title:
Excimer formation of pyrene labeled lipids interpreted by means of
  coarse-grained molecular dynamics simulations

abstract:
  The excimer formation dynamics of pyrene-labeled molecules in lipid bilayers
depends on molecular motion over distances of the order of 1-2 nm. From the
concentration dependence of the excimer photoemission curve, it is possible to
derive a value for the lipid self-diffusion coefficient. This technique has
been intensively used in the past twenty years, leading to rather large
numerical values for self-diffusion compared with other approaches based on
fluorescent probes tracking. In most cases, the interpretation of the
experimental data rely on models for diffusion limited 2d reaction rates, or
comparison with 2d lattice random walks. Our approach uses realistic molecular
dynamics trajectories to reinterpret these experiments. Based on a well
established coarse-grained model for lipid MD simulations (Martini), we show
how to relate simulation results to experimental data on excimer formation. Our
procedure is quite general and is applicable to all diffusion-limited kinetic
processes. Key to our approach is the determination of the acceleration factor
of lipid coarse-grained numerical models compared to reality. We find a
significant reduction of the diffusion coefficient values, in particular when
interleaflet association is taken into account. Our work does not point to
deviation from a diffusion-limited mechanism but indicates that the excimer
formation across bilayer leaflets could be hindered.


title: Illuminating spiky balls and cap bodies

abstract:
  The convex hull of a ball with an exterior point is called a spike (or cap).
A union of finitely many spikes of a ball is called a spiky ball. If a spiky
ball is convex, then we call it a cap body. In this note we upper bound the
illumination numbers of $2$-illuminable spiky balls as well as centrally
symmetric cap bodies. In particular, we prove the Illumination Conjecture for
centrally symmetric cap bodies in sufficiently large dimensions by showing that
any $d$-dimensional centrally symmetric cap body can be illuminated by $<2^d$
directions in Euclidean $d$-space for all $d\geq 20$. Furthermore, we
strengthen the latter result for $1$-unconditionally symmetric cap bodies.


title:
What are the characteristics of highly-selected packages? A case study
  on the npm ecosystem

abstract:
  With the popularity of software ecosystems, the number of open source
components (known as packages) has grown rapidly. Identifying high-quality and
well-maintained packages from a large pool of packages to depend on is a basic
and important problem, as it is beneficial for various applications, such as
package recommendation and package search. However, no systematic and
comprehensive work focuses on addressing this problem except in online
discussions or informal literature and interviews. To fill this gap, in this
paper, we conducted a mixed qualitative and quantitative analysis to understand
how developers identify and select relevant open source packages. In
particular, we started by surveying 118 JavaScript developers from the npm
ecosystem to qualitatively understand the factors that make a package to be
highly-selected within the npm ecosystem. The survey results showed that
JavaScript developers believe that highly-selected packages are
well-documented, receive a high number of stars on GitHub, have a large number
of downloads, and do not suffer from vulnerabilities. Then, we conducted an
experiment to quantitatively validate the developers' perception of the factors
that make a highly-selected package. In this analysis, we collected and mined
historical data from 2,527 packages divided into highly-selected and not
highly-selected packages. For each package in the dataset, we collected
quantitative data to present the factors studied in the developers' survey.
Next, we used regression analysis to quantitatively investigate which of the
studied factors are the most important. Our regression analysis complements our
survey results about highly-selected packages. In particular, the results
showed that highly-selected packages tend to be correlated by the number of
downloads, stars, and how large the package's readme file is.


title:
Production of Higgs boson in association with a pair of fermions in the
  presence of a circularly polarized laser field

abstract:
  In the centre of mass frame, we have investigated the process of Higgs
production in association with a pair of fermions, $e^{+}e^{-}\rightarrow
f\bar{f} H$, at the leading order inside an intense electromagnetic field with
circular polarization. Our analytical calculations are based on the Narrow
Width Approximation (NWA), which is valid in the leading order. We have
considered only the initial particles inside the laser field as a first step.
In the second part, we have embedded both initial and final particles in the
laser field. We have analyzed the angular distribution of the produced Higgs
boson as a function of the laser parameters in both cases. We have found that
in the case where both initial and final particles are embedded in the laser
field, the order of magnitude of the differential cross-section of the process
$e^{+}e^{-}\rightarrow\mu^{+}\mu^{-}H$ is reduced more significantly, while
that of the process $e^{+}e^{-}\rightarrow\nu\bar{\nu}H$ is enhanced.


title: Multimodal Transformer for Nursing Activity Recognition

abstract:
  In an aging population, elderly patient safety is a primary concern at
hospitals and nursing homes, which demands for increased nurse care. By
performing nurse activity recognition, we can not only make sure that all
patients get an equal desired care, but it can also free nurses from manual
documentation of activities they perform, leading to a fair and safe place of
care for the elderly. In this work, we present a multimodal transformer-based
network, which extracts features from skeletal joints and acceleration data,
and fuses them to perform nurse activity recognition. Our method achieves
state-of-the-art performance of 81.8% accuracy on the benchmark dataset
available for nurse activity recognition from the Nurse Care Activity
Recognition Challenge. We perform ablation studies to show that our fusion
model is better than single modality transformer variants (using only
acceleration or skeleton joints data). Our solution also outperforms
state-of-the-art ST-GCN, GRU and other classical hand-crafted-feature-based
classifier solutions by a margin of 1.6%, on the NCRC dataset. Code is
available at \url{https://github.com/Momilijaz96/MMT_for_NCRC}.


title:
Revealing the properties of void galaxies and their assembly using the
  EAGLE simulation

abstract:
  We explore the properties of central galaxies living in voids using the EAGLE
cosmological hydrodynamic simulations in conjunction with the
\cite{paillas2017} void catalogue. Based on the distance to the closest void,
we define four galaxy samples: inner void, outer void, wall, and skeleton. We
find that inner void galaxies with host halo masses $<10^{12}M_\odot$ have
lower stellar mass and stellar mass fractions than those in denser
environments, and the fraction of galaxies with star formation (SF) activity
and atomic hydrogen (HI) gas decreases with increasing distance to their
closest void, in agreement with observations. To mitigate the influence of
stellar mass, we compare inner void galaxies to subsamples of fixed stellar
(halo) mass. Inner void galaxies with $M_{*}= 10^{[9.0-9.5]}M_\odot$ have
similar SF activity and HI gas fractions, but the lowest quenched galaxy
fraction. Inner void galaxies with $M_{*}= 10^{[9.5-10.5]}M_\odot$ have the
lowest HI gas fraction, the highest quenched fraction, and lowest gas
metallicities compared to galaxies in denser environments. On the other hand,
inner void galaxies with $M_{*}>10^{10.5}M_\odot$ have comparable SF activity
and HI gas fractions to their analogues in denser environments. They retain the
highest metallicity gas that might be linked to physical processes only
fostered in underdense regions. Furthermore, inner void galaxies have the
lowest fraction of positive gas-phase metallicity gradients, which are
typically associated with external processes or feedback events, suggesting
they have more quiet merger histories than galaxies in denser environments. Our
findings provide a unique insight into how galaxies are affected by their
large-scale environment.


title: Orbit harmonics for the union of two orbits

abstract:
  Garsia and Procesi, in their study of Springer's representation, proved that
the cohomology ring of a Springer fiber is isomorphic to the associated graded
ring of the coordinate ring of the $S_n$ orbit of a single point in
$\mathbb{C}^n$. This construction was an essential tool in their analysis of
the Springer representation, and variations of it have reappeared recently in
several other combinatorial and geometric contexts under the name orbit
harmonics. In this article, we analyze the orbit harmonics of a union of two
$S_n$ orbits. We prove that when the coordinate sums of the two orbits are
different, the corresponding graded $S_n$ representation is a direct sum of two
Springer representations, one of which is shifted in degree by 1.


title:
Joint Distribution Matters: Deep Brownian Distance Covariance for
  Few-Shot Classification

abstract:
  Few-shot classification is a challenging problem as only very few training
examples are given for each new task. One of the effective research lines to
address this challenge focuses on learning deep representations driven by a
similarity measure between a query image and few support images of some class.
Statistically, this amounts to measure the dependency of image features, viewed
as random vectors in a high-dimensional embedding space. Previous methods
either only use marginal distributions without considering joint distributions,
suffering from limited representation capability, or are computationally
expensive though harnessing joint distributions. In this paper, we propose a
deep Brownian Distance Covariance (DeepBDC) method for few-shot classification.
The central idea of DeepBDC is to learn image representations by measuring the
discrepancy between joint characteristic functions of embedded features and
product of the marginals. As the BDC metric is decoupled, we formulate it as a
highly modular and efficient layer. Furthermore, we instantiate DeepBDC in two
different few-shot classification frameworks. We make experiments on six
standard few-shot image benchmarks, covering general object recognition,
fine-grained categorization and cross-domain classification. Extensive
evaluations show our DeepBDC significantly outperforms the counterparts, while
establishing new state-of-the-art results. The source code is available at
http://www.peihuali.org/DeepBDC


title: Generalized Tuza's conjecture for random hypergraphs

abstract:
  A celebrated conjecture of Tuza states that in any finite graph the minimum
size of a cover of triangles by edges is at most twice the maximum size of a
set of edge-disjoint triangles. For an $r$-uniform hypergraph ($r$-graph) $G$,
let $\tau(G)$ be the minimum size of a cover of edges by $(r-1)$-sets of
vertices, and let $\nu(G)$ be the maximum size of a set of edges pairwise
intersecting in fewer than $r-1$ vertices. Aharoni and Zerbib proposed the
following generalization of Tuza's conjecture: $$ \text{For any $r$-graph $G$,
$\tau(G)/\nu(G) \leq \lceil(r+1)/2\rceil$.} $$
  Let $H_r(n,p)$ be the uniformly random $r$-graph on $n$ vertices. We show
that, for $r \in \{3, 4, 5\}$ and any $p = p(n)$, $H_r(n,p)$ satisfies the
Aharoni-Zerbib conjecture with high probability (i.e., with probability
approaching 1 as $n \rightarrow \infty$). We also show that there is a $C < 1$
such that, for any $r \geq 6$ and any $p = p(n)$, $\tau(H_r(n, p))/\nu(H_r(n,
p)) \leq C r$ with high probability. Furthermore, we may take $C < 1/2 +
\varepsilon$, for any $\varepsilon > 0$, by restricting to sufficiently large
$r$ (depending on $\varepsilon$).


