title:
Identifying Friction in a Nonlinear Chaotic System Using a Universal
  Adaptive Stabilizer

abstract:
  This paper proposes a friction model parameter identification routine that
can work with highly nonlinear and chaotic systems. The chosen system for this
study is a passively-actuated tilted Furuta pendulum, which is known to have a
highly non linear and coupled model. The pendulum is tilted to ensure the
existence of a stable equilibrium configuration for all its degrees of freedom,
and the link weights are the only external forces applied to the system. A
nonlinear analytical model of the pendulum is derived, and a continuous
friction model considering static friction, dynamic friction, viscous friction,
and the stribeck effect is selected from the literature. A high-gain Universal
Adaptive Stabilizer (UAS) observer is designed to identify friction model
parameters using joint angle measurements. The methodology is tested in
simulation and validated on an experimental setup. Despite the high
nonlinearity of the system, the methodology is proven to converge to the exact
parameter values, in simulation, and to yield qualitative parameter magnitudes
in experiments where the goodness of fit was around 85\% on average. The
discrepancy between the simulation and the experimental results is attributed
to the limitations of the friction model. The main advantage of the proposed
method is the significant reduction in computational needs and the time
required relative to conventional optimization-based identification routines.
The proposed approach yielded more than 99\% reduction in the estimation time
while being considerably more accurate than the optimization approach in every
test performed. One more advantage is that the approach can be easily adapted
to fit other models to experimental data.


title:
A New Approach to Determine the Minimal Polynomials of Binary Modified
  de Bruijn Sequences

abstract:
  A binary modified de Bruijn sequence is an infinite and periodic binary
sequence derived by removing a zero from the longest run of zeros in a binary
de Bruijn sequence. The minimal polynomial of the modified sequence is its
unique least-degree characteristic polynomial. Leveraging on a recent
characterization, we devise a novel general approach to determine the minimal
polynomial. We translate the characterization into a problem of identifying a
Hamiltonian cycle in a specially constructed graph. Along the way, we
demonstrate the usefullness of computational tools from the cycle joining
method in the modified setup.


title:
Interleaving Monte Carlo Tree Search and Self-Supervised Learning for
  Object Retrieval in Clutter

abstract:
  In this study, working with the task of object retrieval in clutter, we have
developed a robot learning framework in which Monte Carlo Tree Search (MCTS) is
first applied to enable a Deep Neural Network (DNN) to learn the intricate
interactions between a robot arm and a complex scene containing many objects,
allowing the DNN to partially clone the behavior of MCTS. In turn, the trained
DNN is integrated into MCTS to help guide its search effort. We call this
approach learning-guided Monte Carlo tree search for Object REtrieval (MORE),
which delivers significant computational efficiency gains and added solution
optimality. MORE is a self-supervised robotics framework/pipeline capable of
working in the real world that successfully embodies the System 2 to System 1
learning philosophy proposed by Kahneman, where learned knowledge, used
properly, can help greatly speed up a time-consuming decision process over
time. Videos and supplementary material can be found at
https://github.com/arc-l/more


title:
SparGE: Sparse Coding-based Patient Similarity Learning via Low-rank
  Constraints and Graph Embedding

abstract:
  Patient similarity assessment (PSA) is pivotal to evidence-based and
personalized medicine, enabled by analyzing the increasingly available
electronic health records (EHRs). However, machine learning approaches for PSA
has to deal with inherent data deficiencies of EHRs, namely missing values,
noise, and small sample sizes. In this work, an end-to-end discriminative
learning framework, called SparGE, is proposed to address these data challenges
of EHR for PSA. SparGE measures similarity by jointly sparse coding and graph
embedding. First, we use low-rank constrained sparse coding to identify and
calculate weight for similar patients, while denoising against missing values.
Then, graph embedding on sparse representations is adopted to measure the
similarity between patient pairs via preserving local relationships defined by
distances. Finally, a global cost function is constructed to optimize related
parameters. Experimental results on two private and public real-world
healthcare datasets, namely SingHEART and MIMIC-III, show that the proposed
SparGE significantly outperforms other machine learning patient similarity
methods.


title: Multi-Criteria Assessment of Shape Quality in CAD Systems of the Future

abstract:
  Unlike many other works, where authors are usually focused on one or two
quality criteria, the current manuscript, which is a generalization of the
article [35] published in Russian, offers a multi-criteria approach to the
assessment of the shape quality of curves that constitute component parts of
the surfaces used for the computer modelling of object shapes in various types
of design. Based on the analysis of point particle motion along a curved path,
requirements for the quality of functional curves are proposed: a high order of
smoothness, a minimum number of curvature extrema, minimization of the maximum
value of curvature and its variation rate, minimization of the potential energy
of the curve, and aesthetic analysis from the standpoint of the laws of
technical aesthetics. The authors do not set themselves the task of giving a
simple and precise mathematical definition of such curves. On the contrary,
this category can include various curves that meet certain quality criteria,
the refinement and addition of which is possible in the near future.
Engineering practice shows that quality criteria can change over time, which
does not diminish the need to develop multi-criteria methods for assessing the
quality of geometric shapes. Technical issues faced during edge rounding in 3D
models that affect the quality of industrial design product shape have been
reviewed as an example of the imperfection of existing CAD systems.


title: Ratchet based ion pumps for selective ion separations

abstract:
  The development of a selective, membrane-based ion separation technology may
prove useful in a wide range of applications such as water treatment, battery
recycling, ion specific chemical sensors, extraction of valuable metals from
sea water, and bio-medical devices. In this work we show that a flashing
ratchet mechanism can be used for high precision ion separation. The suggested
ratchet-based ion pumps utilize a unique feature of ratchets, the frequency
dependent current reversals, to drive ions with the same charge but different
diffusion coefficients in opposite directions. We show that for the prevalent
ions in water, ions with a relative diffusion coefficient difference as small
as 1% can be separated by driving them in opposite directions with a velocity
difference as high as 1.2 mm/s. Since the pumping properties of the ratchet are
determined by an electric input signal, the proposed ion pumps can pave the way
for simple large-scale, fit-to-purpose selective ion separation systems.


title:
For the splitting method of the nonlinear heat equation with initial
  datum in W^1,q

abstract:
  In this paper, we analyze an operator splitting scheme of the nonlinear heat
equation in $\Omega\subset\mathbb{R}^d$ ($d\geq 1$): $\partial_t u = \Delta u +
\lambda |u|^{p-1} u$ in $\Omega\times(0,\infty)$, $u=0$ in
$\partial\Omega\times(0,\infty)$, $u ({\bf x},0) =\phi ({\bf x})$ in $\Omega$.
where $\lambda\in\{-1,1\}$ and $\phi \in W^{1,q}(\Omega)\cap L^{\infty}
(\Omega)$ with $2\leq p < \infty$ and $d(p-1)/2<q<\infty$. We establish the
well-posedness of the approximation of $u$ in $L^r$-space ($r\geq q$), and
furthermore, we derive its convergence rate of order $\mathcal{O}(\tau)$ for a
time step $\tau>0$. Finally, we give some numerical examples to confirm the
reliability of the analyzed result.


title: Quantum-Coherent Nanoscience

abstract:
  For the past three decades, nanoscience has widely affected many areas in
physics, chemistry, and engineering, and has led to numerous fundamental
discoveries as well as applications and products. Concurrently, quantum science
and technology has developed into a cross-disciplinary research endeavour
connecting these same areas and holds a burgeoning commercial promise. Although
quantum physics dictates the behaviour of nanoscale objects, quantum coherence,
which is central to quantum information, communication and sensing has not
played an explicit role in much of nanoscience. This Review describes
fundamental principles and practical applications of quantum coherence in
nanoscale systems, a research area we call quantum-coherent nanoscience. We
structure this manuscript according to specific degrees of freedom that can be
quantum-coherently controlled in a given nanoscale system such as charge, spin,
mechanical motion, and photons. We review the current state of the art and
focus on outstanding challenges and opportunities unlocked by the merging of
nanoscience and coherent quantum operations.


title: Hard X-ray flares and spectral variability in NGC 4395 ULX1

abstract:
  We report the detection of flaring events in NGC 4395 ULX1, a nearby
ultraluminous X-ray source(ULX), for the first time, using recent XMM-NEWTON
observations. The flaring episodes are spectrally harder than the steady
emission periods, resulting in higher fractional variability in the high energy
regime. A thin Keplerian and a slim accretion disk provide the best-fit
continuum for XMM-NEWTON spectra. In all observations, the presence of a broad
Gaussian emission feature around ~0.9 keV suggests a strong wind outflow in
this ULX. The flaring spectra correspond to higher slim disk temperatures due
to higher mass accretion rate under an advection-dominated accretion scenario.
The luminosity-temperature profile in different flux states is consistent with
the theoretical predictions for a slim accretion disk in the case of
super-Eddington accretion onto a stellar-mass compact object. The unperturbed
absorption column and wind outflow line emission during flaring events suggest
that the site of origin of these flares is mainly the advection flow in the
inner region of the accretion disk.


title:
Generalization of the Tavis-Cummings model for multi-level anharmonic
  systems: insights on the second excitation manifold

abstract:
  Confined electromagnetic modes strongly couple to collective excitations in
ensembles of quantum emitters, producing light-matter hybrid states known as
polaritons. Under such conditions, the discrete multilevel spectrum of
molecular systems offers an appealing playground for exploring multiphoton
processes. This work contrasts predictions from the Tavis-Cummings (TC) model,
in which the material is a collection of two-level systems, with the
implications of considering additional energy levels with harmonic and
anharmonic structures. We discuss the exact eigenspectrum, up to the second
excitation manifold, of an arbitrary number $N$ of oscillators collectively
coupled to a single cavity mode in the rotating-wave approximation. Elaborating
on our group-theoretic approach [New J. Phys. 23, 063081 (2021)], we simplify
the brute-force diagonalization of a gigantic $N^2\times N^2$ Hamiltonian
(where $N=10^6-10^{10}$, as experiments suggest) to the diagonalization of, at
most, $4\times4$ matrices. We thoroughly discuss the eigenstates and the
consequences of weak and strong anharmonicities. Furthermore, we find resonant
conditions between bipolaritons and anharmonic transitions where two-photon
absorption can be enhanced. Finally, we conclude that energy shifts in the
polaritonic states induced by anharmonicities become negligible for large $N$.
Thus, calculations with a single or few emitters qualitatively fail to
represent the nonlinear optical response of the collective strong coupling
regime. Our work highlights the rich physics of multilevel anharmonic systems
coupled to cavities absent in standard models of quantum optics. We also
provide concise tabulated expressions for eigenfrequencies and transition
amplitudes, which should serve as a reference for future spectroscopic studies
of molecular polaritons.

