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SHREC’20 Track: Retrieval of digital surfaces with similar geometric reliefs
(Elsevier, 2020-10-01)
This paper presents the methods that have participated in the SHREC’20 contest on retrieval of surface patches with similar geometric reliefs and the analysis of their performance over the benchmark created for this challenge. The goal of the context is to verify the possibility of retrieving 3D models only based on the reliefs that are present on their surface and to compare methods that are suitable for this task. This problem is related to many real world applications, such as the classification of cultural heritage goods or the analysis of different materials. To address this challenge, it is necessary to characterize the local ”geometric pattern” information, possibly forgetting model size and bending. Seven groups participated in this contest and twenty runs were submitted for evaluation. The performances of the methods reveal that good results are achieved with a number of techniques that use different approaches.
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Improving adaptive large neighborhood search: an evaluation of parallel approaches with deep learning integration
(Springer Science+Business Media, 2026-02-01)
This paper introduces a hybrid optimization framework that enhances vehicle routing by integrating Parallel Adaptive Large Neighborhood Search (PALNS) with deep generative modeling. Our method uses Variational Autoencoders (VAEs) to extract latent representations of routing patterns, which dynamically guide neighborhood selection during the search. Unlike conventional heuristics that rely on handcrafted rules, our approach learns from historical solution data to balance exploration and exploitation. We conduct extensive computational experiments on benchmark CVRP instances and real routing data, showing significant improvements in solution quality and steeper convergence curves compared to standalone ALNS and other metaheuristics. While there is modest runtime overhead, the latency is consistent and within practical bounds. The results also highlight interpretability of latent features and their contribution to dynamic search behavior. This work bridges machine learning and large-scale combinatorial optimization, with practical implications for logistics and supply chain applications.
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Does FIFA World Cup Participation Move the Macroeconomy? Evidence from Seven Tournaments Across Non-Host Countries, 1998-2022
(RELX Group (Netherlands), 2026-01-01)
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