Relevant Filters Research Articles

Simulation of sunscreen performance

AbstractSunscreens are used to protect the human skin against harmful effects of solar UV radiation. The most important quantity characterizing sunscreen performance is the sun protection factor (SPF). At the stage of development of new sun protection formulations quick and inexpensive methods for estimation of the UV screening performance are highly desirable. The most convenient approach towards this goal is given by computational simulations. Models for the calculation of the SPF employ the same algorithm as used with in vitro SPF measurements, but replace the transmittance measurement by the calculation of the overall absorbance of the UV filters in an irregular sunscreen film. The simulations require a database with quantitative UV extinction spectra of the relevant UV filters as well as a mathematical description of the film irregularity. The simulation algorithm implies also the consideration of photodegradation properties of the UV filters in the sunscreen composition. Besides using such simulations for designing new sunscreen formulations, the calculations can also support the understanding of sunscreen performance in general.

Spectral analysis comparisons of Fourier-theory-based methods and minimum variance (Capon) methods

In recent years, adaptive (data dependent) methods have been introduced into many areas where Fourier spectral analysis has traditionally been used. Although the data-dependent methods are often advanced as being superior to Fourier methods, they do require some finesse in choosing the order of the relevant filters. In performing comparisons, we have found some concerns about the mappings, particularly when related to cases involving many spectral lines or even continuous spectral signals. Using numerical simulations, several comparisons between Fourier transform procedures and minimum variance method (MVM) have been performed. For multiple frequency signals, the MVM resolves most of the frequency content only for filters that have more degrees of freedom than the number of distinct spectral lines in the signal. In the case of Gaussian spectral approximation, MVM will always underestimate the width, and can misappropriate the location of spectral line in some circumstances. Large filters can be used to improve results with multiple frequency signals, but are computationally inefficient. Significant biases can occur when using MVM to study spectral information or echo power from the atmosphere. Artifacts and artificial narrowing of turbulent layers is one such impact.

Design criteria for visualization of energy consumption: A systematic literature review

Visualizing energy consumption is widely considered an important way to motivate end-users to conserve energy. Designing effective visualizations, however, is a non-trivial software design challenge. In particular, there are no clear criteria for designing visualizations of energy consumption for end-users. This paper presents systematic literature review findings from a total of 22 primary studies selected after applying quality and relevance filters. The results were synthesized using Grounded Theory's open coding and constant comparison procedures and led to the emergence of design criteria for visualization as the central theme across all primary studies. The key categories comprising this central theme include: (a) functional criteria, which include information displayed in the visualization, modes of visualization, and visualization techniques, and (b) non-functional criteria, which include hardware and software considerations such as integrality, extensibility and portability. Together, these criteria provide clear guidelines based on research evidence for software engineers and researchers designing visualizations of energy consumption for end-users.

Endogenous attention modulates attentional and motor interference from distractors: evidence from behavioral and electrophysiological results.

Selective visual attention enhances the processing of relevant stimuli and filters out irrelevant stimuli and/or distractors. However, irrelevant information is sometimes processed, as demonstrated by the Simon effect (Simon and Rudell, 1967). We examined whether fully irrelevant distractors (task and target-irrelevant) produce interference (measured as the Simon effect), and whether endogenous orienting modulated this interference. Despite being fully irrelevant, distractors were attentionally coded (as reflected by the distractor-related N2pc component), and interfered with the processing of the target response (as reflected by the target-related lateralized readiness potential component). Distractors’ attentional capture depended on endogenous attention, and their interference with target responses was modulated by both endogenous attention and distractor location repetition. These results demonstrate both endogenous attentional and motor modulations over the Simon effect produced by fully irrelevant distractors.

Similar processes but different environmental filters for soil bacterial and fungal community composition turnover on a broad spatial scale.

