arXiv subjects

Ke Chen

Publications and source records attributed to Ke Chen.

At least 253 records · Page 14Linked to original sources

arXiv2021

The Peskin problem with $\dot B^1_{\infty,\infty}$ initial data

In this paper we study the Peskin problem in 2D, which describes the dynamics of a 1D closed elastic structure immersed in a steady Stokes flow. We prove the local well-posedness for arbitrary initial configuration in $(C^2)^{\dot B^1_{\infty,\infty}}$ satisfying the well-stretched condition, and the global well-posedness when the initial configuration is sufficiently close to an equilibrium in $\dot B^1_{\infty,\infty}$. Here $(C^2)^{\dot B^1_{\infty,\infty}}$ is the closure of $C^2$ in the Besov space $\dot B^1_{\infty,\infty}$. The global-in-time solution will converge to an equilibrium exponentially as $t\rightarrow+\infty$. This is the first well-posedness result for the Peskin problem with non-Lipschitz initial data.

math.AP↗

arXiv2021

Using Deep Image Prior to Assist Variational Selective Segmentation Deep Learning Algorithms

Variational segmentation algorithms require a prior imposed in the form of a regularisation term to enforce smoothness of the solution. Recently, it was shown in the Deep Image Prior work that the explicit regularisation in a model can be removed and replaced by the implicit regularisation captured by the architecture of a neural network. The Deep Image Prior approach is competitive, but is only tailored to one specific image and does not allow us to predict future images. We propose to incorporate the ideas from Deep Image Prior into a more traditional learning algorithm to allow us to use the implicit regularisation offered by the Deep Image Prior, but still be able to predict future images.

cs.CV↗

arXiv2021

Synthesis and thermal expansion of chalcogenide MAX phase Hf2SeC

Thermal expansion of MAX phases along different directions tended to be different because of the anisotropy of hexagonal crystals. Herein, a new Hf2SeC phase was synthesized and confirmed to be relatively isotropic, whose coefficients of thermal expansion (CTEs) were determined to be 9.73 μK-1 and 10.18 μK-1 along a and c directions. The strong M-S bond endowed Hf2SC and Zr2SC lower CTEs than Hf2SeC and Zr2SeC. A good relationship between the thermal expansion anisotropy and the ratio of elastic stiffness constant c11 and c33 was established. This straightforward approximation could be used to roughly predict the thermal expansion anisotropy of MAX phases.

cond-mat.mtrl-sci↗

arXiv2021

Tunable mid-infrared hyperbolic van der Waals metasurfaces by strong plasmon-phonon polaritons coupling

Hyperbolic metasurfaces based on van der Waals (vdW) materials support propagation of extremely anisotropic polaritons towards nanoscale light compression and manipulation, and thus has great potential in the applications of planar hyperlens, nanolasing, quantum optics and ultrasensitive infrared spectroscopy. Two-dimensional hexagonal boron nitride (h-BN) as a vdW metasurface can manipulate the propagation of hyperbolic polaritons at the level of single atomic layers, possessing higher degree of field confinement and lower losses than the conventional media. However, active manipulation of hyperbolic polaritonic waves in h-BN midinfrared metasurfaces remains elusive. Herein, we provide an effective strategy for constructing tunable mid-infrared hyperbolic vdW metasurfaces (HMSs). They are composed of meta-atoms that are the in-plane heterostructures of thin-layer h-BN and monolayer graphene strips (iHBNG). The strong coupling of h-BN phonons and graphene plasmons enables the large tunability of light fields by tailoring chemical potentials of graphene without frequency shift, which involves topological transitions of polaritonic modes, unidirectional polariton propagation and local-density-of-state enhancement. Simulated visual near-field distributions of iHBNG metasurfaces reveal the unique transformations of hyperbolic polariton propagations, distinguished from that of individual h-BN and graphene metasurfaces. Our findings provide a platform of optical nanomanipulation towards emerging on-chip polaritonic devices.

physics.optics↗

arXiv2021

Sparse Steerable Convolutions: An Efficient Learning of SE(3)-Equivariant Features for Estimation and Tracking of Object Poses in 3D Space

