title: Secure Joint Communication and Sensing

abstract:
  This work considers mitigation of information leakage between communication
and sensing operations in joint communication and sensing systems.
Specifically, a discrete memoryless state-dependent broadcast channel model is
studied in which (i) the presence of feedback enables a transmitter to
simultaneously achieve reliable communication and channel state estimation;
(ii) one of the receivers is treated as an eavesdropper whose state should be
estimated but which should remain oblivious to a part of the transmitted
information. The model abstracts the challenges behind security for joint
communication and sensing if one views the channel state as a characteristic of
the receiver, e.g., its location. For independent identically distributed
(i.i.d.) states, perfect output feedback, and when part of the transmitted
message should be kept secret, a partial characterization of the
secrecy-distortion region is developed. The characterization is exact when the
broadcast channel is either physically-degraded or
reversely-physically-degraded. The characterization is also extended to the
situation in which the entire transmitted message should be kept secret. The
benefits of a joint approach compared to separation-based secure communication
and state-sensing methods are illustrated with a binary joint communication and
sensing model.


title: Localization Operators On Discrete Modulation Spaces

abstract:
  In this paper, we study a class of pseudo-differential operators known as
time-frequency localization operators on $\Z^n$, which depend on a symbol
$\varsigma$ and two windows functions $g_1$ and $g_2$. We define the short-time
Fourier transform on $ \Z^n \times \T^n $ and modulation spaces on $\Z^n$, and
present some basic properties. Then, we use modulation spaces on $\Z^n \times
\T^n$ as appropriate classes for symbols, and study the boundedness and
compactness of the localization operators on modulation spaces on $\Z^n$. Then,
we show that these operators are in the Schatten--von Neumann class. Also, we
obtain the relation between the Landau--Pollak--Slepian type operator and the
localization operator on $\Z^n$. Finally, under suitable conditions on the
symbols, we prove that the localization operators are paracommutators,
paraproducts and Fourier multipliers.


title:
Granular piston-probing in microgravity: powder compression, from
  densification to jamming

abstract:
  The macroscopic response of granular solids is determined by the microscopic
fabric of force chains, which, in turn, is intimately linked to the history of
the solid. To query the influence of gravity on powder flow-behavior, a
granular material is subjected to compression by a piston in a closed
container, on-ground and in microgravity. Results show that piston-probing
densifies the packing, eventually leading to jamming of the material compressed
by the piston, regardless of the gravitational environment. The onset of
jamming is found to appear at lower packing fraction in microgravity
($\varphi^{\textrm{$\mu$-g}}_J = 0.567$) than on-ground
($\varphi^{\text{gnd}}_J = 0.579$). We interpret these findings as the
manifestation of a granular fabric altered by the gravitational force field: in
absence of a secondary load (due to gravitational acceleration) to stimulate
reorganization in a different direction to the major compression stress, the
particles' configuration becomes stable at lower density, as the particles have
no external drive to promote reorganization into a denser packing. This is
coupled with a change in interparticular force balance which takes place under
low gravity, as cohesive interactions becoming predominant. We propose a
combination of microscopic and continuum arguments to rationalize our results.


title:
PyTorch Geometric Signed Directed: A Survey and Software on Graph Neural
  Networks for Signed and Directed Graphs

abstract:
  Signed networks are ubiquitous in many real-world applications (e.g., social
networks encoding trust/distrust relationships, correlation networks arising
from time series data). While many signed networks are directed, there is a
lack of survey papers and software packages on graph neural networks (GNNs)
specially designed for directed networks. In this paper, we present PyTorch
Geometric Signed Directed, a survey and software on GNNs for signed and
directed networks. We review typical tasks, loss functions and evaluation
metrics in the analysis of signed and directed networks, discuss data used in
related experiments, and provide an overview of methods proposed. The deep
learning framework consists of easy-to-use GNN models, synthetic and real-world
data, as well as task-specific evaluation metrics and loss functions for signed
and directed networks. The software is presented in a modular fashion, so that
signed and directed networks can also be treated separately. As an extension
library for PyTorch Geometric, our proposed software is maintained with
open-source releases, detailed documentation, continuous integration, unit
tests and code coverage checks. Our code is publicly available at
\url{https://github.com/SherylHYX/pytorch_geometric_signed_directed}.


title:
Small moments without long-range magnetic ordering in the
  zero-temperature ground state of the double-perovskite iridate Ba$_2$YIrO$_6$

abstract:
  The spin-orbit coupled double perovskite iridate Ba$_2$YIrO$_6$ with $d^4$
occupancy of Ir is considered as a candidate material for a non-magnetic $J=0$
ground state. The issue of existence of such a state in Ba$_2$YIrO$_6$ however
has opened up intense debates both in experimental and theoretical studies. In
this study, we revisit the issue using ab-initio density functional combined
with dynamical mean-field theory to investigate the magnetic properties of
Ba$_2$YIrO$_6$ down to zero temperature. To reach the ground state, a recently
developed impurity solver based on tensor-product states working directly at
zero temperature is employed. We find that Ba$_2$YIrO$_6$ has a small non-zero
magnetic moment, both at $T=0$ K as well as at room temperature. We did not
observe any evidence of magnetic ordering, not even at $T=0$ K. We argue that
the small moments in combination with small exchange couplings lead to many
possible magnetic ordering patterns, whose ground state energies differ just by
a few meV. As a result, this configurational frustration prevents the system
from realizing one of the possible ordering patterns.


title: Entropy, virtual Abelianness, and Shannon orbit equivalence

abstract:
  We prove that if two free p.m.p. $\mathbb{Z}$-actions are Shannon orbit
equivalent then they have the same entropy. The argument also applies more
generally to yield the same conclusion for free p.m.p. actions of finitely
generated virtually Abelian groups. Together with the isomorphism theorems of
Ornstein and Ornstein-Weiss and the entropy invariance results of Austin and
Kerr-Li in the non-virtually-cyclic setting, this shows that two Bernoulli
actions of any non-locally-finite countably infinite amenable group are Shannon
orbit equivalent if and only if they are measure conjugate. We also show, at
the opposite end of the stochastic spectrum, that every $\mathbb{Z}$-odometer
is Shannon orbit equivalent to the universal $\mathbb{Z}$-odometer.


title:
A Benchmark Comparison of Learned Control Policies for Agile Quadrotor
  Flight

abstract:
  Quadrotors are highly nonlinear dynamical systems that require carefully
tuned controllers to be pushed to their physical limits. Recently,
learning-based control policies have been proposed for quadrotors, as they
would potentially allow learning direct mappings from high-dimensional raw
sensory observations to actions. Due to sample inefficiency, training such
learned controllers on the real platform is impractical or even impossible.
Training in simulation is attractive but requires to transfer policies between
domains, which demands trained policies to be robust to such domain gap. In
this work, we make two contributions: (i) we perform the first benchmark
comparison of existing learned control policies for agile quadrotor flight and
show that training a control policy that commands body-rates and thrust results
in more robust sim-to-real transfer compared to a policy that directly
specifies individual rotor thrusts, (ii) we demonstrate for the first time that
such a control policy trained via deep reinforcement learning can control a
quadrotor in real-world experiments at speeds over 45km/h.


title: Ball packings in hyperbolic space

abstract:
  In hyperbolic space density cannot be defined by a limit as we define it in
Euclidean space. We describe the local density bounds for sphere packings and
we discuss the different attempts to define optimal arrangements in hyperbolic
space.


title:
The averaging problem on the past null cone in inhomogeneous dust
  cosmologies

abstract:
  We investigate average properties of light fronts as they propagate from a
finite patch of a source (e.g., the last scattering surface) to a finite patch
of a telescope pointing in generic irrotational dust cosmologies. In this
setting we formulate Einstein's field equations on the light fronts and provide
constraint equations and propagation equations along an observer's past null
cone. For the resulting covariant system of area-averaged equations on the
light fronts, we discuss integral-geometric properties together with closure
conditions for the underdetermined set of equations. We formulate light front
averages of observable quantities, including the effective angular diameter
distance and the cosmological redshift drift. The `backreaction' in the
effective angular diameter distance is quantified in terms of cosmological
area-averaged variables on the screen space of observation, as well as a
`memory function' containing information on the auto-correlation of the
expansion rate of an incoming geodesic null ray bundle along the observer's
past null cone.


title:
Large deviations asymptotics for unbounded additive functionals of
  diffusion processes

abstract:
  We study large deviations asymptotics for unbounded additive functionals of
one-dimensional Langevin diffusions with sub-linear gradient drifts. Our
results allow us to obtain parametric insights on the speed and the rate
functions in terms of the growth rate of the drift and the growth rate of the
additive functional. We find a critical value in terms of these growth
parameters which dictates regions of sub-linear speed for our large deviations
asymptotics. Our proof technique hinges on various constructions of independent
interest, including a suitable decomposition of the diffusion process in terms
of alternating renewal cycles and a decomposition of the paths on excursion.
The key to the sub-linear behavior is a heavy-tailed large deviations
phenomenon arising from the fact that at each regeneration cycle the
accumulated area of the diffusion process displays a semi-exponential behavior.


title: The Lieb-Oxford bound and the optimal transport limit of DFT

abstract:
  We review and illustrate with several examples the connection between the
search for lower bounds for the optimal constant in the Lieb-Oxford inequality
and the optimal transport limit (or strictly-correlated-electrons limit) of
density functional theory. We focus in particular on several contributions from
Elliott Lieb which already hinted at this connection.


title: On the significance of the stress-energy tensor in Finsler spacetimes

abstract:
  We revisit the physical arguments which lead to the definition of the
stress-energy tensor $T$ in the Lorentz-Finsler setting $(M,L)$ starting at
classical Relativity. Both the standard heuristic approach using fluids and the
Lagrangian one are taken into account. In particular, we argue that the
Finslerian breaking of Lorentz symmetry makes $T$ an anisotropic 2-tensor (i.
e., a tensor for each $L$-timelike direction), in contrast with the
energy-momentum vectors defined on $M$. Such a tensor is compared with
different ones obtained by using a Lagrangian approach. The notion of
divergence is revised from a geometric viewpoint and, then, the conservation
laws of $T$ for each observer field are revisited. We introduce a natural {\em
anisotropic Lie bracket derivation}, which leads to a divergence obtained from
the volume element and the non-linear connection associated with $L$ alone. The
computation of this divergence selects the Chern anisotropic connection, thus
giving a geometric interpretation to previous choices in the literature.


