title:
Snowmass2021 Cosmic Frontier: The landscape of cosmic-ray and
  high-energy photon probes of particle dark matter

abstract:
  This white paper discusses the current landscape and prospects for
experiments sensitive to particle dark matter processes producing photons and
cosmic rays. Much of the gamma-ray sky remains unexplored on a level of
sensitivity that would enable the discovery of a dark matter signal. Currently
operating GeV-TeV observatories, such as Fermi-LAT, atmospheric Cherenkov
telescopes, and water Cherenkov detector arrays continue to target several
promising dark matter-rich environments within and beyond the Galaxy. Soon,
several new experiments will continue to explore, with increased sensitivity,
especially extended targets in the sky. This paper reviews the several
near-term and longer-term plans for gamma-ray observatories, from MeV energies
up to hundreds of TeV. Similarly, the X-ray sky has been and continues to be
monitored by decade-old observatories. Upcoming telescopes will further bolster
searches and allow new discovery space for lines from, e.g., sterile neutrinos
and axion-photon conversion.
  Furthermore, this overview discusses currently operating cosmic-ray probes
and the landscape of future experiments that will clarify existing persistent
anomalies in cosmic radiation and spearhead possible new discoveries.
  Finally, the article closes with a discussion of necessary cross section
measurements that need to be conducted at colliders to reduce substantial
uncertainties in interpreting photon and cosmic-ray measurements in space.


title: Topological EEG Nonlinear Dynamics Analysis for Emotion Recognition

abstract:
  Emotional recognition through exploring the electroencephalography (EEG)
characteristics has been widely performed in recent studies. Nonlinear analysis
and feature extraction methods for understanding the complex dynamical
phenomena are associated with the EEG patterns of different emotions. The phase
space reconstruction is a typical nonlinear technique to reveal the dynamics of
the brain neural system. Recently, the topological data analysis (TDA) scheme
has been used to explore the properties of space, which provides a powerful
tool to think over the phase space. In this work, we proposed a topological EEG
nonlinear dynamics analysis approach using the phase space reconstruction (PSR)
technique to convert EEG time series into phase space, and the persistent
homology tool explores the topological properties of the phase space. We
perform the topological analysis of EEG signals in different rhythm bands to
build emotion feature vectors, which shows high distinguishing ability. We
evaluate the approach with two well-known benchmark datasets, the DEAP and
DREAMER datasets. The recognition results achieved accuracies of 99.37% and
99.35% in arousal and valence classification tasks with DEAP, and 99.96%,
99.93%, and 99.95% in arousal, valence, and dominance classifications tasks
with DREAMER, respectively. The performances are supposed to be outperformed
current state-of-art approaches in DREAMER (improved by 1% to 10% depends on
temporal length), while comparable to other related works evaluated in DEAP.
The proposed work is the first investigation in the emotion recognition
oriented EEG topological feature analysis, which brought a novel insight into
the brain neural system nonlinear dynamics analysis and feature extraction.


title: A note on decay property of nonlinear Schr\"odinger equations

abstract:
  In this note, we show the existence of a special solution $u$ to defocusing
cubic NLS in $3d$, which lives in $H^{s}$ for all $s>0$, but scatters to a
linear solution in a very slow way. We prove for this $u$, for all
$\epsilon>0$, one has
$\sup_{t>0}t^{\epsilon}\|u(t)-e^{it\Delta}u^{+}\|_{\dot{H}^{1/2}}=\infty$. Note
that such a slow asymptotic convergence is impossible if one further pose the
initial data of $u(0)$ be in $L^{1}$. We expect that similar construction holds
for the other NLS models.


title: Drift Reduced Navigation with Deep Explainable Features

abstract:
  Modern autonomous vehicles (AVs) often rely on vision, LIDAR, and even
radar-based simultaneous localization and mapping (SLAM) frameworks for precise
localization and navigation. However, modern SLAM frameworks often lead to
unacceptably high levels of drift (i.e., localization error) when AVs observe
few visually distinct features or encounter occlusions due to dynamic
obstacles. This paper argues that minimizing drift must be a key desiderata in
AV motion planning, which requires an AV to take active control decisions to
move towards feature-rich regions while also minimizing conventional control
cost. To do so, we first introduce a novel data-driven perception module that
observes LIDAR point clouds and estimates which features/regions an AV must
navigate towards for drift minimization. Then, we introduce an interpretable
model predictive controller (MPC) that moves an AV toward such feature-rich
regions while avoiding visual occlusions and gracefully trading off drift and
control cost. Our experiments on challenging, dynamic scenarios in the
state-of-the-art CARLA simulator indicate our method reduces drift up to 76.76%
compared to benchmark approaches.


title: Efficient universal shuffle attack for visual object tracking

abstract:
  Recently, adversarial attacks have been applied in visual object tracking to
deceive deep trackers by injecting imperceptible perturbations into video
frames. However, previous work only generates the video-specific perturbations,
which restricts its application scenarios. In addition, existing attacks are
difficult to implement in reality due to the real-time of tracking and the
re-initialization mechanism. To address these issues, we propose an offline
universal adversarial attack called Efficient Universal Shuffle Attack. It
takes only one perturbation to cause the tracker malfunction on all videos. To
improve the computational efficiency and attack performance, we propose a
greedy gradient strategy and a triple loss to efficiently capture and attack
model-specific feature representations through the gradients. Experimental
results show that EUSA can significantly reduce the performance of
state-of-the-art trackers on OTB2015 and VOT2018.


title:
The baryonic-to-halo mass relation from mass and energy cascade in
  self-gravitating collisionless dark matter flow

abstract:
  The relation between properties of galaxies and dark matter halos they reside
in can be valuable to understand structure formation and evolution,
particularly the baryonic-to-halo mass ratio (BHMR) and its dynamic evolution.
We first review some unique properties of self-gravitating collisionless dark
matter flow (SG-CFD), followed by their implications in derivation of BHMR. To
maximize system entropy, the long-range interaction requires a broad size of
halos to be formed. These halos facilitate an inverse mass and energy cascade
from small to large mass scales that involves a constant rate of energy
transfer $\epsilon_u$=$-4.6\times10^{-7}m^2/s^3$. Considering galaxies and
halos with a total baryonic mass $m_b$, halo mass $m_h$, halo virial size
$r_h$, and flat rotation velocity $v_f$, the baryonic-to-halo mass relation can
be analytically derived by combining the baryonic Tully-Fisher relation and
constant rate of energy cascade $\epsilon_u$. We found a maximum BHMR ratio
~0.076 for halos with a critical mass $m_{hc}$~$10^{12}M_{sun}$ at z=0. That
ratio is much lower for both smaller and larger halos such that two regimes can
be identified: i) for incompressible small halos with mass $m_h$<$m_{hc}$, we
have $\epsilon_u$$\propto$$v_f/r_h$, $v_f$$\propto$$r_h$, and
$m_b$$\propto$$m_h^{4/3}$; ii) for large halos with mass $m_h$>$m_{hc}$, we
have $\epsilon_u$$\propto$$v_f^3/r_h$, $v_f$$\propto$$r_h^{1/3}$, and
$m_b$$\propto$$m_h^{4/9}$. Combined with proposed double-$\lambda$ halo mass
function, the average BHMR ratio for all halos (~0.024 at z=0) can be
analytically derived, along with its redshift evolution. The fraction of
baryons in galaxy is ~7.6% at z=0 and increases with time $\propto$$t^{1/3}$.
Most (~92.4%) of the baryons are not in galaxies. The SPARC (Spitzer Photometry
& Accurate Rotation Curves) data with ~175 late-type galaxies were used for the
derivation and comparison.


title: Communication-Efficient Federated Distillation with Active Data Sampling

abstract:
  Federated learning (FL) is a promising paradigm to enable privacy-preserving
deep learning from distributed data. Most previous works are based on federated
average (FedAvg), which, however, faces several critical issues, including a
high communication overhead and the difficulty in dealing with heterogeneous
model architectures. Federated Distillation (FD) is a recently proposed
alternative to enable communication-efficient and robust FL, which achieves
orders of magnitude reduction of the communication overhead compared with
FedAvg and is flexible to handle heterogeneous models at the clients. However,
so far there is no unified algorithmic framework or theoretical analysis for
FD-based methods. In this paper, we first present a generic meta-algorithm for
FD and investigate the influence of key parameters through empirical
experiments. Then, we verify the empirical observations theoretically. Based on
the empirical results and theory, we propose a communication-efficient FD
algorithm with active data sampling to improve the model performance and reduce
the communication overhead. Empirical simulations on benchmark datasets will
demonstrate that our proposed algorithm effectively and significantly reduces
the communication overhead while achieving a satisfactory performance.


title:
Texture Generation Using Dual-Domain Feature Flow with Multi-View
  Hallucinations

abstract:
  We propose a dual-domain generative model to estimate a texture map from a
single image for colorizing a 3D human model. When estimating a texture map, a
single image is insufficient as it reveals only one facet of a 3D object. To
provide sufficient information for estimating a complete texture map, the
proposed model simultaneously generates multi-view hallucinations in the image
domain and an estimated texture map in the texture domain. During the
generating process, each domain generator exchanges features to the other by a
flow-based local attention mechanism. In this manner, the proposed model can
estimate a texture map utilizing abundant multi-view image features from which
multiview hallucinations are generated. As a result, the estimated texture map
contains consistent colors and patterns over the entire region. Experiments
show the superiority of our model for estimating a directly render-able texture
map, which is applicable to 3D animation rendering. Furthermore, our model also
improves an overall generation quality in the image domain for pose and
viewpoint transfer tasks.


title:
Investigation of nonlinear squeeze-film damping involving rarefied gas
  effect in micro-electro-mechanical-systems

abstract:
  In this paper, the nonlinear squeeze-film damping (SFD) involving rarefied
gas effect in the micro-electro-mechanical-systems (MEMS) is investigated.
Considering the motion of structures (beam, cantilever, and membrane) in MEMS,
the dynamic response of structure will be influenced largely by the
squeeze-film damping. In the traditional model, a viscous damping assumption
that damping force is linear with moving velocity is used. As the nonlinear
damping phenomenon is observed for a micro-structure oscillating with a
high-velocity, this assumption is invalid and will generates error result for
predicting the response of micro-structure. In addition, due to the small size
of device and the low pressure of encapsulation, the gas in MEMS usually is
rarefied gas. Therefore, to correctly predict the damping force, the rarefied
gas effect must be considered. To study the nonlinear SFD phenomenon involving
the rarefied gas effect, a kinetic method, namely discrete unified gas kinetic
scheme (DUGKS), is introduced. And based on DUGKS, two solving methods, a
traditional decoupled method (Eulerian scheme) and a coupled framework
(arbitrary Lagrangian-Eulerian scheme), are adopted. With these two methods,
two basic motion forms, linear (perpendicular) and tilting motions of a rigid
micro-beam, are studied with forced and free oscillations.


