title:
Benchmarking Structural Evolution Methods for Training of Machine
  Learned Interatomic Potentials

abstract:
  When creating training data for machine-learned interatomic potentials
(MLIPs), it is common to create initial structures and evolve them using
molecular dynamics to sample a larger configuration space. We benchmark two
other modalities of evolving structures, contour exploration and dimer-method
searches against molecular dynamics for their ability to produce diverse and
robust training density functional theory data sets for MLIPs. We also discuss
the generation of initial structures which are either from known structures or
from random structures in detail to further formalize the structure-sourcing
processes in the future. The polymorph-rich zirconium-oxygen composition space
is used as a rigorous benchmark system for comparing the performance of MLIPs
trained on structures generated from these structural evolution methods. Using
Behler-Parrinello neural networks as our machine-learned interatomic potential
models, we find that contour exploration and the dimer-method searches are
generally superior to molecular dynamics in terms of spatial descriptor
diversity and statistical accuracy.


title: Automatic Facial Skin Feature Detection for Everyone

abstract:
  Automatic assessment and understanding of facial skin condition have several
applications, including the early detection of underlying health problems,
lifestyle and dietary treatment, skin-care product recommendation, etc. Selfies
in the wild serve as an excellent data resource to democratize skin quality
assessment, but suffer from several data collection challenges.The key to
guaranteeing an accurate assessment is accurate detection of different skin
features. We present an automatic facial skin feature detection method that
works across a variety of skin tones and age groups for selfies in the wild. To
be specific, we annotate the locations of acne, pigmentation, and wrinkle for
selfie images with different skin tone colors, severity levels, and lighting
conditions. The annotation is conducted in a two-phase scheme with the help of
a dermatologist to train volunteers for annotation. We employ Unet++ as the
network architecture for feature detection. This work shows that the two-phase
annotation scheme can robustly detect the accurate locations of acne,
pigmentation, and wrinkle for selfie images with different ethnicities, skin
tone colors, severity levels, age groups, and lighting conditions.


title: Self-supervised 360$^{\circ}$ Room Layout Estimation

abstract:
  We present the first self-supervised method to train panoramic room layout
estimation models without any labeled data. Unlike per-pixel dense depth that
provides abundant correspondence constraints, layout representation is sparse
and topological, hindering the use of self-supervised reprojection consistency
on images. To address this issue, we propose Differentiable Layout View
Rendering, which can warp a source image to the target camera pose given the
estimated layout from the target image. As each rendered pixel is
differentiable with respect to the estimated layout, we can now train the
layout estimation model by minimizing reprojection loss. Besides, we introduce
regularization losses to encourage Manhattan alignment, ceiling-floor
alignment, cycle consistency, and layout stretch consistency, which further
improve our predictions. Finally, we present the first self-supervised results
on ZilloIndoor and MatterportLayout datasets. Our approach also shows promising
solutions in data-scarce scenarios and active learning, which would have an
immediate value in the real estate virtual tour software. Code is available at
https://github.com/joshua049/Stereo-360-Layout.


title: Measuring Miner Decentralization in Proof-of-Work Blockchains

abstract:
  Proof of work cryptocurrencies began with the promise of a more egalitarian
future with a decentralized monetary system with no powerful entities in
charge. While this vision is far from realized, these cryptocurrencies are
still touted to be much more decentralized than traditional centralized
systems. While it is well understood that cryptocurrencies are centralized, it
is still unclear what the underlying causes are. This work aims to address this
gap and examines some of the forces behind mining centralization.
  The internals of cryptocurrency mining is very opaque and difficult to study
since it traditionally requires forming relationships with miners, who are
typically reticent to share internal information about their competitive
advantages. This work takes a different approach by combining large-scale
statistical techniques with publicly available blockchain data in order to
answer previously intractable questions. The crux of our analysis technique is
based on the simple observation that some miners can utilize their hashpower
more efficiently due to their position in the network. By teasing out that
effect, we de-bias the mining power distribution to get a more accurate
estimate. Using that de-biased mining power distribution, we can answer
questions about the network position of miners in each cryptocurrency network.
Finally, during the course of this study, we observed some unusual mining
behaviors which we highlight.


title:
Axial U(1) symmetry at high temperatures in $N_f=2+1$ lattice QCD with
  chiral fermions

abstract:
  We study the $U(1)_A$ anomaly in the high-temperature phase of $N_f=2+1$
lattice QCD with chiral fermions. Gauge ensembles are generated with M\"obius
domain-wall (MDW) fermions, and in the measurements the determinant is
reweighted to that of overlap fermions. We report the results for the overlap
Dirac spectrum, $U(1)_A$ susceptibility, and topological susceptibility at
$T=204$ and $175$ MeV.


title: Understanding Graph Convolutional Networks for Text Classification

abstract:
  Graph Convolutional Networks (GCN) have been effective at tasks that have
rich relational structure and can preserve global structure information of a
dataset in graph embeddings. Recently, many researchers focused on examining
whether GCNs could handle different Natural Language Processing tasks,
especially text classification. While applying GCNs to text classification is
well-studied, its graph construction techniques, such as node/edge selection
and their feature representation, and the optimal GCN learning mechanism in
text classification is rather neglected. In this paper, we conduct a
comprehensive analysis of the role of node and edge embeddings in a graph and
its GCN learning techniques in text classification. Our analysis is the first
of its kind and provides useful insights into the importance of each graph
node/edge construction mechanism when applied at the GCN training/testing in
different text classification benchmarks, as well as under its semi-supervised
environment.


title:
Origin of Perpendicular Magnetic Anisotropy in
  Co$_x$Fe$_{3-x}$O$_{4+\delta}$ Thin Films Studied by X-ray Magnetic Circular
  and Linear Dichroisms

abstract:
  We investigate the element-specific spin and orbital states and their roles
on magnetic anisotropy in the Co-ferrite (Co$_x$Fe$_{3-x}$O$_{4+\delta}$ (001))
thin films which exhibit perpendicular magnetic anisotropy (PMA). The origin of
PMA in the low $x$ region ($x$ $<$ 1) can be mainly explained by the large
perpendicular orbital magnetic moments in the Co$^{2+}$ (3$d^7$) states
detected by X-ray magnetic circular and linear dichroisms (XMCD/XMLD). The XMLD
for a PMA film ($x=0.2$) with square hysteresis curve shows the oblate charge
distribution in the Co$^{2+}$ site, which is consistent with the change in
local nearest neighbor distance in Co detected by extended X-ray absorption
fine structure analysis. Our finding reveals that the microscopic origin of PMA
in Co-ferrite comes from the enhanced orbital magnetic moments along
out-of-plane [001] direction through in-plane charge distribution by tensile
strain, which adds the material functionalities in spinel ferrite thin films
from the viewpoint of strain and orbital magnetic moments.


title: AxIoU: An Axiomatically Justified Measure for Video Moment Retrieval

abstract:
  Evaluation measures have a crucial impact on the direction of research.
Therefore, it is of utmost importance to develop appropriate and reliable
evaluation measures for new applications where conventional measures are not
well suited. Video Moment Retrieval (VMR) is one such application, and the
current practice is to use R@$K,\theta$ for evaluating VMR systems. However,
this measure has two disadvantages. First, it is rank-insensitive: It ignores
the rank positions of successfully localised moments in the top-$K$ ranked list
by treating the list as a set. Second, it binarizes the Intersection over Union
(IoU) of each retrieved video moment using the threshold $\theta$ and thereby
ignoring fine-grained localisation quality of ranked moments.
  We propose an alternative measure for evaluating VMR, called Average Max IoU
(AxIoU), which is free from the above two problems. We show that AxIoU
satisfies two important axioms for VMR evaluation, namely, \textbf{Invariance
against Redundant Moments} and \textbf{Monotonicity with respect to the Best
Moment}, and also that R@$K,\theta$ satisfies the first axiom only. We also
empirically examine how AxIoU agrees with R@$K,\theta$, as well as its
stability with respect to change in the test data and human-annotated temporal
boundaries.


title:
Pay Attention to Hidden States for Video Deblurring: Ping-Pong Recurrent
  Neural Networks and Selective Non-Local Attention

abstract:
  Video deblurring models exploit information in the neighboring frames to
remove blur caused by the motion of the camera and the objects. Recurrent
Neural Networks~(RNNs) are often adopted to model the temporal dependency
between frames via hidden states. When motion blur is strong, however, hidden
states are hard to deliver proper information due to the displacement between
different frames. While there have been attempts to update the hidden states,
it is difficult to handle misaligned features beyond the receptive field of
simple modules. Thus, we propose 2 modules to supplement the RNN architecture
for video deblurring. First, we design Ping-Pong RNN~(PPRNN) that acts on
updating the hidden states by referring to the features from the current and
the previous time steps alternately. PPRNN gathers relevant information from
the both features in an iterative and balanced manner by utilizing its
recurrent architecture. Second, we use a Selective Non-Local Attention~(SNLA)
module to additionally refine the hidden state by aligning it with the
positional information from the input frame feature. The attention score is
scaled by the relevance to the input feature to focus on the necessary
information. By paying attention to hidden states with both modules, which have
strong synergy, our PAHS framework improves the representation powers of RNN
structures and achieves state-of-the-art deblurring performance on standard
benchmarks and real-world videos.


title:
Probabilistic Parameter Estimation Using a Gaussian Mixture Density
  Network: Application to X-ray Reflectivity Data Curve Fitting

abstract:
  X-ray reflectivity (XRR) is widely used for thin-film structure analysis, and
XRR data analysis involves minimizing the difference between an XRR curve
calculated from model parameters describing the thin-film structure. This
analysis takes a certain amount of time because it involves many unavoidable
iterations. However, the recently introduced artificial neural network (ANN)
method can dramatically reduce the analysis time in the case of repeated
analyses of similar samples. Here, we demonstrate the analysis of XRR data
using a mixture density network (MDN), which enables probabilistic prediction
while maintaining the advantages of an ANN. First, under the assumption of a
unimodal probability distribution of the output parameter, the trained MDN can
estimate the best-fit parameter and, at the same time, estimate the confidence
interval (CI) corresponding to the error bar of the best-fit parameter. The CI
obtained in this manner is similar to that obtained using the Neumann process,
a well-known statistical method. Next, the MDN method provides several possible
solutions for each parameter in the case of a multimodal distribution of the
output parameters. An unsupervised machine learning method is used to cluster
possible parameter sets in order of high probability. Determining the true
value by examining the candidates of the parameter sets obtained in this manner
can help solve the inherent inverse problem associated with scattering data.


