title:
Joint DoA-Range Estimation Using Space-Frequency Virtual Difference
  Coarray

abstract:
  In this paper, we address the problem of joint direction-of-arrival (DoA) and
range estimation using frequency diverse coprime array (FDCA). By incorporating
the coprime array structure and coprime frequency offsets, a two-dimensional
space-frequency virtual difference coarray corresponding to uniform array and
uniform frequency offset is considered to increase the number of
degrees-of-freedom (DoFs). However, the reconstruction of the doubly-Toeplitz
covariance matrix is computationally prohibitive. To solve this problem, we
propose an interpolation algorithm based on decoupled atomic norm minimization
(DANM), which converts the coarray signal to a simple matrix form. On this
basis, a relaxation-based optimization problem is formulated to achieve joint
DoA-range estimation with enhanced DoFs. The reconstructed coarray signal
enables application of existing subspace-based spectral estimation methods. The
proposed DANM problem is further reformulated as an equivalent
rank-minimization problem which is solved by cyclic rank minimization. This
approach avoids the approximation errors introduced in nuclear norm-based
approach, thereby achieving superior root-mean-square error which is closer to
the Cramer-Rao bound. The effectiveness of proposed method is confirmed by
theoretical analyses and numerical simulations.


title:
Sylvester power and weighted sums on the Frobenius set in arithmetic
  progression

abstract:
  Let $a_1,a_2,\dots,a_k$ be positive integers with
$\gcd(a_1,a_2,\dots,a_k)=1$. Frobenius number is the largest positive integer
that is NOT representable in terms of $a_1,a_2,\dots,a_k$. When $k\ge 3$, there
is no explicit formula in general, but some formulae may exist for special
sequences $a_1,a_2,\dots,a_k$, including, those forming arithmetic progressions
and their modifications. In this paper, we give formulae for the power and
weighted sum of nonrepresentable positive integers. As applications, we show
explicit expressions of these sums for $a_1,a_2,\dots,a_k$ forming arithmetic
progressions.


title:
Spin Conductivity Based on Magnetic Toroidal Quadrupole Hidden in
  Antiferromagnets

abstract:
  We report our theoretical results on spin conductivity in antiferromagnets by
focusing on the role of the magnetic toroidal quadrupole (MTQ) in electron
systems. The MTQ is characterized as a time-reversal-odd rank-2 polar tensor
degree of freedom in electrons, which is distinct from conventional rank-1
magnetic and magnetic toroidal dipoles. Based on a microscopic $sd$ model
analysis for a tetragonal system under both collinear and noncollinear
antiferromagnetic orderings, we clarify that the MTQ becomes a source of an
extrinsic spin conductivity even with neither a uniform magnetization nor
spin-orbit coupling. We also list all the magnetic point groups to accommodate
the MTQs as a primary order parameter as well as the candidate
antiferromagnetic materials.


title: Shortest Unique Palindromic Substring Queries in Semi-dynamic Settings

abstract:
  A palindromic substring $T[i.. j]$ of a string $T$ is said to be a shortest
unique palindromic substring (SUPS) in $T$ for an interval $[p, q]$ if $T[i..
j]$ is a shortest one such that $T[i.. j]$ occurs only once in $T$, and $[i,
j]$ contains $[p, q]$. The SUPS problem is, given a string $T$ of length $n$,
to construct a data structure that can compute all the SUPSs for any given
query interval. It is known that any SUPS query can be answered in $O(\alpha)$
time after $O(n)$-time preprocessing, where $\alpha$ is the number of SUPSs to
output [Inoue et al., 2018]. In this paper, we first show that $\alpha$ is at
most $4$, and the upper bound is tight. Also, we present an algorithm to solve
the SUPS problem for a sliding window that can answer any query in $O(\log\log
W)$ time and update data structures in amortized $O(\log\sigma)$ time, where
$W$ is the size of the window, and $\sigma$ is the alphabet size. Furthermore,
we consider the SUPS problem in the after-edit model and present an efficient
algorithm. Namely, we present an algorithm that uses $O(n)$ time for
preprocessing and answers any $k$ SUPS queries in $O(\log n\log\log n +
k\log\log n)$ time after single character substitution. As a by-product, we
propose a fully-dynamic data structure for range minimum queries (RmQs) with a
constraint where the width of each query range is limited to polylogarithmic.
The constrained RmQ data structure can answer such a query in constant time and
support a single-element edit operation in amortized constant time.


title: Knowledgebra: An Algebraic Learning Framework for Knowledge Graph

abstract:
  Knowledge graph (KG) representation learning aims to encode entities and
relations into dense continuous vector spaces such that knowledge contained in
a dataset could be consistently represented. Dense embeddings trained from KG
datasets benefit a variety of downstream tasks such as KG completion and link
prediction. However, existing KG embedding methods fell short to provide a
systematic solution for the global consistency of knowledge representation. We
developed a mathematical language for KG based on an observation of their
inherent algebraic structure, which we termed as Knowledgebra. By analyzing
five distinct algebraic properties, we proved that the semigroup is the most
reasonable algebraic structure for the relation embedding of a general
knowledge graph. We implemented an instantiation model, SemE, using simple
matrix semigroups, which exhibits state-of-the-art performance on standard
datasets. Moreover, we proposed a regularization-based method to integrate
chain-like logic rules derived from human knowledge into embedding training,
which further demonstrates the power of the developed language. As far as we
know, by applying abstract algebra in statistical learning, this work develops
the first formal language for general knowledge graphs, and also sheds light on
the problem of neural-symbolic integration from an algebraic perspective.


title: Risk-Aware Stability, Ultimate Boundedness, and Positive Invariance

abstract:
  This paper introduces the notions of stability, ultimate boundedness, and
positive invariance for stochastic systems in the view of risk. More
specifically, those notions are defined in terms of the worst-case Conditional
Value-at-Risk (CVaR), which quantifies the worst-case conditional expectation
of losses exceeding a certain threshold over a set of possible uncertainties.
Those notions allow us to focus our attention on the tail behavior of
stochastic systems in the analysis of dynamical systems and the design of
controllers. Furthermore, some event-triggered control strategies that
guarantee ultimate boundedness and positive invariance with specified bounds
are derived using the obtained results and illustrated using numerical
examples.


title:
Differentially Private Distributed Mismatch Tracking Algorithm for
  Constraint-Coupled Resource Allocation Problems

abstract:
  This paper considers privacy-concerned distributed constraint-coupled
resource allocation problems over an undirected network, where each agent holds
a private cost function and obtains the solution via only local communication.
With privacy concerns, we mask the exchanged information with independent
Laplace noise against a potential attacker with potential access to all network
communications. We propose a differentially private distributed mismatch
tracking algorithm (diff-DMAC) to achieve cost-optimal distribution of
resources while preserving privacy. Adopting constant stepsizes, the linear
convergence property of diff-DMAC in mean square is established under the
standard assumptions of Lipschitz gradients and strong convexity. Moreover, it
is theoretically proven that the proposed algorithm is
{\epsilon}-differentially private.And we also show the trade-off between
convergence accuracy and privacy level. Finally, a numerical example is
provided for verification.


title: Accelerating Noisy VQE Optimization with Gaussian Processes

abstract:
  Hybrid variational quantum algorithms, which combine a classical optimizer
with evaluations on a quantum chip, are the most promising candidates to show
quantum advantage on current noisy, intermediate-scale quantum (NISQ) devices.
The classical optimizer is required to perform well in the presence of noise in
the objective function evaluations, or else it becomes the weakest link in the
algorithm. We introduce the use of Gaussian Processes (GP) as surrogate models
to reduce the impact of noise and to provide high quality seeds to escape local
minima, whether real or noise-induced. We build this as a framework on top of
local optimizations, for which we choose Implicit Filtering (ImFil) in this
study. ImFil is a state-of-the-art, gradient-free method, which in comparative
studies has been shown to outperform on noisy VQE problems. The result is a new
method: "GP+ImFil". We show that when noise is present, the GP+ImFil approach
finds results closer to the true global minimum in fewer evaluations than
standalone ImFil, and that it works particularly well for larger dimensional
problems. Using GP to seed local searches in a multi-modal landscape shows
mixed results: although it is capable of improving on ImFil standalone, it does
not do so consistently and would only be preferred over other, more exhaustive,
multistart methods if resources are constrained.


title:
Instantaneous Emission Rate of Electron Transport through a quantum
  point contact

abstract:
  We present a theory to describe the instantaneous emission rate of electron
transport in quantum-coherent conductors. Due to the Pauli exclusion principle,
electron emission events are usually correlated. This makes the emission rate
is not a constant, but depends on the history of the emission process. To
incorporate such history dependence, in this paper we characterize the emission
rate via the conditional intensity function, which has been introduced in the
theory of random point process. The conditional intensity function can be
treated as the instantaneous emission rate observed by an ideal single-electron
detector since a given starting time. We demonstrate the method by studying the
instantaneous emission rate of a single-channel quantum point contact driven by
a constant voltage. As the quantum point contact is opened up, we show that the
emission process evolves from a simple Poisson process close to pinch-off to a
non-renewal process at full transmission. These results shows that the
conditional intensity function can provide an intuitive and unified description
of the emission process in quantum-coherent conductors.


title:
Topology optimization for additive manufacturing with length scale,
  overhang, and building orientation constraints

abstract:
  This paper presents a density-based topology optimization approach
considering additive manufacturing limitations. The presented method considers
the minimum size of parts, the minimum size of cavities, the inability of
printing overhanging parts without the use of sacrificial supporting
structures, and the printing directions. These constraints are geometrically
addressed and implemented. The minimum size on solid and void zones is imposed
through a well-known filtering technique. The sacrificial support material is
reduced using a constraint that limits the maximum overhang angle of parts by
comparing the structural gradient with a critical reference slope. Due to the
local nature of the gradient, the chosen restriction is prone to introduce
parts that meet the structural slope but that may not be self-supporting. The
restriction limits the maximum overhang angle for a user-defined printing
direction, which could reduce structural performance if the orientation is not
properly selected. To ease these challenges, a new approach to reduce the
introduction of such non-self-supporting parts and a novel method that includes
different printing directions in the maximum overhang angle constraint are
presented. The proposed strategy for considering the minimum size of solid and
void phases, maximum overhang angle, and printing direction, is illustrated by
solving a set of 2D benchmark design problems including stiff structures and
compliant mechanisms. We also provide MATLAB codes in the appendix for
educational purposes and for replication of the results.


