A dot that went for a walk: People prefer lines drawn with human-like kinematics.

A dominant theory of embodied aesthetic experience (Freedberg & Gallese, 2007, Trends in Cognitive Sciences, 11, 197) posits that the appreciation of visual art is linked to the artist's movements when creating the artwork, yet a direct link between the kinematics of drawing actions and the aesthetics of drawing outcomes has not been experimentally demonstrated. Across four experiments, we measured aesthetic responses of students from arts and non-arts backgrounds to drawing movements generated from computational models of human writing. Experiment 1 demonstrated that human-like drawing movements with bell-shaped velocity profiles (Sigma Lognormal [SL] and Minimum Jerk [MJ]) are perceived as more natural and pleasant than movements with a uniform profile, and in both Experiments 1 and 2 movements that were perceived as more natural were also preferred. Experiment 3 showed that this effect persists if lower-level dynamic stimulus features are fully matched across experimental and control conditions. Furthermore, aesthetic preference for human-like movements were associated with greater perceptual fluency in Experiment 3, evidenced by unbiased estimations of the duration of natural movements. In Experiment 4, line drawings with visual features consistent with the dynamics of natural, human-like movements were preferred, but only by art students. Our findings directly link the aesthetics of human action to the visual aesthetics of drawings, but highlight the importance of incorporating artistic expertise into embodied accounts of aesthetic experience.

Network Bending: Expressive Manipulation of Generative Models in Multiple Domains.

This paper presents the network bending framework, a new approach for manipulating and interacting with deep generative models. We present a comprehensive set of deterministic transformations that can be inserted as distinct layers into the computational graph of a trained generative neural network and applied during inference. In addition, we present a novel algorithm for analysing the deep generative model and clustering features based on their spatial activation maps. This allows features to be grouped together based on spatial similarity in an unsupervised fashion. This results in the meaningful manipulation of sets of features that correspond to the generation of a broad array of semantically significant features of the generated results. We outline this framework, demonstrating our results on deep generative models for both image and audio domains. We show how it allows for the direct manipulation of semantically meaningful aspects of the generative process as well as allowing for a broad range of expressive outcomes.

CSynth: an interactive modelling and visualization tool for 3D chromatin structure.

MOTIVATION: The 3D structure of chromatin in the nucleus is important for gene expression and regulation. Chromosome conformation capture techniques, such as Hi-C, generate large amounts of data showing interaction points on the genome but these are hard to interpret using standard tools. RESULTS: We have developed CSynth, an interactive 3D genome browser and real-time chromatin restraint-based modeller to visualize models of any chromosome conformation capture (3C) data. Unlike other modelling systems, CSynth allows dynamic interaction with the modelling parameters to allow experimentation and effects on the model. It also allows comparison of models generated from data in different tissues/cell states and the results of third-party 3D modelling outputs. In addition, we include an option to view and manipulate these complicated structures using Virtual Reality (VR) so scientists can immerse themselves in the models for further understanding. This VR component has also proven to be a valuable teaching and a public engagement tool. AVAILABILITYAND IMPLEMENTATION: CSynth is web based and available to use at csynth.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

On Writing and Reading Artistic Computational Ecosystems.

We study the use of the generative systems known as computational ecosystems to convey artistic and narrative aims. These are virtual worlds running on computers, composed of agents that trade units of energy and emulate cycles of life and behaviors adapted from biological life forms. In this article we propose a conceptual framework in order to understand these systems, which are involved in processes of authorship and interpretation that this investigation analyzes in order to identify critical instruments for artistic exploration. We formulate a model of narrative that we call system stories (after Mitchell Whitelaw), characterized by the dynamic network of material and conceptual processes that define these artefacts. They account for narrative constellations with multiple agencies from which meaning and messages emerge. Finally, we present three case studies to explore the potential of this model within an artistic and generative domain, arguing that this understanding expands and enriches the palette of the language of these systems.