title: Similarity learning for wells based on logging data

abstract:
  One of the first steps during the investigation of geological objects is the
interwell correlation. It provides information on the structure of the objects
under study, as it comprises the framework for constructing geological models
and assessing hydrocarbon reserves. Today, the detailed interwell correlation
relies on manual analysis of well-logging data. Thus, it is time-consuming and
of a subjective nature. The essence of the interwell correlation constitutes an
assessment of the similarities between geological profiles. There were many
attempts to automate the process of interwell correlation by means of
rule-based approaches, classic machine learning approaches, and deep learning
approaches in the past. However, most approaches are of limited usage and
inherent subjectivity of experts. We propose a novel framework to solve the
geological profile similarity estimation based on a deep learning model. Our
similarity model takes well-logging data as input and provides the similarity
of wells as output. The developed framework enables (1) extracting patterns and
essential characteristics of geological profiles within the wells and (2) model
training following the unsupervised paradigm without the need for manual
analysis and interpretation of well-logging data. For model testing, we used
two open datasets originating in New Zealand and Norway. Our data-based
similarity models provide high performance: the accuracy of our model is
$0.926$ compared to $0.787$ for baselines based on the popular gradient
boosting approach. With them, an oil\&gas practitioner can improve interwell
correlation quality and reduce operation time.


title:
Measurement disturbance tradeoffs in three-qubit unsupervised quantum
  classification

abstract:
  We consider measurement disturbance tradeoffs in quantum machine learning
protocols which seek to learn about quantum data. We study the simplest example
of a binary classification task, in the unsupervised regime. Specifically, we
investigate how a classification of two qubits, that can each be in one of two
unknown states, affects our ability to perform a subsequent classification on
three qubits when a third is added. Surprisingly, we find a range of strategies
in which a non-trivial first classification does not affect the success rate of
the second classification. There is, however, a non-trivial measurement
disturbance tradeoff between the the success rate of the first and second
classifications, and we fully characterise this tradeoff analytically.


title:
On regular solutions for three-dimensional full compressible
  Navier-Stokes equations with degenerate viscosities and far field vacuum

abstract:
  In this paper, the Cauchy problem for the three-dimensional (3-D) full
compressible Navier-Stokes equations (CNS) with zero thermal conductivity is
considered. First, when shear and bulk viscosity coefficients both depend on
the absolute temperature $\theta$ in a power law ($\theta^\nu$ with $\nu>0$) of
Chapman-Enskog, based on some elaborate analysis of this system's intrinsic
singular structures, we identify one class of initial data admitting a
local-in-time regular solution with far field vacuum in terms of the mass
density $\rho$, velocity $u$ and entropy $S$. Furthermore, it is shown that
within its life span of such a regular solution, the velocity stays in an
inhomogeneous Sobolev space, i.e., $u\in H^3(\mathbb{R}^3)$, $S$ has uniformly
finite lower and upper bounds in the whole space, and the laws of conservation
of total mass, momentum and total energy are all satisfied. Note that due to
the appearance of the vacuum, the momentum equations are degenerate both in the
time evolution and viscous stress tensor, and the physical entropy for
polytropic gases behaves singularly, which make the study on corresponding
well-posedness challenging. For proving the existence, we first introduce an
enlarged reformulated structure by considering some new variables, which can
transfer the degeneracies of the full CNS to the possible singularities of some
special source terms related with $S$, and then carry out some singularly
weighted energy estimates carefully designed for this reformulated system.


title: Fragmentation Functions for $\Xi ^-/\bar{\Xi}^+$ Using Neural Networks

abstract:
  We present a determination of fragmentation functions (FFs) for the octet
baryon $\Xi ^-/\bar{\Xi}^+$ from data for single inclusive electron-positron
annihilation. Our parametrization in this QCD analysis is provided in terms of
a Neural Network (NN). We determine fragmentation functions for $\Xi
^-/\bar{\Xi}^+$ at next-to-leading order and for the first time at
next-to-next-to-leading order in perturbative QCD. We discuss the improvement
of higher-order QCD corrections, the quality of fit, and the comparison of our
theoretical results with the fitted datasets. As an application of our new set
of fragmentation functions, named SHKS22, we present predictions for $\Xi ^- /
\bar{\Xi}^+$ baryon production in proton-proton collisions at the LHC
experiments.


title: Formal verification of iterative convergence of numerical algorithms

abstract:
  Physical systems are usually modeled by differential equations, but solving
these differential equations analytically is often intractable. Instead, the
differential equations can be solved numerically by discretization in a finite
computational domain. The discretized equation is reduced to a large linear
system, whose solution is typically found using an iterative solver. We start
with an initial guess, x_0, and iterate the algorithm to obtain a sequence of
solution vectors, x_m. The iterative algorithm is said to converge to solution
$x$ if and only if x_m converges to $x$.
  Accuracy of the numerical solutions is important, especially in the design of
safety critical systems such as airplanes, cars, or nuclear power plants. It is
therefore important to formally guarantee that the iterative solvers converge
to the "true" solution of the original differential equation. In this paper, we
first formalize the necessary and sufficient conditions for iterative
convergence in the Coq proof assistant. We then extend this result to two
classical iterative methods: Gauss-Seidel iteration and Jacobi iteration. We
formalize conditions for the convergence of the Gauss--Seidel classical
iterative method, based on positive definiteness of the iterative matrix. We
then formally state conditions for convergence of Jacobi iteration and
instantiate it with an example to demonstrate convergence of iterative
solutions to the direct solution of the linear system. We leverage recent
developments of the Coq linear algebra and mathcomp library for our
formalization.


title: Large family candidates for kagome topological superconductors

abstract:
  A group of newly discovered non-magnetic metal kagome structures AV3Sb5 (A=K,
Rb, Cs) have aroused widespread interest in experiment and theory due to their
unusual charge density wave (CDW) and intertwined superconductivity. However,
they all possess weak electron-phonon coupling (EPC) and low superconducting
transition temperature. Here, we performed high-throughput first-principles
calculations on novel kagome candidates with AV3Sb5 prototype structure, and
proposed 24 dynamically stable novel kagome metals. The calculation based on
Bardeen-Cooper-Schrieffer theory shows that most of these metals are
superconductors with much stronger EPC than the reported AV3Sb5 materials, and
their superconducting transition temperatures Tc is between 0.3 and 5.0K.
Additionally, several compounds, such as KZr3Pb5 with the highest Tc, are
identified as Z2 topological metals with clear Dirac cone topological surface
states near Fermi level. NaZr3As5 is shown to have possible CDW phases. Our
results provide rich platforms for exploring various new physics with the
prototype kagome structure, in which the coexistence of superconductivity and
nontrivial topological nature provides promising insights on the discovery of
topological superconductors.


title:
Creating triple-NOON states with ultracold atoms via chaos-assisted
  tunneling

abstract:
  Triple-NOON states are superpositions of the form $e^{i \varphi_1}
|{N,0,0}\rangle + e^{i \varphi_2} |{0,N,0}\rangle + e^{i \varphi_3}
|{0,0,N}\rangle$ involving $N$ bosonic quanta distributed over three modes. We
theoretically show how such highly entangled states can be generated with
interacting ultracold bosonic atoms in a symmetric three-site lattice. The
basic protocol consists in preparing all atoms on one site of the lattice and
then letting the system evolve during a specific time such that collective
tunneling of the atoms to the other two sites takes place. The key point put
forward here is that this evolution time can be reduced by several orders of
magnitude via the application of a periodic driving of the lattice, thereby
rendering this protocol feasible in practice. This driving is suitably tuned
such that classical chaos is generated in the entire accessible phase space
except for the Planck cells that host the states participating at the above
triple-NOON superposition. Chaos-assisted tunneling can then give rise to a
dramatic speed-up of this collective tunneling process, without significantly
affecting the purity of this superposition. A triple-NOON state containing $N =
5$ particles can thereby be realized with $^{87}$Rb atoms on time scales of the
order of a few seconds.


title:
Entropy estimates for uniform attractors of dissipative PDEs with non
  translation-compact external forces

abstract:
  We study the Kolmogorov's entropy of uniform attractors for non-autonomous
dissipative PDEs. The main attention is payed to the case where the external
forces are not translation-compact. We present a new general scheme which
allows us to give the upper bounds of this entropy for various classes of
external forces through the entropy of proper projections of their hulls to the
space of translation-compact functions. This result generalizes well known
estimates of Vishik and Chepyzhov for the translation-compact case. The
obtained results are applied to three model problems: sub-quintic 3D damped
wave equation with Dirichlet boundary conditions, quintic 3D wave equation with
periodic boundary conditions and 2D Navier-Stokes system in a bounded domain.
The examples of finite-dimensional uniform attractors for some special external
forces which are not translation-compact are also given.


title:
Predicting Fuel Consumption in Power Generation Plants using Machine
  Learning and Neural Networks

abstract:
  The instability of power generation from national grids has led industries
(e.g., telecommunication) to rely on plant generators to run their businesses.
However, these secondary generators create additional challenges such as fuel
leakages in and out of the system and perturbations in the fuel level gauges.
Consequently, telecommunication operators have been involved in a constant need
for fuel to supply diesel generators. With the increase in fuel prices due to
socio-economic factors, excessive fuel consumption and fuel pilferage become a
problem, and this affects the smooth run of the network companies. In this
work, we compared four machine learning algorithms (i.e. Gradient Boosting,
Random Forest, Neural Network, and Lasso) to predict the amount of fuel
consumed by a power generation plant. After evaluating the predictive accuracy
of these models, the Gradient Boosting model out-perform the other three
regressor models with the highest Nash efficiency value of 99.1%.


title: Video-driven Neural Physically-based Facial Asset for Production

abstract:
  Production-level workflows for producing convincing 3D dynamic human faces
have long relied on a disarray of labor-intensive tools for geometry and
texture generation, motion capture and rigging, and expression synthesis.
Recent neural approaches automate individual components but the corresponding
latent representations cannot provide artists explicit controls as in
conventional tools. In this paper, we present a new learning-based,
video-driven approach for generating dynamic facial geometries with
high-quality physically-based assets. Two key components are well-structured
latent spaces due to dense temporal samplings from videos and explicit facial
expression controls to regulate the latent spaces. For data collection, we
construct a hybrid multiview-photometric capture stage, coupling with an
ultra-fast video camera to obtain raw 3D facial assets. We then model the
facial expression, geometry and physically-based textures using separate VAEs
with a global MLP-based expression mapping across the latent spaces, to
preserve characteristics across respective attributes while maintaining
explicit controls over geometry and texture. We also introduce to model the
delta information as wrinkle maps for physically-base textures, achieving
high-quality rendering of dynamic textures. We demonstrate our approach in
high-fidelity performer-specific facial capture and cross-identity facial
motion retargeting. In addition, our neural asset along with fast adaptation
schemes can also be deployed to handle in-the-wild videos. Besides, we motivate
the utility of our explicit facial disentangle strategy by providing promising
physically-based editing results like geometry and material editing or winkle
transfer with high realism. Comprehensive experiments show that our technique
provides higher accuracy and visual fidelity than previous video-driven facial
reconstruction and animation methods.


