title: Quadratic matrix inequalities with applications to data-based control

abstract:
  This paper studies several problems related to quadratic matrix inequalities
(QMI's), i.e., inequalities in the semidefinite ordering involving quadratic
functions of matrix variables. In particular, we provide conditions under which
the solution set of a QMI is nonempty, convex, bounded, or has nonempty
interior. We also provide a parameterization of the solution set of a given
QMI. In addition, we state projection results, that characterize a subset of
"structured" solutions to a QMI. Thereafter, we derive matrix versions of the
classical S-lemma and Finsler's lemma, that provide conditions under which all
solutions to one QMI also satisfy another QMI. The results will be compared to
related work in the robust control literature, such as the full block
S-procedure and Petersen's lemma, and it is demonstrated how existing results
can be obtained from the results of this paper as special cases. Finally, we
show how the various results for QMI's can be applied to the problem of
data-driven stabilization. This problem involves finding a stabilizing feedback
controller for an unknown dynamical system on the basis of a finite set of
noisy data samples. We provide general necessary and sufficient conditions for
data-based quadratic stabilization. In addition, we demonstrate how to reduce
the computational complexity of data-based stabilization by leveraging QMI
projection results. This involves separating the computation of the Lyapunov
function and the controller, and also leads to explicit formulas for
data-guided feedback gains.


title:
Evaluation of IoT Self-healing Mechanisms using Fault-Injection in
  Message Brokers

abstract:
  The widespread use of Internet-of-Things (IoT) across different application
domains leads to an increased concern regarding their dependability, especially
as the number of potentially mission-critical systems becomes considerable.
Fault-tolerance has been used to reduce the impact of faults in systems, and
their adoption in IoT is becoming a necessity. This work focuses on how to
exercise fault-tolerance mechanisms by deliberately provoking its malfunction.
We start by describing a proof-of-concept fault-injection add-on to a commonly
used publish/subscribe broker. We then present several experiments mimicking
real-world IoT scenarios, focusing on injecting faults in systems with (and
without) active self-healing mechanisms and comparing their behavior to the
baseline without faults. We observe evidence that fault-injection can be used
to (a) exercise in-place fault-tolerance apparatus, and (b) detect when these
mechanisms are not performing nominally, providing insights into enhancing
in-place fault-tolerance techniques.


title: Multilevel Bayesian Deep Neural Networks

abstract:
  In this article we consider Bayesian inference associated to deep neural
networks (DNNs) and in particular, trace-class neural network (TNN) priors
which were proposed by Sell et al. [39]. Such priors were developed as more
robust alternatives to classical architectures in the context of inference
problems. For this work we develop multilevel Monte Carlo (MLMC) methods for
such models. MLMC is a popular variance reduction technique, with particular
applications in Bayesian statistics and uncertainty quantification. We show how
a particular advanced MLMC method that was introduced in [4] can be applied to
Bayesian inference from DNNs and establish mathematically, that the
computational cost to achieve a particular mean square error, associated to
posterior expectation computation, can be reduced by several orders, versus
more conventional techniques. To verify such results we provide numerous
numerical experiments on model problems arising in machine learning. These
include Bayesian regression, as well as Bayesian classification and
reinforcement learning.


title: Conserving elastic turbulence numerically using artificial diffusivity

abstract:
  To simulate elastic turbulence, where viscoelasticity dominates, numerical
solvers introduce an artificial stress diffusivity term to handle the steep
polymer stress gradients that ensue. This has recently been shown [Gupta &
Vincenzi, J. Fluid Mech. 870, 405-418 (2019); Dzanic, From & Sauret, J. Fluid
Mech. 937, A31 (2022)] to introduce unphysical artifacts with a detrimental
impact on simulations. In this Letter, we propose that artificial diffusion is
limited to regions where stress gradients are steep instead of seeking the
zero-diffusivity limit. Through the cellular forcing and four-roll mill
problem, we demonstrate that this modified artificial diffusivity is devoid of
unphysical artifacts, allowing all features of elastic turbulence to be
retained. Results are found to conform with direct simulations while only
requiring a fraction of the numerical resolution.


title: Tree languages and branched groups

abstract:
  We study the portraits of isometries of rooted trees - the labelling of the
tree, at each vertex, by the permutation of its descendants - in terms of
languages. We characterize regularly branched self-similar groups in terms of
$\omega$-regular languages. We deduce the algorithmic decidability of some
problems, such as the comparison of regularly branched contracting groups, and
their orbit structure on the boundary of the rooted tree.


title: Knowledge Removal in Sampling-based Bayesian Inference

abstract:
  The right to be forgotten has been legislated in many countries, but its
enforcement in the AI industry would cause unbearable costs. When single data
deletion requests come, companies may need to delete the whole models learned
with massive resources. Existing works propose methods to remove knowledge
learned from data for explicitly parameterized models, which however are not
appliable to the sampling-based Bayesian inference, i.e., Markov chain Monte
Carlo (MCMC), as MCMC can only infer implicit distributions. In this paper, we
propose the first machine unlearning algorithm for MCMC. We first convert the
MCMC unlearning problem into an explicit optimization problem. Based on this
problem conversion, an {\it MCMC influence function} is designed to provably
characterize the learned knowledge from data, which then delivers the MCMC
unlearning algorithm. Theoretical analysis shows that MCMC unlearning would not
compromise the generalizability of the MCMC models. Experiments on Gaussian
mixture models and Bayesian neural networks confirm the effectiveness of the
proposed algorithm. The code is available at
\url{https://github.com/fshp971/mcmc-unlearning}.


title:
Exploring ultracold collisions in $^6$Li-$^{53}$Cr Fermi mixtures:
  Feshbach resonances and scattering properties of a novel alkali-transition
  metal system

abstract:
  We investigate ultracold collisions in a mixture of $^6$Li and $^{53}$Cr
fermionic atoms by exploring six different Li-Cr spin channels via loss
spectroscopy. We pinpoint more than 50 interspecies Feshbach resonances
comprised between 0 and 1500 Gauss, to which we unambiguously assign a complete
set of quantum numbers based on full coupled-channel calculations. Our work
provides a thorough characterization of $^{6}$Li-$^{53}$Cr scattering
properties and yields predictions for other isotopic pairs, paving the way to
future few- and many-body studies with quantum mixtures of alkali and
transition metal atoms. Additionally, our study lays the ground for the
production of a new class of ultracold polar molecules possessing both electric
and magnetic dipole moments.


title:
The criticality of reversible quadratic centers at the outer boundary of
  its period annulus

abstract:
  This paper deals with the period function of the reversible quadratic centers
\begin{equation*} X_{\np}=-y(1-x)\partial_x+(x+Dx^2+Fy^2)\partial_y,
\end{equation*} where $\np=(D,F)\in\R^2.$ Compactifying the vector field to
$\Sc^2$, the boundary of the period annulus has two connected components, the
center itself and a polycycle. We call them the inner and outer boundary of the
period annulus, respectively. We are interested in the bifurcation of critical
periodic orbits from the polycycle $\out_\np$ at the outer boundary. A critical
period is an isolated critical point of the period function. The criticality of
the period function at the outer boundary is the maximal number of critical
periodic orbits of $X_\np$ that tend to $\out_{\np_0}$ in the Hausdorff sense
as $\np\to\np_0.$ This notion is akin to the cyclicity in Hilbert's 16th
Problem. Our main result (Theorem A) shows that the criticality at the outer
boundary is at most 2 for all $\np=(D,F)\in\R^2$ outside the segments
$\{-1\}\times [0,1]$ and $\{0\}\times [0,2]$. With regard to the bifurcation
from the inner boundary, Chicone and Jacobs proved in their seminal paper on
the issue that the upper bound is 2 for all $\np\in\R^2.$ In this paper the
techniques are different because, while the period function extends
analytically to the center, it has no smooth extension to the polycycle. We
show that the period function has an asymptotic expansion near the polycycle
with the remainder being uniformly flat with respect to~$\np$ and where the
principal part is given in a monomial scale containing a deformation of the
logarithm. More precisely, Theorem~A follows by obtaining the asymptotic
expansion to fourth order and computing its coefficients, which are not
polynomial in~$\np$ but transcendental.


title: Extended critical regimes of deep neural networks

abstract:
  Deep neural networks (DNNs) have been successfully applied to many real-world
problems, but a complete understanding of their dynamical and computational
principles is still lacking. Conventional theoretical frameworks for analysing
DNNs often assume random networks with coupling weights obeying Gaussian
statistics. However, non-Gaussian, heavy-tailed coupling is a ubiquitous
phenomenon in DNNs. Here, by weaving together theories of heavy-tailed random
matrices and non-equilibrium statistical physics, we develop a new type of mean
field theory for DNNs which predicts that heavy-tailed weights enable the
emergence of an extended critical regime without fine-tuning parameters. In
this extended critical regime, DNNs exhibit rich and complex propagation
dynamics across layers. We further elucidate that the extended criticality
endows DNNs with profound computational advantages: balancing the contraction
as well as expansion of internal neural representations and speeding up
training processes, hence providing a theoretical guide for the design of
efficient neural architectures.

categories: cs.LG cond-mat.dis-nn cond-mat.stat-mech cs.AI stat.ML


title:
The Impact of Acquisitions on Inventors' Turnover in the Biotechnology
  Industry

abstract:
  In high-tech industries, where intellectual property plays a crucial role,
the acquisition of intangible assets and employees' tacit knowledge is an
integral part of the motivation for Mergers and Acquisitions (M&As). Following
the molecular biology revolution, the wave of takeovers in the biotechnology
industry in the Nineties is a well-known example of M&As to absorb new
knowledge. The retention of critical R&D employees embodying valuable knowledge
and potential future innovation is uncertain after an acquisition. While not
all employees might be relevant for the success of the takeover, inventors are
among the most valuable. This is especially true for the acquisition of an
innovative start-up. This paper estimates how likely an inventor working for an
acquired biotechnology company will leave. We find that inventors affected by
acquisitions are 20\% more likely to leave the company by a
difference-in-differences approach matching both firms and inventors.


