title:
Determination of Al occupancy and local structure for
  \b{eta}-(AlxGa1-x)2O3 alloys across nearly full composition range from
  Rietveld analysis

abstract:
  Al occupancy and local structure (bond length and bond angles) for monoclinic
\b{eta}-(AlxGa1-x)2O3 alloys, with Al compositions (x) up to 90%, have been
determined from Rietveld refinement of x-ray diffraction data. Al atom
preferentially occupies octahedron (Oh) atomic site in comparison to
tetrahedron (Td) atomic site. However, sizable number of Td atomic sites i.e.
20% for Al composition of 5% remain occupied by Al atoms, which is found to
increase sharply with Al composition. The Oh atomic sites are not fully
occupied by Al atoms even for Al composition of 90%. The lattice parameters
(band gap) of \b{eta}-(AlxGa1-x)2O3 alloy decrease (increases) linearly with Al
composition, but a change in slope of variation of both lattice parameters and
band gap is observed at around Al composition of 50%. The lattice is found to
be distorted for Al compositions more than 50% as indicated by large change in
the bond angles. The lattice distortion is determined to be the origin for the
observed change in slope for the variation of both lattice parameters and band
gap for monoclinic \b{eta}-(AlxGa1-x)2O3 alloy system. Our results provide an
insight in to the local structure of \b{eta}-(AlxGa1-x)2O3 alloys, which are
required to have better understanding of their physical properties.


title:
Localization properties of the asymptotic density distribution of a
  one-dimensional disordered system

abstract:
  Anderson localization is the ubiquitous phenomenon of inhibition of transport
of classical and quantum waves in a disordered medium. In dimension one, it is
well known that all states are localized, implying that the distribution of an
initially narrow wave-packet released in a disordered potential will, at long
time, decay exponentially on the scale of the localization length. However, the
exact shape of the stationary localized distribution differs from a purely
exponential profile and has been computed almost fifty years ago by Gogolin.
  Using the atomic quantum kicked rotor, a paradigmatic quantum simulator of
Anderson localization physics, we study this asymptotic distribution by two
complementary approaches. First, we discuss the connection of the statistical
properties of the system's localized eigenfunctions and their exponential decay
with the localization length of the Gogolin distribution. Next, we make use of
our experimental platform, realizing an ideal Floquet disordered system, to
measure the long-time probability distribution and highlight the very good
agreement with the analytical prediction compared to the purely exponential one
over 3 orders of magnitude.


title:
Dynamic Grass Color Scale Display Technique Based on Grass Length for
  Green Landscape-Friendly Animation Display

abstract:
  Recently, public displays such as liquid crystal displays (LCDs) are often
used in urban green spaces, however, the display devices can spoil green
landscape of urban green spaces because they look like artificial materials. We
previously proposed a green landscape-friendly grass animation display method
by controlling a pixel-by-pixel grass color dynamically. The grass color can be
changed by moving a green grass length in yellow grass, and the grass animation
display can play simple animations using grayscale images. In the previous
research, the color scale was mapped to the green grass length subjectively,
however, this method has not achieved displaying the grass colors corresponding
to the color scale based on objective evaluations. Here, we introduce a dynamic
grass color scale display technique based on a grass length. In this paper, we
developed a grass color scale setting procedure to map the grass length to the
color scale with five levels through image processing. Through the outdoor
experiment of the grass color scale setting procedure, the color scale can
correspond to the green grass length based on a viewpoint. After the
experiments, we demonstrated a grass animation display to show the animations
with the color scale using the experiment results.


title:
New approaches for studying conformal embeddings and collapsing levels
  for $W$--algebras

abstract:
  In this paper we prove a general result saying that under mild hypothesis an
embedding of an affine vertex algebra into an affine $W$-algebra is conformal
if and only if their central charges coincide. This result extends our previous
result obtained in the case of minimal affine $W$-algebras. We also find a
sufficient condition showing that certain conformal levels are collapsing. This
new condition enables us to show collapsing of $W_k(sl(N), x, f )$ for $f$ of
hook or rectangular type. Our methods can be applied to non-admissible levels.
In particular, we prove Creutzig's conjecture on the conformal embedding in the
hook type $W$-algebra $W_k(sl(n+m), x, f_{m,n})$ of its affine vertex
subalgebra. Quite surprisingly, the problem of showing that certain conformal
levels are not collapsing turns out to be very difficult. In the cases when $k$
is admissible and conformal, we prove that $W_k(sl(n+m), x, f_{m,n})$ is not
collapsing. Then, by generalizing the results on semi-simplicity of conformal
embeddings from our previous papers, we find many cases in which $W_k(sl(n+m),
x, f_{m,n})$ is semi-simple as a module for its affine subalgebra at conformal
level and we provide explicit decompositions.


title:
Convergence Acceleration of Preconditioned CG Solver Based on Error
  Vector Sampling for a Sequence of Linear Systems

abstract:
  In this paper, we focus on solving a sequence of linear systems with an
identical (or similar) coefficient matrix. For this type of problems, we
investigate the subspace correction and deflation methods, which use an
auxiliary matrix (subspace) to accelerate the convergence of the iterative
method. In practical simulations, these acceleration methods typically work
well when the range of the auxiliary matrix contains eigenspaces corresponding
to small eigenvalues of the coefficient matrix. We have developed a new
algebraic auxiliary matrix construction method based on error vector sampling,
in which eigenvectors with small eigenvalues are efficiently identified in a
solution process. The generated auxiliary matrix is used for the convergence
acceleration in the following solution step. Numerical tests confirm that both
subspace correction and deflation methods with the auxiliary matrix can
accelerate the solution process of the iterative solver. Furthermore, we
examine the applicability of our technique to the estimation of the condition
number of the coefficient matrix. The algorithm of preconditioned conjugate
gradient (PCG) method with the condition number estimation is also shown.


title:
Significance of chiral three-nucleon force contact terms for
  understanding of elastic nucleon-deuteron scattering

abstract:
  We investigate the importance of the three-nucleon (3N) force contact terms
in elastic nucleon-deuteron (Nd) scattering by applying the N$^4$LO$^+$ chiral
semi-local momentum space (SMS) regularized nucleon-nucleon (NN) chiral
potential supplemented by N$^2$LO and all subleading N$^4$LO three-nucleon
force (3NF) contact terms. Strength parameters of the contact terms were
obtained by least squares fitting of theoretical predictions to cross section
and analyzing powers data at three energies of the impinging nucleon. Although
the N$^3$LO contributions to the 3N force were completely neglected, the
results calculated with the contact terms multiplied by the fitted strength
parameters yield an improved description of the elastic Nd scattering
observables in a wide range of incoming nucleon energies below the pion
production threshold.


title:
HeterMPC: A Heterogeneous Graph Neural Network for Response Generation
  in Multi-Party Conversations

abstract:
  Recently, various response generation models for two-party conversations have
achieved impressive improvements, but less effort has been paid to multi-party
conversations (MPCs) which are more practical and complicated. Compared with a
two-party conversation where a dialogue context is a sequence of utterances,
building a response generation model for MPCs is more challenging, since there
exist complicated context structures and the generated responses heavily rely
on both interlocutors (i.e., speaker and addressee) and history utterances. To
address these challenges, we present HeterMPC, a heterogeneous graph-based
neural network for response generation in MPCs which models the semantics of
utterances and interlocutors simultaneously with two types of nodes in a graph.
Besides, we also design six types of meta relations with
node-edge-type-dependent parameters to characterize the heterogeneous
interactions within the graph. Through multi-hop updating, HeterMPC can
adequately utilize the structural knowledge of conversations for response
generation. Experimental results on the Ubuntu Internet Relay Chat (IRC)
channel benchmark show that HeterMPC outperforms various baseline models for
response generation in MPCs.


title:
Monte Carlo PINNs: deep learning approach for forward and inverse
  problems involving high dimensional fractional partial differential equations

abstract:
  We introduce a sampling based machine learning approach, Monte Carlo physics
informed neural networks (MC-PINNs), for solving forward and inverse fractional
partial differential equations (FPDEs). As a generalization of physics informed
neural networks (PINNs), our method relies on deep neural network surrogates in
addition to a stochastic approximation strategy for computing the fractional
derivatives of the DNN outputs. A key ingredient in our MC-PINNs is to
construct an unbiased estimation of the physical soft constraints in the loss
function. Our directly sampling approach can yield less overall computational
cost compared to fPINNs proposed in \cite{pang2019fpinns} and thus provide an
opportunity for solving high dimensional fractional PDEs. We validate the
performance of MC-PINNs method via several examples that include high
dimensional integral fractional Laplacian equations, parametric identification
of time-space fractional PDEs, and fractional diffusion equation with random
inputs. The results show that MC-PINNs is flexible and promising to tackle
high-dimensional FPDEs.


title: Quantum fluxes at the inner horizon of a spinning black hole

abstract:
  Rotating or charged classical black holes in isolation possess a special
surface in their interior, the Cauchy horizon, beyond which the evolution of
spacetime (based on the equations of General Relativity) ceases to be
deterministic. In this work, we study the effect of a quantum massless scalar
field on the Cauchy horizon inside a rotating (Kerr) black hole that is
evaporating via the emission of Hawking radiation (corresponding to the field
being in the Unruh state). We calculate the flux components (in Eddington
coordinates) of the renormalized stress-energy tensor of the field on the
Cauchy horizon, as functions of the black hole spin and of the polar angle. We
find that these flux components are generically non-vanishing. Furthermore, we
find that the flux components change sign as these parameters vary. The signs
of the fluxes are important, as they provide an indication of whether the
Cauchy horizon expands or crushes (when backreaction is taken into account).
Regardless of these signs, our results imply that the flux components
generically diverge on the Cauchy horizon when expressed in coordinates which
are regular there. This is the first time that irregularity of the Cauchy
horizon under a semiclassical effect is conclusively shown for
(four-dimensional) spinning black holes.


title: Exact tricritical point from next-to-leading-order stability analysis

abstract:
  In the massive chiral Gross-Neveu model, a phase boundary separates a
homogeneous from an inhomogeneous phase. It consists of two parts, a second
order line and a first order line, joined at a tricritical point. Whereas the
first order phase boundary requires a full, numerical Hartree-Fock calculation,
the second order phase boundary can be determined exactly and with less effort
by a perturbative stability analysis. We extend this stability analysis to
higher order perturbation theory. This enables us to locate the tricritical
point exactly, without need to perform a Hartree-Fock calculation. Divergencies
due to the emergence of spectral gaps in a spatially periodic perturbation are
handled using well established tools from many body theory.


