title: Eager Functions as Processes (long version)

abstract:
  We study Milner's encoding of the call-by-value $\lambda$-calculus into the
$\pi$-calculus. We show that, by tuning the encoding to two subcalculi of the
$\pi$-calculus (Internal $\pi$ and Asynchronous Local $\pi$), the equivalence
on $\lambda$-terms induced by the encoding coincides with Lassen's eager
normalform bisimilarity, extended to handle $\eta$-equality. As behavioural
equivalence in the $\pi$-calculus we consider contextual equivalence and barbed
congruence. We also extend the results to preorders. A crucial technical
ingredient in the proofs is the recently-introduced technique of unique
solutions of equations, further developed in this paper. In this respect, the
paper also intends to be an extended case study on the applicability and
expressiveness of the technique.


title: Knowledge-Integrated Informed AI for National Security

abstract:
  The state of artificial intelligence technology has a rich history that dates
back decades and includes two fall-outs before the explosive resurgence of
today, which is credited largely to data-driven techniques. While AI technology
has and continues to become increasingly mainstream with impact across domains
and industries, it's not without several drawbacks, weaknesses, and potential
to cause undesired effects. AI techniques are numerous with many approaches and
variants, but they can be classified simply based on the degree of knowledge
they capture and how much data they require; two broad categories emerge as
prominent across AI to date: (1) techniques that are primarily, and often
solely, data-driven while leveraging little to no knowledge and (2) techniques
that primarily leverage knowledge and depend less on data. Now, a third
category is starting to emerge that leverages both data and knowledge, that
some refer to as "informed AI." This third category can be a game changer
within the national security domain where there is ample scientific and
domain-specific knowledge that stands ready to be leveraged, and where purely
data-driven AI can lead to serious unwanted consequences.
  This report shares findings from a thorough exploration of AI approaches that
exploit data as well as principled and/or practical knowledge, which we refer
to as "knowledge-integrated informed AI." Specifically, we review illuminating
examples of knowledge integrated in deep learning and reinforcement learning
pipelines, taking note of the performance gains they provide. We also discuss
an apparent trade space across variants of knowledge-integrated informed AI,
along with observed and prominent issues that suggest worthwhile future
research directions. Most importantly, this report suggests how the advantages
of knowledge-integrated informed AI stand to benefit the national security
domain.


title: Optical skin: Sensor-integration-free multimodal flexible sensing

abstract:
  The biological skin enables animals to sense various stimuli. Extensive
efforts have been made recently to develop smart skin-like sensors to extend
the capabilities of biological skins; however, simultaneous sensing of several
types of stimuli in a large area remains challenging because this requires
large-scale sensor integration with numerous wire connections. We propose a
simple, highly sensitive, and multimodal sensing approach, which does not
require integrating multiple sensors. The proposed approach is based on an
optical interference technique, which can encode the information of various
stimuli as a spatial pattern. In contrast to the existing approach, the
proposed approach, combined with a deep neural network, enables us to freely
select the sensing mode according to our purpose. As a key example, we
demonstrate simultaneous sensing mode of three different physical quantities,
contact force, contact location, and temperature, using a single soft material
without requiring complex integration. Another unique property of the proposed
approach is spatially continuous sensing with ultrahigh resolution of few tens
of micrometers, which enables identifying the shape of the object in contact.
Furthermore, we present a haptic soft device for a human-machine interface. The
proposed approach encourages the development of high-performance optical skins.

categories: cs.CV cs.HC cs.LG physics.app-ph physics.optics


title:
Efficient Autoprecoder-based deep learning for massive MU-MIMO Downlink
  under PA Non-Linearities

abstract:
  This paper introduces a new efficient autoprecoder (AP) based deep learning
approach for massive multiple-input multiple-output (mMIMO) downlink systems in
which the base station is equipped with a large number of antennas with
energy-efficient power amplifiers (PAs) and serves multiple user terminals. We
present AP-mMIMO, a new method that jointly eliminates the multiuser
interference and compensates the severe nonlinear (NL) PA distortions. Unlike
previous works, AP-mMIMO has a low computational complexity, making it suitable
for a global energy-efficient system. Specifically, we aim to design the
PA-aware precoder and the receive decoder by leveraging the concept of
autoprecoder, whereas the end-to-end massive multiuser (MU)-MIMO downlink is
designed using a deep neural network (NN). Most importantly, the proposed
AP-mMIMO is suited for the varying block fading channel scenario. To deal with
such scenarios, we consider a two-stage precoding scheme: 1) a NN-precoder is
used to address the PA non-linearities and 2) a linear precoder is used to
suppress the multiuser interference. The NN-precoder and the receive decoder
are trained off-line and when the channel varies, only the linear precoder
changes on-line. This latter is designed by using the widely used zero-forcing
precoding scheme or its lowcomplexity version based on matrix polynomials.
Numerical simulations show that the proposed AP-mMIMO approach achieves
competitive performance with a significantly lower complexity compared to
existing literature. Index Terms-multiuser (MU) precoding, massive
multipleinput multiple-output (MIMO), energy-efficiency, hardware impairment,
power amplifier (PA) nonlinearities, autoprecoder, deep learning, neural
network (NN)


title: A correspondence between the free and interacting field theories

abstract:
  We discover a correspondence between the free field and the interacting
states. This correspondence is firstly given from the fact that the free
propagator can be converted into a tower of propagators for massive states,
when expanded with the Hermite function basis. The equivalence of propagators
reveals that in this particular case the duality can naturally be regarded as
the equivalence of one theory on the plane wave basis to the other on the
Hermite function basis. More generally, the Hermite function basis provides an
alternative quantization process with the creation/annihilation operators that
correspond directly to the interacting fields. Moreover, the Hermite function
basis defines an exact way of dimensional reduction. As an illustration, we
apply this basis on 3+1 dimensional Yang-Mills theory with three dimensional
space being reduced through the Hermite function basis, and if with only the
lowest order Hermite function, the equivalent action becomes the
Banks-Fischler-Shenker-Susskind (BFSS) matrix model.


title:
Reward is not enough: can we liberate AI from the reinforcement learning
  paradigm?

abstract:
  I present arguments against the hypothesis put forward by Silver, Singh,
Precup, and Sutton (
https://www.sciencedirect.com/science/article/pii/S0004370221000862 ) : reward
maximization is not enough to explain many activities associated with natural
and artificial intelligence including knowledge, learning, perception, social
intelligence, evolution, language, generalisation and imitation. I show such
reductio ad lucrum has its intellectual origins in the political economy of
Homo economicus and substantially overlaps with the radical version of
behaviourism. I show why the reinforcement learning paradigm, despite its
demonstrable usefulness in some practical application, is an incomplete
framework for intelligence -- natural and artificial. Complexities of
intelligent behaviour are not simply second-order complications on top of
reward maximisation. This fact has profound implications for the development of
practically usable, smart, safe and robust artificially intelligent agents.


title:
Network Resource Allocation Strategy Based on Deep Reinforcement
  Learning

abstract:
  The traditional Internet has encountered a bottleneck in allocating network
resources for emerging technology needs. Network virtualization (NV) technology
as a future network architecture, the virtual network embedding (VNE) algorithm
it supports shows great potential in solving resource allocation problems.
Combined with the efficient machine learning (ML) algorithm, a neural network
model close to the substrate network environment is constructed to train the
reinforcement learning agent. This paper proposes a two-stage VNE algorithm
based on deep reinforcement learning (DRL) (TS-DRL-VNE) for the problem that
the mapping result of existing heuristic algorithm is easy to converge to the
local optimal solution. For the problem that the existing VNE algorithm based
on ML often ignores the importance of substrate network representation and
training mode, a DRL VNE algorithm based on full attribute matrix (FAM-DRL-VNE)
is proposed. In view of the problem that the existing VNE algorithm often
ignores the underlying resource changes between virtual network requests, a DRL
VNE algorithm based on matrix perturbation theory (MPT-DRL-VNE) is proposed.
Experimental results show that the above algorithm is superior to other
algorithms.


title:
Muon Track Reconstruction in a Segmented Bolometric Array Using
  Multi-Objective Optimization

abstract:
  Recent advances in segmented solid-state detector arrays for rare-event
searches have allowed the technology to approach the ton-scale in detector mass
and the scale of meters in size. Often focused around searches for neutrinoless
double-beta decay or direct dark matter detection, such experiments also have
the capability to search for exotic particles that leave track-like signatures
across their volume. However, the segmented nature of such detector arrays
often sets the spatial resolution and makes the problem of reconstructing
track-like paths non-trivial. In this paper, we present an algorithm that
improves reconstruction of track-like events in segmented detectors using
multi-objective optimization - a computational technique that optimizes more
than one cost function at a time without specifying a quantitative weighting
between them. Such a technique allows the reconstruction of tracks through a
detector and the determination of path-lengths through individual elements.
When combined with the reconstructed energy depositions in each element this
allows for a calculation of the stopping power of track-like particles and
opens the door to searches for particles with abnormal stopping power like
monopoles or lightly-ionizing particles (LIPs). Results are presented which
evaluate the precision of the reconstruction tools as they currently stand
against Monte Carlo generated data. The algorithm is presented in the context
of the CUORE experiment, but has applications to other segmented calorimeter
detectors.


title:
More is Better (Mostly): On the Backdoor Attacks in Federated Graph
  Neural Networks

abstract:
  Graph Neural Networks (GNNs) are a class of deep learning-based methods for
processing graph domain information. GNNs have recently become a widely used
graph analysis method due to their superior ability to learn representations
for complex graph data. However, due to privacy concerns and regulation
restrictions, centralized GNNs can be difficult to apply to data-sensitive
scenarios. Federated learning (FL) is an emerging technology developed for
privacy-preserving settings when several parties need to train a shared global
model collaboratively. Although many research works have applied FL to train
GNNs (Federated GNNs), there is no research on their robustness to backdoor
attacks.
  This paper bridges this gap by conducting two types of backdoor attacks in
Federated GNNs: centralized backdoor attacks (CBA) and distributed backdoor
attacks (DBA). CBA is conducted by embedding the same global trigger during
training for every malicious party, while DBA is conducted by decomposing a
global trigger into separate local triggers and embedding them into the
training dataset of different malicious parties, respectively. Our experiments
show that the DBA attack success rate is higher than CBA in almost all
evaluated cases, while rarely, the DBA attack performance is close to CBA. For
CBA, the attack success rate of all local triggers is similar to the global
trigger even if the training set of the adversarial party is embedded with the
global trigger. To further explore the properties of two backdoor attacks in
Federated GNNs, we evaluate the attack performance for different trigger sizes,
poisoning intensities, and trigger densities, with trigger density being the
most influential.