title:
Linear boundary layer analysis of the near-critical reflection of
  internal gravity waves with different sizes of viscosity and diffusivity

abstract:
  The aim of this work is to make a further step towards the understanding of
the near-critical reflection of internal gravity waves from a slope in the more
general and realistic context where the size of viscosity $\nu$ and the size of
diffusivity $\kappa$ are different. In particular, we provide a systematic
characterization of boundary layers (boundary layer wave packets) decays and
sizes depending on the order of magnitude of viscosity and diffusivity. We can
construct an $L^2$ stable approximate solution to the linear near-critical
reflection problem under the scaling assumption of Dauxois \& Young JFM 1999,
where either viscosity of diffusivity satisfies a precise scaling law in terms
of the criticality parameter.


title: A-Eye: Driving with the Eyes of AI for Corner Case Generation

abstract:
  The overall goal of this work is to enrich training data for automated
driving with so called corner cases. In road traffic, corner cases are
critical, rare and unusual situations that challenge the perception by AI
algorithms. For this purpose, we present the design of a test rig to generate
synthetic corner cases using a human-in-the-loop approach. For the test rig, a
real-time semantic segmentation network is trained and integrated into the
driving simulation software CARLA in such a way that a human can drive on the
network's prediction. In addition, a second person gets to see the same scene
from the original CARLA output and is supposed to intervene with the help of a
second control unit as soon as the semantic driver shows dangerous driving
behavior. Interventions potentially indicate poor recognition of a critical
scene by the segmentation network and then represents a corner case. In our
experiments, we show that targeted enrichment of training data with corner
cases leads to improvements in pedestrian detection in safety relevant episodes
in road traffic.


title:
Tracking moving objects through scattering media via speckle
  correlations

abstract:
  Scattering can rapidly degrade our ability to form an optical image, to the
point where only speckle-like patterns can be measured. Truly non-invasive
imaging through a strongly scattering obstacle is difficult, and usually
reliant on a computationally intensive numerical reconstruction. In this work
we show that, by combining the cross-correlations of the measured speckle
pattern at different times, it is possible to track a moving object with
minimal computational effort and over a large field of view.


title:
Multiplet Effects in the Electronic Correlation of One-Dimensional
  Magnetic Transition-Metal Oxides on Metals

abstract:
  We use the constrained random phase approximation (cRPA) method to calculate
the Hubbard $U$ parameter in four one-dimensional magnetic transition metal
atom oxides of composition XO$_2$ (X = Mn, Fe, Co, Ni) on Ir(100). In addition
to the expected screening of the oxide, i.e., a significant reduction of the
$U$ value by the presence of the metal substrate, we find a strong dependence
on the electronic configuration (multiplet) of the X($d$) orbital. Each
particular electronic configuration attained by atom X is dictated by the O
ligands, as well as by the charge transfer and hybridization with the Ir(100)
substrate. We find that MnO$_2$ and NiO$_2$ chains exhibit two different
screening regimes, while the case of CoO$_2$ is somewhere in between. The
electronic structure of the MnO$_2$ chain remains almost unchanged upon
adsorption. Therefore, in this regime, the additional screening is
predominantly generated by the electrons of the neighboring metal surface
atoms. The screening strength for NiO$_2$/Ir(100) is found to depend on the
Ni($d$) configuration in the adsorbed state. The case of FeO$_2$ shows an
exceptional behavior, as it is the only insulating system in the absence of
metallic substrate and, thus, it has the largest $U$ value. However, this value
is significantly reduced by the two mentioned screening effects after
adsorption.


title: Stochastic Causal Programming for Bounding Treatment Effects

abstract:
  Causal effect estimation is important for numerous tasks in the natural and
social sciences. However, identifying effects is impossible from observational
data without making strong, often untestable assumptions. We consider
algorithms for the partial identification problem, bounding treatment effects
from multivariate, continuous treatments over multiple possible causal models
when unmeasured confounding makes identification impossible. We consider a
framework where observable evidence is matched to the implications of
constraints encoded in a causal model by norm-based criteria. This generalizes
classical approaches based purely on generative models. Casting causal effects
as objective functions in a constrained optimization problem, we combine
flexible learning algorithms with Monte Carlo methods to implement a family of
solutions under the name of stochastic causal programming. In particular, we
present ways by which such constrained optimization problems can be
parameterized without likelihood functions for the causal or the observed data
model, reducing the computational and statistical complexity of the task.


title:
Symmetry breaking yields chimeras in two small populations of
  Kuramoto-type oscillators

abstract:
  Despite their simplicity, networks of coupled phase oscillators can give rise
to intriguing collective dynamical phenomena. However, the symmetries of
globally and identically coupled identical units do not allow solutions where
distinct oscillators are frequency-unlocked -- a necessary condition for the
emergence of chimeras. Thus, forced symmetry breaking is necessary to observe
chimera-type solutions. Here, we consider the bifurcations that arise when full
permutational symmetry is broken for the network to consist of coupled
populations. We consider the smallest possible network composed of four phase
oscillators and elucidate the phase space structure, (partial) integrability
for some parameter values, and how the bifurcations away from full symmetry
lead to frequency-unlocked weak chimera solutions. Since such solutions wind
around a torus they must arise in a global bifurcation scenario. Moreover,
periodic weak chimeras undergo a period doubling cascade leading to chaos. The
resulting chaotic dynamics with distinct frequencies do not rely on amplitude
variation and arise in the smallest networks that support chaos.


title:
Hyper Attention Recurrent Neural Network: Tackling Temporal Covariate
  Shift in Time Series Analysis

abstract:
  Analyzing long time series with RNNs often suffers from infeasible training.
Segmentation is therefore commonly used in data pre-processing. However, in
non-stationary time series, there exists often distribution shift among
different segments. RNN is easily swamped in the dilemma of fitting bias in
these segments due to the lack of global information, leading to poor
generalization, known as Temporal Covariate Shift (TCS) problem, which is only
addressed by a recently proposed RNN-based model. One of the assumptions in TCS
is that the distribution of all divided intervals under the same segment are
identical. This assumption, however, may not be true on high-frequency time
series, such as traffic flow, that also have large stochasticity. Besides,
macro information across long periods isn't adequately considered in the latest
RNN-based methods. To address the above issues, we propose Hyper Attention
Recurrent Neural Network (HARNN) for the modeling of temporal patterns
containing both micro and macro information. An HARNN consists of a meta layer
for parameter generation and an attention-enabled main layer for inference.
High-frequency segments are transformed into low-frequency segments and fed
into the meta layers, while the first main layer consumes the same
high-frequency segments as conventional methods. In this way, each
low-frequency segment in the meta inputs generates a unique main layer,
enabling the integration of both macro information and micro information for
inference. This forces all main layers to predict the same target which fully
harnesses the common knowledge in varied distributions when capturing temporal
patterns. Evaluations on multiple benchmarks demonstrated that our model
outperforms a couple of RNN-based methods on a federation of key metrics.


title:
From agent-based models to the macroscopic description of fake-news
  spread: the role of competence in data-driven applications

abstract:
  Fake news spreading, with the aim of manipulating individuals' perceptions of
facts, is now recognized as a major problem in many democratic societies. Yet,
to date, little has been understood about how fake news spreads on social
networks, what the influence of the education level of individuals is, when
fake news is effective in influencing public opinion, and what interventions
might be successful in mitigating their effect. In this paper, starting from
the recently introduced kinetic multi-agent model with competence by the first
two authors, we propose to derive reduced-order models through the notion of
social closure in the mean-field approximation that has its roots in the
classical hydrodynamic closure of kinetic theory. This approach allows to
obtain simplified models in which the competence and learning of the agents
maintain their role in the dynamics and, at the same time, the structure of
such models is more suitable to be interfaced with data-driven applications.
Examples of different Twitter-based test cases are described and discussed.


title: Hadronic Vacuum Polarization and the MUonE proposal

abstract:
  The MUonE proposal at the CERN SPS consists in extracting the value of the
hadronic vacuum polarization self-energy function (HVP) from its contribution
to the differential cross-section of elastic muon-electron scattering. The HVP
contribution to the muon anomalous magnetic moment can then be obtained from a
weighted integral of the measured HVP self-energy function. This, however,
requires a knowledge of the HVP function in its full integration domain. This
paper discusses a procedure to reconstruct the HVP function in the regions not
directly accessible to measurement. The method is based on the so-called
transfer theorems, due to Flajolet and Odlyzko, which we explain and adapt to
HVP.


title:
On the rate of convergence of a numerical scheme for fractional
  conservation laws with noise

abstract:
  We consider a semi-discrete finite volume scheme for a degenerate fractional
conservation laws driven by a cylindrical Wiener process. Making use of the
bounded variation (BV) estimates, Young measure theory, and a clever adaptation
of classical Kruzkov theory, we provide estimates on the rate of convergence
for approximate solutions to fractional problems. The main difficulty stems
from the degenerate fractional operator, and requires a significant departure
from the existing strategy to establish Kato's type of inequality. Finally, as
an application of this theory, we demonstrate numerical convergence rates.


title: Anti-associative algebras

abstract:
  An anti-associative algebra is a nonassociative algebra whose multiplication
satisfies the identity a(bc)+(ab)c=0. Such algebras are nilpotent. We describe
the free anti-associative algebras with a finite number of generators. Other
types of nonassociative algebras, obtained either by the process of
polarization, such as Jacobi-Jordan algebras, or obtained by deformation
quantization, are associated with this class of algebras. Following Markl-Remms
work, we describe the operads associated with these algebra classes and in
particular the cohomology complexes in relation to deformations.


title:
Trace anomaly of Weyl fermions in the Breitenlohner--Maison scheme for
  $\gamma_*$

abstract:
  We revisit the conformal anomaly for a Weyl fermion in four dimensions that
has generated some debate recently. We employ a perturbative expansion for the
metric around Minkowski space, dimensional regularization and a
Breitenlohner--Maison prescription for the chiral $\gamma$ matrix. We obtain a
vanishing odd-parity contribution for Weyl fermions in four dimensions, while
the even-parity contribution is exactly half the one for a Dirac fermion.


