Content-based retrieval of melodies using artificial neural networks

Human listeners are capable of spontaneously organizing and remembering a continuous stream of musical notes. A listener automatically segments a melody into phrases, from which an entire melody may be learnt and later recognized. This ability makes human listeners ideal for the task of retrieving melodies by content. This research introduces two neural networks, known as SONNETMAP and _ReTREEve, which attempt to model this behaviour. SONNET-MAP functions as a melody segmenter, whereas ReTREEve is specialized towards content-based retrieval (CBR). Typically, CBR systems represent melodies as strings of symbols drawn from a finite alphabet, thereby reducing the retrieval process to the task of approximate string matching. SONNET-MAP and ReTREEwe, which are derived from Nigrin’s SONNET architecture, offer a novel approach to these traditional systems, and indeed CBR in general. Based on melodic grouping cues, SONNETMAP segments a melody into phrases. Parallel SONNET modules form independent, sub-symbolic representations of the pitch and rhythm dimensions of each phrase. These representations are then bound using associative maps, forming a two-dimensional representation of each phrase. This organizational scheme enables SONNET-MAP to segment melodies into phrases using both the pitch and rhythm features of each melody. The boundary points formed by these melodic phrase segments are then utilized to populate the iieTREEve network. ReTREEw is organized in the same parallel fashion as SONNET-MAP. However, in addition, melodic phrases are aggregated by an additional layer; thus forming a two-dimensional, hierarchical memory structure of each entire melody. Melody retrieval is accomplished by matching input queries, whether perfect (for example, a fragment from the original melody) or imperfect (for example, a fragment derived from humming), against learned phrases and phrase sequence templates. Using a sample of fifty melodies composed by The Beatles , results show th a t the use of both pitch and rhythm during the retrieval process significantly improves retrieval results over networks that only use either pitch o r rhythm. Additionally, queries that are aligned along phrase boundaries are retrieved using significantly fewer notes than those that are not, thus indicating the importance of a human-based approach to melody segmentation. Moreover, depending on query degradation, different melodic features prove more adept at retrieval than others. The experiments presented in this thesis represent the largest empirical test of SONNET-based networks ever performed. As far as we are aware, the combined SONNET-MAP and -ReTREEue networks constitute the first self-organizing CBR system capable of automatic segmentation and retrieval of melodies using various features of pitch and rhythm

Doctor of Philosophy

dissertationElectron microscopy can visualize synapses at nanometer resolution, and can thereby capture the fine structure of these contacts. However, this imaging method lacks three key elements: temporal information, protein visualization, and large volume reconstruction. For my dissertation, I developed three methods in electron microscopy that overcame these limitations. First, I developed a method to freeze neurons at any desired time point after a stimulus to study synaptic vesicle cycle. Second, I developed a method to couple super-resolution fluorescence microscopy and electron microscopy to pinpoint the location of proteins in electron micrographs at nanometer resolution. Third, I collaborated with computer scientists to develop methods for semi-automated reconstruction of nervous system. I applied these techniques to answer two fundamental questions in synaptic biology. Which vesicles fuse in response to a stimulus? How are synaptic vesicles recovered at synapses after fusion? Only vesicles that are in direct contact with plasma membrane fuse upon stimulation. The active zone in C. elegans is broad, but primed vesicles are concentrated around the dense projection. Following exocytosis of synaptic vesicles, synaptic vesicle membrane was recovered rapidly at two distinct locations at a synapse: the dense projection and adherens junctions. These studies suggest that there may be a novel form of ultrafast endocytosis

Runtime Verification of Real-Time Applications Using Trace Data and Model Requirements

