title:
Evaluation of Dirichlet Process Gaussian Mixtures for Segmentation on
  Noisy Hyperspectral Images

abstract:
  Image segmentation is a fundamental step for the interpretation of Remote
Sensing Images. Clustering or segmentation methods usually precede the
classification task and are used as support tools for manual labeling. The most
common algorithms, such as k-means, mean-shift, and MRS, require an extra
manual step to find the scale parameter. The segmentation results are severely
affected if the parameters are not correctly tuned and diverge from the optimal
values. Additionally, the search for the optimal scale is a costly task, as it
requires a comprehensive hyper-parameter search. This paper proposes and
evaluates a method for segmentation of Hyperspectral Images using the Dirichlet
Process Gaussian Mixture Model. Our model can self-regulate the parameters
until it finds the optimal values of scale and the number of clusters in a
given dataset. The results demonstrate the potential of our method to find
objects in a Hyperspectral Image while bypassing the burden of manual search of
the optimal parameters. In addition, our model also produces similar results on
noisy datasets, while previous research usually required a pre-processing task
for noise reduction and spectral smoothing.


title:
Bathymetry Inversion using a Deep-Learning-Based Surrogate for Shallow
  Water Equations Solvers

abstract:
  River bathymetry is critical for many aspects of water resources management.
We propose and demonstrate a bathymetry inversion method using a
deep-learning-based surrogate for shallow water equations solvers. The
surrogate uses the convolutional autoencoder with a shared-encoder,
separate-decoder architecture. It encodes the input bathymetry and decodes to
separate outputs for flow-field variables. A gradient-based optimizer is used
to perform bathymetry inversion with the trained surrogate. Two
physically-based constraints on both bed elevation value and slope have to be
added as inversion loss regularizations to obtain usable inversion results.
Using the "L-curve" criterion, a heuristic approach was proposed to determine
the regularization parameters. Both the surrogate model and the inversion
algorithm show good performance. We found the bathymetry inversion process has
two distinctive stages, which resembles the sculptural process of initial
broad-brush calving and final detailing. The inversion loss due to flow
prediction error reaches its minimum in the first stage and remains almost
constant afterward. The bed elevation value and slope regularizations play the
dominant role in the second stage in selecting the most probable solution. We
also found the surrogate architecture (whether with both velocity and water
surface elevation or velocity only as outputs) does not show significant impact
on inversion result.


title:
Phase trajectories of linear homogeneous autonomous dynamical systems of
  the third order

abstract:
  The proposed article is devoted to the study of the problem of constructing
phase trajectories in the vicinity of a singular point. This paper presents a
more expanded view of this problem in comparison with those previously
considered by other authors and suggests a more meaningful and grounded
development of this study. Numerous examples of the application of this
approach to the study of trajectories at a singular point are given.


title: Improved Hamiltonians for Quantum Simulations

abstract:
  The quantum simulation of lattice gauge theories for the foreseeable future
will be hampered by limited resources. The historical success of
Symanzik-improved lattice actions in classical simulations strongly suggests
that improved Hamiltonians will reduce quantum resources for fixed
discretization errors. In this work, we consider Symanzik-improved lattice
Hamiltonians for pure gauge theories and design quantum circuits for
$\mathcal{O}(a^2)$-improved Hamiltonians in terms of primitive group gates. An
explicit demonstration for $\mathbb{Z}_2$ is presented including exploratory
tests using the ibm_perth device.


title:
Distributional Hardness Against Preconditioned Lasso via Erasure-Robust
  Designs

abstract:
  Sparse linear regression with ill-conditioned Gaussian random designs is
widely believed to exhibit a statistical/computational gap, but there is
surprisingly little formal evidence for this belief, even in the form of
examples that are hard for restricted classes of algorithms. Recent work has
shown that, for certain covariance matrices, the broad class of Preconditioned
Lasso programs provably cannot succeed on polylogarithmically sparse signals
with a sublinear number of samples. However, this lower bound only shows that
for every preconditioner, there exists at least one signal that it fails to
recover successfully. This leaves open the possibility that, for example,
trying multiple different preconditioners solves every sparse linear regression
problem.
  In this work, we prove a stronger lower bound that overcomes this issue. For
an appropriate covariance matrix, we construct a single signal distribution on
which any invertibly-preconditioned Lasso program fails with high probability,
unless it receives a linear number of samples.
  Surprisingly, at the heart of our lower bound is a new positive result in
compressed sensing. We show that standard sparse random designs are with high
probability robust to adversarial measurement erasures, in the sense that if
$b$ measurements are erased, then all but $O(b)$ of the coordinates of the
signal are still information-theoretically identifiable. To our knowledge, this
is the first time that partial recoverability of arbitrary sparse signals under
erasures has been studied in compressed sensing.

categories: cs.DS cs.IT cs.LG math.IT math.ST stat.ML stat.TH


title: Almost K\"ahler metrics and pp-wave spacetimes

abstract:
  We establish a one-to-one correspondence between a class of strictly almost
K\"ahler metrics on the one hand, and Lorentzian pp-wave spacetimes on the
other; the latter metrics are well known in general relativity, where they
model radiation propagating at the speed of light. Specifically, we construct
families of complete almost K\"ahler metrics by deforming pp-waves via their
propagation wave vector. The almost K\"ahler metrics we obtain exist in all
dimensions $2n \geq 4$, and are defined on both $\mathbb{R}^{2n}$ and
$\mathbb{S}^1\times\mathbb{S}^1 \times M$, where $M$ is any closed almost
K\"ahler manifold; they are not warped products, and include noncompact
examples with constant negative scalar curvature. Finally, we deepen this
relationship between almost K\"ahler and Lorentzian geometry by utilizing
Penrose's "plane wave limit," by which every spacetime has, locally, a pp-wave
metric as a limit: using Penrose's construction, we show that in all dimensions
$2n \geq 4$, every Lorentzian metric admits, locally, an almost K\"ahler metric
as a limit.


title: On the Maximum $F_5$-free Subhypergraphs of a Random Hypergraph

abstract:
  Denote by $F_5$ the $3$-uniform hypergraph on vertex set $\{1,2,3,4,5\}$ with
hyperedges $\{123,124,345\}$. Balogh, Butterfield, Hu, and Lenz proved that if
$p > K \log n / n$ for some large constant $K$, then every maximum $F_5$-free
subhypergraph of $G^3(n,p)$ is tripartite with high probability, and showed if
$p_0 = 0.1 \sqrt{\log n} / n$, then with high probability there exists a
maximum $F_5$-free subhypergraph of $G^3(n,p_0)$ that is not tripartite. In
this paper, we sharpen the upper bound to be best possible up to a constant
factor. We prove that when $p > C \sqrt{\log n} / n $ for some large constant
$C$, then every maximum $F_5$-free subhypergraph of $G^3(n, p)$ is tripartite
with high probability.


title: Data-driven input reconstruction and experimental validation

abstract:
  This paper addresses a data-driven input reconstruction problem based on
Willems' Fundamental Lemma in which unknown input estimators (UIEs) are
constructed directly from historical I/O data. Given only output measurements,
the inputs are estimated by the UIE, which is shown to asymptotically converge
to the true input without knowing the initial conditions. Both open-loop and
closed-loop UIEs are developed based on Lyapunov conditions and the
Luenberger-observer-type feedback, whose convergence properties are studied. An
experimental study is presented demonstrating the efficacy of the closed-loop
UIE for estimating the occupancy of a building on the EPFL campus via measured
carbon dioxide levels.


title: Universal anomalous turbulent diffusion in quantum fluids

abstract:
  In classical viscous fluids, turbulent eddies are known to be responsible for
the rapid spreading of embedded particles. But in an inviscid quantum fluid
where the turbulence is induced by a chaotic tangle of quantized vortices,
dispersion of the particles is achieved via a non-classical mechanism, i.e.,
their binding to the evolving quantized vortices. However, there is limited
existing knowledge on how the vortices diffuse and spread in turbulent quantum
fluids. Here we report a systematic numerical study of the apparent diffusion
of vortices in a random vortex tangle in superfluid helium-4 using full
Biot-Savart simulation. We reveal that the vortices in pure superfluid exhibit
a universal anomalous diffusion (superdiffusion) at small times, which transits
to normal diffusion at large times. This behavior is found to be caused by a
generic scaling property of the vortex velocity, which should exist in all
quantum fluids where the Biot-Savart law governs the vortex motion. Our
simulation at finite temperatures also nicely reproduces recent experimental
observations. The knowledge obtained from this study may form the foundation
for understanding turbulent transport and universal vortex dynamics in various
condensed-matter and cosmic quantum fluids.

categories: physics.flu-dyn cond-mat.mtrl-sci cond-mat.other nlin.CD


title: The limit cycles in a generalized Rayleigh-Li\'enard oscillator

abstract:
  We compute the cyclicity of open period annuli of the following generalized
Rayleigh-Li\'enard equation $$\ddot{x}+ax+bx^3-(\lambda_1+\lambda_2
x^2+\lambda_3\dot{x}^2+\lambda_4 x^4+\lambda_5\dot{x}^4+\lambda_6
x^6)\dot{x}=0$$ and the equivalent planar system $X_\lambda$, where the
coefficients of the perturbation $\lambda_j$ are independent small parameters
and $a, b$ are fixed nonzero constants. Our main tool is the machinery of the
so called higher-order Poincar\'e-Pontryagin-Melnikov functions (Melnikov
functions $M_n$ for short), combined with the explicit computation of center
conditions and the corresponding Bautin ideal.
  We consider first arbitrary analytic arcs $\varepsilon \to
\lambda(\varepsilon)$ and explicitly compute all possible Melnikov functions
$M_n$ related to the deformation $X_{ \lambda(\varepsilon)} $. At a second step
we obtain exact bounds for the number of the zeros of the Melnikov functions
(complete elliptic integrals depending on parameter) in an appropriate complex
domain, using a modification of Petrov's method.
  To deal with the general case of six-parameter deformations $\lambda \to
X_\lambda$, we compute first the related Bautin ideal. To do this we carefully
study the Melnikov functions up to order three, and then use Nakayama lemma
from Algebraic geometry. The principalization of the Bautin ideal (achieved
after a blow up) reduces finally the study of general deformations $X_{ \lambda
} $ to the study of one-parameter deformations $X_{ \lambda(\varepsilon)} $.