title: Resilient Average Consensus: A Detection and Compensation Approach

abstract:
  We study the problem of resilient average consensus for multi-agent systems
with misbehaving nodes. To protect consensus valuefrom being influenced by
misbehaving nodes, we address this problem by detecting misbehaviors,
mitigating the corresponding adverse impact and achieving the resilient average
consensus. In this paper, general types of misbehaviors are
considered,including deception attacks, accidental faults and link failures. We
characterize the adverse impact of misbehaving nodes in a distributed manner
via two-hop communication information and develop a deterministic
detection-compensation-based consensus (D-DCC) algorithm with a decaying
fault-tolerant error bound. Considering scenarios where information sets are
intermittently available due to link failures, a stochastic extension named
stochastic detection-compensation-based consensus(S-DCC) algorithm is proposed.
We prove that D-DCC and S-DCC allow nodes to asymptotically achieve resilient
averageconsensus exactly and in expectation, respectively. Then, the
Wasserstein distance is introduced to analyze the accuracy ofS-DCC. Finally,
extensive simulations are conducted to verify the effectiveness of the proposed
algorithm


title: Robust and Provable Guarantees for Sparse Random Embeddings

abstract:
  In this work, we improve upon the guarantees for sparse random embeddings, as
they were recently provided and analyzed by Freksen at al. (NIPS'18) and
Jagadeesan (NIPS'19). Specifically, we show that (a) our bounds are explicit as
opposed to the asymptotic guarantees provided previously, and (b) our bounds
are guaranteed to be sharper by practically significant constants across a wide
range of parameters, including the dimensionality, sparsity and dispersion of
the data. Moreover, we empirically demonstrate that our bounds significantly
outperform prior works on a wide range of real-world datasets, such as
collections of images, text documents represented as bags-of-words, and text
sequences vectorized by neural embeddings. Behind our numerical improvements
are techniques of broader interest, which improve upon key steps of previous
analyses in terms of (c) tighter estimates for certain types of quadratic
chaos, (d) establishing extreme properties of sparse linear forms, and (e)
improvements on bounds for the estimation of sums of independent random
variables.


title:
Why Fair Labels Can Yield Unfair Predictions: Graphical Conditions for
  Introduced Unfairness

abstract:
  In addition to reproducing discriminatory relationships in the training data,
machine learning systems can also introduce or amplify discriminatory effects.
We refer to this as introduced unfairness, and investigate the conditions under
which it may arise. To this end, we propose introduced total variation as a
measure of introduced unfairness, and establish graphical conditions under
which it may be incentivised to occur. These criteria imply that adding the
sensitive attribute as a feature removes the incentive for introduced variation
under well-behaved loss functions. Additionally, taking a causal perspective,
introduced path-specific effects shed light on the issue of when specific paths
should be considered fair.


title: Canonical Portfolios: Optimal Asset and Signal Combination

abstract:
  We present a novel framework for analyzing the optimal asset and signal
combination problem. We reformulate the original problem of portfolio selection
from a set of correlated assets and signals into one of selecting from a set of
uncorrelated trading strategies through the lens of Canonical Correlation
Analysis of Hotelling (1936). The new environment of uncorrelated trading
strategies offers a pragmatic simplification to the inherent complicated
structure of our underlying problem that involves interactions between and
within variables. We also operationalize our framework to bridge the gap
between theory and practice and showcase improved risk-adjusted returns
performance of our proposed optimizer over the classic mean-variance optimizer
of Markowitz (1952).


title: Many body study of iron(III) bound human serum transferrin

abstract:
  Transferrins are proteins responsible for transporting metal ions in all
vertebrates. However, the iron binding properties of transferrins remain poorly
understood. Iron, as a transition metal, forms ions with partially-occupied 3d
subshells. The electrons within the 3d orbitals of iron-bound transferrin are
therefore highly localized, and interact with one another in a complex manner
that cannot be fully characterized by considering each electron separately. In
this work, we make use of dynamical mean field theory, a technique that
accounts for the strong interactions between these electrons. This is a higher
level of theory than has ever been used to study transferrins. We present novel
data on the effective spin, multiplet states and optical spectra of iron-bound
human serum transferrin.


title: Giry algebras via the support of a probability measure

abstract:
  We give a fundamental theorem, which using categorical arguments is
elementary to prove, that every measurable function from the space of
probability measures on any measurable space to the natural numbers is
necessarily a countably affine function. Using that result a complete analysis
of the Giry algebras for standard measurable spaces, which includes as a
special case, the Polish spaces, is possible. We show that the subcategory of
standard measurable spaces consisting of the single object of the set of
natural numbers with the hom set of monotonic functions (=countably affine maps
between the natural numbers) is codense in the category of standard measurable
space, which provides the route to prove the existence of the barycenter maps,
and using the fundamental theorem mentioned, it follows that the "barycenter
map" really is a countably affine (measurable) function. In summary, everything
follows from the natural numbers with the powerset $\sigma$-algebra and its
super convex space structure.


title:
Evidence of a primordial isotopic gradient in the inner region of the
  solar protoplanetary disc

abstract:
  Not only do the sampled terrestrial worlds (Earth, Mars, and asteroid 4
Vesta) differ in their mass-independent (nucleosynthetic) isotopic compositions
of many elements (e.g. $\varepsilon^{48}$Ca, $\varepsilon^{50}$Ti,
$\varepsilon^{54}$Cr, $\varepsilon^{92}$Mo), the magnitudes of some of these
isotopic anomalies also appear to correlate with heliocentric distance. While
the isotopic differences between the Earth and Mars may be readily accounted
for by the accretion of mostly local materials in distinct regions of the
protoplanetary disc, it is unclear whether this also applies to asteroid Vesta.
Here we analysed the available data from our numerical simulation database to
determine the formation location of Vesta in the framework of three
planet-formation models: classical, Grand Tack, and Depleted Disc. We find that
Vesta has a high probability of forming locally in the asteroid belt in models
where material mixing in the inner disc is limited; this limited mixing is
implied by the isotopic differences between the Earth and Mars. Based on our
results, we propose several criteria to explain the apparent correlation
between the different nucleosynthetic isotopic compositions of the Earth, Mars,
and Vesta: (1) these planetary bodies accreted their building blocks in
different regions of the disc, (2) the inner disc is characterised by an
isotopic gradient, and (3) the isotopic gradient was preserved during the
formation of these planetary bodies and was not diluted by material mixing in
the disc (e.g. via giant planet migration).


title: Increasing Depth of Neural Networks for Life-long Learning

abstract:
  Increasing neural network depth is a well-known method for improving neural
network performance. Modern deep architectures contain multiple mechanisms that
allow hundreds or even thousands of layers to train. This work is trying to
answer if extending neural network depth may be beneficial in a life-long
learning setting. In particular, we propose a novel method based on adding new
layers on top of existing ones to enable the forward transfer of knowledge and
adapting previously learned representations for new tasks. We utilize a method
of determining the most similar tasks for selecting the best location in our
network to add new nodes with trainable parameters. This approach allows for
creating a tree-like model, where each node is a set of neural network
parameters dedicated to a specific task. The proposed method is inspired by
Progressive Neural Network (PNN) concept, therefore it is rehearsal-free and
benefits from dynamic change of network structure. However, it requires fewer
parameters per task than PNN. Experiments on Permuted MNIST and SplitCIFAR show
that the proposed algorithm is on par with other continual learning methods. We
also perform ablation studies to clarify the contributions of each system part.


title: Neighbors

abstract:
  Two members of a packing are neighbors if they have a common boundary point.
A multitude of problems arises in connection with neighbors in a packing. The
oldest one concerns a dispute between Newton and Gregory about the maximum
number of neighbors a member can have in a packing of congruent balls. Other
problems ask for the average number of neighbors or the maximum number of
mutually neighboring members in a packing. The present work gives a survey of
these problems.


title:
Behaviour-neutral Smart Charging of Plugin Electric Vehicles:
  Reinforcement learning approach

abstract:
  Smart charging aims to satisfy EV charging demand without overloading the
grid. A common approach is to postpone the charging to off-peak hours or
dynamically control the charging speed depending on available grid capacity
using dynamic pricing. However, this requires either accurately predicting the
plugin duration to ensure the EV is charged before it is plugged out or
influencing the driver behaviour to ensure the that a vehicle remains connected
until the EV is charged. In this paper, we introduce a novel approach that uses
reinforcement learning for EV charging and requires neither coordination with
the grid nor predicting the session duration. The algorithm trains on a
driver's personal history of past charging sessions to identify the optimal
charging parameters that minimise the effective charging current while
satisfying the target energy demand. We evaluate the performance on a dataset
of 3.1 million charging sessions from 22,731 domestic charge stations. Our
experiments demonstrate that the approach results in 31\% of aggregate peak
reduction and significant reduction in charging power demand. The results also
reveal two interesting features: Firstly, our approach is able to spread the
charging power in time without knowing the session duration. Secondly, the
proposed approach does not require any coordination with the grid or other
charge points and can be implemented on a standalone charge point reducing the
complexity.


title: One-shot Scene Graph Generation

abstract:
  As a structured representation of the image content, the visual scene graph
(visual relationship) acts as a bridge between computer vision and natural
language processing. Existing models on the scene graph generation task
notoriously require tens or hundreds of labeled samples. By contrast, human
beings can learn visual relationships from a few or even one example. Inspired
by this, we design a task named One-Shot Scene Graph Generation, where each
relationship triplet (e.g., "dog-has-head") comes from only one labeled
example. The key insight is that rather than learning from scratch, one can
utilize rich prior knowledge. In this paper, we propose Multiple Structured
Knowledge (Relational Knowledge and Commonsense Knowledge) for the one-shot
scene graph generation task. Specifically, the Relational Knowledge represents
the prior knowledge of relationships between entities extracted from the visual
content, e.g., the visual relationships "standing in", "sitting in", and "lying
in" may exist between "dog" and "yard", while the Commonsense Knowledge encodes
"sense-making" knowledge like "dog can guard yard". By organizing these two
kinds of knowledge in a graph structure, Graph Convolution Networks (GCNs) are
used to extract knowledge-embedded semantic features of the entities. Besides,
instead of extracting isolated visual features from each entity generated by
Faster R-CNN, we utilize an Instance Relation Transformer encoder to fully
explore their context information. Based on a constructed one-shot dataset, the
experimental results show that our method significantly outperforms existing
state-of-the-art methods by a large margin. Ablation studies also verify the
effectiveness of the Instance Relation Transformer encoder and the Multiple
Structured Knowledge.


