title:
GAF-NAU: Gramian Angular Field encoded Neighborhood Attention U-Net for
  Pixel-Wise Hyperspectral Image Classification

abstract:
  Hyperspectral image (HSI) classification is the most vibrant area of research
in the hyperspectral community due to the rich spectral information contained
in HSI can greatly aid in identifying objects of interest. However, inherent
non-linearity between materials and the corresponding spectral profiles brings
two major challenges in HSI classification: interclass similarity and
intraclass variability. Many advanced deep learning methods have attempted to
address these issues from the perspective of a region/patch-based approach,
instead of a pixel-based alternate. However, the patch-based approaches
hypothesize that neighborhood pixels of a target pixel in a fixed spatial
window belong to the same class. And this assumption is not always true. To
address this problem, we herein propose a new deep learning architecture,
namely Gramian Angular Field encoded Neighborhood Attention U-Net (GAF-NAU),
for pixel-based HSI classification. The proposed method does not require
regions or patches centered around a raw target pixel to perform 2D-CNN based
classification, instead, our approach transforms 1D pixel vector in HSI into 2D
angular feature space using Gramian Angular Field (GAF) and then embed it to a
new neighborhood attention network to suppress irrelevant angular feature while
emphasizing on pertinent features useful for HSI classification task.
Evaluation results on three publicly available HSI datasets demonstrate the
superior performance of the proposed model.


title:
Multi-Component Second-Order Dissipative Relativistic Hydrodynamics with
  Binary Reactive Collisions

abstract:
  We derive the multi-component second-order dissipative relativistic
hydrodynamic equations using the moment-expansion method. By computing the
transport coefficients using hard-sphere interactions, we investigate the role
of multiple components and the reactive collisions. We find that both of these
factors increase the effective cross-section and hence decrease the transport
coefficients and relaxation times. We further compute such transport properties
using leading-order perturbative QCD cross-sections. For both types of
cross-sections, we find that the ratio between vector current relaxation time
and conductivity for a multi-component fluid is notably different from that for
a single-component fluid. Therefore, the current study provides a more
applicable guideline for such a ratio in phenomenological hydrodynamics
simulations.


title:
Optimisation of Thimble Simulations and Quantum Dynamics of Multiple
  Fields in Real Time

abstract:
  We apply the Generalised Thimble approach to the computation of exact path
integrals and correlators in real-time quantum field theory. We first
investigate the details of the numerical implementation and ways of optimizing
the algorithm. We subsequently apply the method to an interacting two-field
system in 0+1 dimensions, illustrating the scope for addressing realistic
physical processes using real-time Generalised Thimble computations.


title:
Investigation of two-photon 2s -> 1s decay in one-electron and one-muon
  ions

abstract:
  We have studied the radiative decay of the 2s state of one-electron and
one-muon ions, where the two-photon mechanism plays an important role. Due to
the nuclear size corrections the radiative decay of the 2s state in the
electron and muon ions is qualitatively different. Based on the accurate
relativistic calculation, we introduced a two-parameter approximation, which
makes it possible to describe the two-photon angular-differential transition
probability for the polarized emitted photons with high accuracy. The emission
of photons with linear and circular polarizations was studied separately. We
also investigated the transition probabilities for the polarized initial and
final states. The investigation was performed for ions with atomic numbers 1 <
Z < 120.


title: Short-time asymptotics for non self-similar stochastic volatility models

abstract:
  We provide a short-time large deviation principle (LDP) for stochastic
volatility models, where the volatility is expressed as a function of a
Volterra process. This LDP holds under suitable conditions, but does not
require any self-similarity assumption on the Volterra process. For this
reason, we are able to apply such LDP to two notable examples of non
self-similar rough volatility models: models where the volatility is given as a
function of a log-modulated fractional Brownian motion [Bayer et al.,
Log-modulated rough stochastic volatility models. SIAM J. Financ. Math, 2021,
12(3), 1257-1284], and models where it is given as a function of a fractional
Ornstein-Uhlenbeck (fOU) process [Gatheral et al., Volatility is rough. Quant.
Finance, 2018, 18(6), 933-949]. In both cases we derive consequences for
short-maturity European option prices and implied volatility surfaces. In the
fOU case we also discuss moderate deviations pricing and simulation results.


title: Reliable data delivery in ICN-IoT environments

abstract:
  In an IoT environment, which is characterized by a multitude of
interconnected smart devices with sensing and computational capabilities, many
applications are (i) content-based, that is, they are only interested in
finding a given type of content rather than the location of data, and (ii)
contextualized, that is, the content is generated and consumed in the proximity
of the user. In this context, the Information-Centric Networking (ICN) paradigm
is an appealing model for efficiently retrieving application data, and the
service decentralization towards the network edge helps to reduce the core
network load being the data produced by IoT devices mainly confined in the area
where they are generated. MobCCN is an ICN-based data delivery protocol that we
designed for operating efficiently in such context [1][2], where static and
mobile IoT devices are enriched with ICN functions. Specifically, MobCCN
leverages an efficient routing and forwarding protocol, exploiting
opportunistic contacts among IoT mobile devices, to fill the Forwarding
Interest Base (FIB) tables so as to correctly forward Interest packets towards
the intended data producers. In this paper, we aim to enhance the reliability
of MobCCN by exploring different retransmissions mechanisms, such as
retransmissions based on number of duplicate Interests that are received for
the same requested content, periodic retransmissions, single path versus
disjoint multi-path forwarding, hysteresis mechanism and combinations of them.
Extensive simulation results show that, among the analysed MobCCN variants, the
one that implements both periodic retransmissions and a hysteresis-based
retransmission process ensures the highest delivery rates (up to 95\%) and the
shortest network path, with a very limited traffic overhead due to
retransmissions.


title:
Working memory inspired hierarchical video decomposition with
  transformative representations

abstract:
  Video decomposition is very important to extract moving foreground objects
from complex backgrounds in computer vision, machine learning, and medical
imaging, e.g., extracting moving contrast-filled vessels from the complex and
noisy backgrounds of X-ray coronary angiography (XCA). However, the challenges
caused by dynamic backgrounds, overlapping heterogeneous environments and
complex noises still exist in video decomposition. To solve these problems,
this study is the first to introduce a flexible visual working memory model in
video decomposition tasks to provide interpretable and high-performance
hierarchical deep architecture, integrating the transformative representations
between sensory and control layers from the perspective of visual and cognitive
neuroscience. Specifically, robust PCA unrolling networks acting as a
structure-regularized sensor layer decompose XCA into sparse/low-rank
structured representations to separate moving contrast-filled vessels from
noisy and complex backgrounds. Then, patch recurrent convolutional LSTM
networks with a backprojection module embody unstructured random
representations of the control layer in working memory, recurrently projecting
spatiotemporally decomposed nonlocal patches into orthogonal subspaces for
heterogeneous vessel retrieval and interference suppression. This video
decomposition deep architecture effectively restores the heterogeneous profiles
of intensity and the geometries of moving objects against the complex
background interferences. Experiments show that the proposed method
significantly outperforms state-of-the-art methods in accurate moving
contrast-filled vessel extraction with excellent flexibility and computational
efficiency.


title:
New directions and perspectives in elastic instability and turbulence in
  various viscoelastic flow geometries without inertia

abstract:
  I shortly describe the main results on elastically driven instabilities and
elastic turbulence in viscoelastic inertia-less flows with curved streamlines.
Then I describe a theory of elastic turbulence and prediction of elastic waves
at Re<<1 and Wi>>1, which speed depends on the elastic stress similarly to the
Alfven waves in magneto-hydrodynamics and in in a contrast to all other fluid
flows with wave speed depending on medium elasticity. Since the established and
testified mechanism of elastic instability of viscoelastic flows with
curvilinear streamlines becomes ineffective at zero curvature, so parallel
shear flows are proved linearly stable, similar to Newtonian parallel shear
flows. However, the linear stability of parallel shear flows does not imply
their global stability. Here I switch to the main subject, namely a recent
development in inertia-less parallel shear channel flow of polymer solutions.
In such flow, we discover an elastically driven instability, elastic
turbulence, elastic waves, and drag reduction down to relaminarization that
contradicts the linear stability prediction. In this regard, I discuss shortly
normal versus non-normal bifurcations in such flows, flow resistance, velocity
and pressure fluctuations, and spatial and spectral velocity as a function of
Wi at a high elasticity number.


title: An Industrial Experience Report on Retro-inspection

abstract:
  To reinforce the quality of code delivery, especially to improve future
coding quality, one global Information and Communication Technology (ICT)
enterprise has institutionalized a retrospective style inspection (namely
retro-inspection), which is similar to Fagan inspection but differs in terms of
stage, participants, etc. This paper reports an industrial case study that aims
to investigate the experiences and lessons from this software practice. To this
end, we collected and analyzed various empirical evidence for data
triangulation. The results reflect that retro-inspection distinguishes itself
from peer code review by identifying more complicated and underlying defects,
providing more indicative and suggestive comments. Many experienced inspectors
indicate defects together with their rationale behind and offer suggestions for
correction and prevention. As a result, retro-inspection can benefit not only
quality assurance (like Fagan inspection), but also internal audit,
inter-division communication, and competence promotion. On the other side, we
identify several lessons of retro-inspection at this stage, e.g., developers'
acceptance and organizers' predicament, for next-step improvement of this
practice. To be specific, some recommendations are discussed for
retro-inspection, e.g., more adequate preparation and more careful publicity.
This study concludes that most of the expected benefits of retro-inspection can
be empirically confirmed in this enterprise and its value on the progress to
continuous maturity can be recognized organization-wide. The experiences on
executing this altered practice in a large enterprise provide reference value
on code quality assurance to other software organizations.


title: Twisted GGP Problems and Conjectures

abstract:
  In an earlier work, we considered a family of restriction problems for
classical groups (over local and global fields) and proposed precise answers to
these problems using the local and global Langlands correspondence. These
restriction problems were formulated in terms of a pair $W \subset V$ of
orthogonal, Hermitian, symplectic, or skew-Hermitian spaces. In this paper, we
consider a twisted variant of these conjectures in one particular case -- that
of a pair of skew-Hermitian spaces $W = V$.


