title:
Speaker Embedding-aware Neural Diarization: an Efficient Framework for
  Overlapping Speech Diarization in Meeting Scenarios

abstract:
  Overlapping speech diarization has been traditionally treated as a
multi-label classification problem. In this paper, we reformulate this task as
a single-label prediction problem by encoding multiple binary labels into a
single label with the power set, which represents the possible combinations of
target speakers. This formulation has two benefits. First, the overlaps of
target speakers are explicitly modeled. Second, threshold selection is no
longer needed. Through this formulation, we propose the speaker embedding-aware
neural diarization (SEND) framework, where a speech encoder, a speaker encoder,
two similarity scorers, and a post-processing network are jointly optimized to
predict the encoded labels according to the similarities between speech
features and speaker embeddings. Experimental results show that SEND has a
stable learning process and can be trained on highly overlapped data without
extra initialization. More importantly, our method achieves the
state-of-the-art performance in real meeting scenarios with fewer model
parameters and lower computational complexity.


title: Steady states and coarsening in one-dimensional driven Allen-Cahn system

abstract:
  We study the steady states and the coarsening dynamics in a one dimensional
driven non-conserved system modelled by the so called driven Allen-Cahn
equation, which is the standard Allen-Cahn equation with an additional driving
force. In particular, we derive equations of motion for the phase boundaries in
a phase ordering system obeying this equation using a nearest neighbour
interaction approach. Using the equations of motion we explore kink binary and
ternary interactions and analyze how the average domain size scale with respect
to time. Further, we employ numerical techniques to perform a bifurcation
analysis of the one-period stationary solutions of the equation. We then
investigate the linear stability of the two-period solutions and thereby
identify and study various coarsening modes.


title:
SWIPT-enabled NOMA in Distributed Antenna System with Imperfect Channel
  State Information for Max-Sum-Rate and Max-Min Fairness

abstract:
  Motivated by the fact that the data rate of non-orthogonal multiple access
(NOMA) can be greatly increased with the help of the distributed antenna system
(DAS), we presents a framework in which the DAS contributes not only to the
data rate but also the energy harvesting of simultaneous wireless information
and power transfer (SWIPT) enabled NOMA. This study considers the sum-rate
maximization problem and the max-min fairness problem for SWIPT-enabled NOMA in
DAS and proposes two different schemes of power splitting and power allocation
for SWIPT and NOMA, respectively, with imperfect channel state information
(CSI). Numerical results validate the theoretical findings and demonstrate that
the proposed framework of using SWIPT-enabled NOMA in DAS achieves the higher
data rates than the existing SWIPT-enabled NOMA while guaranteeing the minimum
harvested energy.


title: Prototypical Verbalizer for Prompt-based Few-shot Tuning

abstract:
  Prompt-based tuning for pre-trained language models (PLMs) has shown its
effectiveness in few-shot learning. Typically, prompt-based tuning wraps the
input text into a cloze question. To make predictions, the model maps the
output words to labels via a verbalizer, which is either manually designed or
automatically built. However, manual verbalizers heavily depend on
domain-specific prior knowledge and human efforts, while finding appropriate
label words automatically still remains challenging.In this work, we propose
the prototypical verbalizer (ProtoVerb) which is built directly from training
data. Specifically, ProtoVerb learns prototype vectors as verbalizers by
contrastive learning. In this way, the prototypes summarize training instances
and are able to enclose rich class-level semantics. We conduct experiments on
both topic classification and entity typing tasks, and the results demonstrate
that ProtoVerb significantly outperforms current automatic verbalizers,
especially when training data is extremely scarce. More surprisingly, ProtoVerb
consistently boosts prompt-based tuning even on untuned PLMs, indicating an
elegant non-tuning way to utilize PLMs. Our codes are avaliable at
https://github.com/thunlp/OpenPrompt.


title:
Beyond a Video Frame Interpolator: A Space Decoupled Learning Approach
  to Continuous Image Transition

abstract:
  Video frame interpolation (VFI) aims to improve the temporal resolution of a
video sequence. Most of the existing deep learning based VFI methods adopt
off-the-shelf optical flow algorithms to estimate the bidirectional flows and
interpolate the missing frames accordingly. Though having achieved a great
success, these methods require much human experience to tune the bidirectional
flows and often generate unpleasant results when the estimated flows are not
accurate. In this work, we rethink the VFI problem and formulate it as a
continuous image transition (CIT) task, whose key issue is to transition an
image from one space to another space continuously. More specifically, we learn
to implicitly decouple the images into a translatable flow space and a
non-translatable feature space. The former depicts the translatable states
between the given images, while the later aims to reconstruct the intermediate
features that cannot be directly translated. In this way, we can easily perform
image interpolation in the flow space and intermediate image synthesis in the
feature space, obtaining a CIT model. The proposed space decoupled learning
(SDL) approach is simple to implement, while it provides an effective framework
to a variety of CIT problems beyond VFI, such as style transfer and image
morphing. Our extensive experiments on a variety of CIT tasks demonstrate the
superiority of SDL to existing methods. The source code and models can be found
at \url{https://github.com/yangxy/SDL}.


title:
Completing Partial Point Clouds with Outliers by Collaborative
  Completion and Segmentation

abstract:
  Most existing point cloud completion methods are only applicable to partial
point clouds without any noises and outliers, which does not always hold in
practice. We propose in this paper an end-to-end network, named CS-Net, to
complete the point clouds contaminated by noises or containing outliers. In our
CS-Net, the completion and segmentation modules work collaboratively to promote
each other, benefited from our specifically designed cascaded structure. With
the help of segmentation, more clean point cloud is fed into the completion
module. We design a novel completion decoder which harnesses the labels
obtained by segmentation together with FPS to purify the point cloud and
leverages KNN-grouping for better generation. The completion and segmentation
modules work alternately share the useful information from each other to
gradually improve the quality of prediction. To train our network, we build a
dataset to simulate the real case where incomplete point clouds contain
outliers. Our comprehensive experiments and comparisons against
state-of-the-art completion methods demonstrate our superiority. We also
compare with the scheme of segmentation followed by completion and their
end-to-end fusion, which also proves our efficacy.


title:
Local-Global Context Aware Transformer for Language-Guided Video
  Segmentation

abstract:
  We explore the task of language-guided video segmentation (LVS). Previous
algorithms mostly adopt 3D CNNs to learn video representation, struggling to
capture long-term context and easily suffering from visual-linguistic
misalignment. In light of this, we present Locater (local-global context aware
Transformer), which augments the Transformer architecture with a finite memory
so as to query the entire video with the language expression in an efficient
manner. The memory is designed to involve two components -- one for
persistently preserving global video content, and one for dynamically gathering
local temporal context and segmentation history. Based on the memorized
local-global context and the particular content of each frame, Locater
holistically and flexibly comprehends the expression as an adaptive query
vector for each frame. The vector is used to query the corresponding frame for
mask generation. The memory also allows Locater to process videos with linear
time complexity and constant size memory, while Transformer-style
self-attention computation scales quadratically with sequence length. To
thoroughly examine the visual grounding capability of LVS models, we contribute
a new LVS dataset, A2D-S+, which is built upon A2D-S dataset but poses
increased challenges in disambiguating among similar objects. Experiments on
three LVS datasets and our A2D-S+ show that Locater outperforms previous
state-of-the-arts. Further, our Locater based solution achieved the 1st place
in the Referring Video Object Segmentation Track of the 3rd Large-scale Video
Object Segmentation Challenge. Our code and dataset are available at:
https://github.com/leonnnop/Locater


title: Solving Infinite-Dimensional Harmonic Lyapunov and Riccati equations

abstract:
  In this paper, we address the problem of solving infinite-dimensional
harmonic algebraic Lyapunov and Riccati equations up to an arbitrary small
error. This question is of major practical importance for analysis and
stabilization of periodic systems including tracking of periodic trajectories.
We first give a closed form of a Floquet factorization in the general setting
of L 2 matrix functions and study the spectral properties of
infinite-dimensional harmonic matrices and their truncated version. This
spectral study allows us to propose a generic and numerically efficient
algorithm to solve infinite-dimensional harmonic algebraic Lyapunov equations
up to an arbitrary small error. We combine this algorithm with the Kleinman
algorithm to solve infinite-dimensional harmonic Riccati equations and we apply
the proposed results to the design of a harmonic LQ control with periodic
trajectory tracking.


title: ContrastMask: Contrastive Learning to Segment Every Thing

abstract:
  Partially-supervised instance segmentation is a task which requests
segmenting objects from novel unseen categories via learning on limited seen
categories with annotated masks thus eliminating demands of heavy annotation
burden. The key to addressing this task is to build an effective class-agnostic
mask segmentation model. Unlike previous methods that learn such models only on
seen categories, in this paper, we propose a new method, named ContrastMask,
which learns a mask segmentation model on both seen and unseen categories under
a unified pixel-level contrastive learning framework. In this framework,
annotated masks of seen categories and pseudo masks of unseen categories serve
as a prior for contrastive learning, where features from the mask regions
(foreground) are pulled together, and are contrasted against those from the
background, and vice versa. Through this framework, feature discrimination
between foreground and background is largely improved, facilitating learning of
the class-agnostic mask segmentation model. Exhaustive experiments on the COCO
dataset demonstrate the superiority of our method, which outperforms previous
state-of-the-arts.


title: VO2 Does Not Host a Photoinduced Long-lived Monoclinic Metallic Phase

abstract:
  Femtosecond laser pulses can trigger phase transitions and sometimes even
achieve transient nonequilibrium states that are not accessible in equilibrium.
Vanadium dioxide (VO2) exhibits a first-order insulator-to-metal transition
accompanied by a monoclinic-to-rutile structural transition near room
temperature. Recently there is extensive debate over whether VO2 hosts a
transient long-lived monoclinic metallic phase with femtosecond
photoexcitation. We developed a new method to synthesize quasi-single-crystal
freestanding VO2 films and reexamined the photoinduced phase transition with
mega-electron-volt ultrafast electron diffraction. Benefitting from the well
separated diffraction spots and high temporal resolution, we found no evidence
for the transient monoclinic metallic phase through the accurate measurements
of the photoinduced evolution of the diffraction intensity in the momentum
space. Based on the laser fluence thresholds measured at various temperatures,
we suggest that the phase transition threshold is thermally driven although the
transition occurs within 200 femtoseconds, highlighting the interplay of the
electron-electron correlation and the lattice vibrations in driving the phase
transition in VO2.