title:
Analysis of entropy production in finitely slow processes between
  nonequilibrium steady states

abstract:
  We investigate entropy production in finitely slow transitions between
nonequilibrium steady states in Markov jump processes using the improved
adiabatic approximation method proposed by Takahashi, Fujii, Hino and Hayakawa
[1]. This method provides systematic improvement of the adiabatic approximation
on infinitely slow transitions from which we obtain nonadiabatic corrections
and has a close relationship with the slow driving perturbation [2, 3]. We
analyze two types of excess entropy production of Sagawa and Hayakawa [4] and
Hatano and Sasa [5] as examples, and confirm that the leading adiabatic
contribution reproduces the known results, and then obtain nonadiabatic
corrections written in terms of thermodynamic metrics defined in protocol
parameter spaces. We also numerically study the resulting excess entropy
production in a two-state system.


title:
Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for
  Pre-trained Language Models

abstract:
  Despite the success, the process of fine-tuning large-scale PLMs brings
prohibitive adaptation costs. In fact, fine-tuning all the parameters of a
colossal model and retaining separate instances for different tasks are
practically infeasible. This necessitates a new branch of research focusing on
the parameter-efficient adaptation of PLMs, dubbed as delta tuning in this
paper. In contrast with the standard fine-tuning, delta tuning only fine-tunes
a small portion of the model parameters while keeping the rest untouched,
largely reducing both the computation and storage costs. Recent studies have
demonstrated that a series of delta tuning methods with distinct tuned
parameter selection could achieve performance on a par with full-parameter
fine-tuning, suggesting a new promising way of stimulating large-scale PLMs. In
this paper, we first formally describe the problem of delta tuning and then
comprehensively review recent delta tuning approaches. We also propose a
unified categorization criterion that divide existing delta tuning methods into
three groups: addition-based, specification-based, and reparameterization-based
methods. Though initially proposed as an efficient method to steer large
models, we believe that some of the fascinating evidence discovered along with
delta tuning could help further reveal the mechanisms of PLMs and even deep
neural networks. To this end, we discuss the theoretical principles underlying
the effectiveness of delta tuning and propose frameworks to interpret delta
tuning from the perspective of optimization and optimal control, respectively.
Furthermore, we provide a holistic empirical study of representative methods,
where results on over 100 NLP tasks demonstrate a comprehensive performance
comparison of different approaches. The experimental results also cover the
analysis of combinatorial, scaling and transferable properties of delta tuning.


title: Less is More: Proxy Datasets in NAS approaches

abstract:
  Neural Architecture Search (NAS) defines the design of Neural Networks as a
search problem. Unfortunately, NAS is computationally intensive because of
various possibilities depending on the number of elements in the design and the
possible connections between them. In this work, we extensively analyze the
role of the dataset size based on several sampling approaches for reducing the
dataset size (unsupervised and supervised cases) as an agnostic approach to
reduce search time. We compared these techniques with four common NAS
approaches in NAS-Bench-201 in roughly 1,400 experiments on CIFAR-100. One of
our surprising findings is that in most cases we can reduce the amount of
training data to 25\%, consequently reducing search time to 25\%, while at the
same time maintaining the same accuracy as if training on the full dataset.
Additionally, some designs derived from subsets out-perform designs derived
from the full dataset by up to 22 p.p. accuracy.


title: PERT: Pre-training BERT with Permuted Language Model

abstract:
  Pre-trained Language Models (PLMs) have been widely used in various natural
language processing (NLP) tasks, owing to their powerful text representations
trained on large-scale corpora. In this paper, we propose a new PLM called PERT
for natural language understanding (NLU). PERT is an auto-encoding model (like
BERT) trained with Permuted Language Model (PerLM). The formulation of the
proposed PerLM is straightforward. We permute a proportion of the input text,
and the training objective is to predict the position of the original token.
Moreover, we also apply whole word masking and N-gram masking to improve the
performance of PERT. We carried out extensive experiments on both Chinese and
English NLU benchmarks. The experimental results show that PERT can bring
improvements over various comparable baselines on some of the tasks, while
others are not. These results indicate that developing more diverse
pre-training tasks is possible instead of masked language model variants.
Several quantitative studies are carried out to better understand PERT, which
might help design PLMs in the future. Resources are available:
https://github.com/ymcui/PERT


title: Hierarchical Memory Learning for Fine-Grained Scene Graph Generation

abstract:
  As far as Scene Graph Generation (SGG), coarse and fine predicates mix in the
dataset due to the crowd-sourced labeling, and the long-tail problem is also
pronounced. Given this tricky situation, many existing SGG methods treat the
predicates equally and learn the model under the supervision of
mixed-granularity predicates in one stage, leading to relatively coarse
predictions. In order to alleviate the negative impact of the suboptimum
mixed-granularity annotation and long-tail effect problems, this paper proposes
a novel Hierarchical Memory Learning (HML) framework to learn the model from
simple to complex, which is similar to the human beings' hierarchical memory
learning process. After the autonomous partition of coarse and fine predicates,
the model is first trained on the coarse predicates and then learns the fine
predicates. In order to realize this hierarchical learning pattern, this paper,
for the first time, formulates the HML framework using the new Concept
Reconstruction (CR) and Model Reconstruction (MR) constraints. It is worth
noticing that the HML framework can be taken as one general optimization
strategy to improve various SGG models, and significant improvement can be
achieved on the SGG benchmark (i.e., Visual Genome).


title:
An approximate equivalence of the GNS representation for the Haar state
  of $SU_{q}(2)$

abstract:
  We prove an approximate equivalence involving the GNS representation on the
$L^{2}$ space of the Haar state of the quantum $SU(2)$ group and the direct
integral of all the infinite dimensional irreducible representations of the
$C^{*}$-algebra $C(SU_{q}(2))$. This gives a Fredholm representation of the
dual quantum group $\widehat{SU_q(2)}$ with coefficients in a $C^*$-algebra in
the sense of Mishchenko. It also gives a KK class via the Cuntz picture in
terms of quasihomomorphisms.


title: Evaporation of liquid coating a fiber

abstract:
  We investigate theoretically and numerically the diffusion-limited
evaporation of a liquid deposited on a fiber in two configurations: a sleeve
and a axisymmetric barrel-shaped droplet. For a sleeve, the local flux depends
on both the aspect ratio and the smallest length of the problem. By using
analytical calculations and 3D finite elements simulations, we predict a
divergence of this flux further localized at the edge as the aspect ratio
increases. The evaporation of axisymmetric drops on a fiber is studied with
numerical simulations. For sufficiently large volumes, we evidence that the
evaporation rate is almost independent of the wetting properties of the liquid,
even for small contact angles, and that the droplets evaporate as spheres of
the same volume.


title: Investigating Coverage Guided Fuzzing with Mutation Testing

abstract:
  Coverage guided fuzzing (CGF) is an effective testing technique which has
detected hundreds of thousands of bugs from various software applications. It
focuses on maximizing code coverage to reveal more bugs during fuzzing.
However, a higher coverage does not necessarily imply a better fault detection
capability. Triggering a bug involves not only exercising the specific program
path but also reaching interesting program states in that path. In this paper,
we use mutation testing to improve CGF in detecting bugs. We use mutation
scores as feedback to guide fuzzing towards detecting bugs rather than just
covering code. To evaluate our approach, we conduct a well-designed experiment
on 5 benchmarks. We choose the state-of-the-art fuzzing technique Zest as
baseline and construct two modified techniques on it using our approach. The
experimental results show that our approach can improve CGF in both code
coverage and bug detection.


title:
Non-Parametric Modeling of Spatio-Temporal Human Activity Based on
  Mobile Robot Observations

abstract:
  This work presents a non-parametric spatio-temporal model for mapping human
activity by mobile autonomous robots in a long-term context. Based on
Variational Gaussian Process Regression, the model incorporates prior
information of spatial and temporal-periodic dependencies to create a
continuous representation of human occurrences. The inhomogeneous data
distribution resulting from movements of the robot is included in the model via
a heteroscedastic likelihood function and can be accounted for as predictive
uncertainty. Using a sparse formulation, data sets over multiple weeks and
several hundred square meters can be used for model creation. The experimental
evaluation, based on multi-week data sets, demonstrates that the proposed
approach outperforms the state of the art both in terms of predictive quality
and subsequent path planning.


title:
A possible consistent model of parity violations in the symmetric
  teleparallel gravity

abstract:
  Many parity violating gravity models suffer from the ghost instability
problem. In this paper we consider a symmetric teleparallel gravity model which
extends the general relativity equivalent model by several parity violating
interactions between the gravitational field and a scalar field. These
interactions exclude higher derivatives and are quadratic in the non-metricity
tensor. Through investigations on the linear cosmological perturbations, our
results show that in general this model suffers from the difficulty caused by
the existence of ghost mode in the vector perturbations. However, in cases
where a special condition is imposed on the coefficients of these parity
violating interactions, this model can be ghost free.


title: An In-Depth Study of Continuous Subgraph Matching (Complete Version)

abstract:
  Continuous subgraph matching (CSM) algorithms find the occurrences of a given
pattern on a stream of data graphs online. A number of incremental CSM
algorithms have been proposed. However, a systematical study on these
algorithms is missing to identify their advantages and disadvantages on a wide
range of workloads. Therefore, we first propose to model CSM as incremental
view maintenance (IVM) to capture the design space of existing algorithms.
Then, we implement six representative CSM algorithms, including IncIsoMatch,
SJ-Tree, Graphflow, IEDyn, TurboFlux, and SymBi, in a common framework based on
IVM. We further conduct extensive experiments to evaluate the overall
performance of competing algorithms as well as study the effectiveness of
individual techniques to pinpoint the key factors leading to the performance
differences. We obtain the following new insights into the performance: (1)
existing algorithms start the search from an edge in the query graph that maps
to an updated data edge, potentially leading to many invalid partial results;
(2) all matching orders are based on simple heuristics, which appear
ineffective at times; (3) index updates dominate the query time on some
queries; and (4) the algorithm with constant delay enumeration bears
significant index update cost. Consequently, no algorithm dominates the others
in all cases. Therefore, we give a few recommendations based on our experiment
results. In particular, the SymBi index is useful for sparse queries or long
running queries. The matching orders of IEDyn and TurboFlux work well on tree
queries, those of Graphflow on dense queries or when both query and data graphs
are sparse, and otherwise, we recommend SymBi's matching orders.