title: Shock Hugoniot of diamond from 3 to 80 TPa

abstract:
  The principal Hugoniot of carbon, initially diamond, was measured from 3 to
80 TPa (30 to 800 million atmospheres), the highest pressure ever achieved,
using radiography of spherically-converging shocks. The shocks were generated
by ablation of a plastic coating by soft x-rays in a laser-heated hohlraum at
the National Ignition Facility (NIF). Experiments were performed with low and
high drive powers, spanning different but overlapping pressure ranges. The
radius-time history of the shock, and the profile of mass density behind, were
determined by profile-matching from a time-resolved x-ray radiograph across the
diameter of the sphere. Above ~50 TPa, the heating induced by the shock was
great enough to ionize a significant fraction of K-shell electrons, reducing
the opacity to the 10.2 keV probe x-rays. The opacity and mass density were
deduced simultaneously using the constraint that the total mass of the sample
was constant. The Hugoniot and opacity were consistent with density functional
theory calculations of the electronic states and equation of state (EOS), and
varied significantly from theoretical Hugoniots based on Thomas-Fermi theory.
Theoretical models used to predict the compressibility of diamond ablator
experiments at the NIF, producing the highest neutron yields so far from
inertial confinement fusion experiments, are qualitatively consistent with our
EOS measurements but appear to overpredict the compressibility slightly. These
measurements help to evaluate theoretical techniques and constrain wide-range
EOS models applicable to white dwarf stars, which are the ultimate evolutionary
form of at least 97% of stars in the galaxy.


title:
First-passage time of run-and-tumble particles with non-instantaneous
  resetting

abstract:
  We study the statistics of the first-passage time of a single run and tumble
particle (RTP) in one spatial dimension, with or without resetting, to a fixed
target located at $L>0$. First, we compute the first-passage time distribution
of a free RTP, without resetting nor in a confining potential, but averaged
over the initial position drawn from an arbitrary distribution $p(x)$. Recent
experiments used a non-instantaneous resetting protocol that motivated us to
study in particular the case where $p(x)$ corresponds to the stationary
non-Boltzmann distribution of an RTP in the presence of a harmonic trap. This
distribution $p(x)$ is characterized by a parameter $\nu>0$, which depends on
the microscopic parameters of the RTP dynamics. We show that the first-passage
time distribution of the free RTP, drawn from this initial distribution,
develops interesting singular behaviours, depending on the parameter $\nu$. We
then switch on resetting, mimicked by thermal relaxation of the RTP in the
presence of a harmonic trap. Resetting leads to a finite mean first-passage
time (MFPT) and we study this as a function of the resetting rate for different
values of the parameters $\nu$ and $b = L/c$ where $c$ is the right edge of the
initial distribution $p(x)$. In the diffusive limit of the RTP dynamics, we
find a rich phase diagram in the $(b,\nu)$ plane, with an interesting
re-entrance phase transition. Away from the diffusive limit, qualitatively
similar rich behaviours emerge for the full RTP dynamics.


title:
Learning (Local) Surrogate Loss Functions for Predict-Then-Optimize
  Problems

abstract:
  Decision-Focused Learning (DFL) is a paradigm for tailoring a predictive
model to a downstream optimisation task that uses its predictions, so that it
can perform better on that specific task. The main technical challenge
associated with DFL is that it requires being able to differentiate through
$argmin$ operations to work. However, these $argmin$ optimisations are often
piecewise constant and, as a result, naively differentiating through them would
provide uninformative gradients. Past work has largely focused on getting
around this issue by handcrafting task-specific surrogates to the original
optimisation problem that provide informative gradients when differentiated
through. However, finding these surrogates can be challenging and the need to
handcraft surrogates for each new task limits the usability of DFL. In
addition, even after applying these relaxation techniques, there are no
guarantees that the resulting surrogates are convex and, as a result, training
a predictive model on them may lead to said model getting stuck in local
minimas.
  In this paper, we provide an approach to learn faithful task-specific
surrogates which (a) only requires access to a black-box oracle that can solve
the optimisation problem and is thus generalizable, and (b) can be convex by
construction and so can be easily optimized over. To the best of our knowledge,
this is the first work on using learning to find good surrogates for DFL. We
evaluate our approach on a budget allocation problem from the literature and
find that our approach outperforms even the hand-crafted (non-convex) surrogate
loss proposed by the original paper. Taking a step back, we hope that the
generality and simplicity of our approach will help lower the barrier
associated with implementing DFL-based solutions in practice. To that end, we
are currently working on extending our experiments to more domains.


title: Euler-symmetric complete intersection in projective space

abstract:
  Euler-symmetric projective varieties, introduced by Baohua Fu and Jun-Muk
Hwang in 2020, are nondegenerate projective varieties admitting many
$\mathbb{C}^{\times}$-actions of Euler type. They are quasi-homogeneous and
uniquely determined by their fundamental forms at a general point. In this
paper, we study complete intersections in projective spaces which are
Euler-symmetric. It is proven that such varieties are complete intersections of
hyperquadrics and the base locus of the second fundamental form at a general
point is again a complete intersection.


title: A unified analysis framework for iterative parallel-in-time algorithms

abstract:
  Parallel-in-time integration has been the focus of intensive research efforts
over the past two decades due to the advent of massively parallel computer
architectures and the scaling limits of purely spatial parallelization. Various
iterative parallel-in-time (PinT) algorithms have been proposed, like Parareal,
PFASST, MGRIT, and Space-Time Multi-Grid (STMG). These methods have been
described using different notations and the convergence estimates that are
available for some of them are difficult to compare. We describe Parareal,
PFASST, MGRIT and STMG for the Dahlquist model problem using a common notation
and give precise convergence estimates using generating functions. This allows
us, for the first time, to directly compare their convergence. We prove that
all four methods eventually converge super-linearly and compare them directly
numerically. Our framework also allows us to find new methods.


title: An Improved Greedy Algorithm for Subset Selection in Linear Estimation

abstract:
  In this paper, we consider a subset selection problem in a spatial field
where we seek to find a set of k locations whose observations provide the best
estimate of the field value at a finite set of prediction locations. The
measurements can be taken at any location in the continuous field, and the
covariance between the field values at different points is given by the widely
used squared exponential covariance function. One approach for observation
selection is to perform a grid discretization of the space and obtain an
approximate solution using the greedy algorithm. The solution quality improves
with a finer grid resolution but at the cost of increased computation. We
propose a method to reduce the computational complexity, or conversely to
increase solution quality, of the greedy algorithm by considering a search
space consisting only of prediction locations and centroids of cliques formed
by the prediction locations. We demonstrate the effectiveness of our proposed
approach in simulation, both in terms of solution quality and runtime.


title: Learning Program Representations for Food Images and Cooking Recipes

abstract:
  In this paper, we are interested in modeling a how-to instructional
procedure, such as a cooking recipe, with a meaningful and rich high-level
representation. Specifically, we propose to represent cooking recipes and food
images as cooking programs. Programs provide a structured representation of the
task, capturing cooking semantics and sequential relationships of actions in
the form of a graph. This allows them to be easily manipulated by users and
executed by agents. To this end, we build a model that is trained to learn a
joint embedding between recipes and food images via self-supervision and
jointly generate a program from this embedding as a sequence. To validate our
idea, we crowdsource programs for cooking recipes and show that: (a) projecting
the image-recipe embeddings into programs leads to better cross-modal retrieval
results; (b) generating programs from images leads to better recognition
results compared to predicting raw cooking instructions; and (c) we can
generate food images by manipulating programs via optimizing the latent code of
a GAN. Code, data, and models are available online.


title:
Depth estimation of tumor invasion in early gastric cancer using
  scattering of circularly polarized light: Monte Carlo Simulation study

abstract:
  Quantitative depth estimation of tumor invasion in early gastric cancer by
scattering of circularly polarized light is computationally investigated using
the Monte Carlo method. Using the optical parameters of the human stomach wall
and its carcinoma, the intensity and circular polarization of light scattered
from pseudo-healthy and cancerous tissues were calculated over a wide spectral
range. Large differences in the circular polarization with opposite signs,
together with the large intensity, are obtained at wavelengths 600 nm and 950
nm. At these two wavelengths, the sampling depth of the biological tissues can
be modulated by tuning the detection angle. In bi-layered pseudo-tissues with a
cancerous layer on a healthy layer and vice versa, the degree of circular
polarization of scattered light shows systematic changes depending on the
thickness and depth of the cancerous layer, which indicates the feasibility of
in vivo quantitative estimation of cancer progression in early gastric cancer.


title:
Explainable Artificial Intelligence in Process Mining: Assessing the
  Explainability-Performance Trade-Off in Outcome-Oriented Predictive Process
  Monitoring

abstract:
  Recently, a shift has been made in the field of Outcome-Oriented Predictive
Process Monitoring (OOPPM) to use models from the eXplainable Artificial
Intelligence paradigm, however the evaluation still occurs mainly through
performance-based metrics not accounting for the implications and lack of
actionability of the explanations. In this paper, we define explainability by
the interpretability of the explanations (through the widely-used XAI
properties parsimony and functional complexity) and the faithfulness of the
explainability model (through monotonicity and level of disagreement). The
introduced properties are analysed along the event, case, and control flow
perspective that are typical of a process-based analysis. This allows to
quantitatively compare, inter alia, inherently created explanations (e.g.,
logistic regression coefficients) with post-hoc explanations (e.g., Shapley
values). Moreover, this paper contributes a guideline named X-MOP to
practitioners to select the appropriate model based on the event log
specifications and the task at hand, by providing insight into how the varying
preprocessing, model complexity and post-hoc explainability techniques typical
in OOPPM influence the explainability of the model. To this end, we benchmark
seven classifiers on thirteen real-life events logs.


