Fiber-Flux Diffusion Density for White Matter Tracts Analysis: Application to Mild Anomalies Localization in Contact Sports Players

We present the concept of fiber-flux density for locally quantifying white matter (WM) fiber bundles. By combining scalar diffusivity measures (e.g., fractional anisotropy) with fiber-flux measurements, we define new local descriptors called Fiber-Flux Diffusion Density (FFDD) vectors. Applying each descriptor throughout fiber bundles allows along-tract coupling of a specific diffusion measure with geometrical properties, such as fiber orientation and coherence. A key step in the proposed framework is the construction of an FFDD dissimilarity measure for sub-voxel alignment of fiber bundles, based on the fast marching method (FMM). The obtained aligned WM tract-profiles enable meaningful inter-subject comparisons and group-wise statistical analysis. We demonstrate our method using two different datasets of contact sports players. Along-tract pairwise comparison as well as group-wise analysis, with respect to non-player healthy controls, reveal significant and spatially-consistent FFDD anomalies. Comparing our method with along-tract FA analysis shows improved sensitivity to subtle structural anomalies in football players over standard FA measurements

Modelling and Simulation of Lily flowers using PDE Surfaces

This paper presents a partial differential equation (PDE)-based surface modelling and simulation framework for lily flowers. We use a PDE-based surface modelling technique to represent shape of a lily flower and PDE-based dynamic simulation to animate blossom and decay processes of lily flowers. To this aim, we first automatically construct the geometry of lily flowers from photos to obtain feature curves. Second, we apply a PDE-based surface modelling technique to generate sweeping surfaces to obtain geometric models of the flowers. Then, we use a physics-driven and data-based method and introduce the flower shapes at the initial and final positions into our proposed dynamic deformation model to generate a realistic deformation of flower blossom and decay. The results demonstrate that our proposed technique can create realistic flower models and their movements and shape changes against time efficiently with a small data size

Characteristics of Pro-c Analogies and Blends between Research Publications

Dr Inventor is a tool that aims to enhance the professional (Pro-c) creativity of researchers by suggesting novel hypotheses, arising from analogies between publications. Dr Inventor processes original research documents using a combination of lexical analysis and cognitive computation to identify novel comparisons that suggest new research hypotheses, with the objective of supporting a novel research publication. Research on analogical reasoning strongly suggests that the value of analogy-based comparisons depends primarily on the strength of the mapping (or counterpart projection) between the two analogs. An evaluation study of a number of computer generated comparisons attracted creativity ratings from a group of practising researchers. This paper explores a variety of theoretically motivated metrics operating on different conceptual spaces, identifying some weak associations with user's creativity ratings. Surprisingly, our results show that metrics focused on the mapping appear to have less relevance to creativity than metrics assessing the inferences (blended space). This paper includes a brief description of a research project currently exploring the best research hypothesis generated during this evaluation. Finally, we explore PCA as a means of specifying a combined multiple metrics from several blending spaces as a basis for detecting comparisons to enhance researchers’ creativity

Expert and Corpus-Based Evaluation of a 3-Space Model of Conceptual Blending

This paper presents the 3-space model of conceptual blending that estimates the figurative similarity between Input spaces 1 and 2 using both their analogical similarity and the interconnecting Generic Space. We describe how our Dr Inventor model is being evaluated as a model of lexically based figurative similarity. We describe distinct but related evaluation tasks focused on 1) identifying novel and quality analogies between computer graphics publications 2) evaluation of machine generated translations of text documents 3) evaluation of documents in a plagiarism corpus. Our results show that Dr Inventor is capable of generating novel comparisons between publications but also appears to be a useful tool for evaluating machine translation systems and for detecting and assessing the level of plagiarism between documents. We also outline another more recent evaluation, using a corpus of patent applications

Novel Correspondence-based Approach for Consistent Human Skeleton Extraction

This paper presents a novel base-points-driven shape correspondence (BSC) approach to extract skeletons of articulated objects from 3D mesh shapes. The skeleton extraction based on BSC approach is more accurate than the traditional direct skeleton extraction methods. Since 3D shapes provide more geometric information, BSC offers the consistent information between the source shape and the target shapes. In this paper, we first extract the skeleton from a template shape such as the source shape automatically. Then, the skeletons of the target shapes of different poses are generated based on the correspondence relationship with source shape. The accuracy of the proposed method is demonstrated by presenting a comprehensive performance evaluation on multiple benchmark datasets. The results of the proposed approach can be applied to various applications such as skeleton-driven animation, shape segmentation and human motion analysis