title: Laplacian Vanishing Theorem for Quantized Singular Liouville Equation

abstract:
  In this article we establish a vanishing theorem for singular Liouville
equation with quantized singular source. If a blowup sequence tends to infinity
near a quantized singular source and the blowup solutions violate the spherical
Harnack inequality around the singular source (non-simple blow-ups), the
Laplacian of a coefficient function must tend to zero. This seems to be the
first second order estimates for Liouville equation with quantized sources and
non-simple blow-ups. This result as well as the key ideas of the proof would be
extremely useful for various applications.


title: Relation Regularized Scene Graph Generation

abstract:
  Scene graph generation (SGG) is built on top of detected objects to predict
object pairwise visual relations for describing the image content abstraction.
Existing works have revealed that if the links between objects are given as
prior knowledge, the performance of SGG is significantly improved. Inspired by
this observation, in this article, we propose a relation regularized network
(R2-Net), which can predict whether there is a relationship between two objects
and encode this relation into object feature refinement and better SGG.
Specifically, we first construct an affinity matrix among detected objects to
represent the probability of a relationship between two objects. Graph
convolution networks (GCNs) over this relation affinity matrix are then used as
object encoders, producing relation-regularized representations of objects.
With these relation-regularized features, our R2-Net can effectively refine
object labels and generate scene graphs. Extensive experiments are conducted on
the visual genome dataset for three SGG tasks (i.e., predicate classification,
scene graph classification, and scene graph detection), demonstrating the
effectiveness of our proposed method. Ablation studies also verify the key
roles of our proposed components in performance improvement.


title:
Stability of planetary systems within the S-star cluster: the Solar
  system analogues

abstract:
  A dynamically relaxed dense cluster comprised of about 40 stars (the
so-called S-stars) inhabits the central region of the Galaxy. Their stars
revolve around the Sgr A$^*$ massive object. To understand the dynamical
evolution of planetary systems in a particular environment like that around Sgr
A$^*$, we carry out direct N--body simulations of planetary systems embedded in
the S--star cluster. In this work, we investigate the short-term stability of
the planets orbiting around S-stars after their close interactions with the
central massive black hole of our galaxy. We find that planetary systems go
through encounters with the SMBH and the nearby stars. We determine the
frequency and the strength of planetary systems' encounters with the nearby
stars as these encounters remarkably increase for systems assigned to S-stars
closer to the SMBH. The SMBH severely destabilizes the planetary systems,
though we noted that the small oscillations in the mutual eccentricity and
inclination of the planetary system could be caused by the planet--planet
coupling and the near-resonance effect between the two planets. We obtain
estimates of the fraction of survivor planets ($\sim 51\%$), and find that
planets stripped from their hosting star are generally captured on close orbits
around Sgr A$^*$. We notify while gas giants are tidally disrupted, terrestrial
planets do not. We estimate that Sgr A$^*$ flares can be due to the tidal
disruption events of \textit{starless} giant planets.


title: Exploiting long-term temporal dynamics for video captioning

abstract:
  Automatically describing videos with natural language is a fundamental
challenge for computer vision and natural language processing. Recently,
progress in this problem has been achieved through two steps: 1) employing 2-D
and/or 3-D Convolutional Neural Networks (CNNs) (e.g. VGG, ResNet or C3D) to
extract spatial and/or temporal features to encode video contents; and 2)
applying Recurrent Neural Networks (RNNs) to generate sentences to describe
events in videos. Temporal attention-based model has gained much progress by
considering the importance of each video frame. However, for a long video,
especially for a video which consists of a set of sub-events, we should
discover and leverage the importance of each sub-shot instead of each frame. In
this paper, we propose a novel approach, namely temporal and spatial LSTM
(TS-LSTM), which systematically exploits spatial and temporal dynamics within
video sequences. In TS-LSTM, a temporal pooling LSTM (TP-LSTM) is designed to
incorporate both spatial and temporal information to extract long-term temporal
dynamics within video sub-shots; and a stacked LSTM is introduced to generate a
list of words to describe the video. Experimental results obtained in two
public video captioning benchmarks indicate that our TS-LSTM outperforms the
state-of-the-art methods.


title:
Bounds on new physics with data of the Dresden-II reactor experiment and
  COHERENT

abstract:
  Coherent elastic neutrino-nucleus scattering was first experimentally
established five years ago by the COHERENT experiment using neutrinos from the
spallation neutron source at Oak Ridge National Laboratory. The first strong
evidence of observation of coherent elastic neutrino-nucleus scattering with
reactor antineutrinos has now been reported by the Dresden-II reactor
experiment, using a Germanium detector. In this paper, we present constraints
on a variety of beyond the Standard Model scenarios using the new Dresden-II
data. In particular, we explore the constraints imposed on neutrino
non-standard interactions, neutrino magnetic moments, and several models with
light scalar or light vector mediators. We also quantify the impact of their
combination with COHERENT (CsI and Ar) data. In doing so, we highlight the
synergies between spallation neutron source and nuclear reactor experiments
regarding beyond the Standard Model searches, as well as the advantages of
combining data obtained with different nuclear targets. We also study the
possible signal from beyond the Standard Model scenarios due to elastic
scattering off electrons (which would pass selection cuts of the COHERENT CsI
and the Dresden-II experiments) and find more stringent constraints in certain
parts of the parameter space than those obtained considering coherent elastic
neutrino-nucleus scattering.


title:
Co-opted marginality, a new type of anti-immigrant discourse on social
  media? Classifying social media messages about immigrants with BERT

abstract:
  The article analyzes public discourse about immigrants in comments sections
of 11 social media community platforms over six months in Singapore.


title: Flipping Plane Spanning Paths

abstract:
  Let $S$ be a planar point set in general position, and let $\mathcal{P}(S)$
be the set of all plane straight-line paths with vertex set $S$. A flip on a
path $P \in \mathcal{P}(S)$ is the operation of replacing an edge $e$ of $P$
with another edge $f$ on $S$ to obtain a new valid path from $\mathcal{P}(S)$.
It is a long-standing open question whether for every given planar point set
$S$, every path from $\mathcal{P}(S)$ can be transformed into any other path
from $\mathcal{P}(S)$ by a sequence of flips. To achieve a better understanding
of this question, we provide positive answers for special classes of point
sets, namely, for wheel sets, ice cream cones, double chains, and double
circles. Moreover, we show for general point sets, it is sufficient to prove
the statement for plane spanning paths whose first edge is fixed.


title:
Multi-service Threats: Attacking and Protecting Network Printers and
  VoIP Phones alike

abstract:
  Printing over a network and calling over VoIP technology are routine at
present. This article investigates to what extent these services can be
attacked using freeware in the real world if they are not configured securely.
In finding out that attacks of high impact, termed the Printjack and Phonejack
families, could be mounted at least from insiders, the article also observes
that secure configurations do not appear to be widely adopted.
  Users with the necessary skills may put existing security measures in place
with printers, but would need novel measures, which the article prototypes,
with phones in order for a pair of peers to call each other securely and
without trusting anyone else, including sysadmins.


title: Some applications of linear algebra and geometry in real life

abstract:
  In this paper, some real-world motivated examples are provided illustrating
the power of linear algebra tools as the product of matrices, determinants,
eigenvalues and eigenvectors. In this sense, some practical applications
related to computer graphics, geometry, areas, volumes are presented, along
with some problems connected to sports and investments.


title:
Macroeconomic Effect of Uncertainty and Financial Shocks: a non-Gaussian
  VAR approach

abstract:
  The Great Recession highlighted the role of financial and uncertainty shocks
as drivers of business cycle fluctuations. However, the fact that uncertainty
shocks may affect economic activity by tightening financial conditions makes
empirically distinguishing these shocks difficult. This paper examines the
macroeconomic effects of the financial and uncertainty shocks in the United
States in an SVAR model that exploits the non-normalities of the time series to
identify the uncertainty and the financial shock. The results show that
macroeconomic uncertainty and financial shocks seem to affect business cycles
independently as well as through dynamic interaction. Uncertainty shocks appear
to tighten financial conditions, whereas there appears to be no causal
relationship between financial conditions and uncertainty. Moreover, the
results suggest that uncertainty shocks may have persistent effects on output
and investment that last beyond the business cycle.


title:
Density correlations from analogue Hawking radiation in the presence of
  atom losses

abstract:
  The sonic analogue of Hawking radiation can now be experimentally recreated
in Bose-Einstein Condensates that contain an acoustic black hole. In these
experiments the signal strength and analogue Hawking temperature increase for
denser condensates, which however also suffer increased atom losses from
inelastic collisions. To determine how these affect analogue Hawking radiation,
we numerically simulate creation of the latter in a Bose-Einstein Condensate in
the presence of atomic losses. In particular we explore modifications of
density-density correlations through which the radiation has been analyzed so
far. We find that losses increase the contrast of the correlation signal, which
we attribute to heating that in turn leads to a component of stimulated
radiation in addition to the spontaneous one. Another indirect consequence is
the modification of the white hole instability pattern.


title:
Stabilization of Co oxide during oxygen evolution in alkaline media by
  the introduction of Mn oxide

abstract:
  Improving the stability of electrocatalysts for the oxygen evolution reaction
(OER) through materials design has received less attention than improving their
catalytic activity. We explored the effect of Mn addition to a cobalt oxide for
stabilizing the catalyst by comparing Na-containing CoOx and (Co0.7Mn0.3)Ox
films electrodeposited in alkaline solution. The obtained disordered films were
classified as layered oxides using X-ray absorption spectroscopy (XAS). The
CoOx films showed a constant decrease in the catalytic activity during cycling,
confirmed by oxygen detection, while that of (Co0.7Mn0.3)Ox slightly increased
as measured by electrochemical metrics. These trends were rationalized based on
XAS analysis of the metal oxidation states, which were Co2.8+ and Mn3.7+ near
the surface after cycling. Thus, adding Mn to CoOx successfully stabilized the
catalyst material and its activity during OER cycling. The development of
stabilization approaches is essential to extend the durability of OER
catalysts.


