We present a refined parametric model for forecasting electricity demand, that performed particularly well in the recent Global Energy Forecasting Competition (GEFCom 2012). We begin by motivating and presenting a simple parametric model, treating electricity demand as a function of temperature and day of the year. We then set out a series of refinements to the model, explaining the rationale for each, and using competition scores to demonstrate that each successive refinement step increases the accuracy of the model's predictions. These refinements include combining models from multiple weather stations, removing outliers from the historical data and special treatment of public holidays.

We study the relationship between the sentiment levels of Twitter users and the evolving network structure that the users created by @-mentioning each other. We use a large dataset of tweets to which we apply three sentiment scoring algorithms, including the open source SentiStrength program. Specifically we make three contributions. Firstly, we find that people who have potentially the largest communication reach (according to a dynamic centrality measure) use sentiment differently than the average user: for example, they use positive sentiment more often and negative sentiment less often. Secondly, we find that when we follow structurally stable Twitter communities over a period of months, their sentiment levels are also stable, and sudden changes in community sentiment from one day to the next can in most cases be traced to external events affecting the community. Thirdly, based on our findings, we create and calibrate a simple agent-based model that is capable of reproducing measures of emotive response comparable with those obtained from our empirical dataset.

Abstract. We present Crowfoot, an automatic verification tool for imperative programs that manipulate procedures dynamically at runtime; these programs use a heap that can store not only data but also code (commands or procedures). Such heaps are often called higher-order store, and allow for instance the creation of new recursions on the fly. One can use higher-order store to model phenomena such as runtime loading and unloading of code, runtime update of code and runtime code generation. Crowfoot's assertion language, based on separation logic, features nested Hoare triples which describe the behaviour of procedures stored on the heap. The tool addresses complex issues like deep frame rules and recursion through the store, and is the first verification tool based on recent developments in the mathematical foundations of Hoare logics with nested triples.

Abstract-We show how dynamic software updates can be modelled using a "higher order store" programming language where procedures can be written to the heap. We then show how such updates can be proved correct with a Hoare-calculus that allows for keeping track of behavioural specifications of such stored procedures.

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