Helsinki University of Technology

About: Helsinki University of Technology is a based out in . It is known for research contribution in the topics: Artificial neural network & Finite element method. The organization has 8962 authors who have published 20136 publications receiving 723787 citations. The organization is also known as: TKK & Teknillinen korkeakoulu.

What is Special about Patent Citations? Differences between Scientific and Patent Citations

Abstract: The emergence of pattent bibliometrics as a new branch of scientometrics necessitates a deeper understanding of the relationship between patents and papers. As this connection is established through the linkage between patents and research papers, one must have a clear idea of similarities and differences between patent and paper citations. This paper will investigate to what extent one can not only apply bibliometric methods to patents but also extend the existing interpretative framework for citations in research papers to the field of patent citations. After pointing out some parallels in the debates about the nature of citations in patents and scientific articles, the paper outlines those parts of bibliometric theory covering scientific citations that could be relevant to patent citations too. Then it highlights the specialties and peculiarities of patent citations. One major conclusion is that the general nature of a common framework for both scientific and patent citations would severely limit its usefulness, but research on academic citations might still be a great source of inspiration to the study of patent citations.

Complex random vectors and ICA models: identifiability, uniqueness, and separability

Abstract: In this paper, the conditions for identifiability, separability and uniqueness of linear complex valued independent component analysis (ICA) models are established. These results extend the well-known conditions for solving real-valued ICA problems to complex-valued models. Relevant properties of complex random vectors are described in order to extend the Darmois-Skitovich theorem for complex-valued models. This theorem is used to construct a proof of a theorem for each of the above ICA model concepts. Both circular and noncircular complex random vectors are covered. Examples clarifying the above concepts are presented

Inducing the morphological lexicon of a natural language from unannotated text

Abstract: This work presents an algorithm for the unsupervised learning, or induction, of a simple morphology of a natural language. A probabilistic maximum a posteriori model is utilized, which builds hierarchical representations for a set of morphs, which are morpheme-like units discovered from unannotated text corpora. The induced morph lexicon stores parameters related to both the “meaning” and “form” of the morphs it contains. These parameters affect the role of the morphs in words. The model is implemented in a task of unsupervised morpheme segmentation of Finnish and English words. Very good results are obtained for Finnish and almost as good results are obtained in the English task.