title:
Georeferencing of Photovoltaic Modules from Aerial Infrared Videos using
  Structure-from-Motion

abstract:
  To identify abnormal photovoltaic (PV) modules in large-scale PV plants
economically, drone-mounted infrared (IR) cameras and automated video
processing algorithms are frequently used. While most related works focus on
the detection of abnormal modules, little has been done to automatically
localize those modules within the plant. In this work, we use incremental
structure-from-motion to automatically obtain geocoordinates of all PV modules
in a plant based on visual cues and the measured GPS trajectory of the drone.
In addition, we extract multiple IR images of each PV module. Using our method,
we successfully map 99.3 % of the 35084 modules in four large-scale and one
rooftop plant and extract over 2.2 million module images. As compared to our
previous work, extraction misses 18 times less modules (one in 140 modules as
compared to one in eight). Furthermore, two or three plant rows can be
processed simultaneously, increasing module throughput and reducing flight
duration by a factor of 2.1 and 3.7, respectively. Comparison with an accurate
orthophoto of one of the large-scale plants yields a root mean square error of
the estimated module geocoordinates of 5.87 m and a relative error within each
plant row of 0.22 m to 0.82 m. Finally, we use the module geocoordinates and
extracted IR images to visualize distributions of module temperatures and
anomaly predictions of a deep learning classifier on a map. While the
temperature distribution helps to identify disconnected strings, we also find
that its detection accuracy for module anomalies reaches, or even exceeds, that
of a deep learning classifier for seven out of ten common anomaly types. The
software is published at https://github.com/LukasBommes/PV-Hawk.


title: Critical quantum thermometry and its feasibility in spin systems

abstract:
  In this work, we study temperature sensing with finite-sized strongly
correlated systems exhibiting quantum phase transitions. We use the quantum
Fisher information (QFI) approach to quantify the sensitivity in the
temperature estimation, and apply a finite-size scaling framework to link this
sensitivity to critical exponents of the system around critical points. We
numerically calculate the QFI around the critical points for two
experimentally-realizable systems: the spin-1 Bose-Einstein condensate and the
spin-chain Heisenberg XXZ model in the presence of an external magnetic field.
Our results confirm finite-size scaling properties of the QFI. Furthermore, we
discuss experimentally-accessible observables that (nearly) saturate the QFI at
the critical points for these two systems.


title:
Distilling Robust and Non-Robust Features in Adversarial Examples by
  Information Bottleneck

abstract:
  Adversarial examples, generated by carefully crafted perturbation, have
attracted considerable attention in research fields. Recent works have argued
that the existence of the robust and non-robust features is a primary cause of
the adversarial examples, and investigated their internal interactions in the
feature space. In this paper, we propose a way of explicitly distilling feature
representation into the robust and non-robust features, using Information
Bottleneck. Specifically, we inject noise variation to each feature unit and
evaluate the information flow in the feature representation to dichotomize
feature units either robust or non-robust, based on the noise variation
magnitude. Through comprehensive experiments, we demonstrate that the distilled
features are highly correlated with adversarial prediction, and they have
human-perceptible semantic information by themselves. Furthermore, we present
an attack mechanism intensifying the gradient of non-robust features that is
directly related to the model prediction, and validate its effectiveness of
breaking model robustness.


title: Tilings of the Sphere by Congruent Quadrilaterals or Triangles

abstract:
  We completely classify edge-to-edge tilings of the sphere by congruent
quadrilaterals. As part of the classification, we also present a modern version
of the classification of edge-to-edge tilings of the sphere by congruent
triangles.


title: Adversarial Learning to Reason in an Arbitrary Logic

abstract:
  Existing approaches to learning to prove theorems focus on particular logics
and datasets. In this work, we propose Monte-Carlo simulations guided by
reinforcement learning that can work in an arbitrarily specified logic, without
any human knowledge or set of problems. Since the algorithm does not need any
training dataset, it is able to learn to work with any logical foundation, even
when there is no body of proofs or even conjectures available. We practically
demonstrate the feasibility of the approach in multiple logical systems. The
approach is stronger than training on randomly generated data but weaker than
the approaches trained on tailored axiom and conjecture sets. It however allows
us to apply machine learning to automated theorem proving for many logics,
where no such attempts have been tried to date, such as intuitionistic logic or
linear logic.


title:
Masking Adversarial Damage: Finding Adversarial Saliency for Robust and
  Sparse Network

abstract:
  Adversarial examples provoke weak reliability and potential security issues
in deep neural networks. Although adversarial training has been widely studied
to improve adversarial robustness, it works in an over-parameterized regime and
requires high computations and large memory budgets. To bridge adversarial
robustness and model compression, we propose a novel adversarial pruning
method, Masking Adversarial Damage (MAD) that employs second-order information
of adversarial loss. By using it, we can accurately estimate adversarial
saliency for model parameters and determine which parameters can be pruned
without weakening adversarial robustness. Furthermore, we reveal that model
parameters of initial layer are highly sensitive to the adversarial examples
and show that compressed feature representation retains semantic information
for the target objects. Through extensive experiments on three public datasets,
we demonstrate that MAD effectively prunes adversarially trained networks
without loosing adversarial robustness and shows better performance than
previous adversarial pruning methods.


title: P4RROT: Generating P4 Code for the Application Layer

abstract:
  Throughput and latency critical applications could often benefit of
performing computations close to the client. To enable this, distributed
computing paradigms such as edge computing have recently emerged. However, with
the advent of programmable data planes, computations cannot only be performed
by servers but they can be offloaded to network switches. Languages like P4
enable to flexibly reprogram the entire packet processing pipeline. Though
these devices promise high throughput and ultra-low response times,
implementing application-layer tasks in the data plane programming language P4
is still challenging for an application developer who is not familiar with
networking domain. In this paper, we first identify and examine obstacles and
pain points one can experience when offloading server-based computations to the
network. Then we present P4RROT, a code generator (in form of a library) which
allows to overcome these limitations by providing a user-friendly API to
describe computations to be offloaded. After discussing the design choices
behind P4RROT, we introduce our proof-of-concept implementation for two P4
targets: Netronome SmartNIC and BMv2.


title:
Dynamics of N-spot rings with oscillatory tails in a three-component
  reaction-diffusion system

abstract:
  In two-dimensional space, we investigate the slow dynamics of multiple
localized spots with oscillatory tails in a specific three-component
reaction-diffusion system, whose key feature is that the spots attract or repel
each other alternatively according to their mutual distances, leading to rather
complex patterns. One fundamental pattern is the ring pattern, consisting of
$N$ equally distributed spots on a circle with a certain radius. Depending on
the parameters of the system, stationary or moving (i.e., traveling and
rotating) $N$-spot rings can be observed. In order to understand the emergence
of these patterns, we describe the dynamics of $N$ spots by a set of reduced
ordinary differential equations (ODEs) encoding the information of each spot's
location and velocity. On the basis of the reduced system, we analytically
study the existence and stability of stationary and moving $N$-spot ring
solutions, similar to the collective motion of self-propelled particles.
Numerical simulations are provided to verify our results with fixed parameters.


title:
Neural Network-augmented Kalman Filtering for Robust Online Speech
  Dereverberation in Noisy Reverberant Environments

abstract:
  In this paper, a neural network-augmented algorithm for noise-robust online
dereverberation with a Kalman filtering variant of the weighted prediction
error (WPE) method is proposed. The filter stochastic variations are predicted
by a deep neural network (DNN) trained end-to-end using the filter residual
error and signal characteristics. The presented framework allows for robust
dereverberation on a single-channel noisy reverberant dataset similar to
WHAMR!. The Kalman filtering WPE introduces distortions in the enhanced signal
when predicting the filter variations from the residual error only, if the
target speech power spectral density is not perfectly known and the observation
is noisy. The proposed approach avoids these distortions by correcting the
filter variations estimation in a data-driven way, increasing the robustness of
the method to noisy scenarios. Furthermore, it yields a strong dereverberation
and denoising performance compared to a DNN-supported recursive least squares
variant of WPE, especially for highly noisy inputs.


title:
Localization of Electronic States in Hybrid Nano-Ribbons in the
  Non-Perturbative Regime

abstract:
  We investigate the localization of low-energy single quasi-particle states in
the 7/9-hybrid nanoribbon system in the presence of strong interactions and
within a finite volume. We consider two scenarios, the first being the Hubbard
model at half-filling and perform quantum Monte Carlo simulations for a range
$U$ that includes the strongly correlated regime. In the second case we add a
nearest-neighbor superconducting pairing $\Delta$ and take the symmetric line
limit, where $\Delta$ is equal in magnitude to the hopping parameter $t$. In
this limit the quasi-particle spectrum and wavefunctions can be directly solved
for general onsite interaction $U$. In both cases we extract the site-dependent
quasi-particle wavefunction densities and demonstrate that localization
persists in these non-perturbative regimes under particular scenarios.


title:
Towards Multi-Scale Speaking Style Modelling with Hierarchical Context
  Information for Mandarin Speech Synthesis

abstract:
  Previous works on expressive speech synthesis focus on modelling the
mono-scale style embedding from the current sentence or context, but the
multi-scale nature of speaking style in human speech is neglected. In this
paper, we propose a multi-scale speaking style modelling method to capture and
predict multi-scale speaking style for improving the naturalness and
expressiveness of synthetic speech. A multi-scale extractor is proposed to
extract speaking style embeddings at three different levels from the
ground-truth speech, and explicitly guide the training of a multi-scale style
predictor based on hierarchical context information. Both objective and
subjective evaluations on a Mandarin audiobooks dataset demonstrate that our
proposed method can significantly improve the naturalness and expressiveness of
the synthesized speech.


title:
Universal Representations: A Unified Look at Multiple Task and Domain
  Learning

abstract:
  We propose a unified look at jointly learning multiple vision tasks and
visual domains through universal representations, a single deep neural network.
Learning multiple problems simultaneously involves minimizing a weighted sum of
multiple loss functions with different magnitudes and characteristics and thus
results in unbalanced state of one loss dominating the optimization and poor
results compared to learning a separate model for each problem. To this end, we
propose distilling knowledge of multiple task/domain-specific networks into a
single deep neural network after aligning its representations with the
task/domain-specific ones through small capacity adapters. We rigorously show
that universal representations achieve state-of-the-art performances in
learning of multiple dense prediction problems in NYU-v2 and Cityscapes,
multiple image classification problems from diverse domains in Visual Decathlon
Dataset and cross-domain few-shot learning in MetaDataset. Finally we also
conduct multiple analysis through ablation and qualitative studies.