title:
SADN: Learned Light Field Image Compression with Spatial-Angular
  Decorrelation

abstract:
  Light field image becomes one of the most promising media types for immersive
video applications. In this paper, we propose a novel end-to-end
spatial-angular-decorrelated network (SADN) for high-efficiency light field
image compression. Different from the existing methods that exploit either
spatial or angular consistency in the light field image, SADN decouples the
angular and spatial information by dilation convolution and stride convolution
in spatial-angular interaction, and performs feature fusion to compress spatial
and angular information jointly. To train a stable and robust algorithm, a
large-scale dataset consisting of 7549 light field images is proposed and
built. The proposed method provides 2.137 times and 2.849 times higher
compression efficiency relative to H.266/VVC and H.265/HEVC inter coding,
respectively. It also outperforms the end-to-end image compression networks by
an average of 79.6% bitrate saving with much higher subjective quality and
light field consistency.


title: Authenticated time for detecting GNSS attacks

abstract:
  Information cross-validation can be a powerful tool to detect manipulated,
dubious GNSS data. A promising approach is to leverage time obtained over
networks a mobile device can connect to, and detect discrepancies between the
GNSS-provided time and the network time. The challenge lies in having reliably
both accurate and trustworthy network time as the basis for the GNSS attack
detection. Here, we provide a concrete proposal that leverages, together with
the network time servers, the nearly ubiquitous IEEE 802.11 (Wi-Fi)
infrastructure. Our framework supports application-layer, secure and robust
real time broadcasting by Wi-Fi Access Points (APs), based on hash chains and
infrequent digital signatures verification to minimize computational and
communication overhead, allowing mobile nodes to efficiently obtain
authenticated and rich time information as they roam. We pair this method with
Network Time Security (NTS), for enhanced resilience through multiple sources,
available, ideally, simultaneously. We analyze the performance of our scheme in
a dedicated setup, gauging the overhead for authenticated time data (Wi-Fi
timestamped beacons and NTS). The results show that it is possible to provide
security for the external to GNSS time sources, with minimal overhead for
authentication and integrity, even when the GNSS-equipped nodes are mobile, and
thus have short interactions with the Wi-Fi infrastructure and possibly
intermittent Internet connectivity, as well as limited resources.


title:
Stable approximation of functions from equispaced samples via Jacobi
  frames

abstract:
  In this paper, we study the Jacobi frame approximation with equispaced
samples and derive an error estimation. We observe numerically that the
approximation accuracy gradually decreases as the extended domain parameter
$\gamma$ increases in the uniform norm, especially for differentiable
functions. In addition, we show that when the indexes of Jacobi polynomials
$\alpha$ and $\beta$ are larger (for example $\max\{\alpha,\beta\} > 10$), it
leads to a divergence behavior on the frame approximation error decay.


title:
A Novel Soft Shape-shifting Robot with Track-based Locomotion for
  In-pipe Inspection

abstract:
  With the advent of soft robotics the research community has been exploring
how to exploit the inherent adaptability of soft elastic materials to overcome
the limitations of systems based on rigid materials. A proof-of-concept design
of a crawling robot for pipe inspection and preliminary analysis of its
locomotion capabilities are presented in this work. The novelty of the proposed
design is the combination of silicone based elements of different stiffness to
enable global shape reconfiguration and whole-body track-based locomotion. The
proposed system makes use of a plurality of multi-materials elastic tracks
actuated by an on-board motor through a worm gear that pairs with all of them,
causing their constant eversion from the inside to the outside of the chassis
of the robot. Two toroidal inflatable chambers surround the chassis of the
robot while passing through the loops created by the tracks. Upon inflation of
the chambers the tracks are deformed, changing the overall diameter of the
system. This feature allows the system to adjust to the local diameter of the
pipe navigated, enabling also active contact force control between the tracks
and the pipe walls. We demonstrate how the proposed system efficiently moves
through rigid pipes of different diameters, both straight and curved,
incrementing its outer diameter up to 100% of his original size. Maximum
navigation speed and stall force applied are evaluated. With two front cameras
embedded, the proposed robotic system can represent a cost-effective and
easy-to-control solution for inspection applications, when adaptability and
compliance are critical requirements.


title:
Cyber-Physical Risk Assessment for False Data Injection Attacks
  Considering Moving Target Defences

abstract:
  In this paper, we examine the factors that influence the success of false
data injection (FDI) attacks in the context of both cyber and physical styles
of reinforcement. Many works consider the FDI attack in the context of the
ability to change a measurement in a static system only. However, successful
attacks will require first intrusion into a system followed by construction of
an attack vector that can bypass bad data detection (BDD). In this way, we
develop a full service framework for FDI risk assessment. The framework
considers both the costs of system intrusion via a weighted graph assessment in
combination with a physical, line overload-based vulnerability assessment. We
present our simulations on a IEEE 14-bus system with an overlain RTU network to
model the true risk of intrusion. The cyber model considers multiple methods of
entry for the FDI attack including meter intrusion, RTU intrusion and combined
style attacks. Post-intrusion our physical reinforcement model analyses the
required level of topology divergence to protect against a branch overload from
an optimised attack vector.


title: KuaiRec: A Fully-observed Dataset for Recommender Systems

abstract:
  Recommender systems are usually developed and evaluated on the historical
user-item logs. However, most offline recommendation datasets are highly sparse
and contain various biases, which hampers the evaluation of recommendation
policies. Existing efforts aim to improve the data quality by collecting users'
preferences on randomly selected items (e.g., Yahoo! and Coat). However, they
still suffer from the high variance issue caused by the sparsely observed data.
To fundamentally solve the problem, we present KuaiRec, a fully-observed
dataset collected from the social video-sharing mobile App, Kuaishou. The
feedback of 1,411 users on almost all of the 3,327 videos is explicitly
observed. To the best of our knowledge, this is the first real-world
fully-observed dataset with millions of user-item interactions in
recommendation.
  To demonstrate the advantage of KuaiRec, we leverage it to explore the key
questions in evaluating conversational recommender systems. The experimental
results show that two factors in traditional partially-observed data -- the
data density and the exposure bias -- greatly affect the evaluation results.
This entails the significance of our fully-observed data in researching many
directions in recommender systems, e.g., the unbiased recommendation,
interactive/conversational recommendation, and evaluation. We release the
dataset and the pipeline implementation for evaluation at
https://chongminggao.github.io/KuaiRec/.


title:
Boundary Lipschitz regularity of solutions for general semilinear
  elliptic equations in divergence form

abstract:
  In this paper, we study the nonhomogeneous Dirichlet problem concerning
general semilinear elliptic equations in divergence form. We establish that the
boundary Lipschitz regularity of solutions under some more weaker conditions on
the coefficients, the boundary, the boundary function and the nonhomogeneous
term. In particular, we assume that the nonhomogeneous term satisfies Dini
continuity condition and Lipschitz Newtonian potential condition, which will be
the optimal conditions to obtain the boundary Lipschitz regularity of
solutions.


title:
NLO QCD and EW corrections to vector-boson scattering into $\rm W^+W^-$
  at the LHC

abstract:
  We present the full next-to-leading-order electroweak and QCD corrections to
vector-boson scattering into a pair of off-shell opposite-sign W bosons
decaying into leptons of different flavour at the LHC. We include full
leading-order predictions for the irreducible background. Explicitly, we
investigate the process ${\rm pp \to e^+ \nu_e\mu^-\bar\nu_\mu jj} + X$ at
leading orders $O(\alpha^6)$, $O(\rm\alpha_s\alpha^5)$,
$O(\rm\alpha_s^2\alpha^4)$, supplemented by the loop-induced
$O(\rm\alpha_s^4\alpha^4)$ contribution, and at next-to-leading orders
$O(\alpha^7)$ and $O(\rm\alpha_s\alpha^6)$ in two setups providing fiducial
cross sections as well as differential distributions. We take full account of
photon-induced next-to-leading-order contributions, which prove to be non
negligible. With $-11.4\%$ and $-6.7\%$ in the two setups, the electroweak
corrections are smaller than for other vector-boson-scattering processes. This
can be traced back to the presence of the Higgs-boson resonance in the fiducial
phase space, whose effects we analyse within an additional unphysical, but
manifestly gauge-invariant setup. The QCD corrections amount to $-5.1\%$ and
$-21.6\%$ in the two setups. The large size of the latter correction, compared
to other vector-boson scattering processes, is explained by a very restrictive
definition of its fiducial phase space.


title:
GAN'SDA Wrap: Geographic And Network Structured Data on surfaces that
  Wrap around

abstract:
  There are many methods for projecting spherical maps onto the plane.
Interactive versions of these projections allow the user to centre the region
of interest. However, the effects of such interaction have not previously been
evaluated. In a study with 120 participants we find interaction provides
significantly more accurate area, direction and distance estimation in such
projections. The surface of 3D sphere and torus topologies provides a
continuous surface for uninterrupted network layout. But how best to project
spherical network layouts to 2D screens has not been studied, nor have such
spherical network projections been compared to torus projections. Using the
most successful interactive sphere projections from our first study, we compare
spherical, standard and toroidal layouts of networks for cluster and path
following tasks with 96 participants, finding benefits for both spherical and
toroidal layouts over standard network layouts in terms of accuracy for cluster
understanding tasks.


title: P-class is a proper subclass of NP-class; and more

abstract:
  We may give rise to some questions related to the mathematical structures of
$P$-class and $NP$-class. We have seen that one is a proper subclass of the
other. Here we disclose more that $P$- class turns out to be the proper
distributive sublattice of the $NP$- class.


title:
UncertaINR: Uncertainty Quantification of End-to-End Implicit Neural
  Representations for Computed Tomography

abstract:
  Implicit neural representations (INRs) have achieved impressive results for
scene reconstruction and computer graphics, where their performance has
primarily been assessed on reconstruction accuracy. However, in medical
imaging, where the reconstruction problem is underdetermined and model
predictions inform high-stakes diagnoses, uncertainty quantification of INR
inference is critical. To that end, we study UncertaINR: a Bayesian
reformulation of INR-based image reconstruction, for computed tomography (CT).
We test several Bayesian deep learning implementations of UncertaINR and find
that they achieve well-calibrated uncertainty, while retaining accuracy
competitive with other classical, INR-based, and CNN-based reconstruction
techniques. In contrast to the best-performing prior approaches, UncertaINR
does not require a large training dataset, but only a handful of validation
images.


