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.
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
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`.
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).
[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
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.
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.
[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
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.
Calculates Thermal Neutron Scattering Kernel.
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
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.
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.
Robotic Intelligence Kernel: Visualization
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.
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
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...
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...
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
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
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.
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,...
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
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.
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.
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......
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)
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 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.
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.
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
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.
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
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
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.
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 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
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.
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
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...
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.
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 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.
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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2012-10-02
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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.
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...
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
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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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....
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2013-04-23
... 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...
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
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.
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.
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
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%.
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
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
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...
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...
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...
KITTEN Lightweight Kernel 0.1 Beta
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
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
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
PET Image Reconstruction Using Kernel Method
Wang, Guobao; Qi, Jinyi
2014-01-01
Image reconstruction from low-count PET projection data is challenging because the inverse problem is ill-posed. Prior information can be used to improve image quality. Inspired by the kernel methods in machine learning, this paper proposes a kernel based method that models PET image intensity in each pixel as a function of a set of features obtained from prior information. The kernel-based image model is incorporated into the forward model of PET projection data and the coefficients can be readily estimated by the maximum likelihood (ML) or penalized likelihood image reconstruction. A kernelized expectation-maximization (EM) algorithm is presented to obtain the ML estimate. Computer simulations show that the proposed approach can achieve better bias versus variance trade-off and higher contrast recovery for dynamic PET image reconstruction than the conventional maximum likelihood method with and without post-reconstruction denoising. Compared with other regularization-based methods, the kernel method is easier to implement and provides better image quality for low-count data. Application of the proposed kernel method to a 4D dynamic PET patient dataset showed promising results. PMID:25095249
PET image reconstruction using kernel method.
Wang, Guobao; Qi, Jinyi
2015-01-01
Image reconstruction from low-count positron emission tomography (PET) projection data is challenging because the inverse problem is ill-posed. Prior information can be used to improve image quality. Inspired by the kernel methods in machine learning, this paper proposes a kernel based method that models PET image intensity in each pixel as a function of a set of features obtained from prior information. The kernel-based image model is incorporated into the forward model of PET projection data and the coefficients can be readily estimated by the maximum likelihood (ML) or penalized likelihood image reconstruction. A kernelized expectation-maximization algorithm is presented to obtain the ML estimate. Computer simulations show that the proposed approach can achieve better bias versus variance trade-off and higher contrast recovery for dynamic PET image reconstruction than the conventional maximum likelihood method with and without post-reconstruction denoising. Compared with other regularization-based methods, the kernel method is easier to implement and provides better image quality for low-count data. Application of the proposed kernel method to a 4-D dynamic PET patient dataset showed promising results. PMID:25095249
Evaluating the Gradient of the Thin Wire Kernel
NASA Technical Reports Server (NTRS)
Wilton, Donald R.; Champagne, Nathan J.
2008-01-01
Recently, a formulation for evaluating the thin wire kernel was developed that employed a change of variable to smooth the kernel integrand, canceling the singularity in the integrand. Hence, the typical expansion of the wire kernel in a series for use in the potential integrals is avoided. The new expression for the kernel is exact and may be used directly to determine the gradient of the wire kernel, which consists of components that are parallel and radial to the wire axis.
Feature expectation heightens visual sensitivity during fine orientation discrimination
Cheadle, Sam; Egner, Tobias; Wyart, Valentin; Wu, Claire; Summerfield, Christopher
2015-01-01
Attending to a stimulus enhances the sensitivity of perceptual decisions. However, it remains unclear how perceptual sensitivity varies according to whether a feature is expected or unexpected. Here, observers made fine discrimination judgments about the orientation of visual gratings embedded in low spatial-frequency noise, and psychophysical reverse correlation was used to estimate decision ‘kernels' that revealed how visual features influenced choices. Orthogonal cues alerted subjects to which of two spatial locations was likely to be probed (spatial attention cue) and which of two oriented gratings was likely to occur (feature expectation cue). When an expected (relative to unexpected) feature occurred, decision kernels shifted away from the category boundary, allowing observers to capitalize on more informative, “off-channel” stimulus features. By contrast, the spatial attention cue had a multiplicative influence on decision kernels, consistent with an increase in response gain. Feature expectation thus heightens sensitivity to the most informative visual features, independent of selective attention. PMID:26505967
Feature expectation heightens visual sensitivity during fine orientation discrimination.
Cheadle, Sam; Egner, Tobias; Wyart, Valentin; Wu, Claire; Summerfield, Christopher
2015-01-01
Attending to a stimulus enhances the sensitivity of perceptual decisions. However, it remains unclear how perceptual sensitivity varies according to whether a feature is expected or unexpected. Here, observers made fine discrimination judgments about the orientation of visual gratings embedded in low spatial-frequency noise, and psychophysical reverse correlation was used to estimate decision 'kernels' that revealed how visual features influenced choices. Orthogonal cues alerted subjects to which of two spatial locations was likely to be probed (spatial attention cue) and which of two oriented gratings was likely to occur (feature expectation cue). When an expected (relative to unexpected) feature occurred, decision kernels shifted away from the category boundary, allowing observers to capitalize on more informative, "off-channel" stimulus features. By contrast, the spatial attention cue had a multiplicative influence on decision kernels, consistent with an increase in response gain. Feature expectation thus heightens sensitivity to the most informative visual features, independent of selective attention. PMID:26505967
Capacity of very noisy communication channels based on Fisher information
NASA Astrophysics Data System (ADS)
Duan, Fabing; Chapeau-Blondeau, François; Abbott, Derek
2016-06-01
We generalize the asymptotic capacity expression for very noisy communication channels to now include coloured noise. For the practical scenario of a non-optimal receiver, we consider the common case of a correlation receiver. Due to the central limit theorem and the cumulative characteristic of a correlation receiver, we model this channel noise as additive Gaussian noise. Then, the channel capacity proves to be directly related to the Fisher information of the noise distribution and the weak signal energy. The conditions for occurrence of a noise-enhanced capacity effect are discussed, and the capacity difference between this noisy communication channel and other nonlinear channels is clarified.
Capacity of very noisy communication channels based on Fisher information.
Duan, Fabing; Chapeau-Blondeau, François; Abbott, Derek
2016-01-01
We generalize the asymptotic capacity expression for very noisy communication channels to now include coloured noise. For the practical scenario of a non-optimal receiver, we consider the common case of a correlation receiver. Due to the central limit theorem and the cumulative characteristic of a correlation receiver, we model this channel noise as additive Gaussian noise. Then, the channel capacity proves to be directly related to the Fisher information of the noise distribution and the weak signal energy. The conditions for occurrence of a noise-enhanced capacity effect are discussed, and the capacity difference between this noisy communication channel and other nonlinear channels is clarified. PMID:27306041
Capacity of very noisy communication channels based on Fisher information
Duan, Fabing; Chapeau-Blondeau, François; Abbott, Derek
2016-01-01
We generalize the asymptotic capacity expression for very noisy communication channels to now include coloured noise. For the practical scenario of a non-optimal receiver, we consider the common case of a correlation receiver. Due to the central limit theorem and the cumulative characteristic of a correlation receiver, we model this channel noise as additive Gaussian noise. Then, the channel capacity proves to be directly related to the Fisher information of the noise distribution and the weak signal energy. The conditions for occurrence of a noise-enhanced capacity effect are discussed, and the capacity difference between this noisy communication channel and other nonlinear channels is clarified. PMID:27306041
Asymmetric biclustering with constrained von Mises-Fisher models
NASA Astrophysics Data System (ADS)
Watanabe, Kazuho; Wu, Hsiang-Yun; Takahashi, Shigeo; Fujishiro, Issei
2016-03-01
As a probability distribution on the high-dimensional sphere, the von Mises-Fisher (vMF) distribution is widely used for directional statistics and data analysis methods based on correlation. We consider a constrained vMF distribution for block modeling, which provides a probabilistic model of an asymmetric biclustering method that uses correlation as the similarity measure of data features. We derive the variational Bayesian inference algorithm for the mixture of the constrained vMF distributions. It is applied to a multivariate data visualization method implemented with enhanced parallel coordinate plots.
Azimi-Sadjadi, Mahmood R; Salazar, Jaime; Srinivasan, Saravanakumar
2009-07-01
This paper presents an adaptable content-based image retrieval (CBIR) system developed using regularization theory, kernel-based machines, and Fisher information measure. The system consists of a retrieval subsystem that carries out similarity matching using image-dependant information, multiple mapping subsystems that adaptively modify the similarity measures, and a relevance feedback mechanism that incorporates user information. The adaptation process drives the retrieval error to zero in order to exactly meet either an existing multiclass classification model or the user high-level concepts using reference-model or relevance feedback learning, respectively. To facilitate the selection of the most informative query images during relevance feedback learning a new method based upon the Fisher information is introduced. Model-reference and relevance feedback learning mechanisms are thoroughly tested on a domain-specific image database that encompasses a wide range of underwater objects captured using an electro-optical sensor. Benchmarking results with two other relevance feedback learning methods are also provided. PMID:19447718
NASA Astrophysics Data System (ADS)
Niu, Yanmin; Wang, Xuchu
2011-02-01
This paper presents a new face recognition method that extracts multiple discriminant features based on multiscale image enhancement technique and kernel-based orthogonal feature extraction improvements with several interesting characteristics. First, it can extract more discriminative multiscale face feature than traditional pixel-based or Gabor-based feature. Second, it can effectively deal with the small sample size problem as well as feature correlation problem by using eigenvalue decomposition on scatter matrices. Finally, the extractor handles nonlinearity efficiently by using kernel trick. Multiple recognition experiments on open face data set with comparison to several related methods show the effectiveness and superiority of the proposed method.
Nonconvexity of the relative entropy for Markov dynamics: A Fisher information approach
NASA Astrophysics Data System (ADS)
Polettini, Matteo; Esposito, Massimiliano
2013-07-01
We show via counterexamples that relative entropy between the solution of a Markovian master equation and the steady state is not a convex function of time. We thus disprove the hypotheses that a general evolution principle of thermodynamics based on the decrease of the nonadiabatic entropy production could hold. However, we argue that a large separation of typical decay times is necessary for nonconvex solutions to occur, making concave transients extremely short lived with respect to the main relaxation modes. We describe a general method based on the Fisher information matrix to discriminate between generators that admit nonconvex solutions and those that do not. While initial conditions leading to concave transients are shown to be extremely fine-tuned, by our method we are able to select nonconvex initial conditions that are arbitrarily close to the steady state. Convexity does occur when the system is close to satisfying detailed balance or, more generally, when certain normality conditions of the decay modes are satisfied. Our results circumscribe the range of validity of a conjecture by Maes [Phys. Rev. Lett.PRLTAO0031-900710.1103/PhysRevLett.107.010601 107, 010601 (2011)] regarding monotonicity of the large deviation rate functional for the occupation probability, showing that while the conjecture might hold in the long-time limit, the conditions for Lyapunov's second criterion for stability are not met.
On a (β, q)-generalized Fisher information and inequalities involving q-Gaussian distributions
NASA Astrophysics Data System (ADS)
Bercher, J.-F.
2012-06-01
In the present paper, we would like to draw attention to a possible generalized Fisher information that fits well in the formalism of nonextensive thermostatistics. This generalized Fisher information is defined for densities on {R}n. Just as the maximum Rényi or Tsallis entropy subject to an elliptic moment constraint is a generalized q-Gaussian, we show that the minimization of the generalized Fisher information also leads a generalized q-Gaussian. This yields a generalized Cramér-Rao inequality. In addition, we show that the generalized Fisher information naturally pops up in a simple inequality that links the generalized entropies, the generalized Fisher information, and an elliptic moment. Finally, we give an extended Stam inequality. In this series of results, the extremal functions are the generalized q-Gaussians. Thus, these results complement the classical characterization of the generalized q-Gaussian and introduce a generalized Fisher information as a new information measure in nonextensive thermostatistics.
Chen, Lili; Zhang, Xi; Wang, Hui
2015-05-01
Obstructive sleep apnea (OSA) is a common sleep disorder that often remains undiagnosed, leading to an increased risk of developing cardiovascular diseases. Polysomnogram (PSG) is currently used as a golden standard for screening OSA. However, because it is time consuming, expensive and causes discomfort, alternative techniques based on a reduced set of physiological signals are proposed to solve this problem. This study proposes a convenient non-parametric kernel density-based approach for detection of OSA using single-lead electrocardiogram (ECG) recordings. Selected physiologically interpretable features are extracted from segmented RR intervals, which are obtained from ECG signals. These features are fed into the kernel density classifier to detect apnea event and bandwidths for density of each class (normal or apnea) are automatically chosen through an iterative bandwidth selection algorithm. To validate the proposed approach, RR intervals are extracted from ECG signals of 35 subjects obtained from a sleep apnea database ( http://physionet.org/cgi-bin/atm/ATM ). The results indicate that the kernel density classifier, with two features for apnea event detection, achieves a mean accuracy of 82.07 %, with mean sensitivity of 83.23 % and mean specificity of 80.24 %. Compared with other existing methods, the proposed kernel density approach achieves a comparably good performance but by using fewer features without significantly losing discriminant power, which indicates that it could be widely used for home-based screening or diagnosis of OSA. PMID:25732075
Analysis of heat kernel highlights the strongly modular and heat-preserving structure of proteins
NASA Astrophysics Data System (ADS)
Livi, Lorenzo; Maiorino, Enrico; Pinna, Andrea; Sadeghian, Alireza; Rizzi, Antonello; Giuliani, Alessandro
2016-01-01
In this paper, we study the structure and dynamical properties of protein contact networks with respect to other biological networks, together with simulated archetypal models acting as probes. We consider both classical topological descriptors, such as modularity and statistics of the shortest paths, and different interpretations in terms of diffusion provided by the discrete heat kernel, which is elaborated from the normalized graph Laplacians. A principal component analysis shows high discrimination among the network types, by considering both the topological and heat kernel based vector characterizations. Furthermore, a canonical correlation analysis demonstrates the strong agreement among those two characterizations, providing thus an important justification in terms of interpretability for the heat kernel. Finally, and most importantly, the focused analysis of the heat kernel provides a way to yield insights on the fact that proteins have to satisfy specific structural design constraints that the other considered networks do not need to obey. Notably, the heat trace decay of an ensemble of varying-size proteins denotes subdiffusion, a peculiar property of proteins.
Diffusion on a hypersphere: application to the Wright-Fisher model
NASA Astrophysics Data System (ADS)
Maruyama, Kishiko; Itoh, Yoshiaki
2016-04-01
The eigenfunction expansion by Gegenbauer polynomials for the diffusion on a hypersphere is transformed into the diffusion for the Wright-Fisher model with a particular mutation rate. We use the Ito calculus considering stochastic differential equations. The expansion gives a simple interpretation of the Griffiths eigenfunction expansion for the Wright-Fisher model. Our representation is useful to simulate the Wright-Fisher model as well as Brownian motion on a hypersphere.
Describing variations of the Fisher-matrix across parameter space
NASA Astrophysics Data System (ADS)
Schäfer, Björn Malte; Reischke, Robert
2016-08-01
Forecasts in cosmology, both with Monte Carlo Markov-chain methods and with the Fisher-matrix formalism, depend on the choice of the fiducial model because both the signal strength of any observable and the model non-linearities linking observables to cosmological parameters vary in the general case. In this paper we propose a method for extrapolating Fisher-forecasts across the space of cosmological parameters by constructing a suitable basis. We demonstrate the validity of our method with constraints on a standard dark energy model extrapolated from a ΛCDM-model, as can be expected from two-bin weak lensing tomography with an Euclid-like survey, in the parameter pairs (Ωm, σ8), (Ωm, w0) and (w0, wa). Our numerical results include very accurate extrapolations across a wide range of cosmological parameters in terms of shape, size and orientation of the parameter likelihood, and a decomposition of the change of the likelihood contours into modes, which are straightforward to interpret in a geometrical way. We find that in particular the variation of the dark energy figure of merit is well captured by our formalism.
Comparing quantum cloning: A Fisher-information perspective
NASA Astrophysics Data System (ADS)
Song, Hongting; Luo, Shunlong; Li, Nan; Chang, Lina
2013-10-01
Perfect cloning of an unknown quantum state is impossible. Approximate cloning, which is optimal in various senses, has been found in many cases. Paradigmatic examples are Wootters-Zurek cloning and universal cloning. These cloning machines aim at optimal cloning of the full quantum states. However, in practice, what is important and relevant may only involve partial information in quantum states, rather than quantum states themselves. For example, signals are often encoded as parameters in quantum states, whose information content is well synthesized by quantum Fisher information. This raises the basic issue of evaluating the information transferring capability (e.g., distributing quantum Fisher information) of quantum cloning. We assess and compare Wootters-Zurek cloning and universal cloning from this perspective and show that, on average, Wootters-Zurek cloning performs better than universal cloning for the phase (as well as amplitude) parameter, although they are incomparable individually, and universal cloning has many advantages over Wootters-Zurek cloning in other contexts. Physical insights and related issues are further discussed.
Statistical algorithms for a comprehensive test ban treaty discrimination framework
Foote, N.D.; Anderson, D.N.; Higbee, K.T.; Miller, N.E.; Redgate, T.; Rohay, A.C.; Hagedorn, D.N.
1996-10-01
Seismic discrimination is the process of identifying a candidate seismic event as an earthquake or explosion using information from seismic waveform features (seismic discriminants). In the CTBT setting, low energy seismic activity must be detected and identified. A defensible CTBT discrimination decision requires an understanding of false-negative (declaring an event to be an earthquake given it is an explosion) and false-position (declaring an event to be an explosion given it is an earthquake) rates. These rates are derived from a statistical discrimination framework. A discrimination framework can be as simple as a single statistical algorithm or it can be a mathematical construct that integrates many different types of statistical algorithms and CTBT technologies. In either case, the result is the identification of an event and the numerical assessment of the accuracy of an identification, that is, false-negative and false-positive rates. In Anderson et al., eight statistical discrimination algorithms are evaluated relative to their ability to give results that effectively contribute to a decision process and to be interpretable with physical (seismic) theory. These algorithms can be discrimination frameworks individually or components of a larger framework. The eight algorithms are linear discrimination (LDA), quadratic discrimination (QDA), variably regularized discrimination (VRDA), flexible discrimination (FDA), logistic discrimination, K-th nearest neighbor (KNN), kernel discrimination, and classification and regression trees (CART). In this report, the performance of these eight algorithms, as applied to regional seismic data, is documented. Based on the findings in Anderson et al. and this analysis: CART is an appropriate algorithm for an automated CTBT setting.
Fisher information, Rényi entropy power and quantum phase transition in the Dicke model
NASA Astrophysics Data System (ADS)
Nagy, Á.; Romera, E.
2012-07-01
Fisher information, Rényi entropy power and Fisher-Rényi information product are presented for the Dicke model. There is a quantum phase transition in this quantum optical model. It is pointed out that there is an abrupt change in the Fisher information, Rényi entropy power, the Fisher, Shannon and Rényi lengths at the transition point. It is found that these quantities diverge as the characteristic length: | around the critical value of the coupling strength λc for any value of the parameter β.
Federal Register 2010, 2011, 2012, 2013, 2014
2013-01-25
... FR 67396, Fisher Clinical Services, Inc., 7554 Schantz Road, Allentown, Pennsylvania 18106, made application to the Drug Enforcement Administration (DEA) to be registered as an importer of Tapentadol...
Analog forecasting with dynamics-adapted kernels
NASA Astrophysics Data System (ADS)
Zhao, Zhizhen; Giannakis, Dimitrios
2016-09-01
Analog forecasting is a nonparametric technique introduced by Lorenz in 1969 which predicts the evolution of states of a dynamical system (or observables defined on the states) by following the evolution of the sample in a historical record of observations which most closely resembles the current initial data. Here, we introduce a suite of forecasting methods which improve traditional analog forecasting by combining ideas from kernel methods developed in harmonic analysis and machine learning and state-space reconstruction for dynamical systems. A key ingredient of our approach is to replace single-analog forecasting with weighted ensembles of analogs constructed using local similarity kernels. The kernels used here employ a number of dynamics-dependent features designed to improve forecast skill, including Takens’ delay-coordinate maps (to recover information in the initial data lost through partial observations) and a directional dependence on the dynamical vector field generating the data. Mathematically, our approach is closely related to kernel methods for out-of-sample extension of functions, and we discuss alternative strategies based on the Nyström method and the multiscale Laplacian pyramids technique. We illustrate these techniques in applications to forecasting in a low-order deterministic model for atmospheric dynamics with chaotic metastability, and interannual-scale forecasting in the North Pacific sector of a comprehensive climate model. We find that forecasts based on kernel-weighted ensembles have significantly higher skill than the conventional approach following a single analog.
Online Sequential Extreme Learning Machine With Kernels.
Scardapane, Simone; Comminiello, Danilo; Scarpiniti, Michele; Uncini, Aurelio
2015-09-01
The extreme learning machine (ELM) was recently proposed as a unifying framework for different families of learning algorithms. The classical ELM model consists of a linear combination of a fixed number of nonlinear expansions of the input vector. Learning in ELM is hence equivalent to finding the optimal weights that minimize the error on a dataset. The update works in batch mode, either with explicit feature mappings or with implicit mappings defined by kernels. Although an online version has been proposed for the former, no work has been done up to this point for the latter, and whether an efficient learning algorithm for online kernel-based ELM exists remains an open problem. By explicating some connections between nonlinear adaptive filtering and ELM theory, in this brief, we present an algorithm for this task. In particular, we propose a straightforward extension of the well-known kernel recursive least-squares, belonging to the kernel adaptive filtering (KAF) family, to the ELM framework. We call the resulting algorithm the kernel online sequential ELM (KOS-ELM). Moreover, we consider two different criteria used in the KAF field to obtain sparse filters and extend them to our context. We show that KOS-ELM, with their integration, can result in a highly efficient algorithm, both in terms of obtained generalization error and training time. Empirical evaluations demonstrate interesting results on some benchmarking datasets. PMID:25561597
Linear discriminant analysis with misallocation in training samples
NASA Technical Reports Server (NTRS)
Chhikara, R. (Principal Investigator); Mckeon, J.
1982-01-01
Linear discriminant analysis for a two-class case is studied in the presence of misallocation in training samples. A general appraoch to modeling of mislocation is formulated, and the mean vectors and covariance matrices of the mixture distributions are derived. The asymptotic distribution of the discriminant boundary is obtained and the asymptotic first two moments of the two types of error rate given. Certain numerical results for the error rates are presented by considering the random and two non-random misallocation models. It is shown that when the allocation procedure for training samples is objectively formulated, the effect of misallocation on the error rates of the Bayes linear discriminant rule can almost be eliminated. If, however, this is not possible, the use of Fisher rule may be preferred over the Bayes rule.
A discriminant bispectrum feature for surface electromyogram signal classification.
Chen, Xinpu; Zhu, Xiangyang; Zhang, Dingguo
2010-03-01
This paper presents a discriminant bispectrum (DBS) feature extraction approach to surface electromyogram (sEMG) signal classification for prosthetic control. The proposed feature extraction method involves two steps: (1) the bispectrum matrix integration, and (2) the Fisher linear discriminant (FLD) projection. We compare DBS with other conventional features, such as autoregressive coefficients, root mean square, power spectral distribution and time domain statistics. First, the separability of the features is investigated by the visualization of feature distribution in the FLD subspace and quantitative measurement (Davies-Boulder clustering index). Then four linear and non-linear classifiers are used to evaluate the discriminative powers of the features in terms of classification accuracy (CA). The experimental results show that DBS has better performance than other features for identifying the motion patterns of sEMG signals, and the best CA result of DBS is 99.4%. PMID:19955011
Nonparametric entropy estimation using kernel densities.
Lake, Douglas E
2009-01-01
The entropy of experimental data from the biological and medical sciences provides additional information over summary statistics. Calculating entropy involves estimates of probability density functions, which can be effectively accomplished using kernel density methods. Kernel density estimation has been widely studied and a univariate implementation is readily available in MATLAB. The traditional definition of Shannon entropy is part of a larger family of statistics, called Renyi entropy, which are useful in applications that require a measure of the Gaussianity of data. Of particular note is the quadratic entropy which is related to the Friedman-Tukey (FT) index, a widely used measure in the statistical community. One application where quadratic entropy is very useful is the detection of abnormal cardiac rhythms, such as atrial fibrillation (AF). Asymptotic and exact small-sample results for optimal bandwidth and kernel selection to estimate the FT index are presented and lead to improved methods for entropy estimation. PMID:19897106
Tile-Compressed FITS Kernel for IRAF
NASA Astrophysics Data System (ADS)
Seaman, R.
2011-07-01
The Flexible Image Transport System (FITS) is a ubiquitously supported standard of the astronomical community. Similarly, the Image Reduction and Analysis Facility (IRAF), developed by the National Optical Astronomy Observatory, is a widely used astronomical data reduction package. IRAF supplies compatibility with FITS format data through numerous tools and interfaces. The most integrated of these is IRAF's FITS image kernel that provides access to FITS from any IRAF task that uses the basic IMIO interface. The original FITS kernel is a complex interface of purpose-built procedures that presents growing maintenance issues and lacks recent FITS innovations. A new FITS kernel is being developed at NOAO that is layered on the CFITSIO library from the NASA Goddard Space Flight Center. The simplified interface will minimize maintenance headaches as well as add important new features such as support for the FITS tile-compressed (fpack) format.
Fast generation of sparse random kernel graphs
Hagberg, Aric; Lemons, Nathan; Du, Wen -Bo
2015-09-10
The development of kernel-based inhomogeneous random graphs has provided models that are flexible enough to capture many observed characteristics of real networks, and that are also mathematically tractable. We specify a class of inhomogeneous random graph models, called random kernel graphs, that produces sparse graphs with tunable graph properties, and we develop an efficient generation algorithm to sample random instances from this model. As real-world networks are usually large, it is essential that the run-time of generation algorithms scales better than quadratically in the number of vertices n. We show that for many practical kernels our algorithm runs in timemore » at most ο(n(logn)²). As an example, we show how to generate samples of power-law degree distribution graphs with tunable assortativity.« less
Fast generation of sparse random kernel graphs
Hagberg, Aric; Lemons, Nathan; Du, Wen -Bo
2015-09-10
The development of kernel-based inhomogeneous random graphs has provided models that are flexible enough to capture many observed characteristics of real networks, and that are also mathematically tractable. We specify a class of inhomogeneous random graph models, called random kernel graphs, that produces sparse graphs with tunable graph properties, and we develop an efficient generation algorithm to sample random instances from this model. As real-world networks are usually large, it is essential that the run-time of generation algorithms scales better than quadratically in the number of vertices n. We show that for many practical kernels our algorithm runs in time at most ο(n(logn)²). As an example, we show how to generate samples of power-law degree distribution graphs with tunable assortativity.