title:
Shape Derivative for Penalty-Constrained Nonsmooth-Nonconvex
  Optimization: Cohesive Crack Problem

abstract:
  A class of non-smooth and non-convex optimization problems with penalty
constraints linked to variational inequalities (VI) is studied with respect to
its shape differentiability. The specific problem stemming from quasi-brittle
fracture describes an elastic body with a Barenblatt cohesive crack under the
inequality condition of non-penetration at the crack faces. Based on the
Lagrange approach and using smooth penalization with the Lavrentiev
regularization, a formula for the shape derivative is derived. The explicit
formula contains both primal and adjoint states and is useful for finding
descent directions for a gradient algorithm to identify an optimal crack shape
from a boundary measurement. Numerical examples of destructive testing are
presented in 2D.


title: Extremal Eigenvalues of the Paneitz Operator in Conformal Classes

abstract:
  Let $(M^4,g)$ be a closed Riemannian manifold of dimension four. We
investigate the properties of metrics which are critical points of the
eigenvalues of the Paneitz operator when considered as functionals on the space
of conformal metrics with fixed volume. We prove that critical metrics of the
aforementioned functional are associated to a higher-order analogue of harmonic
maps (which we call Paneitz map) into round spheres. We derive other important
properties of such critical metrics and provide examples.


title: Quasistatic Evolution with Unstable Forces

abstract:
  We consider load controlled quasistatic evolution. Well posedness results for
the nonlocal continuum model related to peridynamics are established. We show
local existence and uniqueness of quasistatic evolution for load paths
originating at critical points associated with energy minima. These are local
minima among the convex set of deformations belonging to the strength domain of
the material. The evolution of the displacements however is not constrained to
lie inside the strength domain of the material. The load-controlled evolution
is shown to exhibit energy balance.


title:
DFT+U and Quantum Monte Carlo study of electronic and optical properties
  of AgNiO$_2$ and AgNi$_{1-x}$Co$_{x}$O$_2$ delafossite

abstract:
  The delafossite 2H-AgNiO$_2$ has received a great deal of attention because
of its unique semi-metallic nature, which only AgNiO$_2$ exhibits among the
$d^{10}$-based delafossites, and its antiferromagnetism in the NiO$_2$ coupled
with a lattice distortion. In contrast, AgCoO$_2$ is insulating, and we predict
that substitutional doping of Co on magnetic Ni sites will induce a
metal-insulator transition. The electronic and magnetic structure of AgNiO$_2$
have been widely studied using Density Functional Theory (DFT), but earlier
studies have not included corrections for strong on-site Coulomb interactions.
Using Quantum Monte Carlo (QMC) simulations, we obtain accurate estimates for
the electronic and magnetic properties of AgNiO$_2$. We also conclude that the
rather strong electron correlations in AgNiO$_2$ are underestimated in DFT
unless on-site Coulomb interactions are accounted for beyond typical mean-field
DFT approaches. We also study various phases of AgNi$_x$Co$_{1-x}$O$_2$ and
estimate their formation energies and gap openings under Co substitutional
doping in order to investigate the electronic properties and phase stability of
the intermetallics.


title:
Efficient Reconstruction of Stochastic Pedigrees: Some Steps From Theory
  to Practice

abstract:
  In an extant population, how much information do extant individuals provide
on the pedigree of their ancestors? Recent work by Kim, Mossel, Ramnarayan and
Turner (2020) studied this question under a number of simplifying assumptions,
including random mating, fixed length inheritance blocks and sufficiently large
founding population. They showed that under these conditions if the average
number of offspring is a sufficiently large constant, then it is possible to
recover a large fraction of the pedigree structure and genetic content by an
algorithm they named REC-GEN.
  We are interested in studying the performance of REC-GEN on simulated data
generated according to the model. As a first step, we improve the running time
of the algorithm. However, we observe that even the faster version of the
algorithm does not do well in any simulations in recovering the pedigree beyond
2 generations. We claim that this is due to the inbreeding present in any
setting where the algorithm can be run, even on simulated data. To support the
claim we show that a main step of the algorithm, called ancestral
reconstruction, performs accurately in a idealized setting with no inbreeding
but performs poorly in random mating populations.
  To overcome the poor behavior of REC-GEN we introduce a Belief-Propagation
based heuristic that accounts for the inbreeding and performs much better in
our simulations.


title: Quantum-Inspired Solvers on Mixed-Integer Linear Programming Problem

abstract:
  Mixed-integer linear programming (MILP) plays a crucial role in artificial
intelligence, biochemistry, finance, cryptography, etc. Notwithstanding popular
for decades, the researches of MILP solvers are still limited by the resource
consumption caused by complexity and failure of Moore's Law. Quantum-inspired
Ising machines, as a new computing paradigm, can be used to solve integer
programming problems by reducing them into Ising models. Therefore, it is
necessary to understand the technical evolution of quantum inspired solvers to
break the bottleneck. In this paper, the concept and traditional algorithms for
MILP are introduced. Then, focused on Ising model, the principle and
implementations of annealers and coherent Ising machines are summarized.
Finally, the paper discusses the challenges and opportunities of miniaturized
solvers in the future.


title: The COHERENT Experimental Program

abstract:
  The COHERENT experiment located in Neutrino Alley at the Spallation Neutron
Source (SNS), Oak Ridge National Laboratory (ORNL), has made the world's first
two measurements of coherent elastic neutrino-nucleus scattering (CEvNS), on
CsI and argon, using neutrinos produced at the SNS. The COHERENT collaboration
continues to pursue CEvNS measurements on various targets as well as additional
studies of inelastic neutrino-nucleus interactions, searches for
accelerator-produced dark matter (DM) and physics beyond the Standard Model,
using the uniquely high-quality and high-intensity neutrino source available at
the SNS. This white paper describes primarily COHERENT's ongoing and
near-future program at the SNS First Target Station (FTS). Opportunities
enabled by the SNS Second Target Station (STS) for the study of neutrino
physics and development of novel detector technologies are elaborated in a
separate white paper.


title:
Adaptable Plug and Play Security Operations Center Leveraging a Novel
  Programmable Plugin-based Intrusion Detection and Prevention System

abstract:
  The number of cyber-attacks have substantially increased over the past decade
resulting in huge organizational financial losses. Indeed, it is no longer a
matter of "if" but "when" a security incident will take place. A Security
Operations Center(SOC) adoption will help in the detection, identification,
prevention, and resolution of issues before they end up causing extensive
cyber-related damage. In this paper, our proposed framework is brought about to
address the problem that current open-source SOC implementations are plagued
with. These include lack of ability to be strengthened on the fly, slow
development processes, and their ineptness for continuous timely updates. We,
herein, propose a framework that would offer a fully automated open-source SOC
deployment; otherwise dubbed, a "plug-and-play framework"; full horizontal
scalability incorporating a modular architecture. These underpinning features
are meant to mitigate underlying SOC challenges, which often emerge as a result
of many pre-determined and repeated processes, bolstering their ability for
expansion with new tools. This is on top of enhancing their ability to handle
more servers in the clusters as a single logical unit. We also introduce a new
system of its kind called a Programmable Plugin-based Intrusion Detection and
Prevention System (PPIDPS). This system will extend a SOC's ability to add any
tool to the monitored devices while collecting logs that can trigger alerts
whenever a suspicious behavior is detected.


title:
A Possible Large-scale Alignment of Galaxy Spin Directions -- Analysis
  of 10 Datasets from SDSS, Pan-STARRS, and HST

abstract:
  Multiple observations made by several different telescopes have shown
asymmetry between the number of spiral galaxies rotating in opposite directions
in different parts of the sky. One of the immediate questions regarding the
possible asymmetry of the spin directions is whether the distribution forms a
cosmological-scale axis. This paper analyzes and compares 10 different datasets
published in the past decade, collected by SDSS, Pan-STARRS, and Hubble Space
Telescope. The datasets contain spiral galaxies separated by their spin
direction, and the distribution can show dipole axes. The analysis shows that
the directions of the most probable dipole axes are consistent in datasets that
have similar average redshift, but different between datasets that have
different average redshift. The analysis also shows that the location of the
most probable axis correlates with the average redshift of the galaxies in the
datasets. That is, the location of the most probable axis shifts when the
redshift gets higher, and the correlation is statistically significant. This
provides a certain indication of a drift in a possible axis formed by the
distribution of galaxy spin directions, or a cosmological scale structure that
peaks at a certain distance from Earth.


title:
Tackling the Unique Challenges of Low-Frequency Solar-Polarimetry with
  SKA-Low Precursor : The Algorithm

abstract:
  Coronal magnetic fields are well known to be one of the crucial parameters
defining coronal physics and space weather. However, measuring the global
coronal magnetic fields remains challenging. The polarization properties of
coronal radio emissions are sensitive to coronal magnetic fields. While they
can prove to be useful probes of coronal and heliospheric magnetic fields,
their usage has been limited by technical and algorithmic challenges. We
present a robust algorithm for precise polarization calibration and imaging of
low-radio frequency solar observations and demonstrate it on data from the
Murchison Widefield Array, a Square Kilometer Array (SKA) precursor. This
algorithm is based on the Measurement Equation framework, which forms the basis
of all modern radio interferometric calibration and imaging. It delivers high
dynamic range and fidelity full Stokes solar radio images with instrumental
polarization leakages $<1\%$, on par with general astronomical radio imaging,
and represents the state-of-the-art. Opening up this rewarding, yet unexplored,
phase space will enable multiple novel science investigations and offer
considerable discovery potential. Examples include detection of low-level of
circularly polarization from thermal coronal emission to estimate large-scale
quiescent coronal fields; polarization of faint gyrosynchrotron emissions from
coronal mass ejections for robust estimation of plasma parameters; and
detection of the first-ever linear polarization at these frequencies. This
method has been developed with the SKA in mind and will enable a new era of
high fidelity spectro-polarimetric snapshot solar imaging at low-radio
frequencies.


