Abstract:
  In this review, we describe the application of one of the most popular deep
learning-based language models - BERT. The paper describes the mechanism of
operation of this model, the main areas of its application to the tasks of text
analytics, comparisons with similar models in each task, as well as a
description of some proprietary models. In preparing this review, the data of
several dozen original scientific articles published over the past few years,
which attracted the most attention in the scientific community, were
systematized. This survey will be useful to all students and researchers who
want to get acquainted with the latest advances in the field of natural
language text analysis.


Abstract:
  We revisit the possibilities of accommodating the experimental indications of
the lepton flavor universality violation in $b$-hadron decays in the minimal
scenarios in which the Standard Model is extended by the presence of a single
$\mathcal{O}(1\,\mathrm{TeV})$ leptoquark state. To do so we combine the most
recent low energy flavor physics constraints, including
$R_{K^{(\ast)}}^\mathrm{exp}$ and $R_{D^{(\ast)}}^\mathrm{exp}$, and combine
them with the bounds on the leptoquark masses and their couplings to quarks and
leptons as inferred from the direct searches at the LHC and the studies of the
large $p_T$ tails of the $pp\to \ell\ell$ differential cross section. We find
that none of the scalar leptoquarks of $m_\mathrm{LQ} \simeq 1\div 2$ TeV can
accommodate the $B$-anomalies alone. Only the vector leptoquark, known as
$U_1$, can provide a viable solution which, in the minimal setup, provides an
interesting prediction, i.e. a lower bound to the lepton flavor violating $b\to
s\mu^\pm\tau^\mp$ decay modes, such as $\mathcal{B}(B\to K\mu\tau) \gtrsim
0.7\times 10^{-7}$.


Abstract:
  This work aims to tackle the challenging heterogeneous graph encoding problem
in the text-to-SQL task. Previous methods are typically node-centric and merely
utilize different weight matrices to parameterize edge types, which 1) ignore
the rich semantics embedded in the topological structure of edges, and 2) fail
to distinguish local and non-local relations for each node. To this end, we
propose a Line Graph Enhanced Text-to-SQL (LGESQL) model to mine the underlying
relational features without constructing meta-paths. By virtue of the line
graph, messages propagate more efficiently through not only connections between
nodes, but also the topology of directed edges. Furthermore, both local and
non-local relations are integrated distinctively during the graph iteration. We
also design an auxiliary task called graph pruning to improve the
discriminative capability of the encoder. Our framework achieves
state-of-the-art results (62.8% with Glove, 72.0% with Electra) on the
cross-domain text-to-SQL benchmark Spider at the time of writing.


Abstract:
  In this note, we give an elementary proof of the result given by Schenzel
that there are functorial isomorphisms between local cohomology groups and
\v{C}ech cohomology groups, by using weakly proregular sequences. In [Sch03],
he used notions of derived category theory in his proof, but we do not use them
in this paper.


Abstract:
  We consider the 1d CFT defined by the half-BPS Wilson line in planar
$\mathcal{N}=4$ super Yang-Mills. Using analytic bootstrap methods we derive
the four-point function of the super-displacement operator at fourth order in a
strong coupling expansion. Via AdS/CFT, this corresponds to the first
three-loop correlator in AdS ever computed. To do so we address the operator
mixing problem by considering a family of auxiliary correlators. We further
extract the anomalous dimension of the lightest non-protected operator and find
agreement with the integrability-based numerical result of Grabner, Gromov and
Julius.


Abstract:
  Open source development, to a great extent, is a type of social movement in
which shared ideologies play critical roles. For participants of open source
development, ideology determines how they make sense of things, shapes their
thoughts, actions, and interactions, enables rich social dynamics in their
projects and communities, and hereby realizes profound impacts at both
individual and organizational levels. While software engineering researchers
have been increasingly recognizing ideology's importance in open source
development, the notion of "ideology" has shown significant ambiguity and
vagueness, and resulted in theoretical and empirical confusion. In this
article, we first examine the historical development of ideology's
conceptualization, and its theories in multiple disciplines. Then, we review
the extant software engineering literature related to ideology. We further
argue the imperatives of developing an empirical theory of ideology in open
source development, and propose a research agenda for developing such a theory.
How such a theory could be applied is also discussed.


Abstract:
  We study the possibilities of factorizations of products of nuclear operators
of different types through the Schatten-von Neumann operators in Hilbert spaces
with giving some applications to eigenvalues problems.


Abstract:
  Understanding multivariate extreme events play a crucial role in managing the
risks of complex systems since extremes are governed by their own mechanisms.
Conditional on a given variable exceeding a high threshold (e.g.\ traffic
intensity), knowing which high-impact quantities (e.g\ air pollutant levels)
are the most likely to be extreme in the future is key. This article
investigates the contribution of marginal extreme events on future extreme
events of related quantities. We propose an Extreme Event Propagation framework
to maximise counterfactual causation probabilities between a known cause and
future high-impact quantities. Extreme value theory provides a tool for
modelling upper tails whilst vine copulas are a flexible device for capturing a
large variety of joint extremal behaviours. We optimise for the probabilities
of causation and apply our framework to a London road traffic and air
pollutants dataset. We replicate documented atmospheric mechanisms beyond
linear relationships. This provides a new tool for quantifying the propagation
of extremes in a large variety of applications.


Abstract:
  This paper establishes an extended representation theorem for unit-root VARs.
A specific algebraic technique is devised to recover stationarity from the
solution of the model in the form of a cointegrating transformation. Closed
forms of the results of interest are derived for integrated processes up to the
4-th order. An extension to higher-order processes turns out to be within the
reach on an induction argument.


Abstract:
  Acoustic Echo Cancellation (AEC) whose aim is to suppress the echo originated
from acoustic coupling between loudspeakers and microphones, plays a key role
in voice interaction. Linear adaptive filter (AF) is always used for handling
this problem. However, since there would be some severe effects in real
scenarios, such nonlinear distortions, background noises, and microphone
clipping, it would lead to considerable residual echo, giving poor performance
in practice. In this paper, we propose an end-to-end network structure for echo
cancellation, which is directly done on time-domain audio waveform. It is
transformed to deep representation by temporal convolution, and modelled by
Long Short-Term Memory (LSTM) for considering temporal property. Since time
delay and severe reverberation may exist at the near-end with respect to the
far-end, a local attention is employed for alignment. The network is trained
using multitask learning by employing an auxiliary classification network for
double-talk detection. Experiments show the superiority of our proposed method
in terms of the echo return loss enhancement (ERLE) for single-talk periods and
the perceptual evaluation of speech quality (PESQ) score for double-talk
periods in background noise and nonlinear distortion scenarios.


Abstract:
  We study the Lie point symmetries and the similarity transformations for the
partial differential equations of the nonlinear one-dimensional
magnetohydrodynamic system with the Hall term known as HMHD system. For this
1+1 system of partial differential equations we find that is invariant under
the action of a seventh dimensional Lie algebra. Furthermore, the
one-dimensional optimal system is derived while the Lie invariants are applied
for the derivation of similarity transformations. We present different kinds of
oscillating solutions.


Abstract:
  Mobile robots have disrupted the material handling industry which is
witnessing radical changes. The requirement for enhanced automation across
various industry segments often entails mobile robotic systems operating in
logistics facilities with little/no infrastructure. In such environments,
out-of-box low-cost robotic solutions are desirable. Wireless connectivity
plays a crucial role in successful operation of such mobile robotic systems. A
wireless mesh network of mobile robots is an attractive solution; however, a
number of system-level challenges create unique and stringent service
requirements. The focus of this paper is the role of Bluetooth mesh technology,
which is the latest addition to the Internet-of-Things (IoT) connectivity
landscape, in addressing the challenges of infrastructure-less connectivity for
mobile robotic systems. It articulates the key system-level design challenges
from communication, control, cooperation, coverage, security, and
navigation/localization perspectives, and explores different capabilities of
Bluetooth mesh technology for such challenges. It also provides performance
insights through real-world experimental evaluation of Bluetooth mesh while
investigating its differentiating features against competing solutions.


Abstract:
  Numerical models of weather and climate critically depend on long-term
stability of integrators for systems of hyperbolic conservation laws. While
such stability is often obtained from (physical or numerical) dissipation
terms, physical fidelity of such simulations also depends on properly
preserving conserved quantities, such as energy, of the system. To address this
apparent paradox, we develop a variational integrator for the shallow water
equations that conserves energy, but dissipates potential enstrophy. Our
approach follows the continuous selective decay framework [F. Gay-Balmaz and D.
Holm. Selective decay by Casimir dissipation in inviscid fluids. Nonlinearity,
26(2):495, 2013], which enables dissipating an otherwise conserved quantity
while conserving the total energy. We use this in combination with the
variational discretization method [D. Pavlov, P. Mullen, Y. Tong, E. Kanso, J.
Marsden and M. Desbrun. Structure-preserving discretization of incompressible
fluids. Physica D: Nonlinear Phenomena, 240(6):443-458, 2011] to obtain a
discrete selective decay framework. This is applied to the shallow water
equations, both in the plane and on the sphere, to dissipate the potential
enstrophy. The resulting scheme significantly improves the quality of the
approximate solutions, enabling long-term integrations to be carried out.


Abstract:
  Conceptual abstraction and analogy-making are key abilities underlying
humans' abilities to learn, reason, and robustly adapt their knowledge to new
domains. Despite of a long history of research on constructing AI systems with
these abilities, no current AI system is anywhere close to a capability of
forming humanlike abstractions or analogies. This paper reviews the advantages
and limitations of several approaches toward this goal, including symbolic
methods, deep learning, and probabilistic program induction. The paper
concludes with several proposals for designing challenge tasks and evaluation
measures in order to make quantifiable and generalizable progress in this area.


Abstract:
  We consider the problem of offline reinforcement learning (RL) -- a
well-motivated setting of RL that aims at policy optimization using only
historical data. Despite its wide applicability, theoretical understandings of
offline RL, such as its optimal sample complexity, remain largely open even in
basic settings such as \emph{tabular} Markov Decision Processes (MDPs).
  In this paper, we propose Off-Policy Double Variance Reduction (OPDVR), a new
variance reduction based algorithm for offline RL. Our main result shows that
OPDVR provably identifies an $\epsilon$-optimal policy with
$\widetilde{O}(H^2/d_m\epsilon^2)$ episodes of offline data in the
finite-horizon stationary transition setting, where $H$ is the horizon length
and $d_m$ is the minimal marginal state-action distribution induced by the
behavior policy. This improves over the best known upper bound by a factor of
$H$. Moreover, we establish an information-theoretic lower bound of
$\Omega(H^2/d_m\epsilon^2)$ which certifies that OPDVR is optimal up to
logarithmic factors. Lastly, we show that OPDVR also achieves rate-optimal
sample complexity under alternative settings such as the finite-horizon MDPs
with non-stationary transitions and the infinite horizon MDPs with discounted
rewards.


