title:
A measurement decoupling based fast algorithm for super-resolving point
  sources with multi-cluster structure

abstract:
  We consider the problem of resolving closely spaced point sources in one
dimension from their Fourier data in a bounded domain. Classical subspace
methods (e.g., MUSIC algorithm, Matrix Pencil method, etc.) show great
superiority in resolving closely spaced sources, but their computational cost
is usually heavy. This is especially the case for point sources with a
multi-cluster structure which requires processing large-sized data matrix
resulted from highly sampled measurements. To address this issue, we propose a
fast algorithm termed D-MUSIC, based on a measurement decoupling strategy. We
demonstrate theoretically that for point sources with a known cluster
structure, their measurement can be decoupled into local measurements of each
of the clusters by solving a system of linear equations that are obtained by
using a multipole basis. We further develop a subsampled MUSIC algorithm to
detect the cluster structure and utilize it to decouple the global measurement.
In the end, the MUSIC algorithm was applied to each local measurement to
resolve point sources therein. Compared to the standard MUSIC algorithm, the
proposed algorithm has comparable super-resolving capability while having a
much lower computational complexity.


title: Continuous Integration of Data Histories into Consistent Namespaces

abstract:
  We describe a policy-based approach to the scaling of shared data services,
using a hierarchy of calibrated data pipelines to automate the continuous
integration of data flows. While there is no unique solution to the problem of
time order, we show how to use a fair interleaving to reproduce reliable
`latest version' semantics in a controlled way, by trading locality for
temporal resolution. We thus establish an invariant global ordering from a
spanning tree over all shards, with controlled scalability. This forms a
versioned coordinate system (or versioned namespace) with consistent semantics
and self-protecting rate-limited versioning, analogous to publish-subscribe
addressing schemes for Content Delivery Network (CDN) or Name Data Networking
(NDN) schemes.


title:
Uncertainty Determines the Adequacy of the Mode and the Tractability of
  Decoding in Sequence-to-Sequence Models

abstract:
  In many natural language processing (NLP) tasks the same input (e.g. source
sentence) can have multiple possible outputs (e.g. translations). To analyze
how this ambiguity (also known as intrinsic uncertainty) shapes the
distribution learned by neural sequence models we measure sentence-level
uncertainty by computing the degree of overlap between references in
multi-reference test sets from two different NLP tasks: machine translation
(MT) and grammatical error correction (GEC). At both the sentence- and the
task-level, intrinsic uncertainty has major implications for various aspects of
search such as the inductive biases in beam search and the complexity of exact
search. In particular, we show that well-known pathologies such as a high
number of beam search errors, the inadequacy of the mode, and the drop in
system performance with large beam sizes apply to tasks with high level of
ambiguity such as MT but not to less uncertain tasks such as GEC. Furthermore,
we propose a novel exact $n$-best search algorithm for neural sequence models,
and show that intrinsic uncertainty affects model uncertainty as the model
tends to overly spread out the probability mass for uncertain tasks and
sentences.


title:
Computational exfoliation of atomically thin 1D materials with
  application to Majorana bound states

abstract:
  We introduce a computational database with calculated structural,
thermodynamic, electronic, magnetic, and optical properties of 820
one-dimensional materials. The materials are systematically selected and
exfoliated from experimental databases of crystal structures based on a
dimensionality scoring parameter. The database is furthermore expanded by
chemical element substitution in the materials. The materials are investigated
in both their bulk form and as isolated one-dimensional components. We discuss
the methodology behind the database, give an overview of some of the calculated
properties, and look at patterns and correlations in the data. The database is
furthermore applied in computational screening to identify materials, which
could exhibit Majorana bound states.


title: Finite Sample Inference in Incomplete Models

abstract:
  We propose confidence regions for the parameters of incomplete models with
exact coverage of the true parameter in finite samples. Our confidence region
inverts a test, which generalizes Monte Carlo tests to incomplete models. The
test statistic is a discrete analogue of a new optimal transport
characterization of the sharp identified region. Both test statistic and
critical values rely on simulation drawn from the distribution of latent
variables and are computed using solutions to discrete optimal transport, hence
linear programming problems. We also propose a fast preliminary search in the
parameter space with an alternative, more conservative yet consistent test,
based on a parameter free critical value.


title: Distributed Filtering with Value of Information Censoring

abstract:
  This work presents a distributed estimation algorithm that efficiently uses
the available communication resources. The approach is based on Bayesian
filtering that is distributed across a network by using the logarithmic opinion
pool operator. Communication efficiency is achieved by having only agents with
high Value of Information (VoI) share their estimates, and the algorithm
provides a tunable trade-off between communication resources and estimation
error. Under linear-Gaussian models the algorithm takes the form of a censored
distributed Information filter, which guarantees the consistency of agent
estimates. Importantly, consistent estimates are shown to play a crucial role
in enabling the large reductions in communication usage provided by the VoI
censoring approach. We verify the performance of the proposed method via
complex simulations in a dynamic network topology and by experimental
validation over a real ad-hoc wireless communication network. The results show
the validity of using the proposed method to drastically reduce the
communication costs of distributed estimation tasks.


title:
Characterization of a CdZnTe detector for a low-power CubeSat
  application

abstract:
  We report spectral and imaging performance of a pixelated CdZnTe detector
custom designed for the \emph{MeVCube} project: a small Compton telescope on a
CubeSat platform. \emph{MeVCube} is expected to cover the energy range between
$200\;\mathrm{keV}$ and $4\;\mathrm{MeV}$, with performance comparable to the
last generation of larger satellites. In order to achieve this goal, an energy
resolution of few percent in full width at half maximum (FWHM) and a $3$-D
spatial resolution of few millimeters for the individual detectors are needed.
The severe power constraints present in small satellites require very low power
read-out electronics for the detector. Our read-out is based on the VATA450.3
ASIC developed by \emph{Ideas}, with a power consumption of only
$0.25\;\mathrm{mW/channel}$, which exhibits good performance in terms of
dynamic range, noise and linearity. A $2.0\;\mathrm{cm} \times 2.0\;\mathrm{cm}
\times 1.5\;\mathrm{cm}$ CdZnTe detector, with a custom $8 \times 8$ pixel
anode structure read-out by a VATA450.3 ASIC, has been tested. A preliminary
read-out system for the cathode, based on a discrete \emph{Amptek} A250F charge
sensitive pre-amplifier and a DRS4 ASIC, has been implemented. An energy
resolution around $3\%$ FWHM has been measured at a gamma energy of
$662\;\mathrm{keV}$; at $200\;\mathrm{keV}$ the average energy resolution is
$6.5\%$, decreasing to $\lesssim 2\%$ at energies above $1\;\mathrm{MeV}$. A
$3$-D spatial resolution of $\approx 2\,\mathrm{mm}$ is achieved.


title:
Cooling through parametric modulations and phase-preserving quantum
  measurements

abstract:
  We propose a cooling protocol that uses phase-preserving quantum measurements
and phase-dependent modulations of the trapping potential at parametric
resonance to cool a quantum oscillator to near its quantum-mechanical
ground-state. The sequential measurements and feedback provide a definite phase
reference and stabilize the oscillator in the long-time limit. The protocol is
robust against moderate amounts of dissipation and phase errors in the feedback
loop. Our work has implications for the cooling of mechanical resonators and
the integration of quantum refrigerators into quantum circuits.


title:
Autonomous crater detection on asteroids using a fully-convolutional
  neural network

abstract:
  This paper shows the application of autonomous Crater Detection using the
U-Net, a Fully-Convolutional Neural Network, on Ceres. The U-Net is trained on
optical images of the Moon Global Morphology Mosaic based on data collected by
the LRO and manual crater catalogues. The Moon-trained network will be tested
on Dawn optical images of Ceres: this task is accomplished by means of a
Transfer Learning (TL) approach. The trained model has been fine-tuned using
100, 500 and 1000 additional images of Ceres. The test performance was measured
on 350 never before seen images, reaching a testing accuracy of 96.24%, 96.95%
and 97.19%, respectively. This means that despite the intrinsic differences
between the Moon and Ceres, TL works with encouraging results. The output of
the U-Net contains predicted craters: it will be post-processed applying global
thresholding for image binarization and a template matching algorithm to
extract craters positions and radii in the pixel space. Post-processed craters
will be counted and compared to the ground truth data in order to compute image
segmentation metrics: precision, recall and F1 score. These indices will be
computed, and their effect will be discussed for tasks such as automated crater
cataloguing and optical navigation.


title:
Hierarchies of Critical Points of a Landau-de Gennes Free Energy on
  Three-Dimensional Cuboids

abstract:
  We investigate critical points of a Landau-de Gennes (LdG) free energy in
three-dimensional (3D) cuboids, that model nematic equilibria. We develop a
hybrid saddle dynamics-based algorithm to efficiently compute solution
landscapes of these 3D systems. Our main results concern (a) the construction
of 3D LdG critical points from a database of 2D LdG critical points and (b)
studies of the effects of cross-section size and cuboid height on solution
landscapes. In doing so, we discover multiple-layer 3D LdG critical points
constructed by stacking 3D critical points on top of each other, novel pathways
between distinct energy minima mediated by 3D LdG critical points and novel
metastable escaped solutions, all of which can be tuned for tailor-made static
and dynamic properties of confined nematic liquid crystal systems in 3D.


title:
Unified Collision Model of Coherent and Measurement-based Quantum
  Feedback

abstract:
  We introduce a general framework, based on collision models and discrete
CP-maps, to describe on an equal footing coherent and measurement-based
feedback control of quantum mechanical systems. We apply our framework to
prominent tasks in quantum control, ranging from cooling to Hamiltonian
control. Unlike other proposed comparisons, where coherent feedback always
proves superior, we find that either measurements or coherent manipulations of
the controller can be advantageous depending on the task at hand.
Measurement-based feedback is typically superior in cooling, whilst coherent
feedback is better at assisting quantum operations. Furthermore, we show that
both coherent and measurement-based feedback loops allow one to simulate
arbitrary Hamiltonian evolutions, and discuss their respective effectiveness in
this regard.


