title:
Wind Farm Layout Optimisation using Set Based Multi-objective Bayesian
  Optimisation

abstract:
  Wind energy is one of the cleanest renewable electricity sources and can help
in addressing the challenge of climate change. One of the drawbacks of
wind-generated energy is the large space necessary to install a wind farm; this
arises from the fact that placing wind turbines in a limited area would hinder
their productivity and therefore not be economically convenient. This naturally
leads to an optimisation problem, which has three specific challenges: (1)
multiple conflicting objectives (2) computationally expensive simulation models
and (3) optimisation over design sets instead of design vectors. The first and
second challenges can be addressed by using surrogate-assisted e.g.\ Bayesian
multi-objective optimisation. However, the traditional Bayesian optimisation
cannot be applied as the optimisation function in the problem relies on design
sets instead of design vectors. This paper extends the applicability of
Bayesian multi-objective optimisation to set based optimisation for solving the
wind farm layout problem. We use a set-based kernel in Gaussian process to
quantify the correlation between wind farms (with a different number of
turbines). The results on the given data set of wind energy and direction
clearly show the potential of using set-based Bayesian multi-objective
optimisation.


title:
Cross-modal Learning of Graph Representations using Radar Point Cloud
  for Long-Range Gesture Recognition

abstract:
  Gesture recognition is one of the most intuitive ways of interaction and has
gathered particular attention for human computer interaction. Radar sensors
possess multiple intrinsic properties, such as their ability to work in low
illumination, harsh weather conditions, and being low-cost and compact, making
them highly preferable for a gesture recognition solution. However, most
literature work focuses on solutions with a limited range that is lower than a
meter. We propose a novel architecture for a long-range (1m - 2m) gesture
recognition solution that leverages a point cloud-based cross-learning approach
from camera point cloud to 60-GHz FMCW radar point cloud, which allows learning
better representations while suppressing noise. We use a variant of Dynamic
Graph CNN (DGCNN) for the cross-learning, enabling us to model relationships
between the points at a local and global level and to model the temporal
dynamics a Bi-LSTM network is employed. In the experimental results section, we
demonstrate our model's overall accuracy of 98.4% for five gestures and its
generalization capability.


title: CADG: A Model Based on Cross Attention for Domain Generalization

abstract:
  In Domain Generalization (DG) tasks, models are trained by using only
training data from the source domains to achieve generalization on an unseen
target domain, this will suffer from the distribution shift problem. So it's
important to learn a classifier to focus on the common representation which can
be used to classify on multi-domains, so that this classifier can achieve a
high performance on an unseen target domain as well. With the success of cross
attention in various cross-modal tasks, we find that cross attention is a
powerful mechanism to align the features come from different distributions. So
we design a model named CADG (cross attention for domain generalization),
wherein cross attention plays a important role, to address distribution shift
problem. Such design makes the classifier can be adopted on multi-domains, so
the classifier will generalize well on an unseen domain. Experiments show that
our proposed method achieves state-of-the-art performance on a variety of
domain generalization benchmarks compared with other single model and can even
achieve a better performance than some ensemble-based methods.


title:
EEND-SS: Joint End-to-End Neural Speaker Diarization and Speech
  Separation for Flexible Number of Speakers

abstract:
  In this paper, we present a novel framework that jointly performs speaker
diarization, speech separation, and speaker counting. Our proposed method
combines end-to-end speaker diarization and speech separation methods, namely,
End-to-End Neural Speaker Diarization with Encoder-Decoder-based Attractor
calculation (EEND-EDA) and the Convolutional Time-domain Audio Separation
Network (ConvTasNet) as multi-tasking joint model. We also propose the multiple
1x1 convolutional layer architecture for estimating the separation masks
corresponding to the number of speakers, and a post-processing technique for
refining the separated speech signal with speech activity. Experiments using
LibriMix dataset show that our proposed method outperforms the baselines in
terms of diarization and separation performance for both fixed and flexible
numbers of speakers, as well as speaker counting performance for flexible
numbers of speakers. All materials will be open-sourced and reproducible in
ESPnet toolkit.


title:
Analytic Map of Three-Channel S Matrix -Generalized Uniformization and
  Mittag-Leffler Expansion-

abstract:
  We explore the analytic structure of the three-channel $S$ matrix by
generalizing uniformization and making a single-valued map for the
three-channel $S$ matrix. First, by means of the inverse Jacobi's elliptic
function we construct a transformation from eight Riemann sheets of the
center-of-mass energy squared complex plane onto a torus, on which the
three-channel $S$ matrix is represented single-valued. Secondly, we show that
the Mittag-Leffler expansion, a pole expansion, of the three-channel scattering
amplitude includes not only topologically trivial but also nontrivial
contributions and is given by the Weierstrass zeta function. Finally, we
examine the obtained formula in the context of a simple three-channel model.
Taking a simple non-relativistic effective field theory with contact
interaction for the $S=-2$, $I=0$, $J^P = 0^+$,
$\Lambda\Lambda-N\Xi-\Sigma\Sigma$ coupled-channel scattering, we demonstrate
that the scattering amplitude as a function of the uniformization variable is,
in fact, given by the Mittag-Leffler expansion with the Weierstrass zeta
function and that it is dominated by contributions from neighboring poles.


title:
Traffic4cast at NeurIPS 2021 -- Temporal and Spatial Few-Shot Transfer
  Learning in Gridded Geo-Spatial Processes

abstract:
  The IARAI Traffic4cast competitions at NeurIPS 2019 and 2020 showed that
neural networks can successfully predict future traffic conditions 1 hour into
the future on simply aggregated GPS probe data in time and space bins. We thus
reinterpreted the challenge of forecasting traffic conditions as a movie
completion task. U-Nets proved to be the winning architecture, demonstrating an
ability to extract relevant features in this complex real-world geo-spatial
process. Building on the previous competitions, Traffic4cast 2021 now focuses
on the question of model robustness and generalizability across time and space.
Moving from one city to an entirely different city, or moving from pre-COVID
times to times after COVID hit the world thus introduces a clear domain shift.
We thus, for the first time, release data featuring such domain shifts. The
competition now covers ten cities over 2 years, providing data compiled from
over 10^12 GPS probe data. Winning solutions captured traffic dynamics
sufficiently well to even cope with these complex domain shifts. Surprisingly,
this seemed to require only the previous 1h traffic dynamic history and static
road graph as input.


title:
The order of convergence in the averaging principle for slow-fast
  systems of SDE's in Hilbert spaces

abstract:
  In this work we are concerned with the study of the strong order of
convergence in the averaging principle for slow-fast systems of SDE's in
Hilbert spaces when the stochastic perturbations are general Wiener processes,
i.e their covariance operators are allowed to be not trace class. In particular
we prove that the slow component converges strongly to the averaged one with
order of convergence 1/2 which is known to be optimal. Moreover we apply this
result to a fully coupled slow-fast stochastic reaction diffusion system where
the stochastic perturbation is given by a white noise both in time and space.


title: Manipulation of oral cancer speech using neural articulatory synthesis

abstract:
  We present an articulatory synthesis framework for the synthesis and
manipulation of oral cancer speech for clinical decision making and alleviation
of patient stress. Objective and subjective evaluations demonstrate that the
framework has acceptable naturalness and is worth further investigation. A
subsequent subjective vowel and consonant identification experiment showed that
the articulatory synthesis system can manipulate the articulatory trajectories
so that the synthesised speech reproduces problems present in the ground truth
oral cancer speech.


title: Normed Fiber Functors

abstract:
  By Goldman-Iwahori, the Bruhat-Tits building of the general linear group
$GL_n$ over a local field $\ell$ can be described as the set of non-archimedean
norms on $\ell^n$. Via a Tannakian formalism, we generalize this picture to a
description of the Bruhat-Tits building of an arbitrary reductive group G over
$\ell$ as the set of norms on the standard fiber functor of a special parahoric
integral model of G. We also give a moduli-theoretic description of the
Bruhat-Tits group scheme associated to a point of the building as the group
scheme of tensor automorphisms of the lattice chains defined by the
corresponding norm.


title: Combinatorial multiple Eisenstein series

abstract:
  We construct a family of $q$-series with rational coefficients satisfying a
variant of the extended double shuffle equations, which are a lift of a given
$\mathbb{Q}$-valued solution of the extended double shuffle equations. These
$q$-series will be called combinatorial (bi-)multiple Eisenstein series, and in
depth one they are given by Eisenstein series. The combinatorial multiple
Eisenstein series can be seen as an interpolation between the given
$\mathbb{Q}$-valued solution of the extended double shuffle equations (as
$q\rightarrow 0$) and multiple zeta values (as $q\rightarrow 1$). In
particular, they are $q$-analogues of multiple zeta values closely related to
modular forms. Their definition is inspired by the Fourier expansion of
multiple Eisenstein series introduced by Gangl-Kaneko-Zagier. Our explicit
construction is done on the level of their generating series, which we show to
be a so-called symmetril and swap invariant bimould.


title: On the Possibility of Discovering Exoplanets within our Solar System

abstract:
  It has previously been suggested that the rate of exoplanet discovery,
doubling roughly every 39 months, is indicative of a runaway increase in the
number of exoplanets in our galaxy. In this paper, we posit that - due to the
finite nature of space in the milky way - it will become increasingly likely
that one of these exoplanets will be found within our solar system. We
calculate the odds of this occurring pass 50\% on Friday 9th December 2146. We
go on to suggest novel methods for influencing where this exoplanet may be
discovered, note possible drawbacks of the discovery, and finally explore how
the previously-hypothesized `exoplanet singularity' (both figurative and
literal) could be averted.


title: Deep Hyperspectral Unmixing using Transformer Network

abstract:
  Currently, this paper is under review in IEEE. Transformers have intrigued
the vision research community with their state-of-the-art performance in
natural language processing. With their superior performance, transformers have
found their way in the field of hyperspectral image classification and achieved
promising results. In this article, we harness the power of transformers to
conquer the task of hyperspectral unmixing and propose a novel deep unmixing
model with transformers. We aim to utilize the ability of transformers to
better capture the global feature dependencies in order to enhance the quality
of the endmember spectra and the abundance maps. The proposed model is a
combination of a convolutional autoencoder and a transformer. The hyperspectral
data is encoded by the convolutional encoder. The transformer captures
long-range dependencies between the representations derived from the encoder.
The data are reconstructed using a convolutional decoder. We applied the
proposed unmixing model to three widely used unmixing datasets, i.e., Samson,
Apex, and Washington DC mall and compared it with the state-of-the-art in terms
of root mean squared error and spectral angle distance. The source code for the
proposed model will be made publicly available at
\url{https://github.com/preetam22n/DeepTrans-HSU}.


