Imatge emotiva i irracional de la humanitat en el cinema. Una aplicació pedagògica

El imaginario colectivo actual, reflejado sobre todo en el cine, muestra una profunda transformación de las características esenciales de la humanidad. La racionalidad ha sido desplazada por otros aspectos tradicionalmente relegados a lo irracional, lo animal, lo corporal, como la emotividad, el deseo, las pasiones. La ciencia-ficción y el cine expresan perfectamente esta deriva, pero no son las únicas manifestaciones. Juntamente con algunos ejemplos cinematográficos, este artículo extrae otros provenientes de la filosofía o la literatura modernas, así como de las barbaries sociales contemporáneas -genocidio, colonialismo¿ y alguna otra nota del campo de la neurología científica y la inteligencia artificial.

To select or to weigh: a comparative study of linear combination schemes for superparent-one-dependence estimators

We conduct a large-scale comparative study on linearly combining superparent-one-dependence estimators (SPODEs), a popular family of seminaive Bayesian classifiers. Altogether, 16 model selection and weighing schemes, 58 benchmark data sets, and various statistical tests are employed. This paper's main contributions are threefold. First, it formally presents each scheme's definition, rationale, and time complexity and hence can serve as a comprehensive reference for researchers interested in ensemble learning. Second, it offers bias-variance analysis for each scheme's classification error performance. Third, it identifies effective schemes that meet various needs in practice. This leads to accurate and fast classification algorithms which have an immediate and significant impact on real-world applications. Another important feature of our study is using a variety of statistical tests to evaluate multiple learning methods across multiple data sets.

mGPT: a probabilistic planner based on heuristic search

We describe the version of the GPT planner to be used in the planning competition. This version, called mGPT, solves mdps specified in the ppddllanguage by extracting and using different classes of lower bounds, along with various heuristic-search algorithms. The lower bounds are extracted from deterministic relaxations of the mdp where alternativeprobabilistic effects of an action are mapped into different, independent, deterministic actions. The heuristic-search algorithms, on the other hand, use these lower bounds for focusing the updates and delivering a consistent value function over all states reachable from the initial state with the greedy policy.

Automated identification of minimal myocardial motion for improved image quality on MR angiography at 3 T

OBJECTIVE: Imaging during a period of minimal myocardial motion is of paramount importance for coronary MR angiography (MRA). The objective of our study was to evaluate the utility of FREEZE, a custom-built automated tool for the identification of the period of minimal myocardial motion, in both a moving phantom at 1.5 T and 10 healthy adults (nine men, one woman; mean age, 24.9 years; age range, 21-32 years) at 3 T. CONCLUSION: Quantitative analysis of the moving phantom showed that dimension measurements approached those obtained in the static phantom when using FREEZE. In vitro, vessel sharpness, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were significantly improved when coronary MRA was performed during the software-prescribed period of minimal myocardial motion (p < 0.05). Consistent with these objective findings, image quality assessments by consensus review also improved significantly when using the automated prescription of the period of minimal myocardial motion. The use of FREEZE improves image quality of coronary MRA. Simultaneously, operator dependence can be minimized while the ease of use is improved.

Planning under partial observability by classical replanning: theory and experiments

Planning with partial observability can be formulated as a non-deterministic search problem in belief space. The problem is harder than classical planning as keeping track of beliefs is harder than keeping track of states, and searching for action policies is harder than searching for action sequences. In this work, we develop a framework for partial observability that avoids these limitations and leads to a planner that scales up to larger problems. For this, the class of problems is restricted to those in which 1) the non-unary clauses representing the uncertainty about the initial situation are nvariant, and 2) variables that are hidden in the initial situation do not appear in the body of conditional effects, which are all assumed to be deterministic. We show that such problems can be translated in linear time into equivalent fully observable non-deterministic planning problems, and that an slight extension of this translation renders the problem solvable by means of classical planners. The whole approach is sound and complete provided that in addition, the state-space is connected. Experiments are also reported.

Sistema de community cloud aplicat a la preservació digital personal (personal archiving) PYRAMID

La preservació digital (PD) s'ha convertit en un problema persistent per a tots els que vulguin conservar la seva informació digital, garantir el seu estat i consultar aquest informació en el transcurs del temps. Fins ara només grans institucions amb coneixement expert i eines especialitzades han pogut fer front a aquest problema, però la preservació digital no pot ser abordada per una sola institució o nació. Les biblioteques, arxius i altres institucions de conservació de la memòria comparteixen aquest repte de la mateixa manera que els col•leccionistes i creadors, que ho fan a títol individual.L’objectiu del projecte és crear l'aplicació Pyramid que està concebuda com una eina de suport orientada a l'usuari domèstic (sense coneixements tècnics ni de preservació) per a la preservació a mig i llarg termini de col•leccions digitals, texts i vídeos, tal que funcioni com un antivirus (en BackGround) i preservi la informació sense requerir un cost addicional a l'ordinador i que l'usuari no noti cap molèstia a l'hora de fer les seves tasques diàries

Anàlisi de la incidència de l'esforç d'alta intensitat en la generació d'IncRNA

El present TFM té per objectiu aplicar tècniques d'intel·ligència artificial per analitzar la incidència de l'esforç d'alta intensitat en la generació d'IncRNA.

Hadoop : processament distribuït de gran volum de dades en el núvol d'Apache

Avui en dia es genera un volum increïble de dades de diferents tipus i que provenen de multitud d'orígens. Els sistemes d'emmagatzematge i processament distribuït són els elements tecnològics que fan possible capturar aquest allau de dades i permeten donar-ne un valor a través d'anàlisis diversos. Hadoop, que integra un sistema d'emmagatzematge i processament distribuïts, s'ha convertit en l'estàndard de-facto per a aplicacions que necessiten una gran capacitat d'emmagatzematge, inclús de l'ordre de desenes de PBs. En aquest treball farem un estudi de Hadoop, analitzarem l'eficiència del seu sistema de durabilitat i en proposarem una alternativa.

Comparison and validation of tissue modelization and statistical classification methods in T1-weighted MR brain images.

This paper presents a validation study on statistical nonsupervised brain tissue classification techniques in magnetic resonance (MR) images. Several image models assuming different hypotheses regarding the intensity distribution model, the spatial model and the number of classes are assessed. The methods are tested on simulated data for which the classification ground truth is known. Different noise and intensity nonuniformities are added to simulate real imaging conditions. No enhancement of the image quality is considered either before or during the classification process. This way, the accuracy of the methods and their robustness against image artifacts are tested. Classification is also performed on real data where a quantitative validation compares the methods' results with an estimated ground truth from manual segmentations by experts. Validity of the various classification methods in the labeling of the image as well as in the tissue volume is estimated with different local and global measures. Results demonstrate that methods relying on both intensity and spatial information are more robust to noise and field inhomogeneities. We also demonstrate that partial volume is not perfectly modeled, even though methods that account for mixture classes outperform methods that only consider pure Gaussian classes. Finally, we show that simulated data results can also be extended to real data.