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/
Novel vehicle detection system based on stacked DoG kernel and AdaBoost
Kang, Hyun Ho; Lee, Seo Won; You, Sung Hyun
2018-01-01
This paper proposes a novel vehicle detection system that can overcome some limitations of typical vehicle detection systems using AdaBoost-based methods. The performance of the AdaBoost-based vehicle detection system is dependent on its training data. Thus, its performance decreases when the shape of a target differs from its training data, or the pattern of a preceding vehicle is not visible in the image due to the light conditions. A stacked Difference of Gaussian (DoG)–based feature extraction algorithm is proposed to address this issue by recognizing common characteristics, such as the shadow and rear wheels beneath vehicles—of vehicles under various conditions. The common characteristics of vehicles are extracted by applying the stacked DoG shaped kernel obtained from the 3D plot of an image through a convolution method and investigating only certain regions that have a similar patterns. A new vehicle detection system is constructed by combining the novel stacked DoG feature extraction algorithm with the AdaBoost method. Experiments are provided to demonstrate the effectiveness of the proposed vehicle detection system under different conditions. PMID:29513727
Magnetic resonance imaging of single rice kernels during cooking
Mohoric, A.; Vergeldt, F.J.; Gerkema, E.; Jager, de P.A.; Duynhoven, van J.P.M.; Dalen, van G.; As, van H.
2004-01-01
The RARE imaging method was used to monitor the cooking of single rice kernels in real time and with high spatial resolution in three dimensions. The imaging sequence is optimized for rapid acquisition of signals with short relaxation times using centered out RARE. Short scan time and high spatial
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.
Thermal neutron scattering kernels for sapphire and silicon single crystals
International Nuclear Information System (INIS)
Cantargi, F.; Granada, J.R.; Mayer, R.E.
2015-01-01
Highlights: • Thermal cross section libraries for sapphire and silicon single crystals were generated. • Debye model was used to represent the vibrational frequency spectra to feed the NJOY code. • Sapphire total cross section was measured at Centro Atómico Bariloche. • Cross section libraries were validated with experimental data available. - Abstract: Sapphire and silicon are materials usually employed as filters in facilities with thermal neutron beams. Due to the lack of the corresponding thermal cross section libraries for those materials, necessary in calculations performed in order to optimize beams for specific applications, here we present the generation of new thermal neutron scattering kernels for those materials. The Debye model was used in both cases to represent the vibrational frequency spectra required to feed the NJOY nuclear data processing system in order to produce the corresponding libraries in ENDF and ACE format. These libraries were validated with available experimental data, some from the literature and others obtained at the pulsed neutron source at Centro Atómico Bariloche
Stacking fault tetrahedron induced plasticity in copper single crystal
Energy Technology Data Exchange (ETDEWEB)
Zhang, Liang, E-mail: lz592@uowmail.edu.au [School of Mechanical, Materials and Mechatronic Engineering, University of Wollongong, Wollongong, NSW 2522 (Australia); Lu, Cheng, E-mail: chenglu@uow.edu.au [School of Mechanical, Materials and Mechatronic Engineering, University of Wollongong, Wollongong, NSW 2522 (Australia); Tieu, Kiet; Su, Lihong; Zhao, Xing [School of Mechanical, Materials and Mechatronic Engineering, University of Wollongong, Wollongong, NSW 2522 (Australia); Pei, Linqing [Department of Mechanical Engineering, Chongqing University, Chongqing 400044 (China)
2017-01-05
Stacking fault tetrahedron (SFT) is the most common type of vacancy clustered defects in fcc metals and alloys, and can play an important role in the mechanical properties of metallic materials. In this study, molecular dynamics (MD) simulations were carried out to investigate the incipience of plasticity and the underlying atomic mechanisms in copper single crystals with SFT. Different deformation mechanisms of SFT were reported due to the crystal orientations and loading directions (compression and tension). The results showed that the incipient plasticity in crystals with SFT resulted from the heterogeneous dislocation nucleation from SFT, so the stress required for plastic deformation was less than that needed for perfect single crystals. Three crystal orientations ([1 0 0], [1 1 0] and [1 1 1]) were specified in this study because they can represent most of the typical deformation mechanisms of SFT. MD simulations revealed that the structural transformation of SFT was frequent under the applied loading; a metastable SFT structure and the collapse of SFT were usually observed. The structural transformation resulted in a different reduction of yield stress in compression and tension, and also caused a decreased or reversed compression/tension asymmetry. Compressive stress can result in the unfaulting of Frank loop in some crystal orientations. According to the elastic theory of dislocation, the process of unfaulting was closely related to the size of the dislocation loop and the stacking fault energy.
Development of nondestructive screening methods for single kernel characterization of wheat
DEFF Research Database (Denmark)
Nielsen, J.P.; Pedersen, D.K.; Munck, L.
2003-01-01
predictability. However, by applying an averaging approach, in which single seed replicate measurements are mathematically simulated, a very good NIT prediction model was achieved. This suggests that the single seed NIT spectra contain hardness information, but that a single seed hardness method with higher......The development of nondestructive screening methods for single seed protein, vitreousness, density, and hardness index has been studied for single kernels of European wheat. A single kernel procedure was applied involving, image analysis, near-infrared transmittance (NIT) spectroscopy, laboratory...
Single pass kernel k-means clustering method
Indian Academy of Sciences (India)
In unsupervised classiﬁcation, kernel -means clustering method has been shown to perform better than conventional -means clustering method in ... 518501, India; Department of Computer Science and Engineering, Jawaharlal Nehru Technological University, Anantapur College of Engineering, Anantapur 515002, India ...
Shiju, S.; Sumitra, S.
2017-12-01
In this paper, the multiple kernel learning (MKL) is formulated as a supervised classification problem. We dealt with binary classification data and hence the data modelling problem involves the computation of two decision boundaries of which one related with that of kernel learning and the other with that of input data. In our approach, they are found with the aid of a single cost function by constructing a global reproducing kernel Hilbert space (RKHS) as the direct sum of the RKHSs corresponding to the decision boundaries of kernel learning and input data and searching that function from the global RKHS, which can be represented as the direct sum of the decision boundaries under consideration. In our experimental analysis, the proposed model had shown superior performance in comparison with that of existing two stage function approximation formulation of MKL, where the decision functions of kernel learning and input data are found separately using two different cost functions. This is due to the fact that single stage representation helps the knowledge transfer between the computation procedures for finding the decision boundaries of kernel learning and input data, which inturn boosts the generalisation capacity of the model.
Reciprocating grinders can grind single maize kernels by shaking the kernel in a vial with a ball bearing. This process results in a grind quality that is not satisfactory for many experiments. Tungesten carbide ball bearings are nearly twice as dense as steel, so we compared their grinding performa...
Chen, Lili; Zhang, Xi; Wang, Hui
2015-05-01
Obstructive sleep apnea (OSA) is a common sleep disorder that often remains undiagnosed, leading to an increased risk of developing cardiovascular diseases. Polysomnogram (PSG) is currently used as a golden standard for screening OSA. However, because it is time consuming, expensive and causes discomfort, alternative techniques based on a reduced set of physiological signals are proposed to solve this problem. This study proposes a convenient non-parametric kernel density-based approach for detection of OSA using single-lead electrocardiogram (ECG) recordings. Selected physiologically interpretable features are extracted from segmented RR intervals, which are obtained from ECG signals. These features are fed into the kernel density classifier to detect apnea event and bandwidths for density of each class (normal or apnea) are automatically chosen through an iterative bandwidth selection algorithm. To validate the proposed approach, RR intervals are extracted from ECG signals of 35 subjects obtained from a sleep apnea database ( http://physionet.org/cgi-bin/atm/ATM ). The results indicate that the kernel density classifier, with two features for apnea event detection, achieves a mean accuracy of 82.07 %, with mean sensitivity of 83.23 % and mean specificity of 80.24 %. Compared with other existing methods, the proposed kernel density approach achieves a comparably good performance but by using fewer features without significantly losing discriminant power, which indicates that it could be widely used for home-based screening or diagnosis of OSA.
Nb/NiCu bilayers in single and stacked superconductive tunnel junctions: preliminary results
International Nuclear Information System (INIS)
Pepe, G.P.; Ruotolo, A.; Parlato, L.; Peluso, G.; Ausanio, G.; Carapella, G.; Latempa, R.
2004-01-01
We present preliminary experimental results concerning both single and stacked tunnel junctions in which one of the electrodes was formed by a superconductor/ferromagnet (S/F) bi-layer. In particular, in the stacked configuration a Nb/NiCu bi-layer was used as the intermediate electrode, and it was probed by tunneling on both sides. Tunnel junctions have been characterized in terms of current-voltage characteristics (IVC), and differential conductance. Preliminary steady-state injection-detection measurements performed in the stacked devices at T=4.2 K are also presented and discussed
Single-Event Transgene Product Levels Predict Levels in Genetically Modified Breeding Stacks.
Gampala, Satyalinga Srinivas; Fast, Brandon J; Richey, Kimberly A; Gao, Zhifang; Hill, Ryan; Wulfkuhle, Bryant; Shan, Guomin; Bradfisch, Greg A; Herman, Rod A
2017-09-13
The concentration of transgene products (proteins and double-stranded RNA) in genetically modified (GM) crop tissues is measured to support food, feed, and environmental risk assessments. Measurement of transgene product concentrations in breeding stacks of previously assessed and approved GM events is required by many regulatory authorities to evaluate unexpected transgene interactions that might affect expression. Research was conducted to determine how well concentrations of transgene products in single GM events predict levels in breeding stacks composed of these events. The concentrations of transgene products were compared between GM maize, soybean, and cotton breeding stacks (MON-87427 × MON-89034 × DAS-Ø15Ø7-1 × MON-87411 × DAS-59122-7 × DAS-40278-9 corn, DAS-81419-2 × DAS-44406-6 soybean, and DAS-21023-5 × DAS-24236-5 × SYN-IR102-7 × MON-88913-8 × DAS-81910-7 cotton) and their component single events (MON-87427, MON-89034, DAS-Ø15Ø7-1, MON-87411, DAS-59122-7, and DAS-40278-9 corn, DAS-81419-2, and DAS-44406-6 soybean, and DAS-21023-5, DAS-24236-5, SYN-IR102-7, MON-88913-8, and DAS-81910-7 cotton). Comparisons were made within a crop and transgene product across plant tissue types and were also made across transgene products in each breeding stack for grain/seed. Scatter plots were generated comparing expression in the stacks to their component events, and the percent of variability accounted for by the line of identity (y = x) was calculated (coefficient of identity, I 2 ). Results support transgene concentrations in single events predicting similar concentrations in breeding stacks containing the single events. Therefore, food, feed, and environmental risk assessments based on concentrations of transgene products in single GM events are generally applicable to breeding stacks composed of these events.
Single corn kernel wide-line NMR oil analysis for breeding purpose
Energy Technology Data Exchange (ETDEWEB)
Wilmers, M C.C.; Rettori, C; Vargas, H; Barberis, G E [Universidade Estadual de Campinas (Brazil). Inst. de Fisica; da Silva, W J [Universidade Estadual de Campinas (Brazil). Inst. de Biologia
1978-12-01
The Wide-Line NMR technique was used to determine the oil content in single corn seeds. Using distinct radio frequency (RF) power, a systematic work was done in kernels with about 10% of moisture, and also in artificially dried seeds with approximated 5% of moisture. For nondried seeds NMR spectra showed clearly the presence of three resonances with different RF saturation factor. For dried seeds, the oil concentration determined by NMR was highly correlated (r = 0,997) with that determined by a gravimetric method. The highest discrepancy between the two methods was found to be about 1,3%. When relative measurements are required as in the case of single kernel for recurrent selection program, precision in the individual selected kernel will be about 2,5%. Applying this technique, a first cycle of recurrent selection using S/sub 1/ lines for low and high oil content was performed in an open pollinated variety. Gain from selection was 12.0 and 14.1% in the populations for high and low oil contents, respectively.
Stacking fault energy measurements in WSe2 single crystals using weak-beam techniques
International Nuclear Information System (INIS)
Agarwal, M.K.; Patel, J.V.; Patel, N.G.
1981-01-01
The weak-beam method of electron microscopy is used to observe threefold dislocations in WSe 2 single crystals grown by direct vapour transport method. The widths of the three fold ribbons are used to determine the stacking fault energy in these crystals. Variation of the width of the ribbons with temperature are also studied and discussed. (author)
Afshar, Saeed; George, Libin; Tapson, Jonathan; van Schaik, André; Hamilton, Tara J
2014-01-01
This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a simple spiking neuron model that performs statistical inference and unsupervised learning of spatiotemporal spike patterns. SKAN is the first proposed neuron model to investigate the effects of dynamic synapto-dendritic kernels and demonstrate their computational power even at the single neuron scale. The rule-set defining the neuron is simple: there are no complex mathematical operations such as normalization, exponentiation or even multiplication. The functionalities of SKAN emerge from the real-time interaction of simple additive and binary processes. Like a biological neuron, SKAN is robust to signal and parameter noise, and can utilize both in its operations. At the network scale neurons are locked in a race with each other with the fastest neuron to spike effectively "hiding" its learnt pattern from its neighbors. The robustness to noise, high speed, and simple building blocks not only make SKAN an interesting neuron model in computational neuroscience, but also make it ideal for implementation in digital and analog neuromorphic systems which is demonstrated through an implementation in a Field Programmable Gate Array (FPGA). Matlab, Python, and Verilog implementations of SKAN are available at: http://www.uws.edu.au/bioelectronics_neuroscience/bens/reproducible_research.
Mogensen, J M; Sørensen, S M; Sulyok, M; van der Westhuizen, L; Shephard, G S; Frisvad, J C; Thrane, U; Krska, R; Nielsen, K F
2011-12-01
Fumonisins are important Fusarium mycotoxins mainly found in maize and derived products. This study analysed maize from five subsistence farmers in the former Transkei region of South Africa. Farmers had sorted kernels into good and mouldy quality. A total of 400 kernels from 10 batches were analysed; of these 100 were visually characterised as uninfected and 300 as infected. Of the 400 kernels, 15% were contaminated with 1.84-1428 mg kg(-1) fumonisins, and 4% (n=15) had a fumonisin content above 100 mg kg(-1). None of the visually uninfected maize had detectable amounts of fumonisins. The total fumonisin concentration was 0.28-1.1 mg kg(-1) for good-quality batches and 0.03-6.2 mg kg(-1) for mouldy-quality batches. The high fumonisin content in the batches was apparently caused by a small number (4%) of highly contaminated kernels, and removal of these reduced the average fumonisin content by 71%. Of the 400 kernels, 80 were screened for 186 microbial metabolites by liquid chromatography-tandem mass spectrometry, detecting 17 other fungal metabolites, including fusaric acid, equisetin, fusaproliferin, beauvericin, cyclosporins, agroclavine, chanoclavine, rugulosin and emodin. Fusaric acid in samples without fumonisins indicated the possibility of using non-toxinogenic Fusaria as biocontrol agents to reduce fumonisin exposure, as done for Aspergillus flavus. This is the first report of mycotoxin profiling in single naturally infected maize kernels. © 2011 Taylor & Francis
Directory of Open Access Journals (Sweden)
Yi-Hung Liu
2014-07-01
Full Text Available Electroencephalogram-based emotion recognition (EEG-ER has received increasing attention in the fields of health care, affective computing, and brain-computer interface (BCI. However, satisfactory ER performance within a bi-dimensional and non-discrete emotional space using single-trial EEG data remains a challenging task. To address this issue, we propose a three-layer scheme for single-trial EEG-ER. In the first layer, a set of spectral powers of different EEG frequency bands are extracted from multi-channel single-trial EEG signals. In the second layer, the kernel Fisher’s discriminant analysis method is applied to further extract features with better discrimination ability from the EEG spectral powers. The feature vector produced by layer 2 is called a kernel Fisher’s emotion pattern (KFEP, and is sent into layer 3 for further classification where the proposed imbalanced quasiconformal kernel support vector machine (IQK-SVM serves as the emotion classifier. The outputs of the three layer EEG-ER system include labels of emotional valence and arousal. Furthermore, to collect effective training and testing datasets for the current EEG-ER system, we also use an emotion-induction paradigm in which a set of pictures selected from the International Affective Picture System (IAPS are employed as emotion induction stimuli. The performance of the proposed three-layer solution is compared with that of other EEG spectral power-based features and emotion classifiers. Results on 10 healthy participants indicate that the proposed KFEP feature performs better than other spectral power features, and IQK-SVM outperforms traditional SVM in terms of the EEG-ER accuracy. Our findings also show that the proposed EEG-ER scheme achieves the highest classification accuracies of valence (82.68% and arousal (84.79% among all testing methods.
Kernel PLS Estimation of Single-trial Event-related Potentials
Rosipal, Roman; Trejo, Leonard J.
2004-01-01
Nonlinear kernel partial least squaes (KPLS) regressior, is a novel smoothing approach to nonparametric regression curve fitting. We have developed a KPLS approach to the estimation of single-trial event related potentials (ERPs). For improved accuracy of estimation, we also developed a local KPLS method for situations in which there exists prior knowledge about the approximate latency of individual ERP components. To assess the utility of the KPLS approach, we compared non-local KPLS and local KPLS smoothing with other nonparametric signal processing and smoothing methods. In particular, we examined wavelet denoising, smoothing splines, and localized smoothing splines. We applied these methods to the estimation of simulated mixtures of human ERPs and ongoing electroencephalogram (EEG) activity using a dipole simulator (BESA). In this scenario we considered ongoing EEG to represent spatially and temporally correlated noise added to the ERPs. This simulation provided a reasonable but simplified model of real-world ERP measurements. For estimation of the simulated single-trial ERPs, local KPLS provided a level of accuracy that was comparable with or better than the other methods. We also applied the local KPLS method to the estimation of human ERPs recorded in an experiment on co,onitive fatigue. For these data, the local KPLS method provided a clear improvement in visualization of single-trial ERPs as well as their averages. The local KPLS method may serve as a new alternative to the estimation of single-trial ERPs and improvement of ERP averages.
Evolution of stacking fault tetrahedral and work hardening effect in copper single crystals
Liu, Hai Tao; Zhu, Xiu Fu; Sun, Ya Zhou; Xie, Wen Kun
2017-11-01
Stacking fault tetrahedral (SFT), generated in machining of copper single crystal as one type of subsurface defects, has significant influence on the performance of workpiece. In this study, molecular dynamics (MD) simulation is used to investigate the evolution of stacking fault tetrahedral in nano-cutting of copper single crystal. The result shows that SFT is nucleated at the intersection of differently oriented stacking fault (SF) planes and SFT evolves from the preform only containing incomplete surfaces into a solid defect. The evolution of SFT contains several stress fluctuations until the complete formation. Nano-indentation simulation is then employed on the machined workpiece from nano-cutting, through which the interaction between SFT and later-formed dislocations in subsurface is studied. In the meanwhile, force-depth curves obtained from nano-indentation on pristine and machined workpieces are compared to analyze the mechanical properties. By simulation of nano-cutting and nano-indentation, it is verified that SFT is a reason of the work hardening effect.
SCAP-82, Single Scattering, Albedo Scattering, Point-Kernel Analysis in Complex Geometry
International Nuclear Information System (INIS)
Disney, R.K.; Vogtman, S.E.
1987-01-01
1 - Description of problem or function: SCAP solves for radiation transport in complex geometries using the single or albedo scatter point kernel method. The program is designed to calculate the neutron or gamma ray radiation level at detector points located within or outside a complex radiation scatter source geometry or a user specified discrete scattering volume. Geometry is describable by zones bounded by intersecting quadratic surfaces within an arbitrary maximum number of boundary surfaces per zone. Anisotropic point sources are describable as pointwise energy dependent distributions of polar angles on a meridian; isotropic point sources may also be specified. The attenuation function for gamma rays is an exponential function on the primary source leg and the scatter leg with a build- up factor approximation to account for multiple scatter on the scat- ter leg. The neutron attenuation function is an exponential function using neutron removal cross sections on the primary source leg and scatter leg. Line or volumetric sources can be represented as a distribution of isotropic point sources, with un-collided line-of-sight attenuation and buildup calculated between each source point and the detector point. 2 - Method of solution: A point kernel method using an anisotropic or isotropic point source representation is used, line-of-sight material attenuation and inverse square spatial attenuation between the source point and scatter points and the scatter points and detector point is employed. A direct summation of individual point source results is obtained. 3 - Restrictions on the complexity of the problem: - The SCAP program is written in complete flexible dimensioning so that no restrictions are imposed on the number of energy groups or geometric zones. The geometric zone description is restricted to zones defined by boundary surfaces defined by the general quadratic equation or one of its degenerate forms. The only restriction in the program is that the total
National Aeronautics and Space Administration — This SBIR Phase I project aims to develop an innovative batch fabrication technique to create single crystal PMN-PT stack actuator deformable mirrors (DM) at low...
Automated assembling of single fuel cell units for use in a fuel cell stack
Jalba, C. K.; Muminovic, A.; Barz, C.; Nasui, V.
2017-05-01
The manufacturing of PEMFC stacks (POLYMER ELEKTROLYT MEMBRAN Fuel Cell) is nowadays still done by hand. Over hundreds of identical single components have to be placed accurate together for the construction of a fuel cell stack. Beside logistic problems, higher total costs and disadvantages in weight the high number of components produce a higher statistic interference because of faulty erection or material defects and summation of manufacturing tolerances. The saving of costs is about 20 - 25 %. Furthermore, the total weight of the fuel cells will be reduced because of a new sealing technology. Overall a one minute cycle time has to be aimed per cell at the manufacturing of these single components. The change of the existing sealing concept to a bonded sealing is one of the important requisites to get an automated manufacturing of single cell units. One of the important steps for an automated gluing process is the checking of the glue application by using of an image processing system. After bonding the single fuel cell the sealing and electrical function can be checked, so that only functional and high qualitative cells can get into further manufacturing processes.
Directory of Open Access Journals (Sweden)
Yotam Luz
Full Text Available Spike-Timing Dependent Plasticity (STDP is characterized by a wide range of temporal kernels. However, much of the theoretical work has focused on a specific kernel - the "temporally asymmetric Hebbian" learning rules. Previous studies linked excitatory STDP to positive feedback that can account for the emergence of response selectivity. Inhibitory plasticity was associated with negative feedback that can balance the excitatory and inhibitory inputs. Here we study the possible computational role of the temporal structure of the STDP. We represent the STDP as a superposition of two processes: potentiation and depression. This allows us to model a wide range of experimentally observed STDP kernels, from Hebbian to anti-Hebbian, by varying a single parameter. We investigate STDP dynamics of a single excitatory or inhibitory synapse in purely feed-forward architecture. We derive a mean-field-Fokker-Planck dynamics for the synaptic weight and analyze the effect of STDP structure on the fixed points of the mean field dynamics. We find a phase transition along the Hebbian to anti-Hebbian parameter from a phase that is characterized by a unimodal distribution of the synaptic weight, in which the STDP dynamics is governed by negative feedback, to a phase with positive feedback characterized by a bimodal distribution. The critical point of this transition depends on general properties of the STDP dynamics and not on the fine details. Namely, the dynamics is affected by the pre-post correlations only via a single number that quantifies its overlap with the STDP kernel. We find that by manipulating the STDP temporal kernel, negative feedback can be induced in excitatory synapses and positive feedback in inhibitory. Moreover, there is an exact symmetry between inhibitory and excitatory plasticity, i.e., for every STDP rule of inhibitory synapse there exists an STDP rule for excitatory synapse, such that their dynamics is identical.
The impact of base stacking on the conformations and electrostatics of single-stranded DNA.
Plumridge, Alex; Meisburger, Steve P; Andresen, Kurt; Pollack, Lois
2017-04-20
Single-stranded DNA (ssDNA) is notable for its interactions with ssDNA binding proteins (SSBs) during fundamentally important biological processes including DNA repair and replication. Previous work has begun to characterize the conformational and electrostatic properties of ssDNA in association with SSBs. However, the conformational distributions of free ssDNA have been difficult to determine. To capture the vast array of ssDNA conformations in solution, we pair small angle X-ray scattering with novel ensemble fitting methods, obtaining key parameters such as the size, shape and stacking character of strands with different sequences. Complementary ion counting measurements using inductively coupled plasma atomic emission spectroscopy are employed to determine the composition of the ion atmosphere at physiological ionic strength. Applying this combined approach to poly dA and poly dT, we find that the global properties of these sequences are very similar, despite having vastly different propensities for single-stranded helical stacking. These results suggest that a relatively simple mechanism for the binding of ssDNA to non-specific SSBs may be at play, which explains the disparity in binding affinities observed for these systems. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.
Energy Technology Data Exchange (ETDEWEB)
Jin, Zheming [Argonne National Lab. (ANL), Argonne, IL (United States); Yoshii, Kazutomo [Argonne National Lab. (ANL), Argonne, IL (United States); Finkel, Hal [Argonne National Lab. (ANL), Argonne, IL (United States); Cappello, Franck [Argonne National Lab. (ANL), Argonne, IL (United States)
2017-04-20
Open Computing Language (OpenCL) is a high-level language that enables software programmers to explore Field Programmable Gate Arrays (FPGAs) for application acceleration. The Intel FPGA software development kit (SDK) for OpenCL allows a user to specify applications at a high level and explore the performance of low-level hardware acceleration. In this report, we present the FPGA performance and power consumption results of the single-precision floating-point vector add OpenCL kernel using the Intel FPGA SDK for OpenCL on the Nallatech 385A FPGA board. The board features an Arria 10 FPGA. We evaluate the FPGA implementations using the compute unit duplication and kernel vectorization optimization techniques. On the Nallatech 385A FPGA board, the maximum compute kernel bandwidth we achieve is 25.8 GB/s, approximately 76% of the peak memory bandwidth. The power consumption of the FPGA device when running the kernels ranges from 29W to 42W.
Doping efficiency of single and randomly stacked bilayer graphene by iodine adsorption
Energy Technology Data Exchange (ETDEWEB)
Kim, HoKwon; Renault, Olivier; Rouchon, Denis; Mariolle, Denis; Chevalier, Nicolas [Univ. Grenoble Alpes, F-38000 Grenoble, France and CEA, LETI, MINATEC Campus, F-38054 Grenoble (France); Tyurnina, Anastasia; Simonato, Jean-Pierre; Dijon, Jean [Univ. Grenoble Alpes, F-38000 Grenoble, France and CEA, LITEN, Minatec Campus, F-38054 Grenoble (France)
2014-07-07
We report on the efficiency and thermal stability of p-doping by iodine on single and randomly stacked, weakly coupled bilayer polycrystalline graphene, as directly measured by photoelectron emission microscopy. The doping results in work function value increase of 0.4–0.5 eV, with a higher degree of iodine uptake by the bilayer (2%) as compared to the single layer (1%) suggesting iodine intercalation in the bilayer. The chemistry of iodine is identified accordingly as I{sub 3}{sup −} and I{sub 5}{sup −} poly iodide anionic complexes with slightly higher concentration of I{sub 5}{sup −} in bilayer than monolayer graphene, likely attributed to differences in doping mechanisms. Temperature dependent in-situ annealing of the doped films demonstrated that the doping remains efficient up to 200 °C.
DEFF Research Database (Denmark)
Winning, H.; Viereck, N.; Wollenweber, B.
2009-01-01
at the vegetative growth stage had little effect on the parameters investigated. For the first time, H-1 HR-MAS NMR spectra of grains taken during grain-filling were analysed by an advanced multiway model. In addition to the results from the chemical protein analysis and the H-1 HR-MAS NMR spectra of single kernels...... was to examine the implications of different drought treatments on the protein fractions in grains of winter wheat using H-1 nuclear magnetic resonance spectroscopy followed by chemometric analysis. Triticum aestivum L. cv. Vinjett was studied in a semi-field experiment and subjected to drought episodes either...... at terminal spikelet, during grain-filling or at both stages. Principal component trajectories of the total protein content and the protein fractions of flour as well as the H-1 NMR spectra of single wheat kernels, wheat flour, and wheat methanol extracts were analysed to elucidate the metabolic development...
Takashima, Ryoichi; Takiguchi, Tetsuya; Ariki, Yasuo
2013-02-01
This paper presents a method for discriminating the location of the sound source (talker) using only a single microphone. In a previous work, the single-channel approach for discriminating the location of the sound source was discussed, where the acoustic transfer function from a user's position is estimated by using a hidden Markov model of clean speech in the cepstral domain. In this paper, each cepstral dimension of the acoustic transfer function is newly weighted, in order to obtain the cepstral dimensions having information that is useful for classifying the user's position. Then, this paper proposes a feature-weighting method for the cepstral parameter using multiple kernel learning, defining the base kernels for each cepstral dimension of the acoustic transfer function. The user's position is trained and classified by support vector machine. The effectiveness of this method has been confirmed by sound source (talker) localization experiments performed in different room environments.
Performance of a methane-fueled single-cell SOFC stack at various levels of fuel utilization
International Nuclear Information System (INIS)
Ahmed, K.; Bolden, R.; Ramprakash and Foger, K.
1998-01-01
Fuel-gas mixtures representing 10 to 85% utilization of a methane-steam mixture at S/C=2 were fed to a single cell stack with a Ni-based anode at 875 deg C. Cell voltage and power output were recorded at current densities of 50 to 350 mA/cm 2 . The accompanying anode off-gas composition at some of these conditions were measured using on-line gas chromatograph and compared with the compositions predicted by a thermodynamic model based on the assumption of no carbon formation. Electrical losses were measured at a chosen current density at various levels of fuel utilization by the galvanostatic current-interruption technique. Cell voltage stability was monitored for up to 1000 h at two levels of fuel utilization. The stack performance was simulated using a mathematical model of the stack; the simulations were compared with the stack test data. Copyright (1998) Australasian Ceramic Society
Galindo-de-la-Rosa, J; Arjona, N; Moreno-Zuria, A; Ortiz-Ortega, E; Guerra-Balcázar, M; Ledesma-García, J; Arriaga, L G
2017-06-15
The purpose of this work is to evaluate single and double-cell membraneless microfluidic fuel cells (MMFCs) that operate in the presence of simulated body fluids SBF, human serum and blood enriched with ethanol as fuels. The study was performed using the alcohol dehydrogenase enzyme immobilised by covalent binding through an array composed of carbon Toray paper as support and a layer of poly(methylene blue)/tetrabutylammonium bromide/Nafion and glutaraldehyde (3D bioanode electrode). The single MMFC was tested in a hybrid microfluidic fuel cell using Pt/C as the cathode. A cell voltage of 1.035V and power density of 3.154mWcm -2 were observed, which is the highest performance reported to date. The stability and durability were tested through chronoamperometry and polarisation/performance curves obtained at different days, which demonstrated a slow decrease in the power density on day 10 (14%) and day 20 (26%). Additionally, the cell was tested for ethanol oxidation in simulated body fluid (SBF) with ionic composition similar to human blood plasma. Those tests resulted in 0.93V of cell voltage and a power density close to 1.237mWcm -2 . The double cell MMFC (Stack) was tested using serum and human blood enriched with ethanol. The stack operated with blood in a serial connection showed an excellent cell performance (0.716mWcm -2 ), demonstrating the feasibility of employing human blood as energy source. Copyright © 2017 Elsevier B.V. All rights reserved.
Ultra-thin distributed Bragg reflectors via stacked single-crystal silicon nanomembranes
Energy Technology Data Exchange (ETDEWEB)
Cho, Minkyu; Seo, Jung-Hun; Lee, Jaeseong; Mi, Hongyi; Kim, Munho; Ma, Zhenqiang, E-mail: mazq@engr.wisc.edu [Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706 (United States); Zhao, Deyin; Zhou, Weidong [Nanophotonics Lab, Department of Electrical Engineering, University of Texas at Arlington, Arlington, Texas 76019 (United States); Yin, Xin; Wang, Xudong [Department of Material Science and Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706 (United States)
2015-05-04
In this paper, we report ultra-thin distributed Bragg reflectors (DBRs) via stacked single-crystal silicon (Si) nanomembranes (NMs). Mesh hole-free single-crystal Si NMs were released from a Si-on-insulator substrate and transferred to quartz and Si substrates. Thermal oxidation was applied to the transferred Si NM to form high-quality SiO{sub 2} and thus a Si/SiO{sub 2} pair with uniform and precisely controlled thicknesses. The Si/SiO{sub 2} layers, as smooth as epitaxial grown layers, minimize scattering loss at the interface and in between the layers. As a result, a reflection of 99.8% at the wavelength range from 1350 nm to 1650 nm can be measured from a 2.5-pair DBR on a quartz substrate and 3-pair DBR on a Si substrate with thickness of 0.87 μm and 1.14 μm, respectively. The high reflection, ultra-thin DBRs developed here, which can be applied to almost any devices and materials, holds potential for application in high performance optoelectronic devices and photonics applications.
International Nuclear Information System (INIS)
LaBel, K.A.; Gates, M.M.; Moran, A.K.; Kim, H.S.; Seidleck, C.M.; Marshall, P.; Kinnison, J.; Carkhuff, B.
1996-01-01
This paper presents radiation effects characterization performed by the NASA Goddard Space Flight Center (GSFC) on spaceflight candidate 16Mbit DRAMs. This includes heavy ion, proton, and Co60 irradiations on single-chip devices as well as proton irradiation of a stacked DRAM module. Lastly, a discussion of test methodology is undertaken
Evaluating the pitch bias of CryoSat exploiting stacks of single look ehoes
Scagliola, Michele; Tagliani, Nicolas; Fornari, Marco; Bouzinac, Catherine; Parrinello, Tommaso
2014-05-01
CryoSat was launched on the 8th April 2010 and it is the first European ice mission dedicated to monitoring precise changes in the thickness of polar ice sheets and floating sea ice over a 3-year period. CryoSat carries an innovative radar altimeter called the Synthetic Aperture Interferometric Altimeter (SIRAL), that transmits pulses at a high pulse repetition frequency thus making the received echoes phase coherent and suitable for azimuth processing. The attitude information of the spacecraft is provided by star trackers, that have an internal accuracy of few arc-seconds. By analysis of the CryoSat products, two different studies [1, 2] verified the existence of a bias between the pitch reported by the star trackers and the actual pitch of CryoSat during its flight. However those studies, that use two different methods to evaluate the actual pitch, provided different values for the pitch bias. This poster is aimed at describing a further method to estimated the pitch with which the satellite is actually flying by analysis of the stacks of the single look echoes that are accumulated for a given location of sea surface during the Level1 processing. In fact, over ocean the power of the single look echoes for a given point is shaped by the along-track antenna pattern. As a consequence, estimating the angular direction of pointing of the antenna from the stack, an estimate of the pitch can be obtained. Finally, the bias evaluated starting from the pitch measured with the proposed method is compared with the pitch bias measured in [1, 2]. [1] Galin,N. and Wingham, D., Estimating Pitch Angle of CryoSat-2 using the Power Distribution of the Synthetic Aperture, presented at SAR Altimetry Expert Group Meeting, Southampton UK, June 25-27, 2013. [2] Smith, W.H.F. and Scharroo, R., Retracking range, SWH, sigma-naught, and attitude in CryoSat conventional ocean data. In proceedings of Ocean Surface Topography Science Team Meeting. San Diego, October 19-21, 2011.
International Nuclear Information System (INIS)
Eler, J.C.; Littlefield, L.G.; Tillery, M.I.