title:
The Virasoro structure and the scattering matrix for Liouville conformal
  field theory

abstract:
  In this work, we construct a representation of the Virasoro algebra in the
canonical Hilbert space associated to Liouville conformal field theory. The
study of the Virasoro operators is performed through the introduction of a new
family of Markovian dynamics associated to holomorphic vector fields defined in
the disk. As an output, we show that the Hamiltonian of Liouville conformal
field theory can be diagonalized through the action of the Virasoro algebra.
This enables to show that the scattering matrix of the theory is diagonal and
that the family of the so-called primary fields (which are eigenvectors of the
Hamiltonian) admits an analytic extension to the whole complex plane, as
conjectured in the physics literature.


title: The Sombor index and coindex of two-trees

abstract:
  The Sombor index of a graph $G$, introduced by Ivan Gutman, is defined as the
sum of the weights $\sqrt{d_G(u)^2+d_G(v)^2}$ of all edges $uv$ of $G$, where
$d_G(u)$ denotes the degree of vertex $u$ in $G$. The Sombor coindex is
recently defined as $\bar{SO}(G)=\sum \limits_{uv\notin
E(G)}\sqrt{d_G(u)^2+d_G(v)^2}$. In this paper, the maximum and second maximum
Sombor index, the minimum and second minimum Sombor coindex in two-trees are
determined.


title:
Revisiting the Siegert relation for the partially coherent regime of
  nanolasers

abstract:
  The Siegert relation connects the first- and second-order coherence
properties of light. While strictly valid only in the thermal regime and in the
absence of correlations, this relation is routinely extended to the partially
coherent regime in the study of high-$\beta$ nanolasers, where it aids in the
identification of the lasing threshold. We re-evaluate the use of a generalized
Siegert relation in different device regimes. A full two-time quantum-optical
theory is derived as a reference for obtaining first- and second-order
correlation functions $g^{(1)}(\tau)$ and $g^{(2)}(\tau)$ in the steady state.
We find that even in the partially coherent regime the generalized Siegert
relation is well suited as an approximation to $g^{(2)}(\tau)$ as long as
emitter correlation are negligible, but does not apply well in the quantum
regime of few-emitter nanolasers, or to devices featuring sub- and superradiant
emission.


title: Rational Angles and Tilings of the Sphere by Congruent Quadrilaterals

abstract:
  We use rational solutions of trigonometric diophantine equations to classify
edge-to-edge tilings of the sphere by congruent almost equilateral
quadrilaterals (i.e., edge combination $a^3b$). Although the classification is
completed in a paper by Cheung, Luk and Yan, the method used here is more
systematic and is applicable to other related tiling problems that cannot be
solved by the method in the other paper. We also provide detailed geometric
data for the tilings.


title: Toposes over which essential implies locally connected

abstract:
  We introduce the notion of an EILC topos: a topos $\mathcal{E}$ such that
every essential geometric morphism with codomain $\mathcal{E}$ is locally
connected. We then show that the topos of sheaves on a topological space $X$ is
EILC if $X$ is Hausdorff (or more generally, if $X$ is Jacobson). Further
examples of Grothendieck toposes that are EILC are Boolean \'etendues and
classifying toposes of compact groups. Next, we introduce the weaker notion of
CILC topos: a topos $\mathcal{E}$ such that any geometric morphism $f :
\mathcal{F} \to \mathcal{E}$ is locally connected, as soon as $f^*$ is
cartesian closed. We give some examples of topological spaces $X$ and small
categories $\mathcal{C}$ such that $\mathbf{Sh}(X)$ resp.
$\mathbf{PSh}(\mathcal{C})$ are CILC. Finally, we show that any Boolean
elementary topos is CILC.


title: Optical manipulation with metamaterial structures

abstract:
  Optical tweezers employing forces produced by light underpin important
manipulation tools in many areas of applied and biological physics.
Conventional optical tweezers are based on refractive optics, and they require
excessive auxiliary optical elements to reshape both amplitude and phase, as
well as wavevector and angular momentum of light, and thus impose limitations
to the overall cost and integration of optical systems. Metamaterials provide
both electric and optically induced magnetic response in subwavelength optical
structures, and they are highly beneficial to achieve unprecedented control of
light required for many applications, also opening new opportunities for
optical manipulation. Here, we review the recent advances in the field of
optical tweezers employing the physics and concepts of metamaterials (the
so-called meta-tweezers) and demonstrate that metamaterial structures could not
only advance classical operations with particles, such as trapping,
transporting, and sorting, but they uncover exotic optical forces such as
pulling and lateral forces. Remarkably, apart from manipulation of particles,
metastructures can be powered dynamically by light to realize ingenious
meta-robots. We provide an outlook for future opportunities in this area
ranging from enhanced particle manipulation to meta-robot actuation.


title: Influence of spin-orbit coupling on chemical bonding

abstract:
  The influence of spin-orbit interaction on chemical bonds in elemental solids
and homonuclear dimers is analyzed by means of density-functional-theory
calculations. Employing highly precise all-electron full-potential methodology,
our results represent benchmark quality. Comparison of the scalar- and
fully-relativistic approaches for elemental solids shows that the spin-orbit
interaction may contract or expand the volume of the considered material. The
largest variation of the volume is obtained for Au, Tl, I, Bi, Po and Hg,
exhibiting changes between 1.0--7.6\%. Using the tight-binding model, we show
for diatomic molecules that the nature of this effect lies in the angular
rearrangement of bonding and antibonding orbitals introduced by spin-orbit
coupling. Such an angular rearrangement appears in partially filled $p$- or
$d$-orbitals in heavy elements. Finally, we discuss the impact of the
relativistic effects on the chemical bonding in single-layer iodides and
transition metal dichalcogenides


title: Multi-Objective Evolutionary Beer Optimisation

abstract:
  Food production is a complex process which can benefit from many optimisation
approaches. However, there is growing interest in methods that support
customisation of food properties to satisfy individual consumer preferences.
This paper addresses the personalisation of beer properties. Having identified
components of the production process for craft beers whose production tends to
be less standardised, we introduce a system which enables brewers to map the
desired beer properties into ingredients dosage and combination. Previously
explored approaches include direct use of structural equations as well as
global machine learning methods. We introduce a framework which uses an
evolutionary method supporting multi-objective optimisation. This work
identifies problem-dependent objectives, their associations, and proposes a
workflow to automate the discovery of multiple novel recipes based on
user-defined criteria. The quality of the solutions generated by the
multi-objective optimiser is compared against solutions from multiple runs of
the method, and those of a single objective evolutionary technique. This
comparison provides a road-map allowing the users to choose among more varied
options or to fine-tune one of the favourite identified solution. The
experiments presented here demonstrate the usability of the framework as well
as the transparency of its criteria.


title:
Energy-band echoes: Time-reversed light emission from optically driven
  quasiparticle wavepackets

abstract:
  The at-will control of quantum states is a primary goal of quantum science
and technology. The celebrated Hahn echo exemplifies such quantum-state control
based on a time-reversal process in a few-level system. Here, we propose a
different echo phenomenon associated with the energy-band structure in quantum
many-body systems. We show that the dynamics of quasiparticle wavepackets can
be reversed by a driving electric-field pulse, yielding echoes with the
time-reversed waveform of the optical excitation pulse when the quasiparticles
recombine. The present echoes are observed not only in band insulators but also
in correlated insulators, including a Mott insulator and a
spontaneously-broken-symmetry charge-ordered insulator, in one- and
higher-dimensional systems, irrespective of the integrability of the models.
Analytical expressions reveal the conditions under which the echoes appear, and
they also indicate that the frequency of the echo pulses reflects the
dispersion relation for quasiparticles such as electron-hole pairs,
doublon-holon pairs, and kink-antikink pairs. These findings provide a
framework for all-optical momentum-resolved spectroscopy of the quasiparticles
in quantum many-body systems.


title: Metric polynomial structures on generalized geometry

abstract:
  In this document, we study the interaction between different geometric
structures that can be defined as morphisms of sections of the generalized
tangent bundle $\mathbb TM:= TM\oplus T^*M\to M$. In particular, we show the
behaviour of various generalized polynomial structures with respect to
different metrics on $\mathbb TM$, in the frame of $(\alpha,
\varepsilon)$-metric structures; and the commutation or anti-commutation of
generalized polynomial structures, forming triple structures.


title: Extreme Dilaton Black Holes in 2 +1 Dimensions: Quasinormal modes

abstract:
  We study neutral massless scalar field perturbations around an extreme
dilaton black hole in 2 +1 dimensions: the wave equations of the massless
scalar field is shown to be exactly solvable in terms of Whittaker functions.
Thus, the quasinormal modes are computed exactly and shown to be purely
imaginary: we show the existence of stable and unstable modes. Interestingly,
the quasinormal modes do not depend on the black holes parameters and the
fundamental mode is always unstable and depends only on the parameters of the
test field. Also, we determine the quasinormal frequencies via the improved
asymptotic iteration method which shows a good agreement with the analytical
results.


title: Vector boson scattering from the lattice

abstract:
  We study vector-boson scattering of the physical, gauge-invariant states in a
reduced standard-model setup on the lattice for various parameter sets. To this
end, the phase shift in the scalar channel is determined using a L\"uscher-type
analysis. The results can be readily interpreted in terms of the Higgs
properties and a reunitarized Fr\"ohlich-Morchio-Strocchi analysis at Born
level. The only deviation appears for a Higgs mass below the elastic threshold,
where we find a negative scattering length indicative of the bound-state nature
of the physical scalar degree of freedom. We assess the possible implications
for an experimental detection of the effect.


title:
Does non-linear factorization of financial returns help build better and
  stabler portfolios?