title: Deep Model-Based Super-Resolution with Non-uniform Blur

abstract:
  We propose a state-of-the-art method for super-resolution with non-uniform
blur. Single-image super-resolution methods seek to restore a high-resolution
image from blurred, subsampled, and noisy measurements. Despite their
impressive performance, existing techniques usually assume a uniform blur
kernel. Hence, these techniques do not generalize well to the more general case
of non-uniform blur. Instead, in this paper, we address the more realistic and
computationally challenging case of spatially-varying blur. To this end, we
first propose a fast deep plug-and-play algorithm, based on linearized ADMM
splitting techniques, which can solve the super-resolution problem with
spatially-varying blur. Second, we unfold our iterative algorithm into a single
network and train it end-to-end. In this way, we overcome the intricacy of
manually tuning the parameters involved in the optimization scheme. Our
algorithm presents remarkable performance and generalizes well after a single
training to a large family of spatially-varying blur kernels, noise levels and
scale factors.


title:
Anisotropic Triebel-Lizorkin spaces and wavelet coefficient decay over
  one-parameter dilation groups, II

abstract:
  This paper is a continuation of [arXiv:2104.14361]. It concerns maximal
characterizations of anisotropic Triebel-Lizorkin spaces
$\dot{\mathbf{F}}^{\alpha}_{p,q}$ for the endpoint case of $p = \infty$ and the
full scale of parameters $\alpha \in \mathbb{R}$ and $q \in (0,\infty]$. In
particular, a Peetre-type characterization of the anisotropic Besov space
$\dot{\mathbf{B}}^{\alpha}_{\infty,\infty} =
\dot{\mathbf{F}}^{\alpha}_{\infty,\infty}$ is obtained. As a consequence, it is
shown that there exist dual molecules of frames and Riesz sequences in
$\dot{\mathbf{F}}^{\alpha}_{\infty,q}$.


title:
An analytical solution of Balitsky-Kovchegov equation using homotopy
  perturbation method

abstract:
  An approximate analytical solution of the Balitsky-Kovchegov (BK) equation
using the homotopy perturbation method (HPM) is suggested in this work. We have
carried out our work in perturbative QCD (pQCD) dipole picture of deep
inelastic scattering (DIS). The BK equation in momentum space with some change
of variables and truncation of the BFKL (Balitsky-Fadin-Kuraev-Lipatov) kernel
can be reduced to the FKPP (Fisher-Kolmogorov-Petrovsky-Piscounov) equation
[Munier and Peschanski 2003]. The observed geometric scaling phenomena are
similar to the travelling wave solution of the FKPP equation. We solved the BK
equation using the HPM. The obtained solution in this work also suggests the
travelling wave nature of the measured scattering amplitude N(k, Y) plotted at
various rapidities. The result obtained in this work can be helpful for
different phenomenological studies in high-density QCD.


title: Determination of CME orientation and consequences for their propagation

abstract:
  The configuration of the interplanetary magnetic field and features of the
related ambient solar wind in the ecliptic and meridional plane are different.
Therefore, one can expect that the orientation of the flux-rope axis of a
coronal mass ejection (CME) influences the propagation of the CME itself.
However, the determination of the CME orientation, especially from image data,
remains a challenging task to perform. This study aims to provide a reference
to different CME orientation determination methods in the near-Sun environment.
Also, it aims to investigate the non-radial flow in the sheath region of the
interplanetary CME (ICME) in order to provide the first proxy to relate the
ICME orientation with its propagation. We investigated 22 isolated CME-ICME
events in the period 2008-2015. We determined the CME orientation in the
near-Sun environment using the following: 1) a 3D reconstruction of the CME
with the graduated cylindrical shell (GCS) model applied to coronagraphic
images provided by the STEREO and SOHO missions and; 2) an ellipse fitting
applied to single spacecraft data from SOHO/LASCO C2 and C3 coronagraphs. In
the near-Earth environment, we obtained the orientation of the corresponding
ICME using in situ plasma and field data and also investigated the non-radial
flow (NRF) in its sheath region. The ability of GCS and ellipse fitting to
determine the CME orientation is found to be limited to reliably distinguish
only between the high or low inclination of the events. Most of the CME-ICME
pairs under investigation were found to be characterized by a low inclination.
For the majority of CME-ICME pairs, we obtain consistent estimations of the
tilt from remote and in situ data. The observed NRFs in the sheath region show
a greater y direction to z direction flow ratio for high-inclination events,
indicating that the CME orientation could have an impact on the CME
propagation.


title:
Spin-momentum locking from topological quantum chemistry: applications
  to multifold fermions

abstract:
  In spin-orbit coupled crystals, symmetries can protect multifold degeneracies
with large Chern numbers and Brillouin zone spanning topological surface
states. In this work, we explore the extent to which the nontrivial topology of
chiral multifold fermions impacts the spin texture of bulk states. To do so, we
formulate a definition of spin-momentum locking in terms of reduced density
matrices. Using tools from the theory of topological quantum chemistry, we show
how the reduced density matrix can be determined from the knowledge of the
basis orbitals and band representation forming the multifold fermion. We show
how on-site spin orbit coupling, crystal field splitting, and Wyckoff position
multiplicity compete to determine the spin texture of states near chiral
fermions. We compute the spin texture of multifold fermions in several
representative examples from space groups $P432$ (207) and $P2_13$ (198). We
show that the winding number of the spin around the Fermi surface can take many
different integer values, from zero all the way to $\pm 7$. Finally, we
conclude by showing how to apply our theory to real materials using the example
of PtGa in space group $P2_13$.


title:
Reconfigurable Intelligent Surface for Near Field Communications:
  Beamforming and Sensing

abstract:
  Reconfigurable intelligent surface (RIS) can improve the communications
between a source and a destination. The surface contains metamaterial that is
configured to reflect the incident wave from the source towards the
destination, especially when there is a blockage in between. Recently,
continuous aperture RIS is proved to have better communication performance than
discrete aperture RIS and has received much attention. However, the
conventional continuous aperture RIS is designed to convert the incoming planar
waves into the outgoing planar waves, which is not the optimal reflecting
scheme when the receiver is not a planar array and is located in the near field
of the RIS. In this paper, we consider two types of receivers in the radiating
near field of the RIS: (1) when the receiver is equipped with a uniform linear
array (ULA), we design RIS coefficient to convert planar waves into cylindrical
waves; (2) when the receiver is equipped with a single antenna, we design RIS
coefficient to convert planar waves into spherical waves. Simulation results
demonstrate that the proposed scheme can reduce energy leakage at the receiver
and thus enhance the channel capacity compared to the conventional scheme. More
interestingly, with cylindrical or spherical wave radiation, the power received
by the receiver is a function of its location and attitude, which could be
utilized to sense the location and the attitude of the receiver with
communication signaling.


title: On intriguing sets in five classes of strongly regular graphs

abstract:
  In this paper, we construct intriguing sets in five classes of strongly
regular graphs defined on nonisotropic points of finite classical polar spaces,
and determine their intersection numbers.


title:
Universal three-state nematicity and magneto-optical Kerr effect in the
  charge density waves in AV$_3$Sb$_5$ (A=Cs, Rb, K)

abstract:
  The kagome lattice provides a fascinating playground to study geometrical
frustration, topology and strong correlations. The newly discovered kagome
metals AV$_3$Sb$_5$ (A=K, Rb, Cs) exhibit various interesting phenomena
including topological band structure and superconductivity. Nevertheless, the
nature of the symmetry breaking in the CDW phase is not yet clear, despite the
fact that it is crucial to understand whether the superconductivity is
unconventional. In this work, we perform scanning birefringence microscopy and
find that six-fold rotation symmetry is broken at the onset of the CDW
transition temperature in all three compounds. Spatial imaging and angle
dependence of the birefringence show a universal three nematic domains that are
120$^\circ$ to each other. We propose staggered CDW orders with a relative
$\pi$ phase shift between layers as a possibility to explain the three-state
nematicity in AV$_3$Sb$_5$. We also perform magneto-optical Kerr effect and
circular dichroism measurements on all three compounds, and the onset of the
both signals is at the CDW transition temperature, indicating broken
time-reversal symmetry and the existence of the long-sought loop currents in
the CDW phase. Our work strongly constrains the nature of the CDWs and sheds
light on possible unconventional superconductivity in AV$_3$Sb$_5$.


title:
H\"{o}lder continuity of Oseledets subspaces for linear cocycles on
  Banach spaces

abstract:
  Let $f:X\to X$ be an invertible Lipschitz transformation on a compact metric
space $X$. Given a H\"{o}lder continuous invertible operator cocycles on a
Banach space and an $f$-invariant ergodic measure, this paper establishes the
H\"{o}lder continuity of Oseledets subspaces over a compact set of arbitrarily
large measure. This extends a result in \cite{Simion16} for invertible operator
cocycles on a Banach space. Finally, this paper proves the H\"{o}lder
continuity in the non-invertible case.


title: Regular Functions on the K-Nilpotent Cone

abstract:
  Let $G$ be a complex reductive algebraic group with Lie algebra
$\mathfrak{g}$ and let $G_{\mathbb{R}}$ be a real form of $G$ with maximal
compact subgroup $K_{\mathbb{R}}$. Associated to $G_{\mathbb{R}}$ is a $K
\times \mathbb{C}^{\times}$-invariant subvariety $\mathcal{N}_{\theta}$ of the
(usual) nilpotent cone $\mathcal{N} \subset \mathfrak{g}^*$. In this article,
we will derive a formula for the ring of regular functions
$\mathbb{C}[\mathcal{N}_{\theta}]$ as a representation of $K \times
\mathbb{C}^{\times}$.
  Some motivation comes from Hodge theory. In arXiv:1206.5547, Schmid and
Vilonen use ideas from Saito's theory of mixed Hodge modules to define
canonical good filtrations on many Harish-Chandra modules (including all
standard and irreducible Harish-Chandra modules). Using these filtrations, they
formulate a conjectural description of the unitary dual. If $G_{\mathbb{R}}$ is
split, and $X$ is the spherical principal series representation of
infinitesimal character $0$, then conjecturally $\mathrm{gr}(X) \simeq
\mathbb{C}[\mathcal{N}_{\theta}]$ as representations of $K \times
\mathbb{C}^{\times}$. So a formula for $\mathbb{C}[\mathcal{N}_{\theta}]$ is an
essential ingredient for computing Hodge filtrations.


title: Bipartite graphs with no $K_6$ minor

abstract:
  A theorem of Mader shows that every graph with average degree at least eight
has a $K_6$ minor, and this is false if we replace eight by any smaller
constant. Replacing average degree by minimum degree seems to make little
difference: we do not know whether all graphs with minimum degree at least
seven have $K_6$ minors, but minimum degree six is certainly not enough. For
every $c>0$ there are arbitrarily large graphs with average degree at least
$8-c$ and minimum degree at least six, with no $K_6$ minor.
  But what if we restrict ourselves to bipartite graphs? The first statement
remains true: for every $c>0$ there are arbitrarily large bipartite graphs with
average degree at least $8-c$ and no $K_6$ minor. But surprisingly, going to
minimum degree now makes a significant difference. We will show that every
bipartite graph with minimum degree at least six has a $K_6$ minor. Indeed, it
is enough that every vertex in the larger part of the bipartition has degree at
least six.


