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Rui Huang

Rui Huang appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

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Computer Visioncond-mat.mes-hallcond-mat.mtrl-sciMachine Learningmath.APastro-ph.GAastro-ph.HEcond-mat.stat-mecheess.IVApplicationsArtificial Intelligenceastro-ph.IMcond-mat.softquant-ph

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Published work

35 published item(s)

preprint2026arXiv

Beyond Fixed Formulas: Data-Driven Linear Predictor for Efficient Diffusion Models

To address the high sampling cost of Diffusion Transformers (DiTs), feature caching offers a training-free acceleration method. However, existing methods rely on hand-crafted forecasting formulas that fail under aggressive skipping. We propose L2P (Learnable Linear Predictor), a simple data-driven caching framework that replaces fixed coefficients with learnable per-timestep weights. Rapidly trained in ~20 seconds on a single GPU, L2P accurately reconstructs current features from past trajectories. L2P significantly outperforms existing baselines: it achieves a 4.55x FLOPs reduction and 4.15x latency speedup on FLUX.1-dev, and maintains high visual fidelity under up to 7.18x acceleration on Qwen-Image models, where prior methods show noticeable quality degradation. Our results show learning linear predictors is highly effective for efficient DiT inference. Code is available at https://github.com/Aredstone/L2P-Cache.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Ray-Aware Pointer Memory with Adaptive Updates for Streaming 3D Reconstruction

Dense 3D reconstruction from continuous image streams requires both accurate geometric aggregation and stable long-term memory management. Recent feed-forward reconstruction frameworks integrate observations through persistent memory representations, yet most rely primarily on appearance-based similarity when updating memory. Such appearance-driven integration often leads to redundant accumulation of observations and unstable geometry when viewpoint changes occur. In this work, we propose a ray-aware pointer memory for streaming 3D reconstruction that explicitly models both spatial location and viewing direction within a unified memory representation. Each memory pointer stores its 3D position, associated ray direction, and feature embedding, allowing the system to reason jointly about geometric proximity and viewpoint consistency. Based on this representation, we introduce an adaptive pointer update strategy that replaces traditional fusion-based memory compression with a retain-or-replace mechanism. Instead of averaging nearby observations, the system selectively retains informative pointers while discarding redundant ones, preserving distinctive geometric structures while maintaining bounded memory growth. Furthermore, the joint reasoning over spatial distance and ray-direction discrepancy enables the system to distinguish between local redundancy, novel observations, and potential loop revisits in a unified manner. When loop candidates are detected, pose refinement is triggered to enforce global geometric consistency across the reconstruction. Extensive experiments demonstrate that the proposed ray-aware memory design significantly improves long-term reconstruction stability and camera pose accuracy while maintaining efficient streaming inference. Our approach provides a principled framework for scalable and drift-resistant online 3D reconstruction from image streams.

0 citations0 reviewsNo code linkedOpen access
preprint2024arXiv

Spy-Watermark: Robust Invisible Watermarking for Backdoor Attack

Backdoor attack aims to deceive a victim model when facing backdoor instances while maintaining its performance on benign data. Current methods use manual patterns or special perturbations as triggers, while they often overlook the robustness against data corruption, making backdoor attacks easy to defend in practice. To address this issue, we propose a novel backdoor attack method named Spy-Watermark, which remains effective when facing data collapse and backdoor defense. Therein, we introduce a learnable watermark embedded in the latent domain of images, serving as the trigger. Then, we search for a watermark that can withstand collapse during image decoding, cooperating with several anti-collapse operations to further enhance the resilience of our trigger against data corruption. Extensive experiments are conducted on CIFAR10, GTSRB, and ImageNet datasets, demonstrating that Spy-Watermark overtakes ten state-of-the-art methods in terms of robustness and stealthiness.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Joint Representation Learning for Text and 3D Point Cloud

Recent advancements in vision-language pre-training (e.g. CLIP) have shown that vision models can benefit from language supervision. While many models using language modality have achieved great success on 2D vision tasks, the joint representation learning of 3D point cloud with text remains under-explored due to the difficulty of 3D-Text data pair acquisition and the irregularity of 3D data structure. In this paper, we propose a novel Text4Point framework to construct language-guided 3D point cloud models. The key idea is utilizing 2D images as a bridge to connect the point cloud and the language modalities. The proposed Text4Point follows the pre-training and fine-tuning paradigm. During the pre-training stage, we establish the correspondence of images and point clouds based on the readily available RGB-D data and use contrastive learning to align the image and point cloud representations. Together with the well-aligned image and text features achieved by CLIP, the point cloud features are implicitly aligned with the text embeddings. Further, we propose a Text Querying Module to integrate language information into 3D representation learning by querying text embeddings with point cloud features. For fine-tuning, the model learns task-specific 3D representations under informative language guidance from the label set without 2D images. Extensive experiments demonstrate that our model shows consistent improvement on various downstream tasks, such as point cloud semantic segmentation, instance segmentation, and object detection. The code will be available here: https://github.com/LeapLabTHU/Text4Point

