arXiv subjects

Wenbin Li

Publications and source records attributed to Wenbin Li.

At least 163 records · Page 9Linked to original sources

arXiv2017

Strain-Induced Band Engineering in Monolayer Stanene on Sb(111)

Two-dimensional (2D) allotrope of tin with low buckled honeycomb structure, named as stanene, is proposed to be an ideal 2D topological insulator with a nontrivial gap larger than 0.1 eV. Theoretical works also pointed out the topological property of stanene occurs by strain tuning. In this letter, we report the successful realization of high quality, monolayer stanene film as well as monolayer stanene nanoribbons on Sb(111) surface by molecular beam epitaxy, providing an ideal platform to the study of stanene. More importantly, we observed a continuous evolution of the electronic bands of stanene across a nanoribbon, which are related to the strain field gradient in stanene. Our work experimentally confirmed that strain is an effective method for band engineering in stanene, which is important for fundamental research and application of stanene.

cond-mat.mtrl-sci↗

arXiv2017

Learn to Model Motion from Blurry Footages

It is difficult to recover the motion field from a real-world footage given a mixture of camera shake and other photometric effects. In this paper we propose a hybrid framework by interleaving a Convolutional Neural Network (CNN) and a traditional optical flow energy. We first conduct a CNN architecture using a novel learnable directional filtering layer. Such layer encodes the angle and distance similarity matrix between blur and camera motion, which is able to enhance the blur features of the camera-shake footages. The proposed CNNs are then integrated into an iterative optical flow framework, which enable the capability of modelling and solving both the blind deconvolution and the optical flow estimation problems simultaneously. Our framework is trained end-to-end on a synthetic dataset and yields competitive precision and performance against the state-of-the-art approaches.

cs.CV↗

arXiv2017

Synthesis of Borophene Nanoribbons on Ag(110) Surface

We present the successful synthesis of single-atom-thick borophene nanoribbons (BNRs) by self-assembly of boron on Ag(110) surface. The scanning tunneling microscopy (STM) studies reveal high quality BNRs: all the ribbons are along the [-110] direction of Ag(110), and can run across the steps on the surface. The width of ribbons is distributed in a narrow range around 10.3 nm. High resolution STM images revealed four ordered surface structures in BNRs. Combined with DFT calculations, we found that all the four structures of boron nanoribbons consist of the boron chains with different width, separated by hexagonal hole arrays. The successful synthesis of BNRs enriches the low dimensional allotrope of boron and may promote further applications of borophene

cond-mat.mtrl-sci↗

arXiv2016

OPML: A One-Pass Closed-Form Solution for Online Metric Learning

To achieve a low computational cost when performing online metric learning for large-scale data, we present a one-pass closed-form solution namely OPML in this paper. Typically, the proposed OPML first adopts a one-pass triplet construction strategy, which aims to use only a very small number of triplets to approximate the representation ability of whole original triplets obtained by batch-manner methods. Then, OPML employs a closed-form solution to update the metric for new coming samples, which leads to a low space (i.e., $O(d)$) and time (i.e., $O(d^2)$) complexity, where $d$ is the feature dimensionality. In addition, an extension of OPML (namely COPML) is further proposed to enhance the robustness when in real case the first several samples come from the same class (i.e., cold start problem). In the experiments, we have systematically evaluated our methods (OPML and COPML) on three typical tasks, including UCI data classification, face verification, and abnormal event detection in videos, which aims to fully evaluate the proposed methods on different sample number, different feature dimensionalities and different feature extraction ways (i.e., hand-crafted and deeply-learned). The results show that OPML and COPML can obtain the promising performance with a very low computational cost. Also, the effectiveness of COPML under the cold start setting is experimentally verified.

cs.LG↗

arXiv2016

Visual Stability Prediction and Its Application to Manipulation

Understanding physical phenomena is a key competence that enables humans and animals to act and interact under uncertain perception in previously unseen environments containing novel objects and their configurations. Developmental psychology has shown that such skills are acquired by infants from observations at a very early stage. In this paper, we contrast a more traditional approach of taking a model-based route with explicit 3D representations and physical simulation by an {\em end-to-end} approach that directly predicts stability from appearance. We ask the question if and to what extent and quality such a skill can directly be acquired in a data-driven way---bypassing the need for an explicit simulation at run-time. We present a learning-based approach based on simulated data that predicts stability of towers comprised of wooden blocks under different conditions and quantities related to the potential fall of the towers. We first evaluate the approach on synthetic data and compared the results to human judgments on the same stimuli. Further, we extend this approach to reason about future states of such towers that in turn enables successful stacking.

