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Investigation of childhood trauma as a transdiagnostic risk factor using multimodal machine learning
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Re-'Cycling' Poetry: Structure in the Twentieth-Century English Song Cycle
The genre of song cycle is characterized by significant typological variety and ambiguity, to the extent that a concise working definition of the form doesn’t really exist. This complexity is partly grounded in the historical legacy of the loaded term ‘cycle’ and its inherent connotations of circular succession. Influenced by this notion, much song cycle discourse is underpinned, consciously or unconsciously, by assumptions of unity, on a variety of musical and textual levels, that have typically pointed to the success or failure of works as cycles.
This study seeks to explore several interrelated ideological, methodological and critical strands. Firstly, established singular conceptions of song cycle structure limit the interpretative possibilities available to analysts and performers. Clearly, not all cycles are circles and an openness to alternative patterns, shapes and structures makes space for new understandings of existing works. Secondly, the breadth and diversity of the song cycle repertory requires bespoke methodological approaches that respond to the specific conceptual and contextual circumstances of works and composers to provide the fullest possible account of their potential structure(s). Finally, the intermedial nature of the song cycle demands a sustained and equal preoccupation with both words and music. Close analysis of music and text that is provisional and dynamic provides an effective way of exploring issues of structure.
The repertory of English, twentieth-century song cycles is relatively underrepresented in musicological study. Finzi, Vaughan Williams and Ireland are all well-known composers, yet very little scholarship focuses on their song cycles. Informed by the principles outlined, I argue that the structure of Vaughan Williams’s On Wenlock Edge can be understood as three, paired micro-narratives, that the structure of Finzi’s Earth and Air and Rain is dynamic (having both framing and internal narrative cycle structures) and that the structure of Ireland’s Mother and Child is non-linear and determined by experiential qualities
General Course Catalog [2022/23 academic year]
General Course Catalog, 2022/23 academic yearhttps://repository.stcloudstate.edu/undergencat/1134/thumbnail.jp
Polyelectrolyte complexes embedding reduced graphite oxide
L'abstract è presente nell'allegato / the abstract is in the attachmen
Word-Final /s/ in English
Synopsis:
The complexities of speech production, perception, and comprehension are enormous. Theoretical approaches of these complexities most recently face the challenge of accounting for findings on subphonemic differences. The aim of the present dissertation is to establish a robust foundation of findings on such subphonemic differences.
One rather popular case for differences in subphonemic detail is word-final /s/ and /z/ in English (henceforth S) as it constitutes a number of morphological functions. Using word-final S, three general issues are investigated. First, are there subphonemic durational differences between different types of word-final S? If there are such differences, how can they be accounted for? Second, can such subphonemic durational differences be perceived? Third, do such subphonemic durational differences influence the comprehension of S?
These questions are investigated by five highly controlled studies: a production task, an implementation of Linear Discriminative Learning, a same-different task, and two number-decision tasks. Using not only real words but also pseudowords as target items, potentially confounding effects of lexical storage are controlled for.
Concerning the first issue, the results show that there are indeed durational differences between different types of word-final S. Non-morphemic S is longest in duration, clitic S is shortest in duration, and plural S duration is in-between non-morphemic S and clitic S durations. It appears that the durational differences are connected to a word’s semantic activation diversity and its phonological certainty. Regarding the second issue, subphonemic durational differences in word-final S can be perceived, with higher levels of perceptibility for differences of 35 ms and higher. In regard to the third issue, subphonemic durational differences are found not to influence the speed of comprehension, but show a significant effect on the process of comprehension. The overall results give raise to a revision of various extant models of speech production, perception, and comprehension
Intelligent computing : the latest advances, challenges and future
Computing is a critical driving force in the development of human civilization. In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence and internet-of-things with new computing theories, architectures, methods, systems, and applications. Intelligent computing has greatly broadened the scope of computing, extending it from traditional computing on data to increasingly diverse computing paradigms such as perceptual intelligence, cognitive intelligence, autonomous intelligence, and human computer fusion intelligence. Intelligence and computing have undergone paths of different evolution and development for a long time but have become increasingly intertwined in recent years: intelligent computing is not only intelligence-oriented but also intelligence-driven. Such cross-fertilization has prompted the emergence and rapid advancement of intelligent computing
ON EXPRESSIVENESS, INFERENCE, AND PARAMETER ESTIMATION OF DISCRETE SEQUENCE MODELS
Huge neural autoregressive sequence models have achieved impressive performance across different applications, such as NLP, reinforcement learning, and bioinformatics. However, some lingering problems (e.g., consistency and coherency of generated texts) continue to exist, regardless of the parameter count. In the first part of this thesis, we chart a taxonomy of the expressiveness of various sequence model families (Ch 3). In particular, we put forth complexity-theoretic proofs that string latent-variable sequence models are strictly more expressive than energy-based sequence models, which in turn are more expressive than autoregressive sequence models. Based on these findings, we introduce residual energy-based sequence models, a family of energy-based sequence models (Ch 4) whose sequence weights can be evaluated efficiently, and also perform competitively against autoregressive models. However, we show how unrestricted energy-based sequence models can suffer from uncomputability; and how such a problem is generally unfixable without knowledge of the true sequence distribution (Ch 5).
In the second part of the thesis, we study practical sequence model families and algorithms based on theoretical findings in the first part of the thesis. We introduce neural particle smoothing (Ch 6), a family of approximate sampling methods that work with conditional latent variable models. We also introduce neural finite-state transducers (Ch 7), which extend weighted finite state transducers with the introduction of mark strings, allowing scoring transduction paths in a finite state transducer with a neural network. Finally, we propose neural regular expressions (Ch 8), a family of neural sequence models that are easy to engineer, allowing a user to design flexible weighted relations using Marked FSTs, and combine these weighted relations together with various operations
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