title:
Investigate the plasmonic enhanced solar photothermal effect of gold
  nanorod nanofilm

abstract:
  Gold nanospheres (Au NSs) and gold nanorods (Au NRs) are traditional noble
metal plasmonic nanomaterials. Particularly, Au NRs with tunable longitudinal
plasmon resonance from visible to the near infrared (NIR) range were suitable
for high efficient photothermal applications due to extended light receiving
range. In this work, we synthesized Au NRs and Au NSs of similar volume, and
subsequently developed them into Au NR/PVDF and Au NS/PVDF nanofilm, both of
which exhibited excellent solar photothermal performance evaluated by solar
photothermal experiments. We found that Au NR/PVDF nanofilm showed higher solar
photothermal performance than Au NS/PVDF nanofilm. Through detailed analysis,
such as morphological characterization, optical measurement, and finite element
method (FEM) modeling, we found that the plasmonic coupling effects inside the
aggregated Au NRs nanoclusters contributed to the spectral blue-shifts and
intensified photothermal performance. Compare to Au NS/PVDF nanofilms, Au
NR/PVDF nanofilm exhibited higher efficient light-to-heat conversion rate,
because of the extended light receiving range and high absorbance, as the
result of strong plasmonic interactions inside nanoclusters, which was further
validated by monochromatic laser photothermal experiments and FEM simulations.
Our work proved that the Au NRs have huge potential for plasmonic solar
photothermal applications, and are envisioned for novel plasmonic applications.


title:
Exact two-component Hamiltonians for relativistic quantum chemistry:
  Two-electron picture-change corrections made simple

abstract:
  Based on self-consistent field (SCF) atomic mean-field (amf) quantities, we
present two simple, yet computationally efficient and numerically accurate
matrix-algebraic approaches to correct both scalar-relativistic \textit{and}
spin-orbit two-electron picture-change effects (PCE) arising within an exact
two-component (X2C) Hamiltonian framework. Both approaches, dubbed amfX2C and
e(xtended)amfX2C, allow us to uniquely tailor PCE corrections to mean-field
models, $viz.$ Hartree-Fock or Kohn-Sham DFT, in the latter case also avoiding
the need of a point-wise calculation of exchange-correlation PCE corrections.
We assess the numerical performance of these PCE correction models on spinor
energies of group-18 (closed-shell) and group-16 (open-shell) diatomic
molecules, achieving a consistent $\approx\!10^{-5}$ Hartree accuracy compared
to reference four-component data. Additional tests include SCF calculations of
molecular properties such as absolute contact density and contact density
shifts in copernicium fluoride compounds (CnF$_{n}$, n=2,4,6), as well as
equation-of-motion coupled cluster calculations of X-ray core ionization
energies of $5d$ and $6d$-containing molecules, where we observe an excellent
agreement with reference data. To conclude, we are confident that our (e)amfX2C
PCE correction models constitute a fundamental milestone towards a universal
and reliable relativistic two-component quantum chemical approach, maintaining
the accuracy of the parent four-component one at a fraction of its
computational cost.

categories: physics.chem-ph cond-mat.str-el physics.atom-ph physics.comp-ph quant-ph


title:
Coordination Strategies When Working from Anywhere: A Case Study of Two
  Agile Teams

abstract:
  Effective coordination is the key to successful agile teams. They rely on
frequent interactions and mutual adjustment to manage dependencies between
activities, which traditionally has been solved by co-locating the team. As the
world is adjusting to post-covid work-life, companies are moving towards a
work-from-anywhere approach where workers can choose to what degree they want
to work from home or office. However, little is known about coordination in
such a context. We report findings on developers' emerging strategies when
working-from-anywhere, from an exploratory case study in Norway, including
eight interviews. Our study shows that new strategies for mutual adjustment
emerged as teams experimented with different tools and approaches: developers
chose tasks according to location, tasks with vague requirements are performed
collocated while individual tasks requiring focus are best performed at home;
large meetings are virtual, preserving co-located time for collaborative tasks;
using virtual rooms to maintain unscheduled meetings as they communicate mental
presence to teammates, lowering the threshold for intra-team unscheduled talks.
The strategies can help organizations create a productive and effective
environment for developers.


title:
Did Earth eat its leftovers? Impact ejecta as a component of the late
  veneer

abstract:
  The presence of highly siderophile elements in Earth's mantle indicates that
a small percentage of Earth's mass was delivered after the last giant impact in
a stage of 'late accretion.' There is ongoing debate about the nature of
late-accreted material and the sizes of late-accreted bodies. Earth appears
isotopically most similar to enstatite chondrites and achondrites. It has been
suggested that late accretion must have been dominated by enstatite-like bodies
that originated in the inner disk, rather than ordinary or carbonaceous
chondrites. Here, we examine the provenances of 'leftover' planetesimals
present in the inner disk in the late stages of accretion simulations.
Dynamically excited planet formation produces planets and embryos with similar
provenances, suggesting that the Moon-forming impactor may have had a stable
isotope composition very similar to the proto-Earth. Commonly, some
planetesimal-sized bodies with similar provenances to the Earth-like planets
are left at the end of the main stage of growth. The most chemically-similar
planetesimals are typically fragments of proto-planets ejected millions of
years earlier. If these similar-provenance bodies are later accreted by the
planet, they will represent late-accreted mass that naturally matches Earth's
composition. The planetesimal-sized bodies that exist during the giant impact
phase can have large core mass fractions, with core provenances similar to the
proto-Earth. These bodies are an important potential source for highly
siderophile elements. The range of core fractions in leftover planetesimals
complicates simple inferences as to the mass and origin of late accretion based
on the highly siderophile elements in the mantle.


title: Geometric Filterless Photodetectors for Mid-infrared Spin Light

abstract:
  Free-space circularly polarized light (CPL) detection, requiring polarizers
and waveplates, has been well established, while such spatial degree of freedom
is unfortunately absent in integrated on-chip optoelectronics. So far, those
reported filterless CPL photodetectors suffer from the intrinsic small
discrimination ratio, vulnerability to the non-CPL field components, and low
responsivity. Here, we report a distinct paradigm of geometric photodetectors
in mid-infrared exhibiting colossal discrimination ratio, close-to-perfect
CPL-specific response, a zero-bias responsivity of 392 V/W at room temperature,
and a detectivity of ellipticity down to 0.03$^o$ Hz$^{-1/2}$. Our approach
employs plasmonic nanostructures array with judiciously designed symmetry,
assisted by graphene ribbons to electrically read their near-field optical
information. This geometry-empowered recipe for infrared photodetectors
provides a robust, direct, strict, and high-quality solution to on-chip
filterless CPL detection and unlocks new opportunities for integrated
functional optoelectronic devices.


title: Enhancing superconductivity in MXenes through hydrogenation

abstract:
  Two-dimensional transition metal carbides and nitrides (MXenes) are an
emerging class of atomically-thin superconductors, whose characteristics are
highly prone to tailoring by surface functionalization. Here we explore the use
of hydrogen adatoms to enhance phonon-mediated superconductivity in MXenes,
based on first-principles calculations combined with Eliashberg theory. We
first demonstrate the stability of three different structural models of
hydrogenated Mo- and W-based MXenes. Particularly high critical temperatures of
over 30 K are obtained for hydrogenated Mo$_2$N and W$_2$N. Several mechanisms
responsible for the enhanced electron-phonon coupling are uncovered, namely (i)
hydrogen-induced changes in the phonon spectrum of the host MXene, (ii)
emerging hydrogen-based phonon modes, and (iii) charge transfer from hydrogen
to the MXene layer, boosting the density of states at the Fermi level. Finally,
we demonstrate that hydrogen adatoms are moreover able to induce
superconductivity in MXenes that are not superconducting in pristine form, such
as Nb$_2$C.


title: Impact of mode regularization for quasinormal mode perturbation theories

abstract:
  We give insight into the critical problem of an open resonator that is
subject to a perturbation outside of its cavity region. We utilize the
framework of quasinormal modes (QNMs), which are the natural mode solutions to
the open boundary problem with complex eigenfrequencies. We first highlight
some fundamental problems with currently adopted formulas using QNM
perturbation theory, when perturbations are added outside the resonator
structure and present a first potential step for solving this problem connected
to a regularization of the QNMs. We then show an example for a full
three-dimensional plasmonic resonator of arbitrary shape and complex dispersion
and loss, clearly displaying the divergent nature of the first-order mode
change predicted from QNM perturbation theory. Subsequently, we concentrate on
the illustrative case of a one-dimensional dielectric barrier, where analytical
QNM solutions are possible. We inspect the change of the mode frequency as
function of distance between the cavity and another smaller barrier structure.
The results obtained from a few QNM expansion are compared with exact
analytical solutions from a transfer matrix approach. We show explicitly how
regularization prevents a problematic spatial divergence for QNM perturbations
in the far field, though eventually higher-order effects and multimodes can
also play a role in the full scattering solution, and retaining a pure discrete
QNM picture becomes questionable in such situations, since the input-output
coupling ultimately involves reservoir modes.


title: Embeddings of Trees, Cantor Sets and Solvable Baumslag-Solitar Groups

abstract:
  We characterise when there exists a quasiisometric embedding between two
solvable Baumslag-Solitar groups. This extends the work of Farb and Mosher on
quasiisometries between the same groups. More generally, we characterise when
there can exist a quasiisometric embedding between two treebolic spaces. This
allows us to determine when two treebolic spaces are quasiisometric, confirming
a conjecture of Woess. The question of whether there exists a quasiisometric
embedding between two treebolic spaces turns out to be equivalent to the
question of whether there exists a bilipschitz embedding between two symbolic
Cantor sets, which in turn is equivalent to the question of whether there
exists a rough isometric embedding between two regular rooted trees. Hence we
answer all three of these questions simultaneously. It turns out that the
existence of such embeddings is completely determined by the boundedness of an
intriguing family of integer sequences.


