title:
A Novel Viewport-Adaptive Motion Compensation Technique for Fisheye
  Video

abstract:
  Although fisheye cameras are in high demand in many application areas due to
their large field of view, many image and video signal processing tasks such as
motion compensation suffer from the introduced strong radial distortions. A
recently proposed projection-based approach takes the fisheye projection into
account to improve fisheye motion compensation. However, the approach does not
consider the large field of view of fisheye lenses that requires the
consideration of different motion planes in 3D space. We propose a novel
viewport-adaptive motion compensation technique that applies the motion vectors
in different perspective viewports in order to realize these motion planes.
Thereby, some pixels are mapped to so-called virtual image planes and require
special treatment to obtain reliable mappings between the perspective viewports
and the original fisheye image. While the state-of-the-art ultra wide-angle
compensation is sufficiently accurate, we propose a virtual image plane
compensation that leads to perfect mappings. All in all, we achieve average
gains of +2.40 dB in terms of PSNR compared to the state of the art in fisheye
motion compensation.


title:
Investigation of Pontryagin trace anomaly using Pauli-Villars
  regularization

abstract:
  In this paper, we investigate the Pontryagin trace anomaly for chiral
fermions in a general curved background using Pauli-Villars regularization. We
use both Feynman diagram method and Fujikawa's method to calculate the
parity-odd contribution (Pontryagin term). Our result indicates that the trace
anomaly of energy-momentum tensor for chiral fermions has Pontryagin term
$P=\frac{i}{1536\pi^2}\epsilon_{\nu\sigma\kappa\lambda}R^{\nu\sigma}_{\ \
\rho\mu}R^{\rho\mu\kappa\lambda}$ which agrees with the work of Bonora et al
\cite{Bonora:2014qla}.


title:
Spherical cap discrepancy of perturbed lattices under the Lambert
  projection

abstract:
  Given any full rank lattice and a natural number N , we regard the point set
given by the scaled lattice intersected with the unit square under the Lambert
map to the unit sphere, and show that its spherical cap discrepancy is at most
of order N , with leading coefficient given explicitly and depending on the
lattice only. The proof is established using a lemma that bounds the amount of
intersections of certain curves with fundamental domains that tile R^2 , and
even allows for local perturbations of the lattice without affecting the bound,
proving to be stable for numerical applications. A special case yields the
smallest constant for the leading term of the cap discrepancy for deterministic
algorithms up to date.


title: Suppression of discontinuous phase transitions by particle diffusion

abstract:
  We investigate the phase transitions of the $q$-state Brownian Potts model in
two dimensions (2d) comprising Potts spins that diffuse like Brownian particles
and interact ferromagnetically with other spins within a fixed distance. With
extensive Monte Carlo simulations we find a continuous phase transition from a
paramagnetic to a ferromagnetic phase even for $q>4$. This is in sharp contrast
to the existence of a discontinuous phase transition in the equilibrium
$q$-state Potts model in 2d with $q>4$. We present detailed numerical evidence
for a continuous phase transition and argue that diffusion generated dynamical
positional disorder suppresses phase coexistence leading to a continuous
transition.


title: Observation of long-range orbital transport and giant orbital torque

abstract:
  Modern spintronics relies on the generation of spin currents through
spin-orbit coupling. The spin-current generation has been believed to be
triggered by current-induced orbital dynamics, which governs the angular
momentum transfer from the lattice to the electrons in solids. The fundamental
role of the orbital response in the angular momentum dynamics suggests the
importance of the orbital counterpart of spin currents: orbital currents.
However, evidence for its existence has been elusive. Here, we demonstrate the
generation of giant orbital currents and uncover remarkable features of the
orbital response. We experimentally and theoretically show that orbital
currents propagate over longer distances than spin currents by more than an
order of magnitude in a ferromagnet and nonmagnets. Furthermore, we find that
the orbital current enables electric manipulation of magnetization with
efficiencies significantly higher than the spin counterpart. These findings
open the door to orbitronics that exploits orbital transport and spin-orbital
coupled dynamics in solid-state devices.


title:
Light curve of a hotspot on equatorial orbit around Kerr black hole
  surrounded by reflective firewall

abstract:
  The light curve of an isolated bright spot in a Keplarian orbit is studied to
investigate the signature of the firewall around the event horizon of the black
hole. An increase in total observed flux is found. In addition to that, for
firewall case comparatively a longer time radiation is observed.


title: DistAD: Software Anomaly Detection Based on Execution Trace Distribution

abstract:
  Modern software systems have become increasingly complex, which makes them
difficult to test and validate. Detecting software partial anomalies in complex
systems at runtime can assist with handling unintended software behaviors,
avoiding catastrophic software failures and improving software runtime
availability. These detection techniques aim to identify the manifestation of
faults (anomalies) before they ultimately lead to unavoidable failures, thus,
supporting the following runtime fault-tolerant techniques. In this work, we
propose a novel anomaly detection method named DistAD, which is based on the
distribution of software runtime dynamic execution traces. Unlike other
existing works using key performance indicators, the execution trace is
collected during runtime via intrusive instrumentation. Instrumentation are
controlled following a sampling mechanism to avoid excessive overheads.
Bi-directional Long Short-Term Memory (Bi-LSTM), an architecture of Recurrent
Neural Network (RNN) is used to achieve the anomaly detection. The whole
framework is constructed under a One-Class Neural Network (OCNN) learning mode
which can help eliminate the limits of lacking for enough labeled samples and
the data imbalance issues. A series of controlled experiments are conducted on
a widely used database system named Cassandra to prove the validity and
feasibility of the proposed method. Overheads brought about by the intrusive
probing are also evaluated. The results show that DistAD can achieve more than
70% accuracy and 90% recall (in normal states) with no more than 2 times
overheads compared with unmonitored executions.


title:
On the homotopy colimit decomposition for quotients of moment-angle
  complexes and its applications

abstract:
  In this paper we prove that the quotient of any real or complex moment-angle
complex by any closed subgroup in the naturally acting compact torus on it is
equivariantly homotopy equivalent to the homotopy colimit of a certain toric
diagram. For any partial quotient this homotopy equivalence rises to an
equivariant homeomorphism generalizing the well-known Davis-Januszkiewicz
construction for quasitoric manifolds and small covers. We prove formality of
the corresponding Borel construction space under the natural assumption on the
group action in the complex case leading to the new description of the
equivariant cohomology for the quotients by any coordinate subgroups. We deduce
the Toral Rank Conjecture for the partial quotient of the moment-angle complex
over any skeleton of the simplex by the diagonal circle action from previously
known results, and give an explicit construction of partial quotients having
arbitrary torsion in integral cohomology.


title:
Bounded-error constrained state estimation in presence of sporadic
  measurements

abstract:
  This contribution proposes a recursive set-membership method for the
ellipsoidal state characterization for linear discrete-time models with
additive unknown disturbances vectors, bounded by possibly degenerate zonotopes
and polytopes, impacting respectively, the state evolution equation and the
sporadic measurement vectors, which are expressed as linear inequality and
equality constraints on the state vector. New algorithms are designed
considering the unprecedented fact that, due to equality constraints, the shape
matrix of the ellipsoid characterizing all possible values of the state vector
is non invertible. The two main size minimizing criterions (volume and sum of
squared axes lengths) are examined in the time update step and also in the
observation updating, in addition to a third one, minimizing some error norm
and ensuring the input-to-state stability of the estimation error.


title:
Measurements of jet observables sensitive to $b$-quark fragmentation in
  $t\bar{t}$ events at the LHC with the ATLAS detector

abstract:
  Several observables sensitive to the fragmentation of $b$-quarks into
$b$-hadrons are measured using 36 fb${}^{-1}$ of $\sqrt s = 13$ TeV
proton-proton collision data collected with the ATLAS detector at the LHC. Jets
containing $b$-hadrons are obtained from a sample of dileptonic $t\bar{t}$
events, and the associated set of charged-particle tracks is separated into
those from the primary $pp$ interaction vertex and those from the displaced
$b$-decay secondary vertex. This division is used to construct observables that
characterize the longitudinal and transverse momentum distributions of the
$b$-hadron within the jet. The measurements have been corrected for detector
effects and provide a test of heavy-quark-fragmentation modeling at the LHC in
a system where the top-quark decay products are color-connected to the proton
beam remnants. The unfolded distributions are compared with the predictions of
modern Monte Carlo parton-shower generators, and these predictions are found to
be largely in agreement with the observed data. These measurements complement
similar measurements from $e^+e^-$ collider experiments in which the $b$-quarks
originate from a color-singlet $Z/\gamma^*$.


title: Quantum anomalous Hall effect

abstract:
  The quantum Hall (QH) effect, quantized Hall resistance combined with zero
longitudinal resistance, is the characteristic experimental fingerprint of
Chern insulators - topologically non-trivial states of two-dimensional matter
with broken time-reversal symmetry. In Chern insulators, non-trivial bulk band
topology is expressed by chiral states that carry current along sample edges
without dissipation. The quantum anomalous Hall (QAH) effect refers to QH
effects that occur in the absence of external magnetic fields due to
spontaneously broken time-reversal symmetry. The QAH effect has now been
realized in four different classes of two-dimensional materials: (i) thin films
of magnetically (Cr- and/or V-) doped topological insulator (Bi,Sb)2Te3 family,
(ii) thin films of the intrinsic magnetic topological insulator MnBi2Te4, (iii)
moir\'e materials formed from graphene, and (iv ) moir\'e materials formed from
transition metal dichalcogenides. In this Article, we review the physical
mechanisms responsible for each class of QAH insulator, highlighting both
differences and commonalities, and comment on potential applications of the QAH
effect.


title: Bayesian Structure Learning with Generative Flow Networks

abstract:
  In Bayesian structure learning, we are interested in inferring a distribution
over the directed acyclic graph (DAG) structure of Bayesian networks, from
data. Defining such a distribution is very challenging, due to the
combinatorially large sample space, and approximations based on MCMC are often
required. Recently, a novel class of probabilistic models, called Generative
Flow Networks (GFlowNets), have been introduced as a general framework for
generative modeling of discrete and composite objects, such as graphs. In this
work, we propose to use a GFlowNet as an alternative to MCMC for approximating
the posterior distribution over the structure of Bayesian networks, given a
dataset of observations. Generating a sample DAG from this approximate
distribution is viewed as a sequential decision problem, where the graph is
constructed one edge at a time, based on learned transition probabilities.
Through evaluation on both simulated and real data, we show that our approach,
called DAG-GFlowNet, provides an accurate approximation of the posterior over
DAGs, and it compares favorably against other methods based on MCMC or
variational inference.