As a basic component of SE(3)-equivariant deep feature learning, steerable convolution has recently demonstrated its advantages for 3D semantic analysis. The advantages are, however, brought by expensive computations on dense, volumetric data, which prevent its practical use for efficient processing of 3D data that are inherently sparse. In this paper, we propose a novel design of Sparse Steerable Convolution (SS-Conv) to address the shortcoming; SS-Conv greatly accelerates steerable convolution with sparse tensors, while strictly preserving the property of SE(3)-equivariance. Based on SS-Conv, we propose a general pipeline for precise estimation of object poses, wherein a key design is a Feature-Steering module that takes the full advantage of SE(3)-equivariance and is able to conduct an efficient pose refinement. To verify our designs, we conduct thorough experiments on three tasks of 3D object semantic analysis, including instance-level 6D pose estimation, category-level 6D pose and size estimation, and category-level 6D pose tracking. Our proposed pipeline based on SS-Conv outperforms existing methods on almost all the metrics evaluated by the three tasks. Ablation studies also show the superiority of our SS-Conv over alternative convolutions in terms of both accuracy and efficiency. Our code is released publicly at https://github.com/Gorilla-Lab-SCUT/SS-Conv.

cs.CV↗

arXiv2021

Global Calderón--Zygmund theory for parabolic $p$-Laplacian system: the case $1<p\leq \frac{2n}{n+2}$

The aim of this paper is to establish global Calderón--Zygmund theory to parabolic $p$-Laplacian system: $$ u_t -\operatorname{div}(|\nabla u|^{p-2}\nabla u) = \operatorname{div} (|F|^{p-2}F)~\text{in}~Ω\times (0,T)\subset \mathbb{R}^{n+1}, $$ proving that $$F\in L^q\Rightarrow \nabla u\in L^q,$$ for any $q>\max\{p,\frac{n(2-p)}{2}\}$ and $p>1$. Acerbi and Mingione \cite{Acerbi07} proved this estimate in the case $p>\frac{2n}{n+2}$. In this article we settle the case $1<p\leq \frac{2n}{n+2}$. We also treat systems with discontinuous coefficients having small BMO (bounded mean oscillation) norm.

math.AP↗

arXiv2021

Tensor-structured sketching for constrained least squares

Constrained least squares problems arise in many applications. Their memory and computation costs are expensive in practice involving high-dimensional input data. We employ the so-called "sketching" strategy to project the least squares problem onto a space of a much lower "sketching dimension" via a random sketching matrix. The key idea of sketching is to reduce the dimension of the problem as much as possible while maintaining the approximation accuracy. Tensor structure is often present in the data matrices of least squares, including linearized inverse problems and tensor decompositions. In this work, we utilize a general class of row-wise tensorized sub-Gaussian matrices as sketching matrices in constrained optimizations for the sketching design's compatibility with tensor structures. We provide theoretical guarantees on the sketching dimension in terms of error criterion and probability failure rate. In the context of unconstrained linear regressions, we obtain an optimal estimate for the sketching dimension. For optimization problems with general constraint sets, we show that the sketching dimension depends on a statistical complexity that characterizes the geometry of the underlying problems. Our theories are demonstrated in a few concrete examples, including unconstrained linear regression and sparse recovery problems.

math.OC↗

arXiv2021

Geometry-Aware Self-Training for Unsupervised Domain Adaptationon Object Point Clouds

The point cloud representation of an object can have a large geometric variation in view of inconsistent data acquisition procedure, which thus leads to domain discrepancy due to diverse and uncontrollable shape representation cross datasets. To improve discrimination on unseen distribution of point-based geometries in a practical and feasible perspective, this paper proposes a new method of geometry-aware self-training (GAST) for unsupervised domain adaptation of object point cloud classification. Specifically, this paper aims to learn a domain-shared representation of semantic categories, via two novel self-supervised geometric learning tasks as feature regularization. On one hand, the representation learning is empowered by a linear mixup of point cloud samples with their self-generated rotation labels, to capture a global topological configuration of local geometries. On the other hand, a diverse point distribution across datasets can be normalized with a novel curvature-aware distortion localization. Experiments on the PointDA-10 dataset show that our GAST method can significantly outperform the state-of-the-art methods.

