title: Optimal Myopic Attacks on Nonlinear Estimation

abstract:
  Recent high-profile incidents have exposed security risks in control systems.
Particularly important and safety-critical modules for security analysis are
estimation and control (E&C). Prior works have analyzed the security of E&C for
linear, time-invariant systems; however, there are few analyses of nonlinear
systems despite their broad use. In an effort to facilitate identifying
vulnerabilities in control systems, in this work we establish a class of
optimal attacks on nonlinear E&C. Specifically, we define two attack objectives
and illustrate that realizing the optimal attacks against the widely-adopted
extended Kalman filter with industry-standard anomaly detection is equivalent
to solving convex quadratically-constrained quadratic programs. Given an
appropriate information model for the attacker (i.e., a specified amount of
attacker knowledge), we provide practical relaxations on the optimal attacks to
allow for their computation at runtime. We also show that the difference
between the optimal and relaxed attacks is bounded. Finally, we illustrate the
use of the introduced attack designs on a case-study.


title:
A Machine Learning Approach to Automatic Classification of Eight Sleep
  Disorders

abstract:
  In this research, we attempt to answer the following basic research
questions: Is a machine learning model able to classify all types of sleep
disorders with high accuracy? Among the different modalities of sleep disorder
signals, are some more important than others? Do raw signals improve the
performance of a deep learning model when they are used as inputs? Prior
research showed that most sleep disorders belong to eight categories. To study
the performance of machine learning models in classifying polysomnography
recordings into the eight categories of sleep pathologies, we selected the
Cyclic Alternating Pattern Sleep Database. We developed a multi-channel Deep
Learning model where a set of Convolutional Neural Networks were applied to six
channels of raw signals of different modalities, including three channels of
EEG signals and one channel each of EMG, ECG , and EOG signals. To compare the
performance of the DL model with other models, we designed a model that took
spectral features, instead of raw signals, as its inputs. We first studied the
"importance" issue of signal modalities using the RF algorithm. We found that
ECG contributed most to the important features and EMG second, among the four
signal modalities. We then studied the accuracy performance of the proposed
machine learning models. We verified that the multi-channel DL-R model, which
took raw signals as its inputs, outperformed all other models, with its
sensitivity and specificity scores both being above 95 %. This accuracy
performance is on a par with those published results which dealt with fewer
types of sleep disorders. We adopted two popular heatmap-generating techniques,
with which we confirmed that the DL model's superior performance was owing to
the CNN network's ability to extract potent features from raw signals.


title: QCD Phase Boundary and the Hadrochemical Horizon

abstract:
  I review the physics of the phase boundary between hadronic matter and quark
matter from several different points of view. These include thermodynamics,
statistical physics, and chemical kinetics. In particular, the review focuses
on the role of the chemical freeze-out line and its relation to the concept of
valence quark percolation. The review ends with some recollections of Jean
Cleymans.


title: "Double-path" ferroelectrics and the sign of the piezoelectric response

abstract:
  In this work, we propose a class of ferroelectrics (which we denote
"double-path" ferroelectrics), characterized by two competing polarization
switching paths for which the change in polarization is different and in fact
of opposite sign. Depending on which path is favorable under given conditions,
this leads to different identification of up- and down-polarized states. Since
the sign of piezoelectric response depends on the assignment of up- or
down-polarized state for a specific structure, this means that the material can
exhibit different signs of the piezoelectric response under different
conditions. We focus on HfO$_2$ as a key example. Our first-principles
calculations show that there are two competing paths in HfO$_2$, resulting from
different displacements of the atoms from the initial to the final structures,
and the change in polarization along these two paths is of opposite sign. These
results provide a natural explanation for the recently observed discrepancy in
the signs of piezoelectric responses in HfO$_2$ between theoretical
first-principles calculations and experimental observation. Further, this
allows predictions of how to favor one path over another by changes in
conditions and compositional tuning. This family of materials also includes
other candidates, such as CuInP$_2$S$_6$ and theoretically proposed
LaVO$_3$-SrVO$_3$ superlattice. We finally note that double-path ferroelectrics
possess novel electromechanical properties since the signs of their
piezoelectric responses can be switched.


title:
Solving AC Power Flow with Graph Neural Networks under Realistic
  Constraints

abstract:
  In this paper we propose a graph neural network architecture solving the AC
power flow problem under realistic constraints. While the energy transition is
changing the energy industry to a digitalized and decentralized energy system,
the challenges are increasingly shifting to the distribution grid level to
integrate new loads and generation technologies. To ensure a save and resilient
operation of distribution grids, AC power flow calculations are the means of
choice to determine grid operating limits or analyze grid asset utilization in
planning procedures. In our approach we demonstrate the development of a
framework which makes use of graph neural networks to learn the physical
constraints of the power flow. We present our model architecture on which we
perform unsupervised training to learn a general solution of the AC power flow
formulation that is independent of the specific topologies and supply tasks
used for training. Finally, we demonstrate, validate and discuss our results on
medium voltage benchmark grids.


title:
Surface and electronic structure at atomic length scales of the
  non-symmorphic antiferromagnet Eu$_5$In$_2$Sb$_6$

abstract:
  We performed Scanning Tunneling Microscopy and Spectroscopy (STM/STS)
measurements to investigate the Zintl phase Eu$_5$In$_2$Sb$_6$, a
non-symmorphic antiferromagnet. The theoretical prediction of a non-trivial
Fermi surface topology stabilized by the non-symmorphic symmetry motivated our
research. On the cleaved (010) plane, we obtained striped patterns that can be
correlated to the stacking of the [In$_2$Sb$_6$]$^{10-}$ double chains along
the crystallographic $c$ axis. The attempted cleavage along the $a$ axis
revealed a more complex pattern. We combined the STS measurement on
non-reconstructed (010) and (081) surfaces with DFT calculations to further
elucidate the electronic structure of Eu$_5$In$_2$Sb$_6$. From our
investigations so far, direct experimental evidence of the predicted
topological surface states remains elusive.


title:
XLMRQA: Open-Domain Question Answering on Vietnamese Wikipedia-based
  Textual Knowledge Source

abstract:
  Question answering (QA) is a natural language understanding task within the
fields of information retrieval and information extraction that has attracted
much attention from the computational linguistics and artificial intelligence
research community in recent years because of the strong development of machine
reading comprehension-based models. A reader-based QA system is a high-level
search engine that can find correct answers to queries or questions in
open-domain or domain-specific texts using machine reading comprehension (MRC)
techniques. The majority of advancements in data resources and machine-learning
approaches in the MRC and QA systems, on the other hand, especially in two
resource-rich languages such as English and Chinese. A low-resource language
like Vietnamese has witnessed a scarcity of research on QA systems. This paper
presents XLMRQA, the first Vietnamese QA system using a supervised
transformer-based reader on the Wikipedia-based textual knowledge source (using
the UIT-ViQuAD corpus), outperforming the two robust QA systems using deep
neural network models: DrQA and BERTserini with 24.46% and 6.28%, respectively.
From the results obtained on the three systems, we analyze the influence of
question types on the performance of the QA systems.


title:
Probability monads with submonads of deterministic states - Extended
  version

abstract:
  Probability theory can be studied synthetically as the computational effect
embodied by a commutative monad. In the recently proposed Markov categories,
one works with an abstraction of the Kleisli category and then defines
deterministic morphisms equationally in terms of copying and discarding. The
resulting difference between 'pure' and 'deterministic' leads us to investigate
the 'sober' objects for a probability monad, for which the two concepts
coincide. We propose natural conditions on a probability monad which allow us
to identify the sober objects and define an idempotent sobrification functor.
Our framework applies to many examples of interest, including the Giry monad on
measurable spaces, and allows us to sharpen a previously given version of de
Finetti's theorem for Markov categories.
  This is an extended version of the paper accepted for the Logic In Computer
Science (LICS) conference 2022. In this document we include more mathematical
details, including all the proofs, of the statements and constructions given in
the published version.


title:
Medical Application of Geometric Deep Learning for the Diagnosis of
  Glaucoma

abstract:
  Purpose: (1) To assess the performance of geometric deep learning (PointNet)
in diagnosing glaucoma from a single optical coherence tomography (OCT) 3D scan
of the optic nerve head (ONH); (2) To compare its performance to that obtained
with a standard 3D convolutional neural network (CNN), and with a gold-standard
glaucoma parameter, i.e. retinal nerve fiber layer (RNFL) thickness.
  Methods: 3D raster scans of the ONH were acquired with Spectralis OCT for 477
glaucoma and 2,296 non-glaucoma subjects at the Singapore National Eye Centre.
All volumes were automatically segmented using deep learning to identify 7
major neural and connective tissues including the RNFL, the prelamina, and the
lamina cribrosa (LC). Each ONH was then represented as a 3D point cloud with
1,000 points chosen randomly from all tissue boundaries. To simplify the
problem, all ONH point clouds were aligned with respect to the plane and center
of Bruch's membrane opening. Geometric deep learning (PointNet) was then used
to provide a glaucoma diagnosis from a single OCT point cloud. The performance
of our approach was compared to that obtained with a 3D CNN, and with RNFL
thickness.
  Results: PointNet was able to provide a robust glaucoma diagnosis solely from
the ONH represented as a 3D point cloud (AUC=95%). The performance of PointNet
was superior to that obtained with a standard 3D CNN (AUC=87%) and with that
obtained from RNFL thickness alone (AUC=80%).
  Discussion: We provide a proof-of-principle for the application of geometric
deep learning in the field of glaucoma. Our technique requires significantly
less information as input to perform better than a 3D CNN, and with an AUC
superior to that obtained from RNFL thickness alone. Geometric deep learning
may have wide applicability in the field of Ophthalmology.


title: Interpretability of Machine Learning Methods Applied to Neuroimaging

abstract:
  Deep learning methods have become very popular for the processing of natural
images, and were then successfully adapted to the neuroimaging field. As these
methods are non-transparent, interpretability methods are needed to validate
them and ensure their reliability. Indeed, it has been shown that deep learning
models may obtain high performance even when using irrelevant features, by
exploiting biases in the training set. Such undesirable situations can
potentially be detected by using interpretability methods. Recently, many
methods have been proposed to interpret neural networks. However, this domain
is not mature yet. Machine learning users face two major issues when aiming to
interpret their models: which method to choose, and how to assess its
reliability? Here, we aim at providing answers to these questions by presenting
the most common interpretability methods and metrics developed to assess their
reliability, as well as their applications and benchmarks in the neuroimaging
context. Note that this is not an exhaustive survey: we aimed to focus on the
studies which we found to be the most representative and relevant.


