title: Engineering nanoscale hypersonic phonon transport

abstract:
  Controlling the vibrations in solids is crucial to tailor their mechanical
properties and their interaction with light. Thermal vibrations represent a
source of noise and dephasing for many physical processes at the quantum level.
One strategy to avoid these vibrations is to structure a solid such that it
possesses a phononic stop band, i.e., a frequency range over which there are no
available mechanical modes. Here, we demonstrate the complete absence of
mechanical vibrations at room temperature over a broad spectral window, with a
5.3 GHz wide band gap centered at 8.4 GHz in a patterned silicon nanostructure
membrane measured using Brillouin light scattering spectroscopy. By
constructing a line-defect waveguide, we directly measure GHz localized modes
at room temperature. Our experimental results of thermally excited guided
mechanical modes at GHz frequencies provides an eficient platform for
photon-phonon integration with applications in optomechanics and signal
processing transduction.


title: Effects of orientational order on modulated cylindrical interfaces

abstract:
  Cylindrical interfaces occur in sheared or deformed emulsions and as
biological or technological lipid monolayer or bilayer tubules. Like the
corresponding spherical droplets and vesicles, these cylinder-like surfaces may
host orientaional order with $n$-fold rotational symmetry, for example in the
positions of lipid molecules or of spherical nanoparticles. We examine how that
order interacts with and induces shape modulations of cylindrical interfaces.
While on spherical droplets $2n$ topological defects necessarily exist and can
induce icosahedral droplet shapes, the cylindrical topology is compatible with
a defect-free patterning. Nevertheless, once a modulation is introduced by a
mechanism such as spontaneous curvature, nontrivial patterns of order,
including ones with excess defects, emerge and have nonlinear effects on the
shape of the tube. Examining the equilibrium energetics of the system
analytically and with a lattice-based Markov chain Monte Carlo simulation, we
predict low-temperature morphologies of modulated cylindrical interfaces
hosting orientational order. A shape modulation induces a banded pattern of
alternatingly isotropic and ordered interfacial material. Furthermore
cylindrical systems can be divided into Type I, without defects, and Type II,
which go through a spectrum of defect states with up to $4n$ excess defects.
The character of the curvature-induced shape transition from unmodulated to
modulated cylinders is continuous or discontinuous accordingly.


title:
Gravitational chiral anomaly for spin 3/2 field interacting with spin
  1/2 field

abstract:
  The gravitational chiral quantum anomaly is calculated in the framework of
the extended Rarita-Schwinger-Adler (RSA) field theory, which includes the
interaction with an additional spin 1/2 field. It is shown that the factor in
the gravitational chiral anomaly, normalized to the Dirac field anomaly, is
equal to -19. The resulting value distinguishes the RSA theory from the other
theories of spin 3/2. A direct verification of the conformality of the RSA
theory in the strong interaction limit at the level of one-loop three-point
graphs was made.


title:
Wideband Multi-User MIMO Communications with Frequency Selective RISs:
  Element Response Modeling and Sum-Rate Maximization

abstract:
  Reconfigurable Intelligent Surfaces (RISs) are an emerging technology for
future wireless communication systems, enabling improved coverage in an energy
efficient manner. RISs are usually metasurfaces, constituting of
two-dimensional arrangements of metamaterial elements, whose individual
response is commonly modeled in the literature as an adjustable phase shifter.
However, this model holds only for narrowband communications, and when wideband
transmissions are utilized, one has to account for the frequency selectivity of
metamaterials, whose response usually follows a Lorentzian-like profile. In
this paper, we consider the uplink of a wideband RIS-empowered multi-user
Multiple-Input Multiple-Output (MIMO) wireless system with Orthogonal Frequency
Division Multiplexing (OFDM) signaling, while accounting for the frequency
selectivity of RISs. In particular, we focus on designing the controllable
parameters dictating the Lorentzian response of each RIS metamaterial element,
in order to maximize the achievable sum rate. We devise a scheme combining
block coordinate descent with penalty dual decomposition to tackle the
resulting challenging optimization framework. Our simulation results reveal the
achievable rates one can achieve using realistically frequency selective RISs
in wideband settings, and quantify the performance loss that occurs when using
state-of-the-art methods which assume that the RIS elements behave as
frequency-flat phase shifters.


title: The Ecological Footprint of Neural Machine Translation Systems

abstract:
  Over the past decade, deep learning (DL) has led to significant advancements
in various fields of artificial intelligence, including machine translation
(MT). These advancements would not be possible without the ever-growing volumes
of data and the hardware that allows large DL models to be trained efficiently.
Due to the large amount of computing cores as well as dedicated memory,
graphics processing units (GPUs) are a more effective hardware solution for
training and inference with DL models than central processing units (CPUs).
However, the former is very power demanding. The electrical power consumption
has economical as well as ecological implications.
  This chapter focuses on the ecological footprint of neural MT systems. It
starts from the power drain during the training of and the inference with
neural MT models and moves towards the environment impact, in terms of carbon
dioxide emissions. Different architectures (RNN and Transformer) and different
GPUs (consumer-grate NVidia 1080Ti and workstation-grade NVidia P100) are
compared. Then, the overall CO2 offload is calculated for Ireland and the
Netherlands. The NMT models and their ecological impact are compared to common
household appliances to draw a more clear picture.
  The last part of this chapter analyses quantization, a technique for reducing
the size and complexity of models, as a way to reduce power consumption. As
quantized models can run on CPUs, they present a power-efficient inference
solution without depending on a GPU.


title: NeAT: Neural Adaptive Tomography

abstract:
  In this paper, we present Neural Adaptive Tomography (NeAT), the first
adaptive, hierarchical neural rendering pipeline for multi-view inverse
rendering. Through a combination of neural features with an adaptive explicit
representation, we achieve reconstruction times far superior to existing neural
inverse rendering methods. The adaptive explicit representation improves
efficiency by facilitating empty space culling and concentrating samples in
complex regions, while the neural features act as a neural regularizer for the
3D reconstruction. The NeAT framework is designed specifically for the
tomographic setting, which consists only of semi-transparent volumetric scenes
instead of opaque objects. In this setting, NeAT outperforms the quality of
existing optimization-based tomography solvers while being substantially
faster.


title:
Evaluating the Efficacy of Facebook's Vaccine Misinformation Content
  Removal Policies

abstract:
  Social media platforms have attempted to remove misinformation about vaccines
because it obstructs efforts to end the COVID-19 pandemic. We examined whether
Facebook's vaccine misinformation removal policies were effective. Posts and
engagements in anti-vaccine pages were reduced to 29% and 23% of pre-policy
levels, respectively, but recovered over the subsequent six months. Posts and
engagements in pro-vaccine pages were also reduced -- to 68% and 30% of
pre-policy levels, respectively. Low-credibility content became more prevalent
in anti-vaccine pages and groups, and high-credibility content became less
prevalent in pro-vaccine pages. Links between anti-vaccine pages and
coordinated inauthentic behavior were also reduced. Our results suggest that
Facebook's policies were only partially successful. Facebook's attempts at
self-regulation appear to have been resource intensive, and ineffective in the
long term.


title:
Accretion of the relativistic Vlasov gas onto a moving Schwarzschild
  black hole: Low-temperature limit and numerical aspects

abstract:
  New developments related to our recent study of the accretion of the Vlasov
gas onto a moving Schwarzschild black hole are presented. We discuss the
low-temperature limit of the mas accretion rate and a simple Monte Carlo
simulation used to check the results obtained in this limit. We also comment on
several numerical aspects related with momentum integrals expressing the
particle density current and the particle density.


title:
Lossy Planarization: A Constant-Factor Approximate Kernelization for
  Planar Vertex Deletion

abstract:
  In the F-minor-free deletion problem we want to find a minimum vertex set in
a given graph that intersects all minor models of graphs from the family F. The
Vertex planarization problem is a special case of F-minor-free deletion for the
family F = {K_5, K_{3,3}}. Whenever the family F contains at least one planar
graph, then F-minor-free deletion is known to admit a constant-factor
approximation algorithm and a polynomial kernelization [Fomin, Lokshtanov,
Misra, and Saurabh, FOCS'12]. The Vertex planarization problem is arguably the
simplest setting for which F does not contain a planar graph and the existence
of a constant-factor approximation or a polynomial kernelization remains a
major open problem.
  In this work we show that Vertex planarization admits an algorithm which is a
combination of both approaches. Namely, we present a polynomial A-approximate
kernelization, for some constant A > 1, based on the framework of lossy
kernelization [Lokshtanov, Panolan, Ramanujan, and Saurabh, STOC'17]. Simply
speaking, when given a graph G and integer k, we show how to compute a graph G'
on poly(k) vertices so that any B-approximate solution to G' can be lifted to
an (A*B)-approximate solution to G, as long as A*B*OPT(G) <= k. In order to
achieve this, we develop a framework for sparsification of planar graphs which
approximately preserves all separators and near-separators between subsets of
the given terminal set.
  Our result yields an improvement over the state-of-art approximation
algorithms for Vertex planarization. The problem admits a polynomial-time
O(n^eps)-approximation algorithm, for any eps > 0, and a quasi-polynomial-time
(log n)^O(1) approximation algorithm, both randomized [Kawarabayashi and
Sidiropoulos, FOCS'17]. By pipelining these algorithms with our approximate
kernelization, we improve the approximation factors to respectively O(OPT^eps)
and (log OPT)^O(1).


title:
Crystalline: Lowering the Cost for Developers to Collect and Organize
  Information for Decision Making

abstract:
  Developers perform online sensemaking on a daily basis, such as researching
and choosing libraries and APIs. Prior research has introduced tools that help
developers capture information from various sources and organize it into
structures useful for subsequent decision-making. However, it remains a
laborious process for developers to manually identify and clip content,
maintaining its provenance and synthesizing it with other content. In this
work, we introduce a new system called Crystalline that attempts to
automatically collect and organize information into tabular structures as the
user searches and browses the web. It leverages natural language processing to
automatically group similar criteria together to reduce clutter as well as
passive behavioral signals such as mouse movement and dwell time to infer what
information to collect and how to visualize and prioritize it. Our user study
suggests that developers are able to create comparison tables about 20% faster
with a 60% reduction in operational cost without sacrificing the quality of the
tables.


