arXiv subjects

Lei Wang

Publications and source records attributed to Lei Wang.

At least 1,027 records · Page 57Linked to original sources

arXiv2019

Controlling solid-liquid interfacial energy anisotropy through the isotropic liquid

Although the anisotropy of the solid-liquid interfacial free energy for most alloy systems is very small, it plays a crucial role in the growth rate, morphology and crystallographic growth direction of dendrites. Previous work posited a dendrite orientation transition via compositional additions. In this work we examine experimentally the change in dendrite growth behaviour in the Al-Sm (Samarium) system as a function of solute concentration and study its interfacial properties using molecular dynamics simulations. We observe a dendrite growth direction which changes from <100> to <110> as Sm content increases. The observed change in dendrite orientation is consistent with the simulation results for the variation of the interfacial free energy anisotropy and thus provides definitive confirmation of conjecture in previous works. In addition, our results provide physical insight into the atomic structural origin of the concentration dependent anisotropy, and deepens our fundamental understanding of solid-liquid interfaces in binary alloys.

cond-mat.mtrl-sci↗

arXiv2019

A Semantic-based Medical Image Fusion Approach

It is necessary for clinicians to comprehensively analyze patient information from different sources. Medical image fusion is a promising approach to providing overall information from medical images of different modalities. However, existing medical image fusion approaches ignore the semantics of images, making the fused image difficult to understand. In this work, we propose a new evaluation index to measure the semantic loss of fused image, and put forward a Fusion W-Net (FW-Net) for multimodal medical image fusion. The experimental results are promising: the fused image generated by our approach greatly reduces the semantic information loss, and has better visual effects in contrast to five state-of-art approaches. Our approach and tool have great potential to be applied in the clinical setting.

eess.IV↗

arXiv2019

BenchCouncil's View on Benchmarking AI and Other Emerging Workloads

This paper outlines BenchCouncil's view on the challenges, rules, and vision of benchmarking modern workloads like Big Data, AI or machine learning, and Internet Services. We conclude the challenges of benchmarking modern workloads as FIDSS (Fragmented, Isolated, Dynamic, Service-based, and Stochastic), and propose the PRDAERS benchmarking rules that the benchmarks should be specified in a paper-and-pencil manner, relevant, diverse, containing different levels of abstractions, specifying the evaluation metrics and methodology, repeatable, and scaleable. We believe proposing simple but elegant abstractions that help achieve both efficiency and general-purpose is the final target of benchmarking in future, which may be not pressing. In the light of this vision, we shortly discuss BenchCouncil's related projects.

cs.PF↗

arXiv2019

Differentiable Meta-learning Model for Few-shot Semantic Segmentation

To address the annotation scarcity issue in some cases of semantic segmentation, there have been a few attempts to develop the segmentation model in the few-shot learning paradigm. However, most existing methods only focus on the traditional 1-way segmentation setting (i.e., one image only contains a single object). This is far away from practical semantic segmentation tasks where the K-way setting (K>1) is usually required by performing the accurate multi-object segmentation. To deal with this issue, we formulate the few-shot semantic segmentation task as a learning-based pixel classification problem and propose a novel framework called MetaSegNet based on meta-learning. In MetaSegNet, an architecture of embedding module consisting of the global and local feature branches is developed to extract the appropriate meta-knowledge for the few-shot segmentation. Moreover, we incorporate a linear model into MetaSegNet as a base learner to directly predict the label of each pixel for the multi-object segmentation. Furthermore, our MetaSegNet can be trained by the episodic training mechanism in an end-to-end manner from scratch. Experiments on two popular semantic segmentation datasets, i.e., PASCAL VOC and COCO, reveal the effectiveness of the proposed MetaSegNet in the K-way few-shot semantic segmentation task.

cs.CV↗

arXiv2019

BOPS, Not FLOPS! A New Metric and Roofline Performance Model For Datacenter Computing