title: Independent sequences and freeness criteria

abstract:
  Let $M$ be a module over a Noetherian local ring $A$. We study
$M$-independent sequences of elements of $\mathfrak{m}_A$ in the sense of Lech
and Hanes. The main tool is a new characterization of the $M$-independence of a
sequence in terms of the associated Koszul complex. As applications, we give a
result in linkage theory, a freeness criterion for $M$ in terms of the
existence of a strongly $M$-independent sequence of length $edim(A)$, and
another freeness criterion inspired from the patching method of Calegari and
Geraghty for balanced modules in their 2018 paper.


title:
Symplectic monodromy at radius zero and equimultiplicity of
  $\mu$-constant families

abstract:
  We show that every family of isolated hypersurface singularity with constant
Milnor number has constant multiplicity. To achieve this, we endow the A'Campo
model of "radius zero" monodromy with a symplectic structure. This new approach
allows to generalize a spectral sequence of McLean converging to fixed point
Floer Homology of iterates of the monodromy to a more general setting which is
well suited to study $\mu$-constant families.


title:
Parabolic optimal control problems with combinatorial switching
  constraints -- Part II: Outer approximation algorithm

abstract:
  We consider optimal control problems for partial differential equations where
the controls take binary values but vary over the time horizon, they can thus
be seen as dynamic switches. The switching patterns may be subject to
combinatorial constraints such as, e.g., an upper bound on the total number of
switchings or a lower bound on the time between two switchings. In a companion
paper [arXiv:2203.07121], we describe the $L^p$-closure of the convex hull of
feasible switching patterns as intersection of convex sets derived from
finite-dimensional projections. In this paper, the resulting outer description
is used for the construction of an outer approximation algorithm in function
space, whose iterates are proven to converge strongly in $L^2$ to the global
minimizer of the convexified optimal control problem. The linear-quadratic
subproblems arising in each iteration of the outer approximation algorithm are
solved by means of a semi-smooth Newton method. A numerical example in two
spatial dimensions illustrates the efficiency of the overall algorithm.


title: Learning Invariances with Generalised Input-Convex Neural Networks

abstract:
  Considering smooth mappings from input vectors to continuous targets, our
goal is to characterise subspaces of the input domain, which are invariant
under such mappings. Thus, we want to characterise manifolds implicitly defined
by level sets. Specifically, this characterisation should be of a global
parametric form, which is especially useful for different informed data
exploration tasks, such as building grid-based approximations, sampling points
along the level curves, or finding trajectories on the manifold. However,
global parameterisations can only exist if the level sets are connected. For
this purpose, we introduce a novel and flexible class of neural networks that
generalise input-convex networks. These networks represent functions that are
guaranteed to have connected level sets forming smooth manifolds on the input
space. We further show that global parameterisations of these level sets can be
always found efficiently. Lastly, we demonstrate that our novel technique for
characterising invariances is a powerful generative data exploration tool in
real-world applications, such as computational chemistry.


title:
Anti-Asian Hate Speech Detection via Data Augmented Semantic Relation
  Inference

abstract:
  With the spreading of hate speech on social media in recent years, automatic
detection of hate speech is becoming a crucial task and has attracted attention
from various communities. This task aims to recognize online posts (e.g.,
tweets) that contain hateful information. The peculiarities of languages in
social media, such as short and poorly written content, lead to the difficulty
of learning semantics and capturing discriminative features of hate speech.
Previous studies have utilized additional useful resources, such as sentiment
hashtags, to improve the performance of hate speech detection. Hashtags are
added as input features serving either as sentiment-lexicons or extra context
information. However, our close investigation shows that directly leveraging
these features without considering their context may introduce noise to
classifiers. In this paper, we propose a novel approach to leverage sentiment
hashtags to enhance hate speech detection in a natural language inference
framework. We design a novel framework SRIC that simultaneously performs two
tasks: (1) semantic relation inference between online posts and sentiment
hashtags, and (2) sentiment classification on these posts. The semantic
relation inference aims to encourage the model to encode sentiment-indicative
information into representations of online posts. We conduct extensive
experiments on two real-world datasets and demonstrate the effectiveness of our
proposed framework compared with state-of-the-art representation learning
models.


title: Programming Quantum Hardware via Levenberg Marquardt Machine Learning

abstract:
  Significant challenges remain with the development of macroscopic quantum
computing, hardware problems of noise, decoherence, and scaling, software
problems of error correction, and, most important, algorithm construction.
Finding truly quantum algorithms is quite difficult, and many quantum
algorithms, like Shor prime factoring or phase estimation, require extremely
long circuit depth for any practical application, necessitating error
correction. Machine learning can be used as a systematic method to
nonalgorithmically program quantum computers. Quantum machine learning enables
us to perform computations without breaking down an algorithm into its gate
building blocks, eliminating that difficult step and potentially reducing
unnecessary complexity. In addition, we have shown that our machine learning
approach is robust to both noise and to decoherence, which is ideal for running
on inherently noisy NISQ devices which are limited in the number of qubits
available for error correction. We demonstrated this using a fundamentally non
classical calculation, experimentally estimating the entanglement of an unknown
quantum state. Results from this have been successfully ported to the IBM
hardware and trained using a powerful hybrid reinforcement learning technique
which is a modified Levenberg Marquardt LM method. The LM method is ideally
suited to quantum machine learning as it only requires knowledge of the final
measured output of the quantum computation, not intermediate quantum states
which are generally not accessible. Since it processes all the learning data
simultaneously, it also requires significantly fewer hits on the quantum
hardware. Machine learning is demonstrated with results from simulations and
runs on the IBM Qiskit hardware interface.


title:
The gMOSS: the galaxy survey and galaxy populations of the large
  homogeneous field

abstract:
  We present the gMOSS (Galaxies of Medium-band One-meter Schmidt telescope
Survey) catalog of $\sim$ 19,000 galaxies in 20 filters (4 broadband SDSS and
16 medium-band filters). We observed 2.386 $\mathrm{deg^2}$ on the central part
of the HS47.5-22 field with the 1-m Schmidt telescope of the Byurakan
Astrophysical Observatory. The gMOSS is a complete flux-limited sample of
galaxies with a threshold magnitude of $r$ SDSS $\le$ 22.5 AB. From photometric
measurements with 16 medium-band filters and $u$ SDSS, we get spectral energy
distributions for each object in the field, which are used for further
analysis. Galaxy classification and photometric redshift estimation based on
spectral template matching with ZEBRA software. The obtained redshift accuracy
is $\sigma_\mathrm{{NMAD}} < 0.0043$. Using the SED-fitting CIGALE code, we
obtained the main properties of the stellar population of galaxies, such as
rest-frame $(u - r)_{\mathrm{res}}$ colour, stellar mass, extinction, and
mass-weighted age with a precision of $0.16 \pm 0.07$ mag, $0.14 \pm 0.04$ dex,
$0.27 \pm 0.1$ mag, and $0.08 \pm 0.04$ dex, respectively. Using a
dust-corrected colour-mass diagram, we divided the full sample into populations
of red and blue galaxies and considered the dependencies between stellar mass
and age. Throughout cosmic time, red sequence galaxies remain older and more
massive than blue cloud galaxies. The star formation history of a complete
subsample of galaxies selected in the redshift range $0.05\le z\le0.015$ with
<$\mathrm{log} M \mathrm{>}_\mathrm{[M_\odot]}$>8.3 shows an increase in the
SFRD up to $z\sim3$, under the results obtained in earlier studies.


title: Optimal Reference for DNA Synthesis

abstract:
  In the recent years, DNA has emerged as a potentially viable storage
technology. DNA synthesis, which refers to the task of writing the data into
DNA, is perhaps the most costly part of existing storage systems. Accordingly,
this high cost and low throughput limits the practical use in available DNA
synthesis technologies. It has been found that the homopolymer run (i.e., the
repetition of the same nucleotide) is a major factor affecting the synthesis
and sequencing errors. Quite recently, [26] studied the role of batch
optimization in reducing the cost of large scale DNA synthesis, for a given
pool $\mathcal{S}$ of random quaternary strings of fixed length. Among other
things, it was shown that the asymptotic cost savings of batch optimization are
significantly greater when the strings in $\mathcal{S}$ contain repeats of the
same character (homopolymer run of length one), as compared to the case where
strings are unconstrained.
  Following the lead of [26], in this paper, we take a step forward towards the
theoretical understanding of DNA synthesis, and study the homopolymer run of
length $k\geq1$. Specifically, we are given a set of DNA strands $\mathcal{S}$,
randomly drawn from a natural Markovian distribution modeling a general
homopolymer run length constraint, that we wish to synthesize. For this
problem, we prove that for any $k\geq 1$, the optimal reference strand,
minimizing the cost of DNA synthesis is, perhaps surprisingly, the periodic
sequence $\overline{\mathsf{ACGT}}$. It turns out that tackling the homopolymer
constraint of length $k\geq2$ is a challenging problem; our main technical
contribution is the representation of the DNA synthesis process as a certain
constrained system, for which string techniques can be applied.


title:
Rows from Many Sources: Enriching row completions from Wikidata with a
  pre-trained Language Model

abstract:
  Row completion is the task of augmenting a given table of text and numbers
with additional, relevant rows. The task divides into two steps: subject
suggestion, the task of populating the main column; and gap filling, the task
of populating the remaining columns. We present state-of-the-art results for
subject suggestion and gap filling measured on a standard benchmark
(WikiTables). Our idea is to solve this task by harmoniously combining
knowledge base table interpretation and free text generation. We interpret the
table using the knowledge base to suggest new rows and generate metadata like
headers through property linking. To improve candidate diversity, we synthesize
additional rows using free text generation via GPT-3, and crucially, we exploit
the metadata we interpret to produce better prompts for text generation.
Finally, we verify that the additional synthesized content can be linked to the
knowledge base or a trusted web source such as Wikipedia.


