title: Spectral identification of networks with generalized diffusive coupling

abstract:
  Spectral network identification aims at inferring the eigenvalues of the
Laplacian matrix of a network from measurement data. This allows to capture
global information on the network structure from local measurements at a few
number of nodes. In this paper, we consider the spectral network identification
problem in the generalized setting of a vector-valued diffusive coupling. The
feasibility of this problem is investigated and theoretical results on the
properties of the associated generalized eigenvalue problem are obtained.
Finally, we propose a numerical method to solve the generalized network
identification problem, which relies on dynamic mode decomposition and
leverages the above theoretical results.


title: Unitail: Detecting, Reading, and Matching in Retail Scene

abstract:
  To make full use of computer vision technology in stores, it is required to
consider the actual needs that fit the characteristics of the retail scene.
Pursuing this goal, we introduce the United Retail Datasets (Unitail), a
large-scale benchmark of basic visual tasks on products that challenges
algorithms for detecting, reading, and matching. With 1.8M quadrilateral-shaped
instances annotated, the Unitail offers a detection dataset to align product
appearance better. Furthermore, it provides a gallery-style OCR dataset
containing 1454 product categories, 30k text regions, and 21k transcriptions to
enable robust reading on products and motivate enhanced product matching.
Besides benchmarking the datasets using various state-of-the-arts, we customize
a new detector for product detection and provide a simple OCR-based matching
solution that verifies its effectiveness.


title:
Determination of the recombination coefficient in electrolytic solutions
  from impedance spectroscopy measurements

abstract:
  The dependence of the actual/effective ionic concentration on the nominal
salt concentration is experimentally investigated for aqueous solutions of NaCl
and KCl. The actual ionic bulk density is determined by means of the impedance
spectroscopy technique using two approaches for the data analysis: A first
approach scrutinizing the real part's plateau of the cell's electric impedance
spectrum versus the bulk density of dopant when the impedance is written in the
series representation. A second one via extraction of the ionic density from
simultaneously fitting of the real and imaginary parts of the impedance
spectrum by using the Poisson-Nernst-Planck model. The validity of this
procedure reposes on the hypothesis that both the diffusion coefficient and the
dielectric constant are independent from the salt concentration in the explored
range of concentrations. Our experimental results indicate that a first-order
reaction describes well the dissociation phenomenon. The
dissociation-association ratio of the reaction has been calculated. Deviations
from linearity appear at rather low concentrations of the order of mM.


title:
Interpretable RNA Foundation Model from Unannotated Data for Highly
  Accurate RNA Structure and Function Predictions

abstract:
  Non-coding RNA structure and function are essential to understanding various
biological processes, such as cell signaling, gene expression, and
post-transcriptional regulations. These are all among the core problems in the
RNA field. With the rapid growth of sequencing technology, we have accumulated
a massive amount of unannotated RNA sequences. On the other hand, expensive
experimental observatory results in only limited numbers of annotated data and
3D structures. Hence, it is still challenging to design supervised
computational methods for predicting their structures and functions. Lack of
annotated data and systematic study causes inferior performance. To resolve the
issue, we propose a novel RNA foundation model (RNA-FM) to take advantage of
all the 23 million non-coding RNA sequences through self-supervised learning.
Within this approach, we discover that the pre-trained RNA-FM could infer
sequential and evolutionary information of non-coding RNAs without using any
labels. Furthermore, we demonstrate RNA-FM's effectiveness by applying it to
the downstream secondary/3D structure prediction, protein-RNA binding
preference modeling, and 5' UTR-based mean ribosome loading prediction. The
comprehensive experiments show that the proposed method improves the RNA
structural and functional modeling results significantly and consistently.
Despite only trained with unlabelled data, RNA-FM can serve as the foundational
model for the field.


title:
PERIDOT Codes: Replacing Identifiers, Sequence Numbers and Nonces with
  Permutations

abstract:
  Identifiers and sequence numbers make up a large part of the protocol
overhead in certain low-power wide-area networks. The requirement for
cryptographic nonces in authentication and encryption schemes often demands
excessively long sequence numbers, which leads to an increase in energy
consumption per transmitted packet. In this paper, the novel PERIDOT coding
scheme is proposed. It replaces identifiers and sequence numbers with a code,
based on which receivers can identify transmitters with high confidence.
PERIDOT is based on specially constructed integer permutations assigned to
transmitters. An upper bound on the performance of PERIDOT codes is provided
and methods for constructing particularly suitable permutations are presented.
In practice, PERIDOT can significantly increase intervals between nonce reuses
and, at the same time, reduce power consumption.


title:
Actual Causality and Responsibility Attribution in Decentralized
  Partially Observable Markov Decision Processes

abstract:
  Actual causality and a closely related concept of responsibility attribution
are central to accountable decision making. Actual causality focuses on
specific outcomes and aims to identify decisions (actions) that were critical
in realizing an outcome of interest. Responsibility attribution is
complementary and aims to identify the extent to which decision makers (agents)
are responsible for this outcome. In this paper, we study these concepts under
a widely used framework for multi-agent sequential decision making under
uncertainty: decentralized partially observable Markov decision processes
(Dec-POMDPs). Following recent works in RL that show correspondence between
POMDPs and Structural Causal Models (SCMs), we first establish a connection
between Dec-POMDPs and SCMs. This connection enables us to utilize a language
for describing actual causality from prior work and study existing definitions
of actual causality in Dec-POMDPs. Given that some of the well-known
definitions may lead to counter-intuitive actual causes, we introduce a novel
definition that more explicitly accounts for causal dependencies between
agents' actions. We then turn to responsibility attribution based on actual
causality, where we argue that in ascribing responsibility to an agent it is
important to consider both the number of actual causes in which the agent
participates, as well as its ability to manipulate its own degree of
responsibility. Motivated by these arguments we introduce a family of
responsibility attribution methods that extends prior work, while accounting
for the aforementioned considerations. Finally, through a simulation-based
experiment, we compare different definitions of actual causality and
responsibility attribution methods. The empirical results demonstrate the
qualitative difference between the considered definitions of actual causality
and their impact on attributed responsibility.


title: The affine Springer fiber - sheaf correspondence

abstract:
  Given a semisimple element in the loop Lie algebra of a reductive group, we
construct a quasi-coherent sheaf on a partial resolution of the trigonometric
commuting variety of the Langlands dual group. The construction uses affine
Springer theory and can be thought of as an incarnation of 3d mirror symmetry.
For the group $GL_n$, the corresponding partial resolution is
$\mathrm{Hilb}^n(\mathbb{C}^\times\times \mathbb{C})$. We also consider a
quantization of this construction for homogeneous elements.


title: Quantum dot on a plasma-facing microparticle surface: Thermal balance

abstract:
  Semiconductor nanocrystals, quantum dots, are known to exhibit the
quantum-confined Stark effect which reveals itself in the shift of their
photoluminescence spectra in response to external electric field. It was,
therefore, proposed to use quantum dots deposited on the microparticle surface
for the optical measurement of the charge acquired by the microparticles in
low-temperature plasmas. Thermal balance of a quantum dot residing on the
surface of a microparticle immersed in a plasma is considered in this work. It
is shown for typical plasma parameters that under periodically pulsed plasma
conditions, the spectral shift of the photoluminescence of the quantum dot
caused by the oscillations of its temperature becomes undetectable at the
effective thermal flux characterizing the thermal contact between the quantum
dot and the microparticle $\sim 10^9$~s$^{-1}$. Under these conditions, the
entire spectral shift observed during the period of plasma pulsing should be
attributed to the quantum-confined Stark effect due to the microparticle
charge. Lower-boundary estimate for the effective thermal flux for the direct
contact between the quantum dot and the microparticle is $\sim
10^{12}$~s$^{-1}$.


title: Face identification by means of a neural net classifier

abstract:
  This paper describes a novel face identification method that combines the
eigenfaces theory with the Neural Nets. We use the eigenfaces methodology in
order to reduce the dimensionality of the input image, and a neural net
classifier that performs the identification process. The method presented
recognizes faces in the presence of variations in facial expression, facial
details and lighting conditions. A recognition rate of more than 87% has been
achieved, while the classical method of Turk and Pentland achieves a 75.5%.


title: Building Decision Forest via Deep Reinforcement Learning

abstract:
  Ensemble learning methods whose base classifier is a decision tree usually
belong to the bagging or boosting. However, no previous work has ever built the
ensemble classifier by maximizing long-term returns to the best of our
knowledge. This paper proposes a decision forest building method called
MA-H-SAC-DF for binary classification via deep reinforcement learning. First,
the building process is modeled as a decentralized partial observable Markov
decision process, and a set of cooperative agents jointly constructs all base
classifiers. Second, the global state and local observations are defined based
on informations of the parent node and the current location. Last, the
state-of-the-art deep reinforcement method Hybrid SAC is extended to a
multi-agent system under the CTDE architecture to find an optimal decision
forest building policy. The experiments indicate that MA-H-SAC-DF has the same
performance as random forest, Adaboost, and GBDT on balanced datasets and
outperforms them on imbalanced datasets.


title: Classical dynamics for Loop Gravity: The 2-vertex model

abstract:
  The study of toy models in loop quantum gravity (LQG), defined as truncations
of the full theory, is relevant to both the development of the LQG
phenomenology, in cosmology and astrophysics, and the progress towards the
resolution of the open issues of the theory, in particular the implementation
of the dynamics. Here, we study the dynamics of spin network states of quantum
geometry defined on the family of graphs consisting in 2 vertices linked by an
arbitrary number of edges, or 2-vertex model in short. A symmetry reduced
sector of this model -- to isotropic and homogeneous geometries -- was
successfully studied in the past, where interesting cosmological insights were
found. We now study the evolution of the classical trajectories for this system
in the general case, for arbitrary number of edges with random initial
configurations. We use the spinorial formalism and its clear interpretation of
spin networks in terms of discrete twisted geometries, with the quantum 3d
space made of superpositions of polyhedra glued together by faces of equal
area. Remarkably, oscillatory and divergent regimes are found with a universal
dependence on the coupling constants of the Hamiltonian and independent of the
initial spinors or the number of edges. Furthermore, we explore the evolution
of the associated polyhedra as well as their volumes and areas.


