title: Complexity of Arithmetic in Warded Datalog+-

abstract:
  Warded Datalog+- extends the logic-based language Datalog with existential
quantifiers in rule heads. Existential rules are needed for advanced reasoning
tasks, e.g., ontological reasoning. The theoretical efficiency guarantees of
Warded Datalog+- do not cover extensions crucial for data analytics, such as
arithmetic. Moreover, despite the significance of arithmetic for common data
analytic scenarios, no decidable fragment of any Datalog+- language extended
with arithmetic has been identified. We close this gap by defining a new
language that extends Warded Datalog+- with arithmetic and prove its
P-completeness. Furthermore, we present an efficient reasoning algorithm for
our newly defined language and prove descriptive complexity results for a
recently introduced Datalog fragment with integer arithmetic, thereby closing
an open question. We lay the theoretical foundation for highly expressive
Datalog+- languages that combine the power of advanced recursive rules and
arithmetic while guaranteeing efficient reasoning algorithms for applications
in modern AI systems, such as Knowledge Graphs.


title:
From SU(2)$_5$ to SU(2)$_3$ Wess-Zumino-Witten transitions in a
  frustrated spin-5/2 chain

abstract:
  We investigate the properties of a frustrated spin-5/2 chain with
next-nearest neighbor two and three-site interactions, with two questions in
mind: the nature of the transition into the dimerized phase induced by the
three-site interaction, and the possible presence of a critical floating phase
at intermediate values of the next-nearest neighbor interaction. We provide
strong evidence that the continuous transition into the dimerized phase, which
has been found to be generically in the Wess-Zumino-Witten SU(2)$_{2S}$
universality class up to spin $S=2$, is SU(2)$_5$ only at two isolated points
of the phase diagram, and that it is SU(2)$_3$ in between, in agreement with
the presence of two relevant operators allowed by symmetry for SU(2)$_5$, and
with the conservation of the parity of the level index along the
renormalization flow between SU(2)$_k$ theories with different values of $k$.
We also find that the dimerization induced by the next-nearest neighbor
interaction is a three step process, with first a small partially dimerized
phase followed by a broad critical floating phase with incommensurate
correlations before the fully dimerized phase is reached. Implications for the
iron oxide ${\mathrm{Bi}}_{3}{\mathrm{FeMo}}_{2}{\mathrm{O}}_{12}$ are briefly
discussed.


title: Substructures in Latin squares

abstract:
  We prove several results about substructures in Latin squares. First, we
explain how to adapt our recent work on high-girth Steiner triple systems to
the setting of Latin squares, resolving a conjecture of Linial that there exist
Latin squares with arbitrarily high girth. As a consequence, we see that the
number of order-$n$ Latin squares with no intercalate (i.e., no $2\times2$
Latin subsquare) is at least $(e^{-9/4}n-o(n))^{n^{2}}$. Equivalently,
$\Pr\left[\mathbf{N}=0\right]\ge e^{-n^{2}/4-
(n^{2})}=e^{-(1+o(1))\mathbb{E}\mathbf{N}}$, where $\mathbf{N}$ is the number
of intercalates in a uniformly random order-$n$ Latin square.
  In fact, extending recent work of Kwan, Sah, and Sawhney, we resolve the
general large-deviation problem for intercalates in random Latin squares, up to
constant factors in the exponent: for any constant $0<\delta\le1$ we have
$\Pr[\mathbf{N}\le(1-\delta)\mathbb{E}\mathbf{N}]=\exp(-\Theta(n^{2}))$ and for
any constant $\delta>0$ we have
$\Pr[\mathbf{N}\ge(1+\delta)\mathbb{E}\mathbf{N}]=\exp(-\Theta(n^{4/3}(\log
n)^{2/3}))$.
  Finally, we show that in almost all order-$n$ Latin squares, the number of
cuboctahedra (i.e., the number of pairs of possibly degenerate $2\times2$
subsquares with the same arrangement of symbols) is of order $n^{4}$, which is
the minimum possible. As observed by Gowers and Long, this number can be
interpreted as measuring "how associative" the quasigroup associated with the
Latin square is.


title: Backpropagation Clipping for Deep Learning with Differential Privacy

abstract:
  We present backpropagation clipping, a novel variant of differentially
private stochastic gradient descent (DP-SGD) for privacy-preserving deep
learning. Our approach clips each trainable layer's inputs (during the forward
pass) and its upstream gradients (during the backward pass) to ensure bounded
global sensitivity for the layer's gradient; this combination replaces the
gradient clipping step in existing DP-SGD variants. Our approach is simple to
implement in existing deep learning frameworks. The results of our empirical
evaluation demonstrate that backpropagation clipping provides higher accuracy
at lower values for the privacy parameter $\epsilon$ compared to previous work.
We achieve 98.7% accuracy for MNIST with $\epsilon = 0.07$ and 74% accuracy for
CIFAR-10 with $\epsilon = 3.64$.


title:
Dimensional reduction and the generalized pion in a magnetic field
  within the NJL model

abstract:
  In this work, the mass of the neutral pion is investigated in the presence of
background magnetic fields in the framework of the Nambu--Jona-Lasinio model.
Taking into account the anisotropic four-fermion interactions, a tensor current
arises in the magnetized QCD system, which forms an anomalous magnetic moment
(AMM) coupling in the Dirac equation for the quarks. By solving the gap
equations, we find that the sign of the dynamically generated AMM is opposite
to the sign of the quark's charge and its magnitude is definitely smaller than
the constituent mass. We construct two generalized Nambu-Goldstone pions, which
emerge as combinations of the quantum fluctuations around the conventional
scalar and the emergent tensor chiral condensates. We analytically demonstrate
that the Goldstone nature has been spoiled by the dimensional reduction in the
two-particle state and the corresponding decreasing mass of the lighter
generalized pionic mode is a remnant of the infrared dynamics.


title:
Rigidity properties for some isometric extensions of partially
  hyperbolic actions on the torus

abstract:
  This paper studies local rigidity for some isometric toral extensions of
partially hyperbolic $\mathbb{Z}^k$ ($k\geqslant 2$) actions on the torus. We
prove a $C^\infty$ local rigidity result for such actions, provided that the
smooth perturbations of the actions satisfy the intersection property. We also
give a local rigidity result within a class of volume preserving actions. Our
method mainly uses a generalization of the KAM iterative scheme.


title:
Constraining Anisotropy Birefringence Dispersion in Gravitational Wave
  Propagation with GWTC-3

abstract:
  Successful detections of gravitational waves have presented a new avenue to
explore the nature of gravity. With the cumulating catalog of detected events,
we can perform tests on General Relativity from various aspects with increasing
precision. In this work, we focus on Lorentz symmetry during propagation of
gravitational waves. Considering the dispersion relation in the gauge-invariant
linearized gravity sector of standard-model Extension, the anisotropy,
birefringence and dispersion effects will be induced during propagation of
gravitational waves since the Lorentz violating modification, and cause
dephasings in waveform received by detectors. With the distorted waveform, we
perform full Bayesian inference with confident detections in the last
gravitational wave catalog. We consider two cases associated with the lowest
mass dimension $d=5,6$ which are supposed to have the most significant effects,
and place the constraints on the expansion coefficients characterizing the
Lorentz violating behavior which have 16 independent components for $d=5$ and
18 components for $d=6$. We do not find any evidence for Lorentz violation in
the gravitational wave data, the constraints on the coefficients are in order
of $10^{-15}{\rm m}$ for $d=5$ and $10^{-11}{\rm m^2}$ for $d=6$ respectively.


title: Two-Stage Deep Anomaly Detection with Heterogeneous Time Series Data

abstract:
  We introduce a data-driven anomaly detection framework using a manufacturing
dataset collected from a factory assembly line. Given heterogeneous time series
data consisting of operation cycle signals and sensor signals, we aim at
discovering abnormal events. Motivated by our empirical findings that
conventional single-stage benchmark approaches may not exhibit satisfactory
performance under our challenging circumstances, we propose a two-stage deep
anomaly detection (TDAD) framework in which two different unsupervised learning
models are adopted depending on types of signals. In Stage I, we select anomaly
candidates by using a model trained by operation cycle signals; in Stage II, we
finally detect abnormal events out of the candidates by using another model,
which is suitable for taking advantage of temporal continuity, trained by
sensor signals. A distinguishable feature of our framework is that operation
cycle signals are exploited first to find likely anomalous points, whereas
sensor signals are leveraged to filter out unlikely anomalous points afterward.
Our experiments comprehensively demonstrate the superiority over single-stage
benchmark approaches, the model-agnostic property, and the robustness to
difficult situations.


title:
Hardware calibrated learning to compensate heterogeneity in analog
  RRAM-based Spiking Neural Networks

abstract:
  Spiking Neural Networks (SNNs) can unleash the full power of analog Resistive
Random Access Memories (RRAMs) based circuits for low power signal processing.
Their inherent computational sparsity naturally results in energy efficiency
benefits. The main challenge implementing robust SNNs is the intrinsic
variability (heterogeneity) of both analog CMOS circuits and RRAM technology.
In this work, we assessed the performance and variability of RRAM-based
neuromorphic circuits that were designed and fabricated using a 130\,nm
technology node. Based on these results, we propose a Neuromorphic Hardware
Calibrated (NHC) SNN, where the learning circuits are calibrated on the
measured data. We show that by taking into account the measured heterogeneity
characteristics in the off-chip learning phase, the NHC SNN self-corrects its
hardware non-idealities and learns to solve benchmark tasks with high accuracy.
This work demonstrates how to cope with the heterogeneity of neurons and
synapses for increasing classification accuracy in temporal tasks.


title:
Long-term optical photometry of the PMS stars V2764 Ori and LkH$\alpha$
  301 in the field of the McNeil's Nebula

abstract:
  In this paper, we present results from long-term $V(RI)_{c}$ photometric
observations of the pre-main-sequence stars V2764 Ori and LkH$\alpha$ 301,
located in the field of the McNeil's Nebula within the Orion star-forming
complex. Our observations were performed in the period from August 2004 to
November 2021 with three telescopes and seven different types of CCD cameras.
Photometric observations, especially concerning the long-term behavior of the
stars, are missing in the literature. We present the first photometric
monitoring for them, which cover 17 years. Our data indicate that the
variability of both stars is typical for classical T Tauri stars.


