title:
Broadband photoresponse arising from photo-bolometric effect in
  quasi-one-dimensional Ta2Ni3Se8

abstract:
  In this paper, we report the synthesis of high-quality Ta2Ni3Se8 crystals
free of noble or toxic elements and the fabrication and testing of
photodetectors on the wire samples. A broadband photoresponse from 405 nm to
1550 nm is observed, along with performance parameters including relatively
high photoresponsivity (10 mA W^-1) and specific detectivity (3.5 * 10^7 Jones)
and comparably short response time ({\tau}_rise = 433 ms, {\tau}_decay = 372
ms) for 1064 nm, 0.5 V bias and 1.352 mW mm^-2. Through extensive measurement
and analysis, it is determined that the dominant mechanism for photocurrent
generation is the photo-bolometric effect, which is believed to be responsible
for the very broad spectral detection capability. More importantly, the
pronounced response to 1310 nm and 1550 nm wavelengths manifests its promising
applications in optical communications. Considering the quasi-one-dimensional
structure with layered texture, the potential to build nanodevices on Ta2Ni3Se8
makes it even more important in future electronic and optoelectronic
applications.


title:
A Dynamic Meta-Learning Model for Time-Sensitive Cold-Start
  Recommendations

abstract:
  We present a novel dynamic recommendation model that focuses on users who
have interactions in the past but turn relatively inactive recently. Making
effective recommendations to these time-sensitive cold-start users is critical
to maintain the user base of a recommender system. Due to the sparse recent
interactions, it is challenging to capture these users' current preferences
precisely. Solely relying on their historical interactions may also lead to
outdated recommendations misaligned with their recent interests. The proposed
model leverages historical and current user-item interactions and dynamically
factorizes a user's (latent) preference into time-specific and time-evolving
representations that jointly affect user behaviors. These latent factors
further interact with an optimized item embedding to achieve accurate and
timely recommendations. Experiments over real-world data help demonstrate the
effectiveness of the proposed time-sensitive cold-start recommendation model.


title:
Time evolution of the lepton number of Majorana neutrinos in the
  Schr\"odinger picture versus Heisenberg picture

abstract:
  In this paper, we study the time evolution of the expectation value of
Majorana neutrino with the Schr\"odinger picture.The operators with the
definite lepton number and operators with the definite mass are related to each
other by a Bogolyubov transformation. Then the vacuum with the null lepton
number is also related to the vacuum for the massive operator and it is written
by the superposition of the vacuum for massive field and Majorana pairs
condensed states. We choose the state with a definite lepton number $L$ $=1$
and the momentum ${\bf q}\ne 0$ as an initial state. By writing the state in
terms of the superposition of energy eigenstates, we are able to study the time
evolution of the state in the Schr\"odinger picture.The expectation value of
lepton number operator is computed and it reproduces the same result as that
obtained in the corresponding Heisenberg operator.


title: DST: Dynamic Substitute Training for Data-free Black-box Attack

abstract:
  With the wide applications of deep neural network models in various computer
vision tasks, more and more works study the model vulnerability to adversarial
examples. For data-free black box attack scenario, existing methods are
inspired by the knowledge distillation, and thus usually train a substitute
model to learn knowledge from the target model using generated data as input.
However, the substitute model always has a static network structure, which
limits the attack ability for various target models and tasks. In this paper,
we propose a novel dynamic substitute training attack method to encourage
substitute model to learn better and faster from the target model.
Specifically, a dynamic substitute structure learning strategy is proposed to
adaptively generate optimal substitute model structure via a dynamic gate
according to different target models and tasks. Moreover, we introduce a
task-driven graph-based structure information learning constrain to improve the
quality of generated training data, and facilitate the substitute model
learning structural relationships from the target model multiple outputs.
Extensive experiments have been conducted to verify the efficacy of the
proposed attack method, which can achieve better performance compared with the
state-of-the-art competitors on several datasets.


title:
Kernel Extreme Learning Machine Optimized by the Sparrow Search
  Algorithm for Hyperspectral Image Classification

abstract:
  To improve the classification performance and generalization ability of the
hyperspectral image classification algorithm, this paper uses Multi-Scale Total
Variation (MSTV) to extract the spectral features, local binary pattern (LBP)
to extract spatial features, and feature superposition to obtain the fused
features of hyperspectral images. A new swarm intelligence optimization method
with high convergence and strong global search capability, the Sparrow Search
Algorithm (SSA), is used to optimize the kernel parameters and regularization
coefficients of the Kernel Extreme Learning Machine (KELM). In summary, a
multiscale fusion feature hyperspectral image classification method (MLS-KELM)
is proposed in this paper. The Indian Pines, Pavia University and Houston 2013
datasets were selected to validate the classification performance of MLS-KELM,
and the method was applied to ZY1-02D hyperspectral data. The experimental
results show that MLS-KELM has better classification performance and
generalization ability compared with other popular classification methods, and
MLS-KELM shows its strong robustness in the small sample case.


title:
Neural Global Shutter: Learn to Restore Video from a Rolling Shutter
  Camera with Global Reset Feature

abstract:
  Most computer vision systems assume distortion-free images as inputs. The
widely used rolling-shutter (RS) image sensors, however, suffer from geometric
distortion when the camera and object undergo motion during capture. Extensive
researches have been conducted on correcting RS distortions. However, most of
the existing work relies heavily on the prior assumptions of scenes or motions.
Besides, the motion estimation steps are either oversimplified or
computationally inefficient due to the heavy flow warping, limiting their
applicability. In this paper, we investigate using rolling shutter with a
global reset feature (RSGR) to restore clean global shutter (GS) videos. This
feature enables us to turn the rectification problem into a deblur-like one,
getting rid of inaccurate and costly explicit motion estimation. First, we
build an optic system that captures paired RSGR/GS videos. Second, we develop a
novel algorithm incorporating spatial and temporal designs to correct the
spatial-varying RSGR distortion. Third, we demonstrate that existing
image-to-image translation algorithms can recover clean GS videos from
distorted RSGR inputs, yet our algorithm achieves the best performance with the
specific designs. Our rendered results are not only visually appealing but also
beneficial to downstream tasks. Compared to the state-of-the-art RS solution,
our RSGR solution is superior in both effectiveness and efficiency. Considering
it is easy to realize without changing the hardware, we believe our RSGR
solution can potentially replace the RS solution in taking distortion-free
videos with low noise and low budget.


title: Question-Driven Graph Fusion Network For Visual Question Answering

abstract:
  Existing Visual Question Answering (VQA) models have explored various visual
relationships between objects in the image to answer complex questions, which
inevitably introduces irrelevant information brought by inaccurate object
detection and text grounding. To address the problem, we propose a
Question-Driven Graph Fusion Network (QD-GFN). It first models semantic,
spatial, and implicit visual relations in images by three graph attention
networks, then question information is utilized to guide the aggregation
process of the three graphs, further, our QD-GFN adopts an object filtering
mechanism to remove question-irrelevant objects contained in the image.
Experiment results demonstrate that our QD-GFN outperforms the prior
state-of-the-art on both VQA 2.0 and VQA-CP v2 datasets. Further analysis shows
that both the novel graph aggregation method and object filtering mechanism
play a significant role in improving the performance of the model.


title:
FedGBF: An efficient vertical federated learning framework via gradient
  boosting and bagging

abstract:
  Federated learning, conducive to solving data privacy and security problems,
has attracted increasing attention recently. However, the existing federated
boosting model sequentially builds a decision tree model with the weak base
learner, resulting in redundant boosting steps and high interactive
communication costs. In contrast, the federated bagging model saves time by
building multi-decision trees in parallel, but it suffers from performance
loss. With the aim of obtaining an outstanding performance with less time cost,
we propose a novel model in a vertically federated setting termed as Federated
Gradient Boosting Forest (FedGBF). FedGBF simultaneously integrates the
boosting and bagging's preponderance by building the decision trees in parallel
as a base learner for boosting. Subsequent to FedGBF, the problem of
hyperparameters tuning is rising. Then we propose the Dynamic FedGBF, which
dynamically changes each forest's parameters and thus reduces the complexity.
Finally, the experiments based on the benchmark datasets demonstrate the
superiority of our method.


title:
Deep Speech Based End-to-End Automated Speech Recognition (ASR) for
  Indian-English Accents

abstract:
  Automated Speech Recognition (ASR) is an interdisciplinary application of
computer science and linguistics that enable us to derive the transcription
from the uttered speech waveform. It finds several applications in Military
like High-performance fighter aircraft, helicopters, air-traffic controller.
Other than military speech recognition is used in healthcare, persons with
disabilities and many more. ASR has been an active research area. Several
models and algorithms for speech to text (STT) have been proposed. One of the
most recent is Mozilla Deep Speech, it is based on the Deep Speech research
paper by Baidu. Deep Speech is a state-of-art speech recognition system is
developed using end-to-end deep learning, it is trained using well-optimized
Recurrent Neural Network (RNN) training system utilizing multiple Graphical
Processing Units (GPUs). This training is mostly done using American-English
accent datasets, which results in poor generalizability to other English
accents. India is a land of vast diversity. This can even be seen in the
speech, there are several English accents which vary from state to state. In
this work, we have used transfer learning approach using most recent Deep
Speech model i.e., deepspeech-0.9.3 to develop an end-to-end speech recognition
system for Indian-English accents. This work utilizes fine-tuning and data
argumentation to further optimize and improve the Deep Speech ASR system. Indic
TTS data of Indian-English accents is used for transfer learning and
fine-tuning the pre-trained Deep Speech model. A general comparison is made
among the untrained model, our trained model and other available speech
recognition services for Indian-English Accents.


title:
Active emulsions in living cell membranes driven by contractile stresses
  and transbilayer coupling

abstract:
  The spatiotemporal organisation of proteins and lipids on the cell surface
has direct functional consequences for signaling, sorting and endocytosis.
Earlier studies have shown that multiple types of membrane proteins including
transmembrane proteins that have cytoplasmic actin binding capacity and
lipid-tethered GPI-anchored proteins (GPI-APs) form nanoscale clusters driven
by active contractile flows generated by the actin cortex. To gain insight into
the role of lipids in organizing membrane domains in living cells, we study the
molecular interactions that promote the actively generated nanoclusters of
GPI-APs and transmembrane proteins. This motivates a theoretical description,
wherein a combination of active contractile stresses and transbilayer coupling
drive the creation of active emulsions, mesoscale liquid ordered (lo) domains
of the GPI-APs and lipids, at temperatures greater than equilibrium lipid-phase
segregation. To test these ideas we use spatial imaging of homo-FRET combined
with local membrane order and demonstrate that mesoscopic domains enriched in
nanoclusters of GPI-APs are maintained by cortical actin activity and
transbilayer interactions, and exhibit significant lipid order, consistent with
predictions of the active composite model.


