title: Higher Topological complexity and the Ext Eilenberg-Moore functor

abstract:
  In this paper, using Sullivan's approach to rational homotopy theory of
simply-connected finite type CW complexes, we endow the $\mathbb{Q}$-vector
space
$\mathcal{E}xt_{C^{\ast}(X;\mathbb{Q})}(\mathbb{Q},C^{\ast}(X;\mathbb{Q}))$
with a graded commutative algebra structure. This leads us to introduce the
$\mathcal{E}xt$-version of higher (resp. module, homology) topological
complexity of $X_0$, the rationalisation of $X$ (resp. of $X$ over
$\mathbb{Q}$). We then establish several inequalities between these new
invariants and the ordinary ones for Gorenstein spaces. We also highlight, in
this context, the benefit of Adams-Hilton models (over any coefficients field)
through two cases, the first one when the space is a $2$-cell CW-complex and
the second one when it is a suspension.


title: Enumeration of symmetric arc diagrams

abstract:
  We give recurrence relations for the enumeration of symmetric elements within
four classes of arc diagrams corresponding to certain involutions and set
partitions whose blocks contain no consecutive integers. These arc diagrams are
motivated by the study of RNA secondary structures. For example, classic RNA
secondary structures correspond to 3412-avoiding involutions with no adjacent
transpositions, and structures with base triples may be represented as
partitions with crossings. Our results rely on combinatorial arguments. In
particular, we use Motzkin paths to describe noncrossing arc diagrams that have
no arc connecting two adjacent nodes, and we give an explicit bijection to
ternary words whose length coincides with the sum of their digits. We also
discuss the asymptotic behavior of some of the sequences considered here in
order to quantify the extremely low probability of finding symmetric structures
with a large number of nodes.


title:
Quantum brownian motion induced by an analog FRW spacetime in the
  presence of disclination

abstract:
  In this paper we study the quantum brownian motion of a scalar point particle
in the analog Friedman-Robertson-Walker spacetime in the presence of a
disclination, in a condensed matter system. The analog spacetime is obtained as
an effective description of a Bose-Einstein condensate in terms of quantum
excitations of sound waves, named phonons. The dynamics of the phonons is
described by a massless real scalar field whose modes are also subjected to a
quasi-periodic condition. In this sense, we find exact solutions for the real
scalar field in this scenario and calculate the two-point function which makes
possible to analyze the mean squared velocity dispersion of the particle in all
directions. We, thus, analyze some interesting particular cases and show some
graphs where it is possible to see the consistency of our results.


title:
Variational Quantum Policy Gradients with an Application to Quantum
  Control

abstract:
  Quantum Machine Learning models are composed by Variational Quantum Circuits
(VQCs) in a very natural way. There are already some empirical results proving
that such models provide an advantage in supervised/unsupervised learning
tasks. However, when applied to Reinforcement Learning (RL), less is known. In
this work, we consider Policy Gradients using a hardware-efficient ansatz. We
prove that the complexity of obtaining an {\epsilon}-approximation of the
gradient using quantum hardware scales only logarithmically with the number of
parameters, considering the number of quantum circuits executions. We test the
performance of such models in benchmarking environments and verify empirically
that such quantum models outperform typical classical neural networks used in
those environments, using a fraction of the number of parameters. Moreover, we
propose the utilization of the Fisher Information spectrum to show that the
quantum model is less prone to barren plateaus than its classical counterpart.
As a different use case, we consider the application of such variational
quantum models to the problem of quantum control and show its feasibility in
the quantum-quantum domain.


title:
Geometric Methods for Sampling, Optimisation, Inference and Adaptive
  Agents

abstract:
  In this chapter, we identify fundamental geometric structures that underlie
the problems of sampling, optimisation, inference and adaptive decision-making.
Based on this identification, we derive algorithms that exploit these geometric
structures to solve these problems efficiently. We show that a wide range of
geometric theories emerge naturally in these fields, ranging from
measure-preserving processes, information divergences, Poisson geometry, and
geometric integration. Specifically, we explain how (i) leveraging the
symplectic geometry of Hamiltonian systems enable us to construct (accelerated)
sampling and optimisation methods, (ii) the theory of Hilbertian subspaces and
Stein operators provides a general methodology to obtain robust estimators,
(iii) preserving the information geometry of decision-making yields adaptive
agents that perform active inference. Throughout, we emphasise the rich
connections between these fields; e.g., inference draws on sampling and
optimisation, and adaptive decision-making assesses decisions by inferring
their counterfactual consequences. Our exposition provides a conceptual
overview of underlying ideas, rather than a technical discussion, which can be
found in the references herein.

categories: stat.ML cs.LG math.DG math.OC math.ST stat.TH


title:
Open-Vocabulary One-Stage Detection with Hierarchical Visual-Language
  Knowledge Distillation

abstract:
  Open-vocabulary object detection aims to detect novel object categories
beyond the training set.
  The advanced open-vocabulary two-stage detectors employ instance-level
visual-to-visual knowledge distillation to align the visual space of the
detector with the semantic space of the Pre-trained Visual-Language Model
(PVLM).
  However, in the more efficient one-stage detector, the absence of
class-agnostic object proposals hinders the knowledge distillation on unseen
objects, leading to severe performance degradation.
  In this paper, we propose a hierarchical visual-language knowledge
distillation method, i.e., HierKD, for open-vocabulary one-stage detection.
  Specifically, a global-level knowledge distillation is explored to transfer
the knowledge of unseen categories from the PVLM to the detector.
  Moreover, we combine the proposed global-level knowledge distillation and the
common instance-level knowledge distillation to learn the knowledge of seen and
unseen categories simultaneously.
  Extensive experiments on MS-COCO show that our method significantly surpasses
the previous best one-stage detector with 11.9\% and 6.7\% $AP_{50}$ gains
under the zero-shot detection and generalized zero-shot detection settings, and
reduces the $AP_{50}$ performance gap from 14\% to 7.3\% compared to the best
two-stage detector.


title: Physically Interpretable Machine Learning for nuclear masses

abstract:
  We present a novel approach to modeling the ground state mass of atomic
nuclei based directly on a probabilistic neural network constrained by relevant
physics. Our Physically Interpretable Machine Learning (PIML) approach
incorporates knowledge of physics by using a physically motivated feature space
in addition to a soft physics constraint that is implemented as a penalty to
the loss function. We train our PIML model on a random set of $\sim$20\% of the
Atomic Mass Evaluation (AME) and predict the remaining $\sim$80\%. The success
of our methodology is exhibited by the unprecedented
$\sigma_\textrm{RMS}\sim186$ keV match to data for the training set and
$\sigma_\textrm{RMS}\sim316$ keV for the entire AME with $Z \geq 20$. We show
that our general methodology can be interpreted using feature importance.


title:
On the Fragility of the Basis on the Hamilton-Jacobi-Bellman Equation in
  Economic Dynamics

abstract:
  In this paper, we provide an example of the optimal growth model in which
there exists infinitely many solutions to the Hamilton-Jacobi-Bellman equation
but the value function does not satisfy this equation. We consider the cause of
this phenomenon, and find that the lack of a solution to the original problem
is crucial. We show that under several conditions, there exists a solution to
the original problem if and only if the value function solves the
Hamilton-Jacobi-Bellman equation. Moreover, in this case, the value function is
the unique nondecreasing concave solution to the Hamilton-Jacobi-Bellman
equation. We also show that without our conditions, this uniqueness result does
not hold.


title:
Towards Clinical Practice: Design and Implementation of Convolutional
  Neural Network-Based Assistive Diagnosis System for COVID-19 Case Detection
  from Chest X-Ray Images

abstract:
  One of the critical tools for early detection and subsequent evaluation of
the incidence of lung diseases is chest radiography. This study presents a
real-world implementation of a convolutional neural network (CNN) based Carebot
Covid app to detect COVID-19 from chest X-ray (CXR) images. Our proposed model
takes the form of a simple and intuitive application. Used CNN can be deployed
as a STOW-RS prediction endpoint for direct implementation into DICOM viewers.
The results of this study show that the deep learning model based on DenseNet
and ResNet architecture can detect SARS-CoV-2 from CXR images with precision of
0.981, recall of 0.962 and AP of 0.993.


title: The Dark Side: Security Concerns in Machine Learning for EDA

abstract:
  The growing IC complexity has led to a compelling need for design efficiency
improvement through new electronic design automation (EDA) methodologies. In
recent years, many unprecedented efficient EDA methods have been enabled by
machine learning (ML) techniques. While ML demonstrates its great potential in
circuit design, however, the dark side about security problems, is seldomly
discussed. This paper gives a comprehensive and impartial summary of all
security concerns we have observed in ML for EDA. Many of them are hidden or
neglected by practitioners in this field. In this paper, we first provide our
taxonomy to define four major types of security concerns, then we analyze
different application scenarios and special properties in ML for EDA. After
that, we present our detailed analysis of each security concern with
experiments.


title:
Analysis of a modified Euler scheme for parabolic semilinear stochastic
  PDEs

abstract:
  We propose a modification of the standard linear implicit Euler integrator
for the weak approximation of parabolic semilinear stochastic PDEs driven by
additive space-time white noise. The new method can easily be combined with a
finite difference method for the spatial discretization. The proposed method is
shown to have improved qualitative properties compared with the standard
method. First, for any time-step size, the spatial regularity of the solution
is preserved, at all times. Second, the proposed method preserves the Gaussian
invariant distribution of the infinite dimensional Ornstein--Uhlenbeck process
obtained when the nonlinearity is absent, for any time-step size. The weak
order of convergence of the proposed method is shown to be equal to $1/2$ in a
general setting, like for the standard Euler scheme. A stronger weak
approximation result is obtained when considering the approximation of a Gibbs
invariant distribution, when the nonlinearity is a gradient: one obtains an
approximation in total variation distance of order $1/2$, which does not hold
for the standard method. This is the first result of this type in the
literature. A key point in the analysis is the interpretation of the proposed
modified Euler scheme as the accelerated exponential Euler scheme applied to a
modified stochastic evolution equation. Finally, it is shown that the proposed
method can be applied to design an asymptotic preserving scheme for a class of
slow-fast multiscale systems, and to construct a Markov Chain Monte Carlo
method which is well-defined in infinite dimension. We also revisit the
analysis of the standard and the accelerated exponential Euler scheme, and we
prove new results with approximation in the total variation distance, which
serve to illustrate the behavior of the proposed modified Euler scheme.


