Neural Information Processing Systems http://nips.cc/

Unless otherwise specified all algorithms were timed on single core versions even though, for instance, the proposed method is in places trivially parallelizable (i.e. during forest build). An exception was the grid search across meta-parameters to find the best (optimal) reference model where parallelization was used when required as this stage does not form part of the time comparisons. Hosted on Google Colaboratory they enable the use of hosted or local runtime environments. When tested hosted runtimes were running Python 3.6.9 Please note that while a hosted runtime can be used for ease of replication, all timings reported in the paper were based on using a local runtime environment as previously indicated NOT a hosted environment. The notebooks, when run in the hosted environment will automatically install the required packages developed as part of this work.

An often overlooked problem in medical image segmentation research is the effective selection of training subsets to annotate from a complete set of unlabelled data. Many studies select their training sets at random, which may lead to suboptimal model performance, especially in the minimal supervision setting where each training image has a profound effect on performance outcomes. This work aims to address this issue. We use prototypical contrasting learning and clustering to extract representative and diverse samples for annotation. We improve upon prior works with a bespoke cluster-based image selection process. Additionally, we introduce the concept of unsupervised balanced batch dataloading to medical image segmentation, which aims to improve model learning with minimally annotated data. We evaluated our method on a public skin lesion dataset (ISIC 2018) and compared it to another state-of-the-art data sampling method. Our method achieved superior performance in a low annotation budget scenario.

As robots enter the messy human world so the vital matter of safety takes on a fresh complexion with physical contact becoming inevitable and even desirable. We report on an artistic-exploration of how dancers, working as part of a multidisciplinary team, engaged in contact improvisation exercises to explore the opportunities and challenges of dancing with cobots. We reveal how they employed their honed bodily senses and physical skills to engage with the robots aesthetically and yet safely, interleaving improvised physical manipulations with reflections to grow their knowledge of how the robots behaved and felt. We introduce somatic safety, a holistic mind-body approach in which safety is learned, felt and enacted through bodily contact with robots in addition to being reasoned about. We conclude that robots need to be better designed for people to hold them and might recognise tacit safety cues among people.We propose that safety should be learned through iterative bodily experience interleaved with reflection.

Neural posterior estimation methods based on discrete normalizing flows have become established tools for simulation-based inference (SBI), but scaling them to high-dimensional problems can be challenging.

We propose causal effect estimators based on empirical Fr\'{e}chet means and operator-valued kernels, tailored to functional data spaces. These methods address the challenges of high-dimensionality, sequential ordering, and model complexity while preserving robustness to treatment misspecification. Using structural assumptions, we obtain compact representations of potential outcomes, enabling scalable estimation of causal effects over time and across covariates. We provide both theoretical, regarding the consistency of functional causal effects, as well as empirical comparison of a range of proposed causal effect estimators. Applications to binary treatment settings with functional outcomes illustrate the framework's utility in biomedical monitoring, where outcomes exhibit complex temporal dynamics. Our estimators accommodate scenarios with registered covariates and outcomes, aligning them to the Fr\'{e}chet means, as well as cases requiring higher-order representations to capture intricate covariate-outcome interactions. These advancements extend causal inference to dynamic and non-linear domains, offering new tools for understanding complex treatment effects in functional data settings.

This paper explores the use of a body-worn vibrotactile vest to convey real-time information from robot to operator. Vibrotactile communication could be useful in providing information without compropmising or loading a person's visual or auditory perception. This paper considers applications in Urban Search and Rescue (USAR) scenarios where a human working alongside a robot is likely to be operating in high cognitive load conditions. The focus is on understanding how best to convey information considering different vibrotactile information coding strategies to enhance scene understanding in scenarios where a robot might be operating remotely as a scout. In exploring information representation, this paper introduces Semantic Haptics, using shapes and patterns to represent certain events as if the skin was a screen, and shows how these lead to bettter learnability and interpreation accuracy.

Constitutional AI (CAI) guides LLM behavior using constitutions, but identifying which principles are most effective for model alignment remains an open challenge. We introduce the C3AI framework (\textit{Crafting Constitutions for CAI models}), which serves two key functions: (1) selecting and structuring principles to form effective constitutions before fine-tuning; and (2) evaluating whether fine-tuned CAI models follow these principles in practice. By analyzing principles from AI and psychology, we found that positively framed, behavior-based principles align more closely with human preferences than negatively framed or trait-based principles. In a safety alignment use case, we applied a graph-based principle selection method to refine an existing CAI constitution, improving safety measures while maintaining strong general reasoning capabilities. Interestingly, fine-tuned CAI models performed well on negatively framed principles but struggled with positively framed ones, in contrast to our human alignment results. This highlights a potential gap between principle design and model adherence. Overall, C3AI provides a structured and scalable approach to both crafting and evaluating CAI constitutions.

Algebraic varieties are the geometric shapes defined by systems of polynomial equations; they are ubiquitous across mathematics and science. Amongst these algebraic varieties are Q-Fano varieties: positively curved shapes which have Q-factorial terminal singularities. Q-Fano varieties are of fundamental importance in geometry as they are'atomic pieces' of more complex shapes - the process of breaking a shape into simpler pieces in this sense is called the Minimal Model Programme. Despite their importance, the classification of Q-Fano varieties remains unknown. In this paper we demonstrate that machine learning can be used to understand this classification.