Rigidity theorem for a static triple with half harmonic Weyl curvature
In this paper, we prove that the static triple with half harmonic Weyl curvature and positive scalar curvature must be the standard hemisphere.
math.DG↗
arXiv subjects
Li Chen
Publications and source records attributed to Li Chen.
Combined Mean Field Limit and Non-relativistic Limit of Vlasov-Maxwell Particle System to Vlasov-Poisson System
In this paper we consider the mean field limit and non-relativistic limit of relativistic Vlasov-Maxwell particle system to Vlasov-Poisson equation. With the relativistic Vlasov-Maxwell particle system being a starting point, we carry out the estimates (with respect to $N$ and $c$) between the characteristic equation of both Vlasov-Maxwell particle model and Vlasov-Poisson equation, where the probabilistic method is exploited. In the last step, we take both large $N$ limit and non-relativistic limit (meaning $c$ tending to infinity) to close the argument.
math-ph↗
Spin-exchange-induced exotic superfluids in a Bose-Fermi spinor mixture
We consider a mixture of spin-1/2 bosons and fermions, where only the bosons are subjected to the spin-orbit coupling induced by Raman beams. The fermions, although not directly coupled to the Raman lasers, acquire an effective spin-orbit coupling through the spin-exchange interaction between the two species. Our calculation shows that this is a promising way of obtaining spin-orbit coupled Fermi gas without Raman-induced heating, where the long-sought topological Fermi superfluids and topological bands can be realized. Conversely, we find that the presence of fermions not only provides a new way to create the supersolid stripe phase of the bosons, but more strikingly it can also greatly increase the spatial period of the bosonic density stripes, and hence makes this phase directly observable in the experiment. This system provides a new and practical platform to explore the physics of spin-orbit coupling, which possesses a dynamic nature through the interaction between the two species.
physics.atom-ph↗
Self-Paced Video Data Augmentation with Dynamic Images Generated by Generative Adversarial Networks
There is an urgent need for an effective video classification method by means of a small number of samples. The deficiency of samples could be effectively alleviated by generating samples through Generative Adversarial Networks (GAN), but the generation of videos on a typical category remains to be underexplored since the complex actions and the changeable viewpoints are difficult to simulate. In this paper, we propose a generative data augmentation method for temporal stream of the Temporal Segment Networks with the dynamic image. The dynamic image compresses the motion information of video into a still image, removing the interference factors such as the background. Thus it is easier to generate images with categorical motion information using GAN. We use the generated dynamic images to enhance the features, with regularization achieved as well, thereby to achieve the effect of video augmentation. In order to deal with the uneven quality of generated images, we propose a Self-Paced Selection (SPS) method, which automatically selects the high-quality generated samples to be added to the network training. Our method is verified on two benchmark datasets, HMDB51 and UCF101. The experimental results show that the method can improve the accuracy of video classification under the circumstance of sample insufficiency and sample imbalance.
cs.CV↗
Multi-stage domain adversarial style reconstruction for cytopathological image stain normalization
The different stain styles of cytopathological images have a negative effect on the generalization ability of automated image analysis algorithms. This article proposes a new framework that normalizes the stain style for cytopathological images through a stain removal module and a multi-stage domain adversarial style reconstruction module. We convert colorful images into grayscale images with a color-encoding mask. Using the mask, reconstructed images retain their basic color without red and blue mixing, which is important for cytopathological image interpretation. The style reconstruction module consists of per-pixel regression with intradomain adversarial learning, inter-domain adversarial learning, and optional task-based refining. Per-pixel regression with intradomain adversarial learning establishes the generative network from the decolorized input to the reconstructed output. The interdomain adversarial learning further reduces the difference in stain style. The generation network can be optimized by combining it with the task network. Experimental results show that the proposed techniques help to optimize the generation network. The average accuracy increases from 75.41% to 84.79% after the intra-domain adversarial learning, and to 87.00% after interdomain adversarial learning. Under the guidance of the task network, the average accuracy rate reaches 89.58%. The proposed method achieves unsupervised stain normalization of cytopathological images, while preserving the cell structure, texture structure, and cell color properties of the image. This method overcomes the problem of generalizing the task models between different stain styles of cytopathological images.
