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Motivation Despite RNA-seq reads provide quality scores that represent the probability of calling a correct base, these values are not probabilistically integrated in most alignment algorithms. Based on the quality scores of the reads, we propose to calculate a lower bound of the probability of alignment of any fast alignment algorithm that generates SAM files. This bound is called Fast Bayesian Bound (FBB) and serves as a canonical reference to compare alignment results across different algorithms. This Bayesian Bound intends to provide additional support to the current state-of-the-art aligners, not to replace them. Results We propose a feasible Bayesian bound that uses quality scores of the reads to align them to a genome of reference. Two theorems are provided to efficiently calculate the Bayesian bound that under some conditions becomes the equality. The algorithm reads the SAM files generated by the alignment algorithms using multiple command option values. The program options are mapped into the FBB reference values, and all the aligners can be compared respect to the same accuracy values provided by the FBB. Stranded paired read RNA-seq data was used for evaluation purposes. The errors of the alignments can be calculated based on the information contained in the distance between the pairs given by Theorem 2, and the alignments to the incorrect strand. Most of the algorithms (Bowtie, Bowtie 2, SHRiMP2, Soap 2, Novoalign) provide similar results with subtle variations. Availability and Implementation Current version of the FBB software is provided at https://bitbucket.org/irenerodriguez/fbb. Supplementary information Supplementary data are available at Bioinformatics online.

A chemical detection system made of a gas sensor array and algorithms intended to monitor human activity was tested in a NASA spacecraft cabin simulator. Such a chemical-based monitoring system, if extended to home settings, would allow the autonomous detection of emergency situations, thereby postponing the moving of elderly people to assisted living facilities and improving their quality of life. Moreover, in contrast to other monitoring systems based on wearable sensors or video cameras, a monitoring system based on measuring changes in air composition induced by human activities would be non-invasive and would not raise privacy concerns when installed in homes. The third generation of the JPL sensor array was adapted in a small, compact and portable system and deployed in a spacecraft-like room for four weeks while volunteers were performing daily routines. The system was able to predict the total number of people and the level of activity performed in the room, while detecting unexpectedly high concentrations of volatiles.

We investigate optimal experiment selection to train a classifier on gas sensor arrays to get the maximal possible performance in a limited number of experiments. In gas sensing, while collecting data for a particular sensor array, one has to choose what gas and concentration level is going to be presented in the next experiment. It is an active decision by the operator selecting the experiments and training the classifiers. Can the algorithm be trained sooner rather than later? Can we minimize the costs of collecting the data in terms of the man-hour of the operator and the expenses of the experiment itself? Active control sampling provides a way to deal with the challenge of minimizing the calibration costs and is applicable to any situation where experimental selection is parameterized by an external control variable. Our results indicate that active sampling strategies can only improve a random selection of experiments over a wide range of concentration of gasses. However, random or uninformed selection is fairly close. Additionally, our active sampling methodology reveals that, when there is no prior knowledge about the range of concentrations to which the sensor will be exposed during real operation, sensor must be calibrated over the entire working range, not just high concentrations. In fact, our results show that it is especially important to include low concentrations in the calibration since the lack of these values would dramatically decrease the performance of the system.