title: An Efficient Anchor-free Universal Lesion Detection in CT-scans

abstract:
  Existing universal lesion detection (ULD) methods utilize compute-intensive
anchor-based architectures which rely on predefined anchor boxes, resulting in
unsatisfactory detection performance, especially in small and mid-sized
lesions. Further, these default fixed anchor-sizes and ratios do not generalize
well to different datasets. Therefore, we propose a robust one-stage
anchor-free lesion detection network that can perform well across varying
lesions sizes by exploiting the fact that the box predictions can be sorted for
relevance based on their center rather than their overlap with the object.
Furthermore, we demonstrate that the ULD can be improved by explicitly
providing it the domain-specific information in the form of multi-intensity
images generated using multiple HU windows, followed by self-attention based
feature-fusion and backbone initialization using weights learned via
self-supervision over CT-scans. We obtain comparable results to the
state-of-the-art methods, achieving an overall sensitivity of 86.05% on the
DeepLesion dataset, which comprises of approximately 32K CT-scans with lesions
annotated across various body organs.


title: Event-triggered Observability: A Set-membership Perspective

abstract:
  This work attempts to discuss the observability of linear time-invariant
systems with event-triggered measurements. A new notion of observability,
namely, $\epsilon$-observability is defined with parameter $\epsilon$, which
relates to the worst-case performance of inferring the initial state based on
not only the received measurement but also the implicit information in the
event-triggering conditions at no-event instants. A criterion is developed to
test the proposed $\epsilon$-observability of discrete-time linear systems,
based on which an iterative event-triggered set-membership observer is designed
to evaluate a set containing all possible values of the state. The proposed
set-membership observer is designed as the outer approximation of the
ellipsoids predicted based on previous state estimates and the ellipsoids
inferred by fusing the received measurement and communication conditions, which
is optimal in the sense of trace at each step and is proved to be
asymptotically bounded. The efficiency of the proposed event-triggered
set-membership state observer is verified by numerical experiments.


title: A bound on the cosmic opacity of unparticles from the CMB temperature

abstract:
  Unparticle cosmology gives an unconventional outlook on the dark sector of
cosmology, increasingly challenging $\Lambda\mbox{CDM}$ by $H_0$-tension. This
model derives from a finite temperature broken conformal symmetry of radiation,
described by a non-radiative correction with unknown sign in energy density.
This symmetry breaking has a sign ambiguity, in which corrections about the IR
fixed point are normal or tachyonic. While the first has been ruled out in a
recent study, the latter possesses a late-time temperature $T_c\simeq
4\,T_{CMB}$ associated with $\Omega_{\cal U}\simeq 1$ ($\Omega_{\cal U} \simeq
10^4 \Omega_{CMB}$), where $T_{CMB}$ denotes the temperature of the Cosmic
Microwave Background (CMB). The CMB is hereby exposed to an enormous heat bath.
The age of the Universe constrained by the globular clusters, independently
puts a constraint on any heat exchange from unparticles to the CMB in
$\Lambda\mbox{CDM}$. Accordingly, we estimate the cross section of unparticles
interactions with CMB photons to be $\sigma_{\gamma \mathcal{U}} \lesssim
10^{-40}\ \mbox{m}^2=10^{-3}\ \mbox{nb}$. This bound is in the midway of
photon-neutrino and photon-photon cross sections. It puts unparticles in
late-time cosmology, if present, at the edge of the standard model.


title:
Novel topological phases of a semi-Dirac Chern insulator in presence of
  extended range hopping

abstract:
  We study topological properties and the topological phase transitions therein
for a semi-Dirac Haldane model on a honeycomb lattice in presence of an
extended range (third neighbour) hopping. While in the absence of a third
neighbour hopping, $t_3$, the system exhibits gapless electronic spectrum, its
presence creates an energy gap in the dispersion. However, the nature of the
spectral gap, that is, whether it is trivial or topological needs to be
ascertained. We find that the answer depends on the value of $t_3$, and its
interplay with the value of the onsite potential that breaks the sublattice
symmetry, namely, Semenoff mass ($\Delta$). To elucidate our findings on the
topological phases, we demonstrate two kinds of phase diagrams using the
available parameter space, one in which the phases are shown in the
$\Delta$-$t_3$ plane, and the other one in a more familiar $\Delta$-$\phi$
plane ($\phi$ being the Haldane flux). The phase diagrams depict the presence
of Chern insulating lobes comprising of Chern numbers $\pm2$ and $\pm1$ for
certain values of $t_3$, along with trivial insulating regions (zero Chern
number). Thus there are phase transitions from one topological regime to
another which are characterized by abrupt changes in the values of the Chern
number. To support the existence of the topological phases, we compute the
counter-propagating chiral edge modes in a ribbon geometry. Finally, the
anomalous Hall conductivity shows plateaus either at $e^2/h$ or $2e^2/h$
corresponding to these topological phases.


title: Tensor denoising of high-dimensional MRI data

abstract:
  The signal to noise ratio (SNR) fundamentally limits the information
accessible by magnetic resonance imaging (MRI). This limitation has been
addressed by a host of denoising techniques, recently including so-called
MPPCA: Principal component analysis (PCA) of the signal followed by automated
rank estimation, exploiting the Marchenko-Pastur (MP) distribution of noise
singular values. Operating on matrices comprised by data-patches, this popular
approach objectively identifies noise components and, ideally, allows noise to
be removed without introducing artifacts such as image blurring or non-local
averaging. The MPPCA rank estimation, however, relies on a large number of
noise singular values relative to the number of signal components to avoid such
ill effects. This condition is unlikely to be met when data-patches and
therefore matrices are small, for example due to spatially varying noise. Here,
we introduce tensor MPPCA (tMPPCA) for the purpose of denoising
multidimensional data, for example from multi-contrast acquisitions. Rather
than combining dimensions in matrices, tMPPCA utilizes each dimension of the
multidimensional data's inherent tensor-structure to better characterize noise,
and to recursively estimate signal components. Relative to matrix-based MPPCA,
tMPPCA requires no additional assumptions, and comparing the two in a numerical
phantom and a multi-TE diffusion MRI dataset, tMPPCA dramatically improves
denoising performance. This is particularly true for small data-patches, which
we believe will improve denoising in cases of spatially varying noise.


title:
Volkov-Pankratov states in a 2d material: excited states of a structural
  soliton

abstract:
  We show that partial dislocations, defects that naturally arise in bilayer
graphene, host an analogue of the mass inversion Volkov-Pankratov (VP) states,
the spectrum of excited states at a topologically non-trivial interface. In
contrast to the dislocation states arising from the change in valley Chern
index, such states (i) exist both with and without an interlayer bias, i.e. for
metallic as well as insulating bulk and (ii) have distinct electron and hole
bound states, whose number is proportional to the dislocation width. Recently
discovered at heterojunctions of 3d topological insulators, their existence at
the partial dislocations of a 2d material opens a rich structural playground
for their investigation, and we demonstrate that the dislocation type, i.e.
screw or edge, as well as the dislocation width, both play a decisive role in
the creation of a topological spectrum of exited states.


title: Asymmetric Proxy Loss for Multi-View Acoustic Word Embeddings

abstract:
  Acoustic word embeddings (AWEs) are discriminative representations of speech
segments, and learned embedding space reflects the phonetic similarity between
words. With multi-view learning, where text labels are considered as
supplementary input, AWEs are jointly trained with acoustically grounded word
embeddings (AGWEs). In this paper, we expand the multi-view approach into a
proxy-based framework for deep metric learning by equating AGWEs with proxies.
A simple modification in computing the similarity matrix allows the general
pair weighting to formulate the data-to-proxy relationship. Under the
systematized framework, we propose an asymmetric-proxy loss that combines
different parts of loss functions asymmetrically while keeping their merits. It
follows the assumptions that the optimal function for anchor-positive pairs may
differ from one for anchor-negative pairs, and a proxy may have a different
impact when it substitutes for different positions in the triplet. We present
comparative experiments with various proxy-based losses including our
asymmetric-proxy loss, and evaluate AWEs and AGWEs for word discrimination
tasks on WSJ corpus. The results demonstrate the effectiveness of the proposed
method.


title:
On eigenfunctions and maximal cliques of generalised Paley graphs of
  square order

abstract:
  Let $GP(q^2,m)$ be the $m$-Paley graph defined on the finite field with order
$q^2$. We study eigenfunctions and maximal cliques in generalised Paley graphs
$GP(q^2,m)$, where $m \mid (q+1)$. In particular, we explicitly construct
maximal cliques of size $\frac{q+1}{m}$ or $\frac{q+1}{m}+1$ in $GP(q^2,m)$,
and show the weight-distribution bound on the cardinality of the support of an
eigenfunction is tight for the smallest eigenvalue $-\frac{q+1}{m}$ of
$GP(q^2,m)$. These new results extend the work of Baker et. al and Goryainov et
al. on Paley graphs of square order. We also study the stability of the
Erd\H{o}s-Ko-Rado theorem for $GP(q^2,m)$ (first proved by Sziklai).


title:
Using Adapters to Overcome Catastrophic Forgetting in End-to-End
  Automatic Speech Recognition

abstract:
  Learning a set of tasks in sequence remains a challenge for Artificial Neural
Networks, which, in such scenarios, tend to suffer from Catastrophic Forgetting
(CF). The same applies to End-to-End (E2E) Automatic Speech Recognition (ASR)
models, even for monolingual tasks. In this paper, we aim to overcome CF for
E2E ASR by inserting adapters, small architectures of few parameters which
allow a general model to be fine-tuned to a specific task, into our model. We
make these adapters task-specific, while either freezing or regularizing the
parameters of the model shared by all tasks, thus stimulating the model to
fully exploit the adapter parameters while keeping the shared parameters such
that they work well for all tasks. Our best method is able to close the gap
between worst case (naively fine-tuning) and best case (jointly training on all
tasks) by more than 75% after a set of four monolingual tasks.


title:
Self-interaction in a cosmic dark fluid: The four-kernel rheological
  extension of the equations of state

abstract:
  We establish a new self-consistent model of coupling between the cosmic dark
energy and dark matter in the framework of the rheological approach, which is
based on the representation of the equations of state in terms of integral
operators of the Volterra-type. We elaborate the so-called four-kernel model,
in the framework of which both the dark energy and dark matter pressures are
presented by two integrals containing the energy densities of the dark energy
and dark matter. For the Volterra operators, the kernels of which are
associated with the effects of fading memory, the corresponding isotropic
homogeneous cosmological model is shown to be exactly integrable. We consider
the classification of the model exact solutions, based on the analysis of roots
of the characteristic polynomial associated with the key equation of the
presented model. The scalars of the pressure and energy-density of the dark
energy and dark matter, the Hubble function and acceleration parameter are
presented explicitly as the functions of the dimensionless scale factor. The
scale factor as the function of the cosmological time is found in quadratures
and is described analytically, qualitatively and numerically. Asymptotic
analysis allowed us to classify the models with respect to behavior typical for
the Big Rip, Little Rip and Pseudo Rip (de Sitter type). Two intriguing exact
cosmological solutions are discussed, which describe the super-exponential
expansion and the symmetric bounce. New solutions are presented, which
correspond to the quasi-periodic behavior of the state functions of the dark
fluid and of the geometric characteristics of the Universe.


