Los caracteres biocenóticos de las lagunas basálticas del oeste de Neuquén

Fil: Roig, Virgilio G.. Universidad Nacional de Cuyo. Instituto de BiologíaFil: Cei, José M.. Universidad Nacional de Cuyo. Instituto de Biologí

Hadron structure in tau -> KKpi nu_tau decays

We analyse the hadronization structure of both vector and axial-vector currents leading to tau -> KKpi nu_tau decays. At leading order in the 1/Nc expansion, and considering only the contribution of the lightest resonances, we work out, within the framework of the resonance chiral Lagrangian, the structure of the local vertices involved in those processes. The couplings in the resonance theory are constrained by imposing the asymptotic behaviour of vector and axial-vector spectral functions ruled by QCD. In this way we predict the hadron spectra and conclude that, contrarily to previous assertions, the vector contribution dominates by far over the axial-vector one in all KKpi charge channels.Comment: 32 pages, 7 figure

Analyzing Vision Transformers for Image Classification in Class Embedding Space

Despite the growing use of transformer models in computer vision, a mechanistic understanding of these networks is still needed. This work introduces a method to reverse-engineer Vision Transformers trained to solve image classification tasks. Inspired by previous research in NLP, we demonstrate how the inner representations at any level of the hierarchy can be projected onto the learned class embedding space to uncover how these networks build categorical representations for their predictions. We use our framework to show how image tokens develop class-specific representations that depend on attention mechanisms and contextual information, and give insights on how self-attention and MLP layers differentially contribute to this categorical composition. We additionally demonstrate that this method (1) can be used to determine the parts of an image that would be important for detecting the class of interest, and (2) exhibits significant advantages over traditional linear probing approaches. Taken together, our results position our proposed framework as a powerful tool for mechanistic interpretability and explainability research.Comment: NeurIPS 202