title: On the Chadrasekhar Limit in Generalized Uncertainty Principles

abstract:
  The Chandrasekhar limit for white dwarfs has been confirmed by many
astrophysical observations. However, how to obtain it theoretically in models
which rely on other-than-Heisenberg's Uncertainty principles, which are
predicted by some quantum gravity theories, is not a trivial task. In this
manuscript, we will derive the Chandrasekhar mass assuming an uncertainty
relation proposed in the framework of Doubly Special Relativity, exhibiting
both a minimal length and a maximum momentum. We found that the Chandrasekhar
mass arises when the size of the star becomes arbitrarily small, the same
behavior as in the original Chandrasekhar's derivation, but not when the more
popular Generalized Uncertainty Principle is assumed. We will argue that this
is related to the difference in the classical limit of these two different
generalized uncertainty principles. The role of a maximum momentum in
guaranteeing the stability of the configuration is also analyzed.


title: An endline bilinear restriction estimate for paraboloids

abstract:
  We prove an $L^2\times L^2\to L^q_tL^r_x$ bilinear adjoint Fourier
restriction estimate for $n$-dimensional elliptic paraboloids, with $n\ge 2$
and $1\le q \le \infty$, $1\le r\le 2$ being on the endline
$\frac{1}{q}=\frac{n+1}{2}\bigl(1-\frac{1}{r}\bigr)$ except for the critical
index. This includes the endpoint case when $q=r=\frac{n+3}{n+1}$, a question
left unsettled in Tao \cite{TaoGFA}. Apart from the critical index, it improves
the sharp non-endline result of Lee-Vargas \cite{LeeVargas} to the full range,
confirming a conjecture in the spirit of Foschi and Klainerman \cite{FoKl} on
the elliptic paraboloid. Our proof is accomplished by uniting the
\emph{profound} induction-on-scale tactics based on the wave-table theory and
the method of descent both stemming from \cite{TaoMZ}.


title: A dominance tree approach to systems of cities

abstract:
  Characterizing the spatial organization of urban systems is a challenge which
points to the more general problem of describing marked point processes in
spatial statistics. We propose a non-parametric method that goes beyond
standard tools of point pattern analysis and which is based on a mapping
between the points and a "dominance tree", constructed from a recursive
analysis of their Voronoi tessellation. Using toy models, we show that the
height of a node in this tree encodes both its mark and the structure of its
neighborhood, reflecting its importance in the system. We use historical
population data in France (1876-2018) and the US (1880-2010) and show that the
method highlights multiscale urban dynamics experienced by these countries.
These include non monotonous city trajectories in the US, as revealed by the
evolution of their height in the tree. The method also captures the attraction
basins of cities at successive scales, and while in both countries these basin
sizes become more homogeneous at larger scales, they are also more
heterogeneous in France than in the US. We introduce a simple graphical
representation - the height clock - that monitors the evolution of the role of
each city in its country. Finally, we show that the height of a city in the
tree is less sensitive to different statistical definitions of cities than its
rank in the urban hierarchy.


title:
Particles in Loop Quantum Gravity formalism: a thermodynamical
  description

abstract:
  In this work, we analyse the thermodynamical behavior of massive and massless
particles within Loop Quantum Gravity formalism. We investigate a modified
dispersion relation which suffices to derive all our results of interest in an
\textit{analytical} manner. Initially, we study the massive case where, in
essence, we examine how the mass modifies the thermodynamical properties of the
system. On the other hand, to the massless case, the thermodynamical system
turns out to rely only on a specific thermal state quantity, the temperature.
More so, the analysis of the spectral radiation, the equation of states, the
mean energy, the entropy, and the heat capacity are provided as well for both
cases. Finally, the thermodynamical properties of our particles under
consideration depends on the Riemann zeta function $\xi(s)$.


title: The 4D Einstein-Gauss-Bonnet Theory of Gravity: A Review

abstract:
  We review the topic of 4D Einstein-Gauss-Bonnet gravity, which has been the
subject of considerable interest over the past two years. Our review begins
with a general introduction to Lovelock's theorem, and the subject of
Gauss-Bonnet terms in the action for gravity. These areas are of fundamental
importance for understanding modified theories of gravity, and inform our
subsequent discussion of recent attempts to include the effects of a
Gauss-Bonnet term in four space-time dimensions by re-scaling the appropriate
coupling parameter. We discuss the mathematical complexities involved in
implementing this idea, and review recent attempts at constructing
well-defined, self-consistent theories that enact it. We then move on to
consider the gravitational physics that results from these theories, in the
context of black holes, cosmology, and weak-field gravity. We show that 4D
Einstein-Gauss-Bonnet gravity exhibits a number of interesting phenomena in
each of these areas.


title: $C$-normal weighted composition operators on $H^2$

abstract:
  A bounded linear operator $T$ on a separable complex Hilbert space $H$ is
called $C$-normal if there is a conjugation $C$ on $H$ such that $ CT^\ast
TC=TT^\ast$. Let $\varphi$ be a linear fractional self-map of $\mathbb{D}$. In
this paper, we characterize the necessary and sufficient condition for the
composition operator $C_\varphi$ and weighted composition operator
$W_{\psi,\varphi}$ to be $C$-normal with some conjugations $C$ and a function
$\psi$.


title: Making Concurrency Functional

abstract:
  The article bridges between two major paradigms in computation, the
functional, at basis computation from input to output, and the interactive,
where computation reacts to its environment while underway. Central to any
compositional theory of interaction is the dichotomy between a system and its
environment. Concurrent games and strategies address the dichotomy in fine
detail, very locally, in a distributed fashion, through distinctions between
Player moves (events of the system) and Opponent moves (those of the
environment). A functional approach has to handle the dichotomy much more
ingeniously, through its blunter distinction between input and output. This has
led to a variety of functional approaches, specialised to particular
interactive demands. Through concurrent games we can more clearly see what
separates and connects the differing paradigms, and show how:
  * to lift functions to strategies; the "Scott order" intrinsic to concurrent
games plays a key role in turning functional dependency to causal dependency.
  * several paradigms of functional programming and logic arise naturally as
subcategories of concurrent games, including stable domain theory;
nondeterministic dataflow; geometry of interaction; the dialectica
interpretation; lenses and optics; and their extensions to containers in
dependent lenses and optics.
  * to transfer enrichments of strategies (such as to probabilistic, quantum or
real-number computation) to functional cases.


title: Group divisible designs with block size five

abstract:
  We report some group divisible designs with block size five, including types
$6^{15}$ and $10^{15}$. As a consequence we are able to extend the known
spectrum for 5-GDDs of type $g^u$.


title:
ReCasNet: Improving consistency within the two-stage mitosis detection
  framework

abstract:
  Mitotic count (MC) is an important histological parameter for cancer
diagnosis and grading, but the manual process for obtaining MC from whole-slide
histopathological images is very time-consuming and prone to error. Therefore,
deep learning models have been proposed to facilitate this process. Existing
approaches utilize a two-stage pipeline: the detection stage for identifying
the locations of potential mitotic cells and the classification stage for
refining prediction confidences. However, this pipeline formulation can lead to
inconsistencies in the classification stage due to the poor prediction quality
of the detection stage and the mismatches in training data distributions
between the two stages. In this study, we propose a Refine Cascade Network
(ReCasNet), an enhanced deep learning pipeline that mitigates the
aforementioned problems with three improvements. First, window relocation was
used to reduce the number of poor quality false positives generated during the
detection stage. Second, object re-cropping was performed with another deep
learning model to adjust poorly centered objects. Third, improved data
selection strategies were introduced during the classification stage to reduce
the mismatches in training data distributions. ReCasNet was evaluated on two
large-scale mitotic figure recognition datasets, canine cutaneous mast cell
tumor (CCMCT) and canine mammary carcinoma (CMC), which resulted in up to 4.8%
percentage point improvements in the F1 scores for mitotic cell detection and
44.1% reductions in mean absolute percentage error (MAPE) for MC prediction.
Techniques that underlie ReCasNet can be generalized to other two-stage object
detection networks and should contribute to improving the performances of deep
learning models in broad digital pathology applications.


title: Motion dynamics of inertial pair coupled via frictional interface

abstract:
  Understanding the motion dynamics of two inertial bodies coupled via a
friction interface is essential for a wide range of system and control
applications. Coupling terms within the dynamics of an inertial pair connected
via a passive frictional contact are non-trivial and have long remained
understudied in system communities. This problem is particularly challenging
from the point of view of modeling interaction forces and motion state
variables. This paper deals with a generalized motion problem in systems with a
free (of additional constraints) friction interface, assuming the classical
Coulomb friction with discontinuity at the zero crossing of the velocity. We
formulate the dynamics of motion as a closed form ordinary differential
equation containing the sign operator for mapping both the Coulomb friction and
the switching conditions, and discuss the validity of the model in the
generalized force and motion coordinates. The system has one active degree of
freedom (the driving body) and one passive degree of freedom (the driven body).
We demonstrate the global convergence of trajectories for a free system with no
external excitation forces. Then, an illustrative case study is presented for a
harmonic oscillator with a frictionally coupled second mass that is not
grounded or connected to a fixed frame. This simplified example illustrates a
realization of the proposed (general) modeling framework. Some future
development of the method and related challenges are discussed at the end of
the paper.


title: Combining Modular Skills in Multitask Learning

abstract:
  A modular design encourages neural models to disentangle and recombine
different facets of knowledge to generalise more systematically to new tasks.
In this work, we assume that each task is associated with a subset of latent
discrete skills from a (potentially small) inventory. In turn, skills
correspond to parameter-efficient (sparse / low-rank) model parameterisations.
By jointly learning these and a task-skill allocation matrix, the network for
each task is instantiated as the average of the parameters of active skills. To
favour non-trivial soft partitions of skills across tasks, we experiment with a
series of inductive biases, such as an Indian Buffet Process prior and a
two-speed learning rate. We evaluate our latent-skill model on two main
settings: 1) multitask reinforcement learning for grounded instruction
following on 8 levels of the BabyAI platform; and 2) few-shot adaptation of
pre-trained text-to-text generative models on CrossFit, a benchmark comprising
160 NLP tasks. We find that the modular design of a network significantly
increases sample efficiency in reinforcement learning and few-shot
generalisation in supervised learning, compared to baselines with fully shared,
task-specific, or conditionally generated parameters where knowledge is
entangled across tasks. In addition, we show how discrete skills help
interpretability, as they yield an explicit hierarchy of tasks.


