Training School on Numerical modelling of Ground Penetrating Radar using gprMax.

This report presents a description of training carried out within the Training School on Numerical modelling of Ground Penetrating Radar using gprMax, organized by the COST Action TU1208 and held in Thessaloniki, Greece, on November 9-11, 2015. Report deals with the example of use gprMax, cylinders buried in the half space. The processing of the modelled GPR data done through the commercial software GPRSoft PRO (Geoscanners AB) to show sample of processing procedures applied to detection of buried objects

Temporal graph models fail to capture global temporal dynamics

A recently released Temporal Graph Benchmark is analyzed in the context of Dynamic Link Property Prediction. We outline our observations and propose a trivial optimization-free baseline of "recently popular nodes" outperforming other methods on medium and large-size datasets in the Temporal Graph Benchmark. We propose two measures based on Wasserstein distance which can quantify the strength of short-term and long-term global dynamics of datasets. By analyzing our unexpectedly strong baseline, we show how standard negative sampling evaluation can be unsuitable for datasets with strong temporal dynamics. We also show how simple negative-sampling can lead to model degeneration during training, resulting in impossible to rank, fully saturated predictions of temporal graph networks. We propose improved negative sampling schemes for both training and evaluation and prove their usefulness. We conduct a comparison with a model trained non-contrastively without negative sampling. Our results provide a challenging baseline and indicate that temporal graph network architectures need deep rethinking for usage in problems with significant global dynamics, such as social media, cryptocurrency markets or e-commerce. We open-source the code for baselines, measures and proposed negative sampling schemes