The Search for the Operatic: Bridging the Gap Between the Spectatorial and Performer Positions Through Sketch-making and Embodiment Techniques

The exhibition was of sketches and a photograph from my PhD practice research. The practice-research was comprised of observing opera singers in rehearsal and sketching them as they moved. As well as records of body position, and to some degree dynamic flow, the exhibited sketches were regarded as kinaesthetic responses in and of themselves – responses to the environment of the rehearsal, in particular responding to the sounds of the orchestra. These sketches were, in part, generated through an embodiment of the music, which was occurring in the same moment as the singer was engaged in embodying the music. These sketches were then used as tools that therefore contained kinaesthetic information which could be unlocked through a process of Butoh derived embodiment techniques alongside reference to the sketched image. This ultimately allowed me to move from a spectatorial position to a performance maker position, bringing a sense of the operatic into the non-singing body, whether that was my own or the bodies of other performers. In this way, and combined with rigorous observation of the corporeal restrictions of singing operatically, choreographies were created that employed operatic ways of moving in non-singing bodies and the operatic was extracted from opera and employed in movement based practice. The aspect of the practice-research exhibited is the correspondence between sketched documentation of the singers in rehearsal and photographic documentation of the dancer (researcher) in performance.

Nietzsche's orphans: music and the search for unity in revolutionary Russia, 1905-1921

At the dawn of the twentieth century, Imperial Russia was in the throes of immense social, political and cultural upheaval. The effects of rapid industrialization, rising capitalism and urbanization, as well as the trauma wrought by revolution and war, reverberated through all levels of society and every cultural sphere. In the aftermath of the 1905 revolution, amid a growing sense of panic over the chaos and divisions emerging in modern life, a portion of Russian educated society (obshchestvennost’) looked to the transformative and unifying power of music as a means of salvation from the personal, social and intellectual divisions of the contemporary world. Transcending professional divisions, these “orphans of Nietzsche” comprised a distinct aesthetic group within educated Russian society. While lacking a common political, religious or national outlook, these philosophers, poets, musicians and other educated members of the upper and middle strata were bound together by their shared image of music’s unifying power, itself built upon a synthesis of Russian and European ideas. They yearned for a “musical Orpheus,” a composer capable of restoring wholeness to society through his music. My dissertation is a study in what I call “musical metaphysics,” an examination of the creation, development, crisis and ultimate failure of this Orphic worldview. To begin, I examine the institutional foundations of musical life in late Imperial Russia, as well as the explosion of cultural life in the aftermath of the 1905 Revolution, a vibrant social context which nourished the formation of musical metaphysics. From here, I assess the intellectual basis upon which musical metaphysics rested: central concepts (music, life-transformation, theurgy, unity, genius, nation), as well as the philosophical heritage of Nietzsche and the Christian thinkers Vladimir Solov’ev, Aleksei Khomiakov, Ivan Kireevskii and Lev Tolstoi. Nietzsche’s orphans’ struggle to reconcile an amoral view of reality with a deeply felt sense of religious purpose gave rise to neo-Slavophile interpretations of history, in which the Russian nation (narod) was singled out as the savior of humanity from the materialism of modern life. This nationalizing tendency existed uneasily within the framework of the multi-ethnic empire. From broad social and cultural trends, I turn to detailed analysis of three of Moscow’s most admired contemporary composers, whose individual creative voices intersected with broader social concerns. The music of Aleksandr Scriabin (1871-1915) was associated with images of universal historical progress. Nikolai Medtner (1879-1951) embodied an “Imperial” worldview, in which musical style was imbued with an eternal significance which transcended the divisions of nation. The compositions of Sergei Rachmaninoff (1873-1943) were seen as the expression of a Russian “national” voice. Heightened nationalist sentiment and the impact of the Great War spelled the doom of this musical worldview. Music became an increasingly nationalized sphere within which earlier, Imperial definitions of belonging grew ever more problematic. As the Germanic heritage upon which their vision was partially based came under attack, Nietzsche’s orphans found themselves ever more divided and alienated from society as a whole. Music’s inability to physically transform the world ultimately came to symbolize the failure of Russia’s educated strata to effectively deal with the pressures of a modernizing society. In the aftermath of the 1917 revolutions, music was transformed from a symbol of active, unifying power into a space of memory, a means of commemorating, reinterpreting, and idealizing the lost world of Imperial Russia itself.