title:
Simulator-based explanation and debugging of hazard-triggering events in
  DNN-based safety-critical systems

abstract:
  When Deep Neural Networks (DNNs) are used in safety-critical systems,
engineers should determine the safety risks associated with DNN errors observed
during testing. For DNNs processing images, engineers visually inspect all
error-inducing images to determine common characteristics among them. Such
characteristics correspond to hazard-triggering events (e.g., low illumination)
that are essential inputs for safety analysis. Though informative, such
activity is expensive and error-prone.
  To support such safety analysis practices, we propose SEDE, a technique that
generates readable descriptions for commonalities in error-inducing, real-world
images and improves the DNN through effective retraining. SEDE leverages the
availability of simulators, which are commonly used for cyber-physical systems.
SEDE relies on genetic algorithms to drive simulators towards the generation of
images that are similar to error-inducing, real-world images in the test set;
it then leverages rule learning algorithms to derive expressions that capture
commonalities in terms of simulator parameter values. The derived expressions
are then used to generate additional images to retrain and improve the DNN.
  With DNNs performing in-car sensing tasks, SEDE successfully characterized
hazard-triggering events leading to a DNN accuracy drop. Also, SEDE enabled
retraining to achieve significant improvements in DNN accuracy, up to 18
percentage points.


title: Extracting information on black hole horizons

abstract:
  We present some features of Kerr black hole horizons that are replicated on
orbits accessible to outside observers. We use the concepts of horizon
confinement and replicas to show that outside the outer horizon there exist
photon orbits whose frequencies contain information about the inner horizon and
that can, in principle, be detected through the emission spectra of black
holes. It is shown that such photon orbits exist close to the rotation axis of
the Kerr geometry. We argue that these results could be used to recognize and
further investigate black holes and their horizons


title:
Classical Newtonian Monte-Carlo Calculation of Ionization of Atomic
  Hydrogen by Protons in Energy Range 0.05 to 1.0 MeV

abstract:
  We have designed a Euler approximation Monte-Carlo code for the calculation
of the ionization of atomic hydrogen by protons with energies between 0.05 and
1.0 MeV. This code is used to calculate the total ionization cross sections
s(E), the angular distribution double differential cross sections d2s/ (dW -
dE) for electrons with energies of 150 eV ionized by 300 keV protons and the
energy distribution d2s/ (dW - dE) for the ionized electrons emitted at 10o by
protons of energy 300 keV. Our calculations are compared to experimental values
of these quantities as well as independent calculations. We find excellent
agreement of our calculated cross section and reasonable agreement of our
calculated double differential cross section with independent calculations and
measurements.


title: Highly Radiating Charged Particles in a Strong Electromagnetic Field

abstract:
  We consider highly radiating ultra-relativistic electrons in a strong
external electromagnetic field. High intensity radiative losses and consequent
$e^+e^-$-pair production, appearing in the frame of quantum electrodynamics,
determine indirectly mass operator of electrons in the strong field. We
calculate a leading term of the mass operator at asymptotically high energies
of electrons in a strong constant field. We do not use any perturbation theory
based on a low ratio of this mass operator to the electron mass $m_e$, but only
the original small parameter of quantum electrodynamics, the fine structure
constant, $\alpha$. The analogous %but less nontrivial result is obtained for
the polarization operator for photon propagator. These results resolve at least
partially a very long-lasted controversy originating from traditional and
straightforward application of perturbative approaches for description of
highly radiating ultra-relativistic charged particles in a very strong
electromagnetic fields.


title: Proper Reuse of Image Classification Features Improves Object Detection

abstract:
  A common practice in transfer learning is to initialize the downstream model
weights by pre-training on a data-abundant upstream task. In object detection
specifically, the feature backbone is typically initialized with Imagenet
classifier weights and fine-tuned on the object detection task. Recent works
show this is not strictly necessary under longer training regimes and provide
recipes for training the backbone from scratch. We investigate the opposite
direction of this end-to-end training trend: we show that an extreme form of
knowledge preservation -- freezing the classifier-initialized backbone --
consistently improves many different detection models, and leads to
considerable resource savings. We hypothesize and corroborate experimentally
that the remaining detector components capacity and structure is a crucial
factor in leveraging the frozen backbone. Immediate applications of our
findings include performance improvements on hard cases like detection of
long-tail object classes and computational and memory resource savings that
contribute to making the field more accessible to researchers with access to
fewer computational resources.


title:
Nonlinear interactions of dipolar excitons and polaritons in MoS2
  bilayers

abstract:
  Nonlinear interactions between excitons strongly coupled to light are key for
accessing quantum many-body phenomena in polariton systems. Atomically-thin
two-dimensional semiconductors provide an attractive platform for strong
light-matter coupling owing to many controllable excitonic degrees of freedom.
Among these, the recently emerged exciton hybridization opens access to
unexplored excitonic species, with a promise of enhanced interactions. Here, we
employ hybridized interlayer excitons (hIX) in bilayer MoS2 to achieve highly
nonlinear excitonic and polaritonic effects. Such interlayer excitons possess
an out-of-plane electric dipole as well as an unusually large oscillator
strength allowing observation of dipolar polaritons(dipolaritons) in bilayers
in optical microcavities. Compared to excitons and polaritons in MoS2
monolayers, both hIX and dipolaritons exhibit about 8 times higher
nonlinearity, which is further strongly enhanced when hIX and intralayer
excitons, sharing the same valence band, are excited simultaneously. This gives
rise to a highly nonlinear regime which we describe theoretically by
introducing a concept of hole crowding. The presented insight into many-body
interactions provides new tools for accessing few-polariton quantum
correlations.


title:
GEB+: A benchmark for generic event boundary captioning, grounding and
  text-based retrieval

abstract:
  Cognitive science has shown that humans perceive videos in terms of events
separated by state changes of dominant subjects. State changes trigger new
events and are one of the most useful among the large amount of redundant
information perceived. However, previous research focuses on the overall
understanding of segments without evaluating the fine-grained status changes
inside. In this paper, we introduce a new dataset called Kinetic-GEBC (Generic
Event Boundary Captioning). The dataset consists of over 170k boundaries
associated with captions describing status changes in the generic events in 12K
videos. Upon this new dataset, we propose three tasks supporting the
development of a more fine-grained, robust, and human-like understanding of
videos through status changes. We evaluate many representative baselines in our
dataset, where we also design a new TPD (Temporal-based Pairwise Difference)
Modeling method for current state-of-the-art backbones and achieve significant
performance improvements. Besides, the results show there are still formidable
challenges for current methods in the utilization of different granularities,
representation of visual difference, and the accurate localization of status
changes. Further analysis shows that our dataset can drive developing more
powerful methods to understand status changes and thus improve video level
comprehension.


title:
Robust and Accurate -- Compositional Architectures for Randomized
  Smoothing

abstract:
  Randomized Smoothing (RS) is considered the state-of-the-art approach to
obtain certifiably robust models for challenging tasks. However, current RS
approaches drastically decrease standard accuracy on unperturbed data, severely
limiting their real-world utility. To address this limitation, we propose a
compositional architecture, ACES, which certifiably decides on a per-sample
basis whether to use a smoothed model yielding predictions with guarantees or a
more accurate standard model without guarantees. This, in contrast to prior
approaches, enables both high standard accuracies and significant provable
robustness. On challenging tasks such as ImageNet, we obtain, e.g., $80.0\%$
natural accuracy and $28.2\%$ certifiable accuracy against $\ell_2$
perturbations with $r=1.0$. We release our code and models at
https://github.com/eth-sri/aces.


title:
GrADyS-GS -- A ground station for managing field experiments with
  Autonomous Vehicles and Wireless Sensor Networks

abstract:
  In many kinds of research, collecting data is tailored to individual
research. It is usual to use dedicated and not reusable software to collect
data. GrADyS Ground Station framework (GrADyS-GS) aims to collect data in a
reusable manner with dynamic background tools. This technical report describes
GrADyS-GS, a ground station software designed to connect with various
technologies to control, monitor, and store results of Mobile Internet of
Things field experiments with Autonomous Vehicles (UAV) and Sensor Networks
(WSN). In the GrADyS project GrADyS-GS is used with ESP32-based IoT devices on
the ground and Unmanned Aerial Vehicles (quad-copters) in the air. The
GrADyS-GS tool was created to support the design, development and testing of
simulated movement coordination algorithms for the AVs, testing of customized
Bluetooth Mesh variations, and overall communication, coordination, and
context-awareness field experiments planed in the GraDyS project. Nevertheless,
GrADyS-GS is also a general purpose tool, as it relies on a dynamic and
easy-to-use Python and JavaScript framework that allows easy customization and
(re)utilization in another projects and field experiments with other kinds of
IoT devices, other WSN types and protocols, and other kinds of mobile connected
flying or ground vehicles. So far, GrADyS-GS has been used to start UAV flights
and collects its data in s centralized manner inside GrADyS project.


title: Accelerating Federated Edge Learning via Topology Optimization

abstract:
  Federated edge learning (FEEL) is envisioned as a promising paradigm to
achieve privacy-preserving distributed learning. However, it consumes excessive
learning time due to the existence of straggler devices. In this paper, a novel
topology-optimized federated edge learning (TOFEL) scheme is proposed to tackle
the heterogeneity issue in federated learning and to improve the
communication-and-computation efficiency. Specifically, a problem of jointly
optimizing the aggregation topology and computing speed is formulated to
minimize the weighted summation of energy consumption and latency. To solve the
mixed-integer nonlinear problem, we propose a novel solution method of
penalty-based successive convex approximation, which converges to a stationary
point of the primal problem under mild conditions. To facilitate real-time
decision making, an imitation-learning based method is developed, where deep
neural networks (DNNs) are trained offline to mimic the penalty-based method,
and the trained imitation DNNs are deployed at the edge devices for online
inference. Thereby, an efficient imitate-learning based approach is seamlessly
integrated into the TOFEL framework. Simulation results demonstrate that the
proposed TOFEL scheme accelerates the federated learning process, and achieves
a higher energy efficiency. Moreover, we apply the scheme to 3D object
detection with multi-vehicle point cloud datasets in the CARLA simulator. The
results confirm the superior learning performance of the TOFEL scheme over
conventional designs with the same resource and deadline constraints.


