Blockchain serves as a replicated transactional processing system in a trustless distributed environment. Existing blockchain systems all rely on an explicit ordering step to determine the global order of transactions that are collected from multiple peers. The ordering consensus can be the bottleneck since it must be Byzantine-fault tolerant and can scarcely benefit from parallel execution. In this paper, we propose an ordering-free architecture that makes ordering implicit through deterministic execution. Based on this novel architecture, we develop a permissioned blockchain system NeuChain. A number of key optimizations such as asynchronous block generation and pipelining are leveraged for high throughput and low latency. Several security mechanisms are also designed to make our system robust to malicious attacks. Our geo-distributed experimental results show that NeuChain can achieve 47.2--64.1X throughput improvement over HyperLedger Fabric and 1.6--12.2X throughput improvement over the state-of-the-art high performance blockchains.

In this paper, a novel Structure-Constrained Low-Rank and Partial Sparse Representation algorithm for image classification is proposed. First, a Structure-Constrained Low-Rank dictionary learning algorithm is proposed, which imposes both structure and low-rank restriction on the coefficient matrix. Second, under the assumption that the representation of test sample is sparse and correlated with the learned representation of training samples, we concatenate training samples and test samples to form a data matrix and find a low-rank and sparse representation of the data matrix over learned dictionary by low-rank matrix recovery technique. Experimental results demonstrate the effectiveness of the proposed algorithm.

In this paper, we present a novel local-based algorithm for stereo matching using Gabor-Feature-Image and Confidence-Mask. Various local-based schemes have been proposed in recent years, most of them mainly use color difference as evaluation criterion when constructing the initial cost volume, however, color channel is highly sensitive to noise, illumination changes, etc. Therefore, we develop a new cost function based on Gabor-Feature-Image for obtaining a more accurate matching cost volume. Furthermore, in order to eliminate the matching ambiguities brought by the winnertakes-all method, an effective disparity refinement strategy using Confidence-Mask is implemented to select and refine the less reliable pixels. The proposed algorithm ranks 23th out of over 150 (global-based and local-based) methods on Middlebury data sets, both quantitative and qualitative evaluation show that it is comparable to state-of-the-art local-based stereo matching algorithms.