categories: quant-ph cond-mat.mes-hall physics.atm-clus physics.chem-ph


title:
Determination of $|V_{cb}|$ using N$^3$LO perturbative corrections to
  $\Gamma(B \to X_c \ell \nu)$ and 1S masses

abstract:
  We determine $|V_{cb}|$ using the third-order perturbative series for the
inclusive semileptonic $B$ decay width and for the masses of the bottomonium 1S
states. We use the masses of $\eta_b(1S)$ and $\Upsilon(1S)$ as short-distance
masses and point out that there is a sizable difference of $|V_{cb}|$ between
the two 1S mass schemes. This is the dominant error of our determination and
stems from insufficiency to describe theoretically the observed mass splitting
of the bottomonium 1S states. We also study the significance of the
$\mathcal{O}(\Lambda_{\overline{\rm MS}}^2)$ non-perturbative effects in HQET
with respect to the current perturbative accuracy. Our result $|V_{cb}|=0.0425
(11)$ is consistent with the PDG value determined from the inclusive decays and
has a slightly larger error.


title:
Engineering superconducting qubits to reduce quasiparticles and charge
  noise

abstract:
  In any physical realization of a qubit, identifying, quantifying, and
suppressing mechanisms of decoherence are important steps towards the goal of
engineering a universal quantum computer or a quantum simulator.
Superconducting circuits based on Josephson junctions offer flexibility in
qubit design; however, their performance is adversely affected by
quasiparticles (broken Cooper pairs) whose density, as observed in various
systems, is considerably higher than that expected in thermal equilibrium. A
full understanding of the generation mechanism and a mitigation strategy that
is compatible with scalable, high-coherence devices are therefore highly
desirable. Here we experimentally demonstrate how to control quasiparticle
generation by downsizing the qubit structure, capping it with a metallic cover,
and equipping it with suitable quasiparticle traps. We achieve record low
charge-parity switching rate (<1Hz) in our aluminium devices. At the same time,
the devices display improved stability with respect to discrete charging
events. Our findings support the hypothesis that the generation of
quasiparticles is dominated by the breaking of Cooper pairs at the junction, as
a result of photon absorption mediated by the antenna-like qubit structure. We
thus demonstrate a convenient approach to shape the electromagnetic environment
of superconducting circuits in the sub-terahertz regime, inhibiting decoherence
from quasiparticle poisoning.


title: A note on the concurrent normal conjecture

abstract:
  It is conjectured since long that for any convex body $K \in \mathbb{R}^n$
there exists a point in the interior of $K$ which belongs to at least $2n$
normals from different points on the boundary of $K$. The conjecture is known
to be true for $n=2,3,4$. Motivated by a recent preprint of Y. Martinez-Maure,
we give a short proof of his result: for dimension $n\geq 3$, under mild
conditions, almost every normal through a boundary point to a smooth convex
body $K\in \mathbb{R}^n$ contains an intersection point of at least $6$ normals
from different points on the boundary of $K$.


title: Universal tetramer and pentamer in two-dimensional fermionic mixtures

abstract:
  We study the emergence of universal tetramer and pentamer bound states in the
two-dimensional $(N+1)$ system, which consists $N$ identical heavy fermions
interacting with a light atom. We show that the critical heavy-light mass ratio
to support a ($3+1$) tetramer below the trimer threshold is $3.38$, and to
support a ($4+1$) pentamer below tetramer is $5.14$. While these ground state
tetramer and pentamer are both with zero total angular momentum, they exhibit
very different density distributions and correlations in momentum space, due to
their distinct angular momentum decompositions in the dimer-fermion frame.
These universal bound states can be accessible by a number of Fermi-Fermi
mixtures now realized in cold atoms laboratories, which also suggest novel
few-body correlations dominant in their corresponding many-body systems.


title:
How the Many Worlds Interpretation brings Common Sense to Paradoxical
  Quantum Experiments

abstract:
  The many worlds interpretation of quantum mechanics (MWI) states that the
world we live in is just one among many parallel worlds. It is widely believed
that because of this commitment to parallel worlds, the MWI violates common
sense. Some go so far as to reject the MWI on this basis. This is despite its
myriad of advantages to physics (e.g. consistency with relativity theory,
mathematical simplicity, realism, determinism, etc.). Here, we make the case
that common sense in fact favors the MWI. We argue that causal explanations are
commonsensical only when they are local causal explanations. We present several
quantum mechanical experiments that seem to exhibit nonlocal "action at a
distance". Under the assumption that only one world exists, these experiments
seem immune to local causal explanation. However, we show that the MWI, by
taking all worlds together, can provide local causal explanations of the
experiments. The MWI therefore restores common sense to physical explanation.


title:
Sensing the Breath: A Multimodal Singing Tutoring Interface with Breath
  Guidance

abstract:
  Breath is a significant component in singing performance, which is still
underresearched in most singing-related music interfaces. In this paper, we
present a multimodal system that detects the learner's singing pitch and
breathing states and provides real-time visual tutoring feedback. Specifically,
the breath detector is a wearable belt with pressure sensors and flexible
fabric. It monitors real-time body movement of the abdomen, back waist, and
twin ribs. A breath visualization algorithm is developed to display real-time
breath states, together with the singing pitch contours on an interactive score
interface. User studies show that our system can help users not only gain
deeper breath during singing but also improve pitch accuracy in vocal training,
especially for those with some musical background.


title:
Optimized Potential Initialization for Low-latency Spiking Neural
  Networks

abstract:
  Spiking Neural Networks (SNNs) have been attached great importance due to the
distinctive properties of low power consumption, biological plausibility, and
adversarial robustness. The most effective way to train deep SNNs is through
ANN-to-SNN conversion, which have yielded the best performance in deep network
structure and large-scale datasets. However, there is a trade-off between
accuracy and latency. In order to achieve high precision as original ANNs, a
long simulation time is needed to match the firing rate of a spiking neuron
with the activation value of an analog neuron, which impedes the practical
application of SNN. In this paper, we aim to achieve high-performance converted
SNNs with extremely low latency (fewer than 32 time-steps). We start by
theoretically analyzing ANN-to-SNN conversion and show that scaling the
thresholds does play a similar role as weight normalization. Instead of
introducing constraints that facilitate ANN-to-SNN conversion at the cost of
model capacity, we applied a more direct way by optimizing the initial membrane
potential to reduce the conversion loss in each layer. Besides, we demonstrate
that optimal initialization of membrane potentials can implement expected
error-free ANN-to-SNN conversion. We evaluate our algorithm on the CIFAR-10,
CIFAR-100 and ImageNet datasets and achieve state-of-the-art accuracy, using
fewer time-steps. For example, we reach top-1 accuracy of 93.38\% on CIFAR-10
with 16 time-steps. Moreover, our method can be applied to other ANN-SNN
conversion methodologies and remarkably promote performance when the time-steps
is small.


title:
Stabilization technique applied to curve shortening flow in
  $\mathbb{R}^3$

abstract:
  For the curve shortening flow in $\mathbb{R}^3$ we derive a family of
monotonicity formulae depending on parameter $\lambda$. For $\lambda=1$ our
formula coincides with the classical formula of Huisken, while for
$\lambda\not=1$ the quantities involved depend on the angle between the
position vector and the tangent vector.


title: PERSPECTIVE: Emergent phases in rare earth nickelate heterostructures

abstract:
  The prediction of high $T_c$ superconductivity in layers of LaNiO$_3$ through
orbital engineering has led to extensive research efforts over the last fifteen
years. During this period, a plethora of thin films and heterostructures based
rare-earth nickelate family with perovskite structure has been synthesized and
explored. In this short perspective, we briefly review the complexity of bulk
$RE$NiO$_3$, spotlighting several recent findings of emergent phenomena in
heterostructures containing the interface between $RE$NiO$_3$ and another
transition metal oxide. Finally, we outline potentially interesting future
directions linked to time-domain dynamics to harness new Mott and topological
phases in artificial structures of $RE$NiO$_3$.


title:
Two-component scalar and fermionic dark matter candidates in a generic
  U$(1)_X$ model

abstract:
  We consider a $U(1)_X\otimes \mathbb{Z}_2\otimes \mathbb{Z}'_2$ extension of
the Standard Model (SM), where the $U(1)_X$ charge of an SM field is given by a
linear combination of its hypercharge and B$-$L number. Apart from the SM
particle content, the model contains three right-handed neutrinos (RHNs)
$N_R^i$ and two scalars $\Phi$, $\chi$, all singlets under the SM gauge group
but charged under $U(1)_X$ gauge group. Two of these additional fields, fermion
$N_R^3$ is odd under $\mathbb{Z}_2$ and scalar $\chi$ is odd under
$\mathbb{Z}'_2$ symmetry. Thus both $\chi$ and $N_R^3$ contribute to the
observed dark matter relic density, leading to two-component dark matter
candidates. We study in detail its dark matter properties such as relic density
and direct detection taking into account the constraints coming from collider
studies. We find that in our model, there can be possible annihilation of one
Dark Matter (DM) into the other, which may potentially alter the relic density
in a significant way.


title: Nonequilibrium free energy during polymer chain growth

abstract:
  During fast diffusion-influenced polymerization, nonequilibrium behavior of
the polymer chains and the surrounding reactive monomers has been reported
recently. Based on the laws of thermodynamics, the emerging nonequilibrium
structures should be characterisable by some "extra free energy" (excess over
the equilibrium Helmholtz free energy). Here, we study the nonequilibrium
thermodynamics of chain-growth polymerization of ideal chains in a dispersion
of free reactive monomers, using off-lattice, reactive Brownian Dynamics (R-BD)
computer simulations in conjunction with approximative statistical mechanics
and relative entropy (Gibbs-Shannon and Kullback-Leibler) concepts. In case of
fast growing polymers, we indeed report increased nonequilibrium free energies
of several kBT compared to equilibrium and near-equilibrium, slowly growing
chains. Interestingly, the extra free energy is a non-monotonic function of the
degree of polymerization and thus also of time. Our decomposition of the
thermodynamic contributions shows that the initial dominant extra free energy
is stored in the nonequilibrium inhomogeneous density profiles of the free
monomer gas (showing density depletion and wakes) in the vicinity of the active
center at the propagating polymer end. At later stages of the polymerization
process, we report significant extra contributions stored in the nonequilibrium
polymer conformations. Finally, our study implies a nontrivial relaxation
kinetics and restoring of the extra free energy during the equilibration
process after polymerization.


