Maiter: An Asynchronous Graph Processing Framework for Delta-based Accumulative Iterative Computation

Myriad of graph-based algorithms in machine learning and data mining require parsing relational data iteratively. These algorithms are implemented in a large-scale distributed environment in order to scale to massive data sets. To accelerate these large-scale graph-based iterative computations, we propose delta-based accumulative iterative computation (DAIC). Different from traditional iterative computations, which iteratively update the result based on the result from the previous iteration, DAIC updates the result by accumulating the "changes" between iterations. By DAIC, we can process only the "changes" to avoid the negligible updates. Furthermore, we can perform DAIC asynchronously to bypass the high-cost synchronous barriers in heterogeneous distributed environments. Based on the DAIC model, we design and implement an asynchronous graph processing framework, Maiter. We evaluate Maiter on local cluster as well as on Amazon EC2 Cloud. The results show that Maiter achieves as much as 60x speedup over Hadoop and outperforms other state-of-the-art frameworks.Comment: ScienceCloud 2012, TKDE 201

Eugenol Nanoencapsulated by Sodium Caseinate: Physical, Antimicrobial, and Biophysical Properties

To improve the application of essential oils as natural antimicrobial preservatives, the objective of the present study was to determine physical, antimicrobial, and biophysical properties of eugenol after nanoencapsulation by sodium caseinate (NaCas). Emulsions were prepared by mixing eugenol in 20.0 mg/mL NaCas solution at an overall eugenol content of 5.0–137.9 mg/mL using shear homogenization. Stable emulsions were observed up to 38.5 mg/mL eugenol, which had droplet diameters of smaller than 125 nm at pH 5–9 after ambient storage for up to 30 days. The encapsulated eugenol had similar minimal inhibitory and minimal bactericidal concentrations as free eugenol against Escherichia coli O157:H7 ATCC 43895, Listeria monocytogenes Scott A, and Salmonella Enteritidis but showed better inhibition of E. coli O157:H7 than free eugenol during incubation at 37 °C for 48 h. After 20 min interaction at 21 °C, bacteria treated with encapsulated eugenol had a greater reduction of intracellular ATP and a greater increase of extracellular ATP than free eugenol, suggesting the enhanced permeation of eugenol after nanoencapsulation. However, such overall trend was not observed when examining bacterial morphology and uptake of crystal violet, suggesting the possible membrane adaptation. Findings from this study showed the feasibility of preparing nanoemulsions with high loading of eugenol using NaCas

PointNet++ Grasping: Learning An End-to-end Spatial Grasp Generation Algorithm from Sparse Point Clouds

Grasping for novel objects is important for robot manipulation in unstructured environments. Most of current works require a grasp sampling process to obtain grasp candidates, combined with local feature extractor using deep learning. This pipeline is time-costly, expecially when grasp points are sparse such as at the edge of a bowl. In this paper, we propose an end-to-end approach to directly predict the poses, categories and scores (qualities) of all the grasps. It takes the whole sparse point clouds as the input and requires no sampling or search process. Moreover, to generate training data of multi-object scene, we propose a fast multi-object grasp detection algorithm based on Ferrari Canny metrics. A single-object dataset (79 objects from YCB object set, 23.7k grasps) and a multi-object dataset (20k point clouds with annotations and masks) are generated. A PointNet++ based network combined with multi-mask loss is introduced to deal with different training points. The whole weight size of our network is only about 11.6M, which takes about 102ms for a whole prediction process using a GeForce 840M GPU. Our experiment shows our work get 71.43% success rate and 91.60% completion rate, which performs better than current state-of-art works.Comment: Accepted at the International Conference on Robotics and Automation (ICRA) 202

Grasp Stability Assessment Through Attention-Guided Cross-Modality Fusion and Transfer Learning

Extensive research has been conducted on assessing grasp stability, a crucial prerequisite for achieving optimal grasping strategies, including the minimum force grasping policy. However, existing works employ basic feature-level fusion techniques to combine visual and tactile modalities, resulting in the inadequate utilization of complementary information and the inability to model interactions between unimodal features. This work proposes an attention-guided cross-modality fusion architecture to comprehensively integrate visual and tactile features. This model mainly comprises convolutional neural networks (CNNs), self-attention, and cross-attention mechanisms. In addition, most existing methods collect datasets from real-world systems, which is time-consuming and high-cost, and the datasets collected are comparatively limited in size. This work establishes a robotic grasping system through physics simulation to collect a multimodal dataset. To address the sim-to-real transfer gap, we propose a migration strategy encompassing domain randomization and domain adaptation techniques. The experimental results demonstrate that the proposed fusion framework achieves markedly enhanced prediction performance (approximately 10%) compared to other baselines. Moreover, our findings suggest that the trained model can be reliably transferred to real robotic systems, indicating its potential to address real-world challenges.Comment: Accepted by IROS 202

A New Open Loop Approach for Identifying the Initial Rotor Position of a Permanent Magnet Synchronous Motor

The precision of initial rotor position detection is critical for the start and running performance of permanent magnet synchronous motor (PMSM). This work describes a new open loop approach for identifying the initial position of a PMSM with an incremental encoder, even when a constant load torque is being applied. By giving a testing current with high frequency to the stator winding, the initial rotor position of a PMSM can be detected with reasonable accuracy. The rotor almost does not move during the process of identification. The FFT algorithms are used to remove the phase bias effects in identification. Our approach is quicker and simpler than the conventional approaches

Spatiotemporal Arbitrage of Large-Scale Portable Energy Storage for Grid Congestion Relief

Energy storage has great potential in grid congestion relief. By making large-scale energy storage portable through trucking, its capability to address grid congestion can be greatly enhanced. This paper explores a business model of large-scale portable energy storage for spatiotemporal arbitrage over nodes with congestion. We propose a spatiotemporal arbitrage model to determine the optimal operation and transportation schedules of portable storage. To validate the business model, we simulate the schedules of a Tesla Semi full of Tesla Powerpack doing arbitrage over two nodes in California with local transmission congestion. The results indicate that the contributions of portable storage to congestion relief are much greater than that of stationary storage, and that trucking storage can bring net profit in energy arbitrage applications.Comment: Submitted to IEEE PES GM 2019; 5 pages,4 figure