title:
On the surface plasmonic waves excited by a dipole above anisotropic and
  spatially dispersive two-dimensional surfaces of infinite extent in planarly
  layered media

abstract:
  The surface plasmonic waves excited by a vertical or horizontal oriented
Hertzian dipole above anisotropic and spatially dispersive two-dimensional
surfaces of infinite extent embedded in planarly layered uniaxial media is
investigated using the dyadic Green function approach. The spectral-domain
transmission line analogy Green function formulation and iso-frequency contours
equations are derived. The methods to accurately and efficently evaluate the
two-dimensional Fourier integral arisen from the spatial-domain Green function
computation are also developed. To resolve the numerical inefficiency due to
the highly oscillatory integrand and singularities of surface plasmonic waves
possessing large wavenumber, two numerical strategies, the extrapolation of the
real-axis integration combined with singularity subtraction, and the deformed
vertical integration path, are proposed and applicable to a wide range of
observation distance. As a demonstration of the proposed formulation, we
compute the scattered fields of a vertical dipole above the graphene biased by
drift current which exhibits significant spatial dispersion and show that its
light-matter interaction can be significantly reinforced when placed above
uniaxially epsilon-near-zero substrates. The proposed formulation may provide
methodology for the computational analysis of two-dimensional materials and
surface plasmonic waves.


title:
The detection matrix as a model-agnostic tool to estimate the number of
  degrees of freedom in mechanical systems and engineering structures

abstract:
  Estimating the number of degrees of freedom of a mechanical system or an
engineering structure from the time-series of a small set of sensors is a basic
problem in diagnostics, which, however, is often overlooked when monitoring
their health and integrity. In this work, we demonstrate the applicability of
the network-theoretic concept of detection matrix as a tool to solve this
problem. From this estimation, we illustrate the possibility to identify
damage. The detection matrix, recently introduced by Haehne et al. in the
context of network theory, is assembled from the transient response of a few
nodes as a result of non-zero initial conditions: its rank offers an estimate
of the number of nodes in the network itself. The use of the detection matrix
is completely model-agnostic, whereby it does not require any knowledge of the
system dynamics. Here, we show that, with a few modifications, this same
principle applies to discrete systems, such as spring-mass lattices and
trusses. Moreover, we discuss how damage in one or more members causes the
appearance of distinct jumps in the singular values of this matrix, thereby
opening the door to structural health monitoring applications, without the need
for a complete model reconstruction.


title:
On the Error of Random Sampling: Uniformly Distributed Random Points on
  Parametric Curves

abstract:
  Given a parametric polynomial curve $\gamma:[a,b]\rightarrow \mathbb{R}^n$,
how can we sample a random point $\mathfrak{x}\in \mathrm{im}(\gamma)$ in such
a way that it is distributed uniformly with respect to the arc-length?
Unfortunately, we cannot sample exactly such a point-even assuming we can
perform exact arithmetic operations. So we end up with the following question:
how does the method we choose affect the quality of the approximate sample we
obtain? In practice, there are many answers. However, in theory, there are
still gaps in our understanding. In this paper, we address this question from
the point of view of complexity theory, providing bounds in terms of the size
of the desired error.


title:
Tabula: Efficiently Computing Nonlinear Activation Functions for Secure
  Neural Network Inference

abstract:
  Multiparty computation approaches to secure neural network inference
traditionally rely on garbled circuits for securely executing nonlinear
activation functions. However, garbled circuits require excessive communication
between server and client, impose significant storage overheads, and incur
large runtime penalties. To eliminate these costs, we propose an alternative to
garbled circuits: Tabula, an algorithm based on secure lookup tables. Tabula
leverages neural networks' ability to be quantized and employs a secure lookup
table approach to efficiently, securely, and accurately compute neural network
nonlinear activation functions. Compared to garbled circuits with quantized
inputs, when computing individual nonlinear functions, our experiments show
Tabula uses between $35 \times$-$70 \times$ less communication, is over
$100\times$ faster, and uses a comparable amount of storage. This leads to
significant performance gains over garbled circuits with quantized inputs
during secure inference on neural networks: Tabula reduces overall
communication by up to $9 \times$ and achieves a speedup of up to $50 \times$,
while imposing comparable storage costs.


title:
Future role and economic benefits of hydrogen and synthetic energy
  carriers in Germany: a systematic review of long-term energy scenarios

abstract:
  Determining the development of Germany's energy system by taking the energy
transition objectives into account is the subject of a series of studies. Since
their assumptions and results play a significant role in the political energy
debate for understanding the role of hydrogen and synthetic energy carriers, a
better discussion is needed. This article provides a comparative assessment of
published transition pathways for Germany to assess the role and advantages of
hydrogen and synthetic energy carriers. Twelve energy studies were selected and
37 scenarios for the years 2030 and 2050 were evaluated. Despite the
variations, the two carriers will play an important future role. While their
deployment is expected to have only started by 2030 with a mean demand of 91
TWh/a (4% of the final energy demand) in Germany, they will be an essential
part by 2050 with a mean demand of 480 TWh/a (24% of the final energy demand).
A moderately positive correlation (0.53) between the decarbonisation targets
and the share of hydrogen-based carriers in final energy demand underlines the
relevance for reaching the climate targets. Additionally, value creation
effects of about 5 bn EUR/a in 2030 can be expected for hydrogen-based
carriers. By 2050, these effects will increase to almost 16 bn EUR/a. Hydrogen
is expected to be mainly produced domestically while synthetic fuels are
projected to be mostly imported. Despite of all the advantages, the
construction of the facilities is associated with high costs which should be
not neglected in the discussion.


title:
Systematic, Lyapunov-Based, Safe and Stabilizing Controller Synthesis
  for Constrained Nonlinear Systems

abstract:
  A controller synthesis method for state- and input-constrained nonlinear
systems is presented that seeks continuous piecewise affine (CPA) Lyapunov-like
functions and controllers simultaneously. Non-convex optimization problems are
formulated on triangulated subsets of the admissible states that can be refined
to meet primary control objectives, such as stability and safety, alongside
secondary performance objectives. A multi-stage design is also given that
enlarges the region of attraction (ROA) sequentially while allowing exclusive
performance for each stage. A clear boundary for an invariant subset of
closed-loop system's ROA is obtained from the resulting Lipschitz Lyapunov
function. For control-affine nonlinear systems, the non-convex problem is
formulated as a series of conservative, but well-posed, semi-definite programs.
These decrease the cost function iteratively until the design objectives are
met. Since the resulting CPA Lyapunov-like functions are also Lipschitz control
(or barrier) Lyapunov functions, they can be used in online quadratic
programming to find minimum-norm control inputs. Numerical examples are
provided to demonstrate the effectiveness of the method.


title: Recursive Monte Carlo and Variational Inference with Auxiliary Variables

abstract:
  A key challenge in applying Monte Carlo and variational inference (VI) is the
design of proposals and variational families that are flexible enough to
closely approximate the posterior, but simple enough to admit tractable
densities and variational bounds. This paper presents recursive
auxiliary-variable inference (RAVI), a new framework for exploiting flexible
proposals, for example based on involved simulations or stochastic
optimization, within Monte Carlo and VI algorithms. The key idea is to estimate
intractable proposal densities via meta-inference: additional Monte Carlo or
variational inference targeting the proposal, rather than the model. RAVI
generalizes and unifies several existing methods for inference with expressive
approximating families, which we show correspond to specific choices of
meta-inference algorithm, and provides new theory for analyzing their bias and
variance. We illustrate RAVI's design framework and theorems by using them to
analyze and improve upon Salimans et al. (2015)'s Markov Chain Variational
Inference, and to design a novel sampler for Dirichlet process mixtures,
achieving state-of-the-art results on a standard benchmark dataset from
astronomy and on a challenging data-cleaning task with Medicare hospital data.


title: Finding Bipartite Partitions on Co-Chordal Graphs

abstract:
  In this paper, we show that the biclique partition number ($\text{bp}$) of a
co-chordal (complementary graph of chordal) graph $G = (V, E)$ is less than the
number of maximal cliques ($\text{mc}$) of its complementary graph: a chordal
graph $G^c = (V, E^c)$. We first provide a general framework of the "divided
and conquer" heuristic of finding minimum biclique partition on co-chordal
graphs based on clique trees. Then, an $O[|V|(|V|+|E^c|)]$-time heuristic is
proposed by applying lexicographic breadth-first search. Either heuristic gives
us a biclique partition of $G$ with a size of $\text{mc}(G^c)-1$. Eventually,
we prove that our heuristic can solve the minimum biclique partition problem on
$G$ exactly if its complement $G^c$ is chordal and clique vertex irreducible.
We also show that $\text{mc}(G^c) - 2 \leq \text{bp}(G) \leq \text{mc}(G^c) -
1$ if $G$ is a split graph.


title: Leveraging Pre-trained BERT for Audio Captioning

abstract:
  Audio captioning aims at using natural language to describe the content of an
audio clip. Existing audio captioning systems are generally based on an
encoder-decoder architecture, in which acoustic information is extracted by an
audio encoder and then a language decoder is used to generate the captions.
Training an audio captioning system often encounters the problem of data
scarcity. Transferring knowledge from pre-trained audio models such as
Pre-trained Audio Neural Networks (PANNs) have recently emerged as a useful
method to mitigate this issue. However, there is less attention on exploiting
pre-trained language models for the decoder, compared with the encoder. BERT is
a pre-trained language model that has been extensively used in Natural Language
Processing (NLP) tasks. Nevertheless, the potential of BERT as the language
decoder for audio captioning has not been investigated. In this study, we
demonstrate the efficacy of the pre-trained BERT model for audio captioning.
Specifically, we apply PANNs as the encoder and initialize the decoder from the
public pre-trained BERT models. We conduct an empirical study on the use of
these BERT models for the decoder in the audio captioning model. Our models
achieve competitive results with the existing audio captioning methods on the
AudioCaps dataset.


title:
Algorithmic Regularization in Model-free Overparametrized Asymmetric
  Matrix Factorization

abstract:
  We study the asymmetric matrix factorization problem under a natural
nonconvex formulation with arbitrary overparamatrization. We consider the
model-free setting with no further assumption on the rank or singular values of
the observed matrix, where the global optima provably overfit. We show that
vanilla gradient descent with small random initialization and early stopping
produces the best low-rank approximation of the observed matrix, without any
additional regularization. We provide a sharp analysis on relationship between
the iteration complexity, initialization size, stepsize and final error. In
particular, our complexity bound is almost dimension-free and depends
logarithmically on the final error, and our results have lenient requirements
on the stepsize and initialization. Our bounds improve upon existing work and
show good agreement with numerical experiments.