title: A multipair-free source of entangled photons in the solid state

abstract:
  Unwanted multiphoton emission commonly reduces the degree of entanglement of
photons generated by non-classical light sources and, in turn, hampers their
exploitation in quantum information science and technology. Quantum emitters
have the potential to overcome this hurdle but, so far, the effect of
multiphoton emission on the quality of entanglement has never been addressed in
detail. Here, we tackle this challenge using photon pairs from a
resonantly-driven quantum dot and comparing quantum state tomography and
second-order coherence measurements as a function of the excitation power. We
observe that the relative (absolute) multiphoton emission probability is as low
as $p_m= (5.6 \pm 0.6)10^{-4}$ ($p_2= (1.5 \pm 0.3)10^{-6}$) at the maximum
source brightness, values that lead to a negligible effect on the degree of
entanglement. In stark contrast with probabilistic sources of entangled
photons, we also demonstrate that the multiphoton emission probability and the
degree of entanglement remain practically unchanged against the excitation
power for multiple Rabi cycles, despite we clearly observe oscillations in the
second-order coherence measurements. Our results, explained by a theoretical
model that we develop to estimate the actual multiphoton contribution in the
two-photon density matrix, highlight that quantum dots can be regarded as a
multipair-free source of entangled photons in the solid state.


title:
A Formally and Algorithmically Efficient LULC change Model-Building
  Environment

abstract:
  The use of spatially explicit land use and land cover (LULC) change models is
widespread in environmental sciences and of interest in public decision-help.
However, it appears that these models suffer from significant biases and
shortcomings, the sources of which can be mathematical, conceptual or
algorithmic. We formalize a modeling environment that distinguishes a
calibration-estimation module and an allocation module. We propose an accurate
calibration-estimation method based on kernel density estimation and detail an
unbiased allocation algorithm. Moreover, a method of evaluation of LULC change
models is presented and allows us to compare them on various fronts (accuracy,
biases, computational efficiency). A case study based on a real land use map
but with known (enforced) transition probabilities is used. It appears that the
estimation error of the methods we propose is substantially improved over the
best existing software. Moreover, these methods require the specification of
very few parameters by the user, and are numerically efficient. This article
presents an overview of our LULC change modeling framework; its various formal
and algorithmic constituents will be detailed in forthcoming papers.


title:
Scientific and Technological Text Knowledge Extraction Method of based
  on Word Mixing and GRU

abstract:
  The knowledge extraction task is to extract triple relations (head
entity-relation-tail entity) from unstructured text data. The existing
knowledge extraction methods are divided into "pipeline" method and joint
extraction method. The "pipeline" method is to separate named entity
recognition and entity relationship extraction and use their own modules to
extract them. Although this method has better flexibility, the training speed
is slow. The learning model of joint extraction is an end-to-end model
implemented by neural network to realize entity recognition and relationship
extraction at the same time, which can well preserve the association between
entities and relationships, and convert the joint extraction of entities and
relationships into a sequence annotation problem. In this paper, we propose a
knowledge extraction method for scientific and technological resources based on
word mixture and GRU, combined with word mixture vector mapping method and
self-attention mechanism, to effectively improve the effect of text
relationship extraction for Chinese scientific and technological resources.


title: Notes on self-dual gravity

abstract:
  In this paper, we study self-dual gravity in the Newman-Penrose formalism. We
specify the self-dual solution space from the Newman-Unti solutions. We show
that the asymptotic symmetries of the self-dual gravity are still the
(extended) BMS symmetries. We transform the self-dual Taub-NUT solution into
the Newman-Unti gauge in analytical form.


title: Interpretation of Black Box NLP Models: A Survey

abstract:
  An increasing number of machine learning models have been deployed in domains
with high stakes such as finance and healthcare. Despite their superior
performances, many models are black boxes in nature which are hard to explain.
There are growing efforts for researchers to develop methods to interpret these
black-box models. Post hoc explanations based on perturbations, such as LIME,
are widely used approaches to interpret a machine learning model after it has
been built. This class of methods has been shown to exhibit large instability,
posing serious challenges to the effectiveness of the method itself and harming
user trust. In this paper, we propose S-LIME, which utilizes a hypothesis
testing framework based on central limit theorem for determining the number of
perturbation points needed to guarantee stability of the resulting explanation.
Experiments on both simulated and real world data sets are provided to
demonstrate the effectiveness of our method.


title:
The possible assignments of the scalar $K_0^*(1950)$ and $K_0^*(2130)$
  within the $^3P_0$ model

abstract:
  We have evaluated the strong decays of the $K_0^*(1950)$ and $K_0^*(2130)$
within the $^3P_0$ model, by employing the meson wave functions from the
relativized quark model. By comparing with the experimental measurements, the
$K_0^*(2130)$ could be assigned as $K_0^*(3^3P_0)$, while the $K_0^*(1950)$
seems like an exotic state, because its width can not be reasonably reproduced
within the $^3P_0$ model. We also predict that the $K_0^*(2^3P_0)$ state has a
mass of about $1811$ MeV and a width of about $656$ MeV, while the
$K_0^*(4^3P_0)$ state has a mass of about $2404$ MeV and a width of about $180$
MeV.


title:
Frictional weakening induced by cohesion: the numerical case of cohesive
  granular failures

abstract:
  The failure of 2D numerical cohesive granular steps collapsing under gravity
are simulated for a large range of cohesion. Focussing on the cumulative
displacement of the grains, and defining a displacement threshold, we establish
a sensible criterion for capturing the failure characteristics. We are able to
locate the failure in time and to identify the different stages of the
destabilisation. We find that the onset of the failure is delayed by increasing
cohesion, but its duration becomes shorter. Defining a narrow displacement
interval, a well-defined shear band revealing the failure comes out. Solving
the equilibrium of the failing block, we are able to make successful
predictions for the dependance between failure angle and cohesion, thereby
disclosing two distinct frictional behaviour: while friction remains constant
at small cohesion, it significantly decreases with cohesive properties at
larger cohesion. The results hence reveal two regimes for the behaviour of
cohesive granular matter depending on cohesion strength, revealing a
cohesion-induced weakening mechanism.


title:
Presentations of braid groups of type $A$ arising from
  $(m+2)$-angulations of regular polygons

abstract:
  We describe presentations of braid groups of type $A$ arising from coloured
quivers of mutation type $A$. We show that these can be interpreted
geometrically as generalised triangulations of regular polygons.


title: RobIn: A Robust Interpretable Deep Network for Schizophrenia Diagnosis

abstract:
  Schizophrenia is a severe mental health condition that requires a long and
complicated diagnostic process. However, early diagnosis is vital to control
symptoms. Deep learning has recently become a popular way to analyse and
interpret medical data. Past attempts to use deep learning for schizophrenia
diagnosis from brain-imaging data have shown promise but suffer from a large
training-application gap - it is difficult to apply lab research to the real
world. We propose to reduce this training-application gap by focusing on
readily accessible data. We collect a data set of psychiatric observations of
patients based on DSM-5 criteria. Because similar data is already recorded in
all mental health clinics that diagnose schizophrenia using DSM-5, our method
could be easily integrated into current processes as a tool to assist
clinicians, whilst abiding by formal diagnostic criteria. To facilitate
real-world usage of our system, we show that it is interpretable and robust.
Understanding how a machine learning tool reaches its diagnosis is essential to
allow clinicians to trust that diagnosis. To interpret the framework, we fuse
two complementary attention mechanisms, 'squeeze and excitation' and
'self-attention', to determine global attribute importance and attribute
interactivity, respectively. The model uses these importance scores to make
decisions. This allows clinicians to understand how a diagnosis was reached,
improving trust in the model. Because machine learning models often struggle to
generalise to data from different sources, we perform experiments with
augmented test data to evaluate the model's applicability to the real world. We
find that our model is more robust to perturbations, and should therefore
perform better in a clinical setting. It achieves 98% accuracy with 10-fold
cross-validation.


title:
The temperature and emission measure distribution in the quiet and
  active solar corona: a Bayesian approach

abstract:
  The reconstruction of the differential emission measure (DEM) from
observations of spectral line intensities provides information on the
temperature distribution of the emission measure in the region observed. The
inversion process is known to be highly unstable and it has been necessary to
provide additional constraints, such as requiring that the DEM should be
smooth. However, this is a non-physical constraint. The goal of this analysis
is to make an empirical determination of the ability of a set of emission line
intensities to constrain the reconstruction. Here, a simple model is used, by
means of a Monte-Carlo-Markov-Chain process, to arrive at solutions that
reproduces the observed intensities in a region of the quiet Sun and a solar
active region. These solutions are compared by means of the reduced
chi-squared. The conclusion from this analysis is that the observations are
only capable of constraining model consisting of 4 temperature-emission measure
pairs plus a determination of the standard deviation of the model from the
observed line intensities. A more complex model with 5 temperature-emission
measure pairs does not improve the fit and leads to parameters that are
irrelevant. A more general conclusion is that the information content of a set
of observed emission lines is limited with respect to the determination of the
emission measure distribution.


title:
Human wealth evolution is an accelerating expansion underpinned by a
  decelerating optimization process

abstract:
  Eight centuries of yearly GDP data from three regions of Western Europe,
corresponding to present days UK, France and Sweden, gauge economic
interactions that reflect the societal structures in which they unfold. In
short, they provide an insight on human wealth evolution. Our data analysis is
carried out in two steps. First, a Monte Carlo algorithm is used to fit the GDP
time series to a piecewise continuous function comprising a sequence of
exponentials with different exponents. These arguably correspond to different
social and technological stages of societal organization.
  As a function of time, human wealth evolution features an accelerating trend
and is thus an evolutionary expansion process. The intensity of human
interactions driving the evolutionary process has increased manyfold over the
centuries.
  This motivates our second analysis, where `wall clock time' as independent
variable is replaced with a measure of human interactions intensity $\tau$.
  In terms of this interaction variable $\tau$, human wealth evolution displays
two different logarithmic regimes, both decelerating, and connected by a rapid
cross-over. The latter occurs around the outbreak of World War I and coincides
with the transition in Western Europe from a mainly agricultural to a mainly
industrial and urbanized society. Finally, wealth evolution in terms of $\tau$
shows the hallmarks of record dynamics optimization.