eess.IV↗
Adaptive Wasserstein Hourglass for Weakly Supervised Hand Pose Estimation from Monocular RGB
Insufficient labeled training datasets is one of the bottlenecks of 3D hand pose estimation from monocular RGB images. Synthetic datasets have a large number of images with precise annotations, but the obvious difference with real-world datasets impacts the generalization. Little work has been done to bridge the gap between two domains over their wide difference. In this paper, we propose a domain adaptation method called Adaptive Wasserstein Hourglass (AW Hourglass) for weakly-supervised 3D hand pose estimation, which aims to distinguish the difference and explore the common characteristics (e.g. hand structure) of synthetic and real-world datasets. Learning the common characteristics helps the network focus on pose-related information. The similarity of the characteristics makes it easier to enforce domain-invariant constraints. During training, based on the relation between these common characteristics and 3D pose learned from fully-annotated synthetic datasets, it is beneficial for the network to restore the 3D pose of weakly labeled real-world datasets with the aid of 2D annotations and depth images. While in testing, the network predicts the 3D pose with the input of RGB.
cs.CV↗
Automated Artery Localization and Vessel Wall Segmentation of Magnetic Resonance Vessel Wall Images using Tracklet Refinement and Polar Conversion
Quantitative analysis of vessel wall structures by automated vessel wall segmentation provides useful imaging biomarkers in evaluating atherosclerotic lesions and plaque progression time-efficiently. To quantify vessel wall features, drawing lumen and outer wall contours of the artery of interest is required. To alleviate manual labor in contour drawing, some computer-assisted tools exist, but manual preprocessing steps, such as region of interest identification and boundary initialization are needed. In addition, the prior knowledge of the ring shape of vessel wall is not taken into consideration in designing the segmentation method. In this work, trained on manual vessel wall contours, a fully automated artery localization and vessel wall segmentation system is proposed. A tracklet refinement algorithm is used to robustly identify the centerlines of arteries of interest from a neural network localization architecture. Image patches are extracted from the centerlines and segmented in a polar coordinate system to use 3D information and to overcome problems such as contour discontinuity and interference from neighboring vessels. From a carotid artery dataset with 116 subjects (3406 slices) and a popliteal artery dataset with 5 subjects (289 slices), the proposed system is shown to robustly identify the artery of interest and segment the vessel wall. The proposed system demonstrates better performance on the carotid dataset with a Dice similarity coefficient of 0.824, compared with traditional vessel wall segmentation methods, Dice of 0.576, and traditional convolutional neural network approaches, Dice of 0.747. This vessel wall segmentation system will facilitate research on atherosclerosis and assist radiologists in image review.
eess.IV↗
AU R-CNN: Encoding Expert Prior Knowledge into R-CNN for Action Unit Detection
Detecting action units (AUs) on human faces is challenging because various AUs make subtle facial appearance change over various regions at different scales. Current works have attempted to recognize AUs by emphasizing important regions. However, the incorporation of expert prior knowledge into region definition remains under-exploited, and current AU detection approaches do not use regional convolutional neural networks (R-CNN) with expert prior knowledge to directly focus on AU-related regions adaptively. By incorporating expert prior knowledge, we propose a novel R-CNN based model named AU R-CNN. The proposed solution offers two main contributions: (1) AU R-CNN directly observes different facial regions, where various AUs are located. Specifically, we define an AU partition rule which encodes the expert prior knowledge into the region definition and RoI-level label definition. This design produces considerably better detection performance than existing approaches. (2) We integrate various dynamic models (including convolutional long short-term memory, two stream network, conditional random field, and temporal action localization network) into AU R-CNN and then investigate and analyze the reason behind the performance of dynamic models. Experiment results demonstrate that \textit{only} static RGB image information and no optical flow-based AU R-CNN surpasses the one fused with dynamic models. AU R-CNN is also superior to traditional CNNs that use the same backbone on varying image resolutions. State-of-the-art recognition performance of AU detection is achieved. The complete network is end-to-end trainable. Experiments on BP4D and DISFA datasets show the effectiveness of our approach. The implementation code is available online.
cs.CV↗
Sub-Gaussian heat kernel estimates and quasi Riesz transforms for $1\leq p\leq 2$
On a complete non-compact Riemannian manifold $M$, we prove that a so-called quasi Riesz transform is always $L^p$ bounded for $1<p\leq 2$. If $M$ satisfies the doubling volume property and the sub-Gaussian heat kernel estimate, we prove that the quasi Riesz transform is also of weak type $(1,1)$.