title:
Measurement of atmospheric muon angular distribution using a portable
  setup of liquid scintillator bars

abstract:
  Measurements of cosmogenic particles at various locations and altitudes are
becoming increasingly important in view of worldwide interests in rare signals
for search of new physics. In this work, we report measurement of muon zenith
angle distributions and integrated flux using a portable setup of four
one-meter long liquid scintillator bars. Each scintillator bar is read out from
both sides via photomultiplier tubes followed by an 8-channel Digitizer. We
exploit energy deposition and excellent timing of scintillators to construct
two dimensional tracks and hence angles of charged particles. We use liquid
scintillators since they have an added advantage of pulse shape discrimination
(PSD) which can be used for detecting muon induced particles. The energy
deposition, time window of event and PSD cuts are used to reduce the random as
well as correlated backgrounds. In addition, we propose three track quality
parameters which are applied to obtain a clean muon spectrum. The zenith angle
measurement is performed upto $60^\circ$. With our improved analysis, we
demonstrate that a setup of 3 bars can also be used for quicker and precise
measurements. The vertical muon flux measured is $66.70 \pm 0.36 \pm 1.50$
$/m^2/sr/s$ with $n=2.10 \pm 0.05 \pm 0.25 $ in $\cos^n \theta$ at the location
of Mumbai, India ($19^{\circ}$N, $72.9^{\circ}$E) at Sea level with a muon
momentum above $255$ MeV/$c$. The muon flux has dependence on various factors,
the most prominents are latitudes, altitudes and momentum cut of muon so that
portable setup like this can be a boon for such measurements at various
locations.


title: Noisy Sorting Capacity

abstract:
  Sorting is the task of ordering $n$ elements using pairwise comparisons. It
is well known that $m=\Theta(n\log n)$ comparisons are both necessary and
sufficient when the outcomes of the comparisons are observed with no noise. In
this paper, we study the sorting problem in the presence of noise. Unlike the
common approach in the literature which aims to minimize the number of pairwise
comparisons $m$ to achieve a given desired error probability, our goal is to
characterize the maximal ratio $\frac{n\log n}{m}$ such that the ordering of
the elements can be estimated with a vanishing error probability
asymptotically. The maximal ratio is referred to as the noisy sorting capacity.
In this work, we derive upper and lower bounds on the noisy sorting capacity.
The algorithm that attains the lower bound is based on the well-known
Burnashev--Zigangirov algorithm for coding over channels with feedback. By
comparing with existing algorithms in the literature under the proposed
framework, we show that our algorithm can achieve a strictly larger ratio
asymptotically.


title:
Unconventional interlayer exchange coupling via chiral phonons in
  synthetic magnetic oxide heterostructures

abstract:
  Chiral symmetry breaking of phonons plays an essential role in emergent
quantum phenomena owing to its strong coupling to spin degree of freedom.
However, direct experimental evidence of the chiral phonon-spin coupling is
lacking. In this study, we report a chiral phonon-mediated interlayer exchange
interaction in atomically controlled ferromagnetic metal (SrRuO3)-nonmagnetic
insulator (SrTiO3) heterostructures. Owing to the unconventional interlayer
exchange interaction, we have observed rotation of magnetic moments as a
function of nonmagnetic insulating spacer thickness, resulting in a spin spiral
state. The chiral phonon-spin coupling is further confirmed by phonon Zeeman
effects. The existence of the chiral phonons and their interplay with spins
along with our atomic-scale heterostructure approach open a window to unveil
the crucial roles of chiral phonons in magnetic materials.


title: Deep Learning Algorithm for Threat Detection in Hackers Forum (Deep Web)

abstract:
  In our current society, the inter-connectivity of devices provides easy
access for netizens to utilize cyberspace technology for illegal activities.
The deep web platform is a consummative ecosystem shielded by boundaries of
trust, information sharing, trade-off, and review systems. Domain knowledge is
shared among experts in hacker's forums which contain indicators of compromise
that can be explored for cyberthreat intelligence. Developing tools that can be
deployed for threat detection is integral in securing digital communication in
cyberspace. In this paper, we addressed the use of TOR relay nodes for
anonymizing communications in deep web forums. We propose a novel approach for
detecting cyberthreats using a deep learning algorithm Long Short-Term Memory
(LSTM). The developed model outperformed the experimental results of other
researchers in this problem domain with an accuracy of 94\% and precision of
90\%. Our model can be easily deployed by organizations in securing digital
communications and detection of vulnerability exposure before cyberattack.


title:
Predicting tensorial molecular properties with equivariant
  machine-learning models

abstract:
  Embedding molecular symmetries into machine-learning models is key for
efficient learning of chemico-physical scalar properties, but little evidence
on how to extend the same strategy to tensorial quantities exists. Here we
formulate a scalable equivariant machine-learning model based on local atomic
environment descriptors. We apply it to a series of molecules and show that
accurate predictions can be achieved for a comprehensive list of dielectric and
magnetic tensorial properties of different ranks. These results show that
equivariant models are a promising platform to extend the scope of machine
learning in materials modelling.


title: Atomistic Engineering of Phonons in Functional Oxide Heterostructures

abstract:
  Engineering of phonons, i.e., collective lattice vibrations in crystals, is
essential for manipulating physical properties of materials such as thermal
transport, electron-phonon interaction, confinement of lattice vibration, and
optical polarization. Most approaches to phonon-engineering have been largely
limited to the high-quality heterostructures of typical semiconductors. Yet,
artificial engineering of phonons in a variety of materials with functional
properties, such as complex oxides, will yield unprecedented applications of
coherent tunable phonons in future quantum acoustic devices. In this study, we
demonstrate artificial engineering of phonons in the atomic-scale SrRuO3/SrTiO3
superlattices, wherein tunable phonon modes were observed via confocal Raman
spectroscopy. In particular, the coherent superlattices led to the backfolding
of acoustic phonon dispersion, resulting in zone-folded acoustic phonons in the
THz frequency domain. We could further fine-tune the frequencies from 1 to 2
THz via atomic-scale precision thickness control. In addition, we observed a
polar optical phonon originating from the local inversion symmetry breaking in
the artificial oxide superlattices, exhibiting emergent functionality. Our
approach of atomic-scale heterostructuring of complex oxides will vastly expand
material systems for quantum acoustic devices, especially with the viability of
functionality integration.


title: Topology Optimization with Tetra-kai-decahedra and Spheroidal Masks

abstract:
  A novel meshing scheme, based on regular tetra-kai-decahedron, also referred
to as truncated octahedron, cells is presented for use in spatial topology
optimization. A tetra-kai-decahedron mesh ensures face connectivity between
elements thereby eliminating singular solutions from the solution space.
Various other benefits of implementing the said mesh are also highlighted, and
the corresponding finite element is introduced. Material mask overlay strategy
or MMOS, a feature based method for topology optimization is extended for use
in 3-dimensions (MMOS-3D) via the aforementioned finite element and spheroidal
negative masks. Formulation for density computation and sensitivity analysis
for gradient based optimization is developed. Examples on traditional
structural topology optimization problems are presented with detailed
discussion on efficacy of the proposed approach.


title:
Software Complex for the Numerical Solution of the Isotropic
  Imaginary-Axis Eliashberg Equations

abstract:
  At present, the task of searching for compounds with a high superconducting
transition temperature is a very relevant scientific direction. Usually, the
calculation of is carried out by numerically solving the system of Eliashberg
equations. In this paper, a set of programs for solving this system written in
various forms on the imaginary axis is presented. As an example of the
developed methods applications, calculations results of and thermodynamic
properties of the metallic hydrogen I41/AMD phase and some other substances
under high pressure are presented.


title:
Exceptionally high saturation magnetisation in Eu-doped magnetite
  stabilised by spin-orbit interaction

abstract:
  The significance of the spin-orbit interaction is very well known in
compounds containing heavier elements such as the rare-earth Eu ion. Here,
through density functional calculations, we investigated the effect of the
spin-orbit interaction on the magnetic ground state of Eu doped magnetite
($\mathrm{Fe_3O_4:Eu_{Fe}}$). By examining all possible spin alignments between
Eu and magnetite's Fe, we demonstrate that Eu, which is most stable when doped
at the tetrahedral site, adapts a spin almost opposite the substituted Fe.
Consequently, because of smaller spin cancellation between the cations on the
tetrahedral site ($\mathrm{Fe_{Tet}}$ and $\mathrm{Eu_{Tet}}$) and the cations
on the octahedral sites ($\mathrm{Fe_{Oct}}$), $\mathrm{Fe_3O_4:Eu_{Fe}}$
exhibits a maximum saturation magnetisation of 9.451 $\mu_B/$f.u. which is
significantly larger than that of undoped magnetite (calculated to be 3.929
$\mu_B/$f.u.). We further show that this large magnetisation persists through
additional electron doping. However, additional hole doping, which may
unintentionally occur in Fe deficient magnetite, can reduce the magnetisation
to values smaller than that of the undoped magnetite. The results presented
here can aid in designing highly efficient magnetically recoverable catalysts
for which both magnetite and rare earth dopants are common materials.


title: Deep Hierarchy in Bandits

abstract:
  Mean rewards of actions are often correlated. The form of these correlations
may be complex and unknown a priori, such as the preferences of a user for
recommended products and their categories. To maximize statistical efficiency,
it is important to leverage these correlations when learning. We formulate a
bandit variant of this problem where the correlations of mean action rewards
are represented by a hierarchical Bayesian model with latent variables. Since
the hierarchy can have multiple layers, we call it deep. We propose a
hierarchical Thompson sampling algorithm (HierTS) for this problem, and show
how to implement it efficiently for Gaussian hierarchies. The efficient
implementation is possible due to a novel exact hierarchical representation of
the posterior, which itself is of independent interest. We use this exact
posterior to analyze the Bayes regret of HierTS in Gaussian bandits. Our
analysis reflects the structure of the problem, that the regret decreases with
the prior width, and also shows that hierarchies reduce the regret by
non-constant factors in the number of actions. We confirm these theoretical
findings empirically, in both synthetic and real-world experiments.


