Ovarian Cancer Identification from Mass Spectra by Kernel Fisher Discriminant Analysis
NASA Astrophysics Data System (ADS)
Zhang, Bailing; Pham, Tuan D.; Zhang, Yanchun
2007-11-01
With the development of mass spectrometry technology, such as surface-enhanced laser desorption/ionisation time of flight (SELDI-ToF-MS), it has become possible to diagnose proteomic signatures for different human cancers. Among the many research issues, feature selection and classification of proteomic patterns in serum are very typical in the discrimination of cancer patients from normal individuals. In the past several years, many machine learning methods have been proposed with encouraging results. Most of the published works, however, are based on the direct application of original mass spectra, together with dimension reduction methods like PCA or feature selection methods like T-tests. Because only the peaks of MS data correspond to potential biomarkers, it is more important to study classification methods using the detected peaks, particularly from biology point of view. This paper investigates ovarian cancer identification from the detected MS peaks by applying the Kernel Fisher Discriminant Analysis (KFDA), which has been shown to be effective for classifying high-dimensional data. Comparing with several state-of-the-art methods such as SVM and random forest, KFDA gives the best performance, achieving an accuracy of 96% sensitivity of 97% and specificity of 95% in 10-fold cross-validations.
NASA Astrophysics Data System (ADS)
Wen, Gezheng; Markey, Mia K.
2015-03-01
It is resource-intensive to conduct human studies for task-based assessment of medical image quality and system optimization. Thus, numerical model observers have been developed as a surrogate for human observers. The Hotelling observer (HO) is the optimal linear observer for signal-detection tasks, but the high dimensionality of imaging data results in a heavy computational burden. Channelization is often used to approximate the HO through a dimensionality reduction step, but how to produce channelized images without losing significant image information remains a key challenge. Kernel local Fisher discriminant analysis (KLFDA) uses kernel techniques to perform supervised dimensionality reduction, which finds an embedding transformation that maximizes betweenclass separability and preserves within-class local structure in the low-dimensional manifold. It is powerful for classification tasks, especially when the distribution of a class is multimodal. Such multimodality could be observed in many practical clinical tasks. For example, primary and metastatic lesions may both appear in medical imaging studies, but the distributions of their typical characteristics (e.g., size) may be very different. In this study, we propose to use KLFDA as a novel channelization method. The dimension of the embedded manifold (i.e., the result of KLFDA) is a counterpart to the number of channels in the state-of-art linear channelization. We present a simulation study to demonstrate the potential usefulness of KLFDA for building the channelized HOs (CHOs) and generating reliable decision statistics for clinical tasks. We show that the performance of the CHO with KLFDA channels is comparable to that of the benchmark CHOs.
NASA Astrophysics Data System (ADS)
Shi, Huaitao; Liu, Jianchang; Wu, Yuhou; Zhang, Ke; Zhang, Lixiu; Xue, Peng
2016-04-01
It is pretty significant for fault diagnosis timely and accurately to improve the dependability of industrial processes. In this study, fault diagnosis of nonlinear and large-scale processes by variable-weighted kernel Fisher discriminant analysis (KFDA) based on improved biogeography-based optimisation (IBBO) is proposed, referred to as IBBO-KFDA, where IBBO is used to determine the parameters of variable-weighted KFDA, and variable-weighted KFDA is used to solve the multi-classification overlapping problem. The main contributions of this work are four-fold to further improve the performance of KFDA for fault diagnosis. First, a nonlinear fault diagnosis approach with variable-weighted KFDA is developed for maximising separation between the overlapping fault samples. Second, kernel parameters and features selection of variable-weighted KFDA are simultaneously optimised using IBBO. Finally, a single fitness function that combines erroneous diagnosis rate with feature cost is created, a novel mixed kernel function is introduced to improve the classification capability in the feature space and diagnosis accuracy of the IBBO-KFDA, and serves as the target function in the optimisation problem. Moreover, an IBBO approach is developed to obtain the better quality of solution and faster convergence speed. On the one hand, the proposed IBBO-KFDA method is first used on Tennessee Eastman process benchmark data sets to validate the feasibility and efficiency. On the other hand, IBBO-KFDA is applied to diagnose faults of automation gauge control system. Simulation results demonstrate that IBBO-KFDA can obtain better kernel parameters and feature vectors with a lower computing cost, higher diagnosis accuracy and a better real-time capacity.
Kernel optimization in discriminant analysis.
You, Di; Hamsici, Onur C; Martinez, Aleix M
2011-03-01
Kernel mapping is one of the most used approaches to intrinsically derive nonlinear classifiers. The idea is to use a kernel function which maps the original nonlinearly separable problem to a space of intrinsically larger dimensionality where the classes are linearly separable. A major problem in the design of kernel methods is to find the kernel parameters that make the problem linear in the mapped representation. This paper derives the first criterion that specifically aims to find a kernel representation where the Bayes classifier becomes linear. We illustrate how this result can be successfully applied in several kernel discriminant analysis algorithms. Experimental results, using a large number of databases and classifiers, demonstrate the utility of the proposed approach. The paper also shows (theoretically and experimentally) that a kernel version of Subclass Discriminant Analysis yields the highest recognition rates. PMID:20820072
Volcano clustering determination: Bivariate Gauss vs. Fisher kernels
NASA Astrophysics Data System (ADS)
Cañón-Tapia, Edgardo
2013-05-01
Underlying many studies of volcano clustering is the implicit assumption that vent distribution can be studied by using kernels originally devised for distribution in plane surfaces. Nevertheless, an important change in topology in the volcanic context is related to the distortion that is introduced when attempting to represent features found on the surface of a sphere that are being projected into a plane. This work explores the extent to which different topologies of the kernel used to study the spatial distribution of vents can introduce significant changes in the obtained density functions. To this end, a planar (Gauss) and a spherical (Fisher) kernels are mutually compared. The role of the smoothing factor in these two kernels is also explored with some detail. The results indicate that the topology of the kernel is not extremely influential, and that either type of kernel can be used to characterize a plane or a spherical distribution with exactly the same detail (provided that a suitable smoothing factor is selected in each case). It is also shown that there is a limitation on the resolution of the Fisher kernel relative to the typical separation between data that can be accurately described, because data sets with separations lower than 500 km are considered as a single cluster using this method. In contrast, the Gauss kernel can provide adequate resolutions for vent distributions at a wider range of separations. In addition, this study also shows that the numerical value of the smoothing factor (or bandwidth) of both the Gauss and Fisher kernels has no unique nor direct relationship with the relevant separation among data. In order to establish the relevant distance, it is necessary to take into consideration the value of the respective smoothing factor together with a level of statistical significance at which the contributions to the probability density function will be analyzed. Based on such reference level, it is possible to create a hierarchy of
Kernel PLS-SVC for Linear and Nonlinear Discrimination
NASA Technical Reports Server (NTRS)
Rosipal, Roman; Trejo, Leonard J.; Matthews, Bryan
2003-01-01
A new methodology for discrimination is proposed. This is based on kernel orthonormalized partial least squares (PLS) dimensionality reduction of the original data space followed by support vector machines for classification. Close connection of orthonormalized PLS and Fisher's approach to linear discrimination or equivalently with canonical correlation analysis is described. This gives preference to use orthonormalized PLS over principal component analysis. Good behavior of the proposed method is demonstrated on 13 different benchmark data sets and on the real world problem of the classification finger movement periods versus non-movement periods based on electroencephalogram.
Liu, Yi-Hung; Wu, Chien-Te; Cheng, Wei-Teng; Hsiao, Yu-Tsung; Chen, Po-Ming; Teng, Jyh-Tong
2014-01-01
Electroencephalogram-based emotion recognition (EEG-ER) has received increasing attention in the fields of health care, affective computing, and brain-computer interface (BCI). However, satisfactory ER performance within a bi-dimensional and non-discrete emotional space using single-trial EEG data remains a challenging task. To address this issue, we propose a three-layer scheme for single-trial EEG-ER. In the first layer, a set of spectral powers of different EEG frequency bands are extracted from multi-channel single-trial EEG signals. In the second layer, the kernel Fisher's discriminant analysis method is applied to further extract features with better discrimination ability from the EEG spectral powers. The feature vector produced by layer 2 is called a kernel Fisher's emotion pattern (KFEP), and is sent into layer 3 for further classification where the proposed imbalanced quasiconformal kernel support vector machine (IQK-SVM) serves as the emotion classifier. The outputs of the three layer EEG-ER system include labels of emotional valence and arousal. Furthermore, to collect effective training and testing datasets for the current EEG-ER system, we also use an emotion-induction paradigm in which a set of pictures selected from the International Affective Picture System (IAPS) are employed as emotion induction stimuli. The performance of the proposed three-layer solution is compared with that of other EEG spectral power-based features and emotion classifiers. Results on 10 healthy participants indicate that the proposed KFEP feature performs better than other spectral power features, and IQK-SVM outperforms traditional SVM in terms of the EEG-ER accuracy. Our findings also show that the proposed EEG-ER scheme achieves the highest classification accuracies of valence (82.68%) and arousal (84.79%) among all testing methods. PMID:25061837
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.
Uncorrelated regularized local Fisher discriminant analysis for face recognition
NASA Astrophysics Data System (ADS)
Wang, Zhan; Ruan, Qiuqi; An, Gaoyun
2014-07-01
A local Fisher discriminant analysis can work well for a multimodal problem. However, it often suffers from the undersampled problem, which makes the local within-class scatter matrix singular. We develop a supervised discriminant analysis technique called uncorrelated regularized local Fisher discriminant analysis for image feature extraction. In this technique, the local within-class scatter matrix is approximated by a full-rank matrix that not only solves the undersampled problem but also eliminates the poor impact of small and zero eigenvalues. Statistically uncorrelated features are obtained to remove redundancy. A trace ratio criterion and the corresponding iterative algorithm are employed to globally solve the objective function. Experimental results on four famous face databases indicate that our proposed method is effective and outperforms the conventional dimensionality reduction methods.
Kernel Partial Least Squares for Nonlinear Regression and Discrimination
NASA Technical Reports Server (NTRS)
Rosipal, Roman; Clancy, Daniel (Technical Monitor)
2002-01-01
This paper summarizes recent results on applying the method of partial least squares (PLS) in a reproducing kernel Hilbert space (RKHS). A previously proposed kernel PLS regression model was proven to be competitive with other regularized regression methods in RKHS. The family of nonlinear kernel-based PLS models is extended by considering the kernel PLS method for discrimination. Theoretical and experimental results on a two-class discrimination problem indicate usefulness of the method.
Approximate Fisher Kernels of Non-iid Image Models for Image Categorization.
Cinbis, Ramazan Gokberk; Verbeek, Jakob; Schmid, Cordelia
2016-06-01
The bag-of-words (BoW) model treats images as sets of local descriptors and represents them by visual word histograms. The Fisher vector (FV) representation extends BoW, by considering the first and second order statistics of local descriptors. In both representations local descriptors are assumed to be identically and independently distributed (iid), which is a poor assumption from a modeling perspective. It has been experimentally observed that the performance of BoW and FV representations can be improved by employing discounting transformations such as power normalization. In this paper, we introduce non-iid models by treating the model parameters as latent variables which are integrated out, rendering all local regions dependent. Using the Fisher kernel principle we encode an image by the gradient of the data log-likelihood w.r.t. the model hyper-parameters. Our models naturally generate discounting effects in the representations; suggesting that such transformations have proven successful because they closely correspond to the representations obtained for non-iid models. To enable tractable computation, we rely on variational free-energy bounds to learn the hyper-parameters and to compute approximate Fisher kernels. Our experimental evaluation results validate that our models lead to performance improvements comparable to using power normalization, as employed in state-of-the-art feature aggregation methods. PMID:26441445
Dynamic physiological signal analysis based on Fisher kernels for emotion recognition.
Garcia, Hernan F; Orozco, Álvaro A; Álvarez, Mauricio A
2013-01-01
Emotional behavior is an active area of study in the fields of neuroscience and affective computing. This field has the fundamental role of emotion recognition in the maintenance of physical and mental health. Valence/Arousal levels are two orthogonal, independent dimensions of any emotional stimulus and allows an analysis framework in affective research. In this paper we present our framework for emotional regression based on machine learning techniques. Autoregressive coefficients and hidden markov models on physiological signals, based on Fisher Kernels characterization are presented for mapping variable length sequences to new dimension feature vector space. Then, support vector regression is performed over the Fisher Scores for emotional recognition. Also quantitatively we evaluated the accuracy of the proposed model by acomplishing a hold-out cross validation over the dataset. The experimental results show that the proposed model can effectively perform the regression in comparison with static characterization methods. PMID:24110689
NASA Astrophysics Data System (ADS)
Jiang, Li; Shi, Tielin; Xuan, Jianping
2012-05-01
Generally, the vibration signals of fault bearings are non-stationary and highly nonlinear under complicated operating conditions. Thus, it's a big challenge to extract optimal features for improving classification and simultaneously decreasing feature dimension. Kernel Marginal Fisher analysis (KMFA) is a novel supervised manifold learning algorithm for feature extraction and dimensionality reduction. In order to avoid the small sample size problem in KMFA, we propose regularized KMFA (RKMFA). A simple and efficient intelligent fault diagnosis method based on RKMFA is put forward and applied to fault recognition of rolling bearings. So as to directly excavate nonlinear features from the original high-dimensional vibration signals, RKMFA constructs two graphs describing the intra-class compactness and the inter-class separability, by combining traditional manifold learning algorithm with fisher criteria. Therefore, the optimal low-dimensional features are obtained for better classification and finally fed into the simplest K-nearest neighbor (KNN) classifier to recognize different fault categories of bearings. The experimental results demonstrate that the proposed approach improves the fault classification performance and outperforms the other conventional approaches.
Mao, Zhi-Hua; Yin, Jian-Hua; Zhang, Xue-Xi; Wang, Xiao; Xia, Yang
2016-02-01
Fourier transform infrared spectroscopic imaging (FTIRI) technique can be used to obtain the quantitative information of content and spatial distribution of principal components in cartilage by combining with chemometrics methods. In this study, FTIRI combining with principal component analysis (PCA) and Fisher's discriminant analysis (FDA) was applied to identify the healthy and osteoarthritic (OA) articular cartilage samples. Ten 10-μm thick sections of canine cartilages were imaged at 6.25μm/pixel in FTIRI. The infrared spectra extracted from the FTIR images were imported into SPSS software for PCA and FDA. Based on the PCA result of 2 principal components, the healthy and OA cartilage samples were effectively discriminated by the FDA with high accuracy of 94% for the initial samples (training set) and cross validation, as well as 86.67% for the prediction group. The study showed that cartilage degeneration became gradually weak with the increase of the depth. FTIRI combined with chemometrics may become an effective method for distinguishing healthy and OA cartilages in future. PMID:26977354
Fisher kernel based task boundary retrieval in laparoscopic database with single video query.
Twinanda, Andru Putra; De Mathelin, Michel; Padoy, Nicolas
2014-01-01
As minimally invasive surgery becomes increasingly popular, the volume of recorded laparoscopic videos will increase rapidly. Invaluable information for teaching, assistance during difficult cases, and quality evaluation can be accessed from these videos through a video search engine. Typically, video search engines give a list of the most relevant videos pertaining to a keyword. However, instead of a whole video, one is often only interested in a fraction of the video (e.g. intestine stitching in bypass surgeries). In addition, video search requires semantic tags, yet the large amount of data typically generated hinders the feasibility of manual annotation. To tackle these problems, we propose a coarse-to-fine video indexing approach that looks for the time boundaries of a task in a laparoscopic video based on a video snippet query. We combine our search approach with the Fisher kernel (FK) encoding and show that similarity measures on this encoding are better suited for this problem than traditional similarities, such as dynamic time warping (DTW). Despite visual challenges, such as the presence of smoke, motion blur, and lens impurity, our approach performs very well in finding 3 tasks in 49 bypass videos, 1 task in 23 hernia videos, and also 1 cross-surgery task between 49 bypass and 7 sleeve gastrectomy videos. PMID:25320826
Kar, Arindam; Bhattacharjee, Debotosh; Basu, Dipak Kumar; Nasipuri, Mita; Kundu, Mahantapas
2012-01-01
In this paper a nonlinear Gabor Wavelet Transform (GWT) discriminant feature extraction approach for enhanced face recognition is proposed. Firstly, the low-energized blocks from Gabor wavelet transformed images are extracted. Secondly, the nonlinear discriminating features are analyzed and extracted from the selected low-energized blocks by the generalized Kernel Discriminative Common Vector (KDCV) method. The KDCV method is extended to include cosine kernel function in the discriminating method. The KDCV with the cosine kernels is then applied on the extracted low-energized discriminating feature vectors to obtain the real component of a complex quantity for face recognition. In order to derive positive kernel discriminative vectors, we apply only those kernel discriminative eigenvectors that are associated with nonzero eigenvalues. The feasibility of the low-energized Gabor-block-based generalized KDCV method with cosine kernel function models has been successfully tested for classification using the L(1), L(2) distance measures; and the cosine similarity measure on both frontal and pose-angled face recognition. Experimental results on the FRAV2D and the FERET database demonstrate the effectiveness of this new approach. PMID:23365559
Feature selection of fMRI data based on normalized mutual information and fisher discriminant ratio.
Wang, Yanbin; Ji, Junzhong; Liang, Peipeng
2016-03-17
Pattern classification has been increasingly used in functional magnetic resonance imaging (fMRI) data analysis. However, the classification performance is restricted by the high dimensional property and noises of the fMRI data. In this paper, a new feature selection method (named as "NMI-F") was proposed by sequentially combining the normalized mutual information (NMI) and fisher discriminant ratio. In NMI-F, the normalized mutual information was firstly used to evaluate the relationships between features, and fisher discriminant ratio was then applied to calculate the importance of each feature involved. Two fMRI datasets (task-related and resting state) were used to test the proposed method. It was found that classification base on the NMI-F method could differentiate the brain cognitive and disease states effectively, and the proposed NMI-F method was prior to the other related methods. The current results also have implications to the future studies. PMID:27257882
Penalized Fisher Discriminant Analysis and Its Application to Image-Based Morphometry
Wang, Wei; Mo, Yilin; Ozolek, John A.; Rohde, Gustavo K.
2011-01-01
Image-based morphometry is an important area of pattern recognition research, with numerous applications in science and technology (including biology and medicine). Fisher Linear Discriminant Analysis (FLDA) techniques are often employed to elucidate and visualize important information that discriminates between two or more populations. We demonstrate that the direct application of FLDA can lead to undesirable errors in characterizing such information and that the reason for such errors is not necessarily the ill conditioning in the resulting generalized eigenvalue problem, as usually assumed. We show that the regularized eigenvalue decomposition often used is related to solving a modified FLDA criterion that includes a least-squares-type representation penalty, and derive the relationship explicitly. We demonstrate the concepts by applying this modified technique to several problems in image-based morphometry, and build discriminant representative models for different data sets. PMID:22140290
A Feature Selection Method Based on Fisher's Discriminant Ratio for Text Sentiment Classification
NASA Astrophysics Data System (ADS)
Wang, Suge; Li, Deyu; Wei, Yingjie; Li, Hongxia
With the rapid growth of e-commerce, product reviews on the Web have become an important information source for customers' decision making when they intend to buy some product. As the reviews are often too many for customers to go through, how to automatically classify them into different sentiment orientation categories (i.e. positive/negative) has become a research problem. In this paper, based on Fisher's discriminant ratio, an effective feature selection method is proposed for product review text sentiment classification. In order to validate the validity of the proposed method, we compared it with other methods respectively based on information gain and mutual information while support vector machine is adopted as the classifier. In this paper, 6 subexperiments are conducted by combining different feature selection methods with 2 kinds of candidate feature sets. Under 1006 review documents of cars, the experimental results indicate that the Fisher's discriminant ratio based on word frequency estimation has the best performance with F value 83.3% while the candidate features are the words which appear in both positive and negative texts.
