Revolutionary War and an Amsterdam Privy: The Remarkable Background of a Rhode Island Ship Token

In 2008 the City of Amsterdam Office for Monuments & Archaeology (BMA) excavated a remarkable find from a late 18th-century privy in Amsterdam’s city centre that can be directly linked to the American Revolutionary War, a 1779 Rhode Island Ship Token. Approximately twenty-five examples of this token are known worldwide, but none of them come from an archaeological context. From this Amsterdam find one can examine these tokens from an entirely new aspect, namely the socio-economic context of the owner as well as the period in which the token was used. The Rhode Island Ship Token was a British propaganda piece ridiculing the weakness of the Americans in 1778 and distributed in the Netherlands to create negative views of the American revolutionaries to discourage the Dutch from intervening in the Anglo-American conflict. Whether the artifact from the privy expressed its owner’s political preferences or was simply a curiosity will remain unknown. What we do know is that with the outbreak of the Fourth Anglo-Dutch War in 1780, the tokens had become worthless and that this particular piece ended in a cesspit after a final use as a clothing ornament, a counter for card games, or possibly even as a child’s toy

Path allocation in a three-stage broadband switch with intermediate channel grouping

A method for path allocation for use with three-stage ATM switches that feature multiple channels between the switch modules in adjacent stages is described. The method is suited to hardware implementation using parallelism to achieve a very short execution time. This allows path allocation to be performed anew in each time slot. A detailed description of the necessary hardware is presented. This hardware counts the number of cells requesting each output module, allocates a path through the intermediate stage of the switch to each cell, and generates a routing tag for each cell, indicating the path assigned to i

A three-stage ATM switch with cell-level path allocation

A method is described for performing routing in three-stage asynchronous transfer mode (ATM) switches which feature multiple channels between the switch modules in adjacent stages. The method is suited to hardware implementation using parallelism to achieve a very short execution time. This allows cell-level routing to be performed, whereby routes are updated in each time slot. The algorithm allows a contention-free routing to be performed, so that buffering is not required in the intermediate stage. An algorithm with this property, which preserves the cell sequence, is referred to as a path allocation algorithm. A detailed description of the necessary hardware is presented. This hardware uses a novel circuit to count the number of cells requesting each output module, it allocates a path through the intermediate stage of the switch to each cell, and it generates a routing tag for each cell, indicating the path assigned to it. The method of routing tag assignment described employs a nonblocking copy network. The use of highly parallel hardware reduces the clock rate required of the circuitry, for a given-switch size. The performance of ATM switches using this path allocation algorithm has been evaluated by simulation, and is described

Wrapper Maintenance: A Machine Learning Approach

The proliferation of online information sources has led to an increased use of wrappers for extracting data from Web sources. While most of the previous research has focused on quick and efficient generation of wrappers, the development of tools for wrapper maintenance has received less attention. This is an important research problem because Web sources often change in ways that prevent the wrappers from extracting data correctly. We present an efficient algorithm that learns structural information about data from positive examples alone. We describe how this information can be used for two wrapper maintenance applications: wrapper verification and reinduction. The wrapper verification system detects when a wrapper is not extracting correct data, usually because the Web source has changed its format. The reinduction algorithm automatically recovers from changes in the Web source by identifying data on Web pages so that a new wrapper may be generated for this source. To validate our approach, we monitored 27 wrappers over a period of a year. The verification algorithm correctly discovered 35 of the 37 wrapper changes, and made 16 mistakes, resulting in precision of 0.73 and recall of 0.95. We validated the reinduction algorithm on ten Web sources. We were able to successfully reinduce the wrappers, obtaining precision and recall values of 0.90 and 0.80 on the data extraction task