title:
On optimization of coherent and incoherent controls for two-level
  quantum systems

abstract:
  This article considers some control problems for closed and open two-level
quantum systems. The closed system's dynamics is governed by the Schr\"odinger
equation with coherent control. The open system's dynamics is governed by the
Gorini-Kossakowski-Sudarshan-Lindblad master equation whose Hamiltonian depends
on coherent control and superoperator of dissipation depends on incoherent
control. For the closed system, we consider the problem for generation of the
phase shift gate for some values of phases and final times for which
numerically show that zero coherent control, which is a stationary point of the
objective functional, is not optimal; it gives an example of "underwater rock"
for practical solving problems of quantum control. For the open system, in the
two-stage method which was developed for generic N-level quantum systems in
[Pechen A., Phys. Rev. A., 84, 042106 (2011)] for approximate generation of a
target density matrix, here we consider the two-level systems for which modify
the first ("incoherent") stage by numerically optimizing piecewise constant
incoherent control instead of using constant incoherent control analytically
computed using eigenvalues of the target density matrix. Exact analytical
formulas are derived for the system's state evolution, the objective functions
and their gradients for the modified first stage. These formulas are applied in
the two-step gradient projection method. The numerical simulations show that
the modified first stage's duration can be significantly less than the
unmodified first stage's duration, but at the cost of optimization in the class
of piecewise constant controls.


title: Nonlinear (chiral) p-form electrodynamics

abstract:
  In our previous article Phys. Rev. Lett. 127 (2021) 271601, we announced a
novel 'democratic' Lagrangian formulation of general nonlinear electrodynamics
in four dimensions that features electric and magnetic potentials on equal
footing. Here, we give an expanded and more detailed account of this new
formalism, and then proceed to push it significantly further by building the
corresponding Lagrangian theories of higher form field interactions in
arbitrary dimensions. Special attention is given to interactions of chiral
2k-forms in 4k+2 dimensions, with further details for 2-forms in 6 dimensions
and 4-forms in 10 dimensions. We comment more broadly on the structure of
covariant equations of motion for chiral fields, and on the place of our
Lagrangian theories in this context. The Lagrangian theories we develop are
simple and explicit, and cover a much broader class of interactions than all
past attempts in the literature.


title: Parallel Parking: Optimal Entry and Minimum Slot Dimensions

abstract:
  The problem of path planning for automated parking is usually presented as
finding a collision-free path from initial to goal positions, where three out
of four parking slot edges represent obstacles. We rethink the path planning
problem for parallel parking by decomposing it into two independent parts. The
topic of this paper is finding optimal parking slot entry positions. Path
planning from initial to entry position is out of scope here. We show the
relation between entry positions, parking slot dimensions, and the number of
backward-forward direction changes. This information can be used as an input to
optimize other parts of the automated parking process.


title:
M2R2: Missing-Modality Robust emotion Recognition framework with
  iterative data augmentation

abstract:
  This paper deals with the utterance-level modalities missing problem with
uncertain patterns on emotion recognition in conversation (ERC) task. Present
models generally predict the speaker's emotions by its current utterance and
context, which is degraded by modality missing considerably. Our work proposes
a framework Missing-Modality Robust emotion Recognition (M2R2), which trains
emotion recognition model with iterative data augmentation by learned common
representation. Firstly, a network called Party Attentive Network (PANet) is
designed to classify emotions, which tracks all the speakers' states and
context. Attention mechanism between speaker with other participants and
dialogue topic is used to decentralize dependence on multi-time and multi-party
utterances instead of the possible incomplete one. Moreover, the Common
Representation Learning (CRL) problem is defined for modality-missing problem.
Data imputation methods improved by the adversarial strategy are used here to
construct extra features to augment data. Extensive experiments and case
studies validate the effectiveness of our methods over baselines for
modality-missing emotion recognition on two different datasets.


title: Matrix Representation of Arbitrarily Controlled Quantum Gates

abstract:
  Controlled operations allow for the entanglement of quantum registers. In
particular, a controlled-$U$ gate allows an operation, $U$, to be applied to
the target register and entangle the results to certain values in the control
register. This can be generalised by making use of the classical notion of
conditional statements, where if a value (or state) satisfies some condition
then a sequence of operations can be performed. A method is introduced to
represent these generalised controlled operations that are based on classical
conditional statements. Throughout examples are given to highlight the use of
introduced gates.


title: Theories of "Gender" in NLP Bias Research

abstract:
  The rise of concern around Natural Language Processing (NLP) technologies
containing and perpetuating social biases has led to a rich and rapidly growing
area of research. Gender bias is one of the central biases being analyzed, but
to date there is no comprehensive analysis of how "gender" is theorized in the
field. We survey nearly 200 articles concerning gender bias in NLP to discover
how the field conceptualizes gender both explicitly (e.g. through definitions
of terms) and implicitly (e.g. through how gender is operationalized in
practice). In order to get a better idea of emerging trajectories of thought,
we split these articles into two sections by time.
  We find that the majority of the articles do not make their theorization of
gender explicit, even if they clearly define "bias." Almost none use a model of
gender that is intersectional or inclusive of nonbinary genders; and many
conflate sex characteristics, social gender, and linguistic gender in ways that
disregard the existence and experience of trans, nonbinary, and intersex
people. There is an increase between the two time-sections in statements
acknowledging that gender is a complicated reality, however, very few articles
manage to put this acknowledgment into practice. In addition to analyzing these
findings, we provide specific recommendations to facilitate interdisciplinary
work, and to incorporate theory and methodology from Gender Studies. Our hope
is that this will produce more inclusive gender bias research in NLP.


title: Pfaffian Interaction and $BCD$-quiver Matrix Models

abstract:
  We study matrix models involving Pfaffian interactions as generalizations of
the standard $\beta = 1$ and $\beta = 4$ matrix models. We present the Pfaffian
formulas for the partition function and the characteristic polynomial averages.
We also explore the matrix chain with the Pfaffian interaction, which realizes
the BCD-type quiver matrix models.


title:
On inherent robustness and performance limitations of a class of
  prescribed-time algorithms

abstract:
  Prescribed-time algorithms based on time-varying gains have remarkable
properties, such as finite-time regulation in a time prescribed by the user,
that is the same for every nonzero initial condition, and that holds even under
matched disturbances. However, at the same time, prescribed-time algorithms are
also known to lack robustness to measurement noise. In this note, it is shown
that in the non-scalar case the lack of robustness of some prescribed-time
algorithms is of an extreme form. Specifically, arbitrarily small measurement
noise can cause considerable deviations, divergence, and other detrimental
consequences.


title: Nucleon-antinucleon interaction

abstract:
  A review is presented of the antinucleon-nucleon interaction, and some
related issues such as fundamental symmetries, annihilation mechanisms,
antinucleon-nucleus scattering, antiprotonic atoms and neutron-antineutron
oscillations. The overall perspective is historical but the modern approaches
are also presented.


title: Study of isospin eigenstates of the pentaquark states with strangeness

abstract:
  In the present work, we construct eight color singlet-singlet type five-quark
currents with strangeness to study the pentaquark states of
$\bar{D}\Xi^{\prime}$, $\bar{D}\Xi_c^{*}$, $\bar{D}^{*}\Xi_c^{\prime}$ and
$\bar{D}^{*}\Xi_c^{*}$ via the QCD sum rules. The considered states are isospin
eigenstates with isospin quantum number $I=1$ or $I=0$. Numerical results show
that the central value of the masses extracted from the Borel platforms with
high isospin are slightly above the thresholds of the meson and baryon
constituents and the low isospin ones are a few dozens of $\rm{MeV}$ below,
what's more, the present study supports the assignment of $P_{cs}(4459)$ as
$\bar{D}\Xi_c^{*}$ molecular state with the $IJ^P=0(\frac{3}{2})^-$.
Technically, besides the analyses of the uncertainties of the masses and pole
residues, detailed discussion about the contribution of vacuum condensates
proportional to the mass of $s$ quark are conducted which may present a
reference for the technical application of QCD sum rules in the future work.


title: Quasiprobability distribution of Classical solitons

abstract:
  Quasiprobability distributions are useful in formulating the phase space
analogue of quantum mechanics. We calculate them for the Kink and Sine-Gordan
solitons. The quasiprobability distributions are useful as they can be used to
calculate quantities like charge distributions, current density, and the upper
bound on the quantum speed limit. We calculate the charge distributions and
current densities from the derived quasiprobability distribution for both the
types of solitons.


title: Stable finiteness of endomorphism rings

abstract:
  We combine a combinatorial idea of Benjamin Weiss and some localization
theory of Grothendieck categories to give a short and completely self-contained
proof of the following recent result of Hanfeng Li and Bingbing Liang: Given a
left Noetherian ring $R$ and a sofic group $G$, the group-ring $R[G]$ is stably
finite.


title:
Near-Field Wideband Extremely Large-scale MIMO Transmission with
  Holographic Metasurface Antennas

abstract:
  Extremely large-scale multiple-input multiple-output (XL-MIMO) is the
development trend of future wireless communications. However, the extremely
large-scale antenna array could bring inevitable nearfield and dual-wideband
effects that seriously reduce the transmission performance. This paper proposes
an algorithmic framework to design the beam combining for the near-field
wideband XL-MIMO uplink transmissions assisted by holographic metasurface
antennas (HMAs). Firstly, we introduce a spherical-wave-based channel model
that simultaneously takes into account both the near-field and dual-wideband
effects. Based on such a model, we then formulate the HMA-based beam combining
problem for the proposed XL-MIMO communications, which is challenging due to
the nonlinear coupling of high dimensional HMA weights and baseband combiners.
We further present a sum-mean-square-error-minimization-based algorithmic
framework. Numerical results showcase that the proposed scheme can effectively
alleviate the sum-rate loss caused by the near-field and dual-wideband effects
in HMA-assisted XL-MIMO systems. Meanwhile, the proposed HMA-based scheme can
achieve a higher sum rate than the conventional phase-shifter-based hybrid
analog/digital one with the same array aperture.