title:
Model-agnostic Counterfactual Synthesis Policy for Interactive
  Recommendation

abstract:
  Interactive recommendation is able to learn from the interactive processes
between users and systems to confront the dynamic interests of users. Recent
advances have convinced that the ability of reinforcement learning to handle
the dynamic process can be effectively applied in the interactive
recommendation. However, the sparsity of interactive data may hamper the
performance of the system. We propose to train a Model-agnostic Counterfactual
Synthesis Policy to generate counterfactual data and address the data sparsity
problem by modelling from observation and counterfactual distribution. The
proposed policy can identify and replace the trivial components for any state
in the training process with other agents, which can be deployed in any
RL-based algorithm. The experimental results demonstrate the effectiveness and
generality of our proposed policy.


title:
Unimodal-Concentrated Loss: Fully Adaptive Label Distribution Learning
  for Ordinal Regression

abstract:
  Learning from a label distribution has achieved promising results on ordinal
regression tasks such as facial age and head pose estimation wherein, the
concept of adaptive label distribution learning (ALDL) has drawn lots of
attention recently for its superiority in theory. However, compared with the
methods assuming fixed form label distribution, ALDL methods have not achieved
better performance. We argue that existing ALDL algorithms do not fully exploit
the intrinsic properties of ordinal regression. In this paper, we emphatically
summarize that learning an adaptive label distribution on ordinal regression
tasks should follow three principles. First, the probability corresponding to
the ground-truth should be the highest in label distribution. Second, the
probabilities of neighboring labels should decrease with the increase of
distance away from the ground-truth, i.e., the distribution is unimodal. Third,
the label distribution should vary with samples changing, and even be distinct
for different instances with the same label, due to the different levels of
difficulty and ambiguity. Under the premise of these principles, we propose a
novel loss function for fully adaptive label distribution learning, namely
unimodal-concentrated loss. Specifically, the unimodal loss derived from the
learning to rank strategy constrains the distribution to be unimodal.
Furthermore, the estimation error and the variance of the predicted
distribution for a specific sample are integrated into the proposed
concentrated loss to make the predicted distribution maximize at the
ground-truth and vary according to the predicting uncertainty. Extensive
experimental results on typical ordinal regression tasks including age and head
pose estimation, show the superiority of our proposed unimodal-concentrated
loss compared with existing loss functions.


title:
Effect of interfacial spin mixing conductance on gyromagnetic ratio of
  Gd substituted Y$_{3}$Fe$_{5}$O$_{12}$

abstract:
  Due to its low intrinsic damping, Y$_3$Fe$_5$O$_{12}$ and its substituted
variations are often used for ferromagnetic layer at spin pumping experiment.
Spin pumping is an interfacial spin current generation in the interface of
ferromagnet and non-magnetic metal, governed by spin mixing conductance
parameter $G^{\uparrow\downarrow}$. $G^{\uparrow\downarrow}$ has been shown to
enhance the damping of the ferromagnetic layer. The theory suggested that the
effect of $G^{\uparrow\downarrow}$ on gyromagnetic ratio only come from its
negligible imaginary part. In this article, we show that the different damping
of ferrimagnetic lattices induced by $G^{\uparrow\downarrow}$ can affect the
gyromagnetic ratio of Gd-substituted Y$_3$Fe$_5$O$_{12}$.


title: Speaker verification in mismatch training and testing conditions

abstract:
  This paper presents an exhaustive study about the robustness of several
parameterizations, with a new database specially acquired for the purpose of a
speaker recognition application. This database includes the following
variations: different recording sessions (including telephonic and microphonic
recordings), recording rooms, and languages (it has been obtained from a
bilingual set of speakers). This study has been performed with covariance
matrices in a text independent speaker verification application. It reveals
that the combination of several parameterizations can improve the robustness in
all the scenarios.


title: No arbitrage global parametrization for the eSSVI volatility surface

abstract:
  The article describes a global and arbitrage-free parametrization of the
eSSVI surfaces introduced by Hendriks and Martini in 2019. A robust calibration
of such surfaces has already been proposed by the quantitative research team at
Zeliade in 2019, but it is sequential in expiries and lacks of a global view on
the surface. The alternative calibration suggested in this article is faster
and always guarantees an arbitrage-free fit of market data.


title: Deep neural networks for solving extremely large linear systems

abstract:
  In this paper, we study deep neural networks for solving extremely large
linear systems arising from physically relevant problems. Because of the curse
of dimensionality, it is expensive to store both solution and right hand side
vectors in such extremely large linear systems. Our idea is to employ a neural
network to characterize the solution with parameters being much fewer than the
size of the solution. We present an error analysis of the proposed method
provided that the solution vector can be approximated by the continuous
quantity, which is in the Barron space. Several numerical examples arising from
partial differential equations, queueing problems and probabilistic Boolean
networks are presented to demonstrate that solutions of linear systems with
sizes ranging from septillion ($10^{24}$) to nonillion ($10^{30})$ can be
learned quite accurately.


title:
Scalar and vector gauges unification in de Sitter ambient space
  formalism

abstract:
  We consider the massless minimally coupled scalar field in the de Sitter
ambient space formalism as a gauge potential or connection field. We construct
the scalar gauge theory by helping an arbitrary constant five-vector field
$B^\alpha$ analogous to the standard gauge theory. The Lagrangian density of
the interaction between the scalar and spinor fields is presented in this
framework. The Yukawa potential can be extracted from this Lagrangian in the
null curvature limit by an appropriate choice of a constant five-vector field.
It is shown that the de Sitter ambient space formalism permits us to unify the
gauge scalar and vector fields. By choosing a matrix form for the constant
five-vector field $B^\alpha$, the unification of gauge scalar and vector fields
in the spectral action of noncommutative geometry can be rewritten. We discuss
that the scalar gauge field may be interpreted as the connection fields between
the different de Sitter hyperboloids in the fuzzy quantum geometry due to the
constant five-vector field.


title:
State-feedback Abstractions for Optimal Control of Piecewise-affine
  Systems

abstract:
  In this manuscript, we investigate symbolic abstractions that capture the
behavior of piecewise-affine systems under input constraints and bounded
external noise. This is accomplished by considering local affine feedback
controllers that are jointly designed with the symbolic model, which ensures
that an alternating simulation relation between the system and the abstraction
holds. The resulting symbolic system is called a state-feedback abstraction and
we show that it can be deterministic even when the original piecewise-affine
system is unstable and non-deterministic. One benefit of this approach is the
fact that the input space need not be discretized and the symbolic-input space
is reduced to a finite set of controllers. When ellipsoidal cells and affine
controllers are considered, we present necessary and sufficient conditions
written as a semi-definite program for the existence of a transition and a
robust upper bound on the transition cost. Two examples illustrate particular
aspects of the theory and its applicability.


title:
On the effects of a wide opening in the domain of the (stochastic)
  Allen-Cahn equation and the motion of hybrid zones

abstract:
  We are concerned with a special form of the (stochastic) Allen-Cahn equation,
which can be seen as a model of hybrid zones in population genetics.
Individuals in the population can be of one of three types; $aa$ are fitter
than $AA$, and both are fitter than the $aA$ heterozygotes. The hybrid zone is
the region separating a subpopulation consisting entirely of $aa$ individuals
from one consisting of $AA$ individuals. We investigate the interplay between
the motion of the hybrid zone and the shape of the habitat, both with and
without genetic drift (corresponding to stochastic and deterministic models
respectively). In the deterministic model, we investigate the effect of a wide
opening and provide some explicit sufficient conditions under which the spread
of the advantageous type is halted, and complementary conditions under which it
sweeps through the whole population. As a standing example, we are interested
in the outcome of the advantageous population passing through an isthmus. We
also identify rather precise conditions under which genetic drift breaks down
the structure of the hybrid zone, complementing previous work that identified
conditions on the strength of genetic drift under which the structure of the
hybrid zone is preserved.
  Our results demonstrate that, even in cylindrical domains, it can be
misleading to caricature allele frequencies by one-dimensional travelling
waves, and that the strength of genetic drift plays an important role in
determining the fate of a favoured allele.


title:
A solution approach for the anonymous sharing of sensitive supply chain
  traceability data

abstract:
  Supply chain traceability systems have become central in many industries,
however, traceability data is often commercially sensitive, and firms seek to
keep it confidential to protect their competitive advantage. This is at odds
with calls for greater transparency in supply chains for sustainability and
efficiency. In this research paper, we develop a solution to this conflict. We
demonstrate that sanitised data can be used to confirm the authenticity of
products and anonymously verify the rights of an actor to access detailed
traceability data. Following a design science methodology, we develop a
prototype protocol and verify its performance with traceability data based on
the global standard EPCIS.


title: Towards gain tuning for numerical KKL observers

abstract:
  This paper presents a first step towards tuning observers for nonlinear
systems. Relying on recent results around Kazantzis-Kravaris/Luenberger (KKL)
observers, we propose to design a family of observers parametrized by the
cut-off frequency of a linear filter. We use neural networks to learn the
mapping between the observer and the nonlinear system as a function of this
frequency, and present a novel method to sample the state-space efficiently for
nonlinear regression. We then propose a criterion related to noise sensitivity,
which can be used to tune the observer by choosing the most appropriate
frequency. We illustrate the merits of this approach in numerical simulations.


title: Parabolic representations and generalized Riley polynomials

abstract:
  We generalize R. Riley's study about parabolic representations of two bridge
knot groups to the general knots in $S^3$. We utilize the parabolic quandle
method for general knot diagrams and adopt symplectic quandle for better
investigation. For any knot diagram with a specified crossing $\text{c}$, we
define a generalized Riley polynomial $R_\text{c}(y) \in \mathbb{Q}[y]$ whose
roots correspond to the conjugacy classes of parabolic representations of the
knot group. The $\text{sign-type}$ of parabolic quandle is newly introduced and
we obtain a formula for the obstruction class to lift a boundary unipotent
$\text{SL}_2 \mathbb{C}$-representation. Moreover, we define another polynomial
$g_\text{c}(u)\in\mathbb{Q}[u]$, called {$u$-polynomial}, and prove that
$R_\text{c}(u^2)=\pm g_\text{c}(u)g_\text{c}(-u)$. Based on this result, we
introduce $\text{Riley field}$ and $\text{$u$-field}$ which are closely related
to the invariant trace field. Finally, we present thorough computations of
parabolic representations by our method for the examples of knots, $4_1$,
$7_4$, $8_{18}$, $9_{29}$, and the link $5_1^2$, along with the complex volume
and cusp shape for each representation.


