title:
A Coronal Mass Ejection followed by a prominence eruption and a plasma
  blob as observed by Solar Orbiter

abstract:
  On February 12, 2021 two subsequent eruptions occurred above the West limb,
as seen along the Sun-Earth line. The first event was a typical slow Coronal
Mass Ejection (CME), followed $\sim 7$ hours later by a smaller and collimated
prominence eruption, originating Southward with respect to the CME, followed by
a plasma blob. These events were observed not only by SOHO and STEREO-A
missions, but also by the suite of remote sensing instruments on-board Solar
Orbiter (SolO). This work shows how data acquired by the Full Sun Imager (FSI),
Metis coronagraph, and Heliospheric Imager (SoloHI) from the SolO perspective
can be combined to study the eruptions and the different source regions.
Moreover, we show how Metis data can be analyzed to provide new information
about solar eruptions.
  Different 3D reconstruction methods were applied to the data acquired by
different spacecraft including remote sensing instruments on-board SolO. Images
acquired by both Metis channels in the Visible Light (VL) and H I
Lyman$-\alpha$ line (UV) were combined to derive physical information on the
expanding plasma. The polarization ratio technique was also applied for the
first time to the Metis images acquired in the VL channel. The two eruptions
were followed in 3D from their source region to their expansion in the
intermediate corona. Thanks to the combination of VL and UV Metis data, the
formation of a post-CME Current Sheet (CS) was followed for the first time in
the intermediate corona. The plasma temperature gradient across a post-CME blob
propagating along the CS was also measured for the first time. Application of
the polarization ratio technique to Metis data shows that, thanks to the
combination of four different polarization measurements, the errors are reduced
by $\sim 5-7$\%, thus better constraining the 3D distribution of plasma.


title: Single-Query Verifiable Proof-of-Sequential-Work

abstract:
  We propose a proof-of-sequential-work (PoSW) that can be verified with only a
single query to the random oracle for each random challenge. We propose a PoSW
that allows any verifier, even the one with no parallelism, to verify using
just a single sequential computation on a single challenge. All the existing
PoSWs [6, 3, 1, 4] mandate a prover to compute a sequence of responses from a
random oracle against N -rounds of queries. Then the prover commits this
sequence using a commitment scheme (e.g., Merkle root (like) commitment)
predefined in the PoSWs. Now the verifier asks the prover to provide a set of
proofs against t randomly chosen checkpoints, called as challenges, in the
computed sequence. The verifier finds out the commitment from each of these
proofs spending O(log N ) rounds of queries to the oracle. It can be reduced to
a single round of queries only if the verifier owns O(log N ) parallelism [4].
The verifier in our PoSW demands no parallelism but uses a single query to the
random oracle in order to verify each of the t challenges. The key observation
is that the commitment schemes themselves in the prior works demand O(log N )
oracle queries to verify. So our PoSW asks the prover to undergo an additional
efficient binary operation x on the responses from the random oracle against N
-rounds of queries. The cumulative result of x, represented as a map f , on all
such responses serves the purpose of the commitment. The verifier verifies this
cumulative result.


title: On binomial thinning and mixing

abstract:
  In this paper we consider the notions of binomial thinning, binomial mixing,
their generalizations, certain interplay between them, associated limit
theorems and provide various examples.


title: AD for an Array Language with Nested Parallelism

abstract:
  We present a technique for applying (forward and) reverse-mode automatic
differentiation (AD) on a non-recursive second-order functional array language
that supports nested parallelism and is primarily aimed at efficient GPU
execution. The key idea is to eliminate the need for a "tape" by relying on
redundant execution to bring into each new scope all program variables that may
be needed by the differentiated code. Efficient execution is enabled by the
observation that perfectly-nested scopes do not introduce re-execution, and
such perfect nests are produced by known compiler transformations, e.g.,
flattening. Our technique differentiates loops and bulk-parallel operators,
such as map, reduce, histogram, scan, scatter, by specific rewrite rules, and
aggressively optimizes the resulting nested-parallel code. We report an
experimental evaluation that compares with established AD solutions and
demonstrates competitive performance on nine common benchmarks from recent
applied AD literature.


title: Stokes phenomena, Poisson-Lie groups and quantum groups

abstract:
  Let g be a complex semisimple Lie algebra, G the simply-connected Poisson-Lie
group corresponding to g, and G* its dual. G-valued Stokes phenomena were used
by Boalch [Bo1,Bo2] to give a canonical, analytic linearisation of the Poisson
structure on G*. Ug-valued Stokes phenomena were used by the first author to
construct a twist killing the KZ associator, and therefore give a
transcendental construction of the Drinfeld-Jimbo quantum group U_hg
(arXiv:1601.04076). In the present paper, we show that the former construction
can be obtained as semiclassical limit of the latter. Along the way, we also
show that the R-matrix of U_hg is a Stokes matrix for the dynamical KZ
equations.


title: Reducing and Invariant subspaces under two commuting shift operators

abstract:
  In this article, we characterize reducing and invariant subspaces of the
space of square integrable functions defined in the unit circle and having
values in some Hardy space with multiplicity.
  We consider subspaces that reduce the bilateral shift and at the same time
are invariant under the unilateral shift acting locally. We also study
subspaces that reduce both operators. The conditions obtained are of the type
of the ones in Helson and Beurling-Lax-Halmos theorems on characterizations of
the invariance for the bilateral and unilateral shift. The motivations for our
study were inspired by recently results on Dynamical Sampling in
shift-invariant spaces.


title:
Characterization of Supernovae Based on the Spectral-Temporal Energy
  Distribution: Possible two SN Ib Subtypes

abstract:
  A quantitative data-driven comparison among supernovae (SNe) based on their
spectral time series combined with multi-band photometry is presented. We use
an unsupervised Random Forest algorithm as a metric on a set of 82
well-documented SNe representing all the main spectroscopic types, in order to
embed these in an abstract metric space reflecting shared correlations between
the objects. We visualize the resulting metric space in 3D, revealing strong
agreement with the current spectroscopic classification scheme. The embedding
splits Type Ib supernovae into two groups, with one subgroup exhibiting
broader, less prominent, higher-velocity lines than the other, possibly
suggesting a new SN Ib subclass is required. The method could be to classify
newly discovered SNe according to their distance from known event groups, or
ultimately to devise a new, spectral-temporal classification scheme. Such an
embedding could also depend on hidden parameters which may perhaps be
physically interpretable.


title:
VLAD-VSA: Cross-Domain Face Presentation Attack Detection with
  Vocabulary Separation and Adaptation

abstract:
  For face presentation attack detection (PAD), most of the spoofing cues are
subtle, local image patterns (e.g., local image distortion, 3D mask edge and
cut photo edges). The representations of existing PAD works with simple global
pooling method, however, lose the local feature discriminability. In this
paper, the VLAD aggregation method is adopted to quantize local features with
visual vocabulary locally partitioning the feature space, and hence preserve
the local discriminability. We further propose the vocabulary separation and
adaptation method to modify VLAD for cross-domain PADtask. The proposed
vocabulary separation method divides vocabulary into domain-shared and
domain-specific visual words to cope with the diversity of live and attack
faces under the cross-domain scenario. The proposed vocabulary adaptation
method imitates the maximization step of the k-means algorithm in the
end-to-end training, which guarantees the visual words be close to the center
of assigned local features and thus brings robust similarity measurement. We
give illustrations and extensive experiments to demonstrate the effectiveness
of VLAD with the proposed vocabulary separation and adaptation method on
standard cross-domain PAD benchmarks. The codes are available at
https://github.com/Liubinggunzu/VLAD-VSA.


title: On the use of Sulphur as a tracer for abundances in galaxies

abstract:
  We present a methodology for the use of sulphur as global metallicity tracer
in galaxies, allowing performing a complete abundance analysis using only the
red-to-near infrared spectral region. We have applied it to a compilation of
high-quality data split into two samples: HII regions (DHR) in spiral and
irregular galaxies, and dwarf galaxies dominated by a strong starburst
(HIIGal). Sulphur abundances have been derived by direct methods under the
assumption of an ionisation structure composed of two zones: an intermediate
one where S{++} is originated and a low ionisation one where S{+} is formed.
Ionisation correction factors (ICF) have been calculated from the Ar{2+}/Ar{3+}
ratio and are shown to correlate with the hardness of the radiation field. Only
about 10% of the objects show S{3+} contributions to the total abundance larger
than 30%. A good correlation exists between sulphur abundance and ionising
temperature with low metallicity objects being ionised by hotter stars. No
correlation is found between ionisation parameter and total S/H abundance. Most
of the HIIGal objects show S/O ratios below the solar value and a trend for
increasing S/O ratios with increasing sulphur abundances while DHR objects show
S/O ratios larger than solar and a tendency for lower S/O ratios for higher
metallicities. Finally, we present a calibration of the sulphur abundance
through the S{23} parameter that remains single valued up to sulphur abundances
well beyond the solar value. S{23} is independent of the ionisation parameter
and only weakly dependent on ionising temperature.


title:
Path toward manufacturable superconducting qubits with relaxation times
  exceeding 0.1 ms

abstract:
  As the superconducting qubit platform matures towards ever-larger scales in
the race towards a practical quantum computer, limitations due to qubit
inhomogeneity through lack of process control become apparent. To benefit from
the advanced process control in industry-scale CMOS fabrication facilities,
different processing methods will be required. In particular, the double-angle
evaporation and lift-off techniques used for current, state-of-the art
superconducting qubits are generally incompatible with modern day
manufacturable processes. Here, we demonstrate a fully CMOS compatible qubit
fabrication method, and show results from overlap Josephson junction devices
with long coherence and relaxation times, on par with the state-of-the-art. We
experimentally verify that Argon milling - the critical step during junction
fabrication - and a subtractive etch process nevertheless result in qubits with
average qubit energy relaxation times T1 reaching 70 $\mu$s, with maximum
values exceeding 100 $\mu$s. Furthermore, we show that our results are still
limited by surface losses and not, crucially, by junction losses. The presented
fabrication process therefore heralds an important milestone towards a
manufacturable 300 mm CMOS process for high-coherence superconducting qubits
and has the potential to advance the scaling of superconducting device
architectures.