title:
Effect of right-handed currents and dark side of the solar neutrino
  parameter space to Neutrinoless Double Beta Decay

abstract:
  The Majorana nature of neutrinos will be the confirmed by the observation of
the rare process called as neutrinoless double beta decay process, i.e. the
simultaneous decay of two neutrons in the nucleus of an isotope (A, Z) into two
protons and two electrons without the emission of any neutrinos i.e, $(A, Z)
\to (A, Z + 2) + 2 e^-$. The non-observation of such a decay so far has been
interpreted as a lower limit on the half life of the isotope under
investigation, which puts severe constraints on any new physics giving rise to
LNV in the electron sector. On the other hand, the standard mechanism with
normal ordering and inverted ordering can not saturate the present experimental
limit while quasi-degenerate light neutrinos are strongly disfavored by the
upper limits on the sum of light neutrino masses from cosmological data sets.
In this work, we show that how dark side of the solar neutrino parameter space
and effect of new physics contributions from right-handed currents can saturate
the experimental limit provided by KamLAND-Zen and GERDA.


title: A Keypoint-based Global Association Network for Lane Detection

abstract:
  Lane detection is a challenging task that requires predicting complex
topology shapes of lane lines and distinguishing different types of lanes
simultaneously. Earlier works follow a top-down roadmap to regress predefined
anchors into various shapes of lane lines, which lacks enough flexibility to
fit complex shapes of lanes due to the fixed anchor shapes. Lately, some works
propose to formulate lane detection as a keypoint estimation problem to
describe the shapes of lane lines more flexibly and gradually group adjacent
keypoints belonging to the same lane line in a point-by-point manner, which is
inefficient and time-consuming during postprocessing. In this paper, we propose
a Global Association Network (GANet) to formulate the lane detection problem
from a new perspective, where each keypoint is directly regressed to the
starting point of the lane line instead of point-by-point extension.
Concretely, the association of keypoints to their belonged lane line is
conducted by predicting their offsets to the corresponding starting points of
lanes globally without dependence on each other, which could be done in
parallel to greatly improve efficiency. In addition, we further propose a
Lane-aware Feature Aggregator (LFA), which adaptively captures the local
correlations between adjacent keypoints to supplement local information to the
global association. Extensive experiments on two popular lane detection
benchmarks show that our method outperforms previous methods with F1 score of
79.63% on CULane and 97.71% on Tusimple dataset with high FPS. The code will be
released at https://github.com/Wolfwjs/GANet.


title: Preparing for the Future -- Rethinking Proxy Apps

abstract:
  A considerable amount of research and engineering went into designing proxy
applications, which represent common high-performance computing workloads, to
co-design and evaluate the current generation of supercomputers, e.g., RIKEN's
Supercomputer Fugaku, ANL's Aurora, or ORNL's Frontier. This process was
necessary to standardize the procurement while avoiding duplicated effort at
each HPC center to develop their own benchmarks. Unfortunately, proxy
applications force HPC centers and providers (vendors) into a an undesirable
state of rigidity, in contrast to the fast-moving trends of current technology
and future heterogeneity. To accommodate an extremely-heterogeneous future, we
have to reconsider how to co-design supercomputers during the next decade, and
avoid repeating the past mistakes. This position paper outlines the current
state-of-the-art in system co-design, challenges encountered over the past
years, and a proposed plan to move forward.


title:
Training Entire-Space Models for Target-oriented Opinion Words
  Extraction

abstract:
  Target-oriented opinion words extraction (TOWE) is a subtask of aspect-based
sentiment analysis (ABSA). Given a sentence and an aspect term occurring in the
sentence, TOWE extracts the corresponding opinion words for the aspect term.
TOWE has two types of instance. In the first type, aspect terms are associated
with at least one opinion word, while in the second type, aspect terms do not
have corresponding opinion words. However, previous researches trained and
evaluated their models with only the first type of instance, resulting in a
sample selection bias problem. Specifically, TOWE models were trained with only
the first type of instance, while these models would be utilized to make
inference on the entire space with both the first type of instance and the
second type of instance. Thus, the generalization performance will be hurt.
Moreover, the performance of these models on the first type of instance cannot
reflect their performance on entire space. To validate the sample selection
bias problem, four popular TOWE datasets containing only aspect terms
associated with at least one opinion word are extended and additionally include
aspect terms without corresponding opinion words. Experimental results on these
datasets show that training TOWE models on entire space will significantly
improve model performance and evaluating TOWE models only on the first type of
instance will overestimate model performance.


title:
Energy Extraction via Magnetic Reconnection in the Ergosphere of a
  rotating non-Kerr Black Hole

abstract:
  Magnetic reconnection process in the ergosphere is investigated for a
relativistic plasma around a rotating non-Kerr black hole. For a rotating
non-Kerr black hole immersed in magnetic field generated by an externally
material, anti-parallel magnetic field line could form in the ergosphere due to
the frame dragging. Therefore, magnetic reconnection could occur in the
ergosphere. Such magnetic reconnection may generate negative energy at infinity
by redistributing the angular momentum during the process. The results show
that, with the effect of the deformed parameter, extraction of energy from a
rotating non-Kerr black hole by magnetic reconnection could be enhanced in the
presence of a positive deformed parameter.


title: Properties of solutions of matrix Riccati equations

abstract:
  In this paper we study properties of regular solutions of matrix Riccati
equations. The obtained results are used to study the asymptotic behavior of
solutions of linear systems of ordinary differential equations.


title:
Fragmentation of the $^4$He$_2$ dimer by relativistic highly charged
  projectiles in collisions with small kinetic energy release

abstract:
  We study theoretically the fragmentation of the helium dimer, $^4$He$_2$,
into singly charged ions in collisions with relativistic highly-charged
projectiles. We discuss the main mechanisms driving this process with the focus
on the fragmentation caused by direct ionization of both atomic sites of the
dimer in a single collision with the projectile. This direct mechanism
dominates the He$_2$ $\to $ He$^+$ + He$^+$ breakup events with the kinetic
energies of the emerging ionic fragments below $4$--$5$ eV. We explore the
energy and angular distributions of the He$^+$ ions produced in collisions with
$1$ and $7$ GeV/u U$^{92+}$ projectiles and show that their shape is
significantly affected by relativistic and higher-order effects in the
interaction between the projectile and the dimer. We also show that the shape
of the energy spectrum is quite sensitive to the binding energy of the He$_2$
dimer which can be exploited for its precise determination. The contribution of
the direct mechanism to the total cross section for the He$_2$ fragmentation by
$1$ and $7$ GeV/u U$^{92+}$ was calculated to be $3.65$ Mb and $2.4$ Mb,
respectively, representing roughly half of this cross section.


title: LaMemo: Language Modeling with Look-Ahead Memory

abstract:
  Although Transformers with fully connected self-attentions are powerful to
model long-term dependencies, they are struggling to scale to long texts with
thousands of words in language modeling. One of the solutions is to equip the
model with a recurrence memory. However, existing approaches directly reuse
hidden states from the previous segment that encodes contexts in a
uni-directional way. As a result, this prohibits the memory to dynamically
interact with the current context that provides up-to-date information for
token prediction. To remedy this issue, we propose Look-Ahead Memory (LaMemo)
that enhances the recurrence memory by incrementally attending to the
right-side tokens, and interpolating with the old memory states to maintain
long-term information in the history. LaMemo embraces bi-directional attention
and segment recurrence with an additional computation overhead only linearly
proportional to the memory length. Experiments on widely used language modeling
benchmarks demonstrate its superiority over the baselines equipped with
different types of memory.


title: Curvature-driven instabilities in thin active shells

abstract:
  Spontaneous material shape changes, such as swelling, growth or thermal
expansion, can be used to trigger dramatic elastic instabilities in thin
shells. These instabilities originate in geometric incompatibility between the
preferred extrinsic and intrinsic curvature of the shell, which may be modified
by active deformations through the thickness and in plane respectively. Here,
we solve the simplest possible model of such instabilities, which assumes the
shells are shallow, thin enough to bend but not stretch, and subject to
homogeneous preferred curvatures. We consider separately the cases of zero,
positive and negative Gaussian curvature. We identify two types of
super-critical symmetry breaking instability, in which the shell's principal
curvature spontaneously breaks discrete up-down symmetry and continuous planar
isotropy respectively. These are then augmented by inversion instabilities, in
which the shell jumps sub-critically between up/down broken symmetry states,
and rotation instabilities, in which the curvatures rotate by 90 degrees
between states of broken isotropy without release of energy. Each instability
has a thickness independent threshold value for the preferred extrinsic
curvature proportional to the square-root of Gauss curvature. Finally, we show
that the threshold for the isotropy-breaking instability is the same for deep
spherical caps, in good agreement with recently published data.


title: $T_2$-limited dc Quantum Magnetometry via Flux Modulation

abstract:
  High-sensitivity magnetometry is of critical importance to the fields of
biomagnetism and geomagnetism. However, the magnetometry for the low-frequency
signal detection meets the challenge of sensitivity improvement, due to
multiple types of low-frequency noise sources. In particular, for the
solid-state spin quantum magnetometry, the sensitivity of low frequency
magnetic field has been limited by short $T_2^*$. Here, we demonstrate a
$T_2$-limited dc quantum magnetometry based on the nitrogen-vacancy centers in
diamond. The magnetometry, combining the flux modulation and the spin-echo
protocol, promotes the sensitivity from being limited by $T_2^*$ to $T_2$ of
orders of magnitude longer. The sensitivity of the dc magnetometry of 32 $\rm
pT/Hz^{1/2}$ has been achieved, overwhelmingly improved by 100 folds over the
Ramsey-type method result of 4.6 $\rm nT/Hz^{1/2}$. Further enhancement of the
sensitivity have been systematically analyzed, although challenging but plenty
of room is achievable. Our result sheds light on realization of room
temperature dc quantum magnetomerty with femtotesla-sensitivity in the future.