title:
Passage of millicharged particles in the electron beam-dump: refining
  constraints from SLACmQ and estimating sensitivity of NA64e

abstract:
  Millicharged particles (MCPs) arise in many well-motivated extensions of the
Standard Model and are a popular subject for experimental searches. We
investigate attenuation of the MCP flux produced at accelerator experiments due
to their interactions in the media. Considering, as an example, the dedicated
MCP search at SLACmQ, we demonstrate that this effect can significantly affect
the final sensitivity to the MCP parameter space leaving its essential part
still unexplored. Applying our analysis to the SLACmQ experiment [53], we
correct their exclusion bounds in close accordance with Ref. [54]. We also show
that this newly reopened area with the MCP masses in the range $10^{-4}$ eV -
$1$ GeV and charges $\gtrsim 10^{-5} e$ can be effectively probed by the
NA64$e$ experiment at the CERN SPS. Light MCPs are mostly produced by virtual
photon in electron scattering off nucleus. The main source of heavy MCP is
decays of vector mesons, produced by the electrons on nuclei.


title: KGI: An Integrated Framework for Knowledge Intensive Language Tasks

abstract:
  In a recent work, we presented a novel state-of-the-art approach to zero-shot
slot filling that extends dense passage retrieval with hard negatives and
robust training procedures for retrieval augmented generation models. In this
paper, we propose a system based on an enhanced version of this approach where
we train task specific models for other knowledge intensive language tasks,
such as open domain question answering (QA), dialogue and fact checking. Our
system achieves results comparable to the best models in the KILT leaderboards.
Moreover, given a user query, we show how the output from these different
models can be combined to cross-examine each other. Particularly, we show how
accuracy in dialogue can be improved using the QA model. A short video
demonstrating the system is available here -
\url{https://ibm.box.com/v/kgi-interactive-demo} .


title:
A study of 1000 galaxies with unusually young and massive stars in the
  SDSS: a search for hidden black holes

abstract:
  We select 1076 galaxies with extinction-corrected Halpha equivalent widths
too large to be explained with a Kroupa (2001) IMF, and compare these with a
control sample of galaxies that is matched in stellar mass, redshift and 4000
AA break strength, but with normal Halpha equivalent widths. Our goal is to
study how processes such as black hole growth and energetic feedback processes
from massive stars differ between galaxies with extreme central Halpha emission
and galaxies with normal young central stellar populations. The stellar mass
distribution of Halpha excess galaxies is peaked at 3 \times 10^10 Msun and
almost all fall well within the star-forming locus in the [OIII]/Hbeta versus
[NII]/Halpha BPT disgram. Halpha excess galaxies are twice as likely to exhibit
Halpha line asymmetries and 1.55 times more likely to be detected at 1 GHz in
the VLA FIRST survey compared to control sample galaxies. The radio luminosity
per unit stellar mass decreases with the stellar age of the system. Using
stacked spectra, we demonstrate that [NeV] emission is not present in the very
youngest of the radio-quiet Halpha excess galaxies with detectable Wolf-Rayet
features, suggesting that black hole growth has not yet commenced in such
systems. [NeV] emission is detected in Halpha excess galaxies with radio
detections and the strength of the line correlates with the radio luminosity.
This is the clearest indication for a possible population of black holes that
may be forming in a subset of the Halpha excess population.


title:
Extended Weil representations by some twisted actions: the finite field
  cases

abstract:
  It is well known(cf. Weil, G\'erardin's works) that there are two different
Weil representations of a symplectic group over an odd finite field. By a
twisted action, we show that one can reorganize them as a representation of a
symplectic similitude group. We also discussed the even field case by following
Genestier-Lysenko and Gurevich-Hadani's works on geometric Weil representations
in characteristic two. For this reason, we approach some of their results from
the lattice model, which is inspired by MVW, Prasad ,and Takeda's works.


title:
Fourth-order Schr\"odinger type operator with unbounded coefficients in
  $L^2(\mathbb{R}^N)$

abstract:
  In this paper we study generation results in $L^2(\mathbb{R}^N)$ for the
fourth order Schr\"odinger type operator with unbounded coefficients of the
form $$A=a^{2} \Delta ^2+V^{2}$$ where $a(x)=1+|x|^{\alpha}$ and
$V=|x|^{\beta}$ with $\alpha>0$ and $\beta >(\alpha-2)^+$. We obtain that
$(-A,D(A))$ generates an analytic strongly continuous semigroup in
$L^2(\mathbb{R}^N)$ for $N\geq5$. Moreover, the maximal domain $D(A)$ can be
characterized for $N>8$ by the weighted Sobolev space
  \[ D_2(A)=\{u\in H^{4}(\mathbb{R}^N)\,:\,V^{2}u\in L^{2}(\mathbb{R}^N),
|x|^{2\alpha-h}D^{4-h}u\in L^{2}(\mathbb{R}^N) \text{ for } h=0,1,2,3,4\}. \]


title: Finding all Stable Matchings with Assignment Constraints

abstract:
  In this paper we consider stable matchings that are subject to assignment
constraints. These are matchings that require certain assigned pairs to be
included, insist that some other assigned pairs are not, and, importantly, are
stable. Our main contribution is an algorithm that determines when assignment
constraints are compatible with stability. Whenever a stable matching
consistent with the assignment constraints exists, our algorithm will output
all of them (in polynomial time per solution). This provides market designers
with (i) a tool to test the feasibility of stable matchings with assignment
constraints, and (ii) a separate tool to implement them.


title: Machine Learning aided Precise Indoor Positioning

abstract:
  This study describes a UWB and Machine Learning (ML)-based indoor positioning
system. We propose a simple mathematical strategy to create data to reduce the
job of measurements for fingerprint-based indoor localization systems. A
considerable number of measurements can be avoided this way. The paper compares
and contrasts the performance of four distinct models. Most test locations'
average error may be reduced to less than 150 mm using the best model.


title: The Complexity of Markov Equilibrium in Stochastic Games

abstract:
  We show that computing approximate stationary Markov coarse correlated
equilibria (CCE) in general-sum stochastic games is computationally
intractable, even when there are two players, the game is turn-based, the
discount factor is an absolute constant, and the approximation is an absolute
constant. Our intractability results stand in sharp contrast to normal-form
games where exact CCEs are efficiently computable. A fortiori, our results
imply that there are no efficient algorithms for learning stationary Markov CCE
policies in multi-agent reinforcement learning (MARL), even when the
interaction is two-player and turn-based, and both the discount factor and the
desired approximation of the learned policies is an absolute constant. In turn,
these results stand in sharp contrast to single-agent reinforcement learning
(RL) where near-optimal stationary Markov policies can be efficiently learned.
Complementing our intractability results for stationary Markov CCEs, we provide
a decentralized algorithm (assuming shared randomness among players) for
learning a nonstationary Markov CCE policy with polynomial time and sample
complexity in all problem parameters. Previous work for learning Markov CCE
policies all required exponential time and sample complexity in the number of
players.


title:
ECG Biometric Recognition: Review, System Proposal, and Benchmark
  Evaluation

abstract:
  Electrocardiograms (ECGs) have shown unique patterns to distinguish between
different subjects and present important advantages compared to other biometric
traits, such as difficulty to counterfeit, liveness detection, and ubiquity.
Also, with the success of Deep Learning technologies, ECG biometric recognition
has received increasing interest in recent years. However, it is not easy to
evaluate the improvements of novel ECG proposed methods, mainly due to the lack
of public data and standard experimental protocols. In this study, we perform
extensive analysis and comparison of different scenarios in ECG biometric
recognition. Both verification and identification tasks are investigated, as
well as single- and multi-session scenarios. Finally, we also perform single-
and multi-lead ECG experiments, considering traditional scenarios using
electrodes in the chest and limbs and current user-friendly wearable devices.
  In addition, we present ECGXtractor, a robust Deep Learning technology
trained with an in-house large-scale database and able to operate successfully
across various scenarios and multiple databases. We introduce our proposed
feature extractor, trained with multiple sinus-rhythm heartbeats belonging to
55,967 subjects, and provide a general public benchmark evaluation with
detailed experimental protocol. We evaluate the system performance over four
different databases: i) our in-house database, ii) PTB, iii) ECG-ID, and iv)
CYBHi. With the widely used PTB database, we achieve Equal Error Rates of 0.14%
and 2.06% in verification, and accuracies of 100% and 96.46% in identification,
respectively in single- and multi-session analysis. We release the source code,
experimental protocol details, and pre-trained models in GitHub to advance in
the field.


title: External environmental noise influences on Virgo during O3

abstract:
  Sources of geophysical noise, such as wind, sea waves and earthquakes, can
have an impact on gravitational wave interferometers causing sensitivity
worsening and gaps in data taking. During the 1-year long O3 run (April 1st
2019 to March 27th 2020), the Virgo Collaboration collected a statistically
significant dataset to study the response of the detector to a variety of
environmental conditions. We used these data to correlate environmental
parameters to global detector performance, such as observation range, duty
cycle and control losses. Where possible, we identified weaknesses in the
detector and we elaborated strategies to improve Virgo robustness against
external disturbances for the next run O4, planned to start in summer 2022. In
this article we present preliminary results of this study.


title: Labeling-Free Comparison Testing of Deep Learning Models

abstract:
  Various deep neural networks (DNNs) are developed and reported for their
tremendous success in multiple domains. Given a specific task, developers can
collect massive DNNs from public sources for efficient reusing and avoid
redundant work from scratch. However, testing the performance (e.g., accuracy
and robustness) of multiple DNNs and giving a reasonable recommendation that
which model should be used is challenging regarding the scarcity of labeled
data and demand of domain expertise. Existing testing approaches are mainly
selection-based where after sampling, a few of the test data are labeled to
discriminate DNNs. Therefore, due to the randomness of sampling, the
performance ranking is not deterministic. In this paper, we propose a
labeling-free comparison testing approach to overcome the limitations of
labeling effort and sampling randomness. The main idea is to learn a Bayesian
model to infer the models' specialty only based on predicted labels. To
evaluate the effectiveness of our approach, we undertook exhaustive experiments
on 9 benchmark datasets spanning in the domains of image, text, and source
code, and 165 DNNs. In addition to accuracy, we consider the robustness against
synthetic and natural distribution shifts. The experimental results demonstrate
that the performance of existing approaches degrades under distribution shifts.
Our approach outperforms the baseline methods by up to 0.74 and 0.53 on
Spearman's correlation and Kendall's $\tau$, respectively, regardless of the
dataset and distribution shift. Additionally, we investigated the impact of
model quality (accuracy and robustness) and diversity (standard deviation of
the quality) on the testing effectiveness and observe that there is a higher
chance of a good result when the quality is over 50\% and the diversity is
larger than 18\%.