NASA Astrophysics Data System (ADS)
Pierini, Jorge O.; Restrepo, Juan C.; Lovallo, Michele; Telesca, Luciano
2014-12-01
The Fisher-Shannon (FS) information plane, defined by the Fisher information measure (FIM) and the Shannon entropy power (N X ), was robustly used to investigate the complex dynamics of eight monthly streamflow time series in Colombia. In the FS plane the streamflow series seem to aggregate into two different clusters corresponding to two different climatological regimes in Colombia. Our findings suggest the use of the statistical quantity defined by the FS information plane as a tool to discriminate among different hydrological regimes.
NASA Astrophysics Data System (ADS)
Pierini, Jorge O.; Restrepo, Juan C.; Lovallo, Michele; Telesca, Luciano
2015-04-01
The Fisher-Shannon (FS) information plane, defined by the Fisher information measure (FIM) and the Shannon entropy power (NX), was robustly used to investigate the complex dynamics of eight monthly streamflow time series in Colombia. In the FS plane the streamflow series seem to aggregate into two different clusters corresponding to two different climatological regimes in Colombia. Our findings suggest the use of the statistical quantity defined by the FS information plane as a tool to discriminate among different hydrological regimes.
Fisher's linear discriminant ratio based threshold for moving human detection in thermal video
NASA Astrophysics Data System (ADS)
Sharma, Lavanya; Yadav, Dileep Kumar; Singh, Annapurna
2016-09-01
In video surveillance, the moving human detection in thermal video is a critical phase that filters out redundant information to extract relevant information. The moving object detection is applied on thermal video because it penetrate challenging problems such as dynamic issues of background and illumination variation. In this work, we have proposed a new background subtraction method using Fisher's linear discriminant ratio based threshold. This threshold is investigated automatically during run-time for each pixel of every sequential frame. Automatically means to avoid the involvement of external source such as programmer or user for threshold selection. This threshold provides better pixel classification at run-time. This method handles problems generated due to multiple behavior of background more accurately using Fisher's ratio. It maximizes the separation between object pixel and the background pixel. To check the efficacy, the performance of this work is observed in terms of various parameters depicted in analysis. The experimental results and their analysis demonstrated better performance of proposed method against considered peer methods.
Tropical cyclone center location based on Fisher discriminant and Chan-Vese model
NASA Astrophysics Data System (ADS)
Qiao, Wenfeng; Li, Yuanxiang; Wei, Xian; Shen, Ji
2011-12-01
TC center location is important for weather forecast and TC analysis. However the appearance of TC centers has different shapes and sizes at different time. At different stages of TC lifetime, the difficulty of locating TC center is different. In order to improve the automatism and precision, we present a TC center location scheme for eye TCs and non-eye TCs. Fisher discriminant is used to segment TC so that we can get the binary image automatically and effectively. Since the cloud wall near the non-eye TC center is homocentric circle, Chan-Vese model is used to get TC contour. Experimental results on TCs show that our scheme can achieve an average error within 0.3 degrees in longitude/latitude in comparison with the best tracks by CMA and RSMC.
Tropical cyclone center location based on Fisher discriminant and Chan-Vese model
NASA Astrophysics Data System (ADS)
Qiao, Wenfeng; Li, Yuanxiang; Wei, Xian; Shen, Ji
2012-01-01
TC center location is important for weather forecast and TC analysis. However the appearance of TC centers has different shapes and sizes at different time. At different stages of TC lifetime, the difficulty of locating TC center is different. In order to improve the automatism and precision, we present a TC center location scheme for eye TCs and non-eye TCs. Fisher discriminant is used to segment TC so that we can get the binary image automatically and effectively. Since the cloud wall near the non-eye TC center is homocentric circle, Chan-Vese model is used to get TC contour. Experimental results on TCs show that our scheme can achieve an average error within 0.3 degrees in longitude/latitude in comparison with the best tracks by CMA and RSMC.
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.
Early discriminant method of infected kernel based on the erosion effects of laser ultrasonics
NASA Astrophysics Data System (ADS)
Fan, Chao
2015-07-01
To discriminate the infected kernel of the wheat as early as possible, a new kind of detection method of hidden insects, especially in their egg and larvae stage, was put forward based on the erosion effect of the laser ultrasonic in this paper. The surface of the grain is exposured by the pulsed laser, the energy of which is absorbed and the ultrasonic is excited, and the infected kernel can be recognized by appropriate signal analyzing. Firstly, the detection principle was given based on the classical wave equation and the platform was established. Then, the detected ultrasonic signal was processed both in the time domain and the frequency domain by using FFT and DCT , and six significant features were selected as the characteristic parameters of the signal by the method of stepwise discriminant analysis. Finally, a BP neural network was designed by using these six parameters as the input to classify the infected kernels from the normal ones. Numerous experiments were performed by using twenty wheat varieties, the results shown that the the infected kernels can be recognized effectively, and the false negative error and the false positive error was 12% and 9% respectively, the discriminant method of the infected kernels based on the erosion effect of laser ultrasonics is feasible.
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.…
Kernel-based discriminant image filter learning: application in face recognition
NASA Astrophysics Data System (ADS)
Zhang, Lingchen; Wei, Sui; Qu, Lei
2014-11-01
The extraction of discriminative and robust feature is a crucial issue in pattern recognition and classification. In this paper, we propose a kernel based discriminant image filter learning method (KDIFL) for local feature enhancement and demonstrate its superiority in the application of face recognition. Instead of designing the image filter in a handcraft or analytical way, we propose to learn the image filter so that after filtering the between-class difference is attenuated and the within-class difference is amplified, thus facilitate the following recognition. During filter learning, the kernel trick is employed to cope with the nonlinear feature space problem caused by expression, pose, illumination, and so on. We show that the proposed filter is generalized and it can be concatenated with classic feature descriptors (e.g. LBP) to further increase the discriminability of extracted features. Our extensive experiments on Yale, ORL and AR face databases validate the effectiveness and robustness of the proposed method.
NASA Astrophysics Data System (ADS)
Dong, Longjun; Wesseloo, Johan; Potvin, Yves; Li, Xibing
2016-01-01
Seismic events and blasts generate seismic waveforms that have different characteristics. The challenge to confidently differentiate these two signatures is complex and requires the integration of physical and statistical techniques. In this paper, the different characteristics of blasts and seismic events were investigated by comparing probability density distributions of different parameters. Five typical parameters of blasts and events and the probability density functions of blast time, as well as probability density functions of origin time difference for neighbouring blasts were extracted as discriminant indicators. The Fisher classifier, naive Bayesian classifier and logistic regression were used to establish discriminators. Databases from three Australian and Canadian mines were established for training, calibrating and testing the discriminant models. The classification performances and discriminant precision of the three statistical techniques were discussed and compared. The proposed discriminators have explicit and simple functions which can be easily used by workers in mines or researchers. Back-test, applied results, cross-validated results and analysis of receiver operating characteristic curves in different mines have shown that the discriminator for one of the mines has a reasonably good discriminating performance.
NASA Astrophysics Data System (ADS)
Raichoor, A.; Comparat, J.; Delubac, T.; Kneib, J.-P.; Yèche, Ch.; Zou, H.; Abdalla, F. B.; Dawson, K.; de la Macorra, A.; Fan, X.; Fan, Z.; Jiang, Z.; Jing, Y.; Jouvel, S.; Lang, D.; Lesser, M.; Li, C.; Ma, J.; Newman, J. A.; Nie, J.; Palanque-Delabrouille, N.; Percival, W. J.; Prada, F.; Shen, S.; Wang, J.; Wu, Z.; Zhang, T.; Zhou, X.; Zhou, Z.
2016-01-01
We present a new selection technique of producing spectroscopic target catalogues for massive spectroscopic surveys for cosmology. This work was conducted in the context of the extended Baryon Oscillation Spectroscopic Survey (eBOSS), which will use ~200 000 emission line galaxies (ELGs) at 0.6 ≤ zspec ≤ 1.0 to obtain a precise baryon acoustic oscillation measurement. Our proposed selection technique is based on optical and near-infrared broad-band filter photometry. We used a training sample to define a quantity, the Fisher discriminant (linear combination of colours), which correlates best with the desired properties of the target: redshift and [Oii] flux. The proposed selections are simply done by applying a cut on magnitudes and this Fisher discriminant. We used public data and dedicated SDSS spectroscopy to quantify the redshift distribution and [Oii] flux of our ELG target selections. We demonstrate that two of our selections fulfil the initial eBOSS/ELG redshift requirements: for a target density of 180 deg-2, ~70% of the selected objects have 0.6 ≤ zspec ≤ 1.0 and only ~1% of those galaxies in the range 0.6 ≤ zspec ≤ 1.0 are expected to have a catastrophic zspec estimate. Additionally, the stacked spectra and stacked deep images for those two selections show characteristic features of star-forming galaxies. The proposed approach using the Fisher discriminant could, however, be used to efficiently select other galaxy populations, based on multi-band photometry, providing that spectroscopic information isavailable. This technique could thus be useful for other future massive spectroscopic surveys such as PFS, DESI, and 4MOST.
Bilinear analysis for kernel selection and nonlinear feature extraction.
Yang, Shu; Yan, Shuicheng; Zhang, Chao; Tang, Xiaoou
2007-09-01
This paper presents a unified criterion, Fisher + kernel criterion (FKC), for feature extraction and recognition. This new criterion is intended to extract the most discriminant features in different nonlinear spaces, and then, fuse these features under a unified measurement. Thus, FKC can simultaneously achieve nonlinear discriminant analysis and kernel selection. In addition, we present an efficient algorithm Fisher + kernel analysis (FKA), which utilizes the bilinear analysis, to optimize the new criterion. This FKA algorithm can alleviate the ill-posed problem existed in traditional kernel discriminant analysis (KDA), and usually, has no singularity problem. The effectiveness of our proposed algorithm is validated by a series of face-recognition experiments on several different databases. PMID:18220192
Technology Transfer Automated Retrieval System (TEKTRAN)
Fisher’s linear discriminant (FLD) models for wheat variety classification were developed and validated. The inputs to the FLD models were the capacitance (C), impedance (Z), and phase angle ('), measured at two frequencies. Classification of wheat varieties was obtained as output of the FLD mod...
Optimizing the Classification Performance of Logistic Regression and Fisher's Discriminant Analyses.
ERIC Educational Resources Information Center
Yarnold, Paul R.; And Others
1994-01-01
A methodology is proposed to optimize the training classification performance of any suboptimal model. The method, referred to as univariate optimal discriminant analysis (UniODA), is illustrated through application to a two-group logistic regression analysis with 12 empirical examples. Maximizing percentage accuracy in classification is…
NASA Astrophysics Data System (ADS)
Sullivan, Shane Z.; Schmitt, Paul D.; DeWalt, Emma L.; Muir, Ryan D.; Simpson, Garth J.
2013-03-01
Photon counting represents the Poisson limit in signal to noise, but can often be complicated in imaging applications by detector paralysis, arising from the finite rise / fall time of the detector upon photon absorption. We present here an approach for reducing dead-time by generating a deconvolution digital filter based on optimizing the Fisher linear discriminant. In brief, two classes are defined, one in which a photon event is initiated at the origin of the digital filter, and one in the photon event is non-coincident with the filter origin. Linear discriminant analysis (LDA) is then performed to optimize the digital filter that best resolves the coincident and non-coincident training set data.1 Once trained, implementation of the filter can be performed quickly, significantly reducing dead-time issues and measurement bias in photon counting applications. Experimental demonstration of the LDA-filter approach was performed in fluorescence microscopy measurements using a highly convolved impulse response with considerable ringing. Analysis of the counts supports the capabilities of the filter in recovering deconvolved impulse responses under the conditions considered in the study. Potential additional applications and possible limitations are also considered.
Discriminative Learning for Automatic Staging of Placental Maturity via Multi-layer Fisher Vector
NASA Astrophysics Data System (ADS)
Lei, Baiying; Yao, Yuan; Chen, Siping; Li, Shengli; Li, Wanjun; Ni, Dong; Wang, Tianfu
2015-07-01
Currently, placental maturity is performed using subjective evaluation, which can be unreliable as it is highly dependent on the observations and experiences of clinicians. To address this problem, this paper proposes a method to automatically stage placenta maturity from B-mode ultrasound (US) images based on dense sampling and novel feature descriptors. Specifically, our proposed method first densely extracts features with a regular grid based on dense sampling instead of a few unreliable interest points. Followed by, these features are clustered using generative Gaussian mixture model (GMM) to obtain high order statistics of the features. The clustering representatives (i.e., cluster means) are encoded by Fisher vector (FV) for staging accuracy enhancement. Differing from the previous studies, a multi-layer FV is investigated to exploit the spatial information rather than the single layer FV. Experimental results show that the proposed method with the dense FV has achieved an area under the receiver of characteristics (AUC) of 96.77%, sensitivity and specificity of 98.04% and 93.75% for the placental maturity staging, respectively. Our experimental results also demonstrate that the dense feature outperforms the traditional sparse feature for placental maturity staging.
Discriminative Learning for Automatic Staging of Placental Maturity via Multi-layer Fisher Vector
Lei, Baiying; Yao, Yuan; Chen, Siping; Li, Shengli; Li, Wanjun; Ni, Dong; Wang, Tianfu
2015-01-01
Currently, placental maturity is performed using subjective evaluation, which can be unreliable as it is highly dependent on the observations and experiences of clinicians. To address this problem, this paper proposes a method to automatically stage placenta maturity from B-mode ultrasound (US) images based on dense sampling and novel feature descriptors. Specifically, our proposed method first densely extracts features with a regular grid based on dense sampling instead of a few unreliable interest points. Followed by, these features are clustered using generative Gaussian mixture model (GMM) to obtain high order statistics of the features. The clustering representatives (i.e., cluster means) are encoded by Fisher vector (FV) for staging accuracy enhancement. Differing from the previous studies, a multi-layer FV is investigated to exploit the spatial information rather than the single layer FV. Experimental results show that the proposed method with the dense FV has achieved an area under the receiver of characteristics (AUC) of 96.77%, sensitivity and specificity of 98.04% and 93.75% for the placental maturity staging, respectively. Our experimental results also demonstrate that the dense feature outperforms the traditional sparse feature for placental maturity staging. PMID:26228175
NASA Astrophysics Data System (ADS)
Fomin, Fedor V.
Preprocessing (data reduction or kernelization) as a strategy of coping with hard problems is universally used in almost every implementation. The history of preprocessing, like applying reduction rules simplifying truth functions, can be traced back to the 1950's [6]. A natural question in this regard is how to measure the quality of preprocessing rules proposed for a specific problem. For a long time the mathematical analysis of polynomial time preprocessing algorithms was neglected. The basic reason for this anomaly was that if we start with an instance I of an NP-hard problem and can show that in polynomial time we can replace this with an equivalent instance I' with |I'| < |I| then that would imply P=NP in classical complexity.
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.
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.
Franke, Felix; Quian Quiroga, Rodrigo; Hierlemann, Andreas; Obermayer, Klaus
2015-06-01
Spike sorting, i.e., the separation of the firing activity of different neurons from extracellular measurements, is a crucial but often error-prone step in the analysis of neuronal responses. Usually, three different problems have to be solved: the detection of spikes in the extracellular recordings, the estimation of the number of neurons and their prototypical (template) spike waveforms, and the assignment of individual spikes to those putative neurons. If the template spike waveforms are known, template matching can be used to solve the detection and classification problem. Here, we show that for the colored Gaussian noise case the optimal template matching is given by a form of linear filtering, which can be derived via linear discriminant analysis. This provides a Bayesian interpretation for the well-known matched filter output. Moreover, with this approach it is possible to compute a spike detection threshold analytically. The method can be implemented by a linear filter bank derived from the templates, and can be used for online spike sorting of multielectrode recordings. It may also be applicable to detection and classification problems of transient signals in general. Its application significantly decreases the error rate on two publicly available spike-sorting benchmark data sets in comparison to state-of-the-art template matching procedures. Finally, we explore the possibility to resolve overlapping spikes using the template matching outputs and show that they can be resolved with high accuracy. PMID:25652689
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. PMID:23363107
ERIC Educational Resources Information Center
Meshbane, Alice; Morris, John D.
A method for comparing the cross-validated classification accuracy of Fisher's linear classification functions (FLCFs) and the least absolute deviation is presented under varying data conditions for the two-group classification problem. With this method, separate-group as well as total-sample proportions of current classifications can be compared…
Faria-Machado, Adelia F; Tres, Alba; van Ruth, Saskia M; Antoniassi, Rosemar; Junqueira, Nilton T V; Lopes, Paulo Sergio N; Bizzo, Humberto R
2015-11-18
Pequi is an oleaginous fruit whose edible oil is composed mainly by saturated and monounsaturated fatty acids. The biological and nutritional properties of pequi oil are dependent on its composition, which can change according to the oil source (pulp or kernel). There is little data in the scientific literature concerning the differences between the compositions of pequi kernel and pulp oils. Therefore, in this study, different pequi genotypes were evaluated to determine the fatty acid composition of pulp and kernel oils. PCA and PLS-DA were applied to develop a model to distinguish these oils. For all evaluated genotypes, the major fatty acids of both pulp and kernel oils were oleic and palmitic acids. Despite the apparent similarity between the analyzed samples, it was possible to discriminate pulp and kernel oils by means of their fatty acid composition using chemometrics, as well as the unique pequi genotype without endocarp spines (CPAC-PQ-SE-06). PMID:26506457
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. PMID:23764527
Yang, Renjie; Liu, Rong; Xu, Kexin; Yang, Yanrong
2013-12-01
A new method for discrimination analysis of adulterated milk and pure milk is proposed by combining two-dimensional correlation spectroscopy (2D-COS) with kernel orthogonal projection to latent structure (K-OPLS). Three adulteration types of milk with urea, melamine, and glucose were prepared, respectively. The synchronous 2D spectra of adulterated milk and pure milk samples were calculated. Based on the characteristics of 2D correlation spectra of adulterated milk and pure milk, a discriminant model of urea-tainted milk, melamine-tainted milk, glucose-tainted milk, and pure milk was built by K-OPLS. The classification accuracy rates of unknown samples were 85.7, 92.3, 100, and 87.5%, respectively. The results show that this method has great potential in the rapid discrimination analysis of adulterated milk and pure milk. PMID:24359648
Discriminative clustering via extreme learning machine.
Huang, Gao; Liu, Tianchi; Yang, Yan; Lin, Zhiping; Song, Shiji; Wu, Cheng
2015-10-01
Discriminative clustering is an unsupervised learning framework which introduces the discriminative learning rule of supervised classification into clustering. The underlying assumption is that a good partition (clustering) of the data should yield high discrimination, namely, the partitioned data can be easily classified by some classification algorithms. In this paper, we propose three discriminative clustering approaches based on Extreme Learning Machine (ELM). The first algorithm iteratively trains weighted ELM (W-ELM) classifier to gradually maximize the data discrimination. The second and third methods are both built on Fisher's Linear Discriminant Analysis (LDA); but one approach adopts alternative optimization, while the other leverages kernel k-means. We show that the proposed algorithms can be easily implemented, and yield competitive clustering accuracy on real world data sets compared to state-of-the-art clustering methods. PMID:26143036
Yuan, Ying; Wang, Wei; Chu, Xuan; Xi, Ming-jie
2016-01-01
The feasibility of Fourier transform near infrared (FT-NIR) spectroscopy with spectral range between 833 and 2 500 nm to detect the moldy corn kernels with different levels of mildew was verified in this paper. Firstly, to avoid the influence of noise, moving average smoothing was used for spectral data preprocessing after four common pretreatment methods were compared. Then to improve the prediction performance of the model, SPXY (sample set partitioning based on joint x-y distance) was selected and used for sample set partition. Furthermore, in order to reduce the dimensions of the original spectral data, successive projection algorithm (SPA) was adopted and ultimately 7 characteristic wavelengths were extracted, the characteristic wave-lengths were 833, 927, 1 208, 1 337, 1 454, 1 861, 2 280 nm. The experimental results showed when the spectrum data of the 7 characteristic wavelengths were taken as the input of SVM, the radial basic function (RBF) used as the kernel function, and kernel parameter C = 7 760 469, γ = 0.017 003, the classification accuracies of the established SVM model were 97.78% and 93.33% for the training and testing sets respectively. In addition, the independent validation set was selected in the same standard, and used to verify the model. At last, the classification accuracy of 91.11% for the independent validation set was achieved. The result indicated that it is feasible to identify and classify different degree of moldy corn grain kernels using SPA and SVM, and characteristic wavelengths selected by SPA in this paper also lay a foundation for the online NIR detection of mildew corn kernels. PMID:27228772
Action recognition using spatiotemporal features and hybrid generative/discriminative models
NASA Astrophysics Data System (ADS)
Liu, Jia; Yang, Jie
2012-04-01
We propose a new method for human action recognition based on multiple features and a hybrid generative/discriminative model. Specifically, we propose a new action representation based on computing a rich set of descriptors from Affine-SIFT key point trajectories. A new hybrid generative/discriminative approach based on support vector machine and topic model is proposed using Fisher kernel method for action recognition. Fisher score for the topic model is evaluated by the variational inference algorithm. To obtain efficient and compact representations for actions, we develop a feature fusion method to combine spatial-temporal local motion descriptors and demonstrate how this kernel framework can be used to combine different types of features and models into a single classifier. Our experiments, conducted on a number of popular datasets, show performance improvements over the corresponding generative approach and are competitive with the best results reported in the literature.