title: Machine-guided Exploration and Calibration of Astrophysical Simulations

abstract:
  We apply a novel method with machine learning to calibrate sub-grid models
within numerical simulation codes to achieve convergence with observations and
between different codes. It utilizes active learning and neural density
estimators. The hyper parameters of the machine are calibrated with a
well-defined projectile motion problem. Then, using a set of 22 cosmological
zoom simulations, we tune the parameters of a popular star formation and
feedback model within Enzo to match simulations. The parameters that are
adjusted include the star formation efficiency, coupling of thermal energy from
stellar feedback, and volume into which the energy is deposited. This number
translates to a factor of more than three improvements over manual calibration.
Despite using fewer simulations, we obtain a better agreement to the observed
baryon makeup of a Milky-Way (MW) sized halo. Switching to a different
strategy, we improve the consistency of the recommended parameters from the
machine. Given the success of the calibration, we then apply the technique to
reconcile metal transport between grid-based and particle-based simulation
codes using an isolated galaxy. It is an improvement over manual exploration
while hinting at a less known relation between the diffusion coefficient and
the metal mass in the halo region. The exploration and calibration of the
parameters of the sub-grid models with a machine learning approach is concluded
to be versatile and directly applicable to different problems.


title: SimMatch: Semi-supervised Learning with Similarity Matching

abstract:
  Learning with few labeled data has been a longstanding problem in the
computer vision and machine learning research community. In this paper, we
introduced a new semi-supervised learning framework, SimMatch, which
simultaneously considers semantic similarity and instance similarity. In
SimMatch, the consistency regularization will be applied on both semantic-level
and instance-level. The different augmented views of the same instance are
encouraged to have the same class prediction and similar similarity
relationship respected to other instances. Next, we instantiated a labeled
memory buffer to fully leverage the ground truth labels on instance-level and
bridge the gaps between the semantic and instance similarities. Finally, we
proposed the \textit{unfolding} and \textit{aggregation} operation which allows
these two similarities be isomorphically transformed with each other. In this
way, the semantic and instance pseudo-labels can be mutually propagated to
generate more high-quality and reliable matching targets. Extensive
experimental results demonstrate that SimMatch improves the performance of
semi-supervised learning tasks across different benchmark datasets and
different settings. Notably, with 400 epochs of training, SimMatch achieves
67.2\%, and 74.4\% Top-1 Accuracy with 1\% and 10\% labeled examples on
ImageNet, which significantly outperforms the baseline methods and is better
than previous semi-supervised learning frameworks. Code and pre-trained models
are available at https://github.com/KyleZheng1997/simmatch.


title: C*-algebraic Gauss-Lucas Theorem and C*-algebraic Sendov's Conjecture

abstract:
  Using a result of Robertson \textit{[Proc. Edinburgh Math. Soc. (2), 1976]},
we introduce a notion of differentiation of maps on certain classes of unital
commutative C*-algebras. We then derive C*-algebraic Gauss-Lucas theorem and
formulate C*-algebraic Sendov's conjecture. We verify C*-algebraic Sendov's
conjecture for polynomials of degree 2.


title: New simple Lie superalgebras as queerified associative algebras

abstract:
  Over $\mathbb{C}$, Montgomery superized Herstein's construction of simple Lie
algebras from finite-dimensional associative algebras, found obstructions to
the procedure and applied it to $\mathbb{Z}/2$-graded associative algebra of
differential operators with polynomial coefficients.
  Since the 1990s, Vasiliev and Konstein with their co-authors constructed (via
the Herstein--Montgomery method, having rediscovered it) simple Lie
(super)algebras from the associative (super)algebra such as Vasiliev's higher
spin algebras (a.k.a. algebras of observables of the rational Calogero model)
and algebras of symplectic reflections.
  The "queerification" is another method for cooking a~simple Lie superalgebra
from the simple associative (super)algebra. The above examples of associative
(super)algebras, and Lie (super)algebras of "matrices of complex size" can be
"queerified" by adding new elements resembling Faddeev--Popov ghosts.
  Conjectures: 1) a "queerified" Hamiltonian describes a version of the
Calogero model with $1\vert 1$-dimensional time; 2) metabelean algebras and
inhomogeneous subalgebras of Lie superalgebras naturally widen supersymmetries
in future theories; 3) only graded-commutative algebras can imitate algebras of
functions in a reasonably rich non-commutative Geometry.


title:
Uncertainty-Aware Text-to-Program for Question Answering on Structured
  Electronic Health Records

abstract:
  Question Answering on Electronic Health Records (EHR-QA) has a significant
impact on the healthcare domain, and it is being actively studied. Previous
research on structured EHR-QA focuses on converting natural language queries
into query language such as SQL or SPARQL (NLQ2Query), so the problem scope is
limited to pre-defined data types by the specific query language. In order to
expand the EHR-QA task beyond this limitation to handle multi-modal medical
data and solve complex inference in the future, more primitive systemic
language is needed. In this paper, we design the program-based model
(NLQ2Program) for EHR-QA as the first step towards the future direction. We
tackle MIMICSPARQL*, the graph-based EHR-QA dataset, via a program-based
approach in a semi-supervised manner in order to overcome the absence of gold
programs. Without the gold program, our proposed model shows comparable
performance to the previous state-of-the-art model, which is an NLQ2Query model
(0.9% gain). In addition, for a reliable EHR-QA model, we apply the uncertainty
decomposition method to measure the ambiguity in the input question. We
empirically confirmed data uncertainty is most indicative of the ambiguity in
the input question.


title: On Manin-Schechtman orders related to directed graphs

abstract:
  As a generalization of weak Bruhat orders on permutations, in 1989 Manin and
Schechtman introduced the notion of a higher Bruhat order on the $d$-element
subsets of a set $[n]=\{1,2,\ldots,n\}$. Among other results in this field,
they proved that the set of such orders for $n,d$ fixed, endowed with natural
local transformations, constitutes a poset with one minimal and one maximal
elements. In this paper we consider a wider model, involving the so-called
convex order on certain path systems in an acyclic directed graph, introduce
local transformations, or flips, on such orders and prove that the resulting
structure gives a poset with one minimal and one maximal elements as well,
yielding a generalization of the above-mentioned classical result.


title:
DS3-Net: Difficulty-perceived Common-to-T1ce Semi-Supervised Multimodal
  MRI Synthesis Network

abstract:
  Contrast-enhanced T1 (T1ce) is one of the most essential magnetic resonance
imaging (MRI) modalities for diagnosing and analyzing brain tumors, especially
gliomas. In clinical practice, common MRI modalities such as T1, T2, and fluid
attenuation inversion recovery are relatively easy to access while T1ce is more
challenging considering the additional cost and potential risk of allergies to
the contrast agent. Therefore, it is of great clinical necessity to develop a
method to synthesize T1ce from other common modalities. Current paired image
translation methods typically have the issue of requiring a large amount of
paired data and do not focus on specific regions of interest, e.g., the tumor
region, in the synthesization process. To address these issues, we propose a
Difficulty-perceived common-to-T1ce Semi-Supervised multimodal MRI Synthesis
network (DS3-Net), involving both paired and unpaired data together with
dual-level knowledge distillation. DS3-Net predicts a difficulty map to
progressively promote the synthesis task. Specifically, a pixelwise constraint
and a patchwise contrastive constraint are guided by the predicted difficulty
map. Through extensive experiments on the publiclyavailable BraTS2020 dataset,
DS3-Net outperforms its supervised counterpart in each respect. Furthermore,
with only 5% paired data, the proposed DS3-Net achieves competitive performance
with state-of-theart image translation methods utilizing 100% paired data,
delivering an average SSIM of 0.8947 and an average PSNR of 23.60.


title:
Field free switching through bulk spin-orbit torque in L10-FePt films
  deposited on vicinal substrates

abstract:
  L10-FePt distinguishes itself for its ultrahigh perpendicular magnetic
anisotropy (PMA), which enables memory cells with sufficient thermal stability
to scale down to 3 nm. The recently discovered "bulk" spin-orbit torques in
L10-FePt provide an efficient and scalable way to manipulate the L10-FePt
magnetization. However, the existence of external field during the switching
limits its practical application, and therefore field-free switching of the
L10-FePt is in highly demand. In this manuscript, we demonstrate the field-free
switching of the L10-FePt by growing it on vicinal MgO (001) substrates. This
method is different from previously established strategies, as it does not need
to add other functional layers or create asymmetry in the film structure. We
demonstrate the field-free switching is robust and can withstand strong field
disturbance up to ~1 kOe. The dependence on vicinal angle, film thickness, and
growth temperature demonstrated a wide operation window for the field-free
switching of the L10-FePt. We confirmed that the physical origin of the
field-free switching is the vicinal surface-induced the tilted anisotropy of
L10-FePt. We quantitatively characterize the spin-orbit torques in the L10-FePt
films, and found the spin-orbit torques are not significantly influenced by the
lattice strain from vicinal substrates. Our results extend beyond the
established strategies to realize field-free switching, and potentially could
be applied to other magnetic and antiferromagnetic systems.


title:
Post-Newtonian Jeans Equation for Stationary and Spherically Symmetrical
  Self-Gravitating System

abstract:
  The post-Newtonian Jeans equation for stationary self-gravitating systems is
derived from the post-Newtonian Boltzmann equation in spherical coordinates.
The Jeans equation is coupled with the three Poisson equations from the
post-Newtonian theory. The Poisson equations are functions of the
energy-momentum tensor components which are determined from the post-Newtonian
Maxwell--J\"uttner distribution function. As an application, the effect of a
central massive black hole on the velocity dispersion profile of the host
galaxy is investigated and the influence of the post-Newtonian corrections are
determined.


title: The Fractional Malmheden Theorem

abstract:
  We provide a fractional counterpart of the classical results by Schwarz and
Malmheden on harmonic functions. From that we obtain a representation formula
for $s$-harmonic functions as a linear superposition of weighted classical
harmonic functions which also entails a new proof of the fractional Harnack
inequality. This proof also leads to optimal constants for the fractional
Harnack inequality in the ball.


