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Sample records for crust-mantle system inferred

  1. Long-Term Evolution of the Martian Crust-Mantle System

    NASA Astrophysics Data System (ADS)

    Grott, M.; Baratoux, D.; Hauber, E.; Sautter, V.; Mustard, J.; Gasnault, O.; Ruff, S. W.; Karato, S.-I.; Debaille, V.; Knapmeyer, M.; Sohl, F.; Van Hoolst, T.; Breuer, D.; Morschhauser, A.; Toplis, M. J.

    2013-01-01

    Lacking plate tectonics and crustal recycling, the long-term evolution of the crust-mantle system of Mars is driven by mantle convection, partial melting, and silicate differentiation. Volcanic landforms such as lava flows, shield volcanoes, volcanic cones, pyroclastic deposits, and dikes are observed on the martian surface, and while activity was widespread during the late Noachian and Hesperian, volcanism became more and more restricted to the Tharsis and Elysium provinces in the Amazonian period. Martian igneous rocks are predominantly basaltic in composition, and remote sensing data, in-situ data, and analysis of the SNC meteorites indicate that magma source regions were located at depths between 80 and 150 km, with degrees of partial melting ranging from 5 to 15 %. Furthermore, magma storage at depth appears to be of limited importance, and secular cooling rates of 30 to 40 K Gyr-1 were derived from surface chemistry for the Hesperian and Amazonian periods. These estimates are in general agreement with numerical models of the thermo-chemical evolution of Mars, which predict source region depths of 100 to 200 km, degrees of partial melting between 5 and 20 %, and secular cooling rates of 40 to 50 K Gyr-1. In addition, these model predictions largely agree with elastic lithosphere thickness estimates derived from gravity and topography data. Major unknowns related to the evolution of the crust-mantle system are the age of the shergottites, the planet's initial bulk mantle water content, and its average crustal thickness. Analysis of the SNC meteorites, estimates of the elastic lithosphere thickness, as well as the fact that tidal dissipation takes place in the martian mantle indicate that rheologically significant amounts of water of a few tens of ppm are still present in the interior. However, the exact amount is controversial and estimates range from only a few to more than 200 ppm. Owing to the uncertain formation age of the shergottites it is unclear whether

  2. Metasedimentary and igneous xenoliths from Tallante (Betic Cordillera, Spain): Inferences on crust-mantle interactions and clues for post-collisional volcanism magma sources

    NASA Astrophysics Data System (ADS)

    Bianchini, Gianluca; Braga, Roberto; Langone, Antonio; Natali, Claudio; Tiepolo, Massimo

    2015-04-01

    The deep seated xenolith association exhumed in the Pliocenic volcano of Tallante (Betic Cordillera, Spain) includes protogranular mantle peridotites, felsic (metasedimentary) crustal rocks, as well as cumulus igneous rocks such as norites and amphibole (± phlogopite)-clinopyroxenites. The whole xenolith suite equilibrated at the same pressure (0.7-0.9 GPa) representing the local crust-mantle boundary (MOHO) characterized by extreme lithological heterogeneity. This heterogeneity resulted from orogenic processes that induced the juxtaposition of crustal rocks (variably depleted in fusible components) within mantle domains including metasomes, as it is commonly observed in orogenic mantle massifs of the Mediterranean area. In this contribution, we report new mineral compositions of igneous parageneses recorded in these xenoliths, and we present Sr-Nd isotope data on both igneous and metasedimentary xenoliths that integrate those from the literature. Sr-Nd isotopes coherently indicate a restitic character of the metasedimentary xenoliths, which according to model ages were affected by partial melting in Paleozoic times. Sr-Nd isotopic errorchrons on the igneous xenoliths, on the other hand, qualitatively indicate Tertiary ages, which are corroborated by U-Pb zircon datings of one norite xenolith and two composite xenoliths having zircon-bearing norite veinlets. The new data are discussed proposing that MOHO lithologies of Tallante could provide significant source compositions for the genesis of the Neogene volcanics of the Betic area, which included calcalkaline lavas as well as more potassic products such as lamproites.

  3. Crust-mantle boundaries in the Taiwan - Luzon arc-continent collision system determined from local earthquake tomography and layered Vp models

    NASA Astrophysics Data System (ADS)

    Ustaszewski, K. M.; Wu, Y.; Suppe, J.; Huang, H.; Carena, S.; Chang, C.

    2011-12-01

    We performed 3D mapping of crust-mantle boundaries in the Taiwan-Luzon arc-continent collision zone using a local earthquake tomographic model, providing better insight into the mode of subduction polarity reversal. The mapped crust-mantle discontinuities include three tectonically distinct Mohos. Furthermore, a crust-mantle boundary marks the eastern limit of the Eurasian lower crust against the mantle of the Philippine Sea plate. It dips steeply to the east underneath eastern and southern Taiwan and steepens progressively towards north until it becomes vertical at 23.7°N. From there it continues northward in a slightly overturned orientation, where the limit of the tomographic model at the northern tip of the island prevents further mapping. In order to map several Moho discontinuities, we contoured a surface of constant Vp = 7.5 km s-1 constrained from local earthquake tomography and confined to regions with a minimum of 500 rays per tomography cell. Additional constraints for the Moho were derived from layered (1D) Vp models using P-wave arrivals of local earthquakes recorded at 52 seismic stations, employing a genetic algorithm. The Moho of the Eurasian and the Philippine Sea plates are topologically disconnected across the plate boundary. Beneath southern Taiwan, the Eurasian Moho dips to the E at 50-60°, following the orientation of the plate boundary and continuous with the Benioff zone. Towards north, the Eurasian Moho twists to become subvertical, again together with the plate boundary. At the same time, it steps westward into a more external position underneath the thrust belt, giving way to the north-dipping Philippine Sea plate. The Philippine Sea plate Moho shallows towards the surface along the Longitudinal Valley suture. It forms a synform-like crustal root with an axis parallel to the trend of geological units at surface and it is interpreted as the base of the magmatic Luzon arc. Towards the north, the crustal root deepens from 30 km to about 70

  4. Hydrothermal experiments on serpentinization at crust/mantle boundary

    NASA Astrophysics Data System (ADS)

    Oyanagi, R.; Okamoto, A.; Tsuchiya, N.

    2013-12-01

    Serpentinization commonly proceeds in seafloor hydrothermal systems at mid-ocean ridges, along the bending faults, and at the boundary of wedge mantle and subducting plate. Silica activity are key factors in controlling reaction paths and the rate of serpentinization (e.g., Frost and Beard, 2007; Klein et al., 2009; Ogasawara et al.,2013). However, most of the previous experimental studies focused on bulk solid materials and solutions within the reaction vessel, and local changes of products reaction rate in response to concentration gradient have not been clarified. Ogasawara et al. (2013) conducted hydrothermal experiments in Ol-Opx-H2O system, and modeled the progress of serpentinization by coupled reactions and silica diffusion. In their experiment, reaction product is only serpentine and no talc or brucite were found. In this study, we conducted hydrothermal experiments in olivine (Ol)-quartz (Qtz)-H2O and Ol-plagioclase (Pl)-H2O systems as the analogue of crust/mantle boundary. The condition was 250 degreeC and at a vapor-saturated pressure. Composite powders (composed of Qtz/Ol zone, or Pl/Ol zone) were set in tube-in-tube vessels, and then loaded into autocrave with fluid ( NaOHaq, pH = 13.8 at 25 degreeC ). Runnig time is up to 25 days and maximum water content in the products is 12 wt% H2O. After the experiments, solution chemistry and the extent of serpentinization were analyzed in detail. In the Ol-Qtz-H2O experiments, we observed systematic changes of reaction products in the Ol zone. Smectite and serpentine was formed at Ol-Qtz boundary due to high Na concentration although talc is expected to form in MgO-SiO2-H2O system at Ol-Qtz boundary. Mg/Si ratio of products from EDS analyze shows high Si gradient near the boundary indicate that amount of smectite decreased with increasing distance from the Ol-Qtz boundary and only serpentine zone was observed at ~10mm. At >10mm away from Ol-Qtz boundary, serpentine ( chrysotile nano tubes) and brucite was

  5. Crust-mantle mechanical coupling in Eastern Mediterranean and eastern Turkey.

    PubMed

    Özeren, M Sinan

    2012-05-29

    Present-day crust-mantle coupling in the Eastern Mediterranean and eastern Turkey is studied using the Global Positioning System (GPS) and seismic anisotropy data. The general trend of the shear wave fast-splitting directions in NE Turkey and Lesser Caucaus align well with the geodetic velocities in an absolute plate motion frame of reference pointing to an effective coupling in this part of the region of weak surface deformation. Farther south, underneath the Bitlis Suture, however, there are significant Pn delays with E-W anisotropy axes indicating significant lateral escape. Meanwhile, the GPS reveals very little surface deformation. This mismatch possibly suggests a decoupling along the suture. In the Aegean, the shear wave anisotropy and the Pn anisotropy directions agree with the extensional component of the right-lateral shear strains except under the Crete Basin and other parts of the southern Aegean Sea. This extensional direction matches perfectly also with the southward pulling force vectors across the Hellenic trench; however, the maximum right-lateral shear directions obtained from the GPS data in the Aegean do not match either of these anisotropies. Seismic anisotropy from Rayleigh waves sampled at 15 s, corresponding to the lower crust, match the maximum right-lateral maximum shear directions from the GPS indicating decoupling between the crust and the mantle. This decoupling most likely results from the lateral variations of the gravitational potential energies and the slab-pull forces.

  6. Crust-mantle mechanical coupling in Eastern Mediterranean and Eastern Turkey

    PubMed Central

    Sinan Özeren, M.

