Populations in statistical genetic modelling and inference

What is a population? This review considers how a population may be defined in terms of understanding the structure of the underlying genetics of the individuals involved. The main approach is to consider statistically identifiable groups of randomly mating individuals, which is well defined in theory for any type of (sexual) organism. We discuss generative models using drift, admixture and spatial structure, and the ancestral recombination graph. These are contrasted with statistical models for inference, principle component analysis and other `non-parametric' methods. The relationships between these approaches are explored with both simulated and real-data examples. The state-of-the-art practical software tools are discussed and contrasted. We conclude that populations are a useful theoretical construct that can be well defined in theory and often approximately exist in practice

Accelerated Neural Networks on OpenCL Devices Using SYCL-DNN

Over the past few years machine learning has seen a renewed explosion of interest, following a number of studies showing the effectiveness of neural networks in a range of tasks which had previously been considered incredibly hard. Neural networks' effectiveness in the fields of image recognition and natural language processing stems primarily from the vast amounts of data available to companies and researchers, coupled with the huge amounts of compute power available in modern accelerators such as GPUs, FPGAs and ASICs. There are a number of approaches available to developers for utilizing GPGPU technologies such as SYCL, OpenCL and CUDA, however many applications require the same low level mathematical routines. Libraries dedicated to accelerating these common routines allow developers to easily make full use of the available hardware without requiring low level knowledge of the hardware themselves, however such libraries are often provided by hardware manufacturers for specific hardware such as cuDNN for Nvidia hardware or MIOpen for AMD hardware. SYCL-DNN is a new open-source library dedicated to providing accelerated routines for neural network operations which are hardware and vendor agnostic. Built on top of the SYCL open standard and written entirely in standard C++, SYCL-DNN allows a user to easily accelerate neural network code for a wide range of hardware using a modern C++ interface. The library is tested on AMD's OpenCL for GPU, Intel's OpenCL for CPU and GPU, ARM's OpenCL for Mali GPUs as well as ComputeAorta's OpenCL for R-Car CV engine and host CPU. In this talk we will present performance figures for SYCL-DNN on this range of hardware, and discuss how high performance was achieved on such a varied set of accelerators with such different hardware features.Comment: 4 pages, 3 figures. In International Workshop on OpenCL (IWOCL '19), May 13-15, 2019, Bosto

Overview of gene structure

Throughout the C. elegans sequencing project Genefinder was the primary protein-coding gene prediction program. These initial predictions were manually reviewed by curators as part of a "first-pass annotation" and are actively curated by WormBase staff using a variety of data and information. In the WormBase data release WS133 there are 22,227 protein-coding gene, including 2,575 alternatively-spliced forms. Twenty-eight percent of these have every base of every exon confirmed by transcription evidence while an additional 51% have some bases confirmed. Most of the genes are relatively small covering a genomic region of about 3 kb. The average gene contains 6.4 coding exons accounting for about 26% of the genome. Most exons are small and separated by small introns. The median size of exons is 123 bases, while the most common size for introns is 47 bases. Protein-coding genes are denser on the autosomes than on chromosome X, and denser in the central region of the autosomes than on the arms. There are only 561 annotated pseudogenes but estimates but several estimates put this much higher

Apparent strength conceals instability in a model for the collapse of historical states

An explanation for the political processes leading to the sudden collapse of empires and states would be useful for understanding both historical and contemporary political events. We seek a general description of state collapse spanning eras and cultures, from small kingdoms to continental empires, drawing on a suitably diverse range of historical sources. Our aim is to provide an accessible verbal hypothesis that bridges the gap between mathematical and social methodology. We use game-theory to determine whether factions within a state will accept the political status quo, or wish to better their circumstances through costly rebellion. In lieu of precise data we verify our model using sensitivity analysis. We find that a small amount of dissatisfaction is typically harmless, but can trigger sudden collapse when there is a sufficient buildup of political inequality. Contrary to intuition, a state is predicted to be least stable when its leadership is at the height of its political power and thus most able to exert its influence through external warfare, lavish expense or autocratic decree

High performance methanol-oxygen fuel cell with hollow fiber electrode

A methanol/air-oxygen fuel cell including an electrode formed by open-ended ion-exchange hollow fibers having a layer of catalyst deposited on the inner surface thereof and a first current collector in contact with the catalyst layer. A second current collector external of said fibers is provided which is immersed along with the hollow fiber electrode in an aqueous electrolyte body. Upon passage of air or oxygen through the hollow fiber electrode and introduction of methanol into the aqueous electrolyte, a steady current output is obtained. Two embodiments of the fuel cell are disclosed. In the first embodiment the second metal electrode is displaced away from the hollow fiber in the electrolyte body while in the second embodiment a spiral-wrap electrode is provided about the outer surface of the hollow fiber electrode

Posterior predictive p-values and the convex order

Posterior predictive p-values are a common approach to Bayesian model-checking. This article analyses their frequency behaviour, that is, their distribution when the parameters and the data are drawn from the prior and the model respectively. We show that the family of possible distributions is exactly described as the distributions that are less variable than uniform on [0,1], in the convex order. In general, p-values with such a property are not conservative, and we illustrate how the theoretical worst-case error rate for false rejection can occur in practice. We describe how to correct the p-values to recover conservatism in several common scenarios, for example, when interpreting a single p-value or when combining multiple p-values into an overall score of significance. We also handle the case where the p-value is estimated from posterior samples obtained from techniques such as Markov Chain or Sequential Monte Carlo. Our results place posterior predictive p-values in a much clearer theoretical framework, allowing them to be used with more assurance.Comment: 14 pages, 3 figure

Understanding clustering in type space using field theoretic techniques

The birth/death process with mutation describes the evolution of a population, and displays rich dynamics including clustering and fluctuations. We discuss an analytical `field-theoretical' approach to the birth/death process, using a simple dimensional analysis argument to describe evolution as a `Super-Brownian Motion' in the infinite population limit. The field theory technique provides corrections to this for large but finite population, and an exact description at arbitrary population size. This allows a characterisation of the difference between the evolution of a phenotype, for which strong local clustering is observed, and a genotype for which distributions are more dispersed. We describe the approach with sufficient detail for non-specialists.Comment: Accepted, Bulletin of Mathematical Biolog