1979-01-01
A prototype particulate stack sampler (PPSS) has been developed to improve on the existing EPA Method 5 sampling apparatus. Its primary features are (1) higher sampling rate (56 1/min); (2) display (on demand) of all required variables and calculated values by a microcomputer-based calculating and display system; (3) continuous stack gas moisture determination; (4) a virtual impactor nozzle with 3 μm mass median diameter cutpoint which collects fine and coarse particle fractions on separate glass fiber filters; (5) a variable-area inlet to maintain isokinetic sampling conditions; and (6) conversion to stainless steel components from the glass specified by EPA Method 5. The basic sampling techniques of EPA Method 5 have been retained; however, versatility in the form of optional in-stack filters and general modernization of the stack sampler have been provided in the prototype design. Laboratory testing with monodisperse dye aerosols has shown the present variable inlet, virtual impactor nozzle to have a collection efficiency which is less than 77% and significant wall losses. This is primarily due to lack of symmetry in this rectangular jet impactor and short transition lengths dictated by physical design constraints (required passage of the nozzle through a 7.6 cm (3 in) diameter stack port). Electronic components have shown acceptable service in laboratory testing although no field testing of the prototype under a broad range of temperature, humidity, and SO 2 concentration has been undertaken
Development of a 5.1 T conduction-cooled YBCO coil composed of a stack of 12 single pancakes
Energy Technology Data Exchange (ETDEWEB)
Miyazaki, Hiroshi, E-mail: hiroshi17.miyazaki@toshiba.co.jp [Toshiba Corporation, Power Systems Company, 2-4 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045 (Japan); Iwai, Sadanori; Tosaka, Taizo; Tasaki, Kenji; Hanai, Satoshi; Urata, Masami; Ioka, Shigeru; Ishii, Yusuke [Toshiba Corporation, Power Systems Company, 2-4 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045 (Japan)
2013-01-15
Highlights: ► We confirmed that performance of YBCO coil was improved by using APC wire. ► We made a conduction-cooled YBCO coil composed of a stack of 12 single pancakes. ► The coil had a central magnetic field as high as 5.1 T at 10 K. ► We also tested the coil operation in a 4 T background magnetic field. -- Abstract: We fabricated and tested a 5 T-class conduction-cooled high-temperature superconducting (HTS) coil composed of a stack of 12 single pancake coils wound with YBCO-coated conductors. The length of each single pancake coil was 25 m, and the inner diameter of the coil was 50 mm. The voltage–current characteristics were measured in liquid nitrogen and under conduction-cooled conditions at 10–60 K. At 10 K, the central magnetic field of the 12 stacked pancake coils was as high as 5.1 T at 305 A. We also tested the coil operation in a 4 T background magnetic field.
The coherent interlayer resistance of a single, rotated interface between two stacks of AB graphite
Energy Technology Data Exchange (ETDEWEB)
Habib, K. M. Masum, E-mail: khabib@ee.ucr.edu; Sylvia, Somaia S.; Neupane, Mahesh; Lake, Roger K., E-mail: rlake@ee.ucr.edu [Department of Electrical Engineering, University of California, Riverside, California 92521-0204 (United States); Ge, Supeng [Department of Physics and Astronomy, University of California, Riverside, California 92521-0204 (United States)
2013-12-09
The coherent, interlayer resistance of a misoriented, rotated interface between two stacks of AB graphite is determined for a variety of misorientation angles. The quantum-resistance of the ideal AB stack is on the order of 1 to 10 mΩ μm{sup 2}. For small rotation angles, the coherent interlayer resistance exponentially approaches the ideal quantum resistance at energies away from the charge neutrality point. Over a range of intermediate angles, the resistance increases exponentially with cell size for minimum size unit cells. Larger cell sizes, of similar angles, may not follow this trend. The energy dependence of the interlayer transmission is described.
The coherent interlayer resistance of a single, rotated interface between two stacks of AB graphite
International Nuclear Information System (INIS)
Habib, K. M. Masum; Sylvia, Somaia S.; Neupane, Mahesh; Lake, Roger K.; Ge, Supeng
2013-01-01
The coherent, interlayer resistance of a misoriented, rotated interface between two stacks of AB graphite is determined for a variety of misorientation angles. The quantum-resistance of the ideal AB stack is on the order of 1 to 10 mΩ μm 2 . For small rotation angles, the coherent interlayer resistance exponentially approaches the ideal quantum resistance at energies away from the charge neutrality point. Over a range of intermediate angles, the resistance increases exponentially with cell size for minimum size unit cells. Larger cell sizes, of similar angles, may not follow this trend. The energy dependence of the interlayer transmission is described
Single point aerosol sampling: Evaluation of mixing and probe performance in a nuclear stack
Energy Technology Data Exchange (ETDEWEB)
Rodgers, J.C.; Fairchild, C.I.; Wood, G.O. [Los Alamos National Laboratory, NM (United States)] [and others
1995-02-01
Alternative Reference Methodologies (ARMs) have been developed for sampling of radionuclides from stacks and ducts that differ from the methods required by the U.S. EPA. The EPA methods are prescriptive in selection of sampling locations and in design of sampling probes whereas the alternative methods are performance driven. Tests were conducted in a stack at Los Alamos National Laboratory to demonstrate the efficacy of the ARMs. Coefficients of variation of the velocity tracer gas, and aerosol particle profiles were determined at three sampling locations. Results showed numerical criteria placed upon the coefficients of variation by the ARMs were met at sampling stations located 9 and 14 stack diameters from flow entrance, but not at a location that is 1.5 diameters downstream from the inlet. Experiments were conducted to characterize the transmission of 10 {mu}m aerodynamic equivalent diameter liquid aerosol particles through three types of sampling probes. The transmission ratio (ratio of aerosol concentration at the probe exit plane to the concentration in the free stream) was 107% for a 113 L/min (4-cfm) anisokinetic shrouded probe, but only 20% for an isokinetic probe that follows the EPA requirements. A specially designed isokinetic probe showed a transmission ratio of 63%. The shrouded probe performance would conform to the ARM criteria; however, the isokinetic probes would not.
Energy Technology Data Exchange (ETDEWEB)
Wang, Quanlong [School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001 (China); Center for Precision Engineering, Harbin Institute of Technology, Harbin 150001 (China); Bai, Qingshun [School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001 (China); Chen, Jiaxuan, E-mail: wangquanlong0@hit.edu.cn [Center for Precision Engineering, Harbin Institute of Technology, Harbin 150001 (China); Guo, Yongbo [Center for Precision Engineering, Harbin Institute of Technology, Harbin 150001 (China); Xie, Wenkun [School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001 (China); Center for Precision Engineering, Harbin Institute of Technology, Harbin 150001 (China)
2015-11-15
Graphical abstract: In this paper, molecular dynamics simulation is performed to study the distribution of dislocation defects and local atomic crystal structure of single crystal copper. The stress distribution is investigated which is calculated by virial stress and analyzed by static pressure. The results are shown in (a)–(d). It is indicated that the compressive stress mainly spreads over the shear-slip zone, and the tensile stress is consisted in flank friction zone, shown in (a). The high tensile stress in subsurface is the source of stress, shown in (b). By the driven action of the stress source, the initial stair-rod dislocation nucleates. Then the dislocation climbs along four {1 1 1} planes under the stress driven action, shown in (d). Finally, the SFT is formed by the interaction of the compressive stress and the tensile stress which come from the shear-slip zone and friction zone, respectively. Besides, stair-rod dislocation, stacking faults and dislocation loop are also nucleated in the subsurface, shown in (c). Dislocation distribution, local atomic crystal structure state and stress-induced formation process of SFT by atomic. - Highlights: • A novel defect structure “stress-induced stacking fault tetrahedra” is revealed. • Atomic structural evolution and stress state distribution of the SFT are studied. • The stress-induced formation mechanism of the SFT is proposed. - Abstract: Stacking fault tetrahedra commonly existed in subsurface of deformed face center cubic metals, has great influence on machining precision and surface roughness in nano-cutting. Here we report, a stacking fault tetrahedra is formed in subsurface of workpiece during nano-cutting. The variation of cutting force and subsurface defects distribution are studied by using molecular dynamics simulation. The stress distribution is investigated which is calculated by virial stress and analyzed by static compression. The result shows that the cutting force has a rapidly
International Nuclear Information System (INIS)
Wang, Quanlong; Bai, Qingshun; Chen, Jiaxuan; Guo, Yongbo; Xie, Wenkun
2015-01-01
Graphical abstract: In this paper, molecular dynamics simulation is performed to study the distribution of dislocation defects and local atomic crystal structure of single crystal copper. The stress distribution is investigated which is calculated by virial stress and analyzed by static pressure. The results are shown in (a)–(d). It is indicated that the compressive stress mainly spreads over the shear-slip zone, and the tensile stress is consisted in flank friction zone, shown in (a). The high tensile stress in subsurface is the source of stress, shown in (b). By the driven action of the stress source, the initial stair-rod dislocation nucleates. Then the dislocation climbs along four {1 1 1} planes under the stress driven action, shown in (d). Finally, the SFT is formed by the interaction of the compressive stress and the tensile stress which come from the shear-slip zone and friction zone, respectively. Besides, stair-rod dislocation, stacking faults and dislocation loop are also nucleated in the subsurface, shown in (c). Dislocation distribution, local atomic crystal structure state and stress-induced formation process of SFT by atomic. - Highlights: • A novel defect structure “stress-induced stacking fault tetrahedra” is revealed. • Atomic structural evolution and stress state distribution of the SFT are studied. • The stress-induced formation mechanism of the SFT is proposed. - Abstract: Stacking fault tetrahedra commonly existed in subsurface of deformed face center cubic metals, has great influence on machining precision and surface roughness in nano-cutting. Here we report, a stacking fault tetrahedra is formed in subsurface of workpiece during nano-cutting. The variation of cutting force and subsurface defects distribution are studied by using molecular dynamics simulation. The stress distribution is investigated which is calculated by virial stress and analyzed by static compression. The result shows that the cutting force has a rapidly
A scalable single-chip multi-processor architecture with on-chip RTOS kernel
Theelen, B.D.; Verschueren, A.C.; Reyes Suarez, V.V.; Stevens, M.P.J.; Nunez, A.
2003-01-01
Now that system-on-chip technology is emerging, single-chip multi-processors are becoming feasible. A key problem of designing such systems is the complexity of their on-chip interconnects and memory architecture. It is furthermore unclear at what level software should be integrated. An example of a
Development of a single-photon-counting camera with use of a triple-stacked micro-channel plate.
Yasuda, Naruomi; Suzuki, Hitoshi; Katafuchi, Tetsuro
2016-01-01
At the quantum-mechanical level, all substances (not merely electromagnetic waves such as light and X-rays) exhibit wave–particle duality. Whereas students of radiation science can easily understand the wave nature of electromagnetic waves, the particle (photon) nature may elude them. Therefore, to assist students in understanding the wave–particle duality of electromagnetic waves, we have developed a photon-counting camera that captures single photons in two-dimensional images. As an image intensifier, this camera has a triple-stacked micro-channel plate (MCP) with an amplification factor of 10(6). The ultra-low light of a single photon entering the camera is first converted to an electron through the photoelectric effect on the photocathode. The electron is intensified by the triple-stacked MCP and then converted to a visible light distribution, which is measured by a high-sensitivity complementary metal oxide semiconductor image sensor. Because it detects individual photons, the photon-counting camera is expected to provide students with a complete understanding of the particle nature of electromagnetic waves. Moreover, it measures ultra-weak light that cannot be detected by ordinary low-sensitivity cameras. Therefore, it is suitable for experimental research on scintillator luminescence, biophoton detection, and similar topics.
Directory of Open Access Journals (Sweden)
Timothy Reissman
2012-01-01
Full Text Available We present copper structures composed of multilayer, stacked inductors (MLSIs with tens of micro-Henry inductance for use in low frequency (sub 100 MHz, power converter technology. Unique to this work is the introduction of single-level lithography over the traditional two-level approach to create each inductor layer. The result is a simplified fabrication process which results in a reduction in the number of lithography steps per inductor (metal layer and a reduction in the necessary alignment precision. Additionally, we show that this fabrication process yields strong adhesion amongst the layers, since even after a postprocess abrasion technique at the inner diameter of the inductors, no shearing occurs and connectivity is preserved. In total, three separate structures were fabricated using the single-level lithography approach, each with a three-layered, stacked inductor design but with varied geometries. Measured values for each of the structures were extracted, and the following results were obtained: inductance values of 24.74, 17.25, and 24.74 μH, self-resonances of 9.87, 5.72, and 10.58 MHz, and peak quality factors of 2.26, 2.05, and 4.6, respectively. These values are in good agreement with the lumped parameter model presented.
X-ray diffraction study of stacking faults in a single crystal of 2H SiC
International Nuclear Information System (INIS)
Pandey, D.; Krishna, P.
1977-01-01
The nature of random stacking faults in a heavily disordered single crystal of 2H SiC has been investigated by studying the broadening of x-ray diffraction maxima. The intensity distribution along the 10.1 reciprocal lattice row was recorded on a four-circle, computer-controlled single crystal diffractometer. The 10.1 reflections with 1 even were found to be considerably broadened showing that the stacking faults present are predominantly intrinsic faults ( both growth and deformation faults). A careful study of the half-width values of different 10.1 reflections revealed that the fault probabilities are large. Exact expressions for the diffracted intensity and the observable diffraction effects were obtained and these were then used to calculate the deformation and growth fault probabilities which were found to be 0.20 and 0.11 respectively. It is suggested that several deformation fault configurations result from a clustering of growth faults. The results obtained are compared with those obtained for 2H ZnS crystals. (author)
International Nuclear Information System (INIS)
Kordatos, Apostolis; Kelaidis, Nikolaos; Giamini, Sigiava Aminalragia; Marquez-Velasco, Jose; Xenogiannopoulou, Evangelia; Tsipas, Polychronis; Kordas, George; Dimoulas, Athanasios
2016-01-01
Highlights: • Growth of non-defective few layer graphene on Rh(1 1 1) substrates using an ambient- pressure CVD method. • Control of graphene stacking order via the cool-down rate. • Graphene is grown with a mainly AB-stacking geometry on single-crystalline Rhodium for a slow cool-down rate and non-AB for a very fast cool-down. • Good epitaxial orientation of the surface is presented through the RHEED data and confirmed with ARPES characterization for the lower cool-down rate, where graphene's ΓK direction a perfectly aligned with the ΓK direction of the Rh(1 1 1) single crystal. - Abstract: Graphene synthesis on single crystal Rh(1 1 1) catalytic substrates is performed by Chemical Vapor Deposition (CVD) at 1000 °C and atmospheric pressure. Raman analysis shows full substrate coverage with few layer graphene. It is found that the cool-down rate strongly affects the graphene stacking order. When lowered, the percentage of AB (Bernal) -stacked regions increases, leading to an almost full AB stacking order. When increased, the percentage of AB-stacked graphene regions decreases to a point where almost a full non AB-stacked graphene is grown. For a slow cool-down rate, graphene with AB stacking order and good epitaxial orientation with the substrate is achieved. This is indicated mainly by Raman characterization and confirmed by Reflection high-energy electron diffraction (RHEED) imaging. Additional Scanning Tunneling Microscopy (STM) topography data confirm that the grown graphene is mainly an AB-stacked structure. The electronic structure of the graphene/Rh(1 1 1) system is examined by Angle resolved Photo-Emission Spectroscopy (ARPES), where σ and π bands of graphene, are observed. Graphene's ΓK direction is aligned with the ΓK direction of the substrate, indicating no significant contribution from rotated domains.
Energy Technology Data Exchange (ETDEWEB)
Kordatos, Apostolis [National Center for Scientific Research “Demokritos”, Athens, 15310 (Greece); Kelaidis, Nikolaos, E-mail: n.kelaidis@inn.demokritos.gr [National Center for Scientific Research “Demokritos”, Athens, 15310 (Greece); Giamini, Sigiava Aminalragia [National Center for Scientific Research “Demokritos”, Athens, 15310 (Greece); University of Athens, Department of Physics, Section of Solid State Physics, Athens, 15684 Greece (Greece); Marquez-Velasco, Jose [National Center for Scientific Research “Demokritos”, Athens, 15310 (Greece); National Technical University of Athens, Department of Physics, Athens, 15784 Greece (Greece); Xenogiannopoulou, Evangelia; Tsipas, Polychronis; Kordas, George; Dimoulas, Athanasios [National Center for Scientific Research “Demokritos”, Athens, 15310 (Greece)
2016-04-30
Highlights: • Growth of non-defective few layer graphene on Rh(1 1 1) substrates using an ambient- pressure CVD method. • Control of graphene stacking order via the cool-down rate. • Graphene is grown with a mainly AB-stacking geometry on single-crystalline Rhodium for a slow cool-down rate and non-AB for a very fast cool-down. • Good epitaxial orientation of the surface is presented through the RHEED data and confirmed with ARPES characterization for the lower cool-down rate, where graphene's ΓK direction a perfectly aligned with the ΓK direction of the Rh(1 1 1) single crystal. - Abstract: Graphene synthesis on single crystal Rh(1 1 1) catalytic substrates is performed by Chemical Vapor Deposition (CVD) at 1000 °C and atmospheric pressure. Raman analysis shows full substrate coverage with few layer graphene. It is found that the cool-down rate strongly affects the graphene stacking order. When lowered, the percentage of AB (Bernal) -stacked regions increases, leading to an almost full AB stacking order. When increased, the percentage of AB-stacked graphene regions decreases to a point where almost a full non AB-stacked graphene is grown. For a slow cool-down rate, graphene with AB stacking order and good epitaxial orientation with the substrate is achieved. This is indicated mainly by Raman characterization and confirmed by Reflection high-energy electron diffraction (RHEED) imaging. Additional Scanning Tunneling Microscopy (STM) topography data confirm that the grown graphene is mainly an AB-stacked structure. The electronic structure of the graphene/Rh(1 1 1) system is examined by Angle resolved Photo-Emission Spectroscopy (ARPES), where σ and π bands of graphene, are observed. Graphene's ΓK direction is aligned with the ΓK direction of the substrate, indicating no significant contribution from rotated domains.
DEFF Research Database (Denmark)
Mogensen, Jesper Mølgaard; Sørensen, S.M.; Sulyok, M.
2011-01-01
Fumonisins are important Fusarium mycotoxins mainly found in maize and derived products. This study analysed maize from five subsistence farmers in the former Transkei region of South Africa. Farmers had sorted kernels into good and mouldy quality. A total of 400 kernels from 10 batches were...... analysed; of these 100 were visually characterised as uninfected and 300 as infected. Of the 400 kernels, 15% were contaminated with 1.84-1428 mg kg(-1) fumonisins, and 4% (n = 15) had a fumonisin content above 100 mg kg(-1). None of the visually uninfected maize had detectable amounts of fumonisins....... The total fumonisin concentration was 0.28-1.1 mg kg(-1) for good-quality batches and 0.03-6.2 mg kg(-1) for mouldy-quality batches. The high fumonisin content in the batches was apparently caused by a small number (4%) of highly contaminated kernels, and removal of these reduced the average fumonisin...
Mitigation of artifacts in rtm with migration kernel decomposition
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.
Matsuhata, Hirofumi; Sekiguchi, Takashi
2018-04-01
Morphology of single Shockley-type stacking faults (SFs) generated by recombination enhanced dislocation glide (REDG) in 4H-SiC are discussed and analysed. A complete set of the 12 different dissociated states of basal-plane dislocation loops is obtained using the crystallographic space group operations. From this set, six different double rhombic-shaped SFs are derived. These tables indicate the rules that connect shapes of SFs with the locations of partial dislocations having different core structures, the positions of slip planes in a unit cell, and the Burgers vectors of partial dislocations. We applied these tables for the analysis of SFs generated by the REDG effect reported in the past articles. Shapes, growing process of SFs and perfect dislocations for origins of SFs were well analysed systematically.
Kernel Machine SNP-set Testing under Multiple Candidate Kernels
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
Directory of Open Access Journals (Sweden)
Guangjun Qiu
2018-03-01
Full Text Available The viability and vigor of crop seeds are crucial indicators for evaluating seed quality, and high-quality seeds can increase agricultural yield. The conventional methods for assessing seed viability are time consuming, destructive, and labor intensive. Therefore, a rapid and nondestructive technique for testing seed viability has great potential benefits for agriculture. In this study, single-kernel Fourier transform near-infrared (FT-NIR spectroscopy with a wavelength range of 1000–2500 nm was used to distinguish viable and nonviable supersweet corn seeds. Various preprocessing algorithms coupled with partial least squares discriminant analysis (PLS-DA were implemented to test the performance of classification models. The FT-NIR spectroscopy technique successfully differentiated viable seeds from seeds that were nonviable due to overheating or artificial aging. Correct classification rates for both heat-damaged kernels and artificially aged kernels reached 98.0%. The comprehensive model could also attain an accuracy of 98.7% when combining heat-damaged samples and artificially aged samples into one category. Overall, the FT-NIR technique with multivariate data analysis methods showed great potential capacity in rapidly and nondestructively detecting seed viability in supersweet corn.
Qiu, Guangjun; Lü, Enli; Lu, Huazhong; Xu, Sai; Zeng, Fanguo; Shui, Qin
2018-03-28
The viability and vigor of crop seeds are crucial indicators for evaluating seed quality, and high-quality seeds can increase agricultural yield. The conventional methods for assessing seed viability are time consuming, destructive, and labor intensive. Therefore, a rapid and nondestructive technique for testing seed viability has great potential benefits for agriculture. In this study, single-kernel Fourier transform near-infrared (FT-NIR) spectroscopy with a wavelength range of 1000-2500 nm was used to distinguish viable and nonviable supersweet corn seeds. Various preprocessing algorithms coupled with partial least squares discriminant analysis (PLS-DA) were implemented to test the performance of classification models. The FT-NIR spectroscopy technique successfully differentiated viable seeds from seeds that were nonviable due to overheating or artificial aging. Correct classification rates for both heat-damaged kernels and artificially aged kernels reached 98.0%. The comprehensive model could also attain an accuracy of 98.7% when combining heat-damaged samples and artificially aged samples into one category. Overall, the FT-NIR technique with multivariate data analysis methods showed great potential capacity in rapidly and nondestructively detecting seed viability in supersweet corn.
Davtyan, Arman; Biermanns, Andreas; Loffeld, Otmar; Pietsch, Ullrich
2016-06-01
Coherent x-ray diffraction imaging is used to measure diffraction patterns from individual highly defective nanowires, showing a complex speckle pattern instead of well-defined Bragg peaks. The approach is tested for nanowires of 500 nm diameter and 500 nm height predominately composed by zinc-blende (ZB) and twinned zinc-blende (TZB) phase domains. Phase retrieval is used to reconstruct the measured 2-dimensional intensity patterns recorded from single nanowires with 3.48 nm and 0.98 nm spatial resolution. Whereas the speckle amplitudes and distribution are perfectly reconstructed, no unique solution could be obtained for the phase structure. The number of phase switches is found to be proportional to the number of measured speckles and follows a narrow number distribution. Using data with 0.98 nm spatial resolution the mean number of phase switches is in reasonable agreement with estimates taken from TEM. However, since the resolved phase domain still is 3-4 times larger than a single GaAs bilayer we explain the non-ambiguous phase reconstruction by the fact that depending on starting phase and sequence of subroutines used during the phase retrieval the retrieved phase domain host a different sequence of randomly stacked bilayers. Modelling possible arrangements of bilayer sequences within a phase domain demonstrate that the complex speckle patterns measured can indeed be explained by the random arrangement of the ZB and TZB phase domains.
Directory of Open Access Journals (Sweden)
Ali Sayyed
2015-09-01
Full Text Available The use of mobile nodes to collect data in a Wireless Sensor Network (WSN has gained special attention over the last years. Some researchers explore the use of Unmanned Aerial Vehicles (UAVs as mobile node for such data-collection purposes. Analyzing these works, it is apparent that mobile nodes used in such scenarios are typically equipped with at least two different radio interfaces. The present work presents a Dual-Stack Single-Radio Communication Architecture (DSSRCA, which allows a UAV to communicate in a bidirectional manner with a WSN and a Sink node. The proposed architecture was specifically designed to support different network QoS requirements, such as best-effort and more reliable communications, attending both UAV-to-WSN and UAV-to-Sink communications needs. DSSRCA was implemented and tested on a real UAV, as detailed in this paper. This paper also includes a simulation analysis that addresses bandwidth consumption in an environmental monitoring application scenario. It includes an analysis of the data gathering rate that can be achieved considering different UAV flight speeds. Obtained results show the viability of using a single radio transmitter for collecting data from the WSN and forwarding such data to the Sink node.
Sayyed, Ali; de Araújo, Gustavo Medeiros; Bodanese, João Paulo; Becker, Leandro Buss
2015-09-16
The use of mobile nodes to collect data in a Wireless Sensor Network (WSN) has gained special attention over the last years. Some researchers explore the use of Unmanned Aerial Vehicles (UAVs) as mobile node for such data-collection purposes. Analyzing these works, it is apparent that mobile nodes used in such scenarios are typically equipped with at least two different radio interfaces. The present work presents a Dual-Stack Single-Radio Communication Architecture (DSSRCA), which allows a UAV to communicate in a bidirectional manner with a WSN and a Sink node. The proposed architecture was specifically designed to support different network QoS requirements, such as best-effort and more reliable communications, attending both UAV-to-WSN and UAV-to-Sink communications needs. DSSRCA was implemented and tested on a real UAV, as detailed in this paper. This paper also includes a simulation analysis that addresses bandwidth consumption in an environmental monitoring application scenario. It includes an analysis of the data gathering rate that can be achieved considering different UAV flight speeds. Obtained results show the viability of using a single radio transmitter for collecting data from the WSN and forwarding such data to the Sink node.
Control Transfer in Operating System Kernels
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
Testing pollen of single and stacked insect-resistant Bt-maize on in vitro reared honey bee larvae.
Hendriksma, Harmen P; Härtel, Stephan; Steffan-Dewenter, Ingolf
2011-01-01
The ecologically and economic important honey bee (Apis mellifera) is a key non-target arthropod species in environmental risk assessment (ERA) of genetically modified (GM) crops. Honey bee larvae are directly exposed to transgenic products by the consumption of GM pollen. But most ERA studies only consider responses of adult bees, although Bt-proteins primarily affect the larval phases of target organisms. We adopted an in vitro larvae rearing system, to assess lethal and sublethal effects of Bt-pollen consumption in a standardized eco-toxicological bioassay. The effects of pollen from two Bt-maize cultivars, one expressing a single and the other a total of three Bt-proteins, on the survival and prepupae weight of honey bee larvae were analyzed. The control treatments included pollen from three non-transgenic maize varieties and of Heliconia rostrata. Three days old larvae were fed the realistic exposure dose of 2 mg pollen within the semi-artificial diet. The larvae were monitored over 120 h, until the prepupal stage, where larvae terminate feeding and growing. Neither single nor stacked Bt-maize pollen showed an adverse effect on larval survival and the prepupal weight. In contrast, feeding of H. rostrata pollen caused significant toxic effects. The results of this study indicate that pollen of the tested Bt-varieties does not harm the development of in vitro reared A. mellifera larvae. To sustain the ecosystem service of pollination, Bt-impact on A. mellifera should always be a crucial part of regulatory biosafety assessments. We suggest that our approach of feeding GM pollen on in vitro reared honey bee larvae is well suited of becoming a standard bioassay in regulatory risk assessments schemes of GM crops.
Testing pollen of single and stacked insect-resistant Bt-maize on in vitro reared honey bee larvae.
Directory of Open Access Journals (Sweden)
Harmen P Hendriksma
Full Text Available The ecologically and economic important honey bee (Apis mellifera is a key non-target arthropod species in environmental risk assessment (ERA of genetically modified (GM crops. Honey bee larvae are directly exposed to transgenic products by the consumption of GM pollen. But most ERA studies only consider responses of adult bees, although Bt-proteins primarily affect the larval phases of target organisms. We adopted an in vitro larvae rearing system, to assess lethal and sublethal effects of Bt-pollen consumption in a standardized eco-toxicological bioassay. The effects of pollen from two Bt-maize cultivars, one expressing a single and the other a total of three Bt-proteins, on the survival and prepupae weight of honey bee larvae were analyzed. The control treatments included pollen from three non-transgenic maize varieties and of Heliconia rostrata. Three days old larvae were fed the realistic exposure dose of 2 mg pollen within the semi-artificial diet. The larvae were monitored over 120 h, until the prepupal stage, where larvae terminate feeding and growing. Neither single nor stacked Bt-maize pollen showed an adverse effect on larval survival and the prepupal weight. In contrast, feeding of H. rostrata pollen caused significant toxic effects. The results of this study indicate that pollen of the tested Bt-varieties does not harm the development of in vitro reared A. mellifera larvae. To sustain the ecosystem service of pollination, Bt-impact on A. mellifera should always be a crucial part of regulatory biosafety assessments. We suggest that our approach of feeding GM pollen on in vitro reared honey bee larvae is well suited of becoming a standard bioassay in regulatory risk assessments schemes of GM crops.
Testing Pollen of Single and Stacked Insect-Resistant Bt-Maize on In vitro Reared Honey Bee Larvae
Hendriksma, Harmen P.; Härtel, Stephan; Steffan-Dewenter, Ingolf
2011-01-01
The ecologically and economic important honey bee (Apis mellifera) is a key non-target arthropod species in environmental risk assessment (ERA) of genetically modified (GM) crops. Honey bee larvae are directly exposed to transgenic products by the consumption of GM pollen. But most ERA studies only consider responses of adult bees, although Bt-proteins primarily affect the larval phases of target organisms. We adopted an in vitro larvae rearing system, to assess lethal and sublethal effects of Bt-pollen consumption in a standardized eco-toxicological bioassay. The effects of pollen from two Bt-maize cultivars, one expressing a single and the other a total of three Bt-proteins, on the survival and prepupae weight of honey bee larvae were analyzed. The control treatments included pollen from three non-transgenic maize varieties and of Heliconia rostrata. Three days old larvae were fed the realistic exposure dose of 2 mg pollen within the semi-artificial diet. The larvae were monitored over 120 h, until the prepupal stage, where larvae terminate feeding and growing. Neither single nor stacked Bt-maize pollen showed an adverse effect on larval survival and the prepupal weight. In contrast, feeding of H. rostrata pollen caused significant toxic effects. The results of this study indicate that pollen of the tested Bt-varieties does not harm the development of in vitro reared A. mellifera larvae. To sustain the ecosystem service of pollination, Bt-impact on A. mellifera should always be a crucial part of regulatory biosafety assessments. We suggest that our approach of feeding GM pollen on in vitro reared honey bee larvae is well suited of becoming a standard bioassay in regulatory risk assessments schemes of GM crops. PMID:22194811
Indian Academy of Sciences (India)
Deligne, Mumford and Artin [DM, Ar2]) and consider algebraic stacks, then we can cons- truct the 'moduli ... the moduli scheme and the moduli stack of vector bundles. First I will give ... 1–31. © Printed in India. 1 ...... Cultura, Spain. References.
Stacking with stochastic cooling
Energy Technology Data Exchange (ETDEWEB)
Caspers, Fritz E-mail: Fritz.Caspers@cern.ch; Moehl, Dieter
2004-10-11
Accumulation of large stacks of antiprotons or ions with the aid of stochastic cooling is more delicate than cooling a constant intensity beam. Basically the difficulty stems from the fact that the optimized gain and the cooling rate are inversely proportional to the number of particles 'seen' by the cooling system. Therefore, to maintain fast stacking, the newly injected batch has to be strongly 'protected' from the Schottky noise of the stack. Vice versa the stack has to be efficiently 'shielded' against the high gain cooling system for the injected beam. In the antiproton accumulators with stacking ratios up to 10{sup 5} the problem is solved by radial separation of the injection and the stack orbits in a region of large dispersion. An array of several tapered cooling systems with a matched gain profile provides a continuous particle flux towards the high-density stack core. Shielding of the different systems from each other is obtained both through the spatial separation and via the revolution frequencies (filters). In the 'old AA', where the antiproton collection and stacking was done in one single ring, the injected beam was further shielded during cooling by means of a movable shutter. The complexity of these systems is very high. For more modest stacking ratios, one might use azimuthal rather than radial separation of stack and injected beam. Schematically half of the circumference would be used to accept and cool new beam and the remainder to house the stack. Fast gating is then required between the high gain cooling of the injected beam and the low gain stack cooling. RF-gymnastics are used to merge the pre-cooled batch with the stack, to re-create free space for the next injection, and to capture the new batch. This scheme is less demanding for the storage ring lattice, but at the expense of some reduction in stacking rate. The talk reviews the 'radial' separation schemes and also gives some
Ben Ali, Sina-Elisabeth; Madi, Zita Erika; Hochegger, Rupert; Quist, David; Prewein, Bernhard; Haslberger, Alexander G.; Brandes, Christian
2014-01-01
Genetic mutations must be avoided during the production and use of seeds. In the European Union (EU), Directive 2001/18/EC requires any DNA construct introduced via transformation to be stable. Establishing genetic stability is critical for the approval of genetically modified organisms (GMOs). In this study, genetic stability of two GMOs was examined using high resolution melting (HRM) analysis and real-time polymerase chain reaction (PCR) employing Scorpion primers for amplification. The genetic variability of the transgenic insert and that of the flanking regions in a single oilseed rape variety (GT73) and a stacked maize (MON88017 × MON810) was studied. The GT73 and the 5' region of MON810 showed no instabilities in the examined regions. However; two out of 100 analyzed samples carried a heterozygous point mutation in the 3' region of MON810 in the stacked variety. These results were verified by direct sequencing of the amplified PCR products as well as by sequencing of cloned PCR fragments. The occurrence of the mutation suggests that the 5' region is more suitable than the 3' region for the quantification of MON810. The identification of the single nucleotide polymorphism (SNP) in a stacked event is in contrast to the results of earlier studies of the same MON810 region in a single event where no DNA polymorphism was found. PMID:25365178
System effects in sample self-stacking CZE: Single analyte peak splitting of salt-containing samples
Czech Academy of Sciences Publication Activity Database
Malá, Zdeňka; Gebauer, Petr; Boček, Petr
2009-01-01
Roč. 30, č. 5 (2009), s. 866-874 ISSN 0173-0835 R&D Projects: GA ČR GA203/08/1536; GA AV ČR IAA400310609; GA AV ČR IAA400310703 Institutional research plan: CEZ:AV0Z40310501 Keywords : CZE * peak splitting * self-stacking Subject RIV: CB - Analytical Chemistry, Separation Impact factor: 3.077, year: 2009
Takaya, Tomohisa; Su, Charlene; de La Harpe, Kimberly; Crespo-Hernández, Carlos E; Kohler, Bern
2008-07-29
Excited electronic states created by UV excitation of the diribonucleoside monophosphates ApA, ApG, ApC, ApU, and CpG were studied by the femtosecond transient-absorption technique. Bleach recovery signals recorded at 252 nm show that long-lived excited states are formed in all five dinucleosides. The lifetimes of these states exceed those measured in equimolar mixtures of the constituent mononucleotides by one to two orders of magnitude, indicating that electronic coupling between proximal nucleobases dramatically slows the relaxation of excess electronic energy. The decay rates of the long-lived states decrease with increasing energy of the charge-transfer state produced by transferring an electron from one base to another. The charge-transfer character of the long-lived states revealed by this analysis supports their assignment to excimer or exciplex states. Identical bleach recovery signals were seen for ApA, (A)(4), and poly(A) at delay times >10 ps after photoexcitation. This indicates that excited states localized on a stack of just two bases are the common trap states independent of the number of stacked nucleotides. The fraction of initial excitations that decay to long-lived exciplex states is approximately equal to the fraction of stacked bases determined by NMR measurements. This supports a model in which excitations associated with two stacked bases decay to exciplex states, whereas excitations in unstacked bases decay via ultrafast internal conversion. These results establish the importance of charge transfer-quenching pathways for UV-irradiated RNA and DNA in room-temperature solution.
