京都大学 / 0048 / 新制・課程博士 / 博士(理学) / 甲第20898号 / 理博第4350号 / 新制||理||1624(附属図書館) / 京都大学大学院理学研究科物理学・宇宙物理学専攻 / (主査)教授 田中 貴浩, 准教授 樽家 篤史, 教授 川合 光 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DGAM

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Few researchers have examined the contributing factors to racial identity development for White Americans. In order to better understand White racial identity development, the current study was designed to use Helms’s (1990) theory of White racial identity development to examine the associations between racial attitudes and status profiles of White racial identity, with particular interest in color-blind racial attitudes (i.e., the belief that race is a non-issue in modern society) and belief in a just world (i.e., the view that the world is fair and just). To gain further insight into profiles of White racial identity, additional social attitudes were included in the analyses, including social dominance orientation and internal and external motivation to avoid prejudice, as well as demographic variables. A sample of 350 White American adults recruited from Amazon’s MTurk completed measures of racial identity, racial attitudes, social desirability, and demographic information. K means cluster analyses were conducted to create five status profiles of White identity. Among all study variables, cluster group membership was primarily defined by color-blind racial attitudes, social dominance orientation, and age. Results revealed color-blind racial attitudes were the strongest variables across all five clusters, even those in which the primary racial identity status was autonomy. Belief in a just world, on the other hand, did not appear to be a prominent factor in determining cluster membership in the current study. These results pointed to implications for both research and theory on White racial identity statuses, given that participants who were autonomous were also high in color-blind racial attitudes, which is inconsistent with current conceptualizations of the autonomy ego status. The results indicated the possibility of an ego status prior to autonomy and hold implications for identifying additional statuses of White racial identity within Helms’s (1990) model. The study results hold further implications for future research in the exploration of connections between White racial identity and multicultural counseling competence.

In this dissertation, I evaluate questions related to the role of connectivity in economic and innovation-related processes. Specifically, I utilize conceptual frameworks from the economic geography literature to study the relevance of internal, external, and infrastructural connectivity at a city level.In the first essay, I examine the role of intra-metropolitan connectivity of inventors and evaluate the quality of the associated innovation outputs. I focus on the fact that there exist meaningful demographic differences between people domiciled in city centers and the suburban areas and claim that these heterogeneities serve as sources of diversity and creativity. I suggest empirical evidence that the collaboration of inventors from the two different sub-regions is associated with higher quality innovation outcomes. I further study how firm heterogeneity moderates the effects of this intra-metropolitan connectivity. The findings suggest that local firms and small to mid-sized firms (SMEs) enjoy more benefits because foreign firms and large firms are exploiting their own global network. This paper provides both managerial and practical implications that a metropolitan area may improve its quality of innovation outputs by taking advantage of the urban-suburban connectivity among the inventors. In the second essay, I provide specific guidelines to city planners to evaluate the external connectivity of the associated city. Cities are industry hotspots, playing vital roles as centers of economic development. Each city has different location-specific advantages that can foster different core industries and firms, participating in diverse activities within a global value chain system. Given the increasing rate of globalization at the metro level, it has become paramount for cities to establish and develop economic partnerships with other cities to further growing their regional economies. However, few city planners have clear directions in choosing partner cities, and the decisions are rarely based on appropriate data analysis. Based on the Brookings Institution’s Global City Initiative 2.0 project, and after enhancing it with additional data analyses, I introduce a set of step-by-step guidelines to city planners for finding global partner cities. To provide an actual case, I share our own anecdote regarding how Philadelphia chose potential partner cities in order to attract more FDI in its biopharmaceutical sector and foster innovation activities. I also present evidence that the inadequate ability of local firms to source knowledge from international markets associates with relatively weak economic performance. The comprehensive analyses of the city’s role in the global value chain include from the upstream (Research and Development) and the midstream (FDI, imports, international joint ventures) to the downstream (exports). This case-based paper provides practical implications to city planners by providing ways of understanding the broad global value chain with which the city is involved. In the third essay, I assess the relationship between soft networks and hard networks of global cities. Public transportation systems (PTS) have been developed along with the associated metropolitan area. Scholars in urban studies have emphasized the important roles of PTS in connecting diverse people, regions, activities, and socio-economic consequences. In this paper, I examine the relationship between public transportation systems and the innovation network in four major U.S. cities in the northeast - New York, Boston, Philadelphia, and Pittsburgh. I graphically show that inventor locations, as well as their connectivity, are established along with the city’s public transportation networks. I further stipulate that this relationship has been seriously undermined by the recent pandemic – COVID-19. Even though it is too early to draw a conclusion, I advance propositions that predict how the relationship between public transportation network and inventor connectivity will be changed followed by the pandemic. In conclusion, I claim that a metropolitan area’s internal, external, and infrastructural network significantly affects its competitiveness. Throughout this dissertation, I confirm that both hard networks and soft networks are key to enhancing the economic and innovative performance of the city. / Business Administration/International Business Administration

