utilized and the required times to
allocate the organs are reduced -- sometimes dramatically.


title: On the smallest open Diophantine equations

abstract:
  This paper reports on the current status of the project in which we order all
polynomial Diophantine equations by an appropriate version of "size", and then
solve the equations in that order. We list the "smallest" equations that are
currently open, both unrestricted and in various families, like the smallest
open symmetric, 2-variable or 3-monomial equations. All the equations we
discuss are amazingly simple to write down but some of them seem to be very
difficult to solve.


title: A Generalization of q-Binomial Theorem

abstract:
  By using Liu's $q$-partial differential equations theory, we prove that if an
analytic function in several variables satisfies a system of $q$-partial
differential equations, if and only if it can be expanded in terms of
homogeneous $(q,c)$-Al-Salam-Carlitz polynomials. As an application, we proved
that for $c\neq0$ and $\max \{|cq|,|x|\}<1$, \begin{align*} \sum_{n=0}^{\infty}
\frac{ (a;q)_n }{(cq;q)_n}x^n=(ax/c;q)_{\infty} \sum_{n=0}^{\infty}
\frac{x^n}{(cq;q)_n}, \end{align*} which is a generalization of famous
$q$-binomial theorem or so-called Cauchy theorem.


title:
A RIS-Based Passive DOA Estimation Method for Integrated Sensing and
  Communication System

abstract:
  Integrated sensing and communication (ISAC) system has received growing
attention, especially in the context of B5G/6G development. Combining the
reconfigurable intelligent surface (RIS) with wireless communication process, a
novel passive sensing technique is formulated in this paper to estimate the
direction of arrival (DOA) of the targets, where the control matrix of the RIS
is used to to realize the multiple measurements with only one full-functional
receiving channel. Unlike the existing methods, the interference signals
introduced by wireless communication are also considered. To improve the DOA
estimation, a novel atomic norm-based method is proposed to remove the
interference signals by the sparse reconstruction. The DOA is estimated after
the interference removal by a novel Hankel-based multiple signal classification
(MUSIC) method. Then, an optimization method is also developed for the
measurement matrix to reduce the power interference signals and keep the
measurement matrix's randomness, which guarantees the performance of the sparse
reconstruction. Finally, we derive the theoretical Cram\'{e}r-Rao lower bound
(CRLB) for the proposed system on the DOA estimation. Simulation results show
that the proposed method outperforms the existing methods in the DOA estimation
and shows the corresponding CRLB with different distributions of the sensing
node. The code about the proposed method is available online
https://github.com/chenpengseu/PassiveDOA-ISAC-RIS.git.


title:
Superdiffusive quantum work and adiabatic quantum evolution in finite
  temperature chaotic Fermi systems

abstract:
  We study the full distribution of quantum work in generic, noninteracting,
disordered fermionic nanosystems at finite temperature. We derive an analytical
determinant formula for the characteristic function of work statistics for
quantum quenches starting from a thermal initial state. For work small compared
to the thermal energy of the Fermi gas, work distribution is Gaussian, and the
variance of work is proportional to the average work, while in the low
temperature or large work limit, a non-Gaussian distribution with
superdiffusive work fluctuations is observed. Similarly, the time dependence of
the probability of adiabaticity crosses over from an exponential to a stretched
exponential behavior. For large enough average work, the work distribution
becomes universal, and depends only on the temperature and the mean work. Apart
from initial low temperature transients, work statistics are well captured by a
Markovian energy-space diffusion process of hard-core particles, starting from
a thermal initial state. Our findings can be verified by measurements on
nanoscale circuits or via single qubit interferometry.


title:
Phase-field simulations of the morphology formation in evaporating
  crystalline multicomponent films

abstract:
  In numerous solution-processed thin films, a complex morphology resulting
from liquid-liquid phase separation (LLPS) or from polycrystallization arises
during the drying or subsequent processing steps. The morphology has a strong
influence on the performance of the final device but unfortunately the
process-structure relationship is often poorly and only qualitatively
understood. This is because many different physical mechanisms (miscibility,
evaporation, crystallization, diffusion, advection) are active at potentially
different time scales, and because the kinetics plays a crucial role: the
morphology develops until it is kinetically quenched far from equilibrium. In
order to unravel the various possible structure formation pathways, we propose
a unified theoretical framework that takes into account all these physical
phenomena. This phase-field simulation tool is based on the Cahn-Hilliard
equations for diffusion and the Allen-Cahn equation for crystallization and
evaporation, which are coupled to the equations for the dynamics of the fluid.
We discuss and validate the behavior of the coupled model based on simple test
cases. Furthermore, we illustrate how this framework allows to investigate the
morphology formation in a drying film undergoing evaporation-induced LLPS and
crystallization, which is typically a situation encountered, e.g., in organic
photovoltaics applications.


title: Flavour-kinematic duality for Goldstone modes

abstract:
  Three scalar effective field theories have special properties in terms of
non-linear symmetries, soft limits and on-shell constructability that arise
from their Goldstone nature: the non-linear $\sigma$-model, multi-DBI theory
and the special Galileon. We discuss how these theories are related via
flavour-kinematic duality, analogous to the colour-kinematic duality between
gravity and gauge theories. At the off-shell level, we identify a specific
mapping between the three theories that is crucially dependent on their
non-linear symmetries. Similarly, we demonstrate how the on-shell amplitudes
factorise into BCJ numerators describing flavour and a scalar version of
kinematics, naturally leading to the inclusion of graviton exchange in the
$SO(M,N)$ non-linear $\sigma$-model. Finally, we map those numerators onto each
other, and comment on a similar relation to tensor kinematics. Our results
highlight a common structure that underlies the physics of different Goldstone
modes.


title:
Field-induced ultrafast modulation of Rashba coupling at room
  temperature in ferroelectric $\alpha$-GeTe(111)

abstract:
  Rashba materials have appeared as an ideal playground for spin-to-charge
conversion in prototype spintronics devices. Among them, $\alpha$-GeTe(111) is
a non-centrosymmetric ferroelectric (FE) semiconductor for which a strong
spin-orbit interaction gives rise to giant Rashba coupling. Its room
temperature ferroelectricity was recently demonstrated as a route towards a new
type of highly energy-efficient non-volatile memory device based on switchable
polarization. Currently based on the application of an electric field, the
writing and reading processes could be outperformed by the use of femtosecond
(fs) light pulses requiring exploration of the possible control of
ferroelectricity on this timescale. Here, we probe the room temperature
transient dynamics of the electronic band structure of $\alpha$-GeTe(111) using
time and angle-resolved photoemission spectroscopy (tr-ARPES). Our experiments
reveal an ultrafast modulation of the Rashba coupling mediated on the fs
timescale by a surface photovoltage (SPV), namely an increase corresponding to
a 13 % enhancement of the lattice distortion. This opens the route for the
control of the FE polarization in $\alpha$-GeTe(111) and FE semiconducting
materials in quantum heterostructures.


title: Suppressing chemical corrosions of lithium metal anodes

abstract:
  The lithium (Li) metal anode is essential for next generation high energy
density rechargeable Li metal batteries. Although extensive studies have been
performed to prolong the cycle life of Li metal batteries, the calendar life,
which associates with chemical corrosion of Li metal in liquid electrolytes,
has not been quantitatively understood. Here, by combing the Titration Gas
Chromatography (TGC) method and Cryogenic Focused Ion Beam (Cryo-FIB), we
established a quantitative relationship between the chemical corrosion rate and
electrochemically deposited Li morphology in various liquid electrolyte
systems. We identified that the corrosion rate is dominated by the porosity of
the deposited Li. The larger the porosity of deposited Li has, the faster the
corrosion rate will be. We further proposed strategies to mitigate the chemical
corrosion on Li thus to extend the calendar life of Li metal batteries. By
strictly controlling the stacking pressure during Li plating, Li deposits with
ultra-low porosity can be achieved, suppressing the corrosion rate to 0.08% per
day compared with 1.71% per day of the high-porosity Li.


title:
Crossover Between Quantum and Classical Waves and High Frequency
  Localization Landscapes

abstract:
  Anderson localization is a universal interference phenomenon occurring when a
wave evolves through a random medium and it has been observed in a great
variety of physical systems, either quantum or classical. The recently
developed localization landscape theory offers a computationally affordable way
to obtain useful information on the localized modes, such as their location or
size. Here we examine this theory in the context of classical waves exhibiting
high frequency localization and for which the original localization landscape
approach is no longer informative. Using a Webster's transformation, we convert
a classical wave equation into a Schr\"{o}dinger equation with the same
localization properties. We then compute an adapted localization landscape to
retrieve information on the original classical system. This work offers an
affordable way to access key information on high-frequency mode localization.