title:
NASA/GSFC's Flight Software Core Flight System Implementation For A
  Lunar Surface Imaging Mission

abstract:
  The interest in returning to the Moon for research and exploration has
increased as new tipping point technologies are providing the possibility to do
so. One of these initiatives is the Artemis program by NASA, which plans to
return humans by 2024 to the lunar surface and study water deposits on the
surface. This program will also serve as a practice run to plan the logistics
of sending humans to explore Mars. To return humans safely to the Moon,
multiple technological advances and diverse knowledge about the nature of the
lunar surface are needed. This paper will discuss the design and implementation
of the flight software of EagleCam, a CubeSat camera system based on the free
open-source core Flight System (cFS) architecture developed by NASA's Goddard
Space Flight Center. EagleCam is a payload transported to the Moon by the
Commercial Lunar Payload Services Nova-C lander developed by Intuitive
Machines. The camera system will capture the first third-person view of a
spacecraft performing a Moon landing and collect other scientific data such as
plume interaction with the surface. The complete system is composed of the
CubeSat and the deployer that will eject it. This will be the first time WiFi
protocol is used on the Moon to establish a local communication network.


title: The de Finetti problem with unknown competition

abstract:
  We consider a resource extraction problem which extends the classical de
Finetti problem for a Wiener process to include the case when a competitor, who
is equipped with the possibility to extract all the remaining resources in one
piece, may exist; we interpret this unknown competition as the agent being
subject to possible fraud. This situation is modelled as a
controller-and-stopper non-zero-sum stochastic game with incomplete
information. In order to allow the fraudster to hide his existence, we consider
strategies where his action time is randomised. Under these conditions, we
provide a Nash equilibrium which is fully described in terms of the
corresponding single-player de Finetti problem. In this equilibrium, the agent
and the fraudster use singular strategies in such a way that a two-dimensional
process, which represents available resources and the filtering estimate of
active competition, reflects in a specific direction along a given boundary.


title: OneMax is not the Easiest Function for Fitness Improvements

abstract:
  We study the $(1:s+1)$ success rule for controlling the population size of
the $(1,\lambda)$-EA. It was shown by Hevia Fajardo and Sudholt that this
parameter control mechanism can run into problems for large $s$ if the fitness
landscape is too easy. They conjectured that this problem is worst for the
OneMax benchmark, since in some well-established sense OneMax is known to be
the easiest fitness landscape. In this paper we disprove this conjecture and
show that OneMax is not the easiest fitness landscape with respect to finding
improving steps.
  As a consequence, we show that there exists $s$ and $\varepsilon$ such that
the self-adjusting $(1,\lambda)$-EA with $(1:s+1)$-rule optimizes OneMax
efficiently when started with $\varepsilon n$ zero-bits, but does not find the
optimum in polynomial time on Dynamic BinVal. Hence, we show that there are
landscapes where the problem of the $(1:s+1)$-rule for controlling the
population size of the $(1, \lambda)$-EA is more severe than for OneMax.


title:
From Environmental Sound Representation to Robustness of 2D CNN Models
  Against Adversarial Attacks

abstract:
  This paper investigates the impact of different standard environmental sound
representations (spectrograms) on the recognition performance and adversarial
attack robustness of a victim residual convolutional neural network, namely
ResNet-18. Our main motivation for focusing on such a front-end classifier
rather than other complex architectures is balancing recognition accuracy and
the total number of training parameters. Herein, we measure the impact of
different settings required for generating more informative Mel-frequency
cepstral coefficient (MFCC), short-time Fourier transform (STFT), and discrete
wavelet transform (DWT) representations on our front-end model. This
measurement involves comparing the classification performance over the
adversarial robustness. We demonstrate an inverse relationship between
recognition accuracy and model robustness against six benchmarking attack
algorithms on the balance of average budgets allocated by the adversary and the
attack cost. Moreover, our experimental results have shown that while the
ResNet-18 model trained on DWT spectrograms achieves a high recognition
accuracy, attacking this model is relatively more costly for the adversary than
other 2D representations. We also report some results on different
convolutional neural network architectures such as ResNet-34, ResNet-56,
AlexNet, and GoogLeNet, SB-CNN, and LSTM-based.


title:
Improved Volume-of-Solid formulations for micro-continuum simulation of
  mineral dissolution at the pore-scale

abstract:
  We present two novel Volume-of-Solid (VoS) formulations for micro-continuum
simulation of mineral dissolution at the pore-scale. The traditional VoS
formulation (VoS-psi) uses a diffuse interface localization function psi to
ensure stability and limit diffusion of the reactive surface. The main
limitation of this formulation is that accuracy is strongly dependent on the
choice of the localization function. Our first novel improved formulation
(iVoS) uses the divergence of a reactive flux to localize the reaction at the
fluid-solid interface, so no localization function is required. Our second
novel formulation (VoS-psi') uses a localization function with a parameter that
is fitted to ensure that the reactive surface area is conserved globally. Both
novel methods are validated by comparison with experiments, numerical
simulations using an interface tracking method based on the Arbitrary Eulerian
Lagrangian (ALE) framework, and numerical simulations using the VoS-psi. All
numerical methods are implemented in GeoChemFoam, our reactive transport
toolbox and three benchmark test cases in both synthetic and real pore
geometries are considered: (1) dissolution of a calcite post by acid injection
in a microchannel and experimental comparison, (2) dissolution in a 2D
polydisperse disc micromodel at different dissolution regimes and (3)
dissolution in a Ketton carbonate rock sample and comparison to
\textit{in-situ} micro-CT experiments. We find that the iVoS results match
accurately experimental results and simulation results obtained with the ALE
method, while the VoS-psi method leads to inaccuracies that are mostly
corrected by the VoS-psi' formulation. In addition, the VoS methods are
significantly faster than the ALE method, with a speed-up factor of between 2
and 12.


title:
Least-Squares Methods with Nonconforming Finite Elements for General
  Second-Order Elliptic Equations

abstract:
  In this paper, we study least-squares finite element methods (LSFEM) for
general second-order elliptic equations with nonconforming finite element
approximations. The equation may be indefinite. For the two-field
potential-flux div LSFEM with Crouzeix-Raviart (CR) element approximation, we
present three proofs of the discrete solvability under the condition that mesh
size is small enough. One of the proof is based on the coerciveness of the
original bilinear form. The other two are based on the minimal assumption of
the uniqueness of the solution of the second-order elliptic equation. A
counterexample shows that div least-squares functional does not have norm
equivalence in the sum space of $H^1$ and CR finite element spaces. Thus it
cannot be used as an a posteriori error estimator. Several versions of reliable
and efficient error estimators are proposed for the method. We also propose a
three-filed potential-flux-intensity div-curl least-squares method with general
nonconforming finite element approximations. The norm equivalence in the
abstract nonconforming piecewise $H^1$-space is established for the three-filed
formulation on the minimal assumption of the uniqueness of the solution of the
second-order elliptic equation. The three-filed div-curl nonconforming
formulation thus has no restriction on the mesh size, and the least-squares
functional can be used as the built-in a posteriori error estimator. Under some
restrictive conditions, we also discuss a potential-flux div-curl least-squares
method.


title: On Measuring Model Complexity in Heteroscedastic Linear Regression

abstract:
  Heteroscedasticity is common in real world applications and is often handled
by incorporating case weights into a modeling procedure. Intuitively, models
fitted with different weight schemes would have a different level of complexity
depending on how well the weights match the inverse of error variances.
However, existing statistical theories on model complexity, also known as model
degrees of freedom, were primarily established under the assumption of equal
error variances. In this work, we focus on linear regression procedures and
seek to extend the existing measures to a heteroscedastic setting. Our analysis
of the weighted least squares method reveals some interesting properties of the
extended measures. In particular, we find that they depend on both the weights
used for model fitting and those for model evaluation. Moreover, modeling
heteroscedastic data with optimal weights generally results in fewer degrees of
freedom than with equal weights, and the size of reduction depends on the
unevenness of error variance. This provides additional insights into weighted
modeling procedures that are useful in risk estimation and model selection.


title: $W$ boson mass and muon $g-2$ in a lepton portal dark matter model

abstract:
  We study a lepton portal dark matter model, motivated by the deviation of the
$W$ boson mass reported by the CDF collaboration. We introduce vector-like
leptons and a scalar dark matter (DM) which exclusively couples to the extra
leptons and muon. The one-loop corrections induced by the new particles can
shift the $W$ boson mass. Besides, the discrepancy in the muon anomalous
magnetic moment and the DM density can simultaneously be explained by this
setup, if the vector-like lepton is lighter than 200 GeV and nearly degenerate
with the DM particle. We also see that the constraints on such a light extra
lepton from the collider experiments can be evaded due to the existence of the
DM particle.


title: Composite Code Sparse Autoencoders for first stage retrieval

abstract:
  We propose a Composite Code Sparse Autoencoder (CCSA) approach for
Approximate Nearest Neighbor (ANN) search of document representations based on
Siamese-BERT models. In Information Retrieval (IR), the ranking pipeline is
generally decomposed in two stages: the first stage focus on retrieving a
candidate set from the whole collection. The second stage re-ranks the
candidate set by relying on more complex models. Recently, Siamese-BERT models
have been used as first stage ranker to replace or complement the traditional
bag-of-word models. However, indexing and searching a large document collection
require efficient similarity search on dense vectors and this is why ANN
techniques come into play. Since composite codes are naturally sparse, we first
show how CCSA can learn efficient parallel inverted index thanks to an
uniformity regularizer. Second, CCSA can be used as a binary quantization
method and we propose to combine it with the recent graph based ANN techniques.
Our experiments on MSMARCO dataset reveal that CCSA outperforms IVF with
product quantization. Furthermore, CCSA binary quantization is beneficial for
the index size, and memory usage for the graph-based HNSW method, while
maintaining a good level of recall and MRR. Third, we compare with recent
supervised quantization methods for image retrieval and find that CCSA is able
to outperform them.


