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Sample records for prevent kernel migration

  1. Mitigation of artifacts in rtm with migration kernel decomposition

    KAUST Repository

    Zhan, Ge

    2012-01-01

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

  2. Migration of ThO2 kernels under the influence of a temperature gradient

    International Nuclear Information System (INIS)

    Smith, C.L.

    1976-11-01

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

  3. Transgastric migration of gossypiboma: A preventable complication

    Directory of Open Access Journals (Sweden)

    Mahesh Kumar Goenka

    2015-01-01

    Full Text Available Gossypiboma is term given for retained piece of cotton/sponge during surgery. The incidence of gossypiboma has described as 1 in 1000-3000 surgeries. Incidence is underestimated because of underreporting due to fear of medico-legal litigation and extreme criticism by media. Intraluminal migration is a rare complication of gossypiboma. Small intestine is most common intraluminal site followed by duodenum. Here, we report sixth case of transgastric migration of gossypiboma.

  4. Surface coating for prevention of metallic seed migration in tissues

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Hyunseok; Park, Jong In [Program in Biomedical Radiation Sciences, Department of Transdisciplinary Studies, Graduate School of Convergence Science and Technology, Seoul National University, Seoul 151-742 (Korea, Republic of); Lee, Won Seok; Park, Min [Interdisciplinary Program in Bioengineering, Seoul National University College of Engineering, Seoul 151-742 (Korea, Republic of); Son, Kwang-Jae [Hanaro Applications Research, Korea Atomic Energy Research Institute, Daejeon 305-353 (Korea, Republic of); Bang, Young-bong [Advanced Institutes of Convergence Technology, Seoul National University, Suwon 443-270 (Korea, Republic of); Choy, Young Bin, E-mail: ybchoy@snu.ac.kr, E-mail: sye@snu.ac.kr [Interdisciplinary Program in Bioengineering, Seoul National University College of Engineering, Seoul 110-744 (Korea, Republic of); Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul 110-744 (Korea, Republic of); Institute of Medical and Biological Engineering, Medical Research Center, Seoul National University, Seoul 110-744 (Korea, Republic of); Ye, Sung-Joon, E-mail: ybchoy@snu.ac.kr, E-mail: sye@snu.ac.kr [Program in Biomedical Radiation Sciences, Department of Transdisciplinary Studies, Graduate School of Convergence Science and Technology, Seoul National University, Seoul 151-742 (Korea, Republic of); Advanced Institutes of Convergence Technology, Seoul National University, Suwon 443-270 (Korea, Republic of); Department of Radiation Oncology, Seoul National University Hospital, Seoul 110-744 (Korea, Republic of)

    2015-06-15

    Purpose: In radiotherapy, metallic implants often detach from their deposited sites and migrate to other locations. This undesirable migration could cause inadequate dose coverage for permanent brachytherapy and difficulties in image-guided radiation delivery for patients. To prevent migration of implanted seeds, the authors propose a potential strategy to use a biocompatible and tissue-adhesive material called polydopamine. Methods: In this study, nonradioactive dummy seeds that have the same geometry and composition as commercial I-125 seeds were coated in polydopamine. Using scanning electron microscopy and x-ray photoelectron spectroscopy, the surface of the polydopamine-coated and noncoated seeds was characterized. The detachment stress between the two types of seeds and the tissue was measured. The efficacy of polydopamine-coated seed was investigated through in vitro migration tests by tracing the seed location after tissue implantation and shaking for given times. The cytotoxicity of the polydopamine coating was also evaluated. Results: The results of the coating characterization have shown that polydopamine was successfully coated on the surface of the seeds. In the adhesion test, the polydopamine-coated seeds had 2.1-fold greater detachment stress than noncoated seeds. From the in vitro test, it was determined that the polydopamine-coated seed migrated shorter distances than the noncoated seed. This difference was increased with a greater length of time after implantation. Conclusions: The authors suggest that polydopamine coating is an effective technique to prevent migration of implanted seeds, especially for permanent prostate brachytherapy.

  5. Planet heating prevents inward migration of planetary cores.

    Science.gov (United States)

    Benítez-Llambay, Pablo; Masset, Frédéric; Koenigsberger, Gloria; Szulágyi, Judit

    2015-04-02

    Planetary systems are born in the disks of gas, dust and rocky fragments that surround newly formed stars. Solid content assembles into ever-larger rocky fragments that eventually become planetary embryos. These then continue their growth by accreting leftover material in the disk. Concurrently, tidal effects in the disk cause a radial drift in the embryo orbits, a process known as migration. Fast inward migration is predicted by theory for embryos smaller than three to five Earth masses. With only inward migration, these embryos can only rarely become giant planets located at Earth's distance from the Sun and beyond, in contrast with observations. Here we report that asymmetries in the temperature rise associated with accreting infalling material produce a force (which gives rise to an effect that we call 'heating torque') that counteracts inward migration. This provides a channel for the formation of giant planets and also explains the strong planet-metallicity correlation found between the incidence of giant planets and the heavy-element abundance of the host stars.

  6. HIV Prevention among Mexican Migrants at Different Migration Phases: Exposure to Prevention Messages and Association With Testing Behaviors

    Science.gov (United States)

    Martinez-Donate, Ana P.; Rangel, M. Gudelia; Zhang, Xiao; Simon, Norma-Jean; Rhoads, Natalie; Gonzalez-Fagoaga, J. Eduardo; Gonzalez, Ahmed Asadi

    2016-01-01

    Mobile populations are at increased risk for HIV infection. Exposure to HIV prevention messages at all phases of the migration process may help decrease im/migrants’ HIV risk. We investigated levels of exposure to HIV prevention messages, factors associated with message exposure, and the association between exposure to prevention messages and HIV testing behavior among Mexican im/migrants at different phases of the migration process. We conducted a cross-sectional, probability survey of Mexican im/migrants (N=3,149) traveling through the border city of Tijuana, Mexico. The results indicate limited exposure to prevention messages (57%–75%) and suboptimal last 12-month HIV testing rates (14%–25%) across five migration phases. Compared to pre-departure levels (75%), exposure to messages decreases at all post-departure migration phases (57%–63%, pprevention messages is positively associated with greater odds of HIV testing at the pre-departure, destination, and interception phases. Binational efforts need to be intensified to reach and deliver HIV prevention to Mexican im/migrants across the migration continuum. PMID:26595267

  7. Multibarrier system preventing migration of radionuclides from radioactive waste repository

    Directory of Open Access Journals (Sweden)

    Olszewska Wioleta

    2015-09-01

    Full Text Available Safety of radioactive waste repositories operation is associated with a multibarrier system designed and constructed to isolate and contain the waste from the biosphere. Each of radioactive waste repositories is equipped with system of barriers, which reduces the possibility of release of radionuclides from the storage site. Safety systems may differ from each other depending on the type of repository. They consist of the natural geological barrier provided by host rocks of the repository and its surroundings, and an engineered barrier system (EBS. The EBS may itself comprise a variety of sub-systems or components, such as waste forms, canisters, buffers, backfills, seals and plugs. The EBS plays a major role in providing the required disposal system performance. It is assumed that the metal canisters and system of barriers adequately isolate waste from the biosphere. The evaluation of the multibarrier system is carried out after detailed tests to determine its parameters, and after analysis including mathematical modeling of migration of contaminants. To provide an assurance of safety of radioactive waste repository multibarrier system, detailed long term safety assessments are developed. Usually they comprise modeling of EBS stability, corrosion rate and radionuclide migration in near field in geosphere and biosphere. The principal goal of radionuclide migration modeling is assessment of the radionuclides release paths and rate from the repository, radionuclides concentration in geosphere in time and human exposure to ionizing radiation

  8. The spatial sensitivity of Sp converted waves—scattered-wave kernels and their applications to receiver-function migration and inversion

    Science.gov (United States)

    Mancinelli, N. J.; Fischer, K. M.

    2018-03-01

    We characterize the spatial sensitivity of Sp converted waves to improve constraints on lateral variations in uppermost-mantle velocity gradients, such as the lithosphere-asthenosphere boundary (LAB) and the mid-lithospheric discontinuities. We use SPECFEM2D to generate 2-D scattering kernels that relate perturbations from an elastic half-space to Sp waveforms. We then show that these kernels can be well approximated using ray theory, and develop an approach to calculating kernels for layered background models. As proof of concept, we show that lateral variations in uppermost-mantle discontinuity structure are retrieved by implementing these scattering kernels in the first iteration of a conjugate-directions inversion algorithm. We evaluate the performance of this technique on synthetic seismograms computed for 2-D models with undulations on the LAB of varying amplitude, wavelength and depth. The technique reliably images the position of discontinuities with dips 100-200 km. In cases of mild topography on a shallow LAB, the relative brightness of the LAB and Moho converters approximately agrees with the ratio of velocity contrasts across the discontinuities. Amplitude retrieval degrades at deeper depths. For dominant periods of 4 s, the minimum station spacing required to produce unaliased results is 5 km, but the application of a Gaussian filter can improve discontinuity imaging where station spacing is greater.

  9. Film breakers prevent migration of aqueous potassium hydroxide in fuel cells

    Science.gov (United States)

    Hess, P. D.

    1970-01-01

    Electrolyte film breakers made from polytetrafluoroethylene are installed in the reactant and water vapor removal outlets of each cell and sealed by elastomers. Use of these devices in the water vapor removal cavity outlets prevents loss of KOH solution through film migration during water removal.

  10. RTOS kernel in portable electrocardiograph

    Science.gov (United States)

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

    2011-12-01

    This paper presents the use of a Real Time Operating System (RTOS) on a portable electrocardiograph based on a microcontroller platform. All medical device digital functions are performed by the microcontroller. The electrocardiograph CPU is based on the 18F4550 microcontroller, in which an uCOS-II RTOS can be embedded. The decision associated with the kernel use is based on its benefits, the license for educational use and its intrinsic time control and peripherals management. The feasibility of its use on the electrocardiograph is evaluated based on the minimum memory requirements due to the kernel structure. The kernel's own tools were used for time estimation and evaluation of resources used by each process. After this feasibility analysis, the migration from cyclic code to a structure based on separate processes or tasks able to synchronize events is used; resulting in an electrocardiograph running on one Central Processing Unit (CPU) based on RTOS.

  11. Focused tight dressing does not prevent cochlear implant magnet migration under 1.5 Tesla MRI.

    Science.gov (United States)

    Cuda, D; Murri, A; Succo, G

    2013-04-01

    We report a retrospective case of inner magnet migration, which occurred after 1.5 Tesla MRI scanning in an adult recipient of a bilateral cochlear implant (CI) despite a focused head dressing. The patient, bilaterally implanted with Nucleus 5 CIs (Cochlear LTD, Sydney, Australia), underwent a 1.5 Tesla cholangio-MRI scan for biliary duct pathology. In subsequent days, a focal skin alteration appeared over the left inner coil. Plain skull radiographs showed partial magnet migration on the left side. Surgical exploration confirmed magnet twisting; the magnet was effectively repositioned. Left CI performance was restored to pre-migration level. The wound healed without complications. Thus, focused dressing does not prevent magnet migration in CI recipients undergoing 1.5 Tesla MRI. All patients should be counselled on this potential complication. A minor surgical procedure is required to reposition the magnet. Nevertheless, timely diagnosis is necessary to prevent skin breakdown and subsequent device contamination. Plain skull radiograph is very effective in identifying magnet twisting; it should be performed systematically after MRI or minimally on all suspected cases.

  12. Unaccompanied Children Migrating from Central America: Public Health Implications for Violence Prevention and Intervention.

    Science.gov (United States)

    Estefan, Lianne Fuino; Ports, Katie A; Hipp, Tracy

    2017-04-01

    Unaccompanied children (UC) migrating to the USA from the Central American countries of El Salvador, Guatemala, and Honduras are an underserved population at high risk for health, academic, and social problems. These children experience trauma, violence, and other risk factors that are shared among several types of interpersonal violence. The trauma and violence experienced by many unaccompanied children, and the subsequent implications for their healthy development into adulthood, indicate the critical need for a public health approach to prevention and intervention. This paper provides an overview of the violence experienced by unaccompanied children along their migration journey, the implications of violence and trauma for the health and well-being of the children across their lifespan, prevention and intervention approaches for UC resettled in the USA, and suggestions for adapted interventions to best address the unique needs of this vulnerable population.

  13. Branch migration prevents DNA loss during double-strand break repair.

    Directory of Open Access Journals (Sweden)

    Julia S P Mawer

    2014-08-01

    Full Text Available The repair of DNA double-strand breaks must be accurate to avoid genomic rearrangements that can lead to cell death and disease. This can be accomplished by promoting homologous recombination between correctly aligned sister chromosomes. Here, using a unique system for generating a site-specific DNA double-strand break in one copy of two replicating Escherichia coli sister chromosomes, we analyse the intermediates of sister-sister double-strand break repair. Using two-dimensional agarose gel electrophoresis, we show that when double-strand breaks are formed in the absence of RuvAB, 4-way DNA (Holliday junctions are accumulated in a RecG-dependent manner, arguing against the long-standing view that the redundancy of RuvAB and RecG is in the resolution of Holliday junctions. Using pulsed-field gel electrophoresis, we explain the redundancy by showing that branch migration catalysed by RuvAB and RecG is required for stabilising the intermediates of repair as, when branch migration cannot take place, repair is aborted and DNA is lost at the break locus. We demonstrate that in the repair of correctly aligned sister chromosomes, an unstable early intermediate is stabilised by branch migration. This reliance on branch migration may have evolved to help promote recombination between correctly aligned sister chromosomes to prevent genomic rearrangements.

  14. Fat Body Cells Are Motile and Actively Migrate to Wounds to Drive Repair and Prevent Infection.

    Science.gov (United States)

    Franz, Anna; Wood, Will; Martin, Paul

    2018-02-26

    Adipocytes have many functions in various tissues beyond energy storage, including regulating metabolism, growth, and immunity. However, little is known about their role in wound healing. Here we use live imaging of fat body cells, the equivalent of vertebrate adipocytes in Drosophila, to investigate their potential behaviors and functions following skin wounding. We find that pupal fat body cells are not immotile, as previously presumed, but actively migrate to wounds using an unusual adhesion-independent, actomyosin-driven, peristaltic mode of motility. Once at the wound, fat body cells collaborate with hemocytes, Drosophila macrophages, to clear the wound of cell debris; they also tightly seal the epithelial wound gap and locally release antimicrobial peptides to fight wound infection. Thus, fat body cells are motile cells, enabling them to migrate to wounds to undertake several local functions needed to drive wound repair and prevent infections. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

  15. Gas migration from closed coal mines to the surface. Risk assessment methodology and prevention means

    International Nuclear Information System (INIS)

    Pokryszka, Z.; Tauziede, Ch.; Lagny, C.; Guise, Y.; Gobillot, R.; Planchenault, J.M.; Lagarde, R.

    2005-01-01

    French law as regards renunciation to mining concessions calls for the mining operator to first undertake analyses of the risks represented by their underground mining works. The problem of gas migration to the surface is especially significant in the context of coal mines. This is because mine gas can migrate to the earth's surface, then present significant risks: explosion, suffocation or gas poisoning risks. As part of the scheduled closure of all coal mining operations in France, INERIS has drawn up, at the request of national mining operator Charbonnages de France, a general methodology for assessing the risk linked to gas in the context of closed coal mines. This article presents the principles of this methodology. An application example based on a true case study is then described. This is completed by a presentation of the preventive and monitoring resources recommended and usually applied in order to manage the risk linked to gaseous emissions. (authors)

  16. Migration and chemokine receptor pattern of colitis-preventing DX5+NKT cells.

    Science.gov (United States)

    Hornung, Matthias; Werner, Jens M; Farkas, Stefan; Schlitt, Hans J; Geissler, Edward K

    2011-11-01

    DX5(+)NKT cells are a subpopulation of NKT cells expressing both T cell receptor and NK cell markers that show an immune-regulating function. Transferred DX5(+)NKT cells from immune competent Balb/c mice can prevent or reduce induced colitis in severe combined immunodeficient (SCID) mice. Here, we investigated the in vivo migration of DX5(+)NKT cells and their corresponding chemokine receptor patterns. DX5(+)NKT cells were isolated from spleens of Balb/c mice and transferred into Balb/c SCID mice. After 2 and 8 days, in vivo migration was examined using in vivo microscopy. In addition, the chemokine receptor pattern was analyzed with fluorescence-activated cell sorting (FACS) and the migration assay was performed. Our results show that labeled DX5(+)NKT cells were primarily detectable in mesenteric lymph nodes and spleen after transfer. After 8 days, DX5(+)NKT cells were observed in the colonic tissues, especially the appendix. FACS analysis of chemokine receptors in DX5(+)NKT cells revealed expression of CCR3, CCR6, CCR9, CXCR3, CXCR4, and CXCR6, but no CCR5, CXCR5, or the lymphoid homing receptor CCR7. Stimulation upregulated especially CCR7 expression, and chemokine receptor patterns were different between splenic and liver DX5(+)NKT cells. These data indicate that colitis-preventing DX5(+)NKT cells need to traffic through lymphoid organs to execute their immunological function at the site of inflammation. Furthermore, DX5(+)NKT cells express a specific chemokine receptor pattern with an upregulation of the lymphoid homing receptor CCR7 after activation.

  17. Can arthroscopic rotator cuff repair prevent proximal migration of the humeral head?

    Directory of Open Access Journals (Sweden)

    Pablo Sanz-Ruiz

    2015-12-01

    Full Text Available Introduction: Shoulder arthroscopy has become increasingly used in recent years, especially in rotator cuff repair. The purpose of this study was to determine whether arthroscopic rotator cuff repair could prevent proximal migration of the humeral head. Material and Methods: We performed a retrospective study of 56 patients suffering from shoulder pain. They were divided into two groups, one comprising patients with impingement syndrome who underwent acromioplasty only and another comprising patients with rotator cuff tear who underwent acromioplasty combined with rotator cuff repair. The pre-operative Hirooka angle and the results of the simple shoulder test (SST were compared after 1 year. Results: We found no differences between the groups for the Hirooka angle or SST results. We did find a significant difference (P<0.05 between pre-operative and post-operative SST results. Conclusions: Rotator cuff repair using arthroscopy is a minimally invasive procedure that improves function and prevents proximal migration of the humeral head after 1 year of follow-up. [Arch Clin Exp Surg 2015; 4(4.000: 190-195

  18. Resveratrol Prevents High Fluence Red Light-Emitting Diode Reactive Oxygen Species-Mediated Photoinhibition of Human Skin Fibroblast Migration.

    Directory of Open Access Journals (Sweden)

    Andrew Mamalis

    Full Text Available Skin fibrosis is a significant medical problem that leads to a functional, aesthetic, and psychosocial impact on quality-of-life. Light-emitting diode-generated 633-nm red light (LED-RL is part of the visible light spectrum that is not known to cause DNA damage and is considered a safe, non-invasive, inexpensive, and portable potential alternative to ultraviolet phototherapy that may change the treatment paradigm of fibrotic skin disease.The goal of our study was to investigate the how reactive oxygen species (ROS free radicals generated by high fluence LED-RL inhibit the migration of skin fibroblasts, the main cell type involved in skin fibrosis. Fibroblast migration speed is increased in skin fibrosis, and we studied cellular migration speed of cultured human skin fibroblasts as a surrogate measure of high fluence LED-RL effect on fibroblast function. To ascertain the inhibitory role of LED-RL generated ROS on migration speed, we hypothesized that resveratrol, a potent antioxidant, could prevent the photoinhibitory effects of high fluence LED-RL on fibroblast migration speed.High fluence LED-RL generated ROS were measured by flow cytometry analysis using dihydrorhodamine (DHR. For purposes of comparison, we assessed the effects of ROS generated by hydrogen peroxide (H2O2 on fibroblast migration speed and the ability of resveratrol, a well known antioxidant, to prevent LED-RL and H2O2 generated ROS-associated changes in fibroblast migration speed. To determine whether resveratrol could prevent the high fluence LED-RL ROS-mediated photoinhibition of human skin fibroblast migration, treated cells were incubated with resveratrol at concentrations of 0.0001% and 0.001% for 24 hours, irradiated with high fluences LED-RL of 480, 640, and 800 J/cm2.High fluence LED-RL increases intracellular fibroblast ROS and decreases fibroblast migration speed. LED-RL at 480, 640 and 800 J/cm2 increased ROS levels to 132.8%, 151.0%, and 158.4% relative to matched

  19. Endoscopic suturing for the prevention of stent migration in benign upper gastrointestinal conditions: a comparative multicenter study.

    Science.gov (United States)

    Ngamruengphong, Saowanee; Sharaiha, Reem Z; Sethi, Amrita; Siddiqui, Ali A; DiMaio, Christopher J; Gonzalez, Susana; Im, Jennifer; Rogart, Jason N; Jagroop, Sophia; Widmer, Jessica; Hasan, Raza Abbas; Laique, Sobia; Gonda, Tamas; Poneros, John; Desai, Amit; Tyberg, Amy; Kumbhari, Vivek; El Zein, Mohamad; Abdelgelil, Ahmed; Besharati, Sepideh; Hernaez, Ruben; Okolo, Patrick I; Singh, Vikesh; Kalloo, Anthony N; Kahaleh, Michel; Khashab, Mouen A

    2016-09-01

    Fully covered self-expandable metal stents (FCSEMSs) have increasingly been used in benign upper gastrointestinal (UGI) conditions; however, stent migration remains a major limitation. Endoscopic suture fixation (ESF) may prevent stent migration. The aims of this study were to compare the frequency of stent migration in patients who received endoscopic suturing for stent fixation (ESF group) compared with those who did not (NSF group) and to assess the impact of ESF on clinical outcome. This was a retrospective study of patients who underwent FCSEMS placement for benign UGI diseases. Patients were divided into either the NSF or ESF group. Outcome variables, including stent migration, clinical success (resolution of underlying pathology), and adverse events, were compared. A total of 125 patients (44 in ESF group, 81 in NSF group; 56 benign strictures, 69 leaks/fistulas/perforations) underwent 224 stenting procedures. Stent migration was significantly more common in the NSF group (33 % vs. 16 %; P = 0.03). Time to stent migration was longer in the ESF group (P = 0.02). ESF appeared to protect against stent migration in patients with a history of stent migration (adjusted odds ratio [OR] 0.09; P = 0.002). ESF was also significantly associated with a higher rate of clinical success (60 % vs. 38 %; P = 0.03). Rates of adverse events were similar between the two groups. Endoscopic suturing for stent fixation is safe and associated with a decreased migration rate, particularly in patients with a prior history of stent migration. It may also improve clinical response, likely because of the reduction in stent migration. © Georg Thieme Verlag KG Stuttgart · New York.

  20. Oil, migration, and the political economy of HIV/AIDS prevention in Nigeria's Niger Delta.

    Science.gov (United States)

    Udoh, Isidore A

    2013-01-01

    In most of sub-Saharan Africa, HIV/AIDS is driven by endemic structural problems such as unemployment, poverty, forced migration, sexual exploitation, and concurrent sexual partnerships. In the Niger Delta of Nigeria, the epidemic is exacerbated by recurring regional conflict and negative environmental externalities resulting from 50 years of oil exploration. This article seeks to identify and analyze potential barriers to HIV/AIDS prevention and treatment from oil pollution and other environmental stressors in Nigeria's Niger Delta. We develop a conceptual framework to understand how oil politics and economic systems affect HIV risks in Nigeria. We then evaluate evidence of how environmental exposures can amplify risks. Using 10 semi-structured interviews, with 85 focus group participants, we test the argument that HIV transmission in the Niger Delta is related to a manipulative "divide and rule" power dynamic that characterizes multinational oil companies' role in shaping conflict contours in oil communities. Oil exploration destroys livelihoods, institutions, and values and forces impoverished and illiterate girls and women to migrate or be trafficked to urban centers as child laborers and sex workers. The elevated HIV/AIDS risk in the Niger Delta brings into focus the political economy of resource extraction, globalization, and indigenous, minority rights and struggles.

  1. Removal of NAPLs from the unsaturated zone using steam: prevention of downward migration by injecting mixtures of steam and air

    DEFF Research Database (Denmark)

    Schmidt, R.; Gudbjerg, Jacob; Sonnenborg, Torben Obel

    2002-01-01

    injection technology is presented, where a mixture of steam and air was injected. In twodimensional experiments with unsaturated porous medium contaminated with nonaqueous phase liquids, it was demonstrated how injection of pure steam lead to severe downward migration. Similar experiments, where steam......Steam injection for remediation of porous media contaminated by nonaqueous phase liquids has been shown to be a potentially efficient technology. There is, however, concern that the technique may lead to downward migration of separate phase contaminant. In this work, a modification of the steam...... and air were injected simultaneously, resulted in practically no downward migration and still rapid cleanup was achieved. The processes responsible for the prevention of downward migration when injecting steam–air mixtures were analyzed using a nonisothermal multiphase flow and transport model. Hereby...

  2. Induced thyme product prevents VEGF-induced migration in human umbilical vein endothelial cells.

    Science.gov (United States)

    Krill, Diane; Madden, John; Huncik, Kevin; Moeller, Peter D

    2010-12-17

    Compounds with anti-angiogenic properties are useful in combating cancer by preventing new blood vessel formation to support the tumor. In this report we introduce a rapid method for screening potential anti-angiogenic compounds in a model system that stimulates the production of secondary defense chemicals in plants. This methodology identified an inducible vascular factor (IVF3), which was found to be inhibitory in all of the model systems tested. Thyme plants were exposed to highly vascular mint plants and the methanol extracts were analyzed by reverse phase HPLC. The thyme compounds induced by the invading mint tissue, and not present in the thyme plants grown alone, were tested in a vertical plate assay measuring root length as a quantitative assay for drug sensitivity. The HPLC-purified extract, referred to as IVF3, reduced the growth of root vascular tissue compared to the control and vehicle control, and 50% as well as known angiogenesis inhibitors, VEGF receptor tyrosine kinase inhibitor and amiloride hydrochloride. Extracted compounds that were effective inhibitors of plant roots were assayed in Madin Darby canine kidney epithelial cells (MDCK) for toxicity, and in human umbilical vein endothelial cells (HUVEC) for their effect on migration. IVF3 was effective at limiting HUVEC migration in VEGF-stimulated cultures. In vivo video capture of intersegmental vessel circulation between 48 and 72 h post fertilization in the developing vasculature of zebrafish embryos showed IVF3 also significantly reduced ISV functional circulation. This report demonstrates the anti-angiogenic effects of IVF3 extract in endothelial cells and in an intact vertebrate model for angiogenesis. Copyright © 2010 Elsevier Inc. All rights reserved.

  3. Iterative software kernels

    Energy Technology Data Exchange (ETDEWEB)

    Duff, I.

    1994-12-31

    This workshop focuses on kernels for iterative software packages. Specifically, the three speakers discuss various aspects of sparse BLAS kernels. Their topics are: `Current status of user lever sparse BLAS`; Current status of the sparse BLAS toolkit`; and `Adding matrix-matrix and matrix-matrix-matrix multiply to the sparse BLAS toolkit`.

  4. Prevention of polydimethylsiloxane microsphere migration using a mussel-inspired polydopamine coating for potential application in injection therapy.

    Directory of Open Access Journals (Sweden)

    Eun-Jae Chung

    Full Text Available The use of injectable bulking agents is a feasible alternative procedure for conventional surgical therapy. In this study, poly(dimethylsiloxane (PDMS microspheres coated with polydopamine (PDA were developed as a potential injection agent to prevent migration in vocal fold. Uniform PDMS microspheres are fabricated using a simple fluidic device and then coated with PDA. Cell attachment test reveals that the PDA-coated PDMS (PDA-PDMS substrate favors cell adhesion and attachment. The injected PDA-PDMS microspheres persist without migration on reconstructed axial CT images, whereas, pristine PDMS locally migrates over a period of 12 weeks. The gross appearance of the implants retrieved at 4, 8, 12 and 34 weeks indicates that the PDA-PDMS group maintained their original position without significant migration until 34 weeks after injection. By contrast, there is diffuse local migration of the pristine PDMS group from 4 weeks after injection. The PDA-coated PDMS microspheres can potentially be used as easily injectable, non-absorbable filler without migration.

  5. 3-D waveform tomography sensitivity kernels for anisotropic media

    KAUST Repository

    Djebbi, Ramzi

    2014-01-01

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

  6. Kernels for structured data

    CERN Document Server

    Gärtner, Thomas

    2009-01-01

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

  7. Linux Kernel in a Nutshell

    CERN Document Server

    Kroah-Hartman, Greg

    2009-01-01

    Linux Kernel in a Nutshell covers the entire range of kernel tasks, starting with downloading the source and making sure that the kernel is in sync with the versions of the tools you need. In addition to configuration and installation steps, the book offers reference material and discussions of related topics such as control of kernel options at runtime.

  8. Endovascular AAA exclusion: will stents with hooks and barbs prevent stent-graft migration?

    Science.gov (United States)

    Malina, M; Lindblad, B; Ivancev, K; Lindh, M; Malina, J; Brunkwall, J

    1998-11-01

    To investigate if stents with hooks and barbs will improve stent-graft fixation in the abdominal aorta. Sixteen- to 24-mm-diameter Dacron grafts were deployed inside cadaveric aortas. The grafts were anchored by stents as in endovascular abdominal aortic aneurysm repair. One hundred thirty-seven stent-graft deployments were carried out with modified self-expanding Z-stents with (A) no hooks and barbs (n = 75), (B) 4 5-mm-long hooks and barbs (n = 39), (C) 8 10-mm-long, strengthened hooks and barbs (n = 19), or (D) hooks only (n = 4). Increasing longitudinal traction was applied to determine the displacement force needed to extract the stent-grafts. The radial force of the stents was measured and correlated to the displacement force. The median (interquartile range) displacement force needed to extract grafts anchored by stent A was 2.5 N (2.0 to 3.4), stent B 7.8 N (7.4 to 10.8), and stent C 22.5 N (17.1 to 27.9), p barbs added anchoring strength. During traction, the weaker barbs were distorted or caused intimal tears. The stronger barbs engaged the entire aortic wall. The radial force of the stents had no impact on fixation, while aortic calcification and graft oversizing had marginal effects. Stent barbs and hooks increased the fixation of stent-grafts tenfold, while the radial force of stents had no impact. These data may prove important in future endograft development to prevent stent-graft migration after aneurysm exclusion.

  9. Data-variant kernel analysis

    CERN Document Server

    Motai, Yuichi

    2015-01-01

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

  10. Multidimensional kernel estimation

    CERN Document Server

    Milosevic, Vukasin

    2015-01-01

    Kernel estimation is one of the non-parametric methods used for estimation of probability density function. Its first ROOT implementation, as part of RooFit package, has one major issue, its evaluation time is extremely slow making in almost unusable. The goal of this project was to create a new class (TKNDTree) which will follow the original idea of kernel estimation, greatly improve the evaluation time (using the TKTree class for storing the data and creating different user-controlled modes of evaluation) and add the interpolation option, for 2D case, with the help of the new Delaunnay2D class.

  11. 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...

  12. 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...

  13. Realized kernels in practice

    DEFF Research Database (Denmark)

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

    2009-01-01

    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...

  14. Cryptomphalus aspersa mollusc eggs extract promotes migration and prevents cutaneous ageing in keratinocytes and dermal fibroblasts in vitro.

    Science.gov (United States)

    Espada, J; Matabuena, M; Salazar, N; Lucena, S; Kourani, O; Carrasco, E; Calvo, M; Rodríguez, C; Reyes, E; González, S; Juarranz, A

    2015-02-01

    The search of substances that minimize cutaneous ageing has increased in the last few years. Previous studies have described the regenerative properties of the secretion of the mollusc Cryptomphalus aspersa (C. aspersa) when applied topically. We evaluate the in vitro effects of a new product derived from the eggs of C. aspersa, IFC-CAF, on cell proliferation, migration, distribution of cytoskeletal proteins, production of extracellular components as well as its ability to prevent cutaneous ageing because of intrinsic or extrinsic factors (exposure to UVB) by determination of ageing markers. We have used the human keratinocyte cell line (HaCaT cells), primary dermal fibroblasts (HDF) and senescent dermal fibroblasts (SHDF). The effects of the compound on cell proliferation and on the cell cycle were determined by the MTT colorimetric assay, estimation of total protein and/or trypan blue test and by flow cytometry, respectively. We also studied cell migration using the wound-healing migration assay, whereas ELISA assays, Western Blot and immunofluorescence microscopy were carried out to test the expression of proteins related to cytoskeleton, extracellular matrix and with ageing. We have found that IFC-CAF does not promote proliferation but induces migration of HaCaT, HDF and SHDF in a time- and dose-dependent manner; a better organization of cytoskeletal proteins (F-actin and vimentin) and promotes the production of extracellular components (fibronectin, collagen 1 and MMPs) and the adhesion to cell-substrate vinculin protein. IFC-CAF also prevents cutaneous ageing. The treatment decreases the expression of the ageing-related markers b-Gal, p53 and p16INK4 in SDDF cells, and improves cell survival after UVB irradiation and nuclear repair in HaCaT cells. IFC-CAF has regenerative properties and protects against ageing factors being, therefore, a potential therapeutic agent for treating or preventing skin ageing. © 2014 Society of Cosmetic Scientists and the Soci

  15. Soya-cerebroside, an extract of Cordyceps militaris, suppresses monocyte migration and prevents cartilage degradation in inflammatory animal models

    Science.gov (United States)

    Liu, Shan-Chi; Chiu, Ching-Peng; Tsai, Chun-Hao; Hung, Chun-Yin; Li, Te-Mao; Wu, Yang-Chang; Tang, Chih-Hsin

    2017-01-01

    Pathophysiological events that modulate the progression of structural changes in osteoarthritis (OA) include the secretion of inflammatory molecules, such as proinflammatory cytokines. Interleukin-1beta (IL-1β) is the prototypical inflammatory cytokine that activates OA synovial cells to release cytokines and chemokines in support of the inflammatory response. The monocyte chemoattractant protein-1 (MCP-1/CCL2) is one of the key chemokines that regulate migration and infiltration of monocytes in response to inflammation. We show in this study that IL-1β-induced MCP-1 expression and monocyte migration in OA synovial fibroblasts (OASFs) is effectively inhibited by soya-cerebroside, an extract of Cordyceps militaris. We found that soya-cerebroside up-regulated of microRNA (miR)-432 expression via inhibiting AMPK and AKT signaling pathways in OASFs. Soya-cerebroside also effectively decreased monocyte infiltration and prevented cartilage degradation in a rat inflammatory model. Our findings are the first to demonstrate that soya-cerebroside inhibits monocyte/macrophage infiltration into synoviocytes, attenuating synovial inflammation and preventing cartilage damage by reducing MCP-1 expression in vitro and in vivo. Taken together, we suggest a novel therapeutic strategy based on the use of soya-cerebroside for the management of OA. PMID:28225075

  16. Innovative Capping Technology To Prevent The Migration of Toxic Chemicals From Contaminated Sediments

    Science.gov (United States)

    Capping is a common strategy for decreasing the risk associated with contaminated sediments in lakes and streams. Historically, caps have been designed to physically isolate contaminated sediments and prevent the transport of contaminants from sediments into the water above them...

  17. Multivariate realised kernels

    DEFF Research Database (Denmark)

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

    We propose a multivariate realised kernel to estimate the ex-post covariation of log-prices. We show this new consistent estimator is guaranteed to be positive semi-definite and is robust to measurement noise of certain types and can also handle non-synchronous trading. It is the first estimator...... returns measured over 5 or 10 minutes intervals. We show the new estimator is substantially more precise....

  18. Multivariate realised kernels

    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...... returns measured over 5 or 10 min intervals. We show that the new estimator is substantially more precise....

  19. 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

  20. 3-D sensitivity kernels of the Rayleigh wave ellipticity

    Science.gov (United States)

    Maupin, Valérie

    2017-10-01

    The ellipticity of the Rayleigh wave at the surface depends on the seismic structure beneath and in the vicinity of the seismological station where it is measured. We derive here the expression and compute the 3-D kernels that describe this dependence with respect to S-wave velocity, P-wave velocity and density. Near-field terms as well as coupling to Love waves are included in the expressions. We show that the ellipticity kernels are the difference between the amplitude kernels of the radial and vertical components of motion. They show maximum values close to the station, but with a complex pattern, even when smoothing in a finite-frequency range is used to remove the oscillatory pattern present in mono-frequency kernels. In order to follow the usual data processing flow, we also compute and analyse the kernels of the ellipticity averaged over incoming wave backazimuth. The kernel with respect to P-wave velocity has the simplest lateral variation and is in good agreement with commonly used 1-D kernels. The kernels with respect to S-wave velocity and density are more complex and we have not been able to find a good correlation between the 3-D and 1-D kernels. Although it is clear that the ellipticity is mostly sensitive to the structure within half-a-wavelength of the station, the complexity of the kernels within this zone prevents simple approximations like a depth dependence times a lateral variation to be useful in the inversion of the ellipticity.

  1. Prevention of the wind migration of Semipalatinsk test site contaminated topsoil by inter-polymer complexes

    International Nuclear Information System (INIS)

    Kudaibergenov, S.E.

    2010-01-01

    -polymer complexes with formation of ternary polymer-metal complexes. The laboratory experiments demonstrated that the treatment of the topsoil by dilute aqueous solutions of Inter-polymer complexes leads to effective aggregation of the topsoil particles with diameter of 0.01-0.1 mm from 38% (control) to 91%. The average value of wind erosion of aggregated soil becomes less than 1% at the velocity of wind 5 m.s -1 in comparison with the value of 74% in case of untreated soil. It was also found that the formed ultrathin crust on the surface of soil particles is able to accumulate the radioactive strontium 4 times higher in comparison with untreated ones. The activity of 238 Pu in the topsoil after treatment by solutions of Inter-polymer complexes increases up to 10000-12000 Bq/kg in comparison with the untreated soil particles (5156 Bq/kg). These results clearly show the potential applicability of Inter-polymer complexes as effective soil remediation materials. The so-called “Koshkar-Ata Lake” located 3 km far from Aktau city and 7-8 km from the Caspian Sea also contains the waste of uranium mining industry. Today the dried area of “Koshkar-Ata Lake” is more than 58 sq. km. In Summer time the thin dust with average diameter 0,01-0,1 mm constantly migrates from the dried part of “Koshkar-Ata Lake” to Aktau city as a result of wind erosion (16-17 m/sec). The developed technology can protect the wind migration of thin dust containing highly toxic heavy metals. It can also be applied to combat against the migration of salted dust from the dried bottom of Aral Sea.

  2. The definition of kernel Oz

    OpenAIRE

    Smolka, Gert

    1994-01-01

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

  3. MULTITASKER, Multitasking Kernel for C and FORTRAN Under UNIX

    International Nuclear Information System (INIS)

    Brooks, E.D. III

    1988-01-01

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

  4. Alloimmunization prevents the migration of transfused indium-111-labeled granulocytes to sites of infection

    International Nuclear Information System (INIS)

    Dutcher, J.P.; Schiffer, C.A.; Johnston, G.S.; Papenburg, D.; Daly, P.A.; Aisner, J.; Wiernik, P.H.

