Journal articles on the topic 'Kernel'
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1
Yang, Tianbao, Mehrdad Mahdavi, Rong Jin, Jinfeng Yi, and Steven Hoi. "Online Kernel Selection: Algorithms and Evaluations." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (September 20, 2021): 1197–203. http://dx.doi.org/10.1609/aaai.v26i1.8298.
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Kernel methods have been successfully applied to many machine learning problems. Nevertheless, since the performance of kernel methods depends heavily on the type of kernels being used, identifying good kernels among a set of given kernels is important to the success of kernel methods. A straightforward approach to address this problem is cross-validation by training a separate classifier for each kernel and choosing the best kernel classifier out of them. Another approach is Multiple Kernel Learning (MKL), which aims to learn a single kernel classifier from an optimal combination of multiple kernels. However, both approaches suffer from a high computational cost in computing the full kernel matrices and in training, especially when the number of kernels or the number of training examples is very large. In this paper, we tackle this problem by proposing an efficient online kernel selection algorithm. It incrementally learns a weight for each kernel classifier. The weight for each kernel classifier can help us to select a good kernel among a set of given kernels. The proposed approach is efficient in that (i) it is an online approach and therefore avoids computing all the full kernel matrices before training; (ii) it only updates a single kernel classifier each time by a sampling technique and therefore saves time on updating kernel classifiers with poor performance; (iii) it has a theoretically guaranteed performance compared to the best kernel predictor. Empirical studies on image classification tasks demonstrate the effectiveness of the proposed approach for selecting a good kernel among a set of kernels.
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Wang, Peiyan, and Dongfeng Cai. "Multiple kernel learning by empirical target kernel." International Journal of Wavelets, Multiresolution and Information Processing 18, no. 02 (September 24, 2019): 1950058. http://dx.doi.org/10.1142/s0219691319500589.
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Multiple kernel learning (MKL) aims at learning an optimal combination of base kernels with which an appropriate hypothesis is determined on the training data. MKL has its flexibility featured by automated kernel learning, and also reflects the fact that typical learning problems often involve multiple and heterogeneous data sources. Target kernel is one of the most important parts of many MKL methods. These methods find the kernel weights by maximizing the similarity or alignment between weighted kernel and target kernel. The existing target kernels implement a global manner, which (1) defines the same target value for closer and farther sample pairs, and inappropriately neglects the variation of samples; (2) is independent of training data, and is hardly approximated by base kernels. As a result, maximizing the similarity to the global target kernel could make these pre-specified kernels less effectively utilized, further reducing the classification performance. In this paper, instead of defining a global target kernel, a localized target kernel is calculated for each sample pair from the training data, which is flexible and able to well handle the sample variations. A new target kernel named empirical target kernel is proposed in this research to implement this idea, and three corresponding algorithms are designed to efficiently utilize the proposed empirical target kernel. Experiments are conducted on four challenging MKL problems. The results show that our algorithms outperform other methods, verifying the effectiveness and superiority of the proposed methods.
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Xu, Lixiang, Yuanyan Tang, Bin Luo, Lixin Cui, Xiu Chen, and Jin Xiao. "A combined Weisfeiler–Lehman graph kernel for structured data." International Journal of Wavelets, Multiresolution and Information Processing 16, no. 05 (September 2018): 1850039. http://dx.doi.org/10.1142/s021969131850039x.
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Different graph kernels may correspond to using different notions of similarity or may be using information coming from multiple sources. In this paper, we develop a common method to construct combined graph kernel (CGK) which is based on a family of graph kernels. We define three kinds of CGK. The first one is called the weighted combined graph kernel and is a parametric CGK. The second one is called the accuracy ratio weighted combined graph kernel and is a non-parametric CGK. The third one is called the product combined graph kernel and also belongs to non-parametric CGK. The three kinds of definition of CGK can be applied for constructing CGK based on a family of graph kernels. This family of kernels is demonstrated based on the Weisfeiler–Lehman (WL) sequence of graphs in this paper, including a highly efficient subtree kernel, edge kernel, and shortest path kernel. Experiments demonstrate that our CGK based on WL graph kernels outperforms the corresponding single WL graph kernel on several classification benchmark data sets.
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Mihaylova, Dasha, Aneta Popova, Ivayla Dincheva, and Svetla Pandova. "HS-SPME-GC–MS Profiling of Volatile Organic Compounds and Polar and Lipid Metabolites of the “Stendesto” Plum–Apricot Kernel with Reference to Its Parents." Horticulturae 10, no. 3 (March 7, 2024): 257. http://dx.doi.org/10.3390/horticulturae10030257.
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Plum–apricot hybrids are the successful backcrosses of plums and apricots. Plums and apricots are well-known and preferred by consumers because of their distinct sensory and beneficial health properties. However, kernel consumption remains limited even though kernels are easily accessible. The “Stendesto” hybrid originates from the “Modesto” apricot and the “Stanley” plum. Kernal metabolites exhibited quantitative differences in terms of metabolites identified by gas chromatography–mass spectrometry (GC–MS) analysis and HS-SPME technique profiling. The results revealed a total of 55 different compounds. Phenolic acids, hydrocarbons, organic acids, fatty acids, sugar acids and alcohols, mono- and disaccharides, as well as amino acids were identified in the studied kernels. The hybrid kernel generally inherited all the metabolites present in the parental kernels. Volatile organic compounds were also investigated. Thirty-five compounds identified as aldehydes, alcohols, ketones, furans, acids, esters, and alkanes were present in the studied samples. Considering volatile organic compounds (VOCs), the hybrid kernel had more resemblance to the plum one, bearing that alkanes were only identified in the apricot kernel. The objective of this study was to investigate the volatile composition and metabolic profile of the first Bulgarian plum–apricot hybrid kernels, and to provide comparable data relevant to both parents. With the aid of principal component analysis (PCA) and hierarchical cluster analysis (HCA), differentiation and clustering of the results occurred in terms of the metabolites present in the plum–apricot hybrid kernels with reference to their parental lines. This study is the first providing information about the metabolic profile of variety-defined kernels. It is also a pioneering study on the comprehensive evaluation of fruit hybrids.
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Wang, Ke, Ligang Cheng, and Bin Yong. "Spectral-Similarity-Based Kernel of SVM for Hyperspectral Image Classification." Remote Sensing 12, no. 13 (July 6, 2020): 2154. http://dx.doi.org/10.3390/rs12132154.
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Spectral similarity measures can be regarded as potential metrics for kernel functions, and can be used to generate spectral-similarity-based kernels. However, spectral-similarity-based kernels have not received significant attention from researchers. In this paper, we propose two novel spectral-similarity-based kernels based on spectral angle mapper (SAM) and spectral information divergence (SID) combined with the radial basis function (RBF) kernel: Power spectral angle mapper RBF (Power-SAM-RBF) and normalized spectral information divergence-based RBF (Normalized-SID-RBF) kernels. First, we prove these spectral-similarity-based kernels to be Mercer’s kernels. Second, we analyze their efficiency in terms of local and global kernels. Finally, we consider three hyperspectral datasets to analyze the effectiveness of the proposed spectral-similarity-based kernels. Experimental results demonstrate that the Power-SAM-RBF and SAM-RBF kernels can obtain an impressive performance, particularly the Power-SAM-RBF kernel. For example, when the ratio of the training set is 20 % , the kappa coefficient of Power-SAM-RBF kernel (0.8561) is 1.61 % , 1.32 % , and 1.23 % higher than that of the RBF kernel on the Indian Pines, University of Pavia, and Salinas Valley datasets, respectively. We present three conclusions. First, the superiority of the Power-SAM-RBF kernel compared to other kernels is evident. Second, the Power-SAM-RBF kernel can provide an outstanding performance when the similarity between spectral signatures in the same hyperspectral dataset is either extremely high or extremely low. Third, the Power-SAM-RBF kernel provides even greater benefits compared to other commonly used kernels when the sizes of the training sets increase. In future work, multiple kernels combining with the spectral-similarity-based kernel are expected to be provide better hyperspectral classification.
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Yang, Bo. "Multiple Kernel Feature Fusion Using Kernel Fisher Method." Applied Mechanics and Materials 333-335 (July 2013): 1406–9. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.1406.
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Different from the existing multiple kernel methods which mainly work in implicit kernel space, we propose a novel multiple kernel method in empirical kernel mapping space. In empirical kernel mapping space, the combination of kernels can be treated as the weighted fusion of empirical kernel mapping samples. Based this fact, we developed a multiple kernel Fisher method to realize multiple kernel classification in empirical kernel mapping space. The experiments here illustrate that the proposed multiple kernel fisher method is feasible and effective.