title:
Impact of Dissipation on Universal Fluctuation Dynamics in Open Quantum
  Systems

abstract:
  Recent experimental and theoretical works have uncovered nontrivial quantum
dynamics due to external dissipation. Using an exact numerical method and a
renormalization-group-based analytical technique, we theoretically elucidate
that dissipation drastically alters universal particle-number-fluctuation
dynamics related to surface-roughness growth in non-interacting fermions and
bosons. In a system under dephasing that causes loss of spatial coherence, we
find that a universality class of surface-roughness dynamics changes from the
ballistic class to a class with the Edwards-Wilkinson scaling exponents and an
unconventional scaling function. On the other hand, in a system under
dissipation with in- and out-flow of particles that breaks particle-number
conservation, the universal dynamics is lost.


title:
Generalised Score Distribution: A Two-Parameter Discrete Distribution
  Accurately Describing Responses from Quality of Experience Subjective
  Experiments

abstract:
  Subjective responses from Multimedia Quality Assessment (MQA) experiments are
conventionally analysed with methods not suitable for the data type these
responses represent. Furthermore, obtaining subjective responses is resource
intensive. A method allowing reuse of existing responses would be thus
beneficial. Applying improper data analysis methods leads to difficult to
interpret results. This encourages drawing erroneous conclusions. Building upon
existing subjective responses is resource friendly and helps develop machine
learning (ML) based visual quality predictors. We show that using a discrete
model for analysis of responses from MQA subjective experiments is feasible. We
indicate that our proposed Generalised Score Distribution (GSD) properly
describes response distributions observed in typical MQA experiments. We
highlight interpretability of GSD parameters and indicate that the GSD
outperforms the approach based on sample empirical distribution when it comes
to bootstrapping. We evidence that the GSD outcompetes the state-of-the-art
model both in terms of goodness-of-fit and bootstrapping capabilities. To do
all of that we analyse more than one million subjective responses from more
than 30 subjective experiments. Furthermore, we make the code implementing the
GSD model and related analyses available through our GitHub repository:
https://github.com/Qub3k/subjective-exp-consistency-check


title:
Post-AO high-resolution imaging using the Kraken multi-frame blind
  deconvolution algorithm

abstract:
  In the context of extreme adaptive optics (ExAO) for large telescopes, we
present the Kraken multi-frame blind deconvolution (MFBD) algorithm for
processing high-cadence acquisitions, capable to provide a diffraction-limited
estimation of the source brightness distribution. This is achieved by a data
modeling of each frame in the sequence driven by the estimation of the
instantaneous wavefront at the entrance pupil. Under suitable physical
contraints, numerical convergence is guaranteed by an iteration scheme starting
from a Compact MFBD (CMFBD) which provides a very robust initial guess which
only employs a few frames. We describe the mathematics behind the process and
report the high-resolution reconstruction of the spectroscopic binary {\alpha}
And (16.3 mas separation) acquired with the precursor of SHARK-VIS, the
upcoming high-contrast camera in the visible for the Large Binocular Telescope.


title: DelTact: A Vision-based Tactile Sensor Using Dense Color Pattern

abstract:
  Tactile sensing is an essential perception for robots to complete dexterous
tasks. As a promising tactile sensing technique, vision-based tactile sensors
have been developed to improve robot performance in manipulation and grasping.
Here we propose a new design of vision-based tactile sensor, DelTact, with its
high-resolution sensing abilities of surface contact measurement. The sensor
uses a modular hardware architecture for compactness whilst maintaining a
robust overall design. Moreover, it adopts an improved dense random color
pattern based on the previous version to achieve high accuracy of contact
deformation tracking. In particular, we optimize the color pattern generation
process and select the appropriate pattern for coordinating with a dense
optical flow algorithm in a real-world experimental sensory setting using
various objects for contact. The optical flow obtained from the raw image is
processed to determine shape and force distribution on the contact surface.
This sensor can be easily integrated with a parallel gripper where experimental
results using qualitative and quantitative analysis demonstrate that the sensor
is capable of providing tactile measurements with high temporal and spatial
resolution.


title:
Identifying Self-Admitted Technical Debt in Issue Tracking Systems using
  Machine Learning

abstract:
  Technical debt is a metaphor indicating sub-optimal solutions implemented for
short-term benefits by sacrificing the long-term maintainability and
evolvability of software. A special type of technical debt is explicitly
admitted by software engineers (e.g. using a TODO comment); this is called
Self-Admitted Technical Debt or SATD. Most work on automatically identifying
SATD focuses on source code comments. In addition to source code comments,
issue tracking systems have shown to be another rich source of SATD, but there
are no approaches specifically for automatically identifying SATD in issues. In
this paper, we first create a training dataset by collecting and manually
analyzing 4,200 issues (that break down to 23,180 sections of issues) from
seven open-source projects (i.e., Camel, Chromium, Gerrit, Hadoop, HBase,
Impala, and Thrift) using two popular issue tracking systems (i.e., Jira and
Google Monorail). We then propose and optimize an approach for automatically
identifying SATD in issue tracking systems using machine learning. Our findings
indicate that: 1) our approach outperforms baseline approaches by a wide margin
with regard to the F1-score; 2) transferring knowledge from suitable datasets
can improve the predictive performance of our approach; 3) extracted SATD
keywords are intuitive and potentially indicating types and indicators of SATD;
4) projects using different issue tracking systems have less common SATD
keywords compared to projects using the same issue tracking system; 5) a small
amount of training data is needed to achieve good accuracy.


title:
Investigating the detection rates and inference of gravitational-wave
  and radio emission from black hole neutron star mergers

abstract:
  Black hole neutron star (BHNS) mergers have recently been detected through
their gravitational-wave (GW) emission. BHNS mergers could also produce
electromagnetic (EM) emission as a short gamma-ray burst (sGRB), and/or an sGRB
afterglow upon interaction with the circummerger medium. Here, we make
predictions for the expected detection rates with the Square Kilometre Array
Phase 1 (SKA1) of sGRB radio afterglows associated with BHNS mergers. We also
investigate the benefits of a multimessenger analysis in inferring the
properties of the merging binary. We simulate a population of BHNS mergers and
estimate their sGRB afterglow flux to obtain the detection rates with SKA1. We
investigate how this rate depends on the GW detector sensitivity, the primary
black hole (BH) spin, and the neutron star equation of state. We then perform a
multimessenger Bayesian inference study on a fiducial BHNS merger. We simulate
its sGRB afterglow and GW emission and take systematic errors into account. The
expected rates of a combined GW and radio detection with the current generation
GW detectors are likely low. Due to the much increased sensitivity of future GW
detectors like the Einstein Telescope, the chances of an sGRB localisation and
radio detection increase substantially. The unknown distribution of the BH spin
has a big influence on the detection rates, however, and it is a large source
of uncertainty. Furthermore, for our fiducial BHNS merger we are able to infer
both the binary source parameters as well as the parameters of the sGRB
afterglow simultaneously when combining the GW and radio data. The radio data
provides useful extra information on the binary parameters such as the mass
ratio but this is limited by the systematic errors involved. A better
understanding of the systematics will further increase the amount of
information on the binary parameters that can be extracted from this radio
data.


title:
CohortPlat: Simulation of cohort platform trials investigating
  combination therapies

abstract:
  Platform trials have gained a lot of attention recently as a possible remedy
for time-consuming classical two-arm randomized controlled trials, especially
in early phase drug development. This short article illustrates how to use the
CohortPlat R package to simulate a cohort platform trial, where each cohort
consists of a combination treatment and the respective monotherapies and
standard-of-care. The endpoint is always assumed to be binary. The package
offers extensive flexibility with respect to both platform trial trajectories,
as well as treatment effect scenarios and decision rules. As a special feature,
the package provides a designated function for running multiple such
simulations efficiently in parallel and saving the results in a concise manner.
Many illustrations of code usage are provided.


title: Feature-Style Encoder for Style-Based GAN Inversion

abstract:
  We propose a novel architecture for GAN inversion, which we call
Feature-Style encoder. The style encoder is key for the manipulation of the
obtained latent codes, while the feature encoder is crucial for optimal image
reconstruction. Our model achieves accurate inversion of real images from the
latent space of a pre-trained style-based GAN model, obtaining better
perceptual quality and lower reconstruction error than existing methods. Thanks
to its encoder structure, the model allows fast and accurate image editing.
Additionally, we demonstrate that the proposed encoder is especially
well-suited for inversion and editing on videos. We conduct extensive
experiments for several style-based generators pre-trained on different data
domains. Our proposed method yields state-of-the-art results for style-based
GAN inversion, significantly outperforming competing approaches. Source codes
are available at https://github.com/InterDigitalInc/FeatureStyleEncoder .


title:
Singleton bounds for entanglement-assisted classical and quantum error
  correcting codes

abstract:
  We show that entirely information theoretic methods, based on von Neumann
entropies and their properties, can be used to derive Singleton bounds on the
performance of entanglement-assisted hybrid classical-quantum (EACQ) error
correcting codes. Concretely we show that the triple-rate region of qubits,
cbits and ebits of possible EACQ codes over arbitrary alphabet sizes is
contained in the quantum Shannon theoretic rate region of an associated
memoryless erasure channel, which turns out to be a polytope. We show that a
large part of this region is attainable by certain EACQ codes, whenever the
local alphabet size (i.e. Hilbert space dimension) is large enough, in keeping
with known facts about classical and quantum minimum distance separable (MDS)
codes: in particular all of its extreme points and several important extremal
lines.


title:
Investigations of c-Differential Uniformity of Permutations with Carlitz
  Rank 3

abstract:
  The $c$-differential uniformity is recently proposed to reflect resistance
against some variants of differential attack. Finding functions with low
$c$-differential uniformity is attracting attention from many researchers. For
even characteristic, it is known that permutations of low Carlitz rank have
good cryptographic parameters, for example, low differential uniformity, high
nonlinearity, etc. In this paper we show that permutations with low Carlitz
rank have low $c$-differential uniformity. We also investigate $c$-differential
uniformity of permutations with Carlitz rank 3 in detail.