title: Breaking Blockchain's Communication Barrier with Coded Computation

abstract:
  Although blockchain, the supporting technology of various cryptocurrencies,
has offered a potentially effective framework for numerous decentralized trust
management systems, its performance is still sub-optimal in real-world
networks. With limited bandwidth, the communication complexity for nodes to
process a block scales with the growing network size and hence becomes the
limiting factor of blockchain's performance.
  In this paper, we suggest a re-design of existing blockchain systems, which
addresses the issue of the communication burden. First, by employing techniques
from Coded Computation, our scheme guarantees correct verification of
transactions while reducing the bit complexity dramatically such that it grows
logarithmically with the number of nodes. Second, with the adoption of
techniques from Information Dispersal and State Machine Replication, the system
is resilient to Byzantine faults and achieves linear message complexity. Third,
we propose a novel 2-dimensional sharding strategy, which inherently supports
cross-shard transactions, alleviating the need for complicated communication
protocols between shards, while keeping the computation and storage benefits of
sharding.


title:
Global existence and optimal decay rate of the classical solution to 3-D
  Radiative Hydrodynamics with and without Heat Conductivity

abstract:
  The classical solution of the 3-D radiative hydrodynamics model is studied in
$H^k$-norm under two different conditions, with and without heat conductivity.
We have proved the following results in both cases. First, when the $H^k$ norm
of the initial perturbation around a constant state is sufficiently small and
the integer $k\geq2$, a unique classical solution to such Cauchy problem is
shown to exist. Second, if we further assume that the $L^1$ norm of the initial
perturbation is small too, the i-order($0\leq i\leq k-2$) derivative of the
solutions have the decay rate of $(1+t)^{-\frac 34-\frac i2}$ in $H^2$ norm.
Third, from the results above we can see that for radiative hydrodynamics, the
radiation can do the same job as the heat conduction, which means if the
thermal conductivity coefficient turns to $0$, because of the effect of
radiation, the solvability of the system and decay rate of the solution stay
the same.


title:
Buchdahl Spacetime with Compact Body Solution of Charged Fluid and
  Scalar Field Theory

abstract:
  The present work contains a discussion on compact stellar body that is
influenced by the effects electromagnetic fields. We have tried to discuss a
solution of Einstein-Maxwell field equation with the interpretation of Buchdahl
spacetime type function. The interior fluid density and anisotropic pressures
have been derived for a charged fluid of compact body which is also continuous
with the Reissner-Nordstrom metric exterior solution. We have discussed the
singularity free interior anisotropic fluid under electromagnetic influence.
The thermodynamics energy conditions and their variation with interior radius
have been discussed here. We have also discussed the physical acceptability of
this new model. Finally we have included the scalar field theory corresponding
to the interior fluid that helped us to discuss teh compact body strange star
evolution with time. The generalized entropy evolution of the compact body has
also been introduced in the discussion of interior fluid evolution analysis


title: Echoes from Hairy Black Holes

abstract:
  We study the waveforms of time signals produced by scalar perturbations in
static hairy black holes, in which the perturbations can be governed by a
double-peak effective potential. The inner potential peak would give rise to
echoes, which provide a powerful tool to test the Kerr hypothesis. The
waveforms are constructed in the time and frequency domains, and we find that
the late-time waveforms are determined by the long-lived and sub-long-lived
quasinormal modes, which are trapped in the potential valley and near the
smaller peak, respectively. When the distance between the peaks is
significantly larger than the width of the peaks, a train of decaying echo
pulses is produced by the superposition of the long-lived and sub-long-lived
modes. In certain cases, the echoes can vanish and then reappear. When the
peaks are close enough, one detects far fewer echo signals and a following
sinusoid tail, which is controlled by the long-lived or sub-long-lived mode and
hence decays very slowly.


title: The restricted quantum double of the Yangian

abstract:
  Let $\mathfrak{g}$ be a complex semisimple Lie algebra with associated
Yangian $Y_\hbar\mathfrak{g}$. In the mid-1990s, Khoroshkin and Tolstoy
formulated a conjecture which asserts that the algebra
$\mathrm{D}Y_\hbar\mathfrak{g}$ obtained by doubling the generators of
$Y_\hbar\mathfrak{g}$, called the Yangian double, provides a realization of the
quantum double of the Yangian. We provide a uniform proof of this conjecture
over $\mathbb{C}[\![\hbar]\!]$ which is compatible with the theory of quantized
enveloping algebras. As a byproduct, we identify the universal $R$-matrix of
the Yangian with the canonical element defined by the pairing between the
Yangian and its restricted dual.


title: Stability of the Minimum Energy Path

abstract:
  The minimum energy path (MEP) is the most probable transition path that
connects two equilibrium states of a potential energy landscape. It has been
widely used to study transition mechanisms as well as transition rates in the
fields of chemistry, physics, and materials science. % In this paper, we derive
a novel result establishing the stability of MEPs under perturbations of the
energy landscape. The result also represents a crucial step towards studying
the convergence of numerical discretisations of MEPs.


title: Towards Web Phishing Detection Limitations and Mitigation

abstract:
  Web phishing remains a serious cyber threat responsible for most data
breaches. Machine Learning (ML)-based anti-phishing detectors are seen as an
effective countermeasure, and are increasingly adopted by web-browsers and
software products. However, with an average of 10K phishing links reported per
hour to platforms such as PhishTank and VirusTotal (VT), the deficiencies of
such ML-based solutions are laid bare. We first explore how phishing sites
bypass ML-based detection with a deep dive into 13K phishing pages targeting
major brands such as Facebook. Results show successful evasion is caused by:
(1) use of benign services to obscure phishing URLs; (2) high similarity
between the HTML structures of phishing and benign pages; (3) hiding the
ultimate phishing content within Javascript and running such scripts only on
the client; (4) looking beyond typical credentials and credit cards for new
content such as IDs and documents; (5) hiding phishing content until after
human interaction. We attribute the root cause to the dependency of ML-based
models on the vertical feature space (webpage content). These solutions rely
only on what phishers present within the page itself. Thus, we propose
Anti-SubtlePhish, a more resilient model based on logistic regression. The key
augmentation is the inclusion of a horizontal feature space, which examines
correlation variables between the final render of suspicious pages against what
trusted services have recorded (e.g., PageRank). To defeat (1) and (2), we
correlate information between WHOIS, PageRank, and page analytics. To combat
(3), (4) and (5), we correlate features after rendering the page. Experiments
with 100K phishing/benign sites show promising accuracy (98.8%). We also
obtained 100% accuracy against 0-day phishing pages that were manually crafted,
comparing well to the 0% recorded by VT vendors over the first four days.


title: Comparability digraphs: An analogue of comparability graphs

abstract:
  Comparability graphs are a popular class of graphs. We introduce as the
digraph analogue of comparability graphs the class of comparability digraphs.
We show that many concepts such as implication classes and the knotting graph
for a comparability graph can be naturally extended to a comparability digraph.
We give a characterization of comparability digraphs in terms of their knotting
graphs. Semicomplete comparability digraphs are a prototype of comparability
digraphs. One instrumental technique for analyzing the structure of
comparability graphs is the Triangle Lemma for graphs. We generalize the
Triangle Lemma to semicomplete digraphs. Using the Triangle Lemma for
semicomplete digraphs we prove that if an implication class of a semicomplete
digraph contains no circuit of length 2 then it contains no circuit at all. We
also use it to device an $\mathcal{O}(n^3)$ time recognition algorithm for
semicomplete comparability digraphs where $n$ is the number of vertices of the
input digraph. The correctness of the algorithm implies a characterization for
semicomplete comparability digraphs, akin to that for comparability graphs.


title: BinsFormer: Revisiting Adaptive Bins for Monocular Depth Estimation

abstract:
  Monocular depth estimation is a fundamental task in computer vision and has
drawn increasing attention. Recently, some methods reformulate it as a
classification-regression task to boost the model performance, where continuous
depth is estimated via a linear combination of predicted probability
distributions and discrete bins. In this paper, we present a novel framework
called BinsFormer, tailored for the classification-regression-based depth
estimation. It mainly focuses on two crucial components in the specific task:
1) proper generation of adaptive bins and 2) sufficient interaction between
probability distribution and bins predictions. To specify, we employ the
Transformer decoder to generate bins, novelly viewing it as a direct set-to-set
prediction problem. We further integrate a multi-scale decoder structure to
achieve a comprehensive understanding of spatial geometry information and
estimate depth maps in a coarse-to-fine manner. Moreover, an extra scene
understanding query is proposed to improve the estimation accuracy, which turns
out that models can implicitly learn useful information from an auxiliary
environment classification task. Extensive experiments on the KITTI, NYU, and
SUN RGB-D datasets demonstrate that BinsFormer surpasses state-of-the-art
monocular depth estimation methods with prominent margins. Code and pretrained
models will be made publicly available at
\url{https://github.com/zhyever/Monocular-Depth-Estimation-Toolbox}.


title:
Whose Advantage? Measuring Attention Dynamics across YouTube and Twitter
  on Controversial Topics

abstract:
  The ideological asymmetries have been recently observed in contested online
spaces, where conservative voices seem to be relatively more pronounced even
though liberals are known to have the population advantage on digital
platforms. Most prior research, however, focused on either one single platform
or one single political topic. Whether an ideological group garners more
attention across platforms and/or topics, and how the attention dynamics evolve
over time, have not been explored. In this work, we present a quantitative
study that links collective attention across two social platforms -- YouTube
and Twitter, centered on online activities surrounding popular videos of three
controversial political topics including Abortion, Gun control, and Black Lives
Matter over 16 months. We propose several sets of video-centric metrics to
characterize how online attention is accumulated for different ideological
groups. We find that neither side is on a winning streak: left-leaning videos
are overall more viewed, more engaging, but less tweeted than right-leaning
videos. The attention time series unfold quicker for left-leaning videos, but
span a longer time for right-leaning videos. Network analysis on the early
adopters and tweet cascades show that the information diffusion for
left-leaning videos tends to involve centralized actors; while that for
right-leaning videos starts earlier in the attention lifecycle. In sum, our
findings go beyond the static picture of ideological asymmetries in digital
spaces and provide a set of methods to quantify attention dynamics across
different social platforms.