title:
Time evolution of quantum correlations in presence of state dependent
  bath

abstract:
  The emerging quantum technologies heavily rely on the understanding of
dynamics in open quantum systems. In the Born approximation, the initial
system-bath correlations are often neglected which can be violated in the
strong coupling regimes and quantum state preparation. In order to understand
the influence of initial system-bath correlations, we study the extent to which
these initial correlations and the distance of separation between qubits
influence the dynamics of quantum entanglement and coherence. It is shown that
at low temperatures, the initial correlations have no role to play while at
high temperatures, these correlations strongly influence the dynamics.
Furthermore, we have shown that distance of separation between the qubits in
presence of collective bath helps to maintain entanglement and coherence at
long times.


title: FrequencyLowCut Pooling -- Plug & Play against Catastrophic Overfitting

abstract:
  Over the last years, Convolutional Neural Networks (CNNs) have been the
dominating neural architecture in a wide range of computer vision tasks. From
an image and signal processing point of view, this success might be a bit
surprising as the inherent spatial pyramid design of most CNNs is apparently
violating basic signal processing laws, i.e. Sampling Theorem in their
down-sampling operations. However, since poor sampling appeared not to affect
model accuracy, this issue has been broadly neglected until model robustness
started to receive more attention. Recent work [17] in the context of
adversarial attacks and distribution shifts, showed after all, that there is a
strong correlation between the vulnerability of CNNs and aliasing artifacts
induced by poor down-sampling operations. This paper builds on these findings
and introduces an aliasing free down-sampling operation which can easily be
plugged into any CNN architecture: FrequencyLowCut pooling. Our experiments
show, that in combination with simple and fast FGSM adversarial training, our
hyper-parameter free operator significantly improves model robustness and
avoids catastrophic overfitting.


title:
Provable concept learning for interpretable predictions using
  variational inference

abstract:
  In safety critical applications, practitioners are reluctant to trust neural
networks when no interpretable explanations are available. Many attempts to
provide such explanations revolve around pixel level attributions or use
previously known concepts. In this paper we aim to provide explanations by
provably identifying \emph{high-level, previously unknown concepts}. To this
end, we propose a probabilistic modeling framework to derive (C)oncept
(L)earning and (P)rediction (CLAP) -- a VAE-based classifier that uses visually
interpretable concepts as linear predictors. Assuming that the data generating
mechanism involves predictive concepts, we prove that our method is able to
identify them while attaining optimal classification accuracy. We use synthetic
experiments for validation, and also show that on real-world (PlantVillage and
ChestXRay) datasets, CLAP effectively discovers interpretable factors for
classifying diseases.


title: A Global Modeling Approach for Load Forecasting in Distribution Networks

abstract:
  Efficient load forecasting is needed to ensure better observability in the
distribution networks, whereas such forecasting is made possible by an
increasing number of smart meter installations. Because distribution networks
include a large amount of different loads at various aggregation levels, such
as individual consumers, transformer stations and feeders loads, it is
impractical to develop individual (or so-called local) forecasting models for
each load separately. Furthermore, such local models ignore the strong
dependencies between different loads that might be present due to their spatial
proximity and the characteristics of the distribution network. To address these
issues, this paper proposes a global modeling approach based on deep learning
for efficient forecasting of a large number of loads in distribution networks.
In this way, the computational burden of training a large amount of local
forecasting models can be largely reduced, and the cross-series information
shared among different loads can be utilized. Additionally, an unsupervised
localization mechanism and optimal ensemble construction strategy are also
proposed to localize/personalize the forecasting model to different groups of
loads and to improve the forecasting accuracy further. Comprehensive
experiments are conducted on real-world smart meter data to demonstrate the
superiority of the proposed approach compared to competing methods.


title:
Wavelength-by-wavelength temperature-independent thermal radiation
  utilizing an insulator-metal transition

abstract:
  Both the magnitude and spectrum of the blackbody-radiation distribution
change with temperature. Here, we designed the temperature-dependent spectral
emissivity of a coating to counteract all the changes in the
blackbody-radiation distribution over a certain temperature range, enabled by
the nonhysteretic insulator-to-metal phase transition of SmNiO3. At each
wavelength within the long-wave infrared atmospheric-transparency window, the
thermal radiance of our coating remains nearly constant over a temperature
range of at least 20 {\deg}C. Our approach can conceal thermal gradients and
transient temperature changes from infrared imaging systems, including those
that discriminate by wavelength, such as multispectral and hyperspectral
cameras.

categories: physics.optics cond-mat.mtrl-sci cond-mat.str-el physics.app-ph


title: Physics Informed Shallow Machine Learning for Wind Speed Prediction

abstract:
  The ability to predict wind is crucial for both energy production and weather
forecasting. Mechanistic models that form the basis of traditional forecasting
perform poorly near the ground. In this paper, we take an alternative
data-driven approach based on supervised learning. We analyze a massive dataset
of wind measured from anemometers located at 10 m height in 32 locations in two
central and north west regions of Italy (Abruzzo and Liguria). We train
supervised learning algorithms using the past history of wind to predict its
value at a future time (horizon). Using data from a single location and time
horizon we compare systematically several algorithms where we vary the
input/output variables, the memory of the input and the linear vs non-linear
learning model. We then compare performance of the best algorithms across all
locations and forecasting horizons. We find that the optimal design as well as
its performance vary with the location. We demonstrate that the presence of a
reproducible diurnal cycle provides a rationale to understand this variation.
We conclude with a systematic comparison with state of the art algorithms and
show that, when the model is accurately designed, shallow algorithms are
competitive with more complex deep architectures.


title:
Partitioning a 2-edge-coloured graph of minimum degree $2n/3 + o(n)$
  into three monochromatic cycles

abstract:
  Lehel conjectured in the 1970s that every red and blue edge-coloured complete
graph can be partitioned into two monochromatic cycles. This was confirmed in
2010 by Bessy and Thomass\'e. However, the host graph $G$ does not have to be
complete. It it suffices to require that $G$ has minimum degree at least
$3n/4$, where $n$ is the order of $G$, as was shown recently by Letzter,
confirming a conjecture of Balogh, Bar\'{a}t, Gerbner, Gy\'arf\'as and
S\'ark\"ozy. This degree condition is asymptotically tight.
  Here we continue this line of research, by proving that for every red and
blue edge-colouring of an $n$-vertex graph of minimum degree at least $2n/3 +
o(n)$, there is a partition of the vertex set into three monochromatic cycles.
This approximately verifies a conjecture of Pokrovskiy and is essentially
tight.


title:
Separate and conquer heuristic allows robust mining of contrast sets
  from various types of data

abstract:
  Identifying differences between groups is one of the most important knowledge
discovery problems. The procedure, also known as contrast sets mining, is
applied in a wide range of areas like medicine, industry, or economics. In the
paper we present RuleKit-CS, an algorithm for contrast set mining based on a
sequential covering - a well established heuristic for decision rule induction.
Multiple passes accompanied with an attribute penalization scheme allow
generating contrast sets describing same examples with different attributes,
unlike the standard sequential covering. The ability to identify contrast sets
in regression and survival data sets, the feature not provided by the existing
algorithms, further extends the usability of RuleKit-CS. Experiments on wide
range of data sets confirmed RuleKit-CS to be a useful tool for discovering
differences between defined groups. The algorithm is a part of the RuleKit
suite available at GitHub under GNU AGPL 3 licence
(https://github.com/adaa-polsl/RuleKit).
  Keywords: Contrast sets, Sequential covering, Rule induction, Regression,
Survival, Knowledge discovery


title: Evaluating the Text-to-SQL Capabilities of Large Language Models

abstract:
  We perform an empirical evaluation of Text-to-SQL capabilities of the Codex
language model. We find that, without any finetuning, Codex is a strong
baseline on the Spider benchmark; we also analyze the failure modes of Codex in
this setting. Furthermore, we demonstrate on the GeoQuery and Scholar
benchmarks that a small number of in-domain examples provided in the prompt
enables Codex to perform better than state-of-the-art models finetuned on such
few-shot examples.


title: A quantum Szilard engine for two-level systems coupled to a qubit

abstract:
  The innate complexity of solid state physics exposes superconducting quantum
circuits to interactions with uncontrolled degrees of freedom degrading their
coherence. By using a simple stabilization sequence we show that a
superconducting fluxonium qubit is coupled to a two-level system (TLS)
environment of unknown origin, with a relatively long energy relaxation time
exceeding $50\,\text{ms}$. Implementing a quantum Szilard engine with an active
feedback control loop allows us to decide whether the qubit heats or cools its
TLS environment. The TLSs can be cooled down resulting in a four times lower
qubit population, or they can be heated to manifest themselves as a negative
temperature environment corresponding to a qubit population of $\sim 80\,\%$.
We show that the TLSs and the qubit are each other's dominant loss mechanism
and that the qubit relaxation is independent of the TLS populations.
Understanding and mitigating TLS environments is therefore not only crucial to
improve qubit lifetimes but also to avoid non-Markovian qubit dynamics.


title: ASAS-SN follow-up of IceCube high-energy neutrino alerts

abstract:
  We report on the search for optical counterparts to IceCube neutrino alerts
released between April 2016 and August 2021 with the All-Sky Automated Survey
for SuperNovae (ASAS-SN). Despite the discovery of a diffuse astrophysical
high-energy neutrino flux in 2013, the source of those neutrinos remains
largely unknown. Since 2016, IceCube has published likely-astrophysical
neutrinos as public realtime alerts. Through a combination of normal survey and
triggered target-of-opportunity observations, ASAS-SN obtained images within 1
hour of the neutrino detection for 20% (11) of all observable IceCube alerts
and within one day for another 57% (32). For all observable alerts, we obtained
images within at least two weeks from the neutrino alert. ASAS-SN provides the
only optical follow-up for about 17% of IceCube's neutrino alerts. We recover
the two previously claimed counterparts to neutrino alerts, the flaring-blazar
TXS 0506+056 and the tidal disruption event AT2019dsg. We investigate the light
curves of previously-detected transients in the alert footprints, but do not
identify any further candidate neutrino sources. We also analysed the optical
light curves of Fermi 4FGL sources coincident with high-energy neutrino alerts,
but do not identify any contemporaneous flaring activity. Finally, we derive
constraints on the luminosity functions of neutrino sources for a range of
assumed evolution models.