With the rise of microservice architectures, there is a need for an orchestration tool to manage containers. Kubernetes has emerged as one of the most popular alternatives, adopting widespread usage. But managing multiple Kubernetes clusters on its own have proven to be a challenging task. This difficulty has given rise to multiple cloud based alternatives which help streamline the managing process of a cluster environment and helps maintain an extreme high availability environment that is hard to replicate in an on premise environment. Using these cloud based platforms for hosting and managing ones system is great, but alleviating control of a system to a cloud provider masquerades any illicit behaviour performed on or through the system. The scope of this thesis is on examining optional designs that will automate the process of executing a geographical failover between different locations to better sustain an on premise fault tolerant kubernetes environment. There already exists multiple tools in the area of kubernetes service mesh, but their focus is not primarily on increasing system resilience but to increase security, observability and performance. Linkerd is a sidecar oriented service mesh which supports geographical failover by manually announcing individual services between cluster(s) mirror gateways. Cilium offers an Container Networking Interface (CNI) which performs routing through eBPF and allows for seamless failover between clusters by managing cross cluster service endpoints. Both of the mentioned service mesh providers handle failover from inside the kubernetes cluster. The contributions includes two new peer-to-peer designs that focus on external cluster geographical failover - both designs are compatible with preexisting kubernetes clusters without internal modifications. A fully repli-cated design was then realised into a proof of concept (POC), and tested against a Cilium multi cluster environment on the metric of north to south traffic latency. Due to the nature of the underlying hardware, the tests showed that the POC can be used for external geographical failover and it showed potential performance capabilities in a limited lab scale. As the purpose of this thesis was not to determine the traffic throughput of a geographical failover solution; but to examine different approaches automatic geographical failover can be implemented, the tests were a success. Therefore, this thesis can conclude that there exists several working solutions, and the POC have shown that there are still undiscovered and unimplemented solutions to explore.

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A cluster is a group of computing resources that are connected by a single computer network and are managed as a single system. Clusters potentially have three key advantages over workstations operated in isolation—fault tolerance, load balancing and support for distributed computing. Information sharing among the cluster’s resources affects all phases of cluster administration. The thesis describes a new tool for distributing files within clusters. This tool, the Scalable and Reliable File Transfer Tool (SRFTT), uses Forward Error Correction (FEC) and multiple multicast channels to achieve an efficient reliable file transfer, relative to heterogeneous clusters. SRFTT achieves scalability by avoiding feedback from the receivers. Tests show that, for large files, retransmitting recovery information on multiple multicast channels gives significant performance gains when compared to a single retransmission channel.

As unlabeled data becomes increasingly available, the need for robust data mining techniques increases as well. Clustering is a common data mining tool which seeks to find related, independent patterns in data called clusters. The cluster validation problem addresses the question of how well a given clustering fits the data set. We present CVIC (cluster validation using instance-based confidences) which assigns confidence scores to each individual instance, as opposed to more traditional methods which focus on the clusters themselves. CVIC trains supervised learners to recreate the clustering, and instances are scored based on output from the learners which corresponds to the confidence that the instance was clustered correctly. One consequence of individually validated instances is the ability to direct users to instances in a cluster that are either potentially misclustered or correctly clustered. Instances with low confidences can either be manually inspected or reclustered and instances with high confidences can be automatically labeled. We compare CVIC to three competing methods for assigning confidence scores and show results on CVIC's ability to successfully assign scores that result in higher average precision and recall for detecting misclustered and correctly clustered instances across five clustering algorithms on twenty data sets including handwritten historical image data provided by Ancestry.com.