title:
A Step towards Unveiling the Nature of Three Cataclysmic Variables: LS
  Cam, V902 Mon, and SWIFT J0746.3-1608

abstract:
  We have carried out detailed time-resolved timing analyses of three
cataclysmic variables (CVs) namely LS Cam, V902 Mon, and SWIFT J0746.3-1608,
using the long-baseline, high-cadence optical photometric data from the
Transiting Exoplanet Survey Satellite (TESS). Our analysis of LS Cam
observations hints the presence of a superorbital period of $\sim$
4.025$\pm$0.007 d along with negative and positive superhump periods of $\sim$
3.30 h and 3.70 h, respectively. These results can be explained as an
interaction of nodal and apsidal precession of the accretion disc with orbital
motion. For the other two sources, V902 Mon and SWIFT J0746.3-1608, we have
found evidence of a beat period of 2387.0$\pm$0.6 s and 2409.5$\pm$0.7 s,
respectively, which were not found in earlier studies. Our results presented in
this study indicate the change in the accretion mode during the entire
observing period for both sources. For V902 Mon, an apparent orbital period
derivative of (6.09 $\pm$ 0.60) $\times 10^{-10}$ was also found. Moreover, the
second harmonic of orbital frequency dominates the power spectrum of SWIFT
J0746.3-1608, suggestive of ellipsoidal modulation of the secondary star.
Present analyses suggest that LS Cam could be a superhumping CV whereas V902
Mon and SWIFT J0746.3-1608 are likely to be variable disc-overflow accreting
intermediate polars.


title:
Quantum-Aided Meta-Learning for Bayesian Binary Neural Networks via Born
  Machines

abstract:
  Near-term noisy intermediate-scale quantum circuits can efficiently implement
implicit probabilistic models in discrete spaces, supporting distributions that
are practically infeasible to sample from using classical means. One of the
possible applications of such models, also known as Born machines, is
probabilistic inference, which is at the core of Bayesian methods. This paper
studies the use of Born machines for the problem of training binary Bayesian
neural networks. In the proposed approach, a Born machine is used to model the
variational distribution of the binary weights of the neural network, and data
from multiple tasks is used to reduce training data requirements on new tasks.
The method combines gradient-based meta-learning and variational inference via
Born machines, and is shown in a prototypical regression problem to outperform
conventional joint learning strategies.


title:
PanGu-Bot: Efficient Generative Dialogue Pre-training from Pre-trained
  Language Model

abstract:
  In this paper, we introduce PanGu-Bot, a Chinese pre-trained open-domain
dialogue generation model based on a large pre-trained language model (PLM)
PANGU-alpha (Zeng et al.,2021). Different from other pre-trained dialogue
models trained over a massive amount of dialogue data from scratch, we aim to
build a powerful dialogue model with relatively fewer data and computation
costs by inheriting valuable language capabilities and knowledge from PLMs. To
this end, we train PanGu-Bot from the large PLM PANGU-alpha, which has been
proven well-performed on a variety of Chinese natural language tasks. We
investigate different aspects of responses generated by PanGu-Bot, including
response quality, knowledge, and safety. We show that PanGu-Bot outperforms
state-of-the-art Chinese dialogue systems (CDIALGPT (Wang et al., 2020), EVA
(Zhou et al., 2021)) w.r.t. the above three aspects. We also demonstrate that
PanGu-Bot can be easily deployed to generate emotional responses without
further training. Throughout our empirical analysis, we also point out that the
PanGu-Bot response quality, knowledge correctness, and safety are still far
from perfect, and further explorations are indispensable to building reliable
and smart dialogue systems. Our model and code will be available at
https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/PanGu-Bot
soon.


title: String theory, $\mathcal{N}=4$ SYM and Riemann hypothesis

abstract:
  We discuss new relations among string theory, four-dimensional
$\mathcal{N}=4$ supersymmetric Yang-Mills theory (SYM) and the Riemann
hypothesis. It is known that the Riemann hypothesis is equivalent to an
inequality for the sum of divisors function $\sigma (n)$. Based on previous
results in literature, we focus on the fact that $\sigma (n)$ appears in a
problem of counting supersymmetric states in the $\mathcal{N}=4$ SYM with
$SU(3)$ gauge group: the Schur limit of the superconformal index plays a role
of a generating function of $\sigma (n)$. Then assuming the Riemann hypothesis
gives bounds on information on the $1/8$-BPS states in the $\mathcal{N}=4$ SYM.
The AdS/CFT correspondence further connects the Riemann hypothesis to the type
IIB superstring theory on $AdS_5 \times S^5$. In particular, the Riemann
hypothesis implies a miraculous cancellation among Kaluza-Klein modes of the
supergravity multiplet and D3-branes wrapping supersymmetric cycles in the
string theory. We also discuss possibilities to gain new insights on the
Riemann hypothesis from the physics side.


title:
Evolution of the statistical disintegration of finite nuclei toward high
  energy

abstract:
  We develop a statistical approach for the description of complex nuclei
formation from dynamically produced baryons in high energy heavy-ion reactions.
We consider a finite highly-excited expanding nuclear system formed after
central nucleus-nucleus collisions. This system is sub-divided into primary
equilibrated nucleon clusters. The final nuclei are produced after the decay of
these excited clusters. By the successful comparison with the FOPI experimental
data we prove the possibility of such a local equilibrium in nuclear matter
with the temperature corresponding to the phase coexistence region. The
regularities obtained in this new nuclei production mechanism are shown.


title:
Electron scattering of light new particles from evaporating primordial
  black holes

abstract:
  Primordial black holes are a possible component of dark matter, and a most
promising way of investigating them is through the product of their Hawking
evaporation. As a result of this process, any species lighter than the Hawking
temperature is emitted, including possible new particles beyond the Standard
Model. These can then be detected in lab-based experiments via their
interaction with the Standard Model particles. In a previous work, we have
first proposed and studied this scenario in the presence of an interaction
between the light new species and nucleons. Here we extend this discussion to
include the case of interaction with electrons. We show that the simultaneous
presence of primordial black holes and species lighter than about $100$ MeV can
be constrained by the measurements of direct detection experiments, such as
XENON1T, and water Cherenkov neutrino detectors, such as Super-Kamiokande. Our
results provide a complementary and alternative way of investigation with
respect to cosmological and collider searches.


title: Simulation of coarsening in two-phase systems with dissimilar mobilities

abstract:
  In this work, we apply phase field simulations to examine the coarsening
behavior of morphologically complex two-phase microstructures in which the
phases have highly dissimilar mobilities, a condition approaching that found in
experimental solid-liquid systems. Specifically, we consider a two-phase system
at the critical composition ($50\%$ volume fraction) in which the mobilities of
the two phases differ by a factor of 100. This system is simulated in two and
three dimensions using the Cahn-Hilliard model with a concentration-dependent
mobility, and results are compared to simulations with a constant mobility. A
morphological transition occurs during coarsening of the two-dimensional system
(corresponding to a thin film geometry) with dissimilar mobilities, resulting
in a system of nearly-circular particles of high-mobility phase embedded in a
low-mobility matrix. This morphological transition causes the coarsening rate
constant to decrease over time, which explains why a previous study found lack
of agreement with the theoretical $t^{1/3}$ power law. Three-dimensional
systems with dissimilar mobilities resulted in bicontinuous microstructures
that evolve self-similarly, as determined by quantitative analysis of the
interfacial shape distribution. Coarsening kinetics in three dimensions agreed
closely with the $t^{1/3}$ power law after the initial transient stage. A model
is derived to explain a nearly-linear relationship between the coarsening rate
constant and the variance of scaled mean curvature that is observed during this
transient stage.


title: The Three Little Pigs and the Big Bad Wolf: Case Studies of Peer Review

abstract:
  I present for your appraisal three independent cases of the manuscript
referee process conducted by a venerable peer-reviewed scientific journal. Each
case involves a little pig, who submitted for consideration a theoretical plan
for a house to be constructed presently, in a faraway land. An anonymous big
bad wolf was assigned by the journal to assess the merit of these manuscripts.
The pigs proposed three distinct construction frameworks, which varied in
physical and mathematical sophistication. The first little pig submitted a
model of straw, based on the numerical method of toe-counting. His design
included odd features, such as spilled millet and cloven-hoofprints on the
window sill -- possibly a ploy to distract the wolf from the manuscript's
facile mathematical foundation. The second little pig used a more advanced
approach, employing Newton's classical laws of motion, to propose a house of
sticks. This pig included in her manuscript copious citations by a specific
wolf, possibly aiming to ensure acceptance by flattering the wolf whom she
anticipated would be the referee. The third little pig described an
ostentatious house of bricks based on an elaborate dynamical systems and
stability analysis, possibly scheming to dazzle and impress. The big bad wolf
did not appear moved by any of the pigs' tactics. His recommendations were, for
straw: the minor revision of water-proofing; for sticks: the major revision of
fire-proofing, given concerns surrounding climate change; for bricks:
unequivocal rejection, accompanied by multiple derogatory comments regarding
"high-and-mighty theorists." I describe each case in detail, and suggest that
the wolf's reports might be driven as much by self interest as the manuscripts
themselves -- namely, that at the time the wolf wrote the reviews, he was
rather hungry. Finally, I examine morals learned, if any.


title:
Sensor Deception Attacks Against Initial-State Privacy in Supervisory
  Control Systems

abstract:
  This paper investigates the problem of synthesizing sensor deception
attackers against privacy in the context of supervisory control of
discrete-event systems (DES). We consider a DES plant controlled by a
supervisor, which is subject to sensor deception attacks. Specifically, we
consider an active attacker that can tamper with the observations received by
the supervisor by, e.g., hacking on the communication channel between the
sensors and the supervisor. The privacy requirement of the supervisory control
system is to maintain initial-state opacity, i.e., it does not want to reveal
the fact that it was initiated from a secret state during its operation. On the
other hand, the attacker aims to deceive the supervisor, by tampering with its
observations, such that initial-state opacity is violated due to incorrect
control actions. In this work, we investigate from the attacker's point of view
by presenting an effective approach for synthesizing sensor attack strategies
threatening the privacy of the system. To this end, we propose the All Attack
Structure (AAS) that records state estimates for both the supervisor and the
attacker. This structure serves as a basis for synthesizing a sensor attack
strategy. We also discuss how to simplify the synthesis complexity by
leveraging the structural property of the initial-state privacy requirement. A
running academic example is provided to illustrate the synthesis procedure.