title: A Conditional Perspective on the Logic of Iterated Belief Contraction

abstract:
  In this article, we consider iteration principles for contraction, with the
goal of identifying properties for contractions that respect conditional
beliefs. Therefore, we investigate and evaluate four groups of iteration
principles for contraction which consider the dynamics of conditional beliefs.
For all these principles, we provide semantic characterization theorems and
provide formulations by postulates which highlight how the change of beliefs
and of conditional beliefs is constrained, whenever that is possible. The first
group is similar to the syntactic Darwiche-Pearl postulates. As a second group,
we consider semantic postulates for iteration of contraction by Chopra, Ghose,
Meyer and Wong, and by Konieczny and Pino P\'erez, respectively, and we provide
novel syntactic counterparts. Third, we propose a contraction analogue of the
independence condition by Jin and Thielscher. For the fourth group, we consider
natural and moderate contraction by Nayak. Methodically, we make use of
conditionals for contractions, so-called contractionals and furthermore, we
propose and employ the novel notion of $ \alpha $-equivalence for formulating
some of the new postulates.


title: Optimal classical and quantum real and complex dimension witness

abstract:
  We find the minimal number of independent preparations and measurements
certifying the dimension of a classical or quantum system limited to $d$
states, optionally reduced to the real subspace. As a dimension certificate, we
use the linear independence tested by a determinant. We find the sets of
preparations and measurements that maximize the chance to detect larger space
if the extra contribution is very small. We discuss the practical application
of the test to certify the space logical operations on a quantum computer.


title: Financial Crisis in the Framework of Non-zero Temperature Balance Theory

abstract:
  Financial crises are known as crashes that result in a sudden loss of value
of financial assets in large part and they continue to occur from time to time
surprisingly. In order to discover features of the financial network, the
pairwise interaction of stocks has been considered in many research, but the
existence of the strong correlation of stocks and their collective behavior in
crisis made us address higher-order interactions. Hence, in this study, we
investigate financial networks by triplet interaction in the framework of
balance theory. Due to detecting the contribution of higher-order interactions
in understanding the complex behavior of stocks we take the advantage of the
orders parameters of the higher-order interactions. Looking at real data of
financial market obtained from $S\&P500$ through the lens of balance theory for
the quest of network structure in different periods of time near and far from
crisis reveals the existence of a structural difference of the network that
corresponds to different periods of time. Here, we address two well-known
crises the Great regression (2008) and the Covid-19 recession (2020). Results
show an ordered structure forms on-crisis in the financial network while stocks
behave independently far from a crisis. The formation of the ordered structure
of stocks in crisis makes the network resistant against disorder. The
resistance of the ordered structure against applying a disorder (temperature)
can measure the crisis strength and determine the temperature at which the
network transits. There is a critical temperature, $T_{c}$, in the language of
statistical mechanics and mean-field approach which above, the ordered
structure destroys abruptly and a first-order phase transition occurs. The
stronger the crisis, the higher the critical temperature.


title: AI Research Associate for Early-Stage Scientific Discovery

abstract:
  Artificial intelligence (AI) has been increasingly applied in scientific
activities for decades; however, it is still far from an insightful and
trustworthy collaborator in the scientific process. Most existing AI methods
are either too simplistic to be useful in real problems faced by scientists or
too domain-specialized (even dogmatized), stifling transformative discoveries
or paradigm shifts. We present an AI research associate for early-stage
scientific discovery based on (a) a novel minimally-biased ontology for
physics-based modeling that is context-aware, interpretable, and generalizable
across classical and relativistic physics; (b) automatic search for viable and
parsimonious hypotheses, represented at a high-level (via domain-agnostic
constructs) with built-in invariants, e.g., postulated forms of conservation
principles implied by a presupposed spacetime topology; and (c) automatic
compilation of the enumerated hypotheses to domain-specific, interpretable, and
trainable/testable tensor-based computation graphs to learn phenomenological
relations, e.g., constitutive or material laws, from sparse (and possibly
noisy) data sets.


title: Electrodynamics with quaternionic mass

abstract:
  New electrodynamics with quaternionic mass is found to yields interesting
results. The quaternionic mass involves longitudinal as well as transverse
(vector) masses. Because of these two masses, an application of a magnetic
field in a system induces electric charge and current densities in the system.
Frank Wilczek axion electrodynamics is derived from this new electrodynamics
bearing a quantum signature. A new version of Maxwell-Proca equations that are
invariant under the gauge transformations is derived from the new
electrodynamics. New electric and magnetic fields that are different from the
standard formalism are proposed to describe massive electrodynamics. Axion
electrodynamics are shown to be equivalent to Proca electrodynamics. The energy
and momentum conservation equations are not influenced by the quaternionic
mass.


title:
New composition products for complex harmonic functions, the dynamic
  with respect to these and composition operators induced

abstract:
  In this work we propose composition products in the class of complex harmonic
functions so that the composition of two such functions is again a complex
harmonic function. From here we begin the study of the iterations of the
functions of this class showing briefly their potential to be a topic of future
research. In parallel, we define and study composition operators whose symbols
belong to a Hardy space of complex harmonic functions also introduced in the
work. All this constitutes a previous work for the research of semigroups and
evolutionary families composed of complex harmonic functions.


title:
One-Year In: COVID-19 Research at the International Level in CORD-19
  Data

abstract:
  The appearance of a novel coronavirus in late 2019 radically changed the
community of researchers working on coronaviruses since the 2002 SARS epidemic.
In 2020, coronavirus-related publications grew by 20 times over the previous
two years, with 130,000 more researchers publishing on related topics. The
United States, the United Kingdom and China led dozens of nations working on
coronavirus prior to the pandemic, but leadership consolidated among these
three nations in 2020, which collectively accounted for 50% of all papers,
garnering well more than 60% of citations. China took an early lead on COVID-19
research, but dropped rapidly in production and international participation
through the year. Europe showed an opposite pattern, beginning slowly in
publications but growing in contributions during the year. The share of
internationally collaborative publications dropped from pre-pandemic rates;
single-authored publications grew. For all nations, including China, the number
of publications about COVID track closely with the outbreak of COVID-19 cases.
Lower-income nations participate very little in COVID-19 research in 2020.
Topic maps of internationally collaborative work show the rise of patient care
and public health clusters, two topics that were largely absent from
coronavirus research in the two years prior to 2020. Findings are consistent
with global science as a self-organizing system operating on a reputation-based
dynamic.


title: Two-Dimensional Arbitrary Angle of Arrival in Radar Target Simulation

abstract:
  Automotive radar sensors play a key role in the current development of
advanced driver assistance systems (ADAS). Their ability to detect objects even
under adverse weather conditions makes them indispensable for
environment-sensing tasks in autonomous vehicles. Since an operational failure
presents a potential risk to human life, thorough and practical validation
testing must be performed, requiring an integrative test solution. Radar target
simulators (RTS) are capable of performing over-the-air validation tests by
generating virtual radar echoes that are perceived as targets by the radar
under test (RuT). Since the authenticity and credibility of these targets is
based on the accuracy with which they are created, their simulated position
must be arbitrarily adjustable. In this work, an existing approach to
synthesize virtual radar targets at an arbitrary angle of arrival (AoA) is
extended to cover both, the azimuth and elevation domain. The concept is based
on the superposition of the returning signals from four neighboring RTS
channels. A theoretical model describing the basic principle and its
constraints is developed. In addition, a measurement campaign is conducted to
verify the practical functionality of the proposed approach.


title:
T-NGA: Temporal Network Grafting Algorithm for Learning to Process
  Spiking Audio Sensor Events

abstract:
  Spiking silicon cochlea sensors encode sound as an asynchronous stream of
spikes from different frequency channels. The lack of labeled training datasets
for spiking cochleas makes it difficult to train deep neural networks on the
outputs of these sensors. This work proposes a self-supervised method called
Temporal Network Grafting Algorithm (T-NGA), which grafts a recurrent network
pretrained on spectrogram features so that the network works with the cochlea
event features. T-NGA training requires only temporally aligned audio
spectrograms and event features. Our experiments show that the accuracy of the
grafted network was similar to the accuracy of a supervised network trained
from scratch on a speech recognition task using events from a software spiking
cochlea model. Despite the circuit non-idealities of the spiking silicon
cochlea, the grafted network accuracy on the silicon cochlea spike recordings
was only about 5% lower than the supervised network accuracy using the
N-TIDIGITS18 dataset. T-NGA can train networks to process spiking audio sensor
events in the absence of large labeled spike datasets.


title:
Coulomb correlations and magnetic properties of L1$_0$ FeCo: a DFT+DMFT
  study

abstract:
  We consider electronic correlation effects and their impact on magnetic
properties of tetragonally distorted chemically ordered FeCo alloy (L1$_0$
structure) being a promising candidate to rare-earth-free permanent magnets. We
employ a state-of-the-art method combining the density functional (DFT) and
dynamical mean-field theory (DMFT). According to our results, the predicted
Curie temperature reduces with increase of lattice parameters ratio $c/a$ and
reaches nearly 850~K at ${c/a=1.22}$. For all considered $c/a$ from 1 to
$\sqrt{2}$ we find well-localized magnetic moments on Fe sites, which are
formed due to strong correlations originating from Hund's coupling. At the same
time, magnetism of Co sites is more itinerant with a much less lifetime of
local magnetic moments. However, these short-lived local moments are also
formed due to Hund's exchange. Electronic states at Fe sites are characterized
by non-quasiparticle form of self-energies, while the ones for Co sites are
found to have a Fermi-liquid-like shape with quasiparticle mass enhancement
factor ${m^*/m\sim 1.4}$, corresponding to moderately correlated metal. The
strong electron correlations on Fe sites leading to Hund's metal behaviour can
be explained by peculiarities of density of states, which has pronounced peaks
near the Fermi level, while weaker many-body effects on Co sites can be caused
by stronger deviation from half-filling of their $3d$ states. The obtained
momentum dependence of magnetic susceptibility suggests that the ferromagnetic
ordering is the most favourable one except for the near vicinity of the fcc
structure and the magnetic exchange is expected to be of RKKY-type.