This is the author’s version of a work that was accepted for publication in Chemometrics and Intelligent Laboratory Systems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Chemometrics and Intelligent Laboratory Systems, VOL 157, (2016) DOI 10.1016/j.chemolab.2016.07.004, A method for online decorrelation of chemical sensor signals from the e ects of envi- ronmental humidity and temperature variations is proposed. The goal is to improve the accuracy of electronic nose measurements for continuous monitoring by processing data from simultaneous readings of environmental humidity and temperature. The electronic nose setup built for this study included eight metal-oxide sensors, temperature and humid- ity sensors with a wireless communication link to external computer. This wireless electronic nose was used to monitor air for two years in the residence of one of the authors and it col- lected data continuously during 537 days with a sampling rate of 1 samples per second. To estimate the e ects of variations in air humidity and temperature on the chemical sensors signals, we used a standard energy band model for an n-type metal-oxide (MOX) gas sensor. The main assumption of the model is that variations in sensor conductivity can be expressed as a nonlinear function of changes in the semiconductor energy bands in the presence of external humidity and temperature variations. Fitting this model to the collected data, we con rmed that the most statistically signi cant factors are humidity changes and correlated changes of temperature and humidity. This simple model achieves excellent accuracy with a coe cient of determination R2 close to 1. To show how the humidity-temperature correc- tion model works for gas discrimination, we constructed a model for online discrimination among banana, wine and baseline response. This shows that pattern recognition algorithms improve performance and reliability by including the ltered signal of the chemical sensors., This work has been supported by the California Institute for Telecommunications and Information Technology (CALIT2) under Grant Number 2014CSRO 136. JF acknowledges the support of the Marie Curie Actions and the Agency for Business Competitiveness of the Government of Catalonia (ACCI O) for the grant TECSPR15-1-0031; and the Spanish MINECO program, grant TEC2014-59229-R (SIGVOL). RH, TM, and IR-L acknowledge the partial support by 3a Convocatoria de Proyectos de Cooperacion Interuniversitaria UAM{Banco Santander con EEUU. NR would like to acknowledge partial support by ONR grant N000141612252. TM acknowledges CNPq grant 234817/2014- 3 for partial support. We are also thankful to Flavia Huerta who collected data examples during the summer of 2015.

This is the author’s version of a work that was accepted for publication in Pattern Recognition. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition, Vol. 45, Iss. 12, (2012) DOI: 10.1016/j.patcog.2012.06.002, The increasing size and dimensionality of real-world datasets make it necessary to design efficient algorithms not only in the training process but also in the prediction phase. In applications such as credit card fraud detection, the classifier needs to predict an event in 10 ms at most. In these environments the speed of the prediction constraints heavily outweighs the training costs. We propose a new classification method, called a Hierarchical Linear Support Vector Machine (H-LSVM), based on the construction of an oblique decision tree in which the node split is obtained as a Linear Support Vector Machine. Although other methods have been proposed to break the data space down in subregions to speed up Support Vector Machines, the H-LSVM algorithm represents a very simple and efficient model in training but mainly in prediction for large-scale datasets. Only a few hyperplanes need to be evaluated in the prediction step, no kernel computation is required and the tree structure makes parallelization possible. In experiments with medium and large datasets, the H-LSVM reduces the prediction cost considerably while achieving classification results closer to the non-linear SVM than that of the linear case., The authors would like to thank the anonymous reviewers for their comments that help improve the manuscript. I.R.-L. is supported by an FPU Grant from Universidad Autónoma de Madrid, and partially supported by the Universidad Autónoma de Madrid-IIC Chair and TIN2010-21575-C02-01. R.H. acknowledges partial support by ONRN00014-07-1-0741, USARIEM-W81XWH-10-C-0040 (ELINTRIX) and JPL-2012-1455933.

Rigorous and proper linking of financial databases is a necessary step to test trading strategies incorporating multimodal sources of information. This paper proposes a machine learning solution to match companies in heterogeneous financial databases. Our method, named Financial Attribute Selection Distance (FASD), has two stages, each of them corresponding to one of the two interrelated tasks commonly involved in heterogeneous database matching problems: schema matching and entity matching. FASD's schema matching procedure is based on the Kullback-Leibler divergence of string and numeric attributes. FASD's entity matching solution relies on learning a company distance flexible enough to deal with the numeric and string attribute links found by the schema matching algorithm and incorporate different string matching approaches such as edit-based and token-based metrics. The parameters of the distance are optimized using the F-score as cost function. FASD is able to match the joint COMPUSTAT/CRSP and Institutional Brokers' Estimate System (I/B/E/S) databases with a F-score over 0.94 using only a hundred of manually labeled company links., This research is based upon work supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via the Federal Bureau of Investigations, Finance Division. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon.

