Feature extraction based on semi-supervised kernel Marginal Fisher analysis and its application in bearing fault diagnosis

NASA Astrophysics Data System (ADS)

Jiang, Li; Xuan, Jianping; Shi, Tielin

2013-12-01

Generally, the vibration signals of faulty machinery are non-stationary and nonlinear under complicated operating conditions. Therefore, it is a big challenge for machinery fault diagnosis to extract optimal features for improving classification accuracy. This paper proposes semi-supervised kernel Marginal Fisher analysis (SSKMFA) for feature extraction, which can discover the intrinsic manifold structure of dataset, and simultaneously consider the intra-class compactness and the inter-class separability. Based on SSKMFA, a novel approach to fault diagnosis is put forward and applied to fault recognition of rolling bearings. SSKMFA directly extracts the low-dimensional characteristics from the raw high-dimensional vibration signals, by exploiting the inherent manifold structure of both labeled and unlabeled samples. Subsequently, the optimal low-dimensional features are fed into the simplest K-nearest neighbor (KNN) classifier to recognize different fault categories and severities of bearings. The experimental results demonstrate that the proposed approach improves the fault recognition performance and outperforms the other four feature extraction methods.

Margin-maximizing feature elimination methods for linear and nonlinear kernel-based discriminant functions.

PubMed

Aksu, Yaman; Miller, David J; Kesidis, George; Yang, Qing X

2010-05-01

Feature selection for classification in high-dimensional spaces can improve generalization, reduce classifier complexity, and identify important, discriminating feature "markers." For support vector machine (SVM) classification, a widely used technique is recursive feature elimination (RFE). We demonstrate that RFE is not consistent with margin maximization, central to the SVM learning approach. We thus propose explicit margin-based feature elimination (MFE) for SVMs and demonstrate both improved margin and improved generalization, compared with RFE. Moreover, for the case of a nonlinear kernel, we show that RFE assumes that the squared weight vector 2-norm is strictly decreasing as features are eliminated. We demonstrate this is not true for the Gaussian kernel and, consequently, RFE may give poor results in this case. MFE for nonlinear kernels gives better margin and generalization. We also present an extension which achieves further margin gains, by optimizing only two degrees of freedom--the hyperplane's intercept and its squared 2-norm--with the weight vector orientation fixed. We finally introduce an extension that allows margin slackness. We compare against several alternatives, including RFE and a linear programming method that embeds feature selection within the classifier design. On high-dimensional gene microarray data sets, University of California at Irvine (UCI) repository data sets, and Alzheimer's disease brain image data, MFE methods give promising results.

Spectral identification of melon seeds variety based on k-nearest neighbor and Fisher discriminant analysis

NASA Astrophysics Data System (ADS)

Li, Cuiling; Jiang, Kai; Zhao, Xueguan; Fan, Pengfei; Wang, Xiu; Liu, Chuan

2017-10-01

Impurity of melon seeds variety will cause reductions of melon production and economic benefits of farmers, this research aimed to adopt spectral technology combined with chemometrics methods to identify melon seeds variety. Melon seeds whose varieties were "Yi Te Bai", "Yi Te Jin", "Jing Mi NO.7", "Jing Mi NO.11" and " Yi Li Sha Bai "were used as research samples. A simple spectral system was developed to collect reflective spectral data of melon seeds, including a light source unit, a spectral data acquisition unit and a data processing unit, the detection wavelength range of this system was 200-1100nm with spectral resolution of 0.14 7.7nm. The original reflective spectral data was pre-treated with de-trend (DT), multiple scattering correction (MSC), first derivative (FD), normalization (NOR) and Savitzky-Golay (SG) convolution smoothing methods. Principal Component Analysis (PCA) method was adopted to reduce the dimensions of reflective spectral data and extract principal components. K-nearest neighbour (KNN) and Fisher discriminant analysis (FDA) methods were used to develop discriminant models of melon seeds variety based on PCA. Spectral data pretreatments improved the discriminant effects of KNN and FDA, FDA generated better discriminant results than KNN, both KNN and FDA methods produced discriminant accuracies reaching to 90.0% for validation set. Research results showed that using spectral technology in combination with KNN and FDA modelling methods to identify melon seeds variety was feasible.

Multilevel image recognition using discriminative patches and kernel covariance descriptor

NASA Astrophysics Data System (ADS)

Lu, Le; Yao, Jianhua; Turkbey, Evrim; Summers, Ronald M.

2014-03-01

Computer-aided diagnosis of medical images has emerged as an important tool to objectively improve the performance, accuracy and consistency for clinical workflow. To computerize the medical image diagnostic recognition problem, there are three fundamental problems: where to look (i.e., where is the region of interest from the whole image/volume), image feature description/encoding, and similarity metrics for classification or matching. In this paper, we exploit the motivation, implementation and performance evaluation of task-driven iterative, discriminative image patch mining; covariance matrix based descriptor via intensity, gradient and spatial layout; and log-Euclidean distance kernel for support vector machine, to address these three aspects respectively. To cope with often visually ambiguous image patterns for the region of interest in medical diagnosis, discovery of multilabel selective discriminative patches is desired. Covariance of several image statistics summarizes their second order interactions within an image patch and is proved as an effective image descriptor, with low dimensionality compared with joint statistics and fast computation regardless of the patch size. We extensively evaluate two extended Gaussian kernels using affine-invariant Riemannian metric or log-Euclidean metric with support vector machines (SVM), on two medical image classification problems of degenerative disc disease (DDD) detection on cortical shell unwrapped CT maps and colitis detection on CT key images. The proposed approach is validated with promising quantitative results on these challenging tasks. Our experimental findings and discussion also unveil some interesting insights on the covariance feature composition with or without spatial layout for classification and retrieval, and different kernel constructions for SVM. This will also shed some light on future work using covariance feature and kernel classification for medical image analysis.

Optimal Fisher Discriminant Ratio for an Arbitrary Spatial Light Modulator

NASA Technical Reports Server (NTRS)

Juday, Richard D.

1999-01-01

Optimizing the Fisher ratio is well established in statistical pattern recognition as a means of discriminating between classes. I show how to optimize that ratio for optical correlation intensity by choice of filter on an arbitrary spatial light modulator (SLM). I include the case of additive noise of known power spectral density.

Accurate palm vein recognition based on wavelet scattering and spectral regression kernel discriminant analysis

NASA Astrophysics Data System (ADS)

Elnasir, Selma; Shamsuddin, Siti Mariyam; Farokhi, Sajad

2015-01-01

Palm vein recognition (PVR) is a promising new biometric that has been applied successfully as a method of access control by many organizations, which has even further potential in the field of forensics. The palm vein pattern has highly discriminative features that are difficult to forge because of its subcutaneous position in the palm. Despite considerable progress and a few practical issues, providing accurate palm vein readings has remained an unsolved issue in biometrics. We propose a robust and more accurate PVR method based on the combination of wavelet scattering (WS) with spectral regression kernel discriminant analysis (SRKDA). As the dimension of WS generated features is quite large, SRKDA is required to reduce the extracted features to enhance the discrimination. The results based on two public databases-PolyU Hyper Spectral Palmprint public database and PolyU Multi Spectral Palmprint-show the high performance of the proposed scheme in comparison with state-of-the-art methods. The proposed approach scored a 99.44% identification rate and a 99.90% verification rate [equal error rate (EER)=0.1%] for the hyperspectral database and a 99.97% identification rate and a 99.98% verification rate (EER=0.019%) for the multispectral database.

Local kernel nonparametric discriminant analysis for adaptive extraction of complex structures

NASA Astrophysics Data System (ADS)

Li, Quanbao; Wei, Fajie; Zhou, Shenghan

2017-05-01

The linear discriminant analysis (LDA) is one of popular means for linear feature extraction. It usually performs well when the global data structure is consistent with the local data structure. Other frequently-used approaches of feature extraction usually require linear, independence, or large sample condition. However, in real world applications, these assumptions are not always satisfied or cannot be tested. In this paper, we introduce an adaptive method, local kernel nonparametric discriminant analysis (LKNDA), which integrates conventional discriminant analysis with nonparametric statistics. LKNDA is adept in identifying both complex nonlinear structures and the ad hoc rule. Six simulation cases demonstrate that LKNDA have both parametric and nonparametric algorithm advantages and higher classification accuracy. Quartic unilateral kernel function may provide better robustness of prediction than other functions. LKNDA gives an alternative solution for discriminant cases of complex nonlinear feature extraction or unknown feature extraction. At last, the application of LKNDA in the complex feature extraction of financial market activities is proposed.

Common spatial pattern combined with kernel linear discriminate and generalized radial basis function for motor imagery-based brain computer interface applications

NASA Astrophysics Data System (ADS)

Hekmatmanesh, Amin; Jamaloo, Fatemeh; Wu, Huapeng; Handroos, Heikki; Kilpeläinen, Asko

2018-04-01

Brain Computer Interface (BCI) can be a challenge for developing of robotic, prosthesis and human-controlled systems. This work focuses on the implementation of a common spatial pattern (CSP) base algorithm to detect event related desynchronization patterns. Utilizing famous previous work in this area, features are extracted by filter bank with common spatial pattern (FBCSP) method, and then weighted by a sensitive learning vector quantization (SLVQ) algorithm. In the current work, application of the radial basis function (RBF) as a mapping kernel of linear discriminant analysis (KLDA) method on the weighted features, allows the transfer of data into a higher dimension for more discriminated data scattering by RBF kernel. Afterwards, support vector machine (SVM) with generalized radial basis function (GRBF) kernel is employed to improve the efficiency and robustness of the classification. Averagely, 89.60% accuracy and 74.19% robustness are achieved. BCI Competition III, Iva data set is used to evaluate the algorithm for detecting right hand and foot imagery movement patterns. Results show that combination of KLDA with SVM-GRBF classifier makes 8.9% and 14.19% improvements in accuracy and robustness, respectively. For all the subjects, it is concluded that mapping the CSP features into a higher dimension by RBF and utilization GRBF as a kernel of SVM, improve the accuracy and reliability of the proposed method.

Differential evolution algorithm-based kernel parameter selection for Fukunaga-Koontz Transform subspaces construction

NASA Astrophysics Data System (ADS)

Binol, Hamidullah; Bal, Abdullah; Cukur, Huseyin

2015-10-01

The performance of the kernel based techniques depends on the selection of kernel parameters. That's why; suitable parameter selection is an important problem for many kernel based techniques. This article presents a novel technique to learn the kernel parameters in kernel Fukunaga-Koontz Transform based (KFKT) classifier. The proposed approach determines the appropriate values of kernel parameters through optimizing an objective function constructed based on discrimination ability of KFKT. For this purpose we have utilized differential evolution algorithm (DEA). The new technique overcomes some disadvantages such as high time consumption existing in the traditional cross-validation method, and it can be utilized in any type of data. The experiments for target detection applications on the hyperspectral images verify the effectiveness of the proposed method.

Brain tumor image segmentation using kernel dictionary learning.

PubMed

Jeon Lee; Seung-Jun Kim; Rong Chen; Herskovits, Edward H

2015-08-01

Automated brain tumor image segmentation with high accuracy and reproducibility holds a big potential to enhance the current clinical practice. Dictionary learning (DL) techniques have been applied successfully to various image processing tasks recently. In this work, kernel extensions of the DL approach are adopted. Both reconstructive and discriminative versions of the kernel DL technique are considered, which can efficiently incorporate multi-modal nonlinear feature mappings based on the kernel trick. Our novel discriminative kernel DL formulation allows joint learning of a task-driven kernel-based dictionary and a linear classifier using a K-SVD-type algorithm. The proposed approaches were tested using real brain magnetic resonance (MR) images of patients with high-grade glioma. The obtained preliminary performances are competitive with the state of the art. The discriminative kernel DL approach is seen to reduce computational burden without much sacrifice in performance.

Data-Driven Hierarchical Structure Kernel for Multiscale Part-Based Object Recognition

PubMed Central

Wang, Botao; Xiong, Hongkai; Jiang, Xiaoqian; Zheng, Yuan F.

2017-01-01

Detecting generic object categories in images and videos are a fundamental issue in computer vision. However, it faces the challenges from inter and intraclass diversity, as well as distortions caused by viewpoints, poses, deformations, and so on. To solve object variations, this paper constructs a structure kernel and proposes a multiscale part-based model incorporating the discriminative power of kernels. The structure kernel would measure the resemblance of part-based objects in three aspects: 1) the global similarity term to measure the resemblance of the global visual appearance of relevant objects; 2) the part similarity term to measure the resemblance of the visual appearance of distinctive parts; and 3) the spatial similarity term to measure the resemblance of the spatial layout of parts. In essence, the deformation of parts in the structure kernel is penalized in a multiscale space with respect to horizontal displacement, vertical displacement, and scale difference. Part similarities are combined with different weights, which are optimized efficiently to maximize the intraclass similarities and minimize the interclass similarities by the normalized stochastic gradient ascent algorithm. In addition, the parameters of the structure kernel are learned during the training process with regard to the distribution of the data in a more discriminative way. With flexible part sizes on scale and displacement, it can be more robust to the intraclass variations, poses, and viewpoints. Theoretical analysis and experimental evaluations demonstrate that the proposed multiscale part-based representation model with structure kernel exhibits accurate and robust performance, and outperforms state-of-the-art object classification approaches. PMID:24808345

Kernel-based whole-genome prediction of complex traits: a review.

PubMed

Morota, Gota; Gianola, Daniel

2014-01-01

Prediction of genetic values has been a focus of applied quantitative genetics since the beginning of the 20th century, with renewed interest following the advent of the era of whole genome-enabled prediction. Opportunities offered by the emergence of high-dimensional genomic data fueled by post-Sanger sequencing technologies, especially molecular markers, have driven researchers to extend Ronald Fisher and Sewall Wright's models to confront new challenges. In particular, kernel methods are gaining consideration as a regression method of choice for genome-enabled prediction. Complex traits are presumably influenced by many genomic regions working in concert with others (clearly so when considering pathways), thus generating interactions. Motivated by this view, a growing number of statistical approaches based on kernels attempt to capture non-additive effects, either parametrically or non-parametrically. This review centers on whole-genome regression using kernel methods applied to a wide range of quantitative traits of agricultural importance in animals and plants. We discuss various kernel-based approaches tailored to capturing total genetic variation, with the aim of arriving at an enhanced predictive performance in the light of available genome annotation information. Connections between prediction machines born in animal breeding, statistics, and machine learning are revisited, and their empirical prediction performance is discussed. Overall, while some encouraging results have been obtained with non-parametric kernels, recovering non-additive genetic variation in a validation dataset remains a challenge in quantitative genetics.

Unsupervised Wishart Classfication of Wetlands in Newfoundland, Canada Using Polsar Data Based on Fisher Linear Discriminant Analysis

NASA Astrophysics Data System (ADS)

Mohammadimanesh, F.; Salehi, B.; Mahdianpari, M.; Homayouni, S.

2016-06-01

Polarimetric Synthetic Aperture Radar (PolSAR) imagery is a complex multi-dimensional dataset, which is an important source of information for various natural resources and environmental classification and monitoring applications. PolSAR imagery produces valuable information by observing scattering mechanisms from different natural and man-made objects. Land cover mapping using PolSAR data classification is one of the most important applications of SAR remote sensing earth observations, which have gained increasing attention in the recent years. However, one of the most challenging aspects of classification is selecting features with maximum discrimination capability. To address this challenge, a statistical approach based on the Fisher Linear Discriminant Analysis (FLDA) and the incorporation of physical interpretation of PolSAR data into classification is proposed in this paper. After pre-processing of PolSAR data, including the speckle reduction, the H/α classification is used in order to classify the basic scattering mechanisms. Then, a new method for feature weighting, based on the fusion of FLDA and physical interpretation, is implemented. This method proves to increase the classification accuracy as well as increasing between-class discrimination in the final Wishart classification. The proposed method was applied to a full polarimetric C-band RADARSAT-2 data set from Avalon area, Newfoundland and Labrador, Canada. This imagery has been acquired in June 2015, and covers various types of wetlands including bogs, fens, marshes and shallow water. The results were compared with the standard Wishart classification, and an improvement of about 20% was achieved in the overall accuracy. This method provides an opportunity for operational wetland classification in northern latitude with high accuracy using only SAR polarimetric data.

Fisher Discrimination of Metabolic Changes in Rats Treated with Aspirin and Ibuprofen.

PubMed

Zhang, Jing; Song, Huanchun; Jiang, Shuying; Chen, Zhibin; Tong, Shuhua; Lin, Feiyan; Wen, Congcong; Zhang, Xiuhua; Hu, Lufeng

2017-01-01

Aspirin and ibuprofen are the most frequently prescribed non-steroidal anti-inflammatory drugs in the world. However, both are associated with a variety of toxicities. We applied serum metabonomics and Fisher discrimination for the early diagnosis of its toxic reaction in order to help diagnose these toxicities. A total of 45 rats were randomly divided into Control group, Aspirin group, and Ibuprofen groups. The experiment groups were given intragastric aspirin (15 mg/kg) or ibuprofen (15 mg/kg) for 3 weeks. Liver function tests were performed and blood metabonomics were analyzed by gas chromatography-mass spectrometry. The most important compounds altered were trihydroxybutyric acid and l-alanine in the aspirin group, and acetoacetic acid, l-alanine, and trihydroxybutyric acid in the ibuprofen group. With respect to metabolic profiles, all 3 groups were completely distinct from one another. Fisher discrimination showed that 91.1% of the original grouped cases were correctly classified by the third week. However, only 55.6% of liver function tests were able to classify grouped cases correctly. Trihydroxybutyric acid, l-alanine, and acetoacetic acid were the most significant indicators of altered serum metabolites following intragastric administration of aspirin and ibuprofen in rates. These metabolomic data may be used for classification of aspirin and ibuprofen toxicity. © 2017 S. Karger AG, Basel.

Discriminating oat and groat kernels from other grains using near infrared spectroscopy

USDA-ARS?s Scientific Manuscript database

Oat and groats can be discriminated from other grains such as barley, wheat, rye, and triticale (non-oats) using near infrared spectroscopy. The two instruments tested were the manual version of the ARS-USDA Single Kernel Near Infrared (SKNIR) and the automated QualySense QSorter Explorer high-speed...

Robust Pedestrian Classification Based on Hierarchical Kernel Sparse Representation

PubMed Central

Sun, Rui; Zhang, Guanghai; Yan, Xiaoxing; Gao, Jun

2016-01-01

Vision-based pedestrian detection has become an active topic in computer vision and autonomous vehicles. It aims at detecting pedestrians appearing ahead of the vehicle using a camera so that autonomous vehicles can assess the danger and take action. Due to varied illumination and appearance, complex background and occlusion pedestrian detection in outdoor environments is a difficult problem. In this paper, we propose a novel hierarchical feature extraction and weighted kernel sparse representation model for pedestrian classification. Initially, hierarchical feature extraction based on a CENTRIST descriptor is used to capture discriminative structures. A max pooling operation is used to enhance the invariance of varying appearance. Then, a kernel sparse representation model is proposed to fully exploit the discrimination information embedded in the hierarchical local features, and a Gaussian weight function as the measure to effectively handle the occlusion in pedestrian images. Extensive experiments are conducted on benchmark databases, including INRIA, Daimler, an artificially generated dataset and a real occluded dataset, demonstrating the more robust performance of the proposed method compared to state-of-the-art pedestrian classification methods. PMID:27537888

Robust Pedestrian Classification Based on Hierarchical Kernel Sparse Representation.

PubMed

Sun, Rui; Zhang, Guanghai; Yan, Xiaoxing; Gao, Jun

2016-08-16

Vision-based pedestrian detection has become an active topic in computer vision and autonomous vehicles. It aims at detecting pedestrians appearing ahead of the vehicle using a camera so that autonomous vehicles can assess the danger and take action. Due to varied illumination and appearance, complex background and occlusion pedestrian detection in outdoor environments is a difficult problem. In this paper, we propose a novel hierarchical feature extraction and weighted kernel sparse representation model for pedestrian classification. Initially, hierarchical feature extraction based on a CENTRIST descriptor is used to capture discriminative structures. A max pooling operation is used to enhance the invariance of varying appearance. Then, a kernel sparse representation model is proposed to fully exploit the discrimination information embedded in the hierarchical local features, and a Gaussian weight function as the measure to effectively handle the occlusion in pedestrian images. Extensive experiments are conducted on benchmark databases, including INRIA, Daimler, an artificially generated dataset and a real occluded dataset, demonstrating the more robust performance of the proposed method compared to state-of-the-art pedestrian classification methods.

LZW-Kernel: fast kernel utilizing variable length code blocks from LZW compressors for protein sequence classification.

PubMed

Filatov, Gleb; Bauwens, Bruno; Kertész-Farkas, Attila

2018-05-07

Bioinformatics studies often rely on similarity measures between sequence pairs, which often pose a bottleneck in large-scale sequence analysis. Here, we present a new convolutional kernel function for protein sequences called the LZW-Kernel. It is based on code words identified with the Lempel-Ziv-Welch (LZW) universal text compressor. The LZW-Kernel is an alignment-free method, it is always symmetric, is positive, always provides 1.0 for self-similarity and it can directly be used with Support Vector Machines (SVMs) in classification problems, contrary to normalized compression distance (NCD), which often violates the distance metric properties in practice and requires further techniques to be used with SVMs. The LZW-Kernel is a one-pass algorithm, which makes it particularly plausible for big data applications. Our experimental studies on remote protein homology detection and protein classification tasks reveal that the LZW-Kernel closely approaches the performance of the Local Alignment Kernel (LAK) and the SVM-pairwise method combined with Smith-Waterman (SW) scoring at a fraction of the time. Moreover, the LZW-Kernel outperforms the SVM-pairwise method when combined with BLAST scores, which indicates that the LZW code words might be a better basis for similarity measures than local alignment approximations found with BLAST. In addition, the LZW-Kernel outperforms n-gram based mismatch kernels, hidden Markov model based SAM and Fisher kernel, and protein family based PSI-BLAST, among others. Further advantages include the LZW-Kernel's reliance on a simple idea, its ease of implementation, and its high speed, three times faster than BLAST and several magnitudes faster than SW or LAK in our tests. LZW-Kernel is implemented as a standalone C code and is a free open-source program distributed under GPLv3 license and can be downloaded from https://github.com/kfattila/LZW-Kernel. akerteszfarkas@hse.ru. Supplementary data are available at Bioinformatics Online.

Dimensional feature weighting utilizing multiple kernel learning for single-channel talker location discrimination using the acoustic transfer function.

PubMed

Takashima, Ryoichi; Takiguchi, Tetsuya; Ariki, Yasuo

2013-02-01

This paper presents a method for discriminating the location of the sound source (talker) using only a single microphone. In a previous work, the single-channel approach for discriminating the location of the sound source was discussed, where the acoustic transfer function from a user's position is estimated by using a hidden Markov model of clean speech in the cepstral domain. In this paper, each cepstral dimension of the acoustic transfer function is newly weighted, in order to obtain the cepstral dimensions having information that is useful for classifying the user's position. Then, this paper proposes a feature-weighting method for the cepstral parameter using multiple kernel learning, defining the base kernels for each cepstral dimension of the acoustic transfer function. The user's position is trained and classified by support vector machine. The effectiveness of this method has been confirmed by sound source (talker) localization experiments performed in different room environments.

Subject-based feature extraction by using fisher WPD-CSP in brain-computer interfaces.

PubMed

Yang, Banghua; Li, Huarong; Wang, Qian; Zhang, Yunyuan

2016-06-01

Feature extraction of electroencephalogram (EEG) plays a vital role in brain-computer interfaces (BCIs). In recent years, common spatial pattern (CSP) has been proven to be an effective feature extraction method. However, the traditional CSP has disadvantages of requiring a lot of input channels and the lack of frequency information. In order to remedy the defects of CSP, wavelet packet decomposition (WPD) and CSP are combined to extract effective features. But WPD-CSP method considers less about extracting specific features that are fitted for the specific subject. So a subject-based feature extraction method using fisher WPD-CSP is proposed in this paper. The idea of proposed method is to adapt fisher WPD-CSP to each subject separately. It mainly includes the following six steps: (1) original EEG signals from all channels are decomposed into a series of sub-bands using WPD; (2) average power values of obtained sub-bands are computed; (3) the specified sub-bands with larger values of fisher distance according to average power are selected for that particular subject; (4) each selected sub-band is reconstructed to be regarded as a new EEG channel; (5) all new EEG channels are used as input of the CSP and a six-dimensional feature vector is obtained by the CSP. The subject-based feature extraction model is so formed; (6) the probabilistic neural network (PNN) is used as the classifier and the classification accuracy is obtained. Data from six subjects are processed by the subject-based fisher WPD-CSP, the non-subject-based fisher WPD-CSP and WPD-CSP, respectively. Compared with non-subject-based fisher WPD-CSP and WPD-CSP, the results show that the proposed method yields better performance (sensitivity: 88.7±0.9%, and specificity: 91±1%) and the classification accuracy from subject-based fisher WPD-CSP is increased by 6-12% and 14%, respectively. The proposed subject-based fisher WPD-CSP method can not only remedy disadvantages of CSP by WPD but also discriminate

Modeling adaptive kernels from probabilistic phylogenetic trees.

PubMed

Nicotra, Luca; Micheli, Alessio

2009-01-01

Modeling phylogenetic interactions is an open issue in many computational biology problems. In the context of gene function prediction we introduce a class of kernels for structured data leveraging on a hierarchical probabilistic modeling of phylogeny among species. We derive three kernels belonging to this setting: a sufficient statistics kernel, a Fisher kernel, and a probability product kernel. The new kernels are used in the context of support vector machine learning. The kernels adaptivity is obtained through the estimation of the parameters of a tree structured model of evolution using as observed data phylogenetic profiles encoding the presence or absence of specific genes in a set of fully sequenced genomes. We report results obtained in the prediction of the functional class of the proteins of the budding yeast Saccharomyces cerevisae which favorably compare to a standard vector based kernel and to a non-adaptive tree kernel function. A further comparative analysis is performed in order to assess the impact of the different components of the proposed approach. We show that the key features of the proposed kernels are the adaptivity to the input domain and the ability to deal with structured data interpreted through a graphical model representation.

Discriminant analysis for fast multiclass data classification through regularized kernel function approximation.

PubMed

Ghorai, Santanu; Mukherjee, Anirban; Dutta, Pranab K

2010-06-01

In this brief we have proposed the multiclass data classification by computationally inexpensive discriminant analysis through vector-valued regularized kernel function approximation (VVRKFA). VVRKFA being an extension of fast regularized kernel function approximation (FRKFA), provides the vector-valued response at single step. The VVRKFA finds a linear operator and a bias vector by using a reduced kernel that maps a pattern from feature space into the low dimensional label space. The classification of patterns is carried out in this low dimensional label subspace. A test pattern is classified depending on its proximity to class centroids. The effectiveness of the proposed method is experimentally verified and compared with multiclass support vector machine (SVM) on several benchmark data sets as well as on gene microarray data for multi-category cancer classification. The results indicate the significant improvement in both training and testing time compared to that of multiclass SVM with comparable testing accuracy principally in large data sets. Experiments in this brief also serve as comparison of performance of VVRKFA with stratified random sampling and sub-sampling.

Sepsis mortality prediction with the Quotient Basis Kernel.

PubMed

Ribas Ripoll, Vicent J; Vellido, Alfredo; Romero, Enrique; Ruiz-Rodríguez, Juan Carlos

2014-05-01

This paper presents an algorithm to assess the risk of death in patients with sepsis. Sepsis is a common clinical syndrome in the intensive care unit (ICU) that can lead to severe sepsis, a severe state of septic shock or multi-organ failure. The proposed algorithm may be implemented as part of a clinical decision support system that can be used in combination with the scores deployed in the ICU to improve the accuracy, sensitivity and specificity of mortality prediction for patients with sepsis. In this paper, we used the Simplified Acute Physiology Score (SAPS) for ICU patients and the Sequential Organ Failure Assessment (SOFA) to build our kernels and algorithms. In the proposed method, we embed the available data in a suitable feature space and use algorithms based on linear algebra, geometry and statistics for inference. We present a simplified version of the Fisher kernel (practical Fisher kernel for multinomial distributions), as well as a novel kernel that we named the Quotient Basis Kernel (QBK). These kernels are used as the basis for mortality prediction using soft-margin support vector machines. The two new kernels presented are compared against other generative kernels based on the Jensen-Shannon metric (centred, exponential and inverse) and other widely used kernels (linear, polynomial and Gaussian). Clinical relevance is also evaluated by comparing these results with logistic regression and the standard clinical prediction method based on the initial SAPS score. As described in this paper, we tested the new methods via cross-validation with a cohort of 400 test patients. The results obtained using our methods compare favourably with those obtained using alternative kernels (80.18% accuracy for the QBK) and the standard clinical prediction method, which are based on the basal SAPS score or logistic regression (71.32% and 71.55%, respectively). The QBK presented a sensitivity and specificity of 79.34% and 83.24%, which outperformed the other kernels

Feature Extraction of Electronic Nose Signals Using QPSO-Based Multiple KFDA Signal Processing

PubMed Central

Wen, Tailai; Huang, Daoyu; Lu, Kun; Deng, Changjian; Zeng, Tanyue; Yu, Song; He, Zhiyi

2018-01-01

The aim of this research was to enhance the classification accuracy of an electronic nose (E-nose) in different detecting applications. During the learning process of the E-nose to predict the types of different odors, the prediction accuracy was not quite satisfying because the raw features extracted from sensors’ responses were regarded as the input of a classifier without any feature extraction processing. Therefore, in order to obtain more useful information and improve the E-nose’s classification accuracy, in this paper, a Weighted Kernels Fisher Discriminant Analysis (WKFDA) combined with Quantum-behaved Particle Swarm Optimization (QPSO), i.e., QWKFDA, was presented to reprocess the original feature matrix. In addition, we have also compared the proposed method with quite a few previously existing ones including Principal Component Analysis (PCA), Locality Preserving Projections (LPP), Fisher Discriminant Analysis (FDA) and Kernels Fisher Discriminant Analysis (KFDA). Experimental results proved that QWKFDA is an effective feature extraction method for E-nose in predicting the types of wound infection and inflammable gases, which shared much higher classification accuracy than those of the contrast methods. PMID:29382146

Feature Extraction of Electronic Nose Signals Using QPSO-Based Multiple KFDA Signal Processing.

PubMed

Wen, Tailai; Yan, Jia; Huang, Daoyu; Lu, Kun; Deng, Changjian; Zeng, Tanyue; Yu, Song; He, Zhiyi

2018-01-29

The aim of this research was to enhance the classification accuracy of an electronic nose (E-nose) in different detecting applications. During the learning process of the E-nose to predict the types of different odors, the prediction accuracy was not quite satisfying because the raw features extracted from sensors' responses were regarded as the input of a classifier without any feature extraction processing. Therefore, in order to obtain more useful information and improve the E-nose's classification accuracy, in this paper, a Weighted Kernels Fisher Discriminant Analysis (WKFDA) combined with Quantum-behaved Particle Swarm Optimization (QPSO), i.e., QWKFDA, was presented to reprocess the original feature matrix. In addition, we have also compared the proposed method with quite a few previously existing ones including Principal Component Analysis (PCA), Locality Preserving Projections (LPP), Fisher Discriminant Analysis (FDA) and Kernels Fisher Discriminant Analysis (KFDA). Experimental results proved that QWKFDA is an effective feature extraction method for E-nose in predicting the types of wound infection and inflammable gases, which shared much higher classification accuracy than those of the contrast methods.

SEMI-SUPERVISED OBJECT RECOGNITION USING STRUCTURE KERNEL

PubMed Central

Wang, Botao; Xiong, Hongkai; Jiang, Xiaoqian; Ling, Fan

2013-01-01

Object recognition is a fundamental problem in computer vision. Part-based models offer a sparse, flexible representation of objects, but suffer from difficulties in training and often use standard kernels. In this paper, we propose a positive definite kernel called “structure kernel”, which measures the similarity of two part-based represented objects. The structure kernel has three terms: 1) the global term that measures the global visual similarity of two objects; 2) the part term that measures the visual similarity of corresponding parts; 3) the spatial term that measures the spatial similarity of geometric configuration of parts. The contribution of this paper is to generalize the discriminant capability of local kernels to complex part-based object models. Experimental results show that the proposed kernel exhibit higher accuracy than state-of-art approaches using standard kernels. PMID:23666108

Kernel-Based Relevance Analysis with Enhanced Interpretability for Detection of Brain Activity Patterns

PubMed Central

Alvarez-Meza, Andres M.; Orozco-Gutierrez, Alvaro; Castellanos-Dominguez, German

2017-01-01

We introduce Enhanced Kernel-based Relevance Analysis (EKRA) that aims to support the automatic identification of brain activity patterns using electroencephalographic recordings. EKRA is a data-driven strategy that incorporates two kernel functions to take advantage of the available joint information, associating neural responses to a given stimulus condition. Regarding this, a Centered Kernel Alignment functional is adjusted to learning the linear projection that best discriminates the input feature set, optimizing the required free parameters automatically. Our approach is carried out in two scenarios: (i) feature selection by computing a relevance vector from extracted neural features to facilitating the physiological interpretation of a given brain activity task, and (ii) enhanced feature selection to perform an additional transformation of relevant features aiming to improve the overall identification accuracy. Accordingly, we provide an alternative feature relevance analysis strategy that allows improving the system performance while favoring the data interpretability. For the validation purpose, EKRA is tested in two well-known tasks of brain activity: motor imagery discrimination and epileptic seizure detection. The obtained results show that the EKRA approach estimates a relevant representation space extracted from the provided supervised information, emphasizing the salient input features. As a result, our proposal outperforms the state-of-the-art methods regarding brain activity discrimination accuracy with the benefit of enhanced physiological interpretation about the task at hand. PMID:29056897

Color model and method for video fire flame and smoke detection using Fisher linear discriminant

NASA Astrophysics Data System (ADS)

Wei, Yuan; Jie, Li; Jun, Fang; Yongming, Zhang

2013-02-01

Video fire detection is playing an increasingly important role in our life. But recent research is often based on a traditional RGB color model used to analyze the flame, which may be not the optimal color space for fire recognition. It is worse when we research smoke simply using gray images instead of color ones. We clarify the importance of color information for fire detection. We present a fire discriminant color (FDC) model for flame or smoke recognition based on color images. The FDC models aim to unify fire color image representation and fire recognition task into one framework. With the definition of between-class scatter matrices and within-class scatter matrices of Fisher linear discriminant, the proposed models seek to obtain one color-space-transform matrix and a discriminate projection basis vector by maximizing the ratio of these two scatter matrices. First, an iterative basic algorithm is designed to get one-component color space transformed from RGB. Then, a general algorithm is extended to generate three-component color space for further improvement. Moreover, we propose a method for video fire detection based on the models using the kNN classifier. To evaluate the recognition performance, we create a database including flame, smoke, and nonfire images for training and testing. The test experiments show that the proposed model achieves a flame verification rate receiver operating characteristic (ROC I) of 97.5% at a false alarm rate (FAR) of 1.06% and a smoke verification rate (ROC II) of 91.5% at a FAR of 1.2%, and lots of fire video experiments demonstrate that our method reaches a high accuracy for fire recognition.

After "Fisher": Academic Review and Judicial Scrutiny

ERIC Educational Resources Information Center

La Noue, George R.

2013-01-01

This article describes the outcomes of the case "Fisher v. University of Texas at Austin," in which the plaintiff had accused the University of Texas (UT) of racial discrimination in the admission process. The author believes that the ruling of the court in this case makes it harder to hide race-based measures used in college admissions.…

Discriminative Projection Selection Based Face Image Hashing

NASA Astrophysics Data System (ADS)

Karabat, Cagatay; Erdogan, Hakan

Face image hashing is an emerging method used in biometric verification systems. In this paper, we propose a novel face image hashing method based on a new technique called discriminative projection selection. We apply the Fisher criterion for selecting the rows of a random projection matrix in a user-dependent fashion. Moreover, another contribution of this paper is to employ a bimodal Gaussian mixture model at the quantization step. Our simulation results on three different databases demonstrate that the proposed method has superior performance in comparison to previously proposed random projection based methods.

Kernel-Based Discriminant Techniques for Educational Placement

ERIC Educational Resources Information Center

Lin, Miao-hsiang; Huang, Su-yun; Chang, Yuan-chin

2004-01-01

This article considers the problem of educational placement. Several discriminant techniques are applied to a data set from a survey project of science ability. A profile vector for each student consists of five science-educational indicators. The students are intended to be placed into three reference groups: advanced, regular, and remedial.…

Centered Kernel Alignment Enhancing Neural Network Pretraining for MRI-Based Dementia Diagnosis

PubMed Central

Cárdenas-Peña, David; Collazos-Huertas, Diego; Castellanos-Dominguez, German

2016-01-01

Dementia is a growing problem that affects elderly people worldwide. More accurate evaluation of dementia diagnosis can help during the medical examination. Several methods for computer-aided dementia diagnosis have been proposed using resonance imaging scans to discriminate between patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI) and healthy controls (NC). Nonetheless, the computer-aided diagnosis is especially challenging because of the heterogeneous and intermediate nature of MCI. We address the automated dementia diagnosis by introducing a novel supervised pretraining approach that takes advantage of the artificial neural network (ANN) for complex classification tasks. The proposal initializes an ANN based on linear projections to achieve more discriminating spaces. Such projections are estimated by maximizing the centered kernel alignment criterion that assesses the affinity between the resonance imaging data kernel matrix and the label target matrix. As a result, the performed linear embedding allows accounting for features that contribute the most to the MCI class discrimination. We compare the supervised pretraining approach to two unsupervised initialization methods (autoencoders and Principal Component Analysis) and against the best four performing classification methods of the 2014 CADDementia challenge. As a result, our proposal outperforms all the baselines (7% of classification accuracy and area under the receiver-operating-characteristic curve) at the time it reduces the class biasing. PMID:27148392

Structured Kernel Dictionary Learning with Correlation Constraint for Object Recognition.

PubMed

Wang, Zhengjue; Wang, Yinghua; Liu, Hongwei; Zhang, Hao

2017-06-21

In this paper, we propose a new discriminative non-linear dictionary learning approach, called correlation constrained structured kernel KSVD, for object recognition. The objective function for dictionary learning contains a reconstructive term and a discriminative term. In the reconstructive term, signals are implicitly non-linearly mapped into a space, where a structured kernel dictionary, each sub-dictionary of which lies in the span of the mapped signals from the corresponding class, is established. In the discriminative term, by analyzing the classification mechanism, the correlation constraint is proposed in kernel form, constraining the correlations between different discriminative codes, and restricting the coefficient vectors to be transformed into a feature space, where the features are highly correlated inner-class and nearly independent between-classes. The objective function is optimized by the proposed structured kernel KSVD. During the classification stage, the specific form of the discriminative feature is needless to be known, while the inner product of the discriminative feature with kernel matrix embedded is available, and is suitable for a linear SVM classifier. Experimental results demonstrate that the proposed approach outperforms many state-of-the-art dictionary learning approaches for face, scene and synthetic aperture radar (SAR) vehicle target recognition.

Online selective kernel-based temporal difference learning.

PubMed

Chen, Xingguo; Gao, Yang; Wang, Ruili

2013-12-01

In this paper, an online selective kernel-based temporal difference (OSKTD) learning algorithm is proposed to deal with large scale and/or continuous reinforcement learning problems. OSKTD includes two online procedures: online sparsification and parameter updating for the selective kernel-based value function. A new sparsification method (i.e., a kernel distance-based online sparsification method) is proposed based on selective ensemble learning, which is computationally less complex compared with other sparsification methods. With the proposed sparsification method, the sparsified dictionary of samples is constructed online by checking if a sample needs to be added to the sparsified dictionary. In addition, based on local validity, a selective kernel-based value function is proposed to select the best samples from the sample dictionary for the selective kernel-based value function approximator. The parameters of the selective kernel-based value function are iteratively updated by using the temporal difference (TD) learning algorithm combined with the gradient descent technique. The complexity of the online sparsification procedure in the OSKTD algorithm is O(n). In addition, two typical experiments (Maze and Mountain Car) are used to compare with both traditional and up-to-date O(n) algorithms (GTD, GTD2, and TDC using the kernel-based value function), and the results demonstrate the effectiveness of our proposed algorithm. In the Maze problem, OSKTD converges to an optimal policy and converges faster than both traditional and up-to-date algorithms. In the Mountain Car problem, OSKTD converges, requires less computation time compared with other sparsification methods, gets a better local optima than the traditional algorithms, and converges much faster than the up-to-date algorithms. In addition, OSKTD can reach a competitive ultimate optima compared with the up-to-date algorithms.

Implementing Kernel Methods Incrementally by Incremental Nonlinear Projection Trick.

PubMed

Kwak, Nojun

2016-05-20

Recently, the nonlinear projection trick (NPT) was introduced enabling direct computation of coordinates of samples in a reproducing kernel Hilbert space. With NPT, any machine learning algorithm can be extended to a kernel version without relying on the so called kernel trick. However, NPT is inherently difficult to be implemented incrementally because an ever increasing kernel matrix should be treated as additional training samples are introduced. In this paper, an incremental version of the NPT (INPT) is proposed based on the observation that the centerization step in NPT is unnecessary. Because the proposed INPT does not change the coordinates of the old data, the coordinates obtained by INPT can directly be used in any incremental methods to implement a kernel version of the incremental methods. The effectiveness of the INPT is shown by applying it to implement incremental versions of kernel methods such as, kernel singular value decomposition, kernel principal component analysis, and kernel discriminant analysis which are utilized for problems of kernel matrix reconstruction, letter classification, and face image retrieval, respectively.

Palmprint and Face Multi-Modal Biometric Recognition Based on SDA-GSVD and Its Kernelization

PubMed Central

Jing, Xiao-Yuan; Li, Sheng; Li, Wen-Qian; Yao, Yong-Fang; Lan, Chao; Lu, Jia-Sen; Yang, Jing-Yu

2012-01-01

When extracting discriminative features from multimodal data, current methods rarely concern themselves with the data distribution. In this paper, we present an assumption that is consistent with the viewpoint of discrimination, that is, a person's overall biometric data should be regarded as one class in the input space, and his different biometric data can form different Gaussians distributions, i.e., different subclasses. Hence, we propose a novel multimodal feature extraction and recognition approach based on subclass discriminant analysis (SDA). Specifically, one person's different bio-data are treated as different subclasses of one class, and a transformed space is calculated, where the difference among subclasses belonging to different persons is maximized, and the difference within each subclass is minimized. Then, the obtained multimodal features are used for classification. Two solutions are presented to overcome the singularity problem encountered in calculation, which are using PCA preprocessing, and employing the generalized singular value decomposition (GSVD) technique, respectively. Further, we provide nonlinear extensions of SDA based multimodal feature extraction, that is, the feature fusion based on KPCA-SDA and KSDA-GSVD. In KPCA-SDA, we first apply Kernel PCA on each single modal before performing SDA. While in KSDA-GSVD, we directly perform Kernel SDA to fuse multimodal data by applying GSVD to avoid the singular problem. For simplicity two typical types of biometric data are considered in this paper, i.e., palmprint data and face data. Compared with several representative multimodal biometrics recognition methods, experimental results show that our approaches outperform related multimodal recognition methods and KSDA-GSVD achieves the best recognition performance. PMID:22778600

Palmprint and face multi-modal biometric recognition based on SDA-GSVD and its kernelization.

PubMed

Jing, Xiao-Yuan; Li, Sheng; Li, Wen-Qian; Yao, Yong-Fang; Lan, Chao; Lu, Jia-Sen; Yang, Jing-Yu

2012-01-01

When extracting discriminative features from multimodal data, current methods rarely concern themselves with the data distribution. In this paper, we present an assumption that is consistent with the viewpoint of discrimination, that is, a person's overall biometric data should be regarded as one class in the input space, and his different biometric data can form different Gaussians distributions, i.e., different subclasses. Hence, we propose a novel multimodal feature extraction and recognition approach based on subclass discriminant analysis (SDA). Specifically, one person's different bio-data are treated as different subclasses of one class, and a transformed space is calculated, where the difference among subclasses belonging to different persons is maximized, and the difference within each subclass is minimized. Then, the obtained multimodal features are used for classification. Two solutions are presented to overcome the singularity problem encountered in calculation, which are using PCA preprocessing, and employing the generalized singular value decomposition (GSVD) technique, respectively. Further, we provide nonlinear extensions of SDA based multimodal feature extraction, that is, the feature fusion based on KPCA-SDA and KSDA-GSVD. In KPCA-SDA, we first apply Kernel PCA on each single modal before performing SDA. While in KSDA-GSVD, we directly perform Kernel SDA to fuse multimodal data by applying GSVD to avoid the singular problem. For simplicity two typical types of biometric data are considered in this paper, i.e., palmprint data and face data. Compared with several representative multimodal biometrics recognition methods, experimental results show that our approaches outperform related multimodal recognition methods and KSDA-GSVD achieves the best recognition performance.

Correlated Topic Vector for Scene Classification.

PubMed

Wei, Pengxu; Qin, Fei; Wan, Fang; Zhu, Yi; Jiao, Jianbin; Ye, Qixiang

2017-07-01

Scene images usually involve semantic correlations, particularly when considering large-scale image data sets. This paper proposes a novel generative image representation, correlated topic vector, to model such semantic correlations. Oriented from the correlated topic model, correlated topic vector intends to naturally utilize the correlations among topics, which are seldom considered in the conventional feature encoding, e.g., Fisher vector, but do exist in scene images. It is expected that the involvement of correlations can increase the discriminative capability of the learned generative model and consequently improve the recognition accuracy. Incorporated with the Fisher kernel method, correlated topic vector inherits the advantages of Fisher vector. The contributions to the topics of visual words have been further employed by incorporating the Fisher kernel framework to indicate the differences among scenes. Combined with the deep convolutional neural network (CNN) features and Gibbs sampling solution, correlated topic vector shows great potential when processing large-scale and complex scene image data sets. Experiments on two scene image data sets demonstrate that correlated topic vector improves significantly the deep CNN features, and outperforms existing Fisher kernel-based features.

Improved object optimal synthetic description, modeling, learning, and discrimination by GEOGINE computational kernel

NASA Astrophysics Data System (ADS)

Fiorini, Rodolfo A.; Dacquino, Gianfranco

2005-03-01

GEOGINE (GEOmetrical enGINE), a state-of-the-art OMG (Ontological Model Generator) based on n-D Tensor Invariants for n-Dimensional shape/texture optimal synthetic representation, description and learning, was presented in previous conferences elsewhere recently. Improved computational algorithms based on the computational invariant theory of finite groups in Euclidean space and a demo application is presented. Progressive model automatic generation is discussed. GEOGINE can be used as an efficient computational kernel for fast reliable application development and delivery in advanced biomedical engineering, biometric, intelligent computing, target recognition, content image retrieval, data mining technological areas mainly. Ontology can be regarded as a logical theory accounting for the intended meaning of a formal dictionary, i.e., its ontological commitment to a particular conceptualization of the world object. According to this approach, "n-D Tensor Calculus" can be considered a "Formal Language" to reliably compute optimized "n-Dimensional Tensor Invariants" as specific object "invariant parameter and attribute words" for automated n-Dimensional shape/texture optimal synthetic object description by incremental model generation. The class of those "invariant parameter and attribute words" can be thought as a specific "Formal Vocabulary" learned from a "Generalized Formal Dictionary" of the "Computational Tensor Invariants" language. Even object chromatic attributes can be effectively and reliably computed from object geometric parameters into robust colour shape invariant characteristics. As a matter of fact, any highly sophisticated application needing effective, robust object geometric/colour invariant attribute capture and parameterization features, for reliable automated object learning and discrimination can deeply benefit from GEOGINE progressive automated model generation computational kernel performance. Main operational advantages over previous

Anytime query-tuned kernel machine classifiers via Cholesky factorization

NASA Technical Reports Server (NTRS)

DeCoste, D.

2002-01-01

We recently demonstrated 2 to 64-fold query-time speedups of Support Vector Machine and Kernel Fisher classifiers via a new computational geometry method for anytime output bounds (DeCoste,2002). This new paper refines our approach in two key ways. First, we introduce a simple linear algebra formulation based on Cholesky factorization, yielding simpler equations and lower computational overhead. Second, this new formulation suggests new methods for achieving additional speedups, including tuning on query samples. We demonstrate effectiveness on benchmark datasets.

Evidence-based Kernels: Fundamental Units of Behavioral Influence

PubMed Central

Biglan, Anthony

2008-01-01

This paper describes evidence-based kernels, fundamental units of behavioral influence that appear to underlie effective prevention and treatment for children, adults, and families. A kernel is a behavior–influence procedure shown through experimental analysis to affect a specific behavior and that is indivisible in the sense that removing any of its components would render it inert. Existing evidence shows that a variety of kernels can influence behavior in context, and some evidence suggests that frequent use or sufficient use of some kernels may produce longer lasting behavioral shifts. The analysis of kernels could contribute to an empirically based theory of behavioral influence, augment existing prevention or treatment efforts, facilitate the dissemination of effective prevention and treatment practices, clarify the active ingredients in existing interventions, and contribute to efficiently developing interventions that are more effective. Kernels involve one or more of the following mechanisms of behavior influence: reinforcement, altering antecedents, changing verbal relational responding, or changing physiological states directly. The paper describes 52 of these kernels, and details practical, theoretical, and research implications, including calling for a national database of kernels that influence human behavior. PMID:18712600

Scuba: scalable kernel-based gene prioritization.

PubMed

Zampieri, Guido; Tran, Dinh Van; Donini, Michele; Navarin, Nicolò; Aiolli, Fabio; Sperduti, Alessandro; Valle, Giorgio

2018-01-25

The uncovering of genes linked to human diseases is a pressing challenge in molecular biology and precision medicine. This task is often hindered by the large number of candidate genes and by the heterogeneity of the available information. Computational methods for the prioritization of candidate genes can help to cope with these problems. In particular, kernel-based methods are a powerful resource for the integration of heterogeneous biological knowledge, however, their practical implementation is often precluded by their limited scalability. We propose Scuba, a scalable kernel-based method for gene prioritization. It implements a novel multiple kernel learning approach, based on a semi-supervised perspective and on the optimization of the margin distribution. Scuba is optimized to cope with strongly unbalanced settings where known disease genes are few and large scale predictions are required. Importantly, it is able to efficiently deal both with a large amount of candidate genes and with an arbitrary number of data sources. As a direct consequence of scalability, Scuba integrates also a new efficient strategy to select optimal kernel parameters for each data source. We performed cross-validation experiments and simulated a realistic usage setting, showing that Scuba outperforms a wide range of state-of-the-art methods. Scuba achieves state-of-the-art performance and has enhanced scalability compared to existing kernel-based approaches for genomic data. This method can be useful to prioritize candidate genes, particularly when their number is large or when input data is highly heterogeneous. The code is freely available at https://github.com/gzampieri/Scuba .

A trace ratio maximization approach to multiple kernel-based dimensionality reduction.

PubMed

Jiang, Wenhao; Chung, Fu-lai

2014-01-01

Most dimensionality reduction techniques are based on one metric or one kernel, hence it is necessary to select an appropriate kernel for kernel-based dimensionality reduction. Multiple kernel learning for dimensionality reduction (MKL-DR) has been recently proposed to learn a kernel from a set of base kernels which are seen as different descriptions of data. As MKL-DR does not involve regularization, it might be ill-posed under some conditions and consequently its applications are hindered. This paper proposes a multiple kernel learning framework for dimensionality reduction based on regularized trace ratio, termed as MKL-TR. Our method aims at learning a transformation into a space of lower dimension and a corresponding kernel from the given base kernels among which some may not be suitable for the given data. The solutions for the proposed framework can be found based on trace ratio maximization. The experimental results demonstrate its effectiveness in benchmark datasets, which include text, image and sound datasets, for supervised, unsupervised as well as semi-supervised settings. Copyright © 2013 Elsevier Ltd. All rights reserved.

Kernel-based Linux emulation for Plan 9.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Minnich, Ronald G.

2010-09-01

CNKemu is a kernel-based system for the 9k variant of the Plan 9 kernel. It is designed to provide transparent binary support for programs compiled for IBM's Compute Node Kernel (CNK) on the Blue Gene series of supercomputers. This support allows users to build applications with the standard Blue Gene toolchain, including C++ and Fortran compilers. While the CNK is not Linux, IBM designed the CNK so that the user interface has much in common with the Linux 2.0 system call interface. The Plan 9 CNK emulator hence provides the foundation of kernel-based Linux system call support on Plan 9.more » In this paper we discuss cnkemu's implementation and some of its more interesting features, such as the ability to easily intermix Plan 9 and Linux system calls.« less

Kernel-aligned multi-view canonical correlation analysis for image recognition

NASA Astrophysics Data System (ADS)

Su, Shuzhi; Ge, Hongwei; Yuan, Yun-Hao

2016-09-01

Existing kernel-based correlation analysis methods mainly adopt a single kernel in each view. However, only a single kernel is usually insufficient to characterize nonlinear distribution information of a view. To solve the problem, we transform each original feature vector into a 2-dimensional feature matrix by means of kernel alignment, and then propose a novel kernel-aligned multi-view canonical correlation analysis (KAMCCA) method on the basis of the feature matrices. Our proposed method can simultaneously employ multiple kernels to better capture the nonlinear distribution information of each view, so that correlation features learned by KAMCCA can have well discriminating power in real-world image recognition. Extensive experiments are designed on five real-world image datasets, including NIR face images, thermal face images, visible face images, handwritten digit images, and object images. Promising experimental results on the datasets have manifested the effectiveness of our proposed method.

Local coding based matching kernel method for image classification.

PubMed

Song, Yan; McLoughlin, Ian Vince; Dai, Li-Rong

2014-01-01

This paper mainly focuses on how to effectively and efficiently measure visual similarity for local feature based representation. Among existing methods, metrics based on Bag of Visual Word (BoV) techniques are efficient and conceptually simple, at the expense of effectiveness. By contrast, kernel based metrics are more effective, but at the cost of greater computational complexity and increased storage requirements. We show that a unified visual matching framework can be developed to encompass both BoV and kernel based metrics, in which local kernel plays an important role between feature pairs or between features and their reconstruction. Generally, local kernels are defined using Euclidean distance or its derivatives, based either explicitly or implicitly on an assumption of Gaussian noise. However, local features such as SIFT and HoG often follow a heavy-tailed distribution which tends to undermine the motivation behind Euclidean metrics. Motivated by recent advances in feature coding techniques, a novel efficient local coding based matching kernel (LCMK) method is proposed. This exploits the manifold structures in Hilbert space derived from local kernels. The proposed method combines advantages of both BoV and kernel based metrics, and achieves a linear computational complexity. This enables efficient and scalable visual matching to be performed on large scale image sets. To evaluate the effectiveness of the proposed LCMK method, we conduct extensive experiments with widely used benchmark datasets, including 15-Scenes, Caltech101/256, PASCAL VOC 2007 and 2011 datasets. Experimental results confirm the effectiveness of the relatively efficient LCMK method.