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Deep Semantic Statistics Matching (D2SM) Denoising Network

The ultimate aim of image restoration like denoising is to find an exact correlation between the noisy and clear image domains. But the optimization of end-to-end denoising learning like pixel-wise losses is performed in a sample-to-sample manner, which ignores the intrinsic correlation of images, especially semantics. In this paper, we introduce the Deep Semantic Statistics Matching (D2SM) Denoising Network. It exploits semantic features of pretrained classification networks, then it implicitly matches the probabilistic distribution of clear images at the semantic feature space. By learning to preserve the semantic distribution of denoised images, we empirically find our method significantly improves the denoising capabilities of networks, and the denoised results can be better understood by high-level vision tasks. Comprehensive experiments conducted on the noisy Cityscapes dataset demonstrate the superiority of our method on both the denoising performance and semantic segmentation accuracy. Moreover, the performance improvement observed on our extended tasks including super-resolution and dehazing experiments shows its potentiality as a new general plug-and-play component.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Domain Adaptation via Prompt Learning

Unsupervised domain adaption (UDA) aims to adapt models learned from a well-annotated source domain to a target domain, where only unlabeled samples are given. Current UDA approaches learn domain-invariant features by aligning source and target feature spaces. Such alignments are imposed by constraints such as statistical discrepancy minimization or adversarial training. However, these constraints could lead to the distortion of semantic feature structures and loss of class discriminability. In this paper, we introduce a novel prompt learning paradigm for UDA, named Domain Adaptation via Prompt Learning (DAPL). In contrast to prior works, our approach makes use of pre-trained vision-language models and optimizes only very few parameters. The main idea is to embed domain information into prompts, a form of representations generated from natural language, which is then used to perform classification. This domain information is shared only by images from the same domain, thereby dynamically adapting the classifier according to each domain. By adopting this paradigm, we show that our model not only outperforms previous methods on several cross-domain benchmarks but also is very efficient to train and easy to implement.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Fully Attentional Network for Semantic Segmentation

Recent non-local self-attention methods have proven to be effective in capturing long-range dependencies for semantic segmentation. These methods usually form a similarity map of RC*C (by compressing spatial dimensions) or RHW*HW (by compressing channels) to describe the feature relations along either channel or spatial dimensions, where C is the number of channels, H and W are the spatial dimensions of the input feature map. However, such practices tend to condense feature dependencies along the other dimensions,hence causing attention missing, which might lead to inferior results for small/thin categories or inconsistent segmentation inside large objects. To address this problem, we propose anew approach, namely Fully Attentional Network (FLANet),to encode both spatial and channel attentions in a single similarity map while maintaining high computational efficiency. Specifically, for each channel map, our FLANet can harvest feature responses from all other channel maps, and the associated spatial positions as well, through a novel fully attentional module. Our new method has achieved state-of-the-art performance on three challenging semantic segmentation datasets,i.e., 83.6%, 46.99%, and 88.5% on the Cityscapes test set,the ADE20K validation set, and the PASCAL VOC test set,respectively.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Interlayer Coupling and Strain Localization in Small-Twist-Angle Graphene Flakes

Twisted bilayer graphene (TBG) exhibits a wide range of intriguing physical properties, such as superconductivity, ferromagnetism, and superlubricity. Depending on the twist angle, periodic moiré superlattices form in twisted bilayer graphene, with inhomogeneous interlayer coupling and lattice deformation. For a small twist angle (typically <2°), each moiré supercell contains a large number of atoms (>10,000), making it computationally expensive for first-principles and atomistic modeling. In this work, a finite element method based on a continuum model is used to simulate the inhomogeneous interlayer and intralayer deformations of twisted graphene flakes on a rigid graphene substrate. The van der Waals interactions between the graphene layers are described by a periodic potential energy function, whereas the graphene flake is treated as a continuum membrane with effective elastic properties. Our simulations show that structural relaxation and the induced strain localization are most significant in a relatively large graphene flake at small twist angles, where the strain distribution is highly localized as shear strain solitons along the boundaries between neighboring domains of commensurate AB stacking. Moreover, it is found that there exist many metastable equilibrium configurations at particular twist angles, depending on the flake size. The nonlinear mechanics of twisted bilayer graphene is thus expected to be essential for understanding the strain distributions in the moiré superlattices and the strain effects on other physical properties.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

RenderNet: Visual Relocalization Using Virtual Viewpoints in Large-Scale Indoor Environments

