HS-SPME-GC-MS/MS Method for the Rapid and Sensitive Quantitation of 2-Acetyl-1-pyrroline in Single Rice Kernels.

Science.gov (United States)

Hopfer, Helene; Jodari, Farman; Negre-Zakharov, Florence; Wylie, Phillip L; Ebeler, Susan E

2016-05-25

Demand for aromatic rice varieties (e.g., Basmati) is increasing in the US. Aromatic varieties typically have elevated levels of the aroma compound 2-acetyl-1-pyrroline (2AP). Due to its very low aroma threshold, analysis of 2AP provides a useful screening tool for rice breeders. Methods for 2AP analysis in rice should quantitate 2AP at or below sensory threshold level, avoid artifactual 2AP generation, and be able to analyze single rice kernels in cases where only small sample quantities are available (e.g., breeding trials). We combined headspace solid phase microextraction with gas chromatography tandem mass spectrometry (HS-SPME-GC-MS/MS) for analysis of 2AP, using an extraction temperature of 40 °C and a stable isotopologue as internal standard. 2AP calibrations were linear between the concentrations of 53 and 5380 pg/g, with detection limits below the sensory threshold of 2AP. Forty-eight aromatic and nonaromatic, milled rice samples from three harvest years were screened with the method for their 2AP content, and overall reproducibility, observed for all samples, ranged from 5% for experimental aromatic lines to 33% for nonaromatic lines.

Promising mutant variety of rice evolved through gamma irradiation

International Nuclear Information System (INIS)

Prasad, S.C.; Sinha, S.K.

1980-01-01

Rice occupies a major share in crop production in the Chotanagpur plateau of Bihar State. Uplands are roughly 40% in area where traditional low yielding rice, known as ''gora'' is cultivated as directly sown crop. Despite introduction of high yielding rice varieties, gora group of rices continue to prevail. It is therefore desired to increase the productivity level of the gora rice by mutation breeding. One such mutant known as ''gora mutant'' was obtained through gamma irradiation (10 kR) of variety Brown gora. The maturity of both parent and mutant remaining constant (ie. 100 days), there is some improvement in other characteristics like plant height, tillering capacity and kernel character. The parent being tall, shy in tillering and red bold kernel, the mutant has dwarfish characteristics, profuse tillering habit and white kernel with fine grains. The yielding capacity of mutant derivative is 30-40% higher than the parent Brown gora. This variety is in pre-release stage, and the farmers have taken great liking for it. (author)

7 CFR 868.301 - Definition of milled rice.

Science.gov (United States)

2010-01-01

... 7 Agriculture 7 2010-01-01 2010-01-01 false Definition of milled rice. 868.301 Section 868.301... FOR CERTAIN AGRICULTURAL COMMODITIES United States Standards for Milled Rice Terms Defined § 868.301 Definition of milled rice. Whole or broken kernels of rice (Oryza sativa L.) from which the hulls and at...

7 CFR 868.201 - Definition of rough rice.

Science.gov (United States)

2010-01-01

... 7 Agriculture 7 2010-01-01 2010-01-01 false Definition of rough rice. 868.201 Section 868.201... FOR CERTAIN AGRICULTURAL COMMODITIES United States Standards for Rough Rice Terms Defined § 868.201 Definition of rough rice. Rice (Oryza sativa L.) which consists of 50 percent or more of paddy kernels (see...

Bran data of total flavonoid and total phenolic contents, oxygen radical absorbance capacity, and profiles of proanthocyanidins and whole grain physical traits of 32 red and purple rice varieties

Directory of Open Access Journals (Sweden)

Ming-Hsuan Chen

2016-09-01

Full Text Available Phytochemicals in red and purple bran rice have potential health benefit to humans. We determined the phytochemicals in brans of 32 red and purple global rice varieties. The description of the origin and physical traits of the whole grain (color, length, width, thickness and 100-kernel weight of this germplasm collection are provided along with data of total flavonoid and total phenolic contents, oxygen radical absorbance capacity and total proanthocyanidin contents. The contents and proportions of individual oligomers, from degree of polymerization of monomers to 14-mers, and polymers in bran of these 32 rice varieties are presented (DOI: http://dx.doi.org/10.1016/j.foodchem.2016.04.004 [1]. Keywords: Purple rice, Red rice, Black rice, Proanthocyanidins, Tannins, Flavonoids, Rice bran

Degree of Milling Effect on Cold Water Rice Quality

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Ujjwol Subedi

2018-05-01

Full Text Available The aim of this study was to examine the effects of degree of milling on various rice parameters such as proximate composition, and cooking properties using mathematical model. The experiments were performed in the laboratory of Food Research Division, Nepal Agricultural Research Council. The three different medium type rice varieties of Nepal (Lumle-2, Chhomrong and Machhapuchre-3 were exposed to five different degrees of milling (0%, 6%, 8%, 10% and 12%. The degree of milling (DM level significantly (P≤0.05 affected the milling recovery; head rice yield, nutrient content as well as cooking properties of the rice. Increase in DM resulted in further reduction of protein content, fat content, minerals, milled rice and head rice yield after bran layer was further removed. A positive correlation between DM used in present model, amylose content, kernel elongation and gruel solid loss was observed, however, with an increase in DM; amylose content, kernel elongation and gruel solid loss were found to be increased. Adopting 6 to 8% DM for commercial milling of rice might help to prevent quantitative, qualitative and nutritional loss along with retention of good cooking characteristics.

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

Science.gov (United States)

Wang, Gang; Wang, Yalin

2017-02-15

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

Effect of Xanthan, Mono-di-Glyceride and Cultivar on Physical and Sensory Properties of Canned Rice During Storage

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A. Nasirpour

2014-08-01

Full Text Available Ready-to-eat or canned products have an important place in urban life style. Rice is consumed by half of world population. Problems related to production of canned rice are the coherences of rice kernel, retrogradation of rice starch and physico-chemical changes of rice during storage. Main factors affecting physico-chemical properties of canned rice are rice cultivar, amylose and amylopectin content of kernels, precooking conditions, kernel composition and added stabilizers. In this study, effect of three cultivars, Basmati and two local cultivars, Tarom Mazandaran and Tarom Hashemi, also two types of stabilizers, Xanthan, Mono-di-glyceride, on sensory and physical properties of canned rice were studied. Type and level of stabilizers, cultivars and time had significant effect on texture of cooked rice. Initially during storage, Basmati had the highest firmness and Tarom Mazandaran and Tarom Hashemi had lower firmness. The results of texture analysis indicated that both stabilizers reduced firmness of cooked rice during storage. Adding xhanthan as stabilizer had higher effect on reducing of cooked rice firmness comparing to mono-di-glyceride. Result of color analysis showed that Basmati cultivar was most yellowish and Tarom Mazandaran indicated maximum lightness. Sensory analysis of samples indicated that Basmati had the best score on texture, appearance and acceptability and Tarom Hashemi had the best score on taste and flavor. Type of stabilizer had significant effect on taste and flavor of cooked rice.

Classification of Broken Rice Kernels using 12D Features

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SUNDER ALI KHOWAJA

2016-07-01

Full Text Available Integrating the technological aspect for assessment of rice quality is very much needed for the Asian markets where rice is one of the major exports. Methods based on image analysis has been proposed for automated quality assessment by taking into account some of the textural features. These features are good at classifying when rice grains are scanned in controlled environment but it is not suitable for practical implementation. Rice grains are placed randomly on the scanner which neither maintains the uniformity in intensity regions nor the placement strategy is kept ideal thus resulting in false classification of grains. The aim of this research is to propose a method for extracting set of features which can overcome the said issues. This paper uses morphological features along-with gray level and Hough transform based features to overcome the false classification in the existing methods. RBF (Radial Basis function is used as a classification mechanism to classify between complete grains and broken grains. Furthermore the broken grains are classified into two classes? i.e. acceptable grains and non-acceptable grains. This research also uses image enhancement technique prior to the feature extraction and classification process based on top-hat transformation. The proposed method has been simulated in MATLAB to visually analyze and validate the results.

Resistance of Rice Varieties to the Stored-Product Insect, Sitophilus zeamais (Coleoptera: Curculionidae).

Science.gov (United States)

Antunes, Catarina; Mendes, Raquel; Lima, Arlindo; Barros, Graça; Fields, Paul; Da Costa, Luísa Beirão; Rodrigues, José Carlos; Silva, Maria José; Correia, Augusto Manuel; Carvalho, Maria Otilia

2016-02-01

Four common Portuguese rice varieties--Thaibonnet, Gladio, Albatros, and Eurosis--were tested for their relative susceptibility to Sitophilus zeamais Motschulsky, a common pest of stored rice in Portugal and in tropical countries. Physical (moisture content, hardness, length, and width) and chemical (by attenuated total reflection-Fourier transform infrared spectroscopy) properties of rice kernels were measured. Insect bioassays measured median developmental time, Dobie's index of susceptibility, percentage of damaged grains and weight loss, and progeny developed. This was done for paddy, brown rice, and polished rice for each variety. There were small, but significant, differences in insect resistance among the varieties. However, it was different for paddy and polished rice. In paddy, these differences were correlated with hull damage, and Eurosis was the most susceptible variety. In polished rice, resistance was correlated with hardness, and Thaibonnet was the most susceptible variety. In general, paddy rice was more resistant to insect attack, followed by polished rice and then brown rice. Paddy kernels selected with undamaged hull were completely resistant to attack. Implications for IPM and breeding for resistant varieties are discussed. © The Authors 2015. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Mapping QTLs controlling kernel dimensions in a wheat inter-varietal RIL mapping population.

Science.gov (United States)

Cheng, Ruiru; Kong, Zhongxin; Zhang, Liwei; Xie, Quan; Jia, Haiyan; Yu, Dong; Huang, Yulong; Ma, Zhengqiang

2017-07-01

Seven kernel dimension QTLs were identified in wheat, and kernel thickness was found to be the most important dimension for grain weight improvement. Kernel morphology and weight of wheat (Triticum aestivum L.) affect both yield and quality; however, the genetic basis of these traits and their interactions has not been fully understood. In this study, to investigate the genetic factors affecting kernel morphology and the association of kernel morphology traits with kernel weight, kernel length (KL), width (KW) and thickness (KT) were evaluated, together with hundred-grain weight (HGW), in a recombinant inbred line population derived from Nanda2419 × Wangshuibai, with data from five trials (two different locations over 3 years). The results showed that HGW was more closely correlated with KT and KW than with KL. A whole genome scan revealed four QTLs for KL, one for KW and two for KT, distributed on five different chromosomes. Of them, QKl.nau-2D for KL, and QKt.nau-4B and QKt.nau-5A for KT were newly identified major QTLs for the respective traits, explaining up to 32.6 and 41.5% of the phenotypic variations, respectively. Increase of KW and KT and reduction of KL/KT and KW/KT ratios always resulted in significant higher grain weight. Lines combining the Nanda 2419 alleles of the 4B and 5A intervals had wider, thicker, rounder kernels and a 14% higher grain weight in the genotype-based analysis. A strong, negative linear relationship of the KW/KT ratio with grain weight was observed. It thus appears that kernel thickness is the most important kernel dimension factor in wheat improvement for higher yield. Mapping and marker identification of the kernel dimension-related QTLs definitely help realize the breeding goals.

Relações granulométricas no processo de brunimento de arroz Granulometry relationship in the rice milling process

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Carlos A. S. Luz

2005-04-01

Full Text Available O brunimento é uma das etapas de beneficiamento do arroz que tem grande importância sobre o rendimento de engenho. Neste trabalho, analisaram-se relações granulométricas de frações de grãos na massa de arroz e seus efeitos sobre o rendimento de engenho, durante o processo de brunimento. As amostras usadas foram 78 g de arroz descascado, tendo essas partido de amostras originais de 100 g. Foram testadas cinco relações de composição de amostras a serem brunidas, variando as quantidades de grãos quebrados e de quirera que acompanhavam a massa de grãos inteiros, que foram: 1 somente grãos inteiros; 2 6,16 g de quebrados e 1,04 g de quirera, conforme amostra original; 3 3,60 g de quebrados e 3,60 g de quirera adicionados aos inteiros; 4 7,20 g de quebrados adicionados aos inteiros, e 5 7,20 g de quirera adicionada aos inteiros. Os procedimentos foram executados com uso de engenho de provas e com classificação manual. O rendimento de engenho foi significativamente superior, após a operação de brunimento de arroz, quando existem, na massa a ser brunida, grãos quebrados e quirera, variando entre 3,60 g a 7,20 g e 1,04 g a 7,20 g, respectivamente, comparada ao brunimento da massa composta por 100% de grãos inteiros.Milling yield using five different rates of broken and head rice was tested. The samples were arranged to have different amounts of big and small broken kernels in the mass of grains. The size of each fraction of broken grains was established according to the Brazilian Rules of Rice Classification. The paddy sample had 100 g and the brown rice sample had 78 g. The brown rice was milled by adding different proportions of broken kernels to make five treatments as follows: 1 only whole kernels; 2 6.16 g of big broken kernels and 1.04 g of small broken kernels were used as they appear in the original lot; 3 3.6 g of big broken kernels and 3.6 g of small broken kernels were added to the whole kernels; 4 7.2 g of big broken

Morphological and starch structural characteristics of the Japonica rice mutant variety Seolgaeng for dry-milled flour

Science.gov (United States)

Producing fine, good quality rice flour is more difficult than wheat flour because the rice grain is harder. In this study, we analyzed the relationship between the morphology and starch of kernels from genetically different rice varieties that can be used to make dry-milled flour. The non-glutinous...

Coronary Stent Artifact Reduction with an Edge-Enhancing Reconstruction Kernel - A Prospective Cross-Sectional Study with 256-Slice CT.

Science.gov (United States)

Tan, Stéphanie; Soulez, Gilles; Diez Martinez, Patricia; Larrivée, Sandra; Stevens, Louis-Mathieu; Goussard, Yves; Mansour, Samer; Chartrand-Lefebvre, Carl

2016-01-01

Metallic artifacts can result in an artificial thickening of the coronary stent wall which can significantly impair computed tomography (CT) imaging in patients with coronary stents. The objective of this study is to assess in vivo visualization of coronary stent wall and lumen with an edge-enhancing CT reconstruction kernel, as compared to a standard kernel. This is a prospective cross-sectional study involving the assessment of 71 coronary stents (24 patients), with blinded observers. After 256-slice CT angiography, image reconstruction was done with medium-smooth and edge-enhancing kernels. Stent wall thickness was measured with both orthogonal and circumference methods, averaging thickness from diameter and circumference measurements, respectively. Image quality was assessed quantitatively using objective parameters (noise, signal to noise (SNR) and contrast to noise (CNR) ratios), as well as visually using a 5-point Likert scale. Stent wall thickness was decreased with the edge-enhancing kernel in comparison to the standard kernel, either with the orthogonal (0.97 ± 0.02 versus 1.09 ± 0.03 mm, respectively; pkernel generated less overestimation from nominal thickness compared to the standard kernel, both with the orthogonal (0.89 ± 0.19 versus 1.00 ± 0.26 mm, respectively; pkernel was associated with lower SNR and CNR, as well as higher background noise (all p kernel. Stent visual scores were higher with the edge-enhancing kernel (pkernel generates thinner stent walls, less overestimation from nominal thickness, and better image quality scores than the standard kernel.

Recycling rice husks for high-capacity lithium battery anodes.

Science.gov (United States)

Jung, Dae Soo; Ryou, Myung-Hyun; Sung, Yong Joo; Park, Seung Bin; Choi, Jang Wook

2013-07-23

The rice husk is the outer covering of a rice kernel and protects the inner ingredients from external attack by insects and bacteria. To perform this function while ventilating air and moisture, rice plants have developed unique nanoporous silica layers in their husks through years of natural evolution. Despite the massive amount of annual production near 10(8) tons worldwide, so far rice husks have been recycled only for low-value agricultural items. In an effort to recycle rice husks for high-value applications, we convert the silica to silicon and use it for high-capacity lithium battery anodes. Taking advantage of the interconnected nanoporous structure naturally existing in rice husks, the converted silicon exhibits excellent electrochemical performance as a lithium battery anode, suggesting that rice husks can be a massive resource for use in high-capacity lithium battery negative electrodes.

Robust Kernel (Cross-) Covariance Operators in Reproducing Kernel Hilbert Space toward Kernel Methods

OpenAIRE

Alam, Md. Ashad; Fukumizu, Kenji; Wang, Yu-Ping

2016-01-01

To the best of our knowledge, there are no general well-founded robust methods for statistical unsupervised learning. Most of the unsupervised methods explicitly or implicitly depend on the kernel covariance operator (kernel CO) or kernel cross-covariance operator (kernel CCO). They are sensitive to contaminated data, even when using bounded positive definite kernels. First, we propose robust kernel covariance operator (robust kernel CO) and robust kernel crosscovariance operator (robust kern...

Genetic Architecture of Grain Chalk in Rice and Interactions with a Low Phytic Acid Locus

Science.gov (United States)

Grain quality characteristics have a major impact on the value of the harvested rice crop. In addition to grain dimensions which determine rice grain market classes, translucent milled kernels are also important for assuring the highest grain quality and crop value. Over the last several years, ther...

Genetic manipulation of the γ-aminobutyric acid (GABA) shunt in rice: overexpression of truncated glutamate decarboxylase (GAD2) and knockdown of γ-aminobutyric acid transaminase (GABA-T) lead to sustained and high levels of GABA accumulation in rice kernels.

Science.gov (United States)

Shimajiri, Yasuka; Oonishi, Takayuki; Ozaki, Kae; Kainou, Kumiko; Akama, Kazuhito

2013-06-01

Gamma-aminobutyric acid (GABA) is a non-protein amino acid commonly present in all organisms. Because cellular levels of GABA in plants are mainly regulated by synthesis (glutamate decarboxylase, GAD) and catabolism (GABA-transaminase, GABA-T), we attempted seed-specific manipulation of the GABA shunt to achieve stable GABA accumulation in rice. A truncated GAD2 sequence, one of five GAD genes, controlled by the glutelin (GluB-1) or rice embryo globulin promoters (REG) and GABA-T-based trigger sequences in RNA interference (RNAi) cassettes controlled by one of these promoters as well, was introduced into rice (cv. Koshihikari) to establish stable transgenic lines under herbicide selection using pyriminobac. T₁ and T₂ generations of rice lines displayed high GABA concentrations (2-100 mg/100 g grain). In analyses of two selected lines from the T₃ generation, there was a strong correlation between GABA level and the expression of truncated GAD2, whereas the inhibitory effect of GABA-T expression was relatively weak. In these two lines both with two T-DNA copies, their starch, amylose, and protein levels were slightly lower than non-transformed cv. Koshihikari. Free amino acid analysis of mature kernels of these lines demonstrated elevated levels of GABA (75-350 mg/100 g polished rice) and also high levels of several amino acids, such as Ala, Ser, and Val. Because these lines of seeds could sustain their GABA content after harvest (up to 6 months), the strategy in this study could lead to the accumulation GABA and for these to be sustained in the edible parts. © 2013 Society for Experimental Biology, Association of Applied Biologists and John Wiley & Sons Ltd.

WIDE AND THICK GRAIN 1, which encodes an otubain-like protease with deubiquitination activity, influences grain size and shape in rice.

Science.gov (United States)

Huang, Ke; Wang, Dekai; Duan, Penggen; Zhang, Baolan; Xu, Ran; Li, Na; Li, Yunhai

2017-09-01

Grain size and shape are two crucial traits that influence grain yield and grain appearance in rice. Although several factors that affect grain size have been described in rice, the molecular mechanisms underlying the determination of grain size and shape are still elusive. In this study we report that WIDE AND THICK GRAIN 1 (WTG1) functions as an important factor determining grain size and shape in rice. The wtg1-1 mutant exhibits wide, thick, short and heavy grains and also shows an increased number of grains per panicle. WTG1 determines grain size and shape mainly by influencing cell expansion. WTG1 encodes an otubain-like protease, which shares similarity with human OTUB1. Biochemical analyses indicate that WTG1 is a functional deubiquitinating enzyme, and the mutant protein (wtg1-1) loses this deubiquitinating activity. WTG1 is expressed in developing grains and panicles, and the GFP-WTG1 fusion protein is present in the nucleus and cytoplasm. Overexpression of WTG1 results in narrow, thin, long grains due to narrow and long cells, further supporting the role of WTG1 in determining grain size and shape. Thus, our findings identify the otubain-like protease WTG1 to be an important factor that determines grain size and shape, suggesting that WTG1 has the potential to improve grain size and shape in rice. © 2017 The Authors The Plant Journal © 2017 John Wiley & Sons Ltd.

Development of three allele-specific co-Dominant PCR markers suitable for marker-assisted selection of amylose class and paste viscosity of rice

Science.gov (United States)

Most rice is consumed as whole kernel cooked rice, and the consumer preferences for cooked rice texture and other sensory properties differ among regions of the world. Rice is also used as an ingredient in a multitude of foods by food-processing companies across the globe. These sensory and function...

The influence of Palm Kernel Cake and Rice Bran Fermentation Product Mixture to the Broiler Carcass Quality

Directory of Open Access Journals (Sweden)

Yadi Priabudiman

2011-07-01

Full Text Available The purpose of this research was to study the effect of the use of palm kernel cake (PKC and rice bran (RB fermentation products mixture to the percentage of broiler carcass weight pieces. Research using completely randomized design (CRD with treatments of the fermentation product usage rate of   0% (P0, 10% (P1, 20% (P2, 30% (P3 and 40% (P4 of the total ration of 4replications.  Variables measured were percentage of carcass weight ratio cut    pieces  of carcass weight (carcass front, rear carcass, breast meat, wings, back, and thigh with carcass weight multiplied by 100%. The results showed that the percentage of carcass weight piece front and rear carcass was shown at P3.

Physical, milling, cooking, and pasting characteristics of different rice varieties grown in the valley of Kashmir India

Directory of Open Access Journals (Sweden)

Tanveer Ahmad Rather

2016-12-01

Full Text Available In the present study, three different rice varieties, namely frome chena (FC, safaid chena (SC, and barkat chena (BC, were evaluated for various quality aspects in terms of physical, milling, cooking, and pasting characteristics. Among the three rice varieties SC had the highest thousand kernel weight and length breadth ratio (L/B. While as, BC had the lowest thousand kernel weight and FC had lowest L/B. Bulk density was found to be highest for FC followed by SC and BC. FC had density of 769.01 kg/m3. Milling characteristic in terms of broken percentage and head rice yield showed non-significant difference between the varieties. Head rice yield was below 70% in all the three varieties. All the three varieties took similar time to cook and cooking time varied non-significantly between 23.66 and 25.83 min. L/B ratio after cooking was found to be highest for FC followed by BC and SC. Elongation ratio of rice after cooking did not varied significantly between varieties. Elongation ratio after cooking ranged from 1.60 to 1.70. Pasting profile of rice flour was determined using rapid visco analyzer. Significant difference was observed in pasting profile of studied rice varieties.

Proximate Nutritional Evaluation of Gamma Irradiated Black Rice (Oryza sativa L. cv. Cempo ireng)

Science.gov (United States)

Riyatun; Suharyana; Ramelan, A. H.; Sutarno; Saputra, O. A.; Suryanti, V.

2018-03-01

Black rice is a type of pigmented rice with black bran covering the endosperm of the rice kernel. The main objective of the present study was to provide details information on the proximate composition of third generation of gamma irradiated black rice (Oryza sativa L. cv. Cempo ireng). In respect to the control, generally speaking, there were no significant changes of moisture, lipids, proteins, carbohydrates and fibers contents have been observed for the both gamma irradiated black rice. However, the 200-BR has slightly better nutritional value than that of 300-BR and the control. The mineral contents of 200-BR increased significantly of about 35% than the non-gamma irradiated black rice.

NPS-NRL-Rice-UIUC Collaboration on Navy Atmosphere-Ocean Coupled Models on Many-Core Computer Architectures Annual Report

Science.gov (United States)

2015-09-30

DISTRIBUTION STATEMENT A: Distribution approved for public release; distribution is unlimited. NPS-NRL- Rice -UIUC Collaboration on Navy Atmosphere...portability. There is still a gap in the OCCA support for Fortran programmers who do not have accelerator experience. Activities at Rice /Virginia Tech are...for automated data movement and for kernel optimization using source code analysis and run-time detective work. In this quarter the Rice /Virginia

Effect of soaking and phytase treatment on phytic acid, calcium, iron and zinc in rice fractions

NARCIS (Netherlands)

Liang, J.; Han, B.Z.; Nout, M.J.R.; Hamer, R.J.

2009-01-01

With the aim to maximise phytic acid removal and minimise losses of dry matter and minerals (Ca, Fe, Zn) in rice, three products (whole kernels and flour milled from white and brown rice; and bran, all from the same batch of variety Kenjian 90-31) were soaked in demineralized water at 10 °C (SDW),

Effects of milling on functional properties of rice flour.

Science.gov (United States)

Kadan, R S; Bryant, R J; Miller, J A

2008-05-01

A commercial long-grain rice flour (CRF) and the flours made by using a pin mill and the Udy mill from the same batch of broken second-head white long-grain rice were evaluated for their particle size and functional properties. The purpose of this study was to compare the commercial rice flour milling method to the pin and Udy milling methods used in our laboratory and pilot plant. The results showed that pin milled flour had more uniform particle size than the other 2 milled flours. The chalky kernels found in broken white milled rice were pulverized more into fines in both Udy milled flour and CRF than in the pin milled flour. The excessive amount of fines in flours affected their functional properties, for example, WSI and their potential usage in the novel foods such as rice breads (RB). The RB made from CRF collapsed more than loaves made from pin milled Cypress long-grain flours.

Use of oil palm kernel meal as a supplement material for abalone mushroom (Pleurotus cystidiosus O.K. Miller cultivation

Directory of Open Access Journals (Sweden)

Petcharat, V. and

2004-09-01

Full Text Available The objective of this study was to determine the optimum rate of oil palm kernel meal, for an abalone mushroom (Pleurotus cystidiosus cultivation. Different concentrations of oil palm kernel meal (5- 20% were added to pararubber sawdust and used to grow the abalone mushroom in plastic bags. Growth rate of the mycelia, number of days from watering to harvesting and yield were compared to those on 94% sawdust + 5% rice bran + 1% Ca(OH2. The results showed that 10% oil palm kernel meal was the optimum concentration for abalone mushroom cultivation. Yield on 950 g/bag of 89% sawdust + 10% oil palm kernel meal + 1% Ca(OH2 was 202.12 g/bag (B.E. = 60.79% during 120 days of havesting time. Addition of higher concentration of oil palm kernel meal (15-20% did not increase yield of the basidiocarps.

Effect of processing on residues of chlorpyrifos in stored corn and rice

International Nuclear Information System (INIS)

Tejada, A.W.; Calumpang, S.M.F.; Gambalan, N.B.

1990-01-01

The effect of processing on residues of chlorpyrifos in rice and corn was determined. Chlorpyrifos solution (0.1%) sprayed on jute sacks containing corn resulted in absorption of residues in kernel and cob up to six months. A similar trend was observed in rice. Radiotracer studies revealed very low levels of bound residues (0.2 - 0.8 mg/kg) present in rice only. The usual practice of washing rice and corn before cooking reduced chlorpyrifos residues as much as 57% to 100%. Residues in wash water declined with each washing. Cooking further reduced the residues of chlorpyrifos only when volatilization was possible. Chlorpyrifos appeared to be persistent. Cooking rice in plot with the lid on did not produce any substantial reduction in the chlorpyrifos content. The practice of storing rice and corn in the Philippines does not give rice to chlorpyrifos residues which may exceed the recommended daily intake of 0.01 mg/kg-bw. (Auth.) 13 refs., 12 tabs., 3 figs

Review of the cost components of introducing industrially fortified rice.

Science.gov (United States)

Roks, Eveline

2014-09-01

Micronutrient deficiencies affect over two billion people worldwide, particularly in developing countries. Fortification of staple foods with multiple micronutrients is a cost-effective strategy to increase vitamin and mineral intake. The objective of this paper is to review the cost elements of industrially fortified rice by identifying the costs related to the implementation of rice fortification programs, using the experience of the United Nations World Food Programme in its pilot countries. The actual total costs of rice fortification are not easily captured. Core cost elements include the production of fortified rice kernels, transportation to the point of blending, blending of fortified with unfortified rice, costs related to sales or distribution, quality control and assurance, and additional planning. In the introduction phase, organizations or coalitions seeking to advance rice fortification will face additional costs related to the initiation of rice fortification. In the scale-up phase, greater efficiency in the supply chain and economies of scale can be expected. Different cost elements are normally borne by different stakeholders. This makes the implementation of rice fortification programs a feasible option to reach vulnerable populations with inadequate access to affordable nutrition solutions. © 2014 New York Academy of Sciences.

Development of techniques for storing rough rice in cold regions, 1: Storage of rough rice at country elevator with natural heat radiation in winter

International Nuclear Information System (INIS)

Takekura, K.; Kawamura, S.; Itoh, K.

2003-01-01

An on-farm experiment in which 361 metric tons of rough rice was stored in a silo from November until July was conducted at a country elevator in Hokkaido to develop new techniques for storing rough rice in cold regions. The temperature of the rough rice near the inner silo wall decreased to below ice point (-5°C) due to natural heat radiation in winter, which the temperature of the rough rice in the center of the silo was maintained at almost the same temperature as that at the beginning of storage (5°C). Ventilation in the upper vacant space of the silo prevented moisture condensation on the inside surface of the silo during storage. When the cold rough rice was unloaded from the silo in summer, an unheated forced-air drier was used to increase the temperature of rough rice to above the dew point temperature of surrounding air. During the unloading and rewarming process, the moisture content of the rough rice increased due to moisture condensation on the grain from the air. However, the husks first absorbed the condensation and then the moisture slowly permeated into the brown rice kernel. Thus the rewarming process didn't cause any fissures in the brown rice. The results of the experiment indicate that condensation on rough rice doesn't give rise to any problems

Locally linear approximation for Kernel methods : the Railway Kernel

OpenAIRE

Muñoz, Alberto; González, Javier

2008-01-01

In this paper we present a new kernel, the Railway Kernel, that works properly for general (nonlinear) classification problems, with the interesting property that acts locally as a linear kernel. In this way, we avoid potential problems due to the use of a general purpose kernel, like the RBF kernel, as the high dimension of the induced feature space. As a consequence, following our methodology the number of support vectors is much lower and, therefore, the generalization capab...

An Extreme Learning Machine Based on the Mixed Kernel Function of Triangular Kernel and Generalized Hermite Dirichlet Kernel

Directory of Open Access Journals (Sweden)

Senyue Zhang

2016-01-01

Full Text Available According to the characteristics that the kernel function of extreme learning machine (ELM and its performance have a strong correlation, a novel extreme learning machine based on a generalized triangle Hermitian kernel function was proposed in this paper. First, the generalized triangle Hermitian kernel function was constructed by using the product of triangular kernel and generalized Hermite Dirichlet kernel, and the proposed kernel function was proved as a valid kernel function of extreme learning machine. Then, the learning methodology of the extreme learning machine based on the proposed kernel function was presented. The biggest advantage of the proposed kernel is its kernel parameter values only chosen in the natural numbers, which thus can greatly shorten the computational time of parameter optimization and retain more of its sample data structure information. Experiments were performed on a number of binary classification, multiclassification, and regression datasets from the UCI benchmark repository. The experiment results demonstrated that the robustness and generalization performance of the proposed method are outperformed compared to other extreme learning machines with different kernels. Furthermore, the learning speed of proposed method is faster than support vector machine (SVM methods.

The Genetic Basis of Natural Variation in Kernel Size and Related Traits Using a Four-Way Cross Population in Maize.

Science.gov (United States)

Chen, Jiafa; Zhang, Luyan; Liu, Songtao; Li, Zhimin; Huang, Rongrong; Li, Yongming; Cheng, Hongliang; Li, Xiantang; Zhou, Bo; Wu, Suowei; Chen, Wei; Wu, Jianyu; Ding, Junqiang

2016-01-01

Kernel size is an important component of grain yield in maize breeding programs. To extend the understanding on the genetic basis of kernel size traits (i.e., kernel length, kernel width and kernel thickness), we developed a set of four-way cross mapping population derived from four maize inbred lines with varied kernel sizes. In the present study, we investigated the genetic basis of natural variation in seed size and other components of maize yield (e.g., hundred kernel weight, number of rows per ear, number of kernels per row). In total, ten QTL affecting kernel size were identified, three of which (two for kernel length and one for kernel width) had stable expression in other components of maize yield. The possible genetic mechanism behind the trade-off of kernel size and yield components was discussed.

Kernel Machine SNP-set Testing under Multiple Candidate Kernels

Science.gov (United States)

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

2013-01-01

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

Influence of leaf vein density and thickness on hydraulic conductance and photosynthesis in rice (Oryza sativa L.) during water stress.

Science.gov (United States)

Tabassum, Muhammad Adnan; Zhu, Guanglong; Hafeez, Abdul; Wahid, Muhammad Atif; Shaban, Muhammad; Li, Yong

2016-11-16

The leaf venation architecture is an ideal, highly structured and efficient irrigation system in plant leaves. Leaf vein density (LVD) and vein thickness are the two major properties of this system. Leaf laminae carry out photosynthesis to harvest the maximum biological yield. It is still unknown whether the LVD and/or leaf vein thickness determines the plant hydraulic conductance (K plant ) and leaf photosynthetic rate (A). To investigate this topic, the current study was conducted with two varieties under three PEG-induced water deficit stress (PEG-IWDS) levels. The results showed that PEG-IWDS significantly decreased A, stomatal conductance (g s ), and K plant in both cultivars, though the IR-64 strain showed more severe decreases than the Hanyou-3 strain. PEG-IWDS significantly decreased the major vein thickness, while it had no significant effect on LVD. A, g s and K plant were positively correlated with each other, and they were negatively correlated with LVD. A, g s and K plant were positively correlated with the inter-vein distance and major vein thickness. Therefore, the decreased photosynthesis and hydraulic conductance in rice plants under water deficit conditions are related to the decrease in the major vein thickness.

Compressive Load Resistance Characteristics of Rice Grain

OpenAIRE

Sumpun Chaitep; Chaiy R. Metha Pathawee; Pipatpong Watanawanyoo

2008-01-01

Investigation was made to observe the compressive load property of rice gain both rough rice and brown grain. Six rice varieties (indica and japonica) were examined with the moisture content at 10-12%. A compressive load with reference to a principal axis normal to the thickness of the grain were conducted at selected inclined angles of 0°, 15°, 30°, 45°, 60° and 70°. The result showed the compressive load resistance of rice grain based on its characteristic of yield s...

In vitro gastric digestion of cooked white and brown rice using a dynamic rat stomach model.

Science.gov (United States)

Wu, Peng; Deng, Renpan; Wu, Xuee; Wang, Yong; Dong, Zhizhong; Dhital, Sushil; Chen, Xiao Dong

2017-12-15

The changes in physical, rheological and enzyme-digestive behaviours of cooked white and brown rice, with similar amylose content, were investigated using a dynamic in vitro rat stomach (DIVRS) model and a static soaking method. The brown rice had a higher resistance on disintegration and lower gastric emptying rate with 53% of the brown rice particles retained in the stomach at the end compared to 32% for the white rice. Furthermore, the release rate of maltose from the starch hydrolysis was higher in the white rice throughout the digestion suggesting the lower glycemic potency of the brown rice. These differences could be contributed from the rigid bran layer in the brown rice which would inhibit the moisture absorption into rice kernels, limit textural degradation, and generate higher gastric digesta viscosity leading to lower mixing and mass transfer efficiency. This study suggests that the structural difference could affect physiochemical properties during gastric digestion. Copyright © 2017 Elsevier Ltd. All rights reserved.

Screening of the kernels of Pentadesma butyracea from various ...

African Journals Online (AJOL)

Gwla10

Joseph D. Hounhouigan. 2. 1Laboratoire de .... laboratory. Kernels were washed and dried at 45°C for 72 h before analysis. ... generated values allow calculating the various shape ... (LLYOD Instruments, USA) fit with a 0.42 cm thick blade with a triangular ... vacuum. Extraction was run in triplicate on germ, albumen and.

Effect of Neem Seed Kernel Extracts in the Management of Rice ...

African Journals Online (AJOL)

user

Rice plant is an ideal host for a large number of insect pests in ... For farmers and researchers alike reducing the numbers of these pests is therefore a priority. In the ..... Development of a rational control system against insect ... Integration of some biopesticides and trichogramma chilonis for the sustainable management of.

Small kernel 1 encodes a pentatricopeptide repeat protein required for mitochondrial nad7 transcript editing and seed development in maize (Zea mays) and rice (Oryza sativa).

Science.gov (United States)

Li, Xiao-Jie; Zhang, Ya-Feng; Hou, Mingming; Sun, Feng; Shen, Yun; Xiu, Zhi-Hui; Wang, Xiaomin; Chen, Zong-Liang; Sun, Samuel S M; Small, Ian; Tan, Bao-Cai

2014-09-01

RNA editing modifies cytidines (C) to uridines (U) at specific sites in the transcripts of mitochondria and plastids, altering the amino acid specified by the DNA sequence. Here we report the identification of a critical editing factor of mitochondrial nad7 transcript via molecular characterization of a small kernel 1 (smk1) mutant in Zea mays (maize). Mutations in Smk1 arrest both the embryo and endosperm development. Cloning of Smk1 indicates that it encodes an E-subclass pentatricopeptide repeat (PPR) protein that is targeted to mitochondria. Loss of SMK1 function abolishes the C → U editing at the nad7-836 site, leading to the retention of a proline codon that is edited to encode leucine in the wild type. The smk1 mutant showed dramatically reduced complex-I assembly and NADH dehydrogenase activity, and abnormal biogenesis of the mitochondria. Analysis of the ortholog in Oryza sativa (rice) reveals that rice SMK1 has a conserved function in C → U editing of the mitochondrial nad7-836 site. T-DNA knock-out mutants showed abnormal embryo and endosperm development, resulting in embryo or seedling lethality. The leucine at NAD7-279 is highly conserved from bacteria to flowering plants, and analysis of genome sequences from many plants revealed a molecular coevolution between the requirement for C → U editing at this site and the existence of an SMK1 homolog. These results demonstrate that Smk1 encodes a PPR-E protein that is required for nad7-836 editing, and this editing is critical to NAD7 function in complex-I assembly in mitochondria, and hence to embryo and endosperm development in maize and rice. © 2014 The Authors The Plant Journal © 2014 John Wiley & Sons Ltd.

Data-variant kernel analysis

CERN Document Server

Motai, Yuichi

2015-01-01

Describes and discusses the variants of kernel analysis methods for data types that have been intensely studied in recent years This book covers kernel analysis topics ranging from the fundamental theory of kernel functions to its applications. The book surveys the current status, popular trends, and developments in kernel analysis studies. The author discusses multiple kernel learning algorithms and how to choose the appropriate kernels during the learning phase. Data-Variant Kernel Analysis is a new pattern analysis framework for different types of data configurations. The chapters include

Effects of soaking and acidification on physicochemical properties of calcium-fortified rice.

Science.gov (United States)

Sirisoontaralak, Porntip; Limboon, Pailin; Jatuwong, Sujitra; Chavanalikit, Arusa

2016-06-01

Calcium-fortified rice was prepared by soaking milled rice in calcium lactate solution, steaming and drying, and physicochemical properties were determined to evaluate effects of calcium concentration (0, 30, 50 g L(-1) ), soaking temperature (ambient temperature, 40 °C, 60 °C) and acidification. Calcium-fortified rice had less lightness. More total solid loss was observed, especially at high soaking temperature. Harder texture was detected with increased calcium concentration. Calcium fortification lowered pasting viscosity of milled rice. Panelists accepted all fortified rice; however, only rice soaked at 50 g L(-1) concentration could be claimed as a good source of calcium. Increasing of soaking temperature induced more penetration of calcium to rice kernels but calcium was lost more easily after washing. With addition of acetic acid to the soaking solution, enriched calcium content was comparable to that of high soaking temperature but with better retention after washing and calcium solubility was improved. Acid induced reduction of lightness and cooked rice hardness but increased total solid loss and pasting viscosity. Although the taste of acetic acid remained, panelists still accepted the fortified rice. Calcium-fortified rice (190.47-194.3 mg 100 g(-1) ) could be successfully produced by soaking milled rice in 50 g L(-1) calcium lactate solution at 40 °C or at ambient temperature with acidification. © 2015 Society of Chemical Industry. © 2015 Society of Chemical Industry.

Biodiesel from “Morelos” Rice: Synthesis, Oxidative Stability, and Corrosivity

Directory of Open Access Journals (Sweden)

J. Zuñiga-Díaz

2018-01-01

Full Text Available Rice bran is a by-product of great production worldwide and its use for the synthesis of biodiesel does not affect the food chain and therefore it is an excellent alternative for the production of biofuels with low carbon footprint. In this work, the synthesis of biodiesel was carried out from the raw rice bran oil of a kernel variety called “Morelos rice.” The stability and corrosivity characteristics of biodiesel were determined. Biodiesel stability was determined both under storage conditions and under accelerated oxidation conditions, and its corrosivity was evaluated by electrochemical impedance spectroscopy at 110°C under aerated conditions. The results showed that, due to the high instability of the rice bran, its raw oil had a high content of free fatty acids. The synthesized biodiesel showed excellent stability under storage conditions of up to five months, and its oxidative stability was much higher than that established in international standards. On the other hand, biodiesel showed low corrosivity and this was only significant once oxidative degradation began.

Scatter kernel estimation with an edge-spread function method for cone-beam computed tomography imaging

International Nuclear Information System (INIS)

Li Heng; Mohan, Radhe; Zhu, X Ronald

2008-01-01

The clinical applications of kilovoltage x-ray cone-beam computed tomography (CBCT) have been compromised by the limited quality of CBCT images, which typically is due to a substantial scatter component in the projection data. In this paper, we describe an experimental method of deriving the scatter kernel of a CBCT imaging system. The estimated scatter kernel can be used to remove the scatter component from the CBCT projection images, thus improving the quality of the reconstructed image. The scattered radiation was approximated as depth-dependent, pencil-beam kernels, which were derived using an edge-spread function (ESF) method. The ESF geometry was achieved with a half-beam block created by a 3 mm thick lead sheet placed on a stack of slab solid-water phantoms. Measurements for ten water-equivalent thicknesses (WET) ranging from 0 cm to 41 cm were taken with (half-blocked) and without (unblocked) the lead sheet, and corresponding pencil-beam scatter kernels or point-spread functions (PSFs) were then derived without assuming any empirical trial function. The derived scatter kernels were verified with phantom studies. Scatter correction was then incorporated into the reconstruction process to improve image quality. For a 32 cm diameter cylinder phantom, the flatness of the reconstructed image was improved from 22% to 5%. When the method was applied to CBCT images for patients undergoing image-guided therapy of the pelvis and lung, the variation in selected regions of interest (ROIs) was reduced from >300 HU to <100 HU. We conclude that the scatter reduction technique utilizing the scatter kernel effectively suppresses the artifact caused by scatter in CBCT.

Approximate kernel competitive learning.