title:
Near-Optimal Statistical Query Lower Bounds for Agnostically Learning
  Intersections of Halfspaces with Gaussian Marginals

abstract:
  We consider the well-studied problem of learning intersections of halfspaces
under the Gaussian distribution in the challenging \emph{agnostic learning}
model. Recent work of Diakonikolas et al. (2021) shows that any Statistical
Query (SQ) algorithm for agnostically learning the class of intersections of
$k$ halfspaces over $\mathbb{R}^n$ to constant excess error either must make
queries of tolerance at most $n^{-\tilde{\Omega}(\sqrt{\log k})}$ or must make
$2^{n^{\Omega(1)}}$ queries. We strengthen this result by improving the
tolerance requirement to $n^{-\tilde{\Omega}(\log k)}$. This lower bound is
essentially best possible since an SQ algorithm of Klivans et al. (2008)
agnostically learns this class to any constant excess error using $n^{O(\log
k)}$ queries of tolerance $n^{-O(\log k)}$. We prove two variants of our lower
bound, each of which combines ingredients from Diakonikolas et al. (2021) with
(an extension of) a different earlier approach for agnostic SQ lower bounds for
the Boolean setting due to Dachman-Soled et al. (2014). Our approach also
yields lower bounds for agnostically SQ learning the class of "convex subspace
juntas" (studied by Vempala, 2010) and the class of sets with bounded Gaussian
surface area; all of these lower bounds are nearly optimal since they
essentially match known upper bounds from Klivans et al. (2008).


title: EXCESS workshop: Descriptions of rising low-energy spectra

abstract:
  Many low-threshold experiments observe sharply rising event rates of yet
unknown origins below a few hundred eV, and larger than expected from known
backgrounds. Due to the significant impact of this excess on the dark matter or
neutrino sensitivity of these experiments, a collective effort has been started
to share the knowledge about the individual observations. For this, the EXCESS
Workshop was initiated. In its first iteration in June 2021, ten rare event
search collaborations contributed to this initiative via talks and discussions.
The contributing collaborations were CONNIE, CRESST, DAMIC, EDELWEISS, MINER,
NEWS-G, NUCLEUS, RICOCHET, SENSEI and SuperCDMS. They presented data about
their observed energy spectra and known backgrounds together with details about
the respective measurements. In this paper, we summarize the presented
information and give a comprehensive overview of the similarities and
differences between the distinct measurements. The provided data is furthermore
publicly available on the workshop's data repository together with a plotting
tool for visualization.


title:
AD-NEGF: An End-to-End Differentiable Quantum Transport Simulator for
  Sensitivity Analysis and Inverse Problems

abstract:
  Since proposed in the 70s, the Non-Equilibrium Green Function (NEGF) method
has been recognized as a standard approach to quantum transport simulations.
Although it achieves superiority in simulation accuracy, the tremendous
computational cost makes it unbearable for high-throughput simulation tasks
such as sensitivity analysis, inverse design, etc. In this work, we propose
AD-NEGF, to our best knowledge the first end-to-end differentiable NEGF model
for quantum transport simulations. We implement the entire numerical process in
PyTorch, and design customized backward pass with implicit layer techniques,
which provides gradient information at an affordable cost while guaranteeing
the correctness of the forward simulation. The proposed model is validated with
applications in calculating differential physical quantities, empirical
parameter fitting, and doping optimization, which demonstrates its capacity to
accelerate the material design process by conducting gradient-based parameter
optimization.


title: Social interactions affect discovery processes

abstract:
  Our network of acquaintances determines how we get exposed to ideas,
products, or cultural artworks (books, music, movies, etc.). Though this
principle is part of our common sense, little is known about the specific
pathways through which our peers influence our discovery processes and our
experience of the new. Here, we fill this gap by investigating a data set
containing the whole listening histories of a large, socially connected sample
of users from the online music platform \emph{Last.fm}. We demonstrate that
users exhibit highly heterogeneous discovery rates of new songs and artists and
that their social neighborhood significantly influences their behavior. More
explorative users tend to interact with peers more prone to explore new
content. We capture this phenomenology in a modeling scheme where users are
represented by random walkers exploring a graph of songs or artists and
interacting with each other through their social links. Even starting from a
uniform population of agents (no natural differences among the individuals),
our model predicts the emergence of strong heterogeneous exploration patterns,
with users clustered according to their musical tastes and propensity to
explore. We contend our approach can pave the way to a quantitative approach to
collective discovery processes.


title: Adaptively Exploiting d-Separators with Causal Bandits

abstract:
  Multi-armed bandit problems provide a framework to identify the optimal
intervention over a sequence of repeated experiments. Without additional
assumptions, minimax optimal performance (measured by cumulative regret) is
well-understood. With access to additional observed variables that d-separate
the intervention from the outcome (i.e., they are a d-separator), recent causal
bandit algorithms provably incur less regret. However, in practice it is
desirable to be agnostic to whether observed variables are a d-separator.
Ideally, an algorithm should be adaptive; that is, perform nearly as well as an
algorithm with oracle knowledge of the presence or absence of a d-separator. In
this work, we formalize and study this notion of adaptivity, and provide a
novel algorithm that simultaneously achieves (a) optimal regret when a
d-separator is observed, improving on classical minimax algorithms, and (b)
significantly smaller regret than recent causal bandit algorithms when the
observed variables are not a d-separator. Crucially, our algorithm does not
require any oracle knowledge of whether a d-separator is observed. We also
generalize this adaptivity to other conditions, such as the front-door
criterion.


title:
Characterizations of Adjoint Sobolev Embedding Operators for Inverse
  Problems

abstract:
  We consider the Sobolev embedding operator $E_s : H^s(\Omega) \to
L_2(\Omega)$ and its role in the solution of inverse problems. In particular,
we collect various properties and investigate different characterizations of
its adjoint operator $E_s^*$, which is a common component in both iterative and
variational regularization methods. These include variational representations
and connections to boundary value problems, Fourier and wavelet
representations, as well as connections to spatial filters. Moreover, we
consider characterizations in terms of Fourier series, singular value
decompositions and frame decompositions, as well as representations in finite
dimensional settings. While many of these results are already known to
researchers from different fields, a detailed and general overview or reference
work containing rigorous mathematical proofs is still missing. Hence, in this
paper we aim to fill this gap by collecting, introducing and generalizing a
large number of characterizations of $E_s^*$. The resulting compilation can
serve both as a reference as well as a useful guide for its efficient numerical
implementation in practice.


title:
A Bayesian Population Model for the Observed Dust Attenuation in
  Galaxies

abstract:
  Dust plays a pivotal role in determining the observed spectral energy
distribution (SED) of galaxies. Yet our understanding of dust attenuation is
limited and our observations suffer from the dust-metallicity-age degeneracy in
SED fitting (single galaxies), large individual variances (ensemble
measurements), and the difficulty in properly dealing with uncertainties
(statistical considerations). In this study, we create a population Bayesian
model to rigorously account for correlated variables and non-Gaussian error
distributions and demonstrate the improvement over a simple Bayesian model. We
employ a flexible 5-D linear interpolation model for the parameters that
control dust attenuation curves as a function of stellar mass, star formation
rate (SFR), metallicity, redshift, and inclination. Our setup allows us to
determine the complex relationships between dust attenuation and these galaxy
properties simultaneously. Using Prospector fits of nearly 30,000 3D-HST
galaxies, we find that the attenuation slope ($n$) flattens with increasing
optical depth ($\tau$), though less so than in previous studies. $\tau$
increases strongly with SFR, though when $\log~{\rm SFR}\lesssim 0$, $\tau$
remains roughly constant over a wide range of stellar masses. Edge-on galaxies
tend to have larger $\tau$ than face-on galaxies, but only for $\log~M_*\gtrsim
10$, reflecting the lack of triaxiality for low-mass galaxies. Redshift
evolution of dust attenuation is strongest for low-mass, low-SFR galaxies, with
higher optical depths but flatter curves at high redshift. Finally, $n$ has a
complex relationship with stellar mass, highlighting the intricacies of the
star-dust geometry. We have publicly released software
(https://github.com/Astropianist/DustE) for users to access our population
model.


title:
Cryogenic digital data links for the liquid argon time projection
  chamber

abstract:
  In this paper we present the cryogenic functionality of the components of
data links for the Liquid Argon Time Projection Chamber (LArTPC), a potential
far site detector technology of the Long-Baseline Neutrino Experiment (LBNE).
We have confirmed that an LVDS driver can drive a 20-meter CAT5E twisted pair
up to 1 gigabit per second at the liquid nitrogen temperature (77 K). We have
verified that a commercial-off-the-shelf (COTS) serializer, a laser diode
driver, laser diodes, optical fibers and connectors, and field-programming gate
arrays (FPGA's) continue to function at 77 K. A variety of COTS resistors and
capacitors have been tested at 77 K. All tests we have conducted show that the
cryogenic digital data links for the liquid argon time projection chamber are
promising.


title:
Quantum Gravity and Phenomenology: Dark Matter, Dark Energy, Vacuum
  Selection, Emergent Spacetime, and Wormholes

abstract:
  We discuss the relevance of quantum gravity to the frontier questions in high
energy phenomenology: the problems of dark matter, dark energy, and vacuum
selection as well as the problems of emergent spacetime and wormholes. Dark
matter and dark energy phenomenology, and the problem of vacuum selection are
discussed within the context of string theory as a model of quantum gravity.
Emergent spacetime and wormholes are discussed in a more general context of
effective theories of quantum gravity.


title:
Cosmology and Fundamental Physics in the Era of Gravitational-Wave
  Astronomy

abstract:
  The advent of gravitational-wave (GW) astronomy has presented us with a
completely new means for observing the Universe, allowing us to probe its
structure and evolution like never before. In this thesis, we explore three
distinct but complementary avenues for using GW observations to gain new
insights into cosmology and fundamental physics.
  In chapter 1, we study the astrophysical GW background (AGWB): the cumulative
GW signal arising from a large number of compact binary coalescences (CBCs)
throughout the Universe. Since these compact binaries reside in galaxies, the
AGWB contains anisotropies that trace out the large-scale structure of the
cosmic matter distribution. We investigate the angular power spectrum of the
AGWB, with the goal of developing predictions that can be confronted with
directional AGWB searches.
  In chapter 2, we calculate the nonlinear GW memory emitted by cusps and kinks
on cosmic string loops, which are among the most promising cosmological sources
of GWs. We show that, surprisingly, the cusp memory signal diverges for
sufficiently large loops, indicating a breakdown in the validity of the
weak-field description of the cusp. We then present one tentative possible
solution to this divergence, in which the portion of the string surrounding the
cusp collapses to form a primordial black hole (PBH).
  Finally, in chapter 3 we develop a powerful new method for GW detection based
on precision measurements of the orbits of binary systems. In the presence of a
stochastic GW background (GWB) the trajectories of the binary's components are
perturbed, giving rise to a random walk in the system's orbital parameters over
time. We calculate the sensitivity of binary pulsars and lunar laser ranging to
the GWB through this effect, and show that present data are already sensitive
enough to place the strongest constraints to date in the $\mu$Hz frequency
band.