title: The polar decomposition for the product of three operators

abstract:
  In the setting of adjointable operators on Hilbert $C^*$-modules, this paper
deals with the polar decomposition of the product of three operators. The
relationship between the polar decompositions associated with three operators
is clarified. Based on this relationship, a formula for the polar decomposition
of a multiplicative perturbation of an operator is provided. In addition, some
characterizations of the polar decomposition associated with three operators
are provided.


title: Integrating Prior Knowledge in Post-hoc Explanations

abstract:
  In the field of eXplainable Artificial Intelligence (XAI), post-hoc
interpretability methods aim at explaining to a user the predictions of a
trained decision model. Integrating prior knowledge into such interpretability
methods aims at improving the explanation understandability and allowing for
personalised explanations adapted to each user. In this paper, we propose to
define a cost function that explicitly integrates prior knowledge into the
interpretability objectives: we present a general framework for the
optimization problem of post-hoc interpretability methods, and show that user
knowledge can thus be integrated to any method by adding a compatibility term
in the cost function. We instantiate the proposed formalization in the case of
counterfactual explanations and propose a new interpretability method called
Knowledge Integration in Counterfactual Explanation (KICE) to optimize it. The
paper performs an experimental study on several benchmark data sets to
characterize the counterfactual instances generated by KICE, as compared to
reference methods.


title: Universal compilation for quantum state preparation and tomography

abstract:
  Universal compilation is a training process that compiles a trainable unitary
into a target unitary and it serves vast potential applications from quantum
dynamic simulations to optimal circuits with deep-compressing, device
benchmarking, quantum error mitigation, and so on. Here, we propose a universal
compilation-based variational algorithm for the preparation and tomography of
quantum states in low-depth quantum circuits. We apply the Fubini-Study
distance to be a trainable cost function under various gradient-based
optimizers, including the quantum natural gradient approach. We evaluate the
performance of various unitary topologies and the trainability of different
optimizers for getting high efficiency. In practice, we address different
circuit ansatzes in quantum state preparation, including the linear and
graph-based ansatzes for preparing different entanglement target states such as
representative GHZ and W states. We also discuss the effect of the circuit
depth, barren plateau, readout noise in the model, and the error mitigation
solution. We next evaluate the reconstructing efficiency in quantum state
tomography via various popular circuit ansatzes and reveal the crucial role of
the circuit depth in the robust fidelity. The results are comparable with the
shadow tomography method, a similar fashion in the field. Our work expresses
the adequate capacity of the universal compilation-based variational algorithm
to maximize the efficiency in the quantum state preparation and tomography.
Further, it promises applications in quantum metrology and sensing and is
applicable in the near-term quantum computers for verification of the circuits
fidelity and various quantum computing tasks.


title: Non-Commutative Stochastic Processes and Bi-Free Probability

abstract:
  In this paper, a connection between bi-free probability and the theory of
non-commutative stochastic processes is examined. Specifically it is
demonstrated that the transition operators for non-commutative stochastic
processes can be modelled using technology from bi-free probability. Several
important examples are recovered with this approach and new formula are
obtained for processes with free increments. The benefits of this approach are
also discussed.


title: A simple analytical model of magnetic jets

abstract:
  We propose a simple analytical jet model of magnetic jets, utilizing
conservation laws and some results of published GRMHD jet simulations, and
consider radially-averaged profiles of the physical quantities. We take into
account conversion of the magnetic energy flux to bulk acceleration in jets
formed around rotating black holes assuming the mass continuity equation and
constant jet power, leading to the Bernoulli equation. For assumed profiles of
the bulk Lorentz factor and the radius, this gives the profile of toroidal
magnetic field component along the jet. We then consider the case where the
poloidal field component is connected to a rotating black hole surrounded by an
accretion disc. The formalism recovers the standard formula for the power
extracted from a rotating black hole. We find that the poloidal field strength
dominates over the toroidal one in the comoving frame up to large distances,
which means that jets should be more stable to current-driven kink modes. The
resulting magnetic field profiles can then be used to calculate the jet
synchrotron emission.


title:
Adversarial Training-Aided Time-Varying Channel Prediction for TDD/FDD
  Systems

abstract:
  In this paper, a time-varying channel prediction method based on conditional
generative adversarial network (CPcGAN) is proposed for time division
duplexing/frequency division duplexing (TDD/FDD) systems. CPcGAN utilizes a
discriminator to calculate the divergence between the predicted downlink
channel state information (CSI) and the real sample distributions under a
conditional constraint that is previous uplink CSI. The generator of CPcGAN
learns the function relationship between the conditional constraint and the
predicted downlink CSI and reduces the divergence between predicted CSI and
real CSI. The capability of CPcGAN fitting data distribution can capture the
time-varying and multipath characteristics of the channel well. Considering the
propagation characteristics of real channel, we further develop a channel
prediction error indicator to determine whether the generator reaches the best
state. Simulations show that the CPcGAN can obtain higher prediction accuracy
and lower system bit error rate than the existing methods under the same user
speeds.


title:
Investigating Black-Box Function Recognition Using Hardware Performance
  Counters

abstract:
  This paper presents new methods and results for learning information about
black-box program functions using hardware performance counters (HPC), where an
investigator can only invoke and measure function calls. Important use cases
include analysing compiled libraries, e.g. static and dynamic link libraries,
and trusted execution environment (TEE) applications. We develop a generic
machine learning-based approach to classify a comprehensive set of hardware
events, e.g. branch mis-predictions and instruction retirements, to recognise
standard benchmarking and cryptographic library functions. This includes
various signing, verification and hash functions, and ciphers in numerous modes
of operation. Three major architectures are evaluated using off-the-shelf
Intel/X86-64, ARM, and RISC-V CPUs. Following this, we develop and evaluate two
use cases. Firstly, we show that several known CVE-numbered OpenSSL
vulnerabilities can be detected using HPC differences between patched and
unpatched library versions. Secondly, we demonstrate that standardised
cryptographic functions executing in ARM TrustZone TEE applications can be
recognised using non-secure world HPC measurements. High accuracy was achieved
in all cases (86.22-99.83%) depending on the application, architectural, and
compilation assumptions. Lastly, we discuss mitigations, outstanding
challenges, and directions for future research.


title:
Hybrid ISTA: Unfolding ISTA With Convergence Guarantees Using Free-Form
  Deep Neural Networks

abstract:
  It is promising to solve linear inverse problems by unfolding iterative
algorithms (e.g., iterative shrinkage thresholding algorithm (ISTA)) as deep
neural networks (DNNs) with learnable parameters. However, existing ISTA-based
unfolded algorithms restrict the network architectures for iterative updates
with the partial weight coupling structure to guarantee convergence. In this
paper, we propose hybrid ISTA to unfold ISTA with both pre-computed and learned
parameters by incorporating free-form DNNs (i.e., DNNs with arbitrary feasible
and reasonable network architectures), while ensuring theoretical convergence.
We first develop HCISTA to improve the efficiency and flexibility of classical
ISTA (with pre-computed parameters) without compromising the convergence rate
in theory. Furthermore, the DNN-based hybrid algorithm is generalized to
popular variants of learned ISTA, dubbed HLISTA, to enable a free architecture
of learned parameters with a guarantee of linear convergence. To our best
knowledge, this paper is the first to provide a convergence-provable framework
that enables free-form DNNs in ISTA-based unfolded algorithms. This framework
is general to endow arbitrary DNNs for solving linear inverse problems with
convergence guarantees. Extensive experiments demonstrate that hybrid ISTA can
reduce the reconstruction error with an improved convergence rate in the tasks
of sparse recovery and compressive sensing.


title: Cryptography Is Not Enough: Relay Attacks on Authenticated GNSS Signals

abstract:
  Civilian-GNSS is vulnerable to signal spoofing attacks, and countermeasures
based on cryptographic authentication are being proposed to protect against
these attacks. Both Galileo and GPS are currently testing broadcast
authentication techniques based on the delayed key disclosure to validate the
integrity of navigation messages. These authentication mechanisms have proven
secure against record now and replay later attacks, as navigation messages
become invalid after keys are released. This work analyzes the security
guarantees of cryptographically protected GNSS signals and shows the
possibility of spoofing a receiver to an arbitrary location without breaking
any cryptographic operation. In contrast to prior work, we demonstrate the
ability of an attacker to receive signals close to the victim receiver and
generate spoofing signals for a different target location without modifying the
navigation message contents. Our strategy exploits the essential common
reception and transmission time method used to estimate pseudorange in GNSS
receivers, thereby rendering any cryptographic authentication useless. We
evaluate our attack on a commercial receiver (ublox M9N) and a software-defined
GNSS receiver (GNSS-SDR) using a combination of open-source tools, commercial
GNSS signal generators, and software-defined radio hardware platforms. Our
results show that it is possible to spoof a victim receiver to locations around
4000 km away from the true location without requiring any high-speed
communication networks or modifying the message contents. Through this work, we
further highlight the fundamental limitations in securing a broadcast
signaling-based localization system even if all communications are
cryptographically protected.


