Chen Li, Huilong Zhu, Yongkui Zhang, Xiaogen Yin, Kunpeng Jia, Junjie Li, Guilei Wang, Zhenzhen Kong, Anyan Du, Tengzhi Yang, Liheng Zhao, Lu Xie, Xuezheng Ai, Shishuai Ma, Yangyang Li, Henry H. Radamson, Chen Li, Huilong Zhu, Yongkui Zhang, Xiaogen Yin, Kunpeng Jia, Junjie Li, Guilei Wang, Zhenzhen Kong, Anyan Du, Tengzhi Yang, Liheng Zhao, Lu Xie, Xuezheng Ai, Shishuai Ma, Yangyang Li, Henry H. Radamson
A digital etching method was proposed to achieve excellent control of etching depth. The digital etching characteristics of p+ Si and Si0.7Ge0.3 using the combinations of HNO3 oxidation and BOE oxide removal processes were studied. Experiments showed that oxidation saturates with time due to low activation energy. A physical model was presented to describe the wet oxidation process with nitric acid. The model was calibrated with experimental data and the oxidation saturation time, final oxide thickness, and selectivity between Si0.7Ge0.3 and p+ Si were obtained. The digital etch of laminated Si0.7Ge0.3/p+ Si was also investigated. The depth of the tunnels formed by etching SiGe layers between two Si layers was found in proportion to digital etching cycles. And oxidation would also saturate and the saturated relative etched amount per cycle (REPC) was 0.5 nm (4 monolayers). A corrected selectivity calculation formula was presented. The oxidation model was also calibrated with Si0.7Ge0.3/p+ Si stacks, and selectivity from model was the same with the corrected formula. The model can also be used to analyze process variations and repeatability. And it could act as a guidance for experiment design. Selectivity and repeatability should make a trade-off.
Li Chen
The spread of infectious diseases, rumors, fashions, innovations are complex contagion processes, embedded both in networked and spatial contexts. Here we investigate the pattern dynamics of a complex contagion, where two agents, say $A$ and $B$, interact with each other and diffuse simultaneously in the geographic space. The contagion process for each follows the classical susceptible-infected-susceptible kinetics, and their interaction introduces a potential change in the secondary infection propensity compared to the baseline reproduction ratio $R_0$. We show that nontrivial spatial infection patterns arise, when the susceptible move faster than the infected and the interaction between the two agents is neither too competitive nor too cooperative. Interestingly, the system exhibits pattern hysteresis phenomena that quite different parameter regions allowing for patterns exist in the direction of increasing $R_0$ and in the direction of eradication by its reduction. The latter shows a remarkable enhancement in the contagion prevalence, meaning that the infection eradication now becomes extremely difficult compared to the single-agent scenario and to the coinfection without space. Linearization analysis supports our observations, and we identified the required elements and dynamical mechanism behind the emergence of a pattern. These findings call for further investigation for their close relevance, both in biological and social contagions.
Li Chen
My research lies in the intersection of security and machine learning. This overview summarizes one component of my research: combining computer vision with malware exploit detection for enhanced security solutions. I will present the perspectives of efficacy, reliability and resiliency to formulate threat detection as computer vision problems and develop state-of-the-art image-based malware classification. Representing malware binary as images provides a direct visualization of data samples, reduces the efforts for feature extraction, and consumes the whole binary for holistic structural analysis. Employing transfer learning of deep neural networks effective for large scale image classification to malware classification demonstrates superior classification efficacy compared with classical machine learning algorithms. To enhance reliability of these vision-based malware detectors, interpretation frameworks can be constructed on the malware visual representations and useful for extracting faithful explanation, so that security practitioners have confidence in the model before deployment. In cyber-security applications, we should always assume that a malware writer constantly modifies code to bypass detection. Addressing the resiliency of the malware detectors is equivalently important as efficacy and reliability. Via understanding the attack surfaces of machine learning models used for malware detection, we can greatly improve the robustness of the algorithms to combat malware adversaries in the wild. Finally I will discuss future research directions worth pursuing in this research community.
Li Chen
$L_p$-Christoffel-Minkowski problem arises naturally in the $L_p$-Brunn-Minkowski theory. It connects both curvature measures and area measures of convex bodies and is a fundamental problem in convex geometric analysis. Since the lack of Firey's extension of Brunn-Minkowski inequality and constant rank theorem for $p<1$, the existence and uniqueness of $L_p$-Brunn-Minkowski problem are difficult problems. In this paper, we prove a uniqueness theorem for solutions to $L_p$-Christoffel-Minkowski problem with $p<1$ and constant prescribed data. Our proof is motivated by the idea of Brendle-Choi-Daskaspoulos's work on asymptotic behavior of flows by powers of the Gaussian curvature. One of the highlights of our arguments is that we introduce a new auxiliary function $Z$ which is the key to our proof.