title:
Amplification of Primordial Gravitational Waves by a Geometrically
  Driven non-canonical Reheating Era

abstract:
  In order to describe inflation in general relativity, scalar fields must
inevitably be used, with all the setbacks of that description. On the other
hand, $f(R)$ gravity and other modified gravity theories seem to provide a
unified description of early and late-time dynamics without resorting to scalar
or phantom theories. The question is, can modified gravity affect directly the
mysterious radiation domination era? Addressing this question is the focus in
this work, and we shall consider the case for which in the early stages of the
radiation domination era, namely during the reheating era, the background
equation of state parameter is different from $w=1/3$. As we show, in the
context of $f(R)$ gravity, an abnormal reheating era can affect the primordial
gravitational wave energy spectrum today. Since future interferometers will
exactly probe this era, which consists of subhorizon modes that reentered the
horizon during the early stages of the radiation domination era, the focus in
this work is how a short abnormal reheating era that deviates from the standard
perfect fluid pattern with $w\neq 1/3$, and generated by higher order curvature
terms, can affect the primordial gravitational wave energy spectrum. Using a
WKB approach, we calculate the effect of an $f(R)$ gravity generated abnormal
reheating era, and as we show the primordial gravitational wave spectrum is
significantly amplified, a result which is in contrast to the general
relativistic case, where the effect is minor.


title:
Uniform weak error estimates for an asymptotic preserving scheme applied
  to a class of slow-fast parabolic semilinear SPDEs

abstract:
  We study an asymptotic preserving scheme for the temporal discretization of a
system of parabolic semilinear SPDEs with two time scales. Owing to the
averaging principle, when the time scale separation $\epsilon$ vanishes, the
slow component converges to the solution of a limiting evolution equation,
which is captured when the time-step size $\Delta t$ vanishes by a limiting
scheme. The objective of this work is to prove weak error estimates which are
uniform with respect to $\epsilon$, in terms of $\Delta t$: the scheme
satisfies a uniform accuracy property. This is a non trivial generalization of
a recent article in an infinite dimensional framework. The fast component is
discretized using the modified Euler scheme for SPDEs introduced in a recent
work. Proving the weak error estimates requires delicate analysis of the
regularity properties of solutions of infinite dimensional Kolmogorov
equations.


title: Inverse mean value properties (a survey)

abstract:
  Several mean value identities for harmonic and panharmonic functions are
reviewed along with the corresponding inverse properties. The latter
characterize balls, annuli and strips analytically via these functions.


title:
Qualia as physical measurements: a mathematical model of qualia and pure
  concepts

abstract:
  A space of qualia is defined to be a sober topological space whose points are
the qualia and whose open sets are the pure concepts in the sense of Lewis,
carrying additional algebraic structure that conveys the conscious experience
of subjective time and logical abstraction. This structure is analogous to that
of a space of physical measurements. It is conjectured that qualia and
measurements have the same nature, corresponding to fundamental processes via
which classical information is produced and physically stored, and that
therefore the hard problem of consciousness and the measurement problem are two
facets of the same problem. The space of qualia is independent from any
preexisting notions of spacetime and conscious agent, but its structure caters
for a derived geometric model of observer. Intersubjectivity is based on
relating different observers in a way that leads to a logical version of
quantum superposition.

categories: physics.hist-ph cs.AI physics.soc-ph q-bio.NC quant-ph


title:
Model-based Multi-agent Reinforcement Learning: Recent Progress and
  Prospects

abstract:
  Significant advances have recently been achieved in Multi-Agent Reinforcement
Learning (MARL) which tackles sequential decision-making problems involving
multiple participants. However, MARL requires a tremendous number of samples
for effective training. On the other hand, model-based methods have been shown
to achieve provable advantages of sample efficiency. However, the attempts of
model-based methods to MARL have just started very recently. This paper
presents a review of the existing research on model-based MARL, including
theoretical analyses, algorithms, and applications, and analyzes the advantages
and potential of model-based MARL. Specifically, we provide a detailed taxonomy
of the algorithms and point out the pros and cons for each algorithm according
to the challenges inherent to multi-agent scenarios. We also outline promising
directions for future development of this field.


title:
Lithography free method to synthesize the ultra-low reflection
  inverted-pyramid arrays for ultra-thin silicon solar cell

abstract:
  Silicon inverted pyramids arrays have been suggested as one of the most
promising structure for high-efficient ultrathin solar cells due to their
ability of superior light absorption and low enhancement of surface area.
However, the existing techniques for such fabrication are either expensive or
not able to create appropriate structure. Here, we present a lithography free
method for the fabrication of inverted pyramid arrays by using a modified metal
assisted chemical etching (MACE) method. The size and inter-inverted pyramids
spacing can also be controlled through this method. We used an isotropic
chemical etching technique for this process to control the angle of etching,
which leads to ultra-low reflection, even < 0.5%, of this nanostructure. Using
this specification, we have predicted the expected solar cell parameters, which
exceeds the Lambertian limit. This report provides a new pathway to improve the
efficiency of the ultrathin silicon solar cells at lower cost.


title:
Convergence rates of the stochastic alternating algorithm for
  bi-objective optimization

abstract:
  Stochastic alternating algorithms for bi-objective optimization are
considered when optimizing two conflicting functions for which optimization
steps have to be applied separately for each function. Such algorithms consist
of applying a certain number of steps of gradient or subgradient descent on
each single objective at each iteration. In this paper, we show that stochastic
alternating algorithms achieve a sublinear convergence rate of
$\mathcal{O}(1/T)$, under strong convexity, for the determination of a
minimizer of a weighted-sum of the two functions, parameterized by the number
of steps applied on each of them. An extension to the convex case is presented
for which the rate weakens to $\mathcal{O}(1/\sqrt{T})$. These rates are valid
also in the non-smooth case. Importantly, by varying the proportion of steps
applied to each function, one can determine an approximation to the Pareto
front.


title:
Rashba spin-orbit coupling and quantum-interference effect for a pair of
  spin-correlated electrons in their tunneling and reflection under a step
  potential

abstract:
  We present both theory and numerical-computation results for the transmission
and reflection probability currents of a charged particle across a potential
step in the presence of a Rashba spin-orbit interaction. By varying kinetic
energy and angle of incident electrons or barrier height, different features
associated with tunneling and reflection of electrons are revealed by
inter-spin-channel electron tunnelings and reflections. These unique properties
are further accompanied by spin-state quantum interference of either a
reflected or transmitted pairs of spin-correlated electrons with the same
kinetic energy but in different spin-orbital states. Such distinctive features
are expected to give rise to a lot of applications in both spintronics and
quantum-computation devices.


title: Dynamics of purely kinetic $k$-essence in presence of a perfect fluid

abstract:
  We investigate the dynamics of purely kinetic $k$-essence scalar field in
three different scenarios. We begin with the case of a purely kinetic
$k$-essence theory and try to formulate a late time accelerating universe
model. Then we introduce a perfect fluid in presence of the purely kinetic
$k$-essence field and calculate how the features of the previous model are
modified. We discuss two cases primarily, in the first case the fluid and the
scalar field does not exchange energy and momentum and in the later case the
scalar field and the fluid interact non-minimally and exchange energy and
momentum. Using dynamical analysis technique, we examine the stability of these
systems in the background of a spatially flat
Friedman-Lemaitre-Robertson-Walker (FLRW) universe for a particular chosen
functional form of $k$-essence kinetic energy term. From the evolution of the
effective equation of state parameter of the field-fluid system we conclude
that a non-minimally coupled field-fluid scenario can produce physically
interesting results. We also find that the $k$-essence field can effectively
hide in a radiation background.


title:
Low-noise acoustoelectric microwave amplifiers with net gain in
  continuous operation

abstract:
  Over sixty years ago, it was hypothesized that specially designed acoustic
systems that leveraged the acoustoelectric effect between phonons and charge
carriers could revolutionize radio frequency electronic systems by allowing
nonlinear and nonreciprocal functionalities such as gain and isolation to be
achieved in the acoustic domain. Despite six decades of work, no
acoustoelectric amplifier has been produced that can achieve a large net
(terminal) gain at microwave frequencies with low power consumption and noise
figure. Here we demonstrate a novel three-layer acoustoelectric heterostructure
that enables the first-ever continuously operating acoustoelectric amplifier
with terminal gain at gigahertz frequencies. We achieve a terminal gain of
11.25 dB in a 500 micron long device, operating at 1 GHz with a DC power
dissipation of 19.6 mW. We also realize broadband gain from 0.25-3.4 GHz and
nonreciprocal transmission exceeding 44 dB at 1 GHz. Our acoustic noise figure
is 2.8 dB, which is the lowest ever demonstrated noise figure for an
acoustoelectric amplifier. We discuss generally how to optimize these
acoustoelectric heterostructures and show that it should be immediately
achievable to produce devices with even larger gain in shorter lengths while
simultaneously having lower power consumption and noise figure.


title:
Transparency strategy-based data augmentation for BI-RADS classification
  of mammograms

abstract:
  Image augmentation techniques have been widely investigated to improve the
performance of deep learning (DL) algorithms on mammography classification
tasks. Recent methods have proved the efficiency of image augmentation on data
deficiency or data imbalance issues. In this paper, we propose a novel
transparency strategy to boost the Breast Imaging Reporting and Data System
(BI-RADS) scores of mammograms classifier. The proposed approach utilizes the
Region of Interest (ROI) information to generate more high-risk training
examples from original images. Our extensive experiments were conducted on our
benchmark mammography dataset. The experiment results show that the proposed
approach surpasses current state-of-the-art data augmentation techniques such
as Upsampling or CutMix. The study highlights that the transparency method is
more effective than other augmentation strategies for BI-RADS classification
and can be widely applied for our computer vision tasks.


