Practitioners of historical archaeology can define the impact of ideology (as in Althusser) and disciplines of the self (as in Foucault), thus contributing to the study of capitalism in local historical situations. In describing the origins of modern conditions of exploitation, archaeologists can help to educate those exploited. In the Chesapeake region, historical archaeology has been used to create a more direct tie with the present than is usually the case, a tie that may serve to redistribute the power to interpret the past.

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Topographic and image maps of archaeological landscapes can be made using airborne LiDAR (Light Detection and Ranging) data. Such maps contain more three-dimensional data than conventional maps and may be more spatially accurate. In addition to providing a record of topography, LiDAR images may reveal surface indications of archaeological deposits unnoticed when using more conventional discovery techniques. LiDAR data and derived imagery need to be integrated with existing forms of archaeological data for their full potential to be realized.

Archaeologists have tried to reconstruct patterns of thought, meaning, and ideas, using theories of structuralism, cognition, and ideology. Case studies involving each of the theories are described, and the strengths and weakness of their application to archaeological data are presented. Structuralism is found to yield substantial examples with well-worked treatments of archaeological data. These examples tend to ignore economic context, however. Materialism, especially neo-Marxism, contains thorough definitions of ideology that may be useful to archaeology because they preserve economic context. However, such definitions are new to the field and presently offer few well-worked examples of how to handle archaeological data.

We describe the design and implementation of a compiler that automatically translates ordinary programs written in a subset of ML into code that generates native code at run time. Run-time code generation canmake useof values and invariants that cannot be exploited at compile time, yielding code that is often superior to statically optimal code. But the cost of optimizing and generating code at run time can be prohibitive.Redemonstrate howcompile-time specialization can reduce the cost of run-time code generation by an order of magnitude without greatly tiecting code quality. Several benchmark programs are examined, which exhibit an average cost of only six cycles per instruction generated at run time.