title:
Update on the high speed serializer ASIC development for ATLAS Liquid
  Argon calorimeter upgrade

abstract:
  We have been developing a serializer application-specific integrated circuit
(ASIC) based on a commercial 0.25-{\mu}m silicon-on-sapphire (SOS) CMOS
technology for the ATLAS liquid argon calorimeter front-end electronics
upgrade. The first prototype, a 5 Gbps 16:1 serializer has been designed,
fabricated, and tested in lab environment and in a 200 MeV proton beam. The
test results indicate that the first prototype meets the design goals. The
second prototype, a double-lane, 8 Gbps per lane serializer is under
development. The post-layout simulation indicates that 8 Gbps is achievable. In
this paper we present the design and the test results of the first prototype
and the design and status of the second prototype.


title:
Machine Learning-based Urban Canyon Path Loss Prediction using 28 GHz
  Manhattan Measurements

abstract:
  Large bandwidth at mm-wave is crucial for 5G and beyond but the high path
loss (PL) requires highly accurate PL prediction for network planning and
optimization. Statistical models with slope-intercept fit fall short in
capturing large variations seen in urban canyons, whereas ray-tracing, capable
of characterizing site-specific features, faces challenges in describing
foliage and street clutter and associated reflection/diffraction ray
calculation. Machine learning (ML) is promising but faces three key challenges
in PL prediction: 1) insufficient measurement data; 2) lack of extrapolation to
new streets; 3) overwhelmingly complex features/models. We propose an ML-based
urban canyon PL prediction model based on extensive 28 GHz measurements from
Manhattan where street clutters are modeled via a LiDAR point cloud dataset and
buildings by a mesh-grid building dataset. We extract expert knowledge-driven
street clutter features from the point cloud and aggressively compress
3D-building information using convolutional-autoencoder. Using a new
street-by-street training and testing procedure to improve generalizability,
the proposed model using both clutter and building features achieves a
prediction error (RMSE) of $4.8 \pm 1.1$ dB compared to $10.6 \pm 4.4$ dB and
$6.5 \pm 2.0$ dB for 3GPP LOS and slope-intercept prediction, respectively,
where the standard deviation indicates street-by-street variation. By only
using four most influential clutter features, RMSE of $5.5\pm 1.1$ dB is
achieved.


title: The role of adatoms for the adsorption of F4TCNQ on Au(111)

abstract:
  Organic adlayers on inorganic substrates often contain adatoms, which can be
incorporated within the adsorbed molecular species, forming two-dimensional
metal-organic frameworks at the substrate surface. The interplay between native
adatoms and adsorbed molecules significantly changes various adlayer properties
such as the adsorption geometry, the bond strength between the substrate and
the adsorbed species, or the work function at the interface. Here we use
dispersion-corrected density functional theory to gain insight into the
energetics that drive the incorporation of native adatoms within molecular
adlayers based on the prototypical, experimentally well-characterized system of
F4TCNQ on Au(111). We explain the adatom-induced modifications in the
adsorption geometry and the adsorption energy based on the electronic structure
and charge transfer at the interface. With this, we provide deeper insight into
the general mechanisms causing the incorporation of adatoms within an adlayer
made of a strong acceptor molecular species.


title:
Evolution of the unitary Bose gas for broad to narrow Feshbach
  resonances

abstract:
  We study the post-quench dynamics of unitary Bose gases using a two-channel
model, focusing on the effect of variations in the width of the Feshbach
resonance due to density changes. We generally find that increasing the density
leads to a corresponding increase in the production of closed channel
molecules, a decrease in the build up of quantum depletion and a transition
from linear to quadratic early-time growth of the two-body contact as well as
the condensed pair fraction. Motivated by the presence of closed-channel
molecules in the unitary regime, we study the embedded two-body problem finding
a transition from open to closed-channel dominated dimers due to many-body
effects.


title: R&D Towards Cryogenic Optical Links

abstract:
  A number of critical active and passive components of optical links have been
tested at 77 K or lower temperatures, demonstrating potential development of
optical links operating inside the liquid argon time projection chamber
(LArTPC) detector cryostat. A ring oscillator, individual MOSFETs, and a high
speed 16:1 serializer fabricated in a commercial 0.25-um silicon-on-sapphire
CMOS technology continued to function from room temperature to 4.2 K, 15 K, and
77 K respectively. Three types of laser diodes lase from room temperature to 77
K. Optical fibers and optical connectors exhibited minute attenuation changes
from room temperature to 77 K.


title: Measuring the mass of the black widow PSR J1555-2908

abstract:
  Accurate measurements of the masses of neutron stars are necessary to test
binary evolution models, and to constrain the neutron star equation of state.
In pulsar binaries with no measurable post-Keplerian parameters, this requires
an accurate estimate of the binary system's inclination and the radial velocity
of the companion star by other means than pulsar timing. In this paper, we
present the results of a new method for measuring this radial velocity using
the binary synthesis code Icarus. This method relies on constructing a model
spectrum of a tidally distorted, irradiated star as viewed for a given binary
configuration. This method is applied to optical spectra of the newly
discovered black widow PSR J1555-2908. By modelling the optical spectroscopy
alongside optical photometry, we find that the radial velocity of the companion
star is $397\pm4$ km s$^{-1}$ (errors quoted at 95\% confidence interval), as
well as a binary inclination of $>75^{\rm o}$. Combined with $\gamma$-ray
pulsation timing information, this gives a neutron star mass of
1.67$^{+0.15}_{-0.09}$ M$_\odot$ and a companion mass of
0.060$^{+0.005}_{-0.003}$ M$_\odot$, placing PSR J1555-2908 at the observed
upper limit of what is considered a black widow system.


title:
Probabilistic learning inference of boundary value problem with
  uncertainties based on Kullback-Leibler divergence under implicit constraints

abstract:
  In a first part, we present a mathematical analysis of a general methodology
of a probabilistic learning inference that allows for estimating a posterior
probability model for a stochastic boundary value problem from a prior
probability model. The given targets are statistical moments for which the
underlying realizations are not available. Under these conditions, the
Kullback-Leibler divergence minimum principle is used for estimating the
posterior probability measure. A statistical surrogate model of the implicit
mapping, which represents the constraints, is introduced. The MCMC generator
and the necessary numerical elements are given to facilitate the implementation
of the methodology in a parallel computing framework. In a second part, an
application is presented to illustrate the proposed theory and is also, as
such, a contribution to the three-dimensional stochastic homogenization of
heterogeneous linear elastic media in the case of a non-separation of the
microscale and macroscale. For the construction of the posterior probability
measure by using the probabilistic learning inference, in addition to the
constraints defined by given statistical moments of the random effective
elasticity tensor, the second-order moment of the random normalized residue of
the stochastic partial differential equation has been added as a constraint.
This constraint guarantees that the algorithm seeks to bring the statistical
moments closer to their targets while preserving a small residue.


title:
Superconductivity in high-$T_c$ and related strongly correlated systems
  from variational perspective: Beyond mean field theory

abstract:
  In this review, we single out selected universal features of high-$T_c$ and
related systems, which can be compared with experiment. We start with the
concept of real-space pairing, combined with strong correlations. The
discussion of concrete properties relies on variational approach, based on
renormalized mean-field theory (RMFT) in the form of statistically-consistent
Gutzwiller approximation (SGA), and Diagrammatic Expansion of the Variational
Wave Function (DE-GWF). Two energy scales appear, one involving quasiparticles
close to the Fermi energy, and the other reflecting the correlated state. Those
two regimes are separated by a kink in the dispersion relation, observed in
photoemission. One obtains both the doping dependent properties and
renormalized quasiparticles. The reviewed ground-state characteristics for
high-$T_c$ systems encompass superconductivity, nematicity, charge- (and pair-)
density-wave effects, as well as non-BCS kinetic energy gain in the paired
state, all in quantitative manner. Calculated dynamic properties are: universal
Fermi velocity, Fermi wave-vector, effective mass enhancement, pseudogap, and
$d$-wave gap magnitude. The minimal realistic model is represented by the
$t$-$J$-$U$ Hamiltonian. Inadequacy of the $t$-$J$ and Hubbard models is
discussed. For heavy fermion systems we summarize superconducting, Kondo
insulating, ferro- and anti-ferromagnetic states. We overview also coexistent
ferromagnetic (spin-triplet) superconducting phases observed for
$\mathrm{UGe_2}$. Finally, we extend our scheme to collective spin and charge
fluctuations in high-$T_c$ systems, starting from variational approach,
combined with $1/N$ expansion (beyond random phase approximation). Spectrum of
collective spin and charge excitations is determined for the Hubbard and
$t$-$J$-$U$ models, and compared quantitatively with recent experiments.


title: Stochastic optimal control for nonlinear damped network dynamics

abstract:
  We present a stochastic optimal control problem for a tree network. The
dynamics of the network are governed by transport equations with a special
emphasis on the non-linear damping function. Demand profiles at the network
sinks are modelled by a stochastic differential equations. An explicit optimal
inflow into the network is determined and numerical simulations are presented
to show the effects for different choices of the non-linear damping.


title: Spoiler Susceptibility in Multi-District Party Elections

abstract:
  Electoral spoilers are such agents that there exists a coalition of agents
whose total gain when a putative spoiler is eliminated exceeds that spoiler's
share in the election outcome. So far spoiler effects have been analyzed
primarily in the context of single-winner electoral systems. We consider this
problem in the context of multi-district party elections. We introduce a formal
measure of a party's excess electoral impact, treating "spoilership" as a
manner of degree. This approach allows us to compare multi-winner social choice
rules according to their degree of spoiler susceptibility. We present
experimental results, as well as analytical results for toy models, for seven
classical rules ($k$-Borda, Chamberlin--Courant, Harmonic-Borda,
Jefferson--D'Hondt, PAV, SNTV, and STV). Since the probabilistic models
commonly used in computational social choice have been developed for non-party
elections, we extend them to be able to generate multi-district party
elections.


