In Phantasmal Media: An Approach to Imagination, Computation, and Expression (MIT Press, 2013), D. Fox Harrell considers the expressive power of computational media.
D. Fox Harrell is the director of the Imagination, Computation, and Expression Laboratory at MIT's Computer Science and Artificial Intelligence Laboratory.
The Imagination, Computation, and Expression Laboratory at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) has released a new video game called Grayscale, which is designed to sensitize players to problems of sexism, sexual harassment, and sexual assault in the workplace.
To understand and combat this phenomenon, Harrell founded and directs the MIT Imagination, Computation, and Expression Laboratory (ICE Lab), which seeks to develop new forms of computational narratives, video games, social media, and other types of digital media that empower users and address biases.
In his book Phantasmal Media: An Approach to Imagination, Computation, and Expression, MIT Associate Professor of Digital Media D. Fox Harrell argues that the great expressive potential of computational media comes from the ability to construct and reveal phantasms — blends of cultural ideas and sensory imagination.
As D. Fox Harrell, the director of M.I.T.'s Imagination, Computation, and Expression Laboratory, pointed out to me, art, literature, and the social sciences have long served that purpose quite effectively.
The first study to identify baboon miRNAs employed a combined approach involving genomic computation and microarray expression [ 16].
The procedural arguments wrap up essentially the state of a computation and, certainly, the expression of environment.
In order to simplify the computation and obtain analytical expressions for K and σ d 2 for any HPA model, we propose a polynomial approximation of S. By doing this, we will be able to analytically compute the NLD parameters of Equations 20 and 21 for any HPA conversion characteristics after polynomial fitting.
After manual assessment and quality control, all 80 datasets described above were individually pre-processed (background correction, normalization and computation of expression values) according to methods described by Hubbell et al. (13) using the 'affy' R package from Bioconductor (14), which assures uniformity of the analysis process.
For cDNA microarray and iTRAQ-based proteomics, a common reference sample was constructed by mixing equal aliquots from all six samples facilitating computation of relative expression profiles and expression ratios across tissues.