    1983-01-01

    111In-labeled granulocytes were used to study the effects of histocompatibility factors on the migration of transfused granulocytes to infected sites. Fourteen alloimmunized and 20 nonalloimmunized patients received approximately 10(8) 111In-labeled granulocytes from ABO-compatible, non-HLA-matched donors, and scans were performed over known infected sites. All 14 alloimmunized patients had lymphocytotoxic antibody (LCTAb) and required HLA-matched platelet transfusions. Of the nonalloimmunized patients, 20/20 had positive scans at sites of infection. None of the 20 had LCTAb, 0/17 had a positive lymphocytotoxic crossmatch (LCTXM) with the donor, and 3/18 had a positive leukoagglutinin crossmatch (LAXM). Thus, histocompatibility testing was not found to be important in nonalloimmunized patients. In contrast, only 3/14 alloimmunized patients had positive scans at sites of infection (p . 0.00001 compared to nonalloimmunized patients). One of 3 had a positive LCTXM and 2/3 had a positive LAXM. Of the alloimmunized patients, 10/11 with negative scans had a positive LCTXM and 8/11 had a positive LAXM. Labeled granulocytes failed to reach sites of infection in 11/14 (78%) alloimmunized patients, demonstrating that histocompatibility factors can be of major importance in affecting the outcome of granulocyte transfusions. Granulocytes from random donors are unlikely to be effective in alloimmunized patients. The lack of an adequate crossmatching technique is a major problem limiting the ability to provide granulocyte transfusions for alloimmunized patients

  5. Kernel bundle EPDiff

    DEFF Research Database (Denmark)

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

    2011-01-01

    In the LDDMM framework, optimal warps for image registration are found as end-points of critical paths for an energy functional, and the EPDiff equations describe the evolution along such paths. The Large Deformation Diffeomorphic Kernel Bundle Mapping (LDDKBM) extension of LDDMM allows scale space...... information to be automatically incorporated in registrations and promises to improve the standard framework in several aspects. We present the mathematical foundations of LDDKBM and derive the KB-EPDiff evolution equations, which provide optimal warps in this new framework. To illustrate the resulting...

  6. 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....

  7. 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...

  8. Classification Using the Zipfian Kernel

    Czech Academy of Sciences Publication Activity Database

    Jiřina, Marcel; Jiřina jr., M.

    2015-01-01

    Roč. 32, č. 2 (2015), s. 305-326 ISSN 0176-4268 R&D Projects: GA TA ČR TA01010490 Institutional support: RVO:67985807 Keywords : kernel machine * Zipfian kernel * multivariate data * correlation dimension * harmonic series * classification Subject RIV: JC - Computer Hardware ; Software Impact factor: 1.147, year: 2015

  9. Model Selection in Kernel Ridge Regression

    DEFF Research Database (Denmark)

    Exterkate, Peter

    , 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...... applicable, and we recommend their use instead of the popular polynomial kernels in general settings, in which no information on the data-generating process is available....

  10. Viscosity kernel of molecular fluids

    DEFF Research Database (Denmark)

    Puscasu, Ruslan; Todd, Billy; Daivis, Peter

    2010-01-01

    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......The wave-vector dependent shear viscosities for butane and freely jointed chains have been determined. The transverse momentum density and stress autocorrelation functions have been determined by equilibrium molecular dynamics in both atomic and molecular hydrodynamic formalisms. The density......, 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...

  11. Model selection in kernel ridge regression

    DEFF Research Database (Denmark)

    Exterkate, Peter

    2013-01-01

    . 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...... 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 applicable. Therefore, their use is recommended instead of the popular polynomial kernels in general settings, where no information...

  12. Robotic intelligence kernel

    Science.gov (United States)

    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.

  13. Mixture Density Mercer Kernels: A Method to Learn Kernels

    Data.gov (United States)

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

  14. Resummed memory kernels in generalized system-bath master equations

    Science.gov (United States)

    Mavros, Michael G.; Van Voorhis, Troy

    2014-08-01

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

  15. 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...

  16. Heat kernels and critical limits

    OpenAIRE

    Pickrell, Doug

    2007-01-01

    This paper is an exposition of several questions linking heat kernel measures on infinite dimensional Lie groups, limits associated with critical Sobolev exponents, and Feynmann-Kac measures for sigma models.

  17. Small kernel2

    Science.gov (United States)

    Yang, Yan-Zhuo; Ding, Shuo; Wang, Yong; Li, Cui-Ling; Shen, Yun; Meeley, Robert; McCarty, Donald R; Tan, Bao-Cai

    2017-06-01

    Vitamin B 6 , an essential cofactor for a range of biochemical reactions and a potent antioxidant, plays important roles in plant growth, development, and stress tolerance. Vitamin B 6 deficiency causes embryo lethality in Arabidopsis ( Arabidopsis thaliana ), but the specific role of vitamin B 6 biosynthesis in endosperm development has not been fully addressed, especially in monocot crops, where endosperm constitutes the major portion of the grain. Through molecular characterization of a small kernel2 ( smk2 ) mutant in maize, we reveal that vitamin B 6 has differential effects on embryogenesis and endosperm development in maize. The B 6 vitamer pyridoxal 5'-phosphate (PLP) is drastically reduced in both the smk2 embryo and the endosperm. However, whereas embryogenesis of the smk2 mutant is arrested at the transition stage, endosperm formation is nearly normal. Cloning reveals that Smk2 encodes the glutaminase subunit of the PLP synthase complex involved in vitamin B 6 biosynthesis de novo. Smk2 partially complements the Arabidopsis vitamin B 6 -deficient mutant pdx2.1 and Saccharomyces cerevisiae pyridoxine auxotrophic mutant MML21. Smk2 is constitutively expressed in the maize plant, including developing embryos. Analysis of B 6 vitamers indicates that the endosperm accumulates a large amount of pyridoxamine 5'-phosphate (PMP). These results indicate that vitamin B 6 is essential to embryogenesis but has a reduced role in endosperm development in maize. The vitamin B 6 required for seed development is synthesized in the seed, and the endosperm accumulates PMP probably as a storage form of vitamin B 6 . © 2017 American Society of Plant Biologists. All Rights Reserved.

  18. 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...

  19. 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.

  20. 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...

  1. Kernel learning algorithms for face recognition

    CERN Document Server

    Li, Jun-Bao; Pan, Jeng-Shyang

    2013-01-01

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

  2. Semi-Supervised Kernel PCA

    DEFF Research Database (Denmark)

    Walder, Christian; Henao, Ricardo; Mørup, Morten

    We present three generalisations of Kernel Principal Components Analysis (KPCA) which incorporate knowledge of the class labels of a subset of the data points. The first, MV-KPCA, penalises within class variances similar to Fisher discriminant analysis. The second, LSKPCA is a hybrid of least squ...... 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....

  3. Two-species aggregation processes with migration

    International Nuclear Information System (INIS)

    Ke Jianhong; Zhuang Youyi; Lin Zhenquan

    2004-01-01

    We study the kinetic behaviour of an aggregation-migration model, in which irreversible aggregations occur between any two aggregates of the same species and reversible migrations occur simultaneously between two different species. For a simple model with constant aggregation rate kernels as well as size-dependent migration rate kernels, we find that the evolution behaviour of the system depends crucially on the ratios of the aggregation rate coefficients to the migration rate coefficient. In general, the aggregate size distributions of the aggregation-migration processes always obey a conventional or generalized scaling law. Moreover, both species survive together for some cases, while only one species can be conserved finally for other cases

  4. Metabolisable energy values of whole palm kernel and palm kernel ...

    African Journals Online (AJOL)

    4.12 Kcal/kg DM. 4.36 and 4.13 Kcal/kg DM, respectively were the corresponding values for broiler chickens. No interaction between ingredients and birds was found but there were interactions among the bioavailable energy systems and the bird types. Keywords: Metabolisable energy, palm kernel layers, broilers.

  5. Exendin-4 Prevents Vascular Smooth Muscle Cell Proliferation and Migration by Angiotensin II via the Inhibition of ERK1/2 and JNK Signaling Pathways.

    Directory of Open Access Journals (Sweden)

    Kosuke Nagayama

    Full Text Available Angiotensin II (Ang II is a main pathophysiological culprit peptide for hypertension and atherosclerosis by causing vascular smooth muscle cell (VSMC proliferation and migration. Exendin-4, a glucagon-like peptide-1 (GLP-1 receptor agonist, is currently used for the treatment of type-2 diabetes, and is believed to have beneficial effects for cardiovascular diseases. However, the vascular protective mechanisms of GLP-1 receptor agonists remain largely unexplained. In the present study, we examined the effect of exendin-4 on Ang II-induced proliferation and migration of cultured rat aortic smooth muscle cells (RASMC. The major findings of the present study are as follows: (1 Ang II caused a phenotypic switch of RASMC from contractile type to synthetic proliferative type cells; (2 Ang II caused concentration-dependent RASMC proliferation, which was significantly inhibited by the pretreatment with exendin-4; (3 Ang II caused concentration-dependent RASMC migration, which was effectively inhibited by the pretreatment with exendin-4; (4 exendin-4 inhibited Ang II-induced phosphorylation of ERK1/2 and JNK in a pre-incubation time-dependent manner; and (5 U0126 (an ERK1/2 kinase inhibitor and SP600125 (a JNK inhibitor also inhibited both RASMC proliferation and migration induced by Ang II stimulation. These results suggest that exendin-4 prevented Ang II-induced VSMC proliferation and migration through the inhibition of ERK1/2 and JNK phosphorylation caused by Ang II stimulation. This indicates that GLP-1 receptor agonists should be considered for use in the treatment of cardiovascular diseases in addition to their current use in the treatment of diabetes mellitus.

  6. Chicoric acid prevents PDGF-BB-induced VSMC dedifferentiation, proliferation and migration by suppressing ROS/NFκB/mTOR/P70S6K signaling cascade.

    Science.gov (United States)

    Lu, Qing-Bo; Wan, Ming-Yu; Wang, Pei-Yao; Zhang, Chen-Xing; Xu, Dong-Yan; Liao, Xiang; Sun, Hai-Jian

    2018-04-01

    Phenotypic switch of vascular smooth muscle cells (VSMCs) is characterized by increased expressions of VSMC synthetic markers and decreased levels of VSMC contractile markers, which is an important step for VSMC proliferation and migration during the development and progression of cardiovascular diseases including atherosclerosis. Chicoric acid (CA) is identified to exert powerful cardiovascular protective effects. However, little is known about the effects of CA on VSMC biology. Herein, in cultured VSMCs, we showed that pretreatment with CA dose-dependently suppressed platelet-derived growth factor type BB (PDGF-BB)-induced VSMC phenotypic alteration, proliferation and migration. Mechanistically, PDGF-BB-treated VSMCs exhibited higher mammalian target of rapamycin (mTOR) and P70S6K phosphorylation, which was attenuated by CA pretreatment, diphenyleneiodonium chloride (DPI), reactive oxygen species (ROS) scavenger N-acetyl-l-cysteine (NAC) and nuclear factor-κB (NFκB) inhibitor Bay117082. PDGF-BB-triggered ROS production and p65-NFκB activation were inhibited by CA. In addition, both NAC and DPI abolished PDGF-BB-evoked p65-NFκB nuclear translocation, phosphorylation and degradation of Inhibitor κBα (IκBα). Of note, blockade of ROS/NFκB/mTOR/P70S6K signaling cascade prevented PDGF-BB-evoked VSMC phenotypic transformation, proliferation and migration. CA treatment prevented intimal hyperplasia and vascular remodeling in rat models of carotid artery ligation in vivo. These results suggest that CA impedes PDGF-BB-induced VSMC phenotypic switching, proliferation, migration and neointima formation via inhibition of ROS/NFκB/mTOR/P70S6K signaling cascade. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

  7. Reducing Kernel Development Complexity in Distributed Environments

    OpenAIRE

    Lèbre , Adrien; Lottiaux , Renaud; Focht , Erich; Morin , Christine

    2008-01-01

    Setting up generic and fully transparent distributed services for clusters implies complex and tedious kernel developments. More flexible approaches such as user-space libraries are usually preferred with the drawback of requiring application recompilation. A second approach consists in using specific kernel modules (such as FUSE in Gnu/Linux system) to transfer kernel complexity into user space. In this paper, we present a new way to design and implement kernel distributed services for clust...

  8. Oops! What about a Million Kernel Oopses?

    OpenAIRE

    Guo , Lisong; Senna Tschudin , Peter; Kono , Kenji; Muller , Gilles; Lawall , Julia

    2013-01-01

    When a failure occurs in the Linux kernel, the kernel emits an "oops", summarizing the execution context of the failure. Kernel oopses describe real Linux errors, and thus can help prioritize debugging efforts and motivate the design of tools to improve the reliability of Linux code. Nevertheless, the information is only meaningful if it is representative and can be interpreted correctly. In this paper, we study a repository of kernel oopses collected over 8 months by Red Hat. We consider the...

  9. Wheat curl mite (Acari: Eriophyidae) dispersal and its relationship with kernel red streaking in maize.

    Science.gov (United States)

    Liu, J; Lee, E A; Sears, M K; Schaafsma, A W

    2005-10-01

    Wheat curl mites, Aceria tosichella Keifer, dispersing from wheat (Triticum spp.) to nearby corn (Zea mays L.) fields play a role in the development of kernel red streaking in corn. These studies were undertaken to verify the relationship of wheat curl mite to kernel red streaking, to determine whether wheat is the main source of curl mites dispersing into corn and to determine whether planting corn in temporal or spatial isolation of wheat is a valid management strategy. These studies were conducted on farm fields using sticky traps to monitor mites, followed by sampling mature grain for kernel streaking in southwestern Ontario from 1999 to 2002. The dominant source mites were winter wheat. Mite dispersal occurred during the first 3 wk of winter wheat maturation after the wheat had reached Zadoks stage 87. Mite dispersal corresponded to prevailing winds in the area with the lowest number of mites and the lowest severity of kernel red streaking occurring 60 m from wheat fields planted to the north, south, and east of cornfields and 90 m from wheat fields planted to the west of cornfields. The severity of kernel red streaking was positively correlated with the density of wheat curl mites in corn; however, the correlation was weak and kernel red streaking was still high in many cornfields when few or no mites were present. These findings suggest that wheat curl mite migration into corn is not entirely predictive of the incidence and severity of kernel red streaking.

  10. Derivative Kernels: Numerics and Applications.

    Science.gov (United States)

    Hosseini, Mahdi S; Plataniotis, Konstantinos N

    2017-10-01

    A generalized framework for numerical differentiation (ND) is proposed for constructing a finite impulse response (FIR) filter in closed form. The framework regulates the frequency response of ND filters for arbitrary derivative-order and cutoff frequency selected parameters relying on interpolating power polynomials and maximally flat design techniques. Compared with the state-of-the-art solutions, such as Gaussian kernels, the proposed ND filter is sharply localized in the Fourier domain with ripple-free artifacts. Here, we construct 2D MaxFlat kernels for image directional differentiation to calculate image differentials for arbitrary derivative order, cutoff level and steering angle. The resulted kernel library renders a new solution capable of delivering discrete approximation of gradients, Hessian, and higher-order tensors in numerous applications. We tested the utility of this library on three different imaging applications with main focus on the unsharp masking. The reported results highlight the high efficiency of the 2D MaxFlat kernel and its versatility with respect to robustness and parameter control accuracy.

  11. Veto-Consensus Multiple Kernel Learning

    NARCIS (Netherlands)

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

    2016-01-01

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

  12. Paramecium: An Extensible Object-Based Kernel

    NARCIS (Netherlands)

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

    1995-01-01

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

  13. GRIM : Leveraging GPUs for Kernel integrity monitoring

    NARCIS (Netherlands)

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

    2016-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Senyue Zhang

    2016-01-01

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

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

    Science.gov (United States)

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

    2018-05-01

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

  16. Male labor migration, patriarchy, and the awareness-behavior gap: HIV risks and prevention among migrants' wives in Armenia.

    Science.gov (United States)

    Agadjanian, Victor; Markosyan, Karine

    2017-06-01

    Unlike in most of the world, HIV incidence in the former Soviet Union continues to rise. While international labor migration has been identified as a potentially important contributor to this trend, most attention has been focused on risks of male migrants themselves. This study uses recent household survey data to examine HIV-related perceptions and actions of migrants' left-behind wives in Armenia. Multivariate logistic regression analyses show that migrants' wives are significantly more likely to suspect their husbands of extramarital sex than are non-migrants' wives. The analyses detect greater worries about HIV infection and a higher likelihood of spousal communication on HIV matters among migrants' wives, compared to non-migrants' wives, but these differences are largely explained by the suspicion of husband's extramarital sex. Finally, no difference between the two categories of women in the probability of consistent condom use with husbands is found. These findings are interpreted within the context of patriarchal culture and unequal gender relations in Armenian society as they are further reinforced by male migration. Implications of these findings for policies to increase women's awareness of HIV risks associated with migration and their ability to reduce those risks are discussed.

  17. 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....

  18. RKRD: Runtime Kernel Rootkit Detection

    Science.gov (United States)

    Grover, Satyajit; Khosravi, Hormuzd; Kolar, Divya; Moffat, Samuel; Kounavis, Michael E.

    In this paper we address the problem of protecting computer systems against stealth malware. The problem is important because the number of known types of stealth malware increases exponentially. Existing approaches have some advantages for ensuring system integrity but sophisticated techniques utilized by stealthy malware can thwart them. We propose Runtime Kernel Rootkit Detection (RKRD), a hardware-based, event-driven, secure and inclusionary approach to kernel integrity that addresses some of the limitations of the state of the art. Our solution is based on the principles of using virtualization hardware for isolation, verifying signatures coming from trusted code as opposed to malware for scalability and performing system checks driven by events. Our RKRD implementation is guided by our goals of strong isolation, no modifications to target guest OS kernels, easy deployment, minimal infra-structure impact, and minimal performance overhead. We developed a system prototype and conducted a number of experiments which show that the per-formance impact of our solution is negligible.

  19. Kernel Bayesian ART and ARTMAP.

    Science.gov (United States)

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

    2018-02-01

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

  20. Nonlinear Deep Kernel Learning for Image Annotation.

    Science.gov (United States)

    Jiu, Mingyuan; Sahbi, Hichem

    2017-02-08

    Multiple kernel learning (MKL) is a widely used technique for kernel design. Its principle consists in learning, for a given support vector classifier, the most suitable convex (or sparse) linear combination of standard elementary kernels. However, these combinations are shallow and often powerless to capture the actual similarity between highly semantic data, especially for challenging classification tasks such as image annotation. In this paper, we redefine multiple kernels using deep multi-layer networks. In this new contribution, a deep multiple kernel is recursively defined as a multi-layered combination of nonlinear activation functions, each one involves a combination of several elementary or intermediate kernels, and results into a positive semi-definite deep kernel. We propose four different frameworks in order to learn the weights of these networks: supervised, unsupervised, kernel-based semisupervised and Laplacian-based semi-supervised. When plugged into support vector machines (SVMs), the resulting deep kernel networks show clear gain, compared to several shallow kernels for the task of image annotation. Extensive experiments and analysis on the challenging ImageCLEF photo annotation benchmark, the COREL5k database and the Banana dataset validate the effectiveness of the proposed method.

  1. KNBD: A Remote Kernel Block Server for Linux

    Science.gov (United States)

    Becker, Jeff

    1999-01-01

    I am developing a prototype of a Linux remote disk block server whose purpose is to serve as a lower level component of a parallel file system. Parallel file systems are an important component of high performance supercomputers and clusters. Although supercomputer vendors such as SGI and IBM have their own custom solutions, there has been a void and hence a demand for such a system on Beowulf-type PC Clusters. Recently, the Parallel Virtual File System (PVFS) project at Clemson University has begun to address this need (1). Although their system provides much of the functionality of (and indeed was inspired by) the equivalent file systems in the commercial supercomputer market, their system is all in user-space. Migrating their 10 services to the kernel could provide a performance boost, by obviating the need for expensive system calls. Thanks to Pavel Machek, the Linux kernel has provided the network block device (2) with kernels 2.1.101 and later. You can configure this block device to redirect reads and writes to a remote machine's disk. This can be used as a building block for constructing a striped file system across several nodes.

  2. Theory of reproducing kernels and applications

    CERN Document Server

    Saitoh, Saburou

    2016-01-01

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

  3. kFOIL: Learning simple relational kernels

    OpenAIRE

    Landwehr, Niels; Passerini, Andrea; De Raedt, Luc; Frasconi, Paolo

    2006-01-01

    A novel and simple combination of inductive logic programming with kernel methods is presented. The kFOIL algorithm integrates the well-known inductive logic programming system FOIL with kernel methods. The feature space is constructed by leveraging FOIL search for a set of relevant clauses. The search is driven by the performance obtained by a support vector machine based on the resulting kernel. In this way, kFOIL implements a dynamic propositionalization approach. Both classification an...

  4. Convergence of barycentric coordinates to barycentric kernels

    KAUST Repository

    Kosinka, Jiří

    2016-02-12

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

  5. Prevention of oil migration in palm mid fraction and palm olein using a stabilizer rich in behenic acid.

    Science.gov (United States)

    Peyronel, Fernanda; Campos, Rodrigo; Marangoni, Alejandro G

    2016-10-01

    Oil loss is a common problem in high oil volume fraction fat-structured foods, where trans fatty acids are absent and saturated fatty acids minimized. Usually a stabilizer "fat", rich in behenic and stearic acids is added to these products. This stabilizer significantly reduces oil migration and loss; however, the mechanism by which it exerts its effect is not known. Here we study the structure and oil loss characteristics of blends of palm mid fraction (PMF) and palm olein (PO) mixed with a commercial stabilizer of fully hydrogenated cottonseed and rapeseed oils added at 1.25, 3.5, and 7% (w/w) levels. The addition of as little as 1.25% stabilizer reduced oil migration from 13% to ~2% for the 80:20 PMF/PO blend and from 29% to ~3.5% for 20:80 PMF/PO blend after 14days of storage at 20°C, as judged from a filter paper assay. Polarized light microscopy results demonstrated that addition of stabilizer decreased fat crystal size and lead to the formation of a denser network with smaller pores. The rate of crystallization, determined by monitoring increases in solid fat content as function of time by pulsed NMR, increased upon addition of the stabilizer as indicated by decreases in the half-time of crystallization. Addition of stabilizer lead to an increase in the mechanical strength of the material, as indicated by increases in breaking force. Differential scanning calorimetry showed the formation of a new fraction, intermediate between the high melting fractions in both PMF and PO and the stabilizer upon incorporation of the stabilizer in the blends. We hypothesize that the stabilizer helps the formation of this new fraction which has a higher nucleation rate and thus forms a denser network of small crystals with an improved ability to bind and retain oil. Copyright © 2016. Published by Elsevier Ltd.

  6. A theoretical model of air and steam co-injection to prevent the downward migration of DNAPLs during steam-enhanced extraction

    Science.gov (United States)

    Kaslusky, Scott F.; Udell, Kent S.

    2002-04-01

    When steam is injected into soil containing a dense volatile non-aqueous phase liquid contaminant the DNAPL vaporized within the heated soil region condenses and accumulates ahead of the steam condensation front. If enough DNAPL accumulates, gravitational forces can overcome trapping forces allowing the liquid contaminant to flow downward. By injecting air with steam, a portion of the DNAPL vapor remains suspended in equilibrium with the air, decreasing liquid contaminant accumulation ahead of the steam condensation front, and thus reducing the possibility of downward migration. In this work, a one-dimensional theoretical model is developed to predict the injection ratio of air to steam that will prevent the accumulation of volatile DNAPLs. The contaminated region is modeled as a one-dimensional homogeneous porous medium with an initially uniform distribution of a single component contaminant. Mass and energy balances are combined to determine the injection ratio of air to steam that eliminates accumulation of the contaminant ahead of the steam condensation front, and hence reduces the possibility of downward migration. The minimum injection ratio that eliminates accumulation is defined as the optimum injection ratio. Example calculations are presented for three DNAPLs, carbon tetrachloride (CCl 4), trichloroethylene (TCE), and perchloroethylene (PCE). The optimum injection ratio of air to steam is shown to depend on the initial saturation and the volatility of the liquid contaminant. Numerical simulation results are presented to validate the model, and to illustrate downward migration for ratios less than optimum. Optimum injection ratios determined from numerical simulations are shown to be in good agreement with the theoretical model.

  7. 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

  8. Automatic performance tuning of parallel and accelerated seismic imaging kernels

    KAUST Repository

    Haberdar, Hakan

    2014-01-01

    With the increased complexity and diversity of mainstream high performance computing systems, significant effort is required to tune parallel applications in order to achieve the best possible performance for each particular platform. This task becomes more and more challenging and requiring a larger set of skills. Automatic performance tuning is becoming a must for optimizing applications such as Reverse Time Migration (RTM) widely used in seismic imaging for oil and gas exploration. An empirical search based auto-tuning approach is applied to the MPI communication operations of the parallel isotropic and tilted transverse isotropic kernels. The application of auto-tuning using the Abstract Data and Communication Library improved the performance of the MPI communications as well as developer productivity by providing a higher level of abstraction. Keeping productivity in mind, we opted toward pragma based programming for accelerated computation on latest accelerated architectures such as GPUs using the fairly new OpenACC standard. The same auto-tuning approach is also applied to the OpenACC accelerated seismic code for optimizing the compute intensive kernel of the Reverse Time Migration application. The application of such technique resulted in an improved performance of the original code and its ability to adapt to different execution environments.

  9. Hilbertian kernels and spline functions

    CERN Document Server

    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.

  10. Antibodies trap tissue migrating helminth larvae and prevent tissue damage by driving IL-4Rα-independent alternative differentiation of macrophages.

    Directory of Open Access Journals (Sweden)

    Julia Esser-von Bieren

    Full Text Available Approximately one-third of the world's population suffers from chronic helminth infections with no effective vaccines currently available. Antibodies and alternatively activated macrophages (AAM form crucial components of protective immunity against challenge infections with intestinal helminths. However, the mechanisms by which antibodies target these large multi-cellular parasites remain obscure. Alternative activation of macrophages during helminth infection has been linked to signaling through the IL-4 receptor alpha chain (IL-4Rα, but the potential effects of antibodies on macrophage differentiation have not been explored. We demonstrate that helminth-specific antibodies induce the rapid trapping of tissue migrating helminth larvae and prevent tissue necrosis following challenge infection with the natural murine parasite Heligmosomoides polygyrus bakeri (Hp. Mice lacking antibodies (JH (-/- or activating Fc receptors (FcRγ(-/- harbored highly motile larvae, developed extensive tissue damage and accumulated less Arginase-1 expressing macrophages around the larvae. Moreover, Hp-specific antibodies induced FcRγ- and complement-dependent adherence of macrophages to larvae in vitro, resulting in complete larval immobilization. Antibodies together with helminth larvae reprogrammed macrophages to express wound-healing associated genes, including Arginase-1, and the Arginase-1 product L-ornithine directly impaired larval motility. Antibody-induced expression of Arginase-1 in vitro and in vivo occurred independently of IL-4Rα signaling. In summary, we present a novel IL-4Rα-independent mechanism of alternative macrophage activation that is antibody-dependent and which both mediates anti-helminth immunity and prevents tissue disruption caused by migrating larvae.

  11. 7 CFR 51.1403 - Kernel color classification.

    Science.gov (United States)

    2010-01-01

    ... models of pecan kernels, illustrate the color intensities implied by the terms “golden,” “light brown... Kernel color classification. (a) The skin color of pecan kernels may be described in terms of the color...

  12. Sentiment classification with interpolated information diffusion kernels

    NARCIS (Netherlands)

    Raaijmakers, S.

    2007-01-01

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

  13. 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.

  14. 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)

  15. Modelling Issues in Kernel Ridge Regression

    NARCIS (Netherlands)

    P. Exterkate (Peter)

    2011-01-01

    textabstractKernel 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

  16. Kernel method for corrections to scaling.

    Science.gov (United States)

    Harada, Kenji

    2015-07-01

    Scaling analysis, in which one infers scaling exponents and a scaling function in a scaling law from given data, is a powerful tool for determining universal properties of critical phenomena in many fields of science. However, there are corrections to scaling in many cases, and then the inference problem becomes ill-posed by an uncontrollable irrelevant scaling variable. We propose a new kernel method based on Gaussian process regression to fix this problem generally. We test the performance of the new kernel method for some example cases. In all cases, when the precision of the example data increases, inference results of the new kernel method correctly converge. Because there is no limitation in the new kernel method for the scaling function even with corrections to scaling, unlike in the conventional method, the new kernel method can be widely applied to real data in critical phenomena.

  17. Downregulation of COX-2 and CYP 4A signaling by isoliquiritigenin inhibits human breast cancer metastasis through preventing anoikis resistance, migration and invasion

    Energy Technology Data Exchange (ETDEWEB)

    Zheng, Hao; Li, Ying [Department of Pharmacology, School of Medicine, Wuhan University, Wuhan 430071 (China); Wang, Yuzhong [Key Laboratory for Oral Biomedical Engineering of Ministry of Education, School and Hospital of Stomatology, Wuhan University, Wuhan 430079 (China); Zhao, Haixia [Department of Pharmacology, School of Medicine, Wuhan University, Wuhan 430071 (China); Zhang, Jing [Animal Experimental Center of Wuhan University, Wuhan 430071 (China); Chai, Hongyan [Center for Gene Diagnosis, Zhongnan Hospital, Wuhan University, Wuhan 430071 (China); Tang, Tian [Department of Oncology, Renmin Hospital of Wuhan University, Wuhan 430060 (China); Yue, Jiang [Department of Pharmacology, School of Medicine, Wuhan University, Wuhan 430071 (China); Guo, Austin M., E-mail: Austin_Guo@nymc.edu [Department of Pharmacology, School of Medicine, Wuhan University, Wuhan 430071 (China); Department of Pharmacology, New York Medical College, Valhalla, NY 10595 (United States); Yang, Jing, E-mail: yangjingliu2013@163.com [Department of Pharmacology, School of Medicine, Wuhan University, Wuhan 430071 (China)

    2014-10-01

    Flavonoids exert extensive in vitro anti-invasive and in vivo anti-metastatic activities. Anoikis resistance occurs at multiple key stages of the metastatic cascade. Here, we demonstrate that isoliquiritigenin (ISL), a flavonoid from Glycyrrhiza glabra, inhibits human breast cancer metastasis by preventing anoikis resistance, migration and invasion through downregulating cyclooxygenase (COX)-2 and cytochrome P450 (CYP) 4A signaling. ISL induced anoikis in MDA-MB-231 and BT-549 human breast cancer cells as evidenced by flow cytometry and the detection of caspase cleavage. Moreover, ISL inhibited the mRNA expression of phospholipase A2, COX-2 and CYP 4A and decreased the secretion of prostaglandin E{sub 2} (PGE{sub 2}) and 20-hydroxyeicosatetraenoic acid (20-HETE) in detached MDA-MB-231 cells. In addition, it decreased the levels of phospho-PI3K (Tyr{sup 458}), phospho-PDK (Ser{sup 241}) and phospho-Akt (Thr{sup 308}). Conversely, the exogenous addition of PGE{sub 2}, WIT003 (a 20-HETE analog) and an EP4 agonist (CAY10580) or overexpression of constitutively active Akt reversed ISL-induced anoikis. ISL exerted the in vitro anti-migratory and anti-invasive activities, whereas the addition of PGE{sub 2}, WIT003 and CAY10580 or overexpression of constitutively active Akt reversed the in vitro anti-migratory and anti-invasive activities of ISL in MDA-MB-231 cells. Notably, ISL inhibited the in vivo lung metastasis of MDA-MB-231 cells, together with decreased intratumoral levels of PGE{sub 2}, 20-HETE and phospho-Akt (Thr{sup 308}). In conclusion, ISL inhibits breast cancer metastasis by preventing anoikis resistance, migration and invasion via downregulating COX-2 and CYP 4A signaling. It suggests that ISL could be a promising multi-target agent for preventing breast cancer metastasis, and anoikis could represent a novel mechanism through which flavonoids may exert the anti-metastatic activities. - Highlights: • Isoliquiritigenin induces anoikis and suppresses

  18. Bayesian Kernel Mixtures for Counts.

    Science.gov (United States)

    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.

  19. Priority areas for surveillance and prevention of avian influenza during the water-bird migration season in Pakistan

    Directory of Open Access Journals (Sweden)

    Tariq Abbas

    2011-11-01

    Full Text Available Avian influenza viruses may be introduced into domestic poultry through migratory wild birds, particularly from Pakistan, which is situated across the migratory Indus flyway and holds more than 225 wetlands. To answer the question which areas should be given priority in surveillance and prevention with respect to notifiable avian influenza during the migratory season, a subset of Asian waterbird census data was reviewed. The dataset contains 535 local sites and available counts of waterbirds reported from 1987 to 2007. However, as the majority of the sites are not counted regularly gaps in data matrix appeared. The coordinates of 270 known sites completely fitted the administrative boundaries of the country. These coordinates were geo-processed with polygons of water-bodies and a raster map of predicted poultry density. Pixels representing the estimated number of poultry per km2 were found within a 3 to 9 km range of the census sites (or water-bodies in their proximity. The coordinates were also used to map the maximum reported counts of waterbirds and local clusters of under-sampled sites. A retrospective case-series analysis of previous outbreaks (2006-2008 of influenza A virus, subtype H5N1 was performed, which revealed that 64% of outbreaks, reported to Office International des Epizooties, the World Organization for Animal Health, occurred during the migratory period. This paper highlights the potential use and limitations of the Asian waterbirds census data in the context of avian influenza. The proposed methodology may be used to prioritize districts for surveillance and economize prevention measures provided better data are generated in future.

  20. Neutrophil migration towards C5a and CXCL8 is prevented by non-steroidal anti-inflammatory drugs via inhibition of different pathways

    Science.gov (United States)

    Bertolotto, Maria; Contini, Paola; Ottonello, Luciano; Pende, Aldo; Dallegri, Franco; Montecucco, Fabrizio

    2014-01-01

    BACKGROUND AND PURPOSE Non-steroidal anti-inflammatory drugs (NSAIDs) have been shown to induce PG-independent anti-inflammatory actions. Here, we investigated the role of three different NSAIDs (naproxen, ibuprofen and oxaprozin) on neutrophil responses to CXCL8 and C5a. EXPERIMENTAL APPROACH Human neutrophils were isolated from healthy volunteers by dextran and Ficoll-Hypaque density gradients. Neutrophils were pre-incubated with different concentrations (1–100 µM) of NSAIDs or kinase inhibitors. Neutrophil degranulation into supernatants was tested by elisa and zymography. Neutrophil chemotaxis was determined using Boyden chambers. F-actin polymerization was determined by Alexa-Fluor 488-conjugated phalloidin fluorescent assay. Integrin expression was assessed by flow cytometry. The phosphorylation of intracellular kinases was studied by Western blot. KEY RESULTS Pretreatment with NSAIDs did not affect neutrophil degranulation, but inhibited neutrophil migration and polymerization of F-actin, in response to CXCL8 and C5a. Pretreatment with different NSAIDs prevented C5a-induced integrin (CD11b) up-regulation, while only ibuprofen reduced CXCL8-induced CD11b up-regulation. Pre-incubation with naproxen or oxaprozin, but not ibuprofen, inhibited the PI3K/Akt-dependent chemotactic pathways. Both endogenous (released in cell supernatants) or exogenous (added to cell cultures) PGE2 did not affect C5a- or CXCL8-induced activities. Short-term incubation with NSAIDs did not affect neutrophil PGE2 release. CONCLUSION AND IMPLICATIONS Treatment with NSAIDs reduced C5a- and CXCL8-induced neutrophil migration and F-actin polymerization via different mechanisms. Inhibition by ibuprofen was associated with integrin down-regulation, while naproxen and oxaprozin blocked the PI3K/Akt pathway. Both NSAID actions were independent of COX inhibition and PGE2 release. PMID:24597536

  1. Midterm outcome of EndoAnchors for the prevention of endoleak and stent-graft migration in patients with challenging proximal aortic neck anatomy.

    Science.gov (United States)

    Jordan, William D; de Vries, Jean-Paul P M; Ouriel, Kenneth; Mehta, Manish; Varnagy, David; Moore, William M; Arko, Frank R; Joye, James; Henretta, John

    2015-04-01

    To explore the use of EndoAnchors as an adjunct to endovascular abdominal aortic aneurysm repair for prevention of proximal neck complications in patients with challenging neck anatomy. Over a 28-month period, 208 patients (159 men; mean age 72±8 years) were enrolled in the ANCHOR prospective, multicenter registry (ClinicalTrials.gov; identifier NCT01534819) for prophylaxis against proximal neck complications. Patients were eligible when, in the opinion of the investigators, they were at increased risk for type Ia endoleak or migration owing to a hostile neck (length 28 mm, angulation >60°, mural thrombus or calcium >2 mm in thickness or >180° in circumference, or conical shape). Overall, 123/157 (78.3%) patients met the criteria for a hostile neck according to core laboratory assessment of 157 adequate preoperative computed tomographic (CT) images. Implantation of EndoAnchors was technically successful in 204/208 (98.1%) patients. The frequency of fracture was 0.3% (3/1118); there were no clinical sequelae associated with the fractures. Over the mean 14-month follow-up, 95.2% of patients were alive, and no deaths were attributable to EndoAnchors. There were no ruptures, migrations, or open surgical conversions. Aneurysm-related reinterventions were performed in 8 (3.8%) patients. Among 130 patients with postprocedure contrast CT studies, core laboratory analysis identified 2 (1.5%) patients with type Ia endoleaks. Aneurysm sac diameter decreased >5 mm in 42.9% of patients with CT scans at or beyond 1 year; 1.6% of patients developed sac enlargement >5 mm. Prophylactic EndoAnchor use for challenging aortic neck anatomy was associated with satisfactory midterm results. © The Author(s) 2015.

  2. [Smoke-free by ramadan: experience with a low-threshold prevention offer on smoking cessation for persons with migration background].

    Science.gov (United States)

    Gebhardt, R; Cassens, S; Liecke, F; Rohde, G; Gün, A K; Brücker, R; Pankow, W

    2012-06-28

    Persons with migration background exhibit higher smoking rates in comparison to the general population.These smokers often cannot be reached by prevention measures at the family doctor's office. In summer 2011 the health campaign "Smoke-free by Ramadan" was launched in 11 German cities. Measures included the training of doctors on smoking cessation methods, general bilingual information flyers, and in some cases lectures on smoking, specifically for imams. A number of local events, especially for individuals with Turkish migration background were initiated. For these health events a specially equipped health bus of the BKK-vor-Ort was used, in which visitors were offered following elements: systematic data collection about age, sex and smoking behavior, a test to determine of the severity of nicotine dependence (Fagerström test, FTNA), as well as spirometric lung function test. Smokers were generally motivated to stop smoking. Data were anonymously collected and analysed in a documentation and communication sheet in Turkish language, and test results were handed over to participants on a printed information sheet. Data of 1012 people collected on 8 health days were analysed (70% men, mean age 46.5 years). The percentage of smokers was 41.5% (men) or 30% (women). Of 294 male smokers, according to FTNA 43.6% had low, 24.8% had moderate, and 31.6% strong nicotine dependence; in the 91 female smokers the corresponding rates were 54.9%, 30.8% and 14.3%. The distribution pattern of the dependency levels was statistically significantly different between genders (p = 0.006). Reduced lung function (FEV, smoking cessation was advised to all smokers.

  3. Prevention

    Science.gov (United States)

    ... Error processing SSI file About Heart Disease & Stroke Prevention Heart disease and stroke are an epidemic in ... secondhand smoke. Barriers to Effective Heart Disease & Stroke Prevention Many people with key risk factors for heart ...

  4. Cannabinoid Receptor Type 2 Agonist Attenuates Acute Neurogenic Pulmonary Edema by Preventing Neutrophil Migration after Subarachnoid Hemorrhage in Rats.

