arXiv subjects

Ping Li

Publications and source records attributed to Ping Li.

At least 289 records · Page 16Linked to original sources

arXiv2021

Insight into the Microscopic Structure of an AdS Black Hole from the Quantization

We explore the possible microscopic structure of a charged AdS black hole from the quantized viewpoint. A further study shows that some black holes cannot absorb "energy quantum" under certain conditions from the view of quantization. By the quantization of the black hole horizon area, we show the relation between the number of quanta of area and the microscopic degrees of freedom of the black hole. We also interpret a latent heat of thermodynamical phase transition as a transition between the number of quanta of area of large black hole(LBH) and the number of quanta of area of small black hole(SBH) in the charged AdS black hole. Furthermore, the Ruppeiner scalar curvature connecting with the number of quanta of area is also shown.

hep-th↗

arXiv2021

Rethinking Token-Mixing MLP for MLP-based Vision Backbone

In the past decade, we have witnessed rapid progress in the machine vision backbone. By introducing the inductive bias from the image processing, convolution neural network (CNN) has achieved excellent performance in numerous computer vision tasks and has been established as \emph{de facto} backbone. In recent years, inspired by the great success achieved by Transformer in NLP tasks, vision Transformer models emerge. Using much less inductive bias, they have achieved promising performance in computer vision tasks compared with their CNN counterparts. More recently, researchers investigate using the pure-MLP architecture to build the vision backbone to further reduce the inductive bias, achieving good performance. The pure-MLP backbone is built upon channel-mixing MLPs to fuse the channels and token-mixing MLPs for communications between patches. In this paper, we re-think the design of the token-mixing MLP. We discover that token-mixing MLPs in existing MLP-based backbones are spatial-specific, and thus it is sensitive to spatial translation. Meanwhile, the channel-agnostic property of the existing token-mixing MLPs limits their capability in mixing tokens. To overcome those limitations, we propose an improved structure termed as Circulant Channel-Specific (CCS) token-mixing MLP, which is spatial-invariant and channel-specific. It takes fewer parameters but achieves higher classification accuracy on ImageNet1K benchmark.

cs.CV↗

arXiv2021

A Systematic Collection of Medical Image Datasets for Deep Learning

The astounding success made by artificial intelligence (AI) in healthcare and other fields proves that AI can achieve human-like performance. However, success always comes with challenges. Deep learning algorithms are data-dependent and require large datasets for training. The lack of data in the medical imaging field creates a bottleneck for the application of deep learning to medical image analysis. Medical image acquisition, annotation, and analysis are costly, and their usage is constrained by ethical restrictions. They also require many resources, such as human expertise and funding. That makes it difficult for non-medical researchers to have access to useful and large medical data. Thus, as comprehensive as possible, this paper provides a collection of medical image datasets with their associated challenges for deep learning research. We have collected information of around three hundred datasets and challenges mainly reported between 2013 and 2020 and categorized them into four categories: head & neck, chest & abdomen, pathology & blood, and ``others''. Our paper has three purposes: 1) to provide a most up to date and complete list that can be used as a universal reference to easily find the datasets for clinical image analysis, 2) to guide researchers on the methodology to test and evaluate their methods' performance and robustness on relevant datasets, 3) to provide a ``route'' to relevant algorithms for the relevant medical topics, and challenge leaderboards.