title: Landau Theory for the Mpemba Effect Through Phase Transitions

abstract:
  The Mpemba effect describes the situation in which a hot system cools faster
than an identical copy that is initiated at a colder temperature. In many of
the experimental observations of the effect, e.g. in water and clathrate
hydrates, it is defined by the phase transition timing. However, none of the
theoretical investigations so far considered the timing of the phase
transition, and most of the abstract models used to explore the Mpemba effect
do not have a phase transition. We use the phenomenological Landau theory for
phase transitions to identify the second order phase transition time, and
demonstrate with a concrete example that a Mpemba effect can exist in such
models.


title:
The $W$ boson Mass and Muon $g-2$: Hadronic Uncertainties or New
  Physics?

abstract:
  There are now two single measurements of precision observables that have
major anomalies in the Standard Model: the recent CDF measurement of the $W$
mass shows a $7\sigma$ deviation and the Muon $g-2$ experiment at FNAL
confirmed a long-standing anomaly, implying a $4.2 \sigma$ deviation. Doubts
regarding new physics interpretations of these anomalies could stem from
uncertainties in the common hadronic contributions. We demonstrate that the two
anomalies pull the hadronic contributions in opposite directions by performing
electroweak fits in which the hadronic contribution was allowed to float. The
fits show that including the $g - 2$ measurement worsens the tension with the
CDF measurement and conversely that adjustments that alleviate the CDF tension
worsen the $g-2$ tension beyond $5 \sigma$. This means that if we adopt the CDF
$W$ mass measurement, the case for new physics in either the $W$ mass or muon
$g-2$ is inescapable regardless of the size of the SM hadronic contributions.
Lastly, we demonstrate that a mixed scalar leptoquark extension of the Standard
Model could explain both anomalies simultaneously.


title:
High-accuracy Hamiltonian learning via delocalized quantum state
  evolutions

abstract:
  In this Letter, we propose a method to learn the unknown Hamiltonian
governing the dynamics of a quantum many-body system, using measurements on a
single time-dependent state. We investigate the error scaling of our
reconstruction with respect to the experiment duration, measuring an
exponential decrease during the equilibration time. We prove that the accuracy
of the learning process is maximised for states that are delocalized in the
Hamiltonian eigenstates, capable of exploring a large sample of the Hilbert
space. Finally, we provide examples of our algorithm on simulated quantum
systems.


title:
SnapMode: An Intelligent and Distributed Large-Scale Fashion Image
  Retrieval Platform Based On Big Data and Deep Generative Adversarial Network
  Technologies

abstract:
  Fashion is now among the largest industries worldwide, for it represents
human history and helps tell the worlds story. As a result of the Fourth
Industrial Revolution, the Internet has become an increasingly important source
of fashion information. However, with a growing number of web pages and social
data, it is nearly impossible for humans to manually catch up with the ongoing
evolution and the continuously variable content in this domain. The proper
management and exploitation of big data can pave the way for the substantial
growth of the global economy as well as citizen satisfaction. Therefore,
computer scientists have found it challenging to handle e-commerce fashion
websites by using big data and machine learning technologies. This paper first
proposes a scalable focused Web Crawler engine based on the distributed
computing platforms to extract and process fashion data on e-commerce websites.
The role of the proposed platform is then described in developing a
disentangled feature extraction method by employing deep convolutional
generative adversarial networks (DCGANs) for content-based image indexing and
retrieval. Finally, the state-of-the-art solutions are compared, and the
results of the proposed approach are analyzed on a standard dataset. For the
real-life implementation of the proposed solution, a Web-based application is
developed on Apache Storm, Kafka, Solr, and Milvus platforms to create a
fashion search engine called SnapMode.


title: On the derivations of the Equation of Hydrostatic Equilibrium

abstract:
  We develop and present a geometrical and an analytical derivation of the
equation of hydrostatic equilibrium in spherically symmetric stars with a
generalized stress tensor. The analytical derivation is based on the
Navier-Cauchy equation. We also critically examine the derivation of this
equation found in textbooks on stellar astrophysics and show that there are
errors in many of the derivations presented in textbooks.


title:
Disorder, Multistability, and Mechanical Memory in an Ordered
  Metamaterial

abstract:
  Ordered mechanical systems typically have one or only a few stable rest
configurations, and hence are not usually considered useful for encoding
memory. On the other hand, frustrated magnetic systems can espouse an extensive
manifold of quasi-degenerate configurations. Multistable and history-dependent
responses can occur in mechanical systems with quenched disorder, such as
amorphous materials, crumpled sheets, or random spring networks. We introduce
an ordered, periodic mechanical metamaterial that exhibits an extensive set of
spatially disordered prestressed distortions as a consequence of geometric
frustration. This mechanical system encompasses continuous degrees of freedom,
and is hence richer than the magnetic artificial spin ice whose topological
structure it reproduces. We show how this system exhibits history-dependent
response, as the state that the system reaches following a set of external
manipulations depends on the order in which they were applied. Thus
multistability and potential to store complex memory emerge from geometric
frustration in an ordered mechanical lattice that creates its own disorder.


title: Galileo and a forgotten poem on the 1604 supernova

abstract:
  In October 1604, when SN1604, the last naked-eye visible supernova in our
Galaxy, exploded, Galileo Galilei was the professor of mathematics and
astronomy at the University of Padua, teaching the mechanics of planets. He was
therefore the figure of reference to whom all the doubts and questions that
such an apparition brought with it were addressed. The University of Padua
asked Galilei to outline the situation by exposing in three public conferences
his point of view in order to answer the many questions that raged among the
academic community and the common people. Three conferences that Galilei held
almost immediately between November and December in the Aula Magna of the Bo,
the central building of the University. A month after Galilei's lectures a
treatise on the supernova appeared in Padua. The unknown Antonio Lorenzini,
behind whose name it is easy to see the inspiration of Cesare Cremonini, an
Aristotelian professor of natural philosophy in Padua, published a booklet
entitled Discourse about the new star which debunked the conclusions of
Galilei. One month after was published in Padua by the same editor the Dialogo
de Cecco di Ronchitti da Bruzene in perpuosito de la stella nuova, a booklet in
Paduan dialect replying to Lorenzini; the booklet is attributed to Galileo
himself with the help of the monk Girolamo Spinelli. The Dialogo appears in the
national edition of Galilei's works; it does not appear instead a poem in
octaves by an unknown author, published as an appendix to the first edition of
1605 and immediately replaced in the second edition. We publish it here with a
comment.


title: Gradient estimates under integral Ricci bounds

abstract:
  In this paper we study $W^{1,p}$ global regularity estimates for solutions of
$\Delta u = f$ on Riemannian manifolds. Under integral (lower) bounds on the
Ricci tensor we prove the validity of $L^p$-gradient estimates of the form $||
\nabla u ||_{L^p} \le C (|| u ||_{L^p} + || \Delta u||_{L^p})$. We also
construct a counterexample which proves that the previously known constant
lower bounds on the Ricci curvature are optimal in the pointwise sense. The
relation between $L^p$-gradient estimates and different notions of Sobolev
spaces is also investigated.


title:
Learning Cooperative Dynamic Manipulation Skills from Human
  Demonstration Videos

abstract:
  This article proposes a method for learning and robotic replication of
dynamic collaborative tasks from offline videos. The objective is to extend the
concept of learning from demonstration (LfD) to dynamic scenarios, benefiting
from widely available or easily producible offline videos. To achieve this
goal, we decode important dynamic information, such as the Configuration
Dependent Stiffness (CDS), which reveals the contribution of arm pose to the
arm endpoint stiffness, from a three-dimensional human skeleton model. Next,
through encoding of the CDS via Gaussian Mixture Model (GMM) and decoding via
Gaussian Mixture Regression (GMR), the robot's Cartesian impedance profile is
estimated and replicated. We demonstrate the proposed method in a collaborative
sawing task with leader-follower structure, considering environmental
constraints and dynamic uncertainties. The experimental setup includes two
Panda robots, which replicate the leader-follower roles and the impedance
profiles extracted from a two-persons sawing video.


title:
Hierarchical and Multi-Scale Variational Autoencoder for Diverse and
  Natural Non-Autoregressive Text-to-Speech

abstract:
  This paper proposes a hierarchical and multi-scale variational
autoencoder-based non-autoregressive text-to-speech model (HiMuV-TTS) to
generate natural speech with diverse speaking styles. Recent advances in
non-autoregressive TTS (NAR-TTS) models have significantly improved the
inference speed and robustness of synthesized speech. However, the diversity of
speaking styles and naturalness are needed to be improved. To solve this
problem, we propose the HiMuV-TTS model that first determines the global-scale
prosody and then determines the local-scale prosody via conditioning on the
global-scale prosody and the learned text representation. In addition, we
improve the quality of speech by adopting the adversarial training technique.
Experimental results verify that the proposed HiMuV-TTS model can generate more
diverse and natural speech as compared to TTS models with single-scale
variational autoencoders, and can represent different prosody information in
each scale.


title:
Extended depth of field of diffraction limited imaging system using
  spatial light modulator based intensity compensated polarization coded
  aperture

abstract:
  Reducing the aperture size is a conventional technique to obtain enhanced
image resolution in optics but it is obscured by depleting illumination.
Polarization coded apertures (PCAs) can be employed to circumvent this critical
artifact. We experimentally demonstrate intensity compensated polarization
encrypted apertures, which are designed using the polarization modulation
characteristics of LC-SLM. PCAs are not limited by the aperture size and hence
far-field point spread function (PSF) can be more conveniently recorded using
these PCAs. We experimentally validate that Depth of field (DOF) of a
diffraction-limited lens and axial intensity of binary Fresnel zone plate
(BFZP) is enhanced using PCAs with nominal intensity loss.