Fast Generation of Sparse Random Kernel Graphs
2015-01-01
The development of kernel-based inhomogeneous random graphs has provided models that are flexible enough to capture many observed characteristics of real networks, and that are also mathematically tractable. We specify a class of inhomogeneous random graph models, called random kernel graphs, that produces sparse graphs with tunable graph properties, and we develop an efficient generation algorithm to sample random instances from this model. As real-world networks are usually large, it is essential that the run-time of generation algorithms scales better than quadratically in the number of vertices n. We show that for many practical kernels our algorithm runs in time at most 𝒪(n(logn)2). As a practical example we show how to generate samples of power-law degree distribution graphs with tunable assortativity. PMID:26356296
Classification of Hazelnut Kernels by Using Impact Acoustic Time-Frequency Patterns
NASA Astrophysics Data System (ADS)
Kalkan, Habil; Ince, Nuri Firat; Tewfik, Ahmed H.; Yardimci, Yasemin; Pearson, Tom
2007-12-01
Hazelnuts with damaged or cracked shells are more prone to infection with aflatoxin producing molds ( Aspergillus flavus). These molds can cause cancer. In this study, we introduce a new approach that separates damaged/cracked hazelnut kernels from good ones by using time-frequency features obtained from impact acoustic signals. The proposed technique requires no prior knowledge of the relevant time and frequency locations. In an offline step, the algorithm adaptively segments impact signals from a training data set in time using local cosine packet analysis and a Kullback-Leibler criterion to assess the discrimination power of different segmentations. In each resulting time segment, the signal is further decomposed into subbands using an undecimated wavelet transform. The most discriminative subbands are selected according to the Euclidean distance between the cumulative probability distributions of the corresponding subband coefficients. The most discriminative subbands are fed into a linear discriminant analysis classifier. In the online classification step, the algorithm simply computes the learned features from the observed signal and feeds them to the linear discriminant analysis (LDA) classifier. The algorithm achieved a throughput rate of 45 nuts/s and a classification accuracy of 96% with the 30 most discriminative features, a higher rate than those provided with prior methods.
Experimental study of turbulent flame kernel propagation
Mansour, Mohy; Peters, Norbert; Schrader, Lars-Uve
2008-07-15
Flame kernels in spark ignited combustion systems dominate the flame propagation and combustion stability and performance. They are likely controlled by the spark energy, flow field and mixing field. The aim of the present work is to experimentally investigate the structure and propagation of the flame kernel in turbulent premixed methane flow using advanced laser-based techniques. The spark is generated using pulsed Nd:YAG laser with 20 mJ pulse energy in order to avoid the effect of the electrodes on the flame kernel structure and the variation of spark energy from shot-to-shot. Four flames have been investigated at equivalence ratios, {phi}{sub j}, of 0.8 and 1.0 and jet velocities, U{sub j}, of 6 and 12 m/s. A combined two-dimensional Rayleigh and LIPF-OH technique has been applied. The flame kernel structure has been collected at several time intervals from the laser ignition between 10 {mu}s and 2 ms. The data show that the flame kernel structure starts with spherical shape and changes gradually to peanut-like, then to mushroom-like and finally disturbed by the turbulence. The mushroom-like structure lasts longer in the stoichiometric and slower jet velocity. The growth rate of the average flame kernel radius is divided into two linear relations; the first one during the first 100 {mu}s is almost three times faster than that at the later stage between 100 and 2000 {mu}s. The flame propagation is slightly faster in leaner flames. The trends of the flame propagation, flame radius, flame cross-sectional area and mean flame temperature are related to the jet velocity and equivalence ratio. The relations obtained in the present work allow the prediction of any of these parameters at different conditions. (author)
Full Waveform Inversion Using Waveform Sensitivity Kernels
NASA Astrophysics Data System (ADS)
Schumacher, Florian; Friederich, Wolfgang
2013-04-01
We present a full waveform inversion concept for applications ranging from seismological to enineering contexts, in which the steps of forward simulation, computation of sensitivity kernels, and the actual inversion are kept separate of each other. We derive waveform sensitivity kernels from Born scattering theory, which for unit material perturbations are identical to the Born integrand for the considered path between source and receiver. The evaluation of such a kernel requires the calculation of Green functions and their strains for single forces at the receiver position, as well as displacement fields and strains originating at the seismic source. We compute these quantities in the frequency domain using the 3D spectral element code SPECFEM3D (Tromp, Komatitsch and Liu, 2008) and the 1D semi-analytical code GEMINI (Friederich and Dalkolmo, 1995) in both, Cartesian and spherical framework. We developed and implemented the modularized software package ASKI (Analysis of Sensitivity and Kernel Inversion) to compute waveform sensitivity kernels from wavefields generated by any of the above methods (support for more methods is planned), where some examples will be shown. As the kernels can be computed independently from any data values, this approach allows to do a sensitivity and resolution analysis first without inverting any data. In the context of active seismic experiments, this property may be used to investigate optimal acquisition geometry and expectable resolution before actually collecting any data, assuming the background model is known sufficiently well. The actual inversion step then, can be repeated at relatively low costs with different (sub)sets of data, adding different smoothing conditions. Using the sensitivity kernels, we expect the waveform inversion to have better convergence properties compared with strategies that use gradients of a misfit function. Also the propagation of the forward wavefield and the backward propagation from the receiver
Volatile compound formation during argan kernel roasting.
El Monfalouti, Hanae; Charrouf, Zoubida; Giordano, Manuela; Guillaume, Dominique; Kartah, Badreddine; Harhar, Hicham; Gharby, Saïd; Denhez, Clément; Zeppa, Giuseppe
2013-01-01
Virgin edible argan oil is prepared by cold-pressing argan kernels previously roasted at 110 degrees C for up to 25 minutes. The concentration of 40 volatile compounds in virgin edible argan oil was determined as a function of argan kernel roasting time. Most of the volatile compounds begin to be formed after 15 to 25 minutes of roasting. This suggests that a strictly controlled roasting time should allow the modulation of argan oil taste and thus satisfy different types of consumers. This could be of major importance considering the present booming use of edible argan oil. PMID:23472454
Modified wavelet kernel methods for hyperspectral image classification
NASA Astrophysics Data System (ADS)
Hsu, Pai-Hui; Huang, Xiu-Man
2015-10-01
Hyperspectral images have the capability of acquiring images of earth surface with several hundred of spectral bands. Providing such abundant spectral data should increase the abilities in classifying land use/cover type. However, due to the high dimensionality of hyperspectral data, traditional classification methods are not suitable for hyperspectral data classification. The common method to solve this problem is dimensionality reduction by using feature extraction before classification. Kernel methods such as support vector machine (SVM) and multiple kernel learning (MKL) have been successfully applied to hyperspectral images classification. In kernel methods applications, the selection of kernel function plays an important role. The wavelet kernel with multidimensional wavelet functions can find the optimal approximation of data in feature space for classification. The SVM with wavelet kernels (called WSVM) have been also applied to hyperspectral data and improve classification accuracy. In this study, wavelet kernel method combined multiple kernel learning algorithm and wavelet kernels was proposed for hyperspectral image classification. After the appropriate selection of a linear combination of kernel functions, the hyperspectral data will be transformed to the wavelet feature space, which should have the optimal data distribution for kernel learning and classification. Finally, the proposed methods were compared with the existing methods. A real hyperspectral data set was used to analyze the performance of wavelet kernel method. According to the results the proposed wavelet kernel methods in this study have well performance, and would be an appropriate tool for hyperspectral image classification.
Kernel abortion in maize. II. Distribution of /sup 14/C among kernel carboydrates
Hanft, J.M.; Jones, R.J.
1986-06-01
This study was designed to compare the uptake and distribution of /sup 14/C among fructose, glucose, sucrose, and starch in the cob, pedicel, and endosperm tissues of maize (Zea mays L.) kernels induced to abort by high temperature with those that develop normally. Kernels cultured in vitro at 309 and 35/sup 0/C were transferred to (/sup 14/C)sucrose media 10 days after pollination. Kernels cultured at 35/sup 0/C aborted prior to the onset of linear dry matter accumulation. Significant uptake into the cob, pedicel, and endosperm of radioactivity associated with the soluble and starch fractions of the tissues was detected after 24 hours in culture on atlageled media. After 8 days in culture on (/sup 14/C)sucrose media, 48 and 40% of the radioactivity associated with the cob carbohydrates was found in the reducing sugars at 30 and 35/sup 0/C, respectively. Of the total carbohydrates, a higher percentage of label was associated with sucrose and lower percentage with fructose and glucose in pedicel tissue of kernels cultured at 35/sup 0/C compared to kernels cultured at 30/sup 0/C. These results indicate that sucrose was not cleaved to fructose and glucose as rapidly during the unloading process in the pedicel of kernels induced to abort by high temperature. Kernels cultured at 35/sup 0/C had a much lower proportion of label associated with endosperm starch (29%) than did kernels cultured at 30/sup 0/C (89%). Kernels cultured at 35/sup 0/C had a correspondingly higher proportion of /sup 14/C in endosperm fructose, glucose, and sucrose.
Fisher-Symmetric Informationally Complete Measurements for Pure States
NASA Astrophysics Data System (ADS)
Li, Nan; Ferrie, Christopher; Gross, Jonathan A.; Kalev, Amir; Caves, Carlton M.
2016-05-01
We introduce a new kind of quantum measurement that is defined to be symmetric in the sense of uniform Fisher information across a set of parameters that uniquely represent pure quantum states in the neighborhood of a fiducial pure state. The measurement is locally informationally complete—i.e., it uniquely determines these parameters, as opposed to distinguishing two arbitrary quantum states—and it is maximal in the sense of a multiparameter quantum Cramér-Rao bound. For a d -dimensional quantum system, requiring only local informational completeness allows us to reduce the number of outcomes of the measurement from a minimum close to but below 4 d -3 , for the usual notion of global pure-state informational completeness, to 2 d -1 .
Fisher-Symmetric Informationally Complete Measurements for Pure States.
Li, Nan; Ferrie, Christopher; Gross, Jonathan A; Kalev, Amir; Caves, Carlton M
2016-05-01
We introduce a new kind of quantum measurement that is defined to be symmetric in the sense of uniform Fisher information across a set of parameters that uniquely represent pure quantum states in the neighborhood of a fiducial pure state. The measurement is locally informationally complete-i.e., it uniquely determines these parameters, as opposed to distinguishing two arbitrary quantum states-and it is maximal in the sense of a multiparameter quantum Cramér-Rao bound. For a d-dimensional quantum system, requiring only local informational completeness allows us to reduce the number of outcomes of the measurement from a minimum close to but below 4d-3, for the usual notion of global pure-state informational completeness, to 2d-1. PMID:27203310
Enhancing teleportation of quantum Fisher information by partial measurements
NASA Astrophysics Data System (ADS)
Xiao, Xing; Yao, Yao; Zhong, Wo-Jun; Li, Yan-Ling; Xie, Ying-Mao
2016-01-01
The purport of quantum teleportation is to completely transfer information from one party to another distant partner. However, from the perspective of parameter estimation, it is the information carried by a particular parameter, not the information of total quantum state that needs to be teleported. Due to the inevitable noise in environments, we propose two schemes to enhance quantum Fisher information (QFI) teleportation under amplitude damping noise with the technique of partial measurements. We find that post-partial measurement can greatly enhance the teleported QFI, while the combination of prior partial measurement and post-partial measurement reversal could completely eliminate the effect of decoherence. We show that, somewhat consequentially, enhancing QFI teleportation is more economic than that of improving fidelity teleportation. Our work extends the ability of partial measurements as a quantum technique to battle decoherence in quantum information processing.
Detecting Large Quantum Fisher Information with Finite Measurement Precision
NASA Astrophysics Data System (ADS)
Fröwis, Florian; Sekatski, Pavel; Dür, Wolfgang
2016-03-01
We propose an experimentally accessible scheme to determine the lower bounds on the quantum Fisher information (QFI), which ascertains multipartite entanglement or usefulness for quantum metrology. The scheme is based on comparing the measurement statistics of a state before and after a small unitary rotation. We argue that, in general, the limited resolution of collective observables prevents the detection of large QFI. This can be overcome by performing an additional operation prior to the measurement. We illustrate the power of this protocol for present-day spin-squeezing experiments, where the same operation used for the preparation of the initial spin-squeezed state improves also the measurement precision and hence the lower bound on the QFI by 2 orders of magnitude. We also establish a connection to the Leggett-Garg inequalities. We show how to simulate a variant of the inequalities with our protocol and demonstrate that large QFI is necessary for their violation with coarse-grained detectors.
Fisher symmetry and the geometry of quantum states
NASA Astrophysics Data System (ADS)
Gross, Jonathan A.; Barnum, Howard; Caves, Carlton M.
The quantum Fisher information (QFI) is a valuable tool on account of the achievable lower bound it provides for single-parameter estimation. Due to the existence of incompatible quantum observables, however, the lower bound provided by the QFI cannot be saturated in the general multi-parameter case. A bound demonstrated by Gill and Massar (GM) captures some of the limitations that incompatibility imposes in the multi-parameter case. We further explore the structure of measurements allowed by quantum mechanics, identifying restrictions beyond those given by the QFI and GM bound. These additional restrictions give insight into the geometry of quantum state space and notions of measurement symmetry related to the QFI.
Detailed HIkinematics of Tully-Fisher calibrator galaxies
NASA Astrophysics Data System (ADS)
Ponomareva, Anastasia A.; Verheijen, Marc A. W.; Bosma, Albert
2016-09-01
We present spatially-resolved HI kinematics of 32 spiral galaxies which have Cepheid or/and Tip of the Red Giant Branch distances, and define a calibrator sample for the Tully-Fisher relation. The interferometric HI data for this sample were collected from available archives and supplemented with new GMRT observations. This paper describes an uniform analysis of the HI kinematics of this inhomogeneous data set. Our main result is an atlas for our calibrator sample that presents global HI profiles, integrated HI column-density maps, HI surface density profiles and, most importantly, detailed kinematic information in the form of high-quality rotation curves derived from highly-resolved, two-dimensional velocity fields and position-velocity diagrams.
Fisher information and the thermodynamics of scale-invariant systems
NASA Astrophysics Data System (ADS)
Hernando, A.; Vesperinas, C.; Plastino, A.
2010-02-01
We present a thermodynamic formulation for scale-invariant systems based on the minimization with constraints of the Fisher information measure. In such a way a clear analogy between these systems’ thermal properties and those of gases and fluids is seen to emerge in a natural fashion. We focus our attention on the non-interacting scenario, speaking thus of scale-free ideal gases (SFIGs) and present some empirical evidences regarding such disparate systems as electoral results, city populations and total citations in Physics journals, that seem to indicate that SFIGs do exist. We also illustrate the way in which Zipf’s law can be understood in a thermodynamical context as the surface of a finite system. Finally, we derive an equivalent microscopic description of our systems which totally agrees with previous numerical simulations found in the literature.
Centered Kernel Alignment Enhancing Neural Network Pretraining for MRI-Based Dementia Diagnosis
Cárdenas-Peña, David; Collazos-Huertas, Diego; Castellanos-Dominguez, German
2016-01-01
Dementia is a growing problem that affects elderly people worldwide. More accurate evaluation of dementia diagnosis can help during the medical examination. Several methods for computer-aided dementia diagnosis have been proposed using resonance imaging scans to discriminate between patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI) and healthy controls (NC). Nonetheless, the computer-aided diagnosis is especially challenging because of the heterogeneous and intermediate nature of MCI. We address the automated dementia diagnosis by introducing a novel supervised pretraining approach that takes advantage of the artificial neural network (ANN) for complex classification tasks. The proposal initializes an ANN based on linear projections to achieve more discriminating spaces. Such projections are estimated by maximizing the centered kernel alignment criterion that assesses the affinity between the resonance imaging data kernel matrix and the label target matrix. As a result, the performed linear embedding allows accounting for features that contribute the most to the MCI class discrimination. We compare the supervised pretraining approach to two unsupervised initialization methods (autoencoders and Principal Component Analysis) and against the best four performing classification methods of the 2014 CADDementia challenge. As a result, our proposal outperforms all the baselines (7% of classification accuracy and area under the receiver-operating-characteristic curve) at the time it reduces the class biasing. PMID:27148392
Accuracy of Reduced and Extended Thin-Wire Kernels
Burke, G J
2008-11-24
Some results are presented comparing the accuracy of the reduced thin-wire kernel and an extended kernel with exact integration of the 1/R term of the Green's function and results are shown for simple wire structures.
Fisher Information, Entropy, and the Second and Third Laws of Thermodynamics
We propose Fisher Information as a new calculable thermodynamic property that can be shown to follow the Second and the Third Laws of Thermodynamics. Fisher Information is, however, qualitatively different from entropy and potentially possessing a great deal more structure. Hence...
33 CFR 110.50a - Fishers Island Sound, Stonington, Conn.
Code of Federal Regulations, 2014 CFR
2014-07-01
... 33 Navigation and Navigable Waters 1 2014-07-01 2014-07-01 false Fishers Island Sound, Stonington, Conn. 110.50a Section 110.50a Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY ANCHORAGES ANCHORAGE REGULATIONS Special Anchorage Areas § 110.50a Fishers Island...
33 CFR 110.50a - Fishers Island Sound, Stonington, Conn.
Code of Federal Regulations, 2012 CFR
2012-07-01
... 33 Navigation and Navigable Waters 1 2012-07-01 2012-07-01 false Fishers Island Sound, Stonington, Conn. 110.50a Section 110.50a Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY ANCHORAGES ANCHORAGE REGULATIONS Special Anchorage Areas § 110.50a Fishers Island...
33 CFR 110.50a - Fishers Island Sound, Stonington, Conn.
Code of Federal Regulations, 2010 CFR
2010-07-01
... 33 Navigation and Navigable Waters 1 2010-07-01 2010-07-01 false Fishers Island Sound, Stonington, Conn. 110.50a Section 110.50a Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY ANCHORAGES ANCHORAGE REGULATIONS Special Anchorage Areas § 110.50a Fishers Island...
33 CFR 110.50a - Fishers Island Sound, Stonington, Conn.
Code of Federal Regulations, 2013 CFR
2013-07-01
... 33 Navigation and Navigable Waters 1 2013-07-01 2013-07-01 false Fishers Island Sound, Stonington, Conn. 110.50a Section 110.50a Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY ANCHORAGES ANCHORAGE REGULATIONS Special Anchorage Areas § 110.50a Fishers Island...
33 CFR 110.50a - Fishers Island Sound, Stonington, Conn.
Code of Federal Regulations, 2011 CFR
2011-07-01
... 33 Navigation and Navigable Waters 1 2011-07-01 2011-07-01 false Fishers Island Sound, Stonington, Conn. 110.50a Section 110.50a Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY ANCHORAGES ANCHORAGE REGULATIONS Special Anchorage Areas § 110.50a Fishers Island...
This paper describes the theory, data, and methodology necessary for using Fisher information to assess the sustainability of the San Luis Basin (SLB) regional system over time. Fisher information was originally developed as a measure of the information content in data and is an ...
Fabrication of Uranium Oxycarbide Kernels for HTR Fuel
Charles Barnes; CLay Richardson; Scott Nagley; John Hunn; Eric Shaber
2010-10-01
Babcock and Wilcox (B&W) has been producing high quality uranium oxycarbide (UCO) kernels for Advanced Gas Reactor (AGR) fuel tests at the Idaho National Laboratory. In 2005, 350-µm, 19.7% 235U-enriched UCO kernels were produced for the AGR-1 test fuel. Following coating of these kernels and forming the coated-particles into compacts, this fuel was irradiated in the Advanced Test Reactor (ATR) from December 2006 until November 2009. B&W produced 425-µm, 14% enriched UCO kernels in 2008, and these kernels were used to produce fuel for the AGR-2 experiment that was inserted in ATR in 2010. B&W also produced 500-µm, 9.6% enriched UO2 kernels for the AGR-2 experiments. Kernels of the same size and enrichment as AGR-1 were also produced for the AGR-3/4 experiment. In addition to fabricating enriched UCO and UO2 kernels, B&W has produced more than 100 kg of natural uranium UCO kernels which are being used in coating development tests. Successive lots of kernels have demonstrated consistent high quality and also allowed for fabrication process improvements. Improvements in kernel forming were made subsequent to AGR-1 kernel production. Following fabrication of AGR-2 kernels, incremental increases in sintering furnace charge size have been demonstrated. Recently small scale sintering tests using a small development furnace equipped with a residual gas analyzer (RGA) has increased understanding of how kernel sintering parameters affect sintered kernel properties. The steps taken to increase throughput and process knowledge have reduced kernel production costs. Studies have been performed of additional modifications toward the goal of increasing capacity of the current fabrication line to use for production of first core fuel for the Next Generation Nuclear Plant (NGNP) and providing a basis for the design of a full scale fuel fabrication facility.
Fisher information and asymptotic normality in system identification for quantum Markov chains
Guta, Madalin
2011-06-15
This paper deals with the problem of estimating the coupling constant {theta} of a mixing quantum Markov chain. For a repeated measurement on the chain's output we show that the outcomes' time average has an asymptotically normal (Gaussian) distribution, and we give the explicit expressions of its mean and variance. In particular, we obtain a simple estimator of {theta} whose classical Fisher information can be optimized over different choices of measured observables. We then show that the quantum state of the output together with the system is itself asymptotically Gaussian and compute its quantum Fisher information, which sets an absolute bound to the estimation error. The classical and quantum Fisher information are compared in a simple example. In the vicinity of {theta}=0 we find that the quantum Fisher information has a quadratic rather than linear scaling in output size, and asymptotically the Fisher information is localized in the system, while the output is independent of the parameter.
Kernel Temporal Differences for Neural Decoding
Bae, Jihye; Sanchez Giraldo, Luis G.; Pohlmeyer, Eric A.; Francis, Joseph T.; Sanchez, Justin C.; Príncipe, José C.
2015-01-01
We study the feasibility and capability of the kernel temporal difference (KTD)(λ) algorithm for neural decoding. KTD(λ) is an online, kernel-based learning algorithm, which has been introduced to estimate value functions in reinforcement learning. This algorithm combines kernel-based representations with the temporal difference approach to learning. One of our key observations is that by using strictly positive definite kernels, algorithm's convergence can be guaranteed for policy evaluation. The algorithm's nonlinear functional approximation capabilities are shown in both simulations of policy evaluation and neural decoding problems (policy improvement). KTD can handle high-dimensional neural states containing spatial-temporal information at a reasonable computational complexity allowing real-time applications. When the algorithm seeks a proper mapping between a monkey's neural states and desired positions of a computer cursor or a robot arm, in both open-loop and closed-loop experiments, it can effectively learn the neural state to action mapping. Finally, a visualization of the coadaptation process between the decoder and the subject shows the algorithm's capabilities in reinforcement learning brain machine interfaces. PMID:25866504
Kernel method and linear recurrence system
NASA Astrophysics Data System (ADS)
Hou, Qing-Hu; Mansour, Toufik
2008-06-01
Based on the kernel method, we present systematic methods to solve equation systems on generating functions of two variables. Using these methods, we get the generating functions for the number of permutations which avoid 1234 and 12k(k-1)...3 and permutations which avoid 1243 and 12...k.
7 CFR 981.8 - Inedible kernel.
Code of Federal Regulations, 2010 CFR
2010-01-01
... and Orders; Fruits, Vegetables, Nuts), DEPARTMENT OF AGRICULTURE ALMONDS GROWN IN CALIFORNIA Order... of almond kernel with any defect scored as serious damage, or damage due to mold, gum, shrivel, or brown spot, as defined in the United States Standards for Shelled Almonds, or which has embedded...
7 CFR 981.8 - Inedible kernel.
Code of Federal Regulations, 2011 CFR
2011-01-01
... and Orders; Fruits, Vegetables, Nuts), DEPARTMENT OF AGRICULTURE ALMONDS GROWN IN CALIFORNIA Order... of almond kernel with any defect scored as serious damage, or damage due to mold, gum, shrivel, or brown spot, as defined in the United States Standards for Shelled Almonds, or which has embedded...
7 CFR 981.8 - Inedible kernel.
Code of Federal Regulations, 2014 CFR
2014-01-01
... AND ORDERS; FRUITS, VEGETABLES, NUTS), DEPARTMENT OF AGRICULTURE ALMONDS GROWN IN CALIFORNIA Order... of almond kernel with any defect scored as serious damage, or damage due to mold, gum, shrivel, or brown spot, as defined in the United States Standards for Shelled Almonds, or which has embedded...
7 CFR 981.8 - Inedible kernel.
Code of Federal Regulations, 2012 CFR
2012-01-01
... and Orders; Fruits, Vegetables, Nuts), DEPARTMENT OF AGRICULTURE ALMONDS GROWN IN CALIFORNIA Order... of almond kernel with any defect scored as serious damage, or damage due to mold, gum, shrivel, or brown spot, as defined in the United States Standards for Shelled Almonds, or which has embedded...
7 CFR 981.8 - Inedible kernel.
Code of Federal Regulations, 2013 CFR
2013-01-01
... AND ORDERS; FRUITS, VEGETABLES, NUTS), DEPARTMENT OF AGRICULTURE ALMONDS GROWN IN CALIFORNIA Order... of almond kernel with any defect scored as serious damage, or damage due to mold, gum, shrivel, or brown spot, as defined in the United States Standards for Shelled Almonds, or which has embedded...
INTACT OR UNIT-KERNEL SWEET CORN
This report evaluates process and product modifications in canned and frozen sweet corn manufacture with the objective of reducing the total effluent produced in processing. In particular it evaluates the proposed replacement of process steps that yield cut or whole kernel corn w...
Arbitrary-resolution global sensitivity kernels
NASA Astrophysics Data System (ADS)
Nissen-Meyer, T.; Fournier, A.; Dahlen, F.
