396 research outputs found
Statistical methods for estimating and testing treatment effect for multiple treatment groups in observational studies.
Note: Abstract would not save due to an issue with some of the characters
Short-Packet Downlink Transmission with Non-Orthogonal Multiple Access
This work introduces downlink non-orthogonal multiple access (NOMA) into
short-packet communications. NOMA has great potential to improve fairness and
spectral efficiency with respect to orthogonal multiple access (OMA) for
low-latency downlink transmission, thus making it attractive for the emerging
Internet of Things. We consider a two-user downlink NOMA system with finite
blocklength constraints, in which the transmission rates and power allocation
are optimized. To this end, we investigate the trade-off among the transmission
rate, decoding error probability, and the transmission latency measured in
blocklength. Then, a one-dimensional search algorithm is proposed to resolve
the challenges mainly due to the achievable rate affected by the finite
blocklength and the unguaranteed successive interference cancellation. We also
analyze the performance of OMA as a benchmark to fully demonstrate the benefit
of NOMA. Our simulation results show that NOMA significantly outperforms OMA in
terms of achieving a higher effective throughput subject to the same finite
blocklength constraint, or incurring a lower latency to achieve the same
effective throughput target. Interestingly, we further find that with the
finite blocklength, the advantage of NOMA relative to OMA is more prominent
when the effective throughput targets at the two users become more comparable.Comment: 15 pages, 9 figures. This is a longer version of a paper to appear in
IEEE Transactions on Wireless Communications. Citation Information: X. Sun,
S. Yan, N. Yang, Z. Ding, C. Shen, and Z. Zhong, "Short-Packet Downlink
Transmission with Non-Orthogonal Multiple Access," IEEE Trans. Wireless
Commun., accepted to appear [Online]
https://ieeexplore.ieee.org/document/8345745
The effect of foreign ownership on dividend policy : evidence from China
This study examined the relationship between foreign ownership and dividend policy in the Chinese market. Panel logistic regression was employed to explain the effect of foreign ownership on the choice "to pay" or "not to pay" dividends. Panel model used in this study is constructed by 142 companies’ data with 1988 observations involving foreign ownership listed on the Shenzhen Stock Exchange from 2003 to 2016. Findings indicate that a higher level of foreign ownership is associated with a significantly higher probability of paying dividend. This finding is consistent to agency theory and clientele effect theory. The significant positive result for retained earnings to total equity provides support to the implication stated in the life cycle theory. However, the signaling theory is not supported as the results show an insignificant relationship between cash flow and dividend payment, and between investment opportunities and dividend payment. The findings of this study indicates that foreign shareholders
in the Chinese market have high preference for dividend paying companies, especially for large companies with low leverage. Hence for investors who prefer dividends, they should invest in companies with foreign ownership as the likelihood of these companies to pay dividend is higher
Few-Shot Semantic Relation Prediction across Heterogeneous Graphs
Semantic relation prediction aims to mine the implicit relationships between
objects in heterogeneous graphs, which consist of different types of objects
and different types of links. In real-world scenarios, new semantic relations
constantly emerge and they typically appear with only a few labeled data. Since
a variety of semantic relations exist in multiple heterogeneous graphs, the
transferable knowledge can be mined from some existing semantic relations to
help predict the new semantic relations with few labeled data. This inspires a
novel problem of few-shot semantic relation prediction across heterogeneous
graphs. However, the existing methods cannot solve this problem because they
not only require a large number of labeled samples as input, but also focus on
a single graph with a fixed heterogeneity. Targeting this novel and challenging
problem, in this paper, we propose a Meta-learning based Graph neural network
for Semantic relation prediction, named MetaGS. Firstly, MetaGS decomposes the
graph structure between objects into multiple normalized subgraphs, then adopts
a two-view graph neural network to capture local heterogeneous information and
global structure information of these subgraphs. Secondly, MetaGS aggregates
the information of these subgraphs with a hyper-prototypical network, which can
learn from existing semantic relations and adapt to new semantic relations.
Thirdly, using the well-initialized two-view graph neural network and
hyper-prototypical network, MetaGS can effectively learn new semantic relations
from different graphs while overcoming the limitation of few labeled data.
Extensive experiments on three real-world datasets have demonstrated the
superior performance of MetaGS over the state-of-the-art methods
Consensus-Based Group Task Assignment with Social Impact in Spatial Crowdsourcing
Abstract With the pervasiveness of GPS-enabled smart devices and increased wireless communication technologies, spatial crowdsourcing (SC) has drawn increasing attention in assigning location-sensitive tasks to moving workers. In real-world scenarios, for the complex tasks, SC is more likely to assign each task to more than one worker, called group task assignment (GTA), for the reason that an individual worker cannot complete the task well by herself. It is a challenging issue to assign worker groups the tasks that they are interested in and willing to perform. In this paper, we propose a novel framework for group task assignment based on worker groups’ preferences, which includes two components: social impact-based preference modeling (SIPM) and preference-aware group task assignment (PGTA). SIPM employs a bipartite graph embedding model and the attention mechanism to learn the social impact-based preferences of different worker groups on different task categories. PGTA utilizes an optimal task assignment algorithm based on the tree decomposition technique to maximize the overall task assignments, in which we give higher priorities to the worker groups showing more interests in the tasks. We further optimize the original framework by proposing strategies to improve the effectiveness of group task assignment, wherein a deep learning method and the group consensus are taken into consideration. Extensive empirical studies verify that the proposed techniques and optimization strategies can settle the problem nicely
A Learned Index for Exact Similarity Search in Metric Spaces
Indexing is an effective way to support efficient query processing in large
databases. Recently the concept of learned index has been explored actively to
replace or supplement traditional index structures with machine learning models
to reduce storage and search costs. However, accurate and efficient similarity
query processing in high-dimensional metric spaces remains to be an open
challenge. In this paper, a novel indexing approach called LIMS is proposed to
use data clustering and pivot-based data transformation techniques to build
learned indexes for efficient similarity query processing in metric spaces. The
underlying data is partitioned into clusters such that each cluster follows a
relatively uniform data distribution. Data redistribution is achieved by
utilizing a small number of pivots for each cluster. Similar data are mapped
into compact regions and the mapped values are totally ordinal. Machine
learning models are developed to approximate the position of each data record
on the disk. Efficient algorithms are designed for processing range queries and
nearest neighbor queries based on LIMS, and for index maintenance with dynamic
updates. Extensive experiments on real-world and synthetic datasets demonstrate
the superiority of LIMS compared with traditional indexes and state-of-the-art
learned indexes.Comment: 14 pages, 14 figures, submitted to Transactions on Knowledge and Data
Engineerin
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