Acetylcholine neuromodulation in normal and abnormal learning and memory: vigilance control in waking, sleep, autism, amnesia, and Alzheimer's disease

This article provides a unified mechanistic neural explanation of how learning, recognition, and cognition break down during Alzheimer's disease, medial temporal amnesia, and autism. It also clarifies whey there are often sleep disturbances during these disorders. A key mechanism is how acetylcholine modules vigilance control in cortical layer

Comparative cluster labelling involving external text sources

Giving clear, straightforward names to individual result groups of clustering data is most important in making research usable. This is especially so when clustering is the real outcome of the analysis and not just a tool for data preparation. In this case, the underlying concept of the cluster itself makes the result meaningful and useful. However, a cluster comes alive only in the investigator’s mind since it can be defined or described in words. Our method introduced in this paper aims to facilitate and partly automate this verbal characterisation process. The external text database is joined to the objects of the clustering that adds new, previously unused features to the data set. Clusters are described by labels produced by text mining analytics. The validity of clustering can be characterised by the shape of the final word cloud

Folksonomy: the New Way to Serendipity

Folksonomy expands the collaborative process by allowing contributors to index content. It rests on three powerful properties: the absence of a prior taxonomy, multi-indexation and the absence of thesaurus. It concerns a more exploratory search than an entry in a search engine. Its original relationship-based structure (the three-way relationship between users, content and tags) means that folksonomy allows various modalities of curious explorations: a cultural exploration and a social exploration. The paper has two goals. Firstly, it tries to draw a general picture of the various folksonomy websites. Secundly, since labelling lacks any standardisation, folksonomies are often under threat of invasion by noise. This paper consequently tries to explore the different possible ways of regulating the self-generated indexation process.taxonomy; indexation; innovation and user-created content

Building linguistic corpora from Wikipedia articles and discussions

Wikipedia is a valuable resource, useful as a lingustic corpus or a dataset for many kinds of research. We built corpora from Wikipedia articles and talk pages in the I5 format, a TEI customisation used in the German Reference Corpus (Deutsches Referenzkorpus - DeReKo). Our approach is a two-stage conversion combining parsing using the Sweble parser, and transformation using XSLT stylesheets. The conversion approach is able to successfully generate rich and valid corpora regardless of languages. We also introduce a method to segment user contributions in talk pages into postings

Reasoning & Querying – State of the Art

Various query languages for Web and Semantic Web data, both for practical use and as an area of research in the scientific community, have emerged in recent years. At the same time, the broad adoption of the internet where keyword search is used in many applications, e.g. search engines, has familiarized casual users with using keyword queries to retrieve information on the internet. Unlike this easy-to-use querying, traditional query languages require knowledge of the language itself as well as of the data to be queried. Keyword-based query languages for XML and RDF bridge the gap between the two, aiming at enabling simple querying of semi-structured data, which is relevant e.g. in the context of the emerging Semantic Web. This article presents an overview of the field of keyword querying for XML and RDF

Exhaustive and Efficient Constraint Propagation: A Semi-Supervised Learning Perspective and Its Applications

This paper presents a novel pairwise constraint propagation approach by decomposing the challenging constraint propagation problem into a set of independent semi-supervised learning subproblems which can be solved in quadratic time using label propagation based on k-nearest neighbor graphs. Considering that this time cost is proportional to the number of all possible pairwise constraints, our approach actually provides an efficient solution for exhaustively propagating pairwise constraints throughout the entire dataset. The resulting exhaustive set of propagated pairwise constraints are further used to adjust the similarity matrix for constrained spectral clustering. Other than the traditional constraint propagation on single-source data, our approach is also extended to more challenging constraint propagation on multi-source data where each pairwise constraint is defined over a pair of data points from different sources. This multi-source constraint propagation has an important application to cross-modal multimedia retrieval. Extensive results have shown the superior performance of our approach.Comment: The short version of this paper appears as oral paper in ECCV 201