arXiv subjects

Jianjun Wang

Publications and source records attributed to Jianjun Wang.

At least 37 records · Page 2Linked to original sources

arXiv2023

Guaranteed Tensor Recovery Fused Low-rankness and Smoothness

The tensor data recovery task has thus attracted much research attention in recent years. Solving such an ill-posed problem generally requires to explore intrinsic prior structures underlying tensor data, and formulate them as certain forms of regularization terms for guiding a sound estimate of the restored tensor. Recent research have made significant progress by adopting two insightful tensor priors, i.e., global low-rankness (L) and local smoothness (S) across different tensor modes, which are always encoded as a sum of two separate regularization terms into the recovery models. However, unlike the primary theoretical developments on low-rank tensor recovery, these joint L+S models have no theoretical exact-recovery guarantees yet, making the methods lack reliability in real practice. To this crucial issue, in this work, we build a unique regularization term, which essentially encodes both L and S priors of a tensor simultaneously. Especially, by equipping this single regularizer into the recovery models, we can rigorously prove the exact recovery guarantees for two typical tensor recovery tasks, i.e., tensor completion (TC) and tensor robust principal component analysis (TRPCA). To the best of our knowledge, this should be the first exact-recovery results among all related L+S methods for tensor recovery. Significant recovery accuracy improvements over many other SOTA methods in several TC and TRPCA tasks with various kinds of visual tensor data are observed in extensive experiments. Typically, our method achieves a workable performance when the missing rate is extremely large, e.g., 99.5%, for the color image inpainting task, while all its peers totally fail in such challenging case.

cs.LG↗

arXiv2022

Exact Decomposition of Joint Low Rankness and Local Smoothness Plus Sparse Matrices

It is known that the decomposition in low-rank and sparse matrices (\textbf{L+S} for short) can be achieved by several Robust PCA techniques. Besides the low rankness, the local smoothness (\textbf{LSS}) is a vitally essential prior for many real-world matrix data such as hyperspectral images and surveillance videos, which makes such matrices have low-rankness and local smoothness properties at the same time. This poses an interesting question: Can we make a matrix decomposition in terms of \textbf{L\&LSS +S } form exactly? To address this issue, we propose in this paper a new RPCA model based on three-dimensional correlated total variation regularization (3DCTV-RPCA for short) by fully exploiting and encoding the prior expression underlying such joint low-rank and local smoothness matrices. Specifically, using a modification of Golfing scheme, we prove that under some mild assumptions, the proposed 3DCTV-RPCA model can decompose both components exactly, which should be the first theoretical guarantee among all such related methods combining low rankness and local smoothness. In addition, by utilizing Fast Fourier Transform (FFT), we propose an efficient ADMM algorithm with a solid convergence guarantee for solving the resulting optimization problem. Finally, a series of experiments on both simulations and real applications are carried out to demonstrate the general validity of the proposed 3DCTV-RPCA model.

cs.CV↗

arXiv2021

Low-rank matrix recovery via regularized nuclear norm minimization

In this paper, we theoretically investigate the low-rank matrix recovery problem in the context of the unconstrained regularized nuclear norm minimization (RNNM) framework. Our theoretical findings show that, the RNNM method is able to provide a robust recovery of any matrix $X$ (not necessary to be exactly low-rank) from its few noisy measurements $\textbf{b}=\mathcal{A}(X)+\textbf{n}$ with a bounded constraint $\|\textbf{n}\|_{2}\leqε$, provided that the $tk$-order restricted isometry constant (RIC) of $\mathcal{A}$ satisfies a certain constraint related to $t>0$. Specifically, the obtained recovery condition in the case of $t>4/3$ is found to be same with the sharp condition established previously by Cai and Zhang (2014) to guarantee the exact recovery of any rank-$k$ matrix via the constrained nuclear norm minimization method. More importantly, to the best of our knowledge, we are the first to establish the $tk$-order RIC based coefficient estimate of the robust null space property in the case of $0<t\leq1$.

math.NA↗

arXiv2020

Dependency of Sliding Friction for Two Dimensional Systems on Electronegativity

We study the role of electronegativity in sliding friction for five different two dimensional (2D) monolayer systems using density functional theory (DFT) with van der Waals (vdW) corrections. We show that the friction between the commensurate 2D layered systems depends strongly on the electronegativity difference of the involved atoms. All the 2D layered structures exhibit almost the same magnitude of friction force when sliding along the nonpolar path, independent of the material and the surface structures. In contrast, for sliding friction along the polar path, the friction force obeys a universal linear scaling law as a function of the electronegativity difference of its constituent atoms. Further analyses demonstrate that atomic dipoles in the 2D monolayers induced by the electronegativity difference enhance the corrugation of charge distribution and increase the sliding barrier accordingly. Our studies reveal that electronegativity plays an important role in friction of low dimensional systems, and will provide a strategy for designing nanoscale devices further.

