title:
Global 3D simulation of the upper atmosphere of HD189733b and absorption
  in metastable HeI and Ly{\alpha} lines

abstract:
  A 3D fully self-consistent multi-fluid hydrodynamic aeronomy model is applied
to simulate the hydrogen-helium expanding upper atmosphere of the hot Jupiter
HD189733b, and related absorption in the Lya line and the 10830 A line of
metastable helium. We studied the influence of a high-energy stellar flux,
stellar wind, and Lya cooling to reproduce the available observations. We found
that to fit the width of the absorption profile in 10830 A line the escaping
upper atmosphere of planet should be close to the energy limited escape
achieved with a significantly reduced Lya cooling at the altitudes with HI
density higher than 3*10^6 cm^-3. Based on the preformed simulations, we
constrain the helium abundance in the upper atmosphere of HD189733b by a rather
low value of He/H~0.005. We show that under conditions of a moderate stellar
wind similar to that of the Sun the absorption of Lya line takes place mostly
within the Roche lobe due to thermal broadening at a level of about 7%. At an
order of magnitude stronger wind, a significant absorption of about 15% at high
blue shifted velocities of up to 100 km/s is generated in the bowshock region,
due to Doppler broadening. These blue shifted velocities are still lower than
those (~200 km/s) detected in one of the observations. We explain the
differences between performed observations, though not in all the details, by
the stellar activity and the related fluctuations of the ionizing radiation (in
case of 10830 A line), and stellar wind (in case of Lya line).


title:
Approximation of the invariant measure of stable SDEs by an
  Euler--Maruyama scheme

abstract:
  We study approximations of the invariant measure of a stochastic differential
equation (SDE) driven by an $\alpha$-stable L\'evy process ($1<\alpha<2$) using
the Euler--Maruyama (EM) scheme. By a discrete version of Duhamel's principle
and Bismut's formula in Malliavin calculus, we show that the Wasserstein-$1$
distance of the invariant measures of the solution and the approximation is --
up to a constant -- bounded by $\eta^{\frac2{\alpha}-1}$ where $\eta$ is the
step size of the EM scheme. An explicit calculation for Ornstein--Uhlenbeck
$\alpha$-stable process shows that the rate $\eta^{\frac2{\alpha}-1}$ is sharp.
A concrete example is given, and the relevant simulations are implemented.


title: Adjoint-based optimization of a regional water elevation model

abstract:
  Calibration of unknown model parameters is a crucial task in most ocean model
applications. We present an adjoint-based optimization of a bottom friction
coefficient in North Sea-Baltic Sea simulation. The system is modeled with an
unstructured mesh finite element shallow water model. The adjoint model
provides a gradient of model misfit with respect to spatially-varying bottom
friction field. A gradient-based quasi-Newton optimization method is applied to
minimize the misfit. A key benefit of adjoint-based optimization is that the
cost of does not depend on the number of unknown variables. Adjoint models are
typically very laborious to implement manually. In this work, we leverage a
domain specific language framework in which the discrete adjoint model can be
obtained automatically. The adjoint model is both exactly compatible with the
discrete forward model and computationally efficient. Optimizing
spatially-variable parameter fields is typically an under-determined problem as
there are more degrees of freedom compared to the amount of observational data
which can lead to over-fitting. In this work, we employ Hessian-based
regularization to penalize the curvature of the friction field to overcome this
problem. We simulate the sea surface height (SSH) dynamics in the North
Sea-Baltic Sea for a 3-month period in 2019. We show that the optimization
process is robust and results in significant improvement of the model
performance: SSH performance is comparable or better compared to existing
state-of-the-art Baltic Sea models. The results are especially encouraging in
the Danish Straits region, characterized by complex small-scale topography,
highlighting the benefit of unstructured meshes.


title:
Non-equilibrium phase transitions in coupled nonlinear optical
  resonators

abstract:
  Phase transitions and the associated symmetry breaking are at the heart of
many physical phenomena. Coupled systems with multiple interacting degrees of
freedom provide a fertile ground for emergent dynamics that is otherwise
inaccessible in their solitary counterparts. Here we show that coupled
nonlinear optical resonators can undergo self-organization in their spectrum
leading to a first-order phase transition. We experimentally demonstrate such a
spectral phase transition in time-multiplexed coupled optical parametric
oscillators. We switch the nature of mutual coupling from dispersive to
dissipative and access distinct spectral regimes of the parametric oscillator
dimer. We observe abrupt spectral discontinuity at the first-order transition
point which can pave the way for the realization of novel transition-edge
sensors. Furthermore, we show how non-equilibrium phase transitions can lead to
enhanced sensing, where the applied perturbation is not resolvable by the
underlying linear system. Our results can pave the way for sensing using
nonlinear driven-dissipative systems leading to performance enhancements
without sacrificing sensitivity.


title: Reality-based UTXO Ledger

abstract:
  The Unspent Transaction Output (UTXO) model is commonly used in the field of
Distributed Ledger Technology (DLT) to transfer value between participants. One
of its advantages is that it allows parallel processing of transactions, as
independent transactions can be added in any order. This property of order
invariance and parallelisability has potential benefits in terms of
scalability. However, since the UTXO Ledger is an append-only data structure,
this advantage is compromised through the presence of conflicting transactions.
We propose an extended UTXO Ledger model that optimistically updates the ledger
and keeps track of the dependencies of the possible conflicts. In the presence
of a conflict resolution mechanism, we propose a method to reduce the extended
ledger back to a consistent UTXO Ledger.


title:
Whispering-Gallery Mode Resonator Technique with Microfluidic Channel
  for Permittivity Measurement of Liquids

abstract:
  Studies of biochemical liquids require precise determination of their complex
permittivity. We developed a microwave characterization technique on the basis
of a high-quality whispering-gallery mode (WGM) sapphire resonator with a
microfluidic channel filled with the liquid under test. A novel approach allows
obtaining the complex permittivity of biochemical liquids of
sub-microliter/nanoliter volumes with high accuracy. The method is based on
special procedure of analysis of the interaction of electromagnetic field with
liquid in WGM microfluidic resonator and measurements of both the WGM resonance
frequency shift and the change of the inverse quality factor. The approach is
successfully applied to obtain the complex permittivity in the Ka band of
glucose, albumin bovine serum, lactalbumin and cytochrome C aqueous solutions
using the developed microwave technique.


title:
Divergent Thermal Expansion and Gr\"uneisen Ratio in a Quadrupolar Kondo
  Metal

abstract:
  We report on the low-temperature thermal expansion and magnetostriction of
the single-impurity quadrupolar Kondo candidate
Y$_{1-x}$Pr$_{x}$Ir$_2$Zn$_{20}$. In the dilute limit, we find a quadrupolar
strain that possesses a singular dependence on temperature $T$,
$\varepsilon_{\mathrm{u}} \sim H^2 \log 1/T$, for a small but finite magnetic
field $H$. Together with the previously reported anomalous specific heat $C$,
this implies a quadrupolar Gr\"uneisen ratio $\Gamma_u = \partial_T
\varepsilon_{\mathrm{u}}/C \sim H^2/(T^2 \log 1/T)$ whose divergence for finite
$H$ is consistent with the scenario of a quadrupolar Kondo effect. In addition,
we find a singular behavior of the isotropic strain $\varepsilon_{\mathrm{B}}$
in zero magnetic field resulting in a divergence of both the volume thermal
expansion and the volume Gr\"uneisen parameter. We speculate that this behavior
might be also induced by putative Kondo correlations via elastic
anharmonicities or static strain disorder.


title:
Fermionic Bogomol\`nyi-Prasad-Sommerfield Wilson loops in
  four-dimensional $\mathcal{N}=2$ superconformal gauge theories

abstract:
  In this paper we construct fermionic Bogomol\`nyi-Prasad-Sommerfield (BPS)
Wilson loops in $\mathcal{N}=2$ superconformal $SU(N)\times SU(N)$ quiver
theory and $\mathcal{N}=4$ super Yang-Mills theory. The connections of these
fermionic BPS Wilson have a supermatrix structure. We construct timelike BPS
Wilson lines in Minkowski spacetime and circle BPS Wilson loops in Euclidean
space. These Wilson loops involve dimensionful parameters. For generic values
of parameters, they preserve one real (complex) supercharge in Lorentzian
(Euclidean) signature. Supersymmetry enhancement for Wilson loops happens when
the parameters satisfy certain constraints.


title:
Observation of the noise-driven thermalization of the
  Fermi-Pasta-Ulam-Tsingou recurrence in optical fibers

abstract:
  We report the observation of the thermalization of the
Fermi-Pasta-Ulam-Tsingou recurrence process in optical fibers. We show the
transition from a reversible regime to an irreversible one, revealing a
spectrally thermalized state. To do so, we actively compensate the fiber loss
to make the observation of several recurrences possible. We inject into the
fiber a combination of three coherent continuous waves, which we call Fourier
modes, and a random noise. We enhance the noise-driven modulation instability
process against the coherent one by boosting the input noise power level to
speed up the evolution to the thermalization. The distributions of the Fourier
modes power along the fiber length are recorded thanks to a multi-heterodyne
time-domain reflectometer. At low input noise levels, we observe up to four
recurrences. Whereas, at higher noise levels, the Fourier modes fade into the
noise-driven modulation instability spectrum revealing that the process reached
an irreversible thermalized state.


title:
Extending the weak cosmic censorship conjecture to charged Buchdahl star
  by employing the gedanken experiments

abstract:
  In this paper, we extend the weak cosmic censorship conjecture (WCCC) to non
black hole objects, and test its validity for the Buchdahl star. It turns out
that extremal limit for star is over-extremal for black hole, $Q^2/M^2 \leq
9/8$. By carrying out both linear and non-linear perturbations, we establish
the same result, as that for black hole, for the Buchdahl star as well. That
is, it could be overcharged at the linear perturbation while the result is
overturned when non-linear perturbations are included.


title: Tooling for Time- and Space-efficient git Repository Mining

abstract:
  Software projects under version control grow with each commit, accumulating
up to hundreds of thousands of commits per repository. Especially for such
large projects, the traversal of a repository and data extraction for static
source code analysis poses a trade-off between granularity and speed. We
showcase the command-line tool pyrepositoryminer that combines a set of
optimization approaches for efficient traversal and data extraction from git
repositories while being adaptable to third-party and custom software metrics
and data extractions. The tool is written in Python and combines bare
repository access, in-memory storage, parallelization, caching, change-based
analysis, and optimized communication between the traversal and custom data
extraction components. The tool allows for both metrics written in Python and
external programs for data extraction. A single-thread performance evaluation
based on a basic mining use case shows a mean speedup of 15.6x to other freely
available tools across four mid-sized open source projects. A multi-threaded
execution allows for load distribution among cores and, thus, a mean speedup up
to 86.9x using 12 threads.


