Uses of Complex Wavelets in Deep Convolutional Neural Networks

Image understanding has long been a goal for computer vision. It has proved to be an exceptionally difficult task due to the large amounts of variability that are inherent to objects in a scene. Recent advances in supervised learning methods, particularly convolutional neural networks (CNNs), have pushed forth the frontier of what we have been able to train computers to do. Despite their successes, the mechanics of how these networks are able to recognize objects are little understood, and the networks themselves are often very difficult and time-consuming to train. It is very important that we improve our current approaches in every way possible. A CNN is built from connecting many learned convolutional layers in series. These convolutional layers are fairly crude in terms of signal processing - they are arbitrary taps of a finite impulse response filter, learned through stochastic gradient descent from random initial conditions. We believe that if we reformulate the problem, we may achieve many insights and benefits in training CNNs. Noting that modern CNNs are mostly viewed from and analyzed in the spatial domain, this thesis aims to view the convolutional layers in the frequency domain (viewing things in the frequency domain has proved useful in the past for denoising, filter design, compression and many other tasks). In particular, we use complex wavelets (rather than the Fourier transform or the discrete wavelet transform) as basis functions to reformulate image understanding with deep networks. In this thesis, we explore the most popular and well-developed form of using complex wavelets in deep learning, the ScatterNet from Stephane Mallat. We explore its current limitations by building a DeScatterNet and found that while it has many nice properties, it may not be sensitive to the most appropriate shapes for understanding natural images. We then develop a locally invariant convolutional layer, a combination of a complex wavelet transform, a modulus operation, and a learned mixing. To do this, we derive backpropagation equations and allow gradients to flow back through the (previously fixed) ScatterNet front end. Connecting several such locally invariant layers allows us to build learnable ScatterNet, a more flexible and general form of the ScatterNet (while still maintaining its desired properties). We show that the learnable ScatterNet can provide significant improvements over the regular ScatterNet when being used as a front end for a learning system. Additionally, we show that the locally invariant convolutional layer can directly replace convolutional layers in a deep CNN (and not just at the front-end). The locally invariant convolutional layers naturally downsample the input (because of the complex modulus) while increasing the channel dimension (because of the multiple wavelet orientations used). This is an operation that often happens in a CNN by a combination of a pooling and convolutional layer. It was at these locations in a CNN where the learnable ScatterNet performed best, implying it may be useful as learnable pooling layer. Finally, we develop a system to learn complex weights that act directly on the wavelet coefficients of signals, in place of a convolutional layer. We call this layer the wavelet gain layer and show it can be used alongside convolutional layers. The network designer may then choose to learn in the pixel or wavelet domains. This layer shows a lot of promise and affords more control over what regions of the frequency space we want our layer to learn from. Our experiments show that it can improve on learning in the pixel domain for early layers of a CNN

A Framework for Implementing Machine Learning on Omics Data

The potential benefits of applying machine learning methods to -omics data are becoming increasingly apparent, especially in clinical settings. However, the unique characteristics of these data are not always well suited to machine learning techniques. These data are often generated across different technologies in different labs, and frequently with high dimensionality. In this paper we present a framework for combining -omics data sets, and for handling high dimensional data, making -omics research more accessible to machine learning applications. We demonstrate the success of this framework through integration and analysis of multi-analyte data for a set of 3,533 breast cancers. We then use this data-set to predict breast cancer patient survival for individuals at risk of an impending event, with higher accuracy and lower variance than methods trained on individual data-sets. We hope that our pipelines for data-set generation and transformation will open up -omics data to machine learning researchers. We have made these freely available for noncommercial use at www.ccg.ai.Comment: Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 arXiv:1811.0721