Abstract:
  The loss and gain of volatile elements during planet formation is key for
setting their subsequent climate, geodynamics, and habitability. Two broad
regimes of volatile element transport in and out of planetary building blocks
have been identified: that occurring when the nebula is still present, and that
occurring after it has dissipated. Evidence for volatile element loss in
planetary bodies after the dissipation of the solar nebula is found in the high
Mn to Na abundance ratio of Mars, the Moon, and many of the solar system's
minor bodies. This volatile loss is expected to occur when the bodies are
heated by planetary collisions and short-lived radionuclides, and enter a
global magma ocean stage early in their history. The bulk composition of
exo-planetary bodies can be determined by observing white dwarfs which have
accreted planetary material. The abundances of Na, Mn, and Mg have been
measured for the accreting material in four polluted white dwarf systems.
Whilst the Mn/Na abundances of three white dwarf systems are consistent with
the fractionations expected during nebula condensation, the high Mn/Na
abundance ratio of GD362 means that it is not (>3 sigma). We find that heating
of the planetary system orbiting GD362 during the star's giant branch evolution
is insufficient to produce such a high Mn/Na. We, therefore, propose that
volatile loss occurred in a manner analogous to that of the solar system
bodies, either due to impacts shortly after their formation or from heating by
short-lived radionuclides. We present potential evidence for a magma ocean
stage on the exo-planetary body which currently pollutes the atmosphere of
GD362.


Abstract:
  Patch lattices, introduced by G. Cz\'edli and E.T. Schmidt in 2013, are the
building stones for slim (and so necessarily finite and planar) semimodular
lattices with respect to gluing. Slim semimodular lattices were introduced by
G. Gr\"atzer and E. Knapp in 2007, and they have been intensively studied since
then. Outside lattice theory, these lattices played the main role in adding a
uniqueness part to the classical Jordan--H\"older theorem for groups by G.
Cz\'edli and E.T. Schmidt in 2011, and they also led to results in
combinatorial geometry. In this paper, we prove that slim patch lattices are
exactly the absolute retracts with more than two elements for the category of
slim semimodular lattices with length-preserving lattice embeddings as
morphisms. Also, slim patch lattices are the same as the maximal objects $L$ in
this category such that $|L|>2$. Furthermore, slim patch lattices are
characterized as the algebraically closed lattices $L$ in this category such
that $|L|>2$. Finally, we prove that if we consider $\{0,1\}$-preserving
lattice homomorphisms rather than length-preserving ones, then the absolute
retracts for the class of slim semimodular lattices are the at most 4-element
boolean lattices.


Abstract:
  We study first order phase transitions in Randall-Sundrum models in the early
universe dual to confinement in large-$N$ gauge theories. The transition rate
to the confined phase is suppressed by a factor $\exp(-N^2)$, and may not
complete for $N \gg 1$, instead leading to an eternally inflating phase. To
avoid this fate, the resulting constraint on $N$ makes the RS effective field
theory only marginally under control. We present a mechanism where the IR brane
remains stabilized at very high temperature, so that the theory stays in the
confined phase at all times after inflation and reheating. We call this
mechanism avoided deconfinement. The mechanism involves adding new scalar
fields on the IR brane which provide a stablilizing contribution to the radion
potential at finite temperature, in a spirit similar to Weinberg's symmetry
non-restoration mechanism. Avoided deconfinement allows for a viable cosmology
for theories with parametrically large $N$. Early universe cosmological
phenomena such as WIMP freeze-out, axion abundance, baryogenesis, phase
transitions, and gravitational wave signatures are qualitatively modified.


Abstract:
  This paper presents TS2Vec, a universal framework for learning
representations of time series in an arbitrary semantic level. Unlike existing
methods, TS2Vec performs contrastive learning in a hierarchical way over
augmented context views, which enables a robust contextual representation for
each timestamp. Furthermore, to obtain the representation of an arbitrary
sub-sequence in the time series, we can apply a simple aggregation over the
representations of corresponding timestamps. We conduct extensive experiments
on time series classification tasks to evaluate the quality of time series
representations. As a result, TS2Vec achieves significant improvement over
existing SOTAs of unsupervised time series representation on 125 UCR datasets
and 29 UEA datasets. The learned timestamp-level representations also achieve
superior results in time series forecasting and anomaly detection tasks. A
linear regression trained on top of the learned representations outperforms
previous SOTAs of time series forecasting. Furthermore, we present a simple way
to apply the learned representations for unsupervised anomaly detection, which
establishes SOTA results in the literature. The source code is publicly
available at https://github.com/yuezhihan/ts2vec.


Abstract:
  In this paper we consider $ X(\bar\varphi)$ anisotropic symmetric space $
2\pi$ of periodic functions of $m$ variables, in particular, the generalized
Lorentz space $L_{\bar{\psi},\bar{\tau}}^{*}(\mathbb{T}^{m})$ and
Nikol'skii--Besov's class $S_{X(\bar{\varphi}),\bar{\theta}}^{\bar r}B$. The
article proves an embedding theorem for the Nikol'skii - Besov class in the
generalized Lorentz space and establishes an upper bound for the best
approximations by trigonometric polynomials with harmonic numbers from the
hyperbolic cross of functions from the class
$S_{X(\bar{\varphi}),\bar{\theta}}^{\bar r}B$.


Abstract:
  Early detection and quantification of tumour growth would help clinicians to
prescribe more accurate treatments and provide better surgical planning.
However, the multifactorial and heterogeneous nature of lung tumour progression
hampers identification of growth patterns. In this study, we present a novel
method based on a deep hierarchical generative and probabilistic framework
that, according to radiological guidelines, predicts tumour growth, quantifies
its size and provides a semantic appearance of the future nodule. Unlike
previous deterministic solutions, the generative characteristic of our approach
also allows us to estimate the uncertainty in the predictions, especially
important for complex and doubtful cases. Results of evaluating this method on
an independent test set reported a tumour growth balanced accuracy of 74%, a
tumour growth size MAE of 1.77 mm and a tumour segmentation Dice score of 78%.
These surpassed the performances of equivalent deterministic and alternative
generative solutions (i.e. probabilistic U-Net, Bayesian test dropout and
Pix2Pix GAN) confirming the suitability of our approach.


Abstract:
  Most studies for postselected weak measurement in optomechanical system focus
on using a single photon as a measured system. However, we find that using weak
coherent light instead of a single photon can also amplify the mirror's
position displacement of one photon. In the WVA regime (Weak Value
Amplification regime), the weak value of one photon can lie outside the
eigenvalue spectrum, proportional to the difference of the mirror's position
displacement between the successful and failed postselection and its successful
postselection probability is dependent of the mean photon number of the
coherent light and improved by adjusting it accordingly. Outside the WVA
regime, the amplification limit can reach the level of the vacuum fluctuations,
and when the mean photon number and the optomechanical coupling parameter are
selected appropriately, its successful postselection probability becomes
higher, which is beneficial to observe the maximum amplification value under
the current experimental conditions. This result breaks the constraint that it
is difficult to detect outside the WVA regime. This opens up a new regime for
the study of a single photon nonlinearity in optomechanical system.


Abstract:
  We present a novel approach to the formation controlling of aerial robot
swarms that demonstrates the flocking behavior. The proposed method stems from
the Unmanned Aerial Vehicle (UAV) dynamics; thus, it prevents any unattainable
control inputs from being produced and subsequently leads to feasible
trajectories. By modeling the inter-agent relationships using a pairwise energy
function, we show that interacting robot swarms constitute a Markov Random
Field. Our algorithm builds on the Mean-Field Approximation and incorporates
the collective behavioral rules: cohesion, separation, and velocity alignment.
We follow a distributed control scheme and show that our method can control a
swarm of UAVs to a formation and velocity consensus with real-time collision
avoidance. We validate the proposed method with physical and high-fidelity
simulation experiments.


Abstract:
  Determining which image regions to concentrate on is critical for
Human-Object Interaction (HOI) detection. Conventional HOI detectors focus on
either detected human and object pairs or pre-defined interaction locations,
which limits learning of the effective features. In this paper, we reformulate
HOI detection as an adaptive set prediction problem, with this novel
formulation, we propose an Adaptive Set-based one-stage framework (AS-Net) with
parallel instances and interaction branches. To attain this, we map a trainable
interaction query set to an interaction prediction set with a transformer. Each
query adaptively aggregates the interaction-relevant features from global
contexts through multi-head co-attention. Besides, the training process is
supervised adaptively by matching each ground truth with the interaction
prediction. Furthermore, we design an effective instance-aware attention module
to introduce instructive features from the instance branch into the interaction
branch. Our method outperforms previous state-of-the-art methods without any
extra human pose and language features on three challenging HOI detection
datasets. Especially, we achieve over $31\%$ relative improvement on a
large-scale HICO-DET dataset. Code is available at
https://github.com/yoyomimi/AS-Net.


Abstract:
  Thermal evolution models suggest that the luminosities of both Uranus and
Neptune are inconsistent with the classical assumption of an adiabatic
interior. Such models commonly predict Uranus to be brighter and, recently,
Neptune to be fainter than observed. In this work, we investigate the influence
of a thermally conductive boundary layer on the evolution of Uranus- and
Neptune-like planets. This thermal boundary layer (TBL) is assumed to be
located deep in the planet, and be caused by a steep compositional gradient
between a H-He-dominated outer envelope and an ice-rich inner envelope. We
investigate the effect of TBL thickness, thermal conductivity, and the time of
TBL formation on the planet's cooling behaviour. The calculations were
performed with our recently developed tool based on the Henyey method for
stellar evolution. We make use of state-of-the-art equations of state for
hydrogen, helium, and water, as well as of thermal conductivity data for water
calculated via ab initio methods. We find that even a thin conductive layer of
a few kilometres has a significant influence on the planetary cooling. In our
models, Uranus' measured luminosity can only be reproduced if the planet has
been near equilibrium with the solar incident flux for an extended time. For
Neptune, we find a range of solutions with a near constant effective
temperature at layer thicknesses of 15 km or larger, similar to Uranus. In
addition, we find solutions for thin TBLs of few km and strongly enhanced
thermal conductivity. A $\sim$ 1$~$Gyr later onset of the TBL reduces the
present $\Delta T$ by an order of magnitude to only several 100 K. Our models
suggest that a TBL can significantly influence the present planetary luminosity
in both directions, making it appear either brighter or fainter than the
adiabatic case.


