Selecting the most relevant variables for anaerobic digestion imbalances : Two case studies

In this study, a wrapper approach was applied to objectively select the most important variables related to two different anaerobic digestion imbalances, acidogenic states and foaming. This feature selection method, implemented in artificial neural networks (ANN), was performed using input and output data from a fully instrumented pilot plant (1 m 3 upflow fixed bed digester). Results for acidogenic states showed that pH, volatile fatty acids, and inflow rate were the most relevant variables. Results for foaming showed that inflow rate and total organic carbon were among the relevant variables, both of which were related to the feed loading of the digester. Because there is not a complete agreement on the causes of foaming, these results highlight the role of digester feeding patterns in the development of foaming

Detection of severe obstructive sleep apnea through voice analysis

tThis paper deals with the potential and limitations of using voice and speech processing to detect Obstruc-tive Sleep Apnea (OSA). An extensive body of voice features has been extracted from patients whopresent various degrees of OSA as well as healthy controls. We analyse the utility of a reduced set offeatures for detecting OSA. We apply various feature selection and reduction schemes (statistical rank-ing, Genetic Algorithms, PCA, LDA) and compare various classifiers (Bayesian Classifiers, kNN, SupportVector Machines, neural networks, Adaboost). S-fold crossvalidation performed on 248 subjects showsthat in the extreme cases (that is, 127 controls and 121 patients with severe OSA) voice alone is able todiscriminate quite well between the presence and absence of OSA. However, this is not the case withmild OSA and healthy snoring patients where voice seems to play a secondary role. We found that thebest classification schemes are achieved using a Genetic Algorithm for feature selection/reduction.

La força pot predir el rendiment i la lesionabilitat al bàsquet professional?

Amb l’objectiu de conèixer les relacions existents entre la força, el rendiment esportiu i la lesionabilitat en un equip masculí de bàsquet professional, es realitza un estudi prospectiu, observacional i descriptiu d’anàlisis d’estadístiques (71 partits), test de mig esquat (n = 7) i patologia lesional, monitoritzant la temporada 09/10, on es relacionen les dades obtingudes de cada jugador referents al rendiment esportiu per partit (valoració estadística), les mitjanes de força, velocitat i potència de cada mesocicle i la lesionabilitat. La tècnica estadística utilitzada ha estat la correlació a partir del paràmetre rho de Spearman. Aquestes correlacions entre força i lesionabilitat mostren que a valors de força més elevats hi ha més lesions: amb 80 kg són molt significatives per a lesions totals (LT) i potència (rho = 0,898; p = 0,006), i significatives per força (rho = 0,823; p = 0,023) i velocitat (rho = 0,774; p = 0,041); la velocitat amb 90 kg es relaciona amb lesions time loss (TL) (rho = 0,878; p = 0,009), i la potència amb 100 kg, amb lesions totals (LT) (rho = 0,805; p = 0,029) i V100 (rho = 0,898; p = 0,006) molt significativament. I la relació entre força i rendiment és significativament negativa en 5 dels 7 mesocicles, és a dir, a menys força, més rendiment. En conclusió, durant l’execució del mig esquat, hi ha valors de força adients per rendir millor i lesionar-se menys: de 800 N a 1.050 N i amb càrregues de 80 kg a 90 kg.

An approach to vision-based station keeping for an unmanned underwater vehicle

This paper presents an automatic vision-based system for UUV station keeping. The vehicle is equipped with a down-looking camera, which provides images of the sea-floor. The station keeping system is based on a feature-based motion detection algorithm, which exploits standard correlation and explicit textural analysis to solve the correspondence problem. A visual map of the area surveyed by the vehicle is constructed to increase the flexibility of the system, allowing the vehicle to position itself when it has lost the reference image. The testing platform is the URIS underwater vehicle. Experimental results demonstrating the behavior of the system on a real environment are presented

Inter-rater agreement and reliability of the COSMIN (COnsensus-based Standards for the selection of health status Measurement Instruments) Checklist