title:
Transferable Class-Modelling for Decentralized Source Attribution of
  GAN-Generated Images

abstract:
  GAN-generated deepfakes as a genre of digital images are gaining ground as
both catalysts of artistic expression and malicious forms of deception,
therefore demanding systems to enforce and accredit their ethical use. Existing
techniques for the source attribution of synthetic images identify subtle
intrinsic fingerprints using multiclass classification neural nets limited in
functionality and scalability. Hence, we redefine the deepfake detection and
source attribution problems as a series of related binary classification tasks.
We leverage transfer learning to rapidly adapt forgery detection networks for
multiple independent attribution problems, by proposing a semi-decentralized
modular design to solve them simultaneously and efficiently. Class activation
mapping is also demonstrated as an effective means of feature localization for
model interpretation. Our models are determined via experimentation to be
competitive with current benchmarks, and capable of decent performance on human
portraits in ideal conditions. Decentralized fingerprint-based attribution is
found to retain validity in the presence of novel sources, but is more
susceptible to type II errors that intensify with image perturbations and
attributive uncertainty. We describe both our conceptual framework and model
prototypes for further enhancement when investigating the technical limits of
reactive deepfake attribution.


title: The Hodge conjecture for powers of K3 surfaces of Picard number 16

abstract:
  We study the Hodge conjecture for powers of K3 surfaces and show that if the
Kuga--Satake correspondence is algebraic for a family of K3 surfaces of generic
Picard number 16, then the Hodge conjecture holds for all powers of any K3
surface in that family.


title: Compactness methods in Lieb's work

abstract:
  We review some compactness methods appearing in the work of Lieb, with an
emphasis on the techniques developed around his 1983 article on the optimizers
for the Hardy-Littlewood-Sobolev inequality.


title:
Sparse Fuse Dense: Towards High Quality 3D Detection with Depth
  Completion

abstract:
  Current LiDAR-only 3D detection methods inevitably suffer from the sparsity
of point clouds. Many multi-modal methods are proposed to alleviate this issue,
while different representations of images and point clouds make it difficult to
fuse them, resulting in suboptimal performance. In this paper, we present a
novel multi-modal framework SFD (Sparse Fuse Dense), which utilizes pseudo
point clouds generated from depth completion to tackle the issues mentioned
above. Different from prior works, we propose a new RoI fusion strategy 3D-GAF
(3D Grid-wise Attentive Fusion) to make fuller use of information from
different types of point clouds. Specifically, 3D-GAF fuses 3D RoI features
from the couple of point clouds in a grid-wise attentive way, which is more
fine-grained and more precise. In addition, we propose a SynAugment
(Synchronized Augmentation) to enable our multi-modal framework to utilize all
data augmentation approaches tailored to LiDAR-only methods. Lastly, we
customize an effective and efficient feature extractor CPConv (Color Point
Convolution) for pseudo point clouds. It can explore 2D image features and 3D
geometric features of pseudo point clouds simultaneously. Our method holds the
highest entry on the KITTI car 3D object detection leaderboard, demonstrating
the effectiveness of our SFD. Code will be made publicly available.


title:
Statistical analysis of a hierarchical clustering algorithm with
  outliers

abstract:
  It is well known that the classical single linkage algorithm usually fails to
identify clusters in the presence of outliers. In this paper, we propose a new
version of this algorithm, and we study its mathematical performances. In
particular, we establish an oracle type inequality which ensures that our
procedure allows to recover the clusters with large probability under minimal
assumptions on the distribution of the outliers. We deduce from this inequality
the consistency and some rates of convergence of our algorithm for various
situations. Performances of our approach is also assessed through simulation
studies and a comparison with classical clustering algorithms on simulated data
is also presented.


title:
Modularized Bayesian analyses and cutting feedback in likelihood-free
  inference

abstract:
  There has been much recent interest in modifying Bayesian inference for
misspecified models so that it is useful for specific purposes. One popular
modified Bayesian inference method is "cutting feedback" which can be used when
the model consists of a number of coupled modules, with only some of the
modules being misspecified. Cutting feedback methods represent the full
posterior distribution in terms of conditional and sequential components, and
then modify some terms in such a representation based on the modular structure
for specification or computation of a modified posterior distribution. The main
goal of this is to avoid contamination of inferences for parameters of interest
by misspecified modules. Computation for cut posterior distributions is
challenging, and here we consider cutting feedback for likelihood-free
inference based on Gaussian mixture approximations to the joint distribution of
parameters and data summary statistics. We exploit the fact that marginal and
conditional distributions of a Gaussian mixture are Gaussian mixtures to give
explicit approximations to marginal or conditional posterior distributions so
that we can easily approximate cut posterior analyses. The mixture approach
allows repeated approximation of posterior distributions for different data
based on a single mixture fit, which is important for model checks which aid in
the decision of whether to "cut". A semi-modular approach to likelihood-free
inference where feedback is partially cut is also developed. The benefits of
the method are illustrated in two challenging examples, a collective cell
spreading model and a continuous time model for asset returns with jumps.


title: ISDE : Independence Structure Density Estimation

abstract:
  In this paper, we propose ISDE (Independence Structure Density Estimation),
an algorithm designed to estimate a multivariate density under Kullback-Leibler
loss and the Independence Structure (IS) model. IS tackles the curse of
dimensionality by separating features into independent groups. We explain the
construction of ISDE and present some experiments to show its performance on
synthetic and real-world data. Performance is measured quantitatively by
comparing empirical $\log$-likelihood with other density estimation methods and
qualitatively by analyzing outputted partitions of variables. We also provide
information about complexity and running time.


title: The price of unfairness in linear bandits with biased feedback

abstract:
  Artificial intelligence is increasingly used in a wide range of decision
making scenarios with higher and higher stakes. At the same time, recent work
has highlighted that these algorithms can be dangerously biased, and that their
results often need to be corrected to avoid leading to unfair decisions. In
this paper, we study the problem of sequential decision making with biased
linear bandit feedback. At each round, a player selects an action described by
a covariate and by a sensitive attribute. She receives a reward corresponding
to the covariates of the action that she has chosen, but only observe a biased
evaluation of this reward, where the bias depends on the sensitive attribute.
To tackle this problem, we design a Fair Phased Elimination algorithm. We
establish an upper bound on its worst-case regret, showing that it is smaller
than C$\kappa$ 1/3 * log(T) 1/3 T 2/3 , where C is a numerical constant and
$\kappa$ * an explicit geometrical constant characterizing the difficulty of
bias estimation. The worst case regret is higher than the dT 1/2 log(T) regret
rate obtained under unbiased feedback. We show that this rate cannot be
improved for all instances : we obtain lower bounds on the worst-case regret
for some sets of actions showing that this rate is tight up to a
sub-logarithmic factor. We also obtain gap-dependent upper bounds on the
regret, and establish matching lower bounds for some problem instance.
Interestingly, the gap-dependent rates reveal the existence of non-trivial
instances where the problem is no more difficult than its unbiased counterpart.


title: Anytime-valid Confidence Intervals for Contingency Tables and Beyond

abstract:
  E variables are tools for designing tests that keep their type-I error
guarantees under flexible sampling scenarios such as optional stopping and
continuation. We extend the recently developed E variables for two-sample tests
to general null hypotheses and the corresponding anytime-valid confidence
sequences. Using the 2x2 contingency table (Bernoulli) setting as a running
example, we provide simple implementations of these confidence sequences for
linear and odds-ratio based effect size.


title: Spin Effects In Heavy Ion Collisions at High Energies

abstract:
  In non-central high energy heavy ion collisions, the colliding system
possesses a huge orbital angular momentum along the normal direction of the
reaction plane. Due to the spin orbit interaction in the system, such a huge
orbital angular momentum leads to the spin polarization of quarks and
anti-quarks in the quark matter system produced in the collision. Such an
effect, known as the global polarization effect, was predicted many years ago
and has been confirmed by the STAR collaboration at RHIC. The discovery of the
global polarization effect opens a new avenue in heavy ion physics in general
and in studying the properties of quark-gluon plasma in particular. This talk
will briefly review the original ideas and calculations that lead to the
prediction and summarize progresses and problems in related aspects.


title:
A New Probabilistic Representation of the Alternating Zeta Function and
  a New Selberg-like Integral Evaluation

abstract:
  In this paper, we present two new representations of the alternating Zeta
function. We show that for any s $\in$ C this function can be computed as a
limit of a series of determinant. We then express these determinants as the
expectation of a functional of a random vector with Dixon-Anderson density. The
generalization of this representation to more general alternating series allows
us to evaluate a Selberg-type integral with a generalized Vandermonde
determinant.


title:
Orientation Adaptive Minimal Learning Machine: Application to
  Thiolate-Protected Gold Nanoclusters and Gold-Thiolate Rings

abstract:
  Machine learning (ML) force fields are one of the most common applications of
ML methods in the field of physical and chemical science. In the optimal case,
they are able to reach accuracy close to the first principles methods with
significantly lowered computational cost. However, often the training of the ML
methods rely on full atomic structures alongside their potential energies, and
applying the force information is difficult especially in the case of
kernel-based methods. Here we apply distance-based ML methods to predict force
norms and estimate the directions of the force vectors of the
thiolate-protected $\text{Au}_{38}(\text{SCH}_{3})_{24}$ nanocluster. The
method relies only on local structural information without energy evaluations.
We apply the atomic ML forces on the structure optimization of the
gold-thiolate rings and partial optimization of two known structural isomers of
the $\text{Au}_{38}(\text{SCH}_{3})_{24}$ nanocluster. The results demonstrate
that the method is well-suited for the structural optimizations of the
gold-thiolate systems, where the atomic bonding has a covalent nature in the
ligand shell and at the metal-ligand interface.

