Projected performances: the phenomenology of hybrid theater

Throughout the 20th century, mediatized forms gained prominence and eclipsed the theater as a site of cultural power and popularity. Because of this tension, performance theorists like Peggy Phelan framed the definition of theater through its inherent differences from film and television. Other theorists like Philip Auslander problematized this distinction, particularly due to television’s similarities to live performance. The cinema, however, has remained an opponent to performance, ignored in favor of technologies that more readily promote a sense of “liveness.” In Projected Performances, I argue that film projection is more closely related to performance than previously thought, particularly when viewed in light of their phenomenological similarities. Projection is a live act that generates a kind of presence that approximates what is felt with a live performer. The theatrical setting of most film viewings foregrounds this phenomenological frame, despite the prerecorded nature of the content. Despite the seemingly static nature of film, the exhibition of it is most often decidedly theatrical. Hybrid theater, in which productions incorporate film projection alongside live performers, highlights these similarities in a much more explicit way, creating a unique sensory experience. This blending of effects is evident in theatrical broadcasts like the Metropolitan Opera’s “Live in HD” series, which capitalizes on the liveness of theater to draw people to the cinema. I also investigate hybrid productions that use projected scenery, such as The Woman in White and The Elephant Vanishes, as well as productions that feature projected bodies, like the work of Lemieux.Pilon 4d Art. Finally, I interrogate the use of projections in the monumental spectacles of the opening ceremonies at the 2008 and 2010 Olympics in Beijing and Vancouver, respectively.Throughout, I examine the ways in which these hybrid productions trouble the assumed distinction between performance and media, demonstrating that projection is a kind of performance that can share the stage with live performers without damaging the unique essential qualities of theater

Roughness of molecular property landscapes and its impact on modellability

In molecular discovery and drug design, structure-property relationships and activity landscapes are often qualitatively or quantitatively analyzed to guide the navigation of chemical space. The roughness (or smoothness) of these molecular property landscapes is one of their most studied geometric attributes, as it can characterize the presence of activity cliffs, with rougher landscapes generally expected to pose tougher optimization challenges. Here, we introduce a general, quantitative measure for describing the roughness of molecular property landscapes. The proposed roughness index (ROGI) is loosely inspired by the concept of fractal dimension and strongly correlates with the out-of-sample error achieved by machine learning models on numerous regression tasks.Comment: 17 pages, 6 figures, 2 tables (SI with 17 pages, 16 figures

Genome-wide association study of thyroid-stimulating hormone highlights new genes, pathways and associations with thyroid disease

Thyroid hormones play a critical role in regulation of multiple physiological functions and thyroid dysfunction is associated with substantial morbidity. Here, we use electronic health records to undertake a genome-wide association study of thyroid-stimulating hormone (TSH) levels, with a total sample size of 247,107. We identify 158 novel genetic associations, more than doubling the number of known associations with TSH, and implicate 112 putative causal genes, of which 76 are not previously implicated. A polygenic score for TSH is associated with TSH levels in African, South Asian, East Asian, Middle Eastern and admixed American ancestries, and associated with hypothyroidism and other thyroid disease in South Asians. In Europeans, the TSH polygenic score is associated with thyroid disease, including thyroid cancer and age-of-onset of hypothyroidism and hyperthyroidism. We develop pathway-specific genetic risk scores for TSH levels and use these in phenome-wide association studies to identify potential consequences of pathway perturbation. Together, these findings demonstrate the potential utility of genetic associations to inform future therapeutics and risk prediction for thyroid diseases

Evaluating the roughness of structure-property relationships using pretrained molecular representations

Quantitative structure-property relationships (QSPRs) aid in understanding molecular properties as a function of molecular structure. When the correlation between structure and property weakens, a dataset is described as "rough," but this characteristic is partly a function of the chosen representation. Among possible molecular representations are those from recently-developed "foundation models" for chemistry which learn molecular representation from unlabeled samples via self-supervision. However, the performance of these pretrained representations on property prediction benchmarks is mixed when compared to baseline approaches. We sought to understand these trends in terms of the roughness of the underlying QSPR surfaces. We introduce a reformulation of the roughness index (ROGI), ROGI-XD, to enable comparison of ROGI values across representations and evaluate various pretrained representations and those constructed by simple fingerprints and descriptors. We show that pretrained representations do not produce smoother QSPR surfaces, in agreement with previous empirical results of model accuracy. Our findings suggest that imposing stronger assumptions of smoothness with respect to molecular structure during model pretraining can aid in the downstream generation of smoother QSPR surfaces.Comment: 18 pages, 13 figure

Roughness of Molecular Property Landscapes and Its Impact on Modellability

In molecular discovery and drug design, structure-property relationships and activity landscapes are often qualitatively or quantitatively analyzed to guide the navigation of chemical space. The roughness (or smoothness) of these molecular property landscapes is one of their most studied geometric attributes, as it can characterize the presence of activity cliffs, with rougher landscapes generally expected to pose tougher optimization challenges. Here, we introduce a general, quantitative measure for describing the roughness of molecular property landscapes. The proposed roughness index (ROGI) is loosely inspired by the concept of fractal dimension and strongly correlates with the out-of-sample error achieved by machine learning models on numerous regression tasks

Roughness of Molecular Property Landscapes and Its Impact on Modellability

In molecular discovery and drug design, structure–property relationships and activity landscapes are often qualitatively or quantitatively analyzed to guide the navigation of chemical space. The roughness (or smoothness) of these molecular property landscapes is one of their most studied geometric attributes, as it can characterize the presence of activity cliffs, with rougher landscapes generally expected to pose tougher optimization challenges. Here, we introduce a general, quantitative measure for describing the roughness of molecular property landscapes. The proposed roughness index (ROGI) is loosely inspired by the concept of fractal dimension and strongly correlates with the out-of-sample error achieved by machine learning models on numerous regression tasks