title: POS-BERT: Point Cloud One-Stage BERT Pre-Training

abstract:
  Recently, the pre-training paradigm combining Transformer and masked language
modeling has achieved tremendous success in NLP, images, and point clouds, such
as BERT. However, directly extending BERT from NLP to point clouds requires
training a fixed discrete Variational AutoEncoder (dVAE) before pre-training,
which results in a complex two-stage method called Point-BERT. Inspired by BERT
and MoCo, we propose POS-BERT, a one-stage BERT pre-training method for point
clouds. Specifically, we use the mask patch modeling (MPM) task to perform
point cloud pre-training, which aims to recover masked patches information
under the supervision of the corresponding tokenizer output. Unlike Point-BERT,
its tokenizer is extra-trained and frozen. We propose to use the dynamically
updated momentum encoder as the tokenizer, which is updated and outputs the
dynamic supervision signal along with the training process. Further, in order
to learn high-level semantic representation, we combine contrastive learning to
maximize the class token consistency between different transformation point
clouds. Extensive experiments have demonstrated that POS-BERT can extract
high-quality pre-training features and promote downstream tasks to improve
performance. Using the pre-training model without any fine-tuning to extract
features and train linear SVM on ModelNet40, POS-BERT achieves the
state-of-the-art classification accuracy, which exceeds Point-BERT by 3.5\%. In
addition, our approach has significantly improved many downstream tasks, such
as fine-tuned classification, few-shot classification, part segmentation. The
code and trained-models will be available at:
\url{https://github.com/fukexue/POS-BERT}.


title:
Content-Dependent Fine-Grained Speaker Embedding for Zero-Shot Speaker
  Adaptation in Text-to-Speech Synthesis

abstract:
  Zero-shot speaker adaptation aims to clone an unseen speaker's voice without
any adaptation time and parameters. Previous researches usually use a speaker
encoder to extract a global fixed speaker embedding from reference speech, and
several attempts have tried variable-length speaker embedding. However, they
neglect to transfer the personal pronunciation characteristics related to
phoneme content, leading to poor speaker similarity in terms of detailed
speaking styles and pronunciation habits. To improve the ability of the speaker
encoder to model personal pronunciation characteristics, we propose
content-dependent fine-grained speaker embedding for zero-shot speaker
adaptation. The corresponding local content embeddings and speaker embeddings
are extracted from a reference speech, respectively. Instead of modeling the
temporal relations, a reference attention module is introduced to model the
content relevance between the reference speech and the input text, and to
generate the fine-grained speaker embedding for each phoneme encoder output.
The experimental results show that our proposed method can improve speaker
similarity of synthesized speeches, especially for unseen speakers.


title:
A lightweight three-user secure quantum summation protocol without a
  third party based on single-particle states

abstract:
  In this paper, a lightweight three-user secure quantum summation protocol is
put forward by using single-particle states, which can accomplish the goal that
three users cooperate together to calculate the modulo 2 addition of their
private messages without the help of a third party. This protocol only requires
single-particle states rather than quantum entangled states as the initial
quantum resource, and only needs single-particle measurements and Bell basis
measurements. This protocol needs none of quantum entanglement swapping, the
Pauli operations, the controlled-not (CNOT) operation, the Hadamard gate or a
pre-shared private key sequence. Security analysis proves that this protocol is
secure against both the outside attacks and the participant attacks. Compared
with the existing two-dimensional three-user quantum summation protocols, this
protocol more or less takes advantage over them on the aspects of the initial
quantum resource, users' quantum measurement, the usage of quantum entanglement
swapping, the usage of Pauli operations, the usage of CNOT operation or the
usage of Hadamard gate.


title:
Synthetic five-wave mixing in an integrated microcavity for
  visible-telecom entanglement generation

abstract:
  Nonlinear optics processes lie at the heart of photonics and quantum optics
for their indispensable role in light sources and information processing.
During the past decades, the three- and four-wave mixing ($\chi^{(2)}$ and
$\chi^{(3)}$) effects have been extensively studied, especially in the
micro-/nano-structures by which the photon-photon interaction strength is
greatly enhanced. So far, the high-order nonlinearity beyond the $\chi^{(3)}$
has rarely been studied in dielectric materials due to their weak intrinsic
nonlinear susceptibility, even in high-quality microcavities. Here, an
effective five-wave mixing process ($\chi^{(4)}$) is synthesized for the first
time, by incorporating $\chi^{(2)}$ and $\chi^{(3)}$ processes in a single
microcavity. The coherence of the synthetic $\chi^{(4)}$ is verified by
generating time-energy entangled visible-telecom photon-pairs, which requires
only one drive laser at the telecom waveband. The photon pair generation rate
from the synthetic process shows an enhancement factor over $500$ times upon
intrinsic five-wave mixing. Our work demonstrates a universal approach of
nonlinear synthesis via photonic structure engineering at the mesoscopic scale
rather than material engineering, and thus opens a new avenue for realizing
high-order optical nonlinearities and exploring novel functional photonic
devices.


title: Improving Vision Transformers by Revisiting High-frequency Components

abstract:
  The transformer models have shown promising effectiveness in dealing with
various vision tasks. However, compared with training Convolutional Neural
Network (CNN) models, training Vision Transformer (ViT) models is more
difficult and relies on the large-scale training set. To explain this
observation we make a hypothesis that ViT models are less effective in
capturing the high-frequency components of images than CNN models, and verify
it by a frequency analysis. Inspired by this finding, we first investigate the
effects of existing techniques for improving ViT models from a new frequency
perspective, and find that the success of some techniques (e.g., RandAugment)
can be attributed to the better usage of the high-frequency components. Then,
to compensate for this insufficient ability of ViT models, we propose HAT,
which directly augments high-frequency components of images via adversarial
training. We show that HAT can consistently boost the performance of various
ViT models (e.g., +1.2% for ViT-B, +0.5% for Swin-B), and especially enhance
the advanced model VOLO-D5 to 87.3% that only uses ImageNet-1K data, and the
superiority can also be maintained on out-of-distribution data and transferred
to downstream tasks.


title:
Convergence of the Planewave Approximations for Quantum Incommensurate
  Systems

abstract:
  Incommensurate structures come from stacking the single layers of
low-dimensional materials on top of one another with misalignment such as a
twist in orientation. While these structures are of significant physical
interest, they pose many theoretical challenges due to the loss of periodicity.
This paper studies the spectrum distribution of incommensurate Schr\"{o}dinger
operators. We characterize the density of states for the incommensurate system
and develop novel numerical methods to approximate them. In particular, we (i)
justify the thermodynamic limit of the density of states in the real space
formulation; and (ii) propose efficient numerical schemes to evaluate the
density of states based on planewave approximations and reciprocal space
sampling. We present both rigorous analysis and numerical simulations to
support the reliability and efficiency of our numerical algorithms.


title:
Controllability and observability of linear multi-agent systems over
  matrix-weighted signed networks

abstract:
  In this paper, the controllability and observability of linear multi-agent
systems over matrix-weighted signed networks are analyzed. Firstly, the
definition of equitable partition of matrix-weighted signed multi-agent system
is given, and the upper bound of controllable subspace and a necessary
condition of controllability are obtained by combining the restriction
conditions of the coefficient matrix and matrix weight for the case of fixed
and switching topologies, respectively.The influence of different selection
methods of coefficient matrices on the results is discussed. Secondly, for the
case of heterogeneous systems, the upper bound of controllable subspace and the
necessary condition of controllability are obtained when the dynamics of
individuals in the same cell are the same. Thirdly, sufficient conditions for
controllable and uncontrollable union graphs are obtained by taking advantage
of the concept of switched systems and equitable partitions, respectively.
Finally, necessary condition of observability is obtained in terms of the dual
system and the constraints of the coefficient matrix, and the relationship
between the observability and the controllability of the matrix-weighted signed
multi-agent systems is discussed.


title:
Learning Disentangled Semantic Representations for Zero-Shot
  Cross-Lingual Transfer in Multilingual Machine Reading Comprehension

abstract:
  Multilingual pre-trained models are able to zero-shot transfer knowledge from
rich-resource to low-resource languages in machine reading comprehension (MRC).
However, inherent linguistic discrepancies in different languages could make
answer spans predicted by zero-shot transfer violate syntactic constraints of
the target language. In this paper, we propose a novel multilingual MRC
framework equipped with a Siamese Semantic Disentanglement Model (SSDM) to
disassociate semantics from syntax in representations learned by multilingual
pre-trained models. To explicitly transfer only semantic knowledge to the
target language, we propose two groups of losses tailored for semantic and
syntactic encoding and disentanglement. Experimental results on three
multilingual MRC datasets (i.e., XQuAD, MLQA, and TyDi QA) demonstrate the
effectiveness of our proposed approach over models based on mBERT and XLM-100.
Code is available at:https://github.com/wulinjuan/SSDM_MRC.


title:
Bi-fidelity Modeling of Uncertain and Partially Unknown Systems using
  DeepONets

abstract:
  Recent advances in modeling large-scale complex physical systems have shifted
research focuses towards data-driven techniques. However, generating datasets
by simulating complex systems can require significant computational resources.
Similarly, acquiring experimental datasets can prove difficult as well. For
these systems, often computationally inexpensive, but in general inaccurate,
models, known as the low-fidelity models, are available. In this paper, we
propose a bi-fidelity modeling approach for complex physical systems, where we
model the discrepancy between the true system's response and low-fidelity
response in the presence of a small training dataset from the true system's
response using a deep operator network (DeepONet), a neural network
architecture suitable for approximating nonlinear operators. We apply the
approach to model systems that have parametric uncertainty and are partially
unknown. Three numerical examples are used to show the efficacy of the proposed
approach to model uncertain and partially unknown complex physical systems.


title:
AutoOpt: A Methodological Framework of Automatically Designing
  Metaheuristics for Optimization Problems

abstract:
  Metaheuristics are gradient-free and problem-independent search algorithms.
They have gained huge success in solving various optimization problems in
academia and industry. Automated metaheuristic design is a promising
alternative to human-made design. This paper proposes a general and
comprehensive methodological framework, AutoOpt, for automatically designing
metaheuristics for various optimization problems. AutoOpt consists of: (1) a
bi-level criterion to evaluate the designed algorithms' performance; (2) a
general schema of the decision space from where the algorithms will be
designed; (3) a mixed graph- and real number-based representation to represent
the designed algorithms; and (4) a model-free method to conduct the design
process. AutoOpt benefits academic researchers and practical users struggling
to design metaheuristics for optimization problems. A real-world case study
demonstrates AutoOpt's effectiveness and efficiency.