title: Neural net modeling of equilibria in NSTX-U

abstract:
  Neural networks (NNs) offer a path towards synthesizing and interpreting data
on faster timescales than traditional physics-informed computational models. In
this work we develop two neural networks relevant to equilibrium and shape
control modeling, which are part of a suite of tools being developed for the
National Spherical Torus Experiment-Upgrade (NSTX-U) for fast prediction,
optimization, and visualization of plasma scenarios. The networks include
Eqnet, a free-boundary equilibrium solver trained on the EFIT01 reconstruction
algorithm, and Pertnet, which is trained on the Gspert code and predicts the
non-rigid plasma response, a nonlinear term that arises in shape control
modeling. The equilibrium neural network is trained with different combinations
of inputs and outputs in order to offer flexibility in use cases. In
particular, the NN can use magnetic diagnostics as inputs for equilibrium
prediction thus acting as a reconstruction code, or can use profiles and
external currents as inputs to act as a traditional free-boundary
Grad-Shafranov solver. We report strong performance for both networks
indicating that these models could reliably be used within closed-loop
simulations. Some limitations regarding generalizability and noise are
discussed.


title: Machine Learning Wavefunction

abstract:
  This chapter introduces the main ideas and the most important methods for
representing the electronic wavefunction through machine learning models. The
wavefunction of a N-electron system is an incredibly complicated mathematical
object, and models thereof require enough flexibility to properly describe the
complex interactions between the particles, but at the same time a sufficiently
compact representation to be useful in practice. Machine learning techniques
offer an ideal mathematical framework to satisfy these requirements, and
provide algorithms for their optimization in both supervised and unsupervised
fashions. In this chapter, various examples of machine learning wavefunctions
are presented and their strengths and weaknesses with respect to traditional
quantum chemical approaches are discussed; first in theory, and then in
practice with two case studies.


title:
A superconducting qubit with noise-insensitive plasmon levels and
  decay-protected fluxon states

abstract:
  The inductively shunted transmon (IST) is a superconducting qubit with
exponentially suppressed fluxon transitions and a plasmon spectrum
approximating that of the transmon. It shares many characteristics with the
transmon but offers charge offset insensitivity for all levels and precise flux
tunability with quadratic flux noise suppression. In this work we propose and
realize IST qubits deep in the transmon limit where the large geometric
inductance acts as a mere perturbation. With a flux dispersion of only 5.1 MHz
we reach the 'sweet-spot everywhere' regime of a SQUID device with a stable
coherence time over a full flux quantum. Close to the flux degeneracy point the
device reveals tunneling physics between the two quasi-degenerate ground states
with typical observed lifetimes on the order of minutes. In the future, this
qubit regime could be used to avoid leakage to unconfined transmon states in
high-power read-out or driven-dissipative bosonic qubit realizations. Moreover,
the combination of well controllable plasmon transitions together with stable
fluxon states in a single device might offer a way forward towards improved
qubit encoding schemes.


title:
Chemically-adhesive particles form stronger and stiffer
  magnetorheological fluids

abstract:
  Magnetorheological fluids (MRF) are suspensions of magnetic particles that
solidify in the presence of magnetic fields due to the particles forming chains
along field lines. The magnetic forces between particles dominate the
solidification process and determine the yield stress of the material. Here, we
investigate how reversible chemical links between particles influence MRF yield
stress and stiffness through rheological testing in flow and oscillation mode.
First, we functionalize particles with phosphonate groups that are expected to
link through hydrogen bonding and find that this MRF exhibits up to 40% higher
yield stress and 100% higher stiffness than an MRF composed of unfunctionalized
particles. To explain this, we model the chemical attraction as an adhesion
that supplements dipole-dipole interactions between particles. Interestingly,
we find that the increase in yield stress is largest for dilute suspensions
that are expected to solidify into isolated chains, while the proportional
increase in yield stress is less for MRF with higher concentrations. This is
explained by the higher concentration MRF forming a body-centered tetragonal
(BCT) lattice in which interparticle adhesion forces are no longer aligned with
the field. To explore the possibility of dynamically tuning interparticle
interactions, we functionalize particles with polystyrene polymers with thymine
terminal groups that will only exhibit interparticle hydrogen bonding in the
presence of a linking molecule, namely melamine. We find that MRF formed with
these particles also exhibit up to a 40% increase in yield stress and ~100%
increase in stiffness, but only in the presence of melamine. In addition to
confirming the role of hydrogen bonding in increasing MRF performance, these
results highlight the possibility of dynamically tuning MRF performance using
magnetic fields and chemical modifications.


title: New physics from the polarised light of the cosmic microwave background

abstract:
  Cosmology requires new physics beyond the Standard Model of elementary
particles and fields. What is the fundamental physics behind dark matter and
dark energy? What generated the initial fluctuations in the early Universe?
Polarised light of the cosmic microwave background (CMB) may hold the key to
answers. In this article, we discuss two new developments in this research
area. First, if the physics behind dark matter and dark energy violates parity
symmetry, their coupling to photons rotates the plane of linear polarisation as
the CMB photons travel more than 13 billion years. This effect is known as
`cosmic birefringence': space filled with dark matter and dark energy behaves
as if it were a birefringent material, like a crystal. A tantalising hint for
such a signal has been found with the statistical significance of $3\sigma$.
Next, the period of accelerated expansion in the very early Universe, called
`cosmic inflation', produced a stochastic background of primordial
gravitational waves (GW). What generated GW? The leading idea is vacuum
fluctuations in spacetime, but matter fields could also produce a significant
amplitude of primordial GW. Finding its origin using CMB polarisation opens a
new window into the physics behind inflation. These new scientific targets may
influence how data from future CMB experiments are collected, calibrated, and
analysed.


title: Forming planets around stars with non-solar elemental composition

abstract:
  Stars in the solar neighbourhood have refractory element ratios slightly
different from the Sun. It is unclear how much the condensation of solids and
thus the composition of planets forming around these stars is affected. We aim
to understand the impact of changing the ratios of refractory elements Mg, Si,
and Fe within the range observed in solar type stars within 150~pc on the
composition of planets forming around them. We use the GGchem code to simulate
the condensation of solids in protoplanetary disks with a Minimum Mass Solar
Nebula around main sequence G-type stars in the Solar neighbourhood. We extract
the stellar elemental composition from the Hypatia database. We find that a
lower Mg/Si ratio shifts the condensation sequence from forsterite
(Mg$_2$SiO$_4$) and SiO to enstatite (MgSiO$_3$) and quartz (SiO$_2$); a lower
Fe/S ratio leads to the formation of FeS and FeS$_2$ and little or no
Fe-bearing silicates. Ratios of refractory elements translate directly from the
gas phase to the condensed phase for $T\,<\,1000$~K. However, ratios with
respect to volatile elements (e.g.\ oxygen and sulphur) in the condensates --
the building blocks of planets -- differ from the original stellar composition.
Our study shows that the composition of planets crucially depends on the
abundances of the stellar system under investigation. Our results can have
important implications for planet interiors, which depend strongly on the
degree of oxidation and the sulphur abundance.


title:
A New Method for the Reconstruction of Strongly Lensed Galaxies with
  Resolved Kinematics

abstract:
  Integral field spectroscopy of high-redshift galaxies has become a powerful
tool for understanding their dynamics and evolutionary states. However, in the
case of gravitationally lensed systems, it has proved difficult to model both
lensing and intrinsic kinematics in a way that takes full advantage of the
information available in the spectral domain. In this paper, we introduce a new
method for pixel-based source reconstruction that alters standard
regularization schemes for two-dimensional data in a way that leverages
kinematic information in a physically motivated but flexible fashion, and that
is better suited to the three-dimensional nature of integral field data. To
evaluate the performance of this method, we compare its results to those of a
more traditional two-dimensional non-parametric approach using mock ALMA
observations of a typical high-redshift dusty star-forming galaxy. We find that
3D regularization applied to an entire data cube reconstructs a source's
intensity and velocity structure more accurately than 2D regularization applied
to separate velocity channels. Cubes reconstructed with 3D regularization also
have more uniform noise and resolution properties and are less sensitive to the
signal-to-noise ratio of individual velocity channels than the results of 2D
regularization. Our new approach to modeling integral field observations of
lensed systems can be implemented without making restrictive a priori
assumptions about intrinsic kinematics, and opens the door to new observing
strategies that prioritize spectral resolution over spatial resolution (e.g.,
for multi-configuration arrays like ALMA).


title: MaMaDroid2.0 -- The Holes of Control Flow Graphs

abstract:
  Android malware is a continuously expanding threat to billions of mobile
users around the globe. Detection systems are updated constantly to address
these threats. However, a backlash takes the form of evasion attacks, in which
an adversary changes malicious samples such that those samples will be
misclassified as benign. This paper fully inspects a well-known Android malware
detection system, MaMaDroid, which analyzes the control flow graph of the
application. Changes to the portion of benign samples in the train set and
models are considered to see their effect on the classifier. The changes in the
ratio between benign and malicious samples have a clear effect on each one of
the models, resulting in a decrease of more than 40% in their detection rate.
Moreover, adopted ML models are implemented as well, including 5-NN, Decision
Tree, and Adaboost. Exploration of the six models reveals a typical behavior in
different cases, of tree-based models and distance-based models. Moreover,
three novel attacks that manipulate the CFG and their detection rates are
described for each one of the targeted models. The attacks decrease the
detection rate of most of the models to 0%, with regards to different ratios of
benign to malicious apps. As a result, a new version of MaMaDroid is
engineered. This model fuses the CFG of the app and static analysis of features
of the app. This improved model is proved to be robust against evasion attacks
targeting both CFG-based models and static analysis models, achieving a
detection rate of more than 90% against each one of the attacks.