title: Branch and Price for Submodular Bin Packing

abstract:
  The submodular bin packing (SMBP) problem aims to pack items into a minimal
number of bins for which the capacity utilization function is submodular. The
SMBP is equivalent to the chance-constrained and robust bin packing problems
under various conditions. The SMBP is a hard mixed-integer nonlinear
programming optimization problem. In this paper, we propose a branch-and-price
algorithm to solve this problem. The resulting price subproblems are submodular
knapsack problems, and we propose a tailored exact branch-and-cut algorithm
based on a piece-wise linear relaxation to solve them. To speed up column
generation, we develop a hybrid pricing strategy that can replace the exact
pricing algorithm with a fast pricing heuristic. We test our algorithms on
instances generated as suggested in the literature. The computational results
show the efficiency of our branch-and-price algorithm and the proposed pricing
techniques.


title:
Highly tunable optics across a topological transition in organic
  polymers

abstract:
  Controllable topological phase transitions are appealing as they allow for
tunable single particle electronic properties. Here, by using state-of-the-art
manybody perturbation theory techniques, we show that the topological $Z_2$
phase transition occurring in the single particle spectrum of the recently
synthetized ethynylene bridged polyacene polymers is accompanied by a
topological excitonic phase transition: the band inversion in the non-trivial
phase yields real-space exciton wave functions in which electrons and holes
exchange orbital characters with respect to the trivial phase. The topological
excitonic phase transition results in a broad tunability of the singlet-triplet
splittings, opening appealing perspectives for the occurrence of singlet
fission. Finally, the flatness of the single-particle electronic structure in
the topological non trivial phase leads to negatively dispersing triplet
excitons in a large portion of the Brillouin zone, opening a route for
spontaneously coherent energy transport at room temperature.


title:
Unitary evolution and elements of reality in consecutive quantum
  measurements

abstract:
  Probabilities of the outcomes of consecutive quantum measurements can be
obtained by construction probability amplitudes, thus implying unitary
evolution of the measured system, broken each time a measurement is made. In
practice, the experimenter needs to know all past outcomes at the end of the
experiment, and that requires the presence of probes carrying the corresponding
records. In this picture a composite system+probes can be seen to undergo an
unbroken unitary evolution until the end of the trial, where the state of the
probes is examined. For these two descriptions to agree one requires a
particular type of coupling between a probe and the system, which we discuss in
some details. With this in mind, we consider two different ways to extend the
description of a quantum system's past beyond what is actually measured and
recorded. One is to look for quantities whose values can be ascertained without
altering the existing probabilities. Such "elements of reality" can be found,
yet they suffer from the same drawback as their EPR counterparts. The probes
designed to measure non-commuting operators frustrate each other if set up to
work jointly, so no simultaneous values of such quantities can be established
consistently. The other possibility is to investigate the system's response to
weekly coupled probes. Such weak probes are shown either to reduce to a small
fraction the number of cases where the corresponding values are still
accurately measured, or lead only to the evaluation of the system's probability
amplitudes, or their combinations. It is difficult, we conclude, to see in
quantum mechanics anything other than a formalism for predicting the
likelihoods of the recorded outcomes of actually performed observations.


title:
Graph-in-Graph (GiG): Learning interpretable latent graphs in
  non-Euclidean domain for biological and healthcare applications

abstract:
  Graphs are a powerful tool for representing and analyzing unstructured,
non-Euclidean data ubiquitous in the healthcare domain. Two prominent examples
are molecule property prediction and brain connectome analysis. Importantly,
recent works have shown that considering relationships between input data
samples have a positive regularizing effect for the downstream task in
healthcare applications. These relationships are naturally modeled by a
(possibly unknown) graph structure between input samples. In this work, we
propose Graph-in-Graph (GiG), a neural network architecture for protein
classification and brain imaging applications that exploits the graph
representation of the input data samples and their latent relation. We assume
an initially unknown latent-graph structure between graph-valued input data and
propose to learn end-to-end a parametric model for message passing within and
across input graph samples, along with the latent structure connecting the
input graphs. Further, we introduce a degree distribution loss that helps
regularize the predicted latent relationships structure. This regularization
can significantly improve the downstream task. Moreover, the obtained latent
graph can represent patient population models or networks of molecule clusters,
providing a level of interpretability and knowledge discovery in the input
domain of particular value in healthcare.


title: Role of matter coherence in entanglement due to gravity

abstract:
  We investigate the quantum nature of gravitational interaction in terms of
the coherence of quantum objects. As a basic setting, we consider two
gravitating objects each in a superposition state of two paths. The evolution
of objects is described by the completely positive and trace-preserving (CPTP)
map with a population-preserving property. This property reflects that the
probability of objects being on each path is preserved. We use the
$\ell_1$-norm of coherence to quantify the coherence of objects. The quantum
nature of gravitational interaction is characterized by an entangling map,
which is a CPTP map with the capacity to create entanglement. We introduce the
entangling-map witness as an observable to test whether a given map is
entangling. We show that, whenever the gravitating objects initially have a
finite amount of the $\ell_1$-norm of coherence, the witness tests the
entangling map due to gravity. Interestingly, we find that the witness can
capture the signature of the entangling map due to gravity, even when the
objects do not get entangled. This means that the coherence of gravitating
objects always becomes the source of the entangling map due to gravity. We
further discuss a decoherence effect and an experimental perspective in the
present approach.


title:
CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object
  Detection

abstract:
  In autonomous driving, LiDAR point-clouds and RGB images are two major data
modalities with complementary cues for 3D object detection. However, it is
quite difficult to sufficiently use them, due to large inter-modal
discrepancies. To address this issue, we propose a novel framework, namely
Contrastively Augmented Transformer for multi-modal 3D object Detection
(CAT-Det). Specifically, CAT-Det adopts a two-stream structure consisting of a
Pointformer (PT) branch, an Imageformer (IT) branch along with a Cross-Modal
Transformer (CMT) module. PT, IT and CMT jointly encode intra-modal and
inter-modal long-range contexts for representing an object, thus fully
exploring multi-modal information for detection. Furthermore, we propose an
effective One-way Multi-modal Data Augmentation (OMDA) approach via
hierarchical contrastive learning at both the point and object levels,
significantly improving the accuracy only by augmenting point-clouds, which is
free from complex generation of paired samples of the two modalities. Extensive
experiments on the KITTI benchmark show that CAT-Det achieves a new
state-of-the-art, highlighting its effectiveness.


title:
A new Directional Algebraic Fast Multipole Method based iterative solver
  for the Lippmann-Schwinger equation accelerated with HODLR preconditioner

abstract:
  We present a fast iterative solver for scattering problems in 2D, where a
penetrable object with compact support is considered. By representing the
scattered field as a volume potential in terms of the Green's function, we
arrive at the Lippmann-Schwinger equation in integral form, which is then
discretized using an appropriate quadrature technique. The discretized linear
system is then solved using an iterative solver accelerated by Directional
Algebraic Fast Multipole Method (DAFMM). The DAFMM presented here relies on the
directional admissibility condition of the 2D Helmholtz kernel. And the
construction of low-rank factorizations of the appropriate low-rank matrix
sub-blocks is based on our new Nested Cross Approximation (NCA)~\cite{
arXiv:2203.14832 [math.NA]}. The advantage of our new NCA is that the search
space of so-called far-field pivots is smaller than that of the existing NCAs.
Another significant contribution of this work is the use of HODLR based direct
solver as a preconditioner to further accelerate the iterative solver. In one
of our numerical experiments, the iterative solver does not converge without a
preconditioner. We show that the HODLR preconditioner is capable of solving
problems that the iterative solver can not. Another noteworthy contribution of
this article is that we perform a comparative study of the HODLR based fast
direct solver, DAFMM based fast iterative solver, and HODLR preconditioned
DAFMM based fast iterative solver for the discretized Lippmann-Schwinger
problem. To the best of our knowledge, this work is one of the first to provide
a systematic study and comparison of these different solvers for various
problem sizes and contrast functions. In the spirit of reproducible
computational science, the implementation of the algorithms developed in this
article is made available at
\url{https://github.com/vaishna77/Lippmann_Schwinger_Solver}.


title:
Diverse Preference Augmentation with Multiple Domains for Cold-start
  Recommendations

abstract:
  Cold-start issues have been more and more challenging for providing accurate
recommendations with the fast increase of users and items. Most existing
approaches attempt to solve the intractable problems via content-aware
recommendations based on auxiliary information and/or cross-domain
recommendations with transfer learning. Their performances are often
constrained by the extremely sparse user-item interactions, unavailable side
information, or very limited domain-shared users. Recently, meta-learners with
meta-augmentation by adding noises to labels have been proven to be effective
to avoid overfitting and shown good performance on new tasks. Motivated by the
idea of meta-augmentation, in this paper, by treating a user's preference over
items as a task, we propose a so-called Diverse Preference Augmentation
framework with multiple source domains based on meta-learning (referred to as
MetaDPA) to i) generate diverse ratings in a new domain of interest (known as
target domain) to handle overfitting on the case of sparse interactions, and to
ii) learn a preference model in the target domain via a meta-learning scheme to
alleviate cold-start issues. Specifically, we first conduct multi-source domain
adaptation by dual conditional variational autoencoders and impose a
Multi-domain InfoMax (MDI) constraint on the latent representations to learn
domain-shared and domain-specific preference properties. To avoid overfitting,
we add a Mutually-Exclusive (ME) constraint on the output of decoders to
generate diverse ratings given content data. Finally, these generated diverse
ratings and the original ratings are introduced into the meta-training
procedure to learn a preference meta-learner, which produces good
generalization ability on cold-start recommendation tasks. Experiments on
real-world datasets show our proposed MetaDPA clearly outperforms the current
state-of-the-art baselines.


title: Products of commutator ideals of some Lie-admissible algebras

abstract:
  In this article, we mainly study the products of commutator ideals of
Lie-admissible algebras such as Novikov algebras, bicommutative algebras, and
assosymmetric algebras. More precisely, we first study the properties of the
lower central chains for Novikov algebras and bicommutative algebras. Then we
show that for every Lie nilpotent Novikov algebra or Lie nilpotent
bicommutative algebra $\mathcal{A}$, the ideal of $\mathcal{A}$ generated by
the set $\{ab - ba\mid a, b\in \mathcal{A}\}$ is nilpotent. Finally, we study
properties of the lower central chains for assosymmetric algebras, study the
products of commutator ideals of assosymmetric algebras and show that the
products of commutator ideals have a similar property as that for associative
algebras.