title: Holographic entanglement in spin network states: a focused review

abstract:
  In the longstanding quest to reconcile gravity with quantum mechanics,
profound connections have been unveiled between concepts traditionally
pertaining to quantum information theory, such as entanglement, and
constitutive features of gravity, like holography. Developing and promoting
these connections from the conceptual to the operational level unlocks access
to a powerful set of tools, which can be pivotal towards the formulation of a
consistent theory of quantum gravity. Here, we review recent progress on the
role and applications of quantum informational methods, in particular tensor
networks, for quantum gravity models. We focus on spin network states dual to
finite regions of space, represented as entanglement graphs in the group field
theory approach to quantum gravity, and illustrate how techniques from random
tensor networks can be exploited to investigate their holographic properties.
In particular, spin network states can be interpreted as maps from bulk to
boundary, whose holographic behaviour increases with the inhomogeneity of their
geometric data (up to becoming proper quantum channels). The entanglement
entropy of boundary states, which are obtained by feeding such maps with
suitable bulk states, is then proved to follow a bulk area law, with
corrections due to the entanglement of the bulk state. We further review how
exceeding a certain threshold of bulk entanglement leads to the emergence of a
black hole-like region, revealing intriguing perspectives for quantum
cosmology.

categories: hep-th cond-mat.stat-mech math-ph math.MP quant-ph


title:
Exploratory applications of the Fr\"ohlich-Morchio-Strocchi mechanism in
  quantum gravity

abstract:
  A manifestly diffeomorphism-invariant approach to canonical quantum gravity
requires to use composite operators. These can be considered to be bound states
of matter and/or gravitons, intrinsically non-perturbative objects. An
analytical approach to determine the properties of such bound states could be
the Fr\"ohlich-Morchio-Strocchi mechanism. We explore the necessary technology
by applying it to various $n$-point functions, including geon propagators and
black-hole-particle vertices.


title:
Reinforcement Learning in the Wild: Scalable RL Dispatching Algorithm
  Deployed in Ridehailing Marketplace

abstract:
  In this study, a real-time dispatching algorithm based on reinforcement
learning is proposed and for the first time, is deployed in large scale.
Current dispatching methods in ridehailing platforms are dominantly based on
myopic or rule-based non-myopic approaches. Reinforcement learning enables
dispatching policies that are informed of historical data and able to employ
the learned information to optimize returns of expected future trajectories.
Previous studies in this field yielded promising results, yet have left room
for further improvements in terms of performance gain, self-dependency,
transferability, and scalable deployment mechanisms. The present study proposes
a standalone RL-based dispatching solution that is equipped with multiple
mechanisms to ensure robust and efficient on-policy learning and inference
while being adaptable for full-scale deployment. A new form of value updating
based on temporal difference is proposed that is more adapted to the inherent
uncertainty of the problem. For the driver-order assignment, a customized
utility function is proposed that when tuned based on the statistics of the
market, results in remarkable performance improvement and interpretability. In
addition, for reducing the risk of cancellation after drivers' assignment, an
adaptive graph pruning strategy based on the multi-arm bandit problem is
introduced. The method is evaluated using offline simulation with real data and
yields notable performance improvement. In addition, the algorithm is deployed
online in multiple cities under DiDi's operation for A/B testing and is
launched in one of the major international markets as the primary mode of
dispatch. The deployed algorithm shows over 1.3% improvement in total driver
income from A/B testing. In addition, by causal inference analysis, as much as
5.3% improvement in major performance metrics is detected after full-scale
deployment.


title:
Universal catastrophe time distributions of dynamically unstable
  polymers

abstract:
  Dynamic instability -- the growth, catastrophe, and shrinkage of
quasi-one-dimensional filaments -- has been observed in multiple biopolymers.
Scientists have long understood the catastrophic cessation of growth and
subsequent depolymerization as arising from the interplay of hydrolysis and
polymerization at the tip of the polymer. Here, we show that for a broad class
of catastrophe models, the expected catastrophe time distribution is
exponential. We show that the distribution shape is insensitive to noise, but
that depletion of monomers from a finite pool can dramatically change the
distribution shape by reducing the polymerization rate. We derive a form for
this finite-pool catastrophe time distribution and show that finite-pool
effects can be important even when the depletion of monomers does not greatly
alter the polymerization rate.


title: Low-Rank Approximation with $1/\epsilon^{1/3}$ Matrix-Vector Products

abstract:
  We study iterative methods based on Krylov subspaces for low-rank
approximation under any Schatten-$p$ norm. Here, given access to a matrix $A$
through matrix-vector products, an accuracy parameter $\epsilon$, and a target
rank $k$, the goal is to find a rank-$k$ matrix $Z$ with orthonormal columns
such that $\| A(I -ZZ^\top)\|_{S_p} \leq (1+\epsilon)\min_{U^\top U = I_k}
\|A(I - U U^\top)\|_{S_p}$, where $\|M\|_{S_p}$ denotes the $\ell_p$ norm of
the the singular values of $M$. For the special cases of $p=2$ (Frobenius norm)
and $p = \infty$ (Spectral norm), Musco and Musco (NeurIPS 2015) obtained an
algorithm based on Krylov methods that uses $\tilde{O}(k/\sqrt{\epsilon})$
matrix-vector products, improving on the na\"ive $\tilde{O}(k/\epsilon)$
dependence obtainable by the power method, where $\tilde{O}$ suppresses
poly$(\log(dk/\epsilon))$ factors.
  Our main result is an algorithm that uses only
$\tilde{O}(kp^{1/6}/\epsilon^{1/3})$ matrix-vector products, and works for all
$p \geq 1$. For $p = 2$ our bound improves the previous
$\tilde{O}(k/\epsilon^{1/2})$ bound to $\tilde{O}(k/\epsilon^{1/3})$. Since the
Schatten-$p$ and Schatten-$\infty$ norms are the same up to a $1+ \epsilon$
factor when $p \geq (\log d)/\epsilon$, our bound recovers the result of Musco
and Musco for $p = \infty$. Further, we prove a matrix-vector query lower bound
of $\Omega(1/\epsilon^{1/3})$ for any fixed constant $p \geq 1$, showing that
surprisingly $\tilde{\Theta}(1/\epsilon^{1/3})$ is the optimal complexity for
constant~$k$.
  To obtain our results, we introduce several new techniques, including
optimizing over multiple Krylov subspaces simultaneously, and pinching
inequalities for partitioned operators. Our lower bound for $p \in [1,2]$ uses
the Araki-Lieb-Thirring trace inequality, whereas for $p>2$, we appeal to a
norm-compression inequality for aligned partitioned operators.


title: The factorization problem for Jordan algebras. Applications

abstract:
  We investigate the factorization problem as well as the classifying
complements problem in the setting of Jordan algebras. Matched pairs of Jordan
algebras and the corresponding bicrossed products are introduced. It is shown
that any Jordan algebra which factorizes through two given Jordan algebras is
isomorphic to a bicrossed product associated to a certain matched pair between
the same two Jordan algebras. Furthermore, a new type of deformation of a
Jordan algebra is proposed as the main step towards solving the classifying
complements problem.


title:
Ensemble-based learning of turbulence model from indirect observation
  data

abstract:
  In this work, we propose using an ensemble Kalman method to learn a nonlinear
eddy viscosity model, represented as a tensor basis neural network, from
velocity data. Data-driven turbulence models have emerged as a promising
alternative to traditional models for providing closure mapping from the mean
velocities to Reynolds stresses. Most data-driven models in this category need
full-field Reynolds stress data for training, which not only places stringent
demand on the data generation but also makes the trained model ill-conditioned
and lacks robustness. This difficulty can be alleviated by incorporating the
Reynolds-averaged Navier-Stokes (RANS) solver in the training process. However,
this would necessitate developing adjoint solvers of the RANS model, which can
be challenging. Given such difficulty, we present an ensemble Kalman method
with adaptive step size to train a neural network-based turbulence model by
using indirect observation data. To our knowledge, this is the first such
attempt in turbulence modelling. The ensemble method is first verified on the
flow in a square duct, where it correctly learns the underlying turbulence
models from velocity data. Then, the generalizability of the learned model is
evaluated on a family of separated flows over periodic hills. It is
demonstrated that the turbulence model learned in one flow can predict similar
flows in different geometries.


title:
Unaligned but Safe -- Formally Compensating Performance Limitations for
  Imprecise 2D Object Detection

abstract:
  In this paper, we consider the imperfection within machine learning-based 2D
object detection and its impact on safety. We address a special sub-type of
performance limitations: the prediction bounding box cannot be perfectly
aligned with the ground truth, but the computed Intersection-over-Union metric
is always larger than a given threshold. Under such type of performance
limitation, we formally prove the minimum required bounding box enlargement
factor to cover the ground truth. We then demonstrate that the factor can be
mathematically adjusted to a smaller value, provided that the motion planner
takes a fixed-length buffer in making its decisions. Finally, observing the
difference between an empirically measured enlargement factor and our formally
derived worst-case enlargement factor offers an interesting connection between
the quantitative evidence (demonstrated by statistics) and the qualitative
evidence (demonstrated by worst-case analysis).


title:
Describing complex disease progression using joint latent class models
  for multivariate longitudinal markers and clinical endpoints

abstract:
  Neurodegenerative diseases are characterized by numerous markers of
progression and clinical endpoints. For instance, Multiple System Atrophy
(MSA), a rare neurodegenerative synucleinopathy, is characterized by various
combinations of progressive autonomic failure and motor dysfunction, and a very
poor prognosis. Describing the progression of such complex and
multi-dimensional diseases is particularly difficult. One has to simultaneously
account for the assessment of multivariate markers over time, the occurrence of
clinical endpoints, and a highly suspected heterogeneity between patients. Yet,
such description is crucial for understanding the natural history of the
disease, staging patients diagnosed with the disease, unraveling subphenotypes,
and predicting the prognosis. Through the example of MSA progression, we show
how a latent class approach can help describe complex disease progression
measured by multiple repeated markers and clinical endpoints, and identify
subphenotypes for exploring new pathological hypotheses. The joint latent class
model includes class-specific multivariate mixed models to handle multivariate
repeated biomarkers possibly summarized into latent dimensions and
class-and-cause-specific proportional hazard models to handle time-to-event
data. Maximum likelihood estimation is made available in the lcmm R package. In
the French MSA cohort comprising data of 598 patients during up to 13 years,
five subphenotypes of MSA were identified that differ by the sequence and shape
of biomarkers degradation, and the associated risk of death. In posterior
analyses, the five subphenotypes were used to explore the association between
clinical progression and external imaging and fluid biomarkers, while properly
accounting for the uncertainty in the subphenotypes membership.


