Thermal/structural Tailoring of Engine Blades (T/STAEBL) User's Manual

The Thermal/Structural Tailoring of Engine Blades (T/STAEBL) system is a family of computer programs executed by a control program. The T/STAEBL system performs design optimizations of cooled, hollow turbine blades and vanes. This manual contains an overview of the system, fundamentals of the data block structure, and detailed descriptions of the inputs required by the optimizer. Additionally, the thermal analysis input requirements are described as well as the inputs required to perform a finite element blade vibrations analysis

OVI Emission in the Halos of Edge-on Spiral Galaxies

We have used the Far Ultraviolet Spectroscopic Explorer to search for OVI 1031.926, 1037.617 A emission in the halos of the edge-on spiral galaxies NGC4631 and NGC891. In NGC4631, we detected OVI in emission toward a soft X-ray bubble above a region containing numerous Halpha arcs and filaments. The line-of-sight component of the motion of the OVI gas appears to match the underlying disk rotation. The observed OVI luminosities can account for 0.2-2% of the total energy input from supernovae (assuming a full OVI emitting halo) and yield mass flux cooling rates between 0.48 and 2.8 M_sun/yr depending on the model used in the derivations. Based on these findings, we believe it is likely that we are seeing cooling, galactic fountain gas. No emission was detected from the halo of NGC891, a galaxy in a direction with considerably high foreground Galactic extinction.Comment: accepted for publication in ApJ, 16 pages including 4 figure

Epileptic Seizure Detection Using a Convolutional Neural Network

The availability of electroencephalogram (EEG) data has opened up the possibility for new interesting applications, such as epileptic seizure detection. The detection of epileptic activity is usually performed by an expert based on the analysis of the EEG data. This paper shows how a convolutional neural network (CNN) can be applied to EEG images for a full and accurate classification. The proposed methodology was applied on images reflecting the amplitude of the EEG data over all electrodes. Two groups are considered: (a) healthy subjects and (b) epileptic subjects. Classification results show that CNN has a potential in the classification of EEG signals, as well as the detection of epileptic seizures by reaching 99.48% of overall classification accuracy

From colonial categories to local culture: Evolving state practices of ethnic enumeration in Oceania, 1965-2014

Numerous scholars have examined how governments in particular times and places have classified their populations by ethnicity, but studies that are both cross-national and longitudinal are rare. Using a unique database of census questionnaires, we examine state practices of ethnic enumeration over a 50-year period (1965–2014) in the 24 countries and areas that comprise Oceania. The region’s extraordinary linguistic and cultural diversity, combined with its complex colonial history and indigenous politics, make it an ideal site for comparative analyses. We find a shift from biological conceptions of difference to a more cultural understanding of group identity, exemplified by a sharp rise in language questions and the decline of race-based inquiries. While local identity labels have largely displaced colonial categories, the imprimatur of previous regimes still lingers, particularly in Melanesia. These shifts in official constructions of ethnoracial differences reflect a gradual lessening of colonial influences on demographic practices

Evaluation in artificial intelligence: From task-oriented to ability-oriented measurement