title: A Survey of Historical Document Image Datasets

abstract:
  This paper presents a systematic literature review of image datasets for
document image analysis, focusing on historical documents, such as handwritten
manuscripts and early prints. Finding appropriate datasets for historical
document analysis is a crucial prerequisite to facilitate research using
different machine learning algorithms. However, because of the very large
variety of the actual data (e.g., scripts, tasks, dates, support systems, and
amount of deterioration), the different formats for data and label
representation, and the different evaluation processes and benchmarks, finding
appropriate datasets is a difficult task. This work fills this gap, presenting
a meta-study on existing datasets. After a systematic selection process
(according to PRISMA guidelines), we select 56 studies that are chosen based on
different factors, such as the year of publication, number of methods
implemented in the article, reliability of the chosen algorithms, dataset size,
and journal outlet. We summarize each study by assigning it to one of three
pre-defined tasks: document classification, layout structure, or semantic
analysis. We present the statistics, document type, language, tasks, input
visual aspects, and ground truth information for every dataset. In addition, we
provide the benchmark tasks and results from these papers or recent
competitions. We further discuss gaps and challenges in this domain. We
advocate for providing conversion tools to common formats (e.g., COCO format
for computer vision tasks) and always providing a set of evaluation metrics,
instead of just one, to make results comparable across studies.


title: Tail inference using extreme U-statistics

abstract:
  Extreme U-statistics arise when the kernel of a U-statistic has a high degree
but depends only on its arguments through a small number of top order
statistics. As the kernel degree of the U-statistic grows to infinity with the
sample size, estimators built out of such statistics form an intermediate
family in between those constructed in the block maxima and
peaks-over-threshold frameworks in extreme value analysis. The asymptotic
normality of extreme U-statistics based on location-scale invariant kernels is
established. Although the asymptotic variance corresponds with the one of the
H\'ajek projection, the proof goes beyond considering the first term in
Hoeffding's variance decomposition; instead, a growing number of terms needs to
be incorporated in the proof.
  To show the usefulness of extreme U-statistics, we propose a kernel depending
on the three highest order statistics leading to an unbiased estimator of the
shape parameter of the generalized Pareto distribution. When applied to samples
in the max-domain of attraction of an extreme value distribution, the extreme
U-statistic based on this kernel produces a location-scale invariant estimator
of the extreme value index which is asymptotically normal and whose
finite-sample performance is competitive with that of the pseudo-maximum
likelihood estimator.


title:
Arctic curves of the $6$V model with partial DWBC and double Aztec
  rectangles

abstract:
  Previous numerical studies have shown that in the disordered and
anti-ferroelectric phases the six-vertex ($6$V) model with partial domain wall
boundary conditions (DWBC) exhibits an arctic curve whose exact shape is
unknown. The model is defined on a $s\times n$ square lattice ($s\leq n$). In
this paper, we derive the analytic expression of the arctic curve, for $a=b=1$
and $c=\sqrt{2}$ ($\Delta=0$), while keeping the ratio $s/n \,\in [0,1]$ as a
free parameter. The computation relies on the tangent method. We also consider
domino tilings of double Aztec rectangles and show via the tangent method that,
for particular parameters, the arctic curve is identical to that of the $6$V
model with partial DWBC. Our results are confirmed by extensive numerical
simulations.

categories: cond-mat.stat-mech math-ph math.CO math.MP


title: Personal Knowledge Graphs: Use Cases in e-learning Platforms

abstract:
  Personal Knowledge Graphs (PKGs) are introduced by the semantic web community
as small-sized user-centric knowledge graphs (KGs). PKGs fill the gap of
personalised representation of user data and interests on the top of big,
well-established encyclopedic KGs, such as DBpedia. Inspired by the widely
recent usage of PKGs in the medical domain to represent patient data, this PhD
proposal aims to adopt a similar technique in the educational domain in
e-learning platforms by deploying PKGs to represent users and learners. We
propose a novel PKG development that relies on ontology and interlinks to
Linked Open Data. Hence, adding the dimension of personalisation and
explainability in users' featured data while respecting privacy. This research
design is developed in two use cases: a collaborative search learning platform
and an e-learning platform. Our preliminary results show that e-learning
platforms can get benefited from our approach by providing personalised
recommendations and more user and group-specific data.


title: Semantics-Aware Source Coding in Status Update Systems

abstract:
  We consider a communication system in which the destination receives status
updates from an information source that observes a physical process. The
transmitter performs semantics-empowered filtering as a means to send only the
most "important" samples to the receiver in a timely manner. As a first step,
we explore a simple policy where the transmitter selects to encode only a
fraction of the least frequent realizations of the observed random phenomenon,
treating the remaining ones as not informative. For this timely source coding
problem, we derive the optimal codeword lengths in the sense of maximizing a
semantics-aware utility function and minimizing a quadratic average length
cost. Our numerical results show the optimal number of updates to transmit for
different arrival rates and encoding costs and corroborate that semantic
filtering results in higher performance in terms of timely delivery of
important updates.


title: Differentiable DAG Sampling

abstract:
  We propose a new differentiable probabilistic model over DAGs (DP-DAG).
DP-DAG allows fast and differentiable DAG sampling suited to continuous
optimization. To this end, DP-DAG samples a DAG by successively (1) sampling a
linear ordering of the node and (2) sampling edges consistent with the sampled
linear ordering. We further propose VI-DP-DAG, a new method for DAG learning
from observational data which combines DP-DAG with variational inference.
Hence,VI-DP-DAG approximates the posterior probability over DAG edges given the
observed data. VI-DP-DAG is guaranteed to output a valid DAG at any time during
training and does not require any complex augmented Lagrangian optimization
scheme in contrast to existing differentiable DAG learning approaches. In our
extensive experiments, we compare VI-DP-DAG to other differentiable DAG
learning baselines on synthetic and real datasets. VI-DP-DAG significantly
improves DAG structure and causal mechanism learning while training faster than
competitors.


title:
High-power, frequency-quadrupled UV laser source resonant with the
  $^{1}$S$_{0}$-$^{3}$P$_{1}$ narrow intercombination transition of cadmium at
  326.2 nm

abstract:
  We present a novel high-power, frequency-stabilized UV laser source at
326.2~nm, resonant with the Cd $^{1}$S$_{0}$ - $^{3}$P$_{1}$ narrow
intercombination transition. We have achieved a maximum produced power of 1~W
at 326.2~nm by two successive frequency doubling stages of a narrow-linewidth
(< 1~kHz) seed laser at 1304.8~nm. About 3.4 W of optical power at 652.4 nm is
produced by a visible Raman fiber amplifier (VRFA) that amplifies and generates
the second harmonic of the infrared radiation. The visible light is
subsequently frequency-doubled down to 326.2~nm in a non-linear bow-tie cavity
using a Brewster-cut beta-barium-borate (BBO) crystal, with a maximum
conversion efficiency of around 40$\%$ for 2.5~W coupled red power. Full
characterization of the laser source, together with spectroscopy signals of all
Cd isotopes, spanning more than 4 GHz in the UV, are shown.


title:
On the infinitely generated locus of Frobenius algebras of rings of
  prime characteristic

abstract:
  Let $R$ be a commutative Noetherian ring of prime characteristic $p$. The
main goal of this paper is to study in some detail when \[
\overline{W^R}:=\{\mathfrak{p}\in\operatorname{Spec} (R):\
\mathcal{F}^{E_{\mathfrak{p}}}\text{ is finitely generated as a ring over its
degree zero piece}\} \] is an open set in the Zariski topology, where
$\mathcal{F}^{E_{\mathfrak{p}}}$ denotes the Frobenius algebra attached to the
injective hull of the residue field of $R_{\mathfrak{p}}.$ We show that this is
true when $R$ is a Stanley--Reisner ring; moreover, in this case, we explicitly
compute its closed complement, providing an algorithmic method for doing so.


title: ConTinTin: Continual Learning from Task Instructions

abstract:
  The mainstream machine learning paradigms for NLP often work with two
underlying presumptions. First, the target task is predefined and static; a
system merely needs to learn to solve it exclusively. Second, the supervision
of a task mainly comes from a set of labeled examples. A question arises: how
to build a system that can keep learning new tasks from their instructions?
This work defines a new learning paradigm ConTinTin (Continual Learning from
Task Instructions), in which a system should learn a sequence of new tasks one
by one, each task is explained by a piece of textual instruction. The system is
required to (i) generate the expected outputs of a new task by learning from
its instruction, (ii) transfer the knowledge acquired from upstream tasks to
help solve downstream tasks (i.e., forward-transfer), and (iii) retain or even
improve the performance on earlier tasks after learning new tasks (i.e.,
backward-transfer). This new problem is studied on a stream of more than 60
tasks, each equipped with an instruction. Technically, our method
InstructionSpeak contains two strategies that make full use of task
instructions to improve forward-transfer and backward-transfer: one is to learn
from negative outputs, the other is to re-visit instructions of previous tasks.
To our knowledge, this is the first time to study ConTinTin in NLP. In addition
to the problem formulation and our promising approach, this work also
contributes to providing rich analyses for the community to better understand
this novel learning problem.


title: Multi-focus thermal image fusion

abstract:
  This paper proposes a novel algorithm for multi-focus thermal image fusion.
The algorithm is based on local activity analysis and advanced pre-selection of
images into fusion process. The algorithm improves the object temperature
measurement error up to 5 Celsius degrees. The proposed algorithm is evaluated
by half total error rate, root mean squared error, cross correlation and visual
inspection. To the best of our knowledge, this is the first work devoted to
multi-focus thermal image fusion. For testing of proposed algorithm we acquire
six thermal image set with objects at different focal depth.


title: Recursive Variational Quantum Compiling

abstract:
  Variational quantum compiling algorithms \cite{qaqc} aim to compress quantum
circuits into parameterized ansatzes utilizing a hybrid quantum-classical
approach. In this article a variant of VQC named the recursive variational
quantum compiling algorithm (RVQC) is proposed. In contrast with existing VQC
algorithms, where one is required to execute the full target circuit in order
to compile it, RVQC works by dividing the target circuit into $N$ parts and
recursively compresses one part at a time into the parameterized ansatz. Since
smaller parts are being compressed at a time, the circuit depth required to
implement the algorithm is not dependent on the depth of the target circuit,
but on the depth of the ansatz and the depth of each of the $N$ parts. We are
hence able to compress the target circuit by executing circuits of smaller
depths than the target circuit itself. To show that this approach has an edge
over the standard VQC method for sufficiently large circuit depths, RVQC was
compared with VQC on a noise model of the IBM Santiago device with the goal of
compressing a randomly generated five-qubit circuit of depth 5000. It was shown
that VQC was not able to converge within 500 epochs of training. On the other
hand, RVQC was able to converge to a fidelity ($0.88$) within a total of 500
epochs when splitting the target circuit into $N = 5$ parts.


