Efficient layout of large-scale graphs remains a challenging problem: the force-directed and dimensionality reduction-based methods suffer from high overhead for graph distance and gradient ...computation. In this paper, we present a new graph layout algorithm, called DRGraph, that enhances the nonlinear dimensionality reduction process with three schemes: approximating graph distances by means of a sparse distance matrix, estimating the gradient by using the negative sampling technique, and accelerating the optimization process through a multi-level layout scheme. DRGraph achieves a linear complexity for the computation and memory consumption, and scales up to large-scale graphs with millions of nodes. Experimental results and comparisons with state-of-the-art graph layout methods demonstrate that DRGraph can generate visually comparable layouts with a faster running time and a lower memory requirement.
Roping is a troublesome surface defect of aluminium alloy sheets that hinders their application in the automobile industry. When the aluminium alloy sheets are subjected to tension, the texture ...components of various distributions will lead to non-uniform morphology and thus initiate roping. In the current paper, the correlation of roping defect with texture and surface morphology is probed by experiments and crystal plasticity simulation in four aluminium alloy sheets. Depending on the extension length of roping lines along the rolling direction, short and long roping are identified from the tested aluminium alloy sheets. With crystal plasticity simulation, texture variances for different roping degrees are comparatively analysed. The short roping is induced by short clusters of individual hard components, while the long roping is initiated by the long bands where different hard components are mixed. The experimental and simulation results also indicate that roping does not result from specific textures in the aluminium alloy sheets, and the alternative spatial aggregation of soft and hard texture components plays a predominant role in roping development. A roping intensity index RI is also proposed to quantitatively grade roping intensity and show excellent accuracy and robustness in practice.
Roping is a common macroscopic surface defect in AA6XXX aluminum alloy sheets resulting from the three-dimensional (3-D) spatial distribution of specific textures. The crystal plasticity finite ...element model (CPFEM) has been applied to study roping behavior and achieves good agreement with experimental observations but suffers from expensive computational consumption due to large-area and multi-layer characteristics of the roping phenomenon. In the present work, an artificial neural network-based (ANN) model is developed to predict thickness strain and corresponding surface roping under tensile deformation. A 3-D artificial orientation map generation algorithm is proposed and combined with CPFEM to produce reliable datasets to train, test, and validate the ANN model. The layer-by-layer random texture replacement strategy is adopted for dataset generation and facilitates the ANN model to capture the effects of individual layer textures on the thickness strain. A novel exponential weight loss function is also introduced to solve the imbalance problem of texture components. The ANN-based model demonstrates good prediction capability and generalizability in multiple validation cases with various artificial and experimental textures. The proposed ANN-based model provides an efficient and accurate alternative to the conventional physics-based method for roping analysis in aluminum alloy and can be also applied to similar microstructure-related deformation prediction in other materials.
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Abstract
Covid-19 has changed people’s way of life since its outbreak in December 2019. The transmission of the virus mainly relies on droplets or aerosols produced by infected people while they ...breathe, talk, cough or sneeze. The infection can occur over a long distance, especially indoors. To reduce connectivity between the people indoors such as in restaurants, the application of robots for food delivery may be one of the solutions. In this research, a reinforcement learning algorithm is applied to the motion control and navigation of a robot which can be modified into a robot food runner to maintain social distances in restaurants. In this project, we test the algorithm’s ability to explore the environment and find a suitable path to the target point through learning by comparing its trajectories to the shortest theoretical path. The results obtained through the simulation on Webots show that the algorithm works well to find a destination from a randomly selected starting point even though a random obstacle is presented. However, still it has great potential to be extended to a continuous environment.
•GNN is coupled with RNN to predict the texture and mechanical response.•Microstructure with local interaction is explicitly considered with GNN.•Cross-scale responses for individual grain and ...overall aggregate are captured.•The model demonstrates high efficiency, accuracy and self-consistency.
Machine learning (ML) based methods have achieved preliminary success in the constitutive modeling for single crystals or homogenized polycrystals with remarkable computational efficiency. However, existing ML-based constitutive models neglect grain-level anisotropy, which limits the accurate analysis of local effects. In the current work, a temporal graph neural network (TGNN) model is proposed to simulate cross-scale deformation behaviors of polycrystals under complex loading conditions, with straightforward consideration of microstructure variation and local interaction. The TGNN-based model, a variant of Linearized Minimal State Cells (LMSCs), extends its scope from macroscopic stress response to the mechanical response and orientation evolution of all grains within the aggregate. Specifically, the polycrystalline microstructure is represented with a graph to incorporate essential features of grains, including the spatial connectivity, crystallographic orientation and deformation state. Graph neural network (GNN) is used to capture the spatial correlation of grains, and the features extracted by the GNN are further processed with LMSCs to account for the history-dependent deformation and microstructure evolution. Moreover, the representative volume element (RVE) simulation with crystal plasticity is performed to provide reliable datasets for model establishment. The proposed model demonstrates high efficiency, accuracy and self-consistency in predicting the strain-stress response and orientation evolution at the scale of both individual grain and the overall aggregate under complex loading cases, such as cyclic loading and arbitrary loading.