    Science.gov (United States)

    Fujii, Mutsumi; Sherchan, Prativa; Soejima, Yoshiteru; Doycheva, Desislava; Zhao, Diana; Zhang, John H

    2016-01-01

    We evaluated whether JWH133, a selective cannabinoid type 2 receptor (CB2R) agonist, prevented neurogenic pulmonary edema (NPE) after subarachnoid hemorrhage (SAH) by attenuating inflammation. Adult male rats were assigned to six groups: sham-operated, SAH with vehicle, SAH with JWH133 (0.3, 1.0, or 3.0 mg/kg) treatment 1 h after surgery, and SAH with JWH133 (1.0 mg/kg) at 1 h with a selective CB2R antagonist, SR144528 (3.0 mg/kg). The perforation model of SAH was performed and pulmonary wet-to-dry weight ratio was evaluated 24 and 72 h after surgery. Western blot analyses and immunohistochemistry were evaluated 24 h after surgery. JWH133 (1.0 mg/kg) significantly and most strongly improved lung edema 24 h after SAH. SR144528 administration significantly reversed the effects of JWH133 (1.0 mg/kg). SAH-induced increasing levels of myeloperoxidase (MPO) and decreasing levels of a tight junction (TJ) protein, junctional adhesion molecule (JAM)-A, were ameliorated by JWH133 (1.0 mg/kg) administration 24 h after SAH. Immunohistochemical assessment also confirmed substantial leukocyte infiltration in the outside of vessels in SAH, which were attenuated by JWH133 (1.0 mg/kg) injection. CB2R agonist ameliorated lung permeability by inhibiting leukocyte trafficking and protecting tight junction proteins in the lung of NPE after SAH.

  5. Distance Based Multiple Kernel ELM: A Fast Multiple Kernel Learning Approach

    Directory of Open Access Journals (Sweden)

    Chengzhang Zhu

    2015-01-01

    Full Text Available We propose a distance based multiple kernel extreme learning machine (DBMK-ELM, which provides a two-stage multiple kernel learning approach with high efficiency. Specifically, DBMK-ELM first projects multiple kernels into a new space, in which new instances are reconstructed based on the distance of different sample labels. Subsequently, an l2-norm regularization least square, in which the normal vector corresponds to the kernel weights of a new kernel, is trained based on these new instances. After that, the new kernel is utilized to train and test extreme learning machine (ELM. Extensive experimental results demonstrate the superior performance of the proposed DBMK-ELM in terms of the accuracy and the computational cost.

  6. NLO corrections to the Kernel of the BKP-equations

    International Nuclear Information System (INIS)

    Bartels, J.; Lipatov, L.N.; Vacca, G.P.

    2012-01-01

    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→3 kernel, computed in the tree approximation.

  7. 21 CFR 176.350 - Tamarind seed kernel powder.

    Science.gov (United States)

    2010-04-01

    ... 21 Food and Drugs 3 2010-04-01 2009-04-01 true Tamarind seed kernel powder. 176.350 Section 176... Substances for Use Only as Components of Paper and Paperboard § 176.350 Tamarind seed kernel powder. Tamarind... the provisions of this section. (a) Tamarind seed kernel powder is the ground kernel of tamarind seed...

  8. Higher-order Gaussian kernel in bootstrap boosting algorithm ...

    African Journals Online (AJOL)

    The bootstrap boosting algorithm is a bias reduction scheme. The adoption of higher-order Gaussian kernel in a bootstrap boosting algorithm in kernel density estimation was investigated. The algorithm used the higher-order. Gaussian kernel instead of the regular fixed kernels. A comparison of the scheme with existing ...

  9. 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...

  10. 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 ...

  11. 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 ...

  12. 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 ...

  13. 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.

  14. Digital signal processing with kernel methods

    CERN Document Server

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

    2018-01-01

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

  15. HAYABUSA SPICE KERNELS V1.0

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set includes the complete set of Hayabusa SPICE data files (kernel files'') for the surveying and collection phases of the mission. The SPICE data files,...

  16. Ensemble Approach to Building Mercer Kernels

    Data.gov (United States)

    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...

  17. Multiple Kernel Spectral Regression for Dimensionality Reduction

    Directory of Open Access Journals (Sweden)

    Bing Liu

    2013-01-01

    Full Text Available Traditional manifold learning algorithms, such as locally linear embedding, Isomap, and Laplacian eigenmap, only provide the embedding results of the training samples. To solve the out-of-sample extension problem, spectral regression (SR solves the problem of learning an embedding function by establishing a regression framework, which can avoid eigen-decomposition of dense matrices. Motivated by the effectiveness of SR, we incorporate multiple kernel learning (MKL into SR for dimensionality reduction. The proposed approach (termed MKL-SR seeks an embedding function in the Reproducing Kernel Hilbert Space (RKHS induced by the multiple base kernels. An MKL-SR algorithm is proposed to improve the performance of kernel-based SR (KSR further. Furthermore, the proposed MKL-SR algorithm can be performed in the supervised, unsupervised, and semi-supervised situation. Experimental results on supervised classification and semi-supervised classification demonstrate the effectiveness and efficiency of our algorithm.

  18. MESSENGER SPICE KERNELS V1.0

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set includes the complete set of MESSENGER SPICE data files (''kernel files''), which can be accessed using SPICE software. The SPICE data contains...

  19. CASSINI SPICE KERNELS V1.0

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set includes the complete set of Cassini SPICE data files (kernel files''), which can be accessed using SPICE software. The SPICE data contains geometric...

  20. NEAR SPICE KERNELS V1.0

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set includes the complete set of NEAR SPICE data files (kernel files'), which can be accessed using SPICE software. The SPICE data contain geometric and...

  1. STARDUST SPICE KERNELS V1.0

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set includes the complete set of Stardust SPICE data files (kernel files'), which can be accessed using SPICE software. The SPICE data contains geometric...

  2. MSL SPICE KERNELS V1.0

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set includes the MSL SPICE data files (kernel files''), which can be accessed using SPICE software. The SPICE data contain geometric and other ancillary...

  3. Bandwidth Selection for Weighted Kernel Density Estimation

    OpenAIRE

    Wang, Bin; Wang, Xiaofeng

    2007-01-01

    In the this paper, the authors propose to estimate the density of a targeted population with a weighted kernel density estimator (wKDE) based on a weighted sample. Bandwidth selection for wKDE is discussed. Three mean integrated squared error based bandwidth estimators are introduced and their performance is illustrated via Monte Carlo simulation. The least-squares cross-validation method and the adaptive weight kernel density estimator are also studied. The authors also consider the boundary...

  4. Some Remarks on the Symmetry Kernel Test

    OpenAIRE

    Baszczyńska, Aleksandra

    2013-01-01

    The paper presents chosen statistical tests used to verify the hypothesis of the symmetry of random variable’s distribution. Detailed analysis of the symmetry kernel test is made. The properties of the regarded symmetry kernel test are compared with the other symmetry tests using Monte Carlo methods. The symmetry tests are used, as an example, in analysis of the distribution of the Human Development Index (HDI). W pracy przedstawiono wybrane statystyczne testy wykorzystywane w ...

  5. On the Inclusion Relation of Reproducing Kernel Hilbert Spaces

    OpenAIRE

    Zhang, Haizhang; Zhao, Liang

    2011-01-01

    To help understand various reproducing kernels used in applied sciences, we investigate the inclusion relation of two reproducing kernel Hilbert spaces. Characterizations in terms of feature maps of the corresponding reproducing kernels are established. A full table of inclusion relations among widely-used translation invariant kernels is given. Concrete examples for Hilbert-Schmidt kernels are presented as well. We also discuss the preservation of such a relation under various operations of ...

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

    OpenAIRE

    Sitompul, Monica Angelina

    2015-01-01

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

  7. Prevention

    Science.gov (United States)

    ... Contact Aging & Health A to Z Find a Geriatrics Healthcare Professional Medications & Older Adults Making Your Wishes ... Prevention Hearing Loss Heart Attack High Blood Pressure Nutrition Osteoporosis Shingles Skin Cancer Related News Quitting Smoking, ...

  8. 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.

  9. [Transcultural prevention of alcohol-related disorders : effects of a culture- and migration-sensitive approach in elderly migrants with respect to attitudes and behavior: a cluster randomized controlled trial].

    Science.gov (United States)

    Bermejo, Isaac; Frank, F; Komarahadi, F; Albicker, J; Ries, Z; Kriston, L; Härter, M

    2015-07-01

    For migrants who are older than 50, alcohol frequently becomes a problem. Simultaneously alcohol-related prevention measures only reach this group insufficiently. Therefore, a transcultural concept for preventing alcohol-related disorders in elderly (≥ 45 years) migrants has been developed. The transcultural concept, which consisted of a prevention event as well as a cultural and language-sensitive information booklet, was evaluated in a cluster-randomized controlled trial (n = 310 immigrants). As a control condition there was a prevention event with materials from Deutsche Hauptstelle für Suchtfragen (German Centre for Addiction Issues). Data were obtained before and after the event, as well as after 6 months. All materials were available both in German and in Russian, Italian, Spanish and Turkish. Directly after the event, as well as 6 months thereafter, the transcultural approach was rated significantly better than the general prevention event. 73.4 % of the participants read the cultural and migration-sensitive booklet, whereas only 21.2 % in the control condition (p = 0.0001). Furthermore, significantly more participants of the transcultural approach reported a reduced alcohol consumption (49.4 vs. 16.7 %; p = 0.004) after 6 months. The consideration of diversity with respect to cultural, migration-related, socio demographic und linguistic aspects improves the effectiveness of prevention measures.

  10. Oecophylla longinoda (Hymenoptera: Formicidae) Lead to Increased Cashew Kernel Size and Kernel Quality.

    Science.gov (United States)

    Anato, F M; Sinzogan, A A C; Offenberg, J; Adandonon, A; Wargui, R B; Deguenon, J M; Ayelo, P M; Vayssières, J-F; Kossou, D K

    2017-06-01

    Weaver ants, Oecophylla spp., are known to positively affect cashew, Anacardium occidentale L., raw nut yield, but their effects on the kernels have not been reported. We compared nut size and the proportion of marketable kernels between raw nuts collected from trees with and without ants. Raw nuts collected from trees with weaver ants were 2.9% larger than nuts from control trees (i.e., without weaver ants), leading to 14% higher proportion of marketable kernels. On trees with ants, the kernel: raw nut ratio from nuts damaged by formic acid was 4.8% lower compared with nondamaged nuts from the same trees. Weaver ants provided three benefits to cashew production by increasing yields, yielding larger nuts, and by producing greater proportions of marketable kernel mass. © The Authors 2017. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  11. Mesenchymal Stromal Cells Prevent Allostimulation In Vivo and Control Checkpoints of Th1 Priming: Migration of Human DC to Lymph Nodes and NK Cell Activation.

    Science.gov (United States)

    Consentius, C; Akyüz, L; Schmidt-Lucke, J A; Tschöpe, C; Pinzur, L; Ofir, R; Reinke, P; Volk, H-D; Juelke, K

    2015-10-01

    Although the immunomodulatory potency of mesenchymal stromal cells (MSC) is well established, the mechanisms behind are still not clear. The crosstalk between myeloid dendritic cells (mDC) and natural killer (NK) cells and especially NK cell-derived interferon-gamma (IFN-γ) play a pivotal role in the development of type 1 helper (Th1) cell immune responses. While many studies explored the isolated impact of MSC on either in vitro generated DC, NK, or T cells, there are only few data available on the complex interplay between these cells. Here, we investigated the impact of MSC on the functionality of human mDC and the consequences for NK cell and Th1 priming in vitro and in vivo. In critical limb ischemia patients, who have been treated with allogeneic placenta-derived mesenchymal-like stromal cells (PLX-PAD), no in vivo priming of Th1 responses toward the major histocompatibility complex (MHC) mismatches could be detected. Further in vitro studies revealed that mDC reprogramming could play a central role for these effects. Following crosstalk with MSC, activated mDC acquired a tolerogenic phenotype characterized by reduced migration toward CCR7 ligand and impaired ability to stimulate NK cell-derived IFN-γ production. These effects, which were strongly related to an altered interleukin (IL)-12/IL-10 production by mDC, were accompanied by an effective prevention of Th1 priming in vivo. Our findings provide novel evidence for the regulation of Th1 priming by MSC via modulation of mDC and NK cell crosstalk and show that off-the-shelf produced MHC-mismatched PLX-PAD can be used in patients without any sign of immunogenicity. © 2015 AlphaMed Press.

  12. Kernel based orthogonalization for change detection in hyperspectral images

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg

    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 MNF analyses handle 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. An example shows the successful application of (kernel PCA and) kernel MNF analysis to change detection in HyMap data covering a small agricultural area near Lake Waging-Taching, Bavaria, in Southern Germany. In the change detection...

  13. A comprehensive benchmark of kernel methods to extract protein-protein interactions from literature.

    Directory of Open Access Journals (Sweden)

    Domonkos Tikk

    Full Text Available The most important way of conveying new findings in biomedical research is scientific publication. Extraction of protein-protein interactions (PPIs reported in scientific publications is one of the core topics of text mining in the life sciences. Recently, a new class of such methods has been proposed - convolution kernels that identify PPIs using deep parses of sentences. However, comparing published results of different PPI extraction methods is impossible due to the use of different evaluation corpora, different evaluation metrics, different tuning procedures, etc. In this paper, we study whether the reported performance metrics are robust across different corpora and learning settings and whether the use of deep parsing actually leads to an increase in extraction quality. Our ultimate goal is to identify the one method that performs best in real-life scenarios, where information extraction is performed on unseen text and not on specifically prepared evaluation data. We performed a comprehensive benchmarking of nine different methods for PPI extraction that use convolution kernels on rich linguistic information. Methods were evaluated on five different public corpora using cross-validation, cross-learning, and cross-corpus evaluation. Our study confirms that kernels using dependency trees generally outperform kernels based on syntax trees. However, our study also shows that only the best kernel methods can compete with a simple rule-based approach when the evaluation prevents information leakage between training and test corpora. Our results further reveal that the F-score of many approaches drops significantly if no corpus-specific parameter optimization is applied and that methods reaching a good AUC score often perform much worse in terms of F-score. We conclude that for most kernels no sensible estimation of PPI extraction performance on new text is possible, given the current heterogeneity in evaluation data. Nevertheless, our study

  14. Analog forecasting with dynamics-adapted kernels

    Science.gov (United States)

    Zhao, Zhizhen; Giannakis, Dimitrios

    2016-09-01

    Analog forecasting is a nonparametric technique introduced by Lorenz in 1969 which predicts the evolution of states of a dynamical system (or observables defined on the states) by following the evolution of the sample in a historical record of observations which most closely resembles the current initial data. Here, we introduce a suite of forecasting methods which improve traditional analog forecasting by combining ideas from kernel methods developed in harmonic analysis and machine learning and state-space reconstruction for dynamical systems. A key ingredient of our approach is to replace single-analog forecasting with weighted ensembles of analogs constructed using local similarity kernels. The kernels used here employ a number of dynamics-dependent features designed to improve forecast skill, including Takens’ delay-coordinate maps (to recover information in the initial data lost through partial observations) and a directional dependence on the dynamical vector field generating the data. Mathematically, our approach is closely related to kernel methods for out-of-sample extension of functions, and we discuss alternative strategies based on the Nyström method and the multiscale Laplacian pyramids technique. We illustrate these techniques in applications to forecasting in a low-order deterministic model for atmospheric dynamics with chaotic metastability, and interannual-scale forecasting in the North Pacific sector of a comprehensive climate model. We find that forecasts based on kernel-weighted ensembles have significantly higher skill than the conventional approach following a single analog.

  15. Pattern Classification of Signals Using Fisher Kernels

    Directory of Open Access Journals (Sweden)

    Yashodhan Athavale

    2012-01-01

    Full Text Available The intention of this study is to gauge the performance of Fisher kernels for dimension simplification and classification of time-series signals. Our research work has indicated that Fisher kernels have shown substantial improvement in signal classification by enabling clearer pattern visualization in three-dimensional space. In this paper, we will exhibit the performance of Fisher kernels for two domains: financial and biomedical. The financial domain study involves identifying the possibility of collapse or survival of a company trading in the stock market. For assessing the fate of each company, we have collected financial time-series composed of weekly closing stock prices in a common time frame, using Thomson Datastream software. The biomedical domain study involves knee signals collected using the vibration arthrometry technique. This study uses the severity of cartilage degeneration for classifying normal and abnormal knee joints. In both studies, we apply Fisher Kernels incorporated with a Gaussian mixture model (GMM for dimension transformation into feature space, which is created as a three-dimensional plot for visualization and for further classification using support vector machines. From our experiments we observe that Fisher Kernel usage fits really well for both kinds of signals, with low classification error rates.

  16. Prevention

    DEFF Research Database (Denmark)

    Halken, S; Høst, A

    2001-01-01

    , breastfeeding should be encouraged for 4-6 months. In high-risk infants a documented extensively hydrolysed formula is recommended if exclusive breastfeeding is not possible for the first 4 months of life. There is no evidence for preventive dietary intervention neither during pregnancy nor lactation...... populations. These theories remain to be documented in proper, controlled and prospective studies. Breastfeeding and the late introduction of solid foods (>4 months) is associated with a reduced risk of food allergy, atopic dermatitis, and recurrent wheezing and asthma in early childhood. In all infants....... Preventive dietary restrictions after the age of 4-6 months are not scientifically documented....

  17. Gendering Migration

    Directory of Open Access Journals (Sweden)

    Mirjana Morokvašić

    2014-12-01

    Full Text Available Migration patterns, migration discourse and underlying representations, migrants’ experiences, obligations and duties as well as the expectations relative to their migration are gendered. Since the pioneering feminist migration scholars’ questioning of men as a universal reference and the invisibility of women or their stereotypical representations as dependents in the mainstream production of knowledge on migration, the scholarship has evolved considerably. It is argued in the paper that the ongoing process of cross-fertilization of developments in two separate epistemologies, each initially questioning monolithic and essentialist visions of a “migrant” on one hand and a “woman” on the other, produced a fecund subfield of research “migration and gender”. The paper provides an insight into this, reviewing work on the issues related to gendering different phases of migration. Bridging migration and gender brought to the top of research agendas issues that used to be on the margins, creating new visibilities but leaving out other gendered dimensions of complex realities of migrant experience.

  18. Determining the minimum required uranium carbide content for HTGR UCO fuel kernels

    International Nuclear Information System (INIS)

    McMurray, Jacob W.; Lindemer, Terrence B.; Brown, Nicholas R.; Reif, Tyler J.; Morris, Robert N.; Hunn, John D.

    2017-01-01

    Highlights: • The minimum required uranium carbide content for HTGR UCO fuel kernels is calculated. • More nuclear and chemical factors have been included for more useful predictions. • The effect of transmutation products, like Pu and Np, on the oxygen distribution is included for the first time. - Abstract: Three important failure mechanisms that must be controlled in high-temperature gas-cooled reactor (HTGR) fuel for certain higher burnup applications are SiC layer rupture, SiC corrosion by CO, and coating compromise from kernel migration. All are related to high CO pressures stemming from O release when uranium present as UO 2 fissions and the O is not subsequently bound by other elements. In the HTGR kernel design, CO buildup from excess O is controlled by the inclusion of additional uranium apart from UO 2 in the form of a carbide, UC x and this fuel form is designated UCO. Here general oxygen balance formulas were developed for calculating the minimum UC x content to ensure negligible CO formation for 15.5% enriched UCO taken to 16.1% actinide burnup. Required input data were obtained from CALPHAD (CALculation of PHAse Diagrams) chemical thermodynamic models and the Serpent 2 reactor physics and depletion analysis tool. The results are intended to be more accurate than previous estimates by including more nuclear and chemical factors, in particular the effect of transmuted Pu and Np oxides on the oxygen distribution as the fuel kernel composition evolves with burnup.

  19. The Classification of Diabetes Mellitus Using Kernel k-means

    Science.gov (United States)

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

    2018-01-01

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

  20. OS X and iOS Kernel Programming

    CERN Document Server

    Halvorsen, Ole Henry

    2011-01-01

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

  1. Phenolic constituents of shea (Vitellaria paradoxa) kernels.

    Science.gov (United States)

    Maranz, Steven; Wiesman, Zeev; Garti, Nissim

    2003-10-08

    Analysis of the phenolic constituents of shea (Vitellaria paradoxa) kernels by LC-MS revealed eight catechin compounds-gallic acid, catechin, epicatechin, epicatechin gallate, gallocatechin, epigallocatechin, gallocatechin gallate, and epigallocatechin gallate-as well as quercetin and trans-cinnamic acid. The mean kernel content of the eight catechin compounds was 4000 ppm (0.4% of kernel dry weight), with a 2100-9500 ppm range. Comparison of the profiles of the six major catechins from 40 Vitellaria provenances from 10 African countries showed that the relative proportions of these compounds varied from region to region. Gallic acid was the major phenolic compound, comprising an average of 27% of the measured total phenols and exceeding 70% in some populations. Colorimetric analysis (101 samples) of total polyphenols extracted from shea butter into hexane gave an average of 97 ppm, with the values for different provenances varying between 62 and 135 ppm of total polyphenols.

  2. Kernel Method for Nonlinear Granger Causality

    Science.gov (United States)

    Marinazzo, Daniele; Pellicoro, Mario; Stramaglia, Sebastiano

    2008-04-01

    Important information on the structure of complex systems can be obtained by measuring to what extent the individual components exchange information among each other. The linear Granger approach, to detect cause-effect relationships between time series, has emerged in recent years as a leading statistical technique to accomplish this task. Here we generalize Granger causality to the nonlinear case using the theory of reproducing kernel Hilbert spaces. Our method performs linear Granger causality in the feature space of suitable kernel functions, assuming arbitrary degree of nonlinearity. We develop a new strategy to cope with the problem of overfitting, based on the geometry of reproducing kernel Hilbert spaces. Applications to coupled chaotic maps and physiological data sets are presented.

  3. The scalar field kernel in cosmological spaces

    Energy Technology Data Exchange (ETDEWEB)

    Koksma, Jurjen F; Prokopec, Tomislav [Institute for Theoretical Physics (ITP) and Spinoza Institute, Utrecht University, Postbus 80195, 3508 TD Utrecht (Netherlands); Rigopoulos, Gerasimos I [Helsinki Institute of Physics, University of Helsinki, PO Box 64, FIN-00014 (Finland)], E-mail: J.F.Koksma@phys.uu.nl, E-mail: T.Prokopec@phys.uu.nl, E-mail: gerasimos.rigopoulos@helsinki.fi

    2008-06-21

    We construct the quantum-mechanical evolution operator in the functional Schroedinger picture-the kernel-for a scalar field in spatially homogeneous FLRW spacetimes when the field is (a) free and (b) coupled to a spacetime-dependent source term. The essential element in the construction is the causal propagator, linked to the commutator of two Heisenberg picture scalar fields. We show that the kernels can be expressed solely in terms of the causal propagator and derivatives of the causal propagator. Furthermore, we show that our kernel reveals the standard light cone structure in FLRW spacetimes. We finally apply the result to Minkowski spacetime, to de Sitter spacetime and calculate the forward time evolution of the vacuum in a general FLRW spacetime.

  4. Fast Generation of Sparse Random Kernel Graphs.

    Science.gov (United States)

    Hagberg, Aric; Lemons, Nathan

    2015-01-01

    The development of kernel-based inhomogeneous random graphs has provided models that are flexible enough to capture many observed characteristics of real networks, and that are also mathematically tractable. We specify a class of inhomogeneous random graph models, called random kernel graphs, that produces sparse graphs with tunable graph properties, and we develop an efficient generation algorithm to sample random instances from this model. As real-world networks are usually large, it is essential that the run-time of generation algorithms scales better than quadratically in the number of vertices n. We show that for many practical kernels our algorithm runs in time at most (n(logn)2). As a practical example we show how to generate samples of power-law degree distribution graphs with tunable assortativity.

  5. Robust C-Loss Kernel Classifiers.

    Science.gov (United States)

    Xu, Guibiao; Hu, Bao-Gang; Principe, Jose C

    2018-03-01

    The correntropy-induced loss (C-loss) function has the nice property of being robust to outliers. In this paper, we study the C-loss kernel classifier with the Tikhonov regularization term, which is used to avoid overfitting. After using the half-quadratic optimization algorithm, which converges much faster than the gradient optimization algorithm, we find out that the resulting C-loss kernel classifier is equivalent to an iterative weighted least square support vector machine (LS-SVM). This relationship helps explain the robustness of iterative weighted LS-SVM from the correntropy and density estimation perspectives. On the large-scale data sets which have low-rank Gram matrices, we suggest to use incomplete Cholesky decomposition to speed up the training process. Moreover, we use the representer theorem to improve the sparseness of the resulting C-loss kernel classifier. Experimental results confirm that our methods are more robust to outliers than the existing common classifiers.

  6. Kernel principal component analysis for change detection

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg; Morton, J.C.

    2008-01-01

    Principal component analysis (PCA) is often used to detect change over time in remotely sensed images. A commonly used technique consists of finding the projections along the two eigenvectors for data consisting of two variables which represent the same spectral band covering the same geographical...... 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...

  7. Panel data specifications in nonparametric kernel regression

    DEFF Research Database (Denmark)

    Czekaj, Tomasz Gerard; Henningsen, Arne

    We discuss nonparametric regression models for panel data. A fully nonparametric panel data specification that uses the time variable and the individual identifier as additional (categorical) explanatory variables is considered to be the most suitable. We use this estimator and conventional...... 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...... found the estimates of the fully nonparametric panel data model to be more reliable....

  8. 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

  9. 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

  10. 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

  11. 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...

  12. Pharmacological blockade of aquaporin-1 water channel by AqB013 restricts migration and invasiveness of colon cancer cells and prevents endothelial tube formation in vitro.

    Science.gov (United States)

    Dorward, Hilary S; Du, Alice; Bruhn, Maressa A; Wrin, Joseph; Pei, Jinxin V; Evdokiou, Andreas; Price, Timothy J; Yool, Andrea J; Hardingham, Jennifer E

    2016-02-24

    Aquaporins (AQP) are water channel proteins that enable fluid fluxes across cell membranes, important for homeostasis of the tissue environment and for cell migration. AQP1 knockout mouse models of human cancers showed marked inhibition of tumor-induced angiogenesis, and in pre-clinical studies of colon adenocarcinomas, forced over-expression of AQP1 was shown to increase angiogenesis, invasion and metastasis. We have synthesized small molecule antagonists of AQP1. Our hypothesis is that inhibition of AQP1 will reduce migration and invasiveness of colon cancer cells, and the migration and tube-forming capacity of endothelial cells in vitro. Expression of AQP1 in cell lines was assessed by quantitative (q) PCR, western blot and immunofluorescence, while expression of AQP1 in human colon tumour tissue was assessed by immunohistochemistry. The effect of varying concentrations of the AQP1 inhibitor AqB013 was tested on human colon cancer cell lines expressing high versus low levels of AQP1, using wound closure (migration) assays, matrigel invasion assays, and proliferation assays. The effect of AqB013 on angiogenesis was tested using an endothelial cell tube-formation assay. HT29 colon cancer cells with high AQP1 levels showed significant inhibition of migration compared to vehicle control of 27.9% ± 2.6% (p colon cancer.

  13. Migration chemistry

    International Nuclear Information System (INIS)

    Carlsen, L.

    1992-05-01

    Migration chemistry, the influence of chemical -, biochemical - and physico-chemical reactions on the migration behaviour of pollutants in the environment, is an interplay between the actual natur of the pollutant and the characteristics of the environment, such as pH, redox conditions and organic matter content. The wide selection of possible pollutants in combination with varying geological media, as well as the operation of different chemical -, biochemical - and physico-chemical reactions compleactes the prediction of the influence of these processes on the mobility of pollutants. The report summarizes a wide range of potential pollutants in the terrestrial environment as well as a variety of chemical -, biochemical - and physico-chemical reactions, which can be expected to influence the migration behaviour, comprising diffusion, dispersion, convection, sorption/desorption, precipitation/dissolution, transformations/degradations, biochemical reactions and complex formation. The latter comprises the complexation of metal ions as well as non-polar organics to naturally occurring organic macromolecules. The influence of the single types of processes on the migration process is elucidated based on theoretical studies. The influence of chemical -, biochemical - and physico-chemical reactions on the migration behaviour is unambiguous, as the processes apparently control the transport of pollutants in the terrestrial environment. As the simple, conventional K D concept breaks down, it is suggested that the migration process should be described in terms of the alternative concepts chemical dispersion, average-elution-time and effective retention. (AB) (134 refs.)

  14. Symbol recognition with kernel density matching.

    Science.gov (United States)

    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.

  15. 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.)

  16. 42 Variability Bugs in the Linux Kernel

    DEFF Research Database (Denmark)

    Abal, Iago; Brabrand, Claus; Wasowski, Andrzej

    2014-01-01

    , serving to evaluate tool implementations of feature-sensitive analyses by testing them on real bugs. We present a qualitative study of 42 variability bugs collected from bug-fixing commits to the Linux kernel repository. We analyze each of the bugs, and record the results in a database. In addition, we...

  17. 40 Variability Bugs in the Linux Kernel

    DEFF Research Database (Denmark)

    Abal Rivas, Iago; Brabrand, Claus; Wasowski, Andrzej

    2014-01-01

    is a requirement for goal-oriented research, serving to evaluate tool implementations of feature-sensitive analyses by testing them on real bugs. We present a qualitative study of 40 variability bugs collected from bug-fixing commits to the Linux kernel repository. We investigate each of the 40 bugs, recording...

  18. Flexible Scheduling in Multimedia Kernels: An Overview

    NARCIS (Netherlands)

    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

  19. 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

  20. 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

  1. Graph Bundling by Kernel Density Estimation

    NARCIS (Netherlands)

    Hurter, C.; Ersoy, O.; Telea, A.

    We present a fast and simple method to compute bundled layouts of general graphs. For this, we first transform a given graph drawing into a density map using kernel density estimation. Next, we apply an image sharpening technique which progressively merges local height maxima by moving the convolved

  2. Evaluation of different combinations of palm kernel cake - and cotton ...

    African Journals Online (AJOL)

    ... sole palm kernel cake based diets than those fed combinations of palm kernel cake and cottonseed cake. It is concluded that palm kernel cake alone (without any combination with cottonseed cake) is adequate as protein source in compounding protein supplements for West African Dwarf goats for profitable performance.

  3. Enhanced gluten properties in soft kernel durum wheat

    Science.gov (United States)

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

  4. determination of bio-energy potential of palm kernel shell

    African Journals Online (AJOL)

    88888888

    2012-11-03

    Nov 3, 2012 ... Palm Kernel Shell (PKS) is an economically and environmentally sustainable raw material for ... oil and palm kernel oil production, palm oil fibre, effluent, kernel shell and empty fruit bunch are re- garded as wastes. According to Luangkiattikhun et ... use as concrete reinforcement in construction indus-.

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

    African Journals Online (AJOL)

    ADOWIE PERE

    ABSTRACT: A machine processing method for the separation of cracked palm kernel from the shells using ... Cracked palm kernel is a mixture of kernels, broken shells, dusts and other impurities. In order to produce ... Received 31 September 2017, received in revised form 18 October 2017, accepted 29 November 2017.

  6. An Investigation of Kernel Data Attacks and Countermeasures

    Science.gov (United States)

    2017-02-14

    demonstrate that attackers can stealthily subvert various kernel security mechanism s and develop a new keylogger , which is more stealthy than existing... keyloggers .. By classifying kernel data into different categories and handling them separately, we propose a defense mechanism and evaluate its...a computer system. 15. SUBJECT TERMS Kernel Data, Rootkit, Keylogger , Countermeasure 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF a. REPORT b

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

    Indian Academy of Sciences (India)

    2011-12-02

    Dec 2, 2011 ... Wheat kernel dimensions: how do they contribute to kernel weight at an individual QTL level? FA CUI1, 2†, ANMING DING1†, JUN LI1, 3†, CHUNHUA ZHAO1†, XINGFENG LI1, DESHUN FENG1,. XIUQIN WANG4, LIN WANG1, 5, JURONG GAO1 and HONGGANG WANG1∗. 1State Key Laboratory of Crop ...

  8. Landslide Susceptibility Mapping Based on Particle Swarm Optimization of Multiple Kernel Relevance Vector Machines: Case of a Low Hill Area in Sichuan Province, China

    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.

  9. Rye kernel breakfast increases satiety in the afternoon - an effect of food structure

    Directory of Open Access Journals (Sweden)

    Fredriksson Helena

    2011-04-01

    Full Text Available Abstract Background The structure of whole grain cereals is maintained to varying degrees during processing and preparation of foods. Food structure can influence metabolism, including perceived hunger and satiety. A diet that enhances satiety per calorie may help to prevent excessive calorie intake. The objective of this work was to compare subjective appetite ratings after consumption of intact and milled rye kernels. Methods Two studies were performed using a randomized, cross-over design. Ratings for appetite (hunger, satiety and desire to eat were registered during an 8-h period after consumption of whole and milled rye kernels prepared as breads (study 1, n = 24 and porridges (study 2, n = 20. Sifted wheat bread was used as reference in both study parts and the products were eaten in iso-caloric portions with standardized additional breakfast foods. Breads and porridges were analyzed to determine whether structure (whole vs. milled kernels effected dietary fibre content and composition after preparation of the products. Statistical evaluation of the appetite ratings after intake of the different breakfasts was done by paired t-tests for morning and afternoon ratings separately, with subjects as random effect and type of breakfast and time points as fixed effects. Results All rye breakfasts resulted in higher satiety ratings in the morning and afternoon compared with the iso-caloric reference breakfast with sifted wheat bread. Rye bread with milled or whole kernels affected appetite equally, so no effect of structure was observed. In contrast, after consumption of the rye kernel breakfast, satiety was increased and hunger suppressed in the afternoon compared with the milled rye kernel porridge breakfast. This effect could be related to structural differences alone, because the products were equal in nutritional content including dietary fibre content and composition. Conclusions The study demonstrates that small changes in diet composition

  10. Rye kernel breakfast increases satiety in the afternoon - an effect of food structure.

    Science.gov (United States)

    Isaksson, Hanna; Rakha, Allah; Andersson, Roger; Fredriksson, Helena; Olsson, Johan; Aman, Per

    2011-04-11

    The structure of whole grain cereals is maintained to varying degrees during processing and preparation of foods. Food structure can influence metabolism, including perceived hunger and satiety. A diet that enhances satiety per calorie may help to prevent excessive calorie intake. The objective of this work was to compare subjective appetite ratings after consumption of intact and milled rye kernels. Two studies were performed using a randomized, cross-over design. Ratings for appetite (hunger, satiety and desire to eat) were registered during an 8-h period after consumption of whole and milled rye kernels prepared as breads (study 1, n = 24) and porridges (study 2, n = 20). Sifted wheat bread was used as reference in both study parts and the products were eaten in iso-caloric portions with standardized additional breakfast foods. Breads and porridges were analyzed to determine whether structure (whole vs. milled kernels) effected dietary fibre content and composition after preparation of the products. Statistical evaluation of the appetite ratings after intake of the different breakfasts was done by paired t-tests for morning and afternoon ratings separately, with subjects as random effect and type of breakfast and time points as fixed effects. All rye breakfasts resulted in higher satiety ratings in the morning and afternoon compared with the iso-caloric reference breakfast with sifted wheat bread. Rye bread with milled or whole kernels affected appetite equally, so no effect of structure was observed. In contrast, after consumption of the rye kernel breakfast, satiety was increased and hunger suppressed in the afternoon compared with the milled rye kernel porridge breakfast. This effect could be related to structural differences alone, because the products were equal in nutritional content including dietary fibre content and composition. The study demonstrates that small changes in diet composition such as cereal grain structure have the potential to effect

  11. Research on offense and defense technology for iOS kernel security mechanism

    Science.gov (United States)

    Chu, Sijun; Wu, Hao

    2018-04-01

    iOS is a strong and widely used mobile device system. It's annual profits make up about 90% of the total profits of all mobile phone brands. Though it is famous for its security, there have been many attacks on the iOS operating system, such as the Trident apt attack in 2016. So it is important to research the iOS security mechanism and understand its weaknesses and put forward targeted protection and security check framework. By studying these attacks and previous jailbreak tools, we can see that an attacker could only run a ROP code and gain kernel read and write permissions based on the ROP after exploiting kernel and user layer vulnerabilities. However, the iOS operating system is still protected by the code signing mechanism, the sandbox mechanism, and the not-writable mechanism of the system's disk area. This is far from the steady, long-lasting control that attackers expect. Before iOS 9, breaking these security mechanisms was usually done by modifying the kernel's important data structures and security mechanism code logic. However, after iOS 9, the kernel integrity protection mechanism was added to the 64-bit operating system and none of the previous methods were adapted to the new versions of iOS [1]. But this does not mean that attackers can not break through. Therefore, based on the analysis of the vulnerability of KPP security mechanism, this paper implements two possible breakthrough methods for kernel security mechanism for iOS9 and iOS10. Meanwhile, we propose a defense method based on kernel integrity detection and sensitive API call detection to defense breakthrough method mentioned above. And we make experiments to prove that this method can prevent and detect attack attempts or invaders effectively and timely.

  12. 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.

  13. 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...... models to kernel learning, and means for restoring the generalizability in both kernel Principal Component Analysis and the Support Vector Machine are proposed. Viability is proved on a wide range of benchmark machine learning data sets....... as innerproducts in the model formulation. This dissertation presents research on improving the performance of standard kernel methods like kernel Principal Component Analysis and the Support Vector Machine. Moreover, the goal of the thesis has been two-fold. The first part focuses on the use of kernel Principal...

  14. What's driving migration?

    Science.gov (United States)

    Kane, H

    1995-01-01

    During the 1990s investment in prevention of international or internal migration declined, and crisis intervention increased. The budgets of the UN High Commissioner for Refugees and the UN Development Program remained about the same. The operating assumption is that war, persecution, famine, and environmental and social disintegration are inevitable. Future efforts should be directed to stabilizing populations through investment in sanitation, public health, preventive medicine, land tenure, environmental protection, and literacy. Forces pushing migration are likely to increase in the future. Forces include depletion of natural resources, income disparities, population pressure, and political disruption. The causes of migration are not constant. In the past, migration occurred during conquests, settlement, intermarriage, or religious conversion and was a collective movement. Current migration involves mass movement of individuals and the struggle to survive. There is new pressure to leave poor squatter settlements and the scarcities in land, water, and food. The slave trade between the 1500s and the 1800s linked continents, and only 2-3 million voluntarily crossed national borders. Involuntary migration began in the early 1800s when European feudal systems were in a decline, and people sought freedom. Official refugees, who satisfy the strict 1951 UN definition, increased from 15 million in 1980 to 23 million in 1990 but remained a small proportion of international migrants. Much of the mass movement occurs between developing countries. Migration to developed countries is accompanied by growing intolerance, which is misinformed. China practices a form of "population transfer" in Tibet in order to dilute Tibetan nationalism. Colonization of countries is a new less expensive form of control over territory. Eviction of minorities is another popular strategy in Iraq. Public works projects supported by foreign aid displace millions annually. War and civil conflicts

  15. Implementation of kernels on the Maestro processor

    Science.gov (United States)

    Suh, Jinwoo; Kang, D. I. D.; Crago, S. P.

    Currently, most microprocessors use multiple cores to increase performance while limiting power usage. Some processors use not just a few cores, but tens of cores or even 100 cores. One such many-core microprocessor is the Maestro processor, which is based on Tilera's TILE64 processor. The Maestro chip is a 49-core, general-purpose, radiation-hardened processor designed for space applications. The Maestro processor, unlike the TILE64, has a floating point unit (FPU) in each core for improved floating point performance. The Maestro processor runs at 342 MHz clock frequency. On the Maestro processor, we implemented several widely used kernels: matrix multiplication, vector add, FIR filter, and FFT. We measured and analyzed the performance of these kernels. The achieved performance was up to 5.7 GFLOPS, and the speedup compared to single tile was up to 49 using 49 tiles.