title:
Voice-Based Conversational Agents for self-reporting fluid consumption
  and sleep quality

abstract:
  Intelligent conversational agents and virtual assistants, such as chatbots
and voice assistants, have the potential of augmenting health service capacity
to screen symptoms and deliver healthcare interventions. In this paper, we
developed voice-based conversational agents (VCAs) in the Google Actions
Console to deliver periodic self-assessment health surveys. The focus of this
paper is to accommodate self-monitoring for patients with specific fluid
consumption requirements or sleep disorders. Our VCAs, named FluidMonitor and
Sleepy, have been tested to integrate naturally into a patient's daily
lifestyle for the purpose of providing useful interventions. We show the
functionality of our Google Actions and discuss the considerations for using
VCAs as an at-home self-reporting survey technique. User testing showed
satisfaction with the ease of use, likeability, and burden level of the VCAs.


title: Diode laser overtone spectroscopy of methane at 780 nm

abstract:
  A tunable diode laser spectrometer has been employed to examine the overtone
absorption lines of $^{12}$CH$_4$ in the region around 12780 cm$^{-1}$ (780 nm)
at room temperature. The spectrometer sources are commercially available
tunable diode lasers operating in "free-running" mode. The measurement of the
line positions within 0.01 cm$^{-1}$ has been possible by the use of the
wavelength modulation spectroscopy and the second harmonic detection techniques
that permitted the observation of minimum optical absorbances of the order of
$\approx 10^{-6}$. The weakest observed lines of the band have absorption cross
sections of the order of $1 \times 10^{-25}$ cm$^2$/molecule, that is $\simeq
0.3$ km$^{-1}$/amagat. For some of them self-, air-, He- and H$_2$-broadening
coefficients have been obtained.


title:
Koopman von Neumann mechanics and the Koopman representation: A
  perspective on solving nonlinear dynamical systems with quantum computers

abstract:
  A number of recent studies have proposed that linear representations are
appropriate for solving nonlinear dynamical systems with quantum computers,
which fundamentally act linearly on a wave function in a Hilbert space. Linear
representations, such as the Koopman representation and Koopman von Neumann
mechanics, have regained attention from the dynamical-systems research
community. Here, we aim to present a unified theoretical framework, currently
missing in the literature, with which one can compare and relate existing
methods, their conceptual basis, and their representations. We also aim to show
that, despite the fact that quantum simulation of nonlinear classical systems
may be possible with such linear representations, a necessary projection into a
feasible finite-dimensional space will in practice eventually induce numerical
artifacts which can be hard to eliminate or even control. As a result a
practical, reliable and accurate way to use quantum computation for solving
general nonlinear dynamical systems is still an open problem.


title: Modular many-valued semantics for combined logics

abstract:
  We obtain, for the first time, a modular many-valued semantics for combined
logics, which is built directly from many-valued semantics for the logics being
combined, by means of suitable universal operations over partial
non-deterministic logical matrices. Our constructions preserve
finite-valuedness in the context of multiple-conclusion logics whereas,
unsurprisingly, it may be lost in the context of single-conclusion logics.
Besides illustrating our constructions over a wide range of examples, we also
develop concrete applications of our semantic characterizations, namely
regarding the semantics of strengthening a given many-valued logic with
additional axioms, the study of conditions under which a given logic may be
seen as a combination of simpler syntactically defined fragments whose calculi
can be obtained independently and put together to form a calculus for the whole
logic, and also general conditions for decidability to be preserved by the
combination mechanism.


title:
An Overview of Ultra-WideBand (UWB) Standards(IEEE 802.15.4, FiRa,
  Apple): Interoperability Aspects and Future Research Directions

abstract:
  The increasing popularity of ultra-wideband (UWB) technology for
location-based services such as access control and real-time indoor
track\&tracing, as well as UWB support in new consumer devices such as
smartphones, has resulted in the availability of multiple new UWB radio chips.
However, due to this increase in UWB device availability, the question of which
(industry) standards and configuration factors impact UWB interoperability and
compatibility becomes increasingly important. In this paper, the fundamentals
of UWB compatibility are investigated by first giving an overview of different
UWB radio chips on the market. After that, an overview of UWB standards and
standardisation entities is given. Next, this overview is used to discuss the
focus of these different standards and to identify the differences between
them. We describe compatibility issues and associated interoperability aspects
related to PHY, MAC, and upper layers. For the PHY layer, compatibility is
possible for all UWB chips if the correct settings are configured. For the MAC
layer, the implementation of the multiple access scheme as well as the
localization technique is mostly proprietary. For the device discovery, several
standards are currently being drafted. Finally, future challenges related to
UWB interoperability are discussed.


title:
Long range ionic and short range hydration effects govern strongly
  anisotropic clay nanoparticle interactions

abstract:
  The aggregation of clay particles in aqueous solution is a ubiquitous
everyday process of broad environmental and technological importance. However,
it is poorly understood at the all-important atomistic level since it depends
on a complex and dynamic interplay of solvent-mediated electrostatic,
hydrogen-bonding, and dispersion interactions. With this in mind we have
performed an extensive set of classical molecular dynamics simulations
(included enhanced sampling simulations) on the interactions between model
kaolinite nanoparticles in pure and salty water. Our simulations reveal highly
anisotropic behaviour in which the interaction between the nanoparticles varies
from attractive to repulsive depending on the relative orientation of the
nanoparticles. Detailed analysis reveals that at large separation (>1.5 nm)
this interaction is dominated by electrostatic effects whereas at smaller
separations the nature of the water hydration structure becomes critical. This
study highlights an incredible richness in how clay nanoparticles interact,
which should be accounted for in e.g. coarse grained models of clay
nanoparticle aggregation.


title:
Comparison of the performance and reliability between improved sampling
  strategies for polynomial chaos expansion

abstract:
  As uncertainty and sensitivity analysis of complex models grows ever more
important, the difficulty of their timely realizations highlights a need for
more efficient numerical operations. Non-intrusive Polynomial Chaos methods are
highly efficient and accurate methods of mapping input-output relationships to
investigate complex models. There is substantial potential to increase the
efficacy of the method regarding the selected sampling scheme. We examine
state-of-the-art sampling schemes categorized in space-filling-optimal designs
such as Latin Hypercube sampling and L1-optimal sampling and compare their
empirical performance against standard random sampling. The analysis was
performed in the context of L1 minimization using the least-angle regression
algorithm to fit the GPCE regression models. Due to the random nature of the
sampling schemes, we compared different sampling approaches using statistical
stability measures and evaluated the success rates to construct a surrogate
model with relative errors of $<0.1$\%, $<1$\%, and $<10$\%, respectively. The
sampling schemes are thoroughly investigated by evaluating the y of surrogate
models constructed for various distinct test cases, which represent different
problem classes covering low, medium and high dimensional problems. Finally,
the sampling schemes are tested on an application example to estimate the
sensitivity of the self-impedance of a probe that is used to measure the
impedance of biological tissues at different frequencies. We observed strong
differences in the convergence properties of the methods between the analyzed
test functions.


title:
Stochastic smoothing of the top-K calibrated hinge loss for deep
  imbalanced classification

abstract:
  In modern classification tasks, the number of labels is getting larger and
larger, as is the size of the datasets encountered in practice. As the number
of classes increases, class ambiguity and class imbalance become more and more
problematic to achieve high top-1 accuracy. Meanwhile, Top-K metrics (metrics
allowing K guesses) have become popular, especially for performance reporting.
Yet, proposing top-K losses tailored for deep learning remains a challenge,
both theoretically and practically. In this paper we introduce a stochastic
top-K hinge loss inspired by recent developments on top-K calibrated losses.
Our proposal is based on the smoothing of the top-K operator building on the
flexible "perturbed optimizer" framework. We show that our loss function
performs very well in the case of balanced datasets, while benefiting from a
significantly lower computational time than the state-of-the-art top-K loss
function. In addition, we propose a simple variant of our loss for the
imbalanced case. Experiments on a heavy-tailed dataset show that our loss
function significantly outperforms other baseline loss functions.


title:
HL-LHC Computing Review Stage 2, Common Software Projects: Data Science
  Tools for Analysis

abstract:
  This paper was prepared by the HEP Software Foundation (HSF) PyHEP Working
Group as input to the second phase of the LHCC review of High-Luminosity LHC
(HL-LHC) computing, which took place in November, 2021. It describes the
adoption of Python and data science tools in HEP, discusses the likelihood of
future scenarios, and recommendations for action by the HEP community.


title: Deep End-to-end Causal Inference

abstract:
  Causal inference is essential for data-driven decision making across domains
such as business engagement, medical treatment or policy making. However,
research on causal discovery and inference has evolved separately, and the
combination of the two domains is not trivial. In this work, we develop Deep
End-to-end Causal Inference (DECI), a single flow-based method that takes in
observational data and can perform both causal discovery and inference,
including conditional average treatment effect (CATE) estimation. We provide a
theoretical guarantee that DECI can recover the ground truth causal graph under
mild assumptions. In addition, our method can handle heterogeneous, real-world,
mixed-type data with missing values, allowing for both continuous and discrete
treatment decisions. Moreover, the design principle of our method can
generalize beyond DECI, providing a general End-to-end Causal Inference (ECI)
recipe, which enables different ECI frameworks to be built using existing
methods. Our results show the superior performance of DECI when compared to
relevant baselines for both causal discovery and (C)ATE estimation in over a
thousand experiments on both synthetic datasets and other causal machine
learning benchmark datasets.


title: The Generalized Fibonacci Oscillator as an Open Quantum System

abstract:
  We consider an open quantum system with Hamiltonian $H_S$ whose spectrum is
given by a generalized Fibonacci sequence weakly coupled to a Boson reservoir
in equilibrium at inverse temperature $\beta$. We find the generator of the
reduced system evolution and explicitly compute the stationary state of the
system, that turns out to be unique and faithful, in terms of parameters of the
model. If the system Hamiltonian is generic we show that convergence towards
the invariant state is exponentially fast and compute explicitly the spectral
gap for low temperatures, when quantum features of the system are more
significant, under an additional assumption on the spectrum of $H_S$


title:
Evaluating conditional covariance estimates via a new targeting approach
  and a networks-based analysis

abstract:
  Modeling and forecasting of dynamically varying covariances have received
much attention in the literature. The two most widely used conditional
covariances and correlations models are BEKK and DCC. In this paper, we advance
a new method to introduce targeting in both models to estimate matrices
associated with financial time series. Our approach is based on specific groups
of highly correlated assets in a financial market, and these relationships
remain unaltered over time. Based on the estimated parameters, we evaluate our
targeting method on simulated series by referring to two well-known loss
functions introduced in the literature and Network analysis. We find all the
maximal cliques in correlation graphs to evaluate the effectiveness of our
method. Results from an empirical case study are encouraging, mainly when the
number of assets is not large.


