Demagnetization Analysis in Excel (DAIE). An open source workbook in Excel for viewing and analyzing demagnetization data from paleomagnetic discrete samples and u-channels

The Demagnetization Analysis in Excel (DAIE) program is a single Microsoft Excel file designed for viewing and analyzing stepwise demagnetization data of both discrete and u-channel samples in paleomagnetic studies. DAIE is an Excel workbook and has an open modular structure organized in 10 worksheets. It is designed for easy use and interactive operation; all the commands and choices can be entered using drop-down menus associated to single cells. The standard demagnetization diagrams and various parameters of common use are shown on the same worksheet including selectable parameters and user's choices. The characteristic remanence components may be computed by principal component analysis (PCA) on a selected interval of demagnetization steps. Saving of the PCA data can be done both sample by sample, and automatically by applying the selected choices to all the samples included in the file. The whole workbook is free both for use and editing and it is available for download on a dedicated website

Categorical Dimensions of Human Odor Descriptor Space Revealed by Non-Negative Matrix Factorization

In contrast to most other sensory modalities, the basic perceptual dimensions of olfaction remain unclear. Here, we use non-negative matrix factorization (NMF) – a dimensionality reduction technique – to uncover structure in a panel of odor profiles, with each odor defined as a point in multi-dimensional descriptor space. The properties of NMF are favorable for the analysis of such lexical and perceptual data, and lead to a high-dimensional account of odor space. We further provide evidence that odor dimensions apply categorically. That is, odor space is not occupied homogenously, but rather in a discrete and intrinsically clustered manner. We discuss the potential implications of these results for the neural coding of odors, as well as for developing classifiers on larger datasets that may be useful for predicting perceptual qualities from chemical structures

Subspace procrustes analysis

Postprint (author's final draft