title:
Pentagon Wilson loop with Lagrangian insertion at two loops in
  ${\mathcal N}=4$ super Yang-Mills theory

abstract:
  We compute the two-loop result for the null pentagonal Wilson loop with a
Lagrangian insertion (normalized by the Wilson loop without insertion) in
planar, maximally supersymmetric Yang-Mills theory. This finite observable is
closely related to the Amplituhedron, and it is reminiscent of finite parts of
planar two-loop five-particle scattering amplitudes. We verify that, up to this
loop order, the leading singularities are given by the same conformally
invariant expressions that appear in all-plus pure Yang-Mills amplitudes. The
accompanying weight-four transcendental functions are expressed in terms of the
pentagon functions space known from planar two-loop five-particle amplitudes,
but interestingly only a subset of the functions appears. Being a function of
four dimensionless variables, the observable has interesting asymptotic limits.
We verify that our analytic result is consistent with soft and collinear
limits, and find an intriguingly simple pattern in the multi-Regge limit.
Thanks to the new result we can also conjecturally predict, for general
kinematics, the maximal weight piece of the planar three-loop five-particle
all-plus amplitude in pure Yang-Mills theory. Motivated by the Amplituhedron
geometry, we investigate positivity properties of the integrated answer.
Generalizing previous results at four particles, we find numerical evidence
that the two-loop five-particle result has uniform sign in a kinematic region
suggested by the loop Amplituhedron.


title: DIP: Deep Inverse Patchmatch for High-Resolution Optical Flow

abstract:
  Recently, the dense correlation volume method achieves state-of-the-art
performance in optical flow. However, the correlation volume computation
requires a lot of memory, which makes prediction difficult on high-resolution
images. In this paper, we propose a novel Patchmatch-based framework to work on
high-resolution optical flow estimation. Specifically, we introduce the first
end-to-end Patchmatch based deep learning optical flow. It can get
high-precision results with lower memory benefiting from propagation and local
search of Patchmatch. Furthermore, a new inverse propagation is proposed to
decouple the complex operations of propagation, which can significantly reduce
calculations in multiple iterations. At the time of submission, our method
ranks first on all the metrics on the popular KITTI2015 benchmark, and ranks
second on EPE on the Sintel clean benchmark among published optical flow
methods. Experiment shows our method has a strong cross-dataset generalization
ability that the F1-all achieves 13.73%, reducing 21% from the best published
result 17.4% on KITTI2015. What's more, our method shows a good details
preserving result on the high-resolution dataset DAVIS and consumes 2x less
memory than RAFT.


title:
Using segment-based features of jaw movements to recognize foraging
  activities in grazing cattle

abstract:
  Precision livestock farming optimizes livestock production through the use of
sensor information and communication technologies to support decision making,
proactively and near real-time. Among available technologies to monitor
foraging behavior, the acoustic method has been highly reliable and repeatable,
but can be subject to further computational improvements to increase precision
and specificity of recognition of foraging activities. In this study, an
algorithm called Jaw Movement segment-based Foraging Activity Recognizer
(JMFAR) is proposed. The method is based on the computation and analysis of
temporal, statistical and spectral features of jaw movement sounds for
detection of rumination and grazing bouts. They are called JM-segment features
because they are extracted from a sound segment and expect to capture JM
information of the whole segment rather than individual JMs. Two variants of
the method are proposed and tested: (i) the temporal and statistical features
only JMFAR-ns; and (ii) a feature selection process (JMFAR-sel). The JMFAR was
tested on signals registered in a free grazing environment, achieving an
average weighted F1-score greater than 95%. Then, it was compared with a
state-of-the-art algorithm, showing improved performance for estimation of
grazing bouts (+19%). The JMFAR-ns variant reduced the computational cost by
25.4%, but achieved a slightly lower performance than the JMFAR. The good
performance and low computational cost of JMFAR-ns supports the feasibility of
using this algorithm variant for real-time implementation in low-cost embedded
systems.


title:
Product and sum uncertainty relations based on metric-adjusted skew
  information

abstract:
  The metric-adjusted skew information establishes a connection between the
geometrical formulation of quantum statistics and the measures of quantum
information. We study uncertainty relations in product and summation forms of
metric-adjusted skew information. We present lower bounds on product and
summation uncertainty inequalities based on metric-adjusted skew information
via operator representation of observables. Explicit examples are provided to
back our claims.


title: PriMock57: A Dataset Of Primary Care Mock Consultations

abstract:
  Recent advances in Automatic Speech Recognition (ASR) have made it possible
to reliably produce automatic transcripts of clinician-patient conversations.
However, access to clinical datasets is heavily restricted due to patient
privacy, thus slowing down normal research practices. We detail the development
of a public access, high quality dataset comprising of57 mocked primary care
consultations, including audio recordings, their manual utterance-level
transcriptions, and the associated consultation notes. Our work illustrates how
the dataset can be used as a benchmark for conversational medical ASR as well
as consultation note generation from transcripts.


title:
Cyberbullying detection across social media platforms via platform-aware
  adversarial encoding

abstract:
  Despite the increasing interest in cyberbullying detection, existing efforts
have largely been limited to experiments on a single platform and their
generalisability across different social media platforms have received less
attention. We propose XP-CB, a novel cross-platform framework based on
Transformers and adversarial learning. XP-CB can enhance a Transformer
leveraging unlabelled data from the source and target platforms to come up with
a common representation while preventing platform-specific training. To
validate our proposed framework, we experiment on cyberbullying datasets from
three different platforms through six cross-platform configurations, showing
its effectiveness with both BERT and RoBERTa as the underlying Transformer
models.


title:
Fact-nets: towards a mathematical framework for relational quantum
  mechanics

abstract:
  The relational interpretation of quantum mechanics (RQM) has received a
growing interest since its first formulation in 1996. Usually presented as an
interpretational layer over the usual quantum mechanics formalism, it appears
as a philosophical perspective without proper mathematical counterparts. This
state of affairs has direct consequences on the scientific debate on RQM which
still suffers from misunderstandings and imprecise statements. In an attempt to
clarify those debates, the present paper proposes a radical reformulation of
the mathematical framework of quantum mechanics which is relational from the
start: fact-nets. The core idea is that all statements about the world, facts,
are binary entities involving two systems that can be symmetrically thought of
as observed and observer. We initiate a study of the fact-nets formalism and
outline how it can shed new relational light on some familiar quantum features.


title: Describing model categories througth homotopy tiny objects

abstract:
  Let $\mathcal C$ be a $\mathcal V$-enriched model category. We say that an
object $x$ of $\mathcal C$ is homotopy tiny if the total right derived functor
of $\mathcal C(x, -) : \mathcal{C} \rightarrow {\mathcal V}$ preserves homotopy
weighted colimits. Let $\mathcal C_0$ be a full subcategory of $\mathcal C$ all
of whose objects are homotopy tiny. Our main result says that the homotopy
category of the category generated by $\mathcal C_0$ under weak equivalences
and homotopy weighted colimits is equivalent to the homotopy category of the
category $\mathcal V^{\mathcal C_0^{op}}$ of $\mathcal V$-enriched presheaves
on $\mathcal C_0$ with values in $\mathcal V$. If $\mathcal C$ is generated by
$\mathcal C_0$, then $\mathcal C$ is Quillen equivalent to $\mathcal
V^{\mathcal C_0^{op}}$. Two special cases of our theorem are Schwede-Shipley's
theorem on stable model categories and Elmendorf's theorem on equivariant
spaces.


title:
Relative calibration of the LIGO and Virgo detectors using astrophysical
  events from their third observing run

abstract:
  We explore a method to assess the relative scale of the strain measured in
the different detectors of the gravitational-wave network, using binary black
hole (BBH) events detected during the third observing run (O3). The number of
such signals is becoming sufficiently large to adopt a statistical approach
based on the ratio of the signal-to-noise ratio (SNR) of the events between the
detectors and the number of observed events in each detector. We demonstrate
the principle of the method on simulations of BBH signals and we present its
application to published O3 events reported by the Multi-Band Template Analysis
(MBTA) pipeline. Constraints on the relative calibration of the
gravitational-wave network for O3 are obtained at the level of ~3.5% between
the two LIGO detectors and at the level of ~10% between the LIGO Livingston
detector and the Virgo detector.


title: The isogeometric collocated contact surface approach

abstract:
  We propose a frictionless contact formulation for isogeometric analysis,
which combines a collocated formulation for the contact surfaces with a
standard Galerkin treatment of the bulk. We denote it as isogeometric
Collocated Contact Surface (CCS) formulation. The approach is based on a simple
pointwise enforcement of the contact constraints, performed in this study with
the penalty method. Unlike pointwise (node-to-surface or point-to-surface)
contact algorithms in the Galerkin framework, the CCS formulation passes the
contact patch test to machine precision by naturally exploiting the favorable
properties of isogeometric collocation. Compared with approaches where the
discretization of both bulk and contact surfaces is based on collocation, the
CCS approach does not need enhancements to remove oscillations for highly
non-uniform meshes. With respect to integral contact approaches, the CCS
algorithm is less expensive, easier to code and can be added to a pre-existing
isogeometric analysis code with minimal effort. Numerical examples in both
small and large deformations are investigated to compare the CCS approach with
some available contact formulations and to demonstrate its accuracy.


title: Self-triggered MPC robust to bounded packet loss via a min-max approach

abstract:
  Networked Control Systems typically come with a limited communication
bandwidth and thus require special care when designing the underlying control
and triggering law. A method that allows to consider hard constraints on the
communication traffic as well as on states and inputs is self-triggered model
predictive control (MPC). In this scheme, the optimal length of the sampling
interval is determined proactively using predictions of the system behavior.
However, previous formulations of self-triggered MPC have neglected the
widespread phenomenon of packet loss, such that these approaches might fail in
practice. In this paper, we present a novel self-triggered MPC scheme which is
robust to bounded packet loss by virtue of a min-max optimization problem. We
prove recursive feasibility, constraint satisfaction and convergence to the
origin for any possible packet loss realization, and demonstrate the advantages
of the proposed scheme in a numerical example.