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.
Sparse representation with kernels.
Gao, Shenghua; Tsang, Ivor Wai-Hung; Chia, Liang-Tien
2013-02-01
Recent research has shown the initial success of sparse coding (Sc) in solving many computer vision tasks. Motivated by the fact that kernel trick can capture the nonlinear similarity of features, which helps in finding a sparse representation of nonlinear features, we propose kernel sparse representation (KSR). Essentially, KSR is a sparse coding technique in a high dimensional feature space mapped by an implicit mapping function. We apply KSR to feature coding in image classification, face recognition, and kernel matrix approximation. More specifically, by incorporating KSR into spatial pyramid matching (SPM), we develop KSRSPM, which achieves a good performance for image classification. Moreover, KSR-based feature coding can be shown as a generalization of efficient match kernel and an extension of Sc-based SPM. We further show that our proposed KSR using a histogram intersection kernel (HIK) can be considered a soft assignment extension of HIK-based feature quantization in the feature coding process. Besides feature coding, comparing with sparse coding, KSR can learn more discriminative sparse codes and achieve higher accuracy for face recognition. Moreover, KSR can also be applied to kernel matrix approximation in large scale learning tasks, and it demonstrates its robustness to kernel matrix approximation, especially when a small fraction of the data is used. Extensive experimental results demonstrate promising results of KSR in image classification, face recognition, and kernel matrix approximation. All these applications prove the effectiveness of KSR in computer vision and machine learning tasks. PMID:23014744
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.…
Fisher information and steric effect
NASA Astrophysics Data System (ADS)
Nagy, Á.
2007-11-01
A modified Fisher information, the sum of the one-electron Fisher information, is proposed. It is shown that the modified Fisher information is the original Fisher information plus a sum of differences of quantum and classical variances. A generalization of the Stam's inequality is derived. It is argued that Fisher information provides a measure of steric effect.
... sensory information to the spinal cord and brain. Magnetic resonance (MRI) or other imaging of the brain and/or spinal cord are usually normal. Spinal fluid protein is often elevated. Pure Fisher syndrome is ...
Recurrent Miller Fisher syndrome.
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. PMID:15645989
Analysis of the Fisher solution
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 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'.
Kernel Methods on Riemannian Manifolds with Gaussian RBF Kernels.
Jayasumana, Sadeep; Hartley, Richard; Salzmann, Mathieu; Li, Hongdong; Harandi, Mehrtash
2015-12-01
In this paper, we develop an approach to exploiting kernel methods with manifold-valued data. In many computer vision problems, the data can be naturally represented as points on a Riemannian manifold. Due to the non-Euclidean geometry of Riemannian manifolds, usual Euclidean computer vision and machine learning algorithms yield inferior results on such data. In this paper, we define Gaussian radial basis function (RBF)-based positive definite kernels on manifolds that permit us to embed a given manifold with a corresponding metric in a high dimensional reproducing kernel Hilbert space. These kernels make it possible to utilize algorithms developed for linear spaces on nonlinear manifold-valued data. Since the Gaussian RBF defined with any given metric is not always positive definite, we present a unified framework for analyzing the positive definiteness of the Gaussian RBF on a generic metric space. We then use the proposed framework to identify positive definite kernels on two specific manifolds commonly encountered in computer vision: the Riemannian manifold of symmetric positive definite matrices and the Grassmann manifold, i.e., the Riemannian manifold of linear subspaces of a Euclidean space. We show that many popular algorithms designed for Euclidean spaces, such as support vector machines, discriminant analysis and principal component analysis can be generalized to Riemannian manifolds with the help of such positive definite Gaussian kernels. PMID:26539851
Image texture analysis of crushed wheat kernels
NASA Astrophysics Data System (ADS)
Zayas, Inna Y.; Martin, C. R.; Steele, James L.; Dempster, Richard E.
1992-03-01
The development of new approaches for wheat hardness assessment may impact the grain industry in marketing, milling, and breeding. This study used image texture features for wheat hardness evaluation. Application of digital imaging to grain for grading purposes is principally based on morphometrical (shape and size) characteristics of the kernels. A composite sample of 320 kernels for 17 wheat varieties were collected after testing and crushing with a single kernel hardness characterization meter. Six wheat classes where represented: HRW, HRS, SRW, SWW, Durum, and Club. In this study, parameters which characterize texture or spatial distribution of gray levels of an image were determined and used to classify images of crushed wheat kernels. The texture parameters of crushed wheat kernel images were different depending on class, hardness and variety of the wheat. Image texture analysis of crushed wheat kernels showed promise for use in class, hardness, milling quality, and variety discrimination.
Band-Reweighed Gabor Kernel Embedding for Face Image Representation and Recognition.
Ren, Chuan-Xian; Dai, Dao-Qing; Li, Xiao-Xin; Lai, Zhao-Rong
2014-02-01
Face recognition with illumination or pose variation is a challenging problem in image processing and pattern recognition. A novel algorithm using band-reweighed Gabor kernel embedding to deal with the problem is proposed in this paper. For a given image, it is first transformed by a group of Gabor filters, which output Gabor features using different orientation and scale parameters. Fisher scoring function is used to measure the importance of features in each band, and then, the features with the largest scores are preserved for saving memory requirements. The reduced bands are combined by a vector, which is determined by a weighted kernel discriminant criterion and solved by a constrained quadratic programming method, and then, the weighted sum of these nonlinear bands is defined as the similarity between two images. Compared with existing concatenation-based Gabor feature representation and the uniformly weighted similarity calculation approaches, our method provides a new way to use Gabor features for face recognition and presents a reasonable interpretation for highlighting discriminant orientations and scales. The minimum Mahalanobis distance considering the spatial correlations within the data is exploited for feature matching, and the graphical lasso is used therein for directly estimating the sparse inverse covariance matrix. Experiments using benchmark databases show that our new algorithm improves the recognition results and obtains competitive performance. PMID:26270914
Optical resolution from Fisher information
NASA Astrophysics Data System (ADS)
Motka, L.; Stoklasa, B.; D'Angelo, M.; Facchi, P.; Garuccio, A.; Hradil, Z.; Pascazio, S.; Pepe, F. V.; Teo, Y. S.; Řeháček, J.; Sánchez-Soto, L. L.
2016-05-01
The information gained by performing a measurement on a physical system is most appropriately assessed by the Fisher information, which in fact establishes lower bounds on estimation errors for an arbitrary unbiased estimator. We revisit the basic properties of the Fisher information and demonstrate its potential to quantify the resolution of optical systems. We illustrate this with some conceptually important examples, such as single-slit diffraction, spectroscopy and superresolution techniques.
"Fisher v. Texas": Strictly Disappointing
ERIC Educational Resources Information Center
Nieli, Russell K.
2013-01-01
Russell K. Nieli writes in this opinion paper that as far as the ability of state colleges and universities to use race as a criteria for admission goes, "Fisher v. Texas" was a big disappointment, and failed in the most basic way. Nieli states that although some affirmative action opponents have tried to put a more positive spin on the…
On Fisher Information and Thermodynamics
Fisher information is a measure of the information obtainable by an observer from the observation of reality. However, information is obtainable only when there are patterns or features to observe, and these only exist when there is order. For example, a system in perfect disor...
Technology Transfer Automated Retrieval System (TEKTRAN)
The current US corn grading system accounts for the portion of damaged kernels, which is measured by time-consuming and inaccurate visual inspection. Near infrared spectroscopy (NIRS), a non-destructive and fast analytical method, was tested as a tool for discriminating corn kernels with heat and f...
Atomic Fisher information versus atomic number
NASA Astrophysics Data System (ADS)
Nagy, Á.; Sen, K. D.
2006-12-01
It is shown that the Thomas Fermi Fisher information is negative. A slightly more sophisticated model proposed by Gáspár provides a qualitatively correct expression for the Fisher information: Gáspár's Fisher information is proportional to the two-third power of the atomic number. Accurate numerical calculations show an almost linear dependence on the atomic number.
FINE: fisher information nonparametric embedding.
Carter, Kevin M; Raich, Raviv; Finn, William G; Hero, Alfred O
2009-11-01
We consider the problems of clustering, classification, and visualization of high-dimensional data when no straightforward euclidean representation exists. In this paper, we propose using the properties of information geometry and statistical manifolds in order to define similarities between data sets using the Fisher information distance. We will show that this metric can be approximated using entirely nonparametric methods, as the parameterization and geometry of the manifold is generally unknown. Furthermore, by using multidimensional scaling methods, we are able to reconstruct the statistical manifold in a low-dimensional euclidean space; enabling effective learning on the data. As a whole, we refer to our framework as Fisher Information Nonparametric Embedding (FINE) and illustrate its uses on practical problems, including a biomedical application and document classification. PMID:19762935
Approximate kernel competitive learning.
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. PMID:25528318
NASA Astrophysics Data System (ADS)
Rijnsdorp, Adriaan D.; Poos, Jan Jaap; Quirijns, Floor J.; HilleRisLambers, Reinier; De Wilde, Jan W.; Den Heijer, Willem M.
An analysis of the changes in the Dutch demersal fishing fleet since the 1950s revealed that competitive interactions among vessels and gear types within the constraints imposed by biological, economic and fisheries management factors are the dominant processes governing the dynamics of fishing fleets. Double beam trawling, introduced in the early 1960s, proved a successful fishing method to catch deep burying flatfish, in particular sole. In less than 10 years, the otter trawl fleet was replaced by a highly specialised beam trawling fleet, despite an initial doubling of the loss rate of vessels due to stability problems. Engine power, size of the beam trawl, number of tickler chains and fishing speed rapidly increased and fishing activities expanded into previously lightly fished grounds and seasons. Following the ban on flatfish trawling within the 12 nautical mile zone for vessels of more than 300 hp in 1975 and with the restriction of engine power to 2000 hp in 1987, the beam trawl fleet bifurcated. Changes in the fleet capacity were related to the economic results and showed a cyclic pattern with a period of 6-7 years. The arms race between fishers was fuelled by competitive interactions among fishers: while the catchability of the fleet more than doubled in the ten years following the introduction of the beam trawl, a decline in catchability was observed in reference beam trawlers that remained the same. Vessel performance was not only affected by the technological characteristics but also by the number and characteristics of competing vessels.
Optimization of Polarimetric Contrast Enhancement Based on Fisher Criterion
NASA Astrophysics Data System (ADS)
Deng, Qiming; Chen, Jiong; Yang, Jian
The optimization of polarimetric contrast enhancement (OPCE) is a widely used method for maximizing the received power ratio of a desired target versus an undesired target (clutter). In this letter, a new model of the OPCE is proposed based on the Fisher criterion. By introducing the well known two-class problem of linear discriminant analysis (LDA), the proposed model is to enlarge the normalized distance of mean value between the target and the clutter. In addition, a cross-iterative numerical method is proposed for solving the optimization with a quadratic constraint. Experimental results with the polarimetric SAR (POLSAR) data demonstrate the effectiveness of the proposed method.
Seeing the Fisher Z-Transformation
ERIC Educational Resources Information Center
Bond, Charles F., Jr.; Richardson, Ken
2004-01-01
Since 1915, statisticians have been applying Fisher's Z-transformation to Pearson product-moment correlation coefficients. We offer new geometric interpretations of this transformation. (Contains 9 figures.)
Partial least squares, conjugate gradient and the fisher discriminant
Faber, V.
1996-12-31
The theory of multivariate regression has been extensively studied and is commonly used in many diverse scientific areas. A wide variety of techniques are currently available for solving the problem of multivariate calibration. The volume of literature on this subject is so extensive that understanding which technique to apply can often be very confusing. A common class of techniques for solving linear systems, and consequently applications of linear systems to multivariate analysis, are iterative methods. While common linear system solvers typically involve the factorization of the coefficient matrix A in solving the system Ax = b, this method can be impractical if A is large and sparse. Iterative methods such as Gauss-Seidel, SOR, Chebyshev semi-iterative, and related methods also often depend upon parameters that require calibration and which are sometimes hard to choose properly. An iterative method which surmounts many of these difficulties is the method of conjugate gradient. Algorithms of this type find solutions iteratively, by optimally calculating the next approximation from the residuals.
Protein interaction sentence detection using multiple semantic kernels
2011-01-01
Background Detection of sentences that describe protein-protein interactions (PPIs) in biomedical publications is a challenging and unresolved pattern recognition problem. Many state-of-the-art approaches for this task employ kernel classification methods, in particular support vector machines (SVMs). In this work we propose a novel data integration approach that utilises semantic kernels and a kernel classification method that is a probabilistic analogue to SVMs. Semantic kernels are created from statistical information gathered from large amounts of unlabelled text using lexical semantic models. Several semantic kernels are then fused into an overall composite classification space. In this initial study, we use simple features in order to examine whether the use of combinations of kernels constructed using word-based semantic models can improve PPI sentence detection. Results We show that combinations of semantic kernels lead to statistically significant improvements in recognition rates and receiver operating characteristic (ROC) scores over the plain Gaussian kernel, when applied to a well-known labelled collection of abstracts. The proposed kernel composition method also allows us to automatically infer the most discriminative kernels. Conclusions The results from this paper indicate that using semantic information from unlabelled text, and combinations of such information, can be valuable for classification of short texts such as PPI sentences. This study, however, is only a first step in evaluation of semantic kernels and probabilistic multiple kernel learning in the context of PPI detection. The method described herein is modular, and can be applied with a variety of feature types, kernels, and semantic models, in order to facilitate full extraction of interacting proteins. PMID:21569604
Fisher information geometry of the barycenter map
NASA Astrophysics Data System (ADS)
Itoh, Mitsuhiro; Satoh, Hiroyasu
2015-01-01
We report Fisher information geometry of the barycenter map associated with Busemann function Bθ of an Hadamard manifold X and present its application to Riemannian geometry of X from viewpoint of Fisher information geometry. This report is an improvement of [I-Sat'13] together with a fine investigation of the barycenter map.
Melacci, Stefano; Gori, Marco
2013-11-01
Supervised examples and prior knowledge on regions of the input space have been profitably integrated in kernel machines to improve the performance of classifiers in different real-world contexts. The proposed solutions, which rely on the unified supervision of points and sets, have been mostly based on specific optimization schemes in which, as usual, the kernel function operates on points only. In this paper, arguments from variational calculus are used to support the choice of a special class of kernels, referred to as box kernels, which emerges directly from the choice of the kernel function associated with a regularization operator. It is proven that there is no need to search for kernels to incorporate the structure deriving from the supervision of regions of the input space, because the optimal kernel arises as a consequence of the chosen regularization operator. Although most of the given results hold for sets, we focus attention on boxes, whose labeling is associated with their propositional description. Based on different assumptions, some representer theorems are given that dictate the structure of the solution in terms of box kernel expansion. Successful results are given for problems of medical diagnosis, image, and text categorization. PMID:24051728
Melacci, Stefano; Gori, Marco
2013-04-12
Supervised examples and prior knowledge on regions of the input space have been profitably integrated in kernel machines to improve the performance of classifiers in different real-world contexts. The proposed solutions, which rely on the unified supervision of points and sets, have been mostly based on specific optimization schemes in which, as usual, the kernel function operates on points only. In this paper, arguments from variational calculus are used to support the choice of a special class of kernels, referred to as box kernels, which emerges directly from the choice of the kernel function associated with a regularization operator. It is proven that there is no need to search for kernels to incorporate the structure deriving from the supervision of regions of the input space, since the optimal kernel arises as a consequence of the chosen regularization operator. Although most of the given results hold for sets, we focus attention on boxes, whose labeling is associated with their propositional description. Based on different assumptions, some representer theorems are given which dictate the structure of the solution in terms of box kernel expansion. Successful results are given for problems of medical diagnosis, image, and text categorization. PMID:23589591
Duff, I.
1994-12-31
This workshop focuses on kernels for iterative software packages. Specifically, the three speakers discuss various aspects of sparse BLAS kernels. Their topics are: `Current status of user lever sparse BLAS`; Current status of the sparse BLAS toolkit`; and `Adding matrix-matrix and matrix-matrix-matrix multiply to the sparse BLAS toolkit`.
Fisher information and surrogate figures of merit for the task-based assessment of image quality.
Clarkson, Eric; Shen, Fangfang
2010-10-01
Fisher information can be used as a surrogate for task-based measures of image quality based on ideal observer performance. A new and improved derivation of the Fisher information approximation for ideal-observer detectability is provided. This approximation depends only on the presence of a weak signal and does not depend on Gaussian statistical assumptions. This is also not an asymptotic result and therefore applies to imaging, where there is typically only one dataset, albeit a large one. Applications to statistical mixture models for image data are presented. For Gaussian and Poisson mixture models the results are used to connect reconstruction error with ideal-observer detection performance. When the task is the estimation of signal parameters of a weak signal, the ensemble mean squared error of the posterior mean estimator can also be expanded in powers of the signal amplitude. There is no linear term in this expansion, and it is shown that the quadratic term involves a Fisher information kernel that generalizes the standard Fisher information. Applications to imaging mixture models reveal a close connection between ideal performance on these estimation tasks and detection tasks for the same signals. Finally, for tasks that combine detection and estimation, we may also define a detectability that measures performance on this combined task and an ideal observer that maximizes this detectability. This detectability may also be expanded in powers of the signal amplitude, and the quadratic term again involves the Fisher information kernel. Applications of this approximation to imaging mixture models show a relation with the pure detection and pure estimation tasks for the same signals. PMID:20922022
Fisher information and surrogate figures of merit for the task-based assessment of image quality
Clarkson, Eric; Shen, Fangfang
2010-01-01
Fisher information can be used as a surrogate for task-based measures of image quality based on ideal observer performance. A new and improved derivation of the Fisher information approximation for ideal-observer detectability is provided. This approximation depends only on the presence of a weak signal and does not depend on Gaussian statistical assumptions. This is also not an asymptotic result and therefore applies to imaging, where there is typically only one dataset, albeit a large one. Applications to statistical mixture models for image data are presented. For Gaussian and Poisson mixture models the results are used to connect reconstruction error with ideal-observer detection performance. When the task is the estimation of signal parameters of a weak signal, the ensemble mean squared error of the posterior mean estimator can also be expanded in powers of the signal amplitude. There is no linear term in this expansion, and it is shown that the quadratic term involves a Fisher information kernel that generalizes the standard Fisher information. Applications to imaging mixture models reveal a close connection between ideal performance on these estimation tasks and detection tasks for the same signals. Finally, for tasks that combine detection and estimation, we may also define a detectability that measures performance on this combined task and an ideal observer that maximizes this detectability. This detectability may also be expanded in powers of the signal amplitude, and the quadratic term again involves the Fisher information kernel. Applications of this approximation to imaging mixture models show a relation with the pure detection and pure estimation tasks for the same signals. PMID:20922022
Intelligent classification methods of grain kernels using computer vision analysis
NASA Astrophysics Data System (ADS)
Lee, Choon Young; Yan, Lei; Wang, Tianfeng; Lee, Sang Ryong; Park, Cheol Woo
2011-06-01
In this paper, a digital image analysis method was developed to classify seven kinds of individual grain kernels (common rice, glutinous rice, rough rice, brown rice, buckwheat, common barley and glutinous barley) widely planted in Korea. A total of 2800 color images of individual grain kernels were acquired as a data set. Seven color and ten morphological features were extracted and processed by linear discriminant analysis to improve the efficiency of the identification process. The output features from linear discriminant analysis were used as input to the four-layer back-propagation network to classify different grain kernel varieties. The data set was divided into three groups: 70% for training, 20% for validation, and 10% for testing the network. The classification experimental results show that the proposed method is able to classify the grain kernel varieties efficiently.