title:
Error analysis of a fully discrete scheme for the
  Cahn-Hilliard-Magneto-hydrodynamics problem

abstract:
  In this paper we analyze a fully discrete scheme for a general Cahn-Hilliard
equation coupled with a nonsteady Magneto-hydrodynamics flow, which describes
two immiscible, incompressible and electrically conducting fluids with
different mobilities, fluid viscosities and magnetic diffusivities. A typical
fully discrete scheme, which is comprised of conforming finite element method
and the Euler semi-implicit discretization based on a convex splitting of the
energy of the equation is considered in detail. We prove that our scheme is
unconditionally energy stability and obtain some optimal error estimates for
the concentration field, the chemical potential, the velocity field, the
magnetic field and the pressure. The results of numerical tests are presented
to validate the rates of convergence.


title: Fast and explainable clustering based on sorting

abstract:
  We introduce a fast and explainable clustering method called CLASSIX. It
consists of two phases, namely a greedy aggregation phase of the sorted data
into groups of nearby data points, followed by the merging of groups into
clusters. The algorithm is controlled by two scalar parameters, namely a
distance parameter for the aggregation and another parameter controlling the
minimal cluster size. Extensive experiments are conducted to give a
comprehensive evaluation of the clustering performance on synthetic and
real-world datasets, with various cluster shapes and low to high feature
dimensionality. Our experiments demonstrate that CLASSIX competes with
state-of-the-art clustering algorithms. The algorithm has linear space
complexity and achieves near linear time complexity on a wide range of
problems. Its inherent simplicity allows for the generation of intuitive
explanations of the computed clusters.


title:
Parallel domain discretization algorithm for RBF-FD and other meshless
  numerical methods for solving PDEs

abstract:
  In this paper, we present a novel parallel dimension-independent node
positioning algorithm that is capable of generating nodes with variable
density, suitable for meshless numerical analysis. A very efficient sequential
algorithm based on Poisson disc sampling is parallelized for use on
shared-memory computers, such as modern workstations with multi-core
processors. The parallel algorithm uses a global spatial indexing method with
its data divided into two levels, which allows for an efficient multi-threaded
implementation. The addition of bootstrapping enables the algorithm to use any
number of parallel threads while remaining as general as its sequential
variant. We demonstrate the algorithm performance on six complex 2- and
3-dimensional domains, which are either of non-rectangular shape or have
varying nodal spacing or both. We perform a run-time analysis of the algorithm,
to demonstrate its ability to reach high speedups regardless of the domain and
to show how well it scales on the experimental hardware with 16 processor
cores. We also analyse the algorithm in terms of the effects of domain shape,
quality of point placement, and various parallelization overheads.


title:
Implications of Quantum Gravity for Dark Matter Searches with Atom
  Interferometers

abstract:
  In this brief paper, we show that atom interferometer experiments such as
MAGIS, AION or AEDGE have the potential to not only probe very light dark
matter models, but they will also probe quantum gravity. We show that the
linear coupling of a singlet scalar dark matter particle to electrons or
photons is already ruled out by our current understanding of quantum gravity
coupled to data from torsion pendulum experiments. On the other hand, the
quadratic coupling of scalar dark matter to electrons and photons has a large
viable parameter space which will be probed by these atom interferometers.
Implications for searches of quantum gravity are discussed.


title: Concept Bottleneck Model with Additional Unsupervised Concepts

abstract:
  With the increasing demands for accountability, interpretability is becoming
an essential capability for real-world AI applications. However, most methods
utilize post-hoc approaches rather than training the interpretable model. In
this article, we propose a novel interpretable model based on the concept
bottleneck model (CBM). CBM uses concept labels to train an intermediate layer
as the additional visible layer. However, because the number of concept labels
restricts the dimension of this layer, it is difficult to obtain high accuracy
with a small number of labels. To address this issue, we integrate supervised
concepts with unsupervised ones trained with self-explaining neural networks
(SENNs). By seamlessly training these two types of concepts while reducing the
amount of computation, we can obtain both supervised and unsupervised concepts
simultaneously, even for large-sized images. We refer to the proposed model as
the concept bottleneck model with additional unsupervised concepts (CBM-AUC).
We experimentally confirmed that the proposed model outperformed CBM and SENN.
We also visualized the saliency map of each concept and confirmed that it was
consistent with the semantic meanings.


title: Khovanov multicurves are linear

abstract:
  In previous work we introduced a Khovanov multicurve invariant
$\operatorname{\widetilde{Kh}}$ associated with Conway tangles. Applying ideas
from homological mirror symmetry we show that $\operatorname{\widetilde{Kh}}$
is subject to strong geography restrictions: Every component of the invariant
is linear, in the sense that it admits a lift to a curve homotopic to a
straight line in an appropriate planar cover of the tangle boundary.


title:
ExPoSe: Combining State-Based Exploration with Gradient-Based Online
  Search

abstract:
  A tree-based online search algorithm iteratively simulates trajectories and
updates Q-value information on a set of states represented by a tree structure.
Alternatively, policy gradient based online search algorithms update the
information obtained from simulated trajectories directly onto the parameters
of the policy and has been found to be effective. While tree-based methods
limit the updates from simulations to the states that exist in the tree and do
not interpolate the information to nearby states, policy gradient search
methods do not do explicit exploration. In this paper, we show that it is
possible to combine and leverage the strengths of these two methods for
improved search performance. We examine the key reasons behind the improvement
and propose a simple yet effective online search method, named Exploratory
Policy Gradient Search (ExPoSe), that updates both the parameters of the policy
as well as search information on the states in the trajectory. We conduct
experiments on complex planning problems, which include Sokoban and Hamiltonian
cycle search in sparse graphs and show that combining exploration with policy
gradient improves online search performance.


title:
Hyperplane Arrangements Satisfy (un)Twisted Logarithmic Comparison
  Theorems, Applications to $\mathscr{D}_{X}$-modules

abstract:
  For a reduced hyperplane arrangement we prove the analytic Twisted
Logarithmic Comparison Theorem, subject to mild combinatorial arithmetic
conditions on the weights defining the twist. This gives a quasi-isomorphism
between the twisted logarithmic de Rham complex and the twisted meromorphic de
Rham complex. The latter computes the cohomology of the arrangement's
complement with coefficients from the corresponding rank one local system. We
also prove the algebraic variant (when the arrangement is central), and the
analytic and algebraic (untwisted) Logarithmic Comparison Theorems. The last
item positively resolves an old conjecture of Terao and Yuzvinsky. We also
prove that: every nontrivial rank one local system on the complement can be
computed via these Twisted Logarithmic Comparison Theorems; these computations
are explicit finite dimensional linear algebra. Finally, we give some
$\mathscr{D}_{X}$-module applications: for example, we give a sharp restriction
on the codimension one components of the multivariate Bernstein--Sato ideal
attached to an arbitrary factorization of an arrangement. The bound corresponds
to (and, in the univariate case, gives an independent proof of) M. Saito's
result that the roots of the Bernstein--Sato polynomial of a non-smooth,
central, reduced arrangement live in $(-2 + 1/d, 0).$


title: Minimax rate of consistency for linear models with missing values

abstract:
  Missing values arise in most real-world data sets due to the aggregation of
multiple sources and intrinsically missing information (sensor failure,
unanswered questions in surveys...). In fact, the very nature of missing values
usually prevents us from running standard learning algorithms. In this paper,
we focus on the extensively-studied linear models, but in presence of missing
values, which turns out to be quite a challenging task. Indeed, the Bayes rule
can be decomposed as a sum of predictors corresponding to each missing pattern.
This eventually requires to solve a number of learning tasks, exponential in
the number of input features, which makes predictions impossible for current
real-world datasets. First, we propose a rigorous setting to analyze a
least-square type estimator and establish a bound on the excess risk which
increases exponentially in the dimension. Consequently, we leverage the missing
data distribution to propose a new algorithm, andderive associated adaptive
risk bounds that turn out to be minimax optimal. Numerical experiments
highlight the benefits of our method compared to state-of-the-art algorithms
used for predictions with missing values.


title:
A quantum searching model finding one of the edges of a subgraph in a
  complete graph

abstract:
  Some of the quantum searching models have been given by perturbed quantum
walks. Driving some perturbed quantum walks, we may quickly find one of the
targets with high probability. In this paper, we construct a quantum searching
model finding one of the edges of a given subgraph in a complete graph. How to
construct our model is that we label the arcs by $+1$ or $-1$, and define a
perturbed quantum walk by the sign function on the set of arcs. After that, we
detect one of the edges labeled $-1$ by the induced sign function as fast as
possible. This idea was firstly proposed by Segawa et al. in 2021. They only
addressed the case where the subgraph forms a matching, and obtained by a
combinatorial argument that the time of finding one of the edges of the
subgraph is quadratically faster than a classical searching model. In this
paper, we show that the model is valid for any subgraph, i.e., we obtain by
spectral analysis a quadratic speed-up for finding one of the edges of the
subgraph in a complete graph.


title:
Eyring-Kramers type formulas for some piecewise deterministic Markov
  processes

abstract:
  In this work, we give sharp asymptotic equivalents in the small temperature
regime of the smallest eigenvalues of the generator of some piecewise
deterministic Markov processes (including the ZigZag process and the Bouncy
Particle Sampler process) with refreshment rate $\alpha$ on the one-dimensional
torus T. These asymptotic equivalents are usually called Eyring-Kramers type
formulas in the literature. The case when the refreshment rate $\alpha$
vanishes on T is also considered.


title: Quantifying knowledge synchronisation in the 21st century

abstract:
  Humans acquire and accumulate knowledge through language usage and eagerly
exchange their knowledge for advancement. Although geographical barriers had
previously limited communication, the emergence of information technology has
opened new avenues for knowledge exchange. However, it is unclear which
communication pathway is dominant in the 21st century. Here, we explore the
dominant path of knowledge diffusion in the 21st century using Wikipedia, the
largest communal dataset. We evaluate the similarity of shared knowledge
between population groups, distinguished based on their language usage. When
population groups are more engaged with each other, their knowledge structure
is more similar, where engagement is indicated by socioeconomic connections,
such as cultural, linguistic, and historical features. Moreover, geographical
proximity is no longer a critical requirement for knowledge dissemination.
Furthermore, we integrate our data into a mechanistic model to better
understand the underlying mechanism and suggest that the knowledge "Silk Road"
of the 21st century is based online.