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DIOŞAN, LAURA, ALEXANDRINA ROGOZAN, and JEAN-PIERRE PECUCHET. "LEARNING SVM WITH COMPLEX MULTIPLE KERNELS EVOLVED BY GENETIC PROGRAMMING." International Journal on Artificial Intelligence Tools 19, no. 05 (October 2010): 647–77. http://dx.doi.org/10.1142/s0218213010000352.
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Classic kernel-based classifiers use only a single kernel, but the real-world applications have emphasized the need to consider a combination of kernels — also known as a multiple kernel (MK) — in order to boost the classification accuracy by adapting better to the characteristics of the data. Our purpose is to automatically design a complex multiple kernel by evolutionary means. In order to achieve this purpose we propose a hybrid model that combines a Genetic Programming (GP) algorithm and a kernel-based Support Vector Machine (SVM) classifier. In our model, each GP chromosome is a tree that encodes the mathematical expression of a multiple kernel. The evolutionary search process of the optimal MK is guided by the fitness function (or efficiency) of each possible MK. The complex multiple kernels which are evolved in this manner (eCMKs) are compared to several classic simple kernels (SKs), to a convex linear multiple kernel (cLMK) and to an evolutionary linear multiple kernel (eLMK) on several real-world data sets from UCI repository. The numerical experiments show that the SVM involving the evolutionary complex multiple kernels perform better than the classic simple kernels. Moreover, on the considered data sets, the new multiple kernels outperform both the cLMK and eLMK — linear multiple kernels. These results emphasize the fact that the SVM algorithm requires a combination of kernels more complex than a linear one in order to boost its performance.
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Hwang, Jeongsik, and Sadaaki Miyamoto. "Kernel Functions Derived from Fuzzy Clustering and Their Application to Kernel Fuzzyc-Means." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 1 (January 20, 2011): 90–94. http://dx.doi.org/10.20965/jaciii.2011.p0090.
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Among widely used kernel functions, such as support vector machines, in data analysis, the Gaussian kernel is most often used. This kernel arises in entropy-based fuzzyc-means clustering. There is reason, however, to check whether other types of functions used in fuzzyc-means are also kernels. Using completely monotone functions, we show they can be kernels if a regularization constant proposed by Ichihashi is introduced. We also show how these kernel functions are applied to kernel-based fuzzyc-means clustering, which outperform the Gaussian kernel in a typical example.
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Smith, Michael W., Becky S. Cheary, and Becky L. Carroll. "The Occurrence of Pecan Kernel Necrosis." HortScience 42, no. 6 (October 2007): 1351–56. http://dx.doi.org/10.21273/hortsci.42.6.1351.
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Pecan [Carya illinoinensis (Wangenh.) C. Koch] kernels (cotyledon) of ‘Pawnee’ displayed a consistent malady not described previously that was designated as “kernel necrosis.” The most severe form of the problem was blackened, necrotic tissue engulfing the basal one-half to one-third of the kernel. The mildest form was darkened tissue in the dorsal grove at the basal end of the kernel. The problem was first observable during the gel stage of kernel development. No symptoms of kernel necrosis were visible on the shuck (involucre). Kernel necrosis was more prominent on ‘Pawnee’, ‘Choctaw’, and ‘Oklahoma’ than other cultivars observed. At maturity, nuts with kernel necrosis had a larger volume than nuts with normal kernels. There were few differences in elemental concentrations of normal kernels from a severely affected orchard and an orchard with little kernel necrosis, and none of the differences appeared to be associated with this disorder. ‘Pawnee’ kernels with necrosis had more phosphorus, zinc, and manganese than normal kernels. Basal segments of necrotic kernels had more boron and acetic acid-extractable and water-soluble calcium than distal segments or normal kernels. Higher elemental concentrations in basal segments of necrotic kernels did not appear sufficient to cause tissue damage. Soil from the orchard with severe kernel necrosis had unusually high concentrations of nitrate, expressed as nitrogen (NO3-N), in the soil profile. Groundwater used for irrigation was contaminated with 34 mg·L−1 NO3-N. An experiment on ‘Pawnee’ evaluated three nitrogen (N) rates, 0, 0.8 g·cm2 cross-sectional trunk area applied in March, and 1.6 g + 1.6 g + 1.2 g·cm2 cross-sectional trunk area N applied during the second week in March, first week in June, and first week in September, respectively, on the incidence of kernel necrosis, leaf N concentration, soil NO3 concentration, yield, nut quality, and growth over 5 years. Leaf N was affected by treatment only once during the study. Nitrates accumulated in the soil, increasing 24% in 3 years when no supplemental N was applied, except in the contaminated irrigation water. Kernel necrosis was either unaffected by N treatment or during 1 year, kernel necrosis was highest without supplemental N application. Tree yield, kernel quality, and growth were unaffected by N treatment. Yield fluctuations among years were apparent demonstrating that an abundant N supply did not prevent alternate bearing. Kernel necrosis was a severe problem in one orchard and was identified in several orchards at low frequencies. The cause of kernel necrosis remains unknown.
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Chen, Cuiling, Zhijun Hu, Hongbin Xiao, Junbo Ma, and Zhi Li. "One-Step Clustering with Adaptively Local Kernels and a Neighborhood Kernel." Mathematics 11, no. 18 (September 17, 2023): 3950. http://dx.doi.org/10.3390/math11183950.
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Among the methods of multiple kernel clustering (MKC), some adopt a neighborhood kernel as the optimal kernel, and some use local base kernels to generate an optimal kernel. However, these two methods are not synthetically combined together to leverage their advantages, which affects the quality of the optimal kernel. Furthermore, most existing MKC methods require a two-step strategy to cluster, i.e., first learn an indicator matrix, then executive clustering. This does not guarantee the optimality of the final results. To overcome the above drawbacks, a one-step clustering with adaptively local kernels and a neighborhood kernel (OSC-ALK-ONK) is proposed in this paper, where the two methods are combined together to produce an optimal kernel. In particular, the neighborhood kernel improves the expression capability of the optimal kernel and enlarges its search range, and local base kernels avoid the redundancy of base kernels and promote their variety. Accordingly, the quality of the optimal kernel is enhanced. Further, a soft block diagonal (BD) regularizer is utilized to encourage the indicator matrix to be BD. It is helpful to obtain explicit clustering results directly and achieve one-step clustering, then overcome the disadvantage of the two-step strategy. In addition, extensive experiments on eight data sets and comparisons with six clustering methods show that OSC-ALK-ONK is effective.
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Nishijima, K. A., M. M. Wall, and M. S. Siderhurst. "Demonstrating Pathogenicity of Enterobacter cloacae on Macadamia and Identifying Associated Volatiles of Gray Kernel of Macadamia in Hawaii." Plant Disease 91, no. 10 (October 2007): 1221–28. http://dx.doi.org/10.1094/pdis-91-10-1221.
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Gray kernel is an important disease of macadamia (Macadamia integrifolia) that affects the quality of kernels, causing gray discoloration and a permeating, foul odor. Gray kernel symptoms were produced in raw, in-shell kernels of three cultivars of macadamia that were inoculated with strains of Enterobacter cloacae. Koch's postulates were fulfilled for three strains, demonstrating that E. cloacae is a causal agent of gray kernel. An inoculation protocol was developed to consistently reproduce gray kernel symptoms. Among the E. cloacae strains studied, macadamia strain LK 0802-3 and ginger strain B193-3 produced the highest incidences of disease (65 and 40%, respectively). The other macadamia strain, KN 04-2, produced gray kernel in 21.7% of inoculated nuts. Control treatments had 1.7% gray kernel symptoms. Some abiotic and biotic factors that affected incidence of gray kernel in inoculated kernels were identified. Volatiles of gray and nongray kernel samples also were analyzed. Ethanol and acetic acid were present in nongray and gray kernel samples, whereas volatiles from gray kernel samples included the additional compounds, 3-hydroxy-2-butanone (acetoin), 2,3-butanediol, phenol, and 2-methoxyphenol (guaiacol). This is believed to be the first report of the identification of volatile compounds associated with gray kernel.
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BRANDL, MARIA T., ZHONGLI PAN, STEVEN HUYNH, YI ZHU, and TARA H. MCHUGH. "Reduction of Salmonella Enteritidis Population Sizes on Almond Kernels with Infrared Heat." Journal of Food Protection 71, no. 5 (May 1, 2008): 897–902. http://dx.doi.org/10.4315/0362-028x-71.5.897.