title:
On the fractional Korn inequality in bounded domains: Counterexamples to
  the case $ps<1$

abstract:
  The validity of Korn's first inequality in the fractional setting in bounded
domains has been open. We resolve this problem by proving that in fact Korn's
first inequality holds in the case $ps>1$ for fractional $W^{s,p}_0(\Omega)$
Sobolev fields in open and bounded $C^{1}$-regular domains $\Omega\subset
\mathbb R^n$. Also, in the case $ps<1,$ for any open bounded $C^1$ domain
$\Omega\subset \mathbb R^n$ we construct counterexamples to the inequality,
i.e., Korn's first inequality fails to hold in bounded domains. The proof of
the inequality in the case $ps>1$ follows a standard compactness approach
adopted in the classical case, combined with a Hardy inequality, and a recently
proven Korn second inequality by Mengesha and Scott [\textit{Commun. Math.
Sci.,} Vol. 20, N0. 2, 405--423, 2022]. The counterexamples constructed in the
case $ps<1$ are interpolations of a constant affine rigid motion inside the
domain away from the boundary, and of the zero field close to the boundary.


title: Ternary Logic Design in Topological Quantum Computing

abstract:
  A quantum computer can perform exponentially faster than its classical
counterpart. It works on the principle of superposition. But due to the
decoherence effect, the superposition of a quantum state gets destroyed by the
interaction with the environment. It is a real challenge to completely isolate
a quantum system to make it free of decoherence. This problem can be
circumvented by the use of topological quantum phases of matter. These phases
have quasiparticles excitations called anyons. The anyons are charge-flux
composites and show exotic fractional statistics. When the order of exchange
matters, then the anyons are called non-Abelian anyons. Majorana fermions in
topological superconductors and quasiparticles in some quantum Hall states are
non-Abelian anyons. Such topological phases of matter have a ground state
degeneracy. The fusion of two or more non-Abelian anyons can result in a
superposition of several anyons. The topological quantum gates are implemented
by braiding and fusion of the non-Abelian anyons. The fault-tolerance is
achieved through the topological degrees of freedom of anyons. Such degrees of
freedom are non-local, hence inaccessible to the local perturbations. In this
paper, the Hilbert space for a topological qubit is discussed. The Ising and
Fibonacci anyonic models for binary gates are briefly given. Ternary logic
gates are more compact than their binary counterparts and naturally arise in a
type of anyonic model called the metaplectic anyons. The mathematical model,
for the fusion and braiding matrices of metaplectic anyons, is the quantum
deformation of the recoupling theory. We proposed that the existing quantum
ternary arithmetic gates can be realized by braiding and topological charge
measurement of the metaplectic anyons.


title:
Infinite-energy solutions to energy-critical nonlinear Schr\"odinger
  equations in modulation spaces

abstract:
  We prove new well-posedness results for energy-critical nonlinear
Schr\"odinger equations in modulation spaces, which are larger than the energy
space. First, we remove the $\varepsilon$-derivative loss in $L^p$-smoothing
estimates for the linear Schr\"o\-din\-ger equation, if $p$ is larger than the
Tomas-Stein exponent. Next, we show local well-posedness results for nonlinear
Schr\"odinger equations in modulation spaces containing the scaling critical
$L^2$-based Sobolev space. The proof is carried out via bilinear refinements
and adapted function spaces.


title:
A prescribed scalar and boundary mean curvature problem and the Yamabe
  classification on asymptotically Euclidean manifolds with inner boundary

abstract:
  We consider the problem of finding a metric in a given conformal class with
prescribed non-positive scalar curvature and non-positive boundary mean
curvature on an asymptotically Euclidean manifold with inner boundary. We
obtain a necessary and sufficient condition in terms of a conformal invariant
of the zero sets of the target curvatures for the existence of solutions to the
problem and use this result to establish the Yamabe classification of metrics
in those manifolds with respect to the solvability of the prescribed curvature
problem.


title:
Impact Intensity Estimation of a Quadruped Robot without Using a Force
  Sensor

abstract:
  Estimating the impact intensity is one of the significant tasks of the legged
robot. Accurate feedback of the impact may support the robot to plan a suitable
and efficient trajectory to adapt to unknown complex terrains. Ordinarily, this
task is performed by a force sensor in the robot's foot. In this letter, an
impact intensity estimation without using a force sensor is proposed. An
artificial neural network model is designed to predict the motor torques of the
legs in an instantaneous position in the trajectory without utilizing the
complex kinematic and dynamic models of motion. An unscented Kalman filter is
used during the trajectory to smooth and stabilize the measurement. Based on
the difference between the predicted information and the filtered value, the
state and intensity of the robot foot's impact with the obstacles are
estimated. The simulation and experiment on a quadruped robot are carried out
to verify the effectiveness of the proposed method.


title: Region-aware Attention for Image Inpainting

abstract:
  Recent attention-based image inpainting methods have made inspiring progress
by modeling long-range dependencies within a single image. However, they tend
to generate blurry contents since the correlation between each pixel pairs is
always misled by ill-predicted features in holes. To handle this problem, we
propose a novel region-aware attention (RA) module. By avoiding the directly
calculating corralation between each pixel pair in a single samples and
considering the correlation between different samples, the misleading of
invalid information in holes can be avoided. Meanwhile, a learnable region
dictionary (LRD) is introduced to store important information in the entire
dataset, which not only simplifies correlation modeling, but also avoids
information redundancy. By applying RA in our architecture, our methodscan
generate semantically plausible results with realistic details. Extensive
experiments on CelebA, Places2 and Paris StreetView datasets validate the
superiority of our method compared with existing methods.


title: Selective Kernel Attention for Robust Speaker Verification

abstract:
  Recent state-of-the-art speaker verification architectures adopt multi-scale
processing and frequency-channel attention techniques. However, their full
potential may not have been exploited because these techniques' receptive
fields are fixed where most convolutional layers operate with specified kernel
sizes such as 1, 3 or 5. We aim to further improve this line of research by
introducing a selective kernel attention (SKA) mechanism. The SKA mechanism
allows each convolutional layer to adaptively select the kernel size in a
data-driven fashion based on an attention mechanism that exploits both
frequency and channel domain using the previous layer's output. We propose
three module variants using the SKA mechanism whereby two modules are applied
in front of an ECAPA-TDNN model, and the other is combined with the Res2Net
backbone block. Experimental results demonstrate that our proposed model
consistently outperforms the conventional counterpart on the three different
evaluation protocols in terms of both equal error rate and minimum detection
cost function. In addition, we present a detailed analysis that helps
understand how the SKA module works.


title: Force-directed algorithms for schematic drawings and placement: A survey

abstract:
  Force-directed algorithms have been developed over the last 50 years and used
in many application fields, including information visualisation, biological
network visualisation, sensor networks, routing algorithms, scheduling, and
graph drawing. Our survey provides a comprehensive summary of developments and
a full roadmap for state-of-the-art force-directed algorithms in schematic
drawings and placement. We classified the model of force-directed algorithms
into classical and hybrid. The classical force-directed algorithms are further
classified as follows: (a) accumulated force models, (b) energy function
minimisation models and (c) combinatorial optimisation models. The hybrid
force-directed algorithms are classified as follows: (a) parallel and hardware
accelerated models, (b) multilevel force-directed models and (c)
multidimensional scaling force-directed algorithms. Five categories of
application domains in which force-directed algorithms have been adopted for
schematic drawings and placement are also summarised: (a) aesthetic drawings
for general networks, (b) component placement and scheduling in high-level
synthesis of very-large-scale integration circuits design, (c) information
visualisation, (d) biological network visualisation and (e) node placement and
localisation for sensor networks.


title:
Photon orbits and phase transition for Non-Linear charged Anti-de Sitter
  black holes

abstract:
  In this work, we investigate the relationship between the photon sphere
radius and the first-order phase transition for the charged EPYM AdS black
hole. Through the analysis, we find with a certain condition there exist the
non-monotonic behaviors between the photon sphere radius, the impact parameter,
the non-linear YM charge parameter, temperature, and pressure. And both the
changes of photon sphere radius and impact parameter before and after phase
transition can be regarded as the order parameter, their critical exponents
near the critical point are equal to the same value $1/2$, just like the
ordinary thermal systems. These indicate that there maybe exists a universal
relationship of gravity nearby the critical point for a black hole
thermodynamical system. Furthermore, the effect of impact parameter on the
deflect angle is also investigated.


title: Tur\'an inequalities from Chebyshev to Laguerre polynomials

abstract:
  Let $g$ and $h$ be real-valued arithmetic functions, positive and normalized.
Specific choices within the following general scheme of recursively defined
polynomials \begin{equation*} P_n^{g,h}(x):= \frac{x}{h(n)} \sum_{k=1}^{n} g(k)
\, P_{n-k}^{g,h}(x), \end{equation*} with initial value $P_{0}^{g,h}(x)=1$
encode information about several classical, widely studied polynomials. This
includes Chebyshev polynomials of the second kind, associated Laguerre
polynomials, and the Nekrasov--Okounkov polynomials. In this paper we prove
that for $g(n)=n$ and fixed $h$ we obtain orthogonal polynomial sequences for
positive definite functionals. Let $h(n)=n^s$ with $0 \leq s \leq 1 $. Then the
sequence satisfies Tur\'an inequalities for $x \geq 0$.


title:
A Computational Analysis of Pitch Drift in Unaccompanied Solo Singing
  using DBSCAN Clustering

abstract:
  Unaccompanied vocalists usually change the tuning unintentionally and end up
with a higher or lower pitch than the starting point during a long performance.
This phenomenon is called pitch drift, which is dependent on various elements,
such as the skill of the performer, and the length and difficulty of the
performance. In this paper, we propose a computational method for measuring
pitch drift in the course of an unaccompanied vocal performance, using pitch
histogram and DBSCAN clustering.