title: Analysis and transformations of intensity in singing voice

abstract:
  In this paper we introduce a neural auto-encoder that transforms the voice
intensity in recordings of singing voice. Since most recordings of singing
voice are not annotated with voice intensity we propose a means to estimate the
relative voice intensity from the signal's timbre using a neural intensity
estimator. Two methods to overcome the unknown recording factor that relates
voice intensity to recorded signal power are given: The unknown recording
factor can either be learned alongside the weights of the intensity estimator,
or a special loss function based on the scalar product can be used to only
match the intensity contour of the recorded signal's power. The intensity
models are used to condition a previously introduced bottleneck auto-encoder
that disentangles its input, the mel-spectrogram, from the intensity. We
evaluate the intensity models by their consistency and by their fitness to
provide useful information to the auto-encoder. A perceptive test is carried
out that evaluates the perceived intensity change in transformed recordings and
the synthesis quality. The perceptive test confirms that changing the
conditional input changes the perceived intensity accordingly thus suggesting
that the proposed intensity models encode information about the voice
intensity.


title: Optical control of the complex phase of a quantum ground state amplitude

abstract:
  We discuss how coherent driving of a two-level quantum system can be used to
induce a complex phase on the ground state and we discuss its geometric and
dynamic contributions. While the global phase of a wave function has no
physical significance, coherent dynamics in a two-level subspace provides
relative phases and is an essential building block for more advanced dynamics
in larger systems. In this regard, we note that one must be careful with
intuitive accounts of the phase dynamics as it depends on the interaction
picture applied. To mitigate ambiguities in practical analyses, we suggest to
complement the Bloch sphere picture with the path taken by the ground state
amplitude in the complex plane, and we show how the two-level pure state
dynamics can serve as a starting point for the study of the dynamics explored
in three-level lambda systems, four-level tripod systems, and open quantum
systems.


title: Taxonomy of Attacks on Open-Source Software Supply Chains

abstract:
  The widespread dependency on open-source software makes it a fruitful target
for malicious actors, as demonstrated by recurring attacks. The complexity of
today's open-source supply chains results in a significant attack surface,
giving attackers numerous opportunities to reach the goal of injecting
malicious code into open-source artifacts that is then downloaded and executed
by victims.
  This work proposes a general taxonomy for attacks on open-source supply
chains, independent of specific programming languages or ecosystems, and
covering all supply chain stages from code contributions to package
distribution. Taking the form of an attack tree, it covers 107 unique vectors,
linked to 94 real-world incidents, and mapped to 33 mitigating safeguards.
  User surveys conducted with 17 domain experts and 134 software developers
positively validated the correctness, comprehensiveness and comprehensibility
of the taxonomy, as well as its suitability for various use-cases. Survey
participants also assessed the utility and costs of the identified safeguards,
and whether they are used.


title: On Projectivity in Markov Logic Networks

abstract:
  Markov Logic Networks (MLNs) define a probability distribution on relational
structures over varying domain sizes. Many works have noticed that MLNs, like
many other relational models, do not admit consistent marginal inference over
varying domain sizes. Furthermore, MLNs learnt on a certain domain do not
generalize to new domains of varied sizes. In recent works, connections have
emerged between domain size dependence, lifted inference and learning from
sub-sampled domains. The central idea to these works is the notion of
projectivity. The probability distributions ascribed by projective models
render the marginal probabilities of sub-structures independent of the domain
cardinality. Hence, projective models admit efficient marginal inference,
removing any dependence on the domain size. Furthermore, projective models
potentially allow efficient and consistent parameter learning from sub-sampled
domains. In this paper, we characterize the necessary and sufficient conditions
for a two-variable MLN to be projective. We then isolate a special model in
this class of MLNs, namely Relational Block Model (RBM). We show that, in terms
of data likelihood maximization, RBM is the best possible projective MLN in the
two-variable fragment. Finally, we show that RBMs also admit consistent
parameter learning over sub-sampled domains.


title:
Conserved quantities in general relativity -- the view from null
  infinity

abstract:
  In general relativity, an idealized distant observer is situated at future
null infinity where light rays emitted from the source approach. This article
concerns conserved quantities such as mass, energy-momentum, angular momentum,
and center of mass at future null infinity. The classical definitions of Bondi
mass at future null infinity ascertains the mass radiated away in gravitational
waves distinctively. However, the same question for other conserved quantities
such as angular momentum has been a subtle issue since the discovery of
"supertranslation ambiguity" in the 1960's. Recently, new definitions of
angular momentum and center of mass were proposed and proved to be free of such
ambiguity [12,14]. These new definitions arise as limits of the Chen-Wang-Yau
quasilocal conserved quantities, which are based on the theory of optimal
isometric embedding and quasilocal mass of Wang-Yau. It is the purpose of this
note to discuss these recent developments


title: Solutions of certain meta-Fibonacci recurrences

abstract:
  In this note we investigate the solutions of certain meta-Fibonacci
recurrences of the form $f(n)=f(n-f(n-1))+f(n-2)$ for various sets of initial
conditions. In the case when $f(n)=1$ for $n\leq 1$, we prove that the
resulting integer sequence is closely related to the function counting binary
partitions of a certain type.


title:
Light Non-Abelian Vector Dark Matter Produced Through Vector
  Misalignment

abstract:
  In this paper, we examine the evolution of light, feebly interacting
non-abelian dark gauge bosons dark matter in the early universe. In the region
of the parameter space where the dark gauge coupling is too small for the gauge
bosons to be produced efficiently via the freeze-in mechanism, the observed
relic density of dark matter can be obtained via the vector misalignment
mechanism. Vector misalignment has already been discussed in the case of dark
photon dark matter. In this study, we extend the arguments to non-abelian gauge
bosons. We specifically work on a dark sector scenario with a $SU(2)_R$ gauge
symmetry, spontaneously broken by a scalar $\phi$. For sufficiently light
$\lesssim O( MeV)$ and feebly interacting $g_R \lesssim 10^{-10}$ gauge boson,
$W_R$ become the dark matter candidates. The portal between the dark sector and
the Standard Model (SM) sector, in this study, is provided by the right-handed
electron charged under $SU(2)_R$. After discussing the evolution of $W_R$ in
the early universe, we study the constraints on the model and show the allowed
region of the parameter space. Furthermore, the benchmark that can explain the
XENON1T excess is demonstrated.


title:
Mel-spectrogram features for acoustic vehicle detection and speed
  estimation

abstract:
  The paper addresses acoustic vehicle detection and speed estimation from
single sensor measurements. We predict the vehicle's pass-by instant by
minimizing clipped vehicle-to-microphone distance, which is predicted from the
mel-spectrogram of input audio, in a supervised learning approach. In addition,
mel-spectrogram-based features are used directly for vehicle speed estimation,
without introducing any intermediate features. The results show that the
proposed features can be used for accurate vehicle detection and speed
estimation, with an average error of 7.87 km/h. If we formulate speed
estimation as a classification problem, with a 10 km/h discretization interval,
the proposed method attains the average accuracy of 48.7% for correct class
prediction and 91.0% when an offset of one class is allowed. The proposed
method is evaluated on a dataset of 304 urban-environment on-field recordings
of ten different vehicles.


title:
Multimodal Quasi-AutoRegression: Forecasting the visual popularity of
  new fashion products

abstract:
  Estimating the preferences of consumers is of utmost importance for the
fashion industry as appropriately leveraging this information can be beneficial
in terms of profit. Trend detection in fashion is a challenging task due to the
fast pace of change in the fashion industry. Moreover, forecasting the visual
popularity of new garment designs is even more demanding due to lack of
historical data. To this end, we propose MuQAR, a Multimodal
Quasi-AutoRegressive deep learning architecture that combines two modules: (1)
a multi-modal multi-layer perceptron processing categorical, visual and textual
features of the product and (2) a quasi-autoregressive neural network modelling
the "target" time series of the product's attributes along with the "exogenous"
time series of all other attributes. We utilize computer vision, image
classification and image captioning, for automatically extracting visual
features and textual descriptions from the images of new products. Product
design in fashion is initially expressed visually and these features represent
the products' unique characteristics without interfering with the creative
process of its designers by requiring additional inputs (e.g manually written
texts). We employ the product's target attributes time series as a proxy of
temporal popularity patterns, mitigating the lack of historical data, while
exogenous time series help capture trends among interrelated attributes. We
perform an extensive ablation analysis on two large scale image fashion
datasets, Mallzee and SHIFT15m to assess the adequacy of MuQAR and also use the
Amazon Reviews: Home and Kitchen dataset to assess generalisability to other
domains. A comparative study on the VISUELLE dataset, shows that MuQAR is
capable of competing and surpassing the domain's current state of the art by
4.65% and 4.8% in terms of WAPE and MAE respectively.


title: Riesz and Green energy on projective spaces

abstract:
  In this paper we study Riesz, Green and logarithmic energy on two-point
homogeneous spaces. More precisely we consider the real, the complex, the
quaternionic and the Cayley projective spaces. For each of these spaces we
provide upper estimates for the mentioned energies using determinantal point
processes. Moreover, we determine lower bounds for these energies of the same
order of magnitude.


title:
Disentangled Latent Speech Representation for Automatic Pathological
  Intelligibility Assessment

abstract:
  Speech intelligibility assessment plays an important role in the therapy of
patients suffering from pathological speech disorders. Automatic and objective
measures are desirable to assist therapists in their traditionally subjective
and labor-intensive assessments. In this work, we investigate a novel approach
for obtaining such a measure using the divergence in disentangled latent speech
representations of a parallel utterance pair, obtained from a healthy reference
and a pathological speaker. Experiments on an English database of Cerebral
Palsy patients, using all available utterances per speaker, show high and
significant correlation values (R = -0.9) with subjective intelligibility
measures, while having only minimal deviation (+-0.01) across four different
reference speaker pairs. We also demonstrate the robustness of the proposed
method (R = -0.89 deviating +-0.02 over 1000 iterations) by considering a
significantly smaller amount of utterances per speaker. Our results are among
the first to show that disentangled speech representations can be used for
automatic pathological speech intelligibility assessment, resulting in a
reference speaker pair invariant method, applicable in scenarios with only few
utterances available.