title:
Dielectric properties and stratigraphy of regolith in the lunar South
  Pole-Aitken basin: Observations from the Lunar Penetrating Radar

abstract:
  We examine data obtained by the Lunar Penetrating Radar (LPR) onboard the
Chang'E-4 (CE-4) mission to study the dielectric properties and stratigraphy of
lunar regolith on the far side of the Moon. The data collected from January
2019 to September 2020 were processed to generate a 540 m radargram. The travel
velocity of the radar signal and the permittivity of the regolith were deduced
from hyperbolas in the radargram. As CE-4 LPR detected distinct planar
reflectors, we evaluated the dielectric loss from the maximum penetration depth
based on the radar equation. The derived dielectric properties are compared
with the measurements of Apollo samples and Chang'E-2 microwave radiometer
observations. The results suggest that regolith at the landing site has a
permittivity of 2.64-3.85 and a loss tangent of 0.0032-0.0044, indicating that
the local regolith is composed of a fine-grained, low-loss material that is
much more homogeneous than that found at the Chang'E-3 landing site. The total
thickness of weathered material is 40 m, with several regolith layers and a
buried craternidentified in the reconstructed subsurface structure. These
layers clearly record a series of impact events from the adjacent regions. We
suggest that the top layer is primarily made up of the ejecta from a large
crater 140 km away. In contrast, the material source of other thinner layers
comes from nearby smaller craters.


title: Deep Regression of Muon Energy with a K-Nearest Neighbor Algorithm

abstract:
  Within the context of studies for novel measurement solutions for future
particle physics experiments, we developed a performant kNN-based regressor to
infer the energy of highly-relativistic muons from the pattern of their
radiation losses in a dense and granular calorimeter. The regressor is based on
a pool of weak kNN learners, which learn by adapting weights and biases to each
training event through stochastic gradient descent. The effective number of
parameters optimized by the procedure is in the 60 millions range, thus
comparable to that of large deep learning architectures. We test the
performance of the regressor on the considered application by comparing it to
that of several machine learning algorithms, showing comparable accuracy to
that achieved by boosted decision trees and neural networks.


title: Anderson localization of electromagnetic waves in three dimensions

abstract:
  Anderson localization marks a halt of diffusive wave propagation in
disordered systems. Despite extensive studies over the past 40 years, Anderson
localization of light in three dimensions has remained elusive, leading to the
questions about its very existence. Recent orders-of-magnitude speed-up of
finite-difference time-domain calculations allows us to conduct brute-force
numerical simulations of light transport in fully disordered 3D systems with
unprecedented size and refractive index contrast. We demonstrate
three-dimensional localization of vector electromagnetic waves in random
packings of perfect electric conductor spheres, in sharp contrast to the
absence of localization for dielectric spheres with a refractive index contrast
up to 10. Our work opens a wide range of avenues in both fundamental research
related to Anderson localization and potential applications using 3D localized
states.


title:
Effective divisors and Newton-Okounkov bodies of Hilbert schemes of
  points on toric surfaces

abstract:
  We compute the (unbounded) Newton-Okounkov body of the Hilbert scheme of
points on $\mathbb C^2$. We obtain an upper bound for the Newton-Okounkov body
of the Hilbert scheme of points on any smooth toric surface. We conjecture that
this upper bound coincides with the exact Newton-Okounkov body for the Hilbert
schemes of points on $\mathbb P^2$, $\mathbb P^1\times\mathbb P^1$, and
Hirzebruch surfaces. These results imply upper bounds for the effective cones
of these Hilbert schemes, which are also conjecturally sharp in the above
cases.


title: Recursive Reasoning Graph for Multi-Agent Reinforcement Learning

abstract:
  Multi-agent reinforcement learning (MARL) provides an efficient way for
simultaneously learning policies for multiple agents interacting with each
other. However, in scenarios requiring complex interactions, existing
algorithms can suffer from an inability to accurately anticipate the influence
of self-actions on other agents. Incorporating an ability to reason about other
agents' potential responses can allow an agent to formulate more effective
strategies. This paper adopts a recursive reasoning model in a
centralized-training-decentralized-execution framework to help learning agents
better cooperate with or compete against others. The proposed algorithm,
referred to as the Recursive Reasoning Graph (R2G), shows state-of-the-art
performance on multiple multi-agent particle and robotics games.


title: Reversal in the Stationary Prandtl Equations

abstract:
  We demonstrate the existence of an open set of data which exhibits
\textit{reversal} and \textit{recirculation} for the stationary Prandtl
equations (data is taken in an appropriately defined product space due to the
simultaneous forward and backward causality in the problem). Reversal describes
the development of the solution beyond the Goldstein singularity, and is
characterized by the presence of (spatio-temporal) regions in which $u > 0$ and
$u < 0$. The classical point of view of regarding the system as an evolution in
the tangential direction completely breaks down past the Goldstein singularity.
Instead, to describe the development, we view the problem as a
\textit{mixed-type, non-local, quasilinear, free-boundary} problem across the
curve $\{ u = 0 \}$. In a well-chosen nonlinear and self-similar coordinate
system, we extract a coupled system for the bulk solution and several
modulation variables describing the free boundary. Our work combines and
introduces techniques from mixed-type problems, free-boundary problems,
modulation theory, harmonic analysis, and spectral theory. As a byproduct, we
obtain several new cancellations in the Prandtl equations, and develop several
new estimates tailored to singular integral operators with Airy type kernels.


title:
Region Proposal Rectification Towards Robust Instance Segmentation of
  Biological Images

abstract:
  Top-down instance segmentation framework has shown its superiority in object
detection compared to the bottom-up framework. While it is efficient in
addressing over-segmentation, top-down instance segmentation suffers from
over-crop problem. However, a complete segmentation mask is crucial for
biological image analysis as it delivers important morphological properties
such as shapes and volumes. In this paper, we propose a region proposal
rectification (RPR) module to address this challenging incomplete segmentation
problem. In particular, we offer a progressive ROIAlign module to introduce
neighbor information into a series of ROIs gradually. The ROI features are fed
into an attentive feed-forward network (FFN) for proposal box regression. With
additional neighbor information, the proposed RPR module shows significant
improvement in correction of region proposal locations and thereby exhibits
favorable instance segmentation performances on three biological image datasets
compared to state-of-the-art baseline methods. Experimental results demonstrate
that the proposed RPR module is effective in both anchor-based and anchor-free
top-down instance segmentation approaches, suggesting the proposed method can
be applied to general top-down instance segmentation of biological images.


title:
Mid-IR and VUV Spectroscopic Characterisation of Thermally Processed and
  Electron Irradiated CO2 Astrophysical Ice Analogues

abstract:
  The astrochemistry of CO2 ice analogues has been a topic of intensive
investigation due to the prevalence of CO2 throughout the interstellar medium
and the Solar System, as well as the possibility of it acting as a carbon
feedstock for the synthesis of larger, more complex organic molecules. In order
to accurately discern the physico-chemical processes in which CO2 plays a role,
it is necessary to have laboratory-generated spectra to compare against
observational data acquired by ground- and space-based telescopes. A key factor
which is known to influence the appearance of such spectra is temperature,
especially when the spectra are acquired in the infrared and ultraviolet. In
this present study, we describe the results of a systematic investigation
looking into: (i) the influence of thermal annealing on the mid-IR and VUV
absorption spectra of pure, unirradiated CO2 astrophysical ice analogues
prepared at various temperatures, and (ii) the influence of temperature on the
chemical products of electron irradiation of similar ices. Our results indicate
that both mid-IR and VUV spectra of pure CO2 ices are sensitive to the
structural and chemical changes induced by thermal annealing. Furthermore,
using mid-IR spectroscopy, we have successfully identified the production of
radiolytic daughter molecules as a result of 1 keV electron irradiation and the
influence of temperature over this chemistry. Such results are directly
applicable to studies on the chemistry of interstellar ices, comets, and icy
lunar objects and may also be useful as reference data for forthcoming
observational missions.

categories: astro-ph.GA astro-ph.EP astro-ph.IM physics.chem-ph physics.space-ph


title: Theory of nuclear collective vibrations

abstract:
  We review the theory of nuclear collective vibrations evolved over decades
from phenomenological quasiclassical picture to sophisticated microscopic
approaches. The major focus is put on the underlying microscopic mechanisms of
emergent effects, which define the properties of giant resonances and soft
modes. The response of atomic nuclei to electromagnetic and weak fields is
discussed in detail. Astrophysical implications of the giant resonances and
soft modes are outlined.


title: Variable Selection with the Knockoffs: Composite Null Hypotheses

abstract:
  The Fixed-X knockoff filter is a flexible framework for variable selection
with false discovery rate (FDR) control in linear models with arbitrary
(non-singular) design matrices and it allows for finite-sample selective
inference via the LASSO estimates. In this paper, we extend the theory of the
knockoff procedure to tests with composite null hypotheses, which are usually
more relevant to real-world problems. The main technical challenge lies in
handling composite nulls in tandem with dependent features from arbitrary
designs. We develop two methods for composite inference with the knockoffs,
namely, shifted ordinary least-squares (S-OLS) and feature-response product
perturbation (FRPP), building on new structural properties of test statistics
under composite nulls. We also propose two heuristic variants of the S-OLS
method that outperform the celebrated Benjamini-Hochberg (BH) procedure for
composite nulls, which serves as a heuristic baseline under dependent test
statistics. Finally, we analyze the loss in FDR when the original knockoff
procedure is naively applied on composite tests.


title: Asymptotic Distribution of Random Quadratic Forms

abstract:
  In this paper we characterize all distributional limits of the random
quadratic form $T_n =\sum_{1\le u< v\le n} a_{u, v} X_u X_v$, where $((a_{u,
v}))_{1\le u,v\le n}$ is a $\{0, 1\}$-valued symmetric matrix with zeros on the
diagonal and $X_1, X_2, \ldots, X_n$ are i.i.d.~ mean $0$ variance $1$ random
variables with common distribution function $F$. In particular, we show that
any distributional limit of $S_n:=T_n/\sqrt{\mathrm{Var}[T_n]}$ can be
expressed as the sum of three independent components: a Gaussian, a (possibly)
infinite weighted sum of independent centered chi-squares, and a Gaussian
mixture with a random variance. As a consequence, we prove a fourth moment
theorem for the asymptotic normality of $S_n$, which applies even when $F$ does
not have finite fourth moment. More formally, we show that $S_n$ converges to
$N(0, 1)$ if and only if the fourth moment of $S_n$ (appropriately truncated
when $F$ does not have finite fourth moment) converges to 3 (the fourth moment
of the standard normal distribution).