title: Higher-dimensional Heegaard Floer homology and Hecke algebras

abstract:
  Given a closed oriented surface $\Sigma$ of genus greater than 0, we
construct a map $\mathcal{F}$ from the higher-dimensional Heegaard Floer
homology of the cotangent fibers of $T^*\Sigma$ to the Hecke algebra associated
to $\Sigma$ and show that $\mathcal{F}$ is an isomorphism of algebras. We also
establish analogous results for punctured surfaces.


title: The Shapley Value in Machine Learning

abstract:
  Over the last few years, the Shapley value, a solution concept from
cooperative game theory, has found numerous applications in machine learning.
In this paper, we first discuss fundamental concepts of cooperative game theory
and axiomatic properties of the Shapley value. Then we give an overview of the
most important applications of the Shapley value in machine learning: feature
selection, explainability, multi-agent reinforcement learning, ensemble
pruning, and data valuation. We examine the most crucial limitations of the
Shapley value and point out directions for future research.


title: Pasta phases in neutron stars under strong magnetic fields

abstract:
  In the present work, we consider nuclear matter in the innermost crust of
neutron stars under the presence of a strong magnetic field within the
framework of a relativistic mean-field description. Two models with a different
slope of the symmetry energy are considered in order to discuss the
density-dependence of the equation of state on the crust structure. The
non-homogeneous matter in $\beta$-equilibrium is described within the
coexisting phases method, and the effect of including the anomalous magnetic
moment is discussed. Five different geometries for the pasta structures are
considered. It is shown that strong magnetic fields cause an extension of the
inner crust of the neutron stars, with the occurrence of a series of
disconnected non-homogeneous matter regions above the one existing for a null
magnetic field. Moreover, we observed that in these disconnected regions, for
some values of the magnetic field, all five different cluster geometrical
shapes occur, and the gas density is close to the cluster density. Also, the
pressure at the neutron star crust-core transition much larger than the
pressure obtained for a zero magnetic field. Another noticeable effect of the
presence of strong magnetic fields is the increase of the proton fraction,
favoring the appearance of protons in the gas background.


title: Symmetry properties of Wilson loops with a Lagrangian insertion

abstract:
  Null Wilson loops in $\mathcal{N}=4$ super Yang-Mills are dual to planar
scattering amplitudes. This duality implies hidden symmetries for both objects.
We consider closely related infrared finite observables, defined as the Wilson
loop with a Lagrangian insertion, normalized by the Wilson loop itself. Unlike
ratio and remainder functions studied in the literature, this observable is
non-trivial already for four scattered particles and bears close resemblance to
(finite parts of) scattering processes in non-supersymmetric Yang-Mills theory.
We study the general structure of the Wilson loop with a Lagrangian insertion,
focusing in particular on its leading singularities and their (hidden) symmetry
properties. We find evidence that the leading singularities can be written as
certain Grassmannian integrals. The latter are manifestly dual conformal. They
also have a conformal symmetry, up to total derivatives. We find that,
surprisingly, the conformal symmetry becomes an invariance in the frame where
the Lagrangian insertion point is sent to infinity. Furthermore, we use
integrability methods to study how higher Yangian charges act on the
Grassmannian integral. We evaluate the $n$-particle observable both at tree-
and at one-loop level, finding compact analytic formulas. These results are
explicitly written in the form of conformal leading singularities, multiplied
by transcendental functions. We then compare these formulas to known
expressions for all-plus amplitudes in pure Yang-Mills theory. We find a
remarkable new connection: the Wilson loop with Lagrangian insertion in
$\mathcal{N}=4$ super Yang-Mills appears to predict the maximal weight terms of
the planar pure Yang-Mills all-plus amplitude. We test this relationship for
the two-loop $n$-point Yang-Mills amplitude, as well as for the three-loop
four-point amplitude.


title: On absolutely profinitely solitary lattices in higher rank Lie groups

abstract:
  We establish conditions under which lattices in certain simple Lie groups are
profinitely solitary in the absolute sense, so that the commensurability class
of the profinite completion determines the commensurability class of the group
among finitely generated residually finite groups. While cocompact lattices are
typically not absolutely solitary, we show that noncocompact lattices in
$\operatorname{Sp}(n,\mathbb{R})$, $G_{2(2)}$, $E_8(\mathbb{C})$,
$F_4(\mathbb{C})$, and $G_2(\mathbb{C})$ are absolutely solitary if a
well-known conjecture on Grothendieck rigidity is true.


title:
Vector-borne diseases with non-stationary vector populations: the case
  of growing and decaying populations

abstract:
  Since the last century, deterministic compartmental models have emerged as
powerful tools to predict and control epidemic outbreaks, in many cases helping
to mitigate their impacts. A key quantity for these models is the so-called
Basic Reproduction Number, that measures the number of secondary infections
produced by an initial infected individual in a fully susceptible population.
Some methods have been developed to allow the direct computation of this
quantity provided that some conditions are fulfilled, such that the model has a
pre-pandemic disease-free equilibrium state. This condition is only fulfilled
when the populations are stationary. In the case of vector-borne diseases, this
implies that the vector birth and death rates need to be balanced, what is not
fulfilled in many realistic cases in which the vector population grow or
decrease. Here we develop a vector-borne epidemic model with growing and
decaying vector populations and study the conditions under which the standard
methods to compute $R_0$ work and discuss an alternative when they fail. We
also show that growing vector populations produce a delay in the epidemic
dynamics when compared to the case of the stationary vector population.
Finally, we discuss the conditions under which the model can be reduced to the
SIR model with fewer compartments and parameters, which helps in solving the
problem of parameter unidentifiability of many vector-borne epidemic models.


title: ClidSum: A Benchmark Dataset for Cross-Lingual Dialogue Summarization

abstract:
  We present ClidSum, a benchmark dataset for building cross-lingual
summarization systems on dialogue documents. It consists of 67k+ dialogue
documents from two subsets (i.e., SAMSum and MediaSum) and 112k+ annotated
summaries in different target languages. Based on the proposed ClidSum, we
introduce two benchmark settings for supervised and semi-supervised scenarios,
respectively. We then build various baseline systems in different paradigms
(pipeline and end-to-end) and conduct extensive experiments on ClidSum to
provide deeper analyses. Furthermore, we propose mDialBART which extends
mBART-50 (a multi-lingual BART) via further pre-training. The multiple
objectives used in the further pre-training stage help the pre-trained model
capture the structural characteristics as well as important content in
dialogues and the transformation from source to the target language.
Experimental results show the superiority of mDialBART, as an end-to-end model,
outperforms strong pipeline models on ClidSum. Finally, we discuss specific
challenges that current approaches faced with this task and give multiple
promising directions for future research. We have released the dataset and code
at https://github.com/krystalan/ClidSum.


title:
Theoretical investigation of electronic, optical and thermoelectric
  properties of tellurium doped barium titanate (BTO) through modified Becke
  Johnson exchange potential

abstract:
  The stability, electronic structure, optical and thermoelectric properties of
Te-doped BaTiO3 are investigated by first-principal calculation based on the
density functional theory and Boltzmann transport theory implemented in WIEN2K
and BoltzTraP simulation program. This study is carried out by applying LDA +
TB-mBJ potential. Formation energy of each doped structure is calculated to
examine the stability and feasibility of the synthesis. Incorporating Te into
BaTiO3 efficiently reduces the electronic band gap and the level of band gap
reduction can be controlled by varying the amount of dopant. Hence, the
absorption ability is improved in the visible light. Our findings suggest that
all the doped structures are significantly absorbent and productive with an
optical absorption that exceeds 105 cm-1 in the visible range. In addition,
BaTiO3 revealed a smaller dielectric constant at zero frequency compared to Te
doped-BTO: 8.3%, while the optical energy gap is reduced from 3.692 eV to 1.619
eV by growing Te concentration. Then, optical conductivity and Urbach's
parameters are predicted. The transport properties are assessed as a function
of temperature. It is found that the electrical conductivity is considerably
enhanced with increase properties such as Seebeck coefficient and figure of
merit are also investigated. Our theoretical results can be useful for
thermoelectric and visible light photoelectrical device applications.


title:
Observation of the $\pi^2\sigma^2$-bond linear-chain molecular structure
  in $^{16}$C

abstract:
  Measurements of the $^2$H($^{16}$C,$^{16}$C$^{*}$$\rightarrow^4$He+$^{12}$Be
or $^6$He+$^{10}$Be)$^2$H inelastic excitation and cluster-decay reactions have
been carried out at a beam energy of about 23.5 MeV/u. A specially designed
detection system, including one multi-layer silicon-strip telescope at around
zero degrees, has allowed the high-efficiency three-fold coincident detection
and therefore the event-by-event determination of the energy of the unstable
nucleus beam. The decay paths from the $^{16}$C resonances to various states of
the final $^{10}$Be or $^{12}$Be nucleus are recognized thanks to the
well-resolved $Q$-value spectra. The reconstructed resonances at 16.5(1),
17.3(2), 19.4(1) and 21.6(2) MeV are assigned as the $0^+$, $2^+$, $4^+$ and
$6^+$ members, respectively, of the positive-parity
$(3/2_\pi^-)^2(1/2_\sigma^-)^2$-bond linear-chain molecular band in $^{16}$C,
based on the angular correlation analysis for the 16.5 MeV state and the
excellent agreement of decay patterns between the measurements and theoretical
predictions. Moreover, another intriguing high-lying state was observed at
27.2(1) MeV which decays almost exclusively to the $\sim$6 MeV states of
$^{10}$Be, in line with the newly predicted pure $\sigma$-bond linear-chain
configuration.