title: Minimal non-abelian nodal braiding in ideal metamaterials

abstract:
  Exploring new topological phases and phenomena has become a vital topic in
condensed matter physics and material sciences. It is generally believed that a
pair of band nodes with opposite topological charges will annihilate after
collision. Recent studies reveal that a braided colliding nodal pair can be
stabilized in a multi-gap system with PT or C_2z T symmetry. Beyond the
conventional single-gap abelian band topology, this intriguing phenomenon
exemplifies non-abelian topological charges. Here, we construct ideal acoustic
metamaterials to realize non-abelian braiding with the fewest band nodes. We
experimentally observe an elegant but nontrivial nodal braiding process,
including nodes creation, braiding, collision, and repulsion (i.e., failure to
annihilate), and measure the mirror eigenvalues to elucidate the essential
braiding consequence. The latter, at the level of wavefunctions, is of prime
importance since the braiding physics essentially aims to entangle multi-band
wavefunctions. Furthermore, we experimentally unveil the highly intricate
correspondence between the edge responses and the bulk non-abelian charges.
Notably, all our experimental data perfectly reproduce our numerical
simulations. Our findings pave the way for developing non-abelian topological
physics that is still in its infancy.


title:
A unified surrogate-based scheme for black-box and preference-based
  optimization

abstract:
  Black-box and preference-based optimization algorithms are global
optimization procedures that aim to find the global solutions of an
optimization problem using, respectively, the least amount of function
evaluations or sample comparisons as possible. In the black-box case, the
analytical expression of the objective function is unknown and it can only be
evaluated through a (costly) computer simulation or an experiment. In the
preference-based case, the objective function is still unknown but it
corresponds to the subjective criterion of an individual. So, it is not
possible to quantify such criterion in a reliable and consistent way.
Therefore, preference-based optimization algorithms seek global solutions using
only comparisons between couples of different samples, for which a human
decision-maker indicates which of the two is preferred. Quite often, the
black-box and preference-based frameworks are covered separately and are
handled using different techniques. In this paper, we show that black-box and
preference-based optimization problems are closely related and can be solved
using the same family of approaches, namely surrogate-based methods. Moreover,
we propose the generalized Metric Response Surface (gMRS) algorithm, an
optimization scheme that is a generalization of the popular MSRS framework.
Finally, we provide a convergence proof for the proposed optimization method.


title:
Investigation of Torque Ripple in Voltage Source Inverter driven
  Induction Motor Drive operated with Space Vector based Harmonic Elimination
  Pulse Width Modulation Scheme

abstract:
  The lower order harmonic elimination using space vector pulse width
modulation (SVPWM) technique is possible by introducing the dwell time division
coefficient 'k' in space vector. In this paper, torque ripple analysis is
carried out for space vector based harmonic elimination (SVHE) scheme and the
obtained results are compared with the existing schemes such as conventional
and advanced bus clamping SVPWM schemes. Further, the obtained results are
validated through simulation results. The SVHE scheme gives the lower torque
ripple than the conventional and advance bus clamping schemes at higher
modulation indices.


title:
Towards 3D Scene Reconstruction from Locally Scale-Aligned Monocular
  Video Depth

abstract:
  Existing monocular depth estimation methods have achieved excellent
robustness in diverse scenes, but they can only retrieve affine-invariant
depth, up to an unknown scale and shift. However, in some video-based scenarios
such as video depth estimation and 3D scene reconstruction from a video, the
unknown scale and shift residing in per-frame prediction may cause the depth
inconsistency. To solve this problem, we propose a locally weighted linear
regression method to recover the scale and shift with very sparse anchor
points, which ensures the scale consistency along consecutive frames. Extensive
experiments show that our method can boost the performance of existing
state-of-the-art approaches by 50% at most over several zero-shot benchmarks.
Besides, we merge over 6.3 million RGBD images to train strong and robust depth
models. Our produced ResNet50-backbone model even outperforms the
state-of-the-art DPT ViT-Large model. Combining with geometry-based
reconstruction methods, we formulate a new dense 3D scene reconstruction
pipeline, which benefits from both the scale consistency of sparse points and
the robustness of monocular methods. By performing the simple per-frame
prediction over a video, the accurate 3D scene shape can be recovered.


title:
Variational integrators for non-autonomous systems with applications to
  stabilization of multi-agent formations

abstract:
  Numerical methods that preserve geometric invariants of the system, such as
energy, momentum or the symplectic form, are called geometric integrators.
Variational integrators are an important class of geometric integrators. The
general idea for those variational integrators is to discretize Hamilton's
principle rather than the equations of motion in a way that preserves some of
the invariants of the original system. In this paper we construct variational
integrators with fixed time step for time-dependent Lagrangian systems
modelling an important class of autonomous dissipative systems. These
integrators are derived via a family of discrete Lagrangian functions each one
for a fixed time-step. This allows to recover at each step on the set of
discrete sequences the preservation properties of variational integrators for
autonomous Lagrangian systems, such as symplecticity or backward error analysis
for these systems. We also present a discrete Noether theorem for this class of
systems. Applications of the results are shown for the problem of formation
stabilization of multi-agent systems.


title:
Lattice QCD calculation of the two-photon exchange contribution to the
  muonic-hydrogen Lamb shift

abstract:
  We develop a method for lattice QCD calculation of the two-photon exchange
contribution to the muonic-hydrogen Lamb shift. To demonstrate its feasibility,
we present the first lattice calculation with a gauge ensemble at $m_\pi = 142$
MeV. By adopting the infinite-volume reconstruction method along with an
optimized subtraction scheme, we obtain $\Delta E_{\text{TPE}} =
-28.9(4.9)~\mu\text{eV} + 93.72~\mu\text{eV}/\text{fm}^2 \cdot\langle r_p^2
\rangle$, or $\Delta E_{\text{TPE}} = 37.4(4.9)~\mu$eV, which is consistent
with the previous theoretical results in a range of 20-50 $\mu$eV.


title: A multi-domain virtual network embedding algorithm with delay prediction

abstract:
  Virtual network embedding (VNE) is an crucial part of network virtualization
(NV), which aims to map the virtual networks (VNs) to a shared substrate
network (SN). With the emergence of various delay-sensitive applications, how
to improve the delay performance of the system has become a hot topic in
academic circles. Based on extensive research, we proposed a multi-domain
virtual network embedding algorithm based on delay prediction (DP-VNE).
Firstly, the candidate physical nodes are selected by estimating the delay of
virtual requests, then particle swarm optimization (PSO) algorithm is used to
optimize the mapping process, so as to reduce the delay of the system. The
simulation results show that compared with the other three advanced algorithms,
the proposed algorithm can significantly reduce the system delay while keeping
other indicators unaffected.


title:
Particle Flow with a Hybrid Segmented Crystal and Fiber Dual-Readout
  Calorimeter

abstract:
  In the reconstruction of physics events at future e$^+$e$^-$ colliders the
calorimeter design has a crucial role in the overall detector performance. The
reconstruction of events with many jets in their final state sets stringent
requirements on the jet energy and angular resolutions. The energy resolution
for jets with energy of about 45 GeV is required to be at the 4-5\% level to
enable an efficient separation of the W and Z boson invariant masses. We
demonstrate in this paper how such a performance can be achieved by exploiting
a particle flow algorithm tailored for a hybrid dual-readout calorimeter made
of segmented crystals and fibers. The excellent energy resolution and linearity
of such calorimeter for both photons and neutral hadrons ($3\%/\sqrt{E}$ and
$26\%/\sqrt{E}$, respectively), inherent to the homogeneous crystals and
dual-readout technological choices, provides a powerful handle for the
development of a new approach for particle identification and jet
reconstruction. While the dual-readout particle flow algorithm (DR-PFA)
presented in this paper is at its early stage of development, it already
demonstrates the potential of a hybrid dual-readout calorimeter for jet
reconstruction by improving the jet energy resolution with respect to a
calorimeter-only reconstruction from 6.0\% to about 4.5\% for 45 GeV jets.


title: Multitube monitors: a new-generation of neutron beam monitors

abstract:
  With the renewal of many neutron science instruments and the commissioning of
new neutron facilities, there is a rising demand for improved neutron beam
monitoring systems with reduced beam perturbations and higher counting rate
capability. Fission chambers are the most popular beam monitors; however, their
use on some instruments may be prevented by the background generated by fast
neutrons emitted during neutron captures in 235U and by neutrons scattered in
the material of the fission chamber. Multitube detectors, on the other hand,
offer a good alternative with minimum beam perturbations. The purpose of this
paper is to report and analyse the results of the measurements performed with
several Multitubes used for beam monitoring. We show that the transparency of
Multitube beam monitors is 97.6 +/-0.4 %, and that their detection efficiency
is uniform, with a deviation from the mean value < 0.7%. A counting rate
reduction of 10% due to pile-up effects is measured at a rate of 550 kHz. In
addition to neutron beam intensity monitoring, the Multitube can be configured
for 1-dimensional or 2-dimensional localisation. We present the preliminary
results of these additional functionalities.


title:
Long time self-modulation of nonlinear electromagnetic wave in
  two-dimensional cavity

abstract:
  The vacuum is expected to exhibit electromagnetic nonlinearity. We
demonstrate the properties of nonlinear electromagnetic wave in a
two-dimensional rectangular cavity by calculating the nonlinear correction for
two classical standing waves. We apply the linear approximation in a short
timescale. A part of the nonlinear correction increases with time. In
particular, a one-dimensional second harmonic grows if the cavity size
satisfies a specific condition. We also analyze the nonlinear electromagnetic
wave in a timescale longer than the applicable limit of the linear
approximation. We formulate the self-modulation of the amplitude and phase,
including the effect of static magnetic flux density. In the viewpoint of
energy flow between the two modes of the standing wave, the behavior of
nonlinear electromagnetic wave can be classified into three types. Namely, the
energy flow keeps oscillating, eventually decreases to zero, or never occurs.