    2012-01-01

    Present-day crust-mantle coupling in the Eastern Mediterranean and eastern Turkey is studied using the Global Positioning System (GPS) and seismic anisotropy data. The general trend of the shear wave fast-splitting directions in NE Turkey and Lesser Caucaus align well with the geodetic velocities in an absolute plate motion frame of reference pointing to an effective coupling in this part of the region of weak surface deformation. Farther south, underneath the Bitlis Suture, however, there are significant Pn delays with E-W anisotropy axes indicating significant lateral escape. Meanwhile, the GPS reveals very little surface deformation. This mismatch possibly suggests a decoupling along the suture. In the Aegean, the shear wave anisotropy and the Pn anisotropy directions agree with the extensional component of the right-lateral shear strains except under the Crete Basin and other parts of the southern Aegean Sea. This extensional direction matches perfectly also with the southward pulling force vectors across the Hellenic trench; however, the maximum right-lateral shear directions obtained from the GPS data in the Aegean do not match either of these anisotropies. Seismic anisotropy from Rayleigh waves sampled at 15 s, corresponding to the lower crust, match the maximum right-lateral maximum shear directions from the GPS indicating decoupling between the crust and the mantle. This decoupling most likely results from the lateral variations of the gravitational potential energies and the slab-pull forces. PMID:22592788

  7. Crust-mantle mechanical coupling in Eastern Mediterranean and eastern Turkey.

    PubMed

    Özeren, M Sinan

    2012-05-29

    Present-day crust-mantle coupling in the Eastern Mediterranean and eastern Turkey is studied using the Global Positioning System (GPS) and seismic anisotropy data. The general trend of the shear wave fast-splitting directions in NE Turkey and Lesser Caucaus align well with the geodetic velocities in an absolute plate motion frame of reference pointing to an effective coupling in this part of the region of weak surface deformation. Farther south, underneath the Bitlis Suture, however, there are significant Pn delays with E-W anisotropy axes indicating significant lateral escape. Meanwhile, the GPS reveals very little surface deformation. This mismatch possibly suggests a decoupling along the suture. In the Aegean, the shear wave anisotropy and the Pn anisotropy directions agree with the extensional component of the right-lateral shear strains except under the Crete Basin and other parts of the southern Aegean Sea. This extensional direction matches perfectly also with the southward pulling force vectors across the Hellenic trench; however, the maximum right-lateral shear directions obtained from the GPS data in the Aegean do not match either of these anisotropies. Seismic anisotropy from Rayleigh waves sampled at 15 s, corresponding to the lower crust, match the maximum right-lateral maximum shear directions from the GPS indicating decoupling between the crust and the mantle. This decoupling most likely results from the lateral variations of the gravitational potential energies and the slab-pull forces. PMID:22592788

  8. Early evolution of the crust-mantle system

    NASA Technical Reports Server (NTRS)

    Condie, K. C.

    1985-01-01

    Nd isotopic data indicate that most Archean igneous rocks including compositions ranging from komatiite to tonalite are derived from undepleted or depleted upper mantle sources. If sampling is representative, only a few require enriched sources. A major unresolved question is the fate of the material removed from the upper mantle leaving early depleted sources as residue. One possibility is that widespread depletion of the early mantle resulted from a period of early degassing and magmatism. Rare gas isotopic data, in particular 129Xe/130Xe ratios, seem to demand that the upper mantle was extensively degassed at or before 4.4 b.y. and this led to rapid growth of the atmosphere and oceans. The lower mantle, however, was not significantly degassed during this event. It is likely that such widespread degassing and magmatism of the upper mantle transferred significant quantities of incompatible elements into the uppermost mantle or crust. Once formed, such an enriched fraction should resist recycling into the mantle and collect at or near the Earth's surface. One possibility is that it collects chiefly in a zone of partial melting, analogous to the present low-velocity zone at the base of the lithosphere.

  9. The Crust Mantle Transition Beneath the Variscan Domain of the Iberian Peninsula

    NASA Astrophysics Data System (ADS)

    Carbonell, R.; Díaz, J.; Brown, D.; Palomeras, I.; Martí, D.; Ayarza, P.; Afonso, J.; Simancas, F.; Perez-Estaun, A.; Gallart, J.

    2009-12-01

    The Mohorovičić discontinuity (Moho) was defined by Andrija Mohorovičić in 1910 on the basis of an interpretation of regional earthquake records in Eastern Europe as a relatively abrupt increase in P-wave seismic velocities. The Moho is observed/detected world wide. It is the most important boundary within the Earth's lithosphere. The high resolution subsurface geophysical images have demonstrated that the crust-mantle boundary is a far more complex structure that the initial seismological definition established. The purpose of this contribution is to bring together some of the findings related to the crust-mantle transition beneath the Iberian peninsula and to synthesize these results into a perspective that has global implications. For the last three decades an extensive acquisition of varieties of geophysical and geological data has been carried out. These data include controlled-source seismic (refraction and reflection), natural source seismic, and regional geology. In NW Iberia an analog of the continental crust-mantle transition is exposed within the Paleozoic Cabo Ortegal Complex and can be used for comparison and model building. This provides a unique view into the nature of the crust-mantle transition. From the multi-seismic data the character of the Moho is highly variable, in some areas there are no reflections visible in the normal incidence (e.g. ESCI-BETICS-1), in others reflections are prominent single events (e.g. IBERSEIS) and in still others complex geometric features are observed (ESCI-NORTE). There are also time (depth) differences between the wide-angle and the normal incidence seismic Moho's. Laboratory measurements of P- and S-wave velocities reflect an overall increase from middle to lower crustal velocities in the felsic gneisses and intermediate-to-mafic granulites to mantle velocities in the eclogites and ultramafic rocks. The surface outcrops of Cabo Ortegal complex suggests that the seismic Moho is reached at the contact between

  10. Modeling crust-mantle evolution using radiogenic Sr, Nd, and Pb isotope systematics

    NASA Astrophysics Data System (ADS)

    Kumari, Seema; Paul, Debajyoti

    2015-04-01

    The present-day elemental and isotopic composition of Earth's terrestrial reservoirs can be used as geochemical constraints to study evolution of the crust-mantle system. A flexible open system evolutionary model of the Earth, comprising continental crust (CC), upper depleted mantle (UM) -source of mid-ocean ridge basalts (MORB), and lower mantle (LM) reservoir with a D" layer -source of ocean island basalts (OIB), and incorporating key radioactive isotope systematics (Rb-Sr, Sm-Nd, and U-Th-Pb), is solved numerically at 1 Ma time step for 4.55 Ga, the age of the Earth. The best possible solution is the one that produces the present-day concentrations as well as isotopic ratios in terrestrial reservoirs, compiled from published data. Different crustal growth scenarios (exponential, episodic, early and late growth), proposed in earlier studies, and its effect on the evolution of isotope systematics of terrestrial reservoirs is studied. Model simulations strongly favor a layered mantle structure satisfying majority of the isotopic constraints. In the successful model, which is similar to that proposed by Kellogg et al. (1999), the present-day UM comprises of 60% of mantle mass and extends to a depth 1600 km, whereas the LM becomes non-primitive and more enriched than the bulk silicate Earth, mainly due to addition of recycled crustal material. Modeling suggest that isotopic evolution of reservoirs is affected by the mode of crustal growth. Only two scenarios satisfied majority of the Rb-Sr and Sm-Nd isotopic constraints but failed to reproduce the present-day Pb-isotope systematics; exponential growth of crust (mean age, tc=2.3 Ga) and delayed and episodic growth (no growth for initial 900 Ma, tc=2.05 Ga) proposed by Patchett and Arndt (1986). However, assuming a slightly young Earth (4.45 Ga) better satisfies the Pb-isotope systematics. Although, the delayed crustal growth model satisfied Sr-Nd isotopic constraints, presence of early Hadean crust (4.03 and 4.4 Ga

  11. Constrained potential field modeling of the crustal architecture of the Musgrave Province in central Australia: Evidence for lithospheric strengthening due to crust-mantle boundary uplift

    NASA Astrophysics Data System (ADS)

    Aitken, Alan R. A.; Betts, Peter G.; Weinberg, Roberto F.; Gray, Daniel

    2009-12-01

    We image the crustal architecture of the Musgrave Province with petrophysically constrained forward models of new potential field data. These models image divergent shallow-dipping crustal scale thrusts that, at depth, link with an axial zone defined by steeper, lithospheric scale transpressional shear zones. They also show that to permit a near-surface density distribution that is consistent with petrophysical and geological observations, approximately 15-20 km of crust-mantle boundary uplift is necessary beneath the axial zone. The long-term preservation of this crust-mantle boundary offset implies a change from relatively weak lithosphere to relatively strong lithosphere during the intraplate Petermann Orogeny. To explain this, we propose a model in which uplift of the axial zone of the orogen leads to local lithospheric strengthening as a result of the uplift of mantle rocks into the lower crust, coupled with long-term lithospheric cooling due to the erosion of a radioactive upper crust. Brace-Goetze lithospheric strength models suggest that these processes may have increased the integrated strength of the lithosphere by a factor of 1.4-2.8. Because of this strengthening, this system is self-limiting, and activity will cease when lithospheric strength is sufficient to resist external forces and support isostatic imbalances. A simple force-balance model demonstrates that the force required to uplift the axial zone is tectonically reasonable and that the system can subsequently withstand significant tensional forces. This example shows that crust-mantle boundary uplift coupled with reduced crustal heat production can profoundly affect the long-term strength of the continental lithosphere and may be a critical process in the tectonic stabilization of intraplate regions.

  12. Compositional variations in spinel-hosted pargasite inclusions in the olivine-rich rock from the oceanic crust-mantle boundary zone

    NASA Astrophysics Data System (ADS)

    Tamura, Akihiro; Morishita, Tomoaki; Ishimaru, Satoko; Hara, Kaori; Sanfilippo, Alessio; Arai, Shoji

    2016-05-01

    The crust-mantle boundary zone of the oceanic lithosphere is composed mainly of olivine-rich rocks represented by dunite and troctolite. However, we still do not fully understand the global variations in the boundary zone, and an effective classification of the boundary rocks, in terms of their petrographical features and origin, is an essential step in achieving such an understanding. In this paper, to highlight variations in olivine-rich rocks from the crust-mantle boundary, we describe the compositional variations in spinel-hosted hydrous silicate mineral inclusions in rock samples from the ocean floor near a mid-ocean ridge and trench. Pargasite is the dominant mineral among the inclusions, and all of them are exceptionally rich in incompatible elements. The host spinel grains are considered to be products of melt-peridotite reactions, because their origin cannot be ascribed to simple fractional crystallization of a melt. Trace-element compositions of pargasite inclusions are characteristically different between olivine-rich rock samples, in terms of the degree of Eu and Zr anomalies in the trace-element pattern. When considering the nature of the reaction that produced the inclusion-hosting spinel, the compositional differences between samples were found to reflect a diversity in the origin of the olivine-rich rocks, as for example in whether or not a reaction was accompanied by the fractional crystallization of plagioclase. The differences also reflect the fact that the melt flow system (porous or focused flow) controlled the melt/rock ratios during reaction. The pargasite inclusions provide useful data for constraining the history and origin of the olivine-rich rocks and therefore assist in our understanding of the crust-mantle boundary of the oceanic lithosphere.