title:
Speciesist bias in AI -- How AI applications perpetuate discrimination
  and unfair outcomes against animals

abstract:
  Massive efforts are made to reduce biases in both data and algorithms in
order to render AI applications fair. These efforts are propelled by various
high-profile cases where biased algorithmic decision-making caused harm to
women, people of color, minorities, etc. However, the AI fairness field still
succumbs to a blind spot, namely its insensitivity to discrimination against
animals. This paper is the first to describe the 'speciesist bias' and
investigate it in several different AI systems. Speciesist biases are learned
and solidified by AI applications when they are trained on datasets in which
speciesist patterns prevail. These patterns can be found in image recognition
systems, large language models, and recommender systems. Therefore, AI
technologies currently play a significant role in perpetuating and normalizing
violence against animals. This can only be changed when AI fairness frameworks
widen their scope and include mitigation measures for speciesist biases. This
paper addresses the AI community in this regard and stresses the influence AI
systems can have on either increasing or reducing the violence that is
inflicted on animals, and especially on farmed animals.


title:
Brain representation of perceptual stimuli at different levels of
  awareness

abstract:
  This article questions the widespread assumption that there are brain
representations that will always remain unconscious in the sense of being
inaccessible to individual awareness under any circumstances. This implies that
some part of the knowledge generated by the brain is once and for always
excluded from consciousness and, therefore, from being communicated to the
outside world. This standpoint neglects the possibility that the human brain
might have a capacity for generating metarepresentations of nonconscious
knowledge contents at a given moment in time through context sensitive adaptive
learning, and is somewhat difficult to reconcile with experimental findings
showing that initially subliminal targets can be made available to awareness,
or break through to supraliminal levels of processing, when they are embedded
in an appropriate perceptual object context (relevance condition). Specific
properties of neural network architectures, inspired by the functional
organization of the primate cortex, are able to explain how a human brain could
generate this kind of perceptual learning. Signals or knowledge processed
outside awareness could be made available to awareness through adaptive
resonance of bottom-up and top-down signal exchanges in massively parallel
neural network architectures; in other words, on the basis of statistically
significant signal matches in the domain of time and in the domain of memory
content.


title: How to Verify Identity in the Continuous Variable Quantum System?

abstract:
  Continuous variable quantum cryptography has developed rapidly in recent
decades, but how to verify identity in the continuous variable quantum system
is still an urgent issue to be solved. To solve this problem, we propose a
continuous variable quantum identification (CV-QI) protocol based on the
correlation of two-mode squeezed vacuum state and the continuous variable
teleportation. The bidirectional identity verification between two participants
of the communication can be achieved by the proposed CV-QI protocol. In order
to guarantee the security, we make full use of the decoy state sequences during
the whole process of the proposed CV-QI protocol. Besides, we provide the
security analyses of the proposed CV-QI protocol, and analyses indicate that
the security of the proposed CV-QI protocol is guaranteed.


title:
Deep learning classification of large-scale point clouds: A case study
  on cuneiform tablets

abstract:
  This paper introduces a novel network architecture for the classification of
large-scale point clouds. The network is used to classify metadata from
cuneiform tablets. As more than half a million tablets remain unprocessed, this
can help create an overview of the tablets. The network is tested on a
comparison dataset and obtains state-of-the-art performance. We also introduce
new metadata classification tasks on which the network shows promising results.
Finally, we introduce the novel Maximum Attention visualization, demonstrating
that the trained network focuses on the intended features.


title: A pathwise parameterisation for stochastic transport

abstract:
  In this work we set the stage for a new probabilistic pathwise approach to
effectively calibrate a general class of stochastic nonlinear fluid dynamics
models. We focus on a 2D Euler SALT equation, showing that the driving
stochastic parameter can be calibrated in an optimal way to match a set of
given data. Moreover, we show that this model is robust with respect to the
stochastic parameters.


title: 2-adic point counting on $K3$ surfaces

abstract:
  This article reports on an approach to point counting on algebraic varieties
over finite fields that is based on a detailed investigation of the $2$-adic
orthogonal group. Combining the new approach with $p$-adic methods, we count
the number of points on some $K3$ surfaces over the field $\bbF_{\!p}$, for all
primes $p < 10^8$.


title:
Ab initio quantum theory of mass defect and time dilation in trapped-ion
  optical clocks

abstract:
  We derive a Hamiltonian for the external and internal dynamics of an
electromagnetically bound, charged two-particle system in external
electromagnetic and gravitational fields, including leading order relativistic
corrections. We apply this Hamiltonian to describe the relativistic coupling of
the external and internal dynamics of cold ions in Paul traps, including the
effects of micromotion, excess micromotion and trap imperfections. This
provides a systematic and fully quantum mechanical treatment of relativistic
frequency shifts in atomic clocks based on single trapped ions. Our approach
reproduces well known formulas for the second order Doppler shift for thermal
states, which were previously derived on the basis of semi-classical arguments.
We complement and clarify recent discussions in the literature on the role of
time dilation and mass defect in ion clocks.


title:
NU HLT at CMCL 2022 Shared Task: Multilingual and Crosslingual
  Prediction of Human Reading Behavior in Universal Language Space

abstract:
  In this paper, we present a unified model that works for both multilingual
and crosslingual prediction of reading times of words in various languages. The
secret behind the success of this model is in the preprocessing step where all
words are transformed to their universal language representation via the
International Phonetic Alphabet (IPA). To the best of our knowledge, this is
the first study to favorable exploit this phonological property of language for
the two tasks. Various feature types were extracted covering basic frequencies,
n-grams, information theoretic, and psycholinguistically-motivated predictors
for model training. A finetuned Random Forest model obtained best performance
for both tasks with 3.8031 and 3.9065 MAE scores for mean first fixation
duration (FFDAvg) and mean total reading time (TRTAvg) respectively.


title:
On some equivalent norms in Sobolev spaces on bounded domains and on the
  boundaries

abstract:
  We consider the equivalence of some norms in Sobolev spaces on bounded
domains of the d-dimensional real Euclidean space and also in Sobolev spaces on
the boundaries of those domains.


title:
Brownian motion with time-dependent friction and single-particle
  dynamics in liquids

abstract:
  A microscopic theory of molecular motion in classical monatomic liquids,
proposed by Glass and Rice [Phy. Rev. 176, 239 (1968)], is revisited and
extended to incorporate the dynamic friction in the Brownian description of the
atomic diffusion in a mean-time-dependent harmonic force field. A modified,
non-Markovian Langevin equation is utilized to derive an equation of motion for
the velocity autocorrelation function with time-dependent friction coefficient.
Numerical solution of the equation gives an excellent account of the velocity
autocorrelation function in LJ liquids, liquid alkali and transition metals
over a broad range of density and temperature. Derivation of the equation of
motion leads to a self-consistent expression for the time-dependence of
friction coefficient. Our results demonstrate that the nature of
time-dependence of the friction coefficient changes dramatically with the
liquid density. At low and moderate densities, the dynamic friction decays
exponentially whereas it increases exponentially at high liquid densities. Our
findings provide an opportunity to have a new outlook of the Brownian
description of atomic dynamics in liquids.


title:
Improvement of the parameter measurement accuracy by the
  third-generation gravitational wave detector Einstein Telescope

abstract:
  The Einstein Telescope (ET) has been proposed as one of the third-generation
gravitational wave (GW) detectors. The sensitivity of ET would be a factor of
10 better than the second-generation GW detector, Advanced LIGO (aLIGO); thus,
the GW source parameters could be measured with much better accuracy. In this
work, we show how the precision in parameter estimation can be improved between
aLIGO and ET by comparing the measurement errors. We apply the TaylorF2
waveform model defined in the frequency domain to the Fisher matrix method
which is a semi-analytic approach for estimating GW parameter measurement
errors. We adopt as our sources low-mass binary black holes with the total
masses of $M\leq 16 M_{\odot} $ and the effective spins of $-0.9 \leq \chi_{\rm
eff} \leq 0.9$ and calculate the measurement errors of the mass and the spin
parameters using $10^4$ Monte-Carlo samples randomly distributed in our mass
and spin parameter space. We find that for the same sources ET can achieve
$\sim 14$ times better signal-to-noise ratio than aLIGO and the error ratios
($\sigma_{\lambda, \rm ET}/\sigma_{\lambda, \rm aLIGO}$) for the chirp-mass,
symmetric mass ratio, and effective spin parameters can be lower than $7\%$ for
all binaries. We also consider the equal-mass binary neutron stars with the
component masses of 1, 1.4, and 2 $M_{\odot}$ and find that the error ratios
for the mass and the spin parameters can be lower than $1.5 \%$. In particular,
the measurement error of the tidal deformability $\tilde{\Lambda}$ can also be
significantly reduced by ET, with the error ratio of $3.6 - 6.1 \%$.


title:
Unconventional pressure dependence of the superfluid density in the
  nodeless topological superconductor $\alpha$-PdBi$_2$

abstract:
  We investigated the superconducting properties of the topological
superconductor $\alpha$--PdBi$_2$ at ambient and external pressures up to
1.77~GPa using muon spin rotation ($\mu$SR) experiments. The ambient pressure
measurements evince a fully gapped $s$-wave superconducting state in the bulk
of the specimen. AC magnetic susceptibility and $\mu$SR measurements manifest a
continuous suppression of $T_{\rm c}$ with increasing pressure. In parallel, we
observed a significant decrease of superfluid density by $\sim$20\% upon
application of external pressure. Remarkably, the superfluid density follows
linear relation with $T_{\rm c}$ which was found before in some unconventional
topological superconductors and hole doped cuprates. This finding signals a
possible crossover from BEC to BCS in $\alpha$--PdBi$_2$.