title:
A universal test on spikes in a high-dimensional generalized spiked
  model and its applications

abstract:
  This paper aims to test the number of spikes in a generalized spiked
covariance matrix, the spiked eigenvalues of which may be extremely larger or
smaller than the non-spiked ones. For a high-dimensional problem, we first
propose a general test statistic and derive its central limit theorem by random
matrix theory without a Gaussian population constraint. We then apply the
result to estimate the noise variance and test the equality of the smallest
roots in generalized spiked models. Simulation studies showed that the proposed
test method was correctly sized, and the power outcomes showed the robustness
of our statistic to deviations from a Gaussian population. Moreover, our
estimator of the noise variance resulted in much smaller mean absolute errors
and mean squared errors than existing methods. In contrast to previously
developed methods, we eliminated the strict conditions of diagonal or
block-wise diagonal form of the population covariance matrix and extend the
work to a wider range without the assumption of normality. Thus, the proposed
method is more suitable for real problems.


title:
WCL-BBCD: A Contrastive Learning and Knowledge Graph Approach to Named
  Entity Recognition

abstract:
  Named Entity Recognition task is one of the core tasks of information
extraction.Word ambiguity and word abbreviation are important reasons for the
low recognition rate of named entities. In this paper, we propose a novel named
entity recognition model WCL-BBCD (Word Contrastive Learning with
BERT-BiLSTM-CRF-DBpedia) incorporating the idea of contrastive learning. The
model first trains the sentence pairs in the text, calculate similarity between
words in sentence pairs by cosine similarity, and fine-tunes the BERT model
used for the named entity recognition task through the similarity, so as to
alleviate word ambiguity. Then, the fine-tuned BERT model is combined with the
BiLSTM-CRF model to perform the named entity recognition task. Finally, the
recognition results are corrected in combination with prior knowledge such as
knowledge graphs, so as to alleviate the recognition caused by word
abbreviations low-rate problem. Experimental results show that our model
outperforms other similar model methods on the CoNLL-2003 English dataset and
OntoNotes V5 English dataset.


title:
Low c-Differential Uniformity of the Swapped Inverse Function in Odd
  Characteristic

abstract:
  The study of Boolean functions with low $c$-differential uniformity has
become recently an important topic of research. However, in odd characteristic
case, there are not many results on the ($c$-)differential uniformity of
functions that are not power functions. In this paper, we investigate the
$c$-differential uniformity of the swapped inverse functions in odd
characteristic, and show that their $c$-differential uniformities are at most 6
except for some special case.


title:
Asymmetry and nonlinearity of current-bias characteristics in
  superfluid-normal state junctions of weakly-interacting Bose gases

abstract:
  We uncover current-bias characteristics of superfluid-normal state junctions
with weakly-interacting Bose gases. It is shown that in the presence of a
chemical potential bias the characteristics can strongly be asymmetric for
origin. The salient feature that is absent in the fermionic counterpart arises
from a tunneling process associated with a condensate and a bosonic Andreev
reflection process. It turns out that such processes are intrinsically
nonlinear and therefore do not obey Ohm's law even at a low bias. In addition,
the remaining processes are found to obey Ohm's law and become dominant for
transport driven by a temperature bias.


title:
Generalized quantum cluster algebras: the Laurent phenomenon and upper
  bounds

abstract:
  Generalized quantum cluster algebras introduced in [1] are quantum
deformation of generalized cluster algebras of geometric types. In this paper,
we prove that the Laurent phenomenon holds in these generalized quantum cluster
algebras. We also show that upper bounds coincide with the corresponding
generalized quantum upper cluster algebras under the "coprimality" condition.


title:
Probing heavy Majorana neutrino pair production at ILC in a $U(1)_{\rm
  B-L}$ extension of the Standard Model

abstract:
  We consider a gauged B$-$L (Baryon number minus Lepton number) extension of
the Standard Model (SM), which is anomaly free in the presence of three SM
singlet Right Handed Neutrinos (RHNs). Associated with the $U(1)_{\rm B-L}$
gauge symmetry breaking, the RHNs acquire Majorana masses and then with the
electroweak symmetry breaking, tiny Majorana masses for the SM(-like) neutrinos
are naturally generated by the seesaw mechanism. As a result of the seesaw
mechanism, the heavy mass eigenstates which are mainly composed of the
SM-singlet RHNs obtain suppressed electroweak interactions through small
mixings with the SM neutrinos. To investigate the seesaw mechanism, we study
the pair production of heavy Majorana neutrinos through the $U(1)_{\rm B-L}$
gauge boson $Z^\prime$ at the 250 GeV and 500 GeV International Linear Collider
(ILC). Considering the current and prospective future bounds on the B$-$L model
parameters from the search for a resonant $Z^\prime$ boson production at the
Large Hadron Collider (LHC), we focus on a "smoking-gun" signature of the
Majorana nature of the heavy neutrinos: a final state with a pair of same-sign,
same-flavor leptons, small missing momentum, and four hadronic jets. We
estimate the projected significance of the signature at the ILC.


title:
Parametrix constructions and low frequency resonances for the charged
  Klein-Gordon equation on the \linebreak De Sitter-Reissner-Nordstr\"{o}m
  metric

abstract:
  We show the index 0 property of several realizations of the spectral family
of the charged Klein-Gordon operator on the De Sitter-Reissner-Nordstr\"{o}m
metric using a parametrix construction. We also deduce the absence of
resonances in any compact neighborhood of the 0 energy for large masses or
angular momenta of the field. The results are uniform in the cosmological
constant. This work is a preliminary to the elaboration of a numerical scheme
intended to compute low frequency resonances.


title:
A Heintze-Karcher type inequality for hypersurfaces with capillary
  boundary

abstract:
  In this paper, we establish a Heintze-Karcher type inequality for
hypersurfaces with capillary boundary of contact angle $\theta\in
(0,\frac{\pi}{2})$ in a half space or a half ball, by using solution to a mixed
boundary value problem in Reilly type formula. Consequently, we give a new
proof of Alexandrov type theorem for embedded capillary constant mean curvature
hypersurfaces with contact angle $\theta\in (0,\frac{\pi}{2})$ in a half space
or a half ball.


title:
Going Down the Rabbit Hole: Characterizing the Long Tail of Wikipedia
  Reading Sessions

abstract:
  "Wiki rabbit holes" are informally defined as navigation paths followed by
Wikipedia readers that lead them to long explorations, sometimes involving
unexpected articles. Although wiki rabbit holes are a popular concept in
Internet culture, our current understanding of their dynamics is based on
anecdotal reports only. To bridge this gap, this paper provides a large-scale
quantitative characterization of the navigation traces of readers who fell into
a wiki rabbit hole. First, we represent user sessions as navigation trees and
operationalize the concept of wiki rabbit holes based on the depth of these
trees. Then, we characterize rabbit hole sessions in terms of structural
patterns, time properties, and topical exploration.
  We find that article layout influences the structure of rabbit hole sessions
and that the fraction of rabbit hole sessions is higher during the night.
Moreover, readers are more likely to fall into a rabbit hole starting from
articles about entertainment, sports, politics, and history. Finally, we
observe that, on average, readers tend to stay focused on one topic by
remaining in the semantic neighborhood of the first articles even during rabbit
hole sessions.
  These findings contribute to our understanding of Wikipedia readers'
information needs and user behavior on the Web.


title:
Soccer: a quantitative analysis of team resilience and the miracle of
  Bern

abstract:
  Resilience is the ability to positively respond to adversity. It has been
studied in psychology for several decades, with focus on how individuals
overcome traumata or cope with setbacks and obstacles in their professional
career. Research on resilience in the sport context is rather new. Activities
are based on insights that in highly competitive environments, tiny effects tip
the scales. A key question of measuring resilience in sports is what parameters
to measure. Here a novel concept is proposed to measure the resilience of
soccer teams. The relative frequency of matches is determined, where a soccer
team, which is initially trailing by 2 goals, finally succeeds to win the match
or at least to reach a draw. The analysis is applied to the last 59 seasons of
the German premier soccer league Bundesliga. The empirical data are compared
with a theoretical model derived from Poisson distributions. It is shown how
leading teams in the premier soccer league differ from the average with respect
to resilience, which provides further insights into the hidden secrets of top
soccer teams.


title: Analysis of the $\eta_1(1855)$ as a $K\bar{K}_1(1400)$ molecular state

abstract:
  In this work, we study the radiative and strong decay of $S$-wave
$K\bar{K}_1(1400)$ molecular state within the effective Lagrangians approach
and find the relation between the $K\bar{K}_1(1400)$ molecular state and the
newly observed $\eta_1(1855)$ state by comparing with the BESIII observation.
The prediction indicates that the decay width depends on the model parameter
can reach up to $197.27^{+1.64}_{-1.22}$ MeV, which can be confronted with the
experimental data. If the $\eta_1(1855)$ could be $S$-wave $K\bar{K}_1(1400)$
molecular state, the $K\bar{K}^{*}\pi$ three-body decay provides the dominant
contribution, not the $\eta\eta^{'}$ channel found in the experiment. In
addition, the partial width for $\eta_1(1855)\to\gamma{}\phi$ can reach up to
$10.16^{+0.19}_{-0.35}$ KeV. Those results can be measured in future
experiments and can be used to test the nature of the $\eta_1(1855)$.


title:
A Systematic Review on Affective Computing: Emotion Models, Databases,
  and Recent Advances

abstract:
  Affective computing plays a key role in human-computer interactions,
entertainment, teaching, safe driving, and multimedia integration. Major
breakthroughs have been made recently in the areas of affective computing
(i.e., emotion recognition and sentiment analysis). Affective computing is
realized based on unimodal or multimodal data, primarily consisting of physical
information (e.g., textual, audio, and visual data) and physiological signals
(e.g., EEG and ECG signals). Physical-based affect recognition caters to more
researchers due to multiple public databases. However, it is hard to reveal
one's inner emotion hidden purposely from facial expressions, audio tones, body
gestures, etc. Physiological signals can generate more precise and reliable
emotional results; yet, the difficulty in acquiring physiological signals also
hinders their practical application. Thus, the fusion of physical information
and physiological signals can provide useful features of emotional states and
lead to higher accuracy. Instead of focusing on one specific field of affective
analysis, we systematically review recent advances in the affective computing,
and taxonomize unimodal affect recognition as well as multimodal affective
analysis. Firstly, we introduce two typical emotion models followed by commonly
used databases for affective computing. Next, we survey and taxonomize
state-of-the-art unimodal affect recognition and multimodal affective analysis
in terms of their detailed architectures and performances. Finally, we discuss
some important aspects on affective computing and their applications and
conclude this review with an indication of the most promising future
directions, such as the establishment of baseline dataset, fusion strategies
for multimodal affective analysis, and unsupervised learning models.