title: New operated polynomial identities and Gr\"{o}bner-Shirshov bases

abstract:
  Quite recently, Bremner et al. introduced a new approach to Rota's
Classification Problem and classified some (new) operated polynomial
identities. In this paper, we prove that all operated polynomial identities
classified by Bremner et al. are Gr\"{o}bner-Shirshov.


title: CFT duals of three-dimensional de Sitter gravity

abstract:
  We present a class of dS/CFT correspondence between two-dimensional CFTs and
three-dimensional de Sitter spaces. We argue that such a CFT includes an
SU$(2)$ WZW model in the critical level limit $k\to -2$, which corresponds to
the classical gravity limit. We can generalize this dS/CFT by considering the
SU$(N)$ WZW model in the critical level limit $k\to -N$, dual to the
higher-spin gravity on a three-dimensional de Sitter space. We confirm that
under this proposed duality the classical partition function in the gravity
side can be reproduced from CFT calculations. We also point out a duality
relation known in higher-spin holography provides further evidence. Moreover,
we analyze two-point functions and entanglement entropy in our dS/CFT
correspondence. Possible spectrum and quantum corrections in the gravity theory
are discussed.


title: Graph Neural Network Potential for Magnetic Materials

abstract:
  Machine Learning (ML) interatomic potential has shown its great power in
condensed matter physics. However, ML interatomic potential for a magnetic
system including both structural degrees of freedom and magnetic moments has
not been well developed yet. A spin-dependent ML interatomic potential approach
based on the crystal graph neural network (GNN) has been developed for any
magnetic system. It consists of the Heisenberg edge graph neural network
(HEGNN) and spin-distance edge graph neural network (SEGNN). The network
captures the Heisenberg coefficient variation between different structures and
the fine spin-lattice coupling of high order and multi-body interaction with
high accuracy. In the tests, this method perfectly fitted a high-order spin
Hamiltonian and two complex spin-lattice Hamiltonian and captured the fine
spin-lattice coupling in BiFeO3. In addition, a disturbed structure of BiFeO3
with strain was successfully optimized with the trained potential. Our work has
expanded the powerful ML GNN potentials to magnetic systems, which paves a new
way for large-scale dynamic simulations on spin-lattice coupled systems.


title:
Generation and genericity of the group of absolutely continuous
  homeomorphisms of the interval

abstract:
  We examine the Polish group $H_{+}^{AC}$ of order-preserving
self-homeomorphisms $f$ of the interval $\left[ 0,1 \right]$ for which both $f$
and $f^{-1}$ are absolutely continuous; in particular, we establish two
results. First, we prove that $H_{+}^{AC}$ is topologically $2$-generated; in
fact, it is generically $2$-generated, i.e., there is a dense $G_{\delta}$ set
of pairs $\left( f,g \right) \in H_{+}^{AC} \times H_{+}^{AC}$ for which
$\left\langle f,g \right\rangle$ is dense. Secondly, we prove that $H_{+}^{AC}$
admits a dense $G_{\delta}$ conjugacy class, and we explicitly characterize the
elements thereof.


title:
Hybrid Deep Learning Model using SPCAGAN Augmentation for Insider Threat
  Analysis

abstract:
  Cyberattacks from within an organization's trusted entities are known as
insider threats. Anomaly detection using deep learning requires comprehensive
data, but insider threat data is not readily available due to confidentiality
concerns of organizations. Therefore, there arises demand to generate synthetic
data to explore enhanced approaches for threat analysis. We propose a linear
manifold learning-based generative adversarial network, SPCAGAN, that takes
input from heterogeneous data sources and adds a novel loss function to train
the generator to produce high-quality data that closely resembles the original
data distribution. Furthermore, we introduce a deep learning-based hybrid model
for insider threat analysis. We provide extensive experiments for data
synthesis, anomaly detection, adversarial robustness, and synthetic data
quality analysis using benchmark datasets. In this context, empirical
comparisons show that GAN-based oversampling is competitive with numerous
typical oversampling regimes. For synthetic data generation, our SPCAGAN model
overcame the problem of mode collapse and converged faster than previous GAN
models. Results demonstrate that our proposed approach has a lower error, is
more accurate, and generates substantially superior synthetic insider threat
data than previous models.


title:
Comptonization by Reconnection Plasmoids in Black Hole Coronae II:
  Electron-Ion Plasma

abstract:
  We perform two-dimensional particle-in-cell simulations of magnetic
reconnection in electron-ion plasmas subject to strong Compton cooling and
calculate the X-ray spectra produced by this process. The simulations are
performed for trans-relativistic reconnection with magnetization $1\leq
\sigma\leq 3$ (defined as the ratio of magnetic tension to plasma rest-mass
density), which is expected in the coronae of accretion disks around black
holes. We find that magnetic dissipation proceeds with inefficient energy
exchange between the heated ions and the Compton-cooled electrons. As a result,
most electrons are kept at a low temperature in Compton equilibrium with
radiation, and so thermal Comptonization cannot reach photon energies $\sim
100$ keV observed from accreting black holes. Nevertheless, magnetic
reconnection efficiently generates $\sim 100$ keV photons because of mildly
relativistic bulk motions of the plasmoid chain formed in the reconnection
layer. Comptonization by the plasmoid motions dominates the radiative output
and controls the peak of the radiation spectrum $E_{\rm pk}$. We find $E_{\rm
pk}\sim 40$ keV for $\sigma=1$ and $E_{\rm pk}\sim100$ keV for $\sigma=3$. In
addition to the X-ray peak around 100 keV, the simulations show a non-thermal
MeV tail emitted by a non-thermal electron population generated near X-points
of the reconnection layer. The results are consistent with the typical hard
state of accreting black holes. In particular, we find that the spectrum of
Cygnus~X-1 is well explained by electron-ion reconnection with $\sigma\sim 3$.

categories: astro-ph.HE astro-ph.SR physics.comp-ph physics.flu-dyn physics.plasm-ph


title:
Leveraging Reward Gradients For Reinforcement Learning in Differentiable
  Physics Simulations

abstract:
  In recent years, fully differentiable rigid body physics simulators have been
developed, which can be used to simulate a wide range of robotic systems. In
the context of reinforcement learning for control, these simulators
theoretically allow algorithms to be applied directly to analytic gradients of
the reward function. However, to date, these gradients have proved extremely
challenging to use, and are outclassed by algorithms using no gradient
information at all. In this work we present a novel algorithm, cross entropy
analytic policy gradients, that is able to leverage these gradients to
outperform state of art deep reinforcement learning on a set of challenging
nonlinear control problems.


title:
Two Channel Filter Banks on Arbitrary Graphs with Positive Semi Definite
  Variation Operators

abstract:
  We study the design of filter banks for signals defined on the nodes of
graphs. We propose novel two channel filter banks, that can be applied to
arbitrary graphs, given a positive semi definite variation operator, while
using downsampling operators on arbitrary vertex partitions. The proposed
filter banks also satisfy several desirable properties, including perfect
reconstruction, and critical sampling, while having efficient implementations.
Our results generalize previous approaches only valid for the normalized
Laplacian of bipartite graphs. We consider graph Fourier transforms (GFTs)
given by the generalized eigenvectors of the variation operator. This GFT basis
is orthogonal in an alternative inner product space, which depends on the
choices of downsampling sets and variation operators. We show that the spectral
folding property of the normalized Laplacian of bipartite graphs, at the core
of bipartite filter bank theory, can be generalized for the proposed GFT if the
inner product matrix is chosen properly. We give a probabilistic interpretation
to the proposed filter banks using Gaussian graphical models. We also study
orthogonality properties of tree structured filter banks, and propose a vertex
partition algorithm for downsampling. We show that the proposed filter banks
can be implemented efficiently on 3D point clouds, with hundreds of thousands
of points (nodes), while also improving the color signal representation quality
over competing state of the art approaches.


title:
Entropy production in longitudinally expanding Yang-Mills field with use
  of Husimi function$-$semiclassical approximation

abstract:
  We investigate the possible thermalization process of the highly occupied and
weakly coupled Yang-Mills fields expanding along the beam axis through an
evaluation of the entropy, particle number, and pressure anisotropy. The time
evolution of the system is calculated by solving the equation of motion for the
Wigner function in the semiclassical approximation with initial conditions
mimicking the glasma. For the evaluation of the entropy, we adopt the
Husimi-Wehrl (HW) entropy, which is obtained by using the Husimi function, a
positive semidefinite quantum distribution function given by smearing the
Wigner function. By numerical calculations at $g=0.1$ and $0.2$, the entropy
production is found to occur together with the particle creation in two
distinct stages: In the first stage, the particle number and the entropy at low
longitudinal momenta grow rapidly. In the second stage, the particle number and
the entropy of higher longitudinal momentum modes show slower increase. The
pressure anisotropy remains in our simulation and implies that the system is
still out-of-equilibrium.


title: Compartmental Models for COVID-19 and Control via Policy Interventions

abstract:
  We demonstrate an approach to replicate and forecast the spread of the
SARS-CoV-2 (COVID-19) pandemic using the toolkit of probabilistic programming
languages (PPLs). Our goal is to study the impact of various modeling
assumptions and motivate policy interventions enacted to limit the spread of
infectious diseases. Using existing compartmental models we show how to use
inference in PPLs to obtain posterior estimates for disease parameters. We
improve popular existing models to reflect practical considerations such as the
under-reporting of the true number of COVID-19 cases and motivate the need to
model policy interventions for real-world data. We design an SEI3RD model as a
reusable template and demonstrate its flexibility in comparison to other
models. We also provide a greedy algorithm that selects the optimal series of
policy interventions that are likely to control the infected population subject
to provided constraints. We work within a simple, modular, and reproducible
framework to enable immediate cross-domain access to the state-of-the-art in
probabilistic inference with emphasis on policy interventions. We are not
epidemiologists; the sole aim of this study is to serve as an exposition of
methods, not to directly infer the real-world impact of policy-making for
COVID-19.


title: Optimally scheduling public safety power shutoffs

abstract:
  In an effort to reduce power system-caused wildfires, utilities carry out
public safety power shutoffs (PSPS) in which portions of the grid are
de-energized to mitigate the risk of ignition. The decision to call a PSPS must
balance reducing ignition risks and the negative impact of service
interruptions. In this work, we consider three PSPS scheduling scenarios, which
we model as dynamic programs. In the first two scenarios, we assume that N
PSPSs are budgeted as part of the investment strategy. In the first scenario, a
penalty is incurred for each PSPS declared past the Nth event. In the second,
we assume that some costs can be recovered if the number of PSPSs is below $N$
while still being subject to a penalty if above N. In the third, the system
operator wants to minimize the number of PSPS such that the total expected cost
is below a threshold. We provide optimal or asymptotically optimal closed-form
policies for the first two scenarios. Lastly, we apply the first PSPS model to
critical-peak pricing, and obtain an optimal scheduling policy to reduce the
peak demand based on weather observations.