Science.gov (United States)

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

2015-03-01

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

Evaluation of the antioxidant activity of rice bran extracts using different antioxidant assays

Directory of Open Access Journals (Sweden)

Rehman Bajwa, Jawad -ur-

2006-09-01

Full Text Available In the present work the antioxidant activity of different solvent (100% methanol, 80% methanol, 100% acetone, 80% acetone extracts of rice bran was evaluated following different antioxidant assays and using sunflower oil as oxidation substrate. The rice bran extracts were evaluated from the estimate of % inhibition of peroxidation in linoleic acid system, total phenolics content (TPC and loss of β-carotene in a linoleic acid system. Additionally, crude concentrated rice bran extracts were added into the sunflower oil samples and stored under ambient conditions. The extent of oxidative deterioration was followed by the measurement of peroxide-, p-anisidine-, conjugated diene-, and triene- values. The general order of antioxidant efficacy of rice bran extracts as determined by various antioxidant assays was 80% methanolic extract > 100% methanolic  extract > 80% acetone extract > 100% acetone extract. The results of the present comprehensive analysis demonstrate that rice bran extracts of the Super Kernel variety indigenous to Pakistan are a viable source of natural antioxidants and might be exploited for functional foods and nutraceutical applications.Se evalúa la actividad antioxidante diferentes extractos (100% metanol, 80% metanol, 100% acetona and 80% acetona de salvado de arroz -var. Super Kernel- mediante diferentes ensayos y utilizando aceite de girasol como substrato. Los ensayos utilizados fueron la estimación del % de inhibición de la peroxidación en sistemas con ácido linoleico, el contenido total en compuestos fenólicos y la pérdida de β-caroteno en sistemas con ácido linoleico. Adicionalmente, los concentrados de extractos de salvado de arroz se añadieron a aceite de girasol y las muestras se almacenaron a temperatura ambiente. La extensión de la oxidación se evaluó mediante el índice de peróxidos, el índice de p-anisidina, así como la formación de dienos y trienos conjugados. El orden de la eficacia antioxidante

Classification With Truncated Distance Kernel.

Science.gov (United States)

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

2018-05-01

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

Exact Heat Kernel on a Hypersphere and Its Applications in Kernel SVM

Directory of Open Access Journals (Sweden)

Chenchao Zhao

2018-01-01

Full Text Available Many contemporary statistical learning methods assume a Euclidean feature space. This paper presents a method for defining similarity based on hyperspherical geometry and shows that it often improves the performance of support vector machine compared to other competing similarity measures. Specifically, the idea of using heat diffusion on a hypersphere to measure similarity has been previously proposed and tested by Lafferty and Lebanon [1], demonstrating promising results based on a heuristic heat kernel obtained from the zeroth order parametrix expansion; however, how well this heuristic kernel agrees with the exact hyperspherical heat kernel remains unknown. This paper presents a higher order parametrix expansion of the heat kernel on a unit hypersphere and discusses several problems associated with this expansion method. We then compare the heuristic kernel with an exact form of the heat kernel expressed in terms of a uniformly and absolutely convergent series in high-dimensional angular momentum eigenmodes. Being a natural measure of similarity between sample points dwelling on a hypersphere, the exact kernel often shows superior performance in kernel SVM classifications applied to text mining, tumor somatic mutation imputation, and stock market analysis.

Subsampling Realised Kernels

DEFF Research Database (Denmark)

Barndorff-Nielsen, Ole Eiler; Hansen, Peter Reinhard; Lunde, Asger

2011-01-01

In a recent paper we have introduced the class of realised kernel estimators of the increments of quadratic variation in the presence of noise. We showed that this estimator is consistent and derived its limit distribution under various assumptions on the kernel weights. In this paper we extend our...... that subsampling is impotent, in the sense that subsampling has no effect on the asymptotic distribution. Perhaps surprisingly, for the efficient smooth kernels, such as the Parzen kernel, we show that subsampling is harmful as it increases the asymptotic variance. We also study the performance of subsampled...

Kernel abortion in maize. II. Distribution of 14C among kernel carboydrates

International Nuclear Information System (INIS)

Hanft, J.M.; Jones, R.J.

1986-01-01

This study was designed to compare the uptake and distribution of 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 0 C were transferred to [ 14 C]sucrose media 10 days after pollination. Kernels cultured at 35 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 [ 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 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 0 C compared to kernels cultured at 30 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 0 C had a much lower proportion of label associated with endosperm starch (29%) than did kernels cultured at 30 0 C (89%). Kernels cultured at 35 0 C had a correspondingly higher proportion of 14 C in endosperm fructose, glucose, and sucrose

Piloting a Commercial Model for Fortified Rice: Lessons Learned From Brazil.

Science.gov (United States)

Milani, Peiman; Spohrer, Rebecca; Garrett, Greg; Kreis, Katharine

2016-05-18

Two billion people worldwide have micronutrient deficiencies. Food fortification is a proven intervention to increase essential micronutrient availability in diets without requiring consumer behavioral change. Fortification of rice has high potential reach; however, cost, technology, market, and cultural constraints have prevented its wider adoption. From 2010 to 2014, PATH and Global Alliance for Improved Nutrition implemented a pilot project in Brazil testing a model to scale up rice fortification through commercial channels. The project focused on 5 areas: (1) building fortified rice kernel production capacity; (2) supply chain development; (3) distribution channel and market development; (4) demand generation; and (5) advocacy and knowledge dissemination. Primary data were collected in 2 rounds of quantitative research 6 months apart and conducted in 2 regions in Brazil. Secondary data were sourced from published literature, socioeconomic and demographic data, and sales figures from the project's rice miller partner. Postmortem analysis was conducted by the project team with input from external sources. Although the project successfully launched a fortified rice product and a category brand platform, it was unsuccessful in reaching meaningful scale. Market and industry dynamics affected producers' willingness to launch new fortified products. Consumers' strong attachment to rice combined with a weak understanding of micronutrient malnutrition hampered demand creation efforts. This project showed that a purely commercial approach is insufficient for sustainable scale-up of fortified rice to achieve public health goals in a 3- to 5-year period. © The Author(s) 2016.

Optimized Kernel Entropy Components.

Science.gov (United States)

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

2017-06-01

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

Neglecting rice milling yield and quality underestimates economic losses from high-temperature stress.

Directory of Open Access Journals (Sweden)

Nathaniel B Lyman

Full Text Available Future increases in global surface temperature threaten those worldwide who depend on rice production for their livelihoods and food security. Past analyses of high-temperature stress on rice production have focused on paddy yield and have failed to account for the detrimental impact of high temperatures on milling quality outcomes, which ultimately determine edible (marketable rice yield and market value. Using genotype specific rice yield and milling quality data on six common rice varieties from Arkansas, USA, combined with on-site, half-hourly and daily temperature observations, we show a nonlinear effect of high-temperature stress exposure on yield and milling quality. A 1 °C increase in average growing season temperature reduces paddy yield by 6.2%, total milled rice yield by 7.1% to 8.0%, head rice yield by 9.0% to 13.8%, and total milling revenue by 8.1% to 11.0%, across genotypes. Our results indicate that failure to account for changes in milling quality leads to understatement of the impacts of high temperatures on rice production outcomes. These dramatic losses result from reduced paddy yield and increased percentages of chalky and broken kernels, which together decrease the quantity and market value of milled rice. Recently published estimates show paddy yield reductions of up to 10% across the major rice-producing regions of South and Southeast Asia due to rising temperatures. The results of our study suggest that the often-cited 10% figure underestimates the economic implications of climate change for rice producers, thus potentially threatening future food security for global rice producers and consumers.

It is only a banana-Traveltime sensitivity kernels using the unwrapped phase

KAUST Repository

Djebbi, Ramzi; Alkhalifah, Tariq Ali

2012-01-01

Traveltime sensitivity kernels for finite-frequency traveltimes computed using the Born or Rytov approximations admits hallow banana shaped responses in the plane of propagation and a circular doughnut shaped responses in the cross section. This suggests that finite-frequency traveltimes are insensitive to velocity information along the infinite-frequency ray path, which is obviously inaccurate and creates a disconnect in the traveltime dependency on frequency. Using the instantaneous traveltime of the wavefield, which is capable of unwrapping the phase function, we obtain traveltime sensitivity kernels that have plain banana shape responses, with the thickness of the banana governed by the investigated frequency. This result confirms that the hallow banana shape is simply a result of the wrapping of the phase of the wavefield, in which Born nor Rytov approximations can properly deal with. The instantaneous traveltime can, thus, mitigate the nonlinearity problem encountered in finite-frequency traveltime inversions that may arise from these hallow banana sensitivity kernels.

Effect of increased plant density and fertilizer dose on the yield of rice variety IR-6

International Nuclear Information System (INIS)

Amin, M.; Khan, M.A.; Khan, E.A.; Ramazan, M.

2004-01-01

An experiment to evaluate the effect of increased plant density and fertilizer dose on yield of rice variety IR-6 was conducted at the farm of Faculty of Agriculture, Gomal University Dera Ismail Khan. Increase plant density significantly increase number of panicles per square meter, sterility and straw yield while increased fertilizer dose of NPK increase plant height, sterility, normal kernels, and 1000 grain weight. Interaction of increased plant density and fertilizer dose was found to be non significant except sterility percentage and straw yield. However efforts are required for increasing yield per unit area of rice. (author)

A novel adaptive kernel method with kernel centers determined by a support vector regression approach

NARCIS (Netherlands)

Sun, L.G.; De Visser, C.C.; Chu, Q.P.; Mulder, J.A.

2012-01-01

The optimality of the kernel number and kernel centers plays a significant role in determining the approximation power of nearly all kernel methods. However, the process of choosing optimal kernels is always formulated as a global optimization task, which is hard to accomplish. Recently, an

Determination of Optimal Harvest Time of Chuchung Variety Green Rice(®) (Oryza sativa L.) with High Contents of GABA, γ-Oryzanol, and α-Tocopherol.

Science.gov (United States)

Kim, Hoon; Kim, Oui-Woung; Ha, Ae Wha; Park, Soojin

2016-06-01

In our previous study, an early-maturing variety of rice (Oryza sativa L.), Jinbu can have feature with unique green color, various phytochemicals as well as nutritive components by the optimal early harvesting, called Green Rice(®) (GR). The aims of the present field experiments were to evaluate the changes in the weight of 1,000 kernels, yield, and contents of proximate and bioactive compounds in Chuchung, a mid-late maturing variety, during the pre-harvest maturation of rough rice and to research the appropriate harvest time and potent bioactivity of Chuchung GR. The weights of 1,000 kernels of Chuchung GR dramatically increased until 27 days after heading (DAH). The yields of Chuchung GR declined after 27 DAH and significantly declined to 0.0% after 45 DAH. The caloric value and total mineral contents were higher in the GR than in the full ripe stage, the brown rice (BR). In the GR, the contents of bioactive compounds, such as γ-aminobutyric acid, γ-oryzanol, and α-tocopherol, were much higher (P<0.05) than those in the BR, specifically during 24~27 DAH. Therefore, bioactive Chuchung GR can be produced with a reasonable yield at 24~27 DAH and it could be useful for applications in various nutritive and functional food products.

Determination of Optimal Harvest Time of Chuchung Variety Green Rice® (Oryza sativa L.) with High Contents of GABA, γ-Oryzanol, and α-Tocopherol

Science.gov (United States)

Kim, Hoon; Kim, Oui-Woung; Ha, Ae Wha; Park, Soojin

2016-01-01

In our previous study, an early-maturing variety of rice (Oryza sativa L.), Jinbu can have feature with unique green color, various phytochemicals as well as nutritive components by the optimal early harvesting, called Green Rice® (GR). The aims of the present field experiments were to evaluate the changes in the weight of 1,000 kernels, yield, and contents of proximate and bioactive compounds in Chuchung, a mid-late maturing variety, during the pre-harvest maturation of rough rice and to research the appropriate harvest time and potent bioactivity of Chuchung GR. The weights of 1,000 kernels of Chuchung GR dramatically increased until 27 days after heading (DAH). The yields of Chuchung GR declined after 27 DAH and significantly declined to 0.0% after 45 DAH. The caloric value and total mineral contents were higher in the GR than in the full ripe stage, the brown rice (BR). In the GR, the contents of bioactive compounds, such as γ-aminobutyric acid, γ-oryzanol, and α-tocopherol, were much higher (P<0.05) than those in the BR, specifically during 24~27 DAH. Therefore, bioactive Chuchung GR can be produced with a reasonable yield at 24~27 DAH and it could be useful for applications in various nutritive and functional food products. PMID:27390725

Protein Subcellular Localization with Gaussian Kernel Discriminant Analysis and Its Kernel Parameter Selection.

Science.gov (United States)

Wang, Shunfang; Nie, Bing; Yue, Kun; Fei, Yu; Li, Wenjia; Xu, Dongshu

2017-12-15

Kernel discriminant analysis (KDA) is a dimension reduction and classification algorithm based on nonlinear kernel trick, which can be novelly used to treat high-dimensional and complex biological data before undergoing classification processes such as protein subcellular localization. Kernel parameters make a great impact on the performance of the KDA model. Specifically, for KDA with the popular Gaussian kernel, to select the scale parameter is still a challenging problem. Thus, this paper introduces the KDA method and proposes a new method for Gaussian kernel parameter selection depending on the fact that the differences between reconstruction errors of edge normal samples and those of interior normal samples should be maximized for certain suitable kernel parameters. Experiments with various standard data sets of protein subcellular localization show that the overall accuracy of protein classification prediction with KDA is much higher than that without KDA. Meanwhile, the kernel parameter of KDA has a great impact on the efficiency, and the proposed method can produce an optimum parameter, which makes the new algorithm not only perform as effectively as the traditional ones, but also reduce the computational time and thus improve efficiency.

Some physical and mechanical properties of palm kernel shell (PKS ...

African Journals Online (AJOL)

In this study, some of the mechanical and physical properties of palm kernel shells (PKS) were evaluated. These are moisture content, 7.8325 ± 0.6672%; true density, 1.254 ± 5.292 x 10-3 g/cm3; bulk density, 1.1248g/cm3; mean rupture force along width, and thickness were 3174.52 ± 270.70N and 2806.94 ± 498.45N for ...

7 CFR 981.7 - Edible kernel.

Science.gov (United States)

2010-01-01

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

Kernel versions of some orthogonal transformations

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg

Kernel versions of orthogonal transformations such as principal components 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 this kernel...... function. This means that we need not know the nonlinear mappings explicitly. Kernel principal component analysis (PCA) and kernel minimum noise fraction (MNF) analyses handle nonlinearities by implicitly transforming data into high (even infinite) dimensional feature space via the kernel function...

Model Selection in Kernel Ridge Regression

DEFF Research Database (Denmark)

Exterkate, Peter

Kernel ridge regression is gaining popularity as a data-rich nonlinear forecasting tool, which is applicable in many different contexts. This paper investigates the influence of the choice of kernel and the setting of tuning parameters on forecast accuracy. We review several popular kernels......, including polynomial kernels, the Gaussian kernel, and the Sinc kernel. We interpret the latter two kernels in terms of their smoothing properties, and we relate the tuning parameters associated to all these kernels to smoothness measures of the prediction function and to the signal-to-noise ratio. Based...... on these interpretations, we provide guidelines for selecting the tuning parameters from small grids using cross-validation. A Monte Carlo study confirms the practical usefulness of these rules of thumb. Finally, the flexible and smooth functional forms provided by the Gaussian and Sinc kernels makes them widely...

Penetuan Bilangan Iodin pada Hydrogenated Palm Kernel Oil (HPKO) dan Refined Bleached Deodorized Palm Kernel Oil (RBDPKO)

OpenAIRE

Sitompul, Monica Angelina

2015-01-01

Have been conducted Determination of Iodin Value by method titration to some Hydrogenated Palm Kernel Oil (HPKO) and Refined Bleached Deodorized Palm Kernel Oil (RBDPKO). The result of analysis obtained the Iodin Value in Hydrogenated Palm Kernel Oil (A) = 0,16 gr I2/100gr, Hydrogenated Palm Kernel Oil (B) = 0,20 gr I2/100gr, Hydrogenated Palm Kernel Oil (C) = 0,24 gr I2/100gr. And in Refined Bleached Deodorized Palm Kernel Oil (A) = 17,51 gr I2/100gr, Refined Bleached Deodorized Palm Kernel ...

7 CFR 981.8 - Inedible kernel.

Science.gov (United States)

2010-01-01

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

7 CFR 981.408 - Inedible kernel.

Science.gov (United States)

2010-01-01

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

Model selection in kernel ridge regression

DEFF Research Database (Denmark)

Exterkate, Peter

2013-01-01

Kernel ridge regression is a technique to perform ridge regression with a potentially infinite number of nonlinear transformations of the independent variables as regressors. This method is gaining popularity as a data-rich nonlinear forecasting tool, which is applicable in many different contexts....... The influence of the choice of kernel and the setting of tuning parameters on forecast accuracy is investigated. Several popular kernels are reviewed, including polynomial kernels, the Gaussian kernel, and the Sinc kernel. The latter two kernels are interpreted in terms of their smoothing properties......, and the tuning parameters associated to all these kernels are related to smoothness measures of the prediction function and to the signal-to-noise ratio. Based on these interpretations, guidelines are provided for selecting the tuning parameters from small grids using cross-validation. A Monte Carlo study...

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

Science.gov (United States)

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

2018-05-07

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

Viscosity kernel of molecular fluids

DEFF Research Database (Denmark)

Puscasu, Ruslan; Todd, Billy; Daivis, Peter

2010-01-01

, temperature, and chain length dependencies of the reciprocal and real-space viscosity kernels are presented. We find that the density has a major effect on the shape of the kernel. The temperature range and chain lengths considered here have by contrast less impact on the overall normalized shape. Functional...... forms that fit the wave-vector-dependent kernel data over a large density and wave-vector range have also been tested. Finally, a structural normalization of the kernels in physical space is considered. Overall, the real-space viscosity kernel has a width of roughly 3–6 atomic diameters, which means...

Effect of Intermittent Drying Conditions on Fissuring Percentage and Process Duration of Long and Medium Rough Rice Varieties

Directory of Open Access Journals (Sweden)

A. Ghasemi

2016-02-01

Full Text Available One of the factors which affect the quality of rice during milling is internal fissures created during and after drying operation. In many industrial countries intermittent drying method is hired to reduce the moisture content of rough rice in order to reduce the drying time and maintain the quality of the final product. A high percentage of rice breakage during milling process, at least in Iran, necessitates performing the intermittent drying process and optimize it for Iranian varieties. In this study, the effect of this method (drying-tempering and continuous drying method (no tempering on fissuring percentage of Hashemi (long grain and Koohsar (medium grain varieties was investigated. The experiments were carried out at constant drying and tempering temperature of 60 °C, drying durations of 20, 40 and 60 min, and tempering durations of 0 (continuous drying, 40, 80, 120, 160, 200, 240 min. The results revealed that the tempering process significantly reduced the drying time and fissured kernels percentage. Moreover, for both varieties it was observed that the rice fissuring decreased significantly by continuing the tempering process until certain durations. Overall, for optimization of intermittent drying process in terms of the considered qualitative parameters, i.e. reducing energy consumption and losses, conducting 160 and 200 min tempering process after 40 min drying was found appropriate for Hashemi and Koohsar varieties, respectively. In addition, according to the higher fissuring for Koohsar (medium grain compared to Hashemi (long grain, it can be concluded that physical properties such as kernel slenderness ratio is effective on its fissuring.

Kernel learning algorithms for face recognition

CERN Document Server

Li, Jun-Bao; Pan, Jeng-Shyang

2013-01-01

Kernel Learning Algorithms for Face Recognition covers the framework of kernel based face recognition. This book discusses the advanced kernel learning algorithms and its application on face recognition. This book also focuses on the theoretical deviation, the system framework and experiments involving kernel based face recognition. Included within are algorithms of kernel based face recognition, and also the feasibility of the kernel based face recognition method. This book provides researchers in pattern recognition and machine learning area with advanced face recognition methods and its new

Results from ORNL Characterization of Nominal 350 ?m NUCO Kernels from the BWXT 69300 Composite

International Nuclear Information System (INIS)

Hunn, John D.

2004-01-01

This document is a compilation of characterization data obtained on the nominal 350 (micro)m natural enrichment uranium oxide/uranium carbide kernels (NUCO) produced by BWXT for the Advanced Gas Reactor Fuel dEvelopment and Qualification Program. 5 kg of kernels were produced. G73B-NU-69300R was a 4.9 kg composite. G73B-NU-69301 was a 100 g composite. Size, shape, density, and microstructural analysis were performed on samples riffled from a 100 g sublot (69300R-38) riffled by BWXT from the 69300 composite. Measurements were made using optical microscopy to determine the size and shape of the kernels. Hg porosimetry was performed to measure density. The results are summarized in Table 1-1. Values in the table are for the composite and are calculated at 95% confidence from the measured values of a random sample taken from the 69300R-38 sublot. The NUCO kernel composite met all the specifications in Table 1-1 except the aspect ratio specification. This failure was due in part to broken kernels and in part to very irregularly shaped (bumpy) kernels which apparently came from one batch used for the composite. This abnormally shaped batch made up about 1/4 of the composite. The average open porosity of the kernels was fairly low (0.34 ± 0.14%). There appeared to be some closed porosity throughout the kernels but a quantitative measure was not obtained. A brief study of the microstructure of the kernels in the composite showed an oxide outer layer of varying thickness related to the process batch surrounding a center region of carbide and oxide zones. X-ray diffraction showed a phase distribution of around 69-74 wt% oxide versus 26-31 wt% carbide. Most of the carbide was in the form of uranium monocarbide (UC).

Partial Deconvolution with Inaccurate Blur Kernel.

Science.gov (United States)

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

2017-10-17

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

Kernel methods for deep learning

OpenAIRE

Cho, Youngmin

2012-01-01

We introduce a new family of positive-definite kernels that mimic the computation in large neural networks. We derive the different members of this family by considering neural networks with different activation functions. Using these kernels as building blocks, we also show how to construct other positive-definite kernels by operations such as composition, multiplication, and averaging. We explore the use of these kernels in standard models of supervised learning, such as support vector mach...

7 CFR 981.9 - Kernel weight.

Science.gov (United States)

2010-01-01

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

Veto-Consensus Multiple Kernel Learning

NARCIS (Netherlands)

Zhou, Y.; Hu, N.; Spanos, C.J.

2016-01-01

We propose Veto-Consensus Multiple Kernel Learning (VCMKL), a novel way of combining multiple kernels such that one class of samples is described by the logical intersection (consensus) of base kernelized decision rules, whereas the other classes by the union (veto) of their complements. The

7 CFR 51.2295 - Half kernel.

Science.gov (United States)

2010-01-01

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

QTL Analysis of Kernel-Related Traits in Maize Using an Immortalized F2 Population

Science.gov (United States)

Hu, Yanmin; Li, Weihua; Fu, Zhiyuan; Ding, Dong; Li, Haochuan; Qiao, Mengmeng; Tang, Jihua

2014-01-01

Kernel size and weight are important determinants of grain yield in maize. In this study, multivariate conditional and unconditional quantitative trait loci (QTL), and digenic epistatic analyses were utilized in order to elucidate the genetic basis for these kernel-related traits. Five kernel-related traits, including kernel weight (KW), volume (KV), length (KL), thickness (KT), and width (KWI), were collected from an immortalized F2 (IF2) maize population comprising of 243 crosses performed at two separate locations over a span of two years. A total of 54 unconditional main QTL for these five kernel-related traits were identified, many of which were clustered in chromosomal bins 6.04–6.06, 7.02–7.03, and 10.06–10.07. In addition, qKL3, qKWI6, qKV10a, qKV10b, qKW10a, and qKW7a were detected across multiple environments. Sixteen main QTL were identified for KW conditioned on the other four kernel traits (KL, KWI, KT, and KV). Thirteen main QTL were identified for KV conditioned on three kernel-shape traits. Conditional mapping analysis revealed that KWI and KV had the strongest influence on KW at the individual QTL level, followed by KT, and then KL; KV was mostly strongly influenced by KT, followed by KWI, and was least impacted by KL. Digenic epistatic analysis identified 18 digenic interactions involving 34 loci over the entire genome. However, only a small proportion of them were identical to the main QTL we detected. Additionally, conditional digenic epistatic analysis revealed that the digenic epistasis for KW and KV were entirely determined by their constituent traits. The main QTL identified in this study for determining kernel-related traits with high broad-sense heritability may play important roles during kernel development. Furthermore, digenic interactions were shown to exert relatively large effects on KL (the highest AA and DD effects were 4.6% and 6.7%, respectively) and KT (the highest AA effects were 4.3%). PMID:24586932

Comparative study on nutritional and sensory quality of barnyard and foxtail millet food products with traditional rice products.

Science.gov (United States)

Verma, Suman; Srivastava, Sarita; Tiwari, Neha

2015-08-01

Millets have the potential to contribute to food security and nutrition, but still these are underutilized crops. The present study was undertaken with a view to analyse the physico-chemical, functional and nutritional composition of foxtail millet, barnyard millet and rice and to compare the sensory quality and nutritive value of food products from foxtail and barnyard millet with rice. Analysis of physico- chemical and functional characteristics revealed that the thousand kernel weight of foxtail millet, barnyard millet and rice was 2.5, 3.0 and 18.3 g, respectively and thousand kernel volume was 1.6, 13 2.0 and 7.1 ml, respectively. The water absorption capacity of foxtail millet, barnyard millet and rice was 1.90, 1.96 and 1.98 ml/g, respectively and water solubility index was 2.8, 1.2 and 1.0 %, respectively. Viscosity was measured for foxtail millet (1650.6 cps), barnyard millet (1581 cps) and rice (1668.3 cps). Analysis of nutritional composition showed that the moisture content of foxtail millet, barnyard millet and rice was 9.35, 11.93 and 11.91 %, respectively. The total ash, crude protein, crude fat, crude fibre and carbohydrate of foxtail millet were 3.10, 10.29, 3.06, 4.25 and 69.95 %, respectively, for barnyard millet were 4.27, 6.93, 2.02, 2.98 and 71.87 %, respectively and the corresponding values for rice were 0.59, 6.19, 0.53, 0.21 and 80.58 %, respectively. The energy value for foxtail millet, barnyard millet and rice was 349, 407 and 352 Kcal, respectively. The foxtail millet contained 30.10 mg/100 g calcium and 3.73 mg/100 g iron whereas barnyard millet contained 23.16 mg/100 g calcium and 6.91 mg/100 g iron. Values of 10 mg/100 g calcium and 0.10 mg/100 g iron were observed for rice. The formulated products viz. laddu, halwa and biryani from foxtail millet, barnyard millet and rice (control) were analysed for their sensory qualities. Among the products prepared, there was non significant difference with regard to the

An Approximate Approach to Automatic Kernel Selection.

Science.gov (United States)

Ding, Lizhong; Liao, Shizhong

2016-02-02

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

Iterative software kernels

Energy Technology Data Exchange (ETDEWEB)

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`.

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

Science.gov (United States)

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

2018-05-01

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

A kernel version of spatial factor analysis

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg

2009-01-01

. Schölkopf et al. introduce kernel PCA. Shawe-Taylor and Cristianini is an excellent reference for kernel methods in general. Bishop and Press et al. describe kernel methods among many other subjects. Nielsen and Canty use kernel PCA to detect change in univariate airborne digital camera images. The kernel...... version of PCA handles nonlinearities by implicitly transforming data into high (even infinite) dimensional feature space via the kernel function and then performing a linear analysis in that space. In this paper we shall apply kernel versions of PCA, maximum autocorrelation factor (MAF) analysis...

7 CFR 51.1441 - Half-kernel.

Science.gov (United States)

2010-01-01

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

Analysis of Advanced Fuel Kernel Technology

International Nuclear Information System (INIS)

Oh, Seung Chul; Jeong, Kyung Chai; Kim, Yeon Ku; Kim, Young Min; Kim, Woong Ki; Lee, Young Woo; Cho, Moon Sung

2010-03-01

The reference fuel for prismatic reactor concepts is based on use of an LEU UCO TRISO fissile particle. This fuel form was selected in the early 1980s for large high-temperature gas-cooled reactor (HTGR) concepts using LEU, and the selection was reconfirmed for modular designs in the mid-1980s. Limited existing irradiation data on LEU UCO TRISO fuel indicate the need for a substantial improvement in performance with regard to in-pile gaseous fission product release. Existing accident testing data on LEU UCO TRISO fuel are extremely limited, but it is generally expected that performance would be similar to that of LEU UO 2 TRISO fuel if performance under irradiation were successfully improved. Initial HTGR fuel technology was based on carbide fuel forms. In the early 1980s, as HTGR technology was transitioning from high-enriched uranium (HEU) fuel to LEU fuel. An initial effort focused on LEU prismatic design for large HTGRs resulted in the selection of UCO kernels for the fissile particles and thorium oxide (ThO 2 ) for the fertile particles. The primary reason for selection of the UCO kernel over UO 2 was reduced CO pressure, allowing higher burnup for equivalent coating thicknesses and reduced potential for kernel migration, an important failure mechanism in earlier fuels. A subsequent assessment in the mid-1980s considering modular HTGR concepts again reached agreement on UCO for the fissile particle for a prismatic design. In the early 1990s, plant cost-reduction studies led to a decision to change the fertile material from thorium to natural uranium, primarily because of a lower long-term decay heat level for the natural uranium fissile particles. Ongoing economic optimization in combination with anticipated capabilities of the UCO particles resulted in peak fissile particle burnup projection of 26% FIMA in steam cycle and gas turbine concepts

Local Observed-Score Kernel Equating

Science.gov (United States)

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…

Credit scoring analysis using kernel discriminant

Science.gov (United States)

Widiharih, T.; Mukid, M. A.; Mustafid

2018-05-01

Credit scoring model is an important tool for reducing the risk of wrong decisions when granting credit facilities to applicants. This paper investigate the performance of kernel discriminant model in assessing customer credit risk. Kernel discriminant analysis is a non- parametric method which means that it does not require any assumptions about the probability distribution of the input. The main ingredient is a kernel that allows an efficient computation of Fisher discriminant. We use several kernel such as normal, epanechnikov, biweight, and triweight. The models accuracy was compared each other using data from a financial institution in Indonesia. The results show that kernel discriminant can be an alternative method that can be used to determine who is eligible for a credit loan. In the data we use, it shows that a normal kernel is relevant to be selected for credit scoring using kernel discriminant model. Sensitivity and specificity reach to 0.5556 and 0.5488 respectively.

Milling, Nutritional, Physical and Cooking Properties of Four Basmati Rice Varieties

Directory of Open Access Journals (Sweden)

Pravin Ojha

2018-05-01

Full Text Available Rice is one of the most popular staple foods produced contributing higher most in agriculture gross domestic production in Nepal. Thus, nutritional, physicochemical, and cooking properties of rice might interplay important roles in their production and farming practice, therefore, it is inevitable to understand these characteristic features. However, there has been only limited information available on such properties, therefore we aimed to examine nutritional, physicochemical and cooking properties of four Basmati varieties of rice namely Red Basmati, White Basmati, Black Basmati and Pokhareli Basmati. These rice varieties were purchased from different places in Nepal in paddy form. In this study various parameters associated with milling, nutritional, physical and cooking properties were evaluated. To measure protein contents in rice, Kjeldal method was implied. Among the varieties, the protein content was maximum in Red Basmati (7.74% and minimum in Black Basmati (6.51%. The milled rice percentage and head rice recovery were maximum in Pokhareli Basmati represented by 72.02±0.10 and 67.46±0.42, respectively, while and minimum in White Basmati represented by 68.17±0.50 and 65.11±0.28, respectively. The kernel elongation ratio and volume expansion ratio was maximum in Red Basmati represented by 1.62 and 2.85 respectively. Water uptake ratio was maximum 3.11 in Black Basmati and minimum of 2.18 in Red Basmati. Gruel loss was found lowest 1.05% in Red Basmati and highest represented by 2.40% in Black Basmati. The highest starch iodine blue value of 0.21 was observed in Red Basmati and lowest of 0.12 in Black Basmati. The Red Basmati was found to have the better cooking quality among all varieties.

Effect of rice bran supplementation on cookie baking quality

International Nuclear Information System (INIS)

Younis, A.; Bhatti, M.S.; Ahmed, A

2011-01-01

Rice bran, a by-product obtained during polishing of un-milled rice, contains a large quantity of essential nutrients such as minerals, vitamins, fiber, amino acids and antioxidants. Supplementation of rice bran in cookies can improve their nutritional value. In the present study, cookies were prepared from wheat flour with supplementation of rice bran at the rate of 5, 10, 15 and 20 percent. The rice bran was stabilized with acid and dry heat treatment before supplementation. Chemical analysis of the cookies revealed that there was no significant difference in chemical and physical properties of cookies supplemented with acid stabilized rice bran (ASRB) and heat stabilized rice bran (HSRB). The moisture, crude protein, fat and mineral contents were significantly increased with the increment of rice bran. Average width, thickness and spread factor of cookies also increased with the increase in percentage of rice bran. Sensory evaluation of cookies showed that scores for color of cookies decreased significantly with increase in level of rice bran and sensory scores were significantly higher in the cookies prepared with HSRB. However the decrease was non-significant at 10 percent level of substitution. Highest scores for overall acceptability of supplemented cookies was recorded at 15 percent level of substitution as compared to other treatments. Hence it is concluded from the results that supplementation of HSRB at the rate of 10 percent is more suitable for production of rice bran supplemented cookies. (author)

Dissection of Genetic Factors underlying Wheat Kernel Shape and Size in an Elite × Nonadapted Cross using a High Density SNP Linkage Map

Directory of Open Access Journals (Sweden)

Ajay Kumar

2016-03-01

Full Text Available Wheat kernel shape and size has been under selection since early domestication. Kernel morphology is a major consideration in wheat breeding, as it impacts grain yield and quality. A population of 160 recombinant inbred lines (RIL, developed using an elite (ND 705 and a nonadapted genotype (PI 414566, was extensively phenotyped in replicated field trials and genotyped using Infinium iSelect 90K assay to gain insight into the genetic architecture of kernel shape and size. A high density genetic map consisting of 10,172 single nucleotide polymorphism (SNP markers, with an average marker density of 0.39 cM/marker, identified a total of 29 genomic regions associated with six grain shape and size traits; ∼80% of these regions were associated with multiple traits. The analyses showed that kernel length (KL and width (KW are genetically independent, while a large number (∼59% of the quantitative trait loci (QTL for kernel shape traits were in common with genomic regions associated with kernel size traits. The most significant QTL was identified on chromosome 4B, and could be an ortholog of major rice grain size and shape gene or . Major and stable loci also were identified on the homeologous regions of Group 5 chromosomes, and in the regions of (6A and (7A genes. Both parental genotypes contributed equivalent positive QTL alleles, suggesting that the nonadapted germplasm has a great potential for enhancing the gene pool for grain shape and size. This study provides new knowledge on the genetic dissection of kernel morphology, with a much higher resolution, which may aid further improvement in wheat yield and quality using genomic tools.

Kernel parameter dependence in spatial factor analysis

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg

2010-01-01

kernel PCA. Shawe-Taylor and Cristianini [4] is an excellent reference for kernel methods in general. Bishop [5] and Press et al. [6] describe kernel methods among many other subjects. The kernel version of PCA handles nonlinearities by implicitly transforming data into high (even infinite) dimensional...... feature space via the kernel function and then performing a linear analysis in that space. In this paper we shall apply a kernel version of maximum autocorrelation factor (MAF) [7, 8] analysis to irregularly sampled stream sediment geochemistry data from South Greenland and illustrate the dependence...... of the kernel width. The 2,097 samples each covering on average 5 km2 are analyzed chemically for the content of 41 elements....

Distribution of Phenolic Compounds and Antioxidative Activities of Rice Kernel and Their Relationships with Agronomic Practice

Science.gov (United States)

Kesarwani, Amit; Chiang, Po-Yuan; Chen, Shih-Shiung

2014-01-01

The phenolic and antioxidant activity of ethanolic extract of two Japonica rice cultivars, Taikeng no. 16 (medium and slender grain) and Kaohsiung no. 139 (short and round grain), grown under organic and conventional farming were examined. Analyses shows that Kaohsiung no. 139 contains the highest amount of secondary metabolites and continuous farming can increase its production. Results also suggest that phenolic content under different agronomic practices, has not shown significant differences but organically grown rice has proven to be better in higher accumulation of other secondary metabolites (2,2-diphenyl-1-picrylhydrazyl (DPPH), flavonoid content, and ferrous chelating capacity). In nutshell, genetic traits and environment have significant effect on phenolic compounds and the least variation reported under agronomic practices. PMID:25506072

Distribution of Phenolic Compounds and Antioxidative Activities of Rice Kernel and Their Relationships with Agronomic Practice

Directory of Open Access Journals (Sweden)

Amit Kesarwani

2014-01-01

Full Text Available The phenolic and antioxidant activity of ethanolic extract of two Japonica rice cultivars, Taikeng no. 16 (medium and slender grain and Kaohsiung no. 139 (short and round grain, grown under organic and conventional farming were examined. Analyses shows that Kaohsiung no. 139 contains the highest amount of secondary metabolites and continuous farming can increase its production. Results also suggest that phenolic content under different agronomic practices, has not shown significant differences but organically grown rice has proven to be better in higher accumulation of other secondary metabolites (2,2-diphenyl-1-picrylhydrazyl (DPPH, flavonoid content, and ferrous chelating capacity. In nutshell, genetic traits and environment have significant effect on phenolic compounds and the least variation reported under agronomic practices.

A Study of the Anechoic Performance of Rice Husk-Based, Geometrically Tapered, Hollow Absorbers

Directory of Open Access Journals (Sweden)

Muhammad Nadeem Iqbal

2014-01-01

Full Text Available Although solid, geometrically tapered microwave absorbers are preferred due to their better performance, they are bulky and must have a thickness on the order of λ or more. The goal of this study was to design lightweight absorbers that can reduce the electromagnetic reflections to less than −10 dB. We used a very simple approach; two waste materials, that is, rice husks and tire dust in powder form, were used to fabricate two independent samples. We measured and used their dielectric properties to determine and compare the propagation constants and quarter-wave thickness. The quarter-wave thickness for the tire dust was 3 mm less than that of the rice husk material, but we preferred the rice-husk material. This preference was based on the fact that our goal was to achieve minimum backward reflections, and the rice-husk material, with its low dielectric constant, high loss factor, large attenuation per unit length, and ease of fabrication, provided a better opportunity to achieve that goal. The performance of the absorbers was found to be better (lower than −20 dB, and comparison of the results proved that the hollow design with 58% less weight was a good alternative to the use of solid absorbers.

Multiple Kernel Learning with Data Augmentation

Science.gov (United States)

2016-11-22

JMLR: Workshop and Conference Proceedings 63:49–64, 2016 ACML 2016 Multiple Kernel Learning with Data Augmentation Khanh Nguyen nkhanh@deakin.edu.au...University, Australia Editors: Robert J. Durrant and Kee-Eung Kim Abstract The motivations of multiple kernel learning (MKL) approach are to increase... kernel expres- siveness capacity and to avoid the expensive grid search over a wide spectrum of kernels . A large amount of work has been proposed to

OS X and iOS Kernel Programming

CERN Document Server

Halvorsen, Ole Henry

2011-01-01

OS X and iOS Kernel Programming combines essential operating system and kernel architecture knowledge with a highly practical approach that will help you write effective kernel-level code. You'll learn fundamental concepts such as memory management and thread synchronization, as well as the I/O Kit framework. You'll also learn how to write your own kernel-level extensions, such as device drivers for USB and Thunderbolt devices, including networking, storage and audio drivers. OS X and iOS Kernel Programming provides an incisive and complete introduction to the XNU kernel, which runs iPhones, i

Model selection for Gaussian kernel PCA denoising

DEFF Research Database (Denmark)

Jørgensen, Kasper Winther; Hansen, Lars Kai

2012-01-01

We propose kernel Parallel Analysis (kPA) for automatic kernel scale and model order selection in Gaussian kernel PCA. Parallel Analysis [1] is based on a permutation test for covariance and has previously been applied for model order selection in linear PCA, we here augment the procedure to also...... tune the Gaussian kernel scale of radial basis function based kernel PCA.We evaluate kPA for denoising of simulated data and the US Postal data set of handwritten digits. We find that kPA outperforms other heuristics to choose the model order and kernel scale in terms of signal-to-noise ratio (SNR...

Applicability of spectral indices on thickness identification of oil slick

Science.gov (United States)

Niu, Yanfei; Shen, Yonglin; Chen, Qihao; Liu, Xiuguo

2016-10-01

Hyperspectral remote sensing technology has played a vital role in the identification and monitoring of oil spill events, and amount of spectral indices have been developed. In this paper, the applicability of six frequently-used indices is analyzed, and a combination of spectral indices in aids of support vector machine (SVM) algorithm is used to identify the oil slicks and corresponding thickness. The six spectral indices are spectral rotation (SR), spectral absorption depth (HI), band ratio of blue and green (BG), band ratio of BG and shortwave infrared index (BGN), 555nm and 645nm normalized by the blue band index (NB) and spectral slope (ND). The experimental study is conducted in the Gulf of Mexico oil spill zone, with Airborne Visible Infrared Imaging Spectrometer (AVIRIS) hyperspectral imagery captured in May 17, 2010. The results show that SR index is the best in all six indices, which can effectively distinguish the thickness of the oil slick and identify it from seawater; HI index and ND index can obviously distinguish oil slick thickness; BG, BGN and NB are more suitable to identify oil slick from seawater. With the comparison among different kernel functions of SVM, the classify accuracy show that the polynomial and RBF kernel functions have the best effect on the separation of oil slick thickness and the relatively pure seawater. The applicability of spectral indices of oil slick and the method of oil film thickness identification will in aids of oil/gas exploration and oil spill monitoring.

Paramecium: An Extensible Object-Based Kernel

NARCIS (Netherlands)

van Doorn, L.; Homburg, P.; Tanenbaum, A.S.

1995-01-01

In this paper we describe the design of an extensible kernel, called Paramecium. This kernel uses an object-based software architecture which together with instance naming, late binding and explicit overrides enables easy reconfiguration. Determining which components reside in the kernel protection

Theory of reproducing kernels and applications

CERN Document Server

Saitoh, Saburou

2016-01-01

This book provides a large extension of the general theory of reproducing kernels published by N. Aronszajn in 1950, with many concrete applications. In Chapter 1, many concrete reproducing kernels are first introduced with detailed information. Chapter 2 presents a general and global theory of reproducing kernels with basic applications in a self-contained way. Many fundamental operations among reproducing kernel Hilbert spaces are dealt with. Chapter 2 is the heart of this book. Chapter 3 is devoted to the Tikhonov regularization using the theory of reproducing kernels with applications to numerical and practical solutions of bounded linear operator equations. In Chapter 4, the numerical real inversion formulas of the Laplace transform are presented by applying the Tikhonov regularization, where the reproducing kernels play a key role in the results. Chapter 5 deals with ordinary differential equations; Chapter 6 includes many concrete results for various fundamental partial differential equations. In Chapt...