title:
Comparison of the charge-crystal and charge-glass state in geometrically
  frustrated organic conductors studied by fluctuation spectroscopy

abstract:
  We present a systematic investigation of the low-frequency charge carrier
dynamics in different charge states of the organic conductors
$\theta$-(BEDT-TTF)$_2$$M$Zn(SCN)$_4$ with $M$=Rb,Tl, which result from
quenching or relaxing the charge degrees of freedom on a geometrically
frustrated triangular lattice. Due to strong electronic correlations these
materials exhibit a charge-ordering transition, which can be kinetically
avoided by rapid cooling resulting in a so-called charge-glass state without
long-range order. The combination of fluctuation spectroscopy and a heat pulse
method allows us to study and compare the resistance fluctuations in the
low-resistive quenched and the high-resistive charge-ordered state, revealing
striking differences in the respective noise magnitudes. For both compounds, we
find strongly enhanced resistance fluctuations right at the metal-insulator
transition and a broad noise maximum in the slowly cooled charge-crystal state
with partly dominating two-level processes revealing characteristic activation
energies.


title:
High accuracy, high resolution 235U(n,f) cross section from n_TOF (CERN)
  in the thermal to 10 keV energy range

abstract:
  The 235U(n,f) cross section was measured in a wide energy range (25 meV - 170
keV) at the n_TOF facility at CERN, relative to 6Li(n,t) and 10B(n,alpha)
standard reactions, with high resolution and accuracy, with a setup based on a
stack of six samples and six silicon detectors placed in the neutron beam. In
this paper we report on the results in the region between thermal and 10 keV
neutron energy. A resonance analysis has been performed up to 200 eV, with the
code SAMMY. The resulting fission kernels are compared with the ones extracted
on the basis of the resonance parameters of the most recent major evaluated
data libraries. A comparison of the n_TOF data with the evaluated cross
sections is also performed from thermal to 10 keV neutron energy for the
energy-averaged cross section in energy groups of suitably chosen width. A good
agreement is found in average between the new results and the latest evaluated
data files ENDF-B/VIII and JEFF-3.3, as well as with respect to the IAEA
reference files. However, some discrepancies are still present in some specific
energy regions. The new dataset here presented, characterized by unprecedented
resolution and accuracy, can help improving the evaluations in the Resolved
Resonance Region and up to 10 keV, and reduce the uncertainties that affect
this region.


title:
Periodic solutions to relativistic Kepler problems: a variational
  approach

abstract:
  We study relativistic Kepler problems in the plane. At first, using
non-smooth critical point theory, we show that under a general time-periodic
external force of gradient type there are two infinite families of T-periodic
solutions, parameterized by their winding number around the singularity: the
first family is a sequence of local minima, while the second one comes from a
mountain pass-type geometry of the action functional. Secondly, we investigate
the minimality of the circular and non-circular periodic solutions of the
unforced problem, via Morse index theory and level estimates of the action
functional.


title:
$\theta$ dependence of $T_c$ in 4d SU(3) Yang-Mills theory with
  histogram method and the Lee-Yang zeros in the large $N$ limit

abstract:
  The phase diagram on the $\theta$-$T$ plane in four dimensional SU(3)
Yang-Mills theory is explored. We revisit the $\theta$ dependence of the
deconfinement transition temperature, $T_c(\theta)$, on the lattice through the
constraint effective potential for Polyakov loop. The $\theta$ term is
introduced by the reweighting method, and the critical $\beta$ is determined to
$\theta \sim 0.75$, where the interpolation in $\beta$ is carried out by the
multipoint reweighting method. The $\theta$ dependence of $T_c$ obtained here
turns out to be consistent with the previous result by D'Elia and
Negro~\cite{DElia:2012pvq,DElia:2013uaf}. We also derive $T_c(\theta)$ by
classifying configurations into the high and low temperature phases and
applying the Clausius-Clapeyron equation. It is found that the potential
barrier in the double well potential at $T_c(\theta)$ becomes higher with
$\theta$, which suggests that the first order transition continues robustly
above $\theta \sim 0.75$. Using information obtained here, we try to depict the
expected $\theta$ dependence of the free energy density at $T < T_c(0)$, which
crosses the first order transition line at an intermediate value of $\theta$.
Finally, how the Lee-Yang zeros associated with the spontaneous CP violation
appear is discussed formally in the large $N$ limit, and the locations of them
are found to be $(\theta_R,\theta_I)=\left( (2m+1)\pi, \frac{2n+1}{2\chi V_4}
\right)$ with $n$ and $m$ arbitrary integers.


title: Bounded acyclicity and relative simplicial volume

abstract:
  We provide new vanishing and glueing results for relative simplicial volume,
following up on two current themes in bounded cohomology: The passage from
amenable groups to boundedly acyclic groups and the use of equivariant
topology.
  More precisely, we consider equivariant nerve pairs and relative classifying
spaces for families of subgroups. Typically, we apply this to uniformly
boundedly acyclic families of subgroups. Our methods also lead to vanishing
results for $\ell^2$-Betti numbers of aspherical CW-pairs with small relative
amenable category and to a relative version of a result by Dranishnikov and
Rudyak concerning mapping degrees and the inheritance of freeness of
fundamental groups.


title: Online Decision Transformer

abstract:
  Recent work has shown that offline reinforcement learning (RL) can be
formulated as a sequence modeling problem (Chen et al., 2021; Janner et al.,
2021) and solved via approaches similar to large-scale language modeling.
However, any practical instantiation of RL also involves an online component,
where policies pretrained on passive offline datasets are finetuned via
taskspecific interactions with the environment. We propose Online Decision
Transformers (ODT), an RL algorithm based on sequence modeling that blends
offline pretraining with online finetuning in a unified framework. Our
framework uses sequence-level entropy regularizers in conjunction with
autoregressive modeling objectives for sample-efficient exploration and
finetuning. Empirically, we show that ODT is competitive with the
state-of-the-art in absolute performance on the D4RL benchmark but shows much
more significant gains during the finetuning procedure.


title:
Dissecting the different components of the modest accretion bursts of
  the very young protostar HOPS 373

abstract:
  Observed changes in protostellar brightness can be complicated to interpret.
In our JCMT~Transient monitoring survey, we discovered that a young binary
protostar, HOPS 373, is undergoing a modest $30\%$ brightness increase at 850
$\mu$m, caused by a factor of 1.8$-$3.3 enhancement in the accretion rate. The
initial burst occurred over a few months, with a sharp rise and then shallower
decay. A second rise occurred soon after the decay, and the source is still
bright one year later. The mid-IR emission, the small-scale CO outflow mapped
with ALMA, and the location of variable maser emission indicate that the
variability is associated with the SW component. The near-infrared and NEOWISE
$W1$ and $W2$ emission is located along the blueshifted CO outflow, spatially
offset by $\sim3$ to $4^{\prime\prime}$ from the SW component. The $K$-band
emission imaged by UKIRT shows a compact H$_2$ emission source at the edge of
the outflow, with a tail tracing the outflow back to the source. The $W1$
emission, likely dominated by scattered light, brightens by 0.7 mag, consistent
with expectations based on the sub-mm lightcurve. The signal of continuum
variability in $K$-band and $W2$ is masked by stable H$_2$ emission, as seen in
our Gemini/GNIRS spectrum, and perhaps by CO emission. These differences in
emission sources complicate infrared searches for variability of the youngest
protostars.


title:
Non-Stop & Non-Breakable Code Review Services in a Distributed System:
  Detecting Issues in Real Time

abstract:
  The two most significant bottlenecks in code merging are the build process
and the unit tests. However, as the number of items to be checked in a code
review increases, that code review becomes a bottleneck for code merging as
well. Because of the dependency structure between code review services, an
error in one service affects the entire service. As a result, whenever a
service error occurs, it is crucial to have methods for determining which code
review service has ultimately caused the error. With the goal of achieving a
non-stop & non-breakable code review service, this paper describes an early
error detection method along with a case study of the service.


title:
Explainable Machine Learning for Breakdown Prediction in High Gradient
  RF Cavities

abstract:
  Radio Frequency (RF) breakdowns are one of the most prevalent limiting
factors in RF cavities for particle accelerators. During a breakdown, field
enhancement associated with small deformations on the cavity surface results in
electrical arcs. Such arcs lead to beam aborts, reduce machine availability and
can cause irreparable damage on the RF cavity surface. In this paper, we
propose a machine learning strategy to discover breakdown precursors in CERN's
Compact Linear Collider (CLIC) accelerating structures. By interpreting the
parameters of the learned models with explainable Artificial Intelligence (AI),
we reverse-engineer physical properties for deriving fast, reliable, and simple
rule based models. Based on 6 months of historical data and dedicated
experiments, our models show fractions of data with high influence on the
occurrence of breakdowns. Specifically, it is shown that in many cases a rise
of the vacuum pressure is observed before a breakdown is detected with the
current interlock sensors.


title: Weak and Strong Versions of Effective Transfinite Recursion

abstract:
  Working in the context of reverse mathematics, we give a fine-grained
characterization result on the strength of two possible definitions for
Effective Transfinite Recursion used in literature. Moreover, we show that
$\Pi^0_2$-induction along a well-order $X$ is equivalent to the statement that
the exponentiation of any well-order to the power of $X$ is well-founded.


title: Inference and FDR Control for Simulated Ising Models in High-dimension

abstract:
  This paper studies the consistency and statistical inference of simulated
Ising models in the high dimensional background. Our estimators are based on
the Markov chain Monte Carlo maximum likelihood estimation (MCMC-MLE) method
penalized by the Elastic-net. Under mild conditions that ensure a specific
convergence rate of MCMC method, the $\ell_{1}$ consistency of
Elastic-net-penalized MCMC-MLE is proved. We further propose a decorrelated
score test based on the decorrelated score function and prove the asymptotic
normality of the score function without the influence of many nuisance
parameters under the assumption that accelerates the convergence of the MCMC
method. The one-step estimator for a single parameter of interest is purposed
by linearizing the decorrelated score function to solve its root, as well as
its normality and confidence interval for the true value, therefore, be
established. Finally, we use different algorithms to control the false
discovery rate (FDR) via traditional p-values and novel e-values.