title:
Phase transition dimensionality crossover from two to three dimensions
  in a trapped ultracold atomic Bose gas

abstract:
  The equilibrium properties of a weakly interacting atomic Bose gas across the
Berezinskii-Kosterlitz-Thouless (BKT) and Bose-Einstein condensation (BEC)
phase transitions are numerically investigated through a dimensionality
crossover from two to three dimensions. The crossover is realised by confining
the gas in an experimentally feasible hybridised trap which provides
homogeneity along the planar xy-directions through a box potential in tandem
with a harmonic transverse potential along the transverse z-direction. The
dimensionality is modified by varying the frequency of the harmonic trap from
tight to loose transverse trapping. Our findings, based on a stochastic
(projected) Gross-Pitaevskii equation, showcase a continuous shift in the
character of the phase transition from BKT to BEC, and a monotonic increase of
the identified critical temperature as a function of dimensionality, with the
strongest variation exhibited for small chemical potential values up to
approximately twice the transverse confining potential


title: Hodge stratification in low dimension

abstract:
  We define and study the Hodge stratification for the special fiber of Shimura
varieties defined with the Pappas-Rapoport condition, in the case of low
ramification index ($e \leq 3$). For $e \leq 2$, the Hodge polygon induces a
strong stratification. For $e=3$, one needs to introduce several polygons. They
describe the isomorphism class of the sheaf of differentials with extra
structure, and induce a strong stratification on the variety.


title:
Deviation from Tribimaximal mixing using $A_{4}$ flavour model with five
  extra scalars

abstract:
  A modified neutrino mass model with five extra scalars is constructed using
$A_{4}$ discrete symmetry group. The resultant mass matrix is able to give
necessary deviation from Tribimaximal mixing which reproduces the current
neutrino masses and mixing data with good accuracy. The model gives testable
prediction for the future measurements of the neutrinoless double-beta decay
parameter $|m_{\beta \beta}|$. The analysis is consistent with latest
cosmological bound $\Sigma m_{i}\leq$ 0.12 eV.


title: Temperature-dependent magnetism in Fe foams via spin-lattice dynamics

abstract:
  Spin-lattice dynamics is used to study the magnetic properties of Fe foams.
The temperature dependence of the magnetization in foams is determined as a
function of the fraction of surface atoms in foams, nsurf. The Curie
temperature of foams decreases approximately linearly with nsurf, while the
critical exponent of the magnetization increases considerably more strongly. If
the data are plotted as a function of the fraction of surface atoms, reasonable
agreement with recent data on vacancy-filled Fe crystals and novel data on
void-filled crystals is observed for the critical . Critical temperature and
critical exponent also depend on the coordination of surface atoms. Although
the decrease we find is relatively small, it hints to the possibility of
improved usage of topology to taylor magnetic properties.


title: Degree divisibility in Alperin--McKay correspondences

abstract:
  Let $p$ be a prime, B a $p$-block of a finite group $G$ and $b$ its Brauer
correspondent. According to the Alperin--McKay Conjecture, there exists a
bijection between the set of irreducible characters of height zero of $B$ and
those of $b$. In this paper, we show that such a bijection can be found with
the additional property of being compatible with divisibility of character
degrees whenever $G$ is $p$-solvable.


title:
Physical Modeling using Recurrent Neural Networks with Fast
  Convolutional Layers

abstract:
  Discrete-time modeling of acoustic, mechanical and electrical systems is a
prominent topic in the musical signal processing literature. Such models are
mostly derived by discretizing a mathematical model, given in terms of ordinary
or partial differential equations, using established techniques. Recent work
has applied the techniques of machine-learning to construct such models
automatically from data for the case of systems which have lumped states
described by scalar values, such as electrical circuits. In this work, we
examine how similar techniques are able to construct models of systems which
have spatially distributed rather than lumped states. We describe several novel
recurrent neural network structures, and show how they can be thought of as an
extension of modal techniques. As a proof of concept, we generate synthetic
data for three physical systems and show that the proposed network structures
can be trained with this data to reproduce the behavior of these systems.


title: Another proof of the corona theorem

abstract:
  Let $H^\infty(\Delta)$ be the uniform algebra of bounded analytic functions
on the open unit disc $\Delta$, and let $\mathfrak{M}(H^\infty)$ be the maximal
ideal space of $H^\infty(\Delta)$. By regarding $\Delta$ as an open subset of
$\mathfrak{M}(H^\infty)$, the corona problem asks whether $\Delta$ is dense in
$\mathfrak{M}(H^\infty)$, which was solved affirmatively by L. Carleson.
Extending the cluster value theorem to the case of finitely many functions, we
provide a direct proof of the corona theorem: Let $\phi$ be a homomorphism in
$\mathfrak{M}(H^\infty)$, and let $f_1, f_2, \dots, f_N$ be functions in
$H^\infty(\Delta)$. Then there is a sequence $\{\zeta_j\}$ in $\Delta$
satisfying$f_k(\zeta_j) \rightarrow \phi(f_k)$ for $k=1, 2, \dots, N$. On the
other hand, the corona problem remains unsolved in many general settings, for
instance, certain plane domains, polydiscs and balls, our approach is so
natural that it may be possible to deal with such cases from another point of
view.


title:
Confinement-controlled Water Engenders High Energy Density
  Electrochemical-double-layer Capacitance

abstract:
  The renewable energy sector critically needs low-cost and environmentally
neutral energy storage solutions throughout the entire device life cycle.
However, the limited performance of standard water-based electrochemical
systems prevents their use in certain applications. Meanwhile, recent
fundamental studies revealed dielectric anomalies of water near solid-liquid
interfaces of carbon-based nanomaterials. In contrast to the bulk water
properties, these anomalies of water under nano-confinement and in the presence
of electric fields have not yet been understood and used. Here, we
experimentally study the ability of the interfacial water layer to engender and
store charge in electrochemical double-layer capacitance. We demonstrate the
first prototype of a water-only membrane-electrode assembly. The prototype
exhibits characteristics comparable to existing batteries and supercapacitors
without using electrolytes as ionic carriers. The results provide the impetus
for the development of high-energy-density electrochemical double-layer
capacitors and open up other avenues for ecologically-neutral batteries, fuel
cells, and nanofluidic devices.


title:
Learnable Model Augmentation Self-Supervised Learning for Sequential
  Recommendation

abstract:
  Sequential Recommendation aims to predict the next item based on user
behaviour. Recently, Self-Supervised Learning (SSL) has been proposed to
improve recommendation performance. However, most of existing SSL methods use a
uniform data augmentation scheme, which loses the sequence correlation of an
original sequence. To this end, in this paper, we propose a Learnable Model
Augmentation self-supervised learning for sequential Recommendation (LMA4Rec).
Specifically, LMA4Rec first takes model augmentation as a supplementary method
for data augmentation to generate views. Then, LMA4Rec uses learnable Bernoulli
dropout to implement model augmentation learnable operations. Next,
self-supervised learning is used between the contrastive views to extract
self-supervised signals from an original sequence. Finally, experiments on
three public datasets show that the LMA4Rec method effectively improves
sequential recommendation performance compared with baseline methods.


title: OSSO: Obtaining Skeletal Shape from Outside

abstract:
  We address the problem of inferring the anatomic skeleton of a person, in an
arbitrary pose, from the 3D surface of the body; i.e. we predict the inside
(bones) from the outside (skin). This has many applications in medicine and
biomechanics. Existing state-of-the-art biomechanical skeletons are detailed
but do not easily generalize to new subjects. Additionally, computer vision and
graphics methods that predict skeletons are typically heuristic, not learned
from data, do not leverage the full 3D body surface, and are not validated
against ground truth. To our knowledge, our system, called OSSO (Obtaining
Skeletal Shape from Outside), is the first to learn the mapping from the 3D
body surface to the internal skeleton from real data. We do so using 1000 male
and 1000 female dual-energy X-ray absorptiometry (DXA) scans. To these, we fit
a parametric 3D body shape model (STAR) to capture the body surface and a novel
part-based 3D skeleton model to capture the bones. This provides inside/outside
training pairs. We model the statistical variation of full skeletons using PCA
in a pose-normalized space. We then train a regressor from body shape
parameters to skeleton shape parameters and refine the skeleton to satisfy
constraints on physical plausibility. Given an arbitrary 3D body shape and
pose, OSSO predicts a realistic skeleton inside. In contrast to previous work,
we evaluate the accuracy of the skeleton shape quantitatively on held-out DXA
scans, outperforming the state-of-the-art. We also show 3D skeleton prediction
from varied and challenging 3D bodies. The code to infer a skeleton from a body
shape is available for research at https://osso.is.tue.mpg.de/, and the dataset
of paired outer surface (skin) and skeleton (bone) meshes is available as a
Biobank Returned Dataset. This research has been conducted using the UK Biobank
Resource.


title:
Impact of CDF-II measurement of $M_W$ on the electroweak legacy of the
  LHC Run II

abstract:
  We analyze the impact of the recently released CDF-II measurement of $W$ mass
on the SMEFT analyses of the electroweak precision data as well as Higgs and
electroweak diboson productions. We work in the Hagiwara, Ishihara, Szalapski,
and Zeppenfeld basis in which eight generation-independent operators enter in
the electroweak precision data at tree level and, unlike in the Warsaw basis,
the analysis of that set of data constrains all the eight Wilson coefficients,
without the need of combination with Higgs or electroweak diboson data results.
We show that in the global analysis the determination of the coefficients of
all operators which do not enter the electroweak precision data are barely
affected by the new $M_W$ determination.


title: Fast iterative regularization by reusing data

abstract:
  Discrete inverse problems correspond to solving a system of equations in a
stable way with respect to noise in the data. A typical approach to enforce
uniqueness and select a meaningful solution is to introduce a regularizer.
While for most applications the regularizer is convex, in many cases it is not
smooth nor strongly convex. In this paper, we propose and study two new
iterative regularization methods, based on a primal-dual algorithm, to solve
inverse problems efficiently. Our analysis, in the noise free case, provides
convergence rates for the Lagrangian and the feasibility gap. In the noisy
case, it provides stability bounds and early-stopping rules with theoretical
guarantees. The main novelty of our work is the exploitation of some a priori
knowledge about the solution set, i.e. redundant information. More precisely we
show that the linear systems can be used more than once along the iteration.
Despite the simplicity of the idea, we show that this procedure brings
surprising advantages in the numerical applications. We discuss various
approaches to take advantage of redundant information, that are at the same
time consistent with our assumptions and flexible in the implementation.
Finally, we illustrate our theoretical findings with numerical simulations for
robust sparse recovery and image reconstruction through total variation. We
confirm the efficiency of the proposed procedures, comparing the results with
state-of-the-art methods.