Kernels for structured data

CERN Document Server

Gärtner, Thomas

2009-01-01

This book provides a unique treatment of an important area of machine learning and answers the question of how kernel methods can be applied to structured data. Kernel methods are a class of state-of-the-art learning algorithms that exhibit excellent learning results in several application domains. Originally, kernel methods were developed with data in mind that can easily be embedded in a Euclidean vector space. Much real-world data does not have this property but is inherently structured. An example of such data, often consulted in the book, is the (2D) graph structure of molecules formed by

7 CFR 981.401 - Adjusted kernel weight.

Science.gov (United States)

2010-01-01

... 7 Agriculture 8 2010-01-01 2010-01-01 false Adjusted kernel weight. 981.401 Section 981.401... Administrative Rules and Regulations § 981.401 Adjusted kernel weight. (a) Definition. Adjusted kernel weight... kernels in excess of five percent; less shells, if applicable; less processing loss of one percent for...

Testing Infrastructure for Operating System Kernel Development

DEFF Research Database (Denmark)

Walter, Maxwell; Karlsson, Sven

2014-01-01

Testing is an important part of system development, and to test effectively we require knowledge of the internal state of the system under test. Testing an operating system kernel is a challenge as it is the operating system that typically provides access to this internal state information. Multi......-core kernels pose an even greater challenge due to concurrency and their shared kernel state. In this paper, we present a testing framework that addresses these challenges by running the operating system in a virtual machine, and using virtual machine introspection to both communicate with the kernel...... and obtain information about the system. We have also developed an in-kernel testing API that we can use to develop a suite of unit tests in the kernel. We are using our framework for for the development of our own multi-core research kernel....

7 CFR 51.1403 - Kernel color classification.

Science.gov (United States)

2010-01-01

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

Dissection of Genetic Factors underlying Wheat Kernel Shape and Size in an Elite × Nonadapted Cross using a High Density SNP Linkage Map.

Science.gov (United States)

Kumar, Ajay; Mantovani, E E; Seetan, R; Soltani, A; Echeverry-Solarte, M; Jain, S; Simsek, S; Doehlert, D; Alamri, M S; Elias, E M; Kianian, S F; Mergoum, M

2016-03-01

Wheat kernel shape and size has been under selection since early domestication. Kernel morphology is a major consideration in wheat breeding, as it impacts grain yield and quality. A population of 160 recombinant inbred lines (RIL), developed using an elite (ND 705) and a nonadapted genotype (PI 414566), was extensively phenotyped in replicated field trials and genotyped using Infinium iSelect 90K assay to gain insight into the genetic architecture of kernel shape and size. A high density genetic map consisting of 10,172 single nucleotide polymorphism (SNP) markers, with an average marker density of 0.39 cM/marker, identified a total of 29 genomic regions associated with six grain shape and size traits; ∼80% of these regions were associated with multiple traits. The analyses showed that kernel length (KL) and width (KW) are genetically independent, while a large number (∼59%) of the quantitative trait loci (QTL) for kernel shape traits were in common with genomic regions associated with kernel size traits. The most significant QTL was identified on chromosome 4B, and could be an ortholog of major rice grain size and shape gene or . Major and stable loci also were identified on the homeologous regions of Group 5 chromosomes, and in the regions of (6A) and (7A) genes. Both parental genotypes contributed equivalent positive QTL alleles, suggesting that the nonadapted germplasm has a great potential for enhancing the gene pool for grain shape and size. This study provides new knowledge on the genetic dissection of kernel morphology, with a much higher resolution, which may aid further improvement in wheat yield and quality using genomic tools. Copyright © 2016 Crop Science Society of America.

The definition of kernel Oz

OpenAIRE

Smolka, Gert

1994-01-01

Oz is a concurrent language providing for functional, object-oriented, and constraint programming. This paper defines Kernel Oz, a semantically complete sublanguage of Oz. It was an important design requirement that Oz be definable by reduction to a lean kernel language. The definition of Kernel Oz introduces three essential abstractions: the Oz universe, the Oz calculus, and the actor model. The Oz universe is a first-order structure defining the values and constraints Oz computes with. The ...

Fabrication of Uranium Oxycarbide Kernels for HTR Fuel

International Nuclear Information System (INIS)

Barnes, Charles; Richardson, Clay; Nagley, Scott; Hunn, John; Shaber, Eric

2010-01-01

Babcock and Wilcox (B and 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-(micro)m, 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 and W produced 425-(micro)m, 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 and W also produced 500-(micro)m, 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 and 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.

Anisotropic hydrodynamics with a scalar collisional kernel

Science.gov (United States)

Almaalol, Dekrayat; Strickland, Michael

2018-04-01

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

Object classification and detection with context kernel descriptors

DEFF Research Database (Denmark)

Pan, Hong; Olsen, Søren Ingvor; Zhu, Yaping

2014-01-01

Context information is important in object representation. By embedding context cue of image attributes into kernel descriptors, we propose a set of novel kernel descriptors called Context Kernel Descriptors (CKD) for object classification and detection. The motivation of CKD is to use spatial...... consistency of image attributes or features defined within a neighboring region to improve the robustness of descriptor matching in kernel space. For feature selection, Kernel Entropy Component Analysis (KECA) is exploited to learn a subset of discriminative CKD. Different from Kernel Principal Component...

Ranking Support Vector Machine with Kernel Approximation.

Science.gov (United States)

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

2017-01-01

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

Ranking Support Vector Machine with Kernel Approximation

Directory of Open Access Journals (Sweden)

Kai Chen

2017-01-01

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

Dose point kernels for beta-emitting radioisotopes

International Nuclear Information System (INIS)

Prestwich, W.V.; Chan, L.B.; Kwok, C.S.; Wilson, B.

1986-01-01

Knowledge of the dose point kernel corresponding to a specific radionuclide is required to calculate the spatial dose distribution produced in a homogeneous medium by a distributed source. Dose point kernels for commonly used radionuclides have been calculated previously using as a basis monoenergetic dose point kernels derived by numerical integration of a model transport equation. The treatment neglects fluctuations in energy deposition, an effect which has been later incorporated in dose point kernels calculated using Monte Carlo methods. This work describes new calculations of dose point kernels using the Monte Carlo results as a basis. An analytic representation of the monoenergetic dose point kernels has been developed. This provides a convenient method both for calculating the dose point kernel associated with a given beta spectrum and for incorporating the effect of internal conversion. An algebraic expression for allowed beta spectra has been accomplished through an extension of the Bethe-Bacher approximation, and tested against the exact expression. Simplified expression for first-forbidden shape factors have also been developed. A comparison of the calculated dose point kernel for 32 P with experimental data indicates good agreement with a significant improvement over the earlier results in this respect. An analytic representation of the dose point kernel associated with the spectrum of a single beta group has been formulated. 9 references, 16 figures, 3 tables

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

Science.gov (United States)

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

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

Wigner functions defined with Laplace transform kernels.

Science.gov (United States)

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

2011-10-24

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

Metabolic network prediction through pairwise rational kernels.

Science.gov (United States)

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

2014-09-26

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

Influence Function and Robust Variant of Kernel Canonical Correlation Analysis

OpenAIRE

Alam, Md. Ashad; Fukumizu, Kenji; Wang, Yu-Ping

2017-01-01

Many unsupervised kernel methods rely on the estimation of the kernel covariance operator (kernel CO) or kernel cross-covariance operator (kernel CCO). Both kernel CO and kernel CCO are sensitive to contaminated data, even when bounded positive definite kernels are used. To the best of our knowledge, there are few well-founded robust kernel methods for statistical unsupervised learning. In addition, while the influence function (IF) of an estimator can characterize its robustness, asymptotic ...

The Linux kernel as flexible product-line architecture

NARCIS (Netherlands)

M. de Jonge (Merijn)

2002-01-01

textabstractThe Linux kernel source tree is huge ($>$ 125 MB) and inflexible (because it is difficult to add new kernel components). We propose to make this architecture more flexible by assembling kernel source trees dynamically from individual kernel components. Users then, can select what

Exploiting graph kernels for high performance biomedical relation extraction.

Science.gov (United States)

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

2018-01-30

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

Anatomy and Histochemistry of Roots and Shoots in Wild Rice (Zizania latifolia Griseb.

Directory of Open Access Journals (Sweden)

Chaodong Yang

2014-01-01

Full Text Available Wild rice (Zizania latifolia Griseb. is a famous, perennial, emergent vegetable in China. The current work explores the anatomy and histochemistry of roots, stems, and leaves and the permeability of apoplastic barriers of wild rice. The adventitious roots in wild rice have suberized and lignified endodermis and adjacent, thick-walled cortical layers and suberized and lignified hypodermis, composed of a uniseriate sclerenchyma layer (SC underlying uniseriate exodermis; they also have lysigenous aerenchyma. Stems have a thickened epidermal cuticle, a narrow peripheral mechanical ring (PMR, an outer ring of vascular bundles, and an inner ring of vascular bundles embedded in a multiseriate sclerenchyma ring (SCR. There is evidence of suberin in stem SCR and PMR sclerenchyma cells. Sheathing leaves are characterized by thick cuticles and fibrous bundle sheath extensions. Air spaces in stems and leaves consist of mostly lysigenous aerenchyma and pith cavities in stems. Apoplastic barriers are found in roots and stems.

A point kernel shielding code, PKN-HP, for high energy proton incident

Energy Technology Data Exchange (ETDEWEB)

Kotegawa, Hiroshi [Japan Atomic Energy Research Inst., Tokai, Ibaraki (Japan). Tokai Research Establishment

1996-06-01

A point kernel integral technique code PKN-HP, and the related thick target neutron yield data have been developed to calculate neutron and secondary gamma-ray dose equivalents in ordinary concrete and iron shields for fully stopping length C, Cu and U-238 target neutrons produced by 100 MeV-10 GeV proton incident in a 3-dimensional geometry. The comparisons among calculation results of the present code and other calculation techniques, and measured values showed the usefulness of the code. (author)

Transfer of gaseous iodine from atmosphere to rough rice, brown rice and polished rice

International Nuclear Information System (INIS)

Sumiya, Misako; Uchida, Shigeo; Muramatsu, Yasuyuki; Ohmomo, Yoichiro; Yamaguchi, Shuho; Obata, Hitoshi.

1987-01-01

Experiments were carried out in order to obtain information required for establishing transfer coefficients of gaseous iodine (I 2 ) to rough rice, brown rice and polished rice. The gaseous iodine deposited on young rice plants before the heading period was scarcely found in the rough rice harvested at the full ripe stage. The biological half life of iodine in hull, however, was much slower than that in leaves of 14 days. The translocation of iodine from leaves and stalks to rough rice was not clearly recognized. Therefore, it was deduced that iodine found in brown rice mainly should originate from that deposited on the hull. The distribution ratios of iodine between rough rice and brown rice, and between brown rice and polished rice were 100:4 and 100:30 on 100 grains basis, respectively. If average normalized deposition velocity (V d(m) ) or derived deposition velocity (V s ) are given, the transfer coefficients of gaseous iodine to rough rice (TF r ), brown rice (TF b ) and polished rice (TF p ) could be calculated. (author)

Carcass characteristics and meat quality of lambs that are fed diets with palm kernel cake.

Science.gov (United States)

da Conceição Dos Santos, Rozilda; Gomes, Daiany Iris; Alves, Kaliandra Souza; Mezzomo, Rafael; Oliveira, Luis Rennan Sampaio; Cutrim, Darley Oliveira; Sacramento, Samara Bianca Moraes; de Moura Lima, Elizanne; de Carvalho, Francisco Fernando Ramos

2017-06-01

The aim was to evaluate carcass characteristics, cut yield, and meat quality in lambs that were fed different inclusion levels of palm kernel cake. Forty-five woolless castrated male Santa Inês crossbred sheep with an initial average body weight of 23.16±0.35 kg were used. The experimental design was a completely randomized design with five treatments, with palm kernel cake in the proportions of 0.0%, 7.5%, 15.0%, 22.5%, and 30.0% with nine replications per treatment. After slaughter, the gastrointestinal tract was weighed when it was full, after which it was then emptied. The heart, liver, kidney, pancreas perirenal fat were also collected and weighed. The carcass was split into two identical longitudinal halves and weighed to determine the quantitative and qualitative characteristics. The empty body weight, carcass weight and yield, and fat thickness decreased linearly (pkernel inclusion in the diet. There was no difference (p>0.05) for the rib eye area of animals that were fed palm kernel cake. There was a reduction in the commercial cut weight (p0.05). The sarcomere length decreased linearly (pkernel cake was not observed in other meat quality variables. It is worth noting that the red staining intensity, indicated as A, had a tendency to decrease (p = 0.050). The inclusion of palm kernel cake up to 30% in the diet does not lead to changes in meat quality characteristics, except for sarcomere length. Nevertheless, carcass quantitative characteristics decrease with the use of palm kernel cake.

GRIM : Leveraging GPUs for Kernel integrity monitoring

NARCIS (Netherlands)

Koromilas, Lazaros; Vasiliadis, Giorgos; Athanasopoulos, Ilias; Ioannidis, Sotiris

2016-01-01

Kernel rootkits can exploit an operating system and enable future accessibility and control, despite all recent advances in software protection. A promising defense mechanism against rootkits is Kernel Integrity Monitor (KIM) systems, which inspect the kernel text and data to discover any malicious

7 CFR 51.2296 - Three-fourths half kernel.

Science.gov (United States)

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Three-fourths half kernel. 51.2296 Section 51.2296 Agriculture Regulations of the Department of Agriculture AGRICULTURAL MARKETING SERVICE (Standards...-fourths half kernel. Three-fourths half kernel means a portion of a half of a kernel which has more than...

Examining Potential Boundary Bias Effects in Kernel Smoothing on Equating: An Introduction for the Adaptive and Epanechnikov Kernels.

Science.gov (United States)

Cid, Jaime A; von Davier, Alina A

2015-05-01

Test equating is a method of making the test scores from different test forms of the same assessment comparable. In the equating process, an important step involves continuizing the discrete score distributions. In traditional observed-score equating, this step is achieved using linear interpolation (or an unscaled uniform kernel). In the kernel equating (KE) process, this continuization process involves Gaussian kernel smoothing. It has been suggested that the choice of bandwidth in kernel smoothing controls the trade-off between variance and bias. In the literature on estimating density functions using kernels, it has also been suggested that the weight of the kernel depends on the sample size, and therefore, the resulting continuous distribution exhibits bias at the endpoints, where the samples are usually smaller. The purpose of this article is (a) to explore the potential effects of atypical scores (spikes) at the extreme ends (high and low) on the KE method in distributions with different degrees of asymmetry using the randomly equivalent groups equating design (Study I), and (b) to introduce the Epanechnikov and adaptive kernels as potential alternative approaches to reducing boundary bias in smoothing (Study II). The beta-binomial model is used to simulate observed scores reflecting a range of different skewed shapes.

Adaptive Kernel in Meshsize Boosting Algorithm in KDE ...

African Journals Online (AJOL)

This paper proposes the use of adaptive kernel in a meshsize boosting algorithm in kernel density estimation. The algorithm is a bias reduction scheme like other existing schemes but uses adaptive kernel instead of the regular fixed kernels. An empirical study for this scheme is conducted and the findings are comparatively ...

A kernel version of multivariate alteration detection

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg; Vestergaard, Jacob Schack

2013-01-01

Based on the established methods kernel canonical correlation analysis and multivariate alteration detection we introduce a kernel version of multivariate alteration detection. A case study with SPOT HRV data shows that the kMAD variates focus on extreme change observations.......Based on the established methods kernel canonical correlation analysis and multivariate alteration detection we introduce a kernel version of multivariate alteration detection. A case study with SPOT HRV data shows that the kMAD variates focus on extreme change observations....

Implementing Kernel Methods Incrementally by Incremental Nonlinear Projection Trick.

Science.gov (United States)

Kwak, Nojun

2016-05-20

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

Uranium kernel formation via internal gelation

International Nuclear Information System (INIS)

Hunt, R.D.; Collins, J.L.

2004-01-01

In the 1970s and 1980s, U.S. Department of Energy (DOE) conducted numerous studies on the fabrication of nuclear fuel particles using the internal gelation process. These amorphous kernels were prone to flaking or breaking when gases tried to escape from the kernels during calcination and sintering. These earlier kernels would not meet today's proposed specifications for reactor fuel. In the interim, the internal gelation process has been used to create hydrous metal oxide microspheres for the treatment of nuclear waste. With the renewed interest in advanced nuclear fuel by the DOE, the lessons learned from the nuclear waste studies were recently applied to the fabrication of uranium kernels, which will become tri-isotropic (TRISO) fuel particles. These process improvements included equipment modifications, small changes to the feed formulations, and a new temperature profile for the calcination and sintering. The modifications to the laboratory-scale equipment and its operation as well as small changes to the feed composition increased the product yield from 60% to 80%-99%. The new kernels were substantially less glassy, and no evidence of flaking was found. Finally, key process parameters were identified, and their effects on the uranium microspheres and kernels are discussed. (orig.)

Kernel learning at the first level of inference.

Science.gov (United States)

Cawley, Gavin C; Talbot, Nicola L C

2014-05-01

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

Global Polynomial Kernel Hazard Estimation

DEFF Research Database (Denmark)

Hiabu, Munir; Miranda, Maria Dolores Martínez; Nielsen, Jens Perch

2015-01-01

This paper introduces a new bias reducing method for kernel hazard estimation. The method is called global polynomial adjustment (GPA). It is a global correction which is applicable to any kernel hazard estimator. The estimator works well from a theoretical point of view as it asymptotically redu...

A correlation study of proximate composition, physical and cooking properties of new high yielding and disease resistant rice varieties

Directory of Open Access Journals (Sweden)

Nuzhat Rasool

2015-12-01

Full Text Available The present study was aimed to compare proximate composition, physical, and cooking properties of locally cultivated rice varieties of Kashmir division viz.; SR-1, K-448, and K-39. Various physiochemical properties were studied. The relationship between physical, proximate composition, and cooking properties was determined using Pearson’s correlation. Length–breadth (L/B ratio showed a significant positive correlation with kernel length and negative correlation with thousand kernel weight, with a correlation coefficient (r of 0.893 and −0.855, respectively, (p  0.05. Solid loss in gruel was observed to have a negative correlation with L/B ratio (r = −0.432, p > 0.05, water uptake ratio (r = −0.742, p < 0.05, and cooking time (r = −0.678, p < 0.05. The rice cultivars with higher cooking time showed lower gruel solid loss and vice versa. Water uptake was observed to be positively correlated with L/B ratio (r = 0.768, p < 0.05. Among all the cultivars studied, K-448 variety has potential for consumers’ preference and it could be used for breeding programs for the improvement of valuable grain quality traits.

Quantum tomography, phase-space observables and generalized Markov kernels

International Nuclear Information System (INIS)

Pellonpaeae, Juha-Pekka

2009-01-01

We construct a generalized Markov kernel which transforms the observable associated with the homodyne tomography into a covariant phase-space observable with a regular kernel state. Illustrative examples are given in the cases of a 'Schroedinger cat' kernel state and the Cahill-Glauber s-parametrized distributions. Also we consider an example of a kernel state when the generalized Markov kernel cannot be constructed.

Stress-intensity factors for a thick-walled cylinder containing an annular imbedded or external or internal surface crack

Science.gov (United States)

Erdol, R.; Erdogan, F.

1976-01-01

The elastostatic axisymmetric problem for a long thick-walled cylinder containing a ring-shaped internal or edge crack is considered. Using the standard transform technique the problem is formulated in terms of an integral equation which has a simple Cauchy kernel for the internal crack and a generalized Cauchy kernel for the edge crack as the dominant part. As examples the uniform axial load and the steady-state thermal stress problems have been solved and the related stress intensity factors have been calculated. Among other findings the results show that in the cylinder under uniform axial stress containing an internal crack the stress intensity factor at the inner tip is always greater than that at the outer tip for equal net ligament thicknesses and in the cylinder with an edge crack which is under a state of thermal stress the stress intensity factor is a decreasing function of the crack depth, tending to zero as the crack depth approaches the wall thickness.

Single pass kernel k-means clustering method

Indian Academy of Sciences (India)

paper proposes a simple and faster version of the kernel k-means clustering ... It has been considered as an important tool ... On the other hand, kernel-based clustering methods, like kernel k-means clus- ..... able at the UCI machine learning repository (Murphy 1994). ... All the data sets have only numeric valued features.

Relationship between attenuation coefficients and dose-spread kernels

International Nuclear Information System (INIS)

Boyer, A.L.

1988-01-01

Dose-spread kernels can be used to calculate the dose distribution in a photon beam by convolving the kernel with the primary fluence distribution. The theoretical relationships between various types and components of dose-spread kernels relative to photon attenuation coefficients are explored. These relations can be valuable as checks on the conservation of energy by dose-spread kernels calculated by analytic or Monte Carlo methods

Mixture Density Mercer Kernels: A Method to Learn Kernels

Data.gov (United States)

National Aeronautics and Space Administration — This paper presents a method of generating Mercer Kernels from an ensemble of probabilistic mixture models, where each mixture model is generated from a Bayesian...

Integral equations with contrasting kernels

Directory of Open Access Journals (Sweden)

Theodore Burton

2008-01-01

Full Text Available In this paper we study integral equations of the form $x(t=a(t-\\int^t_0 C(t,sx(sds$ with sharply contrasting kernels typified by $C^*(t,s=\\ln (e+(t-s$ and $D^*(t,s=[1+(t-s]^{-1}$. The kernel assigns a weight to $x(s$ and these kernels have exactly opposite effects of weighting. Each type is well represented in the literature. Our first project is to show that for $a\\in L^2[0,\\infty$, then solutions are largely indistinguishable regardless of which kernel is used. This is a surprise and it leads us to study the essential differences. In fact, those differences become large as the magnitude of $a(t$ increases. The form of the kernel alone projects necessary conditions concerning the magnitude of $a(t$ which could result in bounded solutions. Thus, the next project is to determine how close we can come to proving that the necessary conditions are also sufficient. The third project is to show that solutions will be bounded for given conditions on $C$ regardless of whether $a$ is chosen large or small; this is important in real-world problems since we would like to have $a(t$ as the sum of a bounded, but badly behaved function, and a large well behaved function.

Kernel methods in orthogonalization of multi- and hypervariate data

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg

2009-01-01

A kernel version of maximum autocorrelation factor (MAF) analysis is described very briefly and applied to change detection in remotely sensed hyperspectral image (HyMap) data. The kernel version is 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 are replaced by inner products between nonlinear mappings into higher dimensional feature space of the original data. 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 this kernel function. This means that we need not know the nonlinear mappings explicitly. Kernel PCA and MAF analysis handle nonlinearities by implicitly transforming data into high (even infinite...

Kernel based subspace projection of near infrared hyperspectral images of maize kernels

DEFF Research Database (Denmark)

Larsen, Rasmus; Arngren, Morten; Hansen, Per Waaben

2009-01-01

In this paper we present an exploratory analysis of hyper- spectral 900-1700 nm images of maize kernels. The imaging device is a line scanning hyper spectral camera using a broadband NIR illumi- nation. In order to explore the hyperspectral data we compare a series of subspace projection methods ......- tor transform outperform the linear methods as well as kernel principal components in producing interesting projections of the data.......In this paper we present an exploratory analysis of hyper- spectral 900-1700 nm images of maize kernels. The imaging device is a line scanning hyper spectral camera using a broadband NIR illumi- nation. In order to explore the hyperspectral data we compare a series of subspace projection methods...... including principal component analysis and maximum autocorrelation factor analysis. The latter utilizes the fact that interesting phenomena in images exhibit spatial autocorrelation. However, linear projections often fail to grasp the underlying variability on the data. Therefore we propose to use so...

Sparse Event Modeling with Hierarchical Bayesian Kernel Methods

Science.gov (United States)

2016-01-05

SECURITY CLASSIFICATION OF: The research objective of this proposal was to develop a predictive Bayesian kernel approach to model count data based on...several predictive variables. Such an approach, which we refer to as the Poisson Bayesian kernel model, is able to model the rate of occurrence of... kernel methods made use of: (i) the Bayesian property of improving predictive accuracy as data are dynamically obtained, and (ii) the kernel function

Assessment of clinical image quality in paediatric abdominal CT examinations: dependency on the level of adaptive statistical iterative reconstruction (ASiR) and the type of convolution kernel

International Nuclear Information System (INIS)

Larsson, Joel; Baath, Magnus; Thilander-Klang, Anne; Ledenius, Kerstin; Caisander, Haakan

2016-01-01

The purpose of this study was to investigate the effect of different combinations of convolution kernel and the level of Adaptive Statistical iterative Reconstruction (ASiR TM ) on diagnostic image quality as well as visualisation of anatomical structures in paediatric abdominal computed tomography (CT) examinations. Thirty-five paediatric patients with abdominal pain with non-specified pathology undergoing abdominal CT were included in the study. Transaxial stacks of 5-mm-thick images were retrospectively reconstructed at various ASiR levels, in combination with three convolution kernels. Four paediatric radiologists rated the diagnostic image quality and the delineation of six anatomical structures in a blinded randomised visual grading study. Image quality at a given ASiR level was found to be dependent on the kernel, and a more edge-enhancing kernel benefited from a higher ASiR level. An ASiR level of 70 % together with the Soft TM or Standard TM kernel was suggested to be the optimal combination for paediatric abdominal CT examinations. (authors)

The Classification of Diabetes Mellitus Using Kernel k-means

Science.gov (United States)

Alamsyah, M.; Nafisah, Z.; Prayitno, E.; Afida, A. M.; Imah, E. M.

2018-01-01

Diabetes Mellitus is a metabolic disorder which is characterized by chronicle hypertensive glucose. Automatics detection of diabetes mellitus is still challenging. This study detected diabetes mellitus by using kernel k-Means algorithm. Kernel k-means is an algorithm which was developed from k-means algorithm. Kernel k-means used kernel learning that is able to handle non linear separable data; where it differs with a common k-means. The performance of kernel k-means in detecting diabetes mellitus is also compared with SOM algorithms. The experiment result shows that kernel k-means has good performance and a way much better than SOM.

Physiochemical properties and cooking quality of long and short rice (Oryza sativa) grains

International Nuclear Information System (INIS)

Elbashir, L. T. M.

2005-01-01

Five rice grain samples namely long (American (Parboiled rice), A; Pakistan, P and Thailand, T) and short (Egyptian, E and Sudanese, S) types were investigated for their physicochemical and cooking quality characteristics. Investigations showed that rice grain of the two types had a length of 6.73 mm (A), 7.49 mm (P), 7.05 mm (T), 5.46 mm (E) and 5.64 mm (S); width of 2.08 mm (A), 1.73 mm (P), 2.06 mm (T), 2.66 mm (E) and 2.73 mm (S); thickness of 1.59 mm (A), 1.50 mm (P), 1.67 mm (T), 1.93 mm (E) and 1.83 mm (S) and length/width (L/w) ratio 3.24 (A), 4.35(P), 3.43 (T), 2.06 (E) and 2.07 (S). The L/W ratios obtained were used for determination of grain shapes. The shapes determined were slender for the long type samples and bold for the short type samples. Density values were 1.43 g/ml (A), 1.48 g/ml (P), 1.45 g/ml (T), 1.46 g/ml (E) and 1.46 g/ml (S). Paste viscosity increased from out of scale (A), 12 cm (P), 11.5 cm (T), 5.50 cm (S) to 4.50 cm (E).1000 kernel weight values were 16.67 g (A), 14.53 g (P), 18.89 g (T), 18.47 g (E) and 18.32 (S). Broken ratios obtained were 3.62 (A), 0.31 (P), 1.58 (T), 6.39 (E) and 6.54 (S). Also investigations showed that rice grains contained 8.6%-10.9% moisture, 0.3%-0.6%, ash, 0.22-0.48% fiber, 6.2%- 8.0% protein, and 0.5%-1.0 % oil. Cooking reduced the starch percentages from 59.82%-64.27% to the range of 43.97%-55.47% for both types. For all types of rice amylose seems to be lower (24.00%-31.50%) than amylopectin (31.66%-39.57%). Cooking reduced both amylose (13.83%-18.67%) and amylopectin (26.30%-34.32%) content, however, it increased the amylopectin content (39.64%) of the Sudanese, S sample. Alkali spreading values were 3 (A), 1-2 (P), 1-2 (T), 6-7(E) and 6-7 for (S) and their gelatinization temperature (G T) was classified as high - intermediate (70-74 C degree) for A, high G T (75-79 C degree) for P and T, while E and S were classified as having low G T (55-69 C degree). Gel consistency (GC) values were 86.00 mm, 41

Physiochemical properties and cooking quality of long and short rice (Oryza sativa) grains

Energy Technology Data Exchange (ETDEWEB)

Elbashir, L T. M. [Department of Food Science and Technology, Faculty of Agriculture, University of Khartoum, Khartoum (Sudan)

2005-01-01

Five rice grain samples namely long (American (Parboiled rice), A; Pakistan, P and Thailand, T) and short (Egyptian, E and Sudanese, S) types were investigated for their physicochemical and cooking quality characteristics. Investigations showed that rice grain of the two types had a length of 6.73 mm (A), 7.49 mm (P), 7.05 mm (T), 5.46 mm (E) and 5.64 mm (S); width of 2.08 mm (A), 1.73 mm (P), 2.06 mm (T), 2.66 mm (E) and 2.73 mm (S); thickness of 1.59 mm (A), 1.50 mm (P), 1.67 mm (T), 1.93 mm (E) and 1.83 mm (S) and length/width (L/w) ratio 3.24 (A), 4.35(P), 3.43 (T), 2.06 (E) and 2.07 (S). The L/W ratios obtained were used for determination of grain shapes. The shapes determined were slender for the long type samples and bold for the short type samples. Density values were 1.43 g/ml (A), 1.48 g/ml (P), 1.45 g/ml (T), 1.46 g/ml (E) and 1.46 g/ml (S). Paste viscosity increased from out of scale (A), 12 cm (P), 11.5 cm (T), 5.50 cm (S) to 4.50 cm (E).1000 kernel weight values were 16.67 g (A), 14.53 g (P), 18.89 g (T), 18.47 g (E) and 18.32 (S). Broken ratios obtained were 3.62 (A), 0.31 (P), 1.58 (T), 6.39 (E) and 6.54 (S). Also investigations showed that rice grains contained 8.6%-10.9% moisture, 0.3%-0.6%, ash, 0.22-0.48% fiber, 6.2%- 8.0% protein, and 0.5%-1.0 % oil. Cooking reduced the starch percentages from 59.82%-64.27% to the range of 43.97%-55.47% for both types. For all types of rice amylose seems to be lower (24.00%-31.50%) than amylopectin (31.66%-39.57%). Cooking reduced both amylose (13.83%-18.67%) and amylopectin (26.30%-34.32%) content, however, it increased the amylopectin content (39.64%) of the Sudanese, S sample. Alkali spreading values were 3 (A), 1-2 (P), 1-2 (T), 6-7(E) and 6-7 for (S) and their gelatinization temperature (G T) was classified as high - intermediate (70-74 C degree) for A, high G T (75-79 C degree) for P and T, while E and S were classified as having low G T (55-69 C degree). Gel consistency (GC) values were 86.00 mm, 41

Evaluating the Application of Tissue-Specific Dose Kernels Instead of Water Dose Kernels in Internal Dosimetry : A Monte Carlo Study

NARCIS (Netherlands)

Moghadam, Maryam Khazaee; Asl, Alireza Kamali; Geramifar, Parham; Zaidi, Habib

2016-01-01

Purpose: The aim of this work is to evaluate the application of tissue-specific dose kernels instead of water dose kernels to improve the accuracy of patient-specific dosimetry by taking tissue heterogeneities into consideration. Materials and Methods: Tissue-specific dose point kernels (DPKs) and

Parsimonious Wavelet Kernel Extreme Learning Machine

Directory of Open Access Journals (Sweden)

Wang Qin

2015-11-01

Full Text Available In this study, a parsimonious scheme for wavelet kernel extreme learning machine (named PWKELM was introduced by combining wavelet theory and a parsimonious algorithm into kernel extreme learning machine (KELM. In the wavelet analysis, bases that were localized in time and frequency to represent various signals effectively were used. Wavelet kernel extreme learning machine (WELM maximized its capability to capture the essential features in “frequency-rich” signals. The proposed parsimonious algorithm also incorporated significant wavelet kernel functions via iteration in virtue of Householder matrix, thus producing a sparse solution that eased the computational burden and improved numerical stability. The experimental results achieved from the synthetic dataset and a gas furnace instance demonstrated that the proposed PWKELM is efficient and feasible in terms of improving generalization accuracy and real time performance.

Difference between standard and quasi-conformal BFKL kernels

International Nuclear Information System (INIS)

Fadin, V.S.; Fiore, R.; Papa, A.

2012-01-01

As it was recently shown, the colour singlet BFKL kernel, taken in Möbius representation in the space of impact parameters, can be written in quasi-conformal shape, which is unbelievably simple compared with the conventional form of the BFKL kernel in momentum space. It was also proved that the total kernel is completely defined by its Möbius representation. In this paper we calculated the difference between standard and quasi-conformal BFKL kernels in momentum space and discovered that it is rather simple. Therefore we come to the conclusion that the simplicity of the quasi-conformal kernel is caused mainly by using the impact parameter space.

Experimental investigation of ash deposits characteristics of co-combustion of coal and rice hull using a digital image technique

International Nuclear Information System (INIS)

Qiu, Kunzan; Zhang, Hailong; Zhou, Hao; Zhou, Bin; Li, Letian; Cen, Kefa

2014-01-01

This paper investigated the ash deposit characteristics during the co-firing Da Tong (DA) coal with different proportions of rice hull (0%, 5%, 10%, and 20%, based on weight) in a pilot-scale furnace. The growth of ash deposit with a four-stage mode was presented. The stable thickness values of DA coal, 5% rice hull, 10% rice hull, and 20% rice hull were 0.5, 1.4, 2.9, 5.7 cm, with stable heat flux values of 230, 200, 175, and 125 kW/m 2 , respectively. According to the results of scanning electron microscopy with energy dispersive X-ray analysis (SEM-EDX), the amount of Si in the deposits increased with the increasing proportion of rice hull rich in SiO 2 . The X-ray diffraction (XRD) analysis results indicated that most elements except Si were in the amorphous state because of the formation of eutectics. The stable thicknesses of deposits increased exponentially with the proportion of rice hull. The deposit was loose, easy removable but it reduced the heat transfer significantly. Consequently, sootblowing timely was necessary when co-firing DA coal with rice hull. - Highlights: • Digital image technique was used to monitor deposits growth process. • A type of four stages mode of ash deposit growth was presented. • The heat flux of ash deposits fit a three-stage mode. • The addition of rice hull increased the porosity of deposits

A laser optical method for detecting corn kernel defects

Energy Technology Data Exchange (ETDEWEB)

Gunasekaran, S.; Paulsen, M. R.; Shove, G. C.

1984-01-01

An opto-electronic instrument was developed to examine individual corn kernels and detect various kernel defects according to reflectance differences. A low power helium-neon (He-Ne) laser (632.8 nm, red light) was used as the light source in the instrument. Reflectance from good and defective parts of corn kernel surfaces differed by approximately 40%. Broken, chipped, and starch-cracked kernels were detected with nearly 100% accuracy; while surface-split kernels were detected with about 80% accuracy. (author)

Implementation of pencil kernel and depth penetration algorithms for treatment planning of proton beams

International Nuclear Information System (INIS)

Russell, K.R.; Saxner, M.; Ahnesjoe, A.; Montelius, A.; Grusell, E.; Dahlgren, C.V.

2000-01-01

The implementation of two algorithms for calculating dose distributions for radiation therapy treatment planning of intermediate energy proton beams is described. A pencil kernel algorithm and a depth penetration algorithm have been incorporated into a commercial three-dimensional treatment planning system (Helax-TMS, Helax AB, Sweden) to allow conformal planning techniques using irregularly shaped fields, proton range modulation, range modification and dose calculation for non-coplanar beams. The pencil kernel algorithm is developed from the Fermi-Eyges formalism and Moliere multiple-scattering theory with range straggling corrections applied. The depth penetration algorithm is based on the energy loss in the continuous slowing down approximation with simple correction factors applied to the beam penumbra region and has been implemented for fast, interactive treatment planning. Modelling of the effects of air gaps and range modifying device thickness and position are implicit to both algorithms. Measured and calculated dose values are compared for a therapeutic proton beam in both homogeneous and heterogeneous phantoms of varying complexity. Both algorithms model the beam penumbra as a function of depth in a homogeneous phantom with acceptable accuracy. Results show that the pencil kernel algorithm is required for modelling the dose perturbation effects from scattering in heterogeneous media. (author)

Kernel maximum autocorrelation factor and minimum noise fraction transformations

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg

2010-01-01

in hyperspectral HyMap scanner data covering a small agricultural area, and 3) maize kernel inspection. In the cases shown, the kernel MAF/MNF transformation performs better than its linear counterpart as well as linear and kernel PCA. The leading kernel MAF/MNF variates seem to possess the ability to adapt...

Identification of Fusarium damaged wheat kernels using image analysis

Directory of Open Access Journals (Sweden)

Ondřej Jirsa

2011-01-01

Full Text Available Visual evaluation of kernels damaged by Fusarium spp. pathogens is labour intensive and due to a subjective approach, it can lead to inconsistencies. Digital imaging technology combined with appropriate statistical methods can provide much faster and more accurate evaluation of the visually scabby kernels proportion. The aim of the present study was to develop a discrimination model to identify wheat kernels infected by Fusarium spp. using digital image analysis and statistical methods. Winter wheat kernels from field experiments were evaluated visually as healthy or damaged. Deoxynivalenol (DON content was determined in individual kernels using an ELISA method. Images of individual kernels were produced using a digital camera on dark background. Colour and shape descriptors were obtained by image analysis from the area representing the kernel. Healthy and damaged kernels differed significantly in DON content and kernel weight. Various combinations of individual shape and colour descriptors were examined during the development of the model using linear discriminant analysis. In addition to basic descriptors of the RGB colour model (red, green, blue, very good classification was also obtained using hue from the HSL colour model (hue, saturation, luminance. The accuracy of classification using the developed discrimination model based on RGBH descriptors was 85 %. The shape descriptors themselves were not specific enough to distinguish individual kernels.

Unified heat kernel regression for diffusion, kernel smoothing and wavelets on manifolds and its application to mandible growth modeling in CT images.

Science.gov (United States)

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

2015-05-01

We present a novel kernel regression framework for smoothing scalar surface data using the Laplace-Beltrami eigenfunctions. Starting with the heat kernel constructed from the eigenfunctions, we formulate a new bivariate kernel regression framework as a weighted eigenfunction expansion with the heat kernel as the weights. The new kernel method is mathematically equivalent to isotropic heat diffusion, kernel smoothing and recently popular diffusion wavelets. The numerical implementation is validated on a unit sphere using spherical harmonics. As an illustration, the method is applied to characterize the localized growth pattern of mandible surfaces obtained in CT images between ages 0 and 20 by regressing the length of displacement vectors with respect to a surface template. Copyright © 2015 Elsevier B.V. All rights reserved.

Magnesiothermic reduction of rice husk ash for electromagnetic wave adsorption

Energy Technology Data Exchange (ETDEWEB)

Liu, Shu-Ting; Yan, Kang-kang; Zhang, Yuan hu; Jin, Shi-di; Ye, Ying; Chen, Xue-Gang, E-mail: chenxg83@zju.edu.cn

2015-11-15

The increase in electromagnetic pollution due to the extensive exploitation of electromagnetic (EM) waves in modern technology creates correspondingly urgent need for developing effective EM wave absorbers. In this study, we carried out the magnesiothermic reduced the rice husk ash under different temperatures (400–800 °C) and investigated the electromagnetic wave adsorption of the products. The EM absorbing for all samples are mainly depend on the dielectric loss, which is ascribed to the carbon and silicon carbide content. RA samples (raw rice husk ashed in air and was magesiothermic reduced in different temperatures) exhibit poor dielectric properties, whereas RN samples (raw rice husk ashed in nitrogen and was magesiothermic reduced in different temperatures) with higher content of carbon and silicon carbide display considerable higher dielectric loss values and broader bandwidth for RL<−5 dB and −10 dB. For RN samples, the maximum bandwidth for −5 dB and −10 dB decrease with carbon contents, while the optimum thickness decrease with increasing SiC content. The optimum thickness of RN400–800 for EM absorption is 1.5–2.0 mm, with maximum RL of between −28.9 and −68.4 dB, bandwidth of 6.7–13 GHz for RL<−5 dB and 3.2–6.2 GHz for RL<−10 dB. The magnesiothermic reduction will enhance the potential application of rice husk ash in EM wave absorption and the samples benefited from low bulk density and low thickness. With the advantages of light-weight, high EM wave absorption, low cost, RN400–800 could be promising candidates for light-weight EM wave absorption materials over many conventional EM wave absorbers. - Highlights: • RN400–800 samples are potential light-weight electromagnetic absorbers. • Carbon and SiC are considered as dominating contributions for the dielectric loss. • Magnesiumothermic reduction extends the EM wave absorption potential of RHN.

Digital signal processing with kernel methods

CERN Document Server

Rojo-Alvarez, José Luis; Muñoz-Marí, Jordi; Camps-Valls, Gustavo

2018-01-01

A realistic and comprehensive review of joint approaches to machine learning and signal processing algorithms, with application to communications, multimedia, and biomedical engineering systems Digital Signal Processing with Kernel Methods reviews the milestones in the mixing of classical digital signal processing models and advanced kernel machines statistical learning tools. It explains the fundamental concepts from both fields of machine learning and signal processing so that readers can quickly get up to speed in order to begin developing the concepts and application software in their own research. Digital Signal Processing with Kernel Methods provides a comprehensive overview of kernel methods in signal processing, without restriction to any application field. It also offers example applications and detailed benchmarking experiments with real and synthetic datasets throughout. Readers can find further worked examples with Matlab source code on a website developed by the authors. * Presents the necess...

Higher-Order Hybrid Gaussian Kernel in Meshsize Boosting Algorithm

African Journals Online (AJOL)

In this paper, we shall use higher-order hybrid Gaussian kernel in a meshsize boosting algorithm in kernel density estimation. Bias reduction is guaranteed in this scheme like other existing schemes but uses the higher-order hybrid Gaussian kernel instead of the regular fixed kernels. A numerical verification of this scheme ...

Adaptive Kernel In The Bootstrap Boosting Algorithm In KDE ...

African Journals Online (AJOL)

This paper proposes the use of adaptive kernel in a bootstrap boosting algorithm in kernel density estimation. The algorithm is a bias reduction scheme like other existing schemes but uses adaptive kernel instead of the regular fixed kernels. An empirical study for this scheme is conducted and the findings are comparatively ...

Windows Vista Kernel-Mode: Functions, Security Enhancements and Flaws

Directory of Open Access Journals (Sweden)

Mohammed D. ABDULMALIK

2008-06-01

Full Text Available Microsoft has made substantial enhancements to the kernel of the Microsoft Windows Vista operating system. Kernel improvements are significant because the kernel provides low-level operating system functions, including thread scheduling, interrupt and exception dispatching, multiprocessor synchronization, and a set of routines and basic objects.This paper describes some of the kernel security enhancements for 64-bit edition of Windows Vista. We also point out some weakness areas (flaws that can be attacked by malicious leading to compromising the kernel.