title:
CAiD: Context-Aware Instance Discrimination for Self-supervised Learning
  in Medical Imaging

abstract:
  Recently, self-supervised instance discrimination methods have achieved
significant success in learning visual representations from unlabeled
photographic images. However, given the marked differences between photographic
and medical images, the efficacy of instance-based objectives, focusing on
learning the most discriminative global features in the image (i.e., wheels in
bicycle), remains unknown in medical imaging. Our preliminary analysis showed
that high global similarity of medical images in terms of anatomy hampers
instance discrimination methods for capturing a set of distinct features,
negatively impacting their performance on medical downstream tasks. To
alleviate this limitation, we have developed a simple yet effective
self-supervised framework, called Context-Aware instance Discrimination (CAiD).
CAiD aims to improve instance discrimination learning by providing finer and
more discriminative information encoded from a diverse local context of
unlabeled medical images. We conduct a systematic analysis to investigate the
utility of the learned features from a three-pronged perspective: (i)
generalizability and transferability, (ii) separability in the embedding space,
and (iii) reusability. Our extensive experiments demonstrate that CAiD (1)
enriches representations learned from existing instance discrimination methods;
(2) delivers more discriminative features by adequately capturing finer
contextual information from individual medial images; and (3) improves
reusability of low/mid-level features compared to standard instance
discriminative methods. As open science, all codes and pre-trained models are
available on our GitHub page: https://github.com/JLiangLab/CAiD.


title: Extremal sequences for the unit-weighted Gao constant of $\mathbb Z_n$

abstract:
  For $A\subseteq \mathbb Z_n$, the $A$-weighted Gao constant $E_A(n)$ is
defined to be the smallest natural number $k$, such that any sequence of $k$
elements in $\mathbb Z_n$ has a subsequence of length $n$, whose $A$-weighted
sum is zero. Sequences of length $E_A(n)-1$ in $\mathbb Z_n$, which do not have
any $A$-weighted zero-sum subsequence of length $n$ are called $A$-extremal
sequences for the Gao constant. Such a sequence which has $n-1$ zeroes is said
to be of the standard type. When $A=U(n)$ (units in $\mathbb Z_n$) where $n$ is
odd, we characterize all such sequences and show that they are of the standard
type. When $n$ is even, we give examples of such sequences which are not of the
standard type. We also characterize the $U(n)$-extremal sequences for the Gao
constant, when $n=2^rp$, where $p$ is an odd prime.


title: MVSTER: Epipolar Transformer for Efficient Multi-View Stereo

abstract:
  Learning-based Multi-View Stereo (MVS) methods warp source images into the
reference camera frustum to form 3D volumes, which are fused as a cost volume
to be regularized by subsequent networks. The fusing step plays a vital role in
bridging 2D semantics and 3D spatial associations. However, previous methods
utilize extra networks to learn 2D information as fusing cues, underusing 3D
spatial correlations and bringing additional computation costs. Therefore, we
present MVSTER, which leverages the proposed epipolar Transformer to learn both
2D semantics and 3D spatial associations efficiently. Specifically, the
epipolar Transformer utilizes a detachable monocular depth estimator to enhance
2D semantics and uses cross-attention to construct data-dependent 3D
associations along epipolar line. Additionally, MVSTER is built in a cascade
structure, where entropy-regularized optimal transport is leveraged to
propagate finer depth estimations in each stage. Extensive experiments show
MVSTER achieves state-of-the-art reconstruction performance with significantly
higher efficiency: Compared with MVSNet and CasMVSNet, our MVSTER achieves 34%
and 14% relative improvements on the DTU benchmark, with 80% and 51% relative
reductions in running time. MVSTER also ranks first on Tanks&Temples-Advanced
among all published works. Code is released at https://github.com/JeffWang987.


title: Crowd counting with crowd attention convolutional neural network

abstract:
  Crowd counting is a challenging problem due to the scene complexity and scale
variation. Although deep learning has achieved great improvement in crowd
counting, scene complexity affects the judgement of these methods and they
usually regard some objects as people mistakenly; causing potentially enormous
errors in the crowd counting result. To address the problem, we propose a novel
end-to-end model called Crowd Attention Convolutional Neural Network (CAT-CNN).
Our CAT-CNN can adaptively assess the importance of a human head at each pixel
location by automatically encoding a confidence map. With the guidance of the
confidence map, the position of human head in estimated density map gets more
attention to encode the final density map, which can avoid enormous
misjudgements effectively. The crowd count can be obtained by integrating the
final density map. To encode a highly refined density map, the total crowd
count of each image is classified in a designed classification task and we
first explicitly map the prior of the population-level category to feature
maps. To verify the efficiency of our proposed method, extensive experiments
are conducted on three highly challenging datasets. Results establish the
superiority of our method over many state-of-the-art methods.


title: Search for periodic radio emission of gamma pulsar J0357+3205

abstract:
  The search for radio emission from the gamma pulsar J0357+3205 in the meter
wavelength range was carried out. Periodic emission was found in one of the
1,700 observation sessions. The average pulsar profile is single-component with
a half-width of 20-25 ms. Estimation of the pulsar flux density is 14 mJy.


title: Gravitational Waves from Double White Dwarfs as probes of the Milky Way

abstract:
  Future gravitational wave detectors, such as the Laser Interferometer Space
Antenna (LISA), will be able to resolve a significant number of the ultra
compact stellar-mass binaries in our own Galaxy and its neighborhood. These
will be mostly double white dwarf (DWD) binaries, and their underlying
population characteristics can be directly correlated to the different
properties of the Galaxy. In particular, with LISA we will be able to resolve
$\sim\mathcal{O}(10^4)$ binaries, while the rest will generate a confusion
foreground signal. Both categories can be used to address a number of
astrophysical questions. Analogously to how the total electromagnetic radiation
emitted by a galaxy can be related to the underlying total stellar mass, in
this work we propose a framework to infer the same quantity by investigating
the spectral shape and amplitude of the confusion foreground signal. For a
fixed DWD evolution model, we retrieve percentage-level relative errors on the
total stellar mass, which improves for increasing values of the mass. At the
same time, we find that variations in the Miky Way shape, at a fixed mass and
at scale heights smaller than 500~pc, are not distinguishable based on the
shape of stochastic signal alone. Finally, we utilize the catalogue of
resolvable sources to probe the characteristics of the underlying population of
DWD binaries. We show that the DWD frequency, coalescence time and chirp mass
(up to $<0.7\,$M$_\odot$) distributions can be reconstructed from LISA data
with no bias.

categories: astro-ph.GA gr-qc physics.data-an physics.space-ph


title:
Condition-Invariant and Compact Visual Place Description by
  Convolutional Autoencoder

abstract:
  Visual place recognition (VPR) in condition-varying environments is still an
open problem. Popular solutions are CNN-based image descriptors, which have
been shown to outperform traditional image descriptors based on hand-crafted
visual features. However, there are two drawbacks of current CNN-based
descriptors: a) their high dimension and b) lack of generalization, leading to
low efficiency and poor performance in applications. In this paper, we propose
to use a convolutional autoencoder (CAE) to tackle this problem. We employ a
high-level layer of a pre-trained CNN to generate features, and train a CAE to
map the features to a low-dimensional space to improve the condition invariance
property of the descriptor and reduce its dimension at the same time. We verify
our method in three challenging datasets involving significant illumination
changes, and our method is shown to be superior to the state-of-the-art. For
the benefit of the community, we make public the source code.


title:
Exciton spectroscopy and diffusion in MoSe2-WSe2 lateral
  heterostructures encapsulated in hexagonal boron nitride

abstract:
  Chemical vapor deposition (CVD) allows lateral edge epitaxy of transition
metal dichalcogenide heterostructures with potential applications in
optoelectronics. Critical for carrier and exciton transport is the quality of
the two materials that constitute the monolayer and the nature of the lateral
heterojunction. Important details of the optical properties were inaccessible
in as-grown heterostructure samples due to large inhomogeneous broadening of
the optical transitions. Here we perform optical spectroscopy at T = 4 K and
also at 300 K to access the optical transitions in CVD grown MoSe2-WSe2 lateral
heterostructures that are transferred from the growth-substrate and are
encapsulated in hBN. Photoluminescence (PL), reflectance contrast and Raman
spectroscopy reveal considerably narrowed optical transition linewidth similar
to high quality exfoliated monolayers. In high-resolution transmission electron
microscopy (HRTEM) we find near-atomically sharp junctions with a typical
extent of 3nm for the covalently bonded MoSe2-WSe2. In PL imaging experiments
we find effective excitonic diffusion length that are longer for WSe2 than for
MoSe2 at low T=4 K, whereas at 300 K this trend is reversed.


title:
A Differentially Private Probabilistic Framework for Modeling the
  Variability Across Federated Datasets of Heterogeneous Multi-View
  Observations

abstract:
  We propose a novel federated learning paradigm to model data variability
among heterogeneous clients in multi-centric studies. Our method is expressed
through a hierarchical Bayesian latent variable model, where client-specific
parameters are assumed to be realization from a global distribution at the
master level, which is in turn estimated to account for data bias and
variability across clients. We show that our framework can be effectively
optimized through expectation maximization (EM) over latent master's
distribution and clients' parameters. We also introduce formal differential
privacy (DP) guarantees compatibly with our EM optimization scheme. We tested
our method on the analysis of multi-modal medical imaging data and clinical
scores from distributed clinical datasets of patients affected by Alzheimer's
disease. We demonstrate that our method is robust when data is distributed
either in iid and non-iid manners, even when local parameters perturbation is
included to provide DP guarantees. Moreover, the variability of data, views and
centers can be quantified in an interpretable manner, while guaranteeing
high-quality data reconstruction as compared to state-of-the-art autoencoding
models and federated learning schemes. The code is available at
https://gitlab.inria.fr/epione/federated-multi-views-ppca.


title: Anomalous Sound Detection Based on Machine Activity Detection

abstract:
  We have developed an unsupervised anomalous sound detection method for
machine condition monitoring that utilizes an auxiliary task -- detecting when
the target machine is active. First, we train a model that detects machine
activity by using normal data with machine activity labels and then use the
activity-detection error as the anomaly score for a given sound clip if we have
access to the ground-truth activity labels in the inference phase. If these
labels are not available, the anomaly score is calculated through outlier
detection on the embedding vectors obtained by the activity-detection model.
Solving this auxiliary task enables the model to learn the difference between
the target machine sounds and similar background noise, which makes it possible
to identify small deviations in the target sounds. Experimental results showed
that the proposed method improves the anomaly-detection performance of the
conventional method complementarily by means of an ensemble.