title:
CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep
  Learning

abstract:
  Modern deep neural networks can easily overfit to biased training data
containing corrupted labels or class imbalance. Sample re-weighting methods are
popularly used to alleviate this data bias issue. Most current methods,
however, require to manually pre-specify the weighting schemes as well as their
additional hyper-parameters relying on the characteristics of the investigated
problem and training data. This makes them fairly hard to be generally applied
in practical scenarios, due to their significant complexities and inter-class
variations of data bias situations. To address this issue, we propose a
meta-model capable of adaptively learning an explicit weighting scheme directly
from data. Specifically, by seeing each training class as a separate learning
task, our method aims to extract an explicit weighting function with sample
loss and task/class feature as input, and sample weight as output, expecting to
impose adaptively varying weighting schemes to different sample classes based
on their own intrinsic bias characteristics. Synthetic and real data
experiments substantiate the capability of our method on achieving proper
weighting schemes in various data bias cases, like the class imbalance,
feature-independent and dependent label noise scenarios, and more complicated
bias scenarios beyond conventional cases. Besides, the task-transferability of
the learned weighting scheme is also substantiated, by readily deploying the
weighting function learned on relatively smaller-scale CIFAR-10 dataset on much
larger-scale full WebVision dataset. A performance gain can be readily achieved
compared with previous SOAT ones without additional hyper-parameter tuning and
meta gradient descent step. The general availability of our method for multiple
robust deep learning issues, including partial-label learning, semi-supervised
learning and selective classification, has also been validated.


title: Measuring dissimilarity with diffeomorphism invariance

abstract:
  Measures of similarity (or dissimilarity) are a key ingredient to many
machine learning algorithms. We introduce DID, a pairwise dissimilarity measure
applicable to a wide range of data spaces, which leverages the data's internal
structure to be invariant to diffeomorphisms. We prove that DID enjoys
properties which make it relevant for theoretical study and practical use. By
representing each datum as a function, DID is defined as the solution to an
optimization problem in a Reproducing Kernel Hilbert Space and can be expressed
in closed-form. In practice, it can be efficiently approximated via Nystr\"om
sampling. Empirical experiments support the merits of DID.


title:
Reply to "Comment on 'Bell's Theorem Versus Local Realism in a
  Quaternionic Model of Physical Space'"

abstract:
  In this paper, I respond to a critique of one of my papers previously
published in this journal, entitled "Bell's Theorem Versus Local Realism in a
Quaternionic Model of Physical Space." That paper presents a local-realistic
model of quantum correlations based on a quaternionic 3-sphere, taken as a
physical space in which we are confined to perform all our experiments. The
critique, on the other hand, considers two entirely different models within a
flat Euclidean space, neither related to my quaternionic 3-sphere model. It
then criticizes its own flat space models and claims that it has thereby
criticized the model presented in my paper. Along the way, without providing
evidence or proof, it claims that the results in my paper are based on
mistakes. I demonstrate that there are no mistakes of any kind in my paper. On
the contrary, I bring out a number of elementary mathematical and conceptual
mistakes from the critique and the critiques it relies on.


title: On Lorentzian connections with parallel skew torsion

abstract:
  The paper is devoted to metric connections with parallel skew-symmetric
torsion in Lorentzian signature. This is motivated by recent progress in the
Riemannian signature and by possible applications to type II supergravity
theory. We provide a complete information about holonomy algebras, torsion and
curvature of the considered connections up to the corresponding objects from
the Riemannian signature. Using that we show how to construct all simply
connected Lorentzian naturally reductive homogeneous spaces of arbitrary
dimension from Riemannian naturally reductive homogeneous spaces. This leads to
complete classification of Lorentzian naturally reductive homogeneous spaces in
low dimensions.


title:
Moduli of relative stable maps to $\mathbb{P}^1$: cut-and-paste
  invariants

abstract:
  We study constructible invariants of the moduli space
$\overline{\mathcal{M}}(\boldsymbol{x})$ of stable maps from genus zero curves
to $\mathbb{P}^1$, relative to $0$ and $\infty$, with ramification profiles
specified by ${\boldsymbol{x}\in \mathbb{Z}^n}$. These spaces are central to
the enumerative geometry of $\mathbb{P}^1$, and provide a large family of
birational models of the Deligne--Mumford--Knudsen moduli space
$\overline{\mathcal{M}}_{0,n}$. For the sequence of vectors $\boldsymbol{x}$
corresponding to maps which are maximally ramified over $0$ and unramified over
$\infty$, we prove that a generating function for the topological Euler
characteristics of these spaces satisfies a differential equation which allows
for its recursive calculation. We also show that the class of the moduli space
in the Grothendieck ring of varieties is constant as $\boldsymbol{x}$ varies
within a fixed chamber in the resonance decomposition of $\mathbb{Z}^n$. We
conclude by suggesting several further directions in the study of these spaces,
giving conjectures on (1) the asymptotic behavior of the Euler characteristic
and (2) a potential chamber structure for the Chern numbers.


title:
Mergers of supermassive and intermediate-mass black holes in galactic
  nuclei from disruptions of star clusters

abstract:
  Gravitational waves (GWs) offer an unprecedented opportunity to survey the
sky and detect mergers of compact objects. While intermediate-mass black holes
(IMBHs) have not been detected beyond any reasonable doubt with either
dynamical or accretion signatures, the GW landscape appears very promising.
Mergers of an IMBH with a supermassive black hole (SMBH) will be primary
sources for the planned space-based mission LISA and could be observed up to
the distant Universe. SMBH-IMBH binaries can be formed as a result of the
migration and merger of stellar clusters at the center of galaxies, where an
SMBH lurks. We build for the first time a semi-analytical framework to model
this scenario, and find that the the comoving merger rate of SMBH-IMBH binaries
is $\sim 10^{-3}$ Gpc$^{-3}$ yr$^{-1}$ in the local Universe for a unity IMBH
occupation fraction, scales linearly with it, and has a peak at $z\approx
0.5$-$3$. Our model predicts $\sim 1$ event yr$^{-1}$ within redshift $z\approx
3.5$ if $10\%$ of the inspiralled star clusters hosted an IMBH, while $\sim 10$
events yr$^{-1}$ for a unity occupation fraction. More than $90\%$ of these
systems will be detectable with LISA with a signal-to-noise ratio larger than
$10$, promising to potentially find a family of IMBHs.


title:
Sovereign Cryptocurrencies for People, Corporations, Banks and
  Communities: Foundations for Grassroots Cryptoeconomy

abstract:
  The ecosystem of cryptocurrencies benefits the few and exacerbates economic
inequality. Here, we aim to offer an egalitarian and inclusive alternative by
morphing the concepts, tools and technologies developed by the cryptocurrencies
ecosystem, together with the distributed computing technology that enabled it,
into novel foundations for grassroots cryptoeconomy. The foundations are
layered, with each layer consisting of a cryptoeconomic component and a
computational component, supporting the layers above.
  A key message of this paper is the coupling of the economic and computational
features of the novel consensus-free zone: rich economic functionality with
lean computational implementations. They make the proposed layered
architecture, inclusive of the consensus-based layer, promising foundations for
grassroots cryptoeconomy.


title: Border of the Island of Inversion: Unbound states in $^{29}$Ne

abstract:
  The nucleus $^{29}$Ne is situated at the border of the island of inversion.
Despite significant efforts, no bound low-lying intruder $f_{7/2}$-state, which
would place $^{29}$Ne firmly inside the island of inversion, has yet been
observed. Here, the first investigation of unbound states of $^{29}$Ne is
reported. The states were populated in $^{30}\mathrm{Ne}(p,pn)$ and
$^{30}\mathrm{Na}(p,2p)$ reactions at a beam energy of around $230$
MeV/nucleon, and analyzed in terms of their resonance properties, partial cross
sections and momentum distributions. The momentum distributions are compared to
calculations using the eikonal, direct reaction model, allowing
$\ell$-assignments for the observed states. The lowest-lying resonance at an
excitation energy of 1.48(4) MeV shows clear signs of a significant
$\ell$=3-component, giving first evidence for $f_{7/2}$ single particle
strength in $^{29}$Ne. The excitation energies and strengths of the observed
states are compared to shell-model calculations using the sdpf-u-mix
interaction


title: Long-Time Convergence and Propagation of Chaos for Nonlinear MCMC

abstract:
  In this paper, we study the long-time convergence and uniform strong
propagation of chaos for a class of nonlinear Markov chains for Markov chain
Monte Carlo (MCMC). Our technique is quite simple, making use of recent
contraction estimates for linear Markov kernels and basic techniques from
Markov theory and analysis. Moreover, the same proof strategy applies to both
the long-time convergence and propagation of chaos. We also show, via some
experiments, that these nonlinear MCMC techniques are viable for use in
real-world high-dimensional inference such as Bayesian neural networks.


title: Mutual conversion between a magnetic Neel hopfion and a Neel toron

abstract:
  Three-dimensional (3D) magnetic textures attract great attention from
researchers due to their fascinating structures and dynamic behaviors. Magnetic
hopfion is a prominent example of 3D magnetic textures. Here, we numerically
study the mutual conversion between a Neel-type hopfion and a Neel-type toron
under an external magnetic field. We also investigate the excitation modes of
hopfions and torons in a film with strong perpendicular magnetic anisotropy. It
is found that the Neel-type hopfion could be a stable state in the absence of
the external magnetic field, and its diameter varies with the out-of-plane
magnetic field. The Neel-type hopfion may transform to a Neel-type toron at an
out-of-plane magnetic field of about 20 mT, where the cross section structure
is a Neel-type skyrmion. The hopfion and toron show different excitation modes
in the presence of an in-plane microwave magnetic field. Our results provide a
method to realize the conversion between a Neel-type hopfion and a Neel-type
toron, which also gives a way to distinguish Bloch-type and Neel-type hopfions.


title:
A Wasserstein GAN for Joint Learning of Inpainting and its Spatial
  Optimisation

abstract:
  Classic image inpainting is a restoration method that reconstructs missing
image parts. However, a carefully selected mask of known pixels that yield a
high quality inpainting can also act as a sparse image representation. This
challenging spatial optimisation problem is essential for practical
applications such as compression. So far, it has been almost exclusively
addressed by model-based approaches. First attempts with neural networks seem
promising, but are tailored towards specific inpainting operators or require
postprocessing. To address this issue, we propose the first generative
adversarial network for spatial inpainting data optimisation. In contrast to
previous approaches, it allows joint training of an inpainting generator and a
corresponding mask optimisation network. With a Wasserstein distance, we ensure
that our inpainting results accurately reflect the statistics of natural
images. This yields significant improvements in visual quality and speed over
conventional stochastic models and also outperforms current spatial
optimisation networks.