Energy Technology Data Exchange (ETDEWEB)
Iwai, Sadanori, E-mail: sadanori.iwai@toshiba.co.jp; Miyazaki, Hiroshi; Tosaka, Taizo; Tasaki, Kenji; Urata, Masami; Ioka, Shigeru; Ishii, Yusuke
2013-11-15
Highlights: •We made a coil composed of a stack of four single pancakes wound with YBCO wide tapes. •The coil had a central magnetic field as high as 5.9 T at 20 K. •The effect of the tape width on the central magnetic field was small near coil I{sub c}. •We confirmed that performance of YBCO coil was improved by using wider tape. -- Abstract: We have been developing a conduction-cooled coil wound with YBCO-coated conductors for HTS applications. Previously, we have fabricated a coil composed of a stack of 12 single pancakes wound with 4 mm-wide YBCO tapes. This coil had a central magnetic field as high as 5.1 T at 10 K under conduction-cooled conditions. In the present study, we fabricated and tested a coil composed of a stack of four single pancakes wound with 12 mm-wide YBCO tapes. The total size of the coil and the J{sub c} value of the tapes were almost the same as those of the former coil. At 77 K, the voltage–current characteristics showed a high n-value of 24, confirming that the coil had no degradation. Furthermore, in a conduction-cooled configuration at 20 K to 60 K, the coil showed a high n-value of over 20. At 20 K, the central magnetic field reached 5.9 T at 903 A, which is 1.3-times higher than that of the former coil.
Hayashi, Shohei; Yamashita, Tamotsu; Senzaki, Junji; Miyazato, Masaki; Ryo, Mina; Miyajima, Masaaki; Kato, Tomohisa; Yonezawa, Yoshiyuki; Kojima, Kazutoshi; Okumura, Hajime
2018-04-01
The origin of expanded single Shockley-type stacking faults in forward-current degradation of 4H-SiC p-i-n diodes was investigated by the stress-current test. At a stress-current density lower than 25 A cm-2, triangular stacking faults were formed from basal-plane dislocations in the epitaxial layer. At a stress-current density higher than 350 A cm-2, both triangular and long-zone-shaped stacking faults were formed from basal-plane dislocations that converted into threading edge dislocations near the interface between the epitaxial layer and the substrate. In addition, the conversion depth of basal-plane dislocations that expanded into the stacking fault was inside the substrate deeper than the interface. These results indicate that the conversion depth of basal-plane dislocations strongly affects the threshold stress-current density at which the expansion of stacking faults occurs.
Iwai, Sadanori; Miyazaki, Hiroshi; Tosaka, Taizo; Tasaki, Kenji; Urata, Masami; Ioka, Shigeru; Ishii, Yusuke
2013-11-01
We have been developing a conduction-cooled coil wound with YBCO-coated conductors for HTS applications. Previously, we have fabricated a coil composed of a stack of 12 single pancakes wound with 4 mm-wide YBCO tapes. This coil had a central magnetic field as high as 5.1 T at 10 K under conduction-cooled conditions. In the present study, we fabricated and tested a coil composed of a stack of four single pancakes wound with 12 mm-wide YBCO tapes. The total size of the coil and the Jc value of the tapes were almost the same as those of the former coil. At 77 K, the voltage-current characteristics showed a high n-value of 24, confirming that the coil had no degradation. Furthermore, in a conduction-cooled configuration at 20 K to 60 K, the coil showed a high n-value of over 20. At 20 K, the central magnetic field reached 5.9 T at 903 A, which is 1.3-times higher than that of the former coil.
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.
International Nuclear Information System (INIS)
Sandoval, Miguel A.; Fuentes, Rosalba; Walsh, Frank C.; Nava, José L.; Ponce de León, Carlos
2016-01-01
Highlights: • Computational fluid dynamic simulations in a filter-press stack of three cells. • The fluid velocity was different in each cell due to local turbulence. • The upper cell link pipe of the filter press cell acts as a fluid mixer. • The fluid behaviour tends towards a continuous mixing flow pattern. • Close agreement between simulations and experimental data was achieved. - Abstract: Computational fluid dynamics (CFD) simulations were carried out for single-phase flow in a pre-pilot filter press flow reactor with a stack of three cells. Velocity profiles and streamlines were obtained by solving the Reynolds-Averaged Navier-Stokes (RANS) equations with a standard k − ε turbulence model. The flow behaviour shows the appearance of jet flow at the entrance to each cell. At lengths from 12 to 15 cm along the cells channels, a plug flow pattern is developed at all mean linear flow rates studied here, 1.2 ≤ u ≤ 2.1 cm s −1 . The magnitude of the velocity profiles in each cell was different, due to the turbulence generated by the change of flow direction in the last fluid manifold. Residence time distribution (RTD) simulations indicated that the fluid behaviour tends towards a continuous mixing flow pattern, owing to flow at the output of each cell across the upper cell link pipe, which acts as a mixer. Close agreement between simulations and experimental RTD was obtained.
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...
Approximate kernel competitive learning.
Wu, Jian-Sheng; Zheng, Wei-Shi; Lai, Jian-Huang
2015-03-01
Kernel competitive learning has been successfully used to achieve robust clustering. However, kernel competitive learning (KCL) is not scalable for large scale data processing, because (1) it has to calculate and store the full kernel matrix that is too large to be calculated and kept in the memory and (2) it cannot be computed in parallel. In this paper we develop a framework of approximate kernel competitive learning for processing large scale dataset. The proposed framework consists of two parts. First, it derives an approximate kernel competitive learning (AKCL), which learns kernel competitive learning in a subspace via sampling. We provide solid theoretical analysis on why the proposed approximation modelling would work for kernel competitive learning, and furthermore, we show that the computational complexity of AKCL is largely reduced. Second, we propose a pseudo-parallelled approximate kernel competitive learning (PAKCL) based on a set-based kernel competitive learning strategy, which overcomes the obstacle of using parallel programming in kernel competitive learning and significantly accelerates the approximate kernel competitive learning for large scale clustering. The empirical evaluation on publicly available datasets shows that the proposed AKCL and PAKCL can perform comparably as KCL, with a large reduction on computational cost. Also, the proposed methods achieve more effective clustering performance in terms of clustering precision against related approximate clustering approaches. Copyright © 2014 Elsevier Ltd. All rights reserved.
Generalization Performance of Regularized Ranking With Multiscale Kernels.
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.
Optimized Kernel Entropy Components.
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.
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...
Modeling fuel cell stack systems
Energy Technology Data Exchange (ETDEWEB)
Lee, J H [Los Alamos National Lab., Los Alamos, NM (United States); Lalk, T R [Dept. of Mech. Eng., Texas A and M Univ., College Station, TX (United States)
1998-06-15
A technique for modeling fuel cell stacks is presented along with the results from an investigation designed to test the validity of the technique. The technique was specifically designed so that models developed using it can be used to determine the fundamental thermal-physical behavior of a fuel cell stack for any operating and design configuration. Such models would be useful tools for investigating fuel cell power system parameters. The modeling technique can be applied to any type of fuel cell stack for which performance data is available for a laboratory scale single cell. Use of the technique is demonstrated by generating sample results for a model of a Proton Exchange Membrane Fuel Cell (PEMFC) stack consisting of 125 cells each with an active area of 150 cm{sup 2}. A PEMFC stack was also used in the verification investigation. This stack consisted of four cells, each with an active area of 50 cm{sup 2}. Results from the verification investigation indicate that models developed using the technique are capable of accurately predicting fuel cell stack performance. (orig.)
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`.
Classification With Truncated Distance Kernel.
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.
National Aeronautics and Space Administration — This SBIR Phase I project aims to develop a new manufacturing approach for deformable mirrors (DMs) by batch fabricating the stack actuator array. The innovation...
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
Locally linear approximation for Kernel methods : the Railway Kernel
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...
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
On the "stacking fault" in copper
Fransens, J.R.; Pleiter, F
2003-01-01
The results of a perturbed gamma-gamma angular correlations experiment on In-111 implanted into a properly cut single crystal of copper show that the defect known in the literature as "stacking fault" is not a planar faulted loop but a stacking fault tetrahedron with a size of 10-50 Angstrom.
Analog forecasting with dynamics-adapted kernels
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.
Occurrence of 'super soft' wheat kernel texture in hexaploid and tetraploid wheats
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...
An SVM model with hybrid kernels for hydrological time series
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.
Influence of wheat kernel physical properties on the pulverizing process.
Dziki, Dariusz; Cacak-Pietrzak, Grażyna; Miś, Antoni; Jończyk, Krzysztof; Gawlik-Dziki, Urszula
2014-10-01
The physical properties of wheat kernel were determined and related to pulverizing performance by correlation analysis. Nineteen samples of wheat cultivars about similar level of protein content (11.2-12.8 % w.b.) and obtained from organic farming system were used for analysis. The kernel (moisture content 10 % w.b.) was pulverized by using the laboratory hammer mill equipped with round holes 1.0 mm screen. The specific grinding energy ranged from 120 kJkg(-1) to 159 kJkg(-1). On the basis of data obtained many of significant correlations (p 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.
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
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...
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...
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...
Kernel methods for deep learning
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...
Directory of Open Access Journals (Sweden)
Xianglin ZHU
2014-06-01
Full Text Available To resolve the online detection difficulty of some important state variables in fermentation process with traditional instruments, a soft sensing modeling method based on relevance vector machine (RVM with a hybrid kernel function is presented. Based on the characteristic analysis of two commonly-used kernel functions, that is, local Gaussian kernel function and global polynomial kernel function, a hybrid kernel function combing merits of Gaussian kernel function and polynomial kernel function is constructed. To design optimal parameters of this kernel function, the particle swarm optimization (PSO algorithm is applied. The proposed modeling method is used to predict the value of cell concentration in the Lysine fermentation process. Simulation results show that the presented hybrid-kernel RVM model has a better accuracy and performance than the single kernel RVM model.
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...
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...
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.
Directory of Open Access Journals (Sweden)
Hailun Wang
2017-01-01
Full Text Available Support vector regression algorithm is widely used in fault diagnosis of rolling bearing. A new model parameter selection method for support vector regression based on adaptive fusion of the mixed kernel function is proposed in this paper. We choose the mixed kernel function as the kernel function of support vector regression. The mixed kernel function of the fusion coefficients, kernel function parameters, and regression parameters are combined together as the parameters of the state vector. Thus, the model selection problem is transformed into a nonlinear system state estimation problem. We use a 5th-degree cubature Kalman filter to estimate the parameters. In this way, we realize the adaptive selection of mixed kernel function weighted coefficients and the kernel parameters, the regression parameters. Compared with a single kernel function, unscented Kalman filter (UKF support vector regression algorithms, and genetic algorithms, the decision regression function obtained by the proposed method has better generalization ability and higher prediction accuracy.
Reduced multiple empirical kernel learning machine.
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
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...
Variable Kernel Density Estimation
Terrell, George R.; Scott, David W.
1992-01-01
We investigate some of the possibilities for improvement of univariate and multivariate kernel density estimates by varying the window over the domain of estimation, pointwise and globally. Two general approaches are to vary the window width by the point of estimation and by point of the sample observation. The first possibility is shown to be of little efficacy in one variable. In particular, nearest-neighbor estimators in all versions perform poorly in one and two dimensions, but begin to b...
Steerability of Hermite Kernel
Czech Academy of Sciences Publication Activity Database
Yang, Bo; Flusser, Jan; Suk, Tomáš
2013-01-01
Roč. 27, č. 4 (2013), 1354006-1-1354006-25 ISSN 0218-0014 R&D Projects: GA ČR GAP103/11/1552 Institutional support: RVO:67985556 Keywords : Hermite polynomials * Hermite kernel * steerability * adaptive filtering Subject RIV: JD - Computer Applications, Robotics Impact factor: 0.558, year: 2013 http://library.utia.cas.cz/separaty/2013/ZOI/yang-0394387. pdf
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.
Radez, Dan
2015-01-01
If you need to get started with OpenStack or want to learn more, then this book is your perfect companion. If you're comfortable with the Linux command line, you'll gain confidence in using OpenStack.
Reeves, Adam A.
1977-04-12
Hot stack gases transfer contained heat to a gravity flow of pebbles treated with a catalyst, cooled stacked gases and a sulfuric acid mist is withdrawn from the unit, and heat picked up by the pebbles is transferred to air for combustion or other process. The sulfuric acid (or sulfur, depending on the catalyst) is withdrawn in a recovery unit.
Khedher, Omar
2015-01-01
This book is intended for system administrators, cloud engineers, and system architects who want to deploy a cloud based on OpenStack in a mid- to large-sized IT infrastructure. If you have a fundamental understanding of cloud computing and OpenStack and want to expand your knowledge, then this book is an excellent checkpoint to move forward.
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 ...
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] ...
7 CFR 981.408 - Inedible kernel.
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...
7 CFR 981.8 - Inedible kernel.
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...
DEFF Research Database (Denmark)
Barndorff-Nielsen, Ole Eiler; Hansen, Peter Reinhard; Lunde, Asger
2011-01-01
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 error of certain types and can also handle non-synchronous trading. It is the first estimator...... which has these three properties which are all essential for empirical work in this area. We derive the large sample asymptotics of this estimator and assess its accuracy using a Monte Carlo study. We implement the estimator on some US equity data, comparing our results to previous work which has used...
Clustering via Kernel Decomposition
DEFF Research Database (Denmark)
Have, Anna Szynkowiak; Girolami, Mark A.; Larsen, Jan
2006-01-01
Methods for spectral clustering have been proposed recently which rely on the eigenvalue decomposition of an affinity matrix. In this work it is proposed that the affinity matrix is created based on the elements of a non-parametric density estimator. This matrix is then decomposed to obtain...... posterior probabilities of class membership using an appropriate form of nonnegative matrix factorization. The troublesome selection of hyperparameters such as kernel width and number of clusters can be obtained using standard cross-validation methods as is demonstrated on a number of diverse data sets....
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.
Stacking the Equiangular Spiral
Agrawal, A.; Azabi, Y. O.; Rahman, B. M.
2013-01-01
We present an algorithm that adapts the mature Stack and Draw (SaD) methodology for fabricating the exotic Equiangular Spiral Photonic Crystal Fiber. (ES-PCF) The principle of Steiner chains and circle packing is exploited to obtain a non-hexagonal design using a stacking procedure based on Hexagonal Close Packing. The optical properties of the proposed structure are promising for SuperContinuum Generation. This approach could make accessible not only the equiangular spiral but also other qua...
Directory of Open Access Journals (Sweden)
A. Herz
2016-03-01
Full Text Available Self-assembly of ultrathin Au, W, and Au-W bilayer thin films is investigated using a rapid thermal annealing technique in an inert ambient. The solid-state dewetting of Au films is briefly revisited in order to emphasize the role of initial film thickness. W films deposited onto SiO2 evolve into needle-like nanocrystals rather than forming particle-like agglomerates upon annealing at elevated temperatures. Transmission electron microscopy reveals that such nanocrystals actually consist of tungsten (VI oxide (WO3 which is related to an anisotropic oxide crystal growth out of the thin film. The evolution of W films is highly sensitive to the presence of any residual oxygen. Combination of both the dewetting of Au and the oxide crystal growth of WO3 is realized by using various bilayer film configurations of the immiscible Au and W. At low temperature, Au dewetting is initiated while oxide crystal growth is still suppressed. Depending on the stacking sequence of the Au-W bilayer thin film, W acts either as a substrate or as a passivation layer for the dewetting of Au. Being the ground layer, W changes the wettability of Au which clearly modifies its initial state for the dewetting. Being the top layer, W prevents Au from dewetting regardless of Au film thickness. Moreover, regular pattern formation of Au-WO3 nanoparticles is observed at high temperature demonstrating how bilayer thin film dewetting can create unique nanostructure arrangements.
Energy Technology Data Exchange (ETDEWEB)
Herz, A., E-mail: andreas.herz@tu-ilmenau.de, E-mail: dong.wang@tu-ilmenau.de; Franz, A.; Theska, F.; Hentschel, M.; Kups, Th.; Wang, D., E-mail: andreas.herz@tu-ilmenau.de, E-mail: dong.wang@tu-ilmenau.de; Schaaf, P. [Department of Materials for Electronics and Electrical Engineering, Institute of Materials Science and Engineering and Institute of Micro- and Nanotechnologies MacroNano, TU Ilmenau, D-98693 Ilmenau (Germany)
2016-03-15
Self-assembly of ultrathin Au, W, and Au-W bilayer thin films is investigated using a rapid thermal annealing technique in an inert ambient. The solid-state dewetting of Au films is briefly revisited in order to emphasize the role of initial film thickness. W films deposited onto SiO{sub 2} evolve into needle-like nanocrystals rather than forming particle-like agglomerates upon annealing at elevated temperatures. Transmission electron microscopy reveals that such nanocrystals actually consist of tungsten (VI) oxide (WO{sub 3}) which is related to an anisotropic oxide crystal growth out of the thin film. The evolution of W films is highly sensitive to the presence of any residual oxygen. Combination of both the dewetting of Au and the oxide crystal growth of WO{sub 3} is realized by using various bilayer film configurations of the immiscible Au and W. At low temperature, Au dewetting is initiated while oxide crystal growth is still suppressed. Depending on the stacking sequence of the Au-W bilayer thin film, W acts either as a substrate or as a passivation layer for the dewetting of Au. Being the ground layer, W changes the wettability of Au which clearly modifies its initial state for the dewetting. Being the top layer, W prevents Au from dewetting regardless of Au film thickness. Moreover, regular pattern formation of Au-WO{sub 3} nanoparticles is observed at high temperature demonstrating how bilayer thin film dewetting can create unique nanostructure arrangements.
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...
Bruemmer, David J [Idaho Falls, ID
2009-11-17
A robot platform includes perceptors, locomotors, and a system controller. The system controller executes a robot intelligence kernel (RIK) that includes a multi-level architecture and a dynamic autonomy structure. The multi-level architecture includes a robot behavior level for defining robot behaviors, that incorporate robot attributes and a cognitive level for defining conduct modules that blend an adaptive interaction between predefined decision functions and the robot behaviors. The dynamic autonomy structure is configured for modifying a transaction capacity between an operator intervention and a robot initiative and may include multiple levels with at least a teleoperation mode configured to maximize the operator intervention and minimize the robot initiative and an autonomous mode configured to minimize the operator intervention and maximize the robot initiative. Within the RIK at least the cognitive level includes the dynamic autonomy structure.
International Nuclear Information System (INIS)
Werner, S; Rehbein, S; Guttman, P; Heim, S; Schneider, G
2009-01-01
Fresnel zone plates are the key optical elements for soft and hard x-ray microscopy. For short exposure times and minimum radiation load of the specimen the diffraction efficiency of the zone plate objectives has to be maximized. As the efficiency strongly depends on the height of the diffracting zone structures the achievable aspect ratio of the nanostructures determines these limits. To reach aspect ratios ≥ 20:1 for high efficient optics we propose to superimpose zone plates on top of each other. With this multiplication approach the final aspect ratio is only limited by the number of stacked zone plate layers. For the stack process several nanostructuring process steps have to be developed and/or improved. Our results show for the first time two layers of zone plates stacked on top of each other.
Stochastic stacking without filters
International Nuclear Information System (INIS)
Johnson, R.P.; Marriner, J.
1982-12-01
The rate of accumulation of antiprotons is a critical factor in the design of p anti p colliders. A design of a system to accumulate higher anti p fluxes is presented here which is an alternative to the schemes used at the CERN AA and in the Fermilab Tevatron I design. Contrary to these stacking schemes, which use a system of notch filters to protect the dense core of antiprotons from the high power of the stack tail stochastic cooling, an eddy current shutter is used to protect the core in the region of the stack tail cooling kicker. Without filters one can have larger cooling bandwidths, better mixing for stochastic cooling, and easier operational criteria for the power amplifiers. In the case considered here a flux of 1.4 x 10 8 per sec is achieved with a 4 to 8 GHz bandwidth
Energy Technology Data Exchange (ETDEWEB)
Porter, Reid B [Los Alamos National Laboratory; Hush, Don [Los Alamos National Laboratory
2009-01-01
Just as linear models generalize the sample mean and weighted average, weighted order statistic models generalize the sample median and weighted median. This analogy can be continued informally to generalized additive modeels in the case of the mean, and Stack Filters in the case of the median. Both of these model classes have been extensively studied for signal and image processing but it is surprising to find that for pattern classification, their treatment has been significantly one sided. Generalized additive models are now a major tool in pattern classification and many different learning algorithms have been developed to fit model parameters to finite data. However Stack Filters remain largely confined to signal and image processing and learning algorithms for classification are yet to be seen. This paper is a step towards Stack Filter Classifiers and it shows that the approach is interesting from both a theoretical and a practical perspective.
Mixture Density Mercer Kernels: A Method to Learn Kernels
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...
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)
Moses, E.I.
1992-12-01
A laser pulse stacking method is disclosed. A problem with the prior art has been the generation of a series of laser beam pulses where the outer and inner regions of the beams are generated so as to form radially non-synchronous pulses. Such pulses thus have a non-uniform cross-sectional area with respect to the outer and inner edges of the pulses. The present invention provides a solution by combining the temporally non-uniform pulses in a stacking effect to thus provide a more uniform temporal synchronism over the beam diameter. 2 figs.
Unsupervised multiple kernel learning for heterogeneous data integration.
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.
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...
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. ...
Proteome analysis of the almond kernel (Prunus dulcis).
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.
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...
kernel oil by lipolytic organisms
African Journals Online (AJOL)
USER
2010-08-02
Aug 2, 2010 ... Rancidity of extracted cashew oil was observed with cashew kernel stored at 70, 80 and 90% .... method of American Oil Chemist Society AOCS (1978) using glacial ..... changes occur and volatile products are formed that are.
Multineuron spike train analysis with R-convolution linear combination kernel.
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.
Multivariate and semiparametric kernel regression
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...
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....
Myers, Colton
2015-01-01
If you are a system administrator who manages multiple servers, then you know how difficult it is to keep your infrastructure in line. If you've been searching for an easier way, this book is for you. No prior experience with SaltStack is required.
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...
A dual shared stack for FSLM in Erika Enterprise
Balasubramanian, S.M.N.; Afshar, S.; Gai, P.; Behnam, M.; Bril, R.J.
2017-01-01
Recently, the flexible spin-lock model (FSLM) has been introduced, unifying spin-based and suspension-based resource sharing protocols for real-time multi-core platforms. Unlike the multiprocessor stack resource policy (MSRP), FSLM doesn’t allow tasks on a core to share a single stack, however. In
Ablation of film stacks in solar cell fabrication processes
Harley, Gabriel; Kim, Taeseok; Cousins, Peter John
2013-04-02
A dielectric film stack of a solar cell is ablated using a laser. The dielectric film stack includes a layer that is absorptive in a wavelength of operation of the laser source. The laser source, which fires laser pulses at a pulse repetition rate, is configured to ablate the film stack to expose an underlying layer of material. The laser source may be configured to fire a burst of two laser pulses or a single temporally asymmetric laser pulse within a single pulse repetition to achieve complete ablation in a single step.
Option Valuation with Volatility Components, Fat Tails, and Non-Monotonic Pricing Kernels
DEFF Research Database (Denmark)
Babaoglu, Kadir; Christoffersen, Peter; Heston, Steven L.
We nest multiple volatility components, fat tails and a U-shaped pricing kernel in a single option model and compare their contribution to describing returns and option data. All three features lead to statistically significant model improvements. A U-shaped pricing kernel is economically most im...
Influence Function and Robust Variant of Kernel Canonical Correlation Analysis
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 ...
High power, repetitive stacked Blumlein pulse generators
Energy Technology Data Exchange (ETDEWEB)
Davanloo, F; Borovina, D L; Korioth, J L; Krause, R K; Collins, C B [Univ. of Texas at Dallas, Richardson, TX (United States). Center for Quantum Electronics; Agee, F J [US Air Force Phillips Lab., Kirtland AFB, NM (United States); Kingsley, L E [US Army CECOM, Ft. Monmouth, NJ (United States)
1997-12-31
The repetitive stacked Blumlein pulse power generators developed at the University of Texas at Dallas consist of several triaxial Blumleins stacked in series at one end. The lines are charged in parallel and synchronously commuted with a single switch at the other end. In this way, relatively low charging voltages are multiplied to give a high discharge voltage across an arbitrary load. Extensive characterization of these novel pulsers have been performed over the past few years. Results indicate that they are capable of producing high power waveforms with rise times and repetition rates in the range of 0.5-50 ns and 1-300 Hz, respectively, using a conventional thyratron, spark gap, or photoconductive switch. The progress in the development and use of stacked Blumlein pulse generators is reviewed. The technology and the characteristics of these novel pulsers driving flash x-ray diodes are discussed. (author). 4 figs., 5 refs.
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...
An Approximate Approach to Automatic Kernel Selection.
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.
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...
Locati, Fabio Alessandro
2015-01-01
If you are an OpenStack administrator or developer, or wish to build solutions to protect your OpenStack environment, then this book is for you. Experience of Linux administration and familiarity with different OpenStack components is assumed.
Stacked magnet superconducting bearing
International Nuclear Information System (INIS)
Rigney, T.K. II; Saville, M.P.
1993-01-01
A superconducting bearing is described, comprising: a plurality of permanent magnets magnetized end-to-end and stacked side-by-side in alternating polarity, such that flux lines flow between ends of adjacent magnets; isolating means, disposed between said adjacent magnets, for reducing flux leakage between opposing sides of said adjacent magnets; and a member made of superconducting material having at least one surface in communication with said flux lines
Iridium Interfacial Stack (IRIS)
Spry, David James (Inventor)
2015-01-01
An iridium interfacial stack ("IrIS") and a method for producing the same are provided. The IrIS may include ordered layers of TaSi.sub.2, platinum, iridium, and platinum, and may be placed on top of a titanium layer and a silicon carbide layer. The IrIS may prevent, reduce, or mitigate against diffusion of elements such as oxygen, platinum, and gold through at least some of its layers.
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 learning algorithms for face recognition
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
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...
Optimizing memory-bound SYMV kernel on GPU hardware accelerators
Abdelfattah, Ahmad; Dongarra, Jack; Keyes, David E.; Ltaief, Hatem
2013-01-01
and increasing bandwidth, our preliminary asymptotic results show 3.5x and 2.5x fold speedups over the similar CUBLAS 4.0 kernel, and 7-8% and 30% fold improvement over the Matrix Algebra on GPU and Multicore Architectures (MAGMA) library in single and double
Batched Triangular Dense Linear Algebra Kernels for Very Small Matrix Sizes on GPUs
Charara, Ali; Keyes, David E.; Ltaief, Hatem
2017-01-01
Batched dense linear algebra kernels are becoming ubiquitous in scientific applications, ranging from tensor contractions in deep learning to data compression in hierarchical low-rank matrix approximation. Within a single API call, these kernels are capable of simultaneously launching up to thousands of similar matrix computations, removing the expensive overhead of multiple API calls while increasing the occupancy of the underlying hardware. A challenge is that for the existing hardware landscape (x86, GPUs, etc.), only a subset of the required batched operations is implemented by the vendors, with limited support for very small problem sizes. We describe the design and performance of a new class of batched triangular dense linear algebra kernels on very small data sizes using single and multiple GPUs. By deploying two-sided recursive formulations, stressing the register usage, maintaining data locality, reducing threads synchronization and fusing successive kernel calls, the new batched kernels outperform existing state-of-the-art implementations.
Batched Triangular Dense Linear Algebra Kernels for Very Small Matrix Sizes on GPUs
Charara, Ali
2017-03-06
Batched dense linear algebra kernels are becoming ubiquitous in scientific applications, ranging from tensor contractions in deep learning to data compression in hierarchical low-rank matrix approximation. Within a single API call, these kernels are capable of simultaneously launching up to thousands of similar matrix computations, removing the expensive overhead of multiple API calls while increasing the occupancy of the underlying hardware. A challenge is that for the existing hardware landscape (x86, GPUs, etc.), only a subset of the required batched operations is implemented by the vendors, with limited support for very small problem sizes. We describe the design and performance of a new class of batched triangular dense linear algebra kernels on very small data sizes using single and multiple GPUs. By deploying two-sided recursive formulations, stressing the register usage, maintaining data locality, reducing threads synchronization and fusing successive kernel calls, the new batched kernels outperform existing state-of-the-art implementations.
RTOS kernel in portable electrocardiograph
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.
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....
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...
Le Thanh, Nghi
2017-01-01
The aim of the thesis is to provide a universal website using JavaScript as the main programming language. It also shows the basic parts anyone need to create a web application. The thesis creates a simple CMS using MEAN stack. MEAN is a collection of JavaScript based technologies used to develop web application. It is an acronym for MongoDB, Express, AngularJS and Node.js. It also allows non-technical users to easily update and manage a website’s content. But the application also lets o...
Xie, Yuan
2015-01-01
The emerging three-dimensional (3D) chip architectures, with their intrinsic capability of reducing the wire length, promise attractive solutions to reduce the delay of interconnects in future microprocessors. 3D memory stacking enables much higher memory bandwidth for future chip-multiprocessor design, mitigating the ""memory wall"" problem. In addition, heterogenous integration enabled by 3D technology can also result in innovative designs for future microprocessors. This book first provides a brief introduction to this emerging technology, and then presents a variety of approaches to design
Multiple Kernel Learning with Data Augmentation
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
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....
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
Complex use of cottonseed kernels
Energy Technology Data Exchange (ETDEWEB)
Glushenkova, A I
1977-01-01
A review with 41 references is made on the manufacture of oil, protein, and other products from cottonseed, the effects of gossypol on protein yield and quality and technology of gossypol removal. A process eliminating thermal treatment of the kernels and permitting the production of oil, proteins, phytin, gossypol, sugar, sterols, phosphatides, tocopherols, and residual shells and baggase is described.
Kernel regression with functional response
Ferraty, Frédéric; Laksaci, Ali; Tadj, Amel; Vieu, Philippe
2011-01-01
We consider kernel regression estimate when both the response variable and the explanatory one are functional. The rates of uniform almost complete convergence are stated as function of the small ball probability of the predictor and as function of the entropy of the set on which uniformity is obtained.
GRIM : Leveraging GPUs for Kernel integrity monitoring
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
Paramecium: An Extensible Object-Based Kernel
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
Local Observed-Score Kernel Equating
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…
Veto-Consensus Multiple Kernel Learning
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
Asymmetric Flexible Supercapacitor Stack
Directory of Open Access Journals (Sweden)
Leela Mohana Reddy A
2008-01-01
Full Text Available AbstractElectrical double layer supercapacitor is very significant in the field of electrical energy storage which can be the solution for the current revolution in the electronic devices like mobile phones, camera flashes which needs flexible and miniaturized energy storage device with all non-aqueous components. The multiwalled carbon nanotubes (MWNTs have been synthesized by catalytic chemical vapor deposition technique over hydrogen decrepitated Mischmetal (Mm based AB3alloy hydride. The polymer dispersed MWNTs have been obtained by insitu polymerization and the metal oxide/MWNTs were synthesized by sol-gel method. Morphological characterizations of polymer dispersed MWNTs have been carried out using scanning electron microscopy (SEM, transmission electron microscopy (TEM and HRTEM. An assymetric double supercapacitor stack has been fabricated using polymer/MWNTs and metal oxide/MWNTs coated over flexible carbon fabric as electrodes and nafion®membrane as a solid electrolyte. Electrochemical performance of the supercapacitor stack has been investigated using cyclic voltammetry, galvanostatic charge-discharge, and electrochemical impedance spectroscopy.
Radiation-Tolerant Intelligent Memory Stack - RTIMS
Ng, Tak-kwong; Herath, Jeffrey A.
2011-01-01
This innovation provides reconfigurable circuitry and 2-Gb of error-corrected or 1-Gb of triple-redundant digital memory in a small package. RTIMS uses circuit stacking of heterogeneous components and radiation shielding technologies. A reprogrammable field-programmable gate array (FPGA), six synchronous dynamic random access memories, linear regulator, and the radiation mitigation circuits are stacked into a module of 42.7 42.7 13 mm. Triple module redundancy, current limiting, configuration scrubbing, and single- event function interrupt detection are employed to mitigate radiation effects. The novel self-scrubbing and single event functional interrupt (SEFI) detection allows a relatively soft FPGA to become radiation tolerant without external scrubbing and monitoring hardware
High-Density Stacked Ru Nanocrystals for Nonvolatile Memory Application
International Nuclear Information System (INIS)
Ping, Mao; Zhi-Gang, Zhang; Li-Yang, Pan; Jun, Xu; Pei-Yi, Chen
2009-01-01
Stacked ruthenium (Ru) nanocrystals (NCs) are formed by rapid thermal annealing for the whole gate stacks and embedded in memory structure, which is compatible with conventional CMOS technology. Ru NCs with high density (3 × 10 12 cm −2 ), small size (2–4 nm) and good uniformity both in aerial distribution and morphology are formed. Attributed to the higher surface trap density, a memory window of 5.2 V is obtained with stacked Ru NCs in comparison to that of 3.5 V with single-layer samples. The stacked Ru NCs device also exhibits much better retention performance because of Coulomb blockade and vertical uniformity between stacked Ru NCs
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.
Consistent Valuation across Curves Using Pricing Kernels
Directory of Open Access Journals (Sweden)
Andrea Macrina
2018-03-01
Full Text Available The general problem of asset pricing when the discount rate differs from the rate at which an asset’s cash flows accrue is considered. A pricing kernel framework is used to model an economy that is segmented into distinct markets, each identified by a yield curve having its own market, credit and liquidity risk characteristics. The proposed framework precludes arbitrage within each market, while the definition of a curve-conversion factor process links all markets in a consistent arbitrage-free manner. A pricing formula is then derived, referred to as the across-curve pricing formula, which enables consistent valuation and hedging of financial instruments across curves (and markets. As a natural application, a consistent multi-curve framework is formulated for emerging and developed inter-bank swap markets, which highlights an important dual feature of the curve-conversion factor process. Given this multi-curve framework, existing multi-curve approaches based on HJM and rational pricing kernel models are recovered, reviewed and generalised and single-curve models extended. In another application, inflation-linked, currency-based and fixed-income hybrid securities are shown to be consistently valued using the across-curve valuation method.
Pareto-path multitask multiple kernel learning.
Li, Cong; Georgiopoulos, Michael; Anagnostopoulos, Georgios C
2015-01-01
A traditional and intuitively appealing Multitask Multiple Kernel Learning (MT-MKL) method is to optimize the sum (thus, the average) of objective functions with (partially) shared kernel function, which allows information sharing among the tasks. We point out that the obtained solution corresponds to a single point on the Pareto Front (PF) of a multiobjective optimization problem, which considers the concurrent optimization of all task objectives involved in the Multitask Learning (MTL) problem. Motivated by this last observation and arguing that the former approach is heuristic, we propose a novel support vector machine MT-MKL framework that considers an implicitly defined set of conic combinations of task objectives. We show that solving our framework produces solutions along a path on the aforementioned PF and that it subsumes the optimization of the average of objective functions as a special case. Using the algorithms we derived, we demonstrate through a series of experimental results that the framework is capable of achieving a better classification performance, when compared with other similar MTL approaches.
