title: On Online Attention Dynamics

abstract:
  This work aims at emphasizing a number of questions that, although crucial
since the early days of media studies, have not yet been the object of the
empirical and computational study that they deserve: How does collective
attention concentrate and dissipate in modern communication systems? How do
subjects and sources rise and fall in public debates? How are these dynamics
shaped by media infrastructures? In the perspective of addressing these
questions, this chapter provides a review of the literature on the dynamics of
online content dissemination: our goal is to prepare the ground for the
necessary study, which should comprise empirical investigation, mathematical
modeling, numerical simulation, and rigorous system-theoretic analysis.


title:
GaLactic and Extragalactic All-sky Murchison Widefield Array survey
  eXtended (GLEAM-X) I: Survey Description and Initial Data Release

abstract:
  We describe a new low-frequency wideband radio survey of the southern sky.
Observations covering 72 - 231 MHz and Declinations south of $+30^\circ$ have
been performed with the Murchison Widefield Array "extended" Phase II
configuration over 2018 - 2020 and will be processed to form data products
including continuum and polarisation images and mosaics, multi-frequency
catalogues, transient search data, and ionospheric measurements. From a pilot
field described in this work, we publish an initial data release covering 1,447
sq. deg over 4h < RA < 13h, -32.7deg < Dec < -20.7deg. We process twenty
frequency bands sampling 72 - 231 MHz, with a resolution of $2'$ - $45"$, and
produce a wideband source-finding image across 170 - 231MHz with a
root-mean-square noise of $1.27\pm0.15$ mJy/beam. Source-finding yields 78,967
components, of which 71,320 are fitted spectrally. The catalogue has a
completeness of 98% at $\sim50$mJy, and a reliability of 98.2% at $5\sigma$
rising to 99.7% at $7\sigma$. A catalogue is available from Vizier; images are
made available on AAO Data Central, SkyView, and the PASA Datastore. This is
the first in a series of data releases from the GLEAM-X survey.


title:
Anisotropic Infrared Response and Orientation-dependent Strain-tuning of
  the Electronic Structure in Nb2SiTe4

abstract:
  Two-dimensional materials with tunable in-plane anisotropic infrared response
promise versatile applications in polarized photodetectors and field-effect
transistors. Black phosphorus is a prominent example. However, it suffers from
poor ambient stability. Here, we report the strain-tunable anisotropic infrared
response of a layered material Nb2SiTe4, whose lattice structure is similar to
the 2H-phase transition metal dichalcogenides (TMDCs) with three different
kinds of building units. Strikingly, some of the strain-tunable optical
transitions are crystallographic axis-dependent, even showing opposite shift
when uniaxial strain is applied along two in-plane principal axes. Moreover,
G0W0-BSE calculations show good agreement with the anisotropic extinction
spectra. The optical selection rules are obtained via group theory analysis,
and the strain induced unusual shift trends are well explained by the orbital
coupling analysis. Our comprehensive study suggests that Nb2SiTe4 is a good
candidate for tunable polarization-sensitive optoelectronic devices.


title:
Bounded Memory Adversarial Bandits with Composite Anonymous Delayed
  Feedback

abstract:
  We study the adversarial bandit problem with composite anonymous delayed
feedback. In this setting, losses of an action are split into $d$ components,
spreading over consecutive rounds after the action is chosen. And in each
round, the algorithm observes the aggregation of losses that come from the
latest $d$ rounds. Previous works focus on oblivious adversarial setting, while
we investigate the harder non-oblivious setting. We show non-oblivious setting
incurs $\Omega(T)$ pseudo regret even when the loss sequence is bounded memory.
However, we propose a wrapper algorithm which enjoys $o(T)$ policy regret on
many adversarial bandit problems with the assumption that the loss sequence is
bounded memory. Especially, for $K$-armed bandit and bandit convex
optimization, we have $\mathcal{O}(T^{2/3})$ policy regret bound. We also prove
a matching lower bound for $K$-armed bandit. Our lower bound works even when
the loss sequence is oblivious but the delay is non-oblivious. It answers the
open problem proposed in \cite{wang2021adaptive}, showing that non-oblivious
delay is enough to incur $\tilde{\Omega}(T^{2/3})$ regret.


title:
Why does Self-Supervised Learning for Speech Recognition Benefit Speaker
  Recognition?

abstract:
  Recently, self-supervised learning (SSL) has demonstrated strong performance
in speaker recognition, even if the pre-training objective is designed for
speech recognition. In this paper, we study which factor leads to the success
of self-supervised learning on speaker-related tasks, e.g. speaker verification
(SV), through a series of carefully designed experiments. Our empirical results
on the Voxceleb-1 dataset suggest that the benefit of SSL to SV task is from a
combination of mask speech prediction loss, data scale, and model size, while
the SSL quantizer has a minor impact. We further employ the integrated
gradients attribution method and loss landscape visualization to understand the
effectiveness of self-supervised learning for speaker recognition performance.


title: Two-dimensional forward and backward transition rates

abstract:
  Forward transition rates were originally introduced with the aim to evaluate
life insurance liabilities market-consistently. While this idea turned out to
have its limitations, recent literature repurposes forward transition rates as
a tool for avoiding Markov assumptions in the calculation of life insurance
reserves. While life insurance reserves are some form of conditional
first-order moments, the calculation of conditional second-order moments needs
an extension of the forward transition rate concept from one dimension to two
dimensions. Two-dimensional forward transition rates are also needed for the
calculation of path-dependent life insurance cash-flows as they occur upon
contract modifications. Forward transition rates are designed for doing
prospective calculations, and by a time-symmetric definition of so-called
backward transition rates one can do retrospective calculations.


title: Viscoelastic bubbly media and ultrasonic shear-mode effects

abstract:
  Here we show that in ultrasonic fields the phenomenon of reconversion of
shear-modes into an effective compressional wave has a significant effect for
bubbles in a medium viscosity liquid or weak gel. We present the consequent
extra terms in the effective wavenumber and find the changes in sound velocity
for different bubble radii. At high concentrations of bubbles the inclusion of
shear-mode effects in the multiple-scattering model can help identify bubble
sizes where additional resonance signatures emerge.


title: Masked Spectrogram Prediction For Self-Supervised Audio Pre-Training

abstract:
  Transformer-based models attain excellent results and generalize well when
trained on sufficient amounts of data. However, constrained by the limited data
available in the audio domain, most transformer-based models for audio tasks
are finetuned from pre-trained models in other domains (e.g. image), which has
a notable gap with the audio domain. Other methods explore the self-supervised
learning approaches directly in the audio domain but currently do not perform
well in the downstream tasks. In this paper, we present a novel self-supervised
learning method for transformer-based audio models, called masked spectrogram
prediction (MaskSpec), to learn powerful audio representations from unlabeled
audio data (AudioSet used in this paper). Our method masks random patches of
the input spectrogram and reconstructs the masked regions with an
encoder-decoder architecture. Without using extra model weights or supervision,
experimental results on multiple downstream datasets demonstrate MaskSpec
achieves a significant performance gain against the supervised methods and
outperforms the previous pre-trained models. In particular, our best model
reaches the performance of 0.471 (mAP) on AudioSet, 0.854 (mAP) on OpenMIC2018,
0.982 (accuracy) on ESC-50, 0.976 (accuracy) on SCV2, and 0.823 (accuracy) on
DCASE2019 Task1A respectively.


title:
Dynamic Registration: Joint Ego Motion Estimation and 3D Moving Object
  Detection in Dynamic Environment

abstract:
  Localization in a dynamic environment suffers from moving objects. Removing
dynamic object is crucial in this situation but become tricky when ego-motion
is coupled. In this paper, instead of proposing a new slam framework, we aim at
a more general strategy for a localization scenario. In that case, Dynamic
Registration is available for integrating with any lidar slam system. We
utilize 3D object detection to obtain potential moving objects and remove them
temporarily. Then we proposed Dynamic Registration, to iteratively estimate
ego-motion and segment moving objects until no static object generates. Static
objects are merged with the environment. Finally, we successfully segment
dynamic objects, static environments with static objects, and ego-motion
estimation in a dynamic environment. We evaluate the performance of our
proposed method on KITTI Tracking datasets. Results show stable and consistent
improvements based on other classical registration algorithms.


title:
Run Time Analysis for Random Local Search on Generalized Majority
  Functions

abstract:
  Run time analysis of evolutionary algorithms recently makes significant
progress in linking algorithm performance to algorithm parameters. However,
settings that study the impact of problem parameters are rare. The recently
proposed W-model provides a good framework for such analyses, generating
pseudo-Boolean optimization problems with tunable properties.
  We initiate theoretical research of the W-model by studying how one of its
properties -- neutrality -- influences the run time of random local search.
Neutrality creates plateaus in the search space by first performing a majority
vote for subsets of the solution candidate and then evaluating the
smaller-dimensional string via a low-level fitness function.
  We prove upper bounds for the expected run time of random local search on
this MAJORITY problem for its entire parameter spectrum. To this end, we
provide a theorem, applicable to many optimization algorithms, that links the
run time of MAJORITY with its symmetric version HASMAJORITY, where a sufficient
majority is needed to optimize the subset. We also introduce a generalized
version of classic drift theorems as well as a generalized version of Wald's
equation, both of which we believe to be of independent interest.


title:
A peculiarly long-duration gamma-ray burst from binary neutron star
  merger

abstract:
  Some recent findings have shown that the duration of gamma-ray burst (GRB),
although crucially related to the GRB central engine time scale, is not
determinative in inferring the GRB origins in terms of their progenitors. In
this paper, we report a peculiarly long-duration gamma-ray burst, GRB 211211A,
that is associated with a kilonova in optical and near-infrared bands and is
therefore likely the result of a binary neutron star merger. The burst broadly
resembles the properties of GRB 060614 but with a much higher brightness in its
light curve and harder spectra in both the main and extended emission phases,
making it difficult to be explained as a short GRB with soft extended emission.
Such a genuinely long-duration GRB suggests that merger product is likely a
magnetar, which powers up the burst through magnetic and rotation energy for at
least $\sim 70$ seconds.


title: Rigidity of symplectic translating solitons

abstract:
  We obtain a rigidity result of symplectic translating solitons via the
complex phase map. It indicates that we can remove the bounded second
fundamental form assumption for symplectic translating solitons in [13].


title: Soft noncommutative flag schemes

abstract:
  The construction of soft noncommutative schemes via toric geometry in
arXiv:2108.05328 [math.AG] (D(15.1), NCS(1)) can be generalized and applied to
a commutative scheme with a distinguished atlas of reasonably good affine local
coordinate charts. In the current notes we carry out this exercise for flag
varieties.


