title:
Reductions towards a characteristic free proof of the Canonical Element
  Theorem

abstract:
  We reduce Hochster's Canonical Element Conjecture (theorem since 2016) to a
localization problem in a characteristic free way. We prove the validity of a
new variant of the Canonical Element Theorem (CET) and explain how a
characteristic free deduction of the new variant from the original CET would
provide us with a characteristic free proof of the CET. We show that also the
Balanced Big Cohen-Macaulay Module Theorem can be settled by a characteristic
free proof if the big Cohen-Macaulayness of Hochster's modification module can
be deduced from the existence of a maximal Cohen-Macaulay complex in a
characteristic free way.


title:
Backbone is All Your Need: A Simplified Architecture for Visual Object
  Tracking

abstract:
  Exploiting a general-purpose neural architecture to replace hand-wired
designs or inductive biases has recently drawn extensive interest. However,
existing tracking approaches rely on customized sub-modules and need prior
knowledge for architecture selection, hindering the tracking development in a
more general system. This paper presents a Simplified Tracking architecture
(SimTrack) by leveraging a transformer backbone for joint feature extraction
and interaction. Unlike existing Siamese trackers, we serialize the input
images and concatenate them directly before the one-branch backbone. Feature
interaction in the backbone helps to remove well-designed interaction modules
and produce a more efficient and effective framework. To reduce the information
loss from down-sampling in vision transformers, we further propose a foveal
window strategy, providing more diverse input patches with acceptable
computational costs. Our SimTrack improves the baseline with 2.5%/2.6% AUC
gains on LaSOT/TNL2K and gets results competitive with other specialized
tracking algorithms without bells and whistles.


title: Coarse structure of ultrametric spaces with applications

abstract:
  We show how to decompose all separable ultrametric spaces into a "Lego"
combinations of scaled versions of full simplices. To do this we introduce
metric resolutions of large scale metric spaces, which describe how a space can
be broken up into roughly independent pieces. We use these metric resolutions
to define the coarse disjoint union of large scale metric spaces, which
provides a way of attaching large scale metric spaces to each other in a
"coarsely independent way". We use these notions to construct universal spaces
in the categories of separable and proper metric spaces of asymptotic dimension
$0$, respectively. In doing so we generalize a similar result of Dranishnikov
and Zarichnyi as well as Nag\'orko and Bell. However, the new application is a
universal space for proper metric spaces of asymptotic dimension $0$, something
that eluded those authors. We finish with a description of some countable
groups that can serve as such universal spaces.


title:
Phase transitions in the Blume-Capel model with trimodal and Gaussian
  random fields

abstract:
  We obtain the phase diagram for the infinite range random field Blume capel
(RFBC) model for the symmetric trimodal distribution of the random field (h) in
the temperature (T)-crystal field ($\Delta$) and T-h planes. We find 4 ordered
and 2 disordered phases in the model at T=0. Depending on the disorder
strength, there are 5 different ground state phase diagrams in the $\Delta-$h
plane. The T-$\Delta$ and T-h phase diagrams also can be classified into 5
different categories as a function of the strength of the disorder, consistent
with the 5 different ground state phase diagrams. The phase diagrams at finite
temperature consist of lines of first order and second order transitions,
multicritical points like tricritical point, bicritical points, critical end
point, and also many multi-phase coexistence points. We find re-entrance in the
T-h phase diagram for the symmetric trimodal distribution for some values of
$\Delta$. We also studied the Gaussian distribution of the random field. In
contrast, the ground state for the Gaussian distribution consists of only one
ordered and one disordered phase. At low disorder strength ($\sigma$) the
T$-\Delta$ phase diagram has a line of first order and a line of second order
transitions, separated by a TCP. However at higher $\sigma$ the phase diagram
only has a line of second order transition. In general the phase diagram of the
random field problems is expected to be similar for symmetric distributions as
has been observed for the random field O(n) models. In the case of RFBC we find
that while for Gaussian distribution, the phase diagram is similar to the phase
diagram of the infinite range random field Ising model (RFIM) with the bimodal
random field distribution, for the symmetric trimodal distribution there are
many different T-$\Delta$ and T-h phase diagrams with multiple multicritical
and multi-phase coexistence points.


title: Finite syntomic topology and algebraic cobordism of non-unital algebras

abstract:
  Elmanto, Hoyois, Khan, Sosnilo, and Yakerson invented that the algebraic
cobordism is the sphere spectrum of the $\mathbb{P}^1_+$-stable homotopy
category of framed motivic spectra with finite syntomic correspondence.
Inspired by their works and Dwyer--Kan's hammock localization, we consider the
localization of the stable $\infty$-category of motivic spectra by zero-section
stable finite syntomic surjective morphisms. This paper results that the
localization functor is $\mathbb{A}^1$-homotopy equivalent to the finite
syntomic hyper-sheafification, and the algebraic cobordism is weakly equivalent
to the motivic sphere spectrum after the localization (or the
hyper-sheafification). Furthermore, on the finite syntomic topology, we prove
the tilting equivalence between the algebraic cobordisms for non-unital
integral perfectoid algebras.


title:
SelfTune: Metrically Scaled Monocular Depth Estimation through
  Self-Supervised Learning

abstract:
  Monocular depth estimation in the wild inherently predicts depth up to an
unknown scale. To resolve scale ambiguity issue, we present a learning
algorithm that leverages monocular simultaneous localization and mapping (SLAM)
with proprioceptive sensors. Such monocular SLAM systems can provide metrically
scaled camera poses. Given these metric poses and monocular sequences, we
propose a self-supervised learning method for the pre-trained supervised
monocular depth networks to enable metrically scaled depth estimation. Our
approach is based on a teacher-student formulation which guides our network to
predict high-quality depths. We demonstrate that our approach is useful for
various applications such as mobile robot navigation and is applicable to
diverse environments. Our full system shows improvements over recent
self-supervised depth estimation and completion methods on EuRoC, OpenLORIS,
and ScanNet datasets.


title:
EACELEB: An East Asian Language Speaking Celebrity Dataset for Speaker
  Recognition

abstract:
  Large datasets are very useful for training speaker recognition systems, and
various research groups have constructed several over the years. Voxceleb is a
large dataset for speaker recognition that is extracted from Youtube videos.
This paper presents an audio-visual method for acquiring audio data from
Youtube given the speaker's name as input. The system follows a pipeline
similar to that of the Voxceleb data acquisition method. However, our work
focuses on fast data acquisition by using face-tracking in subsequent frames
once a face has been detected -- this is preferable over face detection for
every frame considering its computational cost. We show that applying audio
diarization to our data after acquiring it can yield equal error rates
comparable to Voxceleb. A secondary set of experiments showed that we could
further decrease the error rate by fine-tuning a pre-trained x-vector system
with the acquired data. Like Voxceleb, the work here focuses primarily on
developing audio for celebrities. However, unlike Voxceleb, our target audio
data is from celebrities in East Asian countries. Finally, we set up a speaker
verification task to evaluate the accuracy of our acquired data. After
diarization and fine-tuning, we achieved an equal error rate of approximately
4\% across our entire dataset.


title:
Iterative Corresponding Geometry: Fusing Region and Depth for Highly
  Efficient 3D Tracking of Textureless Objects

abstract:
  Tracking objects in 3D space and predicting their 6DoF pose is an essential
task in computer vision. State-of-the-art approaches often rely on object
texture to tackle this problem. However, while they achieve impressive results,
many objects do not contain sufficient texture, violating the main underlying
assumption. In the following, we thus propose ICG, a novel probabilistic
tracker that fuses region and depth information and only requires the object
geometry. Our method deploys correspondence lines and points to iteratively
refine the pose. We also implement robust occlusion handling to improve
performance in real-world settings. Experiments on the YCB-Video, OPT, and Choi
datasets demonstrate that, even for textured objects, our approach outperforms
the current state of the art with respect to accuracy and robustness. At the
same time, ICG shows fast convergence and outstanding efficiency, requiring
only 1.3 ms per frame on a single CPU core. Finally, we analyze the influence
of individual components and discuss our performance compared to deep
learning-based methods. The source code of our tracker is publicly available.


title:
Non-generative Generalized Zero-shot Learning via Task-correlated
  Disentanglement and Controllable Samples Synthesis

abstract:
  Synthesizing pseudo samples is currently the most effective way to solve the
Generalized Zero Shot Learning (GZSL) problem. Most models achieve competitive
performance but still suffer from two problems: (1) Feature confounding, the
overall representations confound task-correlated and task-independent features,
and existing models disentangle them in a generative way, but they are
unreasonable to synthesize reliable pseudo samples with limited samples; (2)
Distribution uncertainty, that massive data is needed when existing models
synthesize samples from the uncertain distribution, which causes poor
performance in limited samples of seen classes. In this paper, we propose a
non-generative model to address these problems correspondingly in two modules:
(1) Task-correlated feature disentanglement, to exclude the task-correlated
features from task-independent ones by adversarial learning of domain adaption
towards reasonable synthesis; (2) Controllable pseudo sample synthesis, to
synthesize edge-pseudo and center-pseudo samples with certain characteristics
towards more diversity generated and intuitive transfer. In addation, to
describe the new scene that is the limit seen class samples in the training
process, we further formulate a new ZSL task named the 'Few-shot Seen class and
Zero-shot Unseen class learning' (FSZU). Extensive experiments on four
benchmarks verify that the proposed method is competitive in the GZSL and the
FSZU tasks.


title: Magnon-Cooparons in magnet-superconductor hybrids

abstract:
  Generation and detection of spinfull Cooper pairs in conventional
superconductors has been intensely pursued by designing increasingly complex
magnet-superconductor hybrids. We demonstrate theoretically that magnons with
nonzero wavenumbers universally induce a cloud of spinfull triplet Cooper pairs
around them in an adjacent conventional superconductor. The resulting composite
quasiparticle, termed magnon-cooparon, consists of a spin flip in the magnet
screened by a cloud of the spinfull superfluid condensate. Thus, it inherits a
large effective mass, which can be measured experimentally. Furthermore, we
demonstrate that two magnetic wires deposited on a superconductor serve as a
controllable magnonic directional coupler mediated by the nonlocal and
composite nature of magnon-cooparons. Our analysis predicts a quasiparticle
that enables generation, control, and use of spinfull triplet Cooper pairs in
the simplest magnet-superconductor heterostructures.


