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Representing and reasoning spatial and temporal information is a key research issue in Computer Science and Artificial Intelligence. In this paper, we introduce tools that produce three novel encodings which translate problems in qualitative spatial and temporal reasoning into logic programs for answer set programming solvers. Each encoding reflects a(More)
Air pollution has a direct impact to human health, and data-driven air quality models are useful for evaluating population exposure to air pollutants. In this paper, we propose a novel region-based Gaussian process model for estimating urban air pollution dispersion, and applied it to a large dataset of ultrafine particle (UFP) measurements collected from a(More)
White matter hyperintensities (WMHs) are a frequent finding on T2-weighted MRI of the brain in elderly individuals, but their prevalence and severity in younger asymptomatic populations is less well studied. We report the topography of WMHs on T2-weighted fluid inversion recovery (FLAIR) MRI in 428 individuals aged 44-48 years recruited randomly from a(More)
—Sensing and monitoring of our natural environment are important for sustainability. As sensor systems grow to large scale, it will become infeasible to place all sensors under centralized control. We investigate community sensing, where sensors are controlled by self-interested agents that report their measurements to a center. The center can control the(More)
Deciding consistency of constraint networks is a fundamental problem in qualitative spatial and temporal reasoning. In this paper we introduce a divide-and-conquer method that recursively partitions a given problem into smaller sub-problems in deciding consistency. We identify a key theoretical property of a qualitative calculus that ensures the soundness(More)
As the Internet of Things grows to large scale, its components will increasingly be controlled by self-interested agents. For example, sensor networks will evolve to community sensing where a community of agents combine their data into a single coherent structure. As there is no central quality control, agents need to be incentivized to provide accurate(More)
Identifying complexity results for qualitative spatial or temporal calculi has been an important research topic in the past 15 years. Most interesting calculi have been shown to be at least NP-complete, but if tractable fragments of the cal-culi can be found then efficient reasoning with these calculi is possible. In order to get the most efficient(More)
Constraint networks in qualitative spatial and temporal reasoning are always complete graphs. When one adds an extra element to a given network, previously unknown constraints are derived by intersections and compositions of other constraints, and this may introduce inconsistency to the overall network. Likewise, when combining two consistent networks that(More)
a r t i c l e i n f o a b s t r a c t Constraint networks in qualitative spatial and temporal reasoning (QSTR) typically feature variables defined on infinite domains. Mainstream algorithms for deciding network consistency are based on searching for network refinements whose consistency is known to be tractable, either directly or by using a SAT solver.(More)