title: DRT-based modelling framework for Li-ion cells

abstract:
  The correct assessment of battery states is essential to maximize battery
pack performances while ensuring reliable and safe operation. This work
introduces EIS2MOD, a novel modelling framework for Li-ion cells based on
Distribution of Relaxation Time (DRT). A physically based Electric Circuit
Model (ECM) is developed starting from Electrochemical Impedance Spectroscopy
(EIS) and Open Circuit Voltage (OCV) measurements. DRT is applied to deconvolve
the electrochemical phenomena from the EIS. The presented methodology is based
on: i) DRT calculation from EIS, ii) DRT analysis for ECM configuration and
iii) Model parameters extraction and fitting. The proposed framework is applied
to large format Li-ion pouch cells, which are tested over the whole State of
Charge (SoC) range and a wide temperature range (-10{\deg}C to 35{\deg}C).
Different current profiles have been tested to validate the model, showing its
high accuracy in reproducing the battery cell behavior (e.g. RMSE on the
battery terminals voltage lower than 1.50% for driving cycle simulations at
variable temperature and SoC). An additional advantage of EIS2MOD is its light
computational load thus offering an attractive framework for battery management
system implementation.


title:
Fantastic Style Channels and Where to Find Them: A Submodular Framework
  for Discovering Diverse Directions in GANs

abstract:
  The discovery of interpretable directions in the latent spaces of pre-trained
GAN models has recently become a popular topic. In particular, StyleGAN2 has
enabled various image generation and manipulation tasks due to its rich and
disentangled latent spaces. The discovery of such directions is typically done
either in a supervised manner, which requires annotated data for each desired
manipulation or in an unsupervised manner, which requires a manual effort to
identify the directions. As a result, existing work typically finds only a
handful of directions in which controllable edits can be made. In this study,
we design a novel submodular framework that finds the most representative and
diverse subset of directions in the latent space of StyleGAN2. Our approach
takes advantage of the latent space of channel-wise style parameters, so-called
style space, in which we cluster channels that perform similar manipulations
into groups. Our framework promotes diversity by using the notion of clusters
and can be efficiently solved with a greedy optimization scheme. We evaluate
our framework with qualitative and quantitative experiments and show that our
method finds more diverse and disentangled directions. Our project page can be
found at http://catlab-team.github.io/fantasticstyles.


title:
TegTok: Augmenting Text Generation via Task-specific and Open-world
  Knowledge

abstract:
  Generating natural and informative texts has been a long-standing problem in
NLP. Much effort has been dedicated into incorporating pre-trained language
models (PLMs) with various open-world knowledge, such as knowledge graphs or
wiki pages. However, their ability to access and manipulate the task-specific
knowledge is still limited on downstream tasks, as this type of knowledge is
usually not well covered in PLMs and is hard to acquire. To address the
problem, we propose augmenting TExt Generation via Task-specific and Open-world
Knowledge (TegTok) in a unified framework. Our model selects knowledge entries
from two types of knowledge sources through dense retrieval and then injects
them into the input encoding and output decoding stages respectively on the
basis of PLMs. With the help of these two types of knowledge, our model can
learn what and how to generate. Experiments on two text generation tasks of
dialogue generation and question generation, and on two datasets show that our
method achieves better performance than various baseline models.


title: Tunable photon-induced spatial modulation of free electrons

abstract:
  Spatial modulation of electron beams is an essential tool for various
applications such as nanolithography and imaging, yet its implementations are
severely limited and inherently non-tunable. Conversely, light-driven electron
spatial modulation could potentially allow arbitrary electron wavefront shaping
via the underlying mechanism of photon-induced near-field electron microscopy
(PINEM). Here, we present tunable photon-induced spatial modulation of
electrons through their externally-controlled interaction with surface plasmon
polaritons (SPPs). Using recently developed methods of shaping SPP patterns, we
demonstrate a dynamic control of the electron beam with a variety of
high-quality electron distributions. Intriguingly, by utilizing the intrinsic
interaction nonlinearity, we attain the first observation of 2D spatial Rabi
oscillations and generate electron features below the SPP wavelength. Our work
paves the way to on-demand electron wavefront shaping at ultrafast timescales,
with prospects for aberration correction, nano-fabrication and material
characterization.


title: Towards Practical Certifiable Patch Defense with Vision Transformer

abstract:
  Patch attacks, one of the most threatening forms of physical attack in
adversarial examples, can lead networks to induce misclassification by
modifying pixels arbitrarily in a continuous region. Certifiable patch defense
can guarantee robustness that the classifier is not affected by patch attacks.
Existing certifiable patch defenses sacrifice the clean accuracy of classifiers
and only obtain a low certified accuracy on toy datasets. Furthermore, the
clean and certified accuracy of these methods is still significantly lower than
the accuracy of normal classification networks, which limits their application
in practice. To move towards a practical certifiable patch defense, we
introduce Vision Transformer (ViT) into the framework of Derandomized Smoothing
(DS). Specifically, we propose a progressive smoothed image modeling task to
train Vision Transformer, which can capture the more discriminable local
context of an image while preserving the global semantic information. For
efficient inference and deployment in the real world, we innovatively
reconstruct the global self-attention structure of the original ViT into
isolated band unit self-attention. On ImageNet, under 2% area patch attacks our
method achieves 41.70% certified accuracy, a nearly 1-fold increase over the
previous best method (26.00%). Simultaneously, our method achieves 78.58% clean
accuracy, which is quite close to the normal ResNet-101 accuracy. Extensive
experiments show that our method obtains state-of-the-art clean and certified
accuracy with inferring efficiently on CIFAR-10 and ImageNet.


title:
Vainshtein screening in Horndeski theories nonminimally and kinetically
  coupled to ordinary matter

abstract:
  We study the Vainshtein screening mechanism in Horndeski theories in the
presence of a scalar field $\phi$ nonminimally and kinetically coupled to
ordinary matter field. A general interacting Lagrangian describing this
coupling is characterized by energy transfer $f_1$ and momentum exchange $f_2$.
For a spherically symmetric configurations on top of the cosmological
background, we investigate the static perturbations in linear and nonlinear
regimes with respect to the scalar field perturbation. In the former regime,
the PPN parameter generally deviates from unity as long as the matter coupling
or $G_{4,\phi}$ exists. On the other hand, in the latter regime, we show that
the nonlinear self interaction term of scalar field successfully activates the
Vainshtein mechanism even in the presence of the couplings $f_1$ and $f_2$. The
gravitational potentials recover the Newtonian behavior deep inside the
Vainshtein radius. The bounds on coupling terms not to substantially change the
Vainshtein radius are also given.


title:
Nearly model independent constraints on dense matter equation of state
  in a Bayesian approach

abstract:
  We apply Bayesian approach to construct a large number of minimally
constrained equations of state (EoSs) and study their correlations with a few
selected properties of neutron star (NS). Our set of minimal constraints
includes a few basic properties of saturated nuclear matter and low density
pure neutron matter EoS which is obtained from a precise
next-to-next-to-next-to-leading order (N$^{3}$LO) calculation in chiral
effective field theory. The tidal deformability and radius of NS with mass
$1$-$2M_\odot$ are found to be strongly correlated with the pressure of
$\beta$-equilibrated matter as well as with the symmetry energy at densities
higher than the saturation density ($\rho_0 = 0.16$ fm$^{-3}$) in a nearly
model independent manner. These correlations are employed to parametrize the
pressure for $\beta$-equilibrated matter, around 2$\rho_0$, as a function of
neutron star mass and the corresponding tidal deformability. The maximum mass
of neutron star is also strongly correlated with the pressure of symmetric and
$\beta$-equilibrated matter at densities $\sim$ 4.5$\rho_0$.


title:
Enhancement of the Zakharov-Glassey's method for Blow-Up in nonlinear
  Schroedinger equations

abstract:
  In this paper we give a sharper condition for the blow-up of the solution to
a nonlinear Schroedinger equation with free/Stark/quadratic potential by
improving the well known Zakharov-Glassey's method.


title: Scaling limit of the collision measures of multiple random walks

abstract:
  For an integer $k\ge 2$, let $S^{(1)}, S^{(2)}, \dots, S^{(k)}$ be $k$
independent simple symmetric random walks on $\mathbb{Z}$. A pair $(n,z)$ is
called a collision event if there are at least two distinct random walks,
namely, $S^{(i)},S^{(j)}$ satisfying $S^{(i)}_n= S^{(j)}_n=z$. We show that
under the same scaling as in Donsker's theorem, the sequence of random measures
representing these collision events converges to a non-trivial random measure
on $[0,1]\times \mathbb{R}$. Moreover, the limit random measure can be
characterized using Wiener chaos. The proof is inspired by methods from
statistical mechanics, especially, by a partition function that has been
developed for the study of directed polymers in random environments.


title:
New Converse Bounds on the Mismatched Reliability Function and the
  Mismatch Capacity Using an Auxiliary Genie Receiver

abstract:
  We develop a novel framework for proving converse theorems for channel
coding, which is based on the analysis technique of multicast transmission with
an additional auxiliary receiver, which serves as a "genie-aided-genie" to the
original receiver. The genie provides the original receiver a certain narrowed
list of codewords to choose from that includes the transmitted one. This
technique is used to derive upper bounds on the mismatch capacity as well as
the reliability function with a mismatched decoding metric of discrete
memoryless channels. Unlike previous works, our bounding technique exploits
also the inherent symmetric requirement from the codewords, leading to these
new upper bounds which are tighter. Since the computations of most of the known
bounds on the mismatch capacity are rather complicated, we further present a
method to obtain relaxed bounds that are easier to compute. We conclude by
presenting simpler bounds on the reliability function, and provide sufficient
conditions for their tightness in certain ranges of rates.


title: The Szymczak Functor on the Category of Finite Sets and Finite Relations

abstract:
  Szymczak functor is a tool used to construct Conley index for dynamical
systems with discrete time. We present an algorithmizable classification of
isomorphism classes in the Szymczak category over the category of finite sets
with arbitrary relations as morphisms. The research is the first step towards
the construction of Conley theory for relations.