title:
WIP: Achieving Self-Interference-Free Operation on SDR Platform with
  Critical TDD Turnaround Time

abstract:
  Software Defined Radio (SDR) platforms are valuable for research and
development activities or high-end systems that demand real-time adaptable
wireless protocols. While low latency can be achieved using the dedicated
digital processing unit of a state-of-the-art SDR platform, its Radio Frequency
(RF) front-end often poses a limitation in terms of turnaround time (TT), the
time needed for switching from the receiving to the transmitting mode (or vice
versa). Zero Intermediate Frequency (ZIF) transceivers are favorable for SDR,
but suffer from self-interference even if the device is not currently
transmitting. The strict MAC-layer requirements of Time Division Duplex (TDD)
protocols like Wi-Fi cannot be achieved using configurable ZIF transceivers
without having to compromise receiver sensitivity. Using a novel approach, we
show that the TT using the AD9361 RF front-end can be as low as 640 ns, while
the self-interference is at the same level as achieved by the conventional TDD
mode, which has a TT of at least 55 {\mu}s. As compared to Frequency Division
Duplex (FDD) mode, a decrease of receiver noise floor by about 13 dB in the 2.4
GHz band and by about 4.5 dB in the 5 GHz band is achieved.


title:
Visualization of nuclear many-body correlations with the most probable
  configuration of nucleons

abstract:
  A method to visualize many-body correlations using the information of the
full wave function is presented. The set of nucleon coordinates which maximizes
the square of the wave function, that is, the most probable spatial
configuration of nucleons are visualized. The method is applied to Hartree-Fock
(HF) and HF+BCS wave functions of $p$- and $sd$-shell $N=Z$ even-even nuclei to
analyze the cluster correlations in those systems. It is found that there are
$\alpha$-cluster-like four-body correlations already at the HF level in some of
the nuclei. The effects of pairing on the most probable configuration are also
investigated. The method is useful to analyze the nuclear many-body
correlations, and it suggests a new viewpoint to microscopic nuclear wave
functions.


title: Vision-and-Language Pretrained Models: A Survey

abstract:
  Pretrained models have produced great success in both Computer Vision (CV)
and Natural Language Processing (NLP). This progress leads to learning joint
representations of vision and language pretraining by feeding visual and
linguistic contents into a multi-layer transformer, Visual-Language Pretrained
Models (VLPMs). In this paper, we present an overview of the major advances
achieved in VLPMs for producing joint representations of vision and language.
As the preliminaries, we briefly describe the general task definition and
genetic architecture of VLPMs. We first discuss the language and vision data
encoding methods and then present the mainstream VLPM structure as the core
content. We further summarise several essential pretraining and fine-tuning
strategies. Finally, we highlight three future directions for both CV and NLP
researchers to provide insightful guidance.


title: Rational numbers in $\times b$-invariant sets

abstract:
  Let $b \geq 2$ be an integer and $S$ be a finite non-empty set of primes not
containing divisors of $b$. For any non-dense set $A \subset [0,1)$ such that
$A \cap \mathbb{Q}$ is invariant under $\times b$ operation, we prove the
finiteness of rational numbers in $A$ whose denominators can only be divided by
primes in $S$. A quantitative result on the largest prime divisors of the
denominators of rational numbers in $A$ is also obtained.


title:
Prototype-based Domain Generalization Framework for Subject-Independent
  Brain-Computer Interfaces

abstract:
  Brain-computer interface (BCI) is challenging to use in practice due to the
inter/intra-subject variability of electroencephalography (EEG). The BCI
system, in general, necessitates a calibration technique to obtain
subject/session-specific data in order to tune the model each time the system
is utilized. This issue is acknowledged as a key hindrance to BCI, and a new
strategy based on domain generalization has recently evolved to address it. In
light of this, we've concentrated on developing an EEG classification framework
that can be applied directly to data from unknown domains (i.e. subjects),
using only data acquired from separate subjects previously. For this purpose,
in this paper, we proposed a framework that employs the open-set recognition
technique as an auxiliary task to learn subject-specific style features from
the source dataset while helping the shared feature extractor with mapping the
features of the unseen target dataset as a new unseen domain. Our aim is to
impose cross-instance style in-variance in the same domain and reduce the open
space risk on the potential unseen subject in order to improve the
generalization ability of the shared feature extractor. Our experiments showed
that using the domain information as an auxiliary network increases the
generalization performance.


title: Text Revision by On-the-Fly Representation Optimization

abstract:
  Text revision refers to a family of natural language generation tasks, where
the source and target sequences share moderate resemblance in surface form but
differentiate in attributes, such as text formality and simplicity. Current
state-of-the-art methods formulate these tasks as sequence-to-sequence learning
problems, which rely on large-scale parallel training corpus. In this paper, we
present an iterative in-place editing approach for text revision, which
requires no parallel data. In this approach, we simply fine-tune a pre-trained
Transformer with masked language modeling and attribute classification. During
inference, the editing at each iteration is realized by two-step span
replacement. At the first step, the distributed representation of the text
optimizes on the fly towards an attribute function. At the second step, a text
span is masked and another new one is proposed conditioned on the optimized
representation. The empirical experiments on two typical and important text
revision tasks, text formalization and text simplification, show the
effectiveness of our approach. It achieves competitive and even better
performance than state-of-the-art supervised methods on text simplification,
and gains better performance than strong unsupervised methods on text
formalization \footnote{Code and model are available at
\url{https://github.com/jingjingli01/OREO}}.


title:
Spatio-Temporal-Frequency Graph Attention Convolutional Network for
  Aircraft Recognition Based on Heterogeneous Radar Network

abstract:
  This paper proposes a knowledge-and-data-driven graph neural network-based
collaboration learning model for reliable aircraft recognition in a
heterogeneous radar network. The aircraft recognizability analysis shows that:
(1) the semantic feature of an aircraft is motion patterns driven by the
kinetic characteristics, and (2) the grammatical features contained in the
radar cross-section (RCS) signals present spatial-temporal-frequency (STF)
diversity decided by both the electromagnetic radiation shape and motion
pattern of the aircraft. Then a STF graph attention convolutional network
(STFGACN) is developed to distill semantic features from the RCS signals
received by the heterogeneous radar network. Extensive experiment results
verify that the STFGACN outperforms the baseline methods in terms of detection
accuracy, and ablation experiments are carried out to further show that the
expansion of the information dimension can gain considerable benefits to
perform robustly in the low signal-to-noise ratio region.


title:
The asymptotic of the number of permutations whose cycle lengths are
  prime numbers

abstract:
  Let $A$ be a set of natural numbers and let $S_{n,A}$ be the set of all
permutations of $[n]=\{1,2,...,n\}$ with cycle lengths belonging to $A$.
Furthermore, let $\mid A(n)\mid$ denote the cardinality of the set $A(n)=A\cap
[n]$. The limit $\rho=\lim_{n\to\infty}\mid A(n)\mid/n$ (if it exists) is
called the density of set $A$. It turns out that, as $n\to\infty$, the
cardinality $\mid S_{n,A}\mid$ of the set $S_{n,A}$ essentially depends on
$\rho$. The case $\rho>0$ was studied by several authors under certain
additional conditions on $A$. In 1999, Kolchin noticed that there is a lack
studies on classes of permutations for which $\rho=0$. In this context, he also
proposed investigations on certain particular cases. In this paper, we consider
the permutations whose cycle lengths are prime numbers, that is, we assume that
$A=\mathcal{P}$, where $\mathcal{P}$ denotes the set of all primes. For this
class of permutations, the Prime Number Theorem implies that $\rho=0$. In this
paper, we show that, as $n\to\infty$, the ratio $S_{n,\mathcal{P}}/(n-1)!$
approaches a finite limit and determine its value explicitly. Our method of
proof employs classical Tauberian theorems.


title:
Decoding Neural Correlation of Language-Specific Imagined Speech using
  EEG Signals

abstract:
  Speech impairments due to cerebral lesions and degenerative disorders can be
devastating. For humans with severe speech deficits, imagined speech in the
brain-computer interface has been a promising hope for reconstructing the
neural signals of speech production. However, studies in the EEG-based imagined
speech domain still have some limitations due to high variability in spatial
and temporal information and low signal-to-noise ratio. In this paper, we
investigated the neural signals for two groups of native speakers with two
tasks with different languages, English and Chinese. Our assumption was that
English, a non-tonal and phonogram-based language, would have spectral
differences in neural computation compared to Chinese, a tonal and
ideogram-based language. The results showed the significant difference in the
relative power spectral density between English and Chinese in specific
frequency band groups. Also, the spatial evaluation of Chinese native speakers
in the theta band was distinctive during the imagination task. Hence, this
paper would suggest the key spectral and spatial information of word
imagination with specialized language while decoding the neural signals of
speech.


title: Is Surprisal in Issue Trackers Actionable?

abstract:
  Background. From information theory, surprisal is a measurement of how
unexpected an event is. Statistical language models provide a probabilistic
approximation of natural languages, and because surprisal is constructed with
the probability of an event occuring, it is therefore possible to determine the
surprisal associated with English sentences. The issues and pull requests of
software repository issue trackers give insight into the development process
and likely contain the surprising events of this process.
  Objective. Prior works have identified that unusual events in software
repositories are of interest to developers, and use simple code metrics-based
methods for detecting them. In this study we will propose a new method for
unusual event detection in software repositories using surprisal. With the
ability to find surprising issues and pull requests, we intend to further
analyse them to determine if they actually hold importance in a repository, or
if they pose a significant challenge to address. If it is possible to find bad
surprises early, or before they cause additional troubles, it is plausible that
effort, cost and time will be saved as a result.
  Method. After extracting the issues and pull requests from 5000 of the most
popular software repositories on GitHub, we will train a language model to
represent these issues. We will measure their perceived importance in the
repository, measure their resolution difficulty using several analogues,
measure the surprisal of each, and finally generate inferential statistics to
describe any correlations.


title: Sum expressions for $p$-adic Hecke $L$-functions of totally real fields

abstract:
  As a continuation of our previous work, we establish sum expressions for
$p$-adic Hecke $L$-functions of totally real fields in the sense of Delbourgo,
assuming a totally real analog of Heegner hypothesis. This is done by finding
explicit formulas of the periods of the corresponding $p$-adic measures. As an
application, we extend the Ferrero-Greenberg formula of derivatives of $p$-adic
$L$-functions to this setting.