RÉSUMÉ Surveiller les systèmes multi-cœurs est difficile en raison des processus s'exécutant en parallèle, et pouvant interférer les uns avec les autres lorsqu'il s'agit d'accéder aux ressources du système, ou simplement d'avoir du temps processeur. Un tel système peut avoir à suivre des principes temps réel, ajoutant des contraintes de temps qui invalident les résultats dès qu'une date limite est manquée. Sur ce genre de système, des données précises n'auront ainsi de valeur que si elles peuvent être produites en respectant un délai donné. Le traçage peut fournir une grande quantité d'informations d'exécution de haute précision, à la fois sur le système et les applications. Il est ainsi souvent l'outil le plus précis et fiable pour étudier et analyser des systèmes ainsi contraints. Cependant, les utilisateurs doivent disposer d'une grande expertise du système afin de comprendre les évènements du noyau du système et leur signification. De plus, il peut être très long d'analyser et étudier manuellement de volumineuses traces d'exécution de haute précision. Dans cette thèse, nous proposons les méthodes et algorithmes permettant d'automatiquement détecter et identifier l'origine de comportements inattendus dans des applications, et ce à l'aide de traces de leurs exécutions et de modèles des exigences. Nous décrivons la structure interne des modèles, la méthodologie pour suivre l'exécution d'une application à travers ses évènements de l'espace utilisateur, et les structures de données nécessaires pour vérifier les contraintes. Nous détaillons ensuite le processus utilisé pour détecter et finalement comprendre la source du comportement non désiré. Nous proposons aussi une approche pour construire automatiquement les modèles pour des applications temps réel courantes. L'hypothèse servant de point de départ pour ce travail est que les traces d'exécution du système et de l'application à analyser peuvent être utilisées pour automatiquement suivre l'exécution de cette application, y détecter les anomalies et trouver leurs sources. Les résultats de ce travail sont les concepts, méthodologies et structures de données utilisés pour suivre et contraindre des applications, ainsi que les méthodes et algorithmes qui permettent de détecter et identifier les comportements inattendus dans ces applications. Ces derniers ont été testés sur de réelles applications temps réel, et ont permis avec succès de détecter et identifier l'origine des irrégularités à leur exécution. De plus, nous avons pu automatiquement, et de façon précise, construire des modèles pour ces applications. Cette dernière étape rend l'utilisation des méthodes de traçage beaucoup plus accessible aux utilisateurs non-experts. Le résultat final est que la détection automatique et la localisation automatique de la source des comportements inattendus dans une application est une option viable et fonctionnelle, qui accélère et simplifie le travail des utilisateurs pour analyser les exécutions de leurs applications.----------ABSTRACT Monitoring multi-core systems is hard because of the concurrently running processes that can contend with each other to access resources of the system or CPU time. Such a system may have to follow real-time principles, adding time constraints that invalidate results as soon as a deadline is missed. This means that accurate data will only be valuable if it can be produced in a timely fashion. Tracing is often the most accurate and reliable tool to study and analyze those systems, as it can provide a lot of high precision runtime information about both the system and applications. Nevertheless, a deep level of expertise of the system is required in order for the users to understand the kernel events and their meaning. Moreover, it can be time consuming to manually analyze and study voluminous high precision execution traces. In this thesis, we propose methods and algorithms to automatically detect and identify the origin of unwanted behaviors in applications, by using traces of their execution and models of the requirements. We describe the internal structure of the models, the methodology to follow an application runtime through its userspace events, and the data structures needed to verify constraints. We then detail the process followed to detect and finally understand the root cause of the unwanted behavior. We also propose an approach to automatically build the models for common real-time applications. The hypothesis serving as starting point for this work is that execution traces of both the system and the application to analyze can be used to automatically follow this application's execution, detect its anomalies and find their root causes. The results of this work are the concepts, methodologies and data structures used to follow and constrain applications, as well as the methods and algorithms allowing to detect and identify unexpected behaviors in those applications. These have been applied on actual real-time applications and succeeded in detecting and identifying the root causes of the irregularities in their runtime. Moreover, we were able to automatically and accurately build models for those applications, making it even easier for non-expert users to take advantage of tracing methods. The final result is that automatically detecting and pinpointing the origin of unwanted behaviors, in an application, is a valid and interesting option, making it faster and easier for users to analyze executions of their applications

National-Louis University Undergraduate Catalog, 2000-2002

Undergraduate catalog for the2000-02 school year. Contains campus information as well as program information and course descriptions.https://digitalcommons.nl.edu/coursecatalogs/1018/thumbnail.jp

Factorized solution of power system state estimation

In this thesis a general two-stage factorized solution for nonlinear WLS problems has been developed, with two main applications: a geographically distributed multilevel hierarchical state estimation algorithm, suitable for very large-scale power systems covering multiple control areas; and a factorized multi-stage version, which enhances the convergence speed and reduces the computational effort. In the multilevel hierarchical state estimation, the way the algorithm can be customized to the system decomposition is analyzed, particularizing the methodology for the distribution feeder, substation, and transmission or multi-area system levels. Tests are performed on benchmark and realistic large-scale networks, including the entire European transmission system. The main advantage of this method lies in the possibility of filtering raw measurements at the specific location where they are captured, and then sending only local estimates for further processing by higher level state estimators. This multilevel estimator will be of special interest in upcoming systems, where the increased introduction of ICTs at lower levels and widespread interconnections at the regional transmission level are leading to an explosion of information which could be hardly managed by a single energy management system. In the second case, different approaches are proposed, all of them sharing a first linear stage, clearly showing computational efficiency and enhanced convergence speed compared to the conventional estimator. After a two-stage algorithm, the dissertation develops a bilinear three-stage state estimation factorization which virtually eliminates the need to iterate yielding the same solution as that provided by the Gauss-Newton iterative method. This is also extended to the case in which equality constraints are to be enforcedPremio Extraordinario de Doctorado U

Recovering indigenous inscriptions of meaning from the colonial novel: A re-reading of the spatial archetypes in Joseph Conrad’s Lord Jim

This paper discusses an alternative reading practice of the colonial novel (Zawiah 2003) that puts the re(-) presentation of space in such novels under scrutiny. Informed firstly by Jungian archetypal criticism and secondly, by Gayatri Spivak’s concept of ‘worlding’ (1999), it examines the re-presentation of Malaya’s geospatial features – the sea, mountains, forests – as archetypes in the novel Lord Jim (1900) by Joseph Conrad. These archetypal images, I argue, erase the indigenous meanings already inscribed onto Malaya’s geospatial features, in the colonial project of worlding Malaya. However, by peeling away the layers of Western inscriptions of meaning onto Malaya’s geospatial features, the contemporary, post-colonial reader might recover the various meanings endowed on Malaya by its native inhabitants. This alternative reading practice thus enables the reader to discover the diversity of meanings that can and have been given to geospatial features, as opposed to the West’s unilateral act of worlding other worlds