Theory and Application of Dissociative Electron Capture in Molecular Identification

The coupling of an electron monochromator (EM) to a mass spectrometer (MS) has created a new analytical technique, EM-MS, for the investigation of electrophilic compounds. This method provides a powerful tool for molecular identification of compounds contained in complex matrices, such as environmental samples. EM-MS expands the application and selectivity of traditional MS through the inclusion of a new dimension in the space of molecular characteristics--the electron resonance energy spectrum. However, before this tool can realize its full potential, it will be necessary to create a library of resonance energy scans from standards of the molecules for which EM-MS offers a practical means of detection. Here, an approach supplementing direct measurement with chemical inference and quantum scattering theory is presented to demonstrate the feasibility of directly calculating resonance energy spectra. This approach makes use of the symmetry of the transition-matrix element of the captured electron to discriminate between the spectra of isomers. As a way of validating this approach, the resonance values for twenty-five nitrated aromatic compounds were measured along with their relative abundance. Subsequently, the spectra for the isomers of nitrotoluene were shown to be consistent with the symmetry-based model. The initial success of this treatment suggests that it might be possible to predict negative ion resonances and thus create a library of EM-MS standards.Comment: 18 pages, 7 figure

A provenance task abstraction framework

Visual analytics tools integrate provenance recording to externalize analytic processes or user insights. Provenance can be captured on varying levels of detail, and in turn activities can be characterized from different granularities. However, current approaches do not support inferring activities that can only be characterized across multiple levels of provenance. We propose a task abstraction framework that consists of a three stage approach, composed of (1) initializing a provenance task hierarchy, (2) parsing the provenance hierarchy by using an abstraction mapping mechanism, and (3) leveraging the task hierarchy in an analytical tool. Furthermore, we identify implications to accommodate iterative refinement, context, variability, and uncertainty during all stages of the framework. A use case describes exemplifies our abstraction framework, demonstrating how context can influence the provenance hierarchy to support analysis. The paper concludes with an agenda, raising and discussing challenges that need to be considered for successfully implementing such a framework

A novel approach to task abstraction to make better sense of provenance data

Working Group Report in 'Provenance and Logging for Sense Making' report from Dagstuhl Seminar 18462: Provenance and Logging for Sense Making, Dagstuhl Reports, Volume 8, Issue 1

Knowledge-assisted ranking: A visual analytic application for sports event data

© 2016 IEEE. Organizing sports video data for performance analysis can be challenging, especially in cases involving multiple attributes and when the criteria for sorting frequently changes depending on the user's task. The proposed visual analytic system enables users to specify a sort requirement in a flexible manner without depending on specific knowledge about individual sort keys. The authors use regression techniques to train different analytical models for different types of sorting requirements and use visualization to facilitate knowledge discovery at different stages of the process. They demonstrate the system with a rugby case study to find key instances for analyzing team and player performance. Organizing sports video data for performance analysis can be challenging in cases with multiple attributes, and when sorting frequently changes depending on the user's task. As this video shows, the proposed visual analytic system allows interactive data sorting and exploration

Smart Brushing for Parallel Coordinates

The Parallel Coordinates plot is a popular tool for the visualization of high-dimensional data. One of the main challenges whenusing parallel coordinates is occlusion and overplotting resulting from large data sets. Brushing is a popular approach to address thesechallenges. Since its conception, limited improvements have been made to brushing both in the form of visual design and functionalinteraction. We present a set of novel, smart brushing techniques that enhance the standard interactive brushing of a parallel coordinatesplot. We introduce two new interaction concepts: Higher-order, sketch-based brushing, and smart, data-driven brushing. Higher-orderbrushes support interactive, flexible, n-dimensional pattern searches involving an arbitrary number of dimensions. Smart, data-drivenbrushing provides interactive, real-time guidance to the user during the brushing process based on derived meta-data. In addition, weimplement a selection of novel enhancements and user options that complement the two techniques as well as enhance the explorationand analytical ability of the user. We demonstrate the utility and evaluate the results using a case study with a large, high-dimensional,real-world telecommunication data set and we report domain expert feedback from the data suppliers