109,199 research outputs found
Reformulation in planning
Reformulation of a problem is intended to make the problem more amenable to efficient solution. This is equally true in the special case of reformulating a planning problem. This paper considers various ways in which reformulation can be exploited in planning
From chunks to function-argument structure : a similarity-based approach
Chunk parsing has focused on the recognition of partial constituent structures at the level of individual chunks. Little attention has been paid to the question of how such partial analyses can be combined into larger structures for complete utterances. Such larger structures are not only desirable for a deeper syntactic analysis. They also constitute a necessary prerequisite for assigning function-argument structure. The present paper offers a similaritybased algorithm for assigning functional labels such as subject, object, head, complement, etc. to complete syntactic structures on the basis of prechunked input. The evaluation of the algorithm has concentrated on measuring the quality of functional labels. It was performed on a German and an English treebank using two different annotation schemes at the level of function argument structure. The results of 89.73% correct functional labels for German and 90.40%for English validate the general approach
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
Living Knowledge
Diversity, especially manifested in language and knowledge, is a function of local goals, needs, competences, beliefs, culture, opinions and personal experience. The Living Knowledge project considers diversity as an asset rather than a problem. With the project, foundational ideas emerged from the synergic contribution of different disciplines, methodologies (with which many partners were previously unfamiliar) and technologies flowed in concrete diversity-aware applications such as the Future Predictor and the Media Content Analyser providing users with better structured information while coping with Web scale complexities. The key notions of diversity, fact, opinion and bias have been defined in relation to three methodologies: Media Content Analysis (MCA) which operates from a social sciences perspective; Multimodal Genre Analysis (MGA) which operates from a semiotic perspective and Facet Analysis (FA) which operates from a knowledge representation and organization perspective. A conceptual architecture that pulls all of them together has become the core of the tools for automatic extraction and the way they interact. In particular, the conceptual architecture has been implemented with the Media Content Analyser application. The scientific and technological results obtained are described in the following
An Experimental Digital Library Platform - A Demonstrator Prototype for the DigLib Project at SICS
Within the framework of the Digital Library project at SICS, this thesis describes the implementation of a demonstrator prototype of a digital library (DigLib); an experimental platform integrating several functions in one common interface. It includes descriptions of the structure and formats of the digital library collection, the tailoring of the search engine Dienst, the construction of a keyword extraction tool, and the design and development of the interface. The platform was realised through sicsDAIS, an agent interaction and presentation system, and is to be used for testing and evaluating various tools for information seeking. The platform supports various user interaction strategies by providing: search in bibliographic records (Dienst); an index of keywords (the Keyword Extraction Function (KEF)); and browsing through the hierarchical structure of the collection. KEF was developed for this thesis work, and extracts and presents keywords from Swedish documents. Although based on a comparatively simple algorithm, KEF contributes by supplying a long-felt want in the area of Information Retrieval. Evaluations of the tasks and the interface still remain to be done, but the digital library is very much up and running. By implementing the platform through sicsDAIS, DigLib can deploy additional tools and search engines without interfering with already running modules. If wanted, agents providing other services than SICS can supply, can be plugged in
Template-Cut: A Pattern-Based Segmentation Paradigm
We present a scale-invariant, template-based segmentation paradigm that sets
up a graph and performs a graph cut to separate an object from the background.
Typically graph-based schemes distribute the nodes of the graph uniformly and
equidistantly on the image, and use a regularizer to bias the cut towards a
particular shape. The strategy of uniform and equidistant nodes does not allow
the cut to prefer more complex structures, especially when areas of the object
are indistinguishable from the background. We propose a solution by introducing
the concept of a "template shape" of the target object in which the nodes are
sampled non-uniformly and non-equidistantly on the image. We evaluate it on
2D-images where the object's textures and backgrounds are similar, and large
areas of the object have the same gray level appearance as the background. We
also evaluate it in 3D on 60 brain tumor datasets for neurosurgical planning
purposes.Comment: 8 pages, 6 figures, 3 tables, 6 equations, 51 reference
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