title:
STRPM: A Spatiotemporal Residual Predictive Model for High-Resolution
  Video Prediction

abstract:
  Although many video prediction methods have obtained good performance in
low-resolution (64$\sim$128) videos, predictive models for high-resolution
(512$\sim$4K) videos have not been fully explored yet, which are more
meaningful due to the increasing demand for high-quality videos. Compared with
low-resolution videos, high-resolution videos contain richer appearance
(spatial) information and more complex motion (temporal) information. In this
paper, we propose a Spatiotemporal Residual Predictive Model (STRPM) for
high-resolution video prediction. On the one hand, we propose a Spatiotemporal
Encoding-Decoding Scheme to preserve more spatiotemporal information for
high-resolution videos. In this way, the appearance details for each frame can
be greatly preserved. On the other hand, we design a Residual Predictive Memory
(RPM) which focuses on modeling the spatiotemporal residual features (STRF)
between previous and future frames instead of the whole frame, which can
greatly help capture the complex motion information in high-resolution videos.
In addition, the proposed RPM can supervise the spatial encoder and temporal
encoder to extract different features in the spatial domain and the temporal
domain, respectively. Moreover, the proposed model is trained using generative
adversarial networks (GANs) with a learned perceptual loss (LP-loss) to improve
the perceptual quality of the predictions. Experimental results show that STRPM
can generate more satisfactory results compared with various existing methods.


title:
Combination of Time-domain, Frequency-domain, and Cepstral-domain
  Acoustic Features for Speech Commands Classification

abstract:
  In speech-related classification tasks, frequency-domain acoustic features
such as logarithmic Mel-filter bank coefficients (FBANK) and cepstral-domain
acoustic features such as Mel-frequency cepstral coefficients (MFCC) are often
used. However, time-domain features perform more effectively in some sound
classification tasks which contain non-vocal or weakly speech-related sounds.
We previously proposed a feature called bit sequence representation (BSR),
which is a time-domain binary acoustic feature based on the raw waveform.
Compared with MFCC, BSR performed better in environmental sound detection and
showed comparable accuracy performance in limited-vocabulary speech recognition
tasks. In this paper, we propose a novel improvement BSR feature called
BSR-float16 to represent floating-point values more precisely. We
experimentally demonstrated the complementarity among time-domain,
frequency-domain, and cepstral-domain features using a dataset called Speech
Commands proposed by Google. Therefore, we used a simple back-end score fusion
method to improve the final classification accuracy. The fusion results also
showed better noise robustness.


title: A note on the large values of $|\zeta^{(\ell)}(1+{\rm i}t)|$

abstract:
  We investigate the large values of the derivatives of the Riemann zeta
function $\zeta(s)$ on the 1-line. We give a larger lower bound for
$\max_{t\in[T,2T]}|\zeta^{(\ell)}(1+{\rm i} t)|$, which improves the previous
result established by Yang.


title:
Polarized deep diffractive neural network for classification,
  generation, multiplexing and de-multiplexing of orbital angular momentum
  modes

abstract:
  The multiplexing and de-multiplexing of orbital angular momentum (OAM) beams
are critical issues in optical communication. Optical diffractive neural
networks have been introduced to perform classification, generation,
multiplexing and de-multiplexing of OAM beams. However, conventional
diffractive neural networks cannot handle OAM modes with a varying spatial
distribution of polarization directions. Herein, we propose a polarized optical
deep diffractive neural network that is designed based on the concept of
rectangular micro-structure meta-material. Our proposed polarized optical
diffractive neural network is trained to classify, generate, multiplex and
de-multiplex polarized OAM beams.The simulation results show that our network
framework can successfully classify 14 kinds of orthogonally polarized vortex
beams and de-multiplex the hybrid OAM beams into Gauss beams at two, three and
four spatial positions respectively. 6 polarized OAM beams with identical total
intensity and 8 cylinder vector beams with different topology charges also have
been classified effectively. Additionally, results reveal that the network can
generate hybrid OAM beams with high quality and multiplex two polarized linear
beams into 8 kinds of cylinder vector beams.


title: Kleene Star of the Primes is not Regular in Any Base

abstract:
  Let $P_b$ denote the primes expressed in base-$b$. In this note, we prove
that $P_b^*$ is not regular. This strengthens a classical result that $P_b$ is
not regular, due to Minsky and Papert in 1966.


title: Omni-DETR: Omni-Supervised Object Detection with Transformers

abstract:
  We consider the problem of omni-supervised object detection, which can use
unlabeled, fully labeled and weakly labeled annotations, such as image tags,
counts, points, etc., for object detection. This is enabled by a unified
architecture, Omni-DETR, based on the recent progress on student-teacher
framework and end-to-end transformer based object detection. Under this unified
architecture, different types of weak labels can be leveraged to generate
accurate pseudo labels, by a bipartite matching based filtering mechanism, for
the model to learn. In the experiments, Omni-DETR has achieved state-of-the-art
results on multiple datasets and settings. And we have found that weak
annotations can help to improve detection performance and a mixture of them can
achieve a better trade-off between annotation cost and accuracy than the
standard complete annotation. These findings could encourage larger object
detection datasets with mixture annotations. The code is available at
https://github.com/amazon-research/omni-detr.


title:
A simple suboptimal moving horizon estimation scheme with guaranteed
  robust stability

abstract:
  We propose a suboptimal moving horizon estimation (MHE) scheme for a general
class of nonlinear systems. To this end, we consider an MHE formulation that
optimizes over the trajectory of a robustly stable observer. Assuming that the
observer admits a Lyapunov function, we show that this Lyapunov function is an
M-step Lyapunov function for suboptimal MHE. The presented sufficient
conditions can be easily verified in practice. We illustrate the practicability
of the proposed suboptimal MHE scheme with a standard nonlinear benchmark
example. Here, performing a single iteration is sufficient to significantly
improve the observer's estimation results under valid theoretical guarantees.


title: A Continuum of Physics-Based Lithium-Ion Battery Models Reviewed

abstract:
  Physics-based electrochemical battery models derived from porous electrode
theory are a very powerful tool for understanding lithium-ion batteries, as
well as for improving their design and management. Different model fidelity,
and thus model complexity, is needed for different applications. For example,
in battery design we can afford longer computational times and the use of
powerful computers, while for real-time battery control (e.g. in electric
vehicles) we need to perform very fast calculations using simple devices. For
this reason, simplified models that retain most of the features at a lower
computational cost are widely used. Even though in the literature we often find
these simplified models posed independently, leading to inconsistencies between
models, they can actually be derived from more complicated models using a
unified and systematic framework. In this review, we showcase this reductive
framework, starting from a high-fidelity microscale model and reducing it all
the way down to the Single Particle Model (SPM), deriving in the process other
common models, such as the Doyle-Fuller-Newman (DFN) model. We also provide a
critical discussion on the advantages and shortcomings of each of the models,
which can aid model selection for a particular application. Finally, we provide
an overview of possible extensions to the models, with a special focus on
thermal models. Any of these extensions could be incorporated into the
microscale model and the reductive framework re-applied to lead to a new
generation of simplified, multi-physics models.


title: Global Tracking via Ensemble of Local Trackers

abstract:
  The crux of long-term tracking lies in the difficulty of tracking the target
with discontinuous moving caused by out-of-view or occlusion. Existing
long-term tracking methods follow two typical strategies. The first strategy
employs a local tracker to perform smooth tracking and uses another re-detector
to detect the target when the target is lost. While it can exploit the temporal
context like historical appearances and locations of the target, a potential
limitation of such strategy is that the local tracker tends to misidentify a
nearby distractor as the target instead of activating the re-detector when the
real target is out of view. The other long-term tracking strategy tracks the
target in the entire image globally instead of local tracking based on the
previous tracking results. Unfortunately, such global tracking strategy cannot
leverage the temporal context effectively. In this work, we combine the
advantages of both strategies: tracking the target in a global view while
exploiting the temporal context. Specifically, we perform global tracking via
ensemble of local trackers spreading the full image. The smooth moving of the
target can be handled steadily by one local tracker. When the local tracker
accidentally loses the target due to suddenly discontinuous moving, another
local tracker close to the target is then activated and can readily take over
the tracking to locate the target. While the activated local tracker performs
tracking locally by leveraging the temporal context, the ensemble of local
trackers renders our model the global view for tracking. Extensive experiments
on six datasets demonstrate that our method performs favorably against
state-of-the-art algorithms.


title:
Beamforming Optimization for Active Intelligent Reflecting Surface-Aided
  SWIPT

abstract:
  In this paper, we study an active IRS-aided simultaneous wireless information
and power transfer (SWIPT) system. Specifically, an active IRS is deployed to
assist a multi-antenna access point (AP) to convey information and energy
simultaneously to multiple single-antenna information users (IUs) and energy
users (EUs). Two joint transmit and reflect beamforming optimization problems
are investigated with different practical objectives. The first problem
maximizes the weighted sum-power harvested by the EUs subject to individual
signal-to-interference-plus-noise ratio (SINR) constraints at the IUs, while
the second problem maximizes the weighted sum-rate of the IUs subject to
individual energy harvesting (EH) constraints at the EUs. The optimization
problems are non-convex and difficult to solve optimally. To tackle these two
problems, we first rigorously prove that dedicated energy beams are not
required for their corresponding semidefinite relaxation (SDR) reformulations
and the SDR is tight for the first problem, thus greatly simplifying the AP
precoding design. Then, by capitalizing on the techniques of alternating
optimization (AO), SDR, and successive convex approximation (SCA),
computationally efficient algorithms are developed to obtain suboptimal
solutions of the resulting optimization problems. Simulation results
demonstrate that, given the same total system power budget, significant
performance gains in terms of operating range of wireless power transfer (WPT),
total harvested energy, as well as achievable rate can be obtained by our
proposed designs over benchmark schemes (especially the one adopting a passive
IRS). Moreover, it is advisable to deploy an active IRS in the proximity of the
users for the effective operation of WPT/SWIPT.