abstract:
  A portfolio allocation method based on linear and non-linear latent
constrained conditional factors is presented. The factor loadings are
constrained to always be positive in order to obtain long-only portfolios,
which is not guaranteed by classical factor analysis or PCA. In addition, the
factors are to be uncorrelated among clusters in order to build long-only
portfolios. Our approach is based on modern machine learning tools: convex
Non-negative Matrix Factorization (NMF) and autoencoder neural networks,
designed in a specific manner to enforce the learning of useful hidden data
structure such as correlation between the assets' returns. Our technique finds
lowly correlated linear and non-linear conditional latent factors which are
used to build outperforming global portfolios consisting of cryptocurrencies
and traditional assets, similar to hierarchical clustering method. We study the
dynamics of the derived non-linear factors in order to forecast tail losses of
the portfolios and thus build more stable ones.


title:
Computing expected multiplicities for bag-TIDBs with bounded
  multiplicities

abstract:
  In this work, we study the problem of computing a tuple's expected
multiplicity over probabilistic databases with bag semantics (where each tuple
is associated with a multiplicity) exactly and approximately. We consider
bag-TIDBs where we have a bound $c$ on the maximum multiplicity of each tuple
and tuples are independent probabilistic events (we refer to such databases as
c-TIDBs. We are specifically interested in the fine-grained complexity of
computing expected multiplicities and how it compares to the complexity of
deterministic query evaluation algorithms -- if these complexities are
comparable, it opens the door to practical deployment of probabilistic
databases. Unfortunately, our results imply that computing expected
multiplicities for c-TIDBs based on the results produced by such query
evaluation algorithms introduces super-linear overhead (under parameterized
complexity hardness assumptions/conjectures). We proceed to study approximation
of expected result tuple multiplicities for positive relational algebra queries
($RA^+$) over c-TIDBs and for a non-trivial subclass of block-independent
databases (BIDBs). We develop a sampling algorithm that computes a
1$\pm\epsilon$ approximation of the expected multiplicity of an output tuple in
time linear in the runtime of the corresponding deterministic query for any
$RA^+$ query.


title: On modified extension graphs of a fixed atypicality

abstract:
  In this paper we study extensions between finite-dimensional simple modules
over classical Lie superalgebras $\mathfrak{gl}(m|n), \mathfrak{osp}(M|2n)$ and
$\mathfrak{q}_m$. We consider a simplified version of the extension graph which
is produced from the $Ext^1$-graph by identifying representations obtained by
parity change and removal of the loops. We give a necessary condition for a
pair of vertices to be connected and show that this condition is sufficient in
most of the cases. This condition implies that the image of a
finite-dimensional simple module under the Duflo-Serganova functor has
indecomposable isotypical components. This yields semisimplicity of
Duflo-Serganova functor for $\mathcal{F}in(\mathfrak{gl}(m|n))$ and for
$\mathcal{F}in(\mathfrak{osp}(M|2n))$.


title:
BFRnet: A deep learning-based MR background field removal method for QSM
  of the brain containing significant pathological susceptibility sources

abstract:
  Introduction: Background field removal (BFR) is a critical step required for
successful quantitative susceptibility mapping (QSM). However, eliminating the
background field in brains containing significant susceptibility sources, such
as intracranial hemorrhages, is challenging due to the relatively large scale
of the field induced by these pathological susceptibility sources. Method: This
study proposes a new deep learning-based method, BFRnet, to remove background
field in healthy and hemorrhagic subjects. The network is built with the
dual-frequency octave convolutions on the U-net architecture, trained with
synthetic field maps containing significant susceptibility sources. The BFRnet
method is compared with three conventional BFR methods and one previous deep
learning method using simulated and in vivo brains from 4 healthy and 2
hemorrhagic subjects. Robustness against acquisition field-of-view (FOV)
orientation and brain masking are also investigated. Results: For both
simulation and in vivo experiments, BFRnet led to the best visually appealing
results in the local field and QSM results with the minimum contrast loss and
the most accurate hemorrhage susceptibility measurements among all five
methods. In addition, BFRnet produced the most consistent local field and
susceptibility maps between different sizes of brain masks, while conventional
methods depend drastically on precise brain extraction and further brain edge
erosions. It is also observed that BFRnet performed the best among all BFR
methods for acquisition FOVs oblique to the main magnetic field. Conclusion:
The proposed BFRnet improved the accuracy of local field reconstruction in the
hemorrhagic subjects compared with conventional BFR algorithms. The BFRnet
method was effective for acquisitions of titled orientations and retained whole
brains without edge erosion as often required by traditional BFR methods.


title:
Lepton pair photoproduction in peripheral relativistic heavy-ion
  collisions

abstract:
  We study the lepton pair photoproduction in peripheral heavy-ion collisions
based on the formalism in our previous work [Phys. Rev. D 104, 056011 (2021)].
We present the numerical results for the distributions of the transverse
momentum, azimuthal angle and invariant mass for $e^{+}e^{-}$ and
$\mu^{+}\mu^{-}$ pairs as functions of the impact parameter and other kinematic
variables in Au+Au collisions. Our calculation incorporates the information on
the transverse momentum and polarization of photons which is essential to
describe the experimental data. We observe a broadening effect in the
transverse momentum for lepton pairs with and without smear effects. We also
observe a significant enhancement in the distribution of $\cos(2\varphi)$ for
$\mu^{+}\mu^{-}$ pairs. Our results provide a baseline for future studies of
other higher order corrections beyond Born approximation and medium effects in
the lepton pair production.


title:
Spectral analysis on dissimilarity between turbulent momentum and heat
  transfers in plane Couette turbulence

abstract:
  Nonlinear interaction between different scales in turbulence results in both
interscale and spatial transport of turbulent energy, and the role of such
scale interactions in turbulent heat transfer mechanism is also of practical
importance from the engineering viewpoint. In the present study, we investigate
a turbulent plane Couette flow with passive-scalar heat transfer at the Prandtl
number 0.71, in order to address the similarity/difference between the scale
interactions in the velocity and temperature fields. The
constant-temperature-difference boundary condition is used so that the mean
velocity and temperature profiles are similar, and then the roles of interscale
and spatial transports are compared for the spectral transport budgets of the
turbulent energies and temperature-related statistics. We show that turbulent
heat transfer occurs at relatively small streamwise length scales compared to
momentum transfer, although molecular diffusion is more significant in the
temperature field as the Prandtl number is less than 1. Detailed analysis on
the transport budgets of the temperature-related spectra shows that scale
interactions in temperature field supply more energy to small scales than those
in velocity field. Such significant temperature cascade leads to more energetic
temperature fluctuation at small scales, which eventually results in the
dissimilarity between the spectra of turbulent heat and momentum transfers.


title: Stochastic Chaos and Predictability in Laboratory Earthquakes

abstract:
  Laboratory earthquakes exhibit characteristics of a low dimensional random
attractor with a dimension similar to that of natural slow earthquakes. A model
of stochastic differential equations based on rate and state-dependent friction
explains the laboratory observations. We study the transition from stable
sliding to stickslip events and find that aperiodic behavior can be explained
by small perturbations in the stress state. Friction's nonlinear nature
amplifies small scale perturbations, reducing the predictability of the
otherwise periodic macroscopic dynamics.


title: Thermoelectric Effect in Mott Variable-Range Hopping

abstract:
  On the basis of the Kubo-Luttinger linear response theory combined with the
scaling theory of Anderson localization predicting the energy dependence of
localization length near the mobility edge, we have studied the thermoelectric
response of a disordered system exhibiting Mott variable-range hopping (VRH).
We found that the low-tempetature Seebeck coefficient for VRH conduction in a
$d$-dimensional system varies as $S\propto T^{d/(d+1)}$, which is different
from the widely used expression, $S\propto T^{(d-1)/(d+1)}$, based on the
energy-independent localization length. It is seen that the low-$T$ behavior of
$S(T)$ of the thiospinel CuCrTiS$_4$ is in full agreement with the new scheme.


title: Code Search: A Survey of Techniques for Finding Code

abstract:
  The immense amounts of source code provide ample challenges and opportunities
during software development. To handle the size of code bases, developers
commonly search for code, e.g., when trying to find where a particular feature
is implemented or when looking for code examples to reuse. To support
developers in finding relevant code, various code search engines have been
proposed. This article surveys 30 years of research on code search, giving a
comprehensive overview of challenges and techniques that address them. We
discuss the kinds of queries that code search engines support, how to
preprocess and expand queries, different techniques for indexing and retrieving
code, and ways to rank and prune search results. Moreover, we describe
empirical studies of code search in practice. Based on the discussion of prior
work, we conclude the article with an outline of challenges and opportunities
to be addressed in the future.


title: Data-Centric Green AI: An Exploratory Empirical Study

abstract:
  With the growing availability of large-scale datasets, and the popularization
of affordable storage and computational capabilities, the energy consumed by AI
is becoming a growing concern. To address this issue, in recent years, studies
have focused on demonstrating how AI energy efficiency can be improved by
tuning the model training strategy. Nevertheless, how modifications applied to
datasets can impact the energy consumption of AI is still an open question. To
fill this gap, in this exploratory study, we evaluate if data-centric
approaches can be utilized to improve AI energy efficiency. To achieve our
goal, we conduct an empirical experiment, executed by considering 6 different
AI algorithms, a dataset comprising 5,574 data points, and two dataset
modifications (number of data points and number of features). Our results show
evidence that, by exclusively conducting modifications on datasets, energy
consumption can be drastically reduced (up to 92.16%), often at the cost of a
negligible or even absent accuracy decline. As additional introductory results,
we demonstrate how, by exclusively changing the algorithm used, energy savings
up to two orders of magnitude can be achieved. In conclusion, this exploratory
investigation empirically demonstrates the importance of applying data-centric
techniques to improve AI energy efficiency. Our results call for a research
agenda that focuses on data-centric techniques, to further enable and
democratize Green AI.


title: Orthogonality of measures and states

abstract:
  We give a short proof of the theorem due to Preiss and Rataj stating that
there are no analytic maximal orthogonal families (mofs) of Borel probability
measures on a Polish space. When the underlying space is compact and perfect,
we show that the set of witnesses to non-maximality is comeagre. Our argument
is based on the original proof by Preiss and Rataj, but with significant
simplifications. The proof generalises to show that under $\mathsf{MA} + \neg
\mathsf{CH}$ there are no $\mathbf{\Sigma^1_2}$ mofs, that under $\mathsf{PD}$
there are no projective mofs and that under $\mathsf{AD}$ there are no mofs at
all. We also generalise a result due to Kechris and Sofronidis, stating that
for every analytic orthogonal family of Borel probability measures there is a
product measure orthogonal to all measures in the family, to states on a
certain class of C*-algebras.