General perspective view of the Fisher School Covered Bridge, view ...
General perspective view of the Fisher School Covered Bridge, view looking east along Five Rivers Road. - Fisher School Covered Bridge, Crab Creek Road at Fiver Rivers Road, Fisher, Lincoln County, OR
General topographic view of the Fisher School Covered Bridge, view ...
General topographic view of the Fisher School Covered Bridge, view looking northwest from Crab Creek Road. - Fisher School Covered Bridge, Crab Creek Road at Fiver Rivers Road, Fisher, Lincoln County, OR
Interior of the Fisher School Covered Bridge, view to north ...
Interior of the Fisher School Covered Bridge, view to north showing road deck, guardrail, and howe truss. - Fisher School Covered Bridge, Crab Creek Road at Fiver Rivers Road, Fisher, Lincoln County, OR
General perspective view of the Fisher School Covered Bridge, view ...
General perspective view of the Fisher School Covered Bridge, view looking southwest from Five Rivers Road. - Fisher School Covered Bridge, Crab Creek Road at Fiver Rivers Road, Fisher, Lincoln County, OR
Fisher Information in Ecological Systems
NASA Astrophysics Data System (ADS)
Frieden, B. Roy; Gatenby, Robert A.
Fisher information is being increasingly used as a tool of research into ecological systems. For example the information was shown in Chapter 7 to provide a useful diagnostic of the health of an ecology. In other applications to ecology, extreme physical information (EPI) has been used to derive the population-rate (or Lotka-Volterra) equations of ecological systems, both directly [1] and indirectly (Chapter 5) via the quantum Schrodinger wave equation (SWE). We next build on these results, to derive (i) an uncertainty principle (8.3) of biology, (ii) a simple decision rule (8.18) for predicting whether a given ecology is susceptible to a sudden drop in population (Section 8.1), (iii) the probability law (8.57) or (8.59) on the worldwide occurrence of the masses of living creatures from mice to elephants and beyond (Section 8.2), and (iv) the famous quarter-power laws for the attributes of biological and other systems. The latter approach uses EPI to derive the simultaneous quarter-power behavior of all attributes obeyed by the law, such as metabolism rate, brain size, grazing range, etc. (Section 8.3). This maximal breadth of scope is allowed by its basis in information, which of course applies to all types of quantitative data (Section 1.4.3, Chapter 1).
LETTER: Fisher renormalization for logarithmic corrections
NASA Astrophysics Data System (ADS)
Kenna, Ralph; Hsu, Hsiao-Ping; von Ferber, Christian
2008-10-01
For continuous phase transitions characterized by power-law divergences, Fisher renormalization prescribes how to obtain the critical exponents for a system under constraint from their ideal counterparts. In statistical mechanics, such ideal behaviour at phase transitions is frequently modified by multiplicative logarithmic corrections. Here, Fisher renormalization for the exponents of these logarithms is developed in a general manner. As for the leading exponents, Fisher renormalization at the logarithmic level is seen to be involutory and the renormalized exponents obey the same scaling relations as their ideal analogues. The scheme is tested in lattice animals and the Yang-Lee problem at their upper critical dimensions, where predictions for logarithmic corrections are made.
[Case of Fisher syndrome with ocular flutter].
Nakayasu, Koki; Sakimoto, Tohru; Minami, Masayuki; Shigihara, Syuntaro; Ishikawa, Hiroshi
2010-06-01
We report a case of Fisher syndrome accompanied by ocular flutter. A 19-year-old man presented with diplopia and vertigo, associated with preceding symptoms of common cold. Since symmetric weakness of abduction in both eyes, truncal ataxia, diminution of tendon reflexes, and gaze nystagmus were noted, he was diagnosed as having Fisher syndrome. Ocular flutter also was noticed during horizontal gaze. Serum anti-GQ1b antibody and anti-GM1 antibody were detected. He was followed without therapy and the symptoms resolved. The accompanying ocular flutter may suggest that a central nervous system disorder may also be present in Fisher syndrome. PMID:20593660
Robotic Intelligence Kernel: Communications
Walton, Mike C.
2009-09-16
The INL Robotic Intelligence Kernel-Comms is the communication server that transmits information between one or more robots using the RIK and one or more user interfaces. It supports event handling and multiple hardware communication protocols.
Robotic Intelligence Kernel: Driver
2009-09-16
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.
An evaluation of parturition indices in fishers
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.
Fishers' knowledge and seahorse conservation in Brazil
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
Linearized Kernel Dictionary Learning
NASA Astrophysics Data System (ADS)
Golts, Alona; Elad, Michael
2016-06-01
In this paper we present a new approach of incorporating kernels into dictionary learning. The kernel K-SVD algorithm (KKSVD), which has been introduced recently, shows an improvement in classification performance, with relation to its linear counterpart K-SVD. However, this algorithm requires the storage and handling of a very large kernel matrix, which leads to high computational cost, while also limiting its use to setups with small number of training examples. We address these problems by combining two ideas: first we approximate the kernel matrix using a cleverly sampled subset of its columns using the Nystr\\"{o}m method; secondly, as we wish to avoid using this matrix altogether, we decompose it by SVD to form new "virtual samples," on which any linear dictionary learning can be employed. Our method, termed "Linearized Kernel Dictionary Learning" (LKDL) can be seamlessly applied as a pre-processing stage on top of any efficient off-the-shelf dictionary learning scheme, effectively "kernelizing" it. We demonstrate the effectiveness of our method on several tasks of both supervised and unsupervised classification and show the efficiency of the proposed scheme, its easy integration and performance boosting properties.
Application of Fisher Information to Complex Dynamic Systems (Tucson)
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 ...
Application of Fisher Information to Complex Dynamic Systems
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 ...
A fisher vector representation of GPR data for detecting buried objects
NASA Astrophysics Data System (ADS)
Karem, Andrew; Khalifa, Amine B.; Frigui, Hichem
2016-05-01
We present a new method, based on the Fisher Vector (FV), for detecting buried explosive objects using ground- penetrating radar (GPR) data. First, low-level dense SIFT features are extracted from a grid covering a region of interest (ROIs). ROIs are identified as regions with high-energy along the (down-track, depth) dimensions of the 3-D GPR cube, or with high-energy along the (cross-track, depth) dimensions. Next, we model the training data (in the SIFT feature space) by a mixture of Gaussian components. Then, we construct FV descriptors based on the Fisher Kernel. The Fisher Kernel characterizes low-level features from an ROI by their deviation from a generative model. The deviation is the gradient of the ROI log-likelihood with respect to the generative model parameters. The vectorial representation of all the deviations is called the Fisher Vector. FV is a generalization of the standard Bag of Words (BoW) method, which provides a framework to map a set of local descriptors to a global feature vector. It is more efficient to compute than the BoW since it relies on a significantly smaller codebook. In addition, mapping a GPR signature into one global feature vector using this technique makes it more efficient to classify using simple and fast linear classifiers such as Support Vector Machines. The proposed approach is applied to detect buried explosive objects using GPR data. The selected data were accumulated across multiple dates and multiple test sites by a vehicle mounted mine detector (VMMD) using GPR sensor. This data consist of a diverse set of conventional landmines and other buried explosive objects consisting of varying shapes, metal content, and burial depths. The performance of the proposed approach is analyzed using receiver operating characteristics (ROC) and is compared to other state-of-the-art feature representation methods.
Nonparametric estimation of Fisher information from real data
NASA Astrophysics Data System (ADS)
Har-Shemesh, Omri; Quax, Rick; Miñano, Borja; Hoekstra, Alfons G.; Sloot, Peter M. A.
2016-02-01
The Fisher information matrix (FIM) is a widely used measure for applications including statistical inference, information geometry, experiment design, and the study of criticality in biological systems. The FIM is defined for a parametric family of probability distributions and its estimation from data follows one of two paths: either the distribution is assumed to be known and the parameters are estimated from the data or the parameters are known and the distribution is estimated from the data. We consider the latter case which is applicable, for example, to experiments where the parameters are controlled by the experimenter and a complicated relation exists between the input parameters and the resulting distribution of the data. Since we assume that the distribution is unknown, we use a nonparametric density estimation on the data and then compute the FIM directly from that estimate using a finite-difference approximation to estimate the derivatives in its definition. The accuracy of the estimate depends on both the method of nonparametric estimation and the difference Δ θ between the densities used in the finite-difference formula. We develop an approach for choosing the optimal parameter difference Δ θ based on large deviations theory and compare two nonparametric density estimation methods, the Gaussian kernel density estimator and a novel density estimation using field theory method. We also compare these two methods to a recently published approach that circumvents the need for density estimation by estimating a nonparametric f divergence and using it to approximate the FIM. We use the Fisher information of the normal distribution to validate our method and as a more involved example we compute the temperature component of the FIM in the two-dimensional Ising model and show that it obeys the expected relation to the heat capacity and therefore peaks at the phase transition at the correct critical temperature.
LeFebvre, W.
1994-08-01
For many years, the popular program top has aided system administrations in examination of process resource usage on their machines. Yet few are familiar with the techniques involved in obtaining this information. Most of what is displayed by top is available only in the dark recesses of kernel memory. Extracting this information requires familiarity not only with how bytes are read from the kernel, but also what data needs to be read. The wide variety of systems and variants of the Unix operating system in today`s marketplace makes writing such a program very challenging. This paper explores the tremendous diversity in kernel information across the many platforms and the solutions employed by top to achieve and maintain ease of portability in the presence of such divergent systems.
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.
[Fisher Syndrome and Bickerstaff Brainstem Encephalitis].
Kuwabara, Satoshi
2015-11-01
Fisher syndrome has been regarded as a peculiar inflammatory neuropathy with ophthalmoplegia, ataxia, and areflexia, whereas Bickerstaff brainstem encephalitis has been considered a pure central nervous system disease characterized by ophthalmoplegia, ataxia, and consciousness disturbance. Both disorders share common features including preceding infection, albumin-cytological dissociation, and association with Guillain-Barré syndrome. The discovery of anti-GQ1b IgG antibodies further supports the view that the two disorders represent a single disease spectrum. The lesions in Fisher syndrome and Bickerstaff brainstem encephalitis are presumably determined by the expression of ganglioside GQ1b in the human peripheral and central nervous systems. Bickerstaff brainstem encephalitis is likely to represent a variant of Fisher syndrome with central nervous system involvement. PMID:26560952
Calculates Thermal Neutron Scattering Kernel.
Energy Science and Technology Software Center (ESTSC)
1989-11-10
Version 00 THRUSH computes the thermal neutron scattering kernel by the phonon expansion method for both coherent and incoherent scattering processes. The calculation of the coherent part is suitable only for calculating the scattering kernel for heavy water.
Robotic Intelligence Kernel: Architecture
Energy Science and Technology Software Center (ESTSC)
2009-09-16
The INL Robotic Intelligence Kernel Architecture (RIK-A) is a multi-level architecture that supports a dynamic autonomy structure. The RIK-A is used to coalesce hardware for sensing and action as well as software components for perception, communication, behavior and world modeling into a framework that can be used to create behaviors for humans to interact with the robot.
Robotic Intelligence Kernel: Visualization
Energy Science and Technology Software Center (ESTSC)
2009-09-16
The INL Robotic Intelligence Kernel-Visualization is the software that supports the user interface. It uses the RIK-C software to communicate information to and from the robot. The RIK-V illustrates the data in a 3D display and provides an operating picture wherein the user can task the robot.
NASA Technical Reports Server (NTRS)
Spafford, Eugene H.; Mckendry, Martin S.
1986-01-01
An overview of the internal structure of the Clouds kernel was presented. An indication of how these structures will interact in the prototype Clouds implementation is given. Many specific details have yet to be determined and await experimentation with an actual working system.
Fisher information and Rényi dimensions: A thermodynamical formalism.
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. PMID:27586598
EXERGY AND FISHER INFORMATION AS ECOLOGICAL INDEXES
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...
FISHER INFORMATION AND ECOSYSTEM REGIME CHANGES
Following Fisher’s work, we propose two different expressions for the Fisher Information along with Shannon Information as a means of detecting and assessing shifts between alternative ecosystem regimes. Regime shifts are a consequence of bifurcations in the dynamics of an ecosys...
MC Kernel: Broadband Waveform Sensitivity Kernels for Seismic Tomography
NASA Astrophysics Data System (ADS)
Stähler, Simon C.; van Driel, Martin; Auer, Ludwig; Hosseini, Kasra; Sigloch, Karin; Nissen-Meyer, Tarje
2016-04-01
We present MC Kernel, a software implementation to calculate seismic sensitivity kernels on arbitrary tetrahedral or hexahedral grids across the whole observable seismic frequency band. Seismic sensitivity kernels are the basis for seismic tomography, since they map measurements to model perturbations. Their calculation over the whole frequency range was so far only possible with approximative methods (Dahlen et al. 2000). Fully numerical methods were restricted to the lower frequency range (usually below 0.05 Hz, Tromp et al. 2005). With our implementation, it's possible to compute accurate sensitivity kernels for global tomography across the observable seismic frequency band. These kernels rely on wavefield databases computed via AxiSEM (www.axisem.info), and thus on spherically symmetric models. The advantage is that frequencies up to 0.2 Hz and higher can be accessed. Since the usage of irregular, adapted grids is an integral part of regularisation in seismic tomography, MC Kernel works in a inversion-grid-centred fashion: A Monte-Carlo integration method is used to project the kernel onto each basis function, which allows to control the desired precision of the kernel estimation. Also, it means that the code concentrates calculation effort on regions of interest without prior assumptions on the kernel shape. The code makes extensive use of redundancies in calculating kernels for different receivers or frequency-pass-bands for one earthquake, to facilitate its usage in large-scale global seismic tomography.
Sun, Shuying; Yu, Xiaoqing
2016-03-01
DNA methylation is an epigenetic event that plays an important role in regulating gene expression. It is important to study DNA methylation, especially differential methylation patterns between two groups of samples (e.g. patients vs. normal individuals). With next generation sequencing technologies, it is now possible to identify differential methylation patterns by considering methylation at the single CG site level in an entire genome. However, it is challenging to analyze large and complex NGS data. In order to address this difficult question, we have developed a new statistical method using a hidden Markov model and Fisher's exact test (HMM-Fisher) to identify differentially methylated cytosines and regions. We first use a hidden Markov chain to model the methylation signals to infer the methylation state as Not methylated (N), Partly methylated (P), and Fully methylated (F) for each individual sample. We then use Fisher's exact test to identify differentially methylated CG sites. We show the HMM-Fisher method and compare it with commonly cited methods using both simulated data and real sequencing data. The results show that HMM-Fisher outperforms the current available methods to which we have compared. HMM-Fisher is efficient and robust in identifying heterogeneous DM regions. PMID:26854292
Lee, Myung Hee; Liu, Yufeng
2013-12-01
The continuum regression technique provides an appealing regression framework connecting ordinary least squares, partial least squares and principal component regression in one family. It offers some insight on the underlying regression model for a given application. Moreover, it helps to provide deep understanding of various regression techniques. Despite the useful framework, however, the current development on continuum regression is only for linear regression. In many applications, nonlinear regression is necessary. The extension of continuum regression from linear models to nonlinear models using kernel learning is considered. The proposed kernel continuum regression technique is quite general and can handle very flexible regression model estimation. An efficient algorithm is developed for fast implementation. Numerical examples have demonstrated the usefulness of the proposed technique. PMID:24058224
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.
Iris Image Blur Detection with Multiple Kernel Learning
NASA Astrophysics Data System (ADS)
Pan, Lili; Xie, Mei; Mao, Ling
In this letter, we analyze the influence of motion and out-of-focus blur on both frequency spectrum and cepstrum of an iris image. Based on their characteristics, we define two new discriminative blur features represented by Energy Spectral Density Distribution (ESDD) and Singular Cepstrum Histogram (SCH). To merge the two features for blur detection, a merging kernel which is a linear combination of two kernels is proposed when employing Support Vector Machine. Extensive experiments demonstrate the validity of our method by showing the improved blur detection performance on both synthetic and real datasets.
Sexual selection unhandicapped by the Fisher process.
Grafen, A
1990-06-21
A population genetic model of sexual selection is constructed in which, at equilibrium, males signal their quality by developing costly ornaments, and females pay costs to use the ornaments in mate choice. It is shown that the form of the equilibrium is uninfluenced by the Fisher process, that is, by self-reinforcement of female preferences. This is a working model of the handicap principle applied to sexual selection, and places Zahavi's handicap principle on the same logical footing as the Fisher process, in that each can support sexual selection without the presence of the other. A way of measuring the relative importance of the two processes is suggested that can be applied to both theories and facts. A style of modelling that allows simple genetics and complicated biology to be combined is recommended. PMID:2402152
Olympic Fisher Reintroduction Project: 2010 Progress Report
Lewis, Jeffrey C.; Happe, Patti J.; Jenkins, Kurt J.; Manson, David J.
2010-01-01
The 2010 progress report is a summary of the reintroduction, monitoring, and research efforts undertaken during the third year of the Olympic fisher reintroduction project. Jeffrey C. Lewis of Washington Department of Fish and Wildlife, Patti J. Happe of Olympic National Park, and Kurt J. Jenkins of U. S. Geological Survey are the principal investigators of the monitoring and research program associated with the reintroduction. David J. Manson of Olympic National Park is the lead biological technician.
Olympic Fisher Reintroduction Project- 2009 Progress Report
Lewis, Jeffrey C.; Happe, Patti J.; Jenkins, Kurt J.; Manson, David J.
2009-01-01
The 2009 progress report is a summary of the reintroduction, monitoring, and research efforts undertaken during the first two years of the Olympic fisher reintroduction project. Jeffrey C. Lewis of Washington Department of Fish and Wildlife, Patti J. Happe of Olympic National Park, and Kurt J. Jenkins of U. S. Geological Survey are the principal investigators of the monitoring and research program associated with the reintroduction. David J. Manson of Olympic National Park is the lead biological
Miller Fisher syndrome presenting as palate paralysis.
Noureldine, Mohammad Hassan A; Sweid, Ahmad; Ahdab, Rechdi
2016-09-15
We report a 63-year old patient who presented to our care initially with a hypernasal voice followed by ataxia, ptosis, dysphonia, and paresthesias. The patient's history, physical examination, and additional tests led to a Miller Fisher syndrome (MFS) diagnosis. Palatal paralysis as an inaugurating manifestation of MFS is quite rare and requires special attention from neurologists and otolaryngologists. Although it may present as benign as an acute change in voice, early diagnosis and prompt management may prevent further complications. PMID:27609285
The Fisher Shannon information plane for atoms
NASA Astrophysics Data System (ADS)
Szabó, J. B.; Sen, K. D.; Nagy, Á.
2008-03-01
The Fisher-Shannon information product and plane for atoms are presented analytically assuming Thomas-Fermi-Gáspár statistical model. A comparison with the Hartree-Fock densities reveals that the atomic shell structure is inadequately expressed information theoretically in the statistical model. The shape complexity measure of Lopez et al. is found to have a better large Z dependence than the one obtained from non-relativistic Hartree-Fock densities.
Sir Ronald A. Fisher and the International Biometric Society.