title:
Characterizations of (Jordan) derivation on Banach algebra with local
  actions

abstract:
  Let $\mathcal{A}$ be a unital Banach $*$-algebra and $\mathcal{M}$ be a
unital $*$-$\mathcal{A}$-bimodule. If $W$ is a left separating point of
$\mathcal{M}$, we show that every $*$-derivable mapping at $W$ is a Jordan
derivation, and every $*$-left derivable mapping at $W$ is a Jordan left
derivation under the condition $W \mathcal{A}=\mathcal{A}W$. Moreover we give a
complete description of linear mappings $\delta$ and $\tau$ from $\mathcal{A}$
into $\mathcal{M}$ satisfying $\delta(A)B^*+A\tau(B)^*=0$ for any $A, B\in
\mathcal{A}$ with $AB^*=0$ or $\delta(A)\circ B^*+A\circ\tau(B)^*=0$ for any
$A, B\in \mathcal{A}$ with $A\circ B^*=0$, where $A\circ B=AB+BA$ is the Jordan
product.


title: BioTouchPass: Handwritten Passwords for Touchscreen Biometrics

abstract:
  This work enhances traditional authentication systems based on Personal
Identification Numbers (PIN) and One-Time Passwords (OTP) through the
incorporation of biometric information as a second level of user
authentication. In our proposed approach, users draw each digit of the password
on the touchscreen of the device instead of typing them as usual. A complete
analysis of our proposed biometric system is carried out regarding the
discriminative power of each handwritten digit and the robustness when
increasing the length of the password and the number of enrolment samples. The
new e-BioDigit database, which comprises on-line handwritten digits from 0 to
9, has been acquired using the finger as input on a mobile device. This
database is used in the experiments reported in this work and it is available
together with benchmark results in GitHub. Finally, we discuss specific details
for the deployment of our proposed approach on current PIN and OTP systems,
achieving results with Equal Error Rates (EERs) ca. 4.0% when the attacker
knows the password. These results encourage the deployment of our proposed
approach in comparison to traditional PIN and OTP systems where the attack
would have 100% success rate under the same impostor scenario.


title:
Integration of silicon-vacancy centers in nanodiamonds with an optical
  nanofiber

abstract:
  We experimentally demonstrate the integration of silicon-vacancy centers in
nanodiamonds (SiV-NDs) with an optical nanofiber (ONF). We grow SiV-NDs on seed
NDs dispersed on a quartz substrate using a microwave plasma-assisted chemical
vapor deposition method. First, we search and characterize SiV-NDs on a quartz
substrate using an inverted confocal microscope and an atomic force microscope
(AFM). Subsequently, we pick up SiV-NDs from the quartz substrate and deposit
them on the surface of a free-standing ONF using the AFM tip. The fluorescence
emission spectrum, photon count rate, and intensity correlations for SiV-NDs
are systematically measured.


title:
Predicting Loose-Fitting Garment Deformations Using Bone-Driven Motion
  Networks

abstract:
  We present a learning algorithm that uses bone-driven motion networks to
predict the deformation of loose-fitting garment meshes at interactive rates.
Given a garment, we generate a simulation database and extract virtual bones
from simulated mesh sequences using skin decomposition. At runtime, we
separately compute low- and high-frequency deformations in a sequential manner.
The low-frequency deformations are predicted by transferring body motions to
virtual bones' motions, and the high-frequency deformations are estimated
leveraging the global information of virtual bones' motions and local
information extracted from low-frequency meshes. In addition, our method can
estimate garment deformations caused by variations of the simulation parameters
(e.g., fabric's bending stiffness) using an RBF kernel ensembling trained
networks for different sets of simulation parameters. Through extensive
comparisons, we show that our method outperforms state-of-the-art methods in
terms of prediction accuracy of mesh deformations by about 20% in RMSE and 10%
in Hausdorff distance and STED. The code and data are available at
https://github.com/non-void/VirtualBones.


title: Neural Combinatorial Optimization: a New Player in the Field

abstract:
  Neural Combinatorial Optimization attempts to learn good heuristics for
solving a set of problems using Neural Network models and Reinforcement
Learning. Recently, its good performance has encouraged many practitioners to
develop neural architectures for a wide variety of combinatorial problems.
However, the incorporation of such algorithms in the conventional optimization
framework has raised many questions related to their performance and the
experimental comparison with other methods such as exact algorithms, heuristics
and metaheuristics. This paper presents a critical analysis on the
incorporation of algorithms based on neural networks into the classical
combinatorial optimization framework. Subsequently, a comprehensive study is
carried out to analyse the fundamental aspects of such algorithms, including
performance, transferability, computational cost and generalization to
larger-sized instances. To that end, we select the Linear Ordering Problem as a
case of study, an NP-hard problem, and develop a Neural Combinatorial
Optimization model to optimize it. Finally, we discuss how the analysed aspects
apply to a general learning framework, and suggest new directions for future
work in the area of Neural Combinatorial Optimization algorithms.


title:
Prediction-Based Reachability Analysis for Collision Risk Assessment on
  Highways

abstract:
  Real-time safety systems are crucial components of intelligent vehicles. This
paper introduces a prediction-based collision risk assessment approach on
highways. Given a point mass vehicle dynamics system, a stochastic forward
reachable set considering two-dimensional motion with vehicle state probability
distributions is firstly established. We then develop an acceleration
prediction model, which provides multi-modal probabilistic acceleration
distributions to propagate vehicle states. The collision probability is
calculated by summing up the probabilities of the states where two vehicles
spatially overlap. Simulation results show that the prediction model has
superior performance in terms of vehicle motion position errors, and the
proposed collision detection approach is agile and effective to identify the
collision in cut-in crash events.


title: Learning Label Initialization for Time-Dependent Harmonic Extension

abstract:
  Node classification on graphs can be formulated as the Dirichlet problem on
graphs where the signal is given at the labeled nodes, and the harmonic
extension is done on the unlabeled nodes. This paper considers a time-dependent
version of the Dirichlet problem on graphs and shows how to improve its
solution by learning the proper initialization vector on the unlabeled nodes.
Further, we show that the improved solution is at par with state-of-the-art
methods used for node classification. Finally, we conclude this paper by
discussing the importance of parameter t, pros, and future directions.


title: Some critical point results for Fr\'{e}chet manifolds

abstract:
  We prove a so-called linking theorem and some of its corollaries, namely a
mountain pass theorem and a three critical points theorem for Keller $
C^1$-functional on $ C^1 $- Frechet manifolds. Our approach relies on a
deformation result which is not implemented by considering the negative
pseudo-gradient flows. Furthermore, for mappings between Frechet manifolds we
provide a set of sufficient conditions in terms of the Palais-Smale condition
that indicates when a local diffeomorphism is a global one.


title:
Explicit Solvent Effects on Macromolecular Interactions From a
  Solvent-Augmented Contact Value Theorem

abstract:
  The Derjaguin-Landau-Verywey-Overbeek (DLVO) theory has been a remarkably
accurate framework for the characterization of macromolecular stability in
water solvent. In view of its solvent-implicit nature neglecting the
electrostatics of water molecules with non-negligible charge structure and
concentration, the precision of the DLVO formalism is somewhat puzzling. In
order to shed light on this issue, we derive from our earlier explicit solvent
formalism [S. Buyukdagli el al., Phys. Rev. E, 2013, 87, 063201] a
solvent-augmented contact value theorem and assess the contribution of solvent
molecules to the interaction of charged membranes. We find that in the case of
hydrophobic membranes with fixed charges on the membrane surface, the nearly
exact cancellation of various explicit solvent effects of substantially large
magnitude but opposite sign keeps the intermembrane pressure significantly
close to the double layer force of the DLVO theory. Then, in the case of
hydrophilic surface charge groups within the aqueous region, due to the spatial
separation of the membrane substrate from the location of the fixed charges
where the non-local dielectric response of the structured solvent is sharply
localized, the interfacial field energy and the contact charge densities remain
unaffected by the explicit solvent. As a result, the hydration of the lipid
head groups suppresses the signature of the solvent molecules from the membrane
interaction force.


title:
Inhomogeneous Diophantine approximation for generic homogeneous
  functions

abstract:
  The present paper is a sequel to arXiv:1910.02067 in which results of that
paper are generalized so that they hold in the setting of inhomogeneous
Diophantine approximation. Given any integers $n \geq 2$ and $\ell \geq 1$, any
$\pmb \xi = (\xi_1, \dots, \xi_\ell) \in \mathbb{R}^\ell$, and any homogeneous
function $f = (f_1, \dots , f_\ell): \mathbb{R}^n \to \mathbb{R}^\ell$ that
satisfies a certain nonsingularity assumption, we obtain a biconditional
criterion on the approximating function $\psi = (\psi_1, \dots, \psi_\ell):
\mathbb{R}_{\geq 0} \to (\mathbb{R}_{>0})^\ell$ for a generic element in the
$G$-orbit of $f$ to be (respectively, not to be) $\psi$-approximable at $\pmb
\xi$: that is, for there to exist infinitely many (respectively, only finitely
many) $\mathbf{v} \in \mathbb{Z}^n$ such that $|\xi_j - (f_j \circ
g)(\mathbf{v})| \leq \psi_j(\|\mathbf{v}\|)$ for each $j \in \{1, \dots,
\ell\}$. In this setting, we also obtain a sufficient condition for uniform
approximation. We also consider some examples of $f$ that do not satisfy our
nonsingularity assumptions and prove similar results for these examples. Here,
$G$ can be any closed subgroup of $\mathrm{ASL}_n(\mathbb{R})$ (such as
$\mathrm{ASL}_n(\mathbb{R})$ itself or $\mathrm{SL}_n(\mathbb{R})$) that
satisfies certain axioms introduced by the authors in the aforementioned
previous paper.