title:
Computing critical points for algebraic systems defined by
  hyperoctahedral invariant polynomials

abstract:
  Let $\mathbb{K}$ be a field and $\mathbb{K}[x_1, \dots, x_n]$ be a
multivariate polynomial ring. Given a sequence of polynomials $\mathbf{f} =
(f_1, \dots, f_s)$ and a polynomial $\phi$ in $\mathbb{K}[x_1, \dots, x_n]$
with $s<n$, we consider the problem of computing the set $W(\phi, \mathbf{f})$
of points at which $\mathbf{f}$ vanishes and the Jacobian matrix of
$\mathbf{f}, \phi$ with respect to $x_1, \dots, x_n$ does not have full rank.
This plays an essential role in many application areas.
  In this paper we focus on an important class of polynomials, the ones
invariant under the action of the signed symmetric group $B_n$. We introduce a
notion called {\em hyperoctahedral representation} to represent $B_n$-invariant
sets. We study the invariance properties of the input to split $W(\phi,
\mathbf{f})$ according to the orbits of $B_n$ and then we design an algorithm
whose output being a {hyperoctahedral representation} of $W(\phi, \mathbf{f})$.
The runtime of our algorithm is polynomial in the total number of points
described by the output.


title:
OLxPBench: Real-time, Semantically Consistent, and Domain-specific are
  Essential in Benchmarking, Designing, and Implementing HTAP Systems

abstract:
  As real-time analysis of the new data become increasingly compelling, more
organizations deploy Hybrid Transactional/Analytical Processing (HTAP) systems
to support real-time queries on data recently generated by online transaction
processing. This paper argues that real-time queries, semantically consistent
schema, and domain-specific workloads are essential in benchmarking, designing,
and implementing HTAP systems. However, most state-of-the-art and
state-of-the-practice benchmarks ignore those critical factors. Hence, they are
incommensurable and, at worst, misleading in benchmarking, designing, and
implementing HTAP systems. This paper presents OLxPBench, a composite HTAP
benchmark suite. OLxPBench proposes: (1) the abstraction of a hybrid
transaction, performing a real-time query in-between an online transaction, to
model widely-observed behavior pattern -- making a quick decision while
consulting real-time analysis; (2) a semantically consistent schema to express
the relationships between OLTP and OLAP schema; (3) the combination of
domain-specific and general benchmarks to characterize diverse application
scenarios with varying resource demands. Our evaluations justify the three
design decisions of OLxPBench and pinpoint the bottlenecks of two mainstream
distributed HTAP DBMSs. International Open Benchmark Council (BenchCouncil)
sets up the OLxPBench homepage at https://www.benchcouncil.org/olxpbench/. Its
source code is available from https://github.com/BenchCouncil/olxpbench.git.


abstract:
  In this paper we prove Strichartz estimates for the Dirac equation on
asymptotically flat manifolds. The proof combines the weak dispersive estimates
proved by the first two authors with the Strichartz and smoothing estimates for
the wave and Klein-Gordon flows, exploiting some recent results in the same
geometrical setting.


title:
Neighbor Enhanced Graph Convolutional Networks for Node Classification
  and Recommendation

abstract:
  The recently proposed Graph Convolutional Networks (GCNs) have achieved
significantly superior performance on various graph-related tasks, such as node
classification and recommendation. However, currently researches on GCN models
usually recursively aggregate the information from all the neighbors or
randomly sampled neighbor subsets, without explicitly identifying whether the
aggregated neighbors provide useful information during the graph convolution.
In this paper, we theoretically analyze the affection of the neighbor quality
over GCN models' performance and propose the Neighbor Enhanced Graph
Convolutional Network (NEGCN) framework to boost the performance of existing
GCN models. Our contribution is three-fold. First, we at the first time propose
the concept of neighbor quality for both node classification and recommendation
tasks in a general theoretical framework. Specifically, for node
classification, we propose three propositions to theoretically analyze how the
neighbor quality affects the node classification performance of GCN models.
Second, based on the three proposed propositions, we introduce the graph
refinement process including specially designed neighbor evaluation methods to
increase the neighbor quality so as to boost both the node classification and
recommendation tasks. Third, we conduct extensive node classification and
recommendation experiments on several benchmark datasets. The experimental
results verify that our proposed NEGCN framework can significantly enhance the
performance for various typical GCN models on both node classification and
recommendation tasks.


title:
ALDO2, a multi-function rad-hard linear regulator for SiPM-based HEP
  detectors

abstract:
  ALDO2 is a multi-function, adjustable, low dropout linear regulator designed
in onsemi I3T80 0.35~$\mu$m HV CMOS technology for use in HEP detectors that
adopt silicon photomultipliers (SiPMs). The chip features four independent
regulators, two low voltage channels (max 3.3 V) used to filter and stabilize
the power supply of front-end chips, and two HV channels (max 70 V),
specifically designed to provide the bias voltage to arrays of SiPMs. Each
regulator can be independently shut down and is protected for over-current and
over-temperature. The HV regulators also implement a circuit to monitor the
bias current of the SiPM arrays, allowing to perform I-V curves and thus to
fine-tune the working point of the SiPM arrays during the detector lifetime.
The chip adopts radiation hardening techniques and has been fully qualified up
to a TID of 20 Mrad, a 1-MeV-equivalent neutron fluence of $10^{15}$ cm$^{-2}$,
and with heavy ions up to 40 MeV cm$^2$ mg$^{-1}$ LET and $10^{10}$ cm$^{-2}$
cumulative fluence. The chip will be installed in two CMS detectors in the
HL-LHC phase, the Barrel Timing Detector (BTL) and the High Granularity
Calorimeter (HGCAL).


title:
Energy-Efficient IRS-Aided NOMA Beamforming for 6G Wireless
  Communications

abstract:
  This manuscript presents an energy-efficient alternating optimization
framework based on intelligent reflective surfaces (IRS) aided non-orthogonal
multiple access beamforming (NOMA-BF) system for 6G wireless communications.
Specifically, this work proposes a centralized IRS-enabled design for the
NOMA-BF system to optimize the active beamforming and power allocation
coefficient (PAC) of users at the transmitter in the first stage and passive
beamforming at IRS in the 2nd stage to maximize the energy efficiency (EE) of
the network. However, an increment in the number of supportable users with the
NOMA-BF system will lead to NOMA user interference and inter-cluster
interference (ICI). To mitigate the effect of ICI, first zero-forcing
beamforming along with efficient user clustering algorithm is exploited and
then NOMA user interference is tackled efficiently through a proposed iterative
algorithm that computes PAC of NOMA user through simplified closed-form
expression under the required system constraints. In the 2nd stage, the problem
of passive beamforming is solved through a technique based on
difference-of-convex (DC) programming and successive convex approximation
(SCA). Simulation results demonstrate that the proposed alternating framework
for energy-efficient IRS-assisted NOMA-BF system can achieve convergence within
a few iterations and provide efficient performance in terms of EE of the system
with low complexity.


title:
Adaptive Private-K-Selection with Adaptive K and Application to
  Multi-label PATE

abstract:
  We provide an end-to-end Renyi DP based-framework for differentially private
top-$k$ selection. Unlike previous approaches, which require a data-independent
choice on $k$, we propose to privately release a data-dependent choice of $k$
such that the gap between $k$-th and the $(k+1)$st "quality" is large. This is
achieved by a novel application of the Report-Noisy-Max. Not only does this
eliminate one hyperparameter, the adaptive choice of $k$ also certifies the
stability of the top-$k$ indices in the unordered set so we can release them
using a variant of propose-test-release (PTR) without adding noise. We show
that our construction improves the privacy-utility trade-offs compared to the
previous top-$k$ selection algorithms theoretically and empirically.
Additionally, we apply our algorithm to "Private Aggregation of Teacher
Ensembles (PATE)" in multi-label classification tasks with a large number of
labels and show that it leads to significant performance gains.


title: Quantum polarization mapping of nitrogen-vacancy centers in diamond

abstract:
  Qubit technologies using nitrogen-vacancy color centers in diamond require
precise knowledge of the centers, including the number of emitters within a
diffraction-limited spot and their orientation. However, the number of emitters
is challenging to determine when there is finite background, which affects the
precision of resulting quantum protocols. Here we show the photoluminescence
(PL) intensity and quantum correlation (Hanbury Brown and Twiss) measurements
as a function of polarization for one- and two-emitter systems. The sample was
made by implanting low concentrations of adenine (C5H5N5) into a low nitrogen
chemical vapor deposition diamond. This approach yielded well-spaced regions
with few nitrogen-vacancy centers. By mapping the PL intensity and quantum
correlation as a function of polarization, we can distinguish two emitter
systems from single emitters with background, providing a method to quantify
the background signal at implanted sites, which might be different from
off-site background levels. This approach provides a valuable new mechanism for
characterizing few emitter systems useful for quantum sensing, communication,
and computation tasks.


title:
Continual Normalization: Rethinking Batch Normalization for Online
  Continual Learning

abstract:
  Existing continual learning methods use Batch Normalization (BN) to
facilitate training and improve generalization across tasks. However, the
non-i.i.d and non-stationary nature of continual learning data, especially in
the online setting, amplify the discrepancy between training and testing in BN
and hinder the performance of older tasks. In this work, we study the
cross-task normalization effect of BN in online continual learning where BN
normalizes the testing data using moments biased towards the current task,
resulting in higher catastrophic forgetting. This limitation motivates us to
propose a simple yet effective method that we call Continual Normalization (CN)
to facilitate training similar to BN while mitigating its negative effect.
Extensive experiments on different continual learning algorithms and online
scenarios show that CN is a direct replacement for BN and can provide
substantial performance improvements. Our implementation is available at
\url{https://github.com/phquang/Continual-Normalization}.


title: Virtually expanding dynamics

abstract:
  We introduce a class of discrete dynamical systems that we call
\emph{virtually expanding}. This is an open subset of self-covering maps on a
closed manifold which contains all expanding maps and some partially hyperbolic
volume-expanding maps. We show that the Perron-Frobenius operator is
quasi-compact on a Sobolev space of positive order for such class of dynamical
systems.