title:
Revisiting the Hubble constant, spatial curvature and cosmography with
  time-delay and cosmic chronometer observations

abstract:
  In this work, we use a model-independent approach to determine the Hubble
constant and the spatial curvature simultaneously. We use the cosmic
chronometers in combination with the time delay measurements in this method,
and the cosmography method is used to reconstruct distances from cosmic
chronometer observations instead of the polynomial expansion. This approach
avoids the problems of the physical meaning of polynomial coefficients and the
calibration of "nuisance parameter". Our results show that the measured Hubble
constant $H_0=72.24^{+2.73}_{-2.52} km/s/Mpc$ is in good agreement with that
derived from the local distance ladder measurement. In addition, our results
show that a zero spatial curvature $\Omega_k=0.062^{+0.117}_{-0.078}$ and an
accelerating expansion of the Universe $q_0=-0.645^{+0.126}_{-0.124}$ are
supported by the current time delay of strong lensing system and cosmic
chronometer observations. If we assume a flat universe in advance, we can infer
that the $H_0=70.47^{+1.14}_{-1.15} km/s/Mpc$, falls between the SH0ES and
Plank CMB observation results. Moreover, the convergence and goodness of the
fourth order fitting is worse than the third order fitting case. Finally, the
cosmic expansion history is reconstructed within a wide range of redshifts.


title: ResT V2: Simpler, Faster and Stronger

abstract:
  This paper proposes ResTv2, a simpler, faster, and stronger multi-scale
vision Transformer for visual recognition. ResTv2 simplifies the EMSA structure
in ResTv1 (i.e., eliminating the multi-head interaction part) and employs an
upsample operation to reconstruct the lost medium- and high-frequency
information caused by the downsampling operation. In addition, we explore
different techniques for better apply ResTv2 backbones to downstream tasks. We
found that although combining EMSAv2 and window attention can greatly reduce
the theoretical matrix multiply FLOPs, it may significantly decrease the
computation density, thus causing lower actual speed. We comprehensively
validate ResTv2 on ImageNet classification, COCO detection, and ADE20K semantic
segmentation. Experimental results show that the proposed ResTv2 can outperform
the recently state-of-the-art backbones by a large margin, demonstrating the
potential of ResTv2 as solid backbones. The code and models will be made
publicly available at \url{https://github.com/wofmanaf/ResT}


title:
On the Role of Pre-trained Language Models in Word Ordering: A Case
  Study with BART

abstract:
  Word ordering is a constrained language generation task taking unordered
words as input. Existing work uses linear models and neural networks for the
task, yet pre-trained language models have not been studied in word ordering,
let alone why they help. We use BART as an instance and show its effectiveness
in the task. To explain why BART helps word ordering, we extend analysis with
probing and empirically identify that syntactic dependency knowledge in BART is
a reliable explanation. We also report performance gains with BART in the
related partial tree linearization task, which readily extends our analysis.


title:
Nonclassical correlated deterministic single-photon pairs for a trapped
  atom in bimodal cavities

abstract:
  Single photons and single-photon pairs, inherently nonclassical in their
nature, are fundamental elements of quantum sciences and technologies. Here, we
propose to realize the nonclassical correlated deterministic photon pairs at
the single-photon level for a single atom trapped in bimodal cavities. It is
shown that the photon emissions for bimodal cavities exhibit a high single
photon purity and relatively large intracavity photon numbers, which are
ascribed to the combination of optical Stark shift enhanced energy-spectrum
anharmonicity and quantum interference suppressed two-photon excitation. Our
scheme generates photon-photon pairs with strong photon blockade for cavity
modes via constructing a highly tunable cavity-enhanced deterministic
parametric down-conversion process. Furthermore, we show that the
cross-correlation function between the two cavity modes vastly violates the
Cauchy-Schwarz inequality of the classical boundary, which unambiguously
demonstrates the nonclassicality of the single-photon pairs. Our result reveals
a prominent strategy to generate the high-quality two-mode single-photon
sources with strong nonclassical correlation, which could provide versatile
applications in distribution of quantum networks, long distance quantum
communication, and fundamental tests of quantum physics.


title:
Insight-HXMT dedicated 33-day observation of SGR J1935+2154 II. Burst
  Spectral Catalog

abstract:
  Since April 28, 2020, Insight-HXMT has implemented a dedicated observation on
the magnetar SGR J1935+2154. Thanks to the wide energy band (1-250 keV) and
high sensitivity of Insight-HXMT, we obtained 75 bursts from SGR J1935+2154
during a month-long activity episode after the emission of FRB 200428. Here, we
report the detailed time-integrated spectral analysis of these bursts and the
statistical distribution of the spectral parameters. We find that for
15%(11/75) of SGR J1935+2154 bursts, the CPL model is preferred, and most of
them occurred in the latter part of this active epoch. In the cumulative
fluence distribution, we find that the fluence of bursts in our sample is about
an order of magnitude weaker than that of Fermi/GBM, but follows the same power
law distribution. Finally, we find a burst with similar peak energy to the
time-integrated spectrum of the X-ray burst associated with FRB 200428 (FRB
200428-Associated Burst), but the low energy index is harder.


title: 2D Human Pose Estimation: A Survey

abstract:
  Human pose estimation aims at localizing human anatomical keypoints or body
parts in the input data (e.g., images, videos, or signals). It forms a crucial
component in enabling machines to have an insightful understanding of the
behaviors of humans, and has become a salient problem in computer vision and
related fields. Deep learning techniques allow learning feature representations
directly from the data, significantly pushing the performance boundary of human
pose estimation. In this paper, we reap the recent achievements of 2D human
pose estimation methods and present a comprehensive survey. Briefly, existing
approaches put their efforts in three directions, namely network architecture
design, network training refinement, and post processing. Network architecture
design looks at the architecture of human pose estimation models, extracting
more robust features for keypoint recognition and localization. Network
training refinement tap into the training of neural networks and aims to
improve the representational ability of models. Post processing further
incorporates model-agnostic polishing strategies to improve the performance of
keypoint detection. More than 200 research contributions are involved in this
survey, covering methodological frameworks, common benchmark datasets,
evaluation metrics, and performance comparisons. We seek to provide researchers
with a more comprehensive and systematic review on human pose estimation,
allowing them to acquire a grand panorama and better identify future
directions.


title:
How to Save Lives with Microblogs? Lessons From the Usage of Weibo for
  Requests for Medical Assistance During COVID-19

abstract:
  During recent crises like COVID-19, microblogging platforms have become
popular channels for affected people seeking assistance such as medical
supplies and rescue operations from emergency responders and the public.
Despite this common practice, the affordances of microblogging services for
help-seeking during crises that needs immediate attention are not well
understood. To fill this gap, we analyzed 8K posts from COVID-19 patients or
caregivers requesting urgent medical assistance on Weibo, the largest
microblogging site in China. Our mixed-methods analyses suggest that existing
microblogging functions need to be improved in multiple aspects to sufficiently
facilitate help-seeking in emergencies, including capabilities of search and
tracking requests, ease of use, and privacy protection. We also find that
people tend to stick to certain well-established functions for publishing
requests, even after better alternatives emerge. These findings have
implications for designing microblogging tools to better support help
requesting and responding during crises.


title:
Towards Building a Personalized Dialogue Generator via Implicit User
  Persona Detection

abstract:
  Current works in the generation of personalized dialogue primarily contribute
to the agent avoiding contradictory persona and driving the response more
informative. However, we found that the generated responses from these models
are mostly self-centered with little care for the other party since they ignore
the user's persona. Moreover, we consider high-quality transmission is
essentially built based on apprehending the persona of the other party.
Motivated by this, we propose a novel personalized dialogue generator by
detecting implicit user persona. Because it's difficult to collect a large
number of personas for each user, we attempt to model the user's potential
persona and its representation from the dialogue absence of any external
information. Perception variable and fader variable are conceived utilizing
Conditional Variational Inference. The two latent variables simulate the
process of people being aware of the other party's persona and producing the
corresponding expression in conversation. Finally, Posterior-discriminated
Regularization is presented to enhance the training procedure. Empirical
studies demonstrate that compared with the state-of-the-art methods, ours is
more concerned with the user's persona and outperforms in evaluations.


title:
Revisiting the Adversarial Robustness-Accuracy Tradeoff in Robot
  Learning

abstract:
  Adversarial training (i.e., training on adversarially perturbed input data)
is a well-studied method for making neural networks robust to potential
adversarial attacks during inference. However, the improved robustness does not
come for free but rather is accompanied by a decrease in overall model accuracy
and performance. Recent work has shown that, in practical robot learning
applications, the effects of adversarial training do not pose a fair trade-off
but inflict a net loss when measured in holistic robot performance. This work
revisits the robustness-accuracy trade-off in robot learning by systematically
analyzing if recent advances in robust training methods and theory in
conjunction with adversarial robot learning can make adversarial training
suitable for real-world robot applications. We evaluate a wide variety of robot
learning tasks ranging from autonomous driving in a high-fidelity environment
amenable to sim-to-real deployment, to mobile robot gesture recognition. Our
results demonstrate that, while these techniques make incremental improvements
on the trade-off on a relative scale, the negative side-effects caused by
adversarial training still outweigh the improvements by an order of magnitude.
We conclude that more substantial advances in robust learning methods are
necessary before they can benefit robot learning tasks in practice.


title: Image Captioning In the Transformer Age

abstract:
  Image Captioning (IC) has achieved astonishing developments by incorporating
various techniques into the CNN-RNN encoder-decoder architecture. However,
since CNN and RNN do not share the basic network component, such a
heterogeneous pipeline is hard to be trained end-to-end where the visual
encoder will not learn anything from the caption supervision. This drawback
inspires the researchers to develop a homogeneous architecture that facilitates
end-to-end training, for which Transformer is the perfect one that has proven
its huge potential in both vision and language domains and thus can be used as
the basic component of the visual encoder and language decoder in an IC
pipeline. Meantime, self-supervised learning releases the power of the
Transformer architecture that a pre-trained large-scale one can be generalized
to various tasks including IC. The success of these large-scale models seems to
weaken the importance of the single IC task. However, we demonstrate that IC
still has its specific significance in this age by analyzing the connections
between IC with some popular self-supervised learning paradigms. Due to the
page limitation, we only refer to highly important papers in this short survey
and more related works can be found at
https://github.com/SjokerLily/awesome-image-captioning.