title: Odd Radio Circles as supernovae remnants in the intragroup medium

abstract:
  A measurable fraction ($\sim8$ per cent) of recently discovered arcmin-size
circular diffuse radio sources termed as Odd Radio Circles or ORCs can be
supernovae remnants in the intragroup medium, within the local group and its
immediate neighbour groups of galaxies. This estimate is based on the optical
detection rate of the intragroup supernovae events in the nearby ($z \sim
0.1-0.2$) galaxy groups. A rate of about 5400 intragroup supernovae per million
year is expected within the local and its immediate neighbour groups of
galaxies. For a radio detectability period of about $10^{4}$ years, on average
1.3 intragroup medium supernovae remnants per 1000 square degree are expected
to be detected in the radio surveys with a sensitivity that led to discovery of
ORCs. The angular size, surface brightness and radio flux of the supernova
remnants up to a distance of $\sim3$ Mpc in the intragroup medium can be
expected to be similar to the five known ORCs. The intragroup supernovae
remnants are not residing in the dense and cold interstellar medium of the
galaxies but evolving in low density ($10^{-4}-10^{-5}$ cm$^{-3}$) warm medium
($10^{5}-10^{6}$ K) in galactic halos or beyond, and may find their progenitors
in the diffuse stellar light associated with various tidal streamers
surrounding the Milky-Way and other nearby galaxies.


title: Eddy Covariance: A Scientometric Review (1981-2018)

abstract:
  The history of eddy covariance (EC) measuring system could be dated back to
100 years ago, but it was not until the recent decades that EC gains popularity
and being widely used in global change ecological studies, with explosion of
related work published in papers from various journals. Investigating 8297
literature related with EC from 1981 to 2018, we make a comprehensive and
critical review of scientific development of EC from a scientometric
perspective. First, the paper outlines general bibliometric statistics,
including publication number, country contribution, productive institutions,
active authors, journal distribution, highly cited articles and fund support,
to provide an informative picture of EC studies. Second, research trends are
revealed by network visualization and modeling based on keyword analysis, from
where we could discover the knowledge structure of EC and detect the research
focus and hotspots transitions at different periods. Third, collaboration in EC
research community has been explored. FLUXNET is the largest global network
uniting EC researchers, here we have quantified and evaluated its performance
by using bibliometric indicators of cooperation and citation. Specific
discussions have been given to the historical development of EC, including
technical maturation and application promotion. Considering the current barrier
for collaboration, the review closes by analyzing the reasons hindering data
sharing and makes a prospect of new models for data-intensive collaboration in
the future.


title:
Gastrointestinal Polyps and Tumors Detection Based on Multi-scale
  Feature-fusion with WCE Sequences

abstract:
  Wireless Capsule Endoscopy(WCE) has been widely used for the screening of
gastrointestinal(GI) diseases, especially the small intestine, due to its
advantages of non-invasive and painless imaging of the entire digestive
tract.However, the huge amount of image data captured by WCE makes manual
reading a process that requires a huge amount of tasks and can easily lead to
missed detection and false detection of lesions.Therefore, In this paper, we
propose a \textbf{T}wo-stage \textbf{M}ulti-scale \textbf{F}eature-fusion
learning network(\textbf{TMFNet}) to automatically detect small intestinal
polyps and tumors in WCE image sequences. Specifically, TMFNet consists of
lesion detection network and lesion identification network. Among them, the
former improves the feature extraction module and detection module based on the
traditional Faster R-CNN network, and readjusts the parameters of the anchor in
the region proposal network(RPN) module;the latter combines residual structure
and feature pyramid structure are used to build a small intestinal lesion
recognition network based on feature fusion, for reducing the false positive
rate of the former and improve the overall accuracy.We used 22,335 WCE images
in the experiment, with a total of 123,092 lesion regions used to train the
detection framework of this paper. In the experiment, the detection framework
is trained and tested on the real WCE image dataset provided by the hospital
gastroenterology department. The sensitivity, false positive and accuracy of
the final model on the RPM are 98.81$\%$, 7.43$\%$ and 92.57$\%$,
respectively.Meanwhile,the corresponding results on the lesion images were
98.75$\%$, 5.62$\%$ and 94.39$\%$. The algorithm model proposed in this paper
is obviously superior to other detection algorithms in detection effect and
performance


title:
Zero absolute vorticity plane Couette flow as an hydrodynamic
  representation of quantum energy states under perpendicular magnetic field

abstract:
  Here we extend the Madelung transformation of the Schr\"{o}dinger equation
into a fluid-like form to include the influence of an external electromagnetic
field on a charged particle. The vorticity of the Madelung fluid is then in the
opposite direction to the imposed magnetic field and equal in magnitude to the
cyclotron angular frequency. When the particle motion is confined to a plane,
perpendicular to an imposed magnetic field, the equivalent flow dynamics is
that of zero absolute vorticity obtained in a quasi 2D rotating frame, where
the cyclotron frequency plays a role equivalent to that of the Coriolis
frequency in a rotating frame. We show how the Landau levels and the extended
modes in the integer quantum Hall effect are all mapped into such zero absolute
vorticity-like plane Couette flows, where the latter exhibit a geostrophic-like
balance between the magnetic force and the gradients of the quantum (Bohm)
potential and the electric force.


title:
On Calogero-Moser cellular characters for imprimitive complex reflection
  groups

abstract:
  We study the relationship between Calogero-Moser cellular characters and
characters defined from vectors of a Fock space of type $A_{\infty}$. Using
this interpretation, we show that Lusztig's constructible characters of the
Weyl group of type $B$ are sums of Calogero-Moser cellular characters. We also
give an explicit construction of the character of minimal $b$-invariant of a
given Calogero-Moser family of the complex reflection group $G(l,1,n)$.


title: Model-independent reconstruction of $f(Q)$ non-metric gravity

abstract:
  We consider gravity mediated by non-metricity, with vanishing curvature and
torsion. The gravitational action, including an arbitrary function of the
non-metric scalar, is investigated in view of characterizing the dark energy
effects. In particular, we present a method to reconstruct the $f(Q)$ action
without resorting to \emph{a priori} assumptions on the cosmological model. To
this purpose, we adopt a method based on rational Pad\'e approximations, which
provides a stable behaviour of the cosmographic series at high redshifts,
alleviating the convergence issues proper of the standard approach. We thus
describe how to reconstruct $f(Q)$ through a numerical inversion procedure
based on the current observational bounds on cosmographic parameters. Our
analysis suggests that the best approximation for describing the accelerated
expansion of the universe is represented by a scenario with $f(Q)=\alpha+\beta
Q^{n}$. Finally, possible deviations from the standard $\Lambda$CDM model are
discussed.


title: On Efficiently Acquiring Annotations for Multilingual Models

abstract:
  When tasked with supporting multiple languages for a given problem, two
approaches have arisen: training a model for each language with the annotation
budget divided equally among them, and training on a high-resource language
followed by zero-shot transfer to the remaining languages. In this work, we
show that the strategy of joint learning across multiple languages using a
single model performs substantially better than the aforementioned
alternatives. We also demonstrate that active learning provides additional,
complementary benefits. We show that this simple approach enables the model to
be data efficient by allowing it to arbitrate its annotation budget to query
languages it is less certain on. We illustrate the effectiveness of our
proposed method on a diverse set of tasks: a classification task with 4
languages, a sequence tagging task with 4 languages and a dependency parsing
task with 5 languages. Our proposed method, whilst simple, substantially
outperforms the other viable alternatives for building a model in a
multilingual setting under constrained budgets.


title:
Resonance energy and wave functions of $^{31}$Ne: a calculation using
  supersymmetric quantum mechanics

abstract:
  In this communication, we present an efficient method for computation of
energy and wave function of weakly bound nuclei by the application of
supersymmetric quantum mechanics (SSQM) and bound states in continuum (BIC)
technique. As a case study the scheme is implemented to the two-body ($^{30}$Ne
+ n) cluster model calculation of neutron-rich nucleus $^{31}$Ne. Woods-Saxon
central potential with spin-orbit component is used as the core-nucleon
interaction. The two-body Schr\"{o}dinger equation in relative coordinate is
solved numerically to get the energy and wave function of the low-lying bound
states. A one-parameter family of isospectral potential (IP) is constructed
from the bound state solutions following algebra of SSQM to find energies and
wave functions of the resonance states. In addition to the 2p$_{3/2^-}$ (-0.33
MeV) ground state, two bound excited states: s$_{1/2}$ (-0.30 MeV), $p_{1/2}$
(-0.15 MeV) are also obtained. Few low-lying resonance states: f$_{7/2_1}$
(2.57 MeV), f$_{7/2_2}$ (4.59 MeV), f$_{5/2_1}$ (5.58 MeV), p$_{1/2_1}$(1.432
MeV), p$_{1/2_2}$ (4.165 MeV), p$_{3/2_1}$ (1.431 MeV), p$_{3/2_2}$ (4.205 MeV)
are predicted. Among the predicted resonance states, the f$_{7/2^{-}}$ state
having resonance energy $E_R \simeq 4.59$ MeV is in excellent agreement with
the one found in the literature.


title:
TransRAC: Encoding Multi-scale Temporal Correlation with Transformers
  for Repetitive Action Counting

abstract:
  Counting repetitive actions are widely seen in human activities such as
physical exercise. Existing methods focus on performing repetitive action
counting in short videos, which is tough for dealing with longer videos in more
realistic scenarios. In the data-driven era, the degradation of such
generalization capability is mainly attributed to the lack of long video
datasets. To complement this margin, we introduce a new large-scale repetitive
action counting dataset covering a wide variety of video lengths, along with
more realistic situations where action interruption or action inconsistencies
occur in the video. Besides, we also provide a fine-grained annotation of the
action cycles instead of just counting annotation along with a numerical value.
Such a dataset contains 1,451 videos with about 20,000 annotations, which is
more challenging. For repetitive action counting towards more realistic
scenarios, we further propose encoding multi-scale temporal correlation with
transformers that can take into account both performance and efficiency.
Furthermore, with the help of fine-grained annotation of action cycles, we
propose a density map regression-based method to predict the action period,
which yields better performance with sufficient interpretability. Our proposed
method outperforms state-of-the-art methods on all datasets and also achieves
better performance on the unseen dataset without fine-tuning. The dataset and
code are available.