title:
Analyzing Process-Aware Information System Updates Using Digital Twins
  of Organizations

abstract:
  Digital transformation often entails small-scale changes to information
systems supporting the execution of business processes. These changes may
increase the operational frictions in process execution, which decreases the
process performance. The contributions in the literature providing support to
the tracking and impact analysis of small-scale changes are limited in scope
and functionality. In this paper, we use the recently developed Digital Twins
of Organizations (DTOs) to assess the impact of (process-aware) information
systems updates. More in detail, we model the updates using the configuration
of DTOs and quantitatively assess different types of impacts of information
system updates (structural, operational, and performance-related). We
implemented a prototype of the proposed approach. Moreover, we discuss a case
study involving a standard ERP procure-to-pay business process.


title: On a system of coupled Cahn-Hilliard equations

abstract:
  We consider a system which consists of a Cahn-Hilliard equation coupled with
a Cahn-Hilliard-Oono equation in a bounded domain of $\mathbb{R}^d$, $d = 2,
3$. This system accounts for macrophase and microphase separation in a polymer
mixture through two order parameters $u$ and $v$. The free energy of this
system is a bivariate interaction potential which contains the mixing entropy
of the two order parameters and suitable coupling terms. The equations are
endowed with initial conditions and homogeneous Neumann boundary conditions
both for $u,v$ and for the corresponding chemical potentials. We first prove
that the resulting problem is well posed in a weak sense. Then, in the
conserved case, we establish that the weak solution regularizes
instantaneously. Furthermore, in two spatial dimensions, we show the strict
separation property for $u$ and $v$, namely, they both stay uniformly away from
the pure phases $\pm 1$ in finite time. Finally, we investigate the long-time
behavior of a finite energy solution showing, in particular, that it converges
to a single stationary state.


title: Probing for Labeled Dependency Trees

abstract:
  Probing has become an important tool for analyzing representations in Natural
Language Processing (NLP). For graphical NLP tasks such as dependency parsing,
linear probes are currently limited to extracting undirected or unlabeled parse
trees which do not capture the full task. This work introduces DepProbe, a
linear probe which can extract labeled and directed dependency parse trees from
embeddings while using fewer parameters and compute than prior methods.
Leveraging its full task coverage and lightweight parametrization, we
investigate its predictive power for selecting the best transfer language for
training a full biaffine attention parser. Across 13 languages, our proposed
method identifies the best source treebank 94% of the time, outperforming
competitive baselines and prior work. Finally, we analyze the informativeness
of task-specific subspaces in contextual embeddings as well as which benefits a
full parser's non-linear parametrization provides.


title: On the cyclicity of Kolmogorov polycycles

abstract:
  In this paper we study planar polynomial Kolmogorov's differential systems \[
  X_\mu\quad\sist{xf(x,y;\mu),}{yg(x,y;\mu),} \] with the parameter $\mu$
varying in an open subset $\Lambda\subset\R^N$. Compactifying $X_\mu$ to the
Poincar\'e disc, the boundary of the first quadrant is an invariant triangle
$\Gamma$, that we assume to be a hyperbolic polycycle with exactly three saddle
points at its vertices for all $\mu\in\Lambda.$ We are interested in the
cyclicity of $\Gamma$ inside the family $\{X_\mu\}_{\mu\in\Lambda},$ i.e., the
number of limit cycles that bifurcate from $\Gamma$ as we perturb $\mu.$ In our
main result we define three functions that play the same role for the cyclicity
of the polycycle as the first three Lyapunov quantities for the cyclicity of a
focus. As an application we study two cubic Kolmogorov families, with $N=3$ and
$N=5$, and in both cases we are able to determine the cyclicity of the
polycycle for all $\mu\in\Lambda,$ including those parameters for which the
return map along $\Gamma$ is the identity.


title: Emergent Organization and Polarization due to Active Fluctuations

abstract:
  We introduce and study a model of active Brownian motion with multiplicative
noise describing fluctuations in the self-propulsion or activity. We find that
the standard picture of density accumulation in slow regions is qualitatively
modified by active fluctuations, as stationary density profiles are generally
not determined only by the mean self-propulsion speed landscape. As a result,
activity gradients generically correlate the particle self-propulsion speed and
orientation, leading to emergent polarization at interfaces pointing either
towards dense or dilute regions depending on the amount of noise in the system.
We discuss how these features affect the onset of motility induced phase
separation. Our work provides a foundation for systematic studies of active
matter self-organization in the presence of activity landscapes and active
fluctuations.


title:
Spin-state-controlled chemi-ionization reactions between metastable
  helium atoms and ground-state lithium atoms

abstract:
  We demonstrate the control of $^4$He(2$^3$S$_1$)-$^7$Li(2$^2$S$_{1/2}$)
chemi-ionization reactions by all-optical electron-spin-state preparation of
both atomic species prior to the collision process. Our results demonstrate
that chemi-ionization is strongly suppressed (enhanced) for non-spin-conserving
(spin-conserving) collisions at thermal energies. These findings are in good
agreement with a model based on spin angular momentum coupling of the prepared
atomic states to the quasi-molecular states. Small deviations from the model
indicate the contribution of the $^4\Sigma^+$ channel to the reaction rate
which is in violation of spin conservation.


title: Lie trusses and heaps of Lie affebras

abstract:
  A frame-independent formulation of Lie brackets on affine spaces or {\em Lie
affebras} introduced in [K.\ Grabowska, J.\ Grabowski \& P.\ Urba\'nski, Lie
brackets on affine bundles, {\em Ann.\ Global Anal.\ Geom.} {\bf 24} (2003),
101--130] is given.


title: Focus-and-Detect: A Small Object Detection Framework for Aerial Images

abstract:
  Despite recent advances, object detection in aerial images is still a
challenging task. Specific problems in aerial images makes the detection
problem harder, such as small objects, densely packed objects, objects in
different sizes and with different orientations. To address small object
detection problem, we propose a two-stage object detection framework called
"Focus-and-Detect". The first stage which consists of an object detector
network supervised by a Gaussian Mixture Model, generates clusters of objects
constituting the focused regions. The second stage, which is also an object
detector network, predicts objects within the focal regions. Incomplete Box
Suppression (IBS) method is also proposed to overcome the truncation effect of
region search approach. Results indicate that the proposed two-stage framework
achieves an AP score of 42.06 on VisDrone validation dataset, surpassing all
other state-of-the-art small object detection methods reported in the
literature, to the best of authors' knowledge.


title: The singular support of sheaves is $\gamma$-coisotropic

abstract:
  We prove that the singular support of an element in the derived category of
sheaves is $\gamma$-coisotropic, a notion defined in [Vit22]. We prove that
this implies that it is involutive in the sense of Kashiwara-Schapira, but
being $\gamma$-coisotropic has the advantage to be invariant by symplectic
homeomorphisms (while involutivity is only invariant by $C^1$ diffeomorphisms)
and we give an example of an involutive set that is not $\gamma$-coisotropic.
Along the way we prove a number of results relating the singular support and
the spectral norm $\gamma$ and raise a number of new questions.


title: Effective Explanations for Entity Resolution Models

abstract:
  Entity resolution (ER) aims at matching records that refer to the same
real-world entity. Although widely studied for the last 50 years, ER still
represents a challenging data management problem, and several recent works have
started to investigate the opportunity of applying deep learning (DL)
techniques to solve this problem. In this paper, we study the fundamental
problem of explainability of the DL solution for ER. Understanding the matching
predictions of an ER solution is indeed crucial to assess the trustworthiness
of the DL model and to discover its biases. We treat the DL model as a black
box classifier and - while previous approaches to provide explanations for DL
predictions are agnostic to the classification task. we propose the CERTA
approach that is aware of the semantics of the ER problem. Our approach
produces both saliency explanations, which associate each attribute with a
saliency score, and counterfactual explanations, which provide examples of
values that can flip the prediction. CERTA builds on a probabilistic framework
that aims at computing the explanations evaluating the outcomes produced by
using perturbed copies of the input records. We experimentally evaluate CERTA's
explanations of state-of-the-art ER solutions based on DL models using publicly
available datasets, and demonstrate the effectiveness of CERTA over recently
proposed methods for this problem.


title: Is Geometry Enough for Matching in Visual Localization?

abstract:
  In this paper, we propose to go beyond the well-established approach to
vision-based localization that relies on visual descriptor matching between a
query image and a 3D point cloud. While matching keypoints via visual
descriptors makes localization highly accurate, it has significant storage
demands, raises privacy concerns and increases map maintenance complexity. To
elegantly address those practical challenges for large-scale localization, we
present GoMatch, an alternative to visual-based matching that solely relies on
geometric information for matching image keypoints to maps, represented as sets
of bearing vectors. Our novel bearing vectors representation of 3D points,
significantly relieves the cross-domain challenge in geometric-based matching
that prevented prior work to tackle localization in a realistic environment.
With additional careful architecture design, GoMatch improves over prior
geometric-based matching work with a reduction of ($10.67m, 95.7^{\circ}$) and
($1.43m$, $34.7^{\circ}$) in average median pose errors on Cambridge Landmarks
and 7-Scenes, while requiring as little as $1.5/1.7\%$ of storage capacity in
comparison to the best visual-based matching methods. This confirms its
potential and feasibility for real-world localization and opens the door to
future efforts in advancing city-scale visual localization methods that do not
require storing visual descriptors.


title: MERLIN -- Malware Evasion with Reinforcement LearnINg

abstract:
  In addition to signature-based and heuristics-based detection techniques,
machine learning (ML) is widely used to generalize to new, never-before-seen
malicious software (malware). However, it has been demonstrated that ML models
can be fooled by tricking the classifier into returning the incorrect label.
These studies, for instance, usually rely on a prediction score that is fragile
to gradient-based attacks. In the context of a more realistic situation where
an attacker has very little information about the outputs of a malware
detection engine, modest evasion rates are achieved. In this paper, we propose
a method using reinforcement learning with DQN and REINFORCE algorithms to
challenge two state-of-the-art ML-based detection engines (MalConv \& EMBER)
and a commercial AV classified by Gartner as a leader AV. Our method combines
several actions, modifying a Windows portable execution (PE) file without
breaking its functionalities. Our method also identifies which actions perform
better and compiles a detailed vulnerability report to help mitigate the
evasion. We demonstrate that REINFORCE achieves very good evasion rates even on
a commercial AV with limited available information.