title: Quantum Computers, Predictability, and Free Will

abstract:
  This article focuses on the connection between the possibility of quantum
computers, the predictability of complex quantum systems in nature, and the
issue of free will.


title:
The Peripheral Vortex Biome of Confined Quantum Fluids and Its Influence
  on Vortex Pair Annihilation

abstract:
  The self-annihilation of oppositely charged optical vortices in a quantum
fluid is hindered by nonlinearity and promoted by radial confinement, resulting
in rich life-cycle dynamics of such pairs. The competing effects generate a
biome of peripheral vortices that can directly interact with the original pair
to produce a sequence of surrogation events. Numerical simulation is used to
elucidate the role of the vortex biome as a function of nonlinearity strength
and the initial spacing between the engineered vortices. The results apply
directly to other nonlinear quantum fluids as well and may be useful in the
control of complex condensates in which vortex dynamics produce topologically
protected phases.


title:
Correlation-based feature selection to identify functional dynamics in
  proteins

abstract:
  To interpret molecular dynamics simulations of biomolecular systems,
systematic dimensionality reduction methods are commonly employed. Among
others, this includes principal component analysis (PCA) and time-lagged
independent component analysis (TICA), which aim to maximize the variance and
the timescale of the first components, respectively. A crucial first step of
such an analysis is the identification of suitable and relevant input
coordinates (the so-called features), such as backbone dihedral angles and
interresidue distances. As typically only a small subset of those coordinates
is involved in a specific biomolecular process, it is important to discard the
remaining uncorrelated motions or weakly correlated noise coordinates. This is
because they may exhibit large amplitudes or long timescales and therefore will
be erroneously be considered important by PCA and TICA, respectively. To
discriminate collective motions underlying functional dynamics from
uncorrelated motions, the correlation matrix of the input coordinates is
block-diagonalized by a clustering method. This strategy avoids possible bias
due to presumed functional observables and conformational states or variation
principles that maximize variance or timescales. Considering several linear and
nonlinear correlation measures and various clustering algorithms, it is shown
that the combination of linear correlation and the Leiden community detection
algorithm yields excellent results for all considered model systems. These
include the functional motion of T4 lysozyme to demonstrate the successful
identification of collective motion, as well as the folding of villin headpiece
to highlight the physical interpretation of the correlated motions in terms of
a functional mechanism.


title:
Ultrafast spin dynamics: complementing theoretical analyses by
  quantum-information measures

abstract:
  Theoretical analyses of ultrafast spin dynamics commonly address and discuss
simulated phenomena by means of observables, whereas in quantum information
theory one often utilizes measures of quantum states. In this Paper we report
on possible benefits of quantum information measures in simulations of
ultrafast spin dynamics. For Co/Cu heterostructures illuminated by femtosecond
laser pulses, we discuss the general behaviour of quantum information measures,
in particular distances in Hilbert space and degrees of mixing in the density
matrix. The measures are in particular sensitive to variations of the
polarization of a laser pulse and the sample composition. Moreover, they are
closely related to magnetization and number of excited electrons.


title:
Semi-DRDNet Semi-supervised Detail-recovery Image Deraining Network via
  Unpaired Contrastive Learning

abstract:
  The intricacy of rainy image contents often leads cutting-edge deraining
models to image degradation including remnant rain, wrongly-removed details,
and distorted appearance. Such degradation is further exacerbated when applying
the models trained on synthetic data to real-world rainy images. We raise an
intriguing question -- if leveraging both accessible unpaired clean/rainy yet
real-world images and additional detail repair guidance, can improve the
generalization ability of a deraining model? To answer it, we propose a
semi-supervised detail-recovery image deraining network (termed as
Semi-DRDNet). Semi-DRDNet consists of three branches: 1) for removing rain
streaks without remnants, we present a \textit{squeeze-and-excitation}
(SE)-based rain residual network; 2) for encouraging the lost details to
return, we construct a \textit{structure detail context aggregation}
(SDCAB)-based detail repair network; to our knowledge, this is the first time;
and 3) for bridging the domain gap, we develop a novel contrastive
regularization network to learn from unpaired positive (clean) and negative
(rainy) yet real-world images. As a semi-supervised learning paradigm,
Semi-DRDNet operates smoothly on both synthetic and real-world rainy data in
terms of deraining robustness and detail accuracy. Comparisons on four datasets
show clear visual and numerical improvements of our Semi-DRDNet over thirteen
state-of-the-arts.


title: Fast Fuzzing for Memory Errors

abstract:
  Greybox fuzzing is a proven effective testing method for the detection of
security vulnerabilities and other bugs in modern software systems. Greybox
fuzzing can also be used in combination with a sanitizer, such as
AddressSanitizer (ASAN), to further enhance the detection of certain classes of
bug such as buffer overflow and use-after-free errors. However, sanitizers also
introduce additional performance overheads, and this can degrade the
performance of greybox fuzzing -- measured in the order of 2.36x for fuzzing
with ASAN -- potentially negating the benefit of using a sanitizer in the first
place. Recent research attributes this to extra overheads to additional page
faults that are generated when the disjoint sanitizer metadata is accessed at
runtime.
  In this paper, we present a new design that can detect memory errors without
a proliferation of page faults. The basic idea is to track memory validity
using randomized tokens that are stored directly in the memory itself, rather
than in disjoint metadata. All read/write operations are instrumented to check
for the token, and if present, a memory error will be detected. We implement
our design in the form of the ReZZan -- a sanitizer specifically optimized for
fuzz testing. Since there is no disjoint metadata access, no additional page
faults are generated, minimizing the performance overhead to around 1.14-1.27x
(depending on the configuration).


title:
First look at the multiphase interstellar medium with synthetic
  observations of low-frequency Faraday tomography

abstract:
  Faraday tomography of radio polarimetric data below 200 MHz from LOFAR are
providing us with a new perspective on the diffuse and magnetized interstellar
medium (ISM). Of particular interest is the discovery of Faraday-rotated
synchrotron polarization associated with neutral gas, as traced by atomic
hydrogen (HI) and dust. Here we present the first in-depth numerical study of
these LOFAR results. We produce and analyze comprehensive synthetic
observations of low-frequency synchrotron polarization from MHD simulations of
colliding super shells in the multiphase ISM. We define five distinct gas
phases over more than four orders of magnitude in gas temperature and density,
ranging from hot, and warm, fully ionized gas to cold neutral medium. We focus
on the contribution of each gas phase to synthetic observations of both
rotation measure and synchrotron polarized intensity below 200 MHz. We also
investigate the link between the latter and synthetic observations of optically
thin HI gas. We find that, not only the fully ionized gas but also the warm
partially ionized and neutral phases strongly contribute to the total rotation
measure and polarized intensity. However, the contribution of each phase to the
observables strongly depends on the choice of integration axis and the
orientation of the mean magnetic field with respect to the shell-collision
axis. Strong correlation between HI synthetic data and synchrotron polarized
intensity, reminiscent of LOFAR results, is obtained with lines of sight
perpendicular to the mean magnetic field direction. Our study suggests that
multiphase modelling of MHD processes is needed in order to interpret
observations of the radio sky at low frequency. This work is a first step
toward understanding the complexity of low-frequency synchrotron emission that
will be soon revolutionized by large-scale surveys with LOFAR and the SKA.


title:
Universality but no rigidity for two-dimensional perturbations of almost
  commuting pairs

abstract:
  In this paper we consider two-dimensional dissipative maps of the annulus
which are small perturbations of one-dimensional critical circle maps. It has
been shown earlier that such perturbations admit an attractor which is a
non-smooth topolgical circle - a "critical" circle. We study conjugacies of the
maps that admit such attractors and show that although the maps exhibit
universality - they approach a certain normal form when looked at small scales
- two maps in general can not be smoothly comjugate on their critical
attractors. This result extends the paradigm of "universality but no rigidity"
in two dimensions, discovered by A. De Carvalho, M. Lyubich, M. Martens, to yet
another class of dynamical systems.


title: 3D face reconstruction with dense landmarks

abstract:
  Landmarks often play a key role in face analysis, but many aspects of
identity or expression cannot be represented by sparse landmarks alone. Thus,
in order to reconstruct faces more accurately, landmarks are often combined
with additional signals like depth images or techniques like differentiable
rendering. Can we keep things simple by just using more landmarks? In answer,
we present the first method that accurately predicts 10x as many landmarks as
usual, covering the whole head, including the eyes and teeth. This is
accomplished using synthetic training data, which guarantees perfect landmark
annotations. By fitting a morphable model to these dense landmarks, we achieve
state-of-the-art results for monocular 3D face reconstruction in the wild. We
show that dense landmarks are an ideal signal for integrating face shape
information across frames by demonstrating accurate and expressive facial
performance capture in both monocular and multi-view scenarios. This approach
is also highly efficient: we can predict dense landmarks and fit our 3D face
model at over 150FPS on a single CPU thread.


title: Walk this Way! Entity Walks and Property Walks for RDF2vec

abstract:
  RDF2vec is a knowledge graph embedding mechanism which first extracts
sequences from knowledge graphs by performing random walks, then feeds those
into the word embedding algorithm word2vec for computing vector representations
for entities. In this poster, we introduce two new flavors of walk extraction
coined e-walks and p-walks, which put an emphasis on the structure or the
neighborhood of an entity respectively, and thereby allow for creating
embeddings which focus on similarity or relatedness. By combining the walk
strategies with order-aware and classic RDF2vec, as well as CBOW and skip-gram
word2vec embeddings, we conduct a preliminary evaluation with a total of 12
RDF2vec variants.


title: Homotopy equivalence of topological categories

abstract:
  We prove a version of Quillen's Theorem A for topological categories, and use
it to show that under some mild conditions a full and essentially surjective
functor between topological categories induces a homotopy equivalence of
classifying spaces. Thus we can associate homotopy types to a wide class of
topological stacks of categories.