title:
Do Users Benefit From Interpretable Vision? A User Study, Baseline, And
  Dataset

abstract:
  A variety of methods exist to explain image classification models. However,
whether they provide any benefit to users over simply comparing various inputs
and the model's respective predictions remains unclear. We conducted a user
study (N=240) to test how such a baseline explanation technique performs
against concept-based and counterfactual explanations. To this end, we
contribute a synthetic dataset generator capable of biasing individual
attributes and quantifying their relevance to the model. In a study, we assess
if participants can identify the relevant set of attributes compared to the
ground-truth. Our results show that the baseline outperformed concept-based
explanations. Counterfactual explanations from an invertible neural network
performed similarly as the baseline. Still, they allowed users to identify some
attributes more accurately. Our results highlight the importance of measuring
how well users can reason about biases of a model, rather than solely relying
on technical evaluations or proxy tasks. We open-source our study and dataset
so it can serve as a blue-print for future studies. For code see,
https://github.com/berleon/do_users_benefit_from_interpretable_vision


title: Low Complexity Suboptimal ML Detection for OFDM-IM Systems

abstract:
  Orthogonal frequency division multiplexing with index modulation (OFDM-IM) is
a novel multicarrier scheme, which uses the k out of n subcarriers as active
subcarriers to transmit data. For detecting the subcarrier activation pattern
(SAP) at the receiver, maximum likelihood (ML) detection cannot be used because
of its high computational complexity. Instead, the detector selecting the most
likely active k subcarriers is used, which is called a k largest values (klv)
detector. However, this method degrades the detection performance especially if
the ratio of illegal SAPs to SAPs is high. In this letter, the suboptimal ML
detector is proposed, which is a slight modification of the klv detector.
However, the proposed detector has a similar detection performance compared to
the ML detection, which is suitable for flexible implementation of OFDM-IM
systems.


title: Algorithms and Theory for Supervised Gradual Domain Adaptation

abstract:
  The phenomenon of data distribution evolving over time has been observed in a
range of applications, calling the needs of adaptive learning algorithms. We
thus study the problem of supervised gradual domain adaptation, where labeled
data from shifting distributions are available to the learner along the
trajectory, and we aim to learn a classifier on a target data distribution of
interest. Under this setting, we provide the first generalization upper bound
on the learning error under mild assumptions. Our results are algorithm
agnostic, general for a range of loss functions, and only depend linearly on
the averaged learning error across the trajectory. This shows significant
improvement compared to the previous upper bound for unsupervised gradual
domain adaptation, where the learning error on the target domain depends
exponentially on the initial error on the source domain. Compared with the
offline setting of learning from multiple domains, our results also suggest the
potential benefits of the temporal structure among different domains in
adapting to the target one. Empirically, our theoretical results imply that
learning proper representations across the domains will effectively mitigate
the learning errors. Motivated by these theoretical insights, we propose a
min-max learning objective to learn the representation and classifier
simultaneously. Experimental results on both semi-synthetic and large-scale
real datasets corroborate our findings and demonstrate the effectiveness of our
objectives.


title: On the bundle of null cones

abstract:
  We examine the bundle structure of the field of nowhere vanishing null vector
fields on a (time-oriented) Lorentzian manifold. Sections of what we refer to
as the null tangent, are by definition nowhere vanishing null vector fields. It
is shown that the set of nowhere vanishing null vector fields comes equipped
with a para-associative ternary partial product. Moreover, the null tangent
bundle is an example of a non-polynomial graded bundle.


title:
Numerical study of the transverse stability of line solitons of the
  Zakharov-Kuznetsov equations

abstract:
  We present a detailed numerical study of the transverse stability of line
solitons of two-dimensional, generalized Zakharov-Kusnetsov equations with
various power nonlinearities. In the $L^{2}$-subcritical case, in accordance
with a theorem due to Yamazaki we find a critical velocity, below which the
line soliton is stable. For higher velocities, the numerical results indicate
an instability against the formation of lumps, solitons localized in both
spatial directions. In the $L^2$-critical and supercritical cases, the line
solitons are shown to be numerically stable for small velocities, and strongly
unstable for large velocities, with a blow-up observed in finite time.


title: Symmetrization inequalities on one-dimensional integer lattice

abstract:
  In this paper, we develop a theory of symmetrization on the one dimensional
integer lattice. More precisely, we associate a radially decreasing function
$u^*$ with a function $u$ defined on the integers and prove the corresponding
Polya-Szeg\"{o} inequality. Along the way we also prove the weighted
Polya-Szeg\"{o} inequality for the decreasing rearrangement on the half-line,
i.e., non-negative integers. As a consequence, we prove the discrete weighted
Hardy's inequality with the weight $n^\alpha$ for $1 < \alpha \leq 2$.


title: The Multiple Images of the Plasma Lensing FRB

abstract:
  We investigate the formation of multiple images as the radio signals from
fast radio bursts (FRBs) pass through the plane of a plasma clump. The
exponential model for the plasma clump is adopted to analyze the properties of
the multiple images. By comparing with the classical dispersion relations, we
find that one image has exhibited specific inverse properties to others, such
as their delay times at high frequency is higher than that at low frequency,
owing to the lensing effects of the plasma clump. We demonstrate that these
inverse effects should be observable in some repeating FRBs. Our results
predict deviation in the estimated DM across multiple images, consistent with
the observations of FRB 121102 and FRB 180916.J0158+65. If other plasma lenses
have effects similar to an exponential lens, we find that they should also give
rise to the similar dispersion relation in the multiple images. For some
repeating FRBs, analysis of the differences in time delay and in DM between
multiple images at different frequencies can serve as a method to reveal the
plasma distribution.


title:
Pre-launch characterization of the spectrometer of Hard X-ray Imager
  (HXI) onboard the ASO-S mission

abstract:
  As one of the three payloads of the Advanced Space-based Solar Observatory
(ASO-S), the pre-launch characterization of HXI includes the characterization
of the collimator and the spectrometer. This article focuses on the pre-launch
characterization of HXI's spectrometer, including detection efficiency, energy
resolution, and energy to ADC channel (E-C) relationship. The detection
efficiency characterization necessitates a sufficient number of calibrated
energy points in the HXI observation energy range, with a refined measurement
around the absorption edge of the lanthanum bromide crystal, and is calibrated
with an X-ray beam. The characterization results are consistent with the
simulations and show that the detection efficiency difference between detector
modules is controlled within $\pm3\%$. Radioactive sources $^{133}\textrm{Ba}$
and $^{137}\textrm{Cs}$ are used to calibrate the energy resolution and E-C
relationship. The characterization results show that all detector modules'
energy resolutions are better than $26\%$ at 32 keV, meeting the design
specification. And their E-C relationships vary regularly with voltage. All of
the characterization results indicate that the flight model of the HXI
spectrometer meets the performance requirements and is capable of performing
on-orbit observation activities.


title:
Dijet photoproduction at low $x$ at next-to-leading order and its
  back-to-back limit

abstract:
  We compute the cross section for the inclusive photoproduction of a pair of
jets at next-to-leading order accuracy in the Color Glass Condensate (CGC)
effective theory. The aim is to study the back-to-back limit, to investigate
whether transverse momentum dependent (TMD) factorization can be recovered at
this order. In particular, we focus on the large Sudakov double logarithms,
which are major ingredients of the TMD evolution. Interestingly, the
kinematical improvement of the low-$x$ resummation scheme turns out to be a key
ingredient in the analysis.


title: Is there evidence of a trend in the CO2 airborne fraction?

abstract:
  In a paper recently published in this journal, van Marle et al. (van Marle et
al., 2022) introduce an interesting new data set for land use and land cover
change CO2 emissions (LULCC) that they use to study whether a trend is present
in the airborne fraction (AF), defined as the fraction of CO2 emissions
remaining in the atmosphere. Testing the hypothesis of a trend in the AF has
attracted much attention, with the overall consensus that no statistical
evidence is found for a trend in the data (Knorr, 2009; Gloor et al., 2010;
Raupach et al., 2014; Bennedsen et al., 2019). In their paper, van Marle et al.
analyze the AF as implied by three different LULCC emissions time series (GCP,
H&N, and their new data series). In a Monte Carlo simulation study based on
their new LULCC emissions data, van Marle et al. find evidence of a declining
trend in the AF. In this note, we argue that the statistical analysis presented
in van Marle et al. can be improved in several respects. Specifically, the
Monte Carlo study presented in van Marle et al. is not conducive to determine
whether there is a trend in the AF. Further, we re-examine the evidence for a
trend in the AF by using a variety of different statistical tests. The
statistical evidence for an uninterrupted (positive or negative) trend in the
airborne fraction remains mixed at best. When allowing for a break in the
trend, there is some evidence for upward trends in both subsamples.


