Some Requests for Machine Learning Research from the East African Tech Scene

Based on 46 in-depth interviews with scientists, engineers, and CEOs, this document presents a list of concrete machine research problems, progress on which would directly benefit tech ventures in East Africa.Comment: Presented at NIPS 2018 Workshop on Machine Learning for the Developing Worl

Open Vocabulary Learning on Source Code with a Graph-Structured Cache

Machine learning models that take computer program source code as input typically use Natural Language Processing (NLP) techniques. However, a major challenge is that code is written using an open, rapidly changing vocabulary due to, e.g., the coinage of new variable and method names. Reasoning over such a vocabulary is not something for which most NLP methods are designed. We introduce a Graph-Structured Cache to address this problem; this cache contains a node for each new word the model encounters with edges connecting each word to its occurrences in the code. We find that combining this graph-structured cache strategy with recent Graph-Neural-Network-based models for supervised learning on code improves the models' performance on a code completion task and a variable naming task --- with over $100\%$ relative improvement on the latter --- at the cost of a moderate increase in computation time.Comment: Published in the International Conference on Machine Learning (ICML 2019), 13 page

Characterization of the Biological Role of a Putative Porphyromonas gingivalis RNA-binding Protein

Porphyromonas gingivalis, a gram-negative anaerobic bacterium, is a major etiological agent in the initiation and progression of severe forms of periodontal disease. Oral bacteria like P. gingivalis are subject to continually changing conditions as a consequence of host eating, oral hygiene patterns and subgingival temperatures. As such survival requires an adaptive response to environmental cues, but little is known about the mechanism by which P. gingivalis controls co- and post-transcriptional regulation of RNA levels and potentially protein expression. RNA-binding proteins (RBPs) are evolutionarily conserved across species and are involved in such regulatory mechanisms. However, P. gingivalis currently has no identified RBP. Recently, PG0627 has become an ideal candidate for a putative RBP due to its sequence homology to RBPs across various species. By characterizing PG0627, we can gain better insight into the function of this hypothetical protein and determine if it indeed behaves like an RNA-binding protein. A host of studies were done on a PG0627-deficient P. gingivalis mutant, V3139, in order to determine the biological role of the protein encoded by the gene. Our bioinformatics analysis indicated that PG0627 had sequence homology to several RNA recognition motifs or RBPs. Furthermore, our PG0627-deficient mutant, when compared to W83, exhibited decreased cell-associated iron content, decreased total interactions and invasions with eukaryotic cells, and decreased protease activity. Conversely, our PG0627-deficient mutant displayed slightly increased growth in the presence of nitrosative stress, and in hemin-depleted conditions. In conclusion, our results support that PG0627 is a valid candidate for an RNA-binding protein in P. gingivalis

Deep Learning in Unconventional Domains

Machine learning methods have dramatically improved in recent years thanks to advances in deep learning (LeCun et al., 2015), a set of methods for training high-dimensional, highly-parameterized, nonlinear functions. Yet deep learning progress has been concentrated in the domains of computer vision, vision-based reinforcement learning, and natural language processing. This dissertation is an attempt to extend deep learning into domains where it has thus far had little impact or has never been applied. It presents new deep learning algorithms and state-of-the-art results on tasks in the domains of source-code analysis, relational databases, and tabular data.</p

Simple and Low-Cost Realization of RDS Encoder

This paper presents a simple and easy way of realization of RDS encoder. Autonomous equipment which is capable to generate wanted data stream, modulate that data stream and mixed generated signal with stereo or mono FM composite multiplex signal is simulated and produced. Parts of encoder are described, simulated and measured. The use of RDS makes FM receivers more user-friendly. With this simple and cheap RDS encoder, smaller FM broadcasters have a chance to improve business ability