title:
Towards Large-Scale Interpretable Knowledge Graph Reasoning for Dialogue
  Systems

abstract:
  Users interacting with voice assistants today need to phrase their requests
in a very specific manner to elicit an appropriate response. This limits the
user experience, and is partly due to the lack of reasoning capabilities of
dialogue platforms and the hand-crafted rules that require extensive labor. One
possible way to improve user experience and relieve the manual efforts of
designers is to build an end-to-end dialogue system that can do reasoning
itself while perceiving user's utterances. In this work, we propose a novel
method to incorporate the knowledge reasoning capability into dialogue systems
in a more scalable and generalizable manner. Our proposed method allows a
single transformer model to directly walk on a large-scale knowledge graph to
generate responses. To the best of our knowledge, this is the first work to
have transformer models generate responses by reasoning over differentiable
knowledge graphs. We investigate the reasoning abilities of the proposed method
on both task-oriented and domain-specific chit-chat dialogues. Empirical
results show that this method can effectively and efficiently incorporate a
knowledge graph into a dialogue system with fully-interpretable reasoning
paths.


title:
Learning from Multiple Expert Annotators for Enhancing Anomaly Detection
  in Medical Image Analysis

abstract:
  Building an accurate computer-aided diagnosis system based on data-driven
approaches requires a large amount of high-quality labeled data. In medical
imaging analysis, multiple expert annotators often produce subjective estimates
about "ground truth labels" during the annotation process, depending on their
expertise and experience. As a result, the labeled data may contain a variety
of human biases with a high rate of disagreement among annotators, which
significantly affect the performance of supervised machine learning algorithms.
To tackle this challenge, we propose a simple yet effective approach to combine
annotations from multiple radiology experts for training a deep learning-based
detector that aims to detect abnormalities on medical scans. The proposed
method first estimates the ground truth annotations and confidence scores of
training examples. The estimated annotations and their scores are then used to
train a deep learning detector with a re-weighted loss function to localize
abnormal findings. We conduct an extensive experimental evaluation of the
proposed approach on both simulated and real-world medical imaging datasets.
The experimental results show that our approach significantly outperforms
baseline approaches that do not consider the disagreements among annotators,
including methods in which all of the noisy annotations are treated equally as
ground truth and the ensemble of different models trained on different label
sets provided separately by annotators.


title:
VinDr-PCXR: An open, large-scale chest radiograph dataset for
  interpretation of common thoracic diseases in children

abstract:
  Computer-aided diagnosis systems in adult chest radiography (CXR) have
recently achieved great success thanks to the availability of large-scale,
annotated datasets and the advent of high-performance supervised learning
algorithms. However, the development of diagnostic models for detecting and
diagnosing pediatric diseases in CXR scans is undertaken due to the lack of
high-quality physician-annotated datasets. To overcome this challenge, we
introduce and release VinDr-PCXR, a new pediatric CXR dataset of 9,125 studies
retrospectively collected from a major pediatric hospital in Vietnam between
2020 and 2021. Each scan was manually annotated by a pediatric radiologist who
has more than ten years of experience. The dataset was labeled for the presence
of 36 critical findings and 15 diseases. In particular, each abnormal finding
was identified via a rectangle bounding box on the image. To the best of our
knowledge, this is the first and largest pediatric CXR dataset containing
lesion-level annotations and image-level labels for the detection of multiple
findings and diseases. For algorithm development, the dataset was divided into
a training set of 7,728 and a test set of 1,397. To encourage new advances in
pediatric CXR interpretation using data-driven approaches, we provide a
detailed description of the VinDr-PCXR data sample and make the dataset
publicly available on https://physionet.org/.


title: Photometric Investigation of Contact Binary DY Cet Based on TESS Data

abstract:
  We present a photometric analysis of the TESS light curve of contact binary
system DY Cet and the behavior of its orbital period variation. The light curve
and published radial velocity data analysis was performed using Wilson-Devinney
code. As a result of simultaneous analysis of light curve with radial velocity
data, the masses and radii of the system's components were determined as
M1=1.55(2)Ms, M2=0.55(1)Ms and R1=1.51(2)Rs, R2=0.95(2)Rs, respectively. The
degree of contact (f) and mass ratio (q) of the system were determined as 23%
and 0.355(12), respectively. Orbital period analysis of DY Cet was conducted
for the first time in this study. It was observed that the orbital period has a
sinus-like change with decreasing parabola. To explain the orbital period
change, mass transfer between components is proposed with the assumption of
conservative mass, and the transfer rate was calculated to be dM/dt=1.1x10^(-7)
Ms/yr. A possible third component is suggested for explaining the sinus-like
change, and the mass of the unseen component was determined as 0.13 Ms. The age
of the DY Cet system was estimated as 3.77 Gyr.


title: Does DQN really learn? Exploring adversarial training schemes in Pong

abstract:
  In this work, we study two self-play training schemes, Chainer and Pool, and
show they lead to improved agent performance in Atari Pong compared to a
standard DQN agent -- trained against the built-in Atari opponent. To measure
agent performance, we define a robustness metric that captures how difficult it
is to learn a strategy that beats the agent's learned policy. Through playing
past versions of themselves, Chainer and Pool are able to target weaknesses in
their policies and improve their resistance to attack. Agents trained using
these methods score well on our robustness metric and can easily defeat the
standard DQN agent. We conclude by using linear probing to illuminate what
internal structures the different agents develop to play the game. We show that
training agents with Chainer or Pool leads to richer network activations with
greater predictive power to estimate critical game-state features compared to
the standard DQN agent.


title: The miniJPAS survey: White dwarf science with 56 optical filters

abstract:
  We analyze the white dwarf population in miniJPAS, the first square degree
observed with 56 medium-band, 145 A in width optical filters by the Javalambre
Physics of the accelerating Universe Astrophysical Survey (J-PAS), to provide a
data-based forecast for the white dwarf science with low-resolution (R ~ 50)
photo-spectra. We define the sample of the bluest point-like sources in
miniJPAS with r < 21.5 mag, point-like probability larger than 0.5, (u-r) <
0.80 mag, and (g-i) < 0.25 mag. This sample comprises 33 sources with
spectroscopic information, 11 white dwarfs and 22 QSOs. We estimate the
effective temperature (Teff), the surface gravity, and the composition of the
white dwarf population by a Bayesian fitting to the observed photo-spectra. The
miniJPAS data permit the classification of the observed white dwarfs into
H-dominated and He-dominated with 99% confidence, and the detection of calcium
absorption and polluting metals down to r ~ 21.5 mag at least for sources with
7000 < Teff < 22000 K, the temperature range covered by the white dwarfs in
miniJPAS. The effective temperature is estimated with a 2% uncertainty, close
to the 1% from spectroscopy. A precise estimation of the surface gravity
depends on the available parallax information. In addition, the white dwarf
population at Teff > 7000 K can be segregated from the bluest extragalactic
QSOs, providing a clean sample based on optical photometry alone. The J-PAS
low-resolution photo-spectra provide precise and accurate effective
temperatures and atmospheric compositions for white dwarfs, complementing the
data from Gaia. J-PAS will also detect and characterize new white dwarfs beyond
the Gaia magnitude limit, providing faint candidates for spectroscopic follow
up.


title:
Hierarchical Reinforcement Learning of Locomotion Policies in Response
  to Approaching Objects: A Preliminary Study

abstract:
  Animals such as rabbits and birds can instantly generate locomotion behavior
in reaction to a dynamic, approaching object, such as a person or a rock,
despite having possibly never seen the object before and having limited
perception of the object's properties. Recently, deep reinforcement learning
has enabled complex kinematic systems such as humanoid robots to successfully
move from point A to point B. Inspired by the observation of the innate
reactive behavior of animals in nature, we hope to extend this progress in
robot locomotion to settings where external, dynamic objects are involved whose
properties are partially observable to the robot. As a first step toward this
goal, we build a simulation environment in MuJoCo where a legged robot must
avoid getting hit by a ball moving toward it. We explore whether prior
locomotion experiences that animals typically possess benefit the learning of a
reactive control policy under a proposed hierarchical reinforcement learning
framework. Preliminary results support the claim that the learning becomes more
efficient using this hierarchical reinforcement learning method, even when
partial observability (radius-based object visibility) is taken into account.


title: Explicit formulae for rank zero DT invariants and the OSV conjecture

abstract:
  Fix a Calabi-Yau 3-fold $X$ satisfying the Bogomolov-Gieseker conjecture of
Bayer-Macr\`i-Toda, such as the quintic 3-fold. By two different wall-crossing
arguments we prove two different explicit formulae relating rank 0
Donaldson-Thomas invariants (counting torsion sheaves on $X$ supported on ample
divisors) in terms of rank 1 Donaldson-Thomas invariants (counting ideal
sheaves of curves) and Pandharipande-Thomas invariants. In particular, we prove
a slight modification of Toda's formulation of OSV conjecture for $X$. When $X$
is of Picard rank one, we also give an explicit formula for rank two DT
invariants in terms of rank zero and rank one DT invariants.


title: Interval Dominance based Structural Results for Markov Decision Process

abstract:
  The textbook proof for monotone optimal policies of a Markov decision process
(MDP) requires supermodularity of the rewards and transition probabilities.This
paper uses a sufficient condition for interval dominance (called I)to obtain
structural results for MDPs under more general conditions. We present several
MDP examples where supermodularity does not hold, yet I holds, and so the
optimal policy is monotone; these include sigmoidal rewards and perturbed
bi-diagonal transition matrices. We also consider MDPs with TP3 transition
matrices and concave value functions.


title:
On some H-Galois objects which are distinguished by their polynomial
  H-identities

abstract:
  When k is an algebraically closed field of characteristic 0 and H is a
non-semisimple monomial Hopf algebra, we show that all Galois objects over H
are determined up to H-comodule algebra isomorphism by their polynomial
H-identities, extending a previous result by Kassel.


title:
Differentiable Reasoning over Long Stories -- Assessing Systematic
  Generalisation in Neural Models

abstract:
  Contemporary neural networks have achieved a series of developments and
successes in many aspects; however, when exposed to data outside the training
distribution, they may fail to predict correct answers. In this work, we were
concerned about this generalisation issue and thus analysed a broad set of
models systematically and robustly over long stories. Related experiments were
conducted based on the CLUTRR, which is a diagnostic benchmark suite that can
analyse generalisation of natural language understanding (NLU) systems by
training over small story graphs and testing on larger ones. In order to handle
the multi-relational story graph, we consider two classes of neural models:
"E-GNN", the graph-based models that can process graph-structured data and
consider the edge attributes simultaneously; and "L-Graph", the sequence-based
models which can process linearized version of the graphs. We performed an
extensive empirical evaluation, and we found that the modified recurrent neural
network yield surprisingly accurate results across every systematic
generalisation tasks which outperform the modified graph neural network, while
the latter produced more robust models.