For emerging datacenter (in short, DC) workloads, such as online Internet services or offline data analytics, how to evaluate the upper bound performance and provide apple-to-apple comparisons are fundamental problems. To this end, a unified computation-centric metric is an essential requirement. As the most important computation-centric performance metric, FLOPS has guided computing systems evolutions for many years. However, our observations demonstrate that the average FLOPS efficiency of the DC workloads is only 0.1%, which implies that FLOPS is inappropriate for DC computing. To address the above issue, we propose BOPS (Basic Operations Per Second), which is the average number of BOPs (Basic OPerations) completed per second. We conduct the analysis on the characteristics of seventeen typical DC workloads and extract the minimum representative computation operations set, which is composed of integer and floating point computation operations of arithmetic, comparing and array addressing. Then, we propose the formalized BOPS definition and the BOPS based upper bound performance model. Finally, the BOPS measuring tool is also implemented. We perform experiments with seventeen DC workloads on three typical Intel processors platforms. First, BOPS can reflect the performance gap of different computing systems, the bias between the peak BOPS performance gap and the average DC workloads' wall clock time gap is no more than 10%. Second, the Sort workload can achieve 32% BOPS efficiency on the experimental platform. At last, we present two use cases of BOPS. One is the BOPS based system evaluation, we illustrate that BOPS can compare performance of workloads from multiple domains. The other is BOPS based optimizations. We show that under the guiding of the BOPS based upper bound model, the Sort workload and the Redis workload achieve 4.4X and 1.2X performance improvements respectively.

cs.PF↗

arXiv2019

HPC AI500: A Benchmark Suite for HPC AI Systems

In recent years, with the trend of applying deep learning (DL) in high performance scientific computing, the unique characteristics of emerging DL workloads in HPC raise great challenges in designing, implementing HPC AI systems. The community needs a new yard stick for evaluating the future HPC systems. In this paper, we propose HPC AI500 --- a benchmark suite for evaluating HPC systems that running scientific DL workloads. Covering the most representative scientific fields, each workload from HPC AI500 is based on real-world scientific DL applications. Currently, we choose 14 scientific DL benchmarks from perspectives of application scenarios, data sets, and software stack. We propose a set of metrics for comprehensively evaluating the HPC AI systems, considering both accuracy, performance as well as power and cost. We provide a scalable reference implementation of HPC AI500. HPC AI500 is a part of the open-source AIBench project, the specification and source code are publicly available from \url{http://www.benchcouncil.org/AIBench/index.html}.

cs.PF↗

arXiv2019

Moiré-less Correlations in ABCA Graphene

Atomically thin van der Waals materials stacked with an interlayer twist have proven to be an excellent platform towards achieving gate-tunable correlated phenomena linked to the formation of flat electronic bands. In this work we demonstrate the formation of emergent correlated phases in multilayer rhombohedral graphene - a simple material that also exhibits a flat electronic band but without the need of having a moiré superlattice induced by twisted van der Waals layers. We show that two layers of bilayer graphene that are twisted by an arbitrary tiny angle host large (micron-scale) regions of uniform rhombohedral four-layer (ABCA) graphene that can be independently studied. Scanning tunneling spectroscopy reveals that ABCA graphene hosts an unprecedentedly sharp flat band of 3-5 meV half-width. We demonstrate that when this flat band straddles the Fermi level, a correlated many-body gap emerges with peak-to-peak value of 9.5 meV at charge neutrality. Mean field theoretical calculations indicate that the two primary candidates for the appearance of this broken symmetry state are a charge transfer excitonic insulator and a ferrimagnet. Finally, we show that ABCA graphene hosts surface topological helical edge states at natural interfaces with ABAB graphene which can be turned on and off with gate voltage, implying that small angle twisted double bilayer graphene is an ideal programmable topological quantum material.