title:
Cycle Integrals of the Parson Poincar\'e Series and Intersection Angles
  of Geodesics on Modular Curves

abstract:
  We prove a geometric formula for the cycle integrals of Parson's weight 2k
modular integrals in terms of the intersection angles of geodesics on modular
curves. Our result is an analog for modular integrals of a classical formula
for the cycle integrals of certain hyperbolic Poincar\'e series, due to Katok.
On the other hand, it extends a recent geometric formula of Matsusaka and Duke,
Imamo\={g}lu, and T\'oth for the cycle integrals of weight 2 modular integrals.


title:
Morphological Reinflection with Multiple Arguments: An Extended
  Annotation schema and a Georgian Case Study

abstract:
  In recent years, a flurry of morphological datasets had emerged, most notably
UniMorph, a multi-lingual repository of inflection tables. However, the flat
structure of the current morphological annotation schema makes the treatment of
some languages quirky, if not impossible, specifically in cases of polypersonal
agreement, where verbs agree with multiple arguments using true affixes. In
this paper, we propose to address this phenomenon by expanding the UniMorph
annotation schema to a hierarchical feature structure that naturally
accommodates complex argument marking. We apply this extended schema to one
such language, Georgian, and provide a human-verified, accurate and balanced
morphological dataset for Georgian verbs. The dataset has 4 times more tables
and 6 times more verb forms compared to the existing UniMorph dataset, covering
all possible variants of argument marking, demonstrating the adequacy of our
proposed scheme. Experiments with a standard reinflection model show that
generalization is easy when the data is split at the form level, but extremely
hard when splitting along lemma lines. Expanding the other languages in
UniMorph to this schema is expected to improve both the coverage, consistency
and interpretability of this benchmark.


title:
Physical Inertial Poser (PIP): Physics-aware Real-time Human Motion
  Tracking from Sparse Inertial Sensors

abstract:
  Motion capture from sparse inertial sensors has shown great potential
compared to image-based approaches since occlusions do not lead to a reduced
tracking quality and the recording space is not restricted to be within the
viewing frustum of the camera. However, capturing the motion and global
position only from a sparse set of inertial sensors is inherently ambiguous and
challenging. In consequence, recent state-of-the-art methods can barely handle
very long period motions, and unrealistic artifacts are common due to the
unawareness of physical constraints. To this end, we present the first method
which combines a neural kinematics estimator and a physics-aware motion
optimizer to track body motions with only 6 inertial sensors. The kinematics
module first regresses the motion status as a reference, and then the physics
module refines the motion to satisfy the physical constraints. Experiments
demonstrate a clear improvement over the state of the art in terms of capture
accuracy, temporal stability, and physical correctness.


title: NSR Open Super-string in the proper-time gauge I: Free Field Theory

abstract:
  We study the Neveu-Schwarz-Ramond (NSR) open super-string theory in the
proper-time gauge. The string field action is obtained by evaluating the
Polyakov string path integral. In this study, we focus on the open-string
free-field action, which corresponds to the string path integral on a strip.
Depending on the periodicity of the fermion fields, the open super-string has
two sectors: The Neveu-Schwarz (NS) and Ramond (R) sectors. We can impose the
gauge conditions to fix the (super-) reparametrization invariance on the
two-dimensional metric and its super-partner on the string world sheet to
secure the covariance, in contrast to the light-cone gauge condition.
Accordingly, the proper-time emerges in the NS sector and both proper-time and
its super-partner appear in the R-sector. Integration leads to free-string
field actions in both sectors.


title:
Heaps reduction, decorated diagrams, and the affine Temperley-Lieb
  algebra of type $C$

abstract:
  In this paper we propose a combinatorial framework to study a diagrammatic
representation of the affine Temperley-Lieb algebra of type C introduced by
Ernst. In doing this, we define two procedures, a decoration algorithm on
diagrams and a reduction algorithm on heaps of independent interest. Using this
approach, an explicit algorithmic description of Ernst representation map is
provided from which its faithfulness can be deduced. We also give a
construction of the inverse map.


title:
Random periodic solutions of nonautonomous stochastic feedback systems
  with multiplicative noise

abstract:
  We investigate the dynamical behavior of pull-back trajectories for
nonautonomous stochastic feedback systems with multiplicative noise. We proved
that there exists a random periodic solution of this system and all pull-back
trajectories converge to this random periodic solution as time goes to
infinitely almost surely. Our results can be applied to nonautonomous
stochastic Goodwin negative feedback system, nonautonomous stochastic
Othmer-Tyson positive feedback system and nonautonomous stochastic competitive
systems etc.


title: An introduction to POD-Greedy-Galerkin reduced basis method

abstract:
  Partial differential equations can be used to model many problems in several
fields of application including, e.g., fluid mechanics, heat and mass transfer,
and electromagnetism. Accurate discretization methods (e.g., finite element or
finite volume methods, the so-called full order models) are widely used to
numerically solve these problems. However, when many physical and/or
geometrical parameters are involved, the computational cost required by full
order models becomes prohibitively expensive and this is not acceptable for
real-time computations that are becoming more and more popular for rapid
prototyping. Therefore, there is the need to introduce reduced order methods
(also referred to as reduced basis methods) able to provide, as the input
parameters change, fast and reliable solutions at a reduced computational cost.


title: Gravitational field of slightly deformed naked singularities

abstract:
  We derive a particular approximate solution of Einstein equations, describing
the gravitational field of a mass distribution that slightly deviates from
spherical symmetry. The deviation is described by means of a quadrupole
parameter that is responsible for the appearance of a curvature singularity,
which is not covered by a horizon. We investigate the motion of test particles
in the gravitational field of this naked singularity and show that the
quadrupole parameter affects the properties of Schwarzschild trajectories. By
investigating radial geodesics, we find that no effects of repulsive gravity
are present. We interpreted this result as indicating that repulsive gravity is
non-linear effect.


title:
Capturing Humans in Motion: Temporal-Attentive 3D Human Pose and Shape
  Estimation from Monocular Video

abstract:
  Learning to capture human motion is essential to 3D human pose and shape
estimation from monocular video. However, the existing methods mainly rely on
recurrent or convolutional operation to model such temporal information, which
limits the ability to capture non-local context relations of human motion. To
address this problem, we propose a motion pose and shape network (MPS-Net) to
effectively capture humans in motion to estimate accurate and temporally
coherent 3D human pose and shape from a video. Specifically, we first propose a
motion continuity attention (MoCA) module that leverages visual cues observed
from human motion to adaptively recalibrate the range that needs attention in
the sequence to better capture the motion continuity dependencies. Then, we
develop a hierarchical attentive feature integration (HAFI) module to
effectively combine adjacent past and future feature representations to
strengthen temporal correlation and refine the feature representation of the
current frame. By coupling the MoCA and HAFI modules, the proposed MPS-Net
excels in estimating 3D human pose and shape in the video. Though conceptually
simple, our MPS-Net not only outperforms the state-of-the-art methods on the
3DPW, MPI-INF-3DHP, and Human3.6M benchmark datasets, but also uses fewer
network parameters. The video demos can be found at
https://mps-net.github.io/MPS-Net/.


title: Complete classification of planar p-elasticae

abstract:
  Euler's elastica is defined by a critical point of the total squared
curvature under the fixed length constraint, and its $L^p$-counterpart is
called $p$-elastica. In this paper we completely classify all $p$-elasticae in
the plane and obtain their explicit formulae as well as optimal regularity. To
this end we introduce new types of $p$-elliptic functions which streamline the
whole argument and result. As an application we also classify all closed planar
$p$-elasticae.


title:
Spatially multiplexed single-cavity dual-comb laser for equivalent time
  sampling applications

abstract:
  We present a new method for multiplexing a single laser cavity to support a
pair of noise-correlated, yet cavity length independent modes. These modes
share all intracavity components and take a near-common path, but do not
overlap on any active elements. With a SESAM we passively modelocked both
independent modes and obtain dual optical frequency comb operation. The
demonstrated 80-MHz laser delivers more than 2.4 Watts of average power per
comb with shorter than 140 fs pulses centered at 1052 nm. The relative timing
jitter between these two pulse trains is 2.2 fs [20 Hz, 100 kHz]. In addition
to its high passive stability that suppresses fast timing fluctuations, this
new technique also enables slow feedback on the repetition rate difference to
avoid drifts. We demonstrate a slow feedback loop on the repetition rate
difference via the multiplexing element, yielding {\sigma}=70 {\mu}Hz long-term
stability.


title: Scribble-Supervised LiDAR Semantic Segmentation

abstract:
  Densely annotating LiDAR point clouds remains too expensive and
time-consuming to keep up with the ever growing volume of data. While current
literature focuses on fully-supervised performance, developing efficient
methods that take advantage of realistic weak supervision have yet to be
explored. In this paper, we propose using scribbles to annotate LiDAR point
clouds and release ScribbleKITTI, the first scribble-annotated dataset for
LiDAR semantic segmentation. Furthermore, we present a pipeline to reduce the
performance gap that arises when using such weak annotations. Our pipeline
comprises of three stand-alone contributions that can be combined with any
LiDAR semantic segmentation model to achieve up to 95.7% of the
fully-supervised performance while using only 8% labeled points. Our scribble
annotations and code are available at github.com/ouenal/scribblekitti.


title: Low-scale leptogenesis with flavour and CP symmetries

abstract:
  We consider a type-I seesaw framework with a flavour symmetry from the series
of non-abelian groups $\Delta (3 n^2)$ and $\Delta (6 n^2)$ and a CP symmetry.
Breaking these symmetries non-trivially results in the right-handed neutrinos
being degenerate in mass up to possible (further symmetry-breaking) splittings
$\kappa$ and $\lambda$, while the neutrino Yukawa coupling matrix encodes the
entire flavour structure in the neutrino sector. For a fixed combination of
flavour and CP symmetry and residual groups, this matrix has five real free
parameters. Four of them are set by light neutrino masses and data on lepton
mixing, while $\theta_R$ is related to right-handed neutrinos. We scrutinise
for all four lepton mixing patterns, grouped into Case 1) through Case 3 b.1),
the potential to generate the baryon asymmetry of the Universe (BAU) through
low-scale leptogenesis numerically and analytically. The main results are: a)
the possible correlation of the BAU and the Majorana phases of the PMNS mixing
matrix in certain instances; b) the possibility to generate the correct amount
of BAU for zero $\kappa$ and $\lambda$ as well as for large $\kappa$, depending
on the case and choice of group theory parameters; c) the chance to produce
sufficient BAU for large active-sterile mixing angles, enabling direct
experimental tests at current and future facilities, for $\theta_R$ close to a
special value, potentially protected by an enhanced residual symmetry. We
elucidate these results with representative examples of flavour and CP
symmetries, which all lead to a good agreement with the measured lepton mixing
angles and, possibly, the current hint of the CP phase $\delta$. We identify
the CP-violating combinations relevant for low-scale leptogenesis, and show
that the parametric dependence of the BAU found numerically can be understood
well with their help.