Hadamard Kernel SVM with applications for breast cancer outcome predictions.

PubMed

Jiang, Hao; Ching, Wai-Ki; Cheung, Wai-Shun; Hou, Wenpin; Yin, Hong

2017-12-21

Breast cancer is one of the leading causes of deaths for women. It is of great necessity to develop effective methods for breast cancer detection and diagnosis. Recent studies have focused on gene-based signatures for outcome predictions. Kernel SVM for its discriminative power in dealing with small sample pattern recognition problems has attracted a lot attention. But how to select or construct an appropriate kernel for a specified problem still needs further investigation. Here we propose a novel kernel (Hadamard Kernel) in conjunction with Support Vector Machines (SVMs) to address the problem of breast cancer outcome prediction using gene expression data. Hadamard Kernel outperform the classical kernels and correlation kernel in terms of Area under the ROC Curve (AUC) values where a number of real-world data sets are adopted to test the performance of different methods. Hadamard Kernel SVM is effective for breast cancer predictions, either in terms of prognosis or diagnosis. It may benefit patients by guiding therapeutic options. Apart from that, it would be a valuable addition to the current SVM kernel families. We hope it will contribute to the wider biology and related communities.

Gradient-based adaptation of general gaussian kernels.

PubMed

Glasmachers, Tobias; Igel, Christian

2005-10-01

Gradient-based optimizing of gaussian kernel functions is considered. The gradient for the adaptation of scaling and rotation of the input space is computed to achieve invariance against linear transformations. This is done by using the exponential map as a parameterization of the kernel parameter manifold. By restricting the optimization to a constant trace subspace, the kernel size can be controlled. This is, for example, useful to prevent overfitting when minimizing radius-margin generalization performance measures. The concepts are demonstrated by training hard margin support vector machines on toy data.

Identity Recognition Algorithm Using Improved Gabor Feature Selection of Gait Energy Image

NASA Astrophysics Data System (ADS)

Chao, LIANG; Ling-yao, JIA; Dong-cheng, SHI

2017-01-01

This paper describes an effective gait recognition approach based on Gabor features of gait energy image. In this paper, the kernel Fisher analysis combined with kernel matrix is proposed to select dominant features. The nearest neighbor classifier based on whitened cosine distance is used to discriminate different gait patterns. The approach proposed is tested on the CASIA and USF gait databases. The results show that our approach outperforms other state of gait recognition approaches in terms of recognition accuracy and robustness.

Gabor-based kernel PCA with fractional power polynomial models for face recognition.

PubMed

Liu, Chengjun

2004-05-01

This paper presents a novel Gabor-based kernel Principal Component Analysis (PCA) method by integrating the Gabor wavelet representation of face images and the kernel PCA method for face recognition. Gabor wavelets first derive desirable facial features characterized by spatial frequency, spatial locality, and orientation selectivity to cope with the variations due to illumination and facial expression changes. The kernel PCA method is then extended to include fractional power polynomial models for enhanced face recognition performance. A fractional power polynomial, however, does not necessarily define a kernel function, as it might not define a positive semidefinite Gram matrix. Note that the sigmoid kernels, one of the three classes of widely used kernel functions (polynomial kernels, Gaussian kernels, and sigmoid kernels), do not actually define a positive semidefinite Gram matrix either. Nevertheless, the sigmoid kernels have been successfully used in practice, such as in building support vector machines. In order to derive real kernel PCA features, we apply only those kernel PCA eigenvectors that are associated with positive eigenvalues. The feasibility of the Gabor-based kernel PCA method with fractional power polynomial models has been successfully tested on both frontal and pose-angled face recognition, using two data sets from the FERET database and the CMU PIE database, respectively. The FERET data set contains 600 frontal face images of 200 subjects, while the PIE data set consists of 680 images across five poses (left and right profiles, left and right half profiles, and frontal view) with two different facial expressions (neutral and smiling) of 68 subjects. The effectiveness of the Gabor-based kernel PCA method with fractional power polynomial models is shown in terms of both absolute performance indices and comparative performance against the PCA method, the kernel PCA method with polynomial kernels, the kernel PCA method with fractional power

Time-frequency optimization for discrimination between imagination of right and left hand movements based on two bipolar electroencephalography channels

NASA Astrophysics Data System (ADS)

Yang, Yuan; Chevallier, Sylvain; Wiart, Joe; Bloch, Isabelle

2014-12-01

To enforce a widespread use of efficient and easy to use brain-computer interfaces (BCIs), the inter-subject robustness should be increased and the number of electrodes should be reduced. These two key issues are addressed in this contribution, proposing a novel method to identify subject-specific time-frequency characteristics with a minimal number of electrodes. In this method, two alternative criteria, time-frequency discrimination factor ( TFDF) and F score, are proposed to evaluate the discriminative power of time-frequency regions. Distinct from classical measures (e.g., Fisher criterion, r 2 coefficient), the TFDF is based on the neurophysiologic phenomena, on which the motor imagery BCI paradigm relies, rather than only from statistics. F score is based on the popular Fisher's discriminant and purely data driven; however, it differs from traditional measures since it provides a simple and effective measure for quantifying the discriminative power of a multi-dimensional feature vector. The proposed method is tested on BCI competition IV datasets IIa and IIb for discriminating right and left hand motor imagery. Compared to state-of-the-art methods, our method based on both criteria led to comparable or even better classification results, while using fewer electrodes (i.e., only two bipolar channels, C3 and C4). This work indicates that time-frequency optimization can not only improve the classification performance but also contribute to reducing the number of electrodes required in motor imagery BCIs.

A Kernel-based Lagrangian method for imperfectly-mixed chemical reactions

NASA Astrophysics Data System (ADS)

Schmidt, Michael J.; Pankavich, Stephen; Benson, David A.

2017-05-01

Current Lagrangian (particle-tracking) algorithms used to simulate diffusion-reaction equations must employ a certain number of particles to properly emulate the system dynamics-particularly for imperfectly-mixed systems. The number of particles is tied to the statistics of the initial concentration fields of the system at hand. Systems with shorter-range correlation and/or smaller concentration variance require more particles, potentially limiting the computational feasibility of the method. For the well-known problem of bimolecular reaction, we show that using kernel-based, rather than Dirac delta, particles can significantly reduce the required number of particles. We derive the fixed width of a Gaussian kernel for a given reduced number of particles that analytically eliminates the error between kernel and Dirac solutions at any specified time. We also show how to solve for the fixed kernel size by minimizing the squared differences between solutions over any given time interval. Numerical results show that the width of the kernel should be kept below about 12% of the domain size, and that the analytic equations used to derive kernel width suffer significantly from the neglect of higher-order moments. The simulations with a kernel width given by least squares minimization perform better than those made to match at one specific time. A heuristic time-variable kernel size, based on the previous results, performs on par with the least squares fixed kernel size.

Viscozyme L pretreatment on palm kernels improved the aroma of palm kernel oil after kernel roasting.

PubMed

Zhang, Wencan; Leong, Siew Mun; Zhao, Feifei; Zhao, Fangju; Yang, Tiankui; Liu, Shaoquan

2018-05-01

With an interest to enhance the aroma of palm kernel oil (PKO), Viscozyme L, an enzyme complex containing a wide range of carbohydrases, was applied to alter the carbohydrates in palm kernels (PK) to modulate the formation of volatiles upon kernel roasting. After Viscozyme treatment, the content of simple sugars and free amino acids in PK increased by 4.4-fold and 4.5-fold, respectively. After kernel roasting and oil extraction, significantly more 2,5-dimethylfuran, 2-[(methylthio)methyl]-furan, 1-(2-furanyl)-ethanone, 1-(2-furyl)-2-propanone, 5-methyl-2-furancarboxaldehyde and 2-acetyl-5-methylfuran but less 2-furanmethanol and 2-furanmethanol acetate were found in treated PKO; the correlation between their formation and simple sugar profile was estimated by using partial least square regression (PLS1). Obvious differences in pyrroles and Strecker aldehydes were also found between the control and treated PKOs. Principal component analysis (PCA) clearly discriminated the treated PKOs from that of control PKOs on the basis of all volatile compounds. Such changes in volatiles translated into distinct sensory attributes, whereby treated PKO was more caramelic and burnt after aqueous extraction and more nutty, roasty, caramelic and smoky after solvent extraction. Copyright © 2018 Elsevier Ltd. All rights reserved.

Evidence-Based Kernels: Fundamental Units of Behavioral Influence

ERIC Educational Resources Information Center

Embry, Dennis D.; Biglan, Anthony

2008-01-01

This paper describes evidence-based kernels, fundamental units of behavioral influence that appear to underlie effective prevention and treatment for children, adults, and families. A kernel is a behavior-influence procedure shown through experimental analysis to affect a specific behavior and that is indivisible in the sense that removing any of…

Joint L2,1 Norm and Fisher Discrimination Constrained Feature Selection for Rational Synthesis of Microporous Aluminophosphates.

PubMed

Qi, Miao; Wang, Ting; Yi, Yugen; Gao, Na; Kong, Jun; Wang, Jianzhong

2017-04-01

Feature selection has been regarded as an effective tool to help researchers understand the generating process of data. For mining the synthesis mechanism of microporous AlPOs, this paper proposes a novel feature selection method by joint l 2,1 norm and Fisher discrimination constraints (JNFDC). In order to obtain more effective feature subset, the proposed method can be achieved in two steps. The first step is to rank the features according to sparse and discriminative constraints. The second step is to establish predictive model with the ranked features, and select the most significant features in the light of the contribution of improving the predictive accuracy. To the best of our knowledge, JNFDC is the first work which employs the sparse representation theory to explore the synthesis mechanism of six kinds of pore rings. Numerical simulations demonstrate that our proposed method can select significant features affecting the specified structural property and improve the predictive accuracy. Moreover, comparison results show that JNFDC can obtain better predictive performances than some other state-of-the-art feature selection methods. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

CW-SSIM kernel based random forest for image classification

NASA Astrophysics Data System (ADS)

Fan, Guangzhe; Wang, Zhou; Wang, Jiheng

2010-07-01

Complex wavelet structural similarity (CW-SSIM) index has been proposed as a powerful image similarity metric that is robust to translation, scaling and rotation of images, but how to employ it in image classification applications has not been deeply investigated. In this paper, we incorporate CW-SSIM as a kernel function into a random forest learning algorithm. This leads to a novel image classification approach that does not require a feature extraction or dimension reduction stage at the front end. We use hand-written digit recognition as an example to demonstrate our algorithm. We compare the performance of the proposed approach with random forest learning based on other kernels, including the widely adopted Gaussian and the inner product kernels. Empirical evidences show that the proposed method is superior in its classification power. We also compared our proposed approach with the direct random forest method without kernel and the popular kernel-learning method support vector machine. Our test results based on both simulated and realworld data suggest that the proposed approach works superior to traditional methods without the feature selection procedure.

Improving prediction of heterodimeric protein complexes using combination with pairwise kernel.

PubMed

Ruan, Peiying; Hayashida, Morihiro; Akutsu, Tatsuya; Vert, Jean-Philippe

2018-02-19

Since many proteins become functional only after they interact with their partner proteins and form protein complexes, it is essential to identify the sets of proteins that form complexes. Therefore, several computational methods have been proposed to predict complexes from the topology and structure of experimental protein-protein interaction (PPI) network. These methods work well to predict complexes involving at least three proteins, but generally fail at identifying complexes involving only two different proteins, called heterodimeric complexes or heterodimers. There is however an urgent need for efficient methods to predict heterodimers, since the majority of known protein complexes are precisely heterodimers. In this paper, we use three promising kernel functions, Min kernel and two pairwise kernels, which are Metric Learning Pairwise Kernel (MLPK) and Tensor Product Pairwise Kernel (TPPK). We also consider the normalization forms of Min kernel. Then, we combine Min kernel or its normalization form and one of the pairwise kernels by plugging. We applied kernels based on PPI, domain, phylogenetic profile, and subcellular localization properties to predicting heterodimers. Then, we evaluate our method by employing C-Support Vector Classification (C-SVC), carrying out 10-fold cross-validation, and calculating the average F-measures. The results suggest that the combination of normalized-Min-kernel and MLPK leads to the best F-measure and improved the performance of our previous work, which had been the best existing method so far. We propose new methods to predict heterodimers, using a machine learning-based approach. We train a support vector machine (SVM) to discriminate interacting vs non-interacting protein pairs, based on informations extracted from PPI, domain, phylogenetic profiles and subcellular localization. We evaluate in detail new kernel functions to encode these data, and report prediction performance that outperforms the state-of-the-art.

Increasing accuracy of dispersal kernels in grid-based population models

USGS Publications Warehouse

Slone, D.H.

2011-01-01

Dispersal kernels in grid-based population models specify the proportion, distance and direction of movements within the model landscape. Spatial errors in dispersal kernels can have large compounding effects on model accuracy. Circular Gaussian and Laplacian dispersal kernels at a range of spatial resolutions were investigated, and methods for minimizing errors caused by the discretizing process were explored. Kernels of progressively smaller sizes relative to the landscape grid size were calculated using cell-integration and cell-center methods. These kernels were convolved repeatedly, and the final distribution was compared with a reference analytical solution. For large Gaussian kernels (σ > 10 cells), the total kernel error was <10 &sup-11; compared to analytical results. Using an invasion model that tracked the time a population took to reach a defined goal, the discrete model results were comparable to the analytical reference. With Gaussian kernels that had σ ≤ 0.12 using the cell integration method, or σ ≤ 0.22 using the cell center method, the kernel error was greater than 10%, which resulted in invasion times that were orders of magnitude different than theoretical results. A goal-seeking routine was developed to adjust the kernels to minimize overall error. With this, corrections for small kernels were found that decreased overall kernel error to <10-11 and invasion time error to <5%.

Direct discriminant locality preserving projection with Hammerstein polynomial expansion.

PubMed

Chen, Xi; Zhang, Jiashu; Li, Defang

2012-12-01

Discriminant locality preserving projection (DLPP) is a linear approach that encodes discriminant information into the objective of locality preserving projection and improves its classification ability. To enhance the nonlinear description ability of DLPP, we can optimize the objective function of DLPP in reproducing kernel Hilbert space to form a kernel-based discriminant locality preserving projection (KDLPP). However, KDLPP suffers the following problems: 1) larger computational burden; 2) no explicit mapping functions in KDLPP, which results in more computational burden when projecting a new sample into the low-dimensional subspace; and 3) KDLPP cannot obtain optimal discriminant vectors, which exceedingly optimize the objective of DLPP. To overcome the weaknesses of KDLPP, in this paper, a direct discriminant locality preserving projection with Hammerstein polynomial expansion (HPDDLPP) is proposed. The proposed HPDDLPP directly implements the objective of DLPP in high-dimensional second-order Hammerstein polynomial space without matrix inverse, which extracts the optimal discriminant vectors for DLPP without larger computational burden. Compared with some other related classical methods, experimental results for face and palmprint recognition problems indicate the effectiveness of the proposed HPDDLPP.

A multi-label learning based kernel automatic recommendation method for support vector machine.

PubMed

Zhang, Xueying; Song, Qinbao

2015-01-01

Choosing an appropriate kernel is very important and critical when classifying a new problem with Support Vector Machine. So far, more attention has been paid on constructing new kernels and choosing suitable parameter values for a specific kernel function, but less on kernel selection. Furthermore, most of current kernel selection methods focus on seeking a best kernel with the highest classification accuracy via cross-validation, they are time consuming and ignore the differences among the number of support vectors and the CPU time of SVM with different kernels. Considering the tradeoff between classification success ratio and CPU time, there may be multiple kernel functions performing equally well on the same classification problem. Aiming to automatically select those appropriate kernel functions for a given data set, we propose a multi-label learning based kernel recommendation method built on the data characteristics. For each data set, the meta-knowledge data base is first created by extracting the feature vector of data characteristics and identifying the corresponding applicable kernel set. Then the kernel recommendation model is constructed on the generated meta-knowledge data base with the multi-label classification method. Finally, the appropriate kernel functions are recommended to a new data set by the recommendation model according to the characteristics of the new data set. Extensive experiments over 132 UCI benchmark data sets, with five different types of data set characteristics, eleven typical kernels (Linear, Polynomial, Radial Basis Function, Sigmoidal function, Laplace, Multiquadric, Rational Quadratic, Spherical, Spline, Wave and Circular), and five multi-label classification methods demonstrate that, compared with the existing kernel selection methods and the most widely used RBF kernel function, SVM with the kernel function recommended by our proposed method achieved the highest classification performance.

A Multi-Label Learning Based Kernel Automatic Recommendation Method for Support Vector Machine

PubMed Central

Zhang, Xueying; Song, Qinbao

2015-01-01

Choosing an appropriate kernel is very important and critical when classifying a new problem with Support Vector Machine. So far, more attention has been paid on constructing new kernels and choosing suitable parameter values for a specific kernel function, but less on kernel selection. Furthermore, most of current kernel selection methods focus on seeking a best kernel with the highest classification accuracy via cross-validation, they are time consuming and ignore the differences among the number of support vectors and the CPU time of SVM with different kernels. Considering the tradeoff between classification success ratio and CPU time, there may be multiple kernel functions performing equally well on the same classification problem. Aiming to automatically select those appropriate kernel functions for a given data set, we propose a multi-label learning based kernel recommendation method built on the data characteristics. For each data set, the meta-knowledge data base is first created by extracting the feature vector of data characteristics and identifying the corresponding applicable kernel set. Then the kernel recommendation model is constructed on the generated meta-knowledge data base with the multi-label classification method. Finally, the appropriate kernel functions are recommended to a new data set by the recommendation model according to the characteristics of the new data set. Extensive experiments over 132 UCI benchmark data sets, with five different types of data set characteristics, eleven typical kernels (Linear, Polynomial, Radial Basis Function, Sigmoidal function, Laplace, Multiquadric, Rational Quadratic, Spherical, Spline, Wave and Circular), and five multi-label classification methods demonstrate that, compared with the existing kernel selection methods and the most widely used RBF kernel function, SVM with the kernel function recommended by our proposed method achieved the highest classification performance. PMID:25893896

Support vector machine with a Pearson VII function kernel for discriminating halophilic and non-halophilic proteins.

PubMed

Zhang, Guangya; Ge, Huihua

2013-10-01

Understanding of proteins adaptive to hypersaline environment and identifying them is a challenging task and would help to design stable proteins. Here, we have systematically analyzed the normalized amino acid compositions of 2121 halophilic and 2400 non-halophilic proteins. The results showed that halophilic protein contained more Asp at the expense of Lys, Ile, Cys and Met, fewer small and hydrophobic residues, and showed a large excess of acidic over basic amino acids. Then, we introduce a support vector machine method to discriminate the halophilic and non-halophilic proteins, by using a novel Pearson VII universal function based kernel. In the three validation check methods, it achieved an overall accuracy of 97.7%, 91.7% and 86.9% and outperformed other machine learning algorithms. We also address the influence of protein size on prediction accuracy and found the worse performance for small size proteins might be some significant residues (Cys and Lys) were missing in the proteins. Copyright © 2013 The Authors. Published by Elsevier Ltd.. All rights reserved.

Detection of maize kernels breakage rate based on K-means clustering

NASA Astrophysics Data System (ADS)

Yang, Liang; Wang, Zhuo; Gao, Lei; Bai, Xiaoping

2017-04-01

In order to optimize the recognition accuracy of maize kernels breakage detection and improve the detection efficiency of maize kernels breakage, this paper using computer vision technology and detecting of the maize kernels breakage based on K-means clustering algorithm. First, the collected RGB images are converted into Lab images, then the original images clarity evaluation are evaluated by the energy function of Sobel 8 gradient. Finally, the detection of maize kernels breakage using different pixel acquisition equipments and different shooting angles. In this paper, the broken maize kernels are identified by the color difference between integrity kernels and broken kernels. The original images clarity evaluation and different shooting angles are taken to verify that the clarity and shooting angles of the images have a direct influence on the feature extraction. The results show that K-means clustering algorithm can distinguish the broken maize kernels effectively.

Feasibility of detecting aflatoxin B1 on inoculated maize kernels surface using Vis/NIR hyperspectral imaging.

PubMed

Wang, Wei; Heitschmidt, Gerald W; Windham, William R; Feldner, Peggy; Ni, Xinzhi; Chu, Xuan

2015-01-01

The feasibility of using a visible/near-infrared hyperspectral imaging system with a wavelength range between 400 and 1000 nm to detect and differentiate different levels of aflatoxin B1 (AFB1 ) artificially titrated on maize kernel surface was examined. To reduce the color effects of maize kernels, image analysis was limited to a subset of original spectra (600 to 1000 nm). Residual staining from the AFB1 on the kernels surface was selected as regions of interest for analysis. Principal components analysis (PCA) was applied to reduce the dimensionality of hyperspectral image data, and then a stepwise factorial discriminant analysis (FDA) was performed on latent PCA variables. The results indicated that discriminant factors F2 can be used to separate control samples from all of the other groups of kernels with AFB1 inoculated, whereas the discriminant factors F1 can be used to identify maize kernels with levels of AFB1 as low as 10 ppb. An overall classification accuracy of 98% was achieved. Finally, the peaks of β coefficients of the discrimination factors F1 and F2 were analyzed and several key wavelengths identified for differentiating maize kernels with and without AFB1 , as well as those with differing levels of AFB1 inoculation. Results indicated that Vis/NIR hyperspectral imaging technology combined with the PCA-FDA was a practical method to detect and differentiate different levels of AFB1 artificially inoculated on the maize kernels surface. However, indicated the potential to detect and differentiate naturally occurring toxins in maize kernel. © 2014 Institute of Food Technologists®

Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association Studies

PubMed Central

Manitz, Juliane; Burger, Patricia; Amos, Christopher I.; Chang-Claude, Jenny; Wichmann, Heinz-Erich; Kneib, Thomas; Bickeböller, Heike

2017-01-01

The analysis of genome-wide association studies (GWAS) benefits from the investigation of biologically meaningful gene sets, such as gene-interaction networks (pathways). We propose an extension to a successful kernel-based pathway analysis approach by integrating kernel functions into a powerful algorithmic framework for variable selection, to enable investigation of multiple pathways simultaneously. We employ genetic similarity kernels from the logistic kernel machine test (LKMT) as base-learners in a boosting algorithm. A model to explain case-control status is created iteratively by selecting pathways that improve its prediction ability. We evaluated our method in simulation studies adopting 50 pathways for different sample sizes and genetic effect strengths. Additionally, we included an exemplary application of kernel boosting to a rheumatoid arthritis and a lung cancer dataset. Simulations indicate that kernel boosting outperforms the LKMT in certain genetic scenarios. Applications to GWAS data on rheumatoid arthritis and lung cancer resulted in sparse models which were based on pathways interpretable in a clinical sense. Kernel boosting is highly flexible in terms of considered variables and overcomes the problem of multiple testing. Additionally, it enables the prediction of clinical outcomes. Thus, kernel boosting constitutes a new, powerful tool in the analysis of GWAS data and towards the understanding of biological processes involved in disease susceptibility. PMID:28785300

Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association Studies.

PubMed

Friedrichs, Stefanie; Manitz, Juliane; Burger, Patricia; Amos, Christopher I; Risch, Angela; Chang-Claude, Jenny; Wichmann, Heinz-Erich; Kneib, Thomas; Bickeböller, Heike; Hofner, Benjamin

2017-01-01

The analysis of genome-wide association studies (GWAS) benefits from the investigation of biologically meaningful gene sets, such as gene-interaction networks (pathways). We propose an extension to a successful kernel-based pathway analysis approach by integrating kernel functions into a powerful algorithmic framework for variable selection, to enable investigation of multiple pathways simultaneously. We employ genetic similarity kernels from the logistic kernel machine test (LKMT) as base-learners in a boosting algorithm. A model to explain case-control status is created iteratively by selecting pathways that improve its prediction ability. We evaluated our method in simulation studies adopting 50 pathways for different sample sizes and genetic effect strengths. Additionally, we included an exemplary application of kernel boosting to a rheumatoid arthritis and a lung cancer dataset. Simulations indicate that kernel boosting outperforms the LKMT in certain genetic scenarios. Applications to GWAS data on rheumatoid arthritis and lung cancer resulted in sparse models which were based on pathways interpretable in a clinical sense. Kernel boosting is highly flexible in terms of considered variables and overcomes the problem of multiple testing. Additionally, it enables the prediction of clinical outcomes. Thus, kernel boosting constitutes a new, powerful tool in the analysis of GWAS data and towards the understanding of biological processes involved in disease susceptibility.

Discriminative components of data.

PubMed

Peltonen, Jaakko; Kaski, Samuel

2005-01-01

A simple probabilistic model is introduced to generalize classical linear discriminant analysis (LDA) in finding components that are informative of or relevant for data classes. The components maximize the predictability of the class distribution which is asymptotically equivalent to 1) maximizing mutual information with the classes, and 2) finding principal components in the so-called learning or Fisher metrics. The Fisher metric measures only distances that are relevant to the classes, that is, distances that cause changes in the class distribution. The components have applications in data exploration, visualization, and dimensionality reduction. In empirical experiments, the method outperformed, in addition to more classical methods, a Renyi entropy-based alternative while having essentially equivalent computational cost.

Face recognition based on two-dimensional discriminant sparse preserving projection

NASA Astrophysics Data System (ADS)

Zhang, Dawei; Zhu, Shanan

2018-04-01

In this paper, a supervised dimensionality reduction algorithm named two-dimensional discriminant sparse preserving projection (2DDSPP) is proposed for face recognition. In order to accurately model manifold structure of data, 2DDSPP constructs within-class affinity graph and between-class affinity graph by the constrained least squares (LS) and l1 norm minimization problem, respectively. Based on directly operating on image matrix, 2DDSPP integrates graph embedding (GE) with Fisher criterion. The obtained projection subspace preserves within-class neighborhood geometry structure of samples, while keeping away samples from different classes. The experimental results on the PIE and AR face databases show that 2DDSPP can achieve better recognition performance.

Analyzing Kernel Matrices for the Identification of Differentially Expressed Genes

PubMed Central

Xia, Xiao-Lei; Xing, Huanlai; Liu, Xueqin

2013-01-01

One of the most important applications of microarray data is the class prediction of biological samples. For this purpose, statistical tests have often been applied to identify the differentially expressed genes (DEGs), followed by the employment of the state-of-the-art learning machines including the Support Vector Machines (SVM) in particular. The SVM is a typical sample-based classifier whose performance comes down to how discriminant samples are. However, DEGs identified by statistical tests are not guaranteed to result in a training dataset composed of discriminant samples. To tackle this problem, a novel gene ranking method namely the Kernel Matrix Gene Selection (KMGS) is proposed. The rationale of the method, which roots in the fundamental ideas of the SVM algorithm, is described. The notion of ''the separability of a sample'' which is estimated by performing -like statistics on each column of the kernel matrix, is first introduced. The separability of a classification problem is then measured, from which the significance of a specific gene is deduced. Also described is a method of Kernel Matrix Sequential Forward Selection (KMSFS) which shares the KMGS method's essential ideas but proceeds in a greedy manner. On three public microarray datasets, our proposed algorithms achieved noticeably competitive performance in terms of the B.632+ error rate. PMID:24349110

A framework for optimal kernel-based manifold embedding of medical image data.

PubMed

Zimmer, Veronika A; Lekadir, Karim; Hoogendoorn, Corné; Frangi, Alejandro F; Piella, Gemma

2015-04-01

Kernel-based dimensionality reduction is a widely used technique in medical image analysis. To fully unravel the underlying nonlinear manifold the selection of an adequate kernel function and of its free parameters is critical. In practice, however, the kernel function is generally chosen as Gaussian or polynomial and such standard kernels might not always be optimal for a given image dataset or application. In this paper, we present a study on the effect of the kernel functions in nonlinear manifold embedding of medical image data. To this end, we first carry out a literature review on existing advanced kernels developed in the statistics, machine learning, and signal processing communities. In addition, we implement kernel-based formulations of well-known nonlinear dimensional reduction techniques such as Isomap and Locally Linear Embedding, thus obtaining a unified framework for manifold embedding using kernels. Subsequently, we present a method to automatically choose a kernel function and its associated parameters from a pool of kernel candidates, with the aim to generate the most optimal manifold embeddings. Furthermore, we show how the calculated selection measures can be extended to take into account the spatial relationships in images, or used to combine several kernels to further improve the embedding results. Experiments are then carried out on various synthetic and phantom datasets for numerical assessment of the methods. Furthermore, the workflow is applied to real data that include brain manifolds and multispectral images to demonstrate the importance of the kernel selection in the analysis of high-dimensional medical images. Copyright © 2014 Elsevier Ltd. All rights reserved.

Fisher Matrix Preloaded — FISHER4CAST

NASA Astrophysics Data System (ADS)

Bassett, Bruce A.; Fantaye, Yabebal; Hlozek, Renée; Kotze, Jacques

The Fisher Matrix is the backbone of modern cosmological forecasting. We describe the Fisher4Cast software: A general-purpose, easy-to-use, Fisher Matrix framework. It is open source, rigorously designed and tested and includes a Graphical User Interface (GUI) with automated LATEX file creation capability and point-and-click Fisher ellipse generation. Fisher4Cast was designed for ease of extension and, although written in Matlab, is easily portable to open-source alternatives such as Octave and Scilab. Here we use Fisher4Cast to present new 3D and 4D visualizations of the forecasting landscape and to investigate the effects of growth and curvature on future cosmological surveys. Early releases have been available at since mid-2008. The current release of the code is Version 2.2 which is described here. For ease of reference a Quick Start guide and the code used to produce the figures in this paper are included, in the hope that it will be useful to the cosmology and wider scientific communities.

Kernel Machine SNP-set Testing under Multiple Candidate Kernels

PubMed Central

Wu, Michael C.; Maity, Arnab; Lee, Seunggeun; Simmons, Elizabeth M.; Harmon, Quaker E.; Lin, Xinyi; Engel, Stephanie M.; Molldrem, Jeffrey J.; Armistead, Paul M.

2013-01-01

Joint testing for the cumulative effect of multiple single nucleotide polymorphisms grouped on the basis of prior biological knowledge has become a popular and powerful strategy for the analysis of large scale genetic association studies. The kernel machine (KM) testing framework is a useful approach that has been proposed for testing associations between multiple genetic variants and many different types of complex traits by comparing pairwise similarity in phenotype between subjects to pairwise similarity in genotype, with similarity in genotype defined via a kernel function. An advantage of the KM framework is its flexibility: choosing different kernel functions allows for different assumptions concerning the underlying model and can allow for improved power. In practice, it is difficult to know which kernel to use a priori since this depends on the unknown underlying trait architecture and selecting the kernel which gives the lowest p-value can lead to inflated type I error. Therefore, we propose practical strategies for KM testing when multiple candidate kernels are present based on constructing composite kernels and based on efficient perturbation procedures. We demonstrate through simulations and real data applications that the procedures protect the type I error rate and can lead to substantially improved power over poor choices of kernels and only modest differences in power versus using the best candidate kernel. PMID:23471868

SVM-Based Synthetic Fingerprint Discrimination Algorithm and Quantitative Optimization Strategy

PubMed Central

Chen, Suhang; Chang, Sheng; Huang, Qijun; He, Jin; Wang, Hao; Huang, Qiangui

2014-01-01

Synthetic fingerprints are a potential threat to automatic fingerprint identification systems (AFISs). In this paper, we propose an algorithm to discriminate synthetic fingerprints from real ones. First, four typical characteristic factors—the ridge distance features, global gray features, frequency feature and Harris Corner feature—are extracted. Then, a support vector machine (SVM) is used to distinguish synthetic fingerprints from real fingerprints. The experiments demonstrate that this method can achieve a recognition accuracy rate of over 98% for two discrete synthetic fingerprint databases as well as a mixed database. Furthermore, a performance factor that can evaluate the SVM's accuracy and efficiency is presented, and a quantitative optimization strategy is established for the first time. After the optimization of our synthetic fingerprint discrimination task, the polynomial kernel with a training sample proportion of 5% is the optimized value when the minimum accuracy requirement is 95%. The radial basis function (RBF) kernel with a training sample proportion of 15% is a more suitable choice when the minimum accuracy requirement is 98%. PMID:25347063

Epileptic Seizure Detection with Log-Euclidean Gaussian Kernel-Based Sparse Representation.

PubMed

Yuan, Shasha; Zhou, Weidong; Wu, Qi; Zhang, Yanli

2016-05-01

Epileptic seizure detection plays an important role in the diagnosis of epilepsy and reducing the massive workload of reviewing electroencephalography (EEG) recordings. In this work, a novel algorithm is developed to detect seizures employing log-Euclidean Gaussian kernel-based sparse representation (SR) in long-term EEG recordings. Unlike the traditional SR for vector data in Euclidean space, the log-Euclidean Gaussian kernel-based SR framework is proposed for seizure detection in the space of the symmetric positive definite (SPD) matrices, which form a Riemannian manifold. Since the Riemannian manifold is nonlinear, the log-Euclidean Gaussian kernel function is applied to embed it into a reproducing kernel Hilbert space (RKHS) for performing SR. The EEG signals of all channels are divided into epochs and the SPD matrices representing EEG epochs are generated by covariance descriptors. Then, the testing samples are sparsely coded over the dictionary composed by training samples utilizing log-Euclidean Gaussian kernel-based SR. The classification of testing samples is achieved by computing the minimal reconstructed residuals. The proposed method is evaluated on the Freiburg EEG dataset of 21 patients and shows its notable performance on both epoch-based and event-based assessments. Moreover, this method handles multiple channels of EEG recordings synchronously which is more speedy and efficient than traditional seizure detection methods.

On supervised graph Laplacian embedding CA model & kernel construction and its application

NASA Astrophysics Data System (ADS)

Zeng, Junwei; Qian, Yongsheng; Wang, Min; Yang, Yongzhong

2017-01-01

There are many methods to construct kernel with given data attribute information. Gaussian radial basis function (RBF) kernel is one of the most popular ways to construct a kernel. The key observation is that in real-world data, besides the data attribute information, data label information also exists, which indicates the data class. In order to make use of both data attribute information and data label information, in this work, we propose a supervised kernel construction method. Supervised information from training data is integrated into standard kernel construction process to improve the discriminative property of resulting kernel. A supervised Laplacian embedding cellular automaton model is another key application developed for two-lane heterogeneous traffic flow with the safe distance and large-scale truck. Based on the properties of traffic flow in China, we re-calibrate the cell length, velocity, random slowing mechanism and lane-change conditions and use simulation tests to study the relationships among the speed, density and flux. The numerical results show that the large-scale trucks will have great effects on the traffic flow, which are relevant to the proportion of the large-scale trucks, random slowing rate and the times of the lane space change.

Towards a Holistic Cortical Thickness Descriptor: Heat Kernel-Based Grey Matter Morphology Signatures.

PubMed

Wang, Gang; Wang, Yalin

2017-02-15

In this paper, we propose a heat kernel based regional shape descriptor that may be capable of better exploiting volumetric morphological information than other available methods, thereby improving statistical power on brain magnetic resonance imaging (MRI) analysis. The mechanism of our analysis is driven by the graph spectrum and the heat kernel theory, to capture the volumetric geometry information in the constructed tetrahedral meshes. In order to capture profound brain grey matter shape changes, we first use the volumetric Laplace-Beltrami operator to determine the point pair correspondence between white-grey matter and CSF-grey matter boundary surfaces by computing the streamlines in a tetrahedral mesh. Secondly, we propose multi-scale grey matter morphology signatures to describe the transition probability by random walk between the point pairs, which reflects the inherent geometric characteristics. Thirdly, a point distribution model is applied to reduce the dimensionality of the grey matter morphology signatures and generate the internal structure features. With the sparse linear discriminant analysis, we select a concise morphology feature set with improved classification accuracies. In our experiments, the proposed work outperformed the cortical thickness features computed by FreeSurfer software in the classification of Alzheimer's disease and its prodromal stage, i.e., mild cognitive impairment, on publicly available data from the Alzheimer's Disease Neuroimaging Initiative. The multi-scale and physics based volumetric structure feature may bring stronger statistical power than some traditional methods for MRI-based grey matter morphology analysis. Copyright © 2016 Elsevier Inc. All rights reserved.

Kernel-based least squares policy iteration for reinforcement learning.

PubMed

Xu, Xin; Hu, Dewen; Lu, Xicheng

2007-07-01

In this paper, we present a kernel-based least squares policy iteration (KLSPI) algorithm for reinforcement learning (RL) in large or continuous state spaces, which can be used to realize adaptive feedback control of uncertain dynamic systems. By using KLSPI, near-optimal control policies can be obtained without much a priori knowledge on dynamic models of control plants. In KLSPI, Mercer kernels are used in the policy evaluation of a policy iteration process, where a new kernel-based least squares temporal-difference algorithm called KLSTD-Q is proposed for efficient policy evaluation. To keep the sparsity and improve the generalization ability of KLSTD-Q solutions, a kernel sparsification procedure based on approximate linear dependency (ALD) is performed. Compared to the previous works on approximate RL methods, KLSPI makes two progresses to eliminate the main difficulties of existing results. One is the better convergence and (near) optimality guarantee by using the KLSTD-Q algorithm for policy evaluation with high precision. The other is the automatic feature selection using the ALD-based kernel sparsification. Therefore, the KLSPI algorithm provides a general RL method with generalization performance and convergence guarantee for large-scale Markov decision problems (MDPs). Experimental results on a typical RL task for a stochastic chain problem demonstrate that KLSPI can consistently achieve better learning efficiency and policy quality than the previous least squares policy iteration (LSPI) algorithm. Furthermore, the KLSPI method was also evaluated on two nonlinear feedback control problems, including a ship heading control problem and the swing up control of a double-link underactuated pendulum called acrobot. Simulation results illustrate that the proposed method can optimize controller performance using little a priori information of uncertain dynamic systems. It is also demonstrated that KLSPI can be applied to online learning control by incorporating

Biologically-Inspired Spike-Based Automatic Speech Recognition of Isolated Digits Over a Reproducing Kernel Hilbert Space

PubMed Central

Li, Kan; Príncipe, José C.

2018-01-01

This paper presents a novel real-time dynamic framework for quantifying time-series structure in spoken words using spikes. Audio signals are converted into multi-channel spike trains using a biologically-inspired leaky integrate-and-fire (LIF) spike generator. These spike trains are mapped into a function space of infinite dimension, i.e., a Reproducing Kernel Hilbert Space (RKHS) using point-process kernels, where a state-space model learns the dynamics of the multidimensional spike input using gradient descent learning. This kernelized recurrent system is very parsimonious and achieves the necessary memory depth via feedback of its internal states when trained discriminatively, utilizing the full context of the phoneme sequence. A main advantage of modeling nonlinear dynamics using state-space trajectories in the RKHS is that it imposes no restriction on the relationship between the exogenous input and its internal state. We are free to choose the input representation with an appropriate kernel, and changing the kernel does not impact the system nor the learning algorithm. Moreover, we show that this novel framework can outperform both traditional hidden Markov model (HMM) speech processing as well as neuromorphic implementations based on spiking neural network (SNN), yielding accurate and ultra-low power word spotters. As a proof of concept, we demonstrate its capabilities using the benchmark TI-46 digit corpus for isolated-word automatic speech recognition (ASR) or keyword spotting. Compared to HMM using Mel-frequency cepstral coefficient (MFCC) front-end without time-derivatives, our MFCC-KAARMA offered improved performance. For spike-train front-end, spike-KAARMA also outperformed state-of-the-art SNN solutions. Furthermore, compared to MFCCs, spike trains provided enhanced noise robustness in certain low signal-to-noise ratio (SNR) regime. PMID:29666568

Biologically-Inspired Spike-Based Automatic Speech Recognition of Isolated Digits Over a Reproducing Kernel Hilbert Space.

PubMed

Li, Kan; Príncipe, José C

2018-01-01

This paper presents a novel real-time dynamic framework for quantifying time-series structure in spoken words using spikes. Audio signals are converted into multi-channel spike trains using a biologically-inspired leaky integrate-and-fire (LIF) spike generator. These spike trains are mapped into a function space of infinite dimension, i.e., a Reproducing Kernel Hilbert Space (RKHS) using point-process kernels, where a state-space model learns the dynamics of the multidimensional spike input using gradient descent learning. This kernelized recurrent system is very parsimonious and achieves the necessary memory depth via feedback of its internal states when trained discriminatively, utilizing the full context of the phoneme sequence. A main advantage of modeling nonlinear dynamics using state-space trajectories in the RKHS is that it imposes no restriction on the relationship between the exogenous input and its internal state. We are free to choose the input representation with an appropriate kernel, and changing the kernel does not impact the system nor the learning algorithm. Moreover, we show that this novel framework can outperform both traditional hidden Markov model (HMM) speech processing as well as neuromorphic implementations based on spiking neural network (SNN), yielding accurate and ultra-low power word spotters. As a proof of concept, we demonstrate its capabilities using the benchmark TI-46 digit corpus for isolated-word automatic speech recognition (ASR) or keyword spotting. Compared to HMM using Mel-frequency cepstral coefficient (MFCC) front-end without time-derivatives, our MFCC-KAARMA offered improved performance. For spike-train front-end, spike-KAARMA also outperformed state-of-the-art SNN solutions. Furthermore, compared to MFCCs, spike trains provided enhanced noise robustness in certain low signal-to-noise ratio (SNR) regime.

Analysis of the discriminative methods for diagnosis of benign and malignant solitary pulmonary nodules based on serum markers.

PubMed

Wang, Wanping; Liu, Mingyue; Wang, Jing; Tian, Rui; Dong, Junqiang; Liu, Qi; Zhao, Xianping; Wang, Yuanfang

2014-01-01

Screening indexes of tumor serum markers for benign and malignant solitary pulmonary nodules (SPNs) were analyzed to find the optimum method for diagnosis. Enzyme-linked immunosorbent assays, an automatic immune analyzer and radioimmunoassay methods were used to examine the levels of 8 serum markers in 164 SPN patients, and the sensitivity for differential diagnosis of malignant or benign SPN was compared for detection using a single plasma marker or a combination of markers. The results for serological indicators that closely relate to benign and malignant SPNs were screened using the Fisher discriminant analysis and a non-conditional logistic regression analysis method, respectively. The results were then verified by the k-means clustering analysis method. The sensitivity when using a combination of serum markers to detect SPN was higher than that using a single marker. By Fisher discriminant analysis, cytokeratin 19 fragments (CYFRA21-1), carbohydrate antigen 125 (CA125), squamous cell carcinoma antigen (SCC) and breast cancer antigen (CA153), which relate to the benign and malignant SPNs, were screened. Through non-conditional logistic regression analysis, CYFRA21-1, SCC and CA153 were obtained. Using the k-means clustering analysis, the cophenetic correlation coefficient (0.940) obtained by the Fisher discriminant analysis was higher than that obtained with logistic regression analysis (0.875). This study indicated that the Fisher discriminant analysis functioned better in screening out serum markers to recognize the benign and malignant SPN. The combined detection of CYFRA21-1, CA125, SCC and CA153 is an effective way to distinguish benign and malignant SPN, and will find an important clinical application in the early diagnosis of SPN. © 2014 S. Karger GmbH, Freiburg.

A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach

PubMed Central

Jian, Yulin; Huang, Daoyu; Yan, Jia; Lu, Kun; Huang, Ying; Wen, Tailai; Zeng, Tanyue; Zhong, Shijie; Xie, Qilong

2017-01-01

A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification. PMID:28629202

A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach.

PubMed

Jian, Yulin; Huang, Daoyu; Yan, Jia; Lu, Kun; Huang, Ying; Wen, Tailai; Zeng, Tanyue; Zhong, Shijie; Xie, Qilong

2017-06-19

A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification.

Fisher-Mendel controversy in genetics: scientific argument, intellectual integrity, a fair society, Western falls and bioethical evaluation.

PubMed

Tang, Bing H

2009-10-01

This review article aims to discuss and analyze the background and findings regarding Fisher-Mendel Controversy in Genetics and to elucidate the scientific argument and intellectual integrity involved, as well as their importance in a fair society, and the lesson of Western falls as learned. At the onset of this review, the kernel of Mendel-Fisher Controversy is dissected and then identified. The fact of an organizational restructuring that had never gone towards a happy synchronization for the ensuing years since 1933 is demonstrated. It was at that time after Fisher succeeded Karl Pearson not only as the Francis Galton Professor of Eugenics but also as the chief of the Galton Laboratory at University College, London. The academic style of eugenics in the late 19th and early 20th centuries in the UK is then introduced. Fisher's ideology at that time, with its effects on the human value system and policy-making at that juncture are portrayed. Bioethical assessment is provided. Lessons in history, the emergence of the Eastern phenomenon and the decline of the Western power are outlined.

Approximate kernel competitive learning.

PubMed

Wu, Jian-Sheng; Zheng, Wei-Shi; Lai, Jian-Huang

2015-03-01

Kernel competitive learning has been successfully used to achieve robust clustering. However, kernel competitive learning (KCL) is not scalable for large scale data processing, because (1) it has to calculate and store the full kernel matrix that is too large to be calculated and kept in the memory and (2) it cannot be computed in parallel. In this paper we develop a framework of approximate kernel competitive learning for processing large scale dataset. The proposed framework consists of two parts. First, it derives an approximate kernel competitive learning (AKCL), which learns kernel competitive learning in a subspace via sampling. We provide solid theoretical analysis on why the proposed approximation modelling would work for kernel competitive learning, and furthermore, we show that the computational complexity of AKCL is largely reduced. Second, we propose a pseudo-parallelled approximate kernel competitive learning (PAKCL) based on a set-based kernel competitive learning strategy, which overcomes the obstacle of using parallel programming in kernel competitive learning and significantly accelerates the approximate kernel competitive learning for large scale clustering. The empirical evaluation on publicly available datasets shows that the proposed AKCL and PAKCL can perform comparably as KCL, with a large reduction on computational cost. Also, the proposed methods achieve more effective clustering performance in terms of clustering precision against related approximate clustering approaches. Copyright © 2014 Elsevier Ltd. All rights reserved.

Joint and collaborative representation with local Volterra kernels convolution feature for face recognition

NASA Astrophysics Data System (ADS)

Feng, Guang; Li, Hengjian; Dong, Jiwen; Chen, Xi; Yang, Huiru

2018-04-01

In this paper, we proposed a joint and collaborative representation with Volterra kernel convolution feature (JCRVK) for face recognition. Firstly, the candidate face images are divided into sub-blocks in the equal size. The blocks are extracted feature using the two-dimensional Voltera kernels discriminant analysis, which can better capture the discrimination information from the different faces. Next, the proposed joint and collaborative representation is employed to optimize and classify the local Volterra kernels features (JCR-VK) individually. JCR-VK is very efficiently for its implementation only depending on matrix multiplication. Finally, recognition is completed by using the majority voting principle. Extensive experiments on the Extended Yale B and AR face databases are conducted, and the results show that the proposed approach can outperform other recently presented similar dictionary algorithms on recognition accuracy.

Finite-frequency sensitivity kernels for global seismic wave propagation based upon adjoint methods

NASA Astrophysics Data System (ADS)

Liu, Qinya; Tromp, Jeroen

2008-07-01

We determine adjoint equations and Fréchet kernels for global seismic wave propagation based upon a Lagrange multiplier method. We start from the equations of motion for a rotating, self-gravitating earth model initially in hydrostatic equilibrium, and derive the corresponding adjoint equations that involve motions on an earth model that rotates in the opposite direction. Variations in the misfit function χ then may be expressed as , where δlnm = δm/m denotes relative model perturbations in the volume V, δlnd denotes relative topographic variations on solid-solid or fluid-solid boundaries Σ, and ∇Σδlnd denotes surface gradients in relative topographic variations on fluid-solid boundaries ΣFS. The 3-D Fréchet kernel Km determines the sensitivity to model perturbations δlnm, and the 2-D kernels Kd and Kd determine the sensitivity to topographic variations δlnd. We demonstrate also how anelasticity may be incorporated within the framework of adjoint methods. Finite-frequency sensitivity kernels are calculated by simultaneously computing the adjoint wavefield forward in time and reconstructing the regular wavefield backward in time. Both the forward and adjoint simulations are based upon a spectral-element method. We apply the adjoint technique to generate finite-frequency traveltime kernels for global seismic phases (P, Pdiff, PKP, S, SKS, depth phases, surface-reflected phases, surface waves, etc.) in both 1-D and 3-D earth models. For 1-D models these adjoint-generated kernels generally agree well with results obtained from ray-based methods. However, adjoint methods do not have the same theoretical limitations as ray-based methods, and can produce sensitivity kernels for any given phase in any 3-D earth model. The Fréchet kernels presented in this paper illustrate the sensitivity of seismic observations to structural parameters and topography on internal discontinuities. These kernels form the basis of future 3-D tomographic inversions.

Multiple kernels learning-based biological entity relationship extraction method.

PubMed

Dongliang, Xu; Jingchang, Pan; Bailing, Wang

2017-09-20

Automatic extracting protein entity interaction information from biomedical literature can help to build protein relation network and design new drugs. There are more than 20 million literature abstracts included in MEDLINE, which is the most authoritative textual database in the field of biomedicine, and follow an exponential growth over time. This frantic expansion of the biomedical literature can often be difficult to absorb or manually analyze. Thus efficient and automated search engines are necessary to efficiently explore the biomedical literature using text mining techniques. The P, R, and F value of tag graph method in Aimed corpus are 50.82, 69.76, and 58.61%, respectively. The P, R, and F value of tag graph kernel method in other four evaluation corpuses are 2-5% higher than that of all-paths graph kernel. And The P, R and F value of feature kernel and tag graph kernel fuse methods is 53.43, 71.62 and 61.30%, respectively. The P, R and F value of feature kernel and tag graph kernel fuse methods is 55.47, 70.29 and 60.37%, respectively. It indicated that the performance of the two kinds of kernel fusion methods is better than that of simple kernel. In comparison with the all-paths graph kernel method, the tag graph kernel method is superior in terms of overall performance. Experiments show that the performance of the multi-kernels method is better than that of the three separate single-kernel method and the dual-mutually fused kernel method used hereof in five corpus sets.

A Classification of Remote Sensing Image Based on Improved Compound Kernels of Svm

NASA Astrophysics Data System (ADS)

Zhao, Jianing; Gao, Wanlin; Liu, Zili; Mou, Guifen; Lu, Lin; Yu, Lina

The accuracy of RS classification based on SVM which is developed from statistical learning theory is high under small number of train samples, which results in satisfaction of classification on RS using SVM methods. The traditional RS classification method combines visual interpretation with computer classification. The accuracy of the RS classification, however, is improved a lot based on SVM method, because it saves much labor and time which is used to interpret images and collect training samples. Kernel functions play an important part in the SVM algorithm. It uses improved compound kernel function and therefore has a higher accuracy of classification on RS images. Moreover, compound kernel improves the generalization and learning ability of the kernel.

A particle swarm optimized kernel-based clustering method for crop mapping from multi-temporal polarimetric L-band SAR observations

NASA Astrophysics Data System (ADS)

Tamiminia, Haifa; Homayouni, Saeid; McNairn, Heather; Safari, Abdoreza

2017-06-01

Polarimetric Synthetic Aperture Radar (PolSAR) data, thanks to their specific characteristics such as high resolution, weather and daylight independence, have become a valuable source of information for environment monitoring and management. The discrimination capability of observations acquired by these sensors can be used for land cover classification and mapping. The aim of this paper is to propose an optimized kernel-based C-means clustering algorithm for agriculture crop mapping from multi-temporal PolSAR data. Firstly, several polarimetric features are extracted from preprocessed data. These features are linear polarization intensities, and several statistical and physical based decompositions such as Cloude-Pottier, Freeman-Durden and Yamaguchi techniques. Then, the kernelized version of hard and fuzzy C-means clustering algorithms are applied to these polarimetric features in order to identify crop types. The kernel function, unlike the conventional partitioning clustering algorithms, simplifies the non-spherical and non-linearly patterns of data structure, to be clustered easily. In addition, in order to enhance the results, Particle Swarm Optimization (PSO) algorithm is used to tune the kernel parameters, cluster centers and to optimize features selection. The efficiency of this method was evaluated by using multi-temporal UAVSAR L-band images acquired over an agricultural area near Winnipeg, Manitoba, Canada, during June and July in 2012. The results demonstrate more accurate crop maps using the proposed method when compared to the classical approaches, (e.g. 12% improvement in general). In addition, when the optimization technique is used, greater improvement is observed in crop classification, e.g. 5% in overall. Furthermore, a strong relationship between Freeman-Durden volume scattering component, which is related to canopy structure, and phenological growth stages is observed.

Prediction of cognitive outcome based on the progression of auditory discrimination during coma.

PubMed

Juan, Elsa; De Lucia, Marzia; Tzovara, Athina; Beaud, Valérie; Oddo, Mauro; Clarke, Stephanie; Rossetti, Andrea O

2016-09-01

To date, no clinical test is able to predict cognitive and functional outcome of cardiac arrest survivors. Improvement of auditory discrimination in acute coma indicates survival with high specificity. Whether the degree of this improvement is indicative of recovery remains unknown. Here we investigated if progression of auditory discrimination can predict cognitive and functional outcome. We prospectively recorded electroencephalography responses to auditory stimuli of post-anoxic comatose patients on the first and second day after admission. For each recording, auditory discrimination was quantified and its evolution over the two recordings was used to classify survivors as "predicted" when it increased vs. "other" if not. Cognitive functions were tested on awakening and functional outcome was assessed at 3 months using the Cerebral Performance Categories (CPC) scale. Thirty-two patients were included, 14 "predicted survivors" and 18 "other survivors". "Predicted survivors" were more likely to recover basic cognitive functions shortly after awakening (ability to follow a standardized neuropsychological battery: 86% vs. 44%; p=0.03 (Fisher)) and to show a very good functional outcome at 3 months (CPC 1: 86% vs. 33%; p=0.004 (Fisher)). Moreover, progression of auditory discrimination during coma was strongly correlated with cognitive performance on awakening (phonemic verbal fluency: rs=0.48; p=0.009 (Spearman)). Progression of auditory discrimination during coma provides early indication of future recovery of cognitive functions. The degree of improvement is informative of the degree of functional impairment. If confirmed in a larger cohort, this test would be the first to predict detailed outcome at the single-patient level. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

NMR-based metabolomics approach to study the chronic toxicity of crude ricin from castor bean kernels on rats.