title:
Parsimonious Physics-Informed Random Projection Neural Networks for
  Initial-Value Problems of ODEs and index-1 DAEs

abstract:
  We address a physics-informed neural network based on the concept of random
projections for the numerical solution of IVPs of nonlinear ODEs in
linear-implicit form and index-1 DAEs, which may also arise from the spatial
discretization of PDEs. The scheme has a single hidden layer with appropriately
randomly parametrized Gaussian kernels and a linear output layer, while the
internal weights are fixed to ones. The unknown weights between the hidden and
output layer are computed by Newton's iterations, using the Moore-Penrose
pseudoinverse for low to medium, and sparse QR decomposition with
regularization for medium to large scale systems. To deal with stiffness and
sharp gradients, we propose a variable step size scheme for adjusting the
interval of integration and address a continuation method for providing good
initial guesses for the Newton iterations. Based on previous works on random
projections, we prove the approximation capability of the scheme for ODEs in
the canonical form and index-1 DAEs in the semiexplicit form. The optimal
bounds of the uniform distribution are parsimoniously chosen based on the
bias-variance trade-off. The performance of the scheme is assessed through
seven benchmark problems: four index-1 DAEs, the Robertson model, a model of
five DAEs describing the motion of a bead, a model of six DAEs describing a
power discharge control problem, the chemical Akzo Nobel problem and three
stiff problems, the Belousov-Zhabotinsky, the Allen-Cahn PDE and the
Kuramoto-Sivashinsky PDE. The efficiency of the scheme is compared with three
solvers ode23t, ode23s, ode15s of the MATLAB ODE suite. Our results show that
the proposed scheme outperforms the stiff solvers in several cases, especially
in regimes where high stiffness or sharp gradients arise in terms of numerical
accuracy, while the computational costs are for any practical purposes
comparable.


title:
TrueType Transformer: Character and Font Style Recognition in Outline
  Format

abstract:
  We propose TrueType Transformer (T3), which can perform character and font
style recognition in an outline format. The outline format, such as TrueType,
represents each character as a sequence of control points of stroke contours
and is frequently used in born-digital documents. T3 is organized by a deep
neural network, so-called Transformer. Transformer is originally proposed for
sequential data, such as text, and therefore appropriate for handling the
outline data. In other words, T3 directly accepts the outline data without
converting it into a bitmap image. Consequently, T3 realizes a
resolution-independent classification. Moreover, since the locations of the
control points represent the fine and local structures of the font style, T3 is
suitable for font style classification, where such structures are very
important. In this paper, we experimentally show the applicability of T3 in
character and font style recognition tasks, while observing how the individual
control points contribute to classification results.


title: Why gauge? Conceptual Aspects of Gauge theories

abstract:
  This thesis is about conceptual aspects of gauge theories.
  Gauge theories lie at the heart of modern physics: in particular, they
constitute the standard model of particle physics. At its simplest, the idea of
gauge is that nature is best described using a descriptively redundant
language; the different descriptions are said to be related by a gauge
symmetry. The over-arching question the thesis aims to answer is: how can
descriptive redundancy be fruitful for physics?


title: Domain Generalization via Shuffled Style Assembly for Face Anti-Spoofing

abstract:
  With diverse presentation attacks emerging continually, generalizable face
anti-spoofing (FAS) has drawn growing attention. Most existing methods
implement domain generalization (DG) on the complete representations. However,
different image statistics may have unique properties for the FAS tasks. In
this work, we separate the complete representation into content and style ones.
A novel Shuffled Style Assembly Network (SSAN) is proposed to extract and
reassemble different content and style features for a stylized feature space.
Then, to obtain a generalized representation, a contrastive learning strategy
is developed to emphasize liveness-related style information while suppress the
domain-specific one. Finally, the representations of the correct assemblies are
used to distinguish between living and spoofing during the inferring. On the
other hand, despite the decent performance, there still exists a gap between
academia and industry, due to the difference in data quantity and distribution.
Thus, a new large-scale benchmark for FAS is built up to further evaluate the
performance of algorithms in reality. Both qualitative and quantitative results
on existing and proposed benchmarks demonstrate the effectiveness of our
methods. The codes will be available at https://github.com/wangzhuo2019/SSAN.


title: Quotients of commuting schemes associated to Symmetric Pairs

abstract:
  Let $\mathfrak{g}=\mathfrak{g}_0\oplus \mathfrak{g}_1$ be a $\mathbb
Z_2$-grading of a classical Lie algebra such that $(\mathfrak{g},
\mathfrak{g}_0)$ is a classical symmetric pair. Let $G$ be a classical group
with Lie algebra $\mathfrak{g}$ and let $G_0$ be the connected subgroup of $G$
with ${\rm Lie} (G_0)=\mathfrak g_0$. For $d \geq 2$, let
$\mathfrak{C}^d(\mathfrak{g}_1)$ be the $d$-th commuting scheme associated with
the symmetric pair $(\mathfrak g, \mathfrak g_0)$. In this article, we study
the categorical quotient $\mathfrak{C}^d(\mathfrak{g}_1)//{G_0}$ via the
Chevalley restriction map. As a consequence we show that the categorical
quotient scheme $\mathfrak C^d(\mathfrak g_1)//G_0$ is normal and reduced. As a
part of the proof, we describe a generating set for the algebra
$k[\mathfrak{g}_1^d]^{G_0}$, which are of independent interest.


title:
Dynamics of the collapse of a ferromagnetic skyrmion in a
  centrosymmetric lattice

abstract:
  Time dependence of the size and chirality of a ferromagnetic skyrmion in a
Heisenberg model with the magnetic field on a square lattice has been studied
analytically and numerically. The lattice and the magnetic field generate
strong time dependence of the skyrmion chirality. Due to nonlinearity, the
lattice alone also generates strong intrinsic damping that leads to the
skyrmion collapse via the emission of spin waves. In the absence of the
magnetic field the collapse is slow for a large skyrmion but it becomes
exponentially fast in the presence of the Landau-Lifshitz damping when the
field is turned on. Magnons emitted by a collapsing skyrmion must have a
discrete spectrum due to the quantization of the skyrmion magnetic moment.


title:
Action-angle coordinates for motion in a straight magnetic field with
  constant gradient

abstract:
  The motion of a charged particle in a straight magnetic field ${\bf B} =
B(y)\,\wh{\sf z}$ with a constant perpendicular gradient is solved exactly in
terms of elliptic functions and integrals. The motion can be decomposed in
terms of a periodic motion along the $y$-axis and a drift motion along the
$x$-axis. The periodic motion can be described as a particle trapped in a
symmetric quartic potential in $y$. The canonical transformation from the
canonical coordinates $(y,P_{y})$ to the action-angle coordinates $(J,\theta)$
is solved explicitly in terms of a generating function $S(\theta,J)$ that is
expressed in terms of Jacobi elliptic functions. The presence of a weak
constant electric field ${\bf E} = E_{0}\,\wh{\sf y}$ introduces an asymmetric
component to the quartic potential, and the associated periodic motion is
solved perturbatively up to second order.


title:
EyeLoveGAN: Exploiting domain-shifts to boost network learning with
  cycleGANs

abstract:
  This paper presents our contribution to the REFUGE challenge 2020. The
challenge consisted of three tasks based on a dataset of retinal images:
Segmentation of optic disc and cup, classification of glaucoma, and
localization of fovea. We propose employing convolutional neural networks for
all three tasks. Segmentation is performed using a U-Net, classification is
performed by a pre-trained InceptionV3 network, and fovea detection is
performed by employing stacked hour-glass for heatmap prediction. The challenge
dataset contains images from three different data sources. To enhance
performance, cycleGANs were utilized to create a domain-shift between the data
sources. These cycleGANs move images across domains, thus creating artificial
images which can be used for training.


title:
Pentactions and action representability in the category of reduced
  groups with action

abstract:
  A notion of pentaction of any object in the category $\mathbf{rGr}^{\bullet}$
of reduced groups with action is introduced. The operations are defined in the
set $\mathsf{Pentact}(A)$ of pentactions of an object $A$ of
$\mathbf{rGr}^{\bullet}$. It is proved that if an object $A$ is perfect with
zero weak stabilizer in the sense defined in the paper, then
$\mathsf{Pentact}(A)$ is an object of $\mathbf{rGr}^{\bullet}$, it has a
derived action on $A$, the object $A$ is action representable and
$\mathsf{Pentact}(A)$ represents all actions on $A$.


title:
Knowledge-enriched Attention Network with Group-wise Semantic for Visual
  Storytelling

abstract:
  As a technically challenging topic, visual storytelling aims at generating an
imaginary and coherent story with narrative multi-sentences from a group of
relevant images. Existing methods often generate direct and rigid descriptions
of apparent image-based contents, because they are not capable of exploring
implicit information beyond images. Hence, these schemes could not capture
consistent dependencies from holistic representation, impairing the generation
of reasonable and fluent story. To address these problems, a novel
knowledge-enriched attention network with group-wise semantic model is
proposed. Three main novel components are designed and supported by substantial
experiments to reveal practical advantages. First, a knowledge-enriched
attention network is designed to extract implicit concepts from external
knowledge system, and these concepts are followed by a cascade cross-modal
attention mechanism to characterize imaginative and concrete representations.
Second, a group-wise semantic module with second-order pooling is developed to
explore the globally consistent guidance. Third, a unified one-stage story
generation model with encoder-decoder structure is proposed to simultaneously
train and infer the knowledge-enriched attention network, group-wise semantic
module and multi-modal story generation decoder in an end-to-end fashion.
Substantial experiments on the popular Visual Storytelling dataset with both
objective and subjective evaluation metrics demonstrate the superior
performance of the proposed scheme as compared with other state-of-the-art
methods.


title: A note on branching of $V(\rho)$

abstract:
  Let $\mathfrak{g}$ be a complex simple Lie algebra and let $\mathfrak{g}_0$
be the sub-algebra fixed by a diagram automorphism of $\mathfrak{g}$. Let $G$
be the complex, simply-connected, simple algebraic group with Lie algebra
$\mathfrak{g}$, and let $G_0$ be the connected subgroup of $G$ with Lie algebra
$\mathfrak{g}_0$. Let $\rho$ be the half sum of positive roots of
$\mathfrak{g}$. In this article, we give a necessary and sufficient condition
for a highest weight $\mathfrak{g}_0$-representation $V_0(d\mu)$ to occur in
the representation ${\rm res}_{\mathfrak{g}_0}V(d\rho)$, for any saturation
factor $d$ of the pair $(G_0, G)$.