title: Matter & Mind Matter

abstract:
  As a result of a hundred million years of evolution, living animals have
adapted extremely well to their ecological niche. Such adaptation implies
species-specific interactions with their immediate environment by processing
sensory cues and responding with appropriate behavior. Understanding how living
creatures perform pattern recognition and cognitive tasks is of particular
importance for computing architectures: by studying these information pathways
refined over eons of evolution, researchers may be able to streamline the
process of developing more highly advanced, energy efficient autonomous
systems. With the advent of novel electronic and ionic components along with a
deeper understanding of information pathways in living species, a plethora of
opportunities to develop completely novel information processing avenues are
within reach. Here, we describe the basal information pathways in nervous
systems, from the local neuron level to the entire nervous system network. The
dual importance of local learning rules is addressed, from spike timing
dependent plasticity at the neuron level to the interwoven morphological and
dynamical mechanisms of the global network. Basal biological principles are
highlighted, including phylogenies, ontogenesis, and homeostasis, with
particular emphasis on network topology and dynamics. While in machine learning
system training is performed on virgin networks without any a priori knowledge,
the approach proposed here distinguishes itself unambiguously by employing
growth mechanisms as a guideline to design novel computing architectures.
Including fundamental biological information pathways that explore the
spatiotemporal fundamentals of nervous systems has untapped potential for the
development of entirely novel information processing systems. Finally, a
benchmark for neuromorphic systems is suggested.


title:
On the possibility of incorporation of the iron impurity into $A$ sites
  in SrTiO$_3$

abstract:
  The local environment and oxidation state of the Fe impurity in strontium
titanate are studied using XAFS spectroscopy. The influence of annealing
temperature and deviation from stoichiometry on the possibility of
incorporation of the impurity into the $A$ and $B$~sites of the perovskite
structure is studied. The results obtained from the X-ray diffraction, XANES
spectra, and EXAFS spectra suggest that at high annealing temperature the iron
atoms, at least partially (up to 30\%), enter the $A$~sites in SrTiO$_3$. The
obtained results agree with results of first-principles calculations, according
to which the iron at the $A$~site exhibits strong off-centering (the
displacement of $\sim$1~{\AA}), similar to that previously established in
SrTiO$_3$ samples doped with Mn and Co.


title:
Retrieving Yang--Mills--Higgs fields in Minkowski space from active
  local measurements

abstract:
  We show that we can retrieve a Yang--Mills potential and a Higgs field (up to
gauge) from source-to-solution type data associated with the classical
Yang--Mills--Higgs equations in Minkowski space $\mathbb{R}^{1+3}$. We impose
natural non-degeneracy conditions on the representation for the Higgs field and
on the Lie algebra of the structure group which are satisfied for the case of
the Standard Model. Our approach exploits the non-linear interaction of waves
generated by sources with values in the centre of the Lie algebra showing that
abelian components can be used effectively to recover the Higgs field.


title: Ultra Fast Speech Separation Model with Teacher Student Learning

abstract:
  Transformer has been successfully applied to speech separation recently with
its strong long-dependency modeling capacity using a self-attention mechanism.
However, Transformer tends to have heavy run-time costs due to the deep encoder
layers, which hinders its deployment on edge devices. A small Transformer model
with fewer encoder layers is preferred for computational efficiency, but it is
prone to performance degradation. In this paper, an ultra fast speech
separation Transformer model is proposed to achieve both better performance and
efficiency with teacher student learning (T-S learning). We introduce
layer-wise T-S learning and objective shifting mechanisms to guide the small
student model to learn intermediate representations from the large teacher
model. Compared with the small Transformer model trained from scratch, the
proposed T-S learning method reduces the word error rate (WER) by more than 5%
for both multi-channel and single-channel speech separation on LibriCSS
dataset. Utilizing more unlabeled speech data, our ultra fast speech separation
models achieve more than 10% relative WER reduction.


title:
The Sparse Readout RIGEL Application Specific Integrated Circuit for
  Pixel Silicon Drift Detectors in Soft X-Ray Imaging Space Applications

abstract:
  An Application Specific Integrated Circuit (ASIC), called RIGEL, designed for
the sparse readout of a Silicon Pixel Drift Detector (PixDD) for space
applications is presented.The low leakage current (less than 1 pA at +20
{\deg}C) and anode capacitance (less than 40 fF) of each pixel (300 um x 300
um) of the detector, combined with a low-noise electronics readout, allow to
reach a high spectroscopic resolution performance even at room temperature. The
RIGEL ASIC front-end architecture is composed by a 2-D matrix of 128 readout
pixel cells (RPCs), arranged to host, in a 300 um-sided square area, a central
octagonal pad (for the PixDD anode bump-bonding), and the full-analog
processing chain, providing a full-shaped and stretched signal. In the chip
periphery, the back-end electronics features 16 integrated 10-bits Wilkinson
ADCs, the configuration register and a trigger management circuit. The
characterization of a single RPC has been carried out whose features are: eight
selectable peaking times from 0.5 us to 5 us, an input charge range equivalent
to 30 keV, and a power consumption of less than 550 uW per channel. The RPC has
been tested also with a 4x4 prototype PixDD and 167 eV Full Width at Half
Maximum (FWHM) at the 5.9 keV line of 55Fe at 0{\deg}C and 1.8 us of peaking
time has been measured.


title:
Weak-strong uniqueness and vanishing viscosity for incompressible Euler
  equations in exponential spaces

abstract:
  In the class of admissible weak solutions, we prove a weak-strong uniqueness
result for the incompressible Euler equations assuming that the symmetric part
of the gradient belongs to $L^1_{\rm loc}([0,+\infty);L^{\rm
exp}(\mathbb{R}^d;\mathbb{R}^{d\times d}))$, where $L^{\rm exp}$ denotes the
Orlicz space of exponentially integrable functions. Moreover, under the same
assumptions on the limit solution to the Euler system, we obtain the
convergence of vanishing-viscosity Leray--Hopf weak solutions of the
Navier--Stokes equations.


title:
A Branch-and-Price Approach to a Variant of the Cognitive Radio Resource
  Allocation Problem

abstract:
  Radio-frequency portion of the electromagnetic spectrum is a scarce resource.
Cognitive radio technology has emerged as a promising solution to overcome the
spectrum scarcity bottleneck. Through this technology, secondary users (SUs)
sense the spectrum opportunities free from primary users (PUs), and
opportunistically take advantage of these (temporarily) idle portions, known as
spectrum holes. In this correspondence, we consider a variant of the cognitive
radio resource allocation problem posed by Martinovic et al. in 2017. The
distinguishing feature of this version of the problem is that each SU, due to
its hardware limitations, imposes the requirement that the to-be-aggregated
spectrum holes cannot be arbitrarily far from each other. We call this
restriction as the Maximal Aggregation Range (MAR) constraint, and refer to
this variant of the problem as the MAR-constrained hole assignment problem. The
problem can be formalized as an NP-hard combinatorial optimization problem. We
propose a novel binary integer linear programming (ILP) formulation to the
problem. The number of constraints in this formulation is the number of
spectrum holes plus the number of SUs. On the other hand, the number of binary
decision variables in the formulation can be prohibitively large, as for each
legitimate spectrum allocation to each SU, one variable is needed. We propose a
branch-and-price (B&P) framework to tackle this challenge. This framework is in
fact a branch-and-bound procedure in which at each node of the search tree, we
utilize the so-called (delayed) column generation technique for solving the LP
relaxation of the corresponding subproblem. As evidenced by the numerical
results, the LP relaxation bounds are very tight. This allows for a very
effective pruning of the search space. Compared to the previously suggested
formulations, the proposed technique can require much less computational
effort.


title:
An Empirical Evaluation of Flow Based Programming in the Machine
  Learning Deployment Context

abstract:
  As use of data driven technologies spreads, software engineers are more often
faced with the task of solving a business problem using data-driven methods
such as machine learning (ML) algorithms. Deployment of ML within large
software systems brings new challenges that are not addressed by standard
engineering practices and as a result businesses observe high rate of ML
deployment project failures. Data Oriented Architecture (DOA) is an emerging
approach that can support data scientists and software developers when
addressing such challenges. However, there is a lack of clarity about how DOA
systems should be implemented in practice. This paper proposes to consider
Flow-Based Programming (FBP) as a paradigm for creating DOA applications. We
empirically evaluate FBP in the context of ML deployment on four applications
that represent typical data science projects. We use Service Oriented
Architecture (SOA) as a baseline for comparison. Evaluation is done with
respect to different application domains, ML deployment stages, and code
quality metrics. Results reveal that FBP is a suitable paradigm for data
collection and data science tasks, and is able to simplify data collection and
discovery when compared with SOA. We discuss the advantages of FBP as well as
the gaps that need to be addressed to increase FBP adoption as a standard
design paradigm for DOA.


title: Diffraction from nanocrystal superlattices

abstract:
  Diffraction from a lattice of periodically spaced crystals is a topic of
current interest because of the great development of self-organised
superlattices (SL) of nanocrystals (NC). The self-organisation of NC into SL
has theoretical interest, but especially a rich application prospect, as the
coherent organisation has large effects on a wide range of material properties.
Diffraction is a key method to understand the type and quality of SL ordering.
Hereby the characteristic diffraction signature of a SL of NC - together with
the characteristic types of disorder - are theoretically explored.


title:
RIS-aided Near-Field Localization under Phase-Dependent Amplitude
  Variations

abstract:
  We investigate the problem of reconfigurable intelligent surface (RIS)-aided
near-field localization of a user equipment (UE) served by a base station (BS)
under phase-dependent amplitude variations at each RIS element. Through a
misspecified Cram\'{e}r-Rao bound (MCRB) analysis and a resulting lower bound
(LB) on localization, we show that when the UE is unaware of amplitude
variations (i.e., assumes unit-amplitude responses), severe performance
penalties can arise, especially at high signal-to-noise ratios (SNRs).
Leveraging Jacobi-Anger expansion to decouple range-azimuth-elevation
dimensions, we develop a low-complexity approximated mismatched maximum
likelihood (AMML) estimator, which is asymptotically tight to the LB. To
mitigate performance loss due to model mismatch, we propose to jointly estimate
the UE location and the RIS amplitude model parameters. The corresponding
Cram\'{e}r-Rao bound (CRB) is derived, as well as an iterative refinement
algorithm, which employs the AMML method as a subroutine and alternatingly
updates individual parameters of the RIS amplitude model. Simulation results
indicate fast convergence and performance close to the CRB. The proposed method
can successfully recover the performance loss of the AMML under a wide range of
RIS parameters and effectively calibrate the RIS amplitude model online with
the help of a user that has an a-priori unknown location.