PubMed

Guo, Pingping; Wang, Junsong; Dong, Ge; Wei, Dandan; Li, Minghui; Yang, Minghua; Kong, Lingyi

2014-07-29

Ricin, a large, water soluble toxic glycoprotein, is distributed majorly in the kernels of castor beans (the seeds of Ricinus communis L.) and has been used in traditional Chinese medicine (TCM) or other folk remedies throughout the world. The toxicity of crude ricin (CR) from castor bean kernels was investigated for the first time using an NMR-based metabolomic approach complemented with histopathological inspection and clinical chemistry. The chronic administration of CR could cause kidney and lung impairment, spleen and thymus dysfunction and diminished nutrient intake in rats. An orthogonal signal correction partial least-squares discriminant analysis (OSC-PLSDA) of metabolomic profiles of rat biofluids highlighted a number of metabolic disturbances induced by CR. Long-term CR treatment produced perturbations on energy metabolism, nitrogen metabolism, amino acid metabolism and kynurenine pathway, and evoked oxidative stress. These findings could explain well the CR induced nephrotoxicity and pulmonary toxicity, and provided several potential biomarkers for diagnostics of these toxicities. Such a (1)H NMR based metabolomics approach showed its ability to give a systematic and holistic view of the response of an organism to drugs and is suitable for dynamic studies on the toxicological effects of TCM.

Marginal Fisher analysis and its variants for human gait recognition and content- based image retrieval.

PubMed

Xu, Dong; Yan, Shuicheng; Tao, Dacheng; Lin, Stephen; Zhang, Hong-Jiang

2007-11-01

Dimensionality reduction algorithms, which aim to select a small set of efficient and discriminant features, have attracted great attention for human gait recognition and content-based image retrieval (CBIR). In this paper, we present extensions of our recently proposed marginal Fisher analysis (MFA) to address these problems. For human gait recognition, we first present a direct application of MFA, then inspired by recent advances in matrix and tensor-based dimensionality reduction algorithms, we present matrix-based MFA for directly handling 2-D input in the form of gray-level averaged images. For CBIR, we deal with the relevance feedback problem by extending MFA to marginal biased analysis, in which within-class compactness is characterized only by the distances between each positive sample and its neighboring positive samples. In addition, we present a new technique to acquire a direct optimal solution for MFA without resorting to objective function modification as done in many previous algorithms. We conduct comprehensive experiments on the USF HumanID gait database and the Corel image retrieval database. Experimental results demonstrate that MFA and its extensions outperform related algorithms in both applications.

A Comparative Study of Pairwise Learning Methods Based on Kernel Ridge Regression.

PubMed

Stock, Michiel; Pahikkala, Tapio; Airola, Antti; De Baets, Bernard; Waegeman, Willem

2018-06-12

Many machine learning problems can be formulated as predicting labels for a pair of objects. Problems of that kind are often referred to as pairwise learning, dyadic prediction, or network inference problems. During the past decade, kernel methods have played a dominant role in pairwise learning. They still obtain a state-of-the-art predictive performance, but a theoretical analysis of their behavior has been underexplored in the machine learning literature. In this work we review and unify kernel-based algorithms that are commonly used in different pairwise learning settings, ranging from matrix filtering to zero-shot learning. To this end, we focus on closed-form efficient instantiations of Kronecker kernel ridge regression. We show that independent task kernel ridge regression, two-step kernel ridge regression, and a linear matrix filter arise naturally as a special case of Kronecker kernel ridge regression, implying that all these methods implicitly minimize a squared loss. In addition, we analyze universality, consistency, and spectral filtering properties. Our theoretical results provide valuable insights into assessing the advantages and limitations of existing pairwise learning methods.

Fisher information and Rényi dimensions: A thermodynamical formalism

DOE Office of Scientific and Technical Information (OSTI.GOV)

Godó, B.; Nagy, Á.

The relation between the Fisher information and Rényi dimensions is established: the Fisher information can be expressed as a linear combination of the first and second derivatives of the Rényi dimensions with respect to the Rényi parameter β. The Rényi parameter β is the parameter of the Fisher information. A thermodynamical description based on the Fisher information with β being the inverse temperature is introduced for chaotic systems. The link between the Fisher information and the heat capacity is emphasized, and the Fisher heat capacity is introduced.

Fisher information and Rényi dimensions: A thermodynamical formalism.

PubMed

Godó, B; Nagy, Á

2016-08-01

The relation between the Fisher information and Rényi dimensions is established: the Fisher information can be expressed as a linear combination of the first and second derivatives of the Rényi dimensions with respect to the Rényi parameter β. The Rényi parameter β is the parameter of the Fisher information. A thermodynamical description based on the Fisher information with β being the inverse temperature is introduced for chaotic systems. The link between the Fisher information and the heat capacity is emphasized, and the Fisher heat capacity is introduced.

Fisher information and Rényi entropies in dynamical systems.

PubMed

Godó, B; Nagy, Á

2017-07-01

The link between the Fisher information and Rényi entropies is explored. The relationship is based on a thermodynamical formalism based on Fisher information with a parameter, β, which is interpreted as the inverse temperature. The Fisher heat capacity is defined and found to be sensitive to changes of higher order than the analogous quantity in the conventional formulation.

Front propagation and clustering in the stochastic nonlocal Fisher equation

NASA Astrophysics Data System (ADS)

Ganan, Yehuda A.; Kessler, David A.

2018-04-01

In this work, we study the problem of front propagation and pattern formation in the stochastic nonlocal Fisher equation. We find a crossover between two regimes: a steadily propagating regime for not too large interaction range and a stochastic punctuated spreading regime for larger ranges. We show that the former regime is well described by the heuristic approximation of the system by a deterministic system where the linear growth term is cut off below some critical density. This deterministic system is seen not only to give the right front velocity, but also predicts the onset of clustering for interaction kernels which give rise to stable uniform states, such as the Gaussian kernel, for sufficiently large cutoff. Above the critical cutoff, distinct clusters emerge behind the front. These same features are present in the stochastic model for sufficiently small carrying capacity. In the latter, punctuated spreading, regime, the population is concentrated on clusters, as in the infinite range case, which divide and separate as a result of the stochastic noise. Due to the finite interaction range, if a fragment at the edge of the population separates sufficiently far, it stabilizes as a new cluster, and the processes begins anew. The deterministic cutoff model does not have this spreading for large interaction ranges, attesting to its purely stochastic origins. We show that this mode of spreading has an exponentially small mean spreading velocity, decaying with the range of the interaction kernel.

Front propagation and clustering in the stochastic nonlocal Fisher equation.

PubMed

Ganan, Yehuda A; Kessler, David A

2018-04-01

In this work, we study the problem of front propagation and pattern formation in the stochastic nonlocal Fisher equation. We find a crossover between two regimes: a steadily propagating regime for not too large interaction range and a stochastic punctuated spreading regime for larger ranges. We show that the former regime is well described by the heuristic approximation of the system by a deterministic system where the linear growth term is cut off below some critical density. This deterministic system is seen not only to give the right front velocity, but also predicts the onset of clustering for interaction kernels which give rise to stable uniform states, such as the Gaussian kernel, for sufficiently large cutoff. Above the critical cutoff, distinct clusters emerge behind the front. These same features are present in the stochastic model for sufficiently small carrying capacity. In the latter, punctuated spreading, regime, the population is concentrated on clusters, as in the infinite range case, which divide and separate as a result of the stochastic noise. Due to the finite interaction range, if a fragment at the edge of the population separates sufficiently far, it stabilizes as a new cluster, and the processes begins anew. The deterministic cutoff model does not have this spreading for large interaction ranges, attesting to its purely stochastic origins. We show that this mode of spreading has an exponentially small mean spreading velocity, decaying with the range of the interaction kernel.

Optimized Kernel Entropy Components.

PubMed

Izquierdo-Verdiguier, Emma; Laparra, Valero; Jenssen, Robert; Gomez-Chova, Luis; Camps-Valls, Gustau

2017-06-01

This brief addresses two main issues of the standard kernel entropy component analysis (KECA) algorithm: the optimization of the kernel decomposition and the optimization of the Gaussian kernel parameter. KECA roughly reduces to a sorting of the importance of kernel eigenvectors by entropy instead of variance, as in the kernel principal components analysis. In this brief, we propose an extension of the KECA method, named optimized KECA (OKECA), that directly extracts the optimal features retaining most of the data entropy by means of compacting the information in very few features (often in just one or two). The proposed method produces features which have higher expressive power. In particular, it is based on the independent component analysis framework, and introduces an extra rotation to the eigen decomposition, which is optimized via gradient-ascent search. This maximum entropy preservation suggests that OKECA features are more efficient than KECA features for density estimation. In addition, a critical issue in both the methods is the selection of the kernel parameter, since it critically affects the resulting performance. Here, we analyze the most common kernel length-scale selection criteria. The results of both the methods are illustrated in different synthetic and real problems. Results show that OKECA returns projections with more expressive power than KECA, the most successful rule for estimating the kernel parameter is based on maximum likelihood, and OKECA is more robust to the selection of the length-scale parameter in kernel density estimation.

Adaptive Shape Kernel-Based Mean Shift Tracker in Robot Vision System

PubMed Central

2016-01-01

This paper proposes an adaptive shape kernel-based mean shift tracker using a single static camera for the robot vision system. The question that we address in this paper is how to construct such a kernel shape that is adaptive to the object shape. We perform nonlinear manifold learning technique to obtain the low-dimensional shape space which is trained by training data with the same view as the tracking video. The proposed kernel searches the shape in the low-dimensional shape space obtained by nonlinear manifold learning technique and constructs the adaptive kernel shape in the high-dimensional shape space. It can improve mean shift tracker performance to track object position and object contour and avoid the background clutter. In the experimental part, we take the walking human as example to validate that our method is accurate and robust to track human position and describe human contour. PMID:27379165

GeneFisher-P: variations of GeneFisher as processes in Bio-jETI

PubMed Central

Lamprecht, Anna-Lena; Margaria, Tiziana; Steffen, Bernhard; Sczyrba, Alexander; Hartmeier, Sven; Giegerich, Robert

2008-01-01

Background PCR primer design is an everyday, but not trivial task requiring state-of-the-art software. We describe the popular tool GeneFisher and explain its recent restructuring using workflow techniques. We apply a service-oriented approach to model and implement GeneFisher-P, a process-based version of the GeneFisher web application, as a part of the Bio-jETI platform for service modeling and execution. We show how to introduce a flexible process layer to meet the growing demand for improved user-friendliness and flexibility. Results Within Bio-jETI, we model the process using the jABC framework, a mature model-driven, service-oriented process definition platform. We encapsulate remote legacy tools and integrate web services using jETI, an extension of the jABC for seamless integration of remote resources as basic services, ready to be used in the process. Some of the basic services used by GeneFisher are in fact already provided as individual web services at BiBiServ and can be directly accessed. Others are legacy programs, and are made available to Bio-jETI via the jETI technology. The full power of service-based process orientation is required when more bioinformatics tools, available as web services or via jETI, lead to easy extensions or variations of the basic process. This concerns for instance variations of data retrieval or alignment tools as provided by the European Bioinformatics Institute (EBI). Conclusions The resulting service- and process-oriented GeneFisher-P demonstrates how basic services from heterogeneous sources can be easily orchestrated in the Bio-jETI platform and lead to a flexible family of specialized processes tailored to specific tasks. PMID:18460174

Graph Kernels for Molecular Similarity.

PubMed

Rupp, Matthias; Schneider, Gisbert

2010-04-12

Molecular similarity measures are important for many cheminformatics applications like ligand-based virtual screening and quantitative structure-property relationships. Graph kernels are formal similarity measures defined directly on graphs, such as the (annotated) molecular structure graph. Graph kernels are positive semi-definite functions, i.e., they correspond to inner products. This property makes them suitable for use with kernel-based machine learning algorithms such as support vector machines and Gaussian processes. We review the major types of kernels between graphs (based on random walks, subgraphs, and optimal assignments, respectively), and discuss their advantages, limitations, and successful applications in cheminformatics. Copyright © 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Partial Deconvolution with Inaccurate Blur Kernel.

PubMed

Ren, Dongwei; Zuo, Wangmeng; Zhang, David; Xu, Jun; Zhang, Lei

2017-10-17

Most non-blind deconvolution methods are developed under the error-free kernel assumption, and are not robust to inaccurate blur kernel. Unfortunately, despite the great progress in blind deconvolution, estimation error remains inevitable during blur kernel estimation. Consequently, severe artifacts such as ringing effects and distortions are likely to be introduced in the non-blind deconvolution stage. In this paper, we tackle this issue by suggesting: (i) a partial map in the Fourier domain for modeling kernel estimation error, and (ii) a partial deconvolution model for robust deblurring with inaccurate blur kernel. The partial map is constructed by detecting the reliable Fourier entries of estimated blur kernel. And partial deconvolution is applied to wavelet-based and learning-based models to suppress the adverse effect of kernel estimation error. Furthermore, an E-M algorithm is developed for estimating the partial map and recovering the latent sharp image alternatively. Experimental results show that our partial deconvolution model is effective in relieving artifacts caused by inaccurate blur kernel, and can achieve favorable deblurring quality on synthetic and real blurry images.Most non-blind deconvolution methods are developed under the error-free kernel assumption, and are not robust to inaccurate blur kernel. Unfortunately, despite the great progress in blind deconvolution, estimation error remains inevitable during blur kernel estimation. Consequently, severe artifacts such as ringing effects and distortions are likely to be introduced in the non-blind deconvolution stage. In this paper, we tackle this issue by suggesting: (i) a partial map in the Fourier domain for modeling kernel estimation error, and (ii) a partial deconvolution model for robust deblurring with inaccurate blur kernel. The partial map is constructed by detecting the reliable Fourier entries of estimated blur kernel. And partial deconvolution is applied to wavelet-based and learning-based

Fast Gaussian kernel learning for classification tasks based on specially structured global optimization.

PubMed

Zhong, Shangping; Chen, Tianshun; He, Fengying; Niu, Yuzhen

2014-09-01

For a practical pattern classification task solved by kernel methods, the computing time is mainly spent on kernel learning (or training). However, the current kernel learning approaches are based on local optimization techniques, and hard to have good time performances, especially for large datasets. Thus the existing algorithms cannot be easily extended to large-scale tasks. In this paper, we present a fast Gaussian kernel learning method by solving a specially structured global optimization (SSGO) problem. We optimize the Gaussian kernel function by using the formulated kernel target alignment criterion, which is a difference of increasing (d.i.) functions. Through using a power-transformation based convexification method, the objective criterion can be represented as a difference of convex (d.c.) functions with a fixed power-transformation parameter. And the objective programming problem can then be converted to a SSGO problem: globally minimizing a concave function over a convex set. The SSGO problem is classical and has good solvability. Thus, to find the global optimal solution efficiently, we can adopt the improved Hoffman's outer approximation method, which need not repeat the searching procedure with different starting points to locate the best local minimum. Also, the proposed method can be proven to converge to the global solution for any classification task. We evaluate the proposed method on twenty benchmark datasets, and compare it with four other Gaussian kernel learning methods. Experimental results show that the proposed method stably achieves both good time-efficiency performance and good classification performance. Copyright © 2014 Elsevier Ltd. All rights reserved.

Construction of phylogenetic trees by kernel-based comparative analysis of metabolic networks.

PubMed

Oh, S June; Joung, Je-Gun; Chang, Jeong-Ho; Zhang, Byoung-Tak

2006-06-06

To infer the tree of life requires knowledge of the common characteristics of each species descended from a common ancestor as the measuring criteria and a method to calculate the distance between the resulting values of each measure. Conventional phylogenetic analysis based on genomic sequences provides information about the genetic relationships between different organisms. In contrast, comparative analysis of metabolic pathways in different organisms can yield insights into their functional relationships under different physiological conditions. However, evaluating the similarities or differences between metabolic networks is a computationally challenging problem, and systematic methods of doing this are desirable. Here we introduce a graph-kernel method for computing the similarity between metabolic networks in polynomial time, and use it to profile metabolic pathways and to construct phylogenetic trees. To compare the structures of metabolic networks in organisms, we adopted the exponential graph kernel, which is a kernel-based approach with a labeled graph that includes a label matrix and an adjacency matrix. To construct the phylogenetic trees, we used an unweighted pair-group method with arithmetic mean, i.e., a hierarchical clustering algorithm. We applied the kernel-based network profiling method in a comparative analysis of nine carbohydrate metabolic networks from 81 biological species encompassing Archaea, Eukaryota, and Eubacteria. The resulting phylogenetic hierarchies generally support the tripartite scheme of three domains rather than the two domains of prokaryotes and eukaryotes. By combining the kernel machines with metabolic information, the method infers the context of biosphere development that covers physiological events required for adaptation by genetic reconstruction. The results show that one may obtain a global view of the tree of life by comparing the metabolic pathway structures using meta-level information rather than sequence

A Precise Drunk Driving Detection Using Weighted Kernel Based on Electrocardiogram.

PubMed

Wu, Chung Kit; Tsang, Kim Fung; Chi, Hao Ran; Hung, Faan Hei

2016-05-09

Globally, 1.2 million people die and 50 million people are injured annually due to traffic accidents. These traffic accidents cost $500 billion dollars. Drunk drivers are found in 40% of the traffic crashes. Existing drunk driving detection (DDD) systems do not provide accurate detection and pre-warning concurrently. Electrocardiogram (ECG) is a proven biosignal that accurately and simultaneously reflects human's biological status. In this letter, a classifier for DDD based on ECG is investigated in an attempt to reduce traffic accidents caused by drunk drivers. At this point, it appears that there is no known research or literature found on ECG classifier for DDD. To identify drunk syndromes, the ECG signals from drunk drivers are studied and analyzed. As such, a precise ECG-based DDD (ECG-DDD) using a weighted kernel is developed. From the measurements, 10 key features of ECG signals were identified. To incorporate the important features, the feature vectors are weighted in the customization of kernel functions. Four commonly adopted kernel functions are studied. Results reveal that weighted feature vectors improve the accuracy by 11% compared to the computation using the prime kernel. Evaluation shows that ECG-DDD improved the accuracy by 8% to 18% compared to prevailing methods.

A shock-capturing SPH scheme based on adaptive kernel estimation

NASA Astrophysics Data System (ADS)

Sigalotti, Leonardo Di G.; López, Hender; Donoso, Arnaldo; Sira, Eloy; Klapp, Jaime

2006-02-01

Here we report a method that converts standard smoothed particle hydrodynamics (SPH) into a working shock-capturing scheme without relying on solutions to the Riemann problem. Unlike existing adaptive SPH simulations, the present scheme is based on an adaptive kernel estimation of the density, which combines intrinsic features of both the kernel and nearest neighbor approaches in a way that the amount of smoothing required in low-density regions is effectively controlled. Symmetrized SPH representations of the gas dynamic equations along with the usual kernel summation for the density are used to guarantee variational consistency. Implementation of the adaptive kernel estimation involves a very simple procedure and allows for a unique scheme that handles strong shocks and rarefactions the same way. Since it represents a general improvement of the integral interpolation on scattered data, it is also applicable to other fluid-dynamic models. When the method is applied to supersonic compressible flows with sharp discontinuities, as in the classical one-dimensional shock-tube problem and its variants, the accuracy of the results is comparable, and in most cases superior, to that obtained from high quality Godunov-type methods and SPH formulations based on Riemann solutions. The extension of the method to two- and three-space dimensions is straightforward. In particular, for the two-dimensional cylindrical Noh's shock implosion and Sedov point explosion problems the present scheme produces much better results than those obtained with conventional SPH codes.

Analysis of the Fisher solution

NASA Astrophysics Data System (ADS)

Abdolrahimi, Shohreh; Shoom, Andrey A.

2010-01-01

We study the d-dimensional Fisher solution which represents a static, spherically symmetric, asymptotically flat spacetime with a massless scalar field. The solution has two parameters, the mass M and the “scalar charge” Σ. The Fisher solution has a naked curvature singularity which divides the spacetime manifold into two disconnected parts. The part which is asymptotically flat we call the Fisher spacetime, and another part we call the Fisher universe. The d-dimensional Schwarzschild-Tangherlini solution and the Fisher solution belong to the same theory and are dual to each other. The duality transformation acting in the parameter space (M,Σ) maps the exterior region of the Schwarzschild-Tangherlini black hole into the Fisher spacetime which has a naked timelike singularity, and interior region of the black hole into the Fisher universe, which is an anisotropic expanding-contracting universe and which has two spacelike singularities representing its “big bang” and “big crunch.” The big bang singularity and the singularity of the Fisher spacetime are radially weak in the sense that a 1-dimensional object moving along a timelike radial geodesic can arrive to the singularities intact. At the vicinity of the singularity the Fisher spacetime of nonzero mass has a region where its Misner-Sharp energy is negative. The Fisher universe has a marginally trapped surface corresponding to the state of its maximal expansion in the angular directions. These results and derived relations between geometric quantities of the Fisher spacetime, the Fisher universe, and the Schwarzschild-Tangherlini black hole may suggest that the massless scalar field transforms the black hole event horizon into the naked radially weak disjoint singularities of the Fisher spacetime and the Fisher universe which are “dual to the horizon.”

Analysis of the Fisher solution

DOE Office of Scientific and Technical Information (OSTI.GOV)

Abdolrahimi, Shohreh; Shoom, Andrey A.

2010-01-15

We study the d-dimensional Fisher solution which represents a static, spherically symmetric, asymptotically flat spacetime with a massless scalar field. The solution has two parameters, the mass M and the 'scalar charge' {Sigma}. The Fisher solution has a naked curvature singularity which divides the spacetime manifold into two disconnected parts. The part which is asymptotically flat we call the Fisher spacetime, and another part we call the Fisher universe. The d-dimensional Schwarzschild-Tangherlini solution and the Fisher solution belong to the same theory and are dual to each other. The duality transformation acting in the parameter space (M,{Sigma}) maps the exteriormore » region of the Schwarzschild-Tangherlini black hole into the Fisher spacetime which has a naked timelike singularity, and interior region of the black hole into the Fisher universe, which is an anisotropic expanding-contracting universe and which has two spacelike singularities representing its 'big bang' and 'big crunch'. The big bang singularity and the singularity of the Fisher spacetime are radially weak in the sense that a 1-dimensional object moving along a timelike radial geodesic can arrive to the singularities intact. At the vicinity of the singularity the Fisher spacetime of nonzero mass has a region where its Misner-Sharp energy is negative. The Fisher universe has a marginally trapped surface corresponding to the state of its maximal expansion in the angular directions. These results and derived relations between geometric quantities of the Fisher spacetime, the Fisher universe, and the Schwarzschild-Tangherlini black hole may suggest that the massless scalar field transforms the black hole event horizon into the naked radially weak disjoint singularities of the Fisher spacetime and the Fisher universe which are 'dual to the horizon'.« less

A new EEMD-based scheme for detection of insect damaged wheat kernels using impact acoustics

USDA-ARS?s Scientific Manuscript database

Internally feeding insects inside wheat kernels cause significant, but unseen economic damage to stored grain. In this paper, a new scheme based on ensemble empirical mode decomposition (EEMD) using impact acoustics is proposed for detection of insect-damaged wheat kernels, based on its capability t...

Current distribution of the fisher, Martes pennanti, in California

Treesearch

William J. Zielinski; Thomas E. Kucera; Reginald H. Barrett

1995-01-01

We describe the 1989-1994 distribution of the fisher, Martes pennanti, in California based on results of detection surveys that used either sooted track-plates or cameras. Fishers were detected in two regions of the state: the northwest and the southern Sierra Nevada. Despite considerable survey effort, neither fisher tracks nor photographs were...

Application of SWIR hyperspectral imaging and chemometrics for identification of aflatoxin B1 contaminated maize kernels

NASA Astrophysics Data System (ADS)

Kimuli, Daniel; Wang, Wei; Wang, Wei; Jiang, Hongzhe; Zhao, Xin; Chu, Xuan

2018-03-01

A short-wave infrared (SWIR) hyperspectral imaging system (1000-2500 nm) combined with chemometric data analysis was used to detect aflatoxin B1 (AFB1) on surfaces of 600 kernels of four yellow maize varieties from different States of the USA (Georgia, Illinois, Indiana and Nebraska). For each variety, four AFB1 solutions (10, 20, 100 and 500 ppb) were artificially deposited on kernels and a control group was generated from kernels treated with methanol solution. Principal component analysis (PCA), partial least squares discriminant analysis (PLSDA) and factorial discriminant analysis (FDA) were applied to explore and classify maize kernels according to AFB1 contamination. PCA results revealed partial separation of control kernels from AFB1 contaminated kernels for each variety while no pattern of separation was observed among pooled samples. A combination of standard normal variate and first derivative pre-treatments produced the best PLSDA classification model with accuracy of 100% and 96% in calibration and validation, respectively, from Illinois variety. The best AFB1 classification results came from FDA on raw spectra with accuracy of 100% in calibration and validation for Illinois and Nebraska varieties. However, for both PLSDA and FDA models, poor AFB1 classification results were obtained for pooled samples relative to individual varieties. SWIR spectra combined with chemometrics and spectra pre-treatments showed the possibility of detecting maize kernels of different varieties coated with AFB1. The study further suggests that increase of maize kernel constituents like water, protein, starch and lipid in a pooled sample may have influence on detection accuracy of AFB1 contamination.

Robust kernel representation with statistical local features for face recognition.

PubMed

Yang, Meng; Zhang, Lei; Shiu, Simon Chi-Keung; Zhang, David

2013-06-01

Factors such as misalignment, pose variation, and occlusion make robust face recognition a difficult problem. It is known that statistical features such as local binary pattern are effective for local feature extraction, whereas the recently proposed sparse or collaborative representation-based classification has shown interesting results in robust face recognition. In this paper, we propose a novel robust kernel representation model with statistical local features (SLF) for robust face recognition. Initially, multipartition max pooling is used to enhance the invariance of SLF to image registration error. Then, a kernel-based representation model is proposed to fully exploit the discrimination information embedded in the SLF, and robust regression is adopted to effectively handle the occlusion in face images. Extensive experiments are conducted on benchmark face databases, including extended Yale B, AR (A. Martinez and R. Benavente), multiple pose, illumination, and expression (multi-PIE), facial recognition technology (FERET), face recognition grand challenge (FRGC), and labeled faces in the wild (LFW), which have different variations of lighting, expression, pose, and occlusions, demonstrating the promising performance of the proposed method.

Super-resolution fusion of complementary panoramic images based on cross-selection kernel regression interpolation.

PubMed

Chen, Lidong; Basu, Anup; Zhang, Maojun; Wang, Wei; Liu, Yu

2014-03-20

A complementary catadioptric imaging technique was proposed to solve the problem of low and nonuniform resolution in omnidirectional imaging. To enhance this research, our paper focuses on how to generate a high-resolution panoramic image from the captured omnidirectional image. To avoid the interference between the inner and outer images while fusing the two complementary views, a cross-selection kernel regression method is proposed. First, in view of the complementarity of sampling resolution in the tangential and radial directions between the inner and the outer images, respectively, the horizontal gradients in the expected panoramic image are estimated based on the scattered neighboring pixels mapped from the outer, while the vertical gradients are estimated using the inner image. Then, the size and shape of the regression kernel are adaptively steered based on the local gradients. Furthermore, the neighboring pixels in the next interpolation step of kernel regression are also selected based on the comparison between the horizontal and vertical gradients. In simulation and real-image experiments, the proposed method outperforms existing kernel regression methods and our previous wavelet-based fusion method in terms of both visual quality and objective evaluation.

The Fisher-Markov selector: fast selecting maximally separable feature subset for multiclass classification with applications to high-dimensional data.

PubMed

Cheng, Qiang; Zhou, Hongbo; Cheng, Jie

2011-06-01

Selecting features for multiclass classification is a critically important task for pattern recognition and machine learning applications. Especially challenging is selecting an optimal subset of features from high-dimensional data, which typically have many more variables than observations and contain significant noise, missing components, or outliers. Existing methods either cannot handle high-dimensional data efficiently or scalably, or can only obtain local optimum instead of global optimum. Toward the selection of the globally optimal subset of features efficiently, we introduce a new selector--which we call the Fisher-Markov selector--to identify those features that are the most useful in describing essential differences among the possible groups. In particular, in this paper we present a way to represent essential discriminating characteristics together with the sparsity as an optimization objective. With properly identified measures for the sparseness and discriminativeness in possibly high-dimensional settings, we take a systematic approach for optimizing the measures to choose the best feature subset. We use Markov random field optimization techniques to solve the formulated objective functions for simultaneous feature selection. Our results are noncombinatorial, and they can achieve the exact global optimum of the objective function for some special kernels. The method is fast; in particular, it can be linear in the number of features and quadratic in the number of observations. We apply our procedure to a variety of real-world data, including mid--dimensional optical handwritten digit data set and high-dimensional microarray gene expression data sets. The effectiveness of our method is confirmed by experimental results. In pattern recognition and from a model selection viewpoint, our procedure says that it is possible to select the most discriminating subset of variables by solving a very simple unconstrained objective function which in fact can be

Fishers' knowledge on the coast of Brazil.

PubMed

Begossi, Alpina; Salivonchyk, Svetlana; Lopes, Priscila F M; Silvano, Renato A M

2016-06-01

Although fishers' knowledge has been recently considered into management programmes, there is still the need to establish a better understanding of fishers' perceptions and cognition. Fishers can provide novel information on the biology and ecology of species, which can potentially be used in the management of fisheries. The knowledge fishers have and how they classify nature is empirically based. It is common, for example, to observe that fishers' taxonomy is often represented by the generic level, one of the hierarchical categories of folk classification that is somewhat analogous to the Linnean genus, as it groups organisms of a higher rank than the folk species.In this study we compiled the knowledge fishers have on local fish, such as their folk names, diet and habitat. Five coastal communities widely distributed along the Brazilian coast were studied: two from the northeast (Porto Sauípe and Itacimirim, in Bahia State, n of interviewees = 34), two from the southeast (Itaipu at Niterói and Copacabana at Rio de Janeiro, Rio de Janeiro State, n = 35) and one from the south coast (Pântano do Sul, in Santa Catarina State, n = 23). Fish pictures were randomly ordered and the same order was presented to all interviewees (n = 92), when they were then asked about the species name and classification and its habitat and diet preferences. Fishers make clusters of fish species, usually hierarchically; fishers of the coast of Brazil use mostly primary lexemes (generic names) to name fish; and fishers did not differentiate between scientific species, since the same folk generic name included two different scientific species. Fishers provide information on species to which there is scarce or no information on diet and habitat, such as Rhinobatos percellens (chola guitarfish, arraia viola or cação viola), Sphoeroides dorsalis (marbled puffer, baiacu), Mycteroperca acutirostris (comb grouper, badejo) and Dasyatis guttata (longnose stingray, arraia, arraia

Characterizing nonclassical correlations via local quantum Fisher information

NASA Astrophysics Data System (ADS)

Kim, Sunho; Li, Longsuo; Kumar, Asutosh; Wu, Junde

2018-03-01

We define two ways of quantifying the quantum correlations based on quantum Fisher information (QFI) in order to study the quantum correlations as a resource in quantum metrology. By investigating the hierarchy of measurement-induced Fisher information introduced in Lu et al. [X. M. Lu, S. Luo, and C. H. Oh, Phys. Rev. A 86, 022342 (2012), 10.1103/PhysRevA.86.022342], we show that the presence of quantum correlation can be confirmed by the difference of the Fisher information induced by the measurements of two hierarchies. In particular, the quantitative quantum correlations based on QFI coincide with the geometric discord for pure quantum states.

Kernel-Based Sensor Fusion With Application to Audio-Visual Voice Activity Detection

NASA Astrophysics Data System (ADS)

Dov, David; Talmon, Ronen; Cohen, Israel

2016-12-01

In this paper, we address the problem of multiple view data fusion in the presence of noise and interferences. Recent studies have approached this problem using kernel methods, by relying particularly on a product of kernels constructed separately for each view. From a graph theory point of view, we analyze this fusion approach in a discrete setting. More specifically, based on a statistical model for the connectivity between data points, we propose an algorithm for the selection of the kernel bandwidth, a parameter, which, as we show, has important implications on the robustness of this fusion approach to interferences. Then, we consider the fusion of audio-visual speech signals measured by a single microphone and by a video camera pointed to the face of the speaker. Specifically, we address the task of voice activity detection, i.e., the detection of speech and non-speech segments, in the presence of structured interferences such as keyboard taps and office noise. We propose an algorithm for voice activity detection based on the audio-visual signal. Simulation results show that the proposed algorithm outperforms competing fusion and voice activity detection approaches. In addition, we demonstrate that a proper selection of the kernel bandwidth indeed leads to improved performance.

Nonlinear Deep Kernel Learning for Image Annotation.

PubMed

Jiu, Mingyuan; Sahbi, Hichem

2017-02-08

Multiple kernel learning (MKL) is a widely used technique for kernel design. Its principle consists in learning, for a given support vector classifier, the most suitable convex (or sparse) linear combination of standard elementary kernels. However, these combinations are shallow and often powerless to capture the actual similarity between highly semantic data, especially for challenging classification tasks such as image annotation. In this paper, we redefine multiple kernels using deep multi-layer networks. In this new contribution, a deep multiple kernel is recursively defined as a multi-layered combination of nonlinear activation functions, each one involves a combination of several elementary or intermediate kernels, and results into a positive semi-definite deep kernel. We propose four different frameworks in order to learn the weights of these networks: supervised, unsupervised, kernel-based semisupervised and Laplacian-based semi-supervised. When plugged into support vector machines (SVMs), the resulting deep kernel networks show clear gain, compared to several shallow kernels for the task of image annotation. Extensive experiments and analysis on the challenging ImageCLEF photo annotation benchmark, the COREL5k database and the Banana dataset validate the effectiveness of the proposed method.

Labeled Graph Kernel for Behavior Analysis.

PubMed

Zhao, Ruiqi; Martinez, Aleix M

2016-08-01

Automatic behavior analysis from video is a major topic in many areas of research, including computer vision, multimedia, robotics, biology, cognitive science, social psychology, psychiatry, and linguistics. Two major problems are of interest when analyzing behavior. First, we wish to automatically categorize observed behaviors into a discrete set of classes (i.e., classification). For example, to determine word production from video sequences in sign language. Second, we wish to understand the relevance of each behavioral feature in achieving this classification (i.e., decoding). For instance, to know which behavior variables are used to discriminate between the words apple and onion in American Sign Language (ASL). The present paper proposes to model behavior using a labeled graph, where the nodes define behavioral features and the edges are labels specifying their order (e.g., before, overlaps, start). In this approach, classification reduces to a simple labeled graph matching. Unfortunately, the complexity of labeled graph matching grows exponentially with the number of categories we wish to represent. Here, we derive a graph kernel to quickly and accurately compute this graph similarity. This approach is very general and can be plugged into any kernel-based classifier. Specifically, we derive a Labeled Graph Support Vector Machine (LGSVM) and a Labeled Graph Logistic Regressor (LGLR) that can be readily employed to discriminate between many actions (e.g., sign language concepts). The derived approach can be readily used for decoding too, yielding invaluable information for the understanding of a problem (e.g., to know how to teach a sign language). The derived algorithms allow us to achieve higher accuracy results than those of state-of-the-art algorithms in a fraction of the time. We show experimental results on a variety of problems and datasets, including multimodal data.

Comparing Alternative Kernels for the Kernel Method of Test Equating: Gaussian, Logistic, and Uniform Kernels. Research Report. ETS RR-08-12

ERIC Educational Resources Information Center

Lee, Yi-Hsuan; von Davier, Alina A.

2008-01-01

The kernel equating method (von Davier, Holland, & Thayer, 2004) is based on a flexible family of equipercentile-like equating functions that use a Gaussian kernel to continuize the discrete score distributions. While the classical equipercentile, or percentile-rank, equating method carries out the continuization step by linear interpolation,…

A Bayesian framework based on a Gaussian mixture model and radial-basis-function Fisher discriminant analysis (BayGmmKda V1.1) for spatial prediction of floods

NASA Astrophysics Data System (ADS)

Tien Bui, Dieu; Hoang, Nhat-Duc

2017-09-01

In this study, a probabilistic model, named as BayGmmKda, is proposed for flood susceptibility assessment in a study area in central Vietnam. The new model is a Bayesian framework constructed by a combination of a Gaussian mixture model (GMM), radial-basis-function Fisher discriminant analysis (RBFDA), and a geographic information system (GIS) database. In the Bayesian framework, GMM is used for modeling the data distribution of flood-influencing factors in the GIS database, whereas RBFDA is utilized to construct a latent variable that aims at enhancing the model performance. As a result, the posterior probabilistic output of the BayGmmKda model is used as flood susceptibility index. Experiment results showed that the proposed hybrid framework is superior to other benchmark models, including the adaptive neuro-fuzzy inference system and the support vector machine. To facilitate the model implementation, a software program of BayGmmKda has been developed in MATLAB. The BayGmmKda program can accurately establish a flood susceptibility map for the study region. Accordingly, local authorities can overlay this susceptibility map onto various land-use maps for the purpose of land-use planning or management.

Discriminative analysis of early Alzheimer's disease based on two intrinsically anti-correlated networks with resting-state fMRI.

PubMed

Wang, Kun; Jiang, Tianzi; Liang, Meng; Wang, Liang; Tian, Lixia; Zhang, Xinqing; Li, Kuncheng; Liu, Zhening

2006-01-01

In this work, we proposed a discriminative model of Alzheimer's disease (AD) on the basis of multivariate pattern classification and functional magnetic resonance imaging (fMRI). This model used the correlation/anti-correlation coefficients of two intrinsically anti-correlated networks in resting brains, which have been suggested by two recent studies, as the feature of classification. Pseudo-Fisher Linear Discriminative Analysis (pFLDA) was then performed on the feature space and a linear classifier was generated. Using leave-one-out (LOO) cross validation, our results showed a correct classification rate of 83%. We also compared the proposed model with another one based on the whole brain functional connectivity. Our proposed model outperformed the other one significantly, and this implied that the two intrinsically anti-correlated networks may be a more susceptible part of the whole brain network in the early stage of AD.

A Global Estimate of the Number of Coral Reef Fishers.

PubMed

Teh, Louise S L; Teh, Lydia C L; Sumaila, U Rashid

2013-01-01

Overfishing threatens coral reefs worldwide, yet there is no reliable estimate on the number of reef fishers globally. We address this data gap by quantifying the number of reef fishers on a global scale, using two approaches - the first estimates reef fishers as a proportion of the total number of marine fishers in a country, based on the ratio of reef-related to total marine fish landed values. The second estimates reef fishers as a function of coral reef area, rural coastal population, and fishing pressure. In total, we find that there are 6 million reef fishers in 99 reef countries and territories worldwide, of which at least 25% are reef gleaners. Our estimates are an improvement over most existing fisher population statistics, which tend to omit accounting for gleaners and reef fishers. Our results suggest that slightly over a quarter of the world's small-scale fishers fish on coral reefs, and half of all coral reef fishers are in Southeast Asia. Coral reefs evidently support the socio-economic well-being of numerous coastal communities. By quantifying the number of people who are employed as reef fishers, we provide decision-makers with an important input into planning for sustainable coral reef fisheries at the appropriate scale.

A Global Estimate of the Number of Coral Reef Fishers

PubMed Central

Teh, Louise S. L.; Teh, Lydia C. L.; Sumaila, U. Rashid

2013-01-01

Overfishing threatens coral reefs worldwide, yet there is no reliable estimate on the number of reef fishers globally. We address this data gap by quantifying the number of reef fishers on a global scale, using two approaches - the first estimates reef fishers as a proportion of the total number of marine fishers in a country, based on the ratio of reef-related to total marine fish landed values. The second estimates reef fishers as a function of coral reef area, rural coastal population, and fishing pressure. In total, we find that there are 6 million reef fishers in 99 reef countries and territories worldwide, of which at least 25% are reef gleaners. Our estimates are an improvement over most existing fisher population statistics, which tend to omit accounting for gleaners and reef fishers. Our results suggest that slightly over a quarter of the world’s small-scale fishers fish on coral reefs, and half of all coral reef fishers are in Southeast Asia. Coral reefs evidently support the socio-economic well-being of numerous coastal communities. By quantifying the number of people who are employed as reef fishers, we provide decision-makers with an important input into planning for sustainable coral reef fisheries at the appropriate scale. PMID:23840327

Discrimination of raw and processed Dipsacus asperoides by near infrared spectroscopy combined with least squares-support vector machine and random forests

NASA Astrophysics Data System (ADS)

Xin, Ni; Gu, Xiao-Feng; Wu, Hao; Hu, Yu-Zhu; Yang, Zhong-Lin

2012-04-01

Most herbal medicines could be processed to fulfill the different requirements of therapy. The purpose of this study was to discriminate between raw and processed Dipsacus asperoides, a common traditional Chinese medicine, based on their near infrared (NIR) spectra. Least squares-support vector machine (LS-SVM) and random forests (RF) were employed for full-spectrum classification. Three types of kernels, including linear kernel, polynomial kernel and radial basis function kernel (RBF), were checked for optimization of LS-SVM model. For comparison, a linear discriminant analysis (LDA) model was performed for classification, and the successive projections algorithm (SPA) was executed prior to building an LDA model to choose an appropriate subset of wavelengths. The three methods were applied to a dataset containing 40 raw herbs and 40 corresponding processed herbs. We ran 50 runs of 10-fold cross validation to evaluate the model's efficiency. The performance of the LS-SVM with RBF kernel (RBF LS-SVM) was better than the other two kernels. The RF, RBF LS-SVM and SPA-LDA successfully classified all test samples. The mean error rates for the 50 runs of 10-fold cross validation were 1.35% for RBF LS-SVM, 2.87% for RF, and 2.50% for SPA-LDA. The best classification results were obtained by using LS-SVM with RBF kernel, while RF was fast in the training and making predictions.

7 CFR 981.401 - Adjusted kernel weight.

Code of Federal Regulations, 2012 CFR

2012-01-01

... based on the analysis of a 1,000 gram sample taken from a lot of almonds weighing 10,000 pounds with less than 95 percent kernels, and a 1,000 gram sample taken from a lot of almonds weighing 10,000... percent kernels containing the following: Edible kernels, 530 grams; inedible kernels, 120 grams; foreign...

7 CFR 981.401 - Adjusted kernel weight.

Code of Federal Regulations, 2011 CFR

2011-01-01

... based on the analysis of a 1,000 gram sample taken from a lot of almonds weighing 10,000 pounds with less than 95 percent kernels, and a 1,000 gram sample taken from a lot of almonds weighing 10,000... percent kernels containing the following: Edible kernels, 530 grams; inedible kernels, 120 grams; foreign...

7 CFR 981.401 - Adjusted kernel weight.

Code of Federal Regulations, 2013 CFR

2013-01-01

... based on the analysis of a 1,000 gram sample taken from a lot of almonds weighing 10,000 pounds with less than 95 percent kernels, and a 1,000 gram sample taken from a lot of almonds weighing 10,000... percent kernels containing the following: Edible kernels, 530 grams; inedible kernels, 120 grams; foreign...

7 CFR 981.401 - Adjusted kernel weight.

Code of Federal Regulations, 2010 CFR

2010-01-01

... based on the analysis of a 1,000 gram sample taken from a lot of almonds weighing 10,000 pounds with less than 95 percent kernels, and a 1,000 gram sample taken from a lot of almonds weighing 10,000... percent kernels containing the following: Edible kernels, 530 grams; inedible kernels, 120 grams; foreign...

7 CFR 981.401 - Adjusted kernel weight.

Code of Federal Regulations, 2014 CFR

2014-01-01

... based on the analysis of a 1,000 gram sample taken from a lot of almonds weighing 10,000 pounds with less than 95 percent kernels, and a 1,000 gram sample taken from a lot of almonds weighing 10,000... percent kernels containing the following: Edible kernels, 530 grams; inedible kernels, 120 grams; foreign...

Oligo kernels for datamining on biological sequences: a case study on prokaryotic translation initiation sites

PubMed Central

Meinicke, Peter; Tech, Maike; Morgenstern, Burkhard; Merkl, Rainer

2004-01-01

Background Kernel-based learning algorithms are among the most advanced machine learning methods and have been successfully applied to a variety of sequence classification tasks within the field of bioinformatics. Conventional kernels utilized so far do not provide an easy interpretation of the learnt representations in terms of positional and compositional variability of the underlying biological signals. Results We propose a kernel-based approach to datamining on biological sequences. With our method it is possible to model and analyze positional variability of oligomers of any length in a natural way. On one hand this is achieved by mapping the sequences to an intuitive but high-dimensional feature space, well-suited for interpretation of the learnt models. On the other hand, by means of the kernel trick we can provide a general learning algorithm for that high-dimensional representation because all required statistics can be computed without performing an explicit feature space mapping of the sequences. By introducing a kernel parameter that controls the degree of position-dependency, our feature space representation can be tailored to the characteristics of the biological problem at hand. A regularized learning scheme enables application even to biological problems for which only small sets of example sequences are available. Our approach includes a visualization method for transparent representation of characteristic sequence features. Thereby importance of features can be measured in terms of discriminative strength with respect to classification of the underlying sequences. To demonstrate and validate our concept on a biochemically well-defined case, we analyze E. coli translation initiation sites in order to show that we can find biologically relevant signals. For that case, our results clearly show that the Shine-Dalgarno sequence is the most important signal upstream a start codon. The variability in position and composition we found for that signal is

An Approximate Approach to Automatic Kernel Selection.

PubMed

Ding, Lizhong; Liao, Shizhong

2016-02-02

Kernel selection is a fundamental problem of kernel-based learning algorithms. In this paper, we propose an approximate approach to automatic kernel selection for regression from the perspective of kernel matrix approximation. We first introduce multilevel circulant matrices into automatic kernel selection, and develop two approximate kernel selection algorithms by exploiting the computational virtues of multilevel circulant matrices. The complexity of the proposed algorithms is quasi-linear in the number of data points. Then, we prove an approximation error bound to measure the effect of the approximation in kernel matrices by multilevel circulant matrices on the hypothesis and further show that the approximate hypothesis produced with multilevel circulant matrices converges to the accurate hypothesis produced with kernel matrices. Experimental evaluations on benchmark datasets demonstrate the effectiveness of approximate kernel selection.

[Step Fisher discriminant analysis on severe clinical features of hand foot and mouth disease between enterovirus (EV) 71 and other EV].

PubMed

Ruan, Feng; Tan, Ai-jun; Zhang, Xue-bao; Chen, Xue-qin; Xiao, Song-jian; Ye, Zhong-wen; Wang, Song

2011-07-01

To compare the clinical features of severe hand foot and mouth disease between enterovirus (EV) 71 and other EV to find specific diagnosis index of EV71 severe hand foot and mouth disease. Case definition were adopted from national guideline of hand foot and mouth disease diagnose (Version 2010). Clinical data of severe hand foot and mouth disease came from case history and contents of questionnaire would include the ones between the time of onset and diagnoses being made. EV and EV71, Cox A16 nucleic acid tested were by RT-PCR in stool samples. Clinical features of severe hand foot and mouth disease between EV71 and other EV were compare. There appeared statistical differences between neurologic symptoms such as tremor, myoclonic jerk, listlessness, convulsion and white blood cell counts in CSF (P < 0.05). Results from the step Fisher discriminant analysis showed only tremor and white blood cell had an increase in CSF, with statistically significant differences. The discriminant equation of EV71 was Y = 3.059X(1) + 3.83X(5) - 2.742 and the equation of other EV was Y = 1.634X(1) + 1.623X(5) - 1.693. The specificity of EV71 was 91% and the specificity of other EV was 40%. The increase of clinical features of tremor and white blood cell in CSF could be used as diagnosis index of severe EV71.

A Novel Weighted Kernel PCA-Based Method for Optimization and Uncertainty Quantification

NASA Astrophysics Data System (ADS)

Thimmisetty, C.; Talbot, C.; Chen, X.; Tong, C. H.

2016-12-01

It has been demonstrated that machine learning methods can be successfully applied to uncertainty quantification for geophysical systems through the use of the adjoint method coupled with kernel PCA-based optimization. In addition, it has been shown through weighted linear PCA how optimization with respect to both observation weights and feature space control variables can accelerate convergence of such methods. Linear machine learning methods, however, are inherently limited in their ability to represent features of non-Gaussian stochastic random fields, as they are based on only the first two statistical moments of the original data. Nonlinear spatial relationships and multipoint statistics leading to the tortuosity characteristic of channelized media, for example, are captured only to a limited extent by linear PCA. With the aim of coupling the kernel-based and weighted methods discussed, we present a novel mathematical formulation of kernel PCA, Weighted Kernel Principal Component Analysis (WKPCA), that both captures nonlinear relationships and incorporates the attribution of significance levels to different realizations of the stochastic random field of interest. We also demonstrate how new instantiations retaining defining characteristics of the random field can be generated using Bayesian methods. In particular, we present a novel WKPCA-based optimization method that minimizes a given objective function with respect to both feature space random variables and observation weights through which optimal snapshot significance levels and optimal features are learned. We showcase how WKPCA can be applied to nonlinear optimal control problems involving channelized media, and in particular demonstrate an application of the method to learning the spatial distribution of material parameter values in the context of linear elasticity, and discuss further extensions of the method to stochastic inversion.

Wright-Fisher diffusion bridges.

PubMed

Griffiths, Robert C; Jenkins, Paul A; Spanò, Dario

2017-10-06

The trajectory of the frequency of an allele which begins at x at time 0 and is known to have frequency z at time T can be modelled by the bridge process of the Wright-Fisher diffusion. Bridges when x=z=0 are particularly interesting because they model the trajectory of the frequency of an allele which appears at a time, then is lost by random drift or mutation after a time T. The coalescent genealogy back in time of a population in a neutral Wright-Fisher diffusion process is well understood. In this paper we obtain a new interpretation of the coalescent genealogy of the population in a bridge from a time t∈(0,T). In a bridge with allele frequencies of 0 at times 0 and T the coalescence structure is that the population coalesces in two directions from t to 0 and t to T such that there is just one lineage of the allele under consideration at times 0 and T. The genealogy in Wright-Fisher diffusion bridges with selection is more complex than in the neutral model, but still with the property of the population branching and coalescing in two directions from time t∈(0,T). The density of the frequency of an allele at time t is expressed in a way that shows coalescence in the two directions. A new algorithm for exact simulation of a neutral Wright-Fisher bridge is derived. This follows from knowing the density of the frequency in a bridge and exact simulation from the Wright-Fisher diffusion. The genealogy of the neutral Wright-Fisher bridge is also modelled by branching Pólya urns, extending a representation in a Wright-Fisher diffusion. This is a new very interesting representation that relates Wright-Fisher bridges to classical urn models in a Bayesian setting. Copyright © 2017 Elsevier Inc. All rights reserved.

A Kernel-Based Low-Rank (KLR) Model for Low-Dimensional Manifold Recovery in Highly Accelerated Dynamic MRI.

PubMed

Nakarmi, Ukash; Wang, Yanhua; Lyu, Jingyuan; Liang, Dong; Ying, Leslie

2017-11-01

While many low rank and sparsity-based approaches have been developed for accelerated dynamic magnetic resonance imaging (dMRI), they all use low rankness or sparsity in input space, overlooking the intrinsic nonlinear correlation in most dMRI data. In this paper, we propose a kernel-based framework to allow nonlinear manifold models in reconstruction from sub-Nyquist data. Within this framework, many existing algorithms can be extended to kernel framework with nonlinear models. In particular, we have developed a novel algorithm with a kernel-based low-rank model generalizing the conventional low rank formulation. The algorithm consists of manifold learning using kernel, low rank enforcement in feature space, and preimaging with data consistency. Extensive simulation and experiment results show that the proposed method surpasses the conventional low-rank-modeled approaches for dMRI.

A kernel-based novelty detection scheme for the ultra-fast detection of chirp evoked Auditory Brainstem Responses.

PubMed

Corona-Strauss, Farah I; Delb, Wolfgang; Schick, Bernhard; Strauss, Daniel J

2010-01-01

Auditory Brainstem Responses (ABRs) are used as objective method for diagnostics and quantification of hearing loss. Many methods for automatic recognition of ABRs have been developed, but none of them include the individual measurement setup in the analysis. The purpose of this work was to design a fast recognition scheme for chirp-evoked ABRs that is adjusted to the individual measurement condition using spontaneous electroencephalographic activity (SA). For the classification, the kernel-based novelty detection scheme used features based on the inter-sweep instantaneous phase synchronization as well as energy and entropy relations in the time-frequency domain. This method provided SA discrimination from stimulations above the hearing threshold with a minimum number of sweeps, i.e., 200 individual responses. It is concluded that the proposed paradigm, processing procedures and stimulation techniques improve the detection of ABRs in terms of the degree of objectivity, i.e., automation of procedure, and measurement time.

A new randomized Kaczmarz based kernel canonical correlation analysis algorithm with applications to information retrieval.

PubMed

Cai, Jia; Tang, Yi

2018-02-01

Canonical correlation analysis (CCA) is a powerful statistical tool for detecting the linear relationship between two sets of multivariate variables. Kernel generalization of it, namely, kernel CCA is proposed to describe nonlinear relationship between two variables. Although kernel CCA can achieve dimensionality reduction results for high-dimensional data feature selection problem, it also yields the so called over-fitting phenomenon. In this paper, we consider a new kernel CCA algorithm via randomized Kaczmarz method. The main contributions of the paper are: (1) A new kernel CCA algorithm is developed, (2) theoretical convergence of the proposed algorithm is addressed by means of scaled condition number, (3) a lower bound which addresses the minimum number of iterations is presented. We test on both synthetic dataset and several real-world datasets in cross-language document retrieval and content-based image retrieval to demonstrate the effectiveness of the proposed algorithm. Numerical results imply the performance and efficiency of the new algorithm, which is competitive with several state-of-the-art kernel CCA methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

Protein subcellular localization prediction using multiple kernel learning based support vector machine.