title:
Cavity-Dumping a Single Infrared Pulse from a Free-Electron Laser for
  Two-Color Pump-Probe Experiments

abstract:
  Electromagnetic radiation in the mid- to far-infrared spectral range
represents an indispensable tool for the study of numerous types of collective
excitations in solids and molecules. Short and intense pulses in this THz
spectral range are, however, difficult to obtain. While wide
wavelength-tunability is easily provided by free-electron lasers, the energies
of individual pulses are relatively moderate, on the order of microjoules. Here
we demonstrate a setup that uses cavity-dumping of a free-electron laser to
provide single, picosecond-long pulses in the mid- to far-infrared frequency
range. The duration of the Fourier-limited pulses can be varied by cavity
detuning, and their energy was shown to exceed 100 {\mu}J. Using the
aforementioned infrared pulse as a pump, we have realized a two-color
pump-probe setup facilitating single-shot time-resolved imaging of
magnetization dynamics. We demonstrate the capabilities of the setup first on
thermally-induced demagnetization and magnetic switching of a GdFeCo thin film
and second by showing a single-shot time-resolved detection of resonant
phononic switching of the magnetization in a magnetic garnet.


title: Two-stream Hierarchical Similarity Reasoning for Image-text Matching

abstract:
  Reasoning-based approaches have demonstrated their powerful ability for the
task of image-text matching. In this work, two issues are addressed for
image-text matching. First, for reasoning processing, conventional approaches
have no ability to find and use multi-level hierarchical similarity
information. To solve this problem, a hierarchical similarity reasoning module
is proposed to automatically extract context information, which is then
co-exploited with local interaction information for efficient reasoning.
Second, previous approaches only consider learning single-stream similarity
alignment (i.e., image-to-text level or text-to-image level), which is
inadequate to fully use similarity information for image-text matching. To
address this issue, a two-stream architecture is developed to decompose
image-text matching into image-to-text level and text-to-image level similarity
computation. These two issues are investigated by a unifying framework that is
trained in an end-to-end manner, namely two-stream hierarchical similarity
reasoning network. The extensive experiments performed on the two benchmark
datasets of MSCOCO and Flickr30K show the superiority of the proposed approach
as compared to existing state-of-the-art methods.


title:
A family of orthogonal polynomials corresponding to Jacobi matrices with
  a trace class inverse

abstract:
  Assume that $\{a_{n};\,n\geq0\}$ is a sequence of positive numbers and $\sum
a_{n}^{\,-1}<\infty$. Let $\alpha_{n}=ka_{n}$, $\beta_{n}=a_{n}+k^{2}a_{n-1}$
where $k\in(0,1)$ is a parameter, and let $\{P_{n}(x)\}$ be an orthonormal
polynomial sequence defined by the three-term recurrence \[
\alpha_{0}P_{1}(x)+(\beta_{0}-x)P_{0}(x)=0,\
\alpha_{n}P_{n+1}(x)+(\beta_{n}-x)P_{n}(x)+\alpha_{n-1}P_{n-1}(x)=0 \] for
$n\geq1$, with $P_{0}(x)=1$. Let $J$ be the corresponding Jacobi (tridiagonal)
matrix, i.e. $J_{n,n}=\beta_{n}$, $J_{n,n+1}=J_{n+1,n}=\alpha_{n}$ for
$n\geq0$. Then $J^{-1}$ exists and belongs to the trace class. We derive an
explicit formula for $P_{n}(x)$ as well as for the characteristic function of
$J$ and describe the orthogonality measure for the polynomial sequence. As a
particular case, the modified $q$-Laguerre polynomials are introduced and
studied.


title:
Multi-index Sequential Monte Carlo ratio estimators for Bayesian Inverse
  problems

abstract:
  We consider the problem of estimating expectations with respect to a target
distribution with an unknown normalizing constant, and where even the
unnormalized target needs to be approximated at finite resolution. This setting
is ubiquitous across science and engineering applications, for example in the
context of Bayesian inference where a physics-based model governed by an
intractable partial differential equation (PDE) appears in the likelihood. A
multi-index Sequential Monte Carlo (MISMC) method is used to construct ratio
estimators which provably enjoy the complexity improvements of multi-index
Monte Carlo (MIMC) as well as the efficiency of Sequential Monte Carlo (SMC)
for inference. In particular, the proposed method provably achieves the
canonical complexity of MSE$^{-1}$, while single level methods require
MSE$^{-\xi}$ for $\xi>1$. This is illustrated on examples of Bayesian inverse
problems with an elliptic PDE forward model in $1$ and $2$ spatial dimensions,
where $\xi=5/4$ and $\xi=3/2$, respectively. It is also illustrated on a more
challenging log Gaussian process models, where single level complexity is
approximately $\xi=9/4$ and multilevel Monte Carlo (or MIMC with an
inappropriate index set) gives $\xi = 5/4 + \omega$, for any $\omega > 0$,
whereas our method is again canonical.


title: Temporal Context for Robust Maritime Obstacle Detection

abstract:
  Robust maritime obstacle detection is essential for fully autonomous unmanned
surface vehicles (USVs). The currently widely adopted segmentation-based
obstacle detection methods are prone to misclassification of object reflections
and sun glitter as obstacles, producing many false positive detections,
effectively rendering the methods impractical for USV navigation. However,
water-turbulence-induced temporal appearance changes on object reflections are
very distinctive from the appearance dynamics of true objects. We harness this
property to design WaSR-T, a novel maritime obstacle detection network, that
extracts the temporal context from a sequence of recent frames to reduce
ambiguity. By learning the local temporal characteristics of object reflection
on the water surface, WaSR-T substantially improves obstacle detection accuracy
in the presence of reflections and glitter. Compared with existing single-frame
methods, WaSR-T reduces the number of false positive detections by 41% overall
and by over 53% within the danger zone of the boat, while preserving a high
recall, and achieving new state-of-the-art performance on the challenging MODS
maritime obstacle detection benchmark.


title: Limits of network nonlocality probed by time-like separated observers

abstract:
  In an entanglement swapping scenario, if two sources sharing entangled states
between three parties are independent, local correlations lead to a different
kind of inequalities than the standard Bell inequalities, known as network
local models. A highly demanding task is to find out a way to involve many
players nontrivially in a quantum network since measurements, in general,
disturb the system. To this end, we consider here a novel way of sharing
network nonlocality when two observers initially share close to a maximally
entangled states. We report that by employing unsharp measurements performed by
one of the observers, six pairs can sequentially demonstrate the violation of
bilocal correlations while a maximum of two pairs of observers can exhibit
bi-nonlocality when both the observers perform unsharp measurements. We also
find the critical noise involved in unsharp measurements in each round to
illustrate the bi-nonlocality for a fixed shared entangled state as a resource.
We also establish a connection between entanglement content of the shared
state, quantified via von-Neumann entropy of the local density matrix for pure
states and entanglement of formation for Werner states, and the maximum number
of rounds showing violation of bilocal correlations. By reducing entanglement
content in the elements of the joint measurement by the third party, we observe
that the maximum number reduces to two sequential sharing of bi-nonlocality
even for the maximally entangled state when the settings at each side are taken
to be three and fixed.


title:
Machine Learning-based Beamforming Design for Millimeter Wave IRS
  Communications with Discrete Phase Shifters

abstract:
  In this paper, we investigate an intelligent reflecting surface
(IRS)-assisted millimeter-wave multiple-input single-output downlink wireless
communication system. By jointly calculating the active beamforming at the base
station and the passive beamforming at the IRS, we aim to minimize the transmit
power under the constraint of each user' signal-to-interference-plus-noise
ratio. To solve this problem, we propose a low-complexity machine
learning-based cross-entropy (CE) algorithm to alternately optimize the active
beamforming and the passive beamforming. Specifically, in the alternative
iteration process, the zero-forcing (ZF) method and CE algorithm are applied to
acquire the active beamforming and the passive beamforming, respectively. The
CE algorithm starts with random sampling, by the idea of distribution focusing,
namely shifting the distribution towards a desired one by minimizing CE, and a
near optimal reflection coefficients with adequately high probability can be
obtained. In addition, we extend the original one-bit phase shift at the IRS to
the common case with high-resolution phase shift to enhance the effectiveness
of the algorithms. Simulation results verify that the proposed algorithm can
obtain a near optimal solution with lower computational complexity.


title:
SATLab at SemEval-2022 Task 4: Trying to Detect Patronizing and
  Condescending Language with only Character and Word N-grams

abstract:
  A logistic regression model only fed with character and word n-grams is
proposed for the SemEval-2022 Task 4 on Patronizing and Condescending Language
Detection (PCL). It obtained an average level of performance, well above the
performance of a system that tries to guess without using any knowledge about
the task, but much lower than the best teams. As the proposed model is very
similar to the one that performed well on a task requiring to automatically
identify hate speech and offensive content, this paper confirms the difficulty
of PCL detection.


title:
An addendum to "Mild solutions to semilinear stochastic partial
  differential equations with locally monotone coefficients"

abstract:
  In this addendum we provide an existence and uniqueness result for mild
solutions to semilinear stochastic partial differential equations driven by
Wiener processes and Poisson random measures in the framework of the semigroup
approach with locally monotone coefficients, where the semigroup is allowed to
be pseudo-contractive. This improves an earlier paper of the author, where the
equation was only driven by Wiener processes, and where the semigroup was only
allowed to be a semigroup of contractions.


title: Stellar Triples as a Source for Ba Stars

abstract:
  Barium stars have been studied extensively over the past few decades, yet our
current understanding of how these intriguing objects formed leaves much to be
desired. Many trends observed in systems containing barium stars cannot be
satisfactorily explained by classical binary evolution models, naturally
raising the question of whether triples and other higher-order multiples can
give rise to such exotic objects. In this paper, we study the possibility that
a Roche Lobe overflow from a tertiary in a hierarchical triple system can
potentially lead to surface barium enrichment within the inner binary, while at
the same time causing the inner binary to merge, thereby producing a barium
star. This possibility has the potential to form a large proportion of Barium
stars, as Roche Lobe overflow from a tertiary is typically much more stable for
close orbits than that from a binary companion. Various formation channels and
mechanisms by which this can be achieved are considered, and constraints on
relative formation rates are placed on each scenario. We conclude that a
significant, if not dominant, proportion of barium stars are formed from
hierarchical triple systems, and that further studies are required in this area
before a complete understanding of Barium star populations can be achieved.