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Catalytic infrared (IR) heating was investigated to determine its effect on Salmonella enterica serovar Enteritidis population sizes on raw almond kernels. Using a double-sided catalytic IR heating system, a radiation intensity of 5,458 W/m2 caused a fast temperature increase at the kernel surface and minimal temperature differences between the top and bottom kernel surfaces. Exposure of dry kernels to IR heat for 30, 35 and 45 s resulted in maximum kernel surface temperatures of 90, 102, and 113°C, and when followed by immediate cooling at room temperature, yielded a 0.63-, 1.03-, and 1.51-log reduction in S. enterica population sizes, respectively. The most efficacious decontamination treatment consisted of IR exposure, followed by holding of the kernels at warm temperature for 60 min, which effected a greater than 7.5-log reduction in S. enterica on the kernels. During that treatment, the kernel surface temperature rose to 109°C and gradually decreased to 80°C. Similar IR and holding treatments with lower maximum kernel surface temperatures of 104 and 100°C yielded reductions of 5.3 and 4.2 log CFU/g kernel, respectively. During these treatments, moisture loss from the kernels was minimal and did not exceed 1.06%. Macroscopic observations suggested that kernel quality was not compromised by the IR-holding combination treatment, as skin morphology, meat texture, and kernel color were indistinguishable from those of untreated kernels. Our studies indicate that IR heating technology is an effective dry pasteurization for raw almonds.
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Tan, Yew Ai, and Ainte Kuntom. "Hydrocarbons m Crude Palm Kernel Oil." Journal of AOAC INTERNATIONAL 77, no. 1 (January 1, 1994): 67–73. http://dx.doi.org/10.1093/jaoac/77.1.67.
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Abstract The sources of hydrocarbons in crude palm kernel oil were investigated by a series of laboratory-controlled oil extractions of kernels of varying quality. Site examinations of palm kernel-crushing plants were also conducted to determine possible sources of hydrocarbon contamination of palm kernels throughout the process of kernel extraction. Parallel to these studies, a random survey of crude palm kernel oil (CPKO) produced by different kernel crushers was also carried out to determine the range of hydrocarbon concentrations in locally produced CPKO. This study showed that hydrocarbons can be picked up from sources such as glassware, extracting apparatus, and plastic containers and stoppers. Extraction of oil from low-quality kernels that were both moldy and rancid, broken kernels, and kernels plus added shells also resulted in a higher hydrocarbon level in the final CPKO. Overheating and cooking of the kernels before extraction also contributed to the overall hydrocarbon content. The random survey of hydrocarbon level showed a range of 0.6–7.1 ppm.
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GuiXiang, Chen, Yuan YaHao, Liu ChaoSai, Liu WenLei, and Lu JingRan. "Factors, Harms, and Control of Corn Kernel Breakage: A Review." Annals of Food Processing and Preservation 7, no. 1 (December 15, 2023): 1–13. http://dx.doi.org/10.47739/2573-1033.foodprocessing.1038.
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Corn kernel breakage is an important cause of corn losses. Beginning at harvest, corn kernels are broken by collisions with farm machinery and equipment, drying induces increased kernel breakage susceptibility, and equipment transfers and grain loading generate collisions between corn kernels and with equipment, increasing the amount of broken kernels. Meanwhile, broken kernels increase the risk of insect damage, mold and mildew, as well as aggravate segregation during warehousing, which is detrimental to the safety and quality of corn storage. The causes of corn kernel breakage from harvest to storage are summarized, the water content is a key factor in the post-production quality and quantity of corn, the research results on kernel breakage in recent years are summarized, and suggestions are made for the research related to the reduction of corn kernel breakage rate.
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Mohd Hatta, Noramalina, Zuraini Ali Shah, and Shahreen Kasim. "Evaluate the Performance of SVM Kernel Functions for Multiclass Cancer Classification." International Journal on Data Science 1, no. 1 (May 11, 2020): 37–41. http://dx.doi.org/10.18517/ijods.1.1.37-41.2020.
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Multiclass cancer classification is basically one of the challenging fields in machine learning which a fast growing technology that use human behaviour as examples. Supervised classification such Support Vector Machine (SVM) has been used to classify the dataset on classification by its own function and merely known as kernel function. Kernel function has stated to have a problem especially in selecting their best kernels based on a specific datasets and tasks. Besides, there is an issue stated that the kernels function have a high impossibility to distribute the data in straight line. Here, three basic kernel functions was used and tested with selected dataset and they are linear kernel, polynomial kernel and Radial Basis Function (RBF) kernel function. The three kernels were tested by different dataset to gain the accuracy. For a comparison, this study conducting a test by with and without feature selection in SVM classification kernel function since both tests will give different result and thus give a big meaning to the study.
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Bhattacharya, Riju, Naresh Kumar Nagwani, and Sarsij Tripathi. "A Comparative Study of Graph Kernels and Clustering Algorithms." International Journal of Multimedia Data Engineering and Management 12, no. 1 (January 2021): 33–48. http://dx.doi.org/10.4018/ijmdem.2021010103.
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Graph kernels have evolved as a promising and popular method for graph clustering over the last decade. In this work, comparative study on the five standard graph kernel techniques for graph clustering has been performed. The graph kernels, namely vertex histogram kernel, shortest path kernel, graphlet kernel, k-step random walk kernel, and Weisfeiler-Lehman kernel have been compared for graph clustering. The clustering methods considered for the kernel comparison are hierarchical, k-means, model-based, fuzzy-based, and self-organizing map clustering techniques. The comparative study of kernel methods over the clustering techniques is performed on MUTAG benchmark dataset. Clustering performance is assessed with internal validation performance parameters such as connectivity, Dunn, and the silhouette index. Finally, the comparative analysis is done to facilitate researchers for selecting the appropriate kernel method for effective graph clustering. The proposed methodology elicits k-step random walk and shortest path kernel have performed best among all graph clustering approaches.
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Zhang, Xiao, Shizhong Liao, Jun Xu, and Ji-Rong Wen. "Regret Bounds for Online Kernel Selection in Continuous Kernel Space." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 12 (May 18, 2021): 10931–38. http://dx.doi.org/10.1609/aaai.v35i12.17305.
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Regret bounds of online kernel selection in a finite kernel set have been well studied, having at least an order O( √ NT) of magnitude after T rounds, where N is the number of candidate kernels. But it is still an unsolved problem to achieve sublinear regret bounds of online kernel selection in a continuous kernel space under different learning frameworks. In this paper, to represent different learning frameworks of online kernel selection, we divide online kernel selection approaches in a continuous kernel space into two categories according to the order of selection and training at each round. Then we construct a surrogate hypothesis space that contains all the candidate kernels with bounded norms and inner products, representing the continuously varying hypothesis space. Finally, we decompose the regrets of the proposed online kernel selection categories into different types of instantaneous regrets in the surrogate hypothesis space, and derive optimal regret bounds of order O( √ T) of magnitude under mild assumptions, independent of the cardinality of the continuous kernel space. Empirical studies verified the correctness of the theoretical regret analyses.
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Boac, Josephine M., Mark E. Casada, Lester O. Pordesimo, Frank H. Arthur, Ronaldo G. Maghirang, and Christian D. Mina. "Effect of Internal Insect Infestation on Single Kernel Mass and Particle Density of Corn and Wheat." Applied Engineering in Agriculture 38, no. 3 (2022): 583–88. http://dx.doi.org/10.13031/aea.14858.
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HighlightsSingle kernel mass and particle density were not significantly affected by the number of rice weevils feeding within a corn kernel and lesser grain borers feeding within a wheat kernel.In both corn and wheat, single kernel mass decreased after the larval stage of internally feeding insects.Particle density increased linearly with insect age for both rice weevils in corn and lesser grain borer in wheat.The increasing particle density while the kernel mass was being eroded indicates that the kernel internal void was detected by the gas pycnometer employed for measurement of the true volume of grain kernels.Abstract. To model the dynamics of insect infestation in a grain handling system using the discrete element method (DEM), physical properties of the infested kernels compared to their sound counterparts are needed, specifically particle density and single kernel mass of infested kernels. Thus, the objective of this study was to determine the particle density and single kernel mass of internally infested kernels as affected by insect age. Corn and wheat were infested with internal feeders: rice weevil (RW), Sitophilus oryzae (L.), in corn and lesser grain borer (LGB), Rhyzopertha dominica (F.), in wheat. The internal feeders were allowed to grow and mature inside the kernels and properties were measured for representative samples selected using X-ray imaging approximately 14, 28, 35, and 42 days after the end of a 4-day oviposition period. The measured kernel physical properties were not affected by the number of internal insects per kernel. In both corn and wheat, single kernel mass decreased after the larval stage of internally feeding insects. Single kernel mass decreased from 374 mg in sound corn to 346 mg in corn with pre-emerged RW adults and from 31.4 mg in sound wheat to 25.9 mg in wheat with pre-emerged LGB adults. Particle density increased with insect age for both RW in corn and LGB in wheat with a linear trend. The increasing particle density while the kernel mass eroded indicates that kernel internal void was detected by the gas pycnometer employed for measurement of the true volume of grain kernels. Data obtained from this study enables effective DEM modeling of grain commingling of insect-infested and sound grain kernels in grain handling systems. Keywords: Corn, Insect age, Internal feeders, Insect infestation, Lesser grain borer, Particle density, Rice weevil, Single kernel mass, Wheat.