Abstract:
  In this paper, we want to prove positive mass theorems for ALF and ALG
manifolds with model spaces $\mathbb R^{n-1}\times \mathbb S^1$ and $\mathbb
R^{n-2}\times \mathbb T^2$ respectively in dimensions no greater than $7$
(Theorem \ref{ALFPMT0}). { Different from the compatibility condition for spin
structure in \cite[Theorem 2]{minerbe2008a}, we show that some type of
incompressible condition for $\mathbb S^1$ and $\mathbb T^2$ is enough to
guarantee the nonnegativity of the mass.} As in the asymptotically flat case,
we reduce the desired positive mass theorems to those ones concerning
non-existence of positive scalar curvature metrics on closed manifolds coming
from generalize surgery to $n$-torus. { Finally, we investigate certain fill-in
problems and obtain an optimal bound for total mean curvature of admissible
fill-ins for flat product $2$-torus $\mathbb S^1(l_1)\times \mathbb S^1(l_2)$.}


Abstract:
  Transient stability assessment (TSA) is a cornerstone for resilient
operations of today's interconnected power grids. This paper is a confluence of
quantum computing, data science and machine learning to potentially address the
power system TSA challenge. We devise a quantum TSA (qTSA) method to enable
scalable and efficient data-driven transient stability prediction for bulk
power systems, which is the first attempt to tackle the TSA issue with quantum
computing. Our contributions are three-fold: 1) A low-depth, high
expressibility quantum neural network for accurate and noise-resilient TSA; 2)
A quantum natural gradient descent algorithm for efficient qTSA training; 3) A
systematical analysis on qTSA's performance under various quantum factors. qTSA
underpins a foundation of quantum-enabled and data-driven power grid stability
analytics. It renders the intractable TSA straightforward and effortless in the
Hilbert space, and therefore provides stability information for power system
operations. Extensive experiments on quantum simulators and real quantum
computers verify the accuracy, noise-resilience, scalability and universality
of qTSA.


Abstract:
  Fake news on social media has become a hot topic of research as it negatively
impacts the discourse of real news in the public. Specifically, the ongoing
COVID-19 pandemic has seen a rise of inaccurate and misleading information due
to the surrounding controversies and unknown details at the beginning of the
pandemic. The FakeNews task at MediaEval 2020 tackles this problem by creating
a challenge to automatically detect tweets containing misinformation based on
text and structure from Twitter follower network. In this paper, we present a
simple approach that uses BERT embeddings and a shallow neural network for
classifying tweets using only text, and discuss our findings and limitations of
the approach in text-based misinformation detection.


Abstract:
  We measured $^{35}$Cl abundances in 52 M giants with metallicities between
-0.5 $<$ [Fe/H] $<$ 0.12. Abundances and atmospheric parameters were derived
using infrared spectra from CSHELL on the IRTF and from optical echelle
spectra. We measured Cl abundances by fitting a H$^{35}$Cl molecular feature at
3.6985 $\mu$m with synthetic spectra. We also measured the abundances of O, Ca,
Ti, and Fe using atomic absorption lines. We find that the [Cl/Fe] ratio for
our stars agrees with chemical evolution models of Cl and the [Cl/Ca] ratio is
broadly consistent with the solar ratio over our metallicity range. Both
indicate that Cl is primarily made in core-collapse supernovae with some
contributions from Type Ia SN. We suggest other potential nucleosynthesis
processes, such as the $\nu$-process, are not significant producers of Cl.
Finally, we also find our Cl abundances are consistent with H II and planetary
nebular abundances at a given oxygen abundance, although there is scatter in
the data.


Abstract:
  H\"ormander's propagation of singularities theorem does not fully describe
the propagation of singularities in subelliptic wave equations, due to the
existence of doubly characteristic points. In the present paper, building upon
a visionary conference paper by R. Melrose \cite{Mel86}, we prove that
singularities of subelliptic wave equations only propagate along
null-bicharacteristics and abnormal extremal lifts of singular curves, which
are well-known curves in optimal control theory. We first revisit in depth the
ideas sketched by R. Melrose in \cite{Mel86}, notably providing a full proof of
its main statement. Making more explicit computations, we then explain how
sub-Riemannian geometry and abnormal extremals come into play. This result
shows that abnormal extremals have an important role in the classical-quantum
correspondence between sub-Riemannian geometry and subelliptic operators. As a
consequence, for $x\neq y$ and denoting by $K_G$ the wave kernel, we obtain
that the singular support of the distribution $t\mapsto K_G(t,x,y)$ is included
in the set of lengths of the normal geodesics joining $x$ and $y$, at least up
to the time equal to the minimal length of a singular curve joining $x$ and
$y$.


Abstract:
  The world needs around 150 Pg of negative carbon emissions to mitigate
climate change. Global soils may provide a stable, sizeable reservoir to help
achieve this goal by sequestering atmospheric carbon dioxide as soil organic
carbon (SOC). In turn, SOC can support healthy soils and provide a multitude of
ecosystem benefits. To support SOC sequestration, researchers and policy makers
must be able to precisely measure the amount of SOC in a given plot of land.
SOC measurement is typically accomplished by taking soil cores selected at
random from the plot under study, mixing (compositing) some of them together,
and analyzing (assaying) the composited samples in a laboratory. Compositing
reduces assay costs, which can be substantial. Taking samples is also costly.
Given uncertainties and costs in both sampling and assay along with a desired
estimation precision, there is an optimal composite size that will minimize the
budget required to achieve that precision. Conversely, given a fixed budget,
there is a composite size that minimizes uncertainty. In this paper, we
describe and formalize sampling and assay for SOC and derive the optima for
three commonly used assay methods: dry combustion in an elemental analyzer,
loss-on-ignition, and mid-infrared spectroscopy. We demonstrate the utility of
this approach using data from a soil survey conducted in California. We give
recommendations for practice and provide software to implement our framework.


Abstract:
  The search of close (a<=5 au) giant planet(GP) companions with radial
velocity(RV) around young stars and the estimate of their occurrence rates is
important to constrain the migration timescales. Furthermore, this search will
allow the giant planet occurrence rates to be computed at all separations via
the combination with direct imaging techniques. The RV search around young
stars is a challenge as they are generally faster rotators than older stars of
similar spectral types and they exhibit signatures of spots or pulsation in
their RV time series. Specific analyses are necessary to characterize, and
possibly correct for, this activity. Our aim is to search for planets around
young nearby stars and to estimate the GP occurrence rates for periods up to
1000 days. We used the SOPHIE spectrograph to observe 63 A-M young (<400 Myr)
stars. We used our SAFIR software to compute the RVs and other spectroscopic
observables. We then combined this survey with the HARPS YNS survey to compute
the companion occurrence rates on a total of 120 young A-M stars. We report one
new trend compatible with a planetary companion on HD109647. We also report
HD105693 and HD112097 as binaries, and we confirm the binarity of HD2454,
HD13531, HD17250A, HD28945, HD39587, HD131156, HD 142229, HD186704A, and HD
195943. We constrained for the first time the orbital parameters of HD195943B.
We refute the HD13507 single brown dwarf (BD) companion solution and propose a
double BD companion solution. Based on our sample of 120 young stars, we obtain
a GP occurrence rate of 1_{-0.3}^{+2.2}% for periods lower than 1000 days, and
we obtain an upper limit on BD occurrence rateof 0.9_{-0.9}^{+2}% in the same
period range. We report a possible lack of close (1<P<1000 days) GPs around
young FK stars compared to their older counterparts, with a confidence level of
90%.


Abstract:
  The current status and future prospects of searches for axion-like particles
(ALPs) at colliders, mostly focused on the CERN LHC, are summarized.
Constraints on ALPs with masses above a few GeV that couple to photons, as well
as to Z or Higgs bosons, have been set at the LHC through searches for new
$a\to\gamma\gamma$ resonances in di-, tri-, and four-photon final states.
Inclusive and exclusive diphotons in proton-proton and lead-lead collisions,
pp, PbPb $\to a \to \gamma\gamma (+X)$, as well as exotic Z and Higgs boson
decays, pp $\to \mathrm{Z},\mathrm{H}\to a\gamma \to 3\gamma$ and pp $\to
\mathrm{H}\to aa \to 4\gamma$, have been analyzed. Exclusive searches in PbPb
collisions provide the best exclusion limits for ALP masses $m_a\approx 5-$100
GeV, whereas the other channels are the most competitive ones over $m_a\approx
100$ GeV$-$2.6 TeV. Integrated ALP production cross sections up to $\sim$100 nb
are excluded at 95% confidence level, corresponding to constraints on
axion-photon couplings down to $g_{a\gamma}\approx$ 0.05 TeV$^{-1}$, over broad
mass ranges. Factors of 10$-$100 improvements in these limits are expected at
the LHC approaching $g_{a\gamma}\approx 10^{-3}$ TeV$^{-1}$ over $m_a\approx 1$
GeV$-$5 TeV in the next decade.


Abstract:
  Using only linear optical elements, the creation of dual-rail photonic
entangled states is inherently probabilistic. Known entanglement generation
schemes have low success probabilities, requiring large-scale multiplexing to
achieve near-deterministic operation of quantum information processing
protocols. In this paper, we introduce multiple techniques and methods to
generate photonic entangled states with high probability, which have the
potential to reduce the footprint of Linear Optical Quantum Computing (LOQC)
architectures drastically. Most notably, we are showing how to improve Bell
state preparation from four single photons to up to p=2/3, boost Type-I fusion
to 75% with a dual-rail Bell state ancilla and improve Type-II fusion beyond
the limits of Bell state discrimination.


Abstract:
  Computational meshes arising from shape optimization routines commonly suffer
from decrease of mesh quality or even destruction of the mesh. In this work, we
provide an approach to regularize general shape optimization problems to
increase both shape and volume mesh quality. For this, we employ pre-shape
calculus (cf. arXiv:2012.09124). Existence of regularized solutions is
guaranteed. Further, consistency of modified pre-shape gradient systems is
established. We present pre-shape gradient system modifications, which permit
simultaneous shape optimization with mesh quality improvement. Optimal shapes
to the original problem are left invariant under regularization. The
computational burden of our approach is limited, since additional solution of
possibly larger (non-)linear systems for regularized shape gradients is not
necessary. We implement and compare pre-shape gradient regularization
approaches for a hard to solve 2D problem. As our approach does not depend on
the choice of metrics representing shape gradients, we employ and compare
several different metrics.


