title:
Formation of proto-globular cluster candidates in cosmological
  simulations of dwarf galaxies at $z>4$

abstract:
  We perform cosmological hydrodynamical simulations to study the formation of
proto-globular cluster candidates in progenitors of present-day dwarf galaxies
$(M_{\rm vir} \approx 10^{10}\, {\rm M}_\odot$ at $z=0$) as part of the
"Feedback in Realistic Environment" (FIRE) project. Compact ($r_{1/2}<30$ pc),
relatively massive ($0.5 \times 10^5 \lesssim M_{\star}/{\rm M}_\odot \lesssim
5\times10^5$), self-bound stellar clusters form at $11\gtrsim z \gtrsim 5$ in
progenitors with $M_{\rm vir} \approx 10^9\,{\rm M}_\odot$. Cluster formation
is triggered when at least $10^7\,{\rm M}_\odot$ of dense, turbulent gas
reaches $\Sigma_{\rm gas} \approx 10^4\, {\rm M}_\odot\, {\rm pc}^{-2}$ as a
result of the compressive effects of supernova feedback or from cloud-cloud
collisions. The clusters can survive for $2-3\,{\rm Gyr}$; absent numerical
effects, they would likely survive substantially longer, perhaps to $z=0$. The
longest-lived clusters are those that form at significant distance -- several
hundreds of pc -- from their host galaxy. We therefore predict that globular
clusters forming in progenitors of present-day dwarf galaxies will be offset
from any pre-existing stars within their host dark matter halos as opposed to
deeply embedded within a well-defined galaxy. Properties of the nascent
clusters are consistent with observations of some of the faintest and most
compact high-redshift sources in \textit{Hubble Space Telescope} lensing fields
and are at the edge of what will be detectable as point sources in deep imaging
of non-lensed fields with the \textit{James Webb Space Telescope}. By contrast,
the star clusters' host galaxies will remain undetectable.


title: Freezing Out Fluctuations in Hydro+ Near the QCD Critical Point

abstract:
  We introduce a freeze-out procedure to convert the critical fluctuations in a
droplet of quark-gluon plasma (QGP) that has, as it expanded and cooled, passed
close to a posited critical point on the phase diagram into cumulants of hadron
multiplicities that can subsequently be measured. The procedure connects the
out-of-equilibrium critical fluctuations described in concert with the
hydrodynamic evolution of the droplet of QGP by extended hydrodynamics, known
as Hydro+, with the subsequent kinetic description in terms of observable
hadrons. We introduce a critical scalar isoscalar field sigma whose
fluctuations cause correlations between observed hadrons due to the couplings
of the sigma field to the hadrons via their masses. We match the QGP
fluctuations obtained by solving the Hydro+ equations describing the evolution
of critical fluctuations before freeze-out to the correlations of the sigma
field. In turn, these are imprinted onto correlations and fluctuations in the
multiplicity of hadrons, most importantly protons, after freeze-out via the
generalization of the familiar half-century-old Cooper-Frye freeze-out
prescription which we introduce. The proposed framework allows us to study the
effects of critical slowing down and the consequent deviation of the observable
predictions from equilibrium expectations quantitatively. We also quantify the
suppression of cumulants due to conservation of baryon number. We demonstrate
the procedure in practice by freezing out a Hydro+ simulation in an azimuthally
symmetric and boost invariant background that includes radial flow discussed in
arXiv:1908.08539.


title: A highly settled disk around Oph 163131

abstract:
  High dust density in the midplane of protoplanetary disks is favorable for
efficient grain growth and can allow fast formation of planetesimals and
planets, before disks dissipate. Vertical settling and dust trapping in
pressure maxima are two mechanisms allowing dust to concentrate in
geometrically thin and high density regions. In this work, we aim to study
these mechanisms in the highly inclined protoplanetary disk SSTC2D
J163131.2-242627 (Oph163131, i~84deg). We present new high angular resolution
continuum and 12CO ALMA observations of Oph163131. The gas emission appears
significantly more extended in the vertical and radial direction compared to
the dust emission, consistent with vertical settling and possibly radial drift.
In addition, the new continuum observations reveal two clear rings. The outer
ring, located at ~100 au, is well resolved in the observations, which allows us
to put stringent constraints on the vertical extent of millimeter dust
particles. We model the disk using radiative transfer and find that the scale
height of millimeter sized grains is 0.5au or less at 100au from the central
star. This value is about one order of magnitude smaller than the scale height
of smaller micron-sized dust grains constrained by previous modeling, which
implies that efficient settling of the large grains is occurring in the disk.
When adopting a parametric dust settling prescription, we find that the
observations are consistent with a turbulent viscosity coefficient of about
alpha<=10^-5 at 100au. Finally, we find that the thin dust scale height
measured in Oph163131 is favorable for planetary growth by pebble accretion: a
10 M_Earth planet may grow within less than 10 Myr, even in orbits exceeding
50au.


title:
Data-driven extraction of the substructure of quark and gluon jets in
  proton-proton and heavy-ion collisions

abstract:
  The different modification of quark- and gluon-initiated jets in the
quark-gluon plasma produced in heavy-ion collisions is a long-standing question
that has not yet received a definitive answer from experiments. In particular,
the relative sizes of the modification of quark and gluon jets differ between
theoretical models. Therefore a fully data-driven technique is crucial for an
unbiased extraction of the quark and gluon jet spectra and substructure. We
perform a proof-of-concept study based on proton-proton and heavy-ion collision
events from the PYQUEN generator with statistics accessible in Run 4 of the
Large Hadron Collider. We use a statistical technique called topic modeling to
separate quark and gluon contributions to jet observables. We demonstrate that
jet substructure observables, such as the jet shape and jet fragmentation
function, can be extracted using this data-driven method. These results suggest
the potential for an experimental determination of quark and gluon jet spectra
and their substructure.


title:
Application of Dimensional Reduction in Artificial Neural Networks to
  Improve Emergency Department Triage During Chemical Mass Casualty Incidents

abstract:
  Chemical Mass Casualty Incidents (MCI) place a heavy burden on hospital staff
and resources. Machine Learning (ML) tools can provide efficient decision
support to caregivers. However, ML models require large volumes of data for the
most accurate results, which is typically not feasible in the chaotic nature of
a chemical MCI. This study examines the application of four statistical
dimension reduction techniques: Random Selection, Covariance/Variance,
Pearson's Linear Correlation, and Principle Component Analysis to reduce a
dataset of 311 hazardous chemicals and 79 related signs and symptoms (SSx). An
Artificial Neural Network pipeline was developed to create comparative models.
Results show that the number of signs and symptoms needed to determine a
chemical culprit can be reduced to nearly 40 SSx without losing significant
model accuracy. Evidence also suggests that the application of dimension
reduction methods can improve ANN model performance accuracy.


title:
Towards reconciliation of completely positive open system dynamics with
  the equilibration postulate

abstract:
  Almost every quantum system interacts with a large environment, so the exact
quantum mechanical description of its evolution is impossible. One has to
resort to approximate description, usually in the form of a master equation.
There are at least two basic requirements for such a description: first, it
should preserve the positivity of probabilities; second, it should correctly
describe the equilibration process for systems coupled to a single thermal
bath. Existing two widespread descriptions of evolution fail to satisfy at
least one of those conditions. The so-called Davies master equation, while
preserving the positivity of probabilities, fails to describe thermalization
properly. On the other hand, the Bloch-Redfield master equation violates the
first condition, but it correctly describes equilibration, at least for
off-diagonal elements for several important scenarios. However, is it possible
to have a description of open system dynamics that would share both features?
In this paper, we partially resolve this problem in the weak-coupling limit:
(i) We provide a general form of the proper thermal equilibrium state (the
so-called mean-force state) for an arbitrary open system. (ii) We provide the
solution for the steady-state coherences for a whole class of master equations,
and in particular, we show that the solution coincides with the mean-force
Hamiltonian for the Bloch-Redfield equation. (iii) We consider the cumulant
equation, which is explicitly completely positive, and we show that its
steady-state coherences are the same as one of the Bloch-Redfield dynamics (and
the mean-force state accordingly). (iv) We solve the correction to the diagonal
part of the stationary state for a two-level system both for the Bloch-Redfield
and cumulant equation, showing that the solution of the cumulant is very close
to the mean-force state, whereas the Bloch-Redfield differs significantly.


title:
SIMBAR: Single Image-Based Scene Relighting For Effective Data
  Augmentation For Automated Driving Vision Tasks

abstract:
  Real-world autonomous driving datasets comprise of images aggregated from
different drives on the road. The ability to relight captured scenes to unseen
lighting conditions, in a controllable manner, presents an opportunity to
augment datasets with a richer variety of lighting conditions, similar to what
would be encountered in the real-world. This paper presents a novel image-based
relighting pipeline, SIMBAR, that can work with a single image as input. To the
best of our knowledge, there is no prior work on scene relighting leveraging
explicit geometric representations from a single image. We present qualitative
comparisons with prior multi-view scene relighting baselines. To further
validate and effectively quantify the benefit of leveraging SIMBAR for data
augmentation for automated driving vision tasks, object detection and tracking
experiments are conducted with a state-of-the-art method, a Multiple Object
Tracking Accuracy (MOTA) of 93.3% is achieved with CenterTrack on
SIMBAR-augmented KITTI - an impressive 9.0% relative improvement over the
baseline MOTA of 85.6% with CenterTrack on original KITTI, both models trained
from scratch and tested on Virtual KITTI. For more details and SIMBAR relit
datasets, please visit our project website (https://simbarv1.github.io/).