title: Recollision induced nuclear excitation of $^{229}$Th

abstract:
  Previously we proposed a new approach of exciting the $^{229}$Th nucleus
using laser-driven electron recollision [W. Wang et al., Phys. Rev. Lett. 127,
052501 (2021)]. The current article is aimed to elaborate the method by
explaining further theoretical details and presenting extended new results. The
method has also been improved by adopting the electronic excitation cross
sections calculated recently by Tkalya [E. V. Tkalya, Phys. Rev. Lett. 124,
242501 (2020)]. The new cross sections are obtained from Dirac distorted-wave
calculations instead of from Dirac plane-wave calculations as we used
previously. The distorted-wave cross sections are shown to be 5 to 6 orders of
magnitude higher than the plane-wave results. With the excitation cross
sections updated, the probability of isomeric excitation of $^{229}$Th from
electron recollision is calculated to be on the order of $10^{-12}$ per nucleus
per (femtosecond) laser pulse. Dependency of the excitation probability on
various laser parameters is calculated and discussed, including the laser
intensity, the laser wavelength, and the laser pulse duration.


title:
Five-loop anomalous dimensions of $\phi^Q$ operators in a scalar theory
  with $O(N)$ symmetry

abstract:
  We compute the complete $Q$-dependence of anomalous dimensions of traceless
symmetric tensor operator $\phi^Q$ in $O(N)$ scalar theory to five-loop. The
renormalization factors are extracted from $\phi^Q\rightarrow Q\phi$ form
factors, and the integrand of form factors are constructed with the help of
unitarity cut method. The anomalous dimensions match the known results in
\cite{Badel:2019oxl, Antipin:2020abu}, where the leading and subleading terms
in the large $Q$ limit were obtained using a semiclassical method.


title:
YOLOPose: Transformer-based Multi-Object 6D Pose Estimation using
  Keypoint Regression

abstract:
  6D object pose estimation is a crucial prerequisite for autonomous robot
manipulation applications. The state-of-the-art models for pose estimation are
convolutional neural network (CNN)-based. Lately, Transformers, an architecture
originally proposed for natural language processing, is achieving
state-of-the-art results in many computer vision tasks as well. Equipped with
the multi-head self-attention mechanism, Transformers enable simple
single-stage end-to-end architectures for learning object detection and 6D
object pose estimation jointly. In this work, we propose YOLOPose (short form
for You Only Look Once Pose estimation), a Transformer-based multi-object 6D
pose estimation method based on keypoint regression. In contrast to the
standard heatmaps for predicting keypoints in an image, we directly regress the
keypoints. Additionally, we employ a learnable orientation estimation module to
predict the orientation from the keypoints. Along with a separate translation
estimation module, our model is end-to-end differentiable. Our method is
suitable for real-time applications and achieves results comparable to
state-of-the-art methods.


title:
Search for flavour-changing neutral-current couplings between the top
  quark and the photon with the ATLAS detector at $\sqrt{s} = 13$ TeV

abstract:
  This letter documents a search for flavour-changing neutral currents (FCNCs),
which are strongly suppressed in the Standard Model, in events with a photon
and a top quark with the ATLAS detector. The analysis uses data collected in
$pp$ collisions at $\sqrt{s} = 13$ TeV during Run 2 of the LHC, corresponding
to an integrated luminosity of 139 fb$^{-1}$. Both FCNC top-quark production
and decay are considered. The final state consists of a charged lepton, missing
transverse momentum, a $b$-tagged jet, one high-momentum photon and possibly
additional jets. A multiclass deep neural network is used to classify events
either as signal in one of the two categories, FCNC production or decay, or as
background. No significant excess of events over the background prediction is
observed and 95% CL upper limits are placed on the strength of left- and
right-handed FCNC interactions. The 95% CL bounds on the branching fractions
for the FCNC top-quark decays, estimated from both top-quark production and
decay, are $\mathcal{B}(t\rightarrow u\gamma) < 0.85 \times 10^{-5}$ and
$\mathcal{B}(t\to c\gamma) < 4.2 \times 10^{-5}$ for a left-handed $tq\gamma$
coupling, and $\mathcal{B}(t\to u\gamma) < 1.2 \times 10^{-5}$ and
$\mathcal{B}(t\to c\gamma) < 4.5 \times 10^{-5}$ for a right-handed coupling.


title: Parametric Reshaping of Portraits in Videos

abstract:
  Sharing short personalized videos to various social media networks has become
quite popular in recent years. This raises the need for digital retouching of
portraits in videos. However, applying portrait image editing directly on
portrait video frames cannot generate smooth and stable video sequences. To
this end, we present a robust and easy-to-use parametric method to reshape the
portrait in a video to produce smooth retouched results. Given an input
portrait video, our method consists of two main stages: stabilized face
reconstruction, and continuous video reshaping. In the first stage, we start by
estimating face rigid pose transformations across video frames. Then we jointly
optimize multiple frames to reconstruct an accurate face identity, followed by
recovering face expressions over the entire video. In the second stage, we
first reshape the reconstructed 3D face using a parametric reshaping model
reflecting the weight change of the face, and then utilize the reshaped 3D face
to guide the warping of video frames. We develop a novel signed distance
function based dense mapping method for the warping between face contours
before and after reshaping, resulting in stable warped video frames with
minimum distortions. In addition, we use the 3D structure of the face to
correct the dense mapping to achieve temporal consistency. We generate the
final result by minimizing the background distortion through optimizing a
content-aware warping mesh. Extensive experiments show that our method is able
to create visually pleasing results by adjusting a simple reshaping parameter,
which facilitates portrait video editing for social media and visual effects.


title: Einstein's Oxford Blackboard: A Unique Historical Artefact

abstract:
  Einstein's blackboard is a well-known exhibit at the History of Science
Museum at Oxford University. However, it is much less well known that the
writing on the board provides a neat summary of a work of historic importance,
Einstein's 1931 model of the expanding universe. As a visual representation of
one of the earliest models of the universe to be proposed in the wake of
Hubble's observations of the nebulae, the blackboard provides an intriguing
snapshot of a key moment in modern astronomy and cosmology. In addition, one
line on the blackboard that is not in Einstein's 1931 paper casts useful light
on some anomalies in the calculations of that paper.


title: Real-time Controllable Motion Transition for Characters

abstract:
  Real-time in-between motion generation is universally required in games and
highly desirable in existing animation pipelines. Its core challenge lies in
the need to satisfy three critical conditions simultaneously: quality,
controllability and speed, which renders any methods that need offline
computation (or post-processing) or cannot incorporate (often unpredictable)
user control undesirable. To this end, we propose a new real-time transition
method to address the aforementioned challenges. Our approach consists of two
key components: motion manifold and conditional transitioning. The former
learns the important low-level motion features and their dynamics; while the
latter synthesizes transitions conditioned on a target frame and the desired
transition duration. We first learn a motion manifold that explicitly models
the intrinsic transition stochasticity in human motions via a multi-modal
mapping mechanism. Then, during generation, we design a transition model which
is essentially a sampling strategy to sample from the learned manifold, based
on the target frame and the aimed transition duration. We validate our method
on different datasets in tasks where no post-processing or offline computation
is allowed. Through exhaustive evaluation and comparison, we show that our
method is able to generate high-quality motions measured under multiple
metrics. Our method is also robust under various target frames (with extreme
cases).


title: Tossing Black Hole Spin Axes

abstract:
  The detection of double black hole (BH+BH) mergers provides a unique
possibility to understand their physical properties and origin. To date, the
LIGO/Virgo/KAGRA network of high-frequency gravitational wave observatories
have announced the detection of more than 85 BH+BH merger events (Abbott et al.
2022a). An important diagnostic feature that can be extracted from the data is
the distribution of effective inspiral spins of the BHs. This distribution is
in clear tension with theoretical expectations from both an isolated binary
star origin, which traditionally predicts close-to aligned BH component spins
(Kalogera 2000; Farr et al. 2017), and formation via dynamical interactions in
dense stellar environments that predicts a symmetric distribution of effective
inspiral spins (Mandel & O'Shaughnessy 2010; Rodriguez et al. 2016b). Here it
is demonstrated that isolated binary evolution can convincingly explain the
observed data if BHs have their spin axis tossed during their formation process
in the core collapse of a massive star, similarly to the process evidently
acting in newborn neutron stars. BH formation without spin-axis tossing,
however, cannot reproduce the observed data. Based on simulations with only a
minimum of assumptions, constrains from empirical data can be made on the spin
magnitudes of the first- and second-born BHs, thereby serving to better
understand massive binary star evolution prior to the formation of BHs.


title:
Bifurcation into spectral gaps for strongly indefinite Choquard
  equations

abstract:
  We consider the semilinear elliptic equations $$ \left\{ \begin{array}{ll}
&-\Delta u+V(x)u=\left(I_\alpha\ast |u|^p\right)|u|^{p-2}u+\lambda u\quad
\hbox{for } x\in\mathbb R^N, \\ &u(x) \to 0 \hbox{ as } |x| \to\infty,
\end{array} \right. $$ where $I_\alpha$ is a Riesz potential,
$p\in(\frac{N+\alpha}N,\frac{N+\alpha}{N-2})$, $N\geq3$, and $V $ is continuous
periodic. We assume that $0$ lies in the spectral gap $(a,b)$ of $-\Delta + V$.
We prove the existence of infinitely many geometrically distinct solutions in
$H^1(\mathbb R^N)$ for each $\lambda\in(a, b)$, which bifurcate from $b$ if
$\frac{N+\alpha}N< p < 1 +\frac{2+\alpha}{N}$. Moreover, $b$ is the unique
gap-bifurcation point (from zero) in $[a,b]$. When $\lambda=a$, we find
infinitely many geometrically distinct solutions in $H^2_{loc}(\mathbb R^N)$.
Final remarks are given about the eventual occurrence of a bifurcation from
infinity in $\lambda=a$.


title: OCR Synthetic Benchmark Dataset for Indic Languages

abstract:
  We present the largest publicly available synthetic OCR benchmark dataset for
Indic languages. The collection contains a total of 90k images and their ground
truth for 23 Indic languages. OCR model validation in Indic languages require a
good amount of diverse data to be processed in order to create a robust and
reliable model. Generating such a huge amount of data would be difficult
otherwise but with synthetic data, it becomes far easier. It can be of great
importance to fields like Computer Vision or Image Processing where once an
initial synthetic data is developed, model creation becomes easier. Generating
synthetic data comes with the flexibility to adjust its nature and environment
as and when required in order to improve the performance of the model. Accuracy
for labeled real-time data is sometimes quite expensive while accuracy for
synthetic data can be easily achieved with a good score.


