Almost Sure Central Limit Theory for Self-Normalized Products of Sums of Partial Sums

Let X,X1,X2,… be a sequence of independent and identically distributed random variables in the domain of attraction of a normal law. An almost sure limit theorem for the self-normalized products of sums of partial sums is established

Sufficient and Necessary Conditions of Complete Convergence for Weighted Sums of PNQD Random Variables

The complete convergence for pairwise negative quadrant dependent (PNQD) random variables is studied. So far there has not been the general moment inequality for PNQD sequence, and therefore the study of the limit theory for PNQD sequence is very difficult and challenging. We establish a collection that contains relationship to overcome the difficulties that there is no general moment inequality. Sufficient and necessary conditions of complete convergence for weighted sums of PNQD random variables are obtained. Our results generalize and improve those on complete convergence theorems previously obtained by Baum and Katz (1965) and Wu (2002)

Complete integral convergence for weighted sums of negatively dependent random variables under sub-linear expectations

In the paper, the complete convergence and complete integral convergence for weighted sums of negatively dependent random variables under the sub-linear expectations are established. The results in the paper extend some complete moment convergence theorems from the classical probability space to the situation of sub-linear expectation space

Fabrication and Spectral Properties of Wood-Based Luminescent Nanocomposites

Pressure impregnation pretreatment is a conventional method to fabricate wood-based nanocomposites. In this paper, the wood-based luminescent nanocomposites were fabricated with the method and its spectral properties were investigated. The results show that it is feasible to fabricate wood-based luminescent nanocomposites using microwave modified wood and nanophosphor powders. The luminescent strength is in positive correlation with the amount of phosphor powders dispersed in urea-formaldehyde resin. Phosphors absorb UV and blue light efficiently in the range of 400–470 nm and show a broad band of bluish-green emission centered at 500 nm, which makes them good candidates for potential blue-green luminescent materials

Occupation prediction with multimodal learning from Tweet messages and Google Street View images

Despite the development of various heuristic and machine learning models, social media user occupation predication remains challenging due to limited high-quality ground truth data and difficulties in effectively integrating multiple data sources in different modalities, which can be complementary and contribute to informing the profession or job role of an individual. In response, this study introduces a novel semi-supervised multimodal learning method for Twitter user occupation prediction with a limited number of training samples. Specifically, an unsupervised learning model is first designed to extract textual and visual embeddings from individual tweet messages (textual) and Google Street View images (visual), with the latter capturing the geographical and environmental context surrounding individuals’ residential and workplace areas. Next, these high-dimensional multimodal features are fed into a multilayer transfer learning model for individual occupation classification. The proposed occupation prediction method achieves high evaluation scores for identifying Office workers, Students, and Others or Jobless people, with the F1 score for identifying Office workers surpassing the best previously reported scores for occupation classification using social media data

Modified Biogeography-Based Optimization with Local Search Mechanism

Biogeography-based optimization (BBO) is a new effective population optimization algorithm based on the biogeography theory with inherently insufficient exploration capability. To address this limitation, we proposed a modified BBO with local search mechanism (denoted as MLBBO). In MLBBO, a modified migration operator is integrated into BBO, which can adopt more information from other habitats, to enhance the exploration ability. Then, a local search mechanism is used in BBO to supplement with modified migration operator. Extensive experimental tests are conducted on 27 benchmark functions to show the effectiveness of the proposed algorithm. The simulation results have been compared with original BBO, DE, improved BBO algorithms, and other evolutionary algorithms. Finally, the performance of the modified migration operator and local search mechanism are also discussed