title:
A Dempster-Shafer approach to trustworthy AI with application to fetal
  brain MRI segmentation

abstract:
  Deep learning models for medical image segmentation can fail unexpectedly and
spectacularly for pathological cases and for images acquired at different
centers than those used for training, with labeling errors that violate expert
knowledge about the anatomy and the intensity distribution of the regions to be
segmented. Such errors undermine the trustworthiness of deep learning models
developed for medical image segmentation. Mechanisms with a fallback method for
detecting and correcting such failures are essential for safely translating
this technology into clinics and are likely to be a requirement of future
regulations on artificial intelligence (AI). Here, we propose a principled
trustworthy AI theoretical framework and a practical system that can augment
any backbone AI system using a fallback method and a fail-safe mechanism based
on Dempster-Shafer theory. Our approach relies on an actionable definition of
trustworthy AI. Our method automatically discards the voxel-level labeling
predicted by the backbone AI that are likely to violate expert knowledge and
relies on a fallback atlas-based segmentation method for those voxels. We
demonstrate the effectiveness of the proposed trustworthy AI approach on the
largest reported annotated dataset of fetal T2w MRI consisting of 540 manually
annotated fetal brain 3D MRIs with neurotypical or abnormal brain development
and acquired from 13 sources of data across 6 countries. We show that our
trustworthy AI method improves the robustness of a state-of-the-art backbone AI
for fetal brain MRI segmentation on MRIs acquired across various centers and
for fetuses with various brain abnormalities.


title:
Eliminating Primary Beam Effect in Foreground Subtraction of Neutral
  Hydrogen Intensity Mapping Survey with Deep Learning

abstract:
  In the neutral hydrogen (HI) intensity mapping (IM) survey, the foreground
contamination on the cosmological signals is extremely severe, and the
systematic effects caused by radio telescopes themselves further aggravate the
difficulties in subtracting foreground. In this work, we investigate whether
the deep learning method, concretely the 3D U-Net algorithm here, can play a
crucial role in foreground subtraction when considering the systematic effect
caused by the telescope primary beam. We consider two beam models, i.e., the
Gaussian beam model as a simple case and the Cosine beam model as a
sophisticated case. To make a comparison, and also a combination, with the
U-Net method, we also employ the traditional principal component analysis (PCA)
method in the foreground subtraction. We find that, in the case of the Gaussian
beam, both the PCA and U-Net methods can effectively clean the foreground, but
in the case of the Cosine beam, U-Net performs much better than PCA in cleaning
the foreground. In order to show how well the PCA and U-Net methods can recover
the HI signals, we also derive the angular power spectra, as well as the 2D
power spectrum of HI after performing the foreground subtractions. It is found
that, in the case of Gaussian beam, the concordance with the original HI map
using U-Net is better than that using PCA by $27.4\%$, and in the case of
Cosine beam, the concordance using U-Net is better than that using PCA by
$144.7\%$. Therefore, the U-Net based foreground subtraction can efficiently
eliminate the telescope primary beam effect and shed new light on recovering
the HI power spectrum for future HI IM experiments.


title: On Stability of Two Kinds of Delayed Chemical Reaction Networks

abstract:
  For the networks that are linear conjugate to complex balanced systems, the
delayed version may include two classes of networks: one class is still linear
conjugate to the delayed complex balanced network, the other is not. In this
paper, we prove the existence of the first class of networks, and emphasize the
local asymptotic stability relative to a certain defined invariant set. For the
second class of systems, we define a special subclass and derive the local
asymptotic stability for the subclass. Two examples are provided to illustrate
our results.


title: How Do Graph Networks Generalize to Large and Diverse Molecular Systems?

abstract:
  The predominant method of demonstrating progress of atomic graph neural
networks are benchmarks on small and limited datasets. The implicit hypothesis
behind this approach is that progress on these narrow datasets generalize to
the large diversity of chemistry. This generalizability would be very helpful
for research, but currently remains untested. In this work we test this
assumption by identifying four aspects of complexity in which many datasets are
lacking: 1. Chemical diversity (number of different elements), 2. system size
(number of atoms per sample), 3. dataset size (number of data samples), and 4.
domain shift (similarity of the training and test set). We introduce multiple
subsets of the large Open Catalyst 2020 (OC20) dataset to independently
investigate each of these aspects. We then perform 21 ablation studies and
sensitivity analyses on 9 datasets testing both previously proposed and new
model enhancements. We find that some improvements are consistent between
datasets, but many are not and some even have opposite effects. Based on this
analysis, we identify a smaller dataset that correlates well with the full OC20
dataset, and propose the GemNet-OC model, which outperforms the previous
state-of-the-art on OC20 by 16%, while reducing training time by a factor of
10. Overall, our findings challenge the common belief that graph neural
networks work equally well independent of dataset size and diversity, and
suggest that caution must be exercised when making generalizations based on
narrow datasets.

categories: cs.LG cond-mat.mtrl-sci physics.chem-ph physics.comp-ph


title: A note on a modified fractional Maxwell model

abstract:
  In this paper we consider a modified fractional Maxwell model based on the
application of Hadamard-type fractional derivatives. The model is physically
motivated by the fact that we can take into account at the same time memory
effects and the time-dependence of the viscosity coefficient. We obtain an
ultra-slow relaxation response whose explicit analytic form is given by the
Mittag-Leffler function with a logarithmic argument. We show graphically the
main properties of this relaxation response, also with the asymptotic
behaviour.


title:
Quantum Machine Learning for Software Supply Chain Attacks: How Far Can
  We Go?

abstract:
  Quantum Computing (QC) has gained immense popularity as a potential solution
to deal with the ever-increasing size of data and associated challenges
leveraging the concept of quantum random access memory (QRAM). QC promises
quadratic or exponential increases in computational time with quantum
parallelism and thus offer a huge leap forward in the computation of Machine
Learning algorithms. This paper analyzes speed up performance of QC when
applied to machine learning algorithms, known as Quantum Machine Learning
(QML). We applied QML methods such as Quantum Support Vector Machine (QSVM),
and Quantum Neural Network (QNN) to detect Software Supply Chain (SSC) attacks.
Due to the access limitations of real quantum computers, the QML methods were
implemented on open-source quantum simulators such as IBM Qiskit and TensorFlow
Quantum. We evaluated the performance of QML in terms of processing speed and
accuracy and finally, compared with its classical counterparts. Interestingly,
the experimental results differ to the speed up promises of QC by demonstrating
higher computational time and lower accuracy in comparison to the classical
approaches for SSC attacks.


title: Reinforcement Learning Agents in Colonel Blotto

abstract:
  Models and games are simplified representations of the world. There are many
different kinds of models, all differing in complexity and which aspect of the
world they allow us to further our understanding of. In this paper we focus on
a specific instance of agent-based models, which uses reinforcement learning
(RL) to train the agent how to act in its environment. Reinforcement learning
agents are usually also Markov processes, which is another type of model that
can be used. We test this reinforcement learning agent in a Colonel Blotto
environment1, and measure its performance against Random agents as its
opponent. We find that the RL agent handily beats a single opponent, and still
performs quite well when the number of opponents are increased. We also analyze
the RL agent and look at what strategies it has arrived by looking at the
actions that it has given the highest and lowest Q-values. Interestingly, the
optimal strategy for playing multiple opponents is almost the complete opposite
of the optimal strategy for playing a single opponent.


title:
Vacuum Energy in Modern Cosmology: an analysis of quantum field theory
  in curved spaces and its application to cosmological spacetimes

abstract:
  The last decades have witnessed an unprecedented advancement in our knowledge
of the large scale universe. In particular, increasingly accurate cosmological
observations have allowed us to discover a form of "dark energy", which
presently dominates the expansion of the universe. On the other hand,
fundamental problems in the standard cosmological model point towards the
possibility of a primordial inflationary period. Both these expansion phases
have in common the fact that they should be governed by forms of energy with
properties much similar to those of vacuum energy of classical or quantum
fields. In the meanwhile, quantum field theory in curved spaces (QFTCS) has
proved a rich framework to analyze phenomena of a quantum nature in regimes
where spacetime curvature is relevant, but not too extreme; particularly, it
yields novel insights on the structure and dynamics of quantum vacuum. In this
dissertation, we make a thorough exposition of the fundamentals of QFTCS and
present some of its applications in cosmological spacetimes. Particular
attention is given to the construction of an empirical notion of particles
through an idealized model of particle detectors, and to the phenomenon of
particle creation in expanding FLRW spacetimes. Further, we develop the
procedure of adiabatic renormalization, and use it to compute the renormalized
stress tensor in these spacetimes. For a noninteracting scalar field in de
Sitter spaces, we find that it takes the form of a cosmological constant,
although a quantitatively self-consistent value with the background expansion
can only be found at Planckian densities. We also present a construction of a
simple inflationary model, driven by a self-interacting classical scalar field,
and show how the quantized fluctuations of this field could give rise to a
nearly scale-invariant power spectrum, like the one that is currently observed
in the CMB.


title: DiffSearch: A Scalable and Precise Search Engine for Code Changes

abstract:
  The source code of successful projects is evolving all the time, resulting in
hundreds of thousands of code changes stored in source code repositories. This
wealth of data can be useful, e.g., to find changes similar to a planned code
change or examples of recurring code improvements. This paper presents
DiffSearch, a search engine that, given a query that describes a code change,
returns a set of changes that match the query. The approach is enabled by three
key contributions. First, we present a query language that extends the
underlying programming language with wildcards and placeholders, providing an
intuitive way of formulating queries that is easy to adapt to different
programming languages. Second, to ensure scalability, the approach indexes code
changes in a one-time preprocessing step, mapping them into a feature space,
and then performs an efficient search in the feature space for each query.
Third, to guarantee precision, i.e., that any returned code change indeed
matches the given query, we present a tree-based matching algorithm that checks
whether a query can be expanded to a concrete code change. We present
implementations for Java, JavaScript, and Python, and show that the approach
responds within seconds to queries across one million code changes, has a
recall of 80.7% for Java, 89.6% for Python, and 90.4% for JavaScript, enables
users to find relevant code changes more effectively than a regular
expression-based search, and is helpful for gathering a large-scale dataset of
real-world bug fixes.