title:
Real-Time Scene Text Detection with Differentiable Binarization and
  Adaptive Scale Fusion

abstract:
  Recently, segmentation-based scene text detection methods have drawn
extensive attention in the scene text detection field, because of their
superiority in detecting the text instances of arbitrary shapes and extreme
aspect ratios, profiting from the pixel-level descriptions. However, the vast
majority of the existing segmentation-based approaches are limited to their
complex post-processing algorithms and the scale robustness of their
segmentation models, where the post-processing algorithms are not only isolated
to the model optimization but also time-consuming and the scale robustness is
usually strengthened by fusing multi-scale feature maps directly. In this
paper, we propose a Differentiable Binarization (DB) module that integrates the
binarization process, one of the most important steps in the post-processing
procedure, into a segmentation network. Optimized along with the proposed DB
module, the segmentation network can produce more accurate results, which
enhances the accuracy of text detection with a simple pipeline. Furthermore, an
efficient Adaptive Scale Fusion (ASF) module is proposed to improve the scale
robustness by fusing features of different scales adaptively. By incorporating
the proposed DB and ASF with the segmentation network, our proposed scene text
detector consistently achieves state-of-the-art results, in terms of both
detection accuracy and speed, on five standard benchmarks.


title:
Design of a Canted-cosine-theta orbit corrector for the High Luminosity
  LHC

abstract:
  The High Luminosity LHC requires dipole orbit correctors grouped in double
aperture magnet assemblies. They provide a field of 3.1 T at 100 A in an
aperture of 70 mm. The current standard design is a classical cosine-theta
layout made with ribbon cable. However, the electric insulation of the ribbon
cable is not radiation-resistant enough to withstand the radiation load
expected in the coming years of LHC operation. A new design, based on a
radiation-resistant cable with polyimide insulator, that can replace the
existing orbit correctors at their end-of-life, is needed. The challenge is to
design a magnet that fits directly into the existing positions and that can
operate with the same busbars, passive quench protection, and power supplies as
existing magnets. We propose a self-protected canted-cosine-theta (CCT) design.
We take the opportunity to explore new concepts for the CCT design to produce a
cost-effective and high-quality design with a more sustainable use of
resources. The new orbit corrector design meets high requirements on the field
quality while keeping within the same mechanical volume and maximum excitation
current.
  A collaboration of Swedish universities and Swedish industry has been formed
for the development and production of a prototype magnet following a concurrent
engineering (CE) methodology to reduce the time needed to produce a functional
CCT magnet. The magnet has a 1 m long CCT dipole layout consisting of two
coils. The superconductor is a commercially available 0.33 mm wire with
polyimide insulation in a 6-around-1 cable. The channels in the coil formers,
that determine the CCT layout, allow for 2 x 5 cable layers. A total of 70
windings makes that the coil current can be kept below 100 A. We will present
the detailed design and preliminary quench simulations.


title: Real-Analyticity of Generalized Sine Functions with Two Parameters

abstract:
  We identify the maximal real interval on which $\sin_{p,n}$ is real-analytic
for any real number $p>1$ and any integer $n>1$. We achieve this by first
proving that $\sin_{p,n}$ is analytic at $(1/2){\pi}_{p,n}$ iff $p=m/(m-1)$ for
some integer $m>1$, in which case we determine the radius of convergence of the
Taylor series at $(1/2){\pi}_{p,n}$.


title:
Temperature-related effects in PAMP process as a promising feature
  towards high $^3$He nuclear polarization

abstract:
  We report on further investigation of nuclear hyperpolarization of helium-3
in magnetized plasma at a magnetic field of 3.66 T and at different
experimental conditions in a dual cell of relatively large volume, compared to
those reported in the original PAMP (polarization of atoms in a magnetized
plasma) paper by Maul et al. [Phys. Rev. A. 98, 063405 (2018)]. It was found
that steady state nuclear polarizations obtained by this method strongly
increase with temperature and rf gas discharge power. We managed to reach
almost 8% at the highest possible temperature in our setup which is to date the
highest polarization obtained by this method at 10 mbar. It was also shown that
atomic oxygen in plasma, considered as an impurity, could be a good probe of
plasma parameters relevant for the PAMP process. The polarization build-up
rates with the cell temperature stabilized near room temperature are found to
be of the order of 1 s$^{-1}$ which is by two orders of magnitude faster than
was reported in original PAMP paper for the same filling pressure. These
observations promise a further optimization of experimental conditions towards
strong and fast hyperpolarization by this method.


title:
Multi-Agent Reinforcement Learning for Network Selection and Resource
  Allocation in Heterogeneous multi-RAT Networks

abstract:
  The rapid production of mobile devices along with the wireless applications
boom is continuing to evolve daily. This motivates the exploitation of wireless
spectrum using multiple Radio Access Technologies (multi-RAT) and developing
innovative network selection techniques to cope with such intensive demand
while improving Quality of Service (QoS). Thus, we propose a distributed
framework for dynamic network selection at the edge level, and resource
allocation at the Radio Access Network (RAN) level, while taking into
consideration diverse applications' characteristics. In particular, our
framework employs a deep Multi-Agent Reinforcement Learning (DMARL) algorithm,
that aims to maximize the edge nodes' quality of experience while extending the
battery lifetime of the nodes and leveraging adaptive compression schemes.
Indeed, our framework enables data transfer from the network's edge nodes, with
multi-RAT capabilities, to the cloud in a cost and energy-efficient manner,
while maintaining QoS requirements of different supported applications. Our
results depict that our solution outperforms state-of-the-art techniques of
network selection in terms of energy consumption, latency, and cost.


title: HoneyModels: Machine Learning Honeypots

abstract:
  Machine Learning is becoming a pivotal aspect of many systems today, offering
newfound performance on classification and prediction tasks, but this rapid
integration also comes with new unforeseen vulnerabilities. To harden these
systems the ever-growing field of Adversarial Machine Learning has proposed new
attack and defense mechanisms. However, a great asymmetry exists as these
defensive methods can only provide security to certain models and lack
scalability, computational efficiency, and practicality due to overly
restrictive constraints. Moreover, newly introduced attacks can easily bypass
defensive strategies by making subtle alterations. In this paper, we study an
alternate approach inspired by honeypots to detect adversaries. Our approach
yields learned models with an embedded watermark. When an adversary initiates
an interaction with our model, attacks are encouraged to add this predetermined
watermark stimulating detection of adversarial examples. We show that
HoneyModels can reveal 69.5% of adversaries attempting to attack a Neural
Network while preserving the original functionality of the model. HoneyModels
offer an alternate direction to secure Machine Learning that slightly affects
the accuracy while encouraging the creation of watermarked adversarial samples
detectable by the HoneyModel but indistinguishable from others for the
adversary.


title:
Slow relaxation and aging in the model of randomly connected cycles
  network

abstract:
  We propose a statistical model of a large random network with high
connectivity in order to describe the behavior of {\it E.\,coli} cells after
exposure to acute stress. The building blocks of this network are feedback
cycles typical of the genetic and metabolic networks of a cell. Each node on
the cycles is a spin degree of freedom representing a component in the cell's
network that can be in one of two states - active or inactive. The cycles are
interconnected by regulation or by the exchange of metabolites. Stress is
realized by an external magnetic field that drives the nodes into an inactive
state, and the time the magnetization passes zero value for the first time
represents the first division event of the cell after the stress period. The
numerical and analytical solutions for this first passage problem reproduce the
aging dynamics observed in the experimental data.


title:
$\mathrm{C}^*$-algebras associated to homeomorphisms twisted by vector
  bundles over finite dimensional spaces

abstract:
  In this paper we study Cuntz--Pimsner algebras associated to
$\mathrm{C}^*$-correspondences over commutative $\mathrm{C}^*$-algebras from
the point of view of the $\mathrm{C}^*$-algebra classification programme. We
show that when the correspondence comes from an aperiodic homeomorphism of a
finite-dimensional infinite compact metric space $X$ twisted by a vector
bundle, the resulting Cuntz--Pimsner algebras have finite nuclear dimension.
When the homeomorphism is minimal, this entails classification of these
$\mathrm{C}^*$-algebras by the Elliott invariant. This establishes a dichotomy:
when the vector bundle has rank one, the Cuntz--Pimsner algebra has stable rank
one. Otherwise, it is purely infinite.
  For a Cuntz--Pimsner algebra of a minimal homeomorphism of an infinite
compact metric space $X$ twisted by a line bundle over $X$, we introduce
orbit-breaking subalgebras. With no assumptions on the dimension of $X$, we
show that they are centrally large subalgebras and hence simple and stably
finite. When the dimension of $X$ is finite, they are furthermore
$\mathcal{Z}$-stable and hence classified by the Elliott invariant.


title:
Building prescribed quantitative orbit equivalence with the group of
  integers

abstract:
  Two groups are orbit equivalent if they both admit an action on a same
probability space that share the same orbits. In particular the Ornstein-Weiss
theorem implies that all infinite amenable groups are orbit equivalent to the
group of integers. To refine this notion between infinite amenable groups
Delabie, Koivisto, Le Ma\^itre and Tessera introduced a quantitative version of
orbit equivalence. They furthermore obtained obstructions to the existence of
such equivalence using the isoperimetric profile. In this article we offer to
answer the inverse problem (find a group being orbit equivalent to a prescribed
group with prescribed quantification) in the case of the group of integers
using the so called F{\o}lner tiling shifts introduced by Delabie et al. To do
so we use the diagonal products defined by Brieussel and Zheng giving groups
with prescribed isoperimetric profile.


title:
Next-Generation Local Time Stepping for the ADER-DG Finite Element
  Method

abstract:
  High-frequency ground motion simulations pose a grand challenge in
computational seismology. Two main factors drive this challenge. First, to
account for higher frequencies, we have to extend our numerical models, e.g.,
by considering anelasticity, or by including mountain topography. Second, even
if we were able to keep our models unchanged, simply doubling the frequency
content of a seismic wave propagation solver requires a sixteen-fold increase
in computational resources due to the used four-dimensional space-time domains.
  This work presents the Extreme Scale Discontinuous Galerkin Environment
(EDGE) in the context of high-frequency ground motion simulations. Our
presented enhancements cover the entire spectrum of the unstructured finite
element solver. This includes the incorporation of anelasticity, the
introduction of a next-generation clustered local time stepping scheme, and the
introduction of a completely revised communication scheme. We close the
modeling and simulation loop by presenting our new and rich preprocessing,
which drives the high problem-awareness and numerical efficiency of the core
solver.
  In summary, the presented work allows us to conduct large scale
high-frequency ground motion simulations efficiently, routinely and
conveniently. The soundness of our work is underlined by a set of
high-frequency verification runs using a realistic setting. We conclude the
presentation by studying EDGE's combined algorithmic and computational
efficiency in a demanding setup of the 2014 Mw 5.1 La Habra earthquake. Our
results are compelling and show an improved time-to-solution by over 10x while
scaling strongly from 256 to 1,536 nodes of the Frontera supercomputer with a
parallel efficiency of over 95%.


