Massively-parallel marker-passing in semantic networks

AbstractOne approach to using the information available in a semantic network is the use of marker-passing algorithms, which propagate information through the network to determine relationships between objects. One of the primary arguments in favor of these algorithms are their ability to be implemented in parallel. Despite this, most implementations have been serial and have only sometimes gone so far as to simulate parallelism. In this paper the marker-passing approach is presented. An actual parallel implementation which shows that such programs can be written on commercially available massively parallel machines is also presented

Massively parallel support for a case-based planning system

Case-based planning (CBP), a kind of case-based reasoning, is a technique in which previously generated plans (cases) are stored in memory and can be reused to solve similar planning problems in the future. CBP can save considerable time over generative planning, in which a new plan is produced from scratch. CBP thus offers a potential (heuristic) mechanism for handling intractable problems. One drawback of CBP systems has been the need for a highly structured memory to reduce retrieval times. This approach requires significant domain engineering and complex memory indexing schemes to make these planners efficient. In contrast, our CBP system, CaPER, uses a massively parallel frame-based AI language (PARKA) and can do extremely fast retrieval of complex cases from a large, unindexed memory. The ability to do fast, frequent retrievals has many advantages: indexing is unnecessary; very large case bases can be used; memory can be probed in numerous alternate ways; and queries can be made at several levels, allowing more specific retrieval of stored plans that better fit the target problem with less adaptation. In this paper we describe CaPER's case retrieval techniques and some experimental results showing its good performance, even on large case bases

Semantic Integration Portal

The Semantic Integration Portal is a demonstration of the potential capabilities of Semantic Web applications in a knowledge-rich context. Source data is taken from different online terrorist incident aggregators and marked up according to ontologies specific to those domains. Unlike other semantic web techniques, which scrape the internet for raw data and then mark-up against a standard ontology, the approach here is to allow each data source to have its own domain-specific ontology. This allows the data producers the opportunity to mark up their data in their own way, producing RDF data according to their own ontologies without the need to conform to a standard. A variety of semantic integration techniques can then be applied to these ontologies, both automatic and interactive, allowing data from both sets to be viewed in a suitable application, in this case the mspace browser. Future iterations of the semantic integration portal aim to introduce more automated ontology-mapping techniques, aligning data from a variety of diverse sources with less need for human intervention

Design Index for Deep Neural Networks

AbstractIn this paper, we propose a Deep Neural Networks (DNN) Design Index which would aid a DNN designer during the designing phase of DNNs. We study the designing aspect of DNNs from model-specific and data-specific perspectives with focus on three performance metrics: training time, training error and, validation error. We use a simple example to illustrate the significance of the DNN design index. To validate it, we calculate the design indices for four benchmark problems. This is an elementary work aimed at setting a direction for creating design indices pertaining to deep learning

Fundamental analysis powered by Semantic Web

Abstract—Conducting fundamental analysis within subsets of comparable firms has been demonstrated to provide more reliable inferences and increase the prediction quality in equity research. However, incorporating and representing both firm-specific information and common economic determinants has been widely recognized as the key challenge. This paper investigates how to leverage Semantic Web technologies to assist fundamental analysis by generating flexible and meaningful selections of comparable firms at low costs. We approach the problem by proposing Linked Open Financial Data as the data organization model and ontology modeling for knowledge representation. Results are verified in terms of efficiency with examples of quick mashups, and feasibility by adapting to existing valuation models

Performance Analysis of Mobile Agents for Filtering Data Streams on Wireless Networks

Wireless networks are an ideal environment for mobile agents, since their mobility allows them to move across an unreliable link to reside on a wired host, next to or closer to the resources that they need to use. Furthermore, client-specific data transformations can be moved across the wireless link and run on a wired gateway server, reducing bandwidth demands. In this paper we examine the tradeoffs faced when deciding whether to use mobile agents in a data-filtering application where numerous wireless clients filter information from a large data stream arriving across the wired network. We develop an analytical model and use parameters from filtering experiments conducted during a U.S. Navy Fleet Battle Experiment (FBE) to explore the model\u27s implications

Single photon emission computed tomography (SPECT) of anxiety disorders before and after treatment with citalopram

BACKGROUND: Several studies have now examined the effects of selective serotonin reuptake inhibitor (SSRI) treatment on brain function in a variety of anxiety disorders including obsessive-compulsive disorder (OCD), posttraumatic stress disorder (PTSD), and social anxiety disorder (social phobia) (SAD). Regional changes in cerebral perfusion following SSRI treatment have been shown for all three disorders. The orbitofrontal cortex (OFC) (OCD), caudate (OCD), medial pre-frontal/cingulate (OCD, SAD, PTSD), temporal (OCD, SAD, PTSD) and, thalamic regions (OCD, SAD) are some of those implicated. Some data also suggests that higher perfusion pre-treatment in the anterior cingulate (PTSD), OFC, caudate (OCD) and antero-lateral temporal region (SAD) predicts subsequent treatment response. This paper further examines the notion of overlap in the neurocircuitry of treatment and indeed treatment response across anxiety disorders with SSRI treatment. METHODS: Single photon emission computed tomography (SPECT) using Tc-(99 m )HMPAO to assess brain perfusion was performed on subjects with OCD, PTSD, and SAD before and after 8 weeks (SAD) and 12 weeks (OCD and PTSD) treatment with the SSRI citalopram. Statistical parametric mapping (SPM) was used to compare scans (pre- vs post-medication, and responders vs non-responders) in the combined group of subjects. RESULTS: Citalopram treatment resulted in significant deactivation (p = 0.001) for the entire group in the superior (t = 4.78) and anterior (t = 4.04) cingulate, right thalamus (t = 4.66) and left hippocampus (t = 3.96). Deactivation (p = 0.001) within the left precentral (t = 4.26), right mid-frontal (t = 4.03), right inferior frontal (t = 3.99), left prefrontal (3.81) and right precuneus (t= 3.85) was more marked in treatment responders. No pattern of baseline activation distinguished responders from non-responders to subsequent pharmacotherapy. CONCLUSIONS: Although each of the anxiety disorders may be mediated by different neurocircuits, there is some overlap in the functional neuro-anatomy of their response to SSRI treatment. The current data are consistent with previous work demonstrating the importance of limbic circuits in this spectrum of disorders. These play a crucial role in cognitive-affective processing, are innervated by serotonergic neurons, and changes in their activity during serotonergic pharmacotherapy seem crucial