Background: The COSMIN checklist is a tool for evaluating the methodological quality of studies on measurement properties of health-related patient-reported outcomes. The aim of this study is to determine the inter-rater agreement and reliability of each item score of the COSMIN checklist (n = 114). Methods: 75 articles evaluating measurement properties were randomly selected from the bibliographic database compiled by the Patient-Reported Outcome Measurement Group, Oxford, UK. Raters were asked to assess the methodological quality of three articles, using the COSMIN checklist. In a one-way design, percentage agreement and intraclass kappa coefficients or quadratic-weighted kappa coefficients were calculated for each item. Results: 88 raters participated. Of the 75 selected articles, 26 articles were rated by four to six participants, and 49 by two or three participants. Overall, percentage agreement was appropriate (68% was above 80% agreement), and the kappa coefficients for the COSMIN items were low (61% was below 0.40, 6% was above 0.75). Reasons for low inter-rater agreement were need for subjective judgement, and accustom to different standards, terminology and definitions.Conclusions: Results indicated that raters often choose the same response option, but that it is difficult on item level to distinguish between articles. When using the COSMIN checklist in a systematic review, we recommend getting some training and experience, completing it by two independent raters, and reaching consensus on one final rating. Instructions for using the checklist are improved.

Protocol of the COSMIN study: consensus-based standards for the selection of health measurement instruments

Background: Choosing an adequate measurement instrument depends on the proposed use of the instrument, the concept to be measured, the measurement properties (e.g. internal consistency, reproducibility, content and construct validity, responsiveness, and interpretability), the requirements, the burden for subjects, and costs of the available instruments. As far as measurement properties are concerned, there are no sufficiently specific standards for the evaluation of measurement properties of instruments to measure health status, and also no explicit criteria for what constitutes good measurement properties. In this paper we describe the protocol for the COSMIN study, the objective of which is to develop a checklist that contains COnsensus-based Standards for the selection of health Measurement INstruments, including explicit criteria for satisfying these standards. We will focus on evaluative health related patient-reported outcomes (HR-PROs), i.e. patient-reported health measurement instruments used in a longitudinal design as an outcome measure, excluding health care related PROs, such as satisfaction with care or adherence. The COSMIN standards will be made available in the form of an easily applicable checklist.Method: An international Delphi study will be performed to reach consensus on which and how measurement properties should be assessed, and on criteria for good measurement properties. Two sources of input will be used for the Delphi study: (1) a systematic review of properties, standards and criteria of measurement properties found in systematic reviews of measurement instruments, and (2) an additional literature search of methodological articles presenting a comprehensive checklist of standards and criteria. The Delphi study will consist of four (written) Delphi rounds, with approximately 30 expert panel members with different backgrounds in clinical medicine, biostatistics, psychology, and epidemiology. The final checklist will subsequently be field-tested by assessing the inter-rater reproducibility of the checklist.Discussion: Since the study will mainly be anonymous, problems that are commonly encountered in face-to-face group meetings, such as the dominance of certain persons in the communication process, will be avoided. By performing a Delphi study and involving many experts, the likelihood that the checklist will have sufficient credibility to be accepted and implemented will increase.

A survey on transfer-based approaches to MT using feature structures

Aquest treball és una revisió d'alguns sistemes de Traducció Automàtica que segueixen l'estratègia de Transfer i fan servir estructures de trets com a eina de representació. El treball s'integra dins el projecte MLAP-9315, projecte que investiga la reutilització de les especificacions lingüístiques del projecte EUROTRA per estàndards industrials.

Generalized canonical correlation analysis of matrices with different row and column orders

A Method is offered that makes it possible to apply generalized canonicalcorrelations analysis (CANCOR) to two or more matrices of different row and column order. The new method optimizes the generalized canonical correlationanalysis objective by considering only the observed values. This is achieved byemploying selection matrices. We present and discuss fit measures to assessthe quality of the solutions. In a simulation study we assess the performance of our new method and compare it to an existing procedure called GENCOM,proposed by Green and Carroll. We find that our new method outperforms the GENCOM algorithm both with respect to model fit and recovery of the truestructure. Moreover, as our new method does not require any type of iteration itis easier to implement and requires less computation. We illustrate the methodby means of an example concerning the relative positions of the political parties inthe Netherlands based on provincial data.