Abstract:
  The Tibet AS$\gamma$ experiment has measured $\gamma$-ray flux of supernova
remnant G106.3+2.7 up to 100 TeV, suggesting it {being} potentially a
"PeVatron". Challenge arises when the hadronic scenario requires a hard proton
spectrum (with spectral index $\approx 1.8$), while {usual observations and
numerical simulations prefer} a soft proton spectrum {(with spectral index
$\geq 2$)}. In this paper, we explore an alternative scenario to explain the
$\gamma$-ray spectrum of G106.3+2.7 within the current understanding of
acceleration and escape processes. We consider that the cosmic ray {particles}
are scattered by the turbulence driven via Bell instability. The resulting
hadronic $\gamma$-ray spectrum is novel, dominating the contribution to the
emission above 10\,TeV, and can explain the bizarre broadband spectrum of
G106.3+2.7 in combination with leptonic emission from the remnant.


Abstract:
  Online learning with expert advice is widely used in various machine learning
tasks. It considers the problem where a learner chooses one from a set of
experts to take advice and make a decision. In many learning problems, experts
may be related, henceforth the learner can observe the losses associated with a
subset of experts that are related to the chosen one. In this context, the
relationship among experts can be captured by a feedback graph, which can be
used to assist the learner's decision making. However, in practice, the nominal
feedback graph often entails uncertainties, which renders it impossible to
reveal the actual relationship among experts. To cope with this challenge, the
present work studies various cases of potential uncertainties, and develops
novel online learning algorithms to deal with uncertainties while making use of
the uncertain feedback graph. The proposed algorithms are proved to enjoy
sublinear regret under mild conditions. Experiments on real datasets are
presented to demonstrate the effectiveness of the novel algorithms.


Abstract:
  The recursive expansion of tree level multitrace Einstein-Yang-Mills (EYM)
amplitudes induces a refined graphic expansion, by which any tree-level EYM
amplitude can be expressed as a summation over all possible refined graphs.
Each graph contributes a unique coefficient as well as a proper combination of
color-ordered Yang-Mills (YM) amplitudes. This expansion allows one to evaluate
EYM amplitudes through YM amplitudes, the latter have much simpler structures
in four dimensions than the former. In this paper, we classify the refined
graphs for the expansion of EYM amplitudes into $\text{N}^{\,k}$MHV sectors.
Amplitudes in four dimensions, which involve $k+2$ negative-helicity particles,
at most get non-vanishing contribution from graphs in $\text{N}^{\,k'(k'\leq
k)}$MHV sectors. By the help of this classification, we evaluate the
non-vanishing amplitudes with two negative-helicity particles in four
dimensions. We establish a correspondence between the refined graphs for
single-trace amplitudes with $(g^-_i,g^-_j)$ or $(h^-_i,g^-_j)$ configuration
and the spanning forests of the known Hodges determinant form. Inspired by this
correspondence, we further propose a symmetric formula of double-trace
amplitudes with $(g^-_i,g^-_j)$ configuration. By analyzing the cancellation
between refined graphs in four dimensions, we prove that any other tree
amplitude with two negative-helicity particles has to vanish.


Abstract:
  Control barrier functions (CBFs) have recently become a powerful method for
rendering desired safe sets forward invariant in single- and multi-agent
systems. In the multi-agent case, prior literature has considered scenarios
where all agents cooperate to ensure that the corresponding set remains
invariant. However, these works do not consider scenarios where a subset of the
agents are behaving adversarially with the intent to violate safety bounds. In
addition, prior results on multi-agent CBFs typically assume that control
inputs are continuous and do not consider sampled-data dynamics. This paper
presents a framework for normally-behaving agents in a multi-agent system with
heterogeneous control-affine, sampled-data dynamics to render a safe set
forward invariant in the presence of adversarial agents. The proposed approach
considers several aspects of practical control systems including input
constraints, clock asynchrony and disturbances, and distributed calculation of
control inputs. Our approach also considers functions describing safe sets
having high relative degree with respect to system dynamics. The efficacy of
these results are demonstrated through simulations.


Abstract:
  In this paper, we investigate over-the-air model aggregation in a federated
edge learning (FEEL) system. We introduce a Markovian probability model to
characterize the intrinsic temporal structure of the model aggregation series.
With this temporal probability model, we formulate the model aggregation
problem as to infer the desired aggregated update given all the past
observations from a Bayesian perspective. We develop a message passing based
algorithm, termed temporal-structure-assisted gradient aggregation (TSA-GA), to
fulfil this estimation task with low complexity and near-optimal performance.
We further establish the state evolution (SE) analysis to characterize the
behaviour of the proposed TSA-GA algorithm, and derive an explicit bound of the
expected loss reduction of the FEEL system under certain standard regularity
conditions. In addition, we develop an expectation maximization (EM) strategy
to learn the unknown parameters in the Markovian model. We show that the
proposed TSAGA algorithm significantly outperforms the state-of-the-art, and is
able to achieve comparable learning performance as the error-free benchmark in
terms of both convergence rate and final test accuracy.


Abstract:
  In 2019, Reyes & Wright used the NASA Astrophysics Data System (ADS) to
initiate a comprehensive bibliography for SETI accessible to the public. Since
then, updates to the library have been incomplete, partly due to the difficulty
in managing the large number of false positive publications generated by
searching ADS using simple search terms. In preparation for a recent update,
the scope of the library was revised and reexamined. The scope now includes
social sciences and commensal SETI. Results were curated based on five SETI
keyword searches: "SETI", "technosignature", "Fermi Paradox," "Drake Equation",
and "extraterrestrial intelligence." These keywords returned 553 publications
that merited inclusion in the bibliography that were not previously present. A
curated library of false positive results is now concurrently maintained to
facilitate their exclusion from future searches. A search query and workflow
was developed to capture nearly all SETI-related papers indexed by ADS while
minimizing false positives. These tools will enable efficient, consistent
updates of the SETI library by future curators, and could be adopted for other
bibliography projects as well.


Abstract:
  The article is devoted to the development of numerical methods for solving
saddle point problems and variational inequalities with simplified requirements
for the smoothness conditions of functionals. Recently there were proposed some
notable methods for optimization problems with strongly monotone operators. Our
focus here is on newly proposed techniques for solving strongly convex-concave
saddle point problems. One of the goals of the article is to improve the
obtained estimates of the complexity of introduced algorithms by using
accelerated methods for solving auxiliary problems. The second focus of the
article is introducing an analogue of the boundedness condition for the
operator in the case of arbitrary (not necessarily Euclidean) prox structure.
We propose an analogue of the mirror descent method for solving variational
inequalities with such operators, which is optimal in the considered class of
problems.


Abstract:
  We consider a neutrinophilic $U(1)$ extension of the standard model (SM)
which couples only to SM isosinglet neutral fermions, charged under the new
group. The neutral fermions couple to the SM matter fields through Yukawa
interactions. The neutrinos in the model get their masses from a standard
inverse-seesaw mechanism while an added scalar sector is responsible for the
breaking of the gauged $U(1)$ leading to a light neutral gauge boson ($Z'$),
which has minimal interaction with the SM sector. We study the phenomenology of
having such a light $Z'$ in the context of neutrinophilic interactions as well
as the role of allowing kinetic mixing between the new $U(1)$ group with the SM
hypercharge group. We show that current experimental searches allow for a very
light $Z'$ if it does not couple to SM fields directly and highlight the search
strategies at the LHC. We observe that multilepton final states in the form of
$(4\ell + \slashed{E}_T)$ and $(3\ell + 2j + \slashed{E}_T)$ could be crucial
in discovering such a neutrinophilic gauge boson lying in a mass range of
$200$--$500$ GeV.


Abstract:
  We show that the naive mean-field approximation correctly predicts the
leading term of the logarithmic lower tail probabilities for the number of
copies of a given subgraph in $G(n,p)$ and of arithmetic progressions of a
given length in random subsets of the integers in the entire range of densities
where the mean-field approximation is viable.
  Our main technical result provides sufficient conditions on the maximum
degrees of a uniform hypergraph $\mathcal{H}$ that guarantee that the
logarithmic lower tail probabilities for the number of edges induced by a
binomial random subset of the vertices of $\mathcal{H}$ can be
well-approximated by considering only product distributions. This may be
interpreted as a weak, probabilistic version of the hypergraph container lemma
that is applicable to all sparser-than-average (and not only independent) sets.


Abstract:
  Many studies are devoted to the design of radiomic models for a prediction
task. When no effective model is found, it is often difficult to know whether
the radiomic features do not include information relevant to the task or
because of insufficient data. We propose a downsampling method to answer that
question when considering a classification task into two groups. Using two
large patient cohorts, several experimental configurations involving different
numbers of patients were created. Univariate or multivariate radiomic models
were designed from each configuration. Their performance as reflected by the
Youden index (YI) and Area Under the receiver operating characteristic Curve
(AUC) was compared to the stable performance obtained with the highest number
of patients. A downsampling method is described to predict the YI and AUC
achievable with a large number of patients. Using the multivariate models
involving machine learning, YI and AUC increased with the number of patients
while they decreased for univariate models. The downsampling method better
estimated YI and AUC obtained with the largest number of patients than the YI
and AUC obtained using the number of available patients and identifies the lack
of information relevant to the classification task when no such information
exists.


Abstract:
  The current role of data-driven science is constantly increasing its
importance within Astrophysics, due to the huge amount of multi-wavelength data
collected every day, characterized by complex and high-volume information
requiring efficient and as much as possible automated exploration tools.
Furthermore, to accomplish main and legacy science objectives of future or
incoming large and deep survey projects, such as JWST, LSST and Euclid, a
crucial role is played by an accurate estimation of photometric redshifts,
whose knowledge would permit the detection and analysis of extended and
peculiar sources by disentangling low-z from high-z sources and would
contribute to solve the modern cosmological discrepancies. The recent
photometric redshift data challenges, organized within several survey projects,
like LSST and Euclid, pushed the exploitation of multi-wavelength and
multi-dimensional data observed or ad hoc simulated to improve and optimize the
photometric redshifts prediction and statistical characterization based on both
SED template fitting and machine learning methodologies. But they also provided
a new impetus in the investigation on hybrid and deep learning techniques,
aimed at conjugating the positive peculiarities of different methodologies,
thus optimizing the estimation accuracy and maximizing the photometric range
coverage, particularly important in the high-z regime, where the spectroscopic
ground truth is poorly available. In such a context we summarize what learned
and proposed in more than a decade of research.