title:
Gate-Tunable Transmon Using Selective-Area-Grown
  Superconductor-Semiconductor Hybrid Structures on Silicon

abstract:
  We present a gate-voltage tunable transmon qubit (gatemon) based on planar
InAs nanowires that are selectively grown on a high resistivity silicon
substrate using III-V buffer layers. We show that low loss superconducting
resonators with an internal quality of $2\times 10^5$ can readily be realized
using these substrates after the removal of buffer layers. We demonstrate
coherent control and readout of a gatemon device with a relaxation time,
$T_{1}\approx 700\,\mathrm{ns}$, and dephasing times, $T_2^{\ast}\approx
20\,\mathrm{ns}$ and $T_{\mathrm{2,echo}} \approx 1.3\,\mathrm{\mu s}$.
Further, we infer a high junction transparency of $0.4 - 0.9$ from an analysis
of the qubit anharmonicity.


title:
Efficient computation of states and sensitivities for compound
  structural optimisation problems using a Linear Dependency Aware Solver
  (LDAS)

abstract:
  Real-world structural optimisation problems involve multiple loading
conditions and design constraints, with responses typically depending on states
of discretised governing equations. Generally, one uses gradient-based nested
analysis and design approaches to solve these problems. Herein, solving both
physical and adjoint problems dominates the overall computational effort.
Although not commonly detected, real-world problems can contain linear
dependencies between encountered physical and adjoint loads. Manually keeping
track of such dependencies becomes tedious as design problems become
increasingly involved. To detect and exploit such dependencies, this work
proposes the use of a Linear Dependency Aware Solver (LDAS), which is able to
efficiently detect linear dependencies between all loads to avoid unnecessary
solves entirely and automatically. Illustrative examples are provided that
demonstrate the need and benefits of using an LDAS, including a run-time
experiment.


title: Asynchronous Fully-Decentralized SGD in the Cluster-Based Model

abstract:
  This paper presents fault-tolerant asynchronous Stochastic Gradient Descent
(SGD) algorithms. SGD is widely used for approximating the minimum of a cost
function $Q$, as a core part of optimization and learning algorithms. Our
algorithms are designed for the cluster-based model, which combines
message-passing and shared-memory communication layers. Processes may fail by
crashing, and the algorithm inside each cluster is wait-free, using only reads
and writes.
  For a strongly convex function $Q$, our algorithm tolerates any number of
failures, and provides convergence rate that yields the maximal distributed
acceleration over the optimal convergence rate of sequential SGD.
  For arbitrary functions, the convergence rate has an additional term that
depends on the maximal difference between the parameters at the same iteration.
(This holds under standard assumptions on $Q$.) In this case, the algorithm
obtains the same convergence rate as sequential SGD, up to a logarithmic
factor. This is achieved by using, at each iteration, a multidimensional
approximate agreement algorithm, tailored for the cluster-based model.
  The algorithm for arbitrary functions requires that at least a majority of
the clusters contain at least one nonfaulty process. We prove that this
condition is necessary when optimizing some non-convex functions.


title: Accretion dynamics of ideal fluid in the deformed Kerr Spacetime

abstract:
  We study the properties of a low-angular momentum, inviscid, advective
accretion flow in a deformed Kerr spacetime under the framework of general
theory of relativity. We solve the governing equations that describe the flow
motion in terms of input parameters, namely energy ($E$), angular momentum
($\lambda$), spin ($a_{\rm k}$) and deformation parameter ($\varepsilon$),
respectively. We find that global transonic accretion solutions continue to
exist in non-Kerr spacetime. Depending on the input parameters, accretion flow
is seen to experience shock transition and we find that shocked induced
accretion solutions are available for a wide range of the parameter space in
$\lambda-E$ plane. We examine the modification of the shock parameter space
with $\varepsilon$, and find that as $\varepsilon$ is increased, the effective
region of the parameter space is reduced, and gradually shifted towards the
higher $\lambda$ and lower $E$ domain. In addition, for the first time in the
literature, we notice that accretion flow having zero angular momentum admits
shock transition when spacetime deformation is significantly large.
Interestingly, beyond a critical limit of $\varepsilon_{\rm max}$, the nature
of the central object alters from black hole to naked singularity and we
identify $\varepsilon_{\rm max}$ as function of $a_{\rm k}$. Further, we
examine the accretion solutions and its properties around the naked singularity
as well. Finally, we indicate the implications of the present formalism in the
context of astrophysical applications.


title: Some questions about Conformal Cyclic Cosmology

abstract:
  This article is an extended version of a talk given in Oxford in June 2021 as
part of an online meeting `Ninety minutes of CCC' to mark the 90th birthday of
Sir Roger Penrose. I assemble some questions that I have been asked or have
asked myself about CCC.


title:
Ideal growth in amalgamated powers of nilpotent rings and zeta functions
  of quiver representations

abstract:
  Let $L$ be a nilpotent algebra of class two over a compact discrete valuation
ring $A$ of characteristic zero or of sufficiently large positive
characteristic. Let $q$ be the residue cardinality of $A$. The ideal zeta
function of $L$ is a Dirichlet series enumerating finite-index ideals of $L$.
We prove that there is a rational function in $q$, $q^m$, $q^{-s}$, and
$q^{-ms}$ giving the ideal zeta function of the amalgamation of $m$ copies of
$L$ over the derived subring, for every $m \geq 1$, up to an explicit factor.
More generally, we prove this for the zeta functions of nilpotent quiver
representations of class two defined by Lee and Voll, and in particular for
Dirichlet series counting graded submodules of a graded $A$-module. If the
algebra $L$, or the quiver representation, is defined over $\mathbb{Z}$, then
we obtain a uniform rationality result.


title: Stateful Structural Operational Semantics

abstract:
  Compositionality of denotational semantics is an important concern in
programming semantics. Mathematical operational semantics in the sense of Turi
and Plotkin guarantees compositionality, but seen from the point of view of
stateful computation it applies only to very fine-grained equivalences that
essentially assume unrestricted interference by the environment between any two
statements. We introduce the more restrictive stateful SOS rule format for
stateful languages. We show that compositionality of two more coarse-grained
semantics, respectively given by assuming read-only interference or no
interference between steps, remains an undecidable property even for stateful
SOS. However, further restricting the rule format in a manner inspired by the
cool GSOS formats of Bloom and van Glabbeek, we obtain the streamlined and cool
stateful SOS formats, which respectively guarantee compositionality of the two
more abstract equivalences.


title:
Subelliptic estimates for the $\bar{\partial}$-problem on complex
  algebraic surfaces with isolated singularities

abstract:
  We obtain subelliptic estimates for the $\bar{\partial}$-problem on complex
algebraic surfaces embedded in $\mathbb{C}^n$ with isolated singularities.
$W^{\epsilon}$ Sobolev norms of a form, $f$, for $0< \epsilon < 1$ are
estimated in terms of weighted $L^2$ norms of $\bar{\partial} f$ and
$\bar{\partial}^{\ast}f$, with weights which vanish at the singularities, as
well as weighted $L^2$ norms of $f$, with weights which blow up at the
singularities.


title: Neural Program Repair: Systems, Challenges and Solutions

abstract:
  Automated Program Repair (APR) aims to automatically fix bugs in the source
code. Recently, as advances in Deep Learning (DL) field, there is a rise of
Neural Program Repair (NPR) studies, which formulate APR as a translation task
from buggy code to correct code and adopt neural networks based on
encoder-decoder architecture. Compared with other APR techniques, NPR
approaches have a great advantage in applicability because they do not need any
specification (i.e., a test suite). Although NPR has been a hot research
direction, there isn't any overview on this field yet. In order to help
interested readers understand architectures, challenges and corresponding
solutions of existing NPR systems, we conduct a literature review on latest
studies in this paper. We begin with introducing the background knowledge on
this field. Next, to be understandable, we decompose the NPR procedure into a
series of modules and explicate various design choices on each module.
Furthermore, we identify several challenges and discuss the effect of existing
solutions. Finally, we conclude and provide some promising directions for
future research.


title:
Performance Enhancement of C-V2X Mode 4 Utilizing Multiple Candidate
  Single-subframe Resources

abstract:
  Prioritization of data streams in cellular vehicle-to-everything (C-V2X) may
lead to unfavorable packet delays in low priority streams. This paper studies
the allocation of multiple candidate single-subframe resources (CSRs) per
vehicle as a solution. It proposes a methodology to determine the number of
CSRs for each vehicle based on the number of total vehicles, and to assign the
multiple data streams among them for simultaneous transmission. The numerical
results highlight the achievable delay gains of the proposed approach, and its
negligible impact on packet collisions.


title:
Socialformer: Social Network Inspired Long Document Modeling for
  Document Ranking

abstract:
  Utilizing pre-trained language models has achieved great success for neural
document ranking. Limited by the computational and memory requirements, long
document modeling becomes a critical issue. Recent works propose to modify the
full attention matrix in Transformer by designing sparse attention patterns.
However, most of them only focus on local connections of terms within a
fixed-size window. How to build suitable remote connections between terms to
better model document representation remains underexplored. In this paper, we
propose the model Socialformer, which introduces the characteristics of social
networks into designing sparse attention patterns for long document modeling in
document ranking. Specifically, we consider several attention patterns to
construct a graph like social networks. Endowed with the characteristic of
social networks, most pairs of nodes in such a graph can reach with a short
path while ensuring the sparsity. To facilitate efficient calculation, we
segment the graph into multiple subgraphs to simulate friend circles in social
scenarios. Experimental results confirm the effectiveness of our model on long
document modeling.


title: Dynamical system analysis of Myrzakulov gravity

abstract:
  We perform a dynamical system analysis of Myrzakulov or F(R, T) gravity,
which is a subclass of affinely connected metric theories, where ones uses a
specific but non-special connection, which allows for non-zero curvature and
torsion simultaneously. We consider two classes of models, we extract the
critical points, and we examine their stability properties alongside their
physical features. In the Class 1 models, which possess {\Lambda}CDM cosmology
as a limit, we find the sequence of matter and dark energy eras, and we show
that the Universe will result in a dark-energy dominated critical point for
which dark energy behaves like a cosmological constant. Concerning the
dark-energy equation-of-state parameter we find that it lies in the
quintessence or phantom regime, according to the value of the model parameter.
For the Class 2 models, we again find the dark-energy dominated, de Sitter
late-time attractor, although the scenario does not possess {\Lambda}CDM
cosmology as a limit. The cosmological behavior is richer, and the dark-energy
sector can be quintessence-like, phantom-like, or experience the phantom-divide
crossing during the evolution.