cond-mat.mtrl-sci↗

arXiv2020

The perturbation analysis of nonconvex low-rank matrix robust recovery

In this paper, we bring forward a completely perturbed nonconvex Schatten $p$-minimization to address a model of completely perturbed low-rank matrix recovery. The paper that based on the restricted isometry property generalizes the investigation to a complete perturbation model thinking over not only noise but also perturbation, gives the restricted isometry property condition that guarantees the recovery of low-rank matrix and the corresponding reconstruction error bound. In particular, the analysis of the result reveals that in the case that $p$ decreases $0$ and $a>1$ for the complete perturbation and low-rank matrix, the condition is the optimal sufficient condition $δ_{2r}<1$ \cite{Recht et al 2010}. The numerical experiments are conducted to show better performance, and provides outperformance of the nonconvex Schatten $p$-minimization method comparing with the convex nuclear norm minimization approach in the completely perturbed scenario.

cs.IT↗

arXiv2020

The high-order block RIP for non-convex block-sparse compressed sensing

This paper concentrates on the recovery of block-sparse signals, which is not only sparse but also nonzero elements are arrayed into some blocks (clusters) rather than being arbitrary distributed all over the vector, from linear measurements. We establish high-order sufficient conditions based on block RIP to ensure the exact recovery of every block $s$-sparse signal in the noiseless case via mixed $l_2/l_p$ minimization method, and the stable and robust recovery in the case that signals are not accurately block-sparse in the presence of noise. Additionally, a lower bound on necessary number of random Gaussian measurements is gained for the condition to be true with overwhelming probability. Furthermore, the numerical experiments conducted demonstrate the performance of the proposed algorithm.

cs.IT↗

arXiv2020

An Optimal Condition of Robust Low-rank Matrices Recovery

In this paper we investigate the reconstruction conditions of nuclear norm minimization for low-rank matrix recovery. We obtain sufficient conditions $δ_{tr}<t/(4-t)$ with $0<t<4/3$ to guarantee the robust reconstruction $(z\neq0)$ or exact reconstruction $(z=0)$ of all rank $r$ matrices $X\in\mathbb{R}^{m\times n}$ from $b=\mathcal{A}(X)+z$ via nuclear norm minimization. Furthermore, we not only show that when $t=1$, the upper bound of $δ_r<1/3$ is the same as the result of Cai and Zhang \cite{Cai and Zhang}, but also demonstrate that the gained upper bounds concerning the recovery error are better. Moreover, we prove that the restricted isometry property condition is sharp. Besides, the numerical experiments are conducted to reveal the nuclear norm minimization method is stable and robust for the recovery of low-rank matrix.

cs.IT↗

arXiv2020

On the distributional expansions of powered extremes from Maxwell distribution

In this paper, asymptotic expansions of the distributions and densities of powered extremes for Maxwell samples are considered. The results show that the convergence speeds of normalized partial maxima relies on the powered index. Additionally, compared with previous result, the convergence rate of the distribution of powered extreme from Maxwell samples is faster than that of its extreme. Finally, numerical analysis is conducted to illustrate our findings.

math.PR↗

arXiv2020

An analysis of noise folding for low-rank matrix recovery

Previous work regarding low-rank matrix recovery has concentrated on the scenarios in which the matrix is noise-free and the measurements are corrupted by noise. However, in practical application, the matrix itself is usually perturbed by random noise preceding to measurement. This paper concisely investigates this scenario and evidences that, for most measurement schemes utilized in compressed sensing, the two models are equivalent with the central distinctness that the noise associated with (\ref{eq.3}) is larger by a factor to $mn/M$, where $m,~n$ are the dimension of the matrix and $M$ is the number of measurements. Additionally, this paper discusses the reconstruction of low-rank matrices in the setting, presents sufficient conditions based on the associating null space property to guarantee the robust recovery and obtains the number of measurements. Furthermore, for the non-Gaussian noise scenario, we further explore it and give the corresponding result. The simulation experiments conducted, on the one hand show effect of noise variance on recovery performance, on the other hand demonstrate the verifiability of the proposed model.