  16. 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...

  17. 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.

  18. A Kernel for Protein Secondary Structure Prediction

    OpenAIRE

    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...

  19. Searching and Indexing Genomic Databases via Kernelization

    Directory of Open Access Journals (Sweden)

    Travis eGagie

    2015-02-01

    Full Text Available The rapid advance of DNA sequencing technologies has yielded databases of thousands of genomes. To search and index these databases effectively, it is important that we take advantage of the similarity between those genomes. Several authors have recently suggested searching or indexing only one reference genome and the parts of the other genomes where they differ. In this paper we survey the twenty-year history of this idea and discuss its relation to kernelization in parameterized complexity.

  20. 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......). The MAF projection exploits the fact that interesting phenomena in images typically exhibit spatial autocorrelation. The analysis is based on nearinfrared hyperspectral images of maize grains demonstrating the superiority of the kernelbased MAF method....

  1. Multiple Kernel Learning with Data Augmentation

    Science.gov (United States)

    2016-11-22

    et al., 2010; Sun et al., 2010). Particularly, Sun et al. (2010) developed an efficient method based on sequential minimal optimization (SMO). The...http://www.robots.ox.ac.uk/~vgg/data/ flowers /17/ 58 Multiple Kernel Learning with Data Augmentation Algorithm 2 MKL with Data Augmentation approach for...Maria-Elena Nilsback and Andrew Zisserman. A visual vocabulary for flower classification. In Com- puter Vision and Pattern Recognition, 2006 IEEE Computer

  2. Multiple Kernel Spectral Regression for Dimensionality Reduction

    OpenAIRE

    Liu, Bing; Xia, Shixiong; Zhou, Yong

    2013-01-01

    Traditional manifold learning algorithms, such as locally linear embedding, Isomap, and Laplacian eigenmap, only provide the embedding results of the training samples. To solve the out-of-sample extension problem, spectral regression (SR) solves the problem of learning an embedding function by establishing a regression framework, which can avoid eigen-decomposition of dense matrices. Motivated by the effectiveness of SR, we incorporate multiple kernel learning (MKL) into SR for dimensionality...

  3. Searching and Indexing Genomic Databases via Kernelization.

    Science.gov (United States)

    Gagie, Travis; Puglisi, Simon J

    2015-01-01

    The rapid advance of DNA sequencing technologies has yielded databases of thousands of genomes. To search and index these databases effectively, it is important that we take advantage of the similarity between those genomes. Several authors have recently suggested searching or indexing only one reference genome and the parts of the other genomes where they differ. In this paper, we survey the 20-year history of this idea and discuss its relation to kernelization in parameterized complexity.

  4. 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.

  5. 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/

  6. 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)

  7. Dateline Migration.

    Science.gov (United States)

    Tomasi, Lydio E., Ed.

    1995-01-01

    Presents data on international migration and its effects in and between various countries in North America, Europe, and Africa. Discussions include refugee, immigrant, and migrant worker flows; the legal, political, and social problems surrounding immigrants; alien terrorism and law enforcement problems; and migrant effects on education, social…

  8. Migrating Worker

    DEFF Research Database (Denmark)

    Hansen, Hans

    This is the preliminary report on the results obtained in the Migrating Worker-project. This project was initiated by the Danish Ministry of Finance with the aim of illustrating the effects of the 1408/71 agreement and the bilateral double taxation agreements Denmark has with the countries included...

  9. Signaling in Early Maize Kernel Development.

    Science.gov (United States)

    Doll, Nicolas M; Depège-Fargeix, Nathalie; Rogowsky, Peter M; Widiez, Thomas

    2017-03-06

    Developing the next plant generation within the seed requires the coordination of complex programs driving pattern formation, growth, and differentiation of the three main seed compartments: the embryo (future plant), the endosperm (storage compartment), representing the two filial tissues, and the surrounding maternal tissues. This review focuses on the signaling pathways and molecular players involved in early maize kernel development. In the 2 weeks following pollination, functional tissues are shaped from single cells, readying the kernel for filling with storage compounds. Although the overall picture of the signaling pathways regulating embryo and endosperm development remains fragmentary, several types of molecular actors, such as hormones, sugars, or peptides, have been shown to be involved in particular aspects of these developmental processes. These molecular actors are likely to be components of signaling pathways that lead to transcriptional programming mediated by transcriptional factors. Through the integrated action of these components, multiple types of information received by cells or tissues lead to the correct differentiation and patterning of kernel compartments. In this review, recent advances regarding the four types of molecular actors (hormones, sugars, peptides/receptors, and transcription factors) involved in early maize development are presented. Copyright © 2017 The Author. Published by Elsevier Inc. All rights reserved.

  10. Multiple kernel learning for dimensionality reduction.

    Science.gov (United States)

    Lin, Yen-Yu; Liu, Tyng-Luh; Fuh, Chiou-Shann

    2011-06-01

    In solving complex visual learning tasks, adopting multiple descriptors to more precisely characterize the data has been a feasible way for improving performance. The resulting data representations are typically high-dimensional and assume diverse forms. Hence, finding a way of transforming them into a unified space of lower dimension generally facilitates the underlying tasks such as object recognition or clustering. To this end, the proposed approach (termed MKL-DR) generalizes the framework of multiple kernel learning for dimensionality reduction, and distinguishes itself with the following three main contributions: first, our method provides the convenience of using diverse image descriptors to describe useful characteristics of various aspects about the underlying data. Second, it extends a broad set of existing dimensionality reduction techniques to consider multiple kernel learning, and consequently improves their effectiveness. Third, by focusing on the techniques pertaining to dimensionality reduction, the formulation introduces a new class of applications with the multiple kernel learning framework to address not only the supervised learning problems but also the unsupervised and semi-supervised ones.

  11. Exploiting graph kernels for high performance biomedical relation extraction.

    Science.gov (United States)

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

    2018-01-30

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

  12. 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.

  13. [Study of genetic models of maize kernel traits].

    Science.gov (United States)

    Zhang, H W; Kong, F L

    2000-01-01

    Two sets of NCII mating design including 21 different maize inbreds were used to study the genetic models of five maize kernel traits--kernel length, width, ratio of kernel length and width, kernel thickness and weight per 100 kernels. Ten generations including P1, P2, F1, F2, B1, B2 and their reciprocal crosses RF1, RF2, RB1, RB2 were obtained. Three years' data were obtained and analyzed using mainly two methods: (1) precision identification for single cross and (2) mixed liner model MINQUE approach for diallel design. Method 1 showed that kernel traits were primarily controlled by maternal dominance, endosperm additive and dominance effect (maternal dominance > endosperm additive > endosperm dominance). Cytoplasmic effect was detected in one of the two crosses studied. Method 2 revealed that in the total variance of kernel traits, maternal genotypic effect contributed more than 60%, endosperm genotypic effect contributed less than 40%. Cytoplasmic effect only existed in kernel length and 100 kernel weight, with the range of 10% to 30%. The results indicated that kernel genetic performance was quite largely controlled by maternal genotypic effect.

  14. Effects of packaging materials on storage quality of peanut kernels

    Science.gov (United States)

    Fu, Xiaoji; Xing, Shengping; Xiong, Huiwei; Min, Hua; Zhu, Xuejing; He, Jialin; Mu, Honglei

    2018-01-01

    In order to obtain optimum packaging materials for peanut kernels, the effects of four types of packaging materials on peanut storage quality (coat color, acid value, germination rate, relative damage, and prevention of aflatoxin contamination) were examined. The results showed that packaging materials had a major influence on peanut storage quality indexes. The color of the peanut seed coat packaged in the polyester/aluminum/polyamide/polyethylene (PET/AL/PA/PE) composite film bag did not change significantly during the storage period. Color deterioration was slower with polyamide/polyethylene (PA/PE) packaging materials than with polyethylene (PE) film bags and was slower in PE bags than in the woven bags. The use of PET/AL/PA/PE and PA/PE bags maintained peanut quality and freshness for more than one year and both package types resulted in better germination rates. There were significant differences between the four types of packaging materials in terms of controlling insect pests. The peanuts packaged in the highly permeable woven bags suffered serious invasion from insect pests, while both PET/AL/PA/PE and PA/PE bags effectively prevented insect infection. Peanuts stored in PET/AL/PA/PE and PA/PE bags were also better at preventing and controlling aflatoxin contamination. PMID:29518085

  15. P- and S-wave Receiver Function Imaging with Scattering Kernels

    Science.gov (United States)

    Hansen, S. M.; Schmandt, B.

    2017-12-01

    Full waveform inversion provides a flexible approach to the seismic parameter estimation problem and can account for the full physics of wave propagation using numeric simulations. However, this approach requires significant computational resources due to the demanding nature of solving the forward and adjoint problems. This issue is particularly acute for temporary passive-source seismic experiments (e.g. PASSCAL) that have traditionally relied on teleseismic earthquakes as sources resulting in a global scale forward problem. Various approximation strategies have been proposed to reduce the computational burden such as hybrid methods that embed a heterogeneous regional scale model in a 1D global model. In this study, we focus specifically on the problem of scattered wave imaging (migration) using both P- and S-wave receiver function data. The proposed method relies on body-wave scattering kernels that are derived from the adjoint data sensitivity kernels which are typically used for full waveform inversion. The forward problem is approximated using ray theory yielding a computationally efficient imaging algorithm that can resolve dipping and discontinuous velocity interfaces in 3D. From the imaging perspective, this approach is closely related to elastic reverse time migration. An energy stable finite-difference method is used to simulate elastic wave propagation in a 2D hypothetical subduction zone model. The resulting synthetic P- and S-wave receiver function datasets are used to validate the imaging method. The kernel images are compared with those generated by the Generalized Radon Transform (GRT) and Common Conversion Point stacking (CCP) methods. These results demonstrate the potential of the kernel imaging approach to constrain lithospheric structure in complex geologic environments with sufficiently dense recordings of teleseismic data. This is demonstrated using a receiver function dataset from the Central California Seismic Experiment which shows several

  16. 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...... 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......- tor transform outperform the linear methods as well as kernel principal components in producing interesting projections of the data....

  17. 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...... kernels and did not give acceptable results because of high misclassification. However by using a predefined threshold and classifying entire kernels based on the number of correctly predicted pixels, improved results were achieved (sensitivity and specificity of 0.75 and 0.97). Object-wise classification...... was performed using two methods for feature extraction — score histograms and mean spectra. The model based on score histograms performed better for hard kernel classification (sensitivity and specificity of 0.93 and 0.97), while that of mean spectra gave better results for medium kernels (sensitivity...

  18. 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...... Analysis (KPCA) that only keeps features contributing mostly to image reconstruction, KECA selects the CKD that contribute mostly to the Rényi entropy of the image. These CKD are discriminative as they relate to the density distribution of the histogram of image attributes. We report superior performance...

  19. Visualization of nonlinear kernel models in neuroimaging by sensitivity maps

    DEFF Research Database (Denmark)

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

    show that the performance of linear models is reduced for certain scan labelings/categorizations in this data set, while the nonlinear models provide more flexibility. We show that the sensitivity map can be used to visualize nonlinear versions of kernel logistic regression, the kernel Fisher...... discriminant, and the SVM, and conclude that the sensitivity map is a versatile and computationally efficient tool for visualization of nonlinear kernel models in neuroimaging...

  20. Nonlinear Forecasting with Many Predictors using Kernel Ridge Regression

    OpenAIRE

    Peter Exterkate; Patrick J.F. Groenen; Christiaan Heij; Dick van Dijk

    2011-01-01

    textabstractThis 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 predictive regression model is based on a shrinkage estimator to avoid overfitting. We extend the kernel ridge regression methodology to enable its use for economic time-series forecasting, by ...

  1. A preference for migration

    OpenAIRE

    Stark, Oded

    2007-01-01

    At least to some extent migration behavior is the outcome of a preference for migration. The pattern of migration as an outcome of a preference for migration depends on two key factors: imitation technology and migration feasibility. We show that these factors jointly determine the outcome of a preference for migration and we provide examples that illustrate how the prevalence and transmission of a migration-forming preference yield distinct migration patterns. In particular, the imitation of...

  2. Synergistic anti-tumor actions of luteolin and silibinin prevented cell migration and invasion and induced apoptosis in glioblastoma SNB19 cells and glioblastoma stem cells.

    Science.gov (United States)

    Chakrabarti, Mrinmay; Ray, Swapan K

    2015-12-10

    Glioblastoma is the most lethal brain tumor. Failure of conventional chemotherapies prompted the search for natural compounds for treatment of glioblastoma. Plant-derived flavonoids could be alternative medicine for inhibiting not only glioblastoma cells but also glioblastoma stem cells (GSC). Two plant-derived flavonoids are luteolin (LUT) and silibinin (SIL). We investigated anti-tumor mechanisms of LUT and SIL in different human glioblastoma cells and GSC and found significant synergistic inhibition of human glioblastoma LN18 and SNB19 cells and GSC following treatment with combination of 20µM LUT and 50µM SIL. Combination of 20µM LUT and 50µM SIL was more effective than a conventional chemotherapeutic agent (BCNU or TMZ). We continued our studies with SNB19 cells and GSC and found dramatic inhibition of cell migration from spheroids and also cell invasion through matrigel following treatment with combination of LUT and SIL. This combination was highly effective to block angiogenesis and survival pathways leading to induction of apoptosis. Inhibition of PKCα, XIAP, and iNOS ultimately caused induction of extrinsic and intrinsic pathways of apoptosis. Collectively, synergistic efficacy of LUT and SIL could be a promising therapy to inhibit cell migration and invasion and induce apoptosis in different glioblastoma cells including GSC. Copyright © 2015 Elsevier B.V. All rights reserved.

  3. 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

  4. 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....

  5. 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......) dimensional feature space via the kernel function and then performing a linear analysis in that space. An example shows the successful application of kernel MAF analysis to change detection in HyMap data covering a small agricultural area near Lake Waging-Taching, Bavaria, Germany....

  6. Visualization of nonlinear kernel models in neuroimaging by sensitivity maps

    DEFF Research Database (Denmark)

    Rasmussen, P.M.; Madsen, Kristoffer H; Lund, T.E.

    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...... show that the performance of linear models is reduced for certain scan labelings/categorizations in this data set, while the nonlinear models provide more flexibility. We show that the sensitivity map can be used to visualize nonlinear versions of kernel logistic regression, the kernel Fisher...

  7. Robust visual tracking via speedup multiple kernel ridge regression

    Science.gov (United States)

    Qian, Cheng; Breckon, Toby P.; Li, Hui

    2015-09-01

    Most of the tracking methods attempt to build up feature spaces to represent the appearance of a target. However, limited by the complex structure of the distribution of features, the feature spaces constructed in a linear manner cannot characterize the nonlinear structure well. We propose an appearance model based on kernel ridge regression for visual tracking. Dense sampling is fulfilled around the target image patches to collect the training samples. In order to obtain a kernel space in favor of describing the target appearance, multiple kernel learning is introduced into the selection of kernels. Under the framework, instead of a single kernel, a linear combination of kernels is learned from the training samples to create a kernel space. Resorting to the circulant property of a kernel matrix, a fast interpolate iterative algorithm is developed to seek coefficients that are assigned to these kernels so as to give an optimal combination. After the regression function is learned, all candidate image patches gathered are taken as the input of the function, and the candidate with the maximal response is regarded as the object image patch. Extensive experimental results demonstrate that the proposed method outperforms other state-of-the-art tracking methods.

  8. Kinetic evolutionary behavior of catalysis-select migration

    International Nuclear Information System (INIS)

    Wu Yuan-Gang; Lin Zhen-Quan; Ke Jian-Hong

    2012-01-01

    We propose a catalysis-select migration driven evolution model of two-species (A- and B-species) aggregates, where one unit of species A migrates to species B under the catalysts of species C, while under the catalysts of species D the reaction will become one unit of species B migrating to species A. Meanwhile the catalyst aggregates of species C perform self-coagulation, as do the species D aggregates. We study this catalysis-select migration driven kinetic aggregation phenomena using the generalized Smoluchowski rate equation approach with C species catalysis-select migration rate kernel K(k;i,j) = Kkij and D species catalysis-select migration rate kernel J(k;i,j)= Jkij. The kinetic evolution behaviour is found to be dominated by the competition between the catalysis-select immigration and emigration, in which the competition is between JD 0 and KC 0 (D 0 and C 0 are the initial numbers of the monomers of species D and C, respectively). When JD 0 −KC 0 > 0, the aggregate size distribution of species A satisfies the conventional scaling form and that of species B satisfies a modified scaling form. And in the case of JD 0 −KC 0 0 −KC 0 > 0 case. (interdisciplinary physics and related areas of science and technology)

  9. Bosnia: Migrations

    Directory of Open Access Journals (Sweden)

    Stjepan Pavičić

    2000-12-01

    Full Text Available The paper is a reprint of a very informative review of migrations in Bosnia published almost 60 years ago. The author first notes that the [Slavic] population that first settled Bosnia spoke variants of the ikavian-ţakavian dialect spoken also in neighbouring parts of Croatia (although the interrogative ča itself was not common. From the 13th century the jekavian-štokavian dialect expanded from the Southeast, from areas in modern Montenegro. This change was greatly due to immigration of Vlachs, who had adopted jekavian-štokavian. Although earlier Vlach immigrants had adopted the indigenous ikavian idiom, as well as associating themselves with Catholicism or with the Patarene Bosnian Church, later arrivals spoke jekavian-štokavian and adhered to Eastern Orthodoxy. In the 14th century the former group, living on both sides of the Neretva valley and in the Dinaric range, expanded to areas of Croatia, whereas the Eastern Vlachs had already established themselves on the left bank of the Drina river. By 1450 all Vlachs in Bosnia spoke jekavian-štokavian. In the 15–16th centuries the Ottomans favoured the settlement of Vlachs in Bosnia. The Vlachs served in Ottoman military structures, provided transportation services and were useful in the integration of conquered western and northwestern lands. In general, the establishment of Ottoman rule in Bosnia induced major changes in the population and in migration flows. The author divides this history into three periods. The first lasted from the initial Ottoman conquests to the wars of 1683–1699. At its start in the 15th century almost all Patarenes adopted Islam, especially in areas where the Bosnian Church was strong, but also in areas where Catholicism dominated, where some Catholics embraced Islam. Conversions of Catholics to Islam intensified in the 16th century and throughout the 17th, to a different degree in various regions: a in Central Bosnia conversion was almost total, b along the Sava

  10. 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

  11. Index-free Heat Kernel Coefficients

    OpenAIRE

    van de Ven, Anton E. M.

    1997-01-01

    Using index-free notation, we present the diagonal values of the first five heat kernel coefficients associated with a general Laplace-type operator on a compact Riemannian space without boundary. The fifth coefficient appears here for the first time. For a flat space with a gauge connection, the sixth coefficient is given too. Also provided are the leading terms for any coefficient, both in ascending and descending powers of the Yang-Mills and Riemann curvatures, to the same order as require...

  12. Localized Multiple Kernel Learning A Convex Approach

    Science.gov (United States)

    2016-11-22

    1/2 . Theorem 9 (CLMKL Generalization Error Bounds) Assume that km(x, x) ≤ B, ∀m ∈ NM , x ∈ X . Suppose the loss function ℓ is L- Lipschitz and...mathematical foundation (e.g., Schölkopf and Smola, 2002). The performance of such algorithms, however, crucially depends on the involved kernel function ...approaches to localized MKL (reviewed in Section 1.1) optimize non-convex objective functions . This puts their generalization ability into doubt. Indeed

  13. Learning Rotation for Kernel Correlation Filter

    KAUST Repository

    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.

  14. Towards spatial isolation design in a multi-core real-time kernel targeting safety-critical applications

    DEFF Research Database (Denmark)

    Li, Gang; Top, Søren

    2013-01-01

    In mixed-criticality systems, applications naturally have different safety criticality levels. Partitioning technology is usually used to enable the integration of such mixed criticality applications upon one platform, aiming at reducing hardware, power consumption and especially certification cost....... Partitioning can prevent fault propagation among mixed-criticality applications, if spatial and temporal isolation are adequately ensured. This paper focuses on the solution of spatial isolation in the HARTEX kernel on a multi-core platform in terms of memory, communication between applications and I/O sharing....... According to formulated isolation requirements, a simple partitioning multi-core hardware architecture is proposed using SoC and memory protection units, and the kernel is extended to support spatial isolation between the kernel and applications as well as between applications. Combined design of hardware...

  15. Pareto-path multitask multiple kernel learning.

    Science.gov (United States)

    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.

  16. Index-free heat kernel coefficients

    Science.gov (United States)

    van de Ven, Anton E. M.

    1998-08-01

    Using index-free notation, we present the diagonal values 0264-9381/15/8/014/img1 of the first five heat kernel coefficients 0264-9381/15/8/014/img2 associated with a general Laplace-type operator on a compact Riemannian space without boundary. The fifth coefficient 0264-9381/15/8/014/img3 appears here for the first time. For the special case of a flat space, but with a gauge connection, the sixth coefficient is given too. Also provided are the leading terms for any coefficient, both in ascending and descending powers of the Yang-Mills and Riemann curvatures, to the same order as required for the fourth coefficient. These results are obtained by directly solving the relevant recursion relations, working in the Fock-Schwinger gauge and Riemann normal coordinates. Our procedure is thus non-covariant, but we show that for any coefficient the `gauged', respectively `curved', version is found from the corresponding `non-gauged', respectively `flat', coefficient by making some simple covariant substitutions. These substitutions being understood, the coefficients retain their `flat' form and size. In this sense the fifth and sixth coefficient have only 26 and 75 terms, respectively, allowing us to write them down. Using index-free notation also clarifies the general structure of the heat kernel coefficients. In particular, in flat space we find that from the fifth coefficient onward, certain scalars are absent. This may be relevant for the anomalies of quantum field theories in ten or more dimensions.

  17. 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.

  18. Kernelized rank learning for personalized drug recommendation.

    Science.gov (United States)

    He, Xiao; Folkman, Lukas; Borgwardt, Karsten

    2018-03-08

    Large-scale screenings of cancer cell lines with detailed molecular profiles against libraries of pharmacological compounds are currently being performed in order to gain a better understanding of the genetic component of drug response and to enhance our ability to recommend therapies given a patient's molecular profile. These comprehensive screens differ from the clinical setting in which (1) medical records only contain the response of a patient to very few drugs, (2) drugs are recommended by doctors based on their expert judgment, and (3) selecting the most promising therapy is often more important than accurately predicting the sensitivity to all potential drugs. Current regression models for drug sensitivity prediction fail to account for these three properties. We present a machine learning approach, named Kernelized Rank Learning (KRL), that ranks drugs based on their predicted effect per cell line (patient), circumventing the difficult problem of precisely predicting the sensitivity to the given drug. Our approach outperforms several state-of-the-art predictors in drug recommendation, particularly if the training dataset is sparse, and generalizes to patient data. Our work phrases personalized drug recommendation as a new type of machine learning problem with translational potential to the clinic. The Python implementation of KRL and scripts for running our experiments are available at https://github.com/BorgwardtLab/Kernelized-Rank-Learning. xiao.he@bsse.ethz.ch, lukas.folkman@bsse.ethz.ch. Supplementary data are available at Bioinformatics online.

  19. 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.

  20. Delimiting areas of endemism through kernel interpolation.

    Science.gov (United States)

    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.

  1. 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.

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

    African Journals Online (AJOL)

    A 3-factor experimental design was used to determine the influence of moisture content, roasting duration and temperature on palm kernel and sesame oil colours. Four levels each of these parameters were used. The data obtained were used to develop prediction models for palm kernel and sesame oil colours. Coefficient ...

  3. Evaluation of enzyme supplementation of palm kernel meal-based ...

    African Journals Online (AJOL)

    Journal of Agriculture, Forestry and the Social Sciences ... The results of this study showed that broilers can tolerate 20% inclusion rate of palm kernel meal in their rations without enzyme supplementation and partially replacing maize with palm kernel meal at that level of inclusion can reduce the cost of production of ...

  4. 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...

  5. Efficient methods for robust classification under uncertainty in kernel matrices

    NARCIS (Netherlands)

    Ben-Tal, A.; Bhadra, S.; Bhattacharyya, C.; Nemirovski, A.

    2012-01-01

    In this paper we study the problem of designing SVM classifiers when the kernel matrix, K , is affected by uncertainty. Specifically K is modeled as a positive affine combination of given positive semi definite kernels, with the coefficients ranging in a norm-bounded uncertainty set. We treat the

  6. 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 ...

  7. Homotopy deform method for reproducing kernel space for ...

    Indian Academy of Sciences (India)

    In this paper, the combination of homotopy deform method (HDM) and simplified reproducing kernel method (SRKM) is introduced for solving the boundary value problems (BVPs) of nonlinear differential equations. The solution methodology is based on Adomian decomposition and reproducing kernel method (RKM).

  8. 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.

  9. 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 ...

  10. Extracting Feature Model Changes from the Linux Kernel Using FMDiff

    NARCIS (Netherlands)

    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

  11. 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...

  12. High-power asymptotics of some weighted harmonic Bergman kernels

    Czech Academy of Sciences Publication Activity Database

    Engliš, Miroslav

    2016-01-01

    Roč. 271, č. 5 (2016), s. 1243-1261 ISSN 0022-1236 Institutional support: RVO:67985840 Keywords : Bergman kernel * harmonic Bergman kernel * asymptotic expansion Subject RIV: BA - General Mathematics Impact factor: 1.254, year: 2016 http://www.sciencedirect.com/science/article/pii/S0022123616301513

  13. Efficient Kernel-based 2DPCA for Smile Stages Recognition

    Directory of Open Access Journals (Sweden)

    Fitri Damayanti

    2012-03-01

    Full Text Available Recently, an approach called two-dimensional principal component analysis (2DPCA has been proposed for smile stages representation and recognition. The essence of 2DPCA is that it computes the eigenvectors of the so-called image covariance matrix without matrix-to-vector conversion so the size of the image covariance matrix are much smaller, easier to evaluate covariance matrix, computation cost is reduced and the performance is also improved than traditional PCA. In an effort to improve and perfect the performance of smile stages recognition, in this paper, we propose efficient Kernel based 2DPCA concepts. The Kernelization of 2DPCA can be benefit to develop the nonlinear structures in the input data. This paper discusses comparison of standard Kernel based 2DPCA and efficient Kernel based 2DPCA for smile stages recognition. The results of experiments show that Kernel based 2DPCA achieve better performance in comparison with the other approaches. While the use of efficient Kernel based 2DPCA can speed up the training procedure of standard Kernel based 2DPCA thus the algorithm can achieve much more computational efficiency and remarkably save the memory consuming compared to the standard Kernel based 2DPCA.

  14. Nutritional evaluation of palm kernel meal types: 1. Proximate ...

    African Journals Online (AJOL)

    Studies were conducted to determine the proximate composition and metabolizable energy values of palm kernel meal (PKM) types. The PKM types studied were obtained from Okomu, Presco and Envoy Oil Mills and were either mechanically or solvent extracted using different varieties of palm kernels. Samples of PKM ...

  15. 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...

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

    African Journals Online (AJOL)

    Gwla10

    Pentadesma butyracea Sabine (Clusiaceae) is a ligneous forest species of multipurpose uses. It is widely distributed in Africa from Guinea-Bissau to the West of the Democratic Republic of Congo. This study screened the kernel of P. butyracea on the basis of their physico-chemical properties. Six types of kernels were ...

  17. 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

  18. 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/

  19. 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

  20. Real time kernel performance monitoring with SystemTap

    CERN Multimedia

    CERN. Geneva

    2018-01-01

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

  1. 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.

  2. Nonlinear Forecasting with Many Predictors using Kernel Ridge Regression

    NARCIS (Netherlands)

    P. Exterkate (Peter); P.J.F. Groenen (Patrick); C. Heij (Christiaan); D.J.C. van Dijk (Dick)

    2011-01-01

    textabstractThis 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

  3. On sensitivity kernels for 'wave-equation' transmission tomography

    NARCIS (Netherlands)

    Hoop, Maarten V. de; Hilst, R.D. van der

    2004-01-01

    We combine seismological scattering theory with the theory of distributions to study some properties of sensitivity kernels for finite frequency seismic delay times. The theory to be used for calculating the kernels depends on the way the measurements are made. For example, the sensitivity to the

  4. 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.

  5. 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....

  6. Compactly Supported Basis Functions as Support Vector Kernels for Classification.

    Science.gov (United States)

    Wittek, Peter; Tan, Chew Lim

    2011-10-01

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

  7. Triso coating development progress for uranium nitride kernels

    Energy Technology Data Exchange (ETDEWEB)

    Jolly, Brian C. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Lindemer, Terrence [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Terrani, Kurt A. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

    2015-08-01

    In support of fully ceramic matrix (FCM) fuel development [1-2], coating development work is ongoing at the Oak Ridge National Laboratory (ORNL) to produce tri-structural isotropic (TRISO) coated fuel particles with UN kernels [3]. The nitride kernels are used to increase fissile density in these SiC-matrix fuel pellets with details described elsewhere [4]. The advanced gas reactor (AGR) program at ORNL used fluidized bed chemical vapor deposition (FBCVD) techniques for TRISO coating of UCO (two phase mixture of UO2 and UCx) kernels [5]. Similar techniques were employed for coating of the UN kernels, however significant changes in processing conditions were required to maintain acceptable coating properties due to physical property and dimensional differences between the UCO and UN kernels (Table 1).

  8. A weighted string kernel for protein fold recognition.

    Science.gov (United States)

    Nojoomi, Saghi; Koehl, Patrice

    2017-08-25

    Alignment-free methods for comparing protein sequences have proved to be viable alternatives to approaches that first rely on an alignment of the sequences to be compared. Much work however need to be done before those methods provide reliable fold recognition for proteins whose sequences share little similarity. We have recently proposed an alignment-free method based on the concept of string kernels, SeqKernel (Nojoomi and Koehl, BMC Bioinformatics, 2017, 18:137). In this previous study, we have shown that while Seqkernel performs better than standard alignment-based methods, its applications are potentially limited, because of biases due mostly to sequence length effects. In this study, we propose improvements to SeqKernel that follows two directions. First, we developed a weighted version of the kernel, WSeqKernel. Second, we expand the concept of string kernels into a novel framework for deriving information on amino acids from protein sequences. Using a dataset that only contains remote homologs, we have shown that WSeqKernel performs remarkably well in fold recognition experiments. We have shown that with the appropriate weighting scheme, we can remove the length effects on the kernel values. WSeqKernel, just like any alignment-based sequence comparison method, depends on a substitution matrix. We have shown that this matrix can be optimized so that sequence similarity scores correlate well with structure similarity scores. Starting from no information on amino acid similarity, we have shown that we can derive a scoring matrix that echoes the physico-chemical properties of amino acids. We have made progress in characterizing and parametrizing string kernels as alignment-based methods for comparing protein sequences, and we have shown that they provide a framework for extracting sequence information from structure.

  9. Sonar Validation Study with Migrating Gray Whales

    National Research Council Canada - National Science Library

    Tyack, Peter L

    2005-01-01

    .... Due to a legal challenge to the process used by NMFS in issuing the research permit, testing of the whalefinding sonar was prevented, and the field team collected baseline data on the whale migration...

  10. 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

  11. Learning molecular energies using localized graph kernels

    Science.gov (United States)

    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.

  12. Heat kernel methods for Lifshitz theories

    Science.gov (United States)

    Barvinsky, Andrei O.; Blas, Diego; Herrero-Valea, Mario; Nesterov, Dmitry V.; Pérez-Nadal, Guillem; Steinwachs, Christian F.

    2017-06-01

    We study the one-loop covariant effective action of Lifshitz theories using the heat kernel technique. The characteristic feature of Lifshitz theories is an anisotropic scaling between space and time. This is enforced by the existence of a preferred foliation of space-time, which breaks Lorentz invariance. In contrast to the relativistic case, covariant Lifshitz theories are only invariant under diffeomorphisms preserving the foliation structure. We develop a systematic method to reduce the calculation of the effective action for a generic Lifshitz operator to an algorithm acting on known results for relativistic operators. In addition, we present techniques that drastically simplify the calculation for operators with special properties. We demonstrate the efficiency of these methods by explicit applications.

  13. Modified kernel-based nonlinear feature extraction.

    Energy Technology Data Exchange (ETDEWEB)

    Ma, J. (Junshui); Perkins, S. J. (Simon J.); Theiler, J. P. (James P.); Ahalt, S. (Stanley)

    2002-01-01

    Feature Extraction (FE) techniques are widely used in many applications to pre-process data in order to reduce the complexity of subsequent processes. A group of Kernel-based nonlinear FE ( H E ) algorithms has attracted much attention due to their high performance. However, a serious limitation that is inherent in these algorithms -- the maximal number of features extracted by them is limited by the number of classes involved -- dramatically degrades their flexibility. Here we propose a modified version of those KFE algorithms (MKFE), This algorithm is developed from a special form of scatter-matrix, whose rank is not determined by the number of classes involved, and thus breaks the inherent limitation in those KFE algorithms. Experimental results suggest that MKFE algorithm is .especially useful when the training set is small.

  14. Heat kernel method and its applications

    CERN Document Server

    Avramidi, Ivan G

    2015-01-01

    The heart of the book is the development of a short-time asymptotic expansion for the heat kernel. This is explained in detail and explicit examples of some advanced calculations are given. In addition some advanced methods and extensions, including path integrals, jump diffusion and others are presented. The book consists of four parts: Analysis, Geometry, Perturbations and Applications. The first part shortly reviews of some background material and gives an introduction to PDEs. The second part is devoted to a short introduction to various aspects of differential geometry that will be needed later. The third part and heart of the book presents a systematic development of effective methods for various approximation schemes for parabolic differential equations. The last part is devoted to applications in financial mathematics, in particular, stochastic differential equations. Although this book is intended for advanced undergraduate or beginning graduate students in, it should also provide a useful reference ...

  15. Celluclast 1.5L pretreatment enhanced aroma of palm kernels and oil after kernel roasting.

    Science.gov (United States)

    Zhang, Wencan; Zhao, Fangju; Yang, Tiankui; Zhao, Feifei; Liu, Shaoquan

    2017-12-01

    The aroma of palm kernel oil (PKO) affects its applications. Little information is available on how enzymatic modification of palm kernels (PK) affects PK and PKO aroma after kernel roasting. Celluclast (cellulase) pretreatment of PK resulted in a 2.4-fold increment in the concentration of soluble sugars, with glucose being increased by 6.0-fold. Higher levels of 1.7-, 1.8- and 1.9-fold of O-heterocyclic volatile compounds were found in the treated PK after roasting at 180 °C for 8, 14 and 20 min respectively relative to the corresponding control, with furfural, 5-methyl-2-furancarboxaldehyde, 2-furanmethanol and maltol in particularly higher amounts. Volatile differences between PKOs from control and treated PK were also found, though less obvious owing to the aqueous extraction process. Principal component analysis based on aroma-active compounds revealed that upon the proceeding of roasting, the differentiation between control and treated PK was enlarged while that of corresponding PKOs was less clear-cut. Celluclast pretreatment enabled the medium roasted PK to impart more nutty, roasty and caramelic odor and the corresponding PKO to impart more caramelic but less roasty and burnt notes. Celluclast pretreatment of PK followed by roasting may be a promising new way of improving PKO aroma. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

  16. 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.

  17. Stochastic subset selection for learning with kernel machines.

    Science.gov (United States)

    Rhinelander, Jason; Liu, Xiaoping P

    2012-06-01

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

  18. Deep Restricted Kernel Machines Using Conjugate Feature Duality.

    Science.gov (United States)

    Suykens, Johan A K

    2017-08-01

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

  19. Localized Multiple Kernel Learning Via Sample-Wise Alternating Optimization.

    Science.gov (United States)

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

    2014-01-01

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

  20. 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.

  1. Training Lp norm multiple kernel learning in the primal.

    Science.gov (United States)

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

    2013-10-01

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

  2. 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.

  3. The Globalisation of migration

    OpenAIRE

    Milan Mesić

    2002-01-01

    The paper demonstrates that contemporary international migration is a constitutive part of the globalisation process. After defining the concepts of globalisation and the globalisation of migration, the author discusses six key themes, linking globalisation and international migration (“global cities”, the scale of migration; diversification of migration flows; globalisation of science and education; international migration and citizenship; emigrant communities and new identities). First, in ...

  4. Smoothing internal migration age profiles for comparative research

    Directory of Open Access Journals (Sweden)

    Aude Bernard

    2015-05-01

    Full Text Available Background: Age patterns are a key dimension to compare migration between countries and over time. Comparative metrics can be reliably computed only if data capture the underlying age distribution of migration. Model schedules, the prevailing smoothing method, fit a composite exponential function, but are sensitive to function selection and initial parameter setting. Although non-parametric alternatives exist, their performance is yet to be established. Objective: We compare cubic splines and kernel regressions against model schedules by assessingwhich method provides an accurate representation of the age profile and best performs on metrics for comparing aggregate age patterns. Methods: We use full population microdata for Chile to perform 1,000 Monte-Carlo simulations for nine sample sizes and two spatial scales. We use residual and graphic analysis to assess model performance on the age and intensity at which migration peaks and the evolution of migration age patterns. Results: Model schedules generate a better fit when (1 the expected distribution of the age profile is known a priori, (2 the pre-determined shape of the model schedule adequately describes the true age distribution, and (3 the component curves and initial parameter values can be correctly set. When any of these conditions is not met, kernel regressions and cubic splines offer more reliable alternatives. Conclusions: Smoothing models should be selected according to research aims, age profile characteristics, and sample size. Kernel regressions and cubic splines enable a precise representation of aggregate migration age profiles for most sample sizes, without requiring parameter setting or imposing a pre-determined distribution, and therefore facilitate objective comparison.

  5. SPATIOTEMPORAL DOMAIN DECOMPOSITION FOR MASSIVE PARALLEL COMPUTATION OF SPACE-TIME KERNEL DENSITY

    Directory of Open Access Journals (Sweden)

    A. Hohl

    2015-07-01

    Full Text Available Accelerated processing capabilities are deemed critical when conducting analysis on spatiotemporal datasets of increasing size, diversity and availability. High-performance parallel computing offers the capacity to solve computationally demanding problems in a limited timeframe, but likewise poses the challenge of preventing processing inefficiency due to workload imbalance between computing resources. Therefore, when designing new algorithms capable of implementing parallel strategies, careful spatiotemporal domain decomposition is necessary to account for heterogeneity in the data. In this study, we perform octtree-based adaptive decomposition of the spatiotemporal domain for parallel computation of space-time kernel density. In order to avoid edge effects near subdomain boundaries, we establish spatiotemporal buffers to include adjacent data-points that are within the spatial and temporal kernel bandwidths. Then, we quantify computational intensity of each subdomain to balance workloads among processors. We illustrate the benefits of our methodology using a space-time epidemiological dataset of Dengue fever, an infectious vector-borne disease that poses a severe threat to communities in tropical climates. Our parallel implementation of kernel density reaches substantial speedup compared to sequential processing, and achieves high levels of workload balance among processors due to great accuracy in quantifying computational intensity. Our approach is portable of other space-time analytical tests.

  6. Spatiotemporal Domain Decomposition for Massive Parallel Computation of Space-Time Kernel Density

    Science.gov (United States)

    Hohl, A.; Delmelle, E. M.; Tang, W.