2013-01-01
Background Arguably, genotypes and phenotypes may be linked in functional forms that are not well addressed by the linear additive models that are standard in quantitative genetics. Therefore, developing statistical learning models for predicting phenotypic values from all available molecular information that are capable of capturing complex genetic network architectures is of great importance. Bayesian kernel ridge regression is a non-parametric prediction model proposed for this purpose. Its essence is to create a spatial distance-based relationship matrix called a kernel. Although the set of all single nucleotide polymorphism genotype configurations on which a model is built is finite, past research has mainly used a Gaussian kernel. Results We sought to investigate the performance of a diffusion kernel, which was specifically developed to model discrete marker inputs, using Holstein cattle and wheat data. This kernel can be viewed as a discretization of the Gaussian kernel. The predictive ability of the diffusion kernel was similar to that of non-spatial distance-based additive genomic relationship kernels in the Holstein data, but outperformed the latter in the wheat data. However, the difference in performance between the diffusion and Gaussian kernels was negligible. Conclusions It is concluded that the ability of a diffusion kernel to capture the total genetic variance is not better than that of a Gaussian kernel, at least for these data. Although the diffusion kernel as a choice of basis function may have potential for use in whole-genome prediction, our results imply that embedding genetic markers into a non-Euclidean metric space has very small impact on prediction. Our results suggest that use of the black box Gaussian kernel is justified, given its connection to the diffusion kernel and its similar predictive performance. PMID:23763755
International Nuclear Information System (INIS)
Zhu, X.
1986-01-01
We present a systematic study of composite population kernels for 174 Yb collisions with He, Ar, and Xe buffer gases, using laser-saturation spectroscopy. 174 Yb is chosen as the active species because of the simple structure of its 1 S 0 - 3 P 1 resonance transition (lambda = 556 nm). Elastic collisions are modeled by means of a composite collision kernel, an expression of which is explicitly derived based on arguments of a hard-sphere potential and two-category collisions. The corresponding coupled population-rate equations are solved by iteration to obtain an expression for the saturated-absorption line shape. This expression is fit to the data to obtain information about the composite kernel, along with reasonable values for other parameters. The results confirm that a composite kernel is more general and realistic than a single-component kernel, and the generality in principle and the practical necessity of the former are discussed
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.
Wigner functions defined with Laplace transform kernels.
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
Judge, Gary
2013-01-01
Get to grips with a new technology, understand what it is and what it can do for you, and then get to work with the most important features and tasks. A fast-paced, example-based approach guide for learning BlueStacks.This book is for anyone with a Mac or PC who wants to run Android apps on their computer. Whether you want to play games that are freely available for Android but not your computer, or you want to try apps before you install them on a physical device or use it as a development tool, this book will show you how. No previous experience is needed as this is written in plain English
Credit scoring analysis using kernel discriminant
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.
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....
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....
Assessing Elementary Algebra with STACK
Sangwin, Christopher J.
2007-01-01
This paper concerns computer aided assessment (CAA) of mathematics in which a computer algebra system (CAS) is used to help assess students' responses to elementary algebra questions. Using a methodology of documentary analysis, we examine what is taught in elementary algebra. The STACK CAA system, http://www.stack.bham.ac.uk/, which uses the CAS…
Spherical Torus Center Stack Design
International Nuclear Information System (INIS)
C. Neumeyer; P. Heitzenroeder; C. Kessel; M. Ono; M. Peng; J. Schmidt; R. Woolley; I. Zatz
2002-01-01
The low aspect ratio spherical torus (ST) configuration requires that the center stack design be optimized within a limited available space, using materials within their established allowables. This paper presents center stack design methods developed by the National Spherical Torus Experiment (NSTX) Project Team during the initial design of NSTX, and more recently for studies of a possible next-step ST (NSST) device
Validation of Born Traveltime Kernels
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.
RKRD: Runtime Kernel Rootkit Detection
Grover, Satyajit; Khosravi, Hormuzd; Kolar, Divya; Moffat, Samuel; Kounavis, Michael E.
In this paper we address the problem of protecting computer systems against stealth malware. The problem is important because the number of known types of stealth malware increases exponentially. Existing approaches have some advantages for ensuring system integrity but sophisticated techniques utilized by stealthy malware can thwart them. We propose Runtime Kernel Rootkit Detection (RKRD), a hardware-based, event-driven, secure and inclusionary approach to kernel integrity that addresses some of the limitations of the state of the art. Our solution is based on the principles of using virtualization hardware for isolation, verifying signatures coming from trusted code as opposed to malware for scalability and performing system checks driven by events. Our RKRD implementation is guided by our goals of strong isolation, no modifications to target guest OS kernels, easy deployment, minimal infra-structure impact, and minimal performance overhead. We developed a system prototype and conducted a number of experiments which show that the per-formance impact of our solution is negligible.
Kernel Bayesian ART and ARTMAP.
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.
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.
Electrochemical Detection in Stacked Paper Networks.
Liu, Xiyuan; Lillehoj, Peter B
2015-08-01
Paper-based electrochemical biosensors are a promising technology that enables rapid, quantitative measurements on an inexpensive platform. However, the control of liquids in paper networks is generally limited to a single sample delivery step. Here, we propose a simple method to automate the loading and delivery of liquid samples to sensing electrodes on paper networks by stacking multiple layers of paper. Using these stacked paper devices (SPDs), we demonstrate a unique strategy to fully immerse planar electrodes by aqueous liquids via capillary flow. Amperometric measurements of xanthine oxidase revealed that electrochemical sensors on four-layer SPDs generated detection signals up to 75% higher compared with those on single-layer paper devices. Furthermore, measurements could be performed with minimal user involvement and completed within 30 min. Due to its simplicity, enhanced automation, and capability for quantitative measurements, stacked paper electrochemical biosensors can be useful tools for point-of-care testing in resource-limited settings. © 2015 Society for Laboratory Automation and Screening.
A framework for dense triangular matrix kernels on various manycore architectures
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
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.
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...
Full Piezoelectric Multilayer-Stacked Hybrid Actuation/Transduction Systems
Su, Ji; Jiang, Xiaoning; Zu, Tian-Bing
2011-01-01
The Stacked HYBATS (Hybrid Actuation/Transduction system) demonstrates significantly enhanced electromechanical performance by using the cooperative contributions of the electromechanical responses of multilayer, stacked negative strain components and positive strain components. Both experimental and theoretical studies indicate that, for Stacked HYBATS, the displacement is over three times that of a same-sized conventional flextensional actuator/transducer. The coupled resonance mode between positive strain and negative strain components of Stacked HYBATS is much stronger than the resonance of a single element actuation only when the effective lengths of the two kinds of elements match each other. Compared with the previously invented hybrid actuation system (HYBAS), the multilayer Stacked HYBATS can be designed to provide high mechanical load capability, low voltage driving, and a highly effective piezoelectric constant. The negative strain component will contract, and the positive strain component will expand in the length directions when an electric field is applied on the device. The interaction between the two elements makes an enhanced motion along the Z direction for Stacked-HYBATS. In order to dominate the dynamic length of Stacked-HYBATS by the negative strain component, the area of the cross-section for the negative strain component will be much larger than the total cross-section areas of the two positive strain components. The transverse strain is negative and longitudinal strain positive in inorganic materials, such as ceramics/single crystals. Different piezoelectric multilayer stack configurations can make a piezoelectric ceramic/single-crystal multilayer stack exhibit negative strain or positive strain at a certain direction without increasing the applied voltage. The difference of this innovation from the HYBAS is that all the elements can be made from one-of-a-kind materials. Stacked HYBATS can provide an extremely effective piezoelectric
Optimizing Multiple Kernel Learning for the Classification of UAV Data
Directory of Open Access Journals (Sweden)
Caroline M. Gevaert
2016-12-01
Full Text Available Unmanned Aerial Vehicles (UAVs are capable of providing high-quality orthoimagery and 3D information in the form of point clouds at a relatively low cost. Their increasing popularity stresses the necessity of understanding which algorithms are especially suited for processing the data obtained from UAVs. The features that are extracted from the point cloud and imagery have different statistical characteristics and can be considered as heterogeneous, which motivates the use of Multiple Kernel Learning (MKL for classification problems. In this paper, we illustrate the utility of applying MKL for the classification of heterogeneous features obtained from UAV data through a case study of an informal settlement in Kigali, Rwanda. Results indicate that MKL can achieve a classification accuracy of 90.6%, a 5.2% increase over a standard single-kernel Support Vector Machine (SVM. A comparison of seven MKL methods indicates that linearly-weighted kernel combinations based on simple heuristics are competitive with respect to computationally-complex, non-linear kernel combination methods. We further underline the importance of utilizing appropriate feature grouping strategies for MKL, which has not been directly addressed in the literature, and we propose a novel, automated feature grouping method that achieves a high classification accuracy for various MKL methods.
Characterisation and final disposal behaviour of theoria-based fuel kernels in aqueous phases
International Nuclear Information System (INIS)
Titov, M.
2005-08-01
Two high-temperature reactors (AVR and THTR) operated in Germany have produced about 1 million spent fuel elements. The nuclear fuel in these reactors consists mainly of thorium-uranium mixed oxides, but also pure uranium dioxide and carbide fuels were tested. One of the possible solutions of utilising spent HTR fuel is the direct disposal in deep geological formations. Under such circumstances, the properties of fuel kernels, and especially their leaching behaviour in aqueous phases, have to be investigated for safety assessments of the final repository. In the present work, unirradiated ThO 2 , (Th 0.906 ,U 0.094 )O 2 , (Th 0.834 ,U 0.166 )O 2 and UO 2 fuel kernels were investigated. The composition, crystal structure and surface of the kernels were investigated by traditional methods. Furthermore, a new method was developed for testing the mechanical properties of ceramic kernels. The method was successfully used for the examination of mechanical properties of oxide kernels and for monitoring their evolution during contact with aqueous phases. The leaching behaviour of thoria-based oxide kernels and powders was investigated in repository-relevant salt solutions, as well as in artificial leachates. The influence of different experimental parameters on the kernel leaching stability was investigated. It was shown that thoria-based fuel kernels possess high chemical stability and are indifferent to presence of oxidative and radiolytic species in solution. The dissolution rate of thoria-based materials is typically several orders of magnitude lower than of conventional UO 2 fuel kernels. The life time of a single intact (Th,U)O 2 kernel under aggressive conditions of salt repository was estimated as about hundred thousand years. The importance of grain boundary quality on the leaching stability was demonstrated. Numerical Monte Carlo simulations were performed in order to explain the results of leaching experiments. (orig.)
Environmental assessment of phosphogypsum stacks
International Nuclear Information System (INIS)
Odat, M.; Al-Attar, L.; Raja, G.; Abdul Ghany, B.
2009-01-01
Phosphogypsum is one of the most important by-products of phosphate fertilizer industry. It is kept in large stacks to the west of Homs city. Storing Phosphogypsum as open stacks exposed to various environmental effects, wind and rain, may cause pollution of the surrounding ecosystem (soil, plant, water and air). This study was carried out in order to assess the environmental impact of Phosphogypsum stacks on the surrounding ecosystem. The obtained results show that Phosphogypsum stacks did not increase the concentration of radionuclides, i.e. Radon-222 and Radium-226, the external exposed dose of gamma rays, as well as the concentration of heavy metals in the components of the ecosystem, soil, plant, water and air, as their concentrations did not exceed the permissible limits. However, the concentration of fluorine in the upper layer of soil, located to the east of the Phosphogypsum stacks, increased sufficiently, especially in the dry period of the year. Also, the concentration of fluoride in plants growing up near-by the Phosphogypsum stacks was too high, exceeded the permissible levels. This was reflected in poising plants and animals, feeding on the plants. Consequently, increasing the concentration of fluoride in soil and plants is the main impact of Phosphogypsum stacks on the surrounding ecosystem. Minimising this effect could be achieved by establishing a 50 meter wide protection zone surrounding the Phosphogypsum stacks, which has to be planted with non palatable trees, such as pine and cypress, forming wind barriers. Increasing the concentrations of heavy metals and fluoride in infiltrated water around the stacks was high; hence cautions must be taken to prevent its usage in any application or disposal in adjacent rivers and leaks.(author)
Environmental assessment of phosphogypsum stacks
International Nuclear Information System (INIS)
Odat, M.; Al-Attar, L.; Raja, G.; Abdul Ghany, B.
2008-03-01
Phosphogypsum is one of the most important by-products of phosphate fertilizer industry. It is kept in large stacks to the west of Homs city. Storing Phosphogypsum as open stacks exposed to various environmental effects, wind and rain, may cause pollution of the surrounding ecosystem (soil, plant, water and air). This study was carried out in order to assess the environmental impact of Phosphogypsum stacks on the surrounding ecosystem. The obtained results show that Phosphogypsum stacks did not increase the concentration of radionuclides, i.e. Radon-222 and Radium-226, the external exposed dose of gamma rays, as well as the concentration of heavy metals in the components of the ecosystem, soil, plant, water and air, as their concentrations did not exceed the permissible limits. However, the concentration of fluorine in the upper layer of soil, located to the east of the Phosphogypsum stacks, increased sufficiently, especially in the dry period of the year. Also, the concentration of fluoride in plants growing up near-by the Phosphogypsum stacks was too high, exceeded the permissible levels. This was reflected in poising plants and animals, feeding on the plants. Consequently, increasing the concentration of fluoride in soil and plants is the main impact of Phosphogypsum stacks on the surrounding ecosystem. Minimising this effect could be achieved by establishing a 50 meter wide protection zone surrounding the Phosphogypsum stacks, which has to be planted with non palatable trees, such as pine and cypress, forming wind barriers. Increasing the concentrations of heavy metals and fluoride in infiltrated water around the stacks was high; hence cautions must be taken to prevent its usage in any application or disposal in adjacent rivers and leaks.(author)
Theory of reproducing kernels and applications
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...
Convergence of barycentric coordinates to barycentric kernels
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
Kosinka, Jiří
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.
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....
The Conserved and Unique Genetic Architecture of Kernel Size and Weight in Maize and Rice.
Liu, Jie; Huang, Juan; Guo, Huan; Lan, Liu; Wang, Hongze; Xu, Yuancheng; Yang, Xiaohong; Li, Wenqiang; Tong, Hao; Xiao, Yingjie; Pan, Qingchun; Qiao, Feng; Raihan, Mohammad Sharif; Liu, Haijun; Zhang, Xuehai; Yang, Ning; Wang, Xiaqing; Deng, Min; Jin, Minliang; Zhao, Lijun; Luo, Xin; Zhou, Yang; Li, Xiang; Zhan, Wei; Liu, Nannan; Wang, Hong; Chen, Gengshen; Li, Qing; Yan, Jianbing
2017-10-01
Maize ( Zea mays ) is a major staple crop. Maize kernel size and weight are important contributors to its yield. Here, we measured kernel length, kernel width, kernel thickness, hundred kernel weight, and kernel test weight in 10 recombinant inbred line populations and dissected their genetic architecture using three statistical models. In total, 729 quantitative trait loci (QTLs) were identified, many of which were identified in all three models, including 22 major QTLs that each can explain more than 10% of phenotypic variation. To provide candidate genes for these QTLs, we identified 30 maize genes that are orthologs of 18 rice ( Oryza sativa ) genes reported to affect rice seed size or weight. Interestingly, 24 of these 30 genes are located in the identified QTLs or within 1 Mb of the significant single-nucleotide polymorphisms. We further confirmed the effects of five genes on maize kernel size/weight in an independent association mapping panel with 540 lines by candidate gene association analysis. Lastly, the function of ZmINCW1 , a homolog of rice GRAIN INCOMPLETE FILLING1 that affects seed size and weight, was characterized in detail. ZmINCW1 is close to QTL peaks for kernel size/weight (less than 1 Mb) and contains significant single-nucleotide polymorphisms affecting kernel size/weight in the association panel. Overexpression of this gene can rescue the reduced weight of the Arabidopsis ( Arabidopsis thaliana ) homozygous mutant line in the AtcwINV2 gene (Arabidopsis ortholog of ZmINCW1 ). These results indicate that the molecular mechanisms affecting seed development are conserved in maize, rice, and possibly Arabidopsis. © 2017 American Society of Plant Biologists. All Rights Reserved.
Partial Deconvolution with Inaccurate Blur Kernel.
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
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
Hilbertian kernels and spline functions
Atteia, M
1992-01-01
In this monograph, which is an extensive study of Hilbertian approximation, the emphasis is placed on spline functions theory. The origin of the book was an effort to show that spline theory parallels Hilbertian Kernel theory, not only for splines derived from minimization of a quadratic functional but more generally for splines considered as piecewise functions type. Being as far as possible self-contained, the book may be used as a reference, with information about developments in linear approximation, convex optimization, mechanics and partial differential equations.
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.
Mitigation of artifacts in rtm with migration kernel decomposition
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
Is Stacking Intervention Components Cost-Effective? An Analysis of the Incredible Years Program
Foster, E. Michael; Olchowski, Allison E.; Webster-Stratton, Carolyn H.
2007-01-01
The cost-effectiveness of delivering stacked multiple intervention components for children is compared to implementing single intervention by analyzing the Incredible Years Series program. The result suggests multiple intervention components are more cost-effective than single intervention components.
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.
Ranking Support Vector Machine with Kernel Approximation.
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.
Sentiment classification with interpolated information diffusion kernels
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
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)
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...
Improving the Bandwidth Selection in Kernel Equating
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…
Kernel Korner : The Linux keyboard driver
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
Multiple kernel SVR based on the MRE for remote sensing water depth fusion detection
Wang, Jinjin; Ma, Yi; Zhang, Jingyu
2018-03-01
Remote sensing has an important means of water depth detection in coastal shallow waters and reefs. Support vector regression (SVR) is a machine learning method which is widely used in data regression. In this paper, SVR is used to remote sensing multispectral bathymetry. Aiming at the problem that the single-kernel SVR method has a large error in shallow water depth inversion, the mean relative error (MRE) of different water depth is retrieved as a decision fusion factor with single kernel SVR method, a multi kernel SVR fusion method based on the MRE is put forward. And taking the North Island of the Xisha Islands in China as an experimentation area, the comparison experiments with the single kernel SVR method and the traditional multi-bands bathymetric method are carried out. The results show that: 1) In range of 0 to 25 meters, the mean absolute error(MAE)of the multi kernel SVR fusion method is 1.5m,the MRE is 13.2%; 2) Compared to the 4 single kernel SVR method, the MRE of the fusion method reduced 1.2% (1.9%) 3.4% (1.8%), and compared to traditional multi-bands method, the MRE reduced 1.9%; 3) In 0-5m depth section, compared to the single kernel method and the multi-bands method, the MRE of fusion method reduced 13.5% to 44.4%, and the distribution of points is more concentrated relative to y=x.
Metabolic network prediction through pairwise rational kernels.
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
Adaptive Shape Kernel-Based Mean Shift Tracker in Robot Vision System
Directory of Open Access Journals (Sweden)
Chunmei Liu
2016-01-01
Full Text Available This paper proposes an adaptive shape kernel-based mean shift tracker using a single static camera for the robot vision system. The question that we address in this paper is how to construct such a kernel shape that is adaptive to the object shape. We perform nonlinear manifold learning technique to obtain the low-dimensional shape space which is trained by training data with the same view as the tracking video. The proposed kernel searches the shape in the low-dimensional shape space obtained by nonlinear manifold learning technique and constructs the adaptive kernel shape in the high-dimensional shape space. It can improve mean shift tracker performance to track object position and object contour and avoid the background clutter. In the experimental part, we take the walking human as example to validate that our method is accurate and robust to track human position and describe human contour.
Adaptive Shape Kernel-Based Mean Shift Tracker in Robot Vision System
2016-01-01
This paper proposes an adaptive shape kernel-based mean shift tracker using a single static camera for the robot vision system. The question that we address in this paper is how to construct such a kernel shape that is adaptive to the object shape. We perform nonlinear manifold learning technique to obtain the low-dimensional shape space which is trained by training data with the same view as the tracking video. The proposed kernel searches the shape in the low-dimensional shape space obtained by nonlinear manifold learning technique and constructs the adaptive kernel shape in the high-dimensional shape space. It can improve mean shift tracker performance to track object position and object contour and avoid the background clutter. In the experimental part, we take the walking human as example to validate that our method is accurate and robust to track human position and describe human contour. PMID:27379165
Karmeshu; Gupta, Varun; Kadambari, K V
2011-06-01
A single neuronal model incorporating distributed delay (memory)is proposed. The stochastic model has been formulated as a Stochastic Integro-Differential Equation (SIDE) which results in the underlying process being non-Markovian. A detailed analysis of the model when the distributed delay kernel has exponential form (weak delay) has been carried out. The selection of exponential kernel has enabled the transformation of the non-Markovian model to a Markovian model in an extended state space. For the study of First Passage Time (FPT) with exponential delay kernel, the model has been transformed to a system of coupled Stochastic Differential Equations (SDEs) in two-dimensional state space. Simulation studies of the SDEs provide insight into the effect of weak delay kernel on the Inter-Spike Interval(ISI) distribution. A measure based on Jensen-Shannon divergence is proposed which can be used to make a choice between two competing models viz. distributed delay model vis-á-vis LIF model. An interesting feature of the model is that the behavior of (CV(t))((ISI)) (Coefficient of Variation) of the ISI distribution with respect to memory kernel time constant parameter η reveals that neuron can switch from a bursting state to non-bursting state as the noise intensity parameter changes. The membrane potential exhibits decaying auto-correlation structure with or without damped oscillatory behavior depending on the choice of parameters. This behavior is in agreement with empirically observed pattern of spike count in a fixed time window. The power spectral density derived from the auto-correlation function is found to exhibit single and double peaks. The model is also examined for the case of strong delay with memory kernel having the form of Gamma distribution. In contrast to fast decay of damped oscillations of the ISI distribution for the model with weak delay kernel, the decay of damped oscillations is found to be slower for the model with strong delay kernel.
Bayesian Kernel Mixtures for Counts.
Canale, Antonio; Dunson, David B
2011-12-01
Although Bayesian nonparametric mixture models for continuous data are well developed, there is a limited literature on related approaches for count data. A common strategy is to use a mixture of Poissons, which unfortunately is quite restrictive in not accounting for distributions having variance less than the mean. Other approaches include mixing multinomials, which requires finite support, and using a Dirichlet process prior with a Poisson base measure, which does not allow smooth deviations from the Poisson. As a broad class of alternative models, we propose to use nonparametric mixtures of rounded continuous kernels. An efficient Gibbs sampler is developed for posterior computation, and a simulation study is performed to assess performance. Focusing on the rounded Gaussian case, we generalize the modeling framework to account for multivariate count data, joint modeling with continuous and categorical variables, and other complications. The methods are illustrated through applications to a developmental toxicity study and marketing data. This article has supplementary material online.
New approach for dynamic flow management within the PEMFC stack
International Nuclear Information System (INIS)
Varlam, Mihai; Culcer, Mihai; Carcadea, Elena; Stefanescu, Ioan; Iliescu, Mariana; Enache, Adrian
2009-01-01
An adequate gas and water flow management is a key issue to reach and maintain a higher output power for a PEM fuel cell stack. One of the main aspects which could limit the performance of a PEM fuel cell stack is the weak capability for a non-uniform water distribution management within the fuel cell. The produced water could become a handicap to attain the best working performance by blocking the catalytic surfaces and by preventing the mass transport process. Usually, the excess water is removed in one cell, comparatively to others from the stack and taking into account that all the cells are supplied in parallel from a common air admission pipe, a limitation of gas flow rate within that cell is created. Consequently, this constraint will reduce further the water removal speed. This feedback process will generate finally a drastic decrease of the fuel cell stack performance. A new practical solution to this water and gas non-uniformity of distributions problem is to use a sequential purge procedure of several fuel cell groups inside the stack which could guarantee a right management of water. An experimental setup has been built based on four fuel cell stack. Every fuel cell was connected to a single removal pipe via a solenoid valve. A computer-controlled hardware and software system has been designed and built, in order to generate a given opening-closing sequence for the automatic valve system. (authors)
Glassy carbon based supercapacitor stacks
Energy Technology Data Exchange (ETDEWEB)
Baertsch, M; Braun, A; Koetz, R; Haas, O [Paul Scherrer Inst. (PSI), Villigen (Switzerland)
1997-06-01
Considerable effort is being made to develop electrochemical double layer capacitors (EDLC) that store relatively large quantities of electrical energy and possess at the same time a high power density. Our previous work has shown that glassy carbon is suitable as a material for capacitor electrodes concerning low resistance and high capacity requirements. We present the development of bipolar electrochemical glassy carbon capacitor stacks of up to 3 V. Bipolar stacks are an efficient way to meet the high voltage and high power density requirements for traction applications. Impedance and cyclic voltammogram measurements are reported here and show the frequency response of a 1, 2, and 3 V stack. (author) 3 figs., 1 ref..
Time-predictable Stack Caching
DEFF Research Database (Denmark)
Abbaspourseyedi, Sahar
completely. Thus, in systems with hard deadlines the worst-case execution time (WCET) of the real-time software running on them needs to be bounded. Modern architectures use features such as pipelining and caches for improving the average performance. These features, however, make the WCET analysis more...... addresses, provides an opportunity to predict and tighten the WCET of accesses to data in caches. In this thesis, we introduce the time-predictable stack cache design and implementation within a time-predictable processor. We introduce several optimizations to our design for tightening the WCET while...... keeping the timepredictability of the design intact. Moreover, we provide a solution for reducing the cost of context switching in a system using the stack cache. In design of these caches, we use custom hardware and compiler support for delivering time-predictable stack data accesses. Furthermore...
Putting Priors in Mixture Density Mercer Kernels
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%.
Anisotropic hydrodynamics with a scalar collisional kernel
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.
A Visual Approach to Investigating Shared and Global Memory Behavior of CUDA Kernels
Rosen, Paul
2013-06-01
We present an approach to investigate the memory behavior of a parallel kernel executing on thousands of threads simultaneously within the CUDA architecture. Our top-down approach allows for quickly identifying any significant differences between the execution of the many blocks and warps. As interesting warps are identified, we allow further investigation of memory behavior by visualizing the shared memory bank conflicts and global memory coalescence, first with an overview of a single warp with many operations and, subsequently, with a detailed view of a single warp and a single operation. We demonstrate the strength of our approach in the context of a parallel matrix transpose kernel and a parallel 1D Haar Wavelet transform kernel. © 2013 The Author(s) Computer Graphics Forum © 2013 The Eurographics Association and Blackwell Publishing Ltd.
A Visual Approach to Investigating Shared and Global Memory Behavior of CUDA Kernels
Rosen, Paul
2013-01-01
We present an approach to investigate the memory behavior of a parallel kernel executing on thousands of threads simultaneously within the CUDA architecture. Our top-down approach allows for quickly identifying any significant differences between the execution of the many blocks and warps. As interesting warps are identified, we allow further investigation of memory behavior by visualizing the shared memory bank conflicts and global memory coalescence, first with an overview of a single warp with many operations and, subsequently, with a detailed view of a single warp and a single operation. We demonstrate the strength of our approach in the context of a parallel matrix transpose kernel and a parallel 1D Haar Wavelet transform kernel. © 2013 The Author(s) Computer Graphics Forum © 2013 The Eurographics Association and Blackwell Publishing Ltd.
Optimizing memory-bound SYMV kernel on GPU hardware accelerators
Abdelfattah, Ahmad
2013-01-01
Hardware accelerators are becoming ubiquitous high performance scientific computing. They are capable of delivering an unprecedented level of concurrent execution contexts. High-level programming language extensions (e.g., CUDA), profiling tools (e.g., PAPI-CUDA, CUDA Profiler) are paramount to improve productivity, while effectively exploiting the underlying hardware. We present an optimized numerical kernel for computing the symmetric matrix-vector product on nVidia Fermi GPUs. Due to its inherent memory-bound nature, this kernel is very critical in the tridiagonalization of a symmetric dense matrix, which is a preprocessing step to calculate the eigenpairs. Using a novel design to address the irregular memory accesses by hiding latency and increasing bandwidth, our preliminary asymptotic results show 3.5x and 2.5x fold speedups over the similar CUBLAS 4.0 kernel, and 7-8% and 30% fold improvement over the Matrix Algebra on GPU and Multicore Architectures (MAGMA) library in single and double precision arithmetics, respectively. © 2013 Springer-Verlag.
Energy Technology Data Exchange (ETDEWEB)
Jacobsen, Joachim; Primdahl, S.; Boegh Elmose, H.; Weineisen, H.; Richter, A.
2008-11-15
The aim of the project was to solve the technical challenges in relation to stack functionality in connection with operation of multi stack assemblies under realistic operating conditions. It was the intention to make a targeted effort with the aim of developing a high performance stack technology suitable for both small and large units. An important part of the project was the testing of stack assemblies up to 10 kW power range with relevant fuel and realistic operation condition in the test facility at HC OErstedvaerket. The manufacturing of stacks in the project was as planned a number of stacks (70 kW) for use in demonstration projects both for single stacks and for multi stack assemblies. The start up of the work on the SOFC test facility at HC OErstedsvaerket (HCV) was delayed due to a late delivery of the unit from the PSO 6385 project. A number of unforeseen events during the project have meant that the SOFC test facility at HCV has not until now been ready for performing tests. The experience gained from the operation of a 20 kW Alpha unit in a co-operation between TOFC and Waertsilae now provides an important contribution to the future multi stack assemblies. The work on identification of end user requirements has resulted in a number of different development priorities for the m-CHP and the Distributed Generation market segments. (au)
Stack semantics of type theory
DEFF Research Database (Denmark)
Coquand, Thierry; Mannaa, Bassel; Ruch, Fabian
2017-01-01
We give a model of dependent type theory with one univalent universe and propositional truncation interpreting a type as a stack, generalizing the groupoid model of type theory. As an application, we show that countable choice cannot be proved in dependent type theory with one univalent universe...
Multilayer Piezoelectric Stack Actuator Characterization
Sherrit, Stewart; Jones, Christopher M.; Aldrich, Jack B.; Blodget, Chad; Bao, Xioaqi; Badescu, Mircea; Bar-Cohen, Yoseph
2008-01-01
Future NASA missions are increasingly seeking to use actuators for precision positioning to accuracies of the order of fractions of a nanometer. For this purpose, multilayer piezoelectric stacks are being considered as actuators for driving these precision mechanisms. In this study, sets of commercial PZT stacks were tested in various AC and DC conditions at both nominal and extreme temperatures and voltages. AC signal testing included impedance, capacitance and dielectric loss factor of each actuator as a function of the small-signal driving sinusoidal frequency, and the ambient temperature. DC signal testing includes leakage current and displacement as a function of the applied DC voltage. The applied DC voltage was increased to over eight times the manufacturers' specifications to investigate the correlation between leakage current and breakdown voltage. Resonance characterization as a function of temperature was done over a temperature range of -180C to +200C which generally exceeded the manufacturers' specifications. In order to study the lifetime performance of these stacks, five actuators from one manufacturer were driven by a 60volt, 2 kHz sine-wave for ten billion cycles. The tests were performed using a Lab-View controlled automated data acquisition system that monitored the waveform of the stack electrical current and voltage. The measurements included the displacement, impedance, capacitance and leakage current and the analysis of the experimental results will be presented.
V-stack piezoelectric actuator
Ardelean, Emil V.; Clark, Robert L.
2001-07-01
Aeroelastic control of wings by means of a distributed, trailing-edge control surface is of interest with regards to maneuvers, gust alleviation, and flutter suppression. The use of high energy density, piezoelectric materials as motors provides an appealing solution to this problem. A comparative analysis of the state of the art actuators is currently being conducted. A new piezoelectric actuator design is presented. This actuator meets the requirements for trailing edge flap actuation in both stroke and force. It is compact, simple, sturdy, and leverages stroke geometrically with minimum force penalties while displaying linearity over a wide range of stroke. The V-Stack Piezoelectric Actuator, consists of a base, a lever, two piezoelectric stacks, and a pre-tensioning element. The work is performed alternately by the two stacks, placed on both sides of the lever. Pre-tensioning can be readily applied using a torque wrench, obviating the need for elastic elements and this is for the benefit of the stiffness of the actuator. The characteristics of the actuator are easily modified by changing the base or the stacks. A prototype was constructed and tested experimentally to validate the theoretical model.
Open stack thermal battery tests
Energy Technology Data Exchange (ETDEWEB)
Long, Kevin N. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Roberts, Christine C. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Grillet, Anne M. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Headley, Alexander J. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Fenton, Kyle [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Wong, Dennis [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Ingersoll, David [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2017-04-17
We present selected results from a series of Open Stack thermal battery tests performed in FY14 and FY15 and discuss our findings. These tests were meant to provide validation data for the comprehensive thermal battery simulation tools currently under development in Sierra/Aria under known conditions compared with as-manufactured batteries. We are able to satisfy this original objective in the present study for some test conditions. Measurements from each test include: nominal stack pressure (axial stress) vs. time in the cold state and during battery ignition, battery voltage vs. time against a prescribed current draw with periodic pulses, and images transverse to the battery axis from which cell displacements are computed. Six battery configurations were evaluated: 3, 5, and 10 cell stacks sandwiched between 4 layers of the materials used for axial thermal insulation, either Fiberfrax Board or MinK. In addition to the results from 3, 5, and 10 cell stacks with either in-line Fiberfrax Board or MinK insulation, a series of cell-free “control” tests were performed that show the inherent settling and stress relaxation based on the interaction between the insulation and heat pellets alone.
Holst, Glendon
2016-12-01
Serial section electron microscopy (SSEM) image stacks generated using high throughput microscopy techniques are an integral tool for investigating brain connectivity and cell morphology. FIB or 3View scanning electron microscopes easily generate gigabytes of data. In order to produce analyzable 3D dataset from the imaged volumes, efficient and reliable image segmentation is crucial. Classical manual approaches to segmentation are time consuming and labour intensive. Semiautomatic seeded watershed segmentation algorithms, such as those implemented by ilastik image processing software, are a very powerful alternative, substantially speeding up segmentation times. We have used ilastik effectively for small EM stacks – on a laptop, no less; however, ilastik was unable to carve the large EM stacks we needed to segment because its memory requirements grew too large – even for the biggest workstations we had available. For this reason, we refactored the carving module of ilastik to scale it up to large EM stacks on large workstations, and tested its efficiency. We modified the carving module, building on existing blockwise processing functionality to process data in manageable chunks that can fit within RAM (main memory). We review this refactoring work, highlighting the software architecture, design choices, modifications, and issues encountered.
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 ...
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.
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 ...
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 ...
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...
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...
7 CFR 51.2296 - Three-fourths half kernel.
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...
7 CFR 981.401 - Adjusted kernel weight.
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...
7 CFR 51.1403 - Kernel color classification.
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...