Li Chen, Yunbo Zhang, Han Pu
The (pseudo-)spin degrees of freedom greatly enriches the physics of cold atoms. This is particularly so for systems with high spins (i.e., spin quantum number larger than 1/2). For example, one can construct not only the rank-1 spin vector, but also the rank-2 spin tensor in high spin systems. Here we propose a simple scheme to couple the spin tensor and the center-of-mass orbital angular momentum in a spin-1 cold atom system, and show that this leads to a new quantum phase of the matter: the spin-nematic vortex state that features vorticity in an SU(2) spin-nematic tensor subspace. Under proper conditions, such states are characterized by quantized topological numbers. Our work opens up new avenues of research in topological quantum matter with high spins.
Li Chen, Yan He
In this paper, we consider fully nonlinear equations of Krylov type on Riemannian manifolds with negative curvature which naturally arise in conformal geometry. Moreover, we prove the a priori estimates for solutions to these equations and establish the existence results. Our results can be viewed as an extension of previous results given by Gursky-Viaclovsky and Li-Sheng.
Chen Li, Gim Hee Lee
3D human pose estimation from a single image is an inverse problem due to the inherent ambiguity of the missing depth. Several previous works addressed the inverse problem by generating multiple hypotheses. However, these works are strongly supervised and require ground truth 2D-to-3D correspondences which can be difficult to obtain. In this paper, we propose a weakly supervised deep generative network to address the inverse problem and circumvent the need for ground truth 2D-to-3D correspondences. To this end, we design our network to model a proposal distribution which we use to approximate the unknown multi-modal target posterior distribution. We achieve the approximation by minimizing the KL divergence between the proposal and target distributions, and this leads to a 2D reprojection error and a prior loss term that can be weakly supervised. Furthermore, we determine the most probable solution as the conditional mode of the samples using the mean-shift algorithm. We evaluate our method on three benchmark datasets -- Human3.6M, MPII and MPI-INF-3DHP. Experimental results show that our approach is capable of generating multiple feasible hypotheses and achieves state-of-the-art results compared to existing weakly supervised approaches. Our source code is available at the project website.
Li Chen, Changhui Tan, Lining Tong
We consider a compressible Euler system with singular velocity alignment, known as the Euler-alignment system, describing the flocking behaviors of large animal groups. We establish a local well-posedness theory for the system, as well as a global well-posedness theory for small initial data. We also show the asymptotic flocking behavior, where solutions converge to a constant steady state exponentially in time.
Li Chen, Thomas Hatsukami, Jenq-Neng Hwang, Chun Yuan
Automatically labeling intracranial arteries (ICA) with their anatomical names is beneficial for feature extraction and detailed analysis of intracranial vascular structures. There are significant variations in the ICA due to natural and pathological causes, making it challenging for automated labeling. However, the existing public dataset for evaluation of anatomical labeling is limited. We construct a comprehensive dataset with 729 Magnetic Resonance Angiography scans and propose a Graph Neural Network (GNN) method to label arteries by classifying types of nodes and edges in an attributed relational graph. In addition, a hierarchical refinement framework is developed for further improving the GNN outputs to incorporate structural and relational knowledge about the ICA. Our method achieved a node labeling accuracy of 97.5%, and 63.8% of scans were correctly labeled for all Circle of Willis nodes, on a testing set of 105 scans with both healthy and diseased subjects. This is a significant improvement over available state-of-the-art methods. Automatic artery labeling is promising to minimize manual effort in characterizing the complicated ICA networks and provides valuable information for the identification of geometric risk factors of vascular disease. Our code and dataset are available at https://github.com/clatfd/GNN-ARTLABEL.
Chen Li, Xutan Peng, Hao Peng, Jianxin Li, Lihong Wang, Philip S. Yu, Lifang He
Recently, graph-based algorithms have drawn much attention because of their impressive success in semi-supervised setups. For better model performance, previous studies learn to transform the topology of the input graph. However, these works only focus on optimizing the original nodes and edges, leaving the direction of augmenting existing data unexplored. In this paper, by simulating the generation process of graph signals, we propose a novel heuristic pre-processing technique, namely ELectoral COllege (ELCO), which automatically expands new nodes and edges to refine the label similarity within a dense subgraph. Substantially enlarging the original training set with high-quality generated labeled data, our framework can effectively benefit downstream models. To justify the generality and practicality of ELCO, we couple it with the popular Graph Convolution Network and Graph Attention Network to perform extensive evaluations on three standard datasets. In all setups tested, our method boosts the average score of base models by a large margin of 4.7 points, as well as consistently outperforms the state-of-the-art. We release our code and data on https://github.com/RingBDStack/ELCO to guarantee reproducibility.