    2015-07-01

    Accelerated processing capabilities are deemed critical when conducting analysis on spatiotemporal datasets of increasing size, diversity and availability. High-performance parallel computing offers the capacity to solve computationally demanding problems in a limited timeframe, but likewise poses the challenge of preventing processing inefficiency due to workload imbalance between computing resources. Therefore, when designing new algorithms capable of implementing parallel strategies, careful spatiotemporal domain decomposition is necessary to account for heterogeneity in the data. In this study, we perform octtree-based adaptive decomposition of the spatiotemporal domain for parallel computation of space-time kernel density. In order to avoid edge effects near subdomain boundaries, we establish spatiotemporal buffers to include adjacent data-points that are within the spatial and temporal kernel bandwidths. Then, we quantify computational intensity of each subdomain to balance workloads among processors. We illustrate the benefits of our methodology using a space-time epidemiological dataset of Dengue fever, an infectious vector-borne disease that poses a severe threat to communities in tropical climates. Our parallel implementation of kernel density reaches substantial speedup compared to sequential processing, and achieves high levels of workload balance among processors due to great accuracy in quantifying computational intensity. Our approach is portable of other space-time analytical tests.

  7. Enhanced Data Representation by Kernel Metric Learning for Dementia Diagnosis.

    Science.gov (United States)

    Cárdenas-Peña, David; Collazos-Huertas, Diego; Castellanos-Dominguez, German

    2017-01-01

    Alzheimer's disease (AD) is the kind of dementia that affects the most people around the world. Therefore, an early identification supporting effective treatments is required to increase the life quality of a wide number of patients. Recently, computer-aided diagnosis tools for dementia using Magnetic Resonance Imaging scans have been successfully proposed to discriminate between patients with AD, mild cognitive impairment, and healthy controls. Most of the attention has been given to the clinical data, provided by initiatives as the ADNI, supporting reliable researches on intervention, prevention, and treatments of AD. Therefore, there is a need for improving the performance of classification machines. In this paper, we propose a kernel framework for learning metrics that enhances conventional machines and supports the diagnosis of dementia. Our framework aims at building discriminative spaces through the maximization of center kernel alignment function, aiming at improving the discrimination of the three considered neurological classes. The proposed metric learning performance is evaluated on the widely-known ADNI database using three supervised classification machines ( k -nn, SVM and NNs) for multi-class and bi-class scenarios from structural MRIs. Specifically, from ADNI collection 286 AD patients, 379 MCI patients and 231 healthy controls are used for development and validation of our proposed metric learning framework. For the experimental validation, we split the data into two subsets: 30% of subjects used like a blindfolded assessment and 70% employed for parameter tuning. Then, in the preprocessing stage, each structural MRI scan a total of 310 morphological measurements are automatically extracted from by FreeSurfer software package and concatenated to build an input feature matrix. Obtained test performance results, show that including a supervised metric learning improves the compared baseline classifiers in both scenarios. In the multi-class scenario

  8. Enhanced Data Representation by Kernel Metric Learning for Dementia Diagnosis

    Directory of Open Access Journals (Sweden)

    David Cárdenas-Peña

    2017-07-01

    Full Text Available Alzheimer's disease (AD is the kind of dementia that affects the most people around the world. Therefore, an early identification supporting effective treatments is required to increase the life quality of a wide number of patients. Recently, computer-aided diagnosis tools for dementia using Magnetic Resonance Imaging scans have been successfully proposed to discriminate between patients with AD, mild cognitive impairment, and healthy controls. Most of the attention has been given to the clinical data, provided by initiatives as the ADNI, supporting reliable researches on intervention, prevention, and treatments of AD. Therefore, there is a need for improving the performance of classification machines. In this paper, we propose a kernel framework for learning metrics that enhances conventional machines and supports the diagnosis of dementia. Our framework aims at building discriminative spaces through the maximization of center kernel alignment function, aiming at improving the discrimination of the three considered neurological classes. The proposed metric learning performance is evaluated on the widely-known ADNI database using three supervised classification machines (k-nn, SVM and NNs for multi-class and bi-class scenarios from structural MRIs. Specifically, from ADNI collection 286 AD patients, 379 MCI patients and 231 healthy controls are used for development and validation of our proposed metric learning framework. For the experimental validation, we split the data into two subsets: 30% of subjects used like a blindfolded assessment and 70% employed for parameter tuning. Then, in the preprocessing stage, each structural MRI scan a total of 310 morphological measurements are automatically extracted from by FreeSurfer software package and concatenated to build an input feature matrix. Obtained test performance results, show that including a supervised metric learning improves the compared baseline classifiers in both scenarios. In the multi

  9. Gilkey-de Witt heat kernel expansion and zero modes

    International Nuclear Information System (INIS)

    Alonso-Izquierdo, A.; Mateos Guilarte, J.

    2013-01-01

    In this paper we propose a generalization of the Gilkey-de Witt heat kernel expansion, designed to provide us with a precise estimation of the heat trace of non-negative Schr¨odinger type differential operators with non-trivial kernel over all the domain of its “inverse temperature” variable β. We apply this modified approach to compute effectively the one-loop kink mass shift for some models whose kink fluctuation operator spectrum is unknown and the only alternative to estimate this magnitude is the use of the heat kernel expansion techniques.

  10. Visualization of nonlinear kernel models in neuroimaging by sensitivity maps

    DEFF Research Database (Denmark)

    Rasmussen, P.M.; Madsen, Kristoffer H; Lund, T.E.

    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...

  11. Flour quality and kernel hardness connection in winter wheat

    Directory of Open Access Journals (Sweden)

    Szabó B. P.

    2016-12-01

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

  12. A Truly Jitter-Free Real-Time Kernel

    DEFF Research Database (Denmark)

    Marian, Nicolae; Jiang, Peng

    2008-01-01

    Hardware-Software co-design is a powerful method nowadays for the embedded system development. Reducing time to the market, more accuracy and interactivity with the whole system by the co-design developments are available. The paper considers and investigates a co-design solution applied to a real-time...... kernel (RTK) named HARTEX. The objective was to even more improve the timing performances of the kernel in terms of minimized, constant overhead (jitter free), in an application transparent manner. The co-design solution partitions the kernel between a pure software part, with constant overhead...

  13. Robust Learning With Kernel Mean $p$-Power Error Loss.

    Science.gov (United States)

    Chen, Badong; Xing, Lei; Wang, Xin; Qin, Jing; Zheng, Nanning

    2017-07-25

    Correntropy is a second order statistical measure in kernel space, which has been successfully applied in robust learning and signal processing. In this paper, we define a nonsecond order statistical measure in kernel space, called the kernel mean-p power error (KMPE), including the correntropic loss (C-Loss) as a special case. Some basic properties of KMPE are presented. In particular, we apply the KMPE to extreme learning machine (ELM) and principal component analysis (PCA), and develop two robust learning algorithms, namely ELM-KMPE and PCA-KMPE. Experimental results on synthetic and benchmark data show that the developed algorithms can achieve better performance when compared with some existing methods.

  14. The role of Tre6P and SnRK1 in maize early kernel development and events leading to stress-induced kernel abortion.

    Science.gov (United States)

    Bledsoe, Samuel W; Henry, Clémence; Griffiths, Cara A; Paul, Matthew J; Feil, Regina; Lunn, John E; Stitt, Mark; Lagrimini, L Mark

    2017-04-12

    of kernel excision from the ear triggers a cascade of events starting with the precipitous drop in Tre6P levels. It is proposed that the removal of Tre6P suppression of SnRK1 activity results in transcription of putative SnRK1 target genes, and the metabolic transition from biosynthesis to catabolism. This highlights the importance of Tre6P in the metabolic response to starvation. We also present evidence that sugars can mediate the activation of SnRK1. The precipitous drop in Tre6P corresponds to a large increase in transcription of ZmTPPA.3, indicating that this specific enzyme may be responsible for the de-phosphorylation of Tre6P. The high levels of Tre6P in the immature embryo are likely important for preventing kernel abortion.

  15. Effects of kernel weight and source-limitation on wheat grain yield ...

    African Journals Online (AJOL)

    Also, source levels were manipulated through 50% spikelet removal at anthesis to evaluate cultivar source/sink limitations to kernel growth. The results depicted that grain yield, kernel number per spike and 1000 kernel weight were reduced by 24.1%, 9.2% and 23.7% in warmer environment, respectively. Hence, kernel ...

  16. NEW HORIZONS SPICE KERNELS V1.0

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set includes the complete set of New Horizons (NH) SPICE data files (kernel files'), which can be accessed using SPICE software. The SPICE data contain...

  17. Kernel based collaborative recommender system for e-purchasing

    Indian Academy of Sciences (India)

    Home; Journals; Sadhana; Volume 35; Issue 5. Kernel based collaborative recommender system for -purchasing ... Recommender system a new marketing strategy plays an important role particularly in an electronic commerce environment. Among the various recommender systems, collaborative recommender system ...

  18. Homotopy deform method for reproducing kernel space for ...

    Indian Academy of Sciences (India)

    2016-09-23

    Sep 23, 2016 ... Nonlinear differential equations; the homotopy deform method; the simplified reproducing kernel ... an equivalent integro differential equation. ... an algorithm for solving nonlinear multipoint BVPs by combining homotopy perturbation and variational iteration methods. Most recently, Duan and Rach [12].

  19. Quantitative trait locus (QTL) mapping for 100-kernel weight of ...

    African Journals Online (AJOL)

    hope&shola

    2010-12-06

    Zea mays L.), related to yield. To realize its ... Key words: Maize (Zea mays L.), 100-kernel weight, quantitative trait locus (QTL), recombinant inbred line. (RIL), nitrogen ... cient approach to realize genetic basis of trait, some.

  20. Homotopy deform method for reproducing kernel space for ...

    Indian Academy of Sciences (India)

    2016-09-23

    s12043-016-1269-8. Homotopy deform method for reproducing kernel space for nonlinear boundary value problems. MIN-QIANG XU. ∗ and YING-ZHEN LIN. School of Science, Zhuhai Campus, Beijing Institute of Technology, ...

  1. MARS EXPLORATION ROVER 2 SPICE KERNELS V1.0

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set includes the complete set of Mars Exploration Rover 2 SPICE data files (kernel files'), which can be accessed using SPICE software. The SPICE data...

  2. ROSETTA ORBITER/LANDER SPICE KERNELS V1.0

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set includes the complete set of Rosetta mission SPICE data files (kernel files''), which can be accessed using SPICE software. The SPICE data contains...

  3. MGS MARS SPICE KERNELS V1.0

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set includes the complete set of Mars Global Surveyor SPICE data files (kernel files''), which can be accessed using SPICE software. The SPICE data...

  4. MARS EXPLORATION ROVER 1 SPICE KERNELS V1.0

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set includes the complete set of Mars Exploration Rover 1 SPICE data files (kernel files''), which can be accessed using SPICE software. The SPICE data...

  5. Linear and kernel methods for multi- and hypervariate change detection

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg; Canty, Morton J.

    2010-01-01

    The iteratively re-weighted multivariate alteration detection (IR-MAD) algorithm may be used both for unsuper- vised change detection in multi- and hyperspectral remote sensing imagery as well as for automatic radiometric normalization of multi- or hypervariate multitemporal image sequences...... code exists which allows for fast data exploration and experimentation with smaller datasets. Computationally demanding kernelization of test data with training data and kernel image projections have been programmed to run on massively parallel CUDA-enabled graphics processors, when available, giving....... 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...

  6. DEEP SPACE 1 SPICE KERNELS V1.0

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set includes the complete set of Deep Space 1 (DS1) SPICE data files (kernel files''), which can be accessed using SPICE software. The SPICE data contains...

  7. CLEMENTINE MOON SPICE KERNELS V1.0

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set includes the complete set of Clementine SPICE data files (kernel files''), which can be accessed using SPICE software. The SPICE data contains...

  8. ILO - International Migration Programme.

    Science.gov (United States)

    Boudraa, Miriam

    2011-01-01

    In a wide International Context characterised not only by the economical development but also by the social, cultural, political and individual development, we witness more and more to a exchange between the developed and the developing countries, which can be translated especially in the migration of the work force. In theory, all countries are either countries of origin either countries of transit or destination, and they are all responsible for the rights of migrant workers by promoting the rights, by monitoring and by preventing the abusive conditions. The process of migration of the workforce can be divided into three stages: the first coincides with the period prior to departure, the second is represented by the aftermath of the departure and the period of stay in the country of destination, the third stage corresponds to the return in the country of origin. The workers must be protected throughout this process by the international organizations that perform the catalytic role of communication and exchange between countries, for the only purpose of protecting the rights of immigrant and/or immigrants workers. The responsibility for the protection of workers is divided among the various players in the International Labour Organisation. Every country has to apply measures according to the international standards regarding workers' rights, standards that guide the various countries in the formulation and implementation of their policies and legislation. These standards are suggested by International Conventions, the ILO Conventions and other international instruments such as the human rights instrument. There has been a big step forward once the ILO Fundamental Conventions and Conventions on Migrant Workers where implemented and this implementation represented the use of the Guidelines "ILO Multilateral Framework on Labour Migration".

  9. 7 CFR 981.401 - Adjusted kernel weight.

    Science.gov (United States)

    2010-01-01

    ... weight of delivery 10,000 10,000 2. Percent of edible kernel weight 53.0 84.0 3. Less weight loss in processing 1 1.00 0 4. Less excess moisture of edible kernels (excess moisture×line 2) 1.06 1.68 5. Net... Agriculture Regulations of the Department of Agriculture (Continued) AGRICULTURAL MARKETING SERVICE (Marketing...

  10. Palm kernel shell as aggregate for light weight concrete | Idah ...

    African Journals Online (AJOL)

    The palm kernel, cement, sand and gravel were mixed and cast in steel or cast iron moulds of 150mm2 cubes. The results show that the PK1 with ratio of 1 :2:3: 1· of cement, sand, gravel. and palm kernel shells respectively gave the highest compressive strength of 8.03N/mm2 after 28 days of curing. Comparing the results ...

  11. Mathematical Modelling of Thin Layer Dried Cashew Kernels | Asiru ...

    African Journals Online (AJOL)

    In this paper mathematical models describing thin layer drying of cashew kernels in a batch dryer were presented. The range of drying air temperature was 70 – 110°C. The initial moisture content of the cashew kernels was 9.29% (d.b.) and the final moisture content was in the range of 3.5 to 4.6% dry-basis. Seven different ...

  12. Assessing Gamma kernels and BSS/LSS processes

    DEFF Research Database (Denmark)

    Barndorff-Nielsen, Ole E.

    This paper reviews the roles of gamma type kernels in the theory and modelling for Brownian and Lévy semistationary processes. Applications to financial econometrics and the physics of turbulence are pointed out.......This paper reviews the roles of gamma type kernels in the theory and modelling for Brownian and Lévy semistationary processes. Applications to financial econometrics and the physics of turbulence are pointed out....

  13. Commutators of integral operators with variable kernels on Hardy ...

    Indian Academy of Sciences (India)

    (1.2). Then T ,0 is the singular integral with variable kernel, and we simply write it as T . The. Lp-boundedness of the singular integral operator with variable kernel appears in [1] (see also [3,7]). It turns out that such kind of operators are much more closely related to the elliptic partial differential equations of second order with ...

  14. Local Kernel for Brains Classification in Schizophrenia

    Science.gov (United States)

    Castellani, U.; Rossato, E.; Murino, V.; Bellani, M.; Rambaldelli, G.; Tansella, M.; Brambilla, P.

    In this paper a novel framework for brain classification is proposed in the context of mental health research. A learning by example method is introduced by combining local measurements with non linear Support Vector Machine. Instead of considering a voxel-by-voxel comparison between patients and controls, we focus on landmark points which are characterized by local region descriptors, namely Scale Invariance Feature Transform (SIFT). Then, matching is obtained by introducing the local kernel for which the samples are represented by unordered set of features. Moreover, a new weighting approach is proposed to take into account the discriminative relevance of the detected groups of features. Experiments have been performed including a set of 54 patients with schizophrenia and 54 normal controls on which region of interest (ROI) have been manually traced by experts. Preliminary results on Dorso-lateral PreFrontal Cortex (DLPFC) region are promising since up to 75% of successful classification rate has been obtained with this technique and the performance has improved up to 85% when the subjects have been stratified by sex.

  15. Kernel spectral clustering with memory effect

    Science.gov (United States)

    Langone, Rocco; Alzate, Carlos; Suykens, Johan A. K.

    2013-05-01

    Evolving graphs describe many natural phenomena changing over time, such as social relationships, trade markets, metabolic networks etc. In this framework, performing community detection and analyzing the cluster evolution represents a critical task. Here we propose a new model for this purpose, where the smoothness of the clustering results over time can be considered as a valid prior knowledge. It is based on a constrained optimization formulation typical of Least Squares Support Vector Machines (LS-SVM), where the objective function is designed to explicitly incorporate temporal smoothness. The latter allows the model to cluster the current data well and to be consistent with the recent history. We also propose new model selection criteria in order to carefully choose the hyper-parameters of our model, which is a crucial issue to achieve good performances. We successfully test the model on four toy problems and on a real world network. We also compare our model with Evolutionary Spectral Clustering, which is a state-of-the-art algorithm for community detection of evolving networks, illustrating that the kernel spectral clustering with memory effect can achieve better or equal performances.

  16. KERNEL MAD ALGORITHM FOR RELATIVE RADIOMETRIC NORMALIZATION

    Directory of Open Access Journals (Sweden)

    Y. Bai

    2016-06-01

    Full Text Available The multivariate alteration detection (MAD algorithm is commonly used in relative radiometric normalization. This algorithm is based on linear canonical correlation analysis (CCA which can analyze only linear relationships among bands. Therefore, we first introduce a new version of MAD in this study based on the established method known as kernel canonical correlation analysis (KCCA. The proposed method effectively extracts the non-linear and complex relationships among variables. We then conduct relative radiometric normalization experiments on both the linear CCA and KCCA version of the MAD algorithm with the use of Landsat-8 data of Beijing, China, and Gaofen-1(GF-1 data derived from South China. Finally, we analyze the difference between the two methods. Results show that the KCCA-based MAD can be satisfactorily applied to relative radiometric normalization, this algorithm can well describe the nonlinear relationship between multi-temporal images. This work is the first attempt to apply a KCCA-based MAD algorithm to relative radiometric normalization.

  17. Calpain cleavage prediction using multiple kernel learning.

    Directory of Open Access Journals (Sweden)

    David A DuVerle

    Full Text Available Calpain, an intracellular Ca²⁺-dependent cysteine protease, is known to play a role in a wide range of metabolic pathways through limited proteolysis of its substrates. However, only a limited number of these substrates are currently known, with the exact mechanism of substrate recognition and cleavage by calpain still largely unknown. While previous research has successfully applied standard machine-learning algorithms to accurately predict substrate cleavage by other similar types of proteases, their approach does not extend well to calpain, possibly due to its particular mode of proteolytic action and limited amount of experimental data. Through the use of Multiple Kernel Learning, a recent extension to the classic Support Vector Machine framework, we were able to train complex models based on rich, heterogeneous feature sets, leading to significantly improved prediction quality (6% over highest AUC score produced by state-of-the-art methods. In addition to producing a stronger machine-learning model for the prediction of calpain cleavage, we were able to highlight the importance and role of each feature of substrate sequences in defining specificity: primary sequence, secondary structure and solvent accessibility. Most notably, we showed there existed significant specificity differences across calpain sub-types, despite previous assumption to the contrary. Prediction accuracy was further successfully validated using, as an unbiased test set, mutated sequences of calpastatin (endogenous inhibitor of calpain modified to no longer block calpain's proteolytic action. An online implementation of our prediction tool is available at http://calpain.org.

  18. Proteome analysis of the almond kernel (Prunus dulcis).

    Science.gov (United States)

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

    2016-08-01

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

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

    Directory of Open Access Journals (Sweden)

    Shashi Kumar C.

    2016-12-01

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

  20. Self-organization as an iterative kernel smoothing process.

    Science.gov (United States)

    Mulier, F; Cherkassky, V

    1995-11-01

    Kohonen's self-organizing map, when described in a batch processing mode, can be interpreted as a statistical kernel smoothing problem. The batch SOM algorithm consists of two steps. First, the training data are partitioned according to the Voronoi regions of the map unit locations. Second, the units are updated by taking weighted centroids of the data falling into the Voronoi regions, with the weighing function given by the neighborhood. Then, the neighborhood width is decreased and steps 1, 2 are repeated. The second step can be interpreted as a statistical kernel smoothing problem where the neighborhood function corresponds to the kernel and neighborhood width corresponds to kernel span. To determine the new unit locations, kernel smoothing is applied to the centroids of the Voronoi regions in the topological space. This interpretation leads to some new insights concerning the role of the neighborhood and dimensionality reduction. It also strengthens the algorithm's connection with the Principal Curve algorithm. A generalized self-organizing algorithm is proposed, where the kernel smoothing step is replaced with an arbitrary nonparametric regression method.

  1. [Mexican migration policies after IRCA].

    Science.gov (United States)

    Alba, F

    1999-01-01

    The evolution since 1964 of Mexican government policy regarding migrant workers in the US is discussed. For a decade after the "bracero" program was terminated by the US, the Mexican government attempted to encourage creation of another legal framework for migration, regarded as inevitable whether legal or clandestine. Around 1974-75, a more distant attitude, termed the "policy of no policy," acquired considerable support in Mexican government and academic circles. The no-policy strategy allowed Mexico to achieve certain objectives regarding migration without prompting US intervention in its internal affairs, as for example by a linkage of US migration policy to specific Mexican government actions. The 1986 passage of the US Immigration Reform and Control Act effectively ended the no-policy strategy that had allowed the Mexican government to count on the continued emigration of Mexican workers without compromising its position of promoting respect for migrant rights. The unilateral change in the status quo by the US led to substitution of the "policy of dialogue," a clear signal of the Mexican government's search for a new migration agreement. The policy of dialogue has entailed greater discussion of the two traditional Mexican objectives regarding migration. Some progress has apparently been made concerning migrant rights, but the second and less explicit objective, that of preventing abrupt changes in US immigration policy and in migratory flows, is harder to judge. The atmosphere of freer public debate in Mexico is politicizing migratory policy.

  2. Identifying Chemical-Disease Relationship in Biomedical Text Using a Multiple Kernel Learning-Boosting Method.

    Science.gov (United States)

    Sun, Yueping; Zhang, Yu; Li, Jiao

    2017-01-01

    Chemical-induced disease relations (CID) are crucial in various biomedical tasks. In the CID task of Biocreative V, no classifiers with multiple kernels have been developed. In this study, a multiple kernel learning-boosting (MKLB) method is proposed. Different kernel functions according to different types of features were constucted and boosted, each of which were learned with multiple kernels. Our multiple kernel learning-boosting (MKLB) method achieved a F1 score of 0.5068 without incorporating knowledge bases.

  3. Regulator of Calcineurin 1 Gene Isoform 4, Down-regulated in Hepatocellular Carcinoma, Prevents Proliferation, Migration, and Invasive Activity of Cancer Cells and Metastasis of Orthotopic Tumors by Inhibiting Nuclear Translocation of NFAT1.

    Science.gov (United States)

    Jin, Haojie; Wang, Cun; Jin, Guangzhi; Ruan, Haoyu; Gu, Dishui; Wei, Lin; Wang, Hui; Wang, Ning; Arunachalam, Einthavy; Zhang, Yurong; Deng, Xuan; Yang, Chen; Xiong, Yi; Feng, Hugang; Yao, Ming; Fang, Jingyuan; Gu, Jianren; Cong, Wenming; Qin, Wenxin

    2017-09-01

    xenograft tumors, with fewer metastases and blood vessels, than control HCC cells. In HCC cells, RCAN1.4 inhibited expression of insulin-like growth factor 1 and vascular endothelial growth factor A by reducing calcineurin activity and blocking nuclear translocation of nuclear factor of activated T cells (NFAT1). HCC cells incubated with the calcineurin inhibitor cyclosporin A had decreased nuclear level of NFAT1. HCC cells had hypermethylation of a CpG island in the 5' regulatory region of RCAN1.4, which reduced its expression. RCAN1.4 is down-regulated in HCC tissues, compared with non-tumor liver tissues. RCAN1.4 prevents cell proliferation, migration, and invasion in vitro; overexpressed RCAN1.4 in HCC cells prevents growth, angiogenesis, and metastases of xenograft tumors by inhibiting calcineurin activity and nuclear translocation of NFAT1. Copyright © 2017 AGA Institute. Published by Elsevier Inc. All rights reserved.

  4. A Quantized Kernel Learning Algorithm Using a Minimum Kernel Risk-Sensitive Loss Criterion and Bilateral Gradient Technique

    Directory of Open Access Journals (Sweden)

    Xiong Luo

    2017-07-01

    Full Text Available Recently, inspired by correntropy, kernel risk-sensitive loss (KRSL has emerged as a novel nonlinear similarity measure defined in kernel space, which achieves a better computing performance. After applying the KRSL to adaptive filtering, the corresponding minimum kernel risk-sensitive loss (MKRSL algorithm has been developed accordingly. However, MKRSL as a traditional kernel adaptive filter (KAF method, generates a growing radial basis functional (RBF network. In response to that limitation, through the use of online vector quantization (VQ technique, this article proposes a novel KAF algorithm, named quantized MKRSL (QMKRSL to curb the growth of the RBF network structure. Compared with other quantized methods, e.g., quantized kernel least mean square (QKLMS and quantized kernel maximum correntropy (QKMC, the efficient performance surface makes QMKRSL converge faster and filter more accurately, while maintaining the robustness to outliers. Moreover, considering that QMKRSL using traditional gradient descent method may fail to make full use of the hidden information between the input and output spaces, we also propose an intensified QMKRSL using a bilateral gradient technique named QMKRSL_BG, in an effort to further improve filtering accuracy. Short-term chaotic time-series prediction experiments are conducted to demonstrate the satisfactory performance of our algorithms.

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

    NARCIS (Netherlands)

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

    2016-01-01

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

  6. Lupin kernel fiber consumption modifies fecal microbiota in healthy men as determined by rRNA gene fluorescent in situ hybridization

    NARCIS (Netherlands)

    Smith, Stuart C.; Choy, Rachel; Johnson, Stuart K.; Hall, Ramon S.; Wildeboer-Veloo, Alida C. M.; Welling, Gjalt W.

    Background: Changes in the composition of gastrointestinal microbiota by dietary interventions using pro- and prebiotics provide opportunity for improving health and preventing disease. However, the capacity of lupin kernel fiber (LKFibre), a novel legume-derived food ingredient, to act as a

  7. Protein fold recognition using geometric kernel data fusion.

    Science.gov (United States)

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

    2014-07-01

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

  8. Unsupervised multiple kernel learning for heterogeneous data integration.

    Science.gov (United States)

    Mariette, Jérôme; Villa-Vialaneix, Nathalie

    2018-03-15

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

  9. Local coding based matching kernel method for image classification.

    Directory of Open Access Journals (Sweden)

    Yan Song

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

  10. Towards Fast Reverse Time Migration Kernels using Multi-threaded Wavefront Diamond Tiling

    KAUST Repository

    Malas, T.

    2015-09-13

    Today’s high-end multicore systems are characterized by a deep memory hierarchy, i.e., several levels of local and shared caches, with limited size and bandwidth per core. The ever-increasing gap between the processor and memory speed will further exacerbate the problem and has lead the scientific community to revisit numerical software implementations to better suit the underlying memory subsystem for performance (data reuse) as well as energy efficiency (data locality). The authors propose a novel multi-threaded wavefront diamond blocking (MWD) implementation in the context of stencil computations, which represents the core operation for seismic imaging in oil industry. The stencil diamond formulation introduces temporal blocking for high data reuse in the upper cache levels. The wavefront optimization technique ensures data locality by allowing multiple threads to share common adjacent point stencil. Therefore, MWD is able to take up the aforementioned challenges by alleviating the cache size limitation and releasing pressure from the memory bandwidth. Performance comparisons are shown against the optimized 25-point stencil standard seismic imaging scheme using spatial and temporal blocking and demonstrate the effectiveness of MWD.

  11. Comportamento de cristalização de lipídios estruturados obtidos a partir de gordura de palmiste e óleo de peixe Crystallization behavior of structured lipids produced from palm kernel fat and fish oil

    Directory of Open Access Journals (Sweden)

    Oscar Wilfredo Díaz Gamboa

    2006-07-01

    Full Text Available The aim of this study is to evaluate the crystal structure of binary mixtures of palm kernel fat and fish oil, before and after chemical and enzymatic interesterification. The crystal structure was analyzed by polarized light microscopy. The addition of fish oil didn't change the palm kernel fat crystallization characteristics, spherullites of types A and B being observed. However, due to chemical and enzymatic interesterification, smaller crystals were obtained. There was no difference between chemical and enzymatic interesterification, probably as a function of acyl migration in discontinuous processes catalyzed by lipases.

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

    International Nuclear Information System (INIS)

    Zielinska, S.

    1985-03-01

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

  13. Spine labeling in axial magnetic resonance imaging via integral kernels.

    Science.gov (United States)

    Miles, Brandon; Ben Ayed, Ismail; Hojjat, Seyed-Parsa; Wang, Michael H; Li, Shuo; Fenster, Aaron; Garvin, Gregory J

    2016-12-01

    This study investigates a fast integral-kernel algorithm for classifying (labeling) the vertebra and disc structures in axial magnetic resonance images (MRI). The method is based on a hierarchy of feature levels, where pixel classifications via non-linear probability product kernels (PPKs) are followed by classifications of 2D slices, individual 3D structures and groups of 3D structures. The algorithm further embeds geometric priors based on anatomical measurements of the spine. Our classifier requires evaluations of computationally expensive integrals at each pixel, and direct evaluations of such integrals would be prohibitively time consuming. We propose an efficient computation of kernel density estimates and PPK evaluations for large images and arbitrary local window sizes via integral kernels. Our method requires a single user click for a whole 3D MRI volume, runs nearly in real-time, and does not require an intensive external training. Comprehensive evaluations over T1-weighted axial lumbar spine data sets from 32 patients demonstrate a competitive structure classification accuracy of 99%, along with a 2D slice classification accuracy of 88%. To the best of our knowledge, such a structure classification accuracy has not been reached by the existing spine labeling algorithms. Furthermore, we believe our work is the first to use integral kernels in the context of medical images. Copyright © 2016 Elsevier Ltd. All rights reserved.

  14. Structured Kernel Dictionary Learning with Correlation Constraint for Object Recognition.

    Science.gov (United States)

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

    2017-06-21

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

  15. Interest Extraction Using Relevance Feedback with Kernel Method

    Science.gov (United States)

    Hidekazu, Yanagimoto; Sigeru, Omatu

    In this paper, we propose interest extraction using the relevance feedback with the kernel method. In the field of machine learning, the kernel method has been used. Since the classifier using the kernel method creates a discriminant function in a feature space, the discriminant function is a nonlinear function in a input space. The kernel method is used for the Support Vector Machine (SVM), the Kernel PCA, and so on. The SVM set a discriminant hyperplane between positive data and negative data. Hence, a distance between the hyperplane and a training sample is not important in the SVM. It is difficult to use the SVM to score other samples. Our goal is to create a method which scores the other samples in the feature space. We propose the relevance feedback which is carried out in the feature space. Hence, this relevance feedback can deal with nonlinearity of data. We compare the proposed method with the common relevance feedback using test collection NTCIR2. Finally, we comfirm the proposed method is superior to the common method through simulations.

  16. Mixed kernel function support vector regression for global sensitivity analysis

    Science.gov (United States)

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

    2017-11-01

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

  17. CELLULOSE EXTRACTION FROM PALM KERNEL CAKE USING LIQUID PHASE OXIDATION

    Directory of Open Access Journals (Sweden)

    FARM YAN YAN

    2009-03-01

    Full Text Available Cellulose is widely used in many aspect and industries such as food industry, pharmaceutical, paint, polymers, and many more. Due to the increasing demand in the market, studies and work to produce cellulose are still rapidly developing. In this work, liquid phase oxidation was used to extract cellulose from palm kernel cake to separate hemicellulose, cellulose and lignin. The method is basically a two-step process. Palm kernel cake was pretreated in hot water at 180°C and followed by liquid oxidation process with 30% H2O2 at 60°C at atmospheric pressure. The process parameters are hot water treatment time, ratio of palm kernel cake to H2O2, liquid oxidation reaction temperature and time. Analysis of the process parameters on production cellulose from palm kernel cake was performed by using Response Surface Methodology. The recovered cellulose was further characterized by Fourier Transform Infrared (FTIR. Through the hot water treatment, hemicellulose in the palm kernel cake was successfully recovered as saccharides and thus leaving lignin and cellulose. Lignin was converted to water soluble compounds in liquid oxidation step which contains small molecular weight fatty acid as HCOOH and CH3COOH and almost pure cellulose was recovered.

  18. Semi-supervised learning for ordinal Kernel Discriminant Analysis.

    Science.gov (United States)

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

    2016-12-01

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

  19. Multiple kernel sparse representations for supervised and unsupervised learning.

    Science.gov (United States)

    Thiagarajan, Jayaraman J; Ramamurthy, Karthikeyan Natesan; Spanias, Andreas

    2014-07-01

    In complex visual recognition tasks, it is typical to adopt multiple descriptors, which describe different aspects of the images, for obtaining an improved recognition performance. Descriptors that have diverse forms can be fused into a unified feature space in a principled manner using kernel methods. Sparse models that generalize well to the test data can be learned in the unified kernel space, and appropriate constraints can be incorporated for application in supervised and unsupervised learning. In this paper, we propose to perform sparse coding and dictionary learning in the multiple kernel space, where the weights of the ensemble kernel are tuned based on graph-embedding principles such that class discrimination is maximized. In our proposed algorithm, dictionaries are inferred using multiple levels of 1D subspace clustering in the kernel space, and the sparse codes are obtained using a simple levelwise pursuit scheme. Empirical results for object recognition and image clustering show that our algorithm outperforms existing sparse coding based approaches, and compares favorably to other state-of-the-art methods.

  20. An Efficient Kernel Optimization Method for Radar High-Resolution Range Profile Recognition

    Directory of Open Access Journals (Sweden)

    Chen Bo

    2007-01-01

    Full Text Available A kernel optimization method based on fusion kernel for high-resolution range profile (HRRP is proposed in this paper. Based on the fusion of -norm and -norm Gaussian kernels, our method combines the different characteristics of them so that not only is the kernel function optimized but also the speckle fluctuations of HRRP are restrained. Then the proposed method is employed to optimize the kernel of kernel principle component analysis (KPCA and the classification performance of extracted features is evaluated via support vector machines (SVMs classifier. Finally, experimental results on the benchmark and radar-measured data sets are compared and analyzed to demonstrate the efficiency of our method.

  1. Population, migration and urbanization.

    Science.gov (United States)

    1982-06-01

    Despite recent estimates that natural increase is becoming a more important component of urban growth than rural urban transfer (excess of inmigrants over outmigrants), the share of migration in the total population growth has been consistently increasing in both developed and developing countries. From a demographic perspective, the migration process involves 3 elements: an area of origin which the mover leaves and where he or she is considered an outmigrant; the destination or place of inmigration; and the period over which migration is measured. The 2 basic types of migration are internal and international. Internal migration consists of rural to urban migration, urban to urban migration, rural to rural migration, and urban to rural migration. Among these 4 types of migration various patterns or processes are followed. Migration may be direct when the migrant moves directly from the village to the city and stays there permanently. It can be circular migration, meaning that the migrant moves to the city when it is not planting season and returns to the village when he is needed on the farm. In stage migration the migrant makes a series of moves, each to a city closer to the largest or fastest growing city. Temporary migration may be 1 time or cyclical. The most dominant pattern of internal migration is rural urban. The contribution of migration to urbanization is evident. For example, the rapid urbanization and increase in urban growth from 1960-70 in the Republic of Korea can be attributed to net migration. In Asia the largest component of the population movement consists of individuals and groups moving from 1 rural location to another. Recently, because urban centers could no longer absorb the growing number of migrants from other places, there has been increased interest in the urban to rural population redistribution. This reverse migration also has come about due to slower rates of employment growth in the urban centers and improved economic opportunities

  2. Environmental Disasters and Migration

    OpenAIRE

    Mbaye, Linguère Mously; Zimmermann, Klaus F.

    2015-01-01

    This paper reviews the effect of environmental disasters on migration. Although there is an increase of environmental disasters and migration over the past years, the relationship is complex. While some authors find that environmental disasters increase migration, others show that they have only a marginal or no effect or are even negative. Migration appears to be an insurance mechanism against environmental shocks. Remittances help to decrease households' vulnerability to shocks but also dam...

  3. Regulators of Intestinal Epithelial Migration in Sepsis.

    Science.gov (United States)

    Meng, Mei; Klingensmith, Nathan J; Liang, Zhe; Lyons, John D; Fay, Katherine T; Chen, Ching-Wen; Ford, Mandy L; Coopersmith, Craig M

    2018-02-08

    The gut is a continuously renewing organ, with cell proliferation, migration and death occurring rapidly under basal conditions. Since the impact of critical illness on cell movement from crypt base to villus tip is poorly understood, the purpose of this study was to determine how sepsis alters enterocyte migration. Wild type, transgenic and knockout mice were injected with 5-bromo-2'deoxyuridine (BrdU) to label cells in S phase before and after the onset of cecal ligation and puncture and were sacrificed at pre-determined endpoints to determine distance proliferating cells migrated up the crypt-villus unit. Enterocyte migration rate was decreased from 24-96 hours following sepsis. BrdU was not detectable on villi 6 days after sham laparotomy, meaning all cells had migrated the length of the gut and been exfoliated into its lumen. However, BrdU positive cells were detectable on villi 10 days after sepsis. Multiple components of gut integrity altered enterocyte migration. Sepsis decreased crypt proliferation, which further slowed enterocyte transit as mice injected with BrdU after the onset of sepsis (decreased proliferation) had slower migration than mice injected with BrdU prior to the onset of sepsis (normal proliferation). Decreasing intestinal apoptosis via gut-specific overexpression of Bcl-2 prevented sepsis-induced slowing of enterocyte migration. In contrast, worsened intestinal hyperpermeability by genetic deletion of JAM-A increased enterocyte migration. Sepsis therefore significantly slows enterocyte migration, and intestinal proliferation, apoptosis and permeability all affect migration time, which can potentially be targeted both genetically and pharmacologically.

  4. [Internal migration studies].

    Science.gov (United States)

    Stpiczynski, T

    1986-10-01

    Recent research on internal migration in Poland is reviewed. The basic sources of data, consisting of censuses or surveys, are first described. The author discusses the relationship between migration studies and other sectors of the national economy, and particularly the relationship between migration and income.

  5. The spinor heat kernel in maximally symmetric spaces

    International Nuclear Information System (INIS)

    Camporesi, R.

    1992-01-01

    The heat kernel K(x, x', t) of the iterated Dirac operator on an N-dimensional simply connected maximally symmetric Riemannian manifold is calculated. On the odd-dimensional hyperbolic spaces K is a Minakshisundaram-DeWitt expansion which terminates to the coefficient a (N-1)/2 and is exact. On the odd spheres the heat kernel may be written as an image sum of WKB kernels, each term corresponding to a classical path (geodesic). In the even dimensional case the WKB approximation is not exact, but a closed form of K is derived both in terms of (spherical) eigenfunctions and of a 'sum over classical paths'. The spinor Plancherel measure μ(λ) and ζ function in the hyperbolic case are also calculated. A simple relation between the analytic structure of μ on H N and the degeneracies of the Dirac operator on S N is found. (orig.)