title:
Recent Trends in 2D Object Detection and Applications in Video Event
  Recognition

abstract:
  Object detection serves as a significant step in improving performance of
complex downstream computer vision tasks. It has been extensively studied for
many years now and current state-of-the-art 2D object detection techniques
proffer superlative results even in complex images. In this chapter, we discuss
the geometry-based pioneering works in object detection, followed by the recent
breakthroughs that employ deep learning. Some of these use a monolithic
architecture that takes a RGB image as input and passes it to a feed-forward
ConvNet or vision Transformer. These methods, thereby predict class-probability
and bounding-box coordinates, all in a single unified pipeline. Two-stage
architectures on the other hand, first generate region proposals and then feed
it to a CNN to extract features and predict object category and bounding-box.
We also elaborate upon the applications of object detection in video event
recognition, to achieve better fine-grained video classification performance.
Further, we highlight recent datasets for 2D object detection both in images
and videos, and present a comparative performance summary of various
state-of-the-art object detection techniques.


title: Almost Optimal Proper Learning and Testing Polynomials

abstract:
  We give the first almost optimal polynomial-time proper learning algorithm of
Boolean sparse multivariate polynomial under the uniform distribution. For
$s$-sparse polynomial over $n$ variables and $\epsilon=1/s^\beta$, $\beta>1$,
our algorithm makes $$q_U=\left(\frac{s}{\epsilon}\right)^{\frac{\log
\beta}{\beta}+O(\frac{1}{\beta})}+ \tilde
O\left(s\right)\left(\log\frac{1}{\epsilon}\right)\log n$$ queries. Notice that
our query complexity is sublinear in $1/\epsilon$ and almost linear in $s$. All
previous algorithms have query complexity at least quadratic in $s$ and linear
in $1/\epsilon$.
  We then prove the almost tight lower bound
$$q_L=\left(\frac{s}{\epsilon}\right)^{\frac{\log
\beta}{\beta}+\Omega(\frac{1}{\beta})}+
\Omega\left(s\right)\left(\log\frac{1}{\epsilon}\right)\log n,$$
  Applying the reduction in~\cite{Bshouty19b} with the above algorithm, we give
the first almost optimal polynomial-time tester for $s$-sparse polynomial. Our
tester, for $\beta>3.404$, makes $$\tilde O\left(\frac{s}{\epsilon}\right)$$
queries.


title:
Elastic waveform inversion in the frequency domain for an application in
  mechanized tunneling

abstract:
  The excavation process in mechanized tunneling can be improved by
reconnaissance of the geology ahead. A nondestructive exploration can be
achieved in means of seismic imaging. A full waveform inversion approach, which
works in the frequency domain, is investigated for the application in
tunneling. The approach tries to minimize the difference of seismic records
from field observations and from a discretized ground model by changing the
ground properties. The final ground model might be a representation of the
geology. The used elastic wave modeling approach is described as well as the
application of convolutional perfectly matched layers. The proposed inversion
scheme uses the discrete adjoint gradient method, a multi-scale approach as
well as the L-BFGS method. Numerical parameters are identified as well as a
validation of the forward wave modeling approach is performed in advance to the
inversion of every example. Two-dimensional blind tests with two different
ground scenarios and with two different source and receiver station
configurations are performed and analyzed, where only the seismic records, the
source functions and the ambient ground properties are provided. Finally, an
inversion for a three-dimensional tunnel model is performed and analyzed for
three different source and receiver station configurations.


title:
PSSNet: Planarity-sensible Semantic Segmentation of Large-scale Urban
  Meshes

abstract:
  We introduce a novel deep learning-based framework to interpret 3D urban
scenes represented as textured meshes. Based on the observation that object
boundaries typically align with the boundaries of planar regions, our framework
achieves semantic segmentation in two steps: planarity-sensible
over-segmentation followed by semantic classification. The over-segmentation
step generates an initial set of mesh segments that capture the planar and
non-planar regions of urban scenes. In the subsequent classification step, we
construct a graph that encodes the geometric and photometric features of the
segments in its nodes and the multi-scale contextual features in its edges. The
final semantic segmentation is obtained by classifying the segments using a
graph convolutional network. Experiments and comparisons on two semantic urban
mesh benchmarks demonstrate that our approach outperforms the state-of-the-art
methods in terms of boundary quality, mean IoU (intersection over union), and
generalization ability. We also introduce several new metrics for evaluating
mesh over-segmentation methods dedicated to semantic segmentation, and our
proposed over-segmentation approach outperforms state-of-the-art methods on all
metrics. Our source code is available at
\url{https://github.com/WeixiaoGao/PSSNet}.


title: Quasi-Monostatic Antenna Displacement in Radar Target Simulation

abstract:
  Radar target simulators (RTS) have recently drawn much attention in research
and commercial development, as they are capable of performing over-the-air
validation tests under laboratory conditions by generating virtual radar echoes
that are perceived as targets by a radar under test (RuT). The estimated angle
of arrival (AoA) of such a virtual target is determined by the physical
position of the particular RTS channel that creates it, which must therefore be
considered when planning the setup. A single channel employs two antennas, one
for the reception and the other for the re-transmission of the incoming radar
signal. The antennas are positioned close together, but still spatially
separated, thus an RTS channel can be considered quasi-monostatic, which causes
non-negligible inaccuracies in the angle simulation. In this paper, the authors
examine the analytical implications of this systemic deficiency on the angle
estimation, which provides support for the design and setup of angle-simulating
RTS systems. The mathematical derivations developed are verified by
measurement.


title: Semantic-aware Speech to Text Transmission with Redundancy Removal

abstract:
  Deep learning (DL) based semantic communication methods have been explored
for the efficient transmission of images, text, and speech in recent years. In
contrast to traditional wireless communication methods that focus on the
transmission of abstract symbols, semantic communication approaches attempt to
achieve better transmission efficiency by only sending the semantic-related
information of the source data. In this paper, we consider semantic-oriented
speech to text transmission. We propose a novel end-to-end DL-based
transceiver, which includes an attention-based soft alignment module and a
redundancy removal module to compress the transmitted data. In particular, the
former extracts only the text-related semantic features, and the latter further
drops the semantically redundant content, greatly reducing the amount of
semantic redundancy compared to existing methods. We also propose a two-stage
training scheme, which speeds up the training of the proposed DL model. The
simulation results indicate that our proposed method outperforms current
methods in terms of the accuracy of the received text and transmission
efficiency. Moreover, the proposed method also has a smaller model size and
shorter end-to-end runtime.


title:
Introducing explainable supervised machine learning into interactive
  feedback loops for statistical production system

abstract:
  Statistical production systems cover multiple steps from the collection,
aggregation, and integration of data to tasks like data quality assurance and
dissemination. While the context of data quality assurance is one of the most
promising fields for applying machine learning, the lack of curated and labeled
training data is often a limiting factor.
  The statistical production system for the Centralised Securities Database
features an interactive feedback loop between data collected by the European
Central Bank and data quality assurance performed by data quality managers at
National Central Banks. The quality assurance feedback loop is based on a set
of rule-based checks for raising exceptions, upon which the user either
confirms the data or corrects an actual error.
  In this paper we use the information received from this feedback loop to
optimize the exceptions presented to the National Central Banks thereby
improving the quality of exceptions generated and the time consumed on the
system by the users authenticating those exceptions. For this approach we make
use of explainable supervised machine learning to (a) identify the types of
exceptions and (b) to prioritize which exceptions are more likely to require an
intervention or correction by the NCBs. Furthermore, we provide an explainable
AI taxonomy aiming to identify the different explainable AI needs that arose
during the project.


title: Quantum KdV hierarchy and quasimodular forms

abstract:
  Dubrovin has shown that the spectrum of the quantization (with respect to the
first Poisson structure) of the dispersionless Korteweg--de Vries (KdV)
hierarchy is given by shifted symmetric functions; the latter are related by
the Bloch--Okounkov Theorem to quasimodular forms on the full modular group. We
extend the relation to quasimodular forms to the full quantum KdV hierarchy
(and to the more general Intermediate Long Wave hierarchy). These quantum
integrable hierarchies have been described by Buryak and Rossi in terms of the
Double Ramification cycle in the moduli space of curves. The main tool and
conceptual contribution of the paper is a general effective criterion for
quasimodularity.


title: Abelian subalgebras and ideals of maximal dimension in Zinbiel algebras

abstract:
  In this paper, we compare the abelian subalgebras and ideals of maximal
dimension for finite-dimensional Zinbiel algebras. We study Zinbiel algebras
containing maximal abelian subalgebras of codimension $1$ and supersolvable
Zinbiel algebras in which such subalgebras have codimension $2$, and we also
analyze the case of filiform Zinbiel algebras. We give examples to clarify some
results, including listing the values for $\alpha$ and $\beta$ for the low
dimensional Zinbiel algebras over the complex field that have been classified.


title:
Ionization waves (striations) in a low-current plasma column revisited
  with kinetic and fluid models

abstract:
  A one-dimensional Particle-In-Cell Monte Carlo Collisions (PIC-MCC) method
has been used to model the development and propagation of ionization waves in
neon and argon positive columns. Low current conditions are considered, i.e.
conditions where stepwise ionization or Coulomb collisions are negligible
(linear ionization rate). This self-consistent model describes the development
of self-excited moving striations, reproduces many of the well-known
experimental characteristics (wavelength, spatial resonances, potential drop
over one striation, electron "bunching" effect) of the ionization waves called
p, r and s waves in the literature and sheds light on their physical properties
and on the mechanisms responsible for their existence. These are the first
fully kinetic self-consistent simulations over a large range of conditions
reproducing the development of p, r and s ionization waves. Although the
spatial resonances and the detailed properties of the striations in the
non-linear regime are of kinetic nature, the conditions of existence of the
instability can be obtained and understood from a linear stability analysis of
a three-moment set of quasi-neutral fluid equations where the electron
transport coefficients are expressed as a function of electron temperature and
are obtained from solutions of a 0D Boltzman equation. An essential aspect of
the instability leading to the development of these striations is the
non-Mawellian nature of the electron energy distribution function in the
uniform electric field prior to the instability onset, resulting in an electron
diffusion coefficient in space much larger than the energy diffusion
coefficient.