title: Inferring Pitch from Coarse Spectral Features

abstract:
  Fundamental frequency (F0) has long been treated as the physical definition
of "pitch" in phonetic analysis. But there have been many demonstrations that
F0 is at best an approximation to pitch, both in production and in perception:
pitch is not F0, and F0 is not pitch. Changes in the pitch involve many
articulatory and acoustic covariates; pitch perception often deviates from what
F0 analysis predicts; and in fact, quasi-periodic signals from a single voice
source are often incompletely characterized by an attempt to define a single
time-varying F0. In this paper, we find strong support for the existence of
covariates for pitch in aspects of relatively coarse spectra, in which an
overtone series is not available. Thus linear regression can predict the pitch
of simple vocalizations, produced by an articulatory synthesizer or by human,
from single frames of such coarse spectra. Across speakers, and in more complex
vocalizations, our experiments indicate that the covariates are not quite so
simple, though apparently still available for more sophisticated modeling. On
this basis, we propose that the field needs a better way of thinking about
speech pitch, just as celestial mechanics requires us to go beyond Newton's
point mass approximations to heavenly bodies.


title: Re-Examining Human Annotations for Interpretable NLP

abstract:
  Explanation methods in Interpretable NLP often explain the model's decision
by extracting evidence (rationale) from the input texts supporting the
decision. Benchmark datasets for rationales have been released to evaluate how
good the rationale is. The ground truth rationales in these datasets are often
human annotations obtained via crowd-sourced websites. Valuable as these
datasets are, the details on how those human annotations are obtained are often
not clearly specified. We conduct comprehensive controlled experiments using
crowd-sourced websites on two widely used datasets in Interpretable NLP to
understand how those unsaid details can affect the annotation results.
Specifically, we compare the annotation results obtained from recruiting
workers satisfying different levels of qualification. We also provide
high-quality workers with different instructions for completing the same
underlying tasks. Our results reveal that the annotation quality is highly
subject to the workers' qualification, and workers can be guided to provide
certain annotations by the instructions. We further show that specific
explanation methods perform better when evaluated using the ground truth
rationales obtained by particular instructions. Based on these observations, we
highlight the importance of providing complete details of the annotation
process and call for careful interpretation of any experiment results obtained
using those annotations.


title:
Augmenting Pre-trained Language Models with QA-Memory for Open-Domain
  Question Answering

abstract:
  Retrieval augmented language models have recently become the standard for
knowledge intensive tasks. Rather than relying purely on latent semantics
within the parameters of large neural models, these methods enlist a
semi-parametric memory to encode an index of knowledge for the model to
retrieve over. Most prior work has employed text passages as the unit of
knowledge, which has high coverage at the cost of interpretability,
controllability, and efficiency. The opposite properties arise in other methods
which have instead relied on knowledge base (KB) facts. At the same time, more
recent work has demonstrated the effectiveness of storing and retrieving from
an index of Q-A pairs derived from text \citep{lewis2021paq}. This approach
yields a high coverage knowledge representation that maintains KB-like
properties due to its representations being more atomic units of information.
In this work we push this line of research further by proposing a
question-answer augmented encoder-decoder model and accompanying pretraining
strategy. This yields an end-to-end system that not only outperforms prior QA
retrieval methods on single-hop QA tasks but also enables compositional
reasoning, as demonstrated by strong performance on two multi-hop QA datasets.
Together, these methods improve the ability to interpret and control the model
while narrowing the performance gap with passage retrieval systems.


title:
Real order total variation with applications to the loss functions in
  learning schemes

abstract:
  Loss function are an essential part in modern data-driven approach, such as
bi-level training scheme and machine learnings. In this paper we propose a loss
function consisting of a $r$-order (an)-isotropic total variation semi-norms
$TV^r$, $r\in \mathbb{R}^+$, defined via the Riemann-Liouville (R-L) fractional
derivative. We focus on studying key theoretical properties, such as the lower
semi-continuity and compactness with respect to both the function and the order
of derivative $r$, of such loss functions.


title:
Modal approximation for time-domain elastic scattering from metamaterial
  quasiparticles

abstract:
  This paper aims at quantitatively understanding the elastic wave scattering
due to negative metamaterial structures under wide-band signals in the time
domain. Specifically, we establish the modal expansion for the time-dependent
field scattered by metamaterial quasiparticles in elastodynamics. By Fourier
transform, we first analyze the modal expansion in the time-harmonic regime.
With the presence of quasiparticles, we validate such an expansion in the
static regime via quantitatively analyzing the spectral properties of the
Neumann-Poincar\'{e} operator associated with the elastostatic system. We then
approximate the incident field with a finite number of modes and apply
perturbation theory to obtain such an expansion in the perturbative regime. In
addition, we give polariton resonances as simple poles for the elastic system
with non-zero frequency. Finally, we show that the low-frequency part of the
scattered field in the time domain can be well approximated by using the
resonant modal expansion with sharp error estimates.


title: An improved method for constructing linear codes with small hulls

abstract:
  In this paper, we give a method for constructing linear codes with small
hulls by generalizing the method in \cite{LCD-T-matric}. As a result, we obtain
many optimal Euclidean LCD codes and Hermitian LCD codes, which improve the
previously known lower bound on the largest minimum distance. We also obtain
many optimal codes with one-dimension hull. Furthermore, we give three tables
about formally self-dual LCD codes.


title: Information paradox and island in quasi-de Sitter space

abstract:
  Whereas a static observer in de Sitter (dS) space detects thermal radiation
emitted by the horizon, the dS isometries impose that the radiation is in
equilibrium with the background. This implies that for the static observer to
find the information paradox, the background must be deformed to quasi-dS space
in which the dS isometries are spontaneously broken. We study the condition
that the information paradox arises in quasi-dS space with the monotonically
increasing horizon size which is used to describe the inflationary cosmology.
For this purpose, the dimensional reduction of three-dimensional dS space with
thermal radiation modelled by the JT gravity coupled to CFT is considered. We
argue that when the central charge monotonically increases in time, the
information paradox arises but the conditions for the existence of the island
become more restrictive. As the central charge can be interpreted as the number
of degrees of freedom, the absence of the island in quasi-dS space supports the
entropy argument for the dS swampland conjecture.


title: Continuous frames in n-Hilbert spaces and their tensor products

abstract:
  We introduce the notion of continuous frame in n-Hilbert space which is a
generalization of discrete frame in n-Hilbert space. The tensor product of
Hilbert spaces is a very important topic in mathematics. Here we also introduce
the concept of continuous frame for the tensor products of n-Hilbert spaces.
Further, we study dual continuous frame and continuous Bessel multiplier in
n-Hilbert spaces and their tensor products.


title:
Meteorological indicators of dengue epidemics in non-endemic Northwest
  Argentina

abstract:
  In the last two decades dengue cases increased significantly throughout the
world. In several regions dengue re-emerged, particularly in Latin America,
where dengue cases not only increased but also occurred more frequently. It is
therefore necessary to understand the mechanisms that drive epidemic outbreaks
in non-endemic regions, to help in the design of control strategies. We develop
a stochastic model that includes climate variables, social structure, and
mobility between a non-endemic city and an endemic area. We choose as a case
study the non-endemic city of San Ram{\'o}n de la Nueva Or{\'a}n, located in
Northwest Argentina. Human mobility is intense through the border with Bolivia,
where dengue transmission is sustained during the whole year. City population
was modelled as a meta-population taking into account households and population
data for each patch. Climate variability was considered by including rainfall,
relative humidity and temperature time series into the models. Those climatic
variables were input of a mosquito population ecological model, which in turn
is coupled to an epidemiological model. Different hypotheses regarding people's
mobility between an endemic and non-endemic area are tested, taking into
account the local climatic variation, typical of the non-endemic city.
Simulations are qualitatively consistent with weekly clinical data reported
from 2009 to 2016. Our model results allow to explain the observed pattern of
outbreaks, that alternates large dengue epidemics and several years with
smaller outbreaks. We found that the number of vectors per host and an
effective reproductive number are proxies for large epidemics, both related
with climate variability such as rainfall and temperature, opening the
possibility to test these meteorological variables for forecast purposes.


title:
Robust Cross-Modal Representation Learning with Progressive
  Self-Distillation

abstract:
  The learning objective of vision-language approach of CLIP does not
effectively account for the noisy many-to-many correspondences found in
web-harvested image captioning datasets, which contributes to its compute and
data inefficiency. To address this challenge, we introduce a novel training
framework based on cross-modal contrastive learning that uses progressive
self-distillation and soft image-text alignments to more efficiently learn
robust representations from noisy data. Our model distills its own knowledge to
dynamically generate soft-alignment targets for a subset of images and captions
in every minibatch, which are then used to update its parameters. Extensive
evaluation across 14 benchmark datasets shows that our method consistently
outperforms its CLIP counterpart in multiple settings, including: (a) zero-shot
classification, (b) linear probe transfer, and (c) image-text retrieval,
without incurring added computational cost. Analysis using an ImageNet-based
robustness test-bed reveals that our method offers better effective robustness
to natural distribution shifts compared to both ImageNet-trained models and
CLIP itself. Lastly, pretraining with datasets spanning two orders of magnitude
in size shows that our improvements over CLIP tend to scale with number of
training examples.


title:
Universal quantum computation based on nanoscale skyrmion helicity
  qubits in frustrated magnets

abstract:
  Skyrmions in frustrated magnets have the helicity degree of freedom, where
two different configurations of Bloch-type skyrmions are energetically favored
by the magnetic dipole-dipole interaction and characterized by opposite
helicities. A skyrmion must become a quantum-mechanical object when its radius
is of the order of nanometer. We construct a qubit based on the two-fold
degeneracy of the Bloch-type nanoscale skyrmions in frustrated magnets. It is
shown that the universal quantum computation is possible based on nanoscale
skyrmions in a multilayered system. We explicitly show how to construct the
$\pi /4$ phase-shift gate, the Hadamard gate, and the CNOT gate. The one-qubit
quantum gates are materialized by temporally controlling the electric field and
the spin current. The two-qubit gate is materialized with the use of the
Ising-type exchange coupling by controlling the distance between the skyrmion
centers in two adjacent layers. The merit of the present mechanism is that an
external magnetic field is not necessary. Our results may open a possible way
toward universal quantum computations based on nanoscale topological spin
textures.


title:
Unveiling Pump induced Magnon Mode via its Strong Interaction with
  Walker Modes

abstract:
  We observe a power-dependent anti-crossing of Walker spin-wave modes under a
microwave pump when placing a ferrimagnet in a microwave waveguide that does
not support any discrete photon mode. We interpret the unexpected anti-crossing
as the generation of a pump induced magnon mode that couples strongly to the
Walker modes of ferrimagnet. We realize a remarkable functionality of this
anti-crossing, i.e., a microwave frequency comb, in terms of the cross-Kerr
nonlinearity that mixes the pump and probe frequencies. Such a frequency comb
gets rid of the charge noise from carriers, unlike those produced by diode
mixers, and thus may be useful for coherent information conversion with
ultra-low noise.