Abstract:
  Using a neural network potential (ANI-1ccx) generated from quantum data on a
large data set of molecules and pairs of molecules, isothermal, constant volume
simulations demonstrate that the model can be as accurate as ab initio
molecular dynamics for simulations of pure liquid water and the aqueous
solvation of a methane molecule. No theoretical or experimental data for the
liquid phase is used to train the model, suggesting that the ANI-1ccx approach
is an effective method to link high level ab initio methods to potentials for
large scale simulations.


Abstract:
  IEEE 802.11p standard defines wireless technology protocols that enable
vehicular transportation and manage traffic efficiency. A major challenge in
the development of this technology is ensuring communication reliability in
highly dynamic vehicular environments, where the wireless communication
channels are doubly selective, thus making channel estimation and tracking a
relevant problem to investigate. In this paper, a novel deep learning
(DL)-based weighted interpolation estimator is proposed to accurately estimate
vehicular channels especially in high mobility scenarios. The proposed
estimator is based on modifying the pilot allocation of the IEEE 802.11p
standard so that more transmission data rates are achieved. Extensive numerical
experiments demonstrate that the developed estimator significantly outperforms
the recently proposed DL-based frame-by-frame estimators in different vehicular
scenarios, while substantially reducing the overall computational complexity.


Abstract:
  This work deals with a new generalization of $r$-Stirling numbers using
$l$-tuple of permutations and partitions called $(l,r)$-Stirling numbers of
both kinds. We study various properties of these numbers using combinatorial
interpretations and symmetric functions. Also, we give a limit representation
of the multiple zeta function using $(l,r)$-Stirling of the first kind.


Abstract:
  Low-mass ($M_{\rm{500}}<5\times10^{14}{\rm{M_\odot}}$) galaxy clusters have
been largely unexplored in radio observations, due to the inadequate
sensitivity of existing telescopes. However, the upgraded GMRT (uGMRT) and the
Low Frequency ARray (LoFAR), with unprecedented sensitivity at low frequencies,
have paved the way to closely study less massive clusters than before. We have
started the first large-scale programme to systematically search for diffuse
radio emission from low-mass galaxy clusters, chosen from the Planck
Sunyaev-Zel'dovich cluster catalogue. We report here the detection of diffuse
radio emission from four of the 12 objects in our sample, shortlisted from the
inspection of the LoFAR Two Meter Sky Survey (LoTSS-I), followed up by uGMRT
Band 3 deep observations. The clusters PSZ2~G089 (Abell~1904) and PSZ2~G111
(Abell~1697) are detected with relic-like emission, while PSZ2~G106 is found to
have an intermediate radio halo and PSZ2~G080 (Abell~2018) seems to be a
halo-relic system. PSZ2~G089 and PSZ2~G080 are among the lowest-mass clusters
discovered with a radio relic and a halo-relic system, respectively. A high
($\sim30\%$) detection rate, with powerful radio emission ($P_{1.4\ {\rm
GHz}}\sim10^{23}~{\rm{W~Hz^{-1}}}$) found in most of these objects, opens up
prospects of studying radio emission in galaxy clusters over a wider mass
range, to much lower-mass systems.


Abstract:
  We study the strong magnetic field limit for a nonlinear Iwatsuka-type model,
i.e. a nonlinear Schr\"odinger equation in two spatial dimensions with a
magnetic vector potential that only depends on the $x$-coordinate. Using a
high-frequency averaging technique, we show that this equation can be
effectively described by a nonlocal nonlinear model, which is no longer
dispersive. We also prove that, in this asymptotic regime, inhomogeneous
nonlinearities are confined along the $y$-axis.


Abstract:
  Answering in negative a question of M. Hru\v{s}\'ak, we construct a Borel
ideal not extendable to any $F_\sigma$ ideal and such that it is not
Kat\v{e}tov above the ideal $\mathrm{conv}$.


Abstract:
  Mechanical disorder in solids, which is generated by a broad range of
physical processes and controls various material properties, appears in a wide
variety of forms. Defining unified and measurable dimensionless quantifiers,
allowing quantitative comparison of mechanical disorder across widely different
physical systems, is therefore an important goal. Two such coarse-grained
dimensionless quantifiers (among others) appear in the literature, one is
related to the spectral broadening of discrete phononic bands in finite-size
systems (accessible through computer simulations) and the other is related the
spatial fluctuations of the shear modulus in macroscopically large systems. The
latter has been recently shown to determine the amplitude of wave attenuation
rates in the low-frequency limit (accessible through laboratory experiments).
Here, using two alternative and complementary theoretical approaches linked to
the vibrational spectra of solids, we derive a basic scaling relation between
the two dimensionless quantifiers. This scaling relation, which is supported by
simulational data, shows that the two apparently distinct quantifiers are in
fact intrinsically related, giving rise to a unified quantifier of mechanical
disorder in solids. We further discuss the obtained results in the context of
the unjamming transition taking place in soft sphere packings at low confining
pressures, in addition to their implications for our understanding of the
low-frequency vibrational spectra of disordered solids in general, and in
particular those of glassy systems.


Abstract:
  The problem of Bayesian reduced rank regression is considered in this paper.
We propose, for the first time, to use Langevin Monte Carlo method in this
problem. A spectral scaled Student prior distrbution is used to exploit the
underlying low-rank structure of the coefficient matrix. We show that our
algorithms are significantly faster than the Gibbs sampler in high-dimensional
setting. Simulation results show that our proposed algorithms for Bayesian
reduced rank regression are comparable to the state-of-the-art method where the
rank is chosen by cross validation.


Abstract:
  Multi-regional interaction among neuronal populations underlies the brain's
processing of rich sensory information in our daily lives. Recent neuroscience
and neuroimaging studies have increasingly used naturalistic stimuli and
experimental design to identify such realistic sensory computation in the
brain. However, existing methods for cross-areal interaction analysis with
dimensionality reduction, such as reduced-rank regression and canonical
correlation analysis, have limited applicability and interpretability in
naturalistic settings because they usually do not appropriately 'demix' neural
interactions into those associated with different types of task parameters or
stimulus features (e.g., visual or audio). In this paper, we develop a new
method for cross-areal interaction analysis that uses the rich task or stimulus
parameters to reveal how and what types of information are shared by different
neural populations. The proposed neural demixed shared component analysis
combines existing dimensionality reduction methods with a practical neural
network implementation of functional analysis of variance with latent
variables, thereby efficiently demixing nonlinear effects of continuous and
multimodal stimuli. We also propose a simplifying alternative under the
assumptions of linear effects and unimodal stimuli. To demonstrate our methods,
we analyzed two human neuroimaging datasets of participants watching
naturalistic videos of movies and dance movements. The results demonstrate that
our methods provide new insights into multi-regional interaction in the brain
during naturalistic sensory inputs, which cannot be captured by conventional
techniques.


Abstract:
  Speaker embeddings extracted with deep 2D convolutional neural networks are
typically modeled as projections of first and second order statistics of
channel-frequency pairs onto a linear layer, using either average or attentive
pooling along the time axis. In this paper we examine an alternative pooling
method, where pairwise correlations between channels for given frequencies are
used as statistics. The method is inspired by style-transfer methods in
computer vision, where the style of an image, modeled by the matrix of
channel-wise correlations, is transferred to another image, in order to produce
a new image having the style of the first and the content of the second. By
drawing analogies between image style and speaker characteristics, and between
image content and phonetic sequence, we explore the use of such channel-wise
correlations features to train a ResNet architecture in an end-to-end fashion.
Our experiments on VoxCeleb demonstrate the effectiveness of the proposed
pooling method in speaker recognition.


Abstract:
  For a team of robots to work collaboratively, it is crucial that each robot
have the ability to determine the position of their neighbors, relative to
themselves, in order to execute tasks autonomously. This letter presents an
algorithm for determining the three-dimensional relative position between two
mobile robots, each using nothing more than a single ultra-wideband
transceiver, an accelerometer, a rate gyro, and a magnetometer. A sliding
window filter estimates the relative position at selected keypoints by
combining the distance measurements with acceleration estimates, which each
agent computes using an on-board attitude estimator. The algorithm is
appropriate for real-time implementation, and has been tested in simulation and
experiment, where it comfortably outperforms standard estimators. A positioning
accuracy of less than 1 meter is achieved with inexpensive sensors.


Abstract:
  Room Temperature Ionic Liquids (RTILs) are molten salts which exhibit uniques
physical and chemical properties, commonly harnessed for lubrication and energy
applications. The pure ionic nature of RTIL leads to strong electrostatic
interactions among the liquid, furthermore exalted in the presence of
interfaces and confinement. In this work, we use a tuning-fork based dynamic
Surface Force Tribometer (TF-SFT), which allows probing both the rheological
and the tribological properties of RTILs films confined between a millimetric
sphere and a surface, over a wide range of confinements. When the RTIL is
confined between metallic surfaces, we evidence an abrupt change of its
rheological properties below a threshold confinement. This is reminiscent of a
recently reported confinement induced capillary freezing, here observed with a
wide contact area. In parallel, we probe the tribological response of the film
under imposed nanometric shear deformation and unveil a yielding behaviour of
the interfacial solid phase below this threshold confinement. This is
characterized by a transition from an elastic to a plastic regime, exhibiting
striking similarities with the response of glassy materials. This transition to
yielding of the RTIL in metallic confinement leads overall to a reduction in
friction and offers a self-healing protection of the surfaces avoiding direct
contact, with obvious applications in tribology.


Abstract:
  Recently, it has been shown theoretically that fluorescence microscopy using
random illuminations (RIM) yields a doubled lateral resolution and an improved
optical sectioning. Moreover, an algorithm called algoRIM, based on variance
matching, has been successfully validated on numerous biological applications.
Here, we propose a proof of uniqueness of the RIM variance equation, which
corresponds to a first theoretical validation of algoRIM.


Abstract:
  Reliable evaluation of adversarial defenses is a challenging task, currently
limited to an expert who manually crafts attacks that exploit the defense's
inner workings or approaches based on an ensemble of fixed attacks, none of
which may be effective for the specific defense at hand. Our key observation is
that adaptive attacks are composed of reusable building blocks that can be
formalized in a search space and used to automatically discover attacks for
unknown defenses. We evaluated our approach on 24 adversarial defenses and show
that it outperforms AutoAttack, the current state-of-the-art tool for reliable
evaluation of adversarial defenses: our tool discovered significantly stronger
attacks by producing 3.0\%-50.8\% additional adversarial examples for 10
models, while obtaining attacks with slightly stronger or similar strength for
the remaining models.