The concept of pointwise Fisher consistency (or classification calibration) states necessary and sufficient conditions to have Bayes consistency when a classifier minimizes a surrogate loss function instead of the 0-1 loss. We present a family of multiclass hinge loss functions defined by a continuous control parameter. representing the margin of the positive points of a given class. The parameter. allows shifting from classification uncalibrated to classification calibrated loss functions. Though previous results suggest that increasing the margin of positive points has positive effects on the classification model, other approaches have failed to give increasing weight to the positive examples without losing the classification calibration property. Our lambda-based loss function can give unlimited weight to the positive examples without breaking the classification calibration property. Moreover, when embedding these loss functions into the Support Vector Machine's framework (lambda-SVM), the parameter. defines different regions for the Karush-Kuhn-Tucker conditions. A large margin on positive points also facilitates faster convergence of the Sequential Minimal Optimization algorithm, leading to lower training times than other classification calibrated methods. lambda-SVM allows easy implementation, and its practical use in different datasets not only supports our theoretical analysis, but also provides good classification performance and fast training times., The authors acknowledge the referees' comments and suggestions that helped to improve the manuscript. This research is based upon work supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via the Federal Bureau of Investigations, Finance Division. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. I.R-L acknowledges partial support by Spain's grants TIN2013-42351-P (MINECO) and S2013/ICE-2845 CASI-CAM-CM (Comunidad de Madrid). The authors gratefully acknowledge the use of the facilities of Centro de Computacion Cientifica (CCC) at Universidad Autonoma de Madrid.

The dataset includes the acquired time series of a chemical detection platform exposed to different gas conditions in a turbulent wind tunnel. The chemo-sensory elements were sampling directly the environment. In contrast to traditional approaches that include measurement chambers, open sampling systems are sensitive to dispersion mechanisms of gaseous chemical analytes, namely diffusion, turbulence, and advection, making the identification and monitoring of chemical substances more challenging. The sensing platform included 72 metal-oxide gas sensors that were positioned at 6 different locations of the wind tunnel. At each location, 10 distinct chemical gases were released in the wind tunnel, the sensors were evaluated at 5 different operating temperatures, and 3 different wind speeds were generated in the wind tunnel to induce different levels of turbulence. Moreover, each configuration was repeated 20 times, yielding a dataset of 18,000 measurements. The dataset was collected over a period of 16 months. The data is related to "On the performance of gas sensor arrays in open sampling systems using Inhibitory Support Vector Machines", by Vergara et al.[1]. The dataset can be accessed publicly at the UCI repository upon citation of [1]: http://archive.ics.uci.edu/ml/datasets/Gas+sensor+arrays+in+open+sampling+settings., This work has been supported by the California Institute for Telecommunications and Information Technology (CALIT2) under Grant number 2014 CSRO 136.

To address drift in chemical sensing, an extensive dataset was collected over a period of three years. An array of 16 metal-oxide gas sensors was exposed to six different volatile organic compounds at different concentration levels under tightly-controlled operating conditions. Moreover, the generated dataset is suitable to tackle a variety of challenges in chemical sensing such as sensor drift, sensor failure or system calibration. The data is related to "Chemical gas sensor drift compensation using classifier ensembles", by Vergara et al. [1], and "On the calibration of sensor arrays for pattern recognition using the minimal number of experiments", by Rodriguez-Lujan et al. [2] The dataset can be accessed publicly at the UCI repository upon citation of: http://archive.ics.uci.edu/ml/datasets/Gas+Sensor+Array+Drift+Dataset+at+Different+Concentrations., This work has been supported by the California Institute for Telecommunications and Information Technology (CALIT2) under Grant number 2014 CSRO 136.

A chemical detection platform composed of 8 chemo-resistive gas sensors was exposed to turbulent gas mixtures generated naturally in a wind tunnel. The acquired time series of the sensors are provided. The experimental setup was designed to test gas sensors in realistic environments. Traditionally, chemical detection systems based on chemo-resistive sensors include a gas chamber to control the sample air flow and minimize turbulence. Instead, we utilized a wind tunnel with two independent gas sources that generate two gas plumes. The plumes get naturally mixed along a turbulent flow and reproduce the gas concentration fluctuations observed in natural environments. Hence, the gas sensors can capture the spatio-temporal information contained in the gas plumes. The sensor array was exposed to binary mixtures of ethylene with either methane or carbon monoxide. Volatiles were released at four different rates to induce different concentration levels in the vicinity of the sensor array. Each configuration was repeated 6 times, for a total of 180 measurements. The data is related to "Chemical Discrimination in Turbulent Gas Mixtures with MOX Sensors Validated by Gas Chromatography-Mass Spectrometry", by Fonollosa et al. [1]. The dataset can be accessed publicly at the UCI repository upon citation of [1]: http://archive.ics.uci.edu/ml/datasets/Gas+senso+rarray+exposed+to+turbulent+gas+mixtures., This work has been supported by the California Institute for Telecommunications and Information Technology (CALIT2) under Grant Number 2014 CSRO 136.