title: Time complexity of the Analyst's Traveling Salesman algorithm

abstract:
  The Analyst's Traveling Salesman Problem asks for conditions under which a
(finite or infinite) subset of $\mathbb{R}^N$ is contained on a curve of finite
length. We show that for finite sets, the algorithm constructed by Schul
(2007)and Badger-Naples-Vellis (2019) that solves the Analyst's Traveling
Salesman Problem has polynomial time complexity and we determine the sharp
exponent.


title: Photon-ALP interaction as a measure of initial photon polarization

abstract:
  Axion-like particles (ALPs) are very light, neutral, spin zero bosons
predicted by superstring theory. ALPs interact primarily with two photons and
in the presence of an external magnetic field they generate photon-ALP
oscillations and the change of the polarization state of photons. While well
motivated from a theoretical point of view, hints on ALP existence come from
astrophysics. In this paper, we state and demonstrate some theorems about a
strict relationship between initial photon polarization and photon-ALP
conversion probability - which can be extrapolated by observed astrophysical
spectra - so that, in the presence of ALPs, flux-measuring observatories become
also porarimeters.


title:
Measurement-Device-Independent Quantum Secure Direct Communication with
  User Authentication

abstract:
  Quantum secure direct communication (QSDC) and deterministic secure quantum
communication (DSQC) are two important branches of quantum cryptography, where
one can transmit a secret message securely without encrypting it by a prior
key. In the practical scenario, an adversary can apply detector-side-channel
attacks to get some non-negligible amount of information about the secret
message. Measurement-device-independent (MDI) quantum protocols can remove this
kind of detector-side-channel attack, by introducing an untrusted third party
(UTP), who performs all the measurements during the protocol with imperfect
measurement devices. In this paper, we put forward the first MDI-QSDC protocol
with user identity authentication, where both the sender and the receiver first
check the authenticity of the other party and then exchange the secret message.
Then we extend this to an MDI quantum dialogue (QD) protocol, where both the
parties can send their respective secret messages after verifying the identity
of the other party. Along with this, we also report the first MDI-DSQC protocol
with user identity authentication. Theoretical analyses prove the security of
our proposed protocols against common attacks.


title:
Diffusion approximation for a simple kinetic model with asymmetric
  interface

abstract:
  We study a diffusion approximation for a model of stochastic motion of a
particle in one spatial dimension. The velocity of the particle is constant but
the direction of the motion undergoes random changes with a Poisson clock.
Moreover, the particle interacts with an interface in such a way that it can
randomly be reflected, transmitted, or killed, and the corresponding
probabilities depend on whether the particle arrives at the interface from the
left, or right. We prove that the limit process is a minimal Brownian motion,
if the probability of killing is positive. In the case of no killing, the limit
is a skew Brownian motion.


title:
OpenRAN Gym: An Open Toolbox for Data Collection and Experimentation
  with AI in O-RAN

abstract:
  Open Radio Access Network (RAN) architectures will enable interoperability,
openness, and programmatic data-driven control in next generation cellular
networks. However, developing scalable and efficient data-driven algorithms
that can generalize across diverse deployments and optimize RAN performance is
a complex feat, largely unaddressed as of today. Specifically, the ability to
design efficient data-driven algorithms for network control and inference
requires at a minimum (i) access to large, rich, and heterogeneous datasets;
(ii) testing at scale in controlled but realistic environments, and (iii)
software pipelines to automate data collection and experimentation. To
facilitate these tasks, in this paper we propose OpenRAN Gym, a practical,
open, experimental toolbox that provides end-to-end design, data collection,
and testing workflows for intelligent control in next generation Open RAN
systems. OpenRAN Gym builds on software frameworks for the collection of large
datasets and RAN control, and on a lightweight O-RAN environment for
experimental wireless platforms. We first provide an overview of OpenRAN Gym
and then describe how it can be used to collect data, to design and train
artificial intelligence and machine learning-based O-RAN applications (xApps),
and to test xApps on a softwarized RAN. Then, we provide an example of two
xApps designed with OpenRAN Gym and used to control a large-scale network with
7 base stations and 42 users deployed on the Colosseum testbed. OpenRAN Gym and
its software components are open source and publicly-available to the research
community.


title: Metasurface Magnetless Specular Isolator

abstract:
  We present a (nongyrotropic) metasurface magnetless specular isolator. This
device reflects as a mirror a wave incident under a specified angle in one
direction and absorbs it in the opposite direction. The metasurface is
synthesized in terms of bianisotropic susceptibility tensors, whose
nonreciprocity resides in normal components and exhibits a hybrid electric,
magneto-electric nature. The metaparticle is implemented in the form of a
U-shaped conducting structure loaded by a transistor. The operation principle
of the specular isolator is demonstrated by both full-wave simulation and
experiment, with isolation levels reaching 41 and 38 dB respectively. This
system represents the first realization of a metasurface involving
nonreciprocal normal susceptibilities and features a previously unreported type
of nonreciprocity.


title:
Resurrecting Trust in Facial Recognition: Mitigating Backdoor Attacks in
  Face Recognition to Prevent Potential Privacy Breaches

abstract:
  Biometric data, such as face images, are often associated with sensitive
information (e.g medical, financial, personal government records). Hence, a
data breach in a system storing such information can have devastating
consequences. Deep learning is widely utilized for face recognition (FR);
however, such models are vulnerable to backdoor attacks executed by malicious
parties. Backdoor attacks cause a model to misclassify a particular class as a
target class during recognition. This vulnerability can allow adversaries to
gain access to highly sensitive data protected by biometric authentication
measures or allow the malicious party to masquerade as an individual with
higher system permissions. Such breaches pose a serious privacy threat.
Previous methods integrate noise addition mechanisms into face recognition
models to mitigate this issue and improve the robustness of classification
against backdoor attacks. However, this can drastically affect model accuracy.
We propose a novel and generalizable approach (named BA-BAM: Biometric
Authentication - Backdoor Attack Mitigation), that aims to prevent backdoor
attacks on face authentication deep learning models through transfer learning
and selective image perturbation. The empirical evidence shows that BA-BAM is
highly robust and incurs a maximal accuracy drop of 2.4%, while reducing the
attack success rate to a maximum of 20%. Comparisons with existing approaches
show that BA-BAM provides a more practical backdoor mitigation approach for
face recognition.


title: Moduli spaces of SUSY curves and their operads

abstract:
  This article is dedicated to the generalization of the operad of moduli
spaces of curves to SUSY curves. SUSY curves are algebraic curves with
additional supersymmetric or supergeometric structure. Here, we focus on the
description of the relevant category of graphs and its combinatorics as well as
the construction of dual graphs of SUSY curves and the supermodular operad
taking values in a category of moduli spaces of SUSY curves with Neveu-Schwarz
and Ramond punctures.


title: AF$_2$: Adaptive Focus Framework for Aerial Imagery Segmentation

abstract:
  As a specific semantic segmentation task, aerial imagery segmentation has
been widely employed in high spatial resolution (HSR) remote sensing images
understanding. Besides common issues (e.g. large scale variation) faced by
general semantic segmentation tasks, aerial imagery segmentation has some
unique challenges, the most critical one among which lies in
foreground-background imbalance. There have been some recent efforts that
attempt to address this issue by proposing sophisticated neural network
architectures, since they can be used to extract informative multi-scale
feature representations and increase the discrimination of object boundaries.
Nevertheless, many of them merely utilize those multi-scale representations in
ad-hoc measures but disregard the fact that the semantic meaning of objects
with various sizes could be better identified via receptive fields of diverse
ranges. In this paper, we propose Adaptive Focus Framework (AF$_2$), which
adopts a hierarchical segmentation procedure and focuses on adaptively
utilizing multi-scale representations generated by widely adopted neural
network architectures. Particularly, a learnable module, called Adaptive
Confidence Mechanism (ACM), is proposed to determine which scale of
representation should be used for the segmentation of different objects.
Comprehensive experiments show that AF$_2$ has significantly improved the
accuracy on three widely used aerial benchmarks, as fast as the mainstream
method.


title:
Conformal field theory at the critical point of monolayer-bilayer phases
  of subvalent cations in honeycomb layered materials

abstract:
  The rather intriguing experimental result that a wide class of silver
(Ag)-based layered materials form stable Ag bilayers, each comprising a pair of
triangular sub-lattices, suggests an unexplained bifurcation mechanism for the
honeycomb lattice. It is well-known that the honeycomb lattice of graphene
requires an additional degree of freedom to describe the orbital wave functions
sitting in two different triangular sub-lattices, known as pseudo-spin. In this
paper, we exploit the pseudo-spin degree of freedom in the Ag honeycomb lattice
to propose a bifurcation mechanism for the Ag triangular sub-lattices, which
ultimately requires conformal symmetry breaking within the context of an
idealized model, resulting in a cation monolayer-bilayer phase transition in
exemplars such as ${\rm Ag_2}M_2\rm TeO_6$ (where $M$ = Ni, Co, Cu, Mg, Zn). In
this description, the critical point of the phase transition is described by
Liouville conformal field theory. Our framework is consistent with the
following necessary and sufficient conditions for the observation of stable
bilayers of cations in layered materials: 1) observation of a pair of cation
triangular sub-lattices of the honeycomb lattice; 2) a bond between the
subvalent cation and an anion and; 3) existence of weakly attractive
cation-cation interactions related to allowed cation subvalent states. Since
other materials such as $\rm Tl_2MnTeO_6$ share the aforementioned conditions
with Ag-based systems, the theoretical framework herein also sheds light on the
nature of their bilayer structure, with noteworthy applications in the vast
field of analogue quantum gravity research.