Adaptive model selection using empirical complexities

Given $n$ independent replicates of a jointly distributed pair $(X,Y)\in {\cal R}^d \times {\cal R}$, we wish to select from a fixed sequence of model classes ${\cal F}_1, {\cal F}_2, \ldots$ a deterministic prediction rule $f: {\cal R}^d \to {\cal R}$ whose risk is small. We investigate the possibility of empirically assessingthe {\em complexity} of each model class, that is, the actual difficulty of the estimation problem within each class. The estimated complexities are in turn used to define an adaptive model selection procedure, which is based on complexity penalized empirical risk.The available data are divided into two parts. The first is used to form an empirical cover of each model class, and the second is used to select a candidate rule from each cover based on empirical risk. The covering radii are determined empirically to optimize a tight upper bound on the estimation error. An estimate is chosen from the list of candidates in order to minimize the sum of class complexity and empirical risk. A distinguishing feature of the approach is that the complexity of each model class is assessed empirically, based on the size of its empirical cover.Finite sample performance bounds are established for the estimates, and these bounds are applied to several non-parametric estimation problems. The estimates are shown to achieve a favorable tradeoff between approximation and estimation error, and to perform as well as if the distribution-dependent complexities of the model classes were known beforehand. In addition, it is shown that the estimate can be consistent,and even possess near optimal rates of convergence, when each model class has an infinite VC or pseudo dimension.For regression estimation with squared loss we modify our estimate to achieve a faster rate of convergence.

Feature Extraction from Whole-Sky Ground-Based Images for Cloud-Type Recognition

Several features that can be extracted from digital images of the sky and that can be useful for cloud-type classification of such images are presented. Some features are statistical measurements of image texture, some are based on the Fourier transform of the image and, finally, others are computed from the image where cloudy pixels are distinguished from clear-sky pixels. The use of the most suitable features in an automatic classification algorithm is also shown and discussed. Both the features and the classifier are developed over images taken by two different camera devices, namely, a total sky imager (TSI) and a whole sky imager (WSC), which are placed in two different areas of the world (Toowoomba, Australia; and Girona, Spain, respectively). The performance of the classifier is assessed by comparing its image classification with an a priori classification carried out by visual inspection of more than 200 images from each camera. The index of agreement is 76% when five different sky conditions are considered: clear, low cumuliform clouds, stratiform clouds (overcast), cirriform clouds, and mottled clouds (altocumulus, cirrocumulus). Discussion on the future directions of this research is also presented, regarding both the use of other features and the use of other classification techniques

Application of fuzzy-rule-based postprocessing to correlation methods in pattern recognition

One of the most important problems in optical pattern recognition by correlation is the appearance of sidelobes in the correlation plane, which causes false alarms. We present a method that eliminate sidelobes of up to a given height if certain conditions are satisfied. The method can be applied to any generalized synthetic discriminant function filter and is capable of rejecting lateral peaks that are even higher than the central correlation. Satisfactory results were obtained in both computer simulations and optical implementation.

Correlation postprocessing-based method for the detection of de focused images

We present a method to detect patterns in defocused scenes by means of a joint transform correlator. We describe analytically the correlation plane, and we also introduce an original procedure to recognize the target by postprocessing the correlation plane. The performance of the methodology when the defocused images are corrupted by additive noise is also considered.

Meta-structure correlation in protein space unveils different selection rules for folded and intrinsically disordered proteins

The number of existing protein sequences spans a very small fraction of sequence space. Natural proteins have overcome a strong negative selective pressure to avoid the formation of insoluble aggregates. Stably folded globular proteins and intrinsically disordered proteins (IDP) use alternative solutions to the aggregation problem. While in globular proteins folding minimizes the access to aggregation prone regions IDPs on average display large exposed contact areas. Here, we introduce the concept of average meta-structure correlation map to analyze sequence space. Using this novel conceptual view we show that representative ensembles of folded and ID proteins show distinct characteristics and responds differently to sequence randomization. By studying the way evolutionary constraints act on IDPs to disable a negative function (aggregation) we might gain insight into the mechanisms by which function - enabling information is encoded in IDPs.

A Robust Multiple Feature Approach To Endpoint Detection In Car Environment Based On Advanced Classifiers

In this paper we propose an endpoint detection system based on the use of several features extracted from each speech frame, followed by a robust classifier (i.e Adaboost and Bagging of decision trees, and a multilayer perceptron) and a finite state automata (FSA). We present results for four different classifiers. The FSA module consisted of a 4-state decision logic that filtered false alarms and false positives. We compare the use of four different classifiers in this task. The look ahead of the method that we propose was of 7 frames, which are the number of frames that maximized the accuracy of the system. The system was tested with real signals recorded inside a car, with signal to noise ratio that ranged from 6 dB to 30dB. Finally we present experimental results demonstrating that the system yields robust endpoint detection.