cs.CV↗

arXiv2021

Neural-to-Tree Policy Distillation with Policy Improvement Criterion

While deep reinforcement learning has achieved promising results in challenging decision-making tasks, the main bones of its success --- deep neural networks are mostly black-boxes. A feasible way to gain insight into a black-box model is to distill it into an interpretable model such as a decision tree, which consists of if-then rules and is easy to grasp and be verified. However, the traditional model distillation is usually a supervised learning task under a stationary data distribution assumption, which is violated in reinforcement learning. Therefore, a typical policy distillation that clones model behaviors with even a small error could bring a data distribution shift, resulting in an unsatisfied distilled policy model with low fidelity or low performance. In this paper, we propose to address this issue by changing the distillation objective from behavior cloning to maximizing an advantage evaluation. The novel distillation objective maximizes an approximated cumulative reward and focuses more on disastrous behaviors in critical states, which controls the data shift effect. We evaluate our method on several Gym tasks, a commercial fight game, and a self-driving car simulator. The empirical results show that the proposed method can preserve a higher cumulative reward than behavior cloning and learn a more consistent policy to the original one. Moreover, by examining the extracted rules from the distilled decision trees, we demonstrate that the proposed method delivers reasonable and robust decisions.

cs.LG↗

arXiv2021

GLIB: Towards Automated Test Oracle for Graphically-Rich Applications

Graphically-rich applications such as games are ubiquitous with attractive visual effects of Graphical User Interface (GUI) that offers a bridge between software applications and end-users. However, various types of graphical glitches may arise from such GUI complexity and have become one of the main component of software compatibility issues. Our study on bug reports from game development teams in NetEase Inc. indicates that graphical glitches frequently occur during the GUI rendering and severely degrade the quality of graphically-rich applications such as video games. Existing automated testing techniques for such applications focus mainly on generating various GUI test sequences and check whether the test sequences can cause crashes. These techniques require constant human attention to captures non-crashing bugs such as bugs causing graphical glitches. In this paper, we present the first step in automating the test oracle for detecting non-crashing bugs in graphically-rich applications. Specifically, we propose \texttt{GLIB} based on a code-based data augmentation technique to detect game GUI glitches. We perform an evaluation of \texttt{GLIB} on 20 real-world game apps (with bug reports available) and the result shows that \texttt{GLIB} can achieve 100\% precision and 99.5\% recall in detecting non-crashing bugs such as game GUI glitches. Practical application of \texttt{GLIB} on another 14 real-world games (without bug reports) further demonstrates that \texttt{GLIB} can effectively uncover GUI glitches, with 48 of 53 bugs reported by \texttt{GLIB} having been confirmed and fixed so far.

cs.SE↗

arXiv2021

Near-Room-Temperature Ferromagnetic Behavior of Single-Atom-Thick 2D Iron in Nanolaminated Ternary MAX Phases

Two dimensional (2D) ferromagnetic materials have attracted much attention in the fields of condensed matter physics and materials science, but their synthesis is still a challenge given their limitations on structural stability and susceptibility to oxidization. MAX phases nanolaminated ternary carbides or nitrides possess a unique crystal structure in which single-atom-thick A sublayers are interleaved by two dimensional MX slabs, providing nanostructured templates for designing 2D ferromagnetic materials if the non-magnetic A sublayers can be substituted replaced by magnetic elements. Here, we report three new ternary magnetic MAX phases (Ta2FeC, Ti2FeN and Nb2FeC) with A sublayers of single-atom-thick 2D iron through an isomorphous replacement reaction of MAX precursors (Ta2AlC, Ti2AlN and Nb2AlC) with a Lewis acid salts (FeCl2). All these MAX phases exhibit ferromagnetic (FM) behavior. The Curie temperature (Tc) of Ta2FeC and Nb2FeC MAX phase are 281 K and 291 K, respectively, i.e. close to room temperature. The saturation magnetization of these ternary magnetic MAX phases is almost two orders of magnitude higher than that of V2(Sn,Fe)C MAX phase whose A-site is partial substituted by Fe. Theoretical calculations on magnetic orderings of spin moments of Fe atoms in these nanolaminated magnetic MAX phases reveal that the magnetism can be mainly ascribed to intralayer exchange interaction of the 2D Fe atomic layers. Owning to the richness in composition of MAX phases, there is a large compositional space for constructing functional single-atom-thick 2D layers in materials using these nanolaminated templates.

