Using Agent-Based Modelling to Inform Policy – What Could Possibly Go Wrong?

© 2019, Springer Nature Switzerland AG. Scientific modelling can make things worse, as in the case of the North Atlantic Cod Fisheries Collapse. Some of these failures have been attributed to the simplicity of the models used compared to what they are trying to model. MultiAgent-Based Simulation (MABS) pushes the boundaries of what can be simulated, prompting many to assume that it can usefully inform policy, even in the face of complexity. That said, MABS also brings with it new difficulties and potential confusions. This paper surveys some of the pitfalls that can arise when MABS analysts try to do this. Researchers who claim (or imply) that MABS can reliably predict are criticised in particular. However, an alternative is suggested – that of using MABS for a kind of uncertainty analysis – identifying some of the possible ways a policy can go wrong (or indeed go right). A fisheries example is given. This alternative may widen, rather than narrow, the range of evidence and possibilities that are considered, which could enrich the policy-making process. We call this Reflexive Possibilistic Modelling

Capturing the sounds of an urban greenspace

Acoustic data can be a source of important information about events and the environment in modern cities. To date, much of the focus has been on monitoring noise pollution, but the urban soundscape contains a rich variety of signals about both human and natural phenomena. We describe the CitySounds project, which has installed enclosed sensor kits at several locations across a heavily used urban greenspace in the city of Edinburgh. The acoustic monitoring components regularly capture short clips in real-time of both ultrasonic and audible noises, for example encompassing bats, birds and other wildlife, traffic, and human. The sounds are complemented by collecting other data from sensors, such as temperature and relative humidity. To ensure privacy and compliance with relevant legislation, robust methods render completely unintelligible any traces of voice or conversation that may incidentally be overheard by the sensors. We have adopted a variety of methods to encourage community engagement with the audio data and to communicate the richness of urban soundscapes to a general audience

Optical Flow with Geometric Occlusion Estimation and Fusion of Multiple Frames

Abstract. Optical flow research has made significant progress in recent years and it can now be computed efficiently and accurately for many images. How-ever, complex motions, large displacements, and difficult imaging conditions are still problematic. In this paper, we present a framework for estimating optical flow which leads to improvements on these difficult cases by 1) estimating occlusions and 2) using additional temporal information. First, we divide the image into dis-crete triangles and show how this allows for occluded regions to be naturally esti-mated and directly incorporated into the optimization algorithm. We additionally propose a novel method of dealing with temporal information in image sequences by using “inertial estimates ” of the flow. These estimates are combined using a classifier-based fusion scheme, which significantly improves results. These con-tributions are evaluated on three different optical flow datasets, and we achieve state-of-the-art results on MPI-Sintel.

Bat detective—Deep learning tools for bat acoustic signal detection

Passive acoustic sensing has emerged as a powerful tool for quantifying anthropogenic impacts on biodiversity, especially for echolocating bat species. To better assess bat population trends there is a critical need for accurate, reliable, and open source tools that allow the detection and classification of bat calls in large collections of audio recordings. The majority of existing tools are commercial or have focused on the species classification task, neglecting the important problem of first localizing echolocation calls in audio which is particularly problematic in noisy recordings. We developed a convolutional neural network based open-source pipeline for detecting ultrasonic, full-spectrum, search-phase calls produced by echolocating bats. Our deep learning algorithms were trained on full-spectrum ultrasonic audio collected along road-transects across Europe and labelled by citizen scientists from www.batdetective.org. When compared to other existing algorithms and commercial systems, we show significantly higher detection performance of search-phase echolocation calls with our test sets. As an example application, we ran our detection pipeline on bat monitoring data collected over five years from Jersey (UK), and compared results to a widely-used commercial system. Our detection pipeline can be used for the automatic detection and monitoring of bat populations, and further facilitates their use as indicator species on a large scale. Our proposed pipeline makes only a small number of bat specific design decisions, and with appropriate training data it could be applied to detecting other species in audio. A crucial novelty of our work is showing that with careful, non-trivial, design and implementation considerations, state-of-the-art deep learning methods can be used for accurate and efficient monitoring in audio