title: TracInAD: Measuring Influence for Anomaly Detection

abstract:
  As with many other tasks, neural networks prove very effective for anomaly
detection purposes. However, very few deep-learning models are suited for
detecting anomalies on tabular datasets. This paper proposes a novel
methodology to flag anomalies based on TracIn, an influence measure initially
introduced for explicability purposes. The proposed methods can serve to
augment any unsupervised deep anomaly detection method. We test our approach
using Variational Autoencoders and show that the average influence of a
subsample of training points on a test point can serve as a proxy for
abnormality. Our model proves to be competitive in comparison with
state-of-the-art approaches: it achieves comparable or better performance in
terms of detection accuracy on medical and cyber-security tabular benchmark
data.


title:
New analytic solutions in $f\left( R\right) $-Cosmology from
  Painlev\'{e} analysis

abstract:
  We investigate the integrability properties and existence of analytic
solutions in $f\left( R\right) $-cosmology by using the singularity analysis.
Specifically, for some power-law $f\left( R\right) $-theories of special
interest we apply the ARS algorithm to prove if the field equations possess the
Painlev\'{e} property. Constraints for the free parameters of the power-law
models are constructed and new analytic solutions are derived which are
expressed in terms of Laurent expansions.


title:
Simultaneous Imaging of Widely Differing Particle Concentrations in MPI:
  Problem Statement and Algorithmic Proposal for Improvement

abstract:
  Magnetic Particle Imaging (MPI) is a tomographic imaging technique for
determining the spatial distribution of superparamagnetic nanoparticles.
Current MPI systems are capable of imaging iron masses over a wide dynamic
range of more than four orders of magnitude. In theory, this range could be
further increased using adaptive amplifiers, which prevent signal clipping.
While this applies to a single sample, the dynamic range is severely limited if
several samples with different concentrations or strongly inhomogeneous
particle distributions are considered. One scenario that occurs quite
frequently in pre-clinical applications is that a highly concentrated tracer
bolus in the vascular system "shadows" nearby organs with lower effective
tracer concentrations. The root cause of the problem is the ill-posedness of
the MPI imaging operator, which requires regularization for stable
reconstruction. In this work, we introduce a simple two-step algorithm that
increases the dynamic range by a factor of four. Furthermore, the algorithm
enables spatially adaptive regularization, i.e. highly concentrated signals can
be reconstructed with maximum spatial resolution, while low concentrated
signals are strongly regularized to prevent noise amplification.


title: Half-Positional Objectives Recognized by Deterministic B\"uchi Automata

abstract:
  A central question in the theory of two-player games over graphs is to
understand which objectives are half-positional, that is, which are the
objectives for which the protagonist does not need memory to implement winning
strategies. Objectives for which both players do not need memory have already
been characterized (both in finite and infinite graphs). However, less is known
about half-positional objectives. In particular, no characterization of
half-positionality is known for the central class of omega-regular objectives.
  In this paper, we characterize objectives recognizable by deterministic
B\"uchi automata (a class of omega-regular objectives) that are
half-positional, in both finite and infinite graphs. Our characterization
consists of three natural conditions linked to the language-theoretic notion of
right congruence. Furthermore, this characterization yields a polynomial-time
algorithm to decide half-positionality of an objective recognized by a given
deterministic B\"uchi automaton.


title: Finding patterns in Knowledge Attribution for Transformers

abstract:
  We analyze the Knowledge Neurons framework for the attribution of factual and
relational knowledge to particular neurons in the transformer network. We use a
12-layer multi-lingual BERT model for our experiments. Our study reveals
various interesting phenomena. We observe that mostly factual knowledge can be
attributed to middle and higher layers of the network($\ge 6$). Further
analysis reveals that the middle layers($6-9$) are mostly responsible for
relational information, which is further refined into actual factual knowledge
or the "correct answer" in the last few layers($10-12$). Our experiments also
show that the model handles prompts in different languages, but representing
the same fact, similarly, providing further evidence for effectiveness of
multi-lingual pre-training. Applying the attribution scheme for grammatical
knowledge, we find that grammatical knowledge is far more dispersed among the
neurons than factual knowledge.


title:
A hybrid multi-object segmentation framework with model-based B-splines
  for microbial single cell analysis

abstract:
  In this paper, we propose a hybrid approach for multi-object microbial cell
segmentation. The approach combines an ML-based detection with a geometry-aware
variational-based segmentation using B-splines that are parametrized based on a
geometric model of the cell shape. The detection is done first using YOLOv5. In
a second step, each detected cell is segmented individually. Thus, the
segmentation only needs to be done on a per-cell basis, which makes it amenable
to a variational approach that incorporates prior knowledge on the geometry.
Here, the contour of the segmentation is modelled as closed uniform cubic
B-spline, whose control points are parametrized using the known cell geometry.
Compared to purely ML-based segmentation approaches, which need accurate
segmentation maps as training data that are very laborious to produce, our
method just needs bounding boxes as training data. Still, the proposed method
performs on par with ML-based segmentation approaches usually used in this
context. We study the performance of the proposed method on time-lapse
microscopy data of Corynebacterium glutamicum.


title:
High-pressure II-III phase transition in solid hydrogen: new insights
  from state-of-the-art ab initio calculations

abstract:
  The high-pressure II-III phase transition in solid hydrogen is investigated
using the random phase approximation and diffusion Monte Carlo. Good agreement
between the methods is found confirming that an accurate treatment of exchange
and correlation increases the transition pressure by more than 100 GPa with
respect to semilocal density functional approximations. Using an optimized
hybrid functional, we then reveal a new candidate structure for phase II
generated by a librational instability of the C2/c phase III structure. The
out-of-plane lattice distortion within this phase weakens the in-plane
polarization of C2/c leading to the well-known experimental signatures of the
II-III phase transition such as a sharp shift in vibron frequency, infrared
activity and $c/a$ lattice parameter ratio. Finally, we discuss the zero-point
vibrational energy that plays an important role in stabilizing phase III at
lower pressures.


title:
Improving the Convergence Rates for the Kinetic Fokker-Planck Equation
  by Optimal Control

abstract:
  The long time behavior and detailed convergence analysis of Langevin
equations has received increased attention over the last years. Difficulties
arise from a lack of coercivity, usually termed hypocoercivity, of the
underlying kinetic Fokker-Planck operator which is a consequence of the
partially deterministic nature of a second order stochastic differential
equation. In this manuscript, the effect of controlling the confinement
potential without altering the original invariant measure is investigated. This
leads to an abstract bilinear control system with an unbounded but
infinite-time admissible control operator which, by means of an artificial
diffusion approach, is shown to possess a unique solution. The compactness of
the underlying semigroup is further used to define an infinite-horizon optimal
control problem on an appropriately reduced state space. Under smallness
assumptions on the initial data, feasibility of and existence of a solution to
the optimal control problem are discussed. Numerical results based on a local
approximation based on a shifted Riccati equation illustrate the theoretical
findings.


title:
A Volatility Estimator of Stock Market Indices Based on the Intrinsic
  Entropy Model

abstract:
  Grasping the historical volatility of stock market indices and accurately
estimating are two of the major focuses of those involved in the financial
securities industry and derivative instruments pricing. This paper presents the
results of employing the intrinsic entropy model as a substitute for estimating
the volatility of stock market indices. Diverging from the widely used
volatility models that take into account only the elements related to the
traded prices, namely the open, high, low, and close prices of a trading day
(OHLC), the intrinsic entropy model takes into account the traded volumes
during the considered time frame as well. We adjust the intraday intrinsic
entropy model that we introduced earlier for exchange-traded securities in
order to connect daily OHLC prices with the ratio of the corresponding daily
volume to the overall volume traded in the considered period. The intrinsic
entropy model conceptualizes this ratio as entropic probability or market
credence assigned to the corresponding price level. The intrinsic entropy is
computed using historical daily data for traded market indices (S&P 500, Dow
30, NYSE Composite, NASDAQ Composite, Nikkei 225, and Hang Seng Index). We
compare the results produced by the intrinsic entropy model with the volatility
estimates obtained for the same data sets using widely employed industry
volatility estimators. The intrinsic entropy model proves to consistently
deliver reliable estimates for various time frames while showing peculiarly
high values for the coefficient of variation, with the estimates falling in a
significantly lower interval range compared with those provided by the other
advanced volatility estimators.


title:
Microscopic model of spin flip-flop processes in rare-earth-ion-doped
  crystals

abstract:
  Flip-flop processes due to magnetic dipole-dipole interaction between
neighbouring ions in rare-earth-ion-doped crystals is one of the mechanisms of
relaxation between hyperfine levels. Modeling of this mechanism has so far been
macroscopic, characterized by an average rate describing the relaxation of all
ions. Here however, we present a microscopic model of flip-flop interactions
between individual nuclear spins of dopant ions. Every ion is situated in a
unique local environment in the crystal, where each ion has different distances
and a unique orientation relative to its nearest neighbors, as determined by
the lattice structure. Thus, each ion has a unique flip-flop rate and the
collective relaxation dynamics of all ions in a bulk crystal is a sum of many
exponential decays, giving rise to a distribution of rates rather than a single
average decay rate. We employ this model to calculate flip-flop rates in
Pr$^{3+}$:Y$_2$SiO$_5$ and show experimental measurements of population decay
of the ground state hyperfine levels at $\sim$2 K. We also present a new method
to measure rates of individual transitions from hole burning spectra that
requires significantly fewer fitting parameters in theoretical rate equations
compared to earlier work. Furthermore, we measure the effect of external
magnetic field on the flip-flop rates and observe that the rates slow down by
two orders of magnitude in a field of 5 - 10 mT.


title:
A Global Operational Readiness Review Process: Improving Cloud
  Availability

abstract:
  The ORR (Operational Readiness Review) is a long standing practice to help
insure application or system readiness and improved Availability. In this paper
the ORR is defined and recent examples of its use from Cloud Computing
environments are compared. An emphasis on ORRs used within DevOps environments
is also provided. A detailed presentation of a specific and custom ORR
implementation for a large global IT organization is shared. This includes the
process development approach, key components of the ORR checklist, automation
support provided, and a unique Executive dashboard solution to visualize status
on in-flight releases. Challenges and benefits from this ORR implementation are
provided as well as a detailed comparison with the Google Launch checklist and
its associated PRR/ORR. Finally, suggestions for further improvements,
automation, and usage of the ORR in large-scale industrial settings based on
this real-world experience are elaborated.
  Keywords: Operational Readiness Review, ORR, IT Services, IT Operations,
ITIL, Process Engineering, Reliability, Availability, Software Architecture,
Cloud Computing, Networking, Site Reliability Engineering, DevOps, Agile
Methods, Quality, Defect Prevention, Release Management, Risk Management, Data
Visualization, Organizational Change Management.