categories: physics.comp-ph cond-mat.mtrl-sci physics.atm-clus physics.chem-ph


title:
Constitutive model characterization and discovery using physics-informed
  deep learning

abstract:
  Classically, the mechanical response of materials is described through
constitutive models, often in the form of constrained ordinary differential
equations. These models have a very limited number of parameters, yet, they are
extremely efficient in reproducing complex responses observed in experiments.
Additionally, in their discretized form, they are computationally very
efficient, often resulting in a simple algebraic relation, and therefore they
have been extensively used within large-scale explicit and implicit finite
element models. However, it is very challenging to formulate new constitutive
models, particularly for materials with complex microstructures such as
composites. A recent trend in constitutive modeling leverages complex neural
network architectures to construct complex material responses where a
constitutive model does not yet exist. Whilst very accurate, they suffer from
two deficiencies. First, they are interpolation models and often do poorly in
extrapolation. Second, due to their complex architecture and numerous
parameters, they are inefficient to be used as a constitutive model within a
large-scale finite element model. In this study, we propose a novel approach
based on the physics-informed learning machines for the characterization and
discovery of constitutive models. Unlike data-driven constitutive models, we
leverage foundations of elastoplasticity theory as regularization terms in the
total loss function to find parametric constitutive models that are also
theoretically sound. We demonstrate that our proposed framework can efficiently
identify the underlying constitutive model describing different datasets from
the von Mises family.


title: Towards Robust 2D Convolution for Reliable Visual Recognition

abstract:
  2D convolution (Conv2d), which is responsible for extracting features from
the input image, is one of the key modules of a convolutional neural network
(CNN). However, Conv2d is vulnerable to image corruptions and adversarial
samples. It is an important yet rarely investigated problem that whether we can
design a more robust alternative of Conv2d for more reliable feature
extraction. In this paper, inspired by the recently developed learnable sparse
transform that learns to convert the CNN features into a compact and sparse
latent space, we design a novel building block, denoted by RConv-MK, to
strengthen the robustness of extracted convolutional features. Our method
leverages a set of learnable kernels of different sizes to extract features at
different frequencies and employs a normalized soft thresholding operator to
adaptively remove noises and trivial features at different corruption levels.
Extensive experiments on clean images, corrupted images as well as adversarial
samples validate the effectiveness of the proposed robust module for reliable
visual recognition. The source codes are enclosed in the submission.


title: Conditional coherent control with superconducting artificial atoms

abstract:
  Controlling the flow of quantum information is a fundamental task for quantum
computers, which is unpractical to realize on classical devices. Coherent
devices which can process quantum states are thus required to route the quantum
states yielding the information. In this paper we demonstrate experimentally
the smallest quantum transistor for superconducting processors, composed of
collector and emitter qubits, and the coupler. The interaction strength between
the collector and emitter is controlled by tuning the frequency and the state
of the gate qubit, effectively implementing a quantum switch. From the
truth-table measurement (open-gate fidelity 93.38%, closed-gate fidelity
98.77%), we verify the high performance of the quantum transistor. We also show
that taking into account the third energy level of the qubits is critical to
achieving a high-fidelity transistor. The presented device has a strong
potential for quantum information processes in superconducting platforms.


title:
AdIoTack: Quantifying and Refining Resilience of Decision Tree Ensemble
  Inference Models against Adversarial Volumetric Attacks on IoT Networks

abstract:
  Machine Learning-based techniques have shown success in cyber intelligence.
However, they are increasingly becoming targets of sophisticated data-driven
adversarial attacks resulting in misprediction, eroding their ability to detect
threats on network devices. In this paper, we present AdIoTack, a system that
highlights vulnerabilities of decision trees against adversarial attacks,
helping cybersecurity teams quantify and refine the resilience of their trained
models for monitoring IoT networks. To assess the model for the worst-case
scenario, AdIoTack performs white-box adversarial learning to launch successful
volumetric attacks that decision tree ensemble models cannot flag. Our first
contribution is to develop a white-box algorithm that takes a trained decision
tree ensemble model and the profile of an intended network-based attack on a
victim class as inputs. It then automatically generates recipes that specify
certain packets on top of the indented attack packets (less than 15% overhead)
that together can bypass the inference model unnoticed. We ensure that the
generated attack instances are feasible for launching on IP networks and
effective in their volumetric impact. Our second contribution develops a method
to monitor the network behavior of connected devices actively, inject
adversarial traffic (when feasible) on behalf of a victim IoT device, and
successfully launch the intended attack. Our third contribution prototypes
AdIoTack and validates its efficacy on a testbed consisting of a handful of
real IoT devices monitored by a trained inference model. We demonstrate how the
model detects all non-adversarial volumetric attacks on IoT devices while
missing many adversarial ones. The fourth contribution develops systematic
methods for applying patches to trained decision tree ensemble models,
improving their resilience against adversarial volumetric attacks.


title:
Revisiting black hole hyperaccretion in the center of gamma-ray bursts
  for the lower mass gap

abstract:
  The ultrarelativistic jets triggered by neutrino annihilation processes or
Blandford-Znajek (BZ) mechanisms in stellar-mass black hole (BH) hyperaccretion
systems are generally considered to power gamma-ray bursts (GRBs). Due to the
high accretion rate, the central BHs might grow rapidly on a short timescale,
providing a new way to understand "the lower mass gap" problem. In this paper,
we use the BH hyperaccretion model to investigate BH mass growth based on
observational GRB data. The results show that (i) if the initial BH mass is set
as $3~M_\odot$, the neutrino annihilation processes are capable of fueling the
BHs to escape the lower mass gap for more than half of long-duration GRBs
(LGRBs), while the BZ mechanism is inefficient on triggering BH growths for
LGRBs; (ii) the mean BH mass growths in the case of LGRBs without observable
supernova (SN) association are much larger than these in the case of LGRBs
associated with SNe for both mechanisms, which imply that more massive
progenitors or lower SN explosion energies prevail throughout the former cases;
(iii) for the short-duration GRBs, the mean BH mass growths are satisfied with
the mass supply limitation in the scenario of compact object mergers, but the
hyperaccretion processes are unable to rescue BHs from the gap in binary
neutron star (NS) mergers or the initial BH mass being $3~M_\odot$ after NS-BH
mergers.


title: Sensor fusion in ptychography

abstract:
  Ptychography is a lensless, computational imaging method that utilises
diffraction patterns to determine the amplitude and phase of an object. In
transmission ptychography, the diffraction patterns are recorded by a detector
positioned along the optical axis downstream of the object. The light scattered
at the highest diffraction angle carries information about the finest
structures of the object. We present a setup to simultaneously capture a signal
near the optical axis and a signal scattered at high diffraction angles.
Moreover, we present an algorithm based on a shifted angular spectrum method
and automatic differentiation that utilises this recorded signal. By jointly
reconstructing the object from the resulting low and high diffraction angle
images, the resolution of the reconstructed image is improved remarkably. The
effective numerical aperture of the compound sensor is determined by the
maximum diffraction angle captured by the off axis sensor.


title: Three things everyone should know about Vision Transformers

abstract:
  After their initial success in natural language processing, transformer
architectures have rapidly gained traction in computer vision, providing
state-of-the-art results for tasks such as image classification, detection,
segmentation, and video analysis. We offer three insights based on simple and
easy to implement variants of vision transformers. (1) The residual layers of
vision transformers, which are usually processed sequentially, can to some
extent be processed efficiently in parallel without noticeably affecting the
accuracy. (2) Fine-tuning the weights of the attention layers is sufficient to
adapt vision transformers to a higher resolution and to other classification
tasks. This saves compute, reduces the peak memory consumption at fine-tuning
time, and allows sharing the majority of weights across tasks. (3) Adding
MLP-based patch pre-processing layers improves Bert-like self-supervised
training based on patch masking. We evaluate the impact of these design choices
using the ImageNet-1k dataset, and confirm our findings on the ImageNet-v2 test
set. Transfer performance is measured across six smaller datasets.


title: Transnormal Functions and Focal Varieties on Finsler Manifolds

abstract:
  In this paper, we study transnormal functions and their level sets and focal
varieties on complete Finsler manifolds. We prove that the focal varieties of a
C2 transnormal function are smooth submanifolds and each regular level set is a
tube over either of the focal varieties.


title: ER=EPR, Entanglement Topology and Tensor Networks

abstract:
  This paper discusses ER = EPR, the hypothesis of Susskind and Maldacena that
entangled black holes are connected by an Einstein-Rosen bridge, and that more
generally, quantum entanglement is accompanied by topological connectivity.
Given a background space and a quantum tensor network, we describe how to
construct a new topological space, that welds the network and the background
space together. This construction embodies the principle that quantum
entanglement and topological connectivity are intimately related.


title:
Understanding the Trajectories of Population Decline Across Rural and
  Urban Europe: A Sequence Analysis

abstract:
  Population decline is projected to become widespread in Europe, with the
continental population set to reverse its longstanding trajectory of growth
within the next five years. This represents unfamiliar demographic territory.
Despite this, literature on decline remains sparse and our understanding
porous. Particular epistemological deficiencies stem from a lack of both
cross-national and temporal analyses of population decline. This study seeks to
address these gapsthrough the novel application of sequence and cluster
analysis techniques to examine variations in population decline trajectories
since 2000 in 696 sub-national areas across 33 European territories. The
methodology allows for a holistic understanding of decline trajectories
capturing differences in the ordering, timing, magnitude and spatial structure
of population decline. We identify a typology of population decline
distinguishing seven distinct pathways to depopulation and chart their
geographies. Results revealed differentiated pathways of depopulation in
continental sub-regions, with consistent and rapid declines in the east,
persistent but moderate declines in central Europe, accelerating declines in
the south and decelerating population declines in the west. Results also
revealed differentiated patterns of depopulation across the rural-urban
continuum, with urban and populous areas experiencing deceleration in
population decline, while population decline accelerates or stabilises in rural
areas. Small and mid-sized areas displayed heterogeneous depopulation
trajectories, highlighting the importance of local contextual factors in
influencing trajectories of population decline.