title: The Race to the Vulnerable: Measuring the Log4j Shell Incident

abstract:
  The critical remote-code-execution (RCE) Log4Shell is a severe vulnerability
that was disclosed to the public on December 10, 2021. It exploits a bug in the
wide-spread Log4j library. Any service that uses the library and exposes an
interface to the Internet is potentially vulnerable.
  In this paper, we measure the rush of scanners during the two months after
the disclosure. We use several vantage points to observe both researchers and
attackers. For this purpose, we collect and analyze payloads sent by benign and
malicious communication parties, their origins, and churn. We find that the
initial rush of scanners quickly ebbed. Especially non-malicious scanners were
only interested in the days after the disclosure. In contrast, malicious
scanners continue targeting the vulnerability.


title: Introducing the Welsh Text Summarisation Dataset and Baseline Systems

abstract:
  Welsh is an official language in Wales and is spoken by an estimated 884,300
people (29.2% of the population of Wales). Despite this status and estimated
increase in speaker numbers since the last (2011) census, Welsh remains a
minority language undergoing revitalization and promotion by Welsh Government
and relevant stakeholders. As part of the effort to increase the availability
of Welsh digital technology, this paper introduces the first Welsh
summarisation dataset, which we provide freely for research purposes to help
advance the work on Welsh text summarization. The dataset was created by Welsh
speakers by manually summarising Welsh Wikipedia articles. In addition, the
paper discusses the implementation and evaluation of different summarisation
systems for Welsh. The summarization systems and results will serve as
benchmarks for the development of summarises in other minority language
contexts.


title:
Slotted Aloha with Capture for OWC-based IoT: Design and Analysis in
  Finite Block-Length Regime

abstract:
  In this paper, we propose a Slotted ALOHA (SA)-inspired solution for an
indoor optical wireless communication (OWC)-based Internet of Things (IoT)
system. Assuming that the OWC receiver exploits the capture effect, we are
interested in the derivation of error probability of decoding a short-length
data packet originating from a randomly selected OWC IoT transmitter. The
presented OWC system analysis rests on the derivation of the
signal-to-noise-and-interference-ratio (SINR) statistics and usage of finite
block-length (FBL) information theory, from which relevant error probability
and throughput is derived. Using the derived expressions, we obtain numerical
results which are further utilized to characterize the trade-offs between the
system performance and the OWC system setup parameters. The indoor OWC-based
system geometry plays an important role in the system performance, thus the
presented results can be used as a guideline for the system design to optimize
the performance of the SA-based random access protocol.


title: Electronic band structure screening for Dirac points in Heuslers

abstract:
  The Heusler compounds have provided a playground of material candidates for
various technological applications based on their highly diverse and tunable
properties, controlled by chemical composition and crystal structure. However,
physical exploration of the Heusler chemical space en masse is impossible in
practice, hindering the exploration of the chemical composition vs. proprieties
relationship. Many of these applications are related to the Heuslers electron
transport characteristics, which are embedded in their electronic band
structure (EBS). Here we we created a Heuslers dataset using the Materials
Project (MP) database -- retrieving both chemical composition and their EBSs.
We then used machine learning to develop a model correlating the composition
vs. number of Dirac points in the EBS for Heuslers and also other Cubic
compounds by identifying said Dirac points using an automated algorithm as well
as generating chemical composition and global crystal structure features. Our
ML model captures the overall trend, as well as identifies significant
electronic and global crystal structure features, however, the ML model
suffered from significant variance due to the lack of site specific features.
Future work on a methodology for handling atomic site specific features will
allow ML models to better match the underlying quantum mechanics governing the
properties (also based on site specific properties) and capture the electronic
properties in a more generalized approach.


title:
Rate-Splitting Multiple Access for 6G -- Part I: Principles,
  Applications and Future Works

abstract:
  This letter is the first part of a three-part tutorial focusing on
rate-splitting multiple access (RSMA) for $\mathbf{6}$G. As Part I of the
tutorial, the letter presents the basics of RSMA and its applications in light
of $\mathbf{6}$G. To begin with, we first delineate the design principle and
basic transmission frameworks of downlink and uplink RSMA. We then illustrate
the applications of RSMA for addressing the challenges of various potential
enabling technologies and use cases, consequently making it a promising next
generation multiple access (NGMA) scheme for future networks such as
$\mathbf{6}$G and beyond. We briefly discuss the challenges of RSMA and
conclude the letter. In continuation of Part I, we will focus on the interplay
of RSMA with integrated sensing and communication, and reconfigurable
intelligent surfaces, respectively in Part II and Part III of this tutorial.


title: User-Driven Research of Medical Note Generation Software

abstract:
  A growing body of work uses Natural Language Processing (NLP) methods to
automatically generate medical notes from audio recordings of doctor-patient
consultations. However, there are very few studies on how such systems could be
used in clinical practice, how clinicians would adjust to using them, or how
system design should be influenced by such considerations. In this paper, we
present three rounds of user studies, carried out in the context of developing
a medical note generation system. We present, analyse and discuss the
participating clinicians' impressions and views of how the system ought to be
adapted to be of value to them. Next, we describe a three-week test run of the
system in a live telehealth clinical practice, major findings from which
include (i) the emergence of five different note-taking behaviours; (ii) the
importance of the system generating notes in real time during the consultation;
and (iii) the identification of a number of clinical use cases that could prove
challenging for automatic note generation systems.


title: LUNA: Learning Slot-Turn Alignment for Dialogue State Tracking

abstract:
  Dialogue state tracking (DST) aims to predict the current dialogue state
given the dialogue history. Existing methods generally exploit the utterances
of all dialogue turns to assign value for each slot. This could lead to
suboptimal results due to the information introduced from irrelevant utterances
in the dialogue history, which may be useless and can even cause confusion. To
address this problem, we propose LUNA, a sLot-tUrN Alignment enhanced approach.
It first explicitly aligns each slot with its most relevant utterance, then
further predicts the corresponding value based on this aligned utterance
instead of all dialogue utterances. Furthermore, we design a slot ranking
auxiliary task to learn the temporal correlation among slots which could
facilitate the alignment. Comprehensive experiments are conducted on
multi-domain task-oriented dialogue datasets, i.e., MultiWOZ 2.0, MultiWOZ 2.1,
and MultiWOZ 2.2. The results show that LUNA achieves new state-of-the-art
results on these datasets.


title:
Biologically inspired deep residual networks for computer vision
  applications

abstract:
  Deep neural network has been ensured as a key technology in the field of many
challenging and vigorously researched computer vision tasks. Furthermore,
classical ResNet is thought to be a state-of-the-art convolutional neural
network (CNN) and was observed to capture features which can have good
generalization ability. In this work, we propose a biologically inspired deep
residual neural network where the hexagonal convolutions are introduced along
the skip connections. The performance of different ResNet variants using square
and hexagonal convolution are evaluated with the competitive training strategy
mentioned by [1]. We show that the proposed approach advances the baseline
image classification accuracy of vanilla ResNet architectures on CIFAR-10 and
the same was observed over multiple subsets of the ImageNet 2012 dataset. We
observed an average improvement by 1.35% and 0.48% on baseline top-1 accuracies
for ImageNet 2012 and CIFAR-10, respectively. The proposed biologically
inspired deep residual networks were observed to have improved generalized
performance and this could be a potential research direction to improve the
discriminative ability of state-of-the-art image classification networks.


title:
Direct numerical simulations of shocklet-containing turbulent channel
  counter-flows

abstract:
  Counter-flow or counter-current configurations can maintain high turbulence
intensities and exhibit a significant level of mixing. We have previously
introduced a wall-bounded counter-flow turbulent channel configuration
(Physical Review Fluids, 6(9), p.094603.) as an efficient framework to study
compressibility effects on turbulence. Here, we extend our previous direct
numerical simulation study to a relatively higher Mach number (M=0.7) to
investigate strong compressibility effects (also by reducing the Prandtl number
from Pr=0.7 to 0.2), and the formation and evolution of unsteady shocklet
structures. It is found that the configuration is able to produce highly
turbulent flows with embedded shocklets and significant asymmetry in
probability density functions of dilatation. A peak turbulent Mach number close
to unity is obtained, for which the contribution of the dilatational
dissipation to total dissipation is nevertheless found to be limited to 6%.


title: Automated Imbalanced Classification via Layered Learning

abstract:
  In this paper we address imbalanced binary classification (IBC) tasks.
Applying resampling strategies to balance the class distribution of training
instances is a common approach to tackle these problems. Many state-of-the-art
methods find instances of interest close to the decision boundary to drive the
resampling process. However, under-sampling the majority class may potentially
lead to important information loss. Over-sampling also may increase the chance
of overfitting by propagating the information contained in instances from the
minority class. The main contribution of our work is a new method called ICLL
for tackling IBC tasks which is not based on resampling training observations.
Instead, ICLL follows a layered learning paradigm to model the data in two
stages. In the first layer, ICLL learns to distinguish cases close to the
decision boundary from cases which are clearly from the majority class, where
this dichotomy is defined using a hierarchical clustering analysis. In the
subsequent layer, we use instances close to the decision boundary and instances
from the minority class to solve the original predictive task. A second
contribution of our work is the automatic definition of the layers which
comprise the layered learning strategy using a hierarchical clustering model.
This is a relevant discovery as this process is usually performed manually
according to domain knowledge. We carried out extensive experiments using 100
benchmark data sets. The results show that the proposed method leads to a
better performance relatively to several state-of-the-art methods for IBC.