title: Nearly flat bands in twisted triple bilayer graphene

abstract:
  We investigate the electronic structure of alternating-twist triple
Bernal-stacked bilayer graphene (t3BG) as a function of interlayer coupling
$\omega$, twist angle $\theta$, interlayer potential difference $\Delta$, and
top-bottom bilayers sliding vector $\boldsymbol{\tau}$ for three possible
configurations AB/AB/AB, AB/BA/AB, and AB/AB/BA. The parabolic low-energy band
dispersions in a Bernal-stacked bilayer and gap-opening through a finite
interlayer potential difference $\Delta$ allows the flattening of bands in t3BG
down to $\sim 20$~meV for twist angles $\theta \lesssim 2^{\circ}$ regardless
of the stacking types. The easier isolation of the flat bands and associated
reduction of Coulomb screening thanks to the intrinsic gaps of bilayer graphene
for finite $\Delta$ facilitate the formation of correlation-driven gaps when it
is compared to the metallic phases of twisted trilayer graphene under electric
fields. We obtain the stacking dependent Coulomb energy versus bandwidth $U/W
\gtrsim 1$ ratios in the $\theta$ and $\Delta$ parameter space. We also present
the expected $K$-valley Chern numbers for the lowest-energy nearly flat bands.


title:
Potentialities of Hubble parameter and expansion rate function data to
  alleviate Hubble tension

abstract:
  Taking advantage of Gaussian process (GP), we obtain an improved estimate of
the Hubble constant, $H_0=70.41\pm1.58$ km s$^{-1}$ Mpc$^{-1}$, using Hubble
parameter [$H(z)$] from cosmic chronometers (CCH) and expansion rate function
[$E(z)$], extracted from type Ia supernovae, data. This result is higher than
those obtained by directly reconstructing CCH data with GP. In order to
estimate the potential of future CCH data, we simulate two sets of $H(z)$ data
and use them to constrain $H_0$ by either using GP reconstruction or fitting
them with $E(z)$ data. We find that simulated $H(z)$ data alleviate $H_0$
tension by pushing $H_0$ values higher towards $\sim70$ km s$^{-1}$ Mpc$^{-1}$.
We also find that joint $H(z)$ + $E(z)$ data favor higher values of $H_0$,
which is also confirmed by constraining $H_0$ in the flat concordance model and
2-order Taylor expansion of $H(z)$. In summary, we conclude that more and
better-quality CCH data as well as $E(z)$ data can provide a new and useful
perspective on resolving $H_0$ tension.


title:
Malmsten's integral and some related results: A different approach with
  Special functions

abstract:
  This paper is devoted to a case of the logarithmic integrals studied by
Blagouchine in 2014. It was clarified in his work on the rediscovery of
Malmsten's integrals that the previously called Vardi integral was actually a
particular case of a considered family of integrals, first evaluated by
Malmsten and colleagues in 1842. The approach here is based completely on real
methods. A new proof for the integral presented by Vardi is considered by an
elementary method and other proofs for some related integrals presented by
Blagouchine are considered by methods involving the use of some Special
functions and their properties. In determining a closed form for one of the
integrals presented by Blagouchine, several generalizations for integrals
involving the combinations of some transcedental functions were discovered and
examples were given relating them to known mathematical constants.


title: Implicit-Explicit Error Indicator based on Approximation Order

abstract:
  With the immense computing power at our disposal, the numerical solution of
partial differential equations (PDEs) is becoming a day-to-day task for modern
computational scientists. However, the complexity of real-life problems is such
that tractable solutions do not exist. This makes it difficult to validate the
numerically obtained solution, so good error estimation is crucial in such
cases. It allows the user to identify problematic areas in the computational
domain that may affect the stability and accuracy of the numerical method. Such
areas can then be remedied by either \textit{h}- or \textit{p}-adaptive
procedures. In this paper, we propose to estimate the error of the numerical
solution by solving the same governing problem implicitly and explicitly, using
a different approximation order in each case. We demonstrate the newly proposed
error indicator on the solution of a synthetic two-dimensional Poisson problem
with tractable solution for easier validation. We show that the proposed error
indicator has good potential for locating areas of high error.


title: Sparse Tensor-based Point Cloud Attribute Compression

abstract:
  Recently, numerous learning-based compression methods have been developed
with outstanding performance for the coding of the geometry information of
point clouds. On the contrary, limited explorations have been devoted to point
cloud attribute compression (PCAC). Thus, this study focuses on the PCAC by
applying sparse convolution because of its superior efficiency for representing
the geometry of unorganized points. The proposed method simply stacks sparse
convolutions to construct the variational autoencoder (VAE) framework to
compress the color attributes of a given point cloud. To better encode latent
elements at the bottleneck, we apply the adaptive entropy model with the joint
utilization of hyper prior and autoregressive neighbors to accurately estimate
the bit rate. The qualitative measurement of the proposed method already rivals
the latest G-PCC (or TMC13) version 14 at a similar bit rate. And, our method
shows clear quantitative improvements to G-PCC version 6, and largely
outperforms existing learning-based methods, which promises encouraging
potentials for learnt PCAC.


title:
Autonomous Highway Merging in Mixed Traffic Using Reinforcement Learning
  and Motion Predictive Safety Controller

abstract:
  Deep reinforcement learning (DRL) has a great potential for solving complex
decision-making problems in autonomous driving, especially in mixed-traffic
scenarios where autonomous vehicles and human-driven vehicles (HDVs) drive
together. Safety is a key during both the learning and deploying reinforcement
learning (RL) algorithms process. In this paper, we formulate the on-ramp
merging as a Markov Decision Process (MDP) problem and solve it with an
off-policy RL algorithm, i.e., Soft Actor-Critic for Discrete Action Settings
(SAC-Discrete). In addition, a motion predictive safety controller including a
motion predictor and an action substitution module, is proposed to ensure
driving safety during both training and testing. The motion predictor estimates
the trajectories of the ego vehicle and surrounding vehicles from kinematic
models, and predicts potential collisions. The action substitution module
updates the actions based on safety distance and replaces risky actions, before
sending them to the low-level controller. We train, evaluate and test our
approach on a gym-like highway simulation with three different levels of
traffic modes. The simulation results show that even in harder traffic
densities, our proposed method still significantly reduces collision rate while
maintaining high efficiency, outperforming several state-of-the-art baselines
in the considered on-ramp merging scenarios. The video demo of the evaluation
process can be found at: https://www.youtube.com/watch?v=7FvjbAM4oFw


title: Trion-trion annihilation in monolayer WS$_2$

abstract:
  Strong Coulomb interaction in monolayer transition metal dichalcogenides can
facilitate nontrivial many-body effects among excitonic complexes. Many-body
effects like exciton-exciton annihilation (EEA) have been widely explored in
this material system. However, a similar effect for charged excitons (or
trions), that is, trion-trion annihilation (TTA), is expected to be relatively
suppressed due to repulsive like-charges, and has not been hitherto observed in
such layered semiconductors. By a gate-dependent tuning of the spectral overlap
between the trion and the charged biexciton through an "anti-crossing"-like
behaviour in monolayer WS$_2$, here we present an experimental observation of
an anomalous suppression of the trion emission intensity with an increase in
gate voltage. The results strongly correlate with time-resolved measurements,
and are inferred as a direct evidence of a nontrivial TTA resulting from
non-radiative Auger recombination of a bright trion, and the corresponding
energy resonantly promoting a dark trion to a charged biexciton state. The
extracted Auger coefficient for the process is found to be tunable ten-fold
through a gate-dependent tuning of the spectral overlap.

categories: cond-mat.mes-hall cond-mat.mtrl-sci physics.app-ph physics.optics


title:
STCrowd: A Multimodal Dataset for Pedestrian Perception in Crowded
  Scenes

abstract:
  Accurately detecting and tracking pedestrians in 3D space is challenging due
to large variations in rotations, poses and scales. The situation becomes even
worse for dense crowds with severe occlusions. However, existing benchmarks
either only provide 2D annotations, or have limited 3D annotations with
low-density pedestrian distribution, making it difficult to build a reliable
pedestrian perception system especially in crowded scenes. To better evaluate
pedestrian perception algorithms in crowded scenarios, we introduce a
large-scale multimodal dataset,STCrowd. Specifically, in STCrowd, there are a
total of 219 K pedestrian instances and 20 persons per frame on average, with
various levels of occlusion. We provide synchronized LiDAR point clouds and
camera images as well as their corresponding 3D labels and joint IDs. STCrowd
can be used for various tasks, including LiDAR-only, image-only, and
sensor-fusion based pedestrian detection and tracking. We provide baselines for
most of the tasks. In addition, considering the property of sparse global
distribution and density-varying local distribution of pedestrians, we further
propose a novel method, Density-aware Hierarchical heatmap Aggregation (DHA),
to enhance pedestrian perception in crowded scenes. Extensive experiments show
that our new method achieves state-of-the-art performance for pedestrian
detection on various datasets.


title:
Distortion-Aware Self-Supervised 360{\deg} Depth Estimation from A
  Single Equirectangular Projection Image

abstract:
  360{\deg} images are widely available over the last few years. This paper
proposes a new technique for single 360{\deg} image depth prediction under open
environments. Depth prediction from a 360{\deg} single image is not easy for
two reasons. One is the limitation of supervision datasets - the currently
available dataset is limited to indoor scenes. The other is the problems caused
by Equirectangular Projection Format (ERP), commonly used for 360{\deg} images,
that are coordinate and distortion. There is only one method existing that uses
cube map projection to produce six perspective images and apply self-supervised
learning using motion pictures for perspective depth prediction to deal with
these problems. Different from the existing method, we directly use the ERP
format. We propose a framework of direct use of ERP with coordinate conversion
of correspondences and distortion-aware upsampling module to deal with the ERP
related problems and extend a self-supervised learning method for open
environments. For the experiments, we firstly built a dataset for the
evaluation, and quantitatively evaluate the depth prediction in outdoor scenes.
We show that it outperforms the state-of-the-art technique


title:
MSCCD: Grammar Pluggable Clone Detection Based on ANTLR Parser
  Generation

abstract:
  For various reasons, programming languages continue to multiply and evolve.
It has become necessary to have a multilingual clone detection tool that can
easily expand supported programming languages and detect various code clones is
needed. However, research on multilingual code clone detection has not received
sufficient attention. In this study, we propose MSCCD (Multilingual Syntactic
Code Clone Detector), a grammar pluggable code clone detection tool that uses a
parser generator to generate a code block extractor for the target language.
The extractor then extracts the semantic code blocks from a parse tree. MSCCD
can detect Type-3 clones at various granularities. We evaluated MSCCD's
language extensibility by applying MSCCD to 20 modern languages. Sixteen
languages were perfectly supported, and the remaining four were provided with
the same detection capabilities at the expense of execution time. We evaluated
MSCCD's recall by using BigCloneEval and conducted a manual experiment to
evaluate precision. MSCCD achieved equivalent detection performance equivalent
to state-of-the-art tools.


