Diagnostic tests that can detect pre-clinical or sub-clinical infection, are one of the most powerful tools in our armoury of weapons to control infectious diseases. Considerable effort has been paid to improving diagnostic testing for human, plant and animal diseases, including strategies for targeting the use of diagnostic tests towards individuals who are more likely to be infected. We use machine learning to assess the surrounding risk landscape under which a diagnostic test is applied to augment its interpretation. We develop this to predict the occurrence of bovine tuberculosis incidents in cattle herds, exploiting the availability of exceptionally detailed testing records. We show that, without compromising test specificity, test sensitivity can be improved so that the proportion of infected herds detected improves by over 5 percentage points, or 240 additional infected herds detected in one year beyond those detected by the skin test alone. We also use feature importance testing for assessing the weighting of risk factors. While many factors are associated with increased risk of incidents, of note are several factors that suggest that in some herds there is a higher risk of infection going undetected.

We propose an alternative, explicit state only, approach to concurrent system synthesis. In particular, the focus of this work is on the synthesis of distributed protocols. Given a correctness specification and a protocol skeleton (i.e. incomplete with holes), the goal is to synthesize the holes. At the heart of our technique is a dynamic programming based algorithm that prunes inferred failure candidates. The algorithm exploits the fact that typically only a few transitions are needed to reach an erroneous state in a faulty distributed protocol. Therefore, it is unlikely that every hole to be synthesized is contributing towards the error; thus, faulty protocol candidates where only a subset of holes were used can be used to infer failures of later candidates with a superset of holes. We evaluate the tool using a cache coherence protocol synthesis case study. Specifically, we study a directory based MSI protocol, assuming an unordered interconnect which gives rise to numerous race conditions which must be resolved via introducing transient states - a common cause of complexity and bugs in such protocols. In the case study, we therefore focus on synthesizing the transient state actions (we consider up to 12 holes out of possible 35). With the proposed candidate pruning optimization, we report up to 43x improvement over a naïve candidate enumeration scheme. We make available the tool and C++ library, VerC3.

The generation of near-optimal plans for multi-agent systems with numerical states and temporal actions is computationally challenging. Current off-the-shelf planners can take a very long time before generating a near-optimal solution. In an effort to reduce plan computation time, increase the quality of the resulting plans, and make them more interpretable by humans, we explore collaborative planning techniques that actively involve human users in plan generation. Specifically, we explore a framework in which users provide high-level strategies encoded as soft preferences to guide the low-level search of the planner. Through human subject experimentation, we empirically demonstrate that this approach results in statistically significant improvements to plan quality, without substantially increasing computation time. We also show that the resulting plans achieve greater similarity to those generated by humans with regard to the produced sequences of actions, as compared to plans that do not incorporate user-provided strategies.

In this paper, we verify a modern lazy cache coherence protocol, TSO-CC, against the memory consistency model it was designed for, TSO. We achieve this by first showing a weak simulation relation between TSO-CC (with a fixed number of processors) and a novel finite-state operational model which exhibits the laziness of TSO-CC and satisfies TSO. We then extend this by an existing parameterisation technique, allowing verification for an unbounded number of processors. The approach is executed entirely within a model checker, no external tool is required and very little in-depth knowledge of formal verification methods is required of the verifier.

This paper provides an assessment of synoptic measurements of sea surface salinity (SSS) from the European Space Agency Soil Moisture and Ocean Salinity (SMOS) satellite. Due to the complex nature of the response of L-band signals to SSS, SMOS provides three values of SSS at each grid point from three different forward models. To meet oceanographic requirements for SSS retrieval accuracy, SMOS Level 2 SSS products are averaged over time and space. This paper reports on validation studies in the Atlantic based on monthly Level 3 products on a 1°×1° grid for September 2010. Outside coastal regions, large-scale SSS patterns from SMOS are in general agreement with climatology, Argo, and ocean model output. During September 2010, SSS from descending passes provides reasonable quantitative estimates, while SSS from ascending passes overestimates SSS by over 1 practical salinity unit (psu). The daily mean difference in SSS between ascending and descending passes varies during August-December 2010, reaching a maximum in September. Differences in SMOS SSS from the three models are an order of magnitude smaller than differences between ascending and descending passes. Gridded SMOS SSS data are compared against output from the U.K. Met Office Forecasting Ocean Assimilation Model (FOAM)-Nucleus for European Modelling of the Ocean (NEMO). Basic checks confirm that SSS from FOAM-NEMO is unbiased against Argo and that FOAM-NEMO SSS is a useful independent data source to validate and rapidly identify departures in SMOS SSS. Over the whole Atlantic, SMOS SSS variability against FOAM-NEMO is around 0.9 psu, decreasing to 0.5 psu over the subtropical North Atlantic.