title:
Long lifetime supersolid in a two-component dipolar Bose-Einstein
  condensate

abstract:
  Recent studies on supersolidity in a single-component Bose-Einstein
condensate (BEC) have relied on the Lee-Huang-Yang (LHY) correction for
stabilization of self-bound droplets, which however involves a high density
inside the droplets, limiting the lifetime of the supersolid. Here we propose a
two-component mixture of dipolar and nondipolar BECs, such as an
$^{166}$Er-$^{87}$Rb mixture, to create and stabilize a supersolid without the
LHY correction, which can suppress the atomic loss and may allow observation of
the long-time dynamics of the supersolid. In such a system, supersolidity can
be controlled by the difference in the trap centers between the two components.


title:
Scheduling a single machine with compressible jobs to minimize maximum
  lateness

abstract:
  The problem of scheduling non-simultaneously released jobs with due dates on
a single machine with the objective to minimize the maximum job lateness is
known to be strongly NP-hard. Here we consider an extended model in which the
compression of the job processing times is allowed. The compression is
accomplished at the cost of involving additional emerging resources, whose use,
however, yields some cost. With a given upper limit $U$ on the total allowable
cost, one wishes to minimize the maximum job lateness. This setting is
realistic in practice because by completing some emerging jobs earlier using
some available resources, the objective function value might be decreased. In
particular, for minimizing the maximum job lateness, shortening the processing
time of specific jobs turns out to be very helpful. Although the generalized
problem remains strongly NP-hard and is considerably more complicated than the
generic non-compressible version, given a "sufficient amount" of additional
resources, it might be possible to solve the problem optimally. In particular,
we establish the compression rate for some specific jobs and develop an
algorithm that obtains an optimal solution for the processing times reduced in
this way. Such an approach has an obvious benefit in practice since the
manufacturer can be provided with an information about the required amount of
additional resources in order to solve the problem optimally.


title: Chiral-phonon-induced current in helical crystals

abstract:
  In this study, we theoretically show that in a helical crystal, a current is
induced by chiral phonons representing the microscopic local rotation of atoms.
By treating the rotational motion as a perturbation, we calculate the
time-dependent current by using the adiabatic Berry phase method. The time
average of the current along the helical axis becomes finite in the metallic
phase but it vanishes in the insulating phase. On the other hand, the current
in the hexagonal plane changes with time, but its time average vanishes due to
the threefold rotation space-time symmetry. We show that the time evolutions of
the current follow the space-time symmetries of the helical systems. Moreover,
we explain the reason for the vanishing of the time average of the current in
the insulating phase from the aspect of the Chern number in the parameter
space.


title:
Secondary complementary balancing compressive imaging with a free-space
  balanced amplified photodetector

abstract:
  Single-pixel imaging (SPI) has attracted widespread attention because it
generally uses a non-pixelated photodetector and a digital micromirror device
(DMD) to acquire the object image. Since the modulated patterns seen from two
reflection directions of the DMD are naturally complementary, one can apply
complementary balanced measurements to greatly improve the measurement
signal-to-noise ratio and reconstruction quality. However, the balance between
two reflection arms significantly determines the quality of differential
measurements. In this work, we propose and demonstrate a simple secondary
complementary balancing mechanism to minimize the impact of the imbalance on
the imaging system. In our SPI setup, we used a silicon free-space balanced
amplified photodetector with 5 mm active diameter which could directly output
the difference between two optical input signals in two reflection arms. Both
simulation and experimental results have demonstrated that the use of secondary
complementary balancing can result in a better cancellation of direct current
components of measurements and a better image restoration quality.


title:
Learning Consistency from High-quality Pseudo-labels for Weakly
  Supervised Object Localization

abstract:
  Pseudo-supervised learning methods have been shown to be effective for weakly
supervised object localization tasks. However, the effectiveness depends on the
powerful regularization ability of deep neural networks. Based on the
assumption that the localization network should have similar location
predictions on different versions of the same image, we propose a two-stage
approach to learn more consistent localization. In the first stage, we propose
a mask-based pseudo label generator algorithm, and use the pseudo-supervised
learning method to initialize an object localization network. In the second
stage, we propose a simple and effective method for evaluating the confidence
of pseudo-labels based on classification discrimination, and by learning
consistency from high-quality pseudo-labels, we further refine the localization
network to get better localization performance. Experimental results show that
our proposed approach achieves excellent performance in three benchmark
datasets including CUB-200-2011, ImageNet-1k and Tiny-ImageNet, which
demonstrates its effectiveness.


title: Prediction of five-flavored pentaquarks

abstract:
  We analyze the possibility of the existence of a fully exotic pentaquark
state $udsc\bar{b}$, which is made from the five different flavors
participarting in the strong interactions. We investigate the coupled channel
effects of the $B^{(*)} \Xi_c-B^{(*)} \Xi_c^\prime$ system through $t$-channel
vector meson exchange to search for such states. A $B \Xi_c$ and a $B
\Xi_c^\prime$ bound state are found to have binding energies of about
$10-30$~MeV with $B_s\Lambda_c$ and $B_c\Lambda$ being the possible decay
channels. Similarly, a bound state is formed in the $B^* \Xi_c$ and $B^*
\Xi_c^\prime$ channels, respectively. These states could be searched for
through the $pp\to B_c\Lambda X$ or $pp\to B_s^{(*)}\Lambda_c X$ processes by
the LHCb collaboration.


title: Stability Indices of Non-Hyperbolic Equilibria

abstract:
  We consider families of systems of two-dimensional ordinary differential
equations with the origin $0$ as a non-hyperbolic equilibrium. For any number
$a \in (-\infty, +\infty)$ we show that it is possible to choose a parameter in
these equations such that the stability index $\sigma(0)$ is precisely
$\sigma(0)=a$. In contrast to that, for a hyperbolic equilibrium $x$ it is
known that either $\sigma(x)=-\infty$ or $\sigma(x)=+\infty$. Furthermore, we
discuss a system with an equilibrium that is locally unstable but seems to be
globally attracting, highlighting some subtle differences between the local and
non-local stability indices.


title:
21 new long-term variables in the GX 339-4 field: two years of MeerKAT
  monitoring

abstract:
  We present 21 new long-term variable radio sources found commensally in two
years of weekly MeerKAT monitoring of the low-mass X-ray binary GX 339-4. The
new sources vary on time scales of weeks to months and have a variety of light
curve shapes and spectral index properties. Three of the new variable sources
are coincident with multi-wavelength counterparts; and one of these is
coincident with an optical source in deep MeerLICHT images. For most sources,
we cannot eliminate refractive scintillation of active galactic nuclei as the
cause of the variability. These new variable sources represent $2.2\pm0.5$ per
cent of the unresolved sources in the field, which is consistent with the 1-2
per cent variability found in past radio variability surveys. However, we
expect to find short-term variable sources in the field as well as these 21 new
long-term variable sources. We present the radio light curves and spectral
index variability of the new variable sources, as well as the absolute
astrometry and matches to coincident sources at other wavelengths.


title:
Reinforcement-learning calibration of coherent-state receivers on
  variable-loss optical channels

abstract:
  We study the problem of calibrating a quantum receiver for optical coherent
states when transmitted on a quantum optical channel with variable
transmissivity, a common model for long-distance optical-fiber and
free/deep-space optical communication. We optimize the error probability of
legacy adaptive receivers, such as Kennedy's and Dolinar's, on average with
respect to the channel transmissivity distribution. We then compare our results
with the ultimate error probability attainable by a general quantum device,
computing the Helstrom bound for mixtures of coherent-state hypotheses, for the
first time to our knowledge, and with homodyne measurements. With these tools,
we first analyze the simplest case of two different transmissivity values; we
find that the strategies adopted by adaptive receivers exhibit strikingly new
features as the difference between the two transmissivities increases. Finally,
we employ a recently introduced library of shallow reinforcement learning
methods, demonstrating that an intelligent agent can learn the optimal receiver
setup from scratch by training on repeated communication episodes on the
channel with variable transmissivity and receiving rewards if the
coherent-state message is correctly identified.


title:
Approximation of human flow in urban areas by a network of electric
  circuits : Potential fields and fluctuation-dissipation relations

abstract:
  Owing to the big data the extension of physical laws on nonmaterial has seen
numerous successes, and human mobility is one of the scientific frontier
topics. Recent GPS technology has made it possible to trace detailed
trajectories of millions of people, macroscopic approaches such as the gravity
law for human flow between cities and microscopic approaches of individual
origin-destination distributions are attracting much attention. However, we
need a more general basic model with wide applicability to realize traffic
forecasting and urban planning of metropolis fully utilizing the GPS data.
Here, based on a novel idea of treating moving people as charged particles, we
introduce a sophisticated method to map macroscopic human flows into currents
on an imaginary electric circuit defined over a metropolitan area. Conductance
is found to be nearly proportional to the maximum current in each location and
synchronized human flows in the morning and evening are well described by the
temporal changes of electric potential. Surprisingly, the famous fluctuation
dissipation theorem holds, namely, the variances of currents are proportional
to the conductivities akin to an ordinary material. Especially during the
pandemic, such a tool may offer an invaluable insight for policy-making in
managing human flows.


title:
Proximal Policy Optimization with Adaptive Threshold for Symmetric
  Relative Density Ratio

abstract:
  Deep reinforcement learning (DRL) is one of the promising approaches for
introducing robots into complicated environments. The recent remarkable
progress of DRL stands on regularization of policy, which allows the policy to
improve stably and efficiently. A popular method, so-called proximal policy
optimization (PPO), and its variants constrain density ratio of the latest and
baseline policies when the density ratio exceeds a given threshold. This
threshold can be designed relatively intuitively, and in fact its recommended
value range has been suggested. However, the density ratio is asymmetric for
its center, and the possible error scale from its center, which should be close
to the threshold, would depend on how the baseline policy is given. In order to
maximize the values of regularization of policy, this paper proposes a new PPO
derived using relative Pearson (RPE) divergence, therefore so-called PPO-RPE,
to design the threshold adaptively. In PPO-RPE, the relative density ratio,
which can be formed with symmetry, replaces the raw density ratio. Thanks to
this symmetry, its error scale from center can easily be estimated, hence, the
threshold can be adapted for the estimated error scale. From three simple
benchmark simulations, the importance of algorithm-dependent threshold design
is revealed. By simulating additional four locomotion tasks, it is verified
that the proposed method statistically contributes to task accomplishment by
appropriately restricting the policy updates.