  6. Purification and characterization of riproximin from Ximenia americana fruit kernels.

    Science.gov (United States)

    Bayer, Helene; Ey, Noreen; Wattenberg, Andreas; Voss, Cristina; Berger, Martin R

    2012-03-01

    Highly pure riproximin was isolated from the fruit kernels of Ximenia americana, a defined, seasonally available and potentially unlimited herbal source. The newly established purification procedure included an initial aqueous extraction, removal of lipids with chloroform and subsequent chromatographic purification steps on a strong anion exchange resin and lactosyl-Sepharose. Consistent purity and stable biological properties were shown over several purification batches. The purified, kernel-derived riproximin was characterized in comparison to the African plant material riproximin and revealed highly similar biochemical and biological properties but differences in the electrophoresis pattern and mass spectrometry peptide profile. Our results suggest that although the purified fruit kernel riproximin consists of a mixture of closely related isoforms, it provides a reliable basis for further research and development of this type II ribosome inactivating protein (RIP). Copyright © 2011 Elsevier Inc. All rights reserved.

  7. Reconstruction of noisy and blurred images using blur kernel

    Science.gov (United States)

    Ellappan, Vijayan; Chopra, Vishal

    2017-11-01

    Blur is a common in so many digital images. Blur can be caused by motion of the camera and scene object. In this work we proposed a new method for deblurring images. This work uses sparse representation to identify the blur kernel. By analyzing the image coordinates Using coarse and fine, we fetch the kernel based image coordinates and according to that observation we get the motion angle of the shaken or blurred image. Then we calculate the length of the motion kernel using radon transformation and Fourier for the length calculation of the image and we use Lucy Richardson algorithm which is also called NON-Blind(NBID) Algorithm for more clean and less noisy image output. All these operation will be performed in MATLAB IDE.

  8. Heat Kernel Asymptotics of Zaremba Boundary Value Problem

    Energy Technology Data Exchange (ETDEWEB)

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

    2004-03-15

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

  9. Optimized data fusion for kernel k-means clustering.

    Science.gov (United States)

    Yu, Shi; Tranchevent, Léon-Charles; Liu, Xinhai; Glänzel, Wolfgang; Suykens, Johan A K; De Moor, Bart; Moreau, Yves

    2012-05-01

    This paper presents a novel optimized kernel k-means algorithm (OKKC) to combine multiple data sources for clustering analysis. The algorithm uses an alternating minimization framework to optimize the cluster membership and kernel coefficients as a nonconvex problem. In the proposed algorithm, the problem to optimize the cluster membership and the problem to optimize the kernel coefficients are all based on the same Rayleigh quotient objective; therefore the proposed algorithm converges locally. OKKC has a simpler procedure and lower complexity than other algorithms proposed in the literature. Simulated and real-life data fusion applications are experimentally studied, and the results validate that the proposed algorithm has comparable performance, moreover, it is more efficient on large-scale data sets. (The Matlab implementation of OKKC algorithm is downloadable from http://homes.esat.kuleuven.be/~sistawww/bio/syu/okkc.html.).

  10. Rational kernels for Arabic Root Extraction and Text Classification

    Directory of Open Access Journals (Sweden)

    Attia Nehar

    2016-04-01

    Full Text Available In this paper, we address the problems of Arabic Text Classification and root extraction using transducers and rational kernels. We introduce a new root extraction approach on the basis of the use of Arabic patterns (Pattern Based Stemmer. Transducers are used to model these patterns and root extraction is done without relying on any dictionary. Using transducers for extracting roots, documents are transformed into finite state transducers. This document representation allows us to use and explore rational kernels as a framework for Arabic Text Classification. Root extraction experiments are conducted on three word collections and yield 75.6% of accuracy. Classification experiments are done on the Saudi Press Agency dataset and N-gram kernels are tested with different values of N. Accuracy and F1 report 90.79% and 62.93% respectively. These results show that our approach, when compared with other approaches, is promising specially in terms of accuracy and F1.

  11. Regularized Embedded Multiple Kernel Dimensionality Reduction for Mine Signal Processing

    Science.gov (United States)

    Li, Shuang; Liu, Bing; Zhang, Chen

    2016-01-01

    Traditional multiple kernel dimensionality reduction models are generally based on graph embedding and manifold assumption. But such assumption might be invalid for some high-dimensional or sparse data due to the curse of dimensionality, which has a negative influence on the performance of multiple kernel learning. In addition, some models might be ill-posed if the rank of matrices in their objective functions was not high enough. To address these issues, we extend the traditional graph embedding framework and propose a novel regularized embedded multiple kernel dimensionality reduction method. Different from the conventional convex relaxation technique, the proposed algorithm directly takes advantage of a binary search and an alternative optimization scheme to obtain optimal solutions efficiently. The experimental results demonstrate the effectiveness of the proposed method for supervised, unsupervised, and semisupervised scenarios. PMID:27247562

  12. Regularized Embedded Multiple Kernel Dimensionality Reduction for Mine Signal Processing.

    Science.gov (United States)

    Li, Shuang; Liu, Bing; Zhang, Chen

    2016-01-01

    Traditional multiple kernel dimensionality reduction models are generally based on graph embedding and manifold assumption. But such assumption might be invalid for some high-dimensional or sparse data due to the curse of dimensionality, which has a negative influence on the performance of multiple kernel learning. In addition, some models might be ill-posed if the rank of matrices in their objective functions was not high enough. To address these issues, we extend the traditional graph embedding framework and propose a novel regularized embedded multiple kernel dimensionality reduction method. Different from the conventional convex relaxation technique, the proposed algorithm directly takes advantage of a binary search and an alternative optimization scheme to obtain optimal solutions efficiently. The experimental results demonstrate the effectiveness of the proposed method for supervised, unsupervised, and semisupervised scenarios.

  13. Regularized Embedded Multiple Kernel Dimensionality Reduction for Mine Signal Processing

    Directory of Open Access Journals (Sweden)

    Shuang Li

    2016-01-01

    Full Text Available Traditional multiple kernel dimensionality reduction models are generally based on graph embedding and manifold assumption. But such assumption might be invalid for some high-dimensional or sparse data due to the curse of dimensionality, which has a negative influence on the performance of multiple kernel learning. In addition, some models might be ill-posed if the rank of matrices in their objective functions was not high enough. To address these issues, we extend the traditional graph embedding framework and propose a novel regularized embedded multiple kernel dimensionality reduction method. Different from the conventional convex relaxation technique, the proposed algorithm directly takes advantage of a binary search and an alternative optimization scheme to obtain optimal solutions efficiently. The experimental results demonstrate the effectiveness of the proposed method for supervised, unsupervised, and semisupervised scenarios.

  14. Capturing Option Anomalies with a Variance-Dependent Pricing Kernel

    DEFF Research Database (Denmark)

    Christoffersen, Peter; Heston, Steven; Jacobs, Kris

    2013-01-01

    We develop a GARCH option model with a new pricing kernel allowing for a variance premium. While the pricing kernel is monotonic in the stock return and in variance, its projection onto the stock return is nonmonotonic. A negative variance premium makes it U shaped. We present new semiparametric...... evidence to confirm this U-shaped relationship between the risk-neutral and physical probability densities. The new pricing kernel substantially improves our ability to reconcile the time-series properties of stock returns with the cross-section of option prices. It provides a unified explanation...... for the implied volatility puzzle, the overreaction of long-term options to changes in short-term variance, and the fat tails of the risk-neutral return distribution relative to the physical distribution....

  15. 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...... on the complexification $G_{\\mathbb C}/K_{\\mathbb C}$ of $Ω$, the weight being expressed explicitly in terms of a multivariable $K$-Bessel function on $Ω$. Even in the special case of the symmetric cone $Ω=\\mathbb{R}_+$ these results seem to be new.......We investigate the heat equation corresponding to the Bessel operators on a symmetric cone $Ω=G/K$. These operators form a one-parameter family of elliptic self-adjoint second order differential operators and occur in the Lie algebra action of certain unitary highest weight representations...

  16. Linear and kernel methods for multivariate change detection

    DEFF Research Database (Denmark)

    Canty, Morton J.; Nielsen, Allan Aasbjerg

    2012-01-01

    The iteratively reweighted multivariate alteration detection (IR-MAD) algorithm may be used both for unsupervised change detection in multi- and hyperspectral remote sensing imagery and for automatic radiometric normalization of multitemporal image sequences. Principal components analysis (PCA......), as well as maximum autocorrelation factor (MAF) and minimum noise fraction (MNF) analyses of IR-MAD images, both linear and kernel-based (nonlinear), may further enhance change signals relative to no-change background. IDL (Interactive Data Language) implementations of IR-MAD, automatic radiometric...... normalization, and kernel PCA/MAF/MNF transformations are presented that function as transparent and fully integrated extensions of the ENVI remote sensing image analysis environment. The train/test approach to kernel PCA is evaluated against a Hebbian learning procedure. Matlab code is also available...

  17. Semisupervised kernel marginal Fisher analysis for face recognition.

    Science.gov (United States)

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

    2013-01-01

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

  18. Weighted Feature Gaussian Kernel SVM for Emotion Recognition.

    Science.gov (United States)

    Wei, Wei; Jia, Qingxuan

    2016-01-01

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

  19. Weighted Feature Gaussian Kernel SVM for Emotion Recognition

    Directory of Open Access Journals (Sweden)

    Wei Wei

    2016-01-01

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

  20. Single aflatoxin contaminated corn kernel analysis with fluorescence hyperspectral image

    Science.gov (United States)

    Yao, Haibo; Hruska, Zuzana; Kincaid, Russell; Ononye, Ambrose; Brown, Robert L.; Cleveland, Thomas E.

    2010-04-01

    Aflatoxins are toxic secondary metabolites of the fungi Aspergillus flavus and Aspergillus parasiticus, among others. Aflatoxin contaminated corn is toxic to domestic animals when ingested in feed and is a known carcinogen associated with liver and lung cancer in humans. Consequently, aflatoxin levels in food and feed are regulated by the Food and Drug Administration (FDA) in the US, allowing 20 ppb (parts per billion) limits in food and 100 ppb in feed for interstate commerce. Currently, aflatoxin detection and quantification methods are based on analytical tests including thin-layer chromatography (TCL) and high performance liquid chromatography (HPLC). These analytical tests require the destruction of samples, and are costly and time consuming. Thus, the ability to detect aflatoxin in a rapid, nondestructive way is crucial to the grain industry, particularly to corn industry. Hyperspectral imaging technology offers a non-invasive approach toward screening for food safety inspection and quality control based on its spectral signature. The focus of this paper is to classify aflatoxin contaminated single corn kernels using fluorescence hyperspectral imagery. Field inoculated corn kernels were used in the study. Contaminated and control kernels under long wavelength ultraviolet excitation were imaged using a visible near-infrared (VNIR) hyperspectral camera. The imaged kernels were chemically analyzed to provide reference information for image analysis. This paper describes a procedure to process corn kernels located in different images for statistical training and classification. Two classification algorithms, Maximum Likelihood and Binary Encoding, were used to classify each corn kernel into "control" or "contaminated" through pixel classification. The Binary Encoding approach had a slightly better performance with accuracy equals to 87% or 88% when 20 ppb or 100 ppb was used as classification threshold, respectively.

  1. Rebootless Linux Kernel Patching with Ksplice Uptrack at BNL

    International Nuclear Information System (INIS)

    Hollowell, Christopher; Pryor, James; Smith, Jason

    2012-01-01

    Ksplice/Oracle Uptrack is a software tool and update subscription service which allows system administrators to apply security and bug fix patches to the Linux kernel running on servers/workstations without rebooting them. The RHIC/ATLAS Computing Facility (RACF) at Brookhaven National Laboratory (BNL) has deployed Uptrack on nearly 2,000 hosts running Scientific Linux and Red Hat Enterprise Linux. The use of this software has minimized downtime, and increased our security posture. In this paper, we provide an overview of Ksplice's rebootless kernel patch creation/insertion mechanism, and our experiences with Uptrack.

  2. Aflatoxin detection in whole corn kernels using hyperspectral methods

    Science.gov (United States)

    Casasent, David; Chen, Xue-Wen

    2004-03-01

    Hyperspectral (HS) data for the inspection of whole corn kernels for aflatoxin is considered. The high-dimensionality of HS data requires feature extraction or selection for good classifier generalization. For fast and inexpensive data collection, only several features (λ responses) can be used. These are obtained by feature selection from the full HS response. A new high dimensionality branch and bound (HDBB) feature selection algorithm is used; it is found to be optimum, fast and very efficient. Initial results indicate that HS data is very promising for aflatoxin detection in whole kernel corn.

  3. Source identity and kernel functions for Inozemtsev-type systems

    Energy Technology Data Exchange (ETDEWEB)

    Langmann, Edwin [Department of Theoretical Physics, Royal Institute of Technology KTH, SE-106 91 Stockholm (Sweden); Takemura, Kouichi [Department of Mathematics, Chuo University, 1-13-27 Kasuga, Bunkyo-ku, Tokyo 112-8551 (Japan)

    2012-08-15

    The Inozemtsev Hamiltonian is an elliptic generalization of the differential operator defining the BC{sub N} trigonometric quantum Calogero-Sutherland model, and its eigenvalue equation is a natural many-variable generalization of the Heun differential equation. We present kernel functions for Inozemtsev Hamiltonians and Chalykh-Feigin-Veselov-Sergeev-type deformations thereof. Our main result is a solution of a heat-type equation for a generalized Inozemtsev Hamiltonian which is the source of all these kernel functions. Applications are given, including a derivation of simple exact eigenfunctions and eigenvalues of the Inozemtsev Hamiltonian.

  4. Modelling microwave heating of discrete samples of oil palm kernels

    International Nuclear Information System (INIS)

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

    2016-01-01

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

  5. Supervised Kernel Optimized Locality Preserving Projection with Its Application to Face Recognition and Palm Biometrics

    Directory of Open Access Journals (Sweden)

    Chuang Lin

    2015-01-01

    Full Text Available Kernel Locality Preserving Projection (KLPP algorithm can effectively preserve the neighborhood structure of the database using the kernel trick. We have known that supervised KLPP (SKLPP can preserve within-class geometric structures by using label information. However, the conventional SKLPP algorithm endures the kernel selection which has significant impact on the performances of SKLPP. In order to overcome this limitation, a method named supervised kernel optimized LPP (SKOLPP is proposed in this paper, which can maximize the class separability in kernel learning. The proposed method maps the data from the original space to a higher dimensional kernel space using a data-dependent kernel. The adaptive parameters of the data-dependent kernel are automatically calculated through optimizing an objective function. Consequently, the nonlinear features extracted by SKOLPP have larger discriminative ability compared with SKLPP and are more adaptive to the input data. Experimental results on ORL, Yale, AR, and Palmprint databases showed the effectiveness of the proposed method.

  6. Parameter Selection Method for Support Vector Regression Based on Adaptive Fusion of the Mixed Kernel Function

    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.

  7. The dipole form of the gluon part of the BFKL kernel

    International Nuclear Information System (INIS)

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

    2007-01-01

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

  8. Pest development possibility in storage on the kernels and spikes of spelt wheat (Triticum spelta)

    OpenAIRE

    Almaši, Radmila; Poslončec, Danijela

    2012-01-01

    Storing the spelt wheat (Triticum spelta) depends of the type of storage. If we stored spikes, which contains two kernel of spelt tightly wrapped with tailings, development and reproduction of the most important pests is limited or impossible. Great impacts on the stored pests progeny and percentage of damaged kernels have the number of available kernel and the way of storage (kernels-spikes). Grain moth (Sitotroga cerealella Oliv.) developed more numerous progeny when spelt wheat stores in k...

  9. Effects of substituting groundnut cake with acacia seed kernel meal ...

    African Journals Online (AJOL)

    The study examined the effects of replacing groundnut cake (GNC) with Acacia nilotica seed kernel meal (ASKM) in the diets of broilers and the effects of such on ... Serum metabolites were not affected by the treatment except alkaline phosphatasc and billirubin that were significantly (P < 0.05) lowered by 20% inclusion of ...

  10. Production of glycerol from palm kernel oil | Antia | Nigerian Journal ...

    African Journals Online (AJOL)

    Glycerol production using Palm Kernel Oil (PKO) as a potential raw material was investigated. PKO was optimally hydrolyzed at 268 °C and 500psi (34 atm) pressure using only water. A 96.85 percent maximum yield of the extent of hydrolysis at 61.86 percent water and 38.14 percent oil was achieved The percentage Df ...

  11. Acetolactate Synthase Activity in Developing Maize (Zea mays L.) Kernels

    Science.gov (United States)

    Muhitch, Michael J.

    1988-01-01

    Acetolactate synthase (EC 4.1.3.18) activity was examined in maize (Zea mays L.) endosperm and embryos as a function of kernel development. When assayed using unpurified homogenates, embryo acetolactate synthase activity appeared less sensitive to inhibition by leucine + valine and by the imidazolinone herbicide imazapyr than endosperm acetolactate synthase activity. Evidence is presented to show that pyruvate decarboxylase contributes to apparent acetolactate synthase activity in crude embryo extracts and a modification of the acetolactate synthase assay is proposed to correct for the presence of pyruvate decarboxylase in unpurified plant homogenates. Endosperm acetolactate synthase activity increased rapidly during early kernel development, reaching a maximum of 3 micromoles acetoin per hour per endosperm at 25 days after pollination. In contrast, embryo activity was low in young kernels and steadily increased throughout development to a maximum activity of 0.24 micromole per hour per embryo by 45 days after pollination. The sensitivity of both endosperm and embryo acetolactate synthase activities to feedback inhibition by leucine + valine did not change during kernel development. The results are compared to those found for other enzymes of nitrogen metabolism and discussed with respect to the potential roles of the embryo and endosperm in providing amino acids for storage protein synthesis. PMID:16665871

  12. Structured Kernel Subspace Learning for Autonomous Robot Navigation.

    Science.gov (United States)

    Kim, Eunwoo; Choi, Sungjoon; Oh, Songhwai

    2018-02-14

    This paper considers two important problems for autonomous robot navigation in a dynamic environment, where the goal is to predict pedestrian motion and control a robot with the prediction for safe navigation. While there are several methods for predicting the motion of a pedestrian and controlling a robot to avoid incoming pedestrians, it is still difficult to safely navigate in a dynamic environment due to challenges, such as the varying quality and complexity of training data with unwanted noises. This paper addresses these challenges simultaneously by proposing a robust kernel subspace learning algorithm based on the recent advances in nuclear-norm and l 1 -norm minimization. We model the motion of a pedestrian and the robot controller using Gaussian processes. The proposed method efficiently approximates a kernel matrix used in Gaussian process regression by learning low-rank structured matrix (with symmetric positive semi-definiteness) to find an orthogonal basis, which eliminates the effects of erroneous and inconsistent data. Based on structured kernel subspace learning, we propose a robust motion model and motion controller for safe navigation in dynamic environments. We evaluate the proposed robust kernel learning in various tasks, including regression, motion prediction, and motion control problems, and demonstrate that the proposed learning-based systems are robust against outliers and outperform existing regression and navigation methods.

  13. Music emotion detection using hierarchical sparse kernel machines.

    Science.gov (United States)

    Chin, Yu-Hao; Lin, Chang-Hong; Siahaan, Ernestasia; Wang, Jia-Ching

    2014-01-01

    For music emotion detection, this paper presents a music emotion verification system based on hierarchical sparse kernel machines. With the proposed system, we intend to verify if a music clip possesses happiness emotion or not. There are two levels in the hierarchical sparse kernel machines. In the first level, a set of acoustical features are extracted, and principle component analysis (PCA) is implemented to reduce the dimension. The acoustical features are utilized to generate the first-level decision vector, which is a vector with each element being a significant value of an emotion. The significant values of eight main emotional classes are utilized in this paper. To calculate the significant value of an emotion, we construct its 2-class SVM with calm emotion as the global (non-target) side of the SVM. The probability distributions of the adopted acoustical features are calculated and the probability product kernel is applied in the first-level SVMs to obtain first-level decision vector feature. In the second level of the hierarchical system, we merely construct a 2-class relevance vector machine (RVM) with happiness as the target side and other emotions as the background side of the RVM. The first-level decision vector is used as the feature with conventional radial basis function kernel. The happiness verification threshold is built on the probability value. In the experimental results, the detection error tradeoff (DET) curve shows that the proposed system has a good performance on verifying if a music clip reveals happiness emotion.

  14. Recent sea level change analysed with kernel EOF

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg; Andersen, Ole Baltazar; Knudsen, Per

    2009-01-01

    -2008. Preliminary analysis shows some interesting features related to climate change and particularly the pulsing of the El Niño/Southern Oscillation. Large scale ocean events associated with the El Niño/Southern Oscillation related signals are conveniently concentrated in the first SSH kernel EOF modes....

  15. Effect of Coconut ( cocus Nucifera ) and Palm Kernel ( eleasis ...

    African Journals Online (AJOL)

    Effect of Coconut ( cocus Nucifera ) and Palm Kernel ( eleasis Guinensis ) Oil Supplmented Diets on Serum Lipid Profile of Albino Wistar Rats. ... were fed normal rat pellet. At the end of the feeding period, animals were anaesthetized under chloroform vapor, dissected and blood obtained via cardiac puncture into tubes.

  16. potential use of mangifera indica seed kernel and citrus aurantiifolia ...

    African Journals Online (AJOL)

    HOD

    *Corresponding author tel: + 234 – 803 – 823 – 1628, currently a doctoral student at the Department of Civil. Engineering, Kwame Nkrumah University of Science and Technology, Kumasi, GHANA. POTENTIAL USE OF MANGIFERA INDICA SEED KERNEL AND CITRUS. AURANTIIFOLIA SEED IN WATER DISINFECTION.

  17. Briquetting of Palm Kernel Shell | Ugwu | Journal of Applied ...

    African Journals Online (AJOL)

    In several developing countries, briquettes from agricultural residues contribute significantly to the energy mix especially for small scale and household requirements. In this work, briquettes were produced from Palm kernel shell. This was achieved by carbonising the shell to get the charcoal followed by the pulverization of ...

  18. Hollow microspheres with a tungsten carbide kernel for PEMFC application.

    Science.gov (United States)

    d'Arbigny, Julien Bernard; Taillades, Gilles; Marrony, Mathieu; Jones, Deborah J; Rozière, Jacques

    2011-07-28

    Tungsten carbide microspheres comprising an outer shell and a compact kernel prepared by a simple hydrothermal method exhibit very high surface area promoting a high dispersion of platinum nanoparticles, and an exceptionally high electrochemically active surface area (EAS) stability compared to the usual Pt/C electrocatalysts used for PEMFC application.

  19. Moderate deviations principles for the kernel estimator of ...

    African Journals Online (AJOL)

    Abstract. The aim of this paper is to provide pointwise and uniform moderate deviations principles for the kernel estimator of a nonrandom regression function. Moreover, we give an application of these moderate deviations principles to the construction of condence regions for the regression function. Resume. L'objectif de ...

  20. Matrix kernels for MEG and EEG source localization and imaging

    Energy Technology Data Exchange (ETDEWEB)

    Mosher, J.C.; Lewis, P.S. [Los Alamos National Lab., NM (United States); Leahy, R.M. [University of Southern California, Los Angeles, CA (United States)

    1994-12-31

    The most widely used model for electroencephalography (EEG) and magnetoencephalography (MEG) assumes a quasi-static approximation of Maxwell`s equations and a piecewise homogeneous conductor model. Both models contain an incremental field element that linearly relates an incremental source element (current dipole) to the field or voltage at a distant point. The explicit form of the field element is dependent on the head modeling assumptions and sensor configuration. Proper characterization of this incremental element is crucial to the inverse problem. The field element can be partitioned into the product of a vector dependent on sensor characteristics and a matrix kernel dependent only on head modeling assumptions. We present here the matrix kernels for the general boundary element model (BEM) and for MEG spherical models. We show how these kernels are easily interchanged in a linear algebraic framework that includes sensor specifics such as orientation and gradiometer configuration. We then describe how this kernel is easily applied to ``gain`` or ``transfer`` matrices used in multiple dipole and source imaging models.

  1. On Convergence of Kernel Density Estimates in Particle Filtering

    Czech Academy of Sciences Publication Activity Database

    Coufal, David

    2016-01-01

    Roč. 52, č. 5 (2016), s. 735-756 ISSN 0023-5954 Grant - others:GA ČR(CZ) GA16-03708S; SVV(CZ) 260334/2016 Institutional support: RVO:67985807 Keywords : Fourier analysis * kernel methods * particle filter Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.379, year: 2016

  2. Palm kernel agar: An alternative culture medium for rapid detection ...

    African Journals Online (AJOL)

    The feasibility of using palm kernel agar (PKA) as an alternative culture medium to desiccated coconut agar (DCA), the conventional medium for the recovery of aflatoxigenic fungi from mixed cultures and the detection of aflatoxigenic fungi and direct visual determination of aflatoxins in agricultural commodities was ...

  3. Contribution of granule bound starch synthase in kernel modification

    African Journals Online (AJOL)

    ACSS

    The stability of both GBSS and zein proteins coupled with the use of SDS-PAGE implied that only one was more reliable and required further validation. It was clear from this study, that kernel modification was regulated by complex genetic interactions. Fairly distinct systems such as the starch synthases, zein proteins and ...

  4. Preparation and characterization of active carbon using palm kernel ...

    African Journals Online (AJOL)

    Activated carbons were prepared from Palm kernel shells. Carbonization temperature was 6000C, at a residence time of 5 min for each process. Chemical activation was done by heating a mixture of carbonized material and the activating agents at a temperature of 700C to form a paste, followed by subsequent cooling and ...

  5. Matrix kernels for MEG and EEG source localization and imaging

    International Nuclear Information System (INIS)

    Mosher, J.C.; Lewis, P.S.; Leahy, R.M.

    1994-01-01

    The most widely used model for electroencephalography (EEG) and magnetoencephalography (MEG) assumes a quasi-static approximation of Maxwell's equations and a piecewise homogeneous conductor model. Both models contain an incremental field element that linearly relates an incremental source element (current dipole) to the field or voltage at a distant point. The explicit form of the field element is dependent on the head modeling assumptions and sensor configuration. Proper characterization of this incremental element is crucial to the inverse problem. The field element can be partitioned into the product of a vector dependent on sensor characteristics and a matrix kernel dependent only on head modeling assumptions. We present here the matrix kernels for the general boundary element model (BEM) and for MEG spherical models. We show how these kernels are easily interchanged in a linear algebraic framework that includes sensor specifics such as orientation and gradiometer configuration. We then describe how this kernel is easily applied to ''gain'' or ''transfer'' matrices used in multiple dipole and source imaging models

  6. Disinfection studies of Nahar (Mesua ferrea) seed kernel oil using ...

    African Journals Online (AJOL)

    GREGORY

    2011-12-16

    Dec 16, 2011 ... kinetics. Heterotrophic plate count, using CFU/ml, pour plate method, 35°C / 48 h, plate count agar were employed to evaluate the disinfection and its ... Key words: Nahar (Mesua ferrea) seed kernel oil, extraction, gum Arabic, disinfection, kinetics. ..... Modeling of Chemical Kinetics and Reactor Design.

  7. Physicochemical characteristics of kernel during fruit maturation of ...

    African Journals Online (AJOL)

    Physicochemical characteristics of kernels from four cultivars of coconut were studied with the aim of increasing the value of coconut palm (Cocos nucifera L.), the main income of most equatorial coastal farmers. Studies were undertaken on West African Tall (WAT), Malaysian Yellow Dwarf (MYD), Equatorial Guinea Green ...

  8. General method of boundary correction in kernel regression estimation

    African Journals Online (AJOL)

    Kernel estimators of both density and regression functions are not consistent near the nite end points of their supports. In other words, boundary eects seriously aect the performance of these estimators. In this paper, we combine the transformation and the reflection methods in order to introduce a new general method of ...

  9. Isolation and identification of bioactive compounds from kernel seed ...

    African Journals Online (AJOL)

    The ethanol extract and ethyl acetate fraction of Mangifera indica kernel seed cake inhibited the growth of Staphylococcus aureus and Pseudomonas aeruginosa. The bioactive compounds were isolated and identified by NMR, UV and mass spectrometry as methyl gallate, gallic acid and penta-O-galloylglucose. The

  10. determination of bio-energy potential of palm kernel shell

    African Journals Online (AJOL)

    88888888

    2012-11-03

    Nov 3, 2012 ... Keywords: palm kernel shell, bioenergy, thermogravimetric analysis, pyrolysis, gasification ... tain higher energy density fuels. Fast Pyrolysis is the thermal decomposition of biomass for bio-char, bio- oil and combustible gas production in the absence of ... Calorific Value of Coal and Coke) was used for the.

  11. Characteristics of traditionally processed shea kernels and butter

    NARCIS (Netherlands)

    Honfo, G.F.; Linnemann, A.R.; Akissoe, N.; Soumanou, M.M.; Boekel, van M.A.J.S.

    2013-01-01

    The traditional production of shea butter requires a heat treatment of the nuts. This study compared the end products derived by two commonly used heat treatments, namely smoking and boiling followed by sun-drying. Neither treatment influenced the moisture content of the kernels (8–10%), but the

  12. 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.

  13. Higher dimensional kernel methods | Osemwenkhae | Journal of the ...

    African Journals Online (AJOL)

    The multivariate kernel density estimator (MKDE) for the analysis of data in more than one dimension is presented. This removes the cumbersome nature associated with the interpretation of multivariate results when compared with most common multivariate schemes. The effect of varying the window width in MKDE with the ...

  14. Polynomial kernels for deletion to classes of acyclic digraphs

    NARCIS (Netherlands)

    Mnich, Matthias; van Leeuwen, E.J.

    2017-01-01

    We consider the problem to find a set X of vertices (or arcs) with |X| ≤ k in a given digraph G such that D = G − X is an acyclic digraph. In its generality, this is Directed Feedback Vertex Set (or Directed Feedback Arc Set); the existence of a polynomial kernel for these problems is a notorious

  15. Genetic relationship between plant growth, shoot and kernel sizes in ...

    African Journals Online (AJOL)

    Thirty two hybrids were derived from large (LG) and small (SM) kernel plants by inter-crossing parents that differed for shoot-size at silking [Long-Shoot (LS) and Small-Shoot (SS)]. Each hybrid was grown in replicated experiments at Fargo and Casselton stations, N.D., USA in 2002. Juvenile plant height, leaf number and ...

  16. 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 ...

  17. Mycological deterioration of stored palm kernels recovered from oil ...

    African Journals Online (AJOL)

    Palm kernels obtained from Pioneer Oil Mill Ltd. were stored for eight (8) weeks and examined for their microbiological quality and proximate composition. Seven (7) different fungal species were isolated by serial dilution plate technique. The fungal species included Aspergillus flavus Link; A nidulans Eidem; A niger ...

  18. Employment of kernel methods on wind turbine power performance assessment

    DEFF Research Database (Denmark)

    Skrimpas, Georgios Alexandros; Sweeney, Christian Walsted; Marhadi, Kun S.

    2015-01-01

    A power performance assessment technique is developed for the detection of power production discrepancies in wind turbines. The method employs a widely used nonparametric pattern recognition technique, the kernel methods. The evaluation is based on the trending of an extracted feature from...

  19. Nutritional evaluation of fermented palm kernel cake using red tilapia

    African Journals Online (AJOL)

    The use of palm kernel cake (PKC) and other plant residues in fish feeding especially under extensive aquaculture have been in practice for a long time. On the other hand, the use of microbial-based feedstuff is increasing. In this study, the performance of red tilapia raised on Trichoderma longibrachiatum fermented PKC ...

  20. Single pass kernel k-means clustering method

    Indian Academy of Sciences (India)

    easily implemented and is suitable for large data sets, like those in data mining appli- cations. Experimental results show that, with a small loss of quality, the proposed method can significantly reduce the time taken than the conventional kernel k-means cluster- ing method. The proposed method is also compared with other ...

  1. Cowling–Price Theorem and Characterization of Heat Kernel on ...

    Indian Academy of Sciences (India)

    We extend the uncertainty principle, the Cowling–Price theorem, on non-compact Riemannian symmetric spaces . We establish a characterization of the heat kernel of the Laplace–Beltrami operator on from integral estimates of the Cowling–Price type.

  2. A compact kernel for the calculus of inductive constructions

    Indian Academy of Sciences (India)

    design of the new kernel has been completely revisited since the first release, result- ing in a remarkably ... Our first implementation of Matita, taking us about 10 man years work, spread over a period of. 5 years (Asperti et al 2006) ... Our conclusion is that it is due to a myriad of minor design choices not sufficiently meditated ...

  3. Sparse kernel orthonormalized PLS for feature extraction in large datasets

    DEFF Research Database (Denmark)

    Arenas-García, Jerónimo; Petersen, Kaare Brandt; Hansen, Lars Kai

    2006-01-01

    is tested on a benchmark of UCI data sets, and on the analysis of integrated short-time music features for genre prediction. The upshot is that the method has strong expressive power even with rather few features, is clearly outperforming the ordinary kernel PLS, and therefore is an appealing method...

  4. Wiener kernel analysis of a noise-evoked otoacoustic emission

    NARCIS (Netherlands)

    van Dijk, P; Maat, A; Wit, H P

    1997-01-01

    In one specimen of the frog species, Rana esculenta, the following were measured: (1) a spontaneous otoacoustic emission; (2) a click-evoked otoacoustic emissions; and (3) a noise evoked otoacoustic emission. From the noise evoked emission response, a first-and a second-order Wiener kernel and the

  5. Capturing Option Anomalies with a Variance-Dependent Pricing Kernel

    DEFF Research Database (Denmark)

    Christoffersen, Peter; Heston, Steven; Jacobs, Kris

    2013-01-01

    evidence to confirm this U-shaped relationship between the risk-neutral and physical probability densities. The new pricing kernel substantially improves our ability to reconcile the time-series properties of stock returns with the cross-section of option prices. It provides a unified explanation...

  6. Cowling–Price theorem and characterization of heat kernel on ...

    Indian Academy of Sciences (India)

    R. Narasimhan (Krishtel eMaging) 1461 1996 Oct 15 13:05:22

    Keywords. Hardy's theorem; spherical harmonics; symmetric space; Jacobi function; heat kernel. 1. Introduction. Our starting point in this paper is the classical Hardy's ... solutions of the heat equation of the Laplace–Beltrami operator. .... similar argument with the role of ˜z and zn reversed and the induction hypothesis for d =.

  7. A compact kernel for the calculus of inductive constructions

    Indian Academy of Sciences (India)

    For flexibility reasons, it is useful to remove fixed universes (of the form Typei) in favour of universe variables ... We plan to let the user dynamically configure these aspects of the type system in the next release of. Matita. ..... A minor difference between the old and the new kernel is the management of non dependent binders.

  8. Some engineering properties of shelled and kernel tea ( Camellia ...

    African Journals Online (AJOL)

    Some engineering properties (size dimensions, sphericity, volume, bulk and true densities, friction coefficient, colour characteristics and mechanical behaviour as rupture ... The static coefficients of friction of shelled and kernel tea seeds for the large and small sizes higher values for rubber than the other friction surfaces.

  9. Notes on a storage manager for the Clouds kernel

    Science.gov (United States)

    Pitts, David V.; Spafford, Eugene H.

    1986-01-01

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

  10. FED RAW OR AUTOCLAVED N EEM SEED KERNELS IN DIETS

    African Journals Online (AJOL)

    Autoclaving improved (P<0.05) erythrocyte (RBC) production and cockerels fed diets with 150 g/kg heat- treated neem kernels had superior (P<0.05) packed cell volume (PCV), RBC number and haemogblobin concentration compared to those of birds on basal diet. Neem diets generally induced (P<0.05) lymphocytosis ...

  11. PERI - Auto-tuning Memory Intensive Kernels for Multicore

    Energy Technology Data Exchange (ETDEWEB)

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

    2008-06-24

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

  12. Nutritional evaluation of fermented palm kernel cake using red tilapia

    African Journals Online (AJOL)

    ONOS

    2010-01-25

    Jan 25, 2010 ... The use of palm kernel cake (PKC) and other plant residues in fish feeding especially under extensive aquaculture have been ... significant (P 三 0.05) increase in the levels of phosphorus, calcium and copper in the carcass of fish raised on ... advantageous in the development of supplementary or complete ...

  13. Single pass kernel k-means clustering method

    Indian Academy of Sciences (India)

    This approach has reduced both time complexity and memory requirements. However, the clustering result of this method will be very much deviated form that obtained using the conventional kernel k-means method. This is because of the fact that pseudo cluster centers in the input space may not represent the exact cluster ...

  14. A compact kernel for the calculus of inductive constructions

    Indian Academy of Sciences (India)

    CIC) implemented inside the Matita Interactive Theorem Prover. The design of the new kernel has been completely revisited since the first release, resulting in a remarkably compact implementation of about 2300 lines of OCaml code. The work ...

  15. Calculation of Volterra kernels for solutions of nonlinear differential equations

    NARCIS (Netherlands)

    van Hemmen, JL; Kistler, WM; Thomas, EGF

    2000-01-01

    We consider vector-valued autonomous differential equations of the form x' = f(x) + phi with analytic f and investigate the nonanticipative solution operator phi bar right arrow A(phi) in terms of its Volterra series. We show that Volterra kernels of order > 1 occurring in the series expansion of

  16. Corruption clubs: empirical evidence from kernel density estimates

    NARCIS (Netherlands)

    Herzfeld, T.; Weiss, Ch.

    2007-01-01

    A common finding of many analytical models is the existence of multiple equilibria of corruption. Countries characterized by the same economic, social and cultural background do not necessarily experience the same levels of corruption. In this article, we use Kernel Density Estimation techniques to

  17. Commutators of integral operators with variable kernels on Hardy ...

    Indian Academy of Sciences (India)

    Abstract. Let T ,α (0 ≤ α < n) be the singular and fractional integrals with variable kernel. (x, z), and [b, T ,α] be the commutator generated by T ,α and a Lipschitz function b. In this paper, the authors study the boundedness of [b, T ,α] on the Hardy spaces, under some assumptions such as the Lr -Dini condition. Similar results ...

  18. PERI - auto-tuning memory-intensive kernels for multicore

    International Nuclear Information System (INIS)

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

    2008-01-01

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

  19. Magnetic resonance imaging of single rice kernels during cooking

    NARCIS (Netherlands)

    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

  20. Visualization of nonlinear kernel models in neuroimaging by sensitivity maps

    DEFF Research Database (Denmark)

    Rasmussen, Peter Mondrup; Madsen, Kristoffer Hougaard; Lund, Torben Ellegaard

    2011-01-01

    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...

  1. Biocontrol of Fusarium graminearum Growth and Deoxynivalenol Production in Wheat Kernels with Bacterial Antagonists

    Directory of Open Access Journals (Sweden)

    Cuijuan Shi

    2014-01-01

    Full Text Available Fusarium graminearum is the main causal pathogen affecting small-grain cereals, and it produces deoxynivalenol, a kind of mycotoxin, which displays a wide range of toxic effects in human and animals. Bacterial strains isolated from peanut shells were investigated for their activities against F. graminearum by dual-culture plate and tip-culture assays. Among them, twenty strains exhibited potent inhibition to the growth of F. graminearum, and the inhibition rates ranged from 41.41% to 54.55% in dual-culture plate assay and 92.70% to 100% in tip-culture assay. Furthermore, eighteen strains reduced the production of deoxynivalenol by 16.69% to 90.30% in the wheat kernels assay. Finally, the strains with the strongest inhibitory activity were identified by morphological, physiological, biochemical methods and also 16S rDNA and gyrA gene analysis as Bacillus amyloliquefaciens. The current study highlights the potential application of antagonistic microorganisms and their metabolites in the prevention of fungal growth and mycotoxin production in wheat kernels. As a biological strategy, it might avoid safety problems and nutrition loss which always caused by physical and chemical strategies.