title:
Unsourced Random Access with a Massive MIMO Receiver Using Multiple
  Stages of Orthogonal Pilots

abstract:
  We study the problem of unsourced random access (URA) over Rayleigh
block-fading channels with a receiver equipped with multiple antennas. We
employ multiple stages of orthogonal pilots, each of which is randomly picked
from a codebook. In the proposed scheme, each user encodes its message using a
polar code and appends it to the selected pilot sequences to construct its
transmitted signal. Accordingly, the received signal consists of superposition
of the users' signals each composed of multiple pilot parts and a polar coded
part. We use an iterative approach for decoding the transmitted messages along
with a suitable successive interference cancellation scheme. Performance of the
proposed scheme is illustrated via extensive set of simulation results which
show that it significantly outperforms the existing approaches for URA over
multi-input multi-output fading channels.


title:
Trajectory Forecasting from Detection with Uncertainty-Aware Motion
  Encoding

abstract:
  Trajectory forecasting is critical for autonomous platforms to make safe
planning and actions. Currently, most trajectory forecasting methods assume
that object trajectories have been extracted and directly develop trajectory
predictors based on the ground truth trajectories. However, this assumption
does not hold in practical situations. Trajectories obtained from object
detection and tracking are inevitably noisy, which could cause serious
forecasting errors to predictors built on ground truth trajectories. In this
paper, we propose a trajectory predictor directly based on detection results
without relying on explicitly formed trajectories. Different from the
traditional methods which encode the motion cue of an agent based on its
clearly defined trajectory, we extract the motion information only based on the
affinity cues among detection results, in which an affinity-aware state update
mechanism is designed to take the uncertainty of association into account. In
addition, considering that there could be multiple plausible matching
candidates, we aggregate the states of them. This design relaxes the
undesirable effect of noisy trajectory obtained from data association.
Extensive ablation experiments validate the effectiveness of our method and its
generalization ability on different detectors. Cross-comparison to other
forecasting schemes further proves the superiority of our method. Code will be
released upon acceptance.


title:
MRI Reconstruction via Data Driven Markov Chain with Joint Uncertainty
  Estimation

abstract:
  We introduce a framework that enables efficient sampling from learned
probability distributions for MRI reconstruction. Different from conventional
deep learning-based MRI reconstruction techniques, samples are drawn from the
posterior distribution given the measured k-space using the Markov chain Monte
Carlo (MCMC) method. In addition to the maximum a posteriori (MAP) estimate for
the image, which can be obtained with conventional methods, the minimum mean
square error (MMSE) estimate and uncertainty maps can also be computed. The
data-driven Markov chains are constructed from the generative model learned
from a given image database and are independent of the forward operator that is
used to model the k-space measurement. This provides flexibility because the
method can be applied to k-space acquired with different sampling schemes or
receive coils using the same pre-trained models. Furthermore, we use a
framework based on a reverse diffusion process to be able to utilize advanced
generative models. The performance of the method is evaluated on an open
dataset using 10-fold accelerated acquisition.


title:
Characterization of the electronic ground state of Mg$_2^+$ by PFI-ZEKE
  photoelectron spectroscopy

abstract:
  The $\mathrm{X}^+\ ^2\Sigma_u^+$ ground electronic state of the Mg$_2^+$ ion
has been studied by pulsed-field-ionization zero-kinetic-energy (PFI-ZEKE)
photoelectron spectroscopy. Rotationally cold Mg$_2$ molecules produced in a
laser-ablation supersonic-expansion source were ionized by resonant two-photon
absorption from the $\mathrm{X}\ ^1\Sigma_g^+(v"=0)$ ground vibronic state. The
photoelectron spectra of the $v^+=3-14$ vibrational levels of the
$\mathrm{X}^+$ state were recorded with rotational resolution and their
analysis led to the determination of accurate term values and rotational
constants for these levels. Improved values of the adiabatic ionization energy
of $^{24}$Mg$_2$ ($51\,503.9(4)$ cm$^{-1}$) and of the ground-state
dissociation energy of $^{24}$Mg$_2^+$ ($10 572.3(6)$ cm$^{-1}$) were
determined from experimental data.


title:
Statistical inference in factor analysis for diffusion processes from
  discrete observations

abstract:
  We consider statistical inference in factor analysis for ergodic and
non-ergodic diffusion processes from discrete observations. Factor model based
on high frequency time series data has been mainly discussed in the field of
high dimensional covariance matrix estimation. In this field, the method based
on principal component analysis has been mainly used. However, this method is
effective only for high dimensional model. On the other hand, there is a method
based on the quasi-likelihood. However, since the factor is assumed to be
observable, we cannot use this method when the factor is latent. Thus, the
existing methods are not effective when the factor is latent and the dimension
of the observable variable is not so high. Therefore, we propose an effective
method in the situation.


title:
Machine learning enabled multiple illumination quantitative optoacoustic
  imaging of blood oxygenation in humans

abstract:
  Optoacoustic (OA) imaging is a promising modality for quantifying blood
oxygen saturation (sO$_2$) in various biomedical applications - in diagnosis,
monitoring of organ function or even tumor treatment planning. We present an
accurate and practically feasible real-time capable method for quantitative
imaging of sO$_2$ based on combining multispectral (MS) and multiple
illumination (MI) OA imaging with learned spectral decoloring (LSD). For this
purpose we developed a hybrid real-time MI MS OA imaging setup with ultrasound
(US) imaging capability; we trained gradient boosting machines on MI spectrally
colored absorbed energy spectra generated by generic Monte Carlo simulations,
and used the trained models to estimate sO$_2$ on real OA measurements. We
validated MI-LSD in silico and on in vivo image sequences of radial arteries
and accompanying veins of five healthy human volunteers. We compared the
performance of the method to prior LSD work and conventional linear unmixing.
MI-LSD provided highly accurate results in silico and consistently plausible
results in vivo. This preliminary study shows a potentially high applicability
of quantitative OA oximetry imaging, using our method.


title:
Variable Slope Trapezoidal Circulating Current Injection to Attenuate
  Capacitor Voltage Ripple in Modular Multilevel Converter Based Variable Speed
  Motor Drives Application

abstract:
  The main challenge in using the Modular Multilevel Converter-based
constant-torque variable-speed motor drives is increased sub-module capacitor
voltage ripples (SM-CVR) at lowfundamental frequency operation, due to the
inverse relationship between SM-CVR and operating frequency. To address this
issue, a variable slope trapezoidal circulating current (CC) is injected along
with square wave common-mode voltage (CMV). Compared to sinusoidal CC and
sinusoidal CMV injection, the proposed injection technique can reduce the peak
of the CC in the range of 0% to 50%, resulting in lesser device stress and
improved efficiency. Simulation results of the proposed technique are
presented, and they are further compared with the existing injection techniques
to show the superiority


title: Associated timelike helices in Minkowski 3-space

abstract:
  In this study, some new types of timelike general helices associated to a
non-lightlike curve are introduced in Minkowski 3-space. These new helices are
called associated timelike helices. Some special types of associated timelike
helices are introduced and also by considering the conditions that reference
curve is a non-lightlike helix or a spacelike slant helix, the position vectors
of these new timelike helices are determinate.


title:
Memory effects in the density-wave imbalance in delocalized disordered
  systems

abstract:
  Dynamics of the imbalance in occupations on even and odd sites of a lattice
serves as one of the key characteristics for identification of the many-body
localization transition. In this work, we investigate the long-time behaviour
of the imbalance in disordered one- and two-dimensional many-body systems in
the regime of diffusive or subdiffusive transport. We show that memory effects
originating from a coupling between slow and fast modes lead to a power-law
decay of the imbalance, with the exponent determined by the diffusive (or
subdiffusive) transport law and the spatial dimensionality. Analytical results
are supported by numerical simulations performed on a two-dimensional system in
the regime of weak localization.


title:
Entropic force and real-time dynamics of holographic quarkonium in a
  magnetic field

abstract:
  We continue the study of a recently constructed holographic QCD model
supplemented with magnetic field. We consider the holographic dual of a quark,
anti-quark pair and investigate its entropy beyond the deconfinement phase
transition in terms of interquark distance, temperature and magnetic field. We
obtain a clear magnetic field dependence in the strongly decreasing entropy
near deconfinement and in the entropy variation for growing interquark
separation. We uncover various supporting evidences for inverse magnetic
catalysis. The emergent entropic force is found to become stronger with
magnetic field, promoting the quarkonium dissociation. We also probe the
dynamical dissociation of the quarkonium state, finding a faster dissociation
with magnetic field. At least the static predictions should become amenable to
a qualitative comparison with future lattice data.


title:
A benchmark of state-of-the-art sound event detection systems evaluated
  on synthetic soundscapes

abstract:
  This paper proposes a benchmark of submissions to Detection and
Classification Acoustic Scene and Events 2021 Challenge (DCASE) Task 4
representing a sampling of the state-of-the-art in Sound Event Detection task.
The submissions are evaluated according to the two polyphonic sound detection
score scenarios proposed for the DCASE 2021 Challenge Task 4, which allow to
make an analysis on whether submissions are designed to perform fine-grained
temporal segmentation, coarse-grained temporal segmentation, or have been
designed to be polyvalent on the scenarios proposed. We study the solutions
proposed by participants to analyze their robustness to varying level target to
non-target signal-to-noise ratio and to temporal localization of target sound
events. A last experiment is proposed in order to study the impact of
non-target events on systems outputs. Results show that systems adapted to
provide coarse segmentation outputs are more robust to different target to
non-target signal-to-noise ratio and, with the help of specific data
augmentation methods, they are more robust to time localization of the original
event. Results of the last experiment display that systems tend to spuriously
predict short events when non-target events are present. This is particularly
true for systems that are tailored to have a fine segmentation.


title: Mobility Performance Analysis of Scalable Cell-Free Massive MIMO

abstract:
  While scalable cell-free massive MIMO (CF-mMIMO) shows advantages in static
conditions, the impact of its changing serving access point (AP) set in a
mobile network is not yet addressed. In this paper we first derive the CPU
cluster and AP handover rates of scalable CF-mMIMO as exact numerical results
and tight closed form approximations. We then use our closed form handover rate
result to analyse the mobility-aware throughput. We compare the mobility-aware
spectral efficiency (SE) of scalable CF-mMIMO against distributed MIMO with
pure network- and UE-centric AP selection, for different AP densities and
handover delays. Our results reveal an important trade-off for future dense
networks with low control delay: under moderate to high mobility, scalable
CF-mMIMO maintains its advantage for the 95th-percentile users but at the cost
of degraded median SE.