title: Characterization of Passive CMOS Strip Sensors

abstract:
  Recent advances in CMOS imaging sensor technology , e.g. in CMOS pixel
sensors, have proven that the CMOS process is radiation tolerant enough to cope
with certain radiation levels required for tracking layers in hadron collider
experiments. With the ever-increasing area covered by silicon tracking
detectors cost effective alternatives to the current silicon sensors and more
integrated designs are desirable. This article describes results obtained from
laboratory measurements of silicon strip sensors produced in a passive p-CMOS
process. Electrical characterization and charge collection measurements with a
90Sr source and a laser with infrared wavelength showed no effect of the
stitching process on the performance of the sensor.


title:
Convergence Rate Bounds for the Mirror Descent Method: IQCs and the
  Bregman Divergence

abstract:
  This paper is concerned with convergence analysis for the mirror descent (MD)
method, a well-known algorithm in convex optimization. An analysis framework
via integral quadratic constraints (IQCs) is constructed to analyze the
convergence rate of the MD method with strongly convex objective functions in
both continuous-time and discrete-time. We formulate the problem of finding
convergence rates of the MD algorithms into feasibility problems of linear
matrix inequalities (LMIs) in both schemes. In particular, in continuoustime,
we show that the Bregman divergence function, which is commonly used as a
Lyapunov function for this algorithm, is a special case of the class of
Lyapunov functions associated with the Popov criterion, when the latter is
applied to an appropriate reformulation of the problem. Thus, applying the
Popov criterion and its combination with other IQCs, can lead to convergence
rate bounds with reduced conservatism. We also illustrate via examples that the
convergence rate bounds derived can be tight.


title:
Neutrino Driven Explosions aided by Axion Cooling in Multidimensional
  Simulations of Core-Collapse Supernovae

abstract:
  In this study, we present the first multidimensional core-collapse supernovae
(CCSNe) simulations including QCD axions in order to assess the impact on the
CCSN explosion mechanism. We include axions in our simulations through the
nucleon-nucleon bremsstrahlung emission channel and as a pure energy-sink term
under the assumption that the axions free-stream after being emitted. We
perform both spherically symmetric (1D) and axisymmetric (2D) simulations. In
1D, we utilize a parameterized heating scheme to achieve explosions, whereas in
2D we self-consistently realize explosions through the neutrino heating
mechanism. Our 2D results for a $20 M_\odot$ progenitor show an impact of the
axion emission on the shock behavior and the explosion time when considering
values of the Peccei-Quinn energy scale $f_a \leq 2 \times 10^8$ GeV. The
strong cooling due to the axion emission accelerates the contraction of the
core and leads to more efficient neutrino heating and earlier explosions. For
the axion emission formalism utilized, the values of $f_a$ that impact the
explosion are close to, but in tension with current limits based on the
neutrinos detected from SN1987A. However, given the non-linear behavior of the
emission and the multidimensional nature of CCSNe,we suggest that a
self-consistent, multidimensional approach to simulating CCSNe, including any
late time accretion and cooling, is needed to fully explore the axion bounds
from supernovae and the impact on the CCSN explosion mechanism.


title: Three-manifolds with non-negatively pinched Ricci curvature

abstract:
  We show that every complete non-compact three-manifold with non-negatively
pinched Ricci curvature admits a complete Ricci flow solution for all positive
time, with scale-invariant curvature decay and preservation of pinching.
Combining with recent work of Lott and Deruelle-Schulze-Simon gives a proof of
Hamilton's pinching conjecture without additional hypotheses.


title:
Multi Jet Fusion of Nylon-12: A Viable Method to 3D-print Concentric
  Tube Robots?

abstract:
  In this paper, we present a study on the viability of fabricating Concentric
Tube Robots using Multi Jet Fusion (MJF) of Nylon-12, a type of elastic polymer
commonly used in additive manufacturing. We note that Nylon-12 was already
evaluated for the purpose of building CTRs in prior work, but fabrication was
performed with Selective Laser Sintering (SLS), which produced unsatisfactory
results. Our study is the first study to evaluate the suitability of MJF to
3Dprint CTRs.


title:
Biocomputing Model Using Tripartite Synapses Provides Reliable Neuronal
  Logic Gating with Spike Pattern Diversity

abstract:
  Biocomputing technologies exploit biological communication mechanisms
involving cell-cell signal propagation to perform computations. Researchers
recently worked toward realising logic gates made by neurons to develop novel
devices such as organic neuroprostheses or brain implants made by cells, herein
termed living implants. Several challenges arise from this approach, mainly
associated with the stochastic nature and noise of neuronal communication.
Since astrocytes play a crucial role in the regulation of neurons activity,
there is a possibility whereby astrocytes can be engineered to control synapses
favouring reliable biocomputing. This work proposes a mathematical model of
neuronal logic gates involving neurons and astrocytes, realising OR and AND
gating. We use a shallow coupling of both the Izhikevich and Postnov models to
characterise gating responses with spike pattern variability and astrocyte
synaptic regulation. Logic operation error ratio and accuracy assess the AND
and OR gates' performances at different synaptic Gaussian noise levels. Our
results demonstrate that the astrocyte regulating activity can effectively be
used as a denoising mechanism, paving the way for highly reliable biocomputing
implementations.


title: Deep Learning in Spiking Phasor Neural Networks

abstract:
  Spiking Neural Networks (SNNs) have attracted the attention of the deep
learning community for use in low-latency, low-power neuromorphic hardware, as
well as models for understanding neuroscience. In this paper, we introduce
Spiking Phasor Neural Networks (SPNNs). SPNNs are based on complex-valued Deep
Neural Networks (DNNs), representing phases by spike times. Our model computes
robustly employing a spike timing code and gradients can be formed using the
complex domain. We train SPNNs on CIFAR-10, and demonstrate that the
performance exceeds that of other timing coded SNNs, approaching results with
comparable real-valued DNNs.


title: Local cyclic homology for nonarchimedean Banach algebras

abstract:
  Let V be a complete discrete valuation ring with uniformiser p. We introduce
an invariant of Banach V-algebras called local cyclic homology. This invariant
is related to analytic cyclic homology for complete, bornologically torsionfree
V-algebras. It is shown that local cyclic homology only depends on the
reduction mod p of a Banach V-algebra and that it is homotopy invariant,
matricially stable, and excisive.


title: Relative quantum cohomology under birational transformations

abstract:
  We study how relative quantum cohomology, defined by Tseng--You and
Fan--Wu--You, varies under birational transformations. For toric complete
intersections with simple normal crossings divisors that contain the loci of
indeterminacy, we prove that their respective relative $I$-functions can be
directly identified. For toric complete intersections with smooth divisors, we
prove that their respective relative $I$-functions are related by analytic
continuation. We also study some connections with extremal transitions and FJRW
theory.


title: Chemical evolution of $^{26}$Al and $^{60}$Fe in the Milky Way

abstract:
  We present theoretical mass estimates of $^{26}$Al and $^{60}$Fe throughout
the Galaxy, performed with a numerical chemical evolution model including
detailed nucleosynthesis prescriptions for both stable and radioactive
nuclides. We compared the results for several sets of stellar yields taken from
the literature, either for massive, low and intermediate mass stars, nova
systems (only for $^{26}$Al) and supernovae Type Ia, and then computed the
total masses of $^{26}$Al and $^{60}$Fe in the Galaxy. In particular, we have
studied the bulge and the disc of the Galaxy in a galactocentric radius range
between 0 and 22 kpc. We have assumed that the bulge (between 0 and 2 kpc)
evolved quickly suffering a strong burst of star formation, while the disc
formed more slowly and inside-out, in agreement with previous works, which
reproduced the majority of observational constraints. We have compared our
results with the $^{26}$Al mass observed by the $\gamma$-ray surveys COMPTEL
and INTEGRAL, in order to select the best model. Concerning $^{60}$Fe, for
which we do not have any observed mass value, we have just predicted its mass
to provide a theoretical prediction to be confirmed or disproved by future
observations. We have found that low and intermediate mass stars as well as
Type Ia supernovae contribute negligibly to the two isotopes, while massive
stars are the dominant source. The contribution from novae is, however,
necessary to reproduce the observed mass of $^{26}$Al, and this is a new
result. Our best model predicts $2.12$ M$_{\odot}$ of $^{26}$Al, in agreement
with observations, while for $^{60}$Fe our best mass estimate is around $\sim
1.05$ M$_{\odot}$. We have also predicted the present rate of injection of
$^{26}$Al and $^{60}$Fe in the Galaxy and compared it with previous results,
and we have predicted a larger present time injection rate along the disc,
relative to previous works.


title: Learning Disentangled Representations of Negation and Uncertainty

abstract:
  Negation and uncertainty modeling are long-standing tasks in natural language
processing. Linguistic theory postulates that expressions of negation and
uncertainty are semantically independent from each other and the content they
modify. However, previous works on representation learning do not explicitly
model this independence. We therefore attempt to disentangle the
representations of negation, uncertainty, and content using a Variational
Autoencoder. We find that simply supervising the latent representations results
in good disentanglement, but auxiliary objectives based on adversarial learning
and mutual information minimization can provide additional disentanglement
gains.


title:
A Compositional Approach to Safety-Critical Resilient Control for
  Systems with Coupled Dynamics

abstract:
  Complex, interconnected Cyber-physical Systems (CPS) are increasingly common
in applications including smart grids and transportation. Ensuring safety of
interconnected systems whose dynamics are coupled is challenging because the
effects of faults and attacks in one sub-system can propagate to other
sub-systems and lead to safety violations. In this paper, we study the problem
of safety-critical control for CPS with coupled dynamics when some sub-systems
are subject to failure or attack. We first propose resilient-safety indices
(RSIs) for the faulty or compromised sub-systems that bound the worst-case
impacts of faulty or compromised sub-systems on a set of specified safety
constraints. By incorporating the RSIs, we provide a sufficient condition for
the synthesis of control policies in each failure- and attack- free
sub-systems. The synthesized control policies compensate for the impacts of the
faulty or compromised sub-systems to guarantee safety. We formulate
sum-of-square optimization programs to compute the RSIs and the safety-ensuring
control policies. We present a case study that applies our proposed approach on
the temperature regulation of three coupled rooms. The case study demonstrates
that control policies obtained using our algorithm guarantee system's safety
constraints.