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

    DEFF Research Database (Denmark)

    Christiansen, Asger Nyman; Carstensen, Jens Michael

    We want to test the applicability of kernel based eigen-decomposition methods, compared to the traditional eigen-decomposition methods. We have implemented and tested three kernel based methods methods, namely PCA, MAF and MNF, all using a Gaussian kernel. We tested the methods on a multispectral...

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

    African Journals Online (AJOL)

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

  4. palm kernel husk ash (pkha) as an admixture (accelerator) in concrete

    African Journals Online (AJOL)

    A.W. Otunyo. Department of Civil & Environmental Engineering, University of Port Harcourt, P.M.B. ... Palm kernel husk is a nuisance material in palm oil ... Materials. The following materials were used for the experiment: Palm Kernel husk: Approximately 200kg of palm kernel husk was collected in 5 jute bags from a local ...

  5. Realistic three-dimensional sensitivity kernels for local tomography

    Science.gov (United States)

    Zhao, L.; Jordan, T. H.; Olsen, K. B.

    2003-12-01

    The frequency-dependent travel-time and amplitude anomalies measured at stations in the Southern California Seismic Network from local earthquakes carry rich information on the 3-D subsurface structure in and around the LA Basin (P. Chen et al., this meeting). So far tomography inversions for the LA Basin have been based on travel-time data using approximate Fréchet kernels obtained under high-frequency assumption that breaks down when the spatial dimensions of the structural heterogeneities are comparable to the Fresnel-zone size of the seismic waves. We have developed an algorithm to compute the exact 3-D Fréchet kernels for the frequency-dependent travel-time and amplitude anomalies without relying upon the assumptions of a smooth heterogeneity and a simple reference structure. The algorithm uses a staggered-grid finite-difference method to solve the wave equation in 3-D media and the reciprocity to reduce the number of finite-difference simulations. Numerical results for the travel-time and amplitude Fréchet kernels reveal a number of properties: 1) As expected, the sensitivities of both travel time and amplitude of a finite-bandwidth signal such as the usual P or S wave do not concentrate only on the ray path, but spread out in a volume surrounding it. 2) If the measurement is obtained from the entire waveform of a given arrival, e.g. through the cross-correlation of two waveforms, the travel-time kernel exhibits the counter-intuitive banana-doughnut behavior with minimum on the ray path; whereas the amplitude kernel takes on an intuitive shape of a sausage with maximum on the ray path. 3) In the first Fresnel zone, the travel-time sensitivity is mostly negative, indicating an advance in arrival time for a velocity increase, whereas the amplitude sensitivity is positive, suggesting an amplitude reduction for a wave-speed increase. 4) At stations away from the nodal points, the kernels for measurements obtained on different components of the same phase are

  6. A kernel for open source drug discovery in tropical diseases.

    Science.gov (United States)

    Ortí, Leticia; Carbajo, Rodrigo J; Pieper, Ursula; Eswar, Narayanan; Maurer, Stephen M; Rai, Arti K; Taylor, Ginger; Todd, Matthew H; Pineda-Lucena, Antonio; Sali, Andrej; Marti-Renom, Marc A

    2009-01-01

    Conventional patent-based drug development incentives work badly for the developing world, where commercial markets are usually small to non-existent. For this reason, the past decade has seen extensive experimentation with alternative R&D institutions ranging from private-public partnerships to development prizes. Despite extensive discussion, however, one of the most promising avenues-open source drug discovery-has remained elusive. We argue that the stumbling block has been the absence of a critical mass of preexisting work that volunteers can improve through a series of granular contributions. Historically, open source software collaborations have almost never succeeded without such "kernels". HERE, WE USE A COMPUTATIONAL PIPELINE FOR: (i) comparative structure modeling of target proteins, (ii) predicting the localization of ligand binding sites on their surfaces, and (iii) assessing the similarity of the predicted ligands to known drugs. Our kernel currently contains 143 and 297 protein targets from ten pathogen genomes that are predicted to bind a known drug or a molecule similar to a known drug, respectively. The kernel provides a source of potential drug targets and drug candidates around which an online open source community can nucleate. Using NMR spectroscopy, we have experimentally tested our predictions for two of these targets, confirming one and invalidating the other. The TDI kernel, which is being offered under the Creative Commons attribution share-alike license for free and unrestricted use, can be accessed on the World Wide Web at http://www.tropicaldisease.org. We hope that the kernel will facilitate collaborative efforts towards the discovery of new drugs against parasites that cause tropical diseases.

  7. Migration and income distribution.

    OpenAIRE

    Rodgers G

    1981-01-01

    ILO pub-WEP pub. Working paper based on a conference paper on models for analysis of interrelationships between labour mobility of migrant workers (migration) and income distribution in developing countries - includes a literature survey of empirical research, and covers labour market absorption of migrant rural workers, effects of rural areas-urban areas wage differentials on migration, impact of migration on wages, etc. References. Conference held in Ahmedabad 1981 Jan.

  8. Regional Redistribution and Migration

    DEFF Research Database (Denmark)

    Manasse, Paolo; Schultz, Christian

    We study a model with free migration between a rich and a poor region. Since there is congestion, the rich region has an incentive to give the poor region a transfer in order to reduce immigration. Faced with free migration, the rich region voluntarily chooses a transfer, which turns out...... to be equal to that a social planner would choose. Provided migration occurs in equilibrium, this conclusion holds even in the presence of moderate mobility costs. However, large migration costs will lead to suboptimal transfers in the market solution...

  9. Migration into art

    DEFF Research Database (Denmark)

    Petersen, Anne Ring

    This book addresses a topic of increasing importance to artists, art historians and scholars of cultural studies, migration studies and international relations: migration as a profoundly transforming force that has remodelled artistic and art institutional practices across the world. It explores...... contemporary art's critical engagement with migration and globalisation as a key source for improving our understanding of how these processes transform identities, cultures, institutions and geopolitics. The author explores three interwoven issues of enduring interest: identity and belonging, institutional...... visibility and recognition of migrant artists, and the interrelations between aesthetics and politics, including the balancing of aesthetics, politics and ethics in representations of forced migration....

  10. Growing assisted migration: Synthesis of a climate change adaptation strategy

    Science.gov (United States)

    Mary I. Williams; R. Kasten Dumroese

    2013-01-01

    Assisted migration may be necessary as a climate change adaptation strategy for native plant species that are less adaptive or mobile. Moving plants has been practiced a long time in human history, but movement of species in response to climate change is a new context. First proposed in 1985, assisted migration has gained attention since 2007 as a strategy to prevent...

  11. Composition Kernel: A Software Solution for Constructing a Multi-OS Embedded System

    Directory of Open Access Journals (Sweden)

    Kinebuchi Yuki

    2010-01-01

    Full Text Available Abstract Modern high-end embedded systems require both predictable real-time scheduling and high-level abstraction interface to their OS kernels. Since these features are difficult to be balanced by a single OS, some methods that accommodate multiple different versions of OS kernels, typically real-time OS and general purpose OS, into a single device have been proposed. The hybrid kernel, one of those methods, executes a general purpose OS kernel as a task of real-time OS which can support those features with reasonable engineering effort. However when adapting the approach to various combinations of OS kernels, which is required in the real-world embedded system design, the engineering effort of modifying the kernel becomes not negligible. This article introduce a method called a composition kernel which uses a thin abstraction layer for accommodating kernels without making direct dependencies between them. The authors developed the abstraction layer on an SH-4A processor and executed kernels on top of it. The amount of modifications to the kernels was significantly smaller than that in related work, while introducing only negligible verhead to the performance of the kernels.

  12. Coconut kernel protein modifies the effect of coconut oil on serum lipids.

    Science.gov (United States)

    Padmakumaran Nair, K G; Rajamohan, T; Kurup, P A

    1999-01-01

    Feeding coconut kernel along with coconut oil in human volunteers has been found to reduce serum total and LDL cholesterol when compared to feeding coconut oil alone. This effect of the kernel was also observed in rats. Since many plant proteins have been reported to exert a cholesterol lowering effect, a study was carried out on the effect of isolated kernel protein in rats. Feeding kernel protein resulted in lower levels of cholesterol, phospholipids and triglycerides in the serum and most tissues when compared to casein fed animals. Rats fed kernel protein had (1) increased hepatic degradation of cholesterol to bile acids, (2) increased hepatic cholesterol biosynthesis, and (3) decreased esterification of free cholesterol. In the intestine, however, cholesterogenesis was decreased. The kernel protein also caused decreased lipogenesis in the liver and intestine. This beneficial effect of the kernel protein is attributed to its very low lysine/arginine ratio 2.13% lysine and 24.5% arginine.

  13. Peramalan Crude Palm Oil (CPO Menggunakan Support Vector Regression Kernel Radial Basis

    Directory of Open Access Journals (Sweden)

    Rezzy Eko Caraka

    2017-06-01

    Full Text Available Recently, instead of selecting a kernel has been proposed which uses SVR, where the weight of each kernel is optimized during training. Along this line of research, many pioneering kernel learning algorithms have been proposed. The use of kernels provides a powerful and principled approach to modeling nonlinear patterns through linear patterns in a feature space. Another bene?t is that the design of kernels and linear methods can be decoupled, which greatly facilitates the modularity of machine learning methods. We perform experiments on real data sets crude palm oil prices for application and better illustration using kernel radial basis. We see that evaluation gives a good to fit prediction and actual also good values showing the validity and accuracy of the realized model based on MAPE and R2. Keywords:  Crude Palm Oil; Forecasting; SVR; Radial Basis; Kernel

  14. Optimizing Kernel PCA Using Sparse Representation-Based Classifier for MSTAR SAR Image Target Recognition

    Directory of Open Access Journals (Sweden)

    Chuang Lin

    2013-01-01

    Full Text Available Different kernels cause various class discriminations owing to their different geometrical structures of the data in the feature space. In this paper, a method of kernel optimization by maximizing a measure of class separability in the empirical feature space with sparse representation-based classifier (SRC is proposed to solve the problem of automatically choosing kernel functions and their parameters in kernel learning. The proposed method first adopts a so-called data-dependent kernel to generate an efficient kernel optimization algorithm. Then, a constrained optimization function using general gradient descent method is created to find combination coefficients varied with the input data. After that, optimized kernel PCA (KOPCA is obtained via combination coefficients to extract features. Finally, the sparse representation-based classifier is used to perform pattern classification task. Experimental results on MSTAR SAR images show the effectiveness of the proposed method.

  15. I/O Sharing in a Multi-core Kernel for Mixed-criticality Applications

    DEFF Research Database (Denmark)

    Li, Gang; Top, Søren

    2013-01-01

    -criticality applications with reduced certification cost. In the partitioning architecture of strong spatial and temporal isolation, fault propagation can be prevented among mixed-criticality applications (regarded as partitions). However, I/O sharing between partitions could be the path of fault propagation that hinders...... the partitioning. E.g. a crashed partition generates incorrect outputs to shared I/Os, which affects the functioning of another partition. This paper focuses on a message-based approach of I/O sharing in the HARTEX real-time kernel on a multi-core platform. Based on a simple multi-core partitioning architecture......, a certifiable I/O sharing approach is implemented based on a safe message mechanism, in order to support the partitioning architecture, enable individual certification of mixed-criticality applications and thus achieve minimized total certification cost of the entire system....

  16. Migrating Art History

    DEFF Research Database (Denmark)

    Ørum, Tania

    2012-01-01

    Review of Hiroko Ikegami, The Great Migrator. Robert Rauschenberg and the Global Rise of American Art. Cambridge Mass., The MIT Press, 2010. 277 pages. ISBN 978-0-262-01425-0.......Review of Hiroko Ikegami, The Great Migrator. Robert Rauschenberg and the Global Rise of American Art. Cambridge Mass., The MIT Press, 2010. 277 pages. ISBN 978-0-262-01425-0....

  17. Developments in Australian Migration

    Directory of Open Access Journals (Sweden)

    David Smith

    2016-05-01

    Full Text Available The purpose of this paper is to make a comprehensive assessment of recent developments in international migration to Australia.  In doing so we will be discussing changes in the scale and composition of migration into Australia, the dispersal of migrants throughout Australia and also the influence of policy-decisions and various departmental programmes on this movement.

  18. Geography of European Migration

    Directory of Open Access Journals (Sweden)

    Zhitin Dmitry V.

    2016-08-01

    Full Text Available In recent decades, the role of international migration has increased dramatically in most European countries. The growth in migration has made some authors proclaim the beginning of a second Migration Period that could transform the social and cultural identity of Europe. The article presents an analysis of international migration geography in Europe in the last twenty-five years. The authors identify the main trends in migration, provide migration profiles of European countries, and propose a classification based on the recent changes in the migrant stock. Changes in the migrant stock (total emigration and immigration reflect the level of involvement in international and global processes. They can serve as an indicator of a country’s attractiveness for both foreigners and the country’s citizens. The study shows that European countries are increasingly split into ‘immigrant’ and ‘emigrant’ states. The authors describe spatial patterns of migration. The volume and localisation of migration flows in Europe are affected not only by cultural and historical circumstance, such as a colonial past or a common language. The scale of immigrant influx often does not depend on a donor country’s demographic potential or the level of its socio-economic development. The links between the place of origin and destination are often more complex than it might initially seem. The authors stress the importance of a differentiated immigration policy taking into account ethnic and cultural features of host societies.

  19. Diel vertical migrat..

    African Journals Online (AJOL)

    2002-01-24

    Jan 24, 2002 ... Ringelberg 11964 The positively photo tactic reaction of Daphnia magna. Straus: A contribution to the understanding of diurnal vertical migration. Netherlands Journal of. Sea Research 2: 319—406. Ringelberg J 1980 Introductory remarks: causal and teleological aspects of diurnal migration. ~ In: Kerfoot,.

  20. Migration in Burkina Faso

    NARCIS (Netherlands)

    Wouterse, F.S.

    2007-01-01

    Migration plays an important role in development and as a strategy for poverty reduction. A recent World Bank investigation finds a significant positive relationship between international migration and poverty reduction at the country level (Adams and Page 2003). Burkina Faso, whose conditions for

  1. Samtidskunst og migration

    DEFF Research Database (Denmark)

    Petersen, Anne Ring

    2010-01-01

    "Samtidskunst og migration. En oversigt over faglitteraturen" er en forskningsoversigt der gør status over hvad der hidtil er skrevet inden for det kunsthistoriske område om vor tids billedkunst og migration som politisk, socialt og kulturelt fænomen, primært i forbindelse med immigration til...

  2. The Globalisation of migration

    Directory of Open Access Journals (Sweden)

    Milan Mesić

    2002-04-01

    Full Text Available The paper demonstrates that contemporary international migration is a constitutive part of the globalisation process. After defining the concepts of globalisation and the globalisation of migration, the author discusses six key themes, linking globalisation and international migration (“global cities”, the scale of migration; diversification of migration flows; globalisation of science and education; international migration and citizenship; emigrant communities and new identities. First, in accordance with Saskia Sassen’s analysis, the author rejects the wide-spread notion that unqualified migrants have lost an (important role in »global cities«, i.e. in the centres of the new (global economy. Namely, the post-modern service sector cannot function without the support of a wide range of auxiliary unqualified workers. Second, a critical comparison with traditional overseas mass migration to the USA at the turn of the 19th and 20th centuries indicates that present international migration is, perhaps, less extensive – however it is important to take into consideration various limitations that previously did not exist, and thus the present migration potential is in really greater. Third, globalisation is more evident in a diversification of the forms of migration: the source area of migrants to the New World and Europe has expanded to include new regions in the world; new immigration areas have arisen (the Middle East, new industrial countries of the Far East, South Europe; intra-regional migration has intensified. Forth, globalisation is linked to an increased migration of experts and the pessimistic notion of a brain drain has been replaced by the optimistic idea of a brain gain. Fifth, contemporary international migration has been associated with a crisis of the national model of citizenship. Sixth, the interlinking of (migrant cultural communities regardless of distance and the physical proximity of cultural centres (the

  3. Migration of health workers.

    Science.gov (United States)

    Buchan, James

    2008-01-01

    The discussion and debate stimulated by these papers focused across a range of issues but there were four main areas of questioning: "measuring" and monitoring migration (issues related to comparability, completeness and accuracy of data sets on human resources); the impact of migration of health workers on health systems; the motivations of individual health workers to migrate (the "push" and "pull" factors) and the effect of policies designed either to reduce migration (e.g "self ufficiency") or to stimulate it (e.g active international recruitment). It was recognised that there was a critical need to examine migratory flows within the broader context of all health care labour market dynamics within a country, that increasing migration of health workers was an inevitable consequence of globalisation, and that there was a critical need to improve monitoring so as to better inform policy formulation and policy testing in this area.

  4. Non-intrusive Load Disaggregation Based on Kernel Density Estimation

    Science.gov (United States)

    Sen, Wang; Dongsheng, Yang; Chuchen, Guo; Shengxian, Du

    2017-05-01

    Aiming at the problem of high cost and difficult implementation of high frequency non-intrusive load decomposition method, this paper proposes a new method based on kernel density estimation(KDE) for low frequency NILM (Non-intrusive load monitoring). The method establishes power reference model of electricity load in different working conditions and appliance’s possible combinations first, then probability distribution is calculated as appliances features by kernel density estimation. After that, target power data is divided by step changes, whose distributions will be compared with reference models, and the most similar reference model will be chosen as the decomposed consequence. The proposed approach was tested with data from the GREEND public data set, it showed better performance in terms of energy disaggregation accuracy compared with many traditional NILM approaches. Our results show good performance which can achieve more than 93% accuracy in simulation.

  5. Integral equations with difference kernels on finite intervals

    CERN Document Server

    Sakhnovich, Lev A

    2015-01-01

    This book focuses on solving integral equations with difference kernels on finite intervals. The corresponding problem on the semiaxis was previously solved by N. Wiener–E. Hopf and by M.G. Krein. The problem on finite intervals, though significantly more difficult, may be solved using our method of operator identities. This method is also actively employed in inverse spectral problems, operator factorization and nonlinear integral equations. Applications of the obtained results to optimal synthesis, light scattering, diffraction, and hydrodynamics problems are discussed in this book, which also describes how the theory of operators with difference kernels is applied to stable processes and used to solve the famous M. Kac problems on stable processes. In this second edition these results are extensively generalized and include the case of all Levy processes. We present the convolution expression for the well-known Ito formula of the generator operator, a convolution expression that has proven to be fruitful...

  6. Analysis of Linux kernel as a complex network

    International Nuclear Information System (INIS)

    Gao, Yichao; Zheng, Zheng; Qin, Fangyun

    2014-01-01

    Operating system (OS) acts as an intermediary between software and hardware in computer-based systems. In this paper, we analyze the core of the typical Linux OS, Linux kernel, as a complex network to investigate its underlying design principles. It is found that the Linux Kernel Network (LKN) is a directed network and its out-degree follows an exponential distribution while the in-degree follows a power-law distribution. The correlation between topology and functions is also explored, by which we find that LKN is a highly modularized network with 12 key communities. Moreover, we investigate the robustness of LKN under random failures and intentional attacks. The result shows that the failure of the large in-degree nodes providing basic services will do more damage on the whole system. Our work may shed some light on the design of complex software systems

  7. Kernel Based Nonlinear Dimensionality Reduction and Classification for Genomic Microarray

    Science.gov (United States)

    Li, Xuehua; Shu, Lan

    2008-01-01

    Genomic microarrays are powerful research tools in bioinformatics and modern medicinal research because they enable massively-parallel assays and simultaneous monitoring of thousands of gene expression of biological samples. However, a simple microarray experiment often leads to very high-dimensional data and a huge amount of information, the vast amount of data challenges researchers into extracting the important features and reducing the high dimensionality. In this paper, a nonlinear dimensionality reduction kernel method based locally linear embedding(LLE) is proposed, and fuzzy K-nearest neighbors algorithm which denoises datasets will be introduced as a replacement to the classical LLE's KNN algorithm. In addition, kernel method based support vector machine (SVM) will be used to classify genomic microarray data sets in this paper. We demonstrate the application of the techniques to two published DNA microarray data sets. The experimental results confirm the superiority and high success rates of the presented method. PMID:27879930

  8. Kernel Based Nonlinear Dimensionality Reduction and Classification for Genomic Microarray.

    Science.gov (United States)

    Li, Xuehua; Shu, Lan

    2008-07-15

    Genomic microarrays are powerful research tools in bioinformatics and modern medicinal research because they enable massively-parallel assays and simultaneous monitoring of thousands of gene expression of biological samples. However, a simple microarray experiment often leads to very high-dimensional data and a huge amount of information, the vast amount of data challenges researchers into extracting the important features and reducing the high dimensionality. In this paper, a nonlinear dimensionality reduction kernel method based locally linear embedding(LLE) is proposed, and fuzzy K-nearest neighbors algorithm which denoises datasets will be introduced as a replacement to the classical LLE's KNN algorithm. In addition, kernel method based support vector machine (SVM) will be used to classify genomic microarray data sets in this paper. We demonstrate the application of the techniques to two published DNA microarray data sets. The experimental results confirm the superiority and high success rates of the presented method.

  9. Hydroxocobalamin treatment of acute cyanide poisoning from apricot kernels.

    Science.gov (United States)

    Cigolini, Davide; Ricci, Giogio; Zannoni, Massimo; Codogni, Rosalia; De Luca, Manuela; Perfetti, Paola; Rocca, Giampaolo

    2011-05-24

    Clinical experience with hydroxocobalamin in acute cyanide poisoning via ingestion remains limited. This case concerns a 35-year-old mentally ill woman who consumed more than 20 apricot kernels. Published literature suggests each kernel would have contained cyanide concentrations ranging from 0.122 to 4.09 mg/g (average 2.92 mg/g). On arrival, the woman appeared asymptomatic with a raised pulse rate and slight metabolic acidosis. Forty minutes after admission (approximately 70 min postingestion), the patient experienced headache, nausea and dyspnoea, and was hypotensive, hypoxic and tachypnoeic. Following treatment with amyl nitrite and sodium thiosulphate, her methaemoglobin level was 10%. This prompted the administration of oxygen, which evoked a slight improvement in her vital signs. Hydroxocobalamin was then administered. After 24 h, she was completely asymptomatic with normalised blood pressure and other haemodynamic parameters. This case reinforces the safety and effectiveness of hydroxocobalamin in acute cyanide poisoning by ingestion.

  10. Effective face recognition using bag of features with additive kernels

    Science.gov (United States)

    Yang, Shicai; Bebis, George; Chu, Yongjie; Zhao, Lindu

    2016-01-01

    In past decades, many techniques have been used to improve face recognition performance. The most common and well-studied ways are to use the whole face image to build a subspace based on the reduction of dimensionality. Differing from methods above, we consider face recognition as an image classification problem. The face images of the same person are considered to fall into the same category. Each category and each face image could be both represented by a simple pyramid histogram. Spatial dense scale-invariant feature transform features and bag of features method are used to build categories and face representations. In an effort to make the method more efficient, a linear support vector machine solver, Pegasos, is used for the classification in the kernel space with additive kernels instead of nonlinear SVMs. Our experimental results demonstrate that the proposed method can achieve very high recognition accuracy on the ORL, YALE, and FERET databases.

  11. Finite Gaussian Mixture Approximations to Analytically Intractable Density Kernels

    DEFF Research Database (Denmark)

    Khorunzhina, Natalia; Richard, Jean-Francois

    The objective of the paper is that of constructing finite Gaussian mixture approximations to analytically intractable density kernels. The proposed method is adaptive in that terms are added one at the time and the mixture is fully re-optimized at each step using a distance measure that approxima......The objective of the paper is that of constructing finite Gaussian mixture approximations to analytically intractable density kernels. The proposed method is adaptive in that terms are added one at the time and the mixture is fully re-optimized at each step using a distance measure...... that approximates the corresponding importance sampling variance. All functions of interest are evaluated under Gaussian quadrature rules. Examples include a sequential (filtering) evaluation of the likelihood function of a stochastic volatility model where all relevant densities (filtering, predictive...

  12. Music Emotion Detection Using Hierarchical Sparse Kernel Machines

    Directory of Open Access Journals (Sweden)

    Yu-Hao Chin

    2014-01-01

    Full Text Available For music emotion detection, this paper presents a music emotion verification system based on hierarchical sparse kernel machines. With the proposed system, we intend to verify if a music clip possesses happiness emotion or not. There are two levels in the hierarchical sparse kernel machines. In the first level, a set of acoustical features are extracted, and principle component analysis (PCA is implemented to reduce the dimension. The acoustical features are utilized to generate the first-level decision vector, which is a vector with each element being a significant value of an emotion. The significant values of eight main emotional classes are utilized in this paper. To calculate the significant value of an emotion, we construct its 2-class SVM with calm emotion as the global (non-target side of the SVM. The probability distributions of the adopted acoustical features are calculated and the probability product kernel is applied in the first-level SVMs to obtain first-level decision vector feature. In the second level of the hierarchical system, we merely construct a 2-class relevance vector machine (RVM with happiness as the target side and other emotions as the background side of the RVM. The first-level decision vector is used as the feature with conventional radial basis function kernel. The happiness verification threshold is built on the probability value. In the experimental results, the detection error tradeoff (DET curve shows that the proposed system has a good performance on verifying if a music clip reveals happiness emotion.

  13. Engineering a static verification tool for GPU kernels

    OpenAIRE

    Bardsley, E; Betts, A; Chong, N; Collingbourne, P; Deligiannis, P; Donaldson, AF; Ketema, J; Liew, D; Qadeer, S

    2014-01-01

    We report on practical experiences over the last 2.5 years related to the engineering of GPUVerify, a static verification tool for OpenCL and CUDA GPU kernels, plotting the progress of GPUVerify from a prototype to a fully functional and relatively efficient analysis tool. Our hope is that this experience report will serve the verification community by helping to inform future tooling efforts. ? 2014 Springer International Publishing.

  14. Chebyshev recursion methods: Kernel polynomials and maximum entropy

    Energy Technology Data Exchange (ETDEWEB)

    Silver, R.N.; Roeder, H.; Voter, A.F.; Kress, J.D. [Los Alamos National Lab., NM (United States). Theoretical Div.

    1995-10-01

    The authors describe two Chebyshev recursion methods for calculations with very large sparse Hamiltonians, the kernel polynomial method (KPM) and the maximum entropy method (MEM). They are especially applicable to physical properties involving large numbers of eigenstates, which include densities of states, spectral functions, thermodynamics, total energies, as well as forces for molecular dynamics and Monte Carlo simulations. The authors apply Chebyshev methods to the electronic structure of Si, the thermodynamics of Heisenberg antiferromagnets, and a polaron problem.

  15. Explicit signal to noise ratio in reproducing kernel Hilbert spaces

    DEFF Research Database (Denmark)

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

    2011-01-01

    This paper introduces a nonlinear feature extraction method based on kernels for remote sensing data analysis. The proposed approach is based on the minimum noise fraction (MNF) transform, which maximizes the signal variance while also minimizing the estimated noise variance. We here propose an a...... with PCA, MNF, KPCA, and the previous version of KMNF. Extracted features with the explicit KMNF also improve hyperspectral image classification....

  16. Benchmarking NWP Kernels on Multi- and Many-core Processors

    Science.gov (United States)

    Michalakes, J.; Vachharajani, M.

    2008-12-01

    Increased computing power for weather, climate, and atmospheric science has provided direct benefits for defense, agriculture, the economy, the environment, and public welfare and convenience. Today, very large clusters with many thousands of processors are allowing scientists to move forward with simulations of unprecedented size. But time-critical applications such as real-time forecasting or climate prediction need strong scaling: faster nodes and processors, not more of them. Moreover, the need for good cost- performance has never been greater, both in terms of performance per watt and per dollar. For these reasons, the new generations of multi- and many-core processors being mass produced for commercial IT and "graphical computing" (video games) are being scrutinized for their ability to exploit the abundant fine- grain parallelism in atmospheric models. We present results of our work to date identifying key computational kernels within the dynamics and physics of a large community NWP model, the Weather Research and Forecast (WRF) model. We benchmark and optimize these kernels on several different multi- and many-core processors. The goals are to (1) characterize and model performance of the kernels in terms of computational intensity, data parallelism, memory bandwidth pressure, memory footprint, etc. (2) enumerate and classify effective strategies for coding and optimizing for these new processors, (3) assess difficulties and opportunities for tool or higher-level language support, and (4) establish a continuing set of kernel benchmarks that can be used to measure and compare effectiveness of current and future designs of multi- and many-core processors for weather and climate applications.

  17. Relational kernel-based grasping with numerical features

    OpenAIRE

    Antanas, Laura; Moreno, Plinio; De Raedt, Luc

    2015-01-01

    Object grasping is a key task in robot manipulation. Performing a grasp largely depends on the object properties and grasp constraints. This paper proposes a new statistical relational learning approach to recognize graspable points in object point clouds. We characterize each point with numerical shape features and represent each cloud as a (hyper-) graph by considering qualitative spatial relations between neighboring points. Further, we use kernels on graphs to exploit extended contextual ...

  18. Realistic dispersion kernels applied to cohabitation reaction dispersion equations

    Science.gov (United States)

    Isern, Neus; Fort, Joaquim; Pérez-Losada, Joaquim

    2008-10-01

    We develop front spreading models for several jump distance probability distributions (dispersion kernels). We derive expressions for a cohabitation model (cohabitation of parents and children) and a non-cohabitation model, and apply them to the Neolithic using data from real human populations. The speeds that we obtain are consistent with observations of the Neolithic transition. The correction due to the cohabitation effect is up to 38%.

  19. On linear viscoelasticity within general fractional derivatives without singular kernel

    Directory of Open Access Journals (Sweden)

    Gao Feng

    2017-01-01

    Full Text Available The Riemann-Liouville and Caputo-Liouville fractional derivatives without singular kernel are proposed as mathematical tools to describe the mathematical models in line viscoelasticity in the present article. The fractional mechanical models containing the Maxwell and Kelvin-Voigt elements are graphically discussed with the Laplace transform. The results are accurate and efficient to reveal the complex behaviors of the real materials.

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

    African Journals Online (AJOL)

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

  1. Removal of Lead from Aqueous Solution by Palm Kernel Fibre ...

    African Journals Online (AJOL)

    The sorption of lead on palm kernel fibre, an agricultural waste product, has been studied. The sorption process was studied as a function of initial lead concentration and initial solution pH. The percentage lead removal was found to increase with increasing initial solution pH up to pH 5 and then to decrease as pH was ...

  2. Orthomodular lattices, Foulis Semigroups and Dagger Kernel Categories

    OpenAIRE

    Jacobs, Bart

    2009-01-01

    This paper is a sequel to arXiv:0902.2355 and continues the study of quantum logic via dagger kernel categories. It develops the relation between these categories and both orthomodular lattices and Foulis semigroups. The relation between the latter two notions has been uncovered in the 1960s. The current categorical perspective gives a broader context and reconstructs this relationship between orthomodular lattices and Foulis semigroups as special instance.

  3. Near infrared face recognition using Zernike moments and Hermite kernels

    Czech Academy of Sciences Publication Activity Database

    Farokhi, Sajad; Sheikh, U.U.; Flusser, Jan; Yang, Bo

    2015-01-01

    Roč. 316, č. 1 (2015), s. 234-245 ISSN 0020-0255 R&D Projects: GA ČR(CZ) GA13-29225S Keywords : face recognition * Zernike moments * Hermite kernel * Decision fusion * Near infrared Subject RIV: JD - Computer Applications, Robotics Impact factor: 3.364, year: 2015 http://library.utia.cas.cz/separaty/2015/ZOI/flusser-0444205.pdf

  4. Approximate Minimization of the Regularized Expected Error over Kernel Models

    Czech Academy of Sciences Publication Activity Database

    Kůrková, Věra; Sanguineti, M.

    2008-01-01

    Roč. 33, č. 3 (2008), s. 747-756 ISSN 0364-765X R&D Projects: GA ČR GA201/05/0557; GA ČR GA201/08/1744 Institutional research plan: CEZ:AV0Z10300504 Keywords : suboptimal solutions * expected error * convex functionals * kernel methods * model complexity * rates of convergence Subject RIV: BA - General Mathematics Impact factor: 1.086, year: 2008

  5. Learning with Generalization Capability by Kernel Methods of Bounded Complexity

    Czech Academy of Sciences Publication Activity Database

    Kůrková, Věra; Sanguineti, M.

    2005-01-01

    Roč. 21, č. 3 (2005), s. 350-367 ISSN 0885-064X R&D Projects: GA AV ČR 1ET100300419 Institutional research plan: CEZ:AV0Z10300504 Keywords : supervised learning * generalization * model complexity * kernel methods * minimization of regularized empirical errors * upper bounds on rates of approximate optimization Subject RIV: BA - General Mathematics Impact factor: 1.186, year: 2005

  6. The depression of a graph and k-kernels

    Directory of Open Access Journals (Sweden)

    Schurch Mark

    2014-05-01

    Full Text Available An edge ordering of a graph G is an injection f : E(G → R, the set of real numbers. A path in G for which the edge ordering f increases along its edge sequence is called an f-ascent ; an f-ascent is maximal if it is not contained in a longer f-ascent. The depression of G is the smallest integer k such that any edge ordering f has a maximal f-ascent of length at most k. A k-kernel of a graph G is a set of vertices U ⊆ V (G such that for any edge ordering f of G there exists a maximal f-ascent of length at most k which neither starts nor ends in U. Identifying a k-kernel of a graph G enables one to construct an infinite family of graphs from G which have depression at most k. We discuss various results related to the concept of k-kernels, including an improved upper bound for the depression of trees.

  7. Robust Kernel Clustering Algorithm for Nonlinear System Identification

    Directory of Open Access Journals (Sweden)

    Mohamed Bouzbida

    2017-01-01

    Full Text Available In engineering field, it is necessary to know the model of the real nonlinear systems to ensure its control and supervision; in this context, fuzzy modeling and especially the Takagi-Sugeno fuzzy model has drawn the attention of several researchers in recent decades owing to their potential to approximate nonlinear behavior. To identify the parameters of Takagi-Sugeno fuzzy model several clustering algorithms are developed such as the Fuzzy C-Means (FCM algorithm, Possibilistic C-Means (PCM algorithm, and Possibilistic Fuzzy C-Means (PFCM algorithm. This paper presents a new clustering algorithm for Takagi-Sugeno fuzzy model identification. Our proposed algorithm called Robust Kernel Possibilistic Fuzzy C-Means (RKPFCM algorithm is an extension of the PFCM algorithm based on kernel method, where the Euclidean distance used the robust hyper tangent kernel function. The proposed algorithm can solve the nonlinear separable problems found by FCM, PCM, and PFCM algorithms. Then an optimization method using the Particle Swarm Optimization (PSO method combined with the RKPFCM algorithm is presented to overcome the convergence to a local minimum of the objective function. Finally, validation results of examples are given to demonstrate the effectiveness, practicality, and robustness of our proposed algorithm in stochastic environment.

  8. Aveiro method in reproducing kernel Hilbert spaces under complete dictionary

    Science.gov (United States)

    Mai, Weixiong; Qian, Tao

    2017-12-01

    Aveiro Method is a sparse representation method in reproducing kernel Hilbert spaces (RKHS) that gives orthogonal projections in linear combinations of reproducing kernels over uniqueness sets. It, however, suffers from determination of uniqueness sets in the underlying RKHS. In fact, in general spaces, uniqueness sets are not easy to be identified, let alone the convergence speed aspect with Aveiro Method. To avoid those difficulties we propose an anew Aveiro Method based on a dictionary and the matching pursuit idea. What we do, in fact, are more: The new Aveiro method will be in relation to the recently proposed, the so called Pre-Orthogonal Greedy Algorithm (P-OGA) involving completion of a given dictionary. The new method is called Aveiro Method Under Complete Dictionary (AMUCD). The complete dictionary consists of all directional derivatives of the underlying reproducing kernels. We show that, under the boundary vanishing condition, bring available for the classical Hardy and Paley-Wiener spaces, the complete dictionary enables an efficient expansion of any given element in the Hilbert space. The proposed method reveals new and advanced aspects in both the Aveiro Method and the greedy algorithm.

  9. Spatial Markov Kernels for Image Categorization and Annotation.

    Science.gov (United States)

    Zhiwu Lu; Ip, H H S

    2011-08-01

    This paper presents a novel discriminative stochastic method for image categorization and annotation. We first divide the images into blocks on a regular grid and then generate visual keywords through quantizing the features of image blocks. The traditional Markov chain model is generalized to capture 2-D spatial dependence between visual keywords by defining the notion of "past" as what we have observed in a row-wise raster scan. The proposed spatial Markov chain model can be trained via maximum-likelihood estimation and then be used directly for image categorization. Since this is completely a generative method, we can further improve it through developing new discriminative learning. Hence, spatial dependence between visual keywords is incorporated into kernels in two different ways, for use with a support vector machine in a discriminative approach to the image categorization problem. Moreover, a kernel combination is used to handle rotation and multiscale issues. Experiments on several image databases demonstrate that our spatial Markov kernel method for image categorization can achieve promising results. When applied to image annotation, which can be considered as a multilabel image categorization process, our method also outperforms state-of-the-art techniques.

  10. Kernel Negative ε Dragging Linear Regression for Pattern Classification

    Directory of Open Access Journals (Sweden)

    Yali Peng

    2017-01-01

    Full Text Available Linear regression (LR and its variants have been widely used for classification problems. However, they usually predefine a strict binary label matrix which has no freedom to fit the samples. In addition, they cannot deal with complex real-world applications such as the case of face recognition where samples may not be linearly separable owing to varying poses, expressions, and illumination conditions. Therefore, in this paper, we propose the kernel negative ε dragging linear regression (KNDLR method for robust classification on noised and nonlinear data. First, a technique called negative ε dragging is introduced for relaxing class labels and is integrated into the LR model for classification to properly treat the class margin of conventional linear regressions for obtaining robust result. Then, the data is implicitly mapped into a high dimensional kernel space by using the nonlinear mapping determined by a kernel function to make the data more linearly separable. Finally, our obtained KNDLR method is able to partially alleviate the problem of overfitting and can perform classification well for noised and deformable data. Experimental results show that the KNDLR classification algorithm obtains greater generalization performance and leads to better robust classification decision.

  11. Analyzing kernel matrices for the identification of differentially expressed genes.

    Directory of Open Access Journals (Sweden)

    Xiao-Lei Xia

    Full Text Available One of the most important applications of microarray data is the class prediction of biological samples. For this purpose, statistical tests have often been applied to identify the differentially expressed genes (DEGs, followed by the employment of the state-of-the-art learning machines including the Support Vector Machines (SVM in particular. The SVM is a typical sample-based classifier whose performance comes down to how discriminant samples are. However, DEGs identified by statistical tests are not guaranteed to result in a training dataset composed of discriminant samples. To tackle this problem, a novel gene ranking method namely the Kernel Matrix Gene Selection (KMGS is proposed. The rationale of the method, which roots in the fundamental ideas of the SVM algorithm, is described. The notion of ''the separability of a sample'' which is estimated by performing [Formula: see text]-like statistics on each column of the kernel matrix, is first introduced. The separability of a classification problem is then measured, from which the significance of a specific gene is deduced. Also described is a method of Kernel Matrix Sequential Forward Selection (KMSFS which shares the KMGS method's essential ideas but proceeds in a greedy manner. On three public microarray datasets, our proposed algorithms achieved noticeably competitive performance in terms of the B.632+ error rate.

  12. Optimizing memory-bound SYMV kernel on GPU hardware accelerators

    KAUST Repository

    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.

  13. Dimensionality reduction of hyperspectral images using kernel ICA

    Science.gov (United States)

    Khan, Asif; Kim, Intaek; Kong, Seong G.