title:
Learn from Structural Scope: Improving Aspect-Level Sentiment Analysis
  with Hybrid Graph Convolutional Networks

abstract:
  Aspect-level sentiment analysis aims to determine the sentiment polarity
towards a specific target in a sentence. The main challenge of this task is to
effectively model the relation between targets and sentiments so as to filter
out noisy opinion words from irrelevant targets. Most recent efforts capture
relations through target-sentiment pairs or opinion spans from a word-level or
phrase-level perspective. Based on the observation that targets and sentiments
essentially establish relations following the grammatical hierarchy of
phrase-clause-sentence structure, it is hopeful to exploit comprehensive
syntactic information for better guiding the learning process. Therefore, we
introduce the concept of Scope, which outlines a structural text region related
to a specific target. To jointly learn structural Scope and predict the
sentiment polarity, we propose a hybrid graph convolutional network (HGCN) to
synthesize information from constituency tree and dependency tree, exploring
the potential of linking two syntax parsing methods to enrich the
representation. Experimental results on four public datasets illustrate that
our HGCN model outperforms current state-of-the-art baselines.


title: Plug-and-Play Adaptation for Continuously-updated QA

abstract:
  Language models (LMs) have shown great potential as implicit knowledge bases
(KBs). And for their practical use, knowledge in LMs need to be updated
periodically. However, existing tasks to assess LMs' efficacy as KBs do not
adequately consider multiple large-scale updates. To this end, we first propose
a novel task--Continuously-updated QA (CuQA)--in which multiple large-scale
updates are made to LMs, and the performance is measured with respect to the
success in adding and updating knowledge while retaining existing knowledge. We
then present LMs with plug-in modules that effectively handle the updates.
Experiments conducted on zsRE QA and NQ datasets show that our method
outperforms existing approaches. We find that our method is 4x more effective
in terms of updates/forgets ratio, compared to a fine-tuning baseline.


title: Machines of finite depth: towards a formalization of neural networks

abstract:
  We provide a unifying framework where artificial neural networks and their
architectures can be formally described as particular cases of a general
mathematical construction--machines of finite depth. Unlike neural networks,
machines have a precise definition, from which several properties follow
naturally. Machines of finite depth are modular (they can be combined),
efficiently computable and differentiable. The backward pass of a machine is
again a machine and can be computed without overhead using the same procedure
as the forward pass. We prove this statement theoretically and practically, via
a unified implementation that generalizes several classical
architectures--dense, convolutional, and recurrent neural networks with a rich
shortcut structure--and their respective backpropagation rules.


title:
3-D generalized analytic signal associated with linear canonical
  transform in Clifford biquaternion domain

abstract:
  The analytic signal is a useful mathematical tool. It separates qualitative
and quantitative information of a signal in form of the local phase and local
amplitude. The Clifford Fourier transform (CFT) plays a vital role in the
representation of multidimensional signals. By generalizing the CFT to the
Clifford linear canonical transform (CLCT), we present a new type of Clifford
biquaternionic analytic signal. Due to the advantages of more freedom, the
envelop detection problems of 3D images, with the help of this new analytic
signal, can get a better visual appearance. Synthesis examples are presented to
demonstrate these advantages.


title:
MCRB-based Performance Analysis of 6G Localization under Hardware
  Impairments

abstract:
  Location information is expected to be the key to meeting the needs of
communication and context-aware services in 6G systems. User localization is
achieved based on delay and/or angle estimation using uplink or downlink pilot
signals. However, hardware impairments (HWIs) distort the signals at both the
transmitter and receiver sides and thus affect the localization performance.
While this impact can be ignored at lower frequencies where HWIs are less
severe, modeling and analysis efforts are needed for 6G to evaluate the
localization degradation due to HWIs. In this work, we model various types of
impairments and conduct a misspecified Cram\'er-Rao bound analysis to evaluate
the HWI-induced performance loss. Simulation results with different types of
HWIs show that each HWI leads to a different level of degradation in angle and
delay estimation performance.


title:
Learning Green's functions associated with parabolic partial
  differential equations

abstract:
  Given input-output pairs from a parabolic partial differential equation (PDE)
in any spatial dimension $n\geq 1$, we derive the first theoretically rigorous
scheme for learning the associated Green's function $G$. Until now, rigorously
learning Green's functions associated with parabolic operators has been a major
challenge in the field of scientific machine learning because $G$ may not be
square-integrable when $n>1$, and time-dependent PDEs have transient dynamics.
By combining the hierarchical low-rank structure of $G$ together with the
randomized singular value decomposition, we construct an approximant to $G$
that achieves a relative error of
$\smash{\mathcal{O}(\Gamma_\epsilon^{-1/2}\epsilon)}$ in the $L^1$-norm with
high probability by using at most
$\smash{\mathcal{O}(\epsilon^{-\frac{n+2}{2}}\log(1/\epsilon))}$ input-output
training pairs, where $\Gamma_\epsilon$ is a measure of the quality of the
training dataset for learning $G$, and $\epsilon>0$ is sufficiently small.
Along the way, we extend the low-rank theory of Bebendorf and Hackbusch from
elliptic PDEs in dimension $1\leq n\leq 3$ to parabolic PDEs in any dimensions,
which shows that Green's functions associated with parabolic PDEs admit a
low-rank structure on well-separated domains.


title:
Taylor's Frozen-in Hypothesis for Magnetohydrodynamic turbulence and
  Solar Wind

abstract:
  In hydrodynamics, Taylor's frozen-in hypothesis connects the wavenumber
spectrum to the frequency spectrum of a time series measured in real space. In
this paper, we generalize Taylor's hypothesis to magnetohydrodynamic
turbulence. We analytically derive one-point two-time correlation functions for
Els\"{a}sser variables whose Fourier transform yields the corresponding
frequency spectra, $ E^\pm(f) $. We show that $ E^\pm(f) \propto |{\bf U}_0 \mp
{\bf B}_0|^{2/3} $ in Kolmogorov-like model, and $ E^\pm(f) \propto (B_0 |{\bf
U}_0 \mp {\bf B}_0|)^{1/2} $ in Iroshnikov-Kraichnan model, where $ {\bf U}_0,
{\bf B}_0$ are the mean velocity and mean magnetic fields respectively.

categories: physics.plasm-ph astro-ph.SR physics.flu-dyn physics.space-ph


title:
Evaluation and Learning in Two-Player Symmetric Games via Best and
  Better Responses

abstract:
  Artificial intelligence and robotic competitions are accompanied by a class
of game paradigms in which each player privately commits a strategy to a game
system which simulates the game using the collected joint strategy and then
returns payoffs to players. This paper considers the strategy commitment for
two-player symmetric games in which the players' strategy spaces are identical
and their payoffs are symmetric. First, we introduce two digraph-based metrics
at a meta-level for strategy evaluation in two-agent reinforcement learning,
grounded on sink equilibrium. The metrics rank the strategies of a single
player and determine the set of strategies which are preferred for the private
commitment. Then, in order to find the preferred strategies under the metrics,
we propose two variants of the classical learning algorithm self-play, called
strictly best-response and weakly better-response self-plays. By modeling
learning processes as walks over joint-strategy response digraphs, we prove
that the learnt strategies by two variants are preferred under two metrics,
respectively. The preferred strategies under both two metrics are identified
and adjacency matrices induced by one metric and one variant are connected.
Finally, simulations are provided to illustrate the results.


title: Minimum-Time Quantum Control and the Quantum Brachistochrone Equation

abstract:
  Minimum-time quantum control protocols can be obtained from the quantum
brachistochrone formalism [Carlini, Hosoya, Koike, and Okudaira, Phys. Rev.
Lett. 96, 06053, (2006)]. We point out that the original treatment implicitly
applied the variational calculus with fixed boundary conditions. We argue that
the genuine quantum brachistochrone problem involves a variational problem with
a movable endpoint, contrary to the classical brachistochrone problem. This
formulation not only simplifies the derivation of the quantum brachistochrone
equation but introduces an additional constraint at the endpoint due to the
boundary effect. We present the general solution to the full quantum
brachistochrone equation and discuss its main features. Using it, we prove that
the speed of evolution under constraints is reduced with respect to the
unrestricted case. In addition, we find that solving the quantum
brachistochrone equation is closely connected to solving the dynamics of the
Lagrange multipliers, which is in general governed by nonlinear differential
equations. Their numerical integration allows generating time-extremal
trajectories. Furthermore, when the restricted operators form a closed
subalgebra, the Lagrange multipliers become constant and the optimal
Hamiltonian takes a concise form. The new class of analytically solvable models
for the quantum brachistochrone problem opens up the possibility of applying it
to many-body quantum systems, exploring notions related to geometry such as
quantum speed limits, and advancing significantly the quantum state and gate
preparation for quantum information processing.


title: Modern Baselines for SPARQL Semantic Parsing

abstract:
  In this work, we focus on the task of generating SPARQL queries from natural
language questions, which can then be executed on Knowledge Graphs (KGs). We
assume that gold entity and relations have been provided, and the remaining
task is to arrange them in the right order along with SPARQL vocabulary, and
input tokens to produce the correct SPARQL query. Pre-trained Language Models
(PLMs) have not been explored in depth on this task so far, so we experiment
with BART, T5 and PGNs (Pointer Generator Networks) with BERT embeddings,
looking for new baselines in the PLM era for this task, on DBpedia and Wikidata
KGs. We show that T5 requires special input tokenisation, but produces state of
the art performance on LC-QuAD 1.0 and LC-QuAD 2.0 datasets, and outperforms
task-specific models from previous works. Moreover, the methods enable semantic
parsing for questions where a part of the input needs to be copied to the
output query, thus enabling a new paradigm in KG semantic parsing.


title:
Evaporation Residue Production in Complete and Incomplete Fusion
  Reactions of Ne-20 with Au-197 and Ho-165 at E = 14.9 x AMeV

abstract:
  Velocity distributions of heavy residues (A_Res > A_Tar), produced in
irradiations of Au-197 and Ho-165 with Ne-20 at bombarding energies of E = 208
MeV, have been measured at the velocity filter SHIP. While for the products
from Ne-20 + Ho-165 just a mean value of v/v_CN = 0.91+-0.01 was obtained,
individual residues, attributed to isotopes of astatine and polonium with mass
numbers ranging from A=195 to A=203 could be identified by means of alpha -
spectroscopy in the bombardments Au-197. A linear increase of the mean velocity
from v/v_CN = 0.85 to v/v_CN = 0.93 with decreasing residue mass was observed.
This effect was attributed to an increasing contribution of pre-equilibrium
particle emission to the production of heavy residues. A quantititative
estimation resulted in a 90 % contribution of pre-equilibrium processes to the
production of astatine isotopes, and a 50 % contribution to the polonium
isotopes.