title:
Topologically protected dynamics in three-dimensional nonlinear
  antisymmetric Lotka-Volterra systems

abstract:
  Studies of topological bands and their associated low-dimensional boundary
modes have largely focused on linear systems. This work reports robust
dynamical features of three-dimensional (3D) nonlinear systems in connection
with intriguing topological bands in 3D. Specifically, for a 3D setting of
coupled rock-paper-scissors cycles governed by the antisymmetric Lotka-Volterra
equation (ALVE) that is inherently nonlinear, we unveil distinct
characteristics and robustness of surface polarized masses and analyze them in
connection with the dynamics and topological bands of the linearized
Lotka-Volterra (LV) equation. Our analysis indicated that insights learned from
Weyl semimetal phases with type-I and type-II Weyl singularities based on a
linearized version of the ALVE are still remarkably useful, even though the
system dynamics is far beyond the linear regime. This work indicates the
relevance and importance of the concept of topological boundary modes in
analyzing high-dimensional nonlinear systems and hopes to stimulate another
wave of topological studies in nonlinear systems.


title: Modelling word learning and recognition using visually grounded speech

abstract:
  Background: Computational models of speech recognition often assume that the
set of target words is already given. This implies that these models do not
learn to recognise speech from scratch without prior knowledge and explicit
supervision. Visually grounded speech models learn to recognise speech without
prior knowledge by exploiting statistical dependencies between spoken and
visual input. While it has previously been shown that visually grounded speech
models learn to recognise the presence of words in the input, we explicitly
investigate such a model as a model of human speech recognition.
  Methods: We investigate the time-course of word recognition as simulated by
the model using a gating paradigm to test whether its recognition is affected
by well-known word-competition effects in human speech processing. We
furthermore investigate whether vector quantisation, a technique for discrete
representation learning, aids the model in the discovery and recognition of
words.
  Results/Conclusion: Our experiments show that the model is able to recognise
nouns in isolation and even learns to properly differentiate between plural and
singular nouns. We also find that recognition is influenced by word competition
from the word-initial cohort and neighbourhood density, mirroring word
competition effects in human speech comprehension. Lastly, we find no evidence
that vector quantisation is helpful in discovering and recognising words. Our
gating experiments even show that the vector quantised model requires more of
the input sequence for correct recognition.


title:
$L^p$ regularity of the Bergman projection on generalizations of the
  Hartogs triangle in $\mathbb{C}^{n+1}$

abstract:
  In this paper we investigate a class of domains $\Omega^{n+1}_k =\{(z,w)\in
\mathbb{C}^n\times \mathbb{C}: |z|^k < |w| < 1\}$ for $k \in \mathbb{Z}^+$
which generalizes the Hartogs triangle. we first obtain the new explicit
formulas for the Bergman kernel function on these domains and further give a
range of $p$ values for which the $L^p$ boundedness of the Bergman projection
holds. This range of $p$ is shown to be sharp.


title:
Total of each of the 1-th and 2-th Betti numbers of the join-meet ideal
  of Special Crystal Lattice

abstract:
  The join-meet ideal was introduced by Takayuki Hibi in 1987. It is binomial
ideals that are defined by finite lattices. We study the join-meet ideal of
non-distributive finite lattices that do not always satisfy modular. In
particular, we work on the case of Crystal lattice which is one of them. In
this paper, we give the computational result of a total of each of the 1-th and
2-th of grade Betti numbers of the join-meet ideal of a special Crystal
lattice. The important point about this result is that it is characterized by
the number of combinations of special generators. Moreover, we can consider
from this result that a compatible monomial order suggests a connection to
something good.


title: Asymptotics for a high-energy solution of a supercritical problem

abstract:
  In this paper we deal with the equation \[-\Delta_p u+|u|^{p-2}u=|u|^{q-2}u\]
for $1<p<2$ and $q>p$, under Neumann boundary conditions in the unit ball of
$\mathbb R^N$. We focus on the three positive, radial, and radially
non-decreasing solutions, whose existence for $q$ large is proved in [13]. We
detect the limit profile as $q\to\infty$ of the higher energy solution and show
that, unlike the minimal energy one, it converges to the constant $1$. The
proof requires several tools borrowed from the theory of minimization problems
and accurate a priori estimates of the solutions, which are of independent
interest.


title:
Imaging the Breathing of a Platinum Nanoparticle in Electrochemical
  Environment

abstract:
  Surface strain is widely used in gas phase catalysis and electrocatalysis to
control the binding energies of adsorbates on metal surfaces. However, $in$
$situ$ or $operando$ strain measurements are experimentally challenging,
especially on nanomaterials. Here, we take advantage of the 4$^{th}$ generation
Extremely Brilliant Source at the European Synchrotron Radiation Facility
(ESRF-EBS, Grenoble, France) to quantify the distribution of strain inside a Pt
nanoparticle, and to determine its morphology in an electrochemical
environment. Our results show for the first time evidence of heterogeneous and
potential-dependent strain distribution between highly-coordinated ({100} and
{111} facets) and under-coordinated atoms (edges and corners) as well as
evidence of strain propagation from the surface to the bulk of the
nanoparticle. These results provide dynamic structural insights to better
simulate and design efficient nanocatalysts for energy storage and conversion
applications.


title:
Hyperlink-induced Pre-training for Passage Retrieval in Open-domain
  Question Answering

abstract:
  To alleviate the data scarcity problem in training question answering
systems, recent works propose additional intermediate pre-training for dense
passage retrieval (DPR). However, there still remains a large discrepancy
between the provided upstream signals and the downstream question-passage
relevance, which leads to less improvement. To bridge this gap, we propose the
HyperLink-induced Pre-training (HLP), a method to pre-train the dense retriever
with the text relevance induced by hyperlink-based topology within Web
documents. We demonstrate that the hyperlink-based structures of dual-link and
co-mention can provide effective relevance signals for large-scale pre-training
that better facilitate downstream passage retrieval. We investigate the
effectiveness of our approach across a wide range of open-domain QA datasets
under zero-shot, few-shot, multi-hop, and out-of-domain scenarios. The
experiments show our HLP outperforms the BM25 by up to 7 points as well as
other pre-training methods by more than 10 points in terms of top-20 retrieval
accuracy under the zero-shot scenario. Furthermore, HLP significantly
outperforms other pre-training methods under the other scenarios.


title: Stimulated emission of superradiant atoms in waveguide QED

abstract:
  We investigate the stimulated emission of superradiant atoms coupled to a
waveguide induced by a coherent-state photon pulse. We provide an analytical
result when a short $\pi$ pulse is incident, which shows that the atoms emit
photons coherently into the output pulse, which remains a coherent state in the
short pulse limit. An incident pulse is amplified in phase-preserving manner,
where noise is added almost entirely in the phase direction in phase space.
This property improves the ratio of intensity signal to noise after the
amplification for sufficiently short pulses. This is a unique feature different
from general phase-preserving linear amplifiers, where the signal-to-noise
ratio deteriorates in the amplification process. We also discuss the dependence
of the photon-emission probability on pulse parameters, such as the pulse area
and the duration.


title: Towards Neural Sparse Linear Solvers

abstract:
  Large sparse symmetric linear systems appear in several branches of science
and engineering thanks to the widespread use of the finite element method
(FEM). The fastest sparse linear solvers available implement hybrid iterative
methods. These methods are based on heuristic algorithms to permute rows and
columns or find a preconditioner matrix. In addition, they are inherently
sequential, making them unable to leverage the GPU processing power entirely.
We propose neural sparse linear solvers, a deep learning framework to learn
approximate solvers for sparse symmetric linear systems. Our method relies on
representing a sparse symmetric linear system as an undirected weighted graph.
Such graph representation is inherently permutation-equivariant and
scale-invariant, and it can become the input to a graph neural network trained
to regress the solution. We test neural sparse linear solvers on static linear
analysis problems from structural engineering. Our method is less accurate than
classic algorithms, but it is hardware-independent, fast on GPUs, and
applicable to generic sparse symmetric systems without any additional
hypothesis. Although many limitations remain, this study shows a general
approach to tackle problems involving sparse symmetric matrices using graph
neural networks.


title:
Local work function on Graphene Nanoribbons and on the Au(111)
  herringbone reconstruction

abstract:
  Graphene nanoribbons show exciting electronic properties related to the
exotic nature of the charge carriers and to local confinement as well as
atomic-scale structural details. The local work function provides evidence for
such structural, electronic and chemical variations at surfaces. Kelvin prove
force microscopy (KPFM) can be used to measure the local contact potential
difference (LCPD) between a probe tip and a surface, related to the work
function. Here we use this technique to map the LCPD of graphene nanoribbons
grown on a Au(111) substrate. The LCPD data shows charge transfer between the
graphene nanoribbons and the gold substrate. Our results are corroborated with
density functional theory calculations which verify that the maps reflect from
the doping of nanoribbons. Our results help to understand the relation between
atomic structures and electronic properties.


title: Eliashberg theory of the Jahn-Teller-Hubbard model

abstract:
  We study a multiorbital Hubbard model coupled to local Jahn-Teller phonons to
investigate the superconducting state realized in fullerides. A weak-coupling
approach is employed in combination with a local self-energy approximation. In
addition to the normal and anomalous self-energies of the electrons, we
consider the phonon self-energy, which allows a self-consistent treatment of
the energetics. The frequency dependence of the self-energies and their
characteristic coefficients, such as renormalization factors and dampings, are
investigated in detail using numerical calculations. It is clarified that the
anisotropic phonons play an important role in the stabilization of the
superconducting state. By comparing the full results to those without phonon
self-energies, we show that the superconductivity is stabilized by the
softening of the phonon frequency. The effects of electronic fluctuations are
also considered, which leads to the coupling to orbitons, an analog of plasmons
in the electron gas. This additional contribution further stabilizes the
superconducting state.