title: Supercongruences for sums involving $\binom ak^m$

abstract:
  Let $p$ be an odd prime, and let $a$ be a rational $p$-adic integer with
$a\not\equiv 0\pmod p$. In this paper, using WZ method we establish the
congruences for $\sum_{k=0}^{p-1} \binom ak^2(-1)^k(1-\frac 2ak)$ modulo $p^2$
and $\sum_{k=0}^{p-1} \binom ak^r(1-\frac 2ak)^s$ modulo $p^4$, where
$r\in\{3,4\}$ and $s\in\{1,3\}$.


title:
Superconducting microwave resonators with non-centrosymmetric
  nonlinearity

abstract:
  We investigated both theoretically and experimentally open-ended coplanar
waveguide resonators with rf SQUIDs embedded in the central conductor at
different positions. These rf SQUIDs can be tuned by an external magnetic field
and thus may exhibit the non-centrosymmetric nonlinearity of $\chi^{(2)}$ type
with suppressed Kerr nonlinearity. We demonstrated that this nonlinearity
allows for efficient mixing of $\lambda/2$ and $\lambda$ modes in the cavity
and thus enables various parametric effects with three wave mixing. These
effects are the second harmonic generation, the half tone generation, the
parametric amplification in both degenerate and non-degenerate regimes and
deamplification in degenerate regime.


title: Complete Minors in Complements of Non-Separating Planar Graphs

abstract:
  We prove that the complement of any non-separating planar graph of order
$2n-3$ contains a $K_n$ minor, and argue that the order $2n-3$ is lowest
possible with this property. To illustrate the necessity of the non-separating
hypothesis, we give an example of a planar graph of order 11 whose complement
does not contain a $K_7$ minor. We argue that the complements of planar graphs
of order 11 are intrinsically knotted. We compute the Hadwiger numbers of
complements of wheel graphs.


title: Density Functional Theory for Electronic Excited States

abstract:
  This chapter provides a basic introduction to excited-state extensions of
density functional theory (DFT), including time-dependent (TD-)DFT in both its
linear-response and its explicitly time-dependent formulations. As applied to
the Kohn-Sham DFT ground state, linear-response theory affords an
eigenvalue-type problem for the excitation energies in a basis of
singly-excited Slater determinants, and is widely known simply as "TDDFT"
despite its frequency-domain formulation. This form of TDDFT is the mostly
widely-used quantum-chemical method for excited states, due to a favorable
combination of low cost and reasonable accuracy. The chapter surveys the
accuracy of linear-response TDDFT, which is generally more sensitive to the
details of the exchange-correlation functional as compared to ground-state DFT,
and also describes some known systemic problems exhibited by this approach.
Some of those problems can be corrected on a case-by-case basis using
orbital-optimized, excited-state self-consistent field (SCF) calculations, in
what is known as excited-state Kohn-Sham theory or a "Delta-SCF" procedure, a
class of methods that includes restricted open-shell Kohn-Sham theory. Recent
successes of these approaches are highlighted, including double excitations and
core-level excitations. Finally, explicitly time-dependent (or "real-time")
TDDFT involves propagation of the molecular orbitals in time following an
external perturbation, according to the Kohn-Sham analogue of the
time-dependent Schroedinger equation. The time-dependent approach has been used
to model strong-field electron dynamics, and in the weak-field limit it
provides a route to broadband spectra based on the time evolution of the dipole
moment function. This is useful for describing high-energy excitations (as in
x-ray spectroscopy) and in systems where the density of states is high, as
demonstrated by a few examples.


title: On a curious integer sequence

abstract:
  This note is devoted to study the recurrent numerical sequence defined by:
$a_0 = 0$, $a_n = \frac{n}{2} a_{n - 1} + (n - 1)!$ ($\forall n \geq 1$).
Although, it is immediate that ${(a_n)}_n$ is constituted of rational numbers
with denominators powers of $2$, it is not trivial that ${(a_n)}_n$ is actually
an integer sequence. In this note, we prove this fact by expressing $a_n$ in
terms of the Genocchi numbers and the Stirling numbers of the first kind. We
derive from our main result several corollaries and we conclude with some
remarks and open problems.


title: Toward Fast, Flexible, and Robust Low-Light Image Enhancement

abstract:
  Existing low-light image enhancement techniques are mostly not only difficult
to deal with both visual quality and computational efficiency but also commonly
invalid in unknown complex scenarios. In this paper, we develop a new
Self-Calibrated Illumination (SCI) learning framework for fast, flexible, and
robust brightening images in real-world low-light scenarios. To be specific, we
establish a cascaded illumination learning process with weight sharing to
handle this task. Considering the computational burden of the cascaded pattern,
we construct the self-calibrated module which realizes the convergence between
results of each stage, producing the gains that only use the single basic block
for inference (yet has not been exploited in previous works), which drastically
diminishes computation cost. We then define the unsupervised training loss to
elevate the model capability that can adapt to general scenes. Further, we make
comprehensive explorations to excavate SCI's inherent properties (lacking in
existing works) including operation-insensitive adaptability (acquiring stable
performance under the settings of different simple operations) and
model-irrelevant generality (can be applied to illumination-based existing
works to improve performance). Finally, plenty of experiments and ablation
studies fully indicate our superiority in both quality and efficiency.
Applications on low-light face detection and nighttime semantic segmentation
fully reveal the latent practical values for SCI. The source code is available
at https://github.com/vis-opt-group/SCI.


title: On Opial-type inequalities via fractional calculus

abstract:
  Inequalities play an important role in pure and applied mathematics. In
particular, Opial inequality plays a main role in the study of the existence
and uniqueness of initial and boundary value problems for differential
equations. It has several interesting generalizations. In this work we prove
some new Opial-type inequalities, and we apply them to generalized
Riemann-Liouville-type integral operators.


title: Imperfect draining vortex as analogue extreme compact object

abstract:
  Motivated by recent experimental progress, we study scalar wave propagation
over an imperfect draining vortex, which can serve as an analogue for rotating
and non-rotating extreme compact objects (ECOs). We encapsulate the absorbing
properties of the analogue ECO by means of an effective boundary located around
the analogue horizon. The presence of reflection at the effective boundary,
characterised by a single parameter $\mathcal{K}$, allows for the existence of
bound states located between the effective vortex core and the angular momentum
barrier. The existence of these bound states leads to an enhanced absorption
when the frequency of the incoming wave matches bound state frequencies, which
result in Breit-Wigner type spectral lines in the absorption spectra. We also
investigate the case of rotating analogue ECOs. In this scenario, some of the
bound states undergo superradiant amplification and become unstable. In both
the rotating and non-rotating case, we calculate numerically
transmission/reflection spectra exhibiting the enhanced
absorption/amplification. We complement our numerical study with WKB estimates
as well as an extension of the P\"oschl-Teller toy model which we solve
analytically. Our simple model exhibits distinctive properties which could be
observed in future analogue gravity experiments. We further argue that the
observation of the spectral lines could be a way to characterise the effective
field theory at play in the vicinity of the vortex core.


title: Murmurations of elliptic curves

abstract:
  We investigate the average value of the $p$th Dirichlet coefficients of
elliptic curves for a prime p in a fixed conductor range with given rank.
Plotting this average yields a striking oscillating pattern, the details of
which vary with the rank. Based on this observation, we perform various
data-scientific experiments with the goal of classifying elliptic curves
according to their ranks.


title: Red Dragon: A Redshift-Evolving Gaussian Mixture Model for Galaxies

abstract:
  Precision-era optical cluster cosmology calls for a precise definition of the
red sequence (RS), consistent across redshift. To this end, we present the Red
Dragon algorithm: an error-corrected multivariate Gaussian mixture model (GMM).
Simultaneous use of multiple colors and smooth evolution of GMM parameters
result in a continuous RS and blue cloud (BC) characterization across redshift,
avoiding the discontinuities of red fraction inherent in swapping RS selection
colors. Based on a mid-redshift spectroscopic sample of SDSS galaxies, a RS
defined by Red Dragon selects quenched galaxies (low specific star formation
rate) with a balanced accuracy of over 90%. This approach to galaxy population
assignment gives more natural separations between RS and BC galaxies than hard
cuts in color--magnitude or color--color spaces. The Red Dragon algorithm is
publicly available at bitbucket.org/wkblack/red-dragon-gamma.


title: Multiple EffNet/ResNet Architectures for Melanoma Classification

abstract:
  Melanoma is the most malignant skin tumor and usually cancerates from normal
moles, which is difficult to distinguish benign from malignant in the early
stage. Therefore, many machine learning methods are trying to make auxiliary
prediction. However, these methods attach more attention to the image data of
suspected tumor, and focus on improving the accuracy of image classification,
but ignore the significance of patient-level contextual information for disease
diagnosis in actual clinical diagnosis. To make more use of patient information
and improve the accuracy of diagnosis, we propose a new melanoma classification
model based on EffNet and Resnet. Our model not only uses images within the
same patient but also consider patient-level contextual information for better
cancer prediction. The experimental results demonstrated that the proposed
model achieved 0.981 ACC. Furthermore, we note that the overall ROC value of
the model is 0.976 which is better than the previous state-of-the-art
approaches.


title: New characterizations of the unit vector basis of $c_0$ or $\ell_p$

abstract:
  Motivated by Altshuler's famous characterization of the unit vector basis of
$c_0$ or $\ell_p$ among symmetric bases, we obtain similar characterizations
among democratic bases and among bidemocratic bases. We also prove a separate
characterization of the unit vector basis of $\ell_1$.


title:
A case for using rotation invariant features in state of the art feature
  matchers

abstract:
  The aim of this paper is to demonstrate that a state of the art feature
matcher (LoFTR) can be made more robust to rotations by simply replacing the
backbone CNN with a steerable CNN which is equivariant to translations and
image rotations. It is experimentally shown that this boost is obtained without
reducing performance on ordinary illumination and viewpoint matching sequences.