Spatial scaling of microorganisms has been demonstrated over the last decade. However, the processes and environmental filters shaping soil microbial community structure on a broad spatial scale still need to be refined and ranked. Here, we compared bacterial and fungal community composition turnovers through a biogeographical approach on the same soil sampling design at a broad spatial scale (area range: 13300 to 31000 km2): i) to examine their spatial structuring; ii) to investigate the relative importance of environmental selection and spatial autocorrelation in determining their community composition turnover; and iii) to identify and rank the relevant environmental filters and scales involved in their spatial variations. Molecular fingerprinting of soil bacterial and fungal communities was performed on 413 soils from four French regions of contrasting environmental heterogeneity (Landes<Burgundy≤Brittany<<South-East) using the systematic grid of French Soil Quality Monitoring Network to evaluate the communities’ composition turnovers. The relative importance of processes and filters was assessed by distance-based redundancy analysis. This study demonstrates significant community composition turnover rates for soil bacteria and fungi, which were dependent on the region. Bacterial and fungal community composition turnovers were mainly driven by environmental selection explaining from 10% to 20% of community composition variations, but spatial variables also explained 3% to 9% of total variance. These variables highlighted significant spatial autocorrelation of both communities unexplained by the environmental variables measured and could partly be explained by dispersal limitations. Although the identified filters and their hierarchy were dependent on the region and organism, selection was systematically based on a common group of environmental variables: pH, trophic resources, texture and land use. Spatial autocorrelation was also important at coarse (80 to 120 km radius) and/or medium (40 to 65 km radius) spatial scales, suggesting dispersal limitations at these scales.

Order-driven distortions in auditory relevance filtering systems

Order-driven distortions in auditory relevance filtering systems

Mass Classification in Mammograms Using Selected Geometry and Texture Features, and a New SVM-Based Feature Selection Method

Masses are the primary indications of breast cancer in mammograms, and it is important to classify them as benign or malignant. Benign and malignant masses differ in geometry and texture characteristics. However, not every geometry and texture feature that is extracted contributes to the improvement of classification accuracy; thus, to select the best features from a set is important. In this paper, we examine the feature selection methods for mass classification. We integrate a support vector machine (SVM)-based recursive feature elimination (SVM-RFE) procedure with a normalized mutual information feature selection (NMIFS) to avoid their singular disadvantages (the redundancy in the selected features of the SVM-RFE and the unoptimized classifier for the NMIFS) while retaining their advantages, and we propose a new feature selection method, which is called the SVM-RFE with an NMIFS filter (SRN). In addition to feature selection, we also study the initialization of mass segmentation. Different initialization methods are investigated, and we propose a fuzzy c-means (FCM) clustering, with spatial constraints as the initialization step. In the experiments, 826 regions of interest (ROIs) from the Digital Database for Screening Mammography were used. All 826 were used in the classification experiments, and 413 ROIs were used in the feature selection experiments. Different feature selection methods, including F-score, Relief, SVM-RFE, SVM-RFE with a minimum redundancy-maximum relevance (mRMR) filter [SVM-RFE (mRMR)], and SRN, were used to select features and to compare mass classification results using the selected features. In the classification experiments, the linear discriminant analysis and the SVM classifiers were investigated. The accuracy that is obtained with the SVM classifier using the selected features obtained by the F-score, Relief, SVM-RFE, SVM-RFE (mRMR), and SRN methods are 88%, 88%, 90%, 91%, and 93%, respectively, with a tenfold cross-validation procedure, and 91%, 89%, 92%, 92%, and 94%, respectively, with a leave-one-out (LOO) scheme. We also compared the performance of the different feature selection methods using the receiver operating characteristic analysis and the areas under the curve (AUCs). The AUCs for the F-score, Relief, SVM-RFE, SVM-RFE (mRMR), and SRN methods are 0.9014, 0.8916, 0.9121, 0.9236, and 0.9439, respectively, with a tenfold cross-validation procedure, and are 0.9312, 0.9178, 0.9324, 0.9413, and 0.9615, respectively, with a LOO scheme. Both the accuracy and AUC values show that the proposed SRN feature selection method has the best performance. In addition to the accuracy and the AUC, we also measured the significance between the two best feature selection methods, i.e., the SVM-RFE (mRMR) and the proposed SRN method. Experimental results show that the proposed SRN method is significantly more accurate than the SVM-RFE (mRMR) (p = 0.011).