title:
Design and validation of zero-slack separable manipulator for
  Intracardiac Echocardiography

abstract:
  Clinicians require substantial training and experience to become comfortable
with steering Intracardiac echocardiography (ICE) catheter to localize and
measure the area of treatment to watch for complications while device catheters
are deployed in another access. Thus, it is reasonable that a robotic-assist
system to hold and actively manipulate the ICE catheter could ease the workload
of the physician. Existing commercially-available robotic systems and research
prototypes all use existing commercially available ICE catheters based on
multiple tendon-sheath mechanism (TSM). To motorize the existing TSM-based ICE
catheter, the actuators interface with the outer handle knobs to manipulate
four internal tendons. However, in practice, the actuators are located at a
sterile, safe place far away from the ICE handle. Thus, to interface with
knobs, there exist multiple coupled gear structures between two, leading to a
highly nonlinear behavior (e.g. various slack, elasticity) alongside hysteresis
phenomena in TSM.
  Since ICE catheters are designed for single use, the expensive actuators need
to be located in a safe place so as to be reusable. Moreover, these actuators
should interface as directly as possible with the tendons for accurate tip
controls. In this paper, we introduce a separable ICE catheter robot with four
tendon actuation: one part reusable and another disposable. Moreover, we
propose a practical model and calibration method for our proposed mechanism so
that four tendons are actuated simultaneously allowing for precise tip control
and mitigating issues with conventional devices such as dead-zone and
hysteresis with simple linear compensation. We consider an open-loop controller
since many available ICE catheters are used without position-tracking sensors
at the tip due to costs and single use


title:
Cluster-based ensemble learning for wind power modeling with
  meteorological wind data

abstract:
  Optimal implementation and monitoring of wind energy generation hinge on
reliable power modeling that is vital for understanding turbine control, farm
operational optimization, and grid load balance. Based on the idea of similar
wind condition leads to similar wind power; this paper constructs a modeling
scheme that orderly integrates three types of ensemble learning algorithms,
bagging, boosting, and stacking, and clustering approaches to achieve optimal
power modeling. It also investigates applications of different clustering
algorithms and methodology for determining cluster numbers in wind power
modeling. The results reveal that all ensemble models with clustering exploit
the intrinsic information of wind data and thus outperform models without it by
approximately 15% on average. The model with the best farthest first clustering
is computationally rapid and performs exceptionally well with an improvement of
around 30%. The modeling is further boosted by about 5% by introducing stacking
that fuses ensembles with varying clusters. The proposed modeling framework
thus demonstrates promise by delivering efficient and robust modeling
performance.


title:
Conditions for linear convergence of the gradient method for non-convex
  optimization

abstract:
  In this paper, we derive a new linear convergence rate for the gradient
method with fixed step lengths for non-convex smooth optimization problems
satisfying the Polyak-Lojasiewicz (PL) inequality. We establish that the PL
inequality is a necessary and sufficient condition for linear convergence to
the optimal value for this class of problems. We list some related classes of
functions for which the gradient method may enjoy linear convergence rate.
Moreover, we investigate their relationship with the PL inequality.


title:
Competition of Density Waves and Superconductivity in Twisted Tungsten
  Diselenide

abstract:
  Evidence for correlated insulating and superconducting phases around regions
of high density of states was reported in the strongly spin-orbit coupled
van-der Waals material twisted tungsten diselenide (tWSe$_2$). We investigate
their origin and interplay by using a functional renormalization group approach
that allows to describe superconducting and spin/charge instabilities in an
unbiased way. We map out the phase diagram as function of filling and
perpendicular electric field, and find that the moir\'e Hubbard model for
tWSe$_2$ features mixed-parity superconducting order parameters with $s/f$-wave
and topological $d/p$-wave symmetry next to (incommensurate) density wave
states. Our work systematically characterizes competing interaction-driven
phases in tWSe$_2$ beyond mean-field approximations and provides guidance for
experimental measurements by outlining the fingerprint of correlated states in
interacting susceptibilities.


title:
Knowledge distillation with error-correcting transfer learning for wind
  power prediction

abstract:
  Wind power prediction, especially for turbines, is vital for the operation,
controllability, and economy of electricity companies. Hybrid methodologies
combining advanced data science with weather forecasting have been
incrementally applied to the predictions. Nevertheless, individually modeling
massive turbines from scratch and downscaling weather forecasts to turbine size
are neither easy nor economical. Aiming at it, this paper proposes a novel
framework with mathematical underpinnings for turbine power prediction. This
framework is the first time to incorporate knowledge distillation into energy
forecasting, enabling accurate and economical constructions of turbine models
by learning knowledge from the well-established park model. Besides, park-scale
weather forecasts non-explicitly are mapped to turbines by transfer learning of
predicted power errors, achieving model correction for better performance. The
proposed framework is deployed on five turbines featuring various terrains in
an Arctic wind park, the results are evaluated against the competitors of
ablation investigation. The major findings reveal that the proposed framework,
developed on favorable knowledge distillation and transfer learning parameters
tuning, yields performance boosts from 3.3 % to 23.9 % over its competitors.
This advantage also exists in terms of wind energy physics and computing
efficiency, which are verified by the prediction quality rate and calculation
time.


title: Double-Hashing Algorithm for Frequency Estimation in Data Streams

abstract:
  Frequency estimation of elements is an important task for summarizing data
streams and machine learning applications. The problem is often addressed by
using streaming algorithms with sublinear space data structures. These
algorithms allow processing of large data while using limited data storage.
Commonly used streaming algorithms, such as count-min sketch, have many
advantages, but do not take into account properties of a data stream for
performance optimization. In the present paper we introduce a novel
double-hashing algorithm that provides flexibility to optimize streaming
algorithms depending on the properties of a given stream. In the double-hashing
approach, first a standard streaming algorithm is employed to obtain an
estimate of the element frequencies. This estimate is derived using a fraction
of the stream and allows identification of the heavy hitters. Next, it uses a
modified hash table where the heavy hitters are mapped into individual buckets
and other stream elements are mapped into the remaining buckets. Finally, the
element frequencies are estimated based on the constructed hash table over the
entire data stream with any streaming algorithm. We demonstrate on both
synthetic data and an internet query log dataset that our approach is capable
of improving frequency estimation due to removing heavy hitters from the
hashing process and, thus, reducing collisions in the hash table. Our approach
avoids employing additional machine learning models to identify heavy hitters
and, thus, reduces algorithm complexity and streamlines implementation.
Moreover, because it is not dependent on specific features of the stream
elements for identifying heavy hitters, it is applicable to a large variety of
streams. In addition, we propose a procedure on how to dynamically adjust the
proposed double-hashing algorithm when frequencies of the elements in a stream
are changing over time.


title:
Bayesian Non-Homogeneous Hidden Markov Model with Variable Selection for
  Investigating Drivers of Seizure Risk Cycling

abstract:
  A major issue in the clinical management of epilepsy is the unpredictability
of seizures. Yet, traditional approaches to seizure forecasting and risk
assessment in epilepsy rely heavily on raw seizure frequencies, which are a
stochastic measurement of seizure risk. We consider a Bayesian non-homogeneous
hidden Markov model for unsupervised clustering of zero-inflated seizure count
data. The proposed model allows for a probabilistic estimate of the sequence of
seizure risk states at the individual level. It also offers significant
improvement over prior approaches by incorporating a variable selection prior
for the identification of clinical covariates that drive seizure risk changes
and accommodating highly granular data. For inference, we implement an
efficient sampler that employs stochastic search and data augmentation
techniques. We evaluate model performance on simulated seizure count data. We
then demonstrate the clinical utility of the proposed model by analyzing daily
seizure count data from 133 patients with Dravet syndrome collected through the
Seizure Tracker TM system, a patient-reported electronic seizure diary. We
report on the dynamics of seizure risk cycling, including validation of several
known pharmacologic relationships. We also uncover novel findings
characterizing the presence and volatility of risk states in Dravet syndrome,
which may directly inform counseling to reduce the unpredictability of seizures
for patients with this devastating cause of epilepsy.


title:
End-to-end multi-talker audio-visual ASR using an active speaker
  attention module

abstract:
  This paper presents a new approach for end-to-end audio-visual multi-talker
speech recognition. The approach, referred to here as the visual context
attention model (VCAM), is important because it uses the available video
information to assign decoded text to one of multiple visible faces. This
essentially resolves the label ambiguity issue associated with most
multi-talker modeling approaches which can decode multiple label strings but
cannot assign the label strings to the correct speakers. This is implemented as
a transformer-transducer based end-to-end model and evaluated using a two
speaker audio-visual overlapping speech dataset created from YouTube videos. It
is shown in the paper that the VCAM model improves performance with respect to
previously reported audio-only and audio-visual multi-talker ASR systems.


title: Safe Backstepping with Control Barrier Functions

abstract:
  Complex control systems are often described in a layered fashion, represented
as higher-order systems where the inputs appear after a chain of integrators.
While Control Barrier Functions (CBFs) have proven to be powerful tools for
safety-critical controller design of nonlinear systems, their application to
higher-order systems adds complexity to the controller synthesis process -- it
necessitates dynamically extending the CBF to include higher order terms, which
consequently modifies the safe set in complex ways. We propose an alternative
approach for addressing safety of higher-order systems through Control Barrier
Function Backstepping. Drawing inspiration from the method of Lyapunov
backstepping, we provide a constructive framework for synthesizing
safety-critical controllers and CBFs for higher-order systems from a top-level
dynamics safety specification and controller design. Furthermore, we integrate
the proposed method with Lyapunov backstepping, allowing the tasks of stability
and safety to be expressed individually but achieved jointly. We demonstrate
the efficacy of this approach in simulation.


title:
Hysteresis-Based RL: Robustifying Reinforcement Learning-based Control
  Policies via Hybrid Control

abstract:
  Reinforcement learning (RL) is a promising approach for deriving control
policies for complex systems. As we show in two control problems, the derived
policies from using the Proximal Policy Optimization (PPO) and Deep Q-Network
(DQN) algorithms may lack robustness guarantees. Motivated by these issues, we
propose a new hybrid algorithm, which we call Hysteresis-Based RL (HyRL),
augmenting an existing RL algorithm with hysteresis switching and two stages of
learning. We illustrate its properties in two examples for which PPO and DQN
fail.


