Visual Integration of Data and Model Space in Ensemble Learning

Ensembles of classifier models typically deliver superior performance and can outperform single classifier models given a dataset and classification task at hand. However, the gain in performance comes together with the lack in comprehensibility, posing a challenge to understand how each model affects the classification outputs and where the errors come from. We propose a tight visual integration of the data and the model space for exploring and combining classifier models. We introduce a workflow that builds upon the visual integration and enables the effective exploration of classification outputs and models. We then present a use case in which we start with an ensemble automatically selected by a standard ensemble selection algorithm, and show how we can manipulate models and alternative combinations.Comment: 8 pages, 7 picture

Preparation of Silver Decorated Reduced Graphene Oxide Nanohybrid for Effective Photocatalytic Degradation of Indigo Carmine Dye

Background: Even though silver decorated reduced graphene oxide (Ag-rGO) shows max- imum absorptivity in the UV region, most of the research on the degradation of dyes using Ag-rGO is in the visible region. Therefore the present work focused on the photocatalytic degradation of indigo carmine (IC) dye in the presence of Ag-rGO as a catalyst by UV light irradiation. Methods: In this context, silver-decorated reduced graphene oxide hybrid material was fabricated and explored its potential for the photocatalytic degradation of aqueous IC solution in the UV region. The decoration of Ag nanoparticles on the surface of the rGO nanosheets is evidenced by TEM analysis. The extent of mineralization of the dye was measured by estimating chemical oxygen demand (COD) values before and after irradiation. Results: The synthesized Ag-rGO binary composites displayed excellent photocatalytic activity in 2 Χ 10-5 M IC concentration and 5mg catalyst loading. The optical absorption spectrum of Ag-rGO showed that the energy band-gap was found to be 2.27 eV, which is significantly smaller compared to the band-gap of GO. 5 mg of Ag-rGO was found to be an optimum quantity for the effective degrada- tion of IC dye. The degradation rate increases with the decrease in the concentration of the dye at al- kaline pH conditions. The photocatalytic efficiency was 92% for the second time. Conclusion: The impact of the enhanced reactive species generation was consistent with higher pho- tocatalytic dye degradation. The photocatalytic mechanism has been proposed and the hydroxyl radi- cal was found to be the reactive species responsible for the degradation of dye. The feasibility of reus- ing the photocatalyst showed that the photocatalytic efficiency was very effective for the second tim

Statistical modeling of RNA structure profiling experiments enables parsimonious reconstruction of structure landscapes.

RNA plays key regulatory roles in diverse cellular processes, where its functionality often derives from folding into and converting between structures. Many RNAs further rely on co-existence of alternative structures, which govern their response to cellular signals. However, characterizing heterogeneous landscapes is difficult, both experimentally and computationally. Recently, structure profiling experiments have emerged as powerful and affordable structure characterization methods, which improve computational structure prediction. To date, efforts have centered on predicting one optimal structure, with much less progress made on multiple-structure prediction. Here, we report a probabilistic modeling approach that predicts a parsimonious set of co-existing structures and estimates their abundances from structure profiling data. We demonstrate robust landscape reconstruction and quantitative insights into structural dynamics by analyzing numerous data sets. This work establishes a framework for data-directed characterization of structure landscapes to aid experimentalists in performing structure-function studies

Autoencoders for strategic decision support

In the majority of executive domains, a notion of normality is involved in most strategic decisions. However, few data-driven tools that support strategic decision-making are available. We introduce and extend the use of autoencoders to provide strategically relevant granular feedback. A first experiment indicates that experts are inconsistent in their decision making, highlighting the need for strategic decision support. Furthermore, using two large industry-provided human resources datasets, the proposed solution is evaluated in terms of ranking accuracy, synergy with human experts, and dimension-level feedback. This three-point scheme is validated using (a) synthetic data, (b) the perspective of data quality, (c) blind expert validation, and (d) transparent expert evaluation. Our study confirms several principal weaknesses of human decision-making and stresses the importance of synergy between a model and humans. Moreover, unsupervised learning and in particular the autoencoder are shown to be valuable tools for strategic decision-making