title:
Generalized multichannel optical theorem: Coherent control of the total
  scattering cross section

abstract:
  The optical theorem is a fundamental aspect of quantum scattering theory.
Here, we generalize this theorem to the case where the incident scattering
state is a superposition of internal states of the collision partners,
introducing additional interference contributions and, e.g., providing a route
to control the total integral cross section. As in its standard form, forward
scattering plays an essential role in the generalized optical theorem, but with
interference terms being related to the inelastic forward scattering amplitudes
between states in the initial superposition. Using the resultant control index,
we show that extensive control is possible over ultracold collisions of oxygen
molecules in their rovibrational ground states, and of 85Rb-85Rb collisions,
promising systems for the first experimental demonstration of the quantum
interference control of the total scattering cross section.


title:
ABSNFT: Securitization and Repurchase Scheme for Non-Fungible Tokens
  Based on Game Theoretical Analysis

abstract:
  The Non-Fungible Token (NFT) is viewed as one of the important applications
of blockchain technology. Although NFT has a large market scale and multiple
practical standards, several limitations of the existing mechanism in NFT
markets exist. This work proposes a novel securitization and repurchase scheme
for NFT to overcome these limitations. We first provide an Asset-Backed
Securities (ABS) solution to settle the limitations of non-fungibility of NFT.
Our securitization design aims to enhance the liquidity of NFTs and enable
Oracles and Automatic Market Makers (AMMs) for NFTs. Then we propose a novel
repurchase protocol for a participant owing a portion of NFT to repurchase
other shares to obtain the complete ownership. As participants may
strategically bid during the acquisition process, our repurchase process is
formulated as a Stackelberg game to explore the equilibrium prices. We also
provide solutions to handle difficulties at market such as budget constraints
and lazy bidders.


title: Learning with Neighbor Consistency for Noisy Labels

abstract:
  Recent advances in deep learning have relied on large, labelled datasets to
train high-capacity models. However, collecting large datasets in a time- and
cost-efficient manner often results in label noise. We present a method for
learning from noisy labels that leverages similarities between training
examples in feature space, encouraging the prediction of each example to be
similar to its nearest neighbours. Compared to training algorithms that use
multiple models or distinct stages, our approach takes the form of a simple,
additional regularization term. It can be interpreted as an inductive version
of the classical, transductive label propagation algorithm. We thoroughly
evaluate our method on datasets evaluating both synthetic (CIFAR-10, CIFAR-100)
and realistic (mini-WebVision, Clothing1M, mini-ImageNet-Red) noise, and
achieve competitive or state-of-the-art accuracies across all of them.


title: Modular operads as modules over the Brauer properad

abstract:
  We show that modular operads are equivalent to modules over a certain simple
properad which we call the Brauer properad. Furthermore we show that, in this
setting, the Feynman transform corresponds to the cobar construction for
modules of this kind. To make this precise, we extend the machinery of the bar
and cobar constructions relative to a twisting morphism to modules over a
general properad. This generalizes the classical case of algebras over an
operad and might be of independent interest. As an application, we sketch a
Koszul duality theory for modular operads.


title: Reasoning by Analogy in Mathematical Practice

abstract:
  The testimony and practice of notable mathematicians indicate that there is
an important phenomenological and epistemological difference between
superficial and deep analogies in mathematics. In this paper, we offer a
descriptive theory of analogical reasoning in mathematics, stating general
conditions under which an analogy may provide genuine inductive support to a
mathematical conjecture (over and above fulfilling the merely heuristic role of
'suggesting' a conjecture in the psychological sense). The proposed conditions
generalize the criteria put forward by Hesse (1963) in her influential work on
analogical reasoning in the empirical sciences. By reference to several
case-studies, we argue that the account proposed in this paper does a better
job in vindicating the use of analogical inference in mathematics than the
prominent alternative defended by Bartha (2009). Moreover, our proposal offers
novel insights into the practice of extending to the infinite case mathematical
properties known to hold in finite domains.


title: A Magnetoelectric Memory Device Based on Pseudo-Magnetization

abstract:
  We propose a new type of magnetoelectric memory device that stores magnetic
easy-axis information or pseudo-magnetization, rather than a definite
magnetization direction, in piezoelectric/ferromagnetic (PE/FM)
heterostructures. Theoretically, we show how a PE/FM combination can lead to
non-volatility in pseudo-magnetization exhibiting ferroelectric-like behavior.
The pseudo-magnetization can be manipulated by extremely low voltages
especially when the FM is a low-barrier nanomagnet. Using a circuit model
benchmarked against experiments, we determine the switching energy, delay,
switching probability and retention time of the envisioned 1T/1C memory device
in terms of magnetic and circuit parameters and discuss its thermal stability
in terms of a key parameter called back-voltage vm which is an electrical
measure of the strain-induced magnetic field. Taking advantage of ferromagnetic
resonance (FMR) measurements, we experimentally extract values for vm in CoFeB
films and circular nano-magnets deposited on Pb(Mg1/3Nb2/3)0.7Ti0.3O3 (PMN-PT)
which agree well with the theoretical values. Our experimental findings indeed
indicate the feasibility of the proposed novel device and confirm the assumed
parameters in our modeling effort.


title:
Measurements of cyclotron resonance of the interfacial states in strong
  spin-orbit coupled 2D electron gases proximitized with aluminum

abstract:
  Two-dimensional electron gasses (2DEG) in InAs quantum wells proximitized by
aluminum are promising platforms for topological qubits based on Majorana zero
modes. However, there are still substantial uncertainties associated with the
nature of the electronic states at the interfaces of these systems. It is
challenging to probe the properties of these hybridized states as they are
buried under a relatively thick aluminum layer. In this work, we have
investigated a range of InAs/In$ _{1-\text{x}} $Ga$ _\text{x} $As
heterostructures with Al overlayers using high precision time-domain THz
spectroscopy. Despite the thick metallic overlayer, we observe a prominent
cyclotron resonance in the magnetic field that can be associated with the
response of the interfacial states. Measurements of the THz range complex
Faraday rotation allow the extraction of the sign and magnitude of the
effective mass, the density of charge carriers, and scattering times of the
2DEG despite the close proximity of the aluminum layer. We discuss the
extracted band parameters and connect their values to the known physics of
these materials.


title:
Solutions of the Einstein equations for a black hole surrounded by a
  galactic halo

abstract:
  Various profiles of matter distribution in galactic halos (such as
Navarro-Frenk-White, Burkert, Hernquist, Moore, Taylor-Silk and others) are
considered here as the source term for the Einstein equations. We solve these
equations and find exact solutions which represent the metric of a central
black hole immersed in a galactic halo. Even though in the general case the
solution is numerical, very accurate general analytical metric, which includes
all the particular models, are found in the astrophysically relevant regime,
when the mass of the galaxy is much smaller than the characteristic scale in
the halo.


title:
A Comparison Between Quantile Regression and Linear Regression on
  Empirical Quantiles for Phenological Analysis in Migratory Response to
  Climate Change

abstract:
  It is well established that migratory birds in general have advanced their
arrival times in spring, and in this paper we investigate potential ways of
enhancing the level of detail in future phenological analyses. We perform
single as well as multiple species analyses, using linear models on empirical
quantiles, non-parametric quantile regression and likelihood-based parametric
quantile regression with asymmetric Laplace distributed error terms. We
conclude that non-parametric quantile regression appears most suited for single
as well as multiple species analyses.


title:
Active Visuo-Tactile Interactive Robotic Perception for Accurate Object
  Pose Estimation in Dense Clutter

abstract:
  This work presents a novel active visuo-tactile based framework for robotic
systems to accurately estimate pose of objects in dense cluttered environments.
The scene representation is derived using a novel declutter graph (DG) which
describes the relationship among objects in the scene for decluttering by
leveraging semantic segmentation and grasp affordances networks. The graph
formulation allows robots to efficiently declutter the workspace by
autonomously selecting the next best object to remove and the optimal action
(prehensile or non-prehensile) to perform. Furthermore, we propose a novel
translation-invariant Quaternion filter (TIQF) for active vision and active
tactile based pose estimation. Both active visual and active tactile points are
selected by maximizing the expected information gain. We evaluate our proposed
framework on a system with two robots coordinating on randomized scenes of
dense cluttered objects and perform ablation studies with static vision and
active vision based estimation prior and post decluttering as baselines. Our
proposed active visuo-tactile interactive perception framework shows upto 36%
improvement in pose accuracy compared to the active vision baseline.


title: Metastable diffusions with degenerate drifts

abstract:
  We study the spectrum of the semiclassical Witten Laplacian $\Delta_{f}$
associated to a smooth function $f$ on ${\mathbb R}^d$. We assume that $f$ is a
confining Morse--Bott function. Under this assumption we show that $\Delta_{f}$
admits exponentially small eigenvalues separated from the rest of the spectrum.
Moreover, we establish Eyring-Kramers formula for these eigenvalues. Our
approach is based on microlocal constructions of quasimodes near the critical
submanifolds.


title:
On Sustainability and Survivability in the Matchbox Two-Sector Model: A
  Complete Characterization of Optimal Extinction

abstract:
  We provide a complete characterization of optimal extinction in a two-sector
model of economic growth through three results, surprising in both their
simplicity and intricacy. (i) When the discount factor is below a threshold
identified by the well-known $\delta$-normality condition for the existence of
a stationary optimal stock, the economy's capital becomes extinct in the long
run. (ii) This extinction may be staggered if and only if the investment-good
sector is capital intensive. (iii) We uncover a sequence of thresholds of the
discount factor, identified by a family of rational functions, that represent
bifurcations for optimal postponements on the path to extinction. We also
report various special cases of the model having to do with unsustainable
technologies and equal capital intensities that showcase long-term optimal
growth, all of topical interest and all neglected in the antecedent literature.


