Slender precast voided slabs under walking-induced vibration

Disturbance/discomfort caused by vibrations, induced by pedestrian walking on slabs in residential/office buildings, is a typical design issue for lightweight slender slabs, including prestressed concrete ones. Precast slabs are typically made with pretensioned members which allow for only partial collaboration in the transverse slab direction, which becomes even less effective when they are dry-assembled without cast-in-situ topping since it relies on the arrangement of mutual mechanical connections only. This study investigates through tests and numerical analyses the response of slender precast long-span slabs made with voided members, dry-assembled with mechanical connections, when subjected to vibrations generated by human activities. A parametric set of dynamic modal and time-history analyses encompassing floor member geometry, connection arrangement, mass, and damping, is carried out. The numerical models are validated against results from an experimental test program carried out on two decks of a prototype precast building. The tests and the numerical models allowed to characterize the fundamental dynamic properties of the slab and its vibrational performance, identifying the most efficient technological solutions among those investigated to mitigate human-induced vibrations

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

Stochastic Variational Inference for Hidden Markov Models

Variational inference algorithms have proven successful for Bayesian analysis in large data settings, with recent advances using stochastic variational inference (SVI). However, such methods have largely been studied in independent or exchangeable data settings. We develop an SVI algorithm to learn the parameters of hidden Markov models (HMMs) in a time-dependent data setting. The challenge in applying stochastic optimization in this setting arises from dependencies in the chain, which must be broken to consider minibatches of observations. We propose an algorithm that harnesses the memory decay of the chain to adaptively bound errors arising from edge effects. We demonstrate the effectiveness of our algorithm on synthetic experiments and a large genomics dataset where a batch algorithm is computationally infeasible.Comment: Appears in Advances in Neural Information Processing Systems (NIPS), 201