title:
Holomorphic sections of line bundles on the jet spaces of the Riemann
  sphere

abstract:
  Fix a point $t_0$ in the circle $S^1$. The space $J^k(t_0, \mathbb{P}^1)$ of
$k$-jets at $t_0$ of $C^{\infty}$ maps from $S^1$ to the Riemann sphere
$\mathbb{P}^1$ is a $k+1$ dimensional complex algebraic manifold. We identify a
class of holomorphic sections of line bundles on $J^k(t_0, \mathbb{P}^1)$.


title:
Resonant enhancement of the near-field radiative heat transfer in
  nanoparticles

abstract:
  We numerically study the tuning of the radiative heat transfer between a
spherical InSb nanoparticle in the vicinity of a flat SiC surface assisted by a
static magnetic field. By changing the value of the applied magnetic field, the
dielectric function of the nanosphere becomes anisotropic due to the excitation
of magneto-plasmons. In the dipolar approximation, the plasmon resonance of the
particle splits into two additional satellite resonances that shift to higher
and lower frequencies as the field increases. When one of the particle
resonances overlaps with the phonon-polariton frequency of the SiC surface, an
enhancement of the heat transfer of two orders of magnitude is obtained. To
understand the tuning of the radiative heat transfer, we present a detailed
analysis of the nature of the modes that can be excited (surface, bulk, and
hyperbolic).


title: Null hypersurfaces as wave fronts in Lorentz-Minkowski space

abstract:
  In this paper, we show that "L-complete null hypersurfaces" (i.e. ruled
hypersurfaces foliated by entirety of light-like lines) as wave fronts in the
$(n+1)$-dimensional Lorentz-Minkowski space $\mathbb R^{n+1}_1$ are canonically
induced by hypersurfaces in the $n$-dimensional Euclidean space $\mathbb
R^n_0$. As an application, we show that most of null wave fronts can be
realized as restrictions of certain L-complete null wave fronts. Moreover, we
determine light-like complete null hypersurfaces whose singular sets are
compact.


title:
Fully Decentralized, Scalable Gaussian Processes for Multi-Agent
  Federated Learning

abstract:
  In this paper, we propose decentralized and scalable algorithms for Gaussian
process (GP) training and prediction in multi-agent systems. To decentralize
the implementation of GP training optimization algorithms, we employ the
alternating direction method of multipliers (ADMM). A closed-form solution of
the decentralized proximal ADMM is provided for the case of GP hyper-parameter
training with maximum likelihood estimation. Multiple aggregation techniques
for GP prediction are decentralized with the use of iterative and consensus
methods. In addition, we propose a covariance-based nearest neighbor selection
strategy that enables a subset of agents to perform predictions. The efficacy
of the proposed methods is illustrated with numerical experiments on synthetic
and real data.


title:
WASP-35 and HAT-P-30/WASP-51: re-analysis using TESS and ground-based
  transit photometry

abstract:
  High-precision transit observations provide excellent opportunities for
characterizing the physical properties of exoplanetary systems. These physical
properties supply many pieces of information for unvealing the internal
structure, external atmosphere, and dynamical history of the planets. We
present revised properties of transiting systems WASP-35 and HAT-P-30/WASP-51
through analyzing newly available TESS photometry and ground-based observations
obtained at 1m telescope of Yunnan Observatories as well as from the
literature. The improved system parameters are consistent with the previous
results. Furthermore, we find that HAT-P-30b/WASP-51b's transits show
significant timing variation which cannot be explained by decaying orbit due to
tidal dissipation and the R\o mer effect, while both apsidal precession and an
additional perturbing body could reproduce this signal through our
comprehensive dynamical simulations. Because both of them are valuable targets
which are suitable for transmission spectroscopy, we make some predictions for
atmospheric properties of WASP-35b and HAT-P-30b/WASP-51b based on newly
derived system parameters.


title: Diffusion Maps : Using the Semigroup Property for Parameter Tuning

abstract:
  Diffusion maps (DM) constitute a classic dimension reduction technique, for
data lying on or close to a (relatively) low-dimensional manifold embedded in a
much larger dimensional space. The DM procedure consists in constructing a
spectral parametrization for the manifold from simulated random walks or
diffusion paths on the data set. However, DM is hard to tune in practice. In
particular, the task to set a diffusion time t when constructing the diffusion
kernel matrix is critical. We address this problem by using the semigroup
property of the diffusion operator. We propose a semigroup criterion for
picking t. Experiments show that this principled approach is effective and
robust.


title: De Moivre and Bell polynomials

abstract:
  We survey a family of polynomials that are very useful in all kinds of power
series manipulations, and appearing more frequently in the literature.
Applications to formal power series, generating functions and asymptotic
expansions are described, and we discuss the related work of De Moivre,
Arbogast and Bell.


title: Scaled indium oxide transistors fabricated using atomic layer deposition

abstract:
  In order to continue to improve integrated circuit performance and
functionality, scaled transistors with short channel lengths and low thickness
are needed. But the further scaling of silicon-based devices and the
development of alternative semiconductor channel materials that are compatible
with current fabrication processes is challenging. Here we report
atomic-layer-deposited indium oxide transistors with channel lengths down to 8
nm, channel thicknesses down to 0.5 nm and equivalent dielectric oxide
thickness down to 0.84 nm. Due to the scaled device dimensions and low contact
resistance, the devices exhibit high on-state currents of 3.1 A/mm at a drain
voltage of 0.5 V and a transconductance of 1.5 S/mm at a drain voltage 1 V. Our
devices are a promising alternative channel material for scaled transistors
with back-end-of-line processing compatibility.


title:
Nonadiabatic Correction and Adiabatic Criteria of Noninteracting Quantum
  Dot Systems

abstract:
  We theoretically study nonadiabatic corrections for charge pumping in a
noninteracting electron model of a single-level quantum dot. We derive a
formula for the velocity limit of parameter driving to realize adiabatic
pumping and illustrate its features in the wide-band limit. We discuss the
effect of the nonadiabatic corrections in terms of a typical velocity limit,
which is defined by averaging the velocity limits on the parameter contour. We
also show that the typical velocity limit vanishes when the adiabaticity
breakdown occurs in a quantum dot coupled to reservoirs with a band edge.


title: Winding number and Zak phase in multi-band SSH models

abstract:
  We use multi-band SSH models to demonstrate a prescription for calculating
the correct Zak phase and winding number of multi-band systems. We verify our
prescription by comparing the resultant winding number of a four-band SSH model
with the edge states of a semi-infinite chain which we find by solving the
equation of motion. We then also carry out an extensive comparison with the
numerical results obtained by solving the matrix eigenvalue problem of finite
chains with various number of sites. As a double check of our prescription, we
also confirm the bulk edge correspondence in a six-band SSH model. Similar to
the usual SSH model, the winding numbers associated to the left and right
boundaries in a finite chain may be different if space inversion symmetry is
violated in the system. We believe the prescription we propose here may also be
applied to other 1D multi-band systems.


title: The Orthologic of Epistemic Modals

abstract:
  Epistemic modals have peculiar logical features that are challenging to
account for in a broadly classical framework. For instance, while a sentence of
the form $p\wedge\Diamond\neg p$ ('$p$, but it might be that not $p$') appears
to be a contradiction, $\Diamond\neg p$ does not entail $\neg p$, which would
follow in classical logic. Likewise, the classical laws of distributivity and
disjunctive syllogism fail for epistemic modals. Existing attempts to account
for these facts generally either under- or over-correct. Some predict that
$p\wedge\Diamond\neg p$, a so-called epistemic contradiction, is a
contradiction only in an etiolated sense, under a notion of entailment that
does not always allow us to replace $p\wedge\Diamond\neg p$ with a
contradiction; these theories underpredict the infelicity of embedded epistemic
contradictions. Other theories savage classical logic, eliminating not just
rules that intuitively fail but also rules like non-contradiction, excluded
middle, De Morgan's laws, and disjunction introduction, which intuitively
remain valid for epistemic modals. In this paper, we aim for a middle ground,
developing a semantics and logic for epistemic modals that makes epistemic
contradictions genuine contradictions and that invalidates distributivity and
disjunctive syllogism but that otherwise preserves classical laws that
intuitively remain valid. We start with an algebraic semantics, based on
ortholattices instead of Boolean algebras, and then propose a more concrete
possibility semantics, based on partial possibilities related by compatibility.
Both semantics yield the same consequence relation, which we axiomatize. Then
we show how to extend our semantics to explain parallel phenomena involving
probabilities and conditionals. The goal throughout is to retain what is
desirable about classical logic while accounting for the non-classicality of
epistemic vocabulary.


title: New Classes of Facets for Complementarity Knapsack Problems

abstract:
  The complementarity knapsack problem (CKP) is a knapsack problem with
real-valued variables and complementarity conditions between pairs of its
variables. We extend the polyhedral studies of De Farias et al. for CKP, by
proposing three new families of cutting-planes that are all obtained from a
combinatorial concept known as a pack. Sufficient conditions for these
inequalities to be facet-defining, based on the concept of a maximal switching
pack, are also provided. Moreover, we answer positively a conjecture by De
Farias et al. about the separation complexity of the inequalities introduced in
their work.


title: Veering branched surfaces, surgeries, and geodesic flows

abstract:
  We introduce veering branched surfaces as a dual way of studying veering
triangulations. We then discuss some surgical operations on veering branched
surfaces. Using these, we provide explicit constructions of some veering
branched surfaces whose dual veering triangulations correspond to geodesic
flows of negatively curved surfaces. We construct these veering branched
surfaces on complements of Montesinos links whose double branched covers are
unit tangent bundles of negatively curved orbifolds, and on complements of full
lifts of filling geodesics in unit tangent bundles of negatively curved
surfaces, when the geodesics have no triple intersections and have ($n \geq
4$)-gons as complementary regions. As an application, this provides explicit
Markov partitions of geodesic flows on negatively curved surfaces.
  In an appendix, we classify when it is possible to construct veering
triangulations on drilled unit tangent bundles which correspond to geodesic
flows, by characterizing when there are no perfect fits.