title:
Beating Fabry-P\'erot interference pattern in a magnonic scattering
  junction in the graphene quantum Hall ferromagnet

abstract:
  At filling factor $\nu=0,\pm1$, the ground state of graphene is a particular
SU(4) ferromagnet which hosts a rich phase diagram along with several spin,
pseudospin or "entanglement" magnon modes. Motivated by recent experiments, we
study a $\nu=-1|0|-1$ Fabry-P\'erot magnonic junction. If the ground state at
$\nu=0$ is spin polarized, there exist two spin modes which interfere and
create a beating pattern, while pseudospin modes are reflected. The same
scenario occurs for pseudospin magnon if the $\nu=0$ ground state is pseudospin
polarized. The observation of such an interference pattern would provide
information on the low-energy anisotropies and thus on the ground state.


title:
Supervised Contrastive CSI Representation Learning for Massive MIMO
  Positioning

abstract:
  Similarity metric is crucial for massive MIMO positioning utilizing channel
state information~(CSI). In this letter, we propose a novel massive MIMO CSI
similarity learning method via deep convolutional neural network~(DCNN) and
contrastive learning. A contrastive loss function is designed considering
multiple positive and negative CSI samples drawn from a training dataset. The
DCNN encoder is trained using the loss so that positive samples are mapped to
points close to the anchor's encoding, while encodings of negative samples are
kept away from the anchor's in the representation space. Evaluation results of
fingerprint-based positioning on a real-world CSI dataset show that the learned
similarity metric improves positioning accuracy significantly compared with
other known state-of-the-art methods.


title: Towards Quantum Ray Tracing

abstract:
  Rendering on conventional computers is capable of generating realistic
imagery, but the computational complexity of these light transport algorithms
is a limiting factor of image synthesis. Quantum computers have the potential
to significantly improve rendering performance through reducing the underlying
complexity of the algorithms behind light transport. This paper investigates
hybrid quantum-classical algorithms for ray tracing, a core component of most
rendering techniques. Through a practical implementation of quantum ray tracing
in a 3D environment, we show quantum approaches provide a quadratic improvement
in query complexity compared to the equivalent classical approach. Based on
domain specific knowledge, we then propose algorithms to significantly reduce
the computation required for quantum ray tracing through exploiting image space
coherence and a principled termination criteria for quantum searching. We show
results for both Whitted style ray tracing, and for accelerating ray tracing
operations when performing classical Monte Carlo integration for area lights
and indirect illumination.


title:
Affine Frequency Division Multiplexing for Next Generation Wireless
  Communications

abstract:
  Affine Frequency Division Multiplexing (AFDM), a new chirp-based multicarrier
waveform for high mobility communications, is introduced here. AFDM is based on
discrete affine Fourier transform (DAFT), a generalization of discrete Fourier
transform, which is characterized by two parameters that can be adapted to
better cope with doubly dispersive channels. First, we derive the explicit
input-output relation in the DAFT domain showing the effect of AFDM parameters
in the input-output relation. Second, we show how the DAFT parameters
underlying AFDM have to be set so that the resulting DAFT domain impulse
response conveys a full delay-Doppler representation of the channel. Then, we
show analytically that AFDM can achieve full diversity in doubly dispersive
channels, where full diversity refers to the number of multipath components
separable in either the delay or the Doppler domain, due to its full
delay-Doppler representation. Furthermore, we present a low complexity
detection method taking advantage of zero-padding. We also propose an embedded
pilot-aided channel estimation scheme for AFDM, in which both channel
estimation and data detection are performed within the same AFDM frame.
Finally, simulations corroborate the validity of our analytical results and
show the significant performance gains of AFDM over state-of-the-art
multicarrier schemes in high mobility scenarios.


title: Magnetohydrodynamic Turbulence: Chandrasekhar's Contributions \& Beyond

abstract:
  In the period of 1948-1955, Chandrasekhar wrote four papers on
magnetohydrodynamic (MHD) turbulence, which are the first set of papers in the
area. The field moved on following these pioneering efforts. In this paper, I
will briefly describe important works of MHD turbulence, starting from those by
Chandrasekhar.

categories: physics.plasm-ph astro-ph.SR physics.flu-dyn physics.hist-ph


title: The anomalous $Wtb$ vertex and top-pair production at the LHC

abstract:
  We perform an exclusive study of the anomalous $Wtb$ interaction in the
context of LHC. The limits on $Wtb$ anomalous couplings have been estimated via
the measurements of the top-quark decay width and cross-section as well as
production asymmetries in case the CP is violated. Our investigations reveal
that the upper bounds on $(|C_L|, |C_R|)$ would be of about (1.82, 0.03)
$\times 10^{-4}$ at $2.5 \sigma$ C.L., for the already collected data at the
LHC with $\sqrt{S} = 13$ TeV for an integrated luminosity of 139 fb$^{-1}$. The
corresponding limits for future hadron colliders, namely, High Luminosity LHC
(HL-LHC), High Energy LHC (HE-LHC) and Future Circular Collider (FCC-hh) for
the projected luminosities of 3 ab$^{-1}$, 12 ab$^{-1}$ and 30 ab$^{-1}$ are
found to be to $(0.81, 0.006)\times 10^{-4}$, $(0.44, 0.0017)\times 10^{-4}$
and $(0.21, 0.0004)\times 10^{-4}$ respectively at 2.5$\sigma$ C. L..


title: Speeding Hirschberg Algorithm for Sequence Alignment

abstract:
  The use of Hirschberg algorithm reduces the spatial cost of recovering the
Longest Common Subsequence to linear space. The same technique can be applied
to similar problems like Sequence Alignment. However, the price to pay is a
duplication of temporal cost. We present here a technique to reduce this time
overhead to a negligible amount.


title: GTNet: A Tree-Based Deep Graph Learning Architecture

abstract:
  We propose Graph Tree Networks (GTNets), a deep graph learning architecture
with a new general message passing scheme that originates from the tree
representation of graphs. In the tree representation, messages propagate upward
from the leaf nodes to the root node, and each node preserves its initial
information prior to receiving information from its child nodes (neighbors). We
formulate a general propagation rule following the nature of message passing in
the tree to update a node's feature by aggregating its initial feature and its
neighbor nodes' updated features. Two graph representation learning models are
proposed within this GTNet architecture - Graph Tree Attention Network (GTAN)
and Graph Tree Convolution Network (GTCN), with experimentally demonstrated
state-of-the-art performance on several popular benchmark datasets. Unlike the
vanilla Graph Attention Network (GAT) and Graph Convolution Network (GCN) which
have the "over-smoothing" issue, the proposed GTAN and GTCN models can go deep
as demonstrated by comprehensive experiments and rigorous theoretical analysis.


title:
Polarized Electron Beams from Laser Plasma Acceleration and Their
  Polarimetry

abstract:
  In recent years, Laser Plasma Acceleration (LPA) has become a promising
alternative to conventional RF accelerators. However, so far, it has only been
theoretically shown that generating polarized LPA beams is possible. The LEAP
(Laser Electron Acceleration with Polarization) project at DESY aims to
demonstrate this experimentally for the first time, using a pre-polarized
plasma target. The electron polarization will be measured with photon
transmission polarimetry, which makes use of the production of circularly
polarized bremsstrahlung during the passage of the electron beams through a
suitable converter target. The photon polarization is then measured with the
aid of transmission asymmetry arising from reversing the magnetization
direction of an iron absorber. In this contribution an overview of the LEAP
project is presented, detailing the generation of the polarized electron beams
along with the design and simulation studies of the polarimeter.


title: Faithful coherent states

abstract:
  We propose the notion of faithful coherent states based on the fidelity-based
coherence witness. The criterion for detecting faithful coherent states can be
restricted to a subclass of fidelity-based criterion under unitary
transformations for single and bipartite systems. We can realize these unitary
transformations by using quantum gates and circuits, and establish the
connection of faithful coherence states and coherence distillation, maximum
relative entropy of coherence.


title:
A Scalable Combinatorial Solver for Elastic Geometrically Consistent 3D
  Shape Matching

abstract:
  We present a scalable combinatorial algorithm for globally optimizing over
the space of geometrically consistent mappings between 3D shapes. We use the
mathematically elegant formalism proposed by Windheuser et al. (ICCV 2011)
where 3D shape matching was formulated as an integer linear program over the
space of orientation-preserving diffeomorphisms. Until now, the resulting
formulation had limited practical applicability due to its complicated
constraint structure and its large size. We propose a novel primal heuristic
coupled with a Lagrange dual problem that is several orders of magnitudes
faster compared to previous solvers. This allows us to handle shapes with
substantially more triangles than previously solvable. We demonstrate
compelling results on diverse datasets, and, even showcase that we can address
the challenging setting of matching two partial shapes without availability of
complete shapes. Our code is publicly available at
http://github.com/paul0noah/sm-comb .


title: Quantum Prudent Contracts with Applications to Bitcoin

abstract:
  Smart contracts are cryptographic protocols that are enforced without a
judiciary. Smart contracts are used occasionally in Bitcoin and are prevalent
in Ethereum. Public quantum money improves upon cash we use today, yet the
current constructions do not enable smart contracts. In this work, we define
and introduce quantum payment schemes, and show how to implement prudent
contracts -- a non-trivial subset of the functionality that a network such as
Ethereum provides. Examples discussed include: multi-signature wallets in which
funds can be spent by any 2-out-of-3 owners; restricted accounts that can send
funds only to designated destinations; and "colored coins" that can represent
stocks that can be freely traded, and their owner would receive dividends. Our
approach is not as universal as the one used in Ethereum since we do not reach
a consensus regarding the state of a ledger. We call our proposal prudent
contracts to reflect this. The main building block is either quantum tokens for
digital signatures (Ben-David and Sattath QCrypt'17, Coladangelo et al.
Crypto'21), semi-quantum tokens for digital signatures (Shmueli'22) or one-shot
signatures (Amos et al. STOC'20). The solution has all the benefits of public
quantum money: no mining is necessary, and the security model is standard
(e.g., it is not susceptible to 51\% attacks, as in Bitcoin). Our one-shot
signature construction can be used to upgrade the Bitcoin network to a quantum
payment scheme. Notable advantages of this approach are: transactions are
locally verifiable and without latency, the throughput is unbounded, and most
importantly, it would remove the need for Bitcoin mining. Our approach requires
a universal large-scale quantum computer and long-term quantum memory; hence we
do not expect it to be implementable in the next few years.