title: VRL3: A Data-Driven Framework for Visual Deep Reinforcement Learning

abstract:
  We propose a simple but powerful data-driven framework for solving highly
challenging visual deep reinforcement learning (DRL) tasks. We analyze a number
of major obstacles in taking a data-driven approach, and present a suite of
design principles, training strategies, and critical insights about data-driven
visual DRL. Our framework has three stages: in stage 1, we leverage non-RL
datasets (e.g. ImageNet) to learn task-agnostic visual representations; in
stage 2, we use offline RL data (e.g. a limited number of expert
demonstrations) to convert the task-agnostic representations into more powerful
task-specific representations; in stage 3, we fine-tune the agent with online
RL. On a set of highly challenging hand manipulation tasks with sparse reward
and realistic visual inputs, our framework learns 370%-1200% faster than the
previous SOTA method while using an encoder that is 50 times smaller, fully
demonstrating the potential of data-driven deep reinforcement learning.


title:
Reducing the Gibbs effect in multimodal medical imaging by the Fake
  Nodes Approach

abstract:
  It is a common practice in multimodal medical imaging to undersample the
anatomically-derived segmentation images to measure the mean activity of a
co-acquired functional image. This practice avoids the resampling-related Gibbs
effect that would occur in oversampling the functional image. As sides effect,
waste of time and efforts are produced since the anatomical segmentation at
full resolution is performed in many hours of computations or manual work. In
this work we explain the commonly-used resampling methods and give errors bound
in the cases of continuous and discontinuous signals. Then we propose a Fake
Nodes scheme for image resampling designed to reduce the Gibbs effect when
oversampling the functional image. This new approach is compared to the
traditional counterpart in two significant experiments, both showing that Fake
Nodes resampling gives smaller errors.


title:
A Deep Learning Approach for Repairing Missing Activity Labels in Event
  Logs for Process Mining

abstract:
  Process mining is a relatively new subject that builds a bridge between
traditional process modeling and data mining. Process discovery is one of the
most critical parts of process mining, which aims at discovering process models
automatically from event logs. The performance of existing process discovery
algorithms can be affected when there are missing activity labels in event
logs. Several methods have been proposed to repair missing activity labels, but
their accuracy can drop when a large number of activity labels are missing. In
this paper, we propose an LSTM-based prediction model to predict the missing
activity labels in event logs. The proposed model takes both the prefix and
suffix sequences of the events with missing activity labels as input.
Additional attributes of event logs are also utilized to improve the
performance. Our evaluation of several publicly available datasets shows that
the proposed method performed consistently better than existing methods in
terms of repairing missing activity labels in event logs.


title:
Signatures of Majorana bound states and parity effects in
  two-dimensional chiral p-wave Josephson junctions

abstract:
  We characterize topological features of Josephson junctions formed by coupled
mesoscopic chiral $p$-wave superconducting islands. Through analytic and
numerical studies of the low-lying BdG (Bogoliubov-de Gennes) spectrum, we
identify localized MBS (Majorana bound states) nucleated in Josephson vortices
by the application of a perpendicular magnetic field. Additionally, we
demonstrate the existence of an extended MBS that is delocalized around the
outer perimeter of the coupled islands, which has measurable consequences on
the Josephson supercurrent and phase dynamics of the junction. In particular,
we predict a change in the critical current diffraction pattern in which the
odd integer-flux nodes are lifted in a fermion parity-dependent fashion. We
model the competing stochastic effects of thermal noise and macroscopic quantum
tunneling within the RCSJ framework and show the emergence of a bimodal
critical current distribution. We demonstrate that increasing the parity
transition rate suppresses the bimodal nature of the distribution, thus
strongly emphasizing the non-trivial parity dependent nature of the many-body
ground state. Finally, we consider a trijunction geometry with three islands
and discuss possible schemes to braid Majorana bound states by moving the
Josephson vortices to which they are bound.


title: Rewriting results in the language of compatibility

abstract:
  This is a reply to Muff, S. et al. (2022) Rewriting results sections in the
language of evidence, Trends in Ecology & Evolution 37, 203-210.


title: Least sum of squares of trimmed residuals regression

abstract:
  In the famous least sum of trimmed squares (LTS) of residuals estimator
(Rousseeuw (1984)), residuals are first squared and then trimmed. In this
article, we first trim residuals - using a depth trimming scheme - and then
square the rest of residuals. The estimator that can minimize the sum of
squares of the trimmed residuals, is called an LST estimator.
  It turns out that LST is also a robust alternative to the classic least sum
of squares (LS) of residuals estimator. Indeed, it has a very high finite
sample breakdown point and can resist, asymptotically, up to 50% contamination
without breakdown - in sharp contrast to the 0% of the LS estimator. The
population version of LST is Fisher consistent, and the sample version is
strong and root-n consistent under some conditions. Three approximate
algorithms for computing LST are proposed and tested in synthetic and real data
examples.
  These experiments indicate that two of the algorithms can compute the LST
estimator very fast and with relatively smaller variances, compared with that
of the famous LTS estimator. All the evidence suggests that LST deserves to be
a robust alternative to the LS estimator and is feasible in practice for large
data sets (with possible contamination and outliers) in high dimensions.


title:
The photochemical ring-opening reaction of 1,3-cyclohexadiene: complex
  dynamical evolution of the reactive state

abstract:
  The photochemically induced ring-opening isomerization reaction of
1,3-cyclohexadiene (CHD) to 1,3,5-hexatriene (HT) is a textbook example of a
pericyclic reaction, and has been amply investigated with advanced
spectroscopic techniques. The generally accepted description of the
isomerization pathway starts with a valence excitation to the lowest-lying
bright state, followed by a passage through a conical intersection to a dark
doubly excited state, and finally a branching between either the return to the
ground state of the cyclic molecule or the actual ring-opening reaction leading
to the open-chain isomer. It was traditionally assumed that the dark reactive
state corresponds to the second excited state of CHD at the Franck-Condon
geometry. Here in a joint experimental and computational effort we demonstrate
that the evolution of the excitation-deexcitation process is much more complex
than usually described. In particular, we show that an initially high-lying
electronic state smoothly decreasing in energy along the reaction path plays a
key role in the ring-opening reaction. The conceptual basis of our work is that
the dynamics to consider here is determined by diabatic states, whose
populations are the ones closely related to the observed photoelectron signal.


title: ODEs whose symmetry groups are not fiber-preserving

abstract:
  We observe that, up to conjugation, a majority of symmetric higher order ODEs
(ordinary differential equations) and ODE systems have only fiber-preserving
point symmetries. By exploiting Lie's classification of Lie algebras of vector
fields, we describe all the exceptions to this in the case of scalar ODEs and
systems of ODEs on a pair of functions.
  The scalar ODEs whose symmetry algebra is not fiber preserving can be
expressed via absolute and relative scalar differential invariants, while a
similar description for ODE systems requires us to also invoke conditional
differential invariants and vector-valued relative invariants to deal with
singular orbits of the action.
  Investigating prolongations of the actions, we observe some interesting
relations between different realizations of Lie algebras. We also note that it
may happen that the prolongation of a finite-dimensional Lie algebra acting on
a differential equation never becomes free. An example of an underdetermined
ODE system for which this phenomenon occurs shows limitations of the method of
moving frames.


title:
Manage risks in complex engagements by leveraging organization-wide
  knowledge using Machine Learning

abstract:
  One of the ways for organizations to continuously get better at executing
projects is to learn from their past experience. In large organizations, the
different accounts and business units often work in silos and tapping the rich
knowledge base across the organization is easier said than done. With easy
access to the collective experience spread across the organization, project
teams and business leaders can proactively anticipate and manage risks in new
engagements. Early discovery and timely management of risks is key to success
in the complex engagements of today. In this paper, the authors describe a
Machine Learning based solution deployed with MLOps principles to solve this
problem in an efficient manner.


title:
Understanding planetary context to enable life detection on exoplanets
  and test the Copernican principle

abstract:
  The search for life on exoplanets is motivated by the universal ways in which
life could modify its planetary environment. Atmospheric gases such as oxygen
and methane are promising candidates for such environmental modification due to
the evolutionary benefits their production would confer. However, confirming
that these gases are produced by life, rather than by geochemical or
astrophysical processes, will require a thorough understanding of planetary
context, including the expected counterfactual atmospheric evolution for
lifeless planets. Here, we evaluate current understanding of planetary context
for several candidate biosignatures and their upcoming observability. We review
the contextual framework for oxygen and describe how conjectured abiotic oxygen
scenarios may be testable. In contrast to oxygen, current understanding of how
planetary context controls non-biological methane (CH$_4$) production is
limited, even though CH$_4$ biosignatures in anoxic atmospheres may be readily
detectable with the James Webb Space Telescope. We assess environmental context
for CH$_4$ biosignatures and conclude that abundant atmospheric CH$_4$
coexisting with CO$_2$, and CO:CH$_4$ << 1 is suggestive of biological
production, although precise thresholds are dependent on stellar context and
sparsely characterized abiotic CH$_4$ scenarios. A planetary context framework
is also considered for alternative or agnostic biosignatures. Whatever the
distribution of life in the Universe, observations of terrestrial exoplanets in
coming decades will provide a quantitative understanding of the atmospheric
evolution of lifeless worlds. This knowledge will inform future instrument
requirements to either corroborate the presence of life elsewhere or confirm
its apparent absence.


title:
Theorems of Szeg\H{o}-Verblunsky type in the multivariate and almost
  periodic settings

abstract:
  The classical Szeg\H{o}-Verblunsky theorem relates integrability of the
logarithm of the absolutely continuous part of a probability measure on the
circle to square summability of the sequence of recurrence coefficients for the
orthogonal polynomials determined by the measure. The present paper constructs
orthogonal polynomials on the torus of arbitrary finite dimension in order to
prove theorems of Szeg\H{o}-Verblunsky type in the multivariate and almost
periodic settings. The results are applied to the one-dimensional Schr\"odinger
equation in impedance form to yield a new trace formula valid for piecewise
constant impedance, a case where the classical trace formula breaks down. As a
byproduct, the analysis gives an explicit formula for the Taylor coefficients
of a bounded holomorphic function on the open disk in terms of its continued
fraction expansion.