2007-12-01
Extracting observables out of any part of a seismogram (e.g. including diffracted phases such as Pdiff) necessitates the knowledge of 3-D time-space wavefields for the Green functions that form the backbone of Fréchet sensitivity kernels. While known for a while, this idea is still computationally intractable in 3-D, facing major simulation and storage issues when high-frequency wavefields are considered at the global scale. We recently developed a new "collapsed-dimension" spectral-element method that solves the 3-D system of elastodynamic equations in a 2-D space, based on exploring symmetry considerations of the seismic-wave radiation patterns. We will present the technical background on the computation of waveform kernels, various examples of time- and frequency-dependent sensitivity kernels and subsequently extracted time-window kernels (e.g. banana- doughnuts). Given the computationally light-weighted 2-D nature, we will explore some crucial parameters such as excitation type, source time functions, frequency, azimuth, discontinuity locations, and phase type, i.e. an a priori view into how, when, and where seismograms carry 3-D Earth signature. A once-and-for-all database of 2-D waveforms for various source depths shall then serve as a complete set of global time-space sensitivity for a given spherically symmetric background model, thereby allowing for tomographic inversions with arbitrary frequencies, observables, and phases.
Application of the matrix exponential kernel
NASA Technical Reports Server (NTRS)
Rohach, A. F.
1972-01-01
A point matrix kernel for radiation transport, developed by the transmission matrix method, has been used to develop buildup factors and energy spectra through slab layers of different materials for a point isotropic source. Combinations of lead-water slabs were chosen for examples because of the extreme differences in shielding properties of these two materials.
Monnereau, Iris; Pollnac, Richard
2012-10-01
Lobster fishing (targeting the spiny lobster Panulirus argus) is an important economic activity throughout the Wider Caribbean Region both as a source of income and employment for the local population as well as foreign exchange for national governments. Due to the high unit prices of the product, international lobster trade provides a way to improve the livelihoods of fisheries-dependent populations. The specie harvested is identical throughout the region and end market prices are roughly similar. In this paper we wish to investigate to which extent lobster fishers' job satisfaction differs in three countries in the Caribbean and how these differences can be explained by looking at the national governance arrangements. PMID:22997479
7 CFR 868.254 - Broken kernels determination.
Code of Federal Regulations, 2010 CFR
2010-01-01
... 7 Agriculture 7 2010-01-01 2010-01-01 false Broken kernels determination. 868.254 Section 868.254 Agriculture Regulations of the Department of Agriculture (Continued) GRAIN INSPECTION, PACKERS AND STOCKYARD... Governing Application of Standards § 868.254 Broken kernels determination. Broken kernels shall...
7 CFR 868.304 - Broken kernels determination.
Code of Federal Regulations, 2010 CFR
2010-01-01
... 7 Agriculture 7 2010-01-01 2010-01-01 false Broken kernels determination. 868.304 Section 868.304 Agriculture Regulations of the Department of Agriculture (Continued) GRAIN INSPECTION, PACKERS AND STOCKYARD... Application of Standards § 868.304 Broken kernels determination. Broken kernels shall be determined by the...
Applying Single Kernel Sorting Technology to Developing Scab Resistant Lines
Technology Transfer Automated Retrieval System (TEKTRAN)
We are using automated single-kernel near-infrared (SKNIR) spectroscopy instrumentation to sort fusarium head blight (FHB) infected kernels from healthy kernels, and to sort segregating populations by hardness to enhance the development of scab resistant hard and soft wheat varieties. We sorted 3 r...
21 CFR 176.350 - Tamarind seed kernel powder.
Code of Federal Regulations, 2012 CFR
2012-04-01
... Substances for Use Only as Components of Paper and Paperboard § 176.350 Tamarind seed kernel powder. Tamarind seed kernel powder may be safely used as a component of articles intended for use in producing... 21 Food and Drugs 3 2012-04-01 2012-04-01 false Tamarind seed kernel powder. 176.350 Section...
21 CFR 176.350 - Tamarind seed kernel powder.
Code of Federal Regulations, 2014 CFR
2014-04-01
... 21 Food and Drugs 3 2014-04-01 2014-04-01 false Tamarind seed kernel powder. 176.350 Section 176...) INDIRECT FOOD ADDITIVES: PAPER AND PAPERBOARD COMPONENTS Substances for Use Only as Components of Paper and Paperboard § 176.350 Tamarind seed kernel powder. Tamarind seed kernel powder may be safely used as...
21 CFR 176.350 - Tamarind seed kernel powder.
Code of Federal Regulations, 2013 CFR
2013-04-01
... Substances for Use Only as Components of Paper and Paperboard § 176.350 Tamarind seed kernel powder. Tamarind seed kernel powder may be safely used as a component of articles intended for use in producing... 21 Food and Drugs 3 2013-04-01 2013-04-01 false Tamarind seed kernel powder. 176.350 Section...
21 CFR 176.350 - Tamarind seed kernel powder.
Code of Federal Regulations, 2010 CFR
2010-04-01
... 21 Food and Drugs 3 2010-04-01 2009-04-01 true Tamarind seed kernel powder. 176.350 Section 176... Substances for Use Only as Components of Paper and Paperboard § 176.350 Tamarind seed kernel powder. Tamarind seed kernel powder may be safely used as a component of articles intended for use in...
21 CFR 176.350 - Tamarind seed kernel powder.
Code of Federal Regulations, 2011 CFR
2011-04-01
... 21 Food and Drugs 3 2011-04-01 2011-04-01 false Tamarind seed kernel powder. 176.350 Section 176... Substances for Use Only as Components of Paper and Paperboard § 176.350 Tamarind seed kernel powder. Tamarind seed kernel powder may be safely used as a component of articles intended for use in...
Thermomechanical property of rice kernels studied by DMA
Technology Transfer Automated Retrieval System (TEKTRAN)
The thermomechanical property of the rice kernels was investigated using a dynamic mechanical analyzer (DMA). The length change of rice kernel with a loaded constant force along the major axis direction was detected during temperature scanning. The thermomechanical transition occurred in rice kernel...
7 CFR 51.2125 - Split or broken kernels.
Code of Federal Regulations, 2014 CFR
2014-01-01
... 7 Agriculture 2 2014-01-01 2014-01-01 false Split or broken kernels. 51.2125 Section 51.2125 Agriculture Regulations of the Department of Agriculture AGRICULTURAL MARKETING SERVICE (Standards... § 51.2125 Split or broken kernels. Split or broken kernels means seven-eighths or less of...
7 CFR 51.2125 - Split or broken kernels.
Code of Federal Regulations, 2013 CFR
2013-01-01
... 7 Agriculture 2 2013-01-01 2013-01-01 false Split or broken kernels. 51.2125 Section 51.2125 Agriculture Regulations of the Department of Agriculture AGRICULTURAL MARKETING SERVICE (Standards... § 51.2125 Split or broken kernels. Split or broken kernels means seven-eighths or less of...
7 CFR 868.304 - Broken kernels determination.
Code of Federal Regulations, 2011 CFR
2011-01-01
... 7 Agriculture 7 2011-01-01 2011-01-01 false Broken kernels determination. 868.304 Section 868.304 Agriculture Regulations of the Department of Agriculture (Continued) GRAIN INSPECTION, PACKERS AND STOCKYARD... Application of Standards § 868.304 Broken kernels determination. Broken kernels shall be determined by the...
7 CFR 868.304 - Broken kernels determination.
Code of Federal Regulations, 2014 CFR
2014-01-01
... 7 Agriculture 7 2014-01-01 2014-01-01 false Broken kernels determination. 868.304 Section 868.304 Agriculture Regulations of the Department of Agriculture (Continued) GRAIN INSPECTION, PACKERS AND STOCKYARD... Application of Standards § 868.304 Broken kernels determination. Broken kernels shall be determined by the...
7 CFR 868.254 - Broken kernels determination.
Code of Federal Regulations, 2014 CFR
2014-01-01
... 7 Agriculture 7 2014-01-01 2014-01-01 false Broken kernels determination. 868.254 Section 868.254 Agriculture Regulations of the Department of Agriculture (Continued) GRAIN INSPECTION, PACKERS AND STOCKYARD... Governing Application of Standards § 868.254 Broken kernels determination. Broken kernels shall...
7 CFR 868.254 - Broken kernels determination.
Code of Federal Regulations, 2011 CFR
2011-01-01
... 7 Agriculture 7 2011-01-01 2011-01-01 false Broken kernels determination. 868.254 Section 868.254 Agriculture Regulations of the Department of Agriculture (Continued) GRAIN INSPECTION, PACKERS AND STOCKYARD... Governing Application of Standards § 868.254 Broken kernels determination. Broken kernels shall...
7 CFR 868.304 - Broken kernels determination.
Code of Federal Regulations, 2012 CFR
2012-01-01
... 7 Agriculture 7 2012-01-01 2012-01-01 false Broken kernels determination. 868.304 Section 868.304 Agriculture Regulations of the Department of Agriculture (Continued) GRAIN INSPECTION, PACKERS AND STOCKYARD... Application of Standards § 868.304 Broken kernels determination. Broken kernels shall be determined by the...
7 CFR 868.254 - Broken kernels determination.
Code of Federal Regulations, 2012 CFR
2012-01-01
... 7 Agriculture 7 2012-01-01 2012-01-01 false Broken kernels determination. 868.254 Section 868.254 Agriculture Regulations of the Department of Agriculture (Continued) GRAIN INSPECTION, PACKERS AND STOCKYARD... Governing Application of Standards § 868.254 Broken kernels determination. Broken kernels shall...
Hill, Nicholas A O; Rowcliffe, J Marcus; Koldewey, Heather J; Milner-Gulland, E J
2012-04-01
Alternative occupations are frequently promoted as a means to reduce the number of people exploiting declining fisheries. However, there is little evidence that alternative occupations reduce fisher numbers. Seaweed farming is frequently promoted as a lucrative alternative occupation for artisanal fishers in Southeast Asia. We examined how the introduction of seaweed farming has affected village-level changes in the number of fishers on Danajon Bank, central Philippines, where unsustainable fishing has led to declining fishery yields. To determine how fisher numbers had changed since seaweed farming started, we interviewed the heads of household from 300 households in 10 villages to examine their perceptions of how fisher numbers had changed in their village and the reasons they associated with these changes. We then asked key informants (people with detailed knowledge of village members) to estimate fisher numbers in these villages before seaweed farming began and at the time of the survey. We compared the results of how fisher numbers had changed in each village with the wealth, education, seaweed farm sizes, and other attributes of households in these villages, which we collected through interviews, and with village-level factors such as distance to markets. We also asked people why they either continued to engage in or ceased fishing. In four villages, respondents thought seaweed farming and low fish catches had reduced fisher numbers, at least temporarily. In one of these villages, there was a recent return to fishing due to declines in the price of seaweed and increased theft of seaweed. In another four villages, fisher numbers increased as human population increased, despite the widespread uptake of seaweed farming. Seaweed farming failed for technical reasons in two other villages. Our results suggest seaweed farming has reduced fisher numbers in some villages, a result that may be correlated with socioeconomic status, but the heterogeneity of outcomes is
ERIC Educational Resources Information Center
Kovarsky, Irving
Intended as a guide on discrimination problems and issues for students and practitioners in the area of employment relations, this book interrelates historical, religious, economic, medical, and sociological factors surrounding racial, religious, national, sex, age, and physical and mental discrimination to explain discrimination in employment.…
NASA Astrophysics Data System (ADS)
Lanorte, Antonio; Lasaponara, Rosa; Lovallo, Michele; Telesca, Luciano
2014-02-01
The time dynamics of SPOT-VEGETATION Normalized Difference Vegetation Index (NDVI) time series are analyzed by using the statistical approach of the Fisher-Shannon (FS) information plane to assess and monitor vegetation recovery after fire disturbance. Fisher-Shannon information plane analysis allows us to gain insight into the complex structure of a time series to quantify its degree of organization and order. The analysis was carried out using 10-day Maximum Value Composites of NDVI (MVC-NDVI) with a 1 km × 1 km spatial resolution. The investigation was performed on two test sites located in Galizia (North Spain) and Peloponnese (South Greece), selected for the vast fires which occurred during the summer of 2006 and 2007 and for their different vegetation covers made up mainly of low shrubland in Galizia test site and evergreen forest in Peloponnese. Time series of MVC-NDVI have been analyzed before and after the occurrence of the fire events. Results obtained for both the investigated areas clearly pointed out that the dynamics of the pixel time series before the occurrence of the fire is characterized by a larger degree of disorder and uncertainty; while the pixel time series after the occurrence of the fire are featured by a higher degree of organization and order. In particular, regarding the Peloponneso fire, such discrimination is more evident than in the Galizia fire. This suggests a clear possibility to discriminate the different post-fire behaviors and dynamics exhibited by the different vegetation covers.
Khansari, Maziyar M; O’Neill, William; Penn, Richard; Chau, Felix; Blair, Norman P; Shahidi, Mahnaz
2016-01-01
The conjunctiva is a densely vascularized mucus membrane covering the sclera of the eye with a unique advantage of accessibility for direct visualization and non-invasive imaging. The purpose of this study is to apply an automated quantitative method for discrimination of different stages of diabetic retinopathy (DR) using conjunctival microvasculature images. Fine structural analysis of conjunctival microvasculature images was performed by ordinary least square regression and Fisher linear discriminant analysis. Conjunctival images between groups of non-diabetic and diabetic subjects at different stages of DR were discriminated. The automated method’s discriminate rates were higher than those determined by human observers. The method allowed sensitive and rapid discrimination by assessment of conjunctival microvasculature images and can be potentially useful for DR screening and monitoring. PMID:27446692
Khansari, Maziyar M; O'Neill, William; Penn, Richard; Chau, Felix; Blair, Norman P; Shahidi, Mahnaz
2016-07-01
The conjunctiva is a densely vascularized mucus membrane covering the sclera of the eye with a unique advantage of accessibility for direct visualization and non-invasive imaging. The purpose of this study is to apply an automated quantitative method for discrimination of different stages of diabetic retinopathy (DR) using conjunctival microvasculature images. Fine structural analysis of conjunctival microvasculature images was performed by ordinary least square regression and Fisher linear discriminant analysis. Conjunctival images between groups of non-diabetic and diabetic subjects at different stages of DR were discriminated. The automated method's discriminate rates were higher than those determined by human observers. The method allowed sensitive and rapid discrimination by assessment of conjunctival microvasculature images and can be potentially useful for DR screening and monitoring. PMID:27446692
[Discrimination of Red Tide algae by fluorescence spectra and principle component analysis].
Su, Rong-guo; Hu, Xu-peng; Zhang, Chuan-song; Wang, Xiu-lin
2007-07-01
Fluorescence discrimination technology for 11 species of the Red Tide algae at genus level was constructed by principle component analysis and non-negative least squares. Rayleigh and Raman scattering peaks of 3D fluorescence spectra were eliminated by Delaunay triangulation method. According to the results of Fisher linear discrimination, the first principle component score and the second component score of 3D fluorescence spectra were chosen as discriminant feature and the feature base was established. The 11 algae species were tested, and more than 85% samples were accurately determinated, especially for Prorocentrum donghaiense, Skeletonema costatum, Gymnodinium sp., which have frequently brought Red tide in the East China Sea. More than 95% samples were right discriminated. The results showed that the genus discriminant feature of 3D fluorescence spectra of Red Tide algae given by principle component analysis could work well. PMID:17891964
Kernel weights optimization for error diffusion halftoning method
NASA Astrophysics Data System (ADS)
Fedoseev, Victor
2015-02-01
This paper describes a study to find the best error diffusion kernel for digital halftoning under various restrictions on the number of non-zero kernel coefficients and their set of values. As an objective measure of quality, WSNR was used. The problem of multidimensional optimization was solved numerically using several well-known algorithms: Nelder- Mead, BFGS, and others. The study found a kernel function that provides a quality gain of about 5% in comparison with the best of the commonly used kernel introduced by Floyd and Steinberg. Other kernels obtained allow to significantly reduce the computational complexity of the halftoning process without reducing its quality.
Chare kernel; A runtime support system for parallel computations
Shu, W. ); Kale, L.V. )
1991-03-01
This paper presents the chare kernel system, which supports parallel computations with irregular structure. The chare kernel is a collection of primitive functions that manage chares, manipulative messages, invoke atomic computations, and coordinate concurrent activities. Programs written in the chare kernel language can be executed on different parallel machines without change. Users writing such programs concern themselves with the creation of parallel actions but not with assigning them to specific processors. The authors describe the design and implementation of the chare kernel. Performance of chare kernel programs on two hypercube machines, the Intel iPSC/2 and the NCUBE, is also given.
Management decision making for fisher populations informed by occupancy modeling
Fuller, Angela K.; Linden, Daniel W.; Royle, J. Andrew
2016-01-01
Harvest data are often used by wildlife managers when setting harvest regulations for species because the data are regularly collected and do not require implementation of logistically and financially challenging studies to obtain the data. However, when harvest data are not available because an area had not previously supported a harvest season, alternative approaches are required to help inform management decision making. When distribution or density data are required across large areas, occupancy modeling is a useful approach, and under certain conditions, can be used as a surrogate for density. We collaborated with the New York State Department of Environmental Conservation (NYSDEC) to conduct a camera trapping study across a 70,096-km2 region of southern New York in areas that were currently open to fisher (Pekania [Martes] pennanti) harvest and those that had been closed to harvest for approximately 65 years. We used detection–nondetection data at 826 sites to model occupancy as a function of site-level landscape characteristics while accounting for sampling variation. Fisher occupancy was influenced positively by the proportion of conifer and mixed-wood forest within a 15-km2 grid cell and negatively associated with road density and the proportion of agriculture. Model-averaged predictions indicated high occupancy probabilities (>0.90) when road densities were low (<1 km/km2) and coniferous and mixed forest proportions were high (>0.50). Predicted occupancy ranged 0.41–0.67 in wildlife management units (WMUs) currently open to trapping, which could be used to guide a minimum occupancy threshold for opening new areas to trapping seasons. There were 5 WMUs that had been closed to trapping but had an average predicted occupancy of 0.52 (0.07 SE), and above the threshold of 0.41. These areas are currently under consideration by NYSDEC for opening a conservative harvest season. We demonstrate the use of occupancy modeling as an aid to management
A FURTHER EVALUATION OF PICTURE PROMPTS DURING AUDITORY-VISUAL CONDITIONAL DISCRIMINATION TRAINING
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 more advanced receptive skills. We used a balanced design to compare the effects of picture prompts, pointing prompts, and either trial-and-error learning or a no-reinforcement condition. In addition, we assessed the emergence of vocal tacts for the 2 participants who had prior tact repertoires. Picture prompts enhanced acquisition for all participants, but there were no differential effects on tact emergence. The results support a generality of the effect reported by Fisher et al. and suggest that a variety of learners may benefit from the incorporation of picture prompts into auditory-visual conditional discrimination training. PMID:23322929
ERIC Educational Resources Information Center
Pollnac, Richard B.; Kotowicz, Dawn
2012-01-01
The paper examines job satisfaction among fishers in a tsunami-impacted area on the Andaman coast of Thailand. Following the tsunami, many predicted that fishers would be reluctant to resume their fishing activities. Observations in the fishing communities, however, indicated that as soon as fishers obtained replacements for equipment damaged by…
Difference image analysis: automatic kernel design using information criteria
NASA Astrophysics Data System (ADS)
Bramich, D. M.; Horne, Keith; Alsubai, K. A.; Bachelet, E.; Mislis, D.; Parley, N.
2016-03-01
We present a selection of methods for automatically constructing an optimal kernel model for difference image analysis which require very few external parameters to control the kernel design. Each method consists of two components; namely, a kernel design algorithm to generate a set of candidate kernel models, and a model selection criterion to select the simplest kernel model from the candidate models that provides a sufficiently good fit to the target image. We restricted our attention to the case of solving for a spatially invariant convolution kernel composed of delta basis functions, and we considered 19 different kernel solution methods including six employing kernel regularization. We tested these kernel solution methods by performing a comprehensive set of image simulations and investigating how their performance in terms of model error, fit quality, and photometric accuracy depends on the properties of the reference and target images. We find that the irregular kernel design algorithm employing unregularized delta basis functions, combined with either the Akaike or Takeuchi information criterion, is the best kernel solution method in terms of photometric accuracy. Our results are validated by tests performed on two independent sets of real data. Finally, we provide some important recommendations for software implementations of difference image analysis.
Canine distemper in an isolated population of fishers (Martes pennanti) from California.
Keller, Stefan M; Gabriel, Mourad; Terio, Karen A; Dubovi, Edward J; VanWormer, Elizabeth; Sweitzer, Rick; Barret, Reginald; Thompson, Craig; Purcell, Kathryn; Munson, Linda
2012-10-01
Four fishers (Martes pennanti) from an insular population in the southern Sierra Nevada Mountains, California, USA died as a consequence of an infection with canine distemper virus (CDV) in 2009. Three fishers were found in close temporal and spatial relationship; the fourth fisher died 4 mo later at a 70 km distance from the initial group. Gross lesions were restricted to hyperkeratosis of periocular skin and ulceration of footpads. All animals had necrotizing bronchitis and bronchiolitis with syncytia and intracytoplasmic inclusion bodies. Inclusion bodies were abundant in the epithelia of urinary bladder and epididymis but were infrequent in the renal pelvis and the female genital epithelia. No histopathologic or immunohistochemical evidence for virus spread to the central nervous system was found. One fisher had encephalitis caused by Sarcocystis neurona and another had severe head trauma as a consequence of predation. The H gene nucleotide sequence of the virus isolates from the first three fishers was identical and was 99.6% identical to the isolate from the fourth fisher. Phylogenetically, the isolates clustered with other North American isolates separate from classical European wildlife lineage strains. These data suggest that the European wildlife lineage might consist of two separate subgroups that are genetically distinct and endemic in different geographic regions. The source of infection as well as pertinent transmission routes remained unclear. This is the first report of CDV in fishers and underscores the significance of CDV as a pathogen of management concern. PMID:23060505
Distribution of quantum Fisher information in asymmetric cloning machines
NASA Astrophysics Data System (ADS)
Xiao, Xing; Yao, Yao; Zhou, Lei-Ming; Wang, Xiaoguang
2014-12-01
An unknown quantum state cannot be copied and broadcast freely due to the no-cloning theorem. Approximate cloning schemes have been proposed to achieve the optimal cloning characterized by the maximal fidelity between the original and its copies. Here, from the perspective of quantum Fisher information (QFI), we investigate the distribution of QFI in asymmetric cloning machines which produce two nonidentical copies. As one might expect, improving the QFI of one copy results in decreasing the QFI of the other copy. It is perhaps also unsurprising that asymmetric phase-covariant cloning outperforms universal cloning in distributing QFI since a priori information of the input state has been utilized. However, interesting results appear when we compare the distributabilities of fidelity (which quantifies the full information of quantum states), and QFI (which only captures the information of relevant parameters) in asymmetric cloning machines. Unlike the results of fidelity, where the distributability of symmetric cloning is always optimal for any d-dimensional cloning, we find that any asymmetric cloning outperforms symmetric cloning on the distribution of QFI for d <= 18, whereas some but not all asymmetric cloning strategies could be worse than symmetric ones when d > 18.
Detecting Large Quantum Fisher Information with Finite Measurement Precision.
Fröwis, Florian; Sekatski, Pavel; Dür, Wolfgang
2016-03-01
We propose an experimentally accessible scheme to determine the lower bounds on the quantum Fisher information (QFI), which ascertains multipartite entanglement or usefulness for quantum metrology. The scheme is based on comparing the measurement statistics of a state before and after a small unitary rotation. We argue that, in general, the limited resolution of collective observables prevents the detection of large QFI. This can be overcome by performing an additional operation prior to the measurement. We illustrate the power of this protocol for present-day spin-squeezing experiments, where the same operation used for the preparation of the initial spin-squeezed state improves also the measurement precision and hence the lower bound on the QFI by 2 orders of magnitude. We also establish a connection to the Leggett-Garg inequalities. We show how to simulate a variant of the inequalities with our protocol and demonstrate that large QFI is necessary for their violation with coarse-grained detectors. PMID:26991166
PRECISE TULLY-FISHER RELATIONS WITHOUT GALAXY INCLINATIONS
Obreschkow, D.; Meyer, M.
2013-11-10
Power-law relations between tracers of baryonic mass and rotational velocities of disk galaxies, so-called Tully-Fisher relations (TFRs), offer a wealth of applications in galaxy evolution and cosmology. However, measurements of rotational velocities require galaxy inclinations, which are difficult to measure, thus limiting the range of TFR studies. This work introduces a maximum likelihood estimation (MLE) method for recovering the TFR in galaxy samples with limited or no information on inclinations. The robustness and accuracy of this method is demonstrated using virtual and real galaxy samples. Intriguingly, the MLE reliably recovers the TFR of all test samples, even without using any inclination measurements—that is, assuming a random sin i-distribution for galaxy inclinations. Explicitly, this 'inclination-free MLE' recovers the three TFR parameters (zero-point, slope, scatter) with statistical errors only about 1.5 times larger than the best estimates based on perfectly known galaxy inclinations with zero uncertainty. Thus, given realistic uncertainties, the inclination-free MLE is highly competitive. If inclination measurements have mean errors larger than 10°, it is better not to use any inclinations than to consider the inclination measurements to be exact. The inclination-free MLE opens interesting perspectives for future H I surveys by the Square Kilometer Array and its pathfinders.
Marine protected area improves yield without disadvantaging fishers.
Kerwath, Sven E; Winker, Henning; Götz, Albrecht; Attwood, Colin G
2013-01-01
Potential fishery benefits of Marine Protected Areas (MPAs) are widely acknowledged, yet seldom demonstrated, as fishery data series that straddle MPA establishment are seldom available. Here we postulate, based on a 15-year time series of nation-wide, spatially referenced catch and effort data, that the establishment of the Goukamma MPA (18 km alongshore; 40 km²) benefited the adjacent fishery for roman (Chrysoblephus laticeps), a South African endemic seabream. Roman-directed catch-per-unit-effort (CPUE) in the vicinity of the new MPA immediately increased, contradicting trends across this species' distribution. The increase continued after 5 years, the time lag expected for larval export, effectively doubling the pre-MPA CPUE after 10 years. We find no indication that establishing the MPA caused a systematic drop in total catch or increased travel distances for the fleet. Our results provide rare empirical evidence of rapidly increasing catch rates after MPA implementation without measurable disadvantages for fishers. PMID:23962973
Optimum array design to maximize Fisher information for bearing estimation.
