A Novel Concept Search Method Based on Multiset Word Embedding and Character-Level Synthesis – Semantic representation learning (SE) is a very promising branch of cognitive science that aims to create a formal model for all the semantic information of a language (i.e. it is represented by its semantic states), while using only a small fraction of the words in the language. This paper focuses on an efficient algorithm for semantic representation learning on a single corpus, and for evaluation, via the classification of a set of semantic-semantic queries of a language. The paper proposes a new algorithm for semantic representation learning on a different set of queries, called semantic queries with semantic states, and a new semantic-semantic search method based on the Semantic Semantic Search (SSS) algorithm. Our method is evaluated using three datasets – English-English, French-French and German-English data. The results show that our method does not outperform the state-of-the-art Semantic Semantic search algorithms on these datasets.

The paper shows that a two-dimensional (2D) representation of the problem is an attractive technique for the optimization of quadratic functions. In real data the 2D representation is also suitable to model time-varying information sources. We propose to exploit real-time 3D reconstruction to obtain a 2D reconstruction function for a stochastic function. The stochastic reconstruction parameter is a non-convex (non-linear function) which can be modeled in any non-linear time-scale fashion. We show how our formulation allows us to solve the 2D problem efficiently and efficiently using a stochastic algorithm. It also leads to the design of a scalable system to solve the 2D problem efficiently in practice.

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A Convex Solution to the Positioning Problem with a Coupled Convex-concave-constraint ModelThe paper shows that a two-dimensional (2D) representation of the problem is an attractive technique for the optimization of quadratic functions. In real data the 2D representation is also suitable to model time-varying information sources. We propose to exploit real-time 3D reconstruction to obtain a 2D reconstruction function for a stochastic function. The stochastic reconstruction parameter is a non-convex (non-linear function) which can be modeled in any non-linear time-scale fashion. We show how our formulation allows us to solve the 2D problem efficiently and efficiently using a stochastic algorithm. It also leads to the design of a scalable system to solve the 2D problem efficiently in practice.