title: Room Temperature Giant Magnetocaloric Effect In CrTe1-XSex Alloys

abstract:
  Polycrystalline CrTe1-xSex samples were prepared using the conventional
solid-state reaction technique and characterized by x-rad diffraction,
temperature- and field- dependent magnetization for possible use in
magnetocaloric cooling. Rietveld refinement revealed two phases in all prepared
samples; a minor monoclinic phase that decreases with Se concentration until it
vanishes near 10% of Se, and a major hexagonal one. At high temperature,
CrTe1-xSex is paramagnetic, with ferromagnetic phase transition occurs at Tc
~332, 320, 318, 310, 299, and 295 K for x= 0.00, 0.02, 0.04, 0.06, 0.08, and
0.10, respectively. The magnetocaloric effect analysis (at H=5T) revealed a
maximum magnetic entropy change of about 8.3, 7.9, 8.2, 9.1, 9.2, and 8.5
(J/kg.K) with a relative cooling power (RCP) value of about 555, 568, 547, 638,
694, and 668 (J/kg) around Tc for x= 0.00, 0.02, 0.04, 0.06, 0.08, and 0.10,
respectively. Critical behavior analysis showed that the magnetic isotherms
measured near Tc follow 3D Mean field critical exponents and a second order
phase transition. A rigorous iteration estimated the critical exponents based
on modified Arrott and Kouvel-fisher plots analysis, giving $\beta$ ~ 0.44,
0.51, 0.46, 0.49, 0.42, 0.49 and $\gamma$ ~ 1.02, 1.00, 0.99, 1.08, 1.07, 0.96,
and 1.01 for x= 0.00, 0.02, 0.04, 0.06, 0.08, and 0.10, respectively.


title:
A three-dimensional discrete model for approximating the deformation of
  a viral capsid subjected to lying over a flat surface

abstract:
  In this paper we present a three-dimensional discrete model governing the
deformation of a viral capsid, modelled as a regular icosahedron and subjected
not to cross a given flat rigid surface on which it initially lies in
correspondence of one vertex only. First, we set up the model in the form of a
set of variational inequalities posed over a non-empty, closed and convex
subset of a suitable space. Secondly, we show the existence and uniqueness of
the solution for the proposed model. Finally, we numerically test this model
and we observe that the outputs of the numerical experiments comply with
physics.


title: Audio-Based Deep Learning Frameworks for Detecting COVID-19

abstract:
  This paper evaluates a wide range of audio-based deep learning frameworks
applied to the breathing, cough, and speech sounds for detecting COVID-19. In
general, the audio recording inputs are transformed into low-level spectrogram
features, then they are fed into pre-trained deep learning models to extract
high-level embedding features. Next, the dimension of these high-level
embedding features are reduced before finetuning using Light Gradient Boosting
Machine (LightGBM) as a back-end classification. Our experiments on the Second
DiCOVA Challenge achieved the highest Area Under the Curve (AUC), F1 score,
sensitivity score, and specificity score of 89.03%, 64.41%, 63.33%, and 95.13%,
respectively. Based on these scores, our method outperforms the
state-of-the-art systems, and improves the challenge baseline by 4.33%, 6.00%
and 8.33% in terms of AUC, F1 score and sensitivity score, respectively.


title:
Random Design Variations of Hollow-core Anti-resonant Fibers: A
  Monte-Carlo Study

abstract:
  Hollow-core anti-resonant fibers (HC-ARFs) have earned great attention in the
fiber optics community due to their remarkable light-guiding properties and
broad application spectrum. Particularly nested HC-ARFs have recently reached
competitiveness to standard fibers and even outperform them in certain
categories. Key to their success is a precisely fine-tuned geometry, which
inherently leaves optical characteristics highly susceptible to minimal
structural deviations. When fabricating fibers, these come into play and
manifest themselves in various imperfections to the geometry, ultimately
worsening the fiber performance. In this article, for the first time to the
best of our knowledge, these imperfections have been statistically modeled and
analyzed on their impact to the propagation loss in a Monte-Carlo fashioned
simulation. We have considered randomly varying outer and nested tube wall
thicknesses as well as random tube angle offsets. We found that the loss
increase due to perturbed tube angles dominates that of varying tube
thicknesses by approximately an order of magnitude for FM and two orders of
magnitude for HOM propagation at a wavelength of 1.55 $\mu$m. Moreover, the
higher-order-mode-extinction-ratio (HOMER) is proportional to the intensity of
structural variations, indicating an increase in the `single-modeness' of a
fabricated fiber. Furthermore, a bend condition worsens the loss contribution
of both effects applied jointly dramatically to a value of $+50\%$ at a bend
radius of 4 cm compared to $+5\%$ for a straight fiber. We believe that our
work helps to predict the performance of realistic HC-ARFs after fabrication.


title: Artemis: Articulated Neural Pets with Appearance and Motion synthesis

abstract:
  We, humans, are entering into a virtual era and indeed want to bring animals
to the virtual world as well for companion. Yet, computer-generated (CGI) furry
animals are limited by tedious off-line rendering, let alone interactive motion
control. In this paper, we present ARTEMIS, a novel neural modeling and
rendering pipeline for generating ARTiculated neural pets with appEarance and
Motion synthesIS. Our ARTEMIS enables interactive motion control, real-time
animation, and photo-realistic rendering of furry animals. The core of our
ARTEMIS is a neural-generated (NGI) animal engine, which adopts an efficient
octree-based representation for animal animation and fur rendering. The
animation then becomes equivalent to voxel-level deformation based on explicit
skeletal warping. We further use a fast octree indexing and efficient
volumetric rendering scheme to generate appearance and density features maps.
Finally, we propose a novel shading network to generate high-fidelity details
of appearance and opacity under novel poses from appearance and density feature
maps. For the motion control module in ARTEMIS, we combine state-of-the-art
animal motion capture approach with recent neural character control scheme. We
introduce an effective optimization scheme to reconstruct the skeletal motion
of real animals captured by a multi-view RGB and Vicon camera array. We feed
all the captured motion into a neural character control scheme to generate
abstract control signals with motion styles. We further integrate ARTEMIS into
existing engines that support VR headsets, providing an unprecedented immersive
experience where a user can intimately interact with a variety of virtual
animals with vivid movements and photo-realistic appearance. We make available
our ARTEMIS model and dynamic furry animal dataset at
https://haiminluo.github.io/publication/artemis/.


title:
BlockMeter: An Application Agnostic Performance Measurement Framework
  For Private Blockchain Platforms

abstract:
  Blockchain Technology is an emerging technology with the potential to disrupt
a number of application domains. Though blockchain platforms like Bitcoin and
Ethereum have seen immense success and acceptability, their nature of being
public and anonymous make them unsuitable for many enterprise level use-cases.
To address this issue, Linux Foundation has started an open source umbrella
initiative, known as the Hyperledger Platforms. Under this initiative, a number
of private blockchain platforms have been developed which can be used for
different enterprise level applications. However, the scalability and
performance of these private blockchains must be examined to understand their
suitability for different use-cases. Recent researches and projects on
performance benchmarking for private blockchain systems are very specific to
use-cases and are generally tied to a blockchain platform. In this article, we
presentBlockMeter, an application agnostic performance benchmarking framework
for private blockchain platforms. This framework can be utilised to measure the
key performance matrices of any application deployed on top of an external
private blockchain application in real-time. In this article, we present the
architecture of the framework and discuss its different implementation aspects.
Then, to showcase the applicability of the framework, we use BlockMeter to
evaluate the two most widely used Hyperledger platforms, Hyperledger Fabric and
HyperledgerSawtooth, against a number of use-cases.


title: Scale-free Unconstrained Online Learning for Curved Losses

abstract:
  A sequence of works in unconstrained online convex optimisation have
investigated the possibility of adapting simultaneously to the norm $U$ of the
comparator and the maximum norm $G$ of the gradients. In full generality,
matching upper and lower bounds are known which show that this comes at the
unavoidable cost of an additive $G U^3$, which is not needed when either $G$ or
$U$ is known in advance. Surprisingly, recent results by Kempka et al. (2019)
show that no such price for adaptivity is needed in the specific case of
$1$-Lipschitz losses like the hinge loss. We follow up on this observation by
showing that there is in fact never a price to pay for adaptivity if we
specialise to any of the other common supervised online learning losses: our
results cover log loss, (linear and non-parametric) logistic regression, square
loss prediction, and (linear and non-parametric) least-squares regression. We
also fill in several gaps in the literature by providing matching lower bounds
with an explicit dependence on $U$. In all cases we obtain scale-free
algorithms, which are suitably invariant under rescaling of the data. Our
general goal is to establish achievable rates without concern for computational
efficiency, but for linear logistic regression we also provide an adaptive
method that is as efficient as the recent non-adaptive algorithm by Agarwal et
al. (2021).


title:
Vehicle and License Plate Recognition with Novel Dataset for Toll
  Collection

abstract:
  We propose an automatic framework for toll collection, consisting of three
steps: vehicle type recognition, license plate localization, and reading.
However, each of the three steps becomes non-trivial due to image variations
caused by several factors. The traditional vehicle decorations on the front
cause variations among vehicles of the same type. These decorations make
license plate localization and recognition difficult due to severe background
clutter and partial occlusions. Likewise, on most vehicles, specifically
trucks, the position of the license plate is not consistent. Lastly, for
license plate reading, the variations are induced by non-uniform font styles,
sizes, and partially occluded letters and numbers. Our proposed framework takes
advantage of both data availability and performance evaluation of the backbone
deep learning architectures. We gather a novel dataset, \emph{Diverse Vehicle
and License Plates Dataset (DVLPD)}, consisting of 10k images belonging to six
vehicle types. Each image is then manually annotated for vehicle type, license
plate, and its characters and digits. For each of the three tasks, we evaluate
You Only Look Once (YOLO)v2, YOLOv3, YOLOv4, and FasterRCNN. For real-time
implementation on a Raspberry Pi, we evaluate the lighter versions of YOLO
named Tiny YOLOv3 and Tiny YOLOv4. The best Mean Average Precision (mAP@0.5) of
98.8% for vehicle type recognition, 98.5% for license plate detection, and
98.3% for license plate reading is achieved by YOLOv4, while its lighter
version, i.e., Tiny YOLOv4 obtained a mAP of 97.1%, 97.4%, and 93.7% on vehicle
type recognition, license plate detection, and license plate reading,
respectively. The dataset and the training codes are available at
https://github.com/usama-x930/VT-LPR


title:
Crossing of the branch cut: the topological origin of a universal
  2{\pi}-phase retardation in non-Hermitian metasurfaces

abstract:
  Full wavefront control by photonic components requires that the spatial phase
modulation on an incoming optical beam ranges from 0 to 2{\pi}. Because of
their radiative coupling to the environment, all optical components are
intrinsically non-Hermitian systems, often described by reflection and
transmission matrices with complex eigenfrequencies. Here, we show that
Parity-Time symmetry breaking -- either explicit or spontaneous -- moves the
position of Zero singularities of the reflection or transmission matrices from
the real axis to the upper part of the complex frequency plane. A universal 0
to 2{\pi}-phase gradient of an output channel as a function of the real
frequency excitation is thus realized whenever the discontinuity branch
bridging a Zero and a Pole, i.e a pair of singularities, is crossing the real
axis. This basic understanding is applied to engineer electromagnetic fields at
interfaces, including, but not limited to, metasurfaces. Non-Hermitian
topological features associated with exceptional degeneracies or branch cut
crossing are shown to play a surprisingly pivotal role in the design of
resonant photonic systems.