On the Markovian assumption in the excursion set approach: the approximation of Markovian Velocities

The excursion set approach uses the statistics of the density field smoothed on a wide range of scales, to gain insight into a number of interesting processes in nonlinear structure formation, such as cluster assembly, merging and clustering. The approach treats the curve defined by the height of the overdensity fluctuation field when changing the smoothing scale as a random walk. The steps of the walks are often assumed to be uncorrelated, so that the walk heights are a Markov process, even though this assumption is known to be inaccurate for physically relevant filters. We develop a model in which the walk steps, rather than heights, are a Markov process, and correlations between steps arise because of nearest neighbour interactions. This model is a particular case of a general class, which we call Markov Velocity models. We show how these can approximate the walks generated by arbitrary power spectra and filters, and, unlike walks with Markov heights, provide a very good approximation to physically relevant models. We define a Markov Velocity Monte Carlo algorithm to generate walks whose first crossing distribution is very similar to that of TopHat-smoothed LCDM walks. Finally, we demonstrate that Markov Velocity walks generically exhibit a simple but realistic form of assembly bias, so we expect them to be useful in the construction of more realistic merger history trees.

A hybrid wrapper–filter approach to detect the source(s) of out-of-control signals in multivariate manufacturing process

With modern data-acquisition equipment and on-line computers used during production, it is now common to monitor several correlated quality characteristics simultaneously in multivariate processes. Multivariate control charts (MCC) are important tools for monitoring multivariate processes. One difficulty encountered with multivariate control charts is the identification of the variable or group of variables that cause an out-of-control signal. Expert knowledge either in combination with wrapper-based supervised classifier or a pre-filter with wrapper are the standard approaches to detect the sources of out-of-control signal. However gathering expert knowledge in source identification is costly and may introduce human error. Individual univariate control charts (UCC) and decomposition of T2 statistics are also used in many cases simultaneously to identify the sources, but these either ignore the correlations between the sources or may take more time with the increase of dimensions. The aim of this paper is to develop a source identification approach that does not need any expert-knowledge and can detect out-of-control signal in less computational complexity. We propose, a hybrid wrapper–filter based source identification approach that hybridizes a Mutual Information (MI) based Maximum Relevance (MR) filter ranking heuristic with an Artificial Neural Network (ANN) based wrapper. The Artificial Neural Network Input Gain Measurement Approximation (ANNIGMA) has been combined with MR (MR-ANNIGMA) to utilize the knowledge about the intrinsic pattern of the quality characteristics computed by the filter for directing the wrapper search process. To compute optimal ANNIGMA score, we also propose a Global MR-ANNIGMA using non-functional relationship between variables which is independent of the derivative of the objective function and has a potential to overcome the local optimization problem of ANN training. The novelty of the proposed approaches is that they combine the advantages of both filter and wrapper approaches and do not require any expert knowledge about the sources of the out-of-control signals. Heuristic score based subset generation process also reduces the search space into polynomial growth which in turns reduces computational time. The proposed approaches were tested by exhaustive experiments using both simulated and real manufacturing data and compared to existing methods including independent filter, wrapper and Multivariate EWMA (MEWMA) methods. The results indicate that the proposed approaches can identify the sources of out-of-control signals more accurately than existing approaches.

Comparing evolutionary algorithms and particle filters for Markerless Human Motion Capture

