Abstract Warning Colors by Robert McClure Warning Colors is a piece for orchestra scored for three flutes (second flute doubling alto flute in G and third flute doubling piccolo), two oboes, one english horn in F, two clarinets in Bb, one bass clarinet in Bb, three bassoons, four horns in F, three trumpets in C, two tenor trombones, one bass trombone, tuba, timpani, three percussion, harp, piano doubling celeste, and strings. The title is a phrase used in evolutionary biology in relation to the behavior of mimicry which is the core musical concept of the work. While writing a piece called Desert Miniatures: Insects for three bassoons in the summer of 2012, I learned about a butterfly, the Arizona Red Spotted Purple from the Sonoran Desert in Arizona which employs mimicry. The physical appearance of the butterfly has evolved to resemble another, noxious species of butterfly in the region, the Pipevine Swallowtail. The Red Spotted Purple is attacked far less because it has developed similar warning colors to the Swallowtail that predators have learned to recognize and avoid. Warning Colors employs three types of musical mimicry. The first is harmonic mimicry in which a stable harmony is presented in either the winds or brass. The strings mimic the harmony by sliding around it using microtones. These moments of harmonic mimicry serve as structural pillars. Second, rhythmic mimicry occurs when a melody or line is performed simultaneously against itself, the mimicking melody having different rhythmic values. The two lines intertwine rhythmically, come into unison, and break away from each other in a heterophonic texture. The third, melodic mimicry, occurs when two or more lines mimic a source by matching its contour. However, these mimics are not the product of a simple transposition because they retain their own internal intervallic characteristics. The concept of mimicry informed many of the musical characteristics displayed and heard in Warning Colors.

This dissertation primarily concerns selected structural elements in Bagatelles and Konstellationen. These are pitch/interval, rhythm/meter in Bagatelles, the formal design and its relations with dynamics and texture in Konstellationen, as well as the usage of indeterminacy. There are also selected aspects of performance in regard to extended technique, pedaling, and certain dynamic control problems related to two works in question. Chapter one introduces the historical background of Polish music and the emergence of Poland as one of the leading forces in contemporary music. It also provides the musical background of Moszumanska-Nazar, as well as the stylistic features and representative works in her three compositional periods. Personal interviews and correspondence with the composer provide additional biographical and stylistic insight for this chapter. Chapter two focuses on the aspects of structural procedure. In Bagatelles, the structural elements are: organized pitch sets, the dominance of linear interval, scale pattern, dissonant intervals, as well as the rhythmic pattern and the various metric designs. Konstellationen present most interesting and unusual formal design in that the elements that delineate the form are dynamics, texture and certain pianistic devices, such as the ostinato, trills, abrupt high notes, irregular fast notes, and clusters. Chapter three addresses particularly the aleatoric elements. The study covers areas of pitch, rhythm, and form with a brief introduction of music in indeterminacy. Chapter four turns to several issues pertaining to the performance aspects. These include pedaling, extended techniques, and dynamic control. The last part of this chapter draws conclusions from the observation and analysis of the two works in question.

This composition for Wind Ensemble (like Concert Band but usually only one player on a part) was Scott Master's Thesis for completing a Master of Music degree in Composition at Butler University. Written in 2010, the piece is a combination of styles, philosophies, and techniques, all in balance with each other. Avant-garde and traditional techniques are used; tonality and atonality vie with each other, resulting in a combination of the two; specific musical directions are balanced by a degree of choice available to each participant. “The Memory of Persistence” is about a journey. No specific personal story is presented, nor is the piece programmatic, but the progress and development of the piece could mirror many life situations and be accessible to anyone from that point of view. There is a progression from simple to complex, from innocence to maturity, that is demonstrated in the instrumentation as well as the melodic and harmonic elements. The title of the composition is an allusion to Salvador Dali’s painting called “The Persistence of Memory.” Even the font of the score’s title page is based on Dali’s own handwriting. Dali was a major player in the Surrealism movement of the twentieth century, combining classical elements of art with unusual, surprising, or even outrageous twists. “The Memory of Persistence” does the same thing in a musical setting. Yet, even without knowledge of Dali, the listener can understand the title because the piece demonstrates persistence through difficulty while retaining the memory of the past and incorporating it into current life.