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Odek, Zephania R., Terry J. Siebenmorgen, and Andronikos Mauromoustakos. "Relative Impact of Kernel Thickness and Moisture Content on Rice Fissuring during Drying." Applied Engineering in Agriculture 34, no. 1 (2018): 239–46. http://dx.doi.org/10.13031/aea.12513.
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Abstract.Individual kernel thickness and moisture content (MC) vary within rice panicles. These variations affect the drying characteristics of rice kernels and consequently, the milling yield. This study utilized an X-ray system augmented with an in-situ rice drying apparatus that enabled fissure detection in rough rice kernels during drying and tempering. Rough rice kernels of two long-grain cultivars (Roy J and CL XL745), each at two MC levels (20% and 16%, w.b.), were fractionated into three thickness fractions (thin <1.98 mm, medium 1.98 - 2.03 mm, and thick >2.03 mm). Kernels from each of the 12 sub-lots were dried and tempered under controlled air conditions. Fissured kernel percentages (FKP) were determined from X-ray images taken before, during, and after drying and tempering. Kernel thickness and MC both affected moisture desorption fissuring. Generally, as kernel thickness increased, the FKP increased for high-MC lots. In regards to MC, high-MC lots were more prone to fissuring than the low-MC lots. Overall, these findings highlight the role of kernel properties on fissuring during drying. Keywords: Kernel fissuring, Kernel thickness, Moisture content, Rice drying, X-ray imaging.
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La Bonte, Don R., and John A. Juvik. "Characterization of sugary-1 (su-1) sugary enhancer (se) Kernels in Segregating Sweet Corn Populations." Journal of the American Society for Horticultural Science 115, no. 1 (January 1990): 153–57. http://dx.doi.org/10.21273/jashs.115.1.153.
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A single-kernel, sugar analysis technique was used to study the genetic relationship between morphological and metabolic traits previously associated with expression of the sugary enhancer (se) endosperm mutation in a su-1 sweet corn (Zea mays L.) background. Analysis of sucrose and total carotene content in su-1 kernel populations segregating for se showed that light-yellow kernel color was a reliable phenotypic indicator for kernels homozygous for the se gene. High levels of kernel maltose was not always indicative of su-1 se kernels in mature (55 days after pollination) kernel populations. Characteristic high levels of percent moisture in su-1 se kernels at 28 and 35 days post-pollination were identified as an expression of high sugar content. Kernels homozygous for su-1 se were also found to weigh less at maturity than su-1 Se kernels, and se was found to be partially expressed in a heterozygous condition.
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Baram, Yoram. "Learning by Kernel Polarization." Neural Computation 17, no. 6 (June 1, 2005): 1264–75. http://dx.doi.org/10.1162/0899766053630341.
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Kernels are key components of pattern recognition mechanisms. We propose a universal kernel optimality criterion, which is independent of the classifier to be used. Defining data polarization as a process by which points of different classes are driven to geometrically opposite locations in a confined domain, we propose selecting the kernel parameter values that polarize the data in the associated feature space. Conversely, the kernel is said to be polarized by the data. Kernel polarization gives rise to an unconstrained optimization problem. We show that complete kernel polarization yields consistent classification by kernel-sum classifiers. Tested on real-life data, polarized kernels demonstrate a clear advantage over the Euclidean distance in proximity classifiers. Embedded in a support vectors classifier, kernel polarization is found to yield about the same performance as exhaustive parameter search.
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Qiangrong, Jiang, and Qiu guang. "Graph kernels combined with the neural network on protein classification." Journal of Bioinformatics and Computational Biology 17, no. 05 (October 2019): 1950030. http://dx.doi.org/10.1142/s0219720019500306.
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At present, most of the researches on protein classification are based on graph kernels. The essence of graph kernels is to extract the substructure and use the similarity of substructures as the kernel values. In this paper, we propose a novel graph kernel named vertex-edge similarity kernel (VES kernel) based on mixed matrix, the innovation point of which is taking the adjacency matrix of the graph as the sample vector of each vertex and calculating kernel values by finding the most similar vertex pair of two graphs. In addition, we combine the novel kernel with the neural network and the experimental results show that the combination is better than the existing advanced methods.
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Fujita, Kazuhisa. "Characteristics of Networks Generated by Kernel Growing Neural Gas." International Journal of Artificial Intelligence & Applications 14, no. 5 (September 28, 2023): 25–39. http://dx.doi.org/10.5121/ijaia.2023.14503.
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This research aims to develop kernel GNG, a kernelized version of the growing neural gas (GNG) algorithm, and to investigate the features of the networks generated by the kernel GNG. The GNG is an unsupervised artificial neural network that can transform a dataset into an undirected graph, thereby extracting the features of the dataset as a graph. The GNG is widely used in vector quantization, clustering, and 3D graphics. Kernel methods are often used to map a dataset to feature space, with support vector machines being the most prominent application. This paper introduces the kernel GNG approach and explores the characteristics of the networks generated by kernel GNG. Five kernels, including Gaussian, Laplacian, Cauchy, inverse multiquadric, and log kernels, are used in this study. The results of this study show that the average degree and the average clustering coefficient decrease as the kernel parameter increases for Gaussian, Laplacian, Cauchy, and IMQ kernels. If we avoid more edges and a higher clustering coefficient (or more triangles), the kernel GNG with a larger value of the parameter will be more appropriate.
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Kim, Moo-Geon, Woong-Jin Keum, and Young-Kyoon Kim. "Evaluation of the Volume of Coronary Artery Calcification according to Various Kernels and iBHC Algorithm." Korean Society of Computed Tomographic Technology 24, no. 2 (September 30, 2022): 49–54. http://dx.doi.org/10.31320/jksct.2022.24.2.49.
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The purpose of this study was to measure whether the volume of calcium due to cardiac artery calcification changes by functions different from that of the kernel, and to improve the use of more accurate kernels and functions. A Somatom Force(Siemens Healthineers, Forchheim, Germany) was used as CT equipment. Based on the Sa36 kernel, the Agatston score and volumes of calcium with the Br32, Br36, and Br40 kernels were compared. In addition, variables were added to the Br36 kernel by applying the equipment's unique function, iBHC(Beam Hardening Correction). The image was analyzed using the calcium score function of Aquarius iNtusion Edition ver. 4.4.13 P6 software(Terarecon, California, USA). There was a statistically significant difference between the Sa36 kernel to which the iBHC algorithm was applied and the Agatston scores and volumes of the Br32, Br36, and Br40 kernels to which the iBHC algorithm was not applied. In addition, there was a statistically significant difference between the Sa36 kernel to which the iBHC algorithm was applied, the Br36 kernel to which the iBHC algorithm was applied, and the Agatston score and volume of the Br36 kernel to which the iBHC algorithm was not applied. As a result, it can be seen that the Agatston score and volume of calcium change with the change in the kernel and with the application of the iBHC algorithm. Protocol correction is essential for accurate diagnosis and differentiation of coronary artery disease. To this end, it is necessary to use intermediate-level kernels such as Sa36 kernels and to use careful iBHC algorithms.
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Xu, Bi-Cun, Kai Ming Ting, and Yuan Jiang. "Isolation Graph Kernel." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 12 (May 18, 2021): 10487–95. http://dx.doi.org/10.1609/aaai.v35i12.17255.
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A recent Wasserstein Weisfeiler-Lehman (WWL) Graph Kernel has a distinctive feature: Representing the distribution of Weisfeiler-Lehman (WL)-embedded node vectors of a graph in a histogram that enables a dissimilarity measurement of two graphs using Wasserstein distance. It has been shown to produce better classification accuracy than other graph kernels which do not employ such distribution and Wasserstein distance. This paper introduces an alternative called Isolation Graph Kernel (IGK) that measures the similarity between two attributed graphs. IGK is unique in two aspects among existing graph kernels. First, it is the first graph kernel which employs a distributional kernel in the framework of kernel mean embedding. This avoids the need to use the computationally expensive Wasserstein distance. Second, it is the first graph kernel that incorporates the distribution of attributed nodes (ignoring the edges) in a dataset of graphs. We reveal that this distributional information, extracted in the form of a feature map of Isolation Kernel, is crucial in building an efficient and effective graph kernel. We show that IGK is better than WWL in terms of classification accuracy, and it runs orders of magnitude faster in large datasets when used in the context of SVM classification.
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Wang, Ting Hua, Wen Sheng Zhu, Qiong Zhang, and Hai Hui Xie. "A Composite Kernel for Word Sense Disambiguation." Applied Mechanics and Materials 530-531 (February 2014): 522–25. http://dx.doi.org/10.4028/www.scientific.net/amm.530-531.522.
