Denoising Criterion for Variational Auto-Encoding Framework

Denoising autoencoders (DAE) are trained to reconstruct their clean inputs with noise injected at the input level, while variational autoencoders (VAE) are trained with noise injected in their stochastic hidden layer, with a regularizer that encourages this noise injection. In this paper, we show that injecting noise both in input and in the stochastic hidden layer can be advantageous and we propose a modified variational lower bound as an improved objective function in this setup. When input is corrupted, then the standard VAE lower bound involves marginalizing the encoder conditional distribution over the input noise, which makes the training criterion intractable. Instead, we propose a modified training criterion which corresponds to a tractable bound when input is corrupted. Experimentally, we find that the proposed denoising variational autoencoder (DVAE) yields better average log-likelihood than the VAE and the importance weighted autoencoder on the MNIST and Frey Face datasets.Comment: ICLR conference submissio

13C-Methyl isocyanide as an NMR probe for cytochrome P450 active site

The cytochromes P450 (CYPs) play a central role in many biologically important oxidation reactions, including the metabolism of drugs and other xenobiotic compounds. Because they are often assayed as both drug targets and anti-targets, any tools that provide: (a) confirmation of active site binding and (b) structural data, would be of great utility, especially if data could be obtained in reasonably high throughput. To this end, we have developed an analog of the promiscuous heme ligand, cyanide,with a 13CH3-reporter attached. This 13C-methyl isocyanide ligand binds to bacterial (P450cam) and membrane-bound mammalian (CYP2B4) CYPs. It can be used in a rapid 1D experiment to identify binders, and provides a qualitative measure of structural changes in the active site

Active and Passive Causal Inference Learning

This paper serves as a starting point for machine learning researchers, engineers and students who are interested in but not yet familiar with causal inference. We start by laying out an important set of assumptions that are collectively needed for causal identification, such as exchangeability, positivity, consistency and the absence of interference. From these assumptions, we build out a set of important causal inference techniques, which we do so by categorizing them into two buckets; active and passive approaches. We describe and discuss randomized controlled trials and bandit-based approaches from the active category. We then describe classical approaches, such as matching and inverse probability weighting, in the passive category, followed by more recent deep learning based algorithms. By finishing the paper with some of the missing aspects of causal inference from this paper, such as collider biases, we expect this paper to provide readers with a diverse set of starting points for further reading and research in causal inference and discovery

Muscle fatigue measured with evoked muscle vibrations

Skeletal muscle vibrates laterally during voluntary and evoked muscle contractions. We hypothesized that the vibration amplitude from evoked muscle twitches is directly related to evoked twitch force from fatiguing muscle. To test the hypothesis, vibrations produced by evoked muscle twitches were recorded during short (5-second) rest periods as the muscle was intermittently exercised with voluntary contractions. Trials were performed at 30%, 50%, and 70% of maximal coluntary contraction. Evoked muscle twitches eliminated the problems of motivation and tremor that complicatye sound and vibration measurements during voluntary contractions. Results from the first dorsal interosseus hand muscle in 11 normal adult volunteers from the first dorsal interosseus hand muscle in 11 normal adult volunteers revealed that the vibration amplitude is highly correlated ( r 2 = 0.93, at 70% MVC, r 2 = 0.97, at 50% MVC; r 2 = 0.85, at 30% MVC) with force. Both potentiation and reduction of force with exercise were accompanied by parallel changes in vibration amplitude, as measured with an accelerometer. Compound muscle action potentials did not increase with exercise-induced twitch potentiation, and did not correlate as highly with force during fatigue.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/50153/1/880150308_ftp.pd