title:
Discovery of hot subdwarfs with extremely high carbon and oxygen
  abundances

abstract:
  Helium rich subdwarf O stars (sdOs) are hot compact stars in a pre-white
dwarf evolutionary state. Most of them have effective temperatures and surface
gravities in the range Teff = 40,000-50,000 K and log g = 5.5-6.0. Their
atmospheres are helium dominated. If present at all, C, N, and O are trace
elements. The abundance patterns are explained in terms of nucleosynthesis
during single star evolution (late helium core flash) or a binary He-core white
dwarf merger. Here we announce the discovery of two hot hydrogen-deficient sdOs
(PG1654+322 and PG1528+025) that exhibit unusually strong carbon and oxygen
lines. A non-LTE model atmosphere analysis of spectra obtained with the Large
Binocular Telescope and by the LAMOST survey reveals astonishingly high
abundances of C (~20%) and O (~20%) and that the two stars are located close to
the helium main sequence. Both establish a new spectroscopic class of hot
H-deficient subdwarfs (CO-sdO) and can be identified as the remnants of a
He-core white dwarf that accreted matter of a merging low-mass CO-core white
dwarf. We conclude that the CO-sdOs represent an alternative evolutionary
channel creating PG1159 stars besides the evolution of single stars that
experience a late helium-shell flash.


title:
Blow-up of solutions to relaxed compressible Navier-Stokes equations in
  divergence form

abstract:
  This note shows the blow-up of certain non-small solutions to relaxed
compressible Navier-Stokes equations in divergence form.


title: An evolutionary channel for CO-rich and pulsating He-rich subdwarfs

abstract:
  Recently a new class of hot subluminous stars strongly enriched in C and O
have been discovered (CO-sdOs). These stars show abundances very similar to
those observed in PG1159 stars but at lower temperatures. Moreover, it has been
recently suggested that C and O enrichment might be the key ingredient driving
the pulsations in He-rich hot subdwarf stars (He-sdBVs). Here we argue that
these two types of rare stars can be explained by a variant of one of the main
channels forming hot subdwarf stars. The scenario involves the formation and
merging of a He-core white dwarf and a less massive CO-core white dwarf. We
have constructed a simple merger models and computed their subsequent
evolution. The merger products are in agreement with the surface parameters and
composition of CO-sdOs. In addition, we have performed simulations including
the effects of element diffusion and the excitation of pulsations. These
simulations show that less massive merger products can form stellar structures
that have surface parameters, abundances, and pulsation periods similar to
those displayed by He-sdBVs. We conclude that the proposed scenario, or some
variant of it, offers a very plausible explanation for the formation of
CO-sdOs, pulsating He-sdBs and low-luminosity PG1159 stars.


title:
Direct visualization and effects of atomic-scale defects on the
  optoelectronic properties of hexagonal boron nitride

abstract:
  Hexagonal boron nitride (hBN) is attracting a lot of attention in the last
years, thanks to its many remarkable properties. These include the presence of
single-photon emitters with superior optical properties, which make it an ideal
candidate for a plethora of photonic technologies. However, despite the large
number of experimental results and theoretical calculations, the structure of
the defects responsible for the observed emission is still under debate. In
this work, we visualize individual atomic-scale defects in hBN with atomic
force microscopy under ambient conditions and observe multiple narrow emission
lines from optically stable emitters. This direct observation of the structure
of the defects combined with density functional theory calculations of their
band structures and electronic properties allows us to associate the existence
of several single-photon transitions to the observed defects. Our work sheds
light on the origin of single-photon emission in hBN that is important for the
understanding and tunability of high-quality emitters in optoelectronics and
quantum technologies.


title: An Electro-Optical Trap for Polar Molecules

abstract:
  A detailed treatment of an electro-optical trap for polar molecules, realized
by embedding an optical trap within a uniform electrostatic field, is presented
and the trap's properties analyzed and discussed. The electro-optical trap
offers significant advantages over an optical trap that include an increased
trap depth and conversion of alignment of the trapped molecules to marked
orientation. Tilting the polarization plane of the optical field with respect
to the electrostatic field diminishes both the trap depth and orientation and
lifts the degeneracy of the $\pm M$ states of the trapped molecules. These and
other features of the electro-optical trap are explained in terms of the
eigenproperties of the polar and polarizable molecules subject to the combined
permanent and induced electric dipole interactions at play.


title: Efficient Kernel UCB for Contextual Bandits

abstract:
  In this paper, we tackle the computational efficiency of kernelized UCB
algorithms in contextual bandits. While standard methods require a O(CT^3)
complexity where T is the horizon and the constant C is related to optimizing
the UCB rule, we propose an efficient contextual algorithm for large-scale
problems. Specifically, our method relies on incremental Nystrom approximations
of the joint kernel embedding of contexts and actions. This allows us to
achieve a complexity of O(CTm^2) where m is the number of Nystrom points. To
recover the same regret as the standard kernelized UCB algorithm, m needs to be
of order of the effective dimension of the problem, which is at most
O(\sqrt(T)) and nearly constant in some cases.


title: A Scalable Database for the Storage of Object-Centric Event Logs

abstract:
  Object-centric process mining provides a set of techniques for the analysis
of event data where events are associated to several objects. To store
Object-centric Event Logs (OCELs), the JSON-OCEL and JSON-XML formats have been
recently proposed. However, the proposed implementations of the OCEL are
file-based. This means that the entire file needs to be parsed in order to
apply process mining techniques, such as the discovery of object-centric
process models. In this paper, we propose a database storage for the OCEL
format using the MongoDB document database. Since documents in MongoDB are
equivalent to JSON objects, the current JSON implementation of the standard
could be translated straightforwardly in a series of MongoDB collections.


title:
Interaction between interface and massive states in multivalley
  topological heterostructures

abstract:
  Topological interface states in multivalley systems are studied to unravel
their valley sensitivity. For this purpose, multivalley IV-VI topological
crystalline insulator (TCI) heterostructures are explored using magneto-optical
Landau level spectroscopy up to 34 teslas. We characterize the topological
interface states emerging from the distinct L-valleys in Pb1-xSnxSe multi
quantum wells grown along the [111] direction. It is shown that the shape of
the 2D Fermi surfaces of topological interface states residing at the
TCI/trivial insulator interfaces are strongly affected by the valley anisotropy
of topologically trivial Pb1-yEuySe barriers. This phenomenon is shown to be
due to the deep penetration of the topological interface states into the
barriers. For the valleys tilted with respect to the confinement direction, a
significant interaction between topological states and the conventional massive
quantum well states is observed, evidenced by the resulting large
anti-crossings between Landau levels. These are theoretically well-described by
a k.p model that takes into account tilt and anisotropy of the valleys in two
dimensions. Therefore, our work provides a precise characterization of the
topological interface state valley splitting, as well as an accurate
determination of the anisotropy of their Dirac cone dispersion.


title:
NALABS: Detecting Bad Smells in Natural Language Requirements and Test
  Specifications

abstract:
  In large-scale embedded system development, requirement and test
specifications are often expressed in natural language. In the context of
developing such products, requirement review is performed in many cases
manually using these specifications as a basis for quality assurance.
Low-quality specifications can have expensive consequences during the
requirement engineering process. Especially, if feedback loops during
requirement engineering are long, leading to artifacts that are not easily
maintainable, are hard to understand, and are inefficient to port to other
system variants. We use the idea of smells to specifications expressed in
natural language, defining a set of specifications for bad smells. We developed
a tool called NALABS (NAtural LAnguage Bad Smells), available on
https://github.com/eduardenoiu/NALABS and used for automatically checking
specifications. We discuss some of the decisions made for its implementation,
and future work.


title:
Wasserstein-type distances of two-type continuous-state branching
  processes in L\'{e}vy random environments

abstract:
  Under natural conditions, we proved the exponential ergodicity in Wasserstein
distance of two-type continuous-state branching processes in L\'evy random
environments with immigration. Furthermore, we expressed accurately the
parameters of the exponent. The coupling method and the conditioned branching
property play an important role in the approach. Using the tool of
superprocesses, the ergodicity in total variance distance is also proved.


title:
Numerical-relativity validation of effective-one-body waveforms in the
  intermediate-mass-ratio regime

abstract:
  One of the open problems in developing binary black hole (BBH) waveforms for
gravitational wave astronomy is to model the intermediate mass ratio regime and
connect it to the extreme mass ratio regime. A natural approach is to employ
the effective one body (EOB) approach to the two-body dynamics that, by design,
can cover the entire mass ratio range and naturally incorporates the extreme
mass ratio limit. Here we use recently obtained numerical relativity (NR) data
with mass ratios $m_1/m_2=(7,15,\,32,\,64,\,128)$ to test the accuracy of the
state-of-the-art EOB model TEOBResumS in the intermediate mass ratio regime. We
generally find an excellent EOB/NR consistency around merger and ringdown for
all mass ratios and for all available subdominant multipoles, except for the
$\ell=m=5$ one. This mode can be crucially improved using the new large-mass
ratio NR data of this paper. The EOB/NR inspirals are also consistent with the
estimated NR uncertainties. We also use several NR datasets taken by different
public catalogs to probe the universal behavior of the multipolar hierarchy of
waveform amplitudes at merger, that smoothly connects the equal-mass BBH to the
test-mass result. Interestingly, the universal behavior is strengthened if the
nonoscillatory memory contribution is included in the NR waveform. Future NR
simulations with improved accuracy will be necessary to further probe, and
possibly quantitatively refine, the TEOBResumS transition from late inspiral to
plunge in the intermediate mass ratio regime.