title: An effective introduction to the Markov Chain Monte Carlo method

abstract:
  We present an intuitive, conceptual, but semi-rigorous introduction to the
celebrated Markov Chain Monte Carlo method using a simple model of population
dynamics as our motivation and focusing on a few elementary distributions.
Conceptually, the population flow between cities closely resembles the random
walk of a single walker in a state space. We start from two states, then three
states, and finally the setup is fully generalized to many states of both
discrete and continuous distributions. Despite the mathematical simplicity, the
setup remarkably includes all the essential concepts of Markov Chain Monte
Carlo without loss of generality, e.g., ergodicity, global balance and detailed
balance, proposal or selection probability, acceptance probability, up to the
underlying stochastic matrix, and error analysis. Our teaching experience
suggests that most senior undergraduate students in physics can closely follow
these materials without much difficulty.


title: Is there an infinite field whose multiplicative group is indecomposable?

abstract:
  In an earlier paper, we determined the finite fields with indecomposable
multiplicative groups and conjectured that there is no infinite field whose
multiplicative group is indecomposable. In this paper, we prove this conjecture
for several popular classes of fields, including finitely generated fields,
discrete valued fields, fields of Hahn series, local fields, global fields, and
function fields.


title:
Testing the equality of two coefficients of variation: a new Bayesian
  approach

abstract:
  The use of testing procedures for comparing two coefficients of variation
(CVs) of independent populations is not extensively explored in the Bayesian
context. We propose to address this issue through a test based on a measure of
evidence, the Bayesian Discrepancy Measure, recently introduced in the
literature. Computing the Bayesian Discrepancy Measure is straightforward when
the CVs depend on a single parameter of the distribution. In contrast, it
becomes more difficult when this simplification does not occur since more
parameters are involved, requiring often the use of MCMC methods. We derive the
Bayesian Discrepancy Measure and the related test by considering a variety of
distribution assumptions with multiparametric CVs and apply them to real
datasets. As far as we know, some of the examined problems have not yet been
covered in the literature.


title:
ALICE luminosity determination for Pb$-$Pb collisions at
  $\sqrt{s_{\mathrm{NN}}} = 5.02$ TeV

abstract:
  Luminosity determination within the ALICE experiment is based on the
measurement, in van der Meer scans, of the cross sections for visible processes
involving one or more detectors (visible cross sections). In 2015 and 2018, the
Large Hadron Collider provided Pb$-$Pb collisions at a centre-of-mass energy
per nucleon pair of $\sqrt{s_{\rm NN}} = 5.02$ TeV. Two visible cross sections,
associated with particle detection in the Zero Degree Calorimeter (ZDC) and in
the V0 detector, were measured in a van der Meer scan. This article describes
the experimental set-up and the analysis procedure, and presents the
measurement results. The analysis involves a comprehensive study of
beam-related effects and an improved fitting procedure, compared to previous
ALICE studies, for the extraction of the visible cross section. The resulting
uncertainty of the ZDC-based (V0-based) luminosity measurement for the full
sample is 2.3% (2.2%). The inelastic cross section for hadronic interactions in
Pb$-$Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV, obtained by efficiency
correction of the V0-based visible cross section, was measured to be $7.67 \pm
0.24$ b.


title: WebFace260M: A Benchmark for Million-Scale Deep Face Recognition

abstract:
  Face benchmarks empower the research community to train and evaluate
high-performance face recognition systems. In this paper, we contribute a new
million-scale recognition benchmark, containing uncurated 4M identities/260M
faces (WebFace260M) and cleaned 2M identities/42M faces (WebFace42M) training
data, as well as an elaborately designed time-constrained evaluation protocol.
Firstly, we collect 4M name lists and download 260M faces from the Internet.
Then, a Cleaning Automatically utilizing Self-Training (CAST) pipeline is
devised to purify the tremendous WebFace260M, which is efficient and scalable.
To the best of our knowledge, the cleaned WebFace42M is the largest public face
recognition training set and we expect to close the data gap between academia
and industry. Referring to practical deployments, Face Recognition Under
Inference Time conStraint (FRUITS) protocol and a new test set with rich
attributes are constructed. Besides, we gather a large-scale masked face
sub-set for biometrics assessment under COVID-19. For a comprehensive
evaluation of face matchers, three recognition tasks are performed under
standard, masked and unbiased settings, respectively. Equipped with this
benchmark, we delve into million-scale face recognition problems. A distributed
framework is developed to train face recognition models efficiently without
tampering with the performance. Enabled by WebFace42M, we reduce 40% failure
rate on the challenging IJB-C set and rank 3rd among 430 entries on NIST-FRVT.
Even 10% data (WebFace4M) shows superior performance compared with the public
training sets. Furthermore, comprehensive baselines are established under the
FRUITS-100/500/1000 milliseconds protocols. The proposed benchmark shows
enormous potential on standard, masked and unbiased face recognition scenarios.
Our WebFace260M website is https://www.face-benchmark.org.


title:
Light Deflection by Rotating Regular Black Holes with a Cosmological
  Constant

abstract:
  Using the Gauss-Bonnet theorem, we compute and examine the deflection angle
of light rays by rotating regular black holes with a cosmological constant. By
the help of optical geometries, we first deal with the Hayward black holes with
cosmological contributions. Then, we reconsider the study of the Bardeen
solutions. We inspect the cosmological constant effect on the deflection angle
of light rays. Concretely, we find extra cosmological correction terms
generalizing certain obtained findings. Using graphical analysis, we provide a
comparative discussion with respect to the Kerr solutions. The results confirm
that the non-linear electrodynamic charges affect the space-time geometry by
decreasing the deflection angle of light rays by such cosmological black holes.


title: A Bitstream Feature Based Model for Video Decoding Energy Estimation

abstract:
  In this paper we show that a small amount of bit stream features can be used
to accurately estimate the energy consumption of state-of-the-art software and
hardware accelerated decoder implementations for four different video codecs.
By testing the estimation performance on HEVC, H.264, H.263, and VP9 we show
that the proposed model can be used for any hybrid video codec. We test our
approach on a high amount of different test sequences to prove the general
validity. We show that less than 20 features are sufficient to obtain mean
estimation errors that are smaller than 8%. Finally, an example will show the
performance trade-offs in terms of rate, distortion, and decoding energy for
all tested codecs.


title:
Features of Explainability: How users understand counterfactual and
  causal explanations for categorical and continuous features in XAI

abstract:
  Counterfactual explanations are increasingly used to address
interpretability, recourse, and bias in AI decisions. However, we do not know
how well counterfactual explanations help users to understand a systems
decisions, since no large scale user studies have compared their efficacy to
other sorts of explanations such as causal explanations (which have a longer
track record of use in rule based and decision tree models). It is also unknown
whether counterfactual explanations are equally effective for categorical as
for continuous features, although current methods assume they do. Hence, in a
controlled user study with 127 volunteer participants, we tested the effects of
counterfactual and causal explanations on the objective accuracy of users
predictions of the decisions made by a simple AI system, and participants
subjective judgments of satisfaction and trust in the explanations. We
discovered a dissociation between objective and subjective measures:
counterfactual explanations elicit higher accuracy of predictions than
no-explanation control descriptions but no higher accuracy than causal
explanations, yet counterfactual explanations elicit greater satisfaction and
trust than causal explanations. We also found that users understand
explanations referring to categorical features more readily than those
referring to continuous features. We discuss the implications of these findings
for current and future counterfactual methods in XAI.


title:
Surface waves prediction based on long-range acoustic backscattering in
  a mid-frequency range

abstract:
  New data was obtained for a frequency band that had not been so well-studied
for sea surface probing applications before. During the described 2-weeks sea
experiment 1-3 kHz tonal pulses were emitted from a platform, located on the
northern Black Sea shelf, and Doppler spectrum of reverberation was studied. We
believe that this band is worth further studying due the sound propagation
range is large enough to meet practical needs in coastal zone while the
angle-distance resolution is quite moderate. However it is quite difficult to
interpret the obtained data since backscattering spectrum shape is influenced
by a series of effects and has a complicated link to wind waves and currents
parameters. Backscattering of acoustical signals was received for distances
around 2 nautical miles. Significant wave height, dominant wave frequency were
estimated as the result of such signals processing with the use of machine
learning tools. A decision-tree-based mathematical regression model was trained
to solve the inverse problem. Wind waves prediction is in a good agreement with
direct measurements, made on the platform, and machine learning results allow
physical interpretation.


title:
From point forecasts to multivariate probabilistic forecasts: The
  Schaake shuffle for day-ahead electricity price forecasting

abstract:
  Modeling price risks is crucial for economic decision making in energy
markets. Besides the risk of a single price, the dependence structure of
multiple prices is often relevant. We therefore propose a generic and
easy-to-implement method for creating multivariate probabilistic forecasts
based on univariate point forecasts of day-ahead electricity prices. While each
univariate point forecast refers to one of the day's 24 hours, the multivariate
forecast distribution models dependencies across hours. The proposed method is
based on simple copula techniques and an optional time series component. We
illustrate the method for five benchmark data sets recently provided by Lago et
al. (2020). Furthermore, we demonstrate an example for constructing realistic
prediction intervals for the weighted sum of consecutive electricity prices,
as, e.g., needed for pricing individual load profiles.


title: On minimal ideals in pseudo-finite semigroups

abstract:
  A semigroup $S$ is said to be right pseudo-finite if the universal right
congruence can be generated by a finite set $U\subseteq S\times S$, and there
is a bound on the length of derivations for an arbitrary pair $(s,t)\in S\times
S$ as a consequence of those in $U$. This article explores the existence and
nature of a minimal ideal in a right pseudo-finite semigroup. Continuing the
theme started in an earlier work by Dandan et al., we show that in several
natural classes of monoids, right pseudo-finiteness implies the existence of a
completely simple minimal ideal. This is the case for orthodox monoids,
completely regular monoids and right reversible monoids, which include all
commutative monoids. We also show that certain other conditions imply the
existence of a minimal ideal, which need not be completely simple; notably,
this is the case for semigroups in which one of the Green's pre-orders
$\leq_{\mathcal{L}}$ or $\leq_{\mathcal{J}}$ is left compatible with
multiplication. Finally, we establish a number of examples of pseudo-finite
monoids without a minimal ideal. We develop an explicit construction that
yields such examples with additional desired properties, for instance,
regularity or $\mathcal{J}$-triviality.