title:
Speaker-Aware Mixture of Mixtures Training for Weakly Supervised Speaker
  Extraction

abstract:
  Dominant researches adopt supervised training for speaker extraction, while
the scarcity of ideally clean corpus and channel mismatch problem are rarely
considered. To this end, we propose speaker-aware mixture of mixtures training
(SAMoM), utilizing the consistency of speaker identity among target source,
enrollment utterance and target estimate to weakly supervise the training of a
deep speaker extractor. In SAMoM, the input is constructed by mixing up
different speaker-aware mixtures (SAMs), each contains multiple speakers with
their identities known and enrollment utterances available. Informed by
enrollment utterances, target speech is extracted from the input one by one,
such that the estimated targets can approximate the original SAMs after a remix
in accordance with the identity consistency. Moreover, using SAMoM in a
semi-supervised setting with a certain amount of clean sources enables
application in noisy scenarios. Extensive experiments on Libri2Mix show that
the proposed method achieves promising results without access to any clean
sources (11.06dB SI-SDRi). With a domain adaptation, our approach even
outperformed supervised framework in a cross-domain evaluation on AISHELL-1.


title: The semi-random tree process

abstract:
  The online semi-random graph process is a one-player game which starts with
the empty graph on $n$ vertices. At every round, a player (called Builder) is
presented with a vertex $v$ chosen uniformly at random and constructs an edge
of his choice that is incident to $v$. Inspired by recent advances, we define a
family of generalised online semi-random models. Then, we analyse a particular
instance that shares similar features with the original semi-random graph
process and study the hitting times of classical graph properties.
  More formally, in our model the process also starts with the empty graph on
$n$ vertices. At the beginning of each round, Builder is offered a spanning
tree $T$ of $K_n$ chosen uniformly at random and independently from the
previous rounds. He then chooses an edge from $T$ and adds it (if not yet
present) to the already constructed graph. We show that an "optimal" strategy
ensures whp that the hitting time of a graph with minimum degree $k =
o(n^{1/2})$ is given by $\tfrac{kn}{2} + \tfrac{(2\pi n)^{1/2}}{4} +
o(n^{1/2})$ and we also provide an upper bound of $\tfrac{kn}{2} + O(\max\{k,
n^{1/2}\})$ when $k = o(n)$. While this yields a lower bound for creating a
perfect matching (resp. a Hamiltonian cycle), we describe strategies for
Builder to attain these properties whp in $\tfrac{n}{2} + O((\log n) n^{1/2})$
(resp. in $n + O((\log n) n^{1/2})$) rounds. We also study the hitting time of
an $H$-factor for a fixed graph $H$ possessing an additional tree-like
property, and show that it is given by the trivial deterministic lower bound up
to lower order terms. Another contribution of the work is to show that Builder
may cover all $n$ vertices by copies of any given graph $H$ in $d_{\min}(H) n +
o(n)$ rounds whp, where $d_{\min}(H)$ is the minimum degree of a vertex in $H$,
which is a factor of 2 away from a corresponding deterministic lower bound.


title: Scaling limits for a class of regular $\Xi$-coalescents

abstract:
  The block counting process with initial state $n$ counts the number of blocks
of an exchangeable coalescent ($\Xi$-coalescent) restricted to a sample of size
$n$. This work provides scaling limits for the block counting process of
regular $\Xi$-coalescents that stay infinite, including $\Xi$-coalescents with
dust and a large class of dust-free $\Xi$-coalescents. The main convergence
result states that the block counting process, properly logarithmically scaled,
converges in the Skorohod space to an Ornstein--Uhlenbeck type process as $n$
tends to infinity. The existence of such a scaling depends on a sort of
curvature condition of a particular function well-known from the literature.
This curvature condition is intrinsically related to the behavior of the
measure $\Xi$ near the origin. The method of proof is to show the uniform
convergence of the associated generators. Via Siegmund duality an analogous
result for the fixation line is proven. Several examples are studied.


title:
The Probabilistic Profitable Tour Problem under a specific graph
  structure

abstract:
  Among the most important variants of the traveling salesman problem (TSP) are
those relaxing the constraint that every locus should necessarily get visited,
rather taking into account a revenue (prize) for visiting customers. In the
Profitable Tour Problem (PTP), we seek for a tour visiting a subset of
customers while maximizing net gain (profit) as difference between total
revenue collected from visited customers and incurred traveling costs. The
metric TSP can be modeled as a PTP with large revenues. As such, PTP is
well-known to be NP-hard and also APX-hardness follows. Nevertheless, PTP is
solvable in polynomial time on particular graph structures like lines, trees
and circles. Following recent emphasis on robust optimization, and motivated by
current flourishing of retail delivery services, we study the Probabilistic
Profitable Tour Problem (PPTP), the generalization of PTP where customers will
show up with a known probability, in their respective loci,only after the tour
has been planned. Here, the selection of customers has to be made a priori,
before knowing if a customer will actually submit his request or will not.
While the tour has to be designed without this knowledge, revenues will only be
collected from customers who will require the service. The objective is to
maximize the expected net gain obtained by visiting only the customers that
show up. We provide a polynomial time algorithm computing and characterizing
the space of optimal solutions for the special case of the PPTP where customers
are distributed on a line.


title:
Inverse design of focused vector beams for mode excitation in optical
  nanoantennas

abstract:
  We propose a free-space, inverse design of nanostructure's effective
mode-matching fields via a backward propagation of tightly focused vector beams
to the pupil plane of an aplanatic system of high numerical aperture. First, we
study the nanostructure's eigenmodes without considering any excitation fields
and then extract the modal near fields in the focal plane. Each modal field is
then taken as the desired focal field, the band-limited waves of which are
backward propagated to the pupil plane via a reversal of the Richards--Wolf
vector diffraction formula. The pupil fields can be designed to be genuinely
paraxial by associating the longitudinal electric/magnetic field component with
the radial one on the reference sphere. The inversely designed pupil field in
turn is propagated forwardly into the focal region to generate the designed
focal field, whose distribution over the nanostructure's surface is used to
evaluate the overlap between the designed focal field and the modal fields,
i.e., the modal expansion coefficients. Studies for a silicon nanodisk monomer,
dimer, and tetramer demonstrate the ability of our inverse approach to design
the necessary tightly focused vector field that can effectively and exclusively
match a certain eigenmode of interest. Compared with the forward beam-shaping
method, the inverse design approach tends to yield quantitatively more precise
mode-matching field profiles. This work can have a significant impact on
optical applications that rely on controllable and tunable mode excitation and
light scattering.


title: Crowd counting with segmentation attention convolutional neural network

abstract:
  Deep learning occupies an undisputed dominance in crowd counting. In this
paper, we propose a novel convolutional neural network (CNN) architecture
called SegCrowdNet. Despite the complex background in crowd scenes, the
proposeSegCrowdNet still adaptively highlights the human head region and
suppresses the non-head region by segmentation. With the guidance of an
attention mechanism, the proposed SegCrowdNet pays more attention to the human
head region and automatically encodes the highly refined density map. The crowd
count can be obtained by integrating the density map. To adapt the variation of
crowd counts, SegCrowdNet intelligently classifies the crowd count of each
image into several groups. In addition, the multi-scale features are learned
and extracted in the proposed SegCrowdNet to overcome the scale variations of
the crowd. To verify the effectiveness of our proposed method, extensive
experiments are conducted on four challenging datasets. The results demonstrate
that our proposed SegCrowdNet achieves excellent performance compared with the
state-of-the-art methods.


title:
Absolute Quantification of sp$^{3}$ Defects in Semiconducting
  Single-Wall Carbon Nanotubes by Raman Spectroscopy

abstract:
  The functionalization of semiconducting single-wall carbon nanotubes (SWCNTs)
with luminescent sp$^{3}$ defects creates red-shifted emission features in the
near-infrared and boosts their photoluminescence quantum yields (PLQYs). While
multiple synthetic routes for the selective introduction of sp$^{3}$ defects
have been developed, a convenient metric to precisely quantify the number of
defects on a SWCNT lattice is not available. Here, we present a direct and
simple quantification protocol based on a linear correlation of the integrated
Raman D/G$^{+}$ signal ratios and defect densities as extracted from PLQY
measurements. Corroborated by a statistical analysis of single-nanotube
emission spectra at cryogenic temperature, this method enables the quantitative
evaluation of sp$^{3}$ defect densities in (6,5) SWCNTs with an error of $\pm$
3 defects per micrometer and the determination of oscillator strengths for
different defect types. The developed protocol requires only standard Raman
spectroscopy and is independent of the defect configuration, dispersion solvent
and nanotube length.


title: On basic $2$-arc-transitive graphs

abstract:
  A connected graph $\Gamma=(V,E)$ of valency at least $3$ is called a basic
$2$-arc-transitive graph if its full automorphism group has a subgroup $G$ with
the following properties: (i) $G$ acts transitively on the set of $2$-arcs of
$\Gamma$, and (ii) every minimal normal subgroup of $G$ has at most two orbits
on $V$.
  In her papers [17,18], Praeger proved a connected $2$-arc-transitive graph of
valency at least $3$ is a normal cover of some basic $2$-arc-transitive graph,
and characterized the group-theoretic structures for basic $2$-arc-transitive
graphs.
  Based on Praeger's theorems on $2$-arc-transitive graphs, this paper presents
a further understanding on basic $2$-arc-transitive graphs.


title: Geodesic words in central extension of the Klien bottle group

abstract:
  In this work, we give a complete description of the language of geodesic
words for a central extension of the fundamental group of the Klein Bottle with
respect to the standard two-element generating set. Besides, we prove that
there are no dead ends in the group. Finally, we give a new sufficient
condition for the absence of dead ends.


title:
First-principles Calculation of Magnetocrystalline Anisotropy of
  Y(Co,Fe,Ni,Cu)$_5$ Based on Full-potential KKR Green's Function Method

abstract:
  The performance of permanent magnets YCo$_5$ can be improved by replacing
cobalt with other elements, such as iron, copper, and nickel. In order to
determine its optimum composition, it is necessary to perform systematic
theoretical calculations in a consistent framework. In this study, we
calculated the magnetocrystalline anisotropy constant $K_{\rm u}$ of
Y(Co$_{1-x-y}$Fe$_{x}$Cu$_{y}$)$_3$(Co$_{1-z}$Ni$_{z}$)$_2$ on the basis of the
full-potential Korringa-Kohn-Rostoker Green's function method in conjunction
with the coherent potential approximation. The calculated $K_{\rm u}$ of
YCo$_5$ was smaller than the experimental value because of a missing
enhancement due to orbital polarization. Although the value of $K_{\rm u}$ of
Y(Co$_{1-x-y}$Fe$_{x}$Cu$_{y}$)$_3$(Co$_{1-z}$Ni$_{z}$)$_2$ was systematically
underestimated compared to their experimental counterparts, the doping effect
can be analyzed within a consistent framework. The results have shown that
YFe$_3$Co$_2$ has much higher $K_{\rm u}=5.00$ MJ/m$^3$ than pristine YCo$_5$
($K_{\rm u}=1.82$ MJ/m$^3$), and that nickel as a stabilization element
decreases $K_{\rm u}$ and magnetization in YFe$_3$(Co$_{1-z}$Ni$_z$)$_2$.
However, the anisotropy field of $z\sim0.5$ can compete with the value of
YCo$_5$.


