Uncanny nature: Hybrid stop motion using Blender

This talk gives an overview of the project "Uncanny Nature", which incoporates a style of animation called Hybrid Stop Motion, that combines physical object armatures with virtual copies. The development of the production pipeline (using a mix of Blender, Dragonframe, Photoscan and Arduino) is discussed, as well as the way that Blender was used throughout the production to visualise, model, animate and composite the elements together.

Head motion and Inattention/Hyperactivity share common genetic influences: Implications for fMRI studies of ADHD

Head motion (HM) is a well known confound in analyses of functional MRI (fMRI) data. Neuroimaging researchers therefore typically treat HM as a nuisance covariate in their analyses. Even so, it is possible that HM shares a common genetic influence with the trait of interest. Here we investigate the extent to which this relationship is due to shared genetic factors, using HM extracted from resting-state fMRI and maternal and self report measures of Inattention and Hyperactivity-Impulsivity from the Strengths and Weaknesses of ADHD Symptoms and Normal Behaviour (SWAN) scales. Our sample consisted of healthy young adult twins (N = 627 (63% females) including 95 MZ and 144 DZ twin pairs, mean age 22, who had mother-reported SWAN; N = 725 (58% females) including 101 MZ and 156 DZ pairs, mean age 25, with self reported SWAN). This design enabled us to distinguish genetic from environmental factors in the association between head movement and ADHD scales. HM was moderately correlated with maternal reports of Inattention (r = 0.17, p-value = 7.4E-5) and Hyperactivity-Impulsivity (r = 0.16, p-value = 2.9E-4), and these associations were mainly due to pleiotropic genetic factors with genetic correlations [95% CIs] of rg = 0.24 [0.02, 0.43] and rg = 0.23 [0.07, 0.39]. Correlations between self-reports and HM were not significant, due largely to increased measurement error. These results indicate that treating HM as a nuisance covariate in neuroimaging studies of ADHD will likely reduce power to detect between-group effects, as the implicit assumption of independence between HM and Inattention or Hyperactivity-Impulsivity is not warranted. The implications of this finding are problematic for fMRI studies of ADHD, as failing to apply HM correction is known to increase the likelihood of false positives. We discuss two ways to circumvent this problem: censoring the motion contaminated frames of the RS-fMRI scan or explicitly modeling the relationship between HM and Inattention or Hyperactivity-Impulsivity

Stationary processes for object detection and non-rigid structure-from-motion

Stationary processes are random variables whose value is a signal and whose distribution is invariant to translation in the domain of the signal. They are intimately connected to convolution, and therefore to the Fourier transform, since the covariance matrix of a stationary process is a Toeplitz matrix, and Toeplitz matrices are the expression of convolution as a linear operator. This thesis utilises this connection in the study of i) efficient training algorithms for object detection and ii) trajectory-based non-rigid structure-from-motion.

The pedagogic prosthetic: Augmented learning as content-in-motion in hybrid educational spheres

This article draws on the design and implementation of three mobile learning projects introduced by Flanagan in 2011, 2012 and 2014 engaging a total of 206 participants. The latest of these projects is highlighted in this article. Two other projects provide additional examples of innovative strategies to engage mobile and cloud systems describing how electronic and mobile technology can help facilitate teaching and learning, assessment for learning and assessment as learning, and support communities of practice. The second section explains the theoretical premise supporting the implementation of technology and promulgates a hermeneutic phenomenological approach. The third section discusses mobility, both in terms of the exploration of wearable technology in the prototypes developed as a result of the projects, and the affordances of mobility within pedagogy. Finally the quantitative and qualitative methods in place to evaluate m-learning are explained.