title: Immersive Text Game and Personality Classification

abstract:
  We designed and built a game called \textit{Immersive Text Game}, which
allows the player to choose a story and a character, and interact with other
characters in the story in an immersive manner of dialogues. The game is based
on several latest models, including text generation language model, information
extraction model, commonsense reasoning model, and psychology evaluation model.
In the past, similar text games usually let players choose from limited actions
instead of answering on their own, and not every time what characters said are
determined by the player. Through the combination of these models and elaborate
game mechanics and modes, the player will find some novel experiences as driven
through the storyline.


title:
Multi-Modal Learning Using Physicians Diagnostics for Optical Coherence
  Tomography Classification

abstract:
  In this paper, we propose a framework that incorporates experts diagnostics
and insights into the analysis of Optical Coherence Tomography (OCT) using
multi-modal learning. To demonstrate the effectiveness of this approach, we
create a medical diagnostic attribute dataset to improve disease classification
using OCT. Although there have been successful attempts to deploy machine
learning for disease classification in OCT, such methodologies lack the experts
insights. We argue that injecting ophthalmological assessments as another
supervision in a learning framework is of great importance for the machine
learning process to perform accurate and interpretable classification. We
demonstrate the proposed framework through comprehensive experiments that
compare the effectiveness of combining diagnostic attribute features with
latent visual representations and show that they surpass the state-of-the-art
approach. Finally, we analyze the proposed dual-stream architecture and provide
an insight that determine the components that contribute most to classification
performance.


title: Calibration of Machine Reading Systems at Scale

abstract:
  In typical machine learning systems, an estimate of the probability of the
prediction is used to assess the system's confidence in the prediction. This
confidence measure is usually uncalibrated; i.e.\ the system's confidence in
the prediction does not match the true probability of the predicted output. In
this paper, we present an investigation into calibrating open setting machine
reading systems such as open-domain question answering and claim verification
systems. We show that calibrating such complex systems which contain discrete
retrieval and deep reading components is challenging and current calibration
techniques fail to scale to these settings. We propose simple extensions to
existing calibration approaches that allows us to adapt them to these settings.
Our experimental results reveal that the approach works well, and can be useful
to selectively predict answers when question answering systems are posed with
unanswerable or out-of-the-training distribution questions.


title:
On some H-cleft extensions which are distinguished by their polynomial
  H-identities

abstract:
  Let H be the Taft algebra over a finite commutative ring R. When N is a unit
in R, we show that all H-cleft extensions over R are determined up to
H-comodule algebra isomorphism by their polynomial H-identities.


title: On the eternal non-Markovianity of non-unital quantum channels

abstract:
  The eternally non-Markovian (ENM) Pauli channel is an example of a unital
channel characterized by a negative decay rate for all time $t>0$. Here we
consider the problem of constructing an analogous non-unital channel, and show
that in particular the qubit generalized amplitude damping (GAD) channel cannot
be ENM. We construct a quasi-ENM GAD channel, characterized by a time $t^\ast >
0$, such that the channel is non-Markovian only and for all time $t > t^\ast$.
We point out that the impossibility of the ENM property does not hold for a
general qubit or higher-dimensional non-unital channel.


title:
Automated Detection of Acute Promyelocytic Leukemia in Blood Films and
  Bone Marrow Aspirates with Annotation-free Deep Learning

abstract:
  While optical microscopy inspection of blood films and bone marrow aspirates
by a hematologist is a crucial step in establishing diagnosis of acute
leukemia, especially in low-resource settings where other diagnostic modalities
might not be available, the task remains time-consuming and prone to human
inconsistencies. This has an impact especially in cases of Acute Promyelocytic
Leukemia (APL) that require urgent treatment. Integration of automated
computational hematopathology into clinical workflows can improve the
throughput of these services and reduce cognitive human error. However, a major
bottleneck in deploying such systems is a lack of sufficient cell morphological
object-labels annotations to train deep learning models. We overcome this by
leveraging patient diagnostic labels to train weakly-supervised models that
detect different types of acute leukemia. We introduce a deep learning
approach, Multiple Instance Learning for Leukocyte Identification (MILLIE),
able to perform automated reliable analysis of blood films with minimal
supervision. Without being trained to classify individual cells, MILLIE
differentiates between acute lymphoblastic and myeloblastic leukemia in blood
films. More importantly, MILLIE detects APL in blood films (AUC 0.94+/-0.04)
and in bone marrow aspirates (AUC 0.99+/-0.01). MILLIE is a viable solution to
augment the throughput of clinical pathways that require assessment of blood
film microscopy.


title: Enriching Unsupervised User Embedding via Medical Concepts

abstract:
  Clinical notes in Electronic Health Records (EHR) present rich documented
information of patients to inference phenotype for disease diagnosis and study
patient characteristics for cohort selection. Unsupervised user embedding aims
to encode patients into fixed-length vectors without human supervisions.
Medical concepts extracted from the clinical notes contain rich connections
between patients and their clinical categories. However, existing unsupervised
approaches of user embeddings from clinical notes do not explicitly incorporate
medical concepts. In this study, we propose a concept-aware unsupervised user
embedding that jointly leverages text documents and medical concepts from two
clinical corpora, MIMIC-III and Diabetes. We evaluate user embeddings on both
extrinsic and intrinsic tasks, including phenotype classification, in-hospital
mortality prediction, patient retrieval, and patient relatedness. Experiments
on the two clinical corpora show our approach exceeds unsupervised baselines,
and incorporating medical concepts can significantly improve the baseline
performance.


title:
A Subtle Aspect of Minimal Lengths in the Generalized Uncertainty
  Principle

abstract:
  In this work, we point out an overlooked and subtle feature of the
generalized uncertainty principle (GUP) approach to quantizing gravity: namely
that different pairs of modified operators with the same modified commutator,
$[\hat{X},\hat{P}] = i \hbar (1+\beta p^2)$, may have different physical
consequences such as having no minimal length at all. These differences depend
on how the position and/or momentum operators are modified rather than only on
the resulting modified commutator. This provides guidance when constructing GUP
models since it distinguishes those GUPs that have a minimal length scale, as
suggested by some broad arguments about quantum gravity, versus GUPs without a
minimal length scale.


title:
Explicit User Manipulation in Reinforcement Learning Based Recommender
  Systems

abstract:
  Recommender systems are highly prevalent in the modern world due to their
value to both users and platforms and services that employ them. Generally,
they can improve the user experience and help to increase satisfaction, but
they do not come without risks. One such risk is that of their effect on users
and their ability to play an active role in shaping user preferences. This risk
is more significant for reinforcement learning based recommender systems. These
are capable of learning for instance, how recommended content shown to a user
today may tamper that user's preference for other content recommended in the
future. Reinforcement learning based recommendation systems can thus implicitly
learn to influence users if that means maximizing clicks, engagement, or
consumption. On social news and media platforms, in particular, this type of
behavior is cause for alarm. Social media undoubtedly plays a role in public
opinion and has been shown to be a contributing factor to increased political
polarization. Recommender systems on such platforms, therefore, have great
potential to influence users in undesirable ways. However, it may also be
possible for this form of manipulation to be used intentionally. With
advancements in political opinion dynamics modeling and larger collections of
user data, explicit user manipulation in which the beliefs and opinions of
users are tailored towards a certain end emerges as a significant concern in
reinforcement learning based recommender systems.


title: Red Domination in Perfect Elimination Bipartite Graphs

abstract:
  The $k$ red domination problem for a bipartite graph $G=(X,Y,E)$ is to find a
subset $D \subseteq X$ of cardinality at most $k$ that dominates vertices of
$Y$. The decision version of this problem is NP-complete for general bipartite
graphs but solvable in polynomial time for chordal bipartite graphs. We
strengthen that result by showing that it is NP-complete for perfect
elimination bipartite graphs. We present a tight upper bound on the number of
such sets in bipartite graphs, and show that we can calculate that number in
linear time for convex bipartite graphs. We present a linear space linear delay
enumeration algorithm that needs only linear preprocessing time.


title:
Curvature without metric: the Penrose construction for half-flat
  pp-waves

abstract:
  We derive the Penrose data for half-flat pp-waves and extend his original
construction for the Weyl spinor of plane waves in terms of this data.


title: On the Role of Coherence in Shor's Algorithm

abstract:
  Shor's factoring algorithm provides a super-polynomial speed-up over all
known classical factoring algorithms. Here, we address the question of which
quantum properties fuel this advantage. We investigate a sequential variant of
Shor's algorithm with a fixed overall structure and identify the role of
coherence for this algorithm quantitatively. We analyze this protocol in the
framework of dynamical resource theories, which capture the resource character
of operations that can create and detect coherence. This allows us to derive a
lower and an upper bound on the success probability of the protocol, which
depend on rigorously defined measures of coherence as a dynamical resource. We
compare these bounds with the classical limit of the protocol and conclude that
within the fixed structure that we consider, coherence is the quantum resource
that determines its performance by bounding the success probability from below
and above. Therefore, we shine new light on the fundamental role of coherence
in quantum computation.


title: ICTs and Forced Migration: A Critical Discourse Review

abstract:
  The role of information and communication technologies (ICTs) in relation to
issues of forced migration has come to the attention of the Information Systems
(IS) literature. In this paper we review the interdisciplinary research on the
topic, seeking to identify the main discourses in it.


title:
Some properties of the adjoint of the unbounded operators $TT^*$ and
  $T^*T$

abstract:
  In this note, we mainly investigate the validity of the identities
$(TT^*)^*=TT^*$ and $(T^*T)^*=T^*T$, where $T$ is a densely defined closable
(or symmetric) operator.


title: Extendable orthogonal sets of integral vectors

abstract:
  Motivated by a model in quantum computation we study orthogonal sets of
integral vectors of the same norm that can be extended with new vectors keeping
the norm and the orthogonality. Our approach involves some arithmetic
properties of the quaternions and other hypercomplex numbers.


title:
Transform your Smartphone into a DSLR Camera: Learning the ISP in the
  Wild

abstract:
  We propose a trainable Image Signal Processing (ISP) framework that produces
DSLR quality images given RAW images captured by a smartphone. To address the
color misalignments between training image pairs, we employ a color-conditional
ISP network and optimize a novel parametric color mapping between each input
RAW and reference DSLR image. During inference, we predict the target color
image by designing a color prediction network with efficient Global Context
Transformer modules. The latter effectively leverage global information to
learn consistent color and tone mappings. We further propose a robust masked
aligned loss to identify and discard regions with inaccurate motion estimation
during training. Lastly, we introduce the ISP in the Wild (ISPW) dataset,
consisting of weakly paired phone RAW and DSLR sRGB images. We extensively
evaluate our method, setting a new state-of-the-art on two datasets.