title:
Mapping Superconductivity in High-Pressure Hydrides: The $Superhydra$
  Project

abstract:
  The discovery of high-$T_c$ conventional superconductivity in high-pressure
hydrides has helped establish computational methods as a formidable tool to
guide material discoveries in a field traditionally dominated by serendipitous
experimental search. This paves the way to an ever-increasing use of
data-driven approaches to the study and design of superconductors. In this
work, we propose a new method to generate meaningful datasets of
superconductors, based on element substitution into a small set of
representative structural templates, generated by crystal structure prediction
methods (MultiTemplate-HighThroughput approach). Our approach realizes an
optimal compromise between structural variety and computational efficiency, and
can be easily generalized to other elements and compositions. As a first
application, we apply it to binary hydrides at high pressure, realizing a
database of 880 hypothetical structures, characterized with a set of
electronic, vibrational and chemical descriptors. 139 structures of our
$Superhydra$ Database are superconducting according to the McMillan-Allen-Dynes
approximation. Studying the distribution of $T_c$ and other properties across
the database with advanced statistical and visualization techniques, we are
able to obtain comprehensive material maps of the phase space of binary
hydrides. The $Superhydra$ database can be thought as a first step of a
generalized effort to map conventional superconductivity.


title: The tropological vertex

abstract:
  The theory of the topological vertex was originally proposed by Aganagic,
Klemm, Mari\~no and Vafa as a means to calculate open Gromov-Witten invariants
of toric Calabi-Yau threefolds. In this paper, we place the topological vertex
within the context of relative Gromov-Witten invariants of log Calabi-Yau
manifolds and describe how these invariants can be effectively computed via a
gluing formula for the enumeration of tropical curves in a singular integral
affine space. This richer context allows us to prove that the topological
vertex possesses certain tropical symmetries. These symmetries are captured by
the action of a quantum torus Lie algebra that is related to a quantisation of
the Lie algebra of the tropical vertex group of Gross, Pandharipande and
Siebert. Finally, we demonstrate how this algebra of symmetries leads to an
explicit description of the topological vertex and related Gromov-Witten
invariants.


title: Ordered exponential random walks

abstract:
  We study a $d$-dimensional random walk with exponentially distributed
increments conditioned so that the components stay ordered (in the sense of
Doob). We find explicitly a positive harmonic function $h$ for the killed
process and then construct an ordered process using Doob's $h$-transform. Since
these random walks are not nearest-neighbour, the harmonic function is not the
Vandermonde determinant. The ordered process is related to the departure
process of M/M/1 queues in tandem. We find asymptotics for the tail
probabilities of the time until the components in exponential random walks
become disordered and a local limit theorem. We find the distribution of the
processes of smallest and largest particles as Fredholm determinants.


title: Bivariate vine copula based quantile regression

abstract:
  The statistical analysis of univariate quantiles is a well developed research
topic. However, there is a profound need for research in multivariate
quantiles. We tackle the topic of bivariate quantiles and bivariate quantile
regression using vine copulas. They are graph theoretical models identified by
a sequence of linked trees, which allow for separate modelling of marginal
distributions and the dependence structure. We introduce a novel graph
structure model (given by a tree sequence) specifically designed for a
symmetric treatment of two responses in a predictive regression setting. We
establish computational tractability of the model and a straight forward way of
obtaining different conditional distributions. Using vine copulas the typical
shortfalls of regression, as the need for transformations or interactions of
predictors, collinearity or quantile crossings are avoided. We illustrate the
copula based bivariate quantiles for different copula distributions and provide
a data set example. Further, the data example emphasizes the benefits of the
joint bivariate response modelling in contrast to two separate univariate
regressions or by assuming conditional independence, for bivariate response
data set in the presence of conditional dependence.


title: Power Scaling Law for Optical IRSs and Comparison with Optical Relays

abstract:
  The line-of-sight (LOS) requirement of free-space optical (FSO) systems can
be relaxed by employing optical relays and optical intelligent reflecting
surfaces (IRSs). Unlike radio frequency (RF) IRSs, which typically exhibit a
quadratic power scaling law, the power reflected from FSO IRSs and collected at
the receiver lens may scale quadratically or linearly with the IRS size or may
even saturate at a constant value. We analyze the power scaling law for optical
IRSs and unveil its dependence on the wavelength, transmitter (Tx)-to-IRS and
IRS-to-receiver (Rx) distances, beam waist, and lens size. We compare optical
IRSs in different power scaling regimes with optical relays in terms of the
outage probability, diversity and coding gains, and optimal placement. Our
results show that, at the expense of a higher hardware complexity,
relay-assisted FSO links yield a better outage performance at high
signal-to-noise-ratios (SNRs), but optical IRSs can achieve a higher
performance at low SNRs. Moreover, while it is optimal to place relays
equidistant from Tx and Rx, the optimal location of IRSs depends on the power
scaling regime they operate in.


title:
On the Turbulent Behavior of a Magnetically Confined Plasma Near the
  X-Point

abstract:
  We construct a model for the turbulence near the X-point of a Tokamak device
and, under suitable assumptions, we arrive to a closed equation for the
electric field potential fluctuations. The analytical and numerical analysis is
focused on a reduced two-dimensional formulation of the dynamics, which allows
a direct mapping to the incompressible Navier-Stokes equation. The main merit
of this study is to outline how the turbulence near the X-point, in
correspondence to typical operation conditions of medium and large size
Tokamaks, is dominated by the enstrophy cascade from large to smaller spatial
scales.


title:
Experimental study of the interaction of two laser-driven radiative
  shocks at the PALS laser

abstract:
  Radiative shocks (RS) are complex phenomena which are ubiquitous in
astrophysical environments. The study of such hypersonic shocks in the
laboratory, under controlled conditions, is of primary interest to understand
the physics at play and also to check the ability of numerical simulations to
reproduce the experimental results. In this context, we conducted, at the
Prague Asterix Laser System facility (PALS), the first experiments dedicated to
the study of two counter-propagating radiative shocks propagating at non-equal
speeds up to 25-50 km/s in noble gases at pressures ranging between 0.1 and 0.6
bar. These experiments highlighted the interaction between the two radiative
precursors. This interaction is qualitatively but not quantitatively described
by 1D simulations. Preliminary results obtained with XUV spectroscopy leading
to the estimation of shock temperature and ion charge of the plasma are also
presented.


title:
LDSA: Learning Dynamic Subtask Assignment in Cooperative Multi-Agent
  Reinforcement Learning

abstract:
  Cooperative multi-agent reinforcement learning (MARL) has made prominent
progress in recent years. For training efficiency and scalability, most of the
MARL algorithms make all agents share the same policy or value network.
However, many complex multi-agent tasks require agents with a variety of
specific abilities to handle different subtasks. Sharing parameters
indiscriminately may lead to similar behaviors across all agents, which will
limit the exploration efficiency and be detrimental to the final performance.
To balance the training complexity and the diversity of agents' behaviors, we
propose a novel framework for learning dynamic subtask assignment (LDSA) in
cooperative MARL. Specifically, we first introduce a subtask encoder that
constructs a vector representation for each subtask according to its identity.
To reasonably assign agents to different subtasks, we propose an ability-based
subtask selection strategy, which can dynamically group agents with similar
abilities into the same subtask. Then, we condition the subtask policy on its
representation and agents dealing with the same subtask share their experiences
to train the subtask policy. We further introduce two regularizers to increase
the representation difference between subtasks and avoid agents changing
subtasks frequently to stabilize training, respectively. Empirical results show
that LDSA learns reasonable and effective subtask assignment for better
collaboration and significantly improves the learning performance on the
challenging StarCraft II micromanagement benchmark.


title: Monitoring AI systems: A Problem Analysis, Framework and Outlook

abstract:
  Knowledge-based systems have been used to monitor machines and processes in
the real world. In this paper we propose the use of knowledge-based systems to
monitor other AI systems in operation. We motivate and provide a problem
analysis of this novel setting and subsequently propose a framework that allows
for structuring future research related to this setting. Several directions for
further research are also discussed.


title: Fully reconfigurable optomechanical add-drop filters

abstract:
  Fully reconfigurable add-drop filters (ADFs) have important applications in
optical communication and information processing. Here we demonstrate a broadly
tunable add-drop filter based on a double-disk cavity optomechanical system,
side-coupled with a pair of tapered fiber waveguides. By varying the coupling
rates between the cavity and the two waveguides, we investigate the dependence
of the through (drop) efficiency on the coupling rates, which agrees well with
the theoretical results. By optimizing the cavity-waveguide coupling rates, a
drop efficiency of 89% and a transmission of 1.9% have been achieved.
Benefiting from the large optomechanical coupling coefficient of the
double-disk microcavity, a tuning range of 8 nm has been realized, which is
more than one free spectral range (FSR) of the cavity. This is realized by
changing the air gap of the double disk using a fiber tip, which is controlled
by a piezoelectrical nanostage, with a required voltage of 7 V. As a result,
both the through and drop signals can be resonant with any wavelength within
the transparent window of the cavity material, which indicates that the ADF is
fully reconfigurable.


title:
One Size Does Not Fit All: The Case for Personalised Word Complexity
  Models

abstract:
  Complex Word Identification (CWI) aims to detect words within a text that a
reader may find difficult to understand. It has been shown that CWI systems can
improve text simplification, readability prediction and vocabulary acquisition
modelling. However, the difficulty of a word is a highly idiosyncratic notion
that depends on a reader's first language, proficiency and reading experience.
In this paper, we show that personal models are best when predicting word
complexity for individual readers. We use a novel active learning framework
that allows models to be tailored to individuals and release a dataset of
complexity annotations and models as a benchmark for further research.


title: Neutrino Mixing and Leptogenesis in a $L_e-L_\mu-L_\tau$ model

abstract:
  We present a simple extension of the Standard Model with three right-handed
neutrinos in a SUSY framework, with an additional U(1$)_\text{F}$ abelian
flavor symmetry with a non standard leptonic charge $L_e-L_\mu-L_\tau$ for
lepton doublets and arbitrary right-handed charges. We show that the model is
able to reproduce the experimental values of the mixing angles of the PMNS
matrix and of the $r=\Delta m_\text{sun}^2/\Delta m_\text{atm}^2$ ratio, with
only a moderate fine tuning of the Lagrangian free parameters. The baryon
asymmetry of the Universe is generated via thermal leptogenesis through
CP-violating decays of the heavy right-handed neutrinos. We present a detailed
numerical solution of the relevant Boltzmann equation accounting for the impact
of the distribution of the asymmetry in the various lepton flavors.