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The success of supervised learning approaches to word sensed disambiguation (WSD) is largely dependent on the representation of the context in which an ambiguous word occurs. In practice, different kernel functions can be designed according to different representations since kernels can be well defined on general types of data, such as vectors, sequences, trees, as well as graphs. In this paper, we present a composite kernel, which is a linear combination of two types of kernels, i.e., bag of words (BOW) kernel and sequence kernel, for WSD. The benefit of kernel combination is that it allows to integrate heterogeneous sources of information in a simple and effective way. Empirical evaluation shows that the composite kernel can consistently improve the performance of WSD.
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Russin, J. S., B. Z. Guo, K. M. Tubajika, R. L. Brown, T. E. Cleveland, and N. W. Widstrom. "Comparison of Kernel Wax from Corn Genotypes Resistant or Susceptible to Aspergillus flavus." Phytopathology® 87, no. 5 (May 1997): 529–33. http://dx.doi.org/10.1094/phyto.1997.87.5.529.
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Russin, J. S., Guo, B. Z., Tubajika, K. M., Brown, R. L., Cleveland, T. E., and Widstrom, N. W. 1997. Comparison of kernel wax from corn genotypes resistant or susceptible to Aspergillus flavus. Phytopathology 87: 529-533.Kernels of corn genotype GT-MAS: gk are resistant to Aspergillus flavus. Earlier studies showed that this resistance is due in part to kernel pericarp wax. Experiments were conducted to compare wax from GTMAS: gk kernels with that from kernels of several susceptible commercial hybrids. GT-MAS: gk had more pericarp wax than did the susceptible hybrids. Scanning electron microscopy revealed that GT-MAS: gk kernels appeared rough and showed abundant wax deposits on kernel surfaces. Susceptible kernels appeared much more smooth and lacked the abundant surface deposits observed in GT-MAS: gk. In vitro bioassays showed that kernel wax from GT-MAS: gk reduced A. flavus colony diameter by 35%. Colony diameters on a medium amended with wax from susceptible kernels did not differ from those of controls. Thin-layer chromatography and analyses of chromatograms using NIH Image software showed a distinctive composition for GT-MAS: gk kernel wax. Chromatograms of wax from GT-MAS: gk contained a peak unique to this genotype, but also lacked a peak common to all susceptible hybrids. This is the first report of specific kernel factors involved in resistance to A. flavus in corn.
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Chen, Zhengpu, Carl Wassgren, and Kingsly Ambrose. "A Review of Grain Kernel Damage: Mechanisms, Modeling, and Testing Procedures." Transactions of the ASABE 63, no. 2 (2020): 455–75. http://dx.doi.org/10.13031/trans.13643.
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HighlightsPublished literature on grain kernel damage during handling is reviewed.Types and sources of grain kernel damage are discussed.Factors affecting the level of grain kernel damage are outlined.Models to predict grain kernel damage and corresponding test devices are summarized.Abstract. Grain kernel damage during harvest and handling continues to be a challenge in grain postharvest operations. This damage causes physical and physiological changes to grain, which reduces the grain quality and leads to significant yield loss. During harvesting and handling, grain kernels are subject to complex loading conditions consisting of a combination of impact, shear, and compression forces. The main damage mechanisms include impact, which causes external and internal cracks or even fragmentation of the kernel; attrition, which generates fine material; jamming, which deforms and breaks kernels due to high compressive forces; and fatigue, which produces broken kernels and fine material via repeatedly applied loads. Grain kernel damage accumulates as the grain moves through harvesting and handling operations. Harvesting is the major cause of cracks and breakage, while conveying after drying produces fine material. This article provides a comprehensive review of the types of grain kernel damage, sources of grain kernel damage, factors affecting damage, predictive damage models, and the experimental methods used to assess the damage. This review shows that although there is considerable empirical data focused on kernel damage, there is a lack of generalizable mechanics-based predictive models. Mechanics-based models are desirable because they would be useful for providing guidance on designing and operating grain handling processes to minimize kernel damage and thus improve grain quality. In addition, several damage models developed for non-grain particulate materials based on fracture mechanics are reviewed. With some modifications and detailed property analysis, there is potential for adapting the models developed for inorganic materials to predict grain kernel damage. Keywords: Grain kernel damage, Grain harvesting and handling, Breakage susceptibility, Grain damage prediction.
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PAUW, R. M. DE, and T. N. McCAIG. "UTILIZATION OF SODIUM HYDROXIDE TO ASSESS KERNEL COLOR AND ITS INHERITANCE IN ELEVEN SPRING WHEAT VARIETIES." Canadian Journal of Plant Science 68, no. 2 (April 1, 1988): 323–29. http://dx.doi.org/10.4141/cjps88-042.
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White kernel color in wheat (Triticum aestivum L.) is preferred for the principal foods made from wheat in some countries. In general white-kernelled wheats have a shorter dormancy period than red-kernelled wheats and, therefore, are subject to greater levels of preharvest sprouting damage caused by wet weather. In many countries kernel color serves as the basis for segregating grain into classes. Kernel coat color is controlled by up to three genes. The objective of this study was to investigate the effectiveness of a sodium hydroxide solution (NaOH) to enhance kernel color and thereby to facilitate distinguishing between red colored kernels and white ones in wheat populations segregating for kernel color. Six two-way crosses, with five of them made in reciprocal, a single backcross and a three-way cross were made to produce populations segregating for kernel color. A one-molar NaOH solution with 0.1% surfactant was applied to kernels of parents, F1, and several segregating generations. Kernel color reaction to NaOH was under maternal inheritance. The intensity of kernel color reaction to NaOH tended to be related to the number of genes for kernel color.Key words: Triticum aestivum, kernel color, sodium hydroxide, inheritance
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Liu, Mingming, Yinzeng Liu, Qihuan Wang, Qinghao He, and Duanyang Geng. "Real-Time Detection Technology of Corn Kernel Breakage and Mildew Based on Improved YOLOv5s." Agriculture 14, no. 5 (May 7, 2024): 725. http://dx.doi.org/10.3390/agriculture14050725.
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In order to solve low recognition of corn kernel breakage degree and corn kernel mildew degree during corn kernel harvesting, this paper proposes a real-time detection method for corn kernel breakage and mildew based on improved YOlOv5s, which is referred to as the CST-YOLOv5s model algorithm in this paper. The method continuously obtains images through the discrete uniform sampling device of corn kernels and generates whole corn kernels, breakage corn kernels, and mildew corn kernel dataset samples. We aimed at the problems of high similarity of some corn kernel features in the acquired images and the low precision of corn kernel breakage and mildew recognition. Firstly, the CBAM attention mechanism is added to the backbone network of YOLOv5s to finely allocate and process the feature information, highlighting the features of corn breakage and mildew. Secondly, the pyramid pooling structure SPPCPSC, which integrates cross-stage local networks, is adopted to replace the SPPF in YOLOv5s. SPP and CPSC technologies are used to extract and fuse features of different scales, improving the precision of object detection. Finally, the original prediction head is converted into a transformer prediction head to explore the prediction potential with a multi-head attention mechanism. The experimental results show that the CST-YOLOv5s model has a significant improvement in the detection of corn kernel breakage and mildew. Compared with the original YOLOv5s model, the average precision (AP) of corn kernel breakage and mildew recognition increased by 5.2% and 7.1%, respectively, and the mean average precision (mAP) of all kinds of corn kernel recognition is 96.1%, and the frame rate is 36.7 FPS. Compared with YOLOv4-tiny, YOLOv6n, YOLOv7, YOLOv8s, and YOLOv9-E detection model algorithms, the CST-YOLOv5s model has better overall performance in terms of detection accuracy and speed. This study can provide a reference for real-time detection of breakage and mildew kernels during the harvesting process of corn kernels.
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Zhang, Yi, Lulu Wang, and Liandong Wang. "A Comprehensive Evaluation of Graph Kernels for Unattributed Graphs." Entropy 20, no. 12 (December 18, 2018): 984. http://dx.doi.org/10.3390/e20120984.
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Graph kernels are of vital importance in the field of graph comparison and classification. However, how to compare and evaluate graph kernels and how to choose an optimal kernel for a practical classification problem remain open problems. In this paper, a comprehensive evaluation framework of graph kernels is proposed for unattributed graph classification. According to the kernel design methods, the whole graph kernel family can be categorized in five different dimensions, and then several representative graph kernels are chosen from these categories to perform the evaluation. With plenty of real-world and synthetic datasets, kernels are compared by many criteria such as classification accuracy, F1 score, runtime cost, scalability and applicability. Finally, quantitative conclusions are discussed based on the analyses of the extensive experimental results. The main contribution of this paper is that a comprehensive evaluation framework of graph kernels is proposed, which is significant for graph-classification applications and the future kernel research.