title:
Q-TART: Quickly Training for Adversarial Robustness and
  in-Transferability

abstract:
  Raw deep neural network (DNN) performance is not enough; in real-world
settings, computational load, training efficiency and adversarial security are
just as or even more important. We propose to simultaneously tackle
Performance, Efficiency, and Robustness, using our proposed algorithm Q-TART,
Quickly Train for Adversarial Robustness and in-Transferability. Q-TART follows
the intuition that samples highly susceptible to noise strongly affect the
decision boundaries learned by DNNs, which in turn degrades their performance
and adversarial susceptibility. By identifying and removing such samples, we
demonstrate improved performance and adversarial robustness while using only a
subset of the training data. Through our experiments we highlight Q-TART's high
performance across multiple Dataset-DNN combinations, including ImageNet, and
provide insights into the complementary behavior of Q-TART alongside existing
adversarial training approaches to increase robustness by over 1.3% while using
up to 17.9% less training time.


title: Autonomous Satellite Detection and Tracking using Optical Flow

abstract:
  In this paper, an autonomous method of satellite detection and tracking in
images is implemented using optical flow. Optical flow is used to estimate the
image velocities of detected objects in a series of space images. Given that
most objects in an image will be stars, the overall image velocity from star
motion is used to estimate the image's frame-to-frame motion. Objects seen to
be moving with velocity profiles distinct from the overall image velocity are
then classified as potential resident space objects. The detection algorithm is
exercised using both simulated star images and ground-based imagery of
satellites. Finally, this algorithm will be tested and compared using a
commercial and an open-source software approach to provide the reader with two
different options based on their need.


title: Blending Primitive Policies in Shared Control for Assisted Teleoperation

abstract:
  Movement primitives have the property to accommodate changes in the robot
state while maintaining attraction to the original policy. As such, we
investigate the use of primitives as a blending mechanism by considering that
state deviations from the original policy are caused by user inputs. As the
primitive recovers from the user input, it implicitly blends human and robot
policies without requiring their weightings -- referred to as arbitration. In
this paper, we adopt Dynamical Movement Primitives (DMPs), which allow us to
avoid the need for multiple demonstrations, and are fast enough to enable
numerous instantiations, one for each hypothesis of the human intent. User
studies are presented on assisted teleoperation tasks of reaching multiple
goals and dynamic obstacle avoidance. Comparable performance to conventional
teleoperation was achieved while significantly decreasing human intervention,
often by more than 60%.


title: Ruling out QCD phase transition as a PBH origin of LIGO/Virgo events

abstract:
  The best-motivated scenario for a sizable primordial black hole (PBH)
contribution to the LIGO/Virgo binary black hole mergers invokes the QCD phase
transition, which naturally enhances the probability to form PBH with masses of
stellar scale. We reconsider the expected mass function associated not only to
the QCD phase transition proper, but also the following particle antiparticle
annihilation processes, and analyse the constraints on this scenario from a
number of observations: The specific pattern in cosmic microwave background
(CMB) anisotropies induced by accretion onto PBHs, CMB spectral distortions,
gravitational wave searches, and direct counts of supermassive black holes at
high redshift. We find that the scenario is not viable, unless an ad hoc mass
evolution for the PBH mass function and a cutoff in power-spectrum very close
to the QCD scale are introduced by hand.


title:
Exploring the Distributed Knowledge Congruence in Proxy-data-free
  Federated Distillation

abstract:
  Federated learning (FL) is a distributed machine learning paradigm in which
the server periodically aggregates local model parameters from clients without
assembling their private data.
  Constrained communication and personalization requirements pose severe
challenges to FL. Federated distillation (FD) is proposed to simultaneously
address the above two problems, which exchanges knowledge between the server
and clients, supporting heterogeneous local models while significantly reducing
communication overhead. However, most existing FD methods require a proxy
dataset, which is often unavailable in reality.
  A few recent proxy-data-free FD approaches can eliminate the need for
additional public data, but suffer from remarkable discrepancy among local
knowledge due to model heterogeneity, leading to ambiguous representation on
the server and inevitable accuracy degradation.
  To tackle this issue, we propose a proxy-data-free FD algorithm based on
distributed knowledge congruence (FedDKC). FedDKC leverages well-designed
refinement strategies to narrow local knowledge differences into an acceptable
upper bound, so as to mitigate the negative effects of knowledge incongruence.
  Specifically, from perspectives of peak probability and Shannon entropy of
local knowledge, we design kernel-based knowledge refinement (KKR) and
searching-based knowledge refinement (SKR) respectively, and theoretically
guarantee that the refined-local knowledge can satisfy an approximately-similar
distribution and be regarded as congruent.
  Extensive experiments conducted on three common datasets demonstrate that our
proposed FedDKC significantly outperforms the state-of-the-art (accuracy boosts
in 93.33% comparisons,
  Top-1 accuracy boosts by up to 4.38%, and Top-5 accuracy boosts by up to
10.31%) on various heterogeneous settings while evidently improving the
convergence speed.


title:
Independent determination of the Earth's orbital parameters with solar
  neutrinos in Borexino

abstract:
  Since the beginning of 2012, the Borexino collaboration has been reporting
precision measurements of the solar neutrino fluxes, emitted in the
proton-proton chain and in the Carbon-Nitrogen-Oxygen cycle. The experimental
sensitivity achieved in Phase-II and Phase-III of the Borexino data taking made
it possible to detect the annual modulation of the solar neutrino interaction
rate due to the eccentricity of Earth's orbit, with a statistical significance
greater than 5$\sigma$. This is the first precise measurement of the Earth's
orbital parameters based solely on solar neutrinos and an additional signature
of the solar origin of the Borexino signal. The complete periodogram of the
time series of the Borexino solar neutrino detection rate is also reported,
exploring frequencies between one cycle/year and one cycle/day. No other
significant modulation frequencies are found. The present results were uniquely
made possible by Borexino's decade-long high-precision solar neutrino
detection.

categories: hep-ex astro-ph.EP astro-ph.IM astro-ph.SR hep-ph


title:
Activation Regression for Continuous Domain Generalization with
  Applications to Crop Classification

abstract:
  Geographic variance in satellite imagery impacts the ability of machine
learning models to generalise to new regions. In this paper, we model
geographic generalisation in medium resolution Landsat-8 satellite imagery as a
continuous domain adaptation problem, demonstrating how models generalise
better with appropriate domain knowledge. We develop a dataset spatially
distributed across the entire continental United States, providing macroscopic
insight into the effects of geography on crop classification in multi-spectral
and temporally distributed satellite imagery. Our method demonstrates improved
generalisability from 1) passing geographically correlated climate variables
along with the satellite data to a Transformer model and 2) regressing on the
model features to reconstruct these domain variables. Combined, we provide a
novel perspective on geographic generalisation in satellite imagery and a
simple-yet-effective approach to leverage domain knowledge. Code is available
at: \url{https://github.com/samar-khanna/cropmap}


title:
Maxwell's equations in homogeneous spaces for admissible electromagnetic
  fields

abstract:
  Maxwell's vacuum equations are integrated for admissible electromagnetic
fields in homogeneous spaces. Admissible electromagnetic fields are those for
which the space group generates an algebra of symmetry operators ( integrals of
motion ) that is isomorphic to the algebra of group operators. Two frames
associated with the group of motions are used to obtain systems of ordinary
differential equations to which Maxwell's equations reduce. The solutions are
obtained in quadratures. The potentials of the admissible electromagnetic
fields and the metrics of the spaces contained in the obtained solutions depend
on six arbitrary time functions, so it is possible to use them to integrate
field equations in the theory of gravity.


title: Farmer-Bot: An Interactive Bot for Farmers

abstract:
  The Indian Agricultural sector generates huge employment accounting for over
54% of countrys workforce. Its overall stand in GDP is close to 14%. However,
this sector has been plagued by knowledge and infrastructure deficit,
especially in the rural sectors. Like other sectors, the Indian Agricultural
sector has seen rapid digitization with use of technology and Kisan Call Center
(KCC) is one such example. It is a Government of India initiative launched on
21st January 2004 which is a synthesis of two hitherto separate sectors the
Information Technology and Agriculture sector. However, studies have shown to
have constrains to KCC beneficiaries, especially in light of network congestion
and incomplete knowledge of the call center representatives. With the advent of
new technologies, like first-generation SMS based and next-generation social
media tools like WhatsApp, farmers in India are digitally more connected to the
agricultural information services. Previous studies have shown that the KCC
dataset can be used as a viable alternative for Chat-bot. We will base our
study with the available KCC dataset to build an NLP model by getting the
semantic similarity of the queries made by farmers in the past and use it to
automatically answer future queries. We will attempt to make a WhatsApp based
chat-bot to easily communicate with farmers using RASA as a tool.


title: On generalization of Homotopy Axiom

abstract:
  In the paper \emph{K.~Sigmon} A strong homotopy axiom for Alexander
cohomology, Proc. Amer. Math. Soc. \textbf{31:1} (1972), 271--275 it was proven
that if $G$ is compact topological group or field then in the Homotopy Axiom
for Alexander-Spanier-Kolmogoroff cohomology the parameter segment $[0, 1]$ can
be replaced by any compact connected space $T$. The purpose of the paper is to
shows that the parameter space $T$ can not be replaced in general by locally
compact connected space.


title: Epileptic Seizure Risk Assessment by Multi-Channel Imaging of the EEG

abstract:
  Refractory epileptic patients can suffer a seizure at any moment. Seizure
prediction would substantially improve their lives. In this work, based on
scalp EEG and its transformation into images, the likelihood of an epileptic
seizure occurring at any moment is computed using an average of the softmax
layer output (the likelihood) of a CNN, instead of the output of the
classification layer. Results show that by analyzing the likelihood and
thresholding it, prediction has higher sensitivity or a lower FPR/h. The best
threshold for the likelihood was higher than 50% for 5 patients, and was lower
for the remaining 36. However, more testing is needed, especially in new
seizures, to better assess the real performance of this method. This work is a
proof of concept with a positive outlook.