title:
Galaxy And Mass Assembly (GAMA): Data Release 4 and the z < 0.1 total
  and z < 0.08 morphological galaxy stellar mass functions

abstract:
  In Galaxy And Mass Assembly Data Release 4 (GAMA DR4), we make available our
full spectroscopic redshift sample. This includes 248682 galaxy spectra, and,
in combination with earlier surveys, results in 330542 redshifts across five
sky regions covering ~250deg^2. The redshift density, is the highest available
over such a sustained area, has exceptionally high completeness (95 per cent to
r_KIDS=19.65mag), and is well suited for the study of galaxy mergers, galaxy
groups, and the low redshift (z<0.25) galaxy population. DR4 includes 32
value-added tables or Data Management Units (DMUs) that provide a number of
measured and derived data products including GALEX, ESO KiDS, ESO VIKING, WISE
and Herschel Space Observatory imaging. Within this release, we provide visual
morphologies for 15330 galaxies to z<0.08, photometric redshift estimates for
all 18million objects to r_KIDS~25mag, and stellar velocity dispersions for
111830 galaxies. We conclude by deriving the total galaxy stellar mass function
(GSMF) and its sub-division by morphological class (elliptical, compact-bulge
and disc, diffuse-bulge and disc, and disc only). This extends our previous
measurement of the total GSMF down to 10^6.75 M_sol h^-2_70 and we find a total
stellar mass density of rho_*=(2.97+/-0.04)x10^8 M_sol h_70 Mpc^-3 or
Omega_*=(2.17+/-0.03)x10^-3 h^-1_70. We conclude that at z<0.1, the Universe
has converted 4.9+/-0.1 per cent of the baryonic mass implied by Big Bang
Nucleosynthesis into stars that are gravitationally bound within the galaxy
population.


title:
An empirical measurement of the Halo Mass Function from the combination
  of GAMA DR4, SDSS DR12, and REFLEX II data

abstract:
  We construct the halo mass function (HMF) from the GAMA galaxy group
catalogue over the mass range 10^12.7M_sol to 10^15.5M_sol, and find good
agreement with the expectation from LambdaCDM. In comparison to previous
studies, this result extends the mass range over which the HMF has now been
measured over by an order of magnitude. We combine the GAMA DR4 HMF with
similar data from the SDSS DR12 and REFLEX II surveys, and fit a four-parameter
Murray-Robotham-Power (MRP) function, valid at z~0.1, yielding: a density
normalisation of: log10 (phi Mpc^3)=-3.96[+0.55,-0.82], a high mass turn-over
of: log10(M/M_sol)=14.13[+0.43,-0.40], a low mass power law slope of:
alpha=-1.68[+0.21,-0.24] , and a high mass softening parameter of: beta=
0.63[+0.25,-0.11]. If we fold in the constraint on Omega_M from Planck 2018
Cosmology, we are able to reduce these uncertainties further, but this relies
on the assumption that the power-law trend can be extrapolated from
10^12.7M_sol to zero mass. Throughout, we highlight the effort needed to
improve on our HMF measurement: improved halo mass estimates that do not rely
on calibration to simulations; reduced halo mass uncertainties needed to
mitigate the strong Eddington Bias that arises from the steepness of the HMF
low mass slope; and deeper wider area spectroscopic surveys. To our halo mass
limit of 10^12.7 M_sol, we are directly resolving (`seeing') 41+/-5 per cent of
the total mass density, i.e. Omega_[M>12.7]=0.128+/-0.016, opening the door for
the direct construction of 3D dark matter mass maps at Mpc resolution.


title: The challenge of measuring and mapping the missing baryons

abstract:
  The missing baryon problem may now be resolved, but the exact location and
physical properties of the diffuse component remains unclear. This problem
could be tractable, but requires the combination of new galaxy redshift surveys
with new X-ray and radio facilities.


title:
Lazy-MDPs: Towards Interpretable Reinforcement Learning by Learning When
  to Act

abstract:
  Traditionally, Reinforcement Learning (RL) aims at deciding how to act
optimally for an artificial agent. We argue that deciding when to act is
equally important. As humans, we drift from default, instinctive or memorized
behaviors to focused, thought-out behaviors when required by the situation. To
enhance RL agents with this aptitude, we propose to augment the standard Markov
Decision Process and make a new mode of action available: being lazy, which
defers decision-making to a default policy. In addition, we penalize non-lazy
actions in order to encourage minimal effort and have agents focus on critical
decisions only. We name the resulting formalism lazy-MDPs. We study the
theoretical properties of lazy-MDPs, expressing value functions and
characterizing optimal solutions. Then we empirically demonstrate that policies
learned in lazy-MDPs generally come with a form of interpretability: by
construction, they show us the states where the agent takes control over the
default policy. We deem those states and corresponding actions important since
they explain the difference in performance between the default and the new,
lazy policy. With suboptimal policies as default (pretrained or random), we
observe that agents are able to get competitive performance in Atari games
while only taking control in a limited subset of states.


title: Integrating Language Guidance into Vision-based Deep Metric Learning

abstract:
  Deep Metric Learning (DML) proposes to learn metric spaces which encode
semantic similarities as embedding space distances. These spaces should be
transferable to classes beyond those seen during training. Commonly, DML
methods task networks to solve contrastive ranking tasks defined over binary
class assignments. However, such approaches ignore higher-level semantic
relations between the actual classes. This causes learned embedding spaces to
encode incomplete semantic context and misrepresent the semantic relation
between classes, impacting the generalizability of the learned metric space. To
tackle this issue, we propose a language guidance objective for visual
similarity learning. Leveraging language embeddings of expert- and
pseudo-classnames, we contextualize and realign visual representation spaces
corresponding to meaningful language semantics for better semantic consistency.
Extensive experiments and ablations provide a strong motivation for our
proposed approach and show language guidance offering significant,
model-agnostic improvements for DML, achieving competitive and state-of-the-art
results on all benchmarks. Code available at
https://github.com/ExplainableML/LanguageGuidance_for_DML.


title:
Enumerative aspect of symplectic log Calabi-Yau divisors and almost
  toric fibrations

abstract:
  In this paper we are interested in the isotopy classes of symplectic log
Calabi-Yau divisors in a fixed symplectic rational surface. We give several
equivalent definitions and prove the stability, finiteness and rigidity
results. Motivated by the problem of counting toric actions, we obtain a
general counting formula of symplectic log Calabi-Yau divisors in a restrictive
region of $c_1$-nef cone. A detailed count in the case of 2- and 3-point
blow-ups of complex projective space for all symplectic forms is also given. In
our framework the complexity of the combinatorics of analyzing Delzant polygons
is reduced to the arrangement of homology classes. Then we study its relation
with almost toric fibrations. We raise the problem of realizing all symplectic
log Calabi-Yau divisors by some almost toric fibrations and verify it together
with another conjecture of Symington in a special region.


title:
Establishing significance of gravitational-wave signals from a single
  observatory in the PyCBC offline search

abstract:
  Gravitational-wave observations of compact binary coalescences are allowing
us to see black holes and neutron stars further into the universe and recent
results represent the most sensitive searches for compact objects ever
undertaken. Most searches for gravitational waves from compact binary
coalescence currently rely on detecting coincident triggers from multiple
detectors. In this paper, we describe a new method for extrapolating
significance of single-detector signals beyond the live-time of the analysis.
Using this method, we can recover loud signals which only triggered in a single
detector. We demonstrate this method in a search of O3 data, and recover seven
single-detector events with a false alarm rate less than two per year. These
were the same events as discovered in the GWTC-2.1 and GWTC-3 searches in a
single detector, and all but one event from 3-OGC and 4-OGC. Through a campaign
of injected signals, we estimate that the total time--volume sensitivity
increases by a factor of up to $1.20 \pm 0.02$ at a false alarm rate of one per
two years compared to completely ignoring single-detector events.


title: Subgame-perfect Equilibria in Mean-payoff Games (journal version)

abstract:
  In this paper, we provide an effective characterization of all the
subgame-perfect equilibria in infinite duration games played on finite graphs
with mean-payoff objectives. To this end, we introduce the notion of
requirement, and the notion of negotiation function. We establish that the
plays that are supported by SPEs are exactly those that are consistent with a
fixed point of the negotiation function. Finally, we use that characterization
to prove that the SPE threshold problem, who status was left open in the
literature, is decidable.


title: Non-isotropy Regularization for Proxy-based Deep Metric Learning

abstract:
  Deep Metric Learning (DML) aims to learn representation spaces on which
semantic relations can simply be expressed through predefined distance metrics.
Best performing approaches commonly leverage class proxies as sample stand-ins
for better convergence and generalization. However, these proxy-methods solely
optimize for sample-proxy distances. Given the inherent non-bijectiveness of
used distance functions, this can induce locally isotropic sample
distributions, leading to crucial semantic context being missed due to
difficulties resolving local structures and intraclass relations between
samples. To alleviate this problem, we propose non-isotropy regularization
($\mathbb{NIR}$) for proxy-based Deep Metric Learning. By leveraging
Normalizing Flows, we enforce unique translatability of samples from their
respective class proxies. This allows us to explicitly induce a non-isotropic
distribution of samples around a proxy to optimize for. In doing so, we equip
proxy-based objectives to better learn local structures. Extensive experiments
highlight consistent generalization benefits of $\mathbb{NIR}$ while achieving
competitive and state-of-the-art performance on the standard benchmarks
CUB200-2011, Cars196 and Stanford Online Products. In addition, we find the
superior convergence properties of proxy-based methods to still be retained or
even improved, making $\mathbb{NIR}$ very attractive for practical usage. Code
available at https://github.com/ExplainableML/NonIsotropicProxyDML.