title: AABAC -- Automated Attribute Based Access Control for Genomics Data

abstract:
  The COVID-19 crisis has demonstrated the potential of cutting-edge genomics
research. However, privacy of these sensitive pieces of information is an area
of significant concern for genomics researchers. The current security models
makes it difficult to create flexible and automated data sharing frameworks.
These models also increases the complexity of adding or revoking access without
contacting the data publisher. In this work, we investigate an automated
attribute-based access control (AABAC) model for genomics data over Named Data
Networking (NDN). AABAC secures the data itself rather than the storage
location or transmission channel, provides automated data invalidation, and
automates key retrieval and data validation while maintaining the ability to
control access. We show that AABC when combined with NDN provide a secure and
flexible combination for work with genomics research.


title: Convex Polyhedra in the $3$-Sphere and Tilings of the $2$-Sphere

abstract:
  We show that for every convex polyhedral sphere $P$ in $S^3$, there exist two
canonical, non-edge-to-edge tilings of $S^{2}$ whose tiles are given by all the
faces of $P$ and the dual convex polyhedral sphere $P^*$ to $P$. Under the
identifications of $S^{3}$ with the Lie group $SU(2)$, and of $S^{2}$ with the
unit sphere in the Lie algebra $su(2)$ of $SU(2)$, our result is obtained by
considering the set $\widetilde P$ of outward unit normal vectors to $P$ and
the maps from $\widetilde P$ to $S^{2}$ defined by using the left and right
Maurer-Cartan forms on $SU(2)$.


title:
Giant magnetic in-plane anisotropy and competing instabilities in
  Na3Co2SbO6

abstract:
  We report magnetometry data obtained on twin-free single crystals of
Na3Co2SbO6, which is considered a candidate material for realizing the Kitaev
honeycomb model for quantum spin liquids. Contrary to a common belief that such
materials can be modeled with the symmetries of an ideal honeycomb lattice, our
data reveal a pronounced two-fold symmetry and in-plane anisotropy of over
200%, despite the honeycomb layer's tiny orthorhombic distortion of less than
0.2%. We further use magnetic neutron diffraction to elucidate a rich variety
of field-induced phases observed in the magnetometry. These phases manifest
themselves in the paramagnetic state as diffuse scattering signals associated
with competing ferro- and antiferromagnetic instabilities, consistent with a
theory that also predicts a quantum spin liquid phase nearby. Our results call
for theoretical understanding of the observed in-plane anisotropy, and render
Na3Co2SbO6 a promising ground for finding exotic quantum phases by targeted
external tuning.


title: On equivalence relations induced by Polish groups

abstract:
  The motivation of this article is to introduce a kind of orbit equivalence
relations which can well describe structures and properties of Polish groups
from the perspective of Borel reducibility. Given a Polish group $G$, let
$E(G)$ be the right coset equivalence relation $G^\omega/c(G)$, where $c(G)$ is
the group of all convergent sequences in $G$.
  Let $G$ be a Polish group. (1) $G$ is a discrete countable group containing
at least two elements iff $E(G)\sim_BE_0$; (2) if $G$ is TSI uncountable
non-archimedean, then $E(G)\sim_BE_0^\omega$; (3) $G$ is non-archimedean iff
$E(G)\le_B=^+$; (4) let $G$ be a non-CLI Polish group and $H$ a CLI Polish
group, then $E(G)\not\le_BE_H^Y$ for any Polish $H$-space $Y$; (5) if $H$ is a
non-archimedean Polish group but $G$ is not, then $E(G)\not\le_BE_H^Y$ for any
Polish $H$-space $Y$.
  The notion of $\alpha$-unbalanced Polish group for $\alpha<\omega_1$ is
introduced. Let $G,H$ be Polish groups, $0<\alpha<\omega_1$. If $G$ is
$\alpha$-unbalanced but $H$ is not, then $E(G)\not\le_B E(H)$.
  For any Lie group $G$, denote $G_0$ the connected component of the identity
element $1_G$. Let $G$ and $H$ be two separable TSI Lie groups. If
$E(G)\le_BE(H)$, then there exists a continuous locally injection $S:G_0\to
H_0$. Moreover, if $G_0,H_0$ are abelian, $S$ is a group homomorphism.
Particularly, for $c_0,e_0,c_1,e_1\in{\mathbb N}$, $E({\mathbb
R}^{c_0}\times{\mathbb T}^{e_0})\le_BE({\mathbb R}^{c_1}\times{\mathbb
T}^{e_1})$ iff $e_0\le e_1$ and $c_0+e_0\le c_1+e_1$.


title: Analogue Black Holes in Reactive Molecules

abstract:
  We show that reactive molecules with a unit probability of reaction naturally
provide a simulator of some intriguing black hole physics. The unit reaction at
the short distance acts as an event horizon and delivers a one-way traffic for
matter waves passing through the potential barrier when two molecules interact
by high partial-wave scatterings or dipole-dipole interactions. In particular,
the scattering rate as a function of the incident energy exhibits a
thermal-like distribution near the maximum of the interaction energy in the
same manner as a scalar field scatters with the potential barrier outside the
event horizon of a black hole. Such a thermal-like scattering can be extracted
from the temperature-dependent two-body loss rate measured in experiments on
KRb and other molecules.


title:
Parameter-Efficient Tuning by Manipulating Hidden States of Pretrained
  Language Models For Classification Tasks

abstract:
  Parameter-efficient tuning aims to distill knowledge for downstream tasks by
optimizing a few introduced parameters while freezing the pretrained language
models (PLMs). Continuous prompt tuning which prepends a few trainable vectors
to the embeddings of input is one of these methods and has drawn much attention
due to its effectiveness and efficiency. This family of methods can be
illustrated as exerting nonlinear transformations of hidden states inside PLMs.
However, a natural question is ignored: can the hidden states be directly used
for classification without changing them? In this paper, we aim to answer this
question by proposing a simple tuning method which only introduces three
trainable vectors. Firstly, we integrate all layers hidden states using the
introduced vectors. And then, we input the integrated hidden state(s) to a
task-specific linear classifier to predict categories. This scheme is similar
to the way ELMo utilises hidden states except that they feed the hidden states
to LSTM-based models. Although our proposed tuning scheme is simple, it
achieves comparable performance with prompt tuning methods like P-tuning and
P-tuning v2, verifying that original hidden states do contain useful
information for classification tasks. Moreover, our method has an advantage
over prompt tuning in terms of time and the number of parameters.


title:
Private Sequential Hypothesis Testing for Statisticians: Privacy, Error
  Rates, and Sample Size

abstract:
  The sequential hypothesis testing problem is a class of statistical analyses
where the sample size is not fixed in advance. Instead, the decision-process
takes in new observations sequentially to make real-time decisions for testing
an alternative hypothesis against a null hypothesis until some stopping
criterion is satisfied. In many common applications of sequential hypothesis
testing, the data can be highly sensitive and may require privacy protection;
for example, sequential hypothesis testing is used in clinical trials, where
doctors sequentially collect data from patients and must determine when to stop
recruiting patients and whether the treatment is effective. The field of
differential privacy has been developed to offer data analysis tools with
strong privacy guarantees, and has been commonly applied to machine learning
and statistical tasks.
  In this work, we study the sequential hypothesis testing problem under a
slight variant of differential privacy, known as Renyi differential privacy. We
present a new private algorithm based on Wald's Sequential Probability Ratio
Test (SPRT) that also gives strong theoretical privacy guarantees. We provide
theoretical analysis on statistical performance measured by Type I and Type II
error as well as the expected sample size. We also empirically validate our
theoretical results on several synthetic databases, showing that our algorithms
also perform well in practice. Unlike previous work in private hypothesis
testing that focused only on the classical fixed sample setting, our results in
the sequential setting allow a conclusion to be reached much earlier, and thus
saving the cost of collecting additional samples.


title:
Effect of compositional fluctuation on the survival of bet-hedging
  species

abstract:
  Understanding the coexistence of diverse species in a changing environment is
an important problem in community ecology. Bet-hedging is a strategy that helps
species survive in such changing environments. However, studies of bet-hedging
have often focused on the expected long-term growth rate of the species by
itself, neglecting competition with other coexisting species. Here we study the
extinction risk of a bet-hedging species in competition with others. We show
that there are three contributions to the extinction risk. The first is the
usual demographic fluctuation due to stochastic reproduction and selection
processes in finite populations. The second, due to the fluctuation of
population growth rate caused by environmental changes, may counterintuitively
reduce the extinction risk for small populations. Besides those two, we reveal
a third contribution, which is unique to bet-hedging species that diversify
into multiple phenotypes: The phenotype composition of the population will
fluctuate over time, resulting in increased extinction risk. We compare such
compositional fluctuation to the demographic and environmental contributions,
showing how they have different effects on the extinction risk depending on the
population size, generation overlap, and environmental correlation.


title:
Optimal Round and Sample-Size Complexity for Partitioning in Parallel
  Sorting

abstract:
  State-of-the-art parallel sorting algorithms for distributed-memory
architectures are based on computing a balanced partitioning via sampling and
histogramming. By finding samples that partition the sorted keys into
evenly-sized chunks, these algorithms minimize the number of communication
rounds required. Histogramming (computing positions of samples) guides
sampling, enabling a decrease in the overall number of samples collected. We
derive lower and upper bounds on the number of sampling/histogramming rounds
required to compute a balanced partitioning. We improve on prior results to
demonstrate that when using $p$ processors/parts, $O(\log^* p)$ rounds with
$O(p/\log^* p)$ samples per round suffice. We match that with a lower bound
that shows any algorithm requires at least $\Omega(\log^* p)$ rounds with
$O(p)$ samples per round. Additionally, we prove the $\Omega(p \log p)$ samples
lower bound for one round, showing the optimality of sample sort in this case.
To derive the lower bound, we propose a hard randomized input distribution and
apply classical results from the distribution theory of runs.


