Ackerman CM, Courtney SM. Spatial relations and spatial locations are dissociated within prefrontal and parietal cortex. J Neurophysiol 108: 2419 -2429. First published August 15, 2012 doi:10.1152/jn.01024.2011.-Item-specific spatial information is essential for interacting with objects and for binding multiple features of an object together. Spatial relational information is necessary for implicit tasks such as recognizing objects or scenes from different views but also for explicit reasoning about space such as planning a route with a map and for other distinctively human traits such as tool construction. To better understand how the brain supports these two different kinds of information, we used functional MRI to directly contrast the neural encoding and maintenance of spatial relations with that for item locations in equivalent visual scenes. We found a double dissociation between the two: whereas item-specific processing implicates a frontoparietal attention network, including the superior frontal sulcus and intraparietal sulcus, relational processing preferentially recruits a cognitive control network, particularly lateral prefrontal cortex (PFC) and inferior parietal lobule. Moreover, pattern classification revealed that the actual meaning of the relation can be decoded within these same regions, most clearly in rostrolateral PFC, supporting a hierarchical, representational account of prefrontal organization.fMRI; representation; hierarchical organization; reasoning WHEREAS MOST RESEARCH INTO the neural basis of spatial processing has focused on the representation of object location in eye-, head-, or hand-centered coordinates, in much of daily life and language use, it is the relative positions between objects that is critical. For instance, view-independent object recognition depends on knowledge of the spatial relations between object parts. This could be accomplished implicitly, but explicit representation of the spatial relations between entities in the environment is necessary for communication about space beyond mere pointing, for creating tools and symbols composed of multiple independent elements, and for map-based navigation.The prefrontal cortex (PFC) would be expected to be involved in tasks requiring explicit encoding and maintenance of object locations and spatial relations in working memory. Several theories of PFC organization suggest that more abstract or integrated information is represented more anteriorly than more concrete, sensorimotor information [see Badre (2008) As relations are abstracted away from object-bound features and require integration among multiple objects, such theories would predict that in a direct comparison with object location, spatial relations between objects would be represented in more rostral regions.

A multilayer neural network with an adaptive learning algorithm is designed for short term load forecasting. Extensive studies have been performed on the effect of various factors such as learning rate, momentum, the number of presentations in an iteration, etc. on the efficiency and accuracy of the backpropagation-momentum learning metliod which is employed in the training of artificial neural networks. To speed up the training process, a new learning algorithm for the adaptive training of neural networks is presented. The effectiveness of the neural network with tlie proposed adaptive learning algorithm is demonstrated by short term load forccasting of Taiwan power system. It is found that. once trained by the proposed learning algorithm, the neural network can yield the desired hourly load forecast very efficiently and accurately. Moreover, the proposed adaptive learning algorithm converges much faster than the conventional backpropagationmomentum learning method.

This paper presents a solution to the problem of matching personal names in English to the same names represented in Arabic script. Standard string comparison measures perform poorly on this task due to varying transliteration conventions in both languages and the fact that Arabic script does not usually represent short vowels. Significant improvement is achieved by augmenting the classic Levenshtein edit-distance algorithm with character equivalency classes.

Utilizing off the shelf low cost parts, we have constructed a robot that is small, light, powerful and relatively inexpensive (< $3900). The system is constructed around the Beowulf concept of linking multiple discrete computing units into a single cooperative system. The goal of this project is to demonstrate a new robotics platform with sufficient computing resources to run biologically-inspired vision algorithms in real-time. This is accomplished by connecting two dual-CPU embedded PC motherboards using fast gigabit Ethernet. The motherboards contain integrated Firewire, USB and serial connections to handle camera, servomotor, GPS and other miscellaneous inputs/outputs. Computing systems are mounted on a servomechanism-controlled off-the-shelf "Off Road" RC car. Using the high performance characteristics of the car, the robot can attain relatively high speeds outdoors. The robot is used as a test platform for biologically-inspired as well as traditional robotic algorithms, in outdoor navigation and exploration activities. Leader following using multi blob tracking and segmentation, and navigation using statistical information and decision inference from image spectral information are discussed. The design of the robot is opensource and is constructed in a manner that enhances ease of replication. This is done to facilitate construction and development of mobile robots at research institutions where large financial resources may not be readily available as well as to put robots into the hands of hobbyists and help lead to the next stage in the evolution of robotics, a home hobby robot with potential real world applications.

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