PubMed

Hasan, Md Al Mehedi; Ahmad, Shamim; Molla, Md Khademul Islam

2017-03-28

Predicting the subcellular locations of proteins can provide useful hints that reveal their functions, increase our understanding of the mechanisms of some diseases, and finally aid in the development of novel drugs. As the number of newly discovered proteins has been growing exponentially, which in turns, makes the subcellular localization prediction by purely laboratory tests prohibitively laborious and expensive. In this context, to tackle the challenges, computational methods are being developed as an alternative choice to aid biologists in selecting target proteins and designing related experiments. However, the success of protein subcellular localization prediction is still a complicated and challenging issue, particularly, when query proteins have multi-label characteristics, i.e., if they exist simultaneously in more than one subcellular location or if they move between two or more different subcellular locations. To date, to address this problem, several types of subcellular localization prediction methods with different levels of accuracy have been proposed. The support vector machine (SVM) has been employed to provide potential solutions to the protein subcellular localization prediction problem. However, the practicability of an SVM is affected by the challenges of selecting an appropriate kernel and selecting the parameters of the selected kernel. To address this difficulty, in this study, we aimed to develop an efficient multi-label protein subcellular localization prediction system, named as MKLoc, by introducing multiple kernel learning (MKL) based SVM. We evaluated MKLoc using a combined dataset containing 5447 single-localized proteins (originally published as part of the Höglund dataset) and 3056 multi-localized proteins (originally published as part of the DBMLoc set). Note that this dataset was used by Briesemeister et al. in their extensive comparison of multi-localization prediction systems. Finally, our experimental results indicate that

[Discrimination of Red Tide algae by fluorescence spectra and principle component analysis].

PubMed

Su, Rong-guo; Hu, Xu-peng; Zhang, Chuan-song; Wang, Xiu-lin

2007-07-01

Fluorescence discrimination technology for 11 species of the Red Tide algae at genus level was constructed by principle component analysis and non-negative least squares. Rayleigh and Raman scattering peaks of 3D fluorescence spectra were eliminated by Delaunay triangulation method. According to the results of Fisher linear discrimination, the first principle component score and the second component score of 3D fluorescence spectra were chosen as discriminant feature and the feature base was established. The 11 algae species were tested, and more than 85% samples were accurately determinated, especially for Prorocentrum donghaiense, Skeletonema costatum, Gymnodinium sp., which have frequently brought Red tide in the East China Sea. More than 95% samples were right discriminated. The results showed that the genus discriminant feature of 3D fluorescence spectra of Red Tide algae given by principle component analysis could work well.

Chapter 3: Fisher

Treesearch

Roger A. Powell; William J. Zielinski

1994-01-01

The fisher (Martes pennanti) is a medium-size mammalian carnivore and the largest member of the genus Martes (Anderson 1970) of the family Mustelidae in the order Carnivora. The genus Martes includes five or six other extant species. The fisher has the general body build of a stocky weasel and is long, thin, and...

Predicting activity approach based on new atoms similarity kernel function.

PubMed

Abu El-Atta, Ahmed H; Moussa, M I; Hassanien, Aboul Ella

2015-07-01

Drug design is a high cost and long term process. To reduce time and costs for drugs discoveries, new techniques are needed. Chemoinformatics field implements the informational techniques and computer science like machine learning and graph theory to discover the chemical compounds properties, such as toxicity or biological activity. This is done through analyzing their molecular structure (molecular graph). To overcome this problem there is an increasing need for algorithms to analyze and classify graph data to predict the activity of molecules. Kernels methods provide a powerful framework which combines machine learning with graph theory techniques. These kernels methods have led to impressive performance results in many several chemoinformatics problems like biological activity prediction. This paper presents a new approach based on kernel functions to solve activity prediction problem for chemical compounds. First we encode all atoms depending on their neighbors then we use these codes to find a relationship between those atoms each other. Then we use relation between different atoms to find similarity between chemical compounds. The proposed approach was compared with many other classification methods and the results show competitive accuracy with these methods. Copyright © 2015 Elsevier Inc. All rights reserved.

Multiple kernel SVR based on the MRE for remote sensing water depth fusion detection

NASA Astrophysics Data System (ADS)

Wang, Jinjin; Ma, Yi; Zhang, Jingyu

2018-03-01

Remote sensing has an important means of water depth detection in coastal shallow waters and reefs. Support vector regression (SVR) is a machine learning method which is widely used in data regression. In this paper, SVR is used to remote sensing multispectral bathymetry. Aiming at the problem that the single-kernel SVR method has a large error in shallow water depth inversion, the mean relative error (MRE) of different water depth is retrieved as a decision fusion factor with single kernel SVR method, a multi kernel SVR fusion method based on the MRE is put forward. And taking the North Island of the Xisha Islands in China as an experimentation area, the comparison experiments with the single kernel SVR method and the traditional multi-bands bathymetric method are carried out. The results show that: 1) In range of 0 to 25 meters, the mean absolute error(MAE)of the multi kernel SVR fusion method is 1.5m,the MRE is 13.2%; 2) Compared to the 4 single kernel SVR method, the MRE of the fusion method reduced 1.2% (1.9%) 3.4% (1.8%), and compared to traditional multi-bands method, the MRE reduced 1.9%; 3) In 0-5m depth section, compared to the single kernel method and the multi-bands method, the MRE of fusion method reduced 13.5% to 44.4%, and the distribution of points is more concentrated relative to y=x.

Generalized Fisher matrices

NASA Astrophysics Data System (ADS)

Heavens, A. F.; Seikel, M.; Nord, B. D.; Aich, M.; Bouffanais, Y.; Bassett, B. A.; Hobson, M. P.

2014-12-01

The Fisher Information Matrix formalism (Fisher 1935) is extended to cases where the data are divided into two parts (X, Y), where the expectation value of Y depends on X according to some theoretical model, and X and Y both have errors with arbitrary covariance. In the simplest case, (X, Y) represent data pairs of abscissa and ordinate, in which case the analysis deals with the case of data pairs with errors in both coordinates, but X can be any measured quantities on which Y depends. The analysis applies for arbitrary covariance, provided all errors are Gaussian, and provided the errors in X are small, both in comparison with the scale over which the expected signal Y changes, and with the width of the prior distribution. This generalizes the Fisher Matrix approach, which normally only considers errors in the `ordinate' Y. In this work, we include errors in X by marginalizing over latent variables, effectively employing a Bayesian hierarchical model, and deriving the Fisher Matrix for this more general case. The methods here also extend to likelihood surfaces which are not Gaussian in the parameter space, and so techniques such as DALI (Derivative Approximation for Likelihoods) can be generalized straightforwardly to include arbitrary Gaussian data error covariances. For simple mock data and theoretical models, we compare to Markov Chain Monte Carlo experiments, illustrating the method with cosmological supernova data. We also include the new method in the FISHER4CAST software.

a Comparison Study of Different Kernel Functions for Svm-Based Classification of Multi-Temporal Polarimetry SAR Data

NASA Astrophysics Data System (ADS)

Yekkehkhany, B.; Safari, A.; Homayouni, S.; Hasanlou, M.

2014-10-01

In this paper, a framework is developed based on Support Vector Machines (SVM) for crop classification using polarimetric features extracted from multi-temporal Synthetic Aperture Radar (SAR) imageries. The multi-temporal integration of data not only improves the overall retrieval accuracy but also provides more reliable estimates with respect to single-date data. Several kernel functions are employed and compared in this study for mapping the input space to higher Hilbert dimension space. These kernel functions include linear, polynomials and Radial Based Function (RBF). The method is applied to several UAVSAR L-band SAR images acquired over an agricultural area near Winnipeg, Manitoba, Canada. In this research, the temporal alpha features of H/A/α decomposition method are used in classification. The experimental tests show an SVM classifier with RBF kernel for three dates of data increases the Overall Accuracy (OA) to up to 3% in comparison to using linear kernel function, and up to 1% in comparison to a 3rd degree polynomial kernel function.

A Novel Mittag-Leffler Kernel Based Hybrid Fault Diagnosis Method for Wheeled Robot Driving System.

PubMed

Yuan, Xianfeng; Song, Mumin; Zhou, Fengyu; Chen, Zhumin; Li, Yan

2015-01-01

The wheeled robots have been successfully applied in many aspects, such as industrial handling vehicles, and wheeled service robots. To improve the safety and reliability of wheeled robots, this paper presents a novel hybrid fault diagnosis framework based on Mittag-Leffler kernel (ML-kernel) support vector machine (SVM) and Dempster-Shafer (D-S) fusion. Using sensor data sampled under different running conditions, the proposed approach initially establishes multiple principal component analysis (PCA) models for fault feature extraction. The fault feature vectors are then applied to train the probabilistic SVM (PSVM) classifiers that arrive at a preliminary fault diagnosis. To improve the accuracy of preliminary results, a novel ML-kernel based PSVM classifier is proposed in this paper, and the positive definiteness of the ML-kernel is proved as well. The basic probability assignments (BPAs) are defined based on the preliminary fault diagnosis results and their confidence values. Eventually, the final fault diagnosis result is archived by the fusion of the BPAs. Experimental results show that the proposed framework not only is capable of detecting and identifying the faults in the robot driving system, but also has better performance in stability and diagnosis accuracy compared with the traditional methods.

RTOS kernel in portable electrocardiograph

NASA Astrophysics Data System (ADS)

Centeno, C. A.; Voos, J. A.; Riva, G. G.; Zerbini, C.; Gonzalez, E. A.

2011-12-01

This paper presents the use of a Real Time Operating System (RTOS) on a portable electrocardiograph based on a microcontroller platform. All medical device digital functions are performed by the microcontroller. The electrocardiograph CPU is based on the 18F4550 microcontroller, in which an uCOS-II RTOS can be embedded. The decision associated with the kernel use is based on its benefits, the license for educational use and its intrinsic time control and peripherals management. The feasibility of its use on the electrocardiograph is evaluated based on the minimum memory requirements due to the kernel structure. The kernel's own tools were used for time estimation and evaluation of resources used by each process. After this feasibility analysis, the migration from cyclic code to a structure based on separate processes or tasks able to synchronize events is used; resulting in an electrocardiograph running on one Central Processing Unit (CPU) based on RTOS.

R. A. Fisher: a faith fit for eugenics.

PubMed

Moore, James

2007-03-01

In discussions of 'religion-and-science', faith is usually emphasized more than works, scientists' beliefs more than their deeds. By reversing the priority, a lingering puzzle in the life of Ronald Aylmer Fisher (1890-1962), statistician, eugenicist and founder of the neo-Darwinian synthesis, can be solved. Scholars have struggled to find coherence in Fisher's simultaneous commitment to Darwinism, Anglican Christianity and eugenics. The problem is addressed by asking what practical mode of faith or faithful mode of practice lent unity to his life? Families, it is argued, with their myriad practical, emotional and intellectual challenges, rendered a mathematically-based eugenic Darwinian Christianity not just possible for Fisher, but vital.

Co-existence of Fisheries and Marine Renewable Energy: The Spotlight on Fishers and Fishers' Knowledge (FK)

NASA Astrophysics Data System (ADS)

Campbell, M. S.; Ashley, M.; De Groot, J.; Rodwell, L.

2016-02-01

As an emerging industry, Marine Renewable Energy (MRE) is expected to play a major contributory role if the UK is to successfully reach it's desired target of renewable energy production by 2020. However, due to the competing objectives and priorities of MRE and other industries, for example fisheries, and in the delivering of conservation measures, the demand for space within our marine landscape is increasing, and interactions are inevitable. A semi structured interview was conducted with forty fishers across the UK to elicit further information on the challenges, barriers to progress and priority issues these fishers face in relation to MRE development. The questionnaire also included a fisher assessment of the mitigation agenda developed by de Groot et al. (2014) under the Natural Environment Research Council Marine Renewable Energy Knowledge Exchange Programme ( NERC MREKEP). Qualitative data were extracted and analysed using the text analysis software NVivo8. Fishers identified barriers to progress, and in order of the most important themes included; policy, consultation, trust, lack of knowledge, true representation of all fishers, science vs. fisher observation mismatch and timescales. Priority issues identified in order of importance were; displacement or loss of access, cable disturbance, timings of installation/repairs, effects on the seabed and specifically offshore windfarm (OWF) sitting. The consultation process caused discontent among all fishers interviewed. In relation to working towards a collaborative mitigation agenda, fishers highlighted issues of trust in relation to; trans-boundary management, data management and the consultation process. At all stages of the research, the response rate of the importance of gathering fishers' knowledge (FK) was high. Fishers underlined the importance of this data source in assessing the impacts of MRE on the sectors of the UK fleet. Thus, although at an early stage of development, an initial framework for the

Generation of a novel phase-space-based cylindrical dose kernel for IMRT optimization.

PubMed

Zhong, Hualiang; Chetty, Indrin J

2012-05-01

Improving dose calculation accuracy is crucial in intensity-modulated radiation therapy (IMRT). We have developed a method for generating a phase-space-based dose kernel for IMRT planning of lung cancer patients. Particle transport in the linear accelerator treatment head of a 21EX, 6 MV photon beam (Varian Medical Systems, Palo Alto, CA) was simulated using the EGSnrc/BEAMnrc code system. The phase space information was recorded under the secondary jaws. Each particle in the phase space file was associated with a beamlet whose index was calculated and saved in the particle's LATCH variable. The DOSXYZnrc code was modified to accumulate the energy deposited by each particle based on its beamlet index. Furthermore, the central axis of each beamlet was calculated from the orientation of all the particles in this beamlet. A cylinder was then defined around the central axis so that only the energy deposited within the cylinder was counted. A look-up table was established for each cylinder during the tallying process. The efficiency and accuracy of the cylindrical beamlet energy deposition approach was evaluated using a treatment plan developed on a simulated lung phantom. Profile and percentage depth doses computed in a water phantom for an open, square field size were within 1.5% of measurements. Dose optimized with the cylindrical dose kernel was found to be within 0.6% of that computed with the nontruncated 3D kernel. The cylindrical truncation reduced optimization time by approximately 80%. A method for generating a phase-space-based dose kernel, using a truncated cylinder for scoring dose, in beamlet-based optimization of lung treatment planning was developed and found to be in good agreement with the standard, nontruncated scoring approach. Compared to previous techniques, our method significantly reduces computational time and memory requirements, which may be useful for Monte-Carlo-based 4D IMRT or IMAT treatment planning.

Fisher in Adelaide.

PubMed

Mayo, Oliver

2014-06-01

R. A. Fisher spent much of his final 3 years of life in Adelaide. It was a congenial place to live and work, and he was much in demand as a speaker, in Australia and overseas. It was, however, a difficult time for him because of the sustained criticism of fiducial inference from the early 1950s onwards. The article discusses some of Fisher's work on inference from an Adelaide perspective. It also considers some of the successes arising from this time, in the statistics of field experimentation and in evolutionary genetics. A few personal recollections of Fisher as houseguest are provided. This article is the text of a article presented on August 31, 2012 at the 26th International Biometric Conference, Kobe, Japan. © 2014, The International Biometric Society.

Sensitivity kernels for viscoelastic loading based on adjoint methods

NASA Astrophysics Data System (ADS)

Al-Attar, David; Tromp, Jeroen

2014-01-01

Observations of glacial isostatic adjustment (GIA) allow for inferences to be made about mantle viscosity, ice sheet history and other related parameters. Typically, this inverse problem can be formulated as minimizing the misfit between the given observations and a corresponding set of synthetic data. When the number of parameters is large, solution of such optimization problems can be computationally challenging. A practical, albeit non-ideal, solution is to use gradient-based optimization. Although the gradient of the misfit required in such methods could be calculated approximately using finite differences, the necessary computation time grows linearly with the number of model parameters, and so this is often infeasible. A far better approach is to apply the `adjoint method', which allows the exact gradient to be calculated from a single solution of the forward problem, along with one solution of the associated adjoint problem. As a first step towards applying the adjoint method to the GIA inverse problem, we consider its application to a simpler viscoelastic loading problem in which gravitationally self-consistent ocean loading is neglected. The earth model considered is non-rotating, self-gravitating, compressible, hydrostatically pre-stressed, laterally heterogeneous and possesses a Maxwell solid rheology. We determine adjoint equations and Fréchet kernels for this problem based on a Lagrange multiplier method. Given an objective functional J defined in terms of the surface deformation fields, we show that its first-order perturbation can be written δ J = int _{MS}K_{η }δ ln η dV +int _{t0}^{t1}int _{partial M}K_{dot{σ }} δ dot{σ } dS dt, where δ ln η = δη/η denotes relative viscosity variations in solid regions MS, dV is the volume element, δ dot{σ } is the perturbation to the time derivative of the surface load which is defined on the earth model's surface ∂M and for times [t0, t1] and dS is the surface element on ∂M. The `viscosity

Application of kernel method in fluorescence molecular tomography

NASA Astrophysics Data System (ADS)

Zhao, Yue; Baikejiang, Reheman; Li, Changqing

2017-02-01

Reconstruction of fluorescence molecular tomography (FMT) is an ill-posed inverse problem. Anatomical guidance in the FMT reconstruction can improve FMT reconstruction efficiently. We have developed a kernel method to introduce the anatomical guidance into FMT robustly and easily. The kernel method is from machine learning for pattern analysis and is an efficient way to represent anatomical features. For the finite element method based FMT reconstruction, we calculate a kernel function for each finite element node from an anatomical image, such as a micro-CT image. Then the fluorophore concentration at each node is represented by a kernel coefficient vector and the corresponding kernel function. In the FMT forward model, we have a new system matrix by multiplying the sensitivity matrix with the kernel matrix. Thus, the kernel coefficient vector is the unknown to be reconstructed following a standard iterative reconstruction process. We convert the FMT reconstruction problem into the kernel coefficient reconstruction problem. The desired fluorophore concentration at each node can be calculated accordingly. Numerical simulation studies have demonstrated that the proposed kernel-based algorithm can improve the spatial resolution of the reconstructed FMT images. In the proposed kernel method, the anatomical guidance can be obtained directly from the anatomical image and is included in the forward modeling. One of the advantages is that we do not need to segment the anatomical image for the targets and background.

Accelerating the Original Profile Kernel.

PubMed

Hamp, Tobias; Goldberg, Tatyana; Rost, Burkhard

2013-01-01

One of the most accurate multi-class protein classification systems continues to be the profile-based SVM kernel introduced by the Leslie group. Unfortunately, its CPU requirements render it too slow for practical applications of large-scale classification tasks. Here, we introduce several software improvements that enable significant acceleration. Using various non-redundant data sets, we demonstrate that our new implementation reaches a maximal speed-up as high as 14-fold for calculating the same kernel matrix. Some predictions are over 200 times faster and render the kernel as possibly the top contender in a low ratio of speed/performance. Additionally, we explain how to parallelize various computations and provide an integrative program that reduces creating a production-quality classifier to a single program call. The new implementation is available as a Debian package under a free academic license and does not depend on commercial software. For non-Debian based distributions, the source package ships with a traditional Makefile-based installer. Download and installation instructions can be found at https://rostlab.org/owiki/index.php/Fast_Profile_Kernel. Bugs and other issues may be reported at https://rostlab.org/bugzilla3/enter_bug.cgi?product=fastprofkernel.

Generalized multiple kernel learning with data-dependent priors.

PubMed

Mao, Qi; Tsang, Ivor W; Gao, Shenghua; Wang, Li

2015-06-01

Multiple kernel learning (MKL) and classifier ensemble are two mainstream methods for solving learning problems in which some sets of features/views are more informative than others, or the features/views within a given set are inconsistent. In this paper, we first present a novel probabilistic interpretation of MKL such that maximum entropy discrimination with a noninformative prior over multiple views is equivalent to the formulation of MKL. Instead of using the noninformative prior, we introduce a novel data-dependent prior based on an ensemble of kernel predictors, which enhances the prediction performance of MKL by leveraging the merits of the classifier ensemble. With the proposed probabilistic framework of MKL, we propose a hierarchical Bayesian model to learn the proposed data-dependent prior and classification model simultaneously. The resultant problem is convex and other information (e.g., instances with either missing views or missing labels) can be seamlessly incorporated into the data-dependent priors. Furthermore, a variety of existing MKL models can be recovered under the proposed MKL framework and can be readily extended to incorporate these priors. Extensive experiments demonstrate the benefits of our proposed framework in supervised and semisupervised settings, as well as in tasks with partial correspondence among multiple views.

Intra-individual gait patterns across different time-scales as revealed by means of a supervised learning model using kernel-based discriminant regression.

PubMed

Horst, Fabian; Eekhoff, Alexander; Newell, Karl M; Schöllhorn, Wolfgang I

2017-01-01

Traditionally, gait analysis has been centered on the idea of average behavior and normality. On one hand, clinical diagnoses and therapeutic interventions typically assume that average gait patterns remain constant over time. On the other hand, it is well known that all our movements are accompanied by a certain amount of variability, which does not allow us to make two identical steps. The purpose of this study was to examine changes in the intra-individual gait patterns across different time-scales (i.e., tens-of-mins, tens-of-hours). Nine healthy subjects performed 15 gait trials at a self-selected speed on 6 sessions within one day (duration between two subsequent sessions from 10 to 90 mins). For each trial, time-continuous ground reaction forces and lower body joint angles were measured. A supervised learning model using a kernel-based discriminant regression was applied for classifying sessions within individual gait patterns. Discernable characteristics of intra-individual gait patterns could be distinguished between repeated sessions by classification rates of 67.8 ± 8.8% and 86.3 ± 7.9% for the six-session-classification of ground reaction forces and lower body joint angles, respectively. Furthermore, the one-on-one-classification showed that increasing classification rates go along with increasing time durations between two sessions and indicate that changes of gait patterns appear at different time-scales. Discernable characteristics between repeated sessions indicate continuous intrinsic changes in intra-individual gait patterns and suggest a predominant role of deterministic processes in human motor control and learning. Natural changes of gait patterns without any externally induced injury or intervention may reflect continuous adaptations of the motor system over several time-scales. Accordingly, the modelling of walking by means of average gait patterns that are assumed to be near constant over time needs to be reconsidered in the context of

A Novel Mittag-Leffler Kernel Based Hybrid Fault Diagnosis Method for Wheeled Robot Driving System

PubMed Central

Yuan, Xianfeng; Song, Mumin; Chen, Zhumin; Li, Yan

2015-01-01

The wheeled robots have been successfully applied in many aspects, such as industrial handling vehicles, and wheeled service robots. To improve the safety and reliability of wheeled robots, this paper presents a novel hybrid fault diagnosis framework based on Mittag-Leffler kernel (ML-kernel) support vector machine (SVM) and Dempster-Shafer (D-S) fusion. Using sensor data sampled under different running conditions, the proposed approach initially establishes multiple principal component analysis (PCA) models for fault feature extraction. The fault feature vectors are then applied to train the probabilistic SVM (PSVM) classifiers that arrive at a preliminary fault diagnosis. To improve the accuracy of preliminary results, a novel ML-kernel based PSVM classifier is proposed in this paper, and the positive definiteness of the ML-kernel is proved as well. The basic probability assignments (BPAs) are defined based on the preliminary fault diagnosis results and their confidence values. Eventually, the final fault diagnosis result is archived by the fusion of the BPAs. Experimental results show that the proposed framework not only is capable of detecting and identifying the faults in the robot driving system, but also has better performance in stability and diagnosis accuracy compared with the traditional methods. PMID:26229526

Bands selection and classification of hyperspectral images based on hybrid kernels SVM by evolutionary algorithm

NASA Astrophysics Data System (ADS)

Hu, Yan-Yan; Li, Dong-Sheng

2016-01-01

The hyperspectral images(HSI) consist of many closely spaced bands carrying the most object information. While due to its high dimensionality and high volume nature, it is hard to get satisfactory classification performance. In order to reduce HSI data dimensionality preparation for high classification accuracy, it is proposed to combine a band selection method of artificial immune systems (AIS) with a hybrid kernels support vector machine (SVM-HK) algorithm. In fact, after comparing different kernels for hyperspectral analysis, the approach mixed radial basis function kernel (RBF-K) with sigmoid kernel (Sig-K) and applied the optimized hybrid kernels in SVM classifiers. Then the SVM-HK algorithm used to induce the bands selection of an improved version of AIS. The AIS was composed of clonal selection and elite antibody mutation, including evaluation process with optional index factor (OIF). Experimental classification performance was on a San Diego Naval Base acquired by AVIRIS, the HRS dataset shows that the method is able to efficiently achieve bands redundancy removal while outperforming the traditional SVM classifier.

Uneven adaptive capacity among fishers in a sea of change

PubMed Central

Fuller, Emma; Crona, Beatrice I.

2017-01-01

Fishers worldwide operate in an environment of uncertainty and constant change. Their ability to manage risk associated with such uncertainty and subsequently adapt to change is largely a function of individual circumstances, including their access to different fisheries. However, explicit attention to the heterogeneity of fishers’ connections to fisheries at the level of the individual has been largely ignored. We illustrate the ubiquitous nature of these connections by constructing a typology of commercial fishers in the state of Maine based on the different fisheries that fishers rely on to sustain their livelihoods and find that there are over 600 combinations. We evaluate the adaptive potential of each strategy, using a set of attributes identified by fisheries experts in the state, and find that only 12% of fishers can be classified as being well positioned to adapt in the face of changing socioeconomic and ecological conditions. Sensitivity to the uneven and heterogeneous capacity of fishers to manage risk and adapt to change is critical to devising effective management strategies that broadly support fishers. This will require greater attention to the social-ecological connectivity of fishers across different jurisdictions. PMID:28604775

The pre-image problem in kernel methods.

PubMed

Kwok, James Tin-yau; Tsang, Ivor Wai-hung

2004-11-01

In this paper, we address the problem of finding the pre-image of a feature vector in the feature space induced by a kernel. This is of central importance in some kernel applications, such as on using kernel principal component analysis (PCA) for image denoising. Unlike the traditional method which relies on nonlinear optimization, our proposed method directly finds the location of the pre-image based on distance constraints in the feature space. It is noniterative, involves only linear algebra and does not suffer from numerical instability or local minimum problems. Evaluations on performing kernel PCA and kernel clustering on the USPS data set show much improved performance.

Discriminative graph embedding for label propagation.

PubMed

Nguyen, Canh Hao; Mamitsuka, Hiroshi

2011-09-01

In many applications, the available information is encoded in graph structures. This is a common problem in biological networks, social networks, web communities and document citations. We investigate the problem of classifying nodes' labels on a similarity graph given only a graph structure on the nodes. Conventional machine learning methods usually require data to reside in some Euclidean spaces or to have a kernel representation. Applying these methods to nodes on graphs would require embedding the graphs into these spaces. By embedding and then learning the nodes on graphs, most methods are either flexible with different learning objectives or efficient enough for large scale applications. We propose a method to embed a graph into a feature space for a discriminative purpose. Our idea is to include label information into the embedding process, making the space representation tailored to the task. We design embedding objective functions that the following learning formulations become spectral transforms. We then reformulate these spectral transforms into multiple kernel learning problems. Our method, while being tailored to the discriminative tasks, is efficient and can scale to massive data sets. We show the need of discriminative embedding on some simulations. Applying to biological network problems, our method is shown to outperform baselines.

A ℓ2, 1 norm regularized multi-kernel learning for false positive reduction in Lung nodule CAD.

PubMed

Cao, Peng; Liu, Xiaoli; Zhang, Jian; Li, Wei; Zhao, Dazhe; Huang, Min; Zaiane, Osmar

2017-03-01

The aim of this paper is to describe a novel algorithm for False Positive Reduction in lung nodule Computer Aided Detection(CAD). In this paper, we describes a new CT lung CAD method which aims to detect solid nodules. Specially, we proposed a multi-kernel classifier with a ℓ 2, 1 norm regularizer for heterogeneous feature fusion and selection from the feature subset level, and designed two efficient strategies to optimize the parameters of kernel weights in non-smooth ℓ 2, 1 regularized multiple kernel learning algorithm. The first optimization algorithm adapts a proximal gradient method for solving the ℓ 2, 1 norm of kernel weights, and use an accelerated method based on FISTA; the second one employs an iterative scheme based on an approximate gradient descent method. The results demonstrates that the FISTA-style accelerated proximal descent method is efficient for the ℓ 2, 1 norm formulation of multiple kernel learning with the theoretical guarantee of the convergence rate. Moreover, the experimental results demonstrate the effectiveness of the proposed methods in terms of Geometric mean (G-mean) and Area under the ROC curve (AUC), and significantly outperforms the competing methods. The proposed approach exhibits some remarkable advantages both in heterogeneous feature subsets fusion and classification phases. Compared with the fusion strategies of feature-level and decision level, the proposed ℓ 2, 1 norm multi-kernel learning algorithm is able to accurately fuse the complementary and heterogeneous feature sets, and automatically prune the irrelevant and redundant feature subsets to form a more discriminative feature set, leading a promising classification performance. Moreover, the proposed algorithm consistently outperforms the comparable classification approaches in the literature. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

More than Anecdotes: Fishers' Ecological Knowledge Can Fill Gaps for Ecosystem Modeling.

PubMed

Bevilacqua, Ana Helena V; Carvalho, Adriana R; Angelini, Ronaldo; Christensen, Villy

2016-01-01

Ecosystem modeling applied to fisheries remains hampered by a lack of local information. Fishers' knowledge could fill this gap, improving participation in and the management of fisheries. The same fishing area was modeled using two approaches: based on fishers' knowledge and based on scientific information. For the former, the data was collected by interviews through the Delphi methodology, and for the latter, the data was gathered from the literature. Agreement between the attributes generated by the fishers' knowledge model and scientific model is discussed and explored, aiming to improve data availability, the ecosystem model, and fisheries management. The ecosystem attributes produced from the fishers' knowledge model were consistent with the ecosystem attributes produced by the scientific model, and elaborated using only the scientific data from literature. This study provides evidence that fishers' knowledge may suitably complement scientific data, and may improve the modeling tools for the research and management of fisheries.

Kernel-PCA data integration with enhanced interpretability

PubMed Central

2014-01-01

Background Nowadays, combining the different sources of information to improve the biological knowledge available is a challenge in bioinformatics. One of the most powerful methods for integrating heterogeneous data types are kernel-based methods. Kernel-based data integration approaches consist of two basic steps: firstly the right kernel is chosen for each data set; secondly the kernels from the different data sources are combined to give a complete representation of the available data for a given statistical task. Results We analyze the integration of data from several sources of information using kernel PCA, from the point of view of reducing dimensionality. Moreover, we improve the interpretability of kernel PCA by adding to the plot the representation of the input variables that belong to any dataset. In particular, for each input variable or linear combination of input variables, we can represent the direction of maximum growth locally, which allows us to identify those samples with higher/lower values of the variables analyzed. Conclusions The integration of different datasets and the simultaneous representation of samples and variables together give us a better understanding of biological knowledge. PMID:25032747

Spectral imaging using consumer-level devices and kernel-based regression.

PubMed

Heikkinen, Ville; Cámara, Clara; Hirvonen, Tapani; Penttinen, Niko

2016-06-01

Hyperspectral reflectance factor image estimations were performed in the 400-700 nm wavelength range using a portable consumer-level laptop display as an adjustable light source for a trichromatic camera. Targets of interest were ColorChecker Classic samples, Munsell Matte samples, geometrically challenging tempera icon paintings from the turn of the 20th century, and human hands. Measurements and simulations were performed using Nikon D80 RGB camera and Dell Vostro 2520 laptop screen as a light source. Estimations were performed without spectral characteristics of the devices and by emphasizing simplicity for training sets and estimation model optimization. Spectral and color error images are shown for the estimations using line-scanned hyperspectral images as the ground truth. Estimations were performed using kernel-based regression models via a first-degree inhomogeneous polynomial kernel and a Matérn kernel, where in the latter case the median heuristic approach for model optimization and link function for bounded estimation were evaluated. Results suggest modest requirements for a training set and show that all estimation models have markedly improved accuracy with respect to the DE00 color distance (up to 99% for paintings and hands) and the Pearson distance (up to 98% for paintings and 99% for hands) from a weak training set (Digital ColorChecker SG) case when small representative training data were used in the estimation.

Synthesis and analysis of discriminators under influence of broadband non-Gaussian noise

NASA Astrophysics Data System (ADS)

Artyushenko, V. M.; Volovach, V. I.

2018-01-01

We considered the problems of the synthesis and analysis of discriminators, when the useful signal is exposed to non-Gaussian additive broadband noise. It is shown that in this case, the discriminator of the tracking meter should contain the nonlinear transformation unit, the characteristics of which are determined by the Fisher information relative to the probability density function of the mixture of non-Gaussian broadband noise and mismatch errors. The parameters of the discriminatory and phase characteristics of the discriminators working under the above conditions are obtained. It is shown that the efficiency of non-linear processing depends on the ratio of power of FM noise to the power of Gaussian noise. The analysis of the information loss of signal transformation caused by the linear section of discriminatory characteristics of the unit of nonlinear transformations of the discriminator is carried out. It is shown that the average slope of the nonlinear transformation characteristic is determined by the Fisher information relative to the probability density function of the mixture of non-Gaussian noise and mismatch errors.

Weight-based discrimination: an ubiquitary phenomenon?

PubMed

Sikorski, C; Spahlholz, J; Hartlev, M; Riedel-Heller, S G

2016-02-01

Despite strong indications of a high prevalence of weight-related stigmatization in individuals with obesity, limited attention has been given to the role of weight discrimination in examining the stigma obesity. Studies, up to date, rely on a limited basis of data sets and additional studies are needed to confirm the findings of previous studies. In particular, data for Europe are lacking, and are needed in light of a recent ruling of the European Court of Justice that addressed weight-based discrimination. The data were derived from a large representative telephone survey in Germany (n=3003). The dependent variable, weight-based discrimination, was assessed with a one-item question. The lifetime prevalence of weight discrimination across different sociodemographic variables was determined. Logistic regression models were used to assess the association of independent and dependent variables. A sub-group analysis was conducted analyzing all participants with a body mass index ⩾25 kg m(-)(2). The overall prevalence of weight-based discrimination was 7.3%. Large differences, however, were observed regarding weight status. In normal weight and overweight participants the prevalence was 5.6%, but this number doubled in participants with obesity class I (10.2%), and quadrupled in participants with obesity class II (18.7%) and underweight (19.7%). In participants with obesity class III, every third participant reported accounts of weight-based discrimination (38%). In regression models, after adjustment, the associations of weight status and female gender (odds ratio: 2.59, P<0.001) remained highly significant. Discrimination seems to be an ubiquitary phenomenon at least for some groups that are at special risk, such as heavier individuals and women. Our findings therefore emphasize the need for research and intervention on weight discrimination among adults with obesity, including anti-discrimination legislation.

Improved dense trajectories for action recognition based on random projection and Fisher vectors

NASA Astrophysics Data System (ADS)

Ai, Shihui; Lu, Tongwei; Xiong, Yudian

2018-03-01

As an important application of intelligent monitoring system, the action recognition in video has become a very important research area of computer vision. In order to improve the accuracy rate of the action recognition in video with improved dense trajectories, one advanced vector method is introduced. Improved dense trajectories combine Fisher Vector with Random Projection. The method realizes the reduction of the characteristic trajectory though projecting the high-dimensional trajectory descriptor into the low-dimensional subspace based on defining and analyzing Gaussian mixture model by Random Projection. And a GMM-FV hybrid model is introduced to encode the trajectory feature vector and reduce dimension. The computational complexity is reduced by Random Projection which can drop Fisher coding vector. Finally, a Linear SVM is used to classifier to predict labels. We tested the algorithm in UCF101 dataset and KTH dataset. Compared with existed some others algorithm, the result showed that the method not only reduce the computational complexity but also improved the accuracy of action recognition.

Was RA Fisher Right?

PubMed

Srivastava, Ayush; Srivastava, Anurag; Pandey, Ravindra M

2017-10-01

Randomized controlled trials have become the most respected scientific tool to measure the effectiveness of a medical therapy. The design, conduct and analysis of randomized controlled trials were developed by Sir Ronald A. Fisher, a mathematician in Great Britain. Fisher propounded that the process of randomization would equally distribute all the known and even unknown covariates in the two or more comparison groups, so that any difference observed could be ascribed to treatment effect. Today, we observe that in many situations, this prediction of Fisher does not stand true; hence, adaptive randomization schedules have been designed to adjust for major imbalance in important covariates. Present essay unravels some weaknesses inherent in Fisherian concept of randomized controlled trial.

Metabolic network prediction through pairwise rational kernels.

PubMed

Roche-Lima, Abiel; Domaratzki, Michael; Fristensky, Brian

2014-09-26

Metabolic networks are represented by the set of metabolic pathways. Metabolic pathways are a series of biochemical reactions, in which the product (output) from one reaction serves as the substrate (input) to another reaction. Many pathways remain incompletely characterized. One of the major challenges of computational biology is to obtain better models of metabolic pathways. Existing models are dependent on the annotation of the genes. This propagates error accumulation when the pathways are predicted by incorrectly annotated genes. Pairwise classification methods are supervised learning methods used to classify new pair of entities. Some of these classification methods, e.g., Pairwise Support Vector Machines (SVMs), use pairwise kernels. Pairwise kernels describe similarity measures between two pairs of entities. Using pairwise kernels to handle sequence data requires long processing times and large storage. Rational kernels are kernels based on weighted finite-state transducers that represent similarity measures between sequences or automata. They have been effectively used in problems that handle large amount of sequence information such as protein essentiality, natural language processing and machine translations. We create a new family of pairwise kernels using weighted finite-state transducers (called Pairwise Rational Kernel (PRK)) to predict metabolic pathways from a variety of biological data. PRKs take advantage of the simpler representations and faster algorithms of transducers. Because raw sequence data can be used, the predictor model avoids the errors introduced by incorrect gene annotations. We then developed several experiments with PRKs and Pairwise SVM to validate our methods using the metabolic network of Saccharomyces cerevisiae. As a result, when PRKs are used, our method executes faster in comparison with other pairwise kernels. Also, when we use PRKs combined with other simple kernels that include evolutionary information, the accuracy

Implicit kernel sparse shape representation: a sparse-neighbors-based objection segmentation framework.

PubMed

Yao, Jincao; Yu, Huimin; Hu, Roland

2017-01-01

This paper introduces a new implicit-kernel-sparse-shape-representation-based object segmentation framework. Given an input object whose shape is similar to some of the elements in the training set, the proposed model can automatically find a cluster of implicit kernel sparse neighbors to approximately represent the input shape and guide the segmentation. A distance-constrained probabilistic definition together with a dualization energy term is developed to connect high-level shape representation and low-level image information. We theoretically prove that our model not only derives from two projected convex sets but is also equivalent to a sparse-reconstruction-error-based representation in the Hilbert space. Finally, a "wake-sleep"-based segmentation framework is applied to drive the evolutionary curve to recover the original shape of the object. We test our model on two public datasets. Numerical experiments on both synthetic images and real applications show the superior capabilities of the proposed framework.

Ranking Support Vector Machine with Kernel Approximation

PubMed Central

Dou, Yong

2017-01-01

Learning to rank algorithm has become important in recent years due to its successful application in information retrieval, recommender system, and computational biology, and so forth. Ranking support vector machine (RankSVM) is one of the state-of-art ranking models and has been favorably used. Nonlinear RankSVM (RankSVM with nonlinear kernels) can give higher accuracy than linear RankSVM (RankSVM with a linear kernel) for complex nonlinear ranking problem. However, the learning methods for nonlinear RankSVM are still time-consuming because of the calculation of kernel matrix. In this paper, we propose a fast ranking algorithm based on kernel approximation to avoid computing the kernel matrix. We explore two types of kernel approximation methods, namely, the Nyström method and random Fourier features. Primal truncated Newton method is used to optimize the pairwise L2-loss (squared Hinge-loss) objective function of the ranking model after the nonlinear kernel approximation. Experimental results demonstrate that our proposed method gets a much faster training speed than kernel RankSVM and achieves comparable or better performance over state-of-the-art ranking algorithms. PMID:28293256

Ranking Support Vector Machine with Kernel Approximation.

PubMed

Chen, Kai; Li, Rongchun; Dou, Yong; Liang, Zhengfa; Lv, Qi

2017-01-01

Learning to rank algorithm has become important in recent years due to its successful application in information retrieval, recommender system, and computational biology, and so forth. Ranking support vector machine (RankSVM) is one of the state-of-art ranking models and has been favorably used. Nonlinear RankSVM (RankSVM with nonlinear kernels) can give higher accuracy than linear RankSVM (RankSVM with a linear kernel) for complex nonlinear ranking problem. However, the learning methods for nonlinear RankSVM are still time-consuming because of the calculation of kernel matrix. In this paper, we propose a fast ranking algorithm based on kernel approximation to avoid computing the kernel matrix. We explore two types of kernel approximation methods, namely, the Nyström method and random Fourier features. Primal truncated Newton method is used to optimize the pairwise L2-loss (squared Hinge-loss) objective function of the ranking model after the nonlinear kernel approximation. Experimental results demonstrate that our proposed method gets a much faster training speed than kernel RankSVM and achieves comparable or better performance over state-of-the-art ranking algorithms.

Modified kernel-based nonlinear feature extraction.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ma, J.; Perkins, S. J.; Theiler, J. P.

2002-01-01

Feature Extraction (FE) techniques are widely used in many applications to pre-process data in order to reduce the complexity of subsequent processes. A group of Kernel-based nonlinear FE ( H E ) algorithms has attracted much attention due to their high performance. However, a serious limitation that is inherent in these algorithms -- the maximal number of features extracted by them is limited by the number of classes involved -- dramatically degrades their flexibility. Here we propose a modified version of those KFE algorithms (MKFE), This algorithm is developed from a special form of scatter-matrix, whose rank is not determinedmore » by the number of classes involved, and thus breaks the inherent limitation in those KFE algorithms. Experimental results suggest that MKFE algorithm is .especially useful when the training set is small.« less

In silico toxicity prediction by support vector machine and SMILES representation-based string kernel.

PubMed

Cao, D-S; Zhao, J-C; Yang, Y-N; Zhao, C-X; Yan, J; Liu, S; Hu, Q-N; Xu, Q-S; Liang, Y-Z

2012-01-01

There is a great need to assess the harmful effects or toxicities of chemicals to which man is exposed. In the present paper, the simplified molecular input line entry specification (SMILES) representation-based string kernel, together with the state-of-the-art support vector machine (SVM) algorithm, were used to classify the toxicity of chemicals from the US Environmental Protection Agency Distributed Structure-Searchable Toxicity (DSSTox) database network. In this method, the molecular structure can be directly encoded by a series of SMILES substrings that represent the presence of some chemical elements and different kinds of chemical bonds (double, triple and stereochemistry) in the molecules. Thus, SMILES string kernel can accurately and directly measure the similarities of molecules by a series of local information hidden in the molecules. Two model validation approaches, five-fold cross-validation and independent validation set, were used for assessing the predictive capability of our developed models. The results obtained indicate that SVM based on the SMILES string kernel can be regarded as a very promising and alternative modelling approach for potential toxicity prediction of chemicals.

Perceived racial discrimination and hypertension: a comprehensive systematic review.

PubMed

Dolezsar, Cynthia M; McGrath, Jennifer J; Herzig, Alyssa J M; Miller, Sydney B

2014-01-01

Discrimination is posited to underlie racial disparities in hypertension. Extant literature suggests a possible association between racial discrimination and blood pressure, although inconsistent findings have been reported. The aim of this comprehensive systematic review was to quantitatively evaluate the association between perceived racial discrimination with hypertensive status and systolic, diastolic, and ambulatory blood pressure. Electronic database search of PubMed and PsycINFO (keywords: blood pressure/hypertension/diastolic/systolic, racism/discrimination/prejudice/unfair treatment) was combined with descendancy and ascendancy approaches. Forty-four articles (N = 32,651) met inclusion criteria. Articles were coded for demographics, hypertensive diagnosis, blood pressure measurement, discrimination measure and constructs, study quality, and effect sizes. Random effects meta-analytic models were tested based on Fisher's Z, the derived common effect size metric. Overall, perceived racial discrimination was associated with hypertensive status, Zhypertension = 0.048, 95% CI [.013, .087], but not with resting blood pressure, Zsystolic = 0.011, 95% CI [-.006, .031], Zdiastolic = .016, 95% CI [-.006, .034]. Moderators that strengthened the relation included sex (male), race (Black), age (older), education (lower), and hypertensive status. Perceived discrimination was most strongly associated with nighttime ambulatory blood pressure, especially among Blacks. Despite methodological limitations in the existing literature, there was a small, significant association between perceived discrimination and hypertension. Future studies should consider ambulatory nighttime blood pressure, which may more accurately capture daily variation attributable to experienced racial discrimination. Perceived discrimination may partly explain racial health disparities. 2014 APA, all rights reserved

Kernel K-Means Sampling for Nyström Approximation.

PubMed

He, Li; Zhang, Hong

2018-05-01

A fundamental problem in Nyström-based kernel matrix approximation is the sampling method by which training set is built. In this paper, we suggest to use kernel -means sampling, which is shown in our works to minimize the upper bound of a matrix approximation error. We first propose a unified kernel matrix approximation framework, which is able to describe most existing Nyström approximations under many popular kernels, including Gaussian kernel and polynomial kernel. We then show that, the matrix approximation error upper bound, in terms of the Frobenius norm, is equal to the -means error of data points in kernel space plus a constant. Thus, the -means centers of data in kernel space, or the kernel -means centers, are the optimal representative points with respect to the Frobenius norm error upper bound. Experimental results, with both Gaussian kernel and polynomial kernel, on real-world data sets and image segmentation tasks show the superiority of the proposed method over the state-of-the-art methods.

Rare variant testing across methods and thresholds using the multi-kernel sequence kernel association test (MK-SKAT).

PubMed

Urrutia, Eugene; Lee, Seunggeun; Maity, Arnab; Zhao, Ni; Shen, Judong; Li, Yun; Wu, Michael C

Analysis of rare genetic variants has focused on region-based analysis wherein a subset of the variants within a genomic region is tested for association with a complex trait. Two important practical challenges have emerged. First, it is difficult to choose which test to use. Second, it is unclear which group of variants within a region should be tested. Both depend on the unknown true state of nature. Therefore, we develop the Multi-Kernel SKAT (MK-SKAT) which tests across a range of rare variant tests and groupings. Specifically, we demonstrate that several popular rare variant tests are special cases of the sequence kernel association test which compares pair-wise similarity in trait value to similarity in the rare variant genotypes between subjects as measured through a kernel function. Choosing a particular test is equivalent to choosing a kernel. Similarly, choosing which group of variants to test also reduces to choosing a kernel. Thus, MK-SKAT uses perturbation to test across a range of kernels. Simulations and real data analyses show that our framework controls type I error while maintaining high power across settings: MK-SKAT loses power when compared to the kernel for a particular scenario but has much greater power than poor choices.

Putting Priors in Mixture Density Mercer Kernels

NASA Technical Reports Server (NTRS)

Srivastava, Ashok N.; Schumann, Johann; Fischer, Bernd

2004-01-01

This paper presents a new methodology for automatic knowledge driven data mining based on the theory of Mercer Kernels, which are highly nonlinear symmetric positive definite mappings from the original image space to a very high, possibly infinite dimensional feature space. We describe a new method called Mixture Density Mercer Kernels to learn kernel function directly from data, rather than using predefined kernels. These data adaptive kernels can en- code prior knowledge in the kernel using a Bayesian formulation, thus allowing for physical information to be encoded in the model. We compare the results with existing algorithms on data from the Sloan Digital Sky Survey (SDSS). The code for these experiments has been generated with the AUTOBAYES tool, which automatically generates efficient and documented C/C++ code from abstract statistical model specifications. The core of the system is a schema library which contains template for learning and knowledge discovery algorithms like different versions of EM, or numeric optimization methods like conjugate gradient methods. The template instantiation is supported by symbolic- algebraic computations, which allows AUTOBAYES to find closed-form solutions and, where possible, to integrate them into the code. The results show that the Mixture Density Mercer-Kernel described here outperforms tree-based classification in distinguishing high-redshift galaxies from low- redshift galaxies by approximately 16% on test data, bagged trees by approximately 7%, and bagged trees built on a much larger sample of data by approximately 2%.

A nonlinear quality-related fault detection approach based on modified kernel partial least squares.

PubMed

Jiao, Jianfang; Zhao, Ning; Wang, Guang; Yin, Shen

2017-01-01

In this paper, a new nonlinear quality-related fault detection method is proposed based on kernel partial least squares (KPLS) model. To deal with the nonlinear characteristics among process variables, the proposed method maps these original variables into feature space in which the linear relationship between kernel matrix and output matrix is realized by means of KPLS. Then the kernel matrix is decomposed into two orthogonal parts by singular value decomposition (SVD) and the statistics for each part are determined appropriately for the purpose of quality-related fault detection. Compared with relevant existing nonlinear approaches, the proposed method has the advantages of simple diagnosis logic and stable performance. A widely used literature example and an industrial process are used for the performance evaluation for the proposed method. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Development of a kernel function for clinical data.

PubMed

Daemen, Anneleen; De Moor, Bart

2009-01-01

For most diseases and examinations, clinical data such as age, gender and medical history guides clinical management, despite the rise of high-throughput technologies. To fully exploit such clinical information, appropriate modeling of relevant parameters is required. As the widely used linear kernel function has several disadvantages when applied to clinical data, we propose a new kernel function specifically developed for this data. This "clinical kernel function" more accurately represents similarities between patients. Evidently, three data sets were studied and significantly better performances were obtained with a Least Squares Support Vector Machine when based on the clinical kernel function compared to the linear kernel function.

Kernel Abortion in Maize 1

PubMed Central

Hanft, Jonathan M.; Jones, Robert J.

1986-01-01

Kernels cultured in vitro were induced to abort by high temperature (35°C) and by culturing six kernels/cob piece. Aborting kernels failed to enter a linear phase of dry mass accumulation and had a final mass that was less than 6% of nonaborting field-grown kernels. Kernels induced to abort by high temperature failed to synthesize starch in the endosperm and had elevated sucrose concentrations and low fructose and glucose concentrations in the pedicel during early growth compared to nonaborting kernels. Kernels induced to abort by high temperature also had much lower pedicel soluble acid invertase activities than did nonaborting kernels. These results suggest that high temperature during the lag phase of kernel growth may impair the process of sucrose unloading in the pedicel by indirectly inhibiting soluble acid invertase activity and prevent starch synthesis in the endosperm. Kernels induced to abort by culturing six kernels/cob piece had reduced pedicel fructose, glucose, and sucrose concentrations compared to kernels from field-grown ears. These aborting kernels also had a lower pedicel soluble acid invertase activity compared to nonaborting kernels from the same cob piece and from field-grown ears. The low invertase activity in pedicel tissue of the aborting kernels was probably caused by a lack of substrate (sucrose) for the invertase to cleave due to the intense competition for available assimilates. In contrast to kernels cultured at 35°C, aborting kernels from cob pieces containing all six kernels accumulated starch in a linear fashion. These results indicate that kernels cultured six/cob piece abort because of an inadequate supply of sugar and are similar to apical kernels from field-grown ears that often abort prior to the onset of linear growth. PMID:16664846

Numerical method based on the lattice Boltzmann model for the Fisher equation.

PubMed

Yan, Guangwu; Zhang, Jianying; Dong, Yinfeng

2008-06-01

In this paper, a lattice Boltzmann model for the Fisher equation is proposed. First, the Chapman-Enskog expansion and the multiscale time expansion are used to describe higher-order moment of equilibrium distribution functions and a series of partial differential equations in different time scales. Second, the modified partial differential equation of the Fisher equation with the higher-order truncation error is obtained. Third, comparison between numerical results of the lattice Boltzmann models and exact solution is given. The numerical results agree well with the classical ones.

A Unified Fisher's Ratio Learning Method for Spatial Filter Optimization.

PubMed

Li, Xinyang; Guan, Cuntai; Zhang, Haihong; Ang, Kai Keng

To detect the mental task of interest, spatial filtering has been widely used to enhance the spatial resolution of electroencephalography (EEG). However, the effectiveness of spatial filtering is undermined due to the significant nonstationarity of EEG. Based on regularization, most of the conventional stationary spatial filter design methods address the nonstationarity at the cost of the interclass discrimination. Moreover, spatial filter optimization is inconsistent with feature extraction when EEG covariance matrices could not be jointly diagonalized due to the regularization. In this paper, we propose a novel framework for a spatial filter design. With Fisher's ratio in feature space directly used as the objective function, the spatial filter optimization is unified with feature extraction. Given its ratio form, the selection of the regularization parameter could be avoided. We evaluate the proposed method on a binary motor imagery data set of 16 subjects, who performed the calibration and test sessions on different days. The experimental results show that the proposed method yields improvement in classification performance for both single broadband and filter bank settings compared with conventional nonunified methods. We also provide a systematic attempt to compare different objective functions in modeling data nonstationarity with simulation studies.To detect the mental task of interest, spatial filtering has been widely used to enhance the spatial resolution of electroencephalography (EEG). However, the effectiveness of spatial filtering is undermined due to the significant nonstationarity of EEG. Based on regularization, most of the conventional stationary spatial filter design methods address the nonstationarity at the cost of the interclass discrimination. Moreover, spatial filter optimization is inconsistent with feature extraction when EEG covariance matrices could not be jointly diagonalized due to the regularization. In this paper, we propose a novel

Face recognition using total margin-based adaptive fuzzy support vector machines.

PubMed

Liu, Yi-Hung; Chen, Yen-Ting

2007-01-01

This paper presents a new classifier called total margin-based adaptive fuzzy support vector machines (TAF-SVM) that deals with several problems that may occur in support vector machines (SVMs) when applied to the face recognition. The proposed TAF-SVM not only solves the overfitting problem resulted from the outlier with the approach of fuzzification of the penalty, but also corrects the skew of the optimal separating hyperplane due to the very imbalanced data sets by using different cost algorithm. In addition, by introducing the total margin algorithm to replace the conventional soft margin algorithm, a lower generalization error bound can be obtained. Those three functions are embodied into the traditional SVM so that the TAF-SVM is proposed and reformulated in both linear and nonlinear cases. By using two databases, the Chung Yuan Christian University (CYCU) multiview and the facial recognition technology (FERET) face databases, and using the kernel Fisher's discriminant analysis (KFDA) algorithm to extract discriminating face features, experimental results show that the proposed TAF-SVM is superior to SVM in terms of the face-recognition accuracy. The results also indicate that the proposed TAF-SVM can achieve smaller error variances than SVM over a number of tests such that better recognition stability can be obtained.

Disability-based discrimination and health: findings from an Australian-based population study.

PubMed

Krnjacki, Lauren; Priest, Naomi; Aitken, Zoe; Emerson, Eric; Llewellyn, Gwynnyth; King, Tania; Kavanagh, Anne

2018-04-01

Among working-age Australian adults with a disability, we assess the association between disability-based discrimination and both overall health and psychological distress. Using data from the 2015 Australian Bureau of Statistics Survey of Disability, Ageing and Carers we estimated the proportion of working-age women and men (15-64 years) with disability who report disability-based discrimination by socio-demographic characteristics and assessed the association between disability-based discrimination and self-reported health and psychological distress. Nearly 14% of Australians with disability reported disability-based discrimination in the previous year. Disability-based discrimination was more common among people living in more disadvantaged circumstances (unemployed, low income, lower-status occupations), younger people and people born in English-speaking countries. Disability-based discrimination was associated with higher levels of psychological distress (OR: 2.53, 95%CI: 2.11, 3.02) and poorer self-reported health (OR: 1.63, 95%CI: 1.37, 1.95). Disability-based discrimination is a prevalent, important determinant of health for Australians with disability. Implications for public health: Disability-based discrimination is an under-recognised public health problem that is likely to contribute to disability-based health inequities. Public health policy, research and practice needs to concentrate efforts on developing policy and programs that reduce discrimination experienced by Australians with disability. © 2017 The Authors.