title:
Impacts of Personal Characteristics on User Trust in Conversational
  Recommender Systems

abstract:
  Conversational recommender systems (CRSs) imitate human advisors to assist
users in finding items through conversations and have recently gained
increasing attention in domains such as media and e-commerce. Like in human
communication, building trust in human-agent communication is essential given
its significant influence on user behavior. However, inspiring user trust in
CRSs with a "one-size-fits-all" design is difficult, as individual users may
have their own expectations for conversational interactions (e.g., who, user or
system, takes the initiative), which are potentially related to their personal
characteristics. In this study, we investigated the impacts of three personal
characteristics, namely personality traits, trust propensity, and domain
knowledge, on user trust in two types of text-based CRSs, i.e., user-initiative
and mixed-initiative. Our between-subjects user study (N=148) revealed that
users' trust propensity and domain knowledge positively influenced their trust
in CRSs, and that users with high conscientiousness tended to trust the
mixed-initiative system.


title: Half-Quantized Helical Hinge Currents in Axion Insulators

abstract:
  We propose that half-quantized helical hinge currents manifest as the
fingerprint of the axion insulator (AI). These helical hinge currents
microscopically originate from the lateral Goos-H\"anchen (GH) shift of
massless side-surface Dirac electrons that are totally reflected from the
hinges. Meanwhile, due to the presence of the massive top and bottom surfaces
of the AI, the helical current induced by the GH shift is half-quantized. The
semiclassical wave packet analysis uncovers that the hinge current has a
topological origin and its half-quantization is robust to parameter changes.
Lastly, we propose an experimentally feasible six-terminal device to identify
the half-quantized hinge channels by measuring the nonreciprocal conductances.
Our results advance the understanding of the non-trivial transport and
topological magnetoelectric responses in AIs.


title:
Size-based scheduling vs fairness for datacenter flows: a queuing
  perspective

abstract:
  Contrary to the conclusions of a recent body of work where approximate
shortest remaining processing time first (SRPT) flow scheduling is advocated
for datacenter networks, this paper aims to demonstrate that per-flow fairness
remains a preferable objective. We evaluate abstract queuing models by analysis
and simulation to illustrate the non-optimality of SRPT under the reasonable
assumptions that datacenter flows occur in batches and bursts and not, as
usually assumed, individually at the instants of a Poisson process. Results for
these models have significant implications for the design of bandwidth sharing
strategies for datacenter networks. In particular, we propose a novel "virtual
fair scheduling" algorithm that enforces fairness between batches and is
arguably simple enough to be implemented in high speed devices.


title: On the Birth of the Universe and Time

abstract:
  A theory of the initial state of the universe is proposed within the
framework of the Euclidean quantum theory of gravity. The theory is based on a
quantum representation in which the action functional is implemented as an
operator on the space of wave functionals depending on the 4D space metric and
matter fields. The initial construction object is the eigenvalue of the action
operator in the area of the Origin of the universe with the given values of the
3D metric and matter fields on the boundary. The wave function of the initial
state is plotted as an exponential of this eigenvalue, after a Wick rotation in
the complex plane of the radial variable of the Euclidean 4D space. An estimate
of the initial radius of the universe is proposed.


title:
Learning Disentangled Representation for One-shot Progressive Face
  Swapping

abstract:
  Although face swapping has attracted much attention in recent years, it
remains a challenging problem. The existing methods leverage a large number of
data samples to explore the intrinsic properties of face swapping without
taking into account the semantic information of face images. Moreover, the
representation of the identity information tends to be fixed, leading to
suboptimal face swapping. In this paper, we present a simple yet efficient
method named FaceSwapper, for one-shot face swapping based on Generative
Adversarial Networks. Our method consists of a disentangled representation
module and a semantic-guided fusion module. The disentangled representation
module is composed of an attribute encoder and an identity encoder, which aims
to achieve the disentanglement of the identity and the attribute information.
The identity encoder is more flexible and the attribute encoder contains more
details of the attributes than its competitors. Benefiting from the
disentangled representation, FaceSwapper can swap face images progressively. In
addition, semantic information is introduced into the semantic-guided fusion
module to control the swapped area and model the pose and expression more
accurately. The experimental results show that our method achieves
state-of-the-art results on benchmark datasets with fewer training samples. Our
code is publicly available at https://github.com/liqi-casia/FaceSwapper.


title:
Optimizing beam-ion confinement in ITER by adjusting the toroidal phase
  of the 3-D magnetic fields applied for ELM control

abstract:
  The confinement of Neutral Beam Injection (NBI) particles in the presence of
n=3 Resonant Magnetic Perturbations (RMPs) in 15 MA ITER DT plasmas has been
studied using full orbit ASCOT simulations. Realistic NBI distribution
functions, and 3D wall and equilibria, including the plasma response to the
externally applied 3D fields calculated with MARS-F, have been employed. The
observed total fast-ion losses depend on the poloidal spectra of the applied
n=3 RMP as well as on the absolute toroidal phase of the applied perturbation
with respect to the NBI birth distribution. The absolute toroidal phase of the
RMP perturbation does not affect the ELM control capabilities, which makes it a
key parameter in the confinement optimization. The physics mechanisms
underlying the observed fast-ion losses induced by the applied 3D fields have
been studied in terms of the variation of the particle canonical angular
momentum ($\delta P_{\phi}$) induced by the applied 3D fields. The presented
simulations indicate that the transport is located in an Edge Resonant
Transport Layer (ERTL) as observed previously in ASDEX Upgrade studies.
Similarly, our results indicate that an overlapping of several linear and
nonlinear resonances at the edge of the plasma might be responsible for the
observed fast-ion losses. The results presented here may help to optimize the
RMP configuration with respect to the NBI confinement in future ITER
discharges.


title:
Millimeter-wave Foresight Sensing for Safety and Resilience in
  Autonomous Operations

abstract:
  Robotic platforms are highly programmable, scalable and versatile to complete
several tasks including Inspection, Maintenance and Repair (IMR). Mobile
robotics offer reduced restrictions in operating environments, resulting in
greater flexibility; operation at height, dangerous areas and repetitive tasks.
Cyber physical infrastructures have been identified by the UK Robotics Growth
Partnership as a key enabler in how we utilize and interact with sensors and
machines via the virtual and physical worlds. Cyber Physical Systems (CPS)
allow for robotics and artificial intelligence to adapt and repurpose at pace,
allowing for the addressment of new challenges in CPS. A challenge exists
within robotics to secure an effective partnership in a wide range of areas
which include shared workspaces and Beyond Visual Line of Sight (BVLOS).
Robotic manipulation abilities have improved a robots accessibility via the
ability to open doorways, however, challenges exist in how a robot decides if
it is safe to move into a new workspace. Current sensing methods are limited to
line of sight and are unable to capture data beyond doorways or walls,
therefore, a robot is unable to sense if it is safe to open a door. Another
limitation exists as robots are unable to detect if a human is within a shared
workspace. Therefore, if a human is detected, extended safety precautions can
be taken to ensure the safe autonomous operation of a robot. These challenges
are represented as safety, trust and resilience, inhibiting the successful
advancement of CPS. This paper evaluates the use of frequency modulated
continuous wave radar sensing for human detection and through-wall detection to
increase situational awareness. The results validate the use of the sensor to
detect the difference between a person and infrastructure, and increased
situational awareness for navigation via foresight monitoring through walls.


title:
Hexagonal network of photocurrent enhancement in few-layer
  graphene/InGaN quantum dot junctions

abstract:
  Strain in two-dimensional (2D) materials has attracted particular attention
owing to the remarkable modification of electronic and optical properties.
However, emergent electromechanical phenomena and hidden mechanisms remain
under debate. Here, using scanning photocurrent microscopy, we observe
significant photocurrent enhancement in hybrid vertical junction devices made
of strained few-layer graphene and InGaN quantum dots. Optoelectronic response
and photoluminescence measurements demonstrate a mechanism closely tied to the
flexoelectric effect in few-layer graphene, where the strain can induce a
lateral built-in electric field and assist the separation of electron-hole
pairs. Photocurrent mapping reveals an unprecedentedly ordered hexagonal
network, suggesting the potential to create a superlattice by strain
engineering. Our work provides insights into optoelectronic phenomena in the
presence of strain and paves the way for practical applications associated with
strained 2D materials.


title:
3D-printed facet-attached optical elements for beam shaping in optical
  phased arrays

abstract:
  We demonstrate an optical phased-array (OPA) equipped with a 3D-printed
facet-attached element for shaping and deflection of the emitted beam. The beam
shaper combines freeform refractive surfaces with total-internal-reflection
(TIR) mirrors and is in-situ printed to edge-emitting waveguide facets using
high-resolution multi-photon lithography, thereby ensuring precise alignment
with respect to on-chip waveguide structures. In a proof-of-concept experiment,
we achieve a grating-lobe free steering range of $\pm 30{\deg}$ and a
full-width-halfmaximum (FWHM) beam divergence of approximately $2{\deg}$. The
concept opens an attractive alternative to currently used grating structures
and is applicable to a wide range of integration platforms.


title: Generating Scientific Claims for Zero-Shot Scientific Fact Checking

abstract:
  Automated scientific fact checking is difficult due to the complexity of
scientific language and a lack of significant amounts of training data, as
annotation requires domain expertise. To address this challenge, we propose
scientific claim generation, the task of generating one or more atomic and
verifiable claims from scientific sentences, and demonstrate its usefulness in
zero-shot fact checking for biomedical claims. We propose CLAIMGEN-BART, a new
supervised method for generating claims supported by the literature, as well as
KBIN, a novel method for generating claim negations. Additionally, we adapt an
existing unsupervised entity-centric method of claim generation to biomedical
claims, which we call CLAIMGEN-ENTITY. Experiments on zero-shot fact checking
demonstrate that both CLAIMGEN-ENTITY and CLAIMGEN-BART, coupled with KBIN,
achieve up to 90% performance of fully supervised models trained on manually
annotated claims and evidence. A rigorous evaluation study demonstrates
significant improvement in generated claim and negation quality over existing
baselines


title:
Tensile Strain Induced Enhancement in the Thermoelectric Performance of
  monolayer MoS$_{2}$

abstract:
  First-principles calculations based on density functional theory, coupled
with the semi-classical Boltzmann transport theory, have been performed to
investigate the strain- and temperature-induced tunability of the
thermoelectric properties of monolayer (ML) MoS$_2$. The electronic band gap
reduces with increasing tensile strain, and a semiconductor to metal transition
occurs for $10\%$ biaxial strain. Tensile strains, in most of the cases, are
seen to have a diminishing effect on the thermopower and power factor of
monolayer MoS$_2$, with larger impact on the p-type carriers. Performing
anharmonic phonon calculations, it is seen that tensile strain, in general and
uniaxial tensile strain along the zig-zag direction, in particular,
significantly reduces the thermal conductivity of ML-MoS$_2$. The combined
effect of reduced phonon relaxation time, ZA-optical phonon frequency gap and
the Debye temperature is found to be the driving force behind the lowering of
the thermal conductivity. The large reduction in thermal conductivity and
increase in power factor under the action of tensile strains along the zigzag
direction act in concert to result in an enhanced efficiency and hence,
improved thermoelectric performance. The thermoelectric efficiency of
ML-MoS$_2$ is seen to increase with increasing temperature, suggesting its use
as a high-temperature thermoelectric material. Nearly 75\% enhancement in the
thermoelectric efficiency can be achieved with optimal doping concentration.
We, therefore, highlight the significance of in-plane tensile strains in
improving the thermoelectric performance of ML-MoS$_2$, which could open
avenues for its application in emerging areas in 2D-thermoelectrics.