title:
Stability of the steady states in multidimensional reaction diffusion
  systems arising in combustion theory

abstract:
  We prove that the steady state of a class of multidimensional
reaction-diffusion systems is asymptotically stable at the intersection of
unweighted space and exponentially weighted Sobolev spaces, and pay particular
attention to a special case, namely, systems of equations that arise in
combustion theory. The steady-state solutions considered here are the end
states of the traveling fronts associated with the systems, and thus the
present results complement recent papers \cite{GLS1, GLS2, GLS3, GLSR, GLY}
that study the stability of traveling fronts.


title:
Measuring the distortion of time with relativistic effects in
  large-scale structure

abstract:
  To test the theory of gravity one needs to test, on one hand, how space and
time are distorted by matter and, on the other hand, how matter moves in a
distorted space-time. Current observations provide tight constraints on the
motion of matter, through the so-called redshift-space distortions, but they
only provide a measurement of the sum of the spatial and temporal distortions,
via gravitational lensing. In this Letter, we develop a method to measure the
time distortion on its own. We show that the coming generation of galaxy
surveys, like the Square Kilometer Array, will allow us to measure the
distortion of time with an accuracy of 10-30\%. Such a measurement will be
essential to test deviations from General Relativity in a fully
model-independent way. In particular, it can be used to compare the spatial and
temporal distortions of space-time, that are predicted to be the same in
$\Lambda$CDM but generically differ in modified theories of gravity.


title:
DropTrack -- automatic droplet tracking using deep learning for
  microfluidic applications

abstract:
  Deep neural networks are rapidly emerging as data analysis tools, often
outperforming the conventional techniques used in complex microfluidic systems.
One fundamental analysis frequently desired in microfluidic experiments is
counting and tracking the droplets. Specifically, droplet tracking in dense
emulsions is challenging as droplets move in tightly packed configurations.
Sometimes the individual droplets in these dense clusters are hard to resolve,
even for a human observer. Here, two deep learning-based cutting-edge
algorithms for object detection (YOLO) and object tracking (DeepSORT) are
combined into a single image analysis tool, DropTrack, to track droplets in
microfluidic experiments. DropTrack analyzes input videos, extracts droplets'
trajectories, and infers other observables of interest, such as droplet
numbers. Training an object detector network for droplet recognition with
manually annotated images is a labor-intensive task and a persistent
bottleneck. This work partly resolves this problem by training object detector
networks (YOLOv5) with hybrid datasets containing real and synthetic images. We
present an analysis of a double emulsion experiment as a case study to measure
DropTrack's performance. For our test case, the YOLO networks trained with 60%
synthetic images show similar performance in droplet counting as with the one
trained using 100% real images, meanwhile saving the image annotation work by
60%. DropTrack's performance is measured in terms of mean average precision
(mAP), mean square error in counting the droplets, and inference speed. The
fastest configuration of DropTrack runs inference at about 30 frames per
second, well within the standards for real-time image analysis.


title: Long-range parity non-conserving electron-nucleon interaction

abstract:
  As known, electron vacuum polarization by nuclear Coulomb field produces
Uehling potential with the range $\hbar/2m_e c$. Similarly, neutrino vacuum
polarization by $Z$ boson field produces parity non-conserving (PNC) potential
$W_L(r)$ with the large range $\hbar/2m_{\nu}c$. Mixed $Z-\gamma$ electron
vacuum polarization produces PNC potential with range $\hbar/2m_e c$, which
exceeds the range of the weak interaction by many orders of magnitude. We
calculate contribution of the long-range PNC potentials to the nuclear spin
independent and nuclear spin dependent PNC effects.
  The cases of the single-isotope PNC effects and the ratio of PNC effects in
different isotopes are considered for Ca, Cs, Ba, Sm, Dy, Yb, Hg, Tl, Pb, Bi,
Fr, Ra atoms and ions.


title:
Balancing Multi-Domain Corpora Learning for Open-Domain Response
  Generation

abstract:
  Open-domain conversational systems are assumed to generate equally good
responses on multiple domains. Previous work achieved good performance on the
single corpus, but training and evaluating on multiple corpora from different
domains are less studied. This paper explores methods of generating relevant
responses for each of multiple multi-domain corpora. We first examine
interleaved learning which intermingles multiple corpora as the baseline. We
then investigate two multi-domain learning methods, labeled learning and
multi-task labeled learning, which encode each corpus through a unique corpus
embedding. Furthermore, we propose Domain-specific Frequency (DF), a novel
word-level importance weight that measures the relative importance of a word
for a specific corpus compared to other corpora. Based on DF, we propose
weighted learning, a method that integrates DF to the loss function. We also
adopt DF as a new evaluation metric. Extensive experiments show that our
methods gain significant improvements on both automatic and human evaluation.
We share our code and data for reproducibility


title:
A Boosted-DCA with Power-Sum-DC Decomposition for Linearly Constrained
  Polynomial Program

abstract:
  In this paper, we introduce a difference-of-convex (DC) decomposition for
polynomials based on power-sum representation, which can be established by
solving a sparse linear system. A boosted DCA with exact line search (BDCAe) is
proposed to solve the DC formulation of the linearly constrained polynomial
program. We show that the exact line search is equivalent to finding roots of a
unary polynomial in an interval, which has a closed-form solution in many
applications. The subsequential convergence of BDCAe to a critical point is
proved, and the convergence rate under Kurdyka-Lojasiewicz property is
established. Moreover, a fast dual proximal gradient (FDPG) method is applied
to efficiently solve the resulting convex subproblems. Numerical experiments on
the Mean-Variance-Skewness-Kurtosis (MVSK) portfolio optimization model via
BDCAe, DCA, BDCA with Armijo line search, as well as FMINCON and FILTERSD
solvers are reported, which demonstrates good performance of BDCAe.


title: Byzantine Fault Tolerance in Distributed Machine Learning : a Survey

abstract:
  Byzantine Fault Tolerance (BFT) is among the most challenging problems in
Distributed Machine Learning (DML). Byzantine failures are still difficult to
tackle due to their unrestricted nature; as a result, the possibility of
generating arbitrary data. Extensive research efforts are persistently being
made to put into effect the BFT in DML. Some recent studies have been
undertaken to take into consideration various BFT approaches in DML. However,
certain aspects seem to be limited, such as the few analyzed approaches and the
absence of the techniques classification employed in the studied approaches. In
this paper, we present a survey of recent works surrounding BFT in DML. Mainly
in first-order optimization methods, especially Stochastic Gradient Descent
(SGD). We highlight the key techniques as well as fundamental approaches. We
offer an illustrative description of techniques used in BFT in DML, with a
proposed classification of BFTs approaches in the context of their basic
techniques. This classification is established on specific criteria such as
communication process, optimization method, and topology setting, which
characterize future work methods examining the current challenges of BFT in DML


title:
Intra and Cross-spectrum Iris Presentation Attack Detection in the NIR
  and Visible Domains Using Attention-based and Pixel-wise Supervised Learning

abstract:
  Iris Presentation Attack Detection (PAD) is essential to secure iris
recognition systems. Recent iris PAD solutions achieved good performance by
leveraging deep learning techniques. However, most results were reported under
intra-database scenarios and it is unclear if such solutions can generalize
well across databases and capture spectra. These PAD methods run the risk of
overfitting because of the binary label supervision during the network
training, which serves global information learning but weakens the capture of
local discriminative features. This chapter presents a novel attention-based
deep pixel-wise binary supervision (A-PBS) method. A-PBS utilizes pixel-wise
supervision to capture the fine-grained pixel/patch-level cues and attention
mechanism to guide the network to automatically find regions where most
contribute to an accurate PAD decision. Extensive experiments are performed on
six NIR and one visible-light iris databases to show the effectiveness and
robustness of proposed A-PBS methods. We additionally conduct extensive
experiments under intra-/cross-database and intra-/cross-spectrum for detailed
analysis. The results of our experiments indicates the generalizability of the
A-PBS iris PAD approach.


title: A Fibonacci's complement numeration system

abstract:
  With the two's complement notation of signed integers, the fundamental
arithmetic operations of addition, subtraction, and multiplication are
identical to those for unsigned binary numbers. In this work, we consider a
Fibonacci-equivalent of the two's complement notation. A theorem of Zeckendorf
says that every nonnegative integer can be represented as the sum of distinct
nonconsecutive Fibonacci numbers thus leading to a binary representation of
nonnegative integers where powers of 2 are replaced by Fibonacci numbers in its
evaluation. A transducer provided by Berstel computes the sum of the Zeckendorf
binary representation of two nonnegative integers. In this work, we consider a
numeration system also based on Fibonacci numbers but representing all
integers. As for the two's complement notation, we show that addition of
integers represented in this numeration system can be computed with the Berstel
transducer with three additional transitions. Whether this can be done more
generally is an open question raised by the current work.


title: 500 W rod-type 4x4 multi-core ultrafast fiber laser

abstract:
  We present a coherently-combined femtosecond fiber CPA system based on a
rod-type, Ytterbium-doped, multicore fiber with 4x4 cores. A high average power
of up to 500 W (after combination and compression) could be achieved at 10 MHz
repetition rate with an excellent beam quality. Additionally, <500 fs pulses
with up to 600 uJ of pulse energy were also realized with this setup. This
architecture is intrinsically power scalable by increasing the number of cores
in the fiber.


title:
The exotic charmonium-like resonances in the mass region $(3900-4700)$
  MeV

abstract:
  The resonances, containing $c\bar c$ plus $s\bar s$ (or light $q\bar q$)
quarks in the mass region $(3900-4700)$ MeV, are analyzed in the relativistic
strong coupling theory, with and without channel coupling phenomena. The
conventional charmonium spectrum is presented, being calculated with the
relativistic string Hamiltonian, which does not contain fitting parameters,
while for high excitations the universal flattened confining potential is used.
It is shown that $X(4274),X(4500),X(4700)$ can be identified as $3\,^3P_1,
4\,^3P_0, 5\,^3P_0$ states. The exotic states are considered using the
coupled-channel (recoupling) theory, when two mesons $m_1,m_2$ transfer into
another two mesons $m_3, m_4$ and back (infinite number of times), creating the
four-quark systems. The resonances $X(3875), X(3915), Z_{cs}(3985), X(4140)$
can be explained in this way as the exotic four-quark states in the $S$-wave
decay channels.


