Multi-scale label interaction to improve object detection and segmentation

The project aims at advancing the state of the art in the use of context information for classification of image and video data. The use of context in the classification of images has been showed of great importance to improve the performance of actual object recognition systems. In our project we proposed the concept of Multi-scale Feature Labels as a general and compact method to exploit the local and global context. The feature extraction from the discriminative probability or classification confidence label field is of great novelty. Moreover the use of a multi-scale representation of the feature labels lead to a compact and efficient description of the context. The goal of the project has been also to provide a general-purpose method and prove its suitability in different image/video analysis problem. The two-year project generated 5 journal publications (plus 2 under submission), 10 conference publications (plus 2 under submission) and one patent (plus 1 pending). Of these publications, a relevant number make use of the main result of this project to improve the results in detection and/or segmentation of objects.

Primera fase del proyecto de creación de una herramienta de ticketing web Ibermática multi-cliente y parametrizable

Primera fase del proyecto de creación de una herramienta de ticketing web Ibermática multi-cliente y parametrizable.

Multi-receptor binding profile of clozapine and olanzapine: a structural study based on the new beta (2) adrenergic receptor template

Schizophrenia is a devastating mental disorder that has a largeimpact on the quality of life for those who are afflicted and isvery costly for families and society.[1] Although the etiology ofschizophrenia is still unknown and no cure has yet beenfound, it is treatable, and pharmacological therapy often producessatisfactory results. Among the various antipsychoticdrugs in use, clozapine is widely recognized as one ofthemost clinically effective agents, even if it elicits significant sideeffects such as metabolic disorders and agranulocytosis. Clozapineand the closely related compound olanzapine are goodexamples ofdrug s with a complex multi-receptor profile ;[2]they have affinities toward serotonin, dopamine, a adrenergic,muscarinic, and histamine receptors, among others.

Comparative gene finding in chicken indicates that we are closing in on the set of multi-exonic widely-expressed human genes

The recent availability of the chicken genome sequence poses the question of whether there are human protein-coding genes conserved in chicken that are currently not included in the human gene catalog. Here, we show, using comparative gene finding followed by experimental verification of exon pairs by RT–PCR, that the addition to the multi-exonic subset of this catalog could be as little as 0.2%, suggesting that we may be closing in on the human gene set. Our protocol, however, has two shortcomings: (i) the bioinformatic screening of the predicted genes, applied to filter out false positives, cannot handle intronless genes; and (ii) the experimental verification could fail to identify expression at a specific developmental time. This highlights the importance of developing methods that could provide a reliable estimate of the number of these two types of genes.

X-chromosome tiling path array detection of copy number variants in patients with chromosome X-linked mental retardation

Background: Aproximately 5–10% of cases of mental retardation in males are due to copy number variations (CNV) on the X chromosome. Novel technologies, such as array comparative genomic hybridization (aCGH), may help to uncover cryptic rearrangements in X-linked mental retardation (XLMR) patients. We have constructed an X-chromosome tiling path array using bacterial artificial chromosomes (BACs) and validated it using samples with cytogenetically defined copy number changes. We have studied 54 patients with idiopathic mental retardation and 20 controls subjects. Results: Known genomic aberrations were reliably detected on the array and eight novel submicroscopic imbalances, likely causative for the mental retardation (MR) phenotype, were detected. Putatively pathogenic rearrangements included three deletions and five duplications (ranging between 82 kb to one Mb), all but two affecting genes previously known to be responsible for XLMR. Additionally, we describe different CNV regions with significant different frequencies in XLMR and control subjects (44% vs. 20%). Conclusion:This tiling path array of the human X chromosome has proven successful for the detection and characterization of known rearrangements and novel CNVs in XLMR patients.

Analysis of the multi-copy gene family FAM90A as a copy number variant in different ethnic backgrounds

Copy number variants contribute extensively to inter-individual genomic differences, but little is known about their inter-population variability and diversity. In a previous study (Bosch et al., 2007; 16:2572-2582), we reported that the primate-specific gene family FAM90A, which accounts for as many as 25 members in the human reference assembly, has expanded the number of FAM90A clusters across the hominoid lineage. Here we examined the copy number variability of FAM90A genes in 260 HapMap samples of European, African, and Asian ancestry, and showed significant inter-population differences (p<0.0001). Based on the recent study of Stranger et al. (2007; 315:848-853), we also explored the correlation between copy number variability and expression levels of the FAM90A gene family. Despite the high genomic variability, we found a low correlation between FAM90A copy number and expression levels, which could be due to the action of independent trans-acting factors. Our results show that FAM90A is highly variable in copy number between individuals and between populations. However, this variability has little impact on gene expression levels, thus highlighting the importance of genomic variability for genes located in regions containing segmental duplications.

Capacity-achieving input covariance for single-user multi-antenna channels

We characterize the capacity-achieving input covariance for multi-antenna channels known instantaneously at the receiver and in distribution at the transmitter. Our characterization, valid for arbitrary numbers of antennas, encompasses both the eigenvectors and the eigenvalues. The eigenvectors are found for zero-mean channels with arbitrary fading profiles and a wide range of correlation and keyhole structures. For the eigenvalues, in turn, we present necessary and sufficient conditions as well as an iterative algorithm that exhibits remarkable properties: universal applicability, robustness and rapid convergence. In addition, we identify channel structures for which an isotropic input achieves capacity.