Generalization Performance of Regularized Ranking With Multiscale Kernels.

Science.gov (United States)

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

2016-05-01

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

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

Science.gov (United States)

Tezuka, Taro

2018-06-01

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

An analysis of 1-D smoothed particle hydrodynamics kernels

International Nuclear Information System (INIS)

Fulk, D.A.; Quinn, D.W.

1996-01-01

In this paper, the smoothed particle hydrodynamics (SPH) kernel is analyzed, resulting in measures of merit for one-dimensional SPH. Various methods of obtaining an objective measure of the quality and accuracy of the SPH kernel are addressed. Since the kernel is the key element in the SPH methodology, this should be of primary concern to any user of SPH. The results of this work are two measures of merit, one for smooth data and one near shocks. The measure of merit for smooth data is shown to be quite accurate and a useful delineator of better and poorer kernels. The measure of merit for non-smooth data is not quite as accurate, but results indicate the kernel is much less important for these types of problems. In addition to the theory, 20 kernels are analyzed using the measure of merit demonstrating the general usefulness of the measure of merit and the individual kernels. In general, it was decided that bell-shaped kernels perform better than other shapes. 12 refs., 16 figs., 7 tabs

Putting Priors in Mixture Density Mercer Kernels

Science.gov (United States)

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%.

Impact of deep learning on the normalization of reconstruction kernel effects in imaging biomarker quantification: a pilot study in CT emphysema

Science.gov (United States)

Jin, Hyeongmin; Heo, Changyong; Kim, Jong Hyo

2018-02-01

Differing reconstruction kernels are known to strongly affect the variability of imaging biomarkers and thus remain as a barrier in translating the computer aided quantification techniques into clinical practice. This study presents a deep learning application to CT kernel conversion which converts a CT image of sharp kernel to that of standard kernel and evaluates its impact on variability reduction of a pulmonary imaging biomarker, the emphysema index (EI). Forty cases of low-dose chest CT exams obtained with 120kVp, 40mAs, 1mm thickness, of 2 reconstruction kernels (B30f, B50f) were selected from the low dose lung cancer screening database of our institution. A Fully convolutional network was implemented with Keras deep learning library. The model consisted of symmetric layers to capture the context and fine structure characteristics of CT images from the standard and sharp reconstruction kernels. Pairs of the full-resolution CT data set were fed to input and output nodes to train the convolutional network to learn the appropriate filter kernels for converting the CT images of sharp kernel to standard kernel with a criterion of measuring the mean squared error between the input and target images. EIs (RA950 and Perc15) were measured with a software package (ImagePrism Pulmo, Seoul, South Korea) and compared for the data sets of B50f, B30f, and the converted B50f. The effect of kernel conversion was evaluated with the mean and standard deviation of pair-wise differences in EI. The population mean of RA950 was 27.65 +/- 7.28% for B50f data set, 10.82 +/- 6.71% for the B30f data set, and 8.87 +/- 6.20% for the converted B50f data set. The mean of pair-wise absolute differences in RA950 between B30f and B50f is reduced from 16.83% to 1.95% using kernel conversion. Our study demonstrates the feasibility of applying the deep learning technique for CT kernel conversion and reducing the kernel-induced variability of EI quantification. The deep learning model has a

Combination of mass spectrometry-based targeted lipidomics and supervised machine learning algorithms in detecting adulterated admixtures of white rice.

Science.gov (United States)

Lim, Dong Kyu; Long, Nguyen Phuoc; Mo, Changyeun; Dong, Ziyuan; Cui, Lingmei; Kim, Giyoung; Kwon, Sung Won

2017-10-01

The mixing of extraneous ingredients with original products is a common adulteration practice in food and herbal medicines. In particular, authenticity of white rice and its corresponding blended products has become a key issue in food industry. Accordingly, our current study aimed to develop and evaluate a novel discrimination method by combining targeted lipidomics with powerful supervised learning methods, and eventually introduce a platform to verify the authenticity of white rice. A total of 30 cultivars were collected, and 330 representative samples of white rice from Korea and China as well as seven mixing ratios were examined. Random forests (RF), support vector machines (SVM) with a radial basis function kernel, C5.0, model averaged neural network, and k-nearest neighbor classifiers were used for the classification. We achieved desired results, and the classifiers effectively differentiated white rice from Korea to blended samples with high prediction accuracy for the contamination ratio as low as five percent. In addition, RF and SVM classifiers were generally superior to and more robust than the other techniques. Our approach demonstrated that the relative differences in lysoGPLs can be successfully utilized to detect the adulterated mixing of white rice originating from different countries. In conclusion, the present study introduces a novel and high-throughput platform that can be applied to authenticate adulterated admixtures from original white rice samples. Copyright © 2017 Elsevier Ltd. All rights reserved.

Development Radar Absorber Material using Rice Husk Carbon for Anechoic Chamber Application

Science.gov (United States)

Zulpadrianto, Z.; Yohandri, Y.; Putra, A.

2018-04-01

The developments of radar technology in Indonesia are very strategic due to the vast territory and had a high-level cloud cover more than 55% of the time. The objective of this research is to develop radar technology facility in Indonesia using local natural resources. The target of this research is to present a low cost and satisfy quality of anechoic chambers. Anechoic chamber is a space designed to avoid reflection of EM waves from outside or from within the room. The reflection coefficient of the EM wave is influenced by the medium imposed by the EM wave. In laboratory experimental research has been done the development of material radar absorber using rice husk. The rice husk is activated using HCl and KOH by stirring using a magnetic stirrer for 1 Hours. The results of rice husk activation were measured using a Vector Network Analyzer by varying the thickness of the ingredients and the concentration of the activation agent. The VNA measurement is obtained reflection coefficient of -12dB and. -6.22dB for 1M HCL and KOH at thickness 10mm, respectively.

NLO corrections to the Kernel of the BKP-equations

Energy Technology Data Exchange (ETDEWEB)

Bartels, J. [Hamburg Univ. (Germany). 2. Inst. fuer Theoretische Physik; Fadin, V.S. [Budker Institute of Nuclear Physics, Novosibirsk (Russian Federation); Novosibirskij Gosudarstvennyj Univ., Novosibirsk (Russian Federation); Lipatov, L.N. [Hamburg Univ. (Germany). 2. Inst. fuer Theoretische Physik; Petersburg Nuclear Physics Institute, Gatchina, St. Petersburg (Russian Federation); Vacca, G.P. [INFN, Sezione di Bologna (Italy)

2012-10-02

We present results for the NLO kernel of the BKP equations for composite states of three reggeized gluons in the Odderon channel, both in QCD and in N=4 SYM. The NLO kernel consists of the NLO BFKL kernel in the color octet representation and the connected 3{yields}3 kernel, computed in the tree approximation.

A Fast and Simple Graph Kernel for RDF

NARCIS (Netherlands)

de Vries, G.K.D.; de Rooij, S.

2013-01-01

In this paper we study a graph kernel for RDF based on constructing a tree for each instance and counting the number of paths in that tree. In our experiments this kernel shows comparable classification performance to the previously introduced intersection subtree kernel, but is significantly faster

An SVM model with hybrid kernels for hydrological time series

Science.gov (United States)

Wang, C.; Wang, H.; Zhao, X.; Xie, Q.

2017-12-01

Support Vector Machine (SVM) models have been widely applied to the forecast of climate/weather and its impact on other environmental variables such as hydrologic response to climate/weather. When using SVM, the choice of the kernel function plays the key role. Conventional SVM models mostly use one single type of kernel function, e.g., radial basis kernel function. Provided that there are several featured kernel functions available, each having its own advantages and drawbacks, a combination of these kernel functions may give more flexibility and robustness to SVM approach, making it suitable for a wide range of application scenarios. This paper presents such a linear combination of radial basis kernel and polynomial kernel for the forecast of monthly flowrate in two gaging stations using SVM approach. The results indicate significant improvement in the accuracy of predicted series compared to the approach with either individual kernel function, thus demonstrating the feasibility and advantages of such hybrid kernel approach for SVM applications.

Kernel based eigenvalue-decomposition methods for analysing ham

DEFF Research Database (Denmark)

Christiansen, Asger Nyman; Nielsen, Allan Aasbjerg; Møller, Flemming

2010-01-01

methods, such as PCA, MAF or MNF. We therefore investigated the applicability of kernel based versions of these transformation. This meant implementing the kernel based methods and developing new theory, since kernel based MAF and MNF is not described in the literature yet. The traditional methods only...... have two factors that are useful for segmentation and none of them can be used to segment the two types of meat. The kernel based methods have a lot of useful factors and they are able to capture the subtle differences in the images. This is illustrated in Figure 1. You can see a comparison of the most...... useful factor of PCA and kernel based PCA respectively in Figure 2. The factor of the kernel based PCA turned out to be able to segment the two types of meat and in general that factor is much more distinct, compared to the traditional factor. After the orthogonal transformation a simple thresholding...

Reduced multiple empirical kernel learning machine.

Science.gov (United States)

Wang, Zhe; Lu, MingZhe; Gao, Daqi

2015-02-01

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

Design, development and evaluation of a divergent roller sizer for almond kernels

Directory of Open Access Journals (Sweden)

D Ghanbarian

2015-09-01

Full Text Available Introduction: Iran is one of the major producers of almonds. According to the statistics released by FAO (2011, Iran with more than 110000 tons of almonds is the third in rank throughout the world. However, most Iranian almonds are presented as an unsorted and unpackaged product. Some producers sort their products by hand which is very time-consuming and labor-intensive. So, there is an essential need for suitable grading and packaging machines especially for the export of almond kernels.Grading, which is sometimes called sorting, is basically separating the material in different homogenous groups according to its specific characteristics like size, shape, color and on the basis of quality. Weighing is one of the best methods for grading agricultural products based on size, but due to its high cost and complexity of operations, usage of weigh size sorting machines is practically limited. So, sizing of most agricultural products is accomplished based on their dimensional attributes such as diameter, length, thickness or a combination of them. Field study shows that recently vibrating sizing machines are used for grading almond kernels. This type of sizing machine is huge, expensive, noisy and it consumes a lot of energy. Thus, the main objective of the present study was the design, development and evaluation of a new prototype of an almond kernel sizing machine. Materials and methods: It is important that the machine could resolve defects of existing vibrating machines. It should provide efficient and cost effective sizing for a wide range of kernel sizes and shapes. Furthermore, it should be of simple construction and be able to accept manual feeding. Previously conducted experiments showed that the thickness of the kernel is the most appropriate dimension for its sizing. Among the different types of dimensional sizing machines, the divergent roller grader which grades the products based on their thickness is considered to be one of the simplest

Kernel principal component analysis for change detection

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg; Morton, J.C.

2008-01-01

region acquired at two different time points. If change over time does not dominate the scene, the projection of the original two bands onto the second eigenvector will show change over time. In this paper a kernel version of PCA is used to carry out the analysis. Unlike ordinary PCA, kernel PCA...... with a Gaussian kernel successfully finds the change observations in a case where nonlinearities are introduced artificially....

Enhanced gluten properties in soft kernel durum wheat

Science.gov (United States)

Soft kernel durum wheat is a relatively recent development (Morris et al. 2011 Crop Sci. 51:114). The soft kernel trait exerts profound effects on kernel texture, flour milling including break flour yield, milling energy, and starch damage, and dough water absorption (DWA). With the caveat of reduce...

7 CFR 981.61 - Redetermination of kernel weight.

Science.gov (United States)

2010-01-01

... 7 Agriculture 8 2010-01-01 2010-01-01 false Redetermination of kernel weight. 981.61 Section 981... GROWN IN CALIFORNIA Order Regulating Handling Volume Regulation § 981.61 Redetermination of kernel weight. The Board, on the basis of reports by handlers, shall redetermine the kernel weight of almonds...

Obtaining unique large kernel rice using chemical mutagenesis in tissue culture

International Nuclear Information System (INIS)

Alyoshin, N.E.; Avakyan, E.R.; Alyoshin, E.P.

2001-01-01

Full text: Lines with improved characters have been received by chemical mutagenesis in rice tissue culture. The japonica rice (Oryza sativa L.) varieties 'Krasnodarskii 424', 'Dubovskii 129', 'Slavyanetz', 'Liman', 'Lomello', 'VNIIR 2471' were used for mutation induction. Nnitrozo-N-methylurea (MNH) has been used as a mutagen. Two approaches were applied: 1. Development mutants by mutagenic treatment of seeds 2. Development regenerants from somatic tissue culture. In the first case, dry seeds with removed covering glumes have been treated with a solution of NMH (exposure 24 hours, tested concentrations 0.05%; 0.1%; 0.2%). After treatment seeds have been rinsed and planted into the soil in vessels. The effect of mutagen was very much genotype dependant. The highest frequency of mutants were observed in the following concentrations of MNH: for variety VNIIR 2471 - 0.05-0.1%, for variety Slavyanetz - 0.1%; for Lomello - 0.2%; for Linman - 0.05% and 0.2%. The mutant N 95, which has been selected from variety Liman after treatment with 0.2% concentration of mutagen, had the following improved characters: vegetation period 103 days (110 days for the parent variety); plant height 93.2 cm (98.2 cm - parent variety); length of the main panicle 17.2 cm; 1000 grain mass 44.9 g (39.2 g - parent variety). Mutant line N 101 selected from the same variety Liman after treatment with 0.05% concentration of mutagen mutated also in many characters: vegetation period 103 days; plant height 106 cm; 1000 grain mass was 47.0 g. In the second experiment, a somatic callus of the 2nd passage from varieties Kransnodarskii 424, Dubovskii 129, Slavyanetz, Liman were treated with the solution of mutagen NMH (concentration: 0.05%; 0.1%; 0.2% + 0.1% PABA by 40 minutes at Certomat shaking machine (100 rev./min). The treated callus has been cultivated at MS regeneration media (4 mg 2.4 D + 20 mg /l of sucrose) and MS intermediate media (non-hormonal + PABA) to obtain regenerants. Plant

Adaptive metric kernel regression

DEFF Research Database (Denmark)

Goutte, Cyril; Larsen, Jan

2000-01-01

Kernel smoothing is a widely used non-parametric pattern recognition technique. By nature, it suffers from the curse of dimensionality and is usually difficult to apply to high input dimensions. In this contribution, we propose an algorithm that adapts the input metric used in multivariate...... regression by minimising a cross-validation estimate of the generalisation error. This allows to automatically adjust the importance of different dimensions. The improvement in terms of modelling performance is illustrated on a variable selection task where the adaptive metric kernel clearly outperforms...

Consistent Estimation of Pricing Kernels from Noisy Price Data

OpenAIRE

Vladislav Kargin

2003-01-01

If pricing kernels are assumed non-negative then the inverse problem of finding the pricing kernel is well-posed. The constrained least squares method provides a consistent estimate of the pricing kernel. When the data are limited, a new method is suggested: relaxed maximization of the relative entropy. This estimator is also consistent. Keywords: $\\epsilon$-entropy, non-parametric estimation, pricing kernel, inverse problems.

Bioavailability of Zinc in Wistar Rats Fed with Rice Fortified with Zinc Oxide

Science.gov (United States)

Della Lucia, Ceres Mattos; Santos, Laura Luiza Menezes; Rodrigues, Kellen Cristina da Cruz; Rodrigues, Vivian Cristina da Cruz; Martino, Hércia Stampini Duarte; Pinheiro Sant’Ana, Helena Maria

2014-01-01

The study of zinc bioavailability in foods is important because this mineral intake does not meet the recommended doses for some population groups. Also, the presence of dietary factors that reduce zinc absorption contributes to its deficiency. Rice fortified with micronutrients (Ultra Rice®) is a viable alternative for fortification since this cereal is already inserted into the population habit. The aim of this study was to evaluate the bioavailability of zinc (Zn) in rice fortified with zinc oxide. During 42 days, rats were divided into four groups and fed with diets containing two different sources of Zn (test diet: UR® fortified with zinc oxide, or control diet: zinc carbonate (ZnCO3)), supplying 50% or 100%, respectively, of the recommendations of this mineral for animals. Weight gain, food intake, feed efficiency ratio, weight, thickness and length of femur; retention of zinc, calcium (Ca) and magnesium (Mg) in the femur and the concentrations of Zn in femur, plasma and erythrocytes were evaluated. Control diet showed higher weight gain, feed efficiency ratio, retention of Zn and Zn concentration in the femur (p 0.05) for dietary intake, length and thickness of the femur, erythrocyte and plasmatic Zn between groups. Although rice fortified with zinc oxide showed a lower bioavailability compared to ZnCO3, this food can be a viable alternative to be used as a vehicle for fortification. PMID:24932657

Stable Kernel Representations as Nonlinear Left Coprime Factorizations

NARCIS (Netherlands)

Paice, A.D.B.; Schaft, A.J. van der

1994-01-01

A representation of nonlinear systems based on the idea of representing the input-output pairs of the system as elements of the kernel of a stable operator has been recently introduced. This has been denoted the kernel representation of the system. In this paper it is demonstrated that the kernel

7 CFR 981.60 - Determination of kernel weight.

Science.gov (United States)

2010-01-01

... 7 Agriculture 8 2010-01-01 2010-01-01 false Determination of kernel weight. 981.60 Section 981.60... 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 settlement...

End-use quality of soft kernel durum wheat

Science.gov (United States)

Kernel texture is a major determinant of end-use quality of wheat. Durum wheat has very hard kernels. We developed soft kernel durum wheat via Ph1b-mediated homoeologous recombination. The Hardness locus was transferred from Chinese Spring to Svevo durum wheat via back-crossing. ‘Soft Svevo’ had SKC...

Per-Sample Multiple Kernel Approach for Visual Concept Learning

Directory of Open Access Journals (Sweden)

Ling-Yu Duan

2010-01-01

Full Text Available Learning visual concepts from images is an important yet challenging problem in computer vision and multimedia research areas. Multiple kernel learning (MKL methods have shown great advantages in visual concept learning. As a visual concept often exhibits great appearance variance, a canonical MKL approach may not generate satisfactory results when a uniform kernel combination is applied over the input space. In this paper, we propose a per-sample multiple kernel learning (PS-MKL approach to take into account intraclass diversity for improving discrimination. PS-MKL determines sample-wise kernel weights according to kernel functions and training samples. Kernel weights as well as kernel-based classifiers are jointly learned. For efficient learning, PS-MKL employs a sample selection strategy. Extensive experiments are carried out over three benchmarking datasets of different characteristics including Caltech101, WikipediaMM, and Pascal VOC'07. PS-MKL has achieved encouraging performance, comparable to the state of the art, which has outperformed a canonical MKL.

Per-Sample Multiple Kernel Approach for Visual Concept Learning

Directory of Open Access Journals (Sweden)

Tian Yonghong

2010-01-01

Full Text Available Abstract Learning visual concepts from images is an important yet challenging problem in computer vision and multimedia research areas. Multiple kernel learning (MKL methods have shown great advantages in visual concept learning. As a visual concept often exhibits great appearance variance, a canonical MKL approach may not generate satisfactory results when a uniform kernel combination is applied over the input space. In this paper, we propose a per-sample multiple kernel learning (PS-MKL approach to take into account intraclass diversity for improving discrimination. PS-MKL determines sample-wise kernel weights according to kernel functions and training samples. Kernel weights as well as kernel-based classifiers are jointly learned. For efficient learning, PS-MKL employs a sample selection strategy. Extensive experiments are carried out over three benchmarking datasets of different characteristics including Caltech101, WikipediaMM, and Pascal VOC'07. PS-MKL has achieved encouraging performance, comparable to the state of the art, which has outperformed a canonical MKL.

Discrete non-parametric kernel estimation for global sensitivity analysis

International Nuclear Information System (INIS)

Senga Kiessé, Tristan; Ventura, Anne

2016-01-01

This work investigates the discrete kernel approach for evaluating the contribution of the variance of discrete input variables to the variance of model output, via analysis of variance (ANOVA) decomposition. Until recently only the continuous kernel approach has been applied as a metamodeling approach within sensitivity analysis framework, for both discrete and continuous input variables. Now the discrete kernel estimation is known to be suitable for smoothing discrete functions. We present a discrete non-parametric kernel estimator of ANOVA decomposition of a given model. An estimator of sensitivity indices is also presented with its asymtotic convergence rate. Some simulations on a test function analysis and a real case study from agricultural have shown that the discrete kernel approach outperforms the continuous kernel one for evaluating the contribution of moderate or most influential discrete parameters to the model output. - Highlights: • We study a discrete kernel estimation for sensitivity analysis of a model. • A discrete kernel estimator of ANOVA decomposition of the model is presented. • Sensitivity indices are calculated for discrete input parameters. • An estimator of sensitivity indices is also presented with its convergence rate. • An application is realized for improving the reliability of environmental models.

Relationships of clinical protocols and reconstruction kernels with image quality and radiation dose in a 128-slice CT scanner: Study with an anthropomorphic and water phantom

International Nuclear Information System (INIS)

Paul, Jijo; Krauss, B.; Banckwitz, R.; Maentele, W.; Bauer, R.W.; Vogl, T.J.

2012-01-01

Research highlights: ► Clinical protocol, reconstruction kernel, reconstructed slice thickness, phantom diameter or the density of material it contains directly affects the image quality of DSCT. ► Dual energy protocol shows the lowest DLP compared to all other protocols examined. ► Dual-energy fused images show excellent image quality and the noise is same as that of single- or high-pitch mode protocol images. ► Advanced CT technology improves image quality and considerably reduce radiation dose. ► An important finding is the comparatively higher DLP of the dual-source high-pitch protocol compared to other single- or dual-energy protocols. - Abstract: Purpose: The aim of this study was to explore the relationship of scanning parameters (clinical protocols), reconstruction kernels and slice thickness with image quality and radiation dose in a DSCT. Materials and methods: The chest of an anthropomorphic phantom was scanned on a DSCT scanner (Siemens Somatom Definition flash) using different clinical protocols, including single- and dual-energy modes. Four scan protocols were investigated: 1) single-source 120 kV, 110 mA s, 2) single-source 100 kV, 180 mA s, 3) high-pitch 120 kV, 130 mA s and 4) dual-energy with 100/Sn140 kV, eff.mA s 89, 76. The automatic exposure control was switched off for all the scans and the CTDIvol selected was in between 7.12 and 7.37 mGy. The raw data were reconstructed using the reconstruction kernels B31f, B80f and B70f, and slice thicknesses were 1.0 mm and 5.0 mm. Finally, the same parameters and procedures were used for the scanning of water phantom. Friedman test and Wilcoxon-Matched-Pair test were used for statistical analysis. Results: The DLP based on the given CTDIvol values showed significantly lower exposure for protocol 4, when compared to protocol 1 (percent difference 5.18%), protocol 2 (percent diff. 4.51%), and protocol 3 (percent diff. 8.81%). The highest change in Hounsfield Units was observed with dual

Deep Restricted Kernel Machines Using Conjugate Feature Duality.

Science.gov (United States)

Suykens, Johan A K

2017-08-01

The aim of this letter is to propose a theory of deep restricted kernel machines offering new foundations for deep learning with kernel machines. From the viewpoint of deep learning, it is partially related to restricted Boltzmann machines, which are characterized by visible and hidden units in a bipartite graph without hidden-to-hidden connections and deep learning extensions as deep belief networks and deep Boltzmann machines. From the viewpoint of kernel machines, it includes least squares support vector machines for classification and regression, kernel principal component analysis (PCA), matrix singular value decomposition, and Parzen-type models. A key element is to first characterize these kernel machines in terms of so-called conjugate feature duality, yielding a representation with visible and hidden units. It is shown how this is related to the energy form in restricted Boltzmann machines, with continuous variables in a nonprobabilistic setting. In this new framework of so-called restricted kernel machine (RKM) representations, the dual variables correspond to hidden features. Deep RKM are obtained by coupling the RKMs. The method is illustrated for deep RKM, consisting of three levels with a least squares support vector machine regression level and two kernel PCA levels. In its primal form also deep feedforward neural networks can be trained within this framework.

Improved modeling of clinical data with kernel methods.

Science.gov (United States)

Daemen, Anneleen; Timmerman, Dirk; Van den Bosch, Thierry; Bottomley, Cecilia; Kirk, Emma; Van Holsbeke, Caroline; Valentin, Lil; Bourne, Tom; De Moor, Bart

2012-02-01

Despite the rise of high-throughput technologies, clinical data such as age, gender and medical history guide clinical management for most diseases and examinations. To improve clinical management, available patient information should be fully exploited. This requires appropriate modeling of relevant parameters. When kernel methods are used, traditional kernel functions such as the linear kernel are often applied to the set of clinical parameters. These kernel functions, however, have their disadvantages due to the specific characteristics of clinical data, being a mix of variable types with each variable its own range. We propose a new kernel function specifically adapted to the characteristics of clinical data. The clinical kernel function provides a better representation of patients' similarity by equalizing the influence of all variables and taking into account the range r of the variables. Moreover, it is robust with respect to changes in r. Incorporated in a least squares support vector machine, the new kernel function results in significantly improved diagnosis, prognosis and prediction of therapy response. This is illustrated on four clinical data sets within gynecology, with an average increase in test area under the ROC curve (AUC) of 0.023, 0.021, 0.122 and 0.019, respectively. Moreover, when combining clinical parameters and expression data in three case studies on breast cancer, results improved overall with use of the new kernel function and when considering both data types in a weighted fashion, with a larger weight assigned to the clinical parameters. The increase in AUC with respect to a standard kernel function and/or unweighted data combination was maximum 0.127, 0.042 and 0.118 for the three case studies. For clinical data consisting of variables of different types, the proposed kernel function--which takes into account the type and range of each variable--has shown to be a better alternative for linear and non-linear classification problems

Linear and kernel methods for multi- and hypervariate change detection

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg; Canty, Morton J.

2010-01-01

. 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...

Kernel based orthogonalization for change detection in hyperspectral images

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg

function and all quantities needed in the analysis are expressed in terms of this kernel function. This means that we need not know the nonlinear mappings explicitly. Kernel PCA and MNF analyses handle nonlinearities by implicitly transforming data into high (even infinite) dimensional feature space via...... analysis all 126 spectral bands of the HyMap are included. Changes on the ground are most likely due to harvest having taken place between the two acquisitions and solar effects (both solar elevation and azimuth have changed). Both types of kernel analysis emphasize change and unlike kernel PCA, kernel MNF...

Mitigation of artifacts in rtm with migration kernel decomposition

KAUST Repository

Zhan, Ge; Schuster, Gerard T.

2012-01-01

The migration kernel for reverse-time migration (RTM) can be decomposed into four component kernels using Born scattering and migration theory. Each component kernel has a unique physical interpretation and can be interpreted differently

Semi-Supervised Kernel PCA

DEFF Research Database (Denmark)

Walder, Christian; Henao, Ricardo; Mørup, Morten

We present three generalisations of Kernel Principal Components Analysis (KPCA) which incorporate knowledge of the class labels of a subset of the data points. The first, MV-KPCA, penalises within class variances similar to Fisher discriminant analysis. The second, LSKPCA is a hybrid of least...... squares regression and kernel PCA. The final LR-KPCA is an iteratively reweighted version of the previous which achieves a sigmoid loss function on the labeled points. We provide a theoretical risk bound as well as illustrative experiments on real and toy data sets....

Adaptive Metric Kernel Regression

DEFF Research Database (Denmark)

Goutte, Cyril; Larsen, Jan

1998-01-01

Kernel smoothing is a widely used nonparametric pattern recognition technique. By nature, it suffers from the curse of dimensionality and is usually difficult to apply to high input dimensions. In this paper, we propose an algorithm that adapts the input metric used in multivariate regression...... by minimising a cross-validation estimate of the generalisation error. This allows one to automatically adjust the importance of different dimensions. The improvement in terms of modelling performance is illustrated on a variable selection task where the adaptive metric kernel clearly outperforms the standard...

Studies on the radiation drying method for grain, 2: A good drying method of paddy rice from the viewpoint of the drying rate and the crack generation of rice

International Nuclear Information System (INIS)

Horibe, K.; Nakagawa, K.; Tohjo, T.

1990-01-01

A drying rate of paddy rice in a solar heat drying plant was studied. Solar-heated air at the upper part of a plastic house was blasted to the surface of the layer of paddy rice which was piled on the floor of the house. The drying rate increased with higher wind velocity, but it was found that the velocity was limited to 6m/s by the crack generation of the paddy rice. The effects of the layer thickness, the number of layer agitations and the heat supplied on the drying rate at a given wind velocity (6m/s) were expressed with a multiple regression equation. Then, the equation positively proposed appropriate conditions for effective operation of the plant in fine days

21 CFR 176.350 - Tamarind seed kernel powder.

Science.gov (United States)

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 producing...

ASSESSMENT OF CLINICAL IMAGE QUALITY IN PAEDIATRIC ABDOMINAL CT EXAMINATIONS: DEPENDENCY ON THE LEVEL OF ADAPTIVE STATISTICAL ITERATIVE RECONSTRUCTION (ASiR) AND THE TYPE OF CONVOLUTION KERNEL.

Science.gov (United States)

Larsson, Joel; Båth, Magnus; Ledenius, Kerstin; Caisander, Håkan; Thilander-Klang, Anne

2016-06-01

The purpose of this study was to investigate the effect of different combinations of convolution kernel and the level of Adaptive Statistical iterative Reconstruction (ASiR™) on diagnostic image quality as well as visualisation of anatomical structures in paediatric abdominal computed tomography (CT) examinations. Thirty-five paediatric patients with abdominal pain with non-specified pathology undergoing abdominal CT were included in the study. Transaxial stacks of 5-mm-thick images were retrospectively reconstructed at various ASiR levels, in combination with three convolution kernels. Four paediatric radiologists rated the diagnostic image quality and the delineation of six anatomical structures in a blinded randomised visual grading study. Image quality at a given ASiR level was found to be dependent on the kernel, and a more edge-enhancing kernel benefitted from a higher ASiR level. An ASiR level of 70 % together with the Soft™ or Standard™ kernel was suggested to be the optimal combination for paediatric abdominal CT examinations. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Dense Medium Machine Processing Method for Palm Kernel/ Shell ...

African Journals Online (AJOL)

ADOWIE PERE

Cracked palm kernel is a mixture of kernels, broken shells, dusts and other impurities. In ... machine processing method using dense medium, a separator, a shell collector and a kernel .... efficiency, ease of maintenance and uniformity of.

Smoluchowski coagulation models of sea ice thickness distribution dynamics

Science.gov (United States)

Godlovitch, D.; Illner, R.; Monahan, A.

2011-12-01

Sea ice thickness distributions display a ubiquitous exponential decrease with thickness. This tail characterizes the range of ice thickness produced by mechanical redistribution of ice through the process of ridging, rafting, and shearing. We investigate how well the thickness distribution can be simulated by representing mechanical redistribution as a generalized stacking process. Such processes are naturally described by a well-studied class of models known as Smoluchowski Coagulation Models (SCMs), which describe the dynamics of a population of fixed-mass "particles" which combine in pairs to form a "particle" with the combined mass of the constituent pair at a rate which depends on the mass of the interacting particles. Like observed sea ice thickness distributions, the mass distribution of the populations generated by SCMs has an exponential or quasi-exponential form. We use SCMs to model sea ice, identifying mass-increasing particle combinations with thickness-increasing ice redistribution processes. Our model couples an SCM component with a thermodynamic component and generates qualitatively accurate thickness distributions with a variety of rate kernels. Our results suggest that the exponential tail of the sea ice thickness distribution arises from the nature of the ridging process, rather than specific physical properties of sea ice or the spatial arrangement of floes, and that the relative strengths of the dynamic and thermodynamic processes are key in accurately simulating the rate at which the sea ice thickness tail drops off with thickness.

Multivariate and semiparametric kernel regression

OpenAIRE

Härdle, Wolfgang; Müller, Marlene

1997-01-01

The paper gives an introduction to theory and application of multivariate and semiparametric kernel smoothing. Multivariate nonparametric density estimation is an often used pilot tool for examining the structure of data. Regression smoothing helps in investigating the association between covariates and responses. We concentrate on kernel smoothing using local polynomial fitting which includes the Nadaraya-Watson estimator. Some theory on the asymptotic behavior and bandwidth selection is pro...

Notes on the gamma kernel

DEFF Research Database (Denmark)

Barndorff-Nielsen, Ole E.

The density function of the gamma distribution is used as shift kernel in Brownian semistationary processes modelling the timewise behaviour of the velocity in turbulent regimes. This report presents exact and asymptotic properties of the second order structure function under such a model......, and relates these to results of von Karmann and Horwath. But first it is shown that the gamma kernel is interpretable as a Green’s function....

Calculation of the thermal neutron scattering kernel using the synthetic model. Pt. 2. Zero-order energy transfer kernel

International Nuclear Information System (INIS)

Drozdowicz, K.

1995-01-01

A comprehensive unified description of the application of Granada's Synthetic Model to the slow-neutron scattering by the molecular systems is continued. Detailed formulae for the zero-order energy transfer kernel are presented basing on the general formalism of the model. An explicit analytical formula for the total scattering cross section as a function of the incident neutron energy is also obtained. Expressions of the free gas model for the zero-order scattering kernel and for total scattering kernel are considered as a sub-case of the Synthetic Model. (author). 10 refs

Effect of Abiotic Stresses on the Nondestructive Estimation of Rice Leaf Nitrogen Concentration

Directory of Open Access Journals (Sweden)

Stephan M. Haefele

2010-01-01

Full Text Available Decision support tools for non-destructive estimation of rice crop nitrogen (N status (e.g., chlorophyll meter [SPAD] or leaf color chart [LCC] are an established technology for improved N management in irrigated systems, but their value in rainfed environments with frequent abiotic stresses remains untested. Therefore, we studied the effect of drought, salinity, phosphorus (P deficiency, and sulfur (S deficiency on leaf N estimates derived from SPAD and LCC measurements in a greenhouse experiment. Linear relations between chlorophyll concentration and leaf N concentration based on dry weight (Ndw between SPAD values adjusted for leaf thickness and Ndw and between LCC scores adjusted for leaf thickness and Ndw could be confirmed for all treatments and varieties used. Leaf spectral reflectance measurements did not show a stress-dependent change in the reflectance pattern, indicating that no specific element of the photosynthetic complex was affected by the stresses and at the stress level applied. We concluded that SPAD and LCC are potentially useful tools for improved N management in moderately unfavorable rice environments. However, calibration for the most common rice varieties in the target region is recommended to increase the precision of the leaf N estimates.

Characterization of Gluten-free Bread Prepared From Maize, Rice and Tapioca Flours using the Hydrocolloid Seaweed Agar-Agar

OpenAIRE

Alvarenga, Nuno Bartolomeu; Cebola Lidon, Fernando; Belga, Elisa; Motrena, Patrícia; Guerreiro, Suse; Carvalho, Maria João; Canada, João

2011-01-01

Disponível em livre acesso no sítio do DOAJ em http://recent-science.com/index This work aims to check the rheological, physicochemical and sensory characteristics of gluten-free bread produced with corn, rice and tapioca flours, using the hydrocolloid seaweed agar-agar. Relatively to wheat bread, it was found that the pH was slightly lower in gluten-free bread. In the crust only the brightness remained significantly different between both bread types, but in the kernel, the parameters a*,...

Convergence of barycentric coordinates to barycentric kernels

KAUST Repository

Kosinka, Jiří

2016-02-12

We investigate the close correspondence between barycentric coordinates and barycentric kernels from the point of view of the limit process when finer and finer polygons converge to a smooth convex domain. We show that any barycentric kernel is the limit of a set of barycentric coordinates and prove that the convergence rate is quadratic. Our convergence analysis extends naturally to barycentric interpolants and mappings induced by barycentric coordinates and kernels. We verify our theoretical convergence results numerically on several examples.

Convergence of barycentric coordinates to barycentric kernels

KAUST Repository

Kosinka, Jiří ; Barton, Michael

2016-01-01

We investigate the close correspondence between barycentric coordinates and barycentric kernels from the point of view of the limit process when finer and finer polygons converge to a smooth convex domain. We show that any barycentric kernel is the limit of a set of barycentric coordinates and prove that the convergence rate is quadratic. Our convergence analysis extends naturally to barycentric interpolants and mappings induced by barycentric coordinates and kernels. We verify our theoretical convergence results numerically on several examples.

Hadamard Kernel SVM with applications for breast cancer outcome predictions.

Science.gov (United States)

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

2017-12-21

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

Aflatoxin contamination of developing corn kernels.

Science.gov (United States)

Amer, M A

2005-01-01

Preharvest of corn and its contamination with aflatoxin is a serious problem. Some environmental and cultural factors responsible for infection and subsequent aflatoxin production were investigated in this study. Stage of growth and location of kernels on corn ears were found to be one of the important factors in the process of kernel infection with A. flavus & A. parasiticus. The results showed positive correlation between the stage of growth and kernel infection. Treatment of corn with aflatoxin reduced germination, protein and total nitrogen contents. Total and reducing soluble sugar was increase in corn kernels as response to infection. Sucrose and protein content were reduced in case of both pathogens. Shoot system length, seeding fresh weigh and seedling dry weigh was also affected. Both pathogens induced reduction of starch content. Healthy corn seedlings treated with aflatoxin solution were badly affected. Their leaves became yellow then, turned brown with further incubation. Moreover, their total chlorophyll and protein contents showed pronounced decrease. On the other hand, total phenolic compounds were increased. Histopathological studies indicated that A. flavus & A. parasiticus could colonize corn silks and invade developing kernels. Germination of A. flavus spores was occurred and hyphae spread rapidly across the silk, producing extensive growth and lateral branching. Conidiophores and conidia had formed in and on the corn silk. Temperature and relative humidity greatly influenced the growth of A. flavus & A. parasiticus and aflatoxin production.

Kernel Korner : The Linux keyboard driver

NARCIS (Netherlands)

Brouwer, A.E.

1995-01-01

Our Kernel Korner series continues with an article describing the Linux keyboard driver. This article is not for "Kernel Hackers" only--in fact, it will be most useful to those who wish to use their own keyboard to its fullest potential, and those who want to write programs to take advantage of the

The heating of UO_2 kernels in argon gas medium on the physical properties of sintered UO_2 kernels

International Nuclear Information System (INIS)

Damunir; Sri Rinanti Susilowati; Ariyani Kusuma Dewi

2015-01-01

The heating of UO_2 kernels in argon gas medium on the physical properties of sinter UO_2 kernels was conducted. The heated of the UO_2 kernels was conducted in a sinter reactor of a bed type. The sample used was the UO_2 kernels resulted from the reduction results at 800 °C temperature for 3 hours that had the density of 8.13 g/cm"3; porosity of 0.26; O/U ratio of 2.05; diameter of 1146 μm and sphericity of 1.05. The sample was put into a sinter reactor, then it was vacuumed by flowing the argon gas at 180 mmHg pressure to drain the air from the reactor. After that, the cooling water and argon gas were continuously flowed with the pressure of 5 mPa with 1.5 liter/minutes velocity. The reactor temperature was increased and variated at 1200-1500 °C temperature and for 1-4 hours. The sinters UO_2 kernels resulted from the study were analyzed in term of their physical properties including the density, porosity, diameter, sphericity, and specific surface area. The density was analyzed using pycnometer with CCl_4 solution. The porosity was determined using Haynes equation. The diameters and sphericity were showed using the Dino-lite microscope. The specific surface area was determined using surface area meter Nova-1000. The obtained products showed the the heating of UO_2 kernel in argon gas medium were influenced on the physical properties of sinters UO_2 kernel. The condition of best relatively at 1400 °C temperature and 2 hours time. The product resulted from the study was relatively at its best when heating was conducted at 1400 °C temperature and 2 hours time, produced sinters UO_2 kernel with density of 10.14 gr/ml; porosity of 7 %; diameters of 893 μm; sphericity of 1.07 and specific surface area of 4.68 m"2/g with solidify shrinkage of 22 %. (author)

Mitigation of artifacts in rtm with migration kernel decomposition

KAUST Repository

Zhan, Ge

2012-01-01

The migration kernel for reverse-time migration (RTM) can be decomposed into four component kernels using Born scattering and migration theory. Each component kernel has a unique physical interpretation and can be interpreted differently. In this paper, we present a generalized diffraction-stack migration approach for reducing RTM artifacts via decomposition of migration kernel. The decomposition leads to an improved understanding of migration artifacts and, therefore, presents us with opportunities for improving the quality of RTM images.

Realized kernels in practice

DEFF Research Database (Denmark)

Barndorff-Nielsen, Ole Eiler; Hansen, P. Reinhard; Lunde, Asger

2009-01-01

and find a remarkable level of agreement. We identify some features of the high-frequency data, which are challenging for realized kernels. They are when there are local trends in the data, over periods of around 10 minutes, where the prices and quotes are driven up or down. These can be associated......Realized kernels use high-frequency data to estimate daily volatility of individual stock prices. They can be applied to either trade or quote data. Here we provide the details of how we suggest implementing them in practice. We compare the estimates based on trade and quote data for the same stock...

Sorbents based on carbonized rice peel

International Nuclear Information System (INIS)

Mansurova, R. M.; Taipova, R. A.; Zhylybaeva, N. K.; Mansurov, Z. A.; Bijsenbaev, M. A.

2004-01-01

The process receiving of sorbents based on carbonized rice peel (RP) was received and their sorption properties were investigated. Processing carbonization of samples leading on station, this was developed in laboratory of hybrid technology. Carbonization of samples was realized in nitric atmosphere on 400-8000 deg. C. On raising temperature of carbonization content of carbon in samples is rice, hydrogen and oxygen is reduce as a result isolation of volatility products is discover. The samples carbonized on 650 deg. C (910 m 2 /g) owners with maximum removed surface is discover. On carbonization temperature 600-800 deh. C the sorption of ions, which carbonized by sorbents based on rice peel is run to 95-100 %. Electron-microscopic investigation of samples leaded on EM-125 mechanism by accelerating pressure 100 kV. From electron-microscopic print of original samples of RP it is evident, that sample consists of carbonic fractions of different species: carbonic fiber of rounded fractions, fractions of ellipsoid form and of more thickly carbonic structure. Increasing sizes of pores and modification structure of synthesized sorbent is occur during carbonization process. The RP-samples, which carbonized by 650 deg. C has the higher specific surface. Samples consist of thin carbonic scum and reducing specific surface, by higher temperature

Anatomically-aided PET reconstruction using the kernel method.

Science.gov (United States)

Hutchcroft, Will; Wang, Guobao; Chen, Kevin T; Catana, Ciprian; Qi, Jinyi

2016-09-21

This paper extends the kernel method that was proposed previously for dynamic PET reconstruction, to incorporate anatomical side information into the PET reconstruction model. In contrast to existing methods that incorporate anatomical information using a penalized likelihood framework, the proposed method incorporates this information in the simpler maximum likelihood (ML) formulation and is amenable to ordered subsets. The new method also does not require any segmentation of the anatomical image to obtain edge information. We compare the kernel method with the Bowsher method for anatomically-aided PET image reconstruction through a simulated data set. Computer simulations demonstrate that the kernel method offers advantages over the Bowsher method in region of interest quantification. Additionally the kernel method is applied to a 3D patient data set. The kernel method results in reduced noise at a matched contrast level compared with the conventional ML expectation maximization algorithm.