title: Robust Neonatal Face Detection in Real-world Clinical Settings

abstract:
  Current face detection algorithms are extremely generalized and can obtain
decent accuracy when detecting the adult faces. These approaches are
insufficient when handling outlier cases, for example when trying to detect the
face of a neonate infant whose face composition and expressions are relatively
different than that of the adult. It is furthermore difficult when applied to
detect faces in a complicated setting such as the Neonate Intensive Care Unit.
By training a state-of-the-art face detection model, You-Only-Look-Once, on a
proprietary dataset containing labelled neonate faces in a clinical setting,
this work achieves near real time neonate face detection. Our preliminary
findings show an accuracy of 68.7%, compared to the off the shelf solution
which detected neonate faces with an accuracy of 7.37%. Although further
experiments are needed to validate our model, our results are promising and
prove the feasibility of detecting neonatal faces in challenging real-world
settings. The robust and real-time detection of neonatal faces would benefit
wide range of automated systems (e.g., pain recognition and surveillance) who
currently suffer from the time and effort due to the necessity of manual
annotations. To benefit the research community, we make our trained weights
publicly available at github(https://github.com/ja05haus/trained_neonate_face).


title:
Consistency driven Sequential Transformers Attention Model for Partially
  Observable Scenes

abstract:
  Most hard attention models initially observe a complete scene to locate and
sense informative glimpses, and predict class-label of a scene based on
glimpses. However, in many applications (e.g., aerial imaging), observing an
entire scene is not always feasible due to the limited time and resources
available for acquisition. In this paper, we develop a Sequential Transformers
Attention Model (STAM) that only partially observes a complete image and
predicts informative glimpse locations solely based on past glimpses. We design
our agent using DeiT-distilled and train it with a one-step actor-critic
algorithm. Furthermore, to improve classification performance, we introduce a
novel training objective, which enforces consistency between the class
distribution predicted by a teacher model from a complete image and the class
distribution predicted by our agent using glimpses. When the agent senses only
4% of the total image area, the inclusion of the proposed consistency loss in
our training objective yields 3% and 8% higher accuracy on ImageNet and fMoW
datasets, respectively. Moreover, our agent outperforms previous
state-of-the-art by observing nearly 27% and 42% fewer pixels in glimpses on
ImageNet and fMoW.


title:
Multimodal Clustering with Role Induced Constraints for Speaker
  Diarization

abstract:
  Speaker clustering is an essential step in conventional speaker diarization
systems and is typically addressed as an audio-only speech processing task. The
language used by the participants in a conversation, however, carries
additional information that can help improve the clustering performance. This
is especially true in conversational interactions, such as business meetings,
interviews, and lectures, where specific roles assumed by interlocutors
(manager, client, teacher, etc.) are often associated with distinguishable
linguistic patterns. In this paper we propose to employ a supervised text-based
model to extract speaker roles and then use this information to guide an
audio-based spectral clustering step by imposing must-link and cannot-link
constraints between segments. The proposed method is applied on two different
domains, namely on medical interactions and on podcast episodes, and is shown
to yield improved results when compared to the audio-only approach.


title: Can we dream of a 1-adic Langlands correspondence?

abstract:
  After observing that some constructions and results in the $p$-adic Langlands
programme are somehow independent from $p$, we formulate the hypothesis that
this astonishing uniformity could be explained by a 1-adic Langlands
correspondence.


title:
The (meta)metaphysics of science: the case of non-relativistic quantum
  mechanics

abstract:
  Traditionally, to be a realist about something means believing in the
independent existence of that something. In this line of thought, a scientific
realist is someone who believes in the objective existence of the entities
postulated by our best scientific theories. In metaphysical terms, what does
that mean? In ontological terms, i.e., in terms about what exists, scientific
realism can be understood as involving the adoption of an ontology that is
scientifically informed. But according to some philosophers, the realistic
attitude must go beyond ontology. The way in which this requirement has been
understood involves providing a metaphysics for the entities postulated by
science, that is, answering questions about the nature of what ontology admits
to exist. We discuss how two fashionable approaches face the challenge of
providing a metaphysics for science: a form of naturalism and the
Viking/Toolbox approach. Finally, we present a third way, which adopts the best
of both approaches: the meta-Popperian method, which focuses on discarding the
wrong alternatives, or better saying, the metaphysical profiles incompatible
with certain theories. We present the meta-Popperian method, a metametaphysical
method capable of objectively assessing which metaphysical profiles are
incompatible with certain scientific theories. For this, we will use quantum
mechanics as a case study, presenting some previously obtained results. As our
focus is on methodological questions about the relationship between metaphysics
and science; with this method, we can see how science can be used to avoid
error in metaphysical issues. In our opinion, this would be a way to develop a
productive relationship between science and metaphysics.


title: Hazard Detection And Avoidance For The Nova-C Lander

abstract:
  In early 2022, Intuitive Machines' NOVA-C Lander will touch down on the lunar
surface becoming the first commercial endeavor to visit a celestial body.
NOVA-C will deliver six payloads to the lunar surface with various scientific
and engineering objectives, ushering in a new era of commercial space
exploration and utilization. However, to safely accomplish the mission, the
NOVA-C lander must ensure its landing site is free of hazards larger than 30 cm
and the slope of local terrain at touchdown is less than 10 degrees off
vertical. To accomplish this, NOVA-C utilizes Intuitive Machines' precision
navigation system, coupled with machine vision algorithms for scene reduction
and landing site characterization. A unique aspect to the NOVA-C approach is
the real-time nature of the hazard detection and avoidance algorithms--which
are performed 400 meters above and down range of the intended landing site and
completed within 15 seconds. In this paper, we review the theoretical
foundations for the hazard detection and avoidance algorithms, describe the
practical challenges of implementation on the NOVA-C flight computer, and
present test and analysis results.


title:
Synthetic population of binary Cepheids. I. The effect of metallicity
  and initial parameter distribution on characteristics of Cepheids' companions

abstract:
  The majority of classical Cepheids are binary stars, yet the contribution of
companions' light to the total brightness of the system has been assumed
negligible and lacked a thorough, quantitative evaluation. We present an
extensive study of synthetic populations of binary Cepheids, that aims to
characterize Cepheids' companions (e.g. masses, evolutionary and spectral
types), quantify their contribution to the brightness and color of Cepheid
binaries, and assess the relevance of input parameters on the results. We
introduce a collection of synthetic populations, which vary in metal content,
initial parameter distribution, location of the instability strip edges, and
star formation history. Our synthetic populations are free from the selection
bias, while the percentage of Cepheid binaries is controlled by the binarity
parameter. We successfully reproduce recent theoretical and empirical results:
the percentage of binary Cepheids with main sequence (MS) companions, the
contrast-mass ratio relation for binary Cepheids with MS companions, the
appearance of binary Cepheids with giant evolved companions as outlier data
points above the period-luminosity relation. Moreover, we present the first
estimation of the percentage of binary Cepheids in the Large Magellanic Cloud
and announce the quantification of the effect of binarity on the slope and
zero-point of multiband period-luminosity relations, which will be reported in
the next paper of this series.


title: Summary of Tests of General Relativity with GWTC-3

abstract:
  Observations of gravitational waves (GWs) by the advanced LIGO--Virgo
detectors provide us with ground breaking opportunities to test predictions of
Einstein's theory of general relativity (GR) in the strong field regime. In
this article, we summarise the nine tests of GR performed on the new GW signals
included in the third GW transient catalog, GWTC-3. These tests include overall
and self-consistency checks of the signal with the data; tests of the GW
generation, propagation and polarizations; and probes of the nature of the
remnant object by testing the BH ringdown hypothesis and searching for
post-merger echoes. The results from the new events are combined with those
previously published wherever possible. We do not find any statistically
significant deviation from GR and set the most stringent bounds yet on possible
departures from theory.


title:
NNLO event generation for $\boldsymbol{pp \to Zh \to \ell^+\ell^- b \bar
  b}$ production in the SM effective field theory

abstract:
  We consider associated $Zh$ production with $Z \to \ell^+ \ell^-$ and $h \to
b \bar b$ decays in hadronic collisions. In the framework of the Standard Model
effective field theory (SMEFT) we calculate the QCD corrections to this process
and achieve next-to-next-to-leading order plus parton shower (NNLO$+$PS)
accuracy using the MiNNLO$_{\rm PS}$ method. This precision is obtained for a
subset of six SMEFT operators, including the corrections from effective Yukawa-
and chromomagnetic dipole-type interactions. Missing higher-order QCD effects
associated with the considered dimension-six operators are estimated to have a
relative numerical impact of less than a percent on the total rate once
existing experimental limits on the relevant Wilson coefficients are taken into
account. We provide a dedicated Monte Carlo (MC) code that evaluates the NNLO
SMEFT corrections on-the-fly in the event generation. This MC generator is used
to study the numerical impact of NNLO$+$PS corrections on the kinematic
distributions in $pp \to Zh \to \ell^+ \ell^- b \bar b$ production employing
simple SMEFT benchmark scenarios. We identify the invariant mass $m_{b \bar b}$
of the two $b$-tagged jets as well as the three-invariant jet mass $m_{b \bar b
j}$ as particularly interesting observables to study SMEFT effects. These
distributions receive contributions that change both their normalisation and
shape with the latter modifications depending on the exact jet definition. To
our knowledge SMEFT effects of this type have so far not been discussed in the
literature. The presented MC generator can also serve as a starting point to
obtain NNLO$+$PS accuracy for a suitable enlarged set of effective operators in
the future.


title:
Time-encoded pseudo-continuous arterial spin labeling: increasing SNR in
  ASL angiography

abstract:
  Purpose: Dynamic angiography using arterial spin labeling (ASL) can provide
detailed hemodynamic information. However, the long time-resolved readouts
require small flip angles to preserve ASL signal for later timepoints, limiting
SNR. By using time-encoded ASL to generate temporal information, the readout
can be shortened. Here, the improvements in SNR from using larger flip angles,
made possible by the shorter readout, are quantitatively investigated.
  Methods: The SNR of a conventional (sequential) protocol with 9 readouts and
a 4-by-3 time-encoded protocol with 3 readouts (giving 9 matched timepoints)
were directly compared using simulations and in vivo data. Both protocols were
compared using readouts with constant (CFA) and variable flip angles (VFA),
where the VFA scheme was designed to produce a consistent ASL signal across
readouts. Optimization of the background suppression to minimize physiological
noise across readouts was also explored.
  Results: The time-encoded protocol increased in vivo SNR by 103% and 96% when
using CFAs or VFAs, respectively. Use of VFAs improved SNR compared to CFAs by
25% and 21% for the sequential and time-encoded protocols, respectively. The
VFA scheme also removed signal discontinuities in the time-encoded data.
Preliminary data suggest optimizing the background suppression could improve in
vivo SNR by a further 16%.
  Conclusion: Time-encoding can be used to generate additional temporal
information in ASL angiography. This enables the use of larger flip angles
which can double the SNR compared to a non-time-encoded protocol. The shortened
time-encoded readout can also lead to improved background suppression, reducing
physiological noise and further improving SNR.