  13. A deep crust-mantle boundary in the asteroid 4 Vesta.

    PubMed

    Clenet, Harold; Jutzi, Martin; Barrat, Jean-Alix; Asphaug, Erik I; Benz, Willy; Gillet, Philippe

    2014-07-17

    The asteroid 4 Vesta was recently found to have two large impact craters near its south pole, exposing subsurface material. Modelling suggested that surface material in the northern hemisphere of Vesta came from a depth of about 20 kilometres, whereas the exposed southern material comes from a depth of 60 to 100 kilometres. Large amounts of olivine from the mantle were not seen, suggesting that the outer 100 kilometres or so is mainly igneous crust. Here we analyse the data on Vesta and conclude that the crust-mantle boundary (or Moho) is deeper than 80 kilometres.

  14. A deep crust-mantle boundary in the asteroid 4 Vesta.

    PubMed

    Clenet, Harold; Jutzi, Martin; Barrat, Jean-Alix; Asphaug, Erik I; Benz, Willy; Gillet, Philippe

    2014-07-17

    The asteroid 4 Vesta was recently found to have two large impact craters near its south pole, exposing subsurface material. Modelling suggested that surface material in the northern hemisphere of Vesta came from a depth of about 20 kilometres, whereas the exposed southern material comes from a depth of 60 to 100 kilometres. Large amounts of olivine from the mantle were not seen, suggesting that the outer 100 kilometres or so is mainly igneous crust. Here we analyse the data on Vesta and conclude that the crust-mantle boundary (or Moho) is deeper than 80 kilometres. PMID:25030166

  15. Postcollisional mafic igneous rocks record crust-mantle interaction during continental deep subduction.

    PubMed

    Zhao, Zi-Fu; Dai, Li-Qun; Zheng, Yong-Fei

    2013-01-01

    Findings of coesite and microdiamond in metamorphic rocks of supracrustal protolith led to the recognition of continental subduction to mantle depths. The crust-mantle interaction is expected to take place during subduction of the continental crust beneath the subcontinental lithospheric mantle wedge. This is recorded by postcollisional mafic igneous rocks in the Dabie-Sulu orogenic belt and its adjacent continental margin in the North China Block. These rocks exhibit the geochemical inheritance of whole-rock trace elements and Sr-Nd-Pb isotopes as well as zircon U-Pb ages and Hf-O isotopes from felsic melts derived from the subducted continental crust. Reaction of such melts with the overlying wedge peridotite would transfer the crustal signatures to the mantle sources for postcollisional mafic magmatism. Therefore, postcollisonal mafic igneous rocks above continental subduction zones are an analog to arc volcanics above oceanic subduction zones, providing an additional laboratory for the study of crust-mantle interaction at convergent plate margins. PMID:24301173

  16. Postcollisional mafic igneous rocks record crust-mantle interaction during continental deep subduction

    PubMed Central

    Zhao, Zi-Fu; Dai, Li-Qun; Zheng, Yong-Fei

    2013-01-01

    Findings of coesite and microdiamond in metamorphic rocks of supracrustal protolith led to the recognition of continental subduction to mantle depths. The crust-mantle interaction is expected to take place during subduction of the continental crust beneath the subcontinental lithospheric mantle wedge. This is recorded by postcollisional mafic igneous rocks in the Dabie-Sulu orogenic belt and its adjacent continental margin in the North China Block. These rocks exhibit the geochemical inheritance of whole-rock trace elements and Sr-Nd-Pb isotopes as well as zircon U-Pb ages and Hf-O isotopes from felsic melts derived from the subducted continental crust. Reaction of such melts with the overlying wedge peridotite would transfer the crustal signatures to the mantle sources for postcollisional mafic magmatism. Therefore, postcollisonal mafic igneous rocks above continental subduction zones are an analog to arc volcanics above oceanic subduction zones, providing an additional laboratory for the study of crust-mantle interaction at convergent plate margins. PMID:24301173

  17. Postcollisional mafic igneous rocks record crust-mantle interaction during continental deep subduction.

    PubMed

    Zhao, Zi-Fu; Dai, Li-Qun; Zheng, Yong-Fei

    2013-12-04

    Findings of coesite and microdiamond in metamorphic rocks of supracrustal protolith led to the recognition of continental subduction to mantle depths. The crust-mantle interaction is expected to take place during subduction of the continental crust beneath the subcontinental lithospheric mantle wedge. This is recorded by postcollisional mafic igneous rocks in the Dabie-Sulu orogenic belt and its adjacent continental margin in the North China Block. These rocks exhibit the geochemical inheritance of whole-rock trace elements and Sr-Nd-Pb isotopes as well as zircon U-Pb ages and Hf-O isotopes from felsic melts derived from the subducted continental crust. Reaction of such melts with the overlying wedge peridotite would transfer the crustal signatures to the mantle sources for postcollisional mafic magmatism. Therefore, postcollisonal mafic igneous rocks above continental subduction zones are an analog to arc volcanics above oceanic subduction zones, providing an additional laboratory for the study of crust-mantle interaction at convergent plate margins.

  18. Extension velocity partitioning, rheological crust-mantle and intra-crustal decoupling and tectonically inherited structures: consequences for continental rifting dynamics.

    NASA Astrophysics Data System (ADS)

    Wang, Kun; Mezri, Leila; Burov, Evgueni; Le Pourhiet, Laetitia

    2015-04-01

    We implemented series of systematic thermo-mechanical numerical models testing the importance of the rheological structure and extension rate partitioning for continental rift evolution. It is generally assumed that styles of continental rifting are mainly conditioned by the initial integrated strength of the lithosphere. For example, strong plates are expected to undergo extension in narrow rifting mode, while weak lithospheres would stretch in wide rifting mode. However, we show that this classification is largely insufficient because the notion of the integrated strength ignores the internal rheological structure of the lithosphere that may include several zones of crust-mantle or upper-crust-intermediate (etc) crust decoupling. As well, orogenic crusts characterizing most common sites of continental extension may exhibit inverted lithological sequences, with stronger and denser formerly lower crustal units on top of weaker and lighter upper crustal units. This all may result in the appearance of sharp rheological strength gradients and presence of decoupling zones, which may lead to substantially different evolution of the rift system. Indeed, strong jump-like contrasts in the mechanical properties result in mechanical instabilities while mechanical decoupling between the competent layers results in overall drop of the flexural strength of the system and may also lead to important horizontal flow of the ductile material. In particular, the commonly inferred concept of level of necking (that assumes the existence of a stationary horizontal stretching level during rifting) looses its sense if necking occurs at several distinct levels. In this case, due to different mechanical strength of the rheological layers, several necking levels develop and switch from one depth to another resulting in step-like variations of rifting style and accelerations/decelerations of subsidence during the active phase of rifting. During the post-rifting phase, initially decoupled

  19. Competitive hydration and dehydration at olivine-quartz boundary revealed by hydrothermal experiments: Implications for silica metasomatism at the crust-mantle boundary

    NASA Astrophysics Data System (ADS)

    Oyanagi, Ryosuke; Okamoto, Atsushi; Hirano, Nobuo; Tsuchiya, Noriyoshi

    2015-09-01

    Serpentinization occurs via interactions between mantle peridotite and water that commonly passes through the crust. Given that such a fluid has a high silica activity compared with mantle peridotite, it is thought that serpentinization and silica metasomatism occur simultaneously at the crust-mantle boundary. In this study, we conducted hydrothermal experiments in the olivine (Ol)-quartz (Qtz)-H2O system at 250 °C and vapor-saturated pressure under highly alkaline conditions (NaOHaq, pH = 13.8 at 25 °C) to clarify the mechanism of silica metasomatism at the crust-mantle boundary. Composite powders consisting of a Qtz layer and an Ol layer were set in tube-in-tube vessels. After the experiments, the extents of serpentinization and metasomatic reactions were evaluated as a function of distance from the Ol-Qtz boundary. The mineralogy of the reaction products in the Ol-hosted region changed with increasing distance from the Ol-Qtz boundary, from smectite + serpentine (Smc zone) to serpentine + brucite + magnetite (Brc zone). Olivine hydration proceeded in both zones, but the total H2O content in the products was greater in the Brc zone than in the Smc zone. Mass balance calculations revealed that olivine hydration occurred without any supply of silica in the brucite zone. In contrast, the Smc zone was formed by silica metasomatism via competitive hydration and dehydration reactions. In the Smc zone, smectite formed via the simultaneous progress of olivine hydration and serpentine dehydration, and around the boundary of the Smc and Brc zones, serpentine formation occurred by olivine hydration and brucite dehydration. The relative extent of hydration and dehydration reactions controlled the along-tube variation in the rate of H2O production/consumption and the rate of volume increase. Our findings suggest that the competitive progress of serpentinization and silica metasomatic reactions would cause fluctuations in pore fluid pressure, possibly affecting the

  20. The Moho as a magnetic boundary. [Earth crust-mantle boundary

    NASA Technical Reports Server (NTRS)

    Wasilewski, P. J.; Thomas, H. H.; Mayhew, M. A.

    1979-01-01

    Magnetism in the crust and the upper mantle and magnetic results indicating that the seismic Moho is a magnetic boundary are considered. Mantle derived rocks - peridotites from St. Pauls rocks, dunite xenoliths from the Kaupulehu flow, and peridotite, dunite, and eclogite xenoliths from Roberts Victor and San Carlos diatremes - are weakly magnetic with saturation magnetization values from 0.013 emu/gm to less than 0.001 emu/gm which is equivalent to 0.01 to 0.001 wt% Fe304. Literature on the minerals in mantle xenoliths shows that metals and primary Fe304 are absent, and that complex Cr, Mg, Al, and Fe spinels are dominant. These spinels are non-magnetic at mantle temperatures, and the crust/mantle boundary can be specified as a magnetic mineralogy discontinuity. The new magnetic results indicate that the seismic Moho is a magnetic boundary, the source of magnetization is in the crust, and the maximum Curie isotherm depends on magnetic mineralogy and is located at depths which vary with the regional geothermal gradient.

  1. Three-dimensional interface modelling with two-dimensional seismic data: the Alpine crust-mantle boundary

    USGS Publications Warehouse

    Waldhauser, F.; Kissling, E.; Ansorge, J.; Mueller, St.