title: Maximal dispersion of adaptive random walks

abstract:
  Random walks are the most versatile tool to explore a complex network. These
include maximum entropy random walks (MERWs), which are maximally dispersing
and therefore play a key role as they optimize information spreading. However,
building a MERW comes at the cost of knowing beforehand the global structure of
the network to be explored. Here, we propose an adaptive random walk (ARW),
which instead maximizes dispersion by updating its transition rule on the local
information collected while exploring the network. We show how to derive the
ARW via a large-deviation representation of the MERW as a finite-time rare
event of an unbiased random walk and study its dynamics on synthetic and
real-world networks.


title: Bounds on quantum evolution complexity via lattice cryptography

abstract:
  We address the difference between integrable and chaotic motion in quantum
theory as manifested by the complexity of the corresponding evolution
operators. Complexity is understood here as the shortest geodesic distance
between the time-dependent evolution operator and the origin within the group
of unitaries. (An appropriate `complexity metric' must be used that takes into
account the relative difficulty of performing `nonlocal' operations that act on
many degrees of freedom at once.) While simply formulated and geometrically
attractive, this notion of complexity is numerically intractable save for toy
models with Hilbert spaces of very low dimensions. To bypass this difficulty,
we trade the exact definition in terms of geodesics for an upper bound on
complexity, obtained by minimizing the distance over an explicitly prescribed
infinite set of curves, rather than over all possible curves. Identifying this
upper bound turns out equivalent to the closest vector problem (CVP) previously
studied in integer optimization theory, in particular, in relation to
lattice-based cryptography. Effective approximate algorithms are hence provided
by the existing mathematical considerations, and they can be utilized in our
analysis of the upper bounds on quantum evolution complexity. The resulting
algorithmically implemented complexity bound systematically assigns lower
values to integrable than to chaotic systems, as we demonstrate by explicit
numerical work for Hilbert spaces of dimensions up to ~10^4.

categories: quant-ph cs.DS hep-th math.OC physics.data-an


title: Bonsai: A Generalized Look at Dual Deduplication

abstract:
  Cloud Service Providers (CSPs) offer a vast amount of storage space at
competitive prices to cope with the growing demand for digital data storage.
Dual deduplication is a recent framework designed to improve data compression
on the CSP while keeping clients' data private from the CSP. To achieve this,
clients perform lightweight information-theoretic transformations to their data
prior to upload. We investigate the effectiveness of dual deduplication, and
propose an improvement for the existing state-of-the-art method. We name our
proposal Bonsai as it aims at reducing storage fingerprint and improving
scalability. In detail, Bonsai achieves (1) significant reduction in client
storage, (2) reduction in total required storage (client + CSP), and (3)
reducing the deduplication time on the CSP. Our experiments show that Bonsai
achieves compression rates of 68\% on the cloud and 5\% on the client, while
allowing the cloud to identify deduplications in a time-efficient manner. We
also show that combining our method with universal compressors in the cloud,
e.g., Brotli, can yield better overall compression on the data compared to only
applying the universal compressor or plain Bonsai. Finally, we show that Bonsai
and its variants provide sufficient privacy against an honest-but-curious CPS
that knows the distribution of the Clients' original data.


title:
Real-Time Frequency Selective Reconstruction through Register-Based
  Argmax Calculation

abstract:
  Frequency Selective Reconstruction (FSR) is a state-of-the-art algorithm for
solving diverse image reconstruction tasks, where a subset of pixel values in
the image is missing. However, it entails a high computational complexity due
to its iterative, blockwise procedure to reconstruct the missing pixel values.
Although the complexity of FSR can be considerably decreased by performing its
computations in the frequency domain, the reconstruction procedure still takes
multiple seconds up to multiple minutes depending on the parameterization.
However, FSR has the potential for a massive parallelization greatly improving
its reconstruction time. In this paper, we introduce a novel highly
parallelized formulation of FSR adapted to the capabilities of modern GPUs and
propose a considerably accelerated calculation of the inherent argmax
calculation. Altogether, we achieve a 100-fold speed-up, which enables the
usage of FSR for real-time applications.


title:
High-performance Uncertainty Quantification in Large-scale Virtual
  Clinical Trials of Closed-loop Diabetes Treatment

abstract:
  In this paper, we propose a virtual clinical trial for assessing the
performance and identifying risks in closed-loop diabetes treatments. Virtual
clinical trials enable fast and risk-free tests of many treatment variations
for large populations of fictive patients (represented by mathematical models).
We use closed-loop Monte Carlo simulation, implemented in high-performance
software and hardware, to quantify the uncertainty in treatment performance as
well as to compare the performance in different scenarios or of different
closed-loop treatments. Our software can be used for testing a wide variety of
control strategies ranging from heuristical approaches to nonlinear model
predictive control. We present an example of a virtual clinical trial with one
million patients over 52 weeks, and we use high-performance software and
hardware to conduct the virtual trial in 1 h and 22 min.


title: Ionise hard: interstellar PO$^{+}$ detection

abstract:
  We report the first detection of the phosphorus monoxide ion (PO$^{+}$) in
the interstellar medium. Our unbiased and very sensitive spectral survey
towards the G+0.693$-$0.027 molecular cloud covers four different rotational
transitions of this molecule, two of which ($J$=1$-$0 and $J$=2$-$1) appear
free of contamination from other species. The fit performed, assuming Local
Thermodynamic Equilibrium conditions, yields a column density of
$N$=(6.0$\pm$0.7)$\times$10$^{11}$ cm$^{-2}$. The resulting molecular abundance
with respect to molecular hydrogen is 4.5$\times$10$^{-12}$. The column density
of PO$^{+}$ normalised by the cosmic abundance of P is larger than those of
NO$^{+}$ and SO$^{+}$, normalised by N and S, by factors of 3.6 and 2.3,
respectively. The $N$(PO$^{+}$)/$N$(PO) ratio is 0.12$\pm$0.03, more than one
order of magnitude higher than those of $N$(SO$^{+}$)/$N$(SO) and
$N$(NO$^{+}$)/$N$(NO). These results indicate that P is more efficiently
ionised in the ISM than N and S. We have performed new chemical models that
confirm that the PO$^+$ abundance is strongly enhanced in shocked regions with
high values of cosmic-ray ionisation rates (10$^{-15}-$10$^{-14}$ s$^{-1}$), as
occurs in the G+0.693$-$0.027 molecular cloud. The shocks sputter the
interstellar icy grain mantles, releasing into the gas phase most of their P
content, mainly in the form of PH$_3$, which is converted into atomic P, and
then ionised efficiently by cosmic rays, forming P$^+$. Further reactions with
O$_2$ and OH produce PO$^{+}$. The cosmic-ray ionisation of PO might also
contribute significantly, which would explain the high $N$(PO$^{+}$)/$N$(PO)
observed. The relatively high gas-phase abundance of PO$^{+}$ with respect to
other P-bearing species stresses the relevance of this species in the
interstellar chemistry of P.


title: Enumeration of rooted 3-connected bipartite planar maps

abstract:
  We provide the first solution to the problem of counting rooted 3-connected
bipartite planar maps. Our starting point is the enumeration of bicoloured
planar maps according to the number of edges and monochromatic edges, following
Bernardi and Bousquet-M\'elou [J. Comb. Theory Ser. B, 101 (2011), 315-377].
The decomposition of a map into 2- and 3-connected components allows us to
obtain the generating functions of 2-and 3-connected bicoloured maps. Setting
to zero the variable marking monochromatic edges we obtain the generating
function of 3-connected bipartite maps, which is algebraic of degree 26. We
deduce from it an asymptotic estimate for the number of 3-connected bipartite
planar maps of the form $t \cdot n^{-5/2} \gamma^n$, where $\gamma=\rho^{-1}
\approx 2.40958$ and $\rho \approx 0.41501$ is an algebraic number of degree
10.


title:
Formalizing Oracle Trust Models for blockchain-based business
  applications. An example from the supply chain sector

abstract:
  Blockchain technology truly opened the gate to a wave of unparalleled
innovations; however, despite the rapidly growing load of hype, the integration
into the business, apart from a few applications, seems to be coming at a
slower rate. One reason for that delay may be the need in the real-world
applications for the so-called trust model. Trust models are rarely mentioned
in blockchain application proposals despite their importance, which creates
skepticism about their successful developments. To promote trust model
implementation and help practitioners in its redaction, this article provides
an outline of what a trust model is, why it is essential, and an example of how
it is elaborated. The discussed example comes from a case study of a dairy
company that implemented blockchain for the traceability of its products.
Despite being tailored on a traceability project, the redaction and elements of
the trust model, with few adjustments, could be easily readapted for other
applications.


title:
Linear independence criteria for generalized polylogarithms with
  distinct shifts

abstract:
  For a given rational number $x$ and an integer $s\geq 1$, let us consider a
generalized polylogarithmic function, often called the Lerch function, defined
by $$\Phi_{s}(x,z)= \sum_{k=0}^{\infty}\frac{z^{k+1}}{(k+x+1)^s}\enspace.$$ We
prove the linear independence over any number field $K$ of the numbers $1$ and
$\Phi_{s_j}(x_j,\alpha_i)$ with any choice of distinct shifts $x_1,\ldots, x_d$
with $0\le x_1<\ldots<x_d<1$, as well as any choice of depths $1\leq s_1\leq
r_1,\ldots, 1\leq s_d\leq r_d$, at distinct algebraic numbers
$\alpha_1,\ldots,\alpha_m\in K$ subject to a metric condition. As is usual in
the theory, the points $\alpha_i$ need to be chosen sufficiently close to zero
with respect to a given fixed place $v_0$ of $K$, Archimedean or finite.
  This is the first linear independence result with distinct shifts $x_1,
\ldots, x_d$ that allows values at different points for generalized
polylogarithmic functions. Previous criteria were only for the functions with
one fixed shift or at one point. Further, we establish another linear
independence criterion for values of the generalized polylogarithmic function
with cyclic coefficients. Let $q\geq 1$ be an integer and
$\boldsymbol{a}=(a_1,\ldots, a_q)\in K^q$ be a $q$-tuple whose coordinates
supposed to be cyclic with the period $q$. Consider the generalized
polylogarithmc function with coefficients $$\Phi_{\boldsymbol{a},s}(x,z)=
\sum_{k=0}^{\infty}\frac{a_{k+1\bmod(q)}\cdot z^{k+1}}{(k+x+1)^s}\enspace.$$
Under suitable condition, we show that the values of these functions are
linearly independent over $K$. Our key tool is a new non-vanishing property for
a generalized Wronskian of Hermite type associated to our explicit
constructions of Pad\'e approximants for this family of generalized
polylogarithmic function.


