In order to monitor and make predictions, palaeoceanographers need to manipulate, integrate and analyse paleo time-series that are obtained from a number of independent techniques and instruments which, moreover, are usually produced by different researches and/or laboratories. The result is a collection of datasets with unequal age precision. This situation worsens since, at present, there is not an assembly of integrated tools that permits the palaeontologists to perform a study without having to use applications of different natures (spreadsheets, image editors, time-series analysers, etc.) which are of general purpose and, usually, expensive and platformdependent. Hence, the goal of this project was to develop a platform-independent full featured tool that could simplify the palaeocenographers work, being used during the whole process: from the beginning, merging and correlating different datasets, continuing with interactive analyses, to the end, plotting the results. PaleoPlot is a general tool which could be used for any kind of time series, although it is optimised for the use in the stratigraphy area. Finally, it must be said that PaleoPlot is currently been used as an educational tool on palaeontology courses at the University of Salamanca.
Machine learning for malware classification shows encouraging results, but real deployments suffer from performance degradation as malware authors adapt their techniques to evade detection. This phenomenon, known as concept drift, occurs as new malware examples evolve and become less and less like the original training examples. One promising method to cope with concept drift is classification with rejection in which examples that are likely to be misclassified are instead quarantined until they can be expertly analyzed.We revisit TRANSCEND, a recently proposed framework for performing rejection based on conformal prediction theory. In particular, we provide a formal treatment of TRANSCEND, enabling us to refine conformal evaluation theory-its underlying statistical engine-and gain a better understanding of the theoretical reasons for its effectiveness. In the process, we develop two additional conformal evaluators that match or surpass the performance of the original while significantly decreasing the computational overhead. We evaluate our extension on a large dataset that removes sources of experimental bias present in the original evaluation.Finally, to aid practitioners, we determine the optimal operational settings for a TRANSCEND deployment and show how it can be applied to many popular learning algorithms. These insights support both old and new empirical findings, making TRANSCEND a sound and practical solution for the first time. To this end, we release our implementation of TRANSCEND as open source, to aid the adoption of rejection strategies by the security community.
A Sheaf Neural Network (SNN) is a type of Graph Neural Network (GNN) that operates on a sheaf, an object that equips a graph with vector spaces over its nodes and edges and linear maps between these spaces. SNNs have been shown to have useful theoretical properties that help tackle issues arising from heterophily and over-smoothing. One complication intrinsic to these models is finding a good sheaf for the task to be solved. Previous works proposed two diametrically opposed approaches: manually constructing the sheaf based on domain knowledge and learning the sheaf end-to-end using gradientbased methods. However, domain knowledge is often insufficient, while learning a sheaf could lead to overfitting and significant computational overhead. In this work, we propose a novel way of computing sheaves drawing inspiration from Riemannian geometry: we leverage the manifold assumption to compute manifold-and-graph-aware orthogonal maps, which optimally align the tangent spaces of neighbouring data points. We show that this approach achieves promising results with less computational overhead when compared to previous SNN models. Overall, this work provides an interesting connection between algebraic topology and differential geometry, and we hope that it will spark future research in this direction.
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