title: Performance and limitations of dual-comb based ranging systems

abstract:
  Dual-comb LiDARs have the potential to perform high-resolution ranging at
high speed. Here, through an implementation involving electro-optic modulators
and heterodyne detection, we quantify the ranging systems trade-off between
precision and non-ambiguity range (NAR) using a unique performance factor. We
highlight the influence of the comb amplitude envelope on the precision with a
distance measurement limited by the repetition rate of the optical comb. The
influence of the combs repetition rate on the NAR and on the precision is
illustrated through a setup allowing distance measurement with a tunable NAR.
Finally, we demonstrate the impossibility to resolve different targets,
quantify the impact on the measured distance and develop on the conditions in
which non-linear effects of the interference make the measurement impossible.


title:
Screening promising CsV3Sb5-like kagome materials from systematic
  first-principles evaluation

abstract:
  CsV3Sb5 kagome lattice holds the promise for manifesting electron
correlation, topology and superconducting. However, by far only three
CsV3Sb5-like kagome materials have been experimentally spotted. In this work,
we enlarge this family of materials to 1386 compounds via element species
substitution, and the further screening process suggests that 28 promising
candidates have superior thermodynamic stability, hence they are highly likely
to be synthesized. Moreover, these compounds possess several identical
electronic structures, and can be categorized into five non-magnetic and three
magnetic groups accordingly. It is our hope that this work can greatly expand
the viable phase space of the CsV3Sb5-like materials for investigating or
tuning the novel quantum phenomena in kagome lattice.


title:
Uniformizable singular projective structures on Riemann surface
  orbifolds

abstract:
  This paper is devoted to characterizing complex projective structures defined
on Riemann surface orbifolds and giving rise to injective developing maps
defined on the monodromy covering of the surface (orbifold) in question. The
relevance of these structures stems from several problems involving vector
fields with uniform solutions as well as from problems about "simultaneous
uniformization" for leaves of foliations by Riemann surfaces. In this paper, we
first describe the local structure of the mentioned projective structures
showing, in particular, that they are locally bounded. In the case of Riemann
surface orbifolds of finite type, the previous result will then allow us to
provide a detailed global picture of these projective structures by exploiting
their connection with the class of "bounded covering projective structures".


title:
Information regret tradeoff relation for locating two incoherent optical
  point sources

abstract:
  The optimal quantum measurements for estimating individual parameters might
be incompatible with each other so that they cannot be jointly performed. The
tradeoff between the estimation precision for different parameters can be
characterized by information regret -- the difference between the Fisher
information and its quantum limit. We show that the information-regret-tradeoff
relation can give us not only an intuitive picture of the potential in
improving the joint scheme of estimating the centroid and the separation, but
also some clues to the optimal measurements for the sequential scheme. In
particular, we show that, for two incoherent point sources with a very small
separation, the optimal measurement for the separation must extract little
information about the centroid, and vice versa.


title:
Observation of the doubly charmed baryon decay $\it{\Xi_{cc}^{++}\to
  \Xi_{c}^{'+}\pi^{+}}$

abstract:
  The $\it{\Xi_{cc}^{++}\to \Xi_{c}^{'+}\pi^{+}}$ decay is observed using
proton-proton collisions collected by the LHCb experiment at a centre-of-mass
energy of $13\mathrm{\,Te\kern -0.1em V}$, corresponding to an integrated
luminosity of $5.4\,\mathrm{fb}^{-1}$. The $\it{\Xi_{cc}^{++}\to
\Xi_{c}^{'+}\pi^{+}}$ decay is reconstructed partially, where the photon from
the $\it{\Xi_{c}^{'+} \to \Xi_{c}^{+}\gamma}$ decay is not reconstructed and
the $pK^-\pi^+$ final state of the $\it{\Xi_{c}^{+}}$ baryon is employed. The
$\it{\Xi_{cc}^{++}\to \Xi_{c}^{'+}\pi^{+}}$ branching fraction relative to that
of the $\it{\Xi_{cc}^{++}\to \Xi_{c}^{+}\pi^{+}}$ decay is measured to be $1.41
\pm 0.17 \pm 0.10$, where the first uncertainty is statistical and the second
systematic.


title:
A prototype for pulsar observations at low radio frequencies using log
  periodic dipole antennas

abstract:
  A prototype for dedicated observations of pulsars and other astrophysical
transients in the frequency range 50\,-\,80\,MHz has been recently commissioned
at the Gauribidanur radio observatory near Bangalore in India. The antenna
set-up, analog \& digital receiver systems, and the initial observations are
presented.


title: Bernstein Flows for Flexible Posteriors in Variational Bayes

abstract:
  Variational inference (VI) is a technique to approximate difficult to compute
posteriors by optimization. In contrast to MCMC, VI scales to many
observations. In the case of complex posteriors, however, state-of-the-art VI
approaches often yield unsatisfactory posterior approximations. This paper
presents Bernstein flow variational inference (BF-VI), a robust and easy-to-use
method, flexible enough to approximate complex multivariate posteriors. BF-VI
combines ideas from normalizing flows and Bernstein polynomial-based
transformation models. In benchmark experiments, we compare BF-VI solutions
with exact posteriors, MCMC solutions, and state-of-the-art VI methods
including normalizing flow based VI. We show for low-dimensional models that
BF-VI accurately approximates the true posterior; in higher-dimensional models,
BF-VI outperforms other VI methods. Further, we develop with BF-VI a Bayesian
model for the semi-structured Melanoma challenge data, combining a CNN model
part for image data with an interpretable model part for tabular data, and
demonstrate for the first time how the use of VI in semi-structured models.


title: Notes on switching lemmas

abstract:
  We prove three switching lemmas, for random restrictions for which variables
are set independently; for random restrictions where variables are set in
blocks (both due to Hastad [Hastad 86]); and for a distribution appropriate for
the bijective pigeonhole principle [Beame et al. 94, Krajicek et al. 95]. The
proofs are based on Beame's version [Beame 94] of Razborov's proof of the
switching lemma in [Razborov 93], except using families of weighted
restrictions rather than families of restrictions which are all the same size.
This follows a suggestion of Beame in [Beame 94]. The result is something
between Hastad's and Razborov's methods of proof. We use probabilistic
arguments rather than counting ones, in a similar way to Hastad, but rather
than doing induction on the terms in our formula with an inductive hypothesis
involving conditional probability, as Hastad does, we explicitly build one
function to bound the probabilities for the whole formula.


title:
Numerical schemes for a multi-species BGK model with velocity-dependent
  collision frequency

abstract:
  We consider a kinetic description of a multi-species gas mixture modeled with
Bhatnagar-Gross-Krook (BGK) collision operators, in which the collision
frequency varies not only in time and space but also with the microscopic
velocity. In this model, the Maxwellians typically used in standard BGK
operators are replaced by a generalization of such target functions, which are
defined by a variational procedure \cite{arXiv:2101.09047}. In this paper we
present a numerical method for simulating this model, which uses an
Implicit-Explicit (IMEX) scheme to minimize a certain potential function,
mimicking the Lagrange functional that appears in the theoretical derivation.
We show that theoretical properties such as conservation of mass, total
momentum and total energy as well as positivity of the distribution functions
are preserved by the numerical method, and illustrate its usefulness and
effectiveness with numerical examples.


title: OxDNA to study species interactions

abstract:
  Molecular ecology uses molecular genetic data to answer traditional
ecological questions in biogeography and biodiversity among others. Several
ecological principles, such as the niche hypothesis and the competitive
exclusions, are based on the fact that species compete for resources. More in
general, it is now recognized that species interactions play a crucial role in
determining the coexistence and abundance of species. However, experimentally
controllable platforms, which allow to study and measure competitions among
species, are rare and difficult to implement. In this work, we suggest to
exploit a Molecular Dynamics coarse-grained model to study interactions among
single strands of DNA, representing individuals of different species, which
compete for binding to other oligomers considered as resources. In particular,
the well-established knowledge of DNA-DNA interactions at the nanoscale allows
us to test the hypothesis that the maximum consecutive overlap between pairs of
oligomers measure the species competitive advantages. However, we suggest that
more complex structure also plays a role in the ability of the species to
successfully bind to the target resource oligomer. We complement the
simulations with experiments on populations of DNA strands which qualitatively
confirm our hypotheses. These tools constitute a promising starting point for
further developments concerning the study of controlled, DNA-based, artificial
ecosystems.


title:
Melting curve of magnesium up to 460 GPa from ab initio molecular
  dynamics simulations

abstract:
  Based on ab initio molecular dynamics simulations, we determined the melting
curve of magnesium (Mg) up to about 460 GPa using the solid-liquid coexistence
method. Between about 30 and 100 GPa, our melting curve is noticeably lower
than those from static experiments, but is in good agreement with recent shock
experiments. Our melting curve is generally consistent with the melting points
from first-principles calculations using the small-cell coexistence method up
to about 400 GPa, but is significantly higher at about 450 GPa. We find that at
high pressures of a few hundred GPa, due to the strong softening of interatomic
interactions in the liquid phase, solid-liquid coexistence simulations of Mg
show some characteristics distinctively different from other metal systems,
such as aluminum. For example, at a given volume the pressure and temperature
range for maintaining stable solid-liquid coexistence state can be very small.
The strong softening in the liquid phase also causes the unusual behavior of
reentrant melting to occur at very high pressures. The onset of reentrant
melting is predicted at about 305 GPa, close to that at about 300 GPa from the
small-cell coexistence method. We show that the calculated melting points,
considering reentrant melting, can be excellently fitted to a low-order Kechin
equation, making it possible for us to obtain a first-principles melting curve
of Mg at pressures above 50 GPa for the first time. Similar characteristics in
solid-liquid coexistence simulations, as well as reentrant melting, are also
expected for other systems with strong softening in the liquid phase at high
pressures.


title:
Spatial Reuse in Dense Wireless Areas: A Cross-layer Optimization
  Approach via ADMM

abstract:
  This paper introduces an efficient method for communication resource use in
dense wireless areas where all nodes must communicate with a common destination
node. The proposed method groups nodes based on their \newt{distance from the
destination} and creates a structured multi-hop configuration in which each
group can relay its neighbor's data. \newt{The large number of active radio
nodes and the common direction of communication toward a single destination are
exploited to reuse the limited spectrum resources in spatially separated
groups}. Spectrum allocation constraints among groups are then embedded in a
joint routing and resource allocation framework to optimize the route and
amount of resources allocated to each node. \newt{The solution to this problem
uses coordination among the lower-layers of the wireless-network protocol stack
to outperform conventional approaches where these layers are decoupled.
Furthermore, the structure of this problem is exploited to obtain} a
semi-distributed optimization algorithm based on the alternating direction
method of multipliers (ADMM) where each node can optimize its resources
independently based on local channel information.