title:
XYLayoutLM: Towards Layout-Aware Multimodal Networks For Visually-Rich
  Document Understanding

abstract:
  Recently, various multimodal networks for Visually-Rich Document
Understanding(VRDU) have been proposed, showing the promotion of transformers
by integrating visual and layout information with the text embeddings. However,
most existing approaches utilize the position embeddings to incorporate the
sequence information, neglecting the noisy improper reading order obtained by
OCR tools. In this paper, we propose a robust layout-aware multimodal network
named XYLayoutLM to capture and leverage rich layout information from proper
reading orders produced by our Augmented XY Cut. Moreover, a Dilated
Conditional Position Encoding module is proposed to deal with the input
sequence of variable lengths, and it additionally extracts local layout
information from both textual and visual modalities while generating position
embeddings. Experiment results show that our XYLayoutLM achieves competitive
results on document understanding tasks.


title:
Continuous Time Graph Processes with Known ERGM Equilibria: Contextual
  Review, Extensions, and Synthesis

abstract:
  Graph processes that unfold in continuous time are of obvious theoretical and
practical interest. Particularly useful are those whose long-term behavior
converges to a graph distribution of known form. Here, we review some of the
conditions for such convergence, and provide examples of novel and/or known
processes that do so. These include subfamilies of the well-known stochastic
actor oriented models, as well as continuum extensions of temporal and
separable temporal exponential family random graph models. We also comment on
some related threads in the broader work on network dynamics, which provide
additional context for the continuous time case.


title: Relational Databases Ingestion into a NoSQL Data Warehouse

abstract:
  The digital transformation of companies has led to the evolution of databases
towards Big Data. Our work is part of this context and concerns more
particularly the mechanisms to extract datasets stored in a Data Lake and to
store the data in a Data Warehouse. The latter will allow, in a second time,
decisional analysis. In this paper, we present the extraction mechanism limited
to relational databases. To automate this process, we used the Model Driven
Architecture (MDA), which offers a formalized environment for schema
transformation. From the physical schemas describing a Data Lake, we propose
transformation rules that allow the creation of a Data Warehouse stored on a
document-oriented NoSQL system. An experimentation of the transformation
process has been performed on a medical application.


title:
Towards a L 2 cohomology theory for Hodge modules on infinite covering
  spaces: L 2 constructible cohomology and L 2 de Rham cohomology for coherent
  D-modules

abstract:
  This article constructs Von Neumann invariants for constructible complexes
and coherent D-modules on compact complex manifolds, generalizing the work of
the author on coherent L 2-cohomology. We formulate a conjectural
generalization of Dingoyan's L 2-Mixed Hodge structures in terms of Saito's
Mixed Hodge Modules and give partial results in this direction. 2020 AMS
Classification: 32J27.


title:
Computer Vision and Deep Learning for Fish Classification in Underwater
  Habitats: A Survey

abstract:
  Marine scientists use remote underwater video recording to survey fish
species in their natural habitats. This helps them understand and predict how
fish respond to climate change, habitat degradation, and fishing pressure. This
information is essential for developing sustainable fisheries for human
consumption, and for preserving the environment. However, the enormous volume
of collected videos makes extracting useful information a daunting and
time-consuming task for a human. A promising method to address this problem is
the cutting-edge Deep Learning (DL) technology.DL can help marine scientists
parse large volumes of video promptly and efficiently, unlocking niche
information that cannot be obtained using conventional manual monitoring
methods. In this paper, we provide an overview of the key concepts of DL, while
presenting a survey of literature on fish habitat monitoring with a focus on
underwater fish classification. We also discuss the main challenges faced when
developing DL for underwater image processing and propose approaches to address
them. Finally, we provide insights into the marine habitat monitoring research
domain and shed light on what the future of DL for underwater image processing
may hold. This paper aims to inform a wide range of readers from marine
scientists who would like to apply DL in their research to computer scientists
who would like to survey state-of-the-art DL-based underwater fish habitat
monitoring literature.


title: The classical Jellium and the Laughlin phase

abstract:
  I discuss results bearing on a variational problem of a new type, inspired by
fractional quantum Hall physics. In the latter context, the main result
reviewed herein can be spelled as "the phase of independent quasi-holes
generated from Laughlin's wave-function is stable against external potentials
and weak long-range interactions". The main ingredient of the proof is a
connection between fractional quantum Hall wave-functions and statistical
mechanics problems that generalize the 2D one-component plasma (jellium model).
Universal bounds on the density of such systems, coined "Incompressibility
estimates" are obtained via the construction of screening regions for any
configuration of points with positive electric charges. The latter regions are
patches of constant, negative electric charge density, whose shape is optimized
for the total system (points plus patch) not to generate any electric potential
in its exterior.

categories: math.AP cond-mat.mes-hall cond-mat.stat-mech math-ph math.MP math.PR


title: Forward Compatible Few-Shot Class-Incremental Learning

abstract:
  Novel classes frequently arise in our dynamically changing world, e.g., new
users in the authentication system, and a machine learning model should
recognize new classes without forgetting old ones. This scenario becomes more
challenging when new class instances are insufficient, which is called few-shot
class-incremental learning (FSCIL). Current methods handle incremental learning
retrospectively by making the updated model similar to the old one. By
contrast, we suggest learning prospectively to prepare for future updates, and
propose ForwArd Compatible Training (FACT) for FSCIL. Forward compatibility
requires future new classes to be easily incorporated into the current model
based on the current stage data, and we seek to realize it by reserving
embedding space for future new classes. In detail, we assign virtual prototypes
to squeeze the embedding of known classes and reserve for new ones. Besides, we
forecast possible new classes and prepare for the updating process. The virtual
prototypes allow the model to accept possible updates in the future, which act
as proxies scattered among embedding space to build a stronger classifier
during inference. FACT efficiently incorporates new classes with forward
compatibility and meanwhile resists forgetting of old ones. Extensive
experiments validate FACT's state-of-the-art performance. Code is available at:
https://github.com/zhoudw-zdw/CVPR22-Fact


title:
Potential method and projection theorems for macroscopic Hausdorff
  dimension

abstract:
  The macroscopic Hausdorff dimension Dim H (E) of a set E $\subset$ R d was
introduced by Barlow and Taylor to quantify a "fractal at large scales"
behavior of unbounded, possibly discrete, sets E. We develop a method based on
potential theory in order to estimate this dimension in R d. Then, we apply
this method to obtain Marstrand-like projection theorems: given a set E
$\subset$ R 2 , for almost every $\theta$ $\in$ [0, 2$\pi$], the projection of
E on the straight line passing through 0 with angle $\theta$ has dimension
equal to min(Dim H (E) , 1).


title:
JSRehab: Weaning Common Web Interface Components from JavaScript
  Addiction

abstract:
  Leveraging JavaScript (JS) for User Interface (UI) interactivity has been the
norm on the web for many years. Yet, using JS increases bandwidth and battery
consumption as scripts need to be downloaded and processed by the browser.
Plus, client-side JS may expose visitors to security vulnerabilities such as
Cross-Site Scripting (XSS).This paper introduces a new server-side plugin,
called JSRehab, that automatically rewrites common web interface components by
alternatives that do not require any JavaScript (JS). The main objective of
JSRehab is to drastically reduce-and ultimately remove-the inclusion of JS in a
web page to improve its responsiveness and consume less resources. We report on
our implementation of JS-Rehab for Bootstrap, the most popular UI framework by
far, and evaluate it on a corpus of 100 webpages. We show through manual
validation that it is indeed possible to lower the dependencies of pages on JS
while keeping intact its interactivity and accessibility. We observe that
JSRehab brings energy savings of at least 5 % for the majority of web pages on
the tested devices, while introducing a median on-the-wire overhead of only 5 %
to the HTML payload.


title:
Statistical learning for train delays and influence of winter climate
  and atmospheric icing

abstract:
  This study investigated the climate effect under consecutive winters on the
arrival delay of high-speed passenger trains in northern Sweden. Novel
statistical learning approaches, including inhomogeneous Markov chain model and
stratified Cox model, were adopted to account for the time-varying risks of
train delays. The inhomogeneous Markov chain modelling for the arrival delays
has used several covariates, including weather variables, train operational
direction, and findings from the primary delay analysis through stratified Cox
model. The results showed that the weather variables, such as temperature, snow
depth, ice/snow precipitation, and train operational direction, significantly
impact the arrival delay. The performance of the fitted inhomogeneous Markov
chain model was evaluated by the walk-forward validation method. The averaged
mean absolute errors between the expected rates and the observed rates of the
arrival delay over the train line was obtained at the level of 0.088, which
implies that approximately 9% of trains may be misclassified as having arrival
delays by the fitted model at a measuring point on the train line.


title: Deterministic Conjunction Tracking in Long-term Space Debris Simulations

abstract:
  Numerical simulations are at the center of predicting the space debris
environment of the upcoming decades. In light of debris generating events, such
as continued anti-satellite weapon tests and planned mega-constellations,
accurate predictions of the space debris environment are critical to ensure the
long-term sustainability of critical satellite orbits.
  Given the computational complexity of accurate long-term trajectory
propagation for a large number of particles, numerical models usually rely on
Monte-Carlo approaches for stochastic conjunction assessment. On the other
hand, deterministic methods bear the promise of higher accuracy and can serve
to validate stochastic approaches. However, they pose a substantial challenge
to computational feasibility.
  In this work, we present the architecture and proof of concept results for a
numerical simulation capable of modeling the long term debris evolution over
decades with a deterministic conjunction tracking model. For the simulation, we
developed an efficient propagator in modern C++ accounting for Earth's
gravitational anomalies, solar radiation pressure, and atmospheric drag. We
utilized AutoPAS, a sophisticated particle container, which automatically
selects the most efficient data structures and algorithms.
  We present results from a simulation of 16 024 particles in low-Earth orbit
over 20 years. Overall, conjunctions are tracked for predicted collisions and
close encounters to allow a detailed study of both. We analyze the runtime and
computational cost of the simulation in detail. In summary, the obtained
results show that modern computational tools finally enable deterministic
conjunction tracking and can serve to validate prior results and build
higher-fidelity numerical simulations of the long-term debris environment.


title:
S$^2$SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder
  for Text-to-SQL Parsers

abstract:
  The task of converting a natural language question into an executable SQL
query, known as text-to-SQL, is an important branch of semantic parsing. The
state-of-the-art graph-based encoder has been successfully used in this task
but does not model the question syntax well. In this paper, we propose
S$^2$SQL, injecting Syntax to question-Schema graph encoder for Text-to-SQL
parsers, which effectively leverages the syntactic dependency information of
questions in text-to-SQL to improve the performance. We also employ the
decoupling constraint to induce diverse relational edge embedding, which
further improves the network's performance. Experiments on the Spider and
robustness setting Spider-Syn demonstrate that the proposed approach
outperforms all existing methods when pre-training models are used, resulting
in a performance ranks first on the Spider leaderboard.


