Online Social Networks (OSN) are privileged observation channels for understanding the geospatial facets of many real-world phenomena [1]. Unfortunately, in most cases OSN content lacks explicit and structured geographic information, as in the case of Twitter, where only a minimal fraction (1% to 4%) of messages are natively geotagged [2]. This shortage of explicit geographic information drastically limits the exploitation of OSN data in geospatial Decision Support Systems (DSS) [3]. Conversely, the prompt availability of geotagged content would empower existing systems and would open up the possibility to develop new and better geospatial services and applications [4, 5]. As a practical example of this kind, several social media-based systems have been proposed in recent years for mapping and visualizing situational information in the aftermath of mass disasters - a task dubbed as crisis mapping - in an effort to augment emergency response [6, 7]. These systems, however, demand geotagged data to be placed on crisis maps, which in turn imposes to perform the geoparsing task on the majority of social media content. Explicit geographic information is not only needed in early warning [8, 9] and emergency response systems [10, 11, 12, 13, 14], but also in systems and applications for improving event promotion [15, 16], touristic planning [17, 18, 19], healthcare accessibility [20], news aggregation [21] Post-print of the article published in Decision Support Systems 136, 2020. Please refer to the published version: doi.org/10.1016/j.dss.2020.113346

Advanced slot and winding designs are imperative to create future high performance electrical machines (EM). As a result, the development of methods to design and improve slot filling factor (SFF) has attracted considerable research. Recent developments in manufacturing processes, such as additive manufacturing and alternative materials, has also highlighted a need for novel high-fidelity design techniques to develop high performance complex geometries and topologies. This study therefore introduces a novel physics-informed machine learning (PIML) design optimization process for improving SFF in traction electrical machines used in electric vehicles. A maximum entropy sampling algorithm (MESA) is used to seed a physics-informed Bayesian optimization (PIBO) algorithm, where the target function and its approximations are produced by Gaussian processes (GP)s. The proposed PIBO-MESA is coupled with a 2D finite element model (FEM) to perform a GP-based surrogate and provide the first demonstration of the optimal combination of complex design variables for an electrical machine. Significant computational gains were achieved using the new PIBO-MESA approach, which is 45% faster than existing stochastic methods, such as the non-dominated sorting genetic algorithm II (NSGA-II). The FEM results confirm that the new design optimization process and keystone shaped wires lead to a higher SFF (i.e. by 20%) and electromagnetic improvements (e.g. maximum torque by 12%) with similar resistivity. The newly developed PIBO-MESA design optimization process therefore presents significant benefits in the design of high-performance electric machines, with reduced development time and costs.

Sports betting's recent federal legalisation in the USA coincides with the golden age of machine learning. If bettors can leverage data to reliably predict the probability of an outcome, they can recognise when the bookmaker's odds are in their favour. As sports betting is a multi-billion dollar industry in the USA alone, identifying such opportunities could be extremely lucrative. Many researchers have applied machine learning to the sports outcome prediction problem, generally using accuracy to evaluate the performance of predictive models. We hypothesise that for the sports betting problem, model calibration is more important than accuracy. To test this hypothesis, we train models on NBA data over several seasons and run betting experiments on a single season, using published odds. We show that optimising the predictive model for calibration leads to greater returns than optimising for accuracy, on average (return on investment of $+34.69\%$ versus $-35.17\%$) and in the best case ($+36.93\%$ versus $+5.56\%$). These findings suggest that for sports betting (or any probabilistic decision-making problem), calibration is a more important metric than accuracy. Sports bettors who wish to increase profits should therefore optimise their predictive model for calibration.

Over the past year firms like AggregateIQ and Cambridge Analytica have been credited with using AI-targeted ads on social media to help swing the Brexit referendum and the US presidential election respectively. But a lack of evidence meant we have never known whether the technology exists to make this possible. Now the first study detailing the process from start to finish is finally shedding some light. "This is the first time that I've seen all the dots connected," says Joanna Bryson, an artificial intelligence researcher at the University of Bath, UK. At the heart of the debate is psychographic targeting – the directing of political campaigns at people via social media based on their personality and political interests, with the aid of vast amount of data filtered by artificial intelligence.

Over the past year firms like AggregateIQ and Cambridge Analytica have been credited with using AI-targeted ads on social media to help swing the Brexit referendum and the US presidential election respectively. But a lack of evidence meant we have never known whether the technology exists to make this possible. Now the first study detailing the process from start to finish is finally shedding some light. "This is the first time that I've seen all the dots connected," says Joanna Bryson, an artificial intelligence researcher at the University of Bath, UK. At the heart of the debate is psychographic targeting – the directing of political campaigns at people via social media based on their personality and political interests, with the aid of vast amount of data filtered by artificial intelligence.