    2009-05-01

    Computational burden due to high dimensionality of Hyperspectral images is an obstacle in efficient analysis and processing of Hyperspectral images. In this paper, we use Kernel Independent Component Analysis (KICA) for dimensionality reduction of Hyperspectraql images based on band selection. Commonly used ICA and PCA based dimensionality reduction methods do not consider non linear transformations and assumes that data has non-gaussian distribution. When the relation of source signals (pure materials) and observed Hyperspectral images is nonlinear then these methods drop a lot of information during dimensionality reduction process. Recent research shows that kernel-based methods are effective in nonlinear transformations. KICA is robust technique of blind source separation and can even work on near-gaussina data. We use Kernel Independent Component Analysis (KICA) for the selection of minimum number of bands that contain maximum information for detection in Hyperspectral images. The reduction of bands is basd on the evaluation of weight matrix generated by KICA. From the selected lower number of bands, we generate a new spectral image with reduced dimension and use it for hyperspectral image analysis. We use this technique as preprocessing step in detection and classification of poultry skin tumors. The hyperspectral iamge samples of chicken tumors used contain 65 spectral bands of fluorescence in the visible region of the spectrum. Experimental results show that KICA based band selection has high accuracy than that of fastICA based band selection for dimensionality reduction and analysis for Hyperspectral images.

  14. Ethanolic Walnut Kernel Phenolic Compounds and its Antimicrobial Effect

    Directory of Open Access Journals (Sweden)

    K Ashrafi

    2011-10-01

    Full Text Available Introduction: Food-borne pathogens are causes of poisoning and gastrointestinal infections. In recent years, it is recommended to use natural materials like plant extracts and essences instead of chemical preservatives in food industry. Therefore, the aim of this study was to identify the phenolic compounds of ethanolic walnut kernel and its antimicrobial effect on some food-borne pathogens. Methods: In this experimental study, after collection of walnut kernel, its ethanolic extract was prepared. Then its antimicrobial activity on salmonella typhimurium, shigella disentriae, listeria monocytogenes was examined as Minimum Inhibitory Concentration (MIC and Minimum Bactericidal Concentration (MBC using microdilution method. Chloramphenicol (30µg was used as the reference antimicrobial agent. Total phenols, flavonoids and flavonols were also determined by colorimetric method. Results: The results showed that MIC was between 0.625 and 1.25 mg/ml and MBC was between 1.25 and 2.5mg/ml for ethanolic extract. Total phenols were 365±14.71mg/g gallic acid equivalent, and total flavonoids and flavonols were 285±12.25 and 132± 1.63mg/g rutin equivalent, respectively. Conclusion: These findings showed that walnut kernel has antibacterial effects on three aforementioned bacteria and can substitute for chemical preservatives. More studies, such as examinations in food models are needed to unravel the antimicrobial effects of this plant.

  15. A kernel plus method for quantifying wind turbine performance upgrades

    KAUST Repository

    Lee, Giwhyun

    2014-04-21

    Power curves are commonly estimated using the binning method recommended by the International Electrotechnical Commission, which primarily incorporates wind speed information. When such power curves are used to quantify a turbine\\'s upgrade, the results may not be accurate because many other environmental factors in addition to wind speed, such as temperature, air pressure, turbulence intensity, wind shear and humidity, all potentially affect the turbine\\'s power output. Wind industry practitioners are aware of the need to filter out effects from environmental conditions. Toward that objective, we developed a kernel plus method that allows incorporation of multivariate environmental factors in a power curve model, thereby controlling the effects from environmental factors while comparing power outputs. We demonstrate that the kernel plus method can serve as a useful tool for quantifying a turbine\\'s upgrade because it is sensitive to small and moderate changes caused by certain turbine upgrades. Although we demonstrate the utility of the kernel plus method in this specific application, the resulting method is a general, multivariate model that can connect other physical factors, as long as their measurements are available, with a turbine\\'s power output, which may allow us to explore new physical properties associated with wind turbine performance. © 2014 John Wiley & Sons, Ltd.

  16. Fast 2D Complex Gabor Filter With Kernel Decomposition.

    Science.gov (United States)

    Kim, Jaeyoon; Um, Suhyuk; Min, Dongbo

    2018-04-01

    2D complex Gabor filtering has found numerous applications in the fields of computer vision and image processing. Especially, in some applications, it is often needed to compute 2D complex Gabor filter bank consisting of filtering outputs at multiple orientations and frequencies. Although several approaches for fast Gabor filtering have been proposed, they focus primarily on reducing the runtime for performing filtering once at specific orientation and frequency. To obtain the Gabor filter bank, the existing methods are repeatedly applied with respect to multiple orientations and frequencies. In this paper, we propose a novel approach that efficiently computes the 2D complex Gabor filter bank by reducing the computational redundancy that arises when performing filtering at multiple orientations and frequencies. The proposed method first decomposes the Gabor kernel to allow a fast convolution with the Gaussian kernel in a separable manner. This enables reducing the runtime of the Gabor filter bank by reusing intermediate results computed at a specific orientation. By extending this idea, we also propose a fast approach for 2D localized sliding discrete Fourier transform that uses the Gaussian kernel in order to lend spatial localization ability as in the Gabor filter. Experimental results demonstrate that the proposed method runs faster than the state-of-the-art methods, while maintaining similar filtering quality.

  17. The heat kernel as the pagerank of a graph

    Science.gov (United States)

    Chung, Fan

    2007-01-01

    The concept of pagerank was first started as a way for determining the ranking of Web pages by Web search engines. Based on relations in interconnected networks, pagerank has become a major tool for addressing fundamental problems arising in general graphs, especially for large information networks with hundreds of thousands of nodes. A notable notion of pagerank, introduced by Brin and Page and denoted by PageRank, is based on random walks as a geometric sum. In this paper, we consider a notion of pagerank that is based on the (discrete) heat kernel and can be expressed as an exponential sum of random walks. The heat kernel satisfies the heat equation and can be used to analyze many useful properties of random walks in a graph. A local Cheeger inequality is established, which implies that, by focusing on cuts determined by linear orderings of vertices using the heat kernel pageranks, the resulting partition is within a quadratic factor of the optimum. This is true, even if we restrict the volume of the small part separated by the cut to be close to some specified target value. This leads to a graph partitioning algorithm for which the running time is proportional to the size of the targeted volume (instead of the size of the whole graph).

  18. Kernel regression for fMRI pattern prediction.

    Science.gov (United States)

    Chu, Carlton; Ni, Yizhao; Tan, Geoffrey; Saunders, Craig J; Ashburner, John

    2011-05-15

    This paper introduces two kernel-based regression schemes to decode or predict brain states from functional brain scans as part of the Pittsburgh Brain Activity Interpretation Competition (PBAIC) 2007, in which our team was awarded first place. Our procedure involved image realignment, spatial smoothing, detrending of low-frequency drifts, and application of multivariate linear and non-linear kernel regression methods: namely kernel ridge regression (KRR) and relevance vector regression (RVR). RVR is based on a Bayesian framework, which automatically determines a sparse solution through maximization of marginal likelihood. KRR is the dual-form formulation of ridge regression, which solves regression problems with high dimensional data in a computationally efficient way. Feature selection based on prior knowledge about human brain function was also used. Post-processing by constrained deconvolution and re-convolution was used to furnish the prediction. This paper also contains a detailed description of how prior knowledge was used to fine tune predictions of specific "feature ratings," which we believe is one of the key factors in our prediction accuracy. The impact of pre-processing was also evaluated, demonstrating that different pre-processing may lead to significantly different accuracies. Although the original work was aimed at the PBAIC, many techniques described in this paper can be generally applied to any fMRI decoding works to increase the prediction accuracy. Published by Elsevier Inc.

  19. Multiple Kernel Learning for adaptive graph regularized nonnegative matrix factorization

    KAUST Repository

    Wang, Jim Jing-Yan

    2012-01-01

    Nonnegative Matrix Factorization (NMF) has been continuously evolving in several areas like pattern recognition and information retrieval methods. It factorizes a matrix into a product of 2 low-rank non-negative matrices that will define parts-based, and linear representation of non-negative data. Recently, Graph regularized NMF (GrNMF) is proposed to find a compact representation, which uncovers the hidden semantics and simultaneously respects the intrinsic geometric structure. In GNMF, an affinity graph is constructed from the original data space to encode the geometrical information. In this paper, we propose a novel idea which engages a Multiple Kernel Learning approach into refining the graph structure that reflects the factorization of the matrix and the new data space. The GrNMF is improved by utilizing the graph refined by the kernel learning, and then a novel kernel learning method is introduced under the GrNMF framework. Our approach shows encouraging results of the proposed algorithm in comparison to the state-of-the-art clustering algorithms like NMF, GrNMF, SVD etc.

  20. Averaging kernels for DOAS total-column satellite retrievals

    Directory of Open Access Journals (Sweden)

    H. J. Eskes

    2003-01-01

    Full Text Available The Differential Optical Absorption Spectroscopy (DOAS method is used extensively to retrieve total column amounts of trace gases based on UV-visible measurements of satellite spectrometers, such as ERS-2 GOME. In practice the sensitivity of the instrument to the tracer density is strongly height dependent, especially in the troposphere. The resulting tracer profile dependence may introduce large systematic errors in the retrieved columns that are difficult to quantify without proper additional information, as provided by the averaging kernel (AK. In this paper we discuss the DOAS retrieval method in the context of the general retrieval theory as developed by Rodgers. An expression is derived for the DOAS AK for optically thin absorbers. It is shown that the comparison with 3D chemistry-transport models and independent profile measurements, based on averaging kernels, is no longer influenced by errors resulting from a priori profile assumptions. The availability of averaging kernel information as part of the total column retrieval product is important for the interpretation of the observations, and for applications like chemical data assimilation and detailed satellite validation studies.

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

    NARCIS (Netherlands)

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

    2010-01-01

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

  2. Women in migration.

    Science.gov (United States)

    Morokvasic, M

    1984-01-01

    This special issue reflects the belated but growing scholarly appreciation of the specificity and importance of women in migration. Aside from the sheer numerical significance of female migration documented in this issue, women migrants encounter problems and make special contributions which render comprehension of their specificity critical to an understanding of international migration in general. In an introductory essay, Morokvasic surveys the state of knowledge concerning women in migration. The focus then shifts, in Part II, to regional and national case studies which collectively elucidate the multifaceted dimensions of the women in migration research issue through time and space. In Part III, an international comparison of female immigrants and their labor market characteristics reveals striking similarities but also important differences. The US Canada and Australia can be discretely compared through 5 census-based quantitative analyses. The role of migrant women in the labor market is also the theme of Part IV. But the 5 studies comprising this section are based on survey research or on discernible global trends in migration and employment. Part V is devoted to the theme of female rural to urban migration in the Third World.

  3. Temporary Migration and Economic Assimilation

    OpenAIRE

    Dustmann, Christian

    2000-01-01

    In this paper, I study temporary migrations, and its consequences for immigrants' behaviour. I distinguish between temporary migrations where the return time is exogenous, and temporary migrations where the migrant chooses when to return. I then illustrate the consequences both types of temporary migration have for migrants' behaviour (as opposed to a permanent migration). If migrations are non-permanent, then this has also consequences for the way empirical models need to be specified. The p...

  4. Combined multi-kernel head computed tomography images optimized for depicting both brain parenchyma and bone.

    Science.gov (United States)

    Takagi, Satoshi; Nagase, Hiroyuki; Hayashi, Tatsuya; Kita, Tamotsu; Hayashi, Katsumi; Sanada, Shigeru; Koike, Masayuki

    2014-01-01

    The hybrid convolution kernel technique for computed tomography (CT) is known to enable the depiction of an image set using different window settings. Our purpose was to decrease the number of artifacts in the hybrid convolution kernel technique for head CT and to determine whether our improved combined multi-kernel head CT images enabled diagnosis as a substitute for both brain (low-pass kernel-reconstructed) and bone (high-pass kernel-reconstructed) images. Forty-four patients with nondisplaced skull fractures were included. Our improved multi-kernel images were generated so that pixels of >100 Hounsfield unit in both brain and bone images were composed of CT values of bone images and other pixels were composed of CT values of brain images. Three radiologists compared the improved multi-kernel images with bone images. The improved multi-kernel images and brain images were identically displayed on the brain window settings. All three radiologists agreed that the improved multi-kernel images on the bone window settings were sufficient for diagnosing skull fractures in all patients. This improved multi-kernel technique has a simple algorithm and is practical for clinical use. Thus, simplified head CT examinations and fewer images that need to be stored can be expected.

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

    Science.gov (United States)

    Tezuka, Taro

    2018-06-01

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

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

    Directory of Open Access Journals (Sweden)

    Boćanski Jan

    2010-01-01

    Full Text Available Utilization of heterosis requires the study of combining abilities of potential parents. In view of this, the objective of this paper was to study combining abilities and determine the mode of inheritance and gene effects for the main agronomic character, grain yield, and its components, kernel row number and kernel number per row. Six inbred lines were used in the study, three of which originated in the U.S., while the other three were developed at the Institute of Field and Vegetable Crops in Novi Sad. Kernel row number was inherited by superdominance, partial dominance, complete dominance and intermediacy. The mode of inheritance of kernel number per row and grain yield was superdominance. Additive gene action had the greatest influence on the expression of kernel row number, while the other two traits were influenced the most by nonadditive gene.

  7. Labor migration in Asia.

    Science.gov (United States)

    Martin, P L

    1991-01-01

    "A recent conference sponsored by the United Nations Center for Regional Development (UNCRD) in Nagoya, Japan examined the growing importance of labor migration for four major Asian labor importers (Japan, Hong Kong, Malaysia, and Singapore) and five major labor exporters (Bangladesh, Korea, Pakistan, Philippines, and Thailand).... The conference concluded that international labor migration would increase within Asia because the tight labor markets and rising wages which have stimulated Japanese investment in other Asian nations, for example, have not been sufficient to eliminate migration push and pull forces...." excerpt

  8. Internal migration: why do Filipinos move?

    Science.gov (United States)

    Jolipa, N

    1980-01-01

    scholars and researchers are the following: urbanization is an inevitable and irreversible process, and it is wise to plan for it; the problem is not rapid urbanization but unbalanced urbanization, i.e., the concentration of urbanization in Metro Manila; steps to alter national urban patterns might include establishing a migration guidance office; the need exists for an explicit, firm, and consistent population distribution policy; and solutions that anticipate problems having to do with internal migration and prevent these problems from arising will, in the long run, be more effective than curative solutions.

  9. Samtidskunst og migration

    DEFF Research Database (Denmark)

    Petersen, Anne Ring

    2010-01-01

    "Samtidskunst og migration. En oversigt over faglitteraturen" er en forskningsoversigt der gør status over hvad der hidtil er skrevet inden for det kunsthistoriske område om vor tids billedkunst og migration som politisk, socialt og kulturelt fænomen, primært i forbindelse med immigration til Eur...... Europa og i bredere forstand Vesten. Rapporten er en intern rapport som er lavet i tilknytning til det kollektive forskningsprojekt "Islam i europæisk litteratur" på Afdelingen for Litteraturvidenskab og Moderne Kultur og indleveret til dette projekts leder, Peter Madsen......."Samtidskunst og migration. En oversigt over faglitteraturen" er en forskningsoversigt der gør status over hvad der hidtil er skrevet inden for det kunsthistoriske område om vor tids billedkunst og migration som politisk, socialt og kulturelt fænomen, primært i forbindelse med immigration til...

  10. Migration og etnicitet

    DEFF Research Database (Denmark)

    Christiansen, Connie Carøe

    2004-01-01

    Migration og etnicitet er aktuelle og forbundne fænomener, idet migration øger berøringsfladerne mellem befolkningsgrupper. Etniciteter formes i takt med at grænser drages imellem disse grupper. Imod moderniserings-teoriernes forventning forsvandt etnicitet ikke som en traditionel eller oprindelig...... måde at skabe tilhørsforhold på; globalt set fremstår vor tid istedet som en "migrationens tidsalder", der tilsyneladende også er en tidsalder, hvor kulturelle særtræk, i form af etnicitet, udgør vigtige linjer, hvorefter grupper skilller sig ud fra hinanden. Både migration og etnicitet bringer fokus...... den finder sted i modtagerlandet, men nyere perspektiver på migration, som begreber om medborgerskab, transnationalisme og diaspora er eksponenter for, søger udover den nationalstatslige ramme og inddrager konsekvenserne af migrationen for afsenderlande....

  11. Migration and intervening opportunities.

    Science.gov (United States)

    Denslow, D A; Eaton, P J

    1984-10-01

    An analysis of factors affecting migration is presented. The authors "extend the investigation of the roles of information, intervening opportunities, and psychic costs by focussing on differences in migrant destinations with respect to the deterring effect of distance. [They develop] a reservation-wage model of migration which implies that the distance effect is weaker for high-wage destinations and stronger for low-wage destinations." The model is tested using data for Brazil, Japan, Mexico, the United States, and Venezuela. excerpt

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

    Science.gov (United States)

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

    2018-02-01

    This study aims to find out the nutrient residue of palm kernel cake from mannan extraction on broiler chicken by evaluating physical quality (specific gravity, bulk density and compacted bulk density), chemical quality (proximate analysis and Van Soest Test) and biological test (metabolizable energy). Treatment composed of T0 : palm kernel cake extracted aquadest (control), T1 : palm kernel cake extracted acetic acid (CH3COOH) 1%, T2 : palm kernel cake extracted aquadest + mannanase enzyme 100 u/l and T3 : palm kernel cake extracted acetic acid (CH3COOH) 1% + enzyme mannanase 100 u/l. The results showed that mannan extraction had significant effect (Pchickens. It can be concluded that extraction with aquadest + enzyme mannanase 100 u/l yields the best nutrient quality of palm kernel cake residue for broiler chicken.

  13. Repeat migration and disappointment.

    Science.gov (United States)

    Grant, E K; Vanderkamp, J

    1986-01-01

    This article investigates the determinants of repeat migration among the 44 regions of Canada, using information from a large micro-database which spans the period 1968 to 1971. The explanation of repeat migration probabilities is a difficult task, and this attempt is only partly successful. May of the explanatory variables are not significant, and the overall explanatory power of the equations is not high. In the area of personal characteristics, the variables related to age, sex, and marital status are generally significant and with expected signs. The distance variable has a strongly positive effect on onward move probabilities. Variables related to prior migration experience have an important impact that differs between return and onward probabilities. In particular, the occurrence of prior moves has a striking effect on the probability of onward migration. The variable representing disappointment, or relative success of the initial move, plays a significant role in explaining repeat migration probabilities. The disappointment variable represents the ratio of actural versus expected wage income in the year after the initial move, and its effect on both repeat migration probabilities is always negative and almost always highly significant. The repeat probabilities diminish after a year's stay in the destination region, but disappointment in the most recent year still has a bearing on the delayed repeat probabilities. While the quantitative impact of the disappointment variable is not large, it is difficult to draw comparisons since similar estimates are not available elsewhere.

  14. Neutrophil Reverse Migration Becomes Transparent with Zebrafish

    Directory of Open Access Journals (Sweden)

    Taylor W. Starnes

    2012-01-01

    Full Text Available The precise control of neutrophil-mediated inflammation is critical for both host defense and the prevention of immunopathology. In vivo imaging studies in zebrafish, and more recently in mice, have made the novel observation that neutrophils leave a site of inflammation through a process called neutrophil reverse migration. The application of advanced imaging techniques to the genetically tractable, optically transparent zebrafish larvae was critical for these advances. Still, the mechanisms underlying neutrophil reverse migration and its effects on the resolution or priming of immune responses remain unclear. Here, we review the current knowledge of neutrophil reverse migration, its potential roles in host immunity, and the live imaging tools that make zebrafish a valuable model for increasing our knowledge of neutrophil behavior in vivo.

  15. Lagged kernel machine regression for identifying time windows of susceptibility to exposures of complex mixtures.

    Science.gov (United States)

    Liu, Shelley H; Bobb, Jennifer F; Lee, Kyu Ha; Gennings, Chris; Claus Henn, Birgit; Bellinger, David; Austin, Christine; Schnaas, Lourdes; Tellez-Rojo, Martha M; Hu, Howard; Wright, Robert O; Arora, Manish; Coull, Brent A

    2017-09-06

    The impact of neurotoxic chemical mixtures on children's health is a critical public health concern. It is well known that during early life, toxic exposures may impact cognitive function during critical time intervals of increased vulnerability, known as windows of susceptibility. Knowledge on time windows of susceptibility can help inform treatment and prevention strategies, as chemical mixtures may affect a developmental process that is operating at a specific life phase. There are several statistical challenges in estimating the health effects of time-varying exposures to multi-pollutant mixtures, such as: multi-collinearity among the exposures both within time points and across time points, and complex exposure-response relationships. To address these concerns, we develop a flexible statistical method, called lagged kernel machine regression (LKMR). LKMR identifies critical exposure windows of chemical mixtures, and accounts for complex non-linear and non-additive effects of the mixture at any given exposure window. Specifically, LKMR estimates how the effects of a mixture of exposures change with the exposure time window using a Bayesian formulation of a grouped, fused lasso penalty within a kernel machine regression (KMR) framework. A simulation study demonstrates the performance of LKMR under realistic exposure-response scenarios, and demonstrates large gains over approaches that consider each time window separately, particularly when serial correlation among the time-varying exposures is high. Furthermore, LKMR demonstrates gains over another approach that inputs all time-specific chemical concentrations together into a single KMR. We apply LKMR to estimate associations between neurodevelopment and metal mixtures in Early Life Exposures in Mexico and Neurotoxicology, a prospective cohort study of child health in Mexico City. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  16. Kernel Risk-Sensitive Loss: Definition, Properties and Application to Robust Adaptive Filtering

    OpenAIRE

    Chen, Badong; Xing, Lei; Xu, Bin; Zhao, Haiquan; Zheng, Nanning; Principe, Jose C.

    2016-01-01

    Nonlinear similarity measures defined in kernel space, such as correntropy, can extract higher-order statistics of data and offer potentially significant performance improvement over their linear counterparts especially in non-Gaussian signal processing and machine learning. In this work, we propose a new similarity measure in kernel space, called the kernel risk-sensitive loss (KRSL), and provide some important properties. We apply the KRSL to adaptive filtering and investigate the robustnes...

  17. Review of Palm Kernel Oil Processing And Storage Techniques In South East Nigeria

    OpenAIRE

    Okeke CG; Oluka SI

    2017-01-01

    An assessment of palm kernel processing and storage in South-Eastern Nigeria was carried out by investigative survey approach. The survey basically ascertained the extent of mechanization applicable in the area to enable the palm kernel processors and agricultural policy makers, device the modalities for improving palm kernel processing in the area. According to the results obtained from the study, in Abia state, 85% of the respondents use mechanical method while 15% use manual method in crac...

  18. Human rights and migration policies.

    Science.gov (United States)

    Marmora, L

    1990-01-01

    This paper concerns the history of migration, migration policies, and the rights of migrants in Latin America from 1500 to the present. In the first part of the article, the author identifies and discusses the basic rights of migrants. In the second part, migration policies, migration flows, and the treatment of migrants are examined over time.

  19. Managing migration: the Brazilian case

    OpenAIRE

    Eduardo L. G. Rios-Neto

    2005-01-01

    The objective of this paper is to present the Brazilian migration experience and its relationship with migration management. The article is divided into three parts. First, it reviews some basic facts regarding Brazilian immigration and emigration processes. Second, it focuses on some policy and legal issues related to migration. Finally, it addresses five issues regarding migration management in Brazil.

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

    Directory of Open Access Journals (Sweden)

    Yulin Jian

    2017-06-01

    Full Text Available 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.

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

    Science.gov (United States)

    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.

  2. Pencil kernel correction and residual error estimation for quality-index-based dose calculations

    International Nuclear Information System (INIS)

    Nyholm, Tufve; Olofsson, Joergen; Ahnesjoe, Anders; Georg, Dietmar; Karlsson, Mikael

    2006-01-01

    Experimental data from 593 photon beams were used to quantify the errors in dose calculations using a previously published pencil kernel model. A correction of the kernel was derived in order to remove the observed systematic errors. The remaining residual error for individual beams was modelled through uncertainty associated with the kernel model. The methods were tested against an independent set of measurements. No significant systematic error was observed in the calculations using the derived correction of the kernel and the remaining random errors were found to be adequately predicted by the proposed method

  3. Kernel Principal Component Analysis and its Applications in Face Recognition and Active Shape Models

    OpenAIRE

    Wang, Quan

    2012-01-01

    Principal component analysis (PCA) is a popular tool for linear dimensionality reduction and feature extraction. Kernel PCA is the nonlinear form of PCA, which better exploits the complicated spatial structure of high-dimensional features. In this paper, we first review the basic ideas of PCA and kernel PCA. Then we focus on the reconstruction of pre-images for kernel PCA. We also give an introduction on how PCA is used in active shape models (ASMs), and discuss how kernel PCA can be applied ...

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

    Science.gov (United States)

    Jiang, Wenhao; Chung, Fu-lai

    2014-01-01

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

  5. Participation of cob tissue in the transport of medium components into maize kernels cultured in vitro

    International Nuclear Information System (INIS)

    Felker, F.C.

    1990-01-01

    Maize (Zea mays L.) kernels cultured in vitro while still attached to cob pieces have been used as a model system to study the physiology of kernel development. In this study, the role of the cob tissue in uptake of medium components into kernels was examined. Cob tissue was essential for in vitro kernel growth, and better growth occurred with larger cob/kernel ratios. A symplastically transported fluorescent dye readily permeated the endosperm when supplied in the medium, while an apoplastic dye did not. Slicing the cob tissue to disrupt vascular connections, but not apoplastic continuity, greatly reduced [ 14 C]sucrose uptake into kernels. [ 14 C]Sucrose uptake by cob and kernel tissue was reduced 31% and 68%, respectively, by 5 mM PCMBS. L-[ 14 C]glucose was absorbed much more slowly than D-[ 14 C]glucose. These and other results indicate that phloem loading of sugars occurs in the cob tissue. Passage of medium components through the symplast cob tissue may be a prerequisite for uptake into the kernel. Simple diffusion from the medium to the kernels is unlikely. Therefore, the ability of substances to be transported into cob tissue cells should be considered in formulating culture medium

  6. Distributed smoothed tree kernel for protein-protein interaction extraction from the biomedical literature.

    Directory of Open Access Journals (Sweden)

    Gurusamy Murugesan

    Full Text Available Automatic extraction of protein-protein interaction (PPI pairs from biomedical literature is a widely examined task in biological information extraction. Currently, many kernel based approaches such as linear kernel, tree kernel, graph kernel and combination of multiple kernels has achieved promising results in PPI task. However, most of these kernel methods fail to capture the semantic relation information between two entities. In this paper, we present a special type of tree kernel for PPI extraction which exploits both syntactic (structural and semantic vectors information known as Distributed Smoothed Tree kernel (DSTK. DSTK comprises of distributed trees with syntactic information along with distributional semantic vectors representing semantic information of the sentences or phrases. To generate robust machine learning model composition of feature based kernel and DSTK were combined using ensemble support vector machine (SVM. Five different corpora (AIMed, BioInfer, HPRD50, IEPA, and LLL were used for evaluating the performance of our system. Experimental results show that our system achieves better f-score with five different corpora compared to other state-of-the-art systems.

  7. Kernel modifications and tryptophan content in QPM segregating generations

    Directory of Open Access Journals (Sweden)

    -Ignjatović-Micić Dragana

    2010-01-01

    Full Text Available Maize has poor nutritional value due to deficiency of two essential amino acids - tryptophan and lysine. Although recessive opaque2 (o2 mutation significantly increases their content in the endosperm, incorporation of opaque2 into high yielding cultivars was not commercially successful, because of its numerous agronomic and processing problems due to soft endosperm. Quality protein maize - QPM has lately been introduced as opaque2 maize with improved endosperm hardness and improved agronomic traits, but mostly within tropical and subtropical germplasm. The ongoing breeding project at MRI includes improvement of MRI opaque2 lines and conversion of standard lines to QPM germplasm. The main selection steps in QPM breeding involve assessing kernel modifications and tryptophan level in each generation. Herein, we present the results of the analysis for these traits on F3 and BC1F1 generations of QPM x opaque2, opaque2 x QPM and standard lines x QPM crosses. The results showed that the majority the genotypes had kernel types 2 and 3 (good modifications. The whole grain tryptophan content in F3 and BC1F1 genotypes of crosses between QPM and opaque2 germplasm was at the quality protein level, with a few exceptions. All BC1F1 genotypes of standard lines x QPM had tryptophan content in the range of normal maize, while majority of F3 genotypes had tryptophan content at level of QPM. The progeny (with increased tryptophan levels of QPM and opaque2 crosses had significantly higher tryptophan content compared to the progeny of crosses between standard and QPM lines - 0.098 to 0.114 and 0.080, respectively. All genotypes that had poorly modified kernels and/or low tryptophan content will be discarded from further breeding.

  8. Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models

    Science.gov (United States)

    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

  9. A new kernel discriminant analysis framework for electronic nose recognition

    International Nuclear Information System (INIS)

    Zhang, Lei; Tian, Feng-Chun

    2014-01-01

    Graphical abstract: - Highlights: • This paper proposes a new discriminant analysis framework for feature extraction and recognition. • The principle of the proposed NDA is derived mathematically. • The NDA framework is coupled with kernel PCA for classification. • The proposed KNDA is compared with state of the art e-Nose recognition methods. • The proposed KNDA shows the best performance in e-Nose experiments. - Abstract: Electronic nose (e-Nose) technology based on metal oxide semiconductor gas sensor array is widely studied for detection of gas components. This paper proposes a new discriminant analysis framework (NDA) for dimension reduction and e-Nose recognition. In a NDA, the between-class and the within-class Laplacian scatter matrix are designed from sample to sample, respectively, to characterize the between-class separability and the within-class compactness by seeking for discriminant matrix to simultaneously maximize the between-class Laplacian scatter and minimize the within-class Laplacian scatter. In terms of the linear separability in high dimensional kernel mapping space and the dimension reduction of principal component analysis (PCA), an effective kernel PCA plus NDA method (KNDA) is proposed for rapid detection of gas mixture components by an e-Nose. The NDA framework is derived in this paper as well as the specific implementations of the proposed KNDA method in training and recognition process. The KNDA is examined on the e-Nose datasets of six kinds of gas components, and compared with state of the art e-Nose classification methods. Experimental results demonstrate that the proposed KNDA method shows the best performance with average recognition rate and total recognition rate as 94.14% and 95.06% which leads to a promising feature extraction and multi-class recognition in e-Nose

  10. A point kernel algorithm for microbeam radiation therapy

    Science.gov (United States)

    Debus, Charlotte; Oelfke, Uwe; Bartzsch, Stefan

    2017-11-01

    Microbeam radiation therapy (MRT) is a treatment approach in radiation therapy where the treatment field is spatially fractionated into arrays of a few tens of micrometre wide planar beams of unusually high peak doses separated by low dose regions of several hundred micrometre width. In preclinical studies, this treatment approach has proven to spare normal tissue more effectively than conventional radiation therapy, while being equally efficient in tumour control. So far dose calculations in MRT, a prerequisite for future clinical applications are based on Monte Carlo simulations. However, they are computationally expensive, since scoring volumes have to be small. In this article a kernel based dose calculation algorithm is presented that splits the calculation into photon and electron mediated energy transport, and performs the calculation of peak and valley doses in typical MRT treatment fields within a few minutes. Kernels are analytically calculated depending on the energy spectrum and material composition. In various homogeneous materials peak, valley doses and microbeam profiles are calculated and compared to Monte Carlo simulations. For a microbeam exposure of an anthropomorphic head phantom calculated dose values are compared to measurements and Monte Carlo calculations. Except for regions close to material interfaces calculated peak dose values match Monte Carlo results within 4% and valley dose values within 8% deviation. No significant differences are observed between profiles calculated by the kernel algorithm and Monte Carlo simulations. Measurements in the head phantom agree within 4% in the peak and within 10% in the valley region. The presented algorithm is attached to the treatment planning platform VIRTUOS. It was and is used for dose calculations in preclinical and pet-clinical trials at the biomedical beamline ID17 of the European synchrotron radiation facility in Grenoble, France.

  11. Efficient Kernel-Based Ensemble Gaussian Mixture Filtering

    KAUST Repository

    Liu, Bo

    2015-11-11

    We consider the Bayesian filtering problem for data assimilation following the kernel-based ensemble Gaussian-mixture filtering (EnGMF) approach introduced by Anderson and Anderson (1999). In this approach, the posterior distribution of the system state is propagated with the model using the ensemble Monte Carlo method, providing a forecast ensemble that is then used to construct a prior Gaussian-mixture (GM) based on the kernel density estimator. This results in two update steps: a Kalman filter (KF)-like update of the ensemble members and a particle filter (PF)-like update of the weights, followed by a resampling step to start a new forecast cycle. After formulating EnGMF for any observational operator, we analyze the influence of the bandwidth parameter of the kernel function on the covariance of the posterior distribution. We then focus on two aspects: i) the efficient implementation of EnGMF with (relatively) small ensembles, where we propose a new deterministic resampling strategy preserving the first two moments of the posterior GM to limit the sampling error; and ii) the analysis of the effect of the bandwidth parameter on contributions of KF and PF updates and on the weights variance. Numerical results using the Lorenz-96 model are presented to assess the behavior of EnGMF with deterministic resampling, study its sensitivity to different parameters and settings, and evaluate its performance against ensemble KFs. The proposed EnGMF approach with deterministic resampling suggests improved estimates in all tested scenarios, and is shown to require less localization and to be less sensitive to the choice of filtering parameters.

  12. A class of kernel based real-time elastography algorithms.

    Science.gov (United States)

    Kibria, Md Golam; Hasan, Md Kamrul

    2015-08-01

    In this paper, a novel real-time kernel-based and gradient-based Phase Root Seeking (PRS) algorithm for ultrasound elastography is proposed. The signal-to-noise ratio of the strain image resulting from this method is improved by minimizing the cross-correlation discrepancy between the pre- and post-compression radio frequency signals with an adaptive temporal stretching method and employing built-in smoothing through an exponentially weighted neighborhood kernel in the displacement calculation. Unlike conventional PRS algorithms, displacement due to tissue compression is estimated from the root of the weighted average of the zero-lag cross-correlation phases of the pair of corresponding analytic pre- and post-compression windows in the neighborhood kernel. In addition to the proposed one, the other time- and frequency-domain elastography algorithms (Ara et al., 2013; Hussain et al., 2012; Hasan et al., 2012) proposed by our group are also implemented in real-time using Java where the computations are serially executed or parallely executed in multiple processors with efficient memory management. Simulation results using finite element modeling simulation phantom show that the proposed method significantly improves the strain image quality in terms of elastographic signal-to-noise ratio (SNRe), elastographic contrast-to-noise ratio (CNRe) and mean structural similarity (MSSIM) for strains as high as 4% as compared to other reported techniques in the literature. Strain images obtained for the experimental phantom as well as in vivo breast data of malignant or benign masses also show the efficacy of our proposed method over the other reported techniques in the literature. Copyright © 2015 Elsevier B.V. All rights reserved.

  13. 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 .

  14. Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models.

    Science.gov (United States)

    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.

  15. Completion of the Kernel of the Differentiation Operator

    Directory of Open Access Journals (Sweden)

    Anatoly N. Morozov

    2017-01-01

    Full Text Available When investigating piecewise polynomial approximations in spaces \\(L_p, \\; 0~<~p~<~1,\\ the author considered the spreading of k-th derivative (of the operator from Sobolev spaces \\(W_1 ^ k\\ on spaces that are, in a sense, their successors with a low index less than one. In this article, we continue the study of the properties acquired by the differentiation operator \\(\\Lambda\\ with spreading beyond the space \\(W_1^1\\ $$\\Lambda~:~W_1^1~\\mapsto~L_1,\\; \\Lambda f = f^{\\;'} $$.The study is conducted by introducing the family of spaces \\(Y_p^1, \\; 0

    kernel.During the spreading of the differentiation operator from the space \\( C ^ 1 \\ on the space \\( W_p ^ 1 \\ the kernel does not change. In the article, it is constructively shown that jump functions and singular functions \\(f\\ belong to all spaces \\( Y_p ^ 1 \\ and \\(\\Lambda f = 0.\\ Consequently, the space of the functions of the bounded variation \\(H_1 ^ 1 \\ is contained in each \\( Y_p ^ 1 ,\\ and the differentiation operator on \\(H_1^1\\ satisfies the relation \\(\\Lambda f = f^{\\; '}.\\Also, we come to the conclusion that every function from the added part of the kernel can be logically named singular.

  16. Engineering support vector machine kernels that recognize translation initiation sites.

    Science.gov (United States)

    Zien, A; Rätsch, G; Mika, S; Schölkopf, B; Lengauer, T; Müller, K R

    2000-09-01

    In order to extract protein sequences from nucleotide sequences, it is an important step to recognize points at which regions start that code for proteins. These points are called translation initiation sites (TIS). The task of finding TIS can be modeled as a classification problem. We demonstrate the applicability of support vector machines for this task, and show how to incorporate prior biological knowledge by engineering an appropriate kernel function. With the described techniques the recognition performance can be improved by 26% over leading existing approaches. We provide evidence that existing related methods (e.g. ESTScan) could profit from advanced TIS recognition.

  17. The Application of Tamarind Kernel Powder in the Mango Sauce

    OpenAIRE

    koosamart Wayu; Veerasugwanit Nutha; Jittanit Weerachet

    2016-01-01

    Tamarind seed has been well-known as a perfect source of xyloglucan that has functional properties that can be applied in food products. In this research, the tamarind seeds were processed to be tamarind kernel powder (TKP) and then it was added into the mango sauce as the stabilizer. The aim was to study the effects of using TKP as the stabilizer on the quality of mango sauce in comparison with the application of xanthan gum that is the common stabilizer of sauce. The mango sauce samples wer...

  18. Removal of Lead from Aqueous Solution by Palm Kernel Fibre

    African Journals Online (AJOL)

    NJD

    Effect of pH on Adsorption. A 400 cm3 volume of 90 mg dm–3 lead(II) solution was added to four 1 dm3 flasks and the pH of the solutions adjusted to 3, 4, 5 and 6 using 0.1 M HCl or NaOH, measured with a pH meter. (WTW pH Meter 320, Germany). A 1.0 g sample of palm kernel fibre was added to each 400 cm3 volume of ...

  19. Nonlinear Knowledge in Kernel-Based Multiple Criteria Programming Classifier

    Science.gov (United States)

    Zhang, Dongling; Tian, Yingjie; Shi, Yong

    Kernel-based Multiple Criteria Linear Programming (KMCLP) model is used as classification methods, which can learn from training examples. Whereas, in traditional machine learning area, data sets are classified only by prior knowledge. Some works combine the above two classification principle to overcome the defaults of each approach. In this paper, we propose a model to incorporate the nonlinear knowledge into KMCLP in order to solve the problem when input consists of not only training example, but also nonlinear prior knowledge. In dealing with real world case breast cancer diagnosis, the model shows its better performance than the model solely based on training data.

  20. Acute cyanide poisoning due to apricot kernel ingestion

    Directory of Open Access Journals (Sweden)

    Mehmet Tatli

    2017-02-01

    Full Text Available Cyanide is a toxin and one of the most rapidly acting fatal poisons that human being is aware. If it is not treated promptly, encountering to cyanide poison will lead to die in minutes. Cyanide avoids cellular oxygen usage by inactivating mitochondrial cytochrome oxidase thus inhibits cellular respiration. In this case, we represent a case report describing uncommon cyanide intoxication owing to consumption of a few portion of apricot kernels and its rapid treatment with dicobalt edetate after suspection of cyanide poisoning.