title:
Leveraging Channel Noise for Sampling and Privacy via Quantized
  Federated Langevin Monte Carlo

abstract:
  For engineering applications of artificial intelligence, Bayesian learning
holds significant advantages over standard frequentist learning, including the
capacity to quantify uncertainty. Langevin Monte Carlo (LMC) is an efficient
gradient-based approximate Bayesian learning strategy that aims at producing
samples drawn from the posterior distribution of the model parameters. Prior
work focused on a distributed implementation of LMC over a multi-access
wireless channel via analog modulation. In contrast, this paper proposes
quantized federated LMC (FLMC), which integrates one-bit stochastic
quantization of the local gradients with channel-driven sampling.
Channel-driven sampling leverages channel noise for the purpose of contributing
to Monte Carlo sampling, while also serving the role of privacy mechanism.
Analog and digital implementations of wireless LMC are compared as a function
of differential privacy (DP) requirements, revealing the advantages of the
latter at sufficiently high signal-to-noise ratio.


title:
Universal scaling for disordered viscoelastic matter II: Collapses,
  global behavior and spatio-temporal properties

abstract:
  Disordered viscoelastic materials are ubiquitous and exhibit fascinating
invariant scaling properties. In a companion article, we have presented
comprehensive new results for the critical behavior of the dynamic
susceptibility of disordered elastic systems near the onset of rigidity. Here
we provide additional details of the derivation of the singular scaling forms
of the longitudinal response near both jamming and rigidity percolation. We
then discuss global aspects associated with these forms, and make scaling
collapse plots for both undamped and overdamped dynamics in both the rigid and
floppy phases. We also derive critical exponents, invariant scaling
combinations and analytical formulas for universal scaling functions of several
quantities such as transverse and density responses, elastic moduli,
viscosities, and correlation functions. Finally, we discuss tentative
experimental protocols to measure these behaviors in colloidal suspensions.


title: Functional mixture-of-experts for classification

abstract:
  We develop a mixtures-of-experts (ME) approach to the multiclass
classification where the predictors are univariate functions. It consists of a
ME model in which both the gating network and the experts network are
constructed upon multinomial logistic activation functions with functional
inputs. We perform a regularized maximum likelihood estimation in which the
coefficient functions enjoy interpretable sparsity constraints on targeted
derivatives. We develop an EM-Lasso like algorithm to compute the regularized
MLE and evaluate the proposed approach on simulated and real data.


title: Four-dimensional regular black strings in bilocal gravity

abstract:
  In this paper, we obtain a static black string solution to a bilocal
gravitational source in $3+1$ dimensions. The solution is regular at the origin
and tends asymptotically to the ordinary static uncharged black string solution
of General Relativity. It can present an event horizon and allows an internal
horizon depending on the value of the mass density. A mass remnant associated
with a vanishing Hawking temperature is also found. In order to stabilize the
solution, a perfect cosmological fluid with state parameter $\omega>-1$ should
be present throughout space. However, energy conditions do not exclude an
exotic substance nearby the black string. Finally, we present the stationary
counterpart of the solution and analyze the behavior of the horizons according
to the mass and angular momentum of the spinning object.


title: Sound from rotors near a trailing edge

abstract:
  A method of noise reduction proposed for the next generation of aircraft is
to shield noise from the propulsion system, by positioning the noise source
over a wing or another surface. In this paper, an approximate analysis is
developed for the acoustic field far from a circular source placed near the
edge of a semi-infinite plate, a model problem for shielding of noise by a wing
and for scattering by a trailing edge. The approximation is developed for a
source of small radius and is found to be accurate when compared to full
numerical evaluation of the field.


title: Constraining the Charm-Yukawa coupling at the Large Hadron Collider

abstract:
  We present theoretical results for the sensitivity of charm Yukawa coupling
measurements in future high-luminosity LHC runs in three channels: Vector Boson
Fusion (VBF), W Higgs-strahlung and Z Higgs-strahlung production of a Higgs
boson and its subsequent decay into charm quarks. To reduce the overwhelmingly
large backgrounds and to reduce false positives, we apply a set of simple
kinematic and jet feature cuts and feed neural network data structures of three
types; jet features, jet images and particle level features. To facilitate
straightforward comparison with experimental studies, we express our results in
terms of signal strengths.


title:
Nonlinear Model Predictive Control and System Identification for a
  Dual-hormone Artificial Pancreas

abstract:
  In this work, we present a switching nonlinear model predictive control
(NMPC) algorithm for a dual-hormone artificial pancreas (AP), and we use
maximum likelihood estimation (MLE) to identify model parameters. A
dual-hormone AP consists of a continuous glucose monitor (CGM), a control
algorithm, an insulin pump, and a glucagon pump. The AP is designed with a
heuristic to switch between insulin and glucagon as well as state-dependent
constraints. We extend an existing glucoregulatory model with glucagon and
exercise for simulation, and we use a simpler model for control. We test the AP
(NMPC and MLE) using in silico numerical simulations on 50 virtual people with
type 1 diabetes. The system is identified for each virtual person based on data
generated with the simulation model. The simulations show a mean of 89.3% time
in range (3.9-10 mmol/L) and no hypoglycemic events.


title:
Localization via Multiple Reconfigurable Intelligent Surfaces Equipped
  with Single Receive RF Chains

abstract:
  The extra degrees of freedom resulting from the consideration of
Reconfigurable Intelligent Surfaces (RISs) for smart signal propagation can be
exploited for high accuracy localization and tracking. In this paper,
capitalizing on a recent RIS hardware architecture incorporating a single
receive Radio Frequency (RF) chain for measurement collection, we present a
user localization method with multiple RISs. The proposed method includes an
initial step for direction estimation at each RIS, followed by maximum
likelihood position estimation, which is initialized with a least squares line
intersection technique. Our numerical results showcase the accuracy of the
proposed localization, verifying our theoretical estimation analysis.


title: Unsupervised machine learning correlations in EoS of neutron stars

abstract:
  Neutron stars are compact objects of large interest in the nuclear
astrophysics community. The extreme conditions present in such systems impose
big challenges to our current microscopic models of nuclear structure. Equation
of states (EoS) are frequently derived from sophisticated quantum mechanical
models, such as: relativistic, non-relativistic and many mean-field approaches.
Every single model, in general, contains many parameters such as the NN
interaction strength, particle compositions, etc. These are particular features
of each model and can be represented by numbers and categories in a machine
learning context. Different choices of features will affect EoS properties
leading to different macroscopic properties of the star. In this work we
analyze a selection of EoS containing a variety of different physics models.
One of our objectives is to develop tools that enable a better understanding of
the correlations among the different model features and the outcome produced by
them when employed to model neutron stars.


title:
Background Mixup Data Augmentation for Hand and Object-in-Contact
  Detection

abstract:
  Detecting the positions of human hands and objects-in-contact (hand-object
detection) in each video frame is vital for understanding human activities from
videos. For training an object detector, a method called Mixup, which overlays
two training images to mitigate data bias, has been empirically shown to be
effective for data augmentation. However, in hand-object detection, mixing two
hand-manipulation images produces unintended biases, e.g., the concentration of
hands and objects in a specific region degrades the ability of the hand-object
detector to identify object boundaries. We propose a data-augmentation method
called Background Mixup that leverages data-mixing regularization while
reducing the unintended effects in hand-object detection. Instead of mixing two
images where a hand and an object in contact appear, we mix a target training
image with background images without hands and objects-in-contact extracted
from external image sources, and use the mixed images for training the
detector. Our experiments demonstrated that the proposed method can effectively
reduce false positives and improve the performance of hand-object detection in
both supervised and semi-supervised learning settings.


title:
Reviewing local and integrated energy system models: insights into
  flexibility and robustness challenges

abstract:
  The electrification of heating, cooling, and transportation to reach
decarbonization targets calls for a rapid expansion of renewable technologies.
Due to their decentral and intermittent nature, these technologies require
robust planning that considers non-technical constraints and flexibility
options to be integrated effectively. Energy system models (ESMs) are
frequently used to support decision-makers in this planning process. In this
study, 116 case studies of local, integrated ESMs are systematically reviewed
to identify best-practice approaches to model flexibility and to address
non-technical constraints. Within the sample, storage systems and sector
coupling are the most common types of flexibility. Sector coupling with the
transportation sector, specifically with electric vehicles that could be used
for smart charging or vehicle-to-grid operation, is rarely considered. Social
aspects are generally either completely neglected or modeled exogenously.
Lacking actor heterogeneity, which can lead to unstable results in optimization
models, can be addressed through building-level information. A strong emphasis
on cost is found and while emissions are also frequently reported, additional
metrics such as imports or the share of renewable generation are nearly absent.
To guide future modeling, the paper concludes with a roadmap highlighting
flexibility and robustness options that either represent low-hanging fruit or
have a large impact on results.


title: Quantum Machine Learning for $b$-jet identification

abstract:
  Machine Learning algorithms have played an important role in hadronic jet
classification problems. The large variety of models applied to Large Hadron
Collider data has demonstrated that there is still room for improvement. In
this context Quantum Machine Learning is a new and almost unexplored
methodology, where the intrinsic properties of quantum computation could be
used to exploit particles correlations for improving the jet classification
performance. In this paper, we present a brand new approach to identify if a
jet contains a hadron formed by a $b$ or $\bar{b}$ quark at the moment of
production, based on a Variational Quantum Classifier applied to simulated data
of the LHCb experiment. Quantum models are trained and evaluated using LHCb
simulation. The jet identification performance is compared with a Deep Neural
Network model to assess which method gives the better performance.