title: Interacting particles in an activity landscape

abstract:
  We study interacting active Brownian particles (ABPs) with a space-dependent
swim velocity via simulation and theory. We find that, although an equation of
state exists, a mechanical equilibrium does not apply to ABPs in activity
landscapes. The pressure difference originates in the flux of polar order and
the gradient of swim velocity across the interface between regions of different
activity. In contrast to motility-induced phase separation of ABPs with a
homogeneous swim velocity, a critical point does not exist for an
active-passive patch system, which continuously splits into a dense and a
dilute phase with increasing activity. However, if the global density is so
high that not all particles can be packed onto the inactive patch, then
MIPS-like behavior is restored and the pressure is balanced again.


title:
Leading correction to the local density approximation for exchange in
  large-$Z$ atoms

abstract:
  The large-$Z$ asymptotic expansion of atomic exchange energies has been
useful in determining exact conditions for corrections to the local density
approximation in density functional theory. We find that the necessary
correction is fit well with a leading $Z \ln Z$ term, and find its coefficient
numerically. The gradient expansion approximation also displays such a term,
but with a substantially smaller coefficient. Analytic results in the limit of
vanishing interaction with hydrogenic orbitals (a Bohr atom) are given, leading
to the conjecture that the true coefficients for all atoms are precisely 2.7
times larger than their gradient expansion counterpart. Combined with the
hydrogen atom result, this yields an analytic expression for the
exchange-energy correction which is accurate to $\sim 5\%$ for all $Z$.


title: Extremals for $\alpha$-Strichartz inequalities

abstract:
  A necessary and sufficient condition on the precompactness of extremal
sequences for one dimensional $\alpha$-Strichartz inequalities, equivalently
$\alpha$-Fourier extension estimates, is established based on the profile
decomposition arguments. One of our main tools is an operator-convergence
dislocation property consequence which comes from the van der Corput Lemma. Our
result is valid in asymmetric cases as well. In addition, we obtain the
existence of extremals for non-endpoint $\alpha$-Strichartz inequalities.


title: Categories enriched over oplax monoidal categories

abstract:
  We define a notion of category enriched over an oplax monoidal category $V$,
extending the usual definition of category enriched over a monoidal category.
Even though oplax monoidal structures involve infinitely many functors $V^n\to
V$, defining categories enriched over $V$ only requires the lower arity maps
$(n \leq 3)$, similarly to the monoidal case. The focal point of the enrichment
theory shifts, in the oplax case, from the notion of $V$-category (given by
collections of objects and hom-objects together with composition and unit maps)
to the one of categories enriched over $V$ (genuine categories equipped with
additional structures). One of the merits of the notion of categories enriched
over $V$ is that it becomes straightforward to define enriched functors and
natural transformations. We show moreover that the resulting 2-category
$\mathsf{Cat}_V$ can be put in correspondence (via the theory of distributors)
with the 2-category of modules over $V$. We give an example of such an enriched
category in the framework of operads: every cocomplete symmetric monoidal
category $C$ is enriched over the category of sequences in $C$ endowed with an
oplax monoidal structure stemming from the usual operadic composition product,
whose monoids are still the operads. As an application of the study of the
2-functor $V\mapsto\mathsf{Cat}_V$, we show that when $V$ is also endowed with
a compatible lax monoidal structure - thus forming a lax-oplax duoidal category
- the 2-category $\mathsf{Cat}_V$ inherits a lax 2-monoidal structure, thereby
generalising the corresponding result when the enrichment base is a braided
monoidal category. We illustrate this result by discussing the lax-oplax
structure on the category of $(R^\mathrm{e}, R^\mathrm{e})$-bimodules, whose
bimonoids are the bialgebroids. We also comment on the relations with other
enrichment theories (monoidal, multicategories, skew and lax).


title: An iterative formula of smallest singular value of nonsingular matrices

abstract:
  We obtain an iterative formula that converges incrementally to the smallest
singular value. Similarly, we obtain an iterative formula that decreases to
converge to the largest singular value.


title: Finsler metrics and semi-symmetric compatible linear connections

abstract:
  Finsler metrics are direct generalizations of Riemannian metrics such that
the quadratic Riemannian indicatrices in the tangent spaces of a manifold are
replaced by more general convex bodies as unit spheres. A linear connection on
the base manifold is called compatible with the Finsler metric if the induced
parallel transports preserve the Finslerian length of tangent vectors. Finsler
manifolds admitting compatible linear connections are called generalized
Berwald manifolds. Compatible linear connections are the solutions of the
so-called compatibility equations containing the torsion components as
unknowns. Although there are some theoretical results for the solvability of
the compatibility equations (monochromatic Finsler metrics \cite{BM}, extremal
compatible linear connections, algorithmic solutions \cite{V14}), it is very
hard to solve in general because compatible linear connections may or may not
exist on a Finsler manifold and may or may not be unique. Therefore special
cases are of special interest. One of them is the case of the so-called
semi-symmetric compatible linear connection with decomposable torsion tensor.
It is proved \cite{V10} (see also \cite{V11}) that such a compatible linear
connection must be uniquely determined. The original proof is based on
averaging in the sense that the 1-form in the decomposition of the torsion
tensor can be expressed by integrating differential forms on the tangent
manifold over the Finslerian indicatrices. The integral formulas are very
difficult to compute in practice. We present a new proof for the unicity result
by using linear algebra and some basic facts about convex bodies and an
explicit formula for the solution without integration. Necessary conditions of
the solvability are also formulated in terms of intrinsic equations without
unknown quantities. They are sufficient if and only if the solution depends
only on the position.


title: Fevering Interstellar Ices Have More CH3OD

abstract:
  Mono-deuterated methanol is thought to form during the prestellar core stage
of star formation. Observed variations in the CH2DOH/CH3OD ratio suggest that
its formation is strongly dependent on the surrounding cloud conditions. Thus,
it is a potential tracer of the physical conditions before the onset of star
formation. A single-point physical model representative of a typical prestellar
core is coupled to chemical models to investigate potential formation pathways
towards deuterated methanol at the prestellar stage. Simple addition reactions
of H and D are not able to reproduce observed abundances. The implementation of
an experimentally verified abstraction scheme leads to the efficient formation
of methyl-deuterated methanol, but lacks sufficient formation of
hydroxy-deuterated methanol. CH3OD is most likely formed at a later
evolutionarymstage, potentially from H-D exchange reactions in warm ices
between HDO (and D2O) and CH3OH. The CH2DOH/CH3OD ratio is not an appropriate
tracer of the physical conditions during the prestellar stage, but might be
better suited as a tracer of ice heating.


title:
From asymptotic to closed forms for the Keiper/Li approach to the
  Riemann Hypothesis

abstract:
  The Riemann Hypothesis (RH) - that all nonreal zeros of Riemann's zeta
function shall have real part 1/2 - remains a major open problem. Its most
concrete equivalent is that an infinite sequence of real numbers, the
Keiper--Li constants, shall be everywhere positive (Li's criterion). But those
numbers are analytically elusive and strenuous to compute, hence we seek
simpler variants. The essential sensitivity to RH of that sequence lies in its
asymptotic tail; then, retaining this feature, we can modify the Keiper--Li
scheme to obtain a new sequence in elementary closed form. This makes for a
more explicit analysis, with easier and faster computations. We can moreover
show how the new sequence will signal RH-violating zeros if any, by observing
its analogs for the Davenport--Heilbronn counterexamples to RH.


title:
Chaos from Symmetry: Navier Stokes equations, Beltrami fields and the
  Universal Classifying Crystallographic Group

abstract:
  In this report-article, the general setup to classify and construct
Arnold-Beltrami Flows on three-dimensional torii, previously introduced by one
of us, is further pursued. The idea of a Universal Classifying Group (UCG) is
improved. In particular, we construct for the first time such group for the
hexagonal lattice. Mastering the cubic and hexagonal instances, we can cover
all cases. We upgrade Beltrami flows to a special type of periodic solutions of
the NS equations, presenting the relation between the classification of these
flows with the classification of contact structures. The recent developments in
contact and symplectic geometry, considering singular contact structures, in
the framework of b-manifolds, is also reviewed and we show that the choice of
the critical surface for the b-deformation seems to be strongly related to its
group-theoretical structures. This opens directions of investigation towards a
classification of the critical surfaces or boundaries in terms of the UCG and
subgroups. Furthermore, as a result of this research programme a complete set
of MATHEMATICA Codes (for the cubic and hexagonal cases) have been produced
that are able to construct Beltrami Flows with an arbitrarily large number of
parameters and analyze their hidden symmetry structures. Indeed the main goal
is the systematic organization of the parameter space into group irreps. The
two Codes are a further result, being the unavoidable basis for further
investigations. The presented exact solutions illustrate the new conceptions
and ideas here discussed. The main message is: the more symmetric is the
Beltrami Flow, the highest the probability of an on-set of chaotic
trajectories. In various applications we need chaos on small scales and a more
orderly motion on larger ones. Merging elementary chaotic solutions with large
directional ordered flows is the target for future research.


title:
Multi-particle azimuthal cumulants from transverse momentum conservation
  and collective flow

abstract:
  We calculate the $n$th order of $2k$-particle azimuthal cumulants $c_n\{2k\}$
based on transverse momentum conservation (TMC) and collective flow
$v_n$(n=2,3). We demonstrate that the TMC effect only leads to a nonzero
$c_n\{2k\}$ with the sign of $(-1)^{nk}$ and the magnitude inversely
proportional to $(N-2k)^{nk}$. The interplay between TMC and collective flow
can change the signs of $c_2\{4\}$, $c_3\{2\}$ and $c_3\{4\}$ at some values of
multiplicity $N$, which could provide a good probe to study the onset of
collectivity and search for the substructure of proton in small colliding
systems.


title: A note on second derivative estimates for Monge-Ampe`re type equations

abstract:
  In this note we revisit previous Pogorelov type interior and global second
derivative estimates of the author, F. Jiang and J. Liu for solutions of
Monge-Amp`ere type partial differential equations. Taking account of recent
strict convexity regularity results of Guillen, Kitagawa and Rankin, and
following our earlier work in the optimal transportation case, we remove the
monotonicity assumptions in the more general case of generated Jacobian
equations and consequently in the subsequent application to classical
solvability and global regularity for second boundary value problems.