title:
Moment-based Invariants for Probabilistic Loops with Non-polynomial
  Assignments

abstract:
  We present a method to automatically approximate moment-based invariants of
probabilistic programs with non-polynomial updates of continuous state
variables to accommodate more complex dynamics. Our approach leverages
polynomial chaos expansion to approximate non-linear functional updates as sums
of orthogonal polynomials. We exploit this result to automatically estimate
state-variable moments of all orders in Prob-solvable loops with non-polynomial
updates. We showcase the accuracy of our estimation approach in several
examples, such as the turning vehicle model and the Taylor rule in monetary
policy.


title:
Groups with small multiplicities of fields of values of irreducible
  characters

abstract:
  In this work, we classify all finite groups such that for every field
extension F of \mathbb{Q}, F is the field of values of at most 3 irreducible
characters.


title:
Exam problems that link physical concepts with electrical phenomena in
  living cells

abstract:
  We present a collection of biophysics examination problems that focus on
electrodiffusion, the superposition principle, and currents in RC circuits,
leading towards an understanding of the Hodgkin-Huxley model of action
potentials. These problems link basic concepts in electricity to the physiology
of living cells. The models described in these problems are intended help
students develop an understanding of electrical processes in cells. Our work
shows how introductory-level electricity courses can be taught in parallel with
basic cell (electro)physiology and how basic concepts of electric circuits
apply to cellular phenomena.


title: Future memories are not needed for large classes of POMDPs

abstract:
  Optimal policies for partially observed Markov decision processes (POMDPs)
are history-dependent: Decisions are made based on the entire history of
observation. Memoryless policies, which take decisions based on the last
observation only, are generally considered useless in the literature because we
can construct POMDP instances for which optimal memoryless policies are
arbitrarily worse than history-dependent ones. Our purpose is to challenge this
belief. We show that optimal memoryless policies can be computed efficiently
using mixed integer linear programming (MILP), and perform reasonably well on a
wide range of instances from the literature. When strengthened with valid
inequalities, the linear relaxation of this MILP provides high quality
upper-bounds on the value of an optimal history dependent policy. Furthermore,
when used with a finite horizon POMDP problem with memoryless policies as
rolling optimization problem, a model predictive control approach leads to an
efficient history-dependent policy, which we call the short memory in the
future (SMF) policy. Basically, the SMF policy leverages these memoryless
policies to build an approximation of the Bellman value function. Numerical
experiments show the efficiency of our approach on benchmark instances from the
literature.


title:
A Stochastic Climate Model -- An approach to calibrate the
  Climate-Extended Risk Model (CERM)

abstract:
  The initial Climate-Extended Risk Model (CERM) addresses the estimate of
climate-related financial risk embedded within a bank loan portfolio, through a
climatic extension of the Basel II IRB model. It uses a Gaussian copula model
calibrated with non stationary macro-correlations in order to reflect the
future evolution of climate-related financial risks. In this complementary
article, we propose a stochastic forward-looking methodology to calibrate
climate macro-correlation evolution from scientific climate data, for physical
and transition efforts specifically. We assume a global physical and transition
risk, likened to persistent greenhouse gas (GHG) concentration in the
atmosphere. The economic risk is considered stationary and can therefore be
calibrated with a backward-looking methodology. We present 4 key principles to
model the GDP and we propose to model the economic, physical and transition
effort factors with three interdependent stochastic processes allowing for a
calibration with seven well defined parameters. These parameters can be
calibrated using public data. This new approach means not only to evaluate
climate risks without picking any specific scenario but also allows to fill the
gap between current one year approach of regulatory and economic capital models
and the necessarily long-term view of climate risks by designing a framework to
evaluate the resulting credit loss on each step (typically yearly) of the
transition path. This new approach could prove instrumental in the 2022 context
of central banks weighing the pros and cons of a climate capital charge.


title:
Anomalous zero-field splitting for hole spin qubits in Si and Ge quantum
  dots

abstract:
  An anomalous energy splitting of spin triplet states at zero magnetic field
has recently been measured in germanium quantum dots. This zero-field splitting
could crucially alter the coupling between tunnel-coupled quantum dots, the
basic building blocks of state-of-the-art spin-based quantum processors, with
profound implications for semiconducting quantum computers. We develop an
analytical model linking the zero-field splitting to spin-orbit interactions
that are cubic in momentum. Such interactions naturally emerge in hole
nanostructures, where they can also be tuned by external electric fields, and
we find them to be particularly large in silicon and germanium, resulting in a
significant zero-field splitting in the $\mu$eV range. We confirm our
analytical theory by numerical simulations of different quantum dots, also
including other possible sources of zero-field splitting. Our findings are
applicable to a broad range of current architectures encoding spin qubits and
provide a deeper understanding of these materials, paving the way towards the
next generation of semiconducting quantum processors.


title:
A complementary screening for quantum spin Hall insulators in 2D
  exfoliable materials

abstract:
  Quantum spin Hall insulators are a class of topological materials that has
been extensively studied during the past decade. One of their distinctive
features is the presence of a finite band gap in the bulk and gapless,
topologically protected edge states that are spin-momentum locked. These
materials are characterized by a $\mathbb{Z}_2$ topological order where, in the
2D case, a single topological invariant can be even or odd for a trivial or a
topological material, respectively. Thanks to their interesting properties,
such as the realization of dissipationless spin currents, spin pumping and spin
filtering, they are of great interest in the field of electronics, spintronics
and quantum computing. In this paper we perform an high-throughput screening of
quantum spin Hall insulators starting from a set of 783 2D exfoliable
materials, recently identified from a systematic screening of the ICSD, COD,
and MPDS databases. We find a new $\mathbb{Z}_2$ topological insulator
(Hg$_4$N$_4$S$_4$) as well as 3 already known ones and 7 direct gap metal that
have the potential of becoming Quantum spin Hall insulators under a reasonable
external perturbation.


title:
Study on the ERP Implementation Methodologies on SAP, Oracle NetSuite,
  and Microsoft Dynamics 365: A Review

abstract:
  There are Top three vendors in the ERP market: SAP, Oracle Net Suite and
Microsoft dynamics 365 leading the Global ERP market.While analyzing the ERP
selection and implementation trends, it is critical that any organization
looking to implement an ERP system assesses the vendors through the lens of its
own organization's specific requirements. When choosing the right ERP, a few
things must be taken into consideration like the Time Budget and resources. The
research paper analyses each phase and compares the methodologies of SAP,
Oracle Net Suite and Microsoft Dynamics 365 and suggests the best methodologies
to be practiced for any ERP projects. Like a poorly planned trip, if you don't
have an effective methodology, you can expect a negative impact on your
implementation, solution quality, and business satisfaction. Wrong choice of
methodology may lead to poor decision-making, best practices may not be
followed, and teams may be disjointed in the implementation, which can cause
delays. Choosing the right ERP methodology is the key.Methodology is the
lifeline for successful project implementation.


title: Trigonometric Convexity for the Multidimensional Indicator after Ivanov

abstract:
  Multidimensional indicator after Ivanov is a generalization of the notion of
indicator, that is well-known for analytic functions in one complex variable,
to analytic functions in several complex variables. We prove an analogue of
trigonometric convexity for it. Additionally, we show that our estimate is
sharp. The proof is based on the multidimensional analogue of the sectorial
Fourier inversion formula.


title:
Post-selection in noisy Gaussian boson sampling: part is better than
  whole

abstract:
  Gaussian boson sampling is originally proposed to show quantum advantage with
quantum linear optical elements.Recently, several experimental breakthroughs
based on Gaussian boson sampling pointing to quantum computing supremacy have
been presented.However, due to technical limitations, the computation results
of Gaussian boson sampling are influenced severely by photon loss. Here, we
present an efficient and practical method to reduce the negative effect caused
by photon loss with no hardware modifications. Our method takes the data
post-selection process that discards low-quality data according to our
criterion to improve the performance of the final computational results, say
part is better than whole. Besides improving the robustness of computation
results of current GBS devices, this photon loss mitigation method may also
benefit the further development of GBS-based quantum algorithms.


title:
Uniform a priori estimates for positive solutions of the Lane-Emden
  system in the plane

abstract:
  We prove that positive solutions of the superlinear Lane-Emden system in a
two-dimensional smooth bounded domain are bounded independently of the
exponents in the system, provided the exponents are comparable. As a
consequence, the energy of the solutions is uniformly bounded.


title: Generation of gravitational waves from freely decaying turbulence

abstract:
  We study the stochastic gravitational wave background (SGWB) produced by
freely decaying vortical turbulence in the early Universe. We thoroughly
investigate the time correlation of the velocity field, and hence of the
anisotropic stresses producing the gravitational waves. By direct numerical
simulation, we show that the unequal time correlation function (UETC) of the
Fourier components of the velocity field is Gaussian in the time difference, as
predicted by the "sweeping" decorrelation model. We introduce a decorrelation
model that can be extended to wavelengths around the integral scale of the
flow. Supplemented with the evolution laws of the kinetic energy and of the
integral scale, this provides a new model UETC of the turbulent velocity field
consistent with the simulations. We discuss the UETC as a positive definite
kernel, and propose to use the Gibbs kernel for the velocity UETC as a natural
way to ensure positive definiteness of the SGWB. The SGWB is given by a
4-dimensional integration of the resulting anisotropic stress UETC with the
gravitational wave Green's function. We perform this integration using a Monte
Carlo algorithm based on importance sampling, and find that the result matches
that of the direct numerical simulations. Furthermore, the SGWB obtained from
the numerical integration and from the simulations show close agreement with a
model in which the source is constant in time and abruptly turns off after a
few eddy turnover times. Based on this assumption, we provide an approximate
analytical form for the SGWB spectrum and its scaling with the initial kinetic
energy and integral scale. Finally, we use our model and numerical integration
algorithm to show that including an initial growth phase for the turbulent flow
heavily influences the spectral shape of the SGWB. This highlights the
importance of a complete understanding of the turbulence generation mechanism.