math.AP↗
Computing and Communicating Functions in Disorganized Wireless Networks
For future wireless networks, enormous numbers of interconnections are required, creating a disorganized topology and leading to a great challenge in data aggregation. Instead of collecting data individually, a more efficient technique, computation over multi-access channels (CoMAC), has emerged to compute functions by exploiting the signal-superposition property of wireless channels. However, the implementation of CoMAC in disorganized networks with multiple relays (hops) is still an open problem. In this paper, we combine CoMAC and orthogonal communication in the disorganized network to attain the computation of functions at the fusion center. First, to make the disorganized network more tractable, we reorganize the disorganized network into a hierarchical network with multiple layers that consists of subgroups and groups. In the hierarchical network, we propose multi-layer function computation where CoMAC is applied to each subgroup and orthogonal communication is adopted within each group. By computing and communicating subgroup and group functions over layers, the desired functions are reconstructed at the fusion center. The general computation rate is derived and the performance is further improved through time allocation and power control. The closed-form solutions to optimization are obtained, which suggest that existing CoMAC and orthogonal communication schemes can be generalized.
cs.IT↗
Uniqueness of self-similar solutions to flows by quotient curvatures
In this paper, we consider a family of closed hypersurfaces which shrink self-similarly with speed of quotient curvatures. We show that the only such hypersurfaces are shrinking spheres.
math.DG↗
Oscillatory evolution of collective behavior in evolutionary games played with reinforcement learning
Large-scale cooperation underpins the evolution of ecosystems and the human society, and the collective behaviors by self-organization of multi-agent systems are the key for understanding. As artificial intelligence (AI) prevails in almost all branches of science, it would be of great interest to see what new insights of collective behavior could be obtained from a multi-agent AI system. Here, we introduce a typical reinforcement learning (RL) algorithm -- Q learning into evolutionary game dynamics, where agents pursue optimal action on the basis of the introspectiveness rather than the birth-death or imitation processes in the traditional evolutionary game (EG). We investigate the cooperation prevalence numerically for a general $2\times 2$ game setting. We find that the cooperation prevalence in the multi-agent AI is amazing of an equal level as in the traditional EG in most cases. However, in the snowdrift games with RL we also reveal that explosive cooperation appears in the form of periodic oscillation, and we study the impact of the payoff structure on its emergence. Finally, we show that the periodic oscillation can also be observed in some other EGs with the RL algorithm, such as the rock-paper-scissors game. Our results offer a reference point to understand emergence of cooperation and oscillatory behaviors in nature and society from AI's perspective.
physics.soc-ph↗
MetaAdvDet: Towards Robust Detection of Evolving Adversarial Attacks
Deep neural networks (DNNs) are vulnerable to adversarial attack which is maliciously implemented by adding human-imperceptible perturbation to images and thus leads to incorrect prediction. Existing studies have proposed various methods to detect the new adversarial attacks. However, new attack methods keep evolving constantly and yield new adversarial examples to bypass the existing detectors. It needs to collect tens of thousands samples to train detectors, while the new attacks evolve much more frequently than the high-cost data collection. Thus, this situation leads the newly evolved attack samples to remain in small scales. To solve such few-shot problem with the evolving attack, we propose a meta-learning based robust detection method to detect new adversarial attacks with limited examples. Specifically, the learning consists of a double-network framework: a task-dedicated network and a master network which alternatively learn the detection capability for either seen attack or a new attack. To validate the effectiveness of our approach, we construct the benchmarks with few-shot-fashion protocols based on three conventional datasets, i.e. CIFAR-10, MNIST and Fashion-MNIST. Comprehensive experiments are conducted on them to verify the superiority of our approach with respect to the traditional adversarial attack detection methods.
cs.CV↗
Change-point detection in dynamic networks via graphon estimation
We propose a general approach for change-point detection in dynamic networks. The proposed method is model-free and covers a wide range of dynamic networks. The key idea behind our approach is to effectively utilize the network structure in designing change-point detection algorithms. This is done via an initial step of graphon estimation, where we propose a modified neighborhood smoothing~(MNBS) algorithm for estimating the link probability matrices of a dynamic network. Based on the initial graphon estimation, we then develop a screening and thresholding algorithm for multiple change-point detection in dynamic networks. The convergence rate and consistency for the change-point detection procedure are derived as well as those for MNBS. When the number of nodes is large~(e.g., exceeds the number of temporal points), our approach yields a faster convergence rate in detecting change-points comparing with an algorithm that simply employs averaged information of the dynamic network across time. Numerical experiments demonstrate robust performance of the proposed algorithm for change-point detection under various types of dynamic networks, and superior performance over existing methods is observed. A real data example is provided to illustrate the effectiveness and practical impact of the procedure.