Soar! is a musical composition written for wind ensemble and computer music. The total duration of the work is approximately 10 minutes. Flocking behavior of migratory birds serves as the most prominent influence on the imagery and local structure of the composition. The cyclical nature of the birds' journey inspires palindromic designs in the temporal domain. Aesthetically, Soar! portrays the fluid shapes of the flocks with numerous grains in the sounds. This effect is achieved by giving individual parts high degree of independence, especially in regards to rhythm. Technically, Soar! explores various interactions among instrumental lines in a wind ensemble, constructs overarching symmetrical structures, and integrates a large ensemble with computer music. The conductor acts as the leader at several improvisational moments in Soar! The use of conductor-initiated musical events in the piece can be traced back through the historic lineage of aleatoric compositions since the middle of the twentieth century. [Score is on p. 54-92.]

Remaining useful life (RUL) prediction is a problem that researchers in the prognostics and health management (PHM) community have been studying for decades. Both physics-based and data-driven methods have been investigated, and in recent years, deep learning has gained significant attention. When sufficiently large and diverse datasets are available, deep neural networks can achieve state-of-the-art performance in RUL prediction for a variety of systems. However, for end users to trust the results of these models, especially as they are integrated into safety-critical systems, RUL prediction uncertainty must be captured. This work explores an approach for estimating both epistemic and heteroscedastic aleatoric uncertainties that emerge in RUL prediction deep neural networks and demonstrates that quantifying the overall impact of these uncertainties on predictions reveal valuable insight into model performance. Additionally, a study is carried out to observe the effects of RUL truth data augmentation on perceived uncertainties in the model.

The Diploma thesis Organic composition in 20th century abstract Art, Music and Literature aims to explain the gradual process of liberalization of expressive means in Arts, Music and Literature during the 20th century. It describes the picture's emancipation from the dependence on objective reality, particularly V. Kandinsky's way towards abstract expressionism. In Music it traces A. Schonberg's development of the free atonality, and the stream of consciousness technique of J. Joyce in literature. The thesis' second part will be dealing with various manifestations of the organic composition after WWII- action and informal painting in the USA and Europe with its qualitative change. A musical and literal paralel to the principal of organic order will also be briefly introduced on the example of E. Brown's and J. Cage's aleatoric music and the spontaneous writing of J. Kerouac. Selected artistic personalities will be put into socio-cultural context of their time. As a result there comes a complex picture of the organic composition development process, suitable to be used as a source of information for teachers. The didactical part of the thesis will be focused on the possibility of using the given topic in the educational environment.

