Iris: Interactive all-in-one graphical validation of 3D protein model iterations

Iris validation is a Python package created to represent comprehensive per-residue validation metrics for entire protein chains in a compact, readable and interactive view. These metrics can either be calculated by Iris, or by a third-party program such as MolProbity. We show that those parts of a protein model requiring attention may generate ripples across the metrics on the diagram, immediately catching the modeler's attention. Iris can run as a standalone tool, or be plugged into existing structural biology software to display per-chain model quality at a glance, with a particular emphasis on evaluating incremental changes resulting from the iterative nature of model building and refinement. Finally, the integration of Iris into the CCP4i2 graphical user interface is provided as a showcase of its pluggable design

[Review]Iris Ralph, Packing Death in Australian Literature: Ecosides and Eco-Sides, Routledge, 2022, 174pp.

At first glance, a review of Iris Ralph’s Packing Death in Australian Literature (2020) does not fit neatly into an issue themed ‘Strange/Letters’, for, as Ralph’s acknowledgements page indicates, this book grew out of the inaugural 2005 conference of ASLEC-ANZ (then known as ASLE-ANZ). However, Ralph’s analysis, which ‘addresses plants and animals in Australia and its literature’ (1), is very much about strangeness if we consider that, until fairly recently, the contemplation of the nonhuman was an unfamiliar approach to Australian literary criticism

A Visual Stack Based Paradigm for Visualization Environments

We present a new visual paradigm for Visualization Systems, inspired by stack-based programming. Most current implementations of Visualization systems are based on directional graphs. However directional graphs as a visual representation of execution, though initially quite intuitive, quickly grow cumbersome and difficult to follow under complex examples. Our system presents the user with a simple and compact methodology of visually stacking actions directly on top of data objects as a way of creating filter scripts. We explore and address extensions to the basic paradigm to allow for: multiple data input or data output objects to and from execution action modules, execution thread jumps and loops, encapsulation, and overall execution control. We exploit the dynamic nature of current computer graphic interfaces by utilizing features such as drag-and-drop, color emphasis and object animation to indicate action, looping, message/parameter passing; to furnish an overall better understanding of the resulting laid out execution scripts

On Network Science and Mutual Information for Explaining Deep Neural Networks

In this paper, we present a new approach to interpret deep learning models. By coupling mutual information with network science, we explore how information flows through feedforward networks. We show that efficiently approximating mutual information allows us to create an information measure that quantifies how much information flows between any two neurons of a deep learning model. To that end, we propose NIF, Neural Information Flow, a technique for codifying information flow that exposes deep learning model internals and provides feature attributions.Comment: ICASSP 2020 (shorter version appeared at AAAI-19 Workshop on Network Interpretability for Deep Learning