stat.ME↗
Value-added catalogs of M type stars in LAMOST DR5
In this work, we present new catalogs of M giant and M dwarf stars from the LAMOST DR5. In total, 39,796 M giants and 501,152 M dwarfs are identified from the classification pipeline. The template-fitting results contain M giants with 7 temperature subtypes from M0 to M6, M dwarfs with 18 temperature subtypes from K7.0 to M8.5 and 12 metallicity subclasses from dMr to usdMp. We cross-matched our M-type catalog with the 2MASS and WISE catalog to obtain infrared magnitude and colors. Adopting the distances derived from the parallaxes in \gaia{} DR2, the M_G vs. (G_bp-G_rp)_0 diagram shows that there are also early-type stars and white dwarf-M dwarf binaries included in our M type stars sample, with a contamination rate of about 4.6% for M giants and 0.48% for M dwarfs. We found that CaH spectral indices are an efficient selection criteria for carbon stars. A total of 289 carbon stars were identified from the M giants sample, and further confirmed by LAMOST spectra.
astro-ph.GA↗
The Efficacy of SHIELD under Different Threat Models
In this appraisal paper, we evaluate the efficacy of SHIELD, a compression-based defense framework for countering adversarial attacks on image classification models, which was published at KDD 2018. Here, we consider alternative threat models not studied in the original work, where we assume that an adaptive adversary is aware of the ensemble defense approach, the defensive pre-processing, and the architecture and weights of the models used in the ensemble. We define scenarios with varying levels of threat and empirically analyze the proposed defense by varying the degree of information available to the attacker, spanning from a full white-box attack to the gray-box threat model described in the original work. To evaluate the robustness of the defense against an adaptive attacker, we consider the targeted-attack success rate of the Projected Gradient Descent (PGD) attack, which is a strong gradient-based adversarial attack proposed in adversarial machine learning research. We also experiment with training the SHIELD ensemble from scratch, which is different from re-training using a pre-trained model as done in the original work. We find that the targeted PGD attack has a success rate of 64.3% against the original SHIELD ensemble in the full white box scenario, but this drops to 48.9% if the models used in the ensemble are trained from scratch instead of being retrained. Our experiments further reveal that an ensemble whose models are re-trained indeed have higher correlation in the cosine similarity space, and models that are trained from scratch are less vulnerable to targeted attacks in the white-box and gray-box scenarios.
cs.LG↗
Composite fermions in Fock space: Operator algebra, recursion relations, and order parameters
We develop recursion relations, in particle number, for all (unprojected) Jain composite fermion (CF) wave functions. These recursions generalize a similar recursion originally written down by Read for Laughlin states, in mixed first-second quantized notation. In contrast, our approach is purely second-quantized, giving rise to an algebraic, `pure guiding center' definition of CF states that de-emphasizes first quantized many-body wave functions. Key to the construction is a second-quantized representation of the flux attachment operator that maps any given fermion state to its CF counterpart. An algebra of generators of edge excitations is identified. In particular, in those cases where a well-studied parent Hamiltonian exists, its properties can be entirely understood in the present framework, and the identification of edge state generators can be understood as an instance of `microscopic bosonization'. The intimate connection of Read's original recursion with `non-local order parameters' generalizes to the present situation, and we are able to give explicit second quantized formulas for non-local order parameters associated with CF states.
cond-mat.str-el↗
Insight-HXMT observations of Swift J0243.6+6124 during its 2017-2018 outburst
The recently discovered neutron star transient Swift J0243.6+6124 has been monitored by {\it the Hard X-ray Modulation Telescope} ({\it Insight-\rm HXMT). Based on the obtained data, we investigate the broadband spectrum of the source throughout the outburst. We estimate the broadband flux of the source and search for possible cyclotron line in the broadband spectrum. No evidence of line-like features is, however, found up to $\rm 150~keV$. In the absence of any cyclotron line in its energy spectrum, we estimate the magnetic field of the source based on the observed spin evolution of the neutron star by applying two accretion torque models. In both cases, we get consistent results with $B\rm \sim 10^{13}~G$, $D\rm \sim 6~kpc$ and peak luminosity of $\rm >10^{39}~erg~s^{-1}$ which makes the source the first Galactic ultraluminous X-ray source hosting a neutron star.
astro-ph.HE↗