    1998-01-01

    We present a new approach to determine the 3-D topography and lateral continuity of seismic interfaces using 2-D-derived controlled-source seismic reflector data. The aim of the approach is to give the simplest possible structure consistent with all reflector data and error estimates. We define simplicity of seismic intrafaces by the degree of interface continuity (ie shortest length of offsets) and by the degree of interface roughness (least surface roughness). The method is applied to structural information of the crust-mantle boundary (Moho) obtained from over 250 controlled-source seismic reflection and refraction profiles in the greater Alpine region. The reflected and refracted phases from the Moho interface and their interpretation regarding crustal thickness are reviewed and their reliability weighted. Weights assigned to each reflector element are transformed to depth errors considering Fresnel volumes. The 2-D-derived reflector elements are relocated in space (3-D migration) and interpolation is performed between the observed reflector elements to obtain continuity of model parameters. Interface offsets are intoduced only where required according to the prinipal of simplicity. The resulting 3-D model of the ALpine crust-mantle boundary shows two offsets that eivide the interface into a European, an Adriatic and a Ligurian Moho, with the European Moho subducting below the Adriatic Moho, and with the Adriatic Moho underthrusting the Ligurian Moho. Each sub-interface depicts the smoothest possible (ie simplest) surface, fitting the reflector data within their assigned errors. The results are consistent with previous studies for those regions with dense and reliable controlled-source seismic data. The newly derived Alpine Moho interface, however, surpasses earlier studies by its lateral extent over an area of about 600km by 600km, by quantifying reliability estimates along the interface, and by obeying the priciple of being consistently as simple as possible.

  2. Inference Concerning Physical Systems

    NASA Astrophysics Data System (ADS)

    Wolpert, David H.

    The question of whether the universe "is" just an information- processing system has been extensively studied in physics. To address this issue, the canonical forms of information processing in physical systems - observation, prediction, control and memory - were analyzed in [24]. Those forms of information processing are all inherently epistemological; they transfer information concerning the universe as a whole into a scientist's mind. Accordingly, [24] formalized the logical relationship that must hold between the state of a scientist's mind and the state of the universe containing the scientist whenever one of those processes is successful. This formalization has close analogs in the analysis of Turing machines. In particular, it can be used to define an "informational analog" of algorithmic information complexity. In addition, this formalization allows us to establish existence and impossibility results concerning observation, prediction, control and memory. The impossibility results establish that Laplace was wrong to claim that even in a classical, non-chaotic universe the future can be unerringly predicted, given sufficient knowledge of the present. Alternatively, the impossibility results can be viewed as a non-quantum mechanical "uncertainty principle". Here I present a novel motivation of the formalization introduced in [24] and extend some of the associated impossibility results.

  3. Inherited structure and coupled crust-mantle lithosphere evolution: Numerical models of Central Australia

    NASA Astrophysics Data System (ADS)

    Heron, Philip J.; Pysklywec, Russell N.

    2016-05-01

    Continents have a rich tectonic history that have left lasting crustal impressions. In analyzing Central Australian intraplate orogenesis, complex continental features make it difficult to identify the controls of inherited structure. Here the tectonics of two types of inherited structures (e.g., a thermally enhanced or a rheologically strengthened region) are compared in numerical simulations of continental compression with and without "glacial buzzsaw" erosion. We find that although both inherited structures produce deformation in the upper crust that is confined to areas where material contrasts, patterns of deformation in the deep lithosphere differ significantly. Furthermore, our models infer that glacial buzzsaw erosion has little impact at depth. This tectonic isolation of the mantle lithosphere from glacial processes may further assist in the identification of a controlling inherited structure in intraplate orogenesis. Our models are interpreted in the context of Central Australian tectonics (specifically the Petermann and Alice Springs orogenies).

  4. Distribution and nature of the crust-mantle transition layer deduced from amplitude modeling of wide-angle seismic data along the Izu-Bonin island arc

    NASA Astrophysics Data System (ADS)

    Sato, T.; Kodaira, S.; Takahashi, N.; Miura, S.; Kaneda, Y.

    2007-12-01

    The Izu-Bonin island arc formed by subducting of the Pacific plate beneath the Philippine Sea plate is a region of the crustal growth. This arc is divided into the northern and the southern part by the Sofu-gan tectonic line, due to the difference of the geological and geophysical characters (Yuasa, 1985). In the seismic velocity structure along the volcanic front in the northern Izu-Bonin island arc, it is clarified that this arc has not only the middle and the lower crust but also the 7.2-7.6 km/s layer (crust-mantle transition layer) underlying the lower crust (Kodaira et al., 2007, accepted). However, since this velocity structure along the volcanic front in this arc is calculated by the tomography method, the nature of the crust-mantle transition layer and uppermost mantle is unknown. To understand the nature of the crust-mantle transition layer in the Izu-Bonin island arc on the crustal growth, it is also important to know the seismic reflectivity at the top and bottom of this transition layer in this arc. In this study, we clarify the distribution of the seismic reflectivity at the top and bottom of the crust-mantle transition layer along the volcanic front in this arc using the velocity contrast values at these reflectors estimated by the amplitude modeling of wide-angle data. In 2004 and 2005, seismic refraction/reflection surveys using ocean bottom seismographs (OBSs) and controlled sources were conducted along the volcanic front in the Izu-Bonin island arc from the Sagami Bay to Kaitoku Seamount (Kodaira et al., accepted). In record sections of several OBSs, not only the first arrived phases but also later phases reflected from interfaces in the crust and uppermost mantle can be observed. The velocity contrast values at the top and bottom of this transition layer were estimated from the comparison of the observed and synthetic wave forms computed by a finite difference wave propagation program code "e3d" (Larsen and Grieger, 1998). Along the northern Izu

  5. Rayleigh-wave dispersion reveals crust-mantle decoupling beneath eastern Tibet.

    PubMed

    Legendre, Cédric P; Deschamps, Frédéric; Zhao, Li; Chen, Qi-Fu

    2015-01-01

    The Tibetan Plateau results from the collision of the Indian and Eurasian Plates during the Cenozoic, which produced at least 2,000 km of convergence. Its tectonics is dominated by an eastward extrusion of crustal material that has been explained by models implying either a mechanical decoupling between the crust and the lithosphere, or lithospheric deformation. Discriminating between these end-member models requires constraints on crustal and lithospheric mantle deformations. Distribution of seismic anisotropy may be inferred from the mapping of azimuthal anisotropy of surface waves. Here, we use data from the CNSN to map Rayleigh-wave azimuthal anisotropy in the crust and lithospheric mantle beneath eastern Tibet. Beneath Tibet, the anisotropic patterns at periods sampling the crust support an eastward flow up to 100°E in longitude, and a southward bend between 100°E and 104°E. At longer periods, sampling the lithospheric mantle, the anisotropic structures are consistent with the absolute plate motion. By contrast, in the Sino-Korean and Yangtze cratons, the direction of fast propagation remains unchanged throughout the period range sampling the crust and lithospheric mantle. These observations suggest that the crust and lithospheric mantle are mechanically decoupled beneath eastern Tibet, and coupled beneath the Sino-Korean and Yangtze cratons. PMID:26548657

  6. Rayleigh-wave dispersion reveals crust-mantle decoupling beneath eastern Tibet

    PubMed Central

    Legendre, Cédric P.; Deschamps, Frédéric; Zhao, Li; Chen, Qi-Fu

    2015-01-01

    The Tibetan Plateau results from the collision of the Indian and Eurasian Plates during the Cenozoic, which produced at least 2,000 km of convergence. Its tectonics is dominated by an eastward extrusion of crustal material that has been explained by models implying either a mechanical decoupling between the crust and the lithosphere, or lithospheric deformation. Discriminating between these end-member models requires constraints on crustal and lithospheric mantle deformations. Distribution of seismic anisotropy may be inferred from the mapping of azimuthal anisotropy of surface waves. Here, we use data from the CNSN to map Rayleigh-wave azimuthal anisotropy in the crust and lithospheric mantle beneath eastern Tibet. Beneath Tibet, the anisotropic patterns at periods sampling the crust support an eastward flow up to 100°E in longitude, and a southward bend between 100°E and 104°E. At longer periods, sampling the lithospheric mantle, the anisotropic structures are consistent with the absolute plate motion. By contrast, in the Sino-Korean and Yangtze cratons, the direction of fast propagation remains unchanged throughout the period range sampling the crust and lithospheric mantle. These observations suggest that the crust and lithospheric mantle are mechanically decoupled beneath eastern Tibet, and coupled beneath the Sino-Korean and Yangtze cratons. PMID:26548657

  7. Crust-mantle accommodation of Africa-Eurasia convergence in the NW-Moroccan margin

    NASA Astrophysics Data System (ADS)

    Zlotnik, S.; Jimenez-Munt, I.; Fernandez, M.

    2011-12-01

    Recent studies carried out in NW-Africa indicate prominent variations of the lithosphere-asthenosphere boundary (LAB) depth. The studies combine gravity, geoid, surface heat flow, elevation and seismic data along a profile running from the Tagus Abyssal Plain to the Sahara Platform and crossing the Gorringe Bank, the NW Moroccan Margin and the Atlas Mountains. The resulting mantle density anomalies show a prominent lithospheric mantle thickening beneath the margin (LAB >200 km-depth) followed by thinning beneath the Atlas Mountains (LAB ~90 km-depth). A combination of mantle underthrusting due to oblique convergence together with a viscous dripping fed by lateral mantle dragging can explain the imaged lithospheric structure. The model is consistent with a strong decoupled crustal-mantle mechanical response to the Africa-Eurasia convergence and results in positive/negative dynamic topography in regions with thickened/thinned crust. In this work we go a step further analysing, by means of dynamic numerical simulations, the viscous dragging and the Rayleigh-Taylor-like process. Our goal is to understand the initial lithospheric mantle structure suitable to produce the inferred dynamic process. In addition, we study the key factors controlling the deformation of the lithospheric mantle when submitted to convergence. Using the numerical framework Underworld to carry out the simulations we found the key factors controlling the process. Chief among these factors are lithospheric/mantle viscosity ratio and initial mantle and crustal structure. Nevertheless, the process is not very sensitive to the usual power law parameters for mantle rocks (activation energy and volume, power law exponent, etc.), indicating the importance of the rheology of the upper half of the lithosphere, where the power law is not active. These results allow us to speculate on the past and future evolution of the NW-Moroccan margin which could show the appropriated conditions for subduction initiation.