title:
The Two-Loop Massless Off-Shell QCD Operator Matrix Elements to Finite
  Terms

abstract:
  We calculate the unpolarized and polarized two--loop massless off--shell
operator matrix elements in QCD to $O(\varepsilon)$ in the dimensional
parameter in an automated way. Here we use the method of arbitrary high Mellin
moments and difference ring theory, based on integration-by-parts relations.
This method also constitutes one way to compute the QCD anomalous dimensions.
The presented higher order contributions to these operator matrix elements
occur as building blocks in the corresponding higher order calculations up to
four--loop order. All contributing quantities can be expressed in terms of
harmonic sums in Mellin--$N$ space or by harmonic polylogarithms in $z$--space.
We also perform comparisons to the literature.


title: On a prior based on the Wasserstein information matrix

abstract:
  We introduce a prior for the parameters of univariate continuous
distributions, based on the Wasserstein information matrix, which is invariant
under reparameterisations. We discuss the links between the proposed prior with
information geometry. We present several examples where we can either obtain
this prior in closed-form, or propose a numerically tractable approximation for
cases where the prior is not available in closed-form. We present sufficient
conditions for the propriety of the posterior distribution for general classes
of models. We present a simulation study that shows that the induced posteriors
have good frequentist properties.


title:
Efficient Adapter Transfer of Self-Supervised Speech Models for
  Automatic Speech Recognition

abstract:
  Self-supervised learning (SSL) is a powerful tool that allows learning of
underlying representations from unlabeled data. Transformer based models such
as wav2vec 2.0 and HuBERT are leading the field in the speech domain. Generally
these models are fine-tuned on a small amount of labeled data for a downstream
task such as Automatic Speech Recognition (ASR). This involves re-training the
majority of the model for each task. Adapters are small lightweight modules
which are commonly used in Natural Language Processing (NLP) to adapt
pre-trained models to new tasks. In this paper we propose applying adapters to
wav2vec 2.0 to reduce the number of parameters required for downstream ASR
tasks, and increase scalability of the model to multiple tasks or languages.
Using adapters we can perform ASR while training fewer than 10% of parameters
per task compared to full fine-tuning with little degradation of performance.
Ablations show that applying adapters into just the top few layers of the
pre-trained network gives similar performance to full transfer, supporting the
theory that higher pre-trained layers encode more phonemic information, and
further optimizing efficiency.


title:
Parametrising CCQE uncertainties in the Spectral Function model for
  neutrino oscillation analyses

abstract:
  A substantial fraction of systematic uncertainties in neutrino oscillation
experiments stems from the lack of precision in modeling the nucleus when
describing the neutrino-nucleus interactions. The Spectral Function (SF) model
features a distribution of momenta and removal energies of nucleons inside the
nucleus within the shell-model picture, and also accounts for short-range
correlations between nucleons. These characteristics offer significant
improvements with respect to the more commonly used Fermi gas-based models.
Electron scattering experiments offer a precise probe of the structure of the
nucleus and have been used to both construct and validate the SF model. SF is
thus an interesting reference model for long baseline neutrino experiments.
  Based on constraints from electron scattering data, we develop a set of
parameters that can alter the occupancy of the nuclear shells and the
distribution of the nucleon momentum within each shell. In addition, the impact
of final-state interactions on the outgoing lepton and nucleon kinematics, the
contribution of short-range correlations and the effect of Pauli blocking can
also be modified. In this document, we will first describe the development of
these parameters, partially based on a comparison with electron scattering
data. We then show fits of these parameters to available T2K and MINER$\nu$A
cross-section data and discuss how they can be used to constrain the systematic
uncertainties related to the SF model in neutrino oscillation analyses.


title:
Deep Learning based Channel Estimation for Massive MIMO with Hybrid
  Transceivers

abstract:
  Accurate and efficient estimation of the high dimensional channels is one of
the critical challenges for practical applications of massive multiple-input
multiple-output (MIMO). In the context of hybrid analog-digital (HAD)
transceivers, channel estimation becomes even more complicated due to
information loss caused by limited radio-frequency chains. The conventional
compressive sensing (CS) algorithms usually suffer from unsatisfactory
performance and high computational complexity. In this paper, we propose a
novel deep learning (DL) based framework for uplink channel estimation in HAD
massive MIMO systems. To better exploit the sparsity structure of channels in
the angular domain, a novel angular space segmentation method is proposed,
where the entire angular space is segmented into many small regions and a
dedicated neural network is trained offline for each region. During online
testing, the most suitable network is selected based on the information from
the global positioning system. Inside each neural network, the region-specific
measurement matrix and channel estimator are jointly optimized, which not only
improves the signal measurement efficiency, but also enhances the channel
estimation capability. Simulation results show that the proposed approach
significantly outperforms the state-of-the-art CS algorithms in terms of
estimation performance and computational complexity.


title:
Transient Monte Carlo Simulations for the Optimisation and
  Characterisation of Monolithic Silicon Sensors

abstract:
  An ever-increasing demand for high-performance silicon sensors requires
complex sensor designs that are challenging to simulate and model. The
combination of electrostatic finite element simulations with a transient Monte
Carlo approach provides simultaneous access to precise sensor modelling and
high statistics. The high simulation statistics enable the inclusion of Landau
fluctuations and production of secondary particles, which offers a realistic
simulation scenario. The transient simulation approach is an important tool to
achieve an accurate time-resolved description of the sensor, which is crucial
in the face of novel detector prototypes with increasingly precise timing
capabilities. The simulated time resolution as a function of operating
parameters as well as the full transient pulse can be monitored and assessed,
which offers a new perspective on the optimisation and characterisation of
silicon sensors.
  In this paper, a combination of electrostatic finite-element simulations
using 3D TCAD and transient Monte Carlo simulations with the Allpix Squared
framework are presented for a monolithic CMOS pixel sensor with a small
collection diode, that is characterised by a highly inhomogeneous, complex
electric field. The results are compared to transient 3D TCAD simulations that
offer a precise simulation of the transient behaviour but long computation
times. Additionally, the simulations are benchmarked against test-beam data and
good agreement is found for the performance parameters over a wide range of
different operation conditions.


title:
Mean-field vs RPA calculation of the energy of an impurity immersed in a
  spin 1/2 superfluid

abstract:
  In this article we calculate the energy of an impurity weakly coupled to a
spin 1/2 fermionic superfluid. We show that the divergences resulting from
three-body physics can only be cured using a proper description of the
excitations of the many-body background. We highlight the crucial role played
by interactions between quasiparticles which are overlooked within BCS
(Bardeen-Cooper-Schrieffer) mean-field theory of fermionic superfluidity. By
contrast, we prove that their addition using the Random Phase Approximation
(RPA) allows us to regularize the energy of the impurity. Finally, we show that
these beyond mean-field corrections should be observable by the analysis of the
frequency shift of the impurity center of mass oscillations in an external
confining potential.


title:
SODA: Self-organizing data augmentation in deep neural networks --
  Application to biomedical image segmentation tasks

abstract:
  In practice, data augmentation is assigned a predefined budget in terms of
newly created samples per epoch. When using several types of data augmentation,
the budget is usually uniformly distributed over the set of augmentations but
one can wonder if this budget should not be allocated to each type in a more
efficient way. This paper leverages online learning to allocate on the fly this
budget as part of neural network training. This meta-algorithm can be run at
almost no extra cost as it exploits gradient based signals to determine which
type of data augmentation should be preferred. Experiments suggest that this
strategy can save computation time and thus goes in the way of greener machine
learning practices.


title:
HERMES: Hybrid Error-corrector Model with inclusion of External Signals
  for nonstationary fashion time series

abstract:
  Developing models and algorithms to draw causal inference for time series is
a long standing statistical problem. It is crucial for many applications, in
particular for fashion or retail industries, to make optimal inventory
decisions and avoid massive wastes. By tracking thousands of fashion trends on
social media with state-of-the-art computer vision approaches, we propose a new
model for fashion time series forecasting. Our contribution is twofold. We
first provide publicly the first fashion dataset gathering 10000 weekly fashion
time series. As influence dynamics are the key of emerging trend detection, we
associate with each time series an external weak signal representing behaviors
of influencers. Secondly, to leverage such a complex and rich dataset, we
propose a new hybrid forecasting model. Our approach combines per-time-series
parametric models with seasonal components and a global recurrent neural
network to include sporadic external signals. This hybrid model provides
state-of-the-art results on the proposed fashion dataset, on the weekly time
series of the M4 competition, and illustrates the benefit of the contribution
of external weak signals.


title: Time-correlation functions for odd Langevin systems

abstract:
  We investigate the statistical properties of fluctuations in active systems
that are governed by non-symmetric responses. Both an underdamped Langevin
system with an odd resistance tensor and an overdamped Langevin system with an
odd elastic tensor are studied. For a system in thermal equilibrium, the
time-correlation functions should satisfy time-reversal symmetry and the
anti-symmetric parts of the correlation functions should vanish. For the odd
Langevin systems, however, we find that the anti-symmetric parts of the
time-correlation functions can exist and that they are proportional to either
the odd resistance coefficient or the odd elastic constant. This means that the
time-reversal invariance of the correlation functions is broken due to the
presence of odd responses in active systems. Using the short-time asymptotic
expressions of the time-correlation functions, one can estimate an odd elastic
constant of an active material such as an enzyme or a motor protein.


