Laplacian Denoising Autoencoder

While deep neural networks have been shown to perform remarkably well in many machine learning tasks, labeling a large amount of ground truth data for supervised training is usually very costly to scale. Therefore, learning robust representations with unlabeled data is critical in relieving human effort and vital for many downstream tasks. Recent advances in unsupervised and self-supervised learning approaches for visual data have benefited greatly from domain knowledge. Here we are interested in a more generic unsupervised learning framework that can be easily generalized to other domains. In this paper, we propose to learn data representations with a novel type of denoising autoencoder, where the noisy input data is generated by corrupting latent clean data in the gradient domain. This can be naturally generalized to span multiple scales with a Laplacian pyramid representation of the input data. In this way, the agent learns more robust representations that exploit the underlying data structures across multiple scales. Experiments on several visual benchmarks demonstrate that better representations can be learned with the proposed approach, compared to its counterpart with single-scale corruption and other approaches. Furthermore, we also demonstrate that the learned representations perform well when transferring to other downstream vision tasks

The 3-Hydroxy-2-Butanone Pathway Is Required for Pectobacterium carotovorum Pathogenesis

Pectobacterium species are necrotrophic bacterial pathogens that cause soft rot diseases in potatoes and several other crops worldwide. Gene expression data identified Pectobacterium carotovorum subsp. carotovorum budB, which encodes the α-acetolactate synthase enzyme in the 2,3-butanediol pathway, as more highly expressed in potato tubers than potato stems. This pathway is of interest because volatiles produced by the 2,3-butanediol pathway have been shown to act as plant growth promoting molecules, insect attractants, and, in other bacterial species, affect virulence and fitness. Disruption of the 2,3-butanediol pathway reduced virulence of P. c. subsp. carotovorum WPP14 on potato tubers and impaired alkalinization of growth medium and potato tubers under anaerobic conditions. Alkalinization of the milieu via this pathway may aid in plant cell maceration since Pectobacterium pectate lyases are most active at alkaline pH

Time-domain analysis of distributed networks

A method is presented for numerical inversion of the Laplace transform. The method is an extension of Pade-approximation-based techniques. However, it provides more accurate results at an incremental computational cost. The new method is suitable for transient analysis of general linear networks with lumped, distributed, or mixed parameters. An application to transient analysis of distributed networks is demonstrated using an example of an interconnect circuit