Tracking the relation between gist and item memory over the course of long-term memory consolidation

Our experiences in the world support memories not only of specific episodes but also of the generalities (the ‘gist’) across related experiences. It remains unclear how these two types of memories evolve and influence one another over time. In two experiments, 173 human participants encoded spatial locations from a distribution and reported both item memory (specific locations) and gist memory (center for the locations) across one to two months. Experiment 1 demonstrated that after one month, gist memory was preserved relative to item memory, despite a persistent positive correlation between them. Critically, item memories were biased towards the gist over time. Experiment 2 showed that a spatial outlier item changed this relationship and that the extraction of gist is sensitive to the regularities of items. Our results suggest that the gist starts to guide item memories over longer durations as their relative strengths change.

A New User-Based Incentive Strategy for Improving Bike Sharing Systems’ Performance

The benefits of having a Bike Sharing System (BSS) in a city are numerous. Among other advantages, it promotes a cleaner environment with less traffic and pollution. One major problem the users of such services encounter is that of full or empty stations, causing user dissatisfaction. The objective of this work is to propose a new user-based incentive method to enhance BSS performance. The proposed method relies on a spatial outlier detection algorithm. It consists of adapting the departure and arrival stations of the users to the BSS state by stimulating the users to change their journeys in view of minimizing the number of full and empty stations. Experiments are carried out to compare our proposed method to some existing methods for enhancing the resource availability of BSSs, and they are performed on a real dataset issued from a well-known BSS called Velib. The results show that the proposed strategy improves the availability of BSS resources, even when the collaboration of users is partial.

Robust Mahalanobis-distance based spatial outlier detection on discrete GNSS velocity fields

<p>The GNSS velocity field filtering topic can be identified as a multi-dimensional unsupervised spatial outlier detection problem. In the discussed case, we jointly interpreted the horizontal and vertical velocity fields and its uncertainties as a six dimensional space. To detect and classify the spatial outliers, we performed an orthogonal linear transformation technique called Principal Component Analysis (PCA) to dynamically project the data to a lower dimensional subspace, while redacting the most (~99%) of the explained variance of the input data.</p><p>Therefore, the resulting component space can be seen as an attribute function, which describes the investigated deformation patterns. Then we constructed two subspace mapping functions, respectively the k-nearest neighbor (k-NN) and median based neighbor function with Haversine metric, and the samplewise comparison function which compares the samples with the properties of its k-NN environment. Consequently, the resulting comparison function scores highlights the significantly different observations as outliers. Assuming that the data comes from Multivariate Gaussian Distribution (MVD), we evaluated the corresponding Mahalanobis-distance with the estimation of the robust covariance matrix of the investigated area. Then, as the main result of the Robust Mahalanobis-distance (RMD) based approach, we implemented the binary classification via the p-value and critical Mahalanobis-distance thresholding.</p><p>Compared to the formerly investigated and applied One-Class Support Vector machine (OCSVM) approach, the RMD based solution gives <em>~ 17%</em> more accurate results of the European scaled velocity field filtering (like EPN D1933), as well as it corrects the ambiguities and non-desired features (like overfitting) of the former OCSVM approach.</p><p>The results will be also presented as an interactive web page of the velocity fields of the latest version of EPN D2050 filtered with the introduced RMD approach.</p>