Learning bias in stress windows: Frequency and attestation

[Abstract not available

Computational Modeling of Learning Biases in Stress Typology

This dissertation demonstrates a strong connection between the frequency of stress patterns and their relative learnability under a wide class of learning algorithms. These frequency results follow from hypotheses about the learner\u27s available representations and the distribution of input data. Such hypotheses are combined with a model of learning to derive distinctions between classes of stress patterns, addressing frequency biases not modeled by traditional generative theory. I present a series of results for error-driven learners of constraint-based grammars. These results are shown both for single learners and learners in an iterated learning model. First, I show that with general n-gram constraints, learners show biases in their learning of stress patterns, mirroring frequency effects in the observed typology. These include biases toward full alternation and fixed stress near word edges. I show that these effects arise from the learner\u27s representation of the consistency and distinctiveness of learning data. I formalize this notion within error-driven, constraint-based learners. I show how specific representational assumptions can lead to distinct predictions about frequency, potentially adjudicating between theories. Languages with primary stress placement independent of word parity are shown to be---with the right constraint set---more consistent and thus more readily learned, offering an explanation for their relative frequency. This explanation is especially valuable because, while parity-dependent languages exist, they are a small minority. I continue by showing how such a model predicts biases in the size of stress windows and discuss the role of this approach in deciding the nature of potentially ``accidental gaps. I demonstrate that such a model can incorporate sources of bias outside the learner\u27s representations. I give a model of a perceptual nonfinality effect based on probabilistic misperception. This modification is shown to help account for typological skews in the edge of fixed stress and windows, as well as foot type for iterative stress. The methods used and conclusions drawn in this dissertation are potentially extendable to a wide range of linguistic phenomenon. This foundation is a way of approaching some otherwise-unexplained frequency biases by grounding them in theories of linguistic representation and learning

Modeling Morphological Subgeneralizations

Exceptions to morphological regularities often pattern together phonologically. In the English past tense, exceptions to the regular "Add /-d/" rule frequently inhabit "Islands of Reliability" (Albright & Hayes, 2003), in which a group of words take the same irregular past and also pattern together on a set of phonological characteristics. Adults seem to have implicit knowledge of both the overall pattern (the regular past) and the "subgeneralizations".We model this knowledge of subgeneralizations through the interaction of a structured lexicon and a Maximum Entropy grammar. Words that pattern together with respect to a particular morphological process are grouped into a "bundle", which is indexed to a constraint expressing the change that these words undergo to realize the morpheme. These "operational constraints" compete with markedness and faithfulness in the phonological component. The phonological regularity of a bundle is represented by the average of constraint violations for members. Novel words are assigned a bundle on the basis of similarity to these averages.Our model shows promising correspondence with human data, including biases toward regularity and Island of Reliability effects. The model's joint learning approach to phonology and morphology, as well as an inclusive concept of "context", show promise for future application

Editors' note

This is a brief note about the origin of the papers as presentations at Phonology 2013 held at the University of Massachusetts Amherst, and acknowledgments for help with the conference and proceedings

Construction material processed using lunar simulant in various environments

The manufacture of construction materials from locally available resources in space is an important first step in the establishment of lunar and planetary bases. The objective of the CoMPULSIVE (Construction Material Processed Using Lunar Simulant In Various Environments) experiment is to develop a procedure to produce construction materials by sintering or melting Johnson Space Center Simulant 1 (JSC-1) lunar soil simulant in both earth-based (1-g) and microgravity (approximately 0-g) environments. The characteristics of the resultant materials will be tested to determine its physical and mechanical properties. The physical characteristics include: crystalline, thermal, and electrical properties. The mechanical properties include: compressive tensile, and flexural strengths. The simulant, placed in a sealed graphite crucible, will be heated using a high temperature furnace. The crucible will then be cooled by radiative and forced convective means. The core furnace element consists of space qualified quartz-halogen incandescent lamps with focusing mirrors. Sample temperatures of up to 2200 C are attainable using this heating method

Systems-level interactions between insulin–EGF networks amplify mitogenic signaling

Crosstalk mechanisms have not been studied as thoroughly as individual signaling pathways. We exploit experimental and computational approaches to reveal how a concordant interplay between the insulin and epidermal growth factor (EGF) signaling networks can potentiate mitogenic signaling. In HEK293 cells, insulin is a poor activator of the Ras/ERK (extracellular signal-regulated kinase) cascade, yet it enhances ERK activation by low EGF doses. We find that major crosstalk mechanisms that amplify ERK signaling are localized upstream of Ras and at the Ras/Raf level. Computational modeling unveils how critical network nodes, the adaptor proteins GAB1 and insulin receptor substrate (IRS), Src kinase, and phosphatase SHP2, convert insulin-induced increase in the phosphatidylinositol-3,4,5-triphosphate (PIP3) concentration into enhanced Ras/ERK activity. The model predicts and experiments confirm that insulin-induced amplification of mitogenic signaling is abolished by disrupting PIP3-mediated positive feedback via GAB1 and IRS. We demonstrate that GAB1 behaves as a non-linear amplifier of mitogenic responses and insulin endows EGF signaling with robustness to GAB1 suppression. Our results show the feasibility of using computational models to identify key target combinations and predict complex cellular responses to a mixture of external cues

Specialization Methods and Cataphoricity in Coreference Resolution

This paper investigates two areas of coreference resolution --specialization and cataphoricity. In doing so I attempt to build upon an existing state-of-the-art system to achieve greater performance. Coreference systems utilizing specialization of models and specialization of features have both been previously proposed, but no investigation has been made as to their relative effectiveness or possible interrelationship. In this paper I demonstrate that most readily constructible specialization models are equivalent. The existence of cataphoric mentions is largely ignored in coreference resolution. In this paper I introduce several proposals for countering the performance losses due to cataphora. In particular, I propose a method of cataphoricity classification that largely counters these losses. I present results for several potential methods of using this classifier to create performance gains --particularly joint determination using integer linear programming. These methods are demonstrated to be ineffective, providing guidelines for future work