Classification With Truncated Distance Kernel.

PubMed

Huang, Xiaolin; Suykens, Johan A K; Wang, Shuning; Hornegger, Joachim; Maier, Andreas

2018-05-01

This brief proposes a truncated distance (TL1) kernel, which results in a classifier that is nonlinear in the global region but is linear in each subregion. With this kernel, the subregion structure can be trained using all the training data and local linear classifiers can be established simultaneously. The TL1 kernel has good adaptiveness to nonlinearity and is suitable for problems which require different nonlinearities in different areas. Though the TL1 kernel is not positive semidefinite, some classical kernel learning methods are still applicable which means that the TL1 kernel can be directly used in standard toolboxes by replacing the kernel evaluation. In numerical experiments, the TL1 kernel with a pregiven parameter achieves similar or better performance than the radial basis function kernel with the parameter tuned by cross validation, implying the TL1 kernel a promising nonlinear kernel for classification tasks.

Kernel Temporal Differences for Neural Decoding

PubMed Central

Bae, Jihye; Sanchez Giraldo, Luis G.; Pohlmeyer, Eric A.; Francis, Joseph T.; Sanchez, Justin C.; Príncipe, José C.

2015-01-01

We study the feasibility and capability of the kernel temporal difference (KTD)(λ) algorithm for neural decoding. KTD(λ) is an online, kernel-based learning algorithm, which has been introduced to estimate value functions in reinforcement learning. This algorithm combines kernel-based representations with the temporal difference approach to learning. One of our key observations is that by using strictly positive definite kernels, algorithm's convergence can be guaranteed for policy evaluation. The algorithm's nonlinear functional approximation capabilities are shown in both simulations of policy evaluation and neural decoding problems (policy improvement). KTD can handle high-dimensional neural states containing spatial-temporal information at a reasonable computational complexity allowing real-time applications. When the algorithm seeks a proper mapping between a monkey's neural states and desired positions of a computer cursor or a robot arm, in both open-loop and closed-loop experiments, it can effectively learn the neural state to action mapping. Finally, a visualization of the coadaptation process between the decoder and the subject shows the algorithm's capabilities in reinforcement learning brain machine interfaces. PMID:25866504

Application of Fisher Information to Complex Dynamic Systems

EPA Science Inventory

Fisher information was developed by the statistician Ronald Fisher as a measure of the information obtainable from data being used to fit a related parameter. Starting from the work of Ronald Fisher1 and B. Roy Frieden2, we have developed Fisher information as a measure of order ...

Generalization Performance of Regularized Ranking With Multiscale Kernels.

PubMed

Zhou, Yicong; Chen, Hong; Lan, Rushi; Pan, Zhibin

2016-05-01

The regularized kernel method for the ranking problem has attracted increasing attentions in machine learning. The previous regularized ranking algorithms are usually based on reproducing kernel Hilbert spaces with a single kernel. In this paper, we go beyond this framework by investigating the generalization performance of the regularized ranking with multiscale kernels. A novel ranking algorithm with multiscale kernels is proposed and its representer theorem is proved. We establish the upper bound of the generalization error in terms of the complexity of hypothesis spaces. It shows that the multiscale ranking algorithm can achieve satisfactory learning rates under mild conditions. Experiments demonstrate the effectiveness of the proposed method for drug discovery and recommendation tasks.

Joint recognition and discrimination in nonlinear feature space

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.

1997-09-01

A new general method for linear and nonlinear feature extraction is presented. It is novel since it provides both representation and discrimination while most other methods are concerned with only one of these issues. We call this approach the maximum representation and discrimination feature (MRDF) method and show that the Bayes classifier and the Karhunen- Loeve transform are special cases of it. We refer to our nonlinear feature extraction technique as nonlinear eigen- feature extraction. It is new since it has a closed-form solution and produces nonlinear decision surfaces with higher rank than do iterative methods. Results on synthetic databases are shown and compared with results from standard Fukunaga- Koontz transform and Fisher discriminant function methods. The method is also applied to an automated product inspection problem (discrimination) and to the classification and pose estimation of two similar objects (representation and discrimination).

An information measure for class discrimination. [in remote sensing of crop observation

NASA Technical Reports Server (NTRS)

Shen, S. S.; Badhwar, G. D.

1986-01-01

This article describes a separability measure for class discrimination. This measure is based on the Fisher information measure for estimating the mixing proportion of two classes. The Fisher information measure not only provides a means to assess quantitatively the information content in the features for separating classes, but also gives the lower bound for the variance of any unbiased estimate of the mixing proportion based on observations of the features. Unlike most commonly used separability measures, this measure is not dependent on the form of the probability distribution of the features and does not imply a specific estimation procedure. This is important because the probability distribution function that describes the data for a given class does not have simple analytic forms, such as a Gaussian. Results of applying this measure to compare the information content provided by three Landsat-derived feature vectors for the purpose of separating small grains from other crops are presented.

Mapping Fire Severity Using Imaging Spectroscopy and Kernel Based Image Analysis

NASA Astrophysics Data System (ADS)

Prasad, S.; Cui, M.; Zhang, Y.; Veraverbeke, S.

2014-12-01

Improved spatial representation of within-burn heterogeneity after wildfires is paramount to effective land management decisions and more accurate fire emissions estimates. In this work, we demonstrate feasibility and efficacy of airborne imaging spectroscopy (hyperspectral imagery) for quantifying wildfire burn severity, using kernel based image analysis techniques. Two different airborne hyperspectral datasets, acquired over the 2011 Canyon and 2013 Rim fire in California using the Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) sensor, were used in this study. The Rim Fire, covering parts of the Yosemite National Park started on August 17, 2013, and was the third largest fire in California's history. Canyon Fire occurred in the Tehachapi mountains, and started on September 4, 2011. In addition to post-fire data for both fires, half of the Rim fire was also covered with pre-fire images. Fire severity was measured in the field using Geo Composite Burn Index (GeoCBI). The field data was utilized to train and validate our models, wherein the trained models, in conjunction with imaging spectroscopy data were used for GeoCBI estimation wide geographical regions. This work presents an approach for using remotely sensed imagery combined with GeoCBI field data to map fire scars based on a non-linear (kernel based) epsilon-Support Vector Regression (e-SVR), which was used to learn the relationship between spectra and GeoCBI in a kernel-induced feature space. Classification of healthy vegetation versus fire-affected areas based on morphological multi-attribute profiles was also studied. The availability of pre- and post-fire imaging spectroscopy data over the Rim Fire provided a unique opportunity to evaluate the performance of bi-temporal imaging spectroscopy for assessing post-fire effects. This type of data is currently constrained because of limited airborne acquisitions before a fire, but will become widespread with future spaceborne sensors such as those on

Osteoarthritis Severity Determination using Self Organizing Map Based Gabor Kernel

NASA Astrophysics Data System (ADS)

Anifah, L.; Purnomo, M. H.; Mengko, T. L. R.; Purnama, I. K. E.

2018-02-01

The number of osteoarthritis patients in Indonesia is enormous, so early action is needed in order for this disease to be handled. The aim of this paper to determine osteoarthritis severity based on x-ray image template based on gabor kernel. This research is divided into 3 stages, the first step is image processing that is using gabor kernel. The second stage is the learning stage, and the third stage is the testing phase. The image processing stage is by normalizing the image dimension to be template to 50 □ 200 image. Learning stage is done with parameters initial learning rate of 0.5 and the total number of iterations of 1000. The testing stage is performed using the weights generated at the learning stage. The testing phase has been done and the results were obtained. The result shows KL-Grade 0 has an accuracy of 36.21%, accuracy for KL-Grade 2 is 40,52%, while accuracy for KL-Grade 2 and KL-Grade 3 are 15,52%, and 25,86%. The implication of this research is expected that this research as decision support system for medical practitioners in determining KL-Grade on X-ray images of knee osteoarthritis.

G-Hash: Towards Fast Kernel-based Similarity Search in Large Graph Databases.

PubMed

Wang, Xiaohong; Smalter, Aaron; Huan, Jun; Lushington, Gerald H

2009-01-01

Structured data including sets, sequences, trees and graphs, pose significant challenges to fundamental aspects of data management such as efficient storage, indexing, and similarity search. With the fast accumulation of graph databases, similarity search in graph databases has emerged as an important research topic. Graph similarity search has applications in a wide range of domains including cheminformatics, bioinformatics, sensor network management, social network management, and XML documents, among others.Most of the current graph indexing methods focus on subgraph query processing, i.e. determining the set of database graphs that contains the query graph and hence do not directly support similarity search. In data mining and machine learning, various graph kernel functions have been designed to capture the intrinsic similarity of graphs. Though successful in constructing accurate predictive and classification models for supervised learning, graph kernel functions have (i) high computational complexity and (ii) non-trivial difficulty to be indexed in a graph database.Our objective is to bridge graph kernel function and similarity search in graph databases by proposing (i) a novel kernel-based similarity measurement and (ii) an efficient indexing structure for graph data management. Our method of similarity measurement builds upon local features extracted from each node and their neighboring nodes in graphs. A hash table is utilized to support efficient storage and fast search of the extracted local features. Using the hash table, a graph kernel function is defined to capture the intrinsic similarity of graphs and for fast similarity query processing. We have implemented our method, which we have named G-hash, and have demonstrated its utility on large chemical graph databases. Our results show that the G-hash method achieves state-of-the-art performance for k-nearest neighbor (k-NN) classification. Most importantly, the new similarity measurement and the index

Integrating the Gradient of the Thin Wire Kernel

NASA Technical Reports Server (NTRS)

Champagne, Nathan J.; Wilton, Donald R.

2008-01-01

A formulation for integrating the gradient of the thin wire kernel is presented. This approach employs a new expression for the gradient of the thin wire kernel derived from a recent technique for numerically evaluating the exact thin wire kernel. This approach should provide essentially arbitrary accuracy and may be used with higher-order elements and basis functions using the procedure described in [4].When the source and observation points are close, the potential integrals over wire segments involving the wire kernel are split into parts to handle the singular behavior of the integrand [1]. The singularity characteristics of the gradient of the wire kernel are different than those of the wire kernel, and the axial and radial components have different singularities. The characteristics of the gradient of the wire kernel are discussed in [2]. To evaluate the near electric and magnetic fields of a wire, the integration of the gradient of the wire kernel needs to be calculated over the source wire. Since the vector bases for current have constant direction on linear wire segments, these integrals reduce to integrals of the form

Gaussian mass optimization for kernel PCA parameters

NASA Astrophysics Data System (ADS)

Liu, Yong; Wang, Zulin

2011-10-01

This paper proposes a novel kernel parameter optimization method based on Gaussian mass, which aims to overcome the current brute force parameter optimization method in a heuristic way. Generally speaking, the choice of kernel parameter should be tightly related to the target objects while the variance between the samples, the most commonly used kernel parameter, doesn't possess much features of the target, which gives birth to Gaussian mass. Gaussian mass defined in this paper has the property of the invariance of rotation and translation and is capable of depicting the edge, topology and shape information. Simulation results show that Gaussian mass leads a promising heuristic optimization boost up for kernel method. In MNIST handwriting database, the recognition rate improves by 1.6% compared with common kernel method without Gaussian mass optimization. Several promising other directions which Gaussian mass might help are also proposed at the end of the paper.

A Kernel Embedding-Based Approach for Nonstationary Causal Model Inference.

PubMed

Hu, Shoubo; Chen, Zhitang; Chan, Laiwan

2018-05-01

Although nonstationary data are more common in the real world, most existing causal discovery methods do not take nonstationarity into consideration. In this letter, we propose a kernel embedding-based approach, ENCI, for nonstationary causal model inference where data are collected from multiple domains with varying distributions. In ENCI, we transform the complicated relation of a cause-effect pair into a linear model of variables of which observations correspond to the kernel embeddings of the cause-and-effect distributions in different domains. In this way, we are able to estimate the causal direction by exploiting the causal asymmetry of the transformed linear model. Furthermore, we extend ENCI to causal graph discovery for multiple variables by transforming the relations among them into a linear nongaussian acyclic model. We show that by exploiting the nonstationarity of distributions, both cause-effect pairs and two kinds of causal graphs are identifiable under mild conditions. Experiments on synthetic and real-world data are conducted to justify the efficacy of ENCI over major existing methods.

Improving the Bandwidth Selection in Kernel Equating

ERIC Educational Resources Information Center

Andersson, Björn; von Davier, Alina A.

2014-01-01

We investigate the current bandwidth selection methods in kernel equating and propose a method based on Silverman's rule of thumb for selecting the bandwidth parameters. In kernel equating, the bandwidth parameters have previously been obtained by minimizing a penalty function. This minimization process has been criticized by practitioners…

Fisher information in a quantum-critical environment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sun Zhe; Ma Jian; Lu Xiaoming

2010-08-15

We consider a process of parameter estimation in a spin-j system surrounded by a quantum-critical spin chain. Quantum Fisher information lies at the heart of the estimation task. We employ Ising spin chain in a transverse field as the environment which exhibits a quantum phase transition. Fisher information decays with time almost monotonously when the environment reaches the critical point. By choosing a fixed time or taking the time average, one can see the quantum Fisher information presents a sudden drop at the critical point. Different initial states of the environment are considered. The phenomenon that the quantum Fisher information,more » namely, the precision of estimation, changes dramatically can be used to detect the quantum criticality of the environment. We also introduce a general method to obtain the maximal Fisher information for a given state.« less

Score-moment combined linear discrimination analysis (SMC-LDA) as an improved discrimination method.

PubMed

Han, Jintae; Chung, Hoeil; Han, Sung-Hwan; Yoon, Moon-Young

2007-01-01

A new discrimination method called the score-moment combined linear discrimination analysis (SMC-LDA) has been developed and its performance has been evaluated using three practical spectroscopic datasets. The key concept of SMC-LDA was to use not only the score from principal component analysis (PCA), but also the moment of the spectrum, as inputs for LDA to improve discrimination. Along with conventional score, moment is used in spectroscopic fields as an effective alternative for spectral feature representation. Three different approaches were considered. Initially, the score generated from PCA was projected onto a two-dimensional feature space by maximizing Fisher's criterion function (conventional PCA-LDA). Next, the same procedure was performed using only moment. Finally, both score and moment were utilized simultaneously for LDA. To evaluate discrimination performances, three different spectroscopic datasets were employed: (1) infrared (IR) spectra of normal and malignant stomach tissue, (2) near-infrared (NIR) spectra of diesel and light gas oil (LGO) and (3) Raman spectra of Chinese and Korean ginseng. For each case, the best discrimination results were achieved when both score and moment were used for LDA (SMC-LDA). Since the spectral representation character of moment was different from that of score, inclusion of both score and moment for LDA provided more diversified and descriptive information.

Small convolution kernels for high-fidelity image restoration

NASA Technical Reports Server (NTRS)

Reichenbach, Stephen E.; Park, Stephen K.

1991-01-01

An algorithm is developed for computing the mean-square-optimal values for small, image-restoration kernels. The algorithm is based on a comprehensive, end-to-end imaging system model that accounts for the important components of the imaging process: the statistics of the scene, the point-spread function of the image-gathering device, sampling effects, noise, and display reconstruction. Subject to constraints on the spatial support of the kernel, the algorithm generates the kernel values that restore the image with maximum fidelity, that is, the kernel minimizes the expected mean-square restoration error. The algorithm is consistent with the derivation of the spatially unconstrained Wiener filter, but leads to a small, spatially constrained kernel that, unlike the unconstrained filter, can be efficiently implemented by convolution. Simulation experiments demonstrate that for a wide range of imaging systems these small kernels can restore images with fidelity comparable to images restored with the unconstrained Wiener filter.

DIRProt: a computational approach for discriminating insecticide resistant proteins from non-resistant proteins.

PubMed

Meher, Prabina Kumar; Sahu, Tanmaya Kumar; Banchariya, Anjali; Rao, Atmakuri Ramakrishna

2017-03-24

Insecticide resistance is a major challenge for the control program of insect pests in the fields of crop protection, human and animal health etc. Resistance to different insecticides is conferred by the proteins encoded from certain class of genes of the insects. To distinguish the insecticide resistant proteins from non-resistant proteins, no computational tool is available till date. Thus, development of such a computational tool will be helpful in predicting the insecticide resistant proteins, which can be targeted for developing appropriate insecticides. Five different sets of feature viz., amino acid composition (AAC), di-peptide composition (DPC), pseudo amino acid composition (PAAC), composition-transition-distribution (CTD) and auto-correlation function (ACF) were used to map the protein sequences into numeric feature vectors. The encoded numeric vectors were then used as input in support vector machine (SVM) for classification of insecticide resistant and non-resistant proteins. Higher accuracies were obtained under RBF kernel than that of other kernels. Further, accuracies were observed to be higher for DPC feature set as compared to others. The proposed approach achieved an overall accuracy of >90% in discriminating resistant from non-resistant proteins. Further, the two classes of resistant proteins i.e., detoxification-based and target-based were discriminated from non-resistant proteins with >95% accuracy. Besides, >95% accuracy was also observed for discrimination of proteins involved in detoxification- and target-based resistance mechanisms. The proposed approach not only outperformed Blastp, PSI-Blast and Delta-Blast algorithms, but also achieved >92% accuracy while assessed using an independent dataset of 75 insecticide resistant proteins. This paper presents the first computational approach for discriminating the insecticide resistant proteins from non-resistant proteins. Based on the proposed approach, an online prediction server DIRProt has

Combining features from ERP components in single-trial EEG for discriminating four-category visual objects.

PubMed

Wang, Changming; Xiong, Shi; Hu, Xiaoping; Yao, Li; Zhang, Jiacai

2012-10-01

Categorization of images containing visual objects can be successfully recognized using single-trial electroencephalograph (EEG) measured when subjects view images. Previous studies have shown that task-related information contained in event-related potential (ERP) components could discriminate two or three categories of object images. In this study, we investigated whether four categories of objects (human faces, buildings, cats and cars) could be mutually discriminated using single-trial EEG data. Here, the EEG waveforms acquired while subjects were viewing four categories of object images were segmented into several ERP components (P1, N1, P2a and P2b), and then Fisher linear discriminant analysis (Fisher-LDA) was used to classify EEG features extracted from ERP components. Firstly, we compared the classification results using features from single ERP components, and identified that the N1 component achieved the highest classification accuracies. Secondly, we discriminated four categories of objects using combining features from multiple ERP components, and showed that combination of ERP components improved four-category classification accuracies by utilizing the complementarity of discriminative information in ERP components. These findings confirmed that four categories of object images could be discriminated with single-trial EEG and could direct us to select effective EEG features for classifying visual objects.

Multineuron spike train analysis with R-convolution linear combination kernel.

PubMed

Tezuka, Taro

2018-06-01

A spike train kernel provides an effective way of decoding information represented by a spike train. Some spike train kernels have been extended to multineuron spike trains, which are simultaneously recorded spike trains obtained from multiple neurons. However, most of these multineuron extensions were carried out in a kernel-specific manner. In this paper, a general framework is proposed for extending any single-neuron spike train kernel to multineuron spike trains, based on the R-convolution kernel. Special subclasses of the proposed R-convolution linear combination kernel are explored. These subclasses have a smaller number of parameters and make optimization tractable when the size of data is limited. The proposed kernel was evaluated using Gaussian process regression for multineuron spike trains recorded from an animal brain. It was compared with the sum kernel and the population Spikernel, which are existing ways of decoding multineuron spike trains using kernels. The results showed that the proposed approach performs better than these kernels and also other commonly used neural decoding methods. Copyright © 2018 Elsevier Ltd. All rights reserved.

Unsupervised learning of discriminative edge measures for vehicle matching between nonoverlapping cameras.

PubMed

Shan, Ying; Sawhney, Harpreet S; Kumar, Rakesh

2008-04-01

This paper proposes a novel unsupervised algorithm learning discriminative features in the context of matching road vehicles between two non-overlapping cameras. The matching problem is formulated as a same-different classification problem, which aims to compute the probability of vehicle images from two distinct cameras being from the same vehicle or different vehicle(s). We employ a novel measurement vector that consists of three independent edge-based measures and their associated robust measures computed from a pair of aligned vehicle edge maps. The weight of each measure is determined by an unsupervised learning algorithm that optimally separates the same-different classes in the combined measurement space. This is achieved with a weak classification algorithm that automatically collects representative samples from same-different classes, followed by a more discriminative classifier based on Fisher' s Linear Discriminants and Gibbs Sampling. The robustness of the match measures and the use of unsupervised discriminant analysis in the classification ensures that the proposed method performs consistently in the presence of missing/false features, temporally and spatially changing illumination conditions, and systematic misalignment caused by different camera configurations. Extensive experiments based on real data of over 200 vehicles at different times of day demonstrate promising results.

Embedded real-time operating system micro kernel design

NASA Astrophysics Data System (ADS)

Cheng, Xiao-hui; Li, Ming-qiang; Wang, Xin-zheng

2005-12-01

Embedded systems usually require a real-time character. Base on an 8051 microcontroller, an embedded real-time operating system micro kernel is proposed consisting of six parts, including a critical section process, task scheduling, interruption handle, semaphore and message mailbox communication, clock managent and memory managent. Distributed CPU and other resources are among tasks rationally according to the importance and urgency. The design proposed here provides the position, definition, function and principle of micro kernel. The kernel runs on the platform of an ATMEL AT89C51 microcontroller. Simulation results prove that the designed micro kernel is stable and reliable and has quick response while operating in an application system.

Olympic Fisher Reintroduction Project: Progress report 2008-2011

USGS Publications Warehouse

Jeffrey C. Lewis,; Patti J. Happe,; Jenkins, Kurt J.; Manson, David J.

2012-01-01

This progress report summarizes the final year of activities of Phase I of the Olympic fisher restoration project. The intent of the Olympic fisher reintroduction project is to reestablish a self-sustaining population of fishers on the Olympic Peninsula. To achieve this goal, the Olympic fisher reintroduction project released 90 fishers within Olympic National Park from 2008 to 2010. The reintroduction of fishers to the Olympic Peninsula was designed as an adaptive management project, including the monitoring of released fishers as a means to (1) evaluate reintroduction success, (2) investigate key biological and ecological traits of fishers, and (3) inform future reintroduction, monitoring, and research efforts. This report summarizes reintroduction activities and preliminary research and monitoring results completed through December 2011. The report is non-interpretational in nature. Although we report the status of movement, survival, and home range components of the research, we have not completed final analyses and interpretation of research results. Much of the data collected during the monitoring and research project will be analyzed and interpreted in the doctoral dissertation being developed by Jeff Lewis; the completion of this dissertation is anticipated prior to April 2013. We anticipate that this work, and analyses of other data collected during the project, will result in several peer-reviewed scientific publications in ecological and conservation journals, which collectively will comprise the final reporting of work summarized here. These publications will include papers addressing post-release movements, survival, resource selection, food habits, and age determination of fishers.

Improved dynamical scaling analysis using the kernel method for nonequilibrium relaxation.

PubMed

Echinaka, Yuki; Ozeki, Yukiyasu

2016-10-01

The dynamical scaling analysis for the Kosterlitz-Thouless transition in the nonequilibrium relaxation method is improved by the use of Bayesian statistics and the kernel method. This allows data to be fitted to a scaling function without using any parametric model function, which makes the results more reliable and reproducible and enables automatic and faster parameter estimation. Applying this method, the bootstrap method is introduced and a numerical discrimination for the transition type is proposed.

Design of CT reconstruction kernel specifically for clinical lung imaging

NASA Astrophysics Data System (ADS)

Cody, Dianna D.; Hsieh, Jiang; Gladish, Gregory W.

2005-04-01

In this study we developed a new reconstruction kernel specifically for chest CT imaging. An experimental flat-panel CT scanner was used on large dogs to produce 'ground-truth" reference chest CT images. These dogs were also examined using a clinical 16-slice CT scanner. We concluded from the dog images acquired on the clinical scanner that the loss of subtle lung structures was due mostly to the presence of the background noise texture when using currently available reconstruction kernels. This qualitative evaluation of the dog CT images prompted the design of a new recon kernel. This new kernel consisted of the combination of a low-pass and a high-pass kernel to produce a new reconstruction kernel, called the 'Hybrid" kernel. The performance of this Hybrid kernel fell between the two kernels on which it was based, as expected. This Hybrid kernel was also applied to a set of 50 patient data sets; the analysis of these clinical images is underway. We are hopeful that this Hybrid kernel will produce clinical images with an acceptable tradeoff of lung detail, reliable HU, and image noise.

A combined Fisher and Laplacian score for feature selection in QSAR based drug design using compounds with known and unknown activities.

PubMed

Valizade Hasanloei, Mohammad Amin; Sheikhpour, Razieh; Sarram, Mehdi Agha; Sheikhpour, Elnaz; Sharifi, Hamdollah

2018-02-01

Quantitative structure-activity relationship (QSAR) is an effective computational technique for drug design that relates the chemical structures of compounds to their biological activities. Feature selection is an important step in QSAR based drug design to select the most relevant descriptors. One of the most popular feature selection methods for classification problems is Fisher score which aim is to minimize the within-class distance and maximize the between-class distance. In this study, the properties of Fisher criterion were extended for QSAR models to define the new distance metrics based on the continuous activity values of compounds with known activities. Then, a semi-supervised feature selection method was proposed based on the combination of Fisher and Laplacian criteria which exploits both compounds with known and unknown activities to select the relevant descriptors. To demonstrate the efficiency of the proposed semi-supervised feature selection method in selecting the relevant descriptors, we applied the method and other feature selection methods on three QSAR data sets such as serine/threonine-protein kinase PLK3 inhibitors, ROCK inhibitors and phenol compounds. The results demonstrated that the QSAR models built on the selected descriptors by the proposed semi-supervised method have better performance than other models. This indicates the efficiency of the proposed method in selecting the relevant descriptors using the compounds with known and unknown activities. The results of this study showed that the compounds with known and unknown activities can be helpful to improve the performance of the combined Fisher and Laplacian based feature selection methods.

A combined Fisher and Laplacian score for feature selection in QSAR based drug design using compounds with known and unknown activities

NASA Astrophysics Data System (ADS)

Valizade Hasanloei, Mohammad Amin; Sheikhpour, Razieh; Sarram, Mehdi Agha; Sheikhpour, Elnaz; Sharifi, Hamdollah

2018-02-01

Quantitative structure-activity relationship (QSAR) is an effective computational technique for drug design that relates the chemical structures of compounds to their biological activities. Feature selection is an important step in QSAR based drug design to select the most relevant descriptors. One of the most popular feature selection methods for classification problems is Fisher score which aim is to minimize the within-class distance and maximize the between-class distance. In this study, the properties of Fisher criterion were extended for QSAR models to define the new distance metrics based on the continuous activity values of compounds with known activities. Then, a semi-supervised feature selection method was proposed based on the combination of Fisher and Laplacian criteria which exploits both compounds with known and unknown activities to select the relevant descriptors. To demonstrate the efficiency of the proposed semi-supervised feature selection method in selecting the relevant descriptors, we applied the method and other feature selection methods on three QSAR data sets such as serine/threonine-protein kinase PLK3 inhibitors, ROCK inhibitors and phenol compounds. The results demonstrated that the QSAR models built on the selected descriptors by the proposed semi-supervised method have better performance than other models. This indicates the efficiency of the proposed method in selecting the relevant descriptors using the compounds with known and unknown activities. The results of this study showed that the compounds with known and unknown activities can be helpful to improve the performance of the combined Fisher and Laplacian based feature selection methods.

EXERGY AND FISHER INFORMATION AS ECOLOGICAL INDEXES

EPA Science Inventory

Ecological indices are used to provide summary information about a particular aspect of ecosystem behavior. Many such indices have been proposed and here we investigate two: exergy and Fisher Information. Exergy, a thermodynamically based index, is a measure of maximum amount o...

An evaluation of parturition indices in fishers

USGS Publications Warehouse

Frost, H.C.; York, E.C.; Krohn, W.B.; Elowe, K.D.; Decker, T.A.; Powell, S.M.; Fuller, T.K.

1999-01-01

Fishers (Martes pennanti) are important forest carnivores and furbearers that are susceptible to overharvest. Traditional indices used to monitor fisher populations typically overestimate litter size and proportion of females that give birth. We evaluated the usefulness of 2 indices of reproduction to determine proportion of female fishers that gave birth in a particular year. We used female fishers of known age and reproductive histories to compare appearance of placental scars with incidence of pregnancy and litter size. Microscopic observation of freshly removed reproductive tracts correctly identified pregnant fishers and correctly estimated litter size in 3 of 4 instances, but gross observation of placental scars failed to correctly identify pregnant fishers and litter size. Microscopic observations of reproductive tracts in carcasses that were not fresh also failed to identify pregnant animals and litter size. We evaluated mean sizes of anterior nipples to see if different reproductive classes could be distinguished. Mean anterior nipple size of captive and wild fishers correctly identified current-year breeders from nonbreeders. Former breeders were misclassified in 4 of 13 instances. Presence of placental scars accurately predicted parturition in a small sample size of fishers, but absence of placental scars did not signify that a female did not give birth. In addition to enabling the estimation of parturition rates in live animals more accurately than traditional indices, mean anterior nipple size also provided an estimate of the percentage of adult females that successfully raised young. Though using mean anterior nipple size to index reproductive success looks promising, additional data are needed to evaluate effects of using dried, stretched pelts on nipple size for management purposes.

Kernel learning at the first level of inference.

PubMed

Cawley, Gavin C; Talbot, Nicola L C

2014-05-01

Kernel learning methods, whether Bayesian or frequentist, typically involve multiple levels of inference, with the coefficients of the kernel expansion being determined at the first level and the kernel and regularisation parameters carefully tuned at the second level, a process known as model selection. Model selection for kernel machines is commonly performed via optimisation of a suitable model selection criterion, often based on cross-validation or theoretical performance bounds. However, if there are a large number of kernel parameters, as for instance in the case of automatic relevance determination (ARD), there is a substantial risk of over-fitting the model selection criterion, resulting in poor generalisation performance. In this paper we investigate the possibility of learning the kernel, for the Least-Squares Support Vector Machine (LS-SVM) classifier, at the first level of inference, i.e. parameter optimisation. The kernel parameters and the coefficients of the kernel expansion are jointly optimised at the first level of inference, minimising a training criterion with an additional regularisation term acting on the kernel parameters. The key advantage of this approach is that the values of only two regularisation parameters need be determined in model selection, substantially alleviating the problem of over-fitting the model selection criterion. The benefits of this approach are demonstrated using a suite of synthetic and real-world binary classification benchmark problems, where kernel learning at the first level of inference is shown to be statistically superior to the conventional approach, improves on our previous work (Cawley and Talbot, 2007) and is competitive with Multiple Kernel Learning approaches, but with reduced computational expense. Copyright © 2014 Elsevier Ltd. All rights reserved.

Fisher information of accelerated two-qubit systems

NASA Astrophysics Data System (ADS)

Metwally, N.

2018-02-01

In this paper, Fisher information for an accelerated system initially prepared in the X-state is discussed. An analytical solution, which consists of three parts: classical, the average over all pure states and a mixture of pure states, is derived for the general state and for Werner state. It is shown that the Unruh acceleration has a depleting effect on the Fisher information. This depletion depends on the degree of entanglement of the initial state settings. For the X-state, for some intervals of Unruh acceleration, the Fisher information remains constant, irrespective to the Unruh acceleration. In general, the possibility of estimating the state’s parameters decreases as the acceleration increases. However, the precision of estimation can be maximized for certain values of the Unruh acceleration. We also investigate the contribution of the different parts of the Fisher information on the dynamics of the total Fisher information.

Protein fold recognition using geometric kernel data fusion.

PubMed

Zakeri, Pooya; Jeuris, Ben; Vandebril, Raf; Moreau, Yves

2014-07-01

Various approaches based on features extracted from protein sequences and often machine learning methods have been used in the prediction of protein folds. Finding an efficient technique for integrating these different protein features has received increasing attention. In particular, kernel methods are an interesting class of techniques for integrating heterogeneous data. Various methods have been proposed to fuse multiple kernels. Most techniques for multiple kernel learning focus on learning a convex linear combination of base kernels. In addition to the limitation of linear combinations, working with such approaches could cause a loss of potentially useful information. We design several techniques to combine kernel matrices by taking more involved, geometry inspired means of these matrices instead of convex linear combinations. We consider various sequence-based protein features including information extracted directly from position-specific scoring matrices and local sequence alignment. We evaluate our methods for classification on the SCOP PDB-40D benchmark dataset for protein fold recognition. The best overall accuracy on the protein fold recognition test set obtained by our methods is ∼ 86.7%. This is an improvement over the results of the best existing approach. Moreover, our computational model has been developed by incorporating the functional domain composition of proteins through a hybridization model. It is observed that by using our proposed hybridization model, the protein fold recognition accuracy is further improved to 89.30%. Furthermore, we investigate the performance of our approach on the protein remote homology detection problem by fusing multiple string kernels. The MATLAB code used for our proposed geometric kernel fusion frameworks are publicly available at http://people.cs.kuleuven.be/∼raf.vandebril/homepage/software/geomean.php?menu=5/. © The Author 2014. Published by Oxford University Press.

Kernel-based Joint Feature Selection and Max-Margin Classification for Early Diagnosis of Parkinson’s Disease

NASA Astrophysics Data System (ADS)

Adeli, Ehsan; Wu, Guorong; Saghafi, Behrouz; An, Le; Shi, Feng; Shen, Dinggang

2017-01-01

Feature selection methods usually select the most compact and relevant set of features based on their contribution to a linear regression model. Thus, these features might not be the best for a non-linear classifier. This is especially crucial for the tasks, in which the performance is heavily dependent on the feature selection techniques, like the diagnosis of neurodegenerative diseases. Parkinson’s disease (PD) is one of the most common neurodegenerative disorders, which progresses slowly while affects the quality of life dramatically. In this paper, we use the data acquired from multi-modal neuroimaging data to diagnose PD by investigating the brain regions, known to be affected at the early stages. We propose a joint kernel-based feature selection and classification framework. Unlike conventional feature selection techniques that select features based on their performance in the original input feature space, we select features that best benefit the classification scheme in the kernel space. We further propose kernel functions, specifically designed for our non-negative feature types. We use MRI and SPECT data of 538 subjects from the PPMI database, and obtain a diagnosis accuracy of 97.5%, which outperforms all baseline and state-of-the-art methods.

Kernel-based Joint Feature Selection and Max-Margin Classification for Early Diagnosis of Parkinson’s Disease

PubMed Central

Adeli, Ehsan; Wu, Guorong; Saghafi, Behrouz; An, Le; Shi, Feng; Shen, Dinggang

2017-01-01

Feature selection methods usually select the most compact and relevant set of features based on their contribution to a linear regression model. Thus, these features might not be the best for a non-linear classifier. This is especially crucial for the tasks, in which the performance is heavily dependent on the feature selection techniques, like the diagnosis of neurodegenerative diseases. Parkinson’s disease (PD) is one of the most common neurodegenerative disorders, which progresses slowly while affects the quality of life dramatically. In this paper, we use the data acquired from multi-modal neuroimaging data to diagnose PD by investigating the brain regions, known to be affected at the early stages. We propose a joint kernel-based feature selection and classification framework. Unlike conventional feature selection techniques that select features based on their performance in the original input feature space, we select features that best benefit the classification scheme in the kernel space. We further propose kernel functions, specifically designed for our non-negative feature types. We use MRI and SPECT data of 538 subjects from the PPMI database, and obtain a diagnosis accuracy of 97.5%, which outperforms all baseline and state-of-the-art methods. PMID:28120883

Unified Heat Kernel Regression for Diffusion, Kernel Smoothing and Wavelets on Manifolds and Its Application to Mandible Growth Modeling in CT Images

PubMed Central

Chung, Moo K.; Qiu, Anqi; Seo, Seongho; Vorperian, Houri K.

2014-01-01

We present a novel kernel regression framework for smoothing scalar surface data using the Laplace-Beltrami eigenfunctions. Starting with the heat kernel constructed from the eigenfunctions, we formulate a new bivariate kernel regression framework as a weighted eigenfunction expansion with the heat kernel as the weights. The new kernel regression is mathematically equivalent to isotropic heat diffusion, kernel smoothing and recently popular diffusion wavelets. Unlike many previous partial differential equation based approaches involving diffusion, our approach represents the solution of diffusion analytically, reducing numerical inaccuracy and slow convergence. The numerical implementation is validated on a unit sphere using spherical harmonics. As an illustration, we have applied the method in characterizing the localized growth pattern of mandible surfaces obtained in CT images from subjects between ages 0 and 20 years by regressing the length of displacement vectors with respect to the template surface. PMID:25791435

Historical harvest and incidental capture of fishers in California

Treesearch

Jeffrey C. Lewis; William J. Zielinski

1996-01-01

Recent petitions to list the fisher (Martes pennanti) under the Endangered Species Act have brought attention to fisher conservation. Although commercial trapping of fishers in California ended in 1946, summarizing the commercial harvest data can provide a historical perspective to fisher conservation and may indicate the prevalence of incidental...

Application of Fisher Information to Complex Dynamic Systems (Tucson)

EPA Science Inventory

Fisher information was developed by the statistician Ronald Fisher as a measure of the information obtainable from data being used to fit a related parameter. Starting from the work of Ronald Fisher1 and B. Roy Frieden2, we have developed Fisher information as a measure of order ...

Online learning control using adaptive critic designs with sparse kernel machines.

PubMed

Xu, Xin; Hou, Zhongsheng; Lian, Chuanqiang; He, Haibo

2013-05-01

In the past decade, adaptive critic designs (ACDs), including heuristic dynamic programming (HDP), dual heuristic programming (DHP), and their action-dependent ones, have been widely studied to realize online learning control of dynamical systems. However, because neural networks with manually designed features are commonly used to deal with continuous state and action spaces, the generalization capability and learning efficiency of previous ACDs still need to be improved. In this paper, a novel framework of ACDs with sparse kernel machines is presented by integrating kernel methods into the critic of ACDs. To improve the generalization capability as well as the computational efficiency of kernel machines, a sparsification method based on the approximately linear dependence analysis is used. Using the sparse kernel machines, two kernel-based ACD algorithms, that is, kernel HDP (KHDP) and kernel DHP (KDHP), are proposed and their performance is analyzed both theoretically and empirically. Because of the representation learning and generalization capability of sparse kernel machines, KHDP and KDHP can obtain much better performance than previous HDP and DHP with manually designed neural networks. Simulation and experimental results of two nonlinear control problems, that is, a continuous-action inverted pendulum problem and a ball and plate control problem, demonstrate the effectiveness of the proposed kernel ACD methods.

7 CFR 981.408 - Inedible kernel.

Code of Federal Regulations, 2014 CFR

2014-01-01

... kernel is modified to mean a kernel, piece, or particle of almond kernel with any defect scored as... purposes of determining inedible kernels, pieces, or particles of almond kernels. [59 FR 39419, Aug. 3...

7 CFR 981.408 - Inedible kernel.

Code of Federal Regulations, 2011 CFR

2011-01-01

... kernel is modified to mean a kernel, piece, or particle of almond kernel with any defect scored as... purposes of determining inedible kernels, pieces, or particles of almond kernels. [59 FR 39419, Aug. 3...

7 CFR 981.408 - Inedible kernel.

Code of Federal Regulations, 2012 CFR

2012-01-01

... kernel is modified to mean a kernel, piece, or particle of almond kernel with any defect scored as... purposes of determining inedible kernels, pieces, or particles of almond kernels. [59 FR 39419, Aug. 3...

7 CFR 981.408 - Inedible kernel.

Code of Federal Regulations, 2013 CFR

2013-01-01

... kernel is modified to mean a kernel, piece, or particle of almond kernel with any defect scored as... purposes of determining inedible kernels, pieces, or particles of almond kernels. [59 FR 39419, Aug. 3...

Comparative Evaluation of Pavement Crack Detection Using Kernel-Based Techniques in Asphalt Road Surfaces

NASA Astrophysics Data System (ADS)

Miraliakbari, A.; Sok, S.; Ouma, Y. O.; Hahn, M.

2016-06-01

With the increasing demand for the digital survey and acquisition of road pavement conditions, there is also the parallel growing need for the development of automated techniques for the analysis and evaluation of the actual road conditions. This is due in part to the resulting large volumes of road pavement data captured through digital surveys, and also to the requirements for rapid data processing and evaluations. In this study, the Canon 5D Mark II RGB camera with a resolution of 21 megapixels is used for the road pavement condition mapping. Even though many imaging and mapping sensors are available, the development of automated pavement distress detection, recognition and extraction systems for pavement condition is still a challenge. In order to detect and extract pavement cracks, a comparative evaluation of kernel-based segmentation methods comprising line filtering (LF), local binary pattern (LBP) and high-pass filtering (HPF) is carried out. While the LF and LBP methods are based on the principle of rotation-invariance for pattern matching, the HPF applies the same principle for filtering, but with a rotational invariant matrix. With respect to the processing speeds, HPF is fastest due to the fact that it is based on a single kernel, as compared to LF and LBP which are based on several kernels. Experiments with 20 sample images which contain linear, block and alligator cracks are carried out. On an average a completeness of distress extraction with values of 81.2%, 76.2% and 81.1% have been found for LF, HPF and LBP respectively.

UNICOS Kernel Internals Application Development

NASA Technical Reports Server (NTRS)

Caredo, Nicholas; Craw, James M. (Technical Monitor)

1995-01-01

Having an understanding of UNICOS Kernel Internals is valuable information. However, having the knowledge is only half the value. The second half comes with knowing how to use this information and apply it to the development of tools. The kernel contains vast amounts of useful information that can be utilized. This paper discusses the intricacies of developing utilities that utilize kernel information. In addition, algorithms, logic, and code will be discussed for accessing kernel information. Code segments will be provided that demonstrate how to locate and read kernel structures. Types of applications that can utilize kernel information will also be discussed.

Bivariate discrete beta Kernel graduation of mortality data.

PubMed

Mazza, Angelo; Punzo, Antonio

2015-07-01

Various parametric/nonparametric techniques have been proposed in literature to graduate mortality data as a function of age. Nonparametric approaches, as for example kernel smoothing regression, are often preferred because they do not assume any particular mortality law. Among the existing kernel smoothing approaches, the recently proposed (univariate) discrete beta kernel smoother has been shown to provide some benefits. Bivariate graduation, over age and calendar years or durations, is common practice in demography and actuarial sciences. In this paper, we generalize the discrete beta kernel smoother to the bivariate case, and we introduce an adaptive bandwidth variant that may provide additional benefits when data on exposures to the risk of death are available; furthermore, we outline a cross-validation procedure for bandwidths selection. Using simulations studies, we compare the bivariate approach proposed here with its corresponding univariate formulation and with two popular nonparametric bivariate graduation techniques, based on Epanechnikov kernels and on P-splines. To make simulations realistic, a bivariate dataset, based on probabilities of dying recorded for the US males, is used. Simulations have confirmed the gain in performance of the new bivariate approach with respect to both the univariate and the bivariate competitors.

Fishers' knowledge and seahorse conservation in Brazil

PubMed Central

Rosa, Ierecê ML; Alves, Rômulo RN; Bonifácio, Kallyne M; Mourão, José S; Osório, Frederico M; Oliveira, Tacyana PR; Nottingham, Mara C

2005-01-01

From a conservationist perspective, seahorses are threatened fishes. Concomitantly, from a socioeconomic perspective, they represent a source of income to many fishing communities in developing countries. An integration between these two views requires, among other things, the recognition that seahorse fishers have knowledge and abilities that can assist the implementation of conservation strategies and of management plans for seahorses and their habitats. This paper documents the knowledge held by Brazilian fishers on the biology and ecology of the longsnout seahorse Hippocampus reidi. Its aims were to explore collaborative approaches to seahorse conservation and management in Brazil; to assess fishers' perception of seahorse biology and ecology, in the context evaluating potential management options; to increase fishers' involvement with seahorse conservation in Brazil. Data were obtained through questionnaires and interviews made during field surveys conducted in fishing villages located in the States of Piauí, Ceará, Paraíba, Maranhão, Pernambuco and Pará. We consider the following aspects as positive for the conservation of seahorses and their habitats in Brazil: fishers were willing to dialogue with researchers; although captures and/or trade of brooding seahorses occurred, most interviewees recognized the importance of reproduction to the maintenance of seahorses in the wild (and therefore of their source of income), and expressed concern over population declines; fishers associated the presence of a ventral pouch with reproduction in seahorses (regardless of them knowing which sex bears the pouch), and this may facilitate the construction of collaborative management options designed to eliminate captures of brooding specimens; fishers recognized microhabitats of importance to the maintenance of seahorse wild populations; fishers who kept seahorses in captivity tended to recognize the condtions as poor, and as being a cause of seahorse mortality. PMID

Fishers' knowledge and seahorse conservation in Brazil.

PubMed

Rosa, Ierecê Ml; Alves, Rômulo Rn; Bonifácio, Kallyne M; Mourão, José S; Osório, Frederico M; Oliveira, Tacyana Pr; Nottingham, Mara C

2005-12-08

From a conservationist perspective, seahorses are threatened fishes. Concomitantly, from a socioeconomic perspective, they represent a source of income to many fishing communities in developing countries. An integration between these two views requires, among other things, the recognition that seahorse fishers have knowledge and abilities that can assist the implementation of conservation strategies and of management plans for seahorses and their habitats. This paper documents the knowledge held by Brazilian fishers on the biology and ecology of the longsnout seahorse Hippocampus reidi. Its aims were to explore collaborative approaches to seahorse conservation and management in Brazil; to assess fishers' perception of seahorse biology and ecology, in the context evaluating potential management options; to increase fishers' involvement with seahorse conservation in Brazil. Data were obtained through questionnaires and interviews made during field surveys conducted in fishing villages located in the States of Piauí, Ceará, Paraíba, Maranhão, Pernambuco and Pará. We consider the following aspects as positive for the conservation of seahorses and their habitats in Brazil: fishers were willing to dialogue with researchers; although captures and/or trade of brooding seahorses occurred, most interviewees recognized the importance of reproduction to the maintenance of seahorses in the wild (and therefore of their source of income), and expressed concern over population declines; fishers associated the presence of a ventral pouch with reproduction in seahorses (regardless of them knowing which sex bears the pouch), and this may facilitate the construction of collaborative management options designed to eliminate captures of brooding specimens; fishers recognized microhabitats of importance to the maintenance of seahorse wild populations; fishers who kept seahorses in captivity tended to recognize the condtions as poor, and as being a cause of seahorse mortality.

Assessing Fishers' Support of Striped Bass Management Strategies.

PubMed

Murphy, Robert D; Scyphers, Steven B; Grabowski, Jonathan H

2015-01-01

Incorporating the perspectives and insights of stakeholders is an essential component of ecosystem-based fisheries management, such that policy strategies should account for the diverse interests of various groups of anglers to enhance their efficacy. Here we assessed fishing stakeholders' perceptions on the management of Atlantic striped bass (Morone saxatilis) and receptiveness to potential future regulations using an online survey of recreational and commercial fishers in Massachusetts and Connecticut (USA). Our results indicate that most fishers harbored adequate to positive perceptions of current striped bass management policies when asked to grade their state's management regime. Yet, subtle differences in perceptions existed between recreational and commercial fishers, as well as across individuals with differing levels of fishing experience, resource dependency, and tournament participation. Recreational fishers in both states were generally supportive or neutral towards potential management actions including slot limits (71%) and mandated circle hooks to reduce mortality of released fish (74%), but less supportive of reduced recreational bag limits (51%). Although commercial anglers were typically less supportive of management changes than their recreational counterparts, the majority were still supportive of slot limits (54%) and mandated use of circle hooks (56%). Our study suggests that both recreational and commercial fishers are generally supportive of additional management strategies aimed at sustaining healthy striped bass populations and agree on a variety of strategies. However, both stakeholder groups were less supportive of harvest reductions, which is the most direct measure of reducing mortality available to fisheries managers. By revealing factors that influence stakeholders' support or willingness to comply with management strategies, studies such as ours can help managers identify potential stakeholder support for or conflicts that may

Grey Language Hesitant Fuzzy Group Decision Making Method Based on Kernel and Grey Scale

PubMed Central

Diao, Yuzhu; Hu, Aqin

2018-01-01

Based on grey language multi-attribute group decision making, a kernel and grey scale scoring function is put forward according to the definition of grey language and the meaning of the kernel and grey scale. The function introduces grey scale into the decision-making method to avoid information distortion. This method is applied to the grey language hesitant fuzzy group decision making, and the grey correlation degree is used to sort the schemes. The effectiveness and practicability of the decision-making method are further verified by the industry chain sustainable development ability evaluation example of a circular economy. Moreover, its simplicity and feasibility are verified by comparing it with the traditional grey language decision-making method and the grey language hesitant fuzzy weighted arithmetic averaging (GLHWAA) operator integration method after determining the index weight based on the grey correlation. PMID:29498699

Grey Language Hesitant Fuzzy Group Decision Making Method Based on Kernel and Grey Scale.

PubMed

Li, Qingsheng; Diao, Yuzhu; Gong, Zaiwu; Hu, Aqin

2018-03-02

Based on grey language multi-attribute group decision making, a kernel and grey scale scoring function is put forward according to the definition of grey language and the meaning of the kernel and grey scale. The function introduces grey scale into the decision-making method to avoid information distortion. This method is applied to the grey language hesitant fuzzy group decision making, and the grey correlation degree is used to sort the schemes. The effectiveness and practicability of the decision-making method are further verified by the industry chain sustainable development ability evaluation example of a circular economy. Moreover, its simplicity and feasibility are verified by comparing it with the traditional grey language decision-making method and the grey language hesitant fuzzy weighted arithmetic averaging (GLHWAA) operator integration method after determining the index weight based on the grey correlation.

Searching remote homology with spectral clustering with symmetry in neighborhood cluster kernels.

PubMed

Maulik, Ujjwal; Sarkar, Anasua

2013-01-01

Remote homology detection among proteins utilizing only the unlabelled sequences is a central problem in comparative genomics. The existing cluster kernel methods based on neighborhoods and profiles and the Markov clustering algorithms are currently the most popular methods for protein family recognition. The deviation from random walks with inflation or dependency on hard threshold in similarity measure in those methods requires an enhancement for homology detection among multi-domain proteins. We propose to combine spectral clustering with neighborhood kernels in Markov similarity for enhancing sensitivity in detecting homology independent of "recent" paralogs. The spectral clustering approach with new combined local alignment kernels more effectively exploits the unsupervised protein sequences globally reducing inter-cluster walks. When combined with the corrections based on modified symmetry based proximity norm deemphasizing outliers, the technique proposed in this article outperforms other state-of-the-art cluster kernels among all twelve implemented kernels. The comparison with the state-of-the-art string and mismatch kernels also show the superior performance scores provided by the proposed kernels. Similar performance improvement also is found over an existing large dataset. Therefore the proposed spectral clustering framework over combined local alignment kernels with modified symmetry based correction achieves superior performance for unsupervised remote homolog detection even in multi-domain and promiscuous domain proteins from Genolevures database families with better biological relevance. Source code available upon request. sarkar@labri.fr.

Perceptions of gender-based discrimination during surgical training and practice.

PubMed

Bruce, Adrienne N; Battista, Alexis; Plankey, Michael W; Johnson, Lynt B; Marshall, M Blair

2015-01-01

Women represent 15% of practicing general surgeons. Gender-based discrimination has been implicated as discouraging women from surgery. We sought to determine women's perceptions of gender-based discrimination in the surgical training and working environment. Following IRB approval, we fielded a pilot survey measuring perceptions and impact of gender-based discrimination in medical school, residency training, and surgical practice. It was sent electronically to 1,065 individual members of the Association of Women Surgeons. We received 334 responses from medical students, residents, and practicing physicians with a response rate of 31%. Eighty-seven percent experienced gender-based discrimination in medical school, 88% in residency, and 91% in practice. Perceived sources of gender-based discrimination included superiors, physician peers, clinical support staff, and patients, with 40% emanating from women and 60% from men. The majority of responses indicated perceived gender-based discrimination during medical school, residency, and practice. Gender-based discrimination comes from both sexes and has a significant impact on women surgeons.

Perceptions of gender-based discrimination during surgical training and practice.

PubMed

Bruce, Adrienne N; Battista, Alexis; Plankey, Michael W; Johnson, Lynt B; Marshall, M Blair

2015-01-01

Background Women represent 15% of practicing general surgeons. Gender-based discrimination has been implicated as discouraging women from surgery. We sought to determine women's perceptions of gender-based discrimination in the surgical training and working environment. Methods Following IRB approval, we fielded a pilot survey measuring perceptions and impact of gender-based discrimination in medical school, residency training, and surgical practice. It was sent electronically to 1,065 individual members of the Association of Women Surgeons. Results We received 334 responses from medical students, residents, and practicing physicians with a response rate of 31%. Eighty-seven percent experienced gender-based discrimination in medical school, 88% in residency, and 91% in practice. Perceived sources of gender-based discrimination included superiors, physician peers, clinical support staff, and patients, with 40% emanating from women and 60% from men. Conclusions The majority of responses indicated perceived gender-based discrimination during medical school, residency, and practice. Gender-based discrimination comes from both sexes and has a significant impact on women surgeons.

Weighted Feature Gaussian Kernel SVM for Emotion Recognition

PubMed Central

Jia, Qingxuan

2016-01-01

Emotion recognition with weighted feature based on facial expression is a challenging research topic and has attracted great attention in the past few years. This paper presents a novel method, utilizing subregion recognition rate to weight kernel function. First, we divide the facial expression image into some uniform subregions and calculate corresponding recognition rate and weight. Then, we get a weighted feature Gaussian kernel function and construct a classifier based on Support Vector Machine (SVM). At last, the experimental results suggest that the approach based on weighted feature Gaussian kernel function has good performance on the correct rate in emotion recognition. The experiments on the extended Cohn-Kanade (CK+) dataset show that our method has achieved encouraging recognition results compared to the state-of-the-art methods. PMID:27807443

A novel habitat-based approach to predict impacts of marine protected areas on fishers.