title: Probing Simile Knowledge from Pre-trained Language Models

abstract:
  Simile interpretation (SI) and simile generation (SG) are challenging tasks
for NLP because models require adequate world knowledge to produce predictions.
Previous works have employed many hand-crafted resources to bring
knowledge-related into models, which is time-consuming and labor-intensive. In
recent years, pre-trained language models (PLMs) based approaches have become
the de-facto standard in NLP since they learn generic knowledge from a large
corpus. The knowledge embedded in PLMs may be useful for SI and SG tasks.
Nevertheless, there are few works to explore it. In this paper, we probe simile
knowledge from PLMs to solve the SI and SG tasks in the unified framework of
simile triple completion for the first time. The backbone of our framework is
to construct masked sentences with manual patterns and then predict the
candidate words in the masked position. In this framework, we adopt a secondary
training process (Adjective-Noun mask Training) with the masked language model
(MLM) loss to enhance the prediction diversity of candidate words in the masked
position. Moreover, pattern ensemble (PE) and pattern search (PS) are applied
to improve the quality of predicted words. Finally, automatic and human
evaluations demonstrate the effectiveness of our framework in both SI and SG
tasks.


title: A Method of Query Graph Reranking for Knowledge Base Question Answering

abstract:
  This paper presents a novel reranking method to better choose the optimal
query graph, a sub-graph of knowledge graph, to retrieve the answer for an
input question in Knowledge Base Question Answering (KBQA). Existing methods
suffer from a severe problem that there is a significant gap between top-1
performance and the oracle score of top-n results. To address this problem, our
method divides the choosing procedure into two steps: query graph ranking and
query graph reranking. In the first step, we provide top-n query graphs for
each question. Then we propose to rerank the top-n query graphs by combining
with the information of answer type. Experimental results on two widely used
datasets show that our proposed method achieves the best results on the
WebQuestions dataset and the second best on the ComplexQuestions dataset.


title:
High-performance broadband Faraday rotation spectroscopy of 2D materials
  and thin magnetic films

abstract:
  We present a Faraday rotation spectroscopy (FRS) technique for measurements
on the micron scale. Spectral acquisition speeds of many orders of magnitude
faster than state-of-the-art modulation spectroscopy setups are demonstrated.
The experimental method is based on charge-coupled-device detection, avoiding
speed-limiting components, such as polarization modulators with lock-in
amplifiers. At the same time, FRS spectra are obtained with a sensitivity of 20
$\mu$rad (0.001$^\circ$) over a broad spectral range (525 nm - 800 nm), which
is on par with state-of-the-art polarization-modulation techniques. The new
measurement technique also automatically cancels unwanted Faraday rotation
backgrounds. Using the setup, we perform Faraday rotation spectroscopy of
excitons in a hBN-encapsulated atomically thin semiconductor WS$_2$ under
magnetic fields of up to 1.4 T at room temperature and liquid helium
temperature. We determine the A exciton g-factor of -4.4 $\pm$ 0.3 at room
temperature, and -4.2 $\pm$ 0.2 at liquid helium temperature. In addition, we
perform FRS and hysteresis loop measurements on a 20 nm thick film of an
amorphous magnetic Tb$_{0.2}$Fe$_{0.8}$ alloy.


title:
When Performance is not Enough -- A Multidisciplinary View on Clinical
  Decision Support

abstract:
  Scientific publications about machine learning in healthcare are often about
implementing novel methods and boosting the performance - at least from a
computer science perspective. However, beyond such often short-lived
improvements, much more needs to be taken into consideration if we want to
arrive at a sustainable progress in healthcare. What does it take to actually
implement such a system, make it usable for the domain expert, and possibly
bring it into practical usage? Targeted at Computer Scientists, this work
presents a multidisciplinary view on machine learning in medical decision
support systems and covers information technology, medical, as well as ethical
aspects. Along with an implemented risk prediction system in nephrology,
challenges and lessons learned in a pilot project are presented.


title: SkillSpan: Hard and Soft Skill Extraction from English Job Postings

abstract:
  Skill Extraction (SE) is an important and widely-studied task useful to gain
insights into labor market dynamics. However, there is a lacuna of datasets and
annotation guidelines; available datasets are few and contain crowd-sourced
labels on the span-level or labels from a predefined skill inventory. To
address this gap, we introduce SKILLSPAN, a novel SE dataset consisting of
14.5K sentences and over 12.5K annotated spans. We release its respective
guidelines created over three different sources annotated for hard and soft
skills by domain experts. We introduce a BERT baseline (Devlin et al., 2019).
To improve upon this baseline, we experiment with language models that are
optimized for long spans (Joshi et al., 2020; Beltagy et al., 2020), continuous
pre-training on the job posting domain (Han and Eisenstein, 2019; Gururangan et
al., 2020), and multi-task learning (Caruana, 1997). Our results show that the
domain-adapted models significantly outperform their non-adapted counterparts,
and single-task outperforms multi-task learning.


title:
An overview on extremals and critical points of the Sobolev inequality
  in convex cones

abstract:
  In this survey, we consider the sharp Sobolev inequality in convex cones. We
also prove it by using the optimal transport technique. Then we present some
results related to the Euler-Lagrange equation of the Sobolev inequality: the
so-called critical p-Laplace equation. Finally, we discuss some stability
results related to the Sobolev inequality.


title: Spin liquid state in a rare-earth hyperkagome lattice

abstract:
  Quantum fluctuations enhanced by frustration and subtle interplay between
competing degrees of freedom offer an ideal ground to realize novel states with
fractional quantum numbers in quantum materials that defy standard theoretical
paradigms. Quantum spin liquid (QSL) is a highly entangled state wherein
frustration induced strong quantum fluctuations preclude symmetry breaking
phase transitions down to zero temperature without any order parameter.
Experimental realizations of QSL in quantum materials with spin dimensionality
greater than one is very rare. Here, we present our thermodynamic, nuclear
magnetic resonance, muon spin relaxation and inelastic neutron scattering
studies of a new rare-earth hyperkagome compound Li3Yb3Te2O12 in which Yb3+
ions constitute a three dimensional spin-lattice without any detectable
disorder. Our comprehensive experiments evince neither signature of magnetic
ordering nor spin freezing down to 38 mK that suggest the realization of
dynamic liquid-like ground state in this antiferromagnet. The ground state of
this material is interpreted by a low energy Jeff = 1/2 degrees of freedom with
short range spin correlations. The present results demonstrate a viable basis
to explore spin-orbit driven enigmatic correlated quantum states in a new class
of rare-earth based three dimensional frustrated magnets that may open new
avenues in theoretical and experimental search for spin liquids.


title: Bounds for Synchronizing Markov Decision Processes

abstract:
  We consider Markov decision processes with synchronizing objectives, which
require that a probability mass of $1-\epsilon$ accumulates in a designated set
of target states, either once, always, infinitely often, or always from some
point on, where $\epsilon = 0$ for sure synchronizing, and $\epsilon \to 0$ for
almost-sure and limit-sure synchronizing.
  We introduce two new qualitative modes of synchronizing, where the
probability mass should be either positive, or bounded away from $0$. They can
be viewed as dual synchronizing objectives.
  We present algorithms and tight complexity results for the problem of
deciding if a Markov decision process is positive, or bounded synchronizing,
and we provide explicit bounds on $\epsilon$ in all synchronizing modes. In
particular, we show that deciding positive and bounded synchronizing always
from some point on, is coNP-complete.


title:
Semi-Autonomous Electric Vehicles in Platooning Mode and Their Effects
  on Travel Time: A Framework for Simulation Evaluation

abstract:
  Connected and Automated Vehicles (CAVs) have received a lot of attention in
recent years. However, there are still numerous challenges in this field. In
this paper, we investigated the effects of dynamic-flexible platooning on
travel time by considering real-world trips data. For this purpose we extended
the platooning capabilities of the 3DCoAutosim simulation platform, and
proposed a dynamic-flexible model that we validated by creating use cases on
traffic efficiency. We studied our dynamic-flexible platooning case for three
electric vans with an autonomous leader, a semi-autonomous first follower with
a driver, and an autonomous last follower. Results showed that the model
developed in this study is efficient to investigate the effects of
dynamic-flexible platooning on travel time.


title:
The MeVer DeepFake Detection Service: Lessons Learnt from Developing and
  Deploying in the Wild

abstract:
  Enabled by recent improvements in generation methodologies, DeepFakes have
become mainstream due to their increasingly better visual quality, the increase
in easy-to-use generation tools and the rapid dissemination through social
media. This fact poses a severe threat to our societies with the potential to
erode social cohesion and influence our democracies. To mitigate the threat,
numerous DeepFake detection schemes have been introduced in the literature but
very few provide a web service that can be used in the wild. In this paper, we
introduce the MeVer DeepFake detection service, a web service detecting deep
learning manipulations in images and video. We present the design and
implementation of the proposed processing pipeline that involves a model
ensemble scheme, and we endow the service with a model card for transparency.
Experimental results show that our service performs robustly on the three
benchmark datasets while being vulnerable to Adversarial Attacks. Finally, we
outline our experience and lessons learned when deploying a research system
into production in the hopes that it will be useful to other academic and
industry teams.


title: CATrans: Context and Affinity Transformer for Few-Shot Segmentation

abstract:
  Few-shot segmentation (FSS) aims to segment novel categories given scarce
annotated support images. The crux of FSS is how to aggregate dense
correlations between support and query images for query segmentation while
being robust to the large variations in appearance and context. To this end,
previous Transformer-based methods explore global consensus either on context
similarity or affinity map between support-query pairs. In this work, we
effectively integrate the context and affinity information via the proposed
novel Context and Affinity Transformer (CATrans) in a hierarchical
architecture. Specifically, the Relation-guided Context Transformer (RCT)
propagates context information from support to query images conditioned on more
informative support features. Based on the observation that a huge feature
distinction between support and query pairs brings barriers for context
knowledge transfer, the Relation-guided Affinity Transformer (RAT) measures
attention-aware affinity as auxiliary information for FSS, in which the
self-affinity is responsible for more reliable cross-affinity. We conduct
experiments to demonstrate the effectiveness of the proposed model,
outperforming the state-of-the-art methods.