title:
Ponderomotive Forces, Stokes Drift, and Momentum in Acoustic and
  Electromagnetic Waves

abstract:
  We reveal universal connections between three important phenomena in
classical wave physics: (i) the ponderomotive force acting on the medium
particles in an oscillatory wavefield, (ii) the Stokes drift of free medium
particles in a wave field, and (iii) the canonical wave momentum in a medium.
We analyse these phenomena for (a) longitudinal sound waves in a gas or fluid
and (b) transverse electromagnetic waves interacting with electrons in a plasma
or metal. In both cases, assuming quasi-monochromatic yet arbitrarily
inhomogeneous wavefields, the connections between the ponderomotive force,
Stokes drift velocity, kinetic energy, and canonical wave momentum carried by
the medium particles are given by the same simple expressions. This sheds light
on the nature of these phenomena, their fine interplay, and can be useful for
applications to dynamical transport phenomena in various types of waves.


title:
Short-wavelength Reverberant Wave Systems for Physical Realization of
  Reservoir Computing

abstract:
  Machine learning (ML) has found widespread application over a broad range of
important tasks. To enhance ML performance, researchers have investigated
computational architectures whose physical implementations promise compactness,
high-speed execution, physical robustness, and low energy cost. Here, we
experimentally demonstrate an approach that uses the high sensitivity of
reverberant short wavelength waves for physical realization and enhancement of
computational power of a type of ML known as reservoir computing (RC). The
potential computation power of RC systems increases with their effective size.
We here exploit the intrinsic property of short wavelength reverberant wave
sensitivity to perturbations to expand the effective size of the RC system by
means of spatial and spectral perturbations. Working in the microwave regime,
this scheme is tested experimentally on different ML tasks. Our results
indicate the general applicability of reverberant wave-based implementations of
RC and of our effective reservoir size expansion technique


title:
LDPC codes: tracking non-stationary channel noise using sequential
  variational Bayesian estimates

abstract:
  We present a sequential Bayesian learning method for tracking non-stationary
signal-to-noise ratios in LDPC codes using probabilistic graphical models. We
represent the LDPC code as a cluster graph using a general purpose cluster
graph construction algorithm called the layered trees running intersection
property (LTRIP) algorithm. The channel noise estimator is a global Gamma
cluster, which we extend to allow for Bayesian tracking of non-stationary noise
variation. We evaluate our proposed model on real-world 5G drive test data. Our
results show that our model is capable of tracking non-stationary channel
noise, which outperforms an LDPC code with a fixed knowledge of the actual
average channel noise.


title:
OMAD: On-device Mental Anomaly Detection for Substance and Non-Substance
  Users

abstract:
  Stay at home order during the COVID-19 helps flatten the curve but
ironically, instigate mental health problems among the people who have
Substance Use Disorders. Measuring the electrical activity signals in brain
using off-the-shelf consumer wearable devices such as smart wristwatch and
mapping them in real time to underlying mood, behavioral and emotional changes
play striking roles in postulating mental health anomalies. In this work, we
propose to implement a wearable, {\it On-device Mental Anomaly Detection
(OMAD)} system to detect anomalous behaviors and activities that render to
mental health problems and help clinicians to design effective intervention
strategies. We propose an intrinsic artifact removal model on
Electroencephalogram (EEG) signal to better correlate the fine-grained
behavioral changes. We design model compression technique on the artifact
removal and activity recognition (main) modules. We implement a magnitude-based
weight pruning technique both on convolutional neural network and Multilayer
Perceptron to employ the inference phase on Nvidia Jetson Nano; one of the
tightest resource-constrained devices for wearables. We experimented with three
different combinations of feature extractions and artifact removal approaches.
We evaluate the performance of {\it OMAD} in terms of accuracy, F1 score,
memory usage and running time for both unpruned and compressed models using EEG
data from both control and treatment (alcoholic) groups for different object
recognition tasks. Our artifact removal model and main activity detection model
achieved about $\approx$ 93\% and 90\% accuracy, respectively with significant
reduction in model size (70\%) and inference time (31\%).


title: Methods for Efficient Unfolding of Colored Petri Nets

abstract:
  Colored Petri nets offer a compact and user friendly representation of the
traditional P/T nets and colored nets with finite color ranges can be unfolded
into the underlying P/T nets, however, at the expense of an exponential
explosion in size. We present two novel techniques based on static analysis in
order to reduce the size of unfolded colored nets. The first method identifies
colors that behave equivalently and groups them into equivalence classes,
potentially reducing the number of used colors. The second method
overapproximates the sets of colors that can appear in places and excludes
colors that can never be present in a given place. Both methods are
complementary and the combined approach allows us to significantly reduce the
size of multiple colored Petri nets from the Model Checking Contest benchmark.
We compare the performance of our unfolder with state-of-the-art techniques
implemented in the tools MCC, Spike and ITS-Tools, and while our approach is
competitive w.r.t. unfolding time, it also outperforms the existing approaches
both in the size of unfolded nets as well as in the number of answered model
checking queries from the 2021 Model Checking Contest.


title: Total domination number of middle graphs

abstract:
  A total dominating set of a graph G with no isolated vertices is a subset S
of the vertex set such that every vertex of G is adjacent to a vertex in S. The
total domination number of G is the minimum cardinality of a total dominating
set of G. In this paper, we study the total domination number of middle graphs.
Indeed, we obtain tight bounds for this number in terms of the order of the
graph G. We also compute the total domination number of the middle graph of
some known families of graphs explicitly. Moreover, some Nordhaus-Gaddum-like
relations are presented for the total domination number of middle graphs.


title:
Distributed Optimal Control with Recovered Robustness for Uncertain
  Network Systems: A Complementary Design Approach

abstract:
  This paper considers the distributed robust suboptimal consensus control
problem of linear multi-agent systems, with both H2 and H_infty performance
requirements. A novel two-step complementary design approach is proposed. In
the first step, a distributed control law is designed for the nominal
multi-agent system to achieve consensus with a prescribed H2 performance. In
the second step, an extra control input, depending on some carefully chosen
residual signals indicating the modeling mismatch, is designed to complement
the H2 performance by providing robustness guarantee in terms of H_infty
requirement with respect to disturbances or uncertainties. The proposed
complementary design approach provides an additional degree of freedom for
design, having two separate controls to deal with the H2 performance and the
robustness of consensus, respectively. Thereby, it does not need to make much
trade-off, and can be expected to be much less conservative than the trade-off
design such as the mixed H2/H_infty control method. Besides, this complementary
approach will recover the achievable H2 performance when external disturbances
or uncertainties do not exist. The effectiveness of the theoretical results and
the advantages of the complementary approach are validated via numerical
simulations.


title: Pure and mixed discrete variable Gaussian states

abstract:
  A Gaussian function of discrete variable can be associated in a natural way
to a Gaussian function of continuous variable in each odd-dimensional Hilbert
space. The discrete Fourier transform and discrete Wigner distribution of such
a function can be expressed in terms of the corresponding continuous
counterpart. By extending this continuous-discrete correspondence up to all the
continuous variable Gaussian states, we define some new (to our knowledge) pure
and mixed discrete variable Gaussian states, and investigate some of their
properties.


title: Ensemble learning using individual neonatal data for seizure detection

abstract:
  Sharing medical data between institutions is difficult in practice due to
data protection laws and official procedures within institutions. Therefore,
most existing algorithms are trained on relatively small electroencephalogram
(EEG) data sets which is likely to be detrimental to prediction accuracy. In
this work, we simulate a case when the data can not be shared by splitting the
publicly available data set into disjoint sets representing data in individual
institutions. We propose to train a (local) detector in each institution and
aggregate their individual predictions into one final prediction. Four
aggregation schemes are compared, namely, the majority vote, the mean, the
weighted mean and the Dawid-Skene method. The approach allows different
detector architectures amongst the institutions. The method was validated on an
independent data set using only a subset of EEG channels. The ensemble reaches
accuracy comparable to a single detector trained on all the data when
sufficient amount of data is available in each institution. The weighted mean
aggregation scheme showed best overall performance, it was only marginally
outperformed by the Dawid-Skene method when local detectors approach
performance of a single detector trained on all available data.


title:
Fano effect in a thermally induced transport through a triple quantum
  dot within the Coulomb blockade regime

abstract:
  In the present work we theoretically study thermoelectric transport and heat
transfer in a junction including a $T$-shaped double quantum dot coupled to
nonmagnetic electrodes and supplemented with a third dot in a parallel
configuration. We focus on the combined effect of Coulomb interactions and
quantum interference occurring in the $T$-shaped portion of the considered
triple quantum dot on the thermoelectric electron transport. The transport
through the system is studied within the Coulomb blockade regime in the limit
of strong intra-dot interactions between electrons beyond the linear response
regime. It is shown that under these conditions both charge and heat transfer
through the considered system may be significantly affected by the quantum
interference and inter-dot Coulomb interactions.


title:
Towards a consistent model of the hot quadruple system HD 93206 = QZ
  Carinae - I. Observations and their initial analyses

abstract:
  The hot nine-component system HD 93206, which contains a gravitationally
bounded eclipsing Ac1+Ac2 binary ($P=5.9987$~d) and a spectroscopic Aa1+Aa2
($P=20.734$~d) binary can provide~important insights into the origin and
evolution of massive stars. Using archival and new spectra, and a~rich
collection of ground-based and space photometric observations, we carried out a
detailed study of this object. We provide a much improved description of both
short orbits and a good estimate of the mutual period of both binaries of about
14500~d (i.e. 40 years). For the first time, we detected weak lines of the
fainter component of the 6.0~d eclipsing binary in the optical region of the
spectrum, measured their radial velocities, and derived a mass ratio of $M_{\rm
Ac2}/M_{\rm Ac1}=1.29$, which is the opposite of what was estimated from the
International Ultraviolet explorer (IUE) spectra. We confirm that the eclipsing
subsystem Ac is semi-detached and is therefore in a phase of large-scale mass
transfer between its components. The Roche-lobe filling and spectroscopically
brighter component Ac1 is the less massive of the two and is eclipsed in the
secondary minimum. We show that the bulk of the \ha emission, so far believed
to be associated with the eclipsing system, moves with the primary O9.7I
component Aa1 of the 20.73~d spectroscopic binary. However, the weak emission
in the higher Balmer lines seems to be associated with the accretion disc
around component Ac2. We demonstrate that accurate masses and other basic
physical properties including the distance of this unique system can be
obtained but require a more sophisticated modelling. A~first step in this
direction is presented in the accompanying Paper~II (Bro\v{z} et al.).


