140 research outputs found
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
Almost sure convergence for a class of dependent random variables under sub-linear expectations
This article aimed to investigate the almost sure convergence theorem of widely negative orthant dependent (WNOD) random variables under sub-linear expectation space. The conclusions in this essay are an extension of the corresponding conclusions in the classical probability space
Freshness-aware Resource Allocation for Non-orthogonal Wireless-powered IoT Networks
This paper investigates a wireless-powered Internet of Things (IoT) network
comprising a hybrid access point (HAP) and two devices. The HAP facilitates
downlink wireless energy transfer (WET) for device charging and uplink wireless
information transfer (WIT) to collect status updates from the devices. To keep
the information fresh, concurrent WET and WIT are allowed, and orthogonal
multiple access (OMA) and non-orthogonal multiple access (NOMA) are adaptively
scheduled for WIT. Consequently, we formulate an expected weighted sum age of
information (EWSAoI) minimization problem to adaptively schedule the
transmission scheme, choosing from WET, OMA, NOMA, and WET+OMA, and to allocate
transmit power. To address this, we reformulate the problem as a Markov
decision process (MDP) and develop an optimal policy based on instantaneous AoI
and remaining battery power to determine scheme selection and transmit power
allocation. Extensive results demonstrate the effectiveness of the proposed
policy, and the optimal policy has a distinct decision boundary-switching
property, providing valuable insights for practical system design
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
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