title: Cohomogeneity one manifolds with quasipositive curvature

abstract:
  In this paper we give a classification of cohomogeneity one manifolds
admitting an invariant metric with quasipositive sectional curvature up to two
$7$-dimensional families. The main result carries over almost verbatim from the
classification results in positive curvature carried out by Verdiani and Grove,
Wilking and Ziller. Three main tools used in the positively curved case that we
generalized to quasipositively curved cohomogeneity one manifolds are Wilking's
Chain Theorem, the classification of positively curved fixed point homogeneous
manifolds by Grove and Searle and the Rank Lemma.


title:
Interactivity in Constructive Cryptography : Modeling and Applications
  to Updatable Encryption and Private Information Retrieval

abstract:
  In this work, we extend the Constructive Cryptography (CC) framework
introduced by Maurer in 2011 so as to handle interactive protocols.
  We design and construct a so-called {\em Interactive Server Memory Resource}
(ISMR), that is an augmented version of the basic instantiation of a
client-server protocol in CC, namely the Server Memory Resource. We then apply
our ISMR construction to two types of interactive cryptographic protocols for
remote storage : Updatable Encryption (UE) and Private Information Retrieval
(PIR).
  Concerning UE, our results are a composable version of those protocols,
clarifying the security guarantees achieved by {\em any} UE scheme. Namely, we
give the relevant security notion to consider according to a given leakage
context. Letting USMR denote our ISMR adapted to the UE application, we prove
that $\mathsf{IND}\text{-}\mathsf{UE}\text{-}\mathsf{CPA}$ security is
sufficient for a secure construction of a confidential USMR that hides the age
of ciphertexts; and
$\mathsf{IND}\text{-}(\mathsf{ENC}+\mathsf{UPD})\text{-}\mathsf{CPA}$ security
is sufficient for a secure construction of a confidential USMR in case of
unrestricted leakage. As a consequence, contrary to what was claimed before,
the $\mathsf{IND}\text{-}\mathsf{UE}$ security notion is not always stronger
than the $\mathsf{IND}\text{-}(\mathsf{ENC+UPD})$ one.
  Concerning PIR, we also give a composable version of PIR protocols, yielding
a unique model that unifies different notions of PIR : IT-PIR, C-PIR, one- or
multi- server PIR. Using the flexibility of CC, we are also able to model PIR
variants, such as SPIR.


title:
Enhancing Marine Data Transmission with Socially-Aware Resilient Vessel
  Networks

abstract:
  With the multi-dimensional exploration towards oceans, enormous sensing data
has been generated with significant volume, velocity, variety and
heterogeneity. The resulted Big Marine Data (BMD) thus issue unprecedented
architectural challenges on existing marine communication systems. Current
dominant marine communication technologies, e.g., shore-based cellular
stations, high frequency radio, and expensive satellites, extremely suffer from
short coverage, low bandwidth, insecurity, and unavailable cross-domain
transmission. In this paper, Resilient Vessel Network (RVN) is proposed to
fundamentally enhance BMD transmission. RVNs with widespread self-organized
vessels and opportunistic connections reveal advantages of ubiquity,
resilience, low cost and cross-domain transmission. To efficiently manage
opportunistic vessel-to-vessel (V2V) connections for optimal routing, Social
Network Analysis (SNA) on historical vessel interactions is applied for vessel
familiarity measurement and community detection. The performance of the
proposed community-based routing (CBR) is comprehensively evaluated with real
datasets of fishing vessel trajectories. It is demonstrated that CBR achieves
much lower transmission cost with comparable delivery ratio compared to typical
routing algorithms.


title:
Quantum polar stabilizer codes based on polarization of pure quantum
  channel are bad stabilizer codes for quantum computing

abstract:
  Inspired by classical polar code, whose coding rate can asymptotically
achieve the Shannon capacity, researchers are trying to find its analogue in
quantum information field, which is called quantum polar code. Some researchers
believe that directly quantizing classical polar coding circuits will produce
polarization phenomenon of pure quantum channel, based on which we can design a
quantum polar coding scheme that applies to quantum computing. In this paper,
we try to follow this intuition and propose a class of quantum polar stabilizer
codes for quantum computing. Unfortunately, simulation results shows that this
kind of quantum polar stabilizer codes directly based on quantizing classical
polar coding circuits are bad stabilizer codes for quantum computing.


title:
Repeatedly readable state, spontaneous collapse, and quantum/classical
  boundary

abstract:
  We propose a model to identify the quantum/classical boundary. The model
introduces a spontaneous collapse of state superposition: $\frac{d}{dt}
\rho_{ij} =-\frac{i}{\hbar}[H,\rho]_{ij}-\rho_{ij}/\tau_{ij}$. Different from
other collapse models, the collapsing scale $\tau_{ij}$ here does not contain a
universal parameter, but is specified by the two states $| i\rangle $ and $ |
j\rangle$: If each state is {\em in principle} repeatedly readable (typically
by a QND measurement), then $\tau_{ij}$ is the {\em potentially} needed
measuring time to discriminate the two states, and the collapse occurs
spontaneously {\em without} any actual monitoring. Otherwise,
$\tau_{ij}=\infty$, which means no collapse and everlasting superposition. This
happens if one state is not repeatedly readable, or if the two states cannot
possibly be discriminated in a particular circumstance (for example in the Rabi
oscillation). Detailed analysis shows that for a "trapped Schr{\"o}dinger's
cat", the superposition of $|{\rm here} \rangle$ and $| {\rm there} \rangle $
is forbidden if $E D \gg 4\pi \hbar c$, and allowed if $E D \le 4\pi \hbar c$,
where $D$ is the trap separation and $ E$ is the energy gap, which can be
estimated with $ M v^2$. The model also constrains a "free Schr{\"o}dinger's
cat" to display double-slit interference if $p\theta D\ge 8\hbar$, where $p=
Mv$, $\theta $ is the angle spanned by the two trajectories, and $D$ is the
slit separation. In contrast, this model sets no limit on the coherent length
of massless photon, thus the arm of a Michelson interferometer can be
arbitrarily long. The spontaneous collapse which we propose can occur for an
isolated system, and parallels the decoherence induced by interaction with
environment.


title: Sustainability in Software Architecture: A Systematic Mapping Study

abstract:
  Sustainability is an increasingly-studied topic in software engineering in
general, and in software architecture in particular. There are already a number
of secondary studies addressing sustainability in software engineering, but no
such study focusing explicitly on software architecture. This work aims to fill
this gap by conducting a systematic mapping study on the intersection between
sustainability and software architecture research with the intention of (i)
reflecting on the current state of the art, and (ii) identifying the needs for
further research. Our results show that, overall, existing works have focused
disproportionately on specific aspects of sustainability, and in particular on
the most technical and "inward facing" ones. This comes at the expense of the
holistic perspective required to address a multi-faceted concern such as
sustainability. Furthermore, more reflection-oriented research works, and
better coverage of the activities in the architecting life cycle are required
to further the maturity of the area. Based on our findings we then propose a
research agenda for sustainability-aware software architecture.


title:
A Mountaineering Strategy to Excited States: Revising Reference Values
  with EOM-CC4

abstract:
  In the framework of the computational determination of highly-accurate
vertical excitation energies in small organic compounds, we explore the
possibilities offered by the equation-of-motion formalism relying on the
approximate fourth-order coupled-cluster (CC) method, CC4. We demonstrate,
using an extended set of more than 200 reference values based on CC including
up to quadruples excitations (CCSDTQ), that CC4 is an excellent approximation
to CCSDTQ for excited states with a dominant contribution from single
excitations with an average deviation as small as $0.003$ eV. We next assess
the accuracy of several additive basis set correction schemes, in which
vertical excitation energies obtained with a compact basis set and a high-order
CC method are corrected with lower-order CC calculations performed in larger
basis sets. Such strategies are found to be overall very beneficial, though
their accuracy depend significantly on the actual scheme. Finally, CC4 is
employed to improve several theoretical best estimates of the QUEST database
for molecules containing between four and six (non-hydrogen) atoms, for which
previous estimates were computed at the CCSDT level.

categories: physics.chem-ph cond-mat.mtrl-sci cond-mat.str-el physics.comp-ph


title: On the center conjecture for the cyclotomic KLR algebras

abstract:
  The center conjecture for the cyclotomic KLR algebras $R_\beta^\Lambda$
asserts that the center of $R_\beta^\Lambda$ consists of symmetric elements in
its KLR $x$ and $e(\nu)$ generators. In this paper we show that this conjecture
is equivalent to the injectivity of some natural map
$\bar{\iota}_\beta^{\Lambda,i}$ from the cocenter of $R_\beta^\Lambda$ to the
cocenter of $R_\beta^{\Lambda+\Lambda_i}$ for all $i\in I$ and $\Lambda\in
P^+$. We prove that the map $\bar{\iota}_\beta^{\Lambda,i}$ is given by
multiplication with a center element $z(i,\beta)\in
R_\beta^{\Lambda+\Lambda_i}$ and we explicitly calculate the element
$z(i,\beta)$ in terms of the KLR $x$ and $e(\nu)$ generators. We present an
explicit monomial basis for certain bi-weight spaces of the defining ideal of
$R_\beta^\Lambda$ and of $R_\beta^\Lambda$. For
$\beta=\sum_{j=1}^n\alpha_{i_j}$ with $\alpha_{i_1},\cdots, \alpha_{i_n}$
pairwise distinct, we construct an explicit monomial basis of
$R_\beta^\Lambda$, prove the map $\bar{\iota}_\beta^{\Lambda,i}$ is injective
and thus verify the center conjecture for these $R_\beta^\Lambda$.