Markerless Human Motion Capture is the problem of determining the joints’ angles of a three-dimensional articulated body model that best matches current and past observations acquired by video cameras. The problem of Markerless Human Motion Capture is high-dimensional and requires the use of models with a considerable number of degrees of freedom to appropriately adapt to the human anatomy.Particle filters have become the most popular approach for Markerless Human Motion Capture, despite their difficulty to cope with high-dimensional problems. Although several solutions have been proposed to improve their performance, they still suffer from the curse of dimensionality. As a consequence, it is normally required to impose mobility limitations in the body models employed, or to exploit the hierarchical nature of the human skeleton by partitioning the problem into smaller ones.Evolutionary algorithms, though, are powerful methods for solving continuous optimization problems, specially the high-dimensional ones. Yet, few works have tackled Markerless Human Motion Capture using them. This paper evaluates the performance of three of the most competitive algorithms in continuous optimization – Covariance Matrix Adaptation Evolutionary Strategy, Differential Evolution and Particle Swarm Optimization – with two of the most relevant particle filters proposed in the literature, namely the Annealed Particle Filter and the Partitioned Sampling Annealed Particle Filter.The algorithms have been experimentally compared in the public dataset HumanEva-I by employing two body models with different complexities. Our work also analyzes the performance of the algorithms in hierarchical and holistic approaches, i.e., with and without partitioning the search space. Non-parametric tests run on the results have shown that: (i) the evolutionary algorithms employed outperform their particle filter counterparts in all the cases tested; (ii) they can deal with high-dimensional models thus leading to better accuracy; and (iii) the hierarchical strategy surpasses the holistic one.

Assessing the Mechanisms of Misreporting to Filter Questions in Surveys

To avoid asking respondents questions that do not apply to them, surveys often use filter questions that determine routing into follow-up items. Filter questions can be asked in an interleafed format, in which follow-up questions are asked immediately after each relevant filter, or in a grouped format, in which follow-up questions are asked only after multiple filters have been administered. Most previous investigations of filter questions have found that the grouped format collects more affirmative answers than the interleafed format. This result has been taken to mean that respondents in the interleafed format learn to shorten the questionnaire by answering the filter questions negatively. However, this is only one mechanism that could produce the observed differences between the two formats. Acquiescence, the tendency to answer yes to yes/no questions, could also explain the results. We conducted a telephone survey that linked filter question responses to high-quality administrative data to test two hypotheses about the mechanism of the format effect. We find strong support for motivated underreporting and less support for the acquiescence hypothesis. This is the first clear evidence that the grouped format results in more accurate answers to filter questions. However, we also find that the underreporting phenomenon does not always occur. These findings are relevant to all surveys that use multiple filter questions.

On the characterization of noise filters for self-training semi-supervised in nearest neighbor classification

Semi-supervised classification methods have received much attention as suitable tools to tackle training sets with large amounts of unlabeled data and a small quantity of labeled data. Several semi-supervised learning models have been proposed with different assumptions about the characteristics of the input data. Among them, the self-training process has emerged as a simple and effective technique, which does not require any specific hypotheses about the training data. Despite its effectiveness, the self-training algorithm usually make erroneous predictions, mainly at the initial stages, if noisy examples are labeled and incorporated into the training set.Noise filters are commonly used to remove corrupted data in standard classification. In 2005, Li and Zhou proposed the addition of a statistical filter to the self-training process. Nevertheless, in this approach, filtering methods have to deal with a reduced number of labeled instances and the erroneous predictions it may induce. In this work, we analyze the integration of a wide variety of noise filters into the self-training process to distinguish the most relevant features of filters. We will focus on the nearest neighbor rule as a base classifier and ten different noise filters. We provide an extensive analysis of the performance of these filters considering different ratios of labeled data. The results are contrasted with nonparametric statistical tests that allow us to identify relevant filters, and their main characteristics, in the field of semi-supervised learning.

Algorithm for Maneuvering Target Tracking Based on MATLAB

the theory for maneuvering target tracking is significant to national defense and civil application. The filtering algorithm is one of important components in maneuvering target tracking. After the model of the maneuvering target is built, state vectors in the model are forecast and estimated through relevant filtering algorithms. The Unscented Kalman filtering is a novel filtering algorithm specially used for the nonlinear system, which is characterized by easy implementation, good generality, stable performance and so forth. Compared with the traditional Extended Kalman Filtering algorithm, the filtering algorithm can achieve less tracking error and higher tracking precision.