Using interaction data for improving the offline and online evaluation of search engines

This thesis investigates how web search evaluation can be improved using historical interaction data. Modern search engines combine offline and online evaluation approaches in a sequence of steps that a tested change needs to pass through to be accepted as an improvement and subsequently deployed. We refer to such a sequence of steps as an evaluation pipeline. In this thesis, we consider the evaluation pipeline to contain three sequential steps: an offline evaluation step, an online evaluation scheduling step, and an online evaluation step. In this thesis we show that historical user interaction data can aid in improving the accuracy or efficiency of each of the steps of the web search evaluation pipeline. As a result of these improvements, the overall efficiency of the entire evaluation pipeline is increased. Firstly, we investigate how user interaction data can be used to build accurate offline evaluation methods for query auto-completion mechanisms. We propose a family of offline evaluation metrics for query auto-completion that represents the effort the user has to spend in order to submit their query. The parameters of our proposed metrics are trained against a set of user interactions recorded in the search engine’s query logs. From our experimental study, we observe that our proposed metrics are significantly more correlated with an online user satisfaction indicator than the metrics proposed in the existing literature. Hence, fewer changes will pass the offline evaluation step to be rejected after the online evaluation step. As a result, this would allow us to achieve a higher efficiency of the entire evaluation pipeline. Secondly, we state the problem of the optimised scheduling of online experiments. We tackle this problem by considering a greedy scheduler that prioritises the evaluation queue according to the predicted likelihood of success of a particular experiment. This predictor is trained on a set of online experiments, and uses a diverse set of features to represent an online experiment. Our study demonstrates that a higher number of successful experiments per unit of time can be achieved by deploying such a scheduler on the second step of the evaluation pipeline. Consequently, we argue that the efficiency of the evaluation pipeline can be increased. Next, to improve the efficiency of the online evaluation step, we propose the Generalised Team Draft interleaving framework. Generalised Team Draft considers both the interleaving policy (how often a particular combination of results is shown) and click scoring (how important each click is) as parameters in a data-driven optimisation of the interleaving sensitivity. Further, Generalised Team Draft is applicable beyond domains with a list-based representation of results, i.e. in domains with a grid-based representation, such as image search. Our study using datasets of interleaving experiments performed both in document and image search domains demonstrates that Generalised Team Draft achieves the highest sensitivity. A higher sensitivity indicates that the interleaving experiments can be deployed for a shorter period of time or use a smaller sample of users. Importantly, Generalised Team Draft optimises the interleaving parameters w.r.t. historical interaction data recorded in the interleaving experiments. Finally, we propose to apply the sequential testing methods to reduce the mean deployment time for the interleaving experiments. We adapt two sequential tests for the interleaving experimentation. We demonstrate that one can achieve a significant decrease in experiment duration by using such sequential testing methods. The highest efficiency is achieved by the sequential tests that adjust their stopping thresholds using historical interaction data recorded in diagnostic experiments. Our further experimental study demonstrates that cumulative gains in the online experimentation efficiency can be achieved by combining the interleaving sensitivity optimisation approaches, including Generalised Team Draft, and the sequential testing approaches. Overall, the central contributions of this thesis are the proposed approaches to improve the accuracy or efficiency of the steps of the evaluation pipeline: the offline evaluation frameworks for the query auto-completion, an approach for the optimised scheduling of online experiments, a general framework for the efficient online interleaving evaluation, and a sequential testing approach for the online search evaluation. The experiments in this thesis are based on massive real-life datasets obtained from Yandex, a leading commercial search engine. These experiments demonstrate the potential of the proposed approaches to improve the efficiency of the evaluation pipeline.

Learning Binary Code Representations for Effective and Efficient Image Retrieval

The size of online image datasets is constantly increasing. Considering an image dataset with millions of images, image retrieval becomes a seemingly intractable problem for exhaustive similarity search algorithms. Hashing methods, which encodes high-dimensional descriptors into compact binary strings, have become very popular because of their high efficiency in search and storage capacity. In the first part, we propose a multimodal retrieval method based on latent feature models. The procedure consists of a nonparametric Bayesian framework for learning underlying semantically meaningful abstract features in a multimodal dataset, a probabilistic retrieval model that allows cross-modal queries and an extension model for relevance feedback. In the second part, we focus on supervised hashing with kernels. We describe a flexible hashing procedure that treats binary codes and pairwise semantic similarity as latent and observed variables, respectively, in a probabilistic model based on Gaussian processes for binary classification. We present a scalable inference algorithm with the sparse pseudo-input Gaussian process (SPGP) model and distributed computing. In the last part, we define an incremental hashing strategy for dynamic databases where new images are added to the databases frequently. The method is based on a two-stage classification framework using binary and multi-class SVMs. The proposed method also enforces balance in binary codes by an imbalance penalty to obtain higher quality binary codes. We learn hash functions by an efficient algorithm where the NP-hard problem of finding optimal binary codes is solved via cyclic coordinate descent and SVMs are trained in a parallelized incremental manner. For modifications like adding images from an unseen class, we propose an incremental procedure for effective and efficient updates to the previous hash functions. Experiments on three large-scale image datasets demonstrate that the incremental strategy is capable of efficiently updating hash functions to the same retrieval performance as hashing from scratch.