AtmoVis: Visualization of Air Quality Data

<p>Air quality has an adverse impact on the health of people living in areas with poor quality air. Monitoring is needed to understand the effects of poor air quality. It is difficult to compare measurements to find trends and patterns between different monitoring sites when data is contained in separate data stores. Data visualization can make analyzing air quality more effective by making the data more understandable. The purpose of this research is to design and build a prototype for visualizing spatio-temporal data from multiple sources related to air quality and to evaluate the effectiveness of the prototype against criteria by conducting a user study. The prototype web based visualization system, AtmoVis, has a windowed layout with 6 different visualizations: Heat calendar, line plot, monthly rose, site view, monthly averages and data comparison. A pilot study was performed with 11 participants and used to inform the study protocol before the main user study was performed on 20 participants who were air quality experts or experienced with Geographic Information Systems (GIS). The results of the study demonstrated that the heat calendar, line plot, site view, monthly averages and monthly rose visualizations were effective for analyzing the air quality through AtmoVis. The line plot and the heat calendar were the most effective for temporal data analysis. The interactive web based interface for data exploration with a window layout, provided by AtmoVis, was an effective method for accessing air quality visualizations and inferring relationships among air quality variables at different monitoring sites. AtmoVis could potentially be extended to include other datasets in the future.</p>

AtmoVis: Visualization of Air Quality Data

<p>Air quality has an adverse impact on the health of people living in areas with poor quality air. Monitoring is needed to understand the effects of poor air quality. It is difficult to compare measurements to find trends and patterns between different monitoring sites when data is contained in separate data stores. Data visualization can make analyzing air quality more effective by making the data more understandable. The purpose of this research is to design and build a prototype for visualizing spatio-temporal data from multiple sources related to air quality and to evaluate the effectiveness of the prototype against criteria by conducting a user study. The prototype web based visualization system, AtmoVis, has a windowed layout with 6 different visualizations: Heat calendar, line plot, monthly rose, site view, monthly averages and data comparison. A pilot study was performed with 11 participants and used to inform the study protocol before the main user study was performed on 20 participants who were air quality experts or experienced with Geographic Information Systems (GIS). The results of the study demonstrated that the heat calendar, line plot, site view, monthly averages and monthly rose visualizations were effective for analyzing the air quality through AtmoVis. The line plot and the heat calendar were the most effective for temporal data analysis. The interactive web based interface for data exploration with a window layout, provided by AtmoVis, was an effective method for accessing air quality visualizations and inferring relationships among air quality variables at different monitoring sites. AtmoVis could potentially be extended to include other datasets in the future.</p>

Generic Constraint-based Block Modeling using Constraint Programming

Block modeling has been used extensively in many domains including social science, spatial temporal data analysis and even medical imaging. Original formulations of the problem modeled it as a mixed integer programming problem, but were not scalable. Subsequent work relaxed the discrete optimization requirement, and showed that adding constraints is not straightforward in existing approaches. In this work, we present a new approach based on constraint programming, allowing discrete optimization of block modeling in a manner that is not only scalable, but also allows the easy incorporation of constraints. We introduce a new constraint filtering algorithm that outperforms earlier approaches, in both constrained and unconstrained settings, for an exhaustive search and for a type of local search called Large Neighborhood Search. We show its use in the analysis of real datasets. Finally, we show an application of the CP framework for model selection using the Minimum Description Length principle.

Emerging dynamic memristors for neuromorphic reservoir computing

Reservoir computing (RC), as a brain-inspired neuromorphic computing algorithm, is capable of fast and energy-efficient temporal data analysis and prediction. Hardware implementation of RC systems could significantly reduce the computing...

Dense time-course gene expression profiling of the Drosophila melanogaster innate immune response

ABSTRACTImmune responses need to be initiated rapidly, and maintained as needed, to prevent establishment and growth of infections. Still, immune genes differ in both initiation kinetics and shutdown dynamics. Here, we performed an RNA-seq time course on D. melanogaster with 20 time points post-LPS injection. A combination of methods, including spline fitting, cluster analysis, and Granger Causality inference, allowed detailed dissection of expression profiles and functional annotation of genes through guilt-by-association. We identified antimicrobial peptides as immediate-early response genes with a sustained up-regulation up to five days after stimulation, and genes in the IM family as having early and transient responses. We further observed a strong trade-off with metabolic genes, which strikingly recovered to pre-infection levels before the immune response was fully resolved. This high-dimensional dataset enables the comprehensive study of immune response dynamics through the parallel application of multiple temporal data analysis methods.