Variational Walkback: Learning a Transition Operator as a Stochastic Recurrent Net

We propose a novel method to directly learn a stochastic transition operator whose repeated application provides generated samples. Traditional undirected graphical models approach this problem indirectly by learning a Markov chain model whose stationary distribution obeys detailed balance with respect to a parameterized energy function. The energy function is then modified so the model and data distributions match, with no guarantee on the number of steps required for the Markov chain to converge. Moreover, the detailed balance condition is highly restrictive: energy based models corresponding to neural networks must have symmetric weights, unlike biological neural circuits. In contrast, we develop a method for directly learning arbitrarily parameterized transition operators capable of expressing non-equilibrium stationary distributions that violate detailed balance, thereby enabling us to learn more biologically plausible asymmetric neural networks and more general non-energy based dynamical systems. The proposed training objective, which we derive via principled variational methods, encourages the transition operator to "walk back" in multi-step trajectories that start at data-points, as quickly as possible back to the original data points. We present a series of experimental results illustrating the soundness of the proposed approach, Variational Walkback (VW), on the MNIST, CIFAR-10, SVHN and CelebA datasets, demonstrating superior samples compared to earlier attempts to learn a transition operator. We also show that although each rapid training trajectory is limited to a finite but variable number of steps, our transition operator continues to generate good samples well past the length of such trajectories, thereby demonstrating the match of its non-equilibrium stationary distribution to the data distribution. Source Code: http://github.com/anirudh9119/walkback_nips17Comment: To appear at NIPS 201

Antibacterial Performance of a Cu-bearing Stainless Steel against Microorganisms in Tap Water

This document is the Accepted Manuscript of the following article: Mingjun Li, Li Nan, Dake xu, Guogang Ren, Ke Yang, ‘Antibacterial Performance of a Cu-bearing Stainless Steel against Microorganisms in Tap Water’, Journal of Materials Science & Technology, Vol. 31 (3): 243-251, March 2015, DOI: https://doi.org/10.1016/j.jmst.2014.11.016, made available under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License CC BY NC-ND 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/).Tap water is one of the most commonly used water resources in our daily life. However, the increasing water contamination and the health risk caused by pathogenic bacteria, such as Staphylococcus aureus and Escherichia coli have attracted more attention. The mutualism of different pathogenic bacteria may diminish antibacterial effect of antibacterial agents. It was found that materials used for making pipe and tap played one of the most important roles in promoting bacterial growth. This paper is to report the performance of an innovative type 304 Cu-bearing stainless steel (304CuSS) against microbes in tap water. The investigation methodologies involved were means of heterotrophic plate count, contact angle measurements, scanning electron microscopy for observing the cell and subtract surface morphology, atomic absorption spectrometry for copper ions release study, and confocal laser scanning microscopy used for examining live/dead bacteria on normal 304 stainless steel and 304CuSS. It was found that the surface free energy varied after being immersed in tap water with polar component and Cu ions release. The results showed 304CuSS could effectively kill most of the planktonic bacteria (max 95.9% antibacterial rate), and consequently inhibit bacterial biofilms formation on the surface, contributing to the reduction of pathogenic risk to the surrounding environments.Peer reviewe