In the ever-evolving blockchain ecosystem, decentralized exchanges (DEXs) have seen significant growth, which, however, has also brought challenges of Maximal Extractable Value (MEV). DEXs offer a decentralized platform for cryptocurrency trading. Such trading mechanisms primarily include Constant Function Market Makers (CFMMs) and batch auctions.

In distributed storage systems, replication and erasure code (EC) are common methods for data redundancy. Compared with replication, EC has better storage efficiency, but suffers higher overhead in update. Moreover, consistency and reliability problems caused by concurrent updates bring new challenges to applications of EC. Many works focus on optimizing the EC solution, including algorithm optimization, novel data update method, and so on, but lack the solutions for consistency and reliability problems. In this paper, we introduce a storage system that decouples data updating and EC encoding, namely, decoupled data updating and coding (DDUC), and propose a data placement policy that combines replication and parity blocks. For the ( N, M ) EC system, the data are placed as N groups of M +1 replicas, and redundant data blocks of the same stripe are placed in the parity nodes, so that the parity nodes can autonomously perform local EC encoding. Based on the above policy, a two-phase data update method is implemented in which data are updated in replica mode in phase 1, and the EC encoding is done independently by parity nodes in phase 2. This solves the problem of data reliability degradation caused by concurrent updates while ensuring high concurrency performance. It also uses persistent memory (PMem) hardware features of the byte addressing and eight-byte atomic write to implement a lightweight logging mechanism that improves performance while ensuring data consistency. Experimental results show that the concurrent access performance of the proposed storage system is 1.70–3.73 times that of the state-of-the-art storage system Ceph, and the latency is only 3.4%–5.9% that of Ceph.

Reinforcement learning (RL) has seen tremendous success at solving a variety of problems ranging from industrial automation to games. This paper describes how existing programming languages can be augmented with new features so as to allow developers to exploit the power of modern RL algorithms and implementations.

Matrix factorization (MF) methods have shown great promise in collaborative filtering (CF). Conventional MF methods usually assume that the correlated data is distributed on a linear hyperplane, which is not always the case. Kernel methods are used widely in SVMs to classify linearly non-separable data, as well as in PCA to discover the non-linear embeddings of data. In this paper, we present a novel method to kernelize matrix factorization for collaborative filtering, which is equivalent to performing the low-rank matrix factorization in a possibly much higher dimensional space that is implicitly defined by the kernel function. Inspired by the success of multiple kernel learning (MKL) methods, we also explore the approach of learning multiple kernels from the rating matrix to further improve the accuracy of prediction. Since the right choice of kernel is usually unknown, our proposed multiple kernel matrix factorization method helps to select effective kernel functions from the candidates. Through extensive experiments on real-world datasets, we show that our proposed method captures the nonlinear correlations among data, which results in improved prediction accuracy compared to the state-of-art CF models.