The Linux kernel as flexible product-line architecture
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
Directory of Open Access Journals (Sweden)
Sobrizal Sobrizal
2016-10-01
Full Text Available World demand for superior rice grain quality tends to increase. One of the criteria of appearance quality of rice grain is grain shape. Rice consumers exhibit wide preferences for grain shape, but most Indonesian rice consumers prefer long and slender grain. The objectives of this study were to identify and map a gene for rice slender kernel trait using Oryza glumaepatula introgression lines with O. sativa cv. Taichung 65 genetic background. A segregation analysis of BC4F2 population derived from backcrosses of a donor parent O. glumaepatula into a recurrent parent Taichung 65 showed that the slender kernel was controlled by a single recessive gene. This new identified gene was designated as sk1 (slender kernel 1. Moreover, based on the RFLP analyses using 14 RFLP markers located on chromosomes 2, 8, 9, and 10 in which the O. glumaepatula chromosomal segments were retained in BC4F2 population, the sk1 was located between RFLP markers C679 and C560 on the long arm of chromosome 2, with map distances of 2.8 and 1.5 cM, respectively. The wild rice O. glumaepatula carried a recessive allele for slender kernel. This allele may be useful in breeding of rice with slender kernel types. In addition, the development of plant materials and RFLP map associated with slender kernel in this study is the preliminary works in the effort to isolate this important grain shape gene.
SU-F-SPS-09: Parallel MC Kernel Calculations for VMAT Plan Improvement
International Nuclear Information System (INIS)
Chamberlain, S; French, S; Nazareth, D
2016-01-01
Purpose: Adding kernels (small perturbations in leaf positions) to the existing apertures of VMAT control points may improve plan quality. We investigate the calculation of kernel doses using a parallelized Monte Carlo (MC) method. Methods: A clinical prostate VMAT DICOM plan was exported from Eclipse. An arbitrary control point and leaf were chosen, and a modified MLC file was created, corresponding to the leaf position offset by 0.5cm. The additional dose produced by this 0.5 cm × 0.5 cm kernel was calculated using the DOSXYZnrc component module of BEAMnrc. A range of particle history counts were run (varying from 3 × 10"6 to 3 × 10"7); each job was split among 1, 10, or 100 parallel processes. A particle count of 3 × 10"6 was established as the lower range because it provided the minimal accuracy level. Results: As expected, an increase in particle counts linearly increases run time. For the lowest particle count, the time varied from 30 hours for the single-processor run, to 0.30 hours for the 100-processor run. Conclusion: Parallel processing of MC calculations in the EGS framework significantly decreases time necessary for each kernel dose calculation. Particle counts lower than 1 × 10"6 have too large of an error to output accurate dose for a Monte Carlo kernel calculation. Future work will investigate increasing the number of parallel processes and optimizing run times for multiple kernel calculations.
Methane Steam Reforming over an Ni-YSZ Solid Oxide Fuel Cell Anode in Stack Configuration
DEFF Research Database (Denmark)
Mogensen, David; Grunwaldt, Jan-Dierk; Hendriksen, Peter Vang
2014-01-01
The kinetics of catalytic steam reforming of methane over an Ni-YSZ anode of a solid oxide fuel cell (SOFC) have been investigated with the cell placed in a stack configuration. In order to decrease the degree of conversion, a single cell stack with reduced area was used. Measurements were...
DEFF Research Database (Denmark)
Yoon, Gil Ho; Park, Y.K.; Kim, Y.Y.
2007-01-01
A new topology optimization scheme, called the element stacking method, is developed to better handle design optimization involving material-dependent boundary conditions and selection of elements of different types. If these problems are solved by existing standard approaches, complicated finite...... element models or topology optimization reformulation may be necessary. The key idea of the proposed method is to stack multiple elements on the same discretization pixel and select a single or no element. In this method, stacked elements on the same pixel have the same coordinates but may have...... independent degrees of freedom. Some test problems are considered to check the effectiveness of the proposed stacking method....
Digital signal processing with kernel methods
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...
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.
Ensemble Approach to Building Mercer Kernels
National Aeronautics and Space Administration — 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...
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.)
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.
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 ...
Vertical melting of a stack of membranes
Borelli, M. E. S.; Kleinert, H.; Schakel, A. M. J.
2001-02-01
A stack of tensionless membranes with nonlinear curvature energy and vertical harmonic interaction is studied. At low temperatures, the system forms a lamellar phase. At a critical temperature, the stack disorders vertically in a melting-like transition.
Salt Concentration Differences Alter Membrane Resistance in Reverse Electrodialysis Stacks
Geise, Geoffrey M.
2014-01-14
Membrane ionic resistance is usually measured by immersing the membrane in a salt solution at a single, fixed concentration. While salt concentration is known to affect membrane resistance when the same concentration is used on both sides of the membrane, little is known about membrane resistance when the membrane is placed between solutions of different concentrations, such as in a reverse electrodialysis (RED) stack. Ionic resistance measurements obtained using Selemion CMV and AMV that separated sodium chloride and ammonium bicarbonate solutions of different concentrations were greater than those measured using only the high-concentration solution. Measured RED stack resistances showed good agreement with resistances calculated using an equivalent series resistance model, where the membranes accounted for 46% of the total stack resistance. The high area resistance of the membranes separating different salt concentration solutions has implications for modeling and optimizing membranes used in RED systems.
Helping Students Design HyperCard Stacks.
Dunham, Ken
1995-01-01
Discusses how to teach students to design HyperCard stacks. Highlights include introducing HyperCard, developing storyboards, introducing design concepts and scripts, presenting stacks, evaluating storyboards, and continuing projects. A sidebar presents a HyperCard stack evaluation form. (AEF)
PRECISION COSMOGRAPHY WITH STACKED VOIDS
International Nuclear Information System (INIS)
Lavaux, Guilhem; Wandelt, Benjamin D.
2012-01-01
We present a purely geometrical method for probing the expansion history of the universe from the observation of the shape of stacked voids in spectroscopic redshift surveys. Our method is an Alcock-Paczyński (AP) test based on the average sphericity of voids posited on the local isotropy of the universe. It works by comparing the temporal extent of cosmic voids along the line of sight with their angular, spatial extent. We describe the algorithm that we use to detect and stack voids in redshift shells on the light cone and test it on mock light cones produced from N-body simulations. We establish a robust statistical model for estimating the average stretching of voids in redshift space and quantify the contamination by peculiar velocities. Finally, assuming that the void statistics that we derive from N-body simulations is preserved when considering galaxy surveys, we assess the capability of this approach to constrain dark energy parameters. We report this assessment in terms of the figure of merit (FoM) of the dark energy task force and in particular of the proposed Euclid mission which is particularly suited for this technique since it is a spectroscopic survey. The FoM due to stacked voids from the Euclid wide survey may double that of all other dark energy probes derived from Euclid data alone (combined with Planck priors). In particular, voids seem to outperform baryon acoustic oscillations by an order of magnitude. This result is consistent with simple estimates based on mode counting. The AP test based on stacked voids may be a significant addition to the portfolio of major dark energy probes and its potentialities must be studied in detail.
Agarwal, Nitin; Moreira, Belmiro
2014-01-01
Project Specification CERN is establishing a large scale private cloud based on OpenStack as part of the expansion of the computing infrastructure for storing the data coming out of the Large Hadron Collider (LHC) experiments. As the data coming out of the detectors is increasing continuously that needs to be stored in the data center, we need more physical resources (more money) and since Virtual machines takes lot of CPU and memory overhead and minutes for creating the images, booting u...
Stack Monitor Operating Experience Review
International Nuclear Information System (INIS)
Cadwallader, L.C.; Bruyere, S.A.
2009-01-01
Stack monitors are used to sense radioactive particulates and gases in effluent air being vented from rooms of nuclear facilities. These monitors record the levels and types of effluents to the environment. This paper presents the results of a stack monitor operating experience review of the U.S. Department of Energy (DOE) Occurrence Reporting and Processing System (ORPS) database records from the past 18 years. Regulations regarding these monitors are briefly described. Operating experiences reported by the U.S. DOE and in engineering literature sources were reviewed to determine the strengths and weaknesses of these monitors. Electrical faults, radiation instrumentation faults, and human errors are the three leading causes of failures. A representative 'all modes' failure rate is 1E-04/hr. Repair time estimates vary from an average repair time of 17.5 hours (with spare parts on hand) to 160 hours (without spare parts on hand). These data should support the use of stack monitors in any nuclear facility, including the National Ignition Facility and the international ITER project.
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.
Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models
Cuevas, Jaime; Crossa, José; Montesinos-López, Osval A.; Burgueño, Juan; Pérez-Rodríguez, Paulino; de los Campos, Gustavo
2016-01-01
The phenomenon of genotype × environment (G × E) interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G × E have been recently developed and used in genomic selection of plant breeding programs. Genomic prediction models for assessing multi-environment G × E interaction are extensions of a single-environment model, and have advantages and limitations. In this study, we propose two multi-environment Bayesian genomic models: the first model considers genetic effects (u) that can be assessed by the Kronecker product of variance–covariance matrices of genetic correlations between environments and genomic kernels through markers under two linear kernel methods, linear (genomic best linear unbiased predictors, GBLUP) and Gaussian (Gaussian kernel, GK). The other model has the same genetic component as the first model (u) plus an extra component, f, that captures random effects between environments that were not captured by the random effects u. We used five CIMMYT data sets (one maize and four wheat) that were previously used in different studies. Results show that models with G × E always have superior prediction ability than single-environment models, and the higher prediction ability of multi-environment models with u and f over the multi-environment model with only u occurred 85% of the time with GBLUP and 45% of the time with GK across the five data sets. The latter result indicated that including the random effect f is still beneficial for increasing prediction ability after adjusting by the random effect u. PMID:27793970
Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models
Directory of Open Access Journals (Sweden)
Jaime Cuevas
2017-01-01
Full Text Available The phenomenon of genotype × environment (G × E interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G × E have been recently developed and used in genomic selection of plant breeding programs. Genomic prediction models for assessing multi-environment G × E interaction are extensions of a single-environment model, and have advantages and limitations. In this study, we propose two multi-environment Bayesian genomic models: the first model considers genetic effects ( u that can be assessed by the Kronecker product of variance–covariance matrices of genetic correlations between environments and genomic kernels through markers under two linear kernel methods, linear (genomic best linear unbiased predictors, GBLUP and Gaussian (Gaussian kernel, GK. The other model has the same genetic component as the first model ( u plus an extra component, f, that captures random effects between environments that were not captured by the random effects u . We used five CIMMYT data sets (one maize and four wheat that were previously used in different studies. Results show that models with G × E always have superior prediction ability than single-environment models, and the higher prediction ability of multi-environment models with u and f over the multi-environment model with only u occurred 85% of the time with GBLUP and 45% of the time with GK across the five data sets. The latter result indicated that including the random effect f is still beneficial for increasing prediction ability after adjusting by the random effect u .
Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models.
Cuevas, Jaime; Crossa, José; Montesinos-López, Osval A; Burgueño, Juan; Pérez-Rodríguez, Paulino; de Los Campos, Gustavo
2017-01-05
The phenomenon of genotype × environment (G × E) interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G × E have been recently developed and used in genomic selection of plant breeding programs. Genomic prediction models for assessing multi-environment G × E interaction are extensions of a single-environment model, and have advantages and limitations. In this study, we propose two multi-environment Bayesian genomic models: the first model considers genetic effects [Formula: see text] that can be assessed by the Kronecker product of variance-covariance matrices of genetic correlations between environments and genomic kernels through markers under two linear kernel methods, linear (genomic best linear unbiased predictors, GBLUP) and Gaussian (Gaussian kernel, GK). The other model has the same genetic component as the first model [Formula: see text] plus an extra component, F: , that captures random effects between environments that were not captured by the random effects [Formula: see text] We used five CIMMYT data sets (one maize and four wheat) that were previously used in different studies. Results show that models with G × E always have superior prediction ability than single-environment models, and the higher prediction ability of multi-environment models with [Formula: see text] over the multi-environment model with only u occurred 85% of the time with GBLUP and 45% of the time with GK across the five data sets. The latter result indicated that including the random effect f is still beneficial for increasing prediction ability after adjusting by the random effect [Formula: see text]. Copyright © 2017 Cuevas et al.
Lightweight Stacks of Direct Methanol Fuel Cells
Narayanan, Sekharipuram; Valdez, Thomas
2004-01-01
An improved design concept for direct methanol fuel cells makes it possible to construct fuel-cell stacks that can weigh as little as one-third as much as do conventional bipolar fuel-cell stacks of equal power. The structural-support components of the improved cells and stacks can be made of relatively inexpensive plastics. Moreover, in comparison with conventional bipolar fuel-cell stacks, the improved fuel-cell stacks can be assembled, disassembled, and diagnosed for malfunctions more easily. These improvements are expected to bring portable direct methanol fuel cells and stacks closer to commercialization. In a conventional bipolar fuel-cell stack, the cells are interspersed with bipolar plates (also called biplates), which are structural components that serve to interconnect the cells and distribute the reactants (methanol and air). The cells and biplates are sandwiched between metal end plates. Usually, the stack is held together under pressure by tie rods that clamp the end plates. The bipolar stack configuration offers the advantage of very low internal electrical resistance. However, when the power output of a stack is only a few watts, the very low internal resistance of a bipolar stack is not absolutely necessary for keeping the internal power loss acceptably low.
Aflatoxin contamination of developing corn kernels.
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.
Solid Oxide Fuel Cell Stack Diagnostics
DEFF Research Database (Denmark)
Mosbæk, Rasmus Rode; Barfod, Rasmus Gottrup
As SOFC technology is moving closer to a commercial break through, methods to measure the “state-of-health” of operating stacks are becoming of increasing interest. This requires application of advanced methods for detailed electrical and electrochemical characterization during operation....... An operating stack is subject to compositional gradients in the gaseous reactant streams, and temperature gradients across each cell and across the stack, which complicates detailed analysis. Several experimental stacks from Topsoe Fuel Cell A/S were characterized using Electrochemical Impedance Spectroscopy...... in the hydrogen fuel gas supplied to the stack. EIS was used to examine the long-term behavior and monitor the evolution of the impedance of each of the repeating units and the whole stack. The observed impedance was analyzed in detail for one of the repeating units and the whole stack and the losses reported...
OS X and iOS Kernel Programming
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
The Classification of Diabetes Mellitus Using Kernel k-means
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.
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...
Slip-stacking Dynamics for High-Power Proton Beams at Fermilab
Energy Technology Data Exchange (ETDEWEB)
Eldred, Jeffrey Scott [Indiana Univ., Bloomington, IN (United States)
2015-12-01
Slip-stacking is a particle accelerator configuration used to store two particle beams with different momenta in the same ring. The two beams are longitudinally focused by two radiofrequency (RF) cavities with a small frequency difference between them. Each beam is synchronized to one RF cavity and perturbed by the other RF cavity. Fermilab uses slip-stacking in the Recycler so as to double the power of the 120 GeV proton beam in the Main Injector. This dissertation investigates the dynamics of slip-stacking beams analytically, numerically and experimentally. In the analytic analysis, I find the general trajectory of stable slip-stacking particles and identify the slip-stacking parametric resonances. In the numerical analysis, I characterize the stable phase-space area and model the particle losses. In particular, I evaluate the impact of upgrading the Fermilab Booster cycle-rate from 15 Hz to 20 Hz as part of the Proton Improvement Plan II (PIP-II). The experimental analysis is used to verify my approach to simulating slip-stacking loss. I design a study for measuring losses from the longitudinal single-particle dynamics of slip-stacking as a function of RF cavity voltage and RF frequency separation. I further propose the installation of a harmonic RF cavity and study the dynamics of this novel slip-stacking configuration. I show the harmonic RF cavity cancels out parametric resonances in slip-stacking, reduces emittance growth during slip-stacking, and dramatically enhances the stable phase-space area. The harmonic cavity is expected to reduce slip-stacking losses to far exceed PIP-II requirements. These results raise the possibility of extending slip-stacking beyond the PIP-II era.
Option Valuation with Volatility Components, Fat Tails, and Nonlinear Pricing Kernels
DEFF Research Database (Denmark)
Babaoglu, Kadir Gokhan; Christoffersen, Peter; Heston, Steven
We nest multiple volatility components, fat tails and a U-shaped pricing kernel in a single option model and compare their contribution to describing returns and option data. All three features lead to statistically significant model improvements. A second volatility factor is economically most i...
Eucalyptus-Palm Kernel Oil Blends: A Complete Elimination of Diesel in a 4-Stroke VCR Diesel Engine
Directory of Open Access Journals (Sweden)
Srinivas Kommana
2015-01-01
Full Text Available Fuels derived from biomass are mostly preferred as alternative fuels for IC engines as they are abundantly available and renewable in nature. The objective of the study is to identify the parameters that influence gross indicated fuel conversion efficiency and how they are affected by the use of biodiesel relative to petroleum diesel. Important physicochemical properties of palm kernel oil and eucalyptus blend were experimentally evaluated and found within acceptable limits of relevant standards. As most of vegetable oils are edible, growing concern for trying nonedible and waste fats as alternative to petrodiesel has emerged. In present study diesel fuel is completely replaced by biofuels, namely, methyl ester of palm kernel oil and eucalyptus oil in various blends. Different blends of palm kernel oil and eucalyptus oil are prepared on volume basis and used as operating fuel in single cylinder 4-stroke variable compression ratio diesel engine. Performance and emission characteristics of these blends are studied by varying the compression ratio. In the present experiment methyl ester extracted from palm kernel oil is considered as ignition improver and eucalyptus oil is considered as the fuel. The blends taken are PKE05 (palm kernel oil 95 + eucalyptus 05, PKE10 (palm kernel oil 90 + eucalyptus 10, and PKE15 (palm kernel 85 + eucalyptus 15. The results obtained by operating with these fuels are compared with results of pure diesel; finally the most preferable combination and the preferred compression ratio are identified.
Reproducing kernel method with Taylor expansion for linear Volterra integro-differential equations
Directory of Open Access Journals (Sweden)
Azizallah Alvandi
2017-06-01
Full Text Available This research aims of the present a new and single algorithm for linear integro-differential equations (LIDE. To apply the reproducing Hilbert kernel method, there is made an equivalent transformation by using Taylor series for solving LIDEs. Shown in series form is the analytical solution in the reproducing kernel space and the approximate solution $ u_{N} $ is constructed by truncating the series to $ N $ terms. It is easy to prove the convergence of $ u_{N} $ to the analytical solution. The numerical solutions from the proposed method indicate that this approach can be implemented easily which shows attractive features.
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.
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
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
International Nuclear Information System (INIS)
Takahashi, Akito; Yamamoto, Junji; Ebisuya, Mituo; Sumita, Kenji
1979-01-01
A new method for calculating the anisotropic neutron transport is proposed for the angular spectral analysis of D-T fusion reactor neutronics. The method is based on the transport equation with new type of anisotropic scattering kernels formulated by a single function I sub(i) (μ', μ) instead of polynomial expansion, for instance, Legendre polynomials. In the calculation of angular flux spectra by using scattering kernels with the Legendre polynomial expansion, we often observe the oscillation with negative flux. But in principle this oscillation disappears by this new method. In this work, we discussed anisotropic scattering kernels of the elastic scattering and the inelastic scatterings which excite discrete energy levels. The other scatterings were included in isotropic scattering kernels. An approximation method, with use of the first collision source written by the I sub(i) (μ', μ) function, was introduced to attenuate the ''oscillations'' when we are obliged to use the scattering kernels with the Legendre polynomial expansion. Calculated results with this approximation showed remarkable improvement for the analysis of the angular flux spectra in a slab system of lithium metal with the D-T neutron source. (author)
QTL Mapping of Kernel Number-Related Traits and Validation of One Major QTL for Ear Length in Maize.
Huo, Dongao; Ning, Qiang; Shen, Xiaomeng; Liu, Lei; Zhang, Zuxin
2016-01-01
The kernel number is a grain yield component and an important maize breeding goal. Ear length, kernel number per row and ear row number are highly correlated with the kernel number per ear, which eventually determines the ear weight and grain yield. In this study, two sets of F2:3 families developed from two bi-parental crosses sharing one inbred line were used to identify quantitative trait loci (QTL) for four kernel number-related traits: ear length, kernel number per row, ear row number and ear weight. A total of 39 QTLs for the four traits were identified in the two populations. The phenotypic variance explained by a single QTL ranged from 0.4% to 29.5%. Additionally, 14 overlapping QTLs formed 5 QTL clusters on chromosomes 1, 4, 5, 7, and 10. Intriguingly, six QTLs for ear length and kernel number per row overlapped in a region on chromosome 1. This region was designated qEL1.10 and was validated as being simultaneously responsible for ear length, kernel number per row and ear weight in a near isogenic line-derived population, suggesting that qEL1.10 was a pleiotropic QTL with large effects. Furthermore, the performance of hybrids generated by crossing 6 elite inbred lines with two near isogenic lines at qEL1.10 showed the breeding value of qEL1.10 for the improvement of the kernel number and grain yield of maize hybrids. This study provides a basis for further fine mapping, molecular marker-aided breeding and functional studies of kernel number-related traits in maize.
Vertically stacked nanocellulose tactile sensor.
Jung, Minhyun; Kim, Kyungkwan; Kim, Bumjin; Lee, Kwang-Jae; Kang, Jae-Wook; Jeon, Sanghun
2017-11-16
Paper-based electronic devices are attracting considerable attention, because the paper platform has unique attributes such as flexibility and eco-friendliness. Here we report on what is claimed to be the firstly fully integrated vertically-stacked nanocellulose-based tactile sensor, which is capable of simultaneously sensing temperature and pressure. The pressure and temperature sensors are operated using different principles and are stacked vertically, thereby minimizing the interference effect. For the pressure sensor, which utilizes the piezoresistance principle under pressure, the conducting electrode was inkjet printed on the TEMPO-oxidized-nanocellulose patterned with micro-sized pyramids, and the counter electrode was placed on the nanocellulose film. The pressure sensor has a high sensitivity over a wide range (500 Pa-3 kPa) and a high durability of 10 4 loading/unloading cycles. The temperature sensor combines various materials such as poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT:PSS), silver nanoparticles (AgNPs) and carbon nanotubes (CNTs) to form a thermocouple on the upper nanocellulose layer. The thermoelectric-based temperature sensors generate a thermoelectric voltage output of 1.7 mV for a temperature difference of 125 K. Our 5 × 5 tactile sensor arrays show a fast response, negligible interference, and durable sensing performance.
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...
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...
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.
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
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 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...
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.
Simulations Of Transverse Stacking In The NSLS-II Booster
International Nuclear Information System (INIS)
Fliller, R. III; Shaftan, T.
2011-01-01
The NSLS-II injection system consists of a 200 MeV linac and a 3 GeV booster. The linac needs to deliver 15 nC in 80 - 150 bunches to the booster every minute to achieve current stability goals in the storage ring. This is a very stringent requirement that has not been demonstrated at an operating light source. We have developed a scheme to transversely stack two bunch trains in the NSLS-II booster in order to alleviate the charge requirements on the linac. This scheme has been outlined previously. In this paper we show particle tracking simulations of the tracking scheme. We show simulations of the booster ramp with a stacked beam for a variety of lattice errors and injected beam parameters. In all cases the performance of the proposed stacking method is sufficient to reduce the required charge from the linac. For this reason the injection system of the NSLS-II booster is being designed to include this feature. The NSLS-II injection system consists of a 200 MeV linac and a 3 GeV booster. The injectors must provide 7.5nC in bunch trains 80-150 bunches long every minute for top off operation of the storage ring. Top off then requires that the linac deliver 15nC of charge once losses in the injector chain are taken into consideration. This is a very stringent requirement that has not been demonstrated at an operating light source. For this reason we have developed a method to transversely stack two bunch trains in the booster while maintaining the charge transport efficiency. This stacking scheme has been discussed previously. In this paper we show the simulations of the booster ramp with a single bunch train in the booster. Then we give a brief overview of the stacking scheme. Following, we show the results of stacking two bunch trains in the booster with varying beam emittances and train separations. The behavior of the beam through the ramp is examined showing that it is possible to stack two bunch trains in the booster.
Sparse Event Modeling with Hierarchical Bayesian Kernel Methods
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
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
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.
Consistent Estimation of Pricing Kernels from Noisy Price Data
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.
Physically Connected Stacked Patch Antenna Design with 100% Bandwidth
Klionovski, Kirill; Shamim, Atif
2017-01-01
Typically, stacked patch antennas are parasitically coupled and provide larger bandwidth than a single patch antenna. Here, we show a stacked patch antenna design where square patches with semi-circular cutouts are physically connected to each other. This arrangement provides 100% bandwidth from 23.9–72.2 GHz with consistent high gain (5 dBi or more) across the entire bandwidth. In another variation, a single patch loaded with a superstrate provides 83.5% bandwidth from 25.6–62.3 GHz. The mechanism of bandwidth enhancement is explained through electromagnetic simulations. Measured reflection coefficient, radiation patterns and gain results confirm the extremely wideband performance of the design.
Physically Connected Stacked Patch Antenna Design with 100% Bandwidth
Klionovski, Kirill
2017-11-01
Typically, stacked patch antennas are parasitically coupled and provide larger bandwidth than a single patch antenna. Here, we show a stacked patch antenna design where square patches with semi-circular cutouts are physically connected to each other. This arrangement provides 100% bandwidth from 23.9–72.2 GHz with consistent high gain (5 dBi or more) across the entire bandwidth. In another variation, a single patch loaded with a superstrate provides 83.5% bandwidth from 25.6–62.3 GHz. The mechanism of bandwidth enhancement is explained through electromagnetic simulations. Measured reflection coefficient, radiation patterns and gain results confirm the extremely wideband performance of the design.
Technology leadership : a road map to commercially viable PEMFC stack technology
Energy Technology Data Exchange (ETDEWEB)
Stone, C. [Ballard Power Systems, Burnaby, BC (Canada)
2005-07-01
This abstract discussed recent advances in stack technology by Ballard Power Systems. The technology department of this Canadian-owned company exhibited the capability of a single proton exchange membrane fuel cell (PEMFC) stack design to demonstrate that cost reduction, freeze start capability from -20 degrees C and durability under an automotive dynamic operating cycle are comparable to that experienced by a fuel cell stack in an actual vehicle. A technology road map has been developed by the company to define a path to the commercial viability of the PEMFC stack by 2010. Key target parameters for cost reduction, durability, freeze start and stack power density were described in detail along with demonstrated historical capability and details of how the company will achieve its required targets. refs., tabs., figs.
Behavior of a nuclear power plant ventilation stack for wind loads
International Nuclear Information System (INIS)
Venkatachalapathy, V.
2012-01-01
This paper describes behavior of self supporting tall reinforced concrete (RC) ventilation stack of a nuclear power plant (NPP) for wind loads. Since the static and equivalent dynamic wind loads are inter-dependant on overall size of the stack, proper sizing of the stack geometry is important for reducing wind loads. The present study investigated the influence of engineered backfill soil on lateral response of ventilation stack. Ignoring backfill soil stiffness up to ground height does not allow to predict actual critical wind velocity causing across wind oscillation. The results show that proposed modification in the stack geometry modeled using 2D beam-spring elements is economical than that of single tapered geometry. Shaft diameter reduced in the proposed geometry indicates that there is a scope for overall space savings in the NPP layout. (author)
A small mono-polar direct methanol fuel cell stack with passive operation
Chan, Y. H.; Zhao, T. S.; Chen, R.; Xu, C.
A passive direct methanol fuel cell (DMFC) stack that consists of six unit cells was designed, fabricated, and tested. The stack was tested with different methanol concentrations under ambient conditions. It was found that the stack performance increased when the methanol concentration inside the fuel tank was increased from 2.0 to 6.0 M. The improved performance is primarily due to the increased cell temperature as a result of the exothermic reaction between the permeated methanol and oxygen on the cathode. Moreover, the increased cell temperature enhanced the water evaporation rate on the air-breathing cathode, which significantly reduced water flooding on the cathode and further improved the stack performance. This passive DMFC stack, providing 350 mW at 1.8 V, was successfully applied to power a seagull display kit. The seagull display kit can continuously run for about 4 h on a single charge of 25 cm 3 4.0-M methanol solution.
The untyped stack calculus and Bohm's theorem
Directory of Open Access Journals (Sweden)
Alberto Carraro
2013-03-01
Full Text Available The stack calculus is a functional language in which is in a Curry-Howard correspondence with classical logic. It enjoys confluence but, as well as Parigot's lambda-mu, does not admit the Bohm Theorem, typical of the lambda-calculus. We present a simple extension of stack calculus which is for the stack calculus what Saurin's Lambda-mu is for lambda-mu.
Flexural characteristics of a stack leg
International Nuclear Information System (INIS)
Cook, J.
1979-06-01
A 30 MV tandem Van de Graaff accelerator is at present under construction at Daresbury Laboratory. The insulating stack of the machine is of modular construction, each module being 860 mm in length. Each live section stack module contains 8 insulating legs mounted between bulkhead rings. The design, fabrication (from glass discs bonded to stainless steel discs using an epoxy film adhesive) and testing of the stack legs is described. (U.K.)
Quantum logic in dagger kernel categories
Heunen, C.; Jacobs, B.P.F.
2009-01-01
This paper investigates quantum logic from the perspective of categorical logic, and starts from minimal assumptions, namely the existence of involutions/daggers and kernels. The resulting structures turn out to (1) encompass many examples of interest, such as categories of relations, partial
Quantum logic in dagger kernel categories
Heunen, C.; Jacobs, B.P.F.; Coecke, B.; Panangaden, P.; Selinger, P.
2011-01-01
This paper investigates quantum logic from the perspective of categorical logic, and starts from minimal assumptions, namely the existence of involutions/daggers and kernels. The resulting structures turn out to (1) encompass many examples of interest, such as categories of relations, partial
Symbol recognition with kernel density matching.
Zhang, Wan; Wenyin, Liu; Zhang, Kun
2006-12-01
We propose a novel approach to similarity assessment for graphic symbols. Symbols are represented as 2D kernel densities and their similarity is measured by the Kullback-Leibler divergence. Symbol orientation is found by gradient-based angle searching or independent component analysis. Experimental results show the outstanding performance of this approach in various situations.
Flexible Scheduling in Multimedia Kernels: An Overview
Jansen, P.G.; Scholten, Johan; Laan, Rene; Chow, W.S.
1999-01-01
Current Hard Real-Time (HRT) kernels have their timely behaviour guaranteed on the cost of a rather restrictive use of the available resources. This makes current HRT scheduling techniques inadequate for use in a multimedia environment where we can make a considerable profit by a better and more
Reproducing kernel Hilbert spaces of Gaussian priors
Vaart, van der A.W.; Zanten, van J.H.; Clarke, B.; Ghosal, S.
2008-01-01
We review definitions and properties of reproducing kernel Hilbert spaces attached to Gaussian variables and processes, with a view to applications in nonparametric Bayesian statistics using Gaussian priors. The rate of contraction of posterior distributions based on Gaussian priors can be described
A synthesis of empirical plant dispersal kernels
Czech Academy of Sciences Publication Activity Database
Bullock, J. M.; González, L. M.; Tamme, R.; Götzenberger, Lars; White, S. M.; Pärtel, M.; Hooftman, D. A. P.
2017-01-01
Roč. 105, č. 1 (2017), s. 6-19 ISSN 0022-0477 Institutional support: RVO:67985939 Keywords : dispersal kernel * dispersal mode * probability density function Subject RIV: EH - Ecology, Behaviour OBOR OECD: Ecology Impact factor: 5.813, year: 2016
Analytic continuation of weighted Bergman kernels
Czech Academy of Sciences Publication Activity Database
Engliš, Miroslav
2010-01-01
Roč. 94, č. 6 (2010), s. 622-650 ISSN 0021-7824 R&D Projects: GA AV ČR IAA100190802 Keywords : Bergman kernel * analytic continuation * Toeplitz operator Subject RIV: BA - General Mathematics Impact factor: 1.450, year: 2010 http://www.sciencedirect.com/science/article/pii/S0021782410000942
On convergence of kernel learning estimators
Norkin, V.I.; Keyzer, M.A.
2009-01-01
The paper studies convex stochastic optimization problems in a reproducing kernel Hilbert space (RKHS). The objective (risk) functional depends on functions from this RKHS and takes the form of a mathematical expectation (integral) of a nonnegative integrand (loss function) over a probability
Analytic properties of the Virasoro modular kernel
Energy Technology Data Exchange (ETDEWEB)
Nemkov, Nikita [Moscow Institute of Physics and Technology (MIPT), Dolgoprudny (Russian Federation); Institute for Theoretical and Experimental Physics (ITEP), Moscow (Russian Federation); National University of Science and Technology MISIS, The Laboratory of Superconducting metamaterials, Moscow (Russian Federation)
2017-06-15
On the space of generic conformal blocks the modular transformation of the underlying surface is realized as a linear integral transformation. We show that the analytic properties of conformal block implied by Zamolodchikov's formula are shared by the kernel of the modular transformation and illustrate this by explicit computation in the case of the one-point toric conformal block. (orig.)
Kernel based subspace projection of hyperspectral images
DEFF Research Database (Denmark)
Larsen, Rasmus; Nielsen, Allan Aasbjerg; Arngren, Morten
In hyperspectral image analysis an exploratory approach to analyse the image data is to conduct subspace projections. As linear projections often fail to capture the underlying structure of the data, we present kernel based subspace projections of PCA and Maximum Autocorrelation Factors (MAF...
Kernel Temporal Differences for Neural Decoding
Bae, Jihye; Sanchez Giraldo, Luis G.; Pohlmeyer, Eric A.; Francis, Joseph T.; Sanchez, Justin C.; Príncipe, José C.
2015-01-01
We study the feasibility and capability of the kernel temporal difference (KTD)(λ) algorithm for neural decoding. KTD(λ) is an online, kernel-based learning algorithm, which has been introduced to estimate value functions in reinforcement learning. This algorithm combines kernel-based representations with the temporal difference approach to learning. One of our key observations is that by using strictly positive definite kernels, algorithm's convergence can be guaranteed for policy evaluation. The algorithm's nonlinear functional approximation capabilities are shown in both simulations of policy evaluation and neural decoding problems (policy improvement). KTD can handle high-dimensional neural states containing spatial-temporal information at a reasonable computational complexity allowing real-time applications. When the algorithm seeks a proper mapping between a monkey's neural states and desired positions of a computer cursor or a robot arm, in both open-loop and closed-loop experiments, it can effectively learn the neural state to action mapping. Finally, a visualization of the coadaptation process between the decoder and the subject shows the algorithm's capabilities in reinforcement learning brain machine interfaces. PMID:25866504
Scattering kernels and cross sections working group
International Nuclear Information System (INIS)
Russell, G.; MacFarlane, B.; Brun, T.
1998-01-01
Topics addressed by this working group are: (1) immediate needs of the cold-moderator community and how to fill them; (2) synthetic scattering kernels; (3) very simple synthetic scattering functions; (4) measurements of interest; and (5) general issues. Brief summaries are given for each of these topics
Enhanced gluten properties in soft kernel durum wheat
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...
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 ...
Stable Kernel Representations as Nonlinear Left Coprime Factorizations
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.
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...
21 CFR 176.350 - Tamarind seed kernel powder.
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...
End-use quality of soft kernel durum wheat
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...
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 Fast and Simple Graph Kernel for RDF
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
7 CFR 981.61 - Redetermination of kernel weight.
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...
Directory of Open Access Journals (Sweden)
Al Mehedi Hasan
2017-07-01
subcellular localization prediction to find out which kernel is the best for SVM. We have evaluated our system on a combined dataset containing 5447 single-localized proteins (originally published as part of the Höglund dataset and 3056 multi-localized proteins (originally published as part of the DBMLoc set. This dataset was used by Briesemeister et al. in their extensive comparison of multilocalization prediction system. The experimental results indicate that the system based on SVM with the Laplace kernel, termed LKLoc, not only achieves a higher accuracy than the system using other kernels but also shows significantly better results than those obtained from other top systems (MDLoc, BNCs, YLoc+. The source code of this prediction system is available upon request.