title:
In situ small-angle X-ray scattering reveals strong condensation of DNA
  origami during silicification

abstract:
  The silicification of DNA origami structures increases their mechanical and
thermal stability and provides chemical protection. So far, it is unclear how
silicification affects the internal structure of the DNA origami and whether
the whole DNA framework is embedded or if silica just forms an outer shell. By
using in situ small-angle X-ray scattering (SAXS), we show that the
net-cationic silica precursor TMAPS induces substantial condensation of the DNA
origami, which is further enhanced by the addition of TEOS at early reaction
times to an almost 10 % size reduction. We identify the SAXS Porod invariant as
a reliable, model-free parameter for the evaluation of the amount of silica
formation at a given time. Contrast matching of the DNA double helix Lorentzian
peak reveals that silica growth also occurs on the inner surfaces of the
origami. The less polar silica forming within the origami structure, replacing
more than 40 % of the internal hydration water causes a hydrophobic effect:
origami condensation. In the maximally condensed state, thermal stabilization
of the origami up to 60 {\deg}C could be observed. If the reaction is driven
beyond this point, the overall size of the silicified origami increases again
due to more and more silica deposition on the DNA origami. DNA origami objects
with flat surfaces show a strong tendency towards aggregation during
silicification, presumably driven by the very same entropic forces causing
condensation. Our studies provide novel insights into the silicification
reaction and hints for the formulation of optimized reaction protocols.


title:
Chloroplasts in plant cells show active glassy behavior under low light
  conditions

abstract:
  Plants have developed intricate mechanisms to adapt to changing light
conditions. Besides photo- and he- liotropism -- the differential growth
towards light and the diurnal motion with respect to sunlight -- chloroplast
motion acts as a fast mechanism to change the intracellular structure of leaf
cells. While chloroplasts move towards the sides of the plant cell to avoid
strong light, they accumulate and spread out into a layer on the bottom of the
cell at low light to increase the light absorption efficiency. Although the
motion of chloroplasts has been studied for over a century, the collective
organelle-motion leading to light adapting self-organized structures remains
elusive. Here we study the active motion of chloroplasts under dim light
conditions, leading to an accumulation in a densely packed quasi-2D layer. We
observe burst-like re-arrangements and show that these dynamics resemble
colloidal systems close to the glass transition by tracking individual
chloroplasts. Furthermore, we provide a minimal mathematical model to uncover
relevant system parameters controlling the stability of the dense configuration
of chloroplasts. Our study suggests that the meta-stable caging close to the
glass-transition in the chloroplast mono-layer serves a physiological
relevance. Chloroplasts remain in a spread-out configuration to increase the
light uptake, but can easily fluidize when the activity is increased to
efficiently re-arrange the structure towards an avoidance state. Our research
opens new questions about the role that dynamical phase transitions could play
in self-organized intracellular responses of plant cells towards environmental
cues.


title:
AID: Accuracy Improvement of Analog Discharge-Based in-SRAM
  Multiplication Accelerator

abstract:
  This paper presents a novel circuit (AID) to improve the accuracy of an
energy-efficient in-memory multiplier using a standard 6T-SRAM. The
state-of-the-art discharge-based in-SRAM multiplication accelerators suffer
from a non-linear behavior in their bit-line (BL, BLB) due to the quadratic
nature of the access transistor that leads to a poor signal-to-noise ratio
(SNR). In order to achieve linearity in the BLB voltage, we propose a novel
root function technique on the access transistor's gate that results in
accuracy improvement of on average 10.77 dB SNR compared to state-of-the-art
discharge-based topologies. Our analytical methods and a circuit simulation in
a 65 nm CMOS technology verify that the proposed technique consumes 0.523 pJ
per computation (multiplication, accumulation, and preset) from a power supply
of 1V, which is 51.18% lower compared to other state-of-the-art techniques. We
have performed an extensive Monte Carlo based simulation for a 4x4
multiplication operation, and our novel technique presents less than 0.086
standard deviations for the worst-case incorrect output scenario.


title:
Speed limit for a highly irreversible process and tight finite-time
  Landauer's bound

abstract:
  Landauer's bound is the minimum thermodynamic cost for erasing one bit of
information. As this bound is achievable only for quasistatic processes,
finite-time operation incurs additional energetic costs. We find a tight
finite-time Landauer's bound by establishing a general form of the classical
speed limit. This tight bound well captures the divergent behavior associated
with the additional cost of a highly irreversible process, which scales
differently from a nearly irreversible process. We demonstrate the validity of
this bound via discrete one-bit and coarse-grained bit systems. Our work
implies that more heat dissipation than expected occurs during high-speed
irreversible computation.


title:
Boundary regularity of mixed local-nonlocal operators and its
  application

abstract:
  Let $\Omega$ be a bounded $C^2$ domain in $\mathbb{R}^n$ and $u\in
C(\mathbb{R}^n)$ solves \begin{equation*} \begin{aligned} \Delta u + a Iu +
C_0|Du| \geq -K\quad \text{in}\; \Omega, \quad \Delta u + a Iu - C_0|Du|\leq K
\quad \text{in}\; \Omega, \quad u=0\quad \text{in}\; \Omega^c, \end{aligned}
\end{equation*} in viscosity sense, where $0\leq a\leq A_0$, $C_0, K>0$, and
$I$ is a suitable nonlocal operator. We show that $u/\delta$ is in
$C^{\kappa}(\bar \Omega)$ for some $\kappa\in (0,1)$, where $\delta(x)={\rm
dist}(x, \Omega^c)$. Using this result, we also establish that $u\in C^{1,
\gamma}(\bar\Omega)$. Finally, we apply these results to study an
overdetermined problem for mixed local-nonlocal operators.


title:
Email Spam Detection Using Hierarchical Attention Hybrid Deep Learning
  Method

abstract:
  Email is one of the most widely used ways to communicate, with millions of
people and businesses relying on it to communicate and share knowledge and
information on a daily basis. Nevertheless, the rise in email users has
occurred a dramatic increase in spam emails in recent years. Processing and
managing emails properly for individuals and companies are getting increasingly
difficult. This article proposes a novel technique for email spam detection
that is based on a combination of convolutional neural networks, gated
recurrent units, and attention mechanisms. During system training, the network
is selectively focused on necessary parts of the email text. The usage of
convolution layers to extract more meaningful, abstract, and generalizable
features by hierarchical representation is the major contribution of this
study. Additionally, this contribution incorporates cross-dataset evaluation,
which enables the generation of more independent performance results from the
model's training dataset. According to cross-dataset evaluation results, the
proposed technique advances the results of the present attention-based
techniques by utilizing temporal convolutions, which give us more flexible
receptive field sizes are utilized. The suggested technique's findings are
compared to those of state-of-the-art models and show that our approach
outperforms them.


title: Characterizing metastable states with the help of machine learning

abstract:
  Present-day atomistic simulations generate long trajectories of ever more
complex systems. Analyzing these data, discovering metastable states, and
uncovering their nature is becoming increasingly challenging. In this paper, we
first use the variational approach to conformation dynamics to discover the
slowest dynamical modes of the simulations. This allows the different
metastable states of the system to be located and organized hierarchically. The
physical descriptors that characterize metastable states are discovered by
means of a machine learning method. We show in the cases of two proteins,
Chignolin and Bovine Pancreatic Trypsin Inhibitor, how such analysis can be
effortlessly performed in a matter of seconds. Another strength of our approach
is that it can be applied to the analysis of both unbiased and biased
simulations.


title:
Survival probability of random walks and L\'evy flights with stochastic
  resetting

abstract:
  We perform a thorough analysis of the survival probability of symmetric
random walks with stochastic resetting, defined as the probability for the
walker not to cross the origin up to time $n$. For continuous symmetric
distributions of step lengths with either finite (random walks) or infinite
variance (L\'evy flights), this probability can be expressed in terms of the
survival probability of the walk without resetting, given by Sparre Andersen
theory. It is therefore universal, i.e., independent of the step length
distribution. We analyze this survival probability at depth, deriving both
exact results at finite times and asymptotic late-time results. We also
investigate the case where the step length distribution is symmetric but not
continuous, focussing our attention onto arithmetic distributions generating
random walks on the lattice of integers. We investigate in detail the example
of the simple Polya walk and propose an algebraic approach for lattice walks
with a larger range.


title:
When a completion of the universal enveloping algebra is a Banach
  PI-algebra?

abstract:
  We prove that a Banach algebra $B$ that is a completion of the universal
enveloping algebra of a finite-dimensional complex Lie algebra $\mathfrak{g}$
satisfies a polynomial identity if and only if the nilpotent radical
$\mathfrak{n}$ of $\mathfrak{g}$ is associatively nilpotent in $B$.
Furthermore, this holds if and only if a certain polynomial growth condition is
satisfied on $\mathfrak{n}$.


title: FasterVideo: Efficient Online Joint Object Detection And Tracking

abstract:
  Object detection and tracking in videos represent essential and
computationally demanding building blocks for current and future visual
perception systems. In order to reduce the efficiency gap between available
methods and computational requirements of real-world applications, we propose
to re-think one of the most successful methods for image object detection,
Faster R-CNN, and extend it to the video domain. Specifically, we extend the
detection framework to learn instance-level embeddings which prove beneficial
for data association and re-identification purposes. Focusing on the
computational aspects of detection and tracking, our proposed method reaches a
very high computational efficiency necessary for relevant applications, while
still managing to compete with recent and state-of-the-art methods as shown in
the experiments we conduct on standard object tracking benchmarks


title:
The multi elliptic-breather solutions and their asymptotic analysis for
  the mKdV equation

abstract:
  We mainly construct and analyze the multi elliptic-breather solutions under
the periodic solutions background for the focusing modified Korteweg-de Vries
(mKdV) equation. Based on the Darboux-B\"{a}cklund transformation, we provide a
uniform expression for the multi elliptic-breather solutions by the Jacobi
theta functions. Then, a sufficient condition of the elastic collision among
the breathers is given through the symmetric analysis. As $t\rightarrow\pm
\infty$, the asymptotic behaviors of the multi elliptic-breather solutions are
provided directly in two categories: along the line $l_i^{\pm}$ with different
velocity $v_i$ and on the region $R_{i}^{\pm}$ between the breathers. We also
obtain that the multi elliptic-breather solutions could degenerate into the
constant solution, solitons or breathers under the constant background, as
$k\rightarrow 0^+$. Moreover, we provide some examples of the multi
elliptic-breather solutions to visualize above analysis.


