Local module identification in dynamic networks with correlated noise: the full input case

The identification of local modules in dynamic networks with known topology has recently been addressed by formulating conditions for arriving at consistent estimates of the module dynamics, typically under the assumption of having disturbances that are uncorrelated over the different nodes. The conditions typically reflect the selection of a set of node signals that are taken as predictor inputs in a MISO identification setup. In this paper an extension is made to arrive at an identification setup for the situation that process noises on the different node signals can be correlated with each other. In this situation the local module may need to be embedded in a MIMO identification setup for arriving at a consistent estimate with maximum likelihood properties. This requires the proper treatment of confounding variables. The result is an algorithm that, based on the given network topology and disturbance correlation structure, selects an appropriate set of node signals as predictor inputs and outputs in a MISO or MIMO identification setup. As a first step in the analysis, we restrict attention to the (slightly conservative) situation where the selected output node signals are predicted based on all of their in-neighbor node signals in the network.Comment: Extended version of paper submitted to the 58th IEEE Conf. Decision and Control, Nice, 201

Modelling Word Associations with Word Embeddings for a Guesser Agent in the Taboo City Challenge Competition

In the Taboo City Challenge, artificial agents should guess the names of cities from simple textual hints and are evaluated with games played by humans. Thus, playing the games successfully requires mimicking associations that humans have with geographical locations. In this paper, an architecture is proposed that calculates the associative similarity between a city and a hint from a semantic vector space. The semantic vector space is created using the Skip-gram hierarchical softmax model, from a tailored corpus about travel destinations. We investigate the effect of varying training parameters and introduce a targeted corpus annotation method that significantly improves performance. The results on a dataset of 149 games indicate that the proposed architecture can guess the target city with up to 22.45% accuracy — a substantial improvement over the 4.11% accuracy achieved by the baseline architecture

Decision Making With Risk-Based Weather Warnings

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.We study decisions under different weather warning systems that vary in format and/or information conveyed using a laboratory experiment. Participants have to decide between a safe but costly option (spending to protect from a storm) and a risky option (of not spending for protection). We ran three treatments based upon the severe weather warning system for the UK that the Met Office has been using since 2011, using a risk matrix to communicate the impact and likelihood of an event. In Treatment 1, participants received a colored table with a check in the box of the matrix that showed the likelihood and impact level of the warning. In Treatment 2, participants only had the colored table without a check in the exact box, but with the color of the warning communicated. In Treatment 3, participants only had the color of the warning communicated without seeing the associated table. Overall our work shows that while increasing the information with content of warnings is usually beneficial and increases the trust in the warning system. it must be done with caution since better decisions (judged by higher profits) are not always made with an increase of information

Comparing projections of future changes in runoff and water resources from hydrological and ecosystem models in ISI-MIP

Projections of future changes in runoff can have important implications for water resources and flooding. In this study, runoff projections from ISI-MIP (Inter-sectoral Impact Model Intercomparison Project) simulations forced with HadGEM2-ES bias-corrected climate data under the Representative Concentration Pathway 8.5 have been analysed. Projections of change from the baseline period (1981–2010) to the future (2070–2099) from a number of different ecosystems and hydrological models were studied. The differences between projections from the two types of model were looked at globally and regionally. Typically, across different regions the ecosystem models tended to project larger increases and smaller decreases in runoff than the hydrological models. However, the differences varied both regionally and seasonally. Sensitivity experiments were also used to investigate the contributions of varying CO2 and allowing vegetation distribution to evolve on projected changes in runoff. In two out of four models which had data available from CO2 sensitivity experiments, allowing CO2 to vary was found to increase runoff more than keeping CO2 constant, while in two models runoff decreased. This suggests more uncertainty in runoff responses to elevated CO2 than previously considered. As CO2 effects on evapotranspiration via stomatal conductance and leaf-area index are more commonly included in ecosystems models than in hydrological models, this may partially explain some of the difference between model types. Keeping the vegetation distribution static in JULES runs had much less effect on runoff projections than varying CO2, but this may be more pronounced if looked at over a longer timescale as vegetation changes may take longer to reach a new state

Magnetic hysteresis and rotational hysteresis properties of hydrothermally grown multidomain magnetite

Accepted versio

Ports and nature, striking a new balance

The report offers an overview of the project’s aims and results, and its conclusions and recommendations