title:
Simulating and visualizing COVID-19 contact tracing with Corona-Warn-App
  for increased understanding of its privacy-preserving design

abstract:
  The world is under an ongoing pandemic, COVID-19, of a scale last seen a
century ago. Contact tracing is one of the most critical and highly effective
tools for containing and breaking the chain of infections especially in the
case of infectious respiratory diseases like COVID-19. Thanks to the
technological progress in our times, we now have digital mobile applications
like the Corona-Warn-App for digital contact tracing. However, due to the
invasive nature of contact tracing, it is very important to preserve the
privacy of the users. Privacy preservation is important for increasing trust in
the app and subsequently enabling its widespread usage in a privacy-valuing
population. In this paper, we present a visual simulation of the working of the
Corona-Warn-App to demonstrate how the privacy of its users is preserved, how
they're notified of infectious contacts and how it helps in containing the
spread of COVID-19.


title: Effects of heterogeneity on cancer: a game theory perspective

abstract:
  In this study, we explore interactions between cancer cells by using the
hawk-dove game. We analyze the heterogeneity of tumors by considering games
with populations composed of 2 or 3 types of cells. We determine what
strategies are evolutionarily stable in the 2-type and 3-type population games
and the corresponding expected payoffs. Our results show that the payoff of the
best-off cell in the 2-type population game is higher than that of the best-off
cell in the 3-type population game. Translating these mathematical findings to
the field of oncology suggests that a branching-type tumor pursues a less
aggressive course than a punctuated-type one. Some histological and genomic
data of clear cell renal cell carcinomas are consistent with these results.


title:
SSHA: Video Violence Recognition and Localization Using a
  Semi-Supervised Hard Attention Model

abstract:
  Current human-based surveillance systems are prone to inadequate availability
and reliability. Artificial intelligence-based solutions are compelling,
considering their reliability and precision in the face of an increasing
adaption of surveillance systems. Exceedingly efficient and precise machine
learning models are required to effectively utilize the extensive volume of
high-definition surveillance imagery. This study focuses on improving the
accuracy of the methods and models used in automated surveillance systems to
recognize and localize human violence in video footage. The proposed model uses
an I3D backbone pretrained on the Kinetics dataset and has achieved
state-of-the-art accuracy of 90.4% and 98.7% on RWF and Hockey datasets,
respectively. The semi-supervised hard attention mechanism has enabled the
proposed method to fully capture the available information in a high-resolution
video by processing the necessary video regions in great detail.


title: A report on an ergodic dichotomy

abstract:
  We establish (some directions) of a Ledrappier correspondence between
H\"older cocycles, Patterson-Sullivan measures, etc for word-hyperbolic groups
with metric-Anosov Mineyev flow. We then study Patterson-Sullivan measures for
$\theta$-Anosov representations over a local field and show that these are
parametrized by the $\theta$-critical hypersurface of the representation. We
use these Patterson-Sullivan measures to establish a dichotomy concerning
directions in the interior of the $\theta$-limit cone of the representation in
question: if $u$ is such a half-line, then the subset of $u$-conical limit
points has either total-mass if $|\theta|\leq2$ or zero-mass if
$|\theta|\geq4.$ The case $|\theta|=3$ remains unsettled.


title: Infinite transitivity for automorphism groups of the affine plane

abstract:
  This paper is dedicated to the problem of infinite transitivity for
algebraically generated automorphism groups of the affine plane. We provide a
necessary and sufficient condition of infinite transitivity for a large family
of subgroups generated by one-parameter additive groups.


title:
A Survey on Safety-Critical Driving Scenario Generation -- A
  Methodological Perspective

abstract:
  Autonomous driving systems have witnessed a significant development during
the past years thanks to the advance in machine learning-enabled sensing and
decision-making algorithms. One critical challenge for their massive deployment
in the real world is their safety evaluation. Most existing driving systems are
still trained and evaluated on naturalistic scenarios collected from daily life
or heuristically-generated adversarial ones. However, the large population of
cars, in general, leads to an extremely low collision rate, indicating that the
safety-critical scenarios are rare in the collected real-world data. Thus,
methods to artificially generate scenarios become crucial to measure the risk
and reduce the cost. In this survey, we focus on the algorithms of
safety-critical scenario generation in autonomous driving. We first provide a
comprehensive taxonomy of existing algorithms by dividing them into three
categories: data-driven generation, adversarial generation, and knowledge-based
generation. Then, we discuss useful tools for scenario generation, including
simulation platforms and packages. Finally, we extend our discussion to five
main challenges of current works -- fidelity, efficiency, diversity,
transferability, controllability -- and research opportunities lighted up by
these challenges.


title:
Geometrically Higher Order Unfitted Space-Time Methods for PDEs on
  Moving Domains

abstract:
  In this paper, we propose new geometrically unfitted space-time Finite
Element methods for partial differential equations posed on moving domains of
higher order accuracy in space and time. As a model problem, the
convection-diffusion problem on a moving domain is studied. For geometrically
higher order accuracy, we apply a parametric mapping on a background space-time
tensor-product mesh. Concerning discretisation in time, we consider
discontinuous Galerkin, as well as related continuous (Petrov-)Galerkin and
Galerkin collocation methods. For stabilisation with respect to bad cut
configurations and as an extension mechanism that is required for the latter
two schemes, a ghost penalty stabilisation is employed. The article puts an
emphasis on the techniques that allow to achieve a robust but higher order
geometry handling for smooth domains. We investigate the computational
properties of the respective methods in a series of numerical experiments.
These include studies in different dimensions for different polynomial degrees
in space and time, validating the higher order accuracy in both variables.


title: Flow Time Scheduling and Prefix Beck-Fiala

abstract:
  We relate discrepancy theory with the classic scheduling problems of
minimizing max flow time and total flow time on unrelated machines.
Specifically, we give a general reduction that allows us to transfer
discrepancy bounds in the prefix Beck-Fiala (bounded $\ell_1$-norm) setting to
bounds on the flow time of an optimal schedule.
  Combining our reduction with a deep result proved by Banaszczyk via convex
geometry, give guarantees of $O(\sqrt{\log n})$ and $O(\sqrt{\log n} \log P)$
for max flow time and total flow time, respectively, improving upon the
previous best guarantees of $O(\log n)$ and $O(\log n \log P)$. Apart from the
improved guarantees, the reduction motivates seemingly easy versions of prefix
discrepancy questions: any constant bound on prefix Beck-Fiala where vectors
have sparsity two (sparsity one being trivial) would already yield tight
guarantees for both max flow time and total flow time. While known techniques
solve this case when the entries take values in $\{-1,0,1\}$, we show that they
are unlikely to transfer to the more general $2$-sparse case of bounded
$\ell_1$-norm.


title:
Two-particle Bose-Einstein correlations in pp collisions at ${\sqrt{s} =
  13}$ TeV measured with the ATLAS detector at the LHC

abstract:
  This paper presents studies of Bose-Einstein correlations (BEC) in
proton-proton collisions at a centre-of-mass energy of 13 TeV, using data from
the ATLAS detector at the CERN Large Hadron Collider. Data were collected in a
special low-luminosity configuration with a minimum-bias trigger and a
high-multiplicity track trigger, accumulating integrated luminosities of 151
$\mu$b$^{-1}$ and 8.4 nb$^{-1}$ respectively. The BEC are measured for pairs of
like-sign charged particles, each with $|\eta|$ < 2.5, for two kinematic
ranges: the first with particle $p_T$ > 100 MeV and the second with particle
$p_T$ > 500 MeV. The BEC parameters, characterizing the source radius and
particle correlation strength, are investigated as functions of
charged-particle multiplicity (up to 300) and average transverse momentum of
the pair (up to 1.5 GeV). The double-differential dependence on
charged-particle multiplicity and average transverse momentum of the pair is
also studied. The BEC radius is found to be independent of the charged-particle
multiplicity for high charged-particle multiplicity (above 100), confirming a
previous observation at lower energy. This saturation occurs independent of the
transverse momentum of the pair.


title:
Hyper-differential sensitivity analysis for nonlinear Bayesian inverse
  problems

abstract:
  We consider hyper-differential sensitivity analysis (HDSA) of nonlinear
Bayesian inverse problems governed by PDEs with infinite-dimensional
parameters. In previous works, HDSA has been used to assess the sensitivity of
the solution of deterministic inverse problems to additional model
uncertainties and also different types of measurement data. In the present
work, we extend HDSA to the class of Bayesian inverse problems governed by
PDEs. The focus is on assessing the sensitivity of certain key quantities
derived from the posterior distribution. Specifically, we focus on analyzing
the sensitivity of the MAP point and the Bayes risk and make full use of the
information embedded in the Bayesian inverse problem. After establishing our
mathematical framework for HDSA of Bayesian inverse problems, we present a
detailed computational approach for computing the proposed HDSA indices. We
examine the effectiveness of the proposed approach on a model inverse problem
governed by a PDE for heat conduction.


title:
Uniaxial pressure effects in the two-dimensional van-der-Waals
  ferromagnet CrI$_3$

abstract:
  Magnetoelastic coupling and uniaxial pressure dependencies of the
ferromagnetic ordering temperature in the quasi-two-dimensional layered
van-der-Waals material CrI$_3$ are experimentally studied and quantified by
high-resolution dilatometry. Clear anomalies in the thermal expansion
coefficients at $T_{\rm C}$ imply positive (negative) pressure dependencies
$\partial T_{\rm C}/\partial p_{\rm i}$ for pressure applied along
(perpendicular to) the $c$ axis. The experimental results are backed up by
numerical studies showing that the dominant, intra-layer magnetic coupling
increases upon compression along the $c$ direction and decreases with negative
in-plane strain. In contrast, inter-layer exchange is shown to initially
increase and subsequently decrease upon the application of both out-of-plane
and in-plane compression.