PubMed

Teixeira, João B; Moura, Rodrigo L; Mills, Morena; Klein, Carissa; Brown, Christopher J; Adams, Vanessa M; Grantham, Hedley; Watts, Matthew; Faria, Deborah; Amado-Filho, Gilberto M; Bastos, Alex C; Lourival, Reinaldo; Possingham, Hugh P

2017-06-24

While marine protected areas (MPAs) can simultaneously contribute to biodiversity conservation and fisheries management, the global network is biased towards particular ecosystem types, as it was largely established in an ad hoc fashion. The optimization of trade-offs between biodiversity benefits and socio-economic values increases implementation success and minimizes enforcement costs in the long run, but is often neglected in marine spatial planning (MSP). Although the acquisition of spatially explicit socioeconomic data is often perceived as a costly/secondary step in MSP, it is critical to account for lost opportunities by people whose activities will be restricted, especially fishers. Here we present an easily-reproducible habitat-based approach to estimate the spatial distribution of opportunity cost to fishers in data poor regions, assuming that the most accessible areas have higher values and their designation as no-take zones represents increased loss of fishing opportunities. Our method requires only habitat and bathymetric maps, a list of target species, the location of ports, and the relative importance for each port and/or vessel/gear type. The potential distribution of fishing resources is estimated from bathymetric ranges and benthic habitat distribution, while the relative importance of the different resources is estimated for each port, considering total catches (kg), revenues and/or stakeholder perception. Finally, the model can combine different cost layers to produce a comprehensive cost layer, and also allows for the evaluation of tradeoffs. The development of FishCake was based on data from a contentious conservation-planning arena (Abrolhos Bank, Brazil) in which attempts to expand MPA coverage failed due to fishers' resistance. The opportunity cost approach that we introduce herein allows for the incorporation of economic interests of different stakeholders and evaluation of tradeoffs among different stakeholder groups. The novel approach

A Network-Based Kernel Machine Test for the Identification of Risk Pathways in Genome-Wide Association Studies

PubMed Central

Freytag, Saskia; Manitz, Juliane; Schlather, Martin; Kneib, Thomas; Amos, Christopher I.; Risch, Angela; Chang-Claude, Jenny; Heinrich, Joachim; Bickeböller, Heike

2014-01-01

Biological pathways provide rich information and biological context on the genetic causes of complex diseases. The logistic kernel machine test integrates prior knowledge on pathways in order to analyze data from genome-wide association studies (GWAS). Here, the kernel converts genomic information of two individuals to a quantitative value reflecting their genetic similarity. With the selection of the kernel one implicitly chooses a genetic effect model. Like many other pathway methods, none of the available kernels accounts for topological structure of the pathway or gene-gene interaction types. However, evidence indicates that connectivity and neighborhood of genes are crucial in the context of GWAS, because genes associated with a disease often interact. Thus, we propose a novel kernel that incorporates the topology of pathways and information on interactions. Using simulation studies, we demonstrate that the proposed method maintains the type I error correctly and can be more effective in the identification of pathways associated with a disease than non-network-based methods. We apply our approach to genome-wide association case control data on lung cancer and rheumatoid arthritis. We identify some promising new pathways associated with these diseases, which may improve our current understanding of the genetic mechanisms. PMID:24434848

An iterative kernel based method for fourth order nonlinear equation with nonlinear boundary condition

NASA Astrophysics Data System (ADS)

Azarnavid, Babak; Parand, Kourosh; Abbasbandy, Saeid

2018-06-01

This article discusses an iterative reproducing kernel method with respect to its effectiveness and capability of solving a fourth-order boundary value problem with nonlinear boundary conditions modeling beams on elastic foundations. Since there is no method of obtaining reproducing kernel which satisfies nonlinear boundary conditions, the standard reproducing kernel methods cannot be used directly to solve boundary value problems with nonlinear boundary conditions as there is no knowledge about the existence and uniqueness of the solution. The aim of this paper is, therefore, to construct an iterative method by the use of a combination of reproducing kernel Hilbert space method and a shooting-like technique to solve the mentioned problems. Error estimation for reproducing kernel Hilbert space methods for nonlinear boundary value problems have yet to be discussed in the literature. In this paper, we present error estimation for the reproducing kernel method to solve nonlinear boundary value problems probably for the first time. Some numerical results are given out to demonstrate the applicability of the method.

Chapter 4: Fishers and American martens

Treesearch

K.L. Purcell; C.M. Thompson; W.J. Zielinski

2012-01-01

Fishers (Martes pennanti) and American martens (M. americana) are carnivorous mustelids associated with late-successional forests. The distributions of both species have decreased in the Sierra Nevada and southern Cascade region (Zielinski et al. 2005). Fishers occur primarily in lower elevation (3,500 to 7,000 ft) (1067 to...

The Problem of Education-Based Discrimination

ERIC Educational Resources Information Center

Tannock, Stuart

2008-01-01

While the research, theory and policy literature on race, class and gender discrimination in education is extensive, the problem of education-based discrimination itself has been widely overlooked. Indeed, the dominant ideologies of meritocracy and human capital (into which we are inculcated throughout our lives by schools, media and the state)…

A scalable kernel-based semisupervised metric learning algorithm with out-of-sample generalization ability.

PubMed

Yeung, Dit-Yan; Chang, Hong; Dai, Guang

2008-11-01

In recent years, metric learning in the semisupervised setting has aroused a lot of research interest. One type of semisupervised metric learning utilizes supervisory information in the form of pairwise similarity or dissimilarity constraints. However, most methods proposed so far are either limited to linear metric learning or unable to scale well with the data set size. In this letter, we propose a nonlinear metric learning method based on the kernel approach. By applying low-rank approximation to the kernel matrix, our method can handle significantly larger data sets. Moreover, our low-rank approximation scheme can naturally lead to out-of-sample generalization. Experiments performed on both artificial and real-world data show very promising results.

A new discrete dipole kernel for quantitative susceptibility mapping.

PubMed

Milovic, Carlos; Acosta-Cabronero, Julio; Pinto, José Miguel; Mattern, Hendrik; Andia, Marcelo; Uribe, Sergio; Tejos, Cristian

2018-09-01

Most approaches for quantitative susceptibility mapping (QSM) are based on a forward model approximation that employs a continuous Fourier transform operator to solve a differential equation system. Such formulation, however, is prone to high-frequency aliasing. The aim of this study was to reduce such errors using an alternative dipole kernel formulation based on the discrete Fourier transform and discrete operators. The impact of such an approach on forward model calculation and susceptibility inversion was evaluated in contrast to the continuous formulation both with synthetic phantoms and in vivo MRI data. The discrete kernel demonstrated systematically better fits to analytic field solutions, and showed less over-oscillations and aliasing artifacts while preserving low- and medium-frequency responses relative to those obtained with the continuous kernel. In the context of QSM estimation, the use of the proposed discrete kernel resulted in error reduction and increased sharpness. This proof-of-concept study demonstrated that discretizing the dipole kernel is advantageous for QSM. The impact on small or narrow structures such as the venous vasculature might by particularly relevant to high-resolution QSM applications with ultra-high field MRI - a topic for future investigations. The proposed dipole kernel has a straightforward implementation to existing QSM routines. Copyright © 2018 Elsevier Inc. All rights reserved.

7 CFR 981.7 - Edible kernel.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 8 2010-01-01 2010-01-01 false Edible kernel. 981.7 Section 981.7 Agriculture... Regulating Handling Definitions § 981.7 Edible kernel. Edible kernel means a kernel, piece, or particle of almond kernel that is not inedible. [41 FR 26852, June 30, 1976] ...

In silico and experimental evaluation of DNA-based detection methods for the ability to discriminate almond from other Prunus spp.

PubMed

Brežná, Barbara; Šmíd, Jiří; Costa, Joana; Radvanszky, Jan; Mafra, Isabel; Kuchta, Tomáš

2015-04-01

Ten published DNA-based analytical methods aiming at detecting material of almond (Prunus dulcis) were in silico evaluated for potential cross-reactivity with other stone fruits (Prunus spp.), including peach, apricot, plum, cherry, sour cherry and Sargent cherry. For most assays, the analysis of nucleotide databases suggested none or insufficient discrimination of at least some stone fruits. On the other hand, the assay targeting non-specific lipid transfer protein (Röder et al., 2011, Anal Chim Acta 685:74-83) was sufficiently discriminative, judging from nucleotide alignments. Empirical evaluation was performed for three of the published methods, one modification of a commercial kit (SureFood allergen almond) and one attempted novel method targeting thaumatin-like protein gene. Samples of leaves and kernels were used in the experiments. The empirical results were favourable for the method from Röder et al. (2011) and a modification of SureFood allergen almond kit, both showing cross-reactivity <10(-3) compared to the model almond. Copyright © 2014 Elsevier Ltd. All rights reserved.

Exploiting graph kernels for high performance biomedical relation extraction.

PubMed

Panyam, Nagesh C; Verspoor, Karin; Cohn, Trevor; Ramamohanarao, Kotagiri

2018-01-30

Relation extraction from biomedical publications is an important task in the area of semantic mining of text. Kernel methods for supervised relation extraction are often preferred over manual feature engineering methods, when classifying highly ordered structures such as trees and graphs obtained from syntactic parsing of a sentence. Tree kernels such as the Subset Tree Kernel and Partial Tree Kernel have been shown to be effective for classifying constituency parse trees and basic dependency parse graphs of a sentence. Graph kernels such as the All Path Graph kernel (APG) and Approximate Subgraph Matching (ASM) kernel have been shown to be suitable for classifying general graphs with cycles, such as the enhanced dependency parse graph of a sentence. In this work, we present a high performance Chemical-Induced Disease (CID) relation extraction system. We present a comparative study of kernel methods for the CID task and also extend our study to the Protein-Protein Interaction (PPI) extraction task, an important biomedical relation extraction task. We discuss novel modifications to the ASM kernel to boost its performance and a method to apply graph kernels for extracting relations expressed in multiple sentences. Our system for CID relation extraction attains an F-score of 60%, without using external knowledge sources or task specific heuristic or rules. In comparison, the state of the art Chemical-Disease Relation Extraction system achieves an F-score of 56% using an ensemble of multiple machine learning methods, which is then boosted to 61% with a rule based system employing task specific post processing rules. For the CID task, graph kernels outperform tree kernels substantially, and the best performance is obtained with APG kernel that attains an F-score of 60%, followed by the ASM kernel at 57%. The performance difference between the ASM and APG kernels for CID sentence level relation extraction is not significant. In our evaluation of ASM for the PPI task, ASM

Unconventional protein sources: apricot seed kernels.

PubMed

Gabrial, G N; El-Nahry, F I; Awadalla, M Z; Girgis, S M

1981-09-01

Hamawy apricot seed kernels (sweet), Amar apricot seed kernels (bitter) and treated Amar apricot kernels (bitterness removed) were evaluated biochemically. All kernels were found to be high in fat (42.2--50.91%), protein (23.74--25.70%) and fiber (15.08--18.02%). Phosphorus, calcium, and iron were determined in all experimental samples. The three different apricot seed kernels were used for extensive study including the qualitative determination of the amino acid constituents by acid hydrolysis, quantitative determination of some amino acids, and biological evaluation of the kernel proteins in order to use them as new protein sources. Weanling albino rats failed to grow on diets containing the Amar apricot seed kernels due to low food consumption because of its bitterness. There was no loss in weight in that case. The Protein Efficiency Ratio data and blood analysis results showed the Hamawy apricot seed kernels to be higher in biological value than treated apricot seed kernels. The Net Protein Ratio data which accounts for both weight, maintenance and growth showed the treated apricot seed kernels to be higher in biological value than both Hamawy and Amar kernels. The Net Protein Ratio for the last two kernels were nearly equal.

R. A. Fisher and his advocacy of randomization.

PubMed

Hall, Nancy S

2007-01-01

The requirement of randomization in experimental design was first stated by R. A. Fisher, statistician and geneticist, in 1925 in his book Statistical Methods for Research Workers. Earlier designs were systematic and involved the judgment of the experimenter; this led to possible bias and inaccurate interpretation of the data. Fisher's dictum was that randomization eliminates bias and permits a valid test of significance. Randomization in experimenting had been used by Charles Sanders Peirce in 1885 but the practice was not continued. Fisher developed his concepts of randomizing as he considered the mathematics of small samples, in discussions with "Student," William Sealy Gosset. Fisher published extensively. His principles of experimental design were spread worldwide by the many "voluntary workers" who came from other institutions to Rothamsted Agricultural Station in England to learn Fisher's methods.

Perceptions of gender-based discrimination during surgical training and practice

PubMed Central

Bruce, Adrienne N.; Battista, Alexis; Plankey, Michael W.; Johnson, Lynt B.; Marshall, M. Blair

2015-01-01

Background Women represent 15% of practicing general surgeons. Gender-based discrimination has been implicated as discouraging women from surgery. We sought to determine women's perceptions of gender-based discrimination in the surgical training and working environment. Methods Following IRB approval, we fielded a pilot survey measuring perceptions and impact of gender-based discrimination in medical school, residency training, and surgical practice. It was sent electronically to 1,065 individual members of the Association of Women Surgeons. Results We received 334 responses from medical students, residents, and practicing physicians with a response rate of 31%. Eighty-seven percent experienced gender-based discrimination in medical school, 88% in residency, and 91% in practice. Perceived sources of gender-based discrimination included superiors, physician peers, clinical support staff, and patients, with 40% emanating from women and 60% from men. Conclusions The majority of responses indicated perceived gender-based discrimination during medical school, residency, and practice. Gender-based discrimination comes from both sexes and has a significant impact on women surgeons. PMID:25652117

Searching Remote Homology with Spectral Clustering with Symmetry in Neighborhood Cluster Kernels

PubMed Central

Maulik, Ujjwal; Sarkar, Anasua

2013-01-01

Remote homology detection among proteins utilizing only the unlabelled sequences is a central problem in comparative genomics. The existing cluster kernel methods based on neighborhoods and profiles and the Markov clustering algorithms are currently the most popular methods for protein family recognition. The deviation from random walks with inflation or dependency on hard threshold in similarity measure in those methods requires an enhancement for homology detection among multi-domain proteins. We propose to combine spectral clustering with neighborhood kernels in Markov similarity for enhancing sensitivity in detecting homology independent of “recent” paralogs. The spectral clustering approach with new combined local alignment kernels more effectively exploits the unsupervised protein sequences globally reducing inter-cluster walks. When combined with the corrections based on modified symmetry based proximity norm deemphasizing outliers, the technique proposed in this article outperforms other state-of-the-art cluster kernels among all twelve implemented kernels. The comparison with the state-of-the-art string and mismatch kernels also show the superior performance scores provided by the proposed kernels. Similar performance improvement also is found over an existing large dataset. Therefore the proposed spectral clustering framework over combined local alignment kernels with modified symmetry based correction achieves superior performance for unsupervised remote homolog detection even in multi-domain and promiscuous domain proteins from Genolevures database families with better biological relevance. Source code available upon request. Contact: sarkar@labri.fr. PMID:23457439

Community-based comprehensive intervention for people with schizophrenia in Guangzhou, China: Effects on clinical symptoms, social functioning, internalized stigma and discrimination.

PubMed

Li, Jie; Huang, Yuan-Guang; Ran, Mao-Sheng; Fan, Yu; Chen, Wen; Evans-Lacko, Sara; Thornicroft, Graham

2018-04-01

Comprehensive interventions including components of stigma and discrimination reduction in schizophrenia in low- and middle-income countries (LMICs) are lacking. We developed a community-based comprehensive intervention to evaluate its effects on clinical symptoms, social functioning, internalized stigma and discrimination among patients with schizophrenia. A randomized controlled trial including an intervention group (n = 169) and a control group (n = 158) was performed. The intervention group received comprehensive intervention (strategies against stigma and discrimination, psycho-education, social skills training and cognitive behavioral therapy) and the control group received face to face interview. Both lasted for nine months. Participants were measured at baseline, 6 months and 9 months using the Internalized Stigma of Mental Illness scale (ISMI), Discrimination and Stigma Scale (DISC-12), Global Assessment of Functioning (GAF), Schizophrenia Quality of Life Scale (SQLS), Self-Esteem Scale (SES), Brief Psychiatric Rating Scale (BPRS) and PANSS negative scale (PANSS-N). Insight and medication compliance were evaluated by senior psychiatrists. Data were analyzed by descriptive statistics, t-test, chi-square test or Fisher's exact test. Linear Mixed Models were used to show intervention effectiveness on scales. General Linear Mixed Models with multinomial logistic link function were used to assess the effectiveness on medication compliance and insight. We found a significant reduction on anticipated discrimination, BPRS and PANSS-N total scores, and an elevation on overcoming stigma and GAF in the intervention group after 9 months. These suggested the intervention may be effective in reducing anticipated discrimination, increasing skills overcoming stigma as well as improving clinical symptoms and social functioning in Chinese patients with schizophrenia. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Assessing Fishers' Support of Striped Bass Management Strategies

PubMed Central

Murphy, Robert D.; Scyphers, Steven B.; Grabowski, Jonathan H.

2015-01-01

Incorporating the perspectives and insights of stakeholders is an essential component of ecosystem-based fisheries management, such that policy strategies should account for the diverse interests of various groups of anglers to enhance their efficacy. Here we assessed fishing stakeholders’ perceptions on the management of Atlantic striped bass (Morone saxatilis) and receptiveness to potential future regulations using an online survey of recreational and commercial fishers in Massachusetts and Connecticut (USA). Our results indicate that most fishers harbored adequate to positive perceptions of current striped bass management policies when asked to grade their state’s management regime. Yet, subtle differences in perceptions existed between recreational and commercial fishers, as well as across individuals with differing levels of fishing experience, resource dependency, and tournament participation. Recreational fishers in both states were generally supportive or neutral towards potential management actions including slot limits (71%) and mandated circle hooks to reduce mortality of released fish (74%), but less supportive of reduced recreational bag limits (51%). Although commercial anglers were typically less supportive of management changes than their recreational counterparts, the majority were still supportive of slot limits (54%) and mandated use of circle hooks (56%). Our study suggests that both recreational and commercial fishers are generally supportive of additional management strategies aimed at sustaining healthy striped bass populations and agree on a variety of strategies. However, both stakeholder groups were less supportive of harvest reductions, which is the most direct measure of reducing mortality available to fisheries managers. By revealing factors that influence stakeholders’ support or willingness to comply with management strategies, studies such as ours can help managers identify potential stakeholder support for or conflicts that

Distributed smoothed tree kernel for protein-protein interaction extraction from the biomedical literature

PubMed Central

Murugesan, Gurusamy; Abdulkadhar, Sabenabanu; Natarajan, Jeyakumar

2017-01-01

Automatic extraction of protein-protein interaction (PPI) pairs from biomedical literature is a widely examined task in biological information extraction. Currently, many kernel based approaches such as linear kernel, tree kernel, graph kernel and combination of multiple kernels has achieved promising results in PPI task. However, most of these kernel methods fail to capture the semantic relation information between two entities. In this paper, we present a special type of tree kernel for PPI extraction which exploits both syntactic (structural) and semantic vectors information known as Distributed Smoothed Tree kernel (DSTK). DSTK comprises of distributed trees with syntactic information along with distributional semantic vectors representing semantic information of the sentences or phrases. To generate robust machine learning model composition of feature based kernel and DSTK were combined using ensemble support vector machine (SVM). Five different corpora (AIMed, BioInfer, HPRD50, IEPA, and LLL) were used for evaluating the performance of our system. Experimental results show that our system achieves better f-score with five different corpora compared to other state-of-the-art systems. PMID:29099838

Distributed smoothed tree kernel for protein-protein interaction extraction from the biomedical literature.

PubMed

Murugesan, Gurusamy; Abdulkadhar, Sabenabanu; Natarajan, Jeyakumar

2017-01-01

Automatic extraction of protein-protein interaction (PPI) pairs from biomedical literature is a widely examined task in biological information extraction. Currently, many kernel based approaches such as linear kernel, tree kernel, graph kernel and combination of multiple kernels has achieved promising results in PPI task. However, most of these kernel methods fail to capture the semantic relation information between two entities. In this paper, we present a special type of tree kernel for PPI extraction which exploits both syntactic (structural) and semantic vectors information known as Distributed Smoothed Tree kernel (DSTK). DSTK comprises of distributed trees with syntactic information along with distributional semantic vectors representing semantic information of the sentences or phrases. To generate robust machine learning model composition of feature based kernel and DSTK were combined using ensemble support vector machine (SVM). Five different corpora (AIMed, BioInfer, HPRD50, IEPA, and LLL) were used for evaluating the performance of our system. Experimental results show that our system achieves better f-score with five different corpora compared to other state-of-the-art systems.

7 CFR 981.8 - Inedible kernel.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 8 2010-01-01 2010-01-01 false Inedible kernel. 981.8 Section 981.8 Agriculture... Regulating Handling Definitions § 981.8 Inedible kernel. Inedible kernel means a kernel, piece, or particle of almond kernel with any defect scored as serious damage, or damage due to mold, gum, shrivel, or...

7 CFR 51.1415 - Inedible kernels.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Inedible kernels. 51.1415 Section 51.1415 Agriculture... Standards for Grades of Pecans in the Shell 1 Definitions § 51.1415 Inedible kernels. Inedible kernels means that the kernel or pieces of kernels are rancid, moldy, decayed, injured by insects or otherwise...

Fisher classifier and its probability of error estimation

NASA Technical Reports Server (NTRS)

Chittineni, C. B.

1979-01-01

Computationally efficient expressions are derived for estimating the probability of error using the leave-one-out method. The optimal threshold for the classification of patterns projected onto Fisher's direction is derived. A simple generalization of the Fisher classifier to multiple classes is presented. Computational expressions are developed for estimating the probability of error of the multiclass Fisher classifier.

Fisher statistics for analysis of diffusion tensor directional information.

PubMed

Hutchinson, Elizabeth B; Rutecki, Paul A; Alexander, Andrew L; Sutula, Thomas P

2012-04-30

A statistical approach is presented for the quantitative analysis of diffusion tensor imaging (DTI) directional information using Fisher statistics, which were originally developed for the analysis of vectors in the field of paleomagnetism. In this framework, descriptive and inferential statistics have been formulated based on the Fisher probability density function, a spherical analogue of the normal distribution. The Fisher approach was evaluated for investigation of rat brain DTI maps to characterize tissue orientation in the corpus callosum, fornix, and hilus of the dorsal hippocampal dentate gyrus, and to compare directional properties in these regions following status epilepticus (SE) or traumatic brain injury (TBI) with values in healthy brains. Direction vectors were determined for each region of interest (ROI) for each brain sample and Fisher statistics were applied to calculate the mean direction vector and variance parameters in the corpus callosum, fornix, and dentate gyrus of normal rats and rats that experienced TBI or SE. Hypothesis testing was performed by calculation of Watson's F-statistic and associated p-value giving the likelihood that grouped observations were from the same directional distribution. In the fornix and midline corpus callosum, no directional differences were detected between groups, however in the hilus, significant (p<0.0005) differences were found that robustly confirmed observations that were suggested by visual inspection of directionally encoded color DTI maps. The Fisher approach is a potentially useful analysis tool that may extend the current capabilities of DTI investigation by providing a means of statistical comparison of tissue structural orientation. Copyright © 2012 Elsevier B.V. All rights reserved.

Quasi-kernel polynomials and convergence results for quasi-minimal residual iterations

NASA Technical Reports Server (NTRS)

Freund, Roland W.

1992-01-01

Recently, Freund and Nachtigal have proposed a novel polynominal-based iteration, the quasi-minimal residual algorithm (QMR), for solving general nonsingular non-Hermitian linear systems. Motivated by the QMR method, we have introduced the general concept of quasi-kernel polynomials, and we have shown that the QMR algorithm is based on a particular instance of quasi-kernel polynomials. In this paper, we continue our study of quasi-kernel polynomials. In particular, we derive bounds for the norms of quasi-kernel polynomials. These results are then applied to obtain convergence theorems both for the QMR method and for a transpose-free variant of QMR, the TFQMR algorithm.

7 CFR 981.408 - Inedible kernel.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 8 2010-01-01 2010-01-01 false Inedible kernel. 981.408 Section 981.408 Agriculture... Administrative Rules and Regulations § 981.408 Inedible kernel. Pursuant to § 981.8, the definition of inedible kernel is modified to mean a kernel, piece, or particle of almond kernel with any defect scored as...

Linear discriminant analysis with misallocation in training samples

NASA Technical Reports Server (NTRS)

Chhikara, R. (Principal Investigator); Mckeon, J.

1982-01-01

Linear discriminant analysis for a two-class case is studied in the presence of misallocation in training samples. A general appraoch to modeling of mislocation is formulated, and the mean vectors and covariance matrices of the mixture distributions are derived. The asymptotic distribution of the discriminant boundary is obtained and the asymptotic first two moments of the two types of error rate given. Certain numerical results for the error rates are presented by considering the random and two non-random misallocation models. It is shown that when the allocation procedure for training samples is objectively formulated, the effect of misallocation on the error rates of the Bayes linear discriminant rule can almost be eliminated. If, however, this is not possible, the use of Fisher rule may be preferred over the Bayes rule.

Fishers' knowledge about fish trophic interactions in the southeastern Brazilian coast.

PubMed

Ramires, Milena; Clauzet, Mariana; Barrella, Walter; Rotundo, Matheus M; Silvano, Renato Am; Begossi, Alpina

2015-03-05

Data derived from studies of fishers' local ecological knowledge (LEK) can be invaluable to the proposal of new studies and more appropriate management strategies. This study analyzed the fisher's LEK about trophic relationships of fishes in the southeastern Brazilian coast, comparing fishers' LEK with scientific knowledge to provide new hypotheses. The initial contacts with fishers were made through informal visits in their residences, to explain the research goals, meet fishers and their families, check the number of resident fishers and ask for fishers' consent to participate in the research. After this initial contact, fishers were selected to be included in the interviews through the technique of snowball sampling. The fishers indicated by others who attended the criteria to be included in the research were interviewed by using a semi-structured standard questionnaire. There were interviewed 26 artisanal fishers from three communities of the Ilhabela: Jabaquara, Fome and Serraria. The interviewed fishers showed a detailed knowledge about the trophic interactions of the studied coastal fishes, as fishers mentioned 17 food items for these fishes and six fish and three mammals as fish predators. The most mentioned food items were small fish, shrimps and crabs, while the most mentioned predators were large reef fishes. Fishers also mentioned some predators, such as sea otters, that have not been reported by the biological literature and are poorly known. The LEK of the studied fishers showed a high degree of concordance with the scientific literature regarding fish diet. This study evidenced the value of fishers' LEK to improve fisheries research and management, as well as the needy to increase the collaboration among managers, biologists and fishers.

A fuzzy pattern matching method based on graph kernel for lithography hotspot detection

NASA Astrophysics Data System (ADS)

Nitta, Izumi; Kanazawa, Yuzi; Ishida, Tsutomu; Banno, Koji

2017-03-01

In advanced technology nodes, lithography hotspot detection has become one of the most significant issues in design for manufacturability. Recently, machine learning based lithography hotspot detection has been widely investigated, but it has trade-off between detection accuracy and false alarm. To apply machine learning based technique to the physical verification phase, designers require minimizing undetected hotspots to avoid yield degradation. They also need a ranking of similar known patterns with a detected hotspot to prioritize layout pattern to be corrected. To achieve high detection accuracy and to prioritize detected hotspots, we propose a novel lithography hotspot detection method using Delaunay triangulation and graph kernel based machine learning. Delaunay triangulation extracts features of hotspot patterns where polygons locate irregularly and closely one another, and graph kernel expresses inner structure of graphs. Additionally, our method provides similarity between two patterns and creates a list of similar training patterns with a detected hotspot. Experiments results on ICCAD 2012 benchmarks show that our method achieves high accuracy with allowable range of false alarm. We also show the ranking of the similar known patterns with a detected hotspot.

Transfer Kernel Common Spatial Patterns for Motor Imagery Brain-Computer Interface Classification

PubMed Central

Dai, Mengxi; Liu, Shucong; Zhang, Pengju

2018-01-01

Motor-imagery-based brain-computer interfaces (BCIs) commonly use the common spatial pattern (CSP) as preprocessing step before classification. The CSP method is a supervised algorithm. Therefore a lot of time-consuming training data is needed to build the model. To address this issue, one promising approach is transfer learning, which generalizes a learning model can extract discriminative information from other subjects for target classification task. To this end, we propose a transfer kernel CSP (TKCSP) approach to learn a domain-invariant kernel by directly matching distributions of source subjects and target subjects. The dataset IVa of BCI Competition III is used to demonstrate the validity by our proposed methods. In the experiment, we compare the classification performance of the TKCSP against CSP, CSP for subject-to-subject transfer (CSP SJ-to-SJ), regularizing CSP (RCSP), stationary subspace CSP (ssCSP), multitask CSP (mtCSP), and the combined mtCSP and ssCSP (ss + mtCSP) method. The results indicate that the superior mean classification performance of TKCSP can achieve 81.14%, especially in case of source subjects with fewer number of training samples. Comprehensive experimental evidence on the dataset verifies the effectiveness and efficiency of the proposed TKCSP approach over several state-of-the-art methods. PMID:29743934

Transfer Kernel Common Spatial Patterns for Motor Imagery Brain-Computer Interface Classification.

PubMed

Dai, Mengxi; Zheng, Dezhi; Liu, Shucong; Zhang, Pengju

2018-01-01

Motor-imagery-based brain-computer interfaces (BCIs) commonly use the common spatial pattern (CSP) as preprocessing step before classification. The CSP method is a supervised algorithm. Therefore a lot of time-consuming training data is needed to build the model. To address this issue, one promising approach is transfer learning, which generalizes a learning model can extract discriminative information from other subjects for target classification task. To this end, we propose a transfer kernel CSP (TKCSP) approach to learn a domain-invariant kernel by directly matching distributions of source subjects and target subjects. The dataset IVa of BCI Competition III is used to demonstrate the validity by our proposed methods. In the experiment, we compare the classification performance of the TKCSP against CSP, CSP for subject-to-subject transfer (CSP SJ-to-SJ), regularizing CSP (RCSP), stationary subspace CSP (ssCSP), multitask CSP (mtCSP), and the combined mtCSP and ssCSP (ss + mtCSP) method. The results indicate that the superior mean classification performance of TKCSP can achieve 81.14%, especially in case of source subjects with fewer number of training samples. Comprehensive experimental evidence on the dataset verifies the effectiveness and efficiency of the proposed TKCSP approach over several state-of-the-art methods.

Density Estimation with Mercer Kernels

NASA Technical Reports Server (NTRS)

Macready, William G.

2003-01-01

We present a new method for density estimation based on Mercer kernels. The density estimate can be understood as the density induced on a data manifold by a mixture of Gaussians fit in a feature space. As is usual, the feature space and data manifold are defined with any suitable positive-definite kernel function. We modify the standard EM algorithm for mixtures of Gaussians to infer the parameters of the density. One benefit of the approach is it's conceptual simplicity, and uniform applicability over many different types of data. Preliminary results are presented for a number of simple problems.

Sir Ronald A. Fisher and the International Biometric Society.

PubMed

Billard, Lynne

2014-06-01

The year 2012 marks the 50th anniversary of the death of Sir Ronald A. Fisher, one of the two Fathers of Statistics and a Founder of the International Biometric Society (the "Society"). To celebrate the extraordinary genius of Fisher and the far-sighted vision of Fisher and Chester Bliss in organizing and promoting the formation of the Society, this article looks at the origins and growth of the Society, some of the key players and events, and especially the roles played by Fisher himself as the First President. A fresh look at Fisher, the man rather than the scientific genius is also presented. © 2014, The International Biometric Society.

Recurrent Miller Fisher syndrome.

PubMed

Madhavan, S; Geetha; Bhargavan, P V

2004-07-01

Miller Fisher syndrome (MFS) is a variant of Guillan Barre syndrome characterized by the triad of ophthalmoplegia, ataxia and areflexia. Recurrences are exceptional with Miller Fisher syndrome. We are reporting a case with two episodes of MFS within two years. Initially he presented with partial ophthalmoplegia, ataxia. Second episode was characterized by full-blown presentation characterized by ataxia, areflexia and ophthalmoplegia. CSF analysis was typical during both episodes. Nerve conduction velocity study was fairly within normal limits. MRI of brain was within normal limits. He responded to symptomatic measures initially, then to steroids in the second episode. We are reporting the case due to its rarity.

A kernel adaptive algorithm for quaternion-valued inputs.

PubMed

Paul, Thomas K; Ogunfunmi, Tokunbo

2015-10-01

The use of quaternion data can provide benefit in applications like robotics and image recognition, and particularly for performing transforms in 3-D space. Here, we describe a kernel adaptive algorithm for quaternions. A least mean square (LMS)-based method was used, resulting in the derivation of the quaternion kernel LMS (Quat-KLMS) algorithm. Deriving this algorithm required describing the idea of a quaternion reproducing kernel Hilbert space (RKHS), as well as kernel functions suitable with quaternions. A modified HR calculus for Hilbert spaces was used to find the gradient of cost functions defined on a quaternion RKHS. In addition, the use of widely linear (or augmented) filtering is proposed to improve performance. The benefit of the Quat-KLMS and widely linear forms in learning nonlinear transformations of quaternion data are illustrated with simulations.

Testing in Microbiome-Profiling Studies with MiRKAT, the Microbiome Regression-Based Kernel Association Test

PubMed Central

Zhao, Ni; Chen, Jun; Carroll, Ian M.; Ringel-Kulka, Tamar; Epstein, Michael P.; Zhou, Hua; Zhou, Jin J.; Ringel, Yehuda; Li, Hongzhe; Wu, Michael C.

2015-01-01

High-throughput sequencing technology has enabled population-based studies of the role of the human microbiome in disease etiology and exposure response. Distance-based analysis is a popular strategy for evaluating the overall association between microbiome diversity and outcome, wherein the phylogenetic distance between individuals’ microbiome profiles is computed and tested for association via permutation. Despite their practical popularity, distance-based approaches suffer from important challenges, especially in selecting the best distance and extending the methods to alternative outcomes, such as survival outcomes. We propose the microbiome regression-based kernel association test (MiRKAT), which directly regresses the outcome on the microbiome profiles via the semi-parametric kernel machine regression framework. MiRKAT allows for easy covariate adjustment and extension to alternative outcomes while non-parametrically modeling the microbiome through a kernel that incorporates phylogenetic distance. It uses a variance-component score statistic to test for the association with analytical p value calculation. The model also allows simultaneous examination of multiple distances, alleviating the problem of choosing the best distance. Our simulations demonstrated that MiRKAT provides correctly controlled type I error and adequate power in detecting overall association. “Optimal” MiRKAT, which considers multiple candidate distances, is robust in that it suffers from little power loss in comparison to when the best distance is used and can achieve tremendous power gain in comparison to when a poor distance is chosen. Finally, we applied MiRKAT to real microbiome datasets to show that microbial communities are associated with smoking and with fecal protease levels after confounders are controlled for. PMID:25957468

DNA sequence+shape kernel enables alignment-free modeling of transcription factor binding.

PubMed

Ma, Wenxiu; Yang, Lin; Rohs, Remo; Noble, William Stafford

2017-10-01

Transcription factors (TFs) bind to specific DNA sequence motifs. Several lines of evidence suggest that TF-DNA binding is mediated in part by properties of the local DNA shape: the width of the minor groove, the relative orientations of adjacent base pairs, etc. Several methods have been developed to jointly account for DNA sequence and shape properties in predicting TF binding affinity. However, a limitation of these methods is that they typically require a training set of aligned TF binding sites. We describe a sequence + shape kernel that leverages DNA sequence and shape information to better understand protein-DNA binding preference and affinity. This kernel extends an existing class of k-mer based sequence kernels, based on the recently described di-mismatch kernel. Using three in vitro benchmark datasets, derived from universal protein binding microarrays (uPBMs), genomic context PBMs (gcPBMs) and SELEX-seq data, we demonstrate that incorporating DNA shape information improves our ability to predict protein-DNA binding affinity. In particular, we observe that (i) the k-spectrum + shape model performs better than the classical k-spectrum kernel, particularly for small k values; (ii) the di-mismatch kernel performs better than the k-mer kernel, for larger k; and (iii) the di-mismatch + shape kernel performs better than the di-mismatch kernel for intermediate k values. The software is available at https://bitbucket.org/wenxiu/sequence-shape.git. rohs@usc.edu or william-noble@uw.edu. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

Alumina Concentration Detection Based on the Kernel Extreme Learning Machine.

PubMed

Zhang, Sen; Zhang, Tao; Yin, Yixin; Xiao, Wendong

2017-09-01

The concentration of alumina in the electrolyte is of great significance during the production of aluminum. The amount of the alumina concentration may lead to unbalanced material distribution and low production efficiency and affect the stability of the aluminum reduction cell and current efficiency. The existing methods cannot meet the needs for online measurement because industrial aluminum electrolysis has the characteristics of high temperature, strong magnetic field, coupled parameters, and high nonlinearity. Currently, there are no sensors or equipment that can detect the alumina concentration on line. Most companies acquire the alumina concentration from the electrolyte samples which are analyzed through an X-ray fluorescence spectrometer. To solve the problem, the paper proposes a soft sensing model based on a kernel extreme learning machine algorithm that takes the kernel function into the extreme learning machine. K-fold cross validation is used to estimate the generalization error. The proposed soft sensing algorithm can detect alumina concentration by the electrical signals such as voltages and currents of the anode rods. The predicted results show that the proposed approach can give more accurate estimations of alumina concentration with faster learning speed compared with the other methods such as the basic ELM, BP, and SVM.

Perceived ethnic and language-based discrimination and Latina immigrant women's health.

PubMed

Halim, May Ling; Moy, Keith H; Yoshikawa, Hirokazu

2017-01-01

Perceiving ethnic discrimination can have aversive consequences for health. However, little is known about whether perceiving language-based (how one speaks a second language) discrimination poses the same risks. This study examined whether perceptions of language-based and ethnic discrimination are associated with mental and physical health. Among 132 Mexican and Dominican immigrant women, perceiving ethnic and language-based discrimination each predicted psychological distress and poorer physical health. When examined together, only ethnic discrimination remained a significant predictor. These results emphasize the importance of understanding how perceived ethnic and language-based discrimination play an integral role in the health of Latina immigrant women.

Discriminant projective non-negative matrix factorization.

PubMed

Guan, Naiyang; Zhang, Xiang; Luo, Zhigang; Tao, Dacheng; Yang, Xuejun

2013-01-01

Projective non-negative matrix factorization (PNMF) projects high-dimensional non-negative examples X onto a lower-dimensional subspace spanned by a non-negative basis W and considers W(T) X as their coefficients, i.e., X≈WW(T) X. Since PNMF learns the natural parts-based representation Wof X, it has been widely used in many fields such as pattern recognition and computer vision. However, PNMF does not perform well in classification tasks because it completely ignores the label information of the dataset. This paper proposes a Discriminant PNMF method (DPNMF) to overcome this deficiency. In particular, DPNMF exploits Fisher's criterion to PNMF for utilizing the label information. Similar to PNMF, DPNMF learns a single non-negative basis matrix and needs less computational burden than NMF. In contrast to PNMF, DPNMF maximizes the distance between centers of any two classes of examples meanwhile minimizes the distance between any two examples of the same class in the lower-dimensional subspace and thus has more discriminant power. We develop a multiplicative update rule to solve DPNMF and prove its convergence. Experimental results on four popular face image datasets confirm its effectiveness comparing with the representative NMF and PNMF algorithms.

Discriminant Projective Non-Negative Matrix Factorization

PubMed Central

Guan, Naiyang; Zhang, Xiang; Luo, Zhigang; Tao, Dacheng; Yang, Xuejun

2013-01-01

Projective non-negative matrix factorization (PNMF) projects high-dimensional non-negative examples X onto a lower-dimensional subspace spanned by a non-negative basis W and considers WT X as their coefficients, i.e., X≈WWT X. Since PNMF learns the natural parts-based representation Wof X, it has been widely used in many fields such as pattern recognition and computer vision. However, PNMF does not perform well in classification tasks because it completely ignores the label information of the dataset. This paper proposes a Discriminant PNMF method (DPNMF) to overcome this deficiency. In particular, DPNMF exploits Fisher's criterion to PNMF for utilizing the label information. Similar to PNMF, DPNMF learns a single non-negative basis matrix and needs less computational burden than NMF. In contrast to PNMF, DPNMF maximizes the distance between centers of any two classes of examples meanwhile minimizes the distance between any two examples of the same class in the lower-dimensional subspace and thus has more discriminant power. We develop a multiplicative update rule to solve DPNMF and prove its convergence. Experimental results on four popular face image datasets confirm its effectiveness comparing with the representative NMF and PNMF algorithms. PMID:24376680

A dynamic kernel modifier for linux

DOE Office of Scientific and Technical Information (OSTI.GOV)

Minnich, R. G.

2002-09-03

Dynamic Kernel Modifier, or DKM, is a kernel module for Linux that allows user-mode programs to modify the execution of functions in the kernel without recompiling or modifying the kernel source in any way. Functions may be traced, either function entry only or function entry and exit; nullified; or replaced with some other function. For the tracing case, function execution results in the activation of a watchpoint. When the watchpoint is activated, the address of the function is logged in a FIFO buffer that is readable by external applications. The watchpoints are time-stamped with the resolution of the processor highmore » resolution timers, which on most modem processors are accurate to a single processor tick. DKM is very similar to earlier systems such as the SunOS trace device or Linux TT. Unlike these two systems, and other similar systems, DKM requires no kernel modifications. DKM allows users to do initial probing of the kernel to look for performance problems, or even to resolve potential problems by turning functions off or replacing them. DKM watchpoints are not without cost: it takes about 200 nanoseconds to make a log entry on an 800 Mhz Pentium-Ill. The overhead numbers are actually competitive with other hardware-based trace systems, although it has less 'Los Alamos National Laboratory is operated by the University of California for the National Nuclear Security Administration of the United States Department of Energy under contract W-7405-ENG-36. accuracy than an In-Circuit Emulator such as the American Arium. Once the user has zeroed in on a problem, other mechanisms with a higher degree of accuracy can be used.« less

[Research on the methods for multi-class kernel CSP-based feature extraction].

PubMed

Wang, Jinjia; Zhang, Lingzhi; Hu, Bei

2012-04-01

To relax the presumption of strictly linear patterns in the common spatial patterns (CSP), we studied the kernel CSP (KCSP). A new multi-class KCSP (MKCSP) approach was proposed in this paper, which combines the kernel approach with multi-class CSP technique. In this approach, we used kernel spatial patterns for each class against all others, and extracted signal components specific to one condition from EEG data sets of multiple conditions. Then we performed classification using the Logistic linear classifier. Brain computer interface (BCI) competition III_3a was used in the experiment. Through the experiment, it can be proved that this approach could decompose the raw EEG singles into spatial patterns extracted from multi-class of single trial EEG, and could obtain good classification results.

Reduced multiple empirical kernel learning machine.

PubMed

Wang, Zhe; Lu, MingZhe; Gao, Daqi

2015-02-01

Multiple kernel learning (MKL) is demonstrated to be flexible and effective in depicting heterogeneous data sources since MKL can introduce multiple kernels rather than a single fixed kernel into applications. However, MKL would get a high time and space complexity in contrast to single kernel learning, which is not expected in real-world applications. Meanwhile, it is known that the kernel mapping ways of MKL generally have two forms including implicit kernel mapping and empirical kernel mapping (EKM), where the latter is less attracted. In this paper, we focus on the MKL with the EKM, and propose a reduced multiple empirical kernel learning machine named RMEKLM for short. To the best of our knowledge, it is the first to reduce both time and space complexity of the MKL with EKM. Different from the existing MKL, the proposed RMEKLM adopts the Gauss Elimination technique to extract a set of feature vectors, which is validated that doing so does not lose much information of the original feature space. Then RMEKLM adopts the extracted feature vectors to span a reduced orthonormal subspace of the feature space, which is visualized in terms of the geometry structure. It can be demonstrated that the spanned subspace is isomorphic to the original feature space, which means that the dot product of two vectors in the original feature space is equal to that of the two corresponding vectors in the generated orthonormal subspace. More importantly, the proposed RMEKLM brings a simpler computation and meanwhile needs a less storage space, especially in the processing of testing. Finally, the experimental results show that RMEKLM owns a much efficient and effective performance in terms of both complexity and classification. The contributions of this paper can be given as follows: (1) by mapping the input space into an orthonormal subspace, the geometry of the generated subspace is visualized; (2) this paper first reduces both the time and space complexity of the EKM-based MKL; (3

7 CFR 981.9 - Kernel weight.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 8 2010-01-01 2010-01-01 false Kernel weight. 981.9 Section 981.9 Agriculture Regulations of the Department of Agriculture (Continued) AGRICULTURAL MARKETING SERVICE (Marketing Agreements... Regulating Handling Definitions § 981.9 Kernel weight. Kernel weight means the weight of kernels, including...

Microwave-based treatments of wheat kernels do not abolish gluten epitopes implicated in celiac disease.

PubMed

Gianfrani, Carmen; Mamone, Gianfranco; la Gatta, Barbara; Camarca, Alessandra; Di Stasio, Luigia; Maurano, Francesco; Picascia, Stefania; Capozzi, Vito; Perna, Giuseppe; Picariello, Gianluca; Di Luccia, Aldo

2017-03-01

Microwave based treatment (MWT) of wet wheat kernels induced a striking reduction of gluten, up to <20 ppm as determined by R5-antibodybased ELISA, so that wheat could be labeled as gluten-free. In contrast, analysis of gluten peptides by G12 antibody-based ELISA, mass spectrometry-based proteomics and in vitro assay with T cells of celiac subjects, indicated no difference of antigenicity before and after MWT. SDS-PAGE analysis and Raman spectroscopy demonstrated that MWT simply induced conformational modifications, reducing alcohol solubility of gliadins and altering the access of R5-antibody to the gluten epitopes. Thus, MWT neither destroys gluten nor modifies chemically the toxic epitopes, contradicting the preliminary claims that MWT of wheat kernels detoxifies gluten. This study provides evidence that R5-antibody ELISA alone is not effective to determine gluten in thermally treated wheat products. Gluten epitopes in processed wheat should be monitored using strategies based on combined immunoassays with T cells from celiacs, G12-antibody ELISA after proteolysis and proper molecular characterization. Copyright © 2017 Elsevier Ltd. All rights reserved.

7 CFR 51.2295 - Half kernel.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Half kernel. 51.2295 Section 51.2295 Agriculture... Standards for Shelled English Walnuts (Juglans Regia) Definitions § 51.2295 Half kernel. Half kernel means the separated half of a kernel with not more than one-eighth broken off. ...

Oecophylla longinoda (Hymenoptera: Formicidae) Lead to Increased Cashew Kernel Size and Kernel Quality.

PubMed

Anato, F M; Sinzogan, A A C; Offenberg, J; Adandonon, A; Wargui, R B; Deguenon, J M; Ayelo, P M; Vayssières, J-F; Kossou, D K

2017-06-01

Weaver ants, Oecophylla spp., are known to positively affect cashew, Anacardium occidentale L., raw nut yield, but their effects on the kernels have not been reported. We compared nut size and the proportion of marketable kernels between raw nuts collected from trees with and without ants. Raw nuts collected from trees with weaver ants were 2.9% larger than nuts from control trees (i.e., without weaver ants), leading to 14% higher proportion of marketable kernels. On trees with ants, the kernel: raw nut ratio from nuts damaged by formic acid was 4.8% lower compared with nondamaged nuts from the same trees. Weaver ants provided three benefits to cashew production by increasing yields, yielding larger nuts, and by producing greater proportions of marketable kernel mass. © The Authors 2017. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Dynamic PET Image reconstruction for parametric imaging using the HYPR kernel method

NASA Astrophysics Data System (ADS)

Spencer, Benjamin; Qi, Jinyi; Badawi, Ramsey D.; Wang, Guobao

2017-03-01

Dynamic PET image reconstruction is a challenging problem because of the ill-conditioned nature of PET and the lowcounting statistics resulted from short time-frames in dynamic imaging. The kernel method for image reconstruction has been developed to improve image reconstruction of low-count PET data by incorporating prior information derived from high-count composite data. In contrast to most of the existing regularization-based methods, the kernel method embeds image prior information in the forward projection model and does not require an explicit regularization term in the reconstruction formula. Inspired by the existing highly constrained back-projection (HYPR) algorithm for dynamic PET image denoising, we propose in this work a new type of kernel that is simpler to implement and further improves the kernel-based dynamic PET image reconstruction. Our evaluation study using a physical phantom scan with synthetic FDG tracer kinetics has demonstrated that the new HYPR kernel-based reconstruction can achieve a better region-of-interest (ROI) bias versus standard deviation trade-off for dynamic PET parametric imaging than the post-reconstruction HYPR denoising method and the previously used nonlocal-means kernel.

Facial recognition using multisensor images based on localized kernel eigen spaces.

PubMed

Gundimada, Satyanadh; Asari, Vijayan K

2009-06-01

A feature selection technique along with an information fusion procedure for improving the recognition accuracy of a visual and thermal image-based facial recognition system is presented in this paper. A novel modular kernel eigenspaces approach is developed and implemented on the phase congruency feature maps extracted from the visual and thermal images individually. Smaller sub-regions from a predefined neighborhood within the phase congruency images of the training samples are merged to obtain a large set of features. These features are then projected into higher dimensional spaces using kernel methods. The proposed localized nonlinear feature selection procedure helps to overcome the bottlenecks of illumination variations, partial occlusions, expression variations and variations due to temperature changes that affect the visual and thermal face recognition techniques. AR and Equinox databases are used for experimentation and evaluation of the proposed technique. The proposed feature selection procedure has greatly improved the recognition accuracy for both the visual and thermal images when compared to conventional techniques. Also, a decision level fusion methodology is presented which along with the feature selection procedure has outperformed various other face recognition techniques in terms of recognition accuracy.

Kernel abortion in maize : I. Carbohydrate concentration patterns and Acid invertase activity of maize kernels induced to abort in vitro.

PubMed

Hanft, J M; Jones, R J

1986-06-01

Kernels cultured in vitro were induced to abort by high temperature (35 degrees C) and by culturing six kernels/cob piece. Aborting kernels failed to enter a linear phase of dry mass accumulation and had a final mass that was less than 6% of nonaborting field-grown kernels. Kernels induced to abort by high temperature failed to synthesize starch in the endosperm and had elevated sucrose concentrations and low fructose and glucose concentrations in the pedicel during early growth compared to nonaborting kernels. Kernels induced to abort by high temperature also had much lower pedicel soluble acid invertase activities than did nonaborting kernels. These results suggest that high temperature during the lag phase of kernel growth may impair the process of sucrose unloading in the pedicel by indirectly inhibiting soluble acid invertase activity and prevent starch synthesis in the endosperm. Kernels induced to abort by culturing six kernels/cob piece had reduced pedicel fructose, glucose, and sucrose concentrations compared to kernels from field-grown ears. These aborting kernels also had a lower pedicel soluble acid invertase activity compared to nonaborting kernels from the same cob piece and from field-grown ears. The low invertase activity in pedicel tissue of the aborting kernels was probably caused by a lack of substrate (sucrose) for the invertase to cleave due to the intense competition for available assimilates. In contrast to kernels cultured at 35 degrees C, aborting kernels from cob pieces containing all six kernels accumulated starch in a linear fashion. These results indicate that kernels cultured six/cob piece abort because of an inadequate supply of sugar and are similar to apical kernels from field-grown ears that often abort prior to the onset of linear growth.

Official portrait of Astronaut Anna L. Fisher

NASA Technical Reports Server (NTRS)

1985-01-01

Official portrait of Astronaut Anna L. Fisher. Fisher is posing with her helmet on the table in front of her and the American flag appears over the opposite shoulder (34357); Posing with an empty table in front of her and the American flag behind her (34358).

A fast non-local means algorithm based on integral image and reconstructed similar kernel

NASA Astrophysics Data System (ADS)

Lin, Zheng; Song, Enmin

2018-03-01

Image denoising is one of the essential methods in digital image processing. The non-local means (NLM) denoising approach is a remarkable denoising technique. However, its time complexity of the computation is high. In this paper, we design a fast NLM algorithm based on integral image and reconstructed similar kernel. First, the integral image is introduced in the traditional NLM algorithm. In doing so, it reduces a great deal of repetitive operations in the parallel processing, which will greatly improves the running speed of the algorithm. Secondly, in order to amend the error of the integral image, we construct a similar window resembling the Gaussian kernel in the pyramidal stacking pattern. Finally, in order to eliminate the influence produced by replacing the Gaussian weighted Euclidean distance with Euclidean distance, we propose a scheme to construct a similar kernel with a size of 3 x 3 in a neighborhood window which will reduce the effect of noise on a single pixel. Experimental results demonstrate that the proposed algorithm is about seventeen times faster than the traditional NLM algorithm, yet produce comparable results in terms of Peak Signal-to- Noise Ratio (the PSNR increased 2.9% in average) and perceptual image quality.

7 CFR 51.1441 - Half-kernel.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Half-kernel. 51.1441 Section 51.1441 Agriculture... Standards for Grades of Shelled Pecans Definitions § 51.1441 Half-kernel. Half-kernel means one of the separated halves of an entire pecan kernel with not more than one-eighth of its original volume missing...

Home range characteristics of fishers in California

Treesearch

W. J. Zielinski; R. L. Truex; G. A. Schmidt; F. V. Schlexer; K. N. Schmidt; R. H. Barrett

2004-01-01

The fisher (Martes pennanti) is a forest mustelid that historically occurred in California from the mixed conifer forests of the north coast, east to the southern Cascades, and south throughout the Sierra Nevada. Today fishers in California occur only in 2 disjunct populations in the northwestern mountains and the...

Three-dimensional holoscopic image coding scheme using high-efficiency video coding with kernel-based minimum mean-square-error estimation

NASA Astrophysics Data System (ADS)

Liu, Deyang; An, Ping; Ma, Ran; Yang, Chao; Shen, Liquan; Li, Kai

2016-07-01

Three-dimensional (3-D) holoscopic imaging, also known as integral imaging, light field imaging, or plenoptic imaging, can provide natural and fatigue-free 3-D visualization. However, a large amount of data is required to represent the 3-D holoscopic content. Therefore, efficient coding schemes for this particular type of image are needed. A 3-D holoscopic image coding scheme with kernel-based minimum mean square error (MMSE) estimation is proposed. In the proposed scheme, the coding block is predicted by an MMSE estimator under statistical modeling. In order to obtain the signal statistical behavior, kernel density estimation (KDE) is utilized to estimate the probability density function of the statistical modeling. As bandwidth estimation (BE) is a key issue in the KDE problem, we also propose a BE method based on kernel trick. The experimental results demonstrate that the proposed scheme can achieve a better rate-distortion performance and a better visual rendering quality.

Kernel analysis of partial least squares (PLS) regression models.