title:
An Arduino based heartbeat detection device (ArdMob-ECG) for real-time
  ECG analysis

abstract:
  This technical paper provides a tutorial to build a low-cost (10-100 USD) and
easy to assemble ECG device (ArdMob-ECG) that can be easily used for a variety
of different scientific studies. The advantage of this device is that it
automatically stores the data and has a built-in detection algorithm for
heartbeats. Compared to a clinical ECG, this device entails a serial interface
that can send triggers via USB directly to a computer and software (e.g. Unity,
Matlab) with minimal delay due to its architecture. Its software and hardware
is open-source and publicly available. The performance of the device regarding
sensitivity and specificity is comparable to a professional clinical ECG and is
assessed in this paper. Due to the open-source software, a variety of different
research questions and individual alterations can be adapted using this ECG.
The code as well as the circuit is publicly available and accessible for
everyone to promote a better health system in remote areas, Open Science, and
to boost scientific progress and the development of new paradigms that
ultimately foster innovation.


title:
An Analytical Framework for Control Synthesis of Cyber-Physical Systems
  with Safety Guarantee

abstract:
  Cyber-physical systems (CPS) are required to operate safely under fault and
malicious attacks. The simplex architecture and the recently proposed cyber
resilient architectures, e.g., Byzantine fault tolerant++ (BFT++), provide
safety for CPS under faults and malicious cyber attacks, respectively. However,
these existing architectures make use of different timing parameters and
implementations to provide safety, and are seemingly unrelated. In this paper,
we propose an analytical framework to represent the simplex, BFT++ and other
practical cyber resilient architectures (CRAs). We construct a hybrid system
that models CPS adopting any of these architectures. We derive sufficient
conditions via our proposed framework under which a control policy is
guaranteed to be safe. We present an algorithm to synthesize the control
policy. We validate the proposed framework using a case study on lateral
control of a Boeing 747, and demonstrate that our proposed approach ensures
safety of the system.


title: Balanced Clique Computation in Signed Networks: Concepts and Algorithms

abstract:
  Clique is one of the most fundamental models for cohesive subgraph mining in
network analysis. Existing clique model mainly focuses on unsigned networks.
However, in real world, many applications are modeled as signed networks with
positive and negative edges. As the signed networks hold their own properties
different from the unsigned networks, the existing clique model is inapplicable
for the signed networks. Motivated by this, we propose the balanced clique
model that considers the most fundamental and dominant theory, structural
balance theory, for signed networks. Following the balanced clique model, we
study the maximal balanced clique enumeration problem (MBCE) which computes all
the maximal balanced cliques in a given signed network and the maximum balanced
clique search problem (MBCS) which computes the balanced clique with maximum
size. We show that MBCE problem and MBCS problem are both NP-Hard. For the MBCE
problem, a straightforward solution is to treat the signed network as two
unsigned networks and leverage the off-the-shelf techniques for unsigned
networks. However, such a solution is inefficient for large signed networks. To
address this problem, in this paper, we first propose a new maximal balanced
clique enumeration algorithm by exploiting the unique properties of signed
networks. Based on the new proposed algorithm, we devise two optimization
strategies to further improve the efficiency of the enumeration. For the MBCS
problem, we propose a new search framework based on search space partition. To
further improve the efficiency of the new framework, we propose multiple
optimization strategies regarding to redundant search branches and invalid
candidates. We conduct extensive experiments on large real datasets. The
experimental results demonstrate the efficiency, effectiveness and scalability
of our proposed algorithms for MBCE problem and MBCS problem.


title:
Rational Hodge isometries of hyper-Kahler varieties of K3[n]-type are
  algebraic

abstract:
  Let X and Y be compact hyper-Kahler manifolds deformation equivalence to the
Hilbert scheme of length n subschemes of a K3 surface. A cohomology class in
their product XxY is an analytic correspondence, if it belongs to the subring
generated by Chern classes of coherent analytic sheaves. Let f be a Hodge
isometry of their second rational cohomologies with respect to the
Beauville-Bogomolov-Fujiki pairings. We prove that f is induced by an analytic
correspondence. We furthermore lift f to an analytic correspondence F between
their total rational cohomologies, which is a Hodge isometry with respect to
the Mukai pairings, and which preserves the gradings up to sign. When X and Y
are projective the correspondences f and F are algebraic.


title: Properly Proximal von Neumann Algebras

abstract:
  We introduce the notion of proper proximality for finite von Neumann
algebras, which naturally extends the notion of proper proximality for groups.
Apart from the group von Neumann algebras of properly proximal groups, we
provide a number of additional examples, including examples in the settings of
free products, crossed products, and compact quantum groups. Using this notion,
we answer a question of Popa by showing that the group von Neumann algebra of a
nonamenable inner amenable group cannot embed into a free group factor. We also
introduce a notion of proper proximality for probability measure preserving
actions, which gives an invariant for the orbit equivalence relation. This
gives a new approach for establishing strong ergodicity type properties, and we
use this in the setting of Gaussian actions to expand on solid ergodicity
results first established by Chifan and Ioana, and later generalized by
Boutonnet. The techniques developed also allow us to answer a problem left open
by Anantharaman-Delaroche in 1995, by showing the equivalence between the
Haagerup property and the compact approximation property for II$_1$ factors.


title: The K\"othe dual of mixed Morrey spaces and applications

abstract:
  In this paper, we study the separable and weak convergence of mixed-norm
Lebesgue spaces. Furthermore, we prove that the block space
$\mathcal{B}_{\vec{p}\,'}^{p'_0}(\mathbb{R}^n)$ is the K\"othe dual of the
mixed Morrey space $\mathcal{M}_{\vec{p}}^{p_0}(\mathbb{R}^n)$ by the Fatou
property of these block spaces. The boundedness of the Hardy--Littlewood
maximal function is further obtained on the block space
$\mathcal{B}_{\vec{p}\,'}^{p'_0}(\mathbb{R}^n)$. As applications, the
characterizations of $BMO(\mathbb{R}^n)$ via the commutators of the fractional
integral operator $I_{\alpha}$ on mixed Morrey spaces are proved as well as the
block space $\mathcal{B}_{\vec{p}\,'}^{p'_0}(\mathbb{R}^n)$.


title:
Size distribution of primary submicron particles and larger aggregates
  in solvent induced asphaltene precipitation

abstract:
  Asphaltene precipitation is a crucial phase separation phenomenon in the oil
industry, especially in paraffinic froth treatment to extract bitumen from oil
sands ores. This work reveals the formation of particles at 0.2 to 0.4 um in
radius, defined as a primary sub-micron particle (PSMP), which is ubiquitous
from diffusive mixing between asphaltene solution and any of 23 types of
precipitants examined in our experiments. The yield and quantity of asphaltene
particles are affected not only by the Hildebrand solubility parameter of the
precipitants but also by the diffusion coefficients of the asphaltene solution
and the precipitant. The Population Balance Model (PBM) with the Hildebrand
solubility parameter has been used to model particle size distribution. Good
agreement has been achieved between numerical predictions and the experimental
data. It indicates that the colloid theory can describe the size distribution
of PSMP and larger aggregates. Therefore, this study provides new insight into
the mechanism for the dependence of yield and size distribution of the
precipitated asphaltene particles on the composition of precipitants and adding
inhibitors. Controlling of asphaltene yield and size distribution may be
applied to the process of hydrocarbon separation or asphaltene precipitation
prevention.


title:
Ab-initio study of the effect of bromide mixing into RbPbI$_3$ on the
  structural, electronic and optical properties

abstract:
  The ultra-high efficiency and cost-effective photovoltaics based on halide
preovskites have brought a revolution to ongoing photovoltaic research,
surpassing the expectations of the scientific community. However, structural
stability is a severe issue that hinders their wide-scale integration at the
device level. Compositional engineering with the halide mixing has become an
efficient way to deal with this issue without compromising device efficiency.
Herein, the structural, electronic and optical properties of the bromide mixed
orthorhombic $\rm{{RbPb(I_{1-x}Br_x)_3}}$ (where, $\rm{x}=0.25$, $0.50$ and
$0.75$) are calculated using the density functional theory. The electronic
bandstructure and density of states (DOS) are calculated using both PBE
(Perdew-Burke-Ernzerhof) and TB-mBJ (Tran Blaha modified Becke Johnson)
potential. The lowest energy bandgaps of $2.288$ and $2.986$ eV for bromide
mixing of $\rm{x}=0.50$ are obtained using PBE and TB-mBJ, respectively. In
contrast, the mixed bromide phases possess a smaller effective mass,
facilitating a better carrier transport through the mixed halide. Using PBE,
the excitons appear to be the Mott-Wannier type. However, the TB-mBJ predicts
the exciton to be Frenkel type for bromide mixing of $\rm{x}=0.75$ and a
Mott-Wannier type for all other mixing under study. The spectroscopic limited
maximum efficiency (SLME) is observed to be at the highest values of $14.0$\%
and $4.1$\% for the equal admixture of I and Br using PBE and TB-mBJ,
respectively. The calculated properties are consistent with the reported data
of the similar structures.


title:
Comment on "First-order methods almost always avoid strict saddle
  points"

abstract:
  The analysis on the global stability of Riemannian gradient descent method in
manifold optimization (i.e., it avoids strict saddle points for almost all
initializations) due to Lee et al. (Math. Program. 176:311-337) is corrected.
Moreover, an explicit bound on the step-size is presented by the newly
introduced retraction L-smooth property.


title:
Birth places of extreme ultraviolet waves driven by impingement of solar
  jets upon coronal loops

abstract:
  Solar extreme ultraviolet (EUV) waves are large-scale propagating
disturbances in the corona. It is generally believed that the vital key for the
formation of EUV waves is the rapid expansion of the loops that overlie
erupting cores in solar eruptions, such as coronal mass ejections (CMEs) and
solar jets. However, the details of the interaction between the erupting cores
and overlying loops are not clear, because that the overlying loops are always
instantly opened after the energetic eruptions. Here, we present three typical
jet-driven EUV waves without CME to study the interaction between the jets and
the overlying loops that remained closed during the events. All three jets
emanated from magnetic flux cancelation sites in source regions. Interestingly,
after the interactions between jets and overlying loops, three EUV waves
respectively formed ahead of the top, the near end (close to the jet source),
and the far (another) end of the overlying loops. According to the magnetic
field distribution of the loops extrapolated from Potential Field Source
Surface method, it is confirmed that the birth places of three jet-driven EUV
waves were around the weakest magnetic field strength part of the overlying
loops. We suggest that the jet-driven EUV waves preferentially occur at the
weakest part of the overlying loops, and the location can be subject to the
magnetic field intensity around the ends of the loops.