title: Some stability and exact results in generalized Tur\'an problems

abstract:
  Given graphs $H$ and $F$, the generalized Tur\'an number $\mathrm{ex}(n,H,F)$
is the largest number of copies of $H$ in $n$-vertex $F$-free graphs. Stability
refers to the usual phenomenon that if an $n$-vertex $F$-free graph $G$
contains almost $\mathrm{ex}(n,H,F)$ copies of $H$, than $G$ is in some sense
similar to some extremal graph. We obtain new stability results for generalized
Tur\'an problems and derive several new exact results.


title:
Explaining Deep Convolutional Neural Networks via Latent Visual-Semantic
  Filter Attention

abstract:
  Interpretability is an important property for visual models as it helps
researchers and users understand the internal mechanism of a complex model.
However, generating semantic explanations about the learned representation is
challenging without direct supervision to produce such explanations. We propose
a general framework, Latent Visual Semantic Explainer (LaViSE), to teach any
existing convolutional neural network to generate text descriptions about its
own latent representations at the filter level. Our method constructs a mapping
between the visual and semantic spaces using generic image datasets, using
images and category names. It then transfers the mapping to the target domain
which does not have semantic labels. The proposed framework employs a modular
structure and enables to analyze any trained network whether or not its
original training data is available. We show that our method can generate novel
descriptions for learned filters beyond the set of categories defined in the
training dataset and perform an extensive evaluation on multiple datasets. We
also demonstrate a novel application of our method for unsupervised dataset
bias analysis which allows us to automatically discover hidden biases in
datasets or compare different subsets without using additional labels. The
dataset and code are made public to facilitate further research.


title:
How much can one learn a partial differential equation from its
  solution?

abstract:
  In this work we study the problem about learning a partial differential
equation (PDE) from its solution data. PDEs of various types are used as
examples to illustrate how much the solution data can reveal the PDE operator
depending on the underlying operator and initial data. A data driven and data
adaptive approach based on local regression and global consistency is proposed
for stable PDE identification. Numerical experiments are provided to verify our
analysis and demonstrate the performance of the proposed algorithms.


title: Gaia19bxc: possible polar below the period minimum

abstract:
  Gaia19bxc is a transient detected on 2019 May 9 by the Gaia Photometric
Science Alerts Team. I analyzed the past public Zwicky Transient Facility (ZTF)
data and found that Gaia19bxc has a period of 0.04473647(3) d and two different
maxima in one cycle. This object also showed high and low states in the ZTF
data. Based on the high amplitude (2.0 mag) of the short-term variations, short
period, almost zero color indices between the different ZTF bands and the
absence of a longer period, I classified it to be a likely polar. There has
been no established polar below the period minimum of cataclysmic variables
(CVs) and Gaia19bxc could be the first such an object. CVs below the period
minimum usually have a secondary star with a stripped evolved core and
Gaia19bxc is expected to have a similar secondary. If this is indeed the case,
Gaia19bxc could become a highly magnetized exotic ultracompact binary during
its secular evolution.


title:
A High Capacity Preamble Sequence for Random Access in Beyond 5G
  Networks: Design and Analysis

abstract:
  The widely used Zadoff-Chu sequence (ZC sequence) for random access preamble
in 5G has limitations in terms of the total number of preambles generated,
forcing the reuse of preambles. Hence, the probability of collision of
preambles of UEs increase, resulting in the failure of random access procedure.
To truly qualify beyond 5G networks as green technology, the preamble capacity
should be increased without sacrificing energy efficiency. In this paper, we
propose a new candidate preamble sequence called $mALL$ sequence using the
concept of cover sequences to achieve higher preamble capacity without
degrading the power efficiency and hence minimizing device's carbon footprint.
We compare the performance of $mALL$ sequence with Zadoff-Chu sequence and
other sequences in the literature, such as $mZC$ and $aZC$ sequences. We
evaluate the performance of the preamble sequences in terms of periodic
correlation, detection probability and the effect of diversity combining. Also,
this paper explores the Peak to Average Power Ratio (PAPR) and Cubic Metric(CM)
for these sequences, as these are essential parameters to evaluate energy
efficiency. We show that the preamble capacity of the proposed $mALL$ sequence
is $10^{4}$ times higher than that of legacy ZC sequence without any
deterioration in the detection performance.


title:
Noise-induce coexisting firing patterns in hybrid-synaptic interacting
  networks

abstract:
  Synaptic noise plays a major role in setting up coexistence of various firing
patterns, but the precise mechanisms whereby these synaptic noise contributes
to coexisting firing activities are subtle and remain elusive. To investigate
these mechanisms, neurons with hybrid synaptic interaction in a balanced
neuronal networks have been recently put forward. Here we show that both
synaptic noise intensity and excitatory weights can make a greater contribution
than variance of synaptic noise to the coexistence of firing states with slight
modification parameters. The resulting statistical analysis of both voltage
trajectories and their spike trains reveals two forms of coexisting firing
patterns: time-varying and parameter-varying multistability. The emergence of
time-varying multistability as a format of metstable state has been observed
under suitable parameters settings of noise intensity and excitatory synaptic
weight. While the parameter-varying multistability is accompanied by
coexistence of synchrony state and metastable (or asynchronous firing state)
with slightly varying noise intensity and excitatory weights. Our results offer
a series of precise statistical explanation of the intricate effect of synaptic
noise in neural multistability. This reconciles previous theoretical and
numerical works, and confirms the suitability of various statistical methods to
investigate multistability in a hybrid synaptic interacting neuronal networks.


title: Towards efficient representation identification in supervised learning

abstract:
  Humans have a remarkable ability to disentangle complex sensory inputs (e.g.,
image, text) into simple factors of variation (e.g., shape, color) without much
supervision. This ability has inspired many works that attempt to solve the
following question: how do we invert the data generation process to extract
those factors with minimal or no supervision? Several works in the literature
on non-linear independent component analysis have established this negative
result; without some knowledge of the data generation process or appropriate
inductive biases, it is impossible to perform this inversion. In recent years,
a lot of progress has been made on disentanglement under structural
assumptions, e.g., when we have access to auxiliary information that makes the
factors of variation conditionally independent. However, existing work requires
a lot of auxiliary information, e.g., in supervised classification, it
prescribes that the number of label classes should be at least equal to the
total dimension of all factors of variation. In this work, we depart from these
assumptions and ask: a) How can we get disentanglement when the auxiliary
information does not provide conditional independence over the factors of
variation? b) Can we reduce the amount of auxiliary information required for
disentanglement? For a class of models where auxiliary information does not
ensure conditional independence, we show theoretically and experimentally that
disentanglement (to a large extent) is possible even when the auxiliary
information dimension is much less than the dimension of the true latent
representation.


title:
Self-Supervised Video Representation Learning with Motion-Contrastive
  Perception

abstract:
  Visual-only self-supervised learning has achieved significant improvement in
video representation learning. Existing related methods encourage models to
learn video representations by utilizing contrastive learning or designing
specific pretext tasks. However, some models are likely to focus on the
background, which is unimportant for learning video representations. To
alleviate this problem, we propose a new view called long-range residual frame
to obtain more motion-specific information. Based on this, we propose the
Motion-Contrastive Perception Network (MCPNet), which consists of two branches,
namely, Motion Information Perception (MIP) and Contrastive Instance Perception
(CIP), to learn generic video representations by focusing on the changing areas
in videos. Specifically, the MIP branch aims to learn fine-grained motion
features, and the CIP branch performs contrastive learning to learn overall
semantics information for each instance. Experiments on two benchmark datasets
UCF-101 and HMDB-51 show that our method outperforms current state-of-the-art
visual-only self-supervised approaches.


title:
A First-Principles Study on Electronic, Thermodynamic, and Dielectric
  Properties of Monolayer Ca(OH)2 and Mg(OH)2

abstract:
  We perform first-principles calculations to explore electronic,
thermodynamic, and dielectric properties of two-dimensional (2D) layered,
alkaline-earth hydroxides Ca(OH)2 and Mg(OH)2. We calculate the lattice
parameters, exfoliation energies, and phonon spectra of monolayers and also
investigate the thermal properties of these monolayers such as Helmholtz free
energy, heat capacity at constant volume, and entropy as a function of
temperature. We employ Density Functional Perturbation Theory (DFPT) to
calculate the in-plane and out-of-plane static dielectric constant of the bulk
and monolayer samples. We compute the bandgap and electron affinity values
using the HSE06 functional and estimate the leakage current density of
transistors with monolayer Ca(OH)2 and Mg(OH)2 as dielectrics when combined
with HfS2 and WS2, respectively. Our results show that bilayer Mg(OH)2 (EOT ~
0.60 nm) with a lower solubility in water, offers higher out-of-plane
dielectric constants and lower leakage currents than bilayer Ca(OH)2 (EOT ~
0.56 nm). Additionally, the out-of-plane dielectric constant, leakage current,
and EOT of Mg(OH)2 outperform bilayer h-BN. We verify the applicability of
Anderson's rule and conclude that bilayers of Ca(OH)2 and Mg(OH)2 respectively
paired with lattice-matched monolayer HfS2 and WS2 are effective structural
combinations that could lead to the development of innovative multi-functional
Field Effect Transistors (FETs).


title: Coalescent dynamics of planktonic communities

abstract:
  Planktonic communities are extremely diverse and include a vast number of
rare species. The dynamics of these rare species is best described by
individual-based models. However, individual-based approaches to planktonic
diversity face substantial difficulties, due to the large number of individuals
required to make realistic predictions. In this paper, we study diversity of
planktonic communities by means of a spatial coalescence model, that
incorporates transport by oceanic currents. As a main advantage, our approach
requires simulating a number of individuals equal to the size of the sample one
is interested in, rather than the size of the entire community. By theoretical
analysis and simulations, we explore the conditions upon which our coalescence
model is equivalent to individual-based dynamics. As an application, we use our
model to predict the impact of chaotic advection by oceanic currents on
biodiversity. We conclude that the coalescent approach permits to simulate
marine microbial communities much more efficiently than with individual-based
models.


title:
Strong Solutions for Three-dimensional Nonhomogeneous Incompressible
  Heat Conducting Magnetohydrodynamic Equations with Vacuum

abstract:
  This paper is concerned with a Cauchy problem for the three-dimensional (3D)
nonhomogeneous incompressible heat conducting magnetohydrodynamic (MHD)
equations in the whole space. First of all, we establish a weak Serrin-type
blowup criterion for the strong solutions. It is shown that for the Cauchy
problem of the 3D nonhomogeneous heat conducting MHD equations, the strong
solution exists globally if the velocity satisfies the weak Serrin's condition.
In particular, this criterion is independent of the absolute temperature and
magnetic field. Then as an immediate application, we prove the global existence
and uniqueness of strong solution to the 3D nonhomogeneous heat conducting MHD
equations under some smallness condition on the initial data. In addition, the
initial vacuum is allowed.


