Visualization-Based Mapping of Language Function in the Brain

Cortical language maps, obtained through intraoperative electrical stimulation studies, provide a rich source of information for research on language organization. Previous studies have shown interesting correlations between the distribution of essential language sites and such behavioral indicators as verbal IQ and have provided suggestive evidence for regarding human language cortex as an organization of multiple distributed systems. Noninvasive studies using ECoG, PET, and functional MR lend support to this model; however, there as yet are no studies that integrate these two forms of information. In this paper we describe a method for mapping the stimulation data onto a 3-D MRI-based neuroanatomic model of the individual patient. The mapping is done by comparing an intraoperative photograph of the exposed cortical surface with a computer-based MR visualization of the surface, interactively indicating corresponding stimulation sites, and recording 3-D MR machine coordinates of the indicated sites. Repeatability studies were performed to validate the accuracy of the mapping technique. Six observers—a neurosurgeon, a radiologist, and four computer scientists, independently mapped 218 stimulation sites from 12 patients. The mean distance of a mapping from the mean location of each site was 2.07 mm, with a standard deviation of 1.5 mm, or within 5.07 mm with 95% confidence. Since the surgical sites are accurate within approximately 1 cm, these results show that the visualization-based approach is accurate within the limits of the stimulation maps. When incorporated within the kind of information system envisioned by the Human Brain Project, this anatomically based method will not only provide a key link between noninvasive and invasive approaches to understanding language organization, but will also provide the basis for studying the relationship between language function and anatomical variability

Strategic Planning - Niche Marketing in the Agriculture Industry

The purpose of the research is to improve our understanding of the adaptation process in agriculture at the farm level and the influence through the value chain. The research identified critical managerial decision areas in the strategic planning process of blackcurrant growers in Alberta and the South Island of New Zealand. The work was a comparative study of growers that attempted to determine the correspondence between the results of case study observations and a set of theoretical propositions that were developed from a review of the relevant literature. Results indicate that growers understand their own firm’s core competencies, plan strategically and contingently to maintain flexibility and retain niche advantages. Data gathered on the blackcurrant sectors in Canada and New Zealand provided the contextual basis for the selection and analysis of the grower case studies. The sector analysis reached across the value chain. Among the findings reported was the interesting observation that although niche marketing is an accepted strategy in the marketing literature as a means to adaptive change, and although the flexibility inherent in this approach is critical to the success of traditionally resource-starved small firms, it is not clear that the firms reported on in this study engaged in niche marketing as a planned strategy but rather came upon the opportunity through serendipity. In terms of country comparison, results indicate that there may be some specific factors that contribute to the success of the blackcurrant industry in New Zealand. Closer examination of these factors may be beneficial to assisting the Canadian sector. Keywords: Niche marketing, strategic planning, adaptation flexibility JEL Codes: D81, L1, M31, O13, Q13Niche marketing, strategic planning, adaptation flexibility, Farm Management, Marketing, D81, L1, M31, O13, Q13,

Exploiting links and text structure on the Web : a quantitative approach to improving search quality

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Raising Grain in Next Year Country: Dryland Farming, Drought, and Adaptation in the Golden Triangle, Montana

Climate change has already and will likely continue to impact agriculture in the Western United States, threatening water supplies for both irrigated and rainfed agriculture (Calzadilla et al. 2010; Chambers and Pellant 2008; MacDonald et al. 2010; Pedersen et al. 2009). In the Golden Triangle, a region in north central Montana, known for its dryland grain production, the same is true. There is a need for in-depth, fine-grained, place-based, and qualitative research about the process of climate change adaptation in agriculture (Miller et al. 2013). Drought challenges farmers in the Triangle, which is semiarid and receives 10-15 inches of annual rainfall. As such, with this study, I use drought as a “research window” into the process of agricultural adaptation to climate extremes (Head et al. 2011). During the 2014 growing season, I conducted 15 in-depth interviews with conventional and organic dryland farmers in the Golden Triangle about how they experienced and responded to drought, as well as how they perceived the current, rapid climate change. In response to drought, farmers have adapted by conserving both fiscal resources and soil moisture in numerous ways, often operating within the lines of “conventional” and “organic,” though not always, as this research shows. Many farmers are adopting alternative, sustainable agricultural practices to help build soil organic matter, building their resilience to drought and other climate extremes. Despite these current and evolving changes happening on farms in the Golden Triangle, most farmers do not consider climate change when enacting adaptive changes on their farms

