Predicting history.

Can events be accurately described as historic at the time they are happening? Claims of this sort are in effect predictions about the evaluations of future historians; that is, that they will regard the events in question as significant. Here we provide empirical evidence in support of earlier philosophical arguments1 that such claims are likely to be spurious and that, conversely, many events that will one day be viewed as historic attract little attention at the time. We introduce a conceptual and methodological framework for applying machine learning prediction models to large corpora of digitized historical archives. We find that although such models can correctly identify some historically important documents, they tend to overpredict historical significance while also failing to identify many documents that will later be deemed important, where both types of error increase monotonically with the number of documents under consideration. On balance, we conclude that historical significance is extremely difficult to predict, consistent with other recent work on intrinsic limits to predictability in complex social systems2,3. However, the results also indicate the feasibility of developing 'artificial archivists' to identify potentially historic documents in very large digital corpora.

Multi-Snapshot Imaging for Chromatographic Peak Analysis.

OBJECTIVE: Snapshot imaging has several advantages in automated gel electrophoresis compared with the finish-line method in capillary electrophoresis; this comes at the expense of resolution. A novel signal processing algorithm is proposed enabling a multisnapshot imaging (MSI) modality whose objective is to substantially improve resolution. MSI takes multiple-captures in time as macromolecules are electrophoresed. Peaks from latter snapshots have high resolution, but low signal-to-noise ratio (SNR), while earlier snapshots have low resolution, but high SNR. METHODS: Signals at different capture-times are related by a scale-in-separation, shift-in-separation, and amplitude gain. The proposed method realigns the multiple captures using least-squares and fuses them. The algorithm accounts for the partial waveforms observed as the chromatic peaks exit the sensor's field-of-view. RESULTS: MSI improves resolution by approximately [Formula: see text] on average per minute of additional electrophoresis. CONCLUSIONS: Comprehensive analysis of the resolution quantified on several data sets demonstrates the effectiveness of MSI. SIGNIFICANCE: MSI can double the resolution compared with traditional snap-shot imaging over a typical set of captures.