Visual relocalization has been a widely discussed problem in 3D vision: given a pre-constructed 3D visual map, the 6 DoF (Degrees-of-Freedom) pose of a query image is estimated. Relocalization in large-scale indoor environments enables attractive applications such as augmented reality and robot navigation. However, appearance changes fast in such environments when the camera moves, which is challenging for the relocalization system. To address this problem, we propose a virtual view synthesis-based approach, RenderNet, to enrich the database and refine poses regarding this particular scenario. Instead of rendering real images which requires high-quality 3D models, we opt to directly render the needed global and local features of virtual viewpoints and apply them in the subsequent image retrieval and feature matching operations respectively. The proposed method can largely improve the performance in large-scale indoor environments, e.g., achieving an improvement of 7.1\% and 12.2\% on the Inloc dataset.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Automatic Segmentation of Organs-at-Risk from Head-and-Neck CT using Separable Convolutional Neural Network with Hard-Region-Weighted Loss

Nasopharyngeal Carcinoma (NPC) is a leading form of Head-and-Neck (HAN) cancer in the Arctic, China, Southeast Asia, and the Middle East/North Africa. Accurate segmentation of Organs-at-Risk (OAR) from Computed Tomography (CT) images with uncertainty information is critical for effective planning of radiation therapy for NPC treatment. Despite the stateof-the-art performance achieved by Convolutional Neural Networks (CNNs) for automatic segmentation of OARs, existing methods do not provide uncertainty estimation of the segmentation results for treatment planning, and their accuracy is still limited by several factors, including the low contrast of soft tissues in CT, highly imbalanced sizes of OARs and large inter-slice spacing. To address these problems, we propose a novel framework for accurate OAR segmentation with reliable uncertainty estimation. First, we propose a Segmental Linear Function (SLF) to transform the intensity of CT images to make multiple organs more distinguishable than existing methods based on a simple window width/level that often gives a better visibility of one organ while hiding the others. Second, to deal with the large inter-slice spacing, we introduce a novel 2.5D network (named as 3D-SepNet) specially designed for dealing with clinic HAN CT scans with anisotropic spacing. Thirdly, existing hardness-aware loss function often deal with class-level hardness, but our proposed attention to hard voxels (ATH) uses a voxel-level hardness strategy, which is more suitable to dealing with some hard regions despite that its corresponding class may be easy. Our code is now available at https://github.com/HiLab-git/SepNet.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

An LSTM Approach to Temporal 3D Object Detection in LiDAR Point Clouds

Detecting objects in 3D LiDAR data is a core technology for autonomous driving and other robotics applications. Although LiDAR data is acquired over time, most of the 3D object detection algorithms propose object bounding boxes independently for each frame and neglect the useful information available in the temporal domain. To address this problem, in this paper we propose a sparse LSTM-based multi-frame 3d object detection algorithm. We use a U-Net style 3D sparse convolution network to extract features for each frame's LiDAR point-cloud. These features are fed to the LSTM module together with the hidden and memory features from last frame to predict the 3d objects in the current frame as well as hidden and memory features that are passed to the next frame. Experiments on the Waymo Open Dataset show that our algorithm outperforms the traditional frame by frame approach by 7.5% mAP@0.7 and other multi-frame approaches by 1.2% while using less memory and computation per frame. To the best of our knowledge, this is the first work to use an LSTM for 3D object detection in sparse point clouds.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

An X-ray and SZ bright diffuse source toward M31: a Local Hot Bridge

We report a large-scale ($r\approx 20^\circ$) X-ray and Sunyaev-Zeldovich (SZ)-bright diffuse enhancement toward M31, which might be a Local Hot Bridge connecting the Milky Way (MW) with M31. We subtract the Galactic emission from the all-sky O VII and O VIII emission line measurement survey, and find that the emission of these two ions is enhanced within $r\approx20^\circ$ around M31. The mean emission enhancements are $5.6\pm 1.3$ L.U., and $2.8\pm0.6$ L.U. for O VII and O VIII, respectively ($>4σ$ for both ions). We also extract the SZ signal around M31, which suggests a surface brightness $y$ of $2-4\times10^{-7}$, an enhancement $>2.5σ$ (and a best fit of $5.9σ$). These three measurements trace the hot gas with a temperature $\log~T({\rm K})> 6$, showing similar plateau shapes (flat within $\approx15^\circ$, and zero beyond $\approx30^\circ$). A single-phase assumption leads to a temperature of $\log~T({\rm K})=6.34\pm0.03$, which is determined by the O VII/O VIII line ratio. Combining X-ray and SZ measurements, we suggest that this feature is unlikely to be the hot halo around M31 (too massive) or in the MW (too high pressure and X-ray bright). The plateau shape may be explained by a cylinder connecting the MW and M31 (the Local Hot Bridge). We constrain its length to be about 400 kpc, with a radius of 120 kpc, a density of $\approx 2\times10^{-4}-10^{-3} ~\rm cm^{-3}$, and a metallicity of $0.02-0.1~ Z_\odot$. The baryon mass is $\gtrsim10^{11}~M_\odot$, and the oxygen mass is about $\gtrsim10^8~M_\odot$, which contribute to the baryon or metal budget of the Local Group.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Background Model for the High-Energy Telescope of Insight-HXMT

