Vowel recognition in continuous speech

In continuous speech, the identification of phonemes requires the ability to extract features that are capable of characterizing the acoustic signal. Previous work has shown that relatively high classification accuracy can be obtained from a single spectrum taken during the steady-state portion of the phoneme, assuming that the phonetic environment is held constant. The present study represents an attempt to extend this work to variable phonetic contexts by using dynamic rather than static spectral information. This thesis has four aims: 1) Classify vowels in continuous speech; 2) Find the optimal set of features that best describe the vowel regions; 3) Compare the classification results using a multivariate maximum likelihood distance measure with those of a neural network using the backpropagation model; 4) Examine the classification performance of a Hidden Markov Model given a pathway through phonetic space

Graph theoretical analysis of complex networks in the brain

Since the discovery of small-world and scale-free networks the study of complex systems from a network perspective has taken an enormous flight. In recent years many important properties of complex networks have been delineated. In particular, significant progress has been made in understanding the relationship between the structural properties of networks and the nature of dynamics taking place on these networks. For instance, the 'synchronizability' of complex networks of coupled oscillators can be determined by graph spectral analysis. These developments in the theory of complex networks have inspired new applications in the field of neuroscience. Graph analysis has been used in the study of models of neural networks, anatomical connectivity, and functional connectivity based upon fMRI, EEG and MEG. These studies suggest that the human brain can be modelled as a complex network, and may have a small-world structure both at the level of anatomical as well as functional connectivity. This small-world structure is hypothesized to reflect an optimal situation associated with rapid synchronization and information transfer, minimal wiring costs, as well as a balance between local processing and global integration. The topological structure of functional networks is probably restrained by genetic and anatomical factors, but can be modified during tasks. There is also increasing evidence that various types of brain disease such as Alzheimer's disease, schizophrenia, brain tumours and epilepsy may be associated with deviations of the functional network topology from the optimal small-world pattern

Scattering matrices and expansion coefficients of Martian analogue palagonite particles

We present measurements of ratios of elements of the scattering matrix of Martian analogue palagonite particles for scattering angles ranging from 3 to 174 degrees and a wavelength of 632.8 nm. To facilitate the use of these measurements in radiative transfer calculations we have devised a method that enables us to obtain, from these measurements, a normalized synthetic scattering matrix covering the complete scattering angle range from 0 to 180 degrees. Our method is based on employing the coefficients of the expansions of scattering matrix elements into generalized spherical functions. The synthetic scattering matrix elements and/or the expansion coefficients obtained in this way, can be used to include multiple scattering by these irregularly shaped particles in (polarized) radiative transfer calculations, such as calculations of sunlight that is scattered in the dusty Martian atmosphere.Comment: 34 pages 7 figures 1 tabl

Isolation of extracellular vesicles with combined enrichment methods

Extracellular vesicles (EVs) are currently of tremendous interest in many research disciplines and EVs have potential for development of EV diagnostics or therapeutics. Most well-known single EV isolation methods have their particular advantages and disadvantages in terms of EV purity and EV yield. Combining EV isolation methods provides additional potential to improve the efficacy of both purity and yield. This review assesses the contribution and efficacy of using combined EV isolation methods by performing a two-step systematic literature analysis from all papers applying EV isolation in the year 2019. This resulted in an overview of the various methods being applied for EV isolations. A second database was generated for all studies within the first database that fairly compared multiple EV isolation methods by determining both EV purity and EV yield after isolation. From these databases it is shown that the most used EV isolation methods are not per definition the best methods based on EV purity or EV yield, indicating that more factors play a role in the choice which EV isolation method to choose than only the efficacy of the method. From the included studies it is shown that ~60% of all the included EV isolations were performed with combined EV isolation methods. The majority of EV isolations were performed with differential ultracentrifugation alone or in combination with differential ultrafiltration. When efficacy of EV isolation methods was determined in terms of EV purity and EV yield, combined EV isolation methods clearly outperformed single EV isolation methods, regardless of the type of starting material used. A recommended starting point would be the use of size-exclusion chromatography since this method, especially when combined with low-speed centrifugation, resulted in the highest EV purity, while still providing a reasonable EV yield

From the 'cinematic' to the 'anime-ic': Issues of movement in anime

This is the author's accepted manuscript. The final published article is available from the link below.This article explores the way that movement is formally depicted in anime. Drawing on Thomas Lamarre's concepts of the `cinematic' and the `anime-ic', the article interrogates further the differences in movement and action in anime from traditional filmic form. While often considered in terms of `flatness', anime offers spectacle, character development and, ironically, depth through the very form of movement put to use in such texts.The article questions whether the modes of address at work in anime are unique to this form of animation.Taking into account how the terms `cinematic' and `anime-ic' can be understood (and by extension the cinematic and animatic apparatus), the article also begins to explore how viewers might identify with such images

Localized energy for wave equations with degenerate trapping

Localized energy estimates have become a fundamental tool when studying wave equations in the presence of asymptotically at background geometry. Trapped rays necessitate a loss when compared to the estimate on Minkowski space. A loss of regularity is a common way to incorporate such. When trapping is sufficiently weak, a logarithmic loss of regularity suffices. Here, by studying a warped product manifold introduced by Christianson and Wunsch, we encounter the first explicit example of a situation where an estimate with an algebraic loss of regularity exists and this loss is sharp. Due to the global-in-time nature of the estimate for the wave equation, the situation is more complicated than for the Schr\"{o}dinger equation. An initial estimate with sub-optimal loss is first obtained, where extra care is required due to the low frequency contributions. An improved estimate is then established using energy functionals that are inspired by WKB analysis. Finally, it is shown that the loss cannot be improved by any power by saturating the estimate with a quasimode.Comment: 18 page

Comparing Brain Networks of Different Size and Connectivity Density Using Graph Theory

Graph theory is a valuable framework to study the organization of functional and anatomical connections in the brain. Its use for comparing network topologies, however, is not without difficulties. Graph measures may be influenced by the number of nodes (N) and the average degree (k) of the network. The explicit form of that influence depends on the type of network topology, which is usually unknown for experimental data. Direct comparisons of graph measures between empirical networks with different N and/or k can therefore yield spurious results. We list benefits and pitfalls of various approaches that intend to overcome these difficulties. We discuss the initial graph definition of unweighted graphs via fixed thresholds, average degrees or edge densities, and the use of weighted graphs. For instance, choosing a threshold to fix N and k does eliminate size and density effects but may lead to modifications of the network by enforcing (ignoring) non-significant (significant) connections. Opposed to fixing N and k, graph measures are often normalized via random surrogates but, in fact, this may even increase the sensitivity to differences in N and k for the commonly used clustering coefficient and small-world index. To avoid such a bias we tried to estimate the N,k-dependence for empirical networks, which can serve to correct for size effects, if successful. We also add a number of methods used in social sciences that build on statistics of local network structures including exponential random graph models and motif counting. We show that none of the here-investigated methods allows for a reliable and fully unbiased comparison, but some perform better than others