Sequential Dynamical System (SDS) google
Sequential dynamical systems (SDSs) are a class of graph dynamical systems. They are discrete dynamical systems which generalize many aspects of for example classical cellular automata, and they provide a framework for studying asynchronous processes over graphs. The analysis of SDSs uses techniques from combinatorics, abstract algebra, graph theory, dynamical systems and probability theory. …

Apache Object Oriented Data Technology (OODT) google
Metadata for middleware (and vice versa):
• Transparent access to distributed resources
• Data discovery and query optimization
• Distributed processing and virtual archives
But it’s not just for science! It’s also a software architecture:
• Models for information representation
• Solutions to knowledge capture problems
• Unification of technology, data, and metadata …


Hierarchical Latent Tree Analysis (HLTA) google
In the LDA approach to topic detection, a topic is determined by identifying the words that are used with high frequency when writing about the topic. However, high frequency words in one topic may be also used with high frequency in other topics. Thus they may not be the best words to characterize the topic. In this paper, we propose a new method for topic detection, where a topic is determined by identifying words that appear with high frequency in the topic and low frequency in other topics. We model patterns of word cooccurrence and co-occurrences of those patterns using a hierarchy of discrete latent variables. The states of the latent variables represent clusters of documents and they are interpreted as topics. The words that best distinguish a cluster from other clusters are selected to characterize the topic. Empirical results show that the new method yields topics with clearer thematic characterizations than the alternative approaches. In this work, we introduce semantically higher level latent variables to model co-occurrence of those patterns, resulting in hierarchical latent tree models (HLTMs). The latent variables at higher levels of the hierarchy correspond to more general topics, while the latent variables at lower levels correspond to more specific topics. The proposed method for topic detection is therefore called hierarchical latent tree analysis (HLTA). …

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