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. PMID:24499157
Canonical energy is quantum Fisher information
NASA Astrophysics Data System (ADS)
Lashkari, Nima; Van Raamsdonk, Mark
2016-04-01
In quantum information theory, Fisher Information is a natural metric on the space of perturbations to a density matrix, defined by calculating the relative entropy with the unperturbed state at quadratic order in perturbations. In gravitational physics, Canonical Energy defines a natural metric on the space of perturbations to spacetimes with a Killing horizon. In this paper, we show that the Fisher information metric for perturbations to the vacuum density matrix of a ball-shaped region B in a holographic CFT is dual to the canonical energy metric for perturbations to a corresponding Rindler wedge R B of Anti-de-Sitter space. Positivity of relative entropy at second order implies that the Fisher information metric is positive definite. Thus, for physical perturbations to anti-de-Sitter spacetime, the canonical energy associated to any Rindler wedge must be positive. This second-order constraint on the metric extends the first order result from relative entropy positivity that physical perturbations must satisfy the linearized Einstein's equations.
Cell Development obeys Maximum Fisher Information
Frieden, B. Roy; Gatenby, Robert A.
2014-01-01
Eukaryotic cell development has been optimized by natural selection to obey maximal intracellular flux of messenger proteins. This, in turn, implies maximum Fisher information on angular position about a target nuclear pore complex (NPR). The cell is simply modeled as spherical, with cell membrane (CM) diameter 10μm and concentric nuclear membrane (NM) diameter 6μm. The NM contains ≈ 3000 nuclear pore complexes (NPCs). Development requires messenger ligands to travel from the CM-NPC-DNA target binding sites. Ligands acquire negative charge by phosphorylation, passing through the cytoplasm over Newtonian trajectories toward positively charged NPCs (utilizing positive nuclear localization sequences). The CM-NPC channel obeys maximized mean protein flux F and Fisher information I at the NPC, with first-order δI = 0 and approximate 2nd-order δ2I ≈ 0 stability to environmental perturbations. Many of its predictions are confirmed, including the dominance of protein pathways of from 1–4 proteins, a 4nm size for the EGFR protein and the flux value F ≈1016 proteins/m2-s. After entering the nucleus, each protein ultimately delivers its ligand information to a DNA target site with maximum probability, i.e. maximum Kullback-Liebler entropy HKL. In a smoothness limit HKL → IDNA/2, so that the total CM-NPC-DNA channel obeys maximum Fisher I. Thus maximum information → non-equilibrium, one condition for life. PMID:23747917
A theory of Fisher's reproductive value.
Grafen, Alan
2006-07-01
The formal Darwinism project aims to provide a mathematically rigorous basis for optimisation thinking in relation to natural selection. This paper deals with the situation in which individuals in a population belong to classes, such as sexes, or size and/or age classes. Fisher introduced the concept of reproductive value into biology to help analyse evolutionary processes of populations divided into classes. Here a rigorously defined and very general structure justifies, and shows the unity of concept behind, Fisher's uses of reproductive value as measuring the significance for evolutionary processes of (i) an individual and (ii) a class; (iii) recursively, as calculable for a parent as a sum of its shares in the reproductive values of its offspring; and (iv) as an evolutionary maximand under natural selection. The maximand is the same for all parental classes, and is a weighted sum of offspring numbers, which implies that a tradeoff in one aspect of the phenotype can legitimately be studied separately from other aspects. The Price equation, measure theory, Markov theory and positive operators contribute to the framework, which is then applied to a number of examples, including a new and fully rigorous version of Fisher's sex ratio argument. Classes may be discrete (e.g. sex), continuous (e.g. weight at fledging) or multidimensional with discrete and continuous components (e.g. sex and weight at fledging and adult tarsus length). PMID:16791649
A Global Estimate of the Number of Coral Reef Fishers.
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
A Global Estimate of the Number of Coral Reef Fishers
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
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. PMID:19811718
Kernel Phase and Kernel Amplitude in Fizeau Imaging
NASA Astrophysics Data System (ADS)
Pope, Benjamin J. S.
2016-09-01
Kernel phase interferometry is an approach to high angular resolution imaging which enhances the performance of speckle imaging with adaptive optics. Kernel phases are self-calibrating observables that generalize the idea of closure phases from non-redundant arrays to telescopes with arbitrarily shaped pupils, by considering a matrix-based approximation to the diffraction problem. In this paper I discuss the recent fhistory of kernel phase, in particular in the matrix-based study of sparse arrays, and propose an analogous generalization of the closure amplitude to kernel amplitudes. This new approach can self-calibrate throughput and scintillation errors in optical imaging, which extends the power of kernel phase-like methods to symmetric targets where amplitude and not phase calibration can be a significant limitation, and will enable further developments in high angular resolution astronomy.
Classification of oat and groat kernels using NIR hyperspectral imaging.
Serranti, Silvia; Cesare, Daniela; Marini, Federico; Bonifazi, Giuseppe
2013-01-15
An innovative procedure to classify oat and groat kernels based on coupling hyperspectral imaging (HSI) in the near infrared (NIR) range (1006-1650 nm) and chemometrics was designed, developed and validated. According to market requirements, the amount of groat, that is the hull-less oat kernels, is one of the most important quality characteristics of oats. Hyperspectral images of oat and groat samples have been acquired by using a NIR spectral camera (Specim, Finland) and the resulting data hypercubes were analyzed applying Principal Component Analysis (PCA) for exploratory purposes and Partial Least Squares-Discriminant Analysis (PLS-DA) to build the classification models to discriminate the two kernel typologies. Results showed that it is possible to accurately recognize oat and groat single kernels by HSI (prediction accuracy was almost 100%). The study demonstrated also that good classification results could be obtained using only three wavelengths (1132, 1195 and 1608 nm), selected by means of a bootstrap-VIP procedure, allowing to speed up the classification processing for industrial applications. The developed objective and non-destructive method based on HSI can be utilized for quality control purposes and/or for the definition of innovative sorting logics of oat grains. PMID:23200388
Multiple kernel sparse representations for supervised and unsupervised learning.
Thiagarajan, Jayaraman J; Ramamurthy, Karthikeyan Natesan; Spanias, Andreas
2014-07-01
In complex visual recognition tasks, it is typical to adopt multiple descriptors, which describe different aspects of the images, for obtaining an improved recognition performance. Descriptors that have diverse forms can be fused into a unified feature space in a principled manner using kernel methods. Sparse models that generalize well to the test data can be learned in the unified kernel space, and appropriate constraints can be incorporated for application in supervised and unsupervised learning. In this paper, we propose to perform sparse coding and dictionary learning in the multiple kernel space, where the weights of the ensemble kernel are tuned based on graph-embedding principles such that class discrimination is maximized. In our proposed algorithm, dictionaries are inferred using multiple levels of 1D subspace clustering in the kernel space, and the sparse codes are obtained using a simple levelwise pursuit scheme. Empirical results for object recognition and image clustering show that our algorithm outperforms existing sparse coding based approaches, and compares favorably to other state-of-the-art methods. PMID:24833593
Bruemmer, David J.
2009-11-17
A robot platform includes perceptors, locomotors, and a system controller. The system controller executes a robot intelligence kernel (RIK) that includes a multi-level architecture and a dynamic autonomy structure. The multi-level architecture includes a robot behavior level for defining robot behaviors, that incorporate robot attributes and a cognitive level for defining conduct modules that blend an adaptive interaction between predefined decision functions and the robot behaviors. The dynamic autonomy structure is configured for modifying a transaction capacity between an operator intervention and a robot initiative and may include multiple levels with at least a teleoperation mode configured to maximize the operator intervention and minimize the robot initiative and an autonomous mode configured to minimize the operator intervention and maximize the robot initiative. Within the RIK at least the cognitive level includes the dynamic autonomy structure.
Fisher information of quantum damped harmonic oscillators
NASA Astrophysics Data System (ADS)
Aguiar, V.; Guedes, I.
2015-04-01
We calculate the time-dependent Fisher information in position ({{F}x}) and momentum ({{F}p}) for the lowest lying state ≤ft( n=0 \\right) of two classes of quantum damped (Lane-Emden (LE) and Caldirola-Kanai (CK)) harmonic oscillators. The expressions of {{F}x} and {{F}p} are written in terms of ρ , a c-number quantity satisfying a nonlinear differential equation. Analytical solutions of ρ were obtained. For the LE and CK oscillators, we observe that {{F}x} increases while {{F}p} decreases with increasing time. The product {{F}x}{{F}p} increases and tends to a constant value in the limit t\\to ∞ for the LE oscillator, while it is time-independent for the CK oscillator. Moreover, for the CK oscillator the product {{F}x}{{F}p} decreases as the damping ≤ft( γ \\right) increases. Relations among the Fisher information, Leipnik and Shannon entropies, and the Stam and Cramer-Rao inequalities are given. A discussion on the squeezing phenomenon in position for the oscillators is presented.
Quantum Fisher information in noninertial frames
NASA Astrophysics Data System (ADS)
Yao, Yao; Xiao, Xing; Ge, Li; Wang, Xiao-guang; Sun, Chang-pu
2014-04-01
We investigate the performance of quantum Fisher information (QFI) under the Unruh-Hawking effect, where one of the observers (e.g., Rob) is uniformly accelerated with respect to other partners. In the context of relativistic quantum information theory, we demonstrate that quantum Fisher information, as an important measure of the information content of quantum states, has a rich and subtle physical structure compared with entanglement or Bell nonlocality. In this work, we mainly focus on the parametrized (and arbitrary) pure two-qubit states, where the weight parameter θ and phase parameter ϕ are naturally introduced. Intriguingly, we prove that QFI with respect to θ (Fθ) remains unchanged for both scalar and Dirac fields. Meanwhile, we observe that QFI with respect to ϕ (Fϕ) decreases with the increase of acceleration r but remains finite in the limit of infinite acceleration. More importantly, our results show that the symmetry of Fϕ (with respect to θ =π/4) has been broken by the influence of the Unruh effect for both cases.
Longitudinal view of west channel spans, looking east toward fishers ...
Longitudinal view of west channel spans, looking east toward fishers island. - Pennsylvania Railroad, Selinsgrove Bridge, Spanning Susquehanna River, south of Cherry Island, Selinsgrove, Snyder County, PA
Fisher Information-Based Evaluation of Image Quality for Time-of-Flight PET
Vunckx, Kathleen; Zhou, Lin; Matej, Samuel; Defrise, Michel; Nuyts, Johan
2010-01-01
The use of time-of-flight (TOF) information during positron emission tomography (PET) reconstruction has been found to improve the image quality. In this work we quantified this improvement using two existing methods: (1) a very simple analytical expression only valid for a central point in a large uniform disk source, and (2) efficient analytical approximations for post-filtered maximum likelihood expectation maximization (MLEM) reconstruction with a fixed target resolution, predicting the image quality in a pixel or in a small region of interest based on the Fisher information matrix. Using this latter method the weighting function for filtered backprojection reconstruction of TOF PET data proposed by C. Watson can be derived. The image quality was investigated at different locations in various software phantoms. Simplified as well as realistic phantoms, measured both with TOF PET systems and with a conventional PET system, were simulated. Since the time resolution of the system is not always accurately known, the effect on the image quality of using an inaccurate kernel during reconstruction was also examined with the Fisher information-based method. First, we confirmed with this method that the variance improvement in the center of a large uniform disk source is proportional to the disk diameter and inversely proportional to the time resolution. Next, image quality improvement was observed in all pixels, but in eccentric and high-count regions the contrast-to-noise ratio (CNR) increased less than in central and low- or medium-count regions. Finally, the CNR was seen to decrease when the time resolution was inaccurately modeled (too narrow or too wide) during reconstruction. Although the maximum CNR is not very sensitive to the time resolution error, using an inaccurate TOF kernel tends to introduce artifacts in the reconstructed image. PMID:19709969
Labeled Graph Kernel for Behavior Analysis.
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. PMID:26415154
Code of Federal Regulations, 2011 CFR
2011-01-01
... 7 Agriculture 8 2011-01-01 2011-01-01 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,...
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,...
Prenatal development in fishers (Martes pennanti)
Frost, H.C.; Krohn, W.B.; Bezembluk, E.A.; Lott, R.; Wallace, C.R.
2005-01-01
We evaluated and quantified prenatal growth of fishers (Martes pennanti) using ultrasonography. Seven females gave birth to 21 kits. The first identifiable embryonic structures were seen 42 d prepartum; these appeared to be unimplanted blastocysts or gestational sacs, which subsequently implanted in the uterine horns. Maternal and fetal heart rates were monitored from first detection to birth. Maternal heart rates did not differ among sampling periods, while fetal hearts rates increased from first detection to birth. Head and body differentiation, visible limbs and skeletal ossification were visible by 30, 23 and 21 d prepartum, respectively. Mean diameter of gestational sacs and crown-rump lengths were linearly related to gestational age (P < 0.001). Biparietal and body diameters were also linearly related to gestational age (P < 0.001) and correctly predicted parturition dates within 1-2 d. ?? 2004 Elsevier Inc. All rights reserved.
Trajectory Synthesis for Fisher Information Maximization
Wilson, Andrew D.; Schultz, Jarvis A.; Murphey, Todd D.
2015-01-01
Estimation of model parameters in a dynamic system can be significantly improved with the choice of experimental trajectory. For general nonlinear dynamic systems, finding globally “best” trajectories is typically not feasible; however, given an initial estimate of the model parameters and an initial trajectory, we present a continuous-time optimization method that produces a locally optimal trajectory for parameter estimation in the presence of measurement noise. The optimization algorithm is formulated to find system trajectories that improve a norm on the Fisher information matrix (FIM). A double-pendulum cart apparatus is used to numerically and experimentally validate this technique. In simulation, the optimized trajectory increases the minimum eigenvalue of the FIM by three orders of magnitude, compared with the initial trajectory. Experimental results show that this optimized trajectory translates to an order-of-magnitude improvement in the parameter estimate error in practice. PMID:25598763
Cusp Kernels for Velocity-Changing Collisions
NASA Astrophysics Data System (ADS)
McGuyer, B. H.; Marsland, R., III; Olsen, B. A.; Happer, W.
2012-05-01
We introduce an analytical kernel, the “cusp” kernel, to model the effects of velocity-changing collisions on optically pumped atoms in low-pressure buffer gases. Like the widely used Keilson-Storer kernel [J. Keilson and J. E. Storer, Q. Appl. Math. 10, 243 (1952)QAMAAY0033-569X], cusp kernels are characterized by a single parameter and preserve a Maxwellian velocity distribution. Cusp kernels and their superpositions are more useful than Keilson-Storer kernels, because they are more similar to real kernels inferred from measurements or theory and are easier to invert to find steady-state velocity distributions.
On the Behrens-Fisher Problem: A Review.
ERIC Educational Resources Information Center
Kim, Seock-Ho; Cohen, Allan S.
The Behrens-Fisher problem arises when one seeks to make inferences about the means of two normal populations without assuming the variances are equal. This paper presents a review of fundamental concepts and applications used to address the Behrens-Fisher problem under fiducial, Bayesian, and frequentist approaches. Methods of approximations to…
On the Behrens-Fisher Problem: A Review.
ERIC Educational Resources Information Center
Kim, Seock-Ho; Cohen, Allan S.
1998-01-01
Presents a review of fundamental concepts and applications used to address the Behrens-Fisher problem (W. Behrens, 1929 and R. Fisher, 1935), a problem in testing the difference between two population means, through fiducial, Bayesian, and frequentist approaches. (Contains 86 references.) (SLD)
77 FR 15650 - Fisher House and Other Temporary Lodging
Federal Register 2010, 2011, 2012, 2013, 2014
2012-03-16
...The Department of Veterans Affairs (VA) proposes to amend its regulations concerning Fisher House and other temporary lodging furnished by VA while a veteran is experiencing an episode of care at a VA medical facility. We intend that the proposed rule would update current regulations to better describe the application process for this assistance and clarify the distinctions between Fisher......
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…
R. A. Fisher and his advocacy of randomization.
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. PMID:18175604
Kernel-based whole-genome prediction of complex traits: a review
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. PMID:25360145
Stellar Spectral Subclasses Classification Based on Fisher Criterion and Manifold Learning
NASA Astrophysics Data System (ADS)
Zhong-bao, Liu; Li-peng, Song
2015-08-01
Linear Discriminant Analysis (LDA) and Locality Preserving Projections (LPP) are two widely used feature extraction methods. The advantage of LDA is that it takes the global structure of the data into consideration by maximizing the ratio of the between-class scatter to the within-class scatter. LPP tries to preserve the local structure of the data. The global and local structure of the data are very important in dealing with feature extraction problems but it is regretful that the above two methods cannot fully utilize all the information. In view of this, Modified Discriminant Analysis based on Fisher Criterion and Manifold Learning (MDA) is proposed in this paper. Two important concepts are introduced: Manifold based Within-Class Scatter (MWCS) and Manifold based Between-Class Scatter (MBCS). MDA aims to find an optimal projection matrix by maximizing the ratio of MBCS to MWCS based on Fisher criterion. In this paper, we will investigate the performance of MDA in the stellar spectral subclasses classification. We first reduce the dimension of spectra data by PCA (Principal Component Analysis), LDA, LPP, and MDA, respectively. Then we apply support vector machine (SVM) to classify the four subclasses of K-type spectra, three subclasses of F-type spectra, and three subclasses of G-type spectra from Sloan Digital Sky Survey (SDSS). The comparative experiment results verify MDA can preserve both the local and global structure of the data when embed the original data into much lower dimensional space.
Federal Register 2010, 2011, 2012, 2013, 2014
2012-12-21
... FR 60143, Fisher Clinical Services, Inc., 7554 Schantz Road, Allentown, Pennsylvania 18106, made... Enforcement Administration Importer of Controlled Substances; Notice of Registration; Fisher Clinical Services... registration of Fisher Clinical Services, Inc., to import the basic class of controlled substance is...
High-Speed Tracking with Kernelized Correlation Filters.
Henriques, João F; Caseiro, Rui; Martins, Pedro; Batista, Jorge
2015-03-01
The core component of most modern trackers is a discriminative classifier, tasked with distinguishing between the target and the surrounding environment. To cope with natural image changes, this classifier is typically trained with translated and scaled sample patches. Such sets of samples are riddled with redundancies-any overlapping pixels are constrained to be the same. Based on this simple observation, we propose an analytic model for datasets of thousands of translated patches. By showing that the resulting data matrix is circulant, we can diagonalize it with the discrete Fourier transform, reducing both storage and computation by several orders of magnitude. Interestingly, for linear regression our formulation is equivalent to a correlation filter, used by some of the fastest competitive trackers. For kernel regression, however, we derive a new kernelized correlation filter (KCF), that unlike other kernel algorithms has the exact same complexity as its linear counterpart. Building on it, we also propose a fast multi-channel extension of linear correlation filters, via a linear kernel, which we call dual correlation filter (DCF). Both KCF and DCF outperform top-ranking trackers such as Struck or TLD on a 50 videos benchmark, despite running at hundreds of frames-per-second, and being implemented in a few lines of code (Algorithm 1). To encourage further developments, our tracking framework was made open-source. PMID:26353263
Infrasound analysis using Fisher detector and Hough transform
NASA Astrophysics Data System (ADS)
Averbuch, Gil; Assink, Jelle D.; Smets, Pieter S. M.; Evers, Läslo G.
2016-04-01
Automatic detection of infrasound signals from the International Monitoring System (IMS) from the Comprehensive Nuclear-Test-Ban Treaty requires low rates of both false alarms and missed events. The Fisher detector is a statistical method used for detecting such infrasonic events. The detector aims to detect coherent signals after Beamforming is applied on the recordings. A detection is defined to be above a threshold value of Fisher ratio. The Fisher distribution for such a detection is affected by the SNR. While events with high Fisher ratio and SNR can easily be detected automatically, events with lower Fisher ratios and SNRs might be missed. The Hough transform is a post processing step. It is based on a slope-intercept transform applied to a discretely sampled data, with the goal of finding straight lines (in apparent velocity and back azimuth). Applying it on the results from the Fisher detector is advantageous in case of noisy data, which corresponds to low Fisher ratios and SNRs. Results of the Hough transform on synthetic data with SNR down to 0.7 provided a lower number of missed events. In this work, we will present the results of an automatic detector, based on both methods. Synthetic data with different lengths and SNRs are evaluated. Furthermore, continuous data from the IMS infrasound station I18DK will be analyzed. We will compare the performances of both methods and investigate their ability in reducing the number of missed events.