title: Explainability in Machine Learning: a Pedagogical Perspective

abstract:
  Given the importance of integrating of explainability into machine learning,
at present, there are a lack of pedagogical resources exploring this.
Specifically, we have found a need for resources in explaining how one can
teach the advantages of explainability in machine learning. Often pedagogical
approaches in the field of machine learning focus on getting students prepared
to apply various models in the real world setting, but much less attention is
given to teaching students the various techniques one could employ to explain a
model's decision-making process. Furthermore, explainability can benefit from a
narrative structure that aids one in understanding which techniques are
governed by which questions about the data.
  We provide a pedagogical perspective on how to structure the learning process
to better impart knowledge to students and researchers in machine learning,
when and how to implement various explainability techniques as well as how to
interpret the results. We discuss a system of teaching explainability in
machine learning, by exploring the advantages and disadvantages of various
opaque and transparent machine learning models, as well as when to utilize
specific explainability techniques and the various frameworks used to structure
the tools for explainability. Among discussing concrete assignments, we will
also discuss ways to structure potential assignments to best help students
learn to use explainability as a tool alongside any given machine learning
application.
  Data science professionals completing the course will have a birds-eye view
of a rapidly developing area and will be confident to deploy machine learning
more widely. A preliminary analysis on the effectiveness of a recently
delivered course following the structure presented here is included as evidence
supporting our pedagogical approach.


title: Artificial Intelligence for the Metaverse: A Survey

abstract:
  Along with the massive growth of the Internet from the 1990s until now,
various innovative technologies have been created to bring users breathtaking
experiences with more virtual interactions in cyberspace. Many virtual
environments with thousands of services and applications, from social networks
to virtual gaming worlds, have been developed with immersive experience and
digital transformation, but most are incoherent instead of being integrated
into a platform. In this context, metaverse, a term formed by combining meta
and universe, has been introduced as a shared virtual world that is fueled by
many emerging technologies, such as fifth-generation networks and beyond,
virtual reality, and artificial intelligence (AI). Among such technologies, AI
has shown the great importance of processing big data to enhance immersive
experience and enable human-like intelligence of virtual agents. In this
survey, we make a beneficial effort to explore the role of AI in the foundation
and development of the metaverse. We first deliver a preliminary of AI,
including machine learning algorithms and deep learning architectures, and its
role in the metaverse. We then convey a comprehensive investigation of AI-based
methods concerning six technical aspects that have potentials for the
metaverse: natural language processing, machine vision, blockchain, networking,
digital twin, and neural interface, and being potential for the metaverse.
Subsequently, several AI-aided applications, such as healthcare, manufacturing,
smart cities, and gaming, are studied to be deployed in the virtual worlds.
Finally, we conclude the key contribution of this survey and open some future
research directions in AI for the metaverse.


title: Integration of knowledge and data in machine learning

abstract:
  Scientific research's mandate is to comprehend and explore the world, as well
as to improve it based on experience and knowledge. Knowledge embedding and
knowledge discovery are two significant methods of integrating knowledge and
data. Through knowledge embedding, the barriers between knowledge and data can
be eliminated, and machine learning models with physical common sense can be
established. Meanwhile, humans' understanding of the world is always limited,
and knowledge discovery takes advantage of machine learning to extract new
knowledge from observations. Knowledge discovery can not only assist
researchers to better grasp the nature of physics, but it can also support them
in conducting knowledge embedding research. A closed loop of knowledge
generation and usage are formed by combining knowledge embedding with knowledge
discovery, which can improve the robustness and accuracy of models and uncover
previously unknown scientific principles. This study summarizes and analyzes
extant literature, as well as identifies research gaps and future
opportunities.


title:
UAV Base Station Trajectory Optimization Based on Reinforcement Learning
  in Post-disaster Search and Rescue Operations

abstract:
  Because of disaster, terrestrial base stations (TBS) would be partly crashed.
Some user equipments (UE) would be unserved. Deploying unmanned aerial vehicles
(UAV) as aerial base stations is a method to cover UEs quickly. But existing
methods solely refer to the coverage of UAVs. In those scenarios, they focus on
the deployment of UAVs in the post-disaster area where all TBSs do not work any
longer. There is limited research about the combination of available TBSs and
UAVs. We propose the method to deploy UAVs cooperating with available TBSs as
aerial base stations. And improve the coverage by reinforcement learning.
Besides, in the experiments, we cluster UEs with balanced iterative reducing
and clustering using hierarchies (BIRCH) at first. Finally, achieve base
stations' better coverage to UEs through Q-learning.


title:
Exploring Human Mobility for Multi-Pattern Passenger Prediction: A Graph
  Learning Framework

abstract:
  Traffic flow prediction is an integral part of an intelligent transportation
system and thus fundamental for various traffic-related applications. Buses are
an indispensable way of moving for urban residents with fixed routes and
schedules, which leads to latent travel regularity. However, human mobility
patterns, specifically the complex relationships between bus passengers, are
deeply hidden in this fixed mobility mode. Although many models exist to
predict traffic flow, human mobility patterns have not been well explored in
this regard. To reduce this research gap and learn human mobility knowledge
from this fixed travel behaviors, we propose a multi-pattern passenger flow
prediction framework, MPGCN, based on Graph Convolutional Network (GCN).
Firstly, we construct a novel sharing-stop network to model relationships
between passengers based on bus record data. Then, we employ GCN to extract
features from the graph by learning useful topology information and introduce a
deep clustering method to recognize mobility patterns hidden in bus passengers.
Furthermore, to fully utilize Spatio-temporal information, we propose GCN2Flow
to predict passenger flow based on various mobility patterns. To the best of
our knowledge, this paper is the first work to adopt a multipattern approach to
predict the bus passenger flow from graph learning. We design a case study for
optimizing routes. Extensive experiments upon a real-world bus dataset
demonstrate that MPGCN has potential efficacy in passenger flow prediction and
route optimization.


title: Darwin Among the Cryptocurrencies

abstract:
  The paper highlights some commonalities between the development of
cryptocurrencies and the evolution of ecosystems. Concepts from evolutionary
finance embedded in toy models consistent with stylized facts are employed to
understand what survival of the fittest means in cryptofinance. Stylized facts
for ownership, trading volume and market capitalization of cryptocurrencies are
selectively presented in terms of scaling laws.


title:
Efficient Learning of Safe Driving Policy via Human-AI Copilot
  Optimization

abstract:
  Human intervention is an effective way to inject human knowledge into the
training loop of reinforcement learning, which can bring fast learning and
ensured training safety. Given the very limited budget of human intervention,
it remains challenging to design when and how human expert interacts with the
learning agent in the training. In this work, we develop a novel
human-in-the-loop learning method called Human-AI Copilot Optimization
(HACO).To allow the agent's sufficient exploration in the risky environments
while ensuring the training safety, the human expert can take over the control
and demonstrate how to avoid probably dangerous situations or trivial
behaviors. The proposed HACO then effectively utilizes the data both from the
trial-and-error exploration and human's partial demonstration to train a
high-performing agent. HACO extracts proxy state-action values from partial
human demonstration and optimizes the agent to improve the proxy values
meanwhile reduce the human interventions. The experiments show that HACO
achieves a substantially high sample efficiency in the safe driving benchmark.
HACO can train agents to drive in unseen traffic scenarios with a handful of
human intervention budget and achieve high safety and generalizability,
outperforming both reinforcement learning and imitation learning baselines with
a large margin. Code and demo videos are available at:
https://decisionforce.github.io/HACO/.


title: Electrically controlled heat transport in multilayer graphene

abstract:
  The ability to control heat transport with electrical signals has been an
outstanding challenge due to the lack of efficient electrothermal materials.
Previous attempts have mainly concentrated on phase-change and layered
materials and encountered various problems such as low thermal conductivities
and modest on/off ratios. Here, we demonstrate a graphene-based electrothermal
switch enabling electrically tuneable heat flow. The device uses reversible
electro-intercalation of ions to modulate the in-plane thermal conductivity of
graphene film by over thirteen-fold via electrically tuneable phonon
scattering. We anticipate that our results could provide a realistic pathway
for adaptive thermal transport enabling a new class of electrically driven
thermal devices which would find a broad spectrum of applications in aerospace
and microelectronics.


title:
Higgs boson production at the LHC: fast and precise predictions in QCD
  at higher orders

abstract:
  We present a new numerical program, HTurbo, which provides fast and
numerically precise predictions for Higgs boson production cross sections. The
present version of the code implements the perturbative QCD expansion up to the
next-to-next-to-leading order also combined with the resummation of the large
logarithmic corrections at small transverse momenta up to
next-to-next-to-leading logarithmic accuracy and it includes the Higgs boson
production through gluon fusion and decay in two photons with the full
dependence on the final-state kinematics. Arbitrary kinematical cuts can be
applied to the final states in order to obtain fiducial cross sections and
associated kinematical distributions. We present a benchmark comparison with
the predictions obtained with the numerical programs HRes and HNNLO programs
for which HTurbo represents an improved reimplementation.


title:
Commuting to work and gender-conforming social norms: evidence from
  same-sex couples

abstract:
  We analyze work commute time by sexual orientation of partnered or married
individuals, using the American Community Survey 2008-2019. Women in same-sex
couples have a longer commute to work than working women in different-sex
couples, whereas the commute to work of men in same-sex couples is shorter than
the one of working men in different-sex couples, also after controlling for
demographic characteristics, partner characteristics, location, fertility, and
marital status. These differences are particularly stark among married couples
with children: on average, about 3 minutes more one-way to work for married
mothers in same-sex couples, and almost 2 minutes less for married fathers in
same-sex couples, than their corresponding working parents in different-sex
couples. These gaps among men and women amount to 50 percent, and 100 percent,
respectively, of the gender commuting gap estimated in the literature.
Within-couple gaps in commuting time are also significantly smaller in same-sex
couples. We interpret these differences as evidence that it is
gender-conforming social norms boosted by parenthood that lead women in
different-sex couples to specialize into jobs with a shorter commute while
their male partners or spouses hold jobs with a longer commute.


title: Core Electron Heating By Triggered Ion Acoustic Waves In The Solar Wind

abstract:
  Perihelion passes on Parker Solar Probe orbits six through nine have been
studied to show that solar wind core electrons emerged from 15 solar radii with
a temperature of 55 plus or minus 5 eV, independent of the solar wind speed
which varied from 300 to 800 km/sec. After leaving 15 solar radii and in the
absence of triggered ion acoustic waves at greater distances, the core electron
temperature varied with radial distance, R, in solar radii, as 1900R-4/3
electron volts because of cooling produced by the adiabatic expansion. The
coefficient, 1900, reproduces the minimum core electron perpendicular
temperature observed during the 25 days of observation. In the presence of
triggered ion acoustic waves, the core electrons were isotropically heated as
much as a factor of two above the minimum temperature, 1900R-4/3 eV. Triggered
ion acoustic waves were the only waves observed in coincidence with the
electron core heating. They are the dominant wave mode at frequencies greater
than 100 Hz at solar distances between 15 and 30 solar radii.