title:
Hierarchical deposition and deterministic scale-free networks: a
  visibility algorithm approach

abstract:
  The deterministic growth of an interface formed by the synchronous
hierarchical deposition of particles of unequal size, where the number of
particles in a generation $n$ obeys a hyperbolic scaling law, is studied. In
the framework of a dynamical network generated by a horizontal visibility
algorithm (HVA), the discrete deposition sites of the interface constitute
network nodes, while the edges are determined by means of a mutual visibility
criterion. We show analytically that the resulting sparse network is scale-free
with a dominant degree exponent $\gamma_e = \ln{3}/\ln{2}$ and a transient
exponent $\gamma_o = 1$. Through an exact calculation of the average clustering
coefficient we show that the network inherits the modular hierarchical nature
of the underlying deposition process. It is shown numerically that the
eigenvalue spectrum of the adjacency matrix possesses a fractal structure, the
exact shape of which remains undetermined.


title: Deep Learning for Computational Cytology: A Survey

abstract:
  Computational cytology is a critical, rapid-developing, yet challenging topic
in the field of medical image computing which analyzes the digitized cytology
image by computer-aided technologies for cancer screening. Recently, an
increasing number of deep learning (DL) algorithms have made significant
progress in medical image analysis, leading to the boosting publications of
cytological studies. To investigate the advanced methods and comprehensive
applications, we survey more than 120 publications of DL-based cytology image
analysis in this article. We first introduce various deep learning methods,
including fully supervised, weakly supervised, unsupervised, and transfer
learning. Then, we systematically summarize the public datasets, evaluation
metrics, versatile cytology image analysis applications including
classification, detection, segmentation, and other related tasks. Finally, we
discuss current challenges and potential research directions of computational
cytology.


title: Improved Compression of the Okamura-Seymour Metric

abstract:
  Let $G=(V,E)$ be an undirected unweighted planar graph. Consider a vector
storing the distances from an arbitrary vertex $v$ to all vertices $S = \{ s_1
, s_2 , \ldots , s_k \}$ of a single face in their cyclic order. The pattern of
$v$ is obtained by taking the difference between every pair of consecutive
values of this vector. In STOC'19, Li and Parter used a VC-dimension argument
to show that in planar graphs, the number of distinct patterns, denoted $x$, is
only $O(k^3)$. This resulted in a simple compression scheme requiring $\tilde
O(\min \{ k^4+|T|, k\cdot |T|\})$ space to encode the distances between $S$ and
a subset of terminal vertices $T \subseteq V$. This is known as the
Okamura-Seymour metric compression problem.
  We give an alternative proof of the $x=O(k^3)$ bound that exploits planarity
beyond the VC-dimension argument. Namely, our proof relies on cut-cycle
duality, as well as on the fact that distances among vertices of $S$ are
bounded by $k$. Our method implies the following:
  (1) An $\tilde{O}(x+k+|T|)$ space compression of the Okamura-Seymour metric,
thus improving the compression of Li and Parter to $\tilde O(\min \{k^3+|T|,k
\cdot |T| \})$.
  (2) An optimal $\tilde{O}(k+|T|)$ space compression of the Okamura-Seymour
metric, in the case where the vertices of $T$ induce a connected component in
$G$.
  (3) A tight bound of $x = \Theta(k^2)$ for the family of Halin graphs,
whereas the VC-dimension argument is limited to showing $x=O(k^3)$.


title:
Load Balancing and Resource Allocation in Fog-Assisted 5G Networks: An
  Incentive-based Game Theoretic Approach

abstract:
  Fog-assisted 5G Networks allow the users within the networks to execute their
tasks and processes through fog nodes and cooperation among the fog nodes. As a
result, the delay in task execution reduces as compared to that in case of
independent task execution, where the Base Station (BS) or server is directly
involved. In the practical scenario, the ability to cooperate clearly depends
on the willingness of fog nodes to cooperate. Hence, in this paper, we propose
an incentive-based bargaining approach which encourages the fog nodes to
cooperate among themselves by receiving incentives from the end users
benefitting from the cooperation. Considering the heterogenous nature of users
and fog nodes based on their storage capacity, energy efficiency etc., we aim
to emphasise a fair incentive mechanism which fairly and uniformly distributes
the incentives from user to the participating fog nodes. The proposed
incentive-based cooperative approach reduces the cost of end users as well as
balances the energy consumption of fog nodes. The proposed system model
addresses and models the above approaches and mathematically formulate cost
models for both fog nodes and the end users in a fog-assisted 5G network.


title:
Help Me Explore: Minimal Social Interventions for Graph-Based Autotelic
  Agents

abstract:
  In the quest for autonomous agents learning open-ended repertoires of skills,
most works take a Piagetian perspective: learning trajectories are the results
of interactions between developmental agents and their physical environment.
The Vygotskian perspective, on the other hand, emphasizes the centrality of the
socio-cultural environment: higher cognitive functions emerge from
transmissions of socio-cultural processes internalized by the agent. This paper
argues that both perspectives could be coupled within the learning of autotelic
agents to foster their skill acquisition. To this end, we make two
contributions: 1) a novel social interaction protocol called Help Me Explore
(HME), where autotelic agents can benefit from both individual and socially
guided exploration. In social episodes, a social partner suggests goals at the
frontier of the learning agent knowledge. In autotelic episodes, agents can
either learn to master their own discovered goals or autonomously rehearse
failed social goals; 2) GANGSTR, a graph-based autotelic agent for manipulation
domains capable of decomposing goals into sequences of intermediate sub-goals.
We show that when learning within HME, GANGSTR overcomes its individual
learning limits by mastering the most complex configurations (e.g. stacks of 5
blocks) with only few social interventions.


title:
Dynamics and statefinder analysis of a class of sign-changeable
  interacting dark energy scenarios

abstract:
  We revise the dynamical properties of a class of cosmological models where
the dark sector interacts through an interacting term that changes sign during
evolution. In particular, we obtain the critical points and we investigate the
existence and stability conditions for cosmological solutions, describing
radiation, matter and dark energy dominated eras. We find that all the studied
models admit a stable critical point corresponding to an accelerated phase. We
use background data to find the best fit parameters for one of the studied
models, resulting an interacting parameter with a definite sign within
$1\sigma$ confidence level, consistent with the results of the dynamical system
analysis. We also compute the statefinder parameters and plot the $r-q$ and
$r-s$ planes, where we observe different trajectories when we vary the
interaction parameter for a specific model and when we vary the interacting
scenario. We can in this sense distinguish among models, including
$\Lambda$CDM.


title:
AI-based Robust Resource Allocation in End-to-End Network Slicing under
  Demand and CSI Uncertainties

abstract:
  Network slicing (NwS) is one of the main technologies in the fifth-generation
of mobile communication and beyond (5G+). One of the important challenges in
the NwS is information uncertainty which mainly involves demand and channel
state information (CSI). Demand uncertainty is divided into three types: number
of users requests, amount of bandwidth, and requested virtual network functions
workloads. Moreover, the CSI uncertainty is modeled by three methods:
worst-case, probabilistic, and hybrid. In this paper, our goal is to maximize
the utility of the infrastructure provider by exploiting deep reinforcement
learning algorithms in end-to-end NwS resource allocation under demand and CSI
uncertainties. The proposed formulation is a nonconvex mixed-integer non-linear
programming problem. To perform robust resource allocation in problems that
involve uncertainty, we need a history of previous information. To this end, we
use a recurrent deterministic policy gradient (RDPG) algorithm, a recurrent and
memory-based approach in deep reinforcement learning. Then, we compare the RDPG
method in different scenarios with soft actor-critic (SAC), deep deterministic
policy gradient (DDPG), distributed, and greedy algorithms. The simulation
results show that the SAC method is better than the DDPG, distributed, and
greedy methods, respectively. Moreover, the RDPG method out performs the SAC
approach on average by 70%.


title:
Quantifying information scrambling via Classical Shadow Tomography on
  Programmable Quantum Simulators

abstract:
  We develop techniques to probe the dynamics of quantum information, and
implement them experimentally on an IBM superconducting quantum processor. Our
protocols adapt shadow tomography for the study of time evolution channels
rather than of quantum states, and rely only on single-qubit operations and
measurements. We identify two unambiguous signatures of quantum information
scrambling, neither of which can be mimicked by dissipative processes, and
relate these to many-body teleportation. By realizing quantum chaotic dynamics
in experiment, we measure both signatures, and support our results with
numerical simulations of the quantum system. We additionally investigate
operator growth under this dynamics, and observe behaviour characteristic of
quantum chaos. As our methods require only a single quantum state at a time,
they can be readily applied on a wide variety of quantum simulators.


title: Axial momentum and quantization of the Majorana field

abstract:
  New approach to quantization of the relativistic Majorana field is presented.
It is based on expansion of the field into eigenfunctions of the axial momentum
-- a novel observable introduced recently. Relativistic invariance is used as
the main guiding principle instead of canonical formalism. Hidden structure of
the quantized Majorana field in the form of real Clifford algebra of Hermitian
fermionic operators is unveiled. Generators of the Poincar\'e transformations
in the Fock space are found as solutions of certain operator equations, without
invoking the principle of correspondence with classical conserved quantities.
Also operators of parity $\hat{\mbox{P}}$ and time reversal $\hat{\mbox{T}}$
are constructed.


title: Understanding Twitters behavior during the pandemic: Fake News and Fear

abstract:
  The outbreak of the SARS-CoV-2 novel coronavirus (COVID-19) has been
accompanied by a large amount of misleading and false information about the
virus, especially on social media. During the pandemic social media gained
special interest as it went on to become an important medium of communication.
This made the information being relayed on these platforms especially critical.
In our work, we aim to explore the percentage of fake news being spread on
Twitter as well as measure the sentiment of the public at the same time. We
further study how the sentiment of fear is present among the public. In
addition to that we compare the rate of spread of the virus per day with the
rate of spread of fake news on Twitter. Our study is useful in establishing the
role of Twitter, and social media, during a crisis, and more specifically
during crisis management.


title: Group-Agent Reinforcement Learning

abstract:
  It can largely benefit the reinforcement learning process of each agent if
multiple agents perform their separate reinforcement learning tasks
cooperatively. Different from multi-agent reinforcement learning where multiple
agents are in a common environment and should learn to cooperate or compete
with each other, in this case each agent has its separate environment and only
communicate with others to share knowledge without any cooperative or
competitive behaviour as a learning outcome. In fact, this learning scenario is
not well understood yet and not well formulated. As the first effort, we
propose group-agent reinforcement learning as a formulation of this scenario
and the third type of reinforcement learning problem with respect to
single-agent and multi-agent reinforcement learning. We then propose the first
distributed reinforcement learning framework called DDAL (Decentralised
Distributed Asynchronous Learning) designed for group-agent reinforcement
learning. We show through experiments that DDAL achieved desirable performance
with very stable training and has good scalability.


