Gauge/gravity duality is more than AdS/CFT.  In this talk I will discuss how the holographic dictionary generalises to non-conformal settings, focusing on maximally supersymmetric Yang-Mills theories in diverse dimensions and their Dp-brane supergravity duals. Scaling covariance replaces conformal invariance as the unifying principle on both sides of the duality. On the gravity side, I will show how to systematically organise effective actions and Witten diagram rules for arbitrary correlators of scalar and spin-1 Kaluza-Klein modes. On the field theory side, scale covariance fixes the kinematic structure of 2- and 3-point functions at strong coupling, with the latter admitting closed-form expressions in terms of Appell functions. I will illustrate these results with explicit examples, focussing on 3d MSYM.

The Euler characteristic transform (ECT) is an emerging and powerful framework within topological data analysis for quantifying the geometry of shape. The applicability of ECT has been limited due to its sensitivity to noisy data. Here, we introduce SampEuler, a novel ECT-based shape descriptor designed to achieve enhanced robustness to perturbations. We provide a theoretical analysis establishing the stability of SampEuler and validate these properties empirically through pairwise similarity analyses on a benchmark dataset and showcase it on a thymus dataset. The thymus is a primary lymphoid organ that is essential for the maturation and selection of self-tolerant T cells, and within the thymus, thymic epithelial cells are organized in complex three-dimensional architectures, yet the principles governing their formation, functional organization, and remodeling during age-related involution remain poorly understood. Addressing these questions requires robust and informative shape descriptors capable of capturing subtle architectural changes across developmental stages. We develop and apply SampEuler to a newly generated two-dimensional imaging dataset of mouse thymi spanning multiple age groups, where SampEuler outperforms both persistent homology-based methods and deep learning models in detecting subtle, localized morphological differences associated with aging. To facilitate interpretation, we develop a vectorization and visualization framework for SampEuler, which preserves rich morphological information and enables identification of structural features that distinguish thymi across age groups. Collectively, our results demonstrate that SampEuler provides a robust and interpretable approach for quantifying thymic architecture and reveals age-dependent structural changes that offer new insights into thymic organization and involution.