title: A Survey on Word Meta-Embedding Learning

abstract:
  Meta-embedding (ME) learning is an emerging approach that attempts to learn
more accurate word embeddings given existing (source) word embeddings as the
sole input.
  Due to their ability to incorporate semantics from multiple source embeddings
in a compact manner with superior performance, ME learning has gained
popularity among practitioners in NLP.
  To the best of our knowledge, there exist no prior systematic survey on ME
learning and this paper attempts to fill this need.
  We classify ME learning methods according to multiple factors such as whether
they (a) operate on static or contextualised embeddings, (b) trained in an
unsupervised manner or (c) fine-tuned for a particular task/domain.
  Moreover, we discuss the limitations of existing ME learning methods and
highlight potential future research directions.


title:
Two-particle correlations on transverse rapidity in Au+Au collisions at
  $\sqrt{s_{\rm NN}}=200$ GeV at STAR

abstract:
  Two-particle correlation measurements projected onto two-dimensional,
transverse rapidity coordinates ($y_{T1},y_{T2}$), allow access to dynamical
properties of the QCD medium produced in relativistic heavy-ion collisions that
angular correlation measurements are not sensitive to. We report non-identified
charged-particle correlations for Au + Au minimum-bias collisions at
$\sqrt{s_{\rm NN}}$ = 200 GeV taken by the STAR experiment at the Relativistic
Heavy-Ion Collider (RHIC). Correlations are presented as 2D functions of
transverse rapidity for like-sign, unlike-sign and all charged-particle pairs,
as well as for particle pairs whose relative azimuthal angles lie on the
near-side, the away-side, or at all relative azimuth. The correlations are
constructed using charged particles with transverse momentum $p_T \geq 0.15$
GeV/$c$, pseudorapidity from $-$1 to 1, and azimuthal angles from $-\pi$ to
$\pi$. The significant correlation structures that are observed evolve smoothly
with collision centrality. The major correlation features include a saddle
shape plus a broad peak with maximum near $y_T \approx 3$, corresponding to
$p_T \approx$ 1.5 GeV/$c$. The broad peak is observed in both like- and
unlike-sign charge combinations and in near- and away-side relative azimuthal
angles. The all-charge, all-azimuth correlation measurements are compared with
the theoretical predictions of {\sc hijing} and {\sc epos}. The results
indicate that the correlations for peripheral to mid-central collisions can be
approximately described as a superposition of nucleon + nucleon collisions with
minimal effects from the QCD medium. Strong medium effects are indicated in
mid- to most-central collisions.


title: Evidence for the Domain Wall Nature of Sketched LaAlO3/SrTiO3 Nanowires

abstract:
  The rich electron correlations and highly coherent transport in
reconfigurable devices sketched by a conductive atomic force microscope tip at
the LaAlO3/SrTiO3 interface have enabled the oxide platform an ideal playground
for studying correlated electrons and quantum technological applications. Why
these one-dimensional devices possess enhanced properties over the
two-dimensional interface, however, has remained elusive. Here we provide
evidence that one-dimensional LaAlO3/SrTiO3 nanowires are intrinsically
ferroelastic domain walls by nature through thermodynamic study. We have
observed spreading resistance anomalies under thermo-stimulus and temperature
cycles, with characteristic temperatures matching domain wall polarity. This
information is crucial in understanding the novel phenomena including
superconductivity and high mobility quantum transport.


title: The Moving Mirror model for Fast Radio Bursts

abstract:
  Recent observations of coherent radiation from the Crab pulsar (Bij et al
2021) suggest the emission is driven by an ultra - relativistic ($\gamma \sim
10^4$), cold plasma flow. A relativistically expanding plasma shell can
compress the ambient magnetic field, like a moving mirror, and thus produce
coherent radiation whose wavelength is shorter than that of the ambient medium
by $\gamma^2$. This mechanism has been studied in the past by Colgate and
Noerdelinger (1971), in the context of radio loud supernova explosions. In this
work we propose that a similar mechanism drives the coherent emission in fast
radio bursts. The high Lorenz factors dramatically lower the implied energy and
magnetic field requirements, allowing the spin down energy of regular (or even
recycled), fast spinning pulsars, rather than slow spinning magnetars, to
explain FRBs. We show that this model can explain the frequency and the time
evolution of observed FRBs, as well as their duration, energetics and absence
of panchromatic counterparts. We also predict that the peak frequency of sub
pulses decline with observation time as $\omega_{\rm obs} \propto t_{\rm
obs}^{-1/2}$. Unfortunately, with current capabilities it is not possible to
constrain the shape of the curve $\omega_{\rm obs} \left(t_{\rm obs} \right)$.
Finally, we find that a variation of this model can explain weaker radio
transients, such as the one observed from a galactic magnetar. In this variant,
the shock wave produces low frequency photons which are then Compton scattered
to the GHz range.


title:
Harmonic generation in bent graphene with artificially-enhanced
  spin-orbit coupling

abstract:
  We theoretically investigate the nonlinear response of bent graphene, in the
presence of artificially-enhanced spin-orbit coupling, which can occur either
via adatom deposition, or by placing the sheet of bent graphene in contact with
a spin-orbit active substrate. We discuss the interplay between the spin-orbit
coupling and the artificial magnetic field generated by the bending, for both
the cases of Rashba and intrinsic spin-orbit coupling. For the latter, we
introduce a spin-field interaction Hamiltonian addressing directly the electron
spin as a degree of freedom. Our findings reveal that in this case, by
controlling the amount of spin-orbit coupling, it is possible to significantly
tune the spectrum of the nonlinear signal, achieving, in principle, efficient
conversion of light from THz to UV region.


title: Loss-based Sequential Learning for Active Domain Adaptation

abstract:
  Active domain adaptation (ADA) studies have mainly addressed query selection
while following existing domain adaptation strategies. However, we argue that
it is critical to consider not only query selection criteria but also domain
adaptation strategies designed for ADA scenarios. This paper introduces
sequential learning considering both domain type (source/target) or labelness
(labeled/unlabeled). We first train our model only on labeled target samples
obtained by loss-based query selection. When loss-based query selection is
applied under domain shift, unuseful high-loss samples gradually increase, and
the labeled-sample diversity becomes low. To solve these, we fully utilize
pseudo labels of the unlabeled target domain by leveraging loss prediction. We
further encourage pseudo labels to have low self-entropy and diverse class
distributions. Our model significantly outperforms previous methods as well as
baseline models in various benchmark datasets.


title: $WSe_2$ as transparent top gate for near-field experiments

abstract:
  Independent control of carrier density and out-of-plane displacement field is
essential for accessing novel phenomena in two-dimensional material
heterostructures. While this is achieved with independent top and bottom
metallic gate electrodes in transport experiments, it remains a challenge for
near-field optical studies as the top electrode interferes with the optical
path. Here, we systematically characterize the requirements for a material to
be used as top-gate electrode, and demonstrate experimentally that few-layer
WSe_2 can be used as a transparent, ambipolar top gate electrode in infrared
near-field microscopy. We perform nano-imaging of plasmons in a bilayer
graphene heterostructure and tune the plasmon wavelength using a trilayer WSe_2
gate, achieving a density modulation amplitude exceeding 2 10^{12} cm^{-2}.
Moreover, the observed ambipolar gate-voltage response allows to extract the
energy gap of WSe_2 yielding a value of 1.05 eV. Our results will provide an
additional tuning knob to cryogenic near-field experiments on emerging
phenomena in two-dimensional materials and moir\'e material heterostructures.


title:
Multi-Head Distillation for Continual Unsupervised Domain Adaptation in
  Semantic Segmentation

abstract:
  Unsupervised Domain Adaptation (UDA) is a transfer learning task which aims
at training on an unlabeled target domain by leveraging a labeled source
domain. Beyond the traditional scope of UDA with a single source domain and a
single target domain, real-world perception systems face a variety of scenarios
to handle, from varying lighting conditions to many cities around the world. In
this context, UDAs with several domains increase the challenges with the
addition of distribution shifts within the different target domains. This work
focuses on a novel framework for learning UDA, continuous UDA, in which models
operate on multiple target domains discovered sequentially, without access to
previous target domains. We propose MuHDi, for Multi-Head Distillation, a
method that solves the catastrophic forgetting problem, inherent in continual
learning tasks. MuHDi performs distillation at multiple levels from the
previous model as well as an auxiliary target-specialist segmentation head. We
report both extensive ablation and experiments on challenging multi-target UDA
semantic segmentation benchmarks to validate the proposed learning scheme and
architecture.