  8. Major lunar crustal terranes: Surface expressions and crust-mantle origins

    NASA Astrophysics Data System (ADS)

    Jolliff, Bradley L.; Gillis, Jeffrey J.; Haskin, Larry A.; Korotev, Randy L.; Wieczorek, Mark A.

    2000-02-01

    represent samples from the anorthositic central region of the FHT. Ejecta from deep-penetrating basin impacts outside of the central anorthositic region, however, indicate an increasingly mafic composition with depth. The SPAT, a mafic anomaly of great magnitude, may include material of the upper mantle as well as lower crust; thus it is designated a separate terrane. Whether the SPA basin impact simply uncovered lower crust such as we infer for the FHT remains to be determined.

  9. The crust-mantle interaction in continental subduction channels: Zircon evidence from orogenic peridotite in the Sulu orogen

    NASA Astrophysics Data System (ADS)

    Li, Hai-Yong; Chen, Ren-Xu; Zheng, Yong-Fei; Hu, Zhaochu

    2016-02-01

    A combined secondary ion mass spectrometer and laser ablation-(multicollector)-inductively coupled plasma mass spectrometer study of zircon U-Pb ages, trace elements, and O and Hf isotopes was carried out for orogenic peridotite and its host gneiss in the Sulu orogen. Newly grown zircon domains exhibit weak zoning or no zoning, relatively low Th/U ratios (<0.1), low heavy rare earth element (HREE) contents, steep middle rare earth element-HREE patterns, negative Eu anomalies, and negative to low δ18O values of -11.3 to 0.9‰ and U-Pb ages of 220 ± 2 to 231 ± 4 Ma. Thus, these zircons would have grown from metasomatic fluids during the early exhumation of deeply subducted continental crust. The infiltration of metasomatic fluids into the peridotite is also indicated by the occurrence of hydrous minerals such as amphibole, serpentine, and chlorite. In contrast, relict zircon domains exhibit magmatic zircon characteristics. Their U-Pb ages and trace element and Hf-O isotope compositions are similar to those for protolith zircons from ultrahigh-pressure metamorphic rocks in the Dabie-Sulu orogenic belt. Thus, these relict magmatic zircons would be physically transported into the peridotite by metasomatic fluids originated from the deeply subducted continental crust. Therefore, the peridotite underwent metasomatism by aqueous solutions derived from dehydration of the deeply subducted continental crust during the early exhumation. It is these crustally derived fluids that would have brought not only such chemical components as Zr and Si but also tiny zircon grains from the deeply subducted crustal rocks into the peridotite at the slab-mantle interface in continental subduction channels. As such, the orogenic peridotite records the crust-mantle interaction at the deep continental subduction zone.

  10. Mafic inclusions in Yosemite granites and Lassen Pk lavas: records of complex crust-mantle interactions

    SciTech Connect

    Reid, J.B. Jr.; Flinn, J.E.

    1985-01-01

    This study compares three small-scale magmatic systems dominated by mafic/felsic interaction that appear to be analogs to the evolution of their larger host systems: mafic inclusions from modern Lassen Pk lavas along with inclusions and related synplutonic dike materials from granitoids in the Tuolumne Intrusive Series. Each system represents quickly chilled mafic melt previously contaminated by digestion of rewarmed, super-solidus felsic hosts. Contaminants occur in part as megacrysts of reworked oligoclase with lesser hb and biot. Within each group MgO-variation diagrams for Fe, Ca, Ti, Si are strikingly linear (r>.96); alkalis are decidedly less regular, and many hybrid rocks show a curious, pronounced Na enrichment. Field data, petrography, and best fit modeling suggests this may result from flow concentration of oligoclase xenocrysts within contaminated synplutonic dikes, and is preserved in the inclusions when dike cores chill as pillows in their felsic host. Dissolution of mafic inclusions erases these anomalies and creates a more regular series of two-component mafic-felsic mixtures in the large host system. The inclusions and dikes thus appear to record a variety of late-stage mafic-felsic interactive processes that earlier and on a larger scale created much of the compositional variety of their intermediate host rocks.

  11. Evidence of subduction and crust-mantle mixing from a single diamond

    NASA Astrophysics Data System (ADS)

    Schulze, Daniel J.; Harte, Ben; Valley, John W.; Channer, Dominic M. DeR.

    2004-09-01

    Cathodoluminescence (CL) imaging of polished sections of a diamond from the Guaniamo region of Venezuela suggests a history of the diamond involving two periods of growth separated by a period of resorption and possibly brittle deformation. In situ electron probe analysis of multiple eclogitic garnet inclusions reveals a correlation between garnet composition and location in the stone. An early-formed garnet in the diamond core has higher Ca/(Ca+Mg) and lower Mg/(Mg+Fe) values than later garnets associated with the second period of diamond growth. This variation conforms to an extensive trend of variation in the suite of eclogitic garnets extracted from Venezuelan diamonds. The diamond is zoned in carbon isotope composition (in situ secondary ion mass spectrometry, SIMS, data). The core compositions ( δ13C PDB), corresponding to the first stage of growth, average -17.7‰. The second period of growth is apparently in two sub-sets of CL zones with mean values of -13.0‰ and -7.9‰. Nitrogen contents of diamond are low (30-300 atomic ppm) and do not correlate with carbon isotope composition. Oxygen isotope ratios of the garnet inclusions are elevated substantially above those expected for "common mantle"; δ18O VSMOW of early garnet is approximately +10.5‰ and two late garnets average +8.8‰. The evolutionary trend of magnesium enrichment in garnet is unlikely to represent igneous fractionation. The stable isotope data are consistent with diamond formation in subducted meta-basic rocks that had interacted with sea water at low temperatures at or near the sea floor and contained a substantial biogenic carbon component. During or following subduction, diamonds continued to form in an evolving system that was progressively modified by interaction with mantle material.

  12. Crustal redistribution, crust-mantle recycling and Phanerozoic evolution of the continental crust

    NASA Astrophysics Data System (ADS)

    Clift, Peter D.; Vannucchi, Paola; Morgan, Jason Phipps

    2009-12-01

    We here attempt a global scale mass balance of the continental crust during the Phanerozoic and especially the Cenozoic (65 Ma). Continental crust is mostly recycled back into the mantle as a result of the subduction of sediment in trenches (1.65 km 3/a), by the subduction of eroded forearc basement (1.3 km 3/a) and by the delamination of lower crustal material from orogenic plateaus (ca. 1.1 km 3/a). Subduction of rifted crust in continent-continent collision zones (0.4 km 3/a), and dissolved materials fixed into the oceanic crust (ca. 0.4 km 3/a) are less important crustal sinks. At these rates the entire continental crust could be reworked in around 1.8 Ga. Nd isotope data indicate that ca. 80% of the subducted continental crust is not recycled by melting at shallow levels back into arcs, but is subducted to depth into the upper mantle. Continent-continent collision zones do not generally form new crust, but rather cause crustal loss by subduction and as a result of their physical erosion, which exports crust from the orogen to ocean basins where it may be subducted. Regional sedimentation rates suggest that most orogens have their topography eliminated within 100-200 million years. We estimate that during the Cenozoic the global rivers exported an average of 1.8 km 3/a to the oceans, approximately balancing the subducted loss. Accretion of sediment to active continental margins is a small contribution to crustal construction (ca. 0.3 km 3/a). Similarly, continental large igneous provinces (flood basalts) represent construction of only around 0.12 km 3/a, even after accounting for their intrusive roots. If oceanic plateaus are accreted to continental margins then they would average construction rates of 1.1 km 3/a, meaning that to keep constant crustal volumes, arc magmatism would have to maintain production of around 3.8 km 3/a (or 94 km 3/Ma/km of trench). This slightly exceeds the rates derived from sparse seismic experiments in oceanic arc systems. Although

  13. Nature of the crust-mantle transition layer during the crustal growth along the Izu-Bonin island arc deduced from the seismic amplitude modeling

    NASA Astrophysics Data System (ADS)

    Sato, T.; Kodaira, S.; Takahashi, N.; Miura, S.; Kaneda, Y.

    2008-12-01

    The Izu-Bonin island arc is a typical oceanic island arc formed by subduction of the Pacific plate beneath the Philippine Sea plate and the location that the continental middle crust is produced (e.g., Suyehiro et al., 1996). From the seismic velocity structure, the Izu-Bonin island arc beneath the volcanic front has the 7.2-7.6 km/s layer (crust-mantle transition layer) underlying the lower crust (Kodaira et al., 2007). The crust-mantle transition layer is considered as the composition formed by the interaction between the crust and uppermost mantle during the crustal growth (e.g., Tatsumi et al., 2008). However, since this velocity structure beneath the volcanic front along this arc is calculated by the tomography method, the nature of the crust-mantle transition layer and uppermost mantle and the depth of the Moho are unknown. To understand the nature of this transition layer and the depth of the Moho along this arc, it is also important to know the seismic reflectivity at the top and bottom of this transition layer. In this study, we clarify the distribution of the seismic reflectivity at the top and bottom of the crust-mantle transition layer beneath the volcanic front along this arc using the velocity contrast values at these reflectors estimated by the amplitude modeling of wide-angle data. In 2004 and 2005, seismic refraction/reflection surveys using ocean bottom seismographs (OBSs) and controlled sources were conducted beneath the volcanic front along the Izu-Bonin island arc from Sagami Bay to Kaitoku Seamount (Kodaira et al., 2007). In record sections of several OBSs, not only the first arrival phases but also later phases reflected from interfaces in the crust and uppermost mantle are visible. These later phases can be considered as the reflected from the top and bottom of this transition layer. The velocity contrast values at the top and bottom of this transition layer were estimated from the comparison of the observed and synthetic wave forms computed

  14. An inference engine for embedded diagnostic systems

    NASA Technical Reports Server (NTRS)

    Fox, Barry R.; Brewster, Larry T.

    1987-01-01

    The implementation of an inference engine for embedded diagnostic systems is described. The system consists of two distinct parts. The first is an off-line compiler which accepts a propositional logical statement of the relationship between facts and conclusions and produces data structures required by the on-line inference engine. The second part consists of the inference engine and interface routines which accept assertions of fact and return the conclusions which necessarily follow. Given a set of assertions, it will generate exactly the conclusions which logically follow. At the same time, it will detect any inconsistencies which may propagate from an inconsistent set of assertions or a poorly formulated set of rules. The memory requirements are fixed and the worst case execution times are bounded at compile time. The data structures and inference algorithms are very simple and well understood. The data structures and algorithms are described in detail. The system has been implemented on Lisp, Pascal, and Modula-2.