title:
InterpretTime: a new approach for the systematic evaluation of
  neural-network interpretability in time series classification

abstract:
  We present a novel approach to evaluate the performance of interpretability
methods for time series classification, and propose a new strategy to assess
the similarity between domain experts and machine data interpretation. The
novel approach leverages a new family of synthetic datasets and introduces new
interpretability evaluation metrics. The approach addresses several common
issues encountered in the literature, and clearly depicts how well an
interpretability method is capturing neural network's data usage, providing a
systematic interpretability evaluation framework. The new methodology
highlights the superiority of Shapley Value Sampling and Integrated Gradients
for interpretability in time-series classification tasks.


title: Pregeometric First Order Yang-Mills Theory

abstract:
  The standard description of particles and fundamental interactions is
crucially based on a regular metric background. In the language of differential
geometry, this dependence is encoded into the action via Hodge star
dualization. As a result, the conventional forms of the scalar and Yang-Mills
actions break down in a pregeometric regime where the metric is degenerate.
This suggests the use of first order formalism, where the metric may emerge
from more fundamental constituents and the theory can be consistently extended
to the pregeometric phase. We systematically explore different realizations and
interpretations of first order formalism, finding that a fundamental vector or
spinor substructure brings about continuum magnetization and polarization as
integration constants. This effect is analogous to the description of the
cosmological dark sector in a recent self-dual formulation of gravity, and the
similar form obtained for the first order Yang-Mills theory suggests new paths
toward unification.


title: Stable approximation of Helmholtz solutions by evanescent plane waves

abstract:
  Solutions of the Helmholtz equation are known to be well approximated by
superpositions of propagative plane waves. This observation is the foundation
of successful Trefftz methods. However, when too many plane waves are used, the
computation of the expansion is known to be numerically unstable. We explain
how this effect is due to the presence of exponentially large coefficients in
the expansion and can drastically limit the efficiency of the approach. In this
work, we show that the Helmholtz solutions on a disk can be exactly represented
by a continuous superposition of evanescent plane waves, generalizing the
standard Herglotz representation. Here, by evanescent plane waves, we mean
exponential plane waves with complex-valued propagation vector, whose absolute
value decays exponentially in one direction. In addition, the density in this
representation is proved to be uniformly bounded in a suitable weighted
Lebesgue norm, hence overcoming the instability observed with propagative plane
waves and paving the way for stable discrete expansions. In view of practical
implementations, discretization strategies are investigated. We construct
suitable finite-dimensional sets of evanescent plane waves using sampling
strategies in a parametric domain. Provided one uses sufficient oversampling
and regularization, the resulting approximations are shown to be both
controllably accurate and numerically stable, as supported by numerical
evidence.


title: Tiny Object Tracking: A Large-scale Dataset and A Baseline

abstract:
  Tiny objects, frequently appearing in practical applications, have weak
appearance and features, and receive increasing interests in meany vision
tasks, such as object detection and segmentation. To promote the research and
development of tiny object tracking, we create a large-scale video dataset,
which contains 434 sequences with a total of more than 217K frames. Each frame
is carefully annotated with a high-quality bounding box. In data creation, we
take 12 challenge attributes into account to cover a broad range of viewpoints
and scene complexities, and annotate these attributes for facilitating the
attribute-based performance analysis. To provide a strong baseline in tiny
object tracking, we propose a novel Multilevel Knowledge Distillation Network
(MKDNet), which pursues three-level knowledge distillations in a unified
framework to effectively enhance the feature representation, discrimination and
localization abilities in tracking tiny objects. Extensive experiments are
performed on the proposed dataset, and the results prove the superiority and
effectiveness of MKDNet compared with state-of-the-art methods. The dataset,
the algorithm code, and the evaluation code are available at
https://github.com/mmic-lcl/Datasets-and-benchmark-code.


title:
Spin stiffness, spectral weight, and Landau damping of magnons in
  metallic spiral magnets

abstract:
  We analyze the properties of magnons in metallic electron systems with spiral
magnetic order. Our analysis is based on the random phase approximation for the
susceptibilities of tight binding electrons with a local Hubbard interaction in
two or three dimensions. We identify three magnon branches from poles in the
susceptibilities, one associated with in-plane, the other two associated with
out-of-plane fluctuations of the spiral order parameter. We derive general
expressions for the spin stiffnesses and the spectral weights of the magnon
modes, from which also the magnon velocities can be obtained. Moreover, we
determine the size of the decay rates of the magnons due to Landau damping.
While the decay rate of the in-plane mode is of the order of its excitation
energy, the decay rate of the out-of-plane mode is smaller so that these modes
are asymptotically stable excitations even in the presence of Landau damping.


title: Adaptive Read Thresholds for NAND Flash

abstract:
  A primary source of increased read time on NAND flash comes from the fact
that in the presence of noise, the flash medium must be read several times
using different read threshold voltages for the decoder to succeed. This paper
proposes an algorithm that uses a limited number of re-reads to characterize
the noise distribution and recover the stored information. Both hard and soft
decoding are considered. For hard decoding, the paper attempts to find a read
threshold minimizing bit-error-rate (BER) and derives an expression for the
resulting codeword-error-rate. For soft decoding, it shows that minimizing BER
and minimizing codeword-error-rate are competing objectives in the presence of
a limited number of allowed re-reads, and proposes a trade-off between the two.
  The proposed method does not require any prior knowledge about the noise
distribution, but can take advantage of such information when it is available.
Each read threshold is chosen based on the results of previous reads, following
an optimal policy derived through a dynamic programming backward recursion. The
method and results are studied from the perspective of an SLC Flash memory with
Gaussian noise for each level but the paper explains how the method could be
extended to other scenarios.


title:
A Novel Chaos-based Light-weight Image Encryption Scheme for Multi-modal
  Hearing Aids

abstract:
  Multimodal hearing aids (HAs) aim to deliver more intelligible audio in noisy
environments by contextually sensing and processing data in the form of not
only audio but also visual information (e.g. lip reading). Machine learning
techniques can play a pivotal role for the contextually processing of
multimodal data. However, since the computational power of HA devices is low,
therefore this data must be processed either on the edge or cloud which, in
turn, poses privacy concerns for sensitive user data. Existing literature
proposes several techniques for data encryption but their computational
complexity is a major bottleneck to meet strict latency requirements for
development of future multi-modal hearing aids. To overcome this problem, this
paper proposes a novel real-time audio/visual data encryption scheme based on
chaos-based encryption using the Tangent-Delay Ellipse Reflecting Cavity-Map
System (TD-ERCS) map and Non-linear Chaotic (NCA) Algorithm. The results
achieved against different security parameters, including Correlation
Coefficient, Unified Averaged Changed Intensity (UACI), Key Sensitivity
Analysis, Number of Changing Pixel Rate (NPCR), Mean-Square Error (MSE), Peak
Signal to Noise Ratio (PSNR), Entropy test, and Chi-test, indicate that the
newly proposed scheme is more lightweight due to its lower execution time as
compared to existing schemes and more secure due to increased key-space against
modern brute-force attacks.


title:
Valley-Polarized State Induced $\varphi_0$-Josephson Junction in Twisted
  Bilayer Graphene

abstract:
  Recently, gate-defined Josephson junctions in magic angle twisted bilayer
graphene (MATBG) were studied experimentally and highly unconventional
Fraunhofer patterns were observed. In this work, we show that an
interaction-driven valley-polarized state connecting two superconducting
regions of MATBG would give rise to a long-sought-after purely electric
controlled $\varphi_0$-junction in which the two superconductors acquire a
finite phase difference $\varphi_0$ in the ground state. We point out that the
emergence of the $\varphi_0$-junction stems from the time-reversal symmetry
breaking valley polarization and an intravalley inversion symmetry breaking
term. Importantly, a spatially non-uniform valley polarization order parameter
at the junction can explain the highly unconventional Fraunhofer patterns
observed in the experiment. Our work explores the novel transport properties of
the valley-polarized state and suggests that gate-defined MATBG Josephson
junctions could realize the first purely electric controlled
$\varphi_0$-junctions with applications in superconducting devices.

categories: cond-mat.mes-hall cond-mat.mtrl-sci cond-mat.str-el cond-mat.supr-con


title:
Predictive modeling of microbiological seawater quality classification
  in karst region using cascade model

abstract:
  In this paper, an in-depth analysis of Escherichia coli seawater measurements
during the bathing season in the city of Rijeka, Croatia was conducted.
Submerged sources of groundwater were observed at several measurement locations
which could be the cause for increased E. coli values. This specificity of
karst terrain is usually not considered during the monitoring process, thus a
novel measurement methodology is proposed. A cascade machine learning model is
used to predict coastal water quality based on meteorological data, which
improves the level of accuracy due to data imbalance resulting from rare
occurrences of measurements with reduced water quality. Currently, the cascade
model is employed as a filter method, where measurements not classified as
excellent quality need to be further analyzed. However, with improvements
proposed in the paper, the cascade model could be ultimately used as a
standalone method.


title: Flux ramp modulation based hybrid microwave SQUID multiplexer

abstract:
  We present a hybrid microwave SQUID multiplexer that combines two
frequency-division multiplexing techniques to allow multiplexing a given number
of cryogenic detectors with only a fraction of frequency encoding resonators.
Similar to conventional microwave SQUID multiplexing, our multiplexer relies on
inductively coupling non-hysteretic, unshunted rf-SQUIDs to superconducting
microwave resonators as well as applying flux ramp modulation for output signal
linearization. However, instead of utilizing one resonator per SQUID, we couple
multiple SQUIDs to a common readout resonator and encode the SQUID input
signals in sidebands of the microwave carrier by varying the flux ramp
modulation frequency for each SQUID. We prove the suitability of our approach
using a prototype device and argue by means of fundamental information theory
that our approach is particularly suited for reading out large cryogenic
bolometer arrays.


title: High Fidelity RF Clutter Modeling and Simulation

abstract:
  In this paper, we present a tutorial overview of state-of-the-art radio
frequency (RF) clutter modeling and simulation (M&S) techniques. Traditional
statistical approximation based methods will be reviewed followed by more
accurate physics-based stochastic transfer function clutter models that
facilitate site-specific simulations anywhere on earth. The various factors
that go into the computation of these transfer functions will be presented,
followed by several examples across multiple RF applications. Finally, we
introduce a radar challenge dataset generated using these tools that can enable
testing and benchmarking of all cognitive radar algorithms and techniques.