title:
Distribution of dust ejected from the lunar surface into the Earth-Moon
  system

abstract:
  Aims. An asymmetric dust cloud was detected around the Moon by the Lunar Dust
Experiment on board the Lunar Atmosphere and Dust Environment Explorer mission.
We investigate the dynamics of the grains that escape the Moon and their
configuration in the Earth-Moon system.
  Methods. We use a plausible initial ejecta distribution and mass production
rate for the ejected dust. Various forces, including the solar radiation
pressure and the gravity of the Moon, Earth, and Sun, are considered in the
dynamical model, and direct numerical integrations of trajectories of dust
particles are performed. The final states, the average life spans, and the
fraction of retrograde grains as functions of particle size are computed. The
number density distribution in the Earth-Moon system is obtained through
long-term simulations.
  Results. The average life spans depend on the size of dust particles and show
a rapid increase in the size range between $1\, \mathrm{\mu m}$ and $10\,
\mathrm{\mu m}$. About ${3.6\times10^{-3}\,\mathrm{kg/s}}$ ($\sim2\%$)
particles ejected from the lunar surface escape the gravity of the Moon, and
they form an asymmetric torus between the Earth and the Moon in the range
$[10\,R_\mathrm{E},50\,R_\mathrm{E}]$, which is offset toward the direction of
the Sun. A considerable number of retrograde particles occur in the Earth-Moon
system.


title:
Experimental investigation of a quantum heat engine powered by
  generalized measurements

abstract:
  Generalized measurements may allow the control of its invasiveness on the
quantum system interpolating from a very weak to strong projective action. Such
a measurement can fuel a quantum heat engine or extract work depending on the
system-meter interaction. Here, we performed a proof-of-concept experiment
using Nuclear Magnetic Resonance techniques to investigate a spin quantum heat
engine driven by non-selective generalized (weak) measurements without feedback
control. Our prototype of a quantum thermal device operates with a measurement
protocol and a single heat bath. The protocol is composed of two non-selective
measurement channels with variable measurement strengths, one dedicated to
fueling the device (analogous to a hot heat source) and the other committed to
work extraction. The experimental results highlight that this kind of quantum
thermal device can reach unit efficiency with maximum extracted power by
fine-tuning of the measurement strengths.


title:
Robust PCA for High Dimensional Data based on Characteristic
  Transformation

abstract:
  In this paper, we propose a novel robust Principal Component Analysis (PCA)
for high-dimensional data in the presence of various heterogeneities,
especially the heavy-tailedness and outliers. A transformation motivated by the
characteristic function is constructed to improve the robustness of the
classical PCA. Besides the typical outliers, the proposed method has the unique
advantage of dealing with heavy-tail-distributed data, whose covariances could
be nonexistent (positively infinite, for instance). The proposed approach is
also a case of kernel principal component analysis (KPCA) method and adopts the
robust and non-linear properties via a bounded and non-linear kernel function.
The merits of the new method are illustrated by some statistical properties
including the upper bound of the excess error and the behaviors of the large
eigenvalues under a spiked covariance model. In addition, we show the
advantages of our method over the classical PCA by a variety of simulations. At
last, we apply the new robust PCA to classify mice with different genotypes in
a biological study based on their protein expression data and find that our
method is more accurately on identifying abnormal mice comparing to the
classical PCA.


title:
Normalized solutions of $L^2$-supercritical NLS equations on compact
  metric graphs

abstract:
  This paper is devoted to the existence of non-trivial bound states of
prescribed mass for the mass-supercritical nonlinear Schr\"odinger equation on
compact metric graphs. The investigation is based upon a general variational
principle which combines the monotonicity trick and a min-max theorem with
second order information, and upon the blow-up analysis of bound states with
prescribed mass and bounded Morse index.


title:
Rate equations, spatial moments, and concentration profiles for
  mobile-immobile models with power-law and mixed waiting time distributions

abstract:
  We present a framework for systems in which diffusion-advection transport of
a tracer substance in a mobile zone is interrupted by trapping in an immobile
zone. Our model unifies different model approaches based on distributed-order
diffusion equations, exciton diffusion rate models, and random walk models for
multi-rate mobile-immobile mass transport. We study various forms for the
trapping time dynamics and their effects on the tracer mass in the mobile zone.
Moreover we find the associated breakthrough curves, the tracer density at a
fixed point in space as function of time, as well as the mobile and immobile
concentration profiles and the respective moments of the transport.
Specifically we derive explicit forms for the anomalous transport dynamics and
an asymptotic power-law decay of the mobile mass for a Mittag-Leffler trapping
time distribution. In our analysis we point out that even for exponential
trapping time densities transient anomalous transport is observed. Our results
have direct applications in geophysical contexts but also in biological, soft
matter, and solid state systems.


title:
When does a hypergeometric function ${}_{p\!}F_q$ belong to the
  Laguerre--P\'olya class $LP^+$?

abstract:
  I show that a hypergeometric function
${}_{p}F_q(a_1,\ldots,a_p;b_1,\ldots,b_q;\,\cdot\,)$ with $p \le q$ belongs to
the Laguerre--P\'olya class $LP^+$ for arbitrarily large $b_{p+1},\ldots,b_q >
0$ if and only if, after a possible reordering, the differences $a_i - b_i$ are
nonnegative integers. This result arises as an easy corollary of the case $p=q$
proven two decades ago by Ki and Kim. I also give explicit examples for the
case ${}_{1}F_2$.


title: Style-Based Global Appearance Flow for Virtual Try-On

abstract:
  Image-based virtual try-on aims to fit an in-shop garment into a clothed
person image. To achieve this, a key step is garment warping which spatially
aligns the target garment with the corresponding body parts in the person
image. Prior methods typically adopt a local appearance flow estimation model.
They are thus intrinsically susceptible to difficult body poses/occlusions and
large mis-alignments between person and garment images (see
Fig.~\ref{fig:fig1}). To overcome this limitation, a novel global appearance
flow estimation model is proposed in this work. For the first time, a StyleGAN
based architecture is adopted for appearance flow estimation. This enables us
to take advantage of a global style vector to encode a whole-image context to
cope with the aforementioned challenges. To guide the StyleGAN flow generator
to pay more attention to local garment deformation, a flow refinement module is
introduced to add local context. Experiment results on a popular virtual try-on
benchmark show that our method achieves new state-of-the-art performance. It is
particularly effective in a `in-the-wild' application scenario where the
reference image is full-body resulting in a large mis-alignment with the
garment image (Fig.~\ref{fig:fig1} Top). Code is available at:
\url{https://github.com/SenHe/Flow-Style-VTON}.


title: An Alternative Framework for E-Model Inflation in Supergravity

abstract:
  We present novel realizations of E-model inflation within Supergravity which
are largely associated with the existence of a pole of order one in the kinetic
term of the (gauge-singlet) inflaton superfield. This pole arises due to the
selected logarithmic Kahler potentials K1 and ~K1, which parameterize the same
hyperbolic manifold with scalar curvature R=-2/N, where N>0 is the coefficient
of a logarithmic term. The associated superpotential W exhibits the same R
charge with the inflaton-accompanying superfield and includes all the allowed
renormalizable terms. For K=K1, inflation can be attained for N=2 at the cost
of some tuning regarding the coefficients of the W terms and predicts a
tensor-to-scalar ratio r at the level of 0.001. The tuning can be totally
eluded for K=~K1, which allows for quadratic- and quartic-like models with N
values increasing with r and spectral index ns close or even equal to its
present central observational value.


title: Non-Gaussianity in stochastic transport: phenomenology and modelling

abstract:
  Non-Gaussian shapes, despite a linear form of the mean-squared displacement,
have been observed for the displacement distribution in a large range of
diffusive systems. Stochastic models for such "Brownian yet non-Gaussian"
diffusion will be introduced and discussed. Systems with non-Gaussian,
anomalous diffusion will also be addressed.


title:
A Differentially Private Framework for Deep Learning with Convexified
  Loss Functions

abstract:
  Differential privacy (DP) has been applied in deep learning for preserving
privacy of the underlying training sets. Existing DP practice falls into three
categories - objective perturbation, gradient perturbation and output
perturbation. They suffer from three main problems. First, conditions on
objective functions limit objective perturbation in general deep learning
tasks. Second, gradient perturbation does not achieve a satisfactory
privacy-utility trade-off due to over-injected noise in each epoch. Third, high
utility of the output perturbation method is not guaranteed because of the
loose upper bound on the global sensitivity of the trained model parameters as
the noise scale parameter. To address these problems, we analyse a tighter
upper bound on the global sensitivity of the model parameters. Under a
black-box setting, based on this global sensitivity, to control the overall
noise injection, we propose a novel output perturbation framework by injecting
DP noise into a randomly sampled neuron (via the exponential mechanism) at the
output layer of a baseline non-private neural network trained with a
convexified loss function. We empirically compare the privacy-utility
trade-off, measured by accuracy loss to baseline non-private models and the
privacy leakage against black-box membership inference (MI) attacks, between
our framework and the open-source differentially private stochastic gradient
descent (DP-SGD) approaches on six commonly used real-world datasets. The
experimental evaluations show that, when the baseline models have observable
privacy leakage under MI attacks, our framework achieves a better
privacy-utility trade-off than existing DP-SGD implementations, given an
overall privacy budget $\epsilon \leq 1$ for a large number of queries.


title: Understanding the unstable convergence of gradient descent

abstract:
  Most existing analyses of (stochastic) gradient descent rely on the condition
that for $L$-smooth cost, the step size is less than $2/L$. However, many works
have observed that in machine learning applications step sizes often do not
fulfill this condition, yet (stochastic) gradient descent converges, albeit in
an unstable manner. We investigate this unstable convergence phenomenon from
first principles, and elucidate key causes behind it. We also identify its main
characteristics, and how they interrelate, offering a transparent view backed
by both theory and experiments.


title: Formulae of $\imath$-divided powers in ${\bf U}_q(\mathfrak{sl}_2)$, III

abstract:
  The $\imath$-divided powers (depending on a parity) form the
$\imath$canonical basis for the split rank 1 $\imath$quantum group and they are
a basic ingredient for $\imath$quantum groups of higher rank. We obtain closed
formulae for the structure constants for multiplication of the $\imath$-divided
powers. Closed formulae for the comultiplication of the $\imath$-divided powers
are also obtained. These structure constants are integral and positive.