title:
Data-Driven Robust Control for Discrete Linear Time-Invariant Systems: A
  Descriptor System Approach

abstract:
  Given the recent surge of interest in data-driven control, this paper
proposes a two-step method to study robust data-driven control for a
parameter-unknown linear time-invariant (LTI) system that is affected by
energy-bounded noises. First, two data experiments are designed and
corresponding data are collected, then the investigated system is equivalently
written into a data-based descriptor system with structured parametric
uncertainties. Second, combined with model-based control theory for descriptor
systems, state feedback controllers are designed for such data-based descriptor
system, which stabilize the original LTI system and guarantee the ${H_\infty}$
performance. Finally, a simulation example is provided to illustrate the
effectiveness and merits of our method.


title:
Application of the point-like contact model: Resistance Oscillations of
  the Domain Wall in Magnetic Nanowires and Junctions due to Mean Free Path
  Effects

abstract:
  This work is focused on determining the electrical resistance, which induced
by single domain wall in magnetic nanowire with a negligible defect. The
provided model covers a wide range of nanowire's diameters. The obtained result
demonstrates a few orders rapid reduction of the domain wall resistance
accompanied by its possible deviations versus the diameter growth ranging from
1.2 nm to 15.2 nm. The origin of these deviations, which are also identified as
oscillations, is referred to the non-uniform electron scattering on the domain
wall due to the intermixing electron scattering conditions: ballistic for one
spin channel and quasi-ballistic for other one with opposite spin direction. It
may happen when the domain wall width by value is approximately in between two
lengths: a mean free path with the spin down and spin up. The indirect evidence
of this finding is also coming from the fact that homogeneous nanowires shows
the most valuable domain wall resistance oscillations by magnitude rather than
segmented magnetic nanowires. In addition to the approach, where DW width is
constant, the other reasonable model is used when the domain wall can be
constrained for some conditions. The same results are valid for magnetic
junctions with domain wall. Finally, resistance simulation in the diffusive
range, when a diameter of the nanowire (or point-like junction) is larger than
any of spin resolved mean free path of electrons, successfully follows
experimental data for the single and double domain wall resistances available
in literature.


title: $R$ measurement and QCD studies at future super $\tau-c$ factory

abstract:
  We review status of $R$ measurement and QCD studies at low energy range,
discuss prospects for a super $\tau-$charm factory in $2-7$ GeV. With a
high-luminosity $e^+e^-$ collider, statistics are no longer problem for $R$
measurement and a precision of $2\%$ or even better is foreseen, that will lead
to bring down the uncertainty of hadronic contribution to the QED running
coupling constant $\Delta\alpha_{had}$ and the anomalous magnetic moment of the
muon ($a_{\mu}$); measure the strong coupling constant $\alpha_s$ and the charm
quark mass; improve the measurement of the resonance parameters of heavy
charmonia. Huge data samples in $2-3$ GeV will make it possible to study
excited states of $\rho$, $\omega$ and $\phi$, or exotic $Y(2175)$; measure
electromagnetic form factor of mesons and baryons; and measure fragmentation
functions of hadrons.


title:
Ideal Four Wave Mixing Dynamics in a Nonlinear Schr{\"o}dinger Equation
  Fibre System

abstract:
  Near-ideal four wave mixing dynamics are observed in a nonlinear
Schr{\"o}dinger equation system using a new experimental technique associated
with iterated sequential initial conditions in optical fiber. This novel
approach mitigates against unwanted sideband generation and optical loss,
extending the effective propagation distance by two orders of magnitude,
allowing Kerr-driven coupling dynamics to be seen over 50 km of optical fiber
using only one short fiber segment of 500 m. Our experiments reveal the full
dynamical phase space topology in amplitude and phase, showing characteristic
features of multiple Fermi-Pasta-Ulam recurrence cycles, stationary wave
existence, and the system separatrix boundary. Experiments are shown to be in
excellent quantitative agreement with numerical solutions of the canonical
differential equation system describing the wave evolution.


title:
Speeding up deep neural network-based planning of local car maneuvers
  via efficient B-spline path construction

abstract:
  This paper demonstrates how an efficient representation of the planned path
using B-splines, and a construction procedure that takes advantage of the
neural network's inductive bias, speed up both the inference and training of a
DNN-based motion planner. We build upon our recent work on learning local car
maneuvers from past experience using a DNN architecture, introducing a novel
B-spline path construction method, making it possible to generate local
maneuvers in almost constant time of about 11 ms, respecting a number of
constraints imposed by the environment map and the kinematics of a car-like
vehicle. We evaluate thoroughly the new planner employing the recent Bench-MR
framework to obtain quantitative results showing that our method outperforms
state-of-the-art planners by a large margin in the considered task.


title: Exponentially stable adaptive control. Part I. Time-invariant plants

abstract:
  In this research we consider linear time-invariant plants and assume that the
regressor finite excitation requirement is met. In such case, a new law to
adjust the controller parameters, which ensures the exponential stability of
the classical dynamic model of the tracking error under the condition that its
states are not included in such a law, is proposed in this study. In addition,
it also relaxes a number of classical assumptions and requirements of the
adaptive control theory, i.e. the necessity to know the sign/value of the plant
high-frequency gain, the need of experimentally based choice of the proposed
law adaptive gain value, the requirement to the tracking error transfer
function to be strictly positive real considering the output feedback control.
The applicability of the proposed law to the problems of adaptive state and
output feedback control is shown. The advantages of the proposed method over
the well-known ones are demonstrated.


title: UniVIP: A Unified Framework for Self-Supervised Visual Pre-training

abstract:
  Self-supervised learning (SSL) holds promise in leveraging large amounts of
unlabeled data. However, the success of popular SSL methods has limited on
single-centric-object images like those in ImageNet and ignores the correlation
among the scene and instances, as well as the semantic difference of instances
in the scene. To address the above problems, we propose a Unified
Self-supervised Visual Pre-training (UniVIP), a novel self-supervised framework
to learn versatile visual representations on either single-centric-object or
non-iconic dataset. The framework takes into account the representation
learning at three levels: 1) the similarity of scene-scene, 2) the correlation
of scene-instance, 3) the discrimination of instance-instance. During the
learning, we adopt the optimal transport algorithm to automatically measure the
discrimination of instances. Massive experiments show that UniVIP pre-trained
on non-iconic COCO achieves state-of-the-art transfer performance on a variety
of downstream tasks, such as image classification, semi-supervised learning,
object detection and segmentation. Furthermore, our method can also exploit
single-centric-object dataset such as ImageNet and outperforms BYOL by 2.5%
with the same pre-training epochs in linear probing, and surpass current
self-supervised object detection methods on COCO dataset, demonstrating its
universality and potential.


title: Playing (Almost-)Optimally in Concurrent B\"uchi and co-B\"uchi Games

abstract:
  We study two-player concurrent stochastic games on finite graphs, with
B\"uchi and co-B\"uchi objectives. The goal of the first player is to maximize
the probability of satisfying the given objective. Following Martin's
determinacy theorem for Blackwell games, we know that such games have a value.
Natural questions are then: does there exist an optimal strategy, that is, a
strategy achieving the value of the game? what is the memory required for
playing (almost-)optimally? The situation is rather simple to describe for
turn-based games, where positional pure strategies suffice to play optimally in
games with parity objectives. Concurrency makes the situation intricate and
heterogeneous. For most {\omega}-regular objectives, there do indeed not exist
optimal strategies in general. For some objectives (that we will mention),
infinite memory might also be required for playing optimally or
almost-optimally. We also provide characterizations of local interactions of
the players to ensure positionality of (almost-)optimal strategies for B\"uchi
and co-B\"uchi objectives. This characterization relies on properties of game
forms underpinning the formalism for defining local interactions of the two
players. These well-behaved game forms are like elementary bricks which, when
they behave well in isolation, can be assembled in graph games and ensure the
good property for the whole game.


title: Blind2Unblind: Self-Supervised Image Denoising with Visible Blind Spots

abstract:
  Real noisy-clean pairs on a large scale are costly and difficult to obtain.
Meanwhile, supervised denoisers trained on synthetic data perform poorly in
practice. Self-supervised denoisers, which learn only from single noisy images,
solve the data collection problem. However, self-supervised denoising methods,
especially blindspot-driven ones, suffer sizable information loss during input
or network design. The absence of valuable information dramatically reduces the
upper bound of denoising performance. In this paper, we propose a simple yet
efficient approach called Blind2Unblind to overcome the information loss in
blindspot-driven denoising methods. First, we introduce a global-aware mask
mapper that enables global perception and accelerates training. The mask mapper
samples all pixels at blind spots on denoised volumes and maps them to the same
channel, allowing the loss function to optimize all blind spots at once.
Second, we propose a re-visible loss to train the denoising network and make
blind spots visible. The denoiser can learn directly from raw noise images
without losing information or being trapped in identity mapping. We also
theoretically analyze the convergence of the re-visible loss. Extensive
experiments on synthetic and real-world datasets demonstrate the superior
performance of our approach compared to previous work. Code is available at
https://github.com/demonsjin/Blind2Unblind.


title: Stability of Switched Affine Systems: Arbitrary and Dwell-Time Switching

abstract:
  The dynamical behavior of switched affine systems is known to be more
intricate than that of the well-studied switched linear systems, essentially
due to the existence of distinct equilibrium points for each subsystem. First,
under arbitrary switching rules, the stability analysis must be generally
carried out with respect to a compact set with non-empty interior rather than
to a singleton. We provide a novel proof technique for existence and outer
approximation of attractive invariant sets of a switched affine system, under
the hypothesis of global uniform stability of its linearization. On the other
hand, considering dwell-time switching signals, forward invariant sets need not
exist for this class of switched systems, even for stable ones. Hence, more
general notions of stability/boundedness are introduced and studied,
highlighting the relations of these concepts to the uniform stability of the
linear part of the system under the same class of dwell-time switching signals.
These results reveal the main differences and specificities of switched affine
systems with respect to linear ones, providing a first step for the analysis of
switched systems composed by subsystems not sharing the same equilibrium.
Numerical methods based on linear matrix inequalities and sum-of-squares
programming are presented and illustrate the developed theory.