Tuladhar, Saurav R; Buck, John R
2011-11-01
Source bearing estimation is a common application of linear sensor arrays. The Cramer-Rao bound (CRB) sets a lower bound on the achievable mean square error (MSE) of any unbiased bearing estimate. In the spatially white noise case, the CRB is minimized by placing half of the sensors at each end of the array. However, many realistic ocean environments have a mixture of both white noise and spatially correlated noise. In shallow water environments, the correlated ambient noise can be modeled as cylindrically isotropic. This research designs a fixed aperture linear array to maximize the bearing Fisher information (FI) under these noise conditions. The FI is the inverse of the CRB, so maximizing the FI minimizes the CRB. The elements of the optimum array are located closer to the array ends than uniform spacing, but are not as extreme as in the white noise case. The optimum array results from a trade off between maximizing the array bearing sensitivity and minimizing output noise power variation over the bearing. Depending on the source bearing, the resulting improvement in MSE performance of the optimized array over a uniform array is equivalent to a gain of 2-5 dB in input signal-to-noise ratio. PMID:22087908
Enhancing quantum Fisher information by utilizing uncollapsing measurements
NASA Astrophysics Data System (ADS)
He, Juan; Ding, Zhi-Yong; Ye, Liu
2016-09-01
As an indicator of estimation precision, quantum Fisher information (QFI) lies at the heart of quantum metrology theory. In this work, an effective scheme for enhancing QFI is proposed by utilizing quantum uncollapsing measurements. Two kinds of strategies for the arbitrary two-qubit pure state with weight parameter and phase parameter are implemented under different situations, respectively. We derive the explicit conditions for the optimal measurement strengths, and verify that the QFI can be improved quite well. Meanwhile, due to the relation of quantum correlation and QFI, the maximal value of QFI associated with phase parameter for pure state is always equal to 1. It is worth noting that the optimal measurement strength is only related to the weight parameter, as uncollapsing measurements operation does not induce any disturbance on the value of phase parameter. The scheme also can be extended to improve the parameter estimation precision for an N-qubit pure state. In addition, as an example, the situation of an arbitrary single-qubit state under amplitude damping channel is investigated. It is shown that our scheme also works well for enhancing QFI under decoherence.
Hellmann–Feynman connection for the relative Fisher information
Venkatesan, R.C.; Plastino, A.
2015-08-15
The (i) reciprocity relations for the relative Fisher information (RFI, hereafter) and (ii) a generalized RFI–Euler theorem are self-consistently derived from the Hellmann–Feynman theorem. These new reciprocity relations generalize the RFI–Euler theorem and constitute the basis for building up a mathematical Legendre transform structure (LTS, hereafter), akin to that of thermodynamics, that underlies the RFI scenario. This demonstrates the possibility of translating the entire mathematical structure of thermodynamics into a RFI-based theoretical framework. Virial theorems play a prominent role in this endeavor, as a Schrödinger-like equation can be associated to the RFI. Lagrange multipliers are determined invoking the RFI–LTS link and the quantum mechanical virial theorem. An appropriate ansatz allows for the inference of probability density functions (pdf’s, hereafter) and energy-eigenvalues of the above mentioned Schrödinger-like equation. The energy-eigenvalues obtained here via inference are benchmarked against established theoretical and numerical results. A principled theoretical basis to reconstruct the RFI-framework from the FIM framework is established. Numerical examples for exemplary cases are provided. - Highlights: • Legendre transform structure for the RFI is obtained with the Hellmann–Feynman theorem. • Inference of the energy-eigenvalues of the SWE-like equation for the RFI is accomplished. • Basis for reconstruction of the RFI framework from the FIM-case is established. • Substantial qualitative and quantitative distinctions with prior studies are discussed.
A transition mass in the local Tully-Fisher relation
NASA Astrophysics Data System (ADS)
Simons, Raymond C.; Kassin, Susan A.; Weiner, Benjamin J.; Heckman, Timothy M.; Lee, Janice C.; Lotz, Jennifer M.; Peth, Michael; Tchernyshyov, Kirill
2015-09-01
We study the stellar mass Tully-Fisher relation (TFR; stellar mass versus rotation velocity) for a morphologically blind selection of emission line galaxies in the field at redshifts 0.1 < z < 0.375. Kinematics (σg, Vrot) are measured from emission lines in Keck/DEIMOS spectra and quantitative morphology is measured from V- and I-band Hubble images. We find a transition stellar mass in the TFR, log M*/M⊙ = 9.5. Above this mass, nearly all galaxies are rotation dominated, on average more morphologically disc-like according to quantitative morphology, and lie on a relatively tight TFR. Below this mass, the TFR has significant scatter to low rotation velocity and galaxies can either be rotation-dominated discs on the TFR or asymmetric or compact galaxies which scatter off. We refer to this transition mass as the `mass of disc formation', Mdf because above it all star-forming galaxies form discs (except for a small number of major mergers and highly star-forming systems), whereas below it a galaxy may or may not form a disc.
From Spitzer Galaxy photometry to Tully-Fisher distances
NASA Astrophysics Data System (ADS)
Sorce, J. G.; Tully, R. B.; Courtois, H. M.; Jarrett, T. H.; Neill, J. D.; Shaya, E. J.
2014-10-01
This paper involves a data release of the observational campaign: Cosmicflows with Spitzer (CFS). Surface photometry of the 1270 galaxies constituting the survey is presented. An additional ˜400 galaxies from various other Spitzer surveys are also analysed. CFS complements the Spitzer Survey of Stellar Structure in Galaxies, that provides photometry for an additional 2352 galaxies, by extending observations to low galactic latitudes (|b| < 30°). Among these galaxies are calibrators, selected in the K band, of the Tully-Fisher relation. The addition of new calibrators demonstrates the robustness of the previously released calibration. Our estimate of the Hubble constant using supernova host galaxies is unchanged, H0 = 75.2 ± 3.3 km s-1 Mpc-1. Distance-derived radial peculiar velocities, for the 1935 galaxies with all the available parameters, will be incorporated into a new data release of the Cosmicflows project. The size of the previous catalogue will be increased by 20 per cent, including spatial regions close to the Zone of Avoidance.
On conjugate families and Jeffreys priors for von Mises–Fisher distributions
Hornik, Kurt; Grün, Bettina
2013-01-01
This paper discusses characteristics of standard conjugate priors and their induced posteriors in Bayesian inference for von Mises–Fisher distributions, using either the canonical natural exponential family or the more commonly employed polar coordinate parameterizations. We analyze when standard conjugate priors as well as posteriors are proper, and investigate the Jeffreys prior for the von Mises–Fisher family. Finally, we characterize the proper distributions in the standard conjugate family of the (matrix-valued) von Mises–Fisher distributions on Stiefel manifolds. PMID:23805026
NEW MULTICATEGORY BOOSTING ALGORITHMS BASED ON MULTICATEGORY FISHER-CONSISTENT LOSSES
Zou, Hui; Zhu, Ji; Hastie, Trevor
2016-01-01
Fisher-consistent loss functions play a fundamental role in the construction of successful binary margin-based classifiers. In this paper we establish the Fisher-consistency condition for multicategory classification problems. Our approach uses the margin vector concept which can be regarded as a multicategory generalization of the binary margin. We characterize a wide class of smooth convex loss functions that are Fisher-consistent for multicategory classification. We then consider using the margin-vector-based loss functions to derive multicategory boosting algorithms. In particular, we derive two new multicategory boosting algorithms by using the exponential and logistic regression losses.
A meshfree unification: reproducing kernel peridynamics
NASA Astrophysics Data System (ADS)
Bessa, M. A.; Foster, J. T.; Belytschko, T.; Liu, Wing Kam
2014-06-01
This paper is the first investigation establishing the link between the meshfree state-based peridynamics method and other meshfree methods, in particular with the moving least squares reproducing kernel particle method (RKPM). It is concluded that the discretization of state-based peridynamics leads directly to an approximation of the derivatives that can be obtained from RKPM. However, state-based peridynamics obtains the same result at a significantly lower computational cost which motivates its use in large-scale computations. In light of the findings of this study, an update to the method is proposed such that the limitations regarding application of boundary conditions and the use of non-uniform grids are corrected by using the reproducing kernel approximation.
Wilson Dslash Kernel From Lattice QCD Optimization
Joo, Balint; Smelyanskiy, Mikhail; Kalamkar, Dhiraj D.; Vaidyanathan, Karthikeyan
2015-07-01
Lattice Quantum Chromodynamics (LQCD) is a numerical technique used for calculations in Theoretical Nuclear and High Energy Physics. LQCD is traditionally one of the first applications ported to many new high performance computing architectures and indeed LQCD practitioners have been known to design and build custom LQCD computers. Lattice QCD kernels are frequently used as benchmarks (e.g. 168.wupwise in the SPEC suite) and are generally well understood, and as such are ideal to illustrate several optimization techniques. In this chapter we will detail our work in optimizing the Wilson-Dslash kernels for Intel Xeon Phi, however, as we will show the technique gives excellent performance on regular Xeon Architecture as well.
Searching and Indexing Genomic Databases via Kernelization
Gagie, Travis; Puglisi, Simon J.
2015-01-01
The rapid advance of DNA sequencing technologies has yielded databases of thousands of genomes. To search and index these databases effectively, it is important that we take advantage of the similarity between those genomes. Several authors have recently suggested searching or indexing only one reference genome and the parts of the other genomes where they differ. In this paper, we survey the 20-year history of this idea and discuss its relation to kernelization in parameterized complexity. PMID:25710001
Multiple kernel learning for dimensionality reduction.
Lin, Yen-Yu; Liu, Tyng-Luh; Fuh, Chiou-Shann
2011-06-01
In solving complex visual learning tasks, adopting multiple descriptors to more precisely characterize the data has been a feasible way for improving performance. The resulting data representations are typically high-dimensional and assume diverse forms. Hence, finding a way of transforming them into a unified space of lower dimension generally facilitates the underlying tasks such as object recognition or clustering. To this end, the proposed approach (termed MKL-DR) generalizes the framework of multiple kernel learning for dimensionality reduction, and distinguishes itself with the following three main contributions: first, our method provides the convenience of using diverse image descriptors to describe useful characteristics of various aspects about the underlying data. Second, it extends a broad set of existing dimensionality reduction techniques to consider multiple kernel learning, and consequently improves their effectiveness. Third, by focusing on the techniques pertaining to dimensionality reduction, the formulation introduces a new class of applications with the multiple kernel learning framework to address not only the supervised learning problems but also the unsupervised and semi-supervised ones. PMID:20921580
A Fast Reduced Kernel Extreme Learning Machine.
Deng, Wan-Yu; Ong, Yew-Soon; Zheng, Qing-Hua
2016-04-01
In this paper, we present a fast and accurate kernel-based supervised algorithm referred to as the Reduced Kernel Extreme Learning Machine (RKELM). In contrast to the work on Support Vector Machine (SVM) or Least Square SVM (LS-SVM), which identifies the support vectors or weight vectors iteratively, the proposed RKELM randomly selects a subset of the available data samples as support vectors (or mapping samples). By avoiding the iterative steps of SVM, significant cost savings in the training process can be readily attained, especially on Big datasets. RKELM is established based on the rigorous proof of universal learning involving reduced kernel-based SLFN. In particular, we prove that RKELM can approximate any nonlinear functions accurately under the condition of support vectors sufficiency. Experimental results on a wide variety of real world small instance size and large instance size applications in the context of binary classification, multi-class problem and regression are then reported to show that RKELM can perform at competitive level of generalized performance as the SVM/LS-SVM at only a fraction of the computational effort incurred. PMID:26829605
A Kernel Classification Framework for Metric Learning.
Wang, Faqiang; Zuo, Wangmeng; Zhang, Lei; Meng, Deyu; Zhang, David
2015-09-01
Learning a distance metric from the given training samples plays a crucial role in many machine learning tasks, and various models and optimization algorithms have been proposed in the past decade. In this paper, we generalize several state-of-the-art metric learning methods, such as large margin nearest neighbor (LMNN) and information theoretic metric learning (ITML), into a kernel classification framework. First, doublets and triplets are constructed from the training samples, and a family of degree-2 polynomial kernel functions is proposed for pairs of doublets or triplets. Then, a kernel classification framework is established to generalize many popular metric learning methods such as LMNN and ITML. The proposed framework can also suggest new metric learning methods, which can be efficiently implemented, interestingly, using the standard support vector machine (SVM) solvers. Two novel metric learning methods, namely, doublet-SVM and triplet-SVM, are then developed under the proposed framework. Experimental results show that doublet-SVM and triplet-SVM achieve competitive classification accuracies with state-of-the-art metric learning methods but with significantly less training time. PMID:25347887
Semi-Supervised Kernel Mean Shift Clustering.
Anand, Saket; Mittal, Sushil; Tuzel, Oncel; Meer, Peter
2014-06-01
Mean shift clustering is a powerful nonparametric technique that does not require prior knowledge of the number of clusters and does not constrain the shape of the clusters. However, being completely unsupervised, its performance suffers when the original distance metric fails to capture the underlying cluster structure. Despite recent advances in semi-supervised clustering methods, there has been little effort towards incorporating supervision into mean shift. We propose a semi-supervised framework for kernel mean shift clustering (SKMS) that uses only pairwise constraints to guide the clustering procedure. The points are first mapped to a high-dimensional kernel space where the constraints are imposed by a linear transformation of the mapped points. This is achieved by modifying the initial kernel matrix by minimizing a log det divergence-based objective function. We show the advantages of SKMS by evaluating its performance on various synthetic and real datasets while comparing with state-of-the-art semi-supervised clustering algorithms. PMID:26353281
NASA Astrophysics Data System (ADS)
Pope, Benjamin; Tuthill, Peter; Hinkley, Sasha; Ireland, Michael J.; Greenbaum, Alexandra; Latyshev, Alexey; Monnier, John D.; Martinache, Frantz
2016-01-01
At present, the principal limitation on the resolution and contrast of astronomical imaging instruments comes from aberrations in the optical path, which may be imposed by the Earth's turbulent atmosphere or by variations in the alignment and shape of the telescope optics. These errors can be corrected physically, with active and adaptive optics, and in post-processing of the resulting image. A recently developed adaptive optics post-processing technique, called kernel-phase interferometry, uses linear combinations of phases that are self-calibrating with respect to small errors, with the goal of constructing observables that are robust against the residual optical aberrations in otherwise well-corrected imaging systems. Here, we present a direct comparison between kernel phase and the more established competing techniques, aperture masking interferometry, point spread function (PSF) fitting and bispectral analysis. We resolve the α Ophiuchi binary system near periastron, using the Palomar 200-Inch Telescope. This is the first case in which kernel phase has been used with a full aperture to resolve a system close to the diffraction limit with ground-based extreme adaptive optics observations. Excellent agreement in astrometric quantities is found between kernel phase and masking, and kernel phase significantly outperforms PSF fitting and bispectral analysis, demonstrating its viability as an alternative to conventional non-redundant masking under appropriate conditions.
Detonation discrimination techniques using a near-infrared focal plane array camera
NASA Astrophysics Data System (ADS)
Dills, Anthony N.; Perram, Glen P.; Gustafson, Steven C.
2004-08-01
This research investigates the classification of battlespace detonations, specifically the determination of munitions type and size using image features from an infrared wavelength camera. Experimental data are collected for the detonation of several types of conventional munitions with different high explosive materials and different weights. Key features are identified for discriminating various types and sizes of detonation flashes. These features include statistical parameters derived from the time dependence of fireball size. Using Fisher linear discriminant techniques, these features are projected onto a line such that the projected points are maximally clustered for different classes of detonations. Bayesian decision boundaries for classification are then determined.
The Milky Way, the Local Group & the IR Tully-Fisher Diagram
NASA Technical Reports Server (NTRS)
Malhotra, S.; Spergel, D.; Rhoads, J.; Li, J.
1996-01-01
Using the near infrared fluxes of local group galaxies derived from Cosmic Background Explorer/Diffuse Infrared Background Experiment band maps and published Cepheid distances, we construct Tully-Fisher diagrams for the Local Group.
A Discussion with Suzanne Fisher Staples: The Author as Writer and Cultural Observer.
ERIC Educational Resources Information Center
Sawyer, Walter E.; Sawyer, Jean C.
1993-01-01
Presents an interview with Suzanne Fisher Staples, author of the children's novel, "Shabanu, Daughter of the Wind." Discusses Staples' creative writing process, background, and the writer's role as cultural observer. (HB)
Who's Qualified? Seeing Race in Color-Blind Times: Lessons from Fisher v. University of Texas
ERIC Educational Resources Information Center
Donnor, Jamel K.
2015-01-01
Using Howard Winant's racial dualism theory, this chapter explains how race was discursively operationalized in the recent U.S. Supreme Court higher education antiracial diversity case Fisher v. University of Texas at Austin.
Fisher information for the position-dependent mass Schrödinger system
NASA Astrophysics Data System (ADS)
Falaye, B. J.; Serrano, F. A.; Dong, Shi-Hai
2016-01-01
This study presents the Fisher information for the position-dependent mass Schrödinger equation with hyperbolic potential V (x) = -V0csch2 (ax). The analysis of the quantum-mechanical probability for the ground and exited states (n = 0, 1, 2) has been obtained via the Fisher information. This controls both chemical and physical properties of some molecular systems. The Fisher information is considered only for x > 0 due to the singular point at x = 0. We found that Fisher-information-based uncertainty relation and the Cramer-Rao inequality holds. Some relevant numerical results are presented. The results presented show that the Cramer-Rao and the Heisenberg products in both spaces provide a natural measure for anharmonicity of -V0csch2 (ax).
Fisher information and Shannon entropy of position-dependent mass oscillators
NASA Astrophysics Data System (ADS)
Macedo, D. X.; Guedes, I.
2015-09-01
We calculate the Fisher information and the Shannon entropy for three position-dependent mass oscillators. These systems can be seen as deformed harmonic oscillators in the sense that when the deformation parameter (λ) goes to zero, they are identical to the constant mass harmonic oscillator. For two out of the three oscillators we observe that as λ increases the position Fisher information (Fx) increases while the momentum Fisher information (Fp) decreases. On the other hand, the Shannon entropy always increases for the three systems with increasing λ. Discussion about squeezing effect in either position or momentum due to the λ variation and a relation between the product of Fisher information and the Shannon entropy are also presented.
Federal Register 2010, 2011, 2012, 2013, 2014
2012-08-10
...; Socioeconomics of Commercial Fishers and for Hire Diving and Fishing Operations in the Flower Garden Banks...). In 1996, the Flower Gardens Bank National Marine Sanctuary (FGBNMS) was added to the system of...
Extended Tully-Fisher relations using H I stacking
NASA Astrophysics Data System (ADS)
Meyer, Scott A.; Meyer, Martin; Obreschkow, Danail; Staveley-Smith, Lister
2016-01-01
We present a new technique for the statistical evaluation of the Tully-Fisher relation (TFR) using spectral line stacking. This technique has the potential to extend TFR observations to lower masses and higher redshifts than possible through a galaxy-by-galaxy analysis. It further avoids the need for individual galaxy inclination measurements. To quantify the properties of stacked H I emission lines, we consider a simplistic model of galactic discs with analytically expressible line profiles. Using this model, we compare the widths of stacked profiles with those of individual galaxies. We then follow the same procedure using more realistic mock galaxies drawn from the S3-SAX model (a derivative of the Millennium simulation). Remarkably, when stacking the apparent H I lines of galaxies with similar absolute magnitude and random inclinations, the width of the stack is very similar to the width of the deprojected (= corrected for inclination) and dedispersed (= after removal of velocity dispersion) input lines. Therefore, the ratio between the widths of the stack and the deprojected/dedispersed input lines is approximately constant - about 0.93 - with very little dependence on the gas dispersion, galaxy mass, galaxy morphology and shape of the rotation curve. Finally, we apply our technique to construct a stacked TFR using H I Parkes All-Sky Survey (HIPASS) data which already has a well-defined TFR based on individual detections. We obtain a B-band TFR with a slope of -8.5 ± 0.4 and a K-band relation with a slope of -11.7 ± 0.6 for the HIPASS data set which is consistent with the existing results.
Transit light curves with finite integration time: Fisher information analysis
Price, Ellen M.; Rogers, Leslie A.
2014-10-10
Kepler has revolutionized the study of transiting planets with its unprecedented photometric precision on more than 150,000 target stars. Most of the transiting planet candidates detected by Kepler have been observed as long-cadence targets with 30 minute integration times, and the upcoming Transiting Exoplanet Survey Satellite will record full frame images with a similar integration time. Integrations of 30 minutes affect the transit shape, particularly for small planets and in cases of low signal to noise. Using the Fisher information matrix technique, we derive analytic approximations for the variances and covariances on the transit parameters obtained from fitting light curve photometry collected with a finite integration time. We find that binning the light curve can significantly increase the uncertainties and covariances on the inferred parameters when comparing scenarios with constant total signal to noise (constant total integration time in the absence of read noise). Uncertainties on the transit ingress/egress time increase by a factor of 34 for Earth-size planets and 3.4 for Jupiter-size planets around Sun-like stars for integration times of 30 minutes compared to instantaneously sampled light curves. Similarly, uncertainties on the mid-transit time for Earth and Jupiter-size planets increase by factors of 3.9 and 1.4. Uncertainties on the transit depth are largely unaffected by finite integration times. While correlations among the transit depth, ingress duration, and transit duration all increase in magnitude with longer integration times, the mid-transit time remains uncorrelated with the other parameters. We provide code in Python and Mathematica for predicting the variances and covariances at www.its.caltech.edu/∼eprice.
The different baryonic Tully-Fisher relations at low masses
NASA Astrophysics Data System (ADS)
Brook, Chris B.; Santos-Santos, Isabel; Stinson, Greg
2016-06-01
We compare the Baryonic Tully-Fisher relation (BTFR) of simulations and observations of galaxies ranging from dwarfs to spirals, using various measures of rotational velocity Vrot. We explore the BTFR when measuring Vrot at the flat part of the rotation curve, Vflat, at the extent of H I gas, Vlast, and using 20 per cent (W20) and 50 per cent (W50) of the width of H I line profiles. We also compare with the maximum circular velocity of the parent halo, V_max^DM, within dark matter only simulations. The different BTFRs increasingly diverge as galaxy mass decreases. Using Vlast one obtains a power law over four orders of magnitude in baryonic mass, with slope similar to the observed BTFR. Measuring Vflat gives similar results as Vlast when galaxies with rising rotation curves are excluded. However, higher rotation velocities would be found for low-mass galaxies if the cold gas extended far enough for Vrot to reach a maximum. W20 gives a similar slope as Vlast but with slightly lower values of Vrot for low-mass galaxies, although this may depend on the extent of the gas in your galaxy sample. W50 bends away from these other relations towards low velocities at low masses. By contrast, V_max^DM bends towards high velocities for low-mass galaxies, as cold gas does not extend out to the radius at which haloes reach V_max^DM. Our study highlights the need for careful comparisons between observations and models: one needs to be consistent about the particular method of measuring Vrot, and precise about the radius at which velocities are measured.
Phase space gradient of dissipated work and information: A role of relative Fisher information
Yamano, Takuya
2013-11-15
We show that an information theoretic distance measured by the relative Fisher information between canonical equilibrium phase densities corresponding to forward and backward processes is intimately related to the gradient of the dissipated work in phase space. We present a universal constraint on it via the logarithmic Sobolev inequality. Furthermore, we point out that a possible expression of the lower bound indicates a deep connection in terms of the relative entropy and the Fisher information of the canonical distributions.
Resources and estuarine health: perceptions of elected officials and recreational fishers.
Burger, J; Sanchez, J; McMahon, M; Leonard, J; Lord, C G; Ramos, R; Gochfeld, M
1999-10-29
It is important to understand the perceptions of user groups regarding both the health of our estuaries and environmental problems requiring management. Recreational fishers were interviewed to determine the perceptions of one of the traditional user groups of Barnegat Bay (New Jersey), and elected officials were interviewed to determine if the people charged with making decisions about environmental issues in the bay held similar perceptions. Although relative ratings were similar, there were significant differences in perceptions of the severity of environmental problems, and for the most part, public officials thought the problems were more severe than did the fishers. Personal watercraft (often called Jet Skis) were rated as the most severe problem, followed by chemical pollution, junk, overfishing, street runoff, and boat oil. Small boats, sailboats, wind surfers, and foraging birds were not considered environmental problems by either elected officials or fishermen. The disconnect between the perceptions of the recreational fishers and those of the locally elected public officials suggests that officials may be hearing from some of the more vocal people about problems, rather than from the typical fishers. Both groups felt there were decreases in some of the resources in the bay; over 50% felt the number of fish and crabs had declined, the size of fish and crabs had declined, and the number of turtles had declined. Among recreational fishers, there were almost no differences in perceptions of the severity of environmental problems or in changes in the bay. The problems that were rated the most severe were personal watercraft and overfishing by commercial fishers. Recreational fishers ranked sailboats, wind surfers, and fishing by birds as posing no problem for the bay. Most fishers felt there had been recent major changes in Barnegat Bay, with there now being fewer and smaller fish, fewer and smaller crabs, and fewer turtles. The results suggest that the views
NASA Astrophysics Data System (ADS)
Qiao, Lu; Peng, Yankun; Chao, Kuanglin; Qin, Jianwei
2016-05-01
Meat is the necessary source of essential nutrients for people including protein, fat, and so on. The discrimination of meat species and the determination of meat authenticity have been an important issue in the meat industry. The objective of this study is to realize the fast and accurate identification of three main red meats containing beef, lamb and pork by using near-infrared hyperspectral imaging (HSI) technology. After acquiring the hyperspectral images of meat samples, the calibration of acquired images and selection of the region of interest (ROI) were carried out. Then spectral preprocessing method of standard normal variate correction (SNV) was used to reduce the light scattering and random noise before the spectral analysis. Finally, characteristic wavelengths were extracted by principal component analysis (PCA), and the Fisher linear discriminant method was applied to establish Fisher discriminant functions to identify the meat species. All the samples were collected from different batches in order to improve the coverage of the models. In addition to the validation of sample itself in train set and cross validation, three different meat samples were sliced at the size of 2cm×2cm×2 cm approximately and were spliced together in one interface to be scanned by HSI system. The acquired hyperspectral data was applied to further validate the discriminant model. The results demonstrated that the near-infrared hyperspectral imaging technology could be applied as an effective, rapid and non-destructive discrimination method for main red meats.