title: On the Cayleyness of Praeger-Xu graphs

abstract:
  We give a sufficient and necessary condition for a Praeger-Xu graph to be a
Cayley graph.


title: Quantization in Layer's Input is Matter

abstract:
  In this paper, we will show that the quantization in layer's input is more
important than parameters' quantization for loss function. And the algorithm
which is based on the layer's input quantization error is better than
hessian-based mixed precision layout algorithm.


title:
Cohomogeneity one actions on symmetric spaces of noncompact type and
  higher rank

abstract:
  We develop a new structural result for cohomogeneity one actions on (not
necessarily irreducible) symmetric spaces of noncompact type and arbitrary
rank. We apply this result to classify cohomogeneity one actions on
SL(n,R)/SO(n), n>1, up to orbit equivalence. We also reduce the classification
problem on a reducible space to the classification on each one of its
irreducible factors, which in particular allows to classify cohomogeneity one
actions on any finite product of hyperbolic spaces.


title:
Game of Privacy: Towards Better Federated Platform Collaboration under
  Privacy Restriction

abstract:
  Vertical federated learning (VFL) aims to train models from cross-silo data
with different feature spaces stored on different platforms. Existing VFL
methods usually assume all data on each platform can be used for model
training. However, due to the intrinsic privacy risks of federated learning,
the total amount of involved data may be constrained. In addition, existing VFL
studies usually assume only one platform has task labels and can benefit from
the collaboration, making it difficult to attract other platforms to join in
the collaborative learning. In this paper, we study the platform collaboration
problem in VFL under privacy constraint. We propose to incent different
platforms through a reciprocal collaboration, where all platforms can exploit
multi-platform information in the VFL framework to benefit their own tasks.
With limited privacy budgets, each platform needs to wisely allocate its data
quotas for collaboration with other platforms. Thereby, they naturally form a
multi-party game. There are two core problems in this game, i.e., how to
appraise other platforms' data value to compute game rewards and how to
optimize policies to solve the game. To evaluate the contributions of other
platforms' data, each platform offers a small amount of "deposit" data to
participate in the VFL. We propose a performance estimation method to predict
the expected model performance when involving different amount combinations of
inter-platform data. To solve the game, we propose a platform negotiation
method that simulates the bargaining among platforms and locally optimizes
their policies via gradient descent. Extensive experiments on two real-world
datasets show that our approach can effectively facilitate the collaborative
exploitation of multi-platform data in VFL under privacy restrictions.


title: Transferable and Adaptable Driving Behavior Prediction

abstract:
  While autonomous vehicles still struggle to solve challenging situations
during on-road driving, humans have long mastered the essence of driving with
efficient, transferable, and adaptable driving capability. By mimicking humans'
cognition model and semantic understanding during driving, we propose HATN, a
hierarchical framework to generate high-quality, transferable, and adaptable
predictions for driving behaviors in multi-agent dense-traffic environments.
Our hierarchical method consists of a high-level intention identification
policy and a low-level trajectory generation policy. We introduce a novel
semantic sub-task definition and generic state representation for each
sub-task. With these techniques, the hierarchical framework is transferable
across different driving scenarios. Besides, our model is able to capture
variations of driving behaviors among individuals and scenarios by an online
adaptation module. We demonstrate our algorithms in the task of trajectory
prediction for real traffic data at intersections and roundabouts from the
INTERACTION dataset. Through extensive numerical studies, it is evident that
our method significantly outperformed other methods in terms of prediction
accuracy, transferability, and adaptability. Pushing the state-of-the-art
performance by a considerable margin, we also provide a cognitive view of
understanding the driving behavior behind such improvement. We highlight that
in the future, more research attention and effort are deserved for
transferability and adaptability. It is not only due to the promising
performance elevation of prediction and planning algorithms, but more
fundamentally, they are crucial for the scalable and general deployment of
autonomous vehicles.


title: An observational synthesis of the Taurid meteor complex

abstract:
  We provide an overview of the observational properties of the four major
Taurid showers, namely the Northern and Southern Taurids (#017 NTA and #002
STA), the Beta Taurids (#173 BTA) and the Zeta Perseids (#172 ZPE). Analysing
more than two decades of meteor observations from visual, optical and radar
measurements we present the Taurids average activity, annual variations in
strength, radiant drift and orbital variations as a function of solar longitude
and particle size. The Taurid showers are detected over several weeks in the
spring and autumn, but their annual activity level is generally low (less than
15 visual meteors per hour). We find the STA to be predominant in autumn, while
its twin the ZPE dominates over the BTA in spring. Due to their long duration,
the position of each shower's radiant and orbital elements are variable with
time. Optical measurements have previously recorded enhanced STA activity and
increased fireball rates caused by the return of a swarm of meteoroids trapped
in the 7:2 mean motion resonance with Jupiter. However, we find no presence of
the swarm in radar data, suggesting that small meteoroids are removed from the
resonance faster than fireball-producing meteoroids. We also find the STA to be
enriched in smaller particles early in their activity period. The differences
we identify in our analysis between the showers at different particle sizes
provide strong observational constraints to future dynamical modelling of the
Taurid Meteoroid Complex.


title:
Identifying future protection gaps in Amazon floodplains: a dual-season
  forecasted distribution of the world's largest scaled freshwater fish

abstract:
  The Amazon floodplains represent important surfaces of highly valuable
ecosystems, yet they remain neglected from protected areas. While the
efficiency of the protected area network of the Amazon basin may be jeopardised
by climate change, floodplains are exposed to important consequences of climate
change but are omitted from species distribution models and protection gap
analyses. We modelled the current and future (2070) distribution of the giant
bony-tongue fish Arapaima sp. accounting for climate and habitat requirements,
with consideration of dam presence (already existing and planned constructions)
and hydroperiod (high- and low-water stages). We further quantified the amount
of suitable environment which falls inside and outside the current network of
protected areas to identify spatial conservation gaps. We predict climate
change to cause the decline of environmental suitability by 16.6% during the
high-water stage, and by 19.4% during the low-water stage. We found that about
70% of the suitable environments of Arapaima sp. remain currently unprotected,
which is likely to increase by 5% with future climate change effects. Both
current and projected dam constructions may hamper population flows between the
central and the Bolivian and Peruvian parts of the basin. We highlight
protection gaps mostly in the southwestern part of the basin and recommend the
extension of the current network of protected areas in the floodplains of the
upper Ucayali, Juru\`a and Purus Rivers and their tributaries. This study
showed the importance of taking into account hydroperiods and dispersal
barriers in forecasting the distribution of freshwater fish species, and
stresses the urgent need to integrate floodplains to the protected area
networks.


title: On the Acquisition of Stationary Signals Using Uniform ADCs

abstract:
  In this work, we consider the acquisition of stationary signals using uniform
analog-to-digital converters (ADCs), i.e., employing uniform sampling and
scalar uniform quantization. We jointly optimize the pre-sampling and
reconstruction filters to minimize the time-averaged mean-squared error (TMSE)
in recovering the continuous-time input signal for a fixed sampling rate and
quantizer resolution and obtain closed-form expressions for the minimal
achievable TMSE. We show that the TMSE-minimizing pre-sampling filter omits
aliasing and discards weak frequency components to resolve the remaining ones
with higher resolution when the rate budget is small. In our numerical study,
we validate our results and show that sub-Nyquist sampling often minimizes the
TMSE under tight rate budgets at the output of the ADC.


title:
InPars: Data Augmentation for Information Retrieval using Large Language
  Models

abstract:
  The information retrieval community has recently witnessed a revolution due
to large pretrained transformer models. Another key ingredient for this
revolution was the MS MARCO dataset, whose scale and diversity has enabled
zero-shot transfer learning to various tasks. However, not all IR tasks and
domains can benefit from one single dataset equally. Extensive research in
various NLP tasks has shown that using domain-specific training data, as
opposed to a general-purpose one, improves the performance of neural models. In
this work, we harness the few-shot capabilities of large pretrained language
models as synthetic data generators for IR tasks. We show that models finetuned
solely on our unsupervised dataset outperform strong baselines such as BM25 as
well as recently proposed self-supervised dense retrieval methods. Furthermore,
retrievers finetuned on both supervised and our synthetic data achieve better
zero-shot transfer than models finetuned only on supervised data. Code, models,
and data are available at https://github.com/zetaalphavector/inpars .


title: Deep learning for drug repurposing: methods, databases, and applications

abstract:
  Drug development is time-consuming and expensive. Repurposing existing drugs
for new therapies is an attractive solution that accelerates drug development
at reduced experimental costs, specifically for Coronavirus Disease 2019
(COVID-19), an infectious disease caused by severe acute respiratory syndrome
coronavirus 2 (SARS-CoV-2). However, comprehensively obtaining and productively
integrating available knowledge and big biomedical data to effectively advance
deep learning models is still challenging for drug repurposing in other complex
diseases. In this review, we introduce guidelines on how to utilize deep
learning methodologies and tools for drug repurposing. We first summarized the
commonly used bioinformatics and pharmacogenomics databases for drug
repurposing. Next, we discuss recently developed sequence-based and graph-based
representation approaches as well as state-of-the-art deep learning-based
methods. Finally, we present applications of drug repurposing to fight the
COVID-19 pandemic, and outline its future challenges.


title: EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction

abstract:
  Predicting how a drug-like molecule binds to a specific protein target is a
core problem in drug discovery. An extremely fast computational binding method
would enable key applications such as fast virtual screening or drug
engineering. Existing methods are computationally expensive as they rely on
heavy candidate sampling coupled with scoring, ranking, and fine-tuning steps.
We challenge this paradigm with EquiBind, an SE(3)-equivariant geometric deep
learning model performing direct-shot prediction of both i) the receptor
binding location (blind docking) and ii) the ligand's bound pose and
orientation. EquiBind achieves significant speed-ups and better quality
compared to traditional and recent baselines. Further, we show extra
improvements when coupling it with existing fine-tuning techniques at the cost
of increased running time. Finally, we propose a novel and fast fine-tuning
model that adjusts torsion angles of a ligand's rotatable bonds based on
closed-form global minima of the von Mises angular distance to a given input
atomic point cloud, avoiding previous expensive differential evolution
strategies for energy minimization.


