Artificial intelligence and symbols

The introduction of massive parallelism and the renewed interest in neural networks gives a new need to evaluate the relationship of symbolic processing and artificial intelligence. The physical symbol hypothesis has encountered many difficulties coping with human concepts and common sense. Expert systems are showing more promise for the early stages of learning than for real expertise. There is a need to evaluate more fully the inherent limitations of symbol systems and the potential for programming compared with training. This can give more realistic goals for symbolic systems, particularly those based on logical foundations.     

Distributed Sensing and Control Technology for Intelligent Civil Infrastructure Systems

Abstract: Sensing and control functions are the fundamental technologies on which many civil infrastructure control and monitoring systems are constructed. Early civil infrastructure control and monitoring systems employed a centralized computing model for sensor processing and actuator control. These systems required extensive wiring to route power, sensor, and control signals among the distributed input and output devices. Furthermore, the operation of the entire system depended on one or two computers. Abstractly, these civil infrastructure applications are a realtime data acquisition and control system. Due to the dramatic decrease in computing costs, it is now feasible to construct these real-time systems using dozens or even hundreds of distributed processors linked with a data communication network. This allows the controllers to be located adjacent to sensors and actuators, thereby reducing the need for long lengths of multiconductor wire that is subject to noise and breakage. Also, failure of a single processor does not necessarily cause the entire system to fail. This paper introduces the reader to a technology called LonWorks for constructing distributed sensing and control systems. The LonWorks technology is essentially a data communication network that links together extremely small computers called Neurons. Particular emphasis is placed on describing the communication architecture and emphasizing how the distributed Neuron communication processors are designed to ensure a truly modular and open architecture.     

Improved wetland remote sensing in Yellowstone National Park using classification trees to combine TM imagery and ancillary environmental data

The U.S. Fish and Wildlife Service uses the term palustrine wetland to describe vegetated wetlands traditionally identified as marsh, bog, fen, swamp, or wet meadow. Landsat TM imagery was combined with image texture and ancillary environmental data to model probabilities of palustrine wetland occurrence in Yellowstone National Park using classification trees. Model training and test locations were identified from National Wetlands Inventory maps, and classification trees were built for seven years spanning a range of annual precipitation. At a coarse level, palustrine wetland was separated from upland. At a finer level, five palustrine wetland types were discriminated: aquatic bed (PAB), emergent (PEM), forested (PFO), scrub-shrub (PSS), and unconsolidated shore (PUS). TM-derived variables alone were relatively accurate at separating wetland from upland, but model error rates dropped incrementally as image texture, DEM-derived terrain variables, and other ancillary GIS layers were added. For classification trees making use of all available predictors, average overall test error rates were 7.8% for palustrine wetland/upland models and 17.0% for palustrine wetland type models, with consistent accuracies across years. However, models were prone to wetland over-prediction. While the predominant PEM class was classified with omission and commission error rates less than 14%, we had difficulty identifying the PAB and PSS classes. Ancillary vegetation information greatly improved PSS classification and moderately improved PFO discrimination. Association with geothermal areas distinguished PUS wetlands. Wetland over-prediction was exacerbated by class imbalance in likely combination with spatial and spectral limitations of the TM sensor. Wetland probability surfaces may be more informative than hard classification, and appear to respond to climate-driven wetland variability. The developed method is portable, relatively easy to implement, and should be applicable in other settings and over larger extents.     

A comparison of the dissolution of model compounds and the kerogen of Green River oil shale by oxidation with perchloric acid — A model for the kerogen of Green River oil shale

A stepwise degradation of a kerogen concentrate of Green River oil shale was effected by oxidation with perchloric acid of varying boiling point and concentration. Dissolution of the kerogen concentrate occurred between 160 and 170°C. Several model compounds were also oxidized with perchloric acid. The model compounds were polyethylene, graphite, nylon, and polyethylene oxide. The results indicated that the kerogen was similar to polyethylene oxide. The results of this study and previous work were used to present a model for the structure of the kerogen of Green River oil shale.     

fsh—A functional unix command interpreter

Command interpreters under the UNIX operating system typically employ the syntax and semantics of imperative programming languages. Many of the advantages that functional programming languages hold over imperative languages can be incorporated in a functional command interpreter. Unfortunately, functional command interpreters to date have used the syntax of Backus's FP. The advantages of command expressiveness and composition have not been appreciated. A powerful functional command interpreter, fsh, that overcomes many of the deterrents of previous functional command interpreters is presented. Much of the unnecessary complexity of existing UNIX command interpreters is avoided by following the functional paradigm. fsh has been implemented under the UNIX 4.2BSD and System V operating systems.     

Effects of self-generated facial expressions on mood.

Two experiments were conducted in which participants looked at photographs (Experiment 1, n?=?129) or slides (Experiment 2, n?=?90) of people engaging in positive or negative facial expressions. Participants attempted to communicate these facial expressions as accurately as they could to a video camera while viewing themselves in a mirror or without viewing themselves in a mirror. Participants in a control group maintained neutral facial expressions. Participants experienced increased positive moods when they engaged in positive facial expressions and decreased positive moods when they engaged in negative facial expressions. These effects were enhanced when participants viewed themselves in a mirror. The effects of facial expressions on positive affect were stronger for participants with high private self-consciousness. Results were integrated with research identifying individuals who are responsive to self-produced versus situational cues and with theory and research on self-awareness. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Protein translocation: delivering virulence into the host cell

A wide variety of plant and human bacterial pathogens use a specialized 'type III' protein secretion system to deliver virulence factors into host cells. Appendage-like surface structures have recently been identified on several bacterial pathogens and there are indications that these may be conduits for virulence factor delivery.     

Tributary use and large-scale movements of grass carp in Lake Erie

Infrequent captures of invasive, non-native grass carp (Ctenopharyngodon idella) have occurred in Lake Erie over the last 30+ years, with recent evidence suggesting wild reproduction in the lake’s western basin (WB) is occurring. Information on grass carp movements in the Laurentian Great Lakes is lacking, but an improved understanding of large-scale movements and potential areas of aggregation will help inform control strategies and risk assessment if grass carp spread to other parts of Lake Erie and other Great Lakes. Twenty-three grass carp captured in Lake Erie’s WB were implanted with acoustic transmitters and released. Movements were monitored with acoustic receivers deployed throughout Lake Erie and elsewhere in the Great Lakes. Grass carp dispersed up to 236 km, with approximately 25% of fish dispersing greater than 100 km from their release location. Mean daily movements ranged from <0.01 to 2.49 km/day, with the highest daily averages occurring in the spring and summer. The Sandusky, Detroit, and Maumee Rivers, and Plum Creek were the most heavily used WB tributaries. Seventeen percent of grass carp moved into Lake Erie’s central or eastern basins, although all fish eventually returned to the WB. One fish emigrated from Lake Erie through the Huron-Erie Corridor and into Lake Huron. Based on our results, past assessments may have underestimated the potential for grass carp to spread in the Great Lakes. We recommend focusing grass carp control efforts on Sandusky River and Plum Creek given their high use by tagged fish, and secondarily on Maumee and Detroit Rivers.