cond-mat.mes-hall↗

arXiv2019

Magic continuum in twisted bilayer WSe2

Emergent quantum phases driven by electronic interactions can manifest in materials with narrowly dispersing, i.e. "flat", energy bands. Recently, flat bands have been realized in a variety of graphene-based heterostructures using the tuning parameters of twist angle, layer stacking and pressure, and resulting in correlated insulator and superconducting states. Here we report the experimental observation of similar correlated phenomena in twisted bilayer tungsten diselenide (tWSe2), a semiconducting transition metal dichalcogenide (TMD). Unlike twisted bilayer graphene where the flat band appears only within a narrow range around a "magic angle", we observe correlated states over a continuum of angles, spanning 4 degree to 5.1 degree. A Mott-like insulator appears at half band filling that can be sensitively tuned with displacement field. Hall measurements supported by ab initio calculations suggest that the strength of the insulator is driven by the density of states at half filling, consistent with a 2D Hubbard model in a regime of moderate interactions. At 5.1 degree twist, we observe evidence of superconductivity upon doping away from half filling, reaching zero resistivity around 3 K. Our results establish twisted bilayer TMDs as a model system to study interaction-driven phenomena in flat bands with dynamically tunable interactions.

cond-mat.mes-hall↗

arXiv2019

AIBench: An Industry Standard Internet Service AI Benchmark Suite

Today's Internet Services are undergoing fundamental changes and shifting to an intelligent computing era where AI is widely employed to augment services. In this context, many innovative AI algorithms, systems, and architectures are proposed, and thus the importance of benchmarking and evaluating them rises. However, modern Internet services adopt a microservice-based architecture and consist of various modules. The diversity of these modules and complexity of execution paths, the massive scale and complex hierarchy of datacenter infrastructure, the confidential issues of data sets and workloads pose great challenges to benchmarking. In this paper, we present the first industry-standard Internet service AI benchmark suite---AIBench with seventeen industry partners, including several top Internet service providers. AIBench provides a highly extensible, configurable, and flexible benchmark framework that contains loosely coupled modules. We identify sixteen prominent AI problem domains like learning to rank, each of which forms an AI component benchmark, from three most important Internet service domains: search engine, social network, and e-commerce, which is by far the most comprehensive AI benchmarking effort. On the basis of the AIBench framework, abstracting the real-world data sets and workloads from one of the top e-commerce providers, we design and implement the first end-to-end Internet service AI benchmark, which contains the primary modules in the critical paths of an industry scale application and is scalable to deploy on different cluster scales. The specifications, source code, and performance numbers are publicly available from the benchmark council web site http://www.benchcouncil.org/AIBench/index.html.

cs.CV↗

arXiv2019

Tracking system of Mine Patrol Robot for Low Illumination Environment

Computer vision has received a significant attention in recent years, which is one of the important parts for robots to apperceive external environment. Discriminative Correlation Filter (DCF) based trackers gained more popularity due to their efficiency, however, tracking in low-illumination environments is a challenging problem, not yet successfully addressed in the literature. In this work, we tackle the problems by introducing Low-Illumination Long-term Correlation Tracker (LLCT). First, fused features only including HOG and Color Names are employed to boost the tracking efficiency. Second, we used the standard PCA to reduction scheme in the translation and scale estimation phase for accelerating. Third, we learned a long-term correlation filter to keep the long-term memory ability. Finally, update memory templates with interval updates, then re-match existing and initial templates every few frames to maintain template accuracy. The extensive experiments on popular Object Tracking Benchmark OTB-50 datasets have demonstrated that the proposed tracker outperforms the state-of-the-art trackers significantly achieves a high real-time (33FPS) performance. In addition, the proposed approach can be integrated easily in robot system and the running speed performed well. The experimental results show that the novel tracker performance in low-illumination environment is better than that of general trackers.