title: Mimicking Mergers: Mistaking Black Hole Captures as Mergers

abstract:
  As the number of gravitational wave observations has increased in recent
years, the variety of sources has broadened. Here we investigate whether it is
possible for the current generation of detectors to distinguish between very
short-lived gravitational wave signals from mergers between high-mass black
holes, and the signal produced by a close encounter between two black holes
which results in gravitational capture, and ultimately a merger. We compare the
posterior probability distributions produced by analysing simulated signals
from both types of progenitor events, both under ideal and realistic scenarios.
We show that while, under ideal conditions it is possible to distinguish both
progenitors, under more realistic conditions they are indistinguishable. This
has important implications for the interpretation of such short signals, and we
therefore advocate that these signals be the focus of additional investigation
even when satisfactory results have been achieved from standard analyses.


title:
Hard Homogeneous Spaces from the Class Field Theory of Imaginary
  Hyperelliptic Function Fields

abstract:
  We explore algorithmic aspects of a free and transitive commutative group
action coming from the class field theory of imaginary hyperelliptic function
fields. Namely, the Jacobian of an imaginary hyperelliptic curve defined over
$\mathbb{F}_q$ acts on a subset of isomorphism classes of Drinfeld modules. We
describe an algorithm to compute the group action efficiently. This is a
function field analog of the Couveignes-Rostovtsev-Stolbunov group action. Our
proof-of-concept C++/NTL implementation only requires a fraction of a second on
a standard computer. Also, we state a conjecture - supported by experiments -
which implies that the current fastest algorithm to solve its inverse problem
runs in exponential time. This action is therefore a promising candidate for
the construction of Hard Homogeneous Spaces, which are the building blocks of
several post-quantum cryptographic protocols. This demonstrates the relevance
of using imaginary hyperelliptic curves and Drinfeld modules as an alternative
to the standard setting of imaginary quadratic number fields and elliptic
curves for isogeny-based cryptographic applications. Moreover, our function
field setting enables the use of Kedlaya's algorithm and its variants for
computing the order of the group in polynomial time when $q$ is fixed. No such
polynomial-time algorithm for imaginary quadratic number fields is known. For
$q=2$ and parameters similar to CSIDH-512, we compute this order more than 8500
times faster than the record computation for CSIDH-512 by Beullens, Kleinjung
and Vercauteren.


title: Little-Parks like oscillations in lightly doped cuprate superconductors

abstract:
  Understanding the rich and competing electronic orders in cuprate
superconductors may provide important insight into the mechanism of
high-temperature superconductivity. Here, by measuring Bi2Sr2CaCu2O8+x in the
extremely underdoped regime, we obtain evidence for a distinct type of
ordering, which manifests itself as resistance oscillations at low magnetic
fields (<10 T) and at temperatures around the superconducting transition. By
tuning the doping level p continuously, we reveal that these low-field
oscillations occur only when p<0.1. The oscillation amplitude increases with
decreasing p but the oscillation period stays almost constant. We show that
these low-field oscillations can be well described by assuming a periodic
superconducting structure with a mesh size of about 50 nm. Such a charge order,
which is distinctly different from the well-established charge density wave and
pair density wave, seems to be an unexpected piece of the puzzle on the
correlated physics in cuprates.


title: iCub3 Avatar System

abstract:
  We present an avatar system that enables a human operator to visit a remote
location via iCub3, a new humanoid robot developed at the Italian Institute of
Technology (IIT) paving the way for the next generation of the iCub platforms.
On the one hand, we present the humanoid iCub3 that plays the role of the
robotic avatar. Particular attention is paid to the differences between iCub3
and the classical iCub humanoid robot. On the other hand, we present the set of
technologies of the avatar system at the operator side. They are mainly
composed of iFeel, namely, IIT lightweight non-invasive wearable devices for
motion tracking and haptic feedback, and of non-IIT technologies designed for
virtual reality ecosystems. Finally, we show the effectiveness of the avatar
system by describing a demonstration involving a realtime teleoperation of the
iCub3. The robot is located in Venice, Biennale di Venezia, while the human
operator is at more than 290km distance and located in Genoa, IIT. Using a
standard fiber optic internet connection, the avatar system transports the
operator locomotion, manipulation, voice, and face expressions to the iCub3
with visual, auditory, haptic and touch feedback.


title:
Solving parametric partial differential equations with deep rectified
  quadratic unit neural networks

abstract:
  Implementing deep neural networks for learning the solution maps of
parametric partial differential equations (PDEs) turns out to be more efficient
than using many conventional numerical methods. However, limited theoretical
analyses have been conducted on this approach. In this study, we investigate
the expressive power of deep rectified quadratic unit (ReQU) neural networks
for approximating the solution maps of parametric PDEs. The proposed approach
is motivated by the recent important work of G. Kutyniok, P. Petersen, M.
Raslan and R. Schneider (Gitta Kutyniok, Philipp Petersen, Mones Raslan, and
Reinhold Schneider. A theoretical analysis of deep neural networks and
parametric pdes. Constructive Approximation, pages 1-53, 2021), which uses deep
rectified linear unit (ReLU) neural networks for solving parametric PDEs. In
contrast to the previously established complexity-bound
$\mathcal{O}\left(d^3\log_{2}^{q}(1/ \epsilon) \right)$ for ReLU neural
networks, we derive an upper bound $\mathcal{O}\left(d^3\log_{2}^{q}\log_{2}(1/
\epsilon) \right)$ on the size of the deep ReQU neural network required to
achieve accuracy $\epsilon>0$, where $d$ is the dimension of reduced basis
representing the solutions. Our method takes full advantage of the inherent
low-dimensionality of the solution manifolds and better approximation
performance of deep ReQU neural networks. Numerical experiments are performed
to verify our theoretical result.


title: SMC4PEP: Stochastic Model Checking of Product Engineering Processes

abstract:
  Product Engineering Processes (PEPs) are used for describing complex product
developments in big enterprises such as automotive and avionics industries. The
Business Process Model Notation (BPMN) is a widely used language to encode
interactions among several participants in such PEPs. In this paper, we present
SMC4PEP as a tool to convert graphical representations of a business process
using the BPMN standard to an equivalent discrete-time stochastic control
process called Markov Decision Process (MDP). To this aim, we first follow the
approach described in an earlier investigation to generate a semantically
equivalent business process which is more capable of handling the PEP
complexity. In particular, the interaction between different levels of
abstraction is realized by events rather than direct message flows. Afterwards,
SMC4PEP converts the generated process to an MDP model described by the syntax
of the probabilistic model checking tool PRISM. As such, SMC4PEP provides a
framework for automatic verification and validation of business processes in
particular with respect to requirements from legal standards such as Automotive
SPICE. Moreover, our experimental results confirm a faster verification routine
due to smaller MDP models generated from the alternative event-based BPMN
models.


title: Precise atom manipulation through deep reinforcement learning

abstract:
  Atomic-scale manipulation in scanning tunneling microscopy has enabled the
creation of quantum states of matter based on artificial structures and extreme
miniaturization of computational circuitry based on individual atoms. The
ability to autonomously arrange atomic structures with precision will enable
the scaling up of nanoscale fabrication and expand the range of artificial
structures hosting exotic quantum states. However, the a priori unknown
manipulation parameters, the possibility of spontaneous tip apex changes, and
the difficulty of modeling tip-atom interactions make it challenging to select
manipulation parameters that can achieve atomic precision throughout extended
operations. Here we use deep reinforcement learning (DRL) to control the
real-world atom manipulation process. Several state-of-the-art reinforcement
learning techniques are used jointly to boost data efficiency. The
reinforcement learning agent learns to manipulate Ag adatoms on Ag(111)
surfaces with optimal precision and is integrated with path planning algorithms
to complete an autonomous atomic assembly system. These results demonstrate
that state-of-the-art deep reinforcement learning can offer effective solutions
to real-world challenges in nanofabrication and powerful approaches to
increasingly complex scientific experiments at the atomic scale.


title:
Formation of Coronal Mass Ejection and Post-eruption Flow of Solar Wind
  on 2010 August 18 event

abstract:
  The state of the space environment plays a significant role for forecasting
of geomagnetic storms produced by disturbances of the solar wind (SW). Coronal
mass ejections (CMEs) passing through the heliosphere often have a prolonged
(up to several days) trail with declining speed, which affects propagation of
the subsequent SW streams. We studied the CME and the post-eruption plasma
flows behind the CME rear in the event on 2010 August 18 observed in quadrature
by several space-based instruments. Observations of the eruption in the corona
with EUV telescopes and coronagraphs revealed several discrete outflows
followed by a continuous structureless post-eruption stream. The interplanetary
coronal mass ejection (ICME), associated with this CME, was registered by
PLAsma and SupraThermal Ion Composition (PLASTIC) instrument aboard the Solar
TErrestrial RElations Observatory (STEREO-A) between August 20, 16:14 UT and
August 21, 13:14 UT, after which the SW disturbance was present over 3 days.
Kinematic consideration with the use of the gravitational and Drag-Based models
has shown that the discrete plasma flows can be associated with the ICME,
whereas the post-eruption outflow was arrived in the declining part of the SW
transient. We simulated the Fe-ion charge distributions of the ICME and
post-CME parts of SW using the plasma temperature and density in the ejection
region derived from the Differential Emission Measure analysis. The results
demonstrate that in the studied event the post-ICME trailing region was
associated with the post-eruption flow from the corona, rather then with the
ambient SW entrained by the CME.


title:
A Compositional Algorithm for the Conflict-Free Electric Vehicle Routing
  Problem

abstract:
  The Conflict-Free Electric Vehicle Routing Problem (CF-EVRP) is an extension
of the Vehicle Routing Problem (VRP), a combinatorial optimization problem of
designing routes for vehicles to visit customers such that a cost function,
typically the number of vehicles or the total travelled distance, is minimized.
The problem finds many logistics applications, particularly for highly
automated logistic systems for material handling. The CF-EVRP involves
constraints such as time windows on the delivery to the customers, limited
operating range of the vehicles, and limited capacity on the number of vehicles
that a road segment can accommodate at the same time. In this paper, the
compositional algorithm ComSat for solving the CF-EVRP is presented. The
algorithm iterates through the sub-problems until a globally feasible solution
is found. The proposed algorithm is implemented using an optimizing SMT-solver
and is evaluated against an implementation of a previously presented monolithic
model. The soundness and completeness of the algorithm are proven, and it is
benchmarked on a set of generated problems and found to be able to solve
problems of industrial size.