Learning a peptide-protein binding affinity predictor with kernel ridge regression
2013-01-01
Background The cellular function of a vast majority of proteins is performed through physical interactions with other biomolecules, which, most of the time, are other proteins. Peptides represent templates of choice for mimicking a secondary structure in order to modulate protein-protein interaction. They are thus an interesting class of therapeutics since they also display strong activity, high selectivity, low toxicity and few drug-drug interactions. Furthermore, predicting peptides that would bind to a specific MHC alleles would be of tremendous benefit to improve vaccine based therapy and possibly generate antibodies with greater affinity. Modern computational methods have the potential to accelerate and lower the cost of drug and vaccine discovery by selecting potential compounds for testing in silico prior to biological validation. Results We propose a specialized string kernel for small bio-molecules, peptides and pseudo-sequences of binding interfaces. The kernel incorporates physico-chemical properties of amino acids and elegantly generalizes eight kernels, comprised of the Oligo, the Weighted Degree, the Blended Spectrum, and the Radial Basis Function. We provide a low complexity dynamic programming algorithm for the exact computation of the kernel and a linear time algorithm for it’s approximation. Combined with kernel ridge regression and SupCK, a novel binding pocket kernel, the proposed kernel yields biologically relevant and good prediction accuracy on the PepX database. For the first time, a machine learning predictor is capable of predicting the binding affinity of any peptide to any protein with reasonable accuracy. The method was also applied to both single-target and pan-specific Major Histocompatibility Complex class II benchmark datasets and three Quantitative Structure Affinity Model benchmark datasets. Conclusion On all benchmarks, our method significantly (p-value ≤ 0.057) outperforms the current state-of-the-art methods at predicting
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.
Directory of Open Access Journals (Sweden)
Álvaro López García
2016-01-01
Full Text Available In this document we present an implementation of the Open Grid Forum’s Open Cloud Computing Interface (OCCI for OpenStack, namely ooi (Openstack occi interface, 2015 [1]. OCCI is an open standard for management tasks over cloud resources, focused on interoperability, portability and integration. ooi aims to implement this open interface for the OpenStack cloud middleware, promoting interoperability with other OCCI-enabled cloud management frameworks and infrastructures. ooi focuses on being non-invasive with a vanilla OpenStack installation, not tied to a particular OpenStack release version.
López García, Álvaro; Fernández del Castillo, Enol; Orviz Fernández, Pablo
In this document we present an implementation of the Open Grid Forum's Open Cloud Computing Interface (OCCI) for OpenStack, namely ooi (Openstack occi interface, 2015) [1]. OCCI is an open standard for management tasks over cloud resources, focused on interoperability, portability and integration. ooi aims to implement this open interface for the OpenStack cloud middleware, promoting interoperability with other OCCI-enabled cloud management frameworks and infrastructures. ooi focuses on being non-invasive with a vanilla OpenStack installation, not tied to a particular OpenStack release version.
Taxonomy of multi-focal nematode image stacks by a CNN based image fusion approach.
Liu, Min; Wang, Xueping; Zhang, Hongzhong
2018-03-01
In the biomedical field, digital multi-focal images are very important for documentation and communication of specimen data, because the morphological information for a transparent specimen can be captured in form of a stack of high-quality images. Given biomedical image stacks containing multi-focal images, how to efficiently extract effective features from all layers to classify the image stacks is still an open question. We present to use a deep convolutional neural network (CNN) image fusion based multilinear approach for the taxonomy of multi-focal image stacks. A deep CNN based image fusion technique is used to combine relevant information of multi-focal images within a given image stack into a single image, which is more informative and complete than any single image in the given stack. Besides, multi-focal images within a stack are fused along 3 orthogonal directions, and multiple features extracted from the fused images along different directions are combined by canonical correlation analysis (CCA). Because multi-focal image stacks represent the effect of different factors - texture, shape, different instances within the same class and different classes of objects, we embed the deep CNN based image fusion method within a multilinear framework to propose an image fusion based multilinear classifier. The experimental results on nematode multi-focal image stacks demonstrated that the deep CNN image fusion based multilinear classifier can reach a higher classification rate (95.7%) than that by the previous multilinear based approach (88.7%), even we only use the texture feature instead of the combination of texture and shape features as in the previous work. The proposed deep CNN image fusion based multilinear approach shows great potential in building an automated nematode taxonomy system for nematologists. It is effective to classify multi-focal image stacks. Copyright © 2018 Elsevier B.V. All rights reserved.
Dynamic stack testing and HiL simulation
Energy Technology Data Exchange (ETDEWEB)
Randolf, G. [GRandalytics, Honolulu, HI (United States)
2009-07-01
The applications for fuel cell and stack deployment have changed rapidly over the years, from stationary backup supplies to highly dynamic automotive power systems. As a result, testing must keep up in order to ensure mature products of high quality. A new breed of stack test stations has been designed, based on a newly developed single cell, high dynamic hardware-in-the-loop (HiL) simulator in order to meet the growing demand of realistic fuel cell testing scenarios for aviation and automotive industries. The paper described and illustrated the test station architecture and outline of communication nodes. The paper also described the voltage monitor and presented schematics of voltage monitoring modules. The basic requirements of the architecture that were presented included low latency; flexible communication with simulation targets and other data input/output nodes; scalability to various stack sizes; and, safety and reliability. It was concluded that first tests with the voltage monitoring system not only confirmed the design, high throughput and signal quality, but also suggested another application, namely a stack impedance spectrometer for each individual cell. 1 ref., 3 figs.
Scuba: scalable kernel-based gene prioritization.
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 .
1974-01-01
Stacks of SPS Dipole Magnets ready for installation in the tunnel. The SPS uses a separated function lattice with dipoles for bending and quadrupoles for focusing. The 6.2 m long normal conducting dipoles are of H-type with coils that are bent-up at the ends. There are two types, B1 (total of 360) and B2 (384). Both are for a maximum field of 1.8 Tesla and have the same outer dimensions (450x800 mm2 vxh) but with different gaps (B1: 39x129 mm2, B2: 52x92 mm2) tailored to the beam size. The yoke, made of 1.5 mm thick laminations, consists of an upper and a lower half joined together in the median plane once the coils have been inserted.
California dreaming?[PEM stacks
Energy Technology Data Exchange (ETDEWEB)
Crosse, J.
2002-06-01
Hyundai's Santa Fe FCEV will be on sale by the end of 2002. Hyundai uses PEM stacks that are manufactured by International Fuel Cells (IFC), a division of United Technologies. Santa Fe is equipped with a 65 kW electric powertrain of Enova systems and Shell's new gasoline reformer called Hydrogen Source. Eugene Jang, Senior Engineer - Fuel Cell and Materials at Hyundai stated that the compressor related losses on IFC system are below 3%. The maximum speed offered by the vehicle is estimated as 123km/hr while the petrol equivalent fuel consumption is quoted between 5.6L/100 km and 4.8L/100 km. Santa Fe is a compact vehicle offering better steering response and a pleasant drive. (author)
Directory of Open Access Journals (Sweden)
Yongliang Lin
2016-10-01
Full Text Available In this paper, we propose a multiple kernel relevance vector machine (RVM method based on the adaptive cloud particle swarm optimization (PSO algorithm to map landslide susceptibility in the low hill area of Sichuan Province, China. In the multi-kernel structure, the kernel selection problem can be solved by adjusting the kernel weight, which determines the single kernel contribution of the final kernel mapping. The weights and parameters of the multi-kernel function were optimized using the PSO algorithm. In addition, the convergence speed of the PSO algorithm was increased using cloud theory. To ensure the stability of the prediction model, the result of a five-fold cross-validation method was used as the fitness of the PSO algorithm. To verify the results, receiver operating characteristic curves (ROC and landslide dot density (LDD were used. The results show that the model that used a heterogeneous kernel (a combination of two different kernel functions had a larger area under the ROC curve (0.7616 and a lower prediction error ratio (0.28% than did the other types of kernel models employed in this study. In addition, both the sum of two high susceptibility zone LDDs (6.71/100 km2 and the sum of two low susceptibility zone LDDs (0.82/100 km2 demonstrated that the landslide susceptibility map based on the heterogeneous kernel model was closest to the historical landslide distribution. In conclusion, the results obtained in this study can provide very useful information for disaster prevention and land-use planning in the study area.
Paul, Subir; Nagesh Kumar, D.
2018-04-01
Hyperspectral (HS) data comprises of continuous spectral responses of hundreds of narrow spectral bands with very fine spectral resolution or bandwidth, which offer feature identification and classification with high accuracy. In the present study, Mutual Information (MI) based Segmented Stacked Autoencoder (S-SAE) approach for spectral-spatial classification of the HS data is proposed to reduce the complexity and computational time compared to Stacked Autoencoder (SAE) based feature extraction. A non-parametric dependency measure (MI) based spectral segmentation is proposed instead of linear and parametric dependency measure to take care of both linear and nonlinear inter-band dependency for spectral segmentation of the HS bands. Then morphological profiles are created corresponding to segmented spectral features to assimilate the spatial information in the spectral-spatial classification approach. Two non-parametric classifiers, Support Vector Machine (SVM) with Gaussian kernel and Random Forest (RF) are used for classification of the three most popularly used HS datasets. Results of the numerical experiments carried out in this study have shown that SVM with a Gaussian kernel is providing better results for the Pavia University and Botswana datasets whereas RF is performing better for Indian Pines dataset. The experiments performed with the proposed methodology provide encouraging results compared to numerous existing approaches.
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...
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)
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.
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.
Combined Kernel-Based BDT-SMO Classification of Hyperspectral Fused Images
Directory of Open Access Journals (Sweden)
Fenghua Huang
2014-01-01
Full Text Available To solve the poor generalization and flexibility problems that single kernel SVM classifiers have while classifying combined spectral and spatial features, this paper proposed a solution to improve the classification accuracy and efficiency of hyperspectral fused images: (1 different radial basis kernel functions (RBFs are employed for spectral and textural features, and a new combined radial basis kernel function (CRBF is proposed by combining them in a weighted manner; (2 the binary decision tree-based multiclass SMO (BDT-SMO is used in the classification of hyperspectral fused images; (3 experiments are carried out, where the single radial basis function- (SRBF- based BDT-SMO classifier and the CRBF-based BDT-SMO classifier are used, respectively, to classify the land usages of hyperspectral fused images, and genetic algorithms (GA are used to optimize the kernel parameters of the classifiers. The results show that, compared with SRBF, CRBF-based BDT-SMO classifiers display greater classification accuracy and efficiency.
Kernel Tuning and Nonuniform Influence on Optical and Electrochemical Gaps of Bimetal Nanoclusters.
He, Lizhong; Yuan, Jinyun; Xia, Nan; Liao, Lingwen; Liu, Xu; Gan, Zibao; Wang, Chengming; Yang, Jinlong; Wu, Zhikun
2018-03-14
Fine tuning nanoparticles with atomic precision is exciting and challenging and is critical for tuning the properties, understanding the structure-property correlation and determining the practical applications of nanoparticles. Some ultrasmall thiolated metal nanoparticles (metal nanoclusters) have been shown to be precisely doped, and even the protecting staple metal atom could be precisely reduced. However, the precise addition or reduction of the kernel atom while the other metal atoms in the nanocluster remain the same has not been successful until now, to the best of our knowledge. Here, by carefully selecting the protecting ligand with adequate steric hindrance, we synthesized a novel nanocluster in which the kernel can be regarded as that formed by the addition of two silver atoms to both ends of the Pt@Ag 12 icosohedral kernel of the Ag 24 Pt(SR) 18 (SR: thiolate) nanocluster, as revealed by single crystal X-ray crystallography. Interestingly, compared with the previously reported Ag 24 Pt(SR) 18 nanocluster, the as-obtained novel bimetal nanocluster exhibits a similar absorption but a different electrochemical gap. One possible explanation for this result is that the kernel tuning does not essentially change the electronic structure, but obviously influences the charge on the Pt@Ag 12 kernel, as demonstrated by natural population analysis, thus possibly resulting in the large electrochemical gap difference between the two nanoclusters. This work not only provides a novel strategy to tune metal nanoclusters but also reveals that the kernel change does not necessarily alter the optical and electrochemical gaps in a uniform manner, which has important implications for the structure-property correlation of nanoparticles.
Vector Fields and Flows on Differentiable Stacks
DEFF Research Database (Denmark)
A. Hepworth, Richard
2009-01-01
This paper introduces the notions of vector field and flow on a general differentiable stack. Our main theorem states that the flow of a vector field on a compact proper differentiable stack exists and is unique up to a uniquely determined 2-cell. This extends the usual result on the existence...... of vector fields....
Project W-420 stack monitoring system upgrades
International Nuclear Information System (INIS)
CARPENTER, K.E.
1999-01-01
This project will execute the design, procurement, construction, startup, and turnover activities for upgrades to the stack monitoring system on selected Tank Waste Remediation System (TWRS) ventilation systems. In this plan, the technical, schedule, and cost baselines are identified, and the roles and responsibilities of project participants are defined for managing the Stack Monitoring System Upgrades, Project W-420
40 CFR 61.44 - Stack sampling.
2010-07-01
... 40 Protection of Environment 8 2010-07-01 2010-07-01 false Stack sampling. 61.44 Section 61.44 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) NATIONAL... Firing § 61.44 Stack sampling. (a) Sources subject to § 61.42(b) shall be continuously sampled, during...
Learning OpenStack networking (Neutron)
Denton, James
2014-01-01
If you are an OpenStack-based cloud operator with experience in OpenStack Compute and nova-network but are new to Neutron networking, then this book is for you. Some networking experience is recommended, and a physical network infrastructure is required to provide connectivity to instances and other network resources configured in the book.
Single pass kernel k-means clustering method
Indian Academy of Sciences (India)
2016-08-26
Aug 26, 2016 ... Department of Computer Science and Engineering, Srinivasa Ramanujan Institute of Technology, Anantapur 515701, India; Department of Computer Science and Engineering, Rajeev Gandhi Memorial College of Engineering and Technology, Nandyal 518501, India; Department of Computer Science and ...
Detection of Fusarium in single wheat kernels using spectral Imaging
Polder, G.; Heijden, van der G.W.A.M.; Waalwijk, C.; Young, I.T.
2005-01-01
Fusarium head blight (FHB) is a harmful fungal disease that occurs in small grains. Non-destructive detection of this disease is traditionally done using spectroscopy or image processing. In this paper the combination of these two in the form of spectral imaging is evaluated. Transmission spectral
Status of MCFC stack technology at IHI
Energy Technology Data Exchange (ETDEWEB)
Hosaka, M.; Morita, T.; Matsuyama, T.; Otsubo, M. [Ishikawajima-Harima Heavy Industries Co., Ltd., Tokyo (Japan)
1996-12-31
The molten carbonate fuel cell (MCFC) is a promising option for highly efficient power generation possible to enlarge. IHI has been studying parallel flow MCFC stacks with internal manifolds that have a large electrode area of 1m{sup 2}. IHI will make two 250 kW stacks for MW plant, and has begun to make cell components for the plant. To improve the stability of stack, soft corrugated plate used in the separator has been developed, and a way of gathering current from stacks has been studied. The DC output potential of the plant being very high, the design of electric insulation will be very important. A 20 kW short stack test was conducted in 1995 FY to certificate some of the improvements and components of the MW plant. These activities are presented below.
Modular fuel-cell stack assembly
Patel, Pinakin
2010-07-13
A fuel cell assembly having a plurality of fuel cells arranged in a stack. An end plate assembly abuts the fuel cell at an end of said stack. The end plate assembly has an inlet area adapted to receive an exhaust gas from the stack, an outlet area and a passage connecting the inlet area and outlet area and adapted to carry the exhaust gas received at the inlet area from the inlet area to the outlet area. A further end plate assembly abuts the fuel cell at a further opposing end of the stack. The further end plate assembly has a further inlet area adapted to receive a further exhaust gas from the stack, a further outlet area and a further passage connecting the further inlet area and further outlet area and adapted to carry the further exhaust gas received at the further inlet area from the further inlet area to the further outlet area.
A “4-cell” modular passive DMFC (direct methanol fuel cell) stack for portable applications
International Nuclear Information System (INIS)
Wang, Luwen; He, Mingyan; Hu, Yue; Zhang, Yufeng; Liu, Xiaowei; Wang, Gaofeng
2015-01-01
A “4-cell” modular passive DMFC (direct methanol fuel cell) stack, which can be freely combined and applied to various electronic devices, is designed, fabricated and tested. Two PCB (printed circuit board) based accessories are designed and fabricated for electrically connecting and mechanically assembling the “4-cell” modules. The maximum power density of the “4-cell” module is 27 mW cm −2 at 5 M methanol concentration. The steady-state performances of the modular stacks with different numbers of modules are tested. The extra power loss of the multiple module stacks due to inter-module electrical connections is predicted by mathematical fitting method. The fitting results indicate that the efficiencies of the multiple module stacks are all above 90% up to 10 modules. The dynamic performances of the modular stacks are also investigated for portable applications. The results show that the modular stacks exhibit good responsiveness and reproducibility at high loading current (>100 mA). Finally, the modular stacks are successfully applied to drive the experimental fan and charge the mobile phone. - Highlights: • A “4-cell” modular passive DMFC (direct methanol fuel cell) stack is designed, fabricated and tested. • This modular DMFC stack can assemble more single cells with high efficiency. • The modular stack exhibit good responsiveness and reproducibility for portable application
Nanocomposite Materials of Alternately Stacked C60 Monolayer and Graphene
Directory of Open Access Journals (Sweden)
Makoto Ishikawa
2010-01-01
Full Text Available We synthesized the novel nanocomposite consisting alternately of a stacked single graphene sheet and a C60 monolayer by using the graphite intercalation technique in which alkylamine molecules help intercalate large C60 molecules into the graphite. Moreover, it is found that the intercalated C60 molecules can rotate in between single graphene sheets by using C13 NMR measurements. This preparation method provides a general way for intercalating huge fullerene molecules into graphite, which will lead to promising materials with novel mechanical, physical, and electrical properties.
Nanocomposite Materials of Alternately Stacked C60 Monolayer and Graphene
International Nuclear Information System (INIS)
Ishikawa, M.; Miura, K.; Kamiya, S.; Yoshimoto, S.; Suzuki, M.; Kuwahara, D.; Sasaki, N.
2010-01-01
We synthesized the novel nanocomposite consisting alternately of a stacked single graphene sheet and a C 60 monolayer by using the graphite intercalation technique in which alkylamine molecules help intercalate large C 60 molecules into the graphite. Moreover, it is found that the intercalated C 60 molecules can rotate in between single graphene sheets by using C 13 NMR measurements. This preparation method provides a general way for intercalating huge fullerene molecules into graphite, which will lead to promising materials with novel mechanical, physical, and electrical properties.
Analytic scattering kernels for neutron thermalization studies
International Nuclear Information System (INIS)
Sears, V.F.
1990-01-01
Current plans call for the inclusion of a liquid hydrogen or deuterium cold source in the NRU replacement vessel. This report is part of an ongoing study of neutron thermalization in such a cold source. Here, we develop a simple analytical model for the scattering kernel of monatomic and diatomic liquids. We also present the results of extensive numerical calculations based on this model for liquid hydrogen, liquid deuterium, and mixtures of the two. These calculations demonstrate the dependence of the scattering kernel on the incident and scattered-neutron energies, the behavior near rotational thresholds, the dependence on the centre-of-mass pair correlations, the dependence on the ortho concentration, and the dependence on the deuterium concentration in H 2 /D 2 mixtures. The total scattering cross sections are also calculated and compared with available experimental results
Quantized kernel least mean square algorithm.
Chen, Badong; Zhao, Songlin; Zhu, Pingping; Príncipe, José C
2012-01-01
In this paper, we propose a quantization approach, as an alternative of sparsification, to curb the growth of the radial basis function structure in kernel adaptive filtering. The basic idea behind this method is to quantize and hence compress the input (or feature) space. Different from sparsification, the new approach uses the "redundant" data to update the coefficient of the closest center. In particular, a quantized kernel least mean square (QKLMS) algorithm is developed, which is based on a simple online vector quantization method. The analytical study of the mean square convergence has been carried out. The energy conservation relation for QKLMS is established, and on this basis we arrive at a sufficient condition for mean square convergence, and a lower and upper bound on the theoretical value of the steady-state excess mean square error. Static function estimation and short-term chaotic time-series prediction examples are presented to demonstrate the excellent performance.
Kernel-based tests for joint independence
DEFF Research Database (Denmark)
Pfister, Niklas; Bühlmann, Peter; Schölkopf, Bernhard
2018-01-01
if the $d$ variables are jointly independent, as long as the kernel is characteristic. Based on an empirical estimate of dHSIC, we define three different non-parametric hypothesis tests: a permutation test, a bootstrap test and a test based on a Gamma approximation. We prove that the permutation test......We investigate the problem of testing whether $d$ random variables, which may or may not be continuous, are jointly (or mutually) independent. Our method builds on ideas of the two variable Hilbert-Schmidt independence criterion (HSIC) but allows for an arbitrary number of variables. We embed...... the $d$-dimensional joint distribution and the product of the marginals into a reproducing kernel Hilbert space and define the $d$-variable Hilbert-Schmidt independence criterion (dHSIC) as the squared distance between the embeddings. In the population case, the value of dHSIC is zero if and only...
Wilson Dslash Kernel From Lattice QCD Optimization
Energy Technology Data Exchange (ETDEWEB)
Joo, Balint [Jefferson Lab, Newport News, VA; Smelyanskiy, Mikhail [Parallel Computing Lab, Intel Corporation, California, USA; Kalamkar, Dhiraj D. [Parallel Computing Lab, Intel Corporation, India; Vaidyanathan, Karthikeyan [Parallel Computing Lab, Intel Corporation, India
2015-07-01
Lattice Quantum Chromodynamics (LQCD) is a numerical technique used for calculations in Theoretical Nuclear and High Energy Physics. LQCD is traditionally one of the first applications ported to many new high performance computing architectures and indeed LQCD practitioners have been known to design and build custom LQCD computers. Lattice QCD kernels are frequently used as benchmarks (e.g. 168.wupwise in the SPEC suite) and are generally well understood, and as such are ideal to illustrate several optimization techniques. In this chapter we will detail our work in optimizing the Wilson-Dslash kernels for Intel Xeon Phi, however, as we will show the technique gives excellent performance on regular Xeon Architecture as well.
Probing Temperature Inside Planar SOFC Short Stack, Modules, and Stack Series
Yu, Rong; Guan, Wanbing; Zhou, Xiao-Dong
2017-02-01
Probing temperature inside a solid oxide fuel cell (SOFC) stack lies at the heart of the development of high-performance and stable SOFC systems. In this article, we report our recent work on the direct measurements of the temperature in three types of SOFC systems: a 5-cell short stack, a 30-cell stack module, and a stack series consisting of two 30-cell stack modules. The dependence of temperature on the gas flow rate and current density was studied under a current sweep or steady-state operation. During the current sweep, the temperature inside the 5-cell stack decreased with increasing current, while it increased significantly at the bottom and top of the 30-cell stack. During a steady-state operation, the temperature of the 5-cell stack was stable while it was increased in the 30-cell stack. In the stack series, the maximum temperature gradient reached 190°C when the gas was not preheated. If the gas was preheated and the temperature gradient was reduced to 23°C in the stack series with the presence of a preheating gas and segmented temperature control, this resulted in a low degradation rate.
Wantha, Channarong
2018-02-01
This paper reports on the experimental and simulation studies of the influence of stack geometries and different mean pressures on the cold end temperature of the stack in the thermoacoustic refrigeration system. The stack geometry was tested, including spiral stack, circular pore stack and pin array stack. The results of this study show that the mean pressure of the gas in the system has a significant impact on the cold end temperature of the stack. The mean pressure of the gas in the system corresponds to thermal penetration depth, which results in a better cold end temperature of the stack. The results also show that the cold end temperature of the pin array stack decreases more than that of the spiral stack and circular pore stack geometry by approximately 63% and 70%, respectively. In addition, the thermal area and viscous area of the stack are analyzed to explain the results of such temperatures of thermoacoustic stacks.
Stacking gels: A method for maximising output for pulsed-field gel electrophoresis
Directory of Open Access Journals (Sweden)
Heng See
2009-01-01
Full Text Available Pulsed field gel electrophoresis (PFGE, the gold standard of molecular typing methods, has a major disadvantage of an unusually long electrophoretic time. From the original protocol of 6 days, it was modified to 3 days and subsequently to a single day. We describe the procedure of stacking five to six gels one on top of another in order to increase and maximize the output in a shorter time without compromising the resolution and reproducibility. All the variables that affect pulsed field gels during electrophoresis were taken into consideration. We firstly optimized the parameters to be used and secondly determined whether stacking of five to six gels had any effect on the molecular separation during electrophoresis in comparison with a single gel run. DNA preparation, restriction, electrophoresis, staining and gel documentation was carried out based on previously published methods. Gels were analysed using BioNumerics and dice coefficient and unweighted pair group methods were used to generate dendrograms based on 1.5% tolerance values. Identical band profiles and band resolution-separation were seen in the PFGE patterns with single gel and multiple stacking gels. Cluster analysis further strengthened the fact that results from stacking gels were reproducible and comparable with a single gel run. This method of stacking gels saves time and maximizes the output at the same time. The run time for a single gel was about 28 hours, but with six stacked gels the run time was 54 hours compared with 28 x 6 = 168 hours if they were run separately as single gels thus saving time of 67.86%. Beside the big factor of saving time, stacking gels save resources (electricity, reagents, water, chemicals and working time by increasing the sample throughput in a shorter time without compromising on quality of data. But optimization of working parameters is vital depending on the PFGE system used.
A Kernel for Protein Secondary Structure Prediction
Guermeur , Yann; Lifchitz , Alain; Vert , Régis
2004-01-01
http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=10338&mode=toc; International audience; Multi-class support vector machines have already proved efficient in protein secondary structure prediction as ensemble methods, to combine the outputs of sets of classifiers based on different principles. In this chapter, their implementation as basic prediction methods, processing the primary structure or the profile of multiple alignments, is investigated. A kernel devoted to the task is in...
Scalar contribution to the BFKL kernel
International Nuclear Information System (INIS)
Gerasimov, R. E.; Fadin, V. S.
2010-01-01
The contribution of scalar particles to the kernel of the Balitsky-Fadin-Kuraev-Lipatov (BFKL) equation is calculated. A great cancellation between the virtual and real parts of this contribution, analogous to the cancellation in the quark contribution in QCD, is observed. The reason of this cancellation is discovered. This reason has a common nature for particles with any spin. Understanding of this reason permits to obtain the total contribution without the complicated calculations, which are necessary for finding separate pieces.
Weighted Bergman Kernels for Logarithmic Weights
Czech Academy of Sciences Publication Activity Database
Engliš, Miroslav
2010-01-01
Roč. 6, č. 3 (2010), s. 781-813 ISSN 1558-8599 R&D Projects: GA AV ČR IAA100190802 Keywords : Bergman kernel * Toeplitz operator * logarithmic weight * pseudodifferential operator Subject RIV: BA - General Mathematics Impact factor: 0.462, year: 2010 http://www.intlpress.com/site/pub/pages/journals/items/pamq/content/vols/0006/0003/a008/
Heat kernels and zeta functions on fractals
International Nuclear Information System (INIS)
Dunne, Gerald V
2012-01-01
On fractals, spectral functions such as heat kernels and zeta functions exhibit novel features, very different from their behaviour on regular smooth manifolds, and these can have important physical consequences for both classical and quantum physics in systems having fractal properties. This article is part of a special issue of Journal of Physics A: Mathematical and Theoretical in honour of Stuart Dowker's 75th birthday devoted to ‘Applications of zeta functions and other spectral functions in mathematics and physics’. (paper)
Spam comments prediction using stacking with ensemble learning
Mehmood, Arif; On, Byung-Won; Lee, Ingyu; Ashraf, Imran; Choi, Gyu Sang
2018-01-01
Illusive comments of product or services are misleading for people in decision making. The current methodologies to predict deceptive comments are concerned for feature designing with single training model. Indigenous features have ability to show some linguistic phenomena but are hard to reveal the latent semantic meaning of the comments. We propose a prediction model on general features of documents using stacking with ensemble learning. Term Frequency/Inverse Document Frequency (TF/IDF) features are inputs to stacking of Random Forest and Gradient Boosted Trees and the outputs of the base learners are encapsulated with decision tree to make final training of the model. The results exhibits that our approach gives the accuracy of 92.19% which outperform the state-of-the-art method.
International Nuclear Information System (INIS)
Anon.
1998-01-01
Following the departure of Communism, Hungary adopted the most ambitious privatisation programme of all the eastern European countries. Within a year the state electricity company, MVM, and the oil and gas company, MOL, were prepared for sale and a consequent injection of foreign capital. Control of prices by central government inhibited investment initially but a new legal framework put in place in 1995 introduced a pricing regime more attractive to external investors. Particular interest was shown in the 2,200MW mixed heavy oil and natural gas power plant at Dunamenti on the Danube, characterised by its five stacks of varying height which reflect the changing technology employed at the plant. The bid was won by Tractabel of Belgium who have been highly successful in improving plant efficiency. However, the impact of privatisation is now being felt in uncertainty over fuel supply. Removing such uncertainty in order to maintain existing investment and provide the additional 4000MW of generating capacity needed to keep pace with demand, is a major problem which the incoming government faces. (UK)
Advanced Development of Certified OS Kernels
2015-06-01
Mobile Processes,” ACM Trans. Program. Lang. Syst., vol. 32, no. 5, 2010. 220 [36] S. D. Brookes, “A Semantics for Concurrent Separation Logic,” in...the weights of traces. The logic can be used for interactive stack-bound development or as a backend for verified static analysis tools. For clarity...logic as a backend for static stack-bound analysis tools since they would be required to also prove memory safety. To meet Challenge 3, we
Black Hole Spectroscopy with Coherent Mode Stacking.
Yang, Huan; Yagi, Kent; Blackman, Jonathan; Lehner, Luis; Paschalidis, Vasileios; Pretorius, Frans; Yunes, Nicolás
2017-04-21
The measurement of multiple ringdown modes in gravitational waves from binary black hole mergers will allow for testing the fundamental properties of black holes in general relativity and to constrain modified theories of gravity. To enhance the ability of Advanced LIGO/Virgo to perform such tasks, we propose a coherent mode stacking method to search for a chosen target mode within a collection of multiple merger events. We first rescale each signal so that the target mode in each of them has the same frequency and then sum the waveforms constructively. A crucial element to realize this coherent superposition is to make use of a priori information extracted from the inspiral-merger phase of each event. To illustrate the method, we perform a study with simulated events targeting the ℓ=m=3 ringdown mode of the remnant black holes. We show that this method can significantly boost the signal-to-noise ratio of the collective target mode compared to that of the single loudest event. Using current estimates of merger rates, we show that it is likely that advanced-era detectors can measure this collective ringdown mode with one year of coincident data gathered at design sensitivity.
Smith, Joshua Wyatt; The ATLAS collaboration
2016-01-01
The ATLAS experiment explores new hardware and software platforms that, in the future, may be more suited to its data intensive workloads. One such alternative hardware platform is the ARM architecture, which is designed to be extremely power efficient and is found in most smartphones and tablets. CERN openlab recently installed a small cluster of ARM 64-bit evaluation prototype servers. Each server is based on a single-socket ARM 64-bit system on a chip, with 32 Cortex-A57 cores. In total, each server has 128 GB RAM connected with four fast memory channels. This paper reports on the port of the ATLAS software stack onto these new prototype ARM64 servers. This included building the "external" packages that the ATLAS software relies on. Patches were needed to introduce this new architecture into the build as well as patches that correct for platform specific code that caused failures on non-x86 architectures. These patches were applied such that porting to further platforms will need no or only very little adj...
Testing system for a fuel cells stack
International Nuclear Information System (INIS)
Culcer, Mihai; Iliescu, Mariana; Stefanescu, Ioan; Raceanu, Mircea; Enache, Adrian; Lazar, Roxana Elena
2006-01-01
Hydrogen and electricity together represent one of the most promising ways to realize sustainable energy, whilst fuel cells provide the most efficient conversion devices for converting hydrogen and possibly other fuels into electricity. Thus, the development of fuel cell technology is currently being actively pursued worldwide. Due to its simple operation and other fair characteristics, the Proton Exchange Membrane Fuel Cell (PEMFC) is especially suitable as a replacement for the internal combustion engine. The PEMFC is also being developed for decentralized electricity and heat generation in buildings and mobile applications. Starting with 2001 the Institute of Research - Development for Cryogenics and Isotopic Technologies - ICIT - Rm. Valcea developed research activities supported by the Romanian Ministry of Education and Research within the National Research Program in order to bridge the gap to European competencies in the area of hydrogen and fuel cells. The paper deals with the testing system designed and developed in ICIT Rm. Valcea as a flexible and versatile tool allowing a large scale of parameter settings and measurements on a single cell or on a fuel cells stack onto a wind range of output power values. (authors)
Lithiation-induced shuffling of atomic stacks
Nie, Anmin
2014-09-10
In rechargeable lithium-ion batteries, understanding the atomic-scale mechanism of Li-induced structural evolution occurring at the host electrode materials provides essential knowledge for design of new high performance electrodes. Here, we report a new crystalline-crystalline phase transition mechanism in single-crystal Zn-Sb intermetallic nanowires upon lithiation. Using in situ transmission electron microscopy, we observed that stacks of atomic planes in an intermediate hexagonal (h-)LiZnSb phase are "shuffled" to accommodate the geometrical confinement stress arising from lamellar nanodomains intercalated by lithium ions. Such atomic rearrangement arises from the anisotropic lithium diffusion and is accompanied by appearance of partial dislocations. This transient structure mediates further phase transition from h-LiZnSb to cubic (c-)Li2ZnSb, which is associated with a nearly "zero-strain" coherent interface viewed along the [001]h/[111]c directions. This study provides new mechanistic insights into complex electrochemically driven crystalline-crystalline phase transitions in lithium-ion battery electrodes and represents a noble example of atomic-level structural and interfacial rearrangements.
Exploiting graph kernels for high performance biomedical relation extraction.
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
Akiyama, Hiroshi; Sakata, Kozue; Makiyma, Daiki; Nakamura, Kosuke; Teshima, Reiko; Nakashima, Akie; Ogawa, Asako; Yamagishi, Toru; Futo, Satoshi; Oguchi, Taichi; Mano, Junichi; Kitta, Kazumi
2011-01-01
In many countries, the labeling of grains, feed, and foodstuff is mandatory if the genetically modified (GM) organism content exceeds a certain level of approved GM varieties. We previously developed an individual kernel detection system consisting of grinding individual kernels, DNA extraction from the individually ground kernels, GM detection using multiplex real-time PCR, and GM event detection using multiplex qualitative PCR to analyze the precise commingling level and varieties of GM maize in real sample grains. We performed the interlaboratory study of the DNA extraction with multiple ground samples, multiplex real-time PCR detection, and multiplex qualitative PCR detection to evaluate its applicability, practicality, and ruggedness for the individual kernel detection system of GM maize. DNA extraction with multiple ground samples, multiplex real-time PCR, and multiplex qualitative PCR were evaluated by five laboratories in Japan, and all results from these laboratories were consistent with the expected results in terms of the commingling level and event analysis. Thus, the DNA extraction with multiple ground samples, multiplex real-time PCR, and multiplex qualitative PCR for the individual kernel detection system is applicable and practicable in a laboratory to regulate the commingling level of GM maize grain for GM samples, including stacked GM maize.