title: Autonomous Vehicle Calibration via Linear Optimization

abstract:
  In navigation activities, kinematic parameters of a mobile vehicle play a
significant role. Odometry is most commonly used for dead reckoning. However,
the unrestricted accumulation of errors is a disadvantage using this method. As
a result, it is necessary to calibrate odometry parameters to minimize the
error accumulation. This paper presents a pipeline based on sequential least
square programming to minimize the relative position displacement of an
arbitrary landmark in consecutive time steps of a kinematic vehicle model by
calibrating the parameters of applied model. Results showed that the developed
pipeline produced accurate results with small datasets.


title: Conformer and Blind Noisy Students for Improved Image Quality Assessment

abstract:
  Generative models for image restoration, enhancement, and generation have
significantly improved the quality of the generated images. Surprisingly, these
models produce more pleasant images to the human eye than other methods, yet,
they may get a lower perceptual quality score using traditional perceptual
quality metrics such as PSNR or SSIM. Therefore, it is necessary to develop a
quantitative metric to reflect the performance of new algorithms, which should
be well-aligned with the person's mean opinion score (MOS). Learning-based
approaches for perceptual image quality assessment (IQA) usually require both
the distorted and reference image for measuring the perceptual quality
accurately. However, commonly only the distorted or generated image is
available. In this work, we explore the performance of transformer-based
full-reference IQA models. We also propose a method for IQA based on
semi-supervised knowledge distillation from full-reference teacher models into
blind student models using noisy pseudo-labeled data. Our approaches achieved
competitive results on the NTIRE 2022 Perceptual Image Quality Assessment
Challenge: our full-reference model was ranked 4th, and our blind noisy student
was ranked 3rd among 70 participants, each in their respective track.


title:
LyS_ACoru\~na at SemEval-2022 Task 10: Repurposing Off-the-Shelf Tools
  for Sentiment Analysis as Semantic Dependency Parsing

abstract:
  This paper addressed the problem of structured sentiment analysis using a
bi-affine semantic dependency parser, large pre-trained language models, and
publicly available translation models. For the monolingual setup, we
considered: (i) training on a single treebank, and (ii) relaxing the setup by
training on treebanks coming from different languages that can be adequately
processed by cross-lingual language models. For the zero-shot setup and a given
target treebank, we relied on: (i) a word-level translation of available
treebanks in other languages to get noisy, unlikely-grammatical, but annotated
data (we release as much of it as licenses allow), and (ii) merging those
translated treebanks to obtain training data. In the post-evaluation phase, we
also trained cross-lingual models that simply merged all the English treebanks
and did not use word-level translations, and yet obtained better results.
According to the official results, we ranked 8th and 9th in the monolingual and
cross-lingual setups.


title: On the pole placement of scalar linear delay systems with two delays

abstract:
  This paper concerns some spectral properties of the scalar dynamical system
defined by a linear delay-differential equation with two positive delays. More
precisely, the existing links between the delays and the maximal multiplicity
of the characteristic roots are explored, as well as the dominancy of such
roots compared with the spectrum localization. As a by-product of the analysis,
the pole placement issue is revisited with more emphasis on the role of the
delays as control parameters in defining a partial pole placement guaranteeing
the closed-loop stability with an appropriate decay rate of the corresponding
dynamical system.


title:
A Survey on XAI for Beyond 5G Security: Technical Aspects, Use Cases,
  Challenges and Research Directions

abstract:
  With the advent of 5G commercialization, the need for more reliable, faster,
and intelligent telecommunication systems are envisaged for the next generation
beyond 5G (B5G) radio access technologies. Artificial Intelligence (AI) and
Machine Learning (ML) are not just immensely popular in the service layer
applications but also have been proposed as essential enablers in many aspects
of B5G networks, from IoT devices and edge computing to cloud-based
infrastructures. However, most of the existing surveys in B5G security focus on
the performance of AI/ML models and their accuracy, but they often overlook the
accountability and trustworthiness of the models' decisions. Explainable AI
(XAI) methods are promising techniques that would allow system developers to
identify the internal workings of AI/ML black-box models. The goal of using XAI
in the security domain of B5G is to allow the decision-making processes of the
security of systems to be transparent and comprehensible to stakeholders making
the systems accountable for automated actions. In every facet of the
forthcoming B5G era, including B5G technologies such as RAN, zero-touch network
management, E2E slicing, this survey emphasizes the role of XAI in them and the
use cases that the general users would ultimately enjoy. Furthermore, we
presented the lessons learned from recent efforts and future research
directions on top of the currently conducted projects involving XAI.


title:
Constraining the mass and redshift evolution of the hydrostatic mass
  bias using the gas mass fraction in galaxy clusters

abstract:
  The gas mass fraction in galaxy clusters is a convenient probe to use in
cosmological studies, as it can help derive constraints on a collection of
cosmological parameters. It is however subject to various effects from the
baryonic physics inside galaxy clusters, which may bias the obtained
cosmological constraints. Among different aspects of the baryonic physics, in
this paper we focus on the impact of the hydrostatic equilibrium assumption. We
analyse the hydrostatic mass bias $B$, constraining a possible mass and
redshift evolution of this quantity and its impact on the cosmological
constraints. To that end we consider cluster observations of the {\it
Planck}-ESZ sample and evaluate the gas mass fraction using X-ray counterpart
observations. We show a degeneracy between the redshift dependence of the bias
and cosmological parameters. In particular we find a $3.8 \sigma$ evidence for
a redshift dependence of the bias when assuming a {\it Planck} prior on
$\Omega_m$. On the other hand, assuming a constant mass bias would lead to the
extreme large value of $\Omega_m > 0.849$. We however show that our results are
entirely dependent on the cluster sample we consider. In particular, the mass
and redshift trends that we find for the lowest mass-redshift and highest
mass-redshift clusters of our sample are not compatible. Nevertheless, in all
the analyses we find a value for the amplitude of the bias that is consistent
with $B \sim 0.8$, as expected from hydrodynamical simulations and local
measurements, but still in tension with the low value of $B \sim 0.6$ derived
from the combination of cosmic microwave background primary anisotropies with
cluster number counts.


title:
Maritime Communications: A Survey on Enabling Technologies,
  Opportunities, and Challenges

abstract:
  Water covers 71% of the Earth's surface, where the steady increase in oceanic
activities has promoted the need for reliable maritime communication
technologies. The existing maritime communication systems involve terrestrial,
aerial, and satellite networks. This paper presents a holistic overview of the
different forms of maritime communications and provides the latest advances in
various marine technologies. The paper first introduces the different
techniques used for maritime communications over the RF and optical bands.
Then, we present the channel models for RF and optical bands, modulation and
coding schemes, coverage and capacity, and radio resource management in
maritime communications. After that, the paper presents some emerging use cases
of maritime networks, such as the Internet of Ships (IoS) and the
ship-to-underwater Internet of things (IoT). Finally, we highlight a few
exciting open challenges and identify a set of future research directions for
maritime communication, including bringing broadband connectivity to the deep
sea, using THz and visible light signals for on-board applications, and
data-driven modeling for radio and optical marine propagation.


title:
Uncertainty-Aware Prediction of Battery Energy Consumption for Hybrid
  Electric Vehicles

abstract:
  The usability of vehicles is highly dependent on their energy consumption. In
particular, one of the main factors hindering the mass adoption of electric
(EV), hybrid (HEV), and plug-in hybrid (PHEV) vehicles is range anxiety, which
occurs when a driver is uncertain about the availability of energy for a given
trip. To tackle this problem, we propose a machine learning approach for
modeling the battery energy consumption. By reducing predictive uncertainty,
this method can help increase trust in the vehicle's performance and thus boost
its usability. Most related work focuses on physical and/or chemical models of
the battery that affect the energy consumption. We propose a data-driven
approach which relies on real-world datasets including battery related
attributes. Our approach showed an improvement in terms of predictive
uncertainty as well as in accuracy compared to traditional methods.


title: ADM formulation and Hamiltonian analysis of $f(Q)$ gravity

abstract:
  $f(Q)$ gravity is an extension of the symmetric teleparallel equivalent to
general relativity (STEGR). We demonstrate the Hamiltonian analysis of $f(Q)$
gravity with fixing the coincident gauge condition. Using the standard
Dirac-Bergmann algorithm, we show that $f(Q)$ gravity has eight physical
degrees of freedom. This result reflects that the diffeomorphism symmetry of
$f(Q)$ gravity is completely broken due to the gauge fixing. Moreover, in terms
of the perturbations, we discuss the possible mode decomposition of those
degrees of freedom.


title:
CONCERTO: High-fidelity simulation of millimeter line emissions of
  galaxies and [CII] intensity mapping

abstract:
  The intensity mapping of the [CII] 158um line redshifted to the sub-mm window
is a promising probe of the z>4 star formation and its spatial distribution
into the large-scale structure. To prepare the first-generation experiments
(e.g., CONCERTO), we need realistic simulations of the sub-mm extragalactic sky
in spectroscopy. We present a new version of the SIDES simulation including the
main sub-mm lines around 1mm (CO, [CII], [CI]). This approach successfully
reproduces the observed line luminosity functions. We then use our simulation
to generate CONCERTO-like cubes (125-305GHz) and forecast the power spectra of
the fluctuations caused by the various astrophysical components at those
frequencies. Depending on our assumptions on the relation between star
formation rate and [CII] luminosity, and the star formation history, our
predictions of the z~6 [CII] power spectrum vary by two orders of magnitude.
This highlights how uncertain the predictions are and how important future
measurements will be to improve our understanding of this early epoch. SIDES
can reproduce the CO shot noise recently measured at ~100 GHz by the mmIME
experiment. Finally, we compare the contribution of the different astrophysical
components at various redshift to the power spectra. The continuum is by far
the brightest, by a factor of 3 to 100 depending on the frequency. At 300GHz,
the CO foreground power spectrum is higher than the [CII] one for our base
scenario. At lower frequency, the contrast between [CII] and extragalactic
foregrounds is even worse. Masking the known galaxies from deep surveys should
allow to reduce the foregrounds to 20% of the [CII] power spectrum up to z~6.5.
However, this masking method will not be sufficient at higher redshifts. The
code and the products of our simulation are released publicly and can be used
for both intensity mapping experiments and sub-mm continuum and line surveys.


title: Hilbert irreducibility for varieties with a nef tangent bundle

abstract:
  We prove a fibration property for varieties with Hilbert-type properties and
give applications to rational points on varieties with nef tangent bundle.