title:
Sequential Multi-task Learning with Task Dependency for Appeal Judgment
  Prediction

abstract:
  Legal Judgment Prediction (LJP) aims to automatically predict judgment
results, such as charges, relevant law articles, and the term of penalty. It
plays a vital role in legal assistant systems and has become a popular research
topic in recent years. This paper concerns a worthwhile but not well-studied
LJP task, Appeal judgment Prediction (AJP), which predicts the judgment of an
appellate court on an appeal case based on the textual description of case
facts and grounds of appeal. There are two significant challenges in practice
to solve the AJP task. One is how to model the appeal judgment procedure
appropriately. The other is how to improve the interpretability of the
prediction results. We propose a Sequential Multi-task Learning Framework with
Task Dependency for Appeal Judgement Prediction (SMAJudge) to address these
challenges. SMAJudge utilizes two sequential components to model the complete
proceeding from the lower court to the appellate court and employs an attention
mechanism to make the prediction more explainable, which handles the challenges
of AJP effectively. Experimental results obtained with a dataset consisting of
more than 30K appeal judgment documents have revealed the effectiveness and
superiority of SMAJudge.


title: State of the Art in Artificial Intelligence applied to the Legal Domain

abstract:
  While Artificial Intelligence applied to the legal domain is a topic with
origins in the last century, recent advances in Artificial Intelligence are
posed to revolutionize it. This work presents an overview and contextualizes
the main advances on the field of Natural Language Processing and how these
advances have been used to further the state of the art in legal text analysis.


title: Uncertainty Principles for the Short-time Free Metaplectic Transform

abstract:
  The free metaplectic transformation (FMT) has gained much popularity in
recent times because of its various application in signal processing, paraxial
optical systems, digital algorithms, optical encryption and so on. However, the
FMT is inadequate for localized analysis of non-transient signals, as such, it
is imperative to introduce a unique localized transform coined as the short
time free metaplectic transform (STFMT). In this paper, we investigate the
STFMT. Firstly, we propose the definition of the STFMT, and provide the time
frequency analysis of the proposed transform in the FMT domain. Secondly, we
investigate the basic properties of the proposed transform including the
reconstruction formula, Moyals formula. The emergence of the STFMT definition
and its properties broadens the development of time-frequency representation of
higher-dimensional signals theory to a certain extent. Finally, we extend some
different uncertainty principles (UP) from quantum mechanics including Liebs,
Pitts UP, Heisenbergs uncertainty principle, Hausdorff-Young, Hardys UP,
Beurlings UP, Logarithmic UP, and Nazarovs UP which have already been well
studied in the FMT domain


title:
Sim-to-Real 6D Object Pose Estimation via Iterative Self-training for
  Robotic Bin-picking

abstract:
  In this paper, we propose an iterative self-training framework for
sim-to-real 6D object pose estimation to facilitate cost-effective robotic
grasping. Given a bin-picking scenario, we establish a photo-realistic
simulator to synthesize abundant virtual data, and use this to train an initial
pose estimation network. This network then takes the role of a teacher model,
which generates pose predictions for unlabeled real data. With these
predictions, we further design a comprehensive adaptive selection scheme to
distinguish reliable results, and leverage them as pseudo labels to update a
student model for pose estimation on real data. To continuously improve the
quality of pseudo labels, we iterate the above steps by taking the trained
student model as a new teacher and re-label real data using the refined teacher
model. We evaluate our method on a public benchmark and our newly-released
dataset, achieving an ADD(-S) improvement of 11.49% and 22.62% respectively.
Our method is also able to improve robotic bin-picking success by 19.54%,
demonstrating the potential of iterative sim-to-real solutions for robotic
applications.


title: Recent Advances and New Frontiers in Spiking Neural Networks

abstract:
  In recent years, spiking neural networks (SNNs) have received extensive
attention in brain-inspired intelligence due to their rich spatially-temporal
dynamics, various encoding methods, and event-driven characteristics that
naturally fit the neuromorphic hardware. With the development of SNNs,
brain-inspired intelligence, an emerging research field inspired by brain
science achievements and aiming at artificial general intelligence, is becoming
hot. This paper reviews recent advances and discusses new frontiers in SNNs
from five major research topics, including essential elements (i.e., spiking
neuron models, encoding methods, and topology structures), neuromorphic
datasets, optimization algorithms, software, and hardware frameworks. We hope
our survey can help researchers understand SNNs better and inspire new works to
advance this field.


title: Electric-Field Programmable Spin Arrays for Scalable Quantum Repeaters

abstract:
  Large scale control over thousands of quantum emitters desired by quantum
network technology is limited by power consumption and cross-talk inherent in
current microwave techniques. Here we propose a quantum repeater architecture
based on densely-packed diamond color centers (CCs) in a programmable electrode
array. This 'electric-field programmable spin array' (eFPSA) enables high-speed
spin control of individual CCs with low cross-talk and power dissipation.
Integrated in a slow-light waveguide for efficient optical coupling, the eFPSA
serves as a quantum interface for optically-mediated entanglement. We evaluate
the performance of the eFPSA architecture in comparison to a routing tree
design and show increased entanglement generation rate into thousands of qubits
regime. Our results enable high fidelity control of dense quantum emitter
arrays for scalable networking.


title:
CroCo: Cross-Modal Contrastive learning for localization of Earth
  Observation data

abstract:
  It is of interest to localize a ground-based LiDAR point cloud on remote
sensing imagery. In this work, we tackle a subtask of this problem, i.e. to map
a digital elevation model (DEM) rasterized from aerial LiDAR point cloud on the
aerial imagery. We proposed a contrastive learning-based method that trains on
DEM and high-resolution optical imagery and experiment the framework on
different data sampling strategies and hyperparameters. In the best scenario,
the Top-1 score of 0.71 and Top-5 score of 0.81 are obtained. The proposed
method is promising for feature learning from RGB and DEM for localization and
is potentially applicable to other data sources too. Source code will be
released at https://github.com/wtseng530/AVLocalization.


title: Thermal Desorption of H2O-Ice: From Nanoscale Films to the Bulk

abstract:
  Desorption of H2O films ranging from 53 nanometres to 101 micrometre
thicknesses have been investigated using a quartz-crystal microbalance (QCM)
and temperature-programmed desorption. Three desorption stages are observed
belonging to amorphous solid water (ASW), stacking disordered ice I (ice Isd),
and hexagonal ice I (ice Ih). The desorption of ASW is only visible for the >10
micrometre films and is separated from the ice I desorption by 10-15 K and has
an associated desorption energy of 64 kJ mol-1. The desorption energy of the 53
nanometre film was found to be near 50 kJ mol-1 as also noted in the
literature, but with increasing film thickness the desorption energy of ice I
rises until reaching a plateau around 65-70 kJ mol-1. The reason for the
increased desorption energy is suggested to be due to molecules unable to
desorb due to the thick covering layer of H2O and possibly re-adsorption.
Before complete desorption of ice I which occurs around 220 K for the 100
micrometre film, a two-stage ice I desorption is observed with the QCM for the
10 and 20 micrometre films near 200 K. This event corresponds to the desorption
of ice Isd as corroborated by X-ray diffraction patterns collected upon heating
from 92-260 K at ambient pressure. Cubic ice is not observed as is commonly
stated in the literature as resulting from the crystallisation of ASW.
Therefore, ice Isd is the correct terminology for the initial crystallisation
product of ASW.


title:
BrainGB: A Benchmark for Brain Network Analysis with Graph Neural
  Networks

abstract:
  Mapping the connectome of the human brain using structural or functional
connectivity has become one of the most pervasive paradigms for neuroimaging
analysis. Recently, Graph Neural Networks (GNNs) motivated from geometric deep
learning have attracted broad interest due to their established power for
modeling complex networked data. Despite their established performance in other
fields, there has not yet been a systematic study of how to design effective
GNNs for brain network analysis. To bridge this gap, we present BrainGB, a
benchmark for brain network analysis with GNNs. BrainGB standardizes the
process by 1) summarizing brain network construction pipelines for both
functional and structural neuroimaging modalities and 2) modularizing the
implementation of GNN designs. We conduct extensive experiments on datasets
across cohorts and modalities and recommend a set of general recipes for
effective GNN designs on brain networks. To support open and reproducible
research on GNN-based brain network analysis, we also host the BrainGB website
at https:// brainnet.us/ with models, tutorials, examples, as well as an
out-of-box Python package. We hope that this work will provide useful empirical
evidence and offer insights for future research in this novel and promising
direction.


title:
Two Rings and a Marginally Resolved, 5 AU, Disk Around LkCa 15
  Identified Via Near Infrared Sparse Aperture Masking Interferometry

abstract:
  Sparse aperture masking interferometry (SAM) is a high resolution observing
technique that allows for imaging at and beyond a telescope's diffraction
limit. The technique is ideal for searching for stellar companions at small
separations from their host star; however, previous analysis of SAM
observations of young stars surrounded by dusty disks have had difficulties
disentangling planet and extended disk emission. We analyse VLT/SPHERE-IRDIS
SAM observations of the transition disk LkCa\,15, model the extended disk
emission, probe for planets at small separations, and improve contrast limits
for planets. We fit geometrical models directly to the interferometric
observables and recover previously observed extended disk emission. We use
dynamic nested sampling to estimate uncertainties on our model parameters and
to calculate evidences to perform model comparison. We compare our extended
disk emission models against point source models to robustly conclude that the
system is dominated by extended emission within 50 au. We report detections of
two previously observed asymmetric rings at $\sim$17 au and $\sim$45 au. The
peak brightness location of each model ring is consistent with the previous
observations. We also, for the first time with imaging, robustly recover an
elliptical Gaussian inner disk, previously inferred via SED fitting. This inner
disk has a FWHM of ~5 au and a similar inclination and orientation as the outer
rings. Finally, we recover no clear evidence for candidate planets. By
modelling the extended disk emission, we are able to place a lower limit on the
near infrared companion contrast of at least 1000.