Chemical detection systems based on chemo-resistive sensors usually include a gas chamber to control the sample air flow and to minimize turbulence. However, such a kind of experimental setup does not reproduce the gas concentration fluctuations observed in natural environments and destroys the spatio-temporal information contained in gas plumes. Aiming at reproducing more realistic environments, we utilize a wind tunnel with two independent gas sources that get naturally mixed along a turbulent flow. For the first time, chemo-resistive gas sensors are exposed to dynamic gas mixtures generated with several concentration levels at the sources. Moreover, the ground truth of gas concentrations at the sensor location was estimated by means of gas chromatography-mass spectrometry. We used a support vector machine as a tool to show that chemo-resistive transduction can be utilized to reliably identify chemical components in dynamic turbulent mixtures, as long as sufficient gas concentration coverage is used. We show that in open sampling systems, training the classifiers only on high concentrations of gases produces less effective classification and that it is important to calibrate the classification method with data at low gas concentrations to achieve optimal performance.

In this paper, we analyze the performance of several well-known pattern recognition and dimensionality reduction techniques when applied to mass-spectrometry data for odor biometric identification. Motivated by the successful results of previous works capturing the odor from other parts of the body, this work attempts to evaluate the feasibility of identifying people by the odor emanated from the hands. By formulating this task according to a machine learning scheme, the problem is identified with a small-sample-size supervised classification problem in which the input data is formed by mass spectrograms from the hand odor of 13 subjects captured in different sessions. The high dimensionality of the data makes it necessary to apply feature selection and extraction techniques together with a simple classifier in order to improve the generalization capabilities of the model. Our experimental results achieve recognition rates over 85% which reveals that there exists discriminatory information in the hand odor and points at body odor as a promising biometric identifier.

This is the author’s version of a work that was accepted for publication in Pattern Recognition Letters. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition Letters, Vol. 32, Iss. 11, (2011) DOI: 10.1016/j.patrec.2011.04.007, We reformulate the Quadratic Programming Feature Selection (QPFS) method in a Kernel space to obtain a vector which maximizes the quadratic objective function of QPFS. We demonstrate that the vector obtained by Kernel Quadratic Programming Feature Selection is equivalent to the Kernel Fisher vector and, therefore, a new interpretation of the Kernel Fisher discriminant analysis is given which provides some computational advantages for highly unbalanced datasets., I.R.-L. is supported by an FPU grant from Universidad Autónoma de Madrid, and partially supported by the Universidad Autónoma de Madrid-IIC Chair and TIN 2010-21575-C02-01. RH was partially supported by Grants ONRN00014-07-1-0741, and US Army Medical and Material Command under contract #W81XWH-10-C-0040 in collaboration with Elintrix, Inc.

Identifying a subset of features that preserves classification accuracy is a problem of growing importance, because of the increasing size and dimensionality of real-world data sets. We propose a new feature selection method, named Quadratic Programming Feature Selection (QPFS), that reduces the task to a quadratic optimization problem. In order to limit the computational complexity of solving the optimization problem, QPFS uses the Nystr¨om method for approximate matrix diagonalization. QPFS is thus capable of dealing with very large data sets, for which the use of other methods is computationally expensive. In experiments with small and medium data sets, the QPFS method leads to classification accuracy similar to that of other successful techniques. For large data sets, QPFS is superior in terms of computational efficiency., I.R.-L. is supported by an FPU grant from Universidad Autónoma de Madrid, and partially supported by the Universidad Autónoma de Madrid-IIC Chair. R.H. acknowledges partial support by ONR N00014-07-1-0741