title: Copy Motion From One to Another: Fake Motion Video Generation

abstract:
  One compelling application of artificial intelligence is to generate a video
of a target person performing arbitrary desired motion (from a source person).
While the state-of-the-art methods are able to synthesize a video demonstrating
similar broad stroke motion details, they are generally lacking in texture
details. A pertinent manifestation appears as distorted face, feet, and hands,
and such flaws are very sensitively perceived by human observers. Furthermore,
current methods typically employ GANs with a L2 loss to assess the authenticity
of the generated videos, inherently requiring a large amount of training
samples to learn the texture details for adequate video generation. In this
work, we tackle these challenges from three aspects: 1) We disentangle each
video frame into foreground (the person) and background, focusing on generating
the foreground to reduce the underlying dimension of the network output. 2) We
propose a theoretically motivated Gromov-Wasserstein loss that facilitates
learning the mapping from a pose to a foreground image. 3) To enhance texture
details, we encode facial features with geometric guidance and employ local
GANs to refine the face, feet, and hands. Extensive experiments show that our
method is able to generate realistic target person videos, faithfully copying
complex motions from a source person. Our code and datasets are released at
https://github.com/Sifann/FakeMotion


title:
Hidden behind the obvious: misleading keywords and implicitly abusive
  language on social media

abstract:
  While social media offers freedom of self-expression, abusive language carry
significant negative social impact. Driven by the importance of the issue,
research in the automated detection of abusive language has witnessed growth
and improvement. However, these detection models display a reliance on strongly
indicative keywords, such as slurs and profanity. This means that they can
falsely (1a) miss abuse without such keywords or (1b) flag non-abuse with such
keywords, and that (2) they perform poorly on unseen data. Despite the
recognition of these problems, gaps and inconsistencies remain in the
literature. In this study, we analyse the impact of keywords from dataset
construction to model behaviour in detail, with a focus on how models make
mistakes on (1a) and (1b), and how (1a) and (1b) interact with (2). Through the
analysis, we provide suggestions for future research to address all three
problems.


title:
Large time behavior of solutions to a diffusion approximation radiation
  hydrodynamics model

abstract:
  This paper concerns with the large time behavior of solutions to a diffusion
approximation radiation hydrodynamics model when the initial data is a small
perturbation around an equilibrium state. The global-in-time well-posedness of
solutions is achieved in Sobolev spaces depending on the Littlewood-Paley
decomposition technique together with certain elaborate energy estimates in
frequency space. Moreover, the optimal decay rate of the solution is also
yielded provided the initial data also satisfy an additional $L^1$ condition.
Meanwhile, the similar results of the diffusion approximation system without
the thermal conductivity could be also established.


title:
Textual Entailment for Event Argument Extraction: Zero- and Few-Shot
  with Multi-Source Learning

abstract:
  Recent work has shown that NLP tasks such as Relation Extraction (RE) can be
recasted as Textual Entailment tasks using verbalizations, with strong
performance in zero-shot and few-shot settings thanks to pre-trained entailment
models. The fact that relations in current RE datasets are easily verbalized
casts doubts on whether entailment would be effective in more complex tasks. In
this work we show that entailment is also effective in Event Argument
Extraction (EAE), reducing the need of manual annotation to 50% and 20% in ACE
and WikiEvents respectively, while achieving the same performance as with full
training. More importantly, we show that recasting EAE as entailment alleviates
the dependency on schemas, which has been a road-block for transferring
annotations between domains. Thanks to the entailment, the multi-source
transfer between ACE and WikiEvents further reduces annotation down to 10% and
5% (respectively) of the full training without transfer. Our analysis shows
that the key to good results is the use of several entailment datasets to
pre-train the entailment model. Similar to previous approaches, our method
requires a small amount of effort for manual verbalization: only less than 15
minutes per event argument type is needed, and comparable results can be
achieved with users with different level of expertise.


title: On tournaments combinatorics

abstract:
  In this paper we present a new proof of a proposition presented in the
article: Complexes of tournaments, directionality filtrations and persistent
homology. This paper is part of Joaqu\'in Casta\~neda's undergraduate thesis.


title: Complex-order Reset Control System

abstract:
  According to the well-known loop shaping method for the design of
controllers, the performance of the controllers in terms of step response,
steady-state disturbance rejection and noise attenuation and robustness can be
improved by increasing the gain at lower frequencies and decreasing it at
higher frequencies and increasing the phase margin as much as possible.
However, the inherent properties of linear controllers, the Bode's phase-gain
relation, create a limitation. In theory, a complex-order transfer function can
break the Bode's gain-phase relation; however, such transfer function cannot be
directly implemented and should be approximated. This paper proposes a reset
element and a tuning method to approximate a Complex-Order Controller (CLOC)
and, through a simulation example, shows the benefits of using such a
controller.


title: Configuration Spaces over Singular Spaces -- II. Curvature

abstract:
  This is the second paper of a series on configuration spaces $\Upsilon$ over
singular spaces $X$. Here, we focus on geometric aspects of the extended metric
measure space $(\Upsilon, \mathsf{d}_{\Upsilon}, \mu)$ equipped with the
$L^2$-transportation distance $\mathsf{d}_{\Upsilon}$, and a mixed Poisson
measure $\mu$. Firstly, we establish the essential self-adjointness and the
$L^p$-uniqueness for the Laplacian on $\Upsilon$ lifted from $X$. Secondly, we
prove the equivalence of Bakry-\'Emery curvature bounds on $X$ and on
$\Upsilon$, without any metric assumption on $X$. We further prove the
Evolution Variation Inequality on $\Upsilon$, and introduce the notion of
synthetic Ricci-curvature lower bounds for the extended metric measure space
$\Upsilon$. As an application, we prove the Sobolev-to-Lipschitz property on
$\Upsilon$ over singular spaces $X$, originally conjectured in the case when
$X$ is a manifold by M. R\"ockner and A. Schield. As a further application, we
prove the $L^\infty$-to-$\mathsf{d}_{\Upsilon}$-Lipschitz regularization of the
heat semigroup on $\Upsilon$ and gives a new characterization of the ergodicity
of the corresponding particle systems in terms of optimal transport.


title:
Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive
  Review

abstract:
  With the extremely rapid advances in remote sensing (RS) technology, a great
quantity of Earth observation (EO) data featuring considerable and complicated
heterogeneity is readily available nowadays, which renders researchers an
opportunity to tackle current geoscience applications in a fresh way. With the
joint utilization of EO data, much research on multimodal RS data fusion has
made tremendous progress in recent years, yet these developed traditional
algorithms inevitably meet the performance bottleneck due to the lack of the
ability to comprehensively analyse and interpret these strongly heterogeneous
data. Hence, this non-negligible limitation further arouses an intense demand
for an alternative tool with powerful processing competence. Deep learning
(DL), as a cutting-edge technology, has witnessed remarkable breakthroughs in
numerous computer vision tasks owing to its impressive ability in data
representation and reconstruction. Naturally, it has been successfully applied
to the field of multimodal RS data fusion, yielding great improvement compared
with traditional methods. This survey aims to present a systematic overview in
DL-based multimodal RS data fusion. More specifically, some essential knowledge
about this topic is first given. Subsequently, a literature survey is conducted
to analyse the trends of this field. Some prevalent sub-fields in the
multimodal RS data fusion are then reviewed in terms of the to-be-fused data
modalities, i.e., spatiospectral, spatiotemporal, light detection and
ranging-optical, synthetic aperture radar-optical, and RS-Geospatial Big Data
fusion. Furthermore, We collect and summarize some valuable resources for the
sake of the development in multimodal RS data fusion. Finally, the remaining
challenges and potential future directions are highlighted.


title:
Kompetencer: Fine-grained Skill Classification in Danish Job Postings
  via Distant Supervision and Transfer Learning

abstract:
  Skill Classification (SC) is the task of classifying job competences from job
postings. This work is the first in SC applied to Danish job vacancy data. We
release the first Danish job posting dataset: Kompetencer (en: competences),
annotated for nested spans of competences. To improve upon coarse-grained
annotations, we make use of The European Skills, Competences, Qualifications
and Occupations (ESCO; le Vrang et al., 2014) taxonomy API to obtain
fine-grained labels via distant supervision. We study two setups: The zero-shot
and few-shot classification setting. We fine-tune English-based models and
RemBERT (Chung et al., 2020) and compare them to in-language Danish models. Our
results show RemBERT significantly outperforms all other models in both the
zero-shot and the few-shot setting.


title: A Mapping Approach to Convert MTPs into a Capability and Skill Ontology

abstract:
  Being able to quickly integrate new equipment and functions into an existing
plant is a major goal for both discrete and process manufacturing. But
currently, these two industry domains use different approaches to achieve this
goal. While the Module Type Package (MTP) is getting more and more adapted in
practical applications of process manufacturing, so-called skill-based
manufacturing approaches are favored in the context of discrete manufacturing.
The two approaches are incompatible because their models feature different
contents and they use different technologies. This contribution provides a
comparison of the MTP with a skill-based approach as well as an automated
mapping that can be used to transfer the contents of an MTP into a skill
ontology. Through this mapping an MTP can be semantically lifted in order to
apply functions like querying or reasoning. Furthermore, machines that were
previously described using two incompatible models can now be used in one
production process.


title: Enumeration of partial Lukasiewicz paths

abstract:
  \L{}ukasiewicz paths are lattice paths in $\Bbb{N}^2$ starting at the origin,
ending on the $x$-axis, and consisting of steps in the set $\{(1,k), k\geq
-1\}$. We give generating function and exact value for the number of $n$-length
prefixes (resp. suffixes) of these paths ending at height $k\geq 0$ with a
given type of step. We make a similar study for prefixes of height at most
$t\geq 0$. Using the explicit forms for the paths of bounded height, we
evaluate the average height asymptotically. For fixed $k$ and $n\to\infty$,
this quantity behaves as $\sqrt{\pi n}$. Finally we study (in the same way)
prefixes of alternate \L{}ukasiewicz paths, i.e., \L{}ukasiewicz paths that do
contain two consecutive steps with the same direction.