title:
How relevant is climate change research for climate change policy? An
  empirical analysis based on Overton data

abstract:
  Climate change is an ongoing topic in nearly all areas of society since many
years. A discussion of climate change without referring to scientific results
is not imaginable. This is especially the case for policies since action on the
macro scale is required to avoid costly consequences for society. In this
study, we deal with the question of how research on climate change and policy
are connected. In 2019, the new Overton database of policy documents was
released including links to research papers that are cited by policy documents.
The use of results and recommendations from research on climate change might be
reflected in citations of scientific papers in policy documents. Although we
suspect a lot of uncertainty related to the coverage of policy documents in
Overton, there seems to be an impact of international climate policy cycles on
policy document publication. We observe local peaks in climate policy documents
around major decisions in international climate diplomacy. Our results point
out that IGOs and think tanks -- with a focus on climate change -- have
published more climate change policy documents than expected. We found that
climate change papers that are cited in climate change policy documents
received significantly more citations on average than climate change papers
that are not cited in these documents. Both areas of society (science and
policy) focus on similar climate change research fields: biology, earth
sciences, engineering, and disease sciences. Based on these and other empirical
results in this study, we propose a simple model of policy impact considering a
chain of different document types: the chain starts with scientific assessment
reports (systematic reviews) that lead via science communication documents
(policy briefs, policy reports or plain language summaries) and government
reports to legislative documents.


title: Non-vanishing mod $p$ of theta lifts

abstract:
  We establish the non-vanishing mod $p$ of certain global theta lifts from a
compact orthogonal group $\mathrm{O}_{n}$ over a totally real number field $F$
to a symplectic group $\mathrm{Sp}_{2m}$ over $F$ under mild conditions.


title: Deep Residual Reinforcement Learning based Autonomous Blimp Control

abstract:
  Blimps are well suited to perform long-duration aerial tasks as they are
energy efficient, relatively silent and safe. To address the blimp navigation
and control task, in previous work we developed a hardware and
software-in-the-loop framework and a PID-based controller for large blimps in
the presence of wind disturbance. However, blimps have a deformable structure
and their dynamics are inherently non-linear and time-delayed, making PID
controllers difficult to tune. Thus, often resulting in large tracking errors.
Moreover, the buoyancy of a blimp is constantly changing due to variations in
ambient temperature and pressure. To address these issues, in this paper we
present a learning-based framework based on deep residual reinforcement
learning (DRRL), for the blimp control task. Within this framework, we first
employ a PID controller to provide baseline performance. Subsequently, the DRRL
agent learns to modify the PID decisions by interaction with the environment.
We demonstrate in simulation that DRRL agent consistently improves the PID
performance. Through rigorous simulation experiments, we show that the agent is
robust to changes in wind speed and buoyancy. In real-world experiments, we
demonstrate that the agent, trained only in simulation, is sufficiently robust
to control an actual blimp in windy conditions. We openly provide the source
code of our approach at https://github.com/
robot-perception-group/AutonomousBlimpDRL.


title:
Prediction of single-atom-thick transition metal nitride CrN$_4$ with a
  square-planar network and high-temperature ferromagnetism

abstract:
  Single-atom-thick two-dimensional materials such as graphene usually have a
hexagonal lattice while the square-planar lattice is uncommon in the family of
two-dimensional materials. Here, we demonstrate that single-atom-thick
transition metal nitride CrN$_4$ monolayer is a stable free-standing layer with
a square-planar network. The stability of square-planar geometry is ascribed to
the combination of N-N double bond, Cr-N coordination bond, and $\pi$-d
conjugation, in which the double $\pi$-d conjugation situation is reported for
the first time. This mechanism is entirely different from that of the reported
two-dimensional materials, leading to ultralow formation energy and robust
stability. On the other hand, CrN$_4$ layer has a ferromagnetic ground state,
in which the ferromagnetic coupling between two Cr atoms is mediated by
electrons of the half-filled large $\pi$ orbitals from $\pi$-d conjugation. The
high-temperature ferromagnetism with the Curie temperature above 1000 K is
confirmed from the GGA+U, SCAN, and HSE calculations.


title:
Efficient Runtime Profiling for Black-box Machine Learning Services on
  Sensor Streams

abstract:
  In highly distributed environments such as cloud, edge and fog computing, the
application of machine learning for automating and optimizing processes is on
the rise. Machine learning jobs are frequently applied in streaming conditions,
where models are used to analyze data streams originating from e.g. video
streams or sensory data. Often the results for particular data samples need to
be provided in time before the arrival of next data. Thus, enough resources
must be provided to ensure the just-in-time processing for the specific data
stream. This paper focuses on proposing a runtime modeling strategy for
containerized machine learning jobs, which enables the optimization and
adaptive adjustment of resources per job and component. Our black-box approach
assembles multiple techniques into an efficient runtime profiling method, while
making no assumptions about underlying hardware, data streams, or applied
machine learning jobs. The results show that our method is able to capture the
general runtime behaviour of different machine learning jobs already after a
short profiling phase.


title:
Differentially Private Learning Needs Hidden State (Or Much Faster
  Convergence)

abstract:
  Differential privacy analysis of randomized learning algorithms typically
relies on composition theorems, where the implicit assumption is that the
internal state of the iterative algorithm is revealed to the adversary.
However, by assuming hidden states for DP algorithms (when only the
last-iterate is observable), recent works prove a converging privacy bound for
noisy gradient descent (on strongly convex smooth loss function) that is
significantly smaller than composition bounds after $O(1/\text{step-size})$
epochs. In this paper, we extend this hidden-state analysis to the noisy
mini-batch stochastic gradient descent algorithms on strongly-convex smooth
loss functions. We prove converging R\'enyi DP bounds under various mini-batch
sampling schemes, such as "shuffle and partition" (which are used in practical
implementations of DP-SGD) and "sampling without replacement". We prove that,
in these settings, our privacy bound is much smaller than the composition bound
for training with a large number of iterations (which is the case for learning
from high-dimensional data). Our converging privacy analysis, thus, shows that
differentially private learning, with a tight bound, needs hidden state privacy
analysis or a fast convergence. To complement our theoretical results, we run
experiment on training classification models on MNIST, FMNIST and CIFAR-10
datasets, and observe a better accuracy given fixed privacy budgets, under the
hidden-state analysis.


title: Parameterized Algorithms for Upward Planarity

abstract:
  We obtain new parameterized algorithms for the classical problem of
determining whether a directed acyclic graph admits an upward planar drawing.
Our results include a new fixed-parameter algorithm parameterized by the number
of sources, an XP-algorithm parameterized by treewidth, and a fixed-parameter
algorithm parameterized by treedepth. All three algorithms are obtained using a
novel framework for the problem that combines SPQR tree-decompositions with
parameterized techniques. Our approach unifies and pushes beyond previous
tractability results for the problem on series-parallel digraphs, single-source
digraphs and outerplanar digraphs.


title:
Filamentary Baryons and Where to Find Them: A forecast of synchrotron
  radiation from merger and accretion shocks in the local Cosmic Web

abstract:
  We generate probabilistic predictions of the low-redshift ($z < 0.2$)
synchrotron Cosmic Web for half of the Northern Sky. In particular, we predict
the contribution to the specific intensity function at $\nu_\mathrm{obs} = 150\
\mathrm{MHz}$ from merger shocks in clusters and accretion shocks in filaments,
both of which arise during large-scale structure formation. We assume a
primordial magnetogenesis scenario, but our method is general enough to allow
for an exploration of alternative magnetogenesis scenarios - and even
alternative radiation mechanisms and spectral windows. In the future, by
comparing different predictions, one could infer the most plausible physical
model from a synchrotron Cosmic Web detection. Our method combines Bayesian
large-scale structure reconstructions, snapshots of an MHD cosmological
simulation, a Gaussian random field, and a ray tracing approach. The results
help to select targets for deep observations and can be used in actual
detection experiments. We highlight predictions for the Hercules Cluster, the
Coma Cluster, Abell 2199, Abell 2255, the Lockman Hole and the Ursa Major
Supercluster. At degree-scale resolution, the median specific intensity reaches
$m_{I_\nu} \sim 10^{-1}\ \xi_e\ \mathrm{Jy\ deg^{-2}}$, where $\xi_e$ is the
electron acceleration efficiency.


title: Correlation functions of scalar field theories from homotopy algebras

abstract:
  We present expressions for correlation functions of scalar field theories in
perturbation theory using quantum $A_\infty$ algebras. Our expressions are
highly explicit and can be used for theories both in Euclidean space and in
Minkowski space including quantum mechanics. Correlation functions at a given
order of perturbation theory can be calculated algebraically without using
canonical quantization or the path integral, and we demonstrate it explicitly
for $\varphi^3$ theory. We show that the Schwinger-Dyson equations are
satisfied as an immediate consequence of the form of the expressions based on
quantum $A_\infty$ algebras.


title:
TIDF-DLPM: Term and Inverse Document Frequency based Data Leakage
  Prevention Model

abstract:
  Confidentiality of the data is being endangered as it has been categorized
into false categories which might get leaked to an unauthorized party. For this
reason, various organizations are mainly implementing data leakage prevention
systems (DLPs). Firewalls and intrusion detection systems are being outdated
versions of security mechanisms. The data which are being used, in sending
state or are rest are being monitored by DLPs. The confidential data is
prevented with the help of neighboring contexts and contents of DLPs. In this
paper, a semantic-based approach is used to classify data based on the
statistical data leakage prevention model. To detect involved private data,
statistical analysis is being used to contribute secure mechanisms in the
environment of data leakage. The favored Frequency-Inverse Document Frequency
(TF-IDF) is the facts and details recapture function to arrange documents under
particular topics. The results showcase that a similar statistical DLP approach
could appropriately classify documents in case of extent alteration as well as
interchanged documents.


title:
Temporal Network Epistemology: on Reaching Consensus in Real World
  Setting

abstract:
  This work develops the concept of temporal network epistemology model
enabling the simulation of the learning process in dynamic networks. The
results of the research, conducted on the temporal social network generated
using the CogSNet model and on the static topologies as a reference, indicate a
significant influence of the network temporal dynamics on the outcome and flow
of the learning process. It has been shown that not only the dynamics of
reaching consensus is different compared to baseline models but also that
previously unobserved phenomena appear, such as uninformed agents or different
consensus states for disconnected components. It has been also observed that
sometimes only the change of the network structure can contribute to reaching
consensus. The introduced approach and the experimental results can be used to
better understand the way how human communities collectively solve both complex
problems at the scientific level and to inquire into the correctness of less
complex but common and equally important beliefs' spreading across entire
societies.