title:
CipherDAug: Ciphertext based Data Augmentation for Neural Machine
  Translation

abstract:
  We propose a novel data-augmentation technique for neural machine translation
based on ROT-$k$ ciphertexts. ROT-$k$ is a simple letter substitution cipher
that replaces a letter in the plaintext with the $k$th letter after it in the
alphabet. We first generate multiple ROT-$k$ ciphertexts using different values
of $k$ for the plaintext which is the source side of the parallel data. We then
leverage this enciphered training data along with the original parallel data
via multi-source training to improve neural machine translation. Our method,
CipherDAug, uses a co-regularization-inspired training procedure, requires no
external data sources other than the original training data, and uses a
standard Transformer to outperform strong data augmentation techniques on
several datasets by a significant margin. This technique combines easily with
existing approaches to data augmentation, and yields particularly strong
results in low-resource settings.


title:
Raman Signal Extraction from CARS Spectra Using a Learned-Matrix
  Representation of the Discrete Hilbert Transform

abstract:
  Removing distortions in coherent anti-Stokes Raman scattering (CARS) spectra
due to interference with the nonresonant background (NRB) is vital for
quantitative analysis. Popular computational approaches may generate
significant errors for peaks that extend in any part beyond the recording
window. In this work, we present a learned matrix approach to the discrete
Hilbert transform that is easy to implement, fast, and dramatically improves
accuracy of Raman retrieval for Kramers-Kronig approaches.


title: Quasilinear duality and inversion in Banach spaces

abstract:
  We present a unified approach to the processes of inversion and duality for
quasilinear and $1$-quasilinear maps; in particular, for centralizers and
differentials generated by interpolation methods.


title:
Perturbation Theory vs Simulation: Quasi-linear Scale, Binning Effect,
  and Visualization of Bispectrum

abstract:
  Recently, Perturbation Theory (PT), specifically the Effective Field Theory
of Large Scale Structure (EFTofLSS) and its equivalents, have proven powerful
in analyzing observational data. To further this pursuit, we present a
quantitative analysis for the accuracy of PT modeling by comparing its
analytical prediction to the result from a suite of Quijote simulations.
Specifically, we determine $k_{\rm NL}$, the wavenunmber below which the
analytical prediction matches well with the N-body result, for both leading
order (LO) and next-to-leading order (NLO) power spectrum and bispectrum at
redshifts $z=0$, $0.5$, $1$, $2$, $3$. We also quantify the binning effect in
Fourier space and show that an appropriate correction must be applied to the
analytic predictions in order to compare them with the discrete Fourier
transform results obtained from N-body-simulation or real data. Finally, we
have devised a novel spherical-Bispectrum visualization scheme fully capturing
the scale and configuration dependences. The new scheme facilitates
bispectrum-amplitude comparison, for example, between theory and N-body
results.


title:
Non-Hermitian Rosenzweig-Porter random-matrix ensemble: Obstruction to
  the fractal phase

abstract:
  We study the stability of non-ergodic but extended (NEE) phases in
non-Hermitian systems. For this purpose, we generalize a so-called
Rosenzweig-Porter random-matrix ensemble (RP), known to carry a NEE phase along
with the Anderson localized and ergodic ones, to the non-Hermitian case. We
analyze, both analytically and numerically, the spectral and multifractal
properties of the non-Hermitian case. We show that the ergodic and the
localized phases are stable against the non-Hermitian nature of matrix entries.
However, the stability of the fractal phase depends on the choice of the
diagonal elements. For purely real or imaginary diagonal potential the fractal
phases is intact, while for a generic complex diagonal potential the fractal
phase disappears, giving the way to a localized one.

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


title: A forgotten theorem of Orty\'nski, not far off

abstract:
  We combine techniques of Orty\'nski \cite{orty} and Moreno and Plichko
\cite{moreplic} to show that, for $0<p<\infty$, every subspace of
$\ell_p(\Gamma)$ having density character $\mathfrak m$ has the form
$\ell_p(\mathfrak m, H_m)$ for some family of subspaces $H_m\subset \ell_p$.
The ideas of Orty\'nski are then applied to obtain unique positions for
Lindenstrauss spaces.


title:
Thin-ply thermoplastic composites: from weak to robust transverse
  performance through microstructural and morphological tuning

abstract:
  Thin shell carbon fiber composites have great potential for structures that
require large recoverable deformations, high stiffness and low weight, as in
deployable space structures, biomedical devices and robotics. Despite being
astonishingly flexible in fiber direction, thin shells are highly sensitive to
off-axis loading. High sensitivity to imperfections, manufacturing limitations
and a missing in-depth mechanical understanding hinders the creation of
transversely robust shells. This paper provides crucial insights into the
factors influencing the transverse strength of ultra-thin composites using a
highly accurate manufacturing technique to produce novel thermoplastic thin-ply
(35 {\mu}m) carbon fiber-PEEK plies. The effects of fiber type, microstructure
and polymer morphology are addressed. It was found that a combination of
microstructure tuning and isothermal crystallization can achieve thin shell
composites with a 158% improved transverse performance compared to
state-of-the-art thermosets. Moreover, this work outlines the sensitivity to
all related processing conditions, highlighting the need for accurate control
of all named parameters.


title:
The Kalton-Peck space is the complexification of the real Kalton-Peck
  space

abstract:
  The Kalton-Peck $Z_2$ space is the derived space obtained from the scale of
$\ell_p$ spaces by complex interpolation at $1/2$. If we denote by by
$Z_2^{real}$ the derived space obtained from the scale of $\ell_p$ spaces by
real interpolation at $(1/2, 1/2)$, we show that $Z_2$ is the complexification
of $Z_2^{real}$. We also show that $Z_2^{real}$ shares the most important
properties of $Z_2$: it is isomorphic to its dual, it is singular and contains
no complemented copies of $\ell_2$.


title: Learnable latent embeddings for joint behavioral and neural analysis

abstract:
  Mapping behavioral actions to neural activity is a fundamental goal of
neuroscience. As our ability to record large neural and behavioral data
increases, there is growing interest in modeling neural dynamics during
adaptive behaviors to probe neural representations. In particular, neural
latent embeddings can reveal underlying correlates of behavior, yet, we lack
non-linear techniques that can explicitly and flexibly leverage joint behavior
and neural data. Here, we fill this gap with a novel method, CEBRA, that
jointly uses behavioral and neural data in a hypothesis- or discovery-driven
manner to produce consistent, high-performance latent spaces. We validate its
accuracy and demonstrate our tool's utility for both calcium and
electrophysiology datasets, across sensory and motor tasks, and in simple or
complex behaviors across species. It allows for single and multi-session
datasets to be leveraged for hypothesis testing or can be used label-free.
Lastly, we show that CEBRA can be used for the mapping of space, uncovering
complex kinematic features, and rapid, high-accuracy decoding of natural movies
from visual cortex.


title:
Design of Low Thrust Controlled Maneuvers to Chase and De-orbit the
  Space Debris

abstract:
  Over the several decades, the space debris at LEO has grown rapidly which had
caused a serious threat to the operating satellite in an orbit. To avoid the
risk of collision and protect the LEO environment, the space robotics ADR
concept has been continuously developed for over a decade to chase, capture,
and deorbit space debris. This paper presents the designed small satellite with
dual robotic manipulators. The small satellite is designed based on CubeSat
standards, which uses commercially available products in the market. In this
paper, an approach is detailed for designing the controlled chase and deorbit
maneuver for a small satellite equipped with an RCS thruster. The maneuvers are
comprised of two phases, a. bringing the chaser satellite to the debris orbit
and accelerating it to close proximity of 1m to the debris object by using the
low thrust RCS thruster, and b. Once captured, controlled deorbiting it to 250
km of altitude. A Hohmann transfer concept is used to move our chaser satellite
from the lower orbit to the debris orbit by two impulsive burns. A number of
the scenarios are simulated, where one or more orbital elements are adjusted.
For more than one orbital elements adjustment, the DAG law and the Q law are
utilized. These laws synthesize the three direction thrusts to the single
thrust force for the controlled maneuver. The $\Delta$V requirement at each
maneuver is determined by using the performance parameters of the RCS thruster
intended for a small satellite. The results show that, for long term simulation
of a chaser satellite maneuver to debris object, an optimum DAG law is most
suitable than the Q law, as it can handle the singularity behavior of the
orbital elements caused due by adjustment of one or more elements more
efficiently.

categories: cs.RO astro-ph.EP astro-ph.IM physics.space-ph


title: Drifted escape from the finite interval

abstract:
  Properties of the noise-driven escape kinetics are mainly determined by the
stochastic component of the system dynamics. Nevertheless, the escape dynamics
is also sensitive to deterministic forces. Here, we are exploring properties of
the overdamped drifted escape from finite intervals under the action of
symmetric $\alpha$-stable noises. We show that the properly rescaled mean first
passage time follows the universal pattern as a function of the generalized
P\'ecklet number, which can be used to efficiently discriminate between domains
where drift or random force dominate. Stochastic driving of the $\alpha$-stable
type is capable of diminishing the significance of the drift in the regime when
the drift prevails.


title: Compound matrices in systems and control theory: a tutorial

abstract:
  The multiplicative and additive compounds of a matrix play an important role
in several fields of mathematics including geometry, multi-linear algebra,
combinatorics, and the analysis of nonlinear time-varying dynamical systems.
There is a growing interest in applications of these compounds, and their
generalizations, in systems and control theory. The goal of this tutorial paper
is to provide a gentle and self-contained introduction to these topics with an
emphasis on the geometric interpretation of the compounds, and to describe some
of their recent applications including several non-trivial generalizations of
positive systems, cooperative systems, contracting systems, and more.