title: LS Algebras, Valuations and Schubert Varieties

abstract:
  In this paper, we propose an algebraic approach via Lakshmibai-Seshadri (LS)
algebras to establish a link between standard monomial theories,
Newton-Okounkov bodies and valuations. This is applied to Schubert varieties,
where this approach is compatible with the one using Seshadri stratifications
by the same authors (arXiv:2112.03776), showing that LS paths encode vanishing
multiplicities with respect to the web of Schubert varieties.


title:
Navier-Stokes blow-up rates in certain Besov spaces whose regularity
  exceeds the critical value by $\epsilon \in (1,2]$

abstract:
  For a solution $u$ to the Navier-Stokes equations in spatial dimension $n
\geq 3$ which blows up at a finite time $T > 0$, we prove the blowup estimate
${\|u(t)\|}_{\dot{B}_{p,q}^{s_{p}+\epsilon}(\mathbb{R}^n)}
\gtrsim_{\varphi,p,\epsilon} {(T-t)}^{-\epsilon/2}$ for all $\epsilon \in
(1,2)$ and $p,q \in [1,\frac{2}{2-\epsilon})$, where $s_{p}:=-1+\frac{n}{p}$ is
the scaling-critical regularity, and $\varphi$ is the cutoff function used to
define the Littlewood-Paley projections. For $\epsilon =2$, we prove the same
type of estimate but only for $q=1$:
${\|u(t)\|}_{\dot{B}_{p,1}^{s_{p}+2}(\mathbb{R}^n)}
\gtrsim_{\varphi,p,\epsilon} {(T-t)}^{-\epsilon/2}$ for all $p \in [1,\infty)$.
Under the additional restriction that $p,q \in [1,2]$ and $n=3$, these blowup
estimates are implied by those first proved by Robinson, Sadowski and Silva (J.
Math. Phys., 2012) for $p=q=2$ in the case $\epsilon \in (1,2)$, and by
McCormick, Olson, Robinson, Rodrigo, Vidal-L\'{o}pez and Zhou (SIAM J. Math.
Anal., 2016) for $p=2$ in the case $(\epsilon,q) = (2,1)$.


title: A remark on extremal stability of configuration spaces

abstract:
  We explicitly study the extremal stability of configuration spaces of complex
projective spaces of any dimension, and show that the homology groups are
vanish in extremal stable range. As a consequence, we give an affirmative
answer of the question of Knudsen, Miller and Tosteson.


title:
Design and simulation of an Imaging Neutral Particle Analyzer for the
  ASDEX Upgrade tokamak

abstract:
  An Imaging Neutral Particle Analyser (INPA) diagnostic has been designed for
the ASDEX Upgrade (AUG) tokamak. The AUG INPA diagnostic will measure fast
neutrals escaping the plasma after CX reactions. The neutrals will be ionised
by a 20 nm carbon foil and deflected towards a scintillator by the local
magnetic field. The use of a neutral beam injector (NBI) as active source of
neutrals will provide radially resolved measurements while the use of a
scintillator as active component will allow us to cover the whole plasma along
the NBI line with unprecedented phase-space resolution ($ < $ 12 keV and 8 cm)
and fast temporal response (up to 1 kHz with the high resolution acquisition
system and above 100 kHz with the low resolution one); making it suitable to
study localised fast-ions redistributions in phase-space.


title:
A Note on Existence of Solutions to Control Problems of Semilinear
  Partial Differential Equations

abstract:
  In this paper, we study optimal control problems of semilinear elliptic and
parabolic equations. A tracking cost functional, quadratic in the control and
state variables, is considered. No control constraints are imposed. We prove
that the corresponding state equations are well-posed for controls in $L^2$.
However, it is well-known that in the $L^2$ framework the mappings involved in
the control problem are not Frechet differentiable in general, which makes any
analysis of the optimality conditions challenging. Nevertheless, we prove that
every $L^2$ optimal control belongs to $L^\infty$, and consequently standard
optimality conditions are available.


title:
Hierarchical Nearest Neighbor Graph Embedding for Efficient
  Dimensionality Reduction

abstract:
  Dimensionality reduction is crucial both for visualization and preprocessing
high dimensional data for machine learning. We introduce a novel method based
on a hierarchy built on 1-nearest neighbor graphs in the original space which
is used to preserve the grouping properties of the data distribution on
multiple levels. The core of the proposal is an optimization-free projection
that is competitive with the latest versions of t-SNE and UMAP in performance
and visualization quality while being an order of magnitude faster in run-time.
Furthermore, its interpretable mechanics, the ability to project new data, and
the natural separation of data clusters in visualizations make it a general
purpose unsupervised dimension reduction technique. In the paper, we argue
about the soundness of the proposed method and evaluate it on a diverse
collection of datasets with sizes varying from 1K to 11M samples and dimensions
from 28 to 16K. We perform comparisons with other state-of-the-art methods on
multiple metrics and target dimensions highlighting its efficiency and
performance. Code is available at https://github.com/koulakis/h-nne


title:
Kratt: Developing an Automatic Subject Indexing Tool for The National
  Library of Estonia

abstract:
  Manual subject indexing in libraries is a time-consuming and costly process
and the quality of the assigned subjects is affected by the cataloguer's
knowledge on the specific topics contained in the book. Trying to solve these
issues, we exploited the opportunities arising from artificial intelligence to
develop Kratt: a prototype of an automatic subject indexing tool. Kratt is able
to subject index a book independent of its extent and genre with a set of
keywords present in the Estonian Subject Thesaurus. It takes Kratt
approximately 1 minute to subject index a book, outperforming humans 10-15
times. Although the resulting keywords were not considered satisfactory by the
cataloguers, the ratings of a small sample of regular library users showed more
promise. We also argue that the results can be enhanced by including a bigger
corpus for training the model and applying more careful preprocessing
techniques.


title: A Deep-Discrete Learning Framework for Spherical Surface Registration

abstract:
  Cortical surface registration is a fundamental tool for neuroimaging analysis
that has been shown to improve the alignment of functional regions relative to
volumetric approaches. Classically, image registration is performed by
optimizing a complex objective similarity function, leading to long run times.
This contributes to a convention for aligning all data to a global average
reference frame that poorly reflects the underlying cortical heterogeneity. In
this paper, we propose a novel unsupervised learning-based framework that
converts registration to a multi-label classification problem, where each point
in a low-resolution control grid deforms to one of fixed, finite number of
endpoints. This is learned using a spherical geometric deep learning
architecture, in an end-to-end unsupervised way, with regularization imposed
using a deep Conditional Random Field (CRF). Experiments show that our proposed
framework performs competitively, in terms of similarity and areal distortion,
relative to the most popular classical surface registration algorithms and
generates smoother deformations than other learning-based surface registration
methods, even in subjects with atypical cortical morphology.


title: Unifying cubical and multimodal type theory

abstract:
  In this paper we combine the principled approach to programming with
modalities of multimodal type theory (MTT) with the computationally
well-behaved identity types of cubical type theory (CTT). The result -- cubical
modal type theory (Cubical MTT) -- has the desirable features of both systems.
In fact, the whole is more than the sum of its parts: Cubical MTT validates
desirable extensionality principles for modalities that MTT only supported
through ad hoc means.
  We investigate the semantics of Cubical MTT and provide an axiomatic approach
to producing models of Cubical MTT based on the internal language of topoi and
use it to construct presheaf models. Finally, we demonstrate the practicality
and utility of this axiomatic approach to models by constructing a model of
(cubical) guarded recursion in a cubical version of the topos of trees. We then
use this model to justify an axiomatization of Lob induction and thereby use
Cubical MTT to smoothly reason about guarded recursion.


title:
The application of techniques derived from artificial intelligence to
  the prediction of the solvency of bank customers: case of the application of
  the cart type decision tree (dt)

abstract:
  In this study we applied the CART-type Decision Tree (DT-CART) method derived
from artificial intelligence technique to the prediction of the solvency of
bank customers, for this we used historical data of bank customers. However we
have adopted the process of Data Mining techniques, for this purpose we started
with a data preprocessing in which we clean the data and we deleted all rows
with outliers or missing values as well as rows with empty columns, then we
fixed the variable to be explained (dependent or Target) and we also thought to
eliminate all explanatory (independent) variables that are not significant
using univariate analysis as well as the correlation matrix, then we applied
our CART decision tree method using the SPSS tool. After completing our process
of building our model (AD-CART), we started the process of evaluating and
testing the performance of our model, by which we found that the accuracy and
precision of our model is 71%, so we calculated the error ratios, and we found
that the error rate equal to 29%, this allowed us to conclude that our model at
a fairly good level in terms of precision, predictability and very precisely in
predicting the solvency of our banking customers.


title:
Cnoidal waves for the quintic Klein-Gordon and Schr\"odinger equations:
  Existence and orbital instability

abstract:
  In the present paper, we establish the existence and orbital instability
results of cnoidal periodic waves for the quintic Klein-Gordon and nonlinear
Schr\"odinger equations. The spectral analysis for the corresponding linearized
operator is established by using the Floquet theory. The orbital instability is
determined by applying an abstract result due to Shatah and Strauss.


title:
Giant electromagnetic proximity effect in superconductor/ferromagnet
  superlattices

abstract:
  We show that in superlattices with alternating superconducting (S) and
ferromagnetic (F) layers the spontaneous magnetic field induced in the
superconducting layers due to the electromagnetic proximity effect becomes
dramatically enhanced compared to the previously studied S/F bilayers. The
effect reveals itself for the in-plane orientation of the magnetic moments both
for ferromagnetic and anti-ferromagnetic ordering of the moments in the F
layers. In the finite size samples the magnetic field decays from the sample
surface towards the bulk of the structure, and the decay length strongly
depends on the relative orientation of the sample surface, the layers planes
and magnetic moments in the F layers. The obtained results provide additional
insights into experimental data on the neutron scattering in Nb/Gd
superlattices.