title:
Vocal effort modeling in neural TTS for improving the intelligibility of
  synthetic speech in noise

abstract:
  We present a neural text-to-speech (TTS) method that models natural vocal
effort variation to improve the intelligibility of synthetic speech in the
presence of noise. The method consists of first measuring the spectral tilt of
unlabeled conventional speech data, and then conditioning a neural TTS model
with normalized spectral tilt among other prosodic factors. Changing the
spectral tilt parameter and keeping other prosodic factors unchanged enables
effective vocal effort control at synthesis time independent of other prosodic
factors. By extrapolation of the spectral tilt values beyond what has been seen
in the original data, we can generate speech with high vocal effort levels,
thus improving the intelligibility of speech in the presence of masking noise.
We evaluate the intelligibility and quality of normal speech and speech with
increased vocal effort in the presence of various masking noise conditions, and
compare these to well-known speech intelligibility-enhancing algorithms. The
evaluations show that the proposed method can improve the intelligibility of
synthetic speech with little loss in speech quality.


title:
V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision
  Transformer

abstract:
  In this paper, we investigate the application of Vehicle-to-Everything (V2X)
communication to improve the perception performance of autonomous vehicles. We
present a robust cooperative perception framework with V2X communication using
a novel vision Transformer. Specifically, we build a holistic attention model,
namely V2X-ViT, to effectively fuse information across on-road agents (i.e.,
vehicles and infrastructure). V2X-ViT consists of alternating layers of
heterogeneous multi-agent self-attention and multi-scale window self-attention,
which captures inter-agent interaction and per-agent spatial relationships.
These key modules are designed in a unified Transformer architecture to handle
common V2X challenges, including asynchronous information sharing, pose errors,
and heterogeneity of V2X components. To validate our approach, we create a
large-scale V2X perception dataset using CARLA and OpenCDA. Extensive
experimental results demonstrate that V2X-ViT sets new state-of-the-art
performance for 3D object detection and achieves robust performance even under
harsh, noisy environments. The dataset, source code, and trained models will be
open-sourced.


title:
DeeP-LCC: Data-EnablEd Predictive Leading Cruise Control in Mixed
  Traffic Flow

abstract:
  For the control of connected and autonomous vehicles (CAVs), most existing
methods focus on model-based strategies. They require explicit knowledge of
car-following dynamics of human-driven vehicles that are non-trivial to
identify accurately. In this paper, instead of relying on a parametric
car-following model, we introduce a data-driven non-parametric strategy, called
DeeP-LCC (Data-EnablEd Predictive Leading Cruise Control), to achieve safe and
optimal control of CAVs in mixed traffic. We first utilize Willems' fundamental
lemma to obtain a data-centric representation of mixed traffic behavior. This
is justified by rigorous analysis on controllability and observability
properties of mixed traffic. We then employ a receding horizon strategy to
solve a finite-horizon optimal control problem at each time step, in which
input/output constraints are incorporated for collision-free guarantees.
Numerical experiments validate the performance of DeeP-LCC compared to a
standard predictive controller that requires an accurate model. Extensive
nonlinear traffic simulations further confirm its great potential on improving
traffic efficiency, driving safety, and fuel economy.


title:
Multimodal learning-based inversion models for the space-time
  reconstruction of satellite-derived geophysical fields

abstract:
  For numerous earth observation applications, one may benefit from various
satellite sensors to address the reconstruction of some process or information
of interest. A variety of satellite sensors deliver observation data with
different sampling patterns due satellite orbits and/or their sensitivity to
atmospheric conditions (e.g., clour cover, heavy rains,...). Beyond the ability
to account for irregularly-sampled observations, the definition of model-driven
inversion methods is often limited to specific case-studies where one can
explicitly derive a physical model to relate the different observation sources.
Here, we investigate how end-to-end learning schemes provide new means to
address multimodal inversion problems. The proposed scheme combines a
variational formulation with trainable observation operators, {\em a priori}
terms and solvers. Through an application to space oceanography, we show how
this scheme can successfully extract relevant information from
satellite-derived sea surface temperature images and enhance the reconstruction
of sea surface currents issued from satellite altimetry data.


title: How is a graph not like a manifold?

abstract:
  For an equivariantly formal action of $T^k$ on a smooth manifold $X^{2n}$
with isolated fixed points we investigate the global homological properties of
the graded poset $S_X$ of face submanifolds. We prove that the condition of
$j$-independency of tangent weights at each fixed point implies
$(j+1)$-acyclicity of the skeleta $(S_X)_r$ for $r>j+1$. This result provides a
necessary topological condition for a GKM-graph to be a GKM-graph of some
GKM-manifold. We use particular acyclicity arguments to describe the
equivariant cohomology algebra of an equivariantly formal manifold $X^{2n}$
with an $(n-1)$-independent action of $T^{n-1}$, for $n\geqslant 5$. This
description relates the equivariant cohomology algebra to the face algebra of a
certain simplicial poset. This observation relates actions of complexity one to
the theory of torus manifolds.


title: FUTR3D: A Unified Sensor Fusion Framework for 3D Detection

abstract:
  Sensor fusion is an essential topic in many perception systems, such as
autonomous driving and robotics. Existing multi-modal 3D detection models
usually involve customized designs depending on the sensor combinations or
setups. In this work, we propose the first unified end-to-end sensor fusion
framework for 3D detection, named FUTR3D, which can be used in (almost) any
sensor configuration. FUTR3D employs a query-based Modality-Agnostic Feature
Sampler (MAFS), together with a transformer decoder with a set-to-set loss for
3D detection, thus avoiding using late fusion heuristics and post-processing
tricks. We validate the effectiveness of our framework on various combinations
of cameras, low-resolution LiDARs, high-resolution LiDARs, and Radars. On
NuScenes dataset, FUTR3D achieves better performance over specifically designed
methods across different sensor combinations. Moreover, FUTR3D achieves great
flexibility with different sensor configurations and enables low-cost
autonomous driving. For example, only using a 4-beam LiDAR with cameras, FUTR3D
(56.8 mAP) achieves on par performance with state-of-the-art 3D detection model
CenterPoint (56.6 mAP) using a 32-beam LiDAR.


title: Confidence intervals for nonparametric regression

abstract:
  We demonstrate and discuss nonasymptotic bounds in probability for the cost
of a regression scheme with a general loss function from the perspective of the
Rademacher theory, and for the optimality with respect to the average
$L^{2}$-distance to the underlying conditional expectations of least squares
regression outcomes from the perspective of the Vapnik-Chervonenkis theory.
  The results follow from an analysis involving independent but possibly
nonstationary training samples and can be extended, in a manner that we explain
and illustrate, to relevant cases in which the training sample exhibits
dependence.


title: Equitable Continuous Organizations with Self-Assessed Valuations

abstract:
  Organizations are often unable to align the interests of all stakeholders
with the financial success of the organization (e.g. due to regulation).
However, continuous organizations (COs) introduce a paradigm shift. COs offer
immediate liquidity, are permission-less and can align incentives. CO shares
are issued continuously in the form of tokens via a smart contract on a
blockchain. Token prices are designed to increase as more tokens are minted.
When the share supply is low, near-zero prices make it advantageous to buy and
hold tokens until interest in the CO increases, enabling a profitable sale.
This attribute of COs, known as investment efficiency, is desirable. Yet, it
can yield allocative inefficiency via the "holdout problem," i.e. latecomers
may find a CO more valuable than early tokenholders, but be unable to attain
the same token holdings due to inflated prices. With the aim of increasing
overall equity, we introduce a voting mechanism into COs. We show this balances
allocative and investment efficiency, and may dissuade speculative trading
behaviors, thereby decreasing investment risk.


title:
Breast Cancer Induced Bone Osteolysis Prediction Using Temporal
  Variational Auto-Encoders

abstract:
  Objective and Impact Statement. We adopt a deep learning model for bone
osteolysis prediction on computed tomography (CT) images of murine breast
cancer bone metastases. Given the bone CT scans at previous time steps, the
model incorporates the bone-cancer interactions learned from the sequential
images and generates future CT images. Its ability of predicting the
development of bone lesions in cancer-invading bones can assist in assessing
the risk of impending fractures and choosing proper treatments in breast cancer
bone metastasis. Introduction. Breast cancer often metastasizes to bone, causes
osteolytic lesions, and results in skeletal related events (SREs) including
severe pain and even fatal fractures. Although current imaging techniques can
detect macroscopic bone lesions, predicting the occurrence and progression of
bone lesions remains a challenge. Methods. We adopt a temporal variational
auto-encoder (T-VAE) model that utilizes a combination of variational
auto-encoders and long short-term memory networks to predict bone lesion
emergence on our micro-CT dataset containing sequential images of murine
tibiae. Given the CT scans of murine tibiae at early weeks, our model can learn
the distribution of their future states from data. Results. We test our model
against other deep learning-based prediction models on the bone lesion
progression prediction task. Our model produces much more accurate predictions
than existing models under various evaluation metrics. Conclusion. We develop a
deep learning framework that can accurately predict and visualize the
progression of osteolytic bone lesions. It will assist in planning and
evaluating treatment strategies to prevent SREs in breast cancer patients.


title:
Realization of A Strong Ferroelectric Metal by Nb-doping in Strained
  EuTiO$_{3}$

abstract:
  Ferroelectric (FE) metals have been attracting attention as they possess both
metallicity and ferroelectricity, the two seemingly incompatible physical
properties. An important problem for both fundamental research and potential
applications is how to realize a strong FE metal. One strategy is to dope a
strong FE insulator into an FE metal provided that the induced free electrons
do not destroy FE distortion. Strained EuTiO$_{3}$ film is a strong FE
ferromagnet and a promising candidate for such a strategy. Here, we calculate
the structural, electronic, and polarization properties of Nb-doped strained
EuTiO$_{3}$ film using the hybrid density-functional theory. The results show
that the strained EuTi$_{0.875}$Nb$_{0.125}$O$_{3}$ film is a strong FE metal
as the Nb doping induces metallicity without weakening the FE distortion. The
underlying atomistic mechanism of the coexistence of metallicity and strong
ferroelectricity is discussed. The findings show that combining doping and
strain engineering is a promising way to realize new EuTiO$_{3}$-based strong
FE metals and may be used in other materials as well.


