MirrorGAN
Generating an image from a given text description has two goals: visual realism and semantic consistency. Although significant progress has been made in generating high-quality and visually realistic images using generative adversarial networks, guaranteeing semantic consistency between the text description and visual content remains very challenging. In this paper, we address this problem by proposing a novel global-local attentive and semantic-preserving text-to-image-to-text framework called MirrorGAN. MirrorGAN exploits the idea of learning text-to-image generation by redescription and consists of three modules: a semantic text embedding module (STEM), a global-local collaborative attentive module for cascaded image generation (GLAM), and a semantic text regeneration and alignment module (STREAM). STEM generates word- and sentence-level embeddings. GLAM has a cascaded architecture for generating target images from coarse to fine scales, leveraging both local word attention and global sentence attention to progressively enhance the diversity and semantic consistency of the generated images. STREAM seeks to regenerate the text description from the generated image, which semantically aligns with the given text description. Thorough experiments on two public benchmark datasets demonstrate the superiority of MirrorGAN over other representative state-of-the-art methods. …

Adaptive Probabilistic Principal Component Analysis (A-PPCA)
Using the linear Gaussian latent variable model as a starting point we relax some of the constraints it imposes by deriving a nonparametric latent feature Gaussian variable model. This model introduces additional discrete latent variables to the original structure. The Bayesian nonparametric nature of this new model allows it to adapt complexity as more data is observed and project each data point onto a varying number of subspaces. The linear relationship between the continuous latent and observed variables make the proposed model straightforward to interpret, resembling a locally adaptive probabilistic PCA (A-PPCA). We propose two alternative Gibbs sampling procedures for inference in the new model and demonstrate its applicability on sensor data for passive health monitoring. …

RORPack
This document contains the mathematical introduction to RORPack – a Python software library for robust output tracking and disturbance rejection for linear PDE systems. The RORPack library is open-source and freely available at https://…/rorpack The package contains functionality for automated construction of robust internal model based controllers, simulation of the controlled systems, visualisation of the results, as well as a collection of example cases on robust output regulation of controlled heat and wave equations. …

Sharding
Sharding is the process of splitting up your data so it resides in different tables or often different physical databases. Sharding is helpful when you have some specific set of data that outgrows either storage or reasonable performance within a single database.
SHapley Additive exPlanations – SHAP …