title:
Coherence limitations in the optical control of the singlet-triplet
  qubit in a quantum dot molecule

abstract:
  We analyze the optically driven dynamics of a qubit implemented on a
singlet-triplet subspace of two-electron states in a self-assembled quantum dot
molecule. We study two possible control schemes based on the coupling to an
excited (four-particle) state either by two spectrally separated laser pulses
or by a single spectrally broad pulse. We quantitatively characterize the
imperfections of the qubit operation resulting from non-adiabatic evolution and
from limited spectral selectivity in a real system, as compared to the ideal
adiabatic Raman transfer of occupation in the $\Lambda$-system. Next, we study
the effects of decoherence induced by the coupling to the phonons of the
surrounding crystal lattice and by radiative recombination. As a result, we are
able to identify the optimization trade-offs between different sources of
errors and indicate the most favorable conditions for quantum control of the
singlet-triplet qubit in the two optical control schemes.


title:
Defect detection and segmentation in X-Ray images of magnesium alloy
  castings using the Detectron2 framework

abstract:
  New production techniques have emerged that have made it possible to produce
metal parts with more complex shapes, making the quality control process more
difficult. This implies that the visual and superficial analysis has become
even more inefficient. On top of that, it is also not possible to detect
internal defects that these parts could have. The use of X-Ray images has made
this process much easier, allowing not only to detect superficial defects in a
much simpler way, but also to detect welding or casting defects that could
represent a serious hazard for the physical integrity of the metal parts. On
the other hand, the use of an automatic segmentation approach for detecting
defects would help diminish the dependence of defect detection on the
subjectivity of the factory operators and their time dependence variability.
The aim of this paper is to apply a deep learning system based on Detectron2, a
state-of-the-art library applied to object detection and segmentation in
images, for the identification and segmentation of these defects on X-Ray
images obtained mainly from automotive parts


title:
Spatio-kinematic models of five nova remnants: correlations between nova
  shell axial ratio, expansion velocity, and speed class

abstract:
  We present long-slit intermediate-dispersion spectroscopic observations and
narrow-band direct imaging of four classical nova shells, namely TAur, HRDel,
DQHer and QUVul, and the nova-like source CKVul. These are used to construct
models of their nebular remnants using the morpho-kinematic modelling tool
Shape to reveal their 3D shape. All these nova remnants but CKVul can be
described by prolate ellipsoidal shells with different eccentricity degree,
from the spherical QUVul to the highly elongated shell with an equatorial
component HRDel. On the other hand, CKVul shows a more complex structure, with
two pairs of nested bipolar lobes. The spatio-kinematic properties of the
ellipsoidal nova shells derived from our models include their true axial
ratios. This parameter is expected to correlate with the expansion velocity and
decline time C3 (i.e., their speed class) of a nova as the result the
interaction of the ejecta with the circumstellar material and rotation speed
and magnetic field of the white dwarf. We have compared these three parameters
including data available in the literature for another two nova shells, V533
Her and FH Ser. There is an anti-correlation between the expansion velocity and
the axial ratio and decline time C3 for nova remnants with ellipsoidal
morphology, and a correlation between their axial ratios and decline times C3,
confirming theoretical expectations that the fastest expanding novae have the
smallest axial ratios. We note that the high expansion velocity of the nova
shell HRDel of 615 km/s is inconsistent with its long decline time C3 of 250
days.


title:
Data-Augmentation for Graph Neural Network Learning of the Relaxed
  Energies of Unrelaxed Structures

abstract:
  Computational materials discovery has continually grown in utility over the
past decade due to advances in computing power and crystal structure prediction
algorithms (CSPA). However, the computational cost of the \textit{ab initio}
calculations required by CSPA limits its utility to small unit cells, reducing
the compositional and structural space the algorithms can explore. Past studies
have bypassed many unneeded \textit{ab initio} calculations by utilizing
machine learning methods to predict formation energy and determine the
stability of a material. Specifically, graph neural networks display high
fidelity in predicting formation energy. Traditionally graph neural networks
are trained on large data sets of relaxed structures. Unfortunately, the
geometries of unrelaxed candidate structures produced by CSPA often deviate
from the relaxed state, which leads to poor predictions hindering the model's
ability to filter energetically unfavorable prior to \textit{ab initio}
evaluation. This work shows that the prediction error on relaxed structures
reduces as training progresses, while the prediction error on unrelaxed
structures increases, suggesting an inverse correlation between relaxed and
unrelaxed structure prediction accuracy. To remedy this behavior, we propose a
simple, physically motivated, computationally cheap perturbation technique that
augments training data to improve predictions on unrelaxed structures
dramatically. On our test set consisting of 623 Nb-Sr-H hydride structures, we
found that training a crystal graph convolutional neural networks, utilizing
our augmentation method, reduced the MAE of formation energy prediction by 66\%
compared to training with only relaxed structures. We then show how this error
reduction can accelerates CSPA by improving the model's ability to filter out
energetically unfavorable structures accurately.


title: The inverse and the composition in the set of formal Laurent series

abstract:
  The aim of this article is to investigate the problem of the existence and
computation of inverse series to formal Laurent series, and the composition of
formal Laurent series and its properties. Some necessary and sufficient
conditions for the existence of inverse series to a certain type formal Laurent
series are provided. Moreover, we define a general composition of formal
Laurent series and we investigate the Right Distributive Law and the Chain Rule
for formal Laurent series. Finally, we provide a sufficient condition for the
boundary convergence of formal Laurent series.


title: Bags mediated film atomization in a cough machine

abstract:
  We combine experiments and numerical computations to examine underlying fluid
mechanical processes associated with bioaerosol generation during violent
respiratory manoeuvres, such as coughing or sneezing. Analogous experiments
performed in a cough machine -- consisting of a strong shearing airflow over a
thin liquid film, allow us to illustrate the changes in film topology as it
disintegrates into small droplets. We identify that aerosol generation during
the shearing of the liquid film is mediated by the formation of inflated
bag-like structures. The breakup of these bags is triggered by the appearance
of retracting holes that puncture the bag surface. Consequently, the cascade
from inflated bags to droplets is primarily controlled by the dynamics and
stability of liquid rims bounding these retracting holes. We also reveal the
stabilizing role of fluid viscosity that eventually leads to the generation of
smaller droplets.


title:
Fluid-driven granular dynamics through a consistent multi-resolution
  particle method

abstract:
  Granular dynamics driven by fluid flow is ubiquitous in many industrial and
natural processes, such as fluvial and coastal sediment transport. Yet, their
complex multiphysics nature challenges the accuracy and efficiency of numerical
models. Here, we study the dynamics of rapid fluid-driven granular erosion
through a mesh-free particle method based on the enhanced weakly-compressible
Moving Particle Semi-implicit (MPS) method. To that end, we develop and
validate a new multi-resolution multiphase MPS formulation for the consistent
and conservative form of the governing equations, including particle
stabilization techniques. First, we discuss the numerical accuracy and
convergence of the proposed approximation operators through two numerical
benchmark cases: the multi-viscosity Poiseuille flow and the multi-density
hydrostatic pressure. Then, coupling the developed model with a generalized
rheology equation, we investigate the water dam-break waves over movable beds.
The particle convergence study confirms that the proposed multi-resolution
formulation predicts the analytical solutions with acceptable accuracy and
order of convergence. Validating the multiphase granular flow reveals that the
mechanical behavior of this fluid-driven problem is highly sensitive to the
water-sediment density ratio; the bed with lighter grains experiences extreme
erosion and interface deformations. For the bed with a heavier material but
different geometrical setups, the surge speed and the transport layer thickness
remain almost identical (away from the gate). Furthermore, while the
multi-resolution model accurately estimates the global sediment dynamics, the
single-resolution model underestimates the flow evolution. Overall, the
qualitative and quantitative analysis of results emphasizes the importance of
multi-scale multi-density interactions in fluid-driven modeling.


title: Ordered Reliability Bits Guessing Random Additive Noise Decoding

abstract:
  Error correction techniques traditionally focus on the co-design of
restricted code-structures in tandem with code-specific decoders that are
computationally efficient when decoding long codes in hardware. Modern
applications are, however, driving demand for ultra-reliable low-latency
communications (URLLC), rekindling interest in the performance of shorter,
higher-rate error correcting codes, and raising the possibility of revisiting
universal, code-agnostic decoders.
  To that end, here we introduce a soft-detection variant of Guessing Random
Additive Noise Decoding (GRAND) called Ordered Reliability Bits GRAND that can
accurately decode any moderate redundancy block-code. It is designed with
efficient circuit implementation in mind, and determines accurate decodings
while retaining the original hard detection GRAND algorithm's suitability for a
highly parallelized implementation in hardware.
  ORBGRAND is shown to provide excellent soft decision block error performance
for codes of distinct classes (BCH, CA-Polar and RLC) with modest complexity,
while providing better block error rate performance than CA-SCL, a state of the
art soft detection CA-Polar decoder. ORBGRAND offers the possibility of an
accurate, energy efficient soft detection decoder suitable for delivering URLLC
in a single hardware realization.


title:
Production of multi-oriented polarization for relativistic electron
  beams via a mutable filter for nonlinear Compton scattering

abstract:
  We propose a feasible scenario to directly polarize a relativistic electron
beam and obtain overall polarization in various directions through a filter
mechanism for single-shot collision between an ultrarelativistic unpolarized
electron beam and an ultraintense circularly polarized laser pulse. The
electrons are scattered to a large angular range of several degrees and the
polarization states of the electrons are connected with their spatial position
after the collision. Therefore, we can employ a filter to filter out a part of
the scattered electrons based on their position and obtain high-degree overall
polarization for the filtered beam. Through spin-considered Monte-Carlo
simulations, polarization with a degree up to 62% in arbitrary transverse
directions and longitudinal polarization up to 10% are obtained for the
filtered beams at currently achievable laser intensity. We theoretically
analyze the distribution formation of the scattered electrons and investigate
the influence of different initial parameters through simulations to
demonstrate the robustness of our scheme. This scenario provides a simple and
flexible way to produce relativistic polarized electron beams for various
polarization directions.


