System Architecture Optimization (SAO) problems are typically mixed-discrete,
hierarchical, and expensive to evaluate, motivating the application of Bayesian
optimization, which is known for its sample efficiency. Current approaches for
SAO use Gaussian processes (GPs) operating on encoded design vectors. These
encodings can obscure similarities in component connectivity, making it harder
for the GP to capture performance correlations between evaluated and uneval-
uated architectures and potentially reducing optimization sample efficiency. To
address this, we propose a GP surrogate that incorporates similarities between
architecture graphs through the Weisfeiler–Lehman optimal assignment kernel
to better capture shared structural patterns. We also introduce extensions that
facilitate adapting the neighborhood scale at which architecture graphs are com-
pared, as well as the specificity with which structural patterns are distinguished.
This allows learning those properties during GP fitting rather than tuning them
beforehand, which is infeasible for SAO, as new problems typically lack prior
simulation data. Across four benchmarks against encoding-based GPs, an alter-
native graph-kernel, graph neural networks, and model-free baselines, our method
achieves the best optimization performance on connectivity-driven problems. It
reduces regret by up to 40% and final normalized hypervolume error by up to
50% relative to the strongest encoding-based baseline. This comes at a higher
computational cost, but the additional overhead is offset once architecture evalu-
ations take approximately 4–25 seconds. On the remaining problems, our method
remains competitive with state-of-the-art encoding-based approaches. These find-
ings suggest that graph-based approaches could help engineers find better system
architectures faster when simulations are expensive. ... Read more

Researching hand gestures in real-world social interactions requires very careful analysis. While gesture coding schemes were created with that purpose in mind, they are not widely utilised in research. Moreover, studies on gesture classification rarely focus on the physical nature of movements involved in gesturing, despite the fact that being able to quantify the motion could reveal useful patterns and correlations. To address those points, this research proposes the following approach: using machine learning models to automatically classify physical features of hand gestures, according to a coding scheme. Two such classifiers were created, for the left and right hand respectively. Overall, the results are quite promising - despite a small and imbalanced training set and complex features both models achieved an accuracy of roughly 60%. Moreover, the results indicate that by avoiding some of the simplifications that this research makes, and by using more balanced training data, the accuracy could be significantly increased. This is concrete evidence that machine learning models can indeed be used to classify the physical aspects of hand gestures, as defined
by a coding scheme, in social interactions in the wild.
... Read more