eess.IV↗

arXiv2021

S$^2$-MLP: Spatial-Shift MLP Architecture for Vision

Recently, visual Transformer (ViT) and its following works abandon the convolution and exploit the self-attention operation, attaining a comparable or even higher accuracy than CNNs. More recently, MLP-Mixer abandons both the convolution and the self-attention operation, proposing an architecture containing only MLP layers. To achieve cross-patch communications, it devises an additional token-mixing MLP besides the channel-mixing MLP. It achieves promising results when training on an extremely large-scale dataset. But it cannot achieve as outstanding performance as its CNN and ViT counterparts when training on medium-scale datasets such as ImageNet1K and ImageNet21K. The performance drop of MLP-Mixer motivates us to rethink the token-mixing MLP. We discover that the token-mixing MLP is a variant of the depthwise convolution with a global reception field and spatial-specific configuration. But the global reception field and the spatial-specific property make token-mixing MLP prone to over-fitting. In this paper, we propose a novel pure MLP architecture, spatial-shift MLP (S$^2$-MLP). Different from MLP-Mixer, our S$^2$-MLP only contains channel-mixing MLP. We utilize a spatial-shift operation for communications between patches. It has a local reception field and is spatial-agnostic. It is parameter-free and efficient for computation. The proposed S$^2$-MLP attains higher recognition accuracy than MLP-Mixer when training on ImageNet-1K dataset. Meanwhile, S$^2$-MLP accomplishes as excellent performance as ViT on ImageNet-1K dataset with considerably simpler architecture and fewer FLOPs and parameters.

cs.CV↗

arXiv2021

Closed-Form, Provable, and Robust PCA via Leverage Statistics and Innovation Search

The idea of Innovation Search, which was initially proposed for data clustering, was recently used for outlier detection. In the application of Innovation Search for outlier detection, the directions of innovation were utilized to measure the innovation of the data points. We study the Innovation Values computed by the Innovation Search algorithm under a quadratic cost function and it is proved that Innovation Values with the new cost function are equivalent to Leverage Scores. This interesting connection is utilized to establish several theoretical guarantees for a Leverage Score based robust PCA method and to design a new robust PCA method. The theoretical results include performance guarantees with different models for the distribution of outliers and the distribution of inliers. In addition, we demonstrate the robustness of the algorithms against the presence of noise. The numerical and theoretical studies indicate that while the presented approach is fast and closed-form, it can outperform most of the existing algorithms.

stat.ML↗

arXiv2021

Efficient Discontinuous Galerkin Scheme for Analyzing Nanostructured Photoconductive Devices

Incorporation of plasmonic nanostructures in the design of photoconductive devices (PCDs) has significantly improved their optical-to-terahertz conversion efficiency. However, this improvement comes at the cost of increased complexity for the design and simulation of these devices. Indeed, accurate and efficient modeling of multiphysics processes and intricate device geometries of nanostructured PCDs is challenging due to the high computational cost resulting from multiple characteristic scales in time and space. In this work, a discontinuous Galerkin (DG)-based unit-cell scheme for efficient simulation of PCDs with periodic nanostructures is proposed. The scheme considers two physical stages of the device and models them using two coupled systems: a system of Poisson and drift-diffusion equations describing the nonequilibrium steady state, and a system of Maxwell and drift-diffusion equations describing the transient stage. A "potential-drop" boundary condition is enforced on the opposing boundaries of the unit cell to mimic the effect of the bias voltage. Periodic boundary conditions are used for carrier densities and electromagnetic fields. The unit-cell model described by these coupled equations and boundary conditions is discretized using DG methods. Numerical results demonstrate that the proposed DG-based unit-cell scheme has the same accuracy in predicting the THz photocurrent as the DG framework that takes into account the whole device, while it significantly reduces the computational cost.

cs.CE↗

arXiv2021

High-quality Low-dose CT Reconstruction Using Convolutional Neural Networks with Spatial and Channel Squeeze and Excitation

Low-dose computed tomography (CT) allows the reduction of radiation risk in clinical applications at the expense of image quality, which deteriorates the diagnosis accuracy of radiologists. In this work, we present a High-Quality Imaging network (HQINet) for the CT image reconstruction from Low-dose computed tomography (CT) acquisitions. HQINet was a convolutional encoder-decoder architecture, where the encoder was used to extract spatial and temporal information from three contiguous slices while the decoder was used to recover the spacial information of the middle slice. We provide experimental results on the real projection data from low-dose CT Image and Projection Data (LDCT-and-Projection-data), demonstrating that the proposed approach yielded a notable improvement of the performance in terms of image quality, with a rise of 5.5dB in terms of peak signal-to-noise ratio (PSNR) and 0.29 in terms of mutual information (MI).

eess.IV↗

arXiv2021

Electronic structures and topological phases of magnetic layered materials MnBi2Te4, MnBi2Se4 and MnSb2Te4