Li Chen, Ni Xiang
In this paper, we prove some rigidity theorems for the entire 2-convex solutions of 2-Hessian equation in Euclidean space. As an application, we obtain a Bernstein type theorem for global special Lagrangian graphs.
Rizhou Liang, Jiqiang Zhang, Guozhong Zheng, Li Chen
Social hierarchy is important that can not be ignored in human socioeconomic activities and in the animal world. Here we incorporate this factor into the evolutionary game to see what impact it could have on the cooperation outcome. The probabilistic strategy adoption between two players is then not only determined by their payoffs, but also by their hierarchy difference -- players in the high rank are more likely to reproduce their strategies than the peers in the low rank. Through simulating the evolution of Prisoners' dilemma game with three hierarchical distributions, we find that the levels of cooperation are enhanced in all cases, and the enhancement is optimal in the uniform case. The enhancement is due to the fact that the presence of hierarchy facilitates the formation of cooperation clusters with high-rank players acting as the nucleation cores. This mechanism remains valid on Barabási-Albert scale-free networks, in particular the cooperation enhancement is maximal when the hubs are of higher social ranks. We also study a two-hierarchy model, where similar cooperation promotion is revealed and some theoretical analyses are provided. Our finding may partially explain why the social hierarchy is so ubiquitous on this planet.
Xutan Peng, Chen Li, Zhi Cai, Faqiang Shi, Yidan Liu, Jianxin Li
Deep learning researches on the transformation problems for image and text have raised great attention. However, present methods for music feature transfer using neural networks are far from practical application. In this paper, we initiate a novel system for transferring the texture of music, and release it as an open source project. Its core algorithm is composed of a converter which represents sounds as texture spectra, a corresponding reconstructor and a feed-forward transfer network. We evaluate this system from multiple perspectives, and experimental results reveal that it achieves convincing results in both sound effects and computational performance.
Rizhou Liang, Qinqin Wang, Jiqiang Zhang, Guozhong Zheng, Lin Ma, Li Chen
We study the evolution of two mutually interacting games with both pairwise games as well as the public goods game on different topologies. On 2d square lattices, we reveal that the game-game interaction can promote the cooperation prevalence in all cases, and the cooperation-defection phase transitions even become absent and fairly high cooperation is expected when the interaction goes to be very strong. A mean-field theory is developed that points out new dynamical routes arising therein. Detailed analysis shows indeed that there are rich categories of interactions in either individual or bulk scenario: invasion, neutral, and catalyzed types; their combination puts cooperators at a persistent advantage position, which boosts the cooperation. The robustness of the revealed reciprocity is strengthened by the studies of model variants, including asymmetrical or time-varying interactions, games of different types, games with time-scale separation, different updating rules etc. The structural complexities of the underlying population, such as Newman--Watts small world networks, Erdős--Rényi random networks, and Barabási--Albert networks, also do not alter the working of the dynamical reciprocity. In particular, as the number of games engaged increases, the cooperation level continuously improves in general. Our work thus uncovers a new class of cooperation mechanism and indicates the great potential for human cooperation where concurrent issues are so often seen in the real world.
Pascal Auscher, Li Chen, José María Martell, Cruz Prisuelos-Arribas
We study the solvability of the regularity problem for degenerate elliptic operators in the block case for data in weighted spaces. More precisely, let $L_w$ be a degenerate elliptic operator with degeneracy given by a fixed weight $w\in A_2(dx)$ in $\mathbb{R}^n$, and consider the associated block second order degenerate elliptic problem in the upper-half space $\mathbb{R}_+^{n+1}$. We obtain non-tangential bounds for the full gradient of the solution of the block case operator given by the Poisson semigroup in terms of the gradient of the boundary data. All this is done in the spaces $L^p(vdw)$ where $v$ is a Muckenhoupt weight with respect to the underlying natural weighted space $(\mathbb{R}^n, wdx)$. We recover earlier results in the non-degenerate case (when $w\equiv 1$, and with or without weight $v$). Our strategy is also different and more direct thanks in particular to recent observations on change of angles in weighted square function estimates and non-tangential maximal functions. Our method gives as a consequence the (unweighted) $L^2(dx)$-solvability of the regularity problem for the block operator \[ \mathbb{L}_αu(x,t) = -|x|^α \mathrm{div}_x \big(|x|^{-α}\,A(x) \nabla_x u(x,t)\big)-\partial_{t}^2 u(x,t) \] for any complex-valued uniformly elliptic matrix $A$ and for all $-ε<α<\frac{2\,n}{n+2}$, where $ε$ depends just on the dimension and the ellipticity constants of $A$.