Des mesures de confiance calibrées et fiables sont un prérequis pour la plupart des systèmes de perception robotique car elles sont nécessaires aux modules de fusion de capteurs et de planification qui interviennent plus en aval. Cela est particulièrement vrai dans le cas d’applications où la sécurité est essentielle, comme les voitures à conduite autonome. Dans le contexte de l’apprentissage profond, l’incertitude prédictive est classée en incertitude épistémique et incertitude aléatoire. Il existe également une incertitude distributionnelle associée aux données hors distribution. L’incertitude aléatoire représente l’ambiguïté inhérente aux données d’entrée et est généralement irréductible par nature. Plusieurs méthodes existent pour estimer cette incertitude au moyen de structures de réseau modifiées ou de fonctions de perte. Cependant, en général, ces méthodes manquent de calibration, ce qui signifie que les incertitudes estimées ne représentent pas fidèlement l’incertitude des données empiriques. Les approches actuelles pour calibrer l’incertitude aléatoire nécessitent soit un "ensemble de données de calibration", soit de modifier les paramètres du modèle après l’apprentissage. De plus, de nombreuses approches ajoutent des opérations supplémentaires lors de l’inférence. Pour pallier à ces problèmes, nous proposons une méthode simple et efficace d’entraînement d’un régresseur neuronal calibré, conçue à partir des premiers principes de la calibration. Notre idée maîtresse est que la calibration ne peut être réalisée qu’en imposant des contraintes sur plusieurs exemples, comme ceux d’un mini-batch, contrairement aux approches existantes qui n’imposent des contraintes que sur la base d’un échantillon. En obligeant la distribution des sorties du régresseur neuronal (la distribution de la proposition) à ressembler à unedistribution cible en minimisant une divergence f , nous obtenons des modèles nettement mieuxcalibrés par rapport aux approches précédentes. Notre approche, f -Cal, est simple à mettre en œuvre ou à ajouter aux modèles existants et surpasse les méthodes de calibration existantes dansles tâches réelles à grande échelle de détection d’objets et d’estimation de la profondeur. f -Cal peut être mise en œuvre en 10-15 lignes de code PyTorch et peut être intégrée à n’importe quel régresseur neuronal probabiliste, de façon peu invasive. Nous explorons également l’estimation de l’incertitude distributionnelle pour la détection d’objets, et employons des méthodes conçues pour les systèmes de classification. Nous établissons un problème d’arrière-plan hors distribution qui entrave l’applicabilité des méthodes d’incertitude distributionnelle dans la détection d’objets. / Calibrated and reliable confidence measures are a prerequisite for most robotics perception systems since they are needed by sensor fusion and planning components downstream. This is particularly true in the case of safety-critical applications such as self-driving cars. In the context of deep learning, the sources of predictive uncertainty are categorized into epistemic and aleatoric uncertainty. There is also distributional uncertainty associated with out of distribution data. Epistemic uncertainty, also known as knowledge uncertainty, arises because of noise in the model structure and parameters, and can be reduced with more labeled data. Aleatoric uncertainty represents the inherent ambiguity in the input data and is generally irreducible in nature. Several methods exist for estimating aleatoric uncertainty through modified network structures or loss functions. However, in general, these methods lack calibration, meaning that the estimated uncertainties do not represent the empirical data uncertainty accurately. Current approaches to calibrate aleatoric uncertainty either require a held out calibration dataset or to modify the model parameters post-training. Moreover, many approaches add extra computation during inference time. To alleviate these issues, this thesis proposes a simple and effective method for training a calibrated neural regressor, designed from the first principles of calibration. Our key insight is that calibration can be achieved by imposing constraints across multiple examples, such as those in a mini-batch, as opposed to existing approaches that only impose constraints on a per-sample basis. By enforcing the distribution of outputs of the neural regressor (the proposal distribution) to resemble a target distribution by minimizing an f-divergence, we obtain significantly better-calibrated models compared to prior approaches. Our approach, f-Cal, is simple to implement or add to existing models and outperforms existing calibration methods on the large-scale real-world tasks of object detection and depth estimation. f-Cal can be implemented in 10-15 lines of PyTorch code, and can be integrated with any probabilistic neural regressor in a minimally invasive way. This thesis also explores the estimation of distributional uncertainty for object detection, and employ methods designed for classification setups. In particular, we attempt to detect out of distribution (OOD) samples, examples which are not part of training data distribution. I establish a background-OOD problem which hampers applicability of distributional uncertainty methods in object detection specifically.

Leo Brouwer's Sonata Mitología de las Aguas No. I for flute and guitar, first published in 2017, has taken its place as an important twenty-first-century addition to the flute and guitar duo repertory. I provide a brief historical context for the work, followed by preparation guides for guitar alone and duo passages. My preparation guides include exercises and rehearsal strategies, focusing on those passages of the work that include controlled indeterminacy. The study of indeterminacy in music is unusual in the pedagogy of the classical guitarist; this leaves guitarists unprepared for dealing with pieces, especially chamber works, that use improvisation or aleatoric music as a primary element. I take a multifaceted approach to facilitate the realization of the indeterminate sections of the work; this includes demonstrations of my traditional music notation transcriptions and other rehearsal strategies and the application of music performance study systems by James Thurmond and Marcel Tabuteau. This document aims to provide guidance to creating an organic, natural aesthetic in the actualization of Brouwer's groundbreaking work.