cs.CV↗

arXiv2019

End-to-end speech enhancement based on discrete cosine transform

Previous speech enhancement methods focus on estimating the short-time spectrum of speech signals due to its short-term stability. However, these methods often only estimate the clean magnitude spectrum and reuse the noisy phase when resynthesize speech signals, which is unlikely a valid short-time Fourier transform (STFT). Recently, DNN based speech enhancement methods mainly joint estimation of the magnitude and phase spectrum. These methods usually give better performance than magnitude spectrum estimation but need much larger computation and memory overhead. In this paper, we propose using the Discrete Cosine Transform (DCT) to reconstruct a valid short-time spectrum. Under the U-net structure, we enhance the real spectrogram and finally achieve perfect performance.

cs.SD↗

arXiv2019

A kernel-independent treecode based on barycentric Lagrange interpolation

A kernel-independent treecode (KITC) is presented for fast summation of particle interactions. The method employs barycentric Lagrange interpolation at Chebyshev points to approximate well-separated particle-cluster interactions. The KITC requires only kernel evaluations, is suitable for non-oscillatory kernels, and it utilizes a scale-invariance property of barycentric Lagrange interpolation. For a given level of accuracy, the treecode reduces the operation count for pairwise interactions from $O(N^2)$ to $O(N\log N)$, where $N$ is the number of particles in the system. The algorithm is demonstrated for systems of regularized Stokeslets and rotlets in 3D, and numerical results show the treecode performance in terms of error, CPU time, and memory consumption. The KITC is a relatively simple algorithm with low memory consumption, and this enables a straightforward OpenMP parallelization.

math.NA↗

arXiv2019

Quantized thermal Hall conductance from edge current calculations in lattice models

The quantized thermal Hall effect is an important probe for detecting chiral topological order and revealing the nature of chiral gapless edge states. The standard Kubo formula approach for the thermal Hall conductance $κ_{xy}$ based on the linear-response theory faces difficulties in practical application due to the lack of a reliable numerical method for calculating dynamical quantities in microscopic models at finite temperature. In this work, we propose an approach for calculating $κ_{xy}$ in two-dimensional lattice models displaying chiral topological order. Our approach targets at the edge current localized at the boundary which involves only thermal averages of local operators in equilibrium, thus drastically lowering the barrier for the calculation of $κ_{xy}$. We use the chiral $p$-wave superconductor (with and without disorder) and the Hofstadter model as benchmark examples to illustrate several sources of finite-size effects, and we suggest the infinite (or sufficiently long) strip as the best geometry for carrying out numerical simulations.

cond-mat.str-el↗

arXiv2019

Subjective and Objective De-raining Quality Assessment Towards Authentic Rain Image

Images acquired by outdoor vision systems easily suffer poor visibility and annoying interference due to the rainy weather, which brings great challenge for accurately understanding and describing the visual contents. Recent researches have devoted great efforts on the task of rain removal for improving the image visibility. However, there is very few exploration about the quality assessment of de-rained image, even it is crucial for accurately measuring the performance of various de-raining algorithms. In this paper, we first create a de-raining quality assessment (DQA) database that collects 206 authentic rain images and their de-rained versions produced by 6 representative single image rain removal algorithms. Then, a subjective study is conducted on our DQA database, which collects the subject-rated scores of all de-rained images. To quantitatively measure the quality of de-rained image with non-uniform artifacts, we propose a bi-directional feature embedding network (B-FEN) which integrates the features of global perception and local difference together. Experiments confirm that the proposed method significantly outperforms many existing universal blind image quality assessment models. To help the research towards perceptually preferred de-raining algorithm, we will publicly release our DQA database and B-FEN source code on https://github.com/wqb-uestc.