title: Dencentralized learning in the presence of low-rank noise

abstract:
  Observations collected by agents in a network may be unreliable due to
observation noise or interference. This paper proposes a distributed algorithm
that allows each node to improve the reliability of its own observation by
relying solely on local computations and interactions with immediate neighbors,
assuming that the field (graph signal) monitored by the network lies in a
low-dimensional subspace and that a low-rank noise is present in addition to
the usual full-rank noise. While oblique projections can be used to project
measurements onto a low-rank subspace along a direction that is oblique to the
subspace, the resulting solution is not distributed. Starting from the
centralized solution, we propose an algorithm that performs the oblique
projection of the overall set of observations onto the signal subspace in an
iterative and distributed manner. We then show how the oblique projection
framework can be extended to handle distributed learning and adaptation
problems over networks.


title:
Stacked Hybrid-Attention and Group Collaborative Learning for Unbiased
  Scene Graph Generation

abstract:
  Scene Graph Generation, which generally follows a regular encoder-decoder
pipeline, aims to first encode the visual contents within the given image and
then parse them into a compact summary graph. Existing SGG approaches generally
not only neglect the insufficient modality fusion between vision and language,
but also fail to provide informative predicates due to the biased relationship
predictions, leading SGG far from practical. Towards this end, in this paper,
we first present a novel Stacked Hybrid-Attention network, which facilitates
the intra-modal refinement as well as the inter-modal interaction, to serve as
the encoder. We then devise an innovative Group Collaborative Learning strategy
to optimize the decoder. Particularly, based upon the observation that the
recognition capability of one classifier is limited towards an extremely
unbalanced dataset, we first deploy a group of classifiers that are expert in
distinguishing different subsets of classes, and then cooperatively optimize
them from two aspects to promote the unbiased SGG. Experiments conducted on VG
and GQA datasets demonstrate that, we not only establish a new state-of-the-art
in the unbiased metric, but also nearly double the performance compared with
two baselines.


title:
Grasp Pre-shape Selection by Synthetic Training: Eye-in-hand Shared
  Control on the Hannes Prosthesis

abstract:
  We consider the task of object grasping with a prosthetic hand capable of
multiple grasp types. In this setting, communicating the intended grasp type
often requires a high user cognitive load which can be reduced adopting shared
autonomy frameworks. Among these, so-called eye-in-hand systems automatically
control the hand aperture and pre-shaping before the grasp, based on visual
input coming from a camera on the wrist. In this work, we present an
eye-in-hand learning-based approach for hand pre-shape classification from RGB
sequences. In order to reduce the need for tedious data collection sessions for
training the system, we devise a pipeline for rendering synthetic visual
sequences of hand trajectories for the purpose. We tackle the peculiarity of
the eye-in-hand setting by means of a model for the human arm trajectories,
with domain randomization over relevant visual elements. We develop a
sensorized setup to acquire real human grasping sequences for benchmarking and
show that, compared on practical use cases, models trained with our synthetic
dataset achieve better generalization performance than models trained on real
data. We finally integrate our model on the Hannes prosthetic hand and show its
practical effectiveness. Our code, real and synthetic datasets will be released
upon acceptance.


title:
Are You Robert or RoBERTa? Deceiving Online Authorship Attribution
  Models Using Neural Text Generators

abstract:
  Recently, there has been a rise in the development of powerful pre-trained
natural language models, including GPT-2, Grover, and XLM. These models have
shown state-of-the-art capabilities towards a variety of different NLP tasks,
including question answering, content summarisation, and text generation.
Alongside this, there have been many studies focused on online authorship
attribution (AA). That is, the use of models to identify the authors of online
texts. Given the power of natural language models in generating convincing
texts, this paper examines the degree to which these language models can
generate texts capable of deceiving online AA models. Experimenting with both
blog and Twitter data, we utilise GPT-2 language models to generate texts using
the existing posts of online users. We then examine whether these AI-based text
generators are capable of mimicking authorial style to such a degree that they
can deceive typical AA models. From this, we find that current AI-based text
generators are able to successfully mimic authorship, showing capabilities
towards this on both datasets. Our findings, in turn, highlight the current
capacity of powerful natural language models to generate original online posts
capable of mimicking authorial style sufficiently to deceive popular AA
methods; a key finding given the proposed role of AA in real world applications
such as spam-detection and forensic investigation.


title:
Limiting measures and energy growth for sequences of solutions to
  Taubes's Seiberg-Witten equations

abstract:
  We consider sequences of solutions $(\psi_n,A_n)_{n=1}^\infty$ to Taubes's
modified Seiberg-Witten equations, associated with a fixed volume-preserving
vector field $X$ on a 3-manifold and corresponding to arbitrarily large values
of the strength parameter $r_n \to \infty$. In Taubes's work, the asymptotic
behavior of these solutions is related to the dynamics of $X$. We consider the
rather unexplored case of sequences of solutions whose energy is not uniformly
bounded as $n\to\infty$. Our first main result shows that when the energy grows
more slowly than $r_n^{1/2}$, the limiting nodal set of the solutions converges
to an invariant set of the vector field $X$. The main tool we use is a novel
maximum principle for the solutions with the key property that it remains valid
in the unbounded energy case. As a byproduct, in the usual case of sequences of
solutions with bounded energy, we obtain a new, more straightforward proof of
Taubes's result on the existence of periodic orbits that does not involve a
local analysis or the vortex equations. Our second main result proves that,
contrary to what happens in the bounded energy case, when the energy is
unbounded there are no local restrictions to the limiting measures that may
arise in the modified Seiberg-Witten equations. Furthermore, we obtain a
connection about the dimension of the support of the limiting measure (as
expressed through a $d$-Frostman property) and the energy growth of the
sequence of local solutions we construct.


title:
Network approach reveals the spatiotemporal influence of traffic to air
  pollution under the COVID-19

abstract:
  Air pollution causes widespread environmental and health problems and
severely hinders the life quality of urban residents. Traffic is a critical for
human life and its emissions are a major source of pollution, aggravating urban
air pollution. However, the complex interaction between the traffic emissions
and the air pollution in the cities has not yet been revealed. In particular,
the spread of the COVID-19 has caused various cities to implement different
traffic restriction policies according to the local epidemic situation, which
provides the possibility to explore the relationship between urban traffic and
air pollution. Here we explore the influence of traffic to air pollution by
reconstructing a multi-layer complex network base on traffic index and air
quality index. We uncover that air quality in Beijing-Tianjin-Hebei (BTH),
Chengdu-Chongqing Economic Circle (CCS) and Central China (CC) regions are
significantly influenced by the surrounding traffic conditions after the
outbreak. Under different fights against the epidemic stages, the influence of
traffic in other cities on the air pollution reached the maximum in stage 2
(also called Initial Progress in Containing the Virus). For BTH and CC regions,
the impact of traffic on air quality becomes larger in the first two stages and
then decreases, while for CC, the significant impact occurs in Phase 3 among
regions. For other regions, however, the changes are not evident. Our presented
network-based framework provides a new perspective in the field of
transportation and environment, and maybe helpful to guide the government to
formulate air pollution mitigation and traffic restriction policies.


title:
Model Averaging based Semiparametric Modelling for Conditional Quantile
  Prediction

abstract:
  In real data analysis, the underlying model is usually unknown, modelling
strategy plays a key role in the success of data analysis. Stimulated by the
idea of model averaging, we propose a novel semiparametric modelling strategy
for conditional quantile prediction, without assuming the underlying model is
any specific parametric or semiparametric model. Thanks the optimality of the
selected weights by cross-validation, the proposed modelling strategy results
in a more accurate prediction than that based on some commonly used
semiparametric models, such as the varying coefficient models and additive
models. Asymptotic properties are established of the proposed modelling
strategy together with its estimation procedure. Intensive simulation studies
are conducted to demonstrate how well the proposed method works, compared with
its alternatives under various circumstances. The results show the proposed
method indeed leads to more accurate predictions than its alternatives.
Finally, the proposed modelling strategy together with its prediction procedure
are applied to the Boston housing data, which result in more accurate
predictions of the quantiles of the house prices than that based on some
commonly used alternative methods, therefore, present us a more accurate
picture of the housing market in Boston.


title:
The effect of spanwise heterogeneous roughness on mixed convection in
  turbulent channels

abstract:
  Turbulent mixed convection in channel flows with heterogeneous roughness are
studied using direct numerical simulations. The bulk Richardson number $Ri_b$,
characterizing the relative importance between buoyancy and shear, is varied to
cover the flow regimes of forced, mixed and natural convection. The
heterogeneous roughness consists of streamwise-aligned ridges, which in case of
forced convection is known to form secondary motions, and the ridge spacing $S$
is systematically varied. While secondary motions are hardly affected by weak
buoyancy effects, they are broken by streamwise rolls under mild convection.
The present results show that heterogeneous roughness has a significant
influence on the flow organization of mixed convection. The appearance of
streamwise rolls is significantly reduced for densely spaced ridges, since the
strength of rolls is reduced by the ridges. This occurs especially between the
roll-to-cell transition range, where a change of the roughness properties has a
comparable effect as a change of $Ri_b$ for homogeneous wall conditions.
Examinations of higher Reynolds number cases, show that the observed
transitions are shifted towards more convective conditions with higher $Re$.
The streamwise rolls for the specific combination of $S = 4\delta$ and $Ri_b =
1$ or $Ri_b = 3.2$ exhibit very slow dynamics, which is masked in long-time
averages. For these cases the up- and downdrafts slowly move in time over the
entire channel instead of being fixed in space. The orientation of the
convective cells over heterogeneous roughness becomes increasingly
perpendicular to the ridges as $S$ decreases, which is caused by the increasing
lateral drag.