First-principles calculations are performed to study the electronic structures and topological phases of magnetic layered materials MnBi2Te4, MnBi2Se4 and MnSb2Te4 under different film thicknesses, strains and spin-orbit coupling (SOC) strengths. All these compounds energetically prefer the antiferromagnetic (AFM) state. MnBi2Te4 and MnSb2Te4 bulks are AFM topological insulators (TIs) in the AFM state, while they become Weyl semimetals in the ferromagnetic (FM) state. MnBi2Se4 is trivially insulating in both the AFM and FM states, but it becomes an AFM TI or a Weyl semimetal with increasing SOC strength or applying compressive strains. Under equilibrium lattice constants, the FM MnBi2Te4 slabs thicker than two septuple layers (SLs), the AFM MnBi2Te4 slabs thicker than three SLs and the FM MnSb2Te4 slabs thicker than five SLs are all Chern insulators. In addition, Chern insulators can also be obtained by compressing the in-plane lattice constants of the FM MnBi2Se4 slabs thicker than four SLs and the FM MnSb2Te4 slabs of three or four SLs, but cannot be obtained using the same method in the AFM slabs of these two materials. In-plane tensile strains about 1% to 2% turn the Chern insulators into trivial insulators.

physics.comp-ph↗

arXiv2021

Quantization Algorithms for Random Fourier Features

The method of random projection (RP) is the standard technique in machine learning and many other areas, for dimensionality reduction, approximate near neighbor search, compressed sensing, etc. Basically, RP provides a simple and effective scheme for approximating pairwise inner products and Euclidean distances in massive data. Closely related to RP, the method of random Fourier features (RFF) has also become popular, for approximating the Gaussian kernel. RFF applies a specific nonlinear transformation on the projected data from random projections. In practice, using the (nonlinear) Gaussian kernel often leads to better performance than the linear kernel (inner product), partly due to the tuning parameter $(γ)$ introduced in the Gaussian kernel. Recently, there has been a surge of interest in studying properties of RFF. After random projections, quantization is an important step for efficient data storage, computation, and transmission. Quantization for RP has also been extensive studied in the literature. In this paper, we focus on developing quantization algorithms for RFF. The task is in a sense challenging due to the tuning parameter $γ$ in the Gaussian kernel. For example, the quantizer and the quantized data might be tied to each specific tuning parameter $γ$. Our contribution begins with an interesting discovery, that the marginal distribution of RFF is actually free of the Gaussian kernel parameter $γ$. This small finding significantly simplifies the design of the Lloyd-Max (LM) quantization scheme for RFF in that there would be only one LM quantizer for RFF (regardless of $γ$). We also develop a variant named LM$^2$-RFF quantizer, which in certain cases is more accurate. Experiments confirm that the proposed quantization schemes perform well.

stat.ML↗

arXiv2021

Simulation on the Transparency of Electrons and Ion Back Flow for a Time Projection Chamber based on Staggered Multiple THGEMs

The IBF and the transparent rate of electrons are two essential indicators of TPC, which affect the energy resolution and counting rate respectively. In this paper, we propose several novel strategies of staggered multi-THGEM to suppress IBF, where the geometry of the first layer THGEM will be optimized to increase the electron transparent rate. By Garfield++ simulation, the electron transparency rate can be more than 90% of single THGEM with a optimized large hole. By simulating these configurations of triple and quadruple THGEM structures, we conclude that the IBF can be reduced to 0.2% level in an optimized configuration denoted as "ACBA". This strategy for staggered THGEM could have potential applications in future TPC projects.

physics.ins-det↗

arXiv2021

Absorption of massless scalar field by furry black holes in de Rham-Gabadadze-Tolley theory

We study the absorption of massless scalar field by two types furry charged black holes in de Rham-Gabadadze-Tolley (dRGT) theory. The absorption cross section is calculated in high frequency limit $σ_{hf}$ and low frequency limit $σ_{lf}$. We show that the high frequency limit $σ_{hf}$ is the area of shadow and the low frequency limit $σ_{lf}$ is the area of horizon. The ratio $R_{f}=\frac{σ_{hf}}{σ_{lf}}$ is used to measure the impact of charges on the absorption spectra of furry black hole. If the black hole possess an extra charge except mass, the interval value of absorption ratio $R_{f}$ is different: $[1,\frac{27}{16}]$ for electric charge, $[0.7675,\frac{27}{16}]$ for positive charge and $[\frac{27}{16},3.1835]$ for negative charge. We also use a numerical method to compute the absorption cross section in the finite frequency domain. A series of numerical results are presented.