title:
A Comparative Evaluation Of Transformer Models For De-Identification Of
  Clinical Text Data

abstract:
  Objective: To comparatively evaluate several transformer model architectures
at identifying protected health information (PHI) in the i2b2/UTHealth 2014
clinical text de-identification challenge corpus.
  Methods: The i2b2/UTHealth 2014 corpus contains N=1304 clinical notes
obtained from N=296 patients. Using a transfer learning framework, we fine-tune
several transformer model architectures on the corpus, including: BERT-base,
BERT-large, ROBERTA-base, ROBERTA-large, ALBERT-base and ALBERT-xxlarge. During
fine-tuning we vary the following model hyper-parameters: batch size, number
training epochs, learning rate and weight decay. We fine tune models on a
training data set, we evaluate and select optimally performing models on an
independent validation dataset, and lastly assess generalization performance on
a held-out test dataset. We assess model performance in terms of accuracy,
precision (positive predictive value), recall (sensitivity) and F1 score
(harmonic mean of precision and recall). We are interested in overall model
performance (PHI identified vs. PHI not identified), as well as PHI-specific
model performance.
  Results: We observe that the ROBERTA-large models perform best at identifying
PHI in the i2b2/UTHealth 2014 corpus, achieving >99% overall accuracy and 96.7%
recall/precision on the heldout test corpus. Performance was good across many
PHI classes; however, accuracy/precision/recall decreased for identification of
the following entity classes: professions, organizations, ages, and certain
locations.
  Conclusions: Transformers are a promising model class/architecture for
clinical text de-identification. With minimal hyper-parameter tuning
transformers afford researchers/clinicians the opportunity to obtain (near)
state-of-the-art performance.


title: Hate Speech Classification Using SVM and Naive BAYES

abstract:
  The spread of hatred that was formerly limited to verbal communications has
rapidly moved over the Internet. Social media and community forums that allow
people to discuss and express their opinions are becoming platforms for the
spreading of hate messages. Many countries have developed laws to avoid online
hate speech. They hold the companies that run the social media responsible for
their failure to eliminate hate speech. But as online content continues to
grow, so does the spread of hate speech However, manual analysis of hate speech
on online platforms is infeasible due to the huge amount of data as it is
expensive and time consuming. Thus, it is important to automatically process
the online user contents to detect and remove hate speech from online media.
Many recent approaches suffer from interpretability problem which means that it
can be difficult to understand why the systems make the decisions they do.
Through this work, some solutions for the problem of automatic detection of
hate messages were proposed using Support Vector Machine (SVM) and Na\"ive
Bayes algorithms. This achieved near state-of-the-art performance while being
simpler and producing more easily interpretable decisions than other methods.
Empirical evaluation of this technique has resulted in a classification
accuracy of approximately 99% and 50% for SVM and NB respectively over the test
set.
  Keywords: classification; hate speech; feature extraction, algorithm,
supervised learning


title:
Reflective Fiber Faults Detection and Characterization Using
  Long-Short-Term Memory

abstract:
  To reduce operation-and-maintenance expenses (OPEX) and to ensure optical
network survivability, optical network operators need to detect and diagnose
faults in a timely manner and with high accuracy. With the rapid advancement of
telemetry technology and data analysis techniques, data-driven approaches
leveraging telemetry data to tackle the fault diagnosis problem have been
gaining popularity due to their quick implementation and deployment. In this
paper, we propose a novel multi-task learning model based on long short-term
memory (LSTM) to detect, locate, and estimate the reflectance of fiber
reflective faults (events) including the connectors and the mechanical splices
by extracting insights from monitored data obtained by the optical time domain
reflectometry (OTDR) principle commonly used for troubleshooting of fiber optic
cables or links. The experimental results prove that the proposed method: (i)
achieves a good detection capability and high localization accuracy within
short measurement time even for low SNR values; and (ii) outperforms
conventionally employed techniques.


title: Machine Learning-based Anomaly Detection in Optical Fiber Monitoring

abstract:
  Secure and reliable data communication in optical networks is critical for
high-speed Internet. However, optical fibers, serving as the data transmission
medium providing connectivity to billons of users worldwide, are prone to a
variety of anomalies resulting from hard failures (e.g., fiber cuts) and
malicious physical attacks (e.g., optical eavesdropping (fiber tapping)) etc.
Such anomalies may cause network disruption and thereby inducing huge financial
and data losses, or compromise the confidentiality of optical networks by
gaining unauthorized access to the carried data, or gradually degrade the
network operations. Therefore, it is highly required to implement efficient
anomaly detection, diagnosis, and localization schemes for enhancing the
availability and reliability of optical networks. In this paper, we propose a
data driven approach to accurately and quickly detect, diagnose, and localize
fiber anomalies including fiber cuts, and optical eavesdropping attacks. The
proposed method combines an autoencoder-based anomaly detection and an
attention-based bidirectional gated recurrent unit algorithm, whereby the
former is used for fault detection and the latter is adopted for fault
diagnosis and localization once an anomaly is detected by the autoencoder. We
verify the efficiency of our proposed approach by experiments under various
anomaly scenarios using real operational data. The experimental results
demonstrate that: (i) the autoencoder detects any fiber fault or anomaly with
an F1 score of 96.86%; and (ii) the attention-based bidirectional gated
recurrent unit algorithm identifies the the detected anomalies with an average
accuracy of 98.2%, and localizes the faults with an average root mean square
error of 0.19 m.


title:
Heat transport and rectification via quantum statistical and coherence
  asymmetries

abstract:
  Recent experiments at the nanoscales confirm that thermal rectifiers, the
thermal equivalent of electrical diodes, can operate in the quantum regime. We
present a thorough investigation of the effect of different particle exchange
statistics, coherence, and collective interactions on the quantum heat
transport of rectifiers with two-terminal junctions. Using a collision model
approach to describe the open system dynamics, we obtain a general expression
of the nonlinear heat flow that fundamentally deviates from the Landauer
formula whenever quantum statistical or coherence asymmetries are present in
the bath particles. Building on this, we show that heat rectification is
possible even with symmetric medium-bath couplings if the two baths differ in
quantum statistics or coherence. Furthermore, the associated thermal
conductance vanishes exponentially at low temperatures as in the
Coulomb-blockade effect. However, at high temperatures it acquires a power-law
behavior depending on the quantum statistics. Our results can be significant
for heat management in hybrid open quantum systems or solid-state thermal
circuits.


title: Egocentric Human-Object Interaction Detection Exploiting Synthetic Data

abstract:
  We consider the problem of detecting Egocentric HumanObject Interactions
(EHOIs) in industrial contexts. Since collecting and labeling large amounts of
real images is challenging, we propose a pipeline and a tool to generate
photo-realistic synthetic First Person Vision (FPV) images automatically
labeled for EHOI detection in a specific industrial scenario. To tackle the
problem of EHOI detection, we propose a method that detects the hands, the
objects in the scene, and determines which objects are currently involved in an
interaction. We compare the performance of our method with a set of
state-of-the-art baselines. Results show that using a synthetic dataset
improves the performance of an EHOI detection system, especially when few real
data are available. To encourage research on this topic, we publicly release
the proposed dataset at the following url:
https://iplab.dmi.unict.it/EHOI_SYNTH/.


title:
Network state Estimation using Raw Video Analysis: vQoS-GAN based
  non-intrusive Deep Learning Approach

abstract:
  Content based providers transmits real time complex signal such as video data
from one region to another. During this transmission process, the signals
usually end up distorted or degraded where the actual information present in
the video is lost. This normally happens in the streaming video services
applications. Hence there is a need to know the level of degradation that
happened in the receiver side. This video degradation can be estimated by
network state parameters like data rate and packet loss values. Our proposed
solution vQoS GAN (video Quality of Service Generative Adversarial Network) can
estimate the network state parameters from the degraded received video data
using a deep learning approach of semi supervised generative adversarial
network algorithm. A robust and unique design of deep learning network model
has been trained with the video data along with data rate and packet loss class
labels and achieves over 95 percent of training accuracy. The proposed semi
supervised generative adversarial network can additionally reconstruct the
degraded video data to its original form for a better end user experience.


title: Efficient extraction of resonant states in systems with defects

abstract:
  We introduce a new numerical method to compute resonances induced by
localized defects in crystals. This method solves an integral equation in the
defect region to compute analytic continuations of resolvents. Such an approach
enables one to express the resonance in terms of a "resonance source", a
function that is strictly localized within the defect region. The kernel of the
integral equation, to be applied on such a source term, is the Green function
of the perfect crystal, which we show can be computed efficiently by a complex
deformation of the Brillouin zone, named Brillouin Complex Deformation (BCD),
thereby extending to reciprocal space the concept of complex coordinate
transformations.


title: Streamable Neural Audio Synthesis With Non-Causal Convolutions

abstract:
  Deep learning models are mostly used in an offline inference fashion.
However, this strongly limits the use of these models inside audio generation
setups, as most creative workflows are based on real-time digital signal
processing. Although approaches based on recurrent networks can be naturally
adapted to this buffer-based computation, the use of convolutions still poses
some serious challenges. To tackle this issue, the use of causal streaming
convolutions have been proposed. However, this requires specific complexified
training and can impact the resulting audio quality.
  In this paper, we introduce a new method allowing to produce non-causal
streaming models. This allows to make any convolutional model compatible with
real-time buffer-based processing. As our method is based on a post-training
reconfiguration of the model, we show that it is able to transform models
trained without causal constraints into a streaming model. We show how our
method can be adapted to fit complex architectures with parallel branches. To
evaluate our method, we apply it on the recent RAVE model, which provides
high-quality real-time audio synthesis. We test our approach on multiple music
and speech datasets and show that it is faster than overlap-add methods, while
having no impact on the generation quality. Finally, we introduce two
open-source implementation of our work as Max/MSP and PureData externals, and
as a VST audio plugin. This allows to endow traditional digital audio
workstation with real-time neural audio synthesis on a laptop CPU.


title: A decomposition method for lasso problems with zero-sum constraint

abstract:
  In this paper, we consider lasso problems with zero-sum constraint, commonly
required for the analysis of compositional data in high-dimensional spaces. A
novel algorithm is proposed to solve these problems, combining a tailored
active-set technique, to identify the zero variables in the optimal solution,
with a 2-coordinate descent scheme. At every iteration, the algorithm chooses
between two different strategies: the first one requires to compute the whole
gradient of the smooth term of the objective function and is more accurate in
the active-set estimate, while the second one only uses partial derivatives and
is computationally more efficient. Global convergence to optimal solutions is
proved and numerical results are provided on synthetic and real datasets,
showing the effectiveness of the proposed method. The software is publicly
available.