title:
Real semi-stable degenerations, real-oriented blow-ups and straightening
  corners

abstract:
  We study totally real semi-stable degenerations (and more generally, smooth
semi-stable degenerations). Our goal is to describe the homeomorphism type of
the real locus $\mathbf{R} X_t$ of the general fibre in terms of the special
fibre. We give a general homeomorphism statement via the real-oriented blow-up
of the family. Using this, we give more explicit descriptions of $\mathbf{R}
X_t$ as a stratified space glued from (covers of) strata of the special fibre.
We also give relative versions of the statements and consider the example of
toric degenerations in order to link the technique to tropicalisation.


title:
Variational corner transfer matrix renormalization group method for
  classical statistical models

abstract:
  In the context of tensor network states, we for the first time reformulate
the corner transfer matrix renormalization group (CTMRG) method into a
variational bilevel optimization algorithm. The solution of the optimization
problem corresponds to the fixed-point environment pursued in the conventional
CTMRG method, from which the partition function of a classical statistical
model, represented by an infinite tensor network, can be efficiently evaluated.
The validity of this variational idea is demonstrated by the high-precision
calculation of the residual entropy of the dimer model, and is further verified
by investigating several typical phase transitions in classical spin models,
where the obtained critical points and critical exponents all agree with the
best known results in literature. Its extension to three-dimensional tensor
networks or quantum lattice models is straightforward, as also discussed
briefly.


title:
Estimating bulk and edge topological indices in finite open chiral
  chains

abstract:
  We develop a formalism to extend, simultaneously, the usual definition of
bulk and edge indices from topological insulators to the case of a finite
sample with open boundary conditions, and provide a physical interpretation of
these quantities. We then show that they converge exponentially fast to an
integer value when we increase the system size, and also that bulk and edge
quantities coincide at finite size. The theorem applies to any non-homogeneous
system such as disordered or defect configurations. We focus on one-dimensional
chains with chiral symmetry, such as the Su-Schrieffer-Heeger model, but the
proof actually only requires the Hamiltonian to be short-range and with a
spectral gap in the bulk. The definition of bulk and edge indices relies on a
finite-size version of the switch-function formalism where the Fermi projector
is smoothed in energy using a carefully chosen regularization parameter.


title:
Triangle Singularities in the $T^+_{cc}\to D^{*+}D^0\to\pi^+D^0D^0$
  decay width

abstract:
  The values of the masses of the particles involved in the decay of
$T^+_{cc}\to D^{*+}D^0\to\pi^+D^0D^0$ suggest that due to the final state
interactions in the transition vertex $T^+_{cc}\to D^{*+}D^0 $ there may be
triangle logarithmic singularities. We discuss their possible role and show
that the tree approximation for calculating the decay widths
$T^+_{cc}\to(D^{*+}D^0+D^{*0}D^+)\to\pi^+D^0D^0$, $\pi^0D^0 D^+$, $\gamma
D^0D^+$ is quite sufficient at the current level of measurement accuracy.


title:
Estimating the Effects of Educational System Consolidation: The Case of
  China's Rural School Closure Initiative

abstract:
  Global trends of fertility decline, population aging, and rural outmigration
are creating pressures to consolidate school systems, with the rationale that
economies of scale will enable higher quality education to be delivered in an
efficient manner, despite longer travel distances for students. Yet, few
studies have considered the implications of system consolidation for
educational access and inequality, outside of the context of developed
countries. We estimate the impact of educational infrastructure consolidation
on educational attainment using the case of China's rural primary school
closure policies in the early 2000s. We use data from a large household survey
covering 728 villages in 7 provinces, and exploit variation in villages' year
of school closure and children's ages at closure to identify the causal impact
of school closure. For girls exposed to closure during their primary school
ages, we find an average decrease of 0.60 years of schooling by 2011, when
children's mean age was 17 years old. Negative effects strengthen with time
since closure. For boys, there is no corresponding significant effect.
Different effects by gender may be related to greater sensitivity of girls'
enrollment to distance and greater responsiveness of boys' enrollment to
quality.


title:
Sequential Cooperative Energy and Time-Optimal Lane Change Maneuvers for
  Highway Traffic

abstract:
  We derive optimal control policies for a Connected Automated Vehicle (CAV)
and cooperating neighboring CAVs to carry out a lane change maneuver consisting
of a longitudinal phase where the CAV properly positions itself relative to the
cooperating neighbors and a lateral phase where it safely changes lanes. In
contrast to prior work on this problem, where the CAV "selfishly" seeks to
minimize its maneuver time, we seek to ensure that the fast-lane traffic flow
is minimally disrupted (through a properly defined metric) and that highway
throughput is improved by optimally selecting the cooperating vehicles. We show
that analytical solutions for the optimal trajectories can be derived and are
guaranteed to satisfy safety constraints for all vehicles involved in the
maneuver. When feasible solutions do not exist, we include a time relaxation
method trading off a longer maneuver time with reduced disruption. Our analysis
is also extended to multiple sequential maneuvers. Simulation results where the
controllers are implemented show their effectiveness in terms of safety
guarantees and up to 35% throughput improvement compared to maneuvers with no
vehicle cooperation.


title: $k$NN-NER: Named Entity Recognition with Nearest Neighbor Search

abstract:
  Inspired by recent advances in retrieval augmented methods in
NLP~\citep{khandelwal2019generalization,khandelwal2020nearest,meng2021gnn}, in
this paper, we introduce a $k$ nearest neighbor NER ($k$NN-NER) framework,
which augments the distribution of entity labels by assigning $k$ nearest
neighbors retrieved from the training set. This strategy makes the model more
capable of handling long-tail cases, along with better few-shot learning
abilities. $k$NN-NER requires no additional operation during the training
phase, and by interpolating $k$ nearest neighbors search into the vanilla NER
model, $k$NN-NER consistently outperforms its vanilla counterparts: we achieve
a new state-of-the-art F1-score of 72.03 (+1.25) on the Chinese Weibo dataset
and improved results on a variety of widely used NER benchmarks. Additionally,
we show that $k$NN-NER can achieve comparable results to the vanilla NER model
with 40\% less amount of training data. Code available at
\url{https://github.com/ShannonAI/KNN-NER}.


title:
Searching and identifying leptoquarks through low-energy polarized
  scattering processes $e^-p\to e^-\Lambda_c$

abstract:
  We investigate the potential for discovering and identifying the different
leptoquark (LQ) models in the charm sector through the low-energy polarized
scattering processes $\vec{e}^{\,-}p\to e^-\Lambda_c$, $e^-\vec{p}\to
e^-\Lambda_c$, and $\vec{e}^{\,-}\vec{p}\to e^-\Lambda_c$. Considering only the
longitudinally polarized processes, we show that the LQ models can be
disentangled from each other by measuring the four spin asymmetries, $A_{L}^e$,
$A_{L}^p$, $A_{L3}^{ep}$, and $A_{L6}^{ep}$, constructed in terms of the
polarized cross sections. Although it is challenging to accomplish the same
goal with the transversely polarized processes, we find that investing them in
the future experiments is especially beneficial, since they can directly probe
into the imaginary part of the Wilson coefficients in the general low-energy
effective Lagrangian. With our properly designed experimental setups, it is
also demonstrated that promising event rates can be expected for all these
processes and, even in the worst-case scenario -- no LQ signals are observed at
all, they can still provide competitive potential for constraining the new
physics, compared with those from the conventional charmed-hadron weak decays
and the high-$p_T$ dilepton invariant mass tails at high-energy colliders.


title:
A Computationally Informed Realisation Algorithm for Lithium-Ion
  Batteries Implemented with LiiBRA.jl

abstract:
  Real-time battery modelling advancements have quickly become a requirement as
the adoption of battery electric vehicles (BEVs) has rapidly increased. In this
paper an open-source, improved discrete realisation algorithm, implemented in
Julia for creation and simulation of reduced-order, real-time capable
physics-based models is presented. This work reduces the Doyle-Fuller-Newman
electrochemical model into continuous-form transfer functions and introduces a
computationally informed discrete realisation algorithm (CI-DRA) to generate
the reduced-order models. Further improvements in conventional offline model
creation are obtained as well as achieving in-vehicle capable model creation
for ARM based computing architectures. Furthermore, a sensitivity analysis on
the resultant computational time is completed as well as experimental
validation of a worldwide harmonised light vehicle test procedure (WLTP) for a
LG Chem. M50 21700 parameterisation. A performance comparison to the
conventional Matlab implemented discrete realisation algorithm (DRA) is
completed showcasing a mean computational time improvement of 88%. Finally, an
ARM based compilation is investigated for in-vehicle model generation and shows
a modest performance reduction of 43% when compared to the x86 implementation
while still generating accurate models within 5.5 seconds.


title:
A Cooperative Optimal Control Framework for Connected and Automated
  Vehicles in Mixed Traffic Using Social Value Orientation

abstract:
  In this paper, we develop a socially cooperative optimal control framework
for connected and automated vehicles (CAVs) in mixed traffic using social value
orientation (SVO). In our approach, we formulate the interaction between a CAV
and a human-driven vehicle (HDV) as a simultaneous game to facilitate the
derivation of a Nash equilibrium. In the imposed game, each vehicle minimizes a
weighted sum of its egoistic objective and a cooperative objective. The SVO
angles are used to quantify preferences of the vehicles toward the egoistic and
cooperative objectives which lead to an appropriate design of weighting factors
in a multi-objective optimal control problem. We prove that by solving the
proposed optimal control problem, a Nash equilibrium can be obtained. To
estimate the SVO angle of the HDV, we develop a receding horizon estimation
based on maximum entropy inverse reinforcement learning. The effectiveness of
the proposed approach is demonstrated by numerical simulations at a highway
on-ramp merging scenario.


title: Dynamic programming in convex stochastic optimization

abstract:
  This paper studies the dynamic programming principle for general convex
stochastic optimization problems introduced by Rockafellar and Wets in [30]. We
extend the applicability of the theory by relaxing compactness and boundedness
assumptions. In the context of financial mathematics, the relaxed assumption
are satisfied under the well-known no-arbitrage condition and the reasonable
asymptotic elasticity condition of the utility function. Besides financial
mathematics, we obtain several new results in linear and nonlinear stochastic
programming and stochastic optimal control.


