An analysis of the historical and biographical influences on the music of Francis Poulenc as portrayed in his Les Soirées de Nazelles

As a composer, Francis Poulenc has been under-appreciated and overlooked by many contemporary performers and teachers. Poulenc wrote music from his heart, paying little heed to formal systems or musical fashions of his day. In matters of harmony, he was content to use conventional chromatic harmony, but in a manner uniquely his own. Poulenc’s compositions always reflected his personality and Les Soirées de Nazelles is no exception. The many facets of Poulenc’s personality cannot be separated from his music and they contribute to its wit and charm. This document is an examination of Poulenc’s background, personality, and influences, and how these aspects appear and affect Les Soirées de Nazelles. Chapter One describes the cultural atmosphere of Paris at the turn of the century. Chapter Two traces Poulenc’s background. Chapter Three looks at teachers and composers who were influential in his life. The focus of Chapter Four is on Poulenc’s personality and is presented from the perspective of several people who were part of his inner circle. Elements of Poulenc’s compositional style as they apply to Les Soirées de Nazelles are the focus of Chapter Five. Chapter Six is an analytical view of the individual movements of Les Soirées de Nazelles. As Poulenc’s most technically challenging work for solo piano, Les Soirées de Nazelles should only be performed by advanced pianists, and only then by performers who are willing to seek to understand the complex nature of its composer. It is an excellent representative, not only of Poulenc’s compositional style, but also of the change of attitudes in French music at the turn-of-the century

Latent Fingerprint Recognition: Fusion of Local and Global Embeddings

One of the most challenging problems in fingerprint recognition continues to be establishing the identity of a suspect associated with partial and smudgy fingerprints left at a crime scene (i.e., latent prints or fingermarks). Despite the success of fixed-length embeddings for rolled and slap fingerprint recognition, the features learned for latent fingerprint matching have mostly been limited to local minutiae-based embeddings and have not directly leveraged global representations for matching. In this paper, we combine global embeddings with local embeddings for state-of-the-art latent to rolled matching accuracy with high throughput. The combination of both local and global representations leads to improved recognition accuracy across NIST SD 27, NIST SD 302, MSP, MOLF DB1/DB4, and MOLF DB2/DB4 latent fingerprint datasets for both closed-set (84.11%, 54.36%, 84.35%, 70.43%, 62.86% rank-1 retrieval rate, respectively) and open-set (0.50, 0.74, 0.44, 0.60, 0.68 FNIR at FPIR=0.02, respectively) identification scenarios on a gallery of 100K rolled fingerprints. Not only do we fuse the complimentary representations, we also use the local features to guide the global representations to focus on discriminatory regions in two fingerprint images to be compared. This leads to a multi-stage matching paradigm in which subsets of the retrieved candidate lists for each probe image are passed to subsequent stages for further processing, resulting in a considerable reduction in latency (requiring just 0.068 ms per latent to rolled comparison on a AMD EPYC 7543 32-Core Processor, roughly 15K comparisons per second). Finally, we show the generalizability of the fused representations for improving authentication accuracy across several rolled, plain, and contactless fingerprint datasets

Centering: A Framework for Modelling the Coherence of Discourse

Our original paper (Grosz, Joshi, and Weinstein, 1983) on centering claimed that certain entities mentioned in an utterance were more central than others and that this property imposed constraints on a speaker\u27s use of different types of referring expression. Centering was proposed as a model that accounted for this phenomenon. We argued that the compatibility of centering properties of an utterance with choice of referring expression affected the coherence of discourse. Subsequently, we expanded the ideas presented therein. We defined various centering constructs and proposed two centering rules in terms of these constructs. A draft manuscript describing this elaborated centering framework and presenting some initial theoretical claims has been in wide circulation since 1986. This draft (Grosz, Joshi, and Weinstein 1986, hereafter, GJW86) has led to a number of papers by others on this topic and has been extensively cited, but has never been published. We have been urged to publish the more detailed description of the centering framework and theory proposed in GJW86 so that an official version would be archivally available. The task of completing and revising this draft became more daunting as time passed and more and more papers appeared on centering. Many of these papers proposed extensions to or revisions of the theory and attempted to answer questions posed in GJW86. It has become ever more clear that it would be useful to have a definitive statement of the original motivations for centering, the basic definitions underlying the centering framework, and the original theoretical claims. This paper attempts to meet that need. To accomplish this goal, we have chosen to remove descriptions of many open research questions posed in GJW86 as well as solutions that were only partially developed. We have also greatly shortened the discussion of criteria for and constraints on a possible semantic theory as a foundation for this work

Prosody Modelling in Concept-to-Speech Generation: Methodological Issues

We explore three issues for the development of concept-to-speech (CTS) systems. We identify information available in a language-generation system that has the potential to impact prosody; investigate the role played by different corpora in CTS prosody modelling; and explore different methodologies for learning how linguistic features impact prosody. Our major focus is on the comparison of two machine learning methodologies: generalized rule induction and memory-based learning. We describe this work in the context of multimedia abstract generation of intensive care (MAGIC) data, a system that produces multimedia brings of the status of patients who have just undergone a bypass operation