Embedded real-time operating system micro kernel design

Science.gov (United States)

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

2005-12-01

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

Stability of gluten free sweet biscuit elaborated with rice bran, broken rice and okara

Directory of Open Access Journals (Sweden)

Bruna Oliveira TAVARES

2016-01-01

Full Text Available Abstract A challenge to the food sector has been the development of new products incorporating co-products from the food processing industry with minimal impact on their pre-determined structures and adding nutritional quality. In order to add value and develop alternatives for the use of co-products generated during the agroindustrial processing, this work aimed to study the stability of gluten-free sweet biscuits developed with soybean okara, rice bran and broken rice. The formulations were elaborated with increasing percentages of these ingredients and compared with the standard (commercial sweet biscuit for ten months. The analyses were: weight, diameters (internal and external, thickness, specific volume, instrumental parameters of color, texture, scanning electron microscopy, water activity, proximal composition and isoflavones. The experimental sweet biscuits had characteristics of color, weight, volume and diameters (internal and external very similar to the commercial, whereas texture, lipids and energy value decreased, and aw, moisture and protein increased during storage. The sweet biscuits showed the same stability when compared to the standard, and the

Kernel Temporal Differences for Neural Decoding

Science.gov (United States)

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

Collision kernels in the eikonal approximation for Lennard-Jones interaction potential

International Nuclear Information System (INIS)

Zielinska, S.

1985-03-01

The velocity changing collisions are conveniently described by collisional kernels. These kernels depend on an interaction potential and there is a necessity for evaluating them for realistic interatomic potentials. Using the collision kernels, we are able to investigate the redistribution of atomic population's caused by the laser light and velocity changing collisions. In this paper we present the method of evaluating the collision kernels in the eikonal approximation. We discuss the influence of the potential parameters Rsub(o)sup(i), epsilonsub(o)sup(i) on kernel width for a given atomic state. It turns out that unlike the collision kernel for the hard sphere model of scattering the Lennard-Jones kernel is not so sensitive to changes of Rsub(o)sup(i) as the previous one. Contrary to the general tendency of approximating collisional kernels by the Gaussian curve, kernels for the Lennard-Jones potential do not exhibit such a behaviour. (author)

Classification of maize kernels using NIR hyperspectral imaging

DEFF Research Database (Denmark)

Williams, Paul; Kucheryavskiy, Sergey V.

2016-01-01

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...... and specificity of 0.95 and 0.93). Both feature extraction methods can be recommended for classification of maize kernels on production scale....

Influence of wheat kernel physical properties on the pulverizing process.

Science.gov (United States)

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 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.

Evolution kernel for the Dirac field

International Nuclear Information System (INIS)

Baaquie, B.E.

1982-06-01

The evolution kernel for the free Dirac field is calculated using the Wilson lattice fermions. We discuss the difficulties due to which this calculation has not been previously performed in the continuum theory. The continuum limit is taken, and the complete energy eigenfunctions as well as the propagator are then evaluated in a new manner using the kernel. (author)

Gradient-based adaptation of general gaussian kernels.

Science.gov (United States)

Glasmachers, Tobias; Igel, Christian

2005-10-01

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

Analog forecasting with dynamics-adapted kernels

Science.gov (United States)

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.

Open Problem: Kernel methods on manifolds and metric spaces

DEFF Research Database (Denmark)

Feragen, Aasa; Hauberg, Søren

2016-01-01

Radial kernels are well-suited for machine learning over general geodesic metric spaces, where pairwise distances are often the only computable quantity available. We have recently shown that geodesic exponential kernels are only positive definite for all bandwidths when the input space has strong...... linear properties. This negative result hints that radial kernel are perhaps not suitable over geodesic metric spaces after all. Here, however, we present evidence that large intervals of bandwidths exist where geodesic exponential kernels have high probability of being positive definite over finite...... datasets, while still having significant predictive power. From this we formulate conjectures on the probability of a positive definite kernel matrix for a finite random sample, depending on the geometry of the data space and the spread of the sample....

Genetic dissection of the maize kernel development process via conditional QTL mapping for three developing kernel-related traits in an immortalized F2 population.

Science.gov (United States)

Zhang, Zhanhui; Wu, Xiangyuan; Shi, Chaonan; Wang, Rongna; Li, Shengfei; Wang, Zhaohui; Liu, Zonghua; Xue, Yadong; Tang, Guiliang; Tang, Jihua

2016-02-01

Kernel development is an important dynamic trait that determines the final grain yield in maize. To dissect the genetic basis of maize kernel development process, a conditional quantitative trait locus (QTL) analysis was conducted using an immortalized F2 (IF2) population comprising 243 single crosses at two locations over 2 years. Volume (KV) and density (KD) of dried developing kernels, together with kernel weight (KW) at different developmental stages, were used to describe dynamic changes during kernel development. Phenotypic analysis revealed that final KW and KD were determined at DAP22 and KV at DAP29. Unconditional QTL mapping for KW, KV and KD uncovered 97 QTLs at different kernel development stages, of which qKW6b, qKW7a, qKW7b, qKW10b, qKW10c, qKV10a, qKV10b and qKV7 were identified under multiple kernel developmental stages and environments. Among the 26 QTLs detected by conditional QTL mapping, conqKW7a, conqKV7a, conqKV10a, conqKD2, conqKD7 and conqKD8a were conserved between the two mapping methodologies. Furthermore, most of these QTLs were consistent with QTLs and genes for kernel development/grain filling reported in previous studies. These QTLs probably contain major genes associated with the kernel development process, and can be used to improve grain yield and quality through marker-assisted selection.

Kernel-based noise filtering of neutron detector signals

International Nuclear Information System (INIS)

Park, Moon Ghu; Shin, Ho Cheol; Lee, Eun Ki

2007-01-01

This paper describes recently developed techniques for effective filtering of neutron detector signal noise. In this paper, three kinds of noise filters are proposed and their performance is demonstrated for the estimation of reactivity. The tested filters are based on the unilateral kernel filter, unilateral kernel filter with adaptive bandwidth and bilateral filter to show their effectiveness in edge preservation. Filtering performance is compared with conventional low-pass and wavelet filters. The bilateral filter shows a remarkable improvement compared with unilateral kernel and wavelet filters. The effectiveness and simplicity of the unilateral kernel filter with adaptive bandwidth is also demonstrated by applying it to the reactivity measurement performed during reactor start-up physics tests

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

Science.gov (United States)

Jiang, Wenhao; Chung, Fu-lai

2014-01-01

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

Predictive Model Equations for Palm Kernel (Elaeis guneensis J ...

African Journals Online (AJOL)

Estimated error of ± 0.18 and ± 0.2 are envisaged while applying the models for predicting palm kernel and sesame oil colours respectively. Keywords: Palm kernel, Sesame, Palm kernel, Oil Colour, Process Parameters, Model. Journal of Applied Science, Engineering and Technology Vol. 6 (1) 2006 pp. 34-38 ...

Heat kernel analysis for Bessel operators on symmetric cones

DEFF Research Database (Denmark)

Möllers, Jan

2014-01-01

. The heat kernel is explicitly given in terms of a multivariable $I$-Bessel function on $Ω$. Its corresponding heat kernel transform defines a continuous linear operator between $L^p$-spaces. The unitary image of the $L^2$-space under the heat kernel transform is characterized as a weighted Bergmann space...

A multi-scale kernel bundle for LDDMM

DEFF Research Database (Denmark)

Sommer, Stefan Horst; Nielsen, Mads; Lauze, Francois Bernard

2011-01-01

The Large Deformation Diffeomorphic Metric Mapping framework constitutes a widely used and mathematically well-founded setup for registration in medical imaging. At its heart lies the notion of the regularization kernel, and the choice of kernel greatly affects the results of registrations...

Modelling lateral beam quality variations in pencil kernel based photon dose calculations

International Nuclear Information System (INIS)

Nyholm, T; Olofsson, J; Ahnesjoe, A; Karlsson, M

2006-01-01

Standard treatment machines for external radiotherapy are designed to yield flat dose distributions at a representative treatment depth. The common method to reach this goal is to use a flattening filter to decrease the fluence in the centre of the beam. A side effect of this filtering is that the average energy of the beam is generally lower at a distance from the central axis, a phenomenon commonly referred to as off-axis softening. The off-axis softening results in a relative change in beam quality that is almost independent of machine brand and model. Central axis dose calculations using pencil beam kernels show no drastic loss in accuracy when the off-axis beam quality variations are neglected. However, for dose calculated at off-axis positions the effect should be considered, otherwise errors of several per cent can be introduced. This work proposes a method to explicitly include the effect of off-axis softening in pencil kernel based photon dose calculations for arbitrary positions in a radiation field. Variations of pencil kernel values are modelled through a generic relation between half value layer (HVL) thickness and off-axis position for standard treatment machines. The pencil kernel integration for dose calculation is performed through sampling of energy fluence and beam quality in sectors of concentric circles around the calculation point. The method is fully based on generic data and therefore does not require any specific measurements for characterization of the off-axis softening effect, provided that the machine performance is in agreement with the assumed HVL variations. The model is verified versus profile measurements at different depths and through a model self-consistency check, using the dose calculation model to estimate HVL values at off-axis positions. A comparison between calculated and measured profiles at different depths showed a maximum relative error of 4% without explicit modelling of off-axis softening. The maximum relative error

Training Lp norm multiple kernel learning in the primal.

Science.gov (United States)

Liang, Zhizheng; Xia, Shixiong; Zhou, Yong; Zhang, Lei

2013-10-01

Some multiple kernel learning (MKL) models are usually solved by utilizing the alternating optimization method where one alternately solves SVMs in the dual and updates kernel weights. Since the dual and primal optimization can achieve the same aim, it is valuable in exploring how to perform Lp norm MKL in the primal. In this paper, we propose an Lp norm multiple kernel learning algorithm in the primal where we resort to the alternating optimization method: one cycle for solving SVMs in the primal by using the preconditioned conjugate gradient method and other cycle for learning the kernel weights. It is interesting to note that the kernel weights in our method can obtain analytical solutions. Most importantly, the proposed method is well suited for the manifold regularization framework in the primal since solving LapSVMs in the primal is much more effective than solving LapSVMs in the dual. In addition, we also carry out theoretical analysis for multiple kernel learning in the primal in terms of the empirical Rademacher complexity. It is found that optimizing the empirical Rademacher complexity may obtain a type of kernel weights. The experiments on some datasets are carried out to demonstrate the feasibility and effectiveness of the proposed method. Copyright © 2013 Elsevier Ltd. All rights reserved.

Coupling individual kernel-filling processes with source-sink interactions into GREENLAB-Maize.

Science.gov (United States)

Ma, Yuntao; Chen, Youjia; Zhu, Jinyu; Meng, Lei; Guo, Yan; Li, Baoguo; Hoogenboom, Gerrit

2018-02-13

Failure to account for the variation of kernel growth in a cereal crop simulation model may cause serious deviations in the estimates of crop yield. The goal of this research was to revise the GREENLAB-Maize model to incorporate source- and sink-limited allocation approaches to simulate the dry matter accumulation of individual kernels of an ear (GREENLAB-Maize-Kernel). The model used potential individual kernel growth rates to characterize the individual potential sink demand. The remobilization of non-structural carbohydrates from reserve organs to kernels was also incorporated. Two years of field experiments were conducted to determine the model parameter values and to evaluate the model using two maize hybrids with different plant densities and pollination treatments. Detailed observations were made on the dimensions and dry weights of individual kernels and other above-ground plant organs throughout the seasons. Three basic traits characterizing an individual kernel were compared on simulated and measured individual kernels: (1) final kernel size; (2) kernel growth rate; and (3) duration of kernel filling. Simulations of individual kernel growth closely corresponded to experimental data. The model was able to reproduce the observed dry weight of plant organs well. Then, the source-sink dynamics and the remobilization of carbohydrates for kernel growth were quantified to show that remobilization processes accompanied source-sink dynamics during the kernel-filling process. We conclude that the model may be used to explore options for optimizing plant kernel yield by matching maize management to the environment, taking into account responses at the level of individual kernels. © The Author(s) 2018. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Stochastic subset selection for learning with kernel machines.

Science.gov (United States)

Rhinelander, Jason; Liu, Xiaoping P

2012-06-01

Kernel machines have gained much popularity in applications of machine learning. Support vector machines (SVMs) are a subset of kernel machines and generalize well for classification, regression, and anomaly detection tasks. The training procedure for traditional SVMs involves solving a quadratic programming (QP) problem. The QP problem scales super linearly in computational effort with the number of training samples and is often used for the offline batch processing of data. Kernel machines operate by retaining a subset of observed data during training. The data vectors contained within this subset are referred to as support vectors (SVs). The work presented in this paper introduces a subset selection method for the use of kernel machines in online, changing environments. Our algorithm works by using a stochastic indexing technique when selecting a subset of SVs when computing the kernel expansion. The work described here is novel because it separates the selection of kernel basis functions from the training algorithm used. The subset selection algorithm presented here can be used in conjunction with any online training technique. It is important for online kernel machines to be computationally efficient due to the real-time requirements of online environments. Our algorithm is an important contribution because it scales linearly with the number of training samples and is compatible with current training techniques. Our algorithm outperforms standard techniques in terms of computational efficiency and provides increased recognition accuracy in our experiments. We provide results from experiments using both simulated and real-world data sets to verify our algorithm.

RTOS kernel in portable electrocardiograph

Science.gov (United States)

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.

RTOS kernel in portable electrocardiograph

International Nuclear Information System (INIS)

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

2011-01-01

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.

RKRD: Runtime Kernel Rootkit Detection

Science.gov (United States)

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.

Denoising by semi-supervised kernel PCA preimaging

DEFF Research Database (Denmark)

Hansen, Toke Jansen; Abrahamsen, Trine Julie; Hansen, Lars Kai

2014-01-01

Kernel Principal Component Analysis (PCA) has proven a powerful tool for nonlinear feature extraction, and is often applied as a pre-processing step for classification algorithms. In denoising applications Kernel PCA provides the basis for dimensionality reduction, prior to the so-called pre-imag...

Sentiment classification with interpolated information diffusion kernels

NARCIS (Netherlands)

Raaijmakers, S.

2007-01-01

Information diffusion kernels - similarity metrics in non-Euclidean information spaces - have been found to produce state of the art results for document classification. In this paper, we present a novel approach to global sentiment classification using these kernels. We carry out a large array of

Linear and kernel methods for multivariate change detection

DEFF Research Database (Denmark)

Canty, Morton J.; Nielsen, Allan Aasbjerg

2012-01-01

), as well as maximum autocorrelation factor (MAF) and minimum noise fraction (MNF) analyses of IR-MAD images, both linear and kernel-based (nonlinear), may further enhance change signals relative to no-change background. IDL (Interactive Data Language) implementations of IR-MAD, automatic radiometric...... normalization, and kernel PCA/MAF/MNF transformations are presented that function as transparent and fully integrated extensions of the ENVI remote sensing image analysis environment. The train/test approach to kernel PCA is evaluated against a Hebbian learning procedure. Matlab code is also available...... that allows fast data exploration and experimentation with smaller datasets. New, multiresolution versions of IR-MAD that accelerate convergence and that further reduce no-change background noise are introduced. Computationally expensive matrix diagonalization and kernel image projections are programmed...

Panel data specifications in nonparametric kernel regression

DEFF Research Database (Denmark)

Czekaj, Tomasz Gerard; Henningsen, Arne

parametric panel data estimators to analyse the production technology of Polish crop farms. The results of our nonparametric kernel regressions generally differ from the estimates of the parametric models but they only slightly depend on the choice of the kernel functions. Based on economic reasoning, we...

Scuba: scalable kernel-based gene prioritization.

Science.gov (United States)

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

2018-01-25

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

MULTITASKER, Multitasking Kernel for C and FORTRAN Under UNIX

International Nuclear Information System (INIS)

Brooks, E.D. III

1988-01-01

1 - Description of program or function: MULTITASKER implements a multitasking kernel for the C and FORTRAN programming languages that runs under UNIX. The kernel provides a multitasking environment which serves two purposes. The first is to provide an efficient portable environment for the development, debugging, and execution of production multiprocessor programs. The second is to provide a means of evaluating the performance of a multitasking program on model multiprocessor hardware. The performance evaluation features require no changes in the application program source and are implemented as a set of compile- and run-time options in the kernel. 2 - Method of solution: The FORTRAN interface to the kernel is identical in function to the CRI multitasking package provided for the Cray XMP. This provides a migration path to high speed (but small N) multiprocessors once the application has been coded and debugged. With use of the UNIX m4 macro preprocessor, source compatibility can be achieved between the UNIX code development system and the target Cray multiprocessor. The kernel also provides a means of evaluating a program's performance on model multiprocessors. Execution traces may be obtained which allow the user to determine kernel overhead, memory conflicts between various tasks, and the average concurrency being exploited. The kernel may also be made to switch tasks every cpu instruction with a random execution ordering. This allows the user to look for unprotected critical regions in the program. These features, implemented as a set of compile- and run-time options, cause extra execution overhead which is not present in the standard production version of the kernel

Multiple kernel boosting framework based on information measure for classification

International Nuclear Information System (INIS)

Qi, Chengming; Wang, Yuping; Tian, Wenjie; Wang, Qun

2016-01-01

The performance of kernel-based method, such as support vector machine (SVM), is greatly affected by the choice of kernel function. Multiple kernel learning (MKL) is a promising family of machine learning algorithms and has attracted many attentions in recent years. MKL combines multiple sub-kernels to seek better results compared to single kernel learning. In order to improve the efficiency of SVM and MKL, in this paper, the Kullback–Leibler kernel function is derived to develop SVM. The proposed method employs an improved ensemble learning framework, named KLMKB, which applies Adaboost to learning multiple kernel-based classifier. In the experiment for hyperspectral remote sensing image classification, we employ feature selected through Optional Index Factor (OIF) to classify the satellite image. We extensively examine the performance of our approach in comparison to some relevant and state-of-the-art algorithms on a number of benchmark classification data sets and hyperspectral remote sensing image data set. Experimental results show that our method has a stable behavior and a noticeable accuracy for different data set.

Kernel Methods for Mining Instance Data in Ontologies

Science.gov (United States)

Bloehdorn, Stephan; Sure, York

The amount of ontologies and meta data available on the Web is constantly growing. The successful application of machine learning techniques for learning of ontologies from textual data, i.e. mining for the Semantic Web, contributes to this trend. However, no principal approaches exist so far for mining from the Semantic Web. We investigate how machine learning algorithms can be made amenable for directly taking advantage of the rich knowledge expressed in ontologies and associated instance data. Kernel methods have been successfully employed in various learning tasks and provide a clean framework for interfacing between non-vectorial data and machine learning algorithms. In this spirit, we express the problem of mining instances in ontologies as the problem of defining valid corresponding kernels. We present a principled framework for designing such kernels by means of decomposing the kernel computation into specialized kernels for selected characteristics of an ontology which can be flexibly assembled and tuned. Initial experiments on real world Semantic Web data enjoy promising results and show the usefulness of our approach.

Biasing anisotropic scattering kernels for deep-penetration Monte Carlo calculations

International Nuclear Information System (INIS)

Carter, L.L.; Hendricks, J.S.

1983-01-01

The exponential transform is often used to improve the efficiency of deep-penetration Monte Carlo calculations. This technique is usually implemented by biasing the distance-to-collision kernel of the transport equation, but leaving the scattering kernel unchanged. Dwivedi obtained significant improvements in efficiency by biasing an isotropic scattering kernel as well as the distance-to-collision kernel. This idea is extended to anisotropic scattering, particularly the highly forward Klein-Nishina scattering of gamma rays

Investigating differences in light stable isotopes between Thai jasmine rice and Sungyod rice

Science.gov (United States)

Kukusamude, C.; Kongsri, S.

2017-10-01

We report the differences in light stable isotopes between two kinds of Thai rice (Thai jasmine and Sungyod rice). Thai jasmine rice and Sungyod rice were cultivated in the northeast and the south of Thailand. Light isotopes including 13C, 15N and 18O of Thai jasmine rice and Sungyod rice samples were carried out using isotope ratio mass spectrometry (IRMS). Thai jasmine rice (Khao Dawk Mali 105) was cultivated from Thung Kula Rong Hai area, whereas Sungyod rice was cultivated from Phathalung province. Hypothesis testing of difference of each isotope between Thai jasmine rice and Sungyod rice was also studied. The study was the feasibility test whether the light stable isotopes can be the variables to identify Thai jasmine rice and Sungyod rice. The result shows that there was difference in the isotope patterns of Thai jasmine rice and Sungyod rice. Our results may provide the useful information in term of stable isotope profiles of Thai rice.

The integral first collision kernel method for gamma-ray skyshine analysis[Skyshine; Gamma-ray; First collision kernel; Monte Carlo calculation

Energy Technology Data Exchange (ETDEWEB)

Sheu, R.-D.; Chui, C.-S.; Jiang, S.-H. E-mail: shjiang@mx.nthu.edu.tw

2003-12-01

A simplified method, based on the integral of the first collision kernel, is presented for performing gamma-ray skyshine calculations for the collimated sources. The first collision kernels were calculated in air for a reference air density by use of the EGS4 Monte Carlo code. These kernels can be applied to other air densities by applying density corrections. The integral first collision kernel (IFCK) method has been used to calculate two of the ANSI/ANS skyshine benchmark problems and the results were compared with a number of other commonly used codes. Our results were generally in good agreement with others but only spend a small fraction of the computation time required by the Monte Carlo calculations. The scheme of the IFCK method for dealing with lots of source collimation geometry is also presented in this study.

Generation of gamma-ray streaming kernels through cylindrical ducts via Monte Carlo method

International Nuclear Information System (INIS)

Kim, Dong Su

1992-02-01

Since radiation streaming through penetrations is often the critical consideration in protection against exposure of personnel in a nuclear facility, it has been of great concern in radiation shielding design and analysis. Several methods have been developed and applied to the analysis of the radiation streaming in the past such as ray analysis method, single scattering method, albedo method, and Monte Carlo method. But they may be used for order-of-magnitude calculations and where sufficient margin is available, except for the Monte Carlo method which is accurate but requires a lot of computing time. This study developed a Monte Carlo method and constructed a data library of solutions using the Monte Carlo method for radiation streaming through a straight cylindrical duct in concrete walls of a broad, mono-directional, monoenergetic gamma-ray beam of unit intensity. The solution named as plane streaming kernel is the average dose rate at duct outlet and was evaluated for 20 source energies from 0 to 10 MeV, 36 source incident angles from 0 to 70 degrees, 5 duct radii from 10 to 30 cm, and 16 wall thicknesses from 0 to 100 cm. It was demonstrated that average dose rate due to an isotropic point source at arbitrary positions can be well approximated using the plane streaming kernel with acceptable error. Thus, the library of the plane streaming kernels can be used for the accurate and efficient analysis of radiation streaming through a straight cylindrical duct in concrete walls due to arbitrary distributions of gamma-ray sources

Alkaline coal fly ash amendments are recommended for improving rice-peanut crops

Energy Technology Data Exchange (ETDEWEB)

Swain, D.K.; Ghosh, B.C. [Agricultural and Food Engineering Department, Indi an Inst. of Technology, Kharagpur, West Bengal (India); Rautaray, S.K. [RRLRRS, Gerua Via-Hajo, Dist-Kamrup, Assam (India)

2007-05-15

A field experiment investigating amendments of organic material including farmyard manure, paper factory sludge and crop residues combined with fly ash, lime and chemical fertilizer in a rice-peanut cropping system was conducted during 1997-98 and 1998-99 at the Indian Institute of Technology, Kharagpur, India. The soil was an acid lateritic (Halustaf) sandy loam. For rice, an N:P:K level of 90:26.2:33.3 kg/ha was supplied through the organic materials and chemical fertilizer to all the treatments except control and fly ash alone. The required quantities of organic materials were added to supply 30 kg N/ha and the balance amount of N, P and K was supplied through chemical fertilizer. Amendment materials as per fertilization treatments were incorporated to individual plots 15 days before planting of rice during the rainy season. The residual effects were studied on the following peanut crop with application of N:P:K at 30:26.2:33.3 kg/ha through chemical fertilizer alone in all treatments, apart from the control. An application of fly ash at 10 t/ha in combination with chemical fertilizer and organic materials increased the grain yield of rice by 11% compared to chemical fertilizer alone. The residual effect of both lime and fly ash applications combined with direct application of chemical fertilizer increased peanut yields by 30% and 24%, respectively, compared to chemical fertilizer alone. Treatments with fly ash or lime increased P and K uptake in both the crops and oil content in peanut kernel compared to those without the amendments. Alkaline coal fly ash proved to be a better amendment than lime for improving productivity of an acid lateritic soil and enriching the soil with P and K.

A kernel adaptive algorithm for quaternion-valued inputs.

Science.gov (United States)

Paul, Thomas K; Ogunfunmi, Tokunbo

2015-10-01

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

Improving the Bandwidth Selection in Kernel Equating

Science.gov (United States)

Andersson, Björn; von Davier, Alina A.

2014-01-01

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

Point kernels and superposition methods for scatter dose calculations in brachytherapy

International Nuclear Information System (INIS)

Carlsson, A.K.

2000-01-01

Point kernels have been generated and applied for calculation of scatter dose distributions around monoenergetic point sources for photon energies ranging from 28 to 662 keV. Three different approaches for dose calculations have been compared: a single-kernel superposition method, a single-kernel superposition method where the point kernels are approximated as isotropic and a novel 'successive-scattering' superposition method for improved modelling of the dose from multiply scattered photons. An extended version of the EGS4 Monte Carlo code was used for generating the kernels and for benchmarking the absorbed dose distributions calculated with the superposition methods. It is shown that dose calculation by superposition at and below 100 keV can be simplified by using isotropic point kernels. Compared to the assumption of full in-scattering made by algorithms currently in clinical use, the single-kernel superposition method improves dose calculations in a half-phantom consisting of air and water. Further improvements are obtained using the successive-scattering superposition method, which reduces the overestimates of dose close to the phantom surface usually associated with kernel superposition methods at brachytherapy photon energies. It is also shown that scatter dose point kernels can be parametrized to biexponential functions, making them suitable for use with an effective implementation of the collapsed cone superposition algorithm. (author)

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

Science.gov (United States)

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

2013-05-01

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

Wheat kernel dimensions: how do they contribute to kernel weight at ...

Indian Academy of Sciences (India)

2011-12-02

Dec 2, 2011 ... yield components, is greatly influenced by kernel dimensions. (KD), such as ..... six linkage gaps, and it covered 3010.70 cM of the whole genome with an ...... Ersoz E. et al. 2009 The Genetic architecture of maize flowering.

Extraction of rice bran oil from local rice husk

International Nuclear Information System (INIS)

Anwar, J.; Zaman, W.; Salman, M.; Jabeen, N.

2006-01-01

Rice Bran Oil is widely used in pharmaceutical, food and chemical industries due to its unique properties and high medicinal value. In the present work, extraction of rice bran oil from different samples of rice husk collected from local rice shellers by solvent extraction method has been studied. Experiments were conducted using a soxhelt apparatus, to extract rice bran oil using hexane, petroleum ether, ethanol and methanol as the solvents and the yields obtained under different conditions were compared. Batch extraction tests showed that the rate of extraction decreases with time and the solution approaches saturation at an exponential rate. (author)

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

Science.gov (United States)

Zhang, Xueying; Song, Qinbao

2015-01-01

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

Monte Carlo evaluation of a photon pencil kernel algorithm applied to fast neutron therapy treatment planning

Science.gov (United States)

Söderberg, Jonas; Alm Carlsson, Gudrun; Ahnesjö, Anders

2003-10-01

When dedicated software is lacking, treatment planning for fast neutron therapy is sometimes performed using dose calculation algorithms designed for photon beam therapy. In this work Monte Carlo derived neutron pencil kernels in water were parametrized using the photon dose algorithm implemented in the Nucletron TMS (treatment management system) treatment planning system. A rectangular fast-neutron fluence spectrum with energies 0-40 MeV (resembling a polyethylene filtered p(41)+ Be spectrum) was used. Central axis depth doses and lateral dose distributions were calculated and compared with the corresponding dose distributions from Monte Carlo calculations for homogeneous water and heterogeneous slab phantoms. All absorbed doses were normalized to the reference dose at 10 cm depth for a field of radius 5.6 cm in a 30 × 40 × 20 cm3 water test phantom. Agreement to within 7% was found in both the lateral and the depth dose distributions. The deviations could be explained as due to differences in size between the test phantom and that used in deriving the pencil kernel (radius 200 cm, thickness 50 cm). In the heterogeneous phantom, the TMS, with a directly applied neutron pencil kernel, and Monte Carlo calculated absorbed doses agree approximately for muscle but show large deviations for media such as adipose or bone. For the latter media, agreement was substantially improved by correcting the absorbed doses calculated in TMS with the neutron kerma factor ratio and the stopping power ratio between tissue and water. The multipurpose Monte Carlo code FLUKA was used both in calculating the pencil kernel and in direct calculations of absorbed dose in the phantom.

Using the Intel Math Kernel Library on Peregrine | High-Performance

Science.gov (United States)

Computing | NREL the Intel Math Kernel Library on Peregrine Using the Intel Math Kernel Library on Peregrine Learn how to use the Intel Math Kernel Library (MKL) with Peregrine system software. MKL architectures. Core math functions in MKL include BLAS, LAPACK, ScaLAPACK, sparse solvers, fast Fourier

Basic studies on the efficacy of gamma irradiation as insect disinfestation and sterilising techniques for stored rice insects

International Nuclear Information System (INIS)

Abdul Rahim Muda.

1987-01-01

Basic laboratory evaluations on the efficacy of gamma irradiation on the insect sitophilus zeamais, Motch. showed this method of insect control is effective to disinfest both internal and surface infestations in stored milled rice, and substantially reduced reproductive potentials of the weevil. Adult emergence of treated larvae developing within the rice kernel reduced by an average of 82% for treatment doses of 0.05 to 1 kGy. All emerged adults died within 16 days upon emergence at all tested doses. Radiated adult insects showed 100% mortality within 18 days at doses above 0.15 kGy; 26 days at 0.1 kGy and 33 days at 0.05 kGy. However none of the tested doses recorded total immediate mortality after treatment. Significant sterility effects through 93% reduction in F 1 progenies can be achieved by sterilising both parents; but none of the tested doses showed potential for employment as male sterilising technique alone. (author)

TaGS5-3A, a grain size gene selected during wheat improvement for larger kernel and yield.

Science.gov (United States)

Ma, Lin; Li, Tian; Hao, Chenyang; Wang, Yuquan; Chen, Xinhong; Zhang, Xueyong

2016-05-01

Grain size is a dominant component of grain weight in cereals. Earlier studies have shown that OsGS5 plays a major role in regulating both grain size and weight in rice via promotion of cell division. In this study, we isolated TaGS5 homoeologues in wheat and mapped them on chromosomes 3A, 3B and 3D. Temporal and spatial expression analysis showed that TaGS5 homoeologues were preferentially expressed in young spikes and developing grains. Two alleles of TaGS5-3A, TaGS5-3A-T and TaGS5-3A-G were identified in wheat accessions, and a functional marker was developed to discriminate them. Association analysis revealed that TaGS5-3A-T was significantly correlated with larger grain size and higher thousand kernel weight. Biochemical assays showed that TaGS5-3A-T possesses a higher enzymatic activity than TaGS5-3A-G. Transgenic rice lines overexpressing TaGS5-3A-T also exhibited larger grain size and higher thousand kernel weight than TaGS5-3A-G lines, and the transcript levels of cell cycle-related genes in TaGS5-3A-T lines were higher than those in TaGS5-3A-G lines. Furthermore, systematic evolution analysis in diploid, tetraploid and hexaploid wheat showed that TaGS5-3A underwent strong artificial selection during wheat polyploidization events and the frequency changes of two alleles demonstrated that TaGS5-3A-T was favoured in global modern wheat cultivars. These results suggest that TaGS5-3A is a positive regulator of grain size and its favoured allele TaGS5-3A-T exhibits a larger potential application in wheat high-yield breeding. © 2015 Society for Experimental Biology, Association of Applied Biologists and John Wiley & Sons Ltd.

Protein fold recognition using geometric kernel data fusion.

Science.gov (United States)

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

2014-07-01

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

Unsupervised multiple kernel learning for heterogeneous data integration.

Science.gov (United States)

Mariette, Jérôme; Villa-Vialaneix, Nathalie

2018-03-15

Recent high-throughput sequencing advances have expanded the breadth of available omics datasets and the integrated analysis of multiple datasets obtained on the same samples has allowed to gain important insights in a wide range of applications. However, the integration of various sources of information remains a challenge for systems biology since produced datasets are often of heterogeneous types, with the need of developing generic methods to take their different specificities into account. We propose a multiple kernel framework that allows to integrate multiple datasets of various types into a single exploratory analysis. Several solutions are provided to learn either a consensus meta-kernel or a meta-kernel that preserves the original topology of the datasets. We applied our framework to analyse two public multi-omics datasets. First, the multiple metagenomic datasets, collected during the TARA Oceans expedition, was explored to demonstrate that our method is able to retrieve previous findings in a single kernel PCA as well as to provide a new image of the sample structures when a larger number of datasets are included in the analysis. To perform this analysis, a generic procedure is also proposed to improve the interpretability of the kernel PCA in regards with the original data. Second, the multi-omics breast cancer datasets, provided by The Cancer Genome Atlas, is analysed using a kernel Self-Organizing Maps with both single and multi-omics strategies. The comparison of these two approaches demonstrates the benefit of our integration method to improve the representation of the studied biological system. Proposed methods are available in the R package mixKernel, released on CRAN. It is fully compatible with the mixOmics package and a tutorial describing the approach can be found on mixOmics web site http://mixomics.org/mixkernel/. jerome.mariette@inra.fr or nathalie.villa-vialaneix@inra.fr. Supplementary data are available at Bioinformatics online.

Kernel bundle EPDiff

DEFF Research Database (Denmark)

Sommer, Stefan Horst; Lauze, Francois Bernard; Nielsen, Mads

2011-01-01

In the LDDMM framework, optimal warps for image registration are found as end-points of critical paths for an energy functional, and the EPDiff equations describe the evolution along such paths. The Large Deformation Diffeomorphic Kernel Bundle Mapping (LDDKBM) extension of LDDMM allows scale space...

RiceAtlas, a spatial database of global rice calendars and production.

Science.gov (United States)

Laborte, Alice G; Gutierrez, Mary Anne; Balanza, Jane Girly; Saito, Kazuki; Zwart, Sander J; Boschetti, Mirco; Murty, M V R; Villano, Lorena; Aunario, Jorrel Khalil; Reinke, Russell; Koo, Jawoo; Hijmans, Robert J; Nelson, Andrew

2017-05-30

Knowing where, when, and how much rice is planted and harvested is crucial information for understanding the effects of policy, trade, and global and technological change on food security. We developed RiceAtlas, a spatial database on the seasonal distribution of the world's rice production. It consists of data on rice planting and harvesting dates by growing season and estimates of monthly production for all rice-producing countries. Sources used for planting and harvesting dates include global and regional databases, national publications, online reports, and expert knowledge. Monthly production data were estimated based on annual or seasonal production statistics, and planting and harvesting dates. RiceAtlas has 2,725 spatial units. Compared with available global crop calendars, RiceAtlas is nearly ten times more spatially detailed and has nearly seven times more spatial units, with at least two seasons of calendar data, making RiceAtlas the most comprehensive and detailed spatial database on rice calendar and production.

Proteome analysis of the almond kernel (Prunus dulcis).

Science.gov (United States)

Li, Shugang; Geng, Fang; Wang, Ping; Lu, Jiankang; Ma, Meihu

2016-08-01

Almond (Prunus dulcis) is a popular tree nut worldwide and offers many benefits to human health. However, the importance of almond kernel proteins in the nutrition and function in human health requires further evaluation. The present study presents a systematic evaluation of the proteins in the almond kernel using proteomic analysis. The nutrient and amino acid content in almond kernels from Xinjiang is similar to that of American varieties; however, Xinjiang varieties have a higher protein content. Two-dimensional electrophoresis analysis demonstrated a wide distribution of molecular weights and isoelectric points of almond kernel proteins. A total of 434 proteins were identified by LC-MS/MS, and most were proteins that were experimentally confirmed for the first time. Gene ontology (GO) analysis of the 434 proteins indicated that proteins involved in primary biological processes including metabolic processes (67.5%), cellular processes (54.1%), and single-organism processes (43.4%), the main molecular function of almond kernel proteins are in catalytic activity (48.0%), binding (45.4%) and structural molecule activity (11.9%), and proteins are primarily distributed in cell (59.9%), organelle (44.9%), and membrane (22.8%). Almond kernel is a source of a wide variety of proteins. This study provides important information contributing to the screening and identification of almond proteins, the understanding of almond protein function, and the development of almond protein products. © 2015 Society of Chemical Industry. © 2015 Society of Chemical Industry.

Localized Multiple Kernel Learning Via Sample-Wise Alternating Optimization.

Science.gov (United States)

Han, Yina; Yang, Kunde; Ma, Yuanliang; Liu, Guizhong

2014-01-01

Our objective is to train support vector machines (SVM)-based localized multiple kernel learning (LMKL), using the alternating optimization between the standard SVM solvers with the local combination of base kernels and the sample-specific kernel weights. The advantage of alternating optimization developed from the state-of-the-art MKL is the SVM-tied overall complexity and the simultaneous optimization on both the kernel weights and the classifier. Unfortunately, in LMKL, the sample-specific character makes the updating of kernel weights a difficult quadratic nonconvex problem. In this paper, starting from a new primal-dual equivalence, the canonical objective on which state-of-the-art methods are based is first decomposed into an ensemble of objectives corresponding to each sample, namely, sample-wise objectives. Then, the associated sample-wise alternating optimization method is conducted, in which the localized kernel weights can be independently obtained by solving their exclusive sample-wise objectives, either linear programming (for l1-norm) or with closed-form solutions (for lp-norm). At test time, the learnt kernel weights for the training data are deployed based on the nearest-neighbor rule. Hence, to guarantee their generality among the test part, we introduce the neighborhood information and incorporate it into the empirical loss when deriving the sample-wise objectives. Extensive experiments on four benchmark machine learning datasets and two real-world computer vision datasets demonstrate the effectiveness and efficiency of the proposed algorithm.

Control Transfer in Operating System Kernels

Science.gov (United States)

1994-05-13

microkernel system that runs less code in the kernel address space. To realize the performance benefit of allocating stacks in unmapped kseg0 memory, the...review how I modified the Mach 3.0 kernel to use continuations. Because of Mach’s message-passing microkernel structure, interprocess communication was...critical control transfer paths, deeply- nested call chains are undesirable in any case because of the function call overhead. 4.1.3 Microkernel Operating

Bivariate discrete beta Kernel graduation of mortality data.

Science.gov (United States)

Mazza, Angelo; Punzo, Antonio

2015-07-01

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

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

Science.gov (United States)

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

2015-04-01

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

Measurement of Weight of Kernels in a Simulated Cylindrical Fuel Compact for HTGR

International Nuclear Information System (INIS)

Kim, Woong Ki; Lee, Young Woo; Kim, Young Min; Kim, Yeon Ku; Eom, Sung Ho; Jeong, Kyung Chai; Cho, Moon Sung; Cho, Hyo Jin; Kim, Joo Hee

2011-01-01

The TRISO-coated fuel particle for the high temperature gas-cooled reactor (HTGR) is composed of a nuclear fuel kernel and outer coating layers. The coated particles are mixed with graphite matrix to make HTGR fuel element. The weight of fuel kernels in an element is generally measured by the chemical analysis or a gamma-ray spectrometer. Although it is accurate to measure the weight of kernels by the chemical analysis, the samples used in the analysis cannot be put again in the fabrication process. Furthermore, radioactive wastes are generated during the inspection procedure. The gamma-ray spectrometer requires an elaborate reference sample to reduce measurement errors induced from the different geometric shape of test sample from that of reference sample. X-ray computed tomography (CT) is an alternative to measure the weight of kernels in a compact nondestructively. In this study, X-ray CT is applied to measure the weight of kernels in a cylindrical compact containing simulated TRISO-coated particles with ZrO 2 kernels. The volume of kernels as well as the number of kernels in the simulated compact is measured from the 3-D density information. The weight of kernels was calculated from the volume of kernels or the number of kernels. Also, the weight of kernels was measured by extracting the kernels from a compact to review the result of the X-ray CT application

3-D waveform tomography sensitivity kernels for anisotropic media

KAUST Repository

Djebbi, Ramzi

2014-01-01

The complications in anisotropic multi-parameter inversion lie in the trade-off between the different anisotropy parameters. We compute the tomographic waveform sensitivity kernels for a VTI acoustic medium perturbation as a tool to investigate this ambiguity between the different parameters. We use dynamic ray tracing to efficiently handle the expensive computational cost for 3-D anisotropic models. Ray tracing provides also the ray direction information necessary for conditioning the sensitivity kernels to handle anisotropy. The NMO velocity and η parameter kernels showed a maximum sensitivity for diving waves which results in a relevant choice of those parameters in wave equation tomography. The δ parameter kernel showed zero sensitivity; therefore it can serve as a secondary parameter to fit the amplitude in the acoustic anisotropic inversion. Considering the limited penetration depth of diving waves, migration velocity analysis based kernels are introduced to fix the depth ambiguity with reflections and compute sensitivity maps in the deeper parts of the model.

A Fourier-series-based kernel-independent fast multipole method

International Nuclear Information System (INIS)

Zhang Bo; Huang Jingfang; Pitsianis, Nikos P.; Sun Xiaobai

2011-01-01

We present in this paper a new kernel-independent fast multipole method (FMM), named as FKI-FMM, for pairwise particle interactions with translation-invariant kernel functions. FKI-FMM creates, using numerical techniques, sufficiently accurate and compressive representations of a given kernel function over multi-scale interaction regions in the form of a truncated Fourier series. It provides also economic operators for the multipole-to-multipole, multipole-to-local, and local-to-local translations that are typical and essential in the FMM algorithms. The multipole-to-local translation operator, in particular, is readily diagonal and does not dominate in arithmetic operations. FKI-FMM provides an alternative and competitive option, among other kernel-independent FMM algorithms, for an efficient application of the FMM, especially for applications where the kernel function consists of multi-physics and multi-scale components as those arising in recent studies of biological systems. We present the complexity analysis and demonstrate with experimental results the FKI-FMM performance in accuracy and efficiency.