title:
A Framework for Automating Deployment and Evaluation of Blockchain
  Network

abstract:
  Blockchain network deployment and evaluation have become prevalent due to the
demand for private blockchains by enterprises, governments, and edge computing
systems. Whilst a blockchain network's deployment and evaluation are driven by
its architecture, practitioners still need to learn and carry out many
repetitive and error-prone activities to transform architecture into an
operational blockchain network and evaluate it. Greater efficiency could be
gained if practitioners focus solely on the architecture design, a valuable and
hard-to-automate activity, and leave the implementation steps to an automation
framework. This paper proposes an automation framework called NVAL (Network
Deployment and Evaluation Framework), which can deploy and evaluate blockchain
networks based on their architecture specifications. The key idea of NVAL is
reusing and combining the existing automation scripts and utilities of various
blockchain types to deploy and evaluate incoming blockchain network
architectures. We propose a novel meta-model to capture blockchain network
architectures as computer-readable artefacts and employ a state-space search
approach to plan and conduct their deployment and evaluation. An evaluative
case study shows that NVAL successfully combines seven deployment and
evaluation procedures to deploy 65 networks with 12 different architectures and
generate 295 evaluation datasets whilst incurring a negligible processing time
overhead.


title: Magnetization-direction-tunable kagome Weyl line

abstract:
  Kagome magnets provide a fascinating platform for a plethora of topological
quantum phenomena. Here, utilizing angle-resolved photoemission spectroscopy,
we demonstrate Weyl lines with strong out-of-plane dispersion in an A-A stacked
kagome magnet TbxGd1-xMn6Sn6. On the Gd rich side, the Weyl line remains nearly
spin-orbit-gapless due to a remarkable cooperative interplay between Kane-Mele
spin-orbit-coupling, low site symmetry and in-plane magnetic order. Under Tb
substitution, the kagome Weyl line gaps due to a magnetic reorientation to
out-of-plane order. Our results illustrate the magnetic moment direction as an
efficient tuning knob for realizing distinct three-dimensional topological
phases.


title:
Coordinate Invariant User-Guided Constrained Path Planning with Reactive
  Rapidly Expanding Plane-Oriented Escaping Trees

abstract:
  As collaborative robots move closer to human environments, motion generation
and reactive planning strategies that allow for elaborate task execution with
minimal easy-to-implement guidance whilst coping with changes in the
environment is of paramount importance. In this paper, we present a novel
approach for generating real-time motion plans for point-to-point tasks using a
single successful human demonstration. Our approach is based on screw linear
interpolation,which allows us to respect the underlying geometric constraints
that characterize the task and are implicitly present in the demonstration. We
also integrate an original reactive collision avoidance approach with our
planner. We present extensive experimental results to demonstrate that with our
approach,by using a single demonstration of moving one block, we can generate
motion plans for complex tasks like stacking multiple blocks (in a dynamic
environment). Analogous generalization abilities are also shown for tasks like
pouring and loading shelves. For the pouring task, we also show that a
demonstration given for one-armed pouring can be used for planning pouring with
a dual-armed manipulator of different kinematic structure.


title:
A Dynamical System Approach to the Inverse Spectral Problem for Hankel
  Operators: the Case of Compact Operators with Simple Singular Values

abstract:
  Given complex sequences $\{ \lambda_{n} \}_{n=1}^{\infty}$ and $\{ \mu_{n}
\}_{n=1}^{\infty}$ satisfying the following intertwining property: $
|\lambda_1| > |\mu_1| > |\lambda_2| > |\mu_2| > ...> |\lambda_n| > |\mu_n| \to
0, $ we use a dynamical approach to prove that there exists a unique Hankel
operator $\Gamma$ on $\ell^2$, with simple eigenvalues $\{ \lambda_n
\}_{n=1}^{\infty}$ , and $\Gamma S$ with simple eigenvalues $\{ \mu_{n}
\}_{n=1}^{\infty}$ respectively, here $S$ is the shift operator. Specifically,
we introduce abstract Borg's theorm, saying that two sets of real eigenvalues
will uniquely define the spectral measure of compact self-adjoint operators,
under rank-one perturbation. Then we use this Borg's theorem to solve the
inverse spectral problem of Hankel operators, namely reconstruct Hankel
operators from two sets of spectrum. In addition, we give another complex
analysis method to describe the trivial kernel condition $Ker \Gamma = \{ 0 \}$
in terms of $\{ \lambda_n \}_{n=1}^{\infty}$ and $\{ \mu_{n}
\}_{n=1}^{\infty}$.


title:
Nonstationary Temporal Matrix Factorization for Multivariate Time Series
  Forecasting

abstract:
  Modern time series datasets are often high-dimensional, incomplete/sparse,
and nonstationary. These properties hinder the development of scalable and
efficient solutions for time series forecasting and analysis. To address these
challenges, we propose a Nonstationary Temporal Matrix Factorization (NoTMF)
model, in which matrix factorization is used to reconstruct the whole time
series matrix and vector autoregressive (VAR) process is imposed on a properly
differenced copy of the temporal factor matrix. This approach not only
preserves the low-rank property of the data but also offers consistent temporal
dynamics. The learning process of NoTMF involves the optimization of two factor
matrices and a collection of VAR coefficient matrices. To efficiently solve the
optimization problem, we derive an alternating minimization framework, in which
subproblems are solved using conjugate gradient and least squares methods. In
particular, the use of conjugate gradient method offers an efficient routine
and allows us to apply NoTMF on large-scale problems. Through extensive
experiments on Uber movement speed dataset, we demonstrate the superior
accuracy and effectiveness of NoTMF over other baseline models. Our results
also confirm the importance of addressing the nonstationarity of real-world
time series data such as spatiotemporal traffic flow/speed.


title: Continual Sequence Generation with Adaptive Compositional Modules

abstract:
  Continual learning is essential for real-world deployment when there is a
need to quickly adapt the model to new tasks without forgetting knowledge of
old tasks. Existing work on continual sequence generation either always reuses
existing parameters to learn new tasks, which is vulnerable to catastrophic
forgetting on dissimilar tasks, or blindly adds new parameters for every new
task, which could prevent knowledge sharing between similar tasks. To get the
best of both worlds, in this work, we propose continual sequence generation
with adaptive compositional modules to adaptively add modules in transformer
architectures and compose both old and new modules for new tasks. We also
incorporate pseudo experience replay to facilitate knowledge transfer in those
shared modules. Experiment results on various sequences of generation tasks
show that our framework can adaptively add modules or reuse modules based on
task similarity, outperforming state-of-the-art baselines in terms of both
performance and parameter efficiency. We make our code public at
https://github.com/GT-SALT/Adaptive-Compositional-Modules.


title: Oscillations of non-slender tori in the external Hartle-Thorne geometry

abstract:
  We examine the influence of the quadrupole moment of a slowly rotating
neutron star on the oscillations of non-slender accretion tori. We apply
previously developed methods to perform analytical calculations of frequencies
of the radial epicyclic mode of a torus in the specific case of the
Hartle-Thorne geometry. We present here our preliminary results and provide a
brief comparison between the calculated frequencies and the frequencies
previously obtained assuming both standard and linearized Kerr geometry.
Finally, we shortly discuss the consequences for models of high-frequency
quasi-periodic oscillations observed in low-mass X-ray binaries.


title:
Incorporation of Magnesium into GaN Regulated by Intentionally Large
  Amounts of Hydrogen during Growth by MOCVD

abstract:
  Metal organic chemical vapor deposition (MOCVD) of GaN layers doped with Mg
atoms to the recognized optimum level of [Mg] $\sim2 \times 10^{19}$ cm$^{-3}$
has been performed. In a sequence of MOCVD runs, operational conditions,
including temperature and flow rate of precursors, have been maintained except
for a larger flow of hydrogen carrier gas. By employing the largest hydrogen
flow of 25 slm in this study, the performance of the as-grown Mg-doped GaN
layers has been certified by a room-temperature hole concentration of $p\sim2
\times 10^{17}$ cm$^{-3}$. This is a representative value which is typically
obtained by subjecting the Mg-doped GaN layers to a post-growth annealing. The
post-growth annealing is required to render the magnesium acceptors involving
substitutional Mg atoms on a Ga site in the GaN crystal lattice,
Mg$_\mathrm{Ga}$, electrically active by causing thermal dissociation of the
common MgH complexes and hydrogen out diffusion. Eloquent experimental evidence
is presented that the intentionally large amounts of hydrogen during growth of
Mg-doped GaN layers by MOCVD can regulate the incorporation of Mg atoms into
GaN in a significant way so that electrically active isolated Mg$_\mathrm{Ga}$
acceptors can dominate the common MgH complexes.


title: The complexity of the Structure and Classification of Dynamical Systems

abstract:
  This is an expository paper about the Borel complexity of structure and
classification theorems. It sorts several classical problems relative to known
benchmarks of complexity. As a corollary various problems proposed by people
such as von Neumann and Smale are shown to be infeasible using inherently
countable information.


title: Complete Calabi-Yau metrics in the complement of two divisors

abstract:
  We construct new complete Calabi-Yau metrics on the complement of an
anticanonical divisors $D$ in a Fano manifold of dimension at least three, when
$D$ consists of two transversely intersecting smooth divisors. The asymptotic
geometry is modeled on a generalization of the Calabi ansatz, related to the
non-archimedean Monge-Amp\`ere equation.


title:
Driven strongly correlated quantum circuits and Hall states: Unified
  photo-assisted noise and minimal excitations

abstract:
  We show that the photo-assisted noise generated by arbitrary time-dependent
voltages and transmission amplitudes obey a perturbative fluctuation relation
(FR) which fully extends the side-band transmission picture in terms of
many-body eigenstates. This applies for instance to non-equilibrium (NE)
strongly correlated systems or a quantum circuits with a temperature bias,
formed by a tunneling or Josephson junction strongly coupled to an
electromagnetic environment. It applies as well to a quantum point contact
(QPC) in the integer or fractional quantum Hall regime where the FR provides
robust methods to determine the fractional charge that have been implemented
experimentally or to analyze two-particle collisions in a HOM type geometry. We
exploit the FR to revisit the characterisation of minimal excitations generated
by lorentzian pulses for a non-linear dc current.