Short Read (Next-Generation) Sequencing

Rapid advances in DNA sequencing technologies have made it increasingly cost-effective to obtain accurate and timely large-scale genomic sequence data on individuals (short read massively parallel or next generation [next-gen]). A next-gen molecular diagnostic approach that has seen rapid deployment in the clinic over the last year is exome sequencing. Whole exome sequencing covers all protein-coding genes in the genome (≈1.1% of genome), and an exome test for a single patient generates ≈6 gigabases (109 bp) of DNA sequence data. A key challenge facing routine use of next-gen data in patient diagnosis and management is data interpretation. What sequence variant findings are relevant to diagnosis (pathogenic mutations)? What sequence variant findings are relevant to clinical care but not necessarily to patient diagnosis (clinically actionable incidental data)? What sequence information should be stored, and where can it be stored? This review provides a tutorial on current approaches to answering these questions. A recent landmark study showed that application of next-gen sequencing to a large cohort of idiopathic dilated cardiomyopathy patients found ≈27% of patients to show mutations of the titin gene, the most complex gene in the genome (363 exons). We use titin in cardiomyopathy as an exemplar for explaining next-gen sequencing approaches and data interpretation. Decreasing sequencing costs and broad dissemination of next-generation (next-gen) equipment and expertise are increasing availability of massively parallel sequencing of patient DNA samples (short read massively parallel or next-gen sequencing).1,2 Most rapidly expanding is exome sequencing, where all protein-coding sequences (exons) are selected from total genomic DNA and selectively sequenced.3 Alternative approaches to next-gen sequencing include targeted sequencing (TS) and whole genome (complete genome) sequencing. Currently, marketed targeted Sanger sequencing panels using traditional individual exon-by-exon sequencing remain expensive and time consuming, and massively parallel next-gen approaches are beginning to supplant …

Extending Sledgehammer with SMT Solvers

Sledgehammer is a component of Isabelle/HOL that employs resolution-based first-order automatic theorem provers (ATPs) to discharge goals arising in interactive proofs. It heuristically selects relevant facts and, if an ATP is successful, produces a snippet that replays the proof in Isabelle. We extended Sledgehammer to invoke satisfiability modulo theories (SMT) solvers as well, exploiting its relevance filter and parallel architecture. The ATPs and SMT solvers nicely complement each other, and Isabelle users are now pleasantly surprised by SMT proofs for problems beyond the ATPs’ reach.

Extending the State-Space Model to Accommodate Missing Values in Responses and Covariates

This article proposes an extended state-space model for accommodating multivariate panel data. The novel aspect of this contribution is an adjustment to the classical model for multiple subjects that allows missingness in the covariates in addition to the responses. Missing covariate data are handled by a second state-space model nested inside the first to represent unobserved exogenous information. Relevant Kalman filter equations are derived, and explicit expressions are provided for both the E- and M-steps of an expectation-maximization (EM) algorithm, to obtain maximum (Gaussian) likelihood estimates of all model parameters. In the presence of missing data, the resulting EM algorithm becomes computationally intractable, but a simplification of the M-step leads to a new procedure that is shown to be an expectation/conditional maximization (ECM) algorithm under exogeneity of the covariates. Simulation studies reveal that the approach appears to be relatively robust to moderate percentages of missing data, even with fewer subjects and time points, and that estimates are generally consistent with the asymptotics. The methodology is applied to a dataset from a published panel study of elderly patients with impaired respiratory function. Forecasted values thus obtained may serve as an “early-warning” mechanism for identifying patients whose lung function is nearing critical levels. Supplementary materials for this article are available online.

Time-domain analysis of frequency dependent inertial wave forces on cylinders

Mono-pile structures are attractive for small well-head platforms and foundation of offshore wind turbines at moderate water depth. Their diameter of several meters makes them prone to simultaneous occurrence of frequency-dependent inertial forces and non-linear drag. The present paper presents a simple time-domain procedure for the inertial force, in which the frequency dependence is represented via a simple explicit time filter on the wave particle acceleration or velocity. The frequency dependence of the inertia coefficient is known analytically as a function of the wave-number, and the relevant range of waves shorter than about six times the diameter typically corresponds to deep water waves. This permits a universal non-dimensional frequency representation, that is converted to rational form to provide the relevant filter equation. Simple time-domain simulations demonstrate the reduction of the resonant part of the response for natural structural frequencies above the dominating wave frequency.