title: Shiba states in systems with density of states singularities

abstract:
  Magnetic impurities placed in the superconductor can lead to emergence of the
Yu-Shiba-Rusinov bound states. Coupling between the impurity and the substrate
depends on density of states (DOS) at the Fermi level and can be tuned by DOS
singularities. In this paper, we study the role of DOS singularities using the
real space Bogoliubov-de Gennes equations for chosen lattice models. To uncover
the role of these singularities (Dirac point, van Hove singularity, or the flat
band), we study honeycomb, kagome, and Lieb lattices. We show that the
properties of the Shiba state strongly depends on the type of lattice.
Nevertheless some behaviors are generic, e.g. dependence of the critical
magnetic coupling on the DOS at the Fermi level. However, the Shiba states
realized in the Lieb lattice exhibit extraordinary properties, which can be
explained by the presence of a few nonequivalent sublattices. Depending on the
location of the magnetic impurity in the chosen sublattice, the value of
critical magnetic coupling $J_\text{c}$ can be reduced or enhanced when the
flat band is located at the Fermi level. In this context, we also present
differences in the local DOS and coherence lengths for different sublattices in
the Lieb lattice.


title:
A Rich Recipe Representation as Plan to Support Expressive Multi Modal
  Queries on Recipe Content and Preparation Process

abstract:
  Food is not only a basic human necessity but also a key factor driving a
society's health and economic well-being. As a result, the cooking domain is a
popular use-case to demonstrate decision-support (AI) capabilities in service
of benefits like precision health with tools ranging from information retrieval
interfaces to task-oriented chatbots. An AI here should understand concepts in
the food domain (e.g., recipes, ingredients), be tolerant to failures
encountered while cooking (e.g., browning of butter), handle allergy-based
substitutions, and work with multiple data modalities (e.g. text and images).
However, the recipes today are handled as textual documents which makes it
difficult for machines to read, reason and handle ambiguity. This demands a
need for better representation of the recipes, overcoming the ambiguity and
sparseness that exists in the current textual documents. In this paper, we
discuss the construction of a machine-understandable rich recipe representation
(R3), in the form of plans, from the recipes available in natural language. R3
is infused with additional knowledge such as information about allergens and
images of ingredients, possible failures and tips for each atomic cooking step.
To show the benefits of R3, we also present TREAT, a tool for recipe retrieval
which uses R3 to perform multi-modal reasoning on the recipe's content (plan
objects - ingredients and cooking tools), food preparation process (plan
actions and time), and media type (image, text). R3 leads to improved retrieval
efficiency and new capabilities that were hither-to not possible in textual
representation.


title:
Impact of Acoustic Noise on Alzheimer's Disease Detection from Speech:
  Should You Let Baby Cry?

abstract:
  Research related to automatically detecting Alzheimer's disease (AD) is
important, given the high prevalence of AD and the high cost of traditional
methods. Since AD significantly affects the acoustics of spontaneous speech,
speech processing and machine learning (ML) provide promising techniques for
reliably detecting AD. However, speech audio may be affected by different types
of background noise and it is important to understand how the noise influences
the accuracy of ML models detecting AD from speech. In this paper, we study the
effect of fifteen types of noise from five different categories on the
performance of four ML models trained with three types of acoustic
representations. We perform a thorough analysis showing how ML models and
acoustic features are affected by different types of acoustic noise. We show
that acoustic noise is not necessarily harmful - certain types of noise are
beneficial for AD detection models and help increasing accuracy by up to 4.8\%.
We provide recommendations on how to utilize acoustic noise in order to achieve
the best performance results with the ML models deployed in real world.


title: Light-by-Light Scattering at Future $e^+e^-$ Colliders

abstract:
  We study the sensitivity of possible CLIC and FCC-ee measurements of
light-by-light scattering to old and new physics, including the
Heisenberg-Euler Lagrangian in the Standard Model with possible contributions
from loops of additional charged particles or magnetic monopoles, the
Born-Infeld extension of QED, and effective dimension-8 operators involving
four electromagnetic field strengths as could appear in the Standard Model
Effective Field Theory. We find that FCC-ee measurements at 365 GeV and CLIC
measurements at 350 GeV would be sensitive to new physics scales of half a TeV
in the dimension-8 operator coefficients, and that CLIC measurements at 1.4 TeV
or 3 TeV would be sensitive to new physics scales {$\sim 2$ TeV or 5 TeV} at
95\% CL, corresponding to probing loops of new particles with masses up to
$\sim 3.7$ TeV {for large charges and/or multiple species}. Within Born-Infeld
theory, the $95\%$ CL sensitivities would range from $\sim 300$ GeV to 1.3 or
2.8 TeV for the high-energy CLIC options. Measurements of light-by-light
scattering would not exclude monopole production at FCC-hh, except in the
context of Born-Infeld theory.


title: SLNET: A Redistributable Corpus of 3rd-party Simulink Models

abstract:
  MATLAB/Simulink is widely used for model-based design. Engineers create
Simulink models and compile them to embedded code, often to control
safety-critical cyber-physical systems in automotive, aerospace, and healthcare
applications. Despite Simulink's importance, there are few large-scale
empirical Simulink studies, perhaps because there is no large readily available
corpus of third-party open-source Simulink models. To enable empirical Simulink
studies, this paper introduces SLNET, the largest corpus of freely available
third-party Simulink models. SLNET has several advantages over earlier
collections. Specifically, SLNET is 8 times larger than the largest previous
corpus of Simulink models, includes fine-grained metadata, is constructed
automatically, is self-contained, and allows redistribution. SLNET is available
under permissive open-source licenses and contains all of its collection and
analysis tools.


title:
Pre-Training Transformer Decoder for End-to-End ASR Model with Unpaired
  Speech Data

abstract:
  This paper studies a novel pre-training technique with unpaired speech data,
Speech2C, for encoder-decoder based automatic speech recognition (ASR). Within
a multi-task learning framework, we introduce two pre-training tasks for the
encoder-decoder network using acoustic units, i.e., pseudo codes, derived from
an offline clustering model. One is to predict the pseudo codes via masked
language modeling in encoder output, like HuBERT model, while the other lets
the decoder learn to reconstruct pseudo codes autoregressively instead of
generating textual scripts. In this way, the decoder learns to reconstruct
original speech information with codes before learning to generate correct
text. Comprehensive experiments on the LibriSpeech corpus show that the
proposed Speech2C can relatively reduce the word error rate (WER) by 19.2% over
the method without decoder pre-training, and also outperforms significantly the
state-of-the-art wav2vec 2.0 and HuBERT on fine-tuning subsets of 10h and 100h.


title:
A Physical Layer Model for the Performance Evaluation of V2X
  Communication

abstract:
  Recent advancements in V2X communications have greatly increased the
flexibility of the physical and MAC layers. The performance evaluation of V2X
communication systems should take such flexibility into account through a
cross-layer approach, which often leads to complex evaluation processes.
Indeed, many performance evaluations presented in the literature rely on simple
models to abstract the physical layer of the supported technologies. However,
such models are usually not general, i.e., their applications are limited to
specific scenarios or technologies, thus failing to reflect the flexibility of
current V2X communications. Alternative solutions require computationally
intensive simulations at the link level or non-trivial parameter tuning. The
goal of this paper is to develop a new approach for modeling V2X communications
at the physical layer. The approach is general for different technologies and
has been validated through experimental data and system simulations with both
IEEE 802.11p and sidelink LTE-V2X technologies.


title:
Vafa-Witten Theory: Invariants, Floer Homologies, Higgs Bundles, a
  Geometric Langlands Correspondence, and Categorification

abstract:
  We revisit Vafa-Witten theory in the more general setting whereby the
underlying moduli space is not that of instantons, but of the full Vafa-Witten
equations. We physically derive (i) a novel Vafa-Witten four-manifold invariant
associated with this moduli space, (ii) their relation to Gromov-Witten
invariants, (iii) a novel Vafa-Witten Floer homology assigned to three-manifold
boundaries, (iv) a novel Vafa-Witten Atiyah-Floer correspondence, (v) a proof
and generalization of a conjecture by Abouzaid-Manolescu in [1] about the
hypercohomology of a perverse sheaf of vanishing cycles, (vi) a Langlands
duality of these invariants, Floer homologies and hypercohomology, and (vii) a
quantum geometric Langlands correspondence with purely imaginary parameter that
specializes to the classical correspondence in the zero-coupling limit, where
Higgs bundles feature in (ii), (iv), (vi) and (vii). We also explain how these
invariants and homologies will be categorified in the process, and discuss
their higher categorification. We therefore relate differential and enumerative
geometry, topology and geometric representation theory in mathematics, via a
maximally-supersymmetric topological quantum field theory with
electric-magnetic duality in physics.


title:
Optimal supplier of single-error-type entanglement via coherent-state
  transmission

abstract:
  Compared with entanglement with multiple types of noise, entanglement
including only one type of error is a favorable fundamental resource not only
for quantum communication but also for distributed quantum computation. We
consider protocol that presents single-error-type entanglement for distant
qubits via coherent-state transmission over a lossy channel. This protocol is
regarded as a subroutine to serve entanglement for larger protocol to yield a
final output, such as ebits or pbits. In this paper, we provide a subroutine
protocol which achieves the global optimal for typical jointly convex yield
functions monotonically non-decreasing with respect to the singlet fraction,
such as an arbitrary convex function of a singlet fraction and two-way
distillable entanglement/key. Entanglement generation based on remote
non-destructive parity measurement protocol [K. Azuma, H. Takeda, M. Koashi,
and N. Imoto, Phys. Rev. A 85, 062309 (2012)] is identified as such an optimal
subroutine.