Domain transfer multiple kernel learning.
Duan, Lixin; Tsang, Ivor W; Xu, Dong
2012-03-01
Cross-domain learning methods have shown promising results by leveraging labeled patterns from the auxiliary domain to learn a robust classifier for the target domain which has only a limited number of labeled samples. To cope with the considerable change between feature distributions of different domains, we propose a new cross-domain kernel learning framework into which many existing kernel methods can be readily incorporated. Our framework, referred to as Domain Transfer Multiple Kernel Learning (DTMKL), simultaneously learns a kernel function and a robust classifier by minimizing both the structural risk functional and the distribution mismatch between the labeled and unlabeled samples from the auxiliary and target domains. Under the DTMKL framework, we also propose two novel methods by using SVM and prelearned classifiers, respectively. Comprehensive experiments on three domain adaptation data sets (i.e., TRECVID, 20 Newsgroups, and email spam data sets) demonstrate that DTMKL-based methods outperform existing cross-domain learning and multiple kernel learning methods. PMID:21646679
ERIC Educational Resources Information Center
Hunter, Richard W.
1981-01-01
Argues that while a certain level of fairness is necessary in considering the equity of compulsory military service, the most important issue is that of "winning the war." Also asserts that sex, age, and race discrimination are more important than social class discrimination in military service. (Author/GC)
Zone of Avoidance Tully-Fisher Survey
NASA Astrophysics Data System (ADS)
Williams, Wendy; Woudt, Patrick; Kraan-Korteweg, Renee
2009-10-01
We propose to use the Parkes telescope to obtain narrowband HI spectra of a sample of galaxies in the Galactic Zone of Avoidance (ZOA). These observations, combined with high-quality near infrared photometry, will provide both the uniform coverage and accurate distance determinations (via the Tully-Fisher relation) required to map the peculiar velocity flow fields in the ZOA. The mass distribution in this region has a significant effect on the motion of the Local Group. Dynamically important structures, including the Great Attractor and the Local Void, are partially hidden behind our Galaxy. Even the most recent systematic all-sky surveys, such as the 2MASS Redshift Survey (2MRS; Huchra et al. 2005), undersample the ZOA due to stellar crowding and high dust extinction. While statistical reconstruction methods have been used to extrapolate the density field in the ZOA, they are unlikely to truely re?ect the velocity field (Loeb & Narayan 2008). Our project aims for the ?rst time to directly determine the velocity flow fields in this part of the sky. Our sample is taken from the Parkes HIZOA survey (Henning et al. 2005) and is unbiased with respect to extinction and star density.
Fisher matrix optimization of cosmic microwave background interferometers
NASA Astrophysics Data System (ADS)
Liu, Haonan; Bunn, Emory F.
2016-01-01
We describe a method for forecasting errors in interferometric measurements of polarization of the cosmic microwave background (CMB) radiation, based on the use of the Fisher matrix calculated from the visibility covariance and relation matrices. In addition to noise and sample variance, the method can account for many kinds of systematic error by calculating an augmented Fisher matrix, including parameters that characterize the instrument along with the cosmological parameters to be estimated. The method is illustrated with examples of gain errors and errors in polarizer orientation. The augmented Fisher-matrix approach is applicable to a much wider range of problems beyond CMB interferometry.
Fisher Matrix Optimization of Cosmic Microwave Background Interferometry
NASA Astrophysics Data System (ADS)
Liu, Haonan; Bunn, Emory F.
2016-01-01
We describe a method for forecasting errors in interferometric measurements of polarization of the cosmic microwave background (CMB) radiation, based on the use of the Fisher matrix calculated from the visibility covariance and relation matrices. In addition to noise and sample variance, the method can account for many kinds of systematic error by calculating an augmented Fisher matrix, including parameters that characterize the instrument along with the cosmological parameters to be estimated. The method is illustrated with examples of gain errors and errors in polarizer orientation. The augmented Fisher matrix approach is applicable to a much wider range of problems beyond CMB interferometry.
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.
A Kernel Gabor-Based Weighted Region Covariance Matrix for Face Recognition
Qin, Huafeng; Qin, Lan; Xue, Lian; Li, Yantao
2012-01-01
This paper proposes a novel image region descriptor for face recognition, named kernel Gabor-based weighted region covariance matrix (KGWRCM). As different parts are different effectual in characterizing and recognizing faces, we construct a weighting matrix by computing the similarity of each pixel within a face sample to emphasize features. We then incorporate the weighting matrices into a region covariance matrix, named weighted region covariance matrix (WRCM), to obtain the discriminative features of faces for recognition. Finally, to further preserve discriminative features in higher dimensional space, we develop the kernel Gabor-based weighted region covariance matrix (KGWRCM). Experimental results show that the KGWRCM outperforms other algorithms including the kernel Gabor-based region covariance matrix (KGCRM). PMID:22969351
A kernel Gabor-based weighted region covariance matrix for face recognition.
Qin, Huafeng; Qin, Lan; Xue, Lian; Li, Yantao
2012-01-01
This paper proposes a novel image region descriptor for face recognition, named kernel Gabor-based weighted region covariance matrix (KGWRCM). As different parts are different effectual in characterizing and recognizing faces, we construct a weighting matrix by computing the similarity of each pixel within a face sample to emphasize features. We then incorporate the weighting matrices into a region covariance matrix, named weighted region covariance matrix (WRCM), to obtain the discriminative features of faces for recognition. Finally, to further preserve discriminative features in higher dimensional space, we develop the kernel Gabor-based weighted region covariance matrix (KGWRCM). Experimental results show that the KGWRCM outperforms other algorithms including the kernel Gabor-based region covariance matrix (KGCRM). PMID:22969351
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.
Analysing nature's experiment: Fisher's inductive theorem of natural selection.
Edwards, A W F
2016-06-01
The paper by Ewens and Lessard (2015) adds to the progress that has been made in exploring the discrete-generation analytical version of Fisher's Fundamental Theorem of Natural Selection introduced by Ewens (1989). Fisher's continuous-time theorem differs from the version described by Ewens and Lessard by using a different concept of fitness. Ewens and Lessard use the conventional 'viability' concept whereas for Fisher the fitness of a genotype was its relative rate of increase or decrease in the population. The sole purpose of the present paper is to emphasize the alternative inductive nature of Fisher's theorem, as presented by him in 1930, by placing it in the context of his contemporary development of the analysis of variance in agricultural experiments. It is not a general discussion of the theorem itself. PMID:26581894
9. South El Paso St., 203205 (Hotel Fisher), east and ...
9. South El Paso St., 203-205 (Hotel Fisher), east and north facades, west side of street - South El Paso Street Historic District, South El Paso, South Oregon & South Santa Fe Streets, El Paso, El Paso County, TX
Fisher-Shannon analysis of ionization processes and isoelectronic series
Sen, K. D.; Antolin, J.; Angulo, J. C.
2007-09-15
The Fisher-Shannon plane which embodies the Fisher information measure in conjunction with the Shannon entropy is tested in its ability to quantify and compare the informational behavior of the process of atomic ionization. We report the variation of such an information measure and its constituents for a comprehensive set of neutral atoms, and their isoelectronic series including the mononegative ions, using the numerical data generated on 320 atomic systems in position, momentum, and product spaces at the Hartree-Fock level. It is found that the Fisher-Shannon plane clearly reveals shell-filling patterns across the periodic table. Compared to position space, a significantly higher resolution is exhibited in momentum space. Characteristic features in the Fisher-Shannon plane accompanying the ionization process are identified, and the physical reasons for the observed patterns are described.
NASA Astrophysics Data System (ADS)
Wang, Ning; Li, Qiong; El-Latif, Ahmed A. Abd; Peng, Jialiang; Niu, Xiamu
2013-04-01
In recent years, verification based on thermal face images has been extensively studied because of its invariance to illumination and immunity to forgery. However, most of them have not given full consideration to high-verification performance and singular within-class scatter matrix problems. We propose a novel thermal face verification algorithm, which is named two-directional two-dimensional modified Fisher principal component analysis. First, two-dimensional principal component analysis (2-DPCA) is utilized to extract the optimal projective vector in the row direction. Then, 2-D modified Fisher linear discriminant analysis is implemented to overcome the singular within-class scatter matrix problem of the 2-DPCA space in the column direction. Comparative experiments on the natural visible and infrared facial expression thermal face subdatabase demonstrate that the proposed approach outperforms state-of-the-art methods in terms of verification performance.
... Medicine Working Group New Horizons and Research Patient Management Policy and Ethics Issues Quick Links for Patient Care ... genetic discrimination. April 25, 2007, Statement of Administration Policy, Office of Management and Budget Official Statement from the Office of ...
Technology Transfer Automated Retrieval System (TEKTRAN)
Oat (Avena sativa L.) kernels appear to contain much higher polar lipid concentrations than other plant tissues. We have extracted, identified, and quantified polar lipids from 18 oat genotypes grown in replicated plots in three environments in order to determine genotypic or environmental variation...
Accelerating the Original Profile Kernel
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. PMID:23825697
Adaptive wiener image restoration kernel
Yuan, Ding
2007-06-05
A method and device for restoration of electro-optical image data using an adaptive Wiener filter begins with constructing imaging system Optical Transfer Function, and the Fourier Transformations of the noise and the image. A spatial representation of the imaged object is restored by spatial convolution of the image using a Wiener restoration kernel.
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…
7 CFR 981.408 - Inedible kernel.
Code of Federal Regulations, 2013 CFR
2013-01-01
... AND ORDERS; FRUITS, VEGETABLES, NUTS), DEPARTMENT OF AGRICULTURE ALMONDS GROWN IN CALIFORNIA... kernel is modified to mean a kernel, piece, or particle of almond kernel with any defect scored as... Standards for Shelled Almonds, or which has embedded dirt or other foreign material not easily removed...
7 CFR 981.408 - Inedible kernel.
Code of Federal Regulations, 2010 CFR
2010-01-01
... and Orders; Fruits, Vegetables, Nuts), DEPARTMENT OF AGRICULTURE ALMONDS GROWN IN CALIFORNIA... kernel is modified to mean a kernel, piece, or particle of almond kernel with any defect scored as... Standards for Shelled Almonds, or which has embedded dirt or other foreign material not easily removed...
7 CFR 981.408 - Inedible kernel.
Code of Federal Regulations, 2014 CFR
2014-01-01
... AND ORDERS; FRUITS, VEGETABLES, NUTS), DEPARTMENT OF AGRICULTURE ALMONDS GROWN IN CALIFORNIA... kernel is modified to mean a kernel, piece, or particle of almond kernel with any defect scored as... Standards for Shelled Almonds, or which has embedded dirt or other foreign material not easily removed...
7 CFR 981.408 - Inedible kernel.
Code of Federal Regulations, 2011 CFR
2011-01-01
... and Orders; Fruits, Vegetables, Nuts), DEPARTMENT OF AGRICULTURE ALMONDS GROWN IN CALIFORNIA... kernel is modified to mean a kernel, piece, or particle of almond kernel with any defect scored as... Standards for Shelled Almonds, or which has embedded dirt or other foreign material not easily removed...
7 CFR 981.408 - Inedible kernel.
Code of Federal Regulations, 2012 CFR
2012-01-01
... and Orders; Fruits, Vegetables, Nuts), DEPARTMENT OF AGRICULTURE ALMONDS GROWN IN CALIFORNIA... kernel is modified to mean a kernel, piece, or particle of almond kernel with any defect scored as... Standards for Shelled Almonds, or which has embedded dirt or other foreign material not easily removed...
Federal Register 2010, 2011, 2012, 2013, 2014
2011-12-19
... AFFAIRS Agency Information Collection (Regulation on Application for Fisher Houses and Other Temporary Lodging and VHA Fisher House Application) Activity Under OMB Review AGENCY: Veterans Health Administration... Application for Fisher Houses and Other Temporary Lodging and VHA Fisher House Application, VA Forms...
38 CFR 60.20 - Duration of Fisher House or other temporary lodging.
Code of Federal Regulations, 2014 CFR
2014-07-01
... 38 Pensions, Bonuses, and Veterans' Relief 2 2014-07-01 2014-07-01 false Duration of Fisher House... VETERANS AFFAIRS (CONTINUED) FISHER HOUSES AND OTHER TEMPORARY LODGING § 60.20 Duration of Fisher House or other temporary lodging. Fisher House or other temporary lodging may be awarded for the...
38 CFR 60.20 - Duration of Fisher House or other temporary lodging.
Code of Federal Regulations, 2013 CFR
2013-07-01
... 38 Pensions, Bonuses, and Veterans' Relief 2 2013-07-01 2013-07-01 false Duration of Fisher House... VETERANS AFFAIRS (CONTINUED) FISHER HOUSES AND OTHER TEMPORARY LODGING § 60.20 Duration of Fisher House or other temporary lodging. Fisher House or other temporary lodging may be awarded for the...
Local Kernel for Brains Classification in Schizophrenia
NASA Astrophysics Data System (ADS)
Castellani, U.; Rossato, E.; Murino, V.; Bellani, M.; Rambaldelli, G.; Tansella, M.; Brambilla, P.
In this paper a novel framework for brain classification is proposed in the context of mental health research. A learning by example method is introduced by combining local measurements with non linear Support Vector Machine. Instead of considering a voxel-by-voxel comparison between patients and controls, we focus on landmark points which are characterized by local region descriptors, namely Scale Invariance Feature Transform (SIFT). Then, matching is obtained by introducing the local kernel for which the samples are represented by unordered set of features. Moreover, a new weighting approach is proposed to take into account the discriminative relevance of the detected groups of features. Experiments have been performed including a set of 54 patients with schizophrenia and 54 normal controls on which region of interest (ROI) have been manually traced by experts. Preliminary results on Dorso-lateral PreFrontal Cortex (DLPFC) region are promising since up to 75% of successful classification rate has been obtained with this technique and the performance has improved up to 85% when the subjects have been stratified by sex.
Hyperspectral-imaging-based techniques applied to wheat kernels characterization
NASA Astrophysics Data System (ADS)
Serranti, Silvia; Cesare, Daniela; Bonifazi, Giuseppe
2012-05-01
Single kernels of durum wheat have been analyzed by hyperspectral imaging (HSI). Such an approach is based on the utilization of an integrated hardware and software architecture able to digitally capture and handle spectra as an image sequence, as they results along a pre-defined alignment on a surface sample properly energized. The study was addressed to investigate the possibility to apply HSI techniques for classification of different types of wheat kernels: vitreous, yellow berry and fusarium-damaged. Reflectance spectra of selected wheat kernels of the three typologies have been acquired by a laboratory device equipped with an HSI system working in near infrared field (1000-1700 nm). The hypercubes were analyzed applying principal component analysis (PCA) to reduce the high dimensionality of data and for selecting some effective wavelengths. Partial least squares discriminant analysis (PLS-DA) was applied for classification of the three wheat typologies. The study demonstrated that good classification results were obtained not only considering the entire investigated wavelength range, but also selecting only four optimal wavelengths (1104, 1384, 1454 and 1650 nm) out of 121. The developed procedures based on HSI can be utilized for quality control purposes or for the definition of innovative sorting logics of wheat.
R.A. Fisher's contributions to genetical statistics.
Thompson, E A
1990-12-01
R. A. Fisher (1890-1962) was a professor of genetics, and many of his statistical innovations found expression in the development of methodology in statistical genetics. However, whereas his contributions in mathematical statistics are easily identified, in population genetics he shares his preeminence with Sewall Wright (1889-1988) and J. B. S. Haldane (1892-1965). This paper traces some of Fisher's major contributions to the foundations of statistical genetics, and his interactions with Wright and with Haldane which contributed to the development of the subject. With modern technology, both statistical methodology and genetic data are changing. Nonetheless much of Fisher's work remains relevant, and may even serve as a foundation for future research in the statistical analysis of DNA data. For Fisher's work reflects his view of the role of statistics in scientific inference, expressed in 1949: There is no wide or urgent demand for people who will define methods of proof in set theory in the name of improving mathematical statistics. There is a widespread and urgent demand for mathematicians who understand that branch of mathematics known as theoretical statistics, but who are capable also of recognising situations in the real world to which such mathematics is applicable. In recognising features of the real world to which his models and analyses should be applicable, Fisher laid a lasting foundation for statistical inference in genetic analyses. PMID:2085639
On the validity of cosmological Fisher matrix forecasts
Wolz, Laura; Kilbinger, Martin; Weller, Jochen; Giannantonio, Tommaso E-mail: martin.kilbinger@cea.fr E-mail: tommaso@usm.lmu.de
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 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.
Fisher statistics for analysis of diffusion tensor directional information.
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. PMID:22342971
Derivation of aerodynamic kernel functions
NASA Technical Reports Server (NTRS)
Dowell, E. H.; Ventres, C. S.
1973-01-01
The method of Fourier transforms is used to determine the kernel function which relates the pressure on a lifting surface to the prescribed downwash within the framework of Dowell's (1971) shear flow model. This model is intended to improve upon the potential flow aerodynamic model by allowing for the aerodynamic boundary layer effects neglected in the potential flow model. For simplicity, incompressible, steady flow is considered. The proposed method is illustrated by deriving known results from potential flow theory.
Kernel Near Principal Component Analysis
MARTIN, SHAWN B.
2002-07-01
We propose a novel algorithm based on Principal Component Analysis (PCA). First, we present an interesting approximation of PCA using Gram-Schmidt orthonormalization. Next, we combine our approximation with the kernel functions from Support Vector Machines (SVMs) to provide a nonlinear generalization of PCA. After benchmarking our algorithm in the linear case, we explore its use in both the linear and nonlinear cases. We include applications to face data analysis, handwritten digit recognition, and fluid flow.
RKRD: Runtime Kernel Rootkit Detection
NASA Astrophysics Data System (ADS)
Grover, Satyajit; Khosravi, Hormuzd; Kolar, Divya; Moffat, Samuel; Kounavis, Michael E.
In this paper we address the problem of protecting computer systems against stealth malware. The problem is important because the number of known types of stealth malware increases exponentially. Existing approaches have some advantages for ensuring system integrity but sophisticated techniques utilized by stealthy malware can thwart them. We propose Runtime Kernel Rootkit Detection (RKRD), a hardware-based, event-driven, secure and inclusionary approach to kernel integrity that addresses some of the limitations of the state of the art. Our solution is based on the principles of using virtualization hardware for isolation, verifying signatures coming from trusted code as opposed to malware for scalability and performing system checks driven by events. Our RKRD implementation is guided by our goals of strong isolation, no modifications to target guest OS kernels, easy deployment, minimal infra-structure impact, and minimal performance overhead. We developed a system prototype and conducted a number of experiments which show that the per-formance impact of our solution is negligible.
Kernel CMAC with improved capability.
Horváth, Gábor; Szabó, Tamás
2007-02-01
The cerebellar model articulation controller (CMAC) has some attractive features, namely fast learning capability and the possibility of efficient digital hardware implementation. Although CMAC was proposed many years ago, several open questions have been left even for today. The most important ones are about its modeling and generalization capabilities. The limits of its modeling capability were addressed in the literature, and recently, certain questions of its generalization property were also investigated. This paper deals with both the modeling and the generalization properties of CMAC. First, a new interpolation model is introduced. Then, a detailed analysis of the generalization error is given, and an analytical expression of this error for some special cases is presented. It is shown that this generalization error can be rather significant, and a simple regularized training algorithm to reduce this error is proposed. The results related to the modeling capability show that there are differences between the one-dimensional (1-D) and the multidimensional versions of CMAC. This paper discusses the reasons of this difference and suggests a new kernel-based interpretation of CMAC. The kernel interpretation gives a unified framework. Applying this approach, both the 1-D and the multidimensional CMACs can be constructed with similar modeling capability. Finally, this paper shows that the regularized training algorithm can be applied for the kernel interpretations too, which results in a network with significantly improved approximation capabilities. PMID:17278566
Visualizing and Interacting with Kernelized Data.