title: SoK: TEE-assisted Confidential Smart Contract

abstract:
  The blockchain-based smart contract lacks privacy since the contract state
and instruction code are exposed to the public. Combining smart-contract
execution with Trusted Execution Environments (TEEs) provides an efficient
solution, called TEE-assisted smart contracts, for protecting the
confidentiality of contract states. However, the combination approaches are
varied, and a systematic study is absent. Newly released systems may fail to
draw upon the experience learned from existing protocols, such as repeating
known design mistakes or applying TEE technology in insecure ways. In this
paper, we first investigate and categorize the existing systems into two types:
the layer-one solution and layer-two solution. Then, we establish an analysis
framework to capture their common lights, covering the desired properties (for
contract services), threat models, and security considerations (for underlying
systems). Based on our taxonomy, we identify their ideal functionalities and
uncover the fundamental flaws and reasons for the challenges in each
specification design. We believe that this work would provide a guide for the
development of TEE-assisted smart contracts, as well as a framework to evaluate
future TEE-assisted confidential contract systems.


title:
Is it all a cluster game? -- Exploring Out-of-Distribution Detection
  based on Clustering in the Embedding Space

abstract:
  It is essential for safety-critical applications of deep neural networks to
determine when new inputs are significantly different from the training
distribution. In this paper, we explore this out-of-distribution (OOD)
detection problem for image classification using clusters of semantically
similar embeddings of the training data and exploit the differences in distance
relationships to these clusters between in- and out-of-distribution data. We
study the structure and separation of clusters in the embedding space and find
that supervised contrastive learning leads to well-separated clusters while its
self-supervised counterpart fails to do so. In our extensive analysis of
different training methods, clustering strategies, distance metrics, and
thresholding approaches, we observe that there is no clear winner. The optimal
approach depends on the model architecture and selected datasets for in- and
out-of-distribution. While we could reproduce the outstanding results for
contrastive training on CIFAR-10 as in-distribution data, we find standard
cross-entropy paired with cosine similarity outperforms all contrastive
training methods when training on CIFAR-100 instead. Cross-entropy provides
competitive results as compared to expensive contrastive training methods.


title:
Biomimetic Evaluation of an Underwater Soft Hand Through Deep
  Learning-based 3D Pose Reconstruction

abstract:
  Soft robotic hand shows considerable promise for various grasping
applications. However, the sensing and reconstruction of the robot pose will
cause limitation during the design and fabrication. In this work, we present a
novel 3D pose reconstruction approach to analyze the grasping motion of a
bidirectional soft robotic hand using experiment videos. The images from top,
front, back, left, right view were collected using an
one-camera-multiple-mirror imaging device. The coordinate and orientation
information of soft fingers are detected based on deep learning methods. Faster
RCNN model is used to detect the position of fingertips, while U-Net model is
applied to calculate the side boundary of the fingers. Based on the kinematics,
the corresponding coordinate and orientation databases are established. The 3D
pose reconstructed result presents a satisfactory performance and good
accuracy. Using efficacy coefficient method, the finger contribution of the
bending angle and distance between fingers of soft robot hand is analyzed by
compared with that of human hand. The results show that the soft robot hand
perform a human-like motion in both single-direction and bidirectional
grasping.


title: Generalized hydrodynamics of the KdV soliton gas

abstract:
  We establish the explicit correspondence between the theory of soliton gases
in classical integrable dispersive hydrodynamics, and generalized hydrodynamics
(GHD), the hydrodynamic theory for many-body quantum and classical integrable
systems. This is done by constructing the GHD description of the soliton gas
for the Korteweg-de Vries (KdV) equation. We further predict the exact form of
the free energy density and flux, and of the static correlation matrices of
conserved charges and currents, for the soliton gas. For this purpose, we
identify the solitons' statistics with that of classical particles, and confirm
the resulting GHD static correlation matrices by numerical simulations of the
soliton gas. Finally, we express conjectured dynamical correlation functions
for the soliton gas by simply borrowing the GHD results. In principle, other
conjectures are also immediately available, such as diffusion and
large-deviation functions for fluctuations of soliton transport.

categories: nlin.PS cond-mat.stat-mech math-ph math.MP


title:
Multilingual Pre-training with Language and Task Adaptation for
  Multilingual Text Style Transfer

abstract:
  We exploit the pre-trained seq2seq model mBART for multilingual text style
transfer. Using machine translated data as well as gold aligned English
sentences yields state-of-the-art results in the three target languages we
consider. Besides, in view of the general scarcity of parallel data, we propose
a modular approach for multilingual formality transfer, which consists of two
training strategies that target adaptation to both language and task. Our
approach achieves competitive performance without monolingual task-specific
parallel data and can be applied to other style transfer tasks as well as to
other languages.


title:
PMIC: Improving Multi-Agent Reinforcement Learning with Progressive
  Mutual Information Collaboration

abstract:
  Learning to collaborate is critical in multi-agent reinforcement learning
(MARL). A number of previous works promote collaboration by maximizing the
correlation of agents' behaviors, which is typically characterised by mutual
information (MI) in different forms. However, in this paper, we reveal that
strong correlation can emerge from sub-optimal collaborative behaviors, and
simply maximizing the MI can, surprisingly, hinder the learning towards better
collaboration. To address this issue, we propose a novel MARL framework, called
Progressive Mutual Information Collaboration (PMIC), for more effective
MI-driven collaboration. In PMIC, we use a new collaboration criterion measured
by the MI between global states and joint actions. Based on the criterion, the
key idea of PMIC is maximizing the MI associated with superior collaborative
behaviors and minimizing the MI associated with inferior ones. The two MI
objectives play complementary roles by facilitating learning towards better
collaborations while avoiding falling into sub-optimal ones. Specifically, PMIC
stores and progressively maintains sets of superior and inferior interaction
experiences, from which dual MI neural estimators are established. Experiments
on a wide range of MARL benchmarks show the superior performance of PMIC
compared with other algorithms.


title: Fast Timing With Silicon Carbide Low Gain Avalanche Detectors

abstract:
  4H-Silicon Carbide, when considered as a material for the fabrication of Low
Gain Avalanche Detectors for particle timing and position measurement, offers
potential advantages over Silicon. We discuss an ongoing study of this material
aimed at the fabrication and test of prototype fast timing sensors. This work
is well aligned with technical directions identified in the recent Department
of Energy study, Basic Research Needs for High Energy Physics Detector Research
and Development.


title:
Zero-Shot Dependency Parsing with Worst-Case Aware Automated Curriculum
  Learning

abstract:
  Large multilingual pretrained language models such as mBERT and XLM-RoBERTa
have been found to be surprisingly effective for cross-lingual transfer of
syntactic parsing models (Wu and Dredze 2019), but only between related
languages. However, source and training languages are rarely related, when
parsing truly low-resource languages. To close this gap, we adopt a method from
multi-task learning, which relies on automated curriculum learning, to
dynamically optimize for parsing performance on outlier languages. We show that
this approach is significantly better than uniform and size-proportional
sampling in the zero-shot setting.


title: LEVEN: A Large-Scale Chinese Legal Event Detection Dataset

abstract:
  Recognizing facts is the most fundamental step in making judgments, hence
detecting events in the legal documents is important to legal case analysis
tasks. However, existing Legal Event Detection (LED) datasets only concern
incomprehensive event types and have limited annotated data, which restricts
the development of LED methods and their downstream applications. To alleviate
these issues, we present LEVEN a large-scale Chinese LEgal eVENt detection
dataset, with 8,116 legal documents and 150,977 human-annotated event mentions
in 108 event types. Not only charge-related events, LEVEN also covers general
events, which are critical for legal case understanding but neglected in
existing LED datasets. To our knowledge, LEVEN is the largest LED dataset and
has dozens of times the data scale of others, which shall significantly promote
the training and evaluation of LED methods. The results of extensive
experiments indicate that LED is challenging and needs further effort.
Moreover, we simply utilize legal events as side information to promote
downstream applications. The method achieves improvements of average 2.2 points
precision in low-resource judgment prediction, and 1.5 points mean average
precision in unsupervised case retrieval, which suggests the fundamentality of
LED. The source code and dataset can be obtained from
https://github.com/thunlp/LEVEN.


title:
How darknet market users learned to worry more and love PGP: Analysis of
  security advice on darknet marketplaces

abstract:
  Darknet marketplaces, accessible through, Tor are where users can buy illicit
goods, and learn to hide from law enforcement. We surveyed the advice on these
markets and found valid security advice mixed up with paranoid threat models
and a reliance on privacy tools dismissed as unusable by the mainstream.


title:
The Immirzi field in modified gravity: superentropic black holes with
  scalar hair

abstract:
  In the context of f(R) generalizations to the Holst action, endowed with a
dynamical Immirzi field, we derive an analytic solution describing
asymptotically anti-de Sitter black holes with hyperbolic horizon. These
exhibit a scalar hair of the second kind which ultimately depends on the
Immirzi field radial behaviour. In particular, we show how the Immirzi field
modifies the usual entropy law associated to the black hole. We also verify
that the Immirzi field boils down to a constant value in the asymptotic region,
thus restoring the standard loop quantum gravity picture. We finally prove the
violation of the reverse isoperimetric inequality, resulting in the
superentropic nature of the black hole, and we discuss in detail the
thermodynamic stability of the solution.


title:
Learning to Generate Synthetic Training Data using Gradient Matching and
  Implicit Differentiation

abstract:
  Using huge training datasets can be costly and inconvenient. This article
explores various data distillation techniques that can reduce the amount of
data required to successfully train deep networks. Inspired by recent ideas, we
suggest new data distillation techniques based on generative teaching networks,
gradient matching, and the Implicit Function Theorem. Experiments with the
MNIST image classification problem show that the new methods are
computationally more efficient than previous ones and allow to increase the
performance of models trained on distilled data.


title:
Is cosmic birefringence due to dark energy or dark matter? A tomographic
  approach

abstract:
  A pseudoscalar "axionlike" field, $\phi$, may explain the $3\sigma$ hint of
cosmic birefringence observed in the $EB$ power spectrum of the cosmic
microwave background (CMB) polarization data. Is $\phi$ dark energy or dark
matter? A tomographic approach can answer this question. The effective mass of
dark energy field responsible for the accelerated expansion of the Universe
today must be smaller than $m_\phi\simeq 10^{-33}$ eV. If $m_\phi \gtrsim
10^{-32}$ eV, $\phi$ starts evolving before the epoch of reionization and we
should observe different amounts of birefringence from the $EB$ power spectrum
at low ($l\lesssim 10$) and high multipoles. Such an observation, which
requires a full-sky satellite mission, would rule out $\phi$ being dark energy.
If $m_\phi \gtrsim 10^{-28}$ eV, $\phi$ starts oscillating during the epoch of
recombination, leaving a distinct signature in the $EB$ power spectrum at high
multipoles, which can be measured precisely by ground-based CMB observations.
Our tomographic approach relies on the shape of the $EB$ power spectrum and is
less sensitive to miscalibration of polarization angles.


