Creativity, Exploration and Control in Musical Parameter Spaces.

PhDThis thesis investigates the use of multidimensional control of synthesis parameters in electronic music, and the impact of controller mapping techniques on creativity. The theoretical contribution of this work, the EARS model, provides a rigorous application of creative cognition research to this topic. EARS provides a cognitive model of creative interaction with technology, retrodicting numerous prior findings in musical interaction research. The model proposes four interaction modes, and characterises them in terms of parameter-space traversal mechanisms. Recommendations for properties of controller-synthesiser mappings that support each of the modes are given. This thesis proposes a generalisation of Fitts' law that enables throughput-based evaluation of multi-dimensional control devices. Three experiments were run that studied musicians performing sound design tasks with various interfaces. Mappings suited to three of the four EARS modes were quantitatively evaluated. Experiment one investigated the notion of a `divergent interface'. A mapping geometry that caters to early-stage exploratory creativity was developed, and evaluated via a publicly available tablet application. Dimension reduction of a 10D synthesiser parameter space to 2D surface was achieved using Hilbert space-filling curves. Interaction data indicated that this divergent mapping was used for early-stage creativity, and that the traditional sliders were used for late-stage one tuning. Experiment two established a `minimal experimental paradigm' for sound design interface evaluation. This experiment showed that multidimensional controllers were faster than 1D sliders for locating a target sound in two and three timbre dimensions. iv The final study tested a novel embodied interaction technique: ViBEAMP. This system utilised a hand tracker and a 3D visualisation to train users to control 6 synthesis parameters simultaneously. Throughput was recorded as triple that of six sliders, and working memory load was signiffcantly reduced. This experiment revealed that musical, time-targeted interactions obey a different speed-accuracy trade-of law from accuracy-targeted interactions.Electronic Engineering and Computer Science at Queen Mar

Wastewater sequencing reveals early cryptic SARS-CoV-2 variant transmission.

As SARS-CoV-2 continues to spread and evolve, detecting emerging variants early is critical for public health interventions. Inferring lineage prevalence by clinical testing is infeasible at scale, especially in areas with limited resources, participation, or testing and/or sequencing capacity, which can also introduce biases1-3. SARS-CoV-2 RNA concentration in wastewater successfully tracks regional infection dynamics and provides less biased abundance estimates than clinical testing4,5. Tracking virus genomic sequences in wastewater would improve community prevalence estimates and detect emerging variants. However, two factors limit wastewater-based genomic surveillance: low-quality sequence data and inability to estimate relative lineage abundance in mixed samples. Here we resolve these critical issues to perform a high-resolution, 295-day wastewater and clinical sequencing effort, in the controlled environment of a large university campus and the broader context of the surrounding county. We developed and deployed improved virus concentration protocols and deconvolution software that fully resolve multiple virus strains from wastewater. We detected emerging variants of concern up to 14 days earlier in wastewater samples, and identified multiple instances of virus spread not captured by clinical genomic surveillance. Our study provides a scalable solution for wastewater genomic surveillance that allows early detection of SARS-CoV-2 variants and identification of cryptic transmission