title:
Quantum Machine Learning Framework for Virtual Screening in Drug
  Discovery: a Prospective Quantum Advantage

abstract:
  Machine Learning (ML) for Ligand Based Virtual Screening (LB-VS) is an
important in-silico tool for discovering new drugs in a faster and
cost-effective manner, especially for emerging diseases such as COVID-19. In
this paper, we propose a general-purpose framework combining a classical
Support Vector Classifier (SVC) algorithm with quantum kernel estimation for
LB-VS on real-world databases, and we argue in favor of its prospective quantum
advantage. Indeed, we heuristically prove that our quantum integrated workflow
can, at least in some relevant instances, provide a tangible advantage compared
to state-of-art classical algorithms operating on the same datasets, showing
strong dependence on target and features selection method. Finally, we test our
algorithm on IBM Quantum processors using ADRB2 and COVID-19 datasets, showing
that hardware simulations provide results in line with the predicted
performances and can surpass classical equivalents.


title:
Proper tangential vector field for the wall shear stress evaluation in
  numerical blood flow simulations in human carotis tree

abstract:
  In numerical simulations of the cardiovascular system the realistic depiction
of the blood vessel is essential. The vessel morphology plays a critical role
in the underlying hemodynamical computation, for which 3D geometries are
obtained via imaging techniques from patients CTA or MRI scans. To evaluate the
patients current risks suffering vessel wall damage or an ischaemic stroke,
areas of interest are closely observed. A main indicator for these defects is
the stress the blood is exerting on the surrounding vessel tissue. The wall
shear stress (WSS) and its cyclic evaluation as the oscillatory shear index
(OSI) can be used as an indicator for future medical procedures. As a result of
low resolution imaging and craggy stenotic surface areas, the geometry model's
mesh is often non smooth on the surface areas. The automatically generated
tangential vector fields of such a surface topology are therefore corrupted.
Making an interpretation of the orientation dependent WSS not only difficult
but partially unreliable. In order to smooth out the erratic surface tangential
field we apply a projection of the vessel's center-line tangents of the to the
surface, based on a method introduced by Morbiducci et al. The validation of
our approach is made by comparing the orientation focused evaluation of the WSS
to the generic method with automatically generated tangential vectors. The
results of this work focus on the carotid bifurcation area and a distal
stenosis area in the internal carotid artery.


title:
Convolutional Neural Networks for Searching Superflares from Pixel-level
  Data of TESS

abstract:
  In this work, six convolutional neural networks (CNNs) have been trained
based on %different feature images and arrays from the database including
15,638 superflare candidates on solar-type stars, which are collected from the
three-years observations of Transiting Exoplanet Survey Satellite ({\em TESS}).
These networks are used to replace the artificially visual inspection, which
was a direct way to search for superflares, and exclude false positive events
in recent years. Unlike other methods, which only used stellar light curves to
search superflare signals, we try to identify superflares through {\em TESS}
pixel-level data with lower risks of mixing false positive events, and give
more reliable identification results for statistical analysis. The evaluated
accuracy of each network is around 95.57\%. After applying ensemble learning to
these networks, stacking method promotes accuracy to 97.62\% with 100\%
classification rate, and voting method promotes accuracy to 99.42\% with
relatively lower classification rate at 92.19\%. We find that superflare
candidates with short duration and low peak amplitude have lower identification
precision, as their superflare-features are hard to be identified. The database
including 71,732 solar-type stars and 15,638 superflare candidates from {\em
TESS} with corresponding feature images and arrays, and trained CNNs in this
work are public available.

categories: astro-ph.SR astro-ph.EP astro-ph.HE astro-ph.IM


title: Engagement Detection with Multi-Task Training in E-Learning Environments

abstract:
  Recognition of user interaction, in particular engagement detection, became
highly crucial for online working and learning environments, especially during
the COVID-19 outbreak. Such recognition and detection systems significantly
improve the user experience and efficiency by providing valuable feedback. In
this paper, we propose a novel Engagement Detection with Multi-Task Training
(ED-MTT) system which minimizes mean squared error and triplet loss together to
determine the engagement level of students in an e-learning environment. The
performance of this system is evaluated and compared against the
state-of-the-art on a publicly available dataset as well as videos collected
from real-life scenarios. The results show that ED-MTT achieves 6% lower MSE
than the best state-of-the-art performance with highly acceptable training time
and lightweight feature extraction.


title:
Effect of Tamm surface states on hot electron generation and Landau
  damping in nanostructures metal-semiconductor

abstract:
  The hot electron generation in plasmonic nanoparticles is the key to
efficient plasmonic photocatalysis. In the paper, we study theoretically for
the first time the effect of Tamm states (TSs) at the interface
metal-semiconductor on hot electron generation and Landau damping (LD) in metal
nanoparticles. TSs can lead to resonant hot electron generation and to the LD
rate enhanced by several times. The resonant hot electron generation is
reinforced by the transition absorption due to the jump of the permittivity at
the metal-semiconductor interface.


title:
Effective model for studying optical properties of lead-halide
  perovskites

abstract:
  We use general symmetry-based arguments to construct an effective model
suitable for studying optical properties of lead-halide perovskites. To build
the model, we identify an atomic-level interaction between electromagnetic
fields and the spin degree of freedom that should be added to a
minimally-coupled $\mathbf{k\cdot p}$ Hamiltonian. As an application, we study
two basic optical characteristics of the material: the Verdet constant and the
refractive index.


title:
A New Diffusive Representation for Fractional Derivatives and its
  Application

abstract:
  Diffusive representations of fractional derivatives have proven to be useful
tools in the construction of fast and memory efficient numerical methods for
solving fractional differential equations. A common challenge in many of the
known variants of this approach is that they require the numerical
approximation of some integrals over an unbounded integral whose integrand
decays rather slowly which implies that their numerical handling is difficult
and costly. We present a novel variant of such a diffusive representation. This
form also requires the numerical approximation of an integral over an unbounded
domain, but the integrand decays much faster. This allows to use well
established quadrature rules with much better convergence properties.


title:
Complex Networks Analysis of the Energy Landscape of the Low
  Autocorrelation Binary Sequences Problem

abstract:
  We provide an up-to-date view of the structure of the energy landscape of the
low autocorrelation binary sequences problem, a typical representative of the
$NP$-hard class. To study the landscape features of interest we use the local
optima network methodology through exhaustive extraction of the optima graphs
for problem sizes up to $24$. Several metrics are used to characterize the
networks: number and type of optima, optima basins structure, degree and
strength distributions, shortests paths to the global optima, and random
walk-based centrality of optima. Taken together, these metrics provide a
quantitative and coherent explanation for the difficulty of the low
autocorrelation binary sequences problem and provide information that could be
exploited by optimization heuristics for this problem, as well as for a number
of other problems having a similar configuration space structure.


title:
The connection between regular black holes in nonlinear electrodynamics
  and semi-classical dust collapse

abstract:
  There exist a correspondence between black holes in non linear
electrodynamics (NLED) and gravitational collapse of homogeneous dust with
semi-classical corrections in the strong curvature regime that to our knowledge
has not been noticed until now. We discuss the nature of such correspondence
and explore what insights may be gained from considering black holes in NLED in
the context of semi-classical dust collapse and vice-versa.


title: Fair and Argumentative Language Modeling for Computational Argumentation

abstract:
  Although much work in NLP has focused on measuring and mitigating
stereotypical bias in semantic spaces, research addressing bias in
computational argumentation is still in its infancy. In this paper, we address
this research gap and conduct a thorough investigation of bias in argumentative
language models. To this end, we introduce ABBA, a novel resource for bias
measurement specifically tailored to argumentation. We employ our resource to
assess the effect of argumentative fine-tuning and debiasing on the intrinsic
bias found in transformer-based language models using a lightweight
adapter-based approach that is more sustainable and parameter-efficient than
full fine-tuning. Finally, we analyze the potential impact of language model
debiasing on the performance in argument quality prediction, a downstream task
of computational argumentation. Our results show that we are able to
successfully and sustainably remove bias in general and argumentative language
models while preserving (and sometimes improving) model performance in
downstream tasks. We make all experimental code and data available at
https://github.com/umanlp/FairArgumentativeLM.


title:
Automatic detection of small-scale EUV brightenings observed by the
  Solar Orbiter/EUI

abstract:
  Context. Accurate detections of frequent small-scale extreme ultraviolet
(EUV) brightenings are essential to the investigation of the physical processes
heating the corona. Aims. We detected small-scale brightenings, termed
campfires, using their morphological and intensity structures as observed in
coronal EUV imaging observations for statistical analysis. Methods. We applied
a method based on Zernike moments and a support vector machine classifier to
automatically identify and track campfires observed by Solar Orbiter/Extreme
Ultraviolet Imager (EUI) and SDO/AIA. Results. This method detected 8678
campfires (with length scales between 400 km and 4000 km) from a sequence of 50
High Resolution EUV telescope (HRIEUV) 174{\AA} images. From 21 near
co-temporal AIA images covering the same field of view as EUI, we found 1131
campfires, 58% of which were also detected in HRIEUV images. In contrast, about
16% of campfires recognized in HRIEUV were detected by AIA. We obtain a
campfire birthrate of 2*10-16m-2s-1. About 40% of campfires show a duration
longer than 5 s, having been observed in at least two HRIEUV images. We find
that 27% of campfires were found in coronal bright points and the remaining 73%
have occurred out of coronal bright points. We detected 23 EUI campfires with a
duration greater than 245 s. We found that about 80% of campfires are formed at
supergranular boundaries, and the features with the highest total intensities
are generated at network junctions and intense H I Lyman-{\alpha} emission
regions observed by EUI/HRILya. The probability distribution functions for the
total intensity, peak intensity, and projected area of campfires follow a power
law behavior with absolute indices between 2 and 3. This self-similar behavior
is a possible signature of self-organization, or even self-organized
criticality, in the campfire formation process.


title:
A Generic Image Retrieval Method for Date Estimation of Historical
  Document Collections

abstract:
  Date estimation of historical document images is a challenging problem, with
several contributions in the literature that lack of the ability to generalize
from one dataset to others. This paper presents a robust date estimation system
based in a retrieval approach that generalizes well in front of heterogeneous
collections. we use a ranking loss function named smooth-nDCG to train a
Convolutional Neural Network that learns an ordination of documents for each
problem. One of the main usages of the presented approach is as a tool for
historical contextual retrieval. It means that scholars could perform
comparative analysis of historical images from big datasets in terms of the
period where they were produced. We provide experimental evaluation on
different types of documents from real datasets of manuscript and newspaper
images.


