深層ニューラルネットワークによる病理診断支援のためのドメイン知識の段階的転移に関する研究

筑波大学 (University of Tsukuba)201

Latitude, longitude, and beyond:mining mobile objects' behavior

Rapid advancements in Micro-Electro-Mechanical Systems (MEMS), and wireless communications, have resulted in a surge in data generation. Mobility data is one of the various forms of data, which are ubiquitously collected by different location sensing devices. Extensive knowledge about the behavior of humans and wildlife is buried in raw mobility data. This knowledge can be used for realizing numerous viable applications ranging from wildlife movement analysis, to various location-based recommendation systems, urban planning, and disaster relief. With respect to what mentioned above, in this thesis, we mainly focus on providing data analytics for understanding the behavior and interaction of mobile entities (humans and animals). To this end, the main research question to be addressed is: How can behaviors and interactions of mobile entities be determined from mobility data acquired by (mobile) wireless sensor nodes in an accurate and efficient manner? To answer the above-mentioned question, both application requirements and technological constraints are considered in this thesis. On the one hand, applications requirements call for accurate data analytics to uncover hidden information about individual behavior and social interaction of mobile entities, and to deal with the uncertainties in mobility data. Technological constraints, on the other hand, require these data analytics to be efficient in terms of their energy consumption and to have low memory footprint, and processing complexity

Applying the principles of spatial modelling to the management of biodiversity in the fragmented landscapes of south-western Australia

Biodiversity conservation throughout the world is challenged by the impacts of a changing climate on fragmented landscapes. To mitigate these threats, conservation managers require models which can demonstrate the consequences of both negative impacts and management actions. This need can be addressed through spatial modelling applications. Unfortunately, throughout much of the world, spatial modelling is forgone, being seen as requiring skills and resources beyond the means of many conservation planners and managers. This thesis seeks to address this dilemma by delivering criteria for a successful modelling application and by providing case studies which demonstrate how appropriate modelling can be undertaken without highly specialised skills or prohibitively expensive software and equipment. In this way it facilitates the delivery of better targeted and, consequently more effective, management actions. For my case studies I have used the south-western corner of Australia as a demonstration landscape. This region is recognised internationally as a “biodiversity hotspot,” not only for the biological richness and uniqueness of species but also for the level of threat to which they are subject. Like many landscapes throughout the world, much or this region’s natural biota exists in fragmented, fragile and degraded patches and is therefore highly vulnerable to the anticipated impacts of anthropogenic global warming. In this thesis I have: 1) examined the principles of spatial modelling and reviewed how spatial modelling has been applied to conservation management in this region, 2) conducted examples of different forms of spatial modelling using actual regional conservation management issues, and 3) demonstrated how these examples can be incorporated into conservation management planning. My key findings are: Spatial modelling provides users with an opportunity to effectively test hypotheses, thereby informing the planning process and improving conservation outcomes. Where spatial modelling is omitted from the process, knowledge gaps are often addressed by the axiomatic and by assumption. This is contrary to the principles of effective adaptive management. Modelling tools are inherently more effective when selected for their capacity to meet a planning objective rather than where projects are tailored to meet a model’s capacity. The coordinated use of multiple tools can often provide a more robust understanding of the consequences impacts and mitigating actions. All tools and data sets used should be utilised with a clear and acknowledged understanding of their suitability, strengths and limitations. A wide range of spatial modelling tools (and data sets) are freely and readily available to conservation managers. Most of these come with excellent tutorials and support services. Data gaps can often be addressed through targeted field observations, obtained through complimentary planning processes, or synthesised from accessible data sets. There is a very large body of peer reviewed literature demonstrating means by which others have applied existing modelling tools, or developed tools themselves, to meet a wide range of applications. Accessing this literature is an excellent means of building spatial modelling capacity. New and improved tools, methodologies and data sets are constantly being developed. A failure to implement effective spatial modelling is becoming increasing difficult to justify