title: Decay No More: A Persistent Twitter Dataset for Learning Social Meaning

abstract:
  With the proliferation of social media, many studies resort to social media
to construct datasets for developing social meaning understanding systems. For
the popular case of Twitter, most researchers distribute tweet IDs without the
actual text contents due to the data distribution policy of the platform. One
issue is that the posts become increasingly inaccessible over time, which leads
to unfair comparisons and a temporal bias in social media research. To
alleviate this challenge of data decay, we leverage a paraphrase model to
propose a new persistent English Twitter dataset for social meaning (PTSM).
PTSM consists of $17$ social meaning datasets in $10$ categories of tasks. We
experiment with two SOTA pre-trained language models and show that our PTSM can
substitute the actual tweets with paraphrases with marginal performance loss.


title:
Confidence Estimation Transformer for Long-term Renewable Energy
  Forecasting in Reinforcement Learning-based Power Grid Dispatching

abstract:
  The expansion of renewable energy could help realizing the goals of peaking
carbon dioxide emissions and carbon neutralization. Some existing grid
dispatching methods integrating short-term renewable energy prediction and
reinforcement learning (RL) have been proved to alleviate the adverse impact of
energy fluctuations risk. However, these methods omit the long-term output
prediction, which leads to stability and security problems on the optimal power
flow. This paper proposes a confidence estimation Transformer for long-term
renewable energy forecasting in reinforcement learning-based power grid
dispatching (Conformer-RLpatching). Conformer-RLpatching predicts long-term
active output of each renewable energy generator with an enhanced Transformer
to boost the performance of hybrid energy grid dispatching. Furthermore, a
confidence estimation method is proposed to reduce the prediction error of
renewable energy. Meanwhile, a dispatching necessity evaluation mechanism is
put forward to decide whether the active output of a generator needs to be
adjusted. Experiments carried out on the SG-126 power grid simulator show that
Conformer-RLpatching achieves great improvement over the second best algorithm
DDPG in security score by 25.8% and achieves a better total reward compared
with the golden medal team in the power grid dispatching competition sponsored
by State Grid Corporation of China under the same simulation environment. Codes
are outsourced in https://github.com/buptlxh/Conformer-RLpatching.


title: On an exponential sum related to the M\"{o}bius function

abstract:
  Let $\mu(n)$ be the M\"{o}bius function and $e(\alpha)=e^{2\pi i\alpha}$. In
this paper, we study upper bounds of the classical sum
$$S(x,\alpha):=\sum_{1\leq n\leq x}\mu(n)e(\alpha n).$$ We can improve some
classical results of Baker and Harman \cite{BH}.


title: Reaction Network Analysis of Metabolic Insulin Signaling

abstract:
  Absolute concentration robustness (ACR) and concordance are novel concepts in
the theory of robustness and stability within Chemical Reaction Network Theory.
ACR characterizes the invariance of the concentrations of a species at all
positive equilibria of a kinetic system. In this paper, we have extended G.
Shinar and M. Feinberg's reaction network analysis approach to a large model of
a biochemical system based on recent advances in decomposing reaction networks.
Our extended approach is applied to the insulin signaling system, an important
metabolic system, established by Sedaghat and colleagues in 2002. We have shown
that the positive dependence of the insulin metabolic signaling network ensures
the existence of rate constants under which the mass action system has positive
equilibria. We have also shown that the reaction network is concordant and that
the system has ACR in 8 species, which restricts the variability of the
positive equilibria. This result suggests that this "structural source of
robustness" may be an important factor in the system's overall robustness.
Moreover, we have obtained the finest independent decomposition of the insulin
metabolic signaling network (which has 35 reactions) consisting of 10
subnetworks, and have shown that the stability of the equilibria is only within
the subnetworks and not for the whole network. Interestingly, we have verified
that, for the rate constants used in the study of Sedaghat and colleagues, the
insulin-regulated glucose transporter GLUT4 (an important component in glucose
energy metabolism) has stable ACR.


title: Learning Pixel-Level Distinctions for Video Highlight Detection

abstract:
  The goal of video highlight detection is to select the most attractive
segments from a long video to depict the most interesting parts of the video.
Existing methods typically focus on modeling relationship between different
video segments in order to learning a model that can assign highlight scores to
these segments; however, these approaches do not explicitly consider the
contextual dependency within individual segments. To this end, we propose to
learn pixel-level distinctions to improve the video highlight detection. This
pixel-level distinction indicates whether or not each pixel in one video
belongs to an interesting section. The advantages of modeling such fine-level
distinctions are two-fold. First, it allows us to exploit the temporal and
spatial relations of the content in one video, since the distinction of a pixel
in one frame is highly dependent on both the content before this frame and the
content around this pixel in this frame. Second, learning the pixel-level
distinction also gives a good explanation to the video highlight task regarding
what contents in a highlight segment will be attractive to people. We design an
encoder-decoder network to estimate the pixel-level distinction, in which we
leverage the 3D convolutional neural networks to exploit the temporal context
information, and further take advantage of the visual saliency to model the
spatial distinction. State-of-the-art performance on three public benchmarks
clearly validates the effectiveness of our framework for video highlight
detection.


title:
Impedance modelling and collective effects in the Future Circular
  e$^+$e$^-$ Collider with 4 IPs

abstract:
  The FCC-ee impedance model is being constantly updated closely following the
vacuum chamber design and parameters evolution. In particular, at present, a
thicker NEG coating of 150 nm (instead of previous 100 nm) has been suggested
by the vacuum experts, and a more realistic impedance model of the bellows has
been investigated. Moreover, also the transverse impedance has been updated by
considering the same sources as for the longitudinal case. Therefore, the
FCC-ee impedance database is getting more complete and the impedance model is
being refined. In this paper we describe the presently available machine
coupling impedance in both longitudinal and transverse planes, and study the
impedance-driven single bunch instabilities (with and without beam-beam
interaction) for the new FCC-ee parameter set with 4 interaction points (IPs).
The results are compared with the previously obtained ones and a further
possible mitigation of the beam-beam head-tail instability (X-Z instability) is
proposed.


title:
W49N MCN-a: a disk accreting massive protostar embedded in an
  early-phase hot molecular core

abstract:
  We present ALMA archival data for 219-235 GHz continuum and line observations
toward the hot molecular core (HMC) W49N MCN-a (UCHII region J1) at a
resolution of ~0."3. The dust continuum emission, showing an elongated
structure of 1."40x0."95 (PA=43.5deg) perpendicular to the outflow seen in SiO
and SO, represents a rotating flattened envelope, or torus, with a radius of
7,800au inclined at 47.5deg or larger. The emissions from CH3CN and 11
molecular lines exhibit a consistent velocity gradient as a result of rotation.
The magnitude of each velocity gradient is different, reflecting that each line
samples a specific radial region. This allows us to derive a rotation curve as
Vrot prop R^0.44+-0.11 for 2,400au < R < 14,000au, giving the dynamical mass as
Mdyn = 57.0+24.5-17.1 (R [au]/3, 000)^1.88 Msun. The envelope mass
independently estimated from the dustemission is 910Msun (for Tdust =180K) for
R<7,800au and 32Msun (for Tdust=300K) for R<1,700 au. The dynamical mass
formula agrees well with these mass estimates within an uncertainty of a factor
of three in the latter. The envelope is self-gravitating and is unstable to
form spiral arms and fragments, allowing rapid accretion to the inner radii
with a rate of order 10^-2 Msun yr^-1, although inward motion was not detected.
The envelope may become a non self-gravitating Keplerian disk at R<(300-1,000)
au. The formula is also consistent with the total mass ~10^4 Msun of the entire
HMC 0.15 pc (31,000 au) in radius. Multiple transitions of CH3CN, HNCO and
CH3OH provide the rotation temperatures, suggesting that the central source of
MCN-a has an intrinsic bolometric luminosity of ~10^6 Lsun. These results have
revealed the structure and kinematics of MCN-a at its intermediate radii. With
no broad-line H30alpha emission detected, MCN-a may be in the earliest phase of
massive star formation.


title:
ME-GCN: Multi-dimensional Edge-Embedded Graph Convolutional Networks for
  Semi-supervised Text Classification

abstract:
  Compared to sequential learning models, graph-based neural networks exhibit
excellent ability in capturing global information and have been used for
semi-supervised learning tasks. Most Graph Convolutional Networks are designed
with the single-dimensional edge feature and failed to utilise the rich edge
information about graphs. This paper introduces the ME-GCN (Multi-dimensional
Edge-enhanced Graph Convolutional Networks) for semi-supervised text
classification. A text graph for an entire corpus is firstly constructed to
describe the undirected and multi-dimensional relationship of word-to-word,
document-document, and word-to-document. The graph is initialised with
corpus-trained multi-dimensional word and document node representation, and the
relations are represented according to the distance of those words/documents
nodes. Then, the generated graph is trained with ME-GCN, which considers the
edge features as multi-stream signals, and each stream performs a separate
graph convolutional operation. Our ME-GCN can integrate a rich source of graph
edge information of the entire text corpus. The results have demonstrated that
our proposed model has significantly outperformed the state-of-the-art methods
across eight benchmark datasets.


title:
A New Framework for Fast Automated Phonological Reconstruction Using
  Trimmed Alignments and Sound Correspondence Patterns

abstract:
  Computational approaches in historical linguistics have been increasingly
applied during the past decade and many new methods that implement parts of the
traditional comparative method have been proposed. Despite these increased
efforts, there are not many easy-to-use and fast approaches for the task of
phonological reconstruction. Here we present a new framework that combines
state-of-the-art techniques for automated sequence comparison with novel
techniques for phonetic alignment analysis and sound correspondence pattern
detection to allow for the supervised reconstruction of word forms in ancestral
languages. We test the method on a new dataset covering six groups from three
different language families. The results show that our method yields promising
results while at the same time being not only fast but also easy to apply and
expand.