title:
More on homotopy continuation method and discounted zero-sum stochastic
  game with ARAT structure

abstract:
  In this paper, we introduce a homotopy function to trace the trajectory by
applying modified homotopy continuation method for finding the solution of
two-person zero-sum discounted stochastic ARAT game. We show that the algorithm
has the higher order of convergence. For the proposed algorithm, the homotopy
path approaching the solution is smooth and bounded. Two numerical examples are
illustrated to show the effectiveness of the proposed algorithm.


title:
Correlation between nuclear temperatures and symmetry energy in
  sub-saturation

abstract:
  A study of the correlation between nuclear temperatures and symmetry energy
is presented for heavy-ion collisions at intermediate energies via the
isospin-dependent quantum molecular-dynamics model. It is found that different
symmetry energy parameters will change the density and kinetic energy
distribution of the hot nuclei. More importantly, nuclear temperatures that are
based on kinetic energy properties can be used to study symmetry energy
information.


title:
MonoJSG: Joint Semantic and Geometric Cost Volume for Monocular 3D
  Object Detection

abstract:
  Due to the inherent ill-posed nature of 2D-3D projection, monocular 3D object
detection lacks accurate depth recovery ability. Although the deep neural
network (DNN) enables monocular depth-sensing from high-level learned features,
the pixel-level cues are usually omitted due to the deep convolution mechanism.
To benefit from both the powerful feature representation in DNN and pixel-level
geometric constraints, we reformulate the monocular object depth estimation as
a progressive refinement problem and propose a joint semantic and geometric
cost volume to model the depth error. Specifically, we first leverage neural
networks to learn the object position, dimension, and dense normalized 3D
object coordinates. Based on the object depth, the dense coordinates patch
together with the corresponding object features is reprojected to the image
space to build a cost volume in a joint semantic and geometric error manner.
The final depth is obtained by feeding the cost volume to a refinement network,
where the distribution of semantic and geometric error is regularized by direct
depth supervision. Through effectively mitigating depth error by the refinement
framework, we achieve state-of-the-art results on both the KITTI and Waymo
datasets.


title: An elementary analysis of ridge regression with random design

abstract:
  In this note, we provide an elementary analysis of the prediction error of
ridge regression with random design. The proof is short and self-contained. In
particular, it bypasses the use of Rudelson's deviation inequality for
covariance matrices, through a combination of exchangeability arguments, matrix
perturbation and operator convexity.


title: Geographic Adaptation of Pretrained Language Models

abstract:
  Geographic linguistic features are commonly used to improve the performance
of pretrained language models (PLMs) on NLP tasks where geographic knowledge is
intuitively beneficial (e.g., geolocation prediction and dialect feature
prediction). Existing work, however, leverages such geographic information in
task-specific fine-tuning, failing to incorporate it into PLMs' geo-linguistic
knowledge, which would make it transferable across different tasks. In this
work, we introduce an approach to task-agnostic geoadaptation of PLMs that
forces the PLM to learn associations between linguistic phenomena and
geographic locations. More specifically, geoadaptation is an intermediate
training step that couples masked language modeling and geolocation prediction
in a dynamic multitask learning setup. In our experiments, we geoadapt BERTi\'c
-- a PLM for Bosnian, Croatian, Montenegrin, and Serbian (BCMS) -- using a
corpus of geotagged BCMS tweets. Evaluation on three different tasks, namely
unsupervised (zero-shot) and supervised geolocation prediction and
(unsupervised) prediction of dialect features, shows that our geoadaptation
approach is very effective: e.g., we obtain new state-of-the-art performance in
supervised geolocation prediction and report massive gains over geographically
uninformed PLMs on zero-shot geolocation prediction.


title: EDTER: Edge Detection with Transformer

abstract:
  Convolutional neural networks have made significant progresses in edge
detection by progressively exploring the context and semantic features.
However, local details are gradually suppressed with the enlarging of receptive
fields. Recently, vision transformer has shown excellent capability in
capturing long-range dependencies. Inspired by this, we propose a novel
transformer-based edge detector, \emph{Edge Detection TransformER (EDTER)}, to
extract clear and crisp object boundaries and meaningful edges by exploiting
the full image context information and detailed local cues simultaneously.
EDTER works in two stages. In Stage I, a global transformer encoder is used to
capture long-range global context on coarse-grained image patches. Then in
Stage II, a local transformer encoder works on fine-grained patches to excavate
the short-range local cues. Each transformer encoder is followed by an
elaborately designed Bi-directional Multi-Level Aggregation decoder to achieve
high-resolution features. Finally, the global context and local cues are
combined by a Feature Fusion Module and fed into a decision head for edge
prediction. Extensive experiments on BSDS500, NYUDv2, and Multicue demonstrate
the superiority of EDTER in comparison with state-of-the-arts.


title: Quantum state reduction, and Newtonian twistor theory

abstract:
  We discuss the equivalence principle in quantum mechanics in the context of
Newton--Cartan geometry, and non--relativistic twistor theory.


title: In-Context Learning for Few-Shot Dialogue State Tracking

abstract:
  Collecting and annotating task-oriented dialogues is time-consuming and
costly. Thus, few-shot learning for dialogue tasks presents an exciting
opportunity. In this work, we propose an in-context (IC) learning framework for
few-shot dialogue state tracking (DST), where a large pre-trained language
model (LM) takes a test instance and a few annotated examples as input, and
directly decodes the dialogue states without any parameter updates. This makes
the LM more flexible and scalable compared to prior few-shot DST work when
adapting to new domains and scenarios. We study ways to formulate dialogue
context into prompts for LMs and propose an efficient approach to retrieve
dialogues as exemplars given a test instance and a selection pool of few-shot
examples. To better leverage the pre-trained LMs, we also reformulate DST into
a text-to-SQL problem. Empirical results on MultiWOZ 2.1 and 2.4 show that our
method IC-DST outperforms previous fine-tuned state-of-the-art models in
few-shot settings.


title: PMAL: Open Set Recognition via Robust Prototype Mining

abstract:
  Open Set Recognition (OSR) has been an emerging topic. Besides recognizing
predefined classes, the system needs to reject the unknowns. Prototype learning
is a potential manner to handle the problem, as its ability to improve
intra-class compactness of representations is much needed in discrimination
between the known and the unknowns. In this work, we propose a novel Prototype
Mining And Learning (PMAL) framework. It has a prototype mining mechanism
before the phase of optimizing embedding space, explicitly considering two
crucial properties, namely high-quality and diversity of the prototype set.
Concretely, a set of high-quality candidates are firstly extracted from
training samples based on data uncertainty learning, avoiding the interference
from unexpected noise. Considering the multifarious appearance of objects even
in a single category, a diversity-based strategy for prototype set filtering is
proposed. Accordingly, the embedding space can be better optimized to
discriminate therein the predefined classes and between known and unknowns.
Extensive experiments verify the two good characteristics (i.e., high-quality
and diversity) embraced in prototype mining, and show the remarkable
performance of the proposed framework compared to state-of-the-arts.


title: Undersmoothing Causal Estimators with Generative Trees

abstract:
  Inferring individualised treatment effects from observational data can unlock
the potential for targeted interventions. It is, however, hard to infer these
effects from observational data. One major problem that can arise is covariate
shift where the data (outcome) conditional distribution remains the same but
the covariate (input) distribution changes between the training and test set.
In an observational data setting, this problem is materialised in control and
treated units coming from different distributions. A common solution is to
augment learning methods through reweighing schemes (e.g. propensity scores).
These are needed due to model misspecification, but might hurt performance in
the individual case. In this paper, we explore a novel generative tree based
approach that tackles model misspecification directly, helping downstream
estimators achieve better robustness. We show empirically that the choice of
model class can indeed significantly affect the final performance and that
reweighing methods can struggle in individualised effect estimation. Our
proposed approach is competitive with reweighing methods on average treatment
effects while performing significantly better on individualised treatment
effects.


title: Morse Theoretic Signal Compression and Reconstruction on Chain Complexes

abstract:
  At the intersection of Topological Data Analysis (TDA) and machine learning,
the field of cellular signal processing has advanced rapidly in recent years.
In this context, each signal on the cells of a complex is processed using the
combinatorial Laplacian, and the resultant Hodge decomposition. Meanwhile,
discrete Morse theory has been widely used to speed up computations by reducing
the size of complexes while preserving their global topological properties. In
this paper, we provide an approach to signal compression and reconstruction on
chain complexes that leverages the tools of algebraic discrete Morse theory.
The main goal is to reduce and reconstruct a based chain complex together with
a set of signals on its cells via deformation retracts, preserving as much as
possible the global topological structure of both the complex and the signals.
We first prove that any deformation retract of real degree-wise
finite-dimensional based chain complexes is equivalent to a Morse matching. We
will then study how the signal changes under particular types of Morse
matching, showing its reconstruction error is trivial on specific components of
the Hodge decomposition. Furthermore, we provide an algorithm to compute Morse
matchings with minimal reconstruction error.


title: Regularity of symbolic powers of certain graphs

abstract:
  Let $G_{n,r}$ denote the graph with $n$ vertices $\{x_1,\ldots,x_n\}$ in
cyclic order and for each vertex $x_i$ consider the set
$A_i=\{x_{i-r},\ldots,x_{i-1},x_{i+1},x_{i+2},\ldots, x_{i+r}\},$ where
$x_{i-j}$ is the vertex $x_{n+i-j}$, whenever $i<j$ and $0\leq r\leq
\Bigl\lfloor\dfrac{n}{2}\Bigr\rfloor -1$. In $G_{n,r}$, every vertex $x_i$ is
adjacent to all the vertices of $V(G_{n,r})\backslash A_i$. Let $I=I(G_{n,r})$
be the edge ideal of $G_{n,r}$. We show that Minh's conjecture is true for $I,$
i.e. regularity of ordinary powers and symbolic powers of $I$ are equal. We
compute the Waldschmidt constant and resurgence for the whole class.


