Revisiting Precision and Recall Definition for Generative Model Evaluation

In this article we revisit the definition of Precision-Recall (PR) curves for generative models proposed by Sajjadi et al. (arXiv:1806.00035). Rather than providing a scalar for generative quality, PR curves distinguish mode-collapse (poor recall) and bad quality (poor precision). We first generalize their formulation to arbitrary measures, hence removing any restriction to finite support. We also expose a bridge between PR curves and type I and type II error rates of likelihood ratio classifiers on the task of discriminating between samples of the two distributions. Building upon this new perspective, we propose a novel algorithm to approximate precision-recall curves, that shares some interesting methodological properties with the hypothesis testing technique from Lopez-Paz et al (arXiv:1610.06545). We demonstrate the interest of the proposed formulation over the original approach on controlled multi-modal datasets.Comment: ICML 201

Learning from the learners' experience: e-Learning@greenwich post-conference reflections

This publication comprises papers from presenters who, having made a conference presentation, were invited to author an academic paper about their work

Regional economic impacts of a plant disease incursion using a general equilibrium approach

The present study uses a dynamic multiregional computable general equilibrium (CGE) model to estimate the micro- andmacroeconomic effects of a hypothetical disease or pest outbreak. Our example is a Karnal bunt incursion in wheat in Western Australia. The extent of the incursion, the impact of the disease or pest on plant yields, the response of buyers, the costs of eradication and the time path of the scenario contribute to outcomes at the industry, regional, state and national levels. We decompose the contribution of these individual direct effects to the overall impact of the incursion. This might provide some guidance regarding areas for priority in attempting to eradicate or minimise the impacts of a disease or pest. The study also introduces a theory of dynamic regional labour adjustment in which economic events may lead to both real wage differentials and worker migration between regions.Crop Production/Industries,

Training Behavior of Sparse Neural Network Topologies

Improvements in the performance of deep neural networks have often come through the design of larger and more complex networks. As a result, fast memory is a significant limiting factor in our ability to improve network performance. One approach to overcoming this limit is the design of sparse neural networks, which can be both very large and efficiently trained. In this paper we experiment training on sparse neural network topologies. We test pruning-based topologies, which are derived from an initially dense network whose connections are pruned, as well as RadiX-Nets, a class of network topologies with proven connectivity and sparsity properties. Results show that sparse networks obtain accuracies comparable to dense networks, but extreme levels of sparsity cause instability in training, which merits further study.Comment: 6 pages. Presented at the 2019 IEEE High Performance Extreme Computing (HPEC) Conference. Received "Best Paper" awar

Comparison of Bayesian and frequentist group-sequential clinical trial designs

Background: There is a growing interest in the use of Bayesian adaptive designs in late-phase clinical trials. This includes the use of stopping rules based on Bayesian analyses in which the frequentist type I error rate is controlled as in frequentist group-sequential designs. Methods: This paper presents a practical comparison of Bayesian and frequentist group-sequential tests. Focussing on the setting in which data can be summarised by normally distributed test statistics, we evaluate and compare boundary values and operating characteristics. Results: Although Bayesian and frequentist group-sequential approaches are based on fundamentally different paradigms, in a single arm trial or two-arm comparative trial with a prior distribution specified for the treatment difference, Bayesian and frequentist group-sequential tests can have identical stopping rules if particular critical values with which the posterior probability is compared or particular spending function values are chosen. If the Bayesian critical values at different looks are restricted to be equal, O’Brien and Fleming’s design corresponds to a Bayesian design with an exceptionally informative negative prior, Pocock’s design to a Bayesian design with a non-informative prior and frequentist designs with a linear alpha spending function are very similar to Bayesian designs with slightly informative priors. This contrasts with the setting of a comparative trial with independent prior distributions specified for treatment effects in different groups. In this case Bayesian and frequentist group-sequential tests cannot have the same stopping rule as the Bayesian stopping rule depends on the observed means in the two groups and not just on their difference. In this setting the Bayesian test can only be guaranteed to control the type I error for a specified range of values of the control group treatment effect. Conclusions: Comparison of frequentist and Bayesian designs can encourage careful thought about design parameters and help to ensure appropriate design choices are made

Molecular recognition and its underlying mechanisms in molecularly imprinted polymers

Molecular recognition in molecularly imprinting polymers (MIPs) is governed by two mechanisms: pre-organization of functional groups and shape specificity of the binding site. While pre-organization of functional groups has been studied extensively, shape specificity of the binding site has not been rigorously explored. The goal of this research is to determine the influence of shape specificity on molecular recognition in MIPs (Chapter 2). Once shape selectivity was proven to play a vital role in molecular recognition, it was important to determine if pre-organization of functional groups or shape specificity was the dominating factor in determining molecular recognition in the binding site (Chapter 3). Chapters 4 and 5 contain research that is not directly related to shape selectivity or pre-organization of functional groups in MIPs, but is nevertheless important to the field of molecular imprinting and synthetic methodology. A survey of commercially available basic functional monomers was conducted with the goal of making MIPs with acidic compounds as templates. The effect of particle size and flow rate on binding selectivity was investigated for both classic ethylene glycol dimethacrylate (EGDMA)/methacrylic acid (MAA) MIPs and new 2-(methacryloylamine)ethyl-2-methacrylate (NOBE) MIPs. Thin-layer and centrifugally accelerated radial chromatographic experiments were done with MIPs as the stationary phase. A preliminary investigation into the use of quaternary ammonium salts as templates in MIP experiments was conducted. Synthetic methodology involving palladium catalyzed cross couplings is detailed in Chapter 5

Auctions with Options to Re-auction

We examine a dynamic model of English auctions with independent private values. There is a single object for sale and it is not possible for the seller, who has a value of zero for the object, to commit not to sell in the future if a sale is not accomplished today. The seller may be able to commit to a reserve price, or make a cheap-talk announcement of a reserve price and secretly bid for the object herself in order to re-auction it in a later round with a new set of bidders. Bidders are "short-lived" in the sense that at the end of each round all existing bidders vanish and new bidders start arriving. This framework allows us to obtain existing results for one shot-auctions as special cases. This framework also allows us to capture some of the features of thick internet auctions and to obtain some new insights on the role of commitment, on optimal length and on socially optimal reserve prices that are not apparent from a one-shot auction perspective.