Rapidly shifting environmental baselines among fishers of the Gulf of California.
Sáenz-Arroyo, Andrea; Roberts, Callum M; Torre, Jorge; Cariño-Olvera, Micheline; Enríquez-Andrade, Roberto R
2005-09-22
Shifting environmental baselines are inter-generational changes in perception of the state of the environment. As one generation replaces another, people's perceptions of what is natural change even to the extent that they no longer believe historical anecdotes of past abundance or size of species. Although widely accepted, this phenomenon has yet to be quantitatively tested. Here we survey three generations of fishers from Mexico's Gulf of California (N=108), where fish populations have declined steeply over the last 60 years, to investigate how far and fast their environmental baselines are shifting. Compared to young fishers, old fishers named five times as many species and four times as many fishing sites as once being abundant/productive but now depleted (Kruskal-Wallis tests, both p<0.001) with no evidence of a slowdown in rates of loss experienced by younger compared to older generations (Kruskal-Wallis test, n.s. in both cases). Old fishers caught up to 25 times as many Gulf grouper Mycteroperca jordani as young fishers on their best ever fishing day (regression r(2)=0.62, p<0.001). Despite times of plentiful large fish still being within living memory, few young fishers appreciated that large species had ever been common or nearshore sites productive. Such rapid shifts in perception of what is natural help explain why society is tolerant of the creeping loss of biodiversity. They imply a large educational hurdle in efforts to reset expectations and targets for conservation. PMID:16191603
Rapidly shifting environmental baselines among fishers of the Gulf of California
Sáenz-Arroyo, Andrea; Roberts, Callum M; Torre, Jorge; Cariño-Olvera, Micheline; Enríquez-Andrade, Roberto R
2005-01-01
Shifting environmental baselines are inter-generational changes in perception of the state of the environment. As one generation replaces another, people's perceptions of what is natural change even to the extent that they no longer believe historical anecdotes of past abundance or size of species. Although widely accepted, this phenomenon has yet to be quantitatively tested. Here we survey three generations of fishers from Mexico's Gulf of California (N=108), where fish populations have declined steeply over the last 60 years, to investigate how far and fast their environmental baselines are shifting. Compared to young fishers, old fishers named five times as many species and four times as many fishing sites as once being abundant/productive but now depleted (Kruskal–Wallis tests, both p<0.001) with no evidence of a slowdown in rates of loss experienced by younger compared to older generations (Kruskal–Wallis test, n.s. in both cases). Old fishers caught up to 25 times as many Gulf grouper Mycteroperca jordani as young fishers on their best ever fishing day (regression r2=0.62, p<0.001). Despite times of plentiful large fish still being within living memory, few young fishers appreciated that large species had ever been common or nearshore sites productive. Such rapid shifts in perception of what is natural help explain why society is tolerant of the creeping loss of biodiversity. They imply a large educational hurdle in efforts to reset expectations and targets for conservation. PMID:16191603
Multiple kernel learning for sparse representation-based classification.
Shrivastava, Ashish; Patel, Vishal M; Chellappa, Rama
2014-07-01
In this paper, we propose a multiple kernel learning (MKL) algorithm that is based on the sparse representation-based classification (SRC) method. Taking advantage of the nonlinear kernel SRC in efficiently representing the nonlinearities in the high-dimensional feature space, we propose an MKL method based on the kernel alignment criteria. Our method uses a two step training method to learn the kernel weights and sparse codes. At each iteration, the sparse codes are updated first while fixing the kernel mixing coefficients, and then the kernel mixing coefficients are updated while fixing the sparse codes. These two steps are repeated until a stopping criteria is met. The effectiveness of the proposed method is demonstrated using several publicly available image classification databases and it is shown that this method can perform significantly better than many competitive image classification algorithms. PMID:24835226
Small convolution kernels for high-fidelity image restoration
NASA Technical Reports Server (NTRS)
Reichenbach, Stephen E.; Park, Stephen K.
1991-01-01
An algorithm is developed for computing the mean-square-optimal values for small, image-restoration kernels. The algorithm is based on a comprehensive, end-to-end imaging system model that accounts for the important components of the imaging process: the statistics of the scene, the point-spread function of the image-gathering device, sampling effects, noise, and display reconstruction. Subject to constraints on the spatial support of the kernel, the algorithm generates the kernel values that restore the image with maximum fidelity, that is, the kernel minimizes the expected mean-square restoration error. The algorithm is consistent with the derivation of the spatially unconstrained Wiener filter, but leads to a small, spatially constrained kernel that, unlike the unconstrained filter, can be efficiently implemented by convolution. Simulation experiments demonstrate that for a wide range of imaging systems these small kernels can restore images with fidelity comparable to images restored with the unconstrained Wiener filter.
Justice and Reverse Discrimination.
ERIC Educational Resources Information Center
Goldman, Alan H.
Defining reverse discrimination as hiring or admissions decisions based on normally irrelevant criteria, this book develops principles of rights, compensation, and equal opportunity applicable to the reverse discrimination issue. The introduction defines the issue and discusses deductive and inductive methodology as applied to reverse…
Reverse Discrimination: Recent Cases.
ERIC Educational Resources Information Center
Steinhilber, August W.
This paper discusses reverse discrimination cases with particular emphasis on Bakke v. Regents of University of California and those cases which preceded it. A brief history is given of court cases used by opponents and proponents in the discussion of reverse discrimination. Legal theory and a discussion of court cases that preceded Bakke follow.…
Deconinck, Matthieu E.
2010-06-15
We show how one can solve the problem of discriminating between qubit states. We use the quantum state discrimination duality theorem and the Bloch sphere representation of qubits, which allows for an easy geometric and analytical representation of the optimal guessing strategies.
Microscale acceleration history discriminators
Polosky, Marc A.; Plummer, David W.
2002-01-01
A new class of micromechanical acceleration history discriminators is claimed. These discriminators allow the precise differentiation of a wide range of acceleration-time histories, thereby allowing adaptive events to be triggered in response to the severity (or lack thereof) of an external environment. Such devices have applications in airbag activation, and other safety and surety applications.
NASA Technical Reports Server (NTRS)
Koshak, William J.
2010-01-01
This viewgraph presentation describes the significant progress made in the flash-type discrimination algorithm development. The contents include: 1) Highlights of Progress for GLM-R3 Flash-Type discrimination Algorithm Development; 2) Maximum Group Area (MGA) Data; 3) Retrieval Errors from Simulations; and 4) Preliminary Global-scale Retrieval.
Monte Carlo Code System for Electron (Positron) Dose Kernel Calculations.
CHIBANI, OMAR
1999-05-12
Version 00 KERNEL performs dose kernel calculations for an electron (positron) isotropic point source in an infinite homogeneous medium. First, the auxiliary code PRELIM is used to prepare cross section data for the considered medium. Then the KERNEL code simulates the transport of electrons and bremsstrahlung photons through the medium until all particles reach their cutoff energies. The deposited energy is scored in concentric spherical shells at a radial distance ranging from zero to twice the source particle range.
Scale-invariant Lipatov kernels from t-channel unitarity
Coriano, C.; White, A.R.
1994-11-14
The Lipatov equation can be regarded as a reggeon Bethe-Salpeter equation in which higher-order reggeon interactions give higher-order kernels. Infra-red singular contributions in a general kernel are produced by t-channel nonsense states and the allowed kinematic forms are determined by unitarity. Ward identity and infra-red finiteness gauge invariance constraints then determine the corresponding scale-invariant part of a general higher-order kernel.
Robust kernel collaborative representation for face recognition
NASA Astrophysics Data System (ADS)
Huang, Wei; Wang, Xiaohui; Ma, Yanbo; Jiang, Yuzheng; Zhu, Yinghui; Jin, Zhong
2015-05-01
One of the greatest challenges of representation-based face recognition is that the training samples are usually insufficient. In other words, the training set usually does not include enough samples to show varieties of high-dimensional face images caused by illuminations, facial expressions, and postures. When the test sample is significantly different from the training samples of the same subject, the recognition performance will be sharply reduced. We propose a robust kernel collaborative representation based on virtual samples for face recognition. We think that the virtual training set conveys some reasonable and possible variations of the original training samples. Hence, we design a new object function to more closely match the representation coefficients generated from the original and virtual training sets. In order to further improve the robustness, we implement the corresponding representation-based face recognition in kernel space. It is noteworthy that any kind of virtual training samples can be used in our method. We use noised face images to obtain virtual face samples. The noise can be approximately viewed as a reflection of the varieties of illuminations, facial expressions, and postures. Our work is a simple and feasible way to obtain virtual face samples to impose Gaussian noise (and other types of noise) specifically to the original training samples to obtain possible variations of the original samples. Experimental results on the FERET, Georgia Tech, and ORL face databases show that the proposed method is more robust than two state-of-the-art face recognition methods, such as CRC and Kernel CRC.
Influence of wheat kernel physical properties on the pulverizing process.
Dziki, Dariusz; Cacak-Pietrzak, Grażyna; Miś, Antoni; Jończyk, Krzysztof; Gawlik-Dziki, Urszula
2014-10-01
The physical properties of wheat kernel were determined and related to pulverizing performance by correlation analysis. Nineteen samples of wheat cultivars about similar level of protein content (11.2-12.8 % w.b.) and obtained from organic farming system were used for analysis. The kernel (moisture content 10 % w.b.) was pulverized by using the laboratory hammer mill equipped with round holes 1.0 mm screen. The specific grinding energy ranged from 120 kJkg(-1) to 159 kJkg(-1). On the basis of data obtained many of significant correlations (p < 0.05) were found between wheat kernel physical properties and pulverizing process of wheat kernel, especially wheat kernel hardness index (obtained on the basis of Single Kernel Characterization System) and vitreousness significantly and positively correlated with the grinding energy indices and the mass fraction of coarse particles (> 0.5 mm). Among the kernel mechanical properties determined on the basis of uniaxial compression test only the rapture force was correlated with the impact grinding results. The results showed also positive and significant relationships between kernel ash content and grinding energy requirements. On the basis of wheat physical properties the multiple linear regression was proposed for predicting the average particle size of pulverized kernel. PMID:25328207
A short- time beltrami kernel for smoothing images and manifolds.
Spira, Alon; Kimmel, Ron; Sochen, Nir
2007-06-01
We introduce a short-time kernel for the Beltrami image enhancing flow. The flow is implemented by "convolving" the image with a space dependent kernel in a similar fashion to the solution of the heat equation by a convolution with a Gaussian kernel. The kernel is appropriate for smoothing regular (flat) 2-D images, for smoothing images painted on manifolds, and for simultaneously smoothing images and the manifolds they are painted on. The kernel combines the geometry of the image and that of the manifold into one metric tensor, thus enabling a natural unified approach for the manipulation of both. Additionally, the derivation of the kernel gives a better geometrical understanding of the Beltrami flow and shows that the bilateral filter is a Euclidean approximation of it. On a practical level, the use of the kernel allows arbitrarily large time steps as opposed to the existing explicit numerical schemes for the Beltrami flow. In addition, the kernel works with equal ease on regular 2-D images and on images painted on parametric or triangulated manifolds. We demonstrate the denoising properties of the kernel by applying it to various types of images and manifolds. PMID:17547140
Isolation of bacterial endophytes from germinated maize kernels.
Rijavec, Tomaz; Lapanje, Ales; Dermastia, Marina; Rupnik, Maja
2007-06-01
The germination of surface-sterilized maize kernels under aseptic conditions proved to be a suitable method for isolation of kernel-associated bacterial endophytes. Bacterial strains identified by partial 16S rRNA gene sequencing as Pantoea sp., Microbacterium sp., Frigoribacterium sp., Bacillus sp., Paenibacillus sp., and Sphingomonas sp. were isolated from kernels of 4 different maize cultivars. Genus Pantoea was associated with a specific maize cultivar. The kernels of this cultivar were often overgrown with the fungus Lecanicillium aphanocladii; however, those exhibiting Pantoea growth were never colonized with it. Furthermore, the isolated bacterium strain inhibited fungal growth in vitro. PMID:17668041
A Kernel-based Account of Bibliometric Measures
NASA Astrophysics Data System (ADS)
Ito, Takahiko; Shimbo, Masashi; Kudo, Taku; Matsumoto, Yuji
The application of kernel methods to citation analysis is explored. We show that a family of kernels on graphs provides a unified perspective on the three bibliometric measures that have been discussed independently: relatedness between documents, global importance of individual documents, and importance of documents relative to one or more (root) documents (relative importance). The framework provided by the kernels establishes relative importance as an intermediate between relatedness and global importance, in which the degree of `relativity,' or the bias between relatedness and importance, is naturally controlled by a parameter characterizing individual kernels in the family.
Optimized Derivative Kernels for Gamma Ray Spectroscopy
Vlachos, D. S.; Kosmas, O. T.; Simos, T. E.
2007-12-26
In gamma ray spectroscopy, the photon detectors measure the number of photons with energy that lies in an interval which is called a channel. This accumulation of counts produce a measuring function that its deviation from the ideal one may produce high noise in the unfolded spectrum. In order to deal with this problem, the ideal accumulation function is interpolated with the use of special designed derivative kernels. Simulation results are presented which show that this approach is very effective even in spectra with low statistics.
Oil point pressure of Indian almond kernels
NASA Astrophysics Data System (ADS)
Aregbesola, O.; Olatunde, G.; Esuola, S.; Owolarafe, O.
2012-07-01
The effect of preprocessing conditions such as moisture content, heating temperature, heating time and particle size on oil point pressure of Indian almond kernel was investigated. Results showed that oil point pressure was significantly (P < 0.05) affected by above mentioned parameters. It was also observed that oil point pressure reduced with increase in heating temperature and heating time for both coarse and fine particles. Furthermore, an increase in moisture content resulted in increased oil point pressure for coarse particles while there was a reduction in oil point pressure with increase in moisture content for fine particles.
Verification of Chare-kernel programs
Bhansali, S.; Kale, L.V. )
1989-01-01
Experience with concurrent programming has shown that concurrent programs can conceal bugs even after extensive testing. Thus, there is a need for practical techniques which can establish the correctness of parallel programs. This paper proposes a method for showing how to prove the partial correctness of programs written in the Chare-kernel language, which is a language designed to support the parallel execution of computation with irregular structures. The proof is based on the lattice proof technique and is divided into two parts. The first part is concerned with the program behavior within a single chare instance, whereas the second part captures the inter-chare interaction.
The baryonic Tully-Fisher relation and galactic outflows
NASA Astrophysics Data System (ADS)
Dutton, Aaron A.
2012-08-01
Most of the baryons in the Universe are not in the form of stars and cold gas in galaxies. Galactic outflows driven by supernovae/stellar winds are the leading mechanisms for explaining this fact. The scaling relation between galaxy mass and outer rotation velocity (also known as the baryonic Tully-Fisher relation, BTF) has recently been used as evidence against this viewpoint. We use a Λ cold dark matter (ΛCDM)-based semi-analytic disc galaxy formation model to investigate these claims. In our model, galaxies with less efficient star formation and higher gas fractions are more efficient at ejecting gas from galaxies. This somewhat counter intuitive result is due to the (observational) fact that galaxies with less efficient star formation and higher gas fractions tend to live in dark matter haloes with lower circular velocities, from which less energy is required to escape the potential well. In our model the intrinsic scatter in the BTF is ≃0.15 dex, and mostly reflects scatter in dark halo concentration. The scatter is largely independent of galaxy structure because of the large radius within which galaxy rotation velocities are measured. The observed scatter, equal to ≃0.24 dex, is dominated by measurement errors. The best estimate for the intrinsic scatter is that it is less than 0.15 dex, and thus our ΛCDM-based model (which does not include all possible sources of scatter) is only just consistent with this. Future observations of the BTF scatter could be made with a more stringent measurement of the intrinsic scatter, and thus provide a strong constraint to galaxy formation models. In our model, gas-rich galaxies, at fixed virial velocity (Vvir), with lower stellar masses have lower baryonic masses. This is consistent with the expectation that galaxies with lower stellar masses have had less energy available to drive an outflow. However, when the outer rotation velocity (Vflat) is used the correlation has the opposite sign, with a slope in agreement
The different baryonic Tully–Fisher relations at low masses
Brook, Chris B.; Santos-Santos, Isabel; Stinson, Greg
2016-01-01
We compare the Baryonic Tully–Fisher relation (BTFR) of simulations and observations of galaxies ranging from dwarfs to spirals, using various measures of rotational velocity Vrot. We explore the BTFR when measuring Vrot at the flat part of the rotation curve, Vflat, at the extent of H i gas, Vlast, and using 20 per cent (W20) and 50 per cent (W50) of the width of H i line profiles. We also compare with the maximum circular velocity of the parent halo, \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$V_{\\rm max}^{\\rm DM}$\\end{document}, within dark matter only simulations. The different BTFRs increasingly diverge as galaxy mass decreases. Using Vlast one obtains a power law over four orders of magnitude in baryonic mass, with slope similar to the observed BTFR. Measuring Vflat gives similar results as Vlast when galaxies with rising rotation curves are excluded. However, higher rotation velocities would be found for low-mass galaxies if the cold gas extended far enough for Vrot to reach a maximum. W20 gives a similar slope as Vlast but with slightly lower values of Vrot for low-mass galaxies, although this may depend on the extent of the gas in your galaxy sample. W50 bends away from these other relations towards low velocities at low masses. By contrast, \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$V_{\\rm max}^{\\rm DM}$\\end{document} bends towards high velocities for low-mass galaxies, as cold gas does not extend out to the radius at which haloes reach \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage
Prediction of kernel density of corn using single-kernel near infrared spectroscopy
Technology Transfer Automated Retrieval System (TEKTRAN)
Corn hardness as is an important property for dry and wet-millers, food processors and corn breeders developing hybrids for specific markets. Of the several methods used to measure hardness, kernel density measurements are one of the more repeatable methods to quantify hardness. Near infrared spec...
Quadratic negative evidence discrimination
Anderson, D.N.; Redgate, T.; Anderson, K.K.; Rohay, A.C.; Ryan, F.M.
1997-05-01
This paper develops regional discrimination methods which use information inherent in phase magnitudes that are unmeasurable due to small amplitudes and/or high noise levels. The methods are enhancements to teleseismic techniques proposed by, and are extended to regional discrimination. Events observed at teleseismic distances are effectively identified with the M{sub s} vs m{sub b} discriminant because relative to the pressure wave energy (m{sub b}) of an event, an earthquake generates more shear wave energy (M{sub s}) than does an explosion. For some teleseismic events, the M{sub s} magnitude is difficult to measure and is known only to be below a threshold . With M{sub s} unmeasurable, the M{sub s} vs m{sub b} discriminant cannot be formed. However, if the M{sub s} is sufficiently small relative to a measured m{sub b}, then the event is still likely to be an explosion. The methods presented in this report are developed for a single seismic station, and make use of empirical evidence in the regional L{sub g} vs p{sub g} discriminant. The L{sub g} vs p{sub g} discriminant is analogous to the teleseismic M{sub s} vs m{sub b} discriminant.
Frequency discriminator/phase detector
NASA Technical Reports Server (NTRS)
Crow, R. B.
1974-01-01
Circuit provides dual function of frequency discriminator/phase detector which reduces frequency acquisition time without adding to circuit complexity. Both frequency discriminators, in evaluated frequency discriminator/phase detector circuits, are effective two decades above and below center frequency.
Linear and kernel methods for multi- and hypervariate change detection
NASA Astrophysics Data System (ADS)
Nielsen, Allan A.; Canty, Morton J.
2010-10-01
The iteratively re-weighted multivariate alteration detection (IR-MAD) algorithm may be used both for unsuper- vised change detection in multi- and hyperspectral remote sensing imagery as well as for automatic radiometric normalization of multi- or hypervariate multitemporal image sequences. Principal component analysis (PCA) as well as maximum autocorrelation factor (MAF) and minimum noise fraction (MNF) analyses of IR-MAD images, both linear and kernel-based (which are nonlinear), may further enhance change signals relative to no-change background. The kernel versions are based on a dual formulation, also termed Q-mode analysis, in which the data enter into the analysis via inner products in the Gram matrix only. In the kernel version the inner products of the original data are replaced by inner products between nonlinear mappings into higher dimensional feature space. Via kernel substitution, also known as the kernel trick, these inner products between the mappings are in turn replaced by a kernel function and all quantities needed in the analysis are expressed in terms of the kernel function. This means that we need not know the nonlinear mappings explicitly. Kernel principal component analysis (PCA), kernel MAF and kernel MNF analyses handle nonlinearities by implicitly transforming data into high (even innite) dimensional feature space via the kernel function and then performing a linear analysis in that space. In image analysis the Gram matrix is often prohibitively large (its size is the number of pixels in the image squared). In this case we may sub-sample the image and carry out the kernel eigenvalue analysis on a set of training data samples only. To obtain a transformed version of the entire image we then project all pixels, which we call the test data, mapped nonlinearly onto the primal eigenvectors. IDL (Interactive Data Language) implementations of IR-MAD, automatic radiometric normalization and kernel PCA/MAF/MNF transformations have been written
Scientific Computing Kernels on the Cell Processor
Williams, Samuel W.; Shalf, John; Oliker, Leonid; Kamil, Shoaib; Husbands, Parry; Yelick, Katherine
2007-04-04
The slowing pace of commodity microprocessor performance improvements combined with ever-increasing chip power demands has become of utmost concern to computational scientists. As a result, the high performance computing community is examining alternative architectures that address the limitations of modern cache-based designs. In this work, we examine the potential of using the recently-released STI Cell processor as a building block for future high-end computing systems. Our work contains several novel contributions. First, we introduce a performance model for Cell and apply it to several key scientific computing kernels: dense matrix multiply, sparse matrix vector multiply, stencil computations, and 1D/2D FFTs. The difficulty of programming Cell, which requires assembly level intrinsics for the best performance, makes this model useful as an initial step in algorithm design and evaluation. Next, we validate the accuracy of our model by comparing results against published hardware results, as well as our own implementations on a 3.2GHz Cell blade. Additionally, we compare Cell performance to benchmarks run on leading superscalar (AMD Opteron), VLIW (Intel Itanium2), and vector (Cray X1E) architectures. Our work also explores several different mappings of the kernels and demonstrates a simple and effective programming model for Cell's unique architecture. Finally, we propose modest microarchitectural modifications that could significantly increase the efficiency of double-precision calculations. Overall results demonstrate the tremendous potential of the Cell architecture for scientific computations in terms of both raw performance and power efficiency.
Stable Local Volatility Calibration Using Kernel Splines
NASA Astrophysics Data System (ADS)
Coleman, Thomas F.; Li, Yuying; Wang, Cheng
2010-09-01
We propose an optimization formulation using L1 norm to ensure accuracy and stability in calibrating a local volatility function for option pricing. Using a regularization parameter, the proposed objective function balances the calibration accuracy with the model complexity. Motivated by the support vector machine learning, the unknown local volatility function is represented by a kernel function generating splines and the model complexity is controlled by minimizing the 1-norm of the kernel coefficient vector. In the context of the support vector regression for function estimation based on a finite set of observations, this corresponds to minimizing the number of support vectors for predictability. We illustrate the ability of the proposed approach to reconstruct the local volatility function in a synthetic market. In addition, based on S&P 500 market index option data, we demonstrate that the calibrated local volatility surface is simple and resembles the observed implied volatility surface in shape. Stability is illustrated by calibrating local volatility functions using market option data from different dates.
Transcriptome analysis of Ginkgo biloba kernels
He, Bing; Gu, Yincong; Xu, Meng; Wang, Jianwen; Cao, Fuliang; Xu, Li-an
2015-01-01
Ginkgo biloba is a dioecious species native to China with medicinally and phylogenetically important characteristics; however, genomic resources for this species are limited. In this study, we performed the first transcriptome sequencing for Ginkgo kernels at five time points using Illumina paired-end sequencing. Approximately 25.08-Gb clean reads were obtained, and 68,547 unigenes with an average length of 870 bp were generated by de novo assembly. Of these unigenes, 29,987 (43.74%) were annotated in publicly available plant protein database. A total of 3,869 genes were identified as significantly differentially expressed, and enrichment analysis was conducted at different time points. Furthermore, metabolic pathway analysis revealed that 66 unigenes were responsible for terpenoid backbone biosynthesis, with up to 12 up-regulated unigenes involved in the biosynthesis of ginkgolide and bilobalide. Differential gene expression analysis together with real-time PCR experiments indicated that the synthesis of bilobalide may have interfered with the ginkgolide synthesis process in the kernel. These data can remarkably expand the existing transcriptome resources of Ginkgo, and provide a valuable platform to reveal more on developmental and metabolic mechanisms of this species. PMID:26500663
Delimiting Areas of Endemism through Kernel Interpolation
Oliveira, Ubirajara; Brescovit, Antonio D.; Santos, Adalberto J.
2015-01-01
We propose a new approach for identification of areas of endemism, the Geographical Interpolation of Endemism (GIE), based on kernel spatial interpolation. This method differs from others in being independent of grid cells. This new approach is based on estimating the overlap between the distribution of species through a kernel interpolation of centroids of species distribution and areas of influence defined from the distance between the centroid and the farthest point of occurrence of each species. We used this method to delimit areas of endemism of spiders from Brazil. To assess the effectiveness of GIE, we analyzed the same data using Parsimony Analysis of Endemism and NDM and compared the areas identified through each method. The analyses using GIE identified 101 areas of endemism of spiders in Brazil GIE demonstrated to be effective in identifying areas of endemism in multiple scales, with fuzzy edges and supported by more synendemic species than in the other methods. The areas of endemism identified with GIE were generally congruent with those identified for other taxonomic groups, suggesting that common processes can be responsible for the origin and maintenance of these biogeographic units. PMID:25611971
Aligning Biomolecular Networks Using Modular Graph Kernels
NASA Astrophysics Data System (ADS)
Towfic, Fadi; Greenlee, M. Heather West; Honavar, Vasant
Comparative analysis of biomolecular networks constructed using measurements from different conditions, tissues, and organisms offer a powerful approach to understanding the structure, function, dynamics, and evolution of complex biological systems. We explore a class of algorithms for aligning large biomolecular networks by breaking down such networks into subgraphs and computing the alignment of the networks based on the alignment of their subgraphs. The resulting subnetworks are compared using graph kernels as scoring functions. We provide implementations of the resulting algorithms as part of BiNA, an open source biomolecular network alignment toolkit. Our experiments using Drosophila melanogaster, Saccharomyces cerevisiae, Mus musculus and Homo sapiens protein-protein interaction networks extracted from the DIP repository of protein-protein interaction data demonstrate that the performance of the proposed algorithms (as measured by % GO term enrichment of subnetworks identified by the alignment) is competitive with some of the state-of-the-art algorithms for pair-wise alignment of large protein-protein interaction networks. Our results also show that the inter-species similarity scores computed based on graph kernels can be used to cluster the species into a species tree that is consistent with the known phylogenetic relationships among the species.
