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We present an approach to similarity-based retrieval from knowledge bases that takes into account both the structure and semantics of knowledge base fragments. Those fragments, or analogues, are represented as sparse binary vectors that allow a computationally efficient estimation of structural and semantic similarity by the vector dot product. We present the representation scheme and experimental results for the knowledge base that was previously used for testing of leading analogical retrieval models MAC/FAC and ARCS. The experiments show that the proposed single-stage approach provides results compatible with or better than the results of two-stage models MAC/FAC and ARCS in terms of recall and precision. We argue that the proposed representation scheme is useful for large-scale knowledge bases and free-structured database applications.

The results obtained demonstrate that the lasing dynamics reflects the current dynamics formed as a result of complex nonlinear couplings within the laser-thyristor heterostructure. The observed specific features mainly result from the appearance of new channels for generation of excess carriers in the p-base. These channels enhance the main optical activation channel formed by the photogeneration due to the absorption of the spontaneous emission from the active region of the laser part of the heterostructure. The additional channels of excess carrier generation may have an optical nature in the case of scattered laser light upon appearance of new high-Q modes. For nearly critical blocked voltages, generation of carriers can be initiated by an avalanche multiplication of photogenerated carriers.