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Sabitha, N., D. Mohan Reddy, D. Lokanatha Reddy, P. Sudhakar, and B. Ravindra Reddy. "Association Analysis over Seasons among Morphological, Physiological and Yield Components with Kernel Yield in Maize (Zea mays L.)." Journal of Advances in Biology & Biotechnology 27, no. 5 (April 2, 2024): 151–56. http://dx.doi.org/10.9734/jabb/2024/v27i5774.
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Forty five single cross hybrids made from 10 inbred lines of maize through diallel mating design were evaluated for three seasons viz. rabi, summer and kharif from 2016-17 to 2017-18.Kernel yield had consistent significant and positive associations with SPAD meter readings, specific leaf area, cob length, cob girth, number of kernel rows cob-1,number of kernels row-1,100 kernel weight and harvest index in rabi, summer and kharif seasons. Similar trend of positive and significant association of kernel yield with all the above characters were recorded at genotypic level indicating existence a close relationship among these characters. Days to 50% tasseling, days to 50% silking, days to maturity and specific leaf weight showed consistent negative and significant correlations with kernel yield both at phenotypic and genotypic level indicating yield penalty with increase in days to 50% tasselling, days to 50% silking and specific leaf weight because more of vegetative growth and less time for reproductive growth which consequently lead to less kernel yield. The associations of anthesis-silking interval with kernel yield were consistently negative and non-significant in all the three seasons at phenotypic level revealing that narrow interval of anthesis silking interval facilates good seed setting. Plant height showed either negative and significant or negative but non-significant association with kernel yield across seasons suggesting that any increase in plant height may lead to reduction in kernel yield and thus medium plant height is desirable for recording higher kernel yields in Maize. Based on the results of character association analysis it was concluded that SPAD chlorophyll meter readings, specific leaf area, cob length, cob girth, number of kernel rows cob-1, number of kernels row-1, 100 kernel weight and harvest index might be given due importance, while formulating selection indices aimed at kernel yield improvement as these characters had showed consistently positive and significant associations with kernel yield. A plant with medium plant height and duration coupled with higher SPAD meter readings, specific leaf area, cob length, cob girth, number of kernel rows cob-1,number of kernels row-1,100 kernel weight and harvest index is desirable for getting higher kernel yield in maize.
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Price, Stanton R., Derek T. Anderson, Timothy C. Havens, and Steven R. Price. "Kernel Matrix-Based Heuristic Multiple Kernel Learning." Mathematics 10, no. 12 (June 11, 2022): 2026. http://dx.doi.org/10.3390/math10122026.
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Kernel theory is a demonstrated tool that has made its way into nearly all areas of machine learning. However, a serious limitation of kernel methods is knowing which kernel is needed in practice. Multiple kernel learning (MKL) is an attempt to learn a new tailored kernel through the aggregation of a set of valid known kernels. There are generally three approaches to MKL: fixed rules, heuristics, and optimization. Optimization is the most popular; however, a shortcoming of most optimization approaches is that they are tightly coupled with the underlying objective function and overfitting occurs. Herein, we take a different approach to MKL. Specifically, we explore different divergence measures on the values in the kernel matrices and in the reproducing kernel Hilbert space (RKHS). Experiments on benchmark datasets and a computer vision feature learning task in explosive hazard detection demonstrate the effectiveness and generalizability of our proposed methods.
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Tong, Anh, Toan M. Tran, Hung Bui, and Jaesik Choi. "Learning Compositional Sparse Gaussian Processes with a Shrinkage Prior." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 11 (May 18, 2021): 9906–14. http://dx.doi.org/10.1609/aaai.v35i11.17190.
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Choosing a proper set of kernel functions is an important problem in learning Gaussian Process (GP) models since each kernel structure has different model complexity and data fitness. Recently, automatic kernel composition methods provide not only accurate prediction but also attractive interpretability through search-based methods. However, existing methods suffer from slow kernel composition learning. To tackle large-scaled data, we propose a new sparse approximate posterior for GPs, MultiSVGP, constructed from groups of inducing points associated with individual additive kernels in compositional kernels. We demonstrate that this approximation provides a better fit to learn compositional kernels given empirical observations. We also provide theoretically justification on error bound when compared to the traditional sparse GP. In contrast to the search-based approach, we present a novel probabilistic algorithm to learn a kernel composition by handling the sparsity in the kernel selection with Horseshoe prior. We demonstrate that our model can capture characteristics of time series with significant reductions in computational time and have competitive regression performance on real-world data sets.
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Wu, Yuching, and Jianzhuang Xiao. "The Multiscale Spectral Stochastic Finite Element Method for Chloride Diffusion in Recycled Aggregate Concrete." International Journal of Computational Methods 15, no. 01 (September 27, 2017): 1750078. http://dx.doi.org/10.1142/s0219876217500785.
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In this study, the multiscale stochastic finite element method (MsSFEM) was developed based on a novel digital image kernel to make analysis for chloride diffusion in recycled aggregate concrete (RAC). It is significant to study the chloride diffusivity in RAC, because when RAC was applied in coastal areas, chloride-induced rebar corrosion became a common problem for concrete infrastructures. The MsSFEM was an efficient tool to examine the effect of microscopic randomness of RAC on the chloride diffusivity. Based on the proposed digital image kernel, the Karhunen–Loeve expansion and the polynomial chaos were used in the stochastic homogenization process. To investigate advantages and disadvantages of both generation and application of the proposed digital image kernel, it was compared with many other kernels. The comparisons were made between the method to develop the digital image kernel, which is called the pixel-matrix method, and other methods, and between the application of the kernel and various other kernels. It was shown that the proposed digital image kernel is superior to other kernels in many aspects.
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Yururdurmaz, Cengiz, Mehmet Çağatay Çerikçi, Rukiye Kara, and Ali Turan. "DETERMINATION OF SUITABLE WINTER CHICKPEA (CICER ARIETINUM L.) CULTIVARS UNDER KAHRAMANMARAŞ CONDITIONS." Current Trends in Natural Sciences 10, no. 19 (July 31, 2021): 246–51. http://dx.doi.org/10.47068/ctns.2021.v10i19.032.
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This study was conducted with the chickpea cultivars of Işık-05, Azkan, Sarı 98, Hisar, Çakır, Aydın 92, Yaşa-05, Menemen 92, Cevdetbey, Çağatay, Aksu and two local cultivars over the experimental fields of Kahramanmaraş Eastern Mediterranean Transitional Zone Agricultural Research of Institute in 2014-2015 cropping years. Experiments were conducted in randomized blocks design with 3 replications. Quality traits of plant height, the first pod height, number of branches per plant, number of pods per plant, number of kernels per plant, kernel weight per plant, kernel yield, 100-kernel weights were investigated. The differences in plant height, the first pod height, number of branches per plant, number of pods per plant, number of kernels per plant, kernel weight per plant, kernel yield and 100-kernel weight of the genotypes were found to be significant. Kernel yields of the genotypes varied between 425.40 - 267.93 kg da-1 with the greatest value from Çakır cultivar and the lowest value from Hisar cultivar.
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Grabowski, A., R. Siuda, L. Lenc, and S. Grundas. "Evaluation of single-kernel density of scab-damaged winter wheat." International Agrophysics 26, no. 2 (April 1, 2012): 129–35. http://dx.doi.org/10.2478/v10247-012-0019-5.
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Evaluation of single-kernel density of scab-damaged winter wheatMeasurements of single-kernel mass and volume made on healthy (control) and scab-damaged samples of grain of three winter wheat varieties never resulted in lower values of mean single-kernel density for scab-damaged grain. This finding, contrary to common opinion, can be explained as being a result of the comparable magnitude of relative decrease (due to infestation) of two features (mass and volume) that define single-kernel density. The discrepancy between results presented in this paper (kernel volume was determined with an air pycnometer) and the results in some other reports (liquid pycnometers used) can result from the different methods applied for kernel volume measurements: when a liquid medium is used the surface tension effect tends to overestimate the volume, especially for scabby kernels that are known to be shrivellediepossessing voids and pores at the surface that the liquid cannot penetrate. As a consequence kernel density of scabby kernels can be significantly underestimated.
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Matsui, Kota, Wataru Kumagai, Kenta Kanamori, Mitsuaki Nishikimi, and Takafumi Kanamori. "Variable Selection for Nonparametric Learning with Power Series Kernels." Neural Computation 31, no. 8 (August 2019): 1718–50. http://dx.doi.org/10.1162/neco_a_01212.