title: Dark photon kinetic mixing effects for CDF W mass excess

abstract:
  A new $U(1)_X$ gauge boson $X$ primarily interacting with a dark sector can
have renormalizable kinetic mixing with the standard model (SM) $U(1)_Y$ gauge
boson $Y$. This mixing besides introduces interactions of dark photon and dark
sector with SM particles, it also modifies interactions among SM particles. The
modified interactions can be casted into the oblique $S$, $T$ and $U$
parameters. We find that with the dark photon mass larger than the $Z$ boson
mass, the kinetic mixing effects can reduce the tension of the W mass excess
problem reported recently by CDF from $7\sigma$ deviation to within $3 \sigma$
compared with theory prediction. If there is non-abelian kinetic mixing between
$U(1)_X$ and $SU(2)_L$ gauge bosons, in simple renormalizable models of this
type a triplet Higgs is required to generate the mixing. We find that this
triplet with a vacuum expectation value of order 5 GeV can naturally explain
the W mass excess.


title:
Study of charged particle production at high $p_{\rm T}$ using event
  topology in pp, p$-$Pb and Pb$-$Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$
  TeV

abstract:
  This letter reports measurements which characterize the underlying event
associated with hard scatterings at mid-pseudorapidity ($|\eta|<0.8$) in pp,
p$-$Pb and Pb$-$Pb collisions at centre-of-mass energy per nucleon pair,
$\sqrt{s_{\rm NN}}=5.02$ TeV. The measurements are performed with ALICE at the
LHC. The hard scatterings are identified by the leading particle defined as the
charged particle with the largest transverse momentum ($p_{\rm T}$) in the
collision and having $8 < p_{\rm T} < 15$ GeV/$c$. The $p_{\rm T}$ spectra of
associated particles ($0.5 \leq p_{\rm T}<6$ GeV/$c$) are measured in different
azimuthal regions defined with respect to the leading particle direction:
toward, transverse, and away. The associated charged particle yields in the
transverse region are subtracted from those of the away and toward regions. The
remaining jet-like yields are reported as a function of the multiplicity
measured at forward rapidities. The measurements show a suppression of the
jet-like yield in the away region and an enhancement of high-$p_{\rm T}$
associated particles in the toward region in central Pb$-$Pb collisions, as
compared to minimum-bias pp collisions. These observations are consistent with
previous measurements that used two-particle correlations, and with an
interpretation in terms of parton energy loss in a high-density quark gluon
plasma. These yield modifications vanish in peripheral Pb$-$Pb collisions and
are not observed in either high-multiplicity pp or p$-$Pb collisions.


title:
Detection of CO emission lines in the dayside atmospheres of WASP-33b
  and WASP-189b with GIANO

abstract:
  Ultra-hot Jupiters (UHJs) are expected to possess temperature inversion
layers in their dayside atmospheres. Recent thermal emission observations have
discovered several atomic and molecular species along with temperature
inversions in UHJs. We observed the thermal emission spectra of two UHJs
(WASP-33b and WASP-189b) with the GIANO-B high-resolution near-infrared
spectrograph. Using the cross-correlation technique, we detected carbon
monoxide (CO) in the dayside atmospheres of both planets. The detected CO lines
are in emission, which agrees with previous discoveries of iron emission lines
and temperature inversions in the two planets. This is the first detection of
CO lines in emission with high-resolution spectroscopy. Further retrieval work
combining the CO lines with other spectral features will enable a comprehensive
understanding of the atmospheric properties such as temperature structures and
C/O ratios. The detected CO and iron emission lines of WASP-189b have
redshifted radial velocities of several km/s, which likely originate from a
dayside to nightside wind in its atmosphere. Such a redshifted velocity has not
been detected for the emission lines of WASP-33b, suggesting that the
atmospheric circulation patterns of the two UHJs may be different.


title: Physical, subjective and analogical probability

abstract:
  The aim of this paper is to show that the concept of probability is best
understood by dividing this concept into two different types of probability,
namely physical probability and analogical probability. Loosely speaking, a
physical probability is a probability that applies to the outcomes of an
experiment that have been judged as being equally likely on the basis of
physical symmetry. Physical probabilities are arguably in some sense
'objective' and possess all the standard properties of the concept of
probability. On the other hand, an analogical probability is defined by making
an analogy between the uncertainty surrounding an event of interest and the
uncertainty surrounding an event that has a physical probability. Analogical
probabilities are undeniably subjective probabilities and are not obliged to
have all the standard mathematical properties possessed by physical
probabilities, e.g. they may not have the property of additivity or obey the
standard definition of conditional probability. Nevertheless, analogical
probabilities have extra properties, which are not possessed by physical
probabilities, that assist in their direct elicitation, general derivation,
comparison and justification. More specifically, these properties facilitate
the application of analogical probability to real-world problems that can not
be adequately resolved by using only physical probability, e.g. probabilistic
inference about hypotheses on the basis of observed data. Careful definitions
are given of the concepts that are introduced and, where appropriate, examples
of the application of these concepts are presented for additional clarity.


title:
A Multi-Person Video Dataset Annotation Method of Spatio-Temporally
  Actions

abstract:
  Spatio-temporal action detection is an important and challenging problem in
video understanding. However, the application of the existing large-scale
spatio-temporal action datasets in specific fields is limited, and there is
currently no public tool for making spatio-temporal action datasets, it takes a
lot of time and effort for researchers to customize the spatio-temporal action
datasets, so we propose a multi-Person video dataset Annotation Method of
spatio-temporally actions.First, we use ffmpeg to crop the videos and frame the
videos; then use yolov5 to detect human in the video frame, and then use deep
sort to detect the ID of the human in the video frame. By processing the
detection results of yolov5 and deep sort, we can get the annotation file of
the spatio-temporal action dataset to complete the work of customizing the
spatio-temporal action dataset.
https://github.com/Whiffe/Custom-ava-dataset_Custom-Spatio-Temporally-Action-Video-Dataset


title:
On the nodal set of solutions to some sublinear equations without
  homogeneity

abstract:
  We investigate the structure of the nodal set of solutions to \[ -\Delta u =
\lambda_+(u^+)^{p-1}-\lambda_-(u^-)^{q-1} \] where $1 \le p<q<2$, $\lambda_+
>0$, $\lambda_- \ge 0$. The equation is characterized by the sublinear
inhomogeneous character of the right hand-side, which makes difficult to adapt
in a standard way classical tools from free-boundary problems, such as
monotonicity formulas and blow-up arguments. Our main results are: the local
behavior of solutions close to the nodal set; the complete classification of
the admissible vanishing orders, and estimates on the Hausdorff dimension of
the singular set, for local minimizers; the existence of degenerate (not
locally minimal) solutions.


title:
Automated analysis of fibrous cap in intravascular optical coherence
  tomography images of coronary arteries

abstract:
  Thin-cap fibroatheroma (TCFA) and plaque rupture have been recognized as the
most frequent risk factor for thrombosis and acute coronary syndrome.
Intravascular optical coherence tomography (IVOCT) can identify TCFA and assess
cap thickness, which provides an opportunity to assess plaque vulnerability. We
developed an automated method that can detect lipidous plaque and assess
fibrous cap thickness in IVOCT images. This study analyzed a total of 4,360
IVOCT image frames of 77 lesions among 41 patients. To improve segmentation
performance, preprocessing included lumen segmentation, pixel-shifting, and
noise filtering on the raw polar (r, theta) IVOCT images. We used the
DeepLab-v3 plus deep learning model to classify lipidous plaque pixels. After
lipid detection, we automatically detected the outer border of the fibrous cap
using a special dynamic programming algorithm and assessed the cap thickness.
Our method provided excellent discriminability of lipid plaque with a
sensitivity of 85.8% and A-line Dice coefficient of 0.837. By comparing lipid
angle measurements between two analysts following editing of our automated
software, we found good agreement by Bland-Altman analysis (difference 6.7+/-17
degree; mean 196 degree). Our method accurately detected the fibrous cap from
the detected lipid plaque. Automated analysis required a significant
modification for only 5.5% frames. Furthermore, our method showed a good
agreement of fibrous cap thickness between two analysts with Bland-Altman
analysis (4.2+/-14.6 micron; mean 175 micron), indicating little bias between
users and good reproducibility of the measurement. We developed a fully
automated method for fibrous cap quantification in IVOCT images, resulting in
good agreement with determinations by analysts. The method has great potential
to enable highly automated, repeatable, and comprehensive evaluations of TCFAs.


title: Recurrent Lorentzian Weyl spaces and Riccati equation

abstract:
  We describe the local form of all non-closed Lorentzian Weyl structures
$(M,c,\nabla)$ with recurrent curvature tensor. If the dimension of the
manifold is bigger than 3, then the conformal structure is flat, and recurrent
structures are in one-to-one correspondence with the solutions to the Riccati
equation. The recurrent curvature tensor turns out to be a weighted parallel
tensor.


title:
Linearised Calder\'on problem: Reconstruction and Lipschitz stability
  for infinite-dimensional spaces of unbounded perturbations

abstract:
  We investigate a linearised Calder\'on problem in a two-dimensional bounded
simply connected $C^{1,\alpha}$ domain $\Omega$. After extending the linearised
problem for $L^2(\Omega)$ perturbations, we orthogonally decompose $L^2(\Omega)
= \oplus_{k=0}^\infty \mathcal{H}_k$ and prove Lipschitz stability on each of
the infinite-dimensional $\mathcal{H}_k$ subspaces. In particular,
$\mathcal{H}_0$ is the space of square-integrable harmonic perturbations. This
appears to be the first Lipschitz stability result for infinite-dimensional
spaces of perturbations in the context of the (linearised) Calder\'on problem.
Previous optimal estimates with respect to the operator norm of the data map
have been of the logarithmic-type in infinite-dimensional settings. The
remarkable improvement is enabled by using the Hilbert-Schmidt norm for the
Neumann-to-Dirichlet boundary map and its Fr\'echet derivative with respect to
the conductivity coefficient. We also derive a direct reconstruction method
that inductively yields the orthogonal projections of a general $L^2(\Omega)$
perturbation onto the $\mathcal{H}_k$ spaces. If the perturbation is in the
subspace $\oplus_{k=0}^K \mathcal{H}_k$, then the reconstruction method only
requires data corresponding to $2K+2$ specific Neumann boundary values.


title: Interactions in Information Spread

abstract:
  Large quantities of data flow on the internet. When a user decides to help
the spread of a piece of information (by retweeting, liking, posting content),
most research works assumes she does so according to information's content,
publication date, the user's position in the network, the platform used, etc.
However, there is another aspect that has received little attention in the
literature: the information interaction. The idea is that a user's choice is
partly conditioned by the previous pieces of information she has been exposed
to. In this document, we review the works done on interaction modeling and
underline several aspects of interactions that complicate their study. Then, we
present an approach seemingly fit to answer those challenges and detail a
dedicated interaction model based on it. We show our approach fits the problem
better than existing methods, and present leads for future works. Throughout
the text, we show that taking interactions into account improves our
comprehension of information interaction processes in real-world datasets, and
argue that this aspect of information spread is should not be neglected when
modeling spreading processes.