title: Using Orientation to Distinguish Overlapping Chromosomes

abstract:
  A difficult step in the process of karyotyping is segmenting chromosomes that
touch or overlap. In an attempt to automate the process, previous studies
turned to Deep Learning methods, with some formulating the task as a semantic
segmentation problem. These models treat separate chromosome instances as
semantic classes, which we show to be problematic, since it is uncertain which
chromosome should be classed as #1 and #2. Assigning class labels based on
comparison rules, such as the shorter/longer chromosome alleviates, but does
not fully resolve the issue. Instead, we separate the chromosome instances in a
second stage, predicting the orientation of the chromosomes by the model and
use it as one of the key distinguishing factors of the chromosomes. We
demonstrate this method to be effective. Furthermore, we introduce a novel
Double-Angle representation that a neural network can use to predict the
orientation. The representation maps any direction and its reverse to the same
point. Lastly, we present a new expanded synthetic dataset, which is based on
Pommier's dataset, but addresses its issues with insufficient separation
between its training and testing sets.


title:
GX-Plug: a Middleware for Plugging Accelerators to Distributed Graph
  Processing

abstract:
  Recently, research communities highlight the necessity of formulating a
scalability continuum for large-scale graph processing, which gains the
scale-out benefits from distributed graph systems, and the scale-up benefits
from high-performance accelerators. To this end, we propose a middleware,
called the GX-plug, for the ease of integrating the merits of both. As a
middleware, the GX-plug is versatile in supporting different runtime
environments, computation models, and programming models. More, for improving
the middleware performance, we study a series of techniques, including pipeline
shuffle, synchronization caching and skipping, and workload balancing, for
intra-, inter-, and beyond-iteration optimizations, respectively. Experiments
show that our middleware efficiently plugs accelerators to representative
distributed graph systems, e.g., GraphX and Powergraph, with up-to 20x
acceleration ratio.


abstract:
  Domain generalization (DG) aims to improve the generalization performance for
an unseen target domain by using the knowledge of multiple seen source domains.
Mainstream DG methods typically assume that the domain label of each source
sample is known a priori, which is challenged to be satisfied in many
real-world applications. In this paper, we study a practical problem of
compound DG, which relaxes the discrete domain assumption to the mixed source
domains setting. On the other hand, current DG algorithms prioritize the focus
on semantic invariance across domains (one-vs-one), while paying less attention
to the holistic semantic structure (many-vs-many). Such holistic semantic
structure, referred to as meta-knowledge here, is crucial for learning
generalizable representations. To this end, we present Compound Domain
Generalization via Meta-Knowledge Encoding (COMEN), a general approach to
automatically discover and model latent domains in two steps. Firstly, we
introduce Style-induced Domain-specific Normalization (SDNorm) to re-normalize
the multi-modal underlying distributions, thereby dividing the mixture of
source domains into latent clusters. Secondly, we harness the prototype
representations, the centroids of classes, to perform relational modeling in
the embedding space with two parallel and complementary modules, which
explicitly encode the semantic structure for the out-of-distribution
generalization. Experiments on four standard DG benchmarks reveal that COMEN
exceeds the state-of-the-art performance without the need of domain
supervision.


title: Benchmark Calculations of Multiloop Pseudofermion fRG

abstract:
  The pseudofermion functional renormalization group (pffRG) is a computational
method for determining zero-temperature phase diagrams of frustrated quantum
magnets. In a recent methodological advance, the commonly employed Katanin
truncation of the flow equations was extended to include multiloop corrections,
thereby capturing additional contributions from the three-particle vertex [
arXiv:2011.01268 , arXiv:2011.01269 ]. This development has also stimulated
significant progress in the numerical implementation of pffRG, allowing one to
track the evolution of pseudofermion vertices under the renormalization group
flow with unprecedented accuracy. However, cutting-edge solvers differ in their
integration algorithms, heuristics to discretize Matsubara frequency grids, and
more. To lend confidence in the numerical robustness of state-of-the-art
multiloop pffRG codes, we present and compare results produced with two
independently developed and algorithmically distinct solvers for Heisenberg
models on three-dimensional lattice geometries. Using the cubic lattice
Heisenberg (anti)ferromagnet with nearest and next-nearest neighbor
interactions as a generic benchmark model, we find the two codes to
quantitatively agree, often up to several orders of magnitude in digital
precision, both on the level of spin-spin correlation functions and
renormalized fermionic vertices for varying loop orders. These benchmark
calculations further substantiate the usage of multiloop pffRG solvers to
tackle unconventional forms of quantum magnetism.


title:
Constraining and constructing mass distributions of Primordial Black
  Holes from 21cm signal

abstract:
  The evaporation of Primordial Black Hole (PBH) via Hawking radiation
influences the evolution of Inter Galactic Medium by heating up the latter and
consequently affects the 21cm signal originated from the neutral Hydrogen
atoms. In this work, we have considered EDGES observational data of 21cm line
corresponding to cosmic dawn era to constrain the mass and the abundance of
PBHs. In this context, three different PBH mass distributions namely,
monochromatic, power law and lognormal mass distributions are considered to
estimate the effects of PBH evaporation on the 21cm brightness temperature
$T_{21}$. The impacts of Dark Matter - baryon interactions on $T_{21}$ are also
considered in this work along with the influences of PBH evaporation. Further
an attempt has been made in this work to formulate a mass distribution
expression for the PBHs in the context of the 21cm results considered here. The
mass distribution best suited for the present purpose is found to be a
combination of an error function and Owen function. Bounds on Dark Matter mass
are also calculated in this work by considering the PBH mass distributions.


title:
CVF-SID: Cyclic multi-Variate Function for Self-Supervised Image
  Denoising by Disentangling Noise from Image

abstract:
  Recently, significant progress has been made on image denoising with strong
supervision from large-scale datasets. However, obtaining well-aligned
noisy-clean training image pairs for each specific scenario is complicated and
costly in practice. Consequently, applying a conventional supervised denoising
network on in-the-wild noisy inputs is not straightforward. Although several
studies have challenged this problem without strong supervision, they rely on
less practical assumptions and cannot be applied to practical situations
directly. To address the aforementioned challenges, we propose a novel and
powerful self-supervised denoising method called CVF-SID based on a Cyclic
multi-Variate Function (CVF) module and a self-supervised image disentangling
(SID) framework. The CVF module can output multiple decomposed variables of the
input and take a combination of the outputs back as an input in a cyclic
manner. Our CVF-SID can disentangle a clean image and noise maps from the input
by leveraging various self-supervised loss terms. Unlike several methods that
only consider the signal-independent noise models, we also deal with
signal-dependent noise components for real-world applications. Furthermore, we
do not rely on any prior assumptions about the underlying noise distribution,
making CVF-SID more generalizable toward realistic noise. Extensive experiments
on real-world datasets show that CVF-SID achieves state-of-the-art
self-supervised image denoising performance and is comparable to other existing
approaches. The code is publicly available from
https://github.com/Reyhanehne/CVF-SID_PyTorch .


title:
Score difficulty analysis for piano performance education based on
  fingering

abstract:
  In this paper, we introduce score difficulty classification as a sub-task of
music information retrieval (MIR), which may be used in music education
technologies, for personalised curriculum generation, and score retrieval. We
introduce a novel dataset for our task, Mikrokosmos-difficulty, containing 147
piano pieces in symbolic representation and the corresponding difficulty labels
derived by its composer B\'ela Bart\'ok and the publishers. As part of our
methodology, we propose piano technique feature representations based on
different piano fingering algorithms. We use these features as input for two
classifiers: a Gated Recurrent Unit neural network (GRU) with attention
mechanism and gradient-boosted trees trained on score segments. We show that
for our dataset fingering based features perform better than a simple baseline
considering solely the notes in the score. Furthermore, the GRU with attention
mechanism classifier surpasses the gradient-boosted trees. Our proposed models
are interpretable and are capable of generating difficulty feedback both
locally, on short term segments, and globally, for whole pieces. Code,
datasets, models, and an online demo are made available for reproducibility


title: The SAGEX Review on Scattering Amplitudes

abstract:
  This is an introduction to, and invitation to read, a series of review
articles on scattering amplitudes in gauge theory, gravity, and superstring
theory. Our aim is to provide an overview of the field, from basic aspects to a
selection of current (2022) research and developments.


title:
The SAGEX Review on Scattering Amplitudes, Chapter 1: Modern
  Fundamentals of Amplitudes

abstract:
  This chapter introduces the foundational elements of scattering amplitudes.
It is meant to be accessible to readers with only a basic understanding of
quantum field theory. Topics covered include: the four-dimensional
spinor-helicity formalism and the colour decomposition of Yang-Mills scattering
amplitudes; the study of soft and collinear limits of Yang-Mills and gravity
amplitudes; the BCFW recursion relation and generalised unitarity, also in the
superamplitudes formalism of $\mathcal{N}{=}4$ supersymmetric Yang-Mills; an
overview of standard and hidden symmetries of the $S$-matrix of
$\mathcal{N}{=}4$ supersymmetric Yang-Mills, such as the conformal, dual
conformal and Yangian symmetries; and a brief excursus on form factors of
protected and non-protected operators in Yang-Mills theory. Several examples
and explicit calculations are also provided.


title:
The SAGEX Review on Scattering Amplitudes, Chapter 2: An Invitation to
  Color-Kinematics Duality and the Double Copy

abstract:
  Advances in scattering amplitudes have exposed previously-hidden
color-kinematics and double-copy structures in theories ranging from gauge and
gravity theories to effective field theories such as chiral perturbation theory
and the Born-Infeld model. These novel structures both simplify higher-order
calculations and pose tantalizing questions related to a unified framework
underlying relativistic quantum theories. This introductory mini-review article
invites further exploration of these topics. After a brief introduction to
color-kinematics duality and the double copy as they emerges at tree and
loop-level in gauge and gravity theories, we present two distinct examples: 1)
an introduction to the web of double-copy-constructible theories, and 2) a
discussion on the application of the double copy to calculation relevant to
gravitational-wave physics.


