3D Generative Model Latent Disentanglement via Local Eigenprojection

Designing realistic digital humans is extremely complex. Most data-driven generative models used to simplify the creation of their underlying geometric shape do not offer control over the generation of local shape attributes. In this paper, we overcome this limitation by introducing a novel loss function grounded in spectral geometry and applicable to different neural-network-based generative models of 3D head and body meshes. Encouraging the latent variables of mesh variational autoencoders (VAEs) or generative adversarial networks (GANs) to follow the local eigenprojections of identity attributes, we improve latent disentanglement and properly decouple the attribute creation. Experimental results show that our local eigenprojection disentangled (LED) models not only offer improved disentanglement with respect to the state-of-the-art, but also maintain good generation capabilities with training times comparable to the vanilla implementations of the models. Our code and pre-trained models are available at github.com/simofoti/LocalEigenprojDisentangled

Cerebellar involvement in cognitive flexibility

The aim of the present study was to investigate whether the cerebellar structures are involved in functions requiring cognitive flexibility abilities. The flexibility of the hemicerebellectomized and control animals in learning a four-choice learning task, adapting to ever-changing response rules was investigated. While in the initial phase of the task both experimental groups exhibited similar performances, only the control animals significantly improved their performance as the sessions went by. The lack of improvement in lesioned animals' performance rendered their responses particularly defective in the final phases of the task, when conversely intact animals performed best, exploiting their "learning to learn" ability. The findings demonstrate the defective influence of the cerebellar lesion on the acquisition, not the execution, of new responses. The results underline the crucial role of the cerebellum in mediating cognitive flexibility behaviors

A new misfit function for multimodal inversion of surface waves

Higher-mode contribution is important in surface-wave inversion because it allows more information to be exploited, increases investigation depth, and improves model resolution. A new misfit function for multimodal inversion of surface waves, based on the Haskell-Thomson matrix method, allows higher modes to be taken into account without the need to associate experimental data points to a specific mode, thus avoiding mode-misidentification errors in the retrieved velocity profiles. Computing cost is reduced by avoiding the need for calculating synthetic apparent or modal dispersion curves. Based on several synthetic and real examples with inversion results from the classical and the proposed methods, we find that correct velocity models can be retrieved through the multimodal inversion when higher modes are superimposed in the apparent dispersion-curve or when it is not trivial to determine a priori to which mode each data point of the experimental dispersion curve belongs. The main drawback of the method is related to the presence of several local minima in the misfit function. This feature makes the choice of a consistent initial model very importan