title:
An ALE residual distribution scheme for the unsteady Euler equations
  over triangular grids with local mesh adaptation

abstract:
  This work presents a novel interpolation-free mesh adaptation technique for
the Euler equations within the arbitrary Lagrangian Eulerian framework. For the
spatial discretization, we consider a residual distribution scheme, which
provides a pretty simple way to achieve high order accuracy on unstructured
grids. Thanks to a special interpretation of the mesh connectivity changes as a
series of fictitious continuous deformations, we can enforce by construction
the so-called geometric conservation law, which helps to avoid spurious
oscillations while solving the governing equations over dynamic domains. This
strategy preserves the numerical properties of the underlying,
fixed-connectivity scheme, such as conservativeness and stability, as it avoids
an explicit interpolation of the solution between different grids. The proposed
approach is validated through the two-dimensional simulations of steady and
unsteady flow problems over unstructured grids.


title: Deep-learning-enabled Brain Hemodynamic Mapping Using Resting-state fMRI

abstract:
  Cerebrovascular disease is a leading cause of death globally. Prevention and
early intervention are known to be the most effective forms of its management.
Non-invasive imaging methods hold great promises for early stratification, but
at present lack the sensitivity for personalized prognosis. Resting-state
functional magnetic resonance imaging (rs-fMRI), a powerful tool previously
used for mapping neural activity, is available in most hospitals. Here we show
that rs-fMRI can be used to map cerebral hemodynamic function and delineate
impairment. By exploiting time variations in breathing pattern during rs-fMRI,
deep learning enables reproducible mapping of cerebrovascular reactivity (CVR)
and bolus arrive time (BAT) of the human brain using resting-state CO2
fluctuations as a natural 'contrast media'. The deep-learning network was
trained with CVR and BAT maps obtained with a reference method of
CO2-inhalation MRI, which included data from young and older healthy subjects
and patients with Moyamoya disease and brain tumors. We demonstrate the
performance of deep-learning cerebrovascular mapping in the detection of
vascular abnormalities, evaluation of revascularization effects, and vascular
alterations in normal aging. In addition, cerebrovascular maps obtained with
the proposed method exhibited excellent reproducibility in both healthy
volunteers and stroke patients. Deep-learning resting-state vascular imaging
has the potential to become a useful tool in clinical cerebrovascular imaging.


title: Parameterized safety verification of round-based shared-memory systems

abstract:
  We consider the parameterized verification problem for distributed algorithms
where the goal is to develop techniques to prove the correctness of a given
algorithm regardless of the number of participating processes. Motivated by an
asynchronous binary consensus algorithm [J. Aspnes, Fast deterministic
consensus in a noisy environment. Journal of Algorithms, 2002], we consider
round-based distributed algorithms communicating with shared memory. A
particular challenge in these systems is that 1) the number of processes is
unbounded, and, more importantly, 2) there is a fresh set of registers at each
round. A~verification algorithm thus needs to manage both sources of infinity.
In this setting, we prove that the safety verification problem, which consists
in deciding whether all possible executions avoid a given error state, is
PSPACE-complete. For~negative instances of the safety verification problem,
we~also provide exponential lower and upper bounds on the minimal number of
processes needed for an error execution and on the minimal round on which the
error state can be covered.


title:
Universal polarisation energies for defects in monolayer, surface and
  bulk hexagonal boron nitride : A finite-size fragments GW approach

abstract:
  We study defect energy levels in hexagonal boron-nitride with varying number
of layers using a fragment many-body $GW$ formalism, taking as examples the
paradigmatic carbon-dimer and $C_BV_N$ defects. We show that a single layer can
be fragmented in polarizable finite-size areas reproducing faithfully the
effect of the dielectric environment, dramatically facilitating the study at
the many-body level of point defects in the dilute limit. The evolution of
defect energy levels from the monolayer to a $n$-layer system due to increased
screening, labeled polarization energies, follow a simple $({\Delta P}/n +
P_{\infty})$ behavior. The coefficients $\Delta P$ and $P_{\infty}$ are found
to be close-to-universal, with opposite signs for holes and electrons,
characterizing mainly the host and the position of the defect (surface or
bulk), but hardly the defect type. Our results rationalize the evolution of
defect energy levels with layers number, allowing to safely extrapolate results
obtained for the monolayer to few-layers, surface or bulk \textit{h}-BN. The
present many-body fragment approach further opens the door to studying
disordered 2D layers.


title:
Optimal day-ahead offering strategy for large producers based on market
  price response learning

abstract:
  In day-ahead electricity markets based on uniform marginal pricing, small
variations in the offering and bidding curves may substantially modify the
resulting market outcomes. In this work, we deal with the problem of finding
the optimal offering curve for a risk-averse profit-maximizing generating
company (GENCO) in a data-driven context. In particular, a large GENCO's market
share may imply that her offering strategy can alter the marginal price
formation, which can be used to increase profit. We tackle this problem from a
novel perspective. First, we propose a optimization-based methodology to
summarize each GENCO's step-wise supply curves into a subset of representative
price-energy blocks. Then, the relationship between the market price and the
resulting energy block offering prices is modeled through a Bayesian linear
regression approach, which also allows us to generate stochastic scenarios for
the sensibility of the market towards the GENCO strategy, represented by the
regression coefficient probabilistic distributions. Finally, this predictive
model is embedded in the stochastic optimization model by employing a
constraint learning approach. Results show how allowing the GENCO to deviate
from her true marginal costs renders significant changes in her profits and the
market marginal price. Furthermore, these results have also been tested in an
out-of-sample validation setting, showing how this optimal offering strategy is
also effective in a real-world market contest.


title:
Incorporating Explicit Knowledge in Pre-trained Language Models for
  Passage Re-ranking

abstract:
  Passage re-ranking is to obtain a permutation over the candidate passage set
from retrieval stage. Re-rankers have been boomed by Pre-trained Language
Models (PLMs) due to their overwhelming advantages in natural language
understanding. However, existing PLM based re-rankers may easily suffer from
vocabulary mismatch and lack of domain specific knowledge. To alleviate these
problems, explicit knowledge contained in knowledge graph is carefully
introduced in our work. Specifically, we employ the existing knowledge graph
which is incomplete and noisy, and first apply it in passage re-ranking task.
To leverage a reliable knowledge, we propose a novel knowledge graph
distillation method and obtain a knowledge meta graph as the bridge between
query and passage. To align both kinds of embedding in the latent space, we
employ PLM as text encoder and graph neural network over knowledge meta graph
as knowledge encoder. Besides, a novel knowledge injector is designed for the
dynamic interaction between text and knowledge encoder. Experimental results
demonstrate the effectiveness of our method especially in queries requiring
in-depth domain knowledge.


title: HyperNCA: Growing Developmental Networks with Neural Cellular Automata

abstract:
  In contrast to deep reinforcement learning agents, biological neural networks
are grown through a self-organized developmental process. Here we propose a new
hypernetwork approach to grow artificial neural networks based on neural
cellular automata (NCA). Inspired by self-organising systems and
information-theoretic approaches to developmental biology, we show that our
HyperNCA method can grow neural networks capable of solving common
reinforcement learning tasks. Finally, we explore how the same approach can be
used to build developmental metamorphosis networks capable of transforming
their weights to solve variations of the initial RL task.


title:
On the uniqueness of nondegenerate blowups for the motion by curvature
  of networks

abstract:
  In this note we prove uniqueness of nondegenerate compact blowups for the
motion by curvature of planar networks. The proof follows ideas introduced in
"Lojasiewicz-Simon inequalities for minimal networks: stability and
convergence" for the study of stability properties of critical points of the
length functional and it is based on the application of a Lojasiewicz-Simon
gradient inequality.


title:
Eternal vs singular observers in interacting dark energy-dark matter
  models

abstract:
  Interacting dark energy-dark matter models have been widely analyzed in the
literature in an attempt to find traces of new physics beyond the usual
cosmological ($\Lambda$CDM) models. Such a coupling between both dark
components is usually introduced in a phenomenological way through a flux in
the continuity equation. However, models with a Lagrangian formulation are also
possible. A class of the latter assumes a conformal/disformal coupling that
leads to a fifth force on the dark matter component, which consequently does
not follow the same geodesics as the other (baryonic, radiation, and dark
energy) matter sources. Here we analyze how the usual cosmological
singularities of the standard matter frame are seen from the dark matter one,
concluding that by choosing an appropriate coupling, dark matter observers will
see no singularities but a non-beginning, non-ending universe. By considering
two simple phenomenological models we show that such a type of coupling can fit
observational data as well as the usual $\Lambda$CDM model.


