Jigsaw: investigative analysis on text document collections through visualization

This article describes the Jigsaw system for helping investigative analysis across collections of text documents. Jigsaw provides multiple visualizations of the documents and the entities within them to help investigators discern embedded stories and plots. Our early focus within Jigsaw has not been on legal documents and E-discovery, but we feel that the system may have potential in these areas as well. This article illustrates Jigsaw’s views and operations using Enron email archives as example documents

CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning

In open-ended environments, autonomous learning agents must set their own goals and build their own curriculum through an intrinsically motivated exploration. They may consider a large diversity of goals, aiming to discover what is controllable in their environments, and what is not. Because some goals might prove easy and some impossible, agents must actively select which goal to practice at any moment, to maximize their overall mastery on the set of learnable goals. This paper proposes CURIOUS, an algorithm that leverages 1) a modular Universal Value Function Approximator with hindsight learning to achieve a diversity of goals of different kinds within a unique policy and 2) an automated curriculum learning mechanism that biases the attention of the agent towards goals maximizing the absolute learning progress. Agents focus sequentially on goals of increasing complexity, and focus back on goals that are being forgotten. Experiments conducted in a new modular-goal robotic environment show the resulting developmental self-organization of a learning curriculum, and demonstrate properties of robustness to distracting goals, forgetting and changes in body properties.Comment: Accepted at ICML 201

Speculative practices : utilizing InfoVis to explore untapped literary collections

Funding: Canadian Social Sciences and Humanities Research CouncilIn this paper we exemplify how information visualization supports speculative thinking, hypotheses testing, and preliminary interpretation processes as part of literary research. While InfoVis has become a buzz topic in the digital humanities, skepticism remains about how effectively it integrates into and expands on traditional humanities research approaches. From an InfoVis perspective, we lack case studies that show the specific design challenges that make literary studies and humanities research at large a unique application area for information visualization. We examine these questions through our case study of the Speculative W@nderverse, a visualization tool that was designed to enable the analysis and exploration of an untapped literary collection consisting of thousands of science fiction short stories. We present the results of two empirical studies that involved general-interest readers and literary scholars who used the evolving visualization prototype as part of their research for over a year. Our findings suggest a design space for visualizing literary collections that is defined by (1) their academic and public relevance, (2) the tension between qualitative vs. quantitative methods of interpretation, (3) result- vs. process-driven approaches to InfoVis, and (4) the unique material and visual qualities of cultural collections. Through the Speculative W@nderverse we demonstrate how visualization can bridge these sometimes contradictory perspectives by cultivating curiosity and providing entry points into literary collections while, at the same time, supporting multiple aspects of humanities research processes.PostprintPeer reviewe

Ranking and Selecting Multi-Hop Knowledge Paths to Better Predict Human Needs

To make machines better understand sentiments, research needs to move from polarity identification to understanding the reasons that underlie the expression of sentiment. Categorizing the goals or needs of humans is one way to explain the expression of sentiment in text. Humans are good at understanding situations described in natural language and can easily connect them to the character's psychological needs using commonsense knowledge. We present a novel method to extract, rank, filter and select multi-hop relation paths from a commonsense knowledge resource to interpret the expression of sentiment in terms of their underlying human needs. We efficiently integrate the acquired knowledge paths in a neural model that interfaces context representations with knowledge using a gated attention mechanism. We assess the model's performance on a recently published dataset for categorizing human needs. Selectively integrating knowledge paths boosts performance and establishes a new state-of-the-art. Our model offers interpretability through the learned attention map over commonsense knowledge paths. Human evaluation highlights the relevance of the encoded knowledge

McScatter: a Simple Three-Body Scattering Package with Stellar Evolution

We describe a simple computer package which illustrates a method of combining stellar dynamics with stellar evolution. Though the method is intended for elaborate applications (especially the dynamical evolution of rich star clusters) it is illustrated here in the context of three-body scattering, i.e. interactions between a binary star and a field of single stars. We describe the interface between the dynamics and the two independent packages which describe the internal evolution of single stars and binaries. We also give an example application, and introduce a stand alone utility for the visual presentation of simulation results.Comment: 16 pages, Accepted for publication in New Astronomy. Source codes available at: http://manybody.org/manybody/McScatter.html and http://www.manybody.org/manybody/roche.htm