cs.IT↗

arXiv2019

RIP-based performance guarantee for low-tubal-rank tensor recovery

The essential task of tensor data analysis focuses on the tensor decomposition and the corresponding notion of rank. In this paper, by introducing the notion of tensor Singular Value Decomposition (t-SVD), we establish a Regularized Tensor Nuclear Norm Minimization (RTNNM) model for low-tubal-rank tensor recovery. As we know that many variants of the Restricted Isometry Property (RIP) have proven to be crucial analysis tools for sparse recovery. In the t-SVD framework, we initiatively define a novel tensor Restricted Isometry Property (t-RIP). Furthermore, we show that any third-order tensor $\boldsymbol{\mathcal{X}}$ can stably be recovered from few linear noise measurements under some certain t-RIP conditions via the RTNNM model. We note that, as far as the authors are aware, such kind of result has not previously been reported in the literature.

cs.IT↗

arXiv2019

Tensor Restricted Isometry Property Analysis For a Large Class of Random Measurement Ensembles

In previous work, theoretical analysis based on the tensor Restricted Isometry Property (t-RIP) established the robust recovery guarantees of a low-tubal-rank tensor. The obtained sufficient conditions depend strongly on the assumption that the linear measurement maps satisfy the t-RIP. In this paper, by exploiting the probabilistic arguments, we prove that such linear measurement maps exist under suitable conditions on the number of measurements in terms of the tubal rank r and the size of third-order tensor n1, n2, n3. And the obtained minimal possible number of linear measurements is nearly optimal compared with the degrees of freedom of a tensor with tubal rank r. Specially, we consider a random sub-Gaussian distribution that includes Gaussian, Bernoulli and all bounded distributions and construct a large class of linear maps that satisfy a t-RIP with high probability. Moreover, the validity of the required number of measurements is verified by numerical experiments.

stat.ML↗

arXiv2019

Hydrogen Polarity of Interfacial Water Regulates Heterogeneous Ice Nucleation

Using all-atomic molecular dynamics(MD) simulations, we show that various substrates could induce interfacial water (IW) to form the same ice-like oxygen lattice but different hydrogen polarity order, and regulate the heterogeneous ice nucleation on the IW. We develop an efficient MD method to probe the shape, structure of ice nuclei and the corresponding supercooling temperatures. We find that the polarization of hydrogens in IW increases the surface tension between the ice nucleus and the IW, thus lifts the free energy barrier of heterogeneous ice nucleation. The results show that not only the oxygen lattice order but the hydrogen disorder of IW on substrates are required to effectively facilitate the freezing of atop water.

physics.comp-ph↗

arXiv2019

Colloidal Quantum Dot Tandem Solar Cells Using CVD Graphene as An Atomically Thin Intermediate Recombination Layer

Two-terminal tandem cell architectures are believed to be an effective way to further improve the power conversion efficiency in solution processed photovoltaics. To design an efficient tandem solar cell, two key issues need to be considered. Firstly, subcells with well-matched currents and complementary absorption characteristics are a prerequisite for high efficiency. Secondly identifying the appropriate intermediate layer (IML) to connect the subcells is necessary to minimize the optical and electronic losses. PbS colloidal quantum dots (CQDs) are a notable choice for the subcells due to their low cost, solution processibility and remarkable wide range band gap tunability. Single layer Graphene (Gr) has been proposed to be a promising IML due to its high transparency and conductivity. Here, as a proof of concept, we demonstrate a solution processed two terminal PbS CQDs tandem solar cell employing chemical vapor deposited Gr as the IML. In doing so, we report a PbS CQD cell comprising subcells with bandgaps of 1.4 and 0.95 eV that delivers power conversion efficiency in excess of 7%, substantially higher than previously reported CQD tandem cells.

physics.app-ph↗

arXiv2019

Deterministic Analysis of Weighted BPDN With Partially Known Support Information

In this paper, with the aid of the powerful Restricted Isometry Constant (RIC), a deterministic (or say non-stochastic) analysis, which includes a series of sufficient conditions (related to the RIC order) and their resultant error estimates, is established for the weighted Basis Pursuit De-Noising (BPDN) to guarantee the robust signal recovery when Partially Known Support Information (PKSI) of the signal is available. Specifically, the obtained conditions extend nontrivially the ones induced recently for the traditional constrained weighted $\ell_{1}$-minimization model to those for its unconstrained counterpart, i.e., the weighted BPDN. The obtained error estimates are also comparable to the analogous ones induced previously for the robust recovery of the signals with PKSI from some constrained models. Moreover, these results to some degree may well complement the recent investigation of the weighted BPDN which is based on the stochastic analysis.