gr-qc↗

arXiv2020

A Fourier Analytical Approach to Estimation of Smooth Functions in Gaussian Shift Model

Let $\mathbf{x}_j = \mathbfθ + \mathbfε_j$, $j=1,\dots,n$ be i.i.d. copies of a Gaussian random vector $\mathbf{x}\sim\mathcal{N}(\mathbfθ,\mathbfΣ)$ with unknown mean $\mathbfθ \in \mathbb{R}^d$ and unknown covariance matrix $\mathbfΣ\in \mathbb{R}^{d\times d}$. The goal of this article is to study the estimation of $f(\mathbfθ)$ where $f$ is a given smooth function of which smoothness is characterized by a Besov-type norm. The problem of interest resides in the high dimensional regime where the intrinsic dimension can grow with the sample size $n$. Inspired by the classical work of A. N. Kolmogorov on unbiased estimation and Littlewood-Paley theory, we develop a new estimator based on a Fourier analytical approach that achieves effective bias reduction. Asymptotic normality and efficiency are proved when the smoothness index of $f$ is above certain threshold which was discovered recently by Koltchinskii et. al. (2018) for a Hölder type class. Numerical simulations are presented to validate our analysis. The simplicity of implementation and its superiority over the plug-in approach indicate the new estimator can be applied to a broad range of real world applications.

math.ST↗

arXiv2020

Discovery of the Superconductivity in 3d Element Ni doped IrTe$_2$

IrTe$_2$ with large spin-orbital coupling (SOC) shows a CDW-like first order structural phase transition from high-temperature trigonal phase to low-temperature monoclinic phase at 270 K, accompanying with a large jump in transport and magnetic measurement as well as in heat capacity. Here, the 3d element Ni has been doped into IrTe$_2$ by growing Ir$_{1-x}$Ni$_x$Te$_2$ single crystals. Both XRD and XPS results reveal that the Ni atoms have substituted for Ir, which is consistent with the calculation result. Like the CDW behaviour, the structural phase transition shows competition and coexistence with the superconductivity. The monoclinic phase transition has been suppressed gradually with the increase of the doping amount of Ni, at last giving rise to the stabilization of the trigonal phase with superconductivity. Within 0.1$\leq$x$\leq$0.2, Ir$_{1-x}$Ni$_x$Te$_2$ shows the superconductive behaviour with T$_c$ around 2.6K. The superconductivity shows anisotropy with dimensionless anisotropy parameter $γ$=$ξ_{//}$$/$$ξ_{\perp}$$\sim$ 2. Even Ni element shows ferromagnetic behaviour, Ir$_{1-x}$Ni$_x$Te$_2$ only shows weak paramagnetism, no ferromagnetic order is observed in it, which is coincident with the calculation result that their up and down spin density of states compensate each other well. In addition, for other 3d elements Fe, Co and Mn doped IrTe$_2$, only Ir$_{1-x}$Mn$_x$Te$_2$ owns magnetism with magnetic moment of 3.0$μ$$_B$ to the supercell, theoretically.

cond-mat.supr-con↗

arXiv2020

An Optimistic Acceleration of AMSGrad for Nonconvex Optimization

We propose a new variant of AMSGrad, a popular adaptive gradient based optimization algorithm widely used for training deep neural networks. Our algorithm adds prior knowledge about the sequence of consecutive mini-batch gradients and leverages its underlying structure making the gradients sequentially predictable. By exploiting the predictability and ideas from optimistic online learning, the proposed algorithm can accelerate the convergence and increase sample efficiency. After establishing a tighter upper bound under some convexity conditions on the regret, we offer a complimentary view of our algorithm which generalizes the offline and stochastic version of nonconvex optimization. In the nonconvex case, we establish a non-asymptotic convergence bound independently of the initialization. We illustrate the practical speedup on several deep learning models via numerical experiments.