title:
A note on bifurcations from eigenvalues of the Dirichlet-Laplacian with
  arbitrary multiplicity

abstract:
  In this short note, we consider the elliptic problem
  $$
  \lambda \phi + \Delta \phi = \eta|\phi|^\sigma \phi,\quad
\phi\big|_\Omega=0,\quad \lambda, \eta \in \mathbb{C}.
  $$
  The presence of complex coefficients, motivated by the study of complex
Ginzburg-Landau equations, breaks down the variational structure of the
equation. We study the existence of nontrivial solutions as bifurcations from
the trivial solution. More precisely, we characterize the bifurcation branches
starting from eigenvalues of the Dirichlet-Laplacian of arbitrary multiplicity.
This allows us to discuss the nature of such bifurcations in some specific
cases. We conclude with the stability analysis of these branches under the
complex Ginzburg-Landau flow.


title: Bound on optimal local discrimination of multipartite quantum states

abstract:
  We consider the unambiguous discrimination of multipartite quantum states and
provide an upper bound for the maximum success probability of optimal local
discrimination. We also provide a necessary and sufficient condition to realize
the upper bound. We further establish a necessary and sufficient condition for
this upper bound to be saturated. Finally, we illustrate our results using
examples in multidimensional multipartite quantum systems.


title:
The Revisiting Problem in Simultaneous Localization and Mapping: A
  Survey on Visual Loop Closure Detection

abstract:
  Where am I? This is one of the most critical questions that any intelligent
system should answer to decide whether it navigates to a previously visited
area. This problem has long been acknowledged for its challenging nature in
simultaneous localization and mapping (SLAM), wherein the robot needs to
correctly associate the incoming sensory data to the database allowing
consistent map generation. The significant advances in computer vision achieved
over the last 20 years, the increased computational power, and the growing
demand for long-term exploration contributed to efficiently performing such a
complex task with inexpensive perception sensors. In this article, visual loop
closure detection, which formulates a solution based solely on appearance input
data, is surveyed. We start by briefly introducing place recognition and SLAM
concepts in robotics. Then, we describe a loop closure detection system's
structure, covering an extensive collection of topics, including the feature
extraction, the environment representation, the decision-making step, and the
evaluation process. We conclude by discussing open and new research challenges,
particularly concerning the robustness in dynamic environments, the
computational complexity, and scalability in long-term operations. The article
aims to serve as a tutorial and a position paper for newcomers to visual loop
closure detection.


title: Exploring black hole mechanics in cotangent bundle geometries

abstract:
  The classical and continuum limit of a quantum gravitational setting could
lead, at mesoscopic regimes, to a very different notion of geometry w.r.t. the
pseudo-Riemannian one of special and general relativity. A possible way to
characterize this modified space-time notion is by a momentum dependent metric,
in such a way that particles with different energies could probe different
spacetimes. Indeed, doubly special relativity theories, deforming the special
relativistic kinematics while maintaining a relativity principle, have been
understood within a geometrical context, by considering a curved momentum
space. The extension of these momentum spaces to curved spacetimes and its
possible phenomenological implications have been recently investigated.
Following this line of research, we address here the first two laws of black
holes thermodynamics in the context of a cotangent bundle metric, depending on
both momentum and space-time coordinates, compatible with the relativistic
deformed kinematics of doubly special relativity.


title: Transfer Learning with Pre-trained Conditional Generative Models

abstract:
  Transfer learning is crucial in training deep neural networks on new target
tasks. Current transfer learning methods generally assume at least one of (i)
source and target task label spaces must overlap, (ii) source datasets are
available, and (iii) target network architectures are consistent with source
ones. However, these all assumptions are difficult to hold in practical
settings because the target task rarely has the same labels as the source task,
the source dataset access is restricted due to licensing and storage costs, and
the target architecture is often specialized to each task. To transfer source
knowledge without these assumptions, we propose a transfer learning method that
uses deep generative models and is composed of the following two stages: pseudo
pre-training (PP) and pseudo semi-supervised learning (P-SSL). PP trains a
target architecture with a synthesized dataset by using conditional source
generative models. P-SSL applies SSL algorithms to labeled target data and
unlabeled pseudo samples, which are generated by cascading the source
classifier and generative models to condition them with target samples. Our
experimental results indicate that our method can outperform baselines of
scratch training and knowledge distillation.


title: Power Bundle Adjustment for Large-Scale 3D Reconstruction

abstract:
  We present the design and the implementation of a new expansion type
algorithm to solve large-scale bundle adjustment problems. Our approach --
called Power Bundle Adjustment -- is based on the power series expansion of the
inverse Schur complement. This initiates a new family of solvers that we call
inverse expansion methods. We show with the real-world BAL dataset that the
proposed solver challenges the traditional direct and iterative methods. The
solution of the normal equation is significantly accelerated, even for reaching
a very high accuracy. Last but not least, our solver can also complement a
recently presented distributed bundle adjustment framework. We demonstrate that
employing the proposed Power Bundle Adjustment as a sub-problem solver greatly
improves speed and accuracy of the distributed optimization.


title: Learning to Parallelize in a Shared-Memory Environment with Transformers

abstract:
  In past years, the world has switched to many-core and multi-core shared
memory architectures.
  As a result, there is a growing need to utilize these architectures by
introducing shared memory parallelization schemes to software applications.
OpenMP is the most comprehensive API that implements such schemes,
characterized by a readable interface. Nevertheless, introducing OpenMP into
code is challenging due to pervasive pitfalls in management of parallel shared
memory. To facilitate the performance of this task, many source-to-source (S2S)
compilers have been created over the years, tasked with inserting OpenMP
directives into code automatically.
  In addition to having limited robustness to their input format, these
compilers still do not achieve satisfactory coverage and precision in locating
parallelizable code and generating appropriate directives.
  In this work, we propose leveraging recent advances in ML techniques,
specifically in natural language processing (NLP), to replace S2S compilers
altogether.
  We create a database (corpus), Open-OMP, specifically for this goal. Open-OMP
contains over 28,000 code snippets, half of which contain OpenMP directives
while the other half do not need parallelization at all with high probability.
  We use the corpus to train systems to automatically classify code segments in
need of parallelization, as well as suggest individual OpenMP clauses.
  We train several transformer models, named PragFormer, for these tasks, and
show that they outperform statistically-trained baselines and automatic S2S
parallelization compilers in both classifying the overall need for an OpenMP
directive and the introduction of private and reduction clauses.
  Our source code and database are available at:
https://github.com/Scientific-Computing-Lab-NRCN/PragFormer.


title:
Computing quantum correlation functions by Importance Sampling method
  based on path integrals

abstract:
  An importance sampling method based on Generalized Feynman-Kac method has
been used to calculate the mean values of quantum observables from quantum
correlation functions for many body systems both at zero and finite
temperature. Specifically, the expectation of $\langle r_i^n\rangle$, $\langle
r_{ij}^n\rangle$, $\langle r_i^{-n}\rangle$ and $\langle r_{ij}^{-n}\rangle$
for the ground state of the lithium and beryllium and the density matrix, the
partition function, the internal energy and the specific heat of a system of
quantum harmonic oscillators are computed, in good agreement with the best
nonrelativistic values for these quantities. Although the initial results are
encouarging, more experimentation will be needed to improve the other existing
numerical results beyond chemical accuracies specially for the last two
properties for lithium and beryllium. Also more work needs to be done to
improve the trial functions for finite temperature calculations.


title: A Task Programming Implementation for the Particle in Cell Code Smilei

abstract:
  An implementation of the electromagnetic Particle in Cell loop in the code
Smilei using task programming is presented. Through OpenMP, the macro-particles
operations are formulated in terms of tasks. This formulation allows
asynchronous execution respecting the data dependencies of the macro-particle
operations, the most time-consuming part of the code in simulations of interest
for plasma physics. Through some benchmarks it is shown that this formulation
can help mitigating the load imbalance of these operations at the OpenMP thread
level. The improvements in strong scaling for load-imbalanced physical cases
are discussed.


title: Remarks on some integral formulas for $G_2$-structures

abstract:
  For seven-dimensional Riemannian manifolds equipped with a $G_2$-structure,
we show in a full detailed way that all integral formulas and divergence
equations, given by diverse authors, are agree with the ones displayed here in
terms of the intrinsic torsion of the $G_2$-structure. Likewise the components
of such an intrinsic torsion is expressed by means of exterior algebra.


title: Improvements in Higgs-mass predictions with $\texttt{FeynHiggs}$

abstract:
  Recent developments in the public code $\texttt{FeynHiggs}$ for the
prediction of the Standard Model-like Higgs mass in the Minimal Supersymmetric
Standard Model are presented. Improvements in the prediction based on an
effective-field theory concern the cases of multi-scale hierarchies where the
gluino is much heavier than the sfermions or where the additional Higgs bosons
have masses between the electroweak and supersymmetry scales. The fixed-order
part is improved by a re-implementation and extension of the two-loop
corrections, now containing the contributions of orders
$\big(\alpha_t+\alpha_b\big)\cdot\big(\alpha_t+\alpha_b+\alpha_s\big)$ in the
limit of vanishing electroweak gauge couplings for all implemented
renormalization schemes. The updated version has a significantly improved
numerical stability and thus an enhanced range of applicability for scenarios
with heavy supersymmetric particles. All updates will be included in the
upcoming version $\texttt{FeynHiggs-2.19.0}$.


title: On Ramsey numbers of 3-uniform Berge cycles

abstract:
  For an arbitrary graph $G$, a hypergraph $\mathcal{H}$ is called Berge-$G$ if
there is a bijection $\Phi :E(G)\longrightarrow E( \mathcal{H})$ such that for
each $e\in E(G)$, we have $e\subseteq \Phi (e)$. We denote by $\mathcal{B}^rG$,
the family of $r$-uniform Berge-$G$ hypergraphs. For families $\mathcal{H}_1,
\mathcal{H}_2,\ldots, \mathcal{H}_t$ of $r$-uniform hypergraphs, the Ramsey
number $R(\mathcal{H}_1, \mathcal{H}_2,\ldots, \mathcal{H}_t)$ is the smallest
integer $n$ such that in every $t$-hyperedge coloring of $\mathcal{K}_{n}^r$
there is a monochromatic copy of a hypergraph in $\mathcal{H}_i$ of color $i$,
for some $1\leq i\leq t$.
  Recently, the Ramsey problems of Berge hypergraphs have been studied by many
researchers.
  In this paper, we focus on Ramsey number involving $3$-uniform Berge cycles
and we prove that for $n \geq 4$, $
R(\mathcal{B}^3C_n,\mathcal{B}^3C_n,\mathcal{B}^3C_3)=n+1.$ Moreover, for $m
\geq n\geq 6$ and $m\geq 11$, we show that
$R(\mathcal{B}^3K_m,\mathcal{B}^3C_n)= m+\lfloor \frac{n-1}{2}\rfloor -1.$ This
is the first result of Ramsey number for two different families of Berge
hypergraphs.


