Atlas of the global distribution of atmospheric heating during the global weather experiment

Global distributions of atmospheric heating for the annual cycle of the Global Weather Experiment are estimated from the European Centre for Medium-Range Weather Forecasts (ECMWF) Level 3b data set. Distributions of monthly, seasonally, and annually averaged heating are presented for isentropic and isobaric layers within the troposphere and for the troposphere as a whole. The distributions depict a large-scale structure of atmospheric heating that appears spatially and temporally consistent with known features of the global circulation and the seasonal evolution

Generic task problem solvers in Soar

Two trends can be discerned in research in problem solving architectures in the last few years. On one hand, interest in task-specific architectures has grown, wherein types of problems of general utility are identified, and special architectures that support the development of problem solving systems for those types of problems are proposed. These architectures help in the acquisition and specification of knowledge by providing inference methods that are appropriate for the type of problem. However, knowledge based systems which use only one type of problem solving method are very brittle, and adding more types of methods requires a principled approach to integrating them in a flexible way. Contrasting with this trend is the proposal for a flexible, general architecture contained in the work on Soar. Soar has features which make it attractive for flexible use of all potentially relevant knowledge or methods. But as the theory Soar does not make commitments to specific types of problem solvers or provide guidance for their construction. It was investigated how task-specific architectures can be constructed in Soar to retain as many of the advantages as possible of both approaches. Examples were used from the Generic Task approach for building knowledge based systems. Though this approach was developed and applied for a number of problems, the ideas are applicable to other task-specific approaches as well

Integration of Anomalous Data in Multicausal Explanations

This paper describes and evaluates a computational model of anomalous data integration. This model makes use of three factors: entrenchment of the current theory (the amount of data explained), the relative probability of the contradictory explanations (based on conditional probabilities as part of the domain-knowledge), and the availability of alternative explanations based on learning. In an experimental study we found that the enu-enchment of a theory and the availability and likelihood of an alternative explanation influenced solution speed and the correctness of inferred causal explanations. However, in detail, the single levels of both factors were not cleariy distinguishable and did not follow the predictions. These findings suggest that entrenchment itself is not a major factor in determining the difficulty of a task. Instead, we hypothesize that task difficulty is dominated by a person's ability to construct an alternative explanation of a given situation, a factor that is only indirectly related to entrenchment

Why Is Biomedical Informatics Hard? A Fundamental Framework

Building on previous work to define the scientific discipline of biomedical informatics, we present a framework that categorizes fundamental challenges into groups based on data, information, and knowledge, along with the transitions between these levels. We define each level and argue that the framework provides a basis for separating informatics problems from non-informatics problems, identifying fundamental challenges in biomedical informatics, and provides guidance regarding the search for general, reusable solutions to informatics problems. We distinguish between processing data (symbols) and processing meaning. Computational systems, that are the basis for modern information technology (IT), process data. In contrast, many important challenges in biomedicine, such as providing clinical decision support, require processing meaning, not data. Biomedical informatics is hard because of the fundamental mismatch between many biomedical problems and the capabilities of current technology