Analysts commonly investigate the data distributions derived from statistical aggregations of data that are represented by charts, such as histograms and binned scatterplots, to visualize and analyze ...a large-scale dataset. Aggregate queries are implicitly executed through such a process. Datasets are constantly extremely large; thus, the response time should be accelerated by calculating predefined data cubes. However, the queries are limited to the predefined binning schema of preprocessed data cubes. Such limitation hinders analysts' flexible adjustment of visual specifications to investigate the implicit patterns in the data effectively. Particularly, RSATree enables arbitrary queries and flexible binning strategies by leveraging three schemes, namely, an R-tree-based space partitioning scheme to catch the data distribution, a locality-sensitive hashing technique to achieve locality-preserving random access to data items, and a summed area table scheme to support interactive query of aggregated values with a linear computational complexity. This study presents and implements a web-based visual query system that supports visual specification, query, and exploration of large-scale tabular data with user-adjustable granularities. We demonstrate the efficiency and utility of our approach by performing various experiments on real-world datasets and analyzing time and space complexity.
Crash reports are vital for software maintenance since they allow the developers to be informed of the problems encountered in the mobile application. Before fixing, developers need to reproduce the ...crash, which is an extremely time-consuming and tedious task. Existing studies conducted the automatic crash reproduction with the natural language described reproducing steps. Yet we find a non-neglectable portion of crash reports only contain the stack trace when the crash occurs. Such stack-trace-only crashes merely reveal the last GUI page when the crash occurs, and lack step-by-step guidance. Developers tend to spend more effort in understanding the problem and reproducing the crash, and existing techniques cannot work on this, thus calling for a greater need for automatic support. This paper proposes an approach named CrashTranslator to automatically reproduce mobile application crashes directly from the stack trace. It accomplishes this by leveraging a pre-trained Large Language Model to predict the exploration steps for triggering the crash, and designing a reinforcement learning based technique to mitigate the inaccurate prediction and guide the search holistically. We evaluate CrashTranslator on 75 crash reports involving 58 popular Android apps, and it successfully reproduces 61.3% of the crashes, outperforming the state-of-the-art baselines by 109% to 206%. Besides, the average reproducing time is 68.7 seconds, out-performing the baselines by 302% to 1611%. We also evaluate the usefulness of CrashTranslator with promising results.
Visualizing intrinsic structures of high-dimensional data is an essential task in data analysis. Over the past decades, a large number of methods have been proposed. Among all solutions, one ...promising way for enabling effective visual exploration is to construct a k-nearest neighbor (KNN) graph and visualize the graph in a low-dimensional space. Yet, state-of-the-art methods such as the LargeVis still suffer from two main problems when applied to large-scale data: (1) they may produce unappealing visualizations due to the non-convexity of the cost function; (2) visualizing the KNN graph is still time-consuming. In this work, we propose a novel visualization algorithm that leverages a multi-level representation to achieve a high-quality graph layout and employs a cluster-based approximation scheme to accelerate the KNN graph layout. Experiments on various large-scale datasets indicate that our approach achieves a speedup by a factor of five for KNN graph visualization compared to LargeVis and yields aesthetically pleasing visualization results.
Crash reports are vital for software maintenance since they allow the developers to be informed of the problems encountered in the mobile application. Before fixing, developers need to reproduce the ...crash, which is an extremely time-consuming and tedious task. Existing studies conducted the automatic crash reproduction with the natural language described reproducing steps. Yet we find a non-neglectable portion of crash reports only contain the stack trace when the crash occurs. Such stack-trace-only crashes merely reveal the last GUI page when the crash occurs, and lack step-by-step guidance. Developers tend to spend more effort in understanding the problem and reproducing the crash, and existing techniques cannot work on this, thus calling for a greater need for automatic support. This paper proposes an approach named CrashTranslator to automatically reproduce mobile application crashes directly from the stack trace. It accomplishes this by leveraging a pre-trained Large Language Model to predict the exploration steps for triggering the crash, and designing a reinforcement learning based technique to mitigate the inaccurate prediction and guide the search holistically. We evaluate CrashTranslator on 75 crash reports involving 58 popular Android apps, and it successfully reproduces 61.3% of the crashes, outperforming the state-of-the-art baselines by 109% to 206%. Besides, the average reproducing time is 68.7 seconds, outperforming the baselines by 302% to 1611%. We also evaluate the usefulness of CrashTranslator with promising results.
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