The final publication is available at Springer via http://dx.doi.org/ 10.1007/s10462-016-9505-7.The evaluation of artificial intelligence systems and components is crucial for the progress of the discipline. In this paper we describe and critically assess the different ways AI systems are evaluated, and the role of components and techniques in these systems. We first focus on the traditional task-oriented evaluation approach. We identify three kinds of evaluation: human discrimination, problem benchmarks and peer confrontation. We describe some of the limitations of the many evaluation schemes and competitions in these three categories, and follow the progression of some of these tests. We then focus on a less customary (and challenging) ability-oriented evaluation approach, where a system is characterised by its (cognitive) abilities, rather than by the tasks it is designed to solve. We discuss several possibilities: the adaptation of cognitive tests used for humans and animals, the development of tests derived from algorithmic information theory or more integrated approaches under the perspective of universal psychometrics. We analyse some evaluation tests from AI that are better positioned for an ability-oriented evaluation and discuss how their problems and limitations can possibly be addressed with some of the tools and ideas that appear within the paper. Finally, we enumerate a series of lessons learnt and generic guidelines to be used when an AI evaluation scheme is under consideration.I thank the organisers of the AEPIA Summer School On Artificial Intelligence, held in September 2014, for giving me the opportunity to give a lecture on 'AI Evaluation'. This paper was born out of and evolved through that lecture. The information about many benchmarks and competitions discussed in this paper have been contrasted with information from and discussions with many people: M. Bedia, A. Cangelosi, C. Dimitrakakis, I. GarcIa-Varea, Katja Hofmann, W. Langdon, E. Messina, S. Mueller, M. Siebers and C. Soares. Figure 4 is courtesy of F. Martinez-Plumed. Finally, I thank the anonymous reviewers, whose comments have helped to significantly improve the balance and coverage of the paper. This work has been partially supported by the EU (FEDER) and the Spanish MINECO under Grants TIN 2013-45732-C4-1-P, TIN 2015-69175-C4-1-R and by Generalitat Valenciana PROMETEOII2015/013.José Hernández-Orallo (2016). Evaluation in artificial intelligence: From task-oriented to ability-oriented measurement. 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In: Messina ER, Madhavan R (eds) Proceedings of the 2007 workshop on performance metrics for intelligent systems, pp 302–303Gulwani S, Hernández-Orallo J, Kitzelmann E, Muggleton SH, Schmid U, Zorn B (2015) Inductive programming meets the real world. Commun ACM 58(11):90–99Hand DJ (2004) Measurement theory and practice. A Hodder Arnold Publication, LondonHernández-Orallo J (2000a) Beyond the Turing test. J Logic Lang Inf 9(4):447–466Hernández-Orallo J (2000b) On the computational measurement of intelligence factors. In: Meystel A (ed) Performance metrics for intelligent systems workshop. National Institute of Standards and Technology, Gaithersburg, pp 1–8Hernández-Orallo J (2000c) Thesis: computational measures of information gain and reinforcement in inference processes. AI Commun 13(1):49–50Hernández-Orallo J (2010) A (hopefully) non-biased universal environment class for measuring intelligence of biological and artificial systems. 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Cogn Syst Res 27:50–74Hernández-Orallo J, Martínez-Plumed F, Schmid U, Siebers M, Dowe DL (2016) Computer models solving intelligence test problems: progress and implications. Artif Intell 230:74–107Herrmann E, Call J, Hernández-Lloreda MV, Hare B, Tomasello M (2007) Humans have evolved specialized skills of social cognition: the cultural intelligence hypothesis. Science 317(5843):1360–1366Hibbard B (2009) Bias and no free lunch in formal measures of intelligence. J Artif Gen Intell 1(1):54–61Hingston P (2010) A new design for a Turing Test for bots. In: 2010 IEEE symposium on computational intelligence and games (CIG), IEEE, pp 345–350Hingston P (2012) Believable bots: can computers play like people?. Springer, New YorkHo TK, Basu M (2002) Complexity measures of supervised classification problems. IEEE Trans Pattern Anal Mach Intell 24(3):289–300Hutter M (2007) Universal algorithmic intelligence: a mathematical top $$\rightarrow$$ → down approach. 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The Making of Racial and Ethnic Categories: Official Statistics Reconsidered

One of the most striking features of the end of the twentieth century was the resurgence of the ethnic question in public debates, both in developing and in developed countries. Between conflicts and wars interpreted from an ethnic perspective (the Balkans and central Africa), nationalist struggles (the Basque country, Quebec and Belgium), and demands for recognition and political representation by new ethnic minorities resulting from immigration, every country is currently affected by what is commonly known as cultural pluralism (Hobsbawm 1993; Dieckhoff 2000; Faist 2009; Simon and Piché 2013). This ‘ethnic renewal’, to coin the expression used to qualify the growing interest for ethnic diversity in the 1960s in the US, is not only driven by a sort of obsession for cultural differences as an explanation for all kinds of social and political phenomenon. It derives from different legacies: from the increasing diversity of the population of countries that have undergone large immigration flows to the long lasting cohabitation of national minorities within modern Nation states, from the history of slavery to the post-colonial era. This resurgence or extension of the salience of ethnicity in most of the societies around the world can be found not only in public discourses, policy-making, scientific literature and popular representations, but also in the pivotal realm of statistics. Indeed, at the turn of century, an increasing number of countries are processing routinely data on ethnicity or race of their population. This is precisely what this book is about: ethnic and racial classifications in official statistics, as a reflection of the representations of population and an interpretation of social dynamics through different lenses