cond-mat.mtrl-sci↗

arXiv2021

A Generalized Asymmetric Dual-front Model for Active Contours and Image Segmentation

The Voronoi diagram-based dual-front active contour models are known as a powerful and efficient way for addressing the image segmentation and domain partitioning problems. In the basic formulation of the dual-front models, the evolving contours can be considered as the interfaces of adjacent Voronoi regions. Among these dual-front models, a crucial ingredient is regarded as the geodesic metrics by which the geodesic distances and the corresponding Voronoi diagram can be estimated. In this paper, we introduce a type of asymmetric quadratic metrics dual-front model. The metrics considered are built by the integration of the image features and a vector field derived from the evolving contours. The use of the asymmetry enhancement can reduce the risk of contour shortcut or leakage problems especially when the initial contours are far away from the target boundaries or the images have complicated intensity distributions. Moreover, the proposed dual-front model can be applied for image segmentation in conjunction with various region-based homogeneity terms. The numerical experiments on both synthetic and real images show that the proposed dual-front model indeed achieves encouraging results.

cs.CV↗

arXiv2021

Label-Efficient Point Cloud Semantic Segmentation: An Active Learning Approach

Deep learning models are the state-of-the-art methods for semantic point cloud segmentation, the success of which relies on the availability of large-scale annotated datasets. However, it can be extremely time-consuming and prohibitively expensive to compile such datasets. In this work, we propose an active learning approach to maximize model performance given limited annotation budgets. We investigate the appropriate sample granularity for active selection under realistic annotation cost measurement (clicks), and demonstrate that super-point based selection allows for more efficient usage of the limited budget compared to point-level and instance-level selection. We further exploit local consistency constraints to boost the performance of the super-point based approach. We evaluate our methods on two benchmarking datasets (ShapeNet and S3DIS) and the results demonstrate that active learning is an effective strategy to address the high annotation costs in semantic point cloud segmentation.

cs.CV↗

arXiv2021

Towards Uncovering the Intrinsic Data Structures for Unsupervised Domain Adaptation using Structurally Regularized Deep Clustering

Unsupervised domain adaptation (UDA) is to learn classification models that make predictions for unlabeled data on a target domain, given labeled data on a source domain whose distribution diverges from the target one. Mainstream UDA methods strive to learn domain-aligned features such that classifiers trained on the source features can be readily applied to the target ones. Although impressive results have been achieved, these methods have a potential risk of damaging the intrinsic data structures of target discrimination, raising an issue of generalization particularly for UDA tasks in an inductive setting. To address this issue, we are motivated by a UDA assumption of structural similarity across domains, and propose to directly uncover the intrinsic target discrimination via constrained clustering, where we constrain the clustering solutions using structural source regularization that hinges on the very same assumption. Technically, we propose a hybrid model of Structurally Regularized Deep Clustering, which integrates the regularized discriminative clustering of target data with a generative one, and we thus term our method as H-SRDC. Our hybrid model is based on a deep clustering framework that minimizes the Kullback-Leibler divergence between the distribution of network prediction and an auxiliary one, where we impose structural regularization by learning domain-shared classifier and cluster centroids. By enriching the structural similarity assumption, we are able to extend H-SRDC for a pixel-level UDA task of semantic segmentation. We conduct extensive experiments on seven UDA benchmarks of image classification and semantic segmentation. With no explicit feature alignment, our proposed H-SRDC outperforms all the existing methods under both the inductive and transductive settings. We make our implementation codes publicly available at https://github.com/huitangtang/H-SRDC.