title:
Improving Distortion Robustness of Self-supervised Speech Processing
  Tasks with Domain Adaptation

abstract:
  Speech distortions are a long-standing problem that degrades the performance
of supervisely trained speech processing models. It is high time that we
enhance the robustness of speech processing models to obtain good performance
when encountering speech distortions while not hurting the original performance
on clean speech. In this work, we propose to improve the robustness of speech
processing models by domain adversarial training (DAT). We conducted
experiments based on the SUPERB framework on five different speech processing
tasks. In case we do not always have knowledge of the distortion types for
speech data, we analyzed the binary-domain and multi-domain settings, where the
former treats all distorted speech as one domain, and the latter views
different distortions as different domains. In contrast to supervised training
methods, we obtained promising results in target domains where speech data is
distorted with different distortions including new unseen distortions
introduced during testing.


title: A family of triangulated 3-spheres constructed from trees

abstract:
  The search for universality in random triangulations of manifolds, like those
featuring in (Euclidean) Dynamical Triangulations, is central to the random
geometry approach to quantum gravity. In case of the 3-sphere, or any other
manifold of dimension greater than two for that matter, the pursuit is held
back by serious challenges, including the wide open problem of enumerating
triangulations. In an attempt to bypass the toughest challenges we identify a
restricted family of triangulations, of which the enumeration appears less
daunting. In a nutshell, the family consists of triangulated 3-spheres
decorated with a pair of trees, one spanning its tetrahedra and the other its
vertices, with the requirement that after removal of both trees one is left
with a tree-like 2-complex. We prove that these are in bijection with a
combinatorial family of triples of plane trees, satisfying restrictions that
can be succinctly formulated at the level of planar maps. An important
ingredient in the bijection is a step-by-step reconstruction of the
triangulations from triples of trees, that results in a natural subset of the
so-called locally constructible triangulations, for which spherical topology is
guaranteed, through a restriction of the allowed moves. We also provide an
alternative characterization of the family in the framework of discrete Morse
gradients. Finally, several exponential enumerative bounds are deduced from the
triples of trees and some simulation results are presented.


title:
Contribution of the Temperature of the Objects to the Problem of Thermal
  Imaging Focusing

abstract:
  When focusing an image, depth of field, aperture and distance from the camera
to the object, must be taking into account, both, in visible and in infrared
spectrum. Our experiments reveal that in addition, the focusing problem in
thermal spectrum is also hardly dependent of the temperature of the object
itself (and/or the scene).


title: Preliminary experiments on thermal emissivity adjustment for face images

abstract:
  In this paper we summarize several applications based on thermal imaging. We
emphasize the importance of emissivity adjustment for a proper temperature
measurement. A new set of face images acquired at different emissivity values
with steps of 0.01 is also presented and will be distributed for free for
research purposes. Among the utilities, we can mention: a) the possibility to
apply corrections once an image is acquired with a wrong emissivity value and
it is not possible to acquire a new one; b) privacy protection in thermal
images, which can be obtained with a low emissivity factor, which is still
suitable for several applications, but hides the identity of a user; c) image
processing for improving temperature detection in scenes containing objects of
different emissivity.


title: Optimal reinsurance under terminal value constraints

abstract:
  Optimal reinsurance is a perennial problem in insurance. The problem
formulation considered in this paper is closely connected to the optimal
portfolio problem in finance, with some important distinctions. In particular,
the surplus of an insurance company is routinely approximated by a Brownian
motion, as opposed to the geometric Brownian motion used to model assets in
finance. Furthermore, exposure to risk is controlled "downwards" via
reinsurance, rather than "upwards" via risky investments. This leads to
interesting qualitative differences in the optimal solutions.
  In this paper, using the martingale method, we derive the optimal
proportional, non cheap reinsurance control that maximises the quadratic
utility of the terminal value of the insurance surplus. We also consider a
number of realistic constraints on the terminal value: a strict lower boundary,
the probability (Value at Risk) constraint, and the expected shortfall
(conditional Value at Risk) constraints under the $\mathbb{P}$ and $\mathbb{Q}$
measures, respectively. Comparison of the optimal strategies with the optimal
solutions in finance are of particular interest. Results are illustrated.


title: Well-posedness of solutions to stochastic fluid-structure interaction

abstract:
  In this paper we introduce a constructive approach to study well-posedness of
solutions to stochastic fluid-structure interaction with stochastic noise. We
focus on a benchmark problem in stochastic fluid-structure interaction, and
prove the existence of a unique weak solution in the probabilistically strong
sense. The benchmark problem consists of the 2D time-dependent Stokes equations
describing the flow of an incompressible, viscous fluid interacting with a
linearly elastic membrane modeled by the 1D linear wave equation. The membrane
is stochastically forced by the time-dependent white noise. The fluid and the
structure are linearly coupled. The constructive existence proof is based on a
time-discretization via an operator splitting approach. This introduces a
sequence of approximate solutions, which are random variables. We show the
existence of a subsequence of approximate solutions which converges, almost
surely, to a weak solution in the probabilistically strong sense. The proof is
based on uniform energy estimates in terms of the expectation of the energy
norms, which are the backbone for a weak compactness argument giving rise to a
weakly convergent subsequence of probability measures associated with the
approximate solutions. Probabilistic techniques based on the Skorohod
representation theorem and the Gy\"ongy-Krylov lemma are then employed to
obtain almost sure convergence of a subsequence of the random approximate
solutions to a weak solution in the probabilistically strong sense. The result
shows that the deterministic benchmark FSI model is robust to stochastic noise,
even in the presence of rough white noise in time. To the best of our
knowledge, this is the first well-posedness result for fully coupled stochastic
fluid-structure interaction.


title:
Weakly-supervised Temporal Path Representation Learning with Contrastive
  Curriculum Learning -- Extended Version

abstract:
  In step with the digitalization of transportation, we are witnessing a
growing range of path-based smart-city applications, e.g., travel-time
estimation and travel path ranking. A temporal path(TP) that includes temporal
information, e.g., departure time, into the path is fundamental to enable such
applications. In this setting, it is essential to learn generic temporal path
representations(TPRs) that consider spatial and temporal correlations
simultaneously and that can be used in different applications, i.e., downstream
tasks. Existing methods fail to achieve the goal since (i) supervised methods
require large amounts of task-specific labels when training and thus fail to
generalize the obtained TPRs to other tasks; (ii) through unsupervised methods
can learn generic representations, they disregard the temporal aspect, leading
to sub-optimal results. To contend with the limitations of existing solutions,
we propose a Weakly-Supervised Contrastive (WSC) learning model. We first
propose a temporal path encoder that encodes both the spatial and temporal
information of a temporal path into a TPR. To train the encoder, we introduce
weak labels that are easy and inexpensive to obtain and are relevant to
different tasks, e.g., temporal labels indicating peak vs. off-peak hours from
departure times. Based on the weak labels, we construct meaningful positive and
negative temporal path samples by considering both spatial and temporal
information, which facilities training the encoder using contrastive learning
by pulling closer to the positive samples' representations while pushing away
the negative samples' representations. To better guide contrastive learning, we
propose a learning strategy based on Curriculum Learning such that the learning
performs from easy to hard training instances. Experiments studies verify the
effectiveness of the proposed method.


title: Generic Laplace eigenfunctions on metric graphs

abstract:
  It is known, that up to certain pathalogies, a compact metric graph with
standard vertex conditions has a Baire generic set of choices of edge lengths
such that all Laplace eigenvalues are simple and have eigenfunctions that do
not vanish on vertices, \cite{Fri_ijm05,BerLiu_jmaa17}. We provide a new notion
of genericity, using subanalytic sets, that implies both Baire genericity and
full Lebesgue measure. We show that the previous results are also generic in
the stronger sense and that additionally the derivative of an eigenfunction do
not vanish at vertices as well. Moreover, we show that generically an
eigenfunction fails to satisfy any additional vertex condition. Finally, we
show that any two different metric graphs with the same edge lengths would not
share any non-zero eigenvalue, for a generic choice of lengths, except for two
cases of a common symmetry. At the end of the paper we address three open
conjectures for metric graphs that can benefit from the tools introduced in
this paper.


title: Schubert Problems, Positivity and Symbol Letters

abstract:
  We propose a geometrical approach to generate symbol letters of
amplitudes/integrals in planar $\mathcal{N}=4$ Super Yang-Mills theory, known
as {\it Schubert problems}. Beginning with one-loop integrals, we find that
intersections of lines in momentum twistor space are always ordered on a given
line, once the external kinematics $\mathbf{Z}$ is in the positive region
$G_+(4,n)$. Remarkably, cross-ratios of these ordered intersections on a line,
which are guaranteed to be positive now, nicely coincide with symbol letters of
corresponding Feynman integrals, whose positivity is then concluded directly
from such geometrical configurations. In particular, we reproduce from this
approach the $18$ multiplicative independent algebraic letters for $n=8$
amplitudes up to three loops. Finally, we generalize the discussion to two-loop
Schubert problems and, again from ordered points on a line, generate a new kind
of algebraic letters which mix two distinct square roots together. They have
been found recently in the alphabet of two-loop double-box integral with
$n\geq9$, and they are expected to appear in amplitudes at $k+\ell\geq4$.


title:
Quasi-Ergodicity of Transient Patterns in Stochastic Reaction-Diffusion
  Equations

abstract:
  We prove the existence and uniqueness of quasi-stationary and quasi-ergodic
measures for a class of reaction-diffusion equations perturbed by additive
cylindrical noise. To the author's knowledge, this is the first time that
quasi-stationarity \&~quasi-ergodicity of a stochastic partial differential
equation have been proven. Additionally, we prove a quasi-ergodic theorem, and
in a general setting, demonstrate that the rate of convergence to the
quasi-ergodic average in this theorem is, under certain conditions, exponential
in time. These results allow us to qualitatively characterize the behaviour of
solutions to these equations in neighbourhoods of an invariant manifold of the
corresponding deterministic equations at some large time $t>0$, conditioned on
remaining in the neighbourhood at time $t$. In contrast to other approaches
taken to the question of the long-term behaviour of stochastic dynamical
systems in general, and stochastic reaction-diffusion equations in particular,
the approach based on quasi-stationary measures works well outside of the
small-noise regime.