title:
Quantum approximate optimization algorithm for qudit systems with
  long-range interactions

abstract:
  A frequent starting point of quantum computation platforms are two-state
quantum systems, i.e., qubits. However, in the context of integer optimization
problems, relevant to scheduling optimization and operations research, it is
often more resource-efficient to employ quantum systems with more than two
basis states, so-called qudits. Here, we discuss the quantum approximate
optimization algorithm (QAOA) for qudits, layout its implementation in
platforms with long-range interactions between qudits such as trapped ions,
cold atomic mixtures, Rydberg atoms and atoms in cavities. We illustrate how
the QAOA can be used to formulate a variety of integer optimization problems
such as graph coloring problems or electric vehicle (EV) charging optimization.
In addition, we comment on the implementation of constraints and describe three
methods to include these into a quantum circuit of a QAOA by penalty
contributions to the cost Hamiltonian, conditional gates using ancilla qubits,
and a dynamical decoupling strategy. Finally, as a showcase of qudit-based
QAOA, we present numerical results for a charging optimization problem mapped
onto a max-$k$-graph coloring problem. Our work illustrates the flexibility of
qudit systems to solve integer optimization problems.


title:
Local spectroscopic evidence for a nodeless magnetic kagome
  superconductor CeRu$_2$

abstract:
  We report muon spin rotation (${\mu}$SR) experiments on the microscopic
properties of superconductivity and magnetism in the kagome superconductor
CeRu$_2$ with $T_{\rm c}\simeq 5$K. From the measurements of the
temperature-dependent magnetic penetration depth ${\lambda}$, the
superconducting order parameter exhibits an anisotropic $s$-wave gap symmetry.
We further show that the $T_{\rm c}$/$\lambda^{-2}$ ratio is comparable to that
of unconventional superconductors. Furthermore, the powerful combination of
ZF-${\mu}$SR and high-field ${\mu}$SR has been used to uncover magnetic
responses across three characteristic temperatures, identified as $T_1^* \simeq
110$K, $T_2^* \simeq 65$K, and $T_3^* \simeq 40$K. Our experiments classify
CeRu$_{2}$ as an exceedingly rare nodeless magnetic kagome superconductor.


title:
Magnetism, rotation, and nonthermal emission in cool stars -- Average
  magnetic field measurements in 292 M dwarfs

abstract:
  Stellar dynamos generate magnetic fields that are of fundamental importance
to the variability and evolution of Sun-like and low-mass stars, and for the
development of their planetary systems. We report measurements of
surface-average magnetic fields in 292 M dwarfs from a comparison with
radiative transfer calculations; for 260 of them, this is the first measurement
of this kind. Our data were obtained from more than 15,000 high-resolution
spectra taken during the CARMENES project. They reveal a relation between
average field strength, <B>, and Rossby number, $Ro$, resembling the
well-studied rotation-activity relation. Among the slowly rotating stars, we
find that magnetic flux, $\Phi_\textrm{B}$, is proportional to rotation period,
$P$, and among the rapidly rotating stars that average surface fields do not
grow significantly beyond the level set by the available kinetic energy.
Furthermore, we find close relations between nonthermal coronal X-ray emission,
chromospheric H$\alpha$ and Ca H&K emission, and magnetic flux. Taken together,
these relations demonstrate empirically that the rotation-activity relation can
be traced back to a dependence of the magnetic dynamo on rotation. We advocate
the picture that the magnetic dynamo generates magnetic flux on the stellar
surface proportional to rotation rate with a saturation limit set by the
available kinetic energy, and we provide relations for average field strengths
and nonthermal emission that are independent of the choice of the convective
turnover time. We also find that Ca H&K emission saturates at average field
strengths of $\langle B \rangle \approx 800$ G while H$\alpha$ and X-ray
emission grow further with stronger fields in the more rapidly rotating stars.
This is in conflict with the coronal stripping scenario predicting that in the
most rapidly rotating stars coronal plasma would be cooled to chromospheric
temperatures.


title: Parameter Estimation for Quantum Trajectories: Convergence Result

abstract:
  A quantum trajectory describes the evolution of a quantum system undergoing
indirect measurement. In the discrete-time setting, the state of the system is
updated by applying Kraus operators according to the measurement results. From
an experimental perspective, these Kraus operators can depend on unknown
physical parameters p. An interesting and powerful method has been proposed in
[1] to estimate a parameter in a finite set; however, complete results of
convergence were lacking. This article fills this gap by rigorously showing the
consistency of the method, whereas there was only numerical evidence so far.
When the parameter belongs to a continuous set, we propose an algorithm to
approach its value and show simulation results.


title:
Majorization for Starlike and Convex functions with respect to Conjugate
  points

abstract:
  The concept of majorization is now well-known after the beautiful work of
MacGregor, and then followed by Campbell in his sequel of papers. In this
paper, we establish the sharp majorization results for the starlike and convex
functions with respect to the conjugate points.


title:
Polarized Raman Response of Two-Dimensional Quasiperiodic
  Antiferromagnets: Configuration-Interaction versus Green's Function
  Approaches

abstract:
  We study Raman response of Heisenberg antiferromagnets on the
$\mathbf{C}_\mathrm{5v}$ Penrose and $\mathbf{C}_\mathrm{8v}$ Ammann-Beenker
lattices within and beyond the Loudon-Fleury second-order perturbation scheme
intending to explore optical features peculiar to quasiperiodic magnets. Within
the Loudon-Fleury mechanism, we find one and only Raman-active mode of
$\mathrm{E}_2$ symmetry without any dependence on linear incident and scattered
polarizations. Beyond the Loudon-Fleury mechanism, two more symmetry species
$\mathrm{A}_1$ and $\mathrm{A}_2$ are activated via dynamic ring exchange and
chiral spin fluctuations, respectively, which can be extracted by the use of
circular as well as linear polarizations. We employ Green's functions on one
hand and configuration-interaction wavefunctions on the other hand to calculate
the multimagnon contributions to inelastic light scatterings. Demonstrating the
great advantage of the configuration-interaction scheme, we reveal that a major
portion of the Shastry-Shraiman fourth-order Raman intensity is mediated by
multimagnon fluctuations.


title:
A Comparative Study of Machine Learning Methods for X-ray Binary
  Classification

abstract:
  X-ray Binaries (XRBs) consist of a compact object that accretes material from
an orbiting secondary star. The most secure method we have for determining if
the compact object is a black hole is to determine its mass: this is limited to
bright objects, and requires substantial time-intensive spectroscopic
monitoring. With new X-ray sources being discovered with different X-ray
observatories, developing efficient, robust means to classify compact objects
becomes increasingly important. We compare three machine learning
classification methods (Bayesian Gaussian Processes (BGP), K-Nearest Neighbors
(KNN), Support Vector Machines (SVM)) for determining the compact objects as
neutron stars or black holes (BHs) in XRB systems. Each machine learning method
uses spatial patterns which exist between systems of the same type in 3D
Color-Color-Intensity diagrams. We used lightcurves extracted using six years
of data with MAXI/GSC for 44 representative sources. We find that all three
methods are highly accurate in distinguishing pulsing from non-pulsing neutron
stars (NPNS) with 95\% of NPNS and 100\% of pulsars accurately predicted. All
three methods have high accuracy distinguishing BHs from pulsars (92\%) but
continue to confuse BHs with a subclass of NPNS, called the Bursters, with KNN
doing the best at only 50\% accuracy for predicting BHs. The precision of all
three methods is high, providing equivalent results over 5-10 independent runs.
In a future work, we suggest a fourth dimension be incorporated to mitigate the
confusion of BHs with Bursters. This work paves the way towards more robust
methods to efficiently distinguish BHs, NPNS, and pulsars.


title: Mutual insurance for uninsurable income

abstract:
  This paper shows a result counter to prior work: if infinitely lived
individuals' incomes are unobservable, efficient allocations are achieved only
at the expense of invoking inequality. An efficient mutual contract with equal
opportunities is obtained in a closed-form expression.


title: WavFT: Acoustic model finetuning with labelled and unlabelled data

abstract:
  Unsupervised and self-supervised learning methods have leveraged unlabelled
data to improve the pretrained models. However, these methods need
significantly large amount of unlabelled data and the computational cost of
training models with such large amount of data can be prohibitively high. We
address this issue by using unlabelled data during finetuning, instead of
pretraining. We propose acoustic model finetuning (FT) using labelled and
unlabelled data. The model is jointly trained to learn representations to
classify senones, as well as learn contextual acoustic representations. Our
training objective is a combination of cross entropy loss, suitable for
classification task, and contrastive loss, suitable to learn acoustic
representations. The proposed approach outperforms conventional finetuning with
11.2% and 9.19% word error rate relative (WERR) reduction on Gujarati and
Bengali languages respectively.


title:
Computation of Optical Refractive Index Structure Parameter from its
  Statistical Definition Using Radiosonde Data

abstract:
  Knowledge of the optical refractive index structure parameter $C_n^2$ is of
interest for Free Space Optics (FSO) and ground-based optical astronomy, as it
depicts the strength of the expected scintillation on the received optical
waves. Focus is given here to models using meteorological quantities coming
from radiosonde measurements as inputs to estimate the $C_n^2$ profile in the
atmosphere. A model relying on the $C_n^2$ statistical definition is presented
and applied to recent high-density radiosonde profiles at Trappes (France) and
Hilo, HI (USA). It is also compared to thermosonde measurements coming from the
T-REX campaign. This model enables to obtain site-specific average profiles and
to identify isolated turbulent layers using only pressure and temperature
measurements, paving the way for optical site selection.

categories: eess.SP astro-ph.IM physics.ao-ph physics.optics


title:
Revisiting Shallow Discourse Parsing in the PDTB-3: Handling
  Intra-sentential Implicits

abstract:
  In the PDTB-3, several thousand implicit discourse relations were newly
annotated \textit{within} individual sentences, adding to the over 15,000
implicit relations annotated \textit{across} adjacent sentences in the PDTB-2.
Given that the position of the arguments to these \textit{intra-sentential
implicits} is no longer as well-defined as with \textit{inter-sentential
implicits}, a discourse parser must identify both their location and their
sense. That is the focus of the current work. The paper provides a
comprehensive analysis of our results, showcasing model performance under
different scenarios, pointing out limitations and noting future directions.