title:
Gradient flow structure and convergence analysis of the ensemble Kalman
  inversion for nonlinear forward models

abstract:
  The ensemble Kalman inversion (EKI) is a particle based method which has been
introduced as the application of the ensemble Kalman filter to inverse
problems. In practice it has been widely used as derivative-free optimization
method in order to estimate unknown parameters from noisy measurement data. For
linear forward models the EKI can be viewed as gradient flow preconditioned by
a certain sample covariance matrix. Through the preconditioning the resulting
scheme remains in a finite dimensional subspace of the original
high-dimensional (or even infinite dimensional) parameter space and can be
viewed as optimizer restricted to this subspace. For general nonlinear forward
models the resulting EKI flow can only be viewed as gradient flow in
approximation. In this paper we discuss the effect of applying a sample
covariance as preconditioning matrix and quantify the gradient flow structure
of the EKI by controlling the approximation error through the spread in the
particle system. The ensemble collapse on the one side leads to an accurate
gradient approximation, but on the other side to degeneration in the
preconditioning sample covariance matrix. In order to ensure convergence as
optimization method we derive lower as well as upper bounds on the ensemble
collapse. Furthermore, we introduce covariance inflation without breaking the
subspace property intending to reduce the collapse rate of the ensemble such
that the convergence rate improves. In a numerical experiment we apply EKI to a
nonlinear elliptic boundary-value problem and illustrate the dependence of EKI
as derivative-free optimizer on the choice of the initial ensemble.


title:
Doubly-Robust Estimation for Unbiased Learning-to-Rank from
  Position-Biased Click Feedback

abstract:
  Clicks on rankings suffer from position bias: generally items on lower ranks
are less likely to be examined - and thus clicked - by users, in spite of their
actual preferences between items. The prevalent approach to unbiased
click-based Learning-to-Rank (LTR) is based on counterfactual
Inverse-Propensity-Scoring (IPS) estimation. Unique about LTR is the fact that
standard Doubly-Robust (DR) estimation - which combines IPS with regression
predictions - is inapplicable since the treatment variable - indicating whether
a user examined an item - cannot be observed in the data. In this paper, we
introduce a novel DR estimator that uses the expectation of treatment per rank
instead. Our novel DR estimator has more robust unbiasedness conditions than
the existing IPS approach, and in addition, provides enormous decreases in
variance: our experimental results indicate it requires several orders of
magnitude fewer datapoints to converge at optimal performance. For the unbiased
LTR field, our DR estimator contributes both increases in state-of-the-art
performance and the most robust theoretical guarantees of all known LTR
estimators.


title: Algorithms for Domain Wall Fermions

abstract:
  We discuss algorithms for domain wall fermions focussing on accelerating
Hybrid Monte Carlo sampling of gauge configurations. Firstly a new multigrid
algorithm for domain wall solvers and secondly a domain decomposed hybrid monte
carlo approach applied to large subvolumes and optimised for GPU accelerated
nodes. We propose a formulation of DD-RHMC that is suitable for the simulation
of odd numbers of fermions.


title:
Hybrid discrete-continuous truncated Wigner approximation for driven,
  dissipative spin systems

abstract:
  We present a systematic approach for the semiclassical treatment of many-body
dynamics of interacting, open spin systems. Our approach overcomes some of the
shortcomings of the recently developed discrete truncated Wigner approximation
(DTWA) based on Monte-Carlo sampling in a discrete phase space that improves
the classical treatment by accounting for lowest-order quantum fluctuations. We
provide a rigorous derivation of the DTWA by embedding it in a continuous phase
space, thereby introducing a hybrid discrete-continuous truncated Wigner
approximation (DCTWA). We derive a set of operator-differential mappings that
yield an exact equation of motion (EOM) for the continuous SU(2) Wigner
function of spins. The standard DTWA is then recovered by a systematic
neglection of specific terms in this exact EOM. The hybrid approach permits us
to determine the validity conditions and to gain detailed understanding of the
quality of the approximation, paving the way for systematic improvements.
Furthermore, we show that the continuous embedding allows for a straightforward
extension of the method to open spin systems subject to dephasing, losses and
incoherent drive, while preserving the key advantages of the discrete approach,
such as a positive definite Wigner distribution of typical initial states. We
derive exact stochastic differential equations for processes which cannot be
described by the standard DTWA due to the presence of non-classical noise. We
illustrate our approach by applying it to the dissipative dynamics of Rydberg
excitation of one-dimensional arrays of laser-driven atoms and compare it to
exact results for small systems.


title: Rota's basis conjecture holds for random bases of vector spaces

abstract:
  In 1989, Rota conjectured that, given $n$ bases $B_1,\dots,B_n$ of the vector
space $\mathbb{F}^n$ over some field $\mathbb{F}$, one can always decompose the
multi-set $B_1\cup \dots \cup B_n$ into transversal bases. This conjecture
remains wide open despite of a lot of attention. In this paper, we consider the
setting of random bases $B_1,\dots,B_n$. More specifically, our first result
shows that Rota's basis conjecture holds with probability $1-o(1)$ as $n\to
\infty$ if the bases $B_1,\dots,B_n$ are chosen independently uniformly at
random among all bases of $\mathbb{F}_q^n$ for some finite field $\mathbb{F}_q$
(the analogous result is trivially true for an infinite field $\mathbb{F}$). In
other words, the conjecture is true for almost all choices of bases
$B_1,\dots,B_n\subseteq \mathbb{F}_q^n$. Our second, more general, result
concerns random bases $B_1,\dots,B_n\subseteq S^n$ for some given finite subset
$S\subseteq \mathbb{F}$ (in other words, bases $B_1,\dots,B_n$ where all
vectors have entries in $S$). We show that when choosing bases
$B_1,\dots,B_n\subseteq S^n$ independently uniformly at random among all bases
that are subsets of $S^n$, then again Rota's basis conjecture holds with
probability $1-o(1)$ as $n\to \infty$.


title:
Connection of GeV Dark Matter with $(g-2)_{\mu}$ Anomaly in
  $U(1)_{L_{\mu}-L_{\tau}}$

abstract:
  In this work, we investigate the impact of recent anomalous $(g-2)_{\mu}$
measurement with $\sim 4 \sigma$ deviation from the SM, probing its effect on a
light GeV scale fermionic dark matter (DM). The $(g-2)_{\mu}$ anomaly can be
readily explained in the a beyond the SM (BSM) $U(1)_{L_{\mu}-L_{\tau}}$
scenario, where only a portion of hitherto allowed parameter space can explain
the anomaly. This constraint impacts the enhancement of the neutrino floor, the
neutrino background in the DM direct detection experiments. The GeV scale DM is
severely constrained by the $(g-2)_{\mu}$ result as it restricts the
$Z^{\prime}$ mass in the range of $20-200$ MeV. That restriction results in
absence of s-channel resonant annihilation of the GeV scale DM, therefore
resulting in over abundance of the DM relic. Even if t-channel annihilation
aided by large couplings can bring the relic density in the observed range,
direct detection cross section shoots up. Super-GeV DM gets almost ruled out
where as sub-GeV DM gets severely constrained.


title: Follow the Index: A new proposal

abstract:
  Despite all its well-known flaws and calls for its dismissal, the notorious
$h$-index is still used in many instances when awarding grants, or promoting
and hiring scientists. To address this, I set out to devise a better index,
with the twofold aim of taking into account the authors' respective
contributions and considerably reducing the pollution of the scientific
literature. Finally, I present a strategy that is guaranteed to be best for all
researchers.


title:
Laser-probing the rotational cooling of molecular ions by electron
  collisions

abstract:
  We present state-selected measurements of rotational cooling and excitation
rates of CH$^+$ molecular ions by inelastic electron collisions. The
experiments are carried out at the Cryogenic Storage Ring, making use of a
monoenergetic electron beam at matched velocity in combination with
state-sensitive laser-dissociation of the CH$^+$ ions for simultaneous
monitoring of the rotational level populations. Employing storage times of up
to 600 s, we create conditions where electron-induced cooling to the $J = 0$
ground state dominates over radiative relaxation, allowing for the experimental
determination of inelastic electron collision rates to benchmark
state-of-the-art theoretical calculations. On a broader scale, our experiments
pave the way to probe inelastic electron collisions for a variety of molecular
ions relevant in various plasma environments.


title: An Affine Type System with Hindley-Milner Style Type Inference

abstract:
  This article first provides an algorithm W based type inference algorithm for
an affine type system. Then the article further assumes the language equipped
with the above type system uses lazy evaluation, and explores the possibility
of representing the !-modality as a user-defined type synonym with the power of
the newly gained polymorphism.


title:
High-temperature ferromagnetism in two-dimensional material MnSn
  originated from interlayer coupling

abstract:
  MnSn monolayer synthesized recently is a novel two-dimensional ferromagnetic
material with a hexagonal lattice, in which three Mn atom come together to form
a trimer, making it remarkably different from other magnetic two-dimensional
materials. Most impressively, there happens a sharp increase of Curie
temperature from 54 K to 225 K when the number of layers increase from 1 to 3.
However, no quantitative explanation is reported in previous studies. Herein,
by means of first-principle calculations method and Monte carlo method, we
demonstrate that strong interlayer ferromagnetic coupling is the essential role
in enhancing its critical temperature, which act as a magnetic field to
stabilize the ferromagnetism in the MnSn multilayers.


title:
$h^*$-vectors of graph polytopes using activities of dissecting spanning
  trees

abstract:
  Both for symmetric edge polytopes of graphs, and root polytopes of
semi-balanced digraphs, there is a large, natural class of dissections into
simplices: these are dissections where the simplices correspond to certain
(oriented) spanning trees. We show that for any "spanning tree dissection" of
the symmetric edge polytope of a graph, or the root polytope of a semi-balanced
digraph, the $h^*$-vector can be computed as a generating function of certain
activities of the corresponding spanning trees. This also implies that the
distribution of both these activity statistics is independent of the dissection
and other auxiliary data.


title: Neural Q-learning for solving elliptic PDEs

abstract:
  Solving high-dimensional partial differential equations (PDEs) is a major
challenge in scientific computing. We develop a new numerical method for
solving elliptic-type PDEs by adapting the Q-learning algorithm in
reinforcement learning. Our "Q-PDE" algorithm is mesh-free and therefore has
the potential to overcome the curse of dimensionality. Using a neural tangent
kernel (NTK) approach, we prove that the neural network approximator for the
PDE solution, trained with the Q-PDE algorithm, converges to the trajectory of
an infinite-dimensional ordinary differential equation (ODE) as the number of
hidden units $\rightarrow \infty$. For monotone PDE (i.e. those given by
monotone operators, which may be nonlinear), despite the lack of a spectral gap
in the NTK, we then prove that the limit neural network, which satisfies the
infinite-dimensional ODE, converges in $L^2$ to the PDE solution as the
training time $\rightarrow \infty$. More generally, we can prove that any fixed
point of the wide-network limit for the Q-PDE algorithm is a solution of the
PDE (not necessarily under the monotone condition). The numerical performance
of the Q-PDE algorithm is studied for several elliptic PDEs.