Barbosa, A; Paulovich, F V; Paiva, A; Goldenstein, S; Petronetto, F; Nonato, L G
2016-03-01
Kernel-based methods have experienced a substantial progress in the last years, tuning out an essential mechanism for data classification, clustering and pattern recognition. The effectiveness of kernel-based techniques, though, depends largely on the capability of the underlying kernel to properly embed data in the feature space associated to the kernel. However, visualizing how a kernel embeds the data in a feature space is not so straightforward, as the embedding map and the feature space are implicitly defined by the kernel. In this work, we present a novel technique to visualize the action of a kernel, that is, how the kernel embeds data into a high-dimensional feature space. The proposed methodology relies on a solid mathematical formulation to map kernelized data onto a visual space. Our approach is faster and more accurate than most existing methods while still allowing interactive manipulation of the projection layout, a game-changing trait that other kernel-based projection techniques do not have. PMID:26829242
Robust Pedestrian Classification Based on Hierarchical Kernel Sparse Representation.
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
Damage Identification with Linear Discriminant Operators
Farrar, C.R.; Nix, D.A.; Duffey, T.A.; Cornwell, P.J.; Pardoen, G.C.
1999-02-08
This paper explores the application of statistical pattern recognition and machine learning techniques to vibration-based damage detection. First, the damage detection process is described in terms of a problem in statistical pattern recognition. Next, a specific example of a statistical-pattern-recognition-based damage detection process using a linear discriminant operator, ''Fisher's Discriminant'', is applied to the problem of identifying structural damage in a physical system. Accelerometer time histories are recorded from sensors attached to the system as that system is excited using a measured input. Linear Prediction Coding (LPC) coefficients are utilized to convert the accelerometer time-series data into multi-dimensional samples representing the resonances of the system during a brief segment of the time series. Fisher's discriminant is then used to find the linear projection of the LPC data distributions that best separates data from undamaged and damaged systems. The method i s applied to data from concrete bridge columns as the columns are progressively damaged. For this case, the method captures a clear distinction between undamaged and damaged vibration profiles. Further, the method assigns a probability of damage that can be used to rank systems in order of priority for inspection.
Phase space view of quantum mechanical systems and Fisher information
NASA Astrophysics Data System (ADS)
Nagy, Á.
2016-06-01
Pennini and Plastino showed that the form of the Fisher information generated by the canonical distribution function reflects the intrinsic structure of classical mechanics. Now, a quantum mechanical generalization of the Pennini-Plastino theory is presented based on the thermodynamical transcription of the density functional theory. Comparing to the classical case, the phase-space Fisher information contains an extra term due to the position dependence of the temperature. However, for the special case of constant temperature, the expression derived bears resemblance to the classical one. A complete analogy to the classical case is demonstrated for the linear harmonic oscillator.
Pattern recognition descriptor using the Z-Fisher transform
NASA Astrophysics Data System (ADS)
Barajas-García, Carolina; Solorza-Calderón, Selene; Álvarez-Borrego, Josué
2015-09-01
In this work is presented a pattern recognition image descriptor invariant to rotation, scale and translation (RST), which classify images using the Z-Fisher transform. A binary rings mask is generated using the Fourier transform. The normalized analytic Fourier-Mellin amplitude spectrum is filtered with that mask to build 1D signature. The signatures comparison of the problem image and the target are done by the Pearson correlation coefficient (PCC). In general, those PCC values do not satisfy a normal distribution, hence the Fisher's Z distribution is employed to determine the confidence level of the RST invariant descriptor. The descriptor presents a confidence level of 95%.
Nonlinear projection trick in kernel methods: an alternative to the kernel trick.
Kwak, Nojun
2013-12-01
In kernel methods such as kernel principal component analysis (PCA) and support vector machines, the so called kernel trick is used to avoid direct calculations in a high (virtually infinite) dimensional kernel space. In this brief, based on the fact that the effective dimensionality of a kernel space is less than the number of training samples, we propose an alternative to the kernel trick that explicitly maps the input data into a reduced dimensional kernel space. This is easily obtained by the eigenvalue decomposition of the kernel matrix. The proposed method is named as the nonlinear projection trick in contrast to the kernel trick. With this technique, the applicability of the kernel methods is widened to arbitrary algorithms that do not use the dot product. The equivalence between the kernel trick and the nonlinear projection trick is shown for several conventional kernel methods. In addition, we extend PCA-L1, which uses L1-norm instead of L2-norm (or dot product), into a kernel version and show the effectiveness of the proposed approach. PMID:24805227
NASA Astrophysics Data System (ADS)
Hasik, Karel; Kopfová, Jana; Nábělková, Petra; Trofimchuk, Sergei
2016-04-01
We consider the nonlocal KPP-Fisher equation ut (t , x) =uxx (t , x) + u (t , x) (1 - (K * u) (t , x)) which describes the evolution of population density u (t , x) with respect to time t and location x. The non-locality is expressed in terms of the convolution of u (t , ṡ) with kernel K (ṡ) ≥ 0, ∫R K (s) ds = 1. The restrictions K (s), s ≥ 0, and K (s), s ≤ 0, are responsible for interactions of an individual with his left and right neighbors, respectively. We show that these two parts of K play quite different roles as for the existence and uniqueness of traveling fronts to the KPP-Fisher equation. In particular, if the left interaction is dominant, the uniqueness of fronts can be proved, while the dominance of the right interaction can induce the co-existence of monotone and oscillating fronts. We also present a short proof of the existence of traveling waves without assuming various technical restrictions usually imposed on K.
Molecular Hydrodynamics from Memory Kernels.
Lesnicki, Dominika; Vuilleumier, Rodolphe; Carof, Antoine; Rotenberg, Benjamin
2016-04-01
The memory kernel for a tagged particle in a fluid, computed from molecular dynamics simulations, decays algebraically as t^{-3/2}. We show how the hydrodynamic Basset-Boussinesq force naturally emerges from this long-time tail and generalize the concept of hydrodynamic added mass. This mass term is negative in the present case of a molecular solute, which is at odds with incompressible hydrodynamics predictions. Lastly, we discuss the various contributions to the friction, the associated time scales, and the crossover between the molecular and hydrodynamic regimes upon increasing the solute radius. PMID:27104730
Quantum Fisher and skew information for Unruh accelerated Dirac qubit
NASA Astrophysics Data System (ADS)
Banerjee, Subhashish; Alok, Ashutosh Kumar; Omkar, S.
2016-08-01
We develop a Bloch vector representation of the Unruh channel for a Dirac field mode. This is used to provide a unified, analytical treatment of quantum Fisher and skew information for a qubit subjected to the Unruh channel, both in its pure form as well as in the presence of experimentally relevant external noise channels. The time evolution of Fisher and skew information is studied along with the impact of external environment parameters such as temperature and squeezing. The external noises are modelled by both purely dephasing phase damping and the squeezed generalised amplitude damping channels. An interesting interplay between the external reservoir temperature and squeezing on the Fisher and skew information is observed, in particular, for the action of the squeezed generalised amplitude damping channel. It is seen that for some regimes, squeezing can enhance the quantum information against the deteriorating influence of the ambient environment. Similar features are also observed for the analogous study of skew information, highlighting a similar origin of the Fisher and skew information.
Testing the Difference between Dependent Correlations Using the Fisher Z.
ERIC Educational Resources Information Center
Ramseyer, Gary C.
1979-01-01
A procedure is discussed for testing the significance of the difference in two correlated correlation coefficients, using Fisher's Z-Transformation. The procedure is applicable to a wide range of problems involving tests between dependent correlations and has documented mathematical support when its power curves are examined. (MH)
Postcolonial Appalachia: Bhabha, Bakhtin, and Diane Gilliam Fisher's "Kettle Bottom"
ERIC Educational Resources Information Center
Stevenson, Sheryl
2006-01-01
Diane Gilliam Fisher's 2004 award-winning book of poems, "Kettle Bottom," offers students a revealing vantage point for seeing Appalachian regional culture in a postcolonial context. An artful and accessible poetic sequence that was selected as the 2005 summer reading for entering students at Smith College, "Kettle Bottom"…
Detection and Assessment of Ecosystem Regime Shifts from Fisher Information
Ecosystem regime shifts, which are long-term system reorganizations, have profound implications for sustainability. There is a great need for indicators of regime shifts, particularly methods that are applicable to data from real systems. We have developed a form of Fisher info...
Fisher information for inverse problems and trace class operators
NASA Astrophysics Data System (ADS)
Nordebo, S.; Gustafsson, M.; Khrennikov, A.; Nilsson, B.; Toft, J.
2012-12-01
This paper provides a mathematical framework for Fisher information analysis for inverse problems based on Gaussian noise on infinite-dimensional Hilbert space. The covariance operator for the Gaussian noise is assumed to be trace class, and the Jacobian of the forward operator Hilbert-Schmidt. We show that the appropriate space for defining the Fisher information is given by the Cameron-Martin space. This is mainly because the range space of the covariance operator always is strictly smaller than the Hilbert space. For the Fisher information to be well-defined, it is furthermore required that the range space of the Jacobian is contained in the Cameron-Martin space. In order for this condition to hold and for the Fisher information to be trace class, a sufficient condition is formulated based on the singular values of the Jacobian as well as of the eigenvalues of the covariance operator, together with some regularity assumptions regarding their relative rate of convergence. An explicit example is given regarding an electromagnetic inverse source problem with "external" spherically isotropic noise, as well as "internal" additive uncorrelated noise.
Reflections on a Collaboration: Communicating Educational Research in "Fisher"
ERIC Educational Resources Information Center
Garces, Liliana M.
2013-01-01
It was critical that the U.S. Supreme Court have the best empirical evidence available to help inform its decisions in "Fisher." The "amicus" brief filed by 444 researchers from 172 institutions in 42 states was the result of a collaborative effort among members of the social science, educational, and legal communities. In her role as counsel of…
FISHER INFORMATION AND DYNAMIC REGIME CHANGES IN ECOLOGICAL SYSTEMS
Fisher Information and Dynamic Regime Changes in Ecological Systems
Abstract for the 3rd Conference of the International Society for Ecological Informatics
Audrey L. Mayer, Christopher W. Pawlowski, and Heriberto Cabezas
The sustainable nature of particular dynamic...
Fisher information and quantum potential well model for finance
NASA Astrophysics Data System (ADS)
Nastasiuk, V. A.
2015-09-01
The probability distribution function (PDF) for prices on financial markets is derived by extremization of Fisher information. It is shown how on that basis the quantum-like description for financial markets arises and different financial market models are mapped by quantum mechanical ones.
77 FR 59087 - Fisher House and Other Temporary Lodging
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2012-09-26
... Federal Register (77 FR 15650) a proposal to amend its regulations concerning Fisher Houses and other... practice, and the proposed rule emphasized that it would generally reflect current VA practice. See 77 FR... on site for evaluation, donation, and care related to their status as an organ donor for a...
The Fisher-Yates Exact Test and Unequal Sample Sizes
ERIC Educational Resources Information Center
Johnson, Edgar M.
1972-01-01
A computational short cut suggested by Feldman and Klinger for the one-sided Fisher-Yates exact test is clarified and is extended to the calculation of probability values for certain two-sided tests when sample sizes are unequal. (Author)
FISHER INFORMATION AS A METRIC FOR SUSTAINABLE SYSTEM REGIMES
The important question in sustainability is not whether the world is sustainable, but whether a humanly acceptable regime of the world is sustainable. We propose Fisher Information as a metric for the sustainability of dynamic regimes in complex systems. The quantity now known ...
FISHER INFORMATION AS A METRIC FOR SUSTAINABLE REGIMES
The important question in sustainability is not whether the world is sustainable, but whether a humanly acceptable regime of the world is sustainable. We propose Fisher Information as a metric for the sustainability of dynamic regimes in complex systems. The quantity now known ...
An Extended Method of SIRMs Connected Fuzzy Inference Method Using Kernel Method
NASA Astrophysics Data System (ADS)
Seki, Hirosato; Mizuguchi, Fuhito; Watanabe, Satoshi; Ishii, Hiroaki; Mizumoto, Masaharu
The single input rule modules connected fuzzy inference method (SIRMs method) by Yubazaki et al. can decrease the number of fuzzy rules drastically in comparison with the conventional fuzzy inference methods. Moreover, Seki et al. have proposed a functional-type SIRMs method which generalizes the consequent part of the SIRMs method to function. However, these SIRMs methods can not be applied to XOR (Exclusive OR). In this paper, we propose a “kernel-type SIRMs method” which uses the kernel trick to the SIRMs method, and show that this method can treat XOR. Further, a learning algorithm of the proposed SIRMs method is derived by using the steepest descent method, and compared with the one of conventional SIRMs method and kernel perceptron by applying to identification of nonlinear functions, medical diagnostic system and discriminant analysis of Iris data.
Assessing Fishers' Support of Striped Bass Management Strategies.
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
Assessing Fishers' Support of Striped Bass Management Strategies
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
The Tully-Fisher relations for Hickson compact group galaxies
NASA Astrophysics Data System (ADS)
Torres-Flores, S.; Mendes de Oliveira, C.; Plana, H.; Amram, P.; Epinat, B.
2013-07-01
We used K-band photometry, maximum rotational velocities derived from Fabry-Perot data and H I observed and predicted masses to study, for the first time, the K band, stellar and baryonic Tully-Fisher relations for galaxies in Hickson compact groups. We compared these relations with the ones defined for galaxies in less dense environments from the Gassendi HAlpha survey of Spirals and from a sample of gas-rich galaxies. We find that most of the Hickson compact group galaxies lie on the K-band Tully-Fisher relation defined by field galaxies with a few low-mass outliers, namely HCG 49b and HCG 96c, which appear to have had strong recent burst of star formation. The stellar Tully-Fisher relation for compact group galaxies presents a similar dispersion to that of the K-band relation, and it has no significant outliers when a proper computation of the stellar mass is done for the strongly star-forming galaxies. The scatter in these relations can be reduced if the gaseous component is taken into account, i.e. if a baryonic Tully-Fisher relation is considered. In order to explain the positions of the galaxies off the K-band Tully-Fisher relation, we favour a scenario in which their luminosities are brightened due to strong star formation or AGN activity. We argue that strong bursts of star formation can affect the B- and K-band luminosities of HCG 49b and HCG 96c and in the case of the latter also AGN activity may affect the K-band magnitude considerably, without affecting their total masses.
KERNEL PHASE IN FIZEAU INTERFEROMETRY
Martinache, Frantz
2010-11-20
The detection of high contrast companions at small angular separation appears feasible in conventional direct images using the self-calibration properties of interferometric observable quantities. The friendly notion of closure phase, which is key to the recent observational successes of non-redundant aperture masking interferometry used with adaptive optics, appears to be one example of a wide family of observable quantities that are not contaminated by phase noise. In the high-Strehl regime, soon to be available thanks to the coming generation of extreme adaptive optics systems on ground-based telescopes, and already available from space, closure phase like information can be extracted from any direct image, even taken with a redundant aperture. These new phase-noise immune observable quantities, called kernel phases, are determined a priori from the knowledge of the geometry of the pupil only. Re-analysis of archive data acquired with the Hubble Space Telescope NICMOS instrument using this new kernel-phase algorithm demonstrates the power of the method as it clearly detects and locates with milliarcsecond precision a known companion to a star at angular separation less than the diffraction limit.
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2012-12-05
... Enforcement Administration Importer of Controlled Substances; Notice of Application; Fisher Clinical Services..., 2012, Fisher Clinical Services, Inc., 7554 Schantz Road, Allentown, Pennsylvania 18106, made... controlled substance for analytical research and clinical trials. The import of the above listed basic...
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... Enforcement Administration Importer of Controlled Substances; Notice of Application; Fisher Clinical Services... 21, 2013, Fisher Clinical Services, Inc., 7554 Schantz Road, Allentown, Pennsylvania 18106, made... for clinical trials, analytical research and testing. The import of the above listed basic classes...
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Astronaut Anna Fisher practices control of the RMS in a trainer
NASA Technical Reports Server (NTRS)
1984-01-01
Astronaut Anna Lee Fisher, mission specialist for 51-A, practices control of the remote manipulator system (RMS) at a special trainer at JSC. Dr. Fisher is pictured in the manipulator development facility (MDF) of JSC's Shuttle mockup and integration laboratory.
1998-12-25
The Massachusetts Commission Against Discrimination found probable cause to believe that Dr. [name removed] denied [name removed] reproductive services because [name removed] is gay, which [name removed] associates with being at high risk for HIV. [Name removed] claimed that the doctor refused to bank and transport his semen for artificial insemination. [Name removed], the father of one, tested negative and possesses no risk of infecting the would-be mother. The Commission will hold a conciliation session to try and resolve the dispute. If the session is not successful, the Commission will conduct an evidentiary hearing. PMID:11366047
NASA Astrophysics Data System (ADS)
Yang, I.-Chang; Delwiche, Stephen R.; Chen, Suming; Lo, Y. Martin
2009-02-01
Fusarium head blight is a worldwide fungal disease of small cereal grains such as wheat that affects the yield, quality, and safety of food and feed products. The current study was implemented to develop more efficient methods for optically detecting Fusarium-damaged (scabby) kernels from normal (sound) wheat kernels. Through development of a high-power pulsed LED (green and red) inspection system, it was found that Fusarium-damaged and normal wheat kernels have different reflected energy responses. Two parameters (slope and r2) from a regression analysis of the green and red responses were used as input parameters in linear discriminant analysis models. The examined factors affecting accuracy were the orientation of the optical probe, the color contrast between normal and Fusarium-damaged kernels, and the manner in which one LED's response is time-matched to the other LED. Whereas commercial high-speed optical sorters are, on average, 50% efficient at removing mold-damaged kernels, this efficiency can rise to 95% or better under more carefully controlled, kernel-at-rest conditions in the laboratory. The current research on free-falling kernels has demonstrated accuracies (>90% for wheat samples of high visual contrast) that approach those of controlled conditions, which will lead to improvements in high-speed optical sorters.
Liu, Jin; Guo, Ting-ting; Li, Hao-chuan; Jia, Shi-qiang; Yan, Yan-lu; An, Dong; Zhang, Yao; Chen, Shao-jiang
2015-11-01
Doubled haploid (DH) lines are routinely applied in the hybrid maize breeding programs of many institutes and companies for their advantages of complete homozygosity and short breeding cycle length. A key issue in this approach is an efficient screening system to identify haploid kernels from the hybrid kernels crossed with the inducer. At present, haploid kernel selection is carried out manually using the"red-crown" kernel trait (the haploid kernel has a non-pigmented embryo and pigmented endosperm) controlled by the R1-nj gene. Manual selection is time-consuming and unreliable. Furthermore, the color of the kernel embryo is concealed by the pericarp. Here, we establish a novel approach for identifying maize haploid kernels based on visible (Vis) spectroscopy and support vector machine (SVM) pattern recognition technology. The diffuse transmittance spectra of individual kernels (141 haploid kernels and 141 hybrid kernels from 9 genotypes) were collected using a portable UV-Vis spectrometer and integrating sphere. The raw spectral data were preprocessed using smoothing and vector normalization methods. The desired feature wavelengths were selected based on the results of the Kolmogorov-Smirnov test. The wavelengths with p values above 0. 05 were eliminated because the distributions of absorbance data in these wavelengths show no significant difference between haploid and hybrid kernels. Principal component analysis was then performed to reduce the number of variables. The SVM model was evaluated by 9-fold cross-validation. In each round, samples of one genotype were used as the testing set, while those of other genotypes were used as the training set. The mean rate of correct discrimination was 92.06%. This result demonstrates the feasibility of using Vis spectroscopy to identify haploid maize kernels. The method would help develop a rapid and accurate automated screening-system for haploid kernels. PMID:26978947
Code of Federal Regulations, 2011 CFR
2011-01-01
... 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...
Code of Federal Regulations, 2010 CFR
2010-01-01
... 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...
Code of Federal Regulations, 2013 CFR
2013-01-01
..., CERTIFICATION, AND STANDARDS) United States 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...
Code of Federal Regulations, 2012 CFR
2012-01-01
... 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...
Code of Federal Regulations, 2014 CFR
2014-01-01
..., CERTIFICATION, AND STANDARDS) United States 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...