title: A Temporal-Pattern Backdoor Attack to Deep Reinforcement Learning

abstract:
  Deep reinforcement learning (DRL) has made significant achievements in many
real-world applications. But these real-world applications typically can only
provide partial observations for making decisions due to occlusions and noisy
sensors. However, partial state observability can be used to hide malicious
behaviors for backdoors. In this paper, we explore the sequential nature of DRL
and propose a novel temporal-pattern backdoor attack to DRL, whose trigger is a
set of temporal constraints on a sequence of observations rather than a single
observation, and effect can be kept in a controllable duration rather than in
the instant. We validate our proposed backdoor attack to a typical job
scheduling task in cloud computing. Numerous experimental results show that our
backdoor can achieve excellent effectiveness, stealthiness, and sustainability.
Our backdoor's average clean data accuracy and attack success rate can reach
97.8% and 97.5%, respectively.


title: Low-Level Bi-Abduction

abstract:
  The paper proposes a new static analysis designed to handle open programs,
i.e., fragments of programs, with dynamic pointer-linked data structures - in
particular, various kinds of lists - that employ advanced low-level pointer
operations. The goal is to allow such programs be analysed without a need of
writing analysis harnesses that would first initialise the structures being
handled. The approach builds on a special flavour of separation logic and the
approach of bi-abduction. The code of interest is analyzed along the call tree,
starting from its leaves, with each function analysed just once without any
call context, leading to a set of contracts summarizing the behaviour of the
analysed functions. In order to handle the considered programs, methods of
abduction existing in the literature are significantly modified and extended in
the paper. The proposed approach has been implemented in a tool prototype and
successfully evaluated on not large but complex programs.


title:
PI-NLF: A Proportional-Integral Approach for Non-negative Latent Factor
  Analysis

abstract:
  A high-dimensional and incomplete (HDI) matrix frequently appears in various
big-data-related applications, which demonstrates the inherently non-negative
interactions among numerous nodes. A non-negative latent factor (NLF) model
performs efficient representation learning to an HDI matrix, whose learning
process mostly relies on a single latent factor-dependent, non-negative and
multiplicative update (SLF-NMU) algorithm. However, an SLF-NMU algorithm
updates a latent factor based on the current update increment only without
appropriate considerations of past learning information, resulting in slow
convergence. Inspired by the prominent success of a proportional-integral (PI)
controller in various applications, this paper proposes a
Proportional-Integral-incorporated Non-negative Latent Factor (PI-NLF) model
with two-fold ideas: a) establishing an Increment Refinement (IR) mechanism via
considering the past update increments following the principle of a PI
controller; and b) designing an IR-based SLF-NMU (ISN) algorithm to accelerate
the convergence rate of a resultant model. Empirical studies on four HDI
datasets demonstrate that a PI-NLF model outperforms the state-of-the-art
models in both computational efficiency and estimation accuracy for missing
data of an HDI matrix. Hence, this study unveils the feasibility of boosting
the performance of a non-negative learning algorithm through an error feedback
controller.


title:
Investigating molecular transport in the human brain from MRI with
  physics-informed neural networks

abstract:
  In recent years, a plethora of methods combining deep neural networks and
partial differential equations have been developed. A widely known and popular
example are physics-informed neural networks. They solve forward and inverse
problems involving partial differential equations in terms of a neural network
training problem. We apply physics-informed neural networks as well as the
finite element method to estimate the diffusion coefficient governing the long
term, i.e. over days, spread of molecules in the human brain from a novel
magnetic resonance imaging technique. Synthetic testcases are created to
demonstrate that the standard formulation of the physics-informed neural
network faces challenges with noisy measurements in our application. Our
numerical results demonstrate that the residual of the partial differential
equation after training needs to be small in order to obtain accurate recovery
of the diffusion coefficient. To achieve this, we apply several strategies such
as tuning the weights and the norms used in the loss function as well as
residual based adaptive refinement and exchange of residual training points. We
find that the diffusion coefficient estimated with PINNs from magnetic
resonance images becomes consistent with results from a finite element based
approach when the residuum after training becomes small. The observations
presented in this work are an important first step towards solving inverse
problems on observations from large cohorts of patients in a semi-automated
fashion with physics-informed neural networks.


title:
METGEN: A Module-Based Entailment Tree Generation Framework for Answer
  Explanation

abstract:
  Knowing the reasoning chains from knowledge to the predicted answers can help
construct an explainable question answering (QA) system. Advances on QA
explanation propose to explain the answers with entailment trees composed of
multiple entailment steps. While current work proposes to generate entailment
trees with end-to-end generative models, the steps in the generated trees are
not constrained and could be unreliable. In this paper, we propose METGEN, a
Module-based Entailment Tree GENeration framework that has multiple modules and
a reasoning controller. Given a question and several supporting knowledge,
METGEN can iteratively generate the entailment tree by conducting single-step
entailment with separate modules and selecting the reasoning flow with the
controller. As each module is guided to perform a specific type of entailment
reasoning, the steps generated by METGEN are more reliable and valid.
Experiment results on the standard benchmark show that METGEN can outperform
previous state-of-the-art models with only 9% of the parameters.


title: Lorentz symmetry breaking -- classical and quantum aspects

abstract:
  In this book, we review various aspects of the Lorentz symmetry breaking,
both classical and quantum ones, with the special interest to perturbative
generation of Lorentz-breaking terms. We present impacts of Lorentz symmetry
breaking in noncommutative and supersymmetric theories. Also, we discuss the
problem of Lorentz symmetry breaking in a curved space-time. The book is closed
with a review of experimental studies of Lorentz symmetry breaking.


title: J-PLUS: Support Vector Regression to Measure Stellar Parameters

abstract:
  Context. Stellar parameters are among the most important characteristics in
studies of stars, which are based on atmosphere models in traditional methods.
However, time cost and brightness limits restrain the efficiency of spectral
observations. The J-PLUS is an observational campaign that aims to obtain
photometry in 12 bands. Owing to its characteristics, J-PLUS data have become a
valuable resource for studies of stars. Machine learning provides powerful
tools to efficiently analyse large data sets, such as the one from J-PLUS, and
enable us to expand the research domain to stellar parameters. Aims. The main
goal of this study is to construct a SVR algorithm to estimate stellar
parameters of the stars in the first data release of the J-PLUS observational
campaign. Methods. The training data for the parameters regressions is featured
with 12-waveband photometry from J-PLUS, and is cross-identified with
spectrum-based catalogs. These catalogs are from the LAMOST, the APOGEE, and
the SEGUE. We then label them with the stellar effective temperature, the
surface gravity and the metallicity. Ten percent of the sample is held out to
apply a blind test. We develop a new method, a multi-model approach in order to
fully take into account the uncertainties of both the magnitudes and stellar
parameters. The method utilizes more than two hundred models to apply the
uncertainty analysis. Results. We present a catalog of 2,493,424 stars with the
Root Mean Square Error of 160K in the effective temperature regression, 0.35 in
the surface gravity regression and 0.25 in the metallicity regression. We also
discuss the advantages of this multi-model approach and compare it to other
machine-learning methods.


title:
Natural Language Inference with Self-Attention for Veracity Assessment
  of Pandemic Claims

abstract:
  We present a comprehensive work on automated veracity assessment from dataset
creation to developing novel methods based on Natural Language Inference (NLI),
focusing on misinformation related to the COVID-19 pandemic. We first describe
the construction of the novel PANACEA dataset consisting of heterogeneous
claims on COVID-19 and their respective information sources. The dataset
construction includes work on retrieval techniques and similarity measurements
to ensure a unique set of claims. We then propose novel techniques for
automated veracity assessment based on Natural Language Inference including
graph convolutional networks and attention based approaches. We have carried
out experiments on evidence retrieval and veracity assessment on the dataset
using the proposed techniques and found them competitive with SOTA methods, and
provided a detailed discussion.


title:
Cross-section measurements for the production of a $Z$ boson in
  association with high-transverse-momentum jets in $pp$ collisions at
  $\sqrt{s} = 13$ TeV with the ATLAS detector

abstract:
  Cross-section measurements for a $Z$ boson produced in association with
high-transverse-momentum jets ($p_{\mathrm{T}} \geq 100$ GeV) and decaying into
a charged-lepton pair ($e^+e^-,\mu^+\mu^-$) are presented. The measurements are
performed using proton-proton collisions at $\sqrt{s}=13$ TeV corresponding to
an integrated luminosity of $139$ fb$^{-1}$ collected by the ATLAS experiment
at the LHC. Measurements of angular correlations between the $Z$ boson and the
closest jet are performed in events with at least one jet with $p_{\mathrm{T}}
\geq 500$ GeV. Event topologies of particular interest are the collinear
emission of a $Z$ boson in dijet events and a boosted $Z$ boson recoiling
against a jet. Fiducial cross sections are compared with state-of-the-art
theoretical predictions. The data are found to agree with
next-to-next-to-leading-order predictions by NNLOjet and with the
next-to-leading-order multi-leg generators MadGraph5_aMC@NLO and Sherpa.


title:
The Effect of Multi-Generational Selection in Geometric Semantic Genetic
  Programming

abstract:
  Among the evolutionary methods, one that is quite prominent is Genetic
Programming, and, in recent years, a variant called Geometric Semantic Genetic
Programming (GSGP) has shown to be successfully applicable to many real-world
problems. Due to a peculiarity in its implementation, GSGP needs to store all
the evolutionary history, i.e., all populations from the first one. We exploit
this stored information to define a multi-generational selection scheme that is
able to use individuals from older populations. We show that a limited ability
to use "old" generations is actually useful for the search process, thus
showing a zero-cost way of improving the performances of GSGP.