title:
Magnetic order and exchange couplings in the frustrated diamond lattice
  antiferromagnet MnSc$_2$Se$_4$

abstract:
  We report the magnetic properties of $A$-site spinel compound MnSc$_2$Se$_4$.
The macroscopic magnetic measurements uncovers successive magnetic transitions
at $T_{\rm{N1}}$= 2.04 K, followed by two further transitions at
$T_{\rm{N2}}$=1.8 K and $T_{\rm{N3}}$=1.6 K. Neutron powder diffraction reveals
that both, $T_{\rm{N2}} < T < T_{\rm{N1}}$ and $T <T_{\rm{N3}}$, orders are
associated with the propagation vector $k$=(3/4 3/4 0), while the magnetic
structures are collinear amplitude modulated and helical, respectively. Using
neutron powder spectroscopy we demonstrated the effect of substitution of S by
Se on the magnetic exchange. The energy range of the spin-wave excitations is
supressed due to the chemical pressure of the $X$- ion in MnSc$_2X_4$ ($X$=S,
Se) spinels.


title:
Vibrational Control of Cluster Synchronization: Connections with Deep
  Brain Stimulation

abstract:
  Cluster synchronization underlies various functions in the brain. Abnormal
patterns of cluster synchronization are often associated with neurological
disorders. Deep brain stimulation (DBS) is a neurosurgical technique used to
treat several brain diseases, which has been observed to regulate neuronal
synchrony patterns. Despite its widespread use, the mechanisms of DBS remain
largely unknown. In this paper, we hypothesize that DBS plays a role similar to
vibrational control since they both highly rely on high-frequency excitation to
function. Under the framework of Kuramoto-oscillator networks, we study how
vibrations introduced to network connections can stabilize cluster
synchronization. We derive some sufficient conditions and also provide an
effective approach to design vibrational control. Also, a numerical example is
presented to demonstrate our theoretical findings.


title: Learning Audio-Video Modalities from Image Captions

abstract:
  A major challenge in text-video and text-audio retrieval is the lack of
large-scale training data. This is unlike image-captioning, where datasets are
in the order of millions of samples. To close this gap we propose a new video
mining pipeline which involves transferring captions from image captioning
datasets to video clips with no additional manual effort. Using this pipeline,
we create a new large-scale, weakly labelled audio-video captioning dataset
consisting of millions of paired clips and captions. We show that training a
multimodal transformed based model on this data achieves competitive
performance on video retrieval and video captioning, matching or even
outperforming HowTo100M pretraining with 20x fewer clips. We also show that our
mined clips are suitable for text-audio pretraining, and achieve state of the
art results for the task of audio retrieval.


title: Spontaneous Phase Separation of Ternary Fluid Mixtures

abstract:
  We computationally study the spontaneous phase separation of ternary fluid
mixtures using the lattice Boltzmann method, both when all the surface tensions
are equal and when they have different values. Previous theoretical works
typically rely on analysing the sign of the eigenvalues resulting from a simple
linear stability analysis, but we find this does not explain the fluid
morphologies observed. Here, by combining systematic computer simulations over
the full range of the composition space and theoretical analysis on the
eigenvalues and eigenvectors of the unstable modes, we identify four
fundamental phase separation pathways. In particular, we highlight a dominant
but so-far overlooked mechanism involving enrichment and instability of the
minor component at the fluid-fluid interface


title:
High temperature TAP upper bound for the free energy of mean field spin
  glasses

abstract:
  This work proves an upper bound for the free energy of the
Sherrington-Kirkpatrick model and its generalizations in terms of the
Thouless-Anderson-Palmer (TAP) energy. The result applies to models with
spherical or Ising spins and any mixed $p$-spin Hamiltonian with external field
or with a non-linear spike term. The bound is expected to be tight to leading
order at high temperature, and is non-trivial in the presence of an external
field. For the proof a geometric microcanonical method is employed, in which
one covers the spin space with sets, each of which is centered at a
magnetization vector $m$ and whose contribution to the partition function is
bounded in terms of the TAP energy at $m$.


title:
Capacity Higher-Order Statistics Analysis for $\kappa-\mu$ Fading
  Channels with Correlated Shadowing

abstract:
  The proposed research performs an analysis of the capacity higher-order
statistics for a single-input multiple-output multiantenna wireless
communication system equipped with a maximum-ratio combining scheme. It was
assumed that the propagation multipath channel is described with the
$\kappa-\mu$ fading model with the correlated dominant components. Closed-form
and asymptotic expressions were derived and applied to the problem of minimum
capacity reliability (due to channel fluctuations, thus possible rate
deterioration) and corresponding signal-to-noise ratio analysis. The performed
computer simulation, verifying the correctness of the obtained expressions,
along with the generalized $\kappa-\mu$ fading channel with correlated
shadowing, assumed several specific limiting simplified cases: Rayleigh, Rician
and Nakagami-$m$. It was shown that the signal-to-noise ratio (at which minimum
capacity reliability is attained) is achieved at greater values than that of
simplified models, and the absolute value of this minimum can be smaller/higher
than for the degenerate cases depending on the dominant components one-step
correlation coefficient.


title: Alignment of the central galaxies with the environment

abstract:
  In this work, we combine ellipticity and major axis position angle
measurements from the Sloan Digital Sky Server Data Release 16 (SDSS DR16) with
the group finder algorithm of Rodriguez \& Merch\'an to determine the alignment
of the central galaxies with the surrounding structures and satellite galaxies
lying in their group. We use two independent methods: A modified version of the
two-point cross-correlation function and the angle between the central galaxy
orientation and the satellite galaxies relative position. The first method
allows us to study the inner and outer regions of the cluster, while the second
method provides information within the halos. Our results show that central
galaxies present anysotropy in the correlation function up to $\sim 10
h^{-1}Mpc$, which becomes $\sim$10\% stronger for the brightest ones
($^{0.1}M_{r}<-21.5$). When we split the galaxy sample by colour, we find that
red central galaxies are the main contributors to this anisotropy. We also show
that this behaviour does not depend on the group mass or central galaxy
ellipticity. Finally, our results are in agreement with previous findings,
showing that the two-point cross-correlation function is a best tracer of the
galaxy alignments using all galaxies and not only those of the group to which
it belongs. In addition, this feature allows us to explore the behaviour of the
alignment on larger scales.


title: Invariant Measures for a Stochastic Electroconvection Model

abstract:
  We consider a stochastic electroconvection model describing the nonlinear
evolution of a surface charge density in a two-dimensional fluid with additive
stochastic forcing. We prove the existence and uniqueness of solutions and we
show that the corresponding Markov semigroup is weak Feller. We also prove the
existence of invariant measures for the Markov transition kernels associated
with the model.


title: A Beginner's Guide to Line Intensity Mapping Power Spectra

abstract:
  Line intensity mapping (LIM) is an emerging technique in measuring galaxy
evolution and the large-scale structure of the universe. LIM surveys measure
the cumulative emission from all galaxies emitting a given line at a particular
redshift, which trace the distribution of dark matter throughout the universe.
In this proceeding, we provide an introduction to LIM modeling, focusing on
power spectrum calculation. Beyond these calculations, we describe how these
power spectra may be used to constrain properties of galaxy evolution and
large-scale structure cosmology. Throughout, we use the anticipated EXCLAIM
signal of ionized carbon ([CII]) at redshift $z=3$ as a case study. Our goal is
to provide a starting point to non-experts, e.g. upper-level undergraduate and
graduate students familiar with the basics of cosmology, with the tools
necessary to understand the literature and generate LIM power spectrum models
themselves, while also describing a wide array of literature for continued
studies.


title: Assimilation of Satellite Active Fires Data

abstract:
  Wildland fires pose an increasingly serious problem in our society. The
number and severity of these fires has been rising for many years. Wildfires
pose direct threats to life and property as well as threats through ancillary
effects like reduced air quality. The aim of this thesis is to develop
techniques to help combat the impacts of wildfires by improving wildfire
modeling capabilities by using satellite fire observations. Already much work
has been done in this direction by other researchers. Our work seeks to expand
the body of knowledge using mathematically sound methods to utilize information
about wildfires that considers the uncertainties inherent in the satellite
data.
  In this thesis we explore methods for using satellite data to help initialize
and steer wildfire simulations. In particular, we develop a method for
constructing the history of a fire, a new technique for assimilating wildfire
data, and a method for modifying the behavior of a modeled fire by inferring
information about the fuels in the fire domain. These goals rely on being able
to estimate the time a fire first arrived at every location in a geographic
region of interest. Because detailed knowledge of real wildfires is typically
unavailable, the basic procedure for developing and testing the methods in this
thesis will be to first work with simulated data so that the estimates produced
can be compared with known solutions. The methods thus developed are then
applied to real-world scenarios. Analysis of these scenarios shows that the
work with constructing the history of fires and data assimilation improves
improves fire modeling capabilities. The research is significant because it
gives us a better understanding of the capabilities and limitations of using
satellite data to inform wildfire models and it points the way towards new
avenues for modeling fire behavior.


title:
Self-adjoint extensions for a $p^{4}$-corrected Hamiltonian of a
  particle on a finite interval

abstract:
  In the present paper we deal with the issue of finding the self-adjoint
extensions of a $p^4$-corrected Hamiltonian. The importance of this subject
lies on the application of the concepts of quantum mechanics to the
minimal-length scale scenario which describes an effective theory of quantum
gravity. We work in a finite one dimensional interval and we give the explicit
$U(4)$ parametrization that leads to the self-adjoint extensions. Once the
parametrization is known, we can choose appropriate $U(4)$ matrices to model
physical problems. As examples, we discuss the infinite square-well, periodic
conditions, anti-periodic conditions and periodic conditions up to a prescribed
phase. We hope that the parametrization we found will contribute to model other
interesting physical situations in further works.


