Entorno de juegos para el desarrollo de prácticas de Inteligencia Artificial

En este artículo se presenta una herramienta que permite implantar prácticas de aplicación de técnicas de Inteligencia Artificial utilizando un entorno de juegos. Se describirán las principales herramientas ya existentes para este objetivo, analizando sus ventajas e inconvenientes, y se describirá la herramienta aquí presentada y sus principales características. Por último se mostrarán algunos ejemplos de su uso para la definición de una práctica para la asignatura de Inteligencia Artificial

Empirical agent-based modelling of everyday pro-environmental behaviours at work

We report on agent-based modelling work in the LOCAW project (Low Carbon at Work: Modelling Agents and Organisations to Achieve Transition to a Low Carbon Europe). The project explored the effectiveness of various backcasting scenarios conducted with case study organisations in bringing about pro-environmental change in the workforce in the domains of transport, energy use and waste. The model used qualitative representations of workspaces in formalising each scenario, and decision trees learned from questionnaire responses to represent decision-making. We describe the process by which the decision trees were constructed, noting that the use of decision trees in agent-based models requires particular considerations owing to the potential use of explanatory variables in model dynamics. The results of the modelling in various scenarios emphasise the importance of structural environmental changes in facilitating everyday pro-environmental behaviour, but also show there is a role for psychological variables such as norms, values and efficacy. As such, the topology of social interactions is a potentially important driver, raising the interesting prospect that both workplace geography and organisational hierarchy have a role to play in influencing workplace pro-environmental behaviours

Orthodontics in the era of big data analytics

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149344/1/ocr12279_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149344/2/ocr12279.pd

Identification of a biomarker panel for colorectal cancer diagnosis

<p>Abstract</p> <p>Background</p> <p>Malignancies arising in the large bowel cause the second largest number of deaths from cancer in the Western World. Despite progresses made during the last decades, colorectal cancer remains one of the most frequent and deadly neoplasias in the western countries.</p> <p>Methods</p> <p>A genomic study of human colorectal cancer has been carried out on a total of 31 tumoral samples, corresponding to different stages of the disease, and 33 non-tumoral samples. The study was carried out by hybridisation of the tumour samples against a reference pool of non-tumoral samples using Agilent Human 1A 60-mer oligo microarrays. The results obtained were validated by qRT-PCR. In the subsequent bioinformatics analysis, gene networks by means of Bayesian classifiers, variable selection and bootstrap resampling were built. The consensus among all the induced models produced a hierarchy of dependences and, thus, of variables.</p> <p>Results</p> <p>After an exhaustive process of pre-processing to ensure data quality--lost values imputation, probes quality, data smoothing and intraclass variability filtering--the final dataset comprised a total of 8, 104 probes. Next, a supervised classification approach and data analysis was carried out to obtain the most relevant genes. Two of them are directly involved in cancer progression and in particular in colorectal cancer. Finally, a supervised classifier was induced to classify new unseen samples.</p> <p>Conclusions</p> <p>We have developed a tentative model for the diagnosis of colorectal cancer based on a biomarker panel. Our results indicate that the gene profile described herein can discriminate between non-cancerous and cancerous samples with 94.45% accuracy using different supervised classifiers (AUC values in the range of 0.997 and 0.955).</p

Advances in Computational Social Science and Social Simulation

Aquesta conferència és la celebració conjunta de la "10th Artificial Economics Conference AE", la "10th Conference of the European Social Simulation Association ESSA" i la "1st Simulating the Past to Understand Human History SPUHH".Conferència organitzada pel Laboratory for Socio­-Historical Dynamics Simulation (LSDS-­UAB) de la Universitat Autònoma de Barcelona.Readers will find results of recent research on computational social science and social simulation economics, management, sociology,and history written by leading experts in the field. SOCIAL SIMULATION (former ESSA) conferences constitute annual events which serve as an international platform for the exchange of ideas and discussion of cutting edge research in the field of social simulations, both from the theoretical as well as applied perspective, and the 2014 edition benefits from the cross-fertilization of three different research communities into one single event. The volume consists of 122 articles, corresponding to most of the contributions to the conferences, in three different formats: short abstracts (presentation of work-in-progress research), posters (presentation of models and results), and full papers (presentation of social simulation research including results and discussion). The compilation is completed with indexing lists to help finding articles by title, author and thematic content. We are convinced that this book will serve interested readers as a useful compendium which presents in a nutshell the most recent advances at the frontiers of computational social sciences and social simulation researc