title: CO2 removal and 1.5{\deg}C: what, when, where, and how?

abstract:
  The international community aims to limit global warming to 1.5{\deg}C, but
little progress has been made towards a global, cost-efficient, and fair
climate mitigation plan to deploy carbon dioxide removal (CDR) at the Paris
Agreement's scale. Here, we investigate how different CDR options - AR, BECCS,
and DACCS - might be deployed to meet the Paris Agreement's CDR objectives. We
find that international cooperation in climate mitigation policy is key for
deploying the most cost-efficient CDR pathway - comprised of BECCS, mainly
(74%), and AR (26%) -, allowing to take the most advantage of regional
bio-geophysical resources and socio-economic factors, and time variations, and
therefore minimising costs. Importantly, with international cooperation, the
spatio-temporal evolution of the CDR pathway differs greatly from the
socio-economically fair regional allocation of the Paris Agreement's CDR
objectives. With limited, or no international cooperation, we find that the
likelihood of delivering these CDR objectives decreases, as deploying CDR
pathways becomes significantly more challenging and costly - particularly when
leading to the deployment of DACCS. Moreover, we show that developing
international cooperation policy instruments - such as an international market
for negative emissions trading - can deliver, simultaneously, cost-efficient
and equitable CDR at the Paris Agreement's scale, by incentivising
participating nations to meet their share of the Paris Agreement's CDR
objectives, whilst making up for the uneven distribution of CDR potentials
across the world. Crucially, we conclude that international cooperation is
imperative, as soon as possible, to preserve the feasibility and sustainability
of future CDR pathways, and ensure that future generations do not bear the
burden, increasingly costlier, of climate mitigation inaction.


title:
Key interaction patterns in proteins revealed by cluster expansion of
  the partition function

abstract:
  The native conformation of structured proteins is stabilized by a complex
network of interactions. We analyzed the elementary patterns that constitute
such network and ranked them according to their importance in shaping protein
sequence design. To achieve this goal, we employed a cluster expansion of the
partition function in the space of sequences and evaluated numerically the
statistical importance of each cluster. An important feature of this procedure
is that it is applied to a dense, finite system. We found that patterns that
contribute most to the partition function are cycles with even numbers of
nodes, while cliques are typically detrimental. Each cluster also gives a
contribute to the sequence entropy, which is a measure of the evolutionary
designability of a fold. We compared the entropies associated with different
interaction patterns to their abundances in the native structures of real
proteins.


title: Gate-Controlled Skyrmion Chirality

abstract:
  Magnetic skyrmions are localized chiral spin textures, which offer great
promise to store and process information at the nanoscale. In the presence of
asymmetric exchange interactions, their chirality, which governs their
dynamics, is generally considered as an intrinsic parameter set during the
sample deposition. In this work, we experimentally demonstrate that this key
parameter can be controlled by a gate voltage. We observed that the
current-induced skyrmion motion can be reversed by the application of a gate
voltage. This local and dynamical reversal of the skyrmion chirality is due to
a sign inversion of the interfacial Dzyaloshinskii-Moriya interaction that we
attribute to ionic migration of oxygen under gate voltage. Micromagnetic
simulations show that the chirality reversal is a continuous transformation, in
which the skyrmion is conserved. This gate-controlled chirality provides a
local and dynamical degree of freedom, yielding new functionalities to
skyrmion-based logic devices.


title: Approximating the parallel transport of an induced connection

abstract:
  Efficient numerical methods to approximate the parallel transport operators
of the induced connection on a sub-bundle of a vector bundle are presented.
These methods are simpler than naive applications of a Runge--Kutta algorithm,
and have accuracy up to order 4. They have the desirable property of being
insensitive to choices of trivialisation of the sub-bundle. The methods were
developed in order to solve a problem of computing skyrmions using the
Atiyah--Manton--Sutcliffe and Atiyah--Drinfeld--Hitchin--Manin constructions,
but are applicable to a broader range of problems in computational geometry.


title: Capacity Bounds for One-Bit MIMO Gaussian Channels with Analog Combining

abstract:
  The use of 1-bit analog-to-digital converters (ADCs) is seen as a promising
approach to significantly reduce the power consumption and hardware cost of
multiple-input multiple-output (MIMO) receivers. However, the nonlinear
distortion due to 1-bit quantization fundamentally changes the optimal
communication strategy and also imposes a capacity penalty to the system. In
this paper, the capacity of a Gaussian MIMO channel in which the antenna
outputs are processed by an analog linear combiner and then quantized by a set
of zero threshold ADCs is studied. A new capacity upper bound for the zero
threshold case is established that is tighter than the bounds available in the
literature. In addition, we propose an achievability scheme which configures
the analog combiner to create parallel Gaussian channels with phase
quantization at the output. Under this class of analog combiners, an algorithm
is presented that identifies the analog combiner and input distribution that
maximize the achievable rate. Numerical results are provided showing that the
rate of the achievability scheme is tight in the low signal-to-noise ratio
(SNR) regime. Finally, a new 1-bit MIMO receiver architecture which employs
analog temporal and spatial processing is proposed. The proposed receiver
attains the capacity in the high SNR regime.


title: Change in Conceptual Modeling and Systems Reconfiguration

abstract:
  In this paper, we explore the notion of change in systems and software
engineering, emphasizing its philosophical elucidation. Generally, it has been
claimed that change is so pervasive in systems that it almost defeats
description and analysis. In this article, we analyze change using the
conceptual modeling technique called a thinging machine (TM), which reflects
change in terms of the actions of creating, processing, releasing,
transferring, and receiving things. We illustrated change in TM modeling with
an example of a system s reconfiguration of business product handling designed
using business process modeling notation (BPMN). Then we analyze the notion of
change and compare its various definitions in philosophy. Specifically, we
examine Zeno s paradox that involves how to account for change and continuity
together in moving things. The problem is that we cannot assert that an arrow
is actually moving when it has been shot from a bow because the arrow needs to
be at a certain place at each point in time, which by definition cannot contain
any duration at all. In our analysis of this problem, we convert the arrow
trajectory into space units called thimacs. In the TM generic actions, two
types of change are identified: state and progression (PROCESS) changes.
Therefore, when an arrow flows to a TM machine that represents a trajectory
space unit, it is rejected, causing it to bounce away to the outside. That is,
the arrow is transferred, arrives, and is transferred back; therefore, the
arrow is never accepted into a thimac in the trajectory at any moment. The
result of such analysis seems to introduce a logical explanation for the notion
of movement discussed in Zeno s puzzles.


title:
Minimum Sample Size Allocation in Stratified Sampling Under Constraints
  on Variance and Strata Sample Sizes

abstract:
  We derive optimality conditions for the optimal sample allocation problem,
formulated as the determination of the fixed strata sample sizes that minimize
total sample size, under assumed level of the variance of the stratified
$\pi$-estimator and one-sided upper bounds imposed on strata sample sizes. In
this paper, this problem is considered in the context of general stratified
sampling scheme that includes simple random sampling without replacement design
within strata as a special case. Based on established optimality conditions, we
create a new algorithm, the LrNa, that solves the allocation problem defined
above. This new algorithm has its origin in popular recursive Neyman allocation
procedure, the rNa, that is used to solve classical optimal sample allocation
problem (i.e. minimization of the $\pi$-estimator's variance under fixed total
sample size) with only one-sided upper bounds constraints imposed on strata
sample sizes (see e.g. S\"arndal, Swensson, and Wretman (1992, Remark 12.7.1,
p. 466), or Weso{\l}owski, Wieczorkowski, and W\'ojciak (2021)). Ready-to-use
R-implementation of the LrNa is available on CRAN repository at
https://cran.r-project.org/web/packages/stratallo.


title:
Improved proton-transfer barriers with van der Waals density
  functionals: Role of repulsive non-local correlation

abstract:
  Proton-transfer (PT) between organic complexes is a common and important
biochemical process. Unfortunately, PT energy barriers are difficult to
accurately predict using density functional theory (DFT); in particular, the
generalized gradient approximation (GGA) tends to underestimate PT barriers.
Moreover, PT typically occurs in environments where dispersion forces
contribute to the cohesion of the system; thus, a suitable exchange-correlation
functional should accurately describe both dispersion forces and PT barriers.
This paper provides benchmark results for the PT barriers of several density
functionals including several variants of the van der Waals density functional
(vdWDF). The benchmark set comprises small organic molecules with inter- and
intra-molecular PT. The results show that replacing GGA correlation with a
fully non-local vdW-DF correlation increases the PT barriers, making it closer
to the quantum chemical reference values. In contrast, including nonlocal
correlations with the Vydrov-Voorhis (VV) method or dispersion-corrections at
the DFT-D3 or the Tkatchenko-Scheffler (TS) level has barely any impact on the
PT barriers. Hybrid functionals also increase and improve the energies and the
best performance is provided by a hybrid version of the consistent-exchange van
der Waals density functional vdW-DF-cx. For the formic acid dimer PT system, we
analyzed the GGA exchange and non-local correlation contributions. The analysis
shows that the repulsive part of the non-local correlation kernel plays a key
role in the PT energy barriers predicted with vdW-DF.


title: Measurement of static light scattering of bio-particles

abstract:
  Light scattering by small particles is one of the most prevailing and
non-invasive technique for examining the properties of particulate systems
chiefly of biological origin. The sub-micron particles including the
bio-particles were so chosen because of their importance in biology and
biomedical sciences. Light scattering investigation from homogenous, pathogenic
Pseudomonas aeruginosa and non-pathogenic Mycobacterium smegmatis was carried
out at two different wavelength of incident light, by using an original
designed and fabricated polar and azimuth-dependent light scattering setup.
Theoretical scattering plots were generated by using T-matrix approach to
validate our analyses. Simulations of light scattering of these particles were
also carried out using a novel Monte- Carlo simulation technique. The relation
between experimental, theoretical and simulated result is presented in this
paper. We have studied Pseudomonas aeruginosa and Mycobacterium smegmatis, and
attempted for prospect of observing its morphological property by using light
scattering tool.