PubMed

Shinzawa, Hideyuki; Ritthiruangdej, Pitiporn; Ozaki, Yukihiro

2011-05-01

An analytical technique based on kernel matrix representation is demonstrated to provide further chemically meaningful insight into partial least squares (PLS) regression models. The kernel matrix condenses essential information about scores derived from PLS or principal component analysis (PCA). Thus, it becomes possible to establish the proper interpretation of the scores. A PLS model for the total nitrogen (TN) content in multiple Thai fish sauces is built with a set of near-infrared (NIR) transmittance spectra of the fish sauce samples. The kernel analysis of the scores effectively reveals that the variation of the spectral feature induced by the change in protein content is substantially associated with the total water content and the protein hydration. Kernel analysis is also carried out on a set of time-dependent infrared (IR) spectra representing transient evaporation of ethanol from a binary mixture solution of ethanol and oleic acid. A PLS model to predict the elapsed time is built with the IR spectra and the kernel matrix is derived from the scores. The detailed analysis of the kernel matrix provides penetrating insight into the interaction between the ethanol and the oleic acid.

Kernels, Degrees of Freedom, and Power Properties of Quadratic Distance Goodness-of-Fit Tests

PubMed Central

Lindsay, Bruce G.; Markatou, Marianthi; Ray, Surajit

2014-01-01

In this article, we study the power properties of quadratic-distance-based goodness-of-fit tests. First, we introduce the concept of a root kernel and discuss the considerations that enter the selection of this kernel. We derive an easy to use normal approximation to the power of quadratic distance goodness-of-fit tests and base the construction of a noncentrality index, an analogue of the traditional noncentrality parameter, on it. This leads to a method akin to the Neyman-Pearson lemma for constructing optimal kernels for specific alternatives. We then introduce a midpower analysis as a device for choosing optimal degrees of freedom for a family of alternatives of interest. Finally, we introduce a new diffusion kernel, called the Pearson-normal kernel, and study the extent to which the normal approximation to the power of tests based on this kernel is valid. Supplementary materials for this article are available online. PMID:24764609

Integrating semantic information into multiple kernels for protein-protein interaction extraction from biomedical literatures.

PubMed

Li, Lishuang; Zhang, Panpan; Zheng, Tianfu; Zhang, Hongying; Jiang, Zhenchao; Huang, Degen

2014-01-01

Protein-Protein Interaction (PPI) extraction is an important task in the biomedical information extraction. Presently, many machine learning methods for PPI extraction have achieved promising results. However, the performance is still not satisfactory. One reason is that the semantic resources were basically ignored. In this paper, we propose a multiple-kernel learning-based approach to extract PPIs, combining the feature-based kernel, tree kernel and semantic kernel. Particularly, we extend the shortest path-enclosed tree kernel (SPT) by a dynamic extended strategy to retrieve the richer syntactic information. Our semantic kernel calculates the protein-protein pair similarity and the context similarity based on two semantic resources: WordNet and Medical Subject Heading (MeSH). We evaluate our method with Support Vector Machine (SVM) and achieve an F-score of 69.40% and an AUC of 92.00%, which show that our method outperforms most of the state-of-the-art systems by integrating semantic information.

Kernel Abortion in Maize 1

PubMed Central

Hanft, Jonathan M.; Jones, Robert J.

1986-01-01

This study was designed to compare the uptake and distribution of 14C among fructose, glucose, sucrose, and starch in the cob, pedicel, and endosperm tissues of maize (Zea mays L.) kernels induced to abort by high temperature with those that develop normally. Kernels cultured in vitro at 30 and 35°C were transferred to [14C]sucrose media 10 days after pollination. Kernels cultured at 35°C aborted prior to the onset of linear dry matter accumulation. Significant uptake into the cob, pedicel, and endosperm of radioactivity associated with the soluble and starch fractions of the tissues was detected after 24 hours in culture on labeled media. After 8 days in culture on [14C]sucrose media, 48 and 40% of the radioactivity associated with the cob carbohydrates was found in the reducing sugars at 30 and 35°C, respectively. This indicates that some of the sucrose taken up by the cob tissue was cleaved to fructose and glucose in the cob. Of the total carbohydrates, a higher percentage of label was associated with sucrose and a lower percentage with fructose and glucose in pedicel tissue of kernels cultured at 35°C compared to kernels cultured at 30°C. These results indicate that sucrose was not cleaved to fructose and glucose as rapidly during the unloading process in the pedicel of kernels induced to abort by high temperature. Kernels cultured at 35°C had a much lower proportion of label associated with endosperm starch (29%) than did kernels cultured at 30°C (89%). Kernels cultured at 35°C had a correspondingly higher proportion of 14C in endosperm fructose, glucose, and sucrose. These results indicate that starch synthesis in the endosperm is strongly inhibited in kernels induced to abort by high temperature even though there is an adequate supply of sugar. PMID:16664847

A Further Evaluation of Picture Prompts during Auditory-Visual Conditional Discrimination Training

ERIC Educational Resources Information Center

Carp, Charlotte L.; Peterson, Sean P.; Arkel, Amber J.; Petursdottir, Anna I.; Ingvarsson, Einar T.

2012-01-01

This study was a systematic replication and extension of Fisher, Kodak, and Moore (2007), in which a picture prompt embedded into a least-to-most prompting sequence facilitated acquisition of auditory-visual conditional discriminations. Participants were 4 children who had been diagnosed with autism; 2 had limited prior receptive skills, and 2 had…

Structured Kernel Subspace Learning for Autonomous Robot Navigation.

PubMed

Kim, Eunwoo; Choi, Sungjoon; Oh, Songhwai

2018-02-14

This paper considers two important problems for autonomous robot navigation in a dynamic environment, where the goal is to predict pedestrian motion and control a robot with the prediction for safe navigation. While there are several methods for predicting the motion of a pedestrian and controlling a robot to avoid incoming pedestrians, it is still difficult to safely navigate in a dynamic environment due to challenges, such as the varying quality and complexity of training data with unwanted noises. This paper addresses these challenges simultaneously by proposing a robust kernel subspace learning algorithm based on the recent advances in nuclear-norm and l 1 -norm minimization. We model the motion of a pedestrian and the robot controller using Gaussian processes. The proposed method efficiently approximates a kernel matrix used in Gaussian process regression by learning low-rank structured matrix (with symmetric positive semi-definiteness) to find an orthogonal basis, which eliminates the effects of erroneous and inconsistent data. Based on structured kernel subspace learning, we propose a robust motion model and motion controller for safe navigation in dynamic environments. We evaluate the proposed robust kernel learning in various tasks, including regression, motion prediction, and motion control problems, and demonstrate that the proposed learning-based systems are robust against outliers and outperform existing regression and navigation methods.

Local Observed-Score Kernel Equating

ERIC Educational Resources Information Center

Wiberg, Marie; van der Linden, Wim J.; von Davier, Alina A.

2014-01-01

Three local observed-score kernel equating methods that integrate methods from the local equating and kernel equating frameworks are proposed. The new methods were compared with their earlier counterparts with respect to such measures as bias--as defined by Lord's criterion of equity--and percent relative error. The local kernel item response…

Comparing fishers' and scientific estimates of size at maturity and maximum body size as indicators for overfishing.

PubMed

Mclean, Elizabeth L; Forrester, Graham E

2018-04-01

We tested whether fishers' local ecological knowledge (LEK) of two fish life-history parameters, size at maturity (SAM) at maximum body size (MS), was comparable to scientific estimates (SEK) of the same parameters, and whether LEK influenced fishers' perceptions of sustainability. Local ecological knowledge was documented for 82 fishers from a small-scale fishery in Samaná Bay, Dominican Republic, whereas SEK was compiled from the scientific literature. Size at maturity estimates derived from LEK and SEK overlapped for most of the 15 commonly harvested species (10 of 15). In contrast, fishers' maximum size estimates were usually lower than (eight species), or overlapped with (five species) scientific estimates. Fishers' size-based estimates of catch composition indicate greater potential for overfishing than estimates based on SEK. Fishers' estimates of size at capture relative to size at maturity suggest routine inclusion of juveniles in the catch (9 of 15 species), and fishers' estimates suggest that harvested fish are substantially smaller than maximum body size for most species (11 of 15 species). Scientific estimates also suggest that harvested fish are generally smaller than maximum body size (13 of 15), but suggest that the catch is dominated by adults for most species (9 of 15 species), and that juveniles are present in the catch for fewer species (6 of 15). Most Samaná fishers characterized the current state of their fishery as poor (73%) and as having changed for the worse over the past 20 yr (60%). Fishers stated that concern about overfishing, catching small fish, and catching immature fish contributed to these perceptions, indicating a possible influence of catch-size composition on their perceptions. Future work should test this link more explicitly because we found no evidence that the minority of fishers with more positive perceptions of their fishery reported systematically different estimates of catch-size composition than those with the more

A Fast Reduced Kernel Extreme Learning Machine.

PubMed

Deng, Wan-Yu; Ong, Yew-Soon; Zheng, Qing-Hua

2016-04-01

In this paper, we present a fast and accurate kernel-based supervised algorithm referred to as the Reduced Kernel Extreme Learning Machine (RKELM). In contrast to the work on Support Vector Machine (SVM) or Least Square SVM (LS-SVM), which identifies the support vectors or weight vectors iteratively, the proposed RKELM randomly selects a subset of the available data samples as support vectors (or mapping samples). By avoiding the iterative steps of SVM, significant cost savings in the training process can be readily attained, especially on Big datasets. RKELM is established based on the rigorous proof of universal learning involving reduced kernel-based SLFN. In particular, we prove that RKELM can approximate any nonlinear functions accurately under the condition of support vectors sufficiency. Experimental results on a wide variety of real world small instance size and large instance size applications in the context of binary classification, multi-class problem and regression are then reported to show that RKELM can perform at competitive level of generalized performance as the SVM/LS-SVM at only a fraction of the computational effort incurred. Copyright © 2015 Elsevier Ltd. All rights reserved.

Metabolite identification through multiple kernel learning on fragmentation trees.

PubMed

Shen, Huibin; Dührkop, Kai; Böcker, Sebastian; Rousu, Juho

2014-06-15

Metabolite identification from tandem mass spectrometric data is a key task in metabolomics. Various computational methods have been proposed for the identification of metabolites from tandem mass spectra. Fragmentation tree methods explore the space of possible ways in which the metabolite can fragment, and base the metabolite identification on scoring of these fragmentation trees. Machine learning methods have been used to map mass spectra to molecular fingerprints; predicted fingerprints, in turn, can be used to score candidate molecular structures. Here, we combine fragmentation tree computations with kernel-based machine learning to predict molecular fingerprints and identify molecular structures. We introduce a family of kernels capturing the similarity of fragmentation trees, and combine these kernels using recently proposed multiple kernel learning approaches. Experiments on two large reference datasets show that the new methods significantly improve molecular fingerprint prediction accuracy. These improvements result in better metabolite identification, doubling the number of metabolites ranked at the top position of the candidates list. © The Author 2014. Published by Oxford University Press.

Efficient Multiple Kernel Learning Algorithms Using Low-Rank Representation.

PubMed

Niu, Wenjia; Xia, Kewen; Zu, Baokai; Bai, Jianchuan

2017-01-01

Unlike Support Vector Machine (SVM), Multiple Kernel Learning (MKL) allows datasets to be free to choose the useful kernels based on their distribution characteristics rather than a precise one. It has been shown in the literature that MKL holds superior recognition accuracy compared with SVM, however, at the expense of time consuming computations. This creates analytical and computational difficulties in solving MKL algorithms. To overcome this issue, we first develop a novel kernel approximation approach for MKL and then propose an efficient Low-Rank MKL (LR-MKL) algorithm by using the Low-Rank Representation (LRR). It is well-acknowledged that LRR can reduce dimension while retaining the data features under a global low-rank constraint. Furthermore, we redesign the binary-class MKL as the multiclass MKL based on pairwise strategy. Finally, the recognition effect and efficiency of LR-MKL are verified on the datasets Yale, ORL, LSVT, and Digit. Experimental results show that the proposed LR-MKL algorithm is an efficient kernel weights allocation method in MKL and boosts the performance of MKL largely.

Entanglement evaluation with atomic Fisher information

NASA Astrophysics Data System (ADS)

Obada, A.-S. F.; Abdel-Khalek, S.

2010-02-01

In this paper, the concept of atomic Fisher information (AFI) is introduced. The marginal distributions of the AFI are defined. This quantity is used as a parameter of entanglement and compared with linear and atomic Wehrl entropies of the two-level atom. The evolution of the atomic Fisher information and atomic Wehrl entropy for only the pure state (or dissipation-free) of the Jaynes-Cummings model is analyzed. We demonstrate the connections between these measures.

Fast generation of sparse random kernel graphs

DOE PAGES

Hagberg, Aric; Lemons, Nathan; Du, Wen -Bo

2015-09-10

The development of kernel-based inhomogeneous random graphs has provided models that are flexible enough to capture many observed characteristics of real networks, and that are also mathematically tractable. We specify a class of inhomogeneous random graph models, called random kernel graphs, that produces sparse graphs with tunable graph properties, and we develop an efficient generation algorithm to sample random instances from this model. As real-world networks are usually large, it is essential that the run-time of generation algorithms scales better than quadratically in the number of vertices n. We show that for many practical kernels our algorithm runs in timemore » at most ο(n(logn)²). As an example, we show how to generate samples of power-law degree distribution graphs with tunable assortativity.« less

Spatial mode discriminator based on leaky waveguides

NASA Astrophysics Data System (ADS)

Xu, Jing; Liu, Jialing; Shi, Hongkang; Chen, Yuntian

2018-06-01

We propose a conceptually simple and experimentally compatible configuration to discriminate the spatial mode based on leaky waveguides, which are inserted in-between the transmission link. The essence of such a spatial mode discriminator is to introduce the leakage of the power flux on purpose for detection. Importantly, the leaky angle of each individual spatial mode with respect to the propagation direction are different for non-degenerated modes, while the radiation patterns of the degenerated spatial modes in the plane perpendicular to the propagation direction are also distinguishable. Based on these two facts, we illustrate the operation principle of the spatial mode discriminators via two concrete examples; a w-type slab leaky waveguide without degeneracy, and a cylindrical leaky waveguide with degeneracy. The correlation between the leakage angle and the spatial mode distribution for a slab leaky waveguide, as well as differences between the in-plane radiation patterns of degenerated modes in a cylindrical leaky waveguide, are verified numerically and analytically. Such findings can be readily useful in discriminating the spatial modes for optical communication or optical sensing.

Fault detection and diagnosis for gas turbines based on a kernelized information entropy model.

PubMed

Wang, Weiying; Xu, Zhiqiang; Tang, Rui; Li, Shuying; Wu, Wei

2014-01-01

Gas turbines are considered as one kind of the most important devices in power engineering and have been widely used in power generation, airplanes, and naval ships and also in oil drilling platforms. However, they are monitored without man on duty in the most cases. It is highly desirable to develop techniques and systems to remotely monitor their conditions and analyze their faults. In this work, we introduce a remote system for online condition monitoring and fault diagnosis of gas turbine on offshore oil well drilling platforms based on a kernelized information entropy model. Shannon information entropy is generalized for measuring the uniformity of exhaust temperatures, which reflect the overall states of the gas paths of gas turbine. In addition, we also extend the entropy to compute the information quantity of features in kernel spaces, which help to select the informative features for a certain recognition task. Finally, we introduce the information entropy based decision tree algorithm to extract rules from fault samples. The experiments on some real-world data show the effectiveness of the proposed algorithms.

Fault Detection and Diagnosis for Gas Turbines Based on a Kernelized Information Entropy Model

PubMed Central

Wang, Weiying; Xu, Zhiqiang; Tang, Rui; Li, Shuying; Wu, Wei

2014-01-01

Gas turbines are considered as one kind of the most important devices in power engineering and have been widely used in power generation, airplanes, and naval ships and also in oil drilling platforms. However, they are monitored without man on duty in the most cases. It is highly desirable to develop techniques and systems to remotely monitor their conditions and analyze their faults. In this work, we introduce a remote system for online condition monitoring and fault diagnosis of gas turbine on offshore oil well drilling platforms based on a kernelized information entropy model. Shannon information entropy is generalized for measuring the uniformity of exhaust temperatures, which reflect the overall states of the gas paths of gas turbine. In addition, we also extend the entropy to compute the information quantity of features in kernel spaces, which help to select the informative features for a certain recognition task. Finally, we introduce the information entropy based decision tree algorithm to extract rules from fault samples. The experiments on some real-world data show the effectiveness of the proposed algorithms. PMID:25258726

A nonlinear discriminant algorithm for feature extraction and data classification.

PubMed

Santa Cruz, C; Dorronsoro, J R

1998-01-01

This paper presents a nonlinear supervised feature extraction algorithm that combines Fisher's criterion function with a preliminary perceptron-like nonlinear projection of vectors in pattern space. Its main motivation is to combine the approximation properties of multilayer perceptrons (MLP's) with the target free nature of Fisher's classical discriminant analysis. In fact, although MLP's provide good classifiers for many problems, there may be some situations, such as unequal class sizes with a high degree of pattern mixing among them, that may make difficult the construction of good MLP classifiers. In these instances, the features extracted by our procedure could be more effective. After the description of its construction and the analysis of its complexity, we will illustrate its use over a synthetic problem with the above characteristics.

Convolution kernels for multi-wavelength imaging

NASA Astrophysics Data System (ADS)

Boucaud, A.; Bocchio, M.; Abergel, A.; Orieux, F.; Dole, H.; Hadj-Youcef, M. A.

2016-12-01

Astrophysical images issued from different instruments and/or spectral bands often require to be processed together, either for fitting or comparison purposes. However each image is affected by an instrumental response, also known as point-spread function (PSF), that depends on the characteristics of the instrument as well as the wavelength and the observing strategy. Given the knowledge of the PSF in each band, a straightforward way of processing images is to homogenise them all to a target PSF using convolution kernels, so that they appear as if they had been acquired by the same instrument. We propose an algorithm that generates such PSF-matching kernels, based on Wiener filtering with a tunable regularisation parameter. This method ensures all anisotropic features in the PSFs to be taken into account. We compare our method to existing procedures using measured Herschel/PACS and SPIRE PSFs and simulated JWST/MIRI PSFs. Significant gains up to two orders of magnitude are obtained with respect to the use of kernels computed assuming Gaussian or circularised PSFs. A software to compute these kernels is available at https://github.com/aboucaud/pypher

Fisher and marten

Treesearch

Roger A. Powell; Steven W. Buskirk; William J. Zielinski

2003-01-01

The genus Martes is circumboreal in distribution, with extensions into southern (M. gwatkinsii) and southeast Asia as far as 7°S latitude (M. flavigula; Anderson 1970). The fisher (subgenus Pekania) is endemic to the New World and restricted to mesic coniferous forest of the boreal zone and its...

Tile-Based Fisher-Ratio Software for Improved Feature Selection Analysis of Comprehensive Two-Dimensional Gas Chromatography Time-of-Flight Mass Spectrometry Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Marney, Luke C.; Siegler, William C.; Parsons, Brendon A.

Two-dimensional (2D) gas chromatography coupled with time-of-flight mass spectrometry (GC × GC – TOFMS) is a highly capable instrumental platform that produces complex and information-rich multi-dimensional chemical data. The complex data can be overwhelming, especially when many samples (of various sample classes) are analyzed with multiple injections for each sample. Thus, the data must be analyzed in such a way to extract the most meaningful information. The pixel-based and peak table-based algorithmic use of Fisher ratios has been used successfully in the past to reduce the multi-dimensional data down to those chemical compounds that are changing between classes relative tomore » those that are not (i.e., chemical feature selection). We report on the initial development of a computationally fast novel tile-based Fisher-ratio software that addresses challenges due to 2D retention time misalignment without explicitly aligning the data, which is a problem for both pixel-based and peak table- based methods. Concurrently, the tile-based Fisher-ratio software maximizes the sensitivity contrast of true positives against a background of potential false positives and noise. To study this software, eight compounds, plus one internal standard, were spiked into diesel at various concentrations. The tile-based F-ratio software was able to discover all spiked analytes, within the complex diesel sample matrix with thousands of potential false positives, in each possible concentration comparison, even at the lowest absolute spiked analyte concentration ratio of 1.06.« less

An efficient genome-wide association test for multivariate phenotypes based on the Fisher combination function.

PubMed

Yang, James J; Li, Jia; Williams, L Keoki; Buu, Anne

2016-01-05

In genome-wide association studies (GWAS) for complex diseases, the association between a SNP and each phenotype is usually weak. Combining multiple related phenotypic traits can increase the power of gene search and thus is a practically important area that requires methodology work. This study provides a comprehensive review of existing methods for conducting GWAS on complex diseases with multiple phenotypes including the multivariate analysis of variance (MANOVA), the principal component analysis (PCA), the generalizing estimating equations (GEE), the trait-based association test involving the extended Simes procedure (TATES), and the classical Fisher combination test. We propose a new method that relaxes the unrealistic independence assumption of the classical Fisher combination test and is computationally efficient. To demonstrate applications of the proposed method, we also present the results of statistical analysis on the Study of Addiction: Genetics and Environment (SAGE) data. Our simulation study shows that the proposed method has higher power than existing methods while controlling for the type I error rate. The GEE and the classical Fisher combination test, on the other hand, do not control the type I error rate and thus are not recommended. In general, the power of the competing methods decreases as the correlation between phenotypes increases. All the methods tend to have lower power when the multivariate phenotypes come from long tailed distributions. The real data analysis also demonstrates that the proposed method allows us to compare the marginal results with the multivariate results and specify which SNPs are specific to a particular phenotype or contribute to the common construct. The proposed method outperforms existing methods in most settings and also has great applications in GWAS on complex diseases with multiple phenotypes such as the substance abuse disorders.

FISHER'S GEOMETRIC MODEL WITH A MOVING OPTIMUM

PubMed Central

Matuszewski, Sebastian; Hermisson, Joachim; Kopp, Michael

2014-01-01

Fisher's geometric model has been widely used to study the effects of pleiotropy and organismic complexity on phenotypic adaptation. Here, we study a version of Fisher's model in which a population adapts to a gradually moving optimum. Key parameters are the rate of environmental change, the dimensionality of phenotype space, and the patterns of mutational and selectional correlations. We focus on the distribution of adaptive substitutions, that is, the multivariate distribution of the phenotypic effects of fixed beneficial mutations. Our main results are based on an “adaptive-walk approximation,” which is checked against individual-based simulations. We find that (1) the distribution of adaptive substitutions is strongly affected by the ecological dynamics and largely depends on a single composite parameter γ, which scales the rate of environmental change by the “adaptive potential” of the population; (2) the distribution of adaptive substitution reflects the shape of the fitness landscape if the environment changes slowly, whereas it mirrors the distribution of new mutations if the environment changes fast; (3) in contrast to classical models of adaptation assuming a constant optimum, with a moving optimum, more complex organisms evolve via larger adaptive steps. PMID:24898080

Multiscale Support Vector Learning With Projection Operator Wavelet Kernel for Nonlinear Dynamical System Identification.

PubMed

Lu, Zhao; Sun, Jing; Butts, Kenneth

2016-02-03

A giant leap has been made in the past couple of decades with the introduction of kernel-based learning as a mainstay for designing effective nonlinear computational learning algorithms. In view of the geometric interpretation of conditional expectation and the ubiquity of multiscale characteristics in highly complex nonlinear dynamic systems [1]-[3], this paper presents a new orthogonal projection operator wavelet kernel, aiming at developing an efficient computational learning approach for nonlinear dynamical system identification. In the framework of multiresolution analysis, the proposed projection operator wavelet kernel can fulfill the multiscale, multidimensional learning to estimate complex dependencies. The special advantage of the projection operator wavelet kernel developed in this paper lies in the fact that it has a closed-form expression, which greatly facilitates its application in kernel learning. To the best of our knowledge, it is the first closed-form orthogonal projection wavelet kernel reported in the literature. It provides a link between grid-based wavelets and mesh-free kernel-based methods. Simulation studies for identifying the parallel models of two benchmark nonlinear dynamical systems confirm its superiority in model accuracy and sparsity.

Semi-Supervised Marginal Fisher Analysis for Hyperspectral Image Classification

NASA Astrophysics Data System (ADS)

Huang, H.; Liu, J.; Pan, Y.

2012-07-01

The problem of learning with both labeled and unlabeled examples arises frequently in Hyperspectral image (HSI) classification. While marginal Fisher analysis is a supervised method, which cannot be directly applied for Semi-supervised classification. In this paper, we proposed a novel method, called semi-supervised marginal Fisher analysis (SSMFA), to process HSI of natural scenes, which uses a combination of semi-supervised learning and manifold learning. In SSMFA, a new difference-based optimization objective function with unlabeled samples has been designed. SSMFA preserves the manifold structure of labeled and unlabeled samples in addition to separating labeled samples in different classes from each other. The semi-supervised method has an analytic form of the globally optimal solution, and it can be computed based on eigen decomposition. Classification experiments with a challenging HSI task demonstrate that this method outperforms current state-of-the-art HSI-classification methods.

Automated discrimination of dementia spectrum disorders using extreme learning machine and structural T1 MRI features.

PubMed

Jongin Kim; Boreom Lee

2017-07-01

The classification of neuroimaging data for the diagnosis of Alzheimer's Disease (AD) is one of the main research goals of the neuroscience and clinical fields. In this study, we performed extreme learning machine (ELM) classifier to discriminate the AD, mild cognitive impairment (MCI) from normal control (NC). We compared the performance of ELM with that of a linear kernel support vector machine (SVM) for 718 structural MRI images from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The data consisted of normal control, MCI converter (MCI-C), MCI non-converter (MCI-NC), and AD. We employed SVM-based recursive feature elimination (RFE-SVM) algorithm to find the optimal subset of features. In this study, we found that the RFE-SVM feature selection approach in combination with ELM shows the superior classification accuracy to that of linear kernel SVM for structural T1 MRI data.

Multiple Factors Affect Socioeconomics and Wellbeing of Artisanal Sea Cucumber Fishers.

PubMed

Purcell, Steven W; Ngaluafe, Poasi; Foale, Simon J; Cocks, Nicole; Cullis, Brian R; Lalavanua, Watisoni

2016-01-01

Small-scale fisheries are important to livelihoods and subsistence seafood consumption of millions of fishers. Sea cucumbers are fished worldwide for export to Asia, yet few studies have assessed factors affecting socioeconomics and wellbeing among fishers. We interviewed 476 men and women sea cucumber fishers at multiple villages within multiple locations in Fiji, Kiribati, Tonga and New Caledonia using structured questionnaires. Low rates of subsistence consumption confirmed a primary role of sea cucumbers in income security. Prices of sea cucumbers sold by fishers varied greatly among countries, depending on the species. Gender variation in landing prices could be due to women catching smaller sea cucumbers or because some traders take advantage of them. Dissatisfaction with fishery income was common (44% of fishers), especially for i-Kiribati fishers, male fishers, and fishers experiencing difficulty selling their catch, but was uncorrelated with sale prices. Income dissatisfaction worsened with age. The number of livelihood activities averaged 2.2-2.5 across countries, and varied significantly among locations. Sea cucumbers were often a primary source of income to fishers, especially in Tonga. Other common livelihood activities were fishing other marine resources, copra production in Kiribati, agriculture in Fiji, and salaried jobs in New Caledonia. Fishing other coastal and coral reef resources was the most common fall-back livelihood option if fishers were forced to exit the fishery. Our data highlight large disparities in subsistence consumption, gender-related price equity, and livelihood diversity among parallel artisanal fisheries. Improvement of supply chains in dispersed small-scale fisheries appears as a critical need for enhancing income and wellbeing of fishers. Strong evidence for co-dependence among small-scale fisheries, through fall-back livelihood preferences of fishers, suggests that resource managers must mitigate concomitant effects on other

Neural decoding with kernel-based metric learning.

PubMed

Brockmeier, Austin J; Choi, John S; Kriminger, Evan G; Francis, Joseph T; Principe, Jose C

2014-06-01

In studies of the nervous system, the choice of metric for the neural responses is a pivotal assumption. For instance, a well-suited distance metric enables us to gauge the similarity of neural responses to various stimuli and assess the variability of responses to a repeated stimulus-exploratory steps in understanding how the stimuli are encoded neurally. Here we introduce an approach where the metric is tuned for a particular neural decoding task. Neural spike train metrics have been used to quantify the information content carried by the timing of action potentials. While a number of metrics for individual neurons exist, a method to optimally combine single-neuron metrics into multineuron, or population-based, metrics is lacking. We pose the problem of optimizing multineuron metrics and other metrics using centered alignment, a kernel-based dependence measure. The approach is demonstrated on invasively recorded neural data consisting of both spike trains and local field potentials. The experimental paradigm consists of decoding the location of tactile stimulation on the forepaws of anesthetized rats. We show that the optimized metrics highlight the distinguishing dimensions of the neural response, significantly increase the decoding accuracy, and improve nonlinear dimensionality reduction methods for exploratory neural analysis.

Selecting the most relevant brain regions to discriminate Alzheimer's disease patients from healthy controls using multiple kernel learning: A comparison across functional and structural imaging modalities and atlases.

PubMed

Rondina, Jane Maryam; Ferreira, Luiz Kobuti; de Souza Duran, Fabio Luis; Kubo, Rodrigo; Ono, Carla Rachel; Leite, Claudia Costa; Smid, Jerusa; Nitrini, Ricardo; Buchpiguel, Carlos Alberto; Busatto, Geraldo F

2018-01-01

Machine learning techniques such as support vector machine (SVM) have been applied recently in order to accurately classify individuals with neuropsychiatric disorders such as Alzheimer's disease (AD) based on neuroimaging data. However, the multivariate nature of the SVM approach often precludes the identification of the brain regions that contribute most to classification accuracy. Multiple kernel learning (MKL) is a sparse machine learning method that allows the identification of the most relevant sources for the classification. By parcelating the brain into regions of interest (ROI) it is possible to use each ROI as a source to MKL (ROI-MKL). We applied MKL to multimodal neuroimaging data in order to: 1) compare the diagnostic performance of ROI-MKL and whole-brain SVM in discriminating patients with AD from demographically matched healthy controls and 2) identify the most relevant brain regions to the classification. We used two atlases (AAL and Brodmann's) to parcelate the brain into ROIs and applied ROI-MKL to structural (T1) MRI, 18 F-FDG-PET and regional cerebral blood flow SPECT (rCBF-SPECT) data acquired from the same subjects (20 patients with early AD and 18 controls). In ROI-MKL, each ROI received a weight (ROI-weight) that indicated the region's relevance to the classification. For each ROI, we also calculated whether there was a predominance of voxels indicating decreased or increased regional activity (for 18 F-FDG-PET and rCBF-SPECT) or volume (for T1-MRI) in AD patients. Compared to whole-brain SVM, the ROI-MKL approach resulted in better accuracies (with either atlas) for classification using 18 F-FDG-PET (92.5% accuracy for ROI-MKL versus 84% for whole-brain), but not when using rCBF-SPECT or T1-MRI. Although several cortical and subcortical regions contributed to discrimination, high ROI-weights and predominance of hypometabolism and atrophy were identified specially in medial parietal and temporo-limbic cortical regions. Also, the weight of

Vis- and NIR-based instruments for detection of black-tip damaged wheat kernels: A comparative study

USDA-ARS?s Scientific Manuscript database

Black-tip (BT) present in wheat kernels is a non-mycotoxic fungus that attacks the kernels wherein any of a number of molds forms a dark brown or black sooty mold at the tip of the wheat kernel. Three spectrometers covering the spectral ranges 950-1636nm (Spec1), 600-1045nm (Spec2), and 380-780nm (S...

Occupational injuries and diseases among commercial fishers in Finland 1996-2015.

PubMed

Kaustell, Kim O; Mattila, Tiina E A; Rautiainen, Risto H

2016-01-01

Commercial fishing is recognised as one of the most hazardous professions worldwide. In Finland, commercial fishing has some special characteristics, including fishing on ice during frozen waters, and pluriactivity of the fisher family to gain additional income. The goal of this study was to describe injury characteristics among commercial fishers in Finland during the years 1996-2015. With this information, we wish to promote creation of effective safety campaigns and interventions. The data for this study was acquired from The Farmers' Social Insurance Institution, who handles the mandatory pension and occupational injury insurance of Finnish commercial fishers. Descriptive statistics was used to categorise and analyse the data that comprised the anonymized insurance history of 1954 insured fishers and reports on 1135 compensated injuries, 11 fatalities, and 53 occupational disease cases. The results show, that the injury rate of Finnish commercial fishers is high. Forty per cent of the fishing-related injuries occur aboard or when entering or leaving the vessel, while 37% happened ashore, and 11% on sea or lake ice. The most common type of incident is preceded by a slip, trip, or sway followed by a fall to lower level. The injuries result in a median disability length of 21 days. An elevated risk for Finnish (vs. Swedish) speaking, as well as for male fishers was found. The occupational diseases of the studied population were for the most part results of manual, repetitive and/or physically straining work due to e.g. hauling in fishing equipment. Due to small numbers and lack of case data, it is not possible to make any further analysis of the 11 fatalities, which were all drownings. Based on our findings, injury prevention should be targeted, besides preventing fatalities because of drowning, at mitigating the risks for slips, trips, and falls both aboard and ashore.

Sparse Event Modeling with Hierarchical Bayesian Kernel Methods

DTIC Science & Technology

2016-01-05

SECURITY CLASSIFICATION OF: The research objective of this proposal was to develop a predictive Bayesian kernel approach to model count data based on...several predictive variables. Such an approach, which we refer to as the Poisson Bayesian kernel model , is able to model the rate of occurrence of...which adds specificity to the model and can make nonlinear data more manageable. Early results show that the 1. REPORT DATE (DD-MM-YYYY) 4. TITLE

Personal sleep pattern visualization using sequence-based kernel self-organizing map on sound data.

PubMed

Wu, Hongle; Kato, Takafumi; Yamada, Tomomi; Numao, Masayuki; Fukui, Ken-Ichi

2017-07-01

We propose a method to discover sleep patterns via clustering of sound events recorded during sleep. The proposed method extends the conventional self-organizing map algorithm by kernelization and sequence-based technologies to obtain a fine-grained map that visualizes the distribution and changes of sleep-related events. We introduced features widely applied in sound processing and popular kernel functions to the proposed method to evaluate and compare performance. The proposed method provides a new aspect of sleep monitoring because the results demonstrate that sound events can be directly correlated to an individual's sleep patterns. In addition, by visualizing the transition of cluster dynamics, sleep-related sound events were found to relate to the various stages of sleep. Therefore, these results empirically warrant future study into the assessment of personal sleep quality using sound data. Copyright © 2017 Elsevier B.V. All rights reserved.

Maniac talk - Dr. Richard R. Fisher, Director, Heliophysics Division (Emeritus), NASA HQ

NASA Image and Video Library

2016-05-25

Dr. Richard R. Fisher: "As in the case of learning how to perform in any specialized context, I found there were a number of issues I was neither taught nor did I learn from life experience. Over the course of a 50-year career that transitioned from ground-based to space-based, I came to understand that there are specific tools and values that proved vital. Using my own journey, I shall summarize a few of the more useful, to identify and make available things and ideas that helped me with my time with NASA." Dr. Richard R. Fisher, Director, Heliophysics Division (Emeritus), NASA HQ

Wavelet SVM in Reproducing Kernel Hilbert Space for hyperspectral remote sensing image classification

NASA Astrophysics Data System (ADS)

Du, Peijun; Tan, Kun; Xing, Xiaoshi

2010-12-01

Combining Support Vector Machine (SVM) with wavelet analysis, we constructed wavelet SVM (WSVM) classifier based on wavelet kernel functions in Reproducing Kernel Hilbert Space (RKHS). In conventional kernel theory, SVM is faced with the bottleneck of kernel parameter selection which further results in time-consuming and low classification accuracy. The wavelet kernel in RKHS is a kind of multidimensional wavelet function that can approximate arbitrary nonlinear functions. Implications on semiparametric estimation are proposed in this paper. Airborne Operational Modular Imaging Spectrometer II (OMIS II) hyperspectral remote sensing image with 64 bands and Reflective Optics System Imaging Spectrometer (ROSIS) data with 115 bands were used to experiment the performance and accuracy of the proposed WSVM classifier. The experimental results indicate that the WSVM classifier can obtain the highest accuracy when using the Coiflet Kernel function in wavelet transform. In contrast with some traditional classifiers, including Spectral Angle Mapping (SAM) and Minimum Distance Classification (MDC), and SVM classifier using Radial Basis Function kernel, the proposed wavelet SVM classifier using the wavelet kernel function in Reproducing Kernel Hilbert Space is capable of improving classification accuracy obviously.

Understanding spearfishing in a coral reef fishery: Fishers' opportunities, constraints, and decision-making.

PubMed

Pavlowich, Tyler; Kapuscinski, Anne R

2017-01-01

Social and ecological systems come together during the act of fishing. However, we often lack a deep understanding of the fishing process, despite its importance for understanding and managing fisheries. A quantitative, mechanistic understanding of the opportunities fishers encounter, the constraints they face, and how they make decisions within the context of opportunities and constraints will enhance the design of fisheries management strategies to meet linked ecological and social objectives and will improve scientific capacity to predict impacts of different strategies. We examined the case of spearfishing in a Caribbean coral reef fishery. We mounted cameras on fishers' spearguns to observe the fish they encountered, what limited their ability to catch fish, and how they made decisions about which fish to target. We observed spearfishers who dove with and without the assistance of compressed air, and compared the fishing process of each method using content analysis of videos and decision models of fishers' targeting selections. Compressor divers encountered more fish, took less time to catch each fish, and had a higher rate of successful pursuits. We also analyzed differences among taxa in this multispecies fishery, because some taxa are known to be ecologically or economically more valuable than others. Parrotfish are ecologically indispensable for healthy coral reefs, and they were encountered and captured more frequently than any other taxon. Fishers made decisions about which fish to target based on a fish's market value, proximity to the fisher, and taxon. The information uncovered on fishers' opportunities, constraints, and decision making has implications for managing this fishery and others. Moreover, it demonstrates the value of pursuing an improved understanding of the fishing process from the perspective of the fishers.

Design of a multiple kernel learning algorithm for LS-SVM by convex programming.

PubMed

Jian, Ling; Xia, Zhonghang; Liang, Xijun; Gao, Chuanhou

2011-06-01

As a kernel based method, the performance of least squares support vector machine (LS-SVM) depends on the selection of the kernel as well as the regularization parameter (Duan, Keerthi, & Poo, 2003). Cross-validation is efficient in selecting a single kernel and the regularization parameter; however, it suffers from heavy computational cost and is not flexible to deal with multiple kernels. In this paper, we address the issue of multiple kernel learning for LS-SVM by formulating it as semidefinite programming (SDP). Furthermore, we show that the regularization parameter can be optimized in a unified framework with the kernel, which leads to an automatic process for model selection. Extensive experimental validations are performed and analyzed. Copyright © 2011 Elsevier Ltd. All rights reserved.

Spatio-temporal Event Classification using Time-series Kernel based Structured Sparsity

PubMed Central

Jeni, László A.; Lőrincz, András; Szabó, Zoltán; Cohn, Jeffrey F.; Kanade, Takeo

2016-01-01

In many behavioral domains, such as facial expression and gesture, sparse structure is prevalent. This sparsity would be well suited for event detection but for one problem. Features typically are confounded by alignment error in space and time. As a consequence, high-dimensional representations such as SIFT and Gabor features have been favored despite their much greater computational cost and potential loss of information. We propose a Kernel Structured Sparsity (KSS) method that can handle both the temporal alignment problem and the structured sparse reconstruction within a common framework, and it can rely on simple features. We characterize spatio-temporal events as time-series of motion patterns and by utilizing time-series kernels we apply standard structured-sparse coding techniques to tackle this important problem. We evaluated the KSS method using both gesture and facial expression datasets that include spontaneous behavior and differ in degree of difficulty and type of ground truth coding. KSS outperformed both sparse and non-sparse methods that utilize complex image features and their temporal extensions. In the case of early facial event classification KSS had 10% higher accuracy as measured by F1 score over kernel SVM methods1. PMID:27830214

Multiple Factors Affect Socioeconomics and Wellbeing of Artisanal Sea Cucumber Fishers

PubMed Central

Ngaluafe, Poasi; Foale, Simon J.; Cocks, Nicole; Cullis, Brian R.; Lalavanua, Watisoni

2016-01-01

Small-scale fisheries are important to livelihoods and subsistence seafood consumption of millions of fishers. Sea cucumbers are fished worldwide for export to Asia, yet few studies have assessed factors affecting socioeconomics and wellbeing among fishers. We interviewed 476 men and women sea cucumber fishers at multiple villages within multiple locations in Fiji, Kiribati, Tonga and New Caledonia using structured questionnaires. Low rates of subsistence consumption confirmed a primary role of sea cucumbers in income security. Prices of sea cucumbers sold by fishers varied greatly among countries, depending on the species. Gender variation in landing prices could be due to women catching smaller sea cucumbers or because some traders take advantage of them. Dissatisfaction with fishery income was common (44% of fishers), especially for i-Kiribati fishers, male fishers, and fishers experiencing difficulty selling their catch, but was uncorrelated with sale prices. Income dissatisfaction worsened with age. The number of livelihood activities averaged 2.2–2.5 across countries, and varied significantly among locations. Sea cucumbers were often a primary source of income to fishers, especially in Tonga. Other common livelihood activities were fishing other marine resources, copra production in Kiribati, agriculture in Fiji, and salaried jobs in New Caledonia. Fishing other coastal and coral reef resources was the most common fall-back livelihood option if fishers were forced to exit the fishery. Our data highlight large disparities in subsistence consumption, gender-related price equity, and livelihood diversity among parallel artisanal fisheries. Improvement of supply chains in dispersed small-scale fisheries appears as a critical need for enhancing income and wellbeing of fishers. Strong evidence for co-dependence among small-scale fisheries, through fall-back livelihood preferences of fishers, suggests that resource managers must mitigate concomitant effects on

Optimum filter-based discrimination of neutrons and gamma rays

DOE Office of Scientific and Technical Information (OSTI.GOV)

Amiri, Moslem; Prenosil, Vaclav; Cvachovec, Frantisek

2015-07-01

An optimum filter-based method for discrimination of neutrons and gamma-rays in a mixed radiation field is presented. The existing filter-based implementations of discriminators require sample pulse responses in advance of the experiment run to build the filter coefficients, which makes them less practical. Our novel technique creates the coefficients during the experiment and improves their quality gradually. Applied to several sets of mixed neutron and photon signals obtained through different digitizers using stilbene scintillator, this approach is analyzed and its discrimination quality is measured. (authors)

Introducing etch kernels for efficient pattern sampling and etch bias prediction

NASA Astrophysics Data System (ADS)

Weisbuch, François; Lutich, Andrey; Schatz, Jirka

2018-01-01

Successful patterning requires good control of the photolithography and etch processes. While compact litho models, mainly based on rigorous physics, can predict very well the contours printed in photoresist, pure empirical etch models are less accurate and more unstable. Compact etch models are based on geometrical kernels to compute the litho-etch biases that measure the distance between litho and etch contours. The definition of the kernels, as well as the choice of calibration patterns, is critical to get a robust etch model. This work proposes to define a set of independent and anisotropic etch kernels-"internal, external, curvature, Gaussian, z_profile"-designed to represent the finest details of the resist geometry to characterize precisely the etch bias at any point along a resist contour. By evaluating the etch kernels on various structures, it is possible to map their etch signatures in a multidimensional space and analyze them to find an optimal sampling of structures. The etch kernels evaluated on these structures were combined with experimental etch bias derived from scanning electron microscope contours to train artificial neural networks to predict etch bias. The method applied to contact and line/space layers shows an improvement in etch model prediction accuracy over standard etch model. This work emphasizes the importance of the etch kernel definition to characterize and predict complex etch effects.

Improved neutron-gamma discrimination for a 3He neutron detector using subspace learning methods

DOE PAGES

Wang, C. L.; Funk, L. L.; Riedel, R. A.; ...

2017-02-10

3He gas based neutron linear-position-sensitive detectors (LPSDs) have been applied for many neutron scattering instruments. Traditional Pulse-Height Analysis (PHA) for Neutron-Gamma Discrimination (NGD) resulted in the neutron-gamma efficiency ratio on the orders of 10 5-10 6. The NGD ratios of 3He detectors need to be improved for even better scientific results from neutron scattering. Digital Signal Processing (DSP) analyses of waveforms were proposed for obtaining better NGD ratios, based on features extracted from rise-time, pulse amplitude, charge integration, a simplified Wiener filter, and the cross-correlation between individual and template waveforms of neutron and gamma events. Fisher linear discriminant analysis (FLDA)more » and three multivariate analyses (MVAs) of the features were performed. The NGD ratios are improved by about 10 2-10 3 times compared with the traditional PHA method. Finally, our results indicate the NGD capabilities of 3He tube detectors can be significantly improved with subspace-learning based methods, which may result in a reduced data-collection time and better data quality for further data reduction.« less

7 CFR 51.1403 - Kernel color classification.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Kernel color classification. 51.1403 Section 51.1403... STANDARDS) United States Standards for Grades of Pecans in the Shell 1 Kernel Color Classification § 51.1403 Kernel color classification. (a) The skin color of pecan kernels may be described in terms of the color...

Kernel approach to molecular similarity based on iterative graph similarity.

PubMed

Rupp, Matthias; Proschak, Ewgenij; Schneider, Gisbert

2007-01-01

Similarity measures for molecules are of basic importance in chemical, biological, and pharmaceutical applications. We introduce a molecular similarity measure defined directly on the annotated molecular graph, based on iterative graph similarity and optimal assignments. We give an iterative algorithm for the computation of the proposed molecular similarity measure, prove its convergence and the uniqueness of the solution, and provide an upper bound on the required number of iterations necessary to achieve a desired precision. Empirical evidence for the positive semidefiniteness of certain parametrizations of our function is presented. We evaluated our molecular similarity measure by using it as a kernel in support vector machine classification and regression applied to several pharmaceutical and toxicological data sets, with encouraging results.

Automated fine structure image analysis method for discrimination of diabetic retinopathy stage using conjunctival microvasculature images

PubMed Central

Khansari, Maziyar M; O’Neill, William; Penn, Richard; Chau, Felix; Blair, Norman P; Shahidi, Mahnaz

2016-01-01

The conjunctiva is a densely vascularized mucus membrane covering the sclera of the eye with a unique advantage of accessibility for direct visualization and non-invasive imaging. The purpose of this study is to apply an automated quantitative method for discrimination of different stages of diabetic retinopathy (DR) using conjunctival microvasculature images. Fine structural analysis of conjunctival microvasculature images was performed by ordinary least square regression and Fisher linear discriminant analysis. Conjunctival images between groups of non-diabetic and diabetic subjects at different stages of DR were discriminated. The automated method’s discriminate rates were higher than those determined by human observers. The method allowed sensitive and rapid discrimination by assessment of conjunctival microvasculature images and can be potentially useful for DR screening and monitoring. PMID:27446692

Multiple kernel learning in protein-protein interaction extraction from biomedical literature.

PubMed

Yang, Zhihao; Tang, Nan; Zhang, Xiao; Lin, Hongfei; Li, Yanpeng; Yang, Zhiwei

2011-03-01

Knowledge about protein-protein interactions (PPIs) unveils the molecular mechanisms of biological processes. The volume and content of published biomedical literature on protein interactions is expanding rapidly, making it increasingly difficult for interaction database administrators, responsible for content input and maintenance to detect and manually update protein interaction information. The objective of this work is to develop an effective approach to automatic extraction of PPI information from biomedical literature. We present a weighted multiple kernel learning-based approach for automatic PPI extraction from biomedical literature. The approach combines the following kernels: feature-based, tree, graph and part-of-speech (POS) path. In particular, we extend the shortest path-enclosed tree (SPT) and dependency path tree to capture richer contextual information. Our experimental results show that the combination of SPT and dependency path tree extensions contributes to the improvement of performance by almost 0.7 percentage units in F-score and 2 percentage units in area under the receiver operating characteristics curve (AUC). Combining two or more appropriately weighed individual will further improve the performance. Both on the individual corpus and cross-corpus evaluation our combined kernel can achieve state-of-the-art performance with respect to comparable evaluations, with 64.41% F-score and 88.46% AUC on the AImed corpus. As different kernels calculate the similarity between two sentences from different aspects. Our combined kernel can reduce the risk of missing important features. More specifically, we use a weighted linear combination of individual kernels instead of assigning the same weight to each individual kernel, thus allowing the introduction of each kernel to incrementally contribute to the performance improvement. In addition, SPT and dependency path tree extensions can improve the performance by including richer context information

Experimental study of turbulent flame kernel propagation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mansour, Mohy; Peters, Norbert; Schrader, Lars-Uve

2008-07-15

Flame kernels in spark ignited combustion systems dominate the flame propagation and combustion stability and performance. They are likely controlled by the spark energy, flow field and mixing field. The aim of the present work is to experimentally investigate the structure and propagation of the flame kernel in turbulent premixed methane flow using advanced laser-based techniques. The spark is generated using pulsed Nd:YAG laser with 20 mJ pulse energy in order to avoid the effect of the electrodes on the flame kernel structure and the variation of spark energy from shot-to-shot. Four flames have been investigated at equivalence ratios, {phi}{submore » j}, of 0.8 and 1.0 and jet velocities, U{sub j}, of 6 and 12 m/s. A combined two-dimensional Rayleigh and LIPF-OH technique has been applied. The flame kernel structure has been collected at several time intervals from the laser ignition between 10 {mu}s and 2 ms. The data show that the flame kernel structure starts with spherical shape and changes gradually to peanut-like, then to mushroom-like and finally disturbed by the turbulence. The mushroom-like structure lasts longer in the stoichiometric and slower jet velocity. The growth rate of the average flame kernel radius is divided into two linear relations; the first one during the first 100 {mu}s is almost three times faster than that at the later stage between 100 and 2000 {mu}s. The flame propagation is slightly faster in leaner flames. The trends of the flame propagation, flame radius, flame cross-sectional area and mean flame temperature are related to the jet velocity and equivalence ratio. The relations obtained in the present work allow the prediction of any of these parameters at different conditions. (author)« less

PERI - Auto-tuning Memory Intensive Kernels for Multicore

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bailey, David H; Williams, Samuel; Datta, Kaushik

2008-06-24

We present an auto-tuning approach to optimize application performance on emerging multicore architectures. The methodology extends the idea of search-based performance optimizations, popular in linear algebra and FFT libraries, to application-specific computational kernels. Our work applies this strategy to Sparse Matrix Vector Multiplication (SpMV), the explicit heat equation PDE on a regular grid (Stencil), and a lattice Boltzmann application (LBMHD). We explore one of the broadest sets of multicore architectures in the HPC literature, including the Intel Xeon Clovertown, AMD Opteron Barcelona, Sun Victoria Falls, and the Sony-Toshiba-IBM (STI) Cell. Rather than hand-tuning each kernel for each system, we developmore » a code generator for each kernel that allows us to identify a highly optimized version for each platform, while amortizing the human programming effort. Results show that our auto-tuned kernel applications often achieve a better than 4X improvement compared with the original code. Additionally, we analyze a Roofline performance model for each platform to reveal hardware bottlenecks and software challenges for future multicore systems and applications.« less

Effects of snow on fisher and marten distributions in Idaho

Treesearch

Nathan Albrecht; C. Heusser; M. Schwartz; J. Sauder; R. Vinkey

2013-01-01

Studies have suggested that deep snow may limit fisher (Martes pennanti) distribution, and that fisher populations may in turn limit marten (Martes americana) distribution. We tested these hypotheses in the Northern Rocky Mountains of Idaho, a region which differs from previous study areas in its climate and relative fisher and marten abundance, but in which very...

Combustion monitoring of a water tube boiler using a discriminant radial basis network.

PubMed

Sujatha, K; Pappa, N

2011-01-01

This research work includes a combination of Fisher's linear discriminant (FLD) analysis and a radial basis network (RBN) for monitoring the combustion conditions for a coal fired boiler so as to allow control of the air/fuel ratio. For this, two-dimensional flame images are required, which were captured with a CCD camera; the features of the images-average intensity, area, brightness and orientation etc of the flame-are extracted after preprocessing the images. The FLD is applied to reduce the n-dimensional feature size to a two-dimensional feature size for faster learning of the RBN. Also, three classes of images corresponding to different burning conditions of the flames have been extracted from continuous video processing. In this, the corresponding temperatures, and the carbon monoxide (CO) emissions and those of other flue gases have been obtained through measurement. Further, the training and testing of Fisher's linear discriminant radial basis network (FLDRBN), with the data collected, have been carried out and the performance of the algorithms is presented. Copyright © 2010 ISA. Published by Elsevier Ltd. All rights reserved.

Landscape-scale habitat selection by fishers translocated to the Olympic Peninsula of Washington

USGS Publications Warehouse

Lewis, Jeffrey C.; Jenkins, Kurt J.; Happe, Patricia J.; Manson, David J.; McCalmon, Marc

2016-01-01

The fisher was extirpated from much of the Pacific Northwestern United States during the mid- to late-1900s and is now proposed for federal listing as a threatened species in all or part of its west coast range. Following the translocation of 90 fishers from central British Columbia, Canada, to the Olympic Peninsula of Washington State from 2008 to 2010, we investigated the landscape-scale habitat selection of reintroduced fishers across a broad range of forest ages and disturbance histories, providing the first information on habitat relationships of newly reintroduced fishers in coastal coniferous forests in the Pacific Northwest. We developed 17 a priori models to evaluate several habitat-selection hypotheses based on premises of habitat models used to forecast habitat suitability for the reintroduced population. Further, we hypothesized that female fishers, because of their smaller body size than males, greater vulnerability to predation, and specific reproductive requirements, would be more selective than males for mid- to late-seral forest communities, where complex forest structural elements provide secure foraging, resting, and denning sites. We assessed 11 forest structure and landscape characteristics within the home range core-areas used by 19 females and 12 males and within randomly placed pseudo core areas that represented available habitats. We used case-controlled logistic regression to compare the characteristics of used and pseudo core areas and to assess selection by male and female fishers. Females were more selective of core area placement than males. Fifteen of 19 females (79%) and 5 of 12 males (42%) selected core areas within federal lands that encompassed primarily forests with an overstory of mid-sized or large trees. Male fishers exhibited only weak selection for core areas dominated by forests with an overstory of small trees, primarily on land managed for timber production or at high elevations. The amount of natural open area best

Text categorization of biomedical data sets using graph kernels and a controlled vocabulary.