title:
First measurement of antideuteron number fluctuations at energies
  available at the Large Hadron Collider

abstract:
  The first measurement of event-by-event antideuteron number fluctuations in
high energy heavy-ion collisions is presented. The measurements are carried out
at midrapidity ($|\eta| < 0.8$) as a function of collision centrality in
Pb$-$Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV using the ALICE detector.
A significant negative correlation between the produced antiprotons and
antideuterons is observed in all collision centralities. The results are
compared with coalescence calculations, which fail to describe the measurement,
in particular if a correlated production of protons and neutrons is assumed.
Thermal-statistical model calculations describe the data within uncertainties
only for correlation volumes that are different with respect to those
describing proton yields and a similar measurement of net-proton number
fluctuations.


title:
Measurement of the production of charm jets tagged with ${\rm D^0}$
  mesons in pp collisions at $\sqrt{s}$ = 5.02 and 13 TeV

abstract:
  The measurement of the production of charm jets, identified by the presence
of a ${\rm D^0}$ meson in the jet constituents, is presented in proton-proton
collisions at centre-of-mass energies of $\sqrt{s}$ = 5.02 and 13 TeV with the
ALICE detector at the CERN LHC. The ${\rm D^0}$ mesons were reconstructed from
their hadronic decay ${\rm D^0} \rightarrow {\rm K^-}\pi^+$ and the respective
charge conjugate. Jets were reconstructed from ${\rm D^0}$-meson candidates and
charged particles using the anti-$k_{\rm T}$ algorithm, in the jet transverse
momentum range $5<p_{\rm T;chjet}<50$ GeV/$c$, pseudorapidity $|\eta_{\rm jet}|
<0.9-R$, and with the jet resolution parameters $R$ = 0.2, 0.4, 0.6. The
distribution of the jet momentum fraction carried by a ${\rm D^0}$ meson along
the jet axis ($z^{\rm ch}_{||}$) was measured in the range $0.4 < z^{\rm
ch}_{||} < 1.0$ in four ranges of the jet transverse momentum. Comparisons of
results for different collision energies and jet resolution parameters are also
presented. The measurements are compared to predictions from Monte Carlo event
generators based on leading-order and next-to-leading-order perturbative
quantum chromodynamics calculations. A generally good description of the main
features of the data is obtained in spite of a few discrepancies at low $p_{\rm
T;chjet}$. Measurements were also done for $R = 0.3$ at $\sqrt{s}$ = 5.02 TeV
and are shown along with their comparisons to theoretical predictions in an
appendix to this paper.


title:
Thermal and non-thermal X-ray emission from the rotation-powered
  radio/$\gamma$-ray pulsar PSR J1740+1000

abstract:
  We report the results of new XMM-Newton observations of the middle-aged
($\sim$10$^5$ yr) radio pulsar PSR J1740+1000 carried out in 2017-2018. These
long pointings ($\sim$530 ks) show that the non-thermal emission, well
described by a power-law spectrum with photon index $\Gamma=1.80\pm0.17$, is
pulsed with a $\sim$30% pulsed fraction above 2 keV. The thermal emission can
be well fit with the sum of two blackbodies of temperatures $kT_1=70\pm4$ eV,
$kT_2=137\pm7$ eV, $R_1=5.4_{-0.9}^{+1.3}$ km and $R_2=0.70_{-0.13}^{+0.15}$ km
(for a distance of 1.2 kpc). We found no evidence for absorption lines as those
observed in the shorter XMM-Newton observations ($\sim$67 ks) of this pulsar
carried out in 2006. The X-ray thermal and non-thermal components peak in
anti-phase and none of them is seen to coincide in phase with the radio pulse.
This, coupled with the small difference in the emission radii of the two
thermal components, disfavors an interpretation in which the dipolar polar cap
is heated by magnetospheric backward-accelerated particles. Comparison with the
other thermally-emitting isolated neutron stars with spectra well described by
the sum of two components shows that the ratios $T_2$/$T_1$ and $R_2$/$R_1$ are
similar for objects of different classes. The observed values cannot be
reproduced with simple temperature distributions, such as those caused by a
dipolar field, indicating the presence of more complicated thermal maps.


title: A computational study of Gomory-Hu tree algorithms

abstract:
  We present an experimental study of algorithms for computing the Gomory-Hu
tree (aka cut tree) in undirected weighted graphs. We develop a new
implementation based on two popular maxflow algorithms, IBFS and BK. We compare
it with the algorithms from the previous experimental study by Goldberg and
Tsioutsiouliklis (2001) and with the more recent algorithm by Akibo et al.
(2016) (designed for unweighted simple graphs). Results indicate that on some
classes of problems new implementation significantly outperforms previous
methods.


title: Data Parallel Path Tracing in Object Space

abstract:
  We investigate the concept of rendering production-style content with full
path tracing in a data-distributed fashion -- that is, with multiple
collaborating nodes and/or GPUs that each store only part of the model. In
particular, we propose a new approach to tracing rays across different
nodes/GPUs that improves over traditional spatial partitioning, can support
both object-space and spatial partitioning (or any combination thereof), and
that enables multiple techniques for reducing the number of rays sent across
the network. We show that this approach can handle different kinds of model
partitioning strategies, and can ultimately render non-trivial models with full
path tracing even on quite moderate hardware resources with rather low-end
interconnect.


title:
Measurement of the J/$\psi$ polarization with respect to the event plane
  in Pb-Pb collisions at the LHC

abstract:
  The polarization of inclusive J/$\psi$ produced in Pb-Pb collisions at
$\sqrt{s_{\rm NN}}=5.02$ TeV at the LHC was studied by ALICE in the dimuon
channel, via the measurement of the angular distribution of its decay products.
The study was performed in the rapidity region $2.5<y<4$, for three transverse
momentum intervals ($2<p_{\rm T}<4$, $4<p_{\rm T}<6$, $6<p_{\rm T}<10$ GeV/$c$)
and as a function of the centrality of the collision for $2<p_{\rm T}<6$ GeV/c.
For the first time, the polarization was measured with respect to the event
plane of the collision, by considering the angle between the positive-charge
decay muon in the J/$\psi$ rest frame and the axis perpendicular to the
event-plane vector in the laboratory system. A small transverse polarization is
measured, with a significance reaching 3.9$\sigma$ at low $p_{\rm T}$ and for
intermediate centrality values. The polarization could be connected with the
existence of a strong magnetic field in the early stage of quark-gluon plasma
formation in Pb-Pb collisions, as well as with its behaviour as a rotating
fluid with large vorticity.


title:
Gated Multimodal Fusion with Contrastive Learning for Turn-taking
  Prediction in Human-robot Dialogue

abstract:
  Turn-taking, aiming to decide when the next speaker can start talking, is an
essential component in building human-robot spoken dialogue systems. Previous
studies indicate that multimodal cues can facilitate this challenging task.
However, due to the paucity of public multimodal datasets, current methods are
mostly limited to either utilizing unimodal features or simplistic multimodal
ensemble models. Besides, the inherent class imbalance in real scenario, e.g.
sentence ending with short pause will be mostly regarded as the end of turn,
also poses great challenge to the turn-taking decision. In this paper, we first
collect a large-scale annotated corpus for turn-taking with over 5,000 real
human-robot dialogues in speech and text modalities. Then, a novel gated
multimodal fusion mechanism is devised to utilize various information
seamlessly for turn-taking prediction. More importantly, to tackle the data
imbalance issue, we design a simple yet effective data augmentation method to
construct negative instances without supervision and apply contrastive learning
to obtain better feature representations. Extensive experiments are conducted
and the results demonstrate the superiority and competitiveness of our model
over several state-of-the-art baselines.


title:
NNLO QCD corrections in full colour for jet production observables at
  the LHC

abstract:
  Calculations for processes involving a high multiplicity of coloured
particles often employ a leading colour approximation, where only the leading
terms in the expansion of the number of colours $N_c$ and the number of
flavours $n_f$ are retained. This approximation of the full colour result is
motivated by the $1/N_c^2$ suppression of the first subleading terms and by the
increasing complexity of including subleading colour contributions to the
calculation. In this work, we present the calculations using the antenna
subtraction method in the NNLOJET framework for the NNLO QCD corrections at
full colour for several jet observables at the LHC. The single jet inclusive
cross section is calculated doubly differential in transverse momentum and
absolute rapidity and compared with the CMS measurement at 13 TeV. A
calculation for dijet production doubly differential in dijet mass and rapidity
difference is also performed and compared with the ATLAS 7 TeV data. Lastly, a
triply differential dijet cross section in average transverse momentum,
rapidity separation and dijet system boost is calculated and compared with the
CMS 8 TeV data. The impact of the subleading colour contributions to the
leading colour approximation is assessed in detail for all three types of
observables and as a function of the jet cone size. The subleading colour
contributions play a potentially sizable role in the description of the triply
differential distributions, which probe kinematical configurations that are not
easily accessed by any of the other observables.


title:
Evolution and use of data science vocabulary. How much have we changed
  in 13 years?

abstract:
  Here I present an investigation on the evolution and use of vocabulary in
data science in the last 13 years. Based on a rigorous statistical analysis, a
database with 12,787 documents containing the words "data science" in the
title, abstract or keywords is analyzed. It is proposed to classify the
evolution of this discipline in three periods: emergence, growth and boom.
Characteristic words and pioneering documents are identified for each period.
By proposing the distinctive vocabulary and relevant topics of data science and
classified in time periods, these results add value to the scientific community
of this discipline.


title: Selecting and scheduling an optimal subset of road network upgrades

abstract:
  We consider the problem of choosing a subset of proposed road network
upgrades to implement within a fixed budget in order to optimize the benefit in
terms of vehicle hours travelled (VHT), and show how to render the solution of
this problem more tractable by reducing the number of traffic assignment
problems that must be solved. This technique is extended to develop a heuristic
algorithm for finding a schedule of road upgrades over a planning period that
maximizes the bet present value of the resulting VHT reductions.