title:
Influence of refractive index in light scattering measurements of
  biological particles

abstract:
  Investigations on diverse particulate matter can be carried out by
non-invasive and non-destructive analytical means of light scattering
technique. Exploration of small particles using this tool must address the
concept of refractive index accurately. Interpretation of light scattering
results for quantification of morphology of biological particles like viruses
and bacterial cells can only be validated by exact evidence of its refractive
index. Size and shape quantification of such particles by collecting
information from the scattered light, was reported earlier by many researchers
but parameter like refractive index was considered as a standard value employed
from literature or selected as a median value of biological particles which are
alike. A strong analysis is needed on inclusion of appropriate value of
refractive index for revelation of light scattering results. This important
perception is otherwise not considered strongly in similar works reported
earlier. We primarily took an attempt to find the influence of refractive index
in light scattering studies with allusion to biological particles like
Staphylococcus aureus, Escherichia coli, Corona virus (SARS COV 2) etc.


title: Transformation cost spectrum for irregularly sampled time series

abstract:
  Irregularly sampled time series analysis is a common problem in various
disciplines. Since conventional methods are not directly applicable to
irregularly sampled time series, a common interpolation approach is used;
however, this causes data distortion and consequently biases further analyses.
We propose a method that yields a regularly sampled time series spectrum of
costs with minimum information loss. Each time series in this spectrum is a
stationary series and acts as a difference filter. The transformation costs
approach derives the differences between consecutive and arbitrarily sized
segments. After obtaining regular sampling, recurrence plot analysis is
performed to distinguish regime transitions. The approach is applied to a
prototypical model to validate its performance and to different palaeoclimate
proxy data sets located around Africa to identify critical climate transition
periods during the last 5 million years and their characteristic properties.


title: Ontology Matching Through Absolute Orientation of Embedding Spaces

abstract:
  Ontology matching is a core task when creating interoperable and linked open
datasets. In this paper, we explore a novel structure-based mapping approach
which is based on knowledge graph embeddings: The ontologies to be matched are
embedded, and an approach known as absolute orientation is used to align the
two embedding spaces. Next to the approach, the paper presents a first,
preliminary evaluation using synthetic and real-world datasets. We find in
experiments with synthetic data, that the approach works very well on similarly
structured graphs; it handles alignment noise better than size and structural
differences in the ontologies.


title:
Anomalous dispersion and negative-mass dynamics of exciton polaritons in
  an atomically thin semiconductor

abstract:
  Dispersion engineering is a powerful and versatile tool that can vary the
speed of light signals and induce negative-mass effects in the dynamics of
electrons, quasiparticles, and quantum fluids. Here, we demonstrate that
dissipative coupling between bound electron-hole pairs (excitons) and photons
in an optical microcavity can lead to the formation of exciton polaritons with
an inverted dispersion of the lower polariton branch and hence, a negative
mass. We perform a direct measurement of the anomalous dispersion in an
atomically thin WS$_2$ crystal embedded in a planar microcavity, and
demonstrate that the propagation direction of the negative-mass polaritons is
opposite to their momentum. Our study introduces a new concept of non-Hermitian
dispersion engineering for exciton polaritons and shows a pathway for realising
new phases of quantum matter in a solid state.


title:
Checking HateCheck: a cross-functional analysis of behaviour-aware
  learning for hate speech detection

abstract:
  Behavioural testing -- verifying system capabilities by validating
human-designed input-output pairs -- is an alternative evaluation method of
natural language processing systems proposed to address the shortcomings of the
standard approach: computing metrics on held-out data. While behavioural tests
capture human prior knowledge and insights, there has been little exploration
on how to leverage them for model training and development. With this in mind,
we explore behaviour-aware learning by examining several fine-tuning schemes
using HateCheck, a suite of functional tests for hate speech detection systems.
To address potential pitfalls of training on data originally intended for
evaluation, we train and evaluate models on different configurations of
HateCheck by holding out categories of test cases, which enables us to estimate
performance on potentially overlooked system properties. The fine-tuning
procedure led to improvements in the classification accuracy of held-out
functionalities and identity groups, suggesting that models can potentially
generalise to overlooked functionalities. However, performance on held-out
functionality classes and i.i.d. hate speech detection data decreased, which
indicates that generalisation occurs mostly across functionalities from the
same class and that the procedure led to overfitting to the HateCheck data
distribution.


title:
C-NMT: A Collaborative Inference Framework for Neural Machine
  Translation

abstract:
  Collaborative Inference (CI) optimizes the latency and energy consumption of
deep learning inference through the inter-operation of edge and cloud devices.
Albeit beneficial for other tasks, CI has never been applied to the sequence-
to-sequence mapping problem at the heart of Neural Machine Translation (NMT).
In this work, we address the specific issues of collaborative NMT, such as
estimating the latency required to generate the (unknown) output sequence, and
show how existing CI methods can be adapted to these applications. Our
experiments show that CI can reduce the latency of NMT by up to 44% compared to
a non-collaborative approach.


title:
Confidence Score for Unsupervised Foreground Background Separation of
  Document Images

abstract:
  Foreground-background separation is an important problem in document image
analysis. Popular unsupervised binarization methods (such as the Sauvola's
algorithm) employ adaptive thresholding to classify pixels as foreground or
background. In this work, we propose a novel approach for computing confidence
scores of the classification in such algorithms. This score provides an insight
of the confidence level of the prediction. The computational complexity of the
proposed approach is the same as the underlying binarization algorithm. Our
experiments illustrate the utility of the proposed scores in various
applications like document binarization, document image cleanup, and texture
addition.


title: Probing Fundamental Physics With Multi-Modal Cosmic Ray Events

abstract:
  The Horizon-T experiment in Tien Shan is based on the idea of measuring the
time at which EAS disc passes the observation level with nanosecond accuracy.
The detector system consists of ten charged particles registration points
located at distances of up to several hundred meters from each other. The
points are equipped with detectors based on registration of Cherenkov radiation
in glass and registration of scintillation light in polystyrene. The detectors
register the arrival times of charged particles at the observation level with a
resolution of ~2 ns, as required to study the spatial and temporal
characteristics of the EAS and the structure of the multi-modal events
specifically. Over the period of the Horizon-T data taking since 2017 to
present, a large number of multi-modal events were detected. The data has
presented numerous challenges that show the direction towards the further
development of the detector system and of the analysis methods and techniques
that could be applied to these multi-modal events.


title:
KCD: Knowledge Walks and Textual Cues Enhanced Political Perspective
  Detection in News Media

abstract:
  Political perspective detection has become an increasingly important task
that can help combat echo chambers and political polarization. Previous
approaches generally focus on leveraging textual content to identify stances,
while they fail to reason with background knowledge or leverage the rich
semantic and syntactic textual labels in news articles. In light of these
limitations, we propose KCD, a political perspective detection approach to
enable multi-hop knowledge reasoning and incorporate textual cues as
paragraph-level labels. Specifically, we firstly generate random walks on
external knowledge graphs and infuse them with news text representations. We
then construct a heterogeneous information network to jointly model news
content as well as semantic, syntactic and entity cues in news articles.
Finally, we adopt relational graph neural networks for graph-level
representation learning and conduct political perspective detection. Extensive
experiments demonstrate that our approach outperforms state-of-the-art methods
on two benchmark datasets. We further examine the effect of knowledge walks and
textual cues and how they contribute to our approach's data efficiency.


abstract:
  Distributed-order time-fractional wave equations appear in the modeling of
wave propagation in viscoelastic media. The material characteristics of the
medium are modeled through constitutive functions or distributions in the
distributed-order constitutive law. In this work we propose to take positive
Radon measures for the constitutive "functions".
  First, we derive a thermodynamical restriction on the constitutive measures
which is easy to check, and therefore suitable for applications. Then we prove
that the setting with measures in combination with the derived thermodynamical
restriction guarantee existence and uniqueness of solutions for the
distributed-order fractional wave equation. We further discuss the support and
regularity of the fundamental solution, and conclude with a discussion on wave
velocities.


title:
Characterizing nonclassical correlations of tensorizing states in a
  bilocal scenario

abstract:
  In the present paper, we attempt to address the question of "can tensorizing
states have quantum advantages?". To answer this question, we exploit the
notion of measurement-induced nonlocality (MIN) and advocate a fidelity based
nonbilocal measure to capture the nonlocal effects of tensorizing states due to
locally invariant von Neumann projective measurements. We show that the
properties of the fidelity based nonbilocal measure are retrieved from that of
MIN. Analytically, we evaluate the nonbilocal measure for any arbitrary pure
state. The upper bounds of the nonbilocal measure based on fidelity are also
obtained in terms of eigenvalues of correlation matrix. As an illustration, we
have computed the nonbilocality for some popular input states.


title:
Efficient tracking of team sport players with few game-specific
  annotations

abstract:
  One of the requirements for team sports analysis is to track and recognize
players. Many tracking and reidentification methods have been proposed in the
context of video surveillance. They show very convincing results when tested on
public datasets such as the MOT challenge. However, the performance of these
methods are not as satisfactory when applied to player tracking. Indeed, in
addition to moving very quickly and often being occluded, the players wear the
same jersey, which makes the task of reidentification very complex. Some recent
tracking methods have been developed more specifically for the team sport
context. Due to the lack of public data, these methods use private datasets
that make impossible a comparison with them. In this paper, we propose a new
generic method to track team sport players during a full game thanks to few
human annotations collected via a semi-interactive system. Non-ambiguous
tracklets and their appearance features are automatically generated with a
detection and a reidentification network both pre-trained on public datasets.
Then an incremental learning mechanism trains a Transformer to classify
identities using few game-specific human annotations. Finally, tracklets are
linked by an association algorithm. We demonstrate the efficiency of our
approach on a challenging rugby sevens dataset. To overcome the lack of public
sports tracking dataset, we publicly release this dataset at
https://kalisteo.cea.fr/index.php/free-resources/. We also show that our method
is able to track rugby sevens players during a full match, if they are
observable at a minimal resolution, with the annotation of only 6 few seconds
length tracklets per player.