title:
A First-principles study on ABBr3 (A = Cs, Rb, K, Na; B = Ge, Sn) halide
  perovskites for photovoltaic applications

abstract:
  In recent years, halide perovskite-based solar cells have received intensive
attention, and demonstrated power conversion efficiency as high as 25.8%. With
regard to the toxicity of Pb and the instability of organic elements, all
inorganic lead-free perovskites (ILPs) have been extensively studied to achieve
comparable or greater photovoltaic performance. In order to develop ILPs as an
alternative for solar cell applications, we performed first-principles
calculations of ABBr3 perovskites (A = Cs, Rb, K, and Na, and B = Sn, and Ge).
Structural, electronic, and optical properties were systematically studied to
probe the potentiality in photovoltaic applications. All these ILPs exhibited a
direct bandgap in the range of 1.10 to 1.97 eV, highly beneficial for absorbing
solar energy. Furthermore, these ILPs demonstrated significant optical
absorption (over 105 cm-1) in the whole UV-Vis spectrum. These results will be
helpful for designing highly efficient lead-free perovskite solar cells.


title: Smooth over-parameterized solvers for non-smooth structured optimization

abstract:
  Non-smooth optimization is a core ingredient of many imaging or machine
learning pipelines. Non-smoothness encodes structural constraints on the
solutions, such as sparsity, group sparsity, low-rank and sharp edges. It is
also the basis for the definition of robust loss functions and scale-free
functionals such as square-root Lasso. Standard approaches to deal with
non-smoothness leverage either proximal splitting or coordinate descent. These
approaches are effective but usually require parameter tuning, preconditioning
or some sort of support pruning. In this work, we advocate and study a
different route, which operates a non-convex but smooth over-parametrization of
the underlying non-smooth optimization problems. This generalizes quadratic
variational forms that are at the heart of the popular Iterative Reweighted
Least Squares (IRLS). Our main theoretical contribution connects gradient
descent on this reformulation to a mirror descent flow with a varying Hessian
metric. This analysis is crucial to derive convergence bounds that are
dimension-free. This explains the efficiency of the method when using small
grid sizes in imaging. Our main algorithmic contribution is to apply the
Variable Projection (VarPro) method which defines a new formulation by
explicitly minimizing over part of the variables. This leads to a better
conditioning of the minimized functional and improves the convergence of simple
but very efficient gradient-based methods, for instance quasi-Newton solvers.
We exemplify the use of this new solver for the resolution of regularized
regression problems for inverse problems and supervised learning, including
total variation prior and non-convex regularizers.


title: An Intrinsic Entropy Model for Exchange-Traded Securities

abstract:
  This article introduces an intrinsic entropy model that can be used as an
indicator to gauge investor interest in a given exchange-traded security, along
with the state of the general market corroborated by individual security trade
data. Although the syntagma of intrinsic entropy might sound somehow
pleonastic, since entropy itself characterizes the fundamentals of a system, we
would like to make a clear distinction between entropy models based on the
values that a random variable may take and the model that we propose, which
employs actual stock exchange trading data. The model we propose for intrinsic
entropy does not include any exogenous factor that could influence the level of
entropy. The intrinsic entropy signals whether the market is inclined to buy
the security or rather to sell it. We further explore the usage of the
intrinsic entropy model for algorithmic trading, in order to demonstrate the
value of our model in assisting investors in the selection of the intraday
stock portfolio, along with timely generated signals to support the buy / sell
decision making process. The test results provide empirical evidence that the
proposed intrinsic entropy model can be used as an indicator to evaluate the
direction and intensity of intraday trading activity of an exchange-traded
security. The data used for the test consisted of historical intraday
transactions executed on The Bucharest Stock Exchange (BVB).


title:
Integration of Behavioral Economic Models to Optimize ML performance and
  interpretability: a sandbox example

abstract:
  This paper presents a sandbox example of how the integration of models
borrowed from Behavioral Economic (specifically Protection-Motivation Theory)
into ML algorithms (specifically Bayesian Networks) can improve the performance
and interpretability of ML algorithms when applied to Behavioral Data. The
integration of Behavioral Economics knowledge to define the architecture of the
Bayesian Network increases the accuracy of the predictions in 11 percentage
points. Moreover, it simplifies the training process, making unnecessary
training computational efforts to identify the optimal structure of the
Bayesian Network. Finally, it improves the explicability of the algorithm,
avoiding illogical relations among variables that are not supported by previous
behavioral cybersecurity literature. Although preliminary and limited to 0ne
simple model trained with a small dataset, our results suggest that the
integration of behavioral economics and complex ML models may open a promising
strategy to improve the predictive power, training costs and explicability of
complex ML models. This integration will contribute to solve the scientific
issue of ML exhaustion problem and to create a new ML technology with relevant
scientific, technological and market implications.


title:
Restarted randomized surrounding methods for solving large linear
  equations

abstract:
  A class of restarted randomized surrounding methods are presented to
accelerate the surrounding algorithms by restarted techniques for solving the
linear equations. Theoretical analysis prove that the proposed method converges
under the randomized row selection rule and the expectation convergence rate is
also addressed. Numerical experiments further demonstrate that the proposed
algorithms are efficient and outperform the existing method for over-determined
and under-determined linear equation, as well as in the application of image
processing.


title:
Sampling-free obstacle gradients and reactive planning in Neural
  Radiance Fields (NeRF)

abstract:
  This work investigates the use of Neural implicit representations,
specifically Neural Radiance Fields (NeRF), for geometrical queries and motion
planning. We show that by adding the capacity to infer occupancy in a radius to
a pre-trained NeRF, we are effectively learning an approximation to a Euclidean
Signed Distance Field (ESDF). Using backward differentiation of the augmented
network, we obtain an obstacle gradient that is integrated into an obstacle
avoidance policy based on the Riemannian Motion Policies (RMP) framework. Thus,
our findings allow for very fast sampling-free obstacle avoidance planning in
the implicit representation.


title:
Cost-Efficient and QoS-Aware User Association and 3D Placement of 6G
  Aerial Mobile Access Points

abstract:
  6G networks require a flexible infrastructure to dynamically provide
ubiquitous network coverage. Mobile Access Points (MAP) deployment is a
promising solution. In this paper, we formulate the joint 3D MAP deployment and
user association problem over a dynamic network under interference and mobility
constraints. First, we propose an iterative algorithm to optimize the
deployment of MAPs. Our solution efficiently and quickly determines the number,
position and configuration of MAPs for highly dynamic scenarios. MAPs provide
appropriate Quality of Service (QoS) connectivity to mobile ground user in
mmwave or sub-6GHz bands and find their optimal positions in a 3D grid. Each
MAP also implies an energy cost (e.g. for travel) to be minimized. Once all
MAPs deployed, a deep multiagent reinforcement learning algorithm is proposed
to associate multiple users to multiple MAPs under interference constraint.
Each user acts as an independent agent that operates in a fully distributed
architecture and maximizes the network sum-rate.


title: Riemann-Roch for $\overline{\text{Spec}\mathbb Z}$

abstract:
  We prove a Riemann-Roch formula for Arakelov divisors on
$\overline{\text{Spec}\mathbb Z}$ equating the integer valued Euler
characteristic with a simple modification of the traditional expression (i. e.
the degree of the divisor plus log 2). The integer valued topological side
involves besides the ceiling function, the division by log 3. The notion of
dimension and the definition of the cohomologies are based on the universal
arithmetic over the sphere spectrum which we developed using Segal's Gamma
rings. This new perspective allows us to parallel Weil's adelic proof of the
Riemann-Roch formula for function fields including the use of Pontryagin
duality.


title:
A Unified Framework for Verification of Observational Properties for
  Partially-Observed Discrete-Event Systems

abstract:
  In this paper, we investigate property verification problems in
partially-observed discrete-event systems (DES). Particularly, we are
interested in verifying observational properties that are related to the
information-flow of the system. Observational properties considered here
include diagnosability, predictability, detectability and opacity, which have
drawn considerable attentions in the literature. However, in contrast to
existing results, where different verification procedures are developed for
different properties case-by-case, in this work, we provide a unified framework
for verifying all these properties by reducing each of them as an instance of
HyperLTL model checking. Our approach is based on the construction of a Kripke
structure that effectively captures the issue of unobservability as well as the
finite string semantics in partially-observed DES so that HyperLTL model
checking techniques can be suitably applied. Then for each observational
property considered, we explicitly provide the HyperLTL formula to be checked
over the Kripke structure for the purpose of verification. Our approach is
uniform in the sense that all different properties can be verified with the
same model checking engine. Furthermore, our unified framework also brings new
insights for classifying observational properties for partially-observed DES in
terms of their verification complexity.


title:
Noise-like Pulses from an All-Normal-Dispersion Fiber Laser with
  Weakened Spectrum Filtering

abstract:
  Noise-like pulses (NLP) are extremely sought after in many fields. Here, we
experimentally and numerically investigated the generation of noise-like pulses
in an all-normal-dispersion fiber laser with weak spectrum filtering. With the
insertion of the grating as a tunable spectrum filter, the laser operates at a
stable dissipative soliton state with a 3.84 ps duration. Replacing the grating
with a mirror, NLPs with double-scale intensity autocorrelation trace is
ultimately attained. Numerical simulations are performed in detail and
demonstrated that with the absence of a spectrum filter, the stable state
cannot be established but form the random pulse cluster. The random pulse
cluster achieves dynamic stability with suitable feedback, and the NLP is
ultimately generated. The NLP here is directly evolved by the initial noise,
and no other states occur during its evolution. These explorations could deepen
the understanding of NLP and enrich the complex dynamics of the ANDi ultrafast
fiber laser.


title: From deformation theory to tropical geometry

abstract:
  This is a write-up of the author's invited talk at the Eighth International
Congress of Chinese Mathematicians (ICCM) held at Beijing in June 2019. We give
a survey on joint works with Naichung Conan Leung and Ziming Nikolas Ma where
we study how tropical objects arise from asymptotic analysis of the
Maurer-Cartan equation for deformation of complex structures on a semi-flat
Calabi-Yau manifold.


