Bayesian Inference in Estimation of Distribution Algorithms

Metaheuristics such as Estimation of Distribution Algorithms and the Cross-Entropy method use probabilistic modelling and inference to generate candidate solutions in optimization problems. The model fitting task in this class of algorithms has largely been carried out to date based on maximum likelihood. An alternative approach that is prevalent in statistics and machine learning is to use Bayesian inference. In this paper, we provide a framework for the application of Bayesian inference techniques in probabilistic model-based optimization. Based on this framework, a simple continuous Bayesian Estimation of Distribution Algorithm is described. We evaluate and compare this algorithm experimentally with its maximum likelihood equivalent, UMDAG c

Safety margins and adaptive capacity of vegetation to climate change

Vegetation is composed of many individual species whose climatic tolerances can be integrated into spatial analyses of climate change risk. Here, we quantify climate change risk to vegetation at a continental scale by calculating the safety margins for warming and drying (i.e., tolerance to projected change in temperature and precipitation respectively) across plants sharing 100km × 100km grid cells (locations). These safety margins measure how much warmer, or drier, a location could become before its ‘typical’ species exceeds its observed climatic limit. We also analyse the potential adaptive capacity of vegetation to temperature and precipitation change (i.e., likelihood of in situ persistence) using median precipitation and temperature breadth across all species in each location. 47% of vegetation across Australia is potentially at risk from increases in mean annual temperature (MAT) by 2070, with tropical regions most vulnerable. Vegetation at high risk from climate change often also exhibited low adaptive capacity. By contrast, 2% of the continent is at risk from reductions in annual precipitation by 2070. Risk from precipitation change was isolated to the southwest of Western Australia where both the safety margin for drier conditions in the typical species is low, and substantial reductions in MAP are projected

AusTraits, a curated plant trait database for the Australian flora

We introduce the AusTraits database - a compilation of values of plant traits for taxa in the Australian flora (hereafter AusTraits). AusTraits synthesises data on 448 traits across 28,640 taxa from field campaigns, published literature, taxonomic monographs, and individual taxon descriptions. Traits vary in scope from physiological measures of performance (e.g. photosynthetic gas exchange, water-use efficiency) to morphological attributes (e.g. leaf area, seed mass, plant height) which link to aspects of ecological variation. AusTraits contains curated and harmonised individual- and species-level measurements coupled to, where available, contextual information on site properties and experimental conditions. This article provides information on version 3.0.2 of AusTraits which contains data for 997,808 trait-by-taxon combinations. We envision AusTraits as an ongoing collaborative initiative for easily archiving and sharing trait data, which also provides a template for other national or regional initiatives globally to fill persistent gaps in trait knowledge. © 2021, The Author(s). **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “David Cheal" is provided in this record*

Intensity Profile Projection: A Framework for Continuous-Time Representation Learning for Dynamic Networks

We present a new representation learning framework, Intensity Profile Projection, for continuous-time dynamic network data. Given triples $(i,j,t)$, each representing a time-stamped ($t$) interaction between two entities ($i,j$), our procedure returns a continuous-time trajectory for each node, representing its behaviour over time. The framework consists of three stages: estimating pairwise intensity functions, e.g. via kernel smoothing; learning a projection which minimises a notion of intensity reconstruction error; and constructing evolving node representations via the learned projection. The trajectories satisfy two properties, known as structural and temporal coherence, which we see as fundamental for reliable inference. Moreoever, we develop estimation theory providing tight control on the error of any estimated trajectory, indicating that the representations could even be used in quite noise-sensitive follow-on analyses. The theory also elucidates the role of smoothing as a bias-variance trade-off, and shows how we can reduce the level of smoothing as the signal-to-noise ratio increases on account of the algorithm `borrowing strength' across the network.Comment: 37 pages, 10 figure

Spectral embedding of weighted graphs

This paper concerns the statistical analysis of a weighted graph through spectral embedding. Under a latent position model in which the expected adjacency matrix has low rank, we prove uniform consistency and a central limit theorem for the embedded nodes, treated as latent position estimates. In the special case of a weighted stochastic block model, this result implies that the embedding follows a Gaussian mixture model with each component representing a community. We exploit this to formally evaluate different weight representations of the graph using Chernoff information. For example, in a network anomaly detection problem where we observe a p-value on each edge, we recommend against directly embedding the matrix of p-values, and instead using threshold or log p-values, depending on network sparsity and signal strength.Comment: 29 pages, 8 figure

English Medium Instruction and the potential of translanguaging practices in higher education

Abstract In the UAE, although Arabic is the first and official language (L1), English has become the medium of instruction and the language of discourse in higher education in most subject domains. The pedagogical implications of English Medium Instruction (EMI) in the specific context of higher education in the UAE are not well understood, and research is needed to establish what kinds of support speakers of English as a second or additional language (L2) might need to fully access content knowledge in English without burdening learning. Our empirical work hypothesizes that learning content through L2 may lead to more favourable results if the L1 is explicitly drawn upon as a resource in addition to the L2. This study provided undergraduate students with learning materials in three experimental conditions (Arabic-only, English-only, and dual language). Students’ performance was then assessed in three areas of linguistic competence, namely translation into Arabic of a list of English words and phrases, comprehension of an English written text, and translation into Arabic of English words and phrases in context. A series of one way ANOVAs and post-hoc comparisons were carried out to determine differences between the three conditions. The study confirms that overall, for students with an intermediate language level, the presentation of dual language reading materials has a greater impact on their outcomes in comparison with the presentation of reading materials in the L1 or L2 only. This highlights the critical need for raising awareness of translanguaging practices in EMI contexts