cs.IT↗

arXiv2018

Coherence-Based Performance Guarantee of Regularized $\ell_{1}$-Norm Minimization and Beyond

In this paper, we consider recovering the signal $\bm{x}\in\mathbb{R}^{n}$ from its few noisy measurements $\bm{b}=A\bm{x}+\bm{z}$, where $A\in\mathbb{R}^{m\times n}$ with $m\ll n$ is the measurement matrix, and $\bm{z}\in\mathbb{R}^{m}$ is the measurement noise/error. We first establish a coherence-based performance guarantee for a regularized $\ell_{1}$-norm minimization model to recover such signals $\bm{x}$ in the presence of the $\ell_{2}$-norm bounded noise, i.e., $\|\bm{z}\|_{2}\leqε$, and then extend these theoretical results to guarantee the robust recovery of the signals corrupted with the Dantzig Selector (DS) type noise, i.e., $\|A^{T}\bm{z}\|_{\infty}\leqε$, and the structured block-sparse signal recovery in the presence of the bounded noise. To the best of our knowledge, we first extend nontrivially the sharp uniform recovery condition derived by Cai, Wang and Xu (2010) for the constrained $\ell_{1}$-norm minimization model, which takes the form of \begin{align*} μ<\frac{1}{2k-1}, \end{align*} where $μ$ is defined as the (mutual) coherence of $A$, to two unconstrained regularized $\ell_{1}$-norm minimization models to guarantee the robust recovery of any signals (not necessary to be $k$-sparse) under the $\ell_{2}$-norm bounded noise and the DS type noise settings, respectively. Besides, a uniform recovery condition and its two resulting error estimates are also established for the first time to our knowledge, for the robust block-sparse signal recovery using a regularized mixed $\ell_{2}/\ell_{1}$-norm minimization model, and these results well complement the existing theoretical investigation on this model which focuses on the non-uniform recovery conditions and/or the robust signal recovery in presence of the random noise.

math.NA↗

arXiv2018

Directional freezing of binary colloidal suspensions: A model for size fractionation of graphene oxide

The performance of graphene oxide(GO)-based materials strongly depends on the lateral size and size distribution of GO nanosheets. Various methods are employed to prepare GO nanosheets with a narrow size distribution. One of the promising method was proposed recently by directionally freezing of a GO aqueous dispersion at a controlled growth rate of the freezing front. We develop a theoretic model of binary colloids suspension, incorporating both the moving freezing boundary and the preferential adsorption of colloidal particles to the ice phase. We numerically solve the coupled diffusion equations and present state diagrams of size fractionation. Selective trapping of colloids according to their size can be achieved by a suitable choice of the experimental parameters, such as the adsorption rates and the freezing speed.

cond-mat.soft↗

arXiv2017

New sufficient conditions of signal recovery with tight frames via $l_1$-analysis

The paper discusses the recovery of signals in the case that signals are nearly sparse with respect to a tight frame $D$ by means of the $l_1$-analysis approach. We establish several new sufficient conditions regarding the $D$-restricted isometry property to ensure stable reconstruction of signals that are approximately sparse with respect to $D$. It is shown that if the measurement matrix $Φ$ fulfils the condition $δ_{ts}<t/(4-t)$ for $0<t<4/3$, then signals which are approximately sparse with respect to $D$ can be stably recovered by the $l_1$-analysis method. In the case of $D=I$, the bound is sharp, see Cai and Zhang's work \cite{Cai and Zhang 2014}. When $t=1$, the present bound improves the condition $δ_s<0.307$ from Lin et al.'s reuslt to $δ_s<1/3$.

eess.SP↗

arXiv2017

A sharp sufficient condition of block signal recovery via $l_2/l_1$-minimization

This work gains a sharp sufficient condition on the block restricted isometry property for the recovery of sparse signal. Under the certain assumption, the signal with block structure can be stably recovered in the present of noisy case and the block sparse signal can be exactly reconstructed in the noise-free case. Besides, an example is proposed to exhibit the condition is sharp. As byproduct, when $t=1$, the result improves the bound of block restricted isometry constant $δ_{s|\mathcal{I}}$ in Lin and Li (Acta Math. Sin. Engl. Ser. 29(7): 1401-1412, 2013).

eess.SP↗