title:
The SAGEX Review on Scattering Amplitudes, Chapter 3: Mathematical
  structures in Feynman integrals

abstract:
  Dimensionally-regulated Feynman integrals are a cornerstone of all
perturbative computations in quantum field theory. They are known to exhibit a
rich mathematical structure, which has led to the development of powerful new
techniques for their computation. We review some of the most recent advances in
our understanding of the analytic structure of multiloop Feynman integrals in
dimensional regularisation. In particular, we give an overview of modern
approaches to computing Feynman integrals using differential equations, and we
discuss some of the properties of the functions that appear in the solutions.
We then review how dimensional regularisation has a natural mathematical
interpretation in terms of the theory of twisted cohomology groups, and how
many of the well-known ideas about Feynman integrals arise naturally in this
context. This is Chapter 3 of a series of review articles on scattering
amplitudes, of which Chapter 0 [arXiv:2203.13011] presents an overview and
Chapter 4 [arXiv:2203.13015] contains closely related topics.


title:
The SAGEX Review on Scattering Amplitudes, Chapter 4: Multi-loop Feynman
  Integrals

abstract:
  The analytic integration and simplification of multi-loop Feynman integrals
to special functions and constants plays an important role to perform higher
order perturbative calculations in the Standard Model of elementary particles.
In this survey article the most recent and relevant computer algebra and
special function algorithms are presented that are currently used or that may
play an important role to perform such challenging precision calculations in
the future. They are discussed in the context of analytic zero, single and
double scale calculations in the Quantum Field Theories of the Standard Model
and effective field theories, also with classical applications. These
calculations play a central role in the analysis of precision measurements at
present and future colliders to obtain ultimate information for fundamental
physics.


title:
The SAGEX Review on Scattering Amplitudes, Chapter 5: Analytic
  Bootstraps for Scattering Amplitudes and Beyond

abstract:
  One of the main challenges in obtaining predictions for collider experiments
from perturbative quantum field theory, is the direct evaluation of the Feynman
integrals it gives rise to. In this chapter, we review an alternative bootstrap
method that instead efficiently constructs physical quantities by exploiting
their analytic structure. We present in detail the setting where this method
has been originally developed, six- and seven-particle amplitudes in the
large-color limit of $\mathcal{N}=4$ super Yang-Mills theory. We discuss the
class of functions these amplitudes belong to, and the strong clues
mathematical objects known as cluster algebras provide for rendering this
function space both finite and of relatively small dimension at each loop
order. We then describe how to construct this function space, as well as how to
locate the amplitude inside of it with the help of kinematic limits, and apply
the general procedure to a concrete example: The determination of the two-loop
correction to the first nontrivial six-particle amplitude. We also provide an
overview of other areas where the realm of the bootstrap paradigm is expanding,
including other scattering amplitudes, form factors and Feynman integrals, and
point out the analytic properties of potentially wider applicability that it
has revealed.


title:
The SAGEX Review on Scattering Amplitudes, Chapter 6: Ambitwistor
  Strings and Amplitudes from the Worldsheet

abstract:
  Starting with Witten's twistor string, chiral string theories have emerged
that describe field theory amplitudes without the towers of massive states of
conventional strings. These models are known as ambitwistor strings due to
their target space; the space of complexified null geodesics, also called
ambitwistor space. Correlators in these string theories directly yield compact
formulae for tree-level amplitudes and loop integrands, in the form of
worldsheet integrals fully localized on solutions to constraints known as the
scattering equations. In this chapter, we discuss two incarnations of the
ambitwistor string: a 'vector representation' starting in space-time and
structurally resembling the RNS superstring, and a four-dimensional twistorial
version closely related to, but distinct from Witten's original model. The
RNS-like models exist for several theories, with 'heterotic' and type II models
describing super-Yang-Mills and 10d supergravities respectively, and they
manifest the double copy relations directly at the level of the worldsheet
models. In the second half of the chapter, we explain how the underlying models
lead to diverse applications, ranging from extensions to new sectors of
theories, loop amplitudes and to scattering on curved backgrounds. We conclude
with a brief discussion of connections to conventional strings and celestial
holography.


title:
The SAGEX Review on Scattering Amplitudes, Chapter 7: Positive Geometry
  of Scattering Amplitudes

abstract:
  Scattering amplitudes are both a wonderful playground to discover novel ideas
in Quantum Field Theory and simultaneously of immense phenomenological
importance to make precision predictions for e.g.~particle collider observables
and more recently also for gravitational wave signals. In this review chapter,
we give an overview of some of the exciting recent progress on reformulating
QFT in terms of mathematical, geometric quantities, such as polytopes,
associahedra, Grassmanians, and the amplituhedron. In this novel approach,
standard notions of locality and unitarity are derived concepts rather than
fundamental ingredients in the construction which might give us a handle on a
number of open questions in QFT that have evaded an answer for decades. We
first give a basic summary of positive geometry, before discussing the
associahedron -- one of the simplest physically relevant geometric examples --
and its relation to tree-level scattering amplitudes in bi-adjoint $\phi^3$
theory. Our second example is the amplituhedron construction for scattering
amplitudes in planar maximally supersymmetric Yang-Mills theory.


title:
The SAGEX Review on Scattering Amplitudes, Chapter 8: Half BPS
  correlators

abstract:
  Half BPS correlators in N=4 super Yang-Mills theory are key quantities both
in the AdS/CFT correspondence as well as in scattering amplitudes research.
They are dual at strong coupling to quantum gravity amplitudes. At weak
coupling on the other hand they contain all N=4 SYM amplitudes. They have been
found to possess a number of hidden symmetries, for example non-trivial
permutation symmetry of perturbative four-point integrands and a higher
dimensional conformal symmetry. Their study has enjoyed continuous progress
since the discovery of AdS/CFT and they are now some of the best understood
quantities of any four-dimensional quantum field theory.
  In this review we outline the current knowledge of half BPS correlators,
emphasising these two co-existing relations to scattering amplitudes at strong
and weak coupling.


title:
The SAGEX Review on Scattering Amplitudes, Chapter 9: Integrability of
  Amplitudes in Fishnet Theories

abstract:
  We discuss the properties of scattering amplitudes in a conformal bi-scalar
fishnet theory that previously appeared in the study of integrable deformations
of $\mathcal N=4$ SYM. In distinction with the latter theory, the scattering
amplitudes in the conformal fishnet theory do not suffer from infrared
divergences and possess enhanced symmetries. We demonstrate that for the
simplest four-particle scattering the single- and double-trace partial
amplitudes can be computed for arbitrary coupling in the leading color
approximation. We also review the invariance of single-trace components of
multi-particle fishnet amplitudes in the leading color limit under
infinite-dimensional Yangian symmetry.


title:
The SAGEX Review on Scattering Amplitudes, Chapter 10: Modular
  covariance of type IIB string amplitudes and their $\mathcal{N}=4$
  supersymmetric Yang-Mills duals

abstract:
  This article reviews recent developments in the understanding of the
interplay of supersymmetry and modular covariance of scattering amplitudes in
type IIB superstring theory and its holographic image in $\mathcal{N}=4$
supersymmetric Yang-Mills theory (SYM). The first section includes the
determination of exact expressions for BPS interactions in the low-energy
expansion of type IIB superstring amplitudes. The second section concerns
properties of a certain class of integrated correlators in $\mathcal{N}=4$ SYM
with arbitrary classical gauge group that are exactly determined by
supersymmetric localisation. Not only do these reproduce known features of
perturbative and non-perturbative $\mathcal{N}=4$ SYM for any classical gauge
group, but they have large-$N$ expansions that are in accord with expectations
based on the holographic correspondence with superstring theory. The final
section focusses on modular graph functions. These are modular functions that
are closely associated with coefficients in the low-energy expansion of
superstring perturbation theory and have recently received quite a lot of
interest in both the physics and mathematics literature.


title:
The SAGEX Review on Scattering Amplitudes, Chapter 11: Soft Theorems and
  Celestial Amplitudes

abstract:
  The soft limits of scattering amplitudes have been extensively studied due to
their essential role in the computation of physical observables in collider
physics. The universal factorisation that occurs in these kinematic limits has
been shown to be related to conservation laws associated with asymptotic, or
large, gauge symmetries. This connection has led to a deeper understanding of
the symmetries of gauge and gravitational theories and to a reformulation of
scattering amplitudes in a basis of boost eigenstates which makes manifest the
two-dimensional global conformal symmetry of the celestial sphere. The recast,
or celestial, amplitudes possess many of the properties of conformal field
theory correlation functions which has suggested a path towards a holographic
description of asymptotically flat spacetimes. In this review we consider these
interconnected developments in our understanding of soft theorems, asymptotic
symmetries and conformal field theory with a focus on the structure and
symmetries of the celestial amplitudes and their holographic interpretation.


title:
The SAGEX Review on Scattering Amplitudes, Chapter 12: Amplitudes and
  collider physics

abstract:
  We explore how various topics in modern scattering amplitudes research find
application in the description of collider physics processes. After a brief
review of experimentally measured quantities and how they are related to
amplitudes, we summarise recent developments in perturbative QFT, and how they
have impacted our ability to do precision physics with colliders. Next, we
explain how the study of (next-to-)soft radiation is directly relevant to
increasing theoretical precision for key processes at the LHC and related
experiments. Finally, we describe the various techniques that are used to turn
theoretical calculations into something more closely approaching the output of
a particle accelerator.


title:
The SAGEX Review on Scattering Amplitudes, Chapter 13: Post-Minkowskian
  expansion from Scattering Amplitudes

abstract:
  The post-Minkowskian expansion of Einstein's general theory of relativity has
received much attention in recent years due to the possibility of harnessing
the computational power of modern amplitude calculations in such a classical
context. In this brief review, we focus on the post-Minkowskian expansion as
applied to the two-body problem in general relativity without spin, and we
describe how relativistic quantum field theory can be used to greatly simplify
analytical calculations based on the Einstein-Hilbert action. Subtleties
related to the extraction of classical physics from such quantum mechanical
calculations highlight the care which must be taken when both positive and
negative powers of Planck's constant are at play. In the process of obtaining
classical results in both Einstein gravity and supergravity, one learns new
aspects of quantum field theory that are obscured when using units in which
Planck's constant is set to unity. The scattering amplitude approach provides a
self-contained framework for deriving the two-body scattering valid in all
regimes of energy. There is hope that the full impact of amplitude computations
in this field may significantly alter the way in which gravitational wave
predictions will advance in the coming years.