title:
Magnetism of QCD matter and pion mass from tensor-type spin polarization
  and anomalous magnetic moment of quarks

abstract:
  We investigate the magnetism of QCD matter and pion mass under magnetic field
considering the contribution from the tensor-type spin polarization and the
anomalous magnetic moment (AMM) of quarks. It is found that the tensor-type
spin polarization (TSP) induces the magnetic catalysis of chiral condensate and
diamagnetism (negative magnetic susceptibility) of quark matter at low
temperature, both neutral and charged pion masses increase quickly with
magnetic field in the case of TSP. The anomalous magnetic moment (AMM) of
quarks induces magnetic inhibition and a magnetic dependent AMM causes inverse
magnetic catalysis at finite temperature, and the neutral pion mass decreases
with magnetic field while the charged pion mass shows nonmonotonic behavior
with the magnetic field, which is qualitatively in agreement with lattice
result. However, the magnetic susceptibility is positive at low temperature
with AMM. In the current framework, our results show the irreconcilable
contradiction between the diamagnetism and inverse magnetic catalysis.


title: Twisted C-brackets

abstract:
  We consider the double field formulation of the closed bosonic string theory
and calculate the Poisson bracket algebra of the symmetry generators governing
both general coordinate and local gauge transformations. Parameters of both of
these symmetries depend on a double coordinate, defined as a direct sum of the
initial and T-dual coordinate. When no antisymmetric field is present, the
$C$-bracket appears as the Lie bracket generalization in a double theory. With
the introduction of the Kalb-Ramond field, the $B$-twisted $C$-bracket appears,
while with the introduction of the non-commutativity parameter, the
$\theta$-twisted $C$-bracket appears. We present the worldsheet derivation of
these brackets and comment on their relations to analogous twisted Courant
brackets and T-duality.


title:
Gravitationally bound states of two neutral fermions and a Higgs field
  can be parametrically lighter than their constituents

abstract:
  We analyze the localized states of a pair of neutral fermions interacting
with a Higgs field and the metric of spacetime, extending the Einstein-Dirac
formalism introduced by Finster, Smoller, and Yau [Phys. Rev. D 59, 104020
(1999)]. We demonstrate that, when the coupling between the fermions and the
Higgs field is strong, there is a class of bound states in which the total
(ADM) mass no longer increases proportionally to the mass of the constituent
fermions. This phenomenon enables fermionic particles with much larger masses
than in the Higgs-free case to form stable localized states.


title: Neural Models for Output-Space Invariance in Combinatorial Problems

abstract:
  Recently many neural models have been proposed to solve combinatorial puzzles
by implicitly learning underlying constraints using their solved instances,
such as sudoku or graph coloring (GCP). One drawback of the proposed
architectures, which are often based on Graph Neural Networks (GNN), is that
they cannot generalize across the size of the output space from which variables
are assigned a value, for example, set of colors in a GCP, or board-size in
sudoku. We call the output space for the variables as 'value-set'. While many
works have demonstrated generalization of GNNs across graph size, there has
been no study on how to design a GNN for achieving value-set invariance for
problems that come from the same domain. For example, learning to solve 16 x 16
sudoku after being trained on only 9 x 9 sudokus. In this work, we propose
novel methods to extend GNN based architectures to achieve value-set
invariance. Specifically, our model builds on recently proposed Recurrent
Relational Networks. Our first approach exploits the graph-size invariance of
GNNs by converting a multi-class node classification problem into a binary node
classification problem. Our second approach works directly with multiple
classes by adding multiple nodes corresponding to the values in the value-set,
and then connecting variable nodes to value nodes depending on the problem
initialization. Our experimental evaluation on three different combinatorial
problems demonstrates that both our models perform well on our novel problem,
compared to a generic neural reasoner. Between two of our models, we observe an
inherent trade-off: while the binarized model gives better performance when
trained on smaller value-sets, multi-valued model is much more memory
efficient, resulting in improved performance when trained on larger value-sets,
where binarized model fails to train.


title: Hyperbolic space in the Newtonian limit: the cosmological constant

abstract:
  The cosmological constant and the Boltzmann entropy of a Newtonian Universe
filled with a perfect fluid are computed, under the assumption that spatial
sections are copies of 3-dimensional hyperbolic space.


title: Impact of physical-chemistry on the film thinning in surface bubbles

abstract:
  In this paper, we investigate the thinning dynamics of evaporating
surfactant-stabilised surface bubbles by considering the role of the
physical-chemistry of solutions used in the liquid bath. We study the impact of
the surfactant concentration below and above the cmc (critical micelle
concentration) and the role of ambient humidity. First, in a humidity-saturated
atmosphere, we show that if the initial thickness depends on the surfactant
concentration and is limited by the surface elasticity, the drainage dynamics
are very well described from the capillary and gravity contributions. These
dynamics are independent of the surfactant concentration. In a second part, our
study reveals that the physical-chemistry impacts the thinning dynamics through
evaporation. We include in the model the additional contribution due to
evaporation, which shows a good description of the experimental data below the
cmc. Above the cmc, although this model is unsatisfactory at short times, the
dynamics at long times is correctly rendered and we establish that the increase
of the surfactant concentration decreases the impact of evaporation. Finally,
the addition of a hygroscopic compound, glycerol, can be also rationalized by
our model. We demonstrate that glycerol decreases the bubble thinning rate at
ambient humidity, thus increasing their stability.


title:
Phonon study of Jahn-Teller distortion and phase stability in NaMnO$_2$
  for sodium-ion batteries

abstract:
  Cathode materials undergo various phase transitions during the
charge/discharge process, and the structural transitions significantly affect
the battery performance. Although phonon properties can provide a direct clue
for structural stability and transitions, it has been less explored in sodium
cathode materials. Here, using the first-principles calculations, we
investigate phonon and electronic properties of various layered NaMnO$_2$
materials, especially focusing on the dependency of the Jahn-Teller distortion
of Mn$^{3+}$. The phonon dispersion curves show that the O$'$3 and P$'$2
structures with the Jahn-Teller distortion are dynamically stable in contrast
to undistorted O3 and P2 structures. The structural instability of O3 and P2
structures is directly observed from the imaginary phonon frequencies, as
so-called phonon soft modes, whose corresponding displacements are from O atoms
distorting along the local Mn-O bond direction in the MnO$_6$ octahedra. This
is consistent with the experimental stability and a structural transition with
the Jahn-Teller distortion at the high Na concentration. Furthermore, the
orbital-decomposed density of states presents the orbital redistribution by the
Jahn-Teller distortion such as $e_g$-band splitting, and the stability of O$'$3
and P$'$2 is not sensitive to the electron-electron correlation. Our results
demonstrate the importance of phonon analysis to further understand the
structural stability and phase transitions in cathode materials.


title:
A Variational Edge Partition Model for Supervised Graph Representation
  Learning

abstract:
  Graph neural networks (GNNs), which propagate the node features through the
edges and learn how to transform the aggregated features under label
supervision, have achieved great success in supervised feature extraction for
both node-level and graph-level classification tasks. However, GNNs typically
treat the graph structure as given and ignore how the edges are formed. This
paper introduces a graph generative process to model how the observed edges are
generated by aggregating the node interactions over a set of overlapping node
communities, each of which contributes to the edges via a logical OR mechanism.
Based on this generative model, we partition each edge into the summation of
multiple community-specific weighted edges and use them to define
community-specific GNNs. A variational inference framework is proposed to
jointly learn a GNN based inference network that partitions the edges into
different communities, these community-specific GNNs, and a GNN based predictor
that combines community-specific GNNs for the end classification task.
Extensive evaluations on real-world graph datasets have verified the
effectiveness of the proposed method in learning discriminative representations
for both node-level and graph-level classification tasks.


title: Generalised norm resolvent convergence: comparison of different concepts

abstract:
  In this paper, we show that the two concepts of generalised norm resolvent
convergence introduced by Weidmann and the first author of this paper are
equivalent. We also focus on the convergence speed and provide conditions under
which the convergence speed is the same for both concepts. We illustrate the
abstract results by a large number of examples.


title:
Effects of kinematic and magnetic boundary conditions on the dynamics of
  convection-driven plane layer dynamos

abstract:
  Rapidly rotating convection-driven dynamos are investigated under different
kinematic and magnetic boundary conditions using DNS. At a fixed rotation rate,
represented by the Ekman number $E=5\times10^{-7}$, the thermal forcing is
varied from 2 to 20 times its value at the onset of convection
($\mathcal{R}=Ra/Ra_c=2-20$), keeping the fluid properties constant
($Pr=Pr_m=1$). The statistical behavior, force balance and heat transport
characteristics of the dynamos depend on boundary conditions that dictate both
boundary layer and the interior dynamics. At a fixed thermal forcing
($\mathcal{R}=3$), the Ekman plumes in the presence of viscous boundary layers
lead to energetic vortices that result in higher enstrophy and kinetic helicity
with no-slip boundaries compared to free-slip boundaries. The structure and
strength of the magnetic field are also dictated by the boundary conditions.
Though the leading order force balance remains geostrophic, Lorentz force
dominates inside the thermal boundary layer with no-slip, electrically
conducting walls. Here, the Lorentz work term in the turbulent kinetic energy
budget is found to have components that exchange energy from the velocity field
to the magnetic field, and vice-versa. However, with no-slip, insulated walls,
all Lorentz work components perform unidirectional energy transfer to produce
magnetic energy from the kinetic energy of the fluid. The heat transfer
enhancement in dynamos, compared to non-magnetic rotating convection, exhibits
a peak in the range $\mathcal{R}=3-5$. For free-slip conditions, dynamo action
may alter the heat transport by suppressing the formation of large-scale
vortices. However, the highest heat transfer enhancement occurs when the
boundaries are no-slip, electrically conducting walls.