Accurate background estimation is essential for spectral and temporal analysis in astrophysics. In this work, we construct the in-orbit background model for the High-Energy Telescope (HE) of the Hard X-ray Modulation Telescope (dubbed as Insight-HXMT). Based on the two-year blank sky observations of Insight-HXMT/HE, we first investigate the basic properties of the background and find that both the background spectral shape and intensity have long-term evolution at different geographical sites. The entire earth globe is then divided into small grids, each with a typical area of 5x5 square degrees in geographical coordinate system. For each grid, an empirical function is used to describe the long-term evolution of each channel of the background spectrum; the intensity of the background can be variable and a modification factor is introduced to account for this variability by measuring the contemporary flux of the blind detector. For a given pointing observation, the background model is accomplished by integrating over the grids that are passed by the track of the satellite in each orbit. Such a background model is tested with both the blank sky observations and campaigns for observations of a series of celestial sources. The results show an average systematic error of 1.5% for the background energy spectrum (26-100 keV) under a typical exposure of 8 ks, and <3% for background light curve estimation (30-150 keV). Therefore, the background model introduced in this paper is included in the Insight-HXMT software as a standard part specialized for both spectral and temporal analyses.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Disentangle Perceptual Learning through Online Contrastive Learning

Pursuing realistic results according to human visual perception is the central concern in the image transformation tasks. Perceptual learning approaches like perceptual loss are empirically powerful for such tasks but they usually rely on the pre-trained classification network to provide features, which are not necessarily optimal in terms of visual perception of image transformation. In this paper, we argue that, among the features representation from the pre-trained classification network, only limited dimensions are related to human visual perception, while others are irrelevant, although both will affect the final image transformation results. Under such an assumption, we try to disentangle the perception-relevant dimensions from the representation through our proposed online contrastive learning. The resulted network includes the pre-training part and a feature selection layer, followed by the contrastive learning module, which utilizes the transformed results, target images, and task-oriented distorted images as the positive, negative, and anchor samples, respectively. The contrastive learning aims at activating the perception-relevant dimensions and suppressing the irrelevant ones by using the triplet loss, so that the original representation can be disentangled for better perceptual quality. Experiments on various image transformation tasks demonstrate the superiority of our framework, in terms of human visual perception, to the existing approaches using pre-trained networks and empirically designed losses.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Errata on the Calculation of Hot Gas Properties in a Few Li Jiang-Tao's Papers

This is a combination of the errata of seven papers published between 2008 and 2016 with Jiang-Tao Li (JTL) as the first author. All the problems are caused by two mistakes in the original scripts written by JTL used to calculate the physical parameters of the hot gas from X-ray spectral analysis with a thermal plasma code. The mistakes will result in an overestimate of some parameters, such as the electron number density and hot gas mass by a factor of $\sqrt{10}\approx3.162$, and an overestimate of the thermal pressure by a factor of $\approx2.725$. JTL apologizes to the community for the inconvenience caused by these mistakes. We present an update on the text, numbers, figures, and tables of all the seven papers affected by these mistakes. Other papers led by JTL or co-authored papers are not affected.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Global Optimum Search in Quantum Deep Learning

This paper aims to solve machine learning optimization problem by using quantum circuit. Two approaches, namely the average approach and the Partial Swap Test Cut-off method (PSTC) was proposed to search for the global minimum/maximum of two different objective functions. The current cost is $O(\sqrt{|Θ|} N)$, but there is potential to improve PSTC further to $O(\sqrt{|Θ|} \cdot sublinear \ N)$ by enhancing the checking process.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Multi-organ Segmentation via Co-training Weight-averaged Models from Few-organ Datasets

Multi-organ segmentation has extensive applications in many clinical applications. To segment multiple organs of interest, it is generally quite difficult to collect full annotations of all the organs on the same images, as some medical centers might only annotate a portion of the organs due to their own clinical practice. In most scenarios, one might obtain annotations of a single or a few organs from one training set, and obtain annotations of the the other organs from another set of training images. Existing approaches mostly train and deploy a single model for each subset of organs, which are memory intensive and also time inefficient. In this paper, we propose to co-train weight-averaged models for learning a unified multi-organ segmentation network from few-organ datasets. We collaboratively train two networks and let the coupled networks teach each other on un-annotated organs. To alleviate the noisy teaching supervisions between the networks, the weighted-averaged models are adopted to produce more reliable soft labels. In addition, a novel region mask is utilized to selectively apply the consistent constraint on the un-annotated organ regions that require collaborative teaching, which further boosts the performance. Extensive experiments on three public available single-organ datasets LiTS, KiTS, Pancreas and manually-constructed single-organ datasets from MOBA show that our method can better utilize the few-organ datasets and achieves superior performance with less inference computational cost.