title:
On Principal Curve-Based Classifiers and Similarity-Based Selective
  Sampling in Time-Series

abstract:
  Considering the concept of time-dilation, there exist some major issues with
recurrent neural Architectures. Any variation in time spans between input data
points causes performance attenuation in recurrent neural network
architectures. Principal curve-based classifiers have the ability of handling
any kind of variation in time spans. In other words, principal curve-based
classifiers preserve the relativity of time while neural network architecture
violates this property of time. On the other hand, considering the labeling
costs and problems in online monitoring devices, there should be an algorithm
that finds the data points which knowing their labels will cause in better
performance of the classifier. Current selective sampling algorithms have lack
of reliability due to the randomness of the proposed algorithms. This paper
proposes a classifier and also a deterministic selective sampling algorithm
with the same computational steps, both by use of principal curve as their
building block in model definition.


title:
Unsupervised Manga Character Re-identification via Face-body and
  Spatial-temporal Associated Clustering

abstract:
  In the past few years, there has been a dramatic growth in e-manga
(electronic Japanese-style comics). Faced with the booming demand for manga
research and the large amount of unlabeled manga data, we raised a new task,
called unsupervised manga character re-identification. However, the artistic
expression and stylistic limitations of manga pose many challenges to the
re-identification problem. Inspired by the idea that some content-related
features may help clustering, we propose a Face-body and Spatial-temporal
Associated Clustering method (FSAC). In the face-body combination module, a
face-body graph is constructed to solve problems such as exaggeration and
deformation in artistic creation by using the integrity of the image. In the
spatial-temporal relationship correction module, we analyze the appearance
features of characters and design a temporal-spatial-related triplet loss to
fine-tune the clustering. Extensive experiments on a manga book dataset with
109 volumes validate the superiority of our method in unsupervised manga
character re-identification.


title:
Valley polarization fluctuations, bistability, and switching in
  two-dimensional semiconductors

abstract:
  We study theoretically nonlinear valley polarization dynamics of excitons in
atom-thin semiconductors. The presence of significant polarization slows down
valley relaxation due to an effective magnetic field resulting from
exciton-exciton interactions. We address temporal dynamics of valley polarized
excitons and study the steady states of the polarized exciton gas. We
demonstrate bistability of the valley polarization where two steady states with
low and high valley polarization are formed. We study the effects of
fluctuations and noise in such system. We evaluate valley polarization
autocorrelation functions and demonstrate that for a high-polarization regime
the fluctuations are characterized by high amplitude and long relaxation time.
We study the switching between the low- and high-valley polarized states caused
by the noise in the system and demonstrate that the state with high valley
polarization is preferential in a wide range of pumping rates.


title:
Efficient route to achieve superconductivity improvement via
  substitutional La-Ce alloy superhydride at high pressure

abstract:
  The discovery of clathrate superhydrides has approached the long-standing
dream of room-temperature superconductivity and thus inspired their prosperous
research under high pressure. However, how to experimentally optimize these
compelling superhydrides is still a formidable challenge. Here, we find that
half of the Ce atoms in the recently discovered hexagonal close packed (hcp)
CeH9 structure can be randomly replaced by adjacent La, resulting in the
formation of LaH9 unit that is impossible in a binary system. Our experiments
show that hcp (La, Ce)H9 can be synthesized at ~110 GPa and possesses a maximum
Tc of 178 K at higher pressure, which is evidenced by in-situ X-ray diffraction
and electronic transport measurement where a sharp drop of resistivity to zero
and a characteristic decrease of Tc under a magnetic field up to 9 T. More
importantly, the Tc of (La, Ce)H9 is significantly increased by ~50-80 K
compared to CeH9, showing the hitherto highest Tc at megabar pressure. Our
experimental results not only verify the feasibility of improving the
superconductivity of hydrides by introducing other suitable metals, but also
provide important inspiration for finding high-Tc superconductors in various
multinary superhydrides.


title: $q$-adic Expansions of Negative Powers of $p$

abstract:
  In this paper we show that for any two coprime numbers $p,
q\in\mathbb{N}_{\ge 2}$ the $q$-adic expansion of $p^{-n}$ contains all
possible digits in $\{0,1,\cdots, q-1\}$ for all large $n$. As a result, there
are only finitely many $n\in\mathbb{N}$ such that the ternary expansion of
$2^{-n}$ omits the digit $2$. This is an analogue of the Erd\H{o}s' ternary
conjecture, which says that there are only finitely many $n\in\mathbb{N}$ such
that the ternary expansion of $2^n$ omits the digit $2$.


title: Gap probability for the hard edge Pearcey process

abstract:
  The hard edge Pearcey process is universal in random matrix theory and many
other stochastic models. This paper deals with the gap probability for the
thinned/unthinned hard edge Pearcey process over the interval $(0,s)$ by
working on the relevant Fredholm determinants. We establish an integral
representation of the gap probability via a Hamiltonian related a system of
coupled differential equations. Together with some remarkable differential
identities for the Hamiltonian, we derive the large gap asymptotics for the
thinned case, up to and including the constant term. As an application, we also
obtain the asymptotic statistical properties of the counting function for the
hard edge Pearcey process.


title: Plucker Formulas for Plane Algebraic Curves with a Given Newton Polygon

abstract:
  Let $C$ be a generic complex plane plane curve with a given Newton polygon
$P$. We compute the number of its inflection points and bitangents
(equivalently, the number of singularities of the projectively dual curve
$C^\vee$). We also prove that $C^\vee$ has no singularities other than nodes
and cusps for large enough polygons $P$.


title: Stripformer: Strip Transformer for Fast Image Deblurring

abstract:
  Images taken in dynamic scenes may contain unwanted motion blur, which
significantly degrades visual quality. Such blur causes short- and long-range
region-specific smoothing artifacts that are often directional and non-uniform,
which is difficult to be removed. Inspired by the current success of
transformers on computer vision and image processing tasks, we develop,
Stripformer, a transformer-based architecture that constructs intra- and
inter-strip tokens to reweight image features in the horizontal and vertical
directions to catch blurred patterns with different orientations. It stacks
interlaced intra-strip and inter-strip attention layers to reveal blur
magnitudes. In addition to detecting region-specific blurred patterns of
various orientations and magnitudes, Stripformer is also a token-efficient and
parameter-efficient transformer model, demanding much less memory usage and
computation cost than the vanilla transformer but works better without relying
on tremendous training data. Experimental results show that Stripformer
performs favorably against state-of-the-art models in dynamic scene deblurring.


title:
A Skyline and ranking query odissey: a journey from skyline and ranking
  queries up to f-skyline queries

abstract:
  Skyline and ranking queries are two of the most used tools to manage large
data sets. The former is based on non-dominance, while the latter on a scoring
function. Despite their effectiveness, they have some drawbacks like the result
size or the need for a utility function that must be taken into account. To do
this, in the last years, new kinds of queries, called flexible skyline queries,
have been developed. In the present article, a description of skyline and
ranking queries, f-skyline queries and a comparison among them are provided to
highlight the improvements achieved and how some limitations have been
overcome.


title:
Pushing on Personality Detection from Verbal Behavior: A Transformer
  Meets Text Contours of Psycholinguistic Features

abstract:
  Research at the intersection of personality psychology, computer science, and
linguistics has recently focused increasingly on modeling and predicting
personality from language use. We report two major improvements in predicting
personality traits from text data: (1) to our knowledge, the most comprehensive
set of theory-based psycholinguistic features and (2) hybrid models that
integrate a pre-trained Transformer Language Model BERT and Bidirectional Long
Short-Term Memory (BLSTM) networks trained on within-text distributions ('text
contours') of psycholinguistic features. We experiment with BLSTM models (with
and without Attention) and with two techniques for applying pre-trained
language representations from the transformer model - 'feature-based' and
'fine-tuning'. We evaluate the performance of the models we built on two
benchmark datasets that target the two dominant theoretical models of
personality: the Big Five Essay dataset and the MBTI Kaggle dataset. Our
results are encouraging as our models outperform existing work on the same
datasets. More specifically, our models achieve improvement in classification
accuracy by 2.9% on the Essay dataset and 8.28% on the Kaggle MBTI dataset. In
addition, we perform ablation experiments to quantify the impact of different
categories of psycholinguistic features in the respective personality
prediction models.


title:
Spectral Analysis and its applications for a class of scale-free network
  based on the weighted m-clique annex operation

abstract:
  The spectrum of network is an important tool to study the function and
dynamic properties of network, and graph operation and product is an effective
mechanism to construct a specific local and global topological structure. In
this study, a class of weighted $m-$clique annex operation $\tau_m^r(\cdot)$
controlled by scale factor $m$ and weight factor $r$ is defined, through which
an iterative weighted network model $G_t$ with small-world and scale-free
properties is constructed. In particular, when the number of iterations $t$
tends to infinity, the network has transfinite fractal property. Then, through
the iterative features of the network structure, the iterative relationship of
the eigenvalues of the normalized Laplacian matrix corresponding to the network
is studied. Accordingly, some applications of the spectrum of the network,
including the Kenemy constant, Multiplicative Degree-Kirchhoff index and the
number of weighted spanning trees, are further given. In addition, we also
study the effect of the two factors controlling network operation on the
structure and function of the iterative weighted network $G_t$, so that the
network operation can better simulate the real network and have more
application potential in the field of artificial network.


title: Numerical ranges of Foguel operators revisited

abstract:
  The Foguel operator is defined as $F_T=\begin{bmatrix}S^* & T \\ 0 &
S\end{bmatrix}$, where $S$ is the right shift on a Hilbert space $\mathcal H$
and $T$ can be an arbitrary bounded linear operator acting on $\mathcal H$.
Obviously, the numerical range $W(F_0)$ of $F_T$ with $T=0$ is the open unit
disk, and it was suggested by Gau, Wang and Wu in their LAA'2021 paper that
$W(F_{aI})$ for non-zero $a\in\mathbb C$ might be an elliptical disk. In this
paper, we described $W(F_{aI})$ explicitly and, as it happens, it is not.


title: Michael selections and Castaing representations with cadlag functions

abstract:
  Michael's selection theorem implies that a closed convex nonempty-valued
mapping from the Sorgenfrey line to a euclidean space is inner semicontinuous
if and only if the mapping can be represented as the image closure of
right-continuous selections of the mapping. This article gives necessary and
sufficient conditions for the representation to hold for cadlag selections,
i.e., for selections that are right-continuous and have left limits. The
characterization is motivated by continuous time stochastic optimization
problems over cadlag processes. Here, an application to integral functionals of
cadlag functions is given.


