Blang: Bayesian declarative modelling of general data structures and inference via algorithms based on distribution continua

Consider a Bayesian inference problem where a variable of interest does not take values in a Euclidean space. These "non-standard" data structures are in reality fairly common. They are frequently used in problems involving latent discrete factor models, networks, and domain specific problems such as sequence alignments and reconstructions, pedigrees, and phylogenies. In principle, Bayesian inference should be particularly well-suited in such scenarios, as the Bayesian paradigm provides a principled way to obtain confidence assessment for random variables of any type. However, much of the recent work on making Bayesian analysis more accessible and computationally efficient has focused on inference in Euclidean spaces. In this paper, we introduce Blang, a domain specific language and library aimed at bridging this gap. Blang allows users to perform Bayesian analysis on arbitrary data types while using a declarative syntax similar to BUGS. Blang is augmented with intuitive language additions to create data types of the user's choosing. To perform inference at scale on such arbitrary state spaces, Blang leverages recent advances in sequential Monte Carlo and non-reversible Markov chain Monte Carlo methods

Sub-second periodicity in a fast radio burst

Fast radio bursts (FRBs) are millisecond-duration flashes of radio waves that are visible at distances of billions of light-years. The nature of their progenitors and their emission mechanism remain open astrophysical questions. Here we report the detection of the multi-component FRB 20191221A and the identification of a periodic separation of 216.8(1) ms between its components with a significance of 6.5 sigmas. The long (~3 s) duration and nine or more components forming the pulse profile make this source an outlier in the FRB population. Such short periodicity provides strong evidence for a neutron-star origin of the event. Moreover, our detection favours emission arising from the neutron-star magnetosphere, as opposed to emission regions located further away from the star, as predicted by some models.Comment: Updated to conform to the accepted versio

Coordinating Open-Source Software Development

In the recent years a form of software development that was previously dismissed as too ad-hoc and chaotic for serious projects has suddenly taken the front stage. With products such as Apache, Linux, Perl, and others, open-source software has emerged as a viable alternative to traditional approaches to software development. With its globally distributed developer force and extremely rapid code evolution, open source is arguably the extreme in "virtual software projects" [1], and exemplifies many of the advantages and challenges of distributed software development

Project history as a group memory : learning from the past

New members of software development teams must come up-to-speed on a large amount of information before becoming productive, even if they have previous software development experience. Often, this knowledge is gained through mentoring: an experienced colleague monitors the newcomer's progress on his or her first assigned tasks, and provides feedback and advice. The mentor is the person the newcomer turns to for help when stuck; these interactions are typically informal and lightweight, such as quick questions asked over the cubicle divider or at the water cooler. However, these light-weight channels are not always available in virtual teams, where the members of the team are not collocated. Moreover, workers are less likely to help their non-collocated colleagues, making it even harder for a newcomer to come up to speed on a project. The thesis of this dissertation is based on the idea that the collection of all artifacts created in the course of development of a software system implicitly forms a group memory—a repository of information that a work group can use to benefit from its past experience to respond more effectively to the present needs. I call this implicitly-formed group memory a project memory and make three claims: (1) that newcomer software developers can use information from the project memory about past modifications completed on the project to help them effectively perform modification tasks to the system; (2) that the project memory can be built largely automatically, requiring minimal adjustments in work practices of software developers; and (3) that the automatically-built group memory can recommend artifacts useful to the current modification task. To validate the claims of this thesis, I have developed a project memory model and associated tool, called Hipikat, that recommends relevant artifacts from the memory during a software modification task. This dissertation describes the memory model, the implementation of Hipikat, and its use in a series of case studies to validate the thesis claims.Science, Faculty ofComputer Science, Department ofGraduat

Coordinating Open-Source Software Development

In the recent years a form of software development that was previously dismissed as too ad-hoc and chaotic for serious projects has suddenly taken the front stage. With products such as Apache, Linux, Perl, and others, open-source software has emerged as a viable alternative to traditional approaches to software development. With its globally distributed developer force and extremely rapid code evolution, open source is arguably the extreme in "virtual software projects" [1], and exemplifies many of the advantages and challenges of distributed software development