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)
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.
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.
Implementing Kernel Methods Incrementally by Incremental Nonlinear Projection Trick.
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.
Sensitivity kernels for viscoelastic loading based on adjoint methods
Al-Attar, David; Tromp, Jeroen
2014-01-01
Observations of glacial isostatic adjustment (GIA) allow for inferences to be made about mantle viscosity, ice sheet history and other related parameters. Typically, this inverse problem can be formulated as minimizing the misfit between the given observations and a corresponding set of synthetic data. When the number of parameters is large, solution of such optimization problems can be computationally challenging. A practical, albeit non-ideal, solution is to use gradient-based optimization. Although the gradient of the misfit required in such methods could be calculated approximately using finite differences, the necessary computation time grows linearly with the number of model parameters, and so this is often infeasible. A far better approach is to apply the `adjoint method', which allows the exact gradient to be calculated from a single solution of the forward problem, along with one solution of the associated adjoint problem. As a first step towards applying the adjoint method to the GIA inverse problem, we consider its application to a simpler viscoelastic loading problem in which gravitationally self-consistent ocean loading is neglected. The earth model considered is non-rotating, self-gravitating, compressible, hydrostatically pre-stressed, laterally heterogeneous and possesses a Maxwell solid rheology. We determine adjoint equations and Fréchet kernels for this problem based on a Lagrange multiplier method. Given an objective functional J defined in terms of the surface deformation fields, we show that its first-order perturbation can be written δ J = int _{MS}K_{η }δ ln η dV +int _{t0}^{t1}int _{partial M}K_{dot{σ }} δ dot{σ } dS dt, where δ ln η = δη/η denotes relative viscosity variations in solid regions MS, dV is the volume element, δ dot{σ } is the perturbation to the time derivative of the surface load which is defined on the earth model's surface ∂M and for times [t0, t1] and dS is the surface element on ∂M. The `viscosity
A Fast Multiple-Kernel Method With Applications to Detect Gene-Environment Interaction.
Marceau, Rachel; Lu, Wenbin; Holloway, Shannon; Sale, Michèle M; Worrall, Bradford B; Williams, Stephen R; Hsu, Fang-Chi; Tzeng, Jung-Ying
2015-09-01
Kernel machine (KM) models are a powerful tool for exploring associations between sets of genetic variants and complex traits. Although most KM methods use a single kernel function to assess the marginal effect of a variable set, KM analyses involving multiple kernels have become increasingly popular. Multikernel analysis allows researchers to study more complex problems, such as assessing gene-gene or gene-environment interactions, incorporating variance-component based methods for population substructure into rare-variant association testing, and assessing the conditional effects of a variable set adjusting for other variable sets. The KM framework is robust, powerful, and provides efficient dimension reduction for multifactor analyses, but requires the estimation of high dimensional nuisance parameters. Traditional estimation techniques, including regularization and the "expectation-maximization (EM)" algorithm, have a large computational cost and are not scalable to large sample sizes needed for rare variant analysis. Therefore, under the context of gene-environment interaction, we propose a computationally efficient and statistically rigorous "fastKM" algorithm for multikernel analysis that is based on a low-rank approximation to the nuisance effect kernel matrices. Our algorithm is applicable to various trait types (e.g., continuous, binary, and survival traits) and can be implemented using any existing single-kernel analysis software. Through extensive simulation studies, we show that our algorithm has similar performance to an EM-based KM approach for quantitative traits while running much faster. We also apply our method to the Vitamin Intervention for Stroke Prevention (VISP) clinical trial, examining gene-by-vitamin effects on recurrent stroke risk and gene-by-age effects on change in homocysteine level. © 2015 WILEY PERIODICALS, INC.
Genomic-Enabled Prediction in Maize Using Kernel Models with Genotype × Environment Interaction.
Bandeira E Sousa, Massaine; Cuevas, Jaime; de Oliveira Couto, Evellyn Giselly; Pérez-Rodríguez, Paulino; Jarquín, Diego; Fritsche-Neto, Roberto; Burgueño, Juan; Crossa, Jose
2017-06-07
Multi-environment trials are routinely conducted in plant breeding to select candidates for the next selection cycle. In this study, we compare the prediction accuracy of four developed genomic-enabled prediction models: (1) single-environment, main genotypic effect model (SM); (2) multi-environment, main genotypic effects model (MM); (3) multi-environment, single variance G×E deviation model (MDs); and (4) multi-environment, environment-specific variance G×E deviation model (MDe). Each of these four models were fitted using two kernel methods: a linear kernel Genomic Best Linear Unbiased Predictor, GBLUP (GB), and a nonlinear kernel Gaussian kernel (GK). The eight model-method combinations were applied to two extensive Brazilian maize data sets (HEL and USP data sets), having different numbers of maize hybrids evaluated in different environments for grain yield (GY), plant height (PH), and ear height (EH). Results show that the MDe and the MDs models fitted with the Gaussian kernel (MDe-GK, and MDs-GK) had the highest prediction accuracy. For GY in the HEL data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 9 to 32%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 9 to 49%. For GY in the USP data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 0 to 7%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 34 to 70%. For traits PH and EH, gains in prediction accuracy of models with GK compared to models with GB were smaller than those achieved in GY. Also, these gains in prediction accuracy decreased when a more difficult prediction problem was studied. Copyright © 2017 Bandeira e Sousa et al.
Genomic-Enabled Prediction in Maize Using Kernel Models with Genotype × Environment Interaction
Directory of Open Access Journals (Sweden)
Massaine Bandeira e Sousa
2017-06-01
Full Text Available Multi-environment trials are routinely conducted in plant breeding to select candidates for the next selection cycle. In this study, we compare the prediction accuracy of four developed genomic-enabled prediction models: (1 single-environment, main genotypic effect model (SM; (2 multi-environment, main genotypic effects model (MM; (3 multi-environment, single variance G×E deviation model (MDs; and (4 multi-environment, environment-specific variance G×E deviation model (MDe. Each of these four models were fitted using two kernel methods: a linear kernel Genomic Best Linear Unbiased Predictor, GBLUP (GB, and a nonlinear kernel Gaussian kernel (GK. The eight model-method combinations were applied to two extensive Brazilian maize data sets (HEL and USP data sets, having different numbers of maize hybrids evaluated in different environments for grain yield (GY, plant height (PH, and ear height (EH. Results show that the MDe and the MDs models fitted with the Gaussian kernel (MDe-GK, and MDs-GK had the highest prediction accuracy. For GY in the HEL data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 9 to 32%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 9 to 49%. For GY in the USP data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 0 to 7%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 34 to 70%. For traits PH and EH, gains in prediction accuracy of models with GK compared to models with GB were smaller than those achieved in GY. Also, these gains in prediction accuracy decreased when a more difficult prediction problem was studied.
Density of oxidation-induced stacking faults in damaged silicon
Kuper, F.G.; Hosson, J.Th.M. De; Verwey, J.F.
1986-01-01
A model for the relation between density and length of oxidation-induced stacking faults on damaged silicon surfaces is proposed, based on interactions of stacking faults with dislocations and neighboring stacking faults. The model agrees with experiments.
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...
Dynamical stability of slip-stacking particles
Energy Technology Data Exchange (ETDEWEB)
Eldred, Jeffrey; Zwaska, Robert
2014-09-01
We study the stability of particles in slip-stacking configuration, used to nearly double proton beam intensity at Fermilab. We introduce universal area factors to calculate the available phase space area for any set of beam parameters without individual simulation. We find perturbative solutions for stable particle trajectories. We establish Booster beam quality requirements to achieve 97% slip-stacking efficiency. We show that slip-stacking dynamics directly correspond to the driven pendulum and to the system of two standing-wave traps moving with respect to each other.
Text-Filled Stacked Area Graphs
DEFF Research Database (Denmark)
Kraus, Martin
2011-01-01
-filled stacked area graphs; i.e., graphs that feature stacked areas that are filled with small-typed text. Since these graphs allow for computing the text layout automatically, it is possible to include large amounts of textual detail with very little effort. We discuss the most important challenges and some...... solutions for the design of text-filled stacked area graphs with the help of an exemplary visualization of the genres, publication years, and titles of a database of several thousand PC games....
Tunable electro-optic filter stack
Fontecchio, Adam K.; Shriyan, Sameet K.; Bellingham, Alyssa
2017-09-05
A holographic polymer dispersed liquid crystal (HPDLC) tunable filter exhibits switching times of no more than 20 microseconds. The HPDLC tunable filter can be utilized in a variety of applications. An HPDLC tunable filter stack can be utilized in a hyperspectral imaging system capable of spectrally multiplexing hyperspectral imaging data acquired while the hyperspectral imaging system is airborne. HPDLC tunable filter stacks can be utilized in high speed switchable optical shielding systems, for example as a coating for a visor or an aircraft canopy. These HPDLC tunable filter stacks can be fabricated using a spin coating apparatus and associated fabrication methods.
International Nuclear Information System (INIS)
Liu, Xiaowei; Zhang, Bo; Zhang, Yufeng; He, Hong; Li, Jianmin; Wang, Shibo; Yuan, Zhenyu; Deng, Huichao
2010-01-01
An air-breathing 10-cell micro direct methanol fuel cell (µDMFC) stack with four anode feeding patterns is designed, fabricated and tested. For a better understanding of the operational characteristics of both the single cell and the stack, a two-dimensional numerical model is established and calculated. Employing micro-stamping technology, the current collectors of each single cell are microfabricated on the stainless steel plate with a thickness of 300 µm. The single µDMFC is first tested under various operating parameters. On the basis of the simulation and experimental observation of the single cell performance, the µDMFC stack performance is thoroughly analyzed with different anode feeding patterns. The results indicate that the µDMFC stack with pattern B can ensure the uniform performance of each single cell and generate the highest power output. With pattern B, further experiments are carried out to investigate the influence of the anode flow rate on the stack performance. As a result, the µDMFC stack achieves the best performance with the maximum power density of about 24.75 mW cm −2 at 5.0 ml min −1 . Finally, the stack is successfully applied to two electronic devices of different rated power
Three dimensional analysis of planar solid oxide fuel cell stack considering radiation
Energy Technology Data Exchange (ETDEWEB)
Tanaka, T.; Inui, Y.; Urata, A.; Kanno, T. [Department of Electrical and Electronic Engineering, Toyohashi University of Technology, Tempaku-cho, Toyohashi 441-8580 (Japan)
2007-05-15
The authors have been engaged in numerical simulations of the planar type solid oxide fuel cell (SOFC) to make clear the dependence of the cell performance on its operating conditions. Up to now, the authors have already developed the simulation codes for the one channel region and the single cell plate in its cell stack. To calculate accurately the effect of radiation heat transfer from the cell stack surfaces, however, a code that can treat the whole cell stack is necessary. In the present study, therefore, the authors newly develop a three dimensional simulation code of the planar SOFC stack, and the detailed effect of the radiation heat transfer is investigated. It is made clear that the conventional codes are sufficiently accurate, and the newly developed whole cell stack code is not inevitable to predict the maximum cell temperature. This is because the thermal conductivity of the cell materials made of ceramics is very small, and the central part of the cell stack is almost free from the influence of radiation heat transfer. On the other hand, the stack simulation is needed to calculate accurately the cell voltage because the radiation heat transfer reduces it when the ambient temperature is low. The bad influence of low ambient temperature on the voltage is, however, small and relatively high voltage is obtained even when the ambient temperature is very low. (author)
Numerical and experimental studies of stack shunt current for vanadium redox flow battery
International Nuclear Information System (INIS)
Yin, Cong; Guo, Shaoyun; Fang, Honglin; Liu, Jiayi; Li, Yang; Tang, Hao
2015-01-01
Highlights: • A coupled three-dimensional model of VRB cell stack is developed. • Shunt current of the stack is studied with the model and experiment. • Increased electrolyte resistance in channel and manifold lowers the shunt current. • Shunt current loss increases with stack cell number nonlinearly. - Abstract: The stack shunt current of VRB (vanadium redox flow battery) was investigated with experiments and 3D (three-dimensional) simulations. In the proposed model, cell voltages and electrolyte conductivities were calculated based on electrochemical reaction distributions and SOC (state of charge) values, respectively, while coulombic loss was estimated according to shunt current and vanadium ionic crossover through membrane. Shunt current distributions and coulombic efficiency are analyzed in terms of electrolyte conductivities and stack cell numbers. The distributions of cell voltages and shunt currents calculated with proposed model are validated with single cell and short stack tests. The model can be used to optimize VRB stack manifold and channel designs to improve VRB system efficiency
A Tensor-Product-Kernel Framework for Multiscale Neural Activity Decoding and Control
Li, Lin; Brockmeier, Austin J.; Choi, John S.; Francis, Joseph T.; Sanchez, Justin C.; Príncipe, José C.
2014-01-01
Brain machine interfaces (BMIs) have attracted intense attention as a promising technology for directly interfacing computers or prostheses with the brain's motor and sensory areas, thereby bypassing the body. The availability of multiscale neural recordings including spike trains and local field potentials (LFPs) brings potential opportunities to enhance computational modeling by enriching the characterization of the neural system state. However, heterogeneity on data type (spike timing versus continuous amplitude signals) and spatiotemporal scale complicates the model integration of multiscale neural activity. In this paper, we propose a tensor-product-kernel-based framework to integrate the multiscale activity and exploit the complementary information available in multiscale neural activity. This provides a common mathematical framework for incorporating signals from different domains. The approach is applied to the problem of neural decoding and control. For neural decoding, the framework is able to identify the nonlinear functional relationship between the multiscale neural responses and the stimuli using general purpose kernel adaptive filtering. In a sensory stimulation experiment, the tensor-product-kernel decoder outperforms decoders that use only a single neural data type. In addition, an adaptive inverse controller for delivering electrical microstimulation patterns that utilizes the tensor-product kernel achieves promising results in emulating the responses to natural stimulation. PMID:24829569
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.
International Nuclear Information System (INIS)
Birattari, Claudio; Bonardi, Mauro; Gini, Luigi; Groppi, Flavia; Menapace, Enzo
2002-01-01
The experimental values of thin-target excitation functions for the nuclear reactions: nat Os(α, X) 188,189,191 Pt, 192g,194m Ir in the energy range 11 - 38 MeV and nat Mo(p, xn) 94g,95g,95m,96(m+g) Tc in the energy range 5 - 44 MeV are presented. The experimental values were obtained by cyclotron activation followed by off-line HPGe γ-spectrometry and corrected at the End Of an Instantaneous Bombardment, EOIB. In different cases use was made of single foil and stacked foil techniques, which present significantly different advantages and disadvantages. The thin-target yield values can be easily either numerically or analytically integrated, as a function of both incoming particle energy and energy loss in target itself, in order to calculate apriori the thick-target yield of various radionuclides under any different experimental condition. Moreover, the thin-target yields are directly related to the effective cross-sections of various nuclear reaction channels involved. The data are of relevant interest for optimizing cyclotron production of platinum and technetium radionuclides to be used as radiotracers for metallo-biochemical, biomedical, toxicological and environmental studies. (author)
Ray, Sibdas; Das, Aniruddha
2015-06-01
Reaction of 2-ethoxymethyleneamino-2-cyanoacetamide with primary alkyl amines in acetonitrile solvent affords 1-substituted-5-aminoimidazole-4-carboxamides. Single crystal X-ray diffraction studies of these imidazole compounds show that there are both anti-parallel and syn-parallel π-π stackings between two imidazole units in parallel-displaced (PD) conformations and the distance between two π-π stacked imidazole units depends mainly on the anti/ syn-parallel nature and to some extent on the alkyl group attached to N-1 of imidazole; molecules with anti-parallel PD-stacking arrangements of the imidazole units have got vertical π-π stacking distance short enough to impart stabilization whereas the imidazole unit having syn-parallel stacking arrangement have got much larger π-π stacking distances. DFT studies on a pair of anti-parallel imidazole units of such an AICA lead to curves for 'π-π stacking stabilization energy vs. π-π stacking distance' which have got similarity with the 'Morse potential energy diagram for a diatomic molecule' and this affords to find out a minimum π-π stacking distance corresponding to the maximum stacking stabilization energy between the pair of imidazole units. On the other hand, a DFT calculation based curve for 'π-π stacking stabilization energy vs. π-π stacking distance' of a pair of syn-parallel imidazole units is shown to have an exponential nature.
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...
Characterization of Piezoelectric Stacks for Space Applications
Sherrit, Stewart; Jones, Christopher; Aldrich, Jack; Blodget, Chad; Bao, Xiaoqi; Badescu, Mircea; Bar-Cohen, Yoseph
2008-01-01
Future NASA missions are increasingly seeking to actuate mechanisms to precision levels in the nanometer range and below. Co-fired multilayer piezoelectric stacks offer the required actuation precision that is needed for such mechanisms. To obtain performance statistics and determine reliability for extended use, sets of commercial PZT stacks were tested in various AC and DC conditions at both nominal and high temperatures and voltages. In order to study the lifetime performance of these stacks, five actuators were driven sinusoidally for up to ten billion cycles. An automated data acquisition system was developed and implemented to monitor each stack's electrical current and voltage waveforms over the life of the test. As part of the monitoring tests, the displacement, impedance, capacitance and leakage current were measured to assess the operation degradation. This paper presents some of the results of this effort.
The stack on software and sovereignty
Bratton, Benjamin H
2016-01-01
A comprehensive political and design theory of planetary-scale computation proposing that The Stack -- an accidental megastructure -- is both a technological apparatus and a model for a new geopolitical architecture.
Development of Auto-Stacking Warehouse Truck
Directory of Open Access Journals (Sweden)
Kuo-Hsien Hsia
2018-03-01
Full Text Available Warehouse automation is a very important issue for the promotion of traditional industries. For the production of larger and stackable products, it is usually necessary to operate a fork-lifter for the stacking and storage of the products by a skilled person. The general autonomous warehouse-truck does not have the ability of stacking objects. In this paper, we develop a prototype of auto-stacking warehouse-truck that can work without direct operation by a skill person. With command made by an RFID card, the stacker truck can take the packaged product to the warehouse on the prior-planned route and store it in a stacking way in the designated storage area, or deliver the product to the shipping area or into the container from the storage area. It can significantly reduce the manpower requirements of the skilled-person of forklift technician and improve the safety of the warehousing area.
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....
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
Valdez, Thomas I.; Firdosy, S.; Koel, B. E.; Narayanan, S. R.
2005-01-01
Dissolution of ruthenium was observed in the 80-cell stack. Duration testing was performed in single cell MEAs to determine the pathway of cell degradation. EDAX analysis on each of the single cell MEAs has shown that the Johnson Matthey commercial catalyst is stable in DMFC operation for 250 hours, no ruthenium dissolution was observed. Changes in the hydrophobicity of the cathode backing papers was minimum. Electrode polarization analysis revealed that the MEA performance loss is attributed to changes in the cathode catalyst layer. Ruthenium migration does not seem to occur during cell operation but can occur when methanol is absent from the anode compartment, the cathode compartment has access to air, and the cells in the stack are electrically connected to a load (Shunt Currents). The open-to-air cathode stack design allowed for: a) The MEAs to have continual access to oxygen; and b) The stack to sustain shunt currents. Ruthenium dissolution in a DMFC stack can be prevented by: a) Developing an internally manifolded stacks that seal reactant compartments when not in operation; b) Bringing the cell voltages to zero quickly when not in operation; and c) Limiting the total number of cells to 25 in an effort to limit shunt currents.
Exploring online evolution of network stacks
Imai, Pierre
2013-01-01
Network stacks today follow a one-size-fits-all philosophy. They are mostly kept unmodified due to often prohibitive costs of engineering, deploying and administrating customisation of the networking software, with the Internet stack architecture still largely being based on designs and assumptions made for the ARPANET 40 years ago. We venture that heterogeneous and rapidly changing networks of the future require, in order to be successful, run-time self-adaptation mechanisms at different tim...
Methane Steam Reforming over an Ni-YSZ Solid Oxide Fuel Cell Anode in Stack Configuration
Directory of Open Access Journals (Sweden)
D. Mogensen
2014-01-01
Full Text Available The kinetics of catalytic steam reforming of methane over an Ni-YSZ anode of a solid oxide fuel cell (SOFC have been investigated with the cell placed in a stack configuration. In order to decrease the degree of conversion, a single cell stack with reduced area was used. Measurements were performed in the temperature range 600–800°C and the partial pressures of all reactants and products were varied. The obtained rates could be well fitted with a power law expression (r ∝PCH40.7. A simple model is presented which is capable of predicting the methane conversion in a stack configuration from intrinsic kinetics of the anode support material. The predictions are compared with the stack measurements presented here, and good agreement is observed.
Sunil, V.; Venkata siva, G.; Yoganjaneyulu, G.; Ravikumar, V. V.
2017-08-01
The answer for an emission free power source in future is in the form of fuel cells which combine hydrogen and oxygen producing electricity and a harmless by product-water. A proton exchange membrane (PEM) fuel cell is ideal for automotive applications. A single cell cannot supply the essential power for any application. Hence PEM fuel cell stacks are used. The effect of different operating parameters namely: type of convection, type of draught, hydrogen flow rate, hydrogen inlet pressure, ambient temperature and humidity, hydrogen humidity, cell orientation on the performance of air breathing PEM fuel cell stack was analyzed using a computerized fuel cell test station. Then, the fuel cell stack was subjected to different load conditions. It was found that the stack performs very poorly at full capacity (runs only for 30 min. but runs for 3 hours at 50% capacity). Hence, a detailed study was undertaken to maximize the duration of the stack’s performance at peak load.
2015-06-01
implementation of the direct interaction called particle-to-particle kernel for a shared-memory single GPU device using the Compute Unified Device Architecture ...GPU-defined P2P kernel we developed using the Compute Unified Device Architecture (CUDA).9 A brief outline of the rest of this work follows. The...Employed The computing environment used for this work is a 64-node heterogeneous cluster consisting of 48 IBM dx360M4 nodes, each with one Intel Phi
Spatial walk-off compensated beta-barium borate stack for efficient deep-UV generation
Li, Da; Lee, Huai-Chuan; Meissner, Stephanie K.; Meissner, Helmuth E.
2018-02-01
Beta-Barium Borate (β-BBO) crystal is commonly used in nonlinear frequency conversion from visible to deep ultraviolet (DUV). However, in a single crystal BBO, its large spatial walk-off effect will reduce spatial overlap of ordinary and extraordinary beam, and thus degrade the conversion efficiency. To overcome the restrictions in current DUV conversion systems, Onyx applies adhesive-free bonding technique to replace the single crystal BBO with a spatial Walk-off Compensated (WOC) BBO stack, which is capable of correcting the spatial walk-off while retaining a constant nonlinear coefficient in the adjacent bonding layers. As a result, the β-BBO stack will provide good beam quality, high conversion efficiency, and broader acceptance angle and spectral linewidth, when compared with a single crystal of BBO. In this work, we report on performance of a spatial walk-off compensated β-BBO stack with adhesive-free bonding technique, for efficiently converting from the visible to DUV range. The physics behind the WOC BBO stack are demonstrated, followed by simulation of DUV conversion efficiency in an external resonance cavity. We also demonstrate experimentally the beam quality improvement in a 4-layer WOC BBO stack over a single BBO crystal.
Multibands tunneling in AAA-stacked trilayer graphene
Redouani, Ilham; Jellal, Ahmed; Bahaoui, Abdelhadi; Bahlouli, Hocine
2018-04-01
We study the electronic transport through np and npn junctions for AAA-stacked trilayer graphene. Two kinds of gates are considered where the first is a single gate and the second is a double gate. After obtaining the solutions for the energy spectrum, we use the transfer matrix method to determine the three transmission probabilities for each individual cone τ = 0 , ± 1 . We show that the quasiparticles in AAA-stacked trilayer graphene are not only chiral but also labeled by an additional cone index τ. The obtained bands are composed of three Dirac cones that depend on the chirality indexes. We show that there is perfect transmission for normal or near normal incidence, which is a manifestation of the Klein tunneling effect. We analyze also the corresponding total conductance, which is defined as the sum of the conductance channels in each individual cone. Our results are numerically discussed and compared with those obtained for ABA- and ABC-stacked trilayer graphene.
Embedded real-time operating system micro kernel design
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.
Hadamard Kernel SVM with applications for breast cancer outcome predictions.
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.
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....
A Time-predictable Stack Cache
DEFF Research Database (Denmark)
Abbaspour, Sahar; Brandner, Florian; Schoeberl, Martin
2013-01-01
Real-time systems need time-predictable architectures to support static worst-case execution time (WCET) analysis. One architectural feature, the data cache, is hard to analyze when different data areas (e.g., heap allocated and stack allocated data) share the same cache. This sharing leads to le...... of a cache for stack allocated data. Our port of the LLVM C++ compiler supports the management of the stack cache. The combination of stack cache instructions and the hardware implementation of the stack cache is a further step towards timepredictable architectures.......Real-time systems need time-predictable architectures to support static worst-case execution time (WCET) analysis. One architectural feature, the data cache, is hard to analyze when different data areas (e.g., heap allocated and stack allocated data) share the same cache. This sharing leads to less...... precise results of the cache analysis part of the WCET analysis. Splitting the data cache for different data areas enables composable data cache analysis. The WCET analysis tool can analyze the accesses to these different data areas independently. In this paper we present the design and implementation...
StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks
Zhang, Han; Xu, Tao; Li, Hongsheng; Zhang, Shaoting; Wang, Xiaogang; Huang, Xiaolei; Metaxas, Dimitris
2017-01-01
Although Generative Adversarial Networks (GANs) have shown remarkable success in various tasks, they still face challenges in generating high quality images. In this paper, we propose Stacked Generative Adversarial Networks (StackGAN) aiming at generating high-resolution photo-realistic images. First, we propose a two-stage generative adversarial network architecture, StackGAN-v1, for text-to-image synthesis. The Stage-I GAN sketches the primitive shape and colors of the object based on given...
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
Learning Rotation for Kernel Correlation Filter
Hamdi, Abdullah
2017-08-11
Kernel Correlation Filters have shown a very promising scheme for visual tracking in terms of speed and accuracy on several benchmarks. However it suffers from problems that affect its performance like occlusion, rotation and scale change. This paper tries to tackle the problem of rotation by reformulating the optimization problem for learning the correlation filter. This modification (RKCF) includes learning rotation filter that utilizes circulant structure of HOG feature to guesstimate rotation from one frame to another and enhance the detection of KCF. Hence it gains boost in overall accuracy in many of OBT50 detest videos with minimal additional computation.
Research of Performance Linux Kernel File Systems
Directory of Open Access Journals (Sweden)
Andrey Vladimirovich Ostroukh
2015-10-01
Full Text Available The article describes the most common Linux Kernel File Systems. The research was carried out on a personal computer, the characteristics of which are written in the article. The study was performed on a typical workstation running GNU/Linux with below characteristics. On a personal computer for measuring the file performance, has been installed the necessary software. Based on the results, conclusions and proposed recommendations for use of file systems. Identified and recommended by the best ways to store data.
Fixed kernel regression for voltammogram feature extraction
International Nuclear Information System (INIS)
Acevedo Rodriguez, F J; López-Sastre, R J; Gil-Jiménez, P; Maldonado Bascón, S; Ruiz-Reyes, N
2009-01-01
Cyclic voltammetry is an electroanalytical technique for obtaining information about substances under analysis without the need for complex flow systems. However, classifying the information in voltammograms obtained using this technique is difficult. In this paper, we propose the use of fixed kernel regression as a method for extracting features from these voltammograms, reducing the information to a few coefficients. The proposed approach has been applied to a wine classification problem with accuracy rates of over 98%. Although the method is described here for extracting voltammogram information, it can be used for other types of signals
Reciprocity relation for multichannel coupling kernels
International Nuclear Information System (INIS)
Cotanch, S.R.; Satchler, G.R.
1981-01-01
Assuming time-reversal invariance of the many-body Hamiltonian, it is proven that the kernels in a general coupled-channels formulation are symmetric, to within a specified spin-dependent phase, under the interchange of channel labels and coordinates. The theorem is valid for both Hermitian and suitably chosen non-Hermitian Hamiltonians which contain complex effective interactions. While of direct practical consequence for nuclear rearrangement reactions, the reciprocity relation is also appropriate for other areas of physics which involve coupled-channels analysis
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.
DEFF Research Database (Denmark)
Arenas-Garcia, J.; Petersen, K.; Camps-Valls, G.
2013-01-01
correlation analysis (CCA), and orthonormalized PLS (OPLS), as well as their nonlinear extensions derived by means of the theory of reproducing kernel Hilbert spaces (RKHSs). We also review their connections to other methods for classification and statistical dependence estimation and introduce some recent...
Multiple Segmentation of Image Stacks
DEFF Research Database (Denmark)
Smets, Jonathan; Jaeger, Manfred
2014-01-01
We propose a method for the simultaneous construction of multiple image segmentations by combining a recently proposed “convolution of mixtures of Gaussians” model with a multi-layer hidden Markov random field structure. The resulting method constructs for a single image several, alternative...
Kernel learning at the first level of inference.
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.
International Nuclear Information System (INIS)
Murata, K.; Chihara, H.; Koike, C.; Takakura, T.; Imai, Y.; Tsuchiyama, A.; Noguchi, T.
2009-01-01
We carried out experiments of low-temperature infrared spectroscopy and transmission electron microscopy of enstatite (MgSiO 3 ) synthesized by heating of amorphous magnesium silicate. There is a discrepancy between the infrared feature of enstatite obtained in this experiment and that of fine powdered single crystals. This reflects stacking disorder of enstatite. We show that circumstellar dust emission of enstatite is similar to the infrared feature measured in this experiment. This result strongly suggests that circumstellar enstatite has abundant stacking faults and is different from the single crystal.
The Kernel Estimation in Biosystems Engineering
Directory of Open Access Journals (Sweden)
Esperanza Ayuga Téllez
2008-04-01
Full Text Available In many fields of biosystems engineering, it is common to find works in which statistical information is analysed that violates the basic hypotheses necessary for the conventional forecasting methods. For those situations, it is necessary to find alternative methods that allow the statistical analysis considering those infringements. Non-parametric function estimation includes methods that fit a target function locally, using data from a small neighbourhood of the point. Weak assumptions, such as continuity and differentiability of the target function, are rather used than "a priori" assumption of the global target function shape (e.g., linear or quadratic. In this paper a few basic rules of decision are enunciated, for the application of the non-parametric estimation method. These statistical rules set up the first step to build an interface usermethod for the consistent application of kernel estimation for not expert users. To reach this aim, univariate and multivariate estimation methods and density function were analysed, as well as regression estimators. In some cases the models to be applied in different situations, based on simulations, were defined. Different biosystems engineering applications of the kernel estimation are also analysed in this review.
Aligning Biomolecular Networks Using Modular Graph Kernels
Towfic, Fadi; Greenlee, M. Heather West; Honavar, Vasant
Comparative analysis of biomolecular networks constructed using measurements from different conditions, tissues, and organisms offer a powerful approach to understanding the structure, function, dynamics, and evolution of complex biological systems. We explore a class of algorithms for aligning large biomolecular networks by breaking down such networks into subgraphs and computing the alignment of the networks based on the alignment of their subgraphs. The resulting subnetworks are compared using graph kernels as scoring functions. We provide implementations of the resulting algorithms as part of BiNA, an open source biomolecular network alignment toolkit. Our experiments using Drosophila melanogaster, Saccharomyces cerevisiae, Mus musculus and Homo sapiens protein-protein interaction networks extracted from the DIP repository of protein-protein interaction data demonstrate that the performance of the proposed algorithms (as measured by % GO term enrichment of subnetworks identified by the alignment) is competitive with some of the state-of-the-art algorithms for pair-wise alignment of large protein-protein interaction networks. Our results also show that the inter-species similarity scores computed based on graph kernels can be used to cluster the species into a species tree that is consistent with the known phylogenetic relationships among the species.
Formal truncations of connected kernel equations
International Nuclear Information System (INIS)
Dixon, R.M.
1977-01-01
The Connected Kernel Equations (CKE) of Alt, Grassberger and Sandhas (AGS); Kouri, Levin and Tobocman (KLT); and Bencze, Redish and Sloan (BRS) are compared against reaction theory criteria after formal channel space and/or operator truncations have been introduced. The Channel Coupling Class concept is used to study the structure of these CKE's. The related wave function formalism of Sandhas, of L'Huillier, Redish and Tandy and of Kouri, Krueger and Levin are also presented. New N-body connected kernel equations which are generalizations of the Lovelace three-body equations are derived. A method for systematically constructing fewer body models from the N-body BRS and generalized Lovelace (GL) equations is developed. The formally truncated AGS, BRS, KLT and GL equations are analyzed by employing the criteria of reciprocity and two-cluster unitarity. Reciprocity considerations suggest that formal truncations of BRS, KLT and GL equations can lead to reciprocity-violating results. This study suggests that atomic problems should employ three-cluster connected truncations and that the two-cluster connected truncations should be a useful starting point for nuclear systems
Scientific Computing Kernels on the Cell Processor
Energy Technology Data Exchange (ETDEWEB)
Williams, Samuel W.; Shalf, John; Oliker, Leonid; Kamil, Shoaib; Husbands, Parry; Yelick, Katherine
2007-04-04
The slowing pace of commodity microprocessor performance improvements combined with ever-increasing chip power demands has become of utmost concern to computational scientists. As a result, the high performance computing community is examining alternative architectures that address the limitations of modern cache-based designs. In this work, we examine the potential of using the recently-released STI Cell processor as a building block for future high-end computing systems. Our work contains several novel contributions. First, we introduce a performance model for Cell and apply it to several key scientific computing kernels: dense matrix multiply, sparse matrix vector multiply, stencil computations, and 1D/2D FFTs. The difficulty of programming Cell, which requires assembly level intrinsics for the best performance, makes this model useful as an initial step in algorithm design and evaluation. Next, we validate the accuracy of our model by comparing results against published hardware results, as well as our own implementations on a 3.2GHz Cell blade. Additionally, we compare Cell performance to benchmarks run on leading superscalar (AMD Opteron), VLIW (Intel Itanium2), and vector (Cray X1E) architectures. Our work also explores several different mappings of the kernels and demonstrates a simple and effective programming model for Cell's unique architecture. Finally, we propose modest microarchitectural modifications that could significantly increase the efficiency of double-precision calculations. Overall results demonstrate the tremendous potential of the Cell architecture for scientific computations in terms of both raw performance and power efficiency.
Delimiting areas of endemism through kernel interpolation.
Oliveira, Ubirajara; Brescovit, Antonio D; Santos, Adalberto J
2015-01-01
We propose a new approach for identification of areas of endemism, the Geographical Interpolation of Endemism (GIE), based on kernel spatial interpolation. This method differs from others in being independent of grid cells. This new approach is based on estimating the overlap between the distribution of species through a kernel interpolation of centroids of species distribution and areas of influence defined from the distance between the centroid and the farthest point of occurrence of each species. We used this method to delimit areas of endemism of spiders from Brazil. To assess the effectiveness of GIE, we analyzed the same data using Parsimony Analysis of Endemism and NDM and compared the areas identified through each method. The analyses using GIE identified 101 areas of endemism of spiders in Brazil GIE demonstrated to be effective in identifying areas of endemism in multiple scales, with fuzzy edges and supported by more synendemic species than in the other methods. The areas of endemism identified with GIE were generally congruent with those identified for other taxonomic groups, suggesting that common processes can be responsible for the origin and maintenance of these biogeographic units.