categories: math.NA cs.LG cs.NA math.AP math.PR stat.ML


title: Mirror-induced reflection in the frequency domain

abstract:
  Mirrors are ubiquitous in optics and are used to control the propagation of
optical signals in space. Here we propose and demonstrate frequency domain
mirrors that provide reflections of the optical energy in a frequency synthetic
dimension, using electro-optic modulation. First, we theoretically explore the
concept of frequency mirrors with the investigation of propagation loss, and
reflectivity in the frequency domain. Next, we explore the mirror formed
through polarization mode-splitting in a thin-film lithium niobate
micro-resonator. By exciting the Bloch waves of the synthetic frequency crystal
with different wave vectors, we show various states formed by the interference
between forward propagating and reflected waves. Finally, we expand on this
idea, and generate tunable frequency mirrors as well as demonstrate trapped
states formed by these mirrors using coupled lithium niobate micro-resonators.
The ability to control the flow of light in the frequency domain could enable a
wide range of applications, including the study of random walks, boson
sampling, frequency comb sources, optical computation, and topological
photonics. Furthermore, demonstration of optical elements such as cavities,
lasers, and photonic crystals in the frequency domain, may be possible.


title:
Resurgent revivals in bosonic quantum gases: a striking signature of
  many-body quantum interferences

abstract:
  Matter wave revivals depend on a delicate interplay of constructive many-body
quantum interferences in the developing dynamics of an ultracold bosonic system
in an optical lattice. It is shown that the interplay between weak intersite
tunneling and strong onsite interactions can lead to the quantum dynamics of a
density wave displaying several features not found in the mean-field limit:
occupancy oscillations, resurgent revivals, and a (anti-) synchronization of
revival peaks and occupancy oscillation peaks. This implies cooperative
interference effects that alternate between constructive and destructive
features leading to the peak revival behaviors. These many-body quantum
interference phenomena create striking features in various observables, which
are accessible in experimental measurements.


title: Hydroelastomers: soft, tough, highly swelling composites

abstract:
  Inspired by the cellular design of plant tissue, we present a new approach to
make versatile, tough, highly water-swelling composites. We embed highly
swelling hydrogel particles inside tough, water-permeable, elastomeric
matrices. The resulting composites, which we call \emph{hydroelastomers}, show
little softening as they swell, and have excellent fracture properties that
match those of the best-performing, tough hydrogels. Our composites are
straightforward to fabricate, based on commercial materials, and can easily be
molded or extruded to form shapes with complex swelling geometries.
Furthermore, there is a large design space available for making
hydroelastomers, since one can use any hydrogel as the dispersed phase in the
composite, including hydrogels with stimuli-responsiveness. These features
should make hydroelastomers excellent candidates for use in soft robotics and
swelling-based actuation, or as shape-morphing materials, while also being
useful as hydrogel replacements in a wide range of other fields.


title: Finding Balance-Fair Short Paths in Graphs

abstract:
  The computation of short paths in graphs with edge lengths is a pillar of
graph algorithmics and network science. In a more diverse world, however, not
every short path is equally valuable. We contribute to a broader view on path
finding by injecting a natural fairness aspect. Our fairness notion relates to
vertex-colored graphs. Herein, we seek to find short paths in which all colors
should appear with roughly the same frequency. Among other results, we prove
the introduced problems to be computationally intractable (NP-hard and
parameterized hard with respect to the number of colors), while also presenting
an encouraging algorithmic result ("fixed-parameter tractability") related to
the length of the sought solution path.


title: Reduced Material Loss in Thin-film Lithium Niobate Waveguides

abstract:
  Thin-film lithium niobate has shown promise for scalable applications ranging
from single-photon sources to high-bandwidth data communication systems.
Realization of the next generation high-performance classical and quantum
devices, however, requires much lower optical losses than the current state of
the art ($\sim$10 million). Unfortunately, material limitations of ion-sliced
thin-film lithium niobate have not been explored, and therefore it is unclear
how high-quality factor can be achieved in this platform. Here we evaluate the
material limited quality factor of thin-film lithium niobate photonic platform
can be as high as $Q\approx 1.8\times10^{8}$ at telecommunication wavelengths,
corresponding to a propagation loss of 0.2 dB/m.


title: Java Prolog Interface

abstract:
  There are many initiatives in presents-days for interaction between Java and
Prolog programming languages. These initiatives allow combine two programming
paradigms, Object Oriented Programming and Logic Programming. Every proposed
interface has specifics features depending of the final use. The present paper
introduces a new Java Prolog Interface to be use for Prolog persistence
interacting from Java side and functional programming from Prolog side. To
support this interaction, the most advanced solutions implements interlanguages
data type mappings between Java objects and Prolog terms. Java Prolog Interface
is a modern solution that take the best features from existing solutions and
combine all in one. It' s more flexible, adaptive and have an Application
Provider Interface (API) easy to use. JPI implement the javax.script interface
include in Java from version 1.6. The project like existing solutions have an
implementation for the most popular open source Prolog Engines. Is hosted on
GitHub source code management at Prolobjectlink repository and deploy the
resulting artifacts on Maven Central repository. The project have a web page
too hosted on GitHub.


title:
Neutrino follow-up with the Zwicky Transient Facility: Results from the
  first 24 campaigns

abstract:
  The Zwicky Transient Transient Facility (ZTF) performs a systematic neutrino
follow-up program, searching for optical counterparts to high-energy neutrinos
with dedicated Target-of-Opportunity (ToO) observations. Since first light in
March 2018, ZTF has taken prompt observations for 24 high-quality neutrino
alerts from the IceCube Neutrino Observatory, with a median latency of 12.2
hours from initial neutrino detection. From two of these campaigns, we have
already reported tidal disruption event (TDE) AT2019dsg and likely TDE
AT2019fdr as probable counterparts, suggesting that TDEs contribute >7.8% of
the astrophysical neutrino flux. We here present the full results of our
program through to December 2021. No additional candidate neutrino sources were
identified by our program, allowing us to place the first constraints on the
underlying optical luminosity function of astrophysical neutrino sources.
Transients with optical absolutes magnitudes brighter that -21 can contribute
no more than 87% of the total, while transients brighter than -22 can
contribute no more than 58% of the total, neglecting the effect of extinction.
These are the the first observational constraints on the neutrino emission of
bright populations such as superluminous supernovae. None of the neutrinos were
coincident with bright optical AGN flares comparable to that observed for TXS
0506+056/IC170922A, suggesting that most astrophysical neutrinos are not
produced during such optical flares. We highlight the outlook for
electromagnetic neutrino follow-up programs, including the expected potential
for the Rubin Observatory.


title:
$K$-Theory of Cuspidal Curves Over a Perfectoid Base And Formal
  Analogues

abstract:
  In this paper we continue the work of using the recent advances in algebraic
$K$-theory to extend computations done in characteristic $p$ to the mixed
characteristic setting using perfectoid rings. We extend the work of
Hesselholt-Nikolaus in \cite{Hesselholt_Nikolaus} on the algebraic $K$-Theory
of cuspidal curves. We consider both cuspidal curves and the $p$-completion of
cuspidal curves. Along the way we also study the $K$-theory of the
$p$-completed affine line over a perfectoid ring.


title: Gravitizing the Quantum

abstract:
  We discuss a new approach to the problem of quantum gravity in which the
quantum mechanical structures that are traditionally fixed, such as the
Fubini-Study metric in the Hilbert space of states, become dynamical and so
implement the idea of gravitizing the quantum. In particular, in this
formulation of quantum gravity the quantum geometry is still consistent with
the principles of unitarity and also captures fundamental aspects of (quantum)
gravity, such as topology change. Furthermore, we address specific ways of
testing this new approach to quantum gravity by utilizing multi-path
interference and optical lattice atomic clocks.


title:
Imitate and Repurpose: Learning Reusable Robot Movement Skills From
  Human and Animal Behaviors

abstract:
  We investigate the use of prior knowledge of human and animal movement to
learn reusable locomotion skills for real legged robots. Our approach builds
upon previous work on imitating human or dog Motion Capture (MoCap) data to
learn a movement skill module. Once learned, this skill module can be reused
for complex downstream tasks. Importantly, due to the prior imposed by the
MoCap data, our approach does not require extensive reward engineering to
produce sensible and natural looking behavior at the time of reuse. This makes
it easy to create well-regularized, task-oriented controllers that are suitable
for deployment on real robots. We demonstrate how our skill module can be used
for imitation, and train controllable walking and ball dribbling policies for
both the ANYmal quadruped and OP3 humanoid. These policies are then deployed on
hardware via zero-shot simulation-to-reality transfer. Accompanying videos are
available at https://bit.ly/robot-npmp.


title: Prefix Filter: Practically and Theoretically Better Than Bloom

abstract:
  Many applications of approximate membership query data structures, or
filters, require only an incremental filter that supports insertions but not
deletions. However, the design space of incremental filters is missing a "sweet
spot" filter that combines space efficiency, fast queries, and fast insertions.
Incremental filters, such as the Bloom and blocked Bloom filter, are not space
efficient. Dynamic filters (i.e., supporting deletions), such as the cuckoo or
vector quotient filter, are space efficient but do not exhibit consistently
fast insertions and queries.
  In this paper, we propose the prefix filter, an incremental filter that
addresses the above challenge: (1) its space (in bits) is similar to
state-of-the-art dynamic filters; (2) query throughput is high and is
comparable to that of the cuckoo filter; and (3) insert throughput is high with
overall build times faster than those of the vector quotient filter and cuckoo
filter by $1.39\times$-$1.46\times$ and $3.2\times$-$3.5\times$, respectively.
We present a rigorous analysis of the prefix filter that holds also for
practical set sizes (i.e., $n=2^{25}$). The analysis deals with the probability
of failure, false positive rate, and probability that an operation requires
accessing more than a single cache line.