title: A Contribution-based Device Selection Scheme in Federated Learning

abstract:
  In a Federated Learning (FL) setup, a number of devices contribute to the
training of a common model. We present a method for selecting the devices that
provide updates in order to achieve improved generalization, fast convergence,
and better device-level performance. We formulate a min-max optimization
problem and decompose it into a primal-dual setup, where the duality gap is
used to quantify the device-level performance. Our strategy combines
\emph{exploration} of data freshness through a random device selection with
\emph{exploitation} through simplified estimates of device contributions. This
improves the performance of the trained model both in terms of generalization
and personalization. A modified Truncated Monte-Carlo (TMC) method is applied
during the exploitation phase to estimate the device's contribution and lower
the communication overhead. The experimental results show that the proposed
approach has a competitive performance, with lower communication overhead and
competitive personalization performance against the baseline schemes.


title:
Analytic regularity for Navier-Stokes-Korteweg model on pseudo-measure
  spaces

abstract:
  The purpose of this work is to study the existence and analytic smoothing
effect for the compressible Navier- Stokes system with quantum pressure in
pseudo-measure spaces. This system has been considered by B. Haspot and an
analytic smoothing effect for a Korteweg type system was considered by F.
Charve, R. Danchin and J. Xu, both of them in Besov spaces. Here we give a
better lower bound of the radius of analyticity near zero. This work is an
opportunity to deepen the study of partial differential equations in
pseudo-measure spaces by introducing a new functional setting to deal with
non-linear terms. The pseudo-measure spaces are well-adapted to obtain a
point-wise control of solutions, with to study of turbulence as perspective.


title:
Ground-state and thermal entanglements in a non-Hermitian XY system with
  real and imaginary magnetic fields

abstract:
  In this manuscript, we study a non-Hermitian spin-1/2 XY system with two
sites in the presence of an alternating, imaginary and transverse magnetic
field. The eigenvalues and eigenstates of the Hamiltonian are exactly solved.
In addition, the energy spectrum of the system is discussed and an exceptional
point is obtained which distinguishes the parity-time reversal symmetry and
symmetry broken phases. Then we study the ground-state phase diagram which is
illustrated by a unit sphere with the anisotropic parameter gamma, reduced real
and imaginary magnetic fields (h0 and eta0) as variables, and find that there
are two possible ground states. We further discuss the ground-state concurrence
and find that it is only related to gamma and h0 when the value ranges of the
parameters are outside the sphere. Moreover, it only depends on eta0 and is
always the maximum when parameters are inside the sphere. When ground states
are degenerate, the entanglement of the pure state which consists of two ground
states is greater than that of the mixed state. We also study the thermal
entanglement and find that concurrence suddenly changes with temperature and
the reduced external magnetic fields (h0 and eta0). Especially, the change
fades away with increasing gamma. Furthermore, when temperature approaches
zero, there are overlapping parts between the thermal and non-degenerate
ground-state entanglements and it is indicated that the thermal entanglement is
realized by non-degenerate ground states.


title: Adaptive advantage in entanglement-assisted communications

abstract:
  Entanglement is known to boost the efficiency of classical communication. In
distributed computation, for instance, exploiting entanglement can reduce the
number of communicated bits or increase the probability to obtain a correct
answer. Entanglement-assisted classical communication protocols usually consist
of two successive rounds: first a Bell test round, in which the parties measure
their local shares of the entangled state, and then a communication round,
where they exchange classical messages. Here, we go beyond this standard
approach and investigate adaptive uses of entanglement: we allow the receiver
to wait for the arrival of the sender's message before measuring his share of
the entangled state. We first show that such adaptive protocols improve the
success probability in Random Access Codes. Second, we show that once adaptive
measurements are used, an entanglement-assisted bit becomes a strictly stronger
resource than a qubit in prepare-and-measure scenarios. We briefly discuss
extension of these ideas to scenarios involving quantum communication and
identify resource inequalities.


title:
Polygonal functional calculus for operators with finite peripheral
  spectrum

abstract:
  Let $T\colon X\to X$ be a bounded operator on Banach space, whose spectrum
$\sigma(T)$ is included in the closed unit disc $\overline{\mathbb D}$. Assume
that the peripheral spectrum $\sigma(T)\cap{\mathbb T}$ is finite and that $T$
satisfies a resolvent estimate $$\Vert(z-T)^{-1}\Vert\lesssim \max\bigl\{\vert
z -\xi\vert^{-1}\, :\,\xi\in \sigma(T)\cap{\mathbb T}\bigr\}, \qquad
z\in\overline{{\mathbb D}}^c.$$ We prove that $T$ admits a bounded polygonal
functional calculus, that is, an estimate $\Vert\phi(T)\Vert\lesssim
\sup\{\vert\phi(z)\vert\, :\, z\in\Delta\}$ for some polygon
$\Delta\subset{\mathbb D}$ and all polynomials $\phi$, in each of the following
two cases : (i) either $X=L^p$ for some $1<p<\infty$, and $T\colon L^p\to L^p$
is a positive contraction; (ii) or $T$ is polynomially bounded and for all
$\xi\in \sigma(T)\cap{\mathbb T},$ there exists a neighborhood $\mathcal V$ of
$\xi$ such that the set $\{(\xi-z)(z-T)^{-1}\, :\, z\in{\mathcal V}\cap
\overline{{\mathbb D}}^c\}$ is $R$-bounded (here $X$ is arbitrary). Each of
these two results extends a theorem of de Laubenfels concerning polygonal
functional calculus on Hilbert space. Our investigations require the
introduction, for any finite set $E\subset{\mathbb T}$, of a notion of Ritt$_E$
operator which generalises the classical notion of Ritt operator. We study
these Ritt$_E$ operators and their natural functional calculus.


title: Logarithmic Schr\"odinger Equations in Infinite Dimensions

abstract:
  We study the logarithmic Schr\"odinger equation with finite range potential
on $\mathbb{R}^{\mathbb{Z}^d}$. Through a ground-state representation, we
associate and construct a global Gibbs measure and show that it satisfies a
logarithmic Sobolev inequality. We find estimates on the solutions in arbitrary
dimension and prove the existence of weak solutions to the infinite-dimensional
Cauchy problem.


title: Entangled sensor-networks for dark-matter searches

abstract:
  The hypothetical axion particle (of unknown mass) is a leading candidate for
dark matter (DM). Many experiments search for axions with microwave cavities,
where an axion may convert into a cavity photon, leading to a feeble excess in
the output power of the cavity. Recent work has demonstrated that injecting
squeezed vacuum into the cavity can substantially accelerate the axion search.
Here, we go beyond and provide a theoretical framework to leverage the benefits
of quantum squeezing in a network setting consisting of many sensor-cavities.
By forming a local sensor network, the signals among the cavities can be
combined coherently to boost the axion search. Furthermore, injecting
multipartite entanglement across the cavities -- generated by splitting a
squeezed vacuum -- enables a global noise reduction. We explore the performance
advantage of such a local, entangled sensor-network, which enjoys both
coherence between the axion signals and entanglement between the sensors. Our
analyses are pertinent to next-generation DM-axion searches aiming to leverage
a network of sensors and quantum resources in an optimal way. Finally, we
assess the possibility of using a more exotic quantum state, the
Gottesman-Kitaev-Preskill (GKP) state. Despite a constant-factor improvement in
the scan-time relative to a single-mode squeezed-state in the ideal case, the
advantage of employing a GKP state disappears when a practical measurement
scheme is considered.


title: Biased-voter model: how persuasive a small group can be?

abstract:
  We study the voter model dynamics in the presence of confidence and bias. We
assume two types of voters. Unbiased voters whose confidence is indifferent to
the state of the voter and biased voters whose confidence is biased towards a
common fixed preferred state. We study the problem analytically on the complete
graph using mean field theory and on an Erd\H{o}s-R\'enyi random network
topology using the pair approximation, where we assume that the network of
interactions topology is independent of the type of voters. We find that for
the case of a random initial setup, and for sufficiently large number of voters
$N$, the time to consensus increases proportionally to $\log(N)/\gamma v$, with
$\gamma$ the fraction of biased voters and $v$ the parameter quantifying the
bias of the voters ($v=0$ no bias). We verify our analytical results through
numerical simulations. We study this model on a biased-dependent topology of
the network of interactions and examine two distinct, global average-degree
preserving strategies (model I and model II) to obtain such biased-dependent
random topologies starting from the biased-independent random topology case as
the initial setup. Keeping all other parameters constant, in model I,
$\mu_{BU}$, the average number of links among biased (B) and unbiased (U)
voters is varied at the expense of $\mu_{UU}$ and $\mu_{BB}$, i.e. the average
number of links among only unbiased and biased voters respectively. In model
II, $\mu_{BU}$ is kept constant, while $\mu_{BB}$ is varied at the expense of
$\mu_{UU}$. We find that if the agents follow the strategy described by model
II, they can achieve a significant reduction in the time to reach consensus as
well as an increment in the probability to reach consensus to the preferred
state.


title:
Scalable Security Investment Methods for Voltage Stability of Power
  Systems

abstract:
  We develop investment approaches to secure electric power systems against
load attacks that may cause voltage instability. The attacker attempts to alter
the reactive power setpoints of the loads covertly and intelligently to reduce
the voltage stability margin of the grid. The defender, or the system operator,
aims to compensate for this reduction by retuning the reactive power dispatch
of control devices such as shunt capacitor banks. The question is: how much
financial investment should the attacker and the defender plan for to succeed
in their respective objectives? To address this question, we formulate a
cost-based Stackelberg game, where the defender is aware of the attacker's
budget, and a robust-defense sequential algorithm for the realistic case when
the defender is not fully informed about the attacker's resources. We
demonstrate that these methods operate reliably under time-varying load
uncertainties. To provide scalability to large-scale power system models, we
develop a genetic algorithm where both players evolve their candidate solutions
in opposite directions simultaneously. Finally, the proposed methods are
validated using IEEE prototype models, demonstrating that reliable and robust
defense is feasible unless the defender's resources are severely limited
relative to the attacker's resources.


title: Forecasting the abnormal events at well drilling with machine learning

abstract:
  We present a data-driven and physics-informed algorithm for drilling accident
forecasting. The core machine-learning algorithm uses the data from the
drilling telemetry representing the time-series. We have developed a
Bag-of-features representation of the time series that enables the algorithm to
predict the probabilities of six types of drilling accidents in real-time. The
machine-learning model is trained on the 125 past drilling accidents from 100
different Russian oil and gas wells. Validation shows that the model can
forecast 70% of drilling accidents with a false positive rate equals to 40%.
The model addresses partial prevention of the drilling accidents at the well
construction.