title:
Setting the Stage for Planet Formation: Measurements and Implications of
  the Fundamental Disk Properties

abstract:
  The field of planet formation is in an exciting era, where recent
observations of disks around low- to intermediate-mass stars made with state of
the art interferometers and high-contrast optical and IR facilities have
revealed a diversity of substructures, some possibly planet-related. It is
therefore important to understand the physical and chemical nature of the
protoplanetary building blocks, as well as their spatial distribution, to
better understand planet formation. Since PPVI, the field has seen tremendous
improvements in observational capabilities, enabling both surveys of large
samples of disks and high resolution imaging studies of a few bright disks.
Improvements in data quality and sample size have, however, opened up many
fundamental questions about properties such as the mass budget of disks, its
spatial distribution, and its radial extent. Moreover, the vertical structure
of disks has been studied in greater detail with spatially resolved
observations, providing new insights on vertical layering and temperature
stratification, yet also bringing rise to questions about other properties,
such as material transport and viscosity. Each one of these properties - disk
mass, surface density distribution, outer radius, vertical extent, temperature
structure, and transport - is of fundamental interest as they collectively set
the stage for disk evolution and corresponding planet formation theories. In
this chapter, we will review our understanding of the fundamental properties of
disks including the relevant observational techniques to probe their nature,
modelling methods, and the respective caveats. Finally, we discuss the
implications for theories of disk evolution and planet formation underlining
what new questions have since arisen as our observational facilities have
improved.


title: Semi-classical simulation of spin-1 magnets

abstract:
  Theoretical studies of magnets have traditionally concentrated on either
classical spins, or the extreme quantum limit of spin-1/2. However, magnets
built of spin-1 moments are also intrinsically interesting, not least because
they can support quadrupole, as well as dipole moments, on a single site. For
this reason, spin-1 models have been extensively studied as prototypes for
quadrupolar (spin-nematic) order in magnetic insulators, and Fe-based
superconductors. At the same time, because of the presence of quadrupoles, the
classical limit of a spin-1 moment is not an $O(3)$ vector, a fact which must
be taken into account in describing their properties. In this Article we
develop a method to simulate spin-1 magnets based on a $u(3)$ algebra which
treats both dipole and quadrupole moments on equal footing. This approach is
amenable to both classical and quantum calculations, and we develop the
techniques needed to calculate thermodynamic properties through Monte Carlo
simulations and classical low-temperature expansion, and dynamical properties,
through "molecular dynamics" simulations and a multiple-boson expansion. As a
case study, we present detailed analytic and numerical results for the
thermodynamic properties of ferroquadrupolar order on the triangular lattice,
and its associated dynamics. At low temperatures, we show that it is possible
to "correct" for the effects of classical statistics in simulations, and
extrapolate to the zero-temperature quantum results found in flavour-wave
theory.


title: Non-Markovian Vibrational Relaxation Dynamics at Surfaces

abstract:
  Vibrational dynamics of adsorbates near surfaces plays both an important role
for applied surface science and as model lab for studying fundamental problems
of open quantum systems. We employ a previously developed model for the
relaxation of a D-Si-Si bending mode at a D:Si(100)-(2$\times$1) surface,
induced by a "bath" of more than $2000$ phonon modes [U. Lorenz, P. Saalfrank,
Chem. Phys. {\bf 482}, 69 (2017)], to extend previous work along various
directions. First, we use a Hierarchical Effective Mode (HEM) model [E.W.
Fischer, F. Bouakline, M. Werther, P. Saalfrank, J. Chem. Phys. {\bf 153},
064704 (2020)] to study relaxation of higher excited vibrational states than
hitherto done, by solving a high-dimensional system-bath time-dependent
Schr\"odinger equation (TDSE). In the HEM approach, (many) real bath modes are
replaced by (much less) effective bath modes. Accordingly, we are able to
examine scaling laws for vibrational relaxation lifetimes for a realistic
surface science problem. Second, we compare the performance of the multilayer
multiconfigurational time-dependent Hartree (ML-MCTDH) approach with the
recently developed coherent-state based multi-Davydov D2 {\it ansatz} [N. Zhou,
Z. Huang, J. Zhu, V. Chernyak, Y. Zhao, {J. Chem. Phys.} {\bf 143}, 014113
(2015)]. Both approaches work well, with some computational advantages for the
latter in the presented context. Third, we apply open-system density matrix
theory in comparison with basically "exact" solutions of the multi-mode TDSEs.
Specifically, we use an open-system Liouville-von Neumann (LvN) equation
treating vibration-phonon coupling as Markovian dissipation in Lindblad form to
quantify effects beyond the Born-Markov approximation.


title: Hydro-, Magnetohydro-, and Dust-Gas Dynamics of Protoplanetary Disks

abstract:
  The building of planetary systems is controlled by the gas and dust dynamics
of protoplanetary disks. While the gas is simultaneously accreted onto the
central star and dissipated away by winds, dust grains aggregate and collapse
to form planetesimals and eventually planets. This dust and gas dynamics
involves instabilities, turbulence and complex non-linear interactions which
ultimately control the observational appearance and the secular evolution of
these disks.
  This chapter is dedicated to the most recent developments in our
understanding of the dynamics of gaseous and dusty disks, covering hydrodynamic
and magnetohydrodynamic turbulence, gas-dust instabilities, dust clumping and
disk winds. We show how these physical processes have been tested from
observations and highlight standing questions that should be addressed in the
future.


title:
Performance of Coherent Frequency-Shift Keying for Classical
  Communication \\ on Quantum Channels

abstract:
  We evaluate the performance of coherent frequency-shift keying (CFSK)
alphabets for communication on quantum channels. We show that, contrarily to
what previously thought, the square-root-measurement (SRM) is sub-optimal for
discriminating CFSK states. Furthermore, we compute the maximum information
transmission rate of the CFSK alphabet, observing that it employs at least as
many frequency modes as the signal states, and compare it with standard
phase-shift-keying. Finally, we introduce a discretized CFSK alphabet with
higher mode-efficiency, exhibiting comparable error-probability performance
with respect to CFSK and better rate performance. Our results suggest the
existence of a tradeoff between the CFSK reduced error-probability and its mode
efficiency.


title: Norm inflation for the Zakharov system

abstract:
  The Cauchy problem for the classical Zakharov system is shown to be ill-posed
in the sense of norm inflation in a range of Sobolev spaces
$H^s(\mathbb{R}^d)\times H^l(\mathbb{R}^d)$ for all dimensions $d$. This proves
several results on well-posedness, which includes existence of solutions,
uniqueness and continuous dependence on the initial data, to be sharp up to
endpoints.


title: Cross-Modal Perceptionist: Can Face Geometry be Gleaned from Voices?

abstract:
  This work digs into a root question in human perception: can face geometry be
gleaned from one's voices? Previous works that study this question only adopt
developments in image synthesis and convert voices into face images to show
correlations, but working on the image domain unavoidably involves predicting
attributes that voices cannot hint, including facial textures, hairstyles, and
backgrounds. We instead investigate the ability to reconstruct 3D faces to
concentrate on only geometry, which is much more physiologically grounded. We
propose our analysis framework, Cross-Modal Perceptionist, under both
supervised and unsupervised learning. First, we construct a dataset,
Voxceleb-3D, which extends Voxceleb and includes paired voices and face meshes,
making supervised learning possible. Second, we use a knowledge distillation
mechanism to study whether face geometry can still be gleaned from voices
without paired voices and 3D face data under limited availability of 3D face
scans. We break down the core question into four parts and perform visual and
numerical analyses as responses to the core question. Our findings echo those
in physiology and neuroscience about the correlation between voices and facial
structures. The work provides future human-centric cross-modal learning with
explainable foundations. See our project page:
https://choyingw.github.io/works/Voice2Mesh/index.html


title: AdaVocoder: Adaptive Vocoder for Custom Voice

abstract:
  Custom voice is to construct a personal speech synthesis system by adapting
the source speech synthesis model to the target model through the target few
recordings. The solution to constructing a custom voice is to combine an
adaptive acoustic model with a robust vocoder. However, training a robust
vocoder usually requires a multi-speaker dataset, which should include various
age groups and various timbres, so that the trained vocoder can be used for
unseen speakers. Collecting such a multi-speaker dataset is difficult, and the
dataset distribution always has a mismatch with the distribution of the target
speaker dataset. This paper proposes an adaptive vocoder for custom voice from
another novel perspective to solve the above problems. The adaptive vocoder
mainly uses a cross-domain consistency loss to solve the overfitting problem
encountered by the GAN-based neural vocoder in the transfer learning of
few-shot scenes. We construct two adaptive vocoders, AdaMelGAN and AdaHiFi-GAN.
First, We pre-train the source vocoder model on AISHELL3 and CSMSC datasets,
respectively. Then, fine-tune it on the internal dataset VXI-children with few
adaptation data. The empirical results show that a high-quality custom voice
system can be built by combining a adaptive acoustic model with a adaptive
vocoder.


title:
Proofs of some conjectures of Chan-Mao-Osburn on Beck's partition
  statistics

abstract:
  Recently, George Beck introduced two partition statistics $NT(m,j,n)$ and
$M_{\omega}(m,j,n)$, which denote the total number of parts in the partition of
$n$ with rank congruent to $m$ modulo $j$ and the total number of ones in the
partition of $n$ with crank congruent to $m$ modulo $j$, respectively. Andrews
proved a congruence on $NT(m,5,n)$ which was conjectured by Beck. Very
recently, Chan, Mao and Osburn established a number of Andrews-Beck type
congruences and posed several conjectures involving $NT(m,j,n)$ and
$M_{\omega}(m,j,n)$. Some of those conjectures were proved by Chern and Mao. In
this paper, we confirm the remainder three conjectures of Chan-Mao-Osburn and
two conjectures due to Mao. We also present two new conjectures on
$M_{\omega}(m,j,n)$ and $NT(m,j,n)$.