title:
Non-singular T-K axion stars with/without the dynamical bosonic field in
  the presence of negative $\Lambda$ term

abstract:
  In the present work authors derive exact solutions for the relativistic
compact stars in the presence of two fields axion (Dante's Inferno model) and
with/without the complex scalar field (with the quartic self-interaction)
coupled to gravity. The matter source is assumed as the perfect fluid one, and
we also use barotropic/MIT bag EoS to derive energy density and
anisotropic/isotropic pressures from Einstein Field Equations (EFE's). For
simplicity, as the metric potentials we use Tolman-Kuchowicz metric potentials,
which are non-singular and physically acceptable. Unknown variables for the T-K
metric potentials were derived from the junction conditions. To examine the
redshift function, adiabatic index and energy, causality conditions we used
such compact stars as PSR J1416-223, PSR J1903+32, 4U 1820-30, Cen X-3, EXO
1785-248, SAX J1808.4365.


title: Portal Matter and Dark Sector Phenomenology at Colliders

abstract:
  If dark matter (DM) interacts with the Standard Model (SM) via the kinetic
mixing (KM) portal, it necessitates the existence of massive, likely $\gsim 1$
TeV, enabler portal matter (PM) particles that carry both dark and SM quantum
numbers which will appear in vacuum polarization-like loop graphs. Such heavy
states are only directly accessible at high energy colliders and apparently lie
at mass scales beyond the direct kinematic reach of the ILC, CEPC and FCC-ee. A
likely possibility is that these new particles are part of the 'next step'
toward a UV-complete scenario describing both the SM and dark sector physics. A
simple and straightforward example of such a scenario involving a non-abelian
dark sector gauge group is employed in this work to demonstrate some of the
range expected from this new physics. Here we present a broad survey of
existing analyses designed to explore the nature of such PM states in a array
of collider contexts, particularly at the LHC, and point out some of the future
directions where additional work is obviously required in the hunt for new
signatures as well as in model building directions.


title: Systems for Memory Disaggregation: Challenges & Opportunities

abstract:
  Memory disaggregation addresses memory imbalance in a cluster by decoupling
CPU and memory allocations of applications while also increasing the effective
memory capacity for (memory-intensive) applications beyond the local memory
limit imposed by traditional fixed-capacity servers. As the network speeds in
the tightly-knit environments like modern datacenters inch closer to the DRAM
speeds, there has been a recent proliferation of work in this space ranging
from software solutions that pool memory of traditional servers for the shared
use of the cluster to systems targeting the memory disaggregation in the
hardware. In this report, we look at some of these recent memory disaggregation
systems and study the important factors that guide their design, such as the
interface through which the memory is exposed to the application, their runtime
design and relevant optimizations to retain the near-native application
performance, various approaches they employ in managing cluster memory to
maximize utilization, etc. and we analyze the associated trade-offs. We
conclude with a discussion on some open questions and potential future
directions that can render disaggregation more amenable for adoption.


title: Direction-only Orientation Alignment of Leader-Follower Networks

abstract:
  When a team of agents, such as unmanned aerial/underwater vehicles, are
operating in $3$-dimensional space, their coordinated action in pursuit of a
cooperative task generally requires all agents to either share a common
coordinate system or know the orientations of their coordinate axes with regard
to the global coordinate frame. Given the coordinate axes that are initially
unaligned, this work proposes an orientation alignment scheme for multiple
agents with a type of leader-following graph typologies using only inter-agent
directional vectors, and the direction measurements to one or more landmarks of
the first two agents. The directional vectors are expressed in the agents'
body-fixed coordinate frames and the proposed alignment protocol works
exclusively with the directional vectors without the need of a global
coordinate frame common to all agents or the construction of the agents'
orientation matrices. Under the proposed alignment scheme, the orientations of
the agents converge almost globally and asymptotically to the orientation of
the leader agent. Finally, numerical simulations are also given to illustrate
the effectiveness of the proposed method.


title:
A Markov Chain Surrogate Model for a Two-Dimensional Interacting
  Particle System with Internal Collisions

abstract:
  A probabilistic Markov Chain (MC) surrogate model for a two-dimensional
system of interacting particles within a square domain having inherent
symmetries is developed. Particles are assumed to be circular and identical,
colliding with each other and with rigid domain walls via perfectly elastic
collisions. Simulation results over many realizations are used to develop and
evaluate the surrogate MC model. The stationary quantity of interest (QoI) is
the number of particles within each of nine coarser subdomains, delineated via
three internal geometric symmetries. The surrogate model is used to quantify
QoI mean properties and uncertainty as the particle radius is varied.


title:
DeSIRe: Departure coefficient aided Stokes Inversion based on Response
  functions

abstract:
  Future ground-based telescopes, such as the 4-metre class facilities DKIST
and EST, will dramatically improve on current capabilities for simultaneous
multi-line polarimetric observations in a wide range of wavelength bands, from
the near-ultraviolet to the near-infrared. As a result, there will be an
increasing demand for fast diagnostic tools, i.e., inversion codes, that can
infer the physical properties of the solar atmosphere from the vast amount of
data these observatories will produce. The advent of substantially larger
apertures, with the concomitant increase in polarimetric sensitivity, will
drive an increased interest in observing chromospheric spectral lines.
Accordingly, pertinent inversion codes will need to take account of line
formation under general non-local thermodynamic equilibrium (NLTE) conditions.
Several currently available codes can already accomplish this, but they have a
common practical limitation that impairs the speed at which they can invert
polarised spectra, namely that they employ numerical evaluation of the
so-called response functions to changes in the atmospheric parameters, which
makes them less suitable for the analysis of very large data volumes. Here we
present DeSIRe (Departure coefficient aided Stokes Inversion based on Response
functions), an inversion code that integrates the well-known inversion code SIR
with the NLTE radiative transfer solver RH. The DeSIRe runtime benefits from
employing analytical response functions computed in local thermodynamic
equilibrium (through SIR), modified with fixed departure coefficients to
incorporate NLTE effects in chromospheric spectral lines. This publication
describes the operating fundamentals of DeSIRe and describes its behaviour,
robustness, stability, and speed. The code is ready to be used by the solar
community and is being made publicly available.


title:
Evolution of lattice, spin, and charge properties across the phase
  diagram of FeSe$_{1-x}$S$_x$

abstract:
  A Raman scattering study covering the entire substitution range of the
FeSe$_{1-x}$S$_x$ solid solution is presented. Data were taken as a function of
sulfur concentration $x$ for $0\le x \le 1$, of temperature and of scattering
symmetry. All type of excitations including phonons, spins and charges are
analyzed in detail. It is observed that the energy and width of iron-related
B$_{1g}$ phonon mode vary continuously across the entire range of sulfur
substitution. The A$_{1g}$ chalcogenide mode disappears above $x=0.23$ and
reappears at a much higher energy for $x=0.69$. In a similar way the spectral
features appearing at finite doping in A$_{1g}$ symmetry vary discontinuously.
The magnetic excitation centered at approximately 500 cm$^{-1}$ disappears
above $x=0.23$ where the A$_{1g}$ lattice excitations exhibit a discontinuous
change in energy. The low-energy mode associated with fluctuations displays
maximal intensity at the nemato-structural transition and thus tracks the phase
boundary.


title: Uncertainty in fMRI Functional Networks of Autism Brain Imaging Data

abstract:
  In this paper we review the preprocessing pipeline through which fMRI data is
transformed into a network. We discuss three parameters that mostly affect our
understanding of the existence of functional correlations between the brain
regions. In the end, we conclude that the existence of functional correlations
between pairs of the brain's regions can be modeled with probabilistic edges,
not to lose the uncertainty that is inherent in the network generation process.


title: A note on shock wave in the dark matter medium

abstract:
  Employing Newton's stellar balance equation and using the flat rotation
velocity of satellite galaxies, we have found the velocity of sound in the dark
matter medium. What is interesting is that the velocity of satellite galaxies
is very much larger than the velocity of sound in the dark matter medium. This
indicates that there will be shock wave in the dark matter medium due to the
supersonic movement of satellite galaxies.


title: Vortices in quantum droplets of heteronuclear Bose mixtures

abstract:
  We have theoretically investigated the structure of spinning self-bound
droplets made of $^{41}$K-$^{87}$Rb Bose mixture by solving the
Gross-Pitaevskii equation including beyond-mean-field correction in the
Lee-Huang-Yang form. The structure and energetics of vortex formation in the
self-bound mixture have been elucidated, showing that the formation of linear
vortices in the heavier species is energetically favoured over other
configurations. A fake (partially filled) core develops as a consequence in the
other species, resulting in a hole which might be imaged in experiments. The
interplay between vortices and capillary waves, which are the two ways angular
momentum can be stored in a swirling superfluid, is studied in detail by
computing the relation between angular momentum and rotational frequency. The
results show intriguing similarities with the case of a prototypical
superfluid, i.e. $^4$He droplets when set into rotation. A two-branches curve
in the stability diagram, qualitatively similar to the one expected for
classical (incompressible and viscous) rotating liquid droplets, is obtained
when vortices are present in the droplets, while prolate (i.e. non
axi-symmetric) shapes are only permitted in vortex-free droplets.


title: Weak Thermal State Quadrature-Noise Shadow Imaging

abstract:
  In this work, we theoretically and experimentally demonstrate the possibility
to create an image of an opaque object using a few-photon thermal optical
field. We utilize the Quadrature-Noise Shadow Imaging (QSI) technique that
detects the changes in the quadrature-noise statistics of the probe beam after
its interaction with an object. We show that such thermal QSI scheme has an
advantage over the classical differential imaging when the effect of dark
counts is considered. At the same time, the easy availability of thermal
sources for any wavelength makes the method practical for broad range of
applications, not accessible with, e.g. quantum squeezed light. As a proof of
principle, we implement this scheme by two distinct methods: first, with
pseudo-thermal light generated by rotating ground glass (RGG) method and
second, with thermal beam generated by Four-Wave Mixing (FWM) method. The RGG
method shows simplicity and robustness of QSI scheme while the FWM method
validates theoretical signal-to-noise ratio predictions. Finally, we
demonstrate low-light imaging abilities with QSI by imaging a biological
specimen on a CCD camera, detecting just 0.006 photons per pixel on average.


title:
Bootstrapped Representation Learning for Skeleton-Based Action
  Recognition

abstract:
  In this work, we study self-supervised representation learning for 3D
skeleton-based action recognition. We extend Bootstrap Your Own Latent (BYOL)
for representation learning on skeleton sequence data and propose a new data
augmentation strategy including two asymmetric transformation pipelines. We
also introduce a multi-viewpoint sampling method that leverages multiple
viewing angles of the same action captured by different cameras. In the
semi-supervised setting, we show that the performance can be further improved
by knowledge distillation from wider networks, leveraging once more the
unlabeled samples. We conduct extensive experiments on the NTU-60 and NTU-120
datasets to demonstrate the performance of our proposed method. Our method
consistently outperforms the current state of the art on both linear evaluation
and semi-supervised benchmarks.