PubMed

Bleik, Said; Mishra, Meenakshi; Huan, Jun; Song, Min

2013-01-01

Recently, graph representations of text have been showing improved performance over conventional bag-of-words representations in text categorization applications. In this paper, we present a graph-based representation for biomedical articles and use graph kernels to classify those articles into high-level categories. In our representation, common biomedical concepts and semantic relationships are identified with the help of an existing ontology and are used to build a rich graph structure that provides a consistent feature set and preserves additional semantic information that could improve a classifier's performance. We attempt to classify the graphs using both a set-based graph kernel that is capable of dealing with the disconnected nature of the graphs and a simple linear kernel. Finally, we report the results comparing the classification performance of the kernel classifiers to common text-based classifiers.

Out-of-Sample Extensions for Non-Parametric Kernel Methods.

PubMed

Pan, Binbin; Chen, Wen-Sheng; Chen, Bo; Xu, Chen; Lai, Jianhuang

2017-02-01

Choosing suitable kernels plays an important role in the performance of kernel methods. Recently, a number of studies were devoted to developing nonparametric kernels. Without assuming any parametric form of the target kernel, nonparametric kernel learning offers a flexible scheme to utilize the information of the data, which may potentially characterize the data similarity better. The kernel methods using nonparametric kernels are referred to as nonparametric kernel methods. However, many nonparametric kernel methods are restricted to transductive learning, where the prediction function is defined only over the data points given beforehand. They have no straightforward extension for the out-of-sample data points, and thus cannot be applied to inductive learning. In this paper, we show how to make the nonparametric kernel methods applicable to inductive learning. The key problem of out-of-sample extension is how to extend the nonparametric kernel matrix to the corresponding kernel function. A regression approach in the hyper reproducing kernel Hilbert space is proposed to solve this problem. Empirical results indicate that the out-of-sample performance is comparable to the in-sample performance in most cases. Experiments on face recognition demonstrate the superiority of our nonparametric kernel method over the state-of-the-art parametric kernel methods.

Modularized seismic full waveform inversion based on waveform sensitivity kernels - The software package ASKI

NASA Astrophysics Data System (ADS)

Schumacher, Florian; Friederich, Wolfgang; Lamara, Samir; Gutt, Phillip; Paffrath, Marcel

2015-04-01

We present a seismic full waveform inversion concept for applications ranging from seismological to enineering contexts, based on sensitivity kernels for full waveforms. The kernels are derived from Born scattering theory as the Fréchet derivatives of linearized frequency-domain full waveform data functionals, quantifying the influence of elastic earth model parameters and density on the data values. For a specific source-receiver combination, the kernel is computed from the displacement and strain field spectrum originating from the source evaluated throughout the inversion domain, as well as the Green function spectrum and its strains originating from the receiver. By storing the wavefield spectra of specific sources/receivers, they can be re-used for kernel computation for different specific source-receiver combinations, optimizing the total number of required forward simulations. In the iterative inversion procedure, the solution of the forward problem, the computation of sensitivity kernels and the derivation of a model update is held completely separate. In particular, the model description for the forward problem and the description of the inverted model update are kept independent. Hence, the resolution of the inverted model as well as the complexity of solving the forward problem can be iteratively increased (with increasing frequency content of the inverted data subset). This may regularize the overall inverse problem and optimizes the computational effort of both, solving the forward problem and computing the model update. The required interconnection of arbitrary unstructured volume and point grids is realized by generalized high-order integration rules and 3D-unstructured interpolation methods. The model update is inferred solving a minimization problem in a least-squares sense, resulting in Gauss-Newton convergence of the overall inversion process. The inversion method was implemented in the modularized software package ASKI (Analysis of Sensitivity

A lattice Boltzmann model for the Burgers-Fisher equation.

PubMed

Zhang, Jianying; Yan, Guangwu

2010-06-01

A lattice Boltzmann model is developed for the one- and two-dimensional Burgers-Fisher equation based on the method of the higher-order moment of equilibrium distribution functions and a series of partial differential equations in different time scales. In order to obtain the two-dimensional Burgers-Fisher equation, vector sigma(j) has been used. And in order to overcome the drawbacks of "error rebound," a new assumption of additional distribution is presented, where two additional terms, in first order and second order separately, are used. Comparisons with the results obtained by other methods reveal that the numerical solutions obtained by the proposed method converge to exact solutions. The model under new assumption gives better results than that with second order assumption. (c) 2010 American Institute of Physics.

The Flux OSKit: A Substrate for Kernel and Language Research

DTIC Science & Technology

1997-10-01

unclassified Standard Form 298 (Rev. 8-98) Prescribed by ANSI Std Z39-18 tions. Our own microkernel -based OS, Fluke [17], puts almost all of the OSKit to use...kernels distance the language from the hardware; even microkernels and other extensible kernels enforce some default policy which often conflicts with a...be particu- larly useful in these research projects. 6.1.1 The Fluke OS In 1996 we developed an entirely new microkernel - based system called Fluke

Computed tomography coronary stent imaging with iterative reconstruction: a trade-off study between medium kernel and sharp kernel.

PubMed

Zhou, Qijing; Jiang, Biao; Dong, Fei; Huang, Peiyu; Liu, Hongtao; Zhang, Minming

2014-01-01

To evaluate the improvement of iterative reconstruction in image space (IRIS) technique in computed tomographic (CT) coronary stent imaging with sharp kernel, and to make a trade-off analysis. Fifty-six patients with 105 stents were examined by 128-slice dual-source CT coronary angiography (CTCA). Images were reconstructed using standard filtered back projection (FBP) and IRIS with both medium kernel and sharp kernel applied. Image noise and the stent diameter were investigated. Image noise was measured both in background vessel and in-stent lumen as objective image evaluation. Image noise score and stent score were performed as subjective image evaluation. The CTCA images reconstructed with IRIS were associated with significant noise reduction compared to that of CTCA images reconstructed using FBP technique in both of background vessel and in-stent lumen (the background noise decreased by approximately 25.4% ± 8.2% in medium kernel (P kernel (P kernel (P kernel (P kernel showed better visualization of the stent struts and in-stent lumen than that with medium kernel. Iterative reconstruction in image space reconstruction can effectively reduce the image noise and improve image quality. The sharp kernel images constructed with iterative reconstruction are considered the optimal images to observe coronary stents in this study.

Kernel-imbedded Gaussian processes for disease classification using microarray gene expression data

PubMed Central

Zhao, Xin; Cheung, Leo Wang-Kit

2007-01-01

Background Designing appropriate machine learning methods for identifying genes that have a significant discriminating power for disease outcomes has become more and more important for our understanding of diseases at genomic level. Although many machine learning methods have been developed and applied to the area of microarray gene expression data analysis, the majority of them are based on linear models, which however are not necessarily appropriate for the underlying connection between the target disease and its associated explanatory genes. Linear model based methods usually also bring in false positive significant features more easily. Furthermore, linear model based algorithms often involve calculating the inverse of a matrix that is possibly singular when the number of potentially important genes is relatively large. This leads to problems of numerical instability. To overcome these limitations, a few non-linear methods have recently been introduced to the area. Many of the existing non-linear methods have a couple of critical problems, the model selection problem and the model parameter tuning problem, that remain unsolved or even untouched. In general, a unified framework that allows model parameters of both linear and non-linear models to be easily tuned is always preferred in real-world applications. Kernel-induced learning methods form a class of approaches that show promising potentials to achieve this goal. Results A hierarchical statistical model named kernel-imbedded Gaussian process (KIGP) is developed under a unified Bayesian framework for binary disease classification problems using microarray gene expression data. In particular, based on a probit regression setting, an adaptive algorithm with a cascading structure is designed to find the appropriate kernel, to discover the potentially significant genes, and to make the optimal class prediction accordingly. A Gibbs sampler is built as the core of the algorithm to make Bayesian inferences

On the validity of cosmological Fisher matrix forecasts

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wolz, Laura; Kilbinger, Martin; Weller, Jochen

2012-09-01

We present a comparison of Fisher matrix forecasts for cosmological probes with Monte Carlo Markov Chain (MCMC) posterior likelihood estimation methods. We analyse the performance of future Dark Energy Task Force (DETF) stage-III and stage-IV dark-energy surveys using supernovae, baryon acoustic oscillations and weak lensing as probes. We concentrate in particular on the dark-energy equation of state parameters w{sub 0} and w{sub a}. For purely geometrical probes, and especially when marginalising over w{sub a}, we find considerable disagreement between the two methods, since in this case the Fisher matrix can not reproduce the highly non-elliptical shape of the likelihood function.more » More quantitatively, the Fisher method underestimates the marginalized errors for purely geometrical probes between 30%-70%. For cases including structure formation such as weak lensing, we find that the posterior probability contours from the Fisher matrix estimation are in good agreement with the MCMC contours and the forecasted errors only changing on the 5% level. We then explore non-linear transformations resulting in physically-motivated parameters and investigate whether these parameterisations exhibit a Gaussian behaviour. We conclude that for the purely geometrical probes and, more generally, in cases where it is not known whether the likelihood is close to Gaussian, the Fisher matrix is not the appropriate tool to produce reliable forecasts.« less

Improved Online Support Vector Machines Spam Filtering Using String Kernels

NASA Astrophysics Data System (ADS)

Amayri, Ola; Bouguila, Nizar

A major bottleneck in electronic communications is the enormous dissemination of spam emails. Developing of suitable filters that can adequately capture those emails and achieve high performance rate become a main concern. Support vector machines (SVMs) have made a large contribution to the development of spam email filtering. Based on SVMs, the crucial problems in email classification are feature mapping of input emails and the choice of the kernels. In this paper, we present thorough investigation of several distance-based kernels and propose the use of string kernels and prove its efficiency in blocking spam emails. We detail a feature mapping variants in text classification (TC) that yield improved performance for the standard SVMs in filtering task. Furthermore, to cope for realtime scenarios we propose an online active framework for spam filtering.

Anisotropic hydrodynamics with a scalar collisional kernel

NASA Astrophysics Data System (ADS)

Almaalol, Dekrayat; Strickland, Michael

2018-04-01

Prior studies of nonequilibrium dynamics using anisotropic hydrodynamics have used the relativistic Anderson-Witting scattering kernel or some variant thereof. In this paper, we make the first study of the impact of using a more realistic scattering kernel. For this purpose, we consider a conformal system undergoing transversally homogenous and boost-invariant Bjorken expansion and take the collisional kernel to be given by the leading order 2 ↔2 scattering kernel in scalar λ ϕ4 . We consider both classical and quantum statistics to assess the impact of Bose enhancement on the dynamics. We also determine the anisotropic nonequilibrium attractor of a system subject to this collisional kernel. We find that, when the near-equilibrium relaxation-times in the Anderson-Witting and scalar collisional kernels are matched, the scalar kernel results in a higher degree of momentum-space anisotropy during the system's evolution, given the same initial conditions. Additionally, we find that taking into account Bose enhancement further increases the dynamically generated momentum-space anisotropy.

General methodology for simultaneous representation and discrimination of multiple object classes

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.

1998-03-01

We address a new general method for linear and nonlinear feature extraction for simultaneous representation and classification. We call this approach the maximum representation and discrimination feature (MRDF) method. We develop a novel nonlinear eigenfeature extraction technique to represent data with closed-form solutions and use it to derive a nonlinear MRDF algorithm. Results of the MRDF method on synthetic databases are shown and compared with results from standard Fukunaga-Koontz transform and Fisher discriminant function methods. The method is also applied to an automated product inspection problem and for classification and pose estimation of two similar objects under 3D aspect angle variations.

Job Satisfaction among Fishers in the Dominican Republic

ERIC Educational Resources Information Center

Ruiz, Victor

2012-01-01

This paper reflects on the results of a job satisfaction study of small-scale fishers in the Dominican Republic. The survey results suggest that, although fishers are generally satisfied with their occupations, they also have serious concerns. These concerns include anxieties about the level of earnings, the condition of marine resources and the…

Combining Statistical and Geometric Features for Colonic Polyp Detection in CTC Based on Multiple Kernel Learning

PubMed Central

Wang, Shijun; Yao, Jianhua; Petrick, Nicholas; Summers, Ronald M.

2010-01-01

Colon cancer is the second leading cause of cancer-related deaths in the United States. Computed tomographic colonography (CTC) combined with a computer aided detection system provides a feasible approach for improving colonic polyps detection and increasing the use of CTC for colon cancer screening. To distinguish true polyps from false positives, various features extracted from polyp candidates have been proposed. Most of these traditional features try to capture the shape information of polyp candidates or neighborhood knowledge about the surrounding structures (fold, colon wall, etc.). In this paper, we propose a new set of shape descriptors for polyp candidates based on statistical curvature information. These features called histograms of curvature features are rotation, translation and scale invariant and can be treated as complementing existing feature set. Then in order to make full use of the traditional geometric features (defined as group A) and the new statistical features (group B) which are highly heterogeneous, we employed a multiple kernel learning method based on semi-definite programming to learn an optimized classification kernel from the two groups of features. We conducted leave-one-patient-out test on a CTC dataset which contained scans from 66 patients. Experimental results show that a support vector machine (SVM) based on the combined feature set and the semi-definite optimization kernel achieved higher FROC performance compared to SVMs using the two groups of features separately. At a false positive per scan rate of 5, the sensitivity of the SVM using the combined features improved from 0.77 (Group A) and 0.73 (Group B) to 0.83 (p ≤ 0.01). PMID:20953299

Mixed kernel function support vector regression for global sensitivity analysis

NASA Astrophysics Data System (ADS)

Cheng, Kai; Lu, Zhenzhou; Wei, Yuhao; Shi, Yan; Zhou, Yicheng

2017-11-01

Global sensitivity analysis (GSA) plays an important role in exploring the respective effects of input variables on an assigned output response. Amongst the wide sensitivity analyses in literature, the Sobol indices have attracted much attention since they can provide accurate information for most models. In this paper, a mixed kernel function (MKF) based support vector regression (SVR) model is employed to evaluate the Sobol indices at low computational cost. By the proposed derivation, the estimation of the Sobol indices can be obtained by post-processing the coefficients of the SVR meta-model. The MKF is constituted by the orthogonal polynomials kernel function and Gaussian radial basis kernel function, thus the MKF possesses both the global characteristic advantage of the polynomials kernel function and the local characteristic advantage of the Gaussian radial basis kernel function. The proposed approach is suitable for high-dimensional and non-linear problems. Performance of the proposed approach is validated by various analytical functions and compared with the popular polynomial chaos expansion (PCE). Results demonstrate that the proposed approach is an efficient method for global sensitivity analysis.

Assessing Auditory Discrimination Skill of Malay Children Using Computer-based Method.

PubMed

Ting, H; Yunus, J; Mohd Nordin, M Z

2005-01-01

The purpose of this paper is to investigate the auditory discrimination skill of Malay children using computer-based method. Currently, most of the auditory discrimination assessments are conducted manually by Speech-Language Pathologist. These conventional tests are actually general tests of sound discrimination, which do not reflect the client's specific speech sound errors. Thus, we propose computer-based Malay auditory discrimination test to automate the whole process of assessment as well as to customize the test according to the specific speech error sounds of the client. The ability in discriminating voiced and unvoiced Malay speech sounds was studied for the Malay children aged between 7 and 10 years old. The study showed no major difficulty for the children in discriminating the Malay speech sounds except differentiating /g/-/k/ sounds. Averagely the children of 7 years old failed to discriminate /g/-/k/ sounds.

Wigner functions defined with Laplace transform kernels.

PubMed

Oh, Se Baek; Petruccelli, Jonathan C; Tian, Lei; Barbastathis, George

2011-10-24

We propose a new Wigner-type phase-space function using Laplace transform kernels--Laplace kernel Wigner function. Whereas momentum variables are real in the traditional Wigner function, the Laplace kernel Wigner function may have complex momentum variables. Due to the property of the Laplace transform, a broader range of signals can be represented in complex phase-space. We show that the Laplace kernel Wigner function exhibits similar properties in the marginals as the traditional Wigner function. As an example, we use the Laplace kernel Wigner function to analyze evanescent waves supported by surface plasmon polariton. © 2011 Optical Society of America

An Ensemble Approach to Building Mercer Kernels with Prior Information

NASA Technical Reports Server (NTRS)

Srivastava, Ashok N.; Schumann, Johann; Fischer, Bernd

2005-01-01

This paper presents a new methodology for automatic knowledge driven data mining based on the theory of Mercer Kernels, which are highly nonlinear symmetric positive definite mappings from the original image space to a very high, possibly dimensional feature space. we describe a new method called Mixture Density Mercer Kernels to learn kernel function directly from data, rather than using pre-defined kernels. These data adaptive kernels can encode prior knowledge in the kernel using a Bayesian formulation, thus allowing for physical information to be encoded in the model. Specifically, we demonstrate the use of the algorithm in situations with extremely small samples of data. We compare the results with existing algorithms on data from the Sloan Digital Sky Survey (SDSS) and demonstrate the method's superior performance against standard methods. The code for these experiments has been generated with the AUTOBAYES tool, which automatically generates efficient and documented C/C++ code from abstract statistical model specifications. The core of the system is a schema library which contains templates for learning and knowledge discovery algorithms like different versions of EM, or numeric optimization methods like conjugate gradient methods. The template instantiation is supported by symbolic-algebraic computations, which allows AUTOBAYES to find closed-form solutions and, where possible, to integrate them into the code.

Gene Ranking of RNA-Seq Data via Discriminant Non-Negative Matrix Factorization.

PubMed

Jia, Zhilong; Zhang, Xiang; Guan, Naiyang; Bo, Xiaochen; Barnes, Michael R; Luo, Zhigang

2015-01-01

RNA-sequencing is rapidly becoming the method of choice for studying the full complexity of transcriptomes, however with increasing dimensionality, accurate gene ranking is becoming increasingly challenging. This paper proposes an accurate and sensitive gene ranking method that implements discriminant non-negative matrix factorization (DNMF) for RNA-seq data. To the best of our knowledge, this is the first work to explore the utility of DNMF for gene ranking. When incorporating Fisher's discriminant criteria and setting the reduced dimension as two, DNMF learns two factors to approximate the original gene expression data, abstracting the up-regulated or down-regulated metagene by using the sample label information. The first factor denotes all the genes' weights of two metagenes as the additive combination of all genes, while the second learned factor represents the expression values of two metagenes. In the gene ranking stage, all the genes are ranked as a descending sequence according to the differential values of the metagene weights. Leveraging the nature of NMF and Fisher's criterion, DNMF can robustly boost the gene ranking performance. The Area Under the Curve analysis of differential expression analysis on two benchmarking tests of four RNA-seq data sets with similar phenotypes showed that our proposed DNMF-based gene ranking method outperforms other widely used methods. Moreover, the Gene Set Enrichment Analysis also showed DNMF outweighs others. DNMF is also computationally efficient, substantially outperforming all other benchmarked methods. Consequently, we suggest DNMF is an effective method for the analysis of differential gene expression and gene ranking for RNA-seq data.

Automatic face naming by learning discriminative affinity matrices from weakly labeled images.

PubMed

Xiao, Shijie; Xu, Dong; Wu, Jianxin

2015-10-01

Given a collection of images, where each image contains several faces and is associated with a few names in the corresponding caption, the goal of face naming is to infer the correct name for each face. In this paper, we propose two new methods to effectively solve this problem by learning two discriminative affinity matrices from these weakly labeled images. We first propose a new method called regularized low-rank representation by effectively utilizing weakly supervised information to learn a low-rank reconstruction coefficient matrix while exploring multiple subspace structures of the data. Specifically, by introducing a specially designed regularizer to the low-rank representation method, we penalize the corresponding reconstruction coefficients related to the situations where a face is reconstructed by using face images from other subjects or by using itself. With the inferred reconstruction coefficient matrix, a discriminative affinity matrix can be obtained. Moreover, we also develop a new distance metric learning method called ambiguously supervised structural metric learning by using weakly supervised information to seek a discriminative distance metric. Hence, another discriminative affinity matrix can be obtained using the similarity matrix (i.e., the kernel matrix) based on the Mahalanobis distances of the data. Observing that these two affinity matrices contain complementary information, we further combine them to obtain a fused affinity matrix, based on which we develop a new iterative scheme to infer the name of each face. Comprehensive experiments demonstrate the effectiveness of our approach.

Identifying alternate pathways for climate change to impact inland recreational fishers

USGS Publications Warehouse

Hunt, Len M.; Fenichel, Eli P.; Fulton, David C.; Mendelsohn, Robert; Smith, Jordan W.; Tunney, Tyler D.; Lynch, Abigail J.; Paukert, Craig P.; Whitney, James E.

2016-01-01

Fisheries and human dimensions literature suggests that climate change influences inland recreational fishers in North America through three major pathways. The most widely recognized pathway suggests that climate change impacts habitat and fish populations (e.g., water temperature impacting fish survival) and cascades to impact fishers. Climate change also impacts recreational fishers by influencing environmental conditions that directly affect fishers (e.g., increased temperatures in northern climates resulting in extended open water fishing seasons and increased fishing effort). The final pathway occurs from climate change mitigation and adaptation efforts (e.g., refined energy policies result in higher fuel costs, making distant trips more expensive). To address limitations of past research (e.g., assessing climate change impacts for only one pathway at a time and not accounting for climate variability, extreme weather events, or heterogeneity among fishers), we encourage researchers to refocus their efforts to understand and document climate change impacts to inland fishers.

Engaging recreational fishers in management and conservation: global case studies.

PubMed

Granek, E F; Madin, E M P; Brown, M A; Figueira, W; Cameron, D S; Hogan, Z; Kristianson, G; de Villiers, P; Williams, J E; Post, J; Zahn, S; Arlinghaus, R

2008-10-01

Globally, the number of recreational fishers is sizeable and increasing in many countries. Associated with this trend is the potential for negative impacts on fish stocks through exploitation or management measures such as stocking and introduction of non-native fishes. Nevertheless, recreational fishers can be instrumental in successful fisheries conservation through active involvement in, or initiation of, conservation projects to reduce both direct and external stressors contributing to fishery declines. Understanding fishers' concerns for sustained access to the resource and developing methods for their meaningful participation can have positive impacts on conservation efforts. We examined a suite of case studies that demonstrate successful involvement of recreational fishers in conservation and management activities that span developed and developing countries, temperate and tropical regions, marine and freshwater systems, and open- and closed-access fisheries. To illustrate potential benefits and challenges of involving recreational fishers in fisheries management and conservation, we examined the socioeconomic and ecological contexts of each case study. We devised a conceptual framework for the engagement of recreational fishers that targets particular types of involvement (enforcement, advocacy, conservation, management design [type and location], research, and monitoring) on the basis of degree of stakeholder stewardship, scale of the fishery, and source of impacts (internal or external). These activities can be enhanced by incorporating local knowledge and traditions, taking advantage of leadership and regional networks, and creating collaborations among various stakeholder groups, scientists, and agencies to maximize the probability of recreational fisher involvement and project success.

Adaptive kernel regression for freehand 3D ultrasound reconstruction

NASA Astrophysics Data System (ADS)

Alshalalfah, Abdel-Latif; Daoud, Mohammad I.; Al-Najar, Mahasen

2017-03-01

Freehand three-dimensional (3D) ultrasound imaging enables low-cost and flexible 3D scanning of arbitrary-shaped organs, where the operator can freely move a two-dimensional (2D) ultrasound probe to acquire a sequence of tracked cross-sectional images of the anatomy. Often, the acquired 2D ultrasound images are irregularly and sparsely distributed in the 3D space. Several 3D reconstruction algorithms have been proposed to synthesize 3D ultrasound volumes based on the acquired 2D images. A challenging task during the reconstruction process is to preserve the texture patterns in the synthesized volume and ensure that all gaps in the volume are correctly filled. This paper presents an adaptive kernel regression algorithm that can effectively reconstruct high-quality freehand 3D ultrasound volumes. The algorithm employs a kernel regression model that enables nonparametric interpolation of the voxel gray-level values. The kernel size of the regression model is adaptively adjusted based on the characteristics of the voxel that is being interpolated. In particular, when the algorithm is employed to interpolate a voxel located in a region with dense ultrasound data samples, the size of the kernel is reduced to preserve the texture patterns. On the other hand, the size of the kernel is increased in areas that include large gaps to enable effective gap filling. The performance of the proposed algorithm was compared with seven previous interpolation approaches by synthesizing freehand 3D ultrasound volumes of a benign breast tumor. The experimental results show that the proposed algorithm outperforms the other interpolation approaches.

Three-Dimensional Sensitivity Kernels of Z/H Amplitude Ratios of Surface and Body Waves

NASA Astrophysics Data System (ADS)

Bao, X.; Shen, Y.

2017-12-01

The ellipticity of Rayleigh wave particle motion, or Z/H amplitude ratio, has received increasing attention in inversion for shallow Earth structures. Previous studies of the Z/H ratio assumed one-dimensional (1D) velocity structures beneath the receiver, ignoring the effects of three-dimensional (3D) heterogeneities on wave amplitudes. This simplification may introduce bias in the resulting models. Here we present 3D sensitivity kernels of the Z/H ratio to Vs, Vp, and density perturbations, based on finite-difference modeling of wave propagation in 3D structures and the scattering-integral method. Our full-wave approach overcomes two main issues in previous studies of Rayleigh wave ellipticity: (1) the finite-frequency effects of wave propagation in 3D Earth structures, and (2) isolation of the fundamental mode Rayleigh waves from Rayleigh wave overtones and converted Love waves. In contrast to the 1D depth sensitivity kernels in previous studies, our 3D sensitivity kernels exhibit patterns that vary with azimuths and distances to the receiver. The laterally-summed 3D sensitivity kernels and 1D depth sensitivity kernels, based on the same homogeneous reference model, are nearly identical with small differences that are attributable to the single period of the 1D kernels and a finite period range of the 3D kernels. We further verify the 3D sensitivity kernels by comparing the predictions from the kernels with the measurements from numerical simulations of wave propagation for models with various small-scale perturbations. We also calculate and verify the amplitude kernels for P waves. This study shows that both Rayleigh and body wave Z/H ratios provide vertical and lateral constraints on the structure near the receiver. With seismic arrays, the 3D kernels afford a powerful tool to use the Z/H ratios to obtain accurate and high-resolution Earth models.

CLAss-Specific Subspace Kernel Representations and Adaptive Margin Slack Minimization for Large Scale Classification.

PubMed

Yu, Yinan; Diamantaras, Konstantinos I; McKelvey, Tomas; Kung, Sun-Yuan

2018-02-01

In kernel-based classification models, given limited computational power and storage capacity, operations over the full kernel matrix becomes prohibitive. In this paper, we propose a new supervised learning framework using kernel models for sequential data processing. The framework is based on two components that both aim at enhancing the classification capability with a subset selection scheme. The first part is a subspace projection technique in the reproducing kernel Hilbert space using a CLAss-specific Subspace Kernel representation for kernel approximation. In the second part, we propose a novel structural risk minimization algorithm called the adaptive margin slack minimization to iteratively improve the classification accuracy by an adaptive data selection. We motivate each part separately, and then integrate them into learning frameworks for large scale data. We propose two such frameworks: the memory efficient sequential processing for sequential data processing and the parallelized sequential processing for distributed computing with sequential data acquisition. We test our methods on several benchmark data sets and compared with the state-of-the-art techniques to verify the validity of the proposed techniques.

A coarse-to-fine kernel matching approach for mean-shift based visual tracking

NASA Astrophysics Data System (ADS)

Liangfu, L.; Zuren, F.; Weidong, C.; Ming, J.

2009-03-01

Mean shift is an efficient pattern match algorithm. It is widely used in visual tracking fields since it need not perform whole search in the image space. It employs gradient optimization method to reduce the time of feature matching and realize rapid object localization, and uses Bhattacharyya coefficient as the similarity measure between object template and candidate template. This thesis presents a mean shift algorithm based on coarse-to-fine search for the best kernel matching. This paper researches for object tracking with large motion area based on mean shift. To realize efficient tracking of such an object, we present a kernel matching method from coarseness to fine. If the motion areas of the object between two frames are very large and they are not overlapped in image space, then the traditional mean shift method can only obtain local optimal value by iterative computing in the old object window area, so the real tracking position cannot be obtained and the object tracking will be disabled. Our proposed algorithm can efficiently use a similarity measure function to realize the rough location of motion object, then use mean shift method to obtain the accurate local optimal value by iterative computing, which successfully realizes object tracking with large motion. Experimental results show its good performance in accuracy and speed when compared with background-weighted histogram algorithm in the literature.

Ideal regularization for learning kernels from labels.

PubMed

Pan, Binbin; Lai, Jianhuang; Shen, Lixin

2014-08-01

In this paper, we propose a new form of regularization that is able to utilize the label information of a data set for learning kernels. The proposed regularization, referred to as ideal regularization, is a linear function of the kernel matrix to be learned. The ideal regularization allows us to develop efficient algorithms to exploit labels. Three applications of the ideal regularization are considered. Firstly, we use the ideal regularization to incorporate the labels into a standard kernel, making the resulting kernel more appropriate for learning tasks. Next, we employ the ideal regularization to learn a data-dependent kernel matrix from an initial kernel matrix (which contains prior similarity information, geometric structures, and labels of the data). Finally, we incorporate the ideal regularization to some state-of-the-art kernel learning problems. With this regularization, these learning problems can be formulated as simpler ones which permit more efficient solvers. Empirical results show that the ideal regularization exploits the labels effectively and efficiently. Copyright © 2014 Elsevier Ltd. All rights reserved.

Correlation and classification of single kernel fluorescence hyperspectral data with aflatoxin concentration in corn kernels inoculated with Aspergillus flavus spores.

PubMed

Yao, H; Hruska, Z; Kincaid, R; Brown, R; Cleveland, T; Bhatnagar, D

2010-05-01

The objective of this study was to examine the relationship between fluorescence emissions of corn kernels inoculated with Aspergillus flavus and aflatoxin contamination levels within the kernels. Aflatoxin contamination in corn has been a long-standing problem plaguing the grain industry with potentially devastating consequences to corn growers. In this study, aflatoxin-contaminated corn kernels were produced through artificial inoculation of corn ears in the field with toxigenic A. flavus spores. The kernel fluorescence emission data were taken with a fluorescence hyperspectral imaging system when corn kernels were excited with ultraviolet light. Raw fluorescence image data were preprocessed and regions of interest in each image were created for all kernels. The regions of interest were used to extract spectral signatures and statistical information. The aflatoxin contamination level of single corn kernels was then chemically measured using affinity column chromatography. A fluorescence peak shift phenomenon was noted among different groups of kernels with different aflatoxin contamination levels. The fluorescence peak shift was found to move more toward the longer wavelength in the blue region for the highly contaminated kernels and toward the shorter wavelengths for the clean kernels. Highly contaminated kernels were also found to have a lower fluorescence peak magnitude compared with the less contaminated kernels. It was also noted that a general negative correlation exists between measured aflatoxin and the fluorescence image bands in the blue and green regions. The correlation coefficients of determination, r(2), was 0.72 for the multiple linear regression model. The multivariate analysis of variance found that the fluorescence means of four aflatoxin groups, <1, 1-20, 20-100, and >or=100 ng g(-1) (parts per billion), were significantly different from each other at the 0.01 level of alpha. Classification accuracy under a two-class schema ranged from 0.84 to

Adaptive kernel function using line transect sampling

NASA Astrophysics Data System (ADS)

Albadareen, Baker; Ismail, Noriszura

2018-04-01

The estimation of f(0) is crucial in the line transect method which is used for estimating population abundance in wildlife survey's. The classical kernel estimator of f(0) has a high negative bias. Our study proposes an adaptation in the kernel function which is shown to be more efficient than the usual kernel estimator. A simulation study is adopted to compare the performance of the proposed estimators with the classical kernel estimators.

Reconstruction of noisy and blurred images using blur kernel

NASA Astrophysics Data System (ADS)

Ellappan, Vijayan; Chopra, Vishal

2017-11-01

Blur is a common in so many digital images. Blur can be caused by motion of the camera and scene object. In this work we proposed a new method for deblurring images. This work uses sparse representation to identify the blur kernel. By analyzing the image coordinates Using coarse and fine, we fetch the kernel based image coordinates and according to that observation we get the motion angle of the shaken or blurred image. Then we calculate the length of the motion kernel using radon transformation and Fourier for the length calculation of the image and we use Lucy Richardson algorithm which is also called NON-Blind(NBID) Algorithm for more clean and less noisy image output. All these operation will be performed in MATLAB IDE.

Short Message Service (SMS) Command and Control (C2) Awareness in Android-based Smartphones Using Kernel-Level Auditing

DTIC Science & Technology

2012-06-14

Display 480 x 800 pixels (3.7 inches) CPU Qualcomm QSD8250 1GHz Memory (internal) 512MB RAM / 512 MB ROM Kernel version 2.6.35.7-ge0fb012 Figure 3.5: HTC...development and writing). The 34 MSM kernel provided by the AOSP and compatible with the HTC Nexus One’s motherboard and Qualcomm chipset, is used for this...building the kernel is having the prebuilt toolchains and the right kernel for the hardware. Many HTC products use Qualcomm processors which uses the

7 CFR 51.2125 - Split or broken kernels.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Split or broken kernels. 51.2125 Section 51.2125 Agriculture Regulations of the Department of Agriculture AGRICULTURAL MARKETING SERVICE (Standards... kernels. Split or broken kernels means seven-eighths or less of complete whole kernels but which will not...

Robotic Intelligence Kernel: Driver

DOE Office of Scientific and Technical Information (OSTI.GOV)

The INL Robotic Intelligence Kernel-Driver is built on top of the RIK-A and implements a dynamic autonomy structure. The RIK-D is used to orchestrate hardware for sensing and action as well as software components for perception, communication, behavior and world modeling into a single cognitive behavior kernel that provides intrinsic intelligence for a wide variety of unmanned ground vehicle systems.

Fast discrimination of hydroxypropyl methyl cellulose using portable Raman spectrometer and multivariate methods

NASA Astrophysics Data System (ADS)

Song, Biao; Lu, Dan; Peng, Ming; Li, Xia; Zou, Ye; Huang, Meizhen; Lu, Feng

2017-02-01

Raman spectroscopy is developed as a fast and non-destructive method for the discrimination and classification of hydroxypropyl methyl cellulose (HPMC) samples. 44 E series and 41 K series of HPMC samples are measured by a self-developed portable Raman spectrometer (Hx-Raman) which is excited by a 785 nm diode laser and the spectrum range is 200-2700 cm-1 with a resolution (FWHM) of 6 cm-1. Multivariate analysis is applied for discrimination of E series from K series. By methods of principal components analysis (PCA) and Fisher discriminant analysis (FDA), a discrimination result with sensitivity of 90.91% and specificity of 95.12% is achieved. The corresponding receiver operating characteristic (ROC) is 0.99, indicting the accuracy of the predictive model. This result demonstrates the prospect of portable Raman spectrometer for rapid, non-destructive classification and discrimination of E series and K series samples of HPMC.

Bell nozzle kernel analysis program

NASA Technical Reports Server (NTRS)

Elliot, J. J.; Stromstra, R. R.

1969-01-01

Bell Nozzle Kernel Analysis Program computes and analyzes the supersonic flowfield in the kernel, or initial expansion region, of a bell or conical nozzle. It analyzes both plane and axisymmetric geometrices for specified gas properties, nozzle throat geometry and input line.

Derivation of Human Chromatic Discrimination Ability from an Information-Theoretical Notion of Distance in Color Space.

PubMed

da Fonseca, María; Samengo, Inés

2016-12-01

The accuracy with which humans detect chromatic differences varies throughout color space. For example, we are far more precise when discriminating two similar orange stimuli than two similar green stimuli. In order for two colors to be perceived as different, the neurons representing chromatic information must respond differently, and the difference must be larger than the trial-to-trial variability of the response to each separate color. Photoreceptors constitute the first stage in the processing of color information; many more stages are required before humans can consciously report whether two stimuli are perceived as chromatically distinguishable. Therefore, although photoreceptor absorption curves are expected to influence the accuracy of conscious discriminability, there is no reason to believe that they should suffice to explain it. Here we develop information-theoretical tools based on the Fisher metric that demonstrate that photoreceptor absorption properties explain about 87% of the variance of human color discrimination ability, as tested by previous behavioral experiments. In the context of this theory, the bottleneck in chromatic information processing is determined by photoreceptor absorption characteristics. Subsequent encoding stages modify only marginally the chromatic discriminability at the photoreceptor level.

Fishing for space: fine-scale multi-sector maritime activities influence fisher location choice.

PubMed

Tidd, Alex N; Vermard, Youen; Marchal, Paul; Pinnegar, John; Blanchard, Julia L; Milner-Gulland, E J

2015-01-01

The European Union and other states are moving towards Ecosystem Based Fisheries Management to balance food production and security with wider ecosystem concerns. Fishing is only one of several sectors operating within the ocean environment, competing for renewable and non-renewable resources that overlap in a limited space. Other sectors include marine mining, energy generation, recreation, transport and conservation. Trade-offs of these competing sectors are already part of the process but attempts to detail how the seas are being utilised have been primarily based on compilations of data on human activity at large spatial scales. Advances including satellite and shipping automatic tracking enable investigation of factors influencing fishers' choice of fishing grounds at spatial scales relevant to decision-making, including the presence or avoidance of activities by other sectors. We analyse the determinants of English and Welsh scallop-dredging fleet behaviour, including competing sectors, operating in the eastern English Channel. Results indicate aggregate mining activity, maritime traffic, increased fishing costs, and the English inshore 6 and French 12 nautical mile limits negatively impact fishers' likelihood of fishing in otherwise suitable areas. Past success, net-benefits and fishing within the 12 NM predispose fishers to use areas. Systematic conservation planning has yet to be widely applied in marine systems, and the dynamics of spatial overlap of fishing with other activities have not been studied at scales relevant to fisher decision-making. This study demonstrates fisher decision-making is indeed affected by the real-time presence of other sectors in an area, and therefore trade-offs which need to be accounted for in marine planning. As marine resource extraction demands intensify, governments will need to take a more proactive approach to resolving these trade-offs, and studies such as this will be required as the evidential foundation for future

Fisher information framework for time series modeling

NASA Astrophysics Data System (ADS)

Venkatesan, R. C.; Plastino, A.

2017-08-01

A robust prediction model invoking the Takens embedding theorem, whose working hypothesis is obtained via an inference procedure based on the minimum Fisher information principle, is presented. The coefficients of the ansatz, central to the working hypothesis satisfy a time independent Schrödinger-like equation in a vector setting. The inference of (i) the probability density function of the coefficients of the working hypothesis and (ii) the establishing of constraint driven pseudo-inverse condition for the modeling phase of the prediction scheme, is made, for the case of normal distributions, with the aid of the quantum mechanical virial theorem. The well-known reciprocity relations and the associated Legendre transform structure for the Fisher information measure (FIM, hereafter)-based model in a vector setting (with least square constraints) are self-consistently derived. These relations are demonstrated to yield an intriguing form of the FIM for the modeling phase, which defines the working hypothesis, solely in terms of the observed data. Cases for prediction employing time series' obtained from the: (i) the Mackey-Glass delay-differential equation, (ii) one ECG signal from the MIT-Beth Israel Deaconess Hospital (MIT-BIH) cardiac arrhythmia database, and (iii) one ECG signal from the Creighton University ventricular tachyarrhythmia database. The ECG samples were obtained from the Physionet online repository. These examples demonstrate the efficiency of the prediction model. Numerical examples for exemplary cases are provided.

Traditional botanical knowledge of artisanal fishers in southern Brazil

PubMed Central

2013-01-01

Background This study characterized the botanical knowledge of artisanal fishers of the Lami community, Porto Alegre, southern Brazil based on answers to the following question: Is the local botanical knowledge of the artisanal fishers of the rural-urban district of Lami still active, even since the district’s insertion into the metropolitan region of Porto Alegre? Methods This region, which contains a mosaic of urban and rural areas, hosts the Lami Biological Reserve (LBR) and a community of 13 artisanal fisher families. Semi-structured interviews were conducted with 15 fishers, complemented by participatory observation techniques and free-lists; in these interviews, the species of plants used by the community and their indicated uses were identified. Results A total of 111 species belonging to 50 families were identified. No significant differences between the diversities of native and exotic species were found. Seven use categories were reported: medicinal (49%), human food (23.2%), fishing (12.3%), condiments (8%), firewood (5%), mystical purposes (1.45%), and animal food (0.72%). The medicinal species with the highest level of agreement regarding their main uses (AMUs) were Aloe arborescens Mill., Plectranthus barbatus Andrews, Dodonaea viscosa Jacq., Plectranthus ornatus Codd, Eugenia uniflora L., and Foeniculum vulgare Mill. For illness and diseases, most plants were used for problems with the digestive system (20 species), followed by the respiratory system (16 species). This community possesses a wide botanical knowledge, especially of medicinal plants, comparable to observations made in other studies with fishing communities in coastal areas of the Atlantic Forest of Brazil. Conclusions Ethnobotanical studies in rural-urban areas contribute to preserving local knowledge and provide information that aids in conserving the remaining ecosystems in the region. PMID:23898973

7 CFR 51.2296 - Three-fourths half kernel.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Three-fourths half kernel. 51.2296 Section 51.2296 Agriculture Regulations of the Department of Agriculture AGRICULTURAL MARKETING SERVICE (Standards...-fourths half kernel. Three-fourths half kernel means a portion of a half of a kernel which has more than...

Tensor-product kernel-based representation encoding joint MRI view similarity.

PubMed

Alvarez-Meza, A; Cardenas-Pena, D; Castro-Ospina, A E; Alvarez, M; Castellanos-Dominguez, G

2014-01-01

To support 3D magnetic resonance image (MRI) analysis, a marginal image similarity (MIS) matrix holding MR inter-slice relationship along every axis view (Axial, Coronal, and Sagittal) can be estimated. However, mutual inference from MIS view information poses a difficult task since relationships between axes are nonlinear. To overcome this issue, we introduce a Tensor-Product Kernel-based Representation (TKR) that allows encoding brain structure patterns due to patient differences, gathering all MIS matrices into a single joint image similarity framework. The TKR training strategy is carried out into a low dimensional projected space to get less influence of voxel-derived noise. Obtained results for classifying the considered patient categories (gender and age) on real MRI database shows that the proposed TKR training approach outperforms the conventional voxel-wise sum of squared differences. The proposed approach may be useful to support MRI clustering and similarity inference tasks, which are required on template-based image segmentation and atlas construction.

Examining Potential Boundary Bias Effects in Kernel Smoothing on Equating: An Introduction for the Adaptive and Epanechnikov Kernels.

PubMed

Cid, Jaime A; von Davier, Alina A

2015-05-01

Test equating is a method of making the test scores from different test forms of the same assessment comparable. In the equating process, an important step involves continuizing the discrete score distributions. In traditional observed-score equating, this step is achieved using linear interpolation (or an unscaled uniform kernel). In the kernel equating (KE) process, this continuization process involves Gaussian kernel smoothing. It has been suggested that the choice of bandwidth in kernel smoothing controls the trade-off between variance and bias. In the literature on estimating density functions using kernels, it has also been suggested that the weight of the kernel depends on the sample size, and therefore, the resulting continuous distribution exhibits bias at the endpoints, where the samples are usually smaller. The purpose of this article is (a) to explore the potential effects of atypical scores (spikes) at the extreme ends (high and low) on the KE method in distributions with different degrees of asymmetry using the randomly equivalent groups equating design (Study I), and (b) to introduce the Epanechnikov and adaptive kernels as potential alternative approaches to reducing boundary bias in smoothing (Study II). The beta-binomial model is used to simulate observed scores reflecting a range of different skewed shapes.

7 CFR 868.254 - Broken kernels determination.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 7 2010-01-01 2010-01-01 false Broken kernels determination. 868.254 Section 868.254 Agriculture Regulations of the Department of Agriculture (Continued) GRAIN INSPECTION, PACKERS AND STOCKYARD... Governing Application of Standards § 868.254 Broken kernels determination. Broken kernels shall be...

Gaussian processes with optimal kernel construction for neuro-degenerative clinical onset prediction

NASA Astrophysics Data System (ADS)

Canas, Liane S.; Yvernault, Benjamin; Cash, David M.; Molteni, Erika; Veale, Tom; Benzinger, Tammie; Ourselin, Sébastien; Mead, Simon; Modat, Marc

2018-02-01

Gaussian Processes (GP) are a powerful tool to capture the complex time-variations of a dataset. In the context of medical imaging analysis, they allow a robust modelling even in case of highly uncertain or incomplete datasets. Predictions from GP are dependent of the covariance kernel function selected to explain the data variance. To overcome this limitation, we propose a framework to identify the optimal covariance kernel function to model the data.The optimal kernel is defined as a composition of base kernel functions used to identify correlation patterns between data points. Our approach includes a modified version of the Compositional Kernel Learning (CKL) algorithm, in which we score the kernel families using a new energy function that depends both the Bayesian Information Criterion (BIC) and the explained variance score. We applied the proposed framework to model the progression of neurodegenerative diseases over time, in particular the progression of autosomal dominantly-inherited Alzheimer's disease, and use it to predict the time to clinical onset of subjects carrying genetic mutation.

Evaluating the Gradient of the Thin Wire Kernel

NASA Technical Reports Server (NTRS)

Wilton, Donald R.; Champagne, Nathan J.

2008-01-01

Recently, a formulation for evaluating the thin wire kernel was developed that employed a change of variable to smooth the kernel integrand, canceling the singularity in the integrand. Hence, the typical expansion of the wire kernel in a series for use in the potential integrals is avoided. The new expression for the kernel is exact and may be used directly to determine the gradient of the wire kernel, which consists of components that are parallel and radial to the wire axis.

Phylodynamic Inference with Kernel ABC and Its Application to HIV Epidemiology.

PubMed

Poon, Art F Y

2015-09-01

The shapes of phylogenetic trees relating virus populations are determined by the adaptation of viruses within each host, and by the transmission of viruses among hosts. Phylodynamic inference attempts to reverse this flow of information, estimating parameters of these processes from the shape of a virus phylogeny reconstructed from a sample of genetic sequences from the epidemic. A key challenge to phylodynamic inference is quantifying the similarity between two trees in an efficient and comprehensive way. In this study, I demonstrate that a new distance measure, based on a subset tree kernel function from computational linguistics, confers a significant improvement over previous measures of tree shape for classifying trees generated under different epidemiological scenarios. Next, I incorporate this kernel-based distance measure into an approximate Bayesian computation (ABC) framework for phylodynamic inference. ABC bypasses the need for an analytical solution of model likelihood, as it only requires the ability to simulate data from the model. I validate this "kernel-ABC" method for phylodynamic inference by estimating parameters from data simulated under a simple epidemiological model. Results indicate that kernel-ABC attained greater accuracy for parameters associated with virus transmission than leading software on the same data sets. Finally, I apply the kernel-ABC framework to study a recent outbreak of a recombinant HIV subtype in China. Kernel-ABC provides a versatile framework for phylodynamic inference because it can fit a broader range of models than methods that rely on the computation of exact likelihoods. © The Author 2015. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

Data-based diffraction kernels for surface waves from convolution and correlation processes through active seismic interferometry

NASA Astrophysics Data System (ADS)

Chmiel, Malgorzata; Roux, Philippe; Herrmann, Philippe; Rondeleux, Baptiste; Wathelet, Marc

2018-05-01

We investigated the construction of diffraction kernels for surface waves using two-point convolution and/or correlation from land active seismic data recorded in the context of exploration geophysics. The high density of controlled sources and receivers, combined with the application of the reciprocity principle, allows us to retrieve two-dimensional phase-oscillation diffraction kernels (DKs) of surface waves between any two source or receiver points in the medium at each frequency (up to 15 Hz, at least). These DKs are purely data-based as no model calculations and no synthetic data are needed. They naturally emerge from the interference patterns of the recorded wavefields projected on the dense array of sources and/or receivers. The DKs are used to obtain multi-mode dispersion relations of Rayleigh waves, from which near-surface shear velocity can be extracted. Using convolution versus correlation with a grid of active sources is an important step in understanding the physics of the retrieval of surface wave Green's functions. This provides the foundation for future studies based on noise sources or active sources with a sparse spatial distribution.

Robust infrared target tracking using discriminative and generative approaches

NASA Astrophysics Data System (ADS)

Asha, C. S.; Narasimhadhan, A. V.

2017-09-01

The process of designing an efficient tracker for thermal infrared imagery is one of the most challenging tasks in computer vision. Although a lot of advancement has been achieved in RGB videos over the decades, textureless and colorless properties of objects in thermal imagery pose hard constraints in the design of an efficient tracker. Tracking of an object using a single feature or a technique often fails to achieve greater accuracy. Here, we propose an effective method to track an object in infrared imagery based on a combination of discriminative and generative approaches. The discriminative technique makes use of two complementary methods such as kernelized correlation filter with spatial feature and AdaBoost classifier with pixel intesity features to operate in parallel. After obtaining optimized locations through discriminative approaches, the generative technique is applied to determine the best target location using a linear search method. Unlike the baseline algorithms, the proposed method estimates the scale of the target by Lucas-Kanade homography estimation. To evaluate the proposed method, extensive experiments are conducted on 17 challenging infrared image sequences obtained from LTIR dataset and a significant improvement of mean distance precision and mean overlap precision is accomplished as compared with the existing trackers. Further, a quantitative and qualitative assessment of the proposed approach with the state-of-the-art trackers is illustrated to clearly demonstrate an overall increase in performance.

Multi-pose facial correction based on Gaussian process with combined kernel function

NASA Astrophysics Data System (ADS)

Shi, Shuyan; Ji, Ruirui; Zhang, Fan

2018-04-01

In order to improve the recognition rate of various postures, this paper proposes a method of facial correction based on Gaussian Process which build a nonlinear regression model between the front and the side face with combined kernel function. The face images with horizontal angle from -45° to +45° can be properly corrected to front faces. Finally, Support Vector Machine is employed for face recognition. Experiments on CAS PEAL R1 face database show that Gaussian process can weaken the influence of pose changes and improve the accuracy of face recognition to certain extent.

KITTEN Lightweight Kernel 0.1 Beta

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pedretti, Kevin; Levenhagen, Michael; Kelly, Suzanne

2007-12-12

The Kitten Lightweight Kernel is a simplified OS (operating system) kernel that is intended to manage a compute node's hardware resources. It provides a set of mechanisms to user-level applications for utilizing hardware resources (e.g., allocating memory, creating processes, accessing the network). Kitten is much simpler than general-purpose OS kernels, such as Linux or Windows, but includes all of the esssential functionality needed to support HPC (high-performance computing) MPI, PGAS and OpenMP applications. Kitten provides unique capabilities such as physically contiguous application memory, transparent large page support, and noise-free tick-less operation, which enable HPC applications to obtain greater efficiency andmore » scalability than with general purpose OS kernels.« less

Single aflatoxin contaminated corn kernel analysis with fluorescence hyperspectral image

NASA Astrophysics Data System (ADS)

Yao, Haibo; Hruska, Zuzana; Kincaid, Russell; Ononye, Ambrose; Brown, Robert L.; Cleveland, Thomas E.

2010-04-01

Aflatoxins are toxic secondary metabolites of the fungi Aspergillus flavus and Aspergillus parasiticus, among others. Aflatoxin contaminated corn is toxic to domestic animals when ingested in feed and is a known carcinogen associated with liver and lung cancer in humans. Consequently, aflatoxin levels in food and feed are regulated by the Food and Drug Administration (FDA) in the US, allowing 20 ppb (parts per billion) limits in food and 100 ppb in feed for interstate commerce. Currently, aflatoxin detection and quantification methods are based on analytical tests including thin-layer chromatography (TCL) and high performance liquid chromatography (HPLC). These analytical tests require the destruction of samples, and are costly and time consuming. Thus, the ability to detect aflatoxin in a rapid, nondestructive way is crucial to the grain industry, particularly to corn industry. Hyperspectral imaging technology offers a non-invasive approach toward screening for food safety inspection and quality control based on its spectral signature. The focus of this paper is to classify aflatoxin contaminated single corn kernels using fluorescence hyperspectral imagery. Field inoculated corn kernels were used in the study. Contaminated and control kernels under long wavelength ultraviolet excitation were imaged using a visible near-infrared (VNIR) hyperspectral camera. The imaged kernels were chemically analyzed to provide reference information for image analysis. This paper describes a procedure to process corn kernels located in different images for statistical training and classification. Two classification algorithms, Maximum Likelihood and Binary Encoding, were used to classify each corn kernel into "control" or "contaminated" through pixel classification. The Binary Encoding approach had a slightly better performance with accuracy equals to 87% or 88% when 20 ppb or 100 ppb was used as classification threshold, respectively.

7 CFR 868.304 - Broken kernels determination.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 7 Agriculture 7 2011-01-01 2011-01-01 false Broken kernels determination. 868.304 Section 868.304 Agriculture Regulations of the Department of Agriculture (Continued) GRAIN INSPECTION, PACKERS AND STOCKYARD... Application of Standards § 868.304 Broken kernels determination. Broken kernels shall be determined by the use...

7 CFR 868.304 - Broken kernels determination.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 7 2010-01-01 2010-01-01 false Broken kernels determination. 868.304 Section 868.304 Agriculture Regulations of the Department of Agriculture (Continued) GRAIN INSPECTION, PACKERS AND STOCKYARD... Application of Standards § 868.304 Broken kernels determination. Broken kernels shall be determined by the use...

An early "Atkins' Diet": RA Fisher analyses a medical "experiment".

PubMed

Senn, Stephen

2006-04-01

A study on vitamin absorption which RA Fisher analysed for WRG Atkins and co-authored with him is critically examined. The historical background as well as correspondence between Atkins and Fisher is presented.

Study on Energy Productivity Ratio (EPR) at palm kernel oil processing factory: case study on PT-X at Sumatera Utara Plantation

NASA Astrophysics Data System (ADS)

Haryanto, B.; Bukit, R. Br; Situmeang, E. M.; Christina, E. P.; Pandiangan, F.

2018-02-01

The purpose of this study was to determine the performance, productivity and feasibility of the operation of palm kernel processing plant based on Energy Productivity Ratio (EPR). EPR is expressed as the ratio of output to input energy and by-product. Palm Kernel plan is process in palm kernel to become palm kernel oil. The procedure started from collecting data needed as energy input such as: palm kernel prices, energy demand and depreciation of the factory. The energy output and its by-product comprise the whole production price such as: palm kernel oil price and the remaining products such as shells and pulp price. Calculation the equality of energy of palm kernel oil is to analyze the value of Energy Productivity Ratio (EPR) bases on processing capacity per year. The investigation has been done in Kernel Oil Processing Plant PT-X at Sumatera Utara plantation. The value of EPR was 1.54 (EPR > 1), which indicated that the processing of palm kernel into palm kernel oil is feasible to be operated based on the energy productivity.