title: Calder\'on-Zygmund operators on local hardy spaces

abstract:
  We give necessary and sufficient conditions for inhomogeneous
Calder\'on-Zgymund operators to be bounded on the local hardy spaces
$h^p(\mathbb{R}^n)$. We then give applications to local and truncated Riesz
transforms, as well as pseudo-differential operators defined by amplitudes.


title:
Displacement calibration of optical tweezers with absolute gravitational
  acceleration

abstract:
  In recent years, levitated particles of optical traps in vacuum have shown
enormous potential in precision sensor development and searching for new
physics. The accuracy of the calibration relating the detected signal to
absolute displacement of the trapped particle is a critical factor for absolute
measurement performance. In this paper, we suggest and experimentally
demonstrate a novel calibration method for optical tweezers based on
free-falling particles in vacuum, where the gravitational acceleration is
introduced as an absolute reference. Our work provides a calibration protocol
with great certainty and traceability, which is significant in improving the
accuracy of precision sensing based on optically levitated particles.


title:
MIRROR: Differentiable Deep Social Projection for Assistive Human-Robot
  Communication

abstract:
  Communication is a hallmark of intelligence. In this work, we present MIRROR,
an approach to (i) quickly learn human models from human demonstrations, and
(ii) use the models for subsequent communication planning in assistive
shared-control settings. MIRROR is inspired by social projection theory, which
hypothesizes that humans use self-models to understand others. Likewise, MIRROR
leverages self-models learned using reinforcement learning to bootstrap human
modeling. Experiments with simulated humans show that this approach leads to
rapid learning and more robust models compared to existing behavioral cloning
and state-of-the-art imitation learning methods. We also present a
human-subject study using the CARLA simulator which shows that (i) MIRROR is
able to scale to complex domains with high-dimensional observations and
complicated world physics and (ii) provides effective assistive communication
that enabled participants to drive more safely in adverse weather conditions.


title: A Survey for Solving Mixed Integer Programming via Machine Learning

abstract:
  This paper surveys the trend of leveraging machine learning to solve mixed
integer programming (MIP) problems. Theoretically, MIP is an NP-hard problem,
and most of the combinatorial optimization (CO) problems can be formulated as
the MIP. Like other CO problems, the human-designed heuristic algorithms for
MIP rely on good initial solutions and cost a lot of computational resources.
Therefore, we consider applying machine learning methods to solve MIP, since
ML-enhanced approaches can provide the solution based on the typical patterns
from the historical data. In this paper, we first introduce the formulation and
preliminaries of MIP and several traditional algorithms to solve MIP. Then, we
advocate further promoting the different integration of machine learning and
MIP and introducing related learning-based methods, which can be classified
into exact algorithms and heuristic algorithms. Finally, we propose the outlook
for learning-based MIP solvers, direction towards more combinatorial
optimization problems beyond MIP, and also the mutual embrace of traditional
solvers and machine learning components.


title:
Tunable Majorana corner modes by orbital-dependent exchange interaction
  in a two-dimensional topological superconductor

abstract:
  We theoretically study the effect of orbital-dependent exchange field in the
formation of second order topological superconductors. We demonstrate that
changing the orbital difference can induce topological transition and the
Majorana corner modes therein can be manipulated. We further propose to detect
the corner modes via a normal probe terminal. The conductance quantization is
found to be robust to changes of the relevant system parameters.


title:
Robust Estimation of Covariance Matrices: Adversarial Contamination and
  Beyond

abstract:
  We consider the problem of estimating the covariance structure of a random
vector $Y\in \mathbb R^d$ from a sample $Y_1,\ldots,Y_n$. We are interested in
the situation when $d$ is large compared to $n$ but the covariance matrix
$\Sigma$ of interest has (exactly or approximately) low rank. We assume that
the given sample is (a) $\epsilon$-adversarially corrupted, meaning that
$\epsilon$ fraction of the observations could have been replaced by arbitrary
vectors, or that (b) the sample is i.i.d. but the underlying distribution is
heavy-tailed, meaning that the norm of $Y$ possesses only finite fourth
moments. We propose an estimator that is adaptive to the potential low-rank
structure of the covariance matrix as well as to the proportion of contaminated
data, and admits tight deviation guarantees despite rather weak assumptions on
the underlying distribution. Finally, we discuss the algorithms that allow to
approximate the proposed estimator in a numerically efficient way.


title:
Image Encryption using Fractional Integral transforms: Vulnerabilities,
  Threats and Future scope

abstract:
  This paper presents a review on various image encryption schemes based on
fractional transforms for security consideration of image communication. With
the enormous increase in the usage of digital media in almost every sphere from
education to entertainment, the security of sensitive information has been a
concern. Most importantly the user demands for an assured privacy before the
information is shared over open network and insecure channels. As image is the
most frequently used means of communication in digital scenario, there are
various security methods proposed by researchers from time to time. The review
is done in two parts. A brief introduction to various optical signal
processing-based methods is provided in the beginning which is followed by a
conceptual overview of classical DRPE (double random phase encoding) based
optical encryption schemes. Some of the recently published relevant reviews and
cryptanalysis articles are also analysed. The second part of review is
emphasised on the mathematical modelling for encrypting the images in optical
transform domain which is main focus of the presented work. Various schemes are
classified according to the methodology adopted in each of them. A comparative
analysis is presented in tabular form. Based on the observations, the work is
converged to a summary on various challenges and some constructive guidelines
are provided for consideration in future work. Such a narrative review on
encryption algorithm based on various fractional integral transforms in various
architectural schematics is not presented before at one place.


title:
RGB-D SLAM in Indoor Planar Environments with Multiple Large Dynamic
  Objects

abstract:
  This work presents a novel dense RGB-D SLAM approach for dynamic planar
environments that enables simultaneous multi-object tracking, camera
localisation and background reconstruction. Previous dynamic SLAM methods
either rely on semantic segmentation to directly detect dynamic objects; or
assume that dynamic objects occupy a smaller proportion of the camera view than
the static background and can, therefore, be removed as outliers. Our approach,
however, enables dense SLAM when the camera view is largely occluded by
multiple dynamic objects with the aid of camera motion prior. The dynamic
planar objects are separated by their different rigid motions and tracked
independently. The remaining dynamic non-planar areas are removed as outliers
and not mapped into the background. The evaluation demonstrates that our
approach outperforms the state-of-the-art methods in terms of localisation,
mapping, dynamic segmentation and object tracking. We also demonstrate its
robustness to large drift in the camera motion prior.


title: The Power of Multiple Choices in Online Stochastic Matching

abstract:
  We study the power of multiple choices in online stochastic matching. Despite
a long line of research, existing algorithms still only consider two choices of
offline neighbors for each online vertex because of the technical challenge in
analyzing multiple choices. This paper introduces two approaches for designing
and analyzing algorithms that use multiple choices. For unweighted and
vertex-weighted matching, we adopt the online correlated selection (OCS)
technique into the stochastic setting, and improve the competitive ratios to
$0.716$, from $0.711$ and $0.7$ respectively. For edge-weighted matching with
free disposal, we propose the Top Half Sampling algorithm. We directly
characterize the progress of the whole matching instead of individual vertices,
through a differential inequality. This improves the competitive ratio to
$0.706$, breaking the $1-\frac{1}{e}$ barrier in this setting for the first
time in the literature. Finally, for the harder edge-weighted problem without
free disposal, we prove that no algorithms can be $0.703$ competitive,
separating this setting from the aforementioned three.


title: Towards Self-Supervised Category-Level Object Pose and Size Estimation

abstract:
  In this work, we tackle the challenging problem of category-level object pose
and size estimation from a single depth image. Although previous
fully-supervised works have demonstrated promising performance, collecting
ground-truth pose labels is generally time-consuming and labor-intensive.
Instead, we propose a label-free method that learns to enforce the geometric
consistency between category template mesh and observed object point cloud
under a self-supervision manner. Specifically, our method consists of three key
components: differentiable shape deformation, registration, and rendering. In
particular, shape deformation and registration are applied to the template mesh
to eliminate the differences in shape, pose and scale. A differentiable
renderer is then deployed to enforce geometric consistency between point clouds
lifted from the rendered depth and the observed scene for self-supervision. We
evaluate our approach on real-world datasets and find that our approach
outperforms the simple traditional baseline by large margins while being
competitive with some fully-supervised approaches.


title:
Hubble Space Telescope Captures UGC 12591: Bulge/Disk Properties, Star
  Formation and `Missing Baryons' Census in a Very Massive and Fast Spinning
  Hybrid Galaxy

abstract:
  We present Hubble Space Telescope (HST) observations of the nearby (z=
0.023), very massive, highly rotating (488.4$\pm 12.5$ $\kms$) hybrid (S0-Sa)
galaxy UGC~12591, along with observations taken at other wavelengths from UV to
FIR. Deep HST data of the galaxy in V, I and H bands is used to disentangle its
bulge and disk components. The surface brightness distributions show a
dominance of the bulge component over the disk with H-band bulge-to-disc
luminosity ratio $\sim 69\%$. From spectral energy distribution (SED) fitting,
we find an extremely low global star formation rate (SFR) of $\rm \sim 0.1 -
0.2 \Msun y^{-1}$, which is unusually low for this galaxy's huge stellar mass
($\rm M_{\star} \sim 1.6 \times 10^{11} \, \Msun$), that implies a strong
quenching of it's SFR. For at least past $\rm 10^{8}$ years the galaxy has
remained in the quiescent state as a sterile, `red and dead' galaxy. The
fraction of total infra-red dust luminosity contributed by the ISM dust is as
high as $\rm \sim 90\%$, with total dust luminosity $\rm L_{dust} = (0.5-3.5)
\times 10^{10} \Lsun$, dominated by cold dust at equilibrium temperature $\rm
T_{C} \approx 15 K$. UGC~12591 is found host to a super massive black hole
(SMBH) of mass $\rm 6.18\times 10^{8} M_{\sun}$ which at the present moment is
quiescent, i.e., neither we see large ($\rm > 1 kpc$) radio jets nor is the
black hole contributing significantly to the mid-IR SED, ruling out the
presence of strong radiative feedback from a bright AGN.We have obtained a
detailed census of all observable baryons in this galaxy. The star formation
efficiency is very low ($\sim 0.02-0.06$) and within a virial radius the galaxy
has a total observed baryonic mass of $6.87\times10^{11}~\Msun$, amounting to a
baryonic deficiency of $\sim$ $80\%$ relative to the cosmological mean.