title:
Lefschetz properties of Jacobian algebras and singularities of
  hyperplane sections

abstract:
  Let $V:f=0$ be a smooth hypersurface of degree $d \geq 3$ in the complex
projective space $\mathbb{P}^n$, $n \geq 3$. Let $M(f)$ be the associated
Jacobian algebra and $H: \ell=0$ be a hyperplane in $\mathbb{P}^n$. We related
the Lefschetz type properties of the map $\ell: M(f)_k \to M(f)_{k+1}$ induced
by the multiplication by $\ell$ to the singularities of the hyperplane section
$V \cap H$. We also show that a generic hypersurface $V \subset \mathbb{P}^n$
has at least one section $V \cap H$ containing $n$ ordinary double points in
general position. Equivalently, the dual hypersurface $V^{\vee}$ has at least
one normal crossing singularity of multiplicity $n$.


title:
Achieving high-quality van der Waals material on a semiconductor
  substrate

abstract:
  Van der Waals (vdW) materials have emerged as a material class of significant
interest for discovering new physics and for next-generation devices. To date,
most research has been conducted on small exfoliated flakes or on thin films
grown on inert substrates. However, in order to integrate these materials with
existing semiconductor-based devices, they must be synthesized at a wafer scale
on semiconductor substrates. Here, we explore the growth of Bi2Se3, a
topological insulator and prototypical vdW material, on GaAs substrates. We
explore surface treatments and find that an atomically-smooth GaAs surface is
critical to achieving good-quality Bi2Se3 films despite the relatively weak
film/substrate interaction. Calculations indicate that the interface is likely
selenium-terminated and shows no evidence of covalent bonding. These results
clearly demonstrate that the film/substrate interaction is important even in
vdW epitaxy and provide a recipe for the growth of other vdW materials on
semiconductor substrates.


title:
Isothermal Limit of Entropy Solutions of the Euler Equations for
  Isentropic Gas Dynamics

abstract:
  We are concerned with the isothermal limit of entropy solutions in
$L^\infty$, containing the vacuum states, of the Euler equations for isentropic
gas dynamics. We prove that the entropy solutions in $L^\infty$ of the
isentropic Euler equations converge strongly to the corresponding entropy
solutions of the isothermal Euler equations, when the adiabatic exponent
$\gamma \rightarrow 1$. This is achieved by combining careful entropy analysis
and refined kinetic formulation with compensated compactness argument to obtain
the required uniform estimates for the limit. The entropy analysis involves
careful estimates for the relation between the corresponding entropy pairs for
the isentropic and isothermal Euler equations when the adiabatic exponent
$\gamma\to 1$. The kinetic formulation for the entropy solutions of the
isentropic Euler equations with the uniformly bounded initial data is refined,
so that the total variation of the dissipation measures in the formulation is
locally uniformly bounded with respect to $\gamma>1$.


title: Pixle: a fast and effective black-box attack based on rearranging pixels

abstract:
  Recent research has found that neural networks are vulnerable to several
types of adversarial attacks, where the input samples are modified in such a
way that the model produces a wrong prediction that misclassifies the
adversarial sample. In this paper we focus on black-box adversarial attacks,
that can be performed without knowing the inner structure of the attacked
model, nor the training procedure, and we propose a novel attack that is
capable of correctly attacking a high percentage of samples by rearranging a
small number of pixels within the attacked image. We demonstrate that our
attack works on a large number of datasets and models, that it requires a small
number of iterations, and that the distance between the original sample and the
adversarial one is negligible to the human eye.


title:
Influence of protostellar jets and HII regions on the formation and
  evolution of stellar clusters

abstract:
  Context. Understanding the conditions in which stars and stellar clusters
form is of great importance. In particular the role that stellar feedback may
have is still hampered by large uncertainties. Aims. We investigate the role
played by ionising radiation and protostellar outflows during the formation and
evolution of a stellar cluster. To self-consistently take into account gas
accretion, we start with clumps of tens of parsecs in size. Methods. Using an
adaptive mesh refinement code, we run magneto-hydrodynamical numerical
simulations aiming at describing the collapse of massive clumps with either no
stellar feedback or taking into account ionising radiation and/or protostellar
jets. Results. Stellar feedback substantially modifies the protostellar cluster
properties, in several ways. We confirm that protostellar outflows reduce the
star formation rate by a factor of a few, although the outflows do not stop
accretion and likely enough do not modify the final cluster mass. On the other
hand, ionising radiation, once sufficiently massive stars have formed,
efficiently expels the remaining gas and reduces the final cluster mass by a
factor of several. We found that while HII radiation and jets barely change the
distribution of high density gas, the latter increases, at a few places, the
dense gas velocity dispersion again by a factor of several. As we are starting
from a relatively large scale, we found that the clusters whose mass and size
are respectively on the order of a few 1000 M and a fraction of parsec, present
a significant level of rotation. Moreover we found that the sink particles
which mimic the stars themselves, tend to have rotation axis aligned with the
cluster large scale rotation. Finally, computing the classical Q parameter used
to quantify stellar cluster structure, we infer that when jets are included in
the calculation, [...]


title: Role of Gauss-Bonnet corrections in a DGP brane gravitational collapse

abstract:
  An Oppenheimer-Snyder (OS)-type collapse is considered for a
Dvali-Gabadadze-Porrati (DGP) brane, whereas a Gauss-Bonnet (GB) term is
provided for the bulk. We study the combined effect of the DGP induced gravity
plus the GB curvature, regarding any modification of the general relativistic
OS dynamics. Our paper has a twofold objective. On the one hand, we investigate
the nature of singularities that may arise at the collapse end state. It is
shown that all dynamical scenarios for the contracting brane would end in one
of the following cases, depending on conditions imposed: either a central
shell-focusing singularity or what we designate as a "sudden collapse
singularity." On the other hand, we also study the deviations of the exterior
spacetime from the standard Schwarzschild geometry, which emerges in our
modified OS scenario. Our purpose is to investigate whether a black hole always
forms regarding this brane world model. We find situations where a naked
singularity emerges instead.


title:
Posterior Representations for Bayesian Context Trees: Sampling,
  Estimation and Convergence

abstract:
  We revisit the Bayesian Context Trees (BCT) modelling framework for discrete
time series, which was recently found to be very effective in numerous tasks
including model selection, estimation and prediction. A novel representation of
the induced posterior distribution on model space is derived in terms of a
simple branching process, and several consequences of this are explored in
theory and in practice. First, it is shown that the branching process
representation leads to a simple variable-dimensional Monte Carlo sampler for
the joint posterior distribution on models and parameters, which can
efficiently produce independent samples. This sampler is found to be more
efficient than earlier MCMC samplers for the same tasks. Then, the branching
process representation is used to establish the asymptotic consistency of the
BCT posterior, including the derivation of an almost-sure convergence rate.
Finally, an extensive study is carried out on the performance of the induced
Bayesian entropy estimator. Its utility is illustrated through both simulation
experiments and real-world applications, where it is found to outperform
several state-of-the-art methods.


title:
Superconvergence in free probability limit theorems for arbitrary
  triangular arrays

abstract:
  It is known that limit theorems for triangular arrays with identically
distributed rows yields convergence of densities rather than just convergence
in distribution. We show that this superconvergence result holds -- at least at
points at which the limit density is nonzero -- even if the rows of the array
are not identically distributed.


title: Sparse Polynomial Optimisation for Neural Network Verification

abstract:
  The prevalence of neural networks in society is expanding at an increasing
rate. It is becoming clear that providing robust guarantees on systems that use
neural networks is very important, especially in safety-critical applications.
A trained neural network's sensitivity to adversarial attacks is one of its
greatest shortcomings. To provide robust guarantees, one popular method that
has seen success is to bound the activation functions using equality and
inequality constraints. However, there are numerous ways to form these bounds,
providing a trade-off between conservativeness and complexity. Depending on the
complexity of these bounds, the computational time of the optimisation problem
varies, with longer solve times often leading to tighter bounds. We approach
the problem from a different perspective, using sparse polynomial optimisation
theory and the Positivstellensatz, which derives from the field of real
algebraic geometry. The former exploits the natural cascading structure of the
neural network using ideas from chordal sparsity while the later asserts the
emptiness of a semi-algebraic set with a nested family of tests of
non-decreasing accuracy to provide tight bounds. We show that bounds can be
tightened significantly, whilst the computational time remains reasonable. We
compare the solve times of different solvers and show how the accuracy can be
improved at the expense of increased computation time. We show that by using
this sparse polynomial framework the solve time and accuracy can be improved
over other methods for neural network verification with ReLU, sigmoid and tanh
activation functions.


title:
Dikaios: Privacy Auditing of Algorithmic Fairness via Attribute
  Inference Attacks

abstract:
  Machine learning (ML) models have been deployed for high-stakes applications.
Due to class imbalance in the sensitive attribute observed in the datasets, ML
models are unfair on minority subgroups identified by a sensitive attribute,
such as race and sex. In-processing fairness algorithms ensure model
predictions are independent of sensitive attribute. Furthermore, ML models are
vulnerable to attribute inference attacks where an adversary can identify the
values of sensitive attribute by exploiting their distinguishable model
predictions. Despite privacy and fairness being important pillars of
trustworthy ML, the privacy risk introduced by fairness algorithms with respect
to attribute leakage has not been studied. We identify attribute inference
attacks as an effective measure for auditing blackbox fairness algorithms to
enable model builder to account for privacy and fairness in the model design.
We proposed Dikaios, a privacy auditing tool for fairness algorithms for model
builders which leveraged a new effective attribute inference attack that
account for the class imbalance in sensitive attributes through an adaptive
prediction threshold. We evaluated Dikaios to perform a privacy audit of two
in-processing fairness algorithms over five datasets. We show that our
attribute inference attacks with adaptive prediction threshold significantly
outperform prior attacks. We highlighted the limitations of in-processing
fairness algorithms to ensure indistinguishable predictions across different
values of sensitive attributes. Indeed, the attribute privacy risk of these
in-processing fairness schemes is highly variable according to the proportion
of the sensitive attributes in the dataset. This unpredictable effect of
fairness mechanisms on the attribute privacy risk is an important limitation on
their utilization which has to be accounted by the model builder.