title: Conversational Question Answering on Heterogeneous Sources

abstract:
  Conversational question answering (ConvQA) tackles sequential information
needs where contexts in follow-up questions are left implicit. Current ConvQA
systems operate over homogeneous sources of information: either a knowledge
base (KB), or a text corpus, or a collection of tables. This paper addresses
the novel issue of jointly tapping into all of these together, this way
boosting answer coverage and confidence. We present CONVINSE, an end-to-end
pipeline for ConvQA over heterogeneous sources, operating in three stages: i)
learning an explicit structured representation of an incoming question and its
conversational context, ii) harnessing this frame-like representation to
uniformly capture relevant evidences from KB, text, and tables, and iii)
running a fusion-in-decoder model to generate the answer. We construct and
release the first benchmark, ConvMix, for ConvQA over heterogeneous sources,
comprising 3000 real-user conversations with 16000 questions, along with entity
annotations, completed question utterances, and question paraphrases.
Experiments demonstrate the viability and advantages of our method, compared to
state-of-the-art baselines.


title:
Data augmentation and multimodal learning for predicting drug response
  in patient-derived xenografts from gene expressions and histology images

abstract:
  Patient-derived xenografts (PDXs) are an appealing platform for preclinical
drug studies because the in vivo environment of PDXs helps preserve tumor
heterogeneity and usually better mimics drug response of patients with cancer
compared to CCLs. We investigate multimodal neural network (MM-Net) and data
augmentation for drug response prediction in PDXs. The MM-Net learns to predict
response using drug descriptors, gene expressions (GE), and histology
whole-slide images (WSIs) where the multi-modality refers to the tumor
features. We explore whether the integration of WSIs with GE improves
predictions as compared with models that use GE alone. We use two methods to
address the limited number of response values: 1) homogenize drug
representations which allows to combine single-drug and drug-pairs treatments
into a single dataset, 2) augment drug-pair samples by switching the order of
drug features which doubles the sample size of all drug-pair samples. These
methods enable us to combine single-drug and drug-pair treatments, allowing us
to train multimodal and unimodal neural networks (NNs) without changing
architectures or the dataset. Prediction performance of three unimodal NNs
which use GE are compared to assess the contribution of data augmentation
methods. NN that uses the full dataset which includes the original and the
augmented drug-pair treatments as well as single-drug treatments significantly
outperforms NNs that ignore either the augmented drug-pairs or the single-drug
treatments. In assessing the contribution of multimodal learning based on the
MCC metric, MM-Net statistically significantly outperforms all the baselines.
Our results show that data augmentation and integration of histology images
with GE can improve prediction performance of drug response in PDXs.


title: Eigenstate Thermalization Hypothesis and Free Probability

abstract:
  Quantum thermalization is well understood via the Eigenstate Thermalization
Hypothesis (ETH). The general form of ETH, describing all the relevant
correlations of matrix elements, may be derived on the basis of a `typicality'
argument of invariance with respect to local rotations involving nearby energy
levels. In this work, we uncover the close relation between this perspective on
ETH and Free Probability theory, as applied to a thermal ensemble or an energy
shell. This mathematical framework allows one to express in an unambiguous way
high order connected correlation functions (here identified as free cumulants)
in terms of standard correlation functions. This perspective naturally
incorporates the consistency property that local functions of ETH operators
also satisfy ETH. The present results open a direct connection between quantum
thermalization and the mathematical structure of Free Probability, thus
offering the basis for insightful analogies and new developments.

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


title: Dynamics of Fluctuations in the Open Quantum SSEP and Free Probability

abstract:
  The quantum symmetric simple exclusion process (Q-SSEP) is a simple model of
spinless fermions hopping on a one-dimensional chain to neighbouring sites with
random amplitudes. It was proposed as a toy model to study the fluctuations of
coherent (i.e. purely quantum mechanical) effects in out-of-equilibrium
many-body quantum systems. The aim is to describe these coherent effects at a
mesoscopic scale and to establish a universal framework which might be
applicable to a larger class of mesoscopic systems. Here we formulate the
scaling limit of the time dynamics of fluctuating coherences in the open
Q-SSEP, i.e. in the case when the one-dimensional chain is coupled to particle
reservoirs on the boundaries to maintain it out-of-equilibrium. We show that
the fluctuating coherences have a natural interpretation in terms of free
cumulants, a concept from free probability theory which thus seems to be an
appropriate framework to deal with fluctuating quantum many body systems
out-of-equilibrium.

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


title:
A Structural Investigation of the Approximability of Polynomial-Time
  Problems

abstract:
  We initiate the systematic study of a recently introduced polynomial-time
analogue of MaxSNP, which includes a large number of well-studied problems
(including Nearest and Furthest Neighbor in the Hamming metric, Maximum Inner
Product, optimization variants of $k$-XOR and Maximum $k$-Cover). Specifically,
MaxSP$_k$ denotes the class of $O(m^k)$-time problems of the form
$\max_{x_1,\dots, x_k} \#\{y:\phi(x_1,\dots,x_k,y)\}$ where $\phi$ is a
quantifier-free first-order property and $m$ denotes the size of the relational
structure. Assuming central hypotheses about clique detection in hypergraphs
and MAX3SAT, we show that for any MaxSP$_k$ problem definable by a
quantifier-free $m$-edge graph formula $\phi$, the best possible approximation
guarantee in faster-than-exhaustive-search time $O(m^{k-\delta})$ falls into
one of four categories:
  * optimizable to exactness in time $O(m^{k-\delta})$,
  * an (inefficient) approximation scheme, i.e., a $(1+\epsilon)$-approximation
in time $O(m^{k-f(\epsilon)})$,
  * a (fixed) constant-factor approximation in time $O(m^{k-\delta})$, or
  * an $m^\epsilon$-approximation in time $O(m^{k-f(\epsilon)})$.
  We obtain an almost complete characterization of these regimes, for MaxSP$_k$
as well as for an analogously defined minimization class MinSP$_k$. As our main
technical contribution, we rule out approximation schemes for a large class of
problems admitting constant-factor approximations, under the Sparse MAX3SAT
hypothesis posed by (Alman, Vassilevska Williams'20). As general trends for the
problems we consider, we find: (1) Exact optimizability has a simple algebraic
characterization, (2) only few maximization problems do not admit a
constant-factor approximation; these do not even have a subpolynomial-factor
approximation, and (3) constant-factor approximation of minimization problems
is equivalent to deciding whether the optimum is equal to 0.


title: Data-driven prediction and control of extreme events in a chaotic flow

abstract:
  An extreme event is a sudden and violent change in the state of a nonlinear
system. In fluid dynamics, extreme events can have adverse effects on the
system's optimal design and operability, which calls for accurate methods for
their prediction and control. In this paper, we propose a data-driven
methodology for the prediction and control of extreme events in a chaotic shear
flow. The approach is based on echo state networks, which are a type of
reservoir computing that learn temporal correlations within a time-dependent
dataset. The objective is five-fold. First, we exploit ad-hoc metrics from
binary classification to analyse (i) how many of the extreme events predicted
by the network actually occur in the test set (precision), and (ii) how many
extreme events are missed by the network (recall). We apply a principled
strategy for optimal hyperparameter selection, which is key to the networks'
performance. Second, we focus on the time-accurate prediction of extreme
events. We show that echo state networks are able to predict extreme events
well beyond the predictability time, i.e., up to more than five Lyapunov times.
Third, we focus on the long-term prediction of extreme events from a
statistical point of view. By training the networks with datasets that contain
non-converged statistics, we show that the networks are able to learn and
extrapolate the flow's long-term statistics. In other words, the networks are
able to extrapolate in time from relatively short time series. Fourth, we
design a simple and effective control strategy to prevent extreme events from
occurring. The control strategy decreases the occurrence of extreme events up
to one order of magnitude with respect to the uncontrolled system. Finally, we
analyse the robustness of the results for a range of Reynolds numbers. We show
that the networks perform well across a wide range of regimes.


title: Sub-4.7 Scaling Exponent of Polar Codes

abstract:
  Polar code visibly approaches channel capacity in practice and is thereby a
constituent code of the 5G standard. Compared to low-density parity-check code,
however, the performance of short-length polar code has rooms for improvement
that could hinder its adoption by a wider class of applications. As part of the
program that addresses the performance issue at short length, it is crucial to
understand how fast binary memoryless symmetric channels polarize. A number,
called scaling exponent, was defined to measure the speed of polarization and
several estimates of the scaling exponent were given in literature. As of 2022,
the tightest overestimate is 4.714 made by Mondelli, Hassani, and Urbanke in
2015. We lower the overestimate to 4.63.