Transgene Flow from Glufosinate-Resistant Rice to Improved and Weedy Rice in China

Directory of Open Access Journals (Sweden)

Yong-liang LU

2014-09-01

Full Text Available The development of transgenic rice with novel traits in China can increase rice productivity, but transgene flow to improved or weedy rice has become a major concern. We aimed to evaluate the potential maximum frequencies of transgene flow from glufosinate-resistant rice to improved rice cultivars and weedy rice. Treatments were arranged in randomized complete blocks with three replicates. Experiments were conducted between 2009 and 2010 at the Center for Environmental Safety Supervision and Inspection for Genetically Modified Plants, China National Rice Research Institute, Hangzhou, China. Glufosinate-resistant japonica rice 99-1 was the pollen donor. The pollen recipients were two inbred japonica rice (Chunjiang 016 and Xiushui 09, two inbred indica rice (Zhongzu 14 and Zhongzao 22, two indica hybrid rice (Zhongzheyou 1 and Guodao 1, and one weedy indica rice (Taizhou weedy rice. The offspring of recipients were planted in the field and sprayed with a commercial dose of glufosinate. Leaf tissues of survivors were analyzed by polymerase chain reaction to detect the presence of the transgene. The frequency of gene flow ranged from 0 to 0.488%. In 2009, the order of gene flow frequency was as follows: weedy rice > Chunjiang 016 > Xiushui 09 and Zhongzu 14 > Guodao 1, Zhongzheyou 1 and Zhongzao 22. Gene flow frequencies were generally higher in 2009 than in 2010, but did not differ significantly among rice materials. Gene flow frequency was the highest in weedy rice followed by the inbred japonica rice. The risk of gene flow differed significantly between years and year-to-year variance could mask risk differences among pollen recipients. Gene flow was generally lesser in taller pollen recipients than in shorter ones, but plant height only accounted for about 30% of variation in gene flow. When flowering synchrony was maximized, as in this study, low frequencies of gene flow occurred from herbicide-resistant japonica rice to other cultivars and

Resummed memory kernels in generalized system-bath master equations

International Nuclear Information System (INIS)

Mavros, Michael G.; Van Voorhis, Troy

2014-01-01

Generalized master equations provide a concise formalism for studying reduced population dynamics. Usually, these master equations require a perturbative expansion of the memory kernels governing the dynamics; in order to prevent divergences, these expansions must be resummed. Resummation techniques of perturbation series are ubiquitous in physics, but they have not been readily studied for the time-dependent memory kernels used in generalized master equations. In this paper, we present a comparison of different resummation techniques for such memory kernels up to fourth order. We study specifically the spin-boson Hamiltonian as a model system bath Hamiltonian, treating the diabatic coupling between the two states as a perturbation. A novel derivation of the fourth-order memory kernel for the spin-boson problem is presented; then, the second- and fourth-order kernels are evaluated numerically for a variety of spin-boson parameter regimes. We find that resumming the kernels through fourth order using a Padé approximant results in divergent populations in the strong electronic coupling regime due to a singularity introduced by the nature of the resummation, and thus recommend a non-divergent exponential resummation (the “Landau-Zener resummation” of previous work). The inclusion of fourth-order effects in a Landau-Zener-resummed kernel is shown to improve both the dephasing rate and the obedience of detailed balance over simpler prescriptions like the non-interacting blip approximation, showing a relatively quick convergence on the exact answer. The results suggest that including higher-order contributions to the memory kernel of a generalized master equation and performing an appropriate resummation can provide a numerically-exact solution to system-bath dynamics for a general spectral density, opening the way to a new class of methods for treating system-bath dynamics

The dipole form of the gluon part of the BFKL kernel

International Nuclear Information System (INIS)

Fadin, V.S.; Fiore, R.; Grabovsky, A.V.; Papa, A.

2007-01-01

The dipole form of the gluon part of the color singlet BFKL kernel in the next-to-leading order (NLO) is obtained in the coordinate representation by direct transfer from the momentum representation, where the kernel was calculated before. With this paper the transformation of the NLO BFKL kernel to the dipole form, started a few months ago with the quark part of the kernel, is completed

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

Science.gov (United States)

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

2018-02-19

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

Rice production in relation to soil quality under different rice-based cropping systems

Science.gov (United States)

Tran Ba, Linh; Sleutel, Steven; Nguyen Van, Qui; Thi, Guong Vo; Le Van, Khoa; Cornelis, Wim

2016-04-01

Soil quality of shallow paddy soils may be improved by introducing upland crops and thus a more diverse crop cultivation pattern. Yet, the causal relationship between crop performance and enhanced soil traits in rice-upland crop rotations remains elusive. The objectives of this study were to (i) find correlations among soil properties under different rice-upland crop systems and link selected soil properties to rice growth and yield, (ii) present appropriate values of soil parameters for sustainable rice productivity in heavy clay soil, (iii) evaluate the effect of rotating rice with upland crops on rice yield and economic benefit in a long-term experiment. A rice-upland crop rotational field experiment in the Vietnamese Mekong delta was conducted for 10 years using a randomized complete block design with four treatments and four replications. Treatments were: (i) rice-rice-rice (control - conventional system as farmers' practice), (ii) rice-maize-rice, (iii) rice-mung bean-rice, and (iv) rice-mung bean-maize. Soil and plant sampling were performed after harvest of the rice crop at the end of the final winter-spring cropping season (i.e. year 10). Results show differences in rice growth and yield, and economic benefit as an effect of the crop rotation system. These differences were linked with changes in bulk density, soil porosity, soil aggregate stability index, soil penetration resistance, soil macro-porosity, soil organic carbon, acid hydrolysable soil C and soil nutrient elements, especially at soil depth of 20-30 cm. This is evidenced by the strong correlation (P < 0.01) between rice plant parameters, rice yield and soil properties such as bulk density, porosity, penetration resistance, soil organic carbon and Chydrolysable. It turned out that good rice root growth and rice yield corresponded to bulk density values lower than 1.3 Mg m-3, soil porosity higher than 50%, penetration resistance below 1.0 MPa, and soil organic carbon above 25 g kg-1. The optimal

RPAN: rice pan-genome browser for ∼3000 rice genomes.

Science.gov (United States)

Sun, Chen; Hu, Zhiqiang; Zheng, Tianqing; Lu, Kuangchen; Zhao, Yue; Wang, Wensheng; Shi, Jianxin; Wang, Chunchao; Lu, Jinyuan; Zhang, Dabing; Li, Zhikang; Wei, Chaochun

2017-01-25

A pan-genome is the union of the gene sets of all the individuals of a clade or a species and it provides a new dimension of genome complexity with the presence/absence variations (PAVs) of genes among these genomes. With the progress of sequencing technologies, pan-genome study is becoming affordable for eukaryotes with large-sized genomes. The Asian cultivated rice, Oryza sativa L., is one of the major food sources for the world and a model organism in plant biology. Recently, the 3000 Rice Genome Project (3K RGP) sequenced more than 3000 rice genomes with a mean sequencing depth of 14.3×, which provided a tremendous resource for rice research. In this paper, we present a genome browser, Rice Pan-genome Browser (RPAN), as a tool to search and visualize the rice pan-genome derived from 3K RGP. RPAN contains a database of the basic information of 3010 rice accessions, including genomic sequences, gene annotations, PAV information and gene expression data of the rice pan-genome. At least 12 000 novel genes absent in the reference genome were included. RPAN also provides multiple search and visualization functions. RPAN can be a rich resource for rice biology and rice breeding. It is available at http://cgm.sjtu.edu.cn/3kricedb/ or http://www.rmbreeding.cn/pan3k. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

A new discrete dipole kernel for quantitative susceptibility mapping.

Science.gov (United States)

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

2018-09-01

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

Genetic Analysis of Kernel Traits in Maize-Teosinte Introgression Populations

Directory of Open Access Journals (Sweden)

Zhengbin Liu

2016-08-01

Full Text Available Seed traits have been targeted by human selection during the domestication of crop species as a way to increase the caloric and nutritional content of food during the transition from hunter-gather to early farming societies. The primary seed trait under selection was likely seed size/weight as it is most directly related to overall grain yield. Additional seed traits involved in seed shape may have also contributed to larger grain. Maize (Zea mays ssp. mays kernel weight has increased more than 10-fold in the 9000 years since domestication from its wild ancestor, teosinte (Z. mays ssp. parviglumis. In order to study how size and shape affect kernel weight, we analyzed kernel morphometric traits in a set of 10 maize-teosinte introgression populations using digital imaging software. We identified quantitative trait loci (QTL for kernel area and length with moderate allelic effects that colocalize with kernel weight QTL. Several genomic regions with strong effects during maize domestication were detected, and a genetic framework for kernel traits was characterized by complex pleiotropic interactions. Our results both confirm prior reports of kernel domestication loci and identify previously uncharacterized QTL with a range of allelic effects, enabling future research into the genetic basis of these traits.

SU-E-T-154: Calculation of Tissue Dose Point Kernels Using GATE Monte Carlo Simulation Toolkit to Compare with Water Dose Point Kernel

Energy Technology Data Exchange (ETDEWEB)

Khazaee, M [shahid beheshti university, Tehran, Tehran (Iran, Islamic Republic of); Asl, A Kamali [Shahid Beheshti University, Tehran, Iran., Tehran, Tehran (Iran, Islamic Republic of); Geramifar, P [Shariati Hospital, Tehran, Iran., Tehran, Tehran (Iran, Islamic Republic of)

2015-06-15

Purpose: the objective of this study was to assess utilizing water dose point kernel (DPK)instead of tissue dose point kernels in convolution algorithms.to the best of our knowledge, in providing 3D distribution of absorbed dose from a 3D distribution of the activity, the human body is considered equivalent to water. as a Result tissue variations are not considered in patient specific dosimetry. Methods: In this study Gate v7.0 was used to calculate tissue dose point kernel. the beta emitter radionuclides which have taken into consideration in this simulation include Y-90, Lu-177 and P-32 which are commonly used in nuclear medicine. the comparison has been performed for dose point kernels of adipose, bone, breast, heart, intestine, kidney, liver, lung and spleen versus water dose point kernel. Results: In order to validate the simulation the Result of 90Y DPK in water were compared with published results of Papadimitroulas et al (Med. Phys., 2012). The results represented that the mean differences between water DPK and other soft tissues DPKs range between 0.6 % and 1.96% for 90Y, except for lung and bone, where the observed discrepancies are 6.3% and 12.19% respectively. The range of DPK difference for 32P is between 1.74% for breast and 18.85% for bone. For 177Lu, the highest difference belongs to bone which is equal to 16.91%. For other soft tissues the least discrepancy is observed in kidney with 1.68%. Conclusion: In all tissues except for lung and bone, the results of GATE for dose point kernel were comparable to water dose point kernel which demonstrates the appropriateness of applying water dose point kernel instead of soft tissues in the field of nuclear medicine.

SU-E-T-154: Calculation of Tissue Dose Point Kernels Using GATE Monte Carlo Simulation Toolkit to Compare with Water Dose Point Kernel

International Nuclear Information System (INIS)

Khazaee, M; Asl, A Kamali; Geramifar, P

2015-01-01

Purpose: the objective of this study was to assess utilizing water dose point kernel (DPK)instead of tissue dose point kernels in convolution algorithms.to the best of our knowledge, in providing 3D distribution of absorbed dose from a 3D distribution of the activity, the human body is considered equivalent to water. as a Result tissue variations are not considered in patient specific dosimetry. Methods: In this study Gate v7.0 was used to calculate tissue dose point kernel. the beta emitter radionuclides which have taken into consideration in this simulation include Y-90, Lu-177 and P-32 which are commonly used in nuclear medicine. the comparison has been performed for dose point kernels of adipose, bone, breast, heart, intestine, kidney, liver, lung and spleen versus water dose point kernel. Results: In order to validate the simulation the Result of 90Y DPK in water were compared with published results of Papadimitroulas et al (Med. Phys., 2012). The results represented that the mean differences between water DPK and other soft tissues DPKs range between 0.6 % and 1.96% for 90Y, except for lung and bone, where the observed discrepancies are 6.3% and 12.19% respectively. The range of DPK difference for 32P is between 1.74% for breast and 18.85% for bone. For 177Lu, the highest difference belongs to bone which is equal to 16.91%. For other soft tissues the least discrepancy is observed in kidney with 1.68%. Conclusion: In all tissues except for lung and bone, the results of GATE for dose point kernel were comparable to water dose point kernel which demonstrates the appropriateness of applying water dose point kernel instead of soft tissues in the field of nuclear medicine

Scientific opinion on the acute health risks related to the presence of cyanogenic glycosides in raw apricot kernels and products derived from raw apricot kernels

DEFF Research Database (Denmark)

Petersen, Annette

of kernels promoted (10 and 60 kernels/day for the general population and cancer patients, respectively), exposures exceeded the ARfD 17–413 and 3–71 times in toddlers and adults, respectively. The estimated maximum quantity of apricot kernels (or raw apricot material) that can be consumed without exceeding...

Kernel Function Tuning for Single-Layer Neural Networks

Czech Academy of Sciences Publication Activity Database

Vidnerová, Petra; Neruda, Roman

-, accepted 28.11. 2017 (2018) ISSN 2278-0149 R&D Projects: GA ČR GA15-18108S Institutional support: RVO:67985807 Keywords : single-layer neural networks * kernel methods * kernel function * optimisation Subject RIV: IN - Informatics, Computer Science http://www.ijmerr.com/

Physical, chemical and microbiological properties of mixed hydrogenated palm kernel oil and cold-pressed rice bran oil as ingredients in non-dairy creamer

Directory of Open Access Journals (Sweden)

Kunakorn Katsri

2014-02-01

Full Text Available The physical, chemical and microbiological properties of hydrogenated palm kernel oil (PKO and cold-pressed rice bran oil (RBOas ingredients in the production of liquid and powdered non-dairy creamer (coffee whitener were studied. The mixing ratios between hydrogenated PKO and cold-pressed RBO were statistically designed as of 100:0, 90:10,80:20, 70:30, 60:40, 50:50, 40:60, 30:70, 20:80, 10:90 and 0:100.The color, absorbanceand viscosity of the mixtures were investigated. As the ratio of cold-pressed RBO increased, the color became darker (L*of 93.06 to 86.25 and the absorbance significantly increased, while the viscosity of the mixtures of 20:80, 10:90 and 0:100 (54 cp. were the highest amongst the ratios tested.The hydrogenated PKO and cold-pressed RBO mixtures were further chemically tested for fatty acids, -oryzanol, -tocopherol, trans-fat contents andantioxidant activity. There were 10 fatty acids present in hydrogenated PKO with saturated fatty acid being the most predominant. Comparatively, there were only 5 fatty acids found in cold-pressed RBO with monounsaturated fatty acid being the major fatty acid. -Oryzanol and -tocopherol contents were higher with increasingcold-pressed RBO from 0-100% (0 to 1,155.00 mg/100g oil and 0.09 to 30.82 mg/100g oil, respectively. Antioxidant activity was increased with increasing cold-pressed RBO from 0-100% (9.26 to 94.24%.The pure hydrogenated PKO contained higher trans-fat content than that of the 90:10 and 80:20 mixtures (2.73, 1.93 and 1.85mg/100g oil,respectively while other samples had no trans-fat. No microorganisms were present in any of the samples.Therefore, substitution of hydrogenated PKO by cold-pressed RBO from 30-100% would offer more nutritional values and better chemical and physical properties of non-dairy creamer.

Analysis of rice purchase decision on rice consumer in Bandung city

Science.gov (United States)

Kusno, K.; Imannurdin, A.; Syamsiyah, N.; Djuwendah, E.

2018-03-01

This study was conducted at three kinds of purchase location which were traditional market, rice kiosk, and supermarket in Bandung City, with survey data of 108 respondents which were selected by systematic random sampling. The aim of this study is to (1) identify consumer characteristics, (2) identify which atribute is considered by consumer in buying rice, and (3) analyze the relationship between purchase decision and income class. Data were analyzed by descriptive analysis and Chi Square test. The results showed most consumers in the traditional market were middle-educated and lower middle-income, at the rice kiosk, the consumer were generally middle-educated and middle-income, and in the supermarkets, the majority were high-educated and upper middle-income consumers. “Kepulenan” be the first priority of most consumers, but for the lower-middle class, the main priority was price. Thus, in case of scarcity and rice price increase, the government should immediately arrange market operations which targeting to lower-middle class consumers. There was a significant relationship between (1) the quality of rice consumed, (2) the frequency of rice purchase per month, and (3) attitudes toward rice price increase; each with the income class. Although the price of rice increase, consumers of middle and upper-middle were remain loyal to the quality of rice they consumed. This indicates rice market in Bandung city is an ideal market for premium rice so that traders and producers are expected to maintain the quality of rice, such as keep using superior seeds and applying good cultivation based on Good Agricultural Practice (GAP) rules.

Local coding based matching kernel method for image classification.

Directory of Open Access Journals (Sweden)

Yan Song

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

Determination of trace metals in non-conventional oilseeds and oil bearing resources by atomic absorption spectrometry

International Nuclear Information System (INIS)

Anwer, T.; Kazi, T.G.; Bhanger, M.I.; Iqbal, S.; Anwar, F.

2003-01-01

The presence of small amount of trace metals in oil and fats is well known to produce deleterious effect. Crude oils and fat of rice bran varieties (super, 86), mango kernel and muskmelon were evaluated for the determination of Ca, Mg, and Zn by using atomic absorption spectrometric technique. Both rice bran varieties (super, 86) were found to contain high calcium content 12.72, 12.11 micro g/g respectively. In case of Mg, highest content noted in mango kernel 9.91 micro g/g and lowest concentration was in rice bran (super) 2.23 micro g/g. The concentration of Zn was high in rice bran (86) 21.0 micro g/g followed by mango kernel 14.4 micro g/g, rice bran (super) 12.20 micro g/g and muskmelon 8.71 micro g/g. The information gained in present study provides baseline for the stability of these oils. (author)

Multivariate realised kernels

DEFF Research Database (Denmark)

Barndorff-Nielsen, Ole; Hansen, Peter Reinhard; Lunde, Asger

We propose a multivariate realised kernel to estimate the ex-post covariation of log-prices. We show this new consistent estimator is guaranteed to be positive semi-definite and is robust to measurement noise of certain types and can also handle non-synchronous trading. It is the first estimator...

Process for producing metal oxide kernels and kernels so obtained

International Nuclear Information System (INIS)

Lelievre, Bernard; Feugier, Andre.

1974-01-01

The process desbribed is for producing fissile or fertile metal oxide kernels used in the fabrication of fuels for high temperature nuclear reactors. This process consists in adding to an aqueous solution of at least one metallic salt, particularly actinide nitrates, at least one chemical compound capable of releasing ammonia, in dispersing drop by drop the solution thus obtained into a hot organic phase to gel the drops and transform them into solid particles. These particles are then washed, dried and treated to turn them into oxide kernels. The organic phase used for the gel reaction is formed of a mixture composed of two organic liquids, one acting as solvent and the other being a product capable of extracting the anions from the metallic salt of the drop at the time of gelling. Preferably an amine is used as product capable of extracting the anions. Additionally, an alcohol that causes a part dehydration of the drops can be employed as solvent, thus helping to increase the resistance of the particles [fr

Ideal Gas Resonance Scattering Kernel Routine for the NJOY Code

International Nuclear Information System (INIS)

Rothenstein, W.

1999-01-01

In a recent publication an expression for the temperature-dependent double-differential ideal gas scattering kernel is derived for the case of scattering cross sections that are energy dependent. Some tabulations and graphical representations of the characteristics of these kernels are presented in Ref. 2. They demonstrate the increased probability that neutron scattering by a heavy nuclide near one of its pronounced resonances will bring the neutron energy nearer to the resonance peak. This enhances upscattering, when a neutron with energy just below that of the resonance peak collides with such a nuclide. A routine for using the new kernel has now been introduced into the NJOY code. Here, its principal features are described, followed by comparisons between scattering data obtained by the new kernel, and the standard ideal gas kernel, when such comparisons are meaningful (i.e., for constant values of the scattering cross section a 0 K). The new ideal gas kernel for variable σ s 0 (E) at 0 K leads to the correct Doppler-broadened σ s T (E) at temperature T

Geodesic exponential kernels: When Curvature and Linearity Conflict

DEFF Research Database (Denmark)

Feragen, Aase; Lauze, François; Hauberg, Søren

2015-01-01

manifold, the geodesic Gaussian kernel is only positive definite if the Riemannian manifold is Euclidean. This implies that any attempt to design geodesic Gaussian kernels on curved Riemannian manifolds is futile. However, we show that for spaces with conditionally negative definite distances the geodesic...

Real time kernel performance monitoring with SystemTap

CERN Multimedia

CERN. Geneva

2018-01-01

SystemTap is a dynamic method of monitoring and tracing the operation of a running Linux kernel. In this talk I will present a few practical use cases where SystemTap allowed me to turn otherwise complex userland monitoring tasks in simple kernel probes.

Comparative Analysis of Kernel Methods for Statistical Shape Learning

National Research Council Canada - National Science Library

Rathi, Yogesh; Dambreville, Samuel; Tannenbaum, Allen

2006-01-01

.... In this work, we perform a comparative analysis of shape learning techniques such as linear PCA, kernel PCA, locally linear embedding and propose a new method, kernelized locally linear embedding...

PhosphoRice: a meta-predictor of rice-specific phosphorylation sites

Directory of Open Access Journals (Sweden)

Que Shufu

2012-02-01

Full Text Available Abstract Background As a result of the growing body of protein phosphorylation sites data, the number of phosphoprotein databases is constantly increasing, and dozens of tools are available for predicting protein phosphorylation sites to achieve fast automatic results. However, none of the existing tools has been developed to predict protein phosphorylation sites in rice. Results In this paper, the phosphorylation site predictors, NetPhos 2.0, NetPhosK, Kinasephos, Scansite, Disphos and Predphosphos, were integrated to construct meta-predictors of rice-specific phosphorylation sites using several methods, including unweighted voting, unreduced weighted voting, reduced unweighted voting and weighted voting strategies. PhosphoRice, the meta-predictor produced by using weighted voting strategy with parameters selected by restricted grid search and conditional random search, performed the best at predicting phosphorylation sites in rice. Its Matthew's Correlation Coefficient (MCC and Accuracy (ACC reached to 0.474 and 73.8%, respectively. Compared to the best individual element predictor (Disphos_default, PhosphoRice archieved a significant increase in MCC of 0.071 (P Conclusions PhosphoRice is a powerful tool for predicting unidentified phosphorylation sites in rice. Compared to the existing methods, we found that our tool showed greater robustness in ACC and MCC. PhosphoRice is available to the public at http://bioinformatics.fafu.edu.cn/PhosphoRice.

Semi-supervised learning for ordinal Kernel Discriminant Analysis.

Science.gov (United States)

Pérez-Ortiz, M; Gutiérrez, P A; Carbonero-Ruz, M; Hervás-Martínez, C

2016-12-01

Ordinal classification considers those classification problems where the labels of the variable to predict follow a given order. Naturally, labelled data is scarce or difficult to obtain in this type of problems because, in many cases, ordinal labels are given by a user or expert (e.g. in recommendation systems). Firstly, this paper develops a new strategy for ordinal classification where both labelled and unlabelled data are used in the model construction step (a scheme which is referred to as semi-supervised learning). More specifically, the ordinal version of kernel discriminant learning is extended for this setting considering the neighbourhood information of unlabelled data, which is proposed to be computed in the feature space induced by the kernel function. Secondly, a new method for semi-supervised kernel learning is devised in the context of ordinal classification, which is combined with our developed classification strategy to optimise the kernel parameters. The experiments conducted compare 6 different approaches for semi-supervised learning in the context of ordinal classification in a battery of 30 datasets, showing (1) the good synergy of the ordinal version of discriminant analysis and the use of unlabelled data and (2) the advantage of computing distances in the feature space induced by the kernel function. Copyright © 2016 Elsevier Ltd. All rights reserved.

Ideal gas scattering kernel for energy dependent cross-sections

International Nuclear Information System (INIS)

Rothenstein, W.; Dagan, R.

1998-01-01

A third, and final, paper on the calculation of the joint kernel for neutron scattering by an ideal gas in thermal agitation is presented, when the scattering cross-section is energy dependent. The kernel is a function of the neutron energy after scattering, and of the cosine of the scattering angle, as in the case of the ideal gas kernel for a constant bound atom scattering cross-section. The final expression is suitable for numerical calculations

Parameter optimization in the regularized kernel minimum noise fraction transformation

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg; Vestergaard, Jacob Schack

2012-01-01

Based on the original, linear minimum noise fraction (MNF) transformation and kernel principal component analysis, a kernel version of the MNF transformation was recently introduced. Inspired by we here give a simple method for finding optimal parameters in a regularized version of kernel MNF...... analysis. We consider the model signal-to-noise ratio (SNR) as a function of the kernel parameters and the regularization parameter. In 2-4 steps of increasingly refined grid searches we find the parameters that maximize the model SNR. An example based on data from the DLR 3K camera system is given....

Rice microstructure

Science.gov (United States)

An understanding of plant structure is desirable to obtain a clear idea of the overall impact of a crop. A mature rice plant consists of leafy components (left in the field post-harvest) and paddy rice (collected). The rice plant is supported by a hollow stem (culm) with leaf sheaths attached to nod...

On flame kernel formation and propagation in premixed gases

Energy Technology Data Exchange (ETDEWEB)

Eisazadeh-Far, Kian; Metghalchi, Hameed [Northeastern University, Mechanical and Industrial Engineering Department, Boston, MA 02115 (United States); Parsinejad, Farzan [Chevron Oronite Company LLC, Richmond, CA 94801 (United States); Keck, James C. [Massachusetts Institute of Technology, Cambridge, MA 02139 (United States)

2010-12-15

Flame kernel formation and propagation in premixed gases have been studied experimentally and theoretically. The experiments have been carried out at constant pressure and temperature in a constant volume vessel located in a high speed shadowgraph system. The formation and propagation of the hot plasma kernel has been simulated for inert gas mixtures using a thermodynamic model. The effects of various parameters including the discharge energy, radiation losses, initial temperature and initial volume of the plasma have been studied in detail. The experiments have been extended to flame kernel formation and propagation of methane/air mixtures. The effect of energy terms including spark energy, chemical energy and energy losses on flame kernel formation and propagation have been investigated. The inputs for this model are the initial conditions of the mixture and experimental data for flame radii. It is concluded that these are the most important parameters effecting plasma kernel growth. The results of laminar burning speeds have been compared with previously published results and are in good agreement. (author)

Insights from Classifying Visual Concepts with Multiple Kernel Learning

Science.gov (United States)

Binder, Alexander; Nakajima, Shinichi; Kloft, Marius; Müller, Christina; Samek, Wojciech; Brefeld, Ulf; Müller, Klaus-Robert; Kawanabe, Motoaki

2012-01-01

Combining information from various image features has become a standard technique in concept recognition tasks. However, the optimal way of fusing the resulting kernel functions is usually unknown in practical applications. Multiple kernel learning (MKL) techniques allow to determine an optimal linear combination of such similarity matrices. Classical approaches to MKL promote sparse mixtures. Unfortunately, 1-norm regularized MKL variants are often observed to be outperformed by an unweighted sum kernel. The main contributions of this paper are the following: we apply a recently developed non-sparse MKL variant to state-of-the-art concept recognition tasks from the application domain of computer vision. We provide insights on benefits and limits of non-sparse MKL and compare it against its direct competitors, the sum-kernel SVM and sparse MKL. We report empirical results for the PASCAL VOC 2009 Classification and ImageCLEF2010 Photo Annotation challenge data sets. Data sets (kernel matrices) as well as further information are available at http://doc.ml.tu-berlin.de/image_mkl/(Accessed 2012 Jun 25). PMID:22936970

[Nitrogen cycling in rice-duck mutual ecosystem during double cropping rice growth season].

Science.gov (United States)

Zhang, Fan; Chen, Yuan-Quan; Sui, Peng; Gao, Wang-Sheng

2012-01-01

Raising duck in paddy rice field is an evolution of Chinese traditional agriculture. In May-October 2010, a field experiment was conducted in a double cropping rice region of Hunan Province, South-central China to study the nitrogen (N) cycling in rice-duck mutual ecosystem during early rice and late rice growth periods, taking a conventional paddy rice field as the control. Input-output analysis method was adopted. The N output in the early rice-duck mutual ecosystem was 239.5 kg x hm(-2), in which, 12.77 kg x hm(-2) were from ducks, and the N output in the late rice-duck mutual ecosystem was 338.7 kg x hm(-2), in which, 23.35 kg x hm(-2) were from ducks. At the present N input level, there existed soil N deficit during the growth seasons of both early rice and late rice. The N input from duck sub-system was mainly from the feed N, and the cycling rate of the duck feces N recycled within the system was 2.5% during early rice growth season and 3.5% during late rice growth season. After late rice harvested, the soil N sequestration was 178.6 kg x hm(-2).

A method for manufacturing kernels of metallic oxides and the thus obtained kernels

International Nuclear Information System (INIS)

Lelievre Bernard; Feugier, Andre.

1973-01-01

A method is described for manufacturing fissile or fertile metal oxide kernels, consisting in adding at least a chemical compound capable of releasing ammonia to an aqueous solution of actinide nitrates dispersing the thus obtained solution dropwise in a hot organic phase so as to gelify the drops and transform them into solid particles, washing drying and treating said particles so as to transform them into oxide kernels. Such a method is characterized in that the organic phase used in the gel-forming reactions comprises a mixture of two organic liquids, one of which acts as a solvent, whereas the other is a product capable of extracting the metal-salt anions from the drops while the gel forming reaction is taking place. This can be applied to the so-called high temperature nuclear reactors [fr

New Fukui, dual and hyper-dual kernels as bond reactivity descriptors.

Science.gov (United States)

Franco-Pérez, Marco; Polanco-Ramírez, Carlos-A; Ayers, Paul W; Gázquez, José L; Vela, Alberto

2017-06-21

We define three new linear response indices with promising applications for bond reactivity using the mathematical framework of τ-CRT (finite temperature chemical reactivity theory). The τ-Fukui kernel is defined as the ratio between the fluctuations of the average electron density at two different points in the space and the fluctuations in the average electron number and is designed to integrate to the finite-temperature definition of the electronic Fukui function. When this kernel is condensed, it can be interpreted as a site-reactivity descriptor of the boundary region between two atoms. The τ-dual kernel corresponds to the first order response of the Fukui kernel and is designed to integrate to the finite temperature definition of the dual descriptor; it indicates the ambiphilic reactivity of a specific bond and enriches the traditional dual descriptor by allowing one to distinguish between the electron-accepting and electron-donating processes. Finally, the τ-hyper dual kernel is defined as the second-order derivative of the Fukui kernel and is proposed as a measure of the strength of ambiphilic bonding interactions. Although these quantities have never been proposed, our results for the τ-Fukui kernel and for τ-dual kernel can be derived in zero-temperature formulation of the chemical reactivity theory with, among other things, the widely-used parabolic interpolation model.

Quasi-Dual-Packed-Kerneled Au49 (2,4-DMBT)27 Nanoclusters and the Influence of Kernel Packing on the Electrochemical Gap.

Science.gov (United States)

Liao, Lingwen; Zhuang, Shengli; Wang, Pu; Xu, Yanan; Yan, Nan; Dong, Hongwei; Wang, Chengming; Zhao, Yan; Xia, Nan; Li, Jin; Deng, Haiteng; Pei, Yong; Tian, Shi-Kai; Wu, Zhikun

2017-10-02

Although face-centered cubic (fcc), body-centered cubic (bcc), hexagonal close-packed (hcp), and other structured gold nanoclusters have been reported, it was unclear whether gold nanoclusters with mix-packed (fcc and non-fcc) kernels exist, and the correlation between kernel packing and the properties of gold nanoclusters is unknown. A Au 49 (2,4-DMBT) 27 nanocluster with a shell electron count of 22 has now been been synthesized and structurally resolved by single-crystal X-ray crystallography, which revealed that Au 49 (2,4-DMBT) 27 contains a unique Au 34 kernel consisting of one quasi-fcc-structured Au 21 and one non-fcc-structured Au 13 unit (where 2,4-DMBTH=2,4-dimethylbenzenethiol). Further experiments revealed that the kernel packing greatly influences the electrochemical gap (EG) and the fcc structure has a larger EG than the investigated non-fcc structure. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Effects of Extraction Methods on Phytochemicals of Rice Bran Oils Produced from Colored Rice.

Science.gov (United States)

Mingyai, Sukanya; Srikaeo, Khongsak; Kettawan, Aikkarach; Singanusong, Riantong; Nakagawa, Kiyotaka; Kimura, Fumiko; Ito, Junya

2018-02-01

Rice bran oil (RBO) especially from colored rice is rich in phytochemicals and has become popular in food, cosmetic, nutraceutical and pharmaceutical applications owing to its offering health benefits. This study determined the contents of phytochemicals including oryzanols, phytosterols, tocopherols (Toc) and tocotrienols (T3) in RBOs extracted using different methods namely cold-press extraction (CPE), solvent extraction (SE) and supercritical CO 2 extraction (SC-CO 2 ). Two colored rice, Red Jasmine rice (RJM, red rice) and Hom-nin rice (HN, black rice), were studied in comparison with the popular Thai fragrant rice Khao Dawk Mali 105 (KDML 105, white rice). RBOs were found to be the rich source of oryzanols, phytosterols, Toc and T3. Rice varieties had a greater effect on the phytochemicals concentrations than extraction methods. HN rice showed the significantly highest concentration of all phytochemicals, followed by RJM and KDML 105 rice, indicating that colored rice contained high concentration of phytochemicals in the oil than non-colored rice. The RBO samples extracted by the CPE method had a greater concentration of the phytochemicals than those extracted by the SC-CO 2 and SE methods, respectively. In terms of phytochemical contents, HN rice extracted using CPE method was found to be the best.

Optimal kernel shape and bandwidth for atomistic support of continuum stress

International Nuclear Information System (INIS)

Ulz, Manfred H; Moran, Sean J

2013-01-01

The treatment of atomistic scale interactions via molecular dynamics simulations has recently found favour for multiscale modelling within engineering. The estimation of stress at a continuum point on the atomistic scale requires a pre-defined kernel function. This kernel function derives the stress at a continuum point by averaging the contribution from atoms within a region surrounding the continuum point. This averaging volume, and therefore the associated stress at a continuum point, is highly dependent on the bandwidth and shape of the kernel. In this paper we propose an effective and entirely data-driven strategy for simultaneously computing the optimal shape and bandwidth for the kernel. We thoroughly evaluate our proposed approach on copper using three classical elasticity problems. Our evaluation yields three key findings: firstly, our technique can provide a physically meaningful estimation of kernel bandwidth; secondly, we show that a uniform kernel is preferred, thereby justifying the default selection of this kernel shape in future work; and thirdly, we can reliably estimate both of these attributes in a data-driven manner, obtaining values that lead to an accurate estimation of the stress at a continuum point. (paper)

Multivariable Christoffel-Darboux Kernels and Characteristic Polynomials of Random Hermitian Matrices

Directory of Open Access Journals (Sweden)

Hjalmar Rosengren

2006-12-01

Full Text Available We study multivariable Christoffel-Darboux kernels, which may be viewed as reproducing kernels for antisymmetric orthogonal polynomials, and also as correlation functions for products of characteristic polynomials of random Hermitian matrices. Using their interpretation as reproducing kernels, we obtain simple proofs of Pfaffian and determinant formulas, as well as Schur polynomial expansions, for such kernels. In subsequent work, these results are applied in combinatorics (enumeration of marked shifted tableaux and number theory (representation of integers as sums of squares.

A multi-resolution approach to heat kernels on discrete surfaces

KAUST Repository

Vaxman, Amir

2010-07-26

Studying the behavior of the heat diffusion process on a manifold is emerging as an important tool for analyzing the geometry of the manifold. Unfortunately, the high complexity of the computation of the heat kernel - the key to the diffusion process - limits this type of analysis to 3D models of modest resolution. We show how to use the unique properties of the heat kernel of a discrete two dimensional manifold to overcome these limitations. Combining a multi-resolution approach with a novel approximation method for the heat kernel at short times results in an efficient and robust algorithm for computing the heat kernels of detailed models. We show experimentally that our method can achieve good approximations in a fraction of the time required by traditional algorithms. Finally, we demonstrate how these heat kernels can be used to improve a diffusion-based feature extraction algorithm. © 2010 ACM.

Compactly Supported Basis Functions as Support Vector Kernels for Classification.

Science.gov (United States)

Wittek, Peter; Tan, Chew Lim

2011-10-01

Wavelet kernels have been introduced for both support vector regression and classification. Most of these wavelet kernels do not use the inner product of the embedding space, but use wavelets in a similar fashion to radial basis function kernels. Wavelet analysis is typically carried out on data with a temporal or spatial relation between consecutive data points. We argue that it is possible to order the features of a general data set so that consecutive features are statistically related to each other, thus enabling us to interpret the vector representation of an object as a series of equally or randomly spaced observations of a hypothetical continuous signal. By approximating the signal with compactly supported basis functions and employing the inner product of the embedding L2 space, we gain a new family of wavelet kernels. Empirical results show a clear advantage in favor of these kernels.

Market testing and consumer acceptance of irradiated rice (Oryza sativa indica Linn.)

International Nuclear Information System (INIS)

Ungsunantwiwat, Ampai; Sophonsa, Sombut

2001-01-01

Special grade A fragrant rice (Jasmine rice) of 13% moisture content was obtained from a local miller in Bangkok. Low density polyethylene, 29.5 cm in width x 45 cm in length and 200 micron in thickness, was used to pack the rice with a net weight of 5 kg. The irradiated food label was printed on one side of the bag to comply with food control regulations. The color and the ink for marking were tested for gamma radiation compatibility. A total of 800 bags of rice, with a total gross weight of 4,000 kg, were irradiated at a minimum absorbed dose at 0.5 kGy for insect disinfestation. Radiation treatment was carried out using a multi-purpose, carrier type gamma irradiator (Model JS-8900, Serial No. IR-155) located at the Thai Irradiation Center. Irradiated rice was distributed on a weekly basis to food stores in Bangkok and Pathum Thani, as well as to various governmental organizations and interested individuals. The product was sold at 60 bahts per bag (approx. US$ 2.4) to retailers. Various commercial brands of non-irradiated rice of 5 kg size, were available in the market at 52 to 78 bahts per bag (approx. US $ 2.08 to 3.12), depending on quality and brand name. During the distribution, a leaflet of educational information was given to the consumer. A simple questionnaire used in the marketing trial indicated that 72% of the consumers bought irradiated rice because of the good quality of the product based on visual inspection, and 28% of them were willing to try the new product. Most consumers preferred irradiated rice to chemical treatment (fumigation) for insect disinfestation. However, most consumers were not sure if they would like to buy irradiated rice again unless its cooking quality was acceptable. Market testing of irradiated rice in the upper-class market or supermarket was unsuccessful because of limitations in the sale and service conditions. To meet the requirement of the supermarket retailer, irradiated rice had to be supplied on a monthly basis, with

Genome-wide Association Analysis of Kernel Weight in Hard Winter Wheat

Science.gov (United States)

Wheat kernel weight is an important and heritable component of wheat grain yield and a key predictor of flour extraction. Genome-wide association analysis was conducted to identify genomic regions associated with kernel weight and kernel weight environmental response in 8 trials of 299 hard winter ...

A Heterogeneous Multi-core Architecture with a Hardware Kernel for Control Systems

DEFF Research Database (Denmark)

Li, Gang; Guan, Wei; Sierszecki, Krzysztof

2012-01-01

Rapid industrialisation has resulted in a demand for improved embedded control systems with features such as predictability, high processing performance and low power consumption. Software kernel implementation on a single processor is becoming more difficult to satisfy those constraints. This pa......Rapid industrialisation has resulted in a demand for improved embedded control systems with features such as predictability, high processing performance and low power consumption. Software kernel implementation on a single processor is becoming more difficult to satisfy those constraints......). Second, a heterogeneous multi-core architecture is investigated, focusing on its performance in relation to hard real-time constraints and predictable behavior. Third, the hardware implementation of HARTEX is designated to support the heterogeneous multi-core architecture. This hardware kernel has...... several advantages over a similar kernel implemented in software: higher-speed processing capability, parallel computation, and separation between the kernel itself and the applications being run. A microbenchmark has been used to compare the hardware kernel with the software kernel, and compare...

Generalized synthetic kernel approximation for elastic moderation of fast neutrons

International Nuclear Information System (INIS)

Yamamoto, Koji; Sekiya, Tamotsu; Yamamura, Yasunori.

1975-01-01

A method of synthetic kernel approximation is examined in some detail with a view to simplifying the treatment of the elastic moderation of fast neutrons. A sequence of unified kernel (fsub(N)) is introduced, which is then divided into two subsequences (Wsub(n)) and (Gsub(n)) according to whether N is odd (Wsub(n)=fsub(2n-1), n=1,2, ...) or even (Gsub(n)=fsub(2n), n=0,1, ...). The W 1 and G 1 kernels correspond to the usual Wigner and GG kernels, respectively, and the Wsub(n) and Gsub(n) kernels for n>=2 represent generalizations thereof. It is shown that the Wsub(n) kernel solution with a relatively small n (>=2) is superior on the whole to the Gsub(n) kernel solution for the same index n, while both converge to the exact values with increasing n. To evaluate the collision density numerically and rapidly, a simple recurrence formula is derived. In the asymptotic region (except near resonances), this recurrence formula allows calculation with a relatively coarse mesh width whenever hsub(a)<=0.05 at least. For calculations in the transient lethargy region, a mesh width of order epsilon/10 is small enough to evaluate the approximate collision density psisub(N) with an accuracy comparable to that obtained analytically. It is shown that, with the present method, an order of approximation of about n=7 should yield a practically correct solution diviating not more than 1% in collision density. (auth.)

Studies on some agronomic and quality characteristics of 271 induced early mutants of rice (Oryza sativa L. cv. Nizersail)

International Nuclear Information System (INIS)

Rahman, Mostafizur; Miah, A.J.; Mansur, M.A.; Kaul, A.K.

1980-01-01

Nizersail, the most popular, recommended rice variety in Bangladesh, was subjected to gamma-irradiation (10 - 25 kR) or ethyl methane sulfonate (EMS) (0.75 - 1.50%) treatments to obtain the mutants with stiff straw and early maturity. In the M 2 generation, 29 gamma-ray- and 8 EMS-induced mutants were selected mainly for short culm length and earliness. Further selections were made in the segregating M 3 and M 4 populations, and finally 400 plants with short culm length were obtained. These 400 selections were grown in M 5 lines, and 271 of these lines were analyzed for several characters. Heading time had singificantly shifted towards earliness in 40 lines. Yield per plant, 1,000-kernel weight, the length/breadth ratio of kernels, alkali spreading index value (indicator of amylose content) and dye-binding capacity (indicator of protein content) were significantly higher than those in the mother variety in 10 - 30% of the lines examined. However, no positive correlation among these characters was observed. Significant negative correlations were observed between heading time and 1,000-kernel weight, and between yield per plant and dye-binding capacity. These results suggest that the early heading plants may produce fewer tillers with bolder seeds, and that high yielding types may not simultaneously show high protein content. (Kaihara, S.)

Validation of Born Traveltime Kernels

Science.gov (United States)

Baig, A. M.; Dahlen, F. A.; Hung, S.