title: Strong Determinism

abstract:
  A strongly deterministic theory of physics is one that permits exactly one
possible history of the universe. In the words of Penrose (1989), "it is not
just a matter of the future being determined by the past; the entire history of
the universe is fixed, according to some precise mathematical scheme, for all
time." Such an extraordinary feature may appear unattainable in any realistic
and simple theory of physics. In this paper, I propose a definition of strong
determinism and contrast it with those of standard determinism and
super-determinism. Next, I discuss its consequences for explanation, causation,
prediction, fundamental properties, free will, and modality. Finally, I present
the first example of a realistic, simple, and strongly deterministic physical
theory--the Everettian Wentaculus. As a consequence of physical laws, the
history of the Everettian multiverse could not have been different. If the
Everettian Wentaculus is empirically equivalent to other quantum theories, we
can never empirically find out whether or not our world is strongly
deterministic. Even if strong determinism fails to be true, it is closer to the
actual world than we have presumed, with implications for some of the central
topics in philosophy and foundations of physics.


title:
Minimum Cost Multicuts for Incorrect Landmark Edge Detection in
  Pose-graph SLAM

abstract:
  Pose-graph SLAM is the de facto standard framework for constructing
large-scale maps from multi-session experiences of relative observations and
motions during visual robot navigation. It has received increasing attention in
the context of recent advanced SLAM frameworks such as graph neural SLAM. One
remaining challenge is landmark misrecognition errors (i.e., incorrect landmark
edges) that can have catastrophic effects on the inferred pose-graph map. In
this study, we present comprehensive criteria to maximize global consistency in
the pose graph using a new robust graph cut technique. Our key idea is to
formulate the problem as a minimum-cost multi-cut that enables us to optimize
not only landmark correspondences but also the number of landmarks while
allowing for a varying number of landmarks. This makes our proposed approach
invariant against the type of landmark measurement, graph topology, and metric
information, such as the speed of the robot motion. The proposed graph cut
technique was integrated into a practical SLAM framework and verified
experimentally using the public NCLT dataset.


title: An inverse boundary value problem arising in nonlinear acoustics

abstract:
  We consider an inverse problem arising in nonlinear ultrasound imaging. The
propagation of ultrasound waves is modeled by a quasilinear wave equation. We
make measurements at the boundary of the medium encoded in the
Dirichlet-to-Neumann map, and we show that these measurements determine the
nonlinearity.


title:
Focus on the Target's Vocabulary: Masked Label Smoothing for Machine
  Translation

abstract:
  Label smoothing and vocabulary sharing are two widely used techniques in
neural machine translation models. However, we argue that simply applying both
techniques can be conflicting and even leads to sub-optimal performance. When
allocating smoothed probability, original label smoothing treats the
source-side words that would never appear in the target language equally to the
real target-side words, which could bias the translation model. To address this
issue, we propose Masked Label Smoothing (MLS), a new mechanism that masks the
soft label probability of source-side words to zero. Simple yet effective, MLS
manages to better integrate label smoothing with vocabulary sharing. Our
extensive experiments show that MLS consistently yields improvement over
original label smoothing on different datasets, including bilingual and
multilingual translation from both translation quality and model's calibration.
Our code is released at https://github.com/PKUnlp-icler/MLS


title: Progress and prospects in magnetic topological materials

abstract:
  Magnetic topological materials represent a class of compounds whose
properties are strongly influenced by the topology of the electronic
wavefunctions coupled with the magnetic spin configuration. Such materials can
support chiral electronic channels of perfect conduction, and can be used for
an array of applications from information storage and control to
dissipationless spin and charge transport. Here, we review the theoretical and
experimental progress achieved in the field of magnetic topological materials
beginning with the theoretical prediction of the Quantum Anomalous Hall Effect
without Landau levels, and leading to the recent discoveries of magnetic Weyl
semimetals and antiferromagnetic topological insulators. We outline the recent
theoretical progress that resulted in the tabulation, for the first time, of
all magnetic symmetry group representations and topology. We describe several
experiments realizing Chern insulators, Weyl and Dirac magnetic semimetals, and
an array of axionic and higher-order topological phases of matter as well as
survey future perspectives.


title:
Multi-class Token Transformer for Weakly Supervised Semantic
  Segmentation

abstract:
  This paper proposes a new transformer-based framework to learn class-specific
object localization maps as pseudo labels for weakly supervised semantic
segmentation (WSSS). Inspired by the fact that the attended regions of the
one-class token in the standard vision transformer can be leveraged to form a
class-agnostic localization map, we investigate if the transformer model can
also effectively capture class-specific attention for more discriminative
object localization by learning multiple class tokens within the transformer.
To this end, we propose a Multi-class Token Transformer, termed as MCTformer,
which uses multiple class tokens to learn interactions between the class tokens
and the patch tokens. The proposed MCTformer can successfully produce
class-discriminative object localization maps from class-to-patch attentions
corresponding to different class tokens. We also propose to use a patch-level
pairwise affinity, which is extracted from the patch-to-patch transformer
attention, to further refine the localization maps. Moreover, the proposed
framework is shown to fully complement the Class Activation Mapping (CAM)
method, leading to remarkably superior WSSS results on the PASCAL VOC and MS
COCO datasets. These results underline the importance of the class token for
WSSS.


title:
Watch from sky: machine-learning-based multi-UAV network for predictive
  police surveillance

abstract:
  This paper presents the watch-from-sky framework, where multiple unmanned
aerial vehicles (UAVs) play four roles, i.e., sensing, data forwarding,
computing, and patrolling, for predictive police surveillance. Our framework is
promising for crime deterrence because UAVs are useful for collecting and
distributing data and have high mobility. Our framework relies on machine
learning (ML) technology for controlling and dispatching UAVs and predicting
crimes. This paper compares the conceptual model of our framework against the
literature. It also reports a simulation of UAV dispatching using reinforcement
learning and distributed ML inference over a lossy UAV network.


title:
Stellar collisions in globular clusters: the origin of multiple stellar
  populations

abstract:
  Two generations of stars, G1 and G2, typically populate Galactic globular
clusters (GCs). The origin of G2 stars is unclear. We uncover two empirical
dependencies between GC characteristics, which can be explained by the
formation of G2 Main-Sequence (MS) stars due to collision\merging of their
primordial counterparts (G1). A similar genesis of both G2 stars and peculiar
objects like LMXBs and millisecond pulsars is also implied. Indeed, we find a
significant (at a confidence level > 99,9%) anti-correlation between the
fraction of G1 red giants (N_{G1}/N_{tot}) and stellar encounter rates among 51
GCs. Moreover, a Milky Way-like initial mass function (IMF) requires at least
~50% of MS stars located in the mass range [0.1-0.5] M_sun. Unlike cluster mass
loss, stellar collisions\merging retain these G1 stars by converting them into
more massive G2 ones, with mainly M_ms > 0.5 M_sun. This process coupled with a
decreasing relative mass loss with increasing GC masses implies a smaller
(N_{G1}/N_{tot}) in more massive GCs with a shallower present day MF. From data
for 35 GCs, we find that such an anti-correlation is significant at 98.3%
confidence level (Spearman's correlation) for 12 most massive GCs (M_GC >
10^{5.3} M_sun) and it is at a confidence level of 89\% for the 12 least
massive GCs (M_GC < 10^{5.1} M_sun). Other fractions of G1 and G2 stars
observed at the bottom of the MS as compared with the red giant branch in a few
GCs are consistent with the scenario proposed.


title:
A Multi-Document Coverage Reward for RELAXed Multi-Document
  Summarization

abstract:
  Multi-document summarization (MDS) has made significant progress in recent
years, in part facilitated by the availability of new, dedicated datasets and
capacious language models. However, a standing limitation of these models is
that they are trained against limited references and with plain
maximum-likelihood objectives. As for many other generative tasks,
reinforcement learning (RL) offers the potential to improve the training of MDS
models; yet, it requires a carefully-designed reward that can ensure
appropriate leverage of both the reference summaries and the input documents.
For this reason, in this paper we propose fine-tuning an MDS baseline with a
reward that balances a reference-based metric such as ROUGE with coverage of
the input documents. To implement the approach, we utilize RELAX (Grathwohl et
al., 2018), a contemporary gradient estimator which is both low-variance and
unbiased, and we fine-tune the baseline in a few-shot style for both stability
and computational efficiency. Experimental results over the Multi-News and WCEP
MDS datasets show significant improvements of up to +0.95 pp average ROUGE
score and +3.17 pp METEOR score over the baseline, and competitive results with
the literature. In addition, they show that the coverage of the input documents
is increased, and evenly across all documents.


title: The Synthetic Hilbert Space of Laser-Driven Free-Electrons

abstract:
  Recent advances in laser interactions with coherent free electrons have
enabled to shape the electron's quantum state. Each electron becomes a
superposition of energy levels on an infinite quantized ladder, shown to
contain up to thousands of energy levels. We propose to utilize the quantum
nature of such laser-driven free electrons as a synthetic Hilbert space in
which we construct and control qudits (quantum digits). The question that
motivates our work is what qudit states can be accessed using electron-laser
interactions, and whether it is possible to implement any arbitrary quantum
gate. We find how to encode and manipulate free-electron qudit states, focusing
on dimensions which are powers of 2, where the qudit represents multiple qubits
implemented on the same single electron - algebraically separated, but
physically joined. As an example, we prove the possibility to fully control a
4-dimenisonal qudit, and reveal the steps required for full control over any
arbitrary dimension. Our work enriches the range of applications of free
electrons in microscopy and spectroscopy, offering a new platform for
continuous-variable quantum information.


title:
Depthwise Convolution for Multi-Agent Communication with Enhanced
  Mean-Field Approximation

abstract:
  Multi-agent settings remain a fundamental challenge in the reinforcement
learning (RL) domain due to the partial observability and the lack of accurate
real-time interactions across agents. In this paper, we propose a new method
based on local communication learning to tackle the multi-agent RL (MARL)
challenge within a large number of agents coexisting. First, we design a new
communication protocol that exploits the ability of depthwise convolution to
efficiently extract local relations and learn local communication between
neighboring agents. To facilitate multi-agent coordination, we explicitly learn
the effect of joint actions by taking the policies of neighboring agents as
inputs. Second, we introduce the mean-field approximation into our method to
reduce the scale of agent interactions. To more effectively coordinate
behaviors of neighboring agents, we enhance the mean-field approximation by a
supervised policy rectification network (PRN) for rectifying real-time agent
interactions and by a learnable compensation term for correcting the
approximation bias. The proposed method enables efficient coordination as well
as outperforms several baseline approaches on the adaptive traffic signal
control (ATSC) task and the StarCraft II multi-agent challenge (SMAC).