title: EvoSTS Forecasting: Evolutionary Sparse Time-Series Forecasting

abstract:
  In this work, we highlight our novel evolutionary sparse time-series
forecasting algorithm also known as EvoSTS. The algorithm attempts to
evolutionary prioritize weights of Long Short-Term Memory (LSTM) Network that
best minimize the reconstruction loss of a predicted signal using a learned
sparse coded dictionary. In each generation of our evolutionary algorithm, a
set number of children with the same initial weights are spawned. Each child
undergoes a training step and adjusts their weights on the same data. Due to
stochastic back-propagation, the set of children has a variety of weights with
different levels of performance. The weights that best minimize the
reconstruction loss with a given signal dictionary are passed to the next
generation. The predictions from the best-performing weights of the first and
last generation are compared. We found improvements while comparing the weights
of these two generations. However, due to several confounding parameters and
hyperparameter limitations, some of the weights had negligible improvements. To
the best of our knowledge, this is the first attempt to use sparse coding in
this way to optimize time series forecasting model weights, such as those of an
LSTM network.


title:
A Solution to the Temperature Evolution of Multi-well Free-energy
  Landscape

abstract:
  It has been a grand challenge to resolve the temperature evolution of
multi-well free-energy landscape which is fundamentally relevant to phase
transitions and associated critical phenomena as listed by Ginzburg [Rev. Mod.
Phys. 76 (2004) 981]. To address this challenge, here we provide a simple
solution based on a priori concept of Boltzmann thermal mixing among multiple
parabolic potentials. The success and the impact of the present approach have
been extensively demonstrated using a variety of materials, including Nb,
YBa2Cu3O(7-x), Ca3Ru2O7, Ni, BiFeO3, and PbTiO3.


title:
Variation of Chaotic Behaviour of Wind Speed Oscillations Across Indian
  Subcontinent

abstract:
  Modelling the intermittency of wind speed has got significant relevance on
many fields, including wind power generation and distribution. Most of the
available modelling techniques assume the temporal fluctuations in the wind is
stochastic in nature and the underlying dynamics is best described by
statistical methods or the probabilistic distribution. The advent of chaos
theory have changed the perception about irregular fluctuations of dynamic
systems and it has demonstrated that random-like fluctuations can also arise
from deterministic chaotic systems. In this paper, we have analysed the
deterministic nature of apparent random fluctuations seen in the daily average
wind speed with the help of nonlinear time series analysis tools utilising wind
speed data measured at nine typical locations over the Indian subcontinent from
2005 to 2015. The values of significant chaotic quantifiers obtained from the
analysis clearly show the deterministic, low-dimensional and chaotic nature of
wind speed dynamics in all these locations.


title: Panoptic Segmentation using Synthetic and Real Data

abstract:
  Being able to understand the relations between the user and the surrounding
environment is instrumental to assist users in a worksite. For instance,
understanding which objects a user is interacting with from images and video
collected through a wearable device can be useful to inform the worker on the
usage of specific objects in order to improve productivity and prevent
accidents. Despite modern vision systems can rely on advanced algorithms for
object detection, semantic and panoptic segmentation, these methods still
require large quantities of domain-specific labeled data, which can be
difficult to obtain in industrial scenarios. Motivated by this observation, we
propose a pipeline which allows to generate synthetic images from 3D models of
real environments and real objects. The generated images are automatically
labeled and hence effortless to obtain. Exploiting the proposed pipeline, we
generate a dataset comprising synthetic images automatically labeled for
panoptic segmentation. This set is complemented by a small number of manually
labeled real images for fine-tuning. Experiments show that the use of synthetic
images allows to drastically reduce the number of real images needed to obtain
reasonable panoptic segmentation performance.


title:
Avoiding the Great Filter: Predicting the Timeline for Humanity to Reach
  Kardashev Type I Civilization

abstract:
  The level of technological development of any civilization can be gaged in
large part by the amount of energy they produce for their use, but also
encompasses that civilization's stewardship of their home world. Following the
Kardashev definition, a Type I civilization is able to store and use all the
energy available on its planet. In this study, we develop a model based on Carl
Sagan's K formula and use this model to analyze the consumption and energy
supply of the three most important energy sources: fossil fuels (e.g., coal,
oil, natural gas, crude, NGL and feedstocks), nuclear energy and renewable
energy. We also consider environmental limitations suggested by United Nations
Framework Convention on Climate Change, the International Energy Agency, and
those specific to our calculations to predict when humanity will reach the
level of a Kardashev scale Type I civilization. Our findings suggest that the
best estimate for this day will not come until year 2371.


title: A Unified Analysis of Dynamic Interactive Learning

abstract:
  In this paper we investigate the problem of learning evolving concepts over a
combinatorial structure. Previous work by Emamjomeh-Zadeh et al. [2020]
introduced dynamics into interactive learning as a way to model non-static user
preferences in clustering problems or recommender systems. We provide many
useful contributions to this problem. First, we give a framework that captures
both of the models analyzed by [Emamjomeh-Zadeh et al., 2020], which allows us
to study any type of concept evolution and matches the same query complexity
bounds and running time guarantees of the previous models. Using this general
model we solve the open problem of closing the gap between the upper and lower
bounds on query complexity. Finally, we study an efficient algorithm where the
learner simply follows the feedback at each round, and we provide mistake
bounds for low diameter graphs such as cliques, stars, and general o(log n)
diameter graphs by using a Markov Chain model.


title: SemiMultiPose: A Semi-supervised Multi-animal Pose Estimation Framework

abstract:
  Multi-animal pose estimation is essential for studying animals' social
behaviors in neuroscience and neuroethology. Advanced approaches have been
proposed to support multi-animal estimation and achieve state-of-the-art
performance. However, these models rarely exploit unlabeled data during
training even though real world applications have exponentially more unlabeled
frames than labeled frames. Manually adding dense annotations for a large
number of images or videos is costly and labor-intensive, especially for
multiple instances. Given these deficiencies, we propose a novel
semi-supervised architecture for multi-animal pose estimation, leveraging the
abundant structures pervasive in unlabeled frames in behavior videos to enhance
training, which is critical for sparsely-labeled problems. The resulting
algorithm will provide superior multi-animal pose estimation results on three
animal experiments compared to the state-of-the-art baseline and exhibits more
predictive power in sparsely-labeled data regimes.


title:
Longitudinal Complex Dynamics of Labour Markets Reveal Increasing
  Polarisation

abstract:
  In this paper we conduct a longitudinal analysis of the structure of labour
markets in the US over 7 decades of technological, economic and policy change.
We make use of network science, natural language processing and machine
learning to uncover structural changes in the labour market over time. We find
a steady rate of both disappearance of jobs and a shift in the required work
tasks, despite much technological and economic change over this time period.
Machine learning is used to classify jobs as being predominantly cognitive or
physical based on the textual description of the workplace tasks. We also
measure increasing polarisation between these two classes of jobs, linked by
the similarity of tasks, over time that could constrain workers wishing to move
to different jobs.


title:
Leveraging Natural Learning Processing to Uncover Themes in Clinical
  Notes of Patients Admitted for Heart Failure

abstract:
  Heart failure occurs when the heart is not able to pump blood and oxygen to
support other organs in the body as it should. Treatments include medications
and sometimes hospitalization. Patients with heart failure can have both
cardiovascular as well as non-cardiovascular comorbidities. Clinical notes of
patients with heart failure can be analyzed to gain insight into the topics
discussed in these notes and the major comorbidities in these patients. In this
regard, we apply machine learning techniques, such as topic modeling, to
identify the major themes found in the clinical notes specific to the
procedures performed on 1,200 patients admitted for heart failure at the
University of Illinois Hospital and Health Sciences System (UI Health). Topic
modeling revealed five hidden themes in these clinical notes, including one
related to heart disease comorbidities.


title:
Learning and controlling the source-filter representation of speech with
  a variational autoencoder

abstract:
  Understanding and controlling latent representations in deep generative
models is a challenging yet important problem for analyzing, transforming and
generating various types of data. In speech processing, inspiring from the
anatomical mechanisms of phonation, the source-filter model considers that
speech signals are produced from a few independent and physically meaningful
continuous latent factors, among which the fundamental frequency $f_0$ and the
formants are of primary importance. In this work, we show that the
source-filter model of speech production naturally arises in the latent space
of a variational autoencoder (VAE) trained in an unsupervised manner on a
dataset of natural speech signals. Using only a few seconds of labeled speech
signals generated with an artificial speech synthesizer, we experimentally
illustrate that $f_0$ and the formant frequencies are encoded in orthogonal
subspaces of the VAE latent space and we develop a weakly-supervised method to
accurately and independently control these speech factors of variation within
the learned latent subspaces. Without requiring additional information such as
text or human-labeled data, this results in a deep generative model of speech
spectrograms that is conditioned on $f_0$ and the formant frequencies, and
which is applied to the transformation of speech signals.


title:
End-to-end Learning for Joint Depth and Image Reconstruction from
  Diffracted Rotation

abstract:
  Monocular depth estimation is still an open challenge due to the ill-posed
nature of the problem at hand. Deep learning based techniques have been
extensively studied and proved capable of producing acceptable depth estimation
accuracy even if the lack of meaningful and robust depth cues within single RGB
input images severally limits their performance. Coded aperture-based methods
using phase and amplitude masks encode strong depth cues within 2D images by
means of depth-dependent Point Spread Functions (PSFs) at the price of a
reduced image quality. In this paper, we propose a novel end-to-end learning
approach for depth from diffracted rotation. A phase mask that produces a
Rotating Point Spread Function (RPSF) as a function of defocus is jointly
optimized with the weights of a depth estimation neural network. To this aim,
we introduce a differentiable physical model of the aperture mask and exploit
an accurate simulation of the camera imaging pipeline. Our approach requires a
significantly less complex model and less training data, yet it is superior to
existing methods in the task of monocular depth estimation on indoor
benchmarks. In addition, we address the problem of image degradation by
incorporating a non-blind and non-uniform image deblurring module to recover
the sharp all-in-focus image from its RPSF-blurred counterpart.