cs.CV↗

arXiv2016

Dense Motion Estimation for Smoke

Motion estimation for highly dynamic phenomena such as smoke is an open challenge for Computer Vision. Traditional dense motion estimation algorithms have difficulties with non-rigid and large motions, both of which are frequently observed in smoke motion. We propose an algorithm for dense motion estimation of smoke. Our algorithm is robust, fast, and has better performance over different types of smoke compared to other dense motion estimation algorithms, including state of the art and neural network approaches. The key to our contribution is to use skeletal flow, without explicit point matching, to provide a sparse flow. This sparse flow is upgraded to a dense flow. In this paper we describe our algorithm in greater detail, and provide experimental evidence to support our claims.

cs.CV↗

arXiv2016

Nonrigid Optical Flow Ground Truth for Real-World Scenes with Time-Varying Shading Effects

In this paper we present a dense ground truth dataset of nonrigidly deforming real-world scenes. Our dataset contains both long and short video sequences, and enables the quantitatively evaluation for RGB based tracking and registration methods. To construct ground truth for the RGB sequences, we simultaneously capture Near-Infrared (NIR) image sequences where dense markers - visible only in NIR - represent ground truth positions. This allows for comparison with automatically tracked RGB positions and the formation of error metrics. Most previous datasets containing nonrigidly deforming sequences are based on synthetic data. Our capture protocol enables us to acquire real-world deforming objects with realistic photometric effects - such as blur and illumination change - as well as occlusion and complex deformations. A public evaluation website is constructed to allow for ranking of RGB image based optical flow and other dense tracking algorithms, with various statistical measures. Furthermore, we present an RGB-NIR multispectral optical flow model allowing for energy optimization by adoptively combining featured information from both the RGB and the complementary NIR channels. In our experiments we evaluate eight existing RGB based optical flow methods on our new dataset. We also evaluate our hybrid optical flow algorithm by comparing to two existing multispectral approaches, as well as varying our input channels across RGB, NIR and RGB-NIR.

cs.CV↗

arXiv2016

To Fall Or Not To Fall: A Visual Approach to Physical Stability Prediction

Understanding physical phenomena is a key competence that enables humans and animals to act and interact under uncertain perception in previously unseen environments containing novel object and their configurations. Developmental psychology has shown that such skills are acquired by infants from observations at a very early stage. In this paper, we contrast a more traditional approach of taking a model-based route with explicit 3D representations and physical simulation by an end-to-end approach that directly predicts stability and related quantities from appearance. We ask the question if and to what extent and quality such a skill can directly be acquired in a data-driven way bypassing the need for an explicit simulation. We present a learning-based approach based on simulated data that predicts stability of towers comprised of wooden blocks under different conditions and quantities related to the potential fall of the towers. The evaluation is carried out on synthetic data and compared to human judgments on the same stimuli.

cs.CV↗

arXiv2016

Towards the Design of Effective Freehand Gestural Interaction for Interactive TV

As interactive devices become pervasive, people are beginning to looking for more advanced interaction with televisions in the living room. Interactive television has the potential to offer a very engaging experience. But most common user tasks are still challenging with such systems, such as menu selection or text input. And little work has been done on understanding and sup-porting the effective design of freehand interaction with an TV in the living room. In this paper, we perform two studies investi-gating freehand gestural interaction with a consumer level sensor, which is suitable for TV scenarios. In the first study, we inves-tigate a range of design factors for tiled layout menu selection, including wearable feedback, push gesture depth, target size and position in motor space. The results show that tactile and audio feedback have no significant effect on performance and prefer-ence, and these results inform potential designs for high selection performance. In the second study, we investigate a common TV user task of text input using freehand gesture. We design and evaluate two virtual keyboard layouts and three freehand selec-tion methods. Results show that ease of use and error tolerance can be both achieved using a text entry method utilizing a dual circle layout and an expanding target selection technique. Finally, we propose design guidelines for effective, usable and com-fortable freehand gestural interaction for interactive TV based on the findings.