title:
Probing Photon-ALP Oscillations from the Flat Spectrum Radio Quasar
  4C+21.35

abstract:
  Flat spectrum radio quasar (FSRQ) is the most luminous blazar at the GeV
energies. In this paper, we probe the photon-axion-like particle (ALP)
oscillation effect on the latest very-high-energy (VHE) $\gamma$-ray
observations of the FSRQ 4C+21.35 (PKS 1222+216). The $\gamma$-ray spectra are
measured by the collaborations Major Atmospheric Gamma Imaging Cherenkov
Telescopes (MAGIC), Very Energetic Radiation Imaging Telescope Array System
(VERITAS), and Fermi Large Area Telescope (Fermi-LAT), which cover two activity
VHE flares of 4C+21.35 in 2010 and 2014. We show the spectral energy
distributions (SEDs) of these two phases under the null and ALP hypotheses, and
set the combined limit on the ALP parameter space. The 95% $\rm C.L.$ combined
limit set by the FSRQ 4C+21.35 observations measured by MAGIC, VERITAS, and
Fermi-LAT in the $m_a-g_{a\gamma}$ plane is roughly at the photon-ALP coupling
$g_{a\gamma} \gtrsim 8\times 10^{-12} \rm \, GeV^{-1}$ for the ALP mass
$[\,2\times 10^{-10}\, {\rm eV} \lesssim m_a \lesssim 2\times 10^{-8}\, \rm
eV\,]$. Compared with the constraint of NGC 1275 set by Fermi-LAT, no stringent
limit result is derived with the photon-ALP coupling $g_{a\gamma}$ from the
FSRQ 4C+21.35, while this result could slightly broaden the ALP mass $m_a$
limit at the low-mass region.


title:
A Novel Gradient Descent Least Squares (GDLS) Algorithm for Efficient
  SMV Gridless Line Spectrum Estimation with Applications in Tomographic SAR
  Imaging

abstract:
  This paper presents a novel efficient method for gridless line spectrum
estimation problem with single snapshot, namely the gradient descent least
squares (GDLS) method. Conventional single snapshot (a.k.a. single measure
vector or SMV) line spectrum estimation methods either rely on smoothing
techniques that sacrifice the array aperture, or adopt the sparsity constraint
and utilize compressed sensing (CS) method by defining prior grids and
resulting in the off-grid problem. Recently emerged atomic norm minimization
(ANM) methods achieved gridless SMV line spectrum estimation, but its
computational complexity is extremely high; thus it is practically infeasible
in real applications with large problem scales. Our proposed GDLS method
reformulates the line spectrum estimations problem into a least squares (LS)
estimation problem and solves the corresponding objective function via gradient
descent algorithm in an iterative fashion with efficiency. The convergence
guarantee, computational complexity, as well as performance analysis are
discussed in this paper. Numerical simulations and real data experiments show
that the proposed GDLS algorithm outperforms the state-of-the-art methods e.g.,
CS and ANM, in terms of estimation performances. It can completely avoid the
off-grid problem, and its computational complexity is significantly lower than
ANM. Our method has been tested in tomographic SAR (TomoSAR) imaging
applications via simulated and real experiment data. Results show great
potential of the proposed method in terms of better cloud point performance and
eliminating the gridding effect.


title:
Effect of hydrogen gas on magnetic properties of alloys of ferromagnetic
  metals with Pd and its application in hydrogen gas sensing

abstract:
  The mass-production of fuel-cell vehicles and the eventual transition to the
hydrogen economy will require safe, inexpensive and reliable sensors capable of
simultaneously detecting low concentrations of leaking hydrogen and measuring
broad ranges of hydrogen concentration in storage and energy generating
systems. Although several competing sensor technologies can potentially be used
in this role, just a few of them have thus far demonstrated a combination of
all desirable characteristics. This group of devices also includes
magneto-electronic sensors that can detect the presence of hydrogen gas in a
range of hydrogen concentrations from zero to 100% at atmospheric pressure with
the response time approaching the industry standard of one second. The hydrogen
gas sensing mechanism underpinning the operation of magneto-electronic sensors
is based on the physical processes of ferromagnetic resonance, magneto-optical
Kerr effect and anomalous Hall effect that enable one to measure
hydrogen-induced changes in the magnetic properties of structures combining Pd
with one or several ferromagnetic metals such as Co, Fe or Ni. In this chapter,
we overview the physical foundations of emergent ferromagnetic Pd-alloy-based
magneto-electronic hydrogen sensors and compare their characteristics with
those of high-performing multilayer thin film-based counterparts that have
already demonstrated a potential to find commercial applications.

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


title:
Broadband Emission from a Kilonova Ejecta-Pulsar Wind Nebula System:
  Late-Time X-ray Afterglow Rebrightening of GRB 170817A

abstract:
  We study the broadband radiation behavior of a kilonova ejecta-pulsar wind
nebula (PWN) system. In this model, we jointly fit the observations of AT
2017gfo in UV-optical-IR bands and the late-time X-ray afterglow of GRB
170817A. Our work shows that a PWN powered by the remnant neutron star (NS)
post GW170817 event could affect the optical transient AT 2017gfo and
re-brighten the late-time X-ray afterglow of GRB 170817A. The PWN radiation
will regulate the trend of future X-ray observations from a flattening to a
steep decline until some other sources (e.g., a kilonova afterglow) become
dominant. The restricted ranges of the central NS parameters in this work are
consistent with the previous works based on the observations of AT 2017gfo
only. In addition, the new fitting result indicates that the NS wind is highly
magnetized. We point out that the radio and X-ray emission from a kilonova
ejecta-PWN system could be an important electromagnetic feature of binary NS
mergers when a long-lived remnant NS is formed. Therefore, observations of a
kilonova ejecta-PWN system will provide important information to inferring the
nature of a merger remnant.


title:
Spintronic THz emitters based on transition metals and semi-metals/Pt
  multilayers

abstract:
  Spintronic terahertz (THz) emitters (STE) based on the inverse spin Hall
effect in ferromagnetic/heavy metal (FM/HM) heterostructures have become
important sources for THz pulse generation. The design, materials and control
of these interfaces at the nanometer level has become vital to engineer their
THz emission properties.In this work, we present studies of the optimization of
such structures through a multi-pronged approach, taking advantage of material
and interface engineering to enhance the THz spintronic emission. This
includes: the application of multi-stacks of HM/FM junctions and their
application to trilayer structures, the use of spin-sinks to simultaneously
enhance the THz emitted fields and reduce the use of thick Pt layers to reduce
optical absorption, and the use of semi-metals to increase the spin
polarization and thus the THz emission. Through these approaches, significant
enhancements of the THz field can be achieved. Importantly, taking into account
the optical absorption permits to elucidate novel phenomena such as the
relation between the spin diffusion length and the spin-sink using THz
spectroscopy, as well as possibly distinguishing between self and interface
spin-to-charge conversion in semi-metals.


title: Whither the Priors for (Vocal) Interactivity?

abstract:
  Voice-based communication is often cited as one of the most `natural' ways in
which humans and robots might interact, and the recent availability of accurate
automatic speech recognition and intelligible speech synthesis has enabled
researchers to integrate advanced off-the-shelf spoken language technology
components into their robot platforms. Despite this, the resulting interactions
are anything but `natural'. It transpires that simply giving a robot a voice
doesn't mean that a user will know how (or when) to talk to it, and the
resulting `conversations' tend to be stilted, one-sided and short. On the
surface, these difficulties might appear to be fairly trivial consequences of
users' unfamiliarity with robots (and \emph{vice versa}), and that any problems
would be mitigated by long-term use by the human, coupled with `deep learning'
by the robot. However, it is argued here that such communication failures are
indicative of a deeper malaise: a fundamental lack of basic principles --
\emph{priors} -- underpinning not only speech-based interaction in particular,
but (vocal) interactivity in general. This is evidenced not only by the fact
that contemporary spoken language systems already require training data sets
that are orders-of-magnitude greater than that experienced by a young child,
but also by the lack of design principles for creating effective communicative
human-robot interaction. This short position paper identifies some of the key
areas where theoretical insights might help overcome these shortfalls.


title: Oversampling is a necessity for RBF-collocation method of lines

abstract:
  We study a radial basis functions least-squares (RBF-LS), a.k.a. kernel-based
LS, collocation method of lines [arXiv:2109.03409] for solving surface
diffusion problems. Our convergence analysis requires that collocation points
has to be sufficiently dense with respect to the RBF centers. In this paper, we
further study how oversampling ratio, which is the numbers of collocation
points over that of RBF centers for quasi-uniform sets, affects eigenvalue
stability, time stepping sizes taken by Runge-Kutta methods, and accuracy.


title: Maintainable Log Datasets for Evaluation of Intrusion Detection Systems

abstract:
  Intrusion detection systems (IDS) monitor system logs and network traffic to
recognize malicious activities in computer networks. Evaluating and comparing
IDSs with respect to their detection accuracies is thereby essential for their
selection in specific use-cases. Despite a great need, hardly any labeled
intrusion detection datasets are publicly available. As a consequence,
evaluations are often carried out on datasets from real infrastructures, where
analysts cannot control system parameters or generate a reliable ground truth,
or private datasets that prevent reproducibility of results. As a solution, we
present a collection of maintainable log datasets collected in a testbed
representing a small enterprise. Thereby, we employ extensive state machines to
simulate normal user behavior and inject a multi-step attack. For scalable
testbed deployment, we use concepts from model-driven engineering that enable
automatic generation and labeling of an arbitrary number of datasets that
comprise repetitions of attack executions with variations of parameters. In
total, we provide 8 datasets containing 20 distinct types of log files, of
which we label 8 files for 10 unique attack steps. We publish the labeled log
datasets and code for testbed setup and simulation online as open-source to
enable others to reproduce and extend our results.


title: A Survey on Infrared Image and Video Sets

abstract:
  In this survey, we compile a list of publicly available infrared image and
video sets for artificial intelligence and computer vision researchers. We
mainly focus on IR image and video sets which are collected and labelled for
computer vision applications such as object detection, object segmentation,
classification, and motion detection. We categorize 92 different publicly
available or private sets according to their sensor types, image resolution,
and scale. We describe each and every set in detail regarding their collection
purpose, operation environment, optical system properties, and area of
application. We also cover a general overview of fundamental concepts that
relate to IR imagery, such as IR radiation, IR detectors, IR optics and
application fields. We analyse the statistical significance of the entire
corpus from different perspectives. We believe that this survey will be a
guideline for computer vision and artificial intelligence researchers that are
interested in working with the spectra beyond the visible domain.


title:
On some properties of $\omega$-uniqueness in tensor complementarity
  problem

abstract:
  In this article we introduce column adequate tensor in the context of tensor
complementarity problem and consider some important properties. The tensor
complementarity problem is a class of nonlinear complematarity problems with
the involved function being defined by a tensor. We establish the inheritance
property and invariant property of column adequate tensors. We show that
TCP$(q,\mathcal{A})$ has $\omega$-unique solution under some assumptions.