title: On the Evaluation of RGB-D-based Categorical Pose and Shape Estimation

abstract:
  Recently, various methods for 6D pose and shape estimation of objects have
been proposed. Typically, these methods evaluate their pose estimation in terms
of average precision, and reconstruction quality with chamfer distance. In this
work we take a critical look at this predominant evaluation protocol including
metrics and datasets. We propose a new set of metrics, contribute new
annotations for the Redwood dataset and evaluate state-of-the-art methods in a
fair comparison. We find that existing methods do not generalize well to
unconstrained orientations, and are actually heavily biased towards objects
being upright. We contribute an easy-to-use evaluation toolbox with
well-defined metrics, method and dataset interfaces, which readily allows
evaluation and comparison with various state-of-the-art approaches (see
https://github.com/roym899/pose_and_shape_evaluation ).


title:
The geographic proximity effect on domestic cross-sector vis-a-vis
  intra-sector research collaborations

abstract:
  Geographic proximity is acknowledged to be a key factor in research
collaborations. Specifically, it can work as a possible substitute for
institutional proximity. The present study investigates the relevance of the
"proximity" effect for different types of national research collaborations. We
apply a bibliometric approach based on the Italian 2010-2017 scientific
production indexed in the Web of Science. On such dataset, we apply statistical
tools for analyzing if and to what extent geographical distance between
co-authors in the byline of a publication varies across collaboration types,
scientific disciplines, and along time. Results can inform policies aimed at
effectively stimulating cross-sector collaborations, and also bear direct
practical implications for research performance assessments.


title:
Estimation of Evaporator Valve Sizes in Supermarket Refrigeration
  Cabinets

abstract:
  In many applications, e.g. fault diagnostics and optimized control of
supermarket refrigeration systems, it is important to determine the heat demand
of the cabinets. This can easily be achieved by measuring the mass flow through
each cabinet, however, that is expensive and not feasible in large-scale
deployments. Therefore it is important to be able to estimate the valve sizes
from the monitoring data, which is typically measured. The valve size is
measured by an area, which can be used to calculate mass flow through the valve
-- this estimated value is referred to as the valve constant. A novel method
for estimating the cabinet evaporator valve constants is proposed in the
present paper. It is demonstrated using monitoring data from a refrigeration
system in a supermarket consisting of data sampled at a one-minute sampling
rate, however it is shown that a sampling time of around 10-20 minutes is
adequate for the method. Through thermodynamic analysis of a two-stage CO2
refrigeration system, a linear regression model for estimating valve constants
was developed using time series data. The linear regression requires that
transient dynamics are not present in the data, which depends on multiple
factors e.g. the sampling time. If dynamics are not modeled it can be detected
by a significant auto-correlation of the residuals. In order to include the
dynamics in the model, an Auto-Regressive Moving Average model with eXogenous
variables (ARMAX) was applied, and it is shown how it effectively eliminates
the auto-correlation and provides more unbiased estimates, as well as improved
the accuracy estimates. Furthermore, it is shown that the sample time has a
huge impact on the valve estimates. Thus, a method for selecting the optimal
sampling time is introduced. It works individually for each of the evaporators,
by exploring their respective frequency spectrum.


title: Certified Verification of Relational Properties

abstract:
  The use of function contracts to specify the behavior of functions often
remains limited to the scope of a single function call. Relational properties
link several function calls together within a single specification. They can
express more advanced properties of a given function, such as non-interference,
continuity, or monotonicity. They can also relate calls to different functions,
for instance, to show that an optimized implementation is equivalent to its
original counterpart. However, relational properties cannot be expressed and
verified directly in the traditional setting of modular deductive verification.
Self-composition has been proposed to overcome this limitation, but it requires
complex transformations and additional separation hypotheses for real-life
languages with pointers. We propose a novel approach that is not based on code
transformation and avoids those drawbacks. It directly applies a verification
condition generator to produce logical formulas that must be verified to ensure
a given relational property. The approach has been fully formalized and proved
sound in the Coq proof assistant.


title:
Mixed finite element method for a beam equation with the p-biharmonic
  operator

abstract:
  In this paper, we consider a nonlinear beam equation with the p-biharmonic
operator, where $1 < p < \infty$. Using a change of variable, we transform the
problem into a system of differential equations and prove the existence,
uniqueness and regularity of the weak solution by applying the Lax-Milgram
theorem and classical results of functional analysis. We investigate the
discrete formulation for that system and, with the aid of the Brouwer theorem,
we show that the problem has a discrete solution. The uniqueness and stability
of the discrete solution are obtained through classical methods. After
establishing the order of convergence, we apply the mixed finite element method
to obtain an algebraic system of equations. Finally, we implement the
computational codes in Matlab software and perform the comparison between
theory and simulations.


title: Three-body relative equilibria on $\mathbb{S}^2$ I: Euler configurations

abstract:
  Using the properties of the angular momentum, we develop a new geometrical
technique to study relative equilibria for a system of $3$--bodies with
positive masses, moving on the two sphere under the influence of an attractive
potential depending only on the mutual distances among the bodies. With the
above techniques we do an analysis of the relative equilibria for the case of
three bodies when they are moving on the same geodesic (Euler configurations).


title: PAQMAN: A Principled Approach to Active Queue Management

abstract:
  Active Queue Management (AQM) aims to prevent bufferbloat and serial drops in
router and switch FIFO packet buffers that usually employ drop-tail queueing.
AQM describes methods to send proactive feedback to TCP flow sources to
regulate their rate using selective packet drops or markings. Traditionally,
AQM policies relied on heuristics to approximately provide Quality of Service
(QoS) such as a target delay for a given flow. These heuristics are usually
based on simple network and TCP control models together with the monitored
buffer filling. A primary drawback of these heuristics is that their way of
accounting flow characteristics into the feedback mechanism and the
corresponding effect on the state of congestion are not well understood. In
this work, we show that taking a probabilistic model for the flow rates and the
dequeueing pattern, a Semi-Markov Decision Process (SMDP) can be formulated to
obtain an optimal packet dropping policy. This policy-based AQM, denoted
PAQMAN, takes into account a steady-state model of TCP and a target delay for
the flows. Additionally, we present an inference algorithm that builds on TCP
congestion control in order to calibrate the model parameters governing
underlying network conditions. Finally, we evaluate the performance of our
approach using simulation compared to state-of-the-art AQM algorithms.


title: Polarized Hodge Structures for Clemens Manifolds

abstract:
  Let $X$ be a Calabi-Yau threefold. A conifold transition first contracts $X$
along disjoint rational curves with normal bundles of type $(-1,-1)$, and then
smooth the resulting singular complex space $\bar{X}$ to a new compact complex
manifold $Y$. Such $Y$ is called a Clemens manifold and can be non-K\"{a}hler.
We prove that any small smoothing $Y$ of $\bar{X}$ satisfies
$\partial\bar{\partial}$-lemma. We also show that the resulting pure Hodge
structure of weight three on $H^3(Y)$ is polarized by the cup product. These
results answer some questions of R. Friedman.


title: Cyber-Physical Defense in the Quantum Era

abstract:
  Networked-Control Systems (NCSs), a type of cyber-physical systems, consist
of tightly integrated computing, communication and control technologies. While
being very flexible environments, they are vulnerable to computing and
networking attacks. Recent NCSs hacking incidents had major impact. They call
for more research on cyber-physical security. Fears about the use of quantum
computing to break current cryptosystems make matters worse. While the quantum
threat motivated the creation of new disciplines to handle the issue, such as
post-quantum cryptography, other fields have overlooked the existence of
quantum-enabled adversaries. This is the case of cyber-physical defense
research, a distinct but complementary discipline to cyber-physical protection.
Cyber-physical defense refers to the capability to detect and react in response
to cyber-physical attacks. Concretely, it involves the integration of
mechanisms to identify adverse events and prepare response plans, during and
after incidents occur. In this paper, we make the assumption that the
eventually available quantum computer will provide an advantage to adversaries
against defenders, unless they also adopt this technology. We envision the
necessity for a paradigm shift, where an increase of adversarial resources
because of quantum supremacy does not translate into higher likelihood of
disruptions. Consistently with current system design practices in other areas,
such as the use of artificial intelligence for the reinforcement of attack
detection tools, we outline a vision for next generation cyber-physical defense
layers leveraging ideas from quantum computing and machine learning. Through an
example, we show that defenders of NCSs can learn and improve their strategies
to anticipate and recover from attacks.


title:
Gaussian quantum metrology for mode-encoded parameters: general theory
  and imaging applications

abstract:
  Quantum optical metrology aims to identify ultimate sensitivity bounds for
the estimation of parameters econded into quantum states of the electromagnetic
field. In many practical applications, including imaging, microscopy, and
remote sensing, the parameter of interest is not only encoded in the quantum
state of the field, but also in its spatio-temporal distribution, i.e. in its
mode structure. In this mode-encoded parameter estimation setting, we derive an
analytical expression for the quantum Fisher information valid for arbitrary
multimode Gaussian fields. To illustrate the power of our approach, we apply
our results to the estimation of the transverse separation between two point
sources observed with a diffraction-limited imaging system. In this context, we
determine the ultimate resolution provided by mutual coherence and squeezing of
the sources.


title: A Geometrical Interpretation of Okubo Spin Group

abstract:
  In this work we define, for the first time, the affine and projective plane
over the real Okubo algebra, showing a concrete geometrical interpretation of
its Spin group. Okubo algebra is a flexible, composition algebra which is also
a not unital division algebra. Even though Okubo algebra has been known for
more than 40 years, we believe that this is the first time the algebra was used
for affine and projective geometry. After showing that all axioms of affine
geometry are verified, we define a projective plane over Okubo algebra as
completion of the affine plane and directly through the use of Veronese
coordinates. We then present a bijection between the two constructions. Finally
we show a geometric interpretation of Spin(O) as the group of collineations
that preserve the axis of the plane.