title:
The SAGEX Review on Scattering Amplitudes, Chapter 14: Classical Gravity
  from Scattering Amplitudes

abstract:
  Scattering amplitudes have their origin in quantum field theory, but have
wide-ranging applications extending to classical physics. We review a formalism
to connect certain classical observables to scattering amplitudes. An advantage
of this formalism is that it enables us to study implications of the double
copy in classical gravity. We discuss examples of observables including the
total change of a particle's momentum, and the gravitational waveform, during a
scattering encounter. The double copy also allows direct access to classical
solutions in gravity. We review this classical double copy starting from its
linearised level, where it originates in the double copy of three-point
amplitudes. The classical double copy extends elegantly to exact solutions,
making a connection between scattering amplitudes and the geometric formulation
of General Relativity.


title:
The SAGEX Review on Scattering Amplitudes, Chapter 15: The Multi-Regge
  Limit

abstract:
  We review the Regge and multi-Regge limit of scattering amplitudes in gauge
theory, focusing on QCD and its maximally supersymmetric cousin, planar ${\cal
N}=4$ super-Yang-Mills theory. We identify the large logarithms that are
developed in these limits, and the progress that has been made in resumming
them, towards next-to-next-to-leading logarithms for BFKL evolution in QCD, as
well as all-orders proposals in planar ${\cal N}=4$ super-Yang-Mills theory and
the perturbative checks of those proposals. We also cover the application of
single-valued multiple polylogarithms to this important kinematical limit of
particle scattering.


title: Angstrofluidics: walking to the limit

abstract:
  Angstrom-scale fluidic channels are ubiquitous in nature, and play an
important role in regulating cellular traffic, signaling, and responding to
stimuli. Synthetic channels are now a reality with the emergence of several
cutting-edge bottom-up and top-down fabrication methods. In particular, the use
of atomically thin two dimensional (2D) materials and nanotubes as components
to build fluidic conduits has pushed the limits of fabrication to the
Angstrom-scale. Here, we provide an overview of the recent developments in the
fabrication methods for nano- and angstrofluidic channels while categorizing
them on the basis of dimensionality (0D pores, 1D tubes, 2D slits), along with
the latest advances in measurement techniques. We discuss the ionic transport
governed by various stimuli in these channels and draw comparison of ionic
mobility, streaming and osmotic power, with varying pore sizes across all the
dimensionalities. Towards the end of the review, we highlight the unique future
opportunities in the development of smart ionic devices.


title:
Brain-inspired neuronal silencing mechanism to enable high-precision
  sequence identification

abstract:
  Real-time sequence identification is a core use-case of artificial neural
networks (ANNs), ranging from recognizing temporal events to identifying
verification codes. Existing methods apply recurrent neural networks, which
suffer from training difficulties; however, performing this function without
feedback loops remains a challenge. Here, we present an experimental neuronal
long-term plasticity mechanism for high-precision feedforward sequence
identification networks (ID-nets) without feedback loops, wherein input objects
have a given order and timing. This mechanism temporarily silences neurons
following their recent spiking activity. Therefore, transitory objects act on
different dynamically created feedforward sub-networks. ID-nets are
demonstrated to reliably identify 10 handwritten digit sequences, and are
generalized to deep convolutional ANNs with continuous activation nodes trained
on image sequences. Counterintuitively, their classification performance with
limited training datasets is high for sequences, but low for individual
objects. ID-nets are also implemented for writer-dependent recognition, and
suggested as a cryptographic tool for encrypted authentication. The presented
mechanism opens new horizons for advanced ANN algorithms.


title: Geophysics and Stuart vortices on a sphere meet differential geometry

abstract:
  We prove new existence criteria relevant for the non-linear elliptic PDE of
the form $\Delta_{S^2} u=C-he^{u}$, considered on a two dimensional sphere
$S^2$, in the parameter regime $2\leq C<4$. We apply this result, as well as
several previously known results valid when $C<2$, to discuss existence of
solutions of a particular PDE modelling ocean surface currents.


title:
Deuteron VVCS and nuclear structure effects in muonic deuterium at N3LO
  in pionless EFT

abstract:
  We present our studies of the forward unpolarised doubly-virtual Compton
scattering (VVCS) off the deuteron and the closely related two-photon-exchange
($2\gamma$-exchange) corrections to the Lamb shift of muonic deuterium. The
deuteron VVCS amplitude is calculated in the framework of pionless effective
field theory, up to next-to-next-to-next-to-leading order (N3LO) for the
longitudinal and next-to-leading order (NLO) for the transverse amplitude. The
charge elastic form factor of the deuteron, obtained from the residue of the
longitudinal VVCS amplitude, is used to extract the value of the single unknown
two-nucleon one-photon contact coupling that enters the longitudinal amplitude
at N3LO. The obtained deuteron VVCS amplitude serves as a high-precision
model-independent input to examine the $2\gamma$-exchange corrections.
Substantial differences with the recent dispersive evaluations are identified,
namely, the elastic contribution appears to be larger by several standard
deviations, thus ameliorating the current discrepancy between theory and
experiment on the size of $2\gamma$-exchange effects. A correlation between the
values of the deuteron charge and Friar radii is found that can be used to
judge on the quality of a parametrisation of the deuteron charge elastic form
factor. The discrepancy between the theory and the empirical result for the
$2\gamma$-exchange correction in muonic deuterium appears to be completely
eliminated. To further confirm this, we revisit the hydrogen-deuterium isotope
shift in the same framework. Our work provides an alternative self-consistent
and high-precision evaluation of the $2\gamma$-exchange correction in (muonic)
deuterium.


title:
Continuous Emotion Recognition using Visual-audio-linguistic
  information: A Technical Report for ABAW3

abstract:
  We propose a cross-modal co-attention model for continuous emotion
recognition using visual-audio-linguistic information. The model consists of
four blocks. The visual, audio, and linguistic blocks are used to learn the
spatial-temporal features of the multi-modal input. A co-attention block is
designed to fuse the learned features with the multi-head co-attention
mechanism. The visual encoding from the visual block is concatenated with the
attention feature to emphasize the visual information. To make full use of the
data and alleviate over-fitting, cross-validation is carried out on the
training and validation set. The concordance correlation coefficient (CCC)
centering is used to merge the results from each fold. The achieved CCC on the
test set is $0.520$ for valence and $0.602$ for arousal, which significantly
outperforms the baseline method with the corresponding CCC of 0.180 and 0.170
for valence and arousal, respectively. The code is available at
https://github.com/sucv/ABAW3.


title: Multi-modal Emotion Estimation for in-the-wild Videos

abstract:
  In this paper, we briefly introduce our submission to the Valence-Arousal
Estimation Challenge of the 3rd Affective Behavior Analysis in-the-wild (ABAW)
competition. Our method utilizes the multi-modal information, i.e., the visual
and audio information, and employs a temporal encoder to model the temporal
context in the videos. Besides, a smooth processor is applied to get more
reasonable predictions, and a model ensemble strategy is used to improve the
performance of our proposed method. The experiment results show that our method
achieves 65.55% ccc for valence and 70.88% ccc for arousal on the validation
set of the Aff-Wild2 dataset, which prove the effectiveness of our proposed
method.


title: Affine Lie algebras representations induced from Whittaker modules

abstract:
  We use induction from parabolic subalgebras with infinite-dimensional Levi
factor to construct new families of irreducible representations for arbitrary
Affine Kac-Moody algebra. Our first construction defines a functor from the
category of Whittaker modules over the Levi factor of a parabolic subalgebra to
the category of modules over the Affine Lie algebra. The second functor sends
tensor products of a module over the affine part of the Levi factor (in
particular any weight module) and of a Whittaker module over the complement
Heisenberg subalgebra to the Affine Lie algebra modules. Both functors
preserves irreducibility when the central charge is nonzero.


title:
Augment-Connect-Explore: a Paradigm for Visual Action Planning with Data
  Scarcity

abstract:
  Visual action planning particularly excels in applications where the state of
the system cannot be computed explicitly, such as manipulation of deformable
objects, as it enables planning directly from raw images. Even though the field
has been significantly accelerated by deep learning techniques, a crucial
requirement for their success is the availability of a large amount of data. In
this work, we propose the Augment-Connect-Explore (ACE) paradigm to enable
visual action planning in cases of data scarcity.
  We build upon the Latent Space Roadmap (LSR) framework which performs
planning with a graph built in a low dimensional latent space. In particular,
ACE is used to i) Augment the available training dataset by autonomously
creating new pairs of datapoints, ii) create new unobserved Connections among
representations of states in the latent graph, and iii) Explore new regions of
the latent space in a targeted manner. We validate the proposed approach on
both simulated box stacking and real-world folding task showing the
applicability for rigid and deformable object manipulation tasks, respectively.


title:
6G Wireless Communications: From Far-field Beam Steering to Near-field
  Beam Focusing

abstract:
  6G networks will be required to support higher data rates, improved energy
efficiency, lower latency, and more diverse users compared with 5G systems. To
meet these requirements, extremely large antenna arrays and high-frequency
signaling are envisioned to be key physical-layer technologies. The deployment
of extremely large antenna arrays, especially in high-frequency bands,
indicates that future 6G wireless networks are likely to operate in the
radiating near-field (Fresnel) region, as opposed to the traditional far-field
operation of current wireless technologies. In this article, we discuss the
opportunities and challenges that arise in radiating near-field communications.
We begin by discussing the key physical characteristics of near-field
communications, where the standard plane-wave propagation assumption no longer
holds, and clarify its implication on the modelling of wireless channels. Then,
we elaborate on the ability to leverage spherical wavefronts via beam focusing,
highlighting its advantages for 6G systems. We point out several appealing
application scenarios which, with proper design, can benefit from near-field
operation, including interference mitigation in multi-user communications,
accurate localization and focused sensing, as well as wireless power transfer
with minimal energy pollution. We conclude with discussing some of the design
challenges and research directions that are yet to be explored to fully harness
the potential of this emerging paradigm.