title:
Electronic transport in multiterminal chaotic systems with a tunnel
  barrier

abstract:
  We consider the problem of electronic quantum transport through ballistic
mesoscopic systems with chaotic dynamics, connected to a multiterminal
architecture in which one of the terminals has a tunnel barrier. Using a
semiclassical approximation based on matrix integrals, we calculate several
transport statistics, such as average and variance of conductance, average
shot-noise power, among others, that give access to the extreme quantum regime
(small channel numbers in the terminal) for broken and intact time-reversal
symmetry, which the traditional random matrix approach does not access. As an
application, we treat the dephasing regime.


title:
Post-Newtonian gravity and Gaia-like astrometry. Effect of PPN $\gamma$
  uncertainty on parallaxes

abstract:
  Relativistic models of light propagation adopted for high-precision
astrometry are based on the parametrised post-Newtonian formalism, which
provides a framework for examining the effects of a hypothetical violation of
general relativity on astrometric data. Astrometric observations are strongly
affected by the post-Newtonian parameter $\gamma$ describing the strength of
gravitational light deflection. We study both analytically and numerically how
a deviation in the PPN parameter $\gamma$ from unity, which is the value
predicted by general relativity, affects the parallax estimations in Gaia-like
astrometry. Changes in the observable quantities produced by a small variation
in PPN $\gamma$ were calculated analytically. We then considered how such
variations of the observables are reflected in the parallax estimations, and we
performed numerical simulations to check the theoretical predictions. A
variation in the PPN $\gamma$ results in a global shift of parallaxes and we
present a formula describing the parallax bias in terms of the satellite
barycentric distance, the angle between the spin axis and the direction to the
Sun, and the PPN $\gamma$ uncertainty. Numerical simulations of the astrometric
solutions confirm the theoretical result. The up-to-date estimation of PPN
$\gamma$ suggests that a corresponding contribution to the Gaia parallax zero
point unlikely exceeds 0.2 $\mu$as. The numerical simulations indicate that the
parallax shift is strongly dependent on ecliptic latitude. It is argued that
this effect is due to an asymmetry in the Gaia scanning law and this conclusion
is fully validated by additional simulations with a reversed direction of the
precession of the spin axis around the direction to the Sun.


title:
Global Readiness of Language Technology for Healthcare: What would it
  Take to Combat the Next Pandemic?

abstract:
  The COVID-19 pandemic has brought out both the best and worst of language
technology (LT). On one hand, conversational agents for information
dissemination and basic diagnosis have seen widespread use, and arguably, had
an important role in combating the pandemic. On the other hand, it has also
become clear that such technologies are readily available for a handful of
languages, and the vast majority of the global south is completely bereft of
these benefits. What is the state of LT, especially conversational agents, for
healthcare across the world's languages? And, what would it take to ensure
global readiness of LT before the next pandemic? In this paper, we try to
answer these questions through survey of existing literature and resources, as
well as through a rapid chatbot building exercise for 15 Asian and African
languages with varying amount of resource-availability. The study confirms the
pitiful state of LT even for languages with large speaker bases, such as
Sinhala and Hausa, and identifies the gaps that could help us prioritize
research and investment strategies in LT for healthcare.


title:
Implicit Motion-Compensated Network for Unsupervised Video Object
  Segmentation

abstract:
  Unsupervised video object segmentation (UVOS) aims at automatically
separating the primary foreground object(s) from the background in a video
sequence. Existing UVOS methods either lack robustness when there are visually
similar surroundings (appearance-based) or suffer from deterioration in the
quality of their predictions because of dynamic background and inaccurate flow
(flow-based). To overcome the limitations, we propose an implicit
motion-compensated network (IMCNet) combining complementary cues
($\textit{i.e.}$, appearance and motion) with aligned motion information from
the adjacent frames to the current frame at the feature level without
estimating optical flows. The proposed IMCNet consists of an affinity computing
module (ACM), an attention propagation module (APM), and a motion compensation
module (MCM). The light-weight ACM extracts commonality between neighboring
input frames based on appearance features. The APM then transmits global
correlation in a top-down manner. Through coarse-to-fine iterative inspiring,
the APM will refine object regions from multiple resolutions so as to
efficiently avoid losing details. Finally, the MCM aligns motion information
from temporally adjacent frames to the current frame which achieves implicit
motion compensation at the feature level. We perform extensive experiments on
$\textit{DAVIS}_{\textit{16}}$ and $\textit{YouTube-Objects}$. Our network
achieves favorable performance while running at a faster speed compared to the
state-of-the-art methods.


title:
Theoretical analysis of the leptonic decays $B\to \ell \ell
  \ell\bar\nu_{\ell}$: Identical leptons in the final state

abstract:
  We study the effects of the identical leptons in the final state of the
$B^+\to \ell^+ \ell^- \ell^+\bar\nu_{\ell}$ decay. The amplitude of the process
is described by the same form factors as the amplitude of the ${B\to \ell \ell
\ell'\bar\nu'_{\ell}}$ decay for non-identical leptons in the final state.
However, the differential distributions are strongly different, as the ${B^+\to
\ell^+ \ell^- \ell^+\bar\nu_{\ell}}$ amplitude contains both the direct ($M_a$)
and the exchange ($M_b$) diagrams. We calculate a number of the differential
distirbutions. In particular, we propose an interesting observable that can be
readily measured experimentally - the differential distribution over the
invariant mass of the pair of leptons of the same charge, $\ell^+ \ell^+$. The
good news is that the interference between $M_a$ and $M_b$, $d{\cal B}_{ab}$,
is found to be at the level of less than 1\% in all considered differental
distributions and therefore can be neglected in the full kinematical region of
this decay.


title:
The Challenge of Sixfold Integrals: The Closed-Form Evaluation of Newton
  Potentials between Two Cubes

abstract:
  The challenge of explicitly evaluating, in elementary closed form, the weakly
singular sixfold integrals for potentials and forces between two cubes has been
taken up at various places in the mathematics and physics literature. It
created some strikingly specific results, with an aura of arbitrariness, and a
single intricate general procedure due to Hackbusch. Those scattered instances
were mostly addressing the problem heads on, by successive integration while
keeping track of a thicket of primitives generated at intermediate stages.
  In this paper we present a substantially easier and shorter approach, based
on a Laplace transform of the kernel. We clearly exhibit the structure of the
results as obtained by an explicit algorithm, just computing with rational
polynomials. The method extends, up to the evaluation of single integrals, to
higher dimensions. Among other examples, we easily reproduce Fornberg's
startling closed form solution of Trefethen's two-cubes problem and Waldvogel's
symmetric formula for the Newton potential of a rectangular cuboid.


title: Witt invariants from q-series $\hat{Z}$

abstract:
  We present a relation between the Witt invariants of 3-manifolds and the
$\hat{Z}$-invariants. It provides an alternative approach to compute the Witt
invariants of 3-manifolds, which were originally defined geometrically in four
dimensions. We analyze various homology spheres including a hyperbolic manifold
using this method.


title: Scaling theory for the statistics of slip at frictional interfaces

abstract:
  Slip at a frictional interface occurs via intermittent events. Understanding
how these events are nucleated, can propagate, or stop spontaneously remains a
challenge, central to earthquake science and tribology. In the absence of
disorder, rate-and-state approaches predict a diverging nucleation length at
some stress $\sigma^*$, beyond which cracks can propagate. Here we argue that
disorder is a relevant perturbation to this description. We justify why the
distribution of slip contains two parts: a power-law corresponding to
`avalanches', and a `narrow' distribution of system-spanning `fracture' events.
We derive novel scaling relations for avalanches, including a relation between
the stress drop and the spatial extension of a slip event. We compute the
cut-off length beyond which avalanches cannot be stopped by disorder, leading
to a system-spanning fracture, and successfully test these predictions in a
minimal model of frictional interfaces.


title: Molecules in the carbon-rich protoplanetary nebula CRL 2688

abstract:
  We present observations of the carbon-rich protoplanetary nebula (PPN) CRL
2688 made with the Institut de Radioastronomie Millimetrique (IRAM) 30 m
telescope in the 3mm and 2mm bands. In total, 196 transition lines belonging to
38 molecular species and isotopologues are detected, among which, to our best
knowledge, 153 transition lines and 13 species are the first report for this
object. Additionally, in order to contribute to future research, we have
collected observational data on the molecular lines of CRL 2688 from the
literature and compiled them into a single unified catalog. We find that the
molecular abundance of CRL 2688 cannot be explained by the standard model of a
circumstellar envelope. The implications of metal-bearing molecules on
circumstellar chemistry are discussed.


title:
Classification of NEQR Processed Classical Images using Quantum Neural
  Networks (QNN)

abstract:
  A quantum neural network (QNN) is interpreted today as any quantum circuit
with trainable continuous parameters. This work builds on previous works by the
authors and addresses QNN for image classification with Novel Enhanced Quantum
Representation of (NEQR) processed classical data where Principal component
analysis (PCA) and Projected Quantum Kernel features (PQK) were investigated
previously by the authors as a path to quantum advantage for the same classical
dataset. For each of these cases the Fashion-MNIST dataset was downscaled using
PCA to convert into quantum data where the classical NN easily outperformed the
QNN. However, we demonstrated quantum advantage by using PQK where quantum
models achieved more than ~90% accuracy surpassing their classical counterpart
on the same training dataset as in the first case. In this current work, we use
the same dataset fed into a QNN and compare that with performance of a
classical NN model. We built an NEQR model circuit to pre-process the same data
and feed the images into the QNN. Our results showed marginal improvements
(only about ~5.0%) where the QNN performance with NEQR exceeded the performance
of QNN without NEQR. We conclude that given the computational cost and the
massive circuit depth associated with running NEQR, the advantage offered by
this specific Quantum Image Processing (QIMP) algorithm is questionable at
least for classical image dataset. No actual quantum computing hardware
platform exists today that can support the circuit depth needed to run NEQR
even for the reduced image sizes of our toy classical dataset.