The ability to quantify uncertainties associated with neural network predictions is crucial when they are relied upon in decision-making processes, especially in safety-critical applications like radiation therapy. In this paper, a single-model estimator of both epistemic and aleatoric uncertainties in a regression 3D U-net used for radiation dose prediction is presented. To capture epistemic uncertainty, Monte Carlo Dropout is employed, leveraging dropout during test-time inference to obtain a distribution of predictions. The variability among these predictions is used to estimate the model’s epistemic uncertainty. For quantifying aleatoric uncertainty quantile regression, which models conditional quantiles of the output distribution, is used. The method enables the estimation of prediction intervals of a user-specified significance level, where the difference between the upper and lower bound of the interval quantifies the aleatoric uncertainty. The proposed approach is evaluated on two datasets of prostate and breast cancer patient geometries and corresponding radiation doses. Results demonstrate that the quantile regression method provides well-calibrated prediction intervals, allowing for reliable aleatoric uncertainty estimation. Furthermore, the epistemic uncertainty obtained through Monte Carlo Dropout proves effective in identifying out-of-distribution examples, highlighting its usefulness for detecting anomalous cases where the model makes uncertain predictions. / Förmågan att kvantifiera osäkerheter i samband med neurala nätverksprediktioner är avgörande när de åberopas i beslutsprocesser, särskilt i säkerhetskritiska tillämpningar såsom strålterapi. I denna rapport presenteras en en-modellsimplementation för att uppskatta både epistemiska och aleatoriska osäkerheter i ett 3D regressions-U-net som används för att prediktera stråldos. För att fånga epistemisk osäkerhet används Monte Carlo Dropout, som utnyttjar dropout under testtidsinferens för att få en fördelning av prediktioner. Variabiliteten mellan dessa prediktioner används för att uppskatta modellens epistemiska osäkerhet. För att kvantifiera den aleatoriska osäkerheten används kvantilregression, eller quantile regression, som modellerar de betingade kvantilerna i outputfördelningen. Metoden möjliggör uppskattning av prediktionsintervall med en användardefinierad signifikansnivå, där skillnaden mellan intervallets övre och undre gräns kvantifierar den aleatoriska osäkerheten. Den föreslagna metoden utvärderas på två dataset innehållandes geometrier för prostata- och bröstcancerpatienter och korresponderande stråldoser. Resultaten visar på att kvantilregression ger välkalibrerade prediktionsintervall, vilket tillåter en tillförlitlig uppskattning av den aleatoriska osäkerheten. Dessutom visar sig den epistemiska osäkerhet som erhålls genom Monte Carlo Dropout vara användbar för att identifiera datapunkter som inte tillhör samma fördelning som träningsdatan, vilket belyser dess lämplighet för att upptäcka avvikande datapunkter där modellen gör osäkra prediktioner.

With a lineage originating in the days of silent films, visual music, in its current incarnation, is a relatively recent phenomenon when compared to an historically broad field of creative expression. Today it is a time-based audio/visual territory explored and mined by a handful of visual and musical artists. However, an extensive examination of the literature indicates that few of these composers have delved into the associable areas of merging virtual holography and holophony toward visual music composition. It is posited here that such an approach is extremely rich with novel expressive potential and simultaneously with numerous novel challenges. The goal of this study is, through praxis, to instantiate and document an initial exploration into the implementation of holochory toward the creation of visual music compositions. Obviously, engaging holochoric visual music as a means of artistic expression requires an interdisciplinary pipeline. Certainly, this is demonstrated in merging music and visual art into a cohesive form, which is the basis of visual music composition. However, in this study is revealed another form of interdisciplinarity. A major challenge resides with the development of the means to efficiently render the high-resolution stereoscopic images intrinsic to the animation of virtual holograms. Though rendering is a challenge consistent with creating digital animations in general, here the challenge is further exacerbated by the extensive use of multiple reflections and refractions to create complexity from relatively simple geometric objects. This reveals that, with the level of computational technology currently available, the implementation of high-performance computing is the optimal approach. Unifying such diverse areas as music, visual art, and computer science toward a common artistic medium necessitates a methodological approach in which the interdependency between each facet is recognized and engaged. Ultimately, a quadrilateral reciprocative feedback loop, involving the composer's sensibilities in addition to each of the other facets of the compositional process, must be realized in order to facilitate a cohesive methodology leading toward viability. This dissertation provides documentation of methodologies and ideologies undertaken in an initial foray into creating holochoric visual music compositions. Interlaced matrices of contextualization are intended to disseminate the processes involved in deference to composers who will inevitably follow in the wake of this research. Accomplishing such a goal is a quintessential aspect of practice-based research, through which new knowledge is gained during the act of creating. Rather than formulating theoretical perspectives, it is through the praxis of composing holochoric visual music that the constantly arising challenges are recognized, analyzed, and subsequently addressed and resolved in order to ensure progression in the compositional process. Though measuring the success of the resultant compositions is indeed a subjective endeavor, as is the case with all art, the means by which they are achieved is not. The development of such pipelines and processes, and their implementation in practice, are the basic building blocks of further exploration, discovery, and artistic expression. This is the impetus for this document and for my constantly evolving and progressing trajectory as a scholar, artist, composer, and computer scientist. / Doctor of Philosophy / In this paper the author explores the idea that, owing to their shared three-dimensional nature, holophons and holograms are well suited as mediums for visual music composition. This union is ripe with creative opportunity and fraught with challenges in the areas of aesthetics and technical implementation. Squarely situated upon the bleeding edge of phenomenological research and creative practice, this novel medium is nonetheless within reach. Here, one methodological pipeline is delineated that employs the convergence of holography, holophony, and super-computing toward the creation of visual music compositions intended for head mounted displays or large scale 3D/360 projection screens and high-density loudspeaker arrays.