title: The Brezis-Nirenberg problem on non-contractible bounded domains of R^3

abstract:
  In this paper, we study the Brezis-Nirenberg problem on bounded smooth
domains of R3. Using the algebraic topological argument of Bahri-Coron[2] as
implemented in [6] combined with the Brendle[4]- Schoen[8]'s bubble
construction, we solve the problem for non-contractible domains.


title:
Identifying Weaknesses in Machine Translation Metrics Through Minimum
  Bayes Risk Decoding: A Case Study for COMET

abstract:
  Neural metrics have achieved impressive correlation with human judgements in
the evaluation of machine translation systems, but before we can safely
optimise towards such metrics, we should be aware of (and ideally eliminate)
biases towards bad translations that receive high scores. Our experiments show
that sample-based Minimum Bayes Risk decoding can be used to explore and
quantify such weaknesses. When applying this strategy to COMET for en-de and
de-en, we find that COMET models are not sensitive enough to discrepancies in
numbers and named entities. We further show that these biases cannot be fully
removed by simply training on additional synthetic data.


title:
Field-driven skyrmion motion through velocity equipartition between
  skyrmions and a domain wall

abstract:
  Magnetic skyrmions, as a whirling spin texture with axisymmetry, cannot be
propelled by a uniform magnetic field. Therefore, reported skyrmion motions
have been induced using other sorts of stimuli; typically, electric currents in
magnetic metals as well as spin waves, thermal gradient, and field gradient
have manifested their ability to drive skyrmion motion. Here, we demonstrate,
through micromagnetic simulations and analytically, that magnetic skyrmions can
be displaced by a uniform perpendicular magnetic field via a domain-wall (DW)
mediator. For a fixed field strength, the velocity of a skyrmion train evolves
in terms of 1 / (1 + Ns) with Ns denoting the number of skyrmions. Based on
Thiele's model, we reproduce the velocity-Ns relation first identified from
numerical results and reveal that the skyrmion-DW and inter-skyrmion repulsions
offer the direct driving force for skyrmion motion. This study underlines the
role of spin textures' interaction in skyrmion dynamics, and opens an
alternative route for skyrmion manipulation especially relevant to insulating
magnets. Given the correspondence between magnetism and electricity, we
anticipate that the scheme should also work for polar skyrmions in
ferroelectrics.


title: Order-based Structure Learning without Score Equivalence

abstract:
  We consider the structure learning problem with all node variables having the
same error variance, an assumption known to ensure the identifiability of the
causal directed acyclic graph (DAG). We propose an empirical Bayes formulation
of the problem that yields a non-decomposable posterior score for DAG models.
To facilitate efficient posterior computation, we approximate the posterior
probability of each ordering by that of a "best" DAG model, which naturally
leads to an order-based Markov chain Monte Carlo (MCMC) algorithm. Strong
selection consistency for our model is proved under mild high-dimensional
conditions, and the mixing behavior of our sampler is theoretically
investigated. Further, we propose a new iterative top-down algorithm, which
quickly yields an approximate solution to the structure learning problem and
can be used to initialize the MCMC sampler. We demonstrate that our method
outperforms other state-of-the-art algorithms under various simulation
settings, and conclude the paper with a single-cell real-data study
illustrating practical advantages of the proposed method.


title: Renewable energy communities: do they have a business case in Flanders?

abstract:
  Renewable energy communities (RECs) are prominent initiatives to provide end
consumers an active role in the energy sector, raise awareness on the
importance of renewable energy (RE) technologies and increase their share in
the energy system thus reducing greenhouse gas emissions. The economic
viability of RECs though, depends on multiple interdependent factors that
require careful examination for each individual context. This study aims at
investigating the impact of electricity tariffs, ratio of electrification of
heating and transportation sectors, prices of RE technologies and storage
systems, and internal electricity exchange prices on the annual cost for
electricity provision of a REC. A mixed-integer linear model is developed to
minimize energy provision costs for a representative REC in Flanders, Belgium.
The results indicate that RECs have the potential to reduce these costs by 10
to 26% compared to business-as-usual. This cost reduction depends on the type
of electricity tariffs and the level of uptake of flexible assets such as heat
pumps and electric vehicles. The shift towards a higher power component in the
electricity tariff makes electricity storage systems more attractive, which
leads to higher electricity self-consumption. The introduction of flexible
assets adds the possibility to shift demand when tariffs are lower and makes
larger sizes of photovoltaic systems economically viable due to the increase in
the total electricity demand. However, RECs cost reduction compared to
individual smart-homes amounts to only 4% - 6% in the best cases. Uncertainties
stemming from the regulation and the costs of setting up a REC may reduce the
estimated benefits.


title:
Feature-level augmentation to improve robustness of deep neural networks
  to affine transformations

abstract:
  Recent studies revealed that convolutional neural networks do not generalize
well to small image transformations, e.g. rotations by a few degrees or
translations of a few pixels. To improve the robustness to such
transformations, we propose to introduce data augmentation at intermediate
layers of the neural architecture, in addition to the common data augmentation
applied on the input images. By introducing small perturbations to activation
maps (features) at various levels, we develop the capacity of the neural
network to cope with such transformations. We conduct experiments on three
image classification benchmarks (Tiny ImageNet, Caltech-256 and Food-101),
considering two different convolutional architectures (ResNet-18 and
DenseNet-121). When compared with two state-of-the-art stabilization methods,
the empirical results show that our approach consistently attains the best
trade-off between accuracy and mean flip rate.


title: A Closed-Loop MOEMS Accelerometer

abstract:
  In this paper, a closed-loop micro-opto-electro-mechanical system (MOEMS)
accelerometer based on the Fabry-P\'erot (FP) interferometer is presented. The
FP cavity is formed between the end of a cleaved single-mode optical fiber and
the cross-section of a proof mass (PM) which is suspended by four U-shaped
springs. The applied acceleration tends to move the PM in the opposite
direction. The arrays of fixed and movable comb fingers produce an
electrostatic force which keeps the PM in its resting position. The voltage
that can provide this electrostatic force is considered as the output of the
sensor. Using a closed-loop detection method simultaneously increases the
measurement range and sensitivity of the accelerometer. The proposed sensor is
fabricated on a silicon-on-insulator wafer using the bulk micromachining
method. The results of the sensor characterization show that the accelerometer
has a linear response in the range of 5 g. In the closed-loop mode, the
sensitivity and bias stability of the sensor are 1.16 V/g and 40 ug,
respectively.


title:
Effective classification of ecg signals using enhanced convolutional
  neural network in iot

abstract:
  In this paper, a novel ECG monitoring approach based on IoT technology is
suggested. This paper proposes a routing system for IoT healthcare platforms
based on Dynamic Source Routing (DSR) and Routing by Energy and Link Quality
(REL). In addition, the Artificial Neural Network (ANN), Support Vector Machine
(SVM), and Convolution Neural Networks (CNNs)-based approaches for ECG signal
categorization were tested in this study. Deep-ECG will employ a deep CNN to
extract important characteristics, which will then be compared using simple and
fast distance functions in order to classify cardiac problems efficiently. This
work has suggested algorithms for the categorization of ECG data acquired from
mobile watch users in order to identify aberrant data. The Massachusetts
Institute of Technology (MIT) and Beth Israel Hospital (MIT/BIH) Arrhythmia
Database have been used for experimental verification of the suggested
approaches. The results show that the proposed strategy outperforms others in
terms of classification accuracy.


title: DeepCENT: Prediction of Censored Event Time via Deep Learning

abstract:
  With the rapid advances of deep learning, many computational methods have
been developed to analyze nonlinear and complex right censored data via deep
learning approaches. However, the majority of the methods focus on predicting
survival function or hazard function rather than predicting a single valued
time to an event. In this paper, we propose a novel method, DeepCENT, to
directly predict the individual time to an event. It utilizes the deep learning
framework with an innovative loss function that combines the mean square error
and the concordance index. Most importantly, DeepCENT can handle competing
risks, where one type of event precludes the other types of events from being
observed. The validity and advantage of DeepCENT were evaluated using
simulation studies and illustrated with three publicly available cancer data
sets.


title: A simple sum for simplices

abstract:
  We give a vector identity for $n+2$ points in $\mathbb R^n$. It follows as a
corollary that when $n$ is odd the sum of the signed volumes of the
$n$-simplices is zero, and when $n$ is even, the alternating sum of the signed
volumes is zero.


title: Jet-driven viscous locomotion of confined thermoresponsive microgels

abstract:
  We consider the dynamics of micro-sized, asymmetrically-coated
thermoresponsive hydrogel ribbons (microgels) under periodic heating and
cooling in the confined space between two planar surfaces. As the result of the
temperature changes, the volume and thus the shape of the slender microgel
change, which lead to repeated cycles of bending and elastic relaxation, and to
net locomotion. Small devices designed for biomimetic locomotion need to
exploit flows that are not symmetric in time (non-reciprocal) to escape the
constraints of the scallop theorem and undergo net motion. Unlike other
biological slender swimmers, the non-reciprocal bending of the gel centreline
is not sufficient here to explain for the overall swimming motion. We show
instead that the swimming of the gel results from the flux of water
periodically emanating from (or entering) the gel itself due to its shrinking
(or swelling). The associated flows induce viscous stresses that lead to a net
propulsive force on the gel. We derive a theoretical model for this hypothesis
of jet-driven propulsion, which leads to excellent agreement with our
experiments.


title: SUMO: Advanced sleep spindle identification with neural networks

abstract:
  Sleep spindles are neurophysiological phenomena that appear to be linked to
memory formation and other functions of the central nervous system, and that
can be observed in electroencephalographic recordings (EEG) during sleep.
Manually identified spindle annotations in EEG recordings suffer from
substantial intra- and inter-rater variability, even if raters have been highly
trained, which reduces the reliability of spindle measures as a research and
diagnostic tool. The Massive Online Data Annotation (MODA) project has recently
addressed this problem by forming a consensus from multiple such rating
experts, thus providing a corpus of spindle annotations of enhanced quality.
Based on this dataset, we present a U-Net-type deep neural network model to
automatically detect sleep spindles. Our model's performance exceeds that of
the state-of-the-art detector and of most experts in the MODA dataset. We
observed improved detection accuracy in subjects of all ages, including older
individuals whose spindles are particularly challenging to detect reliably. Our
results underline the potential of automated methods to do repetitive
cumbersome tasks with super-human performance.


