Deep neural networks for video classification in ecology

Analyzing large volumes of video data is a challenging and time-consuming task. Automating this process would very valuable, especially in ecological research where massive amounts of video can be used to unlock new avenues of ecological research into the behaviour of animals in their environments. Deep Neural Networks, particularly Deep Convolutional Neural Networks, are a powerful class of models for computer vision. When combined with Recurrent Neural Networks, Deep Convolutional models can be applied to video for frame level video classification. This research studies two datasets: penguins and seals. The purpose of the research is to compare the performance of image-only CNNs, which treat each frame of a video independently, against a combined CNN-RNN approach; and to assess whether incorporating the motion information in the temporal aspect of video improves the accuracy of classifications in these two datasets. Video and image-only models offer similar out-of-sample performance on the simpler seals dataset but the video model led to moderate performance improvements on the more complex penguin action recognition dataset

Developing a computer aided design tool for inclusive design

The purpose of this study was to investigate age-related changes in the performance of a range of movement tasks for integration into a computer aided design (CAD) tool for use in inclusive design

Neuroaesthetics and the Trouble with Beauty

Neuroscience is increasingly being called upon to address issues within the humanities. We discuss challenges that arise, relating to art and beauty, and provide ideas for a way forward.Musi

A bound on Grassmannian codes

We give a new asymptotic upper bound on the size of a code in the Grassmannian space. The bound is better than the upper bounds known previously in the entire range of distances except very large values.Comment: 5 pages, submitte

Shedding light on melanins within in situ human eye melanocytes using 2-photon microscopy profiling techniques.

Choroidal melanocytes (HCMs) are melanin-producing cells in the vascular uvea of the human eye (iris, ciliary body and choroid). These cranial neural crest-derived cells migrate to populate a mesodermal microenvironment, and display cellular functions and extracellular interactions that are biologically distinct to skin melanocytes. HCMs (and melanins) are important in normal human eye physiology with roles including photoprotection, regulation of oxidative damage and immune responses. To extend knowledge of cytoplasmic melanins and melanosomes in label-free HCMs, a non-invasive 'fit-free' approach, combining 2-photon excitation fluorescence lifetimes and emission spectral imaging with phasor plot segmentation was applied. Intracellular melanin-mapped FLIM phasors showed a linear distribution indicating that HCM melanins are a ratio of two fluorophores, eumelanin and pheomelanin. A quantitative histogram of HCM melanins was generated by identifying the image pixel fraction contributed by phasor clusters mapped to varying eumelanin/pheomelanin ratio. Eumelanin-enriched dark HCM regions mapped to phasors with shorter lifetimes and longer spectral emission (580-625 nm) and pheomelanin-enriched lighter pigmented HCM regions mapped to phasors with longer lifetimes and shorter spectral emission (550-585 nm). Overall, we demonstrated that these methods can identify and quantitatively profile the heterogeneous eumelanins/pheomelanins within in situ HCMs, and visualize melanosome spatial distributions, not previously reported for these cells

Frame-by-frame annotation of video recordings using deep neural networks

Funding: Scottish Government (Grant Number(s): Marine Mammal Scientific Support Research Program); Homebrew Films; National Research Foundation of South Africa (Grant Number(s): 105782, 90782).Video data are widely collected in ecological studies, but manual annotation is a challenging and time‐consuming task, and has become a bottleneck for scientific research. Classification models based on convolutional neural networks (CNNs) have proved successful in annotating images, but few applications have extended these to video classification. We demonstrate an approach that combines a standard CNN summarizing each video frame with a recurrent neural network (RNN) that models the temporal component of video. The approach is illustrated using two datasets: one collected by static video cameras detecting seal activity inside coastal salmon nets and another collected by animal‐borne cameras deployed on African penguins, used to classify behavior. The combined RNN‐CNN led to a relative improvement in test set classification accuracy over an image‐only model of 25% for penguins (80% to 85%), and substantially improved classification precision or recall for four of six behavior classes (12–17%). Image‐only and video models classified seal activity with very similar accuracy (88 and 89%), and no seal visits were missed entirely by either model. Temporal patterns related to movement provide valuable information about animal behavior, and classifiers benefit from including these explicitly. We recommend the inclusion of temporal information whenever manual inspection suggests that movement is predictive of class membership.Publisher PDFPeer reviewe

Uncertainty principles for orthonormal sequences

The aim of this paper is to provide complementary quantitative extensions of two results of H.S. Shapiro on the time-frequency concentration of orthonormal sequences in $L^2 (\R)$. More precisely, Shapiro proved that if the elements of an orthonormal sequence and their Fourier transforms are all pointwise bounded by a fixed function in $L^2(\R)$ then the sequence is finite. In a related result, Shapiro also proved that if the elements of an orthonormal sequence and their Fourier transforms have uniformly bounded means and dispersions then the sequence is finite. This paper gives quantitative bounds on the size of the finite orthonormal sequences in Shapiro's uncertainty principles. The bounds are obtained by using prolate sphero\"{i}dal wave functions and combinatorial estimates on the number of elements in a spherical code. Extensions for Riesz bases and different measures of time-frequency concentration are also given

Exploring the Impact of Socio-Technical Core-Periphery Structures in Open Source Software Development

In this paper we apply the social network concept of core-periphery structure to the sociotechnical structure of a software development team. We propose a socio-technical pattern that can be used to locate emerging coordination problems in Open Source projects. With the help of our tool and method called TESNA, we demonstrate a method to monitor the socio-technical core-periphery movement in Open Source projects. We then study the impact of different core-periphery movements on Open Source projects. We conclude that a steady core-periphery shift towards the core is beneficial to the project, whereas shifts away from the core are clearly not good. Furthermore, oscillatory shifts towards and away from the core can be considered as an indication of the instability of the project. Such an analysis can provide developers with a good insight into the health of an Open Source project. Researchers can gain from the pattern theory, and from the method we use to study the core-periphery movements