Chapter 1 : Hybrid Segmentation Methods

We propose a Hybrid Segmentation Engine that consists of component modules, for automated segmentation of radiological patient and the Visible Human data. We integrate boundary-based and region-based segmentation methods to exploit the strength of each method hopefully to cover the weakness of the other method

RFID Technology in Intelligent Tracking Systems in Construction Waste Logistics Using Optimisation Techniques

Construction waste disposal is an urgent issue for protecting our environment. This paper proposes a waste management system and illustrates the work process using plasterboard waste as an example, which creates a hazardous gas when land filled with household waste, and for which the recycling rate is less than 10% in the UK. The proposed system integrates RFID technology, Rule-Based Reasoning, Ant Colony optimization and knowledge technology for auditing and tracking plasterboard waste, guiding the operation staff, arranging vehicles, schedule planning, and also provides evidence to verify its disposal. It h relies on RFID equipment for collecting logistical data and uses digital imaging equipment to give further evidence; the reasoning core in the third layer is responsible for generating schedules and route plans and guidance, and the last layer delivers the result to inform users. The paper firstly introduces the current plasterboard disposal situation and addresses the logistical problem that is now the main barrier to a higher recycling rate, followed by discussion of the proposed system in terms of both system level structure and process structure. And finally, an example scenario will be given to illustrate the system’s utilization

Faculty Impact Statements, 2010

Each issue [in the Research Series] has a distinctive titl

Scale-based surface understanding using diffusion smoothing

The research discussed in this thesis is concerned with surface understanding from the viewpoint of recognition-oriented, scale-related processing based on surface curvatures and diffusion smoothing. Four problems below high level visual processing are investigated: 1) 3-dimensional data smoothing using a diffusion process; 2) Behaviour of shape features across multiple scales, 3) Surface segmentation over multiple scales; and 4) Symbolic description of surface features at multiple scales. In this thesis, the noisy data smoothing problem is treated mathematically as a boundary value problem of the diffusion equation instead of the well-known Gaussian convolution, In such a way, it provides a theoretical basis to uniformly interpret the interrelationships amongst diffusion smoothing, Gaussian smoothing, repeated averaging and spline smoothing. It also leads to solving the problem with a numerical scheme of unconditional stability, which efficiently reduces the computational complexity and preserves the signs of curvatures along the surface boundaries. Surface shapes are classified into eight types using the combinations of the signs of the Gaussian curvature K and mean curvature H, both of which change at different scale levels. Behaviour of surface shape features over multiple scale levels is discussed in terms of the stability of large shape features, the creation, remaining and fading of small shape features, the interaction between large and small features and the structure of behaviour of the nested shape features in the KH sign image. It provides a guidance for tracking the movement of shape features from fine to large scales and for setting up a surface shape description accordingly. A smoothed surface is partitioned into a set of regions based on curvature sign homogeneity. Surface segmentation is posed as a problem of approximating a surface up to the degree of Gaussian and mean curvature signs using the depth data alone How to obtain feasible solutions of this under-determined problem is discussed, which includes the surface curvature sign preservation, the reason that a sculptured surface can be segmented with the KH sign image alone and the selection of basis functions of surface fitting for obtaining the KH sign image or for region growing. A symbolic description of the segmented surface is set up at each scale level. It is composed of a dual graph and a geometrical property list for the segmented surface. The graph describes the adjacency and connectivity among different patches as the topological-invariant properties that allow some object's flexibility, whilst the geometrical property list is added to the graph as constraints that reduce uncertainty. With this organisation, a tower-like surface representation is obtained by tracking the movement of significant features of the segmented surface through different scale levels, from which a stable description can be extracted for inexact matching during object recognition