title:
Publishing a Knowledge Organization System as Linked Data: The Case of
  the Universal Decimal Classification

abstract:
  Linked data (LD) technology is hailed as a long-awaited solution in web-based
information exchange. Linked Open Data (LOD) bring this to another level by
enabling meaningful linking of resources and creating a global, openly
accessible knowledge graph. Our case is the Universal Decimal Classification
(UDC) and the challenges for a KOS service provider to maintain an LD service.
UDC was created during the period 1896--1904 to support systematic organization
and information retrieval of a bibliography. When discussing UDC as LD we make
a distinction between two types of UDC data or two provenances: UDC source
data, and UDC codes as they appear in metadata. To serve the purpose of
supplying semantics one has to front--end UDC LD with a service that can parse
and interpret complex UDC strings. While the use of UDC is free the publishing
and distributing of UDC data is protected by a licence. Publishing of UDC both
as LD and as LOD must be provided for within a complex service that would allow
open access as well as access through a paywall barrier for different levels of
licences. The practical task of publishing the UDC as LOD was informed by the
'10Things guidelines'. The process includes conceptual parts and technological
parts. The transition to a new technology is never a purely mechanical act but
is a research endeavour in its own right. The UDC case has shown the importance
of cross-domain, inter-disciplinary collaboration which needs experts well
situated in multiple knowledge domains.


title: Unveiling the outer dust disc of TW Hya with deep ALMA observations

abstract:
  The radial extent of millimetre dust in protoplanetary discs is often far
smaller than that of their gas, mostly due to processes such as dust growth and
radial drift. However, it has been suggested that current millimetre continuum
observations of discs do not trace their full extent due to limited
sensitivity. In this Letter, we present deep (19 $\mu$Jy beam$^{-1}$) moderate
resolution (0.37") ALMA observations at 1 mm of the nearest protoplanetary
disc, TW Hya. Using the visibility analysis tool `frank', we reveal a
structured millimetre intensity distribution out to 100 au, well beyond
previous estimates of 60-70 au. Our analysis suggests the presence of a new
millimetre continuum gap at 82 au, coincident with similar features seen in
optical/near-infrared scattered light and millimetre molecular line
observations. Examination of the fit residuals confirms the presence of the
previously reported au-scale continuum excess at 52 au (P.A. = 242.5 degrees).
Our results demonstrate the utility of combining deep, moderate resolution
observations with super-resolution analysis techniques to probe the faintest
regions of protoplanetary discs.


title:
Data Determines Distributional Robustness in Contrastive Language Image
  Pre-training (CLIP)

abstract:
  Contrastively trained image-text models such as CLIP, ALIGN, and BASIC have
demonstrated unprecedented robustness to multiple challenging natural
distribution shifts. Since these image-text models differ from previous
training approaches in several ways, an important question is what causes the
large robustness gains. We answer this question via a systematic experimental
investigation. Concretely, we study five different possible causes for the
robustness gains: (i) the training set size, (ii) the training distribution,
(iii) language supervision at training time, (iv) language supervision at test
time, and (v) the contrastive loss function. Our experiments show that the more
diverse training distribution is the main cause for the robustness gains, with
the other factors contributing little to no robustness. Beyond our experimental
results, we also introduce ImageNet-Captions, a version of ImageNet with
original text annotations from Flickr, to enable further controlled experiments
of language-image training.


title:
Neural language models for network configuration: Opportunities and
  reality check

abstract:
  Boosted by deep learning, natural language processing (NLP) techniques have
recently seen spectacular progress, mainly fueled by breakthroughs both in
representation learning with word embeddings (e.g. word2vec) as well as novel
architectures (e.g. transformers). This success quickly invited researchers to
explore the use of NLP techniques to other field, such as computer programming
languages, with the promise to automate tasks in software programming (bug
detection, code synthesis, code repair, cross language translation etc.). By
extension, NLP has potential for application to network configuration languages
as well, for instance considering tasks such as network configuration
verification, synthesis, and cross-vendor translation. In this paper, we survey
recent advances in deep learning applied to programming languages, for the
purpose of code verification, synthesis and translation: in particularly, we
review their training requirements and expected performance, and qualitatively
assess whether similar techniques can benefit corresponding use-cases in
networking.


title: Outdoor Monocular Depth Estimation: A Research Review

abstract:
  Depth estimation is an important task, applied in various methods and
applications of computer vision. While the traditional methods of estimating
depth are based on depth cues and require specific equipment such as stereo
cameras and configuring input according to the approach being used, the focus
at the current time is on a single source, or monocular, depth estimation. The
recent developments in Convolution Neural Networks along with the integration
of classical methods in these deep learning approaches have led to a lot of
advancements in the depth estimation problem. The problem of outdoor depth
estimation, or depth estimation in wild, is a very scarcely researched field of
study. In this paper, we give an overview of the available datasets, depth
estimation methods, research work, trends, challenges, and opportunities that
exist for open research. To our knowledge, no openly available survey work
provides a comprehensive collection of outdoor depth estimation techniques and
research scope, making our work an essential contribution for people looking to
enter this field of study.


title:
Spinning nano-carbon grains: a viable origin for the Anomalous Microwave
  Emission

abstract:
  Context. Excess microwave emission commonly known as `anomalous microwave
emission' (AME) is now routinely detected in the Milky Way. Although its link
with the rotation of interstellar (carbonaceous) nano-grains seems to be
relatively well established at cloud scales, large-scale observations show a
lack of correlation between the different tracers of nano-carbons and AME,
which has led the community to question the viability of this link. Aims. Using
ancillary data and spinning dust models for nano-carbons and nano-silicates, we
explore the extent to which the out of Galactic Plane AME could come from one
carrier or the other. Methods. Contrary to previous large-scale studies, our
method is not built on comparing the correlations of the different dust tracers
with each other, but rather on comparing the poor correlations predicted by the
models with the correlations actually observed. This is based on as realistic
as possible estimates of the gas ionisation state and grain charge as a
function of the local radiation field and gas density. Results. First,
nano-carbon dust can explain all the observations for medium properties in
agreement with the latest findings about the separation of cold and warm
neutral medium in the diffuse ISM. The dispersion in the observations can be
accounted for with little variations in the dust size distribution, abundance
or electric dipole moment. Second, whatever the properties and the abundance of
the nano-silicate dust considered, spinning nano-silicates are excluded as
being the sole source of the AME. Third, the best agreement with the
observations is obtained if the emission of spinning nano-carbons alone is
taken into account. However, a marginal participation of nano-silicates to the
AME production cannot be excluded as long as their abundance does not exceed
$Y_{\rm Si} \sim 1\%$.


title: Trivial source character tables of $\text{SL}_2(q)$, Part II

abstract:
  We compute the trivial source character tables (also called species tables of
the trivial source ring) of the infinite family of finite groups
$\text{SL}_{2}(q)$ for $q$ even, over a large enough field $k$ of positive
characteristic ${\ell}$ not dividing $q$. This article is a continuation of our
article Trivial Source Character Tables of $\text{SL}_{2}(q)$ where we
considered, in particular, the case in which $q$ is odd in
cross-characteristic.


title: A Model of Random Multiple Access in Unlicensed Spectrum Systems

abstract:
  We consider a classical multiple access system with a single transmission
channel, finite number of users (users), and randomized transmission protocol
(ALOHA). We assume that every user sends messages to the base station with
various intensities. Due to the overlapping of messages during their sending
there are restrictions on the time between the messages and a mathematical
model adequate to the physical one becomes quite complicated. In this work, we
propose a simplified mathematical model that is easy to analyze and takes into
account the properties of real systems.


title:
Sea Ice Concentration Estimation Techniques Using Machine Learning: An
  End-To-End Workflow for Estimating Concentration Maps from SAR Images

abstract:
  Sea ice concentration is an important metric used to characterize polar sea
ice behavior. Understanding this behavior and accurately representing it is of
critical importance for climate science research, and also has important uses
in the context of maritime navigation. An end-to-end workflow for generating
learned concentration estimation models from synthetic aperture radar data,
trained on existing passive microwave data, is presented here. A novel
objective function was introduced to account for uncertainty in the passive
microwave measurements, which can be extended to account for arbitrary sources
of error in the training data, and a recent set of in-situ observations was
used to evaluate the reliability of the chosen passive microwave concentration
estimation model. Google Colaboratory was used as the development platform, and
all notebooks, training data, and trained models are available on GitHub. This
chapter is an overview of the most interesting aspects of this investigation,
and a detailed report is also available on GitHub.


title:
Neural Language Taskonomy: Which NLP Tasks are the most Predictive of
  fMRI Brain Activity?

abstract:
  Several popular Transformer based language models have been found to be
successful for text-driven brain encoding. However, existing literature
leverages only pretrained text Transformer models and has not explored the
efficacy of task-specific learned Transformer representations. In this work, we
explore transfer learning from representations learned for ten popular natural
language processing tasks (two syntactic and eight semantic) for predicting
brain responses from two diverse datasets: Pereira (subjects reading sentences
from paragraphs) and Narratives (subjects listening to the spoken stories).
Encoding models based on task features are used to predict activity in
different regions across the whole brain. Features from coreference resolution,
NER, and shallow syntax parsing explain greater variance for the reading
activity. On the other hand, for the listening activity, tasks such as
paraphrase generation, summarization, and natural language inference show
better encoding performance. Experiments across all 10 task representations
provide the following cognitive insights: (i) language left hemisphere has
higher predictive brain activity versus language right hemisphere, (ii)
posterior medial cortex, temporo-parieto-occipital junction, dorsal frontal
lobe have higher correlation versus early auditory and auditory association
cortex, (iii) syntactic and semantic tasks display a good predictive
performance across brain regions for reading and listening stimuli resp.


title: Semigroups of straight left inverse quotients

abstract:
  Let $Q$ be an inverse semigroup. A subsemigroup $S$ of $Q$ is a left I-order
in $Q$ and $Q$ is a semigroup of left I-quotients of $S$ if every element in
$Q$ can be written as $a^{-1}b$, where $a, b \in S$ and $a^{-1}$ is the inverse
of $a$ in the sense of inverse semigroup theory. If we insist on being able to
take $a$ and $b$ to be $\mathcal{R}$-related in $Q$ we say that $S$ is straight
in $Q$ and $Q$ is a semigroup of straight left I-quotients of $S$. We give a
set of necessary and sufficient conditions for a semigroup to be a straight
left I-order. The conditions are in terms of two binary relations,
corresponding to the potential restrictions of $\mathcal{R}$ and $\mathcal{L}$
from an oversemigroup, and an associated partial order. Our approach relies on
the meet structure of the $\mathcal{L}$ of inverse semigroups. We prove that
every finite left I-order is straight and give an example of a left I-order
which is not straight.