title: Imaging topological defects in a non-collinear antiferromagnet

abstract:
  We report on the formation of topological defects emerging from the cycloidal
antiferromagnetic order at the surface of bulk BiFeO$_3$ crystals. Combining
reciprocal and real-space magnetic imaging techniques, we first observe, in a
single ferroelectric domain, the coexistence of antiferromagnetic domains in
which the antiferromagnetic cycloid propagates along different wavevectors. We
then show that the direction of these wavevectors is not strictly locked to the
preferred crystallographic axes as continuous rotations bridge different
wavevectors. At the junctions between the magnetic domains, we observe
topological line defects identical to those found in a broad variety of
lamellar physical systems with rotational symmetries. Our work establishes the
presence of these magnetic objects at room temperature in the multiferroic
antiferromagnet BiFeO$_3$, offering new possibilities for their use in
spintronics.


title: Free groups generated by two parabolic maps

abstract:
  In this paper we consider a group generated by two unipotent parabolic
elements of ${\rm SU}(2,1)$ with distinct fixed points. We give several
conditions that guarantee the group is discrete and free. We also give a result
on the diameter of a finite ${\mathbb R}$-circle in the Heisenberg group.


title: Personalized visual encoding model construction with small data

abstract:
  Encoding models that predict brain response patterns to stimuli are one way
to capture this relationship between variability in bottom-up neural systems
and individual's behavior or pathological state. However, they generally need a
large amount of training data to achieve optimal accuracy. Here, we propose and
test an alternative personalized ensemble encoding model approach to utilize
existing encoding models, to create encoding models for novel individuals with
relatively little stimuli-response data. We show that these personalized
ensemble encoding models trained with small amounts of data for a specific
individual, i.e. ~400 image-response pairs, achieve accuracy not different from
models trained on ~24,000 image-response pairs for the same individual.
Importantly, the personalized ensemble encoding models preserve patterns of
inter-individual variability in the image-response relationship. Additionally,
we use our personalized ensemble encoding model within the recently developed
NeuroGen framework to generate optimal stimuli designed to maximize specific
regions' activations for a specific individual. We show that the
inter-individual differences in face area responses to images of dog vs human
faces observed previously is replicated using NeuroGen with the ensemble
encoding model. Finally, and most importantly, we show the proposed approach is
robust against domain shift by validating on a prospectively collected set of
image-response data in novel individuals with a different scanner and
experimental setup. Our approach shows the potential to use previously
collected, deeply sampled data to efficiently create accurate, personalized
encoding models and, subsequently, personalized optimal synthetic images for
new individuals scanned under different experimental conditions.


title: A many-body Green's function approach to lattice thermal transport

abstract:
  Recent progress in understanding thermal transport in complex crystals has
highlighted the prominent role of heat conduction mediated by interband
tunneling processes, which emerge between overlapping phonon bands (i.e. with
energy differences smaller than their broadenings). These processes have
recently been described in different ways, relying on the Wigner or Green-Kubo
formalism, leading to apparently different results which question the
definition of the heat-current operator. Here, we implement a full quantum
approach based on the Kubo formula, elucidating analogies and differences with
the recently introduced Wigner or Green-Kubo formulations, and extending the
description of thermal transport to the overdamped regime of atomic vibrations,
where the phonon quasiparticle picture breaks down. We rely on first-principles
calculations on complex crystals with ultralow conductivity to compare
numerically the thermal conductivity obtained within the aforementioned
approaches, showing that at least in the quasiparticle regime the differences
are negligible for practical applications.


title:
Beam Management with Orientation and RSRP using Deep Learning for Beyond
  5G Systems

abstract:
  Beam management (BM), i.e., the process of finding and maintaining a suitable
transmit and receive beam pair, can be challenging, particularly in highly
dynamic scenarios. Side-information, e.g., orientation, from on-board sensors
can assist the user equipment (UE) BM. In this work, we use the orientation
information coming from the inertial measurement unit (IMU) for effective BM.
We use a data-driven strategy that fuses the reference signal received power
(RSRP) with orientation information using a recurrent neural network (RNN).
Simulation results show that the proposed strategy performs much better than
the conventional BM and an orientation-assisted BM strategy that utilizes
particle filter in another study. Specifically, the proposed data-driven
strategy improves the beam-prediction accuracy up to 34% and increases mean
RSRP by up to 4.2 dB when the UE orientation changes quickly.


title: Backpropagation Neural Tree

abstract:
  We propose a novel algorithm called Backpropagation Neural Tree (BNeuralT),
which is a stochastic computational dendritic tree. BNeuralT takes random
repeated inputs through its leaves and imposes dendritic nonlinearities through
its internal connections like a biological dendritic tree would do. Considering
the dendritic-tree like plausible biological properties, BNeuralT is a single
neuron neural tree model with its internal sub-trees resembling dendritic
nonlinearities. BNeuralT algorithm produces an ad hoc neural tree which is
trained using a stochastic gradient descent optimizer like gradient descent
(GD), momentum GD, Nesterov accelerated GD, Adagrad, RMSprop, or Adam. BNeuralT
training has two phases, each computed in a depth-first search manner: the
forward pass computes neural tree's output in a post-order traversal, while the
error backpropagation during the backward pass is performed recursively in a
pre-order traversal. A BNeuralT model can be considered a minimal subset of a
neural network (NN), meaning it is a "thinned" NN whose complexity is lower
than an ordinary NN. Our algorithm produces high-performing and parsimonious
models balancing the complexity with descriptive ability on a wide variety of
machine learning problems: classification, regression, and pattern recognition.


title: Functional Mixtures-of-Experts

abstract:
  We consider the statistical analysis of heterogeneous data for clustering and
prediction purposes, in situations where the observations include functions,
typically time series. We extend the modeling with Mixtures-of-Experts (ME), as
a framework of choice in modeling heterogeneity in data for prediction and
clustering with vectorial observations, to this functional data analysis
context. We first present a new family of functional ME (FME) models, in which
the predictors are potentially noisy observations, from entire functions, and
the data generating process of the pair predictor and the real response, is
governed by a hidden discrete variable representing an unknown partition,
leading to complex situations to which the standard ME framework is not
adapted. Second, we provide sparse and interpretable functional representations
of the FME models, thanks to Lasso-like regularizations, notably on the
derivatives of the underlying functional parameters of the model, projected
onto a set of continuous basis functions. We develop dedicated
expectation--maximization algorithms for Lasso-like regularized
maximum-likelihood parameter estimation strategies, to encourage sparse and
interpretable solutions. The proposed FME models and the developed EM-Lasso
algorithms are studied in simulated scenarios and in applications to two real
data sets, and the obtained results demonstrate their performance in accurately
capturing complex nonlinear relationships between the response and the
functional predictor, and in clustering.


title:
Tighter Constraints of Multipartite Systems in terms of General Quantum
  Correlations

abstract:
  Monogamy and polygamy relations characterize the quantum correlation
distributions among multipartite quantum systems. We investigate the monogamy
and polygamy relations satisfied by measures of general quantum correlation. By
using the Hamming weight, we derive new monogamy and polygamy inequalities
satisfied by the $\beta$-th power and the $\alpha$-th power of general quantum
correlations, respectively. We show that these monogamy and polygamy relations
are tighter than the existing ones, such as [Int. J. Theor. Phys. 60, 1455-1470
(2021)]. Taking concurrence and the Tsallis-$q$ entanglement of assistance as
examples, we show the advantages of our results.


title:
PY-Nodes: An ab-initio python code for searching nodes in a material
  using Nelder-Mead's simplex approach

abstract:
  With the discovery of topological semimetals, it has been found that the band
touching points near the Fermi level are of great importance. They give rise to
many exciting phenomena in these materials. Moreover, these points, commonly
known as nodes, are related to several properties of these semimetals. Thus,
the proper estimation of their coordinates is extremely needed for better
understanding of the properties of these materials. We have designed a Python 3
based code named PY-Nodes for efficiently finding the nodes present in a given
material using first-principle approach. The present version of the code is
interfaced with the WIEN2k package. For benchmarking the code, it has been
tested on some famous materials which possess characteristic nodes. These
include - TaAs, a well-known Weyl semimetal, Na$_3$Bi, which is categorized as
Dirac semimetal, CaAgAs, classified as a nodal-line semimetal and YAuPb, which
is claimed to be non-trivial topological semimetal. In case of TaAs, 24 nodes
are obtained from our calculations. On computing their chiralities, it is found
that 12 pairs of nodes having equal and opposite chirality are obtained.
Furthermore, for Na$_3$Bi, a pair of nodes are obtained on the either side of
$\Gamma$-point in the $\boldsymbol{k_3}$ direction. In case of CaAgAs, several
nodes are obtained in the $k_z$=0 plane. These nodes, when plotted in the
$k_x$-$k_y$ plane, form a closed loop which is generally referred to as
nodal-line. Finally, in the case of YAuPb, large number of nodes are obtained
in the vicinity of $\Gamma$-point. The results obtained for these materials are
in good match with the previous works carried out by different research groups.
This assures the reliability and the efficiency of the PY-Nodes code for
estimating the nodes present in a given material.


title:
Relativistic Self-Consistent $GW$: Exact Two-Component Formalism with
  One-Electron Approximation for Solids

abstract:
  We present a formulation of relativistic self-consistent $GW$ for solids
based on the exact two-component formalism with one-electron approximation
(X2C1e) and non-relativistic Coulomb interactions. Our theory allows us to
study scalar relativistic effects, spin-orbit coupling, and the interplay of
relativistic effects with electron correlation without adjustable parameters.
Our all-electron implementation is fully $ab$ $initio$ and does not require a
pseudopotential constructed from atomic calculations. We examine the effect of
the X2C1e approximation by comparison to the established four-component
formalism and reach excellent agreement. The simplicity of X2C1e enables the
construction of higher order theories, such as embedding theories, on top of
perturbative calculations.