title: Sums of linear transformations

abstract:
  We show that if $\mathcal{L}_1$ and $\mathcal{L}_2$ are linear
transformations from $\mathbb{Z}^d$ to $\mathbb{Z}^d$ satisfying certain mild
conditions, then, for any finite subset $A$ of $\mathbb{Z}^d$, $$|\mathcal{L}_1
A+\mathcal{L}_2 A|\geq
\left(|\det(\mathcal{L}_1)|^{1/d}+|\det(\mathcal{L}_2)|^{1/d}\right)^d|A|-
o(|A|).$$ This result corrects and confirms the two-summand case of a
conjecture of Bukh and is best possible up to the lower-order term for many
choices of $\mathcal{L}_1$ and $\mathcal{L}_2$. As an application, we prove a
lower bound for $|A + \lambda \cdot A|$ when $A$ is a finite set of real
numbers and $\lambda$ is an algebraic number. In particular, when $\lambda$ is
of the form $(p/q)^{1/d}$ for some $p, q, d \in \mathbb{N}$, each taken as
small as possible for such a representation, we show that $$|A + \lambda \cdot
A| \geq (p^{1/d} + q^{1/d})^d |A| - o(|A|).$$ This is again best possible up to
the lower-order term and extends a recent result of Krachun and Petrov which
treated the case $\lambda = \sqrt{2}$.


title:
Inducing tensile failure of claystone through thermal pressurization in
  a novel triaxial device

abstract:
  Complex coupled thermo-hydromechanical (THM) loading paths are expected to
occur in clay rocks which serve as host formations for geological radioactive
waste repositories. Exothermic waste packages heat the rock, causing thermal
strains and temperature induced pore pressure build-up. The drifts are designed
in such a way as to limit these effects. One has to anticipate failure and
fracturing of the material, should pore pressures exceed the tensile resistance
of the rock. To characterise the behaviour of the Callovo-Oxfordian claystone
(COx) under effective tension and to quantify the tensile failure criterion, a
laboratory program is carried out in this work. THM loading paths which
correspond to the expected in situ conditions are recreated in the laboratory.
To this end, a special triaxial system was developed, which allows the
independent control of radial and axial stresses, as well as of pore pressure
and temperature of rock specimens. More importantly, the device allows one to
maintain axial effective tension on a specimen. Saturated cylindrical claystone
specimens were tested in undrained conditions under constrained lateral
deformation and under nearly constant axial stress. The specimens were heated
until the induced pore pressures created effective tensile stresses and
ultimately fractured the material. The failure happened at average axial
effective tensile stresses around 3.0 MPa. Fracturing under different lateral
total stresses allows one to describe the failure with a Hoek-Brown or
Fairhurst's generalized Griffith criterion. Measured axial extension strains
are analysed based on a transversely isotropic thermo-poroelastic constitutive
model, which is able to satisfactorily reproduce the observed behaviour.


title:
Towards Representative Subset Selection for Self-Supervised Speech
  Recognition

abstract:
  Self-supervised speech recognition models require considerable labeled
training data for learning high-fidelity representations for Automatic Speech
Recognition (ASR), which hinders their application to low-resource languages.
We consider the task of identifying an optimal subset of training data to
fine-tune self-supervised speech models for ASR. We make a surprising
observation that active learning strategies for sampling harder-to-learn
examples do not perform better than random subset selection for fine-tuning
self-supervised ASR. We then present the COWERAGE algorithm for better subset
selection in self-supervised ASR which is based on our finding that ensuring
the coverage of examples based on training WER in the early training epochs
leads to better generalization performance. Extensive experiments on the
wav2vec 2.0 model and TIMIT dataset show the effectiveness of COWERAGE, with up
to 27% absolute WER improvement over active learning methods. We also report
the connection between training WER and the phonemic cover and demonstrate that
our algorithm ensures inclusion of phonemically diverse examples.


title: Laneformer: Object-aware Row-Column Transformers for Lane Detection

abstract:
  We present Laneformer, a conceptually simple yet powerful transformer-based
architecture tailored for lane detection that is a long-standing research topic
for visual perception in autonomous driving. The dominant paradigms rely on
purely CNN-based architectures which often fail in incorporating relations of
long-range lane points and global contexts induced by surrounding objects
(e.g., pedestrians, vehicles). Inspired by recent advances of the transformer
encoder-decoder architecture in various vision tasks, we move forwards to
design a new end-to-end Laneformer architecture that revolutionizes the
conventional transformers into better capturing the shape and semantic
characteristics of lanes, with minimal overhead in latency. First, coupling
with deformable pixel-wise self-attention in the encoder, Laneformer presents
two new row and column self-attention operations to efficiently mine point
context along with the lane shapes. Second, motivated by the appearing objects
would affect the decision of predicting lane segments, Laneformer further
includes the detected object instances as extra inputs of multi-head attention
blocks in the encoder and decoder to facilitate the lane point detection by
sensing semantic contexts. Specifically, the bounding box locations of objects
are added into Key module to provide interaction with each pixel and query
while the ROI-aligned features are inserted into Value module. Extensive
experiments demonstrate our Laneformer achieves state-of-the-art performances
on CULane benchmark, in terms of 77.1% F1 score. We hope our simple and
effective Laneformer will serve as a strong baseline for future research in
self-attention models for lane detection.


title:
DTA: Physical Camouflage Attacks using Differentiable Transformation
  Network

abstract:
  To perform adversarial attacks in the physical world, many studies have
proposed adversarial camouflage, a method to hide a target object by applying
camouflage patterns on 3D object surfaces. For obtaining optimal physical
adversarial camouflage, previous studies have utilized the so-called neural
renderer, as it supports differentiability. However, existing neural renderers
cannot fully represent various real-world transformations due to a lack of
control of scene parameters compared to the legacy photo-realistic renderers.
In this paper, we propose the Differentiable Transformation Attack (DTA), a
framework for generating a robust physical adversarial pattern on a target
object to camouflage it against object detection models with a wide range of
transformations. It utilizes our novel Differentiable Transformation Network
(DTN), which learns the expected transformation of a rendered object when the
texture is changed while preserving the original properties of the target
object. Using our attack framework, an adversary can gain both the advantages
of the legacy photo-realistic renderers including various physical-world
transformations and the benefit of white-box access by offering
differentiability. Our experiments show that our camouflaged 3D vehicles can
successfully evade state-of-the-art object detection models in the
photo-realistic environment (i.e., CARLA on Unreal Engine). Furthermore, our
demonstration on a scaled Tesla Model 3 proves the applicability and
transferability of our method to the real world.


title:
A Nonlinear Subspace Approach for Parametric Estimation of PDFs from
  Short Data Records with Application to Rayleigh Fading

abstract:
  This paper tackles the issue of real-time parametric estimation of a wide
class of probability density functions from limited datasets. This type of
estimation addresses recent applications that require joint sensing and
actuation. The suggested estimator operates in the nonlinear subspace that the
parameter space of the distribution creates in the measurement sample space.
This enables the estimator to embed a priori available information about the
distribution in the computations to produce parameter estimates that are
induced by signal components belonging only to the correct class of density
functions being considered. It also enables it to nullify the effect of those
components that do not belong to this class on the estimation process. The
estimator can, with high accuracy, compute quickly the parameters of a wide
class of probability density functions from short data records. The approach is
developed and basic proofs of correctness are carried-out for the Rayleigh
distribution, which is used to characterize wireless communication channels
experiencing fast fading in heavily cluttered environments. Simulation results
demonstrate the capabilities of the suggested procedure and the clear
advantages it has over conventional norm-based estimation techniques. The
results also show the ability of the suggested approach to estimate other
density functions including the two-parameter lognormal distribution used to
characterize shadowing in wireless communication.


title:
Burgers' equation revisited: extension of mono-dimensional case on a
  network

abstract:
  The paper deals with the analysis of Burgers' equation on acyclic metric
graphs. The main goal is to establish the existence of weak solutions in the
$TV$ -- class of regularity. A key point is transmission conditions in vertices
obeying the Kirchhoff law. First, we consider positive solutions at arbitrary
acyclic networks and highlight two kinds of vertices, describing two mechanisms
of flow splitting at the vertex. Next we design rules at vertices for solutions
of arbitrary sign for any subgraph of hexagonal grid, which leads to a
construction of general solutions with $TV$ -- regularity for this class of
networks. Introduced transmission conditions are motivated by the change of the
energy estimation.


title: Continued Fractions and Hardy Sums

abstract:
  The classical Dedekind sums $s(d, c)$ can be represented as sums over the
partial quotients of the continued fraction expansion of the rational
$\frac{d}{c}$. Hardy sums, the analog integer-valued sums arising in the
transformation of the logarithms of $\theta$-functions under a subgroup of the
modular group, have been shown to satisfy many properties which mirror the
properties of the classical Dedekind sums. The representation as sums of
partial quotients has, however, been missing so far. We define non-classical
continued fractions and prove that Hardy sums can be expressed as a sums of
partial quotients of these continued fractions. As an application, we give a
new proof that the graph of the Hardy sums is dense in $\mathbf{R} \times
\mathbf{Z}$.


title: Reliably Reproducing Machine-Checked Proofs with the Coq Platform

abstract:
  The Coq Platform is a continuously developed distribution of the Coq proof
assistant together with commonly used libraries, plugins, and external tools
useful in Coq-based formal verification projects. The Coq Platform enables
reproducing and extending Coq artifacts in research, education, and industry,
e.g., formalized mathematics and verified software systems. In this paper, we
describe the background and motivation for the Platform, and outline its
organization and development process. We also compare the Coq Platform to
similar distributions and processes in the proof assistant community, such as
for Isabelle and Lean, and in the wider open source software community.


title: Perspective Flow Aggregation for Data-Limited 6D Object Pose Estimation

abstract:
  Most recent 6D object pose estimation methods, including unsupervised ones,
require many real training images. Unfortunately, for some applications, such
as those in space or deep under water, acquiring real images, even unannotated,
is virtually impossible. In this paper, we propose a method that can be trained
solely on synthetic images, or optionally using a few additional real ones.
Given a rough pose estimate obtained from a first network, it uses a second
network to predict a dense 2D correspondence field between the image rendered
using the rough pose and the real image and infers the required pose
correction. This approach is much less sensitive to the domain shift between
synthetic and real images than state-of-the-art methods. It performs on par
with methods that require annotated real images for training when not using
any, and outperforms them considerably when using as few as twenty real images.