Corn kernel oil and corn fiber oil
Technology Transfer Automated Retrieval System (TEKTRAN)
Unlike most edible plant oils that are obtained directly from oil-rich seeds by either pressing or solvent extraction, corn seeds (kernels) have low levels of oil (4%) and commercial corn oil is obtained from the corn germ (embryo) which is an oil-rich portion of the kernel. Commercial corn oil cou...
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... FR 72409, Fisher Clinical Services, Inc., 7554 Schantz Road, Allentown, Pennsylvania 18106, made... Enforcement Administration Importer of Controlled Substances: Notice of Registration; Fisher Clinical Services... effect on May 1, 1971. DEA has investigated Fisher Clinical Services, Inc., to ensure that the...
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2011-10-12
... AFFAIRS Proposed Information Collection (Regulation on Application for Fisher Houses and Other Temporary Lodging and VHA Fisher House Application); Comment Request AGENCY: Veterans Health Administration... Houses and Other Temporary Lodging and VHA Fisher House Application, VA Forms 10-0408 and 10-0408a....
Bayesian Kernel Mixtures for Counts
Canale, Antonio; Dunson, David B.
2011-01-01
Although Bayesian nonparametric mixture models for continuous data are well developed, there is a limited literature on related approaches for count data. A common strategy is to use a mixture of Poissons, which unfortunately is quite restrictive in not accounting for distributions having variance less than the mean. Other approaches include mixing multinomials, which requires finite support, and using a Dirichlet process prior with a Poisson base measure, which does not allow smooth deviations from the Poisson. As a broad class of alternative models, we propose to use nonparametric mixtures of rounded continuous kernels. An efficient Gibbs sampler is developed for posterior computation, and a simulation study is performed to assess performance. Focusing on the rounded Gaussian case, we generalize the modeling framework to account for multivariate count data, joint modeling with continuous and categorical variables, and other complications. The methods are illustrated through applications to a developmental toxicity study and marketing data. This article has supplementary material online. PMID:22523437
Bayesian Kernel Mixtures for Counts.
Canale, Antonio; Dunson, David B
2011-12-01
Although Bayesian nonparametric mixture models for continuous data are well developed, there is a limited literature on related approaches for count data. A common strategy is to use a mixture of Poissons, which unfortunately is quite restrictive in not accounting for distributions having variance less than the mean. Other approaches include mixing multinomials, which requires finite support, and using a Dirichlet process prior with a Poisson base measure, which does not allow smooth deviations from the Poisson. As a broad class of alternative models, we propose to use nonparametric mixtures of rounded continuous kernels. An efficient Gibbs sampler is developed for posterior computation, and a simulation study is performed to assess performance. Focusing on the rounded Gaussian case, we generalize the modeling framework to account for multivariate count data, joint modeling with continuous and categorical variables, and other complications. The methods are illustrated through applications to a developmental toxicity study and marketing data. This article has supplementary material online. PMID:22523437
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…
A lattice Boltzmann model for the Burgers-Fisher equation.
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. PMID:20590325
Fisher entropy and uncertaintylike relationships in many-body systems
NASA Astrophysics Data System (ADS)
Romera, E.; Angulo, J. C.; Dehesa, J. S.
1999-05-01
General model-independent relationships among radial expectation values of the one-particle densities in position and momentum spaces for any quantum-mechanical system are obtained. They are derived from the Stam uncertainty principle and the recently reported lower bounds to the Fisher information entropy of both densities. The results are usually expressed in terms of some uncertainty products of the system. The accuracy of the bounds is numerically analyzed for neutral atoms within a Hartree-Fock framework.
Traditional botanical knowledge of artisanal fishers in southern Brazil
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
Population Dynamics with Global Regulation: The Conserved Fisher Equation
NASA Astrophysics Data System (ADS)
Newman, T. J.; Kolomeisky, E. B.; Antonovics, J.
2004-06-01
We introduce and study a conserved version of the Fisher equation. Within a population biology context, this model describes spatially extended populations in which the total number of individuals is fixed due to either biotic or environmental factors. We find a rich spectrum of dynamical phases including a pseudotraveling wave and, in the presence of the Allee effect, a phase transition from a locally constrained high density state to a low density fragmented state.
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%.
Fisher Pierce products for improving distribution system reliability
1994-12-31
The challenges facing the electric power utility today in the 1990s has changed significantly from those of even 10 years ago. The proliferation of automation and the personnel computer have heightened the requirements and demands put on the electric distribution system. Today`s customers, fighting to compete in a world market, demand quality, uninterrupted power service. Privatization and the concept of unregulated competition require utilities to streamline to minimize system support costs and optimize power delivery efficiency. Fisher Pierce, serving the electric utility industry for over 50 years, offers a line of products to assist utilities in meeting these challenges. The Fisher Pierce Family of products provide tools for the electric utility to exceed customer service demands. A full line of fault indicating devices are offered to expedite system power restoration both locally and in conjunction with SCADA systems. Fisher Pierce is the largest supplier of roadway lighting controls, manufacturing on a 6 million dollar automated line assuring the highest quality in the world. The distribution system capacitor control line offers intelligent local or radio linked switching control to maintain system voltage and Var levels for quality and cost efficient power delivery under varying customer loads. Additional products, designed to authenticate revenue metering calibration and verify on sight metering service wiring, help optimize the profitability of the utility assuring continuous system service improvements for their customers.
The genetics of speciation: Insights from Fisher's geometric model.
Fraïsse, Christelle; Gunnarsson, P Alexander; Roze, Denis; Bierne, Nicolas; Welch, John J
2016-07-01
Research in speciation genetics has uncovered many robust patterns in intrinsic reproductive isolation, and fitness landscape models have been useful in interpreting these patterns. Here, we examine fitness landscapes based on Fisher's geometric model. Such landscapes are analogous to models of optimizing selection acting on quantitative traits, and have been widely used to study adaptation and the distribution of mutational effects. We show that, with a few modifications, Fisher's model can generate all of the major findings of introgression studies (including "speciation genes" with strong deleterious effects, complex epistasis and asymmetry), and the major patterns in overall hybrid fitnesses (including Haldane's Rule, the speciation clock, heterosis, hybrid breakdown, and male-female asymmetry in the F1). We compare our approach to alternative modeling frameworks that assign fitnesses to genotypes by identifying combinations of incompatible alleles. In some cases, the predictions are importantly different. For example, Fisher's model can explain conflicting empirical results about the rate at which incompatibilities accumulate with genetic divergence. In other cases, the predictions are identical. For example, the quality of reproductive isolation is little affected by the manner in which populations diverge. PMID:27252049
Huang, Lulu; Massa, Lou
2010-01-01
The Kernel Energy Method (KEM) provides a way to calculate the ab-initio energy of very large biological molecules. The results are accurate, and the computational time reduced. However, by use of a list of double kernel interactions a significant additional reduction of computational effort may be achieved, still retaining ab-initio accuracy. A numerical comparison of the indices that name the known double interactions in question, allow one to list higher order interactions having the property of topological continuity within the full molecule of interest. When, that list of interactions is unpacked, as a kernel expansion, which weights the relative importance of each kernel in an expression for the total molecular energy, high accuracy, and a further significant reduction in computational effort results. A KEM molecular energy calculation based upon the HF/STO3G chemical model, is applied to the protein insulin, as an illustration. PMID:21243065
Tully-Fisher relations from an HI-selected sample
NASA Astrophysics Data System (ADS)
Meyer, M. J.; Zwaan, M. A.; Webster, R. L.; Schneider, S.; Staveley-Smith, L.
2008-12-01
The optical and near-infrared Tully-Fisher relations are examined for an HI-selected sample of galaxies. This sample is unique in that membership is simply defined by the overlap of blind 21-cm, optical and near-infrared catalogues, rather than pre-selecting candidate Tully-Fisher galaxies for which to obtain rotational measurements. Three fundamental questions are considered: (i) Does the scatter depend on a third variable? (ii) What is the intrinsic scatter of the relationship? (iii) What is the slope of the z = 0 relationship as a function of wavelength, and can a single slope relationship be obtained by converting to stellar or baryonic mass? The HI Parkes All-Sky Survey (HIPASS) Catalogue (HICAT) provides 21-cm linewidths for 4315 galaxies selected purely on their HI content, and enables the Tully-Fisher relation to be studied without the need to optically pre-select galaxies for which to obtain rotational measures. ESO-LV and the Two Micron All-Sky Survey (2MASS) are used for optical (B, R) and near-infrared (J, H, K) photometry, with samples of up to 351 and 860 galaxies examined, respectively. Third parameter dependencies are tested by correlating offsets from the derived B- and K-band relations with candidate observed and intrinsic variables. A strong dependence is found on purely observational parameters such as galaxy inclination and size. Large-scale galaxy flows are also found to increase observed scatter. Correlations of Tully-Fisher offset with physical properties are found to be strongly dependent on the fitted slope of relation, indicating that if real, these dependencies are weak and largely act in parallel to the observed relations. However, a potential dependence is observed on stellar population (as measured by B-R colour) and star formation rate (measured by far-infrared luminosity). The intrinsic scatter and slope of the Tully-Fisher relation are measured by applying galaxy selection cuts to minimize observational errors. For the B
Kernel map compression for speeding the execution of kernel-based methods.
Arif, Omar; Vela, Patricio A
2011-06-01
The use of Mercer kernel methods in statistical learning theory provides for strong learning capabilities, as seen in kernel principal component analysis and support vector machines. Unfortunately, after learning, the computational complexity of execution through a kernel is of the order of the size of the training set, which is quite large for many applications. This paper proposes a two-step procedure for arriving at a compact and computationally efficient execution procedure. After learning in the kernel space, the proposed extension exploits the universal approximation capabilities of generalized radial basis function neural networks to efficiently approximate and replace the projections onto the empirical kernel map used during execution. Sample applications demonstrate significant compression of the kernel representation with graceful performance loss. PMID:21550884
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... STANDARDS) United States Standards for Shelled English Walnuts (Juglans Regia) Definitions § 51.2296 Three-fourths half kernel. Three-fourths half kernel means a portion of a half of a kernel which has more...
7 CFR 981.401 - Adjusted kernel weight.
Code of Federal Regulations, 2013 CFR
2013-01-01
... weight of delivery 10,000 10,000 2. Percent of edible kernel weight 53.0 84.0 3. Less weight loss in... 7 Agriculture 8 2013-01-01 2013-01-01 false Adjusted kernel weight. 981.401 Section 981.401... Administrative Rules and Regulations § 981.401 Adjusted kernel weight. (a) Definition. Adjusted kernel...
UPDATE OF GRAY KERNEL DISEASE OF MACADAMIA - 2006
Technology Transfer Automated Retrieval System (TEKTRAN)
Gray kernel is an important disease of macadamia that affects the quality of kernels with gray discoloration and a permeating, foul odor that can render entire batches of nuts unmarketable. We report on the successful production of gray kernel in raw macadamia kernels artificially inoculated with s...
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;...
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;...
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;...
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;...
7 CFR 51.2125 - Split or broken kernels.
Code of Federal Regulations, 2011 CFR
2011-01-01
... 7 Agriculture 2 2011-01-01 2011-01-01 false Split or broken kernels. 51.2125 Section 51.2125... STANDARDS) United States Standards for Grades of Shelled Almonds Definitions § 51.2125 Split or broken kernels. Split or broken kernels means seven-eighths or less of complete whole kernels but which will...
7 CFR 51.2125 - Split or broken kernels.
Code of Federal Regulations, 2012 CFR
2012-01-01
... 7 Agriculture 2 2012-01-01 2012-01-01 false Split or broken kernels. 51.2125 Section 51.2125... STANDARDS) United States Standards for Grades of Shelled Almonds Definitions § 51.2125 Split or broken kernels. Split or broken kernels means seven-eighths or less of complete whole kernels but which will...
Retrieval of Brain Tumors by Adaptive Spatial Pooling and Fisher Vector Representation
Huang, Meiyan; Huang, Wei; Jiang, Jun; Zhou, Yujia; Yang, Ru; Zhao, Jie; Feng, Yanqiu; Feng, Qianjin; Chen, Wufan
2016-01-01
Content-based image retrieval (CBIR) techniques have currently gained increasing popularity in the medical field because they can use numerous and valuable archived images to support clinical decisions. In this paper, we concentrate on developing a CBIR system for retrieving brain tumors in T1-weighted contrast-enhanced MRI images. Specifically, when the user roughly outlines the tumor region of a query image, brain tumor images in the database of the same pathological type are expected to be returned. We propose a novel feature extraction framework to improve the retrieval performance. The proposed framework consists of three steps. First, we augment the tumor region and use the augmented tumor region as the region of interest to incorporate informative contextual information. Second, the augmented tumor region is split into subregions by an adaptive spatial division method based on intensity orders; within each subregion, we extract raw image patches as local features. Third, we apply the Fisher kernel framework to aggregate the local features of each subregion into a respective single vector representation and concatenate these per-subregion vector representations to obtain an image-level signature. After feature extraction, a closed-form metric learning algorithm is applied to measure the similarity between the query image and database images. Extensive experiments are conducted on a large dataset of 3604 images with three types of brain tumors, namely, meningiomas, gliomas, and pituitary tumors. The mean average precision can reach 94.68%. Experimental results demonstrate the power of the proposed algorithm against some related state-of-the-art methods on the same dataset. PMID:27273091
Retrieval of Brain Tumors by Adaptive Spatial Pooling and Fisher Vector Representation.
Cheng, Jun; Yang, Wei; Huang, Meiyan; Huang, Wei; Jiang, Jun; Zhou, Yujia; Yang, Ru; Zhao, Jie; Feng, Yanqiu; Feng, Qianjin; Chen, Wufan
2016-01-01
Content-based image retrieval (CBIR) techniques have currently gained increasing popularity in the medical field because they can use numerous and valuable archived images to support clinical decisions. In this paper, we concentrate on developing a CBIR system for retrieving brain tumors in T1-weighted contrast-enhanced MRI images. Specifically, when the user roughly outlines the tumor region of a query image, brain tumor images in the database of the same pathological type are expected to be returned. We propose a novel feature extraction framework to improve the retrieval performance. The proposed framework consists of three steps. First, we augment the tumor region and use the augmented tumor region as the region of interest to incorporate informative contextual information. Second, the augmented tumor region is split into subregions by an adaptive spatial division method based on intensity orders; within each subregion, we extract raw image patches as local features. Third, we apply the Fisher kernel framework to aggregate the local features of each subregion into a respective single vector representation and concatenate these per-subregion vector representations to obtain an image-level signature. After feature extraction, a closed-form metric learning algorithm is applied to measure the similarity between the query image and database images. Extensive experiments are conducted on a large dataset of 3604 images with three types of brain tumors, namely, meningiomas, gliomas, and pituitary tumors. The mean average precision can reach 94.68%. Experimental results demonstrate the power of the proposed algorithm against some related state-of-the-art methods on the same dataset. PMID:27273091
KITTEN Lightweight Kernel 0.1 Beta
Energy Science and Technology Software Center (ESTSC)
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 providesmore » 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 and scalability than with general purpose OS kernels.« less
Biological sequence classification with multivariate string kernels.
Kuksa, Pavel P
2013-01-01
String kernel-based machine learning methods have yielded great success in practical tasks of structured/sequential data analysis. They often exhibit state-of-the-art performance on many practical tasks of sequence analysis such as biological sequence classification, remote homology detection, or protein superfamily and fold prediction. However, typical string kernel methods rely on the analysis of discrete 1D string data (e.g., DNA or amino acid sequences). In this paper, we address the multiclass biological sequence classification problems using multivariate representations in the form of sequences of features vectors (as in biological sequence profiles, or sequences of individual amino acid physicochemical descriptors) and a class of multivariate string kernels that exploit these representations. On three protein sequence classification tasks, the proposed multivariate representations and kernels show significant 15-20 percent improvements compared to existing state-of-the-art sequence classification methods. PMID:24384708
Biological Sequence Analysis with Multivariate String Kernels.
Kuksa, Pavel P
2013-03-01
String kernel-based machine learning methods have yielded great success in practical tasks of structured/sequential data analysis. They often exhibit state-of-the-art performance on many practical tasks of sequence analysis such as biological sequence classification, remote homology detection, or protein superfamily and fold prediction. However, typical string kernel methods rely on analysis of discrete one-dimensional (1D) string data (e.g., DNA or amino acid sequences). In this work we address the multi-class biological sequence classification problems using multivariate representations in the form of sequences of features vectors (as in biological sequence profiles, or sequences of individual amino acid physico-chemical descriptors) and a class of multivariate string kernels that exploit these representations. On a number of protein sequence classification tasks proposed multivariate representations and kernels show significant 15-20\\% improvements compared to existing state-of-the-art sequence classification methods. PMID:23509193
Variational Dirichlet Blur Kernel Estimation.
Zhou, Xu; Mateos, Javier; Zhou, Fugen; Molina, Rafael; Katsaggelos, Aggelos K
2015-12-01
Blind image deconvolution involves two key objectives: 1) latent image and 2) blur estimation. For latent image estimation, we propose a fast deconvolution algorithm, which uses an image prior of nondimensional Gaussianity measure to enforce sparsity and an undetermined boundary condition methodology to reduce boundary artifacts. For blur estimation, a linear inverse problem with normalization and nonnegative constraints must be solved. However, the normalization constraint is ignored in many blind image deblurring methods, mainly because it makes the problem less tractable. In this paper, we show that the normalization constraint can be very naturally incorporated into the estimation process by using a Dirichlet distribution to approximate the posterior distribution of the blur. Making use of variational Dirichlet approximation, we provide a blur posterior approximation that considers the uncertainty of the estimate and removes noise in the estimated kernel. Experiments with synthetic and real data demonstrate that the proposed method is very competitive to the state-of-the-art blind image restoration methods. PMID:26390458
Weighted Bergman Kernels and Quantization}
NASA Astrophysics Data System (ADS)
Engliš, Miroslav
Let Ω be a bounded pseudoconvex domain in CN, φ, ψ two positive functions on Ω such that - log ψ, - log φ are plurisubharmonic, and z∈Ω a point at which - log φ is smooth and strictly plurisubharmonic. We show that as k-->∞, the Bergman kernels with respect to the weights φkψ have an asymptotic expansion
TICK: Transparent Incremental Checkpointing at Kernel Level
Energy Science and Technology Software Center (ESTSC)
2004-10-25
TICK is a software package implemented in Linux 2.6 that allows the save and restore of user processes, without any change to the user code or binary. With TICK a process can be suspended by the Linux kernel upon receiving an interrupt and saved in a file. This file can be later thawed in another computer running Linux (potentially the same computer). TICK is implemented as a Linux kernel module, in the Linux version 2.6.5
Kernel Manifold Alignment for Domain Adaptation.
Tuia, Devis; Camps-Valls, Gustau
2016-01-01
The wealth of sensory data coming from different modalities has opened numerous opportunities for data analysis. The data are of increasing volume, complexity and dimensionality, thus calling for new methodological innovations towards multimodal data processing. However, multimodal architectures must rely on models able to adapt to changes in the data distribution. Differences in the density functions can be due to changes in acquisition conditions (pose, illumination), sensors characteristics (number of channels, resolution) or different views (e.g. street level vs. aerial views of a same building). We call these different acquisition modes domains, and refer to the adaptation problem as domain adaptation. In this paper, instead of adapting the trained models themselves, we alternatively focus on finding mappings of the data sources into a common, semantically meaningful, representation domain. This field of manifold alignment extends traditional techniques in statistics such as canonical correlation analysis (CCA) to deal with nonlinear adaptation and possibly non-corresponding data pairs between the domains. We introduce a kernel method for manifold alignment (KEMA) that can match an arbitrary number of data sources without needing corresponding pairs, just few labeled examples in all domains. KEMA has interesting properties: 1) it generalizes other manifold alignment methods, 2) it can align manifolds of very different complexities, performing a discriminative alignment preserving each manifold inner structure, 3) it can define a domain-specific metric to cope with multimodal specificities, 4) it can align data spaces of different dimensionality, 5) it is robust to strong nonlinear feature deformations, and 6) it is closed-form invertible, which allows transfer across-domains and data synthesis. To authors' knowledge this is the first method addressing all these important issues at once. We also present a reduced-rank version of KEMA for computational