title: Practical Automated Detection of Malicious npm Packages

abstract:
  The npm registry is one of the pillars of the JavaScript and TypeScript
ecosystems, hosting over 1.7 million packages ranging from simple utility
libraries to complex frameworks and entire applications. Due to the
overwhelming popularity of npm, it has become a prime target for malicious
actors, who publish new packages or compromise existing packages to introduce
malware that tampers with or exfiltrates sensitive data from users who install
either these packages or any package that (transitively) depends on them.
Defending against such attacks is essential to maintaining the integrity of the
software supply chain, but the sheer volume of package updates makes
comprehensive manual review infeasible.
  We present Amalfi, a machine-learning based approach for automatically
detecting potentially malicious packages comprised of three complementary
techniques. We start with classifiers trained on known examples of malicious
and benign packages. If a package is flagged as malicious by a classifier, we
then check whether it includes metadata about its source repository, and if so
whether the package can be reproduced from its source code. Packages that are
reproducible from source are not usually malicious, so this step allows us to
weed out false positives. Finally, we also employ a simple textual
clone-detection technique to identify copies of malicious packages that may
have been missed by the classifiers, reducing the number of false negatives.
  Amalfi improves on the state of the art in that it is lightweight, requiring
only a few seconds per package to extract features and run the classifiers, and
gives good results in practice: running it on 96287 package versions published
over the course of one week, we were able to identify 95 previously unknown
malware samples, with a manageable number of false positives.


title:
Implementation Technologies of an Advanced Cloud-based System for
  Distribution Operations

abstract:
  Today's era is characterized as the "digital transformation era". Digital
processes and information systems are used in every aspect of social and
business activity. The use of information technology over the internet is so
extensive that we interact with it daily without even recognizing it. The
technological advances can offer a plethora of improvements for the supply
chain processes, especially in the field of distribution planning and
execution. The scope of this paper is to present the technological content of
an advanced routing and scheduling system for transportation and delivery of
goods. The system focuses on the routing and scheduling problem in urban areas,
as city logistics have become a complex environment for companies to deliver
their goods. The presented system deals with both static and dynamic routing
and scheduling problems. More specifically, the system can create initial
routing plans based on orders, available vehicles, time windows, and traffic
forecasting data. Afterwards, during the execution of the plans, the system can
monitor the fleet, detect deviations from the original plans, and finally,
perform rerouting operations when needed. After a brief presentation of the
system's modules and functionality, the paper describes thoroughly the
technologies used to develop the system. The technological elements of the
system are integrated into a cloud environment offering a system that is easy
to maintain and can effectively support logistics companies' distribution
activities. The system is provided as a Software as a Service with data being
maintained on a central host and processed on the cloud. Therefore, logistics
companies that decide to implement it can achieve faster, more accurate and
more cost-efficient distribution activities while ensuring better customer
service.


title: MaxCut on Permutation Graphs is NP-complete

abstract:
  In this paper, we prove that the MaxCut problem is NP-complete on permutation
graphs, settling a long-standing open problem that appeared in the 1985 column
of the "Ongoing Guide to NP-completeness" by David S. Johnson.


title:
RouteNet-Erlang: A Graph Neural Network for Network Performance
  Evaluation

abstract:
  Network modeling is a fundamental tool in network research, design, and
operation. Arguably the most popular method for modeling is Queuing Theory
(QT). Its main limitation is that it imposes strong assumptions on the packet
arrival process, which typically do not hold in real networks. In the field of
Deep Learning, Graph Neural Networks (GNN) have emerged as a new technique to
build data-driven models that can learn complex and non-linear behavior. In
this paper, we present \emph{RouteNet-Erlang}, a pioneering GNN architecture
designed to model computer networks. RouteNet-Erlang supports complex traffic
models, multi-queue scheduling policies, routing policies and can provide
accurate estimates in networks not seen in the training phase. We benchmark
RouteNet-Erlang against a state-of-the-art QT model, and our results show that
it outperforms QT in all the network scenarios.


title: On the Laistrygonian Nichols algebras that are domains

abstract:
  We consider a class of Nichols algebras $\mathscr{B} (\mathfrak L_q( 1,
\mathscr{G}))$ introduced in arXiv:1606.02521 which are domains and have many
favorable properties like AS-regular and strongly noetherian. We classify their
finite-dimensional simple modules and their point modules.


title: CQELS 2.0: Towards A Unified Framework for Semantic Stream Fusion

abstract:
  We present CQELS 2.0, the second version of Continuous Query Evaluation over
Linked Streams. CQELS 2.0 is a platform-agnostic federated execution framework
towards semantic stream fusion. In this version, we introduce a novel
neural-symbolic stream reasoning component that enables specifying deep neural
network (DNN) based data fusion pipelines via logic rules with learnable
probabilistic degrees as weights. As a platform-agnostic framework, CQELS 2.0
can be implemented for devices with different hardware architectures (from
embedded devices to cloud infrastructures). Moreover, this version also
includes an adaptive federator that allows CQELS instances on different nodes
in a network to coordinate their resources to distribute processing pipelines
by delegating partial workloads to their peers via subscribing continuous
queries


title:
Semi-Structured Query Grounding for Document-Oriented Databases with
  Deep Retrieval and Its Application to Receipt and POI Matching

abstract:
  Semi-structured query systems for document-oriented databases have many real
applications. One particular application that we are interested in is matching
each financial receipt image with its corresponding place of interest (POI,
e.g., restaurant) in the nationwide database. The problem is especially
challenging in the real production environment where many similar or incomplete
entries exist in the database and queries are noisy (e.g., errors in optical
character recognition). In this work, we aim to address practical challenges
when using embedding-based retrieval for the query grounding problem in
semi-structured data. Leveraging recent advancements in deep language encoding
for retrieval, we conduct extensive experiments to find the most effective
combination of modules for the embedding and retrieval of both query and
database entries without any manually engineered component. The proposed model
significantly outperforms the conventional manual pattern-based model while
requiring much less development and maintenance cost. We also discuss some core
observations in our experiments, which could be helpful for practitioners
working on a similar problem in other domains.


title:
ciscNet -- A Single-Branch Cell Instance Segmentation and Classification
  Network

abstract:
  Automated cell nucleus segmentation and classification are required to assist
pathologists in their decision making. The Colon Nuclei Identification and
Counting Challenge 2022 (CoNIC Challenge 2022) supports the development and
comparability of segmentation and classification methods for histopathological
images. In this contribution, we describe our CoNIC Challenge 2022 method
ciscNet to segment, classify and count cell nuclei, and report preliminary
evaluation results. Our code is available at
https://git.scc.kit.edu/ciscnet/ciscnet-conic-2022.


title: A Spatio-Causal Growth Model Explains the Pareto Principle

abstract:
  Under typical growth models, populations quickly lose the ability to select
and sustain effects (gains and losses), as growth leaves their increasing
variation (endogenous and exogeneous) uncontrolled. Under (1) Unconfounded
growth, in contrast, populations preserve the ability to determine which of
their variations are responsible for the gains and losses they observe (and
can, consequently, carry fair selection and optimization processes). Under (2)
Externally-Valid growth, effects generalize across populations' full range of
external variation (and populations can, consequently, expand across
increasingly diverse conditions). The first promotes generalization of effects
over populations, and the second over their environments. These alternative
growth patterns allow systems in complex environments to create sustainable
environments for growth, from their spatial distribution patterns.
  We consider the full growth, from 1840, of American cities and economy. We
use billions of individual-level census records, organized in spatial-levels
ranging from the street level all the way to the national. We demonstrate
populations' combinatorial thresholds for sustainable growth, across locations
and levels. The resulting binomial-exponential model unifies popular
mathematical growth theories, and reveals new connections to the
Fibonacci-Golden ratio, Cooperative Game-Theory, Spatial Experimental-Designs,
Causality, Power-laws, Hyperbolic Geometry, and the Pareto Principle. It,
finally, makes new predictions about the growth of cities, that are hard to
explain with current models.


title:
Incompressible active phases at an interface. I. Formulation and
  axisymmetric odd flows

abstract:
  Inspired by the recent realization of a 2D chiral fluid as an active
monolayer droplet moving atop a 3D Stokesian fluid, we formulate mathematically
its free-boundary dynamics. The surface droplet is described as a general 2D
linear, incompressible, and isotropic fluid, having a viscous shear stress, an
active chiral driving stress, and a Hall stress allowed by the lack of
time-reversal symmetry. The droplet interacts with itself through its driven
internal mechanics and by driving flows in the underlying 3D Stokes phase. We
pose the dynamics as the solution to a singular integral-differential equation,
over the droplet surface, using the mapping from surface stress to surface
velocity for the 3D Stokes equations. Specializing to the case of axisymmetric
droplets, exact representations for the chiral surface flow are given in terms
of solutions to a singular integral equation, solved using both analytical and
numerical techniques. For a disc-shaped monolayer, we additionally employ a
semi-analytical solution that hinges on an orthogonal basis of Bessel functions
and allows for efficient computation of the monolayer velocity field, which
ranges from a nearly solid-body rotation to a unidirectional edge current
depending on the subphase depth and the Saffman-Delbruck length. Except in the
near-wall limit, these solutions have divergent surface shear stresses at
droplet boundaries, a signature of systems with codimension one domains
embedded in a three-dimensional medium. We further investigate the effect of a
Hall viscosity, which couples radial and transverse surface velocity
components, on the dynamics of a closing cavity.

categories: physics.flu-dyn cond-mat.soft math-ph math.MP physics.class-ph


title:
Detection of $d_{1}\otimes d_{2}$ Dimensional Bipartite Entangled State:
  A Graph Theoretical Approach

abstract:
  Braunstein et. al. have started the study of entanglement properties of the
quantum states through graph theoretical approach. Their idea was to start from
a simple unweighted graph $G$ and then they have defined the quantum state from
the Laplacian of the graph $G$. A lot of research had already been done using
the similar idea. We ask here the opposite one i.e can we generate a graph from
the density matrix? To investigate this question, we have constructed a unital
map $\phi$ such that $\phi(\rho)=L_{\rho}+\rho$, where the quantum state is
described by the density operator $\rho$. The entries of $L_{\rho}$ depends on
the entries of the quantum state $\rho$ and the entries are taken in such a way
that $L_{\rho}$ satisfies all the properties of the Laplacian. This make
possible to design a simple connected weighted graph from the Laplacian
$L_{\rho}$. We show that the constructed unital map $\phi$ characterize the
quantum state with respect to its purity by showing that if the determinant of
the matrix $\phi(\rho)-I$ is positive then the quantum state $\rho$ represent a
mixed state. Moreover, we study the positive partial transpose (PPT) criterion
in terms of the spectrum of the density matrix under investigation and the
spectrum of the Laplacian associated with the given density matrix.
Furthermore, we derive the inequality between the minimum eigenvalue of the
density matrix and the weight of the edges of the connected subgraph of a
simple weighted graph to detect the entanglement of $d_{1} \otimes d_{2}$
dimensional bipartite quantum states. Lastly, We have illustrated our results
with few examples.