  15. Xenoliths in ultrapotassic volcanic rocks in the Lhasa block: direct evidence for crust-mantle mixing and metamorphism in the deep crust

    NASA Astrophysics Data System (ADS)

    Wang, Rui; Collins, William J.; Weinberg, Roberto F.; Li, Jin-xiang; Li, Qiu-yun; He, Wen-yan; Richards, Jeremy P.; Hou, Zengqian; Zhou, Li-min; Stern, Richard A.

    2016-07-01

    δ18O (+6 to 7.5 ‰), intermediate (δ18O +8.5 to 9.0 ‰), and high δ18O (+11.0 to 12.0 ‰). The fourth is almost pure andradite with δ18O 10-12 ‰. Both the low and intermediate δ18O groups show significant variation in Fe content, whereas the two high δ18O groups are compositionally homogeneous. We interpret these features to indicate that the low and intermediate δ18O group garnets grew in separate fractionating magmas that were brought together through magma mixing, whereas the high δ18O groups formed under high-grade metamorphic conditions accompanied by metasomatic exchange. The garnets record complex, open-system magmatic and metamorphic processes in a single rock. Based on these features, we consider that ultrapotassic magmas interacted with juvenile 35-20 Ma crust after they intruded in the deep crust (>50 km) at ~13 Ma to form hybridized Miocene granitoid magmas, leaving a refractory residue. The ~13 Ma zircons retain the original, evolved isotopic character of the ultrapotassic magmas, and the garnets record successive stages of the melting and mixing process, along with subsequent high-grade metamorphism followed by low-temperature alteration and brecciation during entrainment and ascent in a late UPV dyke. This is an excellent example of in situ crust-mantle hybridization in the deep Tibetan crust.

  16. Gold solubility and partitioning between sulfide liquid, monosulfide solid solution and hydrous mantle melts: Implications for the formation of Au-rich magmas and crust-mantle differentiation

    NASA Astrophysics Data System (ADS)

    Li, Yuan; Audétat, Andreas

    2013-10-01

    The solubility of Au in sulfur-free vs. sulfide-saturated melts and its partitioning behavior between sulfide liquid (SL), monosulfide solid solution (MSS) and hydrous basanite melt at variable Au activities was investigated in a fO2 range of FMQ-2 to FMQ+1.6 at 1200 °C/1.5 GPa using piston cylinder apparatus. Gold solubility in sulfur-free (<100 μg/g S) melt is low (0.6-1.6 μg/g) and increases with fO2 in a manner consistent with Au dissolution as AuO1/2, whereas in sulfide-saturated melts it is high (13.6 ± 1.7 μg/g) and independent of fO2. Variations in the chlorine content of sulfide-saturated melts (0.2-1.2 wt% Cl) had no measurable effect on Au solubility. Gold partition coefficients between sulfide liquid and silicate melt (DAuSL/SM) are very high, ∼10,000 ± 3000, which is at the upper end of values reported in previous studies. Gold partition coefficients between MSS and silicate melt (DAuMSS/SM) are much lower, 60 ± 10, which is at the lower end of previous values. Both DAuSL/SM and DAuMSS/SM are independent of fO2. The new Au partition coefficients were used in conjunction with previously published Cu and Ag partition coefficients to investigate the role of MSS versus SL during partial melting in the source region of primitive potassic magmas and during crust-mantle differentiation. The high Au content of ore deposits associated with potassic magmas has commonly been explained by the dissolution of Au-rich sulfide liquid, either during partial melting in the mantle source or during partial re-melting of sulfide-bearing cumulates at the crust-mantle boundary. We argue that MSS is the dominant sulfide phase in the mantle source region of these magmas, and thus that their high Au content is a consequence of low MSS-silicate melt partition coefficients rather than of sulfide exhaustion or partial re-melting of sulfide-bearing cumulates. Continental crust is depleted in Au, Ag and Cu relative to mantle melts, which was thought to be due to removal of

  17. Lithium systematics in howardite-eucrite-diogenite meteorites: Implications for crust-mantle evolution of planetary embryos

    NASA Astrophysics Data System (ADS)

    Magna, Tomáš; Šimčíková, Magdalena; Moynier, Frédéric

    2014-01-01

    We present lithium (Li) abundances and isotope compositions in a suite of howardites, eucrites and diogenites (HEDs). These meteorites most likely originated from asteroid Vesta and were delivered to Earth by a series of independent impact events. The Li concentrations show striking differences between Li-poor diogenites plus cumulate eucrites and Li-enriched eucrites whilst howardites have Li abundances intermediate between eucrites and diogenites. Contrary to Li elemental inter-group differences, Li isotope compositions are irresolvable among these individual groups of HED meteorites despite their wildly distinct petrography, attesting to insignificant Li isotope fractionation during formation of a thick basaltic crust by melting of the Vestan mantle. The mean Li isotope composition of Bulk Silicate Vesta is estimated at 3.7 ± 0.6‰ (1σ), intermediate to that of the Earth versus Mars and Moon but identical with these terrestrial bodies within uncertainty. This further validates largely homogeneous inner Solar System solids from the Li isotope perspective and supports the lack of loss of moderately volatile elements from planetary embryos during their magmatic histories because Li does not follow depletion trends inferred from more volatile elements. Pasamonte eucrite has the same Li isotope composition as other eucrites although it may not be directly linked to Vesta. These observations are also important for generating Li elemental and isotope signatures in juvenile basaltic crusts of large terrestrial planets and numerous planetary embryos in the early Solar System. A combination of CV + L chondrites may be less suitable for building Vesta from Li perspective but this may face sampling bias of available data and only further analyses may resolve this issue. Alternatively, significant shift of ∼1‰ towards heavier Li isotope compositions must have occurred during thermal processing of CV + L (2.2-2.8‰) mixture in order to account for the observed Li

  18. Evidence for chondritic Lu/Hf in the early crust - mantle system from Antarctic and Western Australian Eoarchean zircon

    NASA Astrophysics Data System (ADS)

    Hiess, J.; Bennett, V. C.; Black, L.; Eggins, S. M.

    2009-12-01

    The timing and extent of early continental crust formation and mantle differentiation remain contentious issues in Earth Sciences. Here we report new, SHRIMP U-Pb zircon geochronology (n = 142 spot analyses on 69 grains), combined with LA-MC-ICPMS 176Hf determinations (n = 110 spot analyses on 59 grains), in three well-characterized orthogneiss samples of the Napier Complex (Mt Sones and Gage Ridge), East Antarctica and the Narryer Complex, West Australia. Prior to analysis all crystals were extensively documented by cathodoluminesence, reflected and transmitted light imaging to guide beam placement and to identify zones of magmatic oscillatory growth from metamorphic recrystallisation textures. Each sample records complex 207Pb/206Pb age structures that can extend from Hadean through to the Neoarchean, while displaying concordant, reverse-discordant and normal-discordant behaviour. U-Pb systematics within individual grains can be correlated with distinct variations in their measured Lu and Hf isotopic compositions. These features lead to the presence of multiple Lu/Hf arrays within each rock sample evolving towards progressively un-radiogenic values through time. Highly concordant populations of Eoarchean zircon from each rock represent the intrusive age of their igneous protolith at approximately 3.88, 3.85 and 3.73 Ga. Across all three samples these zircon populations typically record initial ɛHf values that lie within error of the Earth’s chondritic uniform reservoir (calculated using λ176Lu of 1.867×10-11 yr-1 and CHUR parameters of Bouvier et al., 2008, EPSL 273: 48-57). These near-chondritic results are consistent with recent LA-MC-ICPMS 176Hf work on primitive Eoarchean TTG zircon from other cratons (e.g. the Itsaq Complex of Greenland, Hiess et al., 2009, GCA 73: 4489-4516) and together argue against voluminous continental crustal growth or significant mantle Lu/Hf differentiation during the Hadean or Eoarchean on a global scale. Two grains of Antarctic zircon with ~25µm spot ages >4 Ga, made on domains of subtle oscillatory zonation, moderate Th/U (>0.36), low common 206Pb (<0.02%) and reverse-discordance (-6% and -9%) record supra-chondritic ɛHf values (c. +2 and +4). These analyses may potentially represent inherited zircon derived from an older depleted mantle reservoir. Alternatively, these positive ɛHf compositions, measured on crystals that also contain recrystallised textures, variable 207Pb/206Pb ages and fractures, may be interpreted as an artifact of Pb disturbance and do not have geological significance. These issues highlight the difficulties in determining precise and accurate initial Hf isotopic compositions in ancient, complex zircon.

  19. Low velocity crustal flow and crust-mantle coupling mechanism in Yunnan, SE Tibet, revealed by 3D S-wave velocity and azimuthal anisotropy

    NASA Astrophysics Data System (ADS)

    Chen, Haopeng; Zhu, Liangbao; Su, Youjin

    2016-08-01

    We used teleseismic data recorded by a permanent seismic network in Yunnan, SE Tibet, and measured the interstation Rayleigh wave phase velocity between 10 and 60 s. A two-step inversion scheme was used to invert for the 3D S-wave velocity and azimuthal anisotropy structure of 10-110 km. The results show that there are two low velocity channels between depths of 20-30 km in Yunnan and that the fast axes are sub-parallel to the strikes of the low velocity channels, which supports the crustal flow model. The azimuthal anisotropy pattern is quite complicated and reveals a complex crust-mantle coupling mechanism in Yunnan. The N-S trending Lüzhijiang Fault separates the Dianzhong Block into two parts. In the western Dianzhong Block, the fast axis of the S-wave changes with depth, which indicates that the crust and the lithospheric mantle are decoupled. In the eastern Dianzhong Block and the western Yangtze Craton, the crust and the lithospheric mantle may be decoupled because of crustal flow, despite a coherent S-wave fast axis at depths of 10-110 km. In addition, the difference between the S-wave fast axis in the lithosphere and the SKS splitting measurement suggests that the lithosphere and the upper mantle are decoupled there. In the Baoshan Block, the stratified anisotropic pattern suggests that the crust and the upper mantle are decoupled.