title:
Passive learning to address nonstationarity in virtual flow metering
  applications

abstract:
  Steady-state process models are common in virtual flow meter applications due
to low computational complexity, and low model development and maintenance
cost. Nevertheless, the prediction performance of steady-state models typically
degrades with time due to the inherent nonstationarity of the underlying
process being modeled. Few studies have investigated how learning methods can
be applied to sustain the prediction accuracy of steady-state virtual flow
meters. This paper explores passive learning, where the model is frequently
calibrated to new data, as a way to address nonstationarity and improve
long-term performance. An advantage with passive learning is that it is
compatible with models used in the industry. Two passive learning methods,
periodic batch learning and online learning, are applied with varying
calibration frequency to train virtual flow meters. Six different model types,
ranging from data-driven to first-principles, are trained on historical
production data from 10 petroleum wells. The results are two-fold: first, in
the presence of frequently arriving measurements, frequent model updating
sustains an excellent prediction performance over time; second, in the presence
of intermittent and infrequently arriving measurements, frequent updating in
addition to the utilization of expert knowledge is essential to increase the
performance accuracy. The investigation may be of interest to experts
developing soft-sensors for nonstationary processes, such as virtual flow
meters.


title:
Introducing the Expohedron for Efficient Pareto-optimal Fairness-Utility
  Amortizations in Repeated Rankings

abstract:
  We consider the problem of computing a sequence of rankings that maximizes
consumer-side utility while minimizing producer-side individual unfairness of
exposure. While prior work has addressed this problem using linear or quadratic
programs on bistochastic matrices, such approaches, relying on Birkhoff-von
Neumann (BvN) decompositions, are too slow to be implemented at large scale.
  In this paper we introduce a geometrical object, a polytope that we call
expohedron, whose points represent all achievable exposures of items for a
Position Based Model (PBM). We exhibit some of its properties and lay out a
Carath\'eodory decomposition algorithm with complexity $O(n^2\log(n))$ able to
express any point inside the expohedron as a convex sum of at most $n$
vertices, where $n$ is the number of items to rank. Such a decomposition makes
it possible to express any feasible target exposure as a distribution over at
most $n$ rankings. Furthermore we show that we can use this polytope to recover
the whole Pareto frontier of the multi-objective fairness-utility optimization
problem, using a simple geometrical procedure with complexity $O(n^2\log(n))$.
Our approach compares favorably to linear or quadratic programming baselines in
terms of algorithmic complexity and empirical runtime and is applicable to any
merit that is a non-decreasing function of item relevance. Furthermore our
solution can be expressed as a distribution over only $n$ permutations, instead
of the $(n-1)^2 + 1$ achieved with BvN decompositions. We perform experiments
on synthetic and real-world datasets, confirming our theoretical results.


title: Towards an Analytical Definition of Sufficient Data

abstract:
  We show that, for each of five datasets of increasing complexity, certain
training samples are more informative of class membership than others. These
samples can be identified a priori to training by analyzing their position in
reduced dimensional space relative to the classes' centroids. Specifically, we
demonstrate that samples nearer the classes' centroids are less informative
than those that are furthest from it. For all five datasets, we show that there
is no statistically significant difference between training on the entire
training set and when excluding up to 2% of the data nearest to each class's
centroid.


title: Weakly Supervised Indoor Localization via Manifold Matching

abstract:
  Inferring the location of a mobile device in an indoor setting is an open
problem of utmost significance. A leading approach that does not require the
deployment of expensive infrastructure is fingerprinting, where a classifier is
trained to predict the location of a device based on its captured signal. The
main caveat of this approach is that acquiring a sufficiently large and
accurate training set may be prohibitively expensive. Here, we propose a weakly
supervised method that only requires the location of a small number of devices.
The localization is done by matching a low-dimensional spectral representation
of the signals to a given sketch of the indoor environment. We test our
approach on simulated and real data and show that it yields an accuracy of a
few meters, which is on par with fully supervised approaches. The simplicity of
our method and its accuracy with minimal supervision makes it ideal for
implementation in indoor localization systems.


title:
Minimization of the Worst-Case Average Energy Consumption in
  UAV-Assisted IoT Networks

abstract:
  The Internet of Things (IoT) brings connectivity to a massive number of
devices that demand energy-efficient solutions to deal with limited battery
capacities, uplink-dominant traffic, and channel impairments. In this work, we
explore the use of Unmanned Aerial Vehicles (UAVs) equipped with configurable
antennas as a flexible solution for serving low-power IoT networks. We
formulate an optimization problem to set the position and antenna beamwidth of
the UAV, and the transmit power of the IoT devices subject to
average-Signal-to-average-Interference-plus-Noise Ratio
($\bar{\text{S}}\overline{\text{IN}}\text{R}$) Quality of Service (QoS)
constraints. We minimize the worst-case average energy consumption of the
latter, thus, targeting the fairest allocation of the energy resources. The
problem is non-convex and highly non-linear; therefore, we re-formulate it as a
series of three geometric programs that can be solved iteratively. Results
reveal the benefits of planning the network compared to a random deployment in
terms of reducing the worst-case average energy consumption. Furthermore, we
show that the target $\bar{\text{S}}\overline{\text{IN}}\text{R}$ is limited by
the number of IoT devices, and highlight the dominant impact of the UAV
hovering height when serving wider areas. Our proposed algorithm outperforms
other optimization benchmarks in terms of minimizing the average energy
consumption at the most energy-demanding IoT device, and convergence time.


title: Uncertainty in Grid Data: A Theory and Comprehensive Robustness Test

abstract:
  This article makes two novel contributions to spatial political and conflict
research using grid data. First, it develops a theory of how uncertainty
specific to grid data affects inference. Second, it introduces a comprehensive
robustness test on sensitivity to this uncertainty, implemented in R. The
uncertainty stems from (1) what is the correct size of grid cells, (2) what is
the correct locations on which to draw dividing lines between these grid cells,
and (3) a greater effect of measurement errors due to finer grid cells. My test
aggregates grid cells into a larger size of choice as the multiple of the
original grid cells. It also enables different starting points of grid cell
aggregation (e.g., whether to start the aggregation from the corner of the
entire map or one grid cell of the original size away from the corner) to shift
the diving lines. I apply my test to Tollefsen, Strand, and Buhaug (2012) to
substantiate its use.


title:
Unsupervised physics-informed disentanglement of multimodal data for
  high-throughput scientific discovery

abstract:
  We introduce physics-informed multimodal autoencoders (PIMA) - a variational
inference framework for discovering shared information in multimodal scientific
datasets representative of high-throughput testing. Individual modalities are
embedded into a shared latent space and fused through a product of experts
formulation, enabling a Gaussian mixture prior to identify shared features.
Sampling from clusters allows cross-modal generative modeling, with a mixture
of expert decoder imposing inductive biases encoding prior scientific knowledge
and imparting structured disentanglement of the latent space. This approach
enables discovery of fingerprints which may be detected in high-dimensional
heterogeneous datasets, avoiding traditional bottlenecks related to
high-fidelity measurement and characterization. Motivated by accelerated
co-design and optimization of materials manufacturing processes, a dataset of
lattice metamaterials from metal additive manufacturing demonstrates accurate
cross modal inference between images of mesoscale topology and mechanical
stress-strain response.


title: Air-Releasable Soft Robots for Explosive Ordnance Disposal

abstract:
  The demining of landmines using drones is challenging; air-releasable
payloads are typically non-intelligent (e.g., water balloons or explosives) and
deploying them at even low altitudes (~6 meter) is inherently inaccurate due to
complex deployment trajectories and constrained visual awareness by the drone
pilot. Soft robotics offers a unique approach for aerial demining, namely due
to the robust, low-cost, and lightweight designs of soft robots. Instead of
non-intelligent payloads, here, we propose the use of air-releasable soft
robots for demining. We developed a full system consisting of an unmanned
aerial vehicle retrofitted to a soft robot carrier including a custom-made
deployment mechanism, and an air-releasable, lightweight (296 g), untethered
soft hybrid robot with integrated electronics that incorporates a new type of a
vacuum-based flasher roller actuator system. We demonstrate a deployment cycle
in which the drone drops the soft robotic hybrid from an altitude of 4.5 m
meters and after which the robot approaches a dummy landmine. By deploying soft
robots at points of interest, we can transition soft robotic technologies from
the laboratory to real-world environments.


title: Online Deep Neural Network for Optimization in Wireless Communications

abstract:
  Recently, deep neural network (DNN) has been widely adopted in the design of
intelligent communication systems thanks to its strong learning ability and low
testing complexity. However, most current offline DNN-based methods still
suffer from unsatisfactory performance, limited generalization ability, and
poor interpretability. In this article, we propose an online DNN-based approach
to solve general optimization problems in wireless communications, where a
dedicated DNN is trained for each data sample. By treating the optimization
variables and the objective function as network parameters and loss function,
respectively, the optimization problem can be solved equivalently through
network training. Thanks to the online optimization nature and meaningful
network parameters, the proposed approach owns strong generalization ability
and interpretability, while its superior performance is demonstrated through a
practical example of joint beamforming in intelligent reflecting surface
(IRS)-aided multi-user multiple-input multiple-output (MIMO) systems.
Simulation results show that the proposed online DNN outperforms conventional
offline DNN and state-of-the-art iterative optimization algorithm, but with low
complexity.


title:
Scalable Multi-Party Privacy-Preserving Gradient Tree Boosting over
  Vertically Partitioned Dataset with Outsourced Computations

abstract:
  Due to privacy concerns, multi-party gradient tree boosting algorithms have
become widely popular amongst machine learning researchers and practitioners.
However, limited existing works have focused on vertically partitioned
datasets, and the few existing works are either not scalable or tend to leak
information. Thus, in this work, we propose SSXGB which is a scalable and
secure multi-party gradient tree boosting framework for vertically partitioned
datasets with partially outsourced computations. Specifically, we employ an
additive homomorphic encryption (HE) scheme for security. We design two
sub-protocols based on the HE scheme to perform non-linear operations
associated with gradient tree boosting algorithms. Next, we propose a secure
training and a secure prediction algorithms under the SSXGB framework. Then we
provide theoretical security and communication analysis for the proposed
framework. Finally, we evaluate the performance of the framework with
experiments using two real-world datasets.