title:
Optimal reservoir computers for forecasting systems of nonlinear
  dynamics

abstract:
  Prediction and analysis of systems of nonlinear dynamics is crucial in many
applications. Here, we study characteristics and optimization of reservoir
computing, a machine learning technique that has gained attention as a suitable
method for this task. By systematically applying Bayesian optimization on
reservoirs we show that reservoirs of low connectivity perform better than or
as well as those of high connectivity in forecasting noiseless Lorenz and
coupled Wilson-Cowan systems. We also show that, unexpectedly, computationally
effective reservoirs of unconnected nodes (RUN) outperform reservoirs of linked
network topologies in predicting these systems. In the presence of noise,
reservoirs of linked nodes perform only slightly better than RUNs. In contrast
to previously reported results, we find that the topology of linked reservoirs
has no significance in the performance of system prediction. Based on our
findings, we give a procedure for designing optimal reservoir computers (RC)
for forecasting dynamical systems. This work paves way for computationally
effective RCs applicable to real-time prediction of signals measured on systems
of nonlinear dynamics such as EEG or MEG signals measured on a brain.


title: A precessing magnetar model for GLEAM-X J162759.5-523504.3

abstract:
  We propose a precessing transient magnetar model for the recently discovered
radio source GLEAM-X J162759.5-523504.3. We identify the observed period of
$\sim 1$ ks as the precession period of the magnetar deformed due to its strong
($B_{\phi} \sim 10^{16}$ G) toroidal field. The resulting deformation of order
$10^{-4}$ implies a spin period of $P_{\rm s} = 0.1$ s. Assuming a strong
dipole field of $B_{\rm d} \sim 10^{14}$ G we predict a period derivative of
$\dot{P}_{\rm s}\sim 10^{-11}$ s/s. We also predict that the precession period
of the magnetar can be observed in the hard X-ray band just as the other three
galactic magnetars exhibit precession.


title: Dynamical tides in superfluid neutron stars

abstract:
  We study the tidal response of a superfluid neutron star in a binary system,
focussing on Newtonian models with superfluid neutrons present throughout the
star's core and the inner crust. Within the two-fluid formalism, we consider
the main aspects that arise from the presence of different regions inside the
star, with particular focus on the various interfaces. Having established the
relevant theory, we determine the tidal excitation of the most relevant
oscillation modes during binary inspiral. Our results suggest that superfluid
physics has a negligible impact on the static tidal deformation. The
overwhelming contribution to the Love number is given by, as for normal matter
stars, the ordinary fundamental mode (f-mode). Strong entrainment, here
described by a phenomenological expression which mimics the large effective
neutron mass expected at the bottom of the crust, is shown to have significant
impact on the superfluid modes, but our results for the dynamical tide are
nevertheless similar to the static limit: the fundamental modes are the ones
most significantly excited by the tidal interaction, with the ordinary f-mode
dominating the superfluid one. We also discuss the strain built up in the
star's crust during binary inspiral, showing that the superfluid f-mode may
(depending on entrainment) reach the limit where the crust breaks, although it
does so after the ordinary f-mode. Overall, our results suggest that the
presence of superfluidity may be difficult to establish from binary neutron
star gravitational-wave signals.


title: Do Magnetars' X-ray luminosities exceed their spin-down luminosities?

abstract:
  Magnetars are X-ray emitting neutron stars that are deemed too luminous to be
spin-powered. The prevailing view that magnetars' X-ray luminosities exceed
their spin-down luminosities is however based on the assumption that the decay
with distance of the flux of the X-rays received from magnetars obeys the
inverse-square law. The results presented here, of testing the hypothesis of
independence of luminosities and distances of magnetars by means of the
Efron-Petrosian statistic, imply that the observational data in the McGill
Magnetar Catalog are consistent with the dependence $S\propto D^{-3/2}$ of the
flux densities $S$ of these objects on their distances $D$ at substantially
higher levels of significance than they are with the dependence $S\propto
D^{-2}$. This is not incompatible with the requirements of the conservation of
energy because the radiation process described in Ardavan [ Mon. Not. R.
Astron. Soc., 507, 4530-4563 (2021)], by which the superluminally moving
current sheet in the magnetosphere of a neutron star is shown to generate the
observed X-ray pulses, is intrinsically transient: the difference in the fluxes
of power across any two spheres centred on the star is balanced by the change
with time of the energy contained inside the shell bounded by those spheres.
Once their over-estimation is rectified, the ratios of X-ray to spin-down
luminosities of known magnetars turn out to be invariably lower than $1$. A
magnetar differs from a spin-powered pulsar only in that it is observed along a
privileged latitudinal direction: the closer is the line of sight to a
direction in which the radiation from the current sheet is focused, the higher
the frequency and the lower the decay rate with distance of the observed
radiation.


title:
Machine Learning and Data Science: Foundations, Concepts, Algorithms,
  and Tools

abstract:
  Today, data is a fuel for businesses to gain important insights and improve
their performance. There is no industry in the world today that does not use
data. But who will get this insight? Who processes all the raw data? Everything
is done by a data analyst or a data scientist.


title:
Inferring the neutron star maximum mass and lower mass gap in neutron
  star-black hole systems with spin

abstract:
  Gravitational-wave (GW) detections of merging neutron star-black hole (NSBH)
systems probe astrophysical neutron star (NS) and black hole (BH) mass
distributions, especially at the transition between NS and BH masses. Of
particular interest are the maximum NS mass, minimum BH mass, and potential
mass gap between them. While previous GW population analyses assumed all NSs
obey the same maximum mass, if rapidly spinning NSs exist, they can extend to
larger maximum masses than nonspinning NSs. In fact, several authors have
proposed that the $\sim2.6\,M_\odot$ object in the event GW190814 -- either the
most massive NS or least massive BH observed to date -- is a rapidly spinning
NS. We therefore infer the NSBH mass distribution jointly with the NS spin
distribution, modeling the NS maximum mass as a function of spin. Using 4
LIGO-Virgo NSBH events including GW190814, if we assume that the NS spin
distribution is uniformly distributed up to the maximum (breakup) spin, we
infer the maximum non-spinning NS mass is $2.7^{+0.5}_{-0.4}\,M_\odot$ (90\%
credibility), while assuming only nonspinning NSs, the NS maximum mass must be
$>2.53 M_\odot$ (90\% credibility). The data support the mass gap's existence,
with a minimum BH mass at $5.4^{+0.7}_{-1.0} M_\odot$. With future
observations, under simplified assumptions, 150 NSBH events may constrain the
maximum nonspinning NS mass to $\pm0.02\,M_\odot$, and we may even measure the
relation between the NS spin and maximum mass entirely from GW data. If rapidly
rotating NSs exist, their spins and masses must be modeled simultaneously to
avoid biasing the NS maximum mass.


title:
Deterministic Non-cooperative Binding in Two-Dimensional Tile Assembly
  Systems Must Have Ultimately Periodic Paths

abstract:
  We consider non-cooperative binding, so-called 'temperature 1', in
deterministic or directed (called here confluent) tile self-assembly systems in
two dimensions and show a necessary and sufficient condition for such system to
have an ultimately periodic assembly path. We prove that an infinite maximal
assembly has an ultimately periodic assembly path if and only if it contains an
infinite assembly path that does not intersect a periodic path in the Z2 grid.
Moreover we show that every infinite assembly must satisfy this condition, and
therefore, contains an ultimately periodic path. This result is obtained
through a super-position and a combination of two paths that produce a new path
with desired properties, a technique that we call co-grow of two paths. The
paper is an updated and improved version of the first part of arXiv 1901.08575.


title:
Design of Flexible Meander Line Antenna for Healthcare for Wireless
  Medical Body Area Networks

abstract:
  A flexible meander line monopole antenna (MMA) is presented in this paper.
The antenna can be worn for on-and off-body applications. The overall dimension
of the MMA is 37 mm x 50 mm x2.37 mm3. The MMA was manufactured and measured,
and the results matched with simulation results. The MMA design shows a
bandwidth of up to 1282.4 (450.5) MHz and provides gains of 3.03 (4.85) dBi in
the lower and upper operating bands, respectively, showing omnidirectional
radiation patterns in free space. While worn on the chest or arm, bandwidths as
high as 688.9 (500.9) MHz and 1261.7 (524.2) MHz, and the gains of 3.80 (4.67)
dBi and 3.00 (4.55) dBi were observed. The experimental measurements of the
read range confirmed the results of the coverage range of up to 11 meters.


title:
Class Distance Weighted Cross-Entropy Loss for Ulcerative Colitis
  Severity Estimation

abstract:
  Endoscopic Mayo score and Ulcerative Colitis Endoscopic Index of Severity are
commonly used scoring systems for the assessment of endoscopic severity of
ulcerative colitis. They are based on assigning a score in relation to the
disease activity, which creates a rank among the levels, making it an ordinal
regression problem. On the other hand, most studies use categorical
cross-entropy loss function, which is not optimal for the ordinal regression
problem, to train the deep learning models. In this study, we propose a novel
loss function called class distance weighted cross-entropy (CDW-CE) that
respects the order of the classes and takes the distance of the classes into
account in calculation of cost. Experimental evaluations show that CDW-CE
outperforms the conventional categorical cross-entropy and CORN framework,
which is designed for the ordinal regression problems. In addition, CDW-CE does
not require any modifications at the output layer and is compatible with the
class activation map visualization techniques.


title:
Surface engineering for cellulose as a boosted Layer-by-Layer assembly:
  excellent flame retardancy and improved durability with introduction of
  bio-based "molecular glue"

abstract:
  Layer-by-Layer (LbL) assembly was attractive as a versatile tool to address
the flammability of cotton, while the washing fastness of LbL coating stayed an
issue. Aiming to tackle this issue, LbL layers consisted of phenylphosphonic
acid (PHA) and 3-aminopropyltriethoxysilane (APTES) was deposited on
polydopamine (PDA)-coated cotton. The prepared cotton reached 31.4% of limiting
oxygen index (LOI), and extinguished immediately after removing the ignitor.
Peak of heat release rate (pHRR) attenuated around 36 % compared with pure
cotton. A combined barrier and quenching mechanisms were proposed. Moreover,
enhanced washing durability (24.1% of LOI) was achieved even after 50 detergent
laundering cycles. A facile, boosted LbL approach with proposed {\pi}-{\pi}
stacking interactions between PDA abundant aromatic structures and benzene ring
in PHA from LbL layers, is first to put forward to construct durable efficient
flame retardant (FR) cotton. This work attempted to enlighten more thoughts and
design for durable FR cotton fabrics.