eess.IV↗

arXiv2019

Region-wise Generative Adversarial ImageInpainting for Large Missing Areas

Recently deep neutral networks have achieved promising performance for filling large missing regions in image inpainting tasks. They usually adopted the standard convolutional architecture over the corrupted image, leading to meaningless contents, such as color discrepancy, blur and artifacts. Moreover, most inpainting approaches cannot well handle the large continuous missing area cases. To address these problems, we propose a generic inpainting framework capable of handling with incomplete images on both continuous and discontinuous large missing areas, in an adversarial manner. From which, region-wise convolution is deployed in both generator and discriminator to separately handle with the different regions, namely existing regions and missing ones. Moreover, a correlation loss is introduced to capture the non-local correlations between different patches, and thus guides the generator to obtain more information during inference. With the help of our proposed framework, we can restore semantically reasonable and visually realistic images. Extensive experiments on three widely-used datasets for image inpainting tasks have been conducted, and both qualitative and quantitative experimental results demonstrate that the proposed model significantly outperforms the state-of-the-art approaches, both on the large continuous and discontinuous missing areas.

cs.CV↗

arXiv2019

Machine Learning Holographic Mapping by Neural Network Renormalization Group

The exact holographic mapping (EHM) provides an explicit duality map between a conformal field theory (CFT) configuration and a massive field propagating on an emergent classical geometry. However, designing the optimal holographic mapping is challenging. Here we introduce the neural network renormalization group as a universal approach to design generic EHM for interacting field theories. Given a field theory action, we train a flow-based hierarchical deep generative neural network to reproduce the boundary field ensemble from uncorrelated bulk field fluctuations. In this way, the neural network develops the optimal renormalization group transformations. Using the machine-designed EHM to map the CFT back to a bulk effective action, we determine the bulk geodesic distance from the residual mutual information. We apply this approach to the complex $ϕ^4$ theory in two-dimensional Euclidian spacetime in its critical phase, and show that the emergent bulk geometry matches the three-dimensional hyperbolic geometry.

cond-mat.dis-nn↗

arXiv2019

A Treecode Algorithm for 3D Stokeslets and Stresslets

The Stokeslet and stresslet kernels are commonly used in boundary element simulations and singularity methods for slow viscous flow. Evaluating the velocity induced by a collection of Stokeslets and stresslets by direct summation requires $O(N^2)$ operations, where $N$ is the system size. The present work develops a treecode algorithm for 3D Stokeslets and stresslets that reduces the cost to $O(N\log N)$. The particles are divided into a hierarchy of clusters, and well-separated particle-cluster interactions are computed by a far-field Cartesian Taylor approximation. The terms in the approximation are contracted to promote efficient computation. Serial and parallel results display the treecode performance for several test cases. In particular the method has relatively simple structure and low memory usage, and this enhances parallel efficiency for large systems.

math.NA↗

arXiv2019

Adaptive Structure-constrained Robust Latent Low-Rank Coding for Image Recovery

In this paper, we propose a robust representation learning model called Adaptive Structure-constrained Low-Rank Coding (AS-LRC) for the latent representation of data. To recover the underlying subspaces more accurately, AS-LRC seamlessly integrates an adaptive weighting based block-diagonal structure-constrained low-rank representation and the group sparse salient feature extraction into a unified framework. Specifically, AS-LRC performs the latent decomposition of given data into a low-rank reconstruction by a block-diagonal codes matrix, a group sparse locality-adaptive salient feature part and a sparse error part. To enforce the block-diagonal structures adaptive to different real datasets for the low-rank recovery, AS-LRC clearly computes an auto-weighting matrix based on the locality-adaptive features and multiplies by the low-rank coefficients for direct minimization at the same time. This encourages the codes to be block-diagonal and can avoid the tricky issue of choosing optimal neighborhood size or kernel width for the weight assignment, suffered in most local geometrical structures-preserving low-rank coding methods. In addition, our AS-LRC selects the L2,1-norm on the projection for extracting group sparse features rather than learning low-rank features by Nuclear-norm regularization, which can make learnt features robust to noise and outliers in samples, and can also make the feature coding process efficient. Extensive visualizations and numerical results demonstrate the effectiveness of our AS-LRC for image representation and recovery.

cs.CV↗