title: Giant diffusion of nanomechanical rotors in a tilted washboard potential

abstract:
  We present an experimental realization of a biased optical periodic potential
in the low friction limit. The noise-induced bistability between locked
(torsional) and running (spinning) states in the rotational motion of a
nanodumbbell is driven by an elliptically polarized light beam tilting the
angular potential. By varying the gas pressure around the point of maximum
intermittency, the rotational effective diffusion coefficient increases by more
than 3 orders of magnitude over free-space diffusion. These experimental
results are in agreement with a simple two-state model that is derived from the
Langevin equation through using timescale separation. Our work provides a new
experimental platform to study the weak thermal noise limit for diffusion in
this system.


title:
Development of a High Intensity Neutron Source at the European
  Spallation Source: The HighNESS project

abstract:
  The European Spallation Source (ESS), presently under construction in Lund,
Sweden, is a multidisciplinary international laboratory that will operate the
world's most powerful pulsed neutron source. Supported by a 3M Euro Research
and Innovation Action within the EU Horizon 2020 program, a design study
(HighNESS) is now underway to develop a second neutron source below the
spallation target. Compared to the first source, located above the spallation
target and designed for high cold and thermal brightness, the new source will
provide higher intensity, and a shift to longer wavelengths in the spectral
regions of cold (2 /- 20 {\AA}), very cold (VCN, 10 /- 120 {\AA}), and ultra
cold (UCN, > 500 {\AA}) neutrons. The core of the second source will consist of
a large liquid deuterium moderator to deliver a high flux of cold neutrons and
to serve secondary VCN and UCN sources, for which different options are under
study. The features of these new sources will boost several areas of condensed
matter research and will provide unique opportunities in fundamental physics.
Part of the HighNESS project is also dedicated to the development of future
instruments that will make use of the new source and will complement the
initial suite of instruments in construction at ESS. The HighNESS project
started in October 2020. In this paper, the ongoing developments and the
results obtained in the first year are described.


title:
Transformation-Invariant Learning of Optimal Individualized Decision
  Rules with Time-to-Event Outcomes

abstract:
  In many important applications of precision medicine, the outcome of interest
is time to an event (e.g., death, relapse of disease) and the primary goal is
to identify the optimal individualized decision rule (IDR) to prolong survival
time. Existing work in this area have been mostly focused on estimating the
optimal IDR to maximize the We propose a new robust framework for estimating an
optimal static or dynamic IDR with time-to-event outcomes based on an
easy-to-interpret quantile criterion. The new method does not need to specify
an outcome regression model and is robust for heavy-tailed distribution. The
estimation problem corresponds to a nonregular M-estimation problem with both
finite and infinite-dimensional nuisance parameters. Employing advanced
empirical process techniques, we establish the statistical theory of the
estimated parameter indexing the optimal IDR. Furthermore, we prove a novel
result that the proposed approach can consistently estimate the optimal value
function under mild conditions even when the optimal IDR is non-unique, which
happens in the challenging setting of exceptional laws. We also propose a
smoothed resampling procedure for inference. The proposed methods are
implemented in the R-package QTOCen. We demonstrate the performance of the
proposed new methods via extensive Monte Carlo studies and a real data
application.restricted mean survival time in the population.


title: Identifying Ambiguous Similarity Conditions via Semantic Matching

abstract:
  Rich semantics inside an image result in its ambiguous relationship with
others, i.e., two images could be similar in one condition but dissimilar in
another. Given triplets like "aircraft" is similar to "bird" than "train",
Weakly Supervised Conditional Similarity Learning (WS-CSL) learns multiple
embeddings to match semantic conditions without explicit condition labels such
as "can fly". However, similarity relationships in a triplet are uncertain
except providing a condition. For example, the previous comparison becomes
invalid once the conditional label changes to "is vehicle". To this end, we
introduce a novel evaluation criterion by predicting the comparison's
correctness after assigning the learned embeddings to their optimal conditions,
which measures how much WS-CSL could cover latent semantics as the supervised
model. Furthermore, we propose the Distance Induced Semantic COndition
VERification Network (DiscoverNet), which characterizes the instance-instance
and triplets-condition relations in a "decompose-and-fuse" manner. To make the
learned embeddings cover all semantics, DiscoverNet utilizes a set module or an
additional regularizer over the correspondence between a triplet and a
condition. DiscoverNet achieves state-of-the-art performance on benchmarks like
UT-Zappos-50k and Celeb-A w.r.t. different criteria.


title:
GPSAF: A Generalized Probabilistic Surrogate-Assisted Framework for
  Constrained Single- and Multi-objective Optimization

abstract:
  Significant effort has been made to solve computationally expensive
optimization problems in the past two decades, and various optimization methods
incorporating surrogates into optimization have been proposed. Most research
focuses on either exploiting the surrogate by defining a utility optimization
problem or customizing an existing optimization method to use one or multiple
approximation models. However, only a little attention has been paid to generic
concepts applicable to different types of algorithms and optimization problems
simultaneously. Thus this paper proposes a generalized probabilistic
surrogate-assisted framework (GPSAF), applicable to a broad category of
unconstrained and constrained, single- and multi-objective optimization
algorithms. The idea is based on a surrogate assisting an existing optimization
method. The assistance is based on two distinct phases, one facilitating
exploration and another exploiting the surrogates. The exploration and
exploitation of surrogates are automatically balanced by performing a
probabilistic knockout tournament among different clusters of solutions. A
study of multiple well-known population-based optimization algorithms is
conducted with and without the proposed surrogate assistance on single- and
multi-objective optimization problems with a maximum solution evaluation budget
of 300 or less. The results indicate the effectiveness of applying GPSAF to an
optimization algorithm and the competitiveness with other surrogate-assisted
algorithms.


title: Reliable Wireless Networking via Soft-Source Information Combining

abstract:
  This paper puts forth a multi-stream networking paradigm, referred to as
soft-source-information-combining (SSIC), to support wireless Internet of
Things (IoT) applications with ultra-reliability requirements. For SSIC
networking, an SSIC dispatcher at the source dispatches duplicates of packets
over multiple streams, which may be established over different physical
wireless networks. If a packet on a stream cannot be decoded due to wireless
interference or noise, the decoder makes available the packet's soft
information. An aggregator then combines the soft information of the duplicates
to boost reliability. Of importance are two challenges: i) how to descramble
the scrambled soft information from different streams to enable correct SSIC;
ii) the construct of an SSIC dispatching and aggregation framework compatible
with commercial network interface cards (NICs) and TCP/IP networks. To address
the challenges, we put forth: i) a soft descrambling (SD) method to minimize
the bit-error rate (BER) and packet-error rate (PER) at the SSIC's output; ii)
an SSIC networking architecture readily deployable over today's TCP/IP networks
without specialized NICs. For concept proving and experimentation, we realized
an SSIC system over two Wi-Fi's physical paths in such a way that all legacy
TCP/IP applications can enjoy the reliability brought forth by SSIC without
modification. Experiments over our testbed corroborate the effectiveness of
SSIC in lowering the packet delivery failure rate and the possibility of SSIC
in providing 99.99% reliable packet delivery for short-range communication.


title: Similarity of materials and data-quality assessment by fingerprinting

abstract:
  Identifying similar materials, i.e., those sharing a certain property or
feature, requires interoperable data of high quality. It also requires means to
measure similarity. We demonstrate how a spectral fingerprint as a descriptor,
combined with a similarity metric, can be used for establishing quantitative
relationships between materials data, thereby serving multiple purposes. This
concerns, for instance, the identification of materials exhibiting electronic
properties similar to a chosen one. The same approach can be used for assessing
uncertainty in data that potentially come from different sources. Selected
examples show how to quantify differences between measured optical spectra or
the impact of methodology and computational parameters on calculated
properties, like the the density of states or excitonic spectra. Moreover,
combining the same fingerprint with a clustering approach allows us to explore
materials spaces in view of finding (un)expected trends or patterns. In all
cases, we provide physical reasoning behind the findings of the automatized
assessment of data.


title: The Power of Filling in Balanced Allocations

abstract:
  It is well known that if $m$ balls (jobs) are placed sequentially into $n$
bins (servers) according to the One-Choice protocol $-$ choose a single bin in
each round and allocate one ball to it $-$ then, for $m \gg n$, the gap between
the maximum and average load diverges. Many refinements of the One-Choice
protocol have been studied that achieve a gap that remains bounded by a
function of $n$, for any $m$. However most of these variations, such as
Two-Choice, are less sample-efficient than One-Choice, in the sense that for
each allocated ball more than one sample is needed (in expectation).
  We introduce a new class of processes which are primarily characterized by
"filling" underloaded bins. A prototypical example is the Packing process: At
each round we only take one bin sample, if the load is below the average load,
then we place as many balls until the average load is reached; otherwise, we
place only one ball. We prove that for any process in this class the gap
between the maximum and average load is $\mathcal{O}(\log n)$ for any number of
balls $m$. For the Packing process, we also prove a matching lower bound. We
also prove that the Packing process is more sample-efficient than One-Choice,
that is, it allocates on average more than one ball per sample. Finally, we
also demonstrate that the upper bound of $\mathcal{O}(\log n)$ on the gap can
be extended to the Caching process (a.k.a. memory protocol) studied by
Mitzenmacher, Prabhakar and Shah (2002).


title: Improving Tokenisation by Alternative Treatment of Spaces

abstract:
  Tokenisation is the first step in almost all NLP tasks, and state-of-the-art
transformer-based language models all use subword tokenisation algorithms to
process input text. Existing algorithms have problems, often producing
tokenisations of limited linguistic validity, and representing equivalent
strings differently depending on their position within a word. We hypothesise
that these problems hinder the ability of transformer-based models to handle
complex words, and suggest that these problems are a result of allowing tokens
to include spaces. We thus experiment with an alternative tokenisation approach
where spaces are always treated as individual tokens. Specifically, we apply
this modification to the BPE and Unigram algorithms. We find that our modified
algorithms lead to improved performance on downstream NLP tasks that involve
handling complex words, whilst having no detrimental effect on performance in
general natural language understanding tasks. Intrinsically, we find our
modified algorithms give more morphologically correct tokenisations, in
particular when handling prefixes. Given the results of our experiments, we
advocate for always treating spaces as individual tokens as an improved
tokenisation method.