Abstract
People with autism spectrum disorder (ASD) find it difficult to recognize and respond to emotions conveyed by the face. Most existing methodologies to teach people with ASD to recognize expressions use still images, and do not take into account that facial expressions have movement. We propose a new approach that uses state of the art technology to solve the problem and to improve interactivity. It is based on an avatar-user interaction model with real time response, which builds upon the patient-therapist relationship: it is designed to be used by the therapist and the patient. The core technology behind it is based on a technique we have developed for real time facial synthesis of 3D characters.

Abstract
People with autism spectrum disorder (ASD) find it difficult to recognize and respond to emotions conveyed by the face. Most existing methodologies to teach people with ASD to recognize expressions use still images, and do not take into account that facial expressions have movement. We propose a new approach that uses state of the art technology to solve the problem and to improve interactivity. It is based on an avatar-user interaction model with real time response, which builds upon the patient-therapist relationship: it is designed to be used by the therapist and the patient. The core technology behind it is based on a technique we have developed for real time facial synthesis of 3D characters.

Abstract
With the advent of GPU programmability, many applications have transferred computational intensive tasks into it. Some of them compute intermediate data comprised by a mixture of relevant and irrelevant elements in respect to further processing tasks. Hence, the ability to discard irrelevant data and preserve the relevant portion is a desired feature, with benefits on further computational effort, memory and communication bandwidth. Parallel stream compaction is an operation that, given a discriminator, is able to output the valid elements discarding the rest. In this paper we contribute two original algorithms for parallel stream compaction on the GPU. We tested and compared our proposals with state-of-art algorithms against different data-sets. Results demonstrate that our proposals can outperform prior algorithms. Result analysis also demonstrate that there is not a best algorithm for all data distributions and that such optimal setting is difficult to be achieved without prior knowledge of the data characteristics.

Abstract
Stream Compaction is an important task to perform in the context of data parallel computing, useful for many applications in Computer Graphics as well as for general purpose computation on graphics hardware. Given a data stream containing irrelevant elements, stream compaction outputs a stream comprised by the relevant elements, discarding the rest. The compaction mechanism has the potential to enable savings on further processing, memory storage and communication bandwidth. Traditionally, stream compaction is defined as a monotonic (or stable) operation in the sense that it preserves the relative order of the data. This is not a full requirement for many applications, therefore we distinguish between monotonic and non-monotonic algorithms. The latter motivated us to introduce the Jumping Jack algorithm as a new algorithm for non-monotonic compaction. In this paper, experimental results are presented and discussed showing that, although simple, the algorithm has interesting properties that enable it to perform faster than existent state-of-the-art algorithms, in many circumstances.

Abstract
Cultural heritage documents are often subject to digitization processes resulting in image material, even for textual contents. It is therefore common, in collections of valuable documents, to have descriptive information generated by the institutions, along with digitized images, transcriptions created by scholars, translations and even miscellaneous annotations. To offer a faceted access to the collection it is necessary to explore these diverse materials, integrate them according to a model that accounts for both metadata and the content and provide a comprehensive retrieval environment. In this work we have applied the MetaMedia multimedia database framework to a collection of ancient documents, processed the documents in their descriptive, textual, and image content and produced a browsing and searching system. The main challenges are the integrated management of metadata and content, the indexing of the image content, and the design of the browsing and searching interface where various views on the data are kept together. Copyright (C) 2009 Catalin Calistru et al.

Abstract
This paper describes the participation of FEUP, from the University of Porto, in the TREC 2009 Blog Track. FEUP participated in the faceted blog distillation task with work focused on the use of temporal features available in the new TREC Blogs08 collection. The approach presented in this paper uses the temporal information available in most individual posts to amplify (or reduce) each post's score. Blog scores, and subsequent ranks, are obtained by combining individual posts' scores. While preparing the runs, no endeavors were made to identify a priori any temporal differences between the three distinct facets.