Semi-automatic selection of summary statistics for ABC model choice

A central statistical goal is to choose between alternative explanatory models of data. In many modern applications, such as population genetics, it is not possible to apply standard methods based on evaluating the likelihood functions of the models, as these are numerically intractable. Approximate Bayesian computation (ABC) is a commonly used alternative for such situations. ABC simulates data x for many parameter values under each model, which is compared to the observed data xobs. More weight is placed on models under which S(x) is close to S(xobs), where S maps data to a vector of summary statistics. Previous work has shown the choice of S is crucial to the efficiency and accuracy of ABC. This paper provides a method to select good summary statistics for model choice. It uses a preliminary step, simulating many x values from all models and fitting regressions to this with the model as response. The resulting model weight estimators are used as S in an ABC analysis. Theoretical results are given to justify this as approximating low dimensional sufficient statistics. A substantive application is presented: choosing between competing coalescent models of demographic growth for Campylobacter jejuni in New Zealand using multi-locus sequence typing data

Lava flow morphology at an erupting andesitic stratovolcano: a satellite perspective on El Reventador, Ecuador

Lava flows pose a significant hazard to infrastructure and property located close to volcanoes, and understanding how flows advance is necessary to manage volcanic hazard during eruptions. Compared to low-silica basaltic flows, flows of andesite composition are infrequently erupted and so relatively few studies of their characteristics and behaviour exist. We use El Reventador, Ecuador as a target to investigate andesitic lava flow properties during a 4.5 year period of extrusive eruption between February 2012 and August 2016. We use satellite radar to map the dimensions of 43 lava flows and look at variations in their emplacement behaviour over time. We find that flows descend the north and south flanks of El Reventador, and were mostly emplaced during durations shorter than the satellite repeat interval of 24 days.Flows ranged in length from 0.3 to 1.7 km, and the length of these flows decreased over the observation period. We measure a decrease in flow volume with time that is correlated with a long-term exponential decrease in eruption rate, and propose that this behaviour is caused by temporary magma storage in the conduit acting as a melt capacitor between the magma reservoir and the surface. We use the dimensions of the flow levees and widths to estimate the flow yield strengths, which were of the order of 10-100 kPa. We observe that some flows were diverted by topographic obstacles, and compare measurements of decreased channel width and increased flow thickness at the obstacles with observations from laboratory experiments. Radar observations, such as those presented here, could be used to map and measure properties of evolving lava flow fields at other remote or difficult to monitor volcanoes

Partition Function Zeros of a Restricted Potts Model on Lattice Strips and Effects of Boundary Conditions

We calculate the partition function $Z(G,Q,v)$ of the $Q$-state Potts model exactly for strips of the square and triangular lattices of various widths $L_y$ and arbitrarily great lengths $L_x$, with a variety of boundary conditions, and with $Q$ and $v$ restricted to satisfy conditions corresponding to the ferromagnetic phase transition on the associated two-dimensional lattices. From these calculations, in the limit $L_x \to \infty$, we determine the continuous accumulation loci ${\cal B}$ of the partition function zeros in the $v$ and $Q$ planes. Strips of the honeycomb lattice are also considered. We discuss some general features of these loci.Comment: 12 pages, 12 figure

Changing assessment practice in engineering: how can understanding lecturer perspectives help?

Assessment in engineering disciplines is typically oriented to demonstrating competence in specific tasks. Even where assessments are intended to have a formative component, little priority may be given to feedback. Engineering departments are often criticized, by their students and by external quality reviewers, for paying insufficient attention to formative assessment. The e3an project set out to build a question bank of peer-reviewed questions for use within electrical and electronic engineering. As a part of this process, a number of engineers from disparate institutions were required to work together in teams, designing a range of assessments for their subject specialisms. The project team observed that lecturers were especially keen to develop formative assessment but that their understanding of what might be required varied considerably. This paper describes the various ways in which the processes of the project have engaged lecturers in actively identifying and developing their conceptions of teaching, learning and assessment in their subject. It reports on an interview study that was conducted with a selection of participants. It is concluded that lecturers' reflections on and understanding of assessment are closely related to the nature of the subject domain and that it is essential when attempting to improve assessment practice to start from the perspective of lecturers in the discipline

Time‐Series Prediction Approaches to Forecasting Deformation in Sentinel‐1 InSAR Data

Time series of displacement are now routinely available from satellite InSAR and are used for flagging anomalous ground motion, but not yet forecasting. We test conventional time series forecasting methods such as SARIMA and supervised machine learning approaches such as LSTM compared to simple function extrapolation. We focus initially on forecasting seasonal signals and begin by characterising the time‐series using sinusoid fitting, seasonal decomposition and autocorrelation functions. We find that the three measures are broadly comparable but identify different types of seasonal characteristic. We use this to select a set of 310 points with highly seasonal characteristics and test the three chosen forecasting methods over prediction windows of 1‐9 months. The lowest overall median RMSE values are obtained for SARIMA when considering short term predictions ( 6 months). Machine learning methods (LSTM) perform less well. We then test the prediction methods on 2000 randomly selected points with a range of seasonalities and find that simple extrapolation of a constant function performed better overall than any of the more sophisticated time series prediction methods. Comparisons between seasonality and RMSE show a small improvement in performance with increasing seasonality. This proof‐of‐concept study demonstrates the potential of time‐series prediction for InSAR data but also highlights the limitations of applying these techniques to non‐periodic signals or individual measurement points. We anticipate future developments, especially to shorter timescales, will have a broad range of potential applications, from infrastructure stability to volcanic eruptions