0 citations0 reviewsNo code linkedOpen access
preprint2018arXiv

Theoretical and numerical studies on global stability of traveling waves with oscillations for time-delayed nonlocal dispersion equations

This paper is concerned with the global stability of non-critical/critical traveling waves with oscillations for time-delayed nonlocal dispersion equations. We first theoretically prove that all traveling waves, especially the critical oscillatory traveling waves, are globally stable in a certain weighted space, where the convergence rates to the non-critical oscillatory traveling waves are time-exponential, and the convergence to the critical oscillatory traveling waves are time-algebraic. Both of the rates are optimal. The approach adopted is the weighted energy method with the fundamental solution theory for time-delayed equations. Secondly, we carry out numerical computations in different cases, which also confirm our theoretical results. Because of oscillations of the solutions and nonlocality of the equation, the numerical results obtained by the regular finite difference scheme are not stable, even worse to be blow-up. In order to overcome these obstacles, we propose a new finite difference scheme by adding artificial viscosities to both sides of the equation, and obtain the desired numerical results.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

A Review on Mechanics and Mechanical Properties of 2D Materials - Graphene and Beyond

Since the first successful synthesis of graphene just over a decade ago, a variety of two-dimensional (2D) materials (e.g., transition metal-dichalcogenides, hexagonal boron-nitride, etc.) have been discovered. Among the many unique and attractive properties of 2D materials, mechanical properties play important roles in manufacturing, integration and performance for their potential applications. Mechanics is indispensable in the study of mechanical properties, both experimentally and theoretically. The coupling between the mechanical and other physical properties (thermal, electronic, optical) is also of great interest in exploring novel applications, where mechanics has to be combined with condensed matter physics to establish a scalable theoretical framework. Moreover, mechanical interactions between 2D materials and various substrate materials are essential for integrated device applications of 2D materials, for which the mechanics of interfaces (adhesion and friction) has to be developed for the 2D materials. Here we review recent theoretical and experimental works related to mechanics and mechanical properties of 2D materials. While graphene is the most studied 2D material to date, we expect continual growth of interest in the mechanics of other 2D materials beyond graphene.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Cavitation of Water by Volume-Controlled Stretching

A liquid subjected to negative pressure is thermodynamically metastable. Confined within a small volume, negative pressure can build up until cavities form spontaneously. The critical negative pressure for cavitation in water has been theoretically predicted to be in the range of -100 to -200 MPa at room temperature, whereas values around -30 MPa have been obtained by many experiments. The discrepancy has yet to be resolved. In this study we perform molecular dynamics simulations to study cavitation of water under volume controlled stretching. It is found that liquid water exhibits a nonlinear elastic compressibility (or stretchability) under hydrostatic tension and remains stable within the confined volume until spontaneous cavitation occurs at a critical strain. Subsequently, as the volume-controlled stretching continues, the cavity grows stably and the hydrostatic tension decreases continuously until the box volume is large enough for another transition to form a water droplet. A modified nucleation theory is proposed to predict the critical condition for cavitation. In particular, a strong dependence of the critical strain and stress for cavitation on the initial liquid volume is predicted by the modified nucleation theory, which may offer a possible explanation for the discrepancies in the values of the critical negative pressure obtained from experiments.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

An Expressive Deep Model for Human Action Parsing from A Single Image

This paper aims at one newly raising task in vision and multimedia research: recognizing human actions from still images. Its main challenges lie in the large variations in human poses and appearances, as well as the lack of temporal motion information. Addressing these problems, we propose to develop an expressive deep model to naturally integrate human layout and surrounding contexts for higher level action understanding from still images. In particular, a Deep Belief Net is trained to fuse information from different noisy sources such as body part detection and object detection. To bridge the semantic gap, we used manually labeled data to greatly improve the effectiveness and efficiency of the pre-training and fine-tuning stages of the DBN training. The resulting framework is shown to be robust to sometimes unreliable inputs (e.g., imprecise detections of human parts and objects), and outperforms the state-of-the-art approaches.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Entropic Effects of Thermal Rippling on van der Waals Interactions between Monolayer Graphene and a Rigid Substrate

