HUMILITY SHAPING SOCIAL JUDGMENTS: THE EFFECT OF HUMILITY ON STEREOTYPING MEDIATED BY EGALITARIAN BELIEFS

Ph.DDOCTOR OF PHILOSOPHY (FASS

Approximation Algorithms for Min-Max Cycle Cover Problems

As a fundamental optimization problem, the vehicle routing problem has wide application backgrounds and has been paid lots of attentions in past decades. In this paper we study its applications in data gathering and wireless energy charging for wireless sensor networks, by devising improved approximation algorithms for it and its variants. The key ingredients in the algorithm design include exploiting the combinatorial properties of the problems and making use of tree decomposition and minimum weighted maximum matching techniques. Specifically, given a metric complete graph G and an integer k > 0, we consider rootless, uncapacitated rooted, and capacitated rooted min-max cycle cover problems in G with an aim to find k rootless (or rooted) edge-disjoint cycles covering the vertices in V such that the maximum cycle weight among the k cycles is minimized. For each of the mentioned problems, we develop an improved approximate solution. That is, for the rootless min-max cycle cover problem, we develop a (5 1/3+ ε)-approximation algorithm; for the uncapacitated rooted min-max cycle cover problem, we devise a (6 1/3 + ε)-approximation algorithm; and for the capacitated rooted min-max cycle cover problem, we propose a (7+ε)-approximation algorithm. These algorithms improve the best existing approximation ratios of the corresponding problems 6+ε , 7+ε , and 13+ε , respectively, where ε is a constant with 0 < ε < 1. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results show that the actual approximation ratios delivered by the proposed algorithms are always no more than 2, much better than their analytical counterparts

Probabilistic odd-even: An adaptive wormhole routing algorithm for 2D mesh network-on-chip

Wormhole routing is a popular routing technique used in network-on-chip. It is efficient but susceptible to deadlock, while deadlock will significantly degrade the network performance of NoC. Most existing adaptive wormhole routings avoid deadlock by reducing the degree of adaptiveness and thus sacrificing network performance. In this paper, we address both deadlock and network performance issues jointly, and propose a probabilistic odd-even (POE) routing algorithm that achieves the minimum packet delivery delay. The proposed POE dynamically adjusts the probabilities of constrained turns that may lead to deadlocks according to the current network conditions, and uses an efficient deadlock detection and recovery scheme when a deadlock happens. By adopting constrained turns adaptively to the network status, it not only reduces the frequency of deadlock and allows the network to be swiftly recovered when it occurs, but also greatly improves the degree of adaptiveness to obtain high network performance. Experimental results show that our method achieves a significant performance improvement both in terms of network throughput and average packet latency compared with the existing methods such as XY, odd-even, abacus turn model and fully adaptive routing algorithm while it only has moderate energy consumption

Scenery deconstruction: a new approach to understanding the historical characteristics of Nanjing cultural landscape

Abstract The “Eight Scenic Views Paintings” represent crucial visual materials for investigating the history of cultural landscapes. However, traditional methods of interpreting materials struggle to discern the inherent connections between different landscape elements. This study proposes an approach for deconstructing historical images, taking the example of the Forty Scenic Views in the Late Ming Dynasty in Nanjing, China. To explore the co-occurrence structure, hierarchical clustering, and correlation features among various elements, various digital humanities quantification methods were applied, including spatial analysis of ArcGIS, co-occurrence and clustering of KH Coder, and correlation analysis of SPSS. This study reveals the characteristics of the landscape construction of Nanjing in the Late Ming: natural landscape as the foundation, artificial landscape as the core, and advocating tradition as the fashion. It also uncovers the landscape order: mountains, waters, and scenic views interweaved and coexisted, as well as nature and culture intertwined and clustered. In addition, multiple information graphs of the subordinate and co-occurrence relationships of 20 landscape elements were constructed, 5 landscape paradigms were extracted, and 36 pairs of related relationships were discovered, deepening the historical understanding of the urban landscape construction of Nanjing in the Late Ming. This paper puts forward the idea of analyzing historical images by digital method, which provides some essential and detailed historical basis for explaining the value of cultural landscape heritage and shaping contemporary urban landscape

Complete chloroplast genome of an invasive marine macroalga Ulva californica (Ulvophyceae, Chlorophyta)

Species belonging to Ulva (Ulvophyceae, Chlorophyta) are one of the major members of invasive seaweeds. Ulva californica Wille 1899 was originally believed to be native to the Pacific coast of North America, while in recent years it has been reported as exotic species, or new record, in Europe, the Mediterranean, Asia, and Oceania. However, the paths of global dispersal of U. californica are unclear. In addition, the species boundary between U. californica and a related species is somewhat disputed. Here, we reported that the complete chloroplast genome of U. californica is 92,126 bp in size, harboring 96 genes (GenBank accession no. MZ561475). The overall base composition was A (37.9%), T (37.4%), C (12.3%), and G (12.4%), similar to those from other Ulva species. The phylogenomic analysis showed that although U. californica was genetically closer to Ulva aragoënsis (Bliding) Maggs 2018 in [Krupnik N et al., 2018], they were clearly distinguishable, supporting the recent opinion that they should be separated into different species. The chloroplast genome data of U. californica would provide plenty resources for phylogeography analysis and monitor on bioinvasion

On-demand energy replenishment for sensor networks via wireless energy transfer

In this paper, we study the use of a wireless charging vehicle (WCV) to replenish energy to sensors in a wireless sensor network so that none of the sensors will run out of its energy, where sensor batteries can be recharged. Specifically, we first propose a flexible on-demand sensor energy charging paradigm that decouples sensor energy replenishment and data collection into separate activities. We then formulate an optimization problem of wireless charging with an aim to maximize the ratio of the amount of energy consumed for charging sensors to the amount of energy consumed on traveling of the WCV as the WCV consumes its energy on both traveling and sensor charging. We also devise a novel algorithm for scheduling the tours of the WCV by jointly considering the residual lifetimes of sensors and the charging ratio of charging tours. We finally evaluate the performance of the proposed algorithm by conducting simulation. Experimental results show that the proposed algorithm is promising, and can improve the energy charging ratio of the WCV significantly

Finding top-k influential users in social networks under the structural diversity model

The influence maximization problem in a large-scale social network is to identify a few influential users such that their influence on the other users in the network is maximized, under a given influence propagation model. One common assumption adopted by two popular influence propagation models is that a user is more likely to be influenced if more his/her friends have already been influenced. This assumption recently however was challenged to be over simplified and inaccurate, as influence propagation process typically is much more complex than that, and the social decision of a user depends more subtly on the network structure, rather than how many his/her influenced friends. Instead, it has been shown that a user is very likely to be influenced by structural diversities of his/her friends. In this paper, we first formulate a novel influence maximization problem under this new structural diversity model. We then propose a constant approximation algorithm for the problem. We finally evaluate the effectiveness of the proposed algorithm by extensive experimental simulations, using different real datasets. Experimental results show that the users identified from a social network by the proposed algorithm have much larger influence than that found by existing algorithmsIt is also acknowledged that the work by Wenzheng Xu was partially supported by 2016 Basic Research Talent Foundation of Sichuan University in China (Grant no. 2082204194050), and the work by Jeffrey Xu Yu was partially supported by Research Grants Council of the Hong Kong SAR, China (Grant no. 14209314