Abstract:
  In this paper we compare the experimental HERA data with the next-to-leading
order approach (NLO) of Ref.[C.~Contreras, E.~Levin, R.~Meneses and
M.~Sanhueza,Eur. Phys. J. C 80 (2020) no.11, 1029). This approach includes the
re-summed NLO corrections to the kernel of the evolution equation, the correct
asymptotic behaviour in the NLO at $\tau = r^2 Q^2_s \,\gg\,1$; the impact
parameter dependence of the saturation scale in accord with the Froissarrt
theorem as well as the non-linear corrections. In this paper, we successfully
describe the experimental data with the quality, which is not worse, than in
the leading order fits with larger number of the phenomenological parameters.
It is demonstrated, that the data could be described, taking into account both
the diffusion on $\ln(k_T)$, which stems from perturbative QCD, and the
Gribov's diffusion in impact parameters. It is shown an ability to describe the
data at rather large values of $\alpha_S$.


Abstract:
  Soft glassy materials such as mayonnaise, wet clays, or dense microgels
display under external shear a solid-to-liquid transition. Such a shear-induced
transition is often associated with a non-monotonic stress response, in the
form of a stress maximum referred to as "stress overshoot". This ubiquitous
phenomenon is characterized by the coordinates of the maximum in terms of
stress $\sigma_\text{M}$ and strain $\gamma_\text{M}$ that both increase as
weak power laws of the applied shear rate. Here we rationalize such power-law
scalings using a continuum model that predicts two different regimes in the
limit of low and high applied shear rates. The corresponding exponents are
directly linked to the steady-state rheology and are both associated with the
nucleation and growth dynamics of a fluidized region. Our work offers a
consistent framework for predicting the transient response of soft glassy
materials upon start-up of shear from the local flow behavior to the global
rheological observables.


Abstract:
  Temporal correspondence - linking pixels or objects across frames - is a
fundamental supervisory signal for the video models. For the panoptic
understanding of dynamic scenes, we further extend this concept to every
segment. Specifically, we aim to learn coarse segment-level matching and fine
pixel-level matching together. We implement this idea by designing two novel
learning objectives. To validate our proposals, we adopt a deep siamese model
and train the model to learn the temporal correspondence on two different
levels (i.e., segment and pixel) along with the target task. At inference time,
the model processes each frame independently without any extra computation and
post-processing. We show that our per-frame inference model can achieve new
state-of-the-art results on Cityscapes-VPS and VIPER datasets. Moreover, due to
its high efficiency, the model runs in a fraction of time (3x) compared to the
previous state-of-the-art approach.


Abstract:
  Lines and circles pose significant scalability challenges in synthetic
geometry. A line with $n$ points implies ${n \choose 3}$ collinearity atoms, or
alternatively, when lines are represented as functions, equality among ${n
\choose 2}$ different lines. Similarly, a circle with $n$ points implies ${n
\choose 4}$ cocyclicity atoms or equality among ${n \choose 3}$ circumcircles.
We introduce a new mathematical concept of $k$-equivalence relations, which
generalizes equality ($k=1$) and includes both lines ($k=2$) and circles
($k=3$), and present an efficient proof-producing procedure to compute the
closure of a $k$-equivalence relation.


Abstract:
  We present a study of the incidence of active galactic nucleus (AGN) in a
sample of major merging systems at 0.3<z<2.5. Galaxies in this merger sample
have projected separations between 3 to 15 kpc and are selected from the
CANDELS/3D-HST catalogs using a peak-finding algorithm. AGNs in mergers and
non-mergers are identified on the basis of their X-ray emission, optical lines,
mid-infrared colors, and radio emission. Among galaxies with adequate
measurements to find potential AGNs, we find a similar fraction of AGNs in
mergers (16.4%) compared to the fraction found in non-merging galaxies (15.4%).
In mergers, this fraction is obtained by assuming that, in unresolved
observations, only one of the merging galaxies is the AGN source. The
similarity between the fractions is possibly due to the higher availability of
cold gas at high redshifts, where the excess of nuclear activity as a result of
merging is less important than at lower redshifts. Star-forming galaxies have a
higher incidence of AGNs than quiescent galaxies. In particular, starbursts in
mergers are the most common sites of AGN activity since they present higher AGN
fractions and black hole accretion rates. We find no clear correlation between
the black hole accretion rate and the galaxy properties (i.e., star-formation
rate, stellar mass) in mergers and non-mergers. However, mergers seem to have a
higher correlation with star formation than non-mergers, which possibly
indicates that the merging process is starting to influence the star formation
and AGN activity even at this pre-coalescence stage.


Abstract:
  We consider the weighted Sobolev spaces associated with non-isotropic
dilations of Calder\'on-Torchinsky and characterize the spaces by the square
functions of Marcinkiewicz type including those defined with repeated uses of
averaging operation.


Abstract:
  We forecast the reionization history constraints, inferred from Lyman-alpha
damping wing absorption features, for a future sample of $\sim 20$ $z \geq 6$
gamma-ray burst (GRB) afterglows. We describe each afterglow spectrum by a
three-parameter model. First, L characterizes the size of the ionized region
(the "bubble size") around a GRB host halo. Second, $\langle{x_{\rm
HI}\rangle}$ is the volume-averaged neutral fraction outside of the ionized
bubble around the GRB, which is approximated as spatially uniform. Finally,
$N_{\mathrm{HI}}$ denotes the column-density of a local damped Lyman-alpha
absorber (DLA) associated with the GRB host galaxy. The size distribution of
ionized regions is extracted from a numerical simulation of reionization, and
evolves strongly across the Epoch of Reionization (EoR). The model DLA column
densities follow the empirical distribution determined from current GRB
afterglow spectra. We use a Fisher matrix formalism to forecast the
$\langle{x_{\rm HI}(z)\rangle}$ constraints that can be obtained from follow-up
spectroscopy of afterglows with SNR = 20 per R=3,000 resolution element at the
continuum. We find that the neutral fraction may be determined to better than
10-15\% (1-$\sigma$) accuracy from this data across multiple independent
redshift bins at $z \sim 6-10$, spanning much of the EoR, although the
precision degrades somewhat near the end of reionization. A more futuristic
survey with $80$ GRB afterglows at $z \geq 6$ can improve the precision here by
a factor of $2$ and extend measurements out to $z \sim 14$. We further discuss
how these constraints may be combined with estimates of the escape fraction of
ionizing photons, derived from the DLA column density distribution towards GRBs
extracted at slightly lower redshift. This combination will help in testing
whether we have an accurate census of the sources that reionized the universe.


Abstract:
  The first chapter is a critical review and a case study in eBusiness, with
special attention to the digital currencies resource and its possibilities. 2.
chapter attempts to incorporate the UTAUT model with perceived risk theory to
explore its impact on the intention to use m-government services. 3. chapter
aims to assess the level of gender inclusivity in the municipal e-procurement
processes in the City of Johannesburg as a case study. It uses a GAD approach.
4. chapter examines the impediments that derail the intensive uptake of
eLearning programmes in a particular higher education institution. The study
adopted an inductive research paradigm that followed a qualitative research
strategy. Data were collected by means of one-on-one in-depth interviews from
selected faculty members at a nominated institution of higher learning. 5.
chapter investigated the role of KMS in enhancing the export performance of
firms operating within the manufacturing sector in Zimbabwe. The study used a
quantitative approach in which a survey questionnaire was distributed to 555
managers drawn from 185 manufacturing firms based in Harare. Data analyses
involved the use of descriptive statistics, Spearman correlations and
regression analysis. In the sixth chapter, a survey was undertaken on 131 SMEs
from the Pelagonija region in order to determine the current level of SME
digitalization within the region. It is aimed to compare with the EU average
and to make conclusions on the impact of the SME digitalization on region GDP
growth as well as revenues collection. The last chapter s purpose was to
develop a measuring and modelling framework, an instrument of IBSQ for the
South African banking sector. Snowball and convenience sampling, both
non-probability techniques were used to recruit participants for the study. A
total of 310 Internet banking customer responses were utilised in the analysis.


Abstract:
  An almost Fuchsian manifold is a hyperbolic 3-manifold of the type $S\times
\mathbb{R}$ which admits a closed minimal surface (homeomorphic to $S$) with
the maximum principal curvature $\lambda_0 <1$, while a weakly almost Fuchsian
manifold is of the same type but it admits a closed minimal surface with
$\lambda_0 <= 1$. We first prove that any weakly almost Fuchsian manifold is in
fact quasi-Fuchsian, and we construct a Canary-Storm type compactification for
the weakly almost Fuchsian space. We apply these results to prove uniform upper
bounds on the volume of the convex core and Hausdorff dimension for the limit
set of weakly almost Fuchsian manifolds and to give examples of quasi-Fuchsian
manifolds which admit unique minimal surfaces (resp. stable minimal surfaces)
without being almost Fuchsian (resp. weakly almost Fuchsian). We also show that
for every $g$ there is an $\epsilon$ depending only on $g$ such that if a
closed hyperbolic 3-manifold fibers over the circle with fiber a surface of
genus $g$, then any embedded minimal surface isotopic to the fiber has the
maximum principal curvature $\lambda_0$ larger than $1+\epsilon$.


Abstract:
  We solve time-harmonic Maxwell's equations in anisotropic, spatially
homogeneous media in intersections of $L^p$-spaces. The material laws are
time-independent. The analysis requires Fourier restriction-extension estimates
for perturbations of Fresnel's wave surface. This surface can be decomposed
into finitely many components of the following three types: smooth surfaces
with non-vanishing Gaussian curvature, smooth surfaces with Gaussian curvature
vanishing along one-dimensional submanifolds, but without flat points, and
surfaces with conical singularities. Our estimates are based on new
Bochner-Riesz estimates with negative index for non-elliptic surfaces.


Abstract:
  Previously we constructed Calabi-Yau threefolds by a differential-geometric
gluing method using Fano threefolds with their smooth anticanonical $K3$
divisors in arXiv:1305.0074. In this article we further consider the
diffeomorphic types of the resulting Calabi-Yau threefolds starting from
different pairs of Fano threefolds of Picard number one.