Graphene monolayer, with extremely low flexural stiffness, displays spontaneous rippling due to thermal fluctuations at a finite temperature. When a graphene membrane is placed on a solid substrate, the adhesive interactions between graphene and the substrate could considerably suppress thermal rippling. On the other hand, the statistical nature of thermal rippling adds an entropic contribution to the graphene-substrate interactions. In this paper we present a statistical mechanics analysis on thermal rippling of monolayer graphene supported on a rigid substrate, assuming a generic form of van der Waals interactions between graphene and substrate at T = 0 K. The rippling amplitude, the equilibrium average separation, and the average interaction energy are predicted simultaneously and compared with molecular dynamics (MD) simulations. While the amplitude of thermal rippling is reduced by adhesive interactions, the entropic contribution leads to an effective repulsion. As a result, the equilibrium average separation increases and the effective adhesion energy decreases with increasing temperature. Moreover, the effect of a biaxial pre-strain in graphene is considered, and a buckling instability is predicted at a critical compressive strain that depends on both the temperature and the adhesive interactions. Limited by the harmonic approximations, the theoretical predictions agree with MD simulations only for relatively small rippling amplitudes but can be extended to account for the anharmonic effects.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Fast Transfer-free Synthesis of High-quality Monolayer Graphene on Insulating Substrates by Simple Rapid Thermal Treatment

The transfer-free synthesis of high-quality, large-area graphene on a given dielectric substrate, which is highly desirable for device applications, remains a significant challenge. In this paper, we report on a simple rapid thermal treatment method for the fast and direct growth of high-quality, large-scale monolayer graphene on a SiO2/Si substrate from solid carbon sources. The stack structure of solid carbon layer/copper film/SiO2 is adopted in the RTT process. The inserted copper film does not only act as an active catalyst for the carbon precursor but also serves as a "filter" that prevents premature carbon dissolution, and thus, contributes to monolayer graphene growth on SiO2/Si. The produced monolayer graphene exhibits high carrier mobility of up to 3000 cm2 V-1s-1 at room temperature and standard half-integer quantum oscillations.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Recognizing Focal Liver Lesions in Contrast-Enhanced Ultrasound with Discriminatively Trained Spatio-Temporal Model

The aim of this study is to provide an automatic computational framework to assist clinicians in diagnosing Focal Liver Lesions (FLLs) in Contrast-Enhancement Ultrasound (CEUS). We represent FLLs in a CEUS video clip as an ensemble of Region-of-Interests (ROIs), whose locations are modeled as latent variables in a discriminative model. Different types of FLLs are characterized by both spatial and temporal enhancement patterns of the ROIs. The model is learned by iteratively inferring the optimal ROI locations and optimizing the model parameters. To efficiently search the optimal spatial and temporal locations of the ROIs, we propose a data-driven inference algorithm by combining effective spatial and temporal pruning. The experiments show that our method achieves promising results on the largest dataset in the literature (to the best of our knowledge), which we have made publicly available.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Two-Tier Prediction of Solar Power Generation with Limited Sensing Resource

This paper considers a typical solar installations scenario with limited sensing resources. In the literature, there exist either day-ahead solar generation prediction methods with limited accuracy, or high accuracy short timescale methods that are not suitable for applications requiring longer term prediction. We propose a two-tier (global-tier and local-tier) prediction method to improve accuracy for long term (24 hour) solar generation prediction using only the historical power data. In global-tier, we examine two popular heuristic methods: weighted k-Nearest Neighbors (k-NN) and Neural Network (NN). In local-tier, the global-tier results are adaptively updated using real-time analytical residual analysis. The proposed method is validated using the UCLA Microgrid with 35kW of solar generation capacity. Experimental results show that the proposed two-tier prediction method achieves higher accuracy compared to day-ahead predictions while providing the same prediction length. The difference in the overall prediction performance using either weighted k-NN based or NN based in the global-tier are carefully discussed and reasoned. Case studies with a typical sunny day and a cloudy day are carried out to demonstrate the effectiveness of the proposed two-tier predictions.

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preprint2014arXiv

Detection of Interlayer Interaction in Few-layer Graphene through Landau Level Spectroscopy

We demonstrate that surface relaxation, which is insignificant in trilayer graphene, starts to manifest in Bernal-stacked tetralayer graphene. Bernal-stacked few-layer graphene has been investigated by analyzing its Landau level spectra through quantum capacitance measurements. We find that in trilayer graphene, the interlayer interaction parameters were similar to that of graphite. However, in tetralayer graphene, the hopping parameters between the bulk and surface bilayers are quite different. This shows a direct evidence for the surface relaxation phenomena. In spite of the fact that the Van der Waals interaction between the carbon layers is thought to be insignificant, we suggest that the interlayer interaction is an important factor in explaining the observed results and the symmetry-breaking effects in graphene sublattice are not negligible.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Exemplar-based Linear Discriminant Analysis for Robust Object Tracking