2001-12-01

Most inversions for Earth structure using seismic traveltimes rely on linear ray theory to translate observed traveltime anomalies into seismic velocity anomalies distributed throughout the mantle. However, ray theory is not an appropriate tool to use when velocity anomalies have scale lengths less than the width of the Fresnel zone. In the presence of these structures, we need to turn to a scattering theory in order to adequately describe all of the features observed in the waveform. By coupling the Born approximation to ray theory, the first order dependence of heterogeneity on the cross-correlated traveltimes (described by the Fréchet derivative or, more colourfully, the banana-doughnut kernel) may be determined. To determine for what range of parameters these banana-doughnut kernels outperform linear ray theory, we generate several random media specified by their statistical properties, namely the RMS slowness perturbation and the scale length of the heterogeneity. Acoustic waves are numerically generated from a point source using a 3-D pseudo-spectral wave propagation code. These waves are then recorded at a variety of propagation distances from the source introducing a third parameter to the problem: the number of wavelengths traversed by the wave. When all of the heterogeneity has scale lengths larger than the width of the Fresnel zone, ray theory does as good a job at predicting the cross-correlated traveltime as the banana-doughnut kernels do. Below this limit, wavefront healing becomes a significant effect and ray theory ceases to be effective even though the kernels remain relatively accurate provided the heterogeneity is weak. The study of wave propagation in random media is of a more general interest and we will also show our measurements of the velocity shift and the variance of traveltime compare to various theoretical predictions in a given regime.

Effect of Palm Kernel Cake Replacement and Enzyme ...

African Journals Online (AJOL)

A feeding trial which lasted for twelve weeks was conducted to study the performance of finisher pigs fed five different levels of palm kernel cake replacement for maize (0%, 40%, 40%, 60%, 60%) in a maize-palm kernel cake based ration with or without enzyme supplementation. It was a completely randomized design ...

DETERMINATION OF SPATIAL INTEGRATION AND SUBSTITUTION OF FOREIGN RICE FOR LOCAL RICE IN GHANA

Directory of Open Access Journals (Sweden)

Philip Kofi ADOM

2014-11-01

Full Text Available This study tested for spatial integration in the rice market and the substitution of imported rice for local rice in Ghana. It is established that the markets for domestic imported rice are well-integrated, but not complete. The imperfect spatial integration of domestic foreign rice markets implies that the market provides opportunities for arbitrage. Price leadership roles are found to be determined by the kind of sub-inter-regional-trade network defined. However, in all, the Accra market emerged as a dominant market leader in the domestic foreign rice market. There is evidence of significant regional substitution of foreign rice for local rice in the long run, but the result is mixed in the short run. The result that local rice is not a perfect substitute for imported rice implies that price disincentive measures such as increasing the import tariffs on foreign rice will only produce a mild effect on increasing the producer price faced by local rice farmers, but aggravate the burden on households’ budget.

[Effects of fish on field resource utilization and rice growth in rice-fish coculture].

Science.gov (United States)

Zhang, Jian; Hu, Liang Liang; Ren, Wei Zheng; Guo, Liang; Wu, Min Fang; Tang, Jian Jun; Chen, Xin

2017-01-01

Rice field can provide habitat for fish and other aquatic animals. Rice-fish coculture can increase rice yield and simultaneously reduce the use of chemicals through reducing rice pest occurrence and nutrient complementary use. However, how fish uses food sources (e.g. phytoplankton, weeds, duckweed, macro-algal and snail) from rice field, and whether the nutrients releasing from those food sources due to fish transforming can improve rice growth are still unknown. Here, we conducted two field experiments to address these questions. One was to investigate the pattern of fish activity in the field using the method of video recording. The other was to examine the utilization of field resources by fish using stable isotope technology. Rice growth and rice yield were also exa-mined. Results showed that fish tended to be more active and significantly expanded the activity range in the rice-fish coculture compared to fish monoculture (fish not living together with rice plants). The contributions of 3 potential aquatic organisms (duckweed, phytoplankton and snail) to fish dietary were 22.7%, 34.8% and 30.0% respectively under rice-fish coculture without feed. Under the treatment with feed, however, the contributions of these 3 aquatic organisms to the fish die-tary were 8.9%, 5.9% and 1.6% respectively. The feed contribution was 71.0%. Rice-fish coculture significantly increased the nitrogen concentration in rice leaves, prolonged tillering stage by 10-12 days and increased rice spike rate and yield. The results suggested that raising fish in paddy field may transform the nutrients contained in field resources to bioavailable for rice plants through fish feeding activity, which can improve rice growth and rice yield.

Efficient Online Subspace Learning With an Indefinite Kernel for Visual Tracking and Recognition

NARCIS (Netherlands)

Liwicki, Stephan; Zafeiriou, Stefanos; Tzimiropoulos, Georgios; Pantic, Maja

2012-01-01

We propose an exact framework for online learning with a family of indefinite (not positive) kernels. As we study the case of nonpositive kernels, we first show how to extend kernel principal component analysis (KPCA) from a reproducing kernel Hilbert space to Krein space. We then formulate an

Flour quality and kernel hardness connection in winter wheat

Directory of Open Access Journals (Sweden)

Szabó B. P.

2016-12-01

Full Text Available Kernel hardness is controlled by friabilin protein and it depends on the relation between protein matrix and starch granules. Friabilin is present in high concentration in soft grain varieties and in low concentration in hard grain varieties. The high gluten, hard wheat our generally contains about 12.0–13.0% crude protein under Mid-European conditions. The relationship between wheat protein content and kernel texture is usually positive and kernel texture influences the power consumption during milling. Hard-textured wheat grains require more grinding energy than soft-textured grains.

Deep kernel learning method for SAR image target recognition

Science.gov (United States)

Chen, Xiuyuan; Peng, Xiyuan; Duan, Ran; Li, Junbao

2017-10-01

With the development of deep learning, research on image target recognition has made great progress in recent years. Remote sensing detection urgently requires target recognition for military, geographic, and other scientific research. This paper aims to solve the synthetic aperture radar image target recognition problem by combining deep and kernel learning. The model, which has a multilayer multiple kernel structure, is optimized layer by layer with the parameters of Support Vector Machine and a gradient descent algorithm. This new deep kernel learning method improves accuracy and achieves competitive recognition results compared with other learning methods.

Influence of differently processed mango seed kernel meal on ...

African Journals Online (AJOL)

Influence of differently processed mango seed kernel meal on performance response of west African ... and TD( consisted spear grass and parboiled mango seed kernel meal with concentrate diet in a ratio of 35:30:35). ... HOW TO USE AJOL.

A framework for dense triangular matrix kernels on various manycore architectures

KAUST Repository

Charara, Ali

2017-06-06

We present a new high-performance framework for dense triangular Basic Linear Algebra Subroutines (BLAS) kernels, ie, triangular matrix-matrix multiplication (TRMM) and triangular solve (TRSM), on various manycore architectures. This is an extension of a previous work on a single GPU by the same authors, presented at the EuroPar\\'16 conference, in which we demonstrated the effectiveness of recursive formulations in enhancing the performance of these kernels. In this paper, the performance of triangular BLAS kernels on a single GPU is further enhanced by implementing customized in-place CUDA kernels for TRMM and TRSM, which are called at the bottom of the recursion. In addition, a multi-GPU implementation of TRMM and TRSM is proposed and we show an almost linear performance scaling, as the number of GPUs increases. Finally, the algorithmic recursive formulation of these triangular BLAS kernels is in fact oblivious to the targeted hardware architecture. We, therefore, port these recursive kernels to homogeneous x86 hardware architectures by relying on the vendor optimized BLAS implementations. Results reported on various hardware architectures highlight a significant performance improvement against state-of-the-art implementations. These new kernels are freely available in the KAUST BLAS (KBLAS) open-source library at https://github.com/ecrc/kblas.

A framework for dense triangular matrix kernels on various manycore architectures

KAUST Repository

Charara, Ali; Keyes, David E.; Ltaief, Hatem

2017-01-01

We present a new high-performance framework for dense triangular Basic Linear Algebra Subroutines (BLAS) kernels, ie, triangular matrix-matrix multiplication (TRMM) and triangular solve (TRSM), on various manycore architectures. This is an extension of a previous work on a single GPU by the same authors, presented at the EuroPar'16 conference, in which we demonstrated the effectiveness of recursive formulations in enhancing the performance of these kernels. In this paper, the performance of triangular BLAS kernels on a single GPU is further enhanced by implementing customized in-place CUDA kernels for TRMM and TRSM, which are called at the bottom of the recursion. In addition, a multi-GPU implementation of TRMM and TRSM is proposed and we show an almost linear performance scaling, as the number of GPUs increases. Finally, the algorithmic recursive formulation of these triangular BLAS kernels is in fact oblivious to the targeted hardware architecture. We, therefore, port these recursive kernels to homogeneous x86 hardware architectures by relying on the vendor optimized BLAS implementations. Results reported on various hardware architectures highlight a significant performance improvement against state-of-the-art implementations. These new kernels are freely available in the KAUST BLAS (KBLAS) open-source library at https://github.com/ecrc/kblas.

PERI - auto-tuning memory-intensive kernels for multicore

International Nuclear Information System (INIS)

Williams, S; Carter, J; Oliker, L; Shalf, J; Yelick, K; Bailey, D; Datta, K

2008-01-01

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

PERI - Auto-tuning Memory Intensive Kernels for Multicore

Energy Technology Data Exchange (ETDEWEB)

Bailey, David H; Williams, Samuel; Datta, Kaushik; Carter, Jonathan; Oliker, Leonid; Shalf, John; Yelick, Katherine; Bailey, David H

2008-06-24

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

Kernel Bayesian ART and ARTMAP.

Science.gov (United States)

Masuyama, Naoki; Loo, Chu Kiong; Dawood, Farhan

2018-02-01

Adaptive Resonance Theory (ART) is one of the successful approaches to resolving "the plasticity-stability dilemma" in neural networks, and its supervised learning model called ARTMAP is a powerful tool for classification. Among several improvements, such as Fuzzy or Gaussian based models, the state of art model is Bayesian based one, while solving the drawbacks of others. However, it is known that the Bayesian approach for the high dimensional and a large number of data requires high computational cost, and the covariance matrix in likelihood becomes unstable. This paper introduces Kernel Bayesian ART (KBA) and ARTMAP (KBAM) by integrating Kernel Bayes' Rule (KBR) and Correntropy Induced Metric (CIM) to Bayesian ART (BA) and ARTMAP (BAM), respectively, while maintaining the properties of BA and BAM. The kernel frameworks in KBA and KBAM are able to avoid the curse of dimensionality. In addition, the covariance-free Bayesian computation by KBR provides the efficient and stable computational capability to KBA and KBAM. Furthermore, Correntropy-based similarity measurement allows improving the noise reduction ability even in the high dimensional space. The simulation experiments show that KBA performs an outstanding self-organizing capability than BA, and KBAM provides the superior classification ability than BAM, respectively. Copyright © 2017 Elsevier Ltd. All rights reserved.

Low-Resolution Tactile Image Recognition for Automated Robotic Assembly Using Kernel PCA-Based Feature Fusion and Multiple Kernel Learning-Based Support Vector Machine

Directory of Open Access Journals (Sweden)

Yi-Hung Liu

2014-01-01

Full Text Available In this paper, we propose a robust tactile sensing image recognition scheme for automatic robotic assembly. First, an image reprocessing procedure is designed to enhance the contrast of the tactile image. In the second layer, geometric features and Fourier descriptors are extracted from the image. Then, kernel principal component analysis (kernel PCA is applied to transform the features into ones with better discriminating ability, which is the kernel PCA-based feature fusion. The transformed features are fed into the third layer for classification. In this paper, we design a classifier by combining the multiple kernel learning (MKL algorithm and support vector machine (SVM. We also design and implement a tactile sensing array consisting of 10-by-10 sensing elements. Experimental results, carried out on real tactile images acquired by the designed tactile sensing array, show that the kernel PCA-based feature fusion can significantly improve the discriminating performance of the geometric features and Fourier descriptors. Also, the designed MKL-SVM outperforms the regular SVM in terms of recognition accuracy. The proposed recognition scheme is able to achieve a high recognition rate of over 85% for the classification of 12 commonly used metal parts in industrial applications.

Design and construction of palm kernel cracking and separation ...

African Journals Online (AJOL)

Design and construction of palm kernel cracking and separation machines. ... Username, Password, Remember me, or Register. DOWNLOAD FULL TEXT Open Access DOWNLOAD FULL TEXT Subscription or Fee Access. Design and construction of palm kernel cracking and separation machines. JO Nordiana, K ...

Variable kernel density estimation in high-dimensional feature spaces

CSIR Research Space (South Africa)

Van der Walt, Christiaan M

2017-02-01

Full Text Available Estimating the joint probability density function of a dataset is a central task in many machine learning applications. In this work we address the fundamental problem of kernel bandwidth estimation for variable kernel density estimation in high...

Heat Kernel Asymptotics of Zaremba Boundary Value Problem

Energy Technology Data Exchange (ETDEWEB)

Avramidi, Ivan G. [Department of Mathematics, New Mexico Institute of Mining and Technology (United States)], E-mail: iavramid@nmt.edu

2004-03-15

The Zaremba boundary-value problem is a boundary value problem for Laplace-type second-order partial differential operators acting on smooth sections of a vector bundle over a smooth compact Riemannian manifold with smooth boundary but with discontinuous boundary conditions, which include Dirichlet boundary conditions on one part of the boundary and Neumann boundary conditions on another part of the boundary. We study the heat kernel asymptotics of Zaremba boundary value problem. The construction of the asymptotic solution of the heat equation is described in detail and the heat kernel is computed explicitly in the leading approximation. Some of the first nontrivial coefficients of the heat kernel asymptotic expansion are computed explicitly.

Graphical analyses of connected-kernel scattering equations

International Nuclear Information System (INIS)

Picklesimer, A.

1982-10-01

Simple graphical techniques are employed to obtain a new (simultaneous) derivation of a large class of connected-kernel scattering equations. This class includes the Rosenberg, Bencze-Redish-Sloan, and connected-kernel multiple scattering equations as well as a host of generalizations of these and other equations. The graphical method also leads to a new, simplified form for some members of the class and elucidates the general structural features of the entire class

An Ensemble Approach to Building Mercer Kernels with Prior Information

Science.gov (United States)

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

2005-01-01

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

Exploration of Shorea robusta (Sal seeds, kernels and its oil

Directory of Open Access Journals (Sweden)

Shashi Kumar C.

2016-12-01

Full Text Available Physical, mechanical, and chemical properties of Shorea robusta seed with wing, seed without wing, and kernel were investigated in the present work. The physico-chemical composition of sal oil was also analyzed. The physico-mechanical properties and proximate composition of seed with wing, seed without wing, and kernel at three moisture contents of 9.50% (w.b, 9.54% (w.b, and 12.14% (w.b, respectively, were studied. The results show that the moisture content of the kernel was highest as compared to seed with wing and seed without wing. The sphericity of the kernel was closer to that of a sphere as compared to seed with wing and seed without wing. The hardness of the seed with wing (32.32, N/mm and seed without wing (42.49, N/mm was lower than the kernels (72.14, N/mm. The proximate composition such as moisture, protein, carbohydrates, oil, crude fiber, and ash content were also determined. The kernel (30.20%, w/w contains higher oil percentage as compared to seed with wing and seed without wing. The scientific data from this work are important for designing of equipment and processes for post-harvest value addition of sal seeds.

A survey of kernel-type estimators for copula and their applications

Science.gov (United States)

Sumarjaya, I. W.

2017-10-01

Copulas have been widely used to model nonlinear dependence structure. Main applications of copulas include areas such as finance, insurance, hydrology, rainfall to name but a few. The flexibility of copula allows researchers to model dependence structure beyond Gaussian distribution. Basically, a copula is a function that couples multivariate distribution functions to their one-dimensional marginal distribution functions. In general, there are three methods to estimate copula. These are parametric, nonparametric, and semiparametric method. In this article we survey kernel-type estimators for copula such as mirror reflection kernel, beta kernel, transformation method and local likelihood transformation method. Then, we apply these kernel methods to three stock indexes in Asia. The results of our analysis suggest that, albeit variation in information criterion values, the local likelihood transformation method performs better than the other kernel methods.

Effects of White Rice, Brown Rice and Germinated Brown Rice on Antioxidant Status of Type 2 Diabetic Rats

OpenAIRE

Imam, Mustapha Umar; Musa, Siti Nor Asma; Azmi, Nur Hanisah; Ismail, Maznah

2012-01-01

Oxidative stress is implicated in the pathogenesis of diabetic complications, and can be increased by diet like white rice (WR). Though brown rice (BR) and germinated brown rice (GBR) have high antioxidant potentials as a result of their bioactive compounds, reports of their effects on oxidative stress-related conditions such as type 2 diabetes are lacking. We hypothesized therefore that if BR and GBR were to improve antioxidant status, they would be better for rice consuming populations inst...

Irradiation performance of coated fuel particles with fission product retaining kernel additives

International Nuclear Information System (INIS)

Foerthmann, R.

1979-10-01

The four irradiation experiments FRJ2-P17, FRJ2-P18, FRJ2-P19, and FRJ2-P20 for testing the efficiency of fission product-retaining kernel additives in coated fuel particles are described. The evaluation of the obtained experimental data led to the following results: - zirconia and alumina kernel additives are not suitable for an effective fission product retention in oxide fuel kernels, - alumina-silica kernel additives reduce the in-pile release of Sr 90 and Ba 140 from BISO-coated particles at temperatures of about 1200 0 C by two orders of magnitude, and the Cs release from kernels by one order of magnitude, - effective transport coefficients including all parameters which contribute to kernel release are given for (Th,U)O 2 mixed oxide kernels and low enriched UO 2 kernels containing 5 wt.% alumina-silica additives: 10g sub(K)/cm 2 s -1 = - 36 028/T + 6,261 (Sr 90), 10g Dsub(K)/cm 2 c -2 = - 29 646/T + 5,826 (Cs 134/137), alumina-silica kernel additives are ineffective for retaining Ag 110 m in coated particles. However, also an intact SiC-interlayer was found not to be effective at temperatures above 1200 0 C, - the penetration of the buffer layer by fission product containing eutectic additive melt during irradiation can be avoided by using additives which consist of alumina and mullite without an excess of silica, - annealing of LASER-failed irradiated particles and the irradiation test FRJ12-P20 indicate that the efficiency of alumina-silica kernel additives is not altered if the coating becomes defect. (orig.) [de

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

Science.gov (United States)

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

2017-01-01

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

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

Science.gov (United States)

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

2017-01-01

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

Dose calculation methods in photon beam therapy using energy deposition kernels

International Nuclear Information System (INIS)

Ahnesjoe, A.

1991-01-01

The problem of calculating accurate dose distributions in treatment planning of megavoltage photon radiation therapy has been studied. New dose calculation algorithms using energy deposition kernels have been developed. The kernels describe the transfer of energy by secondary particles from a primary photon interaction site to its surroundings. Monte Carlo simulations of particle transport have been used for derivation of kernels for primary photon energies form 0.1 MeV to 50 MeV. The trade off between accuracy and calculational speed has been addressed by the development of two algorithms; one point oriented with low computional overhead for interactive use and one for fast and accurate calculation of dose distributions in a 3-dimensional lattice. The latter algorithm models secondary particle transport in heterogeneous tissue by scaling energy deposition kernels with the electron density of the tissue. The accuracy of the methods has been tested using full Monte Carlo simulations for different geometries, and found to be superior to conventional algorithms based on scaling of broad beam dose distributions. Methods have also been developed for characterization of clinical photon beams in entities appropriate for kernel based calculation models. By approximating the spectrum as laterally invariant, an effective spectrum and dose distribution for contaminating charge particles are derived form depth dose distributions measured in water, using analytical constraints. The spectrum is used to calculate kernels by superposition of monoenergetic kernels. The lateral energy fluence distribution is determined by deconvolving measured lateral dose distributions by a corresponding pencil beam kernel. Dose distributions for contaminating photons are described using two different methods, one for estimation of the dose outside of the collimated beam, and the other for calibration of output factors derived from kernel based dose calculations. (au)

Boundary singularity of Poisson and harmonic Bergman kernels

Czech Academy of Sciences Publication Activity Database

Engliš, Miroslav

2015-01-01

Roč. 429, č. 1 (2015), s. 233-272 ISSN 0022-247X R&D Projects: GA AV ČR IAA100190802 Institutional support: RVO:67985840 Keywords : harmonic Bergman kernel * Poisson kernel * pseudodifferential boundary operators Subject RIV: BA - General Mathematics Impact factor: 1.014, year: 2015 http://www.sciencedirect.com/science/article/pii/S0022247X15003170

Optimal Bandwidth Selection in Observed-Score Kernel Equating

Science.gov (United States)

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…

Kernel structures for Clouds

Science.gov (United States)

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.

Methodical characterization of rice ( Oryza sativa bran oil from Pakistan

Directory of Open Access Journals (Sweden)

Mahmood, Zahid

2005-06-01

Full Text Available The hexane-extracted oil content of four varieties of rice (Oryza sativa viz. Super Kernel, 386, 385 and Basmati, bran was ranged 14.70-19.10 %. Other physical and chemical parameters of the extracted oils were as follow: Iodine value 112.40, 109.80, 105.1 and 103.70; refractive index ( 40 °C 1.4650, 1.4680, 1.4657 and 1.4660; density ( 40 °C 0.919, 0.913, 0.909 and 0.911; saponification value 183, 177, 186 and 190; unsaponifiable matter 6.15, 5.60, 4.98 and 5.40 % respectively. Tocopherols ( α, γ and δ in the oils were: 284.00, 175.12, 180.42, 300.06; 83.40, 98.70, 120.70, 90.60; 75.16, 57.20, 39.32, 83.00 mg/kg respectively. The contents of tocotrienols ( α, γ and δ in the oils were: 120.30, 106.00, 95.20, 135.74; 196.00, 125.00, 210.0, 276.41; 72.50, 20.00, 39.30, 64.00 mg/kg respectively. The amount of γ - Oryzanol in the investigated oils was ranged 415.12-802.05 The induction periods (Rancimat, 20 L/h, 120 °C of the crude oils were 6.81, 5.99, 6.39 and 7.40 h respectively. The major sterol fractions of the oils consisted of campesterol ranged (10.10-19.20%, stigmasterol (14.00-19.28 %, b -sitosterol (49.30-58.20 %, and D5 ,avenasterol (8.14-13.05 %. The investigated varieties ( Super Kernel, 386, 385 and Basmati of rice bran oil were found to contain high levels of oleic acid 42.67, 38.59, 40.68 and 36.78 % followed by linoleic and palmitic acids 31.58, 33.80, 28.70, 30.51 and 17.00, 14.88, 19.63, 20.00 % respectively. The contents of myristic, stearic and arachidic acids was 1.50, 2.02, 4.28, 1.00; 2.64, 2.87, 4.02, 7.48; and 1.28, 3.00, 1.00, 1.00 % respectively. A number of parameters of the investigated rice bran oils indigenous to Pakistan were comparable to those of typical rice bran and some other vegetable oils, reported in the literature. The results of the present analysis as compared with those of different vegetable oils demonstrated rice bran to be a potential oil source and thus could be useful

Possibility of using waste tire composites reinforced with rice straw as construction materials.

Science.gov (United States)

Yang, Han-Seung; Kim, Dae-Jun; Lee, Young-Kyu; Kim, Hyun-Joong; Jeon, Jin-Yong; Kang, Chun-Won

2004-10-01

Agricultural lignocellulosic fiber (rice straw)-waste tire particle composite boards were manufactured for use as insulation boards in construction, using the same method as that used in the wood-based panel industry. The manufacturing parameters were: a specific gravity of 0.8 and a rice straw content (10/90, 20/80 and 30/70 by wt.% of rice straw/waste tire particle). A commercial polyurethane adhesive for rubber was used as the composite binder. The water proof, water absorption and thickness swelling properties of the composite boards were better than those of wood particleboard. Furthermore, the flexibility and flexural properties of the composite boards were superior to those of other wood-based panel products. The composite boards also demonstrated good acoustical insulation, electrical insulation, anti-caustic and anti-rot properties. These boards can be used to prevent impact damage, are easily modifiable and are inexpensive. They are able to be used as a substitute for insulation boards and other flexural materials in construction.

Commutators of Integral Operators with Variable Kernels on Hardy ...

Indian Academy of Sciences (India)

Home; Journals; Proceedings – Mathematical Sciences; Volume 115; Issue 4. Commutators of Integral Operators with Variable Kernels on Hardy Spaces. Pu Zhang Kai Zhao. Volume 115 Issue 4 November 2005 pp 399-410 ... Keywords. Singular and fractional integrals; variable kernel; commutator; Hardy space.

Oven-drying reduces ruminal starch degradation in maize kernels

NARCIS (Netherlands)

Ali, M.; Cone, J.W.; Hendriks, W.H.; Struik, P.C.

2014-01-01

The degradation of starch largely determines the feeding value of maize (Zea mays L.) for dairy cows. Normally, maize kernels are dried and ground before chemical analysis and determining degradation characteristics, whereas cows eat and digest fresh material. Drying the moist maize kernels

Mutant-inducing effect of γ-ray irradiation for hybrid rice F1 derived from cross of black glutinous rice x wild rice

International Nuclear Information System (INIS)

Mao Dezhi; Tang Yilan

1998-01-01

The hybrid rice F 1 plant derived from the back crossing of glutinous rice x wild rice was irradiated with γ-ray. The result of investigation to the induced mutant showed that through the selection and backcross, a black glutinous rice strain with the short stem, cold tolerance and high yield was developed. The analysis of the ability of heredity variance showed that the selection was effective for the husk colour, black glutinous and black Indica rice, but ineffective for the white Indica rice and seed setting

Australian wild rice reveals pre-domestication origin of polymorphism deserts in rice genome.

Science.gov (United States)

Krishnan S, Gopala; Waters, Daniel L E; Henry, Robert J

2014-01-01

Rice is a major source of human food with a predominantly Asian production base. Domestication involved selection of traits that are desirable for agriculture and to human consumers. Wild relatives of crop plants are a source of useful variation which is of immense value for crop improvement. Australian wild rices have been isolated from the impacts of domestication in Asia and represents a source of novel diversity for global rice improvement. Oryza rufipogon is a perennial wild progenitor of cultivated rice. Oryza meridionalis is a related annual species in Australia. We have examined the sequence of the genomes of AA genome wild rices from Australia that are close relatives of cultivated rice through whole genome re-sequencing. Assembly of the resequencing data to the O. sativa ssp. japonica cv. Nipponbare shows that Australian wild rices possess 2.5 times more single nucleotide polymorphisms than in the Asian wild rice and cultivated O. sativa ssp. indica. Analysis of the genome of domesticated rice reveals regions of low diversity that show very little variation (polymorphism deserts). Both the perennial and annual wild rice from Australia show a high degree of conservation of sequence with that found in cultivated rice in the same 4.58 Mbp region on chromosome 5, which suggests that some of the 'polymorphism deserts' in this and other parts of the rice genome may have originated prior to domestication due to natural selection. Analysis of genes in the 'polymorphism deserts' indicates that this selection may have been due to biotic or abiotic stress in the environment of early rice relatives. Despite having closely related sequences in these genome regions, the Australian wild populations represent an invaluable source of diversity supporting rice food security.

Australian wild rice reveals pre-domestication origin of polymorphism deserts in rice genome.

Directory of Open Access Journals (Sweden)

Gopala Krishnan S

Full Text Available BACKGROUND: Rice is a major source of human food with a predominantly Asian production base. Domestication involved selection of traits that are desirable for agriculture and to human consumers. Wild relatives of crop plants are a source of useful variation which is of immense value for crop improvement. Australian wild rices have been isolated from the impacts of domestication in Asia and represents a source of novel diversity for global rice improvement. Oryza rufipogon is a perennial wild progenitor of cultivated rice. Oryza meridionalis is a related annual species in Australia. RESULTS: We have examined the sequence of the genomes of AA genome wild rices from Australia that are close relatives of cultivated rice through whole genome re-sequencing. Assembly of the resequencing data to the O. sativa ssp. japonica cv. Nipponbare shows that Australian wild rices possess 2.5 times more single nucleotide polymorphisms than in the Asian wild rice and cultivated O. sativa ssp. indica. Analysis of the genome of domesticated rice reveals regions of low diversity that show very little variation (polymorphism deserts. Both the perennial and annual wild rice from Australia show a high degree of conservation of sequence with that found in cultivated rice in the same 4.58 Mbp region on chromosome 5, which suggests that some of the 'polymorphism deserts' in this and other parts of the rice genome may have originated prior to domestication due to natural selection. CONCLUSIONS: Analysis of genes in the 'polymorphism deserts' indicates that this selection may have been due to biotic or abiotic stress in the environment of early rice relatives. Despite having closely related sequences in these genome regions, the Australian wild populations represent an invaluable source of diversity supporting rice food security.

Perspectives for practical application of the combined fuel kernels in VVER-type reactors

International Nuclear Information System (INIS)

Baranov, V.; Ternovykh, M.; Tikhomirov, G.; Khlunov, A.; Tenishev, A.; Kurina, I.

2011-01-01

The paper considers the main physical processes that take place in fuel kernels under real operation conditions of VVER-type reactors. Main attention is given to the effects induced by combinations of layers with different physical properties inside of fuel kernels on these physical processes. Basic neutron-physical characteristics were calculated for some combined fuel kernels in fuel rods of VVER-type reactors. There are many goals in development of the combined fuel kernels, and these goals define selecting the combinations and compositions of radial layers inside of the kernels. For example, the slower formation of the rim-layer on outer surface of the kernels made of enriched uranium dioxide can be achieved by introduction of inner layer made of natural or depleted uranium dioxide. Other potential goals (lower temperature in the kernel center, better conditions for burn-up of neutron poisons, better retention of toxic materials) could be reached by other combinations of fuel compositions in central and peripheral zones of the fuel kernels. Also, the paper presents the results obtained in experimental manufacturing of the combined fuel pellets. (authors)

Ethanol production from rice on radioactively contaminated field toward sustainable rice farming

International Nuclear Information System (INIS)

Yokoyama, Shinya; Izumi, Bintaro; Oki, Kazuo

2011-01-01

Radioactive species such as 137 Cs were discharged from Fukushima Daiichi Nuclear Power Plant which was severely damaged by the enormous earthquake and tsunami. Cropland has been radioactively contaminated by 137 Cs etc. and it seems impossible to plant rice due to the non-suitability for food. According to the reports, 137 Cs transferred into the rice from soil is less than 1% on the average. Therefore, it is expected that the concentration of 137 Cs in bioethanol will be well below the tentative restriction value even if bioethanol could be produced from the rice. It is proposed that the rice field should be filled with water to avoid the flow of runoff contaminated by radioactive cesium compounds because they are insoluble in aqueous phase and that bioethanol should be produced from the rice in order to maintain the multifunction of rice field and to continue the agriculture. If rice farming is halted and neglected, agricultural function of rice field as well as local community will be permanently destroyed. (author)

Prevalence of Rice Yellow Mottle Virus (RYMV) on Rice Plants ...

African Journals Online (AJOL)

Abstract. Incidence of Rice yellow mottle virus (RYMV) on rice plants (ofada) grown in two local government areas (LGAs) of Ogun State had been evaluated during a two year field survey. Six month old rice plants were observed for symptom expression and leaf samples collected for serological indexing. Of the 60 leaf ...

Occurrence of 'super soft' wheat kernel texture in hexaploid and tetraploid wheats

Science.gov (United States)

Wheat kernel texture is a key trait that governs milling performance, flour starch damage, flour particle size, flour hydration properties, and baking quality. Kernel texture is commonly measured using the Perten Single Kernel Characterization System (SKCS). The SKCS returns texture values (Hardness...

Kernel Methods for Machine Learning with Life Science Applications

DEFF Research Database (Denmark)

Abrahamsen, Trine Julie

Kernel methods refer to a family of widely used nonlinear algorithms for machine learning tasks like classification, regression, and feature extraction. By exploiting the so-called kernel trick straightforward extensions of classical linear algorithms are enabled as long as the data only appear a...

Nonlinear Forecasting With Many Predictors Using Kernel Ridge Regression

DEFF Research Database (Denmark)

Exterkate, Peter; Groenen, Patrick J.F.; Heij, Christiaan

This paper puts forward kernel ridge regression as an approach for forecasting with many predictors that are related nonlinearly to the target variable. In kernel ridge regression, the observed predictor variables are mapped nonlinearly into a high-dimensional space, where estimation of the predi...

Effect of Interplanting with Zero Tillage and Straw Manure on Rice Growth and Rice Quality

Directory of Open Access Journals (Sweden)

Shi-ping LIU

2007-09-01

Full Text Available The interplanting with zero-tillage of rice, i.e. direct sowing rice 10–20 days before wheat harvesting, and remaining about 30-cm high stubble after cutting wheat or rice with no tillage, is a new cultivation technology in wheat-rice rotation system. To study the effects of interplanting with zero tillage and straw manure on rice growth and quality, an experiment was conducted in a wheat-rotation rotation system. Four treatments, i.e. ZIS (Zero-tillage, straw manure and rice interplanting, ZI (Zero-tillage, no straw manure and rice interplanting, PTS (Plowing tillage, straw manure and rice transplanting, and PT (Plowing tillage, no straw manure and rice transplanting, were used. ZIS reduced plant height, leaf area per plant and the biomass of rice plants, but the biomass accumulation of rice at the late stage was quicker than that under conventional transplanting cultivation. In the first year (2002, there was no significant difference in rice yield among the four treatments. However, rice yield decreased in interplanting with zero-tillage in the second year (2003. Compared with the transplanting treatments, the number of filled grains per panicle decreased but 1000-grain weight increased in interplanting with zero-tillage, which were the main factors resulting in higher yield. Interplanting with zero-tillage improved the milling and appearance qualities of rice. The rates of milled and head rice increased while chalky rice rate and chalkiness decreased in interplanting with zero-tillage. Zero-tillage and interplanting also affected rice nutritional and cooking qualities. In 2002, ZIS showed raised protein content, decreased amylose content, softer gel consistency, resulting in improved rice quality. In 2003, zero-tillage and interplanting decreased protein content and showed similar amylose content as compared with transplanting treatments. Moreover, protein content in PTS was obviously increased in comparison with the other three treatments

An overview of the sustainability of rice agroecosystem through rice-fish integration

International Nuclear Information System (INIS)

Ahyaudin Ali

2002-01-01

Rice-fish integration in the rice agroecosystem has been introduced and is expanding in Malaysia. This type of farm integration has resulted in land optimization, thus enabling farmers to grow both fish and rice in one farming system. Introducing fish into the ricefield has also increased seasonal income as well as reduced pesticide use. Although basic ecological knowledge on rice-fish integration has allowed rice-fish integration to be introduced, further research is required to allow for fine tuning of the methodologies used. Thus research on the ecology, management, production methods and the characterization of rice-fish farming system of Malaysia is needed. Further characterization and description is needed on the ecology of the rice-fish farming system of Malaysia in terms of production, food webs, nutrient flow and system diversity. To increase the sustainability efficiency and productivity of the system, implementation of management techniques formulated through research is required. (Author)

Mode of inheritance and combining abilities for kernel row number, kernel number per row and grain yield in maize (Zea mays L.)

NARCIS (Netherlands)

Bocanski, J.; Sreckov, Z.; Nastasic, A.; Ivanovic, M.; Djalovic, I.; Vukosavljev, M.

2010-01-01

Bocanski J., Z. Sreckov, A. Nastasic, M. Ivanovic, I.Djalovic and M. Vukosavljev (2010): Mode of inheritance and combining abilities for kernel row number, kernel number per row and grain yield in maize (Zea mays L.) - Genetika, Vol 42, No. 1, 169- 176. Utilization of heterosis requires the study of

Reproducing Kernels and Coherent States on Julia Sets

Energy Technology Data Exchange (ETDEWEB)

Thirulogasanthar, K., E-mail: santhar@cs.concordia.ca; Krzyzak, A. [Concordia University, Department of Computer Science and Software Engineering (Canada)], E-mail: krzyzak@cs.concordia.ca; Honnouvo, G. [Concordia University, Department of Mathematics and Statistics (Canada)], E-mail: g_honnouvo@yahoo.fr

2007-11-15

We construct classes of coherent states on domains arising from dynamical systems. An orthonormal family of vectors associated to the generating transformation of a Julia set is found as a family of square integrable vectors, and, thereby, reproducing kernels and reproducing kernel Hilbert spaces are associated to Julia sets. We also present analogous results on domains arising from iterated function systems.

Reproducing Kernels and Coherent States on Julia Sets

International Nuclear Information System (INIS)

Thirulogasanthar, K.; Krzyzak, A.; Honnouvo, G.

2007-01-01

We construct classes of coherent states on domains arising from dynamical systems. An orthonormal family of vectors associated to the generating transformation of a Julia set is found as a family of square integrable vectors, and, thereby, reproducing kernels and reproducing kernel Hilbert spaces are associated to Julia sets. We also present analogous results on domains arising from iterated function systems

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

Science.gov (United States)

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

2017-10-01

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

Multiple Kernel Sparse Representation based Orthogonal Discriminative Projection and Its Cost-Sensitive Extension.

Science.gov (United States)

Zhang, Guoqing; Sun, Huaijiang; Xia, Guiyu; Sun, Quansen

2016-07-07

Sparse representation based classification (SRC) has been developed and shown great potential for real-world application. Based on SRC, Yang et al. [10] devised a SRC steered discriminative projection (SRC-DP) method. However, as a linear algorithm, SRC-DP cannot handle the data with highly nonlinear distribution. Kernel sparse representation-based classifier (KSRC) is a non-linear extension of SRC and can remedy the drawback of SRC. KSRC requires the use of a predetermined kernel function and selection of the kernel function and its parameters is difficult. Recently, multiple kernel learning for SRC (MKL-SRC) [22] has been proposed to learn a kernel from a set of base kernels. However, MKL-SRC only considers the within-class reconstruction residual while ignoring the between-class relationship, when learning the kernel weights. In this paper, we propose a novel multiple kernel sparse representation-based classifier (MKSRC), and then we use it as a criterion to design a multiple kernel sparse representation based orthogonal discriminative projection method (MK-SR-ODP). The proposed algorithm aims at learning a projection matrix and a corresponding kernel from the given base kernels such that in the low dimension subspace the between-class reconstruction residual is maximized and the within-class reconstruction residual is minimized. Furthermore, to achieve a minimum overall loss by performing recognition in the learned low-dimensional subspace, we introduce cost information into the dimensionality reduction method. The solutions for the proposed method can be efficiently found based on trace ratio optimization method [33]. Extensive experimental results demonstrate the superiority of the proposed algorithm when compared with the state-of-the-art methods.

Mixed kernel function support vector regression for global sensitivity analysis

Science.gov (United States)

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

2017-11-01

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

Feature Selection and Kernel Learning for Local Learning-Based Clustering.

Science.gov (United States)

Zeng, Hong; Cheung, Yiu-ming

2011-08-01

The performance of the most clustering algorithms highly relies on the representation of data in the input space or the Hilbert space of kernel methods. This paper is to obtain an appropriate data representation through feature selection or kernel learning within the framework of the Local Learning-Based Clustering (LLC) (Wu and Schölkopf 2006) method, which can outperform the global learning-based ones when dealing with the high-dimensional data lying on manifold. Specifically, we associate a weight to each feature or kernel and incorporate it into the built-in regularization of the LLC algorithm to take into account the relevance of each feature or kernel for the clustering. Accordingly, the weights are estimated iteratively in the clustering process. We show that the resulting weighted regularization with an additional constraint on the weights is equivalent to a known sparse-promoting penalty. Hence, the weights of those irrelevant features or kernels can be shrunk toward zero. Extensive experiments show the efficacy of the proposed methods on the benchmark data sets.

Semisupervised kernel marginal Fisher analysis for face recognition.

Science.gov (United States)

Wang, Ziqiang; Sun, Xia; Sun, Lijun; Huang, Yuchun

2013-01-01

Dimensionality reduction is a key problem in face recognition due to the high-dimensionality of face image. To effectively cope with this problem, a novel dimensionality reduction algorithm called semisupervised kernel marginal Fisher analysis (SKMFA) for face recognition is proposed in this paper. SKMFA can make use of both labelled and unlabeled samples to learn the projection matrix for nonlinear dimensionality reduction. Meanwhile, it can successfully avoid the singularity problem by not calculating the matrix inverse. In addition, in order to make the nonlinear structure captured by the data-dependent kernel consistent with the intrinsic manifold structure, a manifold adaptive nonparameter kernel is incorporated into the learning process of SKMFA. Experimental results on three face image databases demonstrate the effectiveness of our proposed algorithm.

Weighted Feature Gaussian Kernel SVM for Emotion Recognition.

Science.gov (United States)

Wei, Wei; Jia, Qingxuan

2016-01-01

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

Fuzzy-based multi-kernel spherical support vector machine for ...

Indian Academy of Sciences (India)

In the proposed classifier, we design a new multi-kernel function based on the fuzzy triangular membership function. Finally, a newly developed multi-kernel function is incorporated into the spherical support vector machine to enhance the performance significantly. The experimental results are evaluated and performance is ...

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

Science.gov (United States)

Binol, Hamidullah; Bal, Abdullah; Cukur, Huseyin

2015-10-01

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

Objective evaluation of whiteness of cooked rice and rice cakes using a portable spectrophotometer.

Science.gov (United States)

Goto, Hajime; Asanome, Noriyuki; Suzuki, Keitaro; Sano, Tomoyoshi; Saito, Hiroshi; Abe, Yohei; Chuba, Masaru; Nishio, Takeshi

2014-03-01

The whiteness of cooked rice and rice cakes was evaluated using a portable spectrophotometer with a whiteness index (WI). Also, by using boiled rice for measurement of Mido values by Mido Meter, it was possible to infer the whiteness of cooked rice without rice cooking. In the analysis of varietal differences of cooked rice, 'Tsuyahime', 'Koshihikari' and 'Koshinokaori' showed high whiteness, while 'Satonoyuki' had inferior whiteness. The whiteness of rice cakes made from 'Koyukimochi' and 'Dewanomochi' was higher than the whiteness of those made from 'Himenomochi' and 'Koganemochi'. While there was a significant correlation (r = 0.84) between WI values and whiteness scores of cooked rice by the sensory test, no correlation was detected between the whiteness scores and Mido values, indicating that the values obtained by a spectrophotometer differ from those obtained by a Mido Meter. Thus, a spectrophotometer may be a novel device for measurement of rice eating quality.

Modelling microwave heating of discrete samples of oil palm kernels

International Nuclear Information System (INIS)

Law, M.C.; Liew, E.L.; Chang, S.L.; Chan, Y.S.; Leo, C.P.