cs.CV↗

arXiv2021

3D AffordanceNet: A Benchmark for Visual Object Affordance Understanding

The ability to understand the ways to interact with objects from visual cues, a.k.a. visual affordance, is essential to vision-guided robotic research. This involves categorizing, segmenting and reasoning of visual affordance. Relevant studies in 2D and 2.5D image domains have been made previously, however, a truly functional understanding of object affordance requires learning and prediction in the 3D physical domain, which is still absent in the community. In this work, we present a 3D AffordanceNet dataset, a benchmark of 23k shapes from 23 semantic object categories, annotated with 18 visual affordance categories. Based on this dataset, we provide three benchmarking tasks for evaluating visual affordance understanding, including full-shape, partial-view and rotation-invariant affordance estimations. Three state-of-the-art point cloud deep learning networks are evaluated on all tasks. In addition we also investigate a semi-supervised learning setup to explore the possibility to benefit from unlabeled data. Comprehensive results on our contributed dataset show the promise of visual affordance understanding as a valuable yet challenging benchmark.

cs.CV↗

arXiv2021

W-PoseNet: Dense Correspondence Regularized Pixel Pair Pose Regression

Solving 6D pose estimation is non-trivial to cope with intrinsic appearance and shape variation and severe inter-object occlusion, and is made more challenging in light of extrinsic large illumination changes and low quality of the acquired data under an uncontrolled environment. This paper introduces a novel pose estimation algorithm W-PoseNet, which densely regresses from input data to 6D pose and also 3D coordinates in model space. In other words, local features learned for pose regression in our deep network are regularized by explicitly learning pixel-wise correspondence mapping onto 3D pose-sensitive coordinates as an auxiliary task. Moreover, a sparse pair combination of pixel-wise features and soft voting on pixel-pair pose predictions are designed to improve robustness to inconsistent and sparse local features. Experiment results on the popular YCB-Video and LineMOD benchmarks show that the proposed W-PoseNet consistently achieves superior performance to the state-of-the-art algorithms.

cs.CV↗

arXiv2021

MAX Phase Zr2SeC and Its Thermal Conduction Behavior

The elemental diversity is crucial to screen out ternary MAX phases with outstanding properties via tuning of bonding types and strength between constitutive atoms. As a matter of fact, the interactions between M and A atoms largely determine the physical and chemical properties of MAX phases. Herein, Se element was experimentally realized to occupy the A site of a MAX phase, Zr2SeC, becoming a new member within this nanolaminated ternary carbide family. Comprehensive characterizations including Rietveld refinement of X-ray Diffraction and atom-resolved transmission electron microscopy techniques were employed to validate this novel MAX phase. The distinct thermal conduction behaviors emerged are attributed to the characteristic interactions between Zr and Se atoms.

cond-mat.mtrl-sci↗

arXiv2021

Reduced Ionic Diffusion by the Dynamic Electron-Ion Collisions in Warm Dense Hydrogen

The dynamic electron-ion collisions play an important rolein determining the static and transport properties of warmdense matter (WDM). Electron force field (eFF) method is applied to study the ionic transport properties of warm densehydrogen. Compared with the results from quantum moleculardynamics and orbital-free molecular dynamics, the ionicdiffusions are largely reduced by involving the dynamic collisions of electrons and ions. This physics is verified by quantum Langevin molecular dynamics (QLMD) simulations, which includes electron-ion collisions induced friction(EI-CIF) into the dynamic equation of ions. Based on these new results, we proposed a model including the correctionof collisions induced friction of the ionic diffusion. The CIF model has been verified to be valid at a wide range ofdensity and temperature. We also compare the results with the Yukawa one component plasma (YOCP) model andEffective OCP (EOCP) model. We proposed to calculate the self-diffusion coefficients using the EOCP model modifiedby the CIF model to introduce the dynamic electron-ion collisions effect.

physics.comp-ph↗