Tracking-by-detection has become an attractive tracking technique, which treats tracking as a category detection problem. However, the task in tracking is to search for a specific object, rather than an object category as in detection. In this paper, we propose a novel tracking framework based on exemplar detector rather than category detector. The proposed tracker is an ensemble of exemplar-based linear discriminant analysis (ELDA) detectors. Each detector is quite specific and discriminative, because it is trained by a single object instance and massive negatives. To improve its adaptivity, we update both object and background models. Experimental results on several challenging video sequences demonstrate the effectiveness and robustness of our tracking algorithm.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Interfacial adhesion between graphene and silicon dioxide by density functional theory with van der Waals corrections

Interfacial adhesion between graphene and a SiO2 substrate is studied by density functional theory (DFT) with dispersion corrections. The results demonstrate the van der Waals (vdW) interaction as the predominate mechanism for the graphene/SiO2 interface. It is found that the interaction strength is strongly influenced by changes of the SiO2 surface structures due to surface reactions with water. The adhesion energy is reduced when the reconstructed SiO2 surface is hydroxylated, and further reduced when covered by a monolayer of adsorbed water molecules. Thus, the effect of humidity may help explain the wide variation of adhesion energies measured in recent experiments between graphene and SiO2. Moreover, it is noted that vdW forces are required to accurately model the graphene/SiO2 interface with DFT and that the adhesion energy is underestimated by empirical force fields commonly used in atomistic simulations.

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preprint2014arXiv

Probing the Electron States and Metal-Insulator Transition Mechanisms in Atomically Thin MoS2 Based on Vertical Heterostructures

The metal-insulator transition (MIT) is one of the remarkable electrical transport properties of atomically thin molybdenum disulphide (MoS2). Although the theory of electron-electron interactions has been used in modeling the MIT phenomena in MoS2, the underlying mechanism and detailed MIT process still remain largely unexplored. Here, we demonstrate that the vertical metal-insulator-semiconductor (MIS) heterostructures built from atomically thin MoS2 (monolayers and multilayers) are ideal capacitor structures for probing the electron states in MoS2. The vertical configuration of MIS heterostructures offers the added advantage of eliminating the influence of large impedance at the band tails and allows the observation of fully excited electron states near the surface of MoS2 over a wide excitation frequency (100 Hz-1 MHz) and temperature range (2 K- 300 K). By combining capacitance and transport measurements, we have observed a percolation-type MIT, driven by density inhomogeneities of electron states, in the vertical heterostructures built from monolayer and multilayer MoS2. In addition, the valence band of thin MoS2 layers and their intrinsic properties such as thickness-dependence screening abilities and band gap widths can be easily accessed and precisely determined through the vertical heterostructures.

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preprint2011arXiv

Planar Traveling Waves For Nonlocal Dispersion Equation With Monostable Nonlinearity

In this paper, we study a class of nonlocal dispersion equation with monostable nonlinearity in $n$-dimensional space u_t - J\ast u +u+d(u(t,x))= \int_{\mathbb{R}^n} f_β(y) b(u(t-τ,x-y)) dy, u(s,x)=u_0(s,x), s\in[-τ,0], \ x\in \mathbb{R}^n} \] where the nonlinear functions $d(u)$ and $b(u)$ possess the monostable characters like Fisher-KPP type, $f_β(x)$ is the heat kernel, and the kernel $J(x)$ satisfies ${\hat J}(ξ)=1-\mathcal{K}|ξ|^α+o(|ξ|^α)$ for $0<α\le 2$. After establishing the existence for both the planar traveling waves $ϕ(x\cdot{\bf e}+ct)$ for $c\ge c_*$ ($c_*$ is the critical wave speed) and the solution $u(t,x)$ for the Cauchy problem, as well as the comparison principles, we prove that, all noncritical planar wavefronts $ϕ(x\cdot{\bf e}+ct)$ are globally stable with the exponential convergence rate $t^{-n/α}e^{-μ_τ}$ for $μ_τ>0$, and the critical wavefronts $ϕ(x\cdot{\bf e}+c_*t)$ are globally stable in the algebraic form $t^{-n/α}$. The adopted approach is Fourier transform and the weighted energy method with a suitably selected weight function. These rates are optimal and the stability results significantly develop the existing studies for nonlocal dispersion equations.