Cosine Modulated Non-Uniform Filter Bank with Improved Computational Efficiency

In this correspondence, a simple design method for multirate near perfect reconstruction (NPR) integer decimated filter bank with non-uniform frequency spacing and linear phase property, that involves optimization of only single parameter, is proposed. It is derived from the uniform cosine modulated filter bank (CMFB) by merging some relevant band pass filters. The design procedure and the structure of the uniform CMFB are mostly preserved in the non-uniform implementation. The parent filter of the filter bank is formulated as an interpolated finite impulse response (IFIR) filter. The IFIR digital filters permit efficient hardware implementations due to less number of multiplier coefficients. Design examples show that the proposed approach provides good performance with less computational complexity at the cost of slight increase in system delay.

Nanocarbonaceous Filters for the Achievement of Highly Sensitive and Selective NO2 Monitoring by Means of Phthalocyanine-Based Resistive Sensors

The most recent results obtained on the development of selective gas sensing devices for nitrogen dioxide (NO2) monitoring associating phthalocyanine-based gas sensor and nanocarbonaceous chemical filter will be described. Since its electrical conductivity is very weakly modulated by reducing gases, copper phthalocyanine (CuPc) is a relevant semiconductor for the development of resistive sensors dedicated to oxidizing pollutants and confers to sensing devices a first level of selectivity. Ozone (O3) being the main interfering pollutant for NO2 monitoring in the context of air quality control, our approach consists in the implementation of a relevant chemical filter highly impervious to O3 and weakly reactive with NO2 placed upstream a CuPc-based sensing device. Because O3 is very reactive with carbon-carbon double bonds, different nanocarbons have been investigated as filter: activated carbons, single and multi-wall carbon nanotubes, nanofibres, nanodiscs. The filtering yields towards the target gases have been for the first time experimentally quantified. We have established the strong influence of the specific surface area of the materials which must be moderate to ensure a selective filtering. By means of graphitization and fluorination treatments, the dangling bonds, the carbonaceous matrix defects and the surface oxygenated groups (SOGs) have been identified as the most active sites of reaction. Amongst all the nanocarbons studied, a mixture of nanocones (30%) and nanodiscs (70%) has exhibited the highest filtering selectivity towards O3 and the best durability. Mechanisms of reaction are proposed and confirmed by complementary characterization techniques (Raman spectroscopy, SEM, EPR, NEXAFS). An original sensing device including carbon nanodiscs as filter and CuPc-based resistive sensor has been developed and exhibits high metrological performances in agreement with the required specifications of air quality control context: high sensitivity in the 0-200 ppb concentration range, high level of repeatability, threshold and resolution close to 10 ppb, total selectivity towards NO2, low sampling time. Strategies to enhance the durability of the filter as well as the sensing device will be finally discussed and the improvements assessed.

Identification of DNA methylation biomarkers from Infinium arrays

Epigenetic modifications of DNA, such as cytosine methylation are differentially abundant in diseases such as cancer. A goal for clinical research is finding sites that are differentially methylated between groups of samples to act as potential biomarkers for disease outcome. However, clinical samples are often limited in availability, represent a heterogeneous collection of cells or are of uncertain clinical class. Array-based methods for identification of methylation provide a cost-effective method to survey a proportion of the methylome at single base resolution. The Illumina Infinium array has become a popular and reliable high throughput method in this field and are proving useful in the identification of biomarkers for disease. Here, we compare a commonly used statistical test with a new intuitive and flexible computational approach to quickly detect differentially methylated sites. The method rapidly identifies and ranks candidate lists with greatest inter-group variability whilst controlling for intra-group variability. Intuitive and biologically relevant filters can be imposed to quickly identify sites and genes of interest.