title:
Phase transitions of non-Abelian charged nodal links in a spring-mass
  system

abstract:
  Although a large class of topological materials have uniformly been
identified using symmetry properties of wave functions, the past two years have
seen the rise of multi-gap topologies beyond this paradigm. Given recent
reports of unexplored features of such phases, platforms that are readily
implementable to realize them are therefore desirable. Here, we demonstrate
that multi-gap topological phase transitions of non-Abelian charged nodal lines
arise in classical phonon waves. By adopting a simple spring-mass system, we
construct nodal lines of a three-band system. The braiding process of the nodal
lines is readily performed by adjusting the spring constants. The generation
and annihilation of the nodal lines are then analyzed using Euler class.
Finally, we retrieve topological transitions from trivial nodal lines to a
nodal link. Our work provides a simple platform that can offer diverse insights
to not only theoretical but also experimental studies on multi-gap topology.


title:
On the Efficiency of Integrating Self-supervised Learning and
  Meta-learning for User-defined Few-shot Keyword Spotting

abstract:
  User-defined keyword spotting is a task to detect new spoken terms defined by
users. This can be viewed as a few-shot learning problem since it is
unreasonable for users to define their desired keywords by providing many
examples. To solve this problem, previous works try to incorporate
self-supervised learning models or apply meta-learning algorithms. But it is
unclear whether self-supervised learning and meta-learning are complementary
and which combination of the two types of approaches is most effective for
few-shot keyword discovery. In this work, we systematically study these
questions by utilizing various self-supervised learning models and combining
them with a wide variety of meta-learning algorithms. Our result shows that
HuBERT combined with Matching network achieves the best result and is robust to
the changes of few-shot examples.


title:
Impact of spatiotemporal heterogeneity in heat pump loads on generation
  and storage requirements

abstract:
  This paper investigates how spatiotemporal heterogeneity in inflexible
residential heat pump loads affects the need for storage and generation in the
electricity system under business-as-usual and low-carbon emissions budgets.
Homogeneous and heterogeneous heat pump loads are generated using
population-weighted average and local temperature, respectively, assuming
complete residential heat pump penetration. The results of a storage and
generation optimal expansion model with network effects for spatiotemporally
homogeneous and heterogeneous load profiles are compared. A case study is
performed using a 3-bus network of London, Manchester, and Glasgow in Britain
for load and weather data for representative weeks. Using heterogeneous heating
demand data changes storage sizing: under a business-as-usual budget, 26% more
total storage is built on an energy and power basis, and this storage is
distributed among all of the buses in the heterogeneous case. Under a
low-carbon budget, total energy storage at all buses increases 2 times on an
energy basis and 40% on a power basis. The energy to power ratio of storage at
each bus also increases when accounting for heterogeneity; this change suggests
that storage will be needed to provide energy support in addition to power
support for electric heating in high-renewable power systems. Accounting for
heterogeneity also increases modeled systems costs, particularly capital costs,
because of the need for higher generation capacity in the largest load center
and coincidence of local peak demand at different buses. These results show the
importance of accounting for heat pump load heterogeneity in power system
planning.


title:
RMS-FlowNet: Efficient and Robust Multi-Scale Scene Flow Estimation for
  Large-Scale Point Clouds

abstract:
  The proposed RMS-FlowNet is a novel end-to-end learning-based architecture
for accurate and efficient scene flow estimation which can operate on point
clouds of high density. For hierarchical scene flow estimation, the existing
methods depend on either expensive Farthest-Point-Sampling (FPS) or
structure-based scaling which decrease their ability to handle a large number
of points. Unlike these methods, we base our fully supervised architecture on
Random-Sampling (RS) for multiscale scene flow prediction. To this end, we
propose a novel flow embedding design which can predict more robust scene flow
in conjunction with RS. Exhibiting high accuracy, our RMS-FlowNet provides a
faster prediction than state-of-the-art methods and works efficiently on
consecutive dense point clouds of more than 250K points at once. Our
comprehensive experiments verify the accuracy of RMS-FlowNet on the established
FlyingThings3D data set with different point cloud densities and validate our
design choices. Additionally, we show that our model presents a competitive
ability to generalize towards the real-world scenes of KITTI data set without
fine-tuning.


title: Inverse problem for a subdiffusion equation with the Caputo derivative

abstract:
  The article investigates an inverse problem of determining the right-hand
side of a subdiffusion equation with Caputo fractional derivative whose
elliptic part has the most general form and is defined on an N-dimensional
torus T N . The Fourier method is used to prove theorems on the existence and
uniqueness of the classical solution of the initial-boundary value problem and
on the unique reconstruction of the unknown right-hand side of the equation.
Requirements for the initial function and for the additional condition are
established under which the classical Fourier method can be applied to the
inverse problem under consideration.


title: Algebraic connectivity of layered path graphs under node deletion

abstract:
  This paper studies the relation between node deletion and the algebraic
connectivity for a graph with a hierarchical structure represented by layers.
The problem is motivated by a mobile robot formation control guided by a
leader. In particular, we consider a scenario in which robots may leave the
network resulting in the removal of the nodes and the associated edges. We show
that the existence of at least one neighbor in the upper layer is crucial for
the algebraic connectivity not to deteriorate by node deletion.


title:
Solutions of Schr\"odinger equations\\ with symmetry in orientation
  preserving tetrahedral group

abstract:
  We consider the nonlinear Schr\"odinger equation \begin{equation*} \Delta u =
\big( 1 +\ve V_1(|y|)\big)u - |u|^{p-1}u
  \quad \text{in} \quad \mathbb{R}^N, \quad N\ge 3, \quad p \in \left(1,
\frac{N+2}{N-2}\right).\end{equation*} The phenomenon of pattern formation has
been a central theme in the study of nonlinear Schr\"odinger equations.
However, the following nonexistence of $O(N)$ symmetry breaking solution is
well-known: if the potential function is radial and radially nondecreasing, any
positive solution must be radial. Therefore, solutions of interesting patterns,
such as those with symmetry in a discrete subgroup of $O(N)$, can only exist
after violating the assumptions. For a potential function that is radial but
asymptotically decreasing, a solution with symmetry merely in a discrete
subgroup of $O(2)$ has been presented. These observations pose the question of
whether patterns of higher dimensions can appear. In this study, the existence
of nonradial solutions whose symmetry group is a discrete subgroup of $O(3)$,
more precisely, the orientation-preserving regular tetrahedral group is shown.


title:
Zero-temperature thermodynamics of dense asymmetric strong-interaction
  matter

abstract:
  Employing constraints derived from the microscopic theory of the strong
interaction, we estimate the zero-temperature phase structure of dense
isospin-asymmetric matter with two quark flavors. We find indications that
strong-interaction matter along trajectories relevant for astrophysical
applications undergoes a first-order phase transition from a
color-superconducting phase to an ungapped quark-matter phase when the density
is increased. Such a phase transition is found to be absent in
isospin-symmetric matter. Moreover, by taking into account constraints from
$\beta$-equilibrium, charge neutrality, and color neutrality, we provide an
estimate for the speed of sound in neutron-star matter. Notably, we observe
that the speed of sound in neutron-star matter exceeds the asymptotic value
associated with the noninteracting quark gas and even increases towards lower
densities across a wide range, in agreement with recent results for
isospin-symmetric matter. Considering results from studies based on chiral
effective field theory at low densities, our findings suggest the existence of
a maximum in the speed of sound for $n/n_0 \lesssim 10$, where $n_0$ is the
nuclear saturation density.


title: Unruh-deWitt detectors in cosmological spacetimes

abstract:
  We analyse the response and thermal behaviour of an Unruh-DeWitt detector as
it travels through cosmological spacetimes, with special reference to the
question of how to define surface gravity and temperature in dynamical
spacetimes. Working within the quantum field theory on curved spacetime
approximation, we consider a detector as it travels along geodesic and
accelerated Kodama trajectories in de Sitter and asymptotically de Sitter FLRW
spacetimes. By modelling the temperature of the detector using the
detailed-balance form of the Kubo-Martin-Schwinger (KMS) conditions as it
thermalises, we can better understand the thermal behaviour of the detector as
it interacts with the quantum field, and use this to compare competing
definitions of surface gravity and temperature that persist in the literature.
These include the approaches of Hayward-Kodama, Ashtekar et al., Fodor et al.,
and Nielsen-Visser. While these are most often examined within the context of a
dynamical black hole, here we shift focus to surface gravity on the evolving
cosmological horizon.


title: Extracting Rules from Neural Networks with Partial Interpretations

abstract:
  We investigate the problem of extracting rules, expressed in Horn logic, from
neural network models. Our work is based on the exact learning model, in which
a learner interacts with a teacher (the neural network model) via queries in
order to learn an abstract target concept, which in our case is a set of Horn
rules. We consider partial interpretations to formulate the queries. These can
be understood as a representation of the world where part of the knowledge
regarding the truthiness of propositions is unknown. We employ Angluin s
algorithm for learning Horn rules via queries and evaluate our strategy
empirically.


title: Exotic cyclic cohomology classes and Lipschitz algebras

abstract:
  We study the noncommutative geometry of algebras of Lipschitz continuous and
H\"older continuous functions where non-classical and novel differential
geometric invariants arise. Indeed, we introduce a new class of Hochschild and
cyclic cohomology classes that pair non-trivially with higher algebraic
$K$-theory yet vanish when restricted to the algebra of smooth functions. Said
cohomology classes provide additional methods to extract numerical invariants
from Connes-Karoubi's relative sequence in $K$-theory.


title: Estimating Separable Matching Models

abstract:
  In this paper we propose two simple methods to estimate models of matching
with transferable and separable utility introduced in Galichon and Salani\'e
(2022). The first method is a minimum distance estimator that relies on the
generalized entropy of matching. The second relies on a reformulation of the
more special but popular Choo and Siow (2006) model; it uses generalized linear
models (GLMs) with two-way fixed effects.