title: Solar Flare Heating with Turbulent Suppression of Thermal Conduction

abstract:
  During solar flares plasma is typically heated to very high temperatures, and
the resulting redistribution of energy via thermal conduction is a primary
mechanism transporting energy throughout the flaring solar atmosphere. The
thermal flux is usually modeled using Spitzer's theory, which is based on local
Coulomb collisions between the electrons carrying the thermal flux and those in
the background. However, often during flares, temperature gradients become
sufficiently steep that the collisional mean free path exceeds the temperature
gradient scale size, so that thermal conduction becomes inherently non-local.
Further, turbulent angular scattering, which is detectable in nonthermal widths
of atomic emission lines, can also act to increase the collision frequency and
so suppress the heat flux. Recent work by Emslie & Bian (2018) extended
Spitzer's theory of thermal conduction to account for both non-locality and
turbulent suppression. We have implemented their theoretical expression for the
heat flux (which is a convolution of the Spitzer flux with a kernel function)
into the RADYN flare-modeling code and performed a parameter study to
understand how the resulting changes in thermal conduction affect flare
dynamics and hence the radiation produced. We find that models with reduced
heat fluxes predict slower bulk flows, less intense line emission, and longer
cooling times. By comparing features of atomic emission lines predicted by the
models with Doppler velocities and nonthermal line widths deduced from a
particular flare observation, we find that models with suppression factors
between 0.3 to 0.5 relative to the Spitzer value best reproduce observed
Doppler velocities across emission lines forming over a wide range of
temperatures. Interestingly, the model that best matches observed nonthermal
line widths has a kappa-type velocity distribution function.


title:
The CARMENES search for exoplanets around M dwarfs. Rotational variation
  in activity indicators of Ross 318, YZ CMi, TYC 3529-1437-1, and EV Lac

abstract:
  The CARMENES instrument is searching for periodic radial-velocity (RV)
variations of M dwarfs, which are induced by orbiting planets. However, there
are other potential sources of such variations, including rotational modulation
caused by stellar activity. We aim to investigate four M dwarfs (Ross 318, YZ
CMi, TYC 3529-1437-1, and EV Lac) with different activity levels and spectral
sub-types. Our goal is to compare the periodicities seen in 22 activity
indicators and the stellar RVs, and to examine their stability over time. For
each star, we calculated GLS periodograms of pseudo-equivalent widths of
chromospheric lines, indices of photospheric bands, the differential line width
as a measure of the width of the average photospheric absorption line, the RV,
the chromatic index that describes the wavelength dependence of the RV, and CCF
parameters. We also calculated periodograms for subsets of the data and
compared our results to TESS photometry. We find the rotation periods of all
four stars to manifest themselves in the RV and photospheric indicators,
particularly the TiO~7050 index, whereas the chromospheric lines show clear
signals only at lower activity levels. For EV Lac and TYC 3529-1437-1, we find
episodes during which indicators vary with the rotation period, and episodes
during which they vary with half the rotation period, similarly to photometric
light curves. The changing periodicities reflect the evolution of stellar
activity features on the stellar surface. We therefore conclude that our
results not only emphasise the importance of carefully analysing indicators
complementary to the RV in RV surveys, but they also suggest that it is also
useful to search for signals in activity indicators in subsets of the dataset,
because an activity signal that is present in the RV may not be visible in the
activity indicators all the time, in particular for the most active stars.


title:
Using Collocated Vision and Tactile Sensors for Visual Servoing and
  Localization

abstract:
  Coordinating proximity and tactile imaging by collocating cameras with
tactile sensors can 1) provide useful information before contact such as object
pose estimates and visually servo a robot to a target with reduced occlusion
and higher resolution compared to head-mounted or external depth cameras, 2)
simplify the contact point and pose estimation problems and help tactile
sensing avoid erroneous matches when a surface does not have significant
texture or has repetitive texture with many possible matches, and 3) use
tactile imaging to further refine contact point and object pose estimation. We
demonstrate our results with objects that have more surface texture than most
objects in standard manipulation datasets. We learn that optic flow needs to be
integrated over a substantial amount of camera travel to be useful in
predicting movement direction. Most importantly, we also learn that state of
the art vision algorithms do not do a good job localizing tactile images on
object models, unless a reasonable prior can be provided from collocated
cameras.


title:
A census of young stellar objects in two line-of-sight star forming
  regions toward IRAS 22147+5948 in the outer Galaxy

abstract:
  (abridged) Star formation in the outer Galaxy, i.e., outside of the Solar
circle, has been lightly studied in part due to low CO brightness of molecular
clouds linked with the negative metallicity gradient. Recent infrared surveys
provide an overview of dust emission in large sections of the Galaxy, but
suffer from cloud confusion and poor spatial resolution at far-infrared
wavelengths. We aim to develop a methodology to identify and classify young
stellar objects (YSOs) in star-forming regions in the outer Galaxy, and use it
to solve a long-standing confusion with the distance and evolutionary status of
IRAS 22147+5948. We use Support Vector Machine learning algorithm to complement
standard color-color and color-magnitude diagrams in search for YSOs in the
IRAS 22147 region using publicly available data from the `Spitzer Mapping of
the Outer Galaxy' survey. The agglomerative hierarchical clustering algorithm
is used to identify clusters, along with the Robitaille et al. (2017) code to
calculate physical properties of individual YSOs. We identify 13 Class I and 13
Class II YSO candidates using the color-color diagrams, and additional 2 and 21
sources, respectively, using the machine learning techniques. Spectral energy
distributions of 23 sources are modelled with a star and a passive disk,
corresponding to Class II objects. Models of 3 sources include envelopes
typical for Class I objects. The objects are grouped in 2 clusters located at
the distance of ~2.2 kpc, and 5 clusters at ~5.6 kpc. The spatial extent of CO,
radio continuum, and dust emission confirms the origin of YSOs in two distinct
star-forming regions along a similar line-of-sight. The outer Galaxy might
serve as a unique laboratory of star formation across environments on condition
that complementary methods and ancillary data are used to properly account for
cloud confusion and distance uncertainties.


title:
The Numerical Assembly Technique for arbitrary planar systems based on
  an alternative homogeneous solution

abstract:
  The Numerical Assembly Technique is extended to investigate arbitrary planar
frame structures with the focus on the computation of natural frequencies. This
allows us to obtain highly accurate results without resorting to spatial
discretization. To this end, we systematically introduce a frame structure as a
set of nodes, beams, bearings, springs, and external loads and formulate the
corresponding boundary and interface conditions. As the underlying homogeneous
solution of the governing equations, we use a novel approach recently presented
in the literature. This greatly improves the numerical stability and allows the
stable computation of very high natural frequencies accurately. We show this
numerically at two frame structures by investigation of the condition number of
the system matrix and also by the use of variable precision arithmetic.


title:
Deep electric field predictions by drift-reduced Braginskii theory with
  plasma-neutral interactions based upon experimental images of boundary
  turbulence

abstract:
  We present 2-dimensional turbulent electric field calculations via
physics-informed deep learning consistent with (i) drift-reduced Braginskii
theory under the framework of an axisymmetric fusion plasma with purely
toroidal field and (ii) experimental estimates of the fluctuating electron
density and temperature obtained from analysis of gas puff imaging of a
discharge on the Alcator C-Mod tokamak. The inclusion of effects from the
locally puffed atomic helium on particle and energy sources within the reduced
plasma turbulence model are found to strengthen correlations between the
electric field and electron pressure. The neutrals are also directly associated
with an observed broadening in the distribution of turbulent field amplitudes
and increased ${\bf E \times B}$ shearing rates.


title: Visibility graphs of animal foraging trajectories

abstract:
  The study of self-propelled particles is a fast-growing research topic where
biologically inspired movement is increasingly becoming of much interest. A
relevant example is the collective motion of social insects, whose variety and
complexity offer fertile grounds for theoretical abstractions. It has been
demonstrated that the collective motion involved in the searching behavior of
termites is consistent with self-similarity, anomalous diffusion and L\'evy
walks. In this work, we use visibility graphs -- a method that maps time series
into graphs and quantifies the signal complexity via graph topological metrics
-- in the context of social insects foraging trajectories extracted from
experiments. Our analysis indicates that the patterns observed for isolated
termites change qualitatively when the termite density is increased, and such
change cannot be explained by jamming effects only, pointing to collective
effects emerging due to non-trivial foraging interactions between insects as
the cause. Moreover, we find that such an onset of complexity is maximized for
intermediate termite densities.


title:
Feedback control for distributed ledgers: An attack mitigation policy
  for DAG-based DLTs

abstract:
  In this paper we present a feedback approach to the design of an attack
mitigation policy for DAG-based Distributed Ledgers. We develop a model to
analyse the behaviour of the ledger under the so called Tips Inflation Attack
and we design a control strategy to counteract this attack strategy. The
efficacy of this approach is showcased through a theoretical analysis, in the
form of two theorems about the stability properties of the ledger with and
without the controller, and extensive Monte Carlo simulations of an agent-based
model of the distributed ledger.