title:
Electron correlations rule phonon-driven instability in single layer
  TiSe$_2$

abstract:
  We investigate the controversial case of charge-density-wave (CDW) order in
single layer 1T-TiSe$_2$ by employing the density functional perturbation
theory with on-site Hubbard interactions. The results emphasize the crucial
role of electron correlations via Hubbard corrections in order to capture the
accurate electronic structure, low- and high-temperature limits of the CDW
phonon mode, and temperature-charge phase diagram, all overestimated with
semi-local functionals. According to our analysis, the CDW order in TiSe$_2$ is
mainly governed by momentum dependent electron-phonon coupling and electron
correlations, while other mechanisms, such as exchange and excitonic
interactions, might only support its formation.


title:
From Stance to Concern: Adaptation of Propositional Analysis to New
  Tasks and Domains

abstract:
  We present a generalized paradigm for adaptation of propositional analysis
(predicate-argument pairs) to new tasks and domains. We leverage an analogy
between stances (belief-driven sentiment) and concerns (topical issues with
moral dimensions/endorsements) to produce an explanatory representation. A key
contribution is the combination of semi-automatic resource building for
extraction of domain-dependent concern types (with 2-4 hours of human labor per
domain) and an entirely automatic procedure for extraction of
domain-independent moral dimensions and endorsement values. Prudent (automatic)
selection of terms from propositional structures for lexical expansion (via
semantic similarity) produces new moral dimension lexicons at three levels of
granularity beyond a strong baseline lexicon. We develop a ground truth (GT)
based on expert annotators and compare our concern detection output to GT, to
yield 231% improvement in recall over baseline, with only a 10% loss in
precision. F1 yields 66% improvement over baseline and 97.8% of human
performance. Our lexically based approach yields large savings over approaches
that employ costly human labor and model building. We provide to the community
a newly expanded moral dimension/value lexicon, annotation guidelines, and GT.


title: On singular variants of the Singer-Hopf Conjecture

abstract:
  We propose singular variants of the Singer-Hopf conjecture, formulated in
terms of the Euler-Mather characteristic, intersection homology Euler
characteristic and, resp., virtual Euler characteristic of a closed irreducible
subvariety of an aspherical complex projective manifold. We prove the
conjecture under the assumption that the cotangent bundle of the ambient
variety is numerically effective (nef), or, more generally, when the ambient
manifold admits a finite morphism to a complex projective manifold with a nef
cotangent bundle.


title:
On the Sparsity of Optimal Linear Decision Rules in Robust Inventory
  Management

abstract:
  We consider the widely-studied class of production-inventory problems from
the seminal work of Ben-Tal et al. (2004) on linear decision rules in robust
optimization. We prove that there always exists an optimal linear decision rule
for this class of problems in which the number of nonzero parameters in the
linear decision rule is equal to a small constant times the number of
parameters in a static decision rule. This result demonstrates that the
celebrated performance of linear decision rules in such robust inventory
management problems can be obtained without sacrificing the simplicity of
static decision rules. From a practical standpoint, our result lays a
theoretical foundation for the growing stream of literature on harnessing
sparsity to develop practicable algorithms for computing optimal linear
decision rules in operational planning problems with many time periods. Our
proof is based on a principled analysis of extreme points of linear programming
formulations, and we show that our proof techniques extend to other fundamental
classes of robust optimization problems from the literature.


title: Lateral Ego-Vehicle Control without Supervision using Point Clouds

abstract:
  Existing vision based supervised approaches to lateral vehicle control are
capable of directly mapping RGB images to the appropriate steering commands.
However, they are prone to suffering from inadequate robustness in real world
scenarios due to a lack of failure cases in the training data. In this paper, a
framework for training a more robust and scalable model for lateral vehicle
control is proposed. The framework only requires an unlabeled sequence of RGB
images. The trained model takes a point cloud as input and predicts the lateral
offset to a subsequent frame from which the steering angle is inferred. The
frame poses are in turn obtained from visual odometry. The point cloud is
conceived by projecting dense depth maps into 3D. An arbitrary number of
additional trajectories from this point cloud can be generated during training.
This is to increase the robustness of the model. Online experiments show that
the performance of our method is superior to that of the supervised model.


title:
On the Connection between Supermassive Black Hole and Galaxy Growth in
  the Reionization Epoch

abstract:
  The correlation between the mass of supermassive black holes (SMBHs;
$\mathcal{M}_{\rm BH}$) and their host galaxies ($\mathcal{M}_\star$) in the
reionization epoch provides valuable constraints on how their growth are
jointly assembled in the early universe. High-redshift quasars typically have a
$\mathcal{M}_{\rm BH}$/$\mathcal{M}_\star$ ratio significantly elevated in
comparison to the local value. However, it is unclear by how much this apparent
offset is driven by observational biases for the most distant quasars. To
address this issue, we model the sample selection and measurement biases for a
compilation of 20 quasars at $z\sim6$ with host properties based on ALMA
observations. We find that the observed distribution of quasars in the
$\mathcal{M}_{\rm BH} - \mathcal{M}_\star$ plane could be fully reproduced by
assuming that the underlying SMBH populations at $z\sim6$ follow the
relationship in the local universe. However, a positive or even a negative
evolution in $\mathcal{M}_{\rm BH}$/$\mathcal{M}_\star$ can also explain the
data, depending on how the intrinsic scatter evolves and the strength of
various systematic uncertainties. This imposes critical requirements on
improving the accuracy of mass measurements and expanding the current sample to
lower $\mathcal{M}_{\rm BH}$ limits to break degeneracies. Interestingly,
quasars that are significant outliers in $\mathcal{M}_{\rm
BH}$/$\mathcal{M}_\star$ tend to move towards the local relation given their
instantaneous BH accretion rate and star formation rate. This may provide
tentative evidence that a self-regulated SMBH-galaxy coevolution scenario is
already in place at $z\sim6$, with AGN feedback being a possible driver.


title:
Oblique Alfv\'en waves in a stellar wind environment with dust particles
  charged by inelastic collisions and by photoionization

abstract:
  The characteristics of Alfv\'en waves propagating in a direction oblique to
the ambient magnetic field in a stellar wind environment are discussed. A
kinetic formulation for a magnetized dusty plasma is adopted considering
Maxwellian distributions of electrons and ions, and immobile dust particles
electrically charged by absorption of plasma particles and by photoionization.
The dispersion relation is numerically solved and the results are compared with
situations previously studied where dust particles were not charged by
photoionization, which is an important process in a stellar wind of a
relatively hot star. We show that the presence of dust causes the shear
Alfv\'en waves to present a region of wavenumber values with zero frequency and
that the minimum wavelength for which the mode becomes dispersive again is
roughly proportional to the radiation intensity to which the dust grains are
exposed. The damping rates of both shear and compressional Alfv\'en waves are
observed to decrease with increasing radiation flux, for the parameters
considered. For the particular case where both modes present a region with null
real frequency when the radiation flux is absent or weak, it is shown that when
the radiation flux is sufficiently strong, the photoionization mechanism may
cause this region to get smaller or even to vanish, for compressional Alfv\'en
waves. In that case, the compressional Alfv\'en waves present non zero
frequency for all wavenumber values, while the shear Alfv\'en waves still
present null frequency in a certain interval of wavenumber values, which gets
smaller with the presence of radiation.


title:
Shaking the causal tree: On the faithfulness and minimality assumptions
  beyond pairwise interactions

abstract:
  Built upon the concept of causal faithfulness, the so-called causal discovery
algorithms have been proposing the breakdown of the contributions of the mutual
information (MI) and conditional mutual information (CMI) to the sets of
variables that reveal causal influences. These algorithms suffer from the lack
of accounting emergent causes when connecting links resulting in a spuriously
embellished view of the organization of complex systems. Here, we first show
that causal emergence information is necessarily contained on CMIs. We also
connect this result with the intrinsic violation of faithfulness. Finally, by
using classical results from Ramsey's theory, we prove that CMIs with large
conditioned datasets contain, necessarily, arbitrary correlations. These
correlations appear only because of the size, and not the nature, of data. They
can be found in "randomly" generated, large enough databases. We also connect
this result with the false illusion of faithfulness. The net result proposes an
update of the well-known causal discovery algorithms, which can, in principle,
detect and isolate emergent causal influences in the network reconstruction
problems undetected so far.


title:
YouTube, The Great Radicalizer? Auditing and Mitigating Ideological
  Biases in YouTube Recommendations

abstract:
  Recommendations algorithms of social media platforms are often criticized for
placing users in "rabbit holes" of (increasingly) ideologically biased content.
Despite these concerns, prior evidence on this algorithmic radicalization is
inconsistent. Furthermore, prior work lacks systematic interventions that
reduce the potential ideological bias in recommendation algorithms. We conduct
a systematic audit of YouTube's recommendation system using a hundred thousand
sock puppets to determine the presence of ideological bias (i.e., are
recommendations aligned with users' ideology), its magnitude (i.e., are users
recommended an increasing number of videos aligned with their ideology), and
radicalization (i.e., are the recommendations progressively more extreme).
Furthermore, we design and evaluate a bottom-up intervention to minimize
ideological bias in recommendations without relying on cooperation from
YouTube. We find that YouTube's recommendations do direct users -- especially
right-leaning users -- to ideologically biased and increasingly radical content
on both homepages and in up-next recommendations. Our intervention effectively
mitigates the observed bias, leading to more recommendations to ideologically
neutral, diverse, and dissimilar content, yet debiasing is especially
challenging for right-leaning users. Our systematic assessment shows that while
YouTube recommendations lead to ideological bias, such bias can be mitigated
through our intervention.


title:
A direct geometry processing cartilage generation method using segmented
  bone models from datasets with poor cartilage visibility

abstract:
  We present a method to generate subject-specific cartilage for the hip joint.
Given bone geometry, our approach is agnostic to image modality, creates
conforming interfaces, and is well suited for finite element analysis. We
demonstrate our method on ten hip joints showing anatomical shape consistency
and well-behaved stress patterns. Our method is fast and may assist in
large-scale biomechanical population studies of the hip joint when manual
segmentation or training data is not feasible.