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In this letter, we propose a variable selection method for general nonparametric kernel-based estimation. The proposed method consists of two-stage estimation: (1) construct a consistent estimator of the target function, and (2) approximate the estimator using a few variables by [Formula: see text]-type penalized estimation. We see that the proposed method can be applied to various kernel nonparametric estimation such as kernel ridge regression, kernel-based density, and density-ratio estimation. We prove that the proposed method has the property of variable selection consistency when the power series kernel is used. Here, the power series kernel is a certain class of kernels containing polynomial and exponential kernels. This result is regarded as an extension of the variable selection consistency for the nonnegative garrote (NNG), a special case of the adaptive Lasso, to the kernel-based estimators. Several experiments, including simulation studies and real data applications, show the effectiveness of the proposed method.
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Chen, Guoxiong, Tatiana Suprunova, Tamar Krugman, Tzion Fahima, and Eviatar Nevo. "Ecogeographic and genetic determinants of kernel weight and colour of wild barley (Hordeum spontaneum) populations in Israel." Seed Science Research 14, no. 2 (May 2004): 137–46. http://dx.doi.org/10.1079/ssr2004163.
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The aim of this study was to establish associations of kernel weight and colour with ecogeographic factors and molecular markers, based on ten wild barley [Hordeum spontaneum (C. Koch) Thell.] populations sampled in Israel across a southward transect of increasing aridity. Kernel weight and colour category were scored using barley kernels (naked caryopsis). Small kernel sizes (0.011 g kernel–1) and dark kernels were found in xeric populations. A higher variation of kernel weight was observed in xeric populations. A higher proportion of variation occurred within, rather than among, populations. Water, temperature and soil factors were associated with kernel size variation. Among 18 simple sequence repeats (SSRs) investigated, HVM14, HVM36, HVM43, BMS64 and BMS90 were associated with kernel weight, and HVM68 with kernel colour. The results indicated that high phenotypic variation and genetic diversity are related to ecological stress, and that the association of phenotypic traits with molecular markers, based on natural plant populations, should be interpreted cautiously due to the high chance of spurious associations between traits and molecular markers.
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Xing, Guangchi, and Tieyuan Zhu. "Decoupled Fréchet kernels based on a fractional viscoacoustic wave equation." GEOPHYSICS 87, no. 1 (December 6, 2021): T61—T70. http://dx.doi.org/10.1190/geo2021-0248.1.
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We have formulated the Fréchet kernel computation using the adjoint-state method based on a fractional viscoacoustic wave equation. We first numerically prove that the 1/2- and the 3/2-order fractional Laplacian operators are self-adjoint. Using this property, we find that the adjoint wave propagator preserves the dispersion and compensates the amplitude, whereas the time-reversed adjoint wave propagator behaves identically to the forward propagator with the same dispersion and dissipation characters. Without introducing rheological mechanisms, this formulation adopts an explicit [Formula: see text] parameterization, which avoids the implicit [Formula: see text] in the conventional viscoacoustic/viscoelastic full-waveform inversion ([Formula: see text]-FWI). In addition, because of the decoupling of operators in the wave equation, the viscoacoustic Fréchet kernel is separated into three distinct contributions with clear physical meanings: lossless propagation, dispersion, and dissipation. We find that the lossless propagation kernel dominates the velocity kernel, whereas the dissipation kernel dominates the attenuation kernel over the dispersion kernel. After validating the Fréchet kernels using the finite-difference method, we conduct a numerical example to demonstrate the capability of the kernels to characterize the velocity and attenuation anomalies. The kernels of different misfit measurements are presented to investigate their different sensitivities. Our results suggest that, rather than the traveltime, the amplitude and the waveform kernels are more suitable to capture attenuation anomalies. These kernels lay the foundation for the multiparameter inversion with the fractional formulation, and the decoupled nature of them promotes our understanding of the significance of different physical processes in [Formula: see text]-FWI.
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Nugroho, Rizki Anjal Puji, Muhamad Syukur, and Willy Bayuardi Suwarno. "Inheritance Of Shape And Kernel Color In Sweet Corn Using JM2 And JM4 Populations." Journal of Tropical Crop Science 5, no. 3 (December 1, 2018): 96–102. http://dx.doi.org/10.29244/jtcs.5.3.96-102.
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Corn kernel is one of the most important characters that correlate with corn yield and quality. Sweet corn kernels can be distinguished by its color which is either yellow and white or pale yellow. Sweet corn breeding by crossing genotypes with different kernel colors will affect the inheritance pattern of kernel color. The aims of this research were to understand the inheritance pattern in sweet corn kernel color by crossing yellow and pale yellow color with red and purple corn kernels using qualitative and quantitative approaches. Genetic materials consisted of P1 (JM2 and JM4) and P2 (Red and Purple) and F1, F2, F3, and F1 reciprocals. P1 consists of JM2 and JM4 with flint shape with yellow and pale yellow color; P2 consists of Red and purple with non-yellow colored kernel and flint shape. The results showed maternal effect influenced the kernel color, but did not affect the kernel shape. Epistatic effects were found in kernel shape but it was co-dominant on kernel color inheritance. Broad-sense heritability values were high for all quantitative variables. Keywords : color, heritability, pale yellow, purple, red, shape
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Shorter, R., and BW Simpson. "Peanut yield and quality variation over harvest dates, and evaluation of some maturity indices in south-eastern Queensland." Australian Journal of Experimental Agriculture 27, no. 3 (1987): 445. http://dx.doi.org/10.1071/ea9870445.
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Yield and quality variations across harvest dates in peanuts (Arachis hypogaea L.) grown under rainfed conditions in south-eastern Queensland in 1978-79 and 1980-81 were investigated. Free arginine percentage of kernels, kernel: hull weight ratio, shell-out percentage, mean individual kernel weight and kernel moisture percentage were monitored during crop development to assess their usefulness as indices of crop maturity. For the Virginia Bunch cultivar, kernel yield ranged from 1862 kg ha-1 at 133 days after sowing (DAS) to 2432 kg ha-l at 168 DAS in 1978-79 and from 687 kg ha-l at 201 DAS to 1618 kg ha-1 at 152 DAS in 1980-81. In both years kernel yield and crop value for Virginia Bunch exhibited bimodal responses to delayed harvesting, with maximum values being obtained at about 150 and 170 DAS. These responses tended to be associated with rainfall distribution and available soil moisture during flowering. None of the maturity indices investigated was sensitive enough to detect the 2 peaks for yield or crop value and therefore would be of no use in determining optimum harvest periods for Virginia Bunch. Red Spanish and White Spanish cultivars, evaluated in 1978-79, produced average kernel yields of 1777 kg ha-l and 1535 kg ha-1 respectively. For these cultivars, differences in yield and crop value over harvest dates were not significant. Although kernel yields did not increase after 133 DAS, the decline in free arginine percentage and the increase in the kerne1:hull weight ratio during the season suggested that these indices may be useful indicators of optimum maturity for spanish-type cultivars.
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Odek, Zephania, Terry J. Siebenmorgen, and Griffiths G. Atungulu. "Validating the Glass Transition Hypothesis in Explaining Fissure Formation in Rough Rice Kernels During the Drying Process." Transactions of the ASABE 64, no. 6 (2021): 1763–70. http://dx.doi.org/10.13031/trans.14595.
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HighlightsX-ray imaging allows visualization of the fissuring that occurs at various drying air conditions.Drying air conditions that create severe intra-kernel material state gradients during drying result in kernel fissuring.The glass transition hypothesis was validated for explaining the fissuring of rice kernels during drying.Abstract. Fissured rice kernels tend to break during milling, leading to milling yield reductions. A hypothesis involving changes in material state properties has been proposed to predict kernel fissuring during the drying process. The hypothesis, referred to as the glass transition hypothesis, has been used to explain kernel fissuring during the drying process and has been supported by various milling studies. However, this hypothesis has not been validated from a fundamental fissuring standpoint. In this study, experiments were performed using drying air temperatures of 45°C, 50°C, 55°C, 60°C, and 65°C with relative humidity values that produced equilibrium moisture contents (EMCs) of 6%, 8%, 10%, 12%, and 14%. These EMCs would position the kernel surface at select regions on a rice material state diagram during drying. At the end of active drying, the kernels were tempered for 2 h at the drying air temperature. Fissures were viewed and detected in these kernels using X-ray imaging. Drying air temperature and EMC combinations that caused sufficient portions of the kernel surface to transition to the glassy region while the core remained in the rubbery region caused severe intra-kernel material state gradients. Such intra-kernel material state gradients caused severe fissuring, thus supporting the glass transition hypothesis in explaining fissure formation. At drying air temperature and EMC combinations that did not cause severe intra-kernel material state gradients, severe fissuring was averted, thus further supporting the glass transition hypothesis. Keywords: Glass transition hypothesis, Material state, Rice quality, State diagram, Tempering, X-ray imaging.