title:
AI-based artistic representation of emotions from EEG signals: a
  discussion on fairness, inclusion, and aesthetics

abstract:
  While Artificial Intelligence (AI) technologies are being progressively
developed, artists and researchers are investigating their role in artistic
practices. In this work, we present an AI-based Brain-Computer Interface (BCI)
in which humans and machines interact to express feelings artistically. This
system and its production of images give opportunities to reflect on the
complexities and range of human emotions and their expressions. In this
discussion, we seek to understand the dynamics of this interaction to reach
better co-existence in fairness, inclusion, and aesthetics.


title:
Spin-transfer-assisted Parametric Pumping of Magnons in Yttrium Iron
  Garnet

abstract:
  The combination of parametric pumping and spin-transfer torque is a powerful
approach that enables high-level control over magnetic excitations in thin-film
ferromagnets. The excitation parameters, such as pumping power and external
field strength, affect the instabilities of individual magnon modes. We
theoretically explore how the simultaneous effects of parametric pumping and
spin transfer torque influence these magnetic instabilities in a thin-film
ferromagnet. Within the LLG framework, we perform micromagnetic simulations of
magnon excitations in yttrium iron garnet by pumping, spin transfer torque, and
a combining the two. Consistent with experimental results, we find that the
magnitude and direction of the spin transfer torque tune the parametric
instability thresholds.


title: Derivatives Risks as Costs in a One-Period Network Model

abstract:
  We present a one-period XVA model encompassing bilateral and centrally
cleared trading in a unified framework with explicit formulas for most
quantities at hand. We illustrate possible uses of this framework for running
stress test exercises on a financial network from a clearing member's
perspective or for optimizing the porting of the portfolio of a defaulted
clearing member.


title:
Maximal $L^p$-regularity for an abstract evolution equation with
  applications to closed-loop boundary feedback control problems

abstract:
  In this paper we present an abstract maximal $L^p$-regularity result up to $T
= \infty$, that is tuned to capture (linear) Partial Differential Equations of
parabolic type, defined on a bounded domain and subject to finite dimensional,
stabilizing, feedback controls acting on (a portion of) the boundary.
Illustrations include, beside a more classical boundary parabolic example, two
more recent settings: (i) the $3d$-Navier-Stokes equations with finite
dimensional, localized, boundary tangential feedback stabilizing controls as
well as Boussinesq systems with finite dimensional, localized, feedback,
stabilizing, Dirichlet boundary control for the thermal equation.


title:
ALM-KD: Knowledge Distillation with noisy labels via adaptive loss
  mixing

abstract:
  Knowledge distillation is a technique where the outputs of a pretrained
model, often known as the teacher model is used for training a student model in
a supervised setting. The teacher model outputs being a richer distribution
over labels should improve the student model's performance as opposed to
training with the usual hard labels. However, the label distribution imposed by
the logits of the teacher network may not be always informative and may lead to
poor student performance. We tackle this problem via the use of an adaptive
loss mixing scheme during KD. Specifically, our method learns an
instance-specific convex combination of the teacher-matching and label
supervision objectives, using meta learning on a validation metric signalling
to the student `how much' of KD is to be used. Through a range of experiments
on controlled synthetic data and real-world datasets, we demonstrate
performance gains obtained using our approach in the standard KD setting as
well as in multi-teacher and self-distillation settings.


title:
Strong effects of uniaxial pressure and short-range correlations in
  Cr2Ge2Te6

abstract:
  Cr$_2$Ge$_2$Te$_6$ is a quasi-2D semiconducting van der Waals ferromagnet
down to the bilayer with great potential for technological applications.
Engineering the critical temperature to achieve room temperature applications
is one of the critical next steps on this path. Here, we report high-resolution
capacitance dilatometry studies on Cr$_2$Ge$_2$Te$_6$ single crystals which
directly prove significant magnetoelastic coupling and provide quantitative
values of the large uniaxial pressure effects on long-range magnetic order
(${\partial}T_{\mathrm{C}}/{\partial}p_{\mathrm{c}}$ = 74 K/GPa and
${\partial}T_{\mathrm{C}}/{\partial}p_{\mathrm{ab}}$ = $-$47 K/GPa) derived
from thermodynamic relations. Moderate in-plane strain is thus sufficient to
strongly enhance ferromagnetism in Cr$_2$Ge$_2$Te$_6$ up to room temperature.
Moreover, unambiguous signs for short-range magnetic order up to 200~K are
found.


title:
Populating and probing protected edge states through topology-entailed
  trivial states

abstract:
  Topological insulators enable non-reciprocal light propagation that is
insensitive to disorder and imperfections. Yet, despite considerable attention
from the photonics community and beyond, the very feature that has inspired
numerous proposals for applications of topological transport also turns out to
be one of the main stumbling blocks for practical implementations: Accessing
topologically protected states is generally assumed to require their protection
to be lifted. We overcome this limitation by topology-entailed trivial (TET)
states that arise from the hybridization of counter-propagating interface
states. We demonstrate selective injection and extraction of light into
topological states as well as long-range coherent light exchange between
spatially separated topological channels. Our results highlight the potential
of TET states as protection-preserving paradigm to manipulate the flow of light
in topological platforms.


title: A useful family of fat-tailed distributions

abstract:
  It is argued that there is a need for fat-tailed distributions that become
thin in the extreme tail. A 3-parameter distribution is introduced that
visually resembles the t-distribution and interpolates between the normal
distribution and the Cauchy distribution. It is fat-tailed, but has all moments
finite, and the moment-generating function exists. It would be useful as an
alternative to the t-distribution for a sensitivity analysis to check the
robustness of results or for computations where finite moments are needed, such
as in option-pricing. It can be motivated probabilistically in at least two
ways, either as the random thinning of a long-tailed distribution, or as random
variation of the variance of a normal distribution. Its properties are
described, algorithms for random-number generation are provided, and examples
of its use in data-fitting given. Some related distributions are also
discussed, including asymmetric and multivariate distributions.


title:
Characterization of Perfect fluid spacetimes obeying
  $f(\mathcal{R})$-gravity equipped with different gradient solitons

abstract:
  The prime object of this article is to study the perfect fluid spacetimes
obeying $f(\mathcal{R})$-gravity, when $\mathcal{B}$-Ricci solitons, gradient
$\mathcal{B}$-Ricci solitons, gradient Einstein Solitons and gradient $m$-quasi
Einstein solitons are its metrics. At first, the existence of
$\mathcal{B}$-Ricci solitons and gradient Einstein Solitons are proved by two
non-trivial example. We establish conditions for which the $\mathcal{B}$-Ricci
solitons are expanding, steady or shrinking. Besides, in the perfect fluid
spacetimes obeying $f(\mathcal{R})$-gravity, when the potential vector field of
$\mathcal{B}$-Ricci soliton is of gradient type, we acquire a Poisson equation.
Moreover, we investigate gradient $\mathcal{B}$-Ricci solitons, gradient
Einstein Solitons and gradient $m$-quasi Einstein solitons in
$f(\mathcal{R})$-gravity, respectively. As a result, we establish some
significant theorems about dark matter era.


title: OCSM : Finding Overlapping Cohesive Subgraphs with Minimum Degree

abstract:
  Cohesive subgraph discovery in a network is one of the fundamental problems
and investigated for several decades. In this paper, we propose the Overlapping
Cohesive Subgraphs with Minimum degree (OCSM) problem which combines three key
concepts for OCSM : (i) edge-based overlapping, (ii) the minimum degree
constraint, and (iii) the graph density. To the best of our knowledge, this is
the first work to identify overlapping cohesive subgraphs with minimum degree
by incorporating the graph density. Since the OCSM problem is NP-hard, we
propose two algorithms: advanced peeling algorithm and seed-based expansion
algorithm. Finally, we show the experimental study with real-world networks to
demonstrate the effectiveness and efficiency of our proposed algorithms.


title: Model predictive control for singular differential-algebraic equations

abstract:
  We study model predictive control for singular differential-algebraic
equations with higher index. This is a novelty when compared to the literature
where only regular differential-algebraic equations with additional assumptions
on the index and/or controllability are considered. By regularization
techniques, we are able to derive an equivalent optimal control problem for an
ordinary differential equation to which well-known model predictive control
techniques can be applied. This allows the construction of terminal constraints
and costs such that the origin is asymptotically stable w.r.t. the resulting
closed-loop system.


title: Confidence Guided Depth Completion Network

abstract:
  The paper proposes an image-guided depth completion method to estimate
accurate dense depth maps with fast computation time. The proposed network has
two-stage structure. The first stage predicts a first depth map. Then, the
second stage further refines the first depth map using the confidence maps. The
second stage consists of two layers, each of which focuses on different regions
and generates a refined depth map and a confidence map. The final depth map is
obtained by combining two depth maps from the second stage using the
corresponding confidence maps. Compared with the top-ranked models on the KITTI
depth completion online leaderboard, the proposed model shows much faster
computation time and competitive performance.


title: Enhanced Higgs pair production from higgsino decay at the HL-LHC

abstract:
  The scenario of multi-sector SUSY breaking predicts pseudo-goldstinos which
are not absorbed by the gravitino and their mass can be as low as ${{\cal O}
(0.1)}$ GeV. Since the interactions of pseudo-goldstinos are not so weak as
gravitino, a produced higgsino can decay to a pseudo-goldstino plus a Higgs
boson insider the detector at the LHC, and thus the higgsino pair production
can lead to the signal of Higgs pair plus missing energy. For the scenario of
natural SUSY which requires rather light higgsinos, such events may sizably
outnumber the Higgs pair events predicted by the SM and be accessible at the
HL-LHC (14 TeV with a luminosity of 3~$\rm{ab}^{-1}$). In this work we examine
the observability of such Higgs pair plus missing energy from the decay of
light higgsinos produced at the HL-LHC. Considering three channels of the
Higgs-pair decay ($bbWW^*$, $bb\gamma\gamma$, $bbbb$), our detailed Monte Carlo
simulations for the signal and backgrounds show that the best channel is
$bbbb+\slash{E}_{T}$, whose statistical significance can reach $5\sigma$ for a
light higgsino allowed by current experiments. This is significantly over the
SM Higgs pair result which is about $1.8\sigma$.