title: Flat Map of a Sphere via Stress Minimization

abstract:
  In this paper we describe a mathematically interesting but relatively minor
improvement to the Gott-Goldberg-Vanderbei (GGV) map projection. This new
projection can be described as what one would get by making a spherical rubber
ball representation of the Earth and then stretching the ball circularly around
the equator until the Northern and Southern hemispheres flatten to a disk. It
is interesting that this new projection is very similar to but not exactly the
same as the GGV projection. And, the mathematics required to solve this
flattening problem is a very nice example of using the calculus of variations
to solve an infinite dimensional optimization problem.


title:
Scandium Nitride as a Gateway III-Nitride Semiconductor for
  Optoelectronic Artificial Synaptic Devices

abstract:
  Traditional computation based on von Neumann architecture is limited by the
time and energy consumption due to data transfer between the storage and the
processing units. The von Neumann architecture is also inefficient in solving
unstructured, probabilistic, and real-time problems. To address these
challenges, a new brain-inspired neuromorphic computational architecture is
required. Due to absence of resistance-capacitance (RC) delay, high bandwidth
and low power consumption, optoelectronic artificial synaptic devices are
highly attractive. Yet stable, scalable, and
complementary-metal-oxide-semiconductor (CMOS)-compatible synapses have not
been demonstrated. In this work, persistence in the photoconductivity of
undoped and magnesium-doped scandium nitride (ScN) is equated to the inhibitory
and excitatory synaptic plasticity of the biological synapses responsible for
memory and learning. Primary functionalities of a biological synapse like
short-term memory (STM), long-term memory (LTM), the transition from
STM-to-LTM, learning and forgetting, frequency-selective optical filtering,
frequency-dependent potentiation and depression, Hebbian learning, and logic
gate operations are demonstrated.


title: An Introduction to Renormalization in Atomic Physics

abstract:
  The purpose of these notes is to provide a pedagogical introduction to the
concept of renormalization in atomic physics. We study quantum dynamics of a
model of a nonrelativistic single electron atom coupled to the quantum
radiation field in the dipole approximation. An interaction between the
electron and the radiation field is regularized, using a suitable cutoff
prescription which eliminates the coupling of the electron to the highly
ultraviolet (UV) field modes. Subsequently, we analyze the corresponding
Heisenberg picture equations of motion, and focus on behavior of physical
observables in the limit of a pointlike electron (i.e., in the limit when the
above mentioned UV cutoff is gradually lifted to infinity). We identify the
radiation reaction force acting on the electron, and show that this force
actually diverges towards infinity when gradually removing the UV cutoff. Such
an analysis leads ultimately to an unique renormalization prescription for the
electron mass, as well as to determination of the Abraham-Lorentz force acting
on the electron. Dependence of the just sketched renormalization procedure upon
the spatial dimension is highlighted. Finally, we present an example
calculation of the atomic level shifts by means of the renormalized
perturbation theory, and formulate also the renormalized mean field theory.


title: A simulation-free seismic survey design by maximizing the spectral gap

abstract:
  Due to the tremendous cost of seismic data acquisition, methods have been
developed to reduce the amount of data acquired by designing optimal missing
trace reconstruction algorithms. These technologies are designed to record as
little data as possible in the field, while providing accurate wavefield
reconstruction in the areas of the survey that are not recorded. This is
achieved by designing randomized subsampling masks that allow for accurate
wavefield reconstruction via matrix completion methods. Motivated by these
recent results, we propose a simulation-free seismic survey design that aims at
improving the quality of a given randomized subsampling using a simulated
annealing algorithm that iteratively increases the spectral gap of the
subsampling mask, a property recently linked to the quality of the
reconstruction. We demonstrate that our proposed method improves the data
reconstruction quality for a fixed subsampling rate on a realistic synthetic
dataset.


title:
Dimensionality Expansion and Transfer Learning for Next Generation
  Energy Management Systems

abstract:
  Electrical management systems (EMS) are playing a central role in enabling
energy savings. They can be deployed within an everyday household where they
monitor and manage appliances and help residents be more energy efficient and
subsequently also more economical. One of they key functionalities of EMS is to
automatically detect and identify appliances within a household through the
process of load monitoring. In this paper, we propose a new transfer learning
approach for building EMS (BEMS) and study the trade-offs in terms of numbers
of samples and target classes in adapting a backbone model during the transfer
process. We also perform a first time analysis of feature expansion through
video-like transformation of time series data for device classification in non
intrusive load monitoring (NILM) and propose a deep learning architecture
enabling accurate appliance identification. We examine the relative performance
of our method on 5 different representative low-frequency datasets and show
that our method performs with an average F1 score of 0.88 on these datasets.


title:
A Transformer-Based Contrastive Learning Approach for Few-Shot Sign
  Language Recognition

abstract:
  Sign language recognition from sequences of monocular images or 2D poses is a
challenging field, not only due to the difficulty to infer 3D information from
2D data, but also due to the temporal relationship between the sequences of
information. Additionally, the wide variety of signs and the constant need to
add new ones on production environments makes it infeasible to use traditional
classification techniques. We propose a novel Contrastive Transformer-based
model, which demonstrate to learn rich representations from body key points
sequences, allowing better comparison between vector embedding. This allows us
to apply these techniques to perform one-shot or few-shot tasks, such as
classification and translation. The experiments showed that the model could
generalize well and achieved competitive results for sign classes never seen in
the training process.


title: Federated Self-supervised Speech Representations: Are We There Yet?

abstract:
  The ubiquity of microphone-enabled devices has lead to large amounts of
unlabelled audio data being produced at the edge. The integration of
self-supervised learning (SSL) and federated learning (FL) into one coherent
system can potentially offer data privacy guarantees while also advancing the
quality and robustness of speech representations. In this paper, we provide a
first-of-its-kind systematic study of the feasibility and complexities for
training speech SSL models under FL scenarios from the perspective of
algorithms, hardware, and systems limits. Despite the high potential of their
combination, we find existing system constraints and algorithmic behaviour make
SSL and FL systems nearly impossible to build today. Yet critically, our
results indicate specific performance bottlenecks and research opportunities
that would allow this situation to be reversed. While our analysis suggests
that, given existing trends in hardware, hybrid SSL and FL speech systems will
not be viable until 2027. We believe this study can act as a roadmap to
accelerate work towards reaching this milestone much earlier.


title:
Markov cohort state-transition model: A multinomial distribution
  representation

abstract:
  Markov cohort state-transition models have been the standard approach for
simulating the prognosis of patients or, more generally, the life trajectories
of individuals over a time period. Current approaches for estimating the
variance of a Markov model using a Monte Carlo sampling or a master equation
representation are computationally expensive and analytically difficult to
express and solve. We introduce an alternative representation of a Markov model
in the form of a multinomial distribution. We derive this representation from
principles and then verify its veracity in a simulation exercise. This
representation provides an exact and fast approach to compute the variance and
a way to estimate transition probabilities in a Bayesian setting.


title:
A Remark on the First Eigenvalue of the Laplace Operator on 1-forms or
  functions for Compact Inner Symmetric Spaces

abstract:
  We remark that on a compact inner symmetric space $G/K$, indowed with the
Riemmannian metric given by the Killing form of $G$ signed-changed, the first
(non-zero) eigenvalue of the Laplace operator on $1$-forms or functions, is the
Casimir eigenvalue of the highest either long or short root of $G$, according
as the highest weight of the isotropy representation is long or short.


title:
Ultraviolet optical horn antennas for label-free detection of single
  proteins

abstract:
  Single-molecule fluorescence techniques have revolutionized our ability to
study proteins. However, the presence of a fluorescent label can alter the
protein structure and/or modify its reaction with other species. To avoid the
need for a fluorescent label, the intrinsic autofluorescence of proteins in the
ultraviolet offers the benefits of fluorescence techniques without introducing
the labelling drawbacks. Unfortunately, the low autofluorescence brightness of
proteins has greatly challenged single molecule detection so far. Here we
introduce optical horn antennas, a dedicated nanophotonic platform enabling the
label-free detection of single proteins in the UV. This design combines
fluorescence plasmonic enhancement, efficient collection up to 85{\textdegree}
angle and background screening. We detect the UV autofluorescence from
immobilized and diffusing single proteins, and monitor protein unfolding and
dissociation upon denaturation. Optical horn antennas open up a unique and
promising form of fluorescence spectroscopy to investigate single proteins in
their native states in real time.


title:
Multilinear smoothing and local well-posedness of a stochastic quadratic
  nonlinear Schr{\"o}dinger equation

abstract:
  In this article, we study a $d$-dimensional stochastic quadratic nonlinear
Schr\"{o}dinger equation (SNLS), driven by a fractional derivative (of order
$-\alpha<0$) of a space-time white noise: $$\left\{ \begin{array}{l}i\partial_t
u-\Delta u= \rho^2 |u|^2 + \langle \nabla \rangle^{-\alpha}\dot{W} \, , \quad
t\in [0,T] \, , \, x\in \mathbb{R}^d \, ,\\ u_0 = \phi\, ,\end{array}\right.$$
where $\rho :\mathbb{R}^d \rightarrow \mathbb{R}$ is a smooth
compactly-supported function. When $\alpha < \frac{d}{2}$, the stochastic
convolution is a function of time with values in a negative-order Sobolev space
and the model has to be interpreted in the Wick sense by means of a
time-dependent renormalization. When $1\leq d \leq 3$, combining both the
classical Strichartz estimates and a deterministic local smoothing, we
establish the local well-posedness of (SNLS) for a small range of $\alpha$, in
the spirit of \cite{Schaeffer1}. Then, we revisit our arguments and establish
multilinear smoothing on the second order stochastic term. This allows us to
improve our local well-posedness result for some $\alpha$. We point out that
this is the first result concerning a Schr\"{o}dinger equation on
$\mathbb{R}^d$ driven by such an irregular noise and whose local well-posedness
results from both a stochastic multilinear smoothing and a deterministic local
one combined with Strichartz inequalities.