title: Winding vectors of topological defects: Multiband Chern numbers

abstract:
  Chern numbers can be calculated within a frame of vortex fields related to
phase conventions of a wave function. In a band protected by gaps the Chern
number is equivalent to the total number of flux carrying vortices. In the
presence of topological defects like Dirac cones this method becomes
problematic, in particular if they lack a well-defined winding number. We
develop a scheme to include topological defects into the vortex field frame. A
winding number is determined by the behavior of the phase in reciprocal space
when encircling the defect's contact point. To address the possible lack of a
winding number we utilize a more general concept of winding vectors. We
demonstrate the usefulness of this ansatz on Dirac cones generated from bands
of the Hofstadter model.


title:
Emission Variation of a Long-period Pulsar Discovered by the
  Five-hundred-meter Aperture Spherical Radio Telescope (FAST)

abstract:
  We report on the variation in the single-pulse emission from PSR J1900+4221
(CRAFTS 19C10) observed at frequency centered at 1.25 GHz using the
Five-hundred-meter Aperture Spherical radio Telescope. The integrated pulse
profile shows two distinct components, referred to here as the leading and
trailing components, with the latter component also containing a third weak
component. The single-pulse sequence reveals different emissions demonstrating
as nulling, regular, and bright pulses, each with a particular abundance and
duration distribution. There also exists pulses that follow a log-normal
distribution suggesting the possibility of another emission, in which the
pulsar is radiating weakly. Changes in the profile shape are seen across
different emissions. We examine the emission variations in the leading and
trailing components collectively and separately, and find moderate correlation
between the two components. The inclination angle is estimated to be about
7{\deg} based on pulse-width, and we discuss that nulling in this pulsar does
not seem to show correlation with age and rotation period.


title:
Tuning the perpendicular anisotropy of ferromagnetic films by thickness,
  width, and profile

abstract:
  Perpendicular magnetic anisotropy was found to be highly sensitive to the
nominal thickness and morphology of laterally heterogeneous CoPd films in the
vicinity of the metal insulator transition. We used the effect to tune the
anisotropy by the width of lithographically patterned stripes with non-uniform
cross-sectional thickness profiles. The phenomenon and the method can be
suitable to control the local anisotropy of spintronic logic circuitry elements
by their shape and lateral dimensions.


title:
Non-Gorenstein locus and almost Gorenstein property of the Ehrhart ring
  of the stable set polytope of a cycle graph

abstract:
  Let $R$ be the Ehrhart ring of the stable set polytope of a cycle graph which
is not Gorenstein. We describe the non-Gorenstein locus of $\mathrm{Spec} R$.
Further, we show that $R$ is almost Gorenstein. Moreover, we show that the
conjecture of Hibi and Tsuchiya is true.


title:
SWAP gate between a Majorana qubit and a parity-protected
  superconducting qubit

abstract:
  High fidelity quantum information processing requires a combination of fast
gates and long-lived quantum memories. In this work, we propose a hybrid
architecture, where a parity-protected superconducting qubit is directly
coupled to a Majorana qubit, which plays the role of a quantum memory. The
superconducting qubit is based upon a $\pi$-periodic Josephson junction
realized with gate-tunable semiconducting wires, where the tunneling of
individual Cooper pairs is suppressed. One of the wires additionally contains
four Majorana zero modes that define a qubit. We demonstrate that this enables
the implementation of a SWAP gate, allowing for the transduction of quantum
information between the topological and conventional qubit. This architecture
combines fast gates, which can be realized with the superconducting qubit, with
a topologically protected Majorana memory.


title: On the Utility of Prediction Sets in Human-AI Teams

abstract:
  Research on human-AI teams usually provides experts with a single label,
which ignores the uncertainty in a model's recommendation. Conformal prediction
(CP) is a well established line of research that focuses on building a
theoretically grounded, calibrated prediction set, which may contain multiple
labels. We explore how such prediction sets impact expert decision-making in
human-AI teams. Our evaluation on human subjects finds that set valued
predictions positively impact experts. However, we notice that the predictive
sets provided by CP can be very large, which leads to unhelpful AI assistants.
To mitigate this, we introduce D-CP, a method to perform CP on some examples
and defer to experts. We prove that D-CP can reduce the prediction set size of
non-deferred examples. We show how D-CP performs in quantitative and in human
subject experiments ($n=120$). Our results suggest that CP prediction sets
improve human-AI team performance over showing the top-1 prediction alone, and
that experts find D-CP prediction sets are more useful than CP prediction sets.


title: Toroidal optical transitions in hydrogen-like atoms

abstract:
  It is commonly believed that electromagnetic spectra of atoms and molecules
can be fully described by interactions of electric and magnetic multipoles.
However, it has recently become clear that interactions between light and
matter also involve toroidal multipoles - toroidal absorption lines have been
observed in electromagnetic metamaterials. Here we show that a new type of
spectroscopy of the hitherto largely neglected toroidal dipolar interaction
becomes feasible if, apart from the classical r{\times}r{\times}p toroidal
dipole density term responsible for the toroidal transitions in metamaterials,
the spin-dependent r{\times}{\sigma} term (that only occurs in relativistic
quantum mechanics) is taken into account. We show that toroidal transitions are
odd under parity and time-reversal symmetries; they can therefore be observed
and distinguished from electric multipole and magnetic dipole transitions.


title:
CS-ROMER: A novel compressed sensing framework for Faraday depth
  reconstruction

abstract:
  The reconstruction of Faraday depth structure from incomplete spectral
polarization radio measurements using the RM Synthesis technique is an
under-constrained problem requiring additional regularisation. In this paper we
present cs-romer: a novel object-oriented compressed sensing framework to
reconstruct Faraday depth signals from spectro-polarization radio data. Unlike
previous compressed sensing applications, this framework is designed to work
directly with data that are irregularly sampled in wavelength-squared space and
to incorporate multiple forms of compressed sensing regularisation. We
demonstrate the framework using simulated data for the JVLA telescope under a
variety of observing conditions, and we introduce a methodology for identifying
the optimal basis function for reconstruction of these data, using an approach
that can also be applied to datasets from other telescopes and over different
frequency ranges. In this work we show that the delta basis function provides
optimal reconstruction for JVLA L-band data and we use this basis with
observations of the low-mass galaxy cluster Abell 1314 in order to reconstruct
the Faraday depth of its constituent cluster galaxies. We use the cs-romer
framework to de-rotate the Galactic Faraday depth contribution directly from
the wavelength-squared data and to handle the spectral behaviour of different
radio sources in a direction-dependent manner. The results of this analysis
show that individual galaxies within Abell 1314 deviate from the behaviour
expected for a Faraday-thin screen such as the intra-cluster medium and instead
suggest that the Faraday rotation exhibited by these galaxies is dominated by
their local environments.


title: Multimodal Detection of Unknown Objects on Roads for Autonomous Driving

abstract:
  Tremendous progress in deep learning over the last years has led towards a
future with autonomous vehicles on our roads. Nevertheless, the performance of
their perception systems is strongly dependent on the quality of the utilized
training data. As these usually only cover a fraction of all object classes an
autonomous driving system will face, such systems struggle with handling the
unexpected. In order to safely operate on public roads, the identification of
objects from unknown classes remains a crucial task. In this paper, we propose
a novel pipeline to detect unknown objects. Instead of focusing on a single
sensor modality, we make use of lidar and camera data by combining state-of-the
art detection models in a sequential manner. We evaluate our approach on the
Waymo Open Perception Dataset and point out current research gaps in anomaly
detection.


title: Robust Subset Selection by Greedy and Evolutionary Pareto Optimization

abstract:
  Subset selection, which aims to select a subset from a ground set to maximize
some objective function, arises in various applications such as influence
maximization and sensor placement. In real-world scenarios, however, one often
needs to find a subset which is robust against (i.e., is good over) a number of
possible objective functions due to uncertainty, resulting in the problem of
robust subset selection. This paper considers robust subset selection with
monotone objective functions, relaxing the submodular property required by
previous studies. We first show that the greedy algorithm can obtain an
approximation ratio of $1-e^{-\beta\opgamma}$, where $\beta$ and $\opgamma$ are
the correlation and submodularity ratios of the objective functions,
respectively; and then propose EPORSS, an evolutionary Pareto optimization
algorithm that can utilize more time to find better subsets. We prove that
EPORSS can also be theoretically grounded, achieving a similar approximation
guarantee to the greedy algorithm. In addition, we derive the lower bound of
$\beta$ for the application of robust influence maximization, and further
conduct experiments to validate the performance of the greedy algorithm and
EPORSS.


title: Exact Paired-Permutation Testing for Structured Test Statistics

abstract:
  Significance testing -- especially the paired-permutation test -- has played
a vital role in developing NLP systems to provide confidence that the
difference in performance between two systems (i.e., the test statistic) is not
due to luck. However, practitioners rely on Monte Carlo approximation to
perform this test due to a lack of a suitable exact algorithm. In this paper,
we provide an efficient exact algorithm for the paired-permutation test for a
family of structured test statistics. Our algorithm runs in $\mathcal{O}(GN
(\log GN )(\log N ))$ time where $N$ is the dataset size and $G$ is the range
of the test statistic. We found that our exact algorithm was $10$x faster than
the Monte Carlo approximation with $20000$ samples on a common dataset.


title: The Price of Hierarchical Clustering

abstract:
  Hierarchical Clustering is a popular tool for understanding the hereditary
properties of a data set. Such a clustering is actually a sequence of
clusterings that starts with the trivial clustering in which every data point
forms its own cluster and then successively merges two existing clusters until
all points are in the same cluster. A hierarchical clustering achieves an
approximation factor of $\alpha$ if the costs of each $k$-clustering in the
hierarchy are at most $\alpha$ times the costs of an optimal $k$-clustering. We
study as cost functions the maximum (discrete) radius of any cluster
($k$-center problem) and the maximum diameter of any cluster ($k$-diameter
problem). In general, the optimal clusterings do not form a hierarchy and hence
an approximation factor of $1$ cannot be achieved. We call the smallest
approximation factor that can be achieved for any instance the price of
hierarchy. For the $k$-diameter problem we improve the upper bound on the price
of hierarchy to $3+2\sqrt{2}\approx 5.83$. Moreover we significantly improve
the lower bounds for $k$-center and $k$-diameter, proving a price of hierarchy
of exactly $4$ and $3+2\sqrt{2}$, respectively.