Abstract:
  Electric field measurements of the Time Domain Sampler (TDS) receiver, part
of the Radio and Plasma Waves (RPW) instrument on board Solar Orbiter, often
exhibit very intense broadband wave emissions at frequencies below 20~kHz in
the spacecraft frame. In this paper, we present a year-long study of
electrostatic fluctuations observed in the solar wind at an interval of
heliocentric distances from 0.5 to 1~AU. The RPW/TDS observations provide a
nearly continuous data set for a statistical study of intense waves below the
local plasma frequency. The on-board and continuously collected and processed
properties of waveform snapshots allow for the mapping plasma waves at
frequencies between 200~Hz and 20~kHz. We used the triggered waveform snapshots
and a Doppler-shifted solution of the dispersion relation for wave mode
identification in order to carry out a detailed spectral and polarization
analysis. Electrostatic ion-acoustic waves are the common wave emissions
observed between the local electron and proton plasma frequency in the soler
wind. The occurrence rate of ion-acoustic waves peaks around perihelion at
distances of 0.5~AU and decreases with increasing distances, with only a few
waves detected per day at 0.9~AU. Waves are more likely to be observed when the
local proton moments and magnetic field are highly variable. A more detailed
analysis of more than 10000 triggered waveform snapshots shows the mean wave
frequency at about 3 kHz and wave amplitude about 2.5 mV/m. The wave amplitude
varies as 1/R^(1.38) with the heliocentric distance. The relative phase
distribution between two components of the E-field shows a mostly linear wave
polarization. Electric field fluctuations are closely aligned with the
directions of the ambient field lines. Only a small number (3%) of ion-acoustic
waves are observed at larger magnetic discontinuities.


Abstract:
  Human mobility is crucial to understand the transmission pattern of COVID-19
on spatially embedded geographic networks. This pattern seems unpredictable,
and the propagation appears unstoppable, resulting in over 350,000 death tolls
in the U.S. by the end of 2020. Here, we create the spatiotemporal inter-county
mobility network using 10 TB (Terabytes) trajectory data of 30 million smart
devices in the U.S. in the first six months of 2020. We investigate its bound
percolation by removing the weakly connected edges. The mobility network
becomes vulnerable and prone to reach its criticality and thus experience
surprisingly abrupt phase transitions. Despite the complex behaviors of the
mobility network, we devised a novel approach to identify a small, manageable
set of recurrent critical bridges, connecting the giant component and the
second-largest component. These adaptive links, located across the United
States, played a key role as valves connecting components in divisions and
regions during the pandemic. Beyond, our numerical results unveil that network
characteristics determine the critical thresholds and the bridge locations. The
findings provide new insights into managing and controlling the connectivity of
mobility networks during unprecedented disruptions. The work can also
potentially offer practical future infectious diseases both globally and
locally.


Abstract:
  In the perovskite oxide SrCrO$_{3}$ the interplay between crystal structure,
strain and orbital ordering enables a transition from a metallic to an
insulating electronic structure under certain conditions. We identified a
narrow window of oxygen partial pressure in which highly strained SrCrO$_{3}$
thin films can be grown using radio-frequency (RF) off-axis magnetron
sputtering on three different substrates,
(LaAlO$_{3}$)$_{0.3}$-(Sr$_{2}$TaAlO$_{6}$)$_{0.7}$ (LSAT), SrTiO$_{3}$ (STO)
and DyScO$_{3}$ (DSO). X-ray diffraction and atomic force microscopy confirmed
the quality of the films and a metal-insulator transition driven by the
substrate induced strain was demonstrated.


Abstract:
  In this survey we provide an overview of our recent results concerning Ricci
de Turck flow on spaces with isolated conical singularities. The crucial
characteristic of the flow is that it preserves the conical singularity. Under
certain conditions, Ricci flat metrics with isolated conical singularities are
stable and positive scalar curvature is preserved under the flow. We also
discuss the relation to Perelman's entropies in the singular setting, and
outline open questions and future reseach directions.


Abstract:
  For given quantum (non-commutative) spaces $\mathbb{P}$ and $\mathbb{O}$ we
study the quantum space of maps $\mathbb{M}_{\mathbb{P},\mathbb{O}}$ from
$\mathbb{P}$ to $\mathbb{O}$. In case of finite quantum spaces these objects
turn out to be behind a large class of maps which generalize the classical
$\mathrm{qc}$-correlations known from quantum information theory to the setting
of quantum input and output sets. We prove a number of important functorial
properties of the mapping
$(\mathbb{P},\mathbb{O})\mapsto\mathbb{M}_{\mathbb{P},\mathbb{O}}$ and use them
to study various operator algebraic properties of the $\mathrm{C}^*$-algebras
$\operatorname{C}(\mathbb{M}_{\mathbb{P},\mathbb{O}})$ such as the lifting
property and residual finite dimensionality. Inside
$\operatorname{C}(\mathbb{M}_{\mathbb{P},\mathbb{O}})$ we construct a universal
operator system $\mathbb{S}_{\mathbb{P},\mathbb{O}}$ related to $\mathbb{P}$
and $\mathbb{O}$ and show, among other things, that the embedding
$\mathbb{S}_{\mathbb{P},\mathbb{O}}\subset\operatorname{C}(\mathbb{M}_{\mathbb{P},\mathbb{O}})$
is hyperrigid, $\operatorname{C}(\mathbb{M}_{\mathbb{P},\mathbb{O}})$ is the
$\mathrm{C}^*$-envelope of $\mathbb{S}_{\mathbb{P},\mathbb{O}}$ and that a
large class of non-signalling correlations on the quantum sets $\mathbb{P}$ and
$\mathbb{O}$ arise from states on
$\operatorname{C}(\mathbb{M}_{\mathbb{P},\mathbb{O}})\otimes_{\rm{max}}\operatorname{C}(\mathbb{M}_{\mathbb{P},\mathbb{O}})$
as well as states on the commuting tensor product
$\mathbb{S}_{\mathbb{P},\mathbb{O}}\otimes_{\rm{c}}\mathbb{S}_{\mathbb{P},\mathbb{O}}$.
Finally we introduce and study the notion of a synchronous correlation with
quantum input and output sets, prove several characterizations of such
correlations and their relation to traces on
$\operatorname{C}(\mathbb{M}_{\mathbb{P},\mathbb{O}})$.


Abstract:
  Along with the increasing popularity of agile software development, software
work has become much more social than ever. Contemporary software teams rely on
a variety of collaborative practices, such as pair programming, the topic of
our study. Many agilists advocated the importance of collocation, face-to-face
interaction, and physical artefacts incorporated in the shared workspace, which
the COVID-19 pandemic made unavailable; most software companies around the
world were forced to send their engineers to work from home. As software
projects and teams overnight turned into dis-tributed collaborations, we
question what happened to the pair programming practice in the work-from-home
mode. This paper reports on a longitudinal study of remote pair programming in
two companies. We conducted 38 interviews with 30 engineers from Norway,
Sweden, and the USA, and used the results of a survey in one of the case
companies. Our study is unique as we collected the data longitudinally in
April/May 2020, Sep/Oct 2020, and Jan/Feb 2021. We found that pair programming
has decreased and some interviewees report not pairing at all for almost a full
year. The experiences of those who paired vary from actively co-editing the
code by using special tools to more passively co-reading and discussing the
code and solutions by sharing the screen. Finally, we found that the interest
in and the use of PP over time, since the first months of forced work from home
to early 2021, has admittedly increased, also as a social practice.


Abstract:
  We study Naruse-Newton coefficients, which are obtained from expanding
descent polynomials in a Newton basis introduced by Jiradilok and McConville.
These coefficients $C_0, C_1, \ldots$ form an integer sequence associated to
each finite set of positive integers. For fixed nonnegative integers $a<b$, we
examine the set $R_{a, b}$ of all ratios $\frac{C_a}{C_b}$ over finite sets of
positive integers. We characterize finite sets for which $\frac{C_a}{C_b}$ is
minimized and provide a construction to prove $R_{a, b}$ is unbounded above. We
use this construction to obtain results on the closure of $R_{a, b}$. We also
examine properties of Naruse-Newton coefficients associated with doubleton
sets, such as unimodality and log-concavity. Finally, we find an explicit
formula for all ratios $\frac{C_a}{C_b}$ of Naruse-Newton coefficients
associated with ribbons of staircase shape.


Abstract:
  The tilted balance among competing interactions can yield a rich variety of
ground states of quantum matter. In most Ce-based heavy fermion systems, this
can often be qualitatively described by the famous Doniach phase diagram, owing
to the competition between the Kondo screening and the
Ruderman-Kittel-Kasuya-Yoshida exchange interaction. Here, we report an unusual
pressure-temperature phase diagram beyond the Doniach one in CeCuP2. At ambient
pressure, CeCuP2 displays typical heavy-fermion behavior, albeit with a very
low carrier density. With lowering temperature, it shows a crossover from a non
Fermi liquid to a Fermi liquid at around 2.4 K. But surprisingly, the Kondo
coherence temperature decreases with increasing pressure, opposite to that in
most Ce-based heavy fermion compounds. Upon further compression, two
superconducting phases are revealed. At 48.0 GPa, the transition temperature
reaches 6.1 K, the highest among all Ce-based heavy fermion superconductors. We
argue for possible roles of valence tuning and fluctuations associated with its
special crystal structure in addition to the hybridization effect. These
unusual phase diagrams suggest that CeCuP2 is a novel platform for studying the
rich heavy fermions physics beyond the conventional Doniach paradigm.


Abstract:
  We present a compression algorithm for parton densities using synthetic
replicas generated from the training of a Generative Adversarial Network (GAN).
The generated replicas are used to further enhance the statistics of a given
Monte Carlo PDF set prior to compression. This results in a compression
methodology that is able to provide a compressed set with smaller number of
replicas and a more adequate representation of the original probability
distribution. We also address the question of whether the GAN could be used as
an alternative mechanism to avoid the fitting of large number of replicas.


Abstract:
  Travellers in autonomous vehicles (AVs) need not to walk to the destination
any more after parking like those in conventional human-driven vehicles (HVs).
Instead, they can drop off directly at the destination and AVs can cruise for
parking autonomously. It is a revolutionary change that such parking autonomy
of AVs may increase the potential parking span substantially and affect the
spatial parking equilibrium. Given this, from urban planners' perspective, it
is of great necessity to reconsider the planning of parking supply along the
city. To this end, this paper is the first to examine the spatial parking
equilibrium considering the mix of AVs and HVs with parking cruising effect. It
is found that the equilibrium solution of travellers' parking location choices
can be biased due to the ignorance of cruising effects. On top of that, the
optimal parking span of AVs at given parking supply should be no less than that
at equilibrium. Besides, the optimal parking planning to minimize the total
parking cost is also explored in a bi-level parking planning design problem
(PPDP). While the optimal differentiated pricing allows the system to achieve
optimal parking distribution, this study suggests that it is beneficial to
encourage AVs to cruise further to park by reserving less than enough parking
areas for AVs.