  20. An Ada inference engine for expert systems

    NASA Technical Reports Server (NTRS)

    Lavallee, David B.

    1986-01-01

    The purpose is to investigate the feasibility of using Ada for rule-based expert systems with real-time performance requirements. This includes exploring the Ada features which give improved performance to expert systems as well as optimizing the tradeoffs or workarounds that the use of Ada may require. A prototype inference engine was built using Ada, and rule firing rates in excess of 500 per second were demonstrated on a single MC68000 processor. The knowledge base uses a directed acyclic graph to represent production lines. The graph allows the use of AND, OR, and NOT logical operators. The inference engine uses a combination of both forward and backward chaining in order to reach goals as quickly as possible. Future efforts will include additional investigation of multiprocessing to improve performance and creating a user interface allowing rule input in an Ada-like syntax. Investigation of multitasking and alternate knowledge base representations will help to analyze some of the performance issues as they relate to larger problems.

  1. Lithology and evolution of the crust-mantle boundary region in the southwestern Basin and Range province

    SciTech Connect

    Wilshire, H.G. )

    1990-01-10

    Seismic transects in this area show a strongly reflective Moho of generally low relief, which, in the area of modern transects, consists of a thin zone (< 2 km thick) of short reflectors. The upper mantle is transparent and has a P{sub n} of 7.8-8.0 km/s similar to much of the western US. A lower crustal zone, 2-13 km thick, has variable internal reflectivity and a relatively low velocity of 6.6-6.8 km/s. Upper mantle peridotite xenoliths show both ductile and brittle deformational features and have structures and composition affected by magmatic intrusion; intrusions form complex dike systems and extensive zones of grain boundary infiltration in peridotite xenoliths. Whereas melt infiltration preceded and followed ductile deformation, brittle deformation, represented by closely spaced joint systems and faults, followed ductile deformation and is related to the youngest magmatic episodes. Lower crustal xenoliths are dominantly igneous-textured pyroxenites and mafic to intermediate gabbros identical to the dikes in peridotite xenoliths. The crustal xenoliths also commonly are jointed, and in addition many show partial melting and have abundant cavities that probably were filled with CO{sub 2}-rich fluids. These rocks are interpreted as products of underplated magmas that were fed through the mantle dike systems and may represent the lowest crustal unit identified in the seismic records. The mafic compositions and high densities of the crustal xenoliths indicate that the low velocity of the lower crust mat be caused in part by fracture systems, partial melts, and high temperatures. The preferred model for the evolution of the lower lithosphere is one in which extension affects the upper mantle as well as the crust and is overlapped in time by multiple magmatic episodes. The earliest magmatic events preceded extension, and later events accompanied and followed extension.

  2. Multistage crust-mantle interactions during the destruction of the North China Craton: Age and composition of the Early Cretaceous intrusions in the Jiaodong Peninsula

    NASA Astrophysics Data System (ADS)

    Tang, Huayun; Zheng, Jianping; Yu, Chunmei; Ping, Xianquan; Ren, Hongwei

    2014-03-01

    In situ Zircon U-Pb ages and Hf-isotopes, whole-rock major- and trace elements, and Sr-Nd isotopic compositions of the Liudusi and Taiboding intrusions in the Jiaodong Peninsula (eastern China) are presented to trace their petrogenesis and relationships to lithosphere evolution. The Liudusi complex, which consists of biotite-bearing gabbro and quartz monzonite, formed at ca 115 Ma. The rocks show shoshonitic alkaline affinities and have crust-like trace-element compositions, superchondritic Zr/Hf and Nb/Ta, but low Rb/Sr and high Ba/Rb ratios, coupled with high initial 87Sr/86Sr, and negative ɛNd(t) (- 15.9) and ɛHf(t) (- 17.8 to - 16.4). The data suggest that the complex was derived from an amphibole-bearing spinel to garnet lherzolitic mantle which is tectonically affiliated to the southeastern margin of the North China Craton (NCC). The parental magma may have experienced fractionation of olivine, clinopyroxene, apatite and Fe-Ti oxides. The Taiboding porphyritic K-feldspar granites (ca 118 Ma) with high SiO2 (72.7-73.7 wt.%) are metaluminous with Nb/Ta ratios (10.7-12.4) similar to that of the average continental crust. They also have highly negative zircon ɛHf(t) (- 21.8 to - 19.3), low ɛNd(t) (- 17.5 to - 15.2) and initial 87Sr/86Sr of 0.70874-0.70883, suggesting that they were dominantly derived from partial melting of the ancient NCC lower crust but with contributions from the mantle. The Liudusi and Taiboding intrusions reflect complex processes involving partial melting of the lithospheric mantle that was modified by a subducted continental crust, lower crust anatexis induced by basaltic underplating, and subsequent magma hybridization in an extensional regime associated with a considerable thinning of the lithosphere in the eastern NCC during the Early Cretaceous. Multistage crust-mantle interactions including (1) lithospheric mantle modification, induced by subduction of the continental crust which would make the lithospheric mantle more susceptible

  3. Inference by replication in densely connected systems

    SciTech Connect

    Neirotti, Juan P.; Saad, David

    2007-10-15

    An efficient Bayesian inference method for problems that can be mapped onto dense graphs is presented. The approach is based on message passing where messages are averaged over a large number of replicated variable systems exposed to the same evidential nodes. An assumption about the symmetry of the solutions is required for carrying out the averages; here we extend the previous derivation based on a replica-symmetric- (RS)-like structure to include a more complex one-step replica-symmetry-breaking-like (1RSB-like) ansatz. To demonstrate the potential of the approach it is employed for studying critical properties of the Ising linear perceptron and for multiuser detection in code division multiple access (CDMA) under different noise models. Results obtained under the RS assumption in the noncritical regime give rise to a highly efficient signal detection algorithm in the context of CDMA; while in the critical regime one observes a first-order transition line that ends in a continuous phase transition point. Finite size effects are also observed. While the 1RSB ansatz is not required for the original problems, it was applied to the CDMA signal detection problem with a more complex noise model that exhibits RSB behavior, resulting in an improvement in performance.

  4. Causal Inferences in the Campbellian Validity System

    ERIC Educational Resources Information Center

    Lund, Thorleif

    2010-01-01

    The purpose of the present paper is to critically examine causal inferences and internal validity as defined by Campbell and co-workers. Several arguments are given against their counterfactual effect definition, and this effect definition should be considered inadequate for causal research in general. Moreover, their defined independence between…

  5. The origin and crust/mantle mass balance of Central Andean ignimbrite magmatism constrained by oxygen and strontium isotopes and erupted volumes

    NASA Astrophysics Data System (ADS)

    Freymuth, Heye; Brandmeier, Melanie; Wörner, Gerhard

    2015-06-01

    Volcanism during the Neogene in the Central Volcanic Zone (CVZ) of the Andes produced (1) stratovolcanoes, (2) rhyodacitic to rhyolitic ignimbrites which reach volumes of generally less than 300 km3 and (3) large-volume monotonous dacitic ignimbrites of up to several thousand cubic kilometres. We present models for the origin of these magma types using O and Sr isotopes to constrain crust/mantle proportions for the large-volume ignimbrites and explore the relationship to the evolution of the Andean crust. Oxygen isotope ratios were measured on phenocrysts in order to avoid the effects of secondary alteration. Our results show a complete overlap in the Sr-O isotope compositions of lavas from stratovolcanoes and low-volume rhyolitic ignimbrites as well as older (>9 Ma) large-volume dacitic ignimbrites. This suggests that the mass balance of crustal and mantle components are largely similar. By contrast, younger (<10 Ma) large-volume dacitic ignimbrites from the southern portion of the Central Andes have distinctly more radiogenic Sr and heavier O isotopes and thus contrast with older dacitic ignimbrites in northernmost Chile and southern Peru. Results of assimilation and fractional crystallization (AFC) models show that the largest chemical changes occur in the lower crust where magmas acquire a base-level geochemical signature that is later modified by middle to upper crustal AFC. Using geospatial analysis, we estimated the volume of these ignimbrite deposits throughout the Central Andes during the Neogene and examined the spatiotemporal pattern of so-called ignimbrite flare-ups. We observe a N-S migration of maximum ages of the onset of large-volume "ignimbrite pulses" through time: Major pulses occurred at 19-24 Ma (e.g. Oxaya, Nazca Group), 13-14 Ma (e.g. Huaylillas and Altos de Pica ignimbrites) and <10 Ma (Altiplano and Puna ignimbrites). Such "flare-ups" represent magmatic production rates of 25 to >70 km3 Ma-1 km-1 (assuming plutonic/volcanic ratios of 1

  6. Parameter inference for biochemical systems that undergo a Hopf bifurcation.

    PubMed

    Kirk, Paul D W; Toni, Tina; Stumpf, Michael P H

    2008-07-01

    The increasingly widespread use of parametric mathematical models to describe biological systems means that the ability to infer model parameters is of great importance. In this study, we consider parameter inferability in nonlinear ordinary differential equation models that undergo a bifurcation, focusing on a simple but generic biochemical reaction model. We systematically investigate the shape of the likelihood function for the model's parameters, analyzing the changes that occur as the model undergoes a Hopf bifurcation. We demonstrate that there exists an intrinsic link between inference and the parameters' impact on the modeled system's dynamical stability, which we hope will motivate further research in this area.

  7. Models for inference in dynamic metacommunity systems

    USGS Publications Warehouse

    Dorazio, Robert M.; Kery, Marc; Royle, J. Andrew; Plattner, Matthias

    2010-01-01

    A variety of processes are thought to be involved in the formation and dynamics of species assemblages. For example, various metacommunity theories are based on differences in the relative contributions of dispersal of species among local communities and interactions of species within local communities. Interestingly, metacommunity theories continue to be advanced without much empirical validation. Part of the problem is that statistical models used to analyze typical survey data either fail to specify ecological processes with sufficient complexity or they fail to account for errors in detection of species during sampling. In this paper, we describe a statistical modeling framework for the analysis of metacommunity dynamics that is based on the idea of adopting a unified approach, multispecies occupancy modeling, for computing inferences about individual species, local communities of species, or the entire metacommunity of species. This approach accounts for errors in detection of species during sampling and also allows different metacommunity paradigms to be specified in terms of species- and location-specific probabilities of occurrence, extinction, and colonization: all of which are estimable. In addition, this approach can be used to address inference problems that arise in conservation ecology, such as predicting temporal and spatial changes in biodiversity for use in making conservation decisions. To illustrate, we estimate changes in species composition associated with the species-specific phenologies of flight patterns of butterflies in Switzerland for the purpose of estimating regional differences in biodiversity.