title:
Estimating the Jacobian matrix of an unknown multivariate function from
  sample values by means of a neural network

abstract:
  We describe, implement and test a novel method for training neural networks
to estimate the Jacobian matrix $J$ of an unknown multivariate function $F$.
The training set is constructed from finitely many pairs $(x,F(x))$ and it
contains no explicit information about $J$. The loss function for
backpropagation is based on linear approximations and on a nearest neighbor
search in the sample data. We formally establish an upper bound on the uniform
norm of the error, in operator norm, between the estimated Jacobian matrix
provided by the algorithm and the actual Jacobian matrix, under natural
assumptions on the function, on the training set and on the loss of the neural
network during training.
  The Jacobian matrix of a multivariate function contains a wealth of
information about the function and it has numerous applications in science and
engineering. The method given here represents a step in moving from black-box
approximations of functions by neural networks to approximations that provide
some structural information about the function in question.


title:
The characteristic polynomial of sums of random permutations and regular
  digraphs

abstract:
  Let $A_n$ be the sum of $d$ permutations matrices of size $n\times n$, each
drawn uniformly at random and independently. We prove that $\det(I_n - z
A_n/\sqrt{d})$ converges when $n\to \infty$ towards a random analytic function
on the unit disk. As an application, we obtain an elementary proof of the
spectral gap of random regular digraphs. Our results are valid both in the
regime where $d$ is fixed and for $d$ slowly growing with $n$.


title: Givens QR Decomposition over Relational Databases

abstract:
  This paper introduces Figaro, an algorithm for computing the upper-triangular
matrix in the QR decomposition of the matrix defined by the natural join over a
relational database. The QR decomposition lies at the core of many linear
algebra techniques and their machine learning applications, including: the
matrix inverse; the least squares; the singular value decomposition; eigenvalue
problems; and the principal component analysis.
  Figaro's main novelty is that it pushes the QR decomposition past the join.
This leads to several desirable properties. For acyclic joins, it takes time
linear in the database size and independent of the join size. Its execution is
equivalent to the application of a sequence of Givens rotations proportional to
the join size. Its number of rounding errors relative to the classical QR
decomposition algorithms is on par with the input size relative to the join
size.
  In experiments with real-world and synthetic databases, Figaro outperforms
both in runtime performance and accuracy the LAPACK libraries openblas and
Intel MKL by a factor proportional to the gap between the join output and input
sizes.


title: LDP-IDS: Local Differential Privacy for Infinite Data Streams

abstract:
  Streaming data collection is essential to real-time data analytics in various
IoTs and mobile device-based systems, which, however, may expose end users'
privacy. Local differential privacy (LDP) is a promising solution to
privacy-preserving data collection and analysis. However, existing few LDP
studies over streams are either applicable to finite streams only or suffering
from insufficient protection. This paper investigates this problem by proposing
LDP-IDS, a novel $w$-event LDP paradigm to provide practical privacy guarantee
for infinite streams at users end, and adapting the popular budget division
framework in centralized differential privacy (CDP). By constructing a unified
error analysi for LDP, we first develop two adatpive budget division-based LDP
methods for LDP-IDS that can enhance data utility via leveraging the
non-deterministic sparsity in streams. Beyond that, we further propose a novel
population division framework that can not only avoid the high sensitivity of
LDP noise to budget division but also require significantly less communication.
Based on the framework, we also present two adaptive population division
methods for LDP-IDS with theoretical analysis. We conduct extensive experiments
on synthetic and real-world datasets to evaluate the effectiveness and
efficiency pf our proposed frameworks and methods. Experimental results
demonstrate that, despite the effectiveness of the adaptive budget division
methods, the proposed population division framework and methods can further
achieve much higher effectiveness and efficiency.


title:
Coupling and selecting constraints in Bayesian optimization under
  uncertainties

abstract:
  We consider chance constrained optimization where it is sought to optimize a
function while complying with constraints, both of which are affected by
uncertainties. The high computational cost of realistic simulations strongly
limits the number of evaluations and makes this type of problems particularly
challenging. In such a context, it is common to rely on Bayesian optimization
algorithms. Assuming, without loss of generality, that the uncertainty comes
from some of the inputs, it becomes possible to build a Gaussian process model
in the joint space of design and uncertain variables. A two-step acquisition
function is then used to provide, both, promising optimization variables
associated to relevant uncertain samples. Our overall contribution is to
correlate the constraints in the GP model and exploit this to optimally decide,
at each iteration, which constraint should be evaluated and at which point. The
coupled Gaussian model of the constraints relies on an output-as-input
encoding. The constraint selection idea is developed by enabling that each
constraint can be evaluated for a different uncertain input, thus improving the
refinement efficiency. Constraints coupling and selection are gradually
implement in 3 algorithm variants which are compared to a reference Bayesian
approach. The results are promising in terms of convergence speed, accuracy and
stability as observed on a 2, a 4 and a 27-dimensional problems.


title:
The MURALES survey. VI. Properties and origin of the extended line
  emission structures in radio galaxies

abstract:
  This is the sixth paper presenting the results of the MUse RAdio Loud
Emission line Snapshot survey (MURALES). We observed 37 radio sources from the
3C sample with z<0.3 and declination <20 degrees with the MUSE optical integral
field spectrograph at the VLT. We here focus on the properties of the extended
emission line regions (EELRs) that can be studied with unprecedented detail
thanks to the depth of these observations. Line emission in the 10 FRIs is, in
most cases, confined to within <4 kpc, while large-scale (>4 kpc) ionized gas
is seen in all but two of the 26 FRIIs. It usually takes the form of elongated
or filamentary structures, typically extending between 10 and 30 kpc, but also
reaching distances of ~80 kpc. We find that 1) the large-scale ionized gas
structures show a tendency to be oriented at large angles from the radio axis,
and 2) the gas on a scale of a few kpc from the nucleus often shows ordered
rotation with a kinematical axis forming a median angle of 65 degrees with the
radio axis. We also discuss the velocity field and ionization properties of the
EELRs. The observed emission line structures appear to be associated with
gaseous "superdisks" formed after a gas rich merger. The different properties
of the EELR can be explained with a combination of the source evolutionary
state and the orientation of the "superdisk" with respect to the radio axis.
The general alignment between the superdisks and radio axis might be produced
by stable and coherent accretion maintained over long timescales.


title: On Distributed Exact Sparse Linear Regression over Networks

abstract:
  In this work, we propose an algorithm for solving exact sparse linear
regression problems over a network in a distributed manner. Particularly, we
consider the problem where data is stored among different computers or agents
that seek to collaboratively find a common regressor with a specified sparsity
k, i.e., the L0-norm is less than or equal to k. Contrary to existing
literature that uses L1 regularization to approximate sparseness, we solve the
problem with exact sparsity k. The main novelty in our proposal lies in showing
a problem formulation with zero duality gap for which we adopt a dual approach
to solve the problem in a decentralized way. This sets a foundational approach
for the study of distributed optimization with explicit sparsity constraints.
We show theoretically and empirically that, under appropriate assumptions,
where each agent solves smaller and local integer programming problems, all
agents will eventually reach a consensus on the same sparse optimal regressor.


title:
Consumption-investment decisions with endogenous reference point and
  drawdown constraint

abstract:
  We propose a consumption-investment decision model where past consumption
peak $h$ plays a crucial role. There are two important consumption levels: the
lowest constrained level and a reference level, at which the risk aversion in
terms of consumption rate is changed. We solve this stochastic control problem
and derive the optimal value function, optimal consumption plan, and optimal
investment strategy in semi-explicit forms. We find five important thresholds
of wealth, all as functions of $h$, and most of them are implicit nonlinear
functions. As can be seen from numerical results, this intuitive and simple
model has significant economic implications and there are at least three
important predictions: the marginal propensity to consume out of wealth is
generally decreasing in wealth but can be increasing with an intermediate level
of wealth; the implied relative risk aversion is a smile in wealth; the welfare
of the poor is more vulnerable to wealth shocks than the wealthy.


title:
Self-adjusting Population Sizes for the $(1, \lambda)$-EA on Monotone
  Functions

abstract:
  We study the $(1,\lambda)$-EA with mutation rate $c/n$ for $c\le 1$, where
the population size is adaptively controlled with the $(1:s+1)$-success rule.
Recently, Hevia Fajardo and Sudholt have shown that this setup with $c=1$ is
efficient on \onemax for $s<1$, but inefficient if $s \ge 18$. Surprisingly,
the hardest part is not close to the optimum, but rather at linear distance. We
show that this behavior is not specific to \onemax. If $s$ is small, then the
algorithm is efficient on all monotone functions, and if $s$ is large, then it
needs superpolynomial time on all monotone functions. In the former case, for
$c<1$ we show a $O(n)$ upper bound for the number of generations and $O(n\log
n)$ for the number of function evaluations, and for $c=1$ we show $O(n\log n)$
generations and $O(n^2\log\log n)$ evaluations. We also show formally that
optimization is always fast, regardless of $s$, if the algorithm starts in
proximity of the optimum. All results also hold in a dynamic environment where
the fitness function changes in each generation.


title:
An Approximate MSE Expression for Maximum Likelihood and Other
  Implicitly Defined Estimators of Non-Random Parameters (extended version)

abstract:
  An approximate mean square error (MSE) expression for the performance
analysis of implicitly defined estimators of non-random parameters is proposed.
An implicitly defined estimator (IDE) declares the minimizer/maximizer of a
selected cost/reward function as the parameter estimate. The maximum likelihood
(ML) and the least squares estimators are among the well known examples of this
class. In this paper, an exact MSE expression for implicitly defined estimators
with a symmetric and unimodal objective function is given. It is shown that the
expression reduces to the Cramer-Rao lower bound (CRLB) and misspecified CRLB
in the large sample size regime for ML and misspecified ML estimation,
respectively. The expression is shown to yield the Ziv-Zakai bound (without the
valley filling function) when it is used in a Bayesian setting, that is, when
an a-priori distribution is assigned to the unknown parameter. In addition,
extension of the suggested expression to the case of nuisance parameters is
studied and some approximations are given to ease the computations for this
case. Numerical results indicate that the suggested MSE expression not only
predicts the estimator performance in the asymptotic region; but it is also
applicable for the threshold region analysis, even for IDEs whose objective
functions do not satisfy the symmetry and unimodality assumptions. Advantages
of the suggested MSE expression are its conceptual simplicity and its
relatively straightforward numerical calculation due to the reduction of the
estimation problem to a binary hypothesis testing problem, similar to the usage
of Ziv-Zakai bounds in random parameter estimation problems.


