Discovery Is Never By Chance: Designing for (Un)Serendipity

Serendipity has a long tradition in the history of science as having played a key role in many significant discoveries. Computer scientists, valuing the role of serendipity in discovery, have attempted to design systems that encourage serendipity. However, that research has focused primarily on only one aspect of serendipity: that of chance encounters. In reality, for serendipity to be valuable chance encounters must be synthesized into insight. In this paper we show, through a formal consideration of serendipity and analysis of how various systems have seized on attributes of interpreting serendipity, that there is a richer space for design to support serendipitous creativity, innovation and discovery than has been tapped to date. We discuss how ideas might be encoded to be shared or discovered by ‘association-hunting’ agents. We propose considering not only the inventor’s role in perceiving serendipity, but also how that inventor’s perception may be enhanced to increase the opportunity for serendipity. We explore the role of environment and how we can better enable serendipitous discoveries to find a home more readily and immediately

Experiencing discovery: Designing for visual delight

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Designing a Predictive Coding System for Electronic Discovery

Not long ago, the concept of using predictive coding and other technologies to assist with the electronic discovery process seemed revolutionary. Da Silva Moore and Global Aerospace stand as the first major cases where judges strongly supported predictive coding.1-2 A recent Indiana case recognized it as a useful method for reducing the amount of potentially relevant evidence that has to be searched and culled.3 Within just a few short years, using predictive coding as part of an electronic discovery process is now considered acceptable and perhaps even expected. It is not difficult to appreciate the advantages of predictive coding and its superiority over a manual process at various steps of electronic discovery, particularly during the review step.4-11 However, questions still remain about the efficacy of the predictive coding process and the tools that are available.12-13 Because the use of predictive coding systems in law is still in its infancy, it presents us with an opportunity to design something that will not only take advantage of the power of big data and computational algorithms, but that will also incorporate design and usability principles to provide an attractive and easy-to-use interface for lawyers to interact with. Predictive coding uses natural language processing and other mathematical models to enhance search results, but the essence of these systems is that they actually learn and the precision of the retrieval improves as additional collections of evidence are entered. Behind-the-scenes will be a repository where all of the evidence for a case resides. Our system will assist the lawyers in reducing the time and cost of an electronic discovery process as well as minimize the chances for mistakes in determining which evidence is relevant to a case and which evidence can be withheld under attorney-client privilege, as attorney work-product or another confidentiality doctrine. 1. Da Silva Moore v. Publicis Groupe & MSL Group, No. 11 Civ. 1279, 2012 WL 607412 (ALC) (AJP) (S.D.N.Y. Feb. 24, 2012). 2. Global Aerospace, Inc. v. Landow Aviation, L.P., No. CL 61040 (Vir. Cir. Ct. Apr. 23, 2012). 3. In re Biomet, 2013 WL 1729682 (N.D. Ind. Apr. 18, 2013). 4. Alison Silverstein and Geoffrey Vance. E-Discovery Myth Busters: Why Predictive Coding is Safe, Successful and Smart. Peer to Peer, Vol. 29, No. 4, December 2013, pp. 66-69. 5. John Papageorge. Predictive Coding Gaining Support in Courts. Indiana Lawyer, January 29-February 11, 2014, p. 8. 6. Adam M. Acosta. Predictive Coding: The Beginning of a New E-Discovery Era. Res Gestae, October 2012, pp. 8-14. 7. Ajith (AJ) Samuel. Analytics Driving the E-Discovery Process. Peer to Peer, Vol. 28, No. 2, June 2012. 8. Richard Acello. Beyond Prediction: Technology-Assisted Review Enters the Lexicon. ABA Journal, August 2012, pp. 37, 70. 9. Barry Murphy. The Rise of Technology-Assisted Review (TAR). Peer to Peer, Vol. 28, No. 2, June 2012, pp. 10. Brian Ingram. Controlling E-Discovery Costs in a Big Data World. Peer to Peer, Vol. 29, No. 1, March 2013. 11. Hal Marcus and Susan Stone. Beyond Predictive Coding - The True Power of Data Analytics [webinar]. International Legal Technology Association, May 19, 2015. 12. Jessica Watts and Gareth Evans. Predictive Coding in the Real World [webinar]. International Legal Technology Association, August 5, 2015. 13. Danielle Bethea. Predictive Coding: Revolutionizing Review or Still Gaining Momentum? Litigation and Practice Support: ITLA White Paper, International Legal Technology Association, June 2014

Rice Galaxy: An open resource for plant science

Background: Rice molecular genetics, breeding, genetic diversity, and allied research (such as rice-pathogen interaction) have adopted sequencing technologies and high-density genotyping platforms for genome variation analysis and gene discovery. Germplasm collections representing rice diversity, improved varieties, and elite breeding materials are accessible through rice gene banks for use in research and breeding, with many having genome sequences and high-density genotype data available. Combining phenotypic and genotypic information on these accessions enables genome-wide association analysis, which is driving quantitative trait loci discovery and molecular marker development. Comparative sequence analyses across quantitative trait loci regions facilitate the discovery of novel alleles. Analyses involving DNA sequences and large genotyping matrices for thousands of samples, however, pose a challenge to non−computer savvy rice researchers. Findings: The Rice Galaxy resource has shared datasets that include high-density genotypes from the 3,000 Rice Genomes project and sequences with corresponding annotations from 9 published rice genomes. The Rice Galaxy web server and deployment installer includes tools for designing single-nucleotide polymorphism assays, analyzing genome-wide association studies, population diversity, rice−bacterial pathogen diagnostics, and a suite of published genomic prediction methods. A prototype Rice Galaxy compliant to Open Access, Open Data, and Findable, Accessible, Interoperable, and Reproducible principles is also presented. Conclusions: Rice Galaxy is a freely available resource that empowers the plant research community to perform state-of-the-art analyses and utilize publicly available big datasets for both fundamental and applied science

Photonic Analogue of Two-dimensional Topological Insulators and Helical One-Way Edge Transport in Bi-Anisotropic Metamaterials

Recent progress in understanding the topological properties of condensed matter has led to the discovery of time-reversal invariant topological insulators. Because of limitations imposed by nature, topologically non-trivial electronic order seems to be uncommon except in small-band-gap semiconductors with strong spin-orbit interactions. In this Article we show that artificial electromagnetic structures, known as metamaterials, provide an attractive platform for designing photonic analogues of topological insulators. We demonstrate that a judicious choice of the metamaterial parameters can create photonic phases that support a pair of helical edge states, and that these edge states enable one-way photonic transport that is robust against disorder.Comment: 13 pages, 3 figure