cs.HC↗

arXiv2016

Video Interpolation using Optical Flow and Laplacian Smoothness

Non-rigid video interpolation is a common computer vision task. In this paper we present an optical flow approach which adopts a Laplacian Cotangent Mesh constraint to enhance the local smoothness. Similar to Li et al., our approach adopts a mesh to the image with a resolution up to one vertex per pixel and uses angle constraints to ensure sensible local deformations between image pairs. The Laplacian Mesh constraints are expressed wholly inside the optical flow optimization, and can be applied in a straightforward manner to a wide range of image tracking and registration problems. We evaluate our approach by testing on several benchmark datasets, including the Middlebury and Garg et al. datasets. In addition, we show application of our method for constructing 3D Morphable Facial Models from dynamic 3D data.

cs.CV↗

arXiv2016

Drift Robust Non-rigid Optical Flow Enhancement for Long Sequences

It is hard to densely track a nonrigid object in long term, which is a fundamental research issue in the computer vision community. This task often relies on estimating pairwise correspondences between images over time where the error is accumulated and leads to a drift issue. In this paper, we introduce a novel optimization framework with an Anchor Patch constraint. It is supposed to significantly reduce overall errors given long sequences containing non-rigidly deformable objects. Our framework can be applied to any dense tracking algorithm, e.g. optical flow. We demonstrate the success of our approach by showing significant error reduction on 6 popular optical flow algorithms applied to a range of real-world nonrigid benchmarks. We also provide quantitative analysis of our approach given synthetic occlusions and image noise.

cs.CV↗

arXiv2016

Blur Robust Optical Flow using Motion Channel

It is hard to estimate optical flow given a realworld video sequence with camera shake and other motion blur. In this paper, we first investigate the blur parameterization for video footage using near linear motion elements. we then combine a commercial 3D pose sensor with an RGB camera, in order to film video footage of interest together with the camera motion. We illustrates that this additional camera motion/trajectory channel can be embedded into a hybrid framework by interleaving an iterative blind deconvolution and warping based optical flow scheme. Our method yields improved accuracy within three other state-of-the-art baselines given our proposed ground truth blurry sequences; and several other realworld sequences filmed by our imaging system.

cs.CV↗

arXiv2015

Experimental Realization of Two-Dimensional Boron Sheets

Boron is the fifth element in the periodic table and possesses rich chemistry second only to carbon. A striking feature of boron is that B12 icosahedral cages occur as the building blocks in bulk boron and many boron compounds. This is in contrast to its neighboring element, carbon, which prefers 2D layered structure (graphite) in its bulk form. On the other hand, boron clusters of medium size have been predicted to be planar or quasi-planar, such as B12+ , B13+, B19-, B36, and so on. This is also in contrast to carbon clusters which exhibit various cage structures (fullerenes). Therefore, boron and carbon can be viewed as a set of complementary chemical systems in their bulk and cluster structures. Now, with the boom of graphene, an intriguing question is that whether boron can also form a monoatomic-layer 2D sheet structure? Here, we report the first successful experimental realization of 2D boron sheets. We have revealed two types of boron sheet structures, corresponding to a triangular boron lattice with different arrangements of the hexagonal holes. Moreover, our boron sheets were found to be relatively stable against oxidization, and interacts only weekly with the substrate. The realization of such a long expected 2D boron sheet could open a door toward boron electronics, in analogous to the carbon electronics based on graphene.

cond-mat.mes-hall↗

arXiv2015

Giant Piezoelectricity in Monolayer Group IV Monochalcogenides: SnSe, SnS, GeSe and GeS

We predict enormous piezoelectric effects in intrinsic monolayer group IV monochalcogenides (MX, M=Sn or Ge, X=Se or S), including SnSe, SnS, GeSe and GeS. Using first-principle simulations based on the modern theory of polarization, we find that their characteristic piezoelectric coefficients are about two orders of magnitude larger than those of other 2D materials, such as MoS2 and GaSe, and bulk quartz and AlN which are widely used in industry. This enhancement is a result of the unique "puckered" C2v symmetry and weaker chemical bonds of monolayer group IV monochalcogenides. Given the achieved experimental advances in fabrication of monolayers, their flexible character and ability to withstand enormous strain, these 2D structures with giant piezoelectric effects may be promising for a broad range of applications, such as nano-sized sensors, piezotronics, and energy harvesting in portable electronic devices.

