Fully asynchronous stochastic coordinate descent: a tight lower bound on the parallelism achieving linear speedup

Mathematical Programming - We seek tight bounds on the viable parallelism in asynchronous implementations of coordinate descent that achieves linear speedup. We focus on asynchronous coordinate...     

Interactions of the vector breathers for the coupled Hirota system with $$4\times 4$$ 4 × 4 Lax pair

Nonlinear Dynamics - In this paper, we investigate the interactions of the vector breathers for the coupled Hirota system with $$4\times 4$$ Lax pair. Firstly, we give the first-order breather...     

Control of a multigroup COVID-19 model with immunity: treatment and test elimination

Nonlinear Dynamics - This paper introduces a multigroup COVID-19 model with immunity, in which the total population of each group is partitioned into five compartments, that is, susceptible,...     

Generalized P(N)-graded Lie superalgebras

We generalize the P(N)-graded Lie superalgebras of Martinez-Zelmanov. This generalization is not so restrictive but suffcient enough so that we are able to have a classification for this generalized P(N)-graded Lie superalgebras. Our result is that the generalized P(N)-graded Lie super-algebra L is centrally isogenous to a matrix Lie superalgebra coordinated by an associative superalgebra with a super-involution. Moreover, L is P(N)-graded if and only if the coordinate algebra R is commutative and the super-involution is trivial. This recovers Martinez-Zelmanov's theorem for type P(N). We also obtain a generalization of Kac's coordinatization via Tits-Kantor-Koecher construction. Actually, the motivation of this generalization comes from the Fermionic-Bosonic module construction.     

游客情感计算的文本大数据挖掘方法比较研究

旅游文本大数据以其方便、快捷和低门槛的特点为游客情感计算提供了极大便利，已经成为旅游大数据的主要来源之一。基于大数据理论和情感理论，以文本大数据为数据源，在全面梳理国内外情感计算相关成果的基础上，利用人工智能中的逻辑/算法编程方法、机器学习方法、深度学习方法对旅游文本大数据进行挖掘，探索最佳的基于文本大数据的游客情感计算方法。研究发现：（1）基于情感词典的游客情感计算模型，其核心是构建情感词典和设计情感计算规则，方法简单，容易实现，适用语料范围广。（2）机器学习，用统计学方法抽取文本中的特征项，具有非线性特征，可靠性较线性特征的情感词典方法高。（3）基于深度学习技术的游客情感计算，效果良好，准确率在85%以上。训练多领域的文本语料易于移植，实用性强，且泛化能力好，较适合大数据时代游客情感计算研究。     

High-efficient surface tailoring via reverse atom transfer radical polymerization and reversible addition-fragmentation chain-transfer polymerization in an aqueous system initiated by a monocenter redox pair

The commonly used multi-center initiation methods always lead to the formation of quantities of homopolymer in the surface tailoring based on reverse atom transfer radical polymerization (ATRP) and reversible addition-fragmentation chain-transfer (RAFT) polymerization. In this study, a monocenter redox pair constructed of silica bearing tert-butyl hydroperoxide groups and ascorbic acid (SiO₂-TBHP/AsAc) was applied to substitute the commonly used initiation method of R-supported RAFT grafting polymerization. All the propagating radicals were restricted on the surface of solid particles during the whole procedure theoretically, resulting in a higher grafting efficiency of 95.1% combined with the “controllable” feature at 10 h. This redox pair was also used to initiate the reverse ATRP in miniemulsion successfully with a grafting efficiency of 86.3% at 10 h. The grafting efficiency obtained under this monocenter initiation method was significantly higher than that of the frequently reported surface modification by reverse ATRP and RAFT polymerization. In addition, the high-efficient surface tailoring was traced and confirmed by nuclear magnetic resonance, Fourier transform infrared, X-ray photoelectron spectroscopy, thermogravimetric analysis, transmission electron microscopy, and other analysis tests. The advantage of this monocenter redox pair will open a new avenue for the potential “high-efficient” surface tailoring of various materials.     

MWCNTs/hydroxypropyl cellulose/polyethylene glycol-based shape-stabilized phase change materials

Journal of Thermal Analysis and Calorimetry - Polyethylene glycol (PEG) is widely used as phase change materials (PCMs). However, the leakage and low thermal conductivity issues restrict its...