A Study on the Growth and Formation of Single Person Households and Their Housing Decisions in Taiwan

The number of single person households has dramatically increased in Taiwan in the past several decades as it has elsewhere in the world, but this phenomenon has been largely neglected in the literature. This research explores the factors that affect the formation of single person households and their housing decisions. Taiwan¡¦s population census data for 1980, 1990 and 2000 are used. Some interesting trends can be found. First of all, people who are married or cohabiting have exhibited an increasing tendency to live alone census by census. This shows the increasing need in a modern society for the husband and wife to live separately due to employment or other reasons. Secondly, unmarried and widowed elderly persons have had an increasing probability of living alone over the decades. Thirdly, the number of female single person households has been increasing rapidly, and there is a higher probability that they are homeowners and also occupying a larger living space than their male counterparts. To sum up, the results of this study show that the demand for housing among single person households will continue to increase as their numbers increase. Their demand for homeownership and living space are also increasing.Single person household formation; Tenure choice; Living space; Binary probit model; Sample selection model

DeepEP: A Deep Learning Framework for Identifying Essential Proteins

Background: Essential proteins are crucial for cellular life and thus, identification of essential proteins is an important topic and a challenging problem for researchers. Recently lots of computational approaches have been proposed to handle this problem. However, traditional centrality methods cannot fully represent the topological features of biological networks. In addition, identifying essential proteins is an imbalanced learning problem; but few current shallow machine learning-based methods are designed to handle the imbalanced characteristics. Results: We develop DeepEP based on a deep learning framework that uses the node2vec technique, multi-scale convolutional neural networks and a sampling technique to identify essential proteins. In DeepEP, the node2vec technique is applied to automatically learn topological and semantic features for each protein in protein-protein interaction (PPI) network. Gene expression profiles are treated as images and multi-scale convolutional neural networks are applied to extract their patterns. In addition, DeepEP uses a sampling method to alleviate the imbalanced characteristics. The sampling method samples the same number of the majority and minority samples in a training epoch, which is not biased to any class in training process. The experimental results show that DeepEP outperforms traditional centrality methods. Moreover, DeepEP is better than shallow machine learning-based methods. Detailed analyses show that the dense vectors which are generated by node2vec technique contribute a lot to the improved performance. It is clear that the node2vec technique effectively captures the topological and semantic properties of PPI network. The sampling method also improves the performance of identifying essential proteins. Conclusion: We demonstrate that DeepEP improves the prediction performance by integrating multiple deep learning techniques and a sampling method. DeepEP is more effective than existing methods

Motivations between First-time and Repeat Business Visitors: A Confirmatory Factor Analysis Approach

Understanding tourism motivations is now seen as a veryuseful tool for tourism marketers to increase their patronage and profits.The purpose of this study is to identify systematic differences ofparticular determinant motivations for business trips across two types ofvisitors. The study used quantitative methodology. The paper employedthe following statistic techniques: the exploratory factor analysis,confirmatory factor analysis, t-test analysis to identify two differentsegments among business visitors. The study found that business visitorsfor a single work related trip were more likely to travel with motives,including the motives of seeking educational values, exploration of thenovel, career enhancement, and opportunity for travel. It is thereforecrucial that tourist managers recognize that repeat visitors were morelikely to traveling with recreational associated reasons than these firsttimers, such as: see new things ， experience different culture andsightseeing. Theoretical and marketing implications were discussed

Analysis of a Cone-Based Distributed Topology Control Algorithm for Wireless Multi-hop Networks

The topology of a wireless multi-hop network can be controlled by varying the transmission power at each node. In this paper, we give a detailed analysis of a cone-based distributed topology control algorithm. This algorithm, introduced in [16], does not assume that nodes have GPS information available; rather it depends only on directional information. Roughly speaking, the basic idea of the algorithm is that a node $u$ transmits with the minimum power $p_{u,\alpha}$ required to ensure that in every cone of degree $\alpha$ around $u$, there is some node that $u$ can reach with power $p_{u,\alpha}$. We show that taking $\alpha = 5\pi/6$ is a necessary and sufficient condition to guarantee that network connectivity is preserved. More precisely, if there is a path from $s$ to $t$ when every node communicates at maximum power, then, if $\alpha <= 5\pi/6$, there is still a path in the smallest symmetric graph $G_\alpha$ containing all edges $(u,v)$ such that $u$ can communicate with $v$ using power $p_{u,\alpha}$. On the other hand, if $\alpha > 5\pi/6$, connectivity is not necessarily preserved. We also propose a set of optimizations that further reduce power consumption and prove that they retain network connectivity. Dynamic reconfiguration in the presence of failures and mobility is also discussed. Simulation results are presented to demonstrate the effectiveness of the algorithm and the optimizations.Comment: 10 page