title: Level statistics of the one-dimensional ionic Hubbard model

abstract:
  In this work we analyze the spectral level statistics of the one-dimensional
ionic Hubbard model, the Hubbard model with an alternating on-site potential.
In particular, we focus on the statistics of the gap ratios between consecutive
energy levels. This quantity is often used in order to signal whether a
many-body system is integrable or chaotic. A chaotic system has typically the
statistics of a Gaussian ensemble of random matrices while the spectral
properties of the integrable system follow a Poisson statistics. We find that
whereas the Hubbard model without alternating potential is known to be
integrable and its spectral properties follow a Poissonian statistics, the
presence of an alternating potential causes a drastic change in the spectral
properties which resemble the one of a Gaussian ensemble of random matrices.
However, to uncover this behavior one has to separately consider the blocks of
all symmetries of the ionic Hubbard model.


title: Spatial Commonsense Graph for Object Localisation in Partial Scenes

abstract:
  We solve object localisation in partial scenes, a new problem of estimating
the unknown position of an object (e.g. where is the bag?) given a partial 3D
scan of a scene. The proposed solution is based on a novel scene graph model,
the Spatial Commonsense Graph (SCG), where objects are the nodes and edges
define pairwise distances between them, enriched by concept nodes and
relationships from a commonsense knowledge base. This allows SCG to better
generalise its spatial inference over unknown 3D scenes. The SCG is used to
estimate the unknown position of the target object in two steps: first, we feed
the SCG into a novel Proximity Prediction Network, a graph neural network that
uses attention to perform distance prediction between the node representing the
target object and the nodes representing the observed objects in the SCG;
second, we propose a Localisation Module based on circular intersection to
estimate the object position using all the predicted pairwise distances in
order to be independent of any reference system. We create a new dataset of
partially reconstructed scenes to benchmark our method and baselines for object
localisation in partial scenes, where our proposed method achieves the best
localisation performance.


title:
Non-metricity as the origin of mass generation in gauge theories of
  scale invariance

abstract:
  We discuss gauge theories of scale invariance beyond the Standard Model (SM)
and Einstein gravity. We show that the non-metricity of their underlying 4D
geometry is at the origin of mass generation and discuss phenomenological
implications. Examples of such theories are Weyl's original quadratic gravity
theory and its Palatini formulation. Non-metricity leads to spontaneous
breaking of this gauged scale symmetry to Einstein gravity. All mass scales:
the Planck scale, the cosmological constant and the mass ($m_\omega$) of the
Weyl gauge boson of scale symmetry ($\omega_\mu$) are proportional to a scalar
field vev that has a (non-metric) geometric origin, in the $\tilde R^2$ term.
With $\omega_\mu$ of geometric origin, the SM Higgs field has a (non-metric)
geometric origin too, being generated by Weyl boson fusion in the early
Universe. This appears as a microscopic realisation of "matter creation from
geometry" discussed in the thermodynamics of open systems applied to cosmology.
Unlike in a locally scale invariant theory (no $\omega_\mu$ present) with an
underlying (metric) pseudo-Riemannian geometry, here there are no ghost degrees
of freedom or additional fields beyond the SM and their underlying
Weyl/Palatini geometry, the cosmological constant is predicted positive and
their connection shares the symmetry of the action. An intuitive picture of
non-metricity in solid state physics is also provided, where it is associated
with point defects (metric anomalies) of the crystalline structure.


title:
Collective states of active matter with stochastic reversals: emergent
  chiral states and spontaneous current switching

abstract:
  We study collective dynamical behavior of active particles with topological
interactions and directional reversals. Surprising phenomena are shown to
emerge as the interaction relaxation time is varied relative to the reversal
rate, such as spontaneous formation of collective chiral states due to phase
synchronization, and collective directional reversals in the presence of
confinement. The results have a direct relevance to modelling and understanding
collective reversals and synchronization phenomena in active matter.


title:
KSoF: The Kassel State of Fluency Dataset -- A Therapy Centered Dataset
  of Stuttering

abstract:
  Stuttering is a complex speech disorder that negatively affects an
individual's ability to communicate effectively. Persons who stutter (PWS)
often suffer considerably under the condition and seek help through therapy.
Fluency shaping is a therapy approach where PWSs learn to modify their speech
to help them to overcome their stutter. Mastering such speech techniques takes
time and practice, even after therapy. Shortly after therapy, success is
evaluated highly, but relapse rates are high. To be able to monitor speech
behavior over a long time, the ability to detect stuttering events and
modifications in speech could help PWSs and speech pathologists to track the
level of fluency. Monitoring could create the ability to intervene early by
detecting lapses in fluency. To the best of our knowledge, no public dataset is
available that contains speech from people who underwent stuttering therapy
that changed the style of speaking. This work introduces the Kassel State of
Fluency (KSoF), a therapy-based dataset containing over 5500 clips of PWSs. The
clips were labeled with six stuttering-related event types: blocks,
prolongations, sound repetitions, word repetitions, interjections, and -
specific to therapy - speech modifications. The audio was recorded during
therapy sessions at the Institut der Kasseler Stottertherapie. The data will be
made available for research purposes upon request.


title: The electron shuttle as an autonomous single-electron source

abstract:
  The majority of experimental realizations of single-electron sources rely on
the periodic manipulation of the tunnel junctions through their gate voltages,
and thus require a high level of control over the system. To circumvent the
necessity of external driving, we utilize the self-oscillatory behavior of the
electron shuttle. By means of waiting time distributions, which had not been
applied to this autonomous system before, we extensively assess the performance
of the shuttle as a single-electron source. We unveil a smooth transition
between three regimes, whereas previous studies based on the time-averaged
electron current only predict a sharp bifurcation. Over the parameter range of
already existing experimental realizations the electron shuttle can perform as
a single-electron source, albeit with moderate precision. We propose an
alternative design of the position-dependent tunneling rates, which
significantly decreases the relative error of charge transmission, and thus
paves the way for the realization of autonomous single-electron sources.


title:
A constrained minimization problem related to two coupled
  pseudo-relativistic Hartree equations

abstract:
  We are concerned with the following constrained minimization problem:
$$e(a_{1},a_{2},\beta) := \inf\left\{E_{a_{1},a_{2},\beta}(u_{1},u_{2}):
\|u_{1}\|_{L^{2}(\mathbb{R}^{3})} = \|u_{2}\|_{L^{2}(\mathbb{R}^{3})} =
1\right\},$$ where $E_{a_{1},a_{2},\beta}$ is the energy functional associated
to two coupled pseudo-relativistic Hartree equations involving three parameters
$a_{1}, a_{2}, \beta$ and two trapping potentials $V_1(x)$ and $V_2(x)$. In
this paper, we obtain the existence of minimizers of $e(a_{1},a_{2},\beta)$ for
possible $a_{1}, a_{2}$ and $\beta$ under suitable conditions on the
potentials, which generalizes the results of the papers [16,17,18] in different
senses.


title:
Faking Fake News for Real Fake News Detection: Propaganda-loaded
  Training Data Generation

abstract:
  While there has been a lot of research and many recent advances in neural
fake news detection, defending against human-written disinformation remains
underexplored. Upon analyzing current approaches for fake news generation and
human-crafted articles, we found that there is a gap between them, which can
explain the poor performance on detecting human-written fake news for detectors
trained on automatically generated data. To address this issue, we propose a
novel framework for generating articles closer to human-written ones.
Specifically, we perform self-critical sequence training with natural language
inference to ensure the validity of the generated articles. We then explicitly
incorporate propaganda techniques into the generated articles to mimic how
humans craft fake news. Eventually, we create a fake news detection training
dataset, PropaNews, which includes 2,256 examples. Our experimental results
show that detectors trained on PropaNews are 7.3% to 12.0% more accurate for
detecting human-written disinformation than for counterparts trained on data
generated by state-of-the-art approaches.


title: A New Guest in the Third Quantized Multiverse

abstract:
  In the present article we point out a conceptual issue of the Third
Quantization formalism for Canonical Quantum Gravity. Based on earlier results
on interuniversal entanglement, the picture of noninteracting
universe-antiuniverse pairs is incomplete. In particular, the variation of the
entanglement entropy with the scale factor, understood as a measure of time,
implies either an interaction between pairs of universes or universes with a
heat bath, i.e. an external field. We propose a simple solution to the problem
by adding a new quantum field to the formalism. Correspondingly, particles of
this field mediate interactions between universes -- depending on the model at
tree level or one loop -- and may further provide a heat bath in the
background.


title:
Noise in supercontinuum generated utilizing PM and non-PM
  tellurite-glass all-normal dispersion fibers

abstract:
  Intensity fluctuations in supercontinuum generation are studied in
polarization-maintaining (PM) and non-PM all-normal dispersion tellurite
photonic crystal fibers. Dispersive Fourier transformation was used to resolve
shot-to-shot spectra generated using 225 fs pump pulses at 1.55 $\mu$m, with
experimental results well-reproduced by vector and scalar numerical
simulations. By comparing the relative intensity noise (RIN) for the PM and
non-PM cases, supported by simulations, we demonstrate the advantage of the
polarization maintaining property of the PM fibers in preserving low-noise
dynamics. We associate the low-noise in the PM case with the suppression of
polarization modulation instability.


title:
Flexible Behavior Trees: In search of the mythical HFSMBTH for
  Collaborative Autonomy in Robotics

abstract:
  In recent years, the model of computation known as Behavior Trees (BT), first
developed in the video game industry, has become more popular in the robotics
community for defining discrete behavior switching. BTs are threatening to
supplant the venerable Hierarchical Finite State Machine (HFSM) model. In this
paper we contrast BT and HFSM, pointing out some potential issues with the BT
form, and advocate for a hybrid model of computation that uses both BT and HFSM
in ways that leverage their individual strengths. The work introduces a new
open-source package for ROS 2 that extends the Flexible Behavior Engine
(FlexBE) to enable interaction with BT-based behaviors within a HFSM in a way
that supports collaborative autonomy. Simulation and hardware demonstrations
illustrate the concepts.