title: $\beta$ decay and evolution of low-lying structure in Ge and As nuclei

abstract:
  A simultaneous calculation for the shape evolution and the related
spectroscopic properties of the low-lying states, and the $\beta$-decay
properties in the even- and odd-mass Ge and As nuclei in the mass
$A\approx70-80$ region, within the framework of the nuclear density functional
theory and the particle-core coupling scheme, is presented. The constrained
self-consistent mean-field calculations using a universal energy density
functional (EDF) and a pairing interaction determines the interacting-boson
Hamiltonian for the even-even core nuclei, and the essential ingredients of the
particle-boson interactions for the odd-nucleon systems, and of the
Gamow-Teller and Fermi transition operators. A rapid structural evolution from
$\gamma$-soft oblate to prolate shapes, as well as the spherical-oblate shape
coexistence around the neutron sub-shell closure $N=40$, is suggested to occur
in the even-even Ge nuclei. The predicted low-energy spectra, electromagnetic
transition rates, and $\beta$-decay $\log{ft}$ values are in a reasonable
agreement with experiment. The predicted $\log{ft}$ values reflect the
structures of the wave functions for the initial and final nuclei of $\beta$
decay, which are, to a large extent, determined by the microscopic input
provided by the underlying EDF calculation.


title: $q$th-root non-Hermitian Floquet topological insulators

abstract:
  Floquet phases of matter have attracted great attention due to their
dynamical and topological nature that are unique to nonequilibrium settings. In
this work, we introduce a generic way of taking any integer $q$th-root of the
evolution operator $U$ that describes Floquet topological matter. As a case
study, we apply our $q$th-rooting procedure to obtain the square- and
cubic-root first- and second-order non-Hermitian Floquet topological
insulators. There, we explicitly demonstrate the presence of multiple edge and
corner modes at fractional quasienergies $\pi/2$, $\pi/3$ and $2\pi/3$, whose
numbers can be large and are highly controllable. Notably, we observe
non-Hermiticity induced fractional-quasienergy corner modes and the coexistence
of non-Hermitian skin effect with fractional-quasienergy edge states. By
repeatedly applying the square- and cubic-rooting procedure, the resulting
$2^n$th- and $3^n$th-root systems are found to possess degenerate edge or
corner modes at quasienergies $\pm(0,1,...2^{n})\pi/2^{n}$ and
$\pm(0,1,...,3^{n})\pi/3^{n}$, respectively, whose numbers are determined by
the bulk topological invariants of the parent system $U$. Our findings thus
establish a framework of constructing an intriguing class of topological matter
in Floquet systems.


title: Time-Optimal Online Replanning for Agile Quadrotor Flight

abstract:
  In this paper, we tackle the problem of flying a quadrotor using time-optimal
control policies that can be replanned online when the environment changes or
when encountering unknown disturbances. This problem is challenging as the
time-optimal trajectories that consider the full quadrotor dynamics are
computationally expensive to generate (order of minutes or even hours). We
introduce a sampling-based method for efficient generation of time-optimal
paths of a point-mass model. These paths are then tracked using a Model
Predictive Contouring Control approach that considers the full quadrotor
dynamics and the single rotor thrust limits. Our combined approach is able to
run in real-time, being the first time-optimal method that is able to adapt to
changes on-the-fly. We showcase our approach's adaption capabilities by flying
a quadrotor at more than 60 km/h in a racing track where gates are moving.
Additionally, we show that our online replanning approach can cope with strong
disturbances caused by winds of up to 68 km/h.


title:
Degrees of Freedom in 3D Linear Large-Scale Antenna Array
  Communications-A Spatial Bandwidth Approach

abstract:
  For wireless communications using linear large-scale antenna arrays, we
define a receiving coordinate system and parameterization strategy to
facilitate the study of the impact of three-dimensional position and rotation
of the arrays on the achievable spatial degrees of freedom (DoF) in
line-of-sight (LOS) channels. An analytical framework based on spatial
bandwidth analysis is developed, under which three elementary problems
corresponding to three basic orthogonal receiving directions are investigated.
For each of them, accurate, simple, and interpretable closed-form
approximations for the achievable spatial DoF are derived, and the spatial
region where a sufficient amount of spatial DoF is expected available is
determined. The expressions can easily be integrated into large-scale
system-level simulations. Some interesting and surprising observations are made
from simulation studies based on the analytical results. For instance, the
spatial bandwidth is shown to be approximately constant in almost the entire
spatial multiplexing region. Moreover, in significant parts of this region, the
optimal receive array orientation is not parallel with the transmitting array.


title: Filtering methods for coupled inverse problems

abstract:
  We are interested in ensemble methods to solve multi-objective optimization
problems. An ensemble Kalman method is proposed to solve a formulation of the
nonlinear problem using a weighted function approach. An analysis of the mean
field limit of the ensemble method yields an explicit update formula for the
weights. Numerical examples show the improved performance of the proposed
method.


title: Modeling of GeV-TeV gamma-ray emission of Cygnus Cocoon

abstract:
  OB-associations and superbubbles being energetically essential galactic
powerhouses are likely to be the important acceleration sites of galactic
cosmic rays (CRs). The emission profile of gamma-ray sources related to
superbubbles and stellar clusters indicates on continuous particle acceleration
by winds of massive stars. One of the most luminous galactic gamma-ray sources
is Cygnus Cocoon superbubble, observed by multiple instruments, such as
Fermi-LAT, ARGO, and, recently, HAWC. We discuss a model of particle
acceleration and transport in a superbubble to explain GeV-TeV gamma-ray
spectrum of Cygnus Cocoon, which has a break at the energy of about 1 TeV. It
is shown that the gamma rays produced by hadronic interactions of high-energy
protons accelerated by an ensemble of shocks from winds of massive stars and
supernovae in the Cygnus Cocoon can explain the observations. The proton
spectral shape at the highest energies depends on the MHD-fluctuation spectrum
in the Cocoon. The viable solutions for Cygnus Cocoon may be applied to some
other associations showing similar behaviour. We briefly discuss the similarity
and differences of particle acceleration processes in extended superbubbles and
compact clusters of young massive stars as represented by Westerlund 1 and 2
gamma-ray sources.


title:
Extorsionware: Exploiting Smart Contract Vulnerabilities for Fun and
  Profit

abstract:
  Smart Contracts (SCs) publicly deployed on blockchain have been shown to
include multiple vulnerabilities, which can be maliciously exploited by users.
In this paper, we present extorsionware, a novel attack exploiting the public
nature of vulnerable SCs to gain control over the victim's SC assets. Thanks to
the control gained over the SC, the attacker obliges the victim to pay a price
to re-gain exclusive control of the SC.


title:
Reinforcement Learning Approach to Clear Paths of Robots in Elevator
  Environment

abstract:
  Efficiently using the space of an elevator for a service robot is very
necessary, due to the need for reducing the amount of time caused by waiting
for the next elevator. To solve this, we propose a hybrid approach that
combines reinforcement learning (RL) with voice interaction for robot
navigation in the scene of entering the elevator. RL provides robots with a
high exploration ability to find a new clear path to enter the elevator
compared to the traditional navigation methods such as Optimal Reciprocal
Collision Avoidance (ORCA). The proposed method allows the robot to take an
active clear path action towards the elevator whilst a crowd of people stands
at the entrance of the elevator wherein there are still lots of space. This is
done by embedding a clear path action (beep) into the RL framework, and the
proposed navigation policy leads the robot to finish tasks efficiently and
safely. Our model approach provides a great improvement in the success rate and
reward of entering the elevator compared to state-of-the-art ORCA and RL
navigation policy without beep.


title: Location-Free Camouflage Generation Network

abstract:
  Camouflage is a common visual phenomenon, which refers to hiding the
foreground objects into the background images, making them briefly invisible to
the human eye. Previous work has typically been implemented by an iterative
optimization process. However, these methods struggle in 1) efficiently
generating camouflage images using foreground and background with arbitrary
structure; 2) camouflaging foreground objects to regions with multiple
appearances (e.g. the junction of the vegetation and the mountains), which
limit their practical application. To address these problems, this paper
proposes a novel Location-free Camouflage Generation Network (LCG-Net) that
fuse high-level features of foreground and background image, and generate
result by one inference. Specifically, a Position-aligned Structure Fusion
(PSF) module is devised to guide structure feature fusion based on the
point-to-point structure similarity of foreground and background, and introduce
local appearance features point-by-point. To retain the necessary identifiable
features, a new immerse loss is adopted under our pipeline, while a background
patch appearance loss is utilized to ensure that the hidden objects look
continuous and natural at regions with multiple appearances. Experiments show
that our method has results as satisfactory as state-of-the-art in the
single-appearance regions and are less likely to be completely invisible, but
far exceed the quality of the state-of-the-art in the multi-appearance regions.
Moreover, our method is hundreds of times faster than previous methods.
Benefitting from the unique advantages of our method, we provide some
downstream applications for camouflage generation, which show its potential.
The related code and dataset will be released at
https://github.com/Tale17/LCG-Net.


title: Lense-Thirring effect on accretion flow from counter-rotating tori

abstract:
  We study the accretion flow from a counter-rotating torus orbiting a central
Kerr black hole (BH). We characterize the flow properties at the turning point
of the accreting matter flow from the orbiting torus, defined by the condition
$u^\phi = 0$ on the flow torodial velocity. The counter-rotating accretion flow
and jet-like flow turning point location along BH rotational axis is given.
Some properties of the counter-rotating flow thickness and counter-rotating
tori energetics are studied.The maximum amount of matter swallowed by the BH
from the counter-rotating tori is determined by the background properties. The
fast spinning BH energetics depends mostly on BH spin rather than on the
properties of the counter-rotating fluids or the tori masses. The turning point
is located in a narrow orbital corona (spherical shell), for photons and matter
flow constituents, surrounding the BH stationary limit (outer ergosurface),
depending on the BH spin-mass ratio and the fluid initial momentum only. The
turning corona for jet-like-flow has larger thickness, it is separated from the
torus flow turning corona and it is closer to the BH stationary limit. Turning
points of matter accreting from torus and from jets are independent explicitly
of the details of the accretion and tori model. The turning corona could be
observable due to an increase of flow luminosity and temperature. The corona is
larger on the BH equatorial plane, where it is the farthest from the central
attractor, and narrower on the BH poles.