stat.ML↗

arXiv2020

Identification of Matrix Joint Block Diagonalization

Given a set $\mathcal{C}=\{C_i\}_{i=1}^m$ of square matrices, the matrix blind joint block diagonalization problem (BJBDP) is to find a full column rank matrix $A$ such that $C_i=AΣ_iA^\text{T}$ for all $i$, where $Σ_i$'s are all block diagonal matrices with as many diagonal blocks as possible. The BJBDP plays an important role in independent subspace analysis (ISA). This paper considers the identification problem for BJBDP, that is, under what conditions and by what means, we can identify the diagonalizer $A$ and the block diagonal structure of $Σ_i$, especially when there is noise in $C_i$'s. In this paper, we propose a ``bi-block diagonalization'' method to solve BJBDP, and establish sufficient conditions under which the method is able to accomplish the task. Numerical simulations validate our theoretical results. To the best of the authors' knowledge, existing numerical methods for BJBDP have no theoretical guarantees for the identification of the exact solution, whereas our method does.

stat.ML↗

arXiv2020

Tensor Completion via Tensor Networks with a Tucker Wrapper

In recent years, low-rank tensor completion (LRTC) has received considerable attention due to its applications in image/video inpainting, hyperspectral data recovery, etc. With different notions of tensor rank (e.g., CP, Tucker, tensor train/ring, etc.), various optimization based numerical methods are proposed to LRTC. However, tensor network based methods have not been proposed yet. In this paper, we propose to solve LRTC via tensor networks with a Tucker wrapper. Here by "Tucker wrapper" we mean that the outermost factor matrices of the tensor network are all orthonormal. We formulate LRTC as a problem of solving a system of nonlinear equations, rather than a constrained optimization problem. A two-level alternative least square method is then employed to update the unknown factors. The computation of the method is dominated by tensor matrix multiplications and can be efficiently performed. Also, under proper assumptions, it is shown that with high probability, the method converges to the exact solution at a linear rate. Numerical simulations show that the proposed algorithm is comparable with state-of-the-art methods.

stat.ML↗

arXiv2020

Extremal graphs and classification of planar graphs by MC-numbers

An edge-coloring of a connected graph $G$ is called a {\em monochromatic connection coloring} (MC-coloring for short) if any two vertices of $G$ are connected by a monochromatic path in $G$. For a connected graph $G$, the {\em monochromatic connection number} (MC-number for short) of $G$, denoted by $mc(G)$, is the maximum number of colors that ensure $G$ has a monochromatic connection coloring by using this number of colors. This concept was introduced by Caro and Yuster in 2011. They proved that $mc(G)\leq m-n+k$ if $G$ is not a $k$-connected graph. In this paper we depict all graphs with $mc(G)=m-n+k+1$ and $mc(G)= m-n+k$ if $G$ is a $k$-connected but not $(k+1)$-connected graph. We also prove that $mc(G)\leq m-n+4$ if $G$ is a planar graph, and classify all planar graphs by their monochromatic connectivity numbers.

math.CO↗

arXiv2020

A Framework of Randomized Selection Based Certified Defenses Against Data Poisoning Attacks

Neural network classifiers are vulnerable to data poisoning attacks, as attackers can degrade or even manipulate their predictions thorough poisoning only a few training samples. However, the robustness of heuristic defenses is hard to measure. Random selection based defenses can achieve certified robustness by averaging the classifiers' predictions on the sub-datasets sampled from the training set. This paper proposes a framework of random selection based certified defenses against data poisoning attacks. Specifically, we prove that the random selection schemes that satisfy certain conditions are robust against data poisoning attacks. We also derive the analytical form of the certified radius for the qualified random selection schemes. The certified radius of bagging derived by our framework is tighter than the previous work. Our framework allows users to improve robustness by leveraging prior knowledge about the training set and the poisoning model. Given higher level of prior knowledge, we can achieve higher certified accuracy both theoretically and practically. According to the experiments on three benchmark datasets: MNIST 1/7, MNIST, and CIFAR-10, our method outperforms the state-of-the-art.

cs.LG↗