title: Hammer PDF: An Intelligent PDF Reader for Scientific Papers

abstract:
  Reading scientific papers has been an essential way for researchers to
acquire academic knowledge, and most papers are in PDF format. However,
existing PDF Readers only support reading, editing, annotating and other basic
functions, and lack of multi-granularity analysis for academic papers.
Specifically, taking a paper as a whole, these PDF Readers cannot access
extended information of the paper, such as related videos, blogs, codes, etc.;
meanwhile, for the content of a paper, these PDF Readers also cannot extract
and display academic details of the paper, such as terms, authors, citations,
etc. In this paper, we introduce Hammer PDF, a novel intelligent PDF Reader for
scientific papers. Beyond basic reading functions, Hammer PDF comes with four
innovative features: (1) locate, mark and interact with spans (e.g., terms)
obtained by information extraction; (2) citation, reference, code, video, blog,
and other extended information are displayed with a paper; (3) built-in Hammer
Scholar, an academic search engine that uses academic information collected
from major academic databases; (4) built-in Q\&A bot to support asking for
interested conference information. Our product helps researchers, especially
those who study computer science, to improve the efficiency and experience of
reading scientific papers. We release Hammer PDF, available for download at
https://pdf.hammerscholar.net/face.


title:
Expression-preserving face frontalization improves visually assisted
  speech processing

abstract:
  Face frontalization consists of synthesizing a frontally-viewed face from an
arbitrarily-viewed one. The main contribution of this paper is a frontalization
methodology that preserves non-rigid facial deformations in order to boost the
performance of visually assisted speech communication. The method alternates
between the estimation of (i)~the rigid transformation (scale, rotation, and
translation) and (ii)~the non-rigid deformation between an arbitrarily-viewed
face and a face model. The method has two important merits: it can deal with
non-Gaussian errors in the data and it incorporates a dynamical face
deformation model. For that purpose, we use the generalized Student
t-distribution in combination with a linear dynamic system in order to account
for both rigid head motions and time-varying facial deformations caused by
speech production. We propose to use the zero-mean normalized cross-correlation
(ZNCC) score to evaluate the ability of the method to preserve facial
expressions. The method is thoroughly evaluated and compared with several state
of the art methods, either based on traditional geometric models or on deep
learning. Moreover, we show that the method, when incorporated into deep
learning pipelines, namely lip reading and speech enhancement, improves word
recognition and speech intelligibilty scores by a considerable margin.
Supplemental material is accessible at
https://team.inria.fr/robotlearn/research/facefrontalization-benchmark/


title:
BMD: A General Class-balanced Multicentric Dynamic Prototype Strategy
  for Source-free Domain Adaptation

abstract:
  Source-free Domain Adaptation (SFDA) aims to adapt a pre-trained source model
to the unlabeled target domain without accessing the well-labeled source data,
which is a much more practical setting due to the data privacy, security, and
transmission issues. To make up for the absence of source data, most existing
methods introduced feature prototype based pseudo-labeling strategies to
realize self-training model adaptation. However, feature prototypes are
obtained by instance-level predictions based feature clustering, which is
category-biased and tends to result in noisy labels since the visual domain
gaps between source and target are usually different between categories. In
addition, we found that a monocentric feature prototype may be ineffective to
represent each category and introduce negative transfer, especially for those
hard-transfer data. To address these issues, we propose a general
class-Balanced Multicentric Dynamic prototype (BMD) strategy for the SFDA task.
Specifically, for each target category, we first introduce a global inter-class
balanced sampling strategy to aggregate potential representative target
samples. Then, we design an intra-class multicentric clustering strategy to
achieve more robust and representative prototypes generation. In contrast to
existing strategies that update the pseudo label at a fixed training period, we
further introduce a dynamic pseudo labeling strategy to incorporate network
update information during model adaptation. Extensive experiments show that the
proposed model-agnostic BMD strategy significantly improves representative SFDA
methods to yield new state-of-the-art results, e.g., improving SHOT from 82.9\%
to 85.8\% on VisDA-C and NRC from 52.6\% to 57.0\% on PointDA. The code is
available at https://github.com/ispc-lab/BMD.


title:
First principles and Monte Carlo studies of adsorption and desorption
  properties from Pd$\rm_{1-x}$Ag$\rm_{{x}}$ surface alloy

abstract:
  The FCC structure of Pd$\rm_{1-x}$Ag$\rm_{x}$ ($\rm{x}=$ 0.25, 0.50, 0.75)
alloys is considered as a fuel cell component in this study. We have looked
into its qualities as a component of a fuel cell to see whether it could be
potentially used as an alternative replacement of the Pt catalyst. We used
Density Functional Theory (DFT) to study H and CO interaction with the surface,
and Kinetic Monte Carlo~(KMC) to study H and CO desorption from the surface.
The bulk modulus and equilibrium crystal structures of Pd$\rm_{1-x}$Ag$\rm_{x}$
alloys were computed using the GPAW code within plane wave basis set $\&$ a PBE
exchange correlation functional treatment. The best values of a lattice
constant for the system are obtained by total energy calculations versus
lattice cell volumes as fitted to the stabilized jellium model. Surface
energies, cohesive energies, and\ binding energy of Pd$\rm_{1-x}$Ag$\rm_{x}$
alloys were computed to analyze the stability properties of structures. Band
structure calculations reveal the electronic and optical properties of these
alloys. The density of states~(DOS) and projected density of states~(PDOS) show
the availability of the eigenstates for occupation. The desorption process is
studied within the Arrhenius type desorption rate $\&$ a temperature
programming. The effects of lateral interactions between adsorbed molecules on
first order desorption (molecular adsorption) $\&$ second order desorption were
taken into account. Adsorption energies of H and CO on Pd$\rm_3$Ag~(111) as
calculated using DFT is used in the process. The outcomes show good qualitative
agreement with literature.


title: An Algebraic Approach to Learning and Grounding

abstract:
  We consider the problem of learning the semantics of composite algebraic
expressions from examples. The outcome is a versatile framework for studying
learning tasks that can be put into the following abstract form: The input is a
partial algebra A and a finite set of samples ({\phi}1, O1), ({\phi}2, O2),
..., each consisting of an algebraic term {\phi}i and a set of objects Oi. The
objective is to simultaneously fill in the missing algebraic operations in A
and ground the variables of every {\phi}i in Oi, so that the combined value of
the terms is optimised. We demonstrate the applicability of this framework
through case studies in grammatical inference, picture-language learning, and
the grounding of logic scene descriptions.


title:
Aggression in Hindi and English Speech: Acoustic Correlates and
  Automatic Identification

abstract:
  In the present paper, we will present the results of an acoustic analysis of
political discourse in Hindi and discuss some of the conventionalised acoustic
features of aggressive speech regularly employed by the speakers of Hindi and
English. The study is based on a corpus of slightly over 10 hours of political
discourse and includes debates on news channel and political speeches. Using
this study, we develop two automatic classification systems for identifying
aggression in English and Hindi speech, based solely on an acoustic model. The
Hindi classifier, trained using 50 hours of annotated speech, and English
classifier, trained using 40 hours of annotated speech, achieve a respectable
accuracy of over 73% and 66% respectively. In this paper, we discuss the
development of this annotated dataset, the experiments for developing the
classifier and discuss the errors that it makes.


title: Information Thermodynamics for Deterministic Chemical Reaction Networks

abstract:
  Information thermodynamics relates the rate of change of mutual information
between two interacting subsystems to their thermodynamics when the joined
system is described by a bipartite stochastic dynamics satisfying local
detailed balance. Here, we expand the scope of information thermodynamics to
deterministic bipartite chemical reaction networks, namely, composed of two
coupled subnetworks sharing species, but not reactions. We do so by introducing
a meaningful notion of mutual information defined for non-normalized
concentration distributions. This allows us to formulate separate second laws
for each subnetwork, which account for their energy and information exchanges,
in complete analogy with stochastic systems. We then use our framework to
investigate the working mechanisms of a model of chemically-driven
self-assembly and an experimental light-driven bimolecular motor. We show that
both systems are constituted by two coupled subnetworks of chemical reactions.
One subnetwork is maintained out of equilibrium by external reservoirs
(chemostats or light sources) and powers the other via energy and information
flows. In doing so, we clarify that the information flow is precisely the
thermodynamic counterpart of an information ratchet mechanism only when no
energy flow is involved.


title: Perturbation Theory in a Microcanonical Ensemble

abstract:
  The microcanonical ensemble is a natural starting point of statistical
mechanics. However, when it comes to perturbation theory in statistical
mechanics, traditionally only the canonical and grand canonical ensembles have
been used. In this article we show how the microcanonical ensemble can be
directly used to carry out perturbation theory for both non-interacting and
interacting systems. We obtain the first non-trivial order answers for the
specific heat of anharmonic oscillators and for the virial expansion in real
gases. They are in exact agreement with the results obtained from the canonical
ensemble.


title:
De la structure des espaces fibr\'es aux \'equations fondamentales de la
  physique. Une introduction

abstract:
  This text describes the fiber bundle structure and shows its universality for
writing the laws of classical physics: newtonian, relativistic and quantum
mechanics.


title:
Non-isothermal diffusion in interconnected discrete-distributed systems:
  a variational approach

abstract:
  Motivated by compartmental analysis in engineering and biophysical systems,
we present a variational framework for the nonequilibrium thermodynamics of
systems involving both distributed and discrete (finite dimensional) subsystems
by specifically using the ideas of interconnected systems. We focus on the
process of non-isothermal diffusion and show how the resulting form of the
entropy equation naturally yields phenomenological expressions for the
diffusion and entropy fluxes between two compartments, which results in
generalized forms of Robin type interface conditions.


title: Large intersection property for limsup sets in metric space

abstract:
  We show that limsup sets generated by a sequence of open sets in compact
Ahlfors $s$-regular space $(X,\mathscr{B},\mu,\rho)$ belong to the classes of
sets with large intersections with index $\lambda$, denoted by
$\mathcal{G}^{\lambda}(X)$, under some conditions. In particular, this provides
a lower bound on Hausdorff dimension of such sets. These results are applied to
obtain that limsup random fractals with indices $\gamma_2$ and $\delta$ belong
to $\mathcal{G}^{s-\delta-\gamma_2}(X)$ almost surely, and random covering sets
with exponentially mixing property belong to $\mathcal{G}^{s_0}(X)$ almost
surely, where $s_0$ equals to the corresponding Hausdorff dimension of covering
sets almost surely. We also investigate the large intersection property of
limsup sets generated by rectangles in metric space.