title: Separable spatio-temporal kriging for fast virtual sensing

abstract:
  Environmental monitoring is a task that requires to surrogate system-wide
information with limited sensor readings. Under the proximity principle, an
environmental monitoring system can be based on the virtual sensing logic and
then rely on distance-based prediction methods, such as $k$-nearest-neighbors,
inverse distance weighted regression and spatio-temporal kriging. The last one
is cumbersome with large datasets, but we show that a suitable separability
assumption reduces its computational cost to an extent broader than considered
insofar. Only spatial interpolation needs to be performed in a centralized way,
while forecasting can be delegated to each sensor. This simplification is
mostly related to the fact that two separate models are involved, one in time
and one in the space domain. Any of the two models can be replaced without
re-estimating the other under a composite likelihood approach. Moreover, the
use of convenient spatial and temporal models eases up computation. We show
that this perspective on kriging allows to perform virtual sensing even in the
case of tall datasets.


title:
Angular Resolution of the Search for Anisotropic Stochastic
  Gravitational-Wave Background with Terrestrial Gravitational-Wave Detectors

abstract:
  We consider an anisotropic search for the stochastic gravitational-wave (GW)
background by decomposing the gravitational-wave sky into its spherical
harmonics components. Previous analyses have used the diffraction limit to
define the highest-order spherical harmonics components used in this search. We
investigate whether the angular resolution of this search is indeed
diffraction-limited by testing our ability to detect and localize simulated GW
signals. We show that while using low-order spherical harmonics modes is
optimal for initially detecting GW sources, the detected sources can be better
localized with higher-order spherical harmonics than expected based on the
diffraction limit argument. Additionally, we discuss how the ability to recover
simulated GW sources is affected by the number of detectors in the network, the
frequency range over which the search is performed, and the method by which the
covariance matrix of the GW skymap is regularized. While we primarily consider
point-source signals in this study, we briefly apply our methodology to
spatially-extended sources and discuss potential future modifications of our
analysis for such signals.


title:
The CO emission in the Taffy Galaxies (UGC 12914/5) at 60pc
  resolution-I: The battle for star formation in the turbulent Taffy Bridge

abstract:
  We present ALMA observations at a spatial resolution of 0.2 arcsec (60 pc) of
CO emission from the Taffy galaxies (UGC 12914/5). The observations are
compared with narrow-band Pa$\alpha$, mid-IR, radio continuum and X-ray
imaging, plus optical spectroscopy. The galaxies have undergone a recent
head-on collision, creating a massive gaseous bridge which is known to be
highly turbulent. The bridge contains a complex web of narrow molecular
filaments and clumps. The majority of the filaments are devoid of star
formation, and fall significantly below the Kennicutt-Schmidt relationship for
normal galaxies, especially for the numerous regions undetected in Pa$\alpha$
emission. Within the loosely connected filaments and clumps of gas we find
regions of high velocity dispersion which appear gravitationally unbound for a
wide range of likely values of $X_{\rm CO}$. Like the "Firecracker" region in
the Antennae system, they would require extremely high external dynamical or
thermal pressure to stop them dissipating rapidly on short crossing timescales
of 2-5~Myrs. We suggest that the clouds may be transient structures within a
highly turbulent multi-phase medium which is strongly suppressing star
formation. Despite the overall turbulence in the system, stars seem to have
formed in compact hotspots within a kpc-sized extragalactic HII region, where
the molecular gas has a lower velocity dispersion than elsewhere, and shows
evidence for a collision with an ionized gas cloud. Like the shocked gas in the
Stephan's Quintet group, the conditions in the Taffy bridge shows how difficult
it is to form stars within a turbulent, multi-phase, gas.


title:
Quantitative convergence of the vectorial Allen-Cahn equation towards
  multiphase mean curvature flow

abstract:
  Phase-field models such as the Allen-Cahn equation may give rise to the
formation and evolution of geometric shapes, a phenomenon that may be analyzed
rigorously in suitable scaling regimes. In its sharp-interface limit, the
vectorial Allen-Cahn equation with a potential with $N\geq 3$ distinct minima
has been conjectured to describe the evolution of branched interfaces by
multiphase mean curvature flow.
  In the present work, we give a rigorous proof for this statement in two and
three ambient dimensions and for a suitable class of potentials: As long as a
strong solution to multiphase mean curvature flow exists, solutions to the
vectorial Allen-Cahn equation with well-prepared initial data converge towards
multiphase mean curvature flow in the limit of vanishing interface width
parameter $\varepsilon\searrow 0$. We even establish the rate of convergence
$O(\varepsilon^{1/2})$.
  Our approach is based on the gradient flow structure of the Allen-Cahn
equation and its limiting motion: Building on the recent concept of "gradient
flow calibrations" for multiphase mean curvature flow, we introduce a notion of
relative entropy for the vectorial Allen-Cahn equation with multi-well
potential. This enables us to overcome the limitations of other approaches,
e.g. avoiding the need for a stability analysis of the Allen-Cahn operator or
additional convergence hypotheses for the energy at positive times.


title: Instability of backoff protocols with arbitrary arrival rates

abstract:
  In contention resolution, multiple processors are trying to coordinate to
send discrete messages through a shared channel with sharply limited
communication. If two processors inadvertently send at the same time, the
messages collide and are not transmitted successfully. An important case is
acknowledgement-based contention resolution, in which processors cannot listen
to the channel at all; all they know is whether or not their own messages have
got through. This situation arises frequently in both networking and cloud
computing. One particularly important example of an acknowledgement-based
contention resolution protocol is binary exponential backoff. Variants of
binary exponential backoff are used in both Ethernet and TCP/IP, and both
Google Drive and AWS instruct their users to implement it to handle busy
periods.
  In queueing models, where each processor has a queue of messages, stable
acknowledgement-based protocols are already known (H{\aa}stad et al., SICOMP
1996). In queue-free models, where each processor has a single message but
processors arrive randomly, it is widely conjectured that no stable
acknowledgement-based protocols exist for any positive arrival rate of
processors. Despite exciting recent results for full-sensing protocols which
assume greater listening capabilities of the processors (see e.g. Bender et al.
STOC 2020 or Chen et al. PODC 2021), this foundational question remains open
even for backoff protocols unless the arrival rate of processors is at least
0.42 (Goldberg et al. SICOMP 2004). We prove the conjecture for all backoff
protocols outside of a tightly-constrained special case, and set out the
remaining technical obstacles to a full proof.


title:
Convex Parameterization of Stabilizing Controllers and its LMI-based
  Computation via Filtering

abstract:
  Various new implicit parameterizations for stabilizing controllers that allow
one to impose structural constraints on the controller have been proposed
lately. They are convex but infinite-dimensional, formulated in the frequency
domain with no available efficient methods for computation. In this paper, we
introduce a kernel version of the Youla parameterization to characterize the
set of stabilizing controllers. It features a single affine constraint, which
allows us to recast the controller parameterization as a novel robust filtering
problem. This makes it possible to derive the first efficient Linear Matrix
Inequality (LMI) implicit parametrization of stabilizing controllers. Our LMI
characterization not only admits efficient numerical computation, but also
guarantees a full-order stabilizing dynamical controller that is efficient for
practical deployment. Numerical experiments demonstrate that our LMI can be
orders of magnitude faster to solve than the existing closed-loop
parameterizations.


title: Approximate Group Fairness for Clustering

abstract:
  We incorporate group fairness into the algorithmic centroid clustering
problem, where $k$ centers are to be located to serve $n$ agents distributed in
a metric space. We refine the notion of proportional fairness proposed in [Chen
et al., ICML 2019] as {\em core fairness}, and $k$-clustering is in the core if
no coalition containing at least $n/k$ agents can strictly decrease their total
distance by deviating to a new center together. Our solution concept is
motivated by the situation where agents are able to coordinate and utilities
are transferable. A string of existence, hardness and approximability results
is provided. Particularly, we propose two dimensions to relax core
requirements: one is on the degree of distance improvement, and the other is on
the size of deviating coalition. For both relaxations and their combination, we
study the extent to which relaxed core fairness can be satisfied in metric
spaces including line, tree and general metric space, and design approximation
algorithms accordingly.


title: Defining the semiclassical limit of the quantum Rabi Hamiltonian

abstract:
  The crossover from quantum to semiclassical behavior in the seminal Rabi
model of light-matter interaction still, surprisingly, lacks a complete and
rigorous understanding. A formalism for deriving the semiclassical model
directly from the quantum Hamiltonian is developed here. Working in a displaced
Fock-state basis $\lvert \alpha, n \rangle$, the semiclassical limit is
obtained by taking $\lvert \alpha \rvert \to \infty$ and the coupling to zero.
This resolves the discrepancy between coherent-state dynamics and semiclassical
Rabi oscillations in both standard and ultrastrong coupling/driving regimes.
Furthermore, it provides a framework for studying the quantum-to-classical
transition, with potential applications in quantum technologies.


title: Joyce structures on spaces of quadratic differentials I

abstract:
  Consider the space parameterising curves of genus g equipped with a quadratic
differential with simple zeroes. We use the geometry of isomonodromic
deformations to construct a complex hyperkahler structure on the total space of
its tangent bundle. This gives a new class of examples of the Joyce structures
introduced in relation to Donaldson-Thomas theory. Our results are related to
recent work in mathematical physics on supersymmetric gauge theories of class
S[A1].


title: AEGNN: Asynchronous Event-based Graph Neural Networks

abstract:
  The best performing learning algorithms devised for event cameras work by
first converting events into dense representations that are then processed
using standard CNNs. However, these steps discard both the sparsity and high
temporal resolution of events, leading to high computational burden and
latency. For this reason, recent works have adopted Graph Neural Networks
(GNNs), which process events as "static" spatio-temporal graphs, which are
inherently "sparse". We take this trend one step further by introducing
Asynchronous, Event-based Graph Neural Networks (AEGNNs), a novel
event-processing paradigm that generalizes standard GNNs to process events as
"evolving" spatio-temporal graphs. AEGNNs follow efficient update rules that
restrict recomputation of network activations only to the nodes affected by
each new event, thereby significantly reducing both computation and latency for
event-by-event processing. AEGNNs are easily trained on synchronous inputs and
can be converted to efficient, "asynchronous" networks at test time. We
thoroughly validate our method on object classification and detection tasks,
where we show an up to a 200-fold reduction in computational complexity
(FLOPs), with similar or even better performance than state-of-the-art
asynchronous methods. This reduction in computation directly translates to an
8-fold reduction in computational latency when compared to standard GNNs, which
opens the door to low-latency event-based processing.