title: A Fast Transformer-based General-Purpose Lossless Compressor

abstract:
  Deep-learning-based compressor has received interests recently due to much
improved compression ratio. However, modern approaches suffer from long
execution time. To ease this problem, this paper targets on cutting down the
execution time of deep-learning-based compressors. Building
history-dependencies sequentially (e.g., recurrent neural networks) is
responsible for long inference latency. Instead, we introduce transformer into
deep learning compressors to build history-dependencies in parallel. However,
existing transformer is too heavy in computation and incompatible to
compression tasks.
  This paper proposes a fast general-purpose lossless compressor, TRACE, by
designing a compression-friendly structure based on a single-layer transformer.
We first design a new metric to advise the selection part of compression model
structures. Byte-grouping and Shared-ffn schemes are further proposed to fully
utilize the capacity of the single-layer transformer. These features allow
TRACE to achieve competitive compression ratio and a much faster speed. In
addition, we further accelerate the compression procedure by designing a
controller to reduce the parameter updating overhead. Experiments show that
TRACE achieves an overall $\sim$3x speedup while keeps a comparable compression
ratio to the state-of-the-art compressors. The source code for TRACE and links
to the datasets are available at
https://github.com/mynotwo/A-Fast-Transformer-based-General-Purpose-LosslessCompressor.


title: A remark on torsors for affine group schemes

abstract:
  We present an elementary proof of the fact that every torsor for an affine
group scheme over an algebraically closed field is trivial. This is related to
the uniqueness of fibre functors on neutral tannakian categories.


title:
Upper and lower bounds of convergence rates for strong solutions of the
  generalized Newtonian fluids with non-standard growth conditions

abstract:
  We consider the motion of an incompressible shear-thickening power-law-like
non-Newtonian fluid in $R^3$ with a variable power-law index. This system of
nonlinear partial differential equations arises in mathematical models of
electrorheological fluids. The aim of this paper is to investigate the
large-time behaviour of the difference $u-\tilde{u}$ where $u$ is a strong
solution of the given equations with the initial data $u_0$ and $\tilde{u}$ is
the strong solution of the same equations with perturbed initial data
$u_0+w_0$. The initial perturbation $w_0$ is not required to be small, but is
assumed to satisfy certain decay condition. In particular, we can show that
$(1+t)^{-\frac{\gamma}{2}}\lesssim
\|u(t)-\tilde{u}(t)\|_2\lesssim(1+t)^{-\frac{\gamma}{2}}$, for sufficiently
large $t>0$, where $\gamma\in(2,\frac{5}{2})$. The proof is based on the
observation that the solution of the linear heat equation describes the
asymptotic behaviour of the solutions of the electrorheological fluids well for
sufficiently large time $t>0$, and the generalized Fourier splitting method
with an iterative argument. Furthermore, it will also be discussed that the
argument used in the present paper can improve the previous results for the
generalized Newtonian fluids with a constant power-law index.


title: SIT: A Bionic and Non-Linear Neuron for Spiking Neural Network

abstract:
  Spiking Neural Networks (SNNs) have piqued researchers' interest because of
their capacity to process temporal information and low power consumption.
However, current state-of-the-art methods limited their biological plausibility
and performance because their neurons are generally built on the simple
Leaky-Integrate-and-Fire (LIF) model. Due to the high level of dynamic
complexity, modern neuron models have seldom been implemented in SNN practice.
In this study, we adopt the Phase Plane Analysis (PPA) technique, a technique
often utilized in neurodynamics field, to integrate a recent neuron model,
namely, the Izhikevich neuron. Based on the findings in the advancement of
neuroscience, the Izhikevich neuron model can be biologically plausible while
maintaining comparable computational cost with LIF neurons. By utilizing the
adopted PPA, we have accomplished putting neurons built with the modified
Izhikevich model into SNN practice, dubbed as the Standardized Izhikevich Tonic
(SIT) neuron. For performance, we evaluate the suggested technique for image
classification tasks in self-built LIF-and-SIT-consisted SNNs, named Hybrid
Neural Network (HNN) on static MNIST, Fashion-MNIST, CIFAR-10 datasets and
neuromorphic N-MNIST, CIFAR10-DVS, and DVS128 Gesture datasets. The
experimental results indicate that the suggested method achieves comparable
accuracy while exhibiting more biologically realistic behaviors on nearly all
test datasets, demonstrating the efficiency of this novel strategy in bridging
the gap between neurodynamics and SNN practice.


title: Seasonality, density dependence and spatial population synchrony

abstract:
  Spatial population synchrony has been the focus of theoretical and empirical
studies for decades, in the hopes of understanding mechanisms and interactions
driving ecological dynamics. In many systems, it is well-known that seasonality
plays a critical role in the density dependence structure of the populations,
yet this has hardly received any attention in synchrony studies. Here, we
propose a protocol that allows to elucidate deterministic and stochastic
sources of spatial synchrony, while accounting for geographic- and
season-specific density dependence. We apply our protocol to seasonally-sampled
time series of sub-arctic gray-sided voles, known for marked spatial synchrony.
Dissociating seasonal density-dependence contributions to the total observed
synchrony reveals differential strength and shape of synchrony patterns by
season. Mild winter weather reveals to be an important driver of vole spatial
synchrony, with lagged effects in the fall. This has direct implications to the
future population dynamics of such species when facing climate change.


title:
The nonuniformity of poleward flux transport on the solar surface: a
  statistical method applied to solar cycles 21-24

abstract:
  The poleward migration of the active regions' magnetic flux on the solar
surface plays an important role in the development of the large-scale field
development, especially the polar field reversal, which is a key process in the
Babcock-Leighton-type solar dynamos. The poleward flux transport is nonuniform,
centered around poleward surges as suggested by previous observations. The
strong, long-lasting surges are related to activity complexes, and often result
in violent polar field reversal. However, the nonuniformity of poleward flux
transport has not been evaluated quantitatively. We propose a statistical
method to analyze the poleward flux transport during solar cycles 21-24 by
considering the frequency distributions of the magnetic field at latitudes of
poleward surges occurrence during solar cycles. The nonuniformity is quantified
as the kurtosis statistics representing the tailedness of the distributions. We
test the method on results of surface flux transport simulations, and apply to
WSO, NSO, MWO, and HMI data. We confirm that the poleward surges are of
significance during solar cycles 21-24 in general. The kurtosis within a solar
cycle is affected by different latitudes of the magnetic field and different
data sources. The southern hemisphere of cycle 24 exhibits the largest
kurtosis, agreeing the super surge concept from previous work. The significant
nonuniformity of poleward flux transport originates from the nonrandomness of
active regions, which favors the activity complexes origin of poleward surges.


title:
Diffusion-free photon upconversion driven by intramolecular
  triplet-triplet annihilation in engineered conjugated chromophores

abstract:
  The photon upconversion based on sensitized triplet-triplet annihilation
(sTTA-UC) is a spin-flip mechanism exploited to recover the energy of dark
triplet states in conjugated systems. In this process a high-energy fluorescent
singlet is created through the collision and fusion of two low-energy triplets
belonging to different diffusing molecules. Its excellent yield in solution
under low excitation intensity and non-coherent light highlighted the huge
potential of sTTA-UC to provide a breakthrough in solar technologies. However,
its diffusion-limited bi-molecular nature limits its efficiency in the solid
state. To overcome this issue, we designed a molecular systems able to host
simultaneously more than one triplet, thus enabling a diffusion-free
intramolecular TTA. We obtain the first direct demonstration of intramolecular
triplet fusion by tailored photoluminescence spectroscopy experiments, thus
opening the way to realize a new family of single molecule upconverters with
huge potential in solar and lightening technologies by accessing the natural
triplets energy reservoir.


title:
Time Series Fault Classification for Wave Propagation Systems with
  Sparse Fault Data

abstract:
  In this work Time Series Classification techniques are investigated, and
especially their applicability in applications where there are significant
differences between the individuals where data is collected, and the
individuals where the classification is evaluated. Classification methods are
applied to a fault classification case, where a key assumption is that data
from a fault free reference case for each specific individual is available. For
the investigated application, wave propagation cause almost chaotic changes of
a measured pressure signal, and physical modeling is difficult. Direct
application of One-Nearest-Neighbor Dynamic Time Warping, a common technique
for this kind of problem, and other machine learning techniques are shown to
fail for this case and new methods to improve the situation are presented. By
using relative features describing the difference from the reference case
rather than the absolute time series, improvements are made compared to
state-of-the-art time series classification algorithms.


title:
Eff the ineffable: on the uncommunicability of a conceptually simple
  contribution to HCI methodology

abstract:
  Given a simple concept that has the potential for a methodological
contribution to the field of HCI, the constraints of submission conventions
within the field may make it impossible to communicate the concept in a manner
that is intelligible to the reader.


title:
An I/O-Efficient Disk-based Graph System for Scalable Second-Order
  Random Walk of Large Graphs

abstract:
  Random walk is widely used in many graph analysis tasks, especially the
first-order random walk. However, as a simplification of real-world problems,
the first-order random walk is poor at modeling higher-order structures in the
data. Recently, second-order random walk-based applications (e.g., Node2vec,
Second-order PageRank) have become attractive. Due to the complexity of the
second-order random walk models and memory limitations, it is not scalable to
run second-order random walk-based applications on a single machine. Existing
disk-based graph systems are only friendly to the first-order random walk
models and suffer from expensive disk I/Os when executing the second-order
random walks. This paper introduces an I/O-efficient disk-based graph system
for the scalable second-order random walk of large graphs, called GraSorw.
First, to eliminate massive light vertex I/Os, we develop a bi-block execution
engine that converts random I/Os into sequential I/Os by applying a new
triangular bi-block scheduling strategy, the bucket-based walk management, and
the skewed walk storage. Second, to improve the I/O utilization, we design a
learning-based block loading model to leverage the advantages of the full-load
and on-demand load methods. Finally, we conducted extensive experiments on six
large real datasets as well as several synthetic datasets. The empirical
results demonstrate that the end-to-end time cost of popular tasks in GraSorw
is reduced by more than one order of magnitude compared to the existing
disk-based graph systems.