title: A Distributed Real-Time Recommender System for Big Data Streams

abstract:
  In today's data-driven world, recommender systems (RS) play a crucial role to
support the decision-making process. As users become continuously connected to
the internet, they become less patient and less tolerant to obsolete
recommendations made by an RS, e.g., movie recommendations on Netflix or books
to read on Amazon. This, in turn, requires continuous training of the RS to
cope with both the online fashion of data and the changing nature of user
tastes and interests, known as concept drift. Streaming (online) RS has to
address three requirements: continuous training and recommendation, handling
concept drifts, and ability to scale. Streaming recommender systems proposed in
the literature mostly, address the first two requirements and do not consider
scalability. That is because they run the training process on a single machine.
Such a machine, no matter how powerful it is, will eventually fail to cope with
the volume of the data, a lesson learned from big data processing. To tackle
the third challenge, we propose a Splitting and Replication mechanism for
building distributed streaming recommender systems. Our mechanism is inspired
by the successful shared-nothing architecture that underpins contemporary big
data processing systems. We have applied our mechanism to two well-known
approaches for online recommender systems, namely, matrix factorization and
item-based collaborative filtering. We have implemented our mechanism on top of
Apache Flink. We conducted experiments comparing the performance of the
baseline (single machine) approach with our distributed approach. Evaluating
different data sets, improvement in processing latency, throughput, and
accuracy have been observed. Our experiments show online recall improvement by
40\% with more than 50\% less memory consumption.


title:
Intersection Prediction from Single 360{\deg} Image via Deep Detection
  of Possible Direction of Travel

abstract:
  Movie-Map, an interactive first-person-view map that engages the user in a
simulated walking experience, comprises short 360{\deg} video segments
separated by traffic intersections that are seamlessly connected according to
the viewer's direction of travel. However, in wide urban-scale areas with
numerous intersecting roads, manual intersection segmentation requires
significant human effort. Therefore, automatic identification of intersections
from 360{\deg} videos is an important problem for scaling up Movie-Map. In this
paper, we propose a novel method that identifies an intersection from
individual frames in 360{\deg} videos. Instead of formulating the intersection
identification as a standard binary classification task with a 360{\deg} image
as input, we identify an intersection based on the number of the possible
directions of travel (PDoT) in perspective images projected in eight directions
from a single 360{\deg} image detected by the neural network for handling
various types of intersections. We constructed a large-scale 360{\deg} Image
Intersection Identification (iii360) dataset for training and evaluation where
360{\deg} videos were collected from various areas such as school campus,
downtown, suburb, and china town and demonstrate that our PDoT-based method
achieves 88\% accuracy, which is significantly better than that achieved by the
direct naive binary classification based method. The source codes and a partial
dataset will be shared in the community after the paper is published.


title:
ConsInstancy: Learning Instance Representations for Semi-Supervised
  Panoptic Segmentation of Concrete Aggregate Particles

abstract:
  We present a semi-supervised method for panoptic segmentation based on
ConsInstancy regularisation, a novel strategy for semi-supervised learning. It
leverages completely unlabelled data by enforcing consistency between predicted
instance representations and semantic segmentations during training in order to
improve the segmentation performance. To this end, we also propose new types of
instance representations that can be predicted by one simple forward path
through a fully convolutional network (FCN), delivering a convenient and
simple-to-train framework for panoptic segmentation. More specifically, we
propose the prediction of a three-dimensional instance orientation map as
intermediate representation and two complementary distance transform maps as
final representation, providing unique instance representations for a panoptic
segmentation. We test our method on two challenging data sets of both, hardened
and fresh concrete, the latter being proposed by the authors in this paper
demonstrating the effectiveness of our approach, outperforming the results
achieved by state-of-the-art methods for semi-supervised segmentation. In
particular, we are able to show that by leveraging completely unlabeled data in
our semi-supervised approach the achieved overall accuracy (OA) is increased by
up to 5% compared to an entirely supervised training using only labeled data.
Furthermore, we exceed the OA achieved by state-of-the-art semi-supervised
methods by up to 1.5%.


title:
"That Is a Suspicious Reaction!": Interpreting Logits Variation to
  Detect NLP Adversarial Attacks

abstract:
  Adversarial attacks are a major challenge faced by current machine learning
research. These purposely crafted inputs fool even the most advanced models,
precluding their deployment in safety-critical applications. Extensive research
in computer vision has been carried to develop reliable defense strategies.
However, the same issue remains less explored in natural language processing.
Our work presents a model-agnostic detector of adversarial text examples. The
approach identifies patterns in the logits of the target classifier when
perturbing the input text. The proposed detector improves the current
state-of-the-art performance in recognizing adversarial inputs and exhibits
strong generalization capabilities across different NLP models, datasets, and
word-level attacks.


title: UniDU: Towards A Unified Generative Dialogue Understanding Framework

abstract:
  With the development of pre-trained language models, remarkable success has
been witnessed in dialogue understanding (DU) direction. However, the current
DU approaches just employ an individual model for each DU task, independently,
without considering the shared knowledge across different DU tasks. In this
paper, we investigate a unified generative dialogue understanding framework,
namely UniDU, to achieve information exchange among DU tasks. Specifically, we
reformulate the DU tasks into unified generative paradigm. In addition, to
consider different training data for each task, we further introduce
model-agnostic training strategy to optimize unified model in a balanced
manner. We conduct the experiments on ten dialogue understanding datasets,
which span five fundamental tasks: dialogue summary, dialogue completion, slot
filling, intent detection and dialogue state tracking. The proposed UniDU
framework outperforms task-specific well-designed methods on all 5 tasks. We
further conduct comprehensive analysis experiments to study the effect factors.
The experimental results also show that the proposed method obtains promising
performance on unseen dialogue domain.


title:
Spectral Unmixing of Hyperspectral Images Based on Block Sparse
  Structure

abstract:
  Spectral unmixing (SU) of hyperspectral images (HSIs) is one of the important
areas in remote sensing (RS) that needs to be carefully addressed in different
RS applications. Despite the high spectral resolution of the hyperspectral
data, the relatively low spatial resolution of the sensors may lead to mixture
of different pure materials within the image pixels. In this case, the spectrum
of a given pixel recorded by the sensor can be a combination of multiple
spectra each belonging to a unique material in that pixel. Spectral unmixing is
then used as a technique to extract the spectral characteristics of the
different materials within the mixed pixels and to recover the spectrum of each
pure spectral signature, called endmember. Block-sparsity exists in
hyperspectral images as a result of spectral similarity between neighboring
pixels. In block-sparse signals, the nonzero samples occur in clusters and the
pattern of the clusters is often supposed to be unavailable as prior
information. This paper presents an innovative spectral unmixing approach for
HSIs based on block-sparse structure and sparse Bayesian learning (SBL)
strategy. To evaluate the performance of the proposed SU algorithm, it is
tested on both synthetic and real hyperspectral data and the quantitative
results are compared to those of other state-of-the-art methods in terms of
abundance angel distance (AAD) and mean square error (MSE). The achieved
results show the superiority of the proposed algorithm over the other competing
methods by a significant margin.


title:
Lipschitz stability of $\gamma$-FOCS and RC canonical Jordan bases of
  real $H$-selfadjoint matrices under small perturbations

abstract:
  In 2008 Bella, Olshevsky and Prasad proved that the flipped orthogonal (FO)
Jordan bases of H-selfadjoint matrices are Lipschitz stable under small
perturbations. In 2022, Dogruer, Minenkova and Olshevsky considered the real
case, and proved that for real H-selfadjoint matrices there exist a more
refined bases called FOCS bases. In addition to flipped orthogonality they also
possess the conjugate symmetric (CS) property. In this paper we prove that
these new FOCS bases are Lipschitz stable under small perturbations as well. We
also establish the Lipschitz stability for the classical real canonical Jordan
bases.


title:
Conservation laws with discontinuous flux function on networks: a
  splitting algorithm

abstract:
  In this article, we present an extension of the splitting algorithm proposed
in [22] to networks of conservation laws with discontinuous flux functions in
the unknown. We start with the discussion of a suitable Riemann solver at the
junction and then describe a strategy how to use the splitting algorithm on the
network. Finally, numerical examples demonstrate the accuracy of the splitting
algorithm by comparisons to the exact solution and other approaches used in the
literature.


title:
Capacitively coupled distinct mechanical resonators for room temperature
  phonon-cavity electromechanics

abstract:
  Coupled electromechanical resonators that can be independently
driven/detected and easily integrated with external circuits are essential for
exploring mechanical modes based signal processing. Here, we present a room
temperature phonon-cavity electromechanical system, consisting of two distinct
resonators: a silicon nitride electromechanical drum capacitively coupled to an
aluminum one. We demonstrate electromechanically induced transparency and
amplification in a two-tone driving scheme and observe the phonon-cavity force
affecting the mechanical damping rates of both movable objects. We also develop
an analytical model based on linearly coupled motion equations, which captures
the optomechanical features in the classical limit and enables to fit
quantitatively our measurements. Our results open up new possibilities in the
study of phonon-cavity based signal processing in the classical and potentially
in the future in the quantum regimes.


title:
Gliding on ice in search of accurate and cost-effective computational
  methods for astrochemistry on grains: the puzzling case of the HCN
  isomerization

abstract:
  The isomerization of hydrogen cyanide to hydrogen isocyanide on icy grain
surfaces is investigated by an accurate composite method (jun-Cheap) rooted in
the coupled cluster ansatz and by density functional approaches. After
benchmarking density functional predictions of both geometries and reaction
energies against jun-Cheap results for the relatively small model system HCN --
(H2O)2 the best performing DFT methods are selected. A large cluster containing
20 water molecules is then employed within a QM/QM$'$ approach to include a
realistic environment mimicking the surface of icy grains. Our results indicate
that four water molecules are directly involved in a proton relay mechanism,
which strongly reduces the activation energy with respect to the direct
hydrogen transfer occurring in the isolated molecule. Further extension of the
size of the cluster up to 192 water molecules in the framework of a three-layer
QM/QM'/MM model has a negligible effect on the energy barrier ruling the
isomerization. Computation of reaction rates by transition state theory
indicates that on icy surfaces the isomerization of HNC to HCN could occur
quite easily even at low temperatures thanks to the reduced activation energy
that can be effectively overcome by tunneling.