title: Toric surfaces with symmetries by reflections

abstract:
  Let $W$ be a reflection group in a plane and $P$ a rational polygon that is
invariant under the $W$-action. The action of $W$ on $P$ induces a $W$-action
on the toric variety $X_P$ associated with $P$. In this paper, we study the
$W$-representation on the cohomology $H^\ast(X_P)$ and show that the invariant
subring $H^\ast(X_P)^W$ is isomorphic to the cohomology ring of the toric
variety associated with the \emph{fundamental region}~$P/W$. As an example, we
provide an explicit description of the main result for the case of the toric
variety associated with the fan of Weyl chambers of type $G_2$.


title:
Efimov resonance position near a narrow Feshbach resonance in
  $^6$Li-$^{133}$Cs mixture

abstract:
  In the vicinity of a narrow Feshbach resonances Efimov features are expected
to be characterized by the resonance's properties rather than the van der Waals
length of the interatomic potential. Although this theoretical prediction is
well-established by now, it still lacks experimental confirmation. Here, we
apply our recently developed three-channel model [Yudkin and Khaykovich, Phys.
Rev. A 103, 063303 (2021)] to the experimental result obtained in a
mass-imbalanced $^6$Li-$^{133}$Cs mixture in the vicinity of the narrowest
resonance explored to date [Johansen at. al. Nat. Phys. 13, 731 (2017)]. We
confirm that the observed position of the Efimov resonance is dictated mainly
by the resonance physics while the influence of the van der Waals tail of the
interatomic potential is minor. We show that the resonance position is strongly
influenced by the presence of another Feshbach resonance which significantly
alters the effective background scattering length at the narrow resonance
position.


title: Six-dimensional light-front Wigner distribution of hadrons

abstract:
  We propose a six-dimensional light-front Wigner distribution for the complete
description of partonic structures of a hadron such as pion and proton, taking
advantage of the recently proposed light-front variable $\tilde{z}$ by Miller
and Brodsky. Quantities derived from the Wigner distribution contain the most
general information of partonic structures, including also new quantities
correlating longitudinal coordinate with transverse momenta or transverse
coordinates, together with spins. The new Wigner distribution can be viewed as
a relativistic version of the original Wigner distribution in hadron physics
and an extension of widely utilized five-dimensional light-front Wigner
distribution.


title:
Self-Supervised Bulk Motion Artifact Removal in Optical Coherence
  Tomography Angiography

abstract:
  Optical coherence tomography angiography (OCTA) is an important imaging
modality in many bioengineering tasks. The image quality of OCTA, however, is
often degraded by Bulk Motion Artifacts (BMA), which are due to micromotion of
subjects and typically appear as bright stripes surrounded by blurred areas.
State-of-the-art methods usually treat BMA removal as a learning-based image
inpainting problem, but require numerous training samples with nontrivial
annotation. In addition, these methods discard the rich structural and
appearance information carried in the BMA stripe region. To address these
issues, in this paper we propose a self-supervised content-aware BMA removal
model. First, the gradient-based structural information and appearance feature
are extracted from the BMA area and injected into the model to capture more
connectivity. Second, with easily collected defective masks, the model is
trained in a self-supervised manner, in which only the clear areas are used for
training while the BMA areas for inference. With the structural information and
appearance feature from noisy image as references, our model can remove larger
BMA and produce better visualizing result. In addition, only 2D images with
defective masks are involved, hence improving the efficiency of our method.
Experiments on OCTA of mouse cortex demonstrate that our model can remove most
BMA with extremely large sizes and inconsistent intensities while previous
methods fail.


title: A Predictive Approach to Bayesian Nonparametric Survival Analysis

abstract:
  Bayesian nonparametric methods are a popular choice for analysing survival
data due to their ability to flexibly model the distribution of survival times.
These methods typically employ a nonparametric prior on the survival function
that is conjugate with respect to right-censored data. Eliciting these priors,
particularly in the presence of covariates, can be challenging and inference
typically relies on computationally intensive Markov chain Monte Carlo schemes.
In this paper, we build on recent work that recasts Bayesian inference as
assigning a predictive distribution on the unseen values of a population
conditional on the observed samples, thus avoiding the need to specify a
complex prior. We describe a copula-based predictive update which admits a
scalable sequential importance sampling algorithm to perform inference that
properly accounts for right-censoring. We provide theoretical justification
through an extension of Doob's consistency theorem and illustrate the method on
a number of simulated and real data sets, including an example with covariates.
Our approach enables analysts to perform Bayesian nonparametric inference
through only the specification of a predictive distribution.


title:
Resummation of small-x double logarithms in QCD: inclusive
  deep-inelastic scattering

abstract:
  We present a comprehensive study of high-energy double logarithms in
inclusive DIS. They appear parametrically as alpha_s^n ln^{2n-k} x at the n-th
order in perturbation theory in the splitting functions for the parton
evolution and the coefficient functions for the hard scattering process, and
represent the leading corrections at small $x$ in the flavour non-singlet case.
We perform their resummation, in terms of modified Bessel functions, to all
orders in full QCD up to NNLL accuracy, and partly to N^3LL and beyond in the
large-n_c limit, and provide fixed-order expansions up to five loops. In the
flavour-singlet sector, where these double logarithms are sub-dominant at small
x compared to single-logarithmic alpha_s^n x^{-1} ln^{n-k} x BFKL
contributions, we construct fixed-order expansions up to five loops at NNLL
accuracy in full QCD. The results elucidate the analytic small-x structure
underlying inclusive DIS results in fixed-order perturbation theory and provide
important information for present and future numerical and analytic
calculations of these quantities.


title:
Performance of dense wireless networks in 5G and beyond using stochastic
  geometry

abstract:
  Device density in cellular networks is expected to increase considerably in
the next future. Accordingly, the access point (AP) will equip massive
multiple-input multiple-output (mMIMO) antennas, using collimated
millimeter-wave (mmW) and sub-THz communications, and increase the bandwidth to
accommodate the growing data rate demands. In this scenario, interference plays
a critical role and, if not characterized and mitigated properly, might limit
the performances of the network. In this context, this paper derives the
statistical properties of the aggregated interference power for a cellular
network equipping a mMIMO cylindrical array. The proposed statistical model
considers the link blockage and other network parameters such as antenna
configuration and device density. The findings show that the characteristic
function (CF) of the aggregated interference power can be regarded as a
weighted mixture of two alpha-stable distributions. Furthermore, by analyzing
the service probability, it is found that there is an optimal configuration of
the array depending on the AP height and device density. The proposed
statistical model can be part of the design of dense networks providing
valuable insights for optimal network deployment


title:
Solving Stochastic Optimization by Newton-type methods with
  Dimension-Adaptive Sparse Grid Quadrature

abstract:
  Stochastic optimisation problems minimise expectations of random cost
functions. We use 'optimise then discretise' method to solve stochastic
optimisation. In our approach, accurate quadrature methods are required to
calculate the objective, gradient or Hessian which are in fact integrals. We
apply the dimension-adaptive sparse grid quadrature to approximate these
integrals when the problem is high dimensional. Dimension-adaptive sparse grid
quadrature shows high accuracy and efficiency in computing an integral with a
smooth integrand. It is a kind of generalisation of the classical sparse grid
method, which refines different dimensions according to their importance. We
show that the dimension-adaptive sparse grid quadrature has better performance
in the optimise then discretise' method than the 'discretise then optimise'
method.


title: A proof of Frankl's conjecture on cross-union families

abstract:
  The families $\mathcal F_0,\ldots,\mathcal F_s$ of $k$-element subsets of
$[n]:=\{1,2,\ldots,n\}$ are called cross-union if there is no choice of $F_0\in
\mathcal F_0, \ldots, F_s\in \mathcal F_s$ such that $F_0\cup\ldots\cup
F_s=[n]$. A natural generalization of the celebrated Erd\H{o}s--Ko--Rado
theorem, due to Frankl and Tokushige, states that for $n\le (s+1)k$ the
geometric mean of $\lvert \mathcal F_i\rvert$ is at most $\binom{n-1}{k}$.
Frankl conjectured that the same should hold for the arithmetic mean under some
mild conditions. We prove Frankl's conjecture in a strong form by showing that
the unique (up to isomorphism) maximizer for the arithmetic mean of cross-union
families is the natural one $\mathcal F_0=\ldots=\mathcal F_s={[n-1]\choose
k}$.


title:
Thermal Conductivity and Theory of Inelastic Scattering of Phonons by
  Collective Fluctuations

abstract:
  We study the intrinsic scattering of phonons by a general quantum degree of
freedom, i.e. a fluctuating "field" $Q$, which may have completely general
correlations, restricted only by unitarity and translational invariance. From
the induced scattering rates, we obtain the consequences on the thermal
conductivity tensor of the phonons. We find that the lowest-order diagonal
scattering rate, which determines the longitudinal conductivity, is controlled
by two-point correlation functions of the $Q$ field, while the off-diagonal
scattering rates involve a minimum of three to four point correlation
functions. We obtain general and explicit forms for these correlations which
isolate the contributions to the Hall conductivity, and provide a general
discussion of the implications of symmetry and equilibrium. We evaluate these
two- and four-point correlation functions and hence the thermal transport for
the illustrative example of an ordered two dimensional antiferromagnet. In this
case the $Q$ field is a composite of magnon operators arising from spin-lattice
coupling. A numerical evaluation of the required integrals demonstrates that
the results satisfy all the necessary symmetry restrictions but otherwise lead
to non-vanishing scattering and Hall effects, and in particular that this
mechanism leads to comparable thermal Hall conductivity for thermal currents
within and normal to the plane of the antiferromagnetism.


title:
Statistical Relational Artificial Intelligence with Relative
  Frequencies: A Contribution to Modelling and Transfer Learning across Domain
  Sizes

abstract:
  Dependencies on the relative frequency of a state in the domain are common
when modelling probabilistic dependencies on relational data. For instance, the
likelihood of a school closure during an epidemic might depend on the
proportion of infected pupils exceeding a threshold. Often, rather than
depending on discrete thresholds, dependencies are continuous: for instance,
the likelihood of any one mosquito bite transmitting an illness depends on the
proportion of carrier mosquitoes. Current approaches usually only consider
probabilities over possible worlds rather than over domain elements themselves.
We introduce two formalisms that explicitly incorporate relative frequencies
into statistical relational artificial intelligence. The first formalism,
Lifted Bayesian Networks for Conditional Probability Logic, expresses discrete
dependencies on probabilistic data. The second formalism, Functional Lifted
Bayesian Networks, expresses continuous dependencies. Incorporating relative
frequencies is not only beneficial to modelling; it also provides a more
rigorous approach to learning problems where training and test or application
domains have different sizes. To this end, we provide a representation of the
asymptotic probability distributions induced by the two formalisms on domains
of increasing sizes. Since that representation has well-understood scaling
behaviour across domain sizes, it can be used to estimate parameters for a
large domain consistently from randomly sampled subpopulations.