title:
Tail variational principle and asymptotic $h$-expansiveness for amenable
  group actions

abstract:
  In this paper we prove the tail variational principle for actions of
countable amenable groups. This allows us to extend some characterizations of
asymptotic $h$-expansiveness from $\mathbb{Z}$-actions to actions of countable
amenable groups.


title:
Divide and Conquer: Text Semantic Matching with Disentangled Keywords
  and Intents

abstract:
  Text semantic matching is a fundamental task that has been widely used in
various scenarios, such as community question answering, information retrieval,
and recommendation. Most state-of-the-art matching models, e.g., BERT, directly
perform text comparison by processing each word uniformly. However, a query
sentence generally comprises content that calls for different levels of
matching granularity. Specifically, keywords represent factual information such
as action, entity, and event that should be strictly matched, while intents
convey abstract concepts and ideas that can be paraphrased into various
expressions. In this work, we propose a simple yet effective training strategy
for text semantic matching in a divide-and-conquer manner by disentangling
keywords from intents. Our approach can be easily combined with pre-trained
language models (PLM) without influencing their inference efficiency, achieving
stable performance improvements against a wide range of PLMs on three
benchmarks.


title:
Two dynamical generated $a_0$ resonances by interactions between vector
  mesons

abstract:
  We study dynamically generated $a_0$ resonances by interactions between
vector mesons including their coupling to channels of pseudoscalar mesons
within coupled-channel approach. Both vector and pseudoscalar mesons are
considered as $t$-channel exchanged mesons for calculating interactions between
vector mesons. Analogous to $a_0(980)$ as a $K\bar K-\pi\eta$ dynamically
generated state, there is an $a_0(1710)$ as a coupled channel dynamically
generated state near $K^*\bar{K}^*$ threshold. The channels involved are
$\rho\phi,K^*\bar{K}^*,\rho\omega, K\bar{K},\pi\eta$. This $a_0$ mainly decays
to $\rho\omega$, $\pi\eta$ and $K\bar{K}$. This pole is much tied to the
coupled channel effect. If we turn off either $\rho\phi$ or $K^*\bar{K}^*$, the
pole disappears. In addition, it is found that $a_0(1450)$ may also be
dynamically generated by $\rho\omega$ interactions due to $\pi$ and $\eta$
exchange.


title:
Almost cyclic regular elements in irreducible representations of simple
  algebraic groups

abstract:
  Let $G$ be a simple linear algebraic group defined over an algebraically
closed field of characteristic $p\geq 0$ and let $\phi$ be a $p$-restricted
irreducible representation of $G$. Let $T$ be a maximal torus of $G$ and $s\in
T$. We say that $s$ is strongly regular if $\alpha(s)\ne\beta(s)$ for all
distinct $T$-roots $\alpha$ and $\beta$ of $G$. Our main result states that if
all but one of the eigenvalues of $\phi(s)$ are of multiplicity 1 then, with a
few specified exceptions, $s$ is strongly regular. This can be viewed as an
extension of our earlier result saying that under the same hypotheses, $s$ must
be regular and all non-zero weights of $\phi$ are of multiplicity 1.


title: A Robust Framework of Chromosome Straightening with ViT-Patch GAN

abstract:
  Chromosomes exhibit non-rigid and non-articulated nature with varying degrees
of curvature. Chromosome straightening is an essential step for subsequent
karyotype construction, pathological diagnosis and cytogenetic map development.
However, robust chromosome straightening remains challenging, due to the
unavailability of training images, distorted chromosome details and shapes
after straightening, as well as poor generalization capability. We propose a
novel architecture, ViT-Patch GAN, consisting of a motion transformation
generator and a Vision Transformer-based patch (ViT-Patch) discriminator. The
generator learns the motion representation of chromosomes for straightening.
With the help of the ViT-Patch discriminator, the straightened chromosomes
retain more shape and banding pattern details. The proposed framework is
trained on a small dataset and is able to straighten chromosome images with
state-of-the-art performance for two large datasets.


title: Domain Adaptation with Factorizable Joint Shift

abstract:
  Existing domain adaptation (DA) usually assumes the domain shift comes from
either the covariates or the labels. However, in real-world applications,
samples selected from different domains could have biases in both the
covariates and the labels. In this paper, we propose a new assumption,
Factorizable Joint Shift (FJS), to handle the co-existence of sampling bias in
covariates and labels. Although allowing for the shift from both sides, FJS
assumes the independence of the bias between the two factors. We provide
theoretical and empirical understandings about when FJS degenerates to prior
assumptions and when it is necessary. We further propose Joint Importance
Aligning (JIA), a discriminative learning objective to obtain joint importance
estimators for both supervised and unsupervised domain adaptation. Our method
can be seamlessly incorporated with existing domain adaptation algorithms for
better importance estimation and weighting on the training data. Experiments on
a synthetic dataset demonstrate the advantage of our method.


title: Geometric Hermite Interpolation in $\mathbb{R}^n$ by Refinements

abstract:
  We describe a general approach for constructing a broad class of operators
approximating high-dimensional curves based on geometric Hermite data. The
geometric Hermite data consists of point samples and their associated tangent
vectors of unit length. Extending the classical Hermite interpolation of
functions, this geometric Hermite problem has become popular in recent years
and has ignited a series of solutions in the 2D plane and 3D space. Here, we
present a method for approximating curves, which is valid in any dimension. A
basic building block of our approach is a Hermite average - a notion introduced
in this paper. We provide an example of such an average and show, via an
illustrative interpolating subdivision scheme, how the limits of the
subdivision scheme inherit geometric properties of the average. Finally, we
prove the convergence of this subdivision scheme, whose limit interpolates the
geometric Hermite data and approximates the sampled curve. We conclude the
paper with various numerical examples that elucidate the advantages of our
approach.


title:
Isometric Embeddings of Bounded Metric Spaces into the Gromov-Hausdorff
  Class

abstract:
  It is shown that any bounded metric space can be isometrically embedded into
the Gromov--Hausdorff metric class GH. This result is a consequence of local
geometry description of the class GH in a sufficiently small neighborhood of a
generic metric space. This description is interesting in itself. The technique
of optimal correspondences and their distortions is used.


title: Solving single photon detector problems

abstract:
  Quantum key distribution(QKD) could be the most significant application in
quantum information theory. Recent years, QKD distance improved dramatically
but is still restricted. The main problem is that the bit error rate(QBER)
caused by single photon detector(SPD) dark counts is out of control as the
distance increases. If this problem can be solved, QKD can be implemented over
arbitrarily long distance. However, previous solutions often result in
impractical requirements such as superconductors while they can only reduce
dark count rate to finite low levels. Here we present a scheme with today's
technologies only, solving SPD problems. Our scheme demonstrates that imperfect
detectors can provide nearly perfect results, namely the QBER caused by dark
counts can be reduced to arbitrarily low while detective efficiency can be
improved to arbitrarily high. As a consequence, QKD distance is not limited by
the imperfect SPD any more. For example, it can be improved from hundreds of
kilometers to thousands without high-technology detectors. Therefore, our
result might change the research point in QKD devices from SPD to others.


title: foREST: A Tree-based Approach for Fuzzing RESTful APIs

abstract:
  Representational state transfer (REST) is a widely employed architecture by
web applications and cloud. Users can invoke such services according to the
specification of their application interfaces, namely RESTful APIs. Existing
approaches for fuzzing RESTful APIs are generally based on classic
API-dependency graphs. However, such dependencies are inefficient for REST
services due to the explosion of dependencies among APIs. In this paper, we
propose a novel tree-based approach that can better capture the essential
dependencies and largely improve the efficiency of RESTful API fuzzing. In
particular, the hierarchical information of the endpoints across multiple APIs
enables us to construct an API tree, and the relationships of tree nodes can
indicate the priority of resource dependencies, \textit{e.g.,} it's more likely
that a node depends on its parent node rather than its offspring or siblings.
In the evaluation part, we first confirm that such a tree-based approach is
more efficient than traditional graph-based approaches. We then apply our tool
to fuzz two real-world RESTful services and compare the performance with two
state-of-the-art tools, EvoMaster and RESTler. Our results show that foREST can
improve the code coverage in all experiments, ranging from 11.5\% to 82.5\%.
Besides, our tool finds 11 new bugs previously unknown.


title:
A new infinite family of irregular algebraic surfaces with canonical map
  of degree 8

abstract:
  In this note we construct an unlimited family of irregular algebraic surfaces
of general type with canonical map of degree $ 8 $, irregularity $ 1 $ and
arbitrarily large geometric genus such that the image of the canonical map is
not a surface of minimal degree.


title: Polarization in Synchrotron Radiations in Curved Spacetime

abstract:
  In this work, we would like to investigate the synchrotron radiation of
charged particle in curved spacetime. In curved spacetime, charged particles in
non-geodesic motions would produce leading synchrotron radiations with
polarizations. Based on the work of DeWitt and Brehme [1], we find a compact
covariant formula of the polarization vector of such synchrotron emissions
which depends linearly on the 4-velocity and 4-acceleration of the charged
sources. This formula could be useful in studying the spacetime geometry and
magnetic configuration from the polarized structures in the EHT images.


title:
Self-supervised Image-specific Prototype Exploration for Weakly
  Supervised Semantic Segmentation

abstract:
  Weakly Supervised Semantic Segmentation (WSSS) based on image-level labels
has attracted much attention due to low annotation costs. Existing methods
often rely on Class Activation Mapping (CAM) that measures the correlation
between image pixels and classifier weight. However, the classifier focuses
only on the discriminative regions while ignoring other useful information in
each image, resulting in incomplete localization maps. To address this issue,
we propose a Self-supervised Image-specific Prototype Exploration (SIPE) that
consists of an Image-specific Prototype Exploration (IPE) and a
General-Specific Consistency (GSC) loss. Specifically, IPE tailors prototypes
for every image to capture complete regions, formed our Image-Specific CAM
(IS-CAM), which is realized by two sequential steps. In addition, GSC is
proposed to construct the consistency of general CAM and our specific IS-CAM,
which further optimizes the feature representation and empowers a
self-correction ability of prototype exploration. Extensive experiments are
conducted on PASCAL VOC 2012 and MS COCO 2014 segmentation benchmark and
results show our SIPE achieves new state-of-the-art performance using only
image-level labels. The code is available at
https://github.com/chenqi1126/SIPE.