title: The Price of Distributed: Rate Loss in the CEO Problem

abstract:
  In the distributed remote (CEO) source coding problem, many separate encoders
observe independently noisy copies of an underlying source. The rate loss is
the difference between the rate required in this distributed setting and the
rate that would be required in a setting where the encoders can fully
cooperate. In this sense, the rate loss characterizes the price of distributed
processing. We survey and extend the known results on the rate loss in various
settings, with a particular emphasis on the case where the noise in the
observations is Gaussian, but the underlying source is general.


title:
On the Utility of Marrying GIN and PMD for Improving Stack Overflow Code
  Snippets

abstract:
  Software developers are increasingly dependent on question and answer portals
and blogs for coding solutions. While such interfaces provide useful
information, there are concerns that code hosted here is often incorrect,
insecure or incomplete. Previous work indeed detected a range of faults in code
provided on Stack Overflow through the use of static analysis. Static analysis
may go a far way towards quickly establishing the health of software code
available online. In addition, mechanisms that enable rapid automated program
improvement may then enhance such code. Accordingly, we present this proof of
concept. We use the PMD static analysis tool to detect performance faults for a
sample of Stack Overflow Java code snippets, before performing mutations on
these snippets using GIN. We then re-analyse the performance faults in these
snippets after the GIN mutations. GIN's RandomSampler was used to perform
17,986 unique line and statement patches on 3,034 snippets where PMD violations
were removed from 770 patched versions. Our outcomes indicate that static
analysis techniques may be combined with automated program improvement methods
to enhance publicly available code with very little resource requirements. We
discuss our planned research agenda in this regard.


title: Magnetism of Al$_x$Fe$_{2-x}$GeO$_5$ with Andalusite Structure

abstract:
  The magnetism of Al$_x$Fe$_{2-x}$GeO$_5$ from $x$ = 0.09 to $x$ = 0.91 with
andalusite structure was examined. The magnetic properties of
Al$_x$Fe$_{2-x}$GeO$_5$ at low temperatures were found to be weak
ferromagnetic-like state for $x$ $<$ 0.3 and spin-glass state for $x$ $>$ 0.3.
The small spontaneous magnetization that appears in the weak ferromagnetic-like
phase would be due to the presence of Dzyaloshinsky-Moriya interaction or to
the difference in the magnitude of the magnetic moment of Fe$^{3+}$ in the
octahedral and trigonal bipyramidal sites. The appearance of the spin-glass
phase indicates that the dilution of Fe ions by Al ions in
Al$_x$Fe$_{2-x}$GeO$_5$ causes the competition between ferromagnetic and
antiferromagnetic interactions. The $x$ dependence of the site occupancy of Fe
ions suggests that Fe$_2$GeO$_5$ with andalusite structure cannot be
synthesized.


title: Note on BDL property of fixed points of primitive morphisms

abstract:
  We consider an infinite word $\boldsymbol{u}$ fixed by a primitive morphism.
We show a necessary condition under which $\boldsymbol{u}$ has a non-trivial
geometric representation which is bounded distance equivalent to a lattice.


title:
Spatial Computing and Intuitive Interaction: Bringing Mixed Reality and
  Robotics Together

abstract:
  Spatial computing -- the ability of devices to be aware of their surroundings
and to represent this digitally -- offers novel capabilities in human-robot
interaction. In particular, the combination of spatial computing and egocentric
sensing on mixed reality devices enables them to capture and understand human
actions and translate these to actions with spatial meaning, which offers
exciting new possibilities for collaboration between humans and robots. This
paper presents several human-robot systems that utilize these capabilities to
enable novel robot use cases: mission planning for inspection, gesture-based
control, and immersive teleoperation. These works demonstrate the power of
mixed reality as a tool for human-robot interaction, and the potential of
spatial computing and mixed reality to drive the future of human-robot
interaction.


title:
PARCEL: Physics-based unsupervised contrastive representation learning
  for parallel MR imaging

abstract:
  With the successful application of deep learning to magnetic resonance (MR)
imaging, parallel imaging techniques based on neural networks have attracted
wide attention. However, in the absence of high-quality, fully sampled datasets
for training, the performance of these methods is limited. To address this
issue, this paper proposes a Physics-bAsed unsupeRvised Contrastive
rEpresentation Learning (PARCEL) method to speed up parallel MR imaging.
Specifically, PARCEL has a parallel framework to contrastively learn two
branches of model-based unrolling networks directly from augmented undersampled
k-space data. A sophisticated co-training loss with three essential components
has been designed to guide the two networks in capturing the inherent features
and representations for MR images. And the final MR image is reconstructed with
the trained contrastive networks. PARCEL was evaluated on in vivo datasets and
compared to five state-of-the-art methods. The results show that PARCEL is able
to learn useful representations for more accurate MR reconstructions without
relying on fully sampled datasets.


title: A novel holographic quantum phase transition and butterfly velocity

abstract:
  In this paper, we make a systematical and in-depth exploration on the phase
structure and the behaviors of butterfly velocity in an
Einstein-Maxwell-dilaton-axions (EMDA) model. Depending on the model parameter,
there are two kinds of mechanisms driving quantum phase transition (QPT) in
this model. One is the infrared (IR) geometry to be renormalization group (RG)
unstable, and the other is the strength of lattice deformation leading to some
kind of bifurcating solution. We also find a novel QPT in the metal phases. The
study on the behavior of the butterfly velocity crossing QPT indicates that the
butterfly velocity or its first derivative exhibiting local extreme depends on
the QPT mechanism. Further, the scaling behaviors of the butterfly velocity in
the zero-temperature limit confirm that different phases are controlled by
different IR geometries. Therefore, the butterfly velocity is a good probe to
QPT and it also provides a possible way to study QPT beyond holography.


title:
Absolute continuity of the solution to stochastic generalized
  Burgers-Huxley equation

abstract:
  The present work deals with the global solvability as well as absolute
continuity of the law of the solution to stochastic generalized Burgers-Huxley
(SGBH) equation driven by multiplicative space-time white noise in a bounded
interval of $\R$. We first prove the existence of a unique local mild solution
to SGBH equation with the help of a truncation argument and contraction mapping
principle. Then global solvability results are obtained by using uniform bounds
of the local mild solution and stopping time arguments. Later, we establish a
comparison theorem for the solution of SGBH equation having higher order
nonlinearities and it plays a crucial role in this work. Then, we discuss the
weak differentiability of the solution to SGBH equation in the Malliavin
calculus sense. Finally, we obtain the absolute continuity of the law of the
solution with respect to the Lebesgue measure on $\mathbb{R}$, and the
existence of density with the aid of comparison theorem and weak
differentiability of the solution.


title:
End-to-End Latency Analysis and Optimal Block Size of Proof-of-Work
  Blockchain Applications

abstract:
  Due to the increasing interest in blockchain technology for fostering secure,
auditable, decentralized applications, a set of challenges associated with this
technology need to be addressed. In this letter, we focus on the delay
associated with Proof-of-Work (PoW)-based blockchain networks, whereby
participants validate the new information to be appended to a distributed
ledger via consensus to confirm transactions. We propose a novel end-to-end
latency model based on batch-service queuing theory that characterizes timers
and forks for the first time. Furthermore, we derive an estimation of optimum
block size analytically. Endorsed by simulation results, we show that the
optimal block size approximation is a consistent method that leads to
close-to-optimal performance by significantly reducing the overheads associated
with blockchain applications.


title: Towards Understanding First-Party Cookie Tracking in the Field

abstract:
  Third-party web tracking is a common, and broadly used technique on the Web.
Almost every step of users' is tracked, analyzed, and later used in different
use cases (e.g., online advertisement). Different defense mechanisms have
emerged to counter these practices (e.g., the recent step of browser vendors to
ban all third-party cookies). However, all of these countermeasures only target
third-party trackers, and ignore the first party because the narrative is that
such monitoring is mostly used to improve the utilized service (e.g.,
analytical services).
  In this paper, we present a large-scale measurement study that analyzes
tracking performed by the first party but utilized by a third party to
circumvent standard tracking preventing techniques (i.e., the first party
performs the tracking in the name of the third party). We visit the top 15,000
websites to analyze first-party cookies used to track users and a technique
called "DNS CNAME cloaking", which can be used by a third party to place
first-party cookies. Using this data, we show that 76% sites in our dataset
effectively utilize such tracking techniques, and in a long-running analysis,
we show that the usage of such cookies increased by more than 50% over 2021.
Furthermore, we shed light on the ecosystem utilizing first-party trackers, and
find that the established trackers already use such tracking, presumably to
avoid tracking blocking.


title:
On the possibility of a significant increase in the storage time of
  ultracold neutrons in traps coated with a liquid helium film

abstract:
  It is shown that rough inner walls of a trap of ultracold neutrons can be
coated with a superfluid helium film much thicker than the depth of penetration
of ultracold neutrons into helium. This coating should reduce the rate of loss
of ultracold neutrons caused by absorption in the walls of the trap by orders
of magnitude. It is demonstrated that triangular roughness is more efficient
than rectangular for the reduction of the rate of loss of ultracold neutrons.
Triangular roughness is more easily implemented technically and such
diffraction gratings are fabricated industrially. Other methods are proposed to
increase the thickness of the protective helium film.


title: On the multiple generations of planetary embryos

abstract:
  Global models of planet formation tend to begin with an initial set of
planetary embryos for the sake simplicity. While this approach gives valuable
insights on the evolution of the initial embryos, the initial distribution
itself is a bold assumption. Limiting oneself to an initial distribution may
neglect essential physics that precedes, or follows said initial distribution.
We wish to investigate the effect of dynamic planetary embryo formation on the
formation of planetary systems. The presented framework begins with an initial
disk of gas, dust and pebbles. The disk evolution, the formation of
planetesimals and the formation of planetary embryos is modeled consistently.
Embryos then grow by pebble, planetesimal and eventually gas accretion. Planet
disk interactions and N-body dynamics with other simultaneously growing embryos
is included in the framework. We show that the formation of planets can occur
in multiple consecutive phases. Earlier generations grow massive by pebble
accretion but are subject to fast type I migration and thus accretion to the
star. The later generations of embryos that form grow to much smaller masses by
planetesimal accretion, as the amount of pebbles in the disk has vanished. The
formation history of planetary systems may be far more complex than an initial
distribution of embryos could reflect. The dynamic formation of planetary
embryos needs to be considered in global models of planet formation to allow
for a complete picture of the systems evolution.