title: Choquet-Based Fuzzy Rough Sets

abstract:
  Fuzzy rough set theory can be used as a tool for dealing with inconsistent
data when there is a gradual notion of indiscernibility between objects. It
does this by providing lower and upper approximations of concepts. In classical
fuzzy rough sets, the lower and upper approximations are determined using the
minimum and maximum operators, respectively. This is undesirable for machine
learning applications, since it makes these approximations sensitive to
outlying samples. To mitigate this problem, ordered weighted average (OWA)
based fuzzy rough sets were introduced. In this paper, we show how the
OWA-based approach can be interpreted intuitively in terms of vague
quantification, and then generalize it to Choquet-based fuzzy rough sets
(CFRS). This generalization maintains desirable theoretical properties, such as
duality and monotonicity. Furthermore, it provides more flexibility for machine
learning applications. In particular, we show that it enables the seamless
integration of outlier detection algorithms, to enhance the robustness of
machine learning algorithms based on fuzzy rough sets.


title:
Ligandformer: A Graph Neural Network for Predicting Compound Property
  with Robust Interpretation

abstract:
  Robust and efficient interpretation of QSAR methods is quite useful to
validate AI prediction rationales with subjective opinion (chemist or biologist
expertise), understand sophisticated chemical or biological process mechanisms,
and provide heuristic ideas for structure optimization in pharmaceutical
industry. For this purpose, we construct a multi-layer self-attention based
Graph Neural Network framework, namely Ligandformer, for predicting compound
property with interpretation. Ligandformer integrates attention maps on
compound structure from different network blocks. The integrated attention map
reflects the machine's local interest on compound structure, and indicates the
relationship between predicted compound property and its structure. This work
mainly contributes to three aspects: 1. Ligandformer directly opens the
black-box of deep learning methods, providing local prediction rationales on
chemical structures. 2. Ligandformer gives robust prediction in different
experimental rounds, overcoming the ubiquitous prediction instability of deep
learning methods. 3. Ligandformer can be generalized to predict different
chemical or biological properties with high performance. Furthermore,
Ligandformer can simultaneously output specific property score and visible
attention map on structure, which can support researchers to investigate
chemical or biological property and optimize structure efficiently. Our
framework outperforms over counterparts in terms of accuracy, robustness and
generalization, and can be applied in complex system study.


title: Groebner fan and embedded resolutions of ideals on toric varieties

abstract:
  We consider the notions of Groebner fan and Newton non-degeneracy for an
ideal on a toric variety, extending the two existing notions for ideals on
affine spaces. We prove, without assumptions on the characteristic of the base
fields, that the "Groebner fan" of such an ideal is actually a polyhedral fan
and that a sub-variety defined by a Newton non-degenerate ideal on a toric
variety $X_\sigma$ admits a toric embedded resolution of singularities
$Z\longrightarrow X_\sigma.$


title: Data-Consistent Local Superresolution for Medical Imaging

abstract:
  In this work we propose a new paradigm of iterative model-based
reconstruction algorithms for providing real-time solution for zooming-in and
refining a region of interest in medical and clinical tomographic (such as
CT/MRI/PET, etc) images. This algorithmic framework is tailor for a clinical
need in medical imaging practice, that after a reconstruction of the full
tomographic image, the clinician may believe that some critical parts of the
image are not clear enough, and may wish to see clearer these
regions-of-interest. A naive approach (which is highly not recommended) would
be performing the global reconstruction of a higher resolution image, which has
two major limitations: firstly, it is computationally inefficient, and
secondly, the image regularization is still applied globally which may
over-smooth some local regions. Furthermore if one wish to fine-tune the
regularization parameter for local parts, it would be computationally
infeasible in practice for the case of using global reconstruction. Our new
iterative approaches for such tasks are based on jointly utilizing the
measurement information, efficient upsampling/downsampling across image spaces,
and locally adjusted image prior for efficient and high-quality
post-processing. The numerical results in low-dose X-ray CT image local zoom-in
demonstrate the effectiveness of our approach.


title:
Observation of rovibrationally coupled bi-modality and speed-dependent
  orientation in DEA dynamics of OCS: reveals partial correlations among point
  group symmetries

abstract:
  Dissociative electron attachment (DEA) to gas-phase carbonyl sulfide (OCS)
has been studied diligently using the time-of-flight (TOF) based
state-of-the-art velocity map imaging (VMI) technique. Three well-resolved DEA
resonances are observed at 5.0, 6.5 and 10.0 eV incident electron energies
along with a weak structure at 8.0 eV. The velocity slice images (VSI), Kinetic
energy (KE) and angular distributions (AD) for the fragmented sulfur anions are
obtained using the wedge slicing technique. The KE distributions for the sulfur
nascent fragments reveal bi-modality with rovibrational signatures. The ADs
substantiate speed-dependent angular anisotropy demand the existence of partial
correlations among three different point group symmetries, confirmed through an
in-plane bending mode of vibration with the axial recoil breakdown. Theoretical
calculations using R-matrix and density functional approaches strongly support
the experimental observations.


title: Gamma-Ray Polarimetry of the Crab Pulsar Observed by POLAR

abstract:
  The X/$\gamma$ ray polarimetry of the Crab pulsar/nebula is believed to hold
crucial information on their emission models. In the past, several missions
have shown evidence of polarized emission from the Crab. The significance of
these measurements remains however limited. New measurements are therefore
required. POLAR is a wide Field of View Compton-scattering polarimeter
(sensitive in 50-500 keV) onboard the Chinese spacelab Tiangong-2 which took
data from September 2016 to April 2017. Although not designed to perform
polarization measurements of pulsars, we present here a novel method which can
be applied to POLAR as well as that of other wide Field of View polarimeters.
The novel polarimetric joint-fitting method for the Crab pulsar observations
with POLAR, allows us to obtain constraining measurements of the pulsar
component. The best fitted values and corresponding 1$\sigma$ deviations for
the averaged phase interval: (PD=$14\substack{+15 \\ -10}$\%,
PA=$108\substack{+33 \\ -54} ^{\circ}$), for Peak 1: (PD=$17\substack{+18 \\
-12}$\%, PA=$174\substack{+39 \\ -36} ^{\circ}$) and for Peak 2:
(PD=$16\substack{+16 \\ -11}$\%, PA=$78\substack{+39 \\ -30} ^{\circ}$).
Further more, the 3$\sigma$ upper limits on the polarization degree are for the
averaged phase interval (55\%), Peak 1 (66\%) and Peak 2 (57\%). Finally, to
illustrate the capabilities of this method in the future, we simulated two
years observation to the Crab pulsar with POLAR-2. The results show that
POLAR-2 is able to confirm the emission to be polarized with $5\sigma$ and
$4\sigma$ confidence level if the Crab pulsar is polarized at $20\%$ and $10\%$
respectively.


title:
A fundamental condition for harmonic analysis in anisotropic generalized
  Orlicz spaces

abstract:
  Anisotropic generalized Orlicz spaces have been investigated in many recent
papers, but the basic assumptions are not as well understood as in the
isotropic case. We study the greatest convex minorant of anisotropic
$\Phi$-functions and prove the equivalence of two widely used conditions in the
theory of generalized Orlicz spaces, usually called (A1) and (M). This provides
a more natural and easily verifiable condition for use in the theory of
anisotropic generalized Orlicz spaces for results such as Jensen's inequality
which we obtain as a corollary.


title: Evaluating Persian Tokenizers

abstract:
  Tokenization plays a significant role in the process of lexical analysis.
Tokens become the input for other natural language processing tasks, like
semantic parsing and language modeling. Natural Language Processing in Persian
is challenging due to Persian's exceptional cases, such as half-spaces. Thus,
it is crucial to have a precise tokenizer for Persian. This article provides a
novel work by introducing the most widely used tokenizers for Persian and
comparing and evaluating their performance on Persian texts using a simple
algorithm with a pre-tagged Persian dependency dataset. After evaluating
tokenizers with the F1-Score, the hybrid version of the Farsi Verb and Hazm
with bounded morphemes fixing showed the best performance with an F1 score of
98.97%.


title: Robust static and dynamic maximum flows

abstract:
  We study the robust maximum flow problem and the robust maximum flow over
time problem where a given number of arcs $\Gamma$ may fail or may be delayed.
Two prominent models have been introduced for these problems: either one
assigns flow to arcs fulfilling weak flow conservation in any scenario, or one
assigns flow to paths where an arc failure or delay affects a whole path. We
provide a unifying framework by presenting novel general models, in which we
assign flow to subpaths. These models contain the known models as special cases
and unify their advantages in order to obtain less conservative robust
solutions. We give a thorough analysis with respect to complexity of the
general models. In particular, we show that the general models are essentially
NP-hard, whereas, e.g. in the static case with $\Gamma = 1$ an optimal solution
can be computed in polynomial time. Further, we answer the open question about
the complexity of the dynamic path model for $\Gamma = 1$. We also compare the
solution quality of the different models. In detail, we show that the general
models have better robust optimal values than the known models and we prove
bounds on these gaps.


title:
Coordinate-Aligned Multi-Camera Collaboration for Active Multi-Object
  Tracking

abstract:
  Active Multi-Object Tracking (AMOT) is a task where cameras are controlled by
a centralized system to adjust their poses automatically and collaboratively so
as to maximize the coverage of targets in their shared visual field. In AMOT,
each camera only receives partial information from its observation, which may
mislead cameras to take locally optimal action. Besides, the global goal, i.e.,
maximum coverage of objects, is hard to be directly optimized. To address the
above issues, we propose a coordinate-aligned multi-camera collaboration system
for AMOT. In our approach, we regard each camera as an agent and address AMOT
with a multi-agent reinforcement learning solution. To represent the
observation of each agent, we first identify the targets in the camera view
with an image detector, and then align the coordinates of the targets in 3D
environment. We define the reward of each agent based on both global coverage
as well as four individual reward terms. The action policy of the agents is
derived with a value-based Q-network. To the best of our knowledge, we are the
first to study the AMOT task. To train and evaluate the efficacy of our system,
we build a virtual yet credible 3D environment, named "Soccer Court", to mimic
the real-world AMOT scenario. The experimental results show that our system
achieves a coverage of 71.88%, outperforming the baseline method by 8.9%.