Determining subunits for sign language recognition by evolutionary cluster-based segmentation of time series

Abstract. The paper considers partitioning time series into subsequences which form homogeneous groups. To determine the cut points an evolutionary optimization procedure based on multicriteria quality assessment of the resulting clusters is applied. The problem is motivated by automatic recognition of signed expressions, based on modeling gestures with subunits, which is similar to modeling speech by means of phonemes. In the paper the problem is formulated, its solution method is proposed and experimentally verified

Data mining and modelling for sign language

Sign languages have received significantly less attention than spoken languages in the research areas of corpus analysis, machine translation, recognition, synthesis and social signal processing, amongst others. This is mainly due to signers being in a clear minority and there being a strong prior belief that sign languages are simply arbitrary gestures. To date, this manifests in the insufficiency of sign language resources available for computational modelling and analysis, with no agreed standards and relatively stagnated advancements compared to spoken language interaction research. Fortunately, the machine learning community has developed methods, such as transfer learning, for dealing with sparse resources, while data mining techniques, such as clustering can provide insights into the data. The work described here utilises such transfer learning techniques to apply neural language model to signed utterances and to compare sign language phonemes, which allows for clustering of similar signs, leading to automated annotation of sign language resources. This thesis promotes the idea that sign language research in computing should rely less on hand-annotated data thus opening up the prospect of using readily available online data (e.g. signed song videos) through the computational modelling and automated annotation techniques presented in this thesis

Molecular characterization and evolutionary plasticity of protein-protein interfaces

Abstract The sequencing of the human genome provides the parts list for understanding cellular processes. However, as 70% of eukaryotic genes work through multi-protein systems, it is only through detailed study of the interactions of these components, that a more complete, systems-level understanding can be gained. This thesis is centred on the establishment of PICCOLO - a comprehensive database of structurally characterized protein interactions. In generating the resource, issues of interface definition, quaternary structure, data redundancy, structural environment and interaction type are addressed. The resource enables a variety of analyses to be performed concerning interface properties including residue propensity, hydropathy, polarity, interface size, sequence entropy and residue contact preference. PICCOLO has been applied to probing the patterns of substitutions that are accepted in protein interfaces across evolution, and whether these patterns are distinguishable from those seen in other structural environments. The derivation of a high-quality set of multiple structural alignments in the form of the database TOCCATA, a prerequisite for such analysis, is described, as well as procedures to derive environment-specific substitution tables. The Blundell group has contributed a series of methods to predict the likely effect of non-synonymous Single Nucleotide Polymorphisms (nsSNPs) on protein stability, function and interactions in order to triage the large volumes of data created from high-throughput genetic screening studies, enabling prioritization of those nsSNPs most likely to be phenotypically detrimental. PICCOLO's contribution to these predictions is described. Historically there has been little focus on protein-protein interactions as drug targets for small-molecule therapeutics. However, alanine-scanning mutagenesis studies have revealed that only a subset of residues contribute the greater part of free energy to binding - so-called "hot-spots". Molecular characterization of hot-spots performed using PICCOLO, probes the molecular basis underlying this important phenomenon leading to the possibility of predictive methods to identify hot-spots 'in silico'

Proceedings of the 2014 Berry Summer Thesis Institute

Thanks to a gift from the Berry Family Foundation and the Berry family, the University Honors Program launched the Berry Summer Thesis Institute in 2012. The institute introduces students in the University Honors Program to intensive research, scholarship opportunities and professional development. Each student pursues a 12-week summer thesis research project under the guidance of a UD faculty mentor. This contains the product of the students\u27 research

Stabilizing Forces in Acoustic Cultural Evolution: Comparing Humans and Birds

Learned acoustic communication systems, like birdsong and spoken human language, can be described from two seemingly contradictory perspectives. On one hand, learned acoustic communication systems can be remarkably consistent. Substantive and descriptive generalizations can be made which hold for a majority of populations within a species. On the other hand, learned acoustic communication systems are often highly variable. The degree of variation is often so great that few, if any, substantive generalizations hold for all populations in a species. Within my dissertation, I explore the interplay of variation and uniformity in three vocal learning species: budgerigars (Melopsittacus undulatus), house finches (Haemorhous mexicanus), and humans (Homo sapiens). Budgerigars are well-known for their versatile mimicry skills, house finch song organization is uniform across populations, and human language has been described as the prime example of variability by some while others see only subtle variations of largely uniform system. For each of these species, I address several questions related to variability and uniformity: What is the typical range of variation? What are the limits of variation? How are those two issues related? And what mechanisms underlie variability and uniformity? In chapter 3, I investigate a potential domain of uniformity in budgerigar warble: the segment. Segments, units divided by acoustic transitions rather than silence, have been largely ignored in non-human animal communication. I find that budgerigars can achieve a high degree of complexity and variability by combining and arranging these small, more stereotyped units. Furthermore, I find that budgerigar segment organization is not only consistent across independent budgerigar populations but is consistent with patterns found in human language. In chapter 4, I investigate variability in house finch song. I present data showing that house finches learn sound patterns which are absent in wild house finch populations. These data suggest that cross-population variation in house finch song is narrower than what is permitted by the house finch song learning program. Finally, in chapter 5, I focus on human language, the most well-described communication system. Here, I research a sound pattern that is absent in the majority of known languages. I find that the rare pattern has independently developed at least six times. In every case, the historical pathway which led to the rare pattern was the same. The historical development in these six linguistic lineages suggests that the overall rarity of the sound pattern is the result of acoustic similarity. These data illuminate the evolutionary forces that give rise to, and limit, variation. The results of this dissertation have wide-ranging implications, from necessary revisions of linguistic theories, to understanding epigenetic interactions, to the application of evolutionary theory to complex behavior. While these projects within the dissertation are all different, evidence from all three projects support the following claims: (i) cross-population commonality is not evidence for what a species is able to learn; (ii) peripheral mechanisms have a strong influence in limiting cross-population variability; and (iii) high degrees of variation can emerge from uniform traits

Washington University Senior Undergraduate Research Digest (WUURD), Spring 2018

From the Washington University Office of Undergraduate Research Digest (WUURD), Vol. 13, 05-01-2018. Published by the Office of Undergraduate Research. Joy Zalis Kiefer, Director of Undergraduate Research and Associate Dean in the College of Arts & Scien

The Talking Heads experiment: Origins of words and meanings

The Talking Heads Experiment, conducted in the years 1999-2001, was the first large-scale experiment in which open populations of situated embodied agents created for the first time ever a new shared vocabulary by playing language games about real world scenes in front of them. The agents could teleport to different physical sites in the world through the Internet. Sites, in Antwerp, Brussels, Paris, Tokyo, London, Cambridge and several other locations were linked into the network. Humans could interact with the robotic agents either on site or remotely through the Internet and thus influence the evolving ontologies and languages of the artificial agents. The present book describes in detail the motivation, the cognitive mechanisms used by the agents, the various installations of the Talking Heads, the experimental results that were obtained, and the interaction with humans. It also provides a perspective on what happened in the field after these initial groundbreaking experiments. The book is invaluable reading for anyone interested in the history of agent-based models of language evolution and the future of Artificial Intelligence