title:
Designing AI for Online-to-Offline Safety Risks with Young Women: The
  Context of Social Matching

abstract:
  In this position paper we draw attention to safety risks against youth and
young adults that originate through the combination of online and in-person
interaction, and opportunities for AI to address these risks. Our context of
study is social matching systems (e.g., Tinder, Bumble), which are used by
young adults for online-to-offline interaction with strangers, and which are
correlated with sexual violence both online and in-person. The paper presents
early insights from an ongoing participatory AI design study in which young
women build directly explainable models for detecting risk associated with
discovered social opportunities, and articulate what AI should do once risk has
been detected. We seek to advocate for participatory AI design as a way to
directly incorporate youth and young adults into the design of a safer
Internet. We also draw attention to challenges with the method.


title: On Electroconvection in Porous Media

abstract:
  We address existence, uniqueness and analyticity of solutions of an
electroconvection model in porous media.


title:
Terahertz sensing of highly absorptive water-methanol mixtures with
  multiple resonances in metamaterials

abstract:
  Ultrasensitive terahertz sensing of highly absorptive aqueous solutions
remains challenging due to strong absorption of water in the terahertz regime.
Here, we experimentally demonstrate a cost-effective metamaterial-based sensor
integrated with terahertz time-domain spectroscopy for highly absorptive
water-methanol mixture sensing. This metamaterial has simple asymmetric wire
structures that support multiple resonances including a fundamental Fano
resonance and higher order dipolar resonance in the terahertz regime. Both the
resonance modes have strong intensity in the transmission spectra which we
exploit for detection of the highly absorptive water-methanol mixtures. The
experimentally characterized sensitivities of the Fano and dipole resonances
for the water-methanol mixtures are found to be 160 and 305 GHz/RIU,
respectively. This method provides a route for readily available
metamaterial-assisted terahertz spectroscopy for ultrasensitive sensing of
highly absorptive chemical and biochemical materials with multiple resonances
and high accuracy.


title:
Human-AI Interaction for User Safety in Social Matching Apps: Involving
  Marginalized Users in Design

abstract:
  In this position paper we intend to advocate for participatory design methods
and mobile social matching apps as ripe contexts for exploring novel human-AI
interactions that benefit marginalized groups. Mobile social matching apps like
Tinder and Bumble use AI to introduce users to each other for rapid
face-to-face meetings. These user discoveries and subsequent interactions pose
disproportionate risk of sexual violence and other harms to marginalized user
demographics, specifically women and the LGBTQIA+ community. We want to extend
the role of AI in these apps to keep users safe while they interact with
strangers across online and offline modalities. To do this, we are using
participatory design methods to empower women and LGBTQIA+ individuals to
envision future human-AI interactions that prioritize their safety during
social matching app-use. In one study, stakeholders identifying as LGBTQIA+ or
women are redesigning dating apps to mediate exchange of sexual consent and
therefore mitigate sexual violence. In the other study, women are designing
multi-purpose, opportunistic social matching apps that foreground women's
safety.


title:
Multidimensional Riemann Problems: Transonic Shock Waves and Free
  Boundary Problems

abstract:
  We are concerned with global solutions of multidimensional Riemann problems
for nonlinear hyperbolic systems of conservation laws, focusing on their global
configurations and structures. We present some recent developments in the
rigorous analysis of two-dimensional Riemann problems involving transonic shock
waves through several prototypes of hyperbolic systems of conservation laws and
discuss some further multidimensional Riemann problems and related problems for
nonlinear partial differential equations. In particular, we present four
different two-dimensional Riemann problems through these prototypes of
hyperbolic systems and show how these Riemann problems can be
reformulated/solved as free boundary problems with transonic shock waves as
free boundaries for the corresponding nonlinear conservation laws of mixed
elliptic-hyperbolic type and related nonlinear partial differential equations.

categories: math.AP math-ph math.MP nlin.PS physics.comp-ph physics.flu-dyn


title:
Tailoring Terahertz Propagation by Phase and Amplitude Control in
  Metasurfaces

abstract:
  Using metasurfaces to control the wave propagation at will has been very
successful over the broad electromagnetic spectrum in recent years. By encoding
specially designed abrupt changes of electromagnetic parameters into
metasurfaces, such as phase and amplitude, nearly arbitrary control over the
output wavefronts could be realized. Constituted by a single- or fewlayer of
planar structures, metasurfaces are straightforward in design and fabrication,
thus promising many realistic applications. Moreover, such control concept can
be further extended to the surface wave regime. In this review, we present our
recent progress on metasurfaces capable of tailoring the propagation of both
free-space and surface terahertz waves. Following an introduction of the basic
concept and theory, a number of unique terahertz metasurfaces are presented,
showing the ability in devising ultra-thin and compact functional terahertz
components.


title: Testing Feedforward Neural Networks Training Programs

abstract:
  Nowadays, we are witnessing an increasing effort to improve the performance
and trustworthiness of Deep Neural Networks (DNNs), with the aim to enable
their adoption in safety critical systems such as self-driving cars. Multiple
testing techniques are proposed to generate test cases that can expose
inconsistencies in the behavior of DNN models. These techniques assume
implicitly that the training program is bug-free and appropriately configured.
However, satisfying this assumption for a novel problem requires significant
engineering work to prepare the data, design the DNN, implement the training
program, and tune the hyperparameters in order to produce the model for which
current automated test data generators search for corner-case behaviors. All
these model training steps can be error-prone. Therefore, it is crucial to
detect and correct errors throughout all the engineering steps of DNN-based
software systems and not only on the resulting DNN model. In this paper, we
gather a catalog of training issues and based on their symptoms and their
effects on the behavior of the training program, we propose practical
verification routines to detect the aforementioned issues, automatically, by
continuously validating that some important properties of the learning dynamics
hold during the training. Then, we design, TheDeepChecker, an end-to-end
property-based debugging approach for DNN training programs. We assess the
effectiveness of TheDeepChecker on synthetic and real-world buggy DL programs
and compare it with Amazon SageMaker Debugger (SMD). Results show that
TheDeepChecker's on-execution validation of DNN-based program's properties
succeeds in revealing several coding bugs and system misconfigurations, early
on and at a low cost. Moreover, TheDeepChecker outperforms the SMD's offline
rules verification on training logs in terms of detection accuracy and DL bugs
coverage.


title: On the Kor\'anyi Spherical maximal function on Heisenberg groups

abstract:
  We prove $L^p\to L^q$ estimates for the local maximal operator associated
with dilates of the K\'oranyi sphere in Heisenberg groups. These estimates are
sharp up to endpoints and imply new bounds on sparse domination for the
corresponding global maximal operator. We also prove sharp $L^p\to L^q$
estimates for spherical means over the Kor\'anyi sphere, which can be used to
improve the sparse domination bounds for the associated lacunary maximal
operator.


title:
Advancements in high refractive index media: from quantum coherence in
  atomic system to deep sub-wavelength coupling in metamaterials

abstract:
  Refractive index enhancement is crucial in the field of lithography, imaging,
optical communications, solar devices and many more. We present a review of
advancements in the process of designing high refractive index metamaterials,
starting from quantum coupling and photonic bandgap materials to metamaterials
utilizing deep subwavelength coupling to achieve ever-high values of refractive
index. A crisp and critical impression of each scheme are presented. The
understanding of evolution of material design from intrinsic electronic states
manipulation to meta-atoms design is not only fascinating but prerequisite to
developing successful devices and applications.


title:
Assisted Shortest Path Planning for a Convoy through a Repairable
  Network

abstract:
  In this article, we consider a multi-agent path planning problem in a
partially impeded environment. The impeded environment is represented by a
graph with select road segments (edges) in disrepair impeding vehicular
movement in the road network. A convoy wishes to travel from a starting
location to a destination while minimizing some accumulated cost. The convoy
may traverse an impeded edge for an additional cost (associated with repairing
the edge) than if it were unimpeded. A second vehicle, referred to as a service
vehicle, is simultaneously deployed with the convoy. The service vehicle
assists the convoy by repairing an edge, reducing the cost for the convoy to
traverse that edge. The convoy is permitted to wait at any vertex to allow the
service vehicle to complete repairing an edge. The service vehicle is permitted
to terminate its path at any vertex. The goal is then to find a pair of paths
so the convoy reaches its destination while minimizing the total time (cost)
the two vehicles are active, including any time the convoy waits. We refer to
this problem as the Assisted Shortest Path Problem (ASPP). We present a
generalized permanent labeling algorithm to find an optimal solution for the
ASPP. We also introduce additional modifications to the labeling algorithm to
significantly improve the computation time and refer to the modified labeling
algorithm as $GPLA^*$. Computational results are presented to illustrate the
effectiveness of $GPLA^*$ in solving the ASPP. We then give concluding remarks
and briefly discuss potential variants of the ASPP for future work.


title:
Improving the convergence order of binary neutron star merger
  simulations in the BSSN formulation

abstract:
  High-accuracy numerical relativity simulations of binary neutron star mergers
are a necessary ingredient for constructing gravitational waveform templates to
analyze and interpret observations of compact object mergers. Numerical
convergence in the post-merger phase of such simulations is challenging to
achieve with many modern codes. In this paper, we study two ways of improving
the convergence properties of binary neutron star merger simulations within the
Baumgarte-Shapiro-Shibata-Nakamura formulation of Einstein's equations. We show
that discontinuities in a particular constraint damping scheme in this
formulation can destroy the post-merger convergence of the simulation. A
continuous prescription, in contrast, ensures convergence until late times. We
additionally study the impact of the equation of state parametrization on the
pre- and post-merger convergence properties of the simulations. In particular,
we compare results for a piecewise polytropic parametrization, which is
commonly used in merger simulations but suffers unphysical discontinuities in
the sound speed, with results using a "generalized" piecewise polytropic
parametrization, which was designed to ensure both continuity and
differentiability of the equation of state. We report on the differences in the
gravitational waves depending on which equation of state parametrization is
used.