title:
Enhancing AES Using Chaos and Logistic Map-Based Key Generation
  Technique for Securing IoT-Based Smart Home

abstract:
  The Internet of Things (IoT) has brought new ways for humans and machines to
communicate with each other over the internet. Though sensor-driven devices
have largely eased our everyday lives, most IoT infrastructures have been
suffering from security challenges. Since the emergence of IoT, lightweight
block ciphers have been a better option for intelligent and sensor-based
applications. When public-key infrastructure dominates worldwide, the symmetric
key encipherment such as Advanced Encryption Standard (AES) shows immense
prospects to sit with the smart home IoT appliances. As investigated, chaos
motivated logistic map shows enormous potential to secure IoT aligned real-time
data communication. The unpredictability and randomness features of the
logistic map in sync with chaos-based scheduling techniques can pave the way to
build a particular dynamic key propagation technique for data confidentiality,
availability and integrity. After being motivated by the security prospects of
AES and chaos cryptography, the paper illustrates a key scheduling technique
using a 3-dimensional S-box (substitution-box). The logistic map algorithm has
been incorporated to enhance security. The proposed approach has applicability
for lightweight IoT devices such as smart home appliances. The work determines
how seeming chaos accelerates the desired key-initiation before message
transmission. The proposed model is evaluated based on the key generation delay
required for the smart-home sensor devices.


title:
Exploring entanglement resource in Si quantum dot systems with
  operational quasiprobability approach

abstract:
  We characterize the quantum entanglement of the realistic two-qubit signals
that are sensitive to charge noises. Our working example is the time response
generated from a silicon double quantum dot (DQD) platform, where a
single-qubit rotation and a two-qubit controlled-NOT operation are conducted
sequentially in time to generate arbitrary entangled states. In order to
characterize the entanglement of two-qubit states, we employ the marginal
operational quasiprobability (OQ) approach that allows negative values of the
probability function if a given state is entangled. While the charge noise,
which is omnipresent in semiconductor devices, severely affects logic
operations implemented in the DQD platform, causing huge degradation in
fidelity of unitary operations as well as resulting two-qubit states, the
pattern in the OQ-driven entanglement strength turns out to be quite invariant,
indicating that the resource of quantum entanglement is not significantly
broken though the physical system is exposed to noise-driven fluctuations in
exchange interaction between quantum dots.


title:
Optimal Energy Management of Series Hybrid Electric Vehicles with Engine
  Start-Stop System

abstract:
  This paper develops energy management (EM) control for series hybrid electric
vehicles (HEVs) that include an engine start-stop system (SSS). The objective
of the control is to optimally split the energy between the sources of the
powertrain and achieve fuel consumption minimization. In contrast to existing
works, a fuel penalty is used to characterize more realistically SSS engine
restarts, to enable more realistic design and testing of control algorithms.
The paper first derives two important analytic results: a) analytic EM optimal
solutions of fundamental and commonly used series HEV frameworks, and b) proof
of optimality of charge sustaining operation in series HEVs. It then proposes a
novel heuristic control strategy, the hysteresis power threshold strategy
(HPTS), by amalgamating simple and effective control rules extracted from the
suite of derived analytic EM optimal solutions. The decision parameters of the
control strategy are small in number and freely tunable. The overall control
performance can be fully optimized for different HEV parameters and driving
cycles by a systematic tuning process, while also targeting charge sustaining
operation. The performance of HPTS is evaluated and benchmarked against
existing methodologies, including dynamic programming (DP) and a recently
proposed state-of-the-art heuristic strategy. The results show the
effectiveness and robustness of the HPTS and also indicate its potential to be
used as the benchmark strategy for high fidelity HEV models, where DP is no
longer applicable due to computational complexity.


title: An Overview of Indian Language Datasets used for Text Summarization

abstract:
  In this paper, we survey Text Summarization (TS) datasets in Indian Languages
(ILs), which are also low-resource languages (LRLs). We seek to answer one
primary question: is the pool of Indian Language Text Summarization (ILTS)
dataset growing or is there a resource poverty? To an-swer the primary
question, we pose two sub-questions that we seek about ILTS datasets: first,
what characteristics: format and domain do ILTS datasets have? Second, how
different are those characteristics of ILTS datasets from high-resource
languages (HRLs) particularly English. We focus on datasets reported in
published ILTS research works during 2012-2022. The survey of ILTS and English
datasets reveals two similarities and one contrast. The two similarities are:
first, the domain of dataset commonly is news (Hermann et al., 2015). The
second similarity is the format of the dataset which is both extractive and
abstractive. The contrast is in how the research in dataset development has
progressed. ILs face a slow speed of development and public release of datasets
as compared with English. We argue that the relatively lower number of ILTS
datasets is because of two reasons: first, absence of a dedicated forum for
developing TS tools and resources; and second, lack of shareable standard
datasets in the public domain.


title: Lee--Yang zeros in the age of Rydberg atoms

abstract:
  Lee--Yang (LY) zeros play a fundamental role in the formulation of
statistical physics in terms of (grand) partition functions, and assume
theoretical significance for the phenomenon of phase transitions. In this
paper, motivated by recent progress in cold Rydberg atom experiments, we
explore the LY zeros in classical Rydberg blockade models. We prove that all
the LY zeros are real for such models with arbitrary blockade radii, and show
how the zeros redistribute as one interpolates between nearest- and
next-nearest-neighbor blockade. We also discuss possible experimental
measurements of these zeros.


title:
Embedded antipodal planes and the minimum weight of the dual code of
  points and lines in projective planes of order $p^2$

abstract:
  The minimum weight of the code generated by the incidence matrix of points
versus lines in a projective plane has been known for over 50 years.
Surprisingly, finding the minimum weight of the dual code of projective planes
of non-prime order is still an open problem, even in the Desarguesian case.
  In this paper, we focus on the case of projective planes of order $p^2$,
where $p$ is prime, and we link the existence of small weight code words in the
dual code to the existence of embedded subplanes and {\em antipodal planes}. In
the Desarguesian case, we can exclude such code words by showing a more general
result that no antipodal plane of order at least 3 can be embedded in a
Desarguesian projective plane.
  Furthermore, we use combinatorial arguments to rule out the existence of code
words in the dual code of points and lines of an arbitrary projective plane of
order $p^2$, $p$ prime, of weight at most $2p^2-2p+4$ using more than two
symbols. In particular, this leads to the result that the dual code of the
Desarguesian projective plane $\mathrm{PG}(2,p^2)$, $p\geq 5$, has minimum
weight at least $2p^2-2p+5$.


title: Sensor Data Validation and Driving Safety in Autonomous Driving Systems

abstract:
  Autonomous driving technology has drawn a lot of attention due to its fast
development and extremely high commercial values. The recent technological leap
of autonomous driving can be primarily attributed to the progress in the
environment perception. Good environment perception provides accurate
high-level environment information which is essential for autonomous vehicles
to make safe and precise driving decisions and strategies. Moreover, such
progress in accurate environment perception would not be possible without deep
learning models and advanced onboard sensors, such as optical sensors (LiDARs
and cameras), radars, GPS. However, the advanced sensors and deep learning
models are prone to recently invented attack methods. For example, LiDARs and
cameras can be compromised by optical attacks, and deep learning models can be
attacked by adversarial examples. The attacks on advanced sensors and deep
learning models can largely impact the accuracy of the environment perception,
posing great threats to the safety and security of autonomous vehicles. In this
thesis, we study the detection methods against the attacks on onboard sensors
and the linkage between attacked deep learning models and driving safety for
autonomous vehicles. To detect the attacks, redundant data sources can be
exploited, since information distortions caused by attacks in victim sensor
data result in inconsistency with the information from other redundant sources.
To study the linkage between attacked deep learning models and driving
safety...


title:
Hafnia for analog memristor: Influence of stoichiometry and crystalline
  structure

abstract:
  The highly non-linear switching behavior of hafnia memristor actually hinders
its wide application in neuromorphic computing. Theoretical understanding into
its switching mechanism has been focused on the processes of conductive
filament generation and rupture, but possible phase transition and
crystallization around the region of conductive filaments (CFs) due to the
variation of O content have been paid less attention to. In this paper,
HfO$\mathrm{_x}$ structural models covering the full stoichiometries from Hf to
HfO$\mathrm{_2}$ were established, and the crystal structure evolution during
the reduction process of hafnia was obtained through first-principles
calculation. The electronic structures and O vacancy migration characteristics
of these structures were analyzed. A criterion was prescribed to predict the
mode of abrupt binary switching or gradual conductance modulation according to
the structure evolution of the CFs. In particular, factors that influence the
merging of tiny conductive channels into strong filaments are intensively
discussed, including the anisotropy of O vacancy migration and the size effect.
The feasibility of Mg doping to achieve robust gradual switching is discussed.


title: Towards (semi) exclusive cross section measurements and modelling

abstract:
  Semi-inclusive neutrino-nucleus scattering, corresponding to the simultaneous
detection of a lepton and one or more hadrons in the final state, is shown to
be much more sensitive to nuclear effects than the inclusive process. The
theoretical description of the semi-inclusive process is illustrated and some
comparisons of relativistic plane wave impulse approximation (RPWIA)
predictions with recent MicroBooNE, T2K and MINERvA data on argon and carbon
are shown. The merit of new variables constructed to enhance particular nuclear
effects is also illustrated and future developments going beyond the RPWIA
approach are outlined.


title:
Chiral asteroseismology: seismic oscillations caused by chiral transport
  in neutron stars and supernovae

abstract:
  We study the novel asteroseismology of the chiral magnetic wave (CMW) in
relativistic electron matter inside neutron stars and core-collapse supernovae
and the chiral vortical wave (CVW) in relativistic neutrino matter at the core
of supernovae. We call the oscillation modes for these chiral waves the chiral
magnetic mode (CM-mode) and chiral vortical mode (CV-mode), respectively. We
derive the dispersion relations of these new modes in the presence of the
chirality flipping due to the finite electron mass. We then estimate the
possible frequencies of these modes and the amplitudes of the resulting
gravitational waves. In particular, since the frequency of the CM-mode depends
on the strength of the magnetic field, gravitational waves for the CM-mode
provide a possible new probe for measuring the magnetic field in neutron stars
and supernovae. This theoretical study opens up an avenue for the "chiral
asteroseismology," which connects the seismic oscillations and associated
gravitational waves at the macroscopic scale to the chirality of microscopic
elementary particles.