title:
A removable temperature sensors holder for cryogenic Rayleigh-B\'enard
  convection cell

abstract:
  A removable holder carrying a set of miniature temperature sensors inside our
Rayleigh-B\'enard convection cylindrical cell has been developed. The sensors
should not disturb the temperature and velocity fields of the buoyancy-driven
turbulent flow of our working fluid - cryogenic helium gas (~ 5 K). The cell,
300 mm both in diameter and height, must withstand the overpressure up to 2.5
bar, which is why all its parts are welded together. The holder consists of a
pair of identical feedthroughs (each with 6 glass-sealed pins), constantan
wires (0.1 mm), Be bronze springs and insulating elements and enables
installing up to 6 sensors simultaneously, with the option to replace the
damaged ones. We already used two such holders and obtained a map of local
temperature and its fluctuations in 12 positions inside the cell.


title:
Thermodynamically consistent versions of approximations used in
  modelling moist air

abstract:
  Some existing approaches to modeling the thermodynamics of moist air make
approximations that break $\textit{thermodynamic consistency}$, such that the
resulting thermodynamics do not obey the 1st and 2nd laws or have other
inconsistencies. Recently, an approach to avoid such inconsistency has been
suggested: the use of $\textit{thermodynamic potentials}$ in terms of their
$\textit{natural variables}$, from which all thermodynamic quantities and
relationships are derived. In this paper, we develop this approach for
$\textit{unapproximated}$ moist air thermodynamics and two widely used
approximations: the constant $\kappa$ approximation and the dry heat capacities
approximation. The consistent constant $\kappa$ approximation is particularly
attractive because it leads to, when using virtual potential temperature
$\theta_v$ as the thermodynamic variable, adiabatic dynamics that depend only
on total mass, independent of the breakdown between water forms. Additionally,
a wide variety of material from different sources in the literature on
thermodynamics in atmospheric modelling is brought together. It is hoped that
this paper provides a comprehensive reference for the use of thermodynamic
potentials in atmospheric modelling, especially for the three systems
considered here.


title: Hydrodynamics of warps in the local model of astrophysical discs

abstract:
  We show how the local approximation of astrophysical discs, which is the
basis for the well known model of the shearing box, can be used to study many
aspects of the dynamics of warped discs. In the local model, inclination of the
orbit of a test particle with respect to the reference orbit corresponds to a
vertical oscillation of the particle at the orbital frequency. Warping of a
disc corresponds to a locally axisymmetric corrugation of the midplane of the
disc that oscillates vertically at the orbital frequency, while evolution of
the warp corresponds to a modulation of the complex amplitude of the vertical
oscillation. We derive a conservation law for this amplitude that is the local
equivalent of the conservation of angular momentum and therefore governs the
evolution of the warp. For lengthscales that are long compared to the vertical
scaleheight of the disc, the known non-resonant and resonant regimes of warp
dynamics, including the diffusive and wavelike regimes of Keplerian discs,
occur in the local model in the same way as in a global view of warped discs.


title:
Semi-FairVAE: Semi-supervised Fair Representation Learning with
  Adversarial Variational Autoencoder

abstract:
  Adversarial learning is a widely used technique in fair representation
learning to remove the biases on sensitive attributes from data
representations. It usually requires to incorporate the sensitive attribute
labels as prediction targets. However, in many scenarios the sensitive
attribute labels of many samples can be unknown, and it is difficult to train a
strong discriminator based on the scarce data with observed attribute labels,
which may lead to generate unfair representations. In this paper, we propose a
semi-supervised fair representation learning approach based on adversarial
variational autoencoder, which can reduce the dependency of adversarial fair
models on data with labeled sensitive attributes. More specifically, we use a
bias-aware model to capture inherent bias information on sensitive attribute by
accurately predicting sensitive attributes from input data, and we use a
bias-free model to learn debiased fair representations by using adversarial
learning to remove bias information from them. The hidden representations
learned by the two models are regularized to be orthogonal. In addition, the
soft labels predicted by the two models are further integrated into a
semi-supervised variational autoencoder to reconstruct the input data, and we
apply an additional entropy regularization to encourage the attribute labels
inferred from the bias-free model to be high-entropy. In this way, the
bias-aware model can better capture attribute information while the bias-free
model is less discriminative on sensitive attributes if the input data is well
reconstructed. Extensive experiments on two datasets for different tasks
validate that our approach can achieve good representation learning fairness
under limited data with sensitive attribute labels.


title:
Wall-modeled large-eddy simulation of three-dimensional turbulent
  boundary layer in a bent square duct

abstract:
  We conduct wall-modeled LES (WMLES) of a pressure-driven three-dimensional
turbulent boundary layer (3DTBL) developing on the floor of a bent square duct
to investigate the predictive capability of three widely used wall models,
namely, a simple equilibrium stress model, an integral nonequilibrium model,
and a PDE nonequilibrium model. The numerical results are compared with the
experiment of Schwarz and Bradshaw (J. Fluid Mech. (1994), vol. 272, pp.
183-210). While the wall-stress magnitudes predicted by the three wall models
are comparable, the PDE nonequilibrium wall model produces a substantially more
accurate prediction of the wall-stress direction, followed by the integral
nonequilibrium wall model. The wall-stress direction from the wall models is
shown to have separable contributions from the equilibrium stress part and the
integrated nonequilibrium effects, where how the latter is modeled differs
among the wall models. The triangular plot of the wall-model solution reveals
different capabilities of the wall models in representing variation of flow
direction along the wall-normal direction. On the contrary, the outer LES
solution is unaffected by the type of wall model used, resulting in nearly
identical predictions of the mean and turbulent statistics in the outer region
for all the wall models. This is explained by the vorticity dynamics and the
inviscid skewing mechanism of generating the mean three-dimensionality.
Finally, the LES solution in the outer layer is used to study the anisotropy of
turbulence. In contrast to the canonical 2D wall turbulence, the Reynolds
stress anisotropy exhibit strong non-monotonic behavior with increasing wall
distance.


title:
Hierarchical model reduction driven by machine learning for parametric
  advection-diffusion-reaction problems in the presence of noisy data

abstract:
  We propose a new approach to generate a reliable reduced model for a
parametric elliptic problem, in the presence of noisy data. The reference model
reduction procedure is the directional HiPOD method, which combines
Hierarchical Model reduction with a standard Proper Orthogonal Decomposition,
according to an offline/online paradigm. In this paper we show that directional
HiPOD looses in terms of accuracy when problem data are affected by noise. This
is due to the interpolation driving the online phase, since it replicates, by
definition, the noise trend. To overcome this limit, we replace interpolation
with Machine Learning fitting models which better discriminate relevant
physical features in the data from irrelevant unstructured noise. The numerical
assessment, although preliminary, confirms the potentialities of the new
approach.


title: End-to-end Learnable Diversity-aware News Recommendation

abstract:
  Diversity is an important factor in providing high-quality personalized news
recommendations. However, most existing news recommendation methods only aim to
optimize recommendation accuracy while ignoring diversity. Reranking is a
widely used post-processing technique to promote the diversity of top
recommendation results. However, the recommendation model is not perfect and
errors may be propagated and amplified in a cascaded recommendation algorithm.
In addition, the recommendation model itself is not diversity-aware, making it
difficult to achieve a good tradeoff between recommendation accuracy and
diversity. In this paper, we propose a news recommendation approach named
LeaDivRec, which is a fully learnable model that can generate diversity-aware
news recommendations in an end-to-end manner. Different from existing news
recommendation methods that are usually based on point- or pair-wise ranking,
in LeaDivRec we propose a more effective list-wise news recommendation model.
More specifically, we propose a permutation Transformer to consider the
relatedness between candidate news and meanwhile can learn different
representations for similar candidate news to help improve recommendation
diversity. We also propose an effective list-wise training method to learn
accurate ranking models. In addition, we propose a diversity-aware
regularization method to further encourage the model to make controllable
diversity-aware recommendations. Extensive experiments on two real-world
datasets validate the effectiveness of our approach in balancing recommendation
accuracy and diversity.


title:
End-to-End Integration of Speech Recognition, Speech Enhancement, and
  Self-Supervised Learning Representation

abstract:
  This work presents our end-to-end (E2E) automatic speech recognition (ASR)
model targetting at robust speech recognition, called Integraded speech
Recognition with enhanced speech Input for Self-supervised learning
representation (IRIS). Compared with conventional E2E ASR models, the proposed
E2E model integrates two important modules including a speech enhancement (SE)
module and a self-supervised learning representation (SSLR) module. The SE
module enhances the noisy speech. Then the SSLR module extracts features from
enhanced speech to be used for speech recognition (ASR). To train the proposed
model, we establish an efficient learning scheme. Evaluation results on the
monaural CHiME-4 task show that the IRIS model achieves the best performance
reported in the literature for the single-channel CHiME-4 benchmark (2.0% for
the real development and 3.9% for the real test) thanks to the powerful
pre-trained SSLR module and the fine-tuned SE module.


title:
FairRank: Fairness-aware Single-tower Ranking Framework for News
  Recommendation

abstract:
  Single-tower models are widely used in the ranking stage of news
recommendation to accurately rank candidate news according to their
fine-grained relatedness with user interest indicated by user behaviors.
However, these models can easily inherit the biases related to users' sensitive
attributes (e.g., demographics) encoded in training click data, and may
generate recommendation results that are unfair to users with certain
attributes. In this paper, we propose FairRank, which is a fairness-aware
single-tower ranking framework for news recommendation. Since candidate news
selection can be biased, we propose to use a shared candidate-aware user model
to match user interest with a real displayed candidate news and a random news,
respectively, to learn a candidate-aware user embedding that reflects user
interest in candidate news and a candidate-invariant user embedding that
indicates intrinsic user interest. We apply adversarial learning to both of
them to reduce the biases brought by sensitive user attributes. In addition, we
use a KL loss to regularize the attribute labels inferred from the two user
embeddings to be similar, which can make the model capture less candidate-aware
bias information. Extensive experiments on two datasets show that FairRank can
improve the fairness of various single-tower news ranking models with minor
performance losses.