title: Extending MAPE-K to support Human-Machine Teaming

abstract:
  The MAPE-K feedback loop has been established as the primary reference model
for self-adaptive and autonomous systems in domains such as autonomous driving,
robotics, and Cyber-Physical Systems. At the same time, the Human Machine
Teaming (HMT) paradigm is designed to promote partnerships between humans and
autonomous machines. It goes far beyond the degree of collaboration expected in
human-on-the-loop and human-in-the-loop systems and emphasizes interactions,
partnership, and teamwork between humans and machines. However, while MAPE-K
enables fully autonomous behavior, it does not explicitly address the
interactions between humans and machines as intended by HMT. In this paper, we
present the MAPE-K-HMT framework which augments the traditional MAPE-K loop
with support for HMT. We identify critical human-machine teaming factors and
describe the infrastructure needed across the various phases of the MAPE-K loop
in order to effectively support HMT. This includes runtime models that are
constructed and populated dynamically across monitoring, analysis, planning,
and execution phases to support human-machine partnerships. We illustrate
MAPE-K-HMT using examples from an autonomous multi-UAV emergency response
system, and present guidelines for integrating HMT into MAPE-K.


title: Revealing the late-time transition of $H_{0}$: relieve the Hubble crisis

abstract:
  The discrepancy between the value of the Hubble constant $H_0$ measured using
the local distance ladder and the value inferred using the cosmic microwave
background is the most serious challenge to the standard $\Lambda$CDM model.
Many models have been suggested to solve or relieve it. Here, we report a
late-time transition of $H_{0}$, i.e., $H_0$ changes from low value to high one
from early to late cosmic time, by investigating the Hubble parameter $H(z)$
data based on the Gaussian process method. This finding effectively reduces the
Hubble crisis by 70\%. Our results are also consistent with the descending
trend of $H_0$ measured by time-delay cosmography of lensed quasars at
1$\sigma$ confidence level. It is interesting that under the $\Lambda$CDM model
and $w$CDM model, this transition disappears, leaving a bump in redshift range
(0.4, 0.5). This demonstrates the model-independent method should be used to
derive $H_0$.


title:
Interpretable Prediction of Pulmonary Hypertension in Newborns using
  Echocardiograms

abstract:
  Pulmonary hypertension (PH) in newborns and infants is a complex condition
associated with several pulmonary, cardiac, and systemic diseases contributing
to morbidity and mortality. Therefore, accurate and early detection of PH is
crucial for successful management. Using echocardiography, the primary
diagnostic tool in pediatrics, human assessment is both time-consuming and
expertise-demanding, raising the need for an automated approach. In this work,
we present an interpretable multi-view video-based deep learning approach to
predict PH for a cohort of 194 newborns using echocardiograms. We use
spatio-temporal convolutional architectures for the prediction of PH from each
view, and aggregate the predictions of the different views using majority
voting. To the best of our knowledge, this is the first work for an automated
assessment of PH in newborns using echocardiograms. Our results show a mean
F1-score of 0.84 for severity prediction and 0.92 for binary detection using
10-fold cross-validation. We complement our predictions with saliency maps and
show that the learned model focuses on clinically relevant cardiac structures,
motivating its usage in clinical practice.


title:
Asymptotic stability of evolution systems of probability measures for
  nonautonomous stochastic systems: Theoretical results and applications

abstract:
  The limiting stability of invariant probability measures of time homogeneous
transition semigroups for autonomous stochastic systems has been extensively
discussed in the literature. In this paper we initially initiate a program to
study the asymptotic stability of evolution systems of probability measures of
time inhomogeneous transition operators for nonautonomous stochastic systems. A
general theoretical result on this topic is established in a Polish space by
establishing some sufficient conditions which can be verified in applications.
Our abstract results are applied to a stochastic lattice reaction-diffusion
equation driven by a time-dependent nonlinear noise. A time-average method and
a mild condition on the time-dependent diffusion function are used to prove
that the limit of every evolution system of probability measures must be an
evolution system of probability measures of the limiting equation. The
theoretical results are expected to be applied to various stochastic lattice
systems/ODEs/PDEs in the future.


title: Semantic system for searching of employees

abstract:
  Many people have stress to leave their job and start a new one because of the
new environment and not enough knowledge about the culture and structure about
the new organization they are going to work in. New employees in company
normally need to integrate in their working place environment quicker to start
doing their job. That makes them ask a lot of questions to their colleagues and
sometimes their colleagues are too busy to answer those questions. In the
literature is defined that this problem could be solved when new employees use
digital system for information as the proposed system for searching of
information. Furthermore, the quality of the returned results from the
searching system is defined as a standard for the efficiency of the searching
systems. Because of this, it is proposed a semantic system for searching
information of employees in a company that will help to better orient new
employees in a company, to know the position and the function of each employee
in the company


title:
Simulating realistic screening clouds around quantum impurities: role of
  spatial anisotropy and disorder

abstract:
  Dynamical quantum impurities in metals induce electronic correlations in real
space that are difficult to simulate due to their multi-scale nature, so that
only s-wave scattering in clean metallic hosts has been investigated so far.
However, screening clouds should show anisotropy due to lack of full rotational
invariance in two- and three-dimensional lattices, while inherent disorder will
also induce spatial inhomogeneities. To tackle these challenges, we present an
efficient and robust algorithm based on the recursive generation of natural
orbitals defined as eigenvectors of the truncated single-particle density
matrix. This method provides well-converged many-body wave functions on
lattices with up to tens of thousands of sites, bypassing some limitations of
other approaches. The algorithm is put to the test by investigating the charge
screening cloud around an interacting resonant level, both on clean and
disordered lattices, achieving accurate spatial resolution from short to long
distances. We thus demonstrate strong anisotropy of spatial correlations around
an adatom in the half-filled square lattice. Taking advantage of the efficiency
of the algorithm, we further compute the disorder-induced distribution of Kondo
temperatures over several thousands of random realizations, at the same time
gaining access to the full spatial profile of the screening cloud in each
sample. While the charge screening cloud is typically shortened due to the
polarization of the impurity by the disorder potential, we surprisingly find
that rare disorder configurations preserve the long range nature of Kondo
correlations in the electronic bath.


title: Two-neutrino double-$\beta$ decay in the mapped interacting boson model

abstract:
  A calculation of two-neutrino double-$\beta$ ($2\nu\beta\beta$) decay matrix
elements within the interacting boson model (IBM) that is based on the nuclear
density functional theory is presented. The constrained self-consistent
mean-field (SCMF) calculation using a universal energy density functional (EDF)
and a pairing interaction provides potential energy surfaces with triaxial
quadrupole degrees of freedom for even-even nuclei corresponding to the initial
and final states of the $2\nu\beta\beta$ decays of interest. The SCMF energy
surface is then mapped onto the bosonic one, and this procedure determines the
IBM Hamiltonian for the even-even nuclei. The same SCMF calculation provides
the essential ingredients of the interacting boson fermion-fermion model
(IBFFM) for the intermediate odd-odd nuclei and the Gamow-Teller and Fermi
transition operators. The EDF-based IBM and IBFFM provide a simultaneous
description of excitation spectra and electromagnetic transition rates for each
nucleus, and single-$\beta$ and $\beta\beta$ decay properties. The calculated
$2\nu\beta\beta$-decay nuclear matrix elements are compared with experiment and
with those from earlier theoretical calculations.


title: pyABC: Efficient and robust easy-to-use approximate Bayesian computation

abstract:
  The Python package pyABC provides a framework for approximate Bayesian
computation (ABC), a likelihood-free parameter inference method popular in many
research areas. At its core, it implements a sequential Monte-Carlo (SMC)
scheme, with various algorithms to adapt to the problem structure and
automatically tune hyperparameters. To scale to computationally expensive
problems, it provides efficient parallelization strategies for multi-core and
distributed systems. The package is highly modular and designed to be easily
usable. In this major update to pyABC, we implement several advanced algorithms
that facilitate efficient and robust inference on a wide range of data and
model types. In particular, we implement algorithms to account for noise, to
adaptively scale-normalize distance metrics, to robustly handle data outliers,
to elucidate informative data points via regression models, to circumvent
summary statistics via optimal transport based distances, and to avoid local
optima in acceptance threshold sequences by predicting acceptance rate curves.
Further, we provide, besides previously existing support of Python and R,
interfaces in particular to the Julia language, the COPASI simulator, and the
PEtab standard.


title: A Method to Detect Quantum Coherent Transport in Memristive Devices

abstract:
  While the size of functional elements in memristors becomes of the orders of
nano-meters or even smaller, the quantum effects in their dynamics can
significantly influence their transport properties, consistent with recent
experimental observations of conductance quantisation in memristors. This
requires the development of experimentally realizable procedures to detect
quantumness of memristors. Here we developed an experimental protocol allowing
us to find evidence that the memristor can be in a superposition of states with
different memristivities. We simulate the nonlinearity induced in the quantum
memristive system via periodic projective measurements, observing how it
manifests itself in the emergence of additional spectral components in the
response to the harmonic signal. Moreover, the response demonstrates a resonant
behaviour when the frequency of the projective measurements commensurates with
the frequency of the input. We demonstrate that observation of such harmonic
mixing can be used as experimental evidence of quantum effects in memristors.


title: On recursion operators for symmetries of the Pavlov-Mikhalev equation

abstract:
  In geometry of nonlinear partial differential equations, recursion operators
that act on symmetries of an equation $\mathcal{E}$ are understood as
B\"{a}cklund auto-transformations of the equation $\mathcal{TE}$ tangent to
$\mathcal{E}$. We apply this approach to a natural two-component extension of
the 3D Pavlov-Mikhalev equation
  \begin{equation*} u_{yy} = u_{tx} + u_yu_{xx} - u_xu_{xy}.
  \end{equation*} We describe the Lie algebra of symmetries for this extension,
construct two recursion operators (one of them was known earlier) and find
their action. We also establish the hereditary property of these operators as
well as their compatibility (in the sense of the Fr\"{o}licher-Nijenhuis
bracket). We find also twelve additional operators which are degenerate in a
sense (we call them \emph{queer}) and discuss their properties. In the
concluding part, a geometrical background of two-component conservation laws
for multi-dimensional equations is exposed together with its relations to
differential coverings.


title: Facial Action Unit Recognition With Multi-models Ensembling

abstract:
  The Affective Behavior Analysis in-the-wild (ABAW) 2022 Competition gives
Affective Computing a large promotion. In this paper, we present our method of
AU challenge in this Competition. We use improved IResnet100 as backbone. Then
we train AU dataset in Aff-Wild2 on three pertained models pretrained by our
private au and expression dataset, and Glint360K respectively. Finally, we
ensemble the results of our models. We achieved F1 score (macro) 0.731 on AU
validation set.