title:
Low-lying mass spectra of excited doubly heavy baryons: Regge relation
  and mass scaling

abstract:
  In picture of heavy-diquark-light-quark endowed with heavy-pair binding, we
explore the low-lying excited baryons $\Xi_{QQ^{\prime}}$ and
$\Omega_{QQ^{\prime}}$ containing two heavy quarks($QQ^{\prime}=cc,bb,bc$) by
applying relations of Regge trajectory and mass scaling to excitations of light
quarks and heavy diquarks, respectively. Two Regge relations, one linear and
the other nonlinear, are constructed based on semi-classical analysis of
massive QCD string so that they contain the short-distance correction due to
heavy-pair interaction when heavy diquark excited internally. Evaluating the
short-distance corrections via the binding energy of the pairs estimated by
that of the heavy-anti-heavy pairs in the observed doubly heavy mesons, we
compute effective masses of heavy diquarks in ground state, 2S- and 1P- waves
and thereby predict low-lying mass spectra of the doubly-heavy baryons up to
the 2S- and 1P- waves of the light quarks and heavy diquarks. The results
indicate that the mass level spacings due to heavy diquark excitations are
generally narrower in contrast with that due to the light quark excitations.


title: Temporal coherence of optical fields in the presence of entanglement

abstract:
  In classical coherence theory, coherence time is typically related to the
bandwidth of the optical field. Narrowing the bandwidth will result in the
lengthening of the coherence time. This will erase temporal distinguishability
of photons due to time delay in pulsed photon interference. However, this is
changed in an SU(1,1)-type quantum interferometer where quantum entanglement is
involved. In this paper, we investigate how the temporal coherence of the
fields in a pulse-pumped SU(1,1) interferometer changes with the bandwidth of
optical filtering. We find that, because of the quantum entanglement, the
coherence of the fields does not improve when optical filtering is applied, in
contrary to the classical coherence theory, and quantum entanglement plays a
crucial role in quantum interference in addition to distinguishability.


title: On Dynamics of \lambda + tan z^2

abstract:
  This article discusses some topological properties of the dynamical plane
($z$-plane) of the holomorphic family of meromorphic maps $\lambda + \tan z^2$
for $ \lambda \in \mathbb C$. In the dynamical plane, we prove that there is no
Herman ring, and the Julia set is a Cantor set for the maps when the parameter
is in the unbounded hyperbolic component contained in the four quadrants in the
complex plane. Julia set is connected for the maps when the parameters are in
other hyperbolic components of the parameter plane.


title:
Electrical sensing of the thermal and light induced spin transition in
  robust contactless spin-crossover/graphene hybrid devices

abstract:
  Hybrid devices based on spin-crossover (SCO)/2D heterostructures grant a
highly sensitive platform to detect the spin transition in the molecular SCO
component and tune the properties of the 2D material. However, the fragility of
the SCO materials upon thermal treatment, light irradiation or contact with
surfaces and the methodologies used for their processing have limited their
applicability. Here, we report an easily processable and robust SCO/2D hybrid
device with outstanding performance based on the sublimable SCO [Fe(Pyrz)2]
molecule deposited over CVD-graphene, which is fully compatible with
electronics industry protocols. Thus, a novel methodology based on growing an
elusive polymorph of [Fe(Pyrz)2] (tetragonal phase) over graphene is developed
that allows us to electrically detect a fast and effective light-induced spin
transition in the devices (~50% yield in 5 minutes). Such performance can be
enhanced even more when a flexible polymeric layer of PMMA is inserted in
between the two active components in a contactless configuration, reaching a
~100 % yield in 5 minutes.


title: Learning to Deblur using Light Field Generated and Real Defocus Images

abstract:
  Defocus deblurring is a challenging task due to the spatially varying nature
of defocus blur. While deep learning approach shows great promise in solving
image restoration problems, defocus deblurring demands accurate training data
that consists of all-in-focus and defocus image pairs, which is difficult to
collect. Naive two-shot capturing cannot achieve pixel-wise correspondence
between the defocused and all-in-focus image pairs. Synthetic aperture of light
fields is suggested to be a more reliable way to generate accurate image pairs.
However, the defocus blur generated from light field data is different from
that of the images captured with a traditional digital camera. In this paper,
we propose a novel deep defocus deblurring network that leverages the strength
and overcomes the shortcoming of light fields. We first train the network on a
light field-generated dataset for its highly accurate image correspondence.
Then, we fine-tune the network using feature loss on another dataset collected
by the two-shot method to alleviate the differences between the defocus blur
exists in the two domains. This strategy is proved to be highly effective and
able to achieve the state-of-the-art performance both quantitatively and
qualitatively on multiple test sets. Extensive ablation studies have been
conducted to analyze the effect of each network module to the final
performance.


title: Influence of coronal hole morphology on the solar wind speed at Earth

abstract:
  It has long been known that the high-speed stream (HSS) peak velocity at
Earth directly depends on the area of the coronal hole (CH) on the Sun.
Different degrees of association between the two parameters have been shown by
many authors. In this study, we revisit this association in greater detail for
a sample of 45 nonpolar CHs during the minimum phase of solar cycle 24. The aim
is to understand how CHs of different properties influence the HSS peak speeds
observed at Earth and draw from this to improve solar wind modeling. The
characteristics of the CHs of our sample were extracted based on the Collection
of Analysis Tools for Coronal Holes (CATCH) which employs an intensity
threshold technique applied to extreme-ultraviolet (EUV) filtergrams. We first
examined all the correlations between the geometric characteristics of the CHs
and the HSS peak speed and duration at Earth, for the entire sample. The CHs
were then categorized in different groups based on morphological criteria, such
as the aspect ratio, the orientation angle and the geometric complexity, a
parameter which is often neglected when the formation of the fast solar wind at
Earth is studied. Our results, confirmed also by the bootstrapping technique,
show that all three aforementioned morphological criteria play a major role in
determining the HSS peak speed at 1 AU. Therefore, they need to be taken into
consideration for empirical models that aim to forecast the fast solar wind at
Earth based on the observed CH solar sources.


title:
A revised sequential quadratic semidefinite programming method for
  nonlinear semidefinite optimization

abstract:
  In 2020, Yamakawa and Okuno proposed a stabilized sequential quadratic
semidefinite programming (SQSDP) method for solving, in particular, degenerate
nonlinear semidefinite optimization problems. The algorithm is shown to
converge globally without a constraint qualification, and it has some nice
properties, including the feasible subproblems, and their possible inexact
computations. In particular, the convergence was established for
approximate-Karush-Kuhn-Tucker (AKKT) and trace-AKKT conditions, which are two
sequential optimality conditions for the nonlinear conic contexts. However,
recently, complementarity-AKKT (CAKKT) conditions were also consider, as an
alternative to the previous mentioned ones, that is more practical. Since few
methods are shown to converge to CAKKT points, at least in conic optimization,
and to complete the study associated to the SQSDP, here we propose a revised
version of the method, maintaining the good properties. We modify the previous
algorithm, prove the global convergence in the sense of CAKKT, and show some
preliminary numerical experiments.


title:
Identifying and diagnosing coherent associations and causalities between
  multi-channels of the gravitational wave detector

abstract:
  The gravitational-wave detector is a very complicated and sensitive
collection of advanced instruments, which is influenced not only by the mutual
interaction between mechanical/electronics systems but also by the surrounding
environment. Thus, it is necessary to categorize and reduce noises from many
channels interconnected by such instruments and environment for achieving the
detection of gravitational waves because it enhances to increase of a
signal-to-noise ratio and reduces false alarms from coincident loud events. For
this reason, it is of great importance to identify some coherent associations
between complicated channels. This study presents a way of identifying (non-)
linear couplings between interconnected channels by using some correlation
coefficients, which are applied to practical issues such as noises by hardware
injection test, lightning strokes, and air compressor vibrations
gravitational-wave detector.


title:
A Robin-Neumann Scheme with Quasi-Newton Acceleration for Partitioned
  Fluid-Structure Interaction

abstract:
  The Dirichlet-Neumann scheme is the most common partitioned algorithm for
fluid-structure interaction (FSI) and offers high flexibility concerning the
solvers employed for the two subproblems. Nevertheless, it is not without
shortcomings: To begin with, the inherent added-mass effect often destabilizes
the numerical solution severely. Moreover, the Dirichlet-Neumann scheme cannot
be applied to FSI problems in which an incompressible fluid is fully enclosed
by Dirichlet boundaries, as it is incapable of satisfying the volume
constraint. In the last decade, interface quasi-Newton methods have proven to
control the added-mass effect and substantially speed up convergence by adding
a Newton-like update step to the Dirichlet-Neumann coupling. They are, however,
without effect on the incompressibility dilemma. As an alternative, the
Robin-Neumann scheme generalizes the fluid's boundary condition to a Robin
condition by including the Cauchy stresses. While this modification in fact
successfully tackles both drawbacks of the Dirichlet-Neumann approach, the
price to be paid is a strong dependency on the Robin weighting parameter, with
very limited a priori knowledge about good choices. This work proposes a
strategy to merge these two ideas and benefit from their combined strengths.
The effectiveness of this new quasi-Newton-accelerated Robin-Neumann scheme is
demonstrated for different FSI simulations and compared to both Robin- and
Dirichlet-Neumann variants.


title:
Combined spectroscopy and intensity interferometry to determine the
  distances of the blue supergiants P Cygni and Rigel

abstract:
  In this paper, we report on the spatial intensity interferometry measurements
within the H$\alpha$ line on two stars: the Luminous Blue Variable (LBV)
supergiant \PCygni\,and the late-type B supergiant Rigel. The experimental
setup has been upgraded to allow simultaneous measurement of two polarization
channels and the zero baseline correlation function. Combined with simultaneous
spectra measurements and based on radiative transfer models calculated with the
code CMFGEN, we were able to fit our measured visibility curves to extract the
stellar distances. Our distance determinations for both \PCygni\ (1.61 $\pm$
0.18 kpc) and Rigel (0.26 $\pm$ 0.02 kpc) agree very well with the values
provided by astrometry with the Gaia and Hipparcos missions, respectively. This
is the first successful step towards extending the application of the Wind
Momentum Luminosity Relation method for distance calibration from an LBV
supergiant to a more normal late-type B supergiant.


title:
The Hutchinson-Barnsley theory for generalized iterated function systems
  by means of infinite iterated function systems

abstract:
  The study of generalized iterated function systems (GIFS) was introduced by
Mihail and Miculescu in 2008. We provide a new approach to study those systems
as the limit of the Hutchinson-Barnsley theory for infinite iterated function
systems (IIFS) which was developed by many authors in the last years. We show
that any attractor of a contractive generalized iterated function system is the
limit w.r.t Hausdorff-Pompeiu metric of attractors of contractive infinite
iterated function systems. We also prove that any Hutchinson measure for a
contractive generalized iterated function system with probabilities is the
limit w.r.t. the Monge-Kantorovich metric of the Hutchinson measures for
contractive infinite iterated function systems with probabilities.