title:
Variation in solar differential rotation and activity in the period
  1964-2016 determined by the Kanzelh\"ohe data set

abstract:
  We determined the differential rotation (DR) parameters $A$ and $B$
(corresponding to the equatorial rotation velocity and the gradient of the
solar DR) by tracing sunspot groups in sunspot drawings of the Kanzelh\"ohe
Observatory for Solar and Environmental Research (KSO; 1964-2008, for solar
cycles (SC) 20-23) and KSO white-light images (2009-2016, for SC 24). We used
different statistical methods and approaches to analyse cycle related
variations, solar cycle phase-related variations and long-term variations of
the DR. $A$ and $B$ show statistically significant periodic variability. The
changes in $A$ related to solar cycle phase are in accordance with previously
reported theoretical and experimental results (higher $A$ during solar minimum,
lower $A$ during the maximum of activity), while changes in $B$ differ from the
theoretical predictions as we observe more negative values of $B$, that is, a
more pronounced DR during activity maximum. The main result of this paper for
the long-term variations in $A$ is the detection of a phase shift between the
activity flip (in the 1970s) and the equatorial rotation velocity flip (in the
early 1990s). During this time period both $A$ and activity show a secular
decreasing trend, indicating their correlation. Therefore, the theoretical
model fails in the phase-shift time period that occurs after the modern
Gleissberg maximum, while in the time period thereafter (after the 1990s),
theoretical and experimental results are consistent. The long-term variations
in $B$ in general yield an anticorrelation of $B$ and activity, as a rise of
$B$ is observed during the entire time period (1964-2016) we analysed, during
which activity decreased. We study for the first time the variation in solar DR
and activity based on 53 years of KSO data. Our results agree well with the
results related to the solar cycle phase from corona observations.


title:
An exploration of dust grain growth within WCd systems using an advected
  scalar dust model

abstract:
  Dust production is one of the more curious phenomena observed in massive
binary systems with interacting winds. The high temperatures, UV photon flux
and violent shocks should destroy any dust grains that condense. However, in
some extreme cases dust production yields of approximately 30% of the total
mass loss rate of the stellar winds have been observed. In order to better
understand this phenomenon a parameter space exploration was performed using a
series of numerical models of dust producing carbon phase Wolf-Rayet (WCd)
systems. These models incorporated a passive scalar dust model simulating dust
growth, destruction and radiative cooling. We find that reasonable dust yields
were produced by these simulations. Significant changes in the dust yield were
caused by changing the mass loss rates of the stars, with a greater mass loss
rate contributing to increased dust yields. Similarly, a close orbit between
the stars also resulted in higher dust yields. Finally, a high velocity wind
shear, which induces Kelvin-Helmholtz (KH) instabilities and wind mixing,
drastically increases the dust yields.


title: Generalized Universal Coding of Integers

abstract:
  Universal coding of integers~(UCI) is a class of variable-length code, such
that the ratio of the expected codeword length to $\max\{1,H(P)\}$ is within a
constant factor, where $H(P)$ is the Shannon entropy of the decreasing
probability distribution $P$. However, if we consider the ratio of the expected
codeword length to $H(P)$, the ratio tends to infinity by using UCI, when
$H(P)$ tends to zero. To solve this issue, this paper introduces a class of
codes, termed generalized universal coding of integers~(GUCI), such that the
ratio of the expected codeword length to $H(P)$ is within a constant factor
$K$. First, the definition of GUCI is proposed and the coding structure of GUCI
is introduced. Next, we propose a class of GUCI $\mathcal{C}$ to achieve the
expansion factor $K_{\mathcal{C}}=2$ and show that the optimal GUCI is in the
range $1\leq K_{\mathcal{C}}^{*}\leq 2$. Then, by comparing UCI and GUCI, we
show that when the entropy is very large or $P(0)$ is not large, there are also
cases where the average codeword length of GUCI is shorter. Finally, the
asymptotically optimal GUCI is presented.


title: Simulation of the LSD Response to the Neutrino Burst from SN 1987A

abstract:
  Using the Geant4 code, we have performed a full-scale simulation of the LSD
response to the neutrino burst from SN1987A. The neutrino flux parameters were
chosen according to one of the models: the standard collapse model or the
rotational supernova explosion model. We showed that, depending on the chosen
parameters, one can either obtain the required number of pulses in the detector
or reproduce their energy spectrum, but not both together. The interaction of
neutrino radiation both with LSD itself and with the material of the
surrounding soil was taken into account in our simulation. We also explored the
hypothesis that the entire unique LSD signal at 2:52 UT was produced by neutron
fluxes from the surrounding granite. However, this hypothesis was not confirmed
by our simulation. The results obtained provide a rich material for possible
interpretations.


title: Strength of Correlations in a Silver Based Cuprate Analogue

abstract:
  AgF2 has been proposed as a cuprate analogue which requires strong
correlation and marked covalence. On the other hand, fluorides are usually
quite ionic and 4d transition metals tend to be less correlated than their 3d
counterparts, which calls for further scrutiny. We combine valence band
photoemission and Auger-Meitner spectroscopy of AgF and AgF2 together with
computations in small clusters to estimate values of the Ag 4d Coulomb
interaction U 4d and charge-transfer energy. Based on these values, AgF2 can be
classified as a charge-transfer correlated insulator according to the
Zaanen-Sawatzky-Allen classification scheme. Thus, we confirm that the material
is a cuprate analogue from the point of view of correlations, suggesting that
it should become a high-temperature superconductor if metallization is achieved
by doping. We present also a computation of the Hubbard U in density functional
"+U" methods and discuss its relation to the Hubbard U in spectroscopies.


title: Gate-Controlled Quantum Dots Based on Two-Dimensional Materials

abstract:
  Two-dimensional (2D) materials are a family of layered materials exhibiting
rich exotic phenomena, such as valley-contrasting physics. Down to
single-particle level, unraveling fundamental physics and potential
applications including quantum information processing in these materials
attracts significant research interests. To unlock these great potentials,
gate-controlled quantum dot architectures have been applied in 2D materials and
their heterostructures. Such systems provide the possibility of electrical
confinement, control, and manipulation of single carriers in these materials.
In this review, efforts in gate-controlled quantum dots in 2D materials are
presented. Following basic introductions to valley degree of freedom and
gate-controlled quantum dot systems, the up-to-date progress in etched and
gate-defined quantum dots in 2D materials, especially in graphene and
transition metal dichalcogenides, is provided. The challenges and opportunities
for future developments in this field, from views of device design, fabrication
scheme, and control technology, are discussed. The rapid progress in this field
not only sheds light on the understanding of spin-valley physics, but also
provides an ideal platform for investigating diverse condensed matter physics
phenomena and realizing quantum computation in the 2D limit.


title:
BYOL for Audio: Exploring Pre-trained General-purpose Audio
  Representations

abstract:
  Pre-trained models are essential as feature extractors in modern machine
learning systems in various domains. In this study, we hypothesize that
representations effective for general audio tasks should provide multiple
aspects of robust features of the input sound. For recognizing sounds
regardless of perturbations such as varying pitch or timbre, features should be
robust to these perturbations. For serving the diverse needs of tasks such as
recognition of emotions or music genres, representations should provide
multiple aspects of these robust features, such as local and global features
and their statistics. To implement our principle, we propose a self-supervised
learning method: Bootstrap Your Own Latent (BYOL) for Audio (BYOL-A, pronounced
"viola"). BYOL-A pre-trains representations of the input sound themselves
invariant to audio data augmentations by minimizing the difference between a
pair of augmented input variants, which makes the learned representations
robust to the perturbations of sounds. In the BYOL-A encoder, the global
pooling calculates representations to form multi-aspect information by
combining statistics of frequency- and channel-wise, local, and global
features. As a result, the learned representations should provide multi-aspect
robust features of the input and serve various needs of diverse tasks. We
evaluated general audio task performance among previous state-of-the-art
methods, and BYOL-A showed competitive results in all tasks with the best
average result of 72.4 %. Besides, BYOL-A sets new records of 57.6 % on
VoxCeleb1 and 63.8 % on CREMA-D. We also conducted extensive ablation
experiments and validated the contributions of BYOL-A components. Our code is
available online.


title:
Deep learning model solves change point detection for multiple change
  types

abstract:
  A change points detection aims to catch an abrupt disorder in data
distribution. Common approaches assume that there are only two fixed
distributions for data: one before and another after a change point. Real-world
data are richer than this assumption. There can be multiple different
distributions before and after a change. We propose an approach that works in
the multiple-distributions scenario. Our approach learn representations for
semi-structured data suitable for change point detection, while a common
classifiers-based approach fails. Moreover, our model is more robust, when
predicting change points. The datasets used for benchmarking are sequences of
images with and without change points in them.


title: Divide & Conquer Imitation Learning

abstract:
  When cast into the Deep Reinforcement Learning framework, many robotics tasks
require solving a long horizon and sparse reward problem, where learning
algorithms struggle. In such context, Imitation Learning (IL) can be a powerful
approach to bootstrap the learning process. However, most IL methods require
several expert demonstrations which can be prohibitively difficult to acquire.
Only a handful of IL algorithms have shown efficiency in the context of an
extreme low expert data regime where a single expert demonstration is
available. In this paper, we present a novel algorithm designed to imitate
complex robotic tasks from the states of an expert trajectory. Based on a
sequential inductive bias, our method divides the complex task into smaller
skills. The skills are learned into a goal-conditioned policy that is able to
solve each skill individually and chain skills to solve the entire task. We
show that our method imitates a non-holonomic navigation task and scales to a
complex simulated robotic manipulation task with very high sample efficiency.