Pattern classification with missing values using multitask learning

In many real-life applications it is important to know how to deal with missing data (incomplete feature vectors). The ability of handling missing data has become a fundamental requirement for pattern classification because inappropriate treatment of missing data may cause large errors or false results on classification. A novel effective neural network is proposed to handle missing values in incomplete patterns with Multitask Learning (MTL). In our approach, a MTL neural network learns in parallel the classification task and the different tasks associated to incomplete features. During the MTL process, missing values are estimated or imputed. Missing data imputation is guided and oriented by the classification task, i.e., imputed values are those that contribute to improve the learning. We prove the robustness of this MTL neural network for handling missing values in classification problems from UCI database.This work will stimulate future works in many directions. Some of them are using different error functions (crossentropy error in discrete tasks, and sum-of-squares error in continuous tasks), adding an EM-model to probability density estimation into the proposed MTL scheme, setting the number of neurons in each subnetwork dynamically using constructive learning, an extensive comparison with other imputation methods, to use this procedure in regression problems, and extending the proposed method to different machines, e.g., Support Vector Machines (SVM)

Plant identification via adaptive combination of transversal filters

For least mean-square (LMS) algorithm applications, it is important to improve the speed of convergence vs the residual error trade-off imposed by the selection of a certain value for the step size. In this paper, we propose to use a mixture approach, adaptively combining two independent LMS filters with large and small step sizes to obtain fast convergence with low misadjustment during stationary periods. Some plant identification simulation examples show the effectiveness of our method when compared to previous variable step size approaches. This combination approach can be straightforwardly extended to other kinds of filters, as it is illustrated with a convex combination of recursive least-squares (RLS) filters.Publicad

¿Son los alumnos capaces de atribuir a los microorganismos algunas transformaciones de los alimentos?

In this paper we investigate if primary school students are able to relate microorganisms with food transformations. The subjects of this study were 343 4th. and 7th. grade children from Lugo (Spain) and the instrument used consisted of four multiple choice questions. The results shed light on the difficulties students have at these levels when they try to apply their ideas to interpret biological phenomena. Finally, we discuss some implications for Science teaching and learning

Some new results in sampling deterministic signals

Whittaker's (or Shannon 's) Sampling Theorem is a well-known interpolation formula that has been extended in many directions. In this paper, we introduce two new formulations: -The first follows Papoulis' Generalized Sampling Expansion for reconstructing a signal from regular samples of N(linear, time-invariant) functionals of it, taking the samples at 1/N the Nyquist rate; but generalizing it for including linear T- periodically time-varying systems. This way is in close relation with works that extend sampling in other directions. -The second generalizes Linden's proof of Kohlenberg's sampling for a bandpass signal, in order to maintain the minimum sampling rate (in the average) and to obtain a separate interpolation of the in-phase and quadrature components of the signal. This follows Grace- Pitt-Brown's theory of bandpass sampling.Peer ReviewedPostprint (published version

Dietary Fat Patterns and Outcomes in Acute Pancreatitis in Spain

Background/Objective: Evidence from basic and clinical studies suggests that unsaturated fatty acids (UFAs) might be relevant mediators of the development of complications in acute pancreatitis (AP). Objective: The aim of this study was to analyze outcomes in patients with AP from regions in Spain with different patterns of dietary fat intake. Materials and Methods: A retrospective analysis was performed with data from 1,655 patients with AP from a Spanish prospective cohort study and regional nutritional data from a Spanish cross-sectional study. Nutritional data considered in the study concern the total lipid consumption, detailing total saturated fatty acids, UFAs and monounsaturated fatty acids (MUFAs) consumption derived from regional data and not from the patient prospective cohort. Two multivariable analysis models were used: (1) a model with the Charlson comorbidity index, sex, alcoholic etiology, and recurrent AP; (2) a model that included these variables plus obesity. Results: In multivariable analysis, patients from regions with high UFA intake had a significantly increased frequency of local complications, persistent organ failure (POF), mortality, and moderate-to-severe disease in the model without obesity and a higher frequency of POF in the model with obesity. Patients from regions with high MUFA intake had significantly more local complications and moderate-to-severe disease; this significance remained for moderate-to-severe disease when obesity was added to the model. Conclusions: Differences in dietary fat patterns could be associated with different outcomes in AP, and dietary fat patterns may be a pre-morbid factor that determines the severity of AP. UFAs, and particulary MUFAs, may influence the pathogenesis of the severity of AP

Aplicación del muestreo enfatizado a la evaluación de transmisiones digitales

The Importance Sampling Technique is a method for reducing the computational effort in Montecarlo simulations for obtaining the relative frequency of an event having a very low probability. While this method is well known in general Operations Research /1//2//3/ and Radar /4//5//6//7//8//9//10/, it has not been considered in digital communication problems. This paper aims to introduce the Importance Sampling concept in this communication context.Peer ReviewedPostprint (published version

Sistemas delta adaptativos con control digital en canales ruidosos

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Further results in designing digital interpolators

One of the two approaches to the design of digital interpolators uses averaged second-order statistics to minimize the mean square interpolation error. This approach has been considered only when the number of samples employed to compute a final interpolated value is even. In the present paper, we generalize systematically this method to consider even or odd numbers of samples in that computation and values of sampling period ratio, with lineal phase non-recursive interpolating filters.Peer ReviewedPostprint (published version

Consensus document on the progression and treatment response criteria in gastroenteropancreatic neuroendocrine tumors

Purpose Gastroenteropancreatic neuroendocrine tumors are a heterogeneous group of low incidence neoplasms characterized by a low proliferative activity and slow growth. Their response to targeted therapies is heterogeneous and often does not lead to tumor shrinkage. Thus, evaluation of the therapeutic response should difer from other kind of tumors. Methods To answer relevant questions about which techniques are best in the assessment of progression or treatment response a RAND/UCLA-based consensus process was implemented. Relevant clinical questions were listed followed by a systematic search of the literature. The expert panel answered all questions with recommendations, combining available evidence and expert opinion. Recommendations were validated through a questionnaire and a participatory meeting. Results Expert recommendations regarding imaging tools for tumor assessment and evaluation of progression were agreed upon. Available imaging techniques were reviewed and recommendations for best patient monitoring practice and the best way to evaluate treatment response were formulated

Designing the quantizing step for a digital generalized sign test detector applied to Radar a first approach

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