title:
Applicability of Software Reliability Growth Models to Open Source
  Software

abstract:
  Software reliability growth models (SRGMs) are based on underlying
assumptions which make them typically more suited for quality evaluation of
closed-source projects and their development lifecycles. Their usage in
open-source software (OSS) projects is a subject of debate. Although the
studies investigating the SRGMs applicability in OSS context do exist, they are
limited by the number of models and projects considered which might lead to
inconclusive results. In this paper, we present an experimental study of SRGMs
applicability to a total of 88 OSS projects, comparing nine SRGMs, looking at
the stability of the best models on the whole projects, on releases, on
different domains and according to different projects attributes. With the aid
of the STRAIT tool, we automated repository mining, data processing and SRGM
analysis for reproducibility. Overall, we found good applicability of SRGMs to
OSS, but with different performance when segmenting the dataset into releases,
domains and considering projects attributes, suggesting that the search for
one-fits-all models is unrealistic, rather recommending to look for the
characteristics of projects and bug fixing processes for the prediction of
applicable models.


title:
Completeness of Sum-Over-Paths for Toffoli-Hadamard and the Clifford
  Hierarchy

abstract:
  The "Sum-Over-Paths" formalism is a way to symbolically manipulate linear
maps that describe quantum systems, and is a tool that is used in formal
verification. We give here a new set of rewrite rules for the formalism, and
show that it is complete for "Toffoli-Hadamard", the simplest approximately
universal fragment of quantum mechanics. We show that the rewriting is
terminating, but not confluent (which is expected from the universality of the
fragment). We do so using the connection between Sum-over-Paths and graphical
language ZH-Calculus, and also show how the axiomatisation translates into the
latter. Finally, we show how to enrich the rewrite system to reach completeness
for the whole Clifford hierarchy.


title:
Soliton v. the gas: Fredholm determinants, analysis, and the rapid
  oscillations behind the kinetic equation

abstract:
  We analyze the case of a dense mKdV soliton gas and its large time behaviour
in the presence of a single trial soliton. We show that the solution can be
expressed in terms of Fredholm determinants as well as in terms of a
Riemann-Hilbert problem. We then show that the solution can be decomposed as
the sum of the background gas solution (an elliptic wave), plus a soliton
solution: the individual expressions are however quite convoluted due to the
interaction dynamics. Additionally, we are able to derive the local phase shift
of the gas after the passage of the soliton, and we can trace the location of
the soliton peak as the dynamics evolves. Finally we show that the soliton
peak, while interacting with the soliton gas, has an oscillatory velocity whose
leading order average value satisfies the kinetic velocity equation analogous
to the one posited by V. Zakharov and G. El.


title:
The interventional Bayesian Gaussian equivalent score for Bayesian
  causal inference with unknown soft interventions

abstract:
  Describing the causal relations governing a system is a fundamental task in
many scientific fields, ideally addressed by experimental studies. However,
obtaining data under intervention scenarios may not always be feasible, while
discovering causal relations from purely observational data is notoriously
challenging. In certain settings, such as genomics, we may have data from
heterogeneous study conditions, with soft (partial) interventions only
pertaining to a subset of the study variables, whose effects and targets are
possibly unknown. Combining data from experimental and observational studies
offers the opportunity to leverage both domains and improve on the
identifiability of causal structures. To this end, we define the interventional
BGe score for a mixture of observational and interventional data, where the
targets and effects of intervention may be unknown. To demonstrate the approach
we compare its performance to other state-of-the-art algorithms, both in
simulations and data analysis applications. Prerogative of our method is that
it takes a Bayesian perspective leading to a full characterisation of the
posterior distribution of the DAG structures. Given a sample of DAGs one can
also automatically derive full posterior distributions of the intervention
effects. Consequently the method effectively captures the uncertainty both in
the structure and the parameter estimates. Codes to reproduce the simulations
and analyses are publicly available at github.com/jackkuipers/iBGe


title:
Time-dependent Spinor field in a static cylindrically-symmetric
  space-time

abstract:
  Within the scope of a static cylindrically symmetric space-time we study the
behavior of a nonlinear spinor field that depends on time and radial
coordinates. It is found that the presence of nontrivial non-diagonal
components of the energy-momentum tensor (EMT) imposes some restriction on both
the metric functions and the spinor field. While for the time independent
spinor field there occur three way restrictions resembling the Bianchi type-I
Universe, in this case things become more complicated.


title:
Holistic Approach to Measure Sample-level Adversarial Vulnerability and
  its Utility in Building Trustworthy Systems

abstract:
  Adversarial attack perturbs an image with an imperceptible noise, leading to
incorrect model prediction. Recently, a few works showed inherent bias
associated with such attack (robustness bias), where certain subgroups in a
dataset (e.g. based on class, gender, etc.) are less robust than others. This
bias not only persists even after adversarial training, but often results in
severe performance discrepancies across these subgroups. Existing works
characterize the subgroup's robustness bias by only checking individual
sample's proximity to the decision boundary. In this work, we argue that this
measure alone is not sufficient and validate our argument via extensive
experimental analysis. It has been observed that adversarial attacks often
corrupt the high-frequency components of the input image. We, therefore,
propose a holistic approach for quantifying adversarial vulnerability of a
sample by combining these different perspectives, i.e., degree of model's
reliance on high-frequency features and the (conventional) sample-distance to
the decision boundary. We demonstrate that by reliably estimating adversarial
vulnerability at the sample level using the proposed holistic metric, it is
possible to develop a trustworthy system where humans can be alerted about the
incoming samples that are highly likely to be misclassified at test time. This
is achieved with better precision when our holistic metric is used over
individual measures. To further corroborate the utility of the proposed
holistic approach, we perform knowledge distillation in a limited-sample
setting. We observe that the student network trained with the subset of samples
selected using our combined metric performs better than both the competing
baselines, viz., where samples are selected randomly or based on their
distances to the decision boundary.


title: Electromagnetic Couplings of Axions

abstract:
  We show that, contrary to assertions in the literature, the main contribution
to the axion-photon coupling need not be quantized in the units proportional to
$e^2$. In particular, we discuss a loophole in the argument for this
quantization and then provide explicit counterexamples. Hence, we construct a
generic axion-photon effective Lagrangian and find that the axion-photon
coupling may be dominated by previously unknown Wilson coefficients. We show
that this result implies a significant modification of conventional axion
electrodynamics and sets new targets for axion experiments. At the core of our
theoretical analysis lies a critical reexamination of the interactions between
axions and magnetic monopoles. We show that, contrary to claims in the
literature, magnetic monopoles need not give mass to axions. Moreover, we find
that a future detection of an axion or axion-like particle with certain
parameters can serve as evidence for the existence of magnetically charged
matter.


title: Light absorption and emission by defects in doped nickel oxide

abstract:
  Nickel oxide is a versatile p-type semiconducting oxide with many
applications in opto-electronic devices, but high doping concentrations are
often required to achieve necessary electrical conductivity. In contrast to
many other transparent oxide semiconductors, even moderate levels of doping of
NiO can lead to significant optical absorption in the visible spectral range,
limiting the application range of the material. This correlation has been
reported extensively in literature, but its origin has been unknown until now.
This work combines experimental data on optical properties from a variety of
NiO samples with results from hybrid density functional theory calculations. It
shows that strong electron-phonon interaction leads to a significant blue shift
(0.6-1 eV) of electronic transitions from the valence band maximum to defect
states by light absorption with respect to the thermodynamic charge transition
levels. This essentially renders NiO a narrow-gap semiconductor by defect band
formation already at moderate doping levels, with strong light absorption for
photon energies of approximately 1 eV. The calculations are also shown to be
fully consistent with experimental data on defect-related light emission in
NiO.


title:
RF Interference in Lens-Based Massive MIMO Systems -- An Application
  Note

abstract:
  We analyze the uplink radio frequency (RF) interference from a multiplicity
of single-antenna user equipments transmitting to a cellular base station (BS)
within the same time-frequency resource. The BS is assumed to operate with a
lens antenna array, which induces additional focusing gain for the incoming
signals. Considering line-of-sight propagation conditions, we characterize the
multiuser RF interference properties via approximation of the mainlobe
interference as well as the effective interferer probability. The results
derived in this application note are foundational to more general multiuser
interference analysis across different propagation conditions, which we present
in a follow up paper.


title:
Estimating the parameters of Hybrid Palatini gravity model with the
  Schwarzschild precession of S2, S38 and S55 stars: case of bulk mass
  distribution

abstract:
  We estimate the parameters of Hybrid Palatini gravity model with the
Schwarzschild precession of S-stars, specifically of S2, S38 and S55 stars. We
also take into account case of bulk mass distribution near Galactic Center. We
assume that the Schwarzschild orbital precession of mentioned S-stars is the
same like in General Relativity (GR) in all studied cases. In 2020 the GRAVITY
Collaboration detected the orbital precession of the S2 star around the
supermassive black hole (SMBH) at the Galactic Center and showed that it is
close to the GR prediction. The astronomical data analysis of S38 and S55
orbits showed that also in these cases orbital precession is close to the GR
prediction. Based on this observational fact, we evaluated parameters of the
Hybrid Palatini Gravity model with the Schwarzschild precession of the S2, S38
and S55 stars and we estimated the range of parameters of Hybrid Palatini
gravity model for which the orbital precession is like in GR for all tree
stars. We also evaluated parameters of the Hybrid Palatini Gravity model in
case of different values of bulk mass density distribution of extended matter.
We believe that proposed method is a useful tool to evaluate parameters of the
gravitational potential at the Galactic Center.


title: Macroscopic Single-Phase Monolayer Borophene on Arbitrary Substrates

abstract:
  A major challenge in the investigation of all 2D materials is the development
of synthesis protocols and tools which would enable their large-scale
production and effective manipulation. The same holds for borophene, where
experiments are still largely limited to in situ characterizations of
small-area samples. In contrast, our work is based on millimeter-sized
borophene sheets, synthesized on an Ir(111) surface in ultrahigh vacuum.
Besides high-quality macroscopic synthesis, as confirmed by low-energy electron
diffraction (LEED) and atomic force microscopy (AFM), we also demonstrate a
successful transfer of borophene from Ir to a Si wafer via electrochemical
delamination process. Comparative Raman spectroscopy, in combination with the
density functional theory (DFT) calculations, proved that borophene's crystal
structure has been preserved in the transfer. Our results demonstrate
successful growth and manipulation of large-scale, single-layer borophene
sheets with minor defects and ambient stability, thus expediting borophene
implementation into more complex systems and devices.