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preprint2010arXiv

Effect of edge structures on elastic modulus and fracture of graphene nanoribbons under uniaxial tension

Based on atomistic simulations, the nonlinear elastic properties of monolayer graphene nanoribbons under quasistatic uniaxial tension are predicted, emphasizing the effect of edge structures (armchair and zigzag, without and with hydrogen passivation). The results of atomistic simulations are interpreted within a theoretical framework of thermodynamics, which enables determination of the nonlinear functions for the strain-dependent edge energy and the hydrogen adsorption energy, for both zigzag and armchair edges. Due to the edge effects, the initial Young's modulus of graphene nanoribbons under infinitesimal strain varies with the edge chirality and the ribbon width. Furthermore, it is found that the nominal strain to fracture is considerably lower for armchair graphene nanoribbons than for zigzag ribbons. Two distinct fracture mechanisms are identified, with homogeneous nucleation for zigzag ribbons and edge-controlled heterogeneous nucleation for armchair ribbons. Hydrogen passivation of the edges is found to have negligible effect on the mechanical properties of zigzag graphene nanoribbons, but its effect is more significant for armchair ribbons.

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preprint2010arXiv

Effects of Mismatch Strain and Substrate Surface Corrugation on Morphology of Supported Monolayer Graphene

Graphene monolayers supported on oxide substrates have been demonstrated with superior charge mobility and thermal transport for potential device applications. Morphological corrugation can strongly influence the transport properties of the supported graphene. In this paper, we theoretically analyze the morphological stability of a graphene monolayer on an oxide substrate, subject to van der Waals interactions and in-plane mismatch strains. First, we define the equilibrium separation and the interfacial adhesion energy as the two key parameters that characterize the van der Waals interaction between a flat monolayer and a flat substrate surface. By a perturbation analysis, a critical compressive mismatch strain is predicted, beyond which the graphene monolayer undergoes strain-induced instability, forming corrugations with increasing amplitude and decreasing wavelength on a perfectly flat surface. When the substrate surface is not perfectly flat, the morphology of graphene depends on both the amplitude and the wavelength of the surface corrugation. A transition from conformal (corrugated) to non-conformal (flat) morphology is predicted. The effects of substrate surface corrugation on the equilibrium mean thickness of the supported graphene and the interfacial adhesion energy are analyzed. Furthermore, by considering both the substrate surface corrugation and the mismatch strain, it is found that, while a tensile mismatch strain reduces the corrugation amplitude of graphene, a corrugated substrate surface promotes strain-induced instability under a compressive strain. These theoretical results suggest possible means to control the morphology of graphene monolayer on oxide substrates by surface patterning and strain engineering.

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preprint2010arXiv

Two-Dimensional Curved Fronts in a Periodic Shear Flow

This paper is devoted to the study of travelling fronts of reaction-diffusion equations with periodic advection in the whole plane $\mathbb R^2$. We are interested in curved fronts satisfying some "conical" conditions at infinity. We prove that there is a minimal speed $c^*$ such that curved fronts with speed $c$ exist if and only if $c\geq c^*$. Moreover, we show that such curved fronts are decreasing in the direction of propagation, that is they are increasing in time. We also give some results about the asymptotic behaviors of the speed with respect to the advection, diffusion and reaction coefficients.

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preprint2009arXiv

Elastic Bending Modulus of Monolayer Graphene

A new formula for elastic bending modulus of monolayer graphene is derived analytically from an empirical potential for solid-state carbon-carbon bonds. Two physical origins are identified for the non-vanishing bending modulus of the atomically thin graphene sheet, one due to the bond angle effect and the other resulting from the bond order term associated with dihedral angles. The analytical prediction compares closely with ab initio energy calculations. Pure bending of graphene monolayers are simulated by a molecular mechanics approach, showing slight nonlinearity and anisotropy in the tangent bending modulus as the bending curvature increases. An intrinsic coupling between bending and in-plane strain is noted for graphene monolayers rolled into carbon nanotubes.

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preprint2009arXiv

Excess energy and deformation along free edges of graphene nanoribbons

Change of the bonding environment at the free edges of graphene monolayer leads to excess edge energy and edge force, depending on the edge morphology (zigzag or armchair). By using a reactive empirical bond-order potential and atomistic simulations, we show that the excess edge energy in free-standing graphene nanoribbons can be partially relaxed by both in-plane and out-of-plane deformation. The excess edge energy and edge force are calculated for graphene nanoribbons with parallel zigzag or armchair edges. Depending on the longitudinal constraint, the compressive edge force leads to either in-plane elongation of the ribbon or out-of-plane buckling deformation. In the former case, the longitudinal strain is inversely proportional to the ribbon width. In the latter case, energy minimization predicts an intrinsic wavelength for edge buckling to be 6.2 nm along the zigzag edge and 8.0 nm along the armchair edge. For graphene nanoribbons of width less than the intrinsic wavelength, interaction between the two free edges becomes significant, leading to anti-phase correlation of the buckling waves.

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