title: Probing Script Knowledge from Pre-Trained Models

abstract:
  Script knowledge is critical for humans to understand the broad daily tasks
and routine activities in the world. Recently researchers have explored the
large-scale pre-trained language models (PLMs) to perform various script
related tasks, such as story generation, temporal ordering of event, future
event prediction and so on. However, it's still not well studied in terms of
how well the PLMs capture the script knowledge. To answer this question, we
design three probing tasks: inclusive sub-event selection, starting sub-event
selection and temporal ordering to investigate the capabilities of PLMs with
and without fine-tuning. The three probing tasks can be further used to
automatically induce a script for each main event given all the possible
sub-events. Taking BERT as a case study, by analyzing its performance on script
induction as well as each individual probing task, we conclude that the
stereotypical temporal knowledge among the sub-events is well captured in BERT,
however the inclusive or starting sub-event knowledge is barely encoded.


title:
Scale Dependencies and Self-Similarity Through Wavelet Scattering
  Covariance

abstract:
  We introduce a scattering covariance matrix which provides non-Gaussian
models of time-series having stationary increments. A complex wavelet transform
computes signal variations at each scale. Dependencies across scales are
captured by the joint covariance across time and scales of complex wavelet
coefficients and their modulus. This covariance is nearly diagonalized by a
second wavelet transform, which defines the scattering covariance. We show that
this set of moments characterizes a wide range of non-Gaussian properties of
multi-scale processes. This is analyzed for a variety of processes, including
fractional Brownian motions, Poisson, multifractal random walks and Hawkes
processes. We prove that self-similar processes have a scattering covariance
matrix which is scale invariant. This property can be estimated numerically and
defines a class of wide-sense self-similar processes. We build maximum entropy
models conditioned by scattering covariance coefficients, and generate new
time-series with a microcanonical sampling algorithm. Applications are shown
for highly non-Gaussian financial and turbulence time-series.

categories: physics.data-an cond-mat.dis-nn cs.LG eess.SP q-fin.MF stat.ML


title:
Doctor XAvIer: Explainable Diagnosis on Physician-Patient Dialogues and
  XAI Evaluation

abstract:
  We introduce Doctor XAvIer, a BERT-based diagnostic system that extracts
relevant clinical data from transcribed patient-doctor dialogues and explains
predictions using feature attribution methods. We present a novel performance
plot and evaluation metric for feature attribution methods: Feature Attribution
Dropping (FAD) curve and its Normalized Area Under the Curve (N-AUC). FAD curve
analysis shows that integrated gradients outperforms Shapley values in
explaining diagnosis classification. Doctor XAvIer outperforms the baseline
with 0.97 F1-score in named entity recognition and symptom pertinence
classification and 0.91 F1-score in diagnosis classification.


title:
Distributed Learning for Vehicular Dynamic Spectrum Access in Autonomous
  Driving

abstract:
  Reliable wireless communication between the autonomously driving cars is one
of the fundamental needs for guaranteeing passenger safety and comfort.
However, when the number of communicating cars increases, the transmission
quality may be significantly degraded due to too high occupancy radio of the
used frequency band. In this paper, we concentrate on the autonomous
vehicle-platooning use-case, where intra-platoon communication is done in the
dynamically selected frequency band, other than nominally devoted for such
purposes. The carrier selection is done in a flexible manner with the support
of the context database located at the roadside unit (edge of wireless
communication infrastructure). However, as the database delivers only context
information to the platoons' leaders, the final decision is made separately by
the individual platoons, following the suggestions made by the artificial
intelligence algorithms. In this work, we concentrate on a lightweight
Q-learning solution, that could be successfully implemented in each car for
dynamic channel selection.


title:
Analysis of Ferroelectric Negative Capacitance-Hybrid MEMS Actuator
  Using Energy-Displacement Landscape

abstract:
  We propose an energy-based framework to analyze the statics and dynamics of a
ferroelectric negative capacitance-hybrid Microelectromechanical System (MEMS)
actuator. A mapping function that relates the charge on the ferroelectric to
displacement of the movable electrode, is used to obtain the Hamiltonian of the
hybrid actuator in terms of displacement. We then use graphical
energy-displacement and phase portrait plots to analyze static pull-in, dynamic
pull-in and pull-out phenomena of the hybrid actuator. Using these, we
illustrate the low-voltage operation of the hybrid actuator to static and step
inputs, as compared to the standalone MEMS actuator. The results obtained are
in agreement with the analytical predictions and numerical simulations. The
proposed framework enables straightforward inclusion of adhesion between the
contacting surfaces, modeled using van der Waals force. We show that the
pull-in voltage is not affected, while the pull-out voltage is reduced due to
adhesion. The proposed framework provides a physics-based tool to design and
analyze negative capacitance based low-voltage MEMS actuators.


title: WordAlchemy: A transformer-based Reverse Dictionary

abstract:
  A reverse dictionary takes a target word's description as input and returns
the words that fit the description. Reverse Dictionaries are useful for new
language learners, anomia patients, and for solving common tip-of-the-tongue
problems (lethologica). Currently, there does not exist any Reverse Dictionary
provider with support for any Indian Language. We present a novel open-source
cross-lingual reverse dictionary system with support for Indian languages. In
this paper, we propose a transformer-based deep learning approach to tackle the
limitations faced by the existing systems using the mT5 model. This
architecture uses the Translation Language Modeling (TLM) technique, rather
than the conventional BERT's Masked Language Modeling (MLM) technique.


title:
Nanodiamond quantum sensors reveal temperature variation associated to
  hippocampal neurons firing

abstract:
  Temperature is one of the most relevant parameters for the regulation of
intracellular processes. Measuring localized subcellular temperature gradients
is fundamental for a deeper understanding of cell function, such as the genesis
of action potentials, and cell metabolism. Here, we detect for the first time
temperature variations (1{\deg}C) associated with potentiation and depletion of
neuronal firing, exploiting a nanoscale thermometer based on optically detected
magnetic resonance in nanodiamonds. Our results provide a tool for assessing
neuronal spiking activity under physiological and pathological conditions and,
conjugated with the high sensitivity of this technique (in perspective
sensitive to < 0.1{\deg}C variations), pave the way to a systematic study of
the generation of localized temperature gradients. Furthermore, they prompt
further studies explaining in detail the physiological mechanism originating
this effect.


title:
Multi-Component Optimization and Efficient Deployment of Neural-Networks
  on Resource-Constrained IoT Hardware

abstract:
  The majority of IoT devices like smartwatches, smart plugs, HVAC controllers,
etc., are powered by hardware with a constrained specification (low memory,
clock speed and processor) which is insufficient to accommodate and execute
large, high-quality models. On such resource-constrained devices, manufacturers
still manage to provide attractive functionalities (to boost sales) by
following the traditional approach of programming IoT devices/products to
collect and transmit data (image, audio, sensor readings, etc.) to their
cloud-based ML analytics platforms. For decades, this online approach has been
facing issues such as compromised data streams, non-real-time analytics due to
latency, bandwidth constraints, costly subscriptions, recent privacy issues
raised by users and the GDPR guidelines, etc. In this paper, to enable
ultra-fast and accurate AI-based offline analytics on resource-constrained IoT
devices, we present an end-to-end multi-component model optimization sequence
and open-source its implementation. Researchers and developers can use our
optimization sequence to optimize high memory, computation demanding models in
multiple aspects in order to produce small size, low latency, low-power
consuming models that can comfortably fit and execute on resource-constrained
hardware. The experimental results show that our optimization components can
produce models that are; (i) 12.06 x times compressed; (ii) 0.13% to 0.27% more
accurate; (iii) Orders of magnitude faster unit inference at 0.06 ms. Our
optimization sequence is generic and can be applied to any state-of-the-art
models trained for anomaly detection, predictive maintenance, robotics, voice
recognition, and machine vision.


title:
INSPIRE: Distributed Bayesian Optimization for ImproviNg SPatIal REuse
  in Dense WLANs

abstract:
  WLANs, which have overtaken wired networks to become the primary means of
connecting devices to the Internet, are prone to performance issues due to the
scarcity of space in the radio spectrum. As a response, IEEE 802.11ax and
subsequent amendments aim at increasing the spatial reuse of a radio channel by
allowing the dynamic update of two key parameters in wireless transmission: the
transmission power (TX_POWER) and the sensitivity threshold (OBSS_PD). In this
paper, we present INSPIRE, a distributed solution performing local Bayesian
optimizations based on Gaussian processes to improve the spatial reuse in
WLANs. INSPIRE makes no explicit assumptions about the topology of WLANs and
favors altruistic behaviors of the access points, leading them to find adequate
configurations of their TX_POWER and OBSS_PD parameters for the "greater good"
of the WLANs. We demonstrate the superiority of INSPIRE over other
state-of-the-art strategies using the ns-3 simulator and two examples inspired
by real-life deployments of dense WLANs. Our results show that, in only a few
seconds, INSPIRE is able to drastically increase the quality of service of
operational WLANs by improving their fairness and throughput.


title: Social Media Sentiment Analysis for Cryptocurrency Market Prediction

abstract:
  In this paper, we explore the usability of different natural language
processing models for the sentiment analysis of social media applied to
financial market prediction, using the cryptocurrency domain as a reference. We
study how the different sentiment metrics are correlated with the price
movements of Bitcoin. For this purpose, we explore different methods to
calculate the sentiment metrics from a text finding most of them not very
accurate for this prediction task. We find that one of the models outperforms
more than 20 other public ones and makes it possible to fine-tune it
efficiently given its interpretable nature. Thus we confirm that interpretable
artificial intelligence and natural language processing methods might be more
valuable practically than non-explainable and non-interpretable ones. In the
end, we analyse potential causal connections between the different sentiment
metrics and the price movements.


title:
Study on emerging applications on data plane and optimization
  possibilities

abstract:
  By programming both the data plane and the control plane, network operators
can adapt their networks to their needs. Thanks to research over the past
decade, this concept has more formulized and more technologically feasible.
However, since control plane programmability came first, it has already been
successfully implemented in the real network and is beginning to pay off.
Today, the data plane programmability is evolving very rapidly to reach this
level, attracting the attention of researchers and developers: Designing data
plane languages, application development on it, formulizing software switches
and architecture that can run data plane codes and the applications, increasing
performance of software switch, and so on. As the control plane and data plane
become more open, many new innovations and technologies are emerging, but some
experts warn that consumers may be confused as to which of the many
technologies to choose. This is a testament to how much innovation is emerging
in the network. This paper outlines some emerging applications on the data
plane and offers opportunities for further improvement and optimization. Our
observations show that most of the implementations are done in a test
environment and have not been tested well enough in terms of performance, but
there are many interesting works, for example, previous control plane solutions
are being implemented in the data plane.