Chen Li, Chad C. Kessens, Ronald S. Fearing, Robert J. Full
Terrestrial animals and robots are susceptible to flipping-over during rapid locomotion in complex terrains. However, small robots are less capable of self-righting from an upside-down orientation compared to small animals like insects. Inspired by the winged discoid cockroach, we designed a new robot that opens its wings to self-right by pushing against the ground. We used this robot to systematically test how self-righting performance depends on wing opening magnitude, speed, and asymmetry, and modeled how kinematic and energetic requirements depend on wing shape and body/wing mass distribution. We discovered that the robot self-rights dynamically using kinetic energy to overcome potential energy barriers, that larger and faster symmetric wing opening increases self-righting performance, and that opening wings asymmetrically increases righting probability when wing opening is small. Our results suggested that the discoid cockroach's winged self-righting is a dynamic maneuver. While the thin, lightweight wings of the discoid cockroach and our robot are energetically sub-optimal for self-righting compared to tall, heavy ones, their ability to open wings saves them substantial energy compared to if they had static shells. Analogous to biological exaptations, our study provided a proof-of-concept for terrestrial robots to use existing morphology in novel ways to overcome new locomotor challenges.
Li Chen
We identify and study a matrix algebra consisting of Pascal-type matrices. The generator of the matrix algebra is shown to well define a canonical bundle map, called the Pascal map on jet bundles, and we use it to give an intrinsic definition of point-wise contact between Hermitian vector bundles in terms of unitary equivalence of the Pascal maps.
Guozhong Zheng, Jiqiang Zhang, Rizhou Liang, Lin Ma, Li Chen
Feb 12, 2022·q-bio.PE·PDF Behavioral experiments on the Ultimatum Game have shown that we human beings have remarkable preference in fair play, contradicting the predictions by the game theory. Most of the existing models seeking for explanations, however, strictly follow the assumption of \emph{Homo economicus} in orthodox Economics that people are self-interested and fully rational to maximize their earnings. Here we relax this assumption by allowing that people probabilistically choose to be "good Samaritans", acting as fair players from time to time. For well-mixed and homogeneously structured populations, we numerically show that as this probability increases the level of fairness undergoes from the low scenario abruptly to the full fairness state, where occasional fair behaviors ($\sim5\%$) are sufficient to drive the whole population to behave in the half-half split manner. We also develop a mean-field theory, which correctly reproduces the first-order phase transition and points out that the bistability is an intrinsic property of this game and small fair acts lead to dramatical change due to its bifurcation structure. Heterogeneously structured populations, however, display continuous fairness transition; surprisingly, very few hub nodes acting as fair players are able to entrain the whole population to the full fairness state. Our results thus reveal the unexpected strength of "good Samaritans", which may constitute a new explanation for the emergence of fairness in our society.
Chen Li, Matthew Dunlop, Georg Stadler
We consider Bayesian inverse problems wherein the unknown state is assumed to be a function with discontinuous structure a priori. A class of prior distributions based on the output of neural networks with heavy-tailed weights is introduced, motivated by existing results concerning the infinite-width limit of such networks. We show theoretically that samples from such priors have desirable discontinuous-like properties even when the network width is finite, making them appropriate for edge-preserving inversion. Numerically we consider deconvolution problems defined on one- and two-dimensional spatial domains to illustrate the effectiveness of these priors; MAP estimation, dimension-robust MCMC sampling and ensemble-based approximations are utilized to probe the posterior distribution. The accuracy of point estimates is shown to exceed those obtained from non-heavy tailed priors, and uncertainty estimates are shown to provide more useful qualitative information.
Chen Li
A smart home energy dataset that records miscellaneous energy consumption data is publicly offered. The proposed energy activity dataset (EAD) has a high data type diversity in contrast to existing load monitoring datasets. In EAD, a simple data point is labeled with the appliance, brand, and event information, whereas a complex data point has an extra application label. Several discoveries have been made on the energy consumption patterns of many appliances. Load curves of the appliances are measured when different events and applications are triggered and utilized. A revised longest-common-subsequence (LCS) similarity measurement algorithm is proposed to calculate energy dataset similarities. Thus, the data quality prior information becomes available before training machine learning models. In addition, a subsample convolutional neural network (SCNN) is put forward. It serves as a non-intrusive optical character recognition (OCR) approach to obtain energy data directly from monitors of power meters. The link for the EAD dataset is: https://drive.google.com/drive/folders/1zn0V6Q8eXXSKxKgcs8ZRValL5VEn3anD