Tectonic discrimination diagrams revisited
NASA Astrophysics Data System (ADS)
Vermeesch, Pieter
2006-06-01
The decision boundaries of most tectonic discrimination diagrams are drawn by eye. Discriminant analysis is a statistically more rigorous way to determine the tectonic affinity of oceanic basalts based on their bulk-rock chemistry. This method was applied to a database of 756 oceanic basalts of known tectonic affinity (ocean island, mid-ocean ridge, or island arc). For each of these training data, up to 45 major, minor, and trace elements were measured. Discriminant analysis assumes multivariate normality. If the same covariance structure is shared by all the classes (i.e., tectonic affinities), the decision boundaries are linear, hence the term linear discriminant analysis (LDA). In contrast with this, quadratic discriminant analysis (QDA) allows the classes to have different covariance structures. To solve the statistical problems associated with the constant-sum constraint of geochemical data, the training data must be transformed to log-ratio space before performing a discriminant analysis. The results can be mapped back to the compositional data space using the inverse log-ratio transformation. An exhaustive exploration of 14,190 possible ternary discrimination diagrams yields the Ti-Si-Sr system as the best linear discrimination diagram and the Na-Nb-Sr system as the best quadratic discrimination diagram. The best linear and quadratic discrimination diagrams using only immobile elements are Ti-V-Sc and Ti-V-Sm, respectively. As little as 5% of the training data are misclassified by these discrimination diagrams. Testing them on a second database of 182 samples that were not part of the training data yields a more reliable estimate of future performance. Although QDA misclassifies fewer training data than LDA, the opposite is generally true for the test data. Therefore LDA is a cruder but more robust classifier than QDA. Another advantage of LDA is that it provides a powerful way to reduce the dimensionality of the multivariate geochemical data in a similar
Technology Transfer Automated Retrieval System (TEKTRAN)
Maize kernel density impacts milling quality of the grain due to kernel hardness. Harder kernels are correlated with higher test weight and are more resistant to breakage during harvest and transport. Softer kernels, in addition to being susceptible to mechanical damage, are also prone to pathogen ...
Landscape-scale habitat selection by fishers translocated to the Olympic Peninsula of Washington
Lewis, Jeffrey C.; Jenkins, Kurt J.; Happe, Patricia J.; Manson, David J.; McCalmon, Marc
2016-01-01
The fisher was extirpated from much of the Pacific Northwestern United States during the mid- to late-1900s and is now proposed for federal listing as a threatened species in all or part of its west coast range. Following the translocation of 90 fishers from central British Columbia, Canada, to the Olympic Peninsula of Washington State from 2008 to 2010, we investigated the landscape-scale habitat selection of reintroduced fishers across a broad range of forest ages and disturbance histories, providing the first information on habitat relationships of newly reintroduced fishers in coastal coniferous forests in the Pacific Northwest. We developed 17 a priori models to evaluate several habitat-selection hypotheses based on premises of habitat models used to forecast habitat suitability for the reintroduced population. Further, we hypothesized that female fishers, because of their smaller body size than males, greater vulnerability to predation, and specific reproductive requirements, would be more selective than males for mid- to late-seral forest communities, where complex forest structural elements provide secure foraging, resting, and denning sites. We assessed 11 forest structure and landscape characteristics within the home range core-areas used by 19 females and 12 males and within randomly placed pseudo core areas that represented available habitats. We used case-controlled logistic regression to compare the characteristics of used and pseudo core areas and to assess selection by male and female fishers. Females were more selective of core area placement than males. Fifteen of 19 females (79%) and 5 of 12 males (42%) selected core areas within federal lands that encompassed primarily forests with an overstory of mid-sized or large trees. Male fishers exhibited only weak selection for core areas dominated by forests with an overstory of small trees, primarily on land managed for timber production or at high elevations. The amount of natural open area best
NASA Astrophysics Data System (ADS)
Wang, Changming; Xiong, Shi; Hu, Xiaoping; Yao, Li; Zhang, Jiacai
2012-10-01
Categorization of images containing visual objects can be successfully recognized using single-trial electroencephalograph (EEG) measured when subjects view images. Previous studies have shown that task-related information contained in event-related potential (ERP) components could discriminate two or three categories of object images. In this study, we investigated whether four categories of objects (human faces, buildings, cats and cars) could be mutually discriminated using single-trial EEG data. Here, the EEG waveforms acquired while subjects were viewing four categories of object images were segmented into several ERP components (P1, N1, P2a and P2b), and then Fisher linear discriminant analysis (Fisher-LDA) was used to classify EEG features extracted from ERP components. Firstly, we compared the classification results using features from single ERP components, and identified that the N1 component achieved the highest classification accuracies. Secondly, we discriminated four categories of objects using combining features from multiple ERP components, and showed that combination of ERP components improved four-category classification accuracies by utilizing the complementarity of discriminative information in ERP components. These findings confirmed that four categories of object images could be discriminated with single-trial EEG and could direct us to select effective EEG features for classifying visual objects.
Tully-Fisher Distances and Motions in the Northern Sky
NASA Astrophysics Data System (ADS)
Courteau, Stephane
1992-10-01
This dissertation consists of a study of large-scale velocity deviations in a uniformly expanding universe. At the onset of this work, galaxy motions in the northern hemisphere, contrary to the impressive sky coverage in the south, were still very poorly mapped. To improve on this situation, we identified a well-defined sample of 380 northern Sb-Sc UGC spirals with a magnitude cutoff of m_B=15.5. Distances are estimated using the Tully-Fisher relation but with rotation velocities measured from long-slit spectra in lieu of 21 cm delta-V's. Surface-brightness profiles and total magnitudes are extracted for 820 r-band images, and (H-alpha +[N II]) rotation curves, fluxes, and rotation profiles are measured from 450 red spectra. The galaxy magnitudes are accurate internally to +-0.022 mag and the delta-V's agree internally to +- 12 km s^-1. A tight linear correlation of optical versus radio velocity widths with a scatter of 24 km s^-1 is also derived. The redshift distribution peaks at 5400 km s^-1, with a tail extending out to 15,000 km s^-1. We find evidence for large-scale motions near the concentration of Perseus-Pisces (PP), but identify a vast region of unperturbed, quiet Hubble flow (QHF) perpendicular to PP. The peculiar velocity (PV) data are corrected for uniform-density Malmquist bias when they are analyzed in estimated distance space; an empirical correction for selection effects mainly due to magnitude cutoff is derived from the QHF region and applied to the PV's in redshift space. Both analyses are consistent in showing the PP supercluster as streaming in bulk towards the Local Group at 392 +- 79 km s^-1 (with respect to the microwave background rest frame). The cause for such motion remains undetermined, but it is most certainly not due to one single object. Gravitational collapse within PP is detected, but the statistical error is large. While we have not calculated a formal bulk motion for the whole volume of space bounded by the Great Attractor and PP
Liu, Ta-Kang; Wang, Yu-Cheng; Chuang, Laurence Zsu-Hsin; Chen, Chih-How
2016-01-01
The abundance of the eastern Taiwan Strait (ETS) population of the Chinese white dolphin (Sousa chinensis) has been estimated to be less than 100 individuals. It is categorized as critically endangered in the IUCN Red List of Threatened Species. Thus, immediate measures of conservation should be taken to protect it from extinction. Currently, the Taiwanese government plans to designate its habitat as a Major Wildlife Habitat (MWH), a type of marine protected area (MPA) for conservation of wildlife species. Although the designation allows continuing the current exploitation, however, it may cause conflicts among multiple stakeholders with competing interests. The study is to explore the attitude and opinions among the stakeholders in order to better manage the MPA. This study employs a semi-structured interview and a questionnaire survey of local fishers. Results from interviews indicated that the subsistence of fishers remains a major problem. It was found that stakeholders have different perceptions of the fishers' attitude towards conservation and also thought that the fishery-related law enforcement could be difficult. Quantitative survey showed that fishers are generally positive towards the conservation of the Chinese white dolphin but are less willing to participate in the planning process. Most fishers considered temporary fishing closure as feasible for conservation. The results of this study provide recommendations for future efforts towards the goal of better conservation for this endangered species. PMID:27526102
Bavinck, Maarten; de Klerk, Leo; van der Plaat, Felice; Ravesteijn, Jorik; Angel, Dominique; Arendsen, Hendrik; van Dijk, Tom; de Hoog, Iris; van Koolwijk, Ant; Tuijtel, Stijn; Zuurendonk, Benjamin
2015-07-01
The tsunami that struck the coasts of India on 26 December 2004 resulted in the large-scale destruction of fisher habitations. The post-tsunami rehabilitation effort in Tamil Nadu was directed towards relocating fisher settlements in the interior. This paper discusses the outcomes of a study on the social effects of relocation in a sample of nine communities along the Coromandel Coast. It concludes that, although the participation of fishing communities in house design and in allocation procedures has been limited, many fisher households are satisfied with the quality of the facilities. The distance of the new settlements to the shore, however, is regarded as an impediment to engaging in the fishing profession, and many fishers are actually moving back to their old locations. This raises questions as to the direction of coastal zone policy in India, as well as to the weight accorded to safety (and other coastal development interests) vis-à-vis the livelihood needs of fishers. PMID:25546250
Introduction to Kernel Methods: Classification of Multivariate Data
NASA Astrophysics Data System (ADS)
Fauvel, M.
2016-05-01
In this chapter, kernel methods are presented for the classification of multivariate data. An introduction example is given to enlighten the main idea of kernel methods. Then emphasis is done on the Support Vector Machine. Structural risk minimization is presented, and linear and non-linear SVM are described. Finally, a full example of SVM classification is given on simulated hyperspectral data.
Comparison of Kernel Equating and Item Response Theory Equating Methods
ERIC Educational Resources Information Center
Meng, Yu
2012-01-01
The kernel method of test equating is a unified approach to test equating with some advantages over traditional equating methods. Therefore, it is important to evaluate in a comprehensive way the usefulness and appropriateness of the Kernel equating (KE) method, as well as its advantages and disadvantages compared with several popular item…
High speed sorting of Fusarium-damaged wheat kernels
Technology Transfer Automated Retrieval System (TEKTRAN)
Recent studies have found that resistance to Fusarium fungal infection can be inherited in wheat from one generation to another. However, there is not yet available a cost effective method to separate Fusarium-damaged wheat kernels from undamaged kernels so that wheat breeders can take advantage of...
Covariant Perturbation Expansion of Off-Diagonal Heat Kernel
NASA Astrophysics Data System (ADS)
Gou, Yu-Zi; Li, Wen-Du; Zhang, Ping; Dai, Wu-Sheng
2016-07-01
Covariant perturbation expansion is an important method in quantum field theory. In this paper an expansion up to arbitrary order for off-diagonal heat kernels in flat space based on the covariant perturbation expansion is given. In literature, only diagonal heat kernels are calculated based on the covariant perturbation expansion.
Evidence-Based Kernels: Fundamental Units of Behavioral Influence
ERIC Educational Resources Information Center
Embry, Dennis D.; Biglan, Anthony
2008-01-01
This paper describes evidence-based kernels, fundamental units of behavioral influence that appear to underlie effective prevention and treatment for children, adults, and families. A kernel is a behavior-influence procedure shown through experimental analysis to affect a specific behavior and that is indivisible in the sense that removing any of…
7 CFR 981.60 - Determination of kernel weight.
Code of Federal Regulations, 2013 CFR
2013-01-01
... AGREEMENTS AND ORDERS; FRUITS, VEGETABLES, NUTS), DEPARTMENT OF AGRICULTURE ALMONDS GROWN IN CALIFORNIA Order Regulating Handling Volume Regulation § 981.60 Determination of kernel weight. (a) Almonds for which settlement is made on kernel weight. All lots of almonds, whether shelled or unshelled, for which...
7 CFR 981.60 - Determination of kernel weight.
Code of Federal Regulations, 2011 CFR
2011-01-01
... Agreements and Orders; Fruits, Vegetables, Nuts), DEPARTMENT OF AGRICULTURE ALMONDS GROWN IN CALIFORNIA Order Regulating Handling Volume Regulation § 981.60 Determination of kernel weight. (a) Almonds for which settlement is made on kernel weight. All lots of almonds, whether shelled or unshelled, for which...
7 CFR 981.60 - Determination of kernel weight.
Code of Federal Regulations, 2012 CFR
2012-01-01
... Agreements and Orders; Fruits, Vegetables, Nuts), DEPARTMENT OF AGRICULTURE ALMONDS GROWN IN CALIFORNIA Order Regulating Handling Volume Regulation § 981.60 Determination of kernel weight. (a) Almonds for which settlement is made on kernel weight. All lots of almonds, whether shelled or unshelled, for which...
7 CFR 981.60 - Determination of kernel weight.
Code of Federal Regulations, 2014 CFR
2014-01-01
... AGREEMENTS AND ORDERS; FRUITS, VEGETABLES, NUTS), DEPARTMENT OF AGRICULTURE ALMONDS GROWN IN CALIFORNIA Order Regulating Handling Volume Regulation § 981.60 Determination of kernel weight. (a) Almonds for which settlement is made on kernel weight. All lots of almonds, whether shelled or unshelled, for which...
7 CFR 981.60 - Determination of kernel weight.
Code of Federal Regulations, 2010 CFR
2010-01-01
... Agreements and Orders; Fruits, Vegetables, Nuts), DEPARTMENT OF AGRICULTURE ALMONDS GROWN IN CALIFORNIA Order Regulating Handling Volume Regulation § 981.60 Determination of kernel weight. (a) Almonds for which settlement is made on kernel weight. All lots of almonds, whether shelled or unshelled, for which...
Integrating the Gradient of the Thin Wire Kernel
NASA Technical Reports Server (NTRS)
Champagne, Nathan J.; Wilton, Donald R.
2008-01-01
A formulation for integrating the gradient of the thin wire kernel is presented. This approach employs a new expression for the gradient of the thin wire kernel derived from a recent technique for numerically evaluating the exact thin wire kernel. This approach should provide essentially arbitrary accuracy and may be used with higher-order elements and basis functions using the procedure described in [4].When the source and observation points are close, the potential integrals over wire segments involving the wire kernel are split into parts to handle the singular behavior of the integrand [1]. The singularity characteristics of the gradient of the wire kernel are different than those of the wire kernel, and the axial and radial components have different singularities. The characteristics of the gradient of the wire kernel are discussed in [2]. To evaluate the near electric and magnetic fields of a wire, the integration of the gradient of the wire kernel needs to be calculated over the source wire. Since the vector bases for current have constant direction on linear wire segments, these integrals reduce to integrals of the form
Polynomial Kernels for Hard Problems on Disk Graphs
NASA Astrophysics Data System (ADS)
Jansen, Bart
Kernelization is a powerful tool to obtain fixed-parameter tractable algorithms. Recent breakthroughs show that many graph problems admit small polynomial kernels when restricted to sparse graph classes such as planar graphs, bounded-genus graphs or H-minor-free graphs. We consider the intersection graphs of (unit) disks in the plane, which can be arbitrarily dense but do exhibit some geometric structure. We give the first kernelization results on these dense graph classes. Connected Vertex Cover has a kernel with 12k vertices on unit-disk graphs and with 3k 2 + 7k vertices on disk graphs with arbitrary radii. Red-Blue Dominating Set parameterized by the size of the smallest color class has a linear-vertex kernel on planar graphs, a quadratic-vertex kernel on unit-disk graphs and a quartic-vertex kernel on disk graphs. Finally we prove that H -Matching on unit-disk graphs has a linear-vertex kernel for every fixed graph H.
Optimal Bandwidth Selection in Observed-Score Kernel Equating
ERIC Educational Resources Information Center
Häggström, Jenny; Wiberg, Marie
2014-01-01
The selection of bandwidth in kernel equating is important because it has a direct impact on the equated test scores. The aim of this article is to examine the use of double smoothing when selecting bandwidths in kernel equating and to compare double smoothing with the commonly used penalty method. This comparison was made using both an equivalent…
Evidence-based Kernels: Fundamental Units of Behavioral Influence
Biglan, Anthony
2008-01-01
This paper describes evidence-based kernels, fundamental units of behavioral influence that appear to underlie effective prevention and treatment for children, adults, and families. A kernel is a behavior–influence procedure shown through experimental analysis to affect a specific behavior and that is indivisible in the sense that removing any of its components would render it inert. Existing evidence shows that a variety of kernels can influence behavior in context, and some evidence suggests that frequent use or sufficient use of some kernels may produce longer lasting behavioral shifts. The analysis of kernels could contribute to an empirically based theory of behavioral influence, augment existing prevention or treatment efforts, facilitate the dissemination of effective prevention and treatment practices, clarify the active ingredients in existing interventions, and contribute to efficiently developing interventions that are more effective. Kernels involve one or more of the following mechanisms of behavior influence: reinforcement, altering antecedents, changing verbal relational responding, or changing physiological states directly. The paper describes 52 of these kernels, and details practical, theoretical, and research implications, including calling for a national database of kernels that influence human behavior. PMID:18712600
Sugar uptake into kernels of tunicate tassel-seed maize
Thomas, P.A.; Felker, F.C.; Crawford, C.G. )
1990-05-01
A maize (Zea mays L.) strain expressing both the tassel-seed (Ts-5) and tunicate (Tu) characters was developed which produces glume-covered kernels on the tassel, often born on 7-10 mm pedicels. Vigorous plants produce up to 100 such kernels interspersed with additional sessile kernels. This floral unit provides a potentially valuable experimental system for studying sugar uptake into developing maize seeds. When detached kernels (with glumes and pedicel intact) are placed in incubation solution, fluid flows up the pedicel and into the glumes, entering the pedicel apoplast near the kernel base. The unusual anatomical features of this maize strain permit experimental access to the pedicel apoplast with much less possibility of kernel base tissue damage than with kernels excised from the cob. ({sup 14}C)Fructose incorporation into soluble and insoluble fractions of endosperm increased for 8 days. Endosperm uptake of sucrose, fructose, and D-glucose was significantly greater than that of L-glucose. Fructose uptake was significantly inhibited by CCCP, DNP, and PCMBS. These results suggest the presence of an active, non-diffusion component of sugar transport in maize kernels.
Harassment, Bias, and Discrimination.
ERIC Educational Resources Information Center
Welliver, Paul W.
1995-01-01
Discusses a new principle which has been added to the AECT (Association for Educational Communications and Technology) Code of Professional Ethics regarding discrimination, harassment, and bias. An example is presented which illustrates a violation of a professional colleague's rights. (LRW)
NASA Astrophysics Data System (ADS)
Labini, Francesco Sylos; Zapperi, Stefano
2007-09-01
Brilliant scientists of all ages should be able to thrive at universities. Mandatory retirement is, therefore, a form of age discrimination, but its removal or postponement can come at a cost to younger faculty members, as observed in Italy.
Mass discrimination during weightlessness
NASA Technical Reports Server (NTRS)
Ross, H.
1981-01-01
An experiment concerned with the ability of astronauts to discriminate between the mass of objects when both the objects and the astronauts are in weightless states is described. The main object of the experiment is to compare the threshold for weight-discrimination on Earth with that for mass-discrimination in orbit. Tests will be conducted premission and postmission and early and late during the mission while the crew is experiencing weightlessness. A comparison of early and late tests inflight and postflight will reveal the rate of adaptation to zero-gravity and 1-g. The mass discrimination box holds 24 balls which the astronaut will compare to one another in a random routine.
Multiplicities of dihedral discriminants
NASA Astrophysics Data System (ADS)
Mayer, Daniel C.
1992-04-01
Given the discriminant {d_k} of a quadratic field k, the number of cyclic relative extensions N\\vert k of fixed odd prime degree p with dihedral absolute Galois group of order 2p, which share a common conductor f, is called the multiplicity of the dihedral discriminant {d_N} = {f^{2(p - 1)}}d_k^p . In this paper, general formulas for multiplicities of dihedral discriminants are derived by analyzing the p-rank of the ring class group mod f of k. For the special case p = 3,{d_k} = - 3 , an elementary proof is given additionally. The theory is illustrated by a discussion of all known discriminants of multiplicity ≥ 5 of totally real and complex cubic fields.
Angular velocity discrimination
NASA Technical Reports Server (NTRS)
Kaiser, Mary K.
1990-01-01
Three experiments designed to investigate the ability of naive observers to discriminate rotational velocities of two simultaneously viewed objects are described. Rotations are constrained to occur about the x and y axes, resulting in linear two-dimensional image trajectories. The results indicate that observers can discriminate angular velocities with a competence near that for linear velocities. However, perceived angular rate is influenced by structural aspects of the stimuli.
Direct Measurement of Wave Kernels in Time-Distance Helioseismology
NASA Technical Reports Server (NTRS)
Duvall, T. L., Jr.
2006-01-01
Solar f-mode waves are surface-gravity waves which propagate horizontally in a thin layer near the photosphere with a dispersion relation approximately that of deep water waves. At the power maximum near 3 mHz, the wavelength of 5 Mm is large enough for various wave scattering properties to be observable. Gizon and Birch (2002,ApJ,571,966)h ave calculated kernels, in the Born approximation, for the sensitivity of wave travel times to local changes in damping rate and source strength. In this work, using isolated small magnetic features as approximate point-sourc'e scatterers, such a kernel has been measured. The observed kernel contains similar features to a theoretical damping kernel but not for a source kernel. A full understanding of the effect of small magnetic features on the waves will require more detailed modeling.
A Robustness Testing Campaign for IMA-SP Partitioning Kernels
NASA Astrophysics Data System (ADS)
Grixti, Stephen; Lopez Trecastro, Jorge; Sammut, Nicholas; Zammit-Mangion, David
2015-09-01
With time and space partitioned architectures becoming increasingly appealing to the European space sector, the dependability of partitioning kernel technology is a key factor to its applicability in European Space Agency projects. This paper explores the potential of the data type fault model, which injects faults through the Application Program Interface, in partitioning kernel robustness testing. This fault injection methodology has been tailored to investigate its relevance in uncovering vulnerabilities within partitioning kernels and potentially contributing towards fault removal campaigns within this domain. This is demonstrated through a robustness testing case study of the XtratuM partitioning kernel for SPARC LEON3 processors. The robustness campaign exposed a number of vulnerabilities in XtratuM, exhibiting the potential benefits of using such a methodology for the robustness assessment of partitioning kernels.
OSKI: A Library of Automatically Tuned Sparse Matrix Kernels
Vuduc, R; Demmel, J W; Yelick, K A
2005-07-19
The Optimized Sparse Kernel Interface (OSKI) is a collection of low-level primitives that provide automatically tuned computational kernels on sparse matrices, for use by solver libraries and applications. These kernels include sparse matrix-vector multiply and sparse triangular solve, among others. The primary aim of this interface is to hide the complex decision-making process needed to tune the performance of a kernel implementation for a particular user's sparse matrix and machine, while also exposing the steps and potentially non-trivial costs of tuning at run-time. This paper provides an overview of OSKI, which is based on our research on automatically tuned sparse kernels for modern cache-based superscalar machines.
Manzan, Maíra Fontes; Lopes, Priscila F M
2015-01-01
Fishers' local ecological knowledge (LEK) is an additional tool to obtain information about cetaceans, regarding their local particularities, fishing interactions, and behavior. However, this knowledge could vary in depth of detail according to the level of interaction that fishers have with a specific species. This study investigated differences in small-scale fishers' LEK regarding the estuarine dolphin (Sotalia guianensis) in three Brazilian northeast coastal communities where fishing is practiced in estuarine lagoons and/or coastal waters and where dolphin-watching tourism varies from incipient to important. The fishers (N = 116) were asked about general characteristics of S. guianensis and their interactions with this dolphin during fishing activities. Compared to lagoon fishers, coastal fishers showed greater knowledge about the species but had more negative interactions with the dolphin during fishing activities. Coastal fishing not only offered the opportunity for fishers to observe a wider variety of the dolphin's behavior, but also implied direct contact with the dolphins, as they are bycaught in coastal gillnets. Besides complementing information that could be used for the management of cetaceans, this study shows that the type of environment most used by fishers also affects the accuracy of the information they provide. When designing studies to gather information on species and/or populations with the support of fishers, special consideration should be given to local particularities such as gear and habitats used within the fishing community. PMID:25399120
Technology Transfer Automated Retrieval System (TEKTRAN)
The objective of this study was to examine the relationship between fluorescence emissions of corn kernels inoculated with Aspergillus flavus and aflatoxin contamination levels within the kernels. The choice of methodology was based on the principle that many biological materials exhibit fluorescenc...
Low Titers of Canine Distemper Virus Antibody in Wild Fishers (Martes pennanti) in the Eastern USA.
Peper, Steven T; Peper, Randall L; Mitcheltree, Denise H; Kollias, George V; Brooks, Robert P; Stevens, Sadie S; Serfass, Thomas L
2016-01-01
Canine distemper virus (CDV) infects species in the order Carnivora. Members of the family Mustelidae are among the species most susceptible to CDV and have a high mortality rate after infection. Assessing an animal's pathogen or disease load prior to any reintroduction project is important to help protect the animal being reintroduced, as well as the wildlife and livestock in the area of relocation. We screened 58 fishers for CDV antibody prior to their release into Pennsylvania, US, as part of a reintroduction program. Five of the 58 (9%) fishers had a weak-positive reaction for CDV antibody at a dilution of 1:16. None of the fishers exhibited any clinical sign of canine distemper while being held prior to release. PMID:26555109
Mean-Field Dynamics and Fisher Information in Matter Wave Interferometry.