title:
Reduced density matrix functional theory from an ab initio
  seniority-zero wave function: Exact and approximate formulations along an
  adiabatic connection path

abstract:
  Currently, there is a growing interest in the development of a new hierarchy
of methods based on the concept of seniority, which has been introduced quite
recently in quantum chemistry. Despite the enormous potential of these methods,
the accurate description of both dynamical and static correlation effects
within a single and in-principle-exact approach remains a challenge. In this
work, we propose an alternative formulation of reduced density-matrix
functional theory (RDMFT) where the (one-electron reduced) density matrix is
mapped onto an ab initio seniority-zero wave function. In this approach, the
exact natural orbitals and their occupancies are determined self-consistently
from an effective seniority-zero calculation. The latter involves a universal
higher-seniority density matrix functional for which an adiabatic connection
(AC) formula is derived and implemented under specific constraints that are
related to the density matrix. The pronounced curvature of the (constrained) AC
integrand, which is numerically observed in prototypical hydrogen chains and
the Helium dimer, indicates that a description of higher-seniority correlations
within second-order perturbation theory is inadequate in this context. Applying
multiple linear interpolations along the AC is a better strategy. Such
information is expected to serve as a guide in the future design of
higher-seniority density-matrix functional approximations.


title:
Asymptotic normality of the least sum of squares of trimmed residuals
  estimator

abstract:
  To enhance the robustness of the classic least sum of squares (LS) of the
residuals estimator, Zuo (2022) introduced the least sum of squares of trimmed
(LST) residuals estimator. The LST enjoys many desired properties and serves
well as a robust alternative to the LS. Its asymptotic properties, including
strong and root-n consistency, have been established whereas the asymptotic
normality is left unaddressed. This article solves this remained problem.


title:
Feedback and control systems for future linear colliders: White Paper
  for Snowmass 2021 Topical Group AF07-RF

abstract:
  Particle accelerators for high energy physics will generate TeV-scale
particle beams in large, multi-Km size machines colliding high brightness beams
at the interaction point [1-4]. The high luminosity in such machines is
achieved by producing very small asymmetric beam size at the interaction point,
with short durations to minimize beam-beam effects. Tuning energy, timing and
position of the beam for optimal performance will require high-precision
controls of amplitude and phase of high-frequency electromagnetic fields and
real-time processing of complex algorithms. The stability of the colliding
beams has a large impact on the collider's effective luminosity. Therefore, the
technology readiness level of diagnostic and control systems will be a major
consideration in the collider design. The technical requirements of such
systems depend on the specifics of beam parameters, such as transverse and
longitudinal dimensions, charge/pulse and beam pulse format, which are driven
by the accelerating technology of choice. While feedback systems with single
bunch position monitor resolution below 50 nm and latency <300 ns have been
demonstrated in beam test facilities, many advanced collider concepts make use
of higher repetition rates, brighter beams and higher accelerating frequencies,
and will require better performance, up to 1-2 order of magnitude, demanding
aggressive R&D to be able to deliver and maintain the targeted luminosity.


title: Behavioral Feedback for Optimal LQG Control

abstract:
  In this work, we revisit the Linear Quadratic Gaussian (LQG) optimal control
problem from a behavioral perspective. Motivated by the suitability of
behavioral models for data-driven control, we begin with a reformulation of the
LQG problem in the space of input-output behaviors and obtain a complete
characterization of the optimal solutions. In particular, we show that the
optimal LQG controller can be expressed as a static behavioral-feedback gain,
thereby eliminating the need for dynamic state estimation characteristic of
state space methods. The static form of the optimal LQG gain also makes it
amenable to its computation by gradient descent, which we investigate via
numerical experiments. Furthermore, we highlight the advantage of this approach
in the data-driven control setting of learning the optimal LQG controller from
expert demonstrations.


title:
A Reinforcement Learning Approach to Sensing Design in
  Resource-Constrained Wireless Networked Control Systems

abstract:
  In this paper, we consider a wireless network of smart sensors (agents) that
monitor a dynamical process and send measurements to a base station that
performs global monitoring and decision-making. Smart sensors are equipped with
both sensing and computation, and can either send raw measurements or process
them prior to transmission. Constrained agent resources raise a fundamental
latency-accuracy trade-off. On the one hand, raw measurements are inaccurate
but fast to produce. On the other hand, data processing on resource-constrained
platforms generates accurate measurements at the cost of non-negligible
computation latency. Further, if processed data are also compressed, latency
caused by wireless communication might be higher for raw measurements. Hence,
it is challenging to decide when and where sensors in the network should
transmit raw measurements or leverage time-consuming local processing. To
tackle this design problem, we propose a Reinforcement Learning approach to
learn an efficient policy that dynamically decides when measurements are to be
processed at each sensor. Effectiveness of our proposed approach is validated
through a numerical simulation with case study on smart sensing motivated by
the Internet of Drones.


title:
THz emission from Fe/Pt spintronic emitters with L1$_{0}$-FePt alloyed
  interface

abstract:
  Recent developments in nanomagnetism and spintronics have enabled the use of
ultrafast spin physics for terahertz (THz) emission. Spintronic THz emitters,
consisting of ferromagnetic FM / non-magnetic (NM) thin film heterostructures,
have demonstrated impressive properties for the use in THz spectroscopy and
have great potential in scientific and industrial applications. In this work,
we focus on the impact of the FM/NM interface on the THz emission by
investigating Fe/Pt bilayers with engineered interfaces. In particular, we
intentionally modify the Fe/Pt interface by inserting an ordered L1$_{0}$-FePt
alloy interlayer. Subsequently, we establish that a Fe/L1$_{0}$-FePt (2\,nm)/Pt
configuration is significantly superior to a Fe/Pt bilayer structure, regarding
THz emission amplitude. The latter depends on the extent of alloying on either
side of the interface. The unique trilayer structure opens new perspectives in
terms of material choices for the next generation of spintronic THz emitters.


title: Structure and Correlations for Harmonically Confined Charges

abstract:
  Coulomb charges confined by a harmonic potential display a rich structure at
strong coupling, both classical and quantum. A simple density functional theory
is reviewed showing the essential role of correlations in forming shell
structure and order within the shells. An overview of previous comparisons with
molecular dynamics and Monte Carlo simulations is summarized and extended.


title: Strategies for Safe Multi-Armed Bandits with Logarithmic Regret and Risk

abstract:
  We investigate a natural but surprisingly unstudied approach to the
multi-armed bandit problem under safety risk constraints. Each arm is
associated with an unknown law on safety risks and rewards, and the learner's
goal is to maximise reward whilst not playing unsafe arms, as determined by a
given threshold on the mean risk.
  We formulate a pseudo-regret for this setting that enforces this safety
constraint in a per-round way by softly penalising any violation, regardless of
the gain in reward due to the same. This has practical relevance to scenarios
such as clinical trials, where one must maintain safety for each round rather
than in an aggregated sense.
  We describe doubly optimistic strategies for this scenario, which maintain
optimistic indices for both safety risk and reward. We show that schema based
on both frequentist and Bayesian indices satisfy tight gap-dependent
logarithmic regret bounds, and further that these play unsafe arms only
logarithmically many times in total. This theoretical analysis is complemented
by simulation studies demonstrating the effectiveness of the proposed schema,
and probing the domains in which their use is appropriate.


title:
Efficient Argument Structure Extraction with Transfer Learning and
  Active Learning

abstract:
  The automation of extracting argument structures faces a pair of challenges
on (1) encoding long-term contexts to facilitate comprehensive understanding,
and (2) improving data efficiency since constructing high-quality argument
structures is time-consuming. In this work, we propose a novel context-aware
Transformer-based argument structure prediction model which, on five different
domains, significantly outperforms models that rely on features or only encode
limited contexts. To tackle the difficulty of data annotation, we examine two
complementary methods: (i) transfer learning to leverage existing annotated
data to boost model performance in a new target domain, and (ii) active
learning to strategically identify a small amount of samples for annotation. We
further propose model-independent sample acquisition strategies, which can be
generalized to diverse domains. With extensive experiments, we show that our
simple-yet-effective acquisition strategies yield competitive results against
three strong comparisons. Combined with transfer learning, substantial F1 score
boost (5-25) can be further achieved during the early iterations of active
learning across domains.


title:
A Networked Competitive Multi-Virus SIR Model: Analysis and
  Observability

abstract:
  This paper proposes a novel discrete-time multi-virus SIR
(susceptible-infected-recovered) model that captures the spread of competing
SIR epidemics over a population network. First, we provide a sufficient
condition for the infection level of all the viruses over the networked model
to converge to zero in exponential time. Second, we propose an observation
model which captures the summation of all the viruses' infection levels in each
node, which represents the individuals who are infected by different viruses
but share similar symptoms. We present a sufficient condition for the model to
be locally observable. We propose a Luenberger observer for the system state
estimation and show via simulations that the estimation error of the Luenberger
observer converges to zero before the viruses die out.


title: Estimating the Heights of Martian Vortices from Mars 2020 MEDA Data

abstract:
  Small convective vortices occur ubiquitously on Mars, frequently as dust
devils, and they produce detectable signals in meteorological data -- in
pressure, temperature, and wind speed and direction. In addition to being
important contributors to the martian dust budget, convective vortices may
serve as probes of the boundary layer, providing clues on convective
instability, boundary layer diurnal evolution, and surface-atmosphere
interactions. Using vortices as boundary layer probes requires a detailed
understanding of the link between their properties and occurrence rates and the
conditions that produce them. Fortunately, the growing cache of data from the
Mars Environmental Dynamics Analyzer (MEDA) instrument suite onboard the Mars
2020 Perseverance rover promises to elucidate these relationships. In this
study, we present a catalog of vortex detections from mission sols 90 through
179 to bolster our previous catalog based on sols 15 through 89. Consistent
with predictions, we find more vortex encounters during this second half of the
mission than from the first half. In addition to analyzing the pressure signals
from these vortex encounters, we also use a Gaussian process analysis to
recover contemporaneous temperature signals. By combining these signals with a
long-established thermodynamics model, we estimate heights of the vortices and
find agreement with previous work and evidence for the diurnal growth and decay
of the martian boundary layer. We also discuss prospects for additional
boundary layer studies using martian vortex encounters.