What motivates academic dishonesty in students? A reinforcement sensitivity theory explanation

BACKGROUND: Academic dishonesty (AD) is an increasing challenge for universities worldwide. The rise of the Internet has further increased opportunities for students to cheat. AIMS: In this study, we investigate the role of personality traits defined within Reinforcement Sensitivity Theory (RST) as potential determinants of AD. RST defines behaviour as resulting from approach (Reward Interest/reactivity, goal-drive, and Impulsivity) and avoidance (behavioural inhibition and Fight-Flight-Freeze) motivations. We further consider the role of deep, surface, or achieving study motivations in mediating/moderating the relationship between personality and AD. SAMPLE: A sample of UK undergraduates (N = 240). METHOD: All participants completed the RST Personality Questionnaire, a short-form version of the study process questionnaire and a measure of engagement in AD, its perceived prevalence, and seriousness. RESULTS: Results showed that RST traits account for additional variance in AD. Mediation analysis suggested that GDP predicted dishonesty indirectly via a surface study approach while the indirect effect via deep study processes suggested dishonesty was not likely. Likelihood of engagement in AD was positively associated with personality traits reflecting Impulsivity and Fight-Flight-Freeze behaviours. Surface study motivation moderated the Impulsivity effect and achieving motivation the FFFS effect such that cheating was even more likely when high levels of these processes were used. CONCLUSIONS: The findings suggest that motivational personality traits defined within RST can explain variance in the likelihood of engaging in dishonest academic behaviours

Chemical Raman Enhancement of Organic Adsorbates on Metal Surfaces

Using a combination of first-principles theory and experiments, we provide a quantitative explanation for chemical contributions to surface-enhanced Raman spectroscopy for a well-studied organic molecule, benzene thiol, chemisorbed on planar Au(111) surfaces. With density functional theory calculations of the static Raman tensor, we demonstrate and quantify a strong mode-dependent modification of benzene thiol Raman spectra by Au substrates. Raman active modes with the largest enhancements result from stronger contributions from Au to their electron-vibron coupling, as quantified through a deformation potential, a well-defined property of each vibrational mode. A straightforward and general analysis is introduced that allows extraction of chemical enhancement from experiments for specific vibrational modes; measured values are in excellent agreement with our calculations.Comment: 5 pages, 4 figures and Supplementary material included as ancillary fil

Rapid localized flank inflation and implications for potential slope instability at Tungurahua volcano, Ecuador

This is the final version. Available on open access from Elsevier via the DOI in this recordHigh rates of volcano surface deformation can be indicative of a forthcoming eruption, but can also relate to slope instability and possible flank collapse. Tungurahua volcano, Ecuador, has been persistently active since 1999 and has previously experienced catastrophic flank failures. During the ongoing eruptive activity, significant surface deformation has been observed, with the highest rates contained within the amphitheatre-shaped scar from the 3000-year-old failure on the west flank However, the cause of this asymmetric deformation and how it might relate to slope stability has not been assessed. Here, for the first time, we present a range of models to test physical processes that might produce asymmetric deformation, which are then applied to slope stability. Our models are informed by InSAR measurements of a deformation episode in November 2015, which show a maximum displacement of ~3.5 cm over a period of ~3 weeks, during which time the volcano also experienced multiple explosions and heightened seismicity. Asymmetric flank material properties, from the rebuilding of the cone, cannot explain the full magnitude and spatial footprint of the observed west flank deformation. The inflation is inferred to be primarily caused by shallow, short35 term, pre-eruptive magma storage that preferentially exploits the 3 ka flank collapse surface. Shallow and rapid pressurization from this inclined deformation source can generate shear stress along the collapse surface, which increases with greater volumes of magma. This may contribute to slope instability during future unrest episodes and promote flank failure, with general application to other volcanoes worldwide displaying asymmetric deformation patterns.Royal SocietyNatural Environment Research Council (NERC)Economic and Social Research Council (ESRC

Identification of climatological sub-regions within the Tully mill area

Identifying optimal nitrogen application rates that reduce nitrogen loss without adversely reducing yields would benefit growers and the environment. In order to identify optimal nitrogen application rates throughout the Tully mill area, it is important to identify sub-regions that share similar topographical, soil, farm management, productivity or climatological attributes. While current SIX EASY STEPS nitrogen guidelines enable a hierarchy of district, soil, block and crop nitrogen requirements for sugarcane, it would be beneficial for management zones to also take spatial climate variability information into account. Unfortunately, spatial climate variability within a region, is generally not considered when developing nitrogen management practices. The objective of this paper was to identify sub-regions within the Tully mill area based on climatological attributes as a first step towards better informing nitrogen management decisions. Rainfall, radiation and temperature data were obtained on a 0.05 by 0.05˚ grid (approximately 5 km by 5 km) for sugarcane-growing areas within the Tully Mill region. A K-means clustering algorithm was then used to cluster these grid cells into distinct sub-regions based on seasonal or annual climate data. Two distinct sub-regions were identified based on total annual rainfall and annual average daily radiation data. These sub-regions were identified as a northern and southern sub-region, divided roughly along the Tully River. The northern sub-region was characterised by lower radiation, lower temperatures and higher rainfall than the southern sub-region. Crop simulation models will now be able to use this knowledge to assess if nitrogen management plans should vary between the two sub-regions in Tully