Abstract:
  We propose a nanoscale device consisting of a double quantum dot with strong
intra- and inter- dot Coulomb repulsions. In this design, the current can only
flow through the lower dot, but is triggered by the gate-controlled occupancy
of the upper dot. At low temperatures, our calculations predict the double dot
to pass through a narrow Kondo regime, resulting in highly sensitive switching
characteristics between three well-defined states : insulating, normal
conduction and resonant conduction.


Abstract:
  In this paper we explore a special class of metric spaces called smocked
metric spaces and study their tangent cones at infinity. We prove that under
the right hypotheses, the rescaled limits of balls converge in both the
Gromov-Hausdorff and Intrinsic Flat sense to normed spaces. This paper will be
applied in upcoming work by Kazaras and Sormani concerning Gromov's conjectures
on the properties of GH and SWIF limits of Riemannian manifolds with positive
scalar curvature.


Abstract:
  We consider training models on private data that are distributed across user
devices. To ensure privacy, we add on-device noise and use secure aggregation
so that only the noisy sum is revealed to the server. We present a
comprehensive end-to-end system, which appropriately discretizes the data and
adds discrete Gaussian noise before performing secure aggregation. We provide a
novel privacy analysis for sums of discrete Gaussians and carefully analyze the
effects of data quantization and modular summation arithmetic. Our theoretical
guarantees highlight the complex tension between communication, privacy, and
accuracy. Our extensive experimental results demonstrate that our solution is
essentially able to match the accuracy to central differential privacy with
less than 16 bits of precision per value.


Abstract:
  Among spin-crossover complexes, Fe-porphyrin (FeP) stands out for molecular
spintronic applications: An intricate, yet favourable balance between ligand
fields, charge transfer, and the Coulomb interaction makes FeP highly
manipulable, while its planar structure facilitates device integration. Here,
we theoretically design a mechanical spin-switch device in which external
strain triggers the intrinsic magneto-structural coupling of FeP through a
purely organic embedding. Exploiting the chemical compatibility and
stretchability of graphene nanoribbon electrodes, we overcome common
reliability and reproducibility issues of conventional inorganic setups. The
competition between the Coulomb interaction and distortion-induced changes in
ligand fields requires methodologies beyond the state-of-the-art: Combining
density functional theory with many-body techniques, we demonstrate
experimentally feasible tensile strain to trigger a low-spin ($S=1$) to
high-spin ($S=2$) crossover. Concomitantly, the current through the device
toggles by over an order of magnitude, adding a fully planar mechanical
current-switch unit to the panoply of molecular spintronics.


Abstract:
  In this paper, we study the topological properties of complex polynomial
Hamiltonian differential systems of degree $n$ having an isochronous center of
Morse type. Firstly, we prove that if the critical level curve possessing an
isochronous center contains only a single singular point, then the vanishing
cycle associated to this center represents a zero homology cycle on the compact
Riemann surface of a generic level curve. Our result provides a positive answer
to a question asked by L. Gavrilov under a quite simple condition and can be
applied to achieve an equivalent description of the Jacobian conjecture on
$\mathbb{C}^2$. Secondly, we obtain a very simple but useful
  necessary condition for isochronicity of Hamiltonian systems, which is that
the $(n+1)$-degree part of the Hamiltonian function must have a factor with
multipicity no less than $(n+1)/2$. Thirdly, we show a relation between
Gavrilov's question and the conjecture proposed by X. Jarque and J.
Villadelprat on the non-isochronicity of real Hamiltonian systems of even
degree $n$.


Abstract:
  This paper analyzes the performance of maximum-ratio transmission
(MRT)/maximum-ratio combining (MRC) scheme in a dual-hop non-orthogonal
multiple access (NOMA) full-duplex (FD) relay networks in the presence of
residual hardware impairments (RHIs). The effects of channel estimation errors
(CEEs) and imperfect successive interference cancellation are also considered
for a realistic performance analysis. In the network, the base station and
multiple users utilize MRT and MRC, respectively, while a dedicated relay
consisting of two antennas, one for receiving and the other for broadcasting,
operates in amplify-and-forward mode. For performance criterion, exact outage
probability (OP) expression is derived for Nakagami-m fading channels.
Furthermore, a tight lower bound and asymptotic expressions are also derived to
provide more insights into the obtained OP in terms of diversity order and
array gain. The obtained numerical results demonstrate the importance of
loop-interference cancellation process at FD relay in order for the
investigated system to perform better than half-duplex-NOMA counterpart. Also,
a performance trade-off between the MRT and MRC schemes is observed in the
presence of CEEs among users. Furthermore, it is shown that RHIs have a
significant effect on the performance of users with lower power coefficients,
however it does not change the diversity order. RHIs and CEEs have the most and
least deterioration effects on the system performance, respectively.


Abstract:
  This paper reviews the first NTIRE challenge on quality enhancement of
compressed video, with a focus on the proposed methods and results. In this
challenge, the new Large-scale Diverse Video (LDV) dataset is employed. The
challenge has three tracks. Tracks 1 and 2 aim at enhancing the videos
compressed by HEVC at a fixed QP, while Track 3 is designed for enhancing the
videos compressed by x265 at a fixed bit-rate. Besides, the quality enhancement
of Tracks 1 and 3 targets at improving the fidelity (PSNR), and Track 2 targets
at enhancing the perceptual quality. The three tracks totally attract 482
registrations. In the test phase, 12 teams, 8 teams and 11 teams submitted the
final results of Tracks 1, 2 and 3, respectively. The proposed methods and
solutions gauge the state-of-the-art of video quality enhancement. The homepage
of the challenge: https://github.com/RenYang-home/NTIRE21_VEnh


Abstract:
  Deep neural networks have achieved promising performance in supervised point
cloud applications, but manual annotation is extremely expensive and
time-consuming in supervised learning schemes. Unsupervised domain adaptation
(UDA) addresses this problem by training a model with only labeled data in the
source domain but making the model generalize well in the target domain.
Existing studies show that self-supervised learning using both source and
target domain data can help improve the adaptability of trained models, but
they all rely on hand-crafted designs of the self-supervised tasks. In this
paper, we propose a learnable self-supervised task and integrate it into a
self-supervision-based point cloud UDA architecture. Specifically, we propose a
learnable nonlinear transformation that transforms a part of a point cloud to
generate abundant and complicated point clouds while retaining the original
semantic information, and the proposed self-supervised task is to reconstruct
the original point cloud from the transformed ones. In the UDA architecture, an
encoder is shared between the networks for the self-supervised task and the
main task of point cloud classification or segmentation, so that the encoder
can be trained to extract features suitable for both the source and the target
domain data. Experiments on PointDA-10 and PointSegDA datasets show that the
proposed method achieves new state-of-the-art performance on both
classification and segmentation tasks of point cloud UDA. Code will be made
publicly available.


Abstract:
  Distant supervision (DS) is a well established technique for creating
large-scale datasets for relation extraction (RE) without using human
annotations. However, research in DS-RE has been mostly limited to the English
language. Constraining RE to a single language inhibits utilization of large
amounts of data in other languages which could allow extraction of more diverse
facts. Very recently, a dataset for multilingual DS-RE has been released.
However, our analysis reveals that the proposed dataset exhibits unrealistic
characteristics such as 1) lack of sentences that do not express any relation,
and 2) all sentences for a given entity pair expressing exactly one relation.
We show that these characteristics lead to a gross overestimation of the model
performance. In response, we propose a new dataset, DiS-ReX, which alleviates
these issues. Our dataset has more than 1.5 million sentences, spanning across
4 languages with 36 relation classes + 1 no relation (NA) class. We also modify
the widely used bag attention models by encoding sentences using mBERT and
provide the first benchmark results on multilingual DS-RE. Unlike the competing
dataset, we show that our dataset is challenging and leaves enough room for
future research to take place in this field.


Abstract:
  Coronal upflows at the edges of active regions (AR), which are a possible
source of slow solar wind, have been found to connect with dynamics in the
transition region. To infer at what scale transition region dynamics connect to
AR upflows, we investigate the statistical properties of the small-scale
dynamics in the transition region underneath the upflows at the edge of AR NOAA
11934. With observations from the Interface Region Imaging Spectragraph (IRIS),
we found that the Si IV 1403\,\AA\ Doppler map consists of numerous
blue-shifted and red-shifted patches mostly with sizes less than 1\,$Mm^2$. The
blue-shifted structures in the transition region tend to be brighter than the
red-shifted ones, but their nonthermal velocities have no significant
difference. With the SWAMIS feature tracking procedure, in IRIS slit-jaw
1400\,\AA\ images we found that dynamic bright dots with an average size of
about 0.3\,$Mm^2$ and lifetimes mostly less than 200\,s spread all over the
region. Most of the bright dots appear to be localised, without clear signature
of propagation of plasma to a long distance on the projection plane. Surge-like
motions with speeds about 15 km/s could be seen in some events at the
boundaries of the upflow region, where the magnetic field appear to be
inclined. We conclude that the transition region dynamics connecting to coronal
upflows should occur in very fine scale, suggesting that the corresponding
coronal upflows should also be highly-structured. It is also plausible that the
transition region dynamics might just act as stimulation at the coronal base
that then drives the upflows in the corona.


Abstract:
  The probabilistic learning on manifolds (PLoM) introduced in 2016 has solved
difficult supervised problems for the ``small data'' limit where the number N
of points in the training set is small. Many extensions have since been
proposed, making it possible to deal with increasingly complex cases. However,
the performance limit has been observed and explained for applications for
which $N$ is very small (50 for example) and for which the dimension of the
diffusion-map basis is close to $N$. For these cases, we propose a novel
extension based on the introduction of a partition in independent random
vectors. We take advantage of this novel development to present improvements of
the PLoM such as a simplified algorithm for constructing the diffusion-map
basis and a new mathematical result for quantifying the concentration of the
probability measure in terms of a probability upper bound. The analysis of the
efficiency of this novel extension is presented through two applications.


Abstract:
  Confidentiality hinders the publication of authentic, labeled datasets of
personal and enterprise data, although they could be useful for evaluating
knowledge graph construction approaches in industrial scenarios. Therefore, our
plan is to synthetically generate such data in a way that it appears as
authentic as possible. Based on our assumption that knowledge workers have
certain habits when they produce or manage data, generation patterns could be
discovered which can be utilized by data generators to imitate real datasets.
In this paper, we initially derived 11 distinct patterns found in real
spreadsheets from industry and demonstrate a suitable generator called Data
Sprout that is able to reproduce them. We describe how the generator produces
spreadsheets in general and what altering effects the implemented patterns
have.