title:
Repeated Extraction of Scrambled Quantum Data: Sustainability of the
  Hayden-Preskill Type Protocols

abstract:
  We introduce and study the problem of scrambler hacking, which is the
procedure of quantum information extraction from and installation on a quantum
scrambler given only partial access. This problem necessarily emerges from a
central topic in contemporary physics - information recovery from systems
undergoing scrambling dynamics, such as the Hayden-Preskill protocol in black
hole studies - because one must replace quantum data with another when
extracting it due to the no-cloning theorem. For large scramblers, we supply
analytical formulas for the optimal hacking fidelity, a quantitative measure of
the effectiveness of scrambler hacking with limited access. In the two-user
scenario where Bob attempts to hack Alice's data, we find that the optimal
fidelity converges to $64/(9\pi^2)\approx0.72$ with increasing Bob's hacking
space relative to Alice's user space. We applied our results to the black hole
information problem and showed that the limited hacking fidelity implies the
reflectivity decay of a black hole as an information mirror, which questions
the solvability of the black hole information paradox through the
Hayden-Preskill type protocol.


title: Three-pion scattering in Chiral Perturbation Theory

abstract:
  We present the results on the relativistic six-pion scattering amplitude at
low energy, calculated at $\mathcal{O}(p^4)$ within the framework of the
massive $\text{O}(N)$ nonlinear sigma model extended to the next-to-leading
order in the chiral counting. For $N=3$, this approach corresponds to the
two(-quark)-flavor Chiral Perturbation Theory. We also present the expressions
for the pion mass, pion decay constant and the four-pion amplitude in the case
of $N$ (meson) flavors at $\mathcal{O}(p^4)$.


title:
Few-shot One-class Domain Adaptation Based on Frequency for Iris
  Presentation Attack Detection

abstract:
  Iris presentation attack detection (PAD) has achieved remarkable success to
ensure the reliability and security of iris recognition systems. Most existing
methods exploit discriminative features in the spatial domain and report
outstanding performance under intra-dataset settings. However, the degradation
of performance is inevitable under cross-dataset settings, suffering from
domain shift. In consideration of real-world applications, a small number of
bonafide samples are easily accessible. We thus define a new domain adaptation
setting called Few-shot One-class Domain Adaptation (FODA), where adaptation
only relies on a limited number of target bonafide samples. To address this
problem, we propose a novel FODA framework based on the expressive power of
frequency information. Specifically, our method integrates frequency-related
information through two proposed modules. Frequency-based Attention Module
(FAM) aggregates frequency information into spatial attention and explicitly
emphasizes high-frequency fine-grained features. Frequency Mixing Module (FMM)
mixes certain frequency components to generate large-scale target-style samples
for adaptation with limited target bonafide samples. Extensive experiments on
LivDet-Iris 2017 dataset demonstrate the proposed method achieves
state-of-the-art or competitive performance under both cross-dataset and
intra-dataset settings.


title:
Deep Page-Level Interest Network in Reinforcement Learning for Ads
  Allocation

abstract:
  A mixed list of ads and organic items is usually displayed in feed and how to
allocate the limited slots to maximize the overall revenue is a key problem.
Meanwhile, modeling user preference with historical behavior is essential in
recommendation and advertising (e.g., CTR prediction and ads allocation). Most
previous works for user behavior modeling only model user's historical
point-level positive feedback (i.e., click), which neglect the page-level
information of feedback and other types of feedback. To this end, we propose
Deep Page-level Interest Network (DPIN) to model the page-level user preference
and exploit multiple types of feedback. Specifically, we introduce four
different types of page-level feedback as input, and capture user preference
for item arrangement under different receptive fields through the multi-channel
interaction module. Through extensive offline and online experiments on Meituan
food delivery platform, we demonstrate that DPIN can effectively model the
page-level user preference and increase the revenue for the platform.


title:
Global well-posedness for two-dimensional flows of viscoelastic
  rate-type fluids with stress diffusion

abstract:
  We consider the system of partial differential equations governing
two-dimensional flows of a robust class of viscoelastic rate-type fluids with
stress diffusion, involving a general objective derivative. The studied system
generalizes the incompressible Navier--Stokes equations for the fluid velocity
$v$ and pressure $p$ by the presence of an additional term in the constitutive
equation for the Cauchy stress expressed in terms of a positive definite tensor
$B$. The tensor $B$ evolves according to a diffusive variant of an equation
that can be viewed as a combination of corresponding counterparts of Oldroyd-B
and Giesekus models. Considering spatially periodic problem, we prove that for
arbitrary initial data and forcing in appropriate $L^2$ spaces, there exists a
unique globally defined weak solution to the equations of motion, and more
regular initial data and forcing launch a more regular solution with $\bs B$
positive definite everywhere.


title:
Weakly Supervised Regional and Temporal Learning for Facial Action Unit
  Recognition

abstract:
  Automatic facial action unit (AU) recognition is a challenging task due to
the scarcity of manual annotations. To alleviate this problem, a large amount
of efforts has been dedicated to exploiting various weakly supervised methods
which leverage numerous unlabeled data. However, many aspects with regard to
some unique properties of AUs, such as the regional and relational
characteristics, are not sufficiently explored in previous works. Motivated by
this, we take the AU properties into consideration and propose two auxiliary AU
related tasks to bridge the gap between limited annotations and the model
performance in a self-supervised manner via the unlabeled data. Specifically,
to enhance the discrimination of regional features with AU relation embedding,
we design a task of RoI inpainting to recover the randomly cropped AU patches.
Meanwhile, a single image based optical flow estimation task is proposed to
leverage the dynamic change of facial muscles and encode the motion information
into the global feature representation. Based on these two self-supervised
auxiliary tasks, local features, mutual relation and motion cues of AUs are
better captured in the backbone network. Furthermore, by incorporating
semi-supervised learning, we propose an end-to-end trainable framework named
weakly supervised regional and temporal learning (WSRTL) for AU recognition.
Extensive experiments on BP4D and DISFA demonstrate the superiority of our
method and new state-of-the-art performances are achieved.


title:
Intermediate-mass black holes born via repeated mergers are unlikely
  thermodynamically stable

abstract:
  Black hole (BH) thermodynamics is one of the most fascinating aspects of BH
physics. While its direct proof is challenging to observe for astrophysical
BHs, gravitational waves (GWs) have recently offered a new indirect powerful
method to probe that it is operating in the Universe. In this paper, we discuss
the thermodynamic stability of IMBHs that are born through repeated mergers of
stellar-mass BHs via GW emission in dense star clusters. We show that $\lesssim
20\%$ of the IMBHs born via repeated mergers are thermodynamically unstable for
a non-charged BH, while $\lesssim 40\%$ are thermodynamically unstable for a
moderately-charged BH. Only in the case of unlikely high charges, $\gtrsim
60\%$ of the IMBHs are thermodynamically stable. With the present and upcoming
GW missions, such as LIGO/Virgo/KAGRA, LISA, DECIGO, and ET that promise to
detect tens, or even hundreds, of IMBH mergers via GW emission in the next
decade, IMBH thermodynamics may soon be tested.


title: Free boundary cluster with Robin condition on the transmission Interface

abstract:
  We formulate and study a variational two-phase free boundary problem with
Robin condition on the interface between the two phases, and we prove existence
and regularity of solutions in dimension two


title: Autoencoder Attractors for Uncertainty Estimation

abstract:
  The reliability assessment of a machine learning model's prediction is an
important quantity for the deployment in safety critical applications. Not only
can it be used to detect novel sceneries, either as out-of-distribution or
anomaly sample, but it also helps to determine deficiencies in the training
data distribution. A lot of promising research directions have either proposed
traditional methods like Gaussian processes or extended deep learning based
approaches, for example, by interpreting them from a Bayesian point of view. In
this work we propose a novel approach for uncertainty estimation based on
autoencoder models: The recursive application of a previously trained
autoencoder model can be interpreted as a dynamical system storing training
examples as attractors. While input images close to known samples will converge
to the same or similar attractor, input samples containing unknown features are
unstable and converge to different training samples by potentially removing or
changing characteristic features. The use of dropout during training and
inference leads to a family of similar dynamical systems, each one being robust
on samples close to the training distribution but unstable on new features.
Either the model reliably removes these features or the resulting instability
can be exploited to detect problematic input samples. We evaluate our approach
on several dataset combinations as well as on an industrial application for
occupant classification in the vehicle interior for which we additionally
release a new synthetic dataset.


title:
A visualisation for conveying the dynamics of iterative eigenvalue
  algorithms over PSD matrices

abstract:
  We propose a new way of visualising the dynamics of iterative eigenvalue
algorithms such as the QR algorithm, over the important special case of PSD
(positive semi-definite) matrices. Many subtle and important properties of such
algorithms are easily found this way. We believe that this may have pedagogical
value to both students and researchers of numerical linear algebra. The fixed
points of iterative algorithms are obtained visually, and their stability is
analysed intuitively. It becomes clear that what it means for an iterative
eigenvalue algorithm to "converge quickly" is an ambiguous question, depending
on whether eigenvalues or eigenvectors are being sought. The presentation is
likely a novel one, and using it, a theorem about the dynamics of general
iterative eigenvalue algorithms is proved. There is an accompanying video
series, currently hosted on Youtube, that has certain advantages in terms of
fully exploiting the interactivity of the visualisation.


title: Schr\"odinger's Black Hole Cat

abstract:
  In the absence of a fully-fledged theory of quantum gravity, we propose a
"bottom-up" framework for exploring quantum-gravitational physics by pairing
two of the most fundamental concepts of quantum theory and general relativity,
namely quantum superposition and spacetime. We show how to describe such
"spacetime superpositions" and explore effects they induce upon quantum matter.
Our approach capitalizes on standard tools of quantum field theory in curved
space, and allows us to calculate physical observables like transition
probabilities for a particle detector residing in curvature-superposed de
Sitter spacetime, or outside a mass-superposed black hole. Crucially, such
scenarios represent genuine quantum superpositions of spacetimes, in contrast
with superpositions of metrics which only differ by a coordinate transformation
and thus are not different according to general relativity.