title: Approximation of Discrete Measures by Finite Point Sets

abstract:
  For a probability measure $\mu$ on $[0,1]$ without discrete component, the
best possible order of approximation by a finite point set in terms of the
star-discrepancy is $\frac{1}{2N}$ as has been proven relatively recently.
However, if $\mu$ contains a discrete component no non-trivial lower bound
holds in general because it is straightforward to construct examples without
any approximation error in this case. This might explain, why the approximation
of discrete measures on $[0,1]$ by finite point sets has so far not been
completely covered in the existing literature. In this note, we close this gap
by giving a complete description of the discrete case. Most importantly, we
prove that for any discrete measure the best possible order of approximation is
for infinitely many $N$ bounded from below by $\frac{1}{cN}$ for some constant
$c \geq 2$ which depends on the measure. This implies, that for a finitely
supported discrete measure on $[0,1]^d$ the known possible order of
approximation $\frac{1}{N}$ is indeed the optimal one.


title: Density profile of ambient circumnuclear medium in Seyfert 1 galaxies

abstract:
  The shape of the ambient circumnuclear medium (ACM) density profile can probe
the history of accretion onto the central supermassive black hole in galaxies
and the circumnuclear environment. However, due to the limitation of the
instrument resolution, the density profiles of the ACM for most of galaxies
remain largely unknown. In this work, we propose a novel method to measure the
ACM density profile of active galactic nucleus (AGN) by the equilibrium between
the radiation pressure on the warm absorbers (WAs, a type of AGN outflows) and
the drag pressure from the ACM. We study the correlation between the outflow
velocity and ionization parameter of WAs in each of the five Seyfert 1 galaxies
(NGC 3227, NGC 3783, NGC 4051, NGC 4593, and NGC 5548), inferring that the
density profile of the ACM is between n\propto r^-1.7 and n \propto r^-2.15 (n
is number density and r is distance) from 0.01 pc to pc scales in these five
AGNs. Our results indicate that the ACM density profile in Seyfert 1 galaxies
is steeper than the prediction by the spherically symmetric Bondi accretion
model and the simulated results of the hot accretion flow, but more in line
with the prediction by the standard thin disk model.


title:
Global sensitivity analysis based on Gaussian-process metamodelling for
  complex biomechanical problems

abstract:
  Biomechanical models often need to describe very complex systems, organs or
diseases, and hence also include a large number of parameters. One of the
attractive features of physics-based models is that in those models (most)
parameters have a clear physical meaning. Nevertheless, the determination of
these parameters is often very elaborate and costly and shows a large scatter
within the population. Hence, it is essential to identify the most important
parameter for a particular problem at hand. In order to distinguish parameters
which have a significant influence on a specific model output from
non-influential parameters, we use sensitivity analysis, in particular the
Sobol method as a global variance-based method. However, the Sobol method
requires a large number of model evaluations, which is prohibitive for
computationally expensive models. We therefore employ Gaussian processes as a
metamodel for the underlying full model. Metamodelling introduces further
uncertainty, which we also quantify. We demonstrate the approach by applying it
to two different problems: nanoparticle-mediated drug delivery in a multiphase
tumour-growth model, and arterial growth and remodelling. Even relatively small
numbers of evaluations of the full model suffice to identify the influential
parameters in both cases and to separate them from non-influential parameters.
The approach also allows the quantification of higher-order interaction
effects. We thus show that a variance-based global sensitivity analysis is
feasible for computationally expensive biomechanical models. Different aspects
of sensitivity analysis are covered including a transparent declaration of the
uncertainties involved in the estimation process. Such a global sensitivity
analysis not only helps to massively reduce costs for experimental
determination of parameters but is also highly beneficial for inverse analysis
of such complex models.


title:
Spectroscopic parameters and electromagnetic form factor of kaon in
  vacuum and a dense medium

abstract:
  The spectroscopic parameters as well as electromagnetic form factor of the
strange particle kaon are investigated in vacuum and a medium with finite
density. The obtained vacuum mass and decay constant, which are consistent with
the existing experimental results, are used to extract the $ Q^2 $ dependence
of the kaon electromagnetic form factor in the interval $Q^2\in [0,10]$ GeV$^2$
in vacuum. The obtained results at lower and intermediate values of $ Q^2 $ are
consistent with the existing experimental data within the presented
uncertainties. The $ Q^2 $ behavior of the electromagnetic form factor of kaon
in vacuum and in the interval $Q^2\in [0,6]$ GeV$^2$ is in a nice agreement
with the existing predictions of the Lattice QCD and the solution of the
Bethe-Salpeter equation for the model of Nambu and Jona-Lasinio (NJL) with
proper-time regularization, as well. The obtained vacuum radius for kaon is
also in a nice agreement with the world's average experimental result. We
extend the analyses to a medium with higher densities and obtain the behavior
of the mass, decay constant, electromagnetic form factor and radius with
respect to density. The obtained results for some of the parameters are
compared with the existing predictions of other models and approaches. The
results obtained in the present study can be useful for future experimental and
theoretical studies both in vacuum and a dense medium.


title: Loss of sustainability in scientific work

abstract:
  For decades the number of scientific publications has been rapidly
increasing, effectively out-dating knowledge at a tremendous rate. Only few
scientific milestones remain relevant and continuously attract citations. Here
we quantify how long scientific work remains being utilized, how long it takes
before today's work is forgotten, and how milestone papers differ from those
forgotten. To answer these questions, we study the complete temporal citation
network of all American Physical Society journals. We quantify the probability
of attracting citations for individual publications based on age and the number
of citations they have received in the past. We capture both aspects, the
forgetting and the tendency to cite already popular works, in a microscopic
generative model for the dynamics of scientific citation networks. We find that
the probability of citing a specific paper declines with age as a power law
with an exponent of $\alpha \sim -1.4$. Whenever a paper in its early years can
be characterized by a scaling exponent above a critical value, $\alpha_c$, the
paper is likely to become "ever-lasting". We validate the model with
out-of-sample predictions, with an accuracy of up to 90% (AUC $\sim 0.9$). The
model also allows us to estimate an expected citation landscape of the future,
predicting that 95% of papers cited in 2050 have yet to be published. The
exponential growth of articles, combined with a power-law type of forgetting
and papers receiving fewer and fewer citations on average, suggests a worrying
tendency toward information overload and raises concerns about scientific
publishing's long-term sustainability.


title:
Generic properties of $3$-dimensional Reeb flows: Birkhoff sections and
  entropy

abstract:
  In this paper we use broken book decompositions to study Reeb flows on closed
$3$-manifolds. We show that if the Liouville measure of a nondegenerate contact
form can be approximated by periodic orbits, then there is a Birkhoff section
for the associated Reeb flow. In view of Irie's equidistribution theorem, this
is shown to imply that the set of contact forms whose Reeb flows have a
Birkhoff section contains an open and dense set in the $C^\infty$-topology. We
also show that the set of contact forms whose Reeb flows have positive
topological entropy is open and dense in the $C^\infty$-topology.


title:
Comparison of Intelligent Approaches for Cycle Time Prediction in
  Injection Moulding of a Medical Device Product

abstract:
  Injection moulding is an increasingly automated industrial process,
particularly when used for the production of high-value precision components
such as polymeric medical devices. In such applications, achieving stringent
product quality demands whilst also ensuring a highly efficient process can be
challenging. Cycle time is one of the most critical factors which directly
affects the throughput rate of the process and hence is a key indicator of
process efficiency. In this work, we examine a production data set from a real
industrial injection moulding process for manufacture of a high precision
medical device. The relationship between the process input variables and the
resulting cycle time is mapped with an artificial neural network (ANN) and an
adaptive neuro-fuzzy system (ANFIS). The predictive performance of different
training methods and neuron numbers in ANN and the impact of model type and the
numbers of membership functions in ANFIS has been investigated. The strengths
and limitations of the approaches are presented and the further research and
development needed to ensure practical on-line use of these methods for dynamic
process optimisation in the industrial process are discussed.


title: The Wiretap Channel for Capacitive PUF-Based Security Enclosures

abstract:
  In order to protect devices from physical manipulations, protective security
enclosures were developed. However, these battery-backed solutions come with a
reduced lifetime, and have to be actively and continuously monitored. In order
to overcome these drawbacks, batteryless capacitive enclosures based on
Physical Unclonable Functions (PUFs) have been developed that generate a
key-encryption-key (KEK) for decryption of the key chain. In order to reproduce
the PUF-key reliably and to compensate the effect of noise and environmental
influences, the key generation includes error correction codes. However,
drilling attacks that aim at partially destroying the enclosure also alter the
PUF-response and are subjected to the same error correction procedures.
Correcting attack effects, however, is highly undesirable as it would destroy
the security concept of the enclosure. In general, designing error correction
codes such that they provide tamper-sensitivity to attacks, while still
correcting noise and environmental effects is a challenging task. We tackle
this problem by first analyzing the behavior of the PUF-response under external
influences and different post-processing parameters. From this, we derive a
system model of the PUF-based enclosure, and construct a wiretap channel
implementation from $q$-ary polar codes. We verify the obtained error
correction scheme in a Monte Carlo simulation and demonstrate that our wiretap
channel implementation achieves a physical layer security of 100 bits for 240
bits of entropy for the PUF-secret. Through this, we further develop capacitive
PUF-based security enclosures and bring them one step closer to their
commercial deployment.


title:
Optical biosensor based on a photonic crystal with a defective layer
  designed to determine the concentration of SARS-CoV-2 in water

abstract:
  We propose a new optical biosensor based on a photonic crystal with a defect
layer, which can determine the SARS-CoV-2 concentration in water by the defect
mode shift. Two models of the dependence of the refractive index of the defect
layer on the concentration of the pathogen in water were considered. The
optimal parameters for the photonic crystal in our device and the optimal
thickness of the defect layer were determined. It was also demonstrated that in
the presence of absorption in the investigated structure it is much more
advantageous to work in the reflection mode compared to the transmission mode.
Finally, the wavelength dependence of the defect mode on the SARS-CoV-2
concentration was obtained and the sensitivity of the sensor was determined.


title:
Faraday isolator based on crystalline silicon (c-Si) for 2 micron laser
  radiation

abstract:
  The magneto-optical properties of single-crystal silicon were investigated as
a function of wavelength and temperature.