  8. Models for inference in dynamic metacommunity systems

    USGS Publications Warehouse

    Dorazio, R.M.; Kery, M.; Royle, J. Andrew; Plattner, M.

    2010-01-01

    A variety of processes are thought to be involved in the formation and dynamics of species assemblages. For example, various metacommunity theories are based on differences in the relative contributions of dispersal of species among local communities and interactions of species within local communities. Interestingly, metacommunity theories continue to be advanced without much empirical validation. Part of the problem is that statistical models used to analyze typical survey data either fail to specify ecological processes with sufficient complexity or they fail to account for errors in detection of species during sampling. In this paper, we describe a statistical modeling framework for the analysis of metacommunity dynamics that is based on the idea of adopting a unified approach, multispecies occupancy modeling, for computing inferences about individual species, local communities of species, or the entire metacommunity of species. This approach accounts for errors in detection of species during sampling and also allows different metacommunity paradigms to be specified in terms of species-and location-specific probabilities of occurrence, extinction, and colonization: all of which are estimable. In addition, this approach can be used to address inference problems that arise in conservation ecology, such as predicting temporal and spatial changes in biodiversity for use in making conservation decisions. To illustrate, we estimate changes in species composition associated with the species-specific phenologies of flight patterns of butterflies in Switzerland for the purpose of estimating regional differences in biodiversity. ?? 2010 by the Ecological Society of America.

  9. LOWER LEVEL INFERENCE CONTROL IN STATISTICAL DATABASE SYSTEMS

    SciTech Connect

    Lipton, D.L.; Wong, H.K.T.

    1984-02-01

    An inference is the process of transforming unclassified data values into confidential data values. Most previous research in inference control has studied the use of statistical aggregates to deduce individual records. However, several other types of inference are also possible. Unknown functional dependencies may be apparent to users who have 'expert' knowledge about the characteristics of a population. Some correlations between attributes may be concluded from 'commonly-known' facts about the world. To counter these threats, security managers should use random sampling of databases of similar populations, as well as expert systems. 'Expert' users of the DATABASE SYSTEM may form inferences from the variable performance of the user interface. Users may observe on-line turn-around time, accounting statistics. the error message received, and the point at which an interactive protocol sequence fails. One may obtain information about the frequency distributions of attribute values, and the validity of data object names from this information. At the back-end of a database system, improved software engineering practices will reduce opportunities to bypass functional units of the database system. The term 'DATA OBJECT' should be expanded to incorporate these data object types which generate new classes of threats. The security of DATABASES and DATABASE SySTEMS must be recognized as separate but related problems. Thus, by increased awareness of lower level inferences, system security managers may effectively nullify the threat posed by lower level inferences.

  10. Inferring connectivity in networked dynamical systems: Challenges using Granger causality

    NASA Astrophysics Data System (ADS)

    Lusch, Bethany; Maia, Pedro D.; Kutz, J. Nathan

    2016-09-01

    Determining the interactions and causal relationships between nodes in an unknown networked dynamical system from measurement data alone is a challenging, contemporary task across the physical, biological, and engineering sciences. Statistical methods, such as the increasingly popular Granger causality, are being broadly applied for data-driven discovery of connectivity in fields from economics to neuroscience. A common version of the algorithm is called pairwise-conditional Granger causality, which we systematically test on data generated from a nonlinear model with known causal network structure. Specifically, we simulate networked systems of Kuramoto oscillators and use the Multivariate Granger Causality Toolbox to discover the underlying coupling structure of the system. We compare the inferred results to the original connectivity for a wide range of parameters such as initial conditions, connection strengths, community structures, and natural frequencies. Our results show a significant systematic disparity between the original and inferred network, unless the true structure is extremely sparse or dense. Specifically, the inferred networks have significant discrepancies in the number of edges and the eigenvalues of the connectivity matrix, demonstrating that they typically generate dynamics which are inconsistent with the ground truth. We provide a detailed account of the dynamics for the Erdős-Rényi network model due to its importance in random graph theory and network science. We conclude that Granger causal methods for inferring network structure are highly suspect and should always be checked against a ground truth model. The results also advocate the need to perform such comparisons with any network inference method since the inferred connectivity results appear to have very little to do with the ground truth system.

  11. On Inference Rules of Logic-Based Information Retrieval Systems.

    ERIC Educational Resources Information Center

    Chen, Patrick Shicheng

    1994-01-01

    Discussion of relevance and the needs of the users in information retrieval focuses on a deductive object-oriented approach and suggests eight inference rules for the deduction. Highlights include characteristics of a deductive object-oriented system, database and data modeling language, implementation, and user interface. (Contains 24…

  12. An expert system shell for inferring vegetation characteristics

    NASA Technical Reports Server (NTRS)

    Harrison, P. Ann; Harrison, Patrick R.

    1992-01-01

    The NASA VEGetation Workbench (VEG) is a knowledge based system that infers vegetation characteristics from reflectance data. The report describes the extensions that have been made to the first generation version of VEG. An interface to a file of unkown cover type data has been constructed. An interface that allows the results of VEG to be written to a file has been implemented. A learning system that learns class descriptions from a data base of historical cover type data and then uses the learned class descriptions to classify an unknown sample has been built. This system has an interface that integrates it into the rest of VEG. The VEG subgoal PROPORTION.GROUND.COVER has been completed and a number of additional techniques that infer the proportion ground cover of a sample have been implemented.

  13. Inference and learning in sparse systems with multiple states

    SciTech Connect

    Braunstein, A.; Ramezanpour, A.; Zhang, P.; Zecchina, R.

    2011-05-15

    We discuss how inference can be performed when data are sampled from the nonergodic phase of systems with multiple attractors. We take as a model system the finite connectivity Hopfield model in the memory phase and suggest a cavity method approach to reconstruct the couplings when the data are separately sampled from few attractor states. We also show how the inference results can be converted into a learning protocol for neural networks in which patterns are presented through weak external fields. The protocol is simple and fully local, and is able to store patterns with a finite overlap with the input patterns without ever reaching a spin-glass phase where all memories are lost.

  14. Evaluation of fuzzy inference systems using fuzzy least squares

    NASA Technical Reports Server (NTRS)

    Barone, Joseph M.

    1992-01-01

    Efforts to develop evaluation methods for fuzzy inference systems which are not based on crisp, quantitative data or processes (i.e., where the phenomenon the system is built to describe or control is inherently fuzzy) are just beginning. This paper suggests that the method of fuzzy least squares can be used to perform such evaluations. Regressing the desired outputs onto the inferred outputs can provide both global and local measures of success. The global measures have some value in an absolute sense, but they are particularly useful when competing solutions (e.g., different numbers of rules, different fuzzy input partitions) are being compared. The local measure described here can be used to identify specific areas of poor fit where special measures (e.g., the use of emphatic or suppressive rules) can be applied. Several examples are discussed which illustrate the applicability of the method as an evaluation tool.

  15. Zircon U-Pb dating, geochemical and Sr-Nd-Hf isotopic characteristics of the Jintonghu monzonitic rocks in western Fujian Province, South China: Implication for Cretaceous crust-mantle interactions and lithospheric extension

    NASA Astrophysics Data System (ADS)

    Li, Bin; Jiang, Shao-Yong; Lu, An-Huai; Zhao, Hai-Xiang; Yang, Tang-Li; Hou, Ming-Lan

    2016-09-01

    Comprehensive petrological, in situ zircon U-Pb dating, Ti-in-zircon temperature and Hf isotopic compositions, whole rock geochemical and Sr-Nd isotopic data are reported for the Jintonghu monzonitic intrusions in the western Fujian Province (Interior Cathaysia Block), South China. The Jintonghu monzonitic intrusions were intruded at 95-96 Ma. Their Sr-Nd-Hf isotopic compositions are similar to the coeval and nearby enriched lithospheric mantle-derived mafic and syenitic rocks, indicating that the Jintonghu monzonitic rocks were likely derived from partial melting of the enriched mantle sources. Their high Nb/Ta ratios (average 21.6) suggest that the metasomatically enriched mantle components were involved, which was attributed to the modification of slab-derived fluid and melt by the subduction of the paleo-Pacific Plate. The presence of mafic xenoliths, together with geochemical and isotopic features indicates a mafic-felsic magma mixing. Furthermore, the Jintonghu intrusions may have experienced orthopyroxene-, biotite- and plagioclase-dominated crystallization. Crust-mantle interaction can be identified as two stages, including that the Early Cretaceous mantle metasomatism and lithospheric extension resulted from the paleo-Pacific slab subduction coupled with slab rollback, and the Late Cretaceous crustal activation and enhanced extension induced by dip-angle subduction and the underplating of mantle-derived mafic magma.

  16. An expert system shell for inferring vegetation characteristics

    NASA Technical Reports Server (NTRS)

    Harrison, P. Ann; Harrison, Patrick R.

    1993-01-01

    The NASA VEGetation Workbench (VEG) is a knowledge based system that infers vegetation characteristics from reflectance data. VEG is described in detail in several references. The first generation version of VEG was extended. In the first year of this contract, an interface to a file of unknown cover type data was constructed. An interface that allowed the results of VEG to be written to a file was also implemented. A learning system that learned class descriptions from a data base of historical cover type data and then used the learned class descriptions to classify an unknown sample was built. This system had an interface that integrated it into the rest of VEG. The VEG subgoal PROPORTION.GROUND.COVER was completed and a number of additional techniques that inferred the proportion ground cover of a sample were implemented. This work was previously described. The work carried out in the second year of the contract is described. The historical cover type database was removed from VEG and stored as a series of flat files that are external to VEG. An interface to the files was provided. The framework and interface for two new VEG subgoals that estimate the atmospheric effect on reflectance data were built. A new interface that allows the scientist to add techniques to VEG without assistance from the developer was designed and implemented. A prototype Help System that allows the user to get more information about each screen in the VEG interface was also added to VEG.

  17. Geochronology and geochemistry of Cretaceous Nanshanping alkaline rocks from the Zijinshan district in Fujian Province, South China: Implications for crust-mantle interaction and lithospheric extension

    NASA Astrophysics Data System (ADS)

    Li, Bin; Jiang, Shao-Yong

    2014-10-01

    In situ