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
In this manuscript we explore the feasibility of using LWUV-VIS-SWNIR (340 - 1100 nm) spectroscopy to classify Saccharomyces cerevisiae colony structures in YP agar and YPD agar, under different growth conditions, such as: i) no alcohol; ii) 1% (nu/nu) Ethanol; iii) 1% (nu/nu) 1-Propanol; iv) 1% (nu/nu) 1-butanol; v) 1 % (nu/nu) Isopropanol; vi) 1% (nu/nu) (+/-)-1-Phenylethanol; vii) 1% (nu/nu) Isoamyl alcohol; viii) 1% (nu/nu) tert-Amyl alcohol (2-Methyl-2-butanol); and ix) 1% (nu/nu) Amyl alcohol. Results show that LWUV-VISSWNIR spectroscopy has the potential for yeasts metabolic state identification once the spectral signatures of colonies differs from each others, being possible to acheive 100% of classification in UV-VIS and VIS-SWNIR. The UV-VIS region present high discriminant information (350-450 nm), and different responses to UV excitation were obtained. Therefore, high precision is obtained because UV-VIS and VIS-NIR exhibit different kinds of information. In the future, high precision analytical chemistry techniques such as mass spectroscopy and molecular biology transcriptomic studies should be performed in order to understand the detailed cell metabolism and genomic phenomena that characterize the yeast colony state.

Abstract
This paper reports on the design, implementation and evaluation of XwQuest, a Web tool for responding to questionnaires on the Web. The distinctive feature of this tool is an XML definition of the questionnaire that focus on questions and admissible answers while avoiding presentation details. This questionnaire definition is processed on the browser side and converted to an Ajax application. Collected responses are periodically sent back to the server and can be retrieved by researchers and processed on a standard spreadsheet program. The paper details the questionnaire language of XwQuest and a generator that converts them into Ajax applications. Two case studies where XwQuest was actually used with good results are also presented. © 2009 IADIS.

Abstract
Collecting and annotating exemplary cases is a costly and critical task that is required in early stages of any classification process. Reducing labeling cost without degrading accuracy calls for a compromise solution which may be achieved with active learning. Common active learning approaches focus on accuracy and assume the availability of a pre-labeled set of exemplary cases covering all classes to learn. This assumption does not necessarily hold. In this paper we study the capabilities of a new active learning approach, d-Confidence, in rapidly covering the case space when compared to the traditional active learning confidence criterion, when the representativeness assumption is not met.. Experimental results also show that; d-Confidence reduces the number of queries required to achieve complete class coverage and tends to improve or maintain classification error.

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
One of the most studied bioprocesses is fermentation by yeasts. Although this is true, there is still the lack of real-time instrumentation that is capable of providing detailed information on metabolic state of fermentations. In this research we explore the possibility of using UV-VIS-SWNIR spectroscopy as a high-output, non-destructive and multivariate methodology of monitoring beer fermentation. We herein report the implementation of the a fibber optics sensor and the capacity for detecting key parameters by partial least squares regression for biomass, extract, pH and total sugars. Results show that UV-VIS-SWNIR is a robust technique for monitoring beer fermentations, being able to provide detailed information spectroscopic fingerprinting of the process. Calibrations were possible to obtain for all the studied parameters with R2 of 0.85 to 0.94 in the UV-VIS region and 0.95 to 0.97 in the VIS-SWNIR region. This preliminary study allowed to conclude that further improvements in experimental methodology and signal processing may turn this technique into a valuable instrument for detailed metabolic studies in biotechnology.