Marine pseudovibrio sp. as a novel source of antimicrobials

Antibiotic resistance among pathogenic microorganisms is becoming ever more common. Unfortunately, the development of new antibiotics which may combat resistance has decreased. Recently, however the oceans and the marine animals that reside there have received increased attention as a potential source for natural product discovery. Many marine eukaryotes interact and form close associations with microorganisms that inhabit their surfaces, many of which can inhibit the attachment, growth or survival of competitor species. It is the bioactive compounds responsible for the inhibition that is of interest to researchers on the hunt for novel bioactives. The genus Pseudovibrio has been repeatedly identified from the bacterial communities isolated from marine surfaces. In addition, antimicrobial activity assays have demonstrated significant antimicrobial producing capabilities throughout the genus. This review will describe the potency, spectrum and possible novelty of the compounds produced by these bacteria, while highlighting the capacity for this genus to produce natural antimicrobial compounds which could be employed to control undesirable bacteria in the healthcare and food production sectors

Probabilistic Future Prediction for Video Scene Understanding

We present a novel deep learning architecture for probabilistic future prediction from video. We predict the future semantics, geometry and motion of complex real-world urban scenes and use this representation to control an autonomous vehicle. This work is the first to jointly predict ego-motion, static scene, and the motion of dynamic agents in a probabilistic manner, which allows sampling consistent, highly probable futures from a compact latent space. Our model learns a representation from RGB video with a spatio-temporal convolutional module. The learned representation can be explicitly decoded to future semantic segmentation, depth, and optical flow, in addition to being an input to a learnt driving policy. To model the stochasticity of the future, we introduce a conditional variational approach which minimises the divergence between the present distribution (what could happen given what we have seen) and the future distribution (what we observe actually happens). During inference, diverse futures are generated by sampling from the present distribution.Toshiba Europe, grant G10045

Subtilomycin: A New Lantibiotic from Bacillus subtilis Strain MMA7 Isolated from the Marine Sponge Haliclona simulans

Bacteriocins are attracting increased attention as an alternative to classic antibiotics in the fight against infectious disease and multidrug resistant pathogens. Bacillus subtilis strain MMA7 isolated from the marine sponge Haliclona simulans displays a broad spectrum antimicrobial activity, which includes Gram-positive and Gram-negative pathogens, as well as several pathogenic Candida species. This activity is in part associated with a newly identified lantibiotic, herein named as subtilomycin. The proposed biosynthetic cluster is composed of six genes, including protein-coding genes for LanB-like dehydratase and LanC-like cyclase modification enzymes, characteristic of the class I lantibiotics. The subtilomycin biosynthetic cluster in B. subtilis strain MMA7 is found in place of the sporulation killing factor (skf) operon, reported in many B. subtilis isolates and involved in a bacterial cannibalistic behaviour intended to delay sporulation. The presence of the subtilomycin biosynthetic cluster appears to be widespread amongst B. subtilis strains isolated from different shallow and deep water marine sponges. Subtilomycin possesses several desirable industrial and pharmaceutical physicochemical properties, including activity over a wide pH range, thermal resistance and water solubility. Additionally, the production of the lantibiotic subtilomycin could be a desirable property should B. subtilis strain MMA7 be employed as a probiotic in aquaculture applications

SETD1A methyltransferase is physically and functionally linked to the DNA damage repair protein RAD18.

SETD1A is a SET domain-containing methyltransferase involved in epigenetic regulation of transcription. It is the main catalytic component of a multiprotein complex that methylates lysine 4 of histone H3, a histone mark associated with gene activation. In humans, six related protein complexes with partly nonredundant cellular functions share several protein subunits but are distinguished by unique catalytic SET-domain proteins. We surveyed physical interactions of the SETD1A-complex using endogenous immunoprecipitation followed by label-free quantitative proteomics on three subunits: SETD1A, RBBP5, and ASH2L. Surprisingly, SETD1A, but not RBBP5 or ASH2L, was found to interact with the DNA damage repair protein RAD18. Reciprocal RAD18 immunoprecipitation experiments confirmed the interaction with SETD1A, whereas size exclusion and protein network analysis suggested an interaction independent of the main SETD1A complex. We found evidence of SETD1A and RAD18 influence on mutual gene expression levels. Further, knockdown of the genes individually showed a DNA damage repair phenotype, whereas simultaneous knockdown resulted in an epistatic effect. This adds to a growing body of work linking epigenetic enzymes to processes involved in genome stability