title:
Detecting changes in dynamic social networks using multiply-labeled
  movement data

abstract:
  The social structure of an animal population can often influence movement and
inform researchers on a species' behavioral tendencies. Animal social networks
can be studied through movement data; however, modern sources of data can have
identification issues that result in multiply-labeled individuals. Since all
available social movement models rely on unique labels, we extend an existing
Bayesian hierarchical movement model in a way that makes use of a latent social
network and accommodates multiply-labeled movement data (MLMD). We apply our
model to drone-measured movement data from Risso's dolphins (Grampus griseus)
and estimate the effects of sonar exposure on the dolphins' social structure.
Our proposed framework can be applied to MLMD for various social movement
applications.


title:
Complementarity of experimental and lattice QCD data on pion parton
  distributions

abstract:
  We extract pion parton distribution functions (PDFs) in a Monte Carlo global
QCD analysis of experimental data together with reduced Ioffe time
pseudo-distributions and matrix elements of current-current correlators
generated from lattice QCD. By including both experimental and lattice QCD
data, our analysis rigorously quantifies both the uncertainties of the pion
PDFs and systematic effects intrinsic to the lattice QCD observables. The
reduced Ioffe time pseudo-distributions significantly decrease the
uncertainties on the PDFs, while the current-current correlators are limited by
the systematic effects associated with the lattice. Consistent with recent
phenomenological determinations, the behavior of the valence quark distribution
of the pion at large momentum fraction is found to be $\sim (1-x)^{ \beta_{\rm
eff}}$ with $\beta_{\rm eff} \approx 1.0-1.2$.


title:
Matters of Gravity: The Newsletter of the Division of Gravitational
  Physics of the American Physical Society. Number 55. April 2022

abstract:
  Contents: APS April Meeting, by Aaron Zimmerman; Cosmic Explorer, the Next
Exploration Step, by Varun Srivastava, Kathryne J. Daniel, and Stefan Ballmer


title:
A Novel Multimodal Approach for Studying the Dynamics of Curiosity in
  Small Group Learning

abstract:
  Curiosity is a vital metacognitive skill in educational contexts, leading to
creativity, and a love of learning. And while many school systems increasingly
undercut curiosity by teaching to the test, teachers are increasingly
interested in how to evoke curiosity in their students to prepare them for a
world in which lifelong learning and reskilling will be more and more
important. One aspect of curiosity that has received little attention, however,
is the role of peers in eliciting curiosity. We present what we believe to be
the first theoretical framework that articulates an integrated socio-cognitive
account of curiosity that ties observable behaviors in peers to underlying
curiosity states. We make a bipartite distinction between individual and
interpersonal functions that contribute to curiosity, and multimodal behaviors
that fulfill these functions. We validate the proposed framework by leveraging
a longitudinal latent variable modeling approach. Findings confirm a positive
predictive relationship between the latent variables of individual and
interpersonal functions and curiosity, with the interpersonal functions
exercising a comparatively stronger influence. Prominent behavioral
realizations of these functions are also discovered in a data-driven manner. We
instantiate the proposed theoretical framework in a set of strategies and
tactics that can be incorporated into learning technologies to indicate, evoke,
and scaffold curiosity. This work is a step towards designing learning
technologies that can recognize and evoke moment-by-moment curiosity during
learning in social contexts and towards a more complete multimodal learning
analytics. The underlying rationale is applicable more generally for developing
computer support for other metacognitive and socio-emotional skills.


title: Origin of intense electron heating in relativistic blast waves

abstract:
  The modeling of gamma-ray burst afterglow emission bears witness to strong
electron heating in the precursor of Weibel-mediated, relativistic
collisionless shock waves propagating in unmagnetized electron-ion plasmas. In
this Letter, we propose a theoretical model, which describes electron heating
via a Joule-like process caused by pitch-angle scattering in the decelerating,
self-induced microturbulence and the coherent charge-separation field induced
by the difference in inertia between electrons and ions. The emergence of this
electric field across the precursor of electron-ion shocks is confirmed by
large-scale particle-in-cell (PIC) simulations. Integrating the model using a
Monte Carlo-Poisson method, we compare the main observables to the PIC
simulations to conclude that the above mechanism can indeed account for the
bulk of electron heating.


title: A Web-Based Tool for Comparative Process Mining

abstract:
  Process mining techniques enable the analysis of a wide variety of processes
using event data. Among the available process mining techniques, most consider
a single process perspective at a time-in the shape of a model or log. In this
paper, we have developed a tool that can compare and visualize the same process
under different constraints, allowing to analyze multiple aspects of the
process. We describe the architecture, structure and use of the tool, and we
provide an open-source full implementation.


title: Unified and Effective Ensemble Knowledge Distillation

abstract:
  Ensemble knowledge distillation can extract knowledge from multiple teacher
models and encode it into a single student model. Many existing methods learn
and distill the student model on labeled data only. However, the teacher models
are usually learned on the same labeled data, and their predictions have high
correlations with groudtruth labels. Thus, they cannot provide sufficient
knowledge complementary to task labels for student teaching. Distilling on
unseen unlabeled data has the potential to enhance the knowledge transfer from
the teachers to the student. In this paper, we propose a unified and effective
ensemble knowledge distillation method that distills a single student model
from an ensemble of teacher models on both labeled and unlabeled data. Since
different teachers may have diverse prediction correctness on the same sample,
on labeled data we weight the predictions of different teachers according to
their correctness. In addition, we weight the distillation loss based on the
overall prediction correctness of the teacher ensemble to distill high-quality
knowledge. On unlabeled data, there is no groundtruth to evaluate prediction
correctness. Fortunately, the disagreement among teachers is an indication of
sample hardness, and thereby we weight the distillation loss based on teachers'
disagreement to emphasize knowledge distillation on important samples.
Extensive experiments on four datasets show the effectiveness of our proposed
ensemble distillation method.


title: Fluctuation-dissipation relations for spiking neurons

abstract:
  Spontaneous fluctuations and stimulus response are essential features of
neural functioning but how they are connected is poorly understood. I derive
fluctuation-dissipation relations (FDR) between the spontaneous spike and
voltage correlations and the firing rate susceptibility for i) the leaky
integrate-and-fire (IF) model with white noise; ii) an IF model with arbitrary
voltage dependence, an adaptation current, and correlated noise. The FDRs can
be used to derive correlation statistics or to infer the system's response from
observations of its spontaneous activity.


title: The coarse geometry of hexagon decomposition graphs

abstract:
  We define and study graphs associated to hexagon decompositions of surfaces
by curves and arcs. One of the variants is shown to be quasi-isometric to the
pants graph, whereas the other variant is quasi-isometric to (a Cayley graph
of) the mapping class group.


title:
Decomposition of Differences in Distribution under Sample Selection and
  the Gender Wage Gap

abstract:
  I address the decomposition of the differences between the distribution of
outcomes of two groups when individuals self-select themselves into
participation. I differentiate between the decomposition for participants and
the entire population, highlighting how the primitive components of the model
affect each of the distributions of outcomes. Additionally, I introduce two
ancillary decompositions that help uncover the sources of differences in the
distribution of unobservables and participation between the two groups. The
estimation is done using existing quantile regression methods, for which I show
how to perform uniformly valid inference. I illustrate these methods by
revisiting the gender wage gap, finding that changes in female participation
and self-selection have been the main drivers for reducing the gap.


title: Constructing all qutrit controlled Clifford+T gates in Clifford+T

abstract:
  For a number of useful quantum circuits, qudit constructions have been found
which reduce resource requirements compared to the best known or best possible
qubit construction. However, many of the necessary qutrit gates in these
constructions have never been explicitly and efficiently constructed in a
fault-tolerant manner. We show how to exactly and unitarily construct any
qutrit multiple-controlled Clifford+T unitary using just Clifford+T gates and
without using ancillae. The T-count to do so is polynomial in the number of
controls $k$, scaling as $O(k^{3.585})$. With our results we can construct
ancilla-free Clifford+T implementations of multiple-controlled T gates as well
as all versions of the qutrit multiple-controlled Toffoli, while the analogous
results for qubits are impossible. As an application of our results, we provide
a procedure to implement any ternary classical reversible function on $n$ trits
as an ancilla-free qutrit unitary using $O(3^n n^{3.585})$ T gates.


title: Magnetic Kagome Superconductor CeRu$_2$

abstract:
  Materials with a kagome lattice provide a platform for searching for new
electronic phases and investigating the interplay between correlation and
topology. Various probes have recently shown that the kagome lattice can host
diverse quantum phases with intertwined orders, including charge density wave
states, bond density wave states, chiral charge order, and, rarely,
superconductivity. However, reports of the coexistence of superconductivity and
magnetic order in kagome materials remain elusive. Here we revisit a magnetic
superconductor CeRu$_2$ with a kagome network formed by Ru atoms. Our
first-principles calculations revealed a kagome flat band near the Fermi
surface, indicative of flat-band magnetism. At ambient pressure, CeRu$_2$
exhibits a superconducting transition temperature ($T_{\text{c}}$) up to ~ 6 K
and a magnetic order at ~ 40 K. Notably, superconductivity and related behavior
can be tuned by adjusting the amount of Ru. We conducted a systematic
investigation of the superconductivity and magnetic order in CeRu$_2$ via
magnetic, resistivity, and structural measurements under pressure up to ~ 168
GPa. An unusual phase diagram that suggests an intriguing interplay between the
compound's superconducting order parameters has been constructed. A
$T_{\text{c}}$ resurgence was observed above pressure of ~ 28 GPa, accompanied
by the sudden appearance of a secondary superconducting transition. Our
experiments have identified tantalizing phase transitions driven by high
pressure and suggest that the superconductivity and magnetism in CeRu$_2$ are
strongly intertwined.