title:
Theory-inspired Parameter Control Benchmarks for Dynamic Algorithm
  Configuration

abstract:
  It has long been observed that the performance of evolutionary algorithms and
other randomized search heuristics can benefit from a non-static choice of the
parameters that steer their optimization behavior. Mechanisms that identify
suitable configurations on the fly ("parameter control") or via a dedicated
training process ("dynamic algorithm configuration") are therefore an important
component of modern evolutionary computation frameworks. Several approaches to
address the dynamic parameter setting problem exist, but we barely understand
which ones to prefer for which applications. As in classical benchmarking,
problem collections with a known ground truth can offer very meaningful
insights in this context. Unfortunately, settings with well-understood control
policies are very rare.
  One of the few exceptions for which we know which parameter settings minimize
the expected runtime is the LeadingOnes problem. We extend this benchmark by
analyzing optimal control policies that can select the parameters only from a
given portfolio of possible values. This also allows us to compute optimal
parameter portfolios of a given size. We demonstrate the usefulness of our
benchmarks by analyzing the behavior of the DDQN reinforcement learning
approach for dynamic algorithm configuration.


title:
Methods and Results for Quantum Optimal Pulse Control on Superconducting
  Qubit Systems

abstract:
  The effective use of current Noisy Intermediate-Scale Quantum (NISQ) devices
is often limited by the noise which is caused by interaction with the
environment and affects the fidelity of quantum gates. In transmon qubit
systems, the quantum gate fidelity can be improved by applying control pulses
that can minimize the effects of the environmental noise. In this work, we
employ physics-guided quantum optimal control strategies to design optimal
pulses driving quantum gates on superconducting qubit systems. We test our
results by conducting experiments on the IBM Q hardware using their OpenPulse
API. We compare the performance of our pulse-optimized quantum gates against
the default quantum gates and show that the optimized pulses improve the
fidelity of the quantum gates, in particular the single-qubit gates. We discuss
the challenges we encountered in our work and point to possible future
improvements.


title:
Normal Mode Analysis of the Linear Viscoelastic Response of Dissipative
  Systems: Application to Vertex Model

abstract:
  We present a semi-analytical method based on normal modes to calculate the
dynamic linear viscoelastic properties of soft systems with various forms of
dissipation. Each normal mode responds with a characteristic relaxation
timescale, and their rheological behavior can be represented with the standard
linear solid model. The method is applied to explore the linear shear rheology
of the vertex model over a broad range of frequencies, which has been widely
used to describe the mechanical properties of epithelial tissues. The
analytical predictions are validated by the numerical simulation results for
the vertex model using several different dissipation mechanisms mimicking
several possible biological scenarios. This work provides insight of the
connection between the dynamical modes and rheological properties of soft
materials and biological tissues.


title:
Finite dimensional boundary uniform stabilization of the Boussinesq
  system in Besov spaces by critical use of Carleman estimate-based inverse
  theory

abstract:
  We consider the d-dimensional Boussinesq system defined on a sufficiently
smooth bounded domain, and subject to a pair $\{ v, \boldsymbol{u} \}$ of
controls localized on $\{ \widetilde{\Gamma}, \omega \}$. Here, $v$ is a scalar
Dirichlet boundary control for the thermal equation, acting on an arbitrary
small connected portion $\widetilde{\Gamma}$ of the boundary $\Gamma = \partial
\Omega$. Instead, $\boldsymbol{u}$ is a $d$-dimensional internal control for
the fluid equation acting on an arbitrary small collar $\omega$ supported by
$\widetilde{\Gamma}$ (Fig 1). The initial conditions for both fluid and heat
equations are taken of low regularity. We then seek to uniformly stabilize such
Boussinesq system in the vicinity of an unstable equilibrium pair, in the
critical setting of correspondingly low regularity spaces, by means of an
explicitly constructed, finite dimensional feedback control pair $\{ v,
\boldsymbol{u} \}$ localized on $\{ \widetilde{\Gamma}, \omega \}$. In
addition, they will be minimal in number, and of reduced dimension: more
precisely, $\boldsymbol{u}$ will be of dimension $(d-1)$, to include
necessarily its $d$\textsuperscript{th} component, and $v$ will be of dimension
$1$. The resulting space of well-posedness and stabilization is a suitable,
tight Besov space for the fluid velocity component (close to
$\boldsymbol{L}^3(\Omega$) for $ d = 3 $) and a corresponding Besov space for
the thermal component, $ q > d $. Unique continuation inverse theorems for
suitably over determined adjoint static problems play a critical role in the
constructive solution. Their proof rests on Carleman-type estimates, a topic
pioneered by M. V. Klibanov since the early 80's, after the 1939- breakthrough
publication \cite{Car}.


title:
Asynchronous Parallel Incremental Block-Coordinate Descent for
  Decentralized Machine Learning

abstract:
  Machine learning (ML) is a key technique for big-data-driven modelling and
analysis of massive Internet of Things (IoT) based intelligent and ubiquitous
computing. For fast-increasing applications and data amounts, distributed
learning is a promising emerging paradigm since it is often impractical or
inefficient to share/aggregate data to a centralized location from distinct
ones. This paper studies the problem of training an ML model over decentralized
systems, where data are distributed over many user devices and the learning
algorithm run on-device, with the aim of relaxing the burden at a central
entity/server. Although gossip-based approaches have been used for this purpose
in different use cases, they suffer from high communication costs, especially
when the number of devices is large. To mitigate this, incremental-based
methods are proposed. We first introduce incremental block-coordinate descent
(I-BCD) for the decentralized ML, which can reduce communication costs at the
expense of running time. To accelerate the convergence speed, an asynchronous
parallel incremental BCD (API-BCD) method is proposed, where multiple
devices/agents are active in an asynchronous fashion. We derive convergence
properties for the proposed methods. Simulation results also show that our
API-BCD method outperforms state of the art in terms of running time and
communication costs.


title:
Short-term Multi-horizon Residential Electric Load Forecasting using
  Deep Learning and Signal Decomposition Methods

abstract:
  With the booming growth of advanced digital technologies, it has become
possible for users as well as distributors of energy to obtain detailed and
timely information about the electricity consumption of households. These
technologies can also be used to forecast the household's electricity
consumption (a.k.a. the load). In this paper, we investigate the use of
Variational Mode Decomposition and deep learning techniques to improve the
accuracy of the load forecasting problem. Although this problem has been
studied in the literature, selecting an appropriate decomposition level and a
deep learning technique providing better forecasting performance have garnered
comparatively less attention. This study bridges this gap by studying the
effect of six decomposition levels and five distinct deep learning networks.
The raw load profiles are first decomposed into intrinsic mode functions using
the Variational Mode Decomposition in order to mitigate their non-stationary
aspect. Then, day, hour, and past electricity consumption data are fed as a
three-dimensional input sequence to a four-level Wavelet Decomposition Network
model. Finally, the forecast sequences related to the different intrinsic mode
functions are combined to form the aggregate forecast sequence. The proposed
method was assessed using load profiles of five Moroccan households from the
Moroccan buildings' electricity consumption dataset (MORED) and was benchmarked
against state-of-the-art time-series models and a baseline persistence model.


title: Image-based eeg classification of brain responses to song recordings

abstract:
  Classifying EEG responses to naturalistic acoustic stimuli is of theoretical
and practical importance, but standard approaches are limited by processing
individual channels separately on very short sound segments (a few seconds or
less). Recent developments have shown classification for music stimuli (~2
mins) by extracting spectral components from EEG and using convolutional neural
networks (CNNs). This paper proposes an efficient method to map raw EEG signals
to individual songs listened for end-to-end classification. EEG channels are
treated as a dimension of a [Channel x Sample] image tile, and images are
classified using CNNs. Our experimental results (88.7%) compete with
state-of-the-art methods (85.0%), yet our classification task is more
challenging by processing longer stimuli that were similar to each other in
perceptual quality, and were unfamiliar to participants. We also adopt a
transfer learning scheme using a pre-trained ResNet-50, confirming the
effectiveness of transfer learning despite image domains unrelated from each
other.


title: Paper-based printed CPW-fed antenna for Wi-Fi applications

abstract:
  A paper-based co-planar waveguide (CPW) fed monopole antenna for Wi-Fi
applications is proposed. The antenna is fabricated by printing a commercial
silver nanoparticle (Ag NP) based ink on photo paper substrate. The antenna is
designed as a single layer for the ease of fabrication, and it is designed to
radiate at two frequencies, 2.4 and 5.8 GHz, which are suitable for Wi-Fi
applications. The printed film exhibits good electrical conductivity, with a
low sheet resistance of 114 m{\Omega}/sq comparable with commonly used
conductive metal lines. The fabricated antenna demonstrates good radiative
properties at both flat and bent conditions. This confirms the good flexible
properties of the antenna making it compatible with mounting on curved
surfaces. The performance of the fabricated antenna is also compared with a
commercial rigid antenna by interfacing with a USB dongle. The printed antenna
demonstrates better performance with respect to signal strength at specific
distances when compared with the commercial antenna. This work demonstrates
that rigid and long commercial antennas can be replaced with paper-based
flexible and cheap antennas and incorporated with wearable technologies.
Additionally, replacing Ag NPs with nanowires provides transparency without
compromising on the electrical properties.


title:
Team Cogitat at NeurIPS 2021: Benchmarks for EEG Transfer Learning
  Competition

abstract:
  Building subject-independent deep learning models for EEG decoding faces the
challenge of strong covariate-shift across different datasets, subjects and
recording sessions. Our approach to address this difficulty is to explicitly
align feature distributions at various layers of the deep learning model, using
both simple statistical techniques as well as trainable methods with more
representational capacity. This follows in a similar vein as covariance-based
alignment methods, often used in a Riemannian manifold context. The methodology
proposed herein won first place in the 2021 Benchmarks in EEG Transfer Learning
(BEETL) competition, hosted at the NeurIPS conference. The first task of the
competition consisted of sleep stage classification, which required the
transfer of models trained on younger subjects to perform inference on multiple
subjects of older age groups without personalized calibration data, requiring
subject-independent models. The second task required to transfer models trained
on the subjects of one or more source motor imagery datasets to perform
inference on two target datasets, providing a small set of personalized
calibration data for multiple test subjects.