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Yang, Yang, Nicola Fink, Tilman Emrich, Dirk Graafen, Rosa Richter, Stefanie Bockius, Elias V. Wolf, et al. "Optimization of Kernel Type and Sharpness Level Improves Objective and Subjective Image Quality for High-Pitch Photon Counting Coronary CT Angiography." Diagnostics 13, no. 11 (June 1, 2023): 1937. http://dx.doi.org/10.3390/diagnostics13111937.
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(1) Background: Photon-counting detector (PCD) CT offers a wide variety of kernels and sharpness levels for image reconstruction. The aim of this retrospective study was to determine optimal settings for coronary CT angiography (CCTA). (2) Methods: Thirty patients (eight female, mean age 63 ± 13 years) underwent PCD-CCTA in a high-pitch mode. Images were reconstructed using three different kernels and four sharpness levels (Br36/40/44/48, Bv36/40/44/48, and Qr36/40/44/48). To analyze objective image quality, the attenuation, image noise, contrast-to-noise ratio (CNR), and vessel sharpness were quantified in proximal and distal coronaries. For subjective image quality, two blinded readers assessed image noise, visually sharp reproduction of coronaries, and the overall image quality using a five-point Likert scale. (3) Results: Attenuation, image noise, CNR, and vessel sharpness significantly differed across kernels (all p < 0.001), with the Br-kernel reaching the highest attenuation. With increasing kernel sharpness, image noise and vessel sharpness increased, whereas CNR continuously decreased. Reconstruction with Br-kernel generally had the highest CNR (Br > Bv > Qr), except Bv-kernel had a superior CNR at sharpness level 40. Bv-kernel had significantly higher vessel sharpness than Br- and Qr-kernel (p < 0.001). Subjective image quality was rated best for kernels Bv40 and Bv36, followed by Br36 and Qr36. (4) Conclusion: Reconstructions with kernel Bv40 are beneficial to achieve optimal image quality in spectral high-pitch CCTA using PCD-CT.
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Da San Martino, Giovanni, Alessandro Sperduti, Fabio Aiolli, and Alessandro Moschitti. "Efficient Online Learning for Mapping Kernels on Linguistic Structures." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3421–28. http://dx.doi.org/10.1609/aaai.v33i01.33013421.
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Kernel methods are popular and effective techniques for learning on structured data, such as trees and graphs. One of their major drawbacks is the computational cost related to making a prediction on an example, which manifests in the classification phase for batch kernel methods, and especially in online learning algorithms. In this paper, we analyze how to speed up the prediction when the kernel function is an instance of the Mapping Kernels, a general framework for specifying kernels for structured data which extends the popular convolution kernel framework. We theoretically study the general model, derive various optimization strategies and show how to apply them to popular kernels for structured data. Additionally, we derive a reliable empirical evidence on semantic role labeling task, which is a natural language classification task, highly dependent on syntactic trees. The results show that our faster approach can clearly improve on standard kernel-based SVMs, which cannot run on very large datasets.
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Sheng, Shaoyang, Min Wu, and Weiqiao Lv. "Dynamic Viscoelastic Behavior of Maize Kernel: Application of Frequency–Temperature Superposition." Foods 13, no. 7 (March 22, 2024): 976. http://dx.doi.org/10.3390/foods13070976.
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Maize kernels were treated using two varieties of drying methodologies, namely combined hot air- and vacuum-drying (HAVD) and natural drying (ND). We performed frequency sweep tests, modified Cole–Cole (MCC) analysis and frequency–temperature superposition (FTS) on these kernels. The kernels’ elastic and viscous properties for ND were higher than those for HAVD. The heterogeneous nature of maize kernel may account for the curvature in MCC plot for the kernel treated by HAVD 75 °C and the failure of FTS. MCC analysis was more sensitive than FTS. The kernel treated by HAVD 75 °C demonstrated thermorheologically simple behavior across the entire temperature range (30–45 °C) in both MCC analysis and FTS. The frequency scale for the kernel treated using HAVD 75 °C was broadened by up to 70,000 Hz. The relaxation processes in the kernel treated by HAVD 75 °C were determined to be mainly associated with subunits of molecules or molecular strands. The data herein could be utilized for maize storage and processing.
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Xu, Hui, Yongguo Yang, Xin Wang, Mingming Liu, Hongxia Xie, and Chujiao Wang. "Multiple Kernel Dimensionality Reduction via Ratio-Trace and Marginal Fisher Analysis." Mathematical Problems in Engineering 2019 (January 14, 2019): 1–8. http://dx.doi.org/10.1155/2019/6941475.
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Traditional supervised multiple kernel learning (MKL) for dimensionality reduction is generally an extension of kernel discriminant analysis (KDA), which has some restrictive assumptions. In addition, they generally are based on graph embedding framework. A more general multiple kernel-based dimensionality reduction algorithm, called multiple kernel marginal Fisher analysis (MKL-MFA), is presented for supervised nonlinear dimensionality reduction combined with ratio-race optimization problem. MKL-MFA aims at relaxing the restrictive assumption that the data of each class is of a Gaussian distribution and finding an appropriate convex combination of several base kernels. To improve the efficiency of multiple kernel dimensionality reduction, the spectral regression frameworks are incorporated into the optimization model. Furthermore, the optimal weights of predefined base kernels can be obtained by solving a different convex optimization. Experimental results on benchmark datasets demonstrate that MKL-MFA outperforms the state-of-the-art supervised multiple kernel dimensionality reduction methods.
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Li, Teng, Yong Dou, Xinwang Liu, Yang Zhao, and Qi Lv. "Multiple kernel clustering with corrupted kernels." Neurocomputing 267 (December 2017): 447–54. http://dx.doi.org/10.1016/j.neucom.2017.06.044.
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Wang, Shaoping, Ang Li, Kuangyu Wen, and Ximing Wu. "Robust kernels for kernel density estimation." Economics Letters 191 (June 2020): 109138. http://dx.doi.org/10.1016/j.econlet.2020.109138.
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Deng, Ping, Bei Cui, Hailan Zhu, Buangurn Phommakoun, Dan Zhang, Yiming Li, Fei Zhao, and Zhong Zhao. "Accumulation Pattern of Amygdalin and Prunasin and Its Correlation with Fruit and Kernel Agronomic Characteristics during Apricot (Prunus armeniaca L.) Kernel Development." Foods 10, no. 2 (February 11, 2021): 397. http://dx.doi.org/10.3390/foods10020397.
Full textAbstract:
To reveal the accumulation pattern of cyanogenic glycosides (amygdalin and prunasin) in bitter apricot kernels to further understand the metabolic mechanisms underlying differential accumulation during kernel development and ripening and explore the association between cyanogenic glycoside accumulation and the physical, chemical and biochemical indexes of fruits and kernels during fruit and kernel development, dynamic changes in physical characteristics (weight, moisture content, linear dimensions, derived parameters) and chemical and biochemical parameters (oil, amygdalin and prunasin contents, β-glucosidase activity) of fruits and kernels from ten apricot (Prunus armeniaca L.) cultivars were systematically studied at 10 day intervals, from 20 days after flowering (DAF) until maturity. High variability in most of physical, chemical and biochemical parameters was found among the evaluated apricot cultivars and at different ripening stages. Kernel oil accumulation showed similar sigmoid patterns. Amygdalin and prunasin levels were undetectable in the sweet kernel cultivars throughout kernel development. During the early stages of apricot fruit development (before 50 DAF), the prunasin level in bitter kernels first increased, then decreased markedly; while the amygdalin level was present in quite small amounts and significantly lower than the prunasin level. From 50 to 70 DAF, prunasin further declined to zero; while amygdalin increased linearly and was significantly higher than the prunasin level, then decreased or increased slowly until full maturity. The cyanogenic glycoside accumulation pattern indicated a shift from a prunasin-dominated to an amygdalin-dominated state during bitter apricot kernel development and ripening. β-glucosidase catabolic enzyme activity was high during kernel development and ripening in all tested apricot cultivars, indicating that β-glucosidase was not important for amygdalin accumulation. Correlation analysis showed a positive correlation of kernel amygdalin content with fruit dimension parameters, kernel oil content and β-glucosidase activity, but no or a weak positive correlation with kernel dimension parameters. Principal component analysis (PCA) showed that the variance accumulation contribution rate of the first three principal components totaled 84.56%, and not only revealed differences in amygdalin and prunasin contents and β-glucosidase activity among cultivars, but also distinguished different developmental stages. The results can help us understand the metabolic mechanisms underlying differential cyanogenic glycoside accumulation in apricot kernels and provide a useful reference for breeding high- or low-amygdalin-content apricot cultivars and the agronomic management, intensive processing and exploitation of bitter apricot kernels.