title:
BBBD: Bounding Box Based Detector for Occlusion Detection and Order
  Recovery

abstract:
  Occlusion handling is one of the challenges of object detection and
segmentation, and scene understanding. Because objects appear differently when
they are occluded in varying degree, angle, and locations. Therefore,
determining the existence of occlusion between objects and their order in a
scene is a fundamental requirement for semantic understanding. Existing works
mostly use deep learning based models to retrieve the order of the instances in
an image or for occlusion detection. This requires labelled occluded data and
it is time consuming. In this paper, we propose a simpler and faster method
that can perform both operations without any training and only requires the
modal segmentation masks. For occlusion detection, instead of scanning the two
objects entirely, we only focus on the intersected area between their bounding
boxes. Similarly, we use the segmentation mask inside the same area to recover
the depth-ordering. When tested on COCOA dataset, our method achieves +8% and
+5% more accuracy than the baselines in order recovery and occlusion detection
respectively.


title: $2$-Spinors via Linear Algebra

abstract:
  We give a streamlined account of $2$-spinors, up to and including the Dirac
equation, using little more than the resources of linear algebra. We prove that
the Dirac bundle is isomorphic to the associated bundles
$\mathrm{SL}_2(\mathbb{C}) \times_{\mathrm{SU}_2} S$ and
$\mathrm{SL}_2(\mathbb{C}) \times_{\mathrm{SU}_2} \bar{S}$. A solution of the
Dirac equation determines a pair of conjugate $2$-spinor fields over the mass
shell $X_m$.


title:
Infrared study of the layered, magnetic insulator
  Mn(Bi$_{0.07}$Sb$_{0.93}$)$_2$Te$_4$ at low temperatures

abstract:
  Topological insulators with intrinsic magnetic ordering, potentially hosting
rare quantum effects, recently attracted extensive attention. MnBi$_2$Te$_4$ is
the first established example. The Sb-doped variant
Mn(Bi$_{1-x}$Sb$_x$)$_2$Te$_4$ shows a great variety of electronic properties
depending on the Sb content~$x$, such as shifts in the Fermi level and the Neel
temperature $T_{\mathrm N}$, and the change of the free charge carrier type
from $n$- to $p$-type at high Sb substitution ratios. Here, we investigate the
effect of magnetic ordering on the bulk electronic structure of
Mn(Bi$_{1-x}$Sb$_x$)$_2$Te$_4$ with high Sb content $x=0.93$ by
temperature-dependent reflectivity measurements over a broad frequency range.
We observe anomalies in the optical response across $T_{\mathrm N}$ when the
antiferromagnetic order sets, which suggests a coupling between the magnetic
ordering and the electronic structure of the material.


title:
Accelerating Robot Learning of Contact-Rich Manipulations: A Curriculum
  Learning Study

abstract:
  The Reinforcement Learning (RL) paradigm has been an essential tool for
automating robotic tasks. Despite the advances in RL, it is still not widely
adopted in the industry due to the need for an expensive large amount of robot
interaction with its environment. Curriculum Learning (CL) has been proposed to
expedite learning. However, most research works have been only evaluated in
simulated environments, from video games to robotic toy tasks. This paper
presents a study for accelerating robot learning of contact-rich manipulation
tasks based on Curriculum Learning combined with Domain Randomization (DR). We
tackle complex industrial assembly tasks with position-controlled robots, such
as insertion tasks. We compare different curricula designs and sampling
approaches for DR. Based on this study, we propose a method that significantly
outperforms previous work, which uses DR only (No CL is used), with less than a
fifth of the training time (samples). Results also show that even when training
only in simulation with toy tasks, our method can learn policies that can be
transferred to the real-world robot. The learned policies achieved success
rates of up to 86\% on real-world complex industrial insertion tasks (with
tolerances of $\pm 0.01~mm$) not seen during the training.


title: Triskels and Symmetries of Mean Global Sea-Level Pressure

abstract:
  The evolution of mean sea-level atmospheric pressure since 1850 is analyzed
using singular spectrum analysis. Maps of the main components (the trends)
reveal striking symmetries of order 3 and 4. The northern hemisphere (NH)
displays a set of three positive features, forming an almost perfect
equilateral triangle. The southern hemisphere (SH) displays a set of three
positive features arranged as an isosceles triangle, with a possible fourth
(weaker) feature. This geometry can be modeled as Taylor-Couette flow of mode 3
(NH) or 4 (SH). The remarkable regularity and order three symmetry of the NH
triskel occurs despite the lack of cylindrical symmetry of the northern
continents. The stronger intensity and larger size of features in the SH is
linked to the presence of the annular AAO. In addition to the dominant trends,
quasi-periodic components of 130, 90, 50, 22, 15, 4, 1.8, 1, 0.5, 0.33, and
0.25 years, i.e. the Jose, Gleissberg, Hale and Schwabe cycles, the annual
cycle and its first three harmonics are identified.


title:
Evolving Generalizable Multigrid-Based Helmholtz Preconditioners with
  Grammar-Guided Genetic Programming

abstract:
  Solving the indefinite Helmholtz equation is not only crucial for the
understanding of many physical phenomena but also represents an
outstandingly-difficult benchmark problem for the successful application of
numerical methods. Here we introduce a new approach for evolving efficient
preconditioned iterative solvers for Helmholtz problems with multi-objective
grammar-guided genetic programming. Our approach is based on a novel
context-free grammar, which enables the construction of multigrid
preconditioners that employ a tailored sequence of operations on each
discretization level. To find solvers that generalize well over the given
domain, we propose a custom method of successive problem difficulty adaption,
in which we evaluate a preconditioner's efficiency on increasingly
ill-conditioned problem instances. We demonstrate our approach's effectiveness
by evolving multigrid-based preconditioners for a two-dimensional indefinite
Helmholtz problem that outperform several human-designed methods for different
wavenumbers up to systems of linear equations with more than a million
unknowns.


title: Query2Particles: Knowledge Graph Reasoning with Particle Embeddings

abstract:
  Answering complex logical queries on incomplete knowledge graphs (KGs) with
missing edges is a fundamental and important task for knowledge graph
reasoning. The query embedding method is proposed to answer these queries by
jointly encoding queries and entities to the same embedding space. Then the
answer entities are selected according to the similarities between the entity
embeddings and the query embedding. As the answers to a complex query are
obtained from a combination of logical operations over sub-queries, the
embeddings of the answer entities may not always follow a uni-modal
distribution in the embedding space. Thus, it is challenging to simultaneously
retrieve a set of diverse answers from the embedding space using a single and
concentrated query representation such as a vector or a hyper-rectangle. To
better cope with queries with diversified answers, we propose Query2Particles
(Q2P), a complex KG query answering method. Q2P encodes each query into
multiple vectors, named particle embeddings. By doing so, the candidate answers
can be retrieved from different areas over the embedding space using the
maximal similarities between the entity embeddings and any of the particle
embeddings. Meanwhile, the corresponding neural logic operations are defined to
support its reasoning over arbitrary first-order logic queries. The experiments
show that Query2Particles achieves state-of-the-art performance on the complex
query answering tasks on FB15k, FB15K-237, and NELL knowledge graphs.


title:
Detecting Backdoor Poisoning Attacks on Deep Neural Networks by Heatmap
  Clustering

abstract:
  Predicitions made by neural networks can be fraudulently altered by so-called
poisoning attacks. A special case are backdoor poisoning attacks. We study
suitable detection methods and introduce a new method called Heatmap
Clustering. There, we apply a $k$-means clustering algorithm on heatmaps
produced by the state-of-the-art explainable AI method Layer-wise relevance
propagation. The goal is to separate poisoned from un-poisoned data in the
dataset. We compare this method with a similar method, called Activation
Clustering, which also uses $k$-means clustering but applies it on the
activation of certain hidden layers of the neural network as input. We test the
performance of both approaches for standard backdoor poisoning attacks,
label-consistent poisoning attacks and label-consistent poisoning attacks with
reduced amplitude stickers. We show that Heatmap Clustering consistently
performs better than Activation Clustering. However, when considering
label-consistent poisoning attacks, the latter method also yields good
detection performance.


title: On a Theorem of Wielandt for fusion systems and localities

abstract:
  A classical theorem of Wielandt states that any two subnormal subgroups of a
finite group $G$ generate a subnormal subgroup of $G$. We prove versions of
this theorem for regular localities and for fusion systems. Along the way we
prove also a purely group-theoretical result which may be of independent
interest.


title:
Diurnal variation of the surface temperature of Mars with the Emirates
  Mars Mission: A comparison with Curiosity and Perseverance rover measurements

abstract:
  The Emirates Mars Infrared Spectrometer (EMIRS) instrument on board the
Emirates Mars Mission (EMM) "Hope", for the first time, is providing us with
the temperature measurements of Mars at all local times covering most of the
planet. It is therefore possible now to compare surface temperature
measurements made from orbit with those from the surface by rovers during the
same time period. We use data of diurnal temperature variation from the Rover
Environmental Monitoring Station (REMS) suite on board the Mars Science
Laboratory (MSL) "Curiosity" rover, and the Mars Environmental Dynamics
Analyzer (MEDA) suite on board the Mars 2020 "Perseverance" rover, between June
and August 2021 and compare them with EMIRS observations. We also compare these
measurements with estimates of the Mars Climate Database (MCD) model. We show
that although the overall trend of temperature variation is in excellent
agreement across missions, EMIRS measurements are systematically lower at night
compared to Mars 2020. We describe a number of factors which could lead to this
discrepancy. We discuss the implications of these results in improving our
understanding of the Martian climate which would lead to better modeling of
local weather prediction, useful for future robotic and potentially crewed
missions to Mars.


title:
Li-Yau gradient estimates on closed manifolds under bakry-emery ricci
  curvature conditions

abstract:
  In this paper, motivated by the work of Qi S. Zhang in [28], we derive Li-Yau
gradient bounds for positive solutions of the f-heat equation on closed
manifolds with Bakry-Emery Ricci curvature bounded below.