title:
Sequence-of-Constraints MPC: Reactive Timing-Optimal Control of
  Sequential Manipulation

abstract:
  Task and Motion Planning has made great progress in solving hard sequential
manipulation problems. However, a gap between such planning formulations and
control methods for reactive execution remains. In this paper we propose a
model predictive control approach dedicated to robustly execute a single
sequence of constraints, which corresponds to a discrete decision sequence of a
TAMP plan. We decompose the overall control problem into three sub-problems
(solving for sequential waypoints, their timing, and a short receding horizon
path) that each is a non-linear program solved online in each MPC cycle. The
resulting control strategy can account for long-term interdependencies of
constraints and reactively plan for a timing-optimal transition through all
constraints. We additionally propose phase backtracking when running
constraints are missed, leading to a fluent re-initiation behavior that is
robust to perturbations and interferences by an experimenter.


title: A Note on A Priori Forecasting and Simplicity Bias in Time Series

abstract:
  To what extent can we forecast a time series without fitting to historical
data? Can universal patterns of probability help in this task? Deep relations
between pattern Kolmogorov complexity and pattern probability have recently
been used to make a priori probability predictions in a variety of systems in
physics, biology and engineering. Here we study simplicity bias (SB) -- an
exponential upper bound decay in pattern probability with increasing complexity
-- in discretised time series extracted from the World Bank Open Data
collection. We predict upper bounds on the probability of discretised series
patterns, without fitting to trends in the data. Thus we perform a kind of
`forecasting without training data'. Additionally we make predictions about
which of two discretised series is more likely with accuracy of ~80%, much
higher than a 50% baseline rate, just by using the complexity of each series.
These results point to a promising perspective on practical time series
forecasting and integration with machine learning methods.


title:
The Effect of Non-Local Electrical Conductivity on Near-Field Radiative
  Heat Transfer between Graphene Sheets

abstract:
  Graphene's near-field radiative heat transfer is determined from its
electrical conductivity, commonly modeled using the local Kubo and Drude
formulas. In this letter, we analyze the non-locality of graphene's electrical
conductivity using the Lindhard model combined with the Mermin relaxation time
approximation. We also study how the variation of electrical conductivity with
wavevector affects near-field radiative conductance between two graphene sheets
separated by a vacuum gap. It is shown that the variation of electrical
conductivity with wavevector, $k_{\rho}$, is appreciable for $k_{\rho}$s
greater than $100k_0$, where $k_0$ is the magnitude of the wavevector in the
free space. The Kubo electrical conductivity provides an accurate estimation of
the spectral radiative conductance between two graphene sheets except for
around the surface-plasmon-polariton frequency of graphene and at separation
gaps smaller than 20 nm where there is a non-negligible contribution from modes
with $k_{\rho}>100k_0$ to the radiative conductance. The Drude formula proves
to be inaccurate for modeling the electrical conductivity and radiative
conductance of graphene except for at temperatures much below the Fermi
temperature and frequencies much smaller than $2{\mu}_c/{\hbar}$, where
${\mu}_c$ and ${\hbar}$ are the chemical potential and reduced Planck's
constant, respectively. It is also shown that the electronic scattering
processes should be considered in the Lindhard model properly, such that the
local electron number is conserved. A substitution of ${\omega}$ by
${\omega}+i{\gamma}$ (${\omega}$, $i$, and ${\gamma}$ being the angular
frequency, imaginary unit, and scattering rate, respectively) in the
collisionless Lindhard model does not satisfy the conservation of the local
electron number and results in significant errors in computing graphene's
electrical conductivity and radiative conductance.

categories: cond-mat.mes-hall cond-mat.mtrl-sci physics.app-ph physics.optics


title: Distance-based approach to quantum coherence and nonclassicality

abstract:
  We provide a coherence-based approach to nonclassical behavior by means of
distance measures. We develop a quantitative relation between coherence and
nonclassicality quantifiers, which establish the nonclassicality as the maximum
quantum-coherence achievable. We compute the coherence of several
representative examples and discuss whether the theory may be extended to
reference observables with continuous spectra.


title:
Determination of the strong-coupling constant from the $Z$-boson
  transverse-momentum distribution

abstract:
  The strong-coupling constant is determined from the low-momentum region of
the transverse-momentum distribution of $Z$ bosons produced through the
Drell-Yan process, using predictions at third order in perturbative QCD. The
analysis employs a measurement performed in proton-proton collisions at a
center-of-mass energy of $\sqrt{s} = 1.96$ TeV with the CDF experiment. The
determined value of the strong coupling at the reference scale corresponding to
the $Z$-boson mass is $\alpha_S(m_Z) = 0.1185^{+0.0014}_{-0.0015}$.


title:
Annotation Efficient Person Re-Identification with Diverse Cluster-Based
  Pair Selection

abstract:
  Person Re-identification (Re-ID) has attracted great attention due to its
promising real-world applications. However, in practice, it is always costly to
annotate the training data to train a Re-ID model, and it still remains
challenging to reduce the annotation cost while maintaining the performance for
the Re-ID task. To solve this problem, we propose the Annotation Efficient
Person Re-Identification method to select image pairs from an alternative pair
set according to the fallibility and diversity of pairs, and train the Re-ID
model based on the annotation. Specifically, we design an annotation and
training framework to firstly reduce the size of the alternative pair set by
clustering all images considering the locality of features, secondly select
images pairs from intra-/inter-cluster samples for human to annotate, thirdly
re-assign clusters according to the annotation, and finally train the model
with the re-assigned clusters. During the pair selection, we seek for valuable
pairs according to pairs' fallibility and diversity, which includes an
intra-cluster criterion to construct image pairs with the most chaotic samples
and the representative samples within clusters, an inter-cluster criterion to
construct image pairs between clusters based on the second-order Wasserstein
distance, and a diversity criterion for clusterbased pair selection. Combining
all criteria above, a greedy strategy is developed to solve the pair selection
problem. Finally, the above
clustering-selecting-annotating-reassigning-training procedure will be repeated
until the annotation budget is reached. Extensive experiments on three widely
adopted Re-ID datasets show that we can greatly reduce the annotation cost
while achieving better performance compared with state-of-the-art works.


title: Externally-driven plasma models as candidates for pulsar radio emission

abstract:
  Coherent radio emission from pulsars originates from excited plasma waves in
an ultra-relativistic and strongly magnetized electron-positron pair plasma
streaming along the open magnetic field lines of the pulsar. Traditional
coherent radio emission models have relied on instabilities in this pair
plasma. Recently alternative models have been suggested. These models appeal to
direct coupling of the external electromagnetic field to the superluminal
O-mode ($lt_2$ mode) during the time-dependent pair cascade process at the
polar gap. The objective of this work is to provide generic constraints on
plasma models based on $lt_2$ mode using realistic pulsar parameters. We find
that the very short timescale associated with pair cascades does not allow
$lt_{2}$ mode to be excited at radio frequencies and the impulsive energy
transfer can only increase the kinetic spread ("temperature") of the pair
plasma particles. Moreover, under homogeneous plasma conditions, plasma waves
on both branches of O-mode (i.e. superluminal $lt_2$ and subluminal $lt_1$)
cannot escape the plasma. In the strongly magnetized pair plasma, only the
extraordinary mode ($t$ mode) can escape freely. We show that any generic
fictitious mechanisms does not result in the wave electric field of $t$ mode to
have predominant orientation either or perpendicular to the magnetic field
plane as observed. Such fictitious mechanisms will inevitably lead to
depolarization of signals and cannot account for the highly polarized single
pulses observed in pulsars. We suggest coherent curvature radiation as a
promising candidate for pulsar radio emission mechanism.


title:
Asymmetric Nuclear Matter and Neutron Star Properties in Relativistic ab
  initio Theory in the Full Dirac Space

abstract:
  The long-standing controversy about the isospin dependence of the effective
Dirac mass in ab-initio calculations of asymmetric nuclear matter is clarified
by solving the Relativistic Brueckner-Hartree-Fock equations in the full Dirac
space. The symmetry energy and its slope parameter at the saturation density
are $E_{\text{sym}}(\rho_0)=33.1$ MeV and $L=65.2$ MeV, in agreement with
empirical and experimental values. Further applications predict the neutron
star radius $R_{1.4M_\odot}\approx 12$ km and the maximum mass of a neutron
star $M_{\text{max}}\leq 2.4M_\odot$.


title:
PQM and the GUP: Implications of Lattice Dynamics and Minimal
  Uncertainties in Quantum Mechanics and Cosmology

abstract:
  We compare two alternative representations of quantum mechanics: Polymer
Quantum Mechanics (PQM), which presents features similar to Loop Quantum
Gravity and Loop Quantum Cosmology, and the Generalized Uncertainty Principle
(GUP) representation, that through a modified Algebra yields an alternative
uncertainty principle similar to those derived in String Theories and Brane
Cosmology. These formalisms can be recast to apparently look similar, but while
the GUP yields an absolute minimal uncertainty on position, PQM implements some
kind of ultraviolet cut-off through a lattice and does not have a minimal
uncertainty. Then we implement them on the anisotropic Bianchi I model in
Misner-like variables on a semiclassical level: PQM always implies a removal of
the singularities, while the GUP fails to do so, highlighting once again how
the two representations are fundamentally incompatible.


title: Designing ML-Resilient Locking at Register-Transfer Level

abstract:
  Various logic-locking schemes have been proposed to protect hardware from
intellectual property piracy and malicious design modifications. Since
traditional locking techniques are applied on the gate-level netlist after
logic synthesis, they have no semantic knowledge of the design function.
Data-driven, machine-learning (ML) attacks can uncover the design flaws within
gate-level locking. Recent proposals on register-transfer level (RTL) locking
have access to semantic hardware information. We investigate the resilience of
ASSURE, a state-of-the-art RTL locking method, against ML attacks. We used the
lessons learned to derive two ML-resilient RTL locking schemes built to
reinforce ASSURE locking. We developed ML-driven security metrics to evaluate
the schemes against an RTL adaptation of the state-of-the-art, ML-based
SnapShot attack.


title:
Asymptotic Bounds for Smoothness Parameter Estimates in Gaussian Process
  Interpolation

abstract:
  It is common to model a deterministic response function, such as the output
of a computer experiment, as a Gaussian process with a Mat\'ern covariance
kernel. The smoothness parameter of a Mat\'ern kernel determines many important
properties of the model in the large data limit, such as the rate of
convergence of the conditional mean to the response function. We prove that the
maximum likelihood and cross-validation estimates of the smoothness parameter
cannot asymptotically undersmooth the truth when the data are obtained on a
fixed bounded subset of $\mathbb{R}^d$. That is, if the data-generating
response function has Sobolev smoothness $\nu_0 + d/2$, then the smoothness
parameter estimates cannot remain below $\nu_0$ as more data are obtained. The
results are based on approximation theory in Sobolev spaces and a general
theorem, proved using reproducing kernel Hilbert space techniques, about sets
of values the parameter estimates cannot take.