title: Improving quantum state detection with adaptive sequential observations

abstract:
  For many quantum systems intended for information processing, one detects the
logical state of a qubit by integrating a continuously observed quantity over
time. For example, ion and atom qubits are typically measured by driving a
cycling transition and counting the number of photons observed from the
resulting fluorescence. Instead of recording only the total observed count in a
fixed time interval, one can observe the photon arrival times and get a state
detection advantage by using the temporal structure in a model such as a Hidden
Markov Model. We study what further advantage may be achieved by applying
pulses to adaptively transform the state during the observation. We give a
three-state example where adaptively chosen transformations yield a clear
advantage, and we compare performances on an ion example, where we see
improvements in some regimes. We provide a software package that can be used
for exploration of temporally resolved strategies with and without adaptively
chosen transformations.


title:
TAC: Optimizing Error-Bounded Lossy Compression for Three-Dimensional
  Adaptive Mesh Refinement Simulations

abstract:
  Today's scientific simulations require a significant reduction of data volume
because of extremely large amounts of data they produce and the limited I/O
bandwidth and storage space. Error-bounded lossy compression has been
considered one of the most effective solutions to the above problem. However,
little work has been done to improve error-bounded lossy compression for
Adaptive Mesh Refinement (AMR) simulation data. Unlike the previous work that
only leverages 1D compression, in this work, we propose to leverage
high-dimensional (e.g., 3D) compression for each refinement level of AMR data.
To remove the data redundancy across different levels, we propose three
pre-process strategies and adaptively use them based on the data
characteristics. Experiments on seven AMR datasets from a real-world
large-scale AMR simulation demonstrate that our proposed approach can improve
the compression ratio by up to 3.3X under the same data distortion, compared to
the state-of-the-art method. In addition, we leverage the flexibility of our
approach to tune the error bound for each level, which achieves much lower data
distortion on two application-specific metrics.


title:
Scattering for Schr\"{o}dinger operators with potentials concentrated
  near a subspace

abstract:
  We study the scattering properties of Schr\"{o}dinger operators with bounded
potentials concentrated near a subspace of $\mathbb{R}^d$. For such operators,
we show the existence of scattering states and characterize their orthogonal
complement as a set of surface states, which consists of states that are
confined to the subspace (such as pure point states) and states that escape it
at a sublinear rate, in a suitable sense. Our proof uses a novel interpretation
of the Enss method in order to obtain a dynamical characterisation of the
orthogonal complement of the scattering states.


title:
Individual Rationality Conditions of Identifying Matching Costs in
  Transferable Utility Matching Games

abstract:
  As the widely applied method for measuring matching assortativeness in a
transferable utility matching game, a matching maximum score estimation is
proposed by \cite{fox2010qe}. This article reveals that combining unmatched
agents, transfers, and individual rationality conditions with sufficiently
large penalty terms makes it possible to identify the coefficient parameter of
a single common constant, i.e., matching costs in the market.


title:
Reliable Geofence Activation with Sparse and Sporadic Location
  Measurements: Extended Version

abstract:
  Geofences are a fundamental tool of location-based services. A geofence is
usually activated by detecting a location measurement inside the geofence
region. However, location measurements such as GPS often appear sporadically on
smartphones, partly due to weak signal, or privacy preservation, because users
may restrict location sensing, or energy conservation, because sensing
locations can consume a significant amount of energy. These unpredictable, and
sometimes long, gaps between measurements mean that entry into a geofence can
go completely undetected. In this paper we argue that short term location
prediction can help alleviate this problem by computing the probability of
entering a geofence in the future. Complicating this prediction approach is the
fact that another location measurement could appear at any time, making the
prediction redundant and wasteful. Therefore, we develop a framework that
accounts for uncertain location predictions and the possibility of new
measurements to trigger geofence activations. Our framework optimizes over the
benefits and costs of correct and incorrect geofence activations, leading to an
algorithm that reacts intelligently to the uncertainties of future movements
and measurements.


title:
A landscape of peaks: The intermittency islands of the stochastic heat
  equation with L\'evy noise

abstract:
  We show that the spatial profile of the solution to the stochastic heat
equation features multiple layers of intermittency islands if the driving noise
is non-Gaussian. On the one hand, as expected, if the noise is sufficiently
heavy-tailed, the largest peaks of the solution will be taller under
multiplicative than under additive noise. On the other hand, surprisingly, as
soon as the noise has a finite moment of order $\frac2d$, where $d$ is the
spatial dimension, the largest peaks will be of the same order for both
additive and multiplicative noise, which is in sharp contrast to the behavior
of the solution under Gaussian noise. However, in this case, a closer
inspection reveals a second layer of peaks, beneath the largest peaks, that is
exclusive to multiplicative noise and that can be observed by sampling the
solution on the lattice. Finally, we compute the macroscopic Hausdorff and
Minkowski dimensions of the intermittency islands of the solution. Under both
additive and multiplicative noise, if it is not too heavy-tailed, the largest
peaks will be self-similar in terms of their large-scale multifractal behavior.
But under multiplicative noise, this type of self-similarity is not present in
the peaks observed on the lattice.


title:
CharacterBERT and Self-Teaching for Improving the Robustness of Dense
  Retrievers on Queries with Typos

abstract:
  Current dense retrievers are not robust to out-of-domain and outlier queries,
i.e. their effectiveness on these queries is much poorer than what one would
expect. In this paper, we consider a specific instance of such queries: queries
that contain typos. We show that a small character level perturbation in
queries (as caused by typos) highly impacts the effectiveness of dense
retrievers. We then demonstrate that the root cause of this resides in the
input tokenization strategy employed by BERT. In BERT, tokenization is
performed using the BERT's WordPiece tokenizer and we show that a token with a
typo will significantly change the token distributions obtained after
tokenization. This distribution change translates to changes in the input
embeddings passed to the BERT-based query encoder of dense retrievers. We then
turn our attention to devising dense retriever methods that are robust to such
queries with typos, while still being as performant as previous methods on
queries without typos. For this, we use CharacterBERT as the backbone encoder
and an efficient yet effective training method, called Self-Teaching (ST), that
distills knowledge from queries without typos into the queries with typos.
Experimental results show that CharacterBERT in combination with ST achieves
significantly higher effectiveness on queries with typos compared to previous
methods. Along with these results and the open-sourced implementation of the
methods, we also provide a new passage retrieval dataset consisting of
real-world queries with typos and associated relevance assessments on the MS
MARCO corpus, thus supporting the research community in the investigation of
effective and robust dense retrievers. Code, experimental results and dataset
are made available at https://github.com/ielab/CharacterBERT-DR.


title: A Sensory Feedback Control Law for Octopus Arm Movements

abstract:
  The main contribution of this paper is a novel sensory feedback control law
for an octopus arm. The control law is inspired by, and helps integrate,
several observations made by biologists. The proposed control law is distinct
from prior work which has mainly focused on open-loop control strategies.
Several analytical results are described including characterization of the
equilibrium and its stability analysis. Numerical simulations demonstrate
life-like motion of the soft octopus arm, qualitatively matching behavioral
experiments. Quantitative comparison with bend propagation experiments helps
provide the first explanation of such canonical motion using a sensory feedback
control law. Several remarks are included that help draw parallels with natural
pursuit strategies such as motion camouflage or classical pursuit.


title: Implicit Feedback for Dense Passage Retrieval: A Counterfactual Approach

abstract:
  In this paper we study how to effectively exploit implicit feedback in Dense
Retrievers (DRs). We consider the specific case in which click data from a
historic click log is available as implicit feedback. We then exploit such
historic implicit interactions to improve the effectiveness of a DR. A key
challenge that we study is the effect that biases in the click signal, such as
position bias, have on the DRs. To overcome the problems associated with the
presence of such bias, we propose the Counterfactual Rocchio (CoRocchio)
algorithm for exploiting implicit feedback in Dense Retrievers. We demonstrate
both theoretically and empirically that dense query representations learnt with
CoRocchio are unbiased with respect to position bias and lead to higher
retrieval effectiveness. We make available the implementations of the proposed
methods and the experimental framework, along with all results at
https://github.com/ielab/Counterfactual-DR.


title:
Attention-Based Applications in Extended Reality to Support Autistic
  Users: A Systematic Review

abstract:
  With the rising prevalence of autism diagnoses, it is essential for research
to understand how to leverage technology to support the diverse nature of
autistic traits. While traditional interventions focused on technology for
medical cure and rehabilitation, recent research aims to understand how
technology can accommodate each unique situation in an efficient and engaging
way. Extended reality (XR) technology has been shown to be effective in
improving attention in autistic users given that it is more engaging and
motivating than other traditional mediums. Here, we conducted a systematic
review of 59 research articles that explored the role of attention in XR
interventions for autistic users. We systematically analyzed demographics,
study design and findings, including autism screening and attention measurement
methods. Furthermore, given methodological inconsistencies in the literature,
we systematically synthesize methods and protocols including screening tools,
physiological and behavioral cues of autism and XR tasks. While there is
substantial evidence for the effectiveness of using XR in attention-based
interventions for autism to support autistic traits, we have identified three
principal research gaps that provide promising research directions to examine
how autistic populations interact with XR. First, our findings highlight the
disproportionate geographic locations of autism studies and underrepresentation
of autistic adults, evidence of gender disparity, and presence of individuals
diagnosed with co-occurring conditions across studies. Second, many studies
used an assortment of standardized and novel tasks and self-report assessments
with limited tested reliability. Lastly, the research lacks evidence of
performance maintenance and transferability.


title:
Shared User Interfaces of Physiological Data: Systematic Review of
  Social Biofeedback Systems and Contexts in HCI

abstract:
  As an emerging interaction paradigm, physiological computing is increasingly
being used to both measure and feed back information about our internal
psychophysiological states. While most applications of physiological computing
are designed for individual use, recent research has explored how biofeedback
can be socially shared between multiple users to augment human-human
communication. Reflecting on the empirical progress in this area of study, this
paper presents a systematic review of 64 studies to characterize the
interaction contexts and effects of social biofeedback systems. Our findings
highlight the importance of physio-temporal and social contextual factors
surrounding physiological data sharing as well as how it can promote
social-emotional competences on three different levels: intrapersonal,
interpersonal, and task-focused. We also present the Social Biofeedback
Interactions framework to articulate the current physiological-social
interaction space. We use this to frame our discussion of the implications and
ethical considerations for future research and design of social biofeedback
interfaces.