title: Estimating the degree of non-Markovianity using quantum machine learning

abstract:
  Several applications of machine learning in quantum physics encompass a
measurement upon the quantum system followed by the training of the machine
learning model with the measurement outcomes. However, recently developed
quantum machine learning models, such as variational quantum circuits (VQCs),
can be implemented directly on the state of the quantum system (quantum data).
Here, we propose to use a qubit as a probe to estimate the degree of
non-Markovianity of the environment. Using VQCs, we find an optimal sequence of
interactions of the qubit with the environment to estimate, with high
precision, the degree of non-Markovianity for the phase damping and amplitude
damping channels. This work contributes to practical quantum applications of
VQCs and delivers a feasible experimental procedure to estimate the degree of
non-Markovianity.


title:
Precision-medicine-toolbox: An open-source python package for
  facilitation of quantitative medical imaging and radiomics analysis

abstract:
  Medical image analysis plays a key role in precision medicine as it allows
the clinicians to identify anatomical abnormalities and it is routinely used in
clinical assessment. Data curation and pre-processing of medical images are
critical steps in the quantitative medical image analysis that can have a
significant impact on the resulting model performance. In this paper, we
introduce a precision-medicine-toolbox that allows researchers to perform data
curation, image pre-processing and handcrafted radiomics extraction (via
Pyradiomics) and feature exploration tasks with Python. With this open-source
solution, we aim to address the data preparation and exploration problem,
bridge the gap between the currently existing packages, and improve the
reproducibility of quantitative medical imaging research.


title:
Realizing symmetry protected topological phase through dimerized
  interactions

abstract:
  We show that in a system of one dimensional spinless fermions a topological
phase and phase transition can emerge only through interaction. By allowing a
dimerized or bond-alternating nearest neighbour interaction we show that the
system exhibits a symmetry protected topological phase while its
non-interacting limit is a gapless state. The non-trivial topological character
appears due to the onset of two degenerate bond-order phases as a function of
dimerized interaction which are found to be topologically distinct from each
other. As a result a topological phase transition occurs between these bond
order phases through a gap closing point. However, in the limit of strong
interaction, the bond order phases are connected through a gapped charge
density wave phase possessing local antiferromagnetic order. The topological
nature is characterized by the edge states, Berry phase and non-local string
order parameter. At the end we provide possible experimental signatures of the
emergent symmetry protected topological phase transition in terms of Thouless
charge pumping and density-density correlation.


title:
Dilute Bose gas with three-body interaction: recent results and open
  questions

abstract:
  We review our recent study on the ground state energy of dilute Bose gases
with three-body interactions. The main feature of our results is the emergence
of the 3D energy-critical Schr\"odinger equation to describe the ground state
energy of a Bose-Einstein condensate, where the nonlinearity strength is
determined by a zero scattering problem. Several open questions will also be
discussed.


title:
Studies on environment-friendly gas mixtures for the Resistive Plate
  Chambers of the ALICE Muon Identifier

abstract:
  Due to their simplicity and comparatively low cost, Resistive Plate Chambers
are gaseous detectors widely used in high-energy and cosmic rays physics, when
large detection areas are needed. However, the best gaseous mixtures are
currently based on tetrafluoroethane, which has the undesirable characteristic
of a large Global Warming Potential (GWP) of about 1400 and, because of this,
it is currently being phased out from industrial use. Tetrafluoropropene (which
has a GWP close to 1) is being considered as a possible replacement. Since
tetrafluoropropene is more electronegative than tetrafluoroethane, it has to be
diluted with gases with a lower attachment coefficient in order to maintain the
operating voltage close to 10 kV. One of the main candidates for this role is
carbon dioxide. In order to ascertain the feasibility and the performance of
tetrafluoropropene-CO2 based mixtures, an R&D program is being carried out
within the ALICE collaboration, employing an array of 72 Bakelite RPCs (Muon
IDentifier, MID) in order to identify muons. Different proportions of
tetrafluoropropene and CO2, with the addition of small quantities of isobutane
and sulphur hexafluoride, have been tested with 50x50 cm 2 RPC prototypes with
2 mm wide gas gap and 2 mm thick Bakelite electrodes. In this contribution,
results from tests with cosmic rays will be presented, together with data
concerning the current drawn by a RPC exposed to the gamma-ray flux of the
Gamma Irradiation Facility (GIF) at CERN.


title:
The Statistics of the Cross-Spectrum and the Spectrum Average:
  Generalization to Multiple Instruments

abstract:
  This article addresses the measurement of the power spectrum of red noise
processes at the lowest frequencies, where the minimum acquisition time is so
long that it is impossible to average on a sequence of data record. Therefore,
averaging is possible only on simultaneous observation of multiple instruments.
This is the case of radio astronomy, which we take as the paradigm, but
examples may be found in other fields such as climatology and geodesy. We
compare the Bayesian confidence interval of the red-noise parameter using two
estimators, the spectrum average and the cross-spectrum. While the spectrum
average is widely used, the cross-spectrum using multiple instruments is rather
uncommon. With two instruments, the cross-spectrum estimator leads to the
Variance-Gamma distribution. A generalization to $q$ devices is provided, with
the example of the observation of millisecond pulsars with 5 radio telescopes.


title: EIC Detector Overview

abstract:
  The Electron Ion Collider will have two interaction regions that can be
instrumented with detectors. The first region will be instrumented as part of
the project and needs to be capable of delivering the physics that has been
outlined by the National Academy of Sciences and ready at the start of beam
commissioning near the end of this decade. Plans for a second complementary
detector to be located at a second interaction region are already in progress
and will hopefully come to fruition just a few years after the first detector
comes online. While the basic parameters of these detectors are being selected
using conventional approaches, the optimization of the detectors is already
being enhanced by making use of advanced optimization techniques.


title:
Composition-independent temperature induced by swift heavy ions in
  insulators and its relation with track formation

abstract:
  A systematic study of the $R_e$-$T_m$ relation ($R_e$ -track radius, $T_m$
-melting temperature) in insulators reveals new features of track formation. A
quantitative relationship is demonstrated between $R_e$ values measured in
various solids when $\langle s_e\rangle$=constant ($\langle s_e\rangle$ -
atomic stopping power). This effect is the consequence of a
composition-independent, identical maximum temperature distribution $\Delta
T(r,0)$, which is induced in various insulators in a narrow cylindrical volume
close to the tracks. The uniformity ceases gradually at larger distances.
$\Delta T(r,0)$ has a Gaussian shape, with a constant width w=4.5 nm, is
independent of materials parameters and it can be determined experimentally by
the measurement of Re in various insulators when $\langle s_e\rangle$=constant.
The consequences of the experimental $\Delta T(r,0)$ distribution are
discussed.


title:
The impact of lexical and grammatical processing on generating code from
  natural language

abstract:
  Considering the seq2seq architecture of TranX for natural language to code
translation, we identify four key components of importance: grammatical
constraints, lexical preprocessing, input representations, and copy mechanisms.
To study the impact of these components, we use a state-of-the-art architecture
that relies on BERT encoder and a grammar-based decoder for which a
formalization is provided. The paper highlights the importance of the lexical
substitution component in the current natural language to code systems.


title:
A Phase-Space Semiclassical Approach for Modeling Nonadiabatic Nuclear
  Dynamics with Electronic Spin

abstract:
  Chemical relaxation phenomena, including photochemistry and electron transfer
processes, form a vigorous area of research in which nonadiabatic dynamics
plays a fundamental role. Here, we show that for nonadiabatic dynamics with two
electronic states and a complex-valued Hamiltonian that does not obey
time-reversal symmetry, the optimal semiclassical approach is to run surface
hopping dynamics on a set of phase-space adiabatic surfaces. In order to
generate such phase-adiabats, one must isolate a proper set of diabats and
apply a phase gauge transformation, before eventually diagonalizing the total
Hamiltonian (which is now parameterized by both R and P). The resulting
algorithm is valid in both the adiabatic and nonadiabatic limits, incorporates
all Berry curvature effects, and allows for the study of semiclassical
nonadiabatic dynamics in the presence of spin-orbit coupling and/or external
magnetic fields.


title:
SmartBelt: A Wearable Microphone Array for Sound Source Localization
  with Haptic Feedback

abstract:
  This paper introduces SmartBelt, a wearable microphone array on a belt that
performs sound source localization and returns the direction of arrival with
respect to the user waist. One of the haptic motors on the belt then vibrates
in the corresponding direction to provide useful feedback to the user. We also
introduce a simple calibration step to adapt the belt to different waist sizes.
Experiments are performed to confirm the accuracy of this wearable sound source
localization system, and results show a Mean Average Error (MAE) of 2.90
degrees, and a correct haptic motor selection with a rate of 92.3%. Results
suggest the device can provide useful haptic feedback, and will be evaluated in
a study with people having hearing impairments.


title: A Proximal Algorithm for Sampling

abstract:
  We consider sampling problems with possibly non-smooth potentials (negative
log-densities). In particular, we study two specific settings of sampling where
the convex potential is either semi-smooth or in composite form as the sum of a
smooth component and a semi-smooth component. To overcome the challenges caused
by the non-smoothness, we propose a Markov chain Monte Carlo algorithm that
resembles proximal methods in optimization for these sampling tasks. The key
component of our method is a sampling scheme for a quadratically regularized
target potential. This scheme relies on rejection sampling with a carefully
designed Gaussian proposal whose center is an approximate minimizer of the
regularized potential. We develop a novel technique (a modified Gaussian
integral) to bound the complexity of this rejection sampling scheme in spite of
the non-smoothness in the potentials. We then combine this scheme with the
alternating sampling framework (ASF), which uses Gibbs sampling on an augmented
distribution, to accomplish the two settings of sampling tasks we consider.
Furthermore, by combining the complexity bound of the rejection sampling we
develop and the remarkable convergence properties of ASF discovered recently,
we are able to establish several non-asymptotic complexity bounds for our
algorithm, in terms of the total number of queries of subgradient of the target
potential. Our algorithm achieves state-of-the-art complexity bounds compared
with all existing methods in the same settings.