title: Magnetic field evolution of the K2 dwarf V471 Tau

abstract:
  Observations of the eclipsing binary system V471 Tau show that the time of
the primary eclipses varies in an apparent periodic way. With growing evidence
that the magnetically active K2 dwarf component might be responsible for
driving the eclipse timing variations (ETVs), it is necessary to monitor the
star throughout the predicted ~ 35 yr activity cycle that putatively fuels the
observed ETVs. We contribute to this goal with this paper by analysing
spectropolarimetric data obtained with ESPaDOnS at the Canada-France-Hawaii
Telescope in December 2014 and January 2015. Using Zeeman-Doppler Imaging, we
reconstruct the distribution of brightness inhomogeneities and large-scale
magnetic field at the surface of the K2 dwarf. Compared to previous tomographic
reconstructions of the star carried out with the same code, we probe a new
phase of the ETVs cycle, offering new constraints for future works exploring
whether a magnetic mechanism operating in the K2 dwarf star is indeed able to
induce the observed ETVs of V471 Tau.


title: Representation Compensation Networks for Continual Semantic Segmentation

abstract:
  In this work, we study the continual semantic segmentation problem, where the
deep neural networks are required to incorporate new classes continually
without catastrophic forgetting. We propose to use a structural
re-parameterization mechanism, named representation compensation (RC) module,
to decouple the representation learning of both old and new knowledge. The RC
module consists of two dynamically evolved branches with one frozen and one
trainable. Besides, we design a pooled cube knowledge distillation strategy on
both spatial and channel dimensions to further enhance the plasticity and
stability of the model. We conduct experiments on two challenging continual
semantic segmentation scenarios, continual class segmentation and continual
domain segmentation. Without any extra computational overhead and parameters
during inference, our method outperforms state-of-the-art performance. The code
is available at \url{https://github.com/zhangchbin/RCIL}.


title:
Robustness Analysis of Classification Using Recurrent Neural Networks
  with Perturbed Sequential Input

abstract:
  For a given stable recurrent neural network (RNN) that is trained to perform
a classification task using sequential inputs, we quantify explicit robustness
bounds as a function of trainable weight matrices. The sequential inputs can be
perturbed in various ways, e.g., streaming images can be deformed due to robot
motion or imperfect camera lens. Using the notion of the Voronoi diagram and
Lipschitz properties of stable RNNs, we provide a thorough analysis and
characterize the maximum allowable perturbations while guaranteeing the full
accuracy of the classification task. We illustrate and validate our theoretical
results using a map dataset with clouds as well as the MNIST dataset.


title: About an extension of the Matsumoto-Yor property

abstract:
  If $\alpha,\beta>0$ are distinct and if $A$ and $B$ are independent
non-degenerate positive random variables such that
  $$S=\tfrac{1}{B}\,\tfrac{\beta A+B}{\alpha A+B}\quad \mbox{and}\quad
T=\tfrac{1}{A}\,\tfrac{\beta A+B}{\alpha A+B}
  $$ are independent, we prove that this happens if and only if the $A$ and $B$
have generalized inverse Gaussian distributions with suitable parameters.
Essentially, this has already been proved in Bao and Noack (2021) with
supplementary hypothesis on existence of smooth densities.
  The sources of these questions are an observation about independence
properties of the exponential Brownian motion due to Matsumoto and Yor (2001)
and a recent work of Croydon and Sasada (2000) on random recursion models
rooted in the discrete Korteweg - de Vries equation, where the above result was
conjectured.
  We also extend the direct result to random matrices proving that a matrix
variate analogue of the above independence property is satisfied by independent
matrix-variate GIG variables. The question of characterization of GIG random
matrices through this independence property remains open.


title:
Existence, uniqueness and ergodicity for the centered Fleming-Viot
  process

abstract:
  Motivated by questions of ergodicity for shift invariant Fleming-Viot
process, we consider the centered Fleming-Viot process $\left(Z_{t}
\right)_{t\geqslant 0}$ defined by $Z_{t} := \tau_{-\left\langle {\rm id},
Y_{t} \right\rangle} \sharp \, Y_{t}$, where $\left(Y_{t}\right)_{t\geqslant
0}$ is the original Fleming-Viot process. Our goal is to characterise the
centered Fleming-Viot process with a martingale problem. To establish the
existence of a solution to this martingale problem, we exploit the original
Fleming-Viot martingale problem and asymptotic expansions. The proof of
uniqueness is based on a weakened version of the duality method, allowing us to
prove uniqueness for initial conditions admitting finite moments. We also
provide counter examples showing that our approach based on the duality method
cannot be expected to give uniqueness for more general initial conditions.
Finally, we establish ergodicity properties with exponential convergence in
total variation for the centered Fleming-Viot process and characterise the
invariant measure.


title: Disentangled Multimodal Representation Learning for Recommendation

abstract:
  Many multimodal recommender systems have been proposed to exploit the rich
side information associated with users or items (e.g., user reviews and item
images) for learning better user and item representations to enhance the
recommendation performance. Studies in psychology show that users have
individual differences in the utilization of different modalities for
organizing information. Therefore, for a certain factor of an item (such as
appearance or quality), the features of different modalities are of different
importance to a user. However, existing methods ignore the fact that different
modalities contribute differently to a user's preferences on various factors of
an item. In light of this, in this paper, we propose a novel Disentangled
Multimodal Representation Learning (DMRL) recommendation model, which can
capture users' attention to different modalities on each factor in user
preference modeling. In particular, we adopt a disentangled representation
technique to ensure the features of different factors in each modality are
independent to each other. A multimodal attention mechanism is then designed to
capture user's modality preference for each factor. Based on the estimated
weights obtained by the attention mechanism, we make recommendation by
combining the preference scores of a user's preferences to each factor of the
target item over different modalities. Extensive evaluations on five real-world
datasets demonstrate the superiority of our method compared with existing
methods.


title: Blind Extraction of Equitable Partitions from Graph Signals

abstract:
  Finding equitable partitions is closely related to the extraction of graph
symmetries and of interest in a variety of applications context such as node
role detection, cluster synchronization, consensus dynamics, and network
control problems. In this work we study a blind identification problem in which
we aim to recover an equitable partition of a network without the knowledge of
the network's edges but based solely on the observations of the outputs of an
unknown graph filter. Specifically, we consider two settings. First, we
consider a scenario in which we can control the input to the graph filter and
present a method to extract the partition inspired by the well known
Weisfeiler-Lehman (color refinement) algorithm. Second, we generalize this idea
to a setting where only observe the outputs to random, low-rank excitations of
the graph filter, and present a simple spectral algorithm to extract the
relevant equitable partitions. Finally, we establish theoretical bounds on the
error that this spectral detection scheme incurs and perform numerical
experiments that illustrate our theoretical results and compare both
algorithms.


title:
Attacks as Defenses: Designing Robust Audio CAPTCHAs Using Attacks on
  Automatic Speech Recognition Systems

abstract:
  Audio CAPTCHAs are supposed to provide a strong defense for online resources;
however, advances in speech-to-text mechanisms have rendered these defenses
ineffective. Audio CAPTCHAs cannot simply be abandoned, as they are
specifically named by the W3C as important enablers of accessibility.
Accordingly, demonstrably more robust audio CAPTCHAs are important to the
future of a secure and accessible Web. We look to recent literature on attacks
on speech-to-text systems for inspiration for the construction of robust,
principle-driven audio defenses. We begin by comparing 20 recent attack papers,
classifying and measuring their suitability to serve as the basis of new
"robust to transcription" but "easy for humans to understand" CAPTCHAs. After
showing that none of these attacks alone are sufficient, we propose a new
mechanism that is both comparatively intelligible (evaluated through a user
study) and hard to automatically transcribe (i.e., $P({\rm transcription}) = 4
\times 10^{-5}$). Finally, we demonstrate that our audio samples have a high
probability of being detected as CAPTCHAs when given to speech-to-text systems
($P({\rm evasion}) = 1.77 \times 10^{-4}$). In so doing, we not only
demonstrate a CAPTCHA that is approximately four orders of magnitude more
difficult to crack, but that such systems can be designed based on the insights
gained from attack papers using the differences between the ways that humans
and computers process audio.


title:
Nationally Representative Individualized Risk Estimation Combining
  Individual Data from Epidemiologic Studies and Representative Surveys with
  Summary Statistics from Disease Registries

abstract:
  Estimating individualized absolute risks is fundamental to clinical
decision-making but are often based on data that does not represent the target
population. Current methods improve external validity by including data from
population registries but require transportability assumptions of model
parameters (relative risks and/or population attributable risks) from
epidemiologic studies to the population. We propose a two-step weighting
procedure to estimate absolute risk of an event (in the absence of competing
events) in the target population without transportability assumptions. The
first step improves external-validity for the cohort by creating
"pseudoweights" for the cohort using a scaled propensity-based kernel-weighting
method, which fractionally distributes sample weights from external probability
reference survey units to cohort units, according to their kernel smoothed
distance in propensity score. The second step poststratifies the pseudoweighted
events in the cohort to a population disease registry by variables available in
the registry. Our approach produces design-consistent absolute risks under
correct specification of the propensity model. Poststratification improves
efficiency and further reduces bias of risk estimates overall and by
demographic variables available in the registry when the true propensity model
is unknown. We apply our methods to develop a nationally representative
all-cause mortality risk model for potential clinical use.


title:
New constraints on the neutron-star mass and radius relation from
  terrestrial nuclear experiments

abstract:
  The determination of the equation of state (EOS) for nuclear matter has been
one of the biggest problems in nuclear astrophysics, because the EOS is
essential for determining the properties of neutron stars. To constrain the
density-dependence of the nuclear symmetry energy, several nuclear experiments,
e.g., reported by the S$\pi$RIT and PREX-II collaborations, have recently been
performed. However, since their uncertainties are still large, additional
constraints such as astronomical observations must be crucial. In addition, it
is interesting to see the effect of their reported value on neutron star
properties. In this study, focusing on the relatively lower density region, we
investigate the allowed area of the neutron-star mass and radius relation by
assuming the constraints from S$\pi$RIT and PREX-II. Each region predicted by
these experiments is still consistent with the allowed area constrained by the
various astronomical observations. Our results show that terrestrial nuclear
experiments must provide further constraints on the EOS for neutron stars,
complementing astronomical observations.