2016-01-01

Highlights: • Microwave (MW) drying of oil palm kernels is experimentally determined and modelled. • MW heating of discrete samples of oil palm kernels (OPKs) is simulated. • OPK heating is due to contact effect, MW interference and heat transfer mechanisms. • Electric field vectors circulate within OPKs sample. • Loosely-packed arrangement improves temperature uniformity of OPKs. - Abstract: Recently, microwave (MW) pre-treatment of fresh palm fruits has showed to be environmentally friendly compared to the existing oil palm milling process as it eliminates the condensate production of palm oil mill effluent (POME) in the sterilization process. Moreover, MW-treated oil palm fruits (OPF) also possess better oil quality. In this work, the MW drying kinetic of the oil palm kernels (OPK) was determined experimentally. Microwave heating/drying of oil palm kernels was modelled and validated. The simulation results show that temperature of an OPK is not the same over the entire surface due to constructive and destructive interferences of MW irradiance. The volume-averaged temperature of an OPK is higher than its surface temperature by 3–7 °C, depending on the MW input power. This implies that point measurement of temperature reading is inadequate to determine the temperature history of the OPK during the microwave heating process. The simulation results also show that arrangement of OPKs in a MW cavity affects the kernel temperature profile. The heating of OPKs were identified to be affected by factors such as local electric field intensity due to MW absorption, refraction, interference, the contact effect between kernels and also heat transfer mechanisms. The thermal gradient patterns of OPKs change as the heating continues. The cracking of OPKs is expected to occur first in the core of the kernel and then it propagates to the kernel surface. The model indicates that drying of OPKs is a much slower process compared to its MW heating. The model is useful

Finite frequency traveltime sensitivity kernels for acoustic anisotropic media: Angle dependent bananas

KAUST Repository

Djebbi, Ramzi

2013-08-19

Anisotropy is an inherent character of the Earth subsurface. It should be considered for modeling and inversion. The acoustic VTI wave equation approximates the wave behavior in anisotropic media, and especially it\\'s kinematic characteristics. To analyze which parts of the model would affect the traveltime for anisotropic traveltime inversion methods, especially for wave equation tomography (WET), we drive the sensitivity kernels for anisotropic media using the VTI acoustic wave equation. A Born scattering approximation is first derived using the Fourier domain acoustic wave equation as a function of perturbations in three anisotropy parameters. Using the instantaneous traveltime, which unwraps the phase, we compute the kernels. These kernels resemble those for isotropic media, with the η kernel directionally dependent. They also have a maximum sensitivity along the geometrical ray, which is more realistic compared to the cross-correlation based kernels. Focusing on diving waves, which is used more often, especially recently in waveform inversion, we show sensitivity kernels in anisotropic media for this case.

Finite frequency traveltime sensitivity kernels for acoustic anisotropic media: Angle dependent bananas

KAUST Repository

Djebbi, Ramzi; Alkhalifah, Tariq Ali

2013-01-01

Anisotropy is an inherent character of the Earth subsurface. It should be considered for modeling and inversion. The acoustic VTI wave equation approximates the wave behavior in anisotropic media, and especially it's kinematic characteristics. To analyze which parts of the model would affect the traveltime for anisotropic traveltime inversion methods, especially for wave equation tomography (WET), we drive the sensitivity kernels for anisotropic media using the VTI acoustic wave equation. A Born scattering approximation is first derived using the Fourier domain acoustic wave equation as a function of perturbations in three anisotropy parameters. Using the instantaneous traveltime, which unwraps the phase, we compute the kernels. These kernels resemble those for isotropic media, with the η kernel directionally dependent. They also have a maximum sensitivity along the geometrical ray, which is more realistic compared to the cross-correlation based kernels. Focusing on diving waves, which is used more often, especially recently in waveform inversion, we show sensitivity kernels in anisotropic media for this case.

Design analysis and performance test of reduction furnace of kernel U3O8

International Nuclear Information System (INIS)

Moch Setyadji; Triyono; Dedy Husnurrofiq

2015-01-01

High Temperature Reactor (HTR) with coated particle fuel is a future reactor (generation IV) because it is not only having high efficiency but also release no fission product into the environment. It has a passive safety principles and negative reactivity. Coated particle fuel for high temperature reactor is made through Sol-Gel process followed by aging, drying, calcination, reduction, sintering and coating process. Research of design analysis of heating system, electrical system, and insulation systems continued construction and performance test of reduction furnace has been done. The aim of this research was to obtain a reduction furnace with adequate performance that can be used to reduce the kernel of U 3 O 8 into UO 2 . The results of the performance test of the reduction furnace showed that heating zones 1-3 can generate heat to a temperature of 900°C with operation time 144 minutes and heat rate of 5.979°C/min. The coefficient of convection in the outer wall of fireproof stone having 7 cm thick was around 30 W/m 2 C. UO 2 kernel resulting from the reduction process has a diameter of between 0.850 to 0.992 mm and qualify as feed sintering process. (author)

Improvement of local varieties of rice (Oryza glaberrima) for resistance to shattering and grain quality by induced mutations

Energy Technology Data Exchange (ETDEWEB)

Cisse, F; Dione, M; Kelly, S [Institute d` Economie Rurale, Station de Recherche Agronomique, Mopti (Mali)

1997-12-01

In Mali, a large area of rice is planted with the local rice, O. glaberrima which under conditions of low rainfall and flood water is more hardy to stress than the introduced O. sativa cultivars. A program to improve the local varieties of O. glaberrima by induced mutations was started in 1988. Ten local varieties were irradiated with 20 and 30 krad. In M{sub 4} of cv. `Gorbal`, irradiated with 20 krads, 13 variants were selected. Five of these were evaluated in M{sub 5} for their agronomic performance. The induced mutants in the remaining 9 varieties were highly sterile with 90% or more sterility in the M{sub 2} and M{sub 3}. Irradiation with 20-30 krad gave high survival (70-95%), and several mutants with white kernel were obtained from the red seeded types. Nearly two-third of the identified mutants had white caryopsis. There is better consumer acceptance of the white seeded type of rice than the red seeded varieties in Mali and the white seeded mutants may have an added premium in the market. The field performance of the M{sub 5} mutants was investigated. Preliminary results showed that some of mutants derived from cv. `Gorbal` were early in maturity and had more panicles per plant, but had a lower 1000-kernel weight, and did not differ from the parent in grain yield. Additional trials are planned to establish potential of the mutants for yield and quality. Three more cultivars of O. glaberrima - `Haira`, `Tombo` and `Yele` - were irradiated with 20 and 60 krads, and gave 75, 81 and 72% seed viability, respectively. M{sub 1} showed reduced plant height. Selection for non-shattering of grains shall be carried out in M{sub 2}. Any plants which are non-shattering but sterile shall be crossed with the parent to recover the mutant types. The taxonomic status of cv. `Gorbal` is not very clear. Isozyme patterns suggest that this cultivar may belong to O. sativa and not to O. glaberrima. To establish its taxonomic status, crosses shall be made with O. sativa and O

Improvement of local varieties of rice (Oryza glaberrima) for resistance to shattering and grain quality by induced mutations

International Nuclear Information System (INIS)

Cisse, F.; Dione, M.; Kelly, S.

1997-01-01

In Mali, a large area of rice is planted with the local rice, O. glaberrima which under conditions of low rainfall and flood water is more hardy to stress than the introduced O. sativa cultivars. A program to improve the local varieties of O. glaberrima by induced mutations was started in 1988. Ten local varieties were irradiated with 20 and 30 krad. In M 4 of cv. 'Gorbal', irradiated with 20 krads, 13 variants were selected. Five of these were evaluated in M 5 for their agronomic performance. The induced mutants in the remaining 9 varieties were highly sterile with 90% or more sterility in the M 2 and M 3 . Irradiation with 20-30 krad gave high survival (70-95%), and several mutants with white kernel were obtained from the red seeded types. Nearly two-third of the identified mutants had white caryopsis. There is better consumer acceptance of the white seeded type of rice than the red seeded varieties in Mali and the white seeded mutants may have an added premium in the market. The field performance of the M 5 mutants was investigated. Preliminary results showed that some of mutants derived from cv. 'Gorbal' were early in maturity and had more panicles per plant, but had a lower 1000-kernel weight, and did not differ from the parent in grain yield. Additional trials are planned to establish potential of the mutants for yield and quality. Three more cultivars of O. glaberrima - 'Haira', 'Tombo' and 'Yele' - were irradiated with 20 and 60 krads, and gave 75, 81 and 72% seed viability, respectively. M 1 showed reduced plant height. Selection for non-shattering of grains shall be carried out in M 2 . Any plants which are non-shattering but sterile shall be crossed with the parent to recover the mutant types. The taxonomic status of cv. 'Gorbal' is not very clear. Isozyme patterns suggest that this cultivar may belong to O. sativa and not to O. glaberrima. To establish its taxonomic status, crosses shall be made with O. sativa and O. glaberrima. (author). 2 tabs

Physical, physicochemical and nutritional characteristics of Bhoja chaul, a traditional ready-to-eat dry heat parboiled rice product processed by an improvised soaking technique.

Science.gov (United States)

Dutta, Himjyoti; Mahanta, Charu Lata; Singh, Vasudeva; Das, Barnali Baruah; Rahman, Narzu

2016-01-15

Bhoja chaul is a traditional whole rice product processed by the dry heat parboiling technique of low amylose/waxy paddy that is eaten after soaking in water and requires no cooking. The essential steps in Bhoja chaul making are soaking paddy in water, roasting with sand, drying and milling. In this study, the product was prepared from a low amylose variety and a waxy rice variety by an improvised laboratory scale technique. Bhoja chaul prepared in the laboratory by this technique was studied for physical, physicochemical, and textural properties. Improvised method shortened the processing time and gave a product with good textural characteristics. Shape of the rice kernels became bolder on processing. RVA studies and DSC endotherms suggested molecular damage and amylose-lipid complex formation by the linear B-chains of amylopectin, respectively. X-ray diffractography indicated formation of partial B-type pattern. Shifting of the crystalline region of the XRD curve towards lower values of Bragg's angle was attributed to the overall increase in inter-planar spacing of the crystalline lamellae. Resistant starch was negligible. Bhoja chaul may be useful for children and people with poor state of digestibility. Copyright © 2014 Elsevier Ltd. All rights reserved.

The influence of maize kernel moisture on the sterilizing effect of gamma rays

International Nuclear Information System (INIS)

Khanymova, T.; Poloni, E.

1980-01-01

The influence of 4 levels of maize kernel moisture (16, 20, 25 and 30%) on gamma-ray sterilizing effect was studied and the after-effect of radiation on the microorganisms at short term storage was followed up. Maize kernels of the hybrid Knezha-36 produced in 1975 were used. Gamma-ray treatment of the kernels was effected by GUBEh-4000 irradiator at doses of 0.2 and 0.3 Mrad and after that they were stored for a month at 12 deg and 25 deg C and controlled moisture conditions. Surface and subepidermal infection of the kernels was determined immediately post irradiation and at the end of the experiment. Non-irradiated kernels were used as controls. Results indicated that the initial kernel moisture has a considerable influence on the sterilizing effect of gamma-rays at the rates used in the experiment and affects to a considerable extent the post-irradiation recovery of organisms. The speed of recovery was highest in the treatment with 30% moisture and lowest in the treatment with 16% kernel moisture. Irradiation of the kernels causes pronounced changes on the surface and subepidermal infection. This was due to the unequal radio resistance to the microbial components and to the modifying effect of the moisture holding capacity. The useful effect of maize kernel irradiation was more prolonged at 12 deg C than at 25 deg C

Puffing of okara/rice blends using a rice cake machine.

Science.gov (United States)

Xie, M; Huff, H; Hsieh, F; Mustapha, A

2008-10-01

Okara is the insoluble byproduct of soymilk and tofu manufactures. It is cheap, high in nutrients, and possesses great potential to be applied to functional human foods. In this study, a puffed okara/rice cake product was developed with blends of okara pellets and parboiled rice. Consumer preference and acceptance tests were conducted for the product. Okara pellets were prepared by grinding the strands obtained from extruding a mixture of dried okara and rice flour (3:2, w/w) with a twin-screw extruder. Okara pellets and parboiled rice were blended in 4 ratios, 90:10, 70:30, 40:60, and 0:100 (w/w), and tempered to 14% and 17% moisture. The blends were puffed at 221, 232, and 243 degrees C for 4, 5, or 6 s. The okara/rice cakes were evaluated for specific volume (SPV), texture, color, and percent weight loss after tumbling. Overall, the decrease in okara content and increase in moisture, heating temperature and time led to greater specific volume (SPV) and hardness, lighter color, and lower percent weight loss after tumbling. The consumer tests indicated that the okara/rice cake containing 70% okara pellets was preferred and the 90% one was liked the least. The possible drivers of liking for the puffed okara/rice cakes could be the okara content, hardness, SPV, bright color, and percent weight loss after tumbling.

Capturing option anomalies with a variance-dependent pricing kernel

NARCIS (Netherlands)

Christoffersen, P.; Heston, S.; Jacobs, K.

2013-01-01

We develop a GARCH option model with a variance premium by combining the Heston-Nandi (2000) dynamic with a new pricing kernel that nests Rubinstein (1976) and Brennan (1979). While the pricing kernel is monotonic in the stock return and in variance, its projection onto the stock return is

Resolvent kernel for the Kohn Laplacian on Heisenberg groups

Directory of Open Access Journals (Sweden)

Neur Eddine Askour

2002-07-01

Full Text Available We present a formula that relates the Kohn Laplacian on Heisenberg groups and the magnetic Laplacian. Then we obtain the resolvent kernel for the Kohn Laplacian and find its spectral density. We conclude by obtaining the Green kernel for fractional powers of the Kohn Laplacian.

Diversity of some endophytic fungi associated with rice black bug Paraeucosmetus pallicornis on rice plant

OpenAIRE

Nur, Amin; La Daha; Nurariaty, Agus; Ade, Rosmana; Muh., Fadlan

2015-01-01

A new rice insect pest was sighted in some rice producing areas of South Sulawesi Province, Indonesia. This pest is rice black bugs Paraeucosmetus pallicornis. The research aimed to isolation of fungi associated with rice black bugs Paraeucosmetus pallicornis, so as to know the cause of a bitter taste to the rice. The isolation of the fungi consist of three kinds of treatment, namely rice black bugs without sterilization, with sterilization and rice black bugs cut and sterilized. The resul...

RiceAtlas, a spatial database of global rice calendars and production

NARCIS (Netherlands)

Laborte, Alice G.; Gutierrez, Mary Anne; Balanza, Jane Girly; Saito, Kazuki; Zwart, Sander; Boschetti, Mirco; Murty, M. V.R.; Villano, Lorena; Aunario, Jorrel Khalil; Reinke, Russell; Koo, Jawoo; Hijmans, Robert J.; Nelson, Andrew

2017-01-01

Knowing where, when, and how much rice is planted and harvested is crucial information for understanding the effects of policy, trade, and global and technological change on food security. We developed RiceAtlas, a spatial database on the seasonal distribution of the world's rice production. It

The Potency of White Rice (Oryza sativa), Black Rice (Oryza sativa L. indica), and Red Rice (Oryza nivara) as Antioxidant and Tyrosinase Inhibitor

Science.gov (United States)

Batubara, I.; Maharni, M.; Sadiah, S.

2017-04-01

Rice is known to have many beneficial biological activities and is often used as “bedak dingin”, a face powder. The content of vitamins, minerals, fiber, and several types of antioxidants, such as ferulic acid, phytic acid, tocopherol, and oryzanols [1-2] are predicted to be potential as a tyrosinase inhibitor. The purpose of this study is to determine the potency of extracts from there types of rice, namely white, red, and black rice as an antioxidant and tyrosinase inhibitor. The rice was extracted with three different solvents, n-hexane, ethyl acetate, and methanol. The results showed that the highest antioxidant activity using 1,1-diphenyl-2-picrylhydrazyl method was found in the methanol extract of black rice (IC50 290 μg/mL). Meanwhile, ethyl acetate extract of white rice has the highest antioxidant activity withphosphomolybdic acid method (41 mmol α-tocopherol equivalents/g sample). Thus, methanol extract of black rice and ethyl acetate extract of white rice are potential as an antioxidant. For tyrosinase inhibitor, n-hexane extract of red rice (IC50 3156 μg/mL) was the most active extract. The active component for radical scavenging is polar compound and for antioxidant by phosphomolybdate method is less polar compounds in black rice methanol extract based on TLC bioautogram. In conclusion, the black rice is the most potent in antioxidant while red rice is for tyrosinase inhibition.

Effect of Rice bran on the Quality of Rice Flour Breads (Gluten-free)

OpenAIRE

仲上, 晴世; 矢部, えん; Haruyo, Nakagami; En, Yabe

2016-01-01

Over recent years progress has been made in the development of substitute foods for allergy patients. One such is rice flour bread. However, typically rice flour bread uses polysaccharide thickener in substitution for the gluten in wheat. Most polysaccharide thickeners are of dietary fiber origin, and the nutritive value is poor. Therefore, in this study, I made rice flour bread adding rice bran in place of polysaccharide thickener. Various nutrients are included in rice bran, including vitam...

A Heterogeneous Multi-core Architecture with a Hardware Kernel for Control Systems

DEFF Research Database (Denmark)

Li, Gang; Guan, Wei; Sierszecki, Krzysztof

2012-01-01

Rapid industrialisation has resulted in a demand for improved embedded control systems with features such as predictability, high processing performance and low power consumption. Software kernel implementation on a single processor is becoming more difficult to satisfy those constraints....... This paper presents a multi-core architecture incorporating a hardware kernel on FPGAs, intended for high performance applications in control engineering domain. First, the hardware kernel is investigated on the basis of a component-based real-time kernel HARTEX (Hard Real-Time Executive for Control Systems...

Pollen source effects on growth of kernel structures and embryo chemical compounds in maize.

Science.gov (United States)

Tanaka, W; Mantese, A I; Maddonni, G A

2009-08-01

Previous studies have reported effects of pollen source on the oil concentration of maize (Zea mays) kernels through modifications to both the embryo/kernel ratio and embryo oil concentration. The present study expands upon previous analyses by addressing pollen source effects on the growth of kernel structures (i.e. pericarp, endosperm and embryo), allocation of embryo chemical constituents (i.e. oil, protein, starch and soluble sugars), and the anatomy and histology of the embryos. Maize kernels with different oil concentration were obtained from pollinations with two parental genotypes of contrasting oil concentration. The dynamics of the growth of kernel structures and allocation of embryo chemical constituents were analysed during the post-flowering period. Mature kernels were dissected to study the anatomy (embryonic axis and scutellum) and histology [cell number and cell size of the scutellums, presence of sub-cellular structures in scutellum tissue (starch granules, oil and protein bodies)] of the embryos. Plants of all crosses exhibited a similar kernel number and kernel weight. Pollen source modified neither the growth period of kernel structures, nor pericarp growth rate. By contrast, pollen source determined a trade-off between embryo and endosperm growth rates, which impacted on the embryo/kernel ratio of mature kernels. Modifications to the embryo size were mediated by scutellum cell number. Pollen source also affected (P embryo chemical compounds. Negative correlations among embryo oil concentration and those of starch (r = 0.98, P embryos with low oil concentration had an increased (P embryo/kernel ratio and allocation of embryo chemicals seems to be related to the early established sink strength (i.e. sink size and sink activity) of the embryos.

Rice peasants and rice research in Colombia

NARCIS (Netherlands)

Spijkers, P.A.N.M.

1983-01-01

Rice has been grown as a food crop in Latin America from early colonial times. In Colombia rice became a prominent subsistence crop especially on the north coast where it has been grown since the 17th century, sometimes also as a commercial crop. During the last twenty years there has been a sharp

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

Directory of Open Access Journals (Sweden)

Ujjwal Maulik

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

Leaf development of cultivated rice and weedy red rice under elevated temperature scenarios

OpenAIRE

Streck,Nereu A.; Uhlmann,Lilian O.; Gabriel,Luana F.

2013-01-01

The objective of this study was to simulate leaf development of cultivated rice genotypes and weedy red rice biotypes in climate change scenarios at Santa Maria, RS, Brazil. A leaf appearance (LAR) model adapted for rice was used to simulate the accumulated leaf number, represented by the Haun Stage, from crop emergence to flag leaf appearance (EM-FL). Three cultivated rice genotypes and two weedy red rice biotypes in six emergence dates were used. The LAR model was run for each emergence dat...

Graphical analyses of connected-kernel scattering equations

International Nuclear Information System (INIS)

Picklesimer, A.

1983-01-01

Simple graphical techniques are employed to obtain a new (simultaneous) derivation of a large class of connected-kernel scattering equations. This class includes the Rosenberg, Bencze-Redish-Sloan, and connected-kernel multiple scattering equations as well as a host of generalizations of these and other equations. The basic result is the application of graphical methods to the derivation of interaction-set equations. This yields a new, simplified form for some members of the class and elucidates the general structural features of the entire class

The global kernel k-means algorithm for clustering in feature space.

Science.gov (United States)

Tzortzis, Grigorios F; Likas, Aristidis C

2009-07-01

Kernel k-means is an extension of the standard k -means clustering algorithm that identifies nonlinearly separable clusters. In order to overcome the cluster initialization problem associated with this method, we propose the global kernel k-means algorithm, a deterministic and incremental approach to kernel-based clustering. Our method adds one cluster at each stage, through a global search procedure consisting of several executions of kernel k-means from suitable initializations. This algorithm does not depend on cluster initialization, identifies nonlinearly separable clusters, and, due to its incremental nature and search procedure, locates near-optimal solutions avoiding poor local minima. Furthermore, two modifications are developed to reduce the computational cost that do not significantly affect the solution quality. The proposed methods are extended to handle weighted data points, which enables their application to graph partitioning. We experiment with several data sets and the proposed approach compares favorably to kernel k -means with random restarts.

Effects of slag-based silicon fertilizer on rice growth and brown-spot resistance.

Science.gov (United States)

Ning, Dongfeng; Song, Alin; Fan, Fenliang; Li, Zhaojun; Liang, Yongchao

2014-01-01

It is well documented that slag-based silicon fertilizers have beneficial effects on the growth and disease resistance of rice. However, their effects vary greatly with sources of slag and are closely related to availability of silicon (Si) in these materials. To date, few researches have been done to compare the differences in plant performance and disease resistance between different slag-based silicon fertilizers applied at the same rate of plant-available Si. In the present study both steel and iron slags were chosen to investigate their effects on rice growth and disease resistance under greenhouse conditions. Both scanning electron microscopy (SEM) and transmission electron microscopy (TEM) were used to examine the effects of slags on ultrastructural changes in leaves of rice naturally infected by Bipolaris oryaze, the causal agent of brown spot. The results showed that both slag-based Si fertilizers tested significantly increased rice growth and yield, but decreased brown spot incidence, with steel slag showing a stronger effect than iron slag. The results of SEM analysis showed that application of slags led to more pronounced cell silicification in rice leaves, more silica cells, and more pronounced and larger papilla as well. The results of TEM analysis showed that mesophyll cells of slag-untreated rice leaf were disorganized, with colonization of the fungus (Bipolaris oryzae), including chloroplast degradation and cell wall alterations. The application of slag maintained mesophyll cells relatively intact and increased the thickness of silicon layer. It can be concluded that applying slag-based fertilizer to Si-deficient paddy soil is necessary for improving both rice productivity and brown spot resistance. The immobile silicon deposited in host cell walls and papillae sites is the first physical barrier for fungal penetration, while the soluble Si in the cytoplasm enhances physiological or induced resistance to fungal colonization.

On methods to increase the security of the Linux kernel

International Nuclear Information System (INIS)

Matvejchikov, I.V.

2014-01-01

Methods to increase the security of the Linux kernel for the implementation of imposed protection tools have been examined. The methods of incorporation into various subsystems of the kernel on the x86 architecture have been described [ru

Relationship between processing score and kernel-fraction particle size in whole-plant corn silage.

Science.gov (United States)

Dias Junior, G S; Ferraretto, L F; Salvati, G G S; de Resende, L C; Hoffman, P C; Pereira, M N; Shaver, R D

2016-04-01

Kernel processing increases starch digestibility in whole-plant corn silage (WPCS). Corn silage processing score (CSPS), the percentage of starch passing through a 4.75-mm sieve, is widely used to assess degree of kernel breakage in WPCS. However, the geometric mean particle size (GMPS) of the kernel-fraction that passes through the 4.75-mm sieve has not been well described. Therefore, the objectives of this study were (1) to evaluate particle size distribution and digestibility of kernels cut in varied particle sizes; (2) to propose a method to measure GMPS in WPCS kernels; and (3) to evaluate the relationship between CSPS and GMPS of the kernel fraction in WPCS. Composite samples of unfermented, dried kernels from 110 corn hybrids commonly used for silage production were kept whole (WH) or manually cut in 2, 4, 8, 16, 32 or 64 pieces (2P, 4P, 8P, 16P, 32P, and 64P, respectively). Dry sieving to determine GMPS, surface area, and particle size distribution using 9 sieves with nominal square apertures of 9.50, 6.70, 4.75, 3.35, 2.36, 1.70, 1.18, and 0.59 mm and pan, as well as ruminal in situ dry matter (DM) digestibilities were performed for each kernel particle number treatment. Incubation times were 0, 3, 6, 12, and 24 h. The ruminal in situ DM disappearance of unfermented kernels increased with the reduction in particle size of corn kernels. Kernels kept whole had the lowest ruminal DM disappearance for all time points with maximum DM disappearance of 6.9% at 24 h and the greatest disappearance was observed for 64P, followed by 32P and 16P. Samples of WPCS (n=80) from 3 studies representing varied theoretical length of cut settings and processor types and settings were also evaluated. Each WPCS sample was divided in 2 and then dried at 60 °C for 48 h. The CSPS was determined in duplicate on 1 of the split samples, whereas on the other split sample the kernel and stover fractions were separated using a hydrodynamic separation procedure. After separation, the

Structured Kernel Dictionary Learning with Correlation Constraint for Object Recognition.

Science.gov (United States)

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

2017-06-21

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

TOWARDS FINDING A NEW KERNELIZED FUZZY C-MEANS CLUSTERING ALGORITHM

Directory of Open Access Journals (Sweden)

Samarjit Das

2014-04-01

Full Text Available Kernelized Fuzzy C-Means clustering technique is an attempt to improve the performance of the conventional Fuzzy C-Means clustering technique. Recently this technique where a kernel-induced distance function is used as a similarity measure instead of a Euclidean distance which is used in the conventional Fuzzy C-Means clustering technique, has earned popularity among research community. Like the conventional Fuzzy C-Means clustering technique this technique also suffers from inconsistency in its performance due to the fact that here also the initial centroids are obtained based on the randomly initialized membership values of the objects. Our present work proposes a new method where we have applied the Subtractive clustering technique of Chiu as a preprocessor to Kernelized Fuzzy CMeans clustering technique. With this new method we have tried not only to remove the inconsistency of Kernelized Fuzzy C-Means clustering technique but also to deal with the situations where the number of clusters is not predetermined. We have also provided a comparison of our method with the Subtractive clustering technique of Chiu and Kernelized Fuzzy C-Means clustering technique using two validity measures namely Partition Coefficient and Clustering Entropy.

A relationship between Gel'fand-Levitan and Marchenko kernels

International Nuclear Information System (INIS)

Kirst, T.; Von Geramb, H.V.; Amos, K.A.

1989-01-01

An integral equation which relates the output kernels of the Gel'fand-Levitan and Marchenko inverse scattering equations is specified. Structural details of this integral equation are studied when the S-matrix is a rational function, and the output kernels are separable in terms of Bessel, Hankel and Jost solutions. 4 refs

Migration of ThO2 kernels under the influence of a temperature gradient

International Nuclear Information System (INIS)

Smith, C.L.

1976-11-01

BISO coated ThO 2 fertile fuel kernels will migrate up the thermal gradients imposed across coated particles during HTGR operation. Thorium dioxide kernel migration has been studied as a function of temperature (1300 to 1700 0 C) and ThO 2 kernel burnup (0.9 to 5.8 percent FIMA) in out-of-pile, postirradiation thermal gradient heating experiments. The studies were conducted to obtain descriptions of migration rates that will be used in core design studies to evaluate the impact of ThO 2 migration on fertile fuel performance in an operating HTGR and to define characteristics needed by any comprehensive model describing ThO 2 kernel migration. The kinetics data generated in these postirradiation studies are consistent with in-pile data collected by investigators at Oak Ridge National Laboratory, which supports use of the more precise postirradiation heating results in HTGR core design studies. Observations of intergranular carbon deposits on the cool side of migrating kernels support the assumption that the kinetics of kernel migration are controlled by solid state diffusion within irradiated ThO 2 kernels. The migration is characterized by a period of no migration (incubation period) followed by migration at the equilibrium rate for ThO 2 . The incubation period decreases with increasing temperature and kernel burnup. The improved understanding of the kinetics of ThO 2 kernel migration provided by this work will contribute to an optimization of HTGR core design and an increased confidence in fuel performance predictions

Nutritional test of rice in rats

International Nuclear Information System (INIS)

Horii, Masaji; Yoshikawa, Seiji

1980-01-01

Behaviors on N derived from rice were followed up by means of 15 N-labeled rice. In the first test, the single unpolished rice diet and the diet of rice and bean lecithin (4.5%) produced urinary excretion of 10 - 12% of 15 N, and that of rice and mannan from devil's tongue (3%), 16 - 20%. The single unpolished rice diet showed slightly more urinary excretion of 15 N, and the other 2 diets showed a similar proportion of 15 N in 3 days. The results indicated that the diet containing mannan from devil's tongue resulted in a poor N absorption by rice, a large quantity of N being excreted over a long period of time. This suggested differences and time lags in the excretion of rice N into the stool and urine depending on the diet constitution. With the unpolished rice diet, a small quantity of rice protein was not absorbed, but was excreted. In the 2nd test with 15 N-polished rice, the urinary excretion rate was 11.44% for a single rice diet, 11.16% for a mixed diet of rice and bean (1:1 in protein), 10.99% for rice and egg yolk, 9.66% for rice, bean and egg yolk and 8.10% for rice and bean lecithin. This decrease in urinary excretion indicated a corresponding increase in absorption of rice protein. (Chiba, N.)

Nutrition quality of extraction mannan residue from palm kernel cake on brolier chicken

Science.gov (United States)

Tafsin, M.; Hanafi, N. D.; Kejora, E.; Yusraini, E.

2018-02-01

This study aims to find out the nutrient residue of palm kernel cake from mannan extraction on broiler chicken by evaluating physical quality (specific gravity, bulk density and compacted bulk density), chemical quality (proximate analysis and Van Soest Test) and biological test (metabolizable energy). Treatment composed of T0 : palm kernel cake extracted aquadest (control), T1 : palm kernel cake extracted acetic acid (CH3COOH) 1%, T2 : palm kernel cake extracted aquadest + mannanase enzyme 100 u/l and T3 : palm kernel cake extracted acetic acid (CH3COOH) 1% + enzyme mannanase 100 u/l. The results showed that mannan extraction had significant effect (P<0.05) in improving the quality of physical and numerically increase the value of crude protein and decrease the value of NDF (Neutral Detergent Fiber). Treatments had highly significant influence (P<0.01) on the metabolizable energy value of palm kernel cake residue in broiler chickens. It can be concluded that extraction with aquadest + enzyme mannanase 100 u/l yields the best nutrient quality of palm kernel cake residue for broiler chicken.

Visualization of nonlinear kernel models in neuroimaging by sensitivity maps

DEFF Research Database (Denmark)

Rasmussen, Peter Mondrup; Hansen, Lars Kai; Madsen, Kristoffer Hougaard

There is significant current interest in decoding mental states from neuroimages. In this context kernel methods, e.g., support vector machines (SVM) are frequently adopted to learn statistical relations between patterns of brain activation and experimental conditions. In this paper we focus...... on visualization of such nonlinear kernel models. Specifically, we investigate the sensitivity map as a technique for generation of global summary maps of kernel classification methods. We illustrate the performance of the sensitivity map on functional magnetic resonance (fMRI) data based on visual stimuli. We...

Explicit signal to noise ratio in reproducing kernel Hilbert spaces

DEFF Research Database (Denmark)

Gomez-Chova, Luis; Nielsen, Allan Aasbjerg; Camps-Valls, Gustavo

2011-01-01

This paper introduces a nonlinear feature extraction method based on kernels for remote sensing data analysis. The proposed approach is based on the minimum noise fraction (MNF) transform, which maximizes the signal variance while also minimizing the estimated noise variance. We here propose...... an alternative kernel MNF (KMNF) in which the noise is explicitly estimated in the reproducing kernel Hilbert space. This enables KMNF dealing with non-linear relations between the noise and the signal features jointly. Results show that the proposed KMNF provides the most noise-free features when confronted...

Reduction of Aflatoxins in Apricot Kernels by Electronic and Manual Color Sorting

Directory of Open Access Journals (Sweden)

Rosanna Zivoli

2016-01-01

Full Text Available The efficacy of color sorting on reducing aflatoxin levels in shelled apricot kernels was assessed. Naturally-contaminated kernels were submitted to an electronic optical sorter or blanched, peeled, and manually sorted to visually identify and sort discolored kernels (dark and spotted from healthy ones. The samples obtained from the two sorting approaches were ground, homogenized, and analysed by HPLC-FLD for their aflatoxin content. A mass balance approach was used to measure the distribution of aflatoxins in the collected fractions. Aflatoxin B1 and B2 were identified and quantitated in all collected fractions at levels ranging from 1.7 to 22,451.5 µg/kg of AFB1 + AFB2, whereas AFG1 and AFG2 were not detected. Excellent results were obtained by manual sorting of peeled kernels since the removal of discolored kernels (2.6%–19.9% of total peeled kernels removed 97.3%–99.5% of total aflatoxins. The combination of peeling and visual/manual separation of discolored kernels is a feasible strategy to remove 97%–99% of aflatoxins accumulated in naturally-contaminated samples. Electronic optical sorter gave highly variable results since the amount of AFB1 + AFB2 measured in rejected fractions (15%–18% of total kernels ranged from 13% to 59% of total aflatoxins. An improved immunoaffinity-based HPLC-FLD method having low limits of detection for the four aflatoxins (0.01–0.05 µg/kg was developed and used to monitor the occurrence of aflatoxins in 47 commercial products containing apricot kernels and/or almonds commercialized in Italy. Low aflatoxin levels were found in 38% of the tested samples and ranged from 0.06 to 1.50 μg/kg for AFB1 and from 0.06 to 1.79 μg/kg for total aflatoxins.

Effects of de-oiled palm kernel cake based fertilizers on sole maize ...

African Journals Online (AJOL)

A study was conducted to determine the effect of de-oiled palm kernel cake based fertilizer formulations on the yield of sole maize and cassava crops. Two de-oiled palm kernel cake based fertilizer formulations A and B were compounded from different proportions of de-oiled palm kernel cake, urea, muriate of potash and ...

Gaussian processes with optimal kernel construction for neuro-degenerative clinical onset prediction

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Canas, Liane S.; Yvernault, Benjamin; Cash, David M.; Molteni, Erika; Veale, Tom; Benzinger, Tammie; Ourselin, Sébastien; Mead, Simon; Modat, Marc

2018-02-01

Gaussian Processes (GP) are a powerful tool to capture the complex time-variations of a dataset. In the context of medical imaging analysis, they allow a robust modelling even in case of highly uncertain or incomplete datasets. Predictions from GP are dependent of the covariance kernel function selected to explain the data variance. To overcome this limitation, we propose a framework to identify the optimal covariance kernel function to model the data.The optimal kernel is defined as a composition of base kernel functions used to identify correlation patterns between data points. Our approach includes a modified version of the Compositional Kernel Learning (CKL) algorithm, in which we score the kernel families using a new energy function that depends both the Bayesian Information Criterion (BIC) and the explained variance score. We applied the proposed framework to model the progression of neurodegenerative diseases over time, in particular the progression of autosomal dominantly-inherited Alzheimer's disease, and use it to predict the time to clinical onset of subjects carrying genetic mutation.

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

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

2018-06-12

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

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

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

2017-06-19

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

Traditional Malian Solid Foods Made from Sorghum and Millet Have Markedly Slower Gastric Emptying than Rice, Potato, or Pasta

Directory of Open Access Journals (Sweden)

Fatimata Cisse

2018-01-01

Full Text Available From anecdotal evidence that traditional African sorghum and millet foods are filling and provide sustained energy, we hypothesized that gastric emptying rates of sorghum and millet foods are slow, particularly compared to non-traditional starchy foods (white rice, potato, wheat pasta. A human trial to study gastric emptying of staple foods eaten in Bamako, Mali was conducted using a carbon-13 (13C-labelled octanoic acid breath test for gastric emptying, and subjective pre-test and satiety response questionnaires. Fourteen healthy volunteers in Bamako participated in a crossover design to test eight starchy staples. A second validation study was done one year later in Bamako with six volunteers to correct for endogenous 13C differences in the starches from different sources. In both trials, traditional sorghum and millet foods (thick porridges and millet couscous had gastric half-emptying times about twice as long as rice, potato, or pasta (p < 0.0001. There were only minor changes due to the 13C correction. Pre-test assessment of millet couscous and rice ranked them as more filling and aligned well with postprandial hunger rankings, suggesting that a preconceived idea of rice being highly satiating may have influenced subjective satiety scoring. Traditional African sorghum and millet foods, whether viscous in the form of a thick porridge or as non-viscous couscous, had distinctly slow gastric emptying, in contrast to the faster emptying of non-traditional starchy foods, which are popular among West African urban consumers.

Fluidization calculation on nuclear fuel kernel coating

International Nuclear Information System (INIS)

Sukarsono; Wardaya; Indra-Suryawan

1996-01-01

The fluidization of nuclear fuel kernel coating was calculated. The bottom of the reactor was in the from of cone on top of the cone there was a cylinder, the diameter of the cylinder for fluidization was 2 cm and at the upper part of the cylinder was 3 cm. Fluidization took place in the cone and the first cylinder. The maximum and the minimum velocity of the gas of varied kernel diameter, the porosity and bed height of varied stream gas velocity were calculated. The calculation was done by basic program

Notes on a storage manager for the Clouds kernel

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Pitts, David V.; Spafford, Eugene H.

1986-01-01

The Clouds project is research directed towards producing a reliable distributed computing system. The initial goal is to produce a kernel which provides a reliable environment with which a distributed operating system can be built. The Clouds kernal consists of a set of replicated subkernels, each of which runs on a machine in the Clouds system. Each subkernel is responsible for the management of resources on its machine; the subkernal components communicate to provide the cooperation necessary to meld the various machines into one kernel. The implementation of a kernel-level storage manager that supports reliability is documented. The storage manager is a part of each subkernel and maintains the secondary storage residing at each machine in the distributed system. In addition to providing the usual data transfer services, the storage manager ensures that data being stored survives machine and system crashes, and that the secondary storage of a failed machine is recovered (made consistent) automatically when the machine is restarted. Since the storage manager is part of the Clouds kernel, efficiency of operation is also a concern.

Metabolite identification through multiple kernel learning on fragmentation trees.

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

2014-06-15

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

Glufosinate herbicide-tolerant (LibertyLink) rice vs. conventional rice in diets for growing-finishing swine.

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Cromwell, G L; Henry, B J; Scott, A L; Gerngross, M F; Dusek, D L; Fletcher, D W

2005-05-01

Genetically modified (GM) rice (LibertyLink, event LLRICE62) that is tolerant to glufosinate ammonium (Liberty) herbicide was compared with a near-isogenic (NI) conventional medium-grain brown rice (cultivar, Bengal) and a commercially milled long-grain brown rice in diets for growing-finishing pigs. The GM and NI rice were grown in 2000. The GM rice was from fields treated (GM+) or not treated (GM-) with glufosinate herbicide. The GM- and NI rice were grown using herbicide regimens typical of southern United States rice production practices. The four rice grains were similar in composition. Growing-finishing pigs (n = 96) were fed fortified rice-soybean meal diets containing the four different rice grains from 25 to 106 kg BW. Diets contained 0.99% lysine initially (growing phase), with lysine decreased to 0.80% (early finishing phase) and 0.65% (late finishing phase), when pigs reached 51 and 77 kg, respectively. The percentage of rice in the four diets was constant during each of the three phases (72.8, 80.0, and 85.8% for the growing, early-finishing, and late-finishing phases, respectively). There were six pen replicates (three pens of barrows and three pens of gilts) and four pigs per pen for each dietary treatment. All pigs were slaughtered at the termination of the study to collect carcass data. At the end of the 98-d experiment, BW gain, feed intake (as-fed basis), and feed:gain ratio did not differ (P > 0.05) for pigs fed the GM+ vs. conventional rice diets, but growth performance traits of pigs fed the GM+ rice diets were superior (P glufosinate herbicide-tolerant rice was similar in composition and nutritional value to conventional rice for growing-finishing pigs.

Asian wild rice is a hybrid swarm with extensive gene flow and feralization from domesticated rice.

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Wang, Hongru; Vieira, Filipe G; Crawford, Jacob E; Chu, Chengcai; Nielsen, Rasmus

2017-06-01

The domestication history of rice remains controversial, with multiple studies reaching different conclusions regarding its origin(s). These studies have generally assumed that populations of living wild rice, O. rufipogon , are descendants of the ancestral population that gave rise to domesticated rice, but relatively little attention has been paid to the origins and history of wild rice itself. Here, we investigate the genetic ancestry of wild rice by analyzing a diverse panel of rice genomes consisting of 203 domesticated and 435 wild rice accessions. We show that most modern wild rice is heavily admixed with domesticated rice through both pollen- and seed-mediated gene flow. In fact, much presumed wild rice may simply represent different stages of feralized domesticated rice. In line with this hypothesis, many presumed wild rice varieties show remnants of the effects of selective sweeps in previously identified domestication genes, as well as evidence of recent selection in flowering genes possibly associated with the feralization process. Furthermore, there is a distinct geographical pattern of gene flow from aus , indica , and japonica varieties into colocated wild rice. We also show that admixture from aus and indica is more recent than gene flow from japonica , possibly consistent with an earlier spread of japonica varieties. We argue that wild rice populations should be considered a hybrid swarm, connected to domesticated rice by continuous and extensive gene flow. © 2017 Wang et al.; Published by Cold Spring Harbor Laboratory Press.

An Economic Risk Analysis of Weed Suppressive Rice Cultivars in Rice Production

Science.gov (United States)

Weeds are a major constraint to rice production. In the United States, most rice cultivars are not inherently weed-suppressive and require substantial herbicide inputs to achieve agronomic and economic viability. Intensive herbicide application in rice also has many potential drawbacks, resulting in...

Probabilistic wind power forecasting based on logarithmic transformation and boundary kernel

International Nuclear Information System (INIS)

Zhang, Yao; Wang, Jianxue; Luo, Xu

2015-01-01

Highlights: • Quantitative information on the uncertainty of wind power generation. • Kernel density estimator provides non-Gaussian predictive distributions. • Logarithmic transformation reduces the skewness of wind power density. • Boundary kernel method eliminates the density leakage near the boundary. - Abstracts: Probabilistic wind power forecasting not only produces the expectation of wind power output, but also gives quantitative information on the associated uncertainty, which is essential for making better decisions about power system and market operations with the increasing penetration of wind power generation. This paper presents a novel kernel density estimator for probabilistic wind power forecasting, addressing two characteristics of wind power which have adverse impacts on the forecast accuracy, namely, the heavily skewed and double-bounded nature of wind power density. Logarithmic transformation is used to reduce the skewness of wind power density, which improves the effectiveness of the kernel density estimator in a transformed scale. Transformations partially relieve the boundary effect problem of the kernel density estimator caused by the double-bounded nature of wind power density. However, the case study shows that there are still some serious problems of density leakage after the transformation. In order to solve this problem in the transformed scale, a boundary kernel method is employed to eliminate the density leak at the bounds of wind power distribution. The improvement of the proposed method over the standard kernel density estimator is demonstrated by short-term probabilistic forecasting results based on the data from an actual wind farm. Then, a detailed comparison is carried out of the proposed method and some existing probabilistic forecasting methods

Kernel based pattern analysis methods using eigen-decompositions for reading Icelandic sagas