cond-mat.mtrl-sci↗

arXiv2015

Piezoelectricity in Two-Dimensional Group III Monochalcogenides

We find that several layer-phase group-III monochalcogenides, including GaS, GaSe and InSe, are piezoelectric in the monolayer form. First-principles calculations reveal that the piezoelectric coefficients of monolayer GaS, GaSe and InSe are on the same order of magnitude as the earlier discovered two-dimensional piezoelectric materials, such as BN and MoS2 monolayers. Our study expands the family of two dimensional piezoelectric materials, suggesting that strong piezoelectric response can occur in a wide range of two dimensional materials with broken inversion symmetry. The co-existence of piezoelectricity and superior photo-sensitivity in these two-dimensional semiconductors enables the integration of electromechanical and optical sensors on the same material platform.

cond-mat.mtrl-sci↗

arXiv2015

Variable Coupling Strength of Silicene on Ag(111)

We performed a scanning tunneling microscopy and spectroscopy (STM/STS) study on the electronic structures of of root(3)Xroot(3)-silicene on Ag(111). We find that the coupling strength of root(3)Xroot(3)-silicene with Ag(111) substrate is variable at different regions, giving rise to notable effects in experiments. These evidences of decoupling or variable interaction of silicene with the substrate are helpful to in-depth understanding of the structure and electronic properties of silicene.

cond-mat.mtrl-sci↗

arXiv2015

Deformation-Driven Diffusion and Plastic Flow in Two-Dimensional Amorphous Granular Pillars

We report a combined experimental and simulation study of deformation-induced diffusion in compacted two-dimensional amorphous granular pillars, in which thermal fluctuations play negligible role. The pillars, consisting of bidisperse cylindrical acetal plastic particles standing upright on a substrate, are deformed uniaxially and quasistatically by a rigid bar moving at a constant speed. The plastic flow and particle rearrangements in the pillars are characterized by computing the best-fit affine transformation strain and non-affine displacement associated with each particle between two stages of deformation. The non-affine displacement exhibits exponential crossover from ballistic to diffusive behavior with respect to the cumulative deviatoric strain, indicating that in athermal granular packings, the cumulative deviatoric strain plays the role of time in thermal systems and drives effective particle diffusion. We further study the size-dependent deformation of the granular pillars by simulation, and find that different-sized pillars follow self-similar shape evolution during deformation. In addition, the yield stress of the pillars increases linearly with pillar size. Formation of transient shear lines in the pillars during deformation becomes more evident as pillar size increases. The width of these elementary shear bands is about twice the diameter of a particle, and does not vary with pillar size.

cond-mat.soft↗

arXiv2014

Envelope function method for electrons in slowly-varying inhomogeneously deformed crystals

We develop a new envelope-function formalism to describe electrons in slowly-varying inhomogeneously strained semiconductor crystals. A coordinate transformation is used to map a deformed crystal back to geometrically undeformed structure with deformed crystal potential. The single-particle Schrödinger equation is solved in the undeformed coordinates using envelope function expansion, wherein electronic wavefunctions are written in terms of strain-parametrized Bloch functions modulated by slowly varying envelope functions. Adopting local approximation of electronic structure, the unknown crystal potential in Schrödinger equation can be replaced by the strain-parametrized Bloch functions and the associated strain-parametrized energy eigenvalues, which can be constructed from unit-cell level ab initio or semi-empirical calculations of homogeneously deformed crystals at a chosen crystal momentum. The Schrödinger equation is then transformed into a coupled differential equation for the envelope functions and solved as a generalized matrix eigenvector problem. As the envelope functions are slowly varying, coarse spatial or Fourier grid can be used to represent the envelope functions, enabling the method to treat relatively large systems. We demonstrate the effectiveness of this method using a one-dimensional model, where we show that the method can achieve high accuracy in the calculation of energy eigenstates with relatively low cost compared to direct diagonalization of Hamiltonian. We further derive envelope function equations that allow the method to be used empirically, in which case certain parameters in the envelope function equations will be fitted to experimental data.

cond-mat.mtrl-sci↗