Haine, Simon A
2016-06-10
There has been considerable recent interest in the mean-field dynamics of various atom-interferometry schemes designed for precision sensing. In the field of quantum metrology, the standard tools for evaluating metrological sensitivity are the classical and quantum Fisher information. In this Letter, we show how these tools can be adapted to evaluate the sensitivity when the behavior is dominated by mean-field dynamics. As an example, we compare the behavior of four recent theoretical proposals for gyroscopes based on matter-wave interference in toroidally trapped geometries. We show that while the quantum Fisher information increases at different rates for the various schemes considered, in all cases it is consistent with the well-known Sagnac phase shift after the matter waves have traversed a closed path. However, we argue that the relevant metric for quantifying interferometric sensitivity is the classical Fisher information, which can vary considerably between the schemes. PMID:27341216
Mean-Field Dynamics and Fisher Information in Matter Wave Interferometry
NASA Astrophysics Data System (ADS)
Haine, Simon A.
2016-06-01
There has been considerable recent interest in the mean-field dynamics of various atom-interferometry schemes designed for precision sensing. In the field of quantum metrology, the standard tools for evaluating metrological sensitivity are the classical and quantum Fisher information. In this Letter, we show how these tools can be adapted to evaluate the sensitivity when the behavior is dominated by mean-field dynamics. As an example, we compare the behavior of four recent theoretical proposals for gyroscopes based on matter-wave interference in toroidally trapped geometries. We show that while the quantum Fisher information increases at different rates for the various schemes considered, in all cases it is consistent with the well-known Sagnac phase shift after the matter waves have traversed a closed path. However, we argue that the relevant metric for quantifying interferometric sensitivity is the classical Fisher information, which can vary considerably between the schemes.
Macroscopic response to microscopic intrinsic noise in three-dimensional Fisher fronts.
Nesic, S; Cuerno, R; Moro, E
2014-10-31
We study the dynamics of three-dimensional Fisher fronts in the presence of density fluctuations. To this end we simulate the Fisher equation subject to stochastic internal noise, and study how the front moves and roughens as a function of the number of particles in the system, N. Our results suggest that the macroscopic behavior of the system is driven by the microscopic dynamics at its leading edge where number fluctuations are dominated by rare events. Contrary to naive expectations, the strength of front fluctuations decays extremely slowly as 1/logN, inducing large-scale fluctuations which we find belong to the one-dimensional Kardar-Parisi-Zhang universality class of kinetically rough interfaces. Hence, we find that there is no weak-noise regime for Fisher fronts, even for realistic numbers of particles in macroscopic systems. PMID:25396356
Galaxy luminosity function and Tully-Fisher relation: reconciled through rotation-curve studies
Cattaneo, Andrea; Salucci, Paolo; Papastergis, Emmanouil E-mail: salucci@sissa.it
2014-03-10
The relation between galaxy luminosity L and halo virial velocity v {sub vir} required to fit the galaxy luminosity function differs from the observed Tully-Fisher relation between L and disk speed v {sub rot}. Because of this, the problem of reproducing the galaxy luminosity function and the Tully-Fisher relation simultaneously has plagued semianalytic models since their inception. Here we study the relation between v {sub rot} and v {sub vir} by fitting observational average rotation curves of disk galaxies binned in luminosity. We show that the v {sub rot}-v {sub vir} relation that we obtain in this way can fully account for this seeming inconsistency. Therefore, the reconciliation of the luminosity function with the Tully-Fisher relation rests on the complex dependence of v {sub rot} on v {sub vir}, which arises because the ratio of stellar mass to dark matter mass is a strong function of halo mass.
Fisher information versus signal-to-noise ratio for a split detector
NASA Astrophysics Data System (ADS)
Knee, George C.; Munro, William J.
2015-07-01
We study the problem of estimating the magnitude of a Gaussian beam displacement using a two-pixel or "split" detector. We calculate the maximum likelihood estimator and compute its asymptotic mean-squared error via the Fisher information. Although the signal-to-noise ratio is known to be simply related to the Fisher information under idealized detection, we find the two measures of precision differ markedly for a split detector. We show that a greater signal-to-noise ratio "before" the detector leads to a greater information penalty, unless adaptive realignment is used. We find that with an initially balanced split detector, tuning the normalized difference in counts to 0.884753 ... gives the highest posterior Fisher information, which provides an improvement by at least a factor of about 2.5 over operating in the usual linear regime. We discuss the implications for weak-value amplification, a popular probabilistic signal amplification technique.
Modified kernel-based nonlinear feature extraction.
Ma, J.; Perkins, S. J.; Theiler, J. P.; Ahalt, S.
2002-01-01
Feature Extraction (FE) techniques are widely used in many applications to pre-process data in order to reduce the complexity of subsequent processes. A group of Kernel-based nonlinear FE ( H E ) algorithms has attracted much attention due to their high performance. However, a serious limitation that is inherent in these algorithms -- the maximal number of features extracted by them is limited by the number of classes involved -- dramatically degrades their flexibility. Here we propose a modified version of those KFE algorithms (MKFE), This algorithm is developed from a special form of scatter-matrix, whose rank is not determined by the number of classes involved, and thus breaks the inherent limitation in those KFE algorithms. Experimental results suggest that MKFE algorithm is .especially useful when the training set is small.
Privacy preserving RBF kernel support vector machine.
Li, Haoran; Xiong, Li; Ohno-Machado, Lucila; Jiang, Xiaoqian
2014-01-01
Data sharing is challenging but important for healthcare research. Methods for privacy-preserving data dissemination based on the rigorous differential privacy standard have been developed but they did not consider the characteristics of biomedical data and make full use of the available information. This often results in too much noise in the final outputs. We hypothesized that this situation can be alleviated by leveraging a small portion of open-consented data to improve utility without sacrificing privacy. We developed a hybrid privacy-preserving differentially private support vector machine (SVM) model that uses public data and private data together. Our model leverages the RBF kernel and can handle nonlinearly separable cases. Experiments showed that this approach outperforms two baselines: (1) SVMs that only use public data, and (2) differentially private SVMs that are built from private data. Our method demonstrated very close performance metrics compared to nonprivate SVMs trained on the private data. PMID:25013805
Point-Kernel Shielding Code System.
1982-02-17
Version 00 QAD-BSA is a three-dimensional, point-kernel shielding code system based upon the CCC-48/QAD series. It is designed to calculate photon dose rates and heating rates using exponential attenuation and infinite medium buildup factors. Calculational provisions include estimates of fast neutron penetration using data computed by the moments method. Included geometry routines can describe complicated source and shield geometries. An internal library contains data for many frequently used structural and shielding materials, enabling the codemore » to solve most problems with only source strengths and problem geometry required as input. This code system adapts especially well to problems requiring multiple sources and sources with asymmetrical geometry. In addition to being edited separately, the total interaction rates from many sources may be edited at each detector point. Calculated photon interaction rates agree closely with those obtained using QAD-P5A.« less
Kernel density estimation using graphical processing unit
NASA Astrophysics Data System (ADS)
Sunarko, Su'ud, Zaki
2015-09-01
Kernel density estimation for particles distributed over a 2-dimensional space is calculated using a single graphical processing unit (GTX 660Ti GPU) and CUDA-C language. Parallel calculations are done for particles having bivariate normal distribution and by assigning calculations for equally-spaced node points to each scalar processor in the GPU. The number of particles, blocks and threads are varied to identify favorable configuration. Comparisons are obtained by performing the same calculation using 1, 2 and 4 processors on a 3.0 GHz CPU using MPICH 2.0 routines. Speedups attained with the GPU are in the range of 88 to 349 times compared the multiprocessor CPU. Blocks of 128 threads are found to be the optimum configuration for this case.
The flare kernel in the impulsive phase
NASA Technical Reports Server (NTRS)
Dejager, C.
1986-01-01
The impulsive phase of a flare is characterized by impulsive bursts of X-ray and microwave radiation, related to impulsive footpoint heating up to 50 or 60 MK, by upward gas velocities (150 to 400 km/sec) and by a gradual increase of the flare's thermal energy content. These phenomena, as well as non-thermal effects, are all related to the impulsive energy injection into the flare. The available observations are also quantitatively consistent with a model in which energy is injected into the flare by beams of energetic electrons, causing ablation of chromospheric gas, followed by convective rise of gas. Thus, a hole is burned into the chromosphere; at the end of impulsive phase of an average flare the lower part of that hole is situated about 1800 km above the photosphere. H alpha and other optical and UV line emission is radiated by a thin layer (approx. 20 km) at the bottom of the flare kernel. The upward rising and outward streaming gas cools down by conduction in about 45 s. The non-thermal effects in the initial phase are due to curtailing of the energy distribution function by escape of energetic electrons. The single flux tube model of a flare does not fit with these observations; instead we propose the spaghetti-bundle model. Microwave and gamma-ray observations suggest the occurrence of dense flare knots of approx. 800 km diameter, and of high temperature. Future observations should concentrate on locating the microwave/gamma-ray sources, and on determining the kernel's fine structure and the related multi-loop structure of the flaring area.
On the Karlin-Kimura approaches to the Wright-Fisher diffusion with fluctuating selection
NASA Astrophysics Data System (ADS)
Huillet, Thierry
2011-02-01
The goal of this work is a comparative study of two Wright-Fisher-like diffusion processes on the interval, one due to Karlin and the other one due to Kimura. Each model accounts for the evolution of one two-locus colony undergoing random mating, under the additional action of selection in a random environment. In other words, we study the effect of disorder on the usual Wright-Fisher model with fixed (nonrandom) selection. There is a drastic qualitative difference between the two models and between the random and nonrandom selection hypotheses. We first present a series of elementary stochastic models and tools that are needed to conduct this study in the context of diffusion process theory, including Kolmogorov backward and forward equations, scale and speed functions, classification of boundaries, and Doob transformation of sample paths using additive functionals. In this spirit, we briefly revisit the neutral Wright-Fisher diffusion and the Wright-Fisher diffusion with nonrandom selection. With these tools at hand, we first deal with the Karlin approach to the Wright-Fisher diffusion model with randomized selection differentials. The specificity of this model is that in the large population case, the boundaries of the state space are natural and hence inaccessible, and so quasi-absorbing only. We supply some limiting properties pertaining to times of hitting of points close to the boundaries. Next, we study the Kimura approach to the Wright-Fisher model with randomized selection, which may be viewed as a modification of the Karlin model, using an appropriate Doob transform which we describe. This model also has natural boundaries, but they turn out to be much more attracting and sticky than in Karlin's version. This leads to a faster approach to the quasi-absorbing states, to a larger time needed to move from the vicinity of one boundary to the other and to a local critical behavior of the branching diffusion obtained after the relevant Doob transformation.
Yates, Katherine L; Schoeman, David S
2013-01-01
Spatial management tools, such as marine spatial planning and marine protected areas, are playing an increasingly important role in attempts to improve marine management and accommodate conflicting needs. Robust data are needed to inform decisions among different planning options, and early inclusion of stakeholder involvement is widely regarded as vital for success. One of the biggest stakeholder groups, and the most likely to be adversely impacted by spatial restrictions, is the fishing community. In order to take their priorities into account, planners need to understand spatial variation in their perceived value of the sea. Here a readily accessible, novel method for quantitatively mapping fishers' spatial access priorities is presented. Spatial access priority mapping, or SAPM, uses only basic functions of standard spreadsheet and GIS software. Unlike the use of remote-sensing data, SAPM actively engages fishers in participatory mapping, documenting rather than inferring their priorities. By so doing, SAPM also facilitates the gathering of other useful data, such as local ecological knowledge. The method was tested and validated in Northern Ireland, where over 100 fishers participated in a semi-structured questionnaire and mapping exercise. The response rate was excellent, 97%, demonstrating fishers' willingness to be involved. The resultant maps are easily accessible and instantly informative, providing a very clear visual indication of which areas are most important for the fishers. The maps also provide quantitative data, which can be used to analyse the relative impact of different management options on the fishing industry and can be incorporated into planning software, such as MARXAN, to ensure that conservation goals can be met at minimum negative impact to the industry. This research shows how spatial access priority mapping can facilitate the early engagement of fishers and the ready incorporation of their priorities into the decision-making process
Lewis, Jeffrey C; Powell, Roger A; Zielinski, William J
2012-01-01
Translocations are frequently used to restore extirpated carnivore populations. Understanding the factors that influence translocation success is important because carnivore translocations can be time consuming, expensive, and controversial. Using population viability software, we modeled reintroductions of the fisher, a candidate for endangered or threatened status in the Pacific states of the US. Our model predicts that the most important factor influencing successful re-establishment of a fisher population is the number of adult females reintroduced (provided some males are also released). Data from 38 translocations of fishers in North America, including 30 reintroductions, 5 augmentations and 3 introductions, show that the number of females released was, indeed, a good predictor of success but that the number of males released, geographic region and proximity of the source population to the release site were also important predictors. The contradiction between model and data regarding males may relate to the assumption in the model that all males are equally good breeders. We hypothesize that many males may need to be released to insure a sufficient number of good breeders are included, probably large males. Seventy-seven percent of reintroductions with known outcomes (success or failure) succeeded; all 5 augmentations succeeded; but none of the 3 introductions succeeded. Reintroductions were instrumental in reestablishing fisher populations within their historical range and expanding the range from its most-contracted state (43% of the historical range) to its current state (68% of the historical range). To increase the likelihood of translocation success, we recommend that managers: 1) release as many fishers as possible, 2) release more females than males (55-60% females) when possible, 3) release as many adults as possible, especially large males, 4) release fishers from a nearby source population, 5) conduct a formal feasibility assessment, and 6) develop
Hallwass, Gustavo; Lopes, Priscila F; Juras, Anastácio A; Silvano, Renato A M
2013-03-01
The long-term impacts of large hydroelectric dams on small-scale fisheries in tropical rivers are poorly known. A promising way to investigate such impacts is to compare and integrate the local ecological knowledge (LEK) of resource users with biological data for the same region. We analyzed the accuracy of fishers' LEK to investigate fisheries dynamics and environmental changes in the Lower Tocantins River (Brazilian Amazon) downstream from a large dam. We estimated fishers' LEK through interviews with 300 fishers in nine villages and collected data on 601 fish landings in five of these villages, 22 years after the dam's establishment (2006-2008). We compared these two databases with each other and with data on fish landings from before the dam's establishment (1981) gathered from the literature. The data obtained based on the fishers' LEK (interviews) and from fisheries agreed regarding the primary fish species caught, the most commonly used type of fishing gear (gill nets) and even the most often used gill net mesh sizes but disagreed regarding seasonal fish abundance. According to the interviewed fishers, the primary environmental changes that occurred after the impoundment were an overall decrease in fish abundance, an increase in the abundance of some fish species and, possibly, the local extinction of a commercial fish species (Semaprochilodus brama). These changes were corroborated by comparing fish landings sampled before and 22 years after the impoundment, which indicated changes in the composition of fish landings and a decrease in the total annual fish production. Our results reinforce the hypothesis that large dams may adversely affect small-scale fisheries downstream and establish a feasible approach for applying fishers' LEK to fisheries management, especially in regions with a low research capacity. PMID:23634590
Lewis, Jeffrey C.; Powell, Roger A.; Zielinski, William J.
2012-01-01
Translocations are frequently used to restore extirpated carnivore populations. Understanding the factors that influence translocation success is important because carnivore translocations can be time consuming, expensive, and controversial. Using population viability software, we modeled reintroductions of the fisher, a candidate for endangered or threatened status in the Pacific states of the US. Our model predicts that the most important factor influencing successful re-establishment of a fisher population is the number of adult females reintroduced (provided some males are also released). Data from 38 translocations of fishers in North America, including 30 reintroductions, 5 augmentations and 3 introductions, show that the number of females released was, indeed, a good predictor of success but that the number of males released, geographic region and proximity of the source population to the release site were also important predictors. The contradiction between model and data regarding males may relate to the assumption in the model that all males are equally good breeders. We hypothesize that many males may need to be released to insure a sufficient number of good breeders are included, probably large males. Seventy-seven percent of reintroductions with known outcomes (success or failure) succeeded; all 5 augmentations succeeded; but none of the 3 introductions succeeded. Reintroductions were instrumental in reestablishing fisher populations within their historical range and expanding the range from its most-contracted state (43% of the historical range) to its current state (68% of the historical range). To increase the likelihood of translocation success, we recommend that managers: 1) release as many fishers as possible, 2) release more females than males (55–60% females) when possible, 3) release as many adults as possible, especially large males, 4) release fishers from a nearby source population, 5) conduct a formal feasibility assessment, and 6
NASA Astrophysics Data System (ADS)
Lasaponara, Rosa; Lanorte, Antonio; Lovallo, Michele; Telesca, Luciano
2015-04-01
Time series can fruitfully support fire monitoring and management from statistical analysis of fire occurrence (Tuia et al. 2008) to danger estimation (lasaponara 2005), damage evaluation (Lanorte et al 2014) and post fire recovery (Lanorte et al. 2014). In this paper, the time dynamics of SPOT-VEGETATION Normalized Difference Vegetation Index (NDVI) time series are analyzed by using the statistical approach of the Fisher-Shannon (FS) information plane to assess and monitor vegetation recovery after fire disturbance. Fisher-Shannon information plane analysis allows us to gain insight into the complex structure of a time series to quantify its degree of organization and order. The analysis was carried out using 10-day Maximum Value Composites of NDVI (MVC-NDVI) with a 1 km × 1 km spatial resolution. The investigation was performed on two test sites located in Galizia (North Spain) and Peloponnese (South Greece), selected for the vast fires which occurred during the summer of 2006 and 2007 and for their different vegetation covers made up mainly of low shrubland in Galizia test site and evergreen forest in Peloponnese. Time series of MVC-NDVI have been analyzed before and after the occurrence of the fire events. Results obtained for both the investigated areas clearly pointed out that the dynamics of the pixel time series before the occurrence of the fire is characterized by a larger degree of disorder and uncertainty; while the pixel time series after the occurrence of the fire are featured by a higher degree of organization and order. In particular, regarding the Peloponneso fire, such discrimination is more evident than in the Galizia fire. This suggests a clear possibility to discriminate the different post-fire behaviors and dynamics exhibited by the different vegetation covers. Reference Lanorte A, R Lasaponara, M Lovallo, L Telesca 2014 Fisher-Shannon information plane analysis of SPOT/VEGETATION Normalized Difference Vegetation Index (NDVI) time series to
False-Positive Serum Botulism Bioassay in Miller-Fisher Syndrome.
Zeylikman, Yuriy; Shah, Vishal; Shah, Umang; Mirsen, Thomas R; Campellone, Joseph V
2015-09-01
We describe a patient with acute progressive weakness and areflexia. Both botulism and Miller-Fisher variant of Guillain-Barré syndrome were initial diagnostic considerations, and she was treated with intravenous immunoglobulin and botulinum antitoxin. A mouse bioassay was positive for botulinum toxin A, although her clinical course, electrodiagnostic studies, and cerebrospinal fluid findings supported Miller-Fisher syndrome. This patient's atypical features offer points of discussion regarding the evaluation of patients with acute neuromuscular weakness and emphasize the limitations of the botulism bioassay. PMID:26301377
A Fisher-gradient complexity in systems with spatio-temporal dynamics
NASA Astrophysics Data System (ADS)
Arbona, A.; Bona, C.; Massó, J.; Miñano, B.; Plastino, A.
2016-04-01
We define a benchmark for definitions of complexity in systems with spatio-temporal dynamics and employ it in the study of Collective Motion. We show that LMC's complexity displays interesting properties in such systems, while a statistical complexity model (SCM) based on autocorrelation reasonably meets our perception of complexity. However this SCM is not as general as desirable, as it does not merely depend on the system's Probability Distribution Function. Inspired by the notion of Fisher information, we develop a SCM candidate, which we call the Fisher-gradient complexity, which exhibits nice properties from the viewpoint of our benchmark.
On bounds for the Fisher-Rao distance between multivariate normal distributions
NASA Astrophysics Data System (ADS)
Strapasson, João E.; Porto, Julianna P. S.; Costa, Sueli I. R.
2015-01-01
Information geometry is approached here by considering the statistical model of multivariate normal distributions as a Riemannian manifold with the natural metric provided by the Fisher information matrix. Explicit forms for the Fisher-Rao distance associated to this metric and for the geodesics of general distribution models are usually very hard to determine. In the case of general multivariate normal distributions lower and upper bounds have been derived. We approach here some of these bounds and introduce a new one discussing their tightness in specific cases.
An information measure for class discrimination. [in remote sensing of crop observation
NASA Technical Reports Server (NTRS)
Shen, S. S.; Badhwar, G. D.
1986-01-01
This article describes a separability measure for class discrimination. This measure is based on the Fisher information measure for estimating the mixing proportion of two classes. The Fisher information measure not only provides a means to assess quantitatively the information content in the features for separating classes, but also gives the lower bound for the variance of any unbiased estimate of the mixing proportion based on observations of the features. Unlike most commonly used separability measures, this measure is not dependent on the form of the probability distribution of the features and does not imply a specific estimation procedure. This is important because the probability distribution function that describes the data for a given class does not have simple analytic forms, such as a Gaussian. Results of applying this measure to compare the information content provided by three Landsat-derived feature vectors for the purpose of separating small grains from other crops are presented.
Topology based Kernels with Application to Inference Problems in Alzheimer’s disease
Pachauri, Deepti; Hinrichs, Chris; Chung, Moo K.; Johnson, Sterling C.; Singh, Vikas
2011-01-01
Alzheimer’s disease (AD) research has recently witnessed a great deal of activity focused on developing new statistical learning tools for automated inference using imaging data. The workhorse for many of these techniques is the Support Vector Machine (SVM) framework (or more generally kernel based methods). Most of these require, as a first step, specification of a kernel matrix between input examples (i.e., images). The inner product between images Ii and Ij in a feature space can generally be written in closed form, and so it is convenient to treat as “given”. However, in certain neuroimaging applications such an assumption becomes problematic. As an example, it is rather challenging to provide a scalar measure of similarity between two instances of highly attributed data such as cortical thickness measures on cortical surfaces. Note that cortical thickness is known to be discriminative for neurological disorders, so leveraging such information in an inference framework, especially within a multi-modal method, is potentially advantageous. But despite being clinically meaningful, relatively few works have successfully exploited this measure for classification or regression. Motivated by these applications, our paper presents novel techniques to compute similarity matrices for such topologically-based attributed data. Our ideas leverage recent developments to characterize signals (e.g., cortical thickness) motivated by the persistence of their topological features, leading to a scheme for simple constructions of kernel matrices. As a proof of principle, on a dataset of 356 subjects from the ADNI study, we report good performance on several statistical inference tasks without any feature selection, dimensionality reduction, or parameter tuning. PMID:21536520
Learning discriminant face descriptor.
Lei, Zhen; Pietikäinen, Matti; Li, Stan Z
2014-02-01
Local feature descriptor is an important module for face recognition and those like Gabor and local binary patterns (LBP) have proven effective face descriptors. Traditionally, the form of such local descriptors is predefined in a handcrafted way. In this paper, we propose a method to learn a discriminant face descriptor (DFD) in a data-driven way. The idea is to learn the most discriminant local features that minimize the difference of the features between images of the same person and maximize that between images from different people. In particular, we propose to enhance the discriminative ability of face representation in three aspects. First, the discriminant image filters are learned. Second, the optimal neighborhood sampling strategy is soft determined. Third, the dominant patterns are statistically constructed. Discriminative learning is incorporated to extract effective and robust features. We further apply the proposed method to the heterogeneous (cross-modality) face recognition problem and learn DFD in a coupled way (coupled DFD or C-DFD) to reduce the gap between features of heterogeneous face images to improve the performance of this challenging problem. Extensive experiments on FERET, CAS-PEAL-R1, LFW, and HFB face databases validate the effectiveness of the proposed DFD learning on both homogeneous and heterogeneous face recognition problems. The DFD improves POEM and LQP by about 4.5 percent on LFW database and the C-DFD enhances the heterogeneous face recognition performance of LBP by over 25 percent. PMID:24356350
Gaussian kernel width optimization for sparse Bayesian learning.
Mohsenzadeh, Yalda; Sheikhzadeh, Hamid
2015-04-01
Sparse kernel methods have been widely used in regression and classification applications. The performance and the sparsity of these methods are dependent on the appropriate choice of the corresponding kernel functions and their parameters. Typically, the kernel parameters are selected using a cross-validation approach. In this paper, a learning method that is an extension of the relevance vector machine (RVM) is presented. The proposed method can find the optimal values of the kernel parameters during the training procedure. This algorithm uses an expectation-maximization approach for updating kernel parameters as well as other model parameters; therefore, the speed of convergence and computational complexity of the proposed method are the same as the standard RVM. To control the convergence of this fully parameterized model, the optimization with respect to the kernel parameters is performed using a constraint on these parameters. The proposed method is compared with the typical RVM and other competing methods to analyze the performance. The experimental results on the commonly used synthetic data, as well as benchmark data sets, demonstrate the effectiveness of the proposed method in reducing the performance dependency on the initial choice of the kernel parameters. PMID:25794377
Classification of maize kernels using NIR hyperspectral imaging.
Williams, Paul J; Kucheryavskiy, Sergey
2016-10-15
NIR hyperspectral imaging was evaluated to classify maize kernels of three hardness categories: hard, medium and soft. Two approaches, pixel-wise and object-wise, were investigated to group kernels according to hardness. The pixel-wise classification assigned a class to every pixel from individual kernels and did not give acceptable results because of high misclassification. However by using a predefined threshold and classifying entire kernels based on the number of correctly predicted pixels, improved results were achieved (sensitivity and specificity of 0.75 and 0.97). Object-wise classification was performed using two methods for feature extraction - score histograms and mean spectra. The model based on score histograms performed better for hard kernel classification (sensitivity and specificity of 0.93 and 0.97), while that of mean spectra gave better results for medium kernels (sensitivity and specificity of 0.95 and 0.93). Both feature extraction methods can be recommended for classification of maize kernels on production scale. PMID:27173544
Effects of sample size on KERNEL home range estimates
Seaman, D.E.; Millspaugh, J.J.; Kernohan, Brian J.; Brundige, Gary C.; Raedeke, Kenneth J.; Gitzen, Robert A.