Delimiting areas of endemism through kernel interpolation.
Directory of Open Access Journals (Sweden)
Ubirajara Oliveira
Full Text Available We propose a new approach for identification of areas of endemism, the Geographical Interpolation of Endemism (GIE, based on kernel spatial interpolation. This method differs from others in being independent of grid cells. This new approach is based on estimating the overlap between the distribution of species through a kernel interpolation of centroids of species distribution and areas of influence defined from the distance between the centroid and the farthest point of occurrence of each species. We used this method to delimit areas of endemism of spiders from Brazil. To assess the effectiveness of GIE, we analyzed the same data using Parsimony Analysis of Endemism and NDM and compared the areas identified through each method. The analyses using GIE identified 101 areas of endemism of spiders in Brazil GIE demonstrated to be effective in identifying areas of endemism in multiple scales, with fuzzy edges and supported by more synendemic species than in the other methods. The areas of endemism identified with GIE were generally congruent with those identified for other taxonomic groups, suggesting that common processes can be responsible for the origin and maintenance of these biogeographic units.
Start-Stop Test Procedures on the PEMFC Stack Level
DEFF Research Database (Denmark)
Mitzel, Jens; Nygaard, Frederik; Veltzé, Sune
The test is addressed to investigate the influence on stack durability of a long stop followed by a restart of a stack. Long stop should be defined as a stop in which the anodic compartment is fully filled by air due to stack leakages. In systems, leakage level of the stack is low and time to fil...
Principles for Instructional Stack Development in HyperCard.
McEneaney, John E.
The purpose of this paper is to provide information about obtaining and using HyperCard stacks that introduce users to principles of stack development. The HyperCard stacks described are available for downloading free of charge from a server at Indiana University South Bend. Specific directions are given for stack use, with advice for beginners. A…
Yekkehkhany, B.; Safari, A.; Homayouni, S.; Hasanlou, M.
2014-10-01
In this paper, a framework is developed based on Support Vector Machines (SVM) for crop classification using polarimetric features extracted from multi-temporal Synthetic Aperture Radar (SAR) imageries. The multi-temporal integration of data not only improves the overall retrieval accuracy but also provides more reliable estimates with respect to single-date data. Several kernel functions are employed and compared in this study for mapping the input space to higher Hilbert dimension space. These kernel functions include linear, polynomials and Radial Based Function (RBF). The method is applied to several UAVSAR L-band SAR images acquired over an agricultural area near Winnipeg, Manitoba, Canada. In this research, the temporal alpha features of H/A/α decomposition method are used in classification. The experimental tests show an SVM classifier with RBF kernel for three dates of data increases the Overall Accuracy (OA) to up to 3% in comparison to using linear kernel function, and up to 1% in comparison to a 3rd degree polynomial kernel function.
EmuStack: An OpenStack-Based DTN Network Emulation Platform (Extended Version
Directory of Open Access Journals (Sweden)
Haifeng Li
2016-01-01
Full Text Available With the advancement of computing and network virtualization technology, the networking research community shows great interest in network emulation. Compared with network simulation, network emulation can provide more relevant and comprehensive details. In this paper, EmuStack, a large-scale real-time emulation platform for Delay Tolerant Network (DTN, is proposed. EmuStack aims at empowering network emulation to become as simple as network simulation. Based on OpenStack, distributed synchronous emulation modules are developed to enable EmuStack to implement synchronous and dynamic, precise, and real-time network emulation. Meanwhile, the lightweight approach of using Docker container technology and network namespaces allows EmuStack to support a (up to hundreds of nodes large-scale topology with only several physical nodes. In addition, EmuStack integrates the Linux Traffic Control (TC tools with OpenStack for managing and emulating the virtual link characteristics which include variable bandwidth, delay, loss, jitter, reordering, and duplication. Finally, experiences with our initial implementation suggest the ability to run and debug experimental network protocol in real time. EmuStack environment would bring qualitative change in network research works.
Extracting Feature Model Changes from the Linux Kernel Using FMDiff
Dintzner, N.J.R.; Van Deursen, A.; Pinzger, M.
2014-01-01
The Linux kernel feature model has been studied as an example of large scale evolving feature model and yet details of its evolution are not known. We present here a classification of feature changes occurring on the Linux kernel feature model, as well as a tool, FMDiff, designed to automatically
Replacement Value of Palm Kernel Meal for Maize on Carcass ...
African Journals Online (AJOL)
This study was conducted to evaluate the effect of replacing maize with palm kernel meal on nutrient composition, fatty acid profile and sensory qualities of the meat of turkeys fed the dietary treatments. Six dietary treatments were formulated using palm kernel meal to replace maize at 0, 20, 40, 60, 80 and 100 percent.
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 ...
Capturing option anomalies with a variance-dependent pricing kernel
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
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...
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.
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.
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...
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...
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 ...
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...
Genetic relationship between plant growth, shoot and kernel sizes in ...
African Journals Online (AJOL)
Maize (Zea mays L.) ear vascular tissue transports nutrients that contribute to grain yield. To assess kernel heritabilities that govern ear development and plant growth, field studies were conducted to determine the combining abilities of parents that differed for kernel-size, grain-filling rates and shoot-size. Thirty two hybrids ...
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
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
Oven-drying reduces ruminal starch degradation in maize kernels
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
Real time kernel performance monitoring with SystemTap
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.
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.
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
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...
Comparison of Kernel Equating and Item Response Theory Equating Methods
Meng, Yu
2012-01-01
The kernel method of test equating is a unified approach to test equating with some advantages over traditional equating methods. Therefore, it is important to evaluate in a comprehensive way the usefulness and appropriateness of the Kernel equating (KE) method, as well as its advantages and disadvantages compared with several popular item…
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
Optimal Bandwidth Selection in Observed-Score Kernel Equating
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…
Computing an element in the lexicographic kernel of a game
Faigle, U.; Kern, Walter; Kuipers, Jeroen
The lexicographic kernel of a game lexicographically maximizes the surplusses $s_{ij}$ (rather than the excesses as would the nucleolus). We show that an element in the lexicographic kernel can be computed efficiently, provided we can efficiently compute the surplusses $s_{ij}(x)$ corresponding to a
Computing an element in the lexicographic kernel of a game
Faigle, U.; Kern, Walter; Kuipers, J.
2002-01-01
The lexicographic kernel of a game lexicographically maximizes the surplusses $s_{ij}$ (rather than the excesses as would the nucleolus). We show that an element in the lexicographic kernel can be computed efficiently, provided we can efficiently compute the surplusses $s_{ij}(x)$ corresponding to a
Forced Air-Breathing PEMFC Stacks
Directory of Open Access Journals (Sweden)
K. S. Dhathathreyan
2012-01-01
Full Text Available Air-breathing fuel cells have a great potential as power sources for various electronic devices. They differ from conventional fuel cells in which the cells take up oxygen from ambient air by active or passive methods. The air flow occurs through the channels due to concentration and temperature gradient between the cell and the ambient conditions. However developing a stack is very difficult as the individual cell performance may not be uniform. In order to make such a system more realistic, an open-cathode forced air-breathing stacks were developed by making appropriate channel dimensions for the air flow for uniform performance in a stack. At CFCT-ARCI (Centre for Fuel Cell Technology-ARC International we have developed forced air-breathing fuel cell stacks with varying capacity ranging from 50 watts to 1500 watts. The performance of the stack was analysed based on the air flow, humidity, stability, and so forth, The major advantage of the system is the reduced number of bipolar plates and thereby reduction in volume and weight. However, the thermal management is a challenge due to the non-availability of sufficient air flow to remove the heat from the system during continuous operation. These results will be discussed in this paper.
Levitation characteristics of HTS tape stacks
Energy Technology Data Exchange (ETDEWEB)
Pokrovskiy, S. V.; Ermolaev, Y. S.; Rudnev, I. A. [National Research Nuclear University MEPhI (Moscow Engineering Physics Institute), Moscow (Russian Federation)
2015-03-15
Due to the considerable development of the technology of second generation high-temperature superconductors and a significant improvement in their mechanical and transport properties in the last few years it is possible to use HTS tapes in the magnetic levitation systems. The advantages of tapes on a metal substrate as compared with bulk YBCO material primarily in the strength, and the possibility of optimizing the convenience of manufacturing elements of levitation systems. In the present report presents the results of the magnetic levitation force measurements between the stack of HTS tapes containing of tapes and NdFeB permanent magnet in the FC and ZFC regimes. It was found a non- linear dependence of the levitation force from the height of the array of stack in both modes: linear growth at small thickness gives way to flattening and constant at large number of tapes in the stack. Established that the levitation force of stacks comparable to that of bulk samples. The numerical calculations using finite element method showed that without the screening of the applied field the levitation force of the bulk superconductor and the layered superconductor stack with a critical current of tapes increased by the filling factor is exactly the same, and taking into account the screening force slightly different.
Energy Technology Data Exchange (ETDEWEB)
Stone, C. [Ballard Power Systems, Burnaby, British Columbia (Canada)
2005-07-01
'Full text:' In February 2005, Ballard announced its most recent advances in PEMFC stack technology. This technology development exhibited, we believe, for the first time the capability of a single PEMFC stack design to demonstrate combined excellence in cost reduction, freeze start capability from -20 C and durability under an automotive OEM defined dynamic operating cycle, comparable to that experienced by a fuel cell stack in an actual vehicle. One month later, building on the above technology leadership demonstration, Ballard announced a technology {sup '}oad map' that defined a path to commercially viability for a PEMFC stack by 2010. The key target parameters for cost reduction, durability, freeze start and stack power density are described in detail along with demonstrated historical capability and a clear path as to how Ballard will achieve the required targets. (author)
International Nuclear Information System (INIS)
Stone, C.
2005-01-01
'Full text:' In February 2005, Ballard announced its most recent advances in PEMFC stack technology. This technology development exhibited, we believe, for the first time the capability of a single PEMFC stack design to demonstrate combined excellence in cost reduction, freeze start capability from -20 C and durability under an automotive OEM defined dynamic operating cycle, comparable to that experienced by a fuel cell stack in an actual vehicle. One month later, building on the above technology leadership demonstration, Ballard announced a technology ' oad map' that defined a path to commercially viability for a PEMFC stack by 2010. The key target parameters for cost reduction, durability, freeze start and stack power density are described in detail along with demonstrated historical capability and a clear path as to how Ballard will achieve the required targets. (author)
Ultrafast convolution/superposition using tabulated and exponential kernels on GPU
Energy Technology Data Exchange (ETDEWEB)
Chen Quan; Chen Mingli; Lu Weiguo [TomoTherapy Inc., 1240 Deming Way, Madison, Wisconsin 53717 (United States)
2011-03-15
Purpose: Collapsed-cone convolution/superposition (CCCS) dose calculation is the workhorse for IMRT dose calculation. The authors present a novel algorithm for computing CCCS dose on the modern graphic processing unit (GPU). Methods: The GPU algorithm includes a novel TERMA calculation that has no write-conflicts and has linear computation complexity. The CCCS algorithm uses either tabulated or exponential cumulative-cumulative kernels (CCKs) as reported in literature. The authors have demonstrated that the use of exponential kernels can reduce the computation complexity by order of a dimension and achieve excellent accuracy. Special attentions are paid to the unique architecture of GPU, especially the memory accessing pattern, which increases performance by more than tenfold. Results: As a result, the tabulated kernel implementation in GPU is two to three times faster than other GPU implementations reported in literature. The implementation of CCCS showed significant speedup on GPU over single core CPU. On tabulated CCK, speedups as high as 70 are observed; on exponential CCK, speedups as high as 90 are observed. Conclusions: Overall, the GPU algorithm using exponential CCK is 1000-3000 times faster over a highly optimized single-threaded CPU implementation using tabulated CCK, while the dose differences are within 0.5% and 0.5 mm. This ultrafast CCCS algorithm will allow many time-sensitive applications to use accurate dose calculation.
3-D waveform tomography sensitivity kernels for anisotropic media
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.
Anatomically-aided PET reconstruction using the kernel method.
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.
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....
Compactly Supported Basis Functions as Support Vector Kernels for Classification.
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.
Stacks with TiN/titanium as the bipolar plate for PEMFCs
International Nuclear Information System (INIS)
Ren, Zhijun; Zhang, Dongming; Wang, Zaiyi
2012-01-01
Proton exchange membrane fuel cell (PEMFC) is a potential alternative for the internal combustion engine. But many problems, such as metallic bipolar plate instead of graphite bipolar plate to decrease the cost, should be solved before its application. Based on the previous results that single cell with TiN/Ti as bipolar plates shows high performance and enough long-time durability, the progress on the stacks with TiN/Ti as bipolar plates is reported in this manuscript. Till now seldom report is focused on stacks because of the complicated processing technique, especially for that with TiN/Ti as bipolar plate. The flow field in the plate is punched from titanium deformation, and two plates are welded by laser welding to form one piece of bipolar plate. The adopted processing techniques for stacks with TiN/Ti as bipolar plate exhibit advantage and feasibility in industry. The power density by weight for the stack is as high as 1353 W kg −1 , although it still has space to be improved. Next work should be focused on the design of flow channel parameters and flow field type based on plastic deformation of metal materials. -- Highlights: ► The progress on the stacks with TiN/Ti as bipolar plates is reported. ► The adopted processing techniques exhibit feasibility in industry. ► The power density by weight for the stack is as high as 1353 W kg −1 .
Dual stacked partial least squares for analysis of near-infrared spectra
Energy Technology Data Exchange (ETDEWEB)
Bi, Yiming [Institute of Automation, Chinese Academy of Sciences, 100190 Beijing (China); Xie, Qiong, E-mail: yimbi@163.com [Institute of Automation, Chinese Academy of Sciences, 100190 Beijing (China); Peng, Silong; Tang, Liang; Hu, Yong; Tan, Jie [Institute of Automation, Chinese Academy of Sciences, 100190 Beijing (China); Zhao, Yuhui [School of Economics and Business, Northeastern University at Qinhuangdao, 066000 Qinhuangdao City (China); Li, Changwen [Food Research Institute of Tianjin Tasly Group, 300410 Tianjin (China)
2013-08-20
Graphical abstract: -- Highlights: •Dual stacking steps are used for multivariate calibration of near-infrared spectra. •A selective weighting strategy is introduced that only a subset of all available sub-models is used for model fusion. •Using two public near-infrared datasets, the proposed method achieved competitive results. •The method can be widely applied in many fields, such as Mid-infrared spectra data and Raman spectra data. -- Abstract: A new ensemble learning algorithm is presented for quantitative analysis of near-infrared spectra. The algorithm contains two steps of stacked regression and Partial Least Squares (PLS), termed Dual Stacked Partial Least Squares (DSPLS) algorithm. First, several sub-models were generated from the whole calibration set. The inner-stack step was implemented on sub-intervals of the spectrum. Then the outer-stack step was used to combine these sub-models. Several combination rules of the outer-stack step were analyzed for the proposed DSPLS algorithm. In addition, a novel selective weighting rule was also involved to select a subset of all available sub-models. Experiments on two public near-infrared datasets demonstrate that the proposed DSPLS with selective weighting rule provided superior prediction performance and outperformed the conventional PLS algorithm. Compared with the single model, the new ensemble model can provide more robust prediction result and can be considered an alternative choice for quantitative analytical applications.
A high-performance aluminum-feed microfluidic fuel cell stack
Wang, Yifei; Leung, Dennis Y. C.
2016-12-01
In this paper, a six-cell microfluidic fuel cell (MFC) stack is demonstrated. Low-cost aluminum is fed directly to the stack, which produces hydrogen fuel on site, through the Al-H2O reaction. This design is not only cost-efficient, but also eliminates the need for hydrogen storage. Unlike the conventional MFC stacks which generally require complex electrolyte distribution and management, the present Al-feed MFC stack requires only a single electrolyte stream, flowing successively through individual cells, which is finally utilized for hydrogen generation. In this manner, the whole system is greatly simplified while the operational robustness is also improved. With 2 M sodium hydroxide solution as electrolyte and kitchen foil Al as fuel, the present six-cell stack (in series) exhibits an open circuit voltage of nearly 6 V and a peak power density of 180.6 mWcm-2 at room temperature. In addition, an energy density of 1 Whg-1(Al) is achieved, which is quite high and comparable with its proton exchange membrane-based counterparts. Finally, pumpless operation of the present stack, together with its practical applications are successfully demonstrated, including lightening LED lights, driving an electric fan, and cell phone charging.
Dual stacked partial least squares for analysis of near-infrared spectra
International Nuclear Information System (INIS)
Bi, Yiming; Xie, Qiong; Peng, Silong; Tang, Liang; Hu, Yong; Tan, Jie; Zhao, Yuhui; Li, Changwen
2013-01-01
Graphical abstract: -- Highlights: •Dual stacking steps are used for multivariate calibration of near-infrared spectra. •A selective weighting strategy is introduced that only a subset of all available sub-models is used for model fusion. •Using two public near-infrared datasets, the proposed method achieved competitive results. •The method can be widely applied in many fields, such as Mid-infrared spectra data and Raman spectra data. -- Abstract: A new ensemble learning algorithm is presented for quantitative analysis of near-infrared spectra. The algorithm contains two steps of stacked regression and Partial Least Squares (PLS), termed Dual Stacked Partial Least Squares (DSPLS) algorithm. First, several sub-models were generated from the whole calibration set. The inner-stack step was implemented on sub-intervals of the spectrum. Then the outer-stack step was used to combine these sub-models. Several combination rules of the outer-stack step were analyzed for the proposed DSPLS algorithm. In addition, a novel selective weighting rule was also involved to select a subset of all available sub-models. Experiments on two public near-infrared datasets demonstrate that the proposed DSPLS with selective weighting rule provided superior prediction performance and outperformed the conventional PLS algorithm. Compared with the single model, the new ensemble model can provide more robust prediction result and can be considered an alternative choice for quantitative analytical applications
ADSORPTION OF COPPER FROM AQUEOUS SOLUTION BY ELAIS GUINEENSIS KERNEL ACTIVATED CARBON
Directory of Open Access Journals (Sweden)
NAJUA DELAILA TUMIN
2008-08-01
Full Text Available In this study, a series of batch laboratory experiments were conducted in order to investigate the feasibility of Elais Guineensis kernel or known as palm kernel shell (PKS-based activated carbon for the removal of copper from aqueous solution by the adsorption process. Investigation was carried out by studying the influence of initial solution pH, adsorbent dosage and initial concentration of copper. The particle size of PKS used was categorized as PKS–M. All batch experiments were carried out at a constant temperature of 30°C (±2°C using mechanical shaker that operated at 100 rpm. The single component equilibrium data was analyzed using Langmuir, Freundlich, Redlich-Peterson, Temkin and Toth adsorption isotherms.
International Nuclear Information System (INIS)
Vogt, A; Soar, G.; Vermaseren, J.A.M.
2010-01-01
We have studied the physical evolution kernels for nine non-singlet observables in deep-inelastic scattering (DIS), semi-inclusive e + e - annihilation and the Drell-Yan (DY) process, and for the flavour-singlet case of the photon- and heavy-top Higgs-exchange structure functions (F 2 , F φ ) in DIS. All known contributions to these kernels show an only single-logarithmic large-x enhancement at all powers of (1-x). Conjecturing that this behaviour persists to (all) higher orders, we have predicted the highest three (DY: two) double logarithms of the higher-order non-singlet coefficient functions and of the four-loop singlet splitting functions. The coefficient-function predictions can be written as exponentiations of 1/N-suppressed contributions in Mellin-N space which, however, are less predictive than the well-known exponentiation of the ln k N terms. (orig.)
Performance and Emission of VCR-CI Engine with palm kernel and eucalyptus blends
Directory of Open Access Journals (Sweden)
Srinivas kommana
2016-09-01
Full Text Available This study aims at complete replacement of conventional diesel fuel by biodiesel. In order to achieve that, palm kernel oil and eucalyptus oil blend has been chosen. Eucalyptus oil was blended with methyl ester of palm kernel oil in 5%, 10% and 15% by volume. Tests were conducted with diesel fuel and blends on a 4 stroke VCR diesel engine for comparative analysis with 220 bar injection pressure and 19:1 compression ratio. All the test fuels were used in computerized 4 stroke single cylinder variable compression ratio engine at five different loads (0, 6, 12, 18 and 24 N m. Present investigation depicts the improved combustion and reduced emissions for the PKO85% + EuO15% blend when compared to diesel at full load conditions.
Improved modeling of clinical data with kernel methods.
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
Progress of MCFC stack technology at Toshiba
Energy Technology Data Exchange (ETDEWEB)
Hori, M.; Hayashi, T.; Shimizu, Y. [Toshiba Corp., Tokyo (Japan)
1996-12-31
Toshiba is working on the development of MCFC stack technology; improvement of cell characteristics, and establishment of separator technology. For the cell technology, Toshiba has concentrated on both the restraints of NiO cathode dissolution and electrolyte loss from cells, which are the critical issues to extend cell life in MCFC, and great progress has been made. On the other hand, recognizing that the separator is one of key elements in accomplishing reliable and cost-competitive MCFC stacks, Toshiba has been accelerating the technology establishment and verification of an advanced type separator. A sub-scale stack with such a separator was provided for an electric generating test, and has been operated for more than 10,000 hours. This paper presents several topics obtained through the technical activities in the MCFC field at Toshiba.
Development of an Integrated Polymer Microfluidic Stack
International Nuclear Information System (INIS)
Datta, Proyag; Hammacher, Jens; Pease, Mark; Gurung, Sitanshu; Goettert, Jost
2006-01-01
Microfluidic is a field of considerable interest. While significant research has been carried out to develop microfluidic components, very little has been done to integrate the components into a complete working system. We present a flexible modular system platform that addresses the requirements of a complete microfluidic system. A microfluidic stack system is demonstrated with the layers of the stack being modular for specific functions. The stack and accompanying infrastructure provides an attractive platform for users to transition their design concepts into a working microfluidic system quickly with very little effort. The concept is demonstrated by using the system to carry out a chemilumiscence experiment. Details regarding the fabrication, assembly and experimental methods are presented
Detailed Electrochemical Characterisation of Large SOFC Stacks
DEFF Research Database (Denmark)
Mosbæk, Rasmus Rode; Hjelm, Johan; Barfod, R.
2012-01-01
application of advanced methods for detailed electrochemical characterisation during operation. An operating stack is subject to steep compositional gradients in the gaseous reactant streams, and significant temperature gradients across each cell and across the stack, which makes it a complex system...... Fuel Cell A/S was characterised in detail using electrochemical impedance spectroscopy. An investigation of the optimal geometrical placement of the current probes and voltage probes was carried out in order to minimise measurement errors caused by stray impedances. Unwanted stray impedances...... are particularly problematic at high frequencies. Stray impedances may be caused by mutual inductance and stray capacitance in the geometrical set-up and do not describe the fuel cell. Three different stack geometries were investigated by electrochemical impedance spectroscopy. Impedance measurements were carried...
Calculation of tritium release from reactor's stack
International Nuclear Information System (INIS)
Akhadi, M.
1996-01-01
Method for calculation of tritium release from nuclear to environment has been discussed. Part of gas effluent contain tritium in form of HTO vapor released from reactor's stack was sampled using silica-gel. The silica-gel was put in the water to withdraw HTO vapor absorbed by silica-gel. Tritium concentration in the water was measured by liquid scintillation counter of Aloka LSC-703. Tritium concentration in the gas effluent and total release of tritium from reactor's stack during certain interval time were calculated using simple mathematic formula. This method has examined for calculation of tritium release from JRR-3M's stack of JAERI, Japan. From the calculation it was obtained the value of tritium release as much as 4.63 x 10 11 Bq during one month. (author)
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
Nonlinearly stacked low noise turbofan stator
Schuster, William B. (Inventor); Nolcheff, Nick A. (Inventor); Gunaraj, John A. (Inventor); Kontos, Karen B. (Inventor); Weir, Donald S. (Inventor)
2009-01-01
A nonlinearly stacked low noise turbofan stator vane having a characteristic curve that is characterized by a nonlinear sweep and a nonlinear lean is provided. The stator is in an axial fan or compressor turbomachinery stage that is comprised of a collection of vanes whose highly three-dimensional shape is selected to reduce rotor-stator and rotor-strut interaction noise while maintaining the aerodynamic and mechanical performance of the vane. The nonlinearly stacked low noise turbofan stator vane reduces noise associated with the fan stage of turbomachinery to improve environmental compatibility.
Stack Monitoring System At PUSPATI TRIGA Reactor
International Nuclear Information System (INIS)
Zamrul Faizad Omar; Mohd Sabri Minhat; Zareen Khan Abdul Jalil Khan; Ridzuan Abdul Mutalib; Khairulezwan Abdul Manan; Nurfarhana Ayuni Joha; Izhar Abu Hussin
2014-01-01
This paper describes the current Stack Monitoring System at PUSPATI TRIGA Reactor (RTP) building. A stack monitoring system is a continuous air monitor placed at the reactor top for monitoring the presence of radioactive gaseous in the effluent air from the RTP building. The system consists of four detectors that provide the reading for background, particulate, Iodine and Noble gas. There is a plan to replace the current system due to frequent fault of the system, thus thorough understanding of the current system is required. Overview of the whole system will be explained in this paper. Some current results would be displayed and moving forward brief plan would be mentioned. (author)
Learning molecular energies using localized graph kernels
Ferré, Grégoire; Haut, Terry; Barros, Kipton
2017-03-01
Recent machine learning methods make it possible to model potential energy of atomic configurations with chemical-level accuracy (as calculated from ab initio calculations) and at speeds suitable for molecular dynamics simulation. Best performance is achieved when the known physical constraints are encoded in the machine learning models. For example, the atomic energy is invariant under global translations and rotations; it is also invariant to permutations of same-species atoms. Although simple to state, these symmetries are complicated to encode into machine learning algorithms. In this paper, we present a machine learning approach based on graph theory that naturally incorporates translation, rotation, and permutation symmetries. Specifically, we use a random walk graph kernel to measure the similarity of two adjacency matrices, each of which represents a local atomic environment. This Graph Approximated Energy (GRAPE) approach is flexible and admits many possible extensions. We benchmark a simple version of GRAPE by predicting atomization energies on a standard dataset of organic molecules.
International Nuclear Information System (INIS)
Choi, Hyeon Chang; Park, Jun Hyub
2005-01-01
In this study, residual stress distribution in multi-stacked film by MEMS (Micro-Electro Mechanical System) process is predicted using Finite Element Method (FEM). We develop a finite element program for REsidual Stress Analysis (RESA) in multi-stacked film. The RESA predicts the distribution of residual stress field in multi-stacked film. Curvatures of multi-stacked film and single layers which consist of the multi-stacked film are used as the input to the RESA. To measure those curvatures is easier than to measure a distribution of residual stress. To verify the RESA, mean stresses and stress gradients of single and multilayers are measured. The mean stresses are calculated from curvatures of deposited wafer by using Stoney's equation. The stress gradients are calculated from the vertical deflection at the end of cantilever beam. To measure the mean stress of each layer in multi-stacked film, we measure the curvature of wafer with the film after etching layer by layer in multi-stacked film
Stochastic subset selection for learning with kernel machines.
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.
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.
Localized Multiple Kernel Learning Via Sample-Wise Alternating Optimization.
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.
Deep Restricted Kernel Machines Using Conjugate Feature Duality.
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.
Training Lp norm multiple kernel learning in the primal.
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.
Stacked spheres and lower bound theorem
Indian Academy of Sciences (India)
BASUDEB DATTA
2011-11-20
Nov 20, 2011 ... Preliminaries. Lower bound theorem. On going work. Definitions. An n-simplex is a convex hull of n + 1 affinely independent points. (called vertices) in some Euclidean space R. N . Stacked spheres and lower bound theorem. Basudeb Datta. Indian Institute of Science. 2 / 27 ...
Contemporary sample stacking in analytical electrophoresis
Czech Academy of Sciences Publication Activity Database
Šlampová, Andrea; Malá, Zdeňka; Pantůčková, Pavla; Gebauer, Petr; Boček, Petr
2013-01-01
Roč. 34, č. 1 (2013), s. 3-18 ISSN 0173-0835 R&D Projects: GA ČR GAP206/10/1219 Institutional support: RVO:68081715 Keywords : biological samples * stacking * trace analysis * zone electrophoresis Subject RIV: CB - Analytical Chemistry, Separation Impact factor: 3.161, year: 2013
SRS reactor stack plume marking tests
International Nuclear Information System (INIS)
Petry, S.F.
1992-03-01
Tests performed in 105-K in 1987 and 1988 demonstrated that the stack plume can successfully be made visible (i.e., marked) by introducing smoke into the stack breech. The ultimate objective of these tests is to provide a means during an emergency evacuation so that an evacuee can readily identify the stack plume and evacuate in the opposite direction, thus minimizing the potential of severe radiation exposure. The EPA has also requested DOE to arrange for more tests to settle a technical question involving the correct calculation of stack downwash. New test canisters were received in 1988 designed to produce more smoke per unit time; however, these canisters have not been evaluated, because normal ventilation conditions have not been reestablished in K Area. Meanwhile, both the authorization and procedure to conduct the tests have expired. The tests can be performed during normal reactor operation. It is recommended that appropriate authorization and procedure approval be obtained to resume testing after K Area restart
Scaling the CERN OpenStack cloud
Bell, T.; Bompastor, B.; Bukowiec, S.; Castro Leon, J.; Denis, M. K.; van Eldik, J.; Fermin Lobo, M.; Fernandez Alvarez, L.; Fernandez Rodriguez, D.; Marino, A.; Moreira, B.; Noel, B.; Oulevey, T.; Takase, W.; Wiebalck, A.; Zilli, S.
2015-12-01
CERN has been running a production OpenStack cloud since July 2013 to support physics computing and infrastructure services for the site. In the past year, CERN Cloud Infrastructure has seen a constant increase in nodes, virtual machines, users and projects. This paper will present what has been done in order to make the CERN cloud infrastructure scale out.
International Nuclear Information System (INIS)
Alberghi, Gian Luigi; Caceres, Elena; Goldstein, Kevin; Lowe, David A. . lowe@het.brown.edu
2001-08-01
We present a candidate supergravity solution for a stacked configuration of stable non-BPS D-branes in Type II string theory compactified on T 4 /Z 2 . This gives a supergravity description of nonabelian tachyon condensation on the brane woldvolume. (author)
Trace interpolation by slant-stack migration
International Nuclear Information System (INIS)
Novotny, M.
1990-01-01
The slant-stack migration formula based on the radon transform is studied with respect to the depth steep Δz of wavefield extrapolation. It can be viewed as a generalized trace-interpolation procedure including wave extrapolation with an arbitrary step Δz. For Δz > 0 the formula yields the familiar plane-wave decomposition, while for Δz > 0 it provides a robust tool for migration transformation of spatially under sampled wavefields. Using the stationary phase method, it is shown that the slant-stack migration formula degenerates into the Rayleigh-Sommerfeld integral in the far-field approximation. Consequently, even a narrow slant-stack gather applied before the diffraction stack can significantly improve the representation of noisy data in the wavefield extrapolation process. The theory is applied to synthetic and field data to perform trace interpolation and dip reject filtration. The data examples presented prove that the radon interpolator works well in the dip range, including waves with mutual stepouts smaller than half the dominant period
Contemporary sample stacking in analytical electrophoresis
Czech Academy of Sciences Publication Activity Database
Malá, Zdeňka; Šlampová, Andrea; Křivánková, Ludmila; Gebauer, Petr; Boček, Petr
2015-01-01
Roč. 36, č. 1 (2015), s. 15-35 ISSN 0173-0835 R&D Projects: GA ČR(CZ) GA13-05762S Institutional support: RVO:68081715 Keywords : biological samples * stacking * trace analysis * zone electrophoresis Subject RIV: CB - Analytical Chemistry, Separation Impact factor: 2.482, year: 2015
40 CFR 61.53 - Stack sampling.
2010-07-01
... 40 Protection of Environment 8 2010-07-01 2010-07-01 false Stack sampling. 61.53 Section 61.53 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) NATIONAL... sampling. (a) Mercury ore processing facility. (1) Unless a waiver of emission testing is obtained under...
40 CFR 61.33 - Stack sampling.
2010-07-01
... 40 Protection of Environment 8 2010-07-01 2010-07-01 false Stack sampling. 61.33 Section 61.33 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) NATIONAL... sampling. (a) Unless a waiver of emission testing is obtained under § 61.13, each owner or operator...
OpenStack cloud computing cookbook
Jackson, Kevin
2013-01-01
A Cookbook full of practical and applicable recipes that will enable you to use the full capabilities of OpenStack like never before.This book is aimed at system administrators and technical architects moving from a virtualized environment to cloud environments with familiarity of cloud computing platforms. Knowledge of virtualization and managing linux environments is expected.
Toward advising SME's on stacked funding
Rauwerda, Kirsten; van Teeffelen, Lex; de Graaf, Frank Jan
2017-01-01
This paper addresses new funding issues faced by SMEs. Over a period of nine months, the authors conducted a preliminary study into the problems surrounding stacked funding faced by SMEs and their financial advisers. The study includes a short literature review, the outcomes of three round table
Photoswitchable Intramolecular H-Stacking of Perylenebisimide
Wang, Jiaobing; Kulago, Artem; Browne, Wesley R.; Feringa, Ben L.
2010-01-01
Dynamic control over the formation of H- or J-type aggregates of chromophores is of fundamental importance for developing responsive organic optoelectronic materials. In this study, the first example of photoswitching between a nonstacked and an intramolecularly H-stacked arrangement of
Optoelectronic interconnects for 3D wafer stacks
Ludwig, David; Carson, John C.; Lome, Louis S.
1996-01-01
Wafer and chip stacking are envisioned as means of providing increased processing power within the small confines of a three-dimensional structure. Optoelectronic devices can play an important role in these dense 3-D processing electronic packages in two ways. In pure electronic processing, optoelectronics can provide a method for increasing the number of input/output communication channels within the layers of the 3-D chip stack. Non-free space communication links allow the density of highly parallel input/output ports to increase dramatically over typical edge bus connections. In hybrid processors, where electronics and optics play a role in defining the computational algorithm, free space communication links are typically utilized for, among other reasons, the increased network link complexity which can be achieved. Free space optical interconnections provide bandwidths and interconnection complexity unobtainable in pure electrical interconnections. Stacked 3-D architectures can provide the electronics real estate and structure to deal with the increased bandwidth and global information provided by free space optical communications. This paper will provide definitions and examples of 3-D stacked architectures in optoelectronics processors. The benefits and issues of these technologies will be discussed.
OpenStack Object Storage (Swift) essentials
Kapadia, Amar; Varma, Sreedhar
2015-01-01
If you are an IT administrator and you want to enter the world of cloud storage using OpenStack Swift, then this book is ideal for you. Basic knowledge of Linux and server technology is beneficial to get the most out of the book.
Gradient-based adaptation of general gaussian kernels.
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.
On weights which admit the reproducing kernel of Bergman type
Directory of Open Access Journals (Sweden)
Zbigniew Pasternak-Winiarski
1992-01-01
Full Text Available In this paper we consider (1 the weights of integration for which the reproducing kernel of the Bergman type can be defined, i.e., the admissible weights, and (2 the kernels defined by such weights. It is verified that the weighted Bergman kernel has the analogous properties as the classical one. We prove several sufficient conditions and necessary and sufficient conditions for a weight to be an admissible weight. We give also an example of a weight which is not of this class. As a positive example we consider the weight μ(z=(Imz2 defined on the unit disk in ℂ.
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...