title: Energy transition scars of driven chaotic systems

abstract:
  Trajectory segments in the energy $E$-shell, which combine to form a closed
curve with segments in another canonically driven energy $E'$-shell, add
oscillatory semiclassical structure to the smooth classical background of the
energy transition probability density. In this, they resemble the contributions
of scars to the spectral Wigner function in the neighbourhood of periodic
orbits in a single static energy shell. The exact representation of the
transition density as an integral over spectral Wigner functions, that was
previously derived for the special case where the system undergoes a reflection
in phase space, is here generalized to arbitrary unitary transformations. If
these are generated continuously by a driving Hamiltonian, there will be a
finite lapse in the driving time for the transition to start, until the
initially nested shells touch each other and then start to overlap.
  The stationary phase evaluation of the multidimensional integral for the
transition density selects the pair of matching trajectory segments on each
shell, which close to form a piecewise smooth compound orbit supporting a
transition scar. Each compound orbit shows up as a fixed point of a product of
mappings that can be interpreted as generalized Poincare maps on the
intersection of the shells. Thus, the closed compound orbits are isolated if
the original Hamiltonian is chaotic. The actions of the compound orbits are
functions of the driving time. Repetitions have no place in the sum over
transition scars, but phase reinforcement can occur if the endpoints of a
contributing segment lie close to the stable and unstable manifolds of an
ordinary Bohr-quantized periodic orbit.


title: MeerKAT uncovers the physics of an Odd Radio Circle

abstract:
  Odd Radio Circles (ORCs) are recently-discovered faint diffuse circles of
radio emission, of unknown cause, surrounding galaxies at moderate redshift ($z
~ 0.2-0.6). Here we present detailed new MeerKAT radio images at 1284 MHz of
the first ORC, originally discovered with the Australian Square Kilometre Array
Pathfinder, with higher resolution (6 arcsec) and sensitivity (~ 2.4 uJy/bm).
  In addition to the new images, which reveal a complex internal structure
consisting of multiple arcs, we also present polarisation and spectral index
maps. Based on these new data, we consider potential mechanisms that may
generate the ORCs.


title: Fully Convolutional Fractional Scaling

abstract:
  We introduce a fully convolutional fractional scaling component, FCFS. Fully
convolutional networks can be applied to any size input and previously did not
support non-integer scaling. Our architecture is simple with an efficient
single layer implementation. Examples and code implementations of three common
scaling methods are published.


title:
Green's function for singular fractional differential equations and
  applications

abstract:
  In this paper, we study the existence of positive solutions for nonlinear
fractional differential equations with a singular weight. We derive Green's
function and corresponding integral operator and then examine the compactness
of the operator. As an application, we prove an existence-result for positive
solutions when a nonlinear term satisfies either superlinear or sublinear
conditions. The proof was mainly employed by Krasnoselski's classical fixed
point theorem.


title:
Isogenies over quadratic fields of elliptic curves with rational
  $j$-invariant

abstract:
  We determine the possible degrees of cyclic isogenies defined over quadratic
fields for non-CM elliptic curves with rational $j$-invariant.


title:
5G-Enabled Pseudonymity for Cooperative Intelligent Transportation
  System

abstract:
  Cooperative Intelligent Transportation Systems (C-ITS) enable communications
between vehicles, road-side infrastructures, and road-users to improve users'
safety and to efficiently manage traffic. Most, if not all, of the intelligent
vehicles-to-everything (V2X) applications, often rely on continuous collection
and sharing of sensitive information such as detailed location information
which raises privacy concerns. In this light, a common approach to concealing
the long-term identity of C-ITS vehicles is using multiple temporary
identifiers, called pseudonyms. However, the legacy pseudonyms management
approach is prone to linking attacks. The introduction of 5G network to V2X
offers enhanced location accuracy, better clock synchronisation, improved
modular service-based architecture, and enhanced security and privacy
preservation controls. Motivated by the above enhancements, we study 5G-enabled
pseudonyms for protecting vehicle identity privacy in C-ITS. We highlight the
gaps in the current standards of pseudonyms management. We further provide
recommendations regarding the pseudonyms management life-cycle.


title: RareGAN: Generating Samples for Rare Classes

abstract:
  We study the problem of learning generative adversarial networks (GANs) for a
rare class of an unlabeled dataset subject to a labeling budget. This problem
is motivated from practical applications in domains including security (e.g.,
synthesizing packets for DNS amplification attacks), systems and networking
(e.g., synthesizing workloads that trigger high resource usage), and machine
learning (e.g., generating images from a rare class). Existing approaches are
unsuitable, either requiring fully-labeled datasets or sacrificing the fidelity
of the rare class for that of the common classes. We propose RareGAN, a novel
synthesis of three key ideas: (1) extending conditional GANs to use labelled
and unlabelled data for better generalization; (2) an active learning approach
that requests the most useful labels; and (3) a weighted loss function to favor
learning the rare class. We show that RareGAN achieves a better
fidelity-diversity tradeoff on the rare class than prior work across different
applications, budgets, rare class fractions, GAN losses, and architectures.


title:
Mitigating Gender Bias in Machine Translation through Adversarial
  Learning

abstract:
  Machine translation and other NLP systems often contain significant biases
regarding sensitive attributes, such as gender or race, that worsen system
performance and perpetuate harmful stereotypes. Recent preliminary research
suggests that adversarial learning can be used as part of a model-agnostic bias
mitigation method that requires no data modifications. However, adapting this
strategy for machine translation and other modern NLP domains requires (1)
restructuring training objectives in the context of fine-tuning pretrained
large language models and (2) developing measures for gender or other protected
variables for tasks in which these attributes must be deduced from the data
itself.
  We present an adversarial learning framework that addresses these challenges
to mitigate gender bias in seq2seq machine translation. Our framework improves
the disparity in translation quality for sentences with male vs. female
entities by 86% for English-German translation and 91% for English-French
translation, with minimal effect on translation quality. The results suggest
that adversarial learning is a promising technique for mitigating gender bias
in machine translation.


title:
Radioactive nuclei in the early Solar System: analysis of the 15
  isotopes produced by core-collapse supernovae

abstract:
  Short-lived radioactive isotopes (SLRs) with half-lives between 0.1 to 100
Myr can be used to probe the origin of the Solar System. In this work, we
examine the core-collapse supernovae production of the 15 SLRs produced:
$^{26}$Al, $^{36}$Cl, $^{41}$Ca, $^{53}$Mn, $^{60}$Fe, $^{92}$Nb, $^{97}$Tc,
$^{98}$Tc, $^{107}$Pd, $^{126}$Sn, $^{129}$I, $^{135}$Cs, $^{146}$Sm,
$^{182}$Hf, and $^{205}$Pb. We probe the impact of the uncertainties of the
core-collapse explosion mechanism by examining a collection of 62 core-collapse
models with initial masses of 15, 20, and 25M$_{\odot}$, explosion energies
between 3.4$\times$10$^{50}$ and 1.8$\times$10$^{52}$ ergs and compact remnant
masses between 1.5M$_{\odot}$and 4.89M$_{\odot}$. We identify the impact of
both explosion energy and remnant mass on the final yields of the SLRs.
Isotopes produced within the innermost regions of the star, such as $^{92}$Nb
and $^{97}$Tc, are the most affected by the remnant mass, $^{92}$Nb varying by
five orders of magnitude. Isotopes synthesised primarily in explosive C-burning
and explosive He-burning, such as $^{60}$Fe, are most affected by explosion
energies. $^{60}$Fe increases by two orders of magnitude from the lowest to the
highest explosion energy in the 15M$_{\odot}$model. The final yield of each
examined SLR is used to compare to literature models.


title: Repairing Brain-Computer Interfaces with Fault-Based Data Acquisition

abstract:
  Brain-computer interfaces (BCIs) decode recorded neural signals from the
brain and/or stimulate the brain with encoded neural signals. BCIs span both
hardware and software and have a wide range of applications in restorative
medicine, from restoring movement through prostheses and robotic limbs to
restoring sensation and communication through spellers. BCIs also have
applications in diagnostic medicine, e.g., providing clinicians with data for
detecting seizures, sleep patterns, or emotions.
  Despite their promise, BCIs have not yet been adopted for long-term,
day-to-day use because of challenges related to reliability and robustness,
which are needed for safe operation in all scenarios. Ensuring safe operation
currently requires hours of manual data collection and recalibration, involving
both patients and clinicians. However, data collection is not targeted at
eliminating specific faults in a BCI. This paper presents a new methodology for
characterizing, detecting, and localizing faults in BCIs. Specifically, it
proposes partial test oracles as a method for detecting faults and slice
functions as a method for localizing faults to characteristic patterns in the
input data or relevant tasks performed by the user. Through targeted data
acquisition and retraining, the proposed methodology improves the correctness
of BCIs. We evaluated the proposed methodology on five BCI applications. The
results show that the proposed methodology (1) precisely localizes faults and
(2) can significantly reduce the frequency of faults through retraining based
on targeted, fault-based data acquisition. These results suggest that the
proposed methodology is a promising step towards repairing faulty BCIs.


title:
SweiNet: Deep Learning Based Uncertainty Quantification for Ultrasound
  Shear Wave Elasticity Imaging

abstract:
  In ultrasound shear wave elasticity (SWE) imaging, a number of algorithms
exist for estimating the shear wave speed (SWS) from spatiotemporal
displacement data. However, no method provides a well-calibrated and practical
uncertainty metric, hindering SWE's clinical adoption and utility in downstream
decision-making. Here, we designed a deep learning SWS estimator that
simultaneously outputs a quantitative and well-calibrated uncertainty value for
each estimate. Our deep neural network (DNN) takes as input a single 2D
spatiotemporal plane of tracked displacement data and outputs the two
parameters $m$ and $\sigma$ of a log-normal probability distribution. For
training and testing, we used in vivo 2D-SWE data of the cervix collected from
30 pregnant subjects, totaling 551 acquisitions and >2 million space-time
plots. Points were grouped by uncertainty into bins to assess uncertainty
calibration: the predicted uncertainty closely matched the root-mean-square
estimation error, with an average absolute percent deviation of 3.84%. We
created a leave-one-out ensemble model that estimated uncertainty with better
calibration (1.45%) than any individual ensemble member on a held-out patient's
data. Lastly, we applied the DNN to an external dataset to evaluate its
generalizability. We have made the trained model, SweiNet, openly available to
provide the research community with a fast SWS estimator that also outputs a
well-calibrated estimate of the predictive uncertainty.