1999-01-01
Kernel methods for estimating home range are being used increasingly in wildlife research, but the effect of sample size on their accuracy is not known. We used computer simulations of 10-200 points/home range and compared accuracy of home range estimates produced by fixed and adaptive kernels with the reference (REF) and least-squares cross-validation (LSCV) methods for determining the amount of smoothing. Simulated home ranges varied from simple to complex shapes created by mixing bivariate normal distributions. We used the size of the 95% home range area and the relative mean squared error of the surface fit to assess the accuracy of the kernel home range estimates. For both measures, the bias and variance approached an asymptote at about 50 observations/home range. The fixed kernel with smoothing selected by LSCV provided the least-biased estimates of the 95% home range area. All kernel methods produced similar surface fit for most simulations, but the fixed kernel with LSCV had the lowest frequency and magnitude of very poor estimates. We reviewed 101 papers published in The Journal of Wildlife Management (JWM) between 1980 and 1997 that estimated animal home ranges. A minority of these papers used nonparametric utilization distribution (UD) estimators, and most did not adequately report sample sizes. We recommend that home range studies using kernel estimates use LSCV to determine the amount of smoothing, obtain a minimum of 30 observations per animal (but preferably a?Y50), and report sample sizes in published results.
Texture analysis in gel electrophoresis images using an integrative kernel-based approach
Fernandez-Lozano, Carlos; Seoane, Jose A.; Gestal, Marcos; Gaunt, Tom R.; Dorado, Julian; Pazos, Alejandro; Campbell, Colin
2016-01-01
Texture information could be used in proteomics to improve the quality of the image analysis of proteins separated on a gel. In order to evaluate the best technique to identify relevant textures, we use several different kernel-based machine learning techniques to classify proteins in 2-DE images into spot and noise. We evaluate the classification accuracy of each of these techniques with proteins extracted from ten 2-DE images of different types of tissues and different experimental conditions. We found that the best classification model was FSMKL, a data integration method using multiple kernel learning, which achieved AUROC values above 95% while using a reduced number of features. This technique allows us to increment the interpretability of the complex combinations of textures and to weight the importance of each particular feature in the final model. In particular the Inverse Difference Moment exhibited the highest discriminating power. A higher value can be associated with an homogeneous structure as this feature describes the homogeneity; the larger the value, the more symmetric. The final model is performed by the combination of different groups of textural features. Here we demonstrated the feasibility of combining different groups of textures in 2-DE image analysis for spot detection. PMID:26758643
Texture analysis in gel electrophoresis images using an integrative kernel-based approach.
Fernandez-Lozano, Carlos; Seoane, Jose A; Gestal, Marcos; Gaunt, Tom R; Dorado, Julian; Pazos, Alejandro; Campbell, Colin
2016-01-01
Texture information could be used in proteomics to improve the quality of the image analysis of proteins separated on a gel. In order to evaluate the best technique to identify relevant textures, we use several different kernel-based machine learning techniques to classify proteins in 2-DE images into spot and noise. We evaluate the classification accuracy of each of these techniques with proteins extracted from ten 2-DE images of different types of tissues and different experimental conditions. We found that the best classification model was FSMKL, a data integration method using multiple kernel learning, which achieved AUROC values above 95% while using a reduced number of features. This technique allows us to increment the interpretability of the complex combinations of textures and to weight the importance of each particular feature in the final model. In particular the Inverse Difference Moment exhibited the highest discriminating power. A higher value can be associated with an homogeneous structure as this feature describes the homogeneity; the larger the value, the more symmetric. The final model is performed by the combination of different groups of textural features. Here we demonstrated the feasibility of combining different groups of textures in 2-DE image analysis for spot detection. PMID:26758643
Improved Prediction of Malaria Degradomes by Supervised Learning with SVM and Profile Kernel
Kuang, Rui; Gu, Jianying; Cai, Hong; Wang, Yufeng
2009-01-01
The spread of drug resistance through malaria parasite populations calls for the development of new therapeutic strategies. However, the seemingly promising genomics-driven target identification paradigm is hampered by the weak annotation coverage. To identify potentially important yet uncharacterized proteins, we apply support vector machines using profile kernels, a supervised discriminative machine learning technique for remote homology detection, as a complement to the traditional alignment based algorithms. In this study, we focus on the prediction of proteases, which have long been considered attractive drug targets because of their indispensable roles in parasite development and infection. Our analysis demonstrates that an abundant and complex repertoire is conserved in five Plasmodium parasite species. Several putative proteases may be important components in networks that mediate cellular processes, including hemoglobin digestion, invasion, trafficking, cell cycle fate, and signal transduction. This catalog of proteases provides a short list of targets for functional characterization and rational inhibitor design. PMID:19057851
DIFFERENTIAL PULSE HEIGHT DISCRIMINATOR
Test, L.D.
1958-11-11
Pulse-height discriminators are described, specifically a differential pulse-height discriminator which is adapted to respond to pulses of a band of amplitudes, but to reject pulses of amplitudes greater or less than tbe preselected band. In general, the discriminator includes a vacuum tube having a plurality of grids adapted to cut off plate current in the tube upon the application of sufficient negative voltage. One grid is held below cutoff, while a positive pulse proportional to the amplltude of each pulse is applled to this grid. Another grid has a negative pulse proportional to the amplitude of each pulse simultaneously applied to it. With this arrangement the tube will only pass pulses which are of sufficlent amplitude to counter the cutoff bias but not of sufficlent amplitude to cutoff the tube.
Two dimensional discriminant neighborhood preserving embedding in face recognition
NASA Astrophysics Data System (ADS)
Pang, Meng; Jiang, Jifeng; Lin, Chuang; Wang, Binghui
2015-03-01
One of the key issues of face recognition is to extract the features of face images. In this paper, we propose a novel method, named two-dimensional discriminant neighborhood preserving embedding (2DDNPE), for image feature extraction and face recognition. 2DDNPE benefits from four techniques, i.e., neighborhood preserving embedding (NPE), locality preserving projection (LPP), image based projection and Fisher criterion. Firstly, NPE and LPP are two popular manifold learning techniques which can optimally preserve the local geometry structures of the original samples from different angles. Secondly, image based projection enables us to directly extract the optimal projection vectors from twodimensional image matrices rather than vectors, which avoids the small sample size problem as well as reserves useful structural information embedded in the original images. Finally, the Fisher criterion applied in 2DDNPE can boost face recognition rates by minimizing the within-class distance, while maximizing the between-class distance. To evaluate the performance of 2DDNPE, several experiments are conducted on the ORL and Yale face datasets. The results corroborate that 2DDNPE outperforms the existing 1D feature extraction methods, such as NPE, LPP, LDA and PCA across all experiments with respect to recognition rate and training time. 2DDNPE also delivers consistently promising results compared with other competing 2D methods such as 2DNPP, 2DLPP, 2DLDA and 2DPCA.
Initial-state splitting kernels in cold nuclear matter
NASA Astrophysics Data System (ADS)
Ovanesyan, Grigory; Ringer, Felix; Vitev, Ivan
2016-09-01
We derive medium-induced splitting kernels for energetic partons that undergo interactions in dense QCD matter before a hard-scattering event at large momentum transfer Q2. Working in the framework of the effective theory SCETG, we compute the splitting kernels beyond the soft gluon approximation. We present numerical studies that compare our new results with previous findings. We expect the full medium-induced splitting kernels to be most relevant for the extension of initial-state cold nuclear matter energy loss phenomenology in both p+A and A+A collisions.
Machine learning algorithms for damage detection: Kernel-based approaches
NASA Astrophysics Data System (ADS)
Santos, Adam; Figueiredo, Eloi; Silva, M. F. M.; Sales, C. S.; Costa, J. C. W. A.
2016-02-01
This paper presents four kernel-based algorithms for damage detection under varying operational and environmental conditions, namely based on one-class support vector machine, support vector data description, kernel principal component analysis and greedy kernel principal component analysis. Acceleration time-series from an array of accelerometers were obtained from a laboratory structure and used for performance comparison. The main contribution of this study is the applicability of the proposed algorithms for damage detection as well as the comparison of the classification performance between these algorithms and other four ones already considered as reliable approaches in the literature. All proposed algorithms revealed to have better classification performance than the previous ones.
Monte Carlo Code System for Electron (Positron) Dose Kernel Calculations.
1999-05-12
Version 00 KERNEL performs dose kernel calculations for an electron (positron) isotropic point source in an infinite homogeneous medium. First, the auxiliary code PRELIM is used to prepare cross section data for the considered medium. Then the KERNEL code simulates the transport of electrons and bremsstrahlung photons through the medium until all particles reach their cutoff energies. The deposited energy is scored in concentric spherical shells at a radial distance ranging from zero to twicemore » the source particle range.« less
Bridging the gap between the KERNEL and RT-11
Hendra, R.G.
1981-06-01
A software package is proposed to allow users of the PL-11 language, and the LSI-11 KERNEL in general, to use their PL-11 programs under RT-11. Further, some general purpose extensions to the KERNEL are proposed that facilitate some number conversions and strong manipulations. A Floating Point Package of procedures to allow full use of the hardware floating point capability of the LSI-11 computers is proposed. Extensions to the KERNEL that allow a user to read, write and delete disc files in the manner of RT-11 is also proposed. A device directory listing routine is also included.
Kernel simplex growing algorithm for hyperspectral endmember extraction
NASA Astrophysics Data System (ADS)
Zhao, Liaoying; Zheng, Junpeng; Li, Xiaorun; Wang, Lijiao
2014-01-01
In order to effectively extract endmembers for hyperspectral imagery where linear mixing model may not be appropriate due to multiple scattering effects, this paper extends the simplex growing algorithm (SGA) to its kernel version. A new simplex volume formula without dimension reduction is used in SGA to form a new simplex growing algorithm (NSGA). The original data are nonlinearly mapped into a high-dimensional space where the scatters can be ignored. To avoid determining complex nonlinear mapping, a kernel function is used to extend the NSGA to kernel NSGA (KNSGA). Experimental results of simulated and real data prove that the proposed KNSGA approach outperforms SGA and NSGA.
Drugs, discrimination and disability.
Gibson, Frances
2009-12-01
Whether addiction to prohibited drugs should be classified as a disability for the purposes of disability discrimination is a controversial question in Australia. The leading Australian case of Marsden v Human Rights Equal Opportunity Commission & Coffs Harbour & District Ex-Servicemen & Women's Memorial Club Ltd (HREOC, No H98/51, 30 August 1999); [2000] FCA 1619 concerned a disability discrimination complaint brought by Mr Marsden as a result of his treatment by the club. The case was brought as a public interest test case by the New South Wales Legal Aid Commission. Mr Marsden was on a methadone program at the time. The reasoning of the decision at the Federal Court opened the way for a finding that dependence on illegal drugs constituted a disability under disability discrimination legislation. The media reaction to the court's decision led to State and federal governments proposing legislation limiting legal protection from discrimination for people addicted to illegal drugs on the basis of their drug use. While the proposed federal legislation lapsed after objections from a coalition of medical, legal and other advocacy groups, the New South Wales legislation still provides that, in employment matters, it is not unlawful to discriminate against a person on the ground of disability if the disability relates to the person's addiction to a prohibited drug and the person is actually addicted to a prohibited drug at the time of the discrimination. The article details the sequence of events in the Marsden case, reflects on the role of public interest litigation in achieving social justice outcomes and suggests that Australia's recent ratification of the Convention on the Rights of Persons with Disabilities on 17 July 2008 should encourage legislators to review legislation which may have a discriminatory effect on people suffering from addictions. PMID:20169800
Code of Federal Regulations, 2010 CFR
2010-07-01
..., or wave action. Note: The Corps of Engineers also has regulations dealing with this section in 33 CFR..., Miss., from its mouth at Kleinston Landing to Fisher Street; navigation. 162.85 Section 162.85... mouth at Kleinston Landing to Fisher Street; navigation. (a) Speed. Excessive speeding is prohibited....
The not-so-benign Miller Fisher syndrome: a variant of the Guilain-Barré syndrome.
Blau, I; Casson, I; Lieberman, A; Weiss, E
1980-06-01
Two patients with Fisher's syndrome of ophthalmoplegia, ataxia, and areflexia experienced severe weakness and respiratory distress. Both patients required tracheostomy and assisted ventilation, but both made a complete recovery. Fisher's syndrome is generally considered to be a benign variant of acute infectious polyneuropathy (the Guillain-Barre syndrome). Our two patients demonstrate that the condition is not always benign. PMID:7387472
Code of Federal Regulations, 2011 CFR
2011-07-01
..., Miss., from its mouth at Kleinston Landing to Fisher Street; navigation. 207.260 Section 207.260... REGULATIONS § 207.260 Yazoo Diversion Canal, Vicksburg, Miss., from its mouth at Kleinston Landing to Fisher... canal at any stage from the mouth of the Yazoo Diversion Canal where it enters into the...
Code of Federal Regulations, 2013 CFR
2013-07-01
..., or wave action. Note: The Corps of Engineers also has regulations dealing with this section in 33 CFR..., Miss., from its mouth at Kleinston Landing to Fisher Street; navigation. 162.85 Section 162.85... mouth at Kleinston Landing to Fisher Street; navigation. (a) Speed. Excessive speeding is prohibited....
NASA Astrophysics Data System (ADS)
Yang, Yuan; Chevallier, Sylvain; Wiart, Joe; Bloch, Isabelle
2014-12-01
To enforce a widespread use of efficient and easy to use brain-computer interfaces (BCIs), the inter-subject robustness should be increased and the number of electrodes should be reduced. These two key issues are addressed in this contribution, proposing a novel method to identify subject-specific time-frequency characteristics with a minimal number of electrodes. In this method, two alternative criteria, time-frequency discrimination factor ( TFDF) and F score, are proposed to evaluate the discriminative power of time-frequency regions. Distinct from classical measures (e.g., Fisher criterion, r 2 coefficient), the TFDF is based on the neurophysiologic phenomena, on which the motor imagery BCI paradigm relies, rather than only from statistics. F score is based on the popular Fisher's discriminant and purely data driven; however, it differs from traditional measures since it provides a simple and effective measure for quantifying the discriminative power of a multi-dimensional feature vector. The proposed method is tested on BCI competition IV datasets IIa and IIb for discriminating right and left hand motor imagery. Compared to state-of-the-art methods, our method based on both criteria led to comparable or even better classification results, while using fewer electrodes (i.e., only two bipolar channels, C3 and C4). This work indicates that time-frequency optimization can not only improve the classification performance but also contribute to reducing the number of electrodes required in motor imagery BCIs.
Quantum discriminant analysis for dimensionality reduction and classification
NASA Astrophysics Data System (ADS)
Cong, Iris; Duan, Luming
2016-07-01
We present quantum algorithms to efficiently perform discriminant analysis for dimensionality reduction and classification over an exponentially large input data set. Compared with the best-known classical algorithms, the quantum algorithms show an exponential speedup in both the number of training vectors M and the feature space dimension N. We generalize the previous quantum algorithm for solving systems of linear equations (2009 Phys. Rev. Lett. 103 150502) to efficiently implement a Hermitian chain product of k trace-normalized N ×N Hermitian positive-semidefinite matrices with time complexity of O({log}(N)). Using this result, we perform linear as well as nonlinear Fisher discriminant analysis for dimensionality reduction over M vectors, each in an N-dimensional feature space, in time O(p {polylog}({MN})/{ε }3), where ɛ denotes the tolerance error, and p is the number of principal projection directions desired. We also present a quantum discriminant analysis algorithm for data classification with time complexity O({log}({MN})/{ε }3).
Liu, Ta-Kang; Wang, Yu-Cheng; Chuang, Laurence Zsu-Hsin; Chen, Chih-How
2016-01-01
The abundance of the eastern Taiwan Strait (ETS) population of the Chinese white dolphin (Sousa chinensis) has been estimated to be less than 100 individuals. It is categorized as critically endangered in the IUCN Red List of Threatened Species. Thus, immediate measures of conservation should be taken to protect it from extinction. Currently, the Taiwanese government plans to designate its habitat as a Major Wildlife Habitat (MWH), a type of marine protected area (MPA) for conservation of wildlife species. Although the designation allows continuing the current exploitation, however, it may cause conflicts among multiple stakeholders with competing interests. The study is to explore the attitude and opinions among the stakeholders in order to better manage the MPA. This study employs a semi-structured interview and a questionnaire survey of local fishers. Results from interviews indicated that the subsistence of fishers remains a major problem. It was found that stakeholders have different perceptions of the fishers’ attitude towards conservation and also thought that the fishery-related law enforcement could be difficult. Quantitative survey showed that fishers are generally positive towards the conservation of the Chinese white dolphin but are less willing to participate in the planning process. Most fishers considered temporary fishing closure as feasible for conservation. The results of this study provide recommendations for future efforts towards the goal of better conservation for this endangered species. PMID:27526102
Discontinuity and Protection of Quantum Fisher Information for a Two-Qubit System
NASA Astrophysics Data System (ADS)
Wang, Guo-You; Yuan, Ji-Bing; Zeng, Hao-Sheng
2015-11-01
We analytically investigate the dynamics of quantum Fisher information of two qubits in independent environments for both uncorrelated and entangled input states. Especially, we observe that, in the non-Markovian regime and resonant case, quantum Fisher information of uncorrelated input state vanishes only at discrete time point whereas that of entangled input state can disappear during finite time interval. It shows that quantum Fisher information, which determines the parameter estimation precision, is strongly affected by the environment memory effects, and the advantage of entanglement-enhance metrology no longer exists even for a very short time. We also note that quantum Fisher information for two kinds of input states can be preserved under appropriate qubit-reservoir detuning. Supported by the National Natural Science Foundation of China under Grant Nos. 11275064, 11075050, the Specialized Research Fund for the Doctoral Program of Higher Education under Grant No. 20124306110003, the Scientific Research Foundation of the Education Department of Hunan Province under Grant No. 13C039, and the Construct Program of the National Key Discipline
Disentangling Similarity Judgments from Pragmatic Judgments: Response to Sloutsky and Fisher (2012)
ERIC Educational Resources Information Center
Noles, Nicholaus S.; Gelman, Susan A.
2012-01-01
Sloutsky and Fisher (2012) attempt to reframe the results presented in Noles and Gelman (2012) as a pure replication of their original work validating the similarity, induction, naming, and categorization (SINC) model. However, their critique fails to engage with the central findings reported in Noles and Gelman, and their reanalysis fails to…
NASA Astrophysics Data System (ADS)
Wang, Zhi-Cheng; Bu, Zhen-Hui
2016-04-01
This paper is concerned with nonplanar traveling fronts in reaction-diffusion equations with combustion nonlinearity and degenerate Fisher-KPP nonlinearity. Our study contains two parts: in the first part we establish the existence of traveling fronts of pyramidal shape in R3, and in the second part we establish the existence and stability of V-shaped traveling fronts in R2.
Reading "The Star Fisher": Toward Critical and Sociological Interpretations of Immigrant Literature.
ERIC Educational Resources Information Center
Lowery, Ruth McKoy
2003-01-01
Proposes a critical-sociological approach to analyzing immigrant literature, noting that for many students, their only contact with immigrants may be through representations in children's literature. Examines how Chinese immigrants to the United States are represented in Laurence Yep's (1992) "The Star Fisher," discussing how the issues of race,…
R. A. Fisher, Lancelot Hogben, and the origin(s) of genotype-environment interaction.
Tabery, James
2008-01-01
This essay examines the origin(s) of genotype-environment interaction, or G x E. "Origin(s)" and not "the origin" because the thesis is that there were actually two distinct concepts of G x E at this beginning: a biometric concept, or G x EB, and a developmental concept, or G x ED. R. A. Fisher, one of the founders of population genetics and the creator of the statistical analysis of variance, introduced the biometric concept as he attempted to resolve one of the main problems in the biometric tradition of biology--partitioning the relative contributions of nature and nurture responsible for variation in a population. Lancelot Hogben, an experimental embryologist and also a statistician, introduced the developmental concept as he attempted to resolve one of the main problems in the developmental tradition of biology--determining the role that developmental relationships between genotype and environment played in the generation of variation. To argue for this thesis, I outline Fisher and Hogben's separate routes to their respective concepts of G x E; then these separate interpretations of G x E are drawn on to explicate a debate between Fisher and Hogben over the importance of G x E, the first installment of a persistent controversy. Finally, Fisher's G x EB and Hogben's G x ED are traced beyond their own work into mid-20th century population and developmental genetics, and then into the infamous IQ Controversy of the 1970s. PMID:19244846
Book review: Biology and conservation of martens, sables, and fishers: A new synthesis
Jenkins, Kurt J.
2013-01-01
Review info: Biology and conservation of martens, sables, and fishers: A new synthesis. Edited by K.B. Aubry, W.J. Zielinski, M.G. Raphael, G. Proulx, and S.W. Buskirk, 2012. ISBN: 978-08014, 580pp.
Fisher Ames and Political Judgment: Reason, Passion, and Vehement Style in the Jay Treaty Speech.
ERIC Educational Resources Information Center
Farrell, James M.
1990-01-01
Analyzes Fisher Ames' fiery speech of 1796 on the Jay Treaty. Demonstrates the influence of Scottish enlightenment thinkers (particularly in moral sense philosophy and faculty psychology) on Ames and his rhetoric. Demonstrates how Ames made a compelling case to shift the standard of political judgment from reason to passion. (SR)
Existence of travelling wave solutions for a Fisher-Kolmogorov system with biomedical applications
NASA Astrophysics Data System (ADS)
Belmonte-Beitia, Juan
2016-07-01
We consider a Fisher-Kolmogorov system with applications in oncology Pérez-García et al. (2015). Of interest is the question of the existence of travelling front solutions of the system. When the speed of the travelling wave is sufficiently large, existence of such fronts is shown using singular geometric perturbation theory.
Wallace, Andrea P. C.; Milner-Gulland, E. J.; Jones, Julia P. G.; Bunnefeld, Nils; Young, Richard; Nicholson, Emily
2015-01-01
Artisanal fisheries are a key source of food and income for millions of people, but if poorly managed, fishing can have declining returns as well as impacts on biodiversity. Management interventions such as spatial and temporal closures can improve fishery sustainability and reduce environmental degradation, but may carry substantial short-term costs for fishers. The Lake Alaotra wetland in Madagascar supports a commercially important artisanal fishery and provides habitat for a Critically Endangered primate and other endemic wildlife of conservation importance. Using detailed data from more than 1,600 fisher catches, we used linear mixed effects models to explore and quantify relationships between catch weight, effort, and spatial and temporal restrictions to identify drivers of fisher behaviour and quantify the potential effect of fishing restrictions on catch. We found that restricted area interventions and fishery closures would generate direct short-term costs through reduced catch and income, and these costs vary between groups of fishers using different gear. Our results show that conservation interventions can have uneven impacts on local people with different fishing strategies. This information can be used to formulate management strategies that minimise the adverse impacts of interventions, increase local support and compliance, and therefore maximise conservation effectiveness. PMID:26107284
Wallace, Andrea P C; Milner-Gulland, E J; Jones, Julia P G; Bunnefeld, Nils; Young, Richard; Nicholson, Emily
2015-01-01
Artisanal fisheries are a key source of food and income for millions of people, but if poorly managed, fishing can have declining returns as well as impacts on biodiversity. Management interventions such as spatial and temporal closures can improve fishery sustainability and reduce environmental degradation, but may carry substantial short-term costs for fishers. The Lake Alaotra wetland in Madagascar supports a commercially important artisanal fishery and provides habitat for a Critically Endangered primate and other endemic wildlife of conservation importance. Using detailed data from more than 1,600 fisher catches, we used linear mixed effects models to explore and quantify relationships between catch weight, effort, and spatial and temporal restrictions to identify drivers of fisher behaviour and quantify the potential effect of fishing restrictions on catch. We found that restricted area interventions and fishery closures would generate direct short-term costs through reduced catch and income, and these costs vary between groups of fishers using different gear. Our results show that conservation interventions can have uneven impacts on local people with different fishing strategies. This information can be used to formulate management strategies that minimise the adverse impacts of interventions, increase local support and compliance, and therefore maximise conservation effectiveness. PMID:26107284
A COMAPRISON OF MERCURY IN MINK AND FISHER IN RHODE ISLAND
Comparison of total mercury concentrations and nitrogen and carbon stable isotope values in muscle tissue and stomach contents of mink (Mustela vison) and fisher (Martes pennanti) from Rhode Island in 2000- 2003 showed results which appeared to reflect dietary differences betwee...
NASA Astrophysics Data System (ADS)
Toranzo, I. V.; López-Rosa, S.; Esquivel, R. O.; Dehesa, J. S.
2015-06-01
Heisenberg-like and Fisher-information-based uncertainty relations which extend and generalize previous similar expressions are obtained for N -fermion d -dimensional systems. The contributions of both spatial and spin degrees of freedom are taken into account. The accuracy of some of these generalized spinned uncertainty-like relations is numerically examined for a large number of atomic and molecular systems.
Exact solutions of the time-fractional Fisher equation by using modified trial equation method
NASA Astrophysics Data System (ADS)
Tandogan, Yusuf Ali; Bildik, Necdet
2016-06-01
In this study, modified trial equation method has been proposed to obtain precise solutions of nonlinear fractional differential equation. Using the modified test equation method, we obtained some new exact solutions of the time fractional nonlinear Fisher equation. The obtained results are classified as a soliton solution, singular solutions, rational function solutions and periodic solutions.
Federal Register 2010, 2011, 2012, 2013, 2014
2010-03-12
... From the Federal Register Online via the Government Publishing Office DEPARTMENT OF LABOR Employment and Training Administration Fisher & Paykel Appliances, Inc., Huntington Beach, CA; Notice of Termination of Investigation Pursuant to Section 221 of the Trade Act of 1974, as amended, an...
38 CFR 60.10 - Eligibility criteria for Fisher House or other temporary lodging.
Code of Federal Regulations, 2013 CFR
2013-07-01
... status as an organ donor for a veteran. VA may also provide Fisher House or other temporary lodging for the donor's accompanying individuals at all phases of the transplant process. (Authority: 38 U.S.C... from the VA health care facility without an overnight stay. (e) Special authority for organ...
38 CFR 60.10 - Eligibility criteria for Fisher House or other temporary lodging.
Code of Federal Regulations, 2014 CFR
2014-07-01
... status as an organ donor for a veteran. VA may also provide Fisher House or other temporary lodging for the donor's accompanying individuals at all phases of the transplant process. (Authority: 38 U.S.C... from the VA health care facility without an overnight stay. (e) Special authority for organ...