Supporting life-long competence development using the TENCompetence infrastructure: a first experiment

This paper describes an experiment to explore the effects of the TENCompetence infrastructure for supporting lifelong competence development which is now in development. This infrastructure provides structured, multi-leveled access to learning materials, based upon competences. People can follow their own learning path, supported by a listing of competences and their components, by competence development plans attached to competences and by the possibility to mark elements as complete. We expected the PCM to have an effect on (1) control of participants of their own learning, and (2) appreciation of their learning route, (3) of the learning resources, (4) of their competence development, and (5) of the possibilities of collaboration. In the experiment, 44 Bulgarian teachers followed a distance learning course on a specific teaching methodology for six weeks. Part of them used the TENCompetence infrastructure, part used an infrastructure which was similar, except for the characterizing elements mentioned above. The results showed that in the experimental condition, more people passed the final competence assess-ment, and people felt more in control of their own learning. No differences between the two groups were found on the amount and appreciation of collaboration and on further measures of competence development.

Immigration and contested nation-building : explaining the political salience of immigration in multi-national societies

Multi-national societies present a complex setting for the politics of immigration, as migration’s linguistic, economic and cultural effects may coincide with existing contestation over nationhood between sub-units and the central state. Empirically, though, political actors only sometimes, and in some places, explicitly connect the politics of immigration to the stakes of multi-level politics. With reference to Canada, Belgium and the United Kingdom, this paper examines the conditions under which political leaders link immigration to ongoing debate about governance in multi-national societies. The paper argues that the distribution of policy competencies in the multi-level system is less important for shaping immigration and integration politics than is the perceived impact (positive or negative) on the sub-unit’s societal culture or its power relationship with the center. Immigration and integration are more often politicized where center and sub-unit hold divergent views on migration and its place in national identity.

A multi-objective model for a multi-period distribution management problem

The problems arising in commercial distribution are complex and involve several players and decision levels. One important decision is relatedwith the design of the routes to distribute the products, in an efficient and inexpensive way.This article deals with a complex vehicle routing problem that can beseen as a new extension of the basic vehicle routing problem. The proposed model is a multi-objective combinatorial optimization problemthat considers three objectives and multiple periods, which models in a closer way the real distribution problems. The first objective is costminimization, the second is balancing work levels and the third is amarketing objective. An application of the model on a small example, with5 clients and 3 days, is presented. The results of the model show the complexity of solving multi-objective combinatorial optimization problems and the contradiction between the several distribution management objective.

Asymptotic robustness in multi-sample analysis of multivariate linear relations

Standard methods for the analysis of linear latent variable models oftenrely on the assumption that the vector of observed variables is normallydistributed. This normality assumption (NA) plays a crucial role inassessingoptimality of estimates, in computing standard errors, and in designinganasymptotic chi-square goodness-of-fit test. The asymptotic validity of NAinferences when the data deviates from normality has been calledasymptoticrobustness. In the present paper we extend previous work on asymptoticrobustnessto a general context of multi-sample analysis of linear latent variablemodels,with a latent component of the model allowed to be fixed across(hypothetical)sample replications, and with the asymptotic covariance matrix of thesamplemoments not necessarily finite. We will show that, under certainconditions,the matrix $\Gamma$ of asymptotic variances of the analyzed samplemomentscan be substituted by a matrix $\Omega$ that is a function only of thecross-product moments of the observed variables. The main advantage of thisis thatinferences based on $\Omega$ are readily available in standard softwareforcovariance structure analysis, and do not require to compute samplefourth-order moments. An illustration with simulated data in the context ofregressionwith errors in variables will be presented.

Multi-sample analysis of moment-structures: Asymptotic validity of inferences based on second-order moments

In moment structure analysis with nonnormal data, asymptotic valid inferences require the computation of a consistent (under general distributional assumptions) estimate of the matrix $\Gamma$ of asymptotic variances of sample second--order moments. Such a consistent estimate involves the fourth--order sample moments of the data. In practice, the use of fourth--order moments leads to computational burden and lack of robustness against small samples. In this paper we show that, under certain assumptions, correct asymptotic inferences can be attained when $\Gamma$ is replaced by a matrix $\Omega$ that involves only the second--order moments of the data. The present paper extends to the context of multi--sample analysis of second--order moment structures, results derived in the context of (simple--sample) covariance structure analysis (Satorra and Bentler, 1990). The results apply to a variety of estimation methods and general type of statistics. An example involving a test of equality of means under covariance restrictions illustrates theoretical aspects of the paper.

Simple models for multi-attribute choice with many alternatives: When it does and does not pay to face tradeoffs with binary attributes

Working Paper no longer available. Please contact the author.

Scaled and adjusted restricted tests in multi-sample analysis of moment structures

We extend to score, Wald and difference test statistics the scaled and adjusted corrections to goodness-of-fit test statistics developed in Satorra and Bentler (1988a,b). The theory is framed in the general context of multisample analysis of moment structures, under general conditions on the distribution of observable variables. Computational issues, as well as the relation of the scaled and corrected statistics to the asymptotic robust ones, is discussed. A Monte Carlo study illustrates thecomparative performance in finite samples of corrected score test statistics.

Cumulative dominance and heuristic performance in binary multi-attribute choice

Several studies have reported high performance of simple decision heuristics multi-attribute decision making. In this paper, we focus on situations where attributes are binary and analyze the performance of Deterministic-Elimination-By-Aspects (DEBA) and similar decision heuristics. We consider non-increasing weights and two probabilistic models for the attribute values: one where attribute values are independent Bernoulli randomvariables; the other one where they are binary random variables with inter-attribute positive correlations. Using these models, we show that good performance of DEBA is explained by the presence of cumulative as opposed to simple dominance. We therefore introduce the concepts of cumulative dominance compliance and fully cumulative dominance compliance and show that DEBA satisfies those properties. We derive a lower bound with which cumulative dominance compliant heuristics will choose a best alternative and show that, even with many attributes, this is not small. We also derive an upper bound for the expected loss of fully cumulative compliance heuristics and show that this is moderateeven when the number of attributes is large. Both bounds are independent of the values ofthe weights.