title:
How Does Embodiment Affect the Human Perception of Computational
  Creativity? An Experimental Study Framework

abstract:
  Which factors influence the human assessment of creativity exhibited by a
computational system is a core question of computational creativity (CC)
research. Recently, the system's embodiment has been put forward as such a
factor, but empirical studies of its effect are lacking. To this end, we
propose an experimental framework which isolates the effect of embodiment on
the perception of creativity from its effect on creativity per se. We not only
manipulate the system's embodiment, but also the perceptual evidence as the
basis for the human creativity assessment. We motivate the core framework with
embodiment and perceptual evidence as independent and the creative process as
controlled variable, and we provide recommendations on measuring the assessment
of creativity as dependent variable. We hope the framework will inspire others
to study the human perception of embodied CC in a principled manner.


title:
An Empirical Analysis of the Use of Real-Time Reachability for the
  Safety Assurance of Autonomous Vehicles

abstract:
  Recent advances in machine learning technologies and sensing have paved the
way for the belief that safe, accessible, and convenient autonomous vehicles
may be realized in the near future. Despite tremendous advances within this
context, fundamental challenges around safety and reliability are limiting
their arrival and comprehensive adoption. Autonomous vehicles are often tasked
with operating in dynamic and uncertain environments. As a result, they often
make use of highly complex components, such as machine learning approaches, to
handle the nuances of sensing, actuation, and control. While these methods are
highly effective, they are notoriously difficult to assure. Moreover, within
uncertain and dynamic environments, design time assurance analyses may not be
sufficient to guarantee safety. Thus, it is critical to monitor the correctness
of these systems at runtime. One approach for providing runtime assurance of
systems with components that may not be amenable to formal analysis is the
simplex architecture, where an unverified component is wrapped with a safety
controller and a switching logic designed to prevent dangerous behavior. In
this paper, we propose using a real-time reachability algorithm for the
implementation of the simplex architecture to assure the safety of a 1/10 scale
open source autonomous vehicle platform known as F1/10. The reachability
algorithm that we leverage (a) provides provable guarantees of safety, and (b)
is used to detect potentially unsafe scenarios. In our approach, the need to
analyze an underlying controller is abstracted away, instead focusing on the
effects of the controller's decisions on the system's future states. We
demonstrate the efficacy of our architecture through a vast set of experiments
conducted both in simulation and on an embedded hardware platform.


title:
Bridging Causal Consistent and Time Reversibility: A Stochastic Process
  Algebraic Approach

abstract:
  Causal consistent reversibility blends causality and reversibility. For a
concurrent system, it says that an action can be undone provided that this has
no consequences, thereby making it possible to bring the system back to a past
consistent state. Time reversibility is instead considered in the performance
evaluation field, mostly for efficient analysis purposes. A continuous-time
Markov chain is time reversible if its stochastic behavior remains the same
when the direction of time is reversed. We study how to bridge these two
theories of reversibility by showing the conditions under which both causal
consistent reversibility and time reversibility can be ensured by construction.
This is done in the setting of a stochastic process calculus, which is then
equipped with a notion of stochastic bisimilarity accounting for both forward
and backward directions.


title: A Falsificationist Account of Artificial Neural Networks

abstract:
  Machine learning operates at the intersection of statistics and computer
science. This raises the question as to its underlying methodology. While much
emphasis has been put on the close link between the process of learning from
data and induction, the falsificationist component of machine learning has
received minor attention. In this paper, we argue that the idea of
falsification is central to the methodology of machine learning. It is commonly
thought that machine learning algorithms infer general prediction rules from
past observations. This is akin to a statistical procedure by which estimates
are obtained from a sample of data. But machine learning algorithms can also be
described as choosing one prediction rule from an entire class of functions. In
particular, the algorithm that determines the weights of an artificial neural
network operates by empirical risk minimization and rejects prediction rules
that lack empirical adequacy. It also exhibits a behavior of implicit
regularization that pushes hypothesis choice toward simpler prediction rules.
We argue that taking both aspects together gives rise to a falsificationist
account of artificial neural networks.


title: Hypertranslations and Hyperrotations

abstract:
  We study the asymptotic symmetries of gravity in flat space. Instead of Bondi
gauge, we work with the recently introduced special double null gauge, in which
$\mathscr{I}^{+}$ and $\mathscr{I}^{-}$ are approached along null directions.
We find four new functions worth of asymptotic diffeomorphisms with finite
surface charges, beyond the familiar supertranslations and superrotations. Two
of these arise from angle-dependent shifts in the $v$-coordinate near
$\mathscr{I}^{+}$. We call these hypertranslations and sub-leading
hypertranslations, with analogous statements in the $u$-coordinate near
$\mathscr{I}^{-}$. There are also two Diff$(S^2)$ transformations, which we
call hyperrotations, that are sub-leading to the Virasoro superrotations. With
power law fall-offs in the null coordinate and the standard metric on the
sphere at leading order, we prove that this is the exhaustive list of
symmetries with finite covariant surface charges in Einstein gravity. We
compute the algebra of the asymptotic Killing vectors under the
Barnich-Troessaert bracket, and find a four-fold infinite generalization of the
BMS algebra.


title:
Autonomy and Intelligence in the Computing Continuum: Challenges,
  Enablers, and Future Directions for Orchestration

abstract:
  Future AI applications require performance, reliability and privacy that the
existing, cloud-dependant system architectures cannot provide. In this article,
we study orchestration in the device-edge-cloud continuum, and focus on AI for
edge, that is, the AI methods used in resource orchestration. We claim that to
support the constantly growing requirements of intelligent applications in the
device-edge-cloud computing continuum, resource orchestration needs to embrace
edge AI and emphasize local autonomy and intelligence. To justify the claim, we
provide a general definition for continuum orchestration, and look at how
current and emerging orchestration paradigms are suitable for the computing
continuum. We describe certain major emerging research themes that may affect
future orchestration, and provide an early vision of an orchestration paradigm
that embraces those research themes. Finally, we survey current key edge AI
methods and look at how they may contribute into fulfilling the vision of
future continuum orchestration.


title: The SU(2) Lie-Poisson Algebra and its Descendants

abstract:
  In this paper, a novel discrete algebra is presented which follows by
combining the SU(2) Lie-Poisson bracket with the discrete Frenet equation.
Physically, the construction describes a discrete piecewise linear string in
R3. The starting point of our derivation is the discrete Frenet frame assigned
at each vertix of the string. Then the link vector that connect the
neighbouring vertices assigns the SU(2) Lie-Poisson bracket. Moreover, the same
bracket defines the transfer matrices of the discrete Frenet equation which
relates two neighbouring frames along the string. The procedure extends in a
self-similar manner to an infinite hierarchy of Poisson structures. As an
example, the first descendant of the SU(2) Lie-Poisson structure is presented
in detail. For this, the spinor representation of the discrete Frenet equation
is employed, as it converts the brackets into a computationally more manageable
form. The final result is a nonlinear, nontrivial and novel Poisson structure
that engages four neighbouring vertices.


title: Hadronic loop effects to excited scalar charmed mesons revisited

abstract:
  We re-examine the hadronic loop effects to the masses of $D^*_0$ and
$D^*_{s0}$ calculated in quark models in the framework of heavy meson chiral
perturbation theory (HMCHPT). The inaccuracy in the choice of the argument of
the chiral loop functions in previous works is corrected. Our calculations
consider the full one-loop corrections that appear at leading order in the
chiral expansion of the effective Lagrangian. Unlike previous approaches, ours
leads to satisfactory results in explaining the low mass of the observed scalar
charm states reported by the Particle Data Group (PDG). It is found that the
mass shift of bare $D^*_{s0}$ ($D^*_{0}$) state is mainly due to the $DK$
($D\pi$) loop corrections for values of the couplings that are compatible with
the measured ones. We show why previous approaches of using HMCHPT in studying
mass shift effects due to chiral loops gave unsatisfactory results.


title: Extreme Values of Permutation Statistics

abstract:
  We investigate extreme values of Mahonian and Eulerian distributions arising
from counting inversions and descents of random elements in finite Coxeter
groups. A triangular array of either of these distributions is constructed from
a sequence of Coxeter groups with increasing ranks. We describe an appropriate
scaling and variance constraints so that the row-wise maximum of this
triangular array is attracted towards the Gumbel extreme value distribution.


title:
Spatio-temporal correlations in turbulence from functional
  renormalisation group

abstract:
  Space-time correlations are central objects in the statistical theory of
turbulence and their modelling plays a crucial role in many applications.
Simple phenomenological arguments, such as the Tennekes-Kraichnan random
sweeping hypothesis, has proven particularly useful in this respect. However,
it remains to be explained how such a simple description can emerge from the
highly complex multi-scale nature of turbulence. Recently, the functional
renormalisation group (FRG) has rooted important progress in this direction for
homogeneous isotropic and stationary turbulence. Indeed, it has allowed for the
rigorous derivation, from the Navier-Stokes equation, of an analytical
expression for any Eulerian multi-point multi-time correlation function in the
limit of large wavenumbers. The corresponding expression for the two-point
function confirms the effect of sweeping at small time-delays and yields its
precise form, while it unveils a crossover to another regime at large time
delays characterised by a slower decay. Moreover, the description of the
spatio-temporal behaviour is extended for all $n$-point correlation functions.
In this paper, we propose an overview of these results and more generally of
the FRG method for turbulence. We give a basic introduction to the FRG and
emphasise how the field-theoretical framework allows one to systematically and
profoundly exploit the symmetries. We show the existence of a fixed-point which
controls turbulence for large-scale forcing, which is accessible owing to the
non-perturbative nature of the FRG. The major part of the paper is devoted to
expounding the spatio-temporal behaviour of correlation functions, and largely
illustrating these results through the analysis of direct numerical simulations
of homogeneous isotropic turbulence, and also scalar turbulence.


title: Filtering and Sampling Object-Centric Event Logs

abstract:
  The scalability of process mining techniques is one of the main challenges to
tackling the massive amount of event data produced every day in enterprise
information systems. To this purpose, filtering and sampling techniques are
proposed to keep a subset of the behavior of the original log and make the
application of process mining techniques feasible. While techniques for
filtering/sampling traditional event logs have been already proposed,
filtering/sampling object-centric event logs is more challenging as the number
of factors (events, objects, object types) to consider is significantly higher.
This paper provides some techniques to filter/sample object-centric event logs.