title: Optically resonant all-dielectric diabolo nanodisks

abstract:
  Optically resonant all-dielectric nanostructures attractively exhibit reduced
losses compared to their plasmonic counterparts; however, achieving strong
field enhancements at the nanoscale, especially within solid-state media, has
remained a significant challenge. In this work, we demonstrate how
subwavelength modifications to a conventional silicon nanodisk enable strong
sub-diffractive and polarization dependent field enhancements in devices
supporting anapole-like modes. We examine the electromagnetic properties of
both individual and arrayed diabolo nanodisks, which are found to exhibit
|E|$^2$/|E$_0$|$^2$ enhancements in the range ~10$^2$-10$^4$, in the high index
medium, depending on geometrical considerations. In addition to supporting a
localized electric field hot-spot similar to those predicted in diabolo
nanostructured photonic crystal cavities and waveguide designs, we identify an
anti-diabolo effect leading to a broadband cold-spot for the orthogonal
polarization. These findings offer the prospect of enhancing or manipulating
light-matter interactions at the nanoscale within an all-dielectric (metal
free) platform for potential applications ranging from nonlinear optics to
quantum light sources, nano-sensing, nanoparticle-manipulation and
active/tunable metasurfaces.


title: Rotating Spirals in segregated reaction-diffusion systems

abstract:
  We give a complete characterization of the boundary traces $\varphi_i$
($i=1,\dots,K$) supporting spiraling waves, rotating with a given angular speed
$\omega$, which appear as singular limits of competition-diffusion systems of
the type \[
  \frac{\partial}{\partial t} u_i -\Delta u_i = \mu u_i -\beta u_i \sum_{j \neq
i} a_{ij} u_j \text{ in } \Omega \times\mathbb{R}^+, \qquad
  u_i = \varphi_i \text{ on $\partial\Omega\times\mathbb{R}^+$}, \qquad
  u_i(\mathbf{x},0) = u_{i,0}(\mathbf{x}) \text{ for $\mathbf{x} \in \Omega$}
\] as $\beta\to +\infty$. Here $\Omega$ is a rotationally invariant planar set
and $a_{ij}>0$ for every $i$ and $j$. We tackle also the homogeneous Dirichlet
and Neumann boundary conditions, as well as entire solutions in the plane. As a
byproduct of our analysis we detect explicit families of eternal, entire
solutions of the pure heat equation, parameterized by $\omega\in\mathbb{R}$,
which reduce to homogeneous harmonic polynomials for $\omega=0$.


title: Beyond the Erd\H{o}s discrepancy problem in function fields

abstract:
  We characterize the limiting behavior of partial sums of multiplicative
functions $f:\mathbb{F}_q[t]\to S^1$. In contrast to the number field setting,
the characterization depends crucially on whether the notion of discrepancy is
defined using long intervals, short intervals, or lexicographic intervals.
  Concerning the notion of short interval discrepancy, we show that a
completely multiplicative $f:\mathbb{F}_q[t]\to\{-1,+1\}$ with $q$ odd has
bounded short interval sums if and only if $f$ coincides with a "modified"
Dirichlet character to a prime power modulus. This confirms the function field
version of a conjecture over $\mathbb{Z}$ that such modified characters are
extremal with respect to the growth rate of partial sums.
  Regarding the lexicographic discrepancy, we prove that the discrepancy of a
completely multiplicative sequence is always infinite if we define it using a
natural lexicographic ordering of $\mathbb{F}_{q}[t]$. This answers a question
of Liu and Wooley.
  Concerning the long sum discrepancy, it was observed by the Polymath 5
collaboration that the Erd\H{o}s discrepancy problem admits infinitely many
completely multiplicative counterexamples on $\mathbb{F}_q[t]$. Nevertheless,
we are able to classify the counterexamples if we restrict to the class of
modified Dirichlet characters. In this setting, we determine the precise growth
rate of the discrepancy, which is still unknown for the analogous problem over
the integers.


title:
Coordinated Sum-Rate Maximization in Multicell MU-MIMO with Deep
  Unrolling

abstract:
  Coordinated weighted sum-rate maximization in multicell MIMO networks with
intra- and intercell interference and local channel state at the base stations
is recognized as an important yet difficult problem. A classical, locally
optimal solution is obtained by the weighted minimum mean squared error (WMMSE)
algorithm which facilitates a distributed implementation in multicell networks.
However, it often suffers from slow convergence and therefore large
communication overhead. To obtain more practical solutions, the
unrolling/unfolding of traditional iterative algorithms gained significant
attention. In this work, we demonstrate a complete unfolding of the WMMSE
algorithm for transceiver design in multicell MU-MIMO interference channels
with local channel state information. The resulting architecture termed
GCN-WMMSE applies ideas from graph signal processing and is agnostic to
different wireless network topologies, while exhibiting a low number of
trainable parameters and high efficiency w.r.t. training data. It significantly
reduces the number of required iterations while achieving performance similar
to the WMMSE algorithm, alleviating the overhead in a distributed deployment.
Additionally, we review previous architectures based on unrolling the WMMSE
algorithm and compare them to GCN-WMMSE in their specific applicable domains.


title:
L3DAS22 Challenge: Learning 3D Audio Sources in a Real Office
  Environment

abstract:
  The L3DAS22 Challenge is aimed at encouraging the development of machine
learning strategies for 3D speech enhancement and 3D sound localization and
detection in office-like environments. This challenge improves and extends the
tasks of the L3DAS21 edition. We generated a new dataset, which maintains the
same general characteristics of L3DAS21 datasets, but with an extended number
of data points and adding constrains that improve the baseline model's
efficiency and overcome the major difficulties encountered by the participants
of the previous challenge. We updated the baseline model of Task 1, using the
architecture that ranked first in the previous challenge edition. We wrote a
new supporting API, improving its clarity and ease-of-use. In the end, we
present and discuss the results submitted by all participants. L3DAS22
Challenge website: www.l3das.com/icassp2022.


title:
$C_T$ for monodromy defects of fields on odd dimensional spheres for
  higher derivative propagation

abstract:
  The central charge $C_T$ is computed for scalar and Dirac fields propagating
according to GJMS-type kinetic operators acting on odd $d$-dimensional spheres
in the presence of a spherical monodromy. The relation of $C_T$ to the
derivatives of the free energy on the conically deformed sphere via the
Perlmutter factor leads to a numerical quadrature. The variation of $C_T$ with
the monodromy flux, $\delta$, displays sign changes, exactly as in even
dimensions. Closed forms for $C_T$ are derived when $\delta$ equals 0 or 1/2
with the derivative order either even or odd and shown to agree with existing,
even $d$ expressions. The infinite $d$ limits are also derived in these special
cases.


title:
On Smooth Perturbations of Chebysh\"ev Polynomials and
  $\bar\partial$-Riemann-Hilbert Method

abstract:
  $\bar\partial$-extension of the matrix Riemann-Hilbert method is used to
study asymptotics of the polynomials $P_n(z)$ satisfying orthogonality
relations \[ \int_{-1}^1 x^lP_n(x)\frac{\rho(x)dx}{\sqrt{1-x^2}}=0, \quad
l\in\{0,\ldots,n-1\}, \] where $\rho(x)$ is a positive $m$ times continuously
differentiable function on $[-1,1]$, $m\geq3$.


title: Correlation decay for finite lattice gauge theories at weak coupling

abstract:
  In the setting of lattice gauge theories with finite (possibly non-Abelian)
gauge groups at weak coupling, we prove exponential decay of correlations for a
wide class of gauge invariant functions, which in particular includes arbitrary
functions of Wilson loop observables.


title: Same Cause; Different Effects in the Brain

abstract:
  To study information processing in the brain, neuroscientists manipulate
experimental stimuli while recording participant brain activity. They can then
use encoding models to find out which brain "zone" (e.g. which region of
interest, volume pixel or electrophysiology sensor) is predicted from the
stimulus properties. Given the assumptions underlying this setup, when stimulus
properties are predictive of the activity in a zone, these properties are
understood to cause activity in that zone.
  In recent years, researchers have used neural networks to construct
representations that capture the diverse properties of complex stimuli, such as
natural language or natural images. Encoding models built using these
high-dimensional representations are often able to significantly predict the
activity in large swathes of cortex, suggesting that the activity in all these
brain zones is caused by stimulus properties captured in the representation. It
is then natural to ask: "Is the activity in these different brain zones caused
by the stimulus properties in the same way?" In neuroscientific terms, this
corresponds to asking if these different zones process the stimulus properties
in the same way.
  Here, we propose a new framework that enables researchers to ask if the
properties of a stimulus affect two brain zones in the same way. We use
simulated data and two real fMRI datasets with complex naturalistic stimuli to
show that our framework enables us to make such inferences. Our inferences are
strikingly consistent between the two datasets, indicating that the proposed
framework is a promising new tool for neuroscientists to understand how
information is processed in the brain.


title: A Tutorial on Adversarial Learning Attacks and Countermeasures

abstract:
  Machine learning algorithms are used to construct a mathematical model for a
system based on training data. Such a model is capable of making highly
accurate predictions without being explicitly programmed to do so. These
techniques have a great many applications in all areas of the modern digital
economy and artificial intelligence. More importantly, these methods are
essential for a rapidly increasing number of safety-critical applications such
as autonomous vehicles and intelligent defense systems. However, emerging
adversarial learning attacks pose a serious security threat that greatly
undermines further such systems. The latter are classified into four types,
evasion (manipulating data to avoid detection), poisoning (injection malicious
training samples to disrupt retraining), model stealing (extraction), and
inference (leveraging over-generalization on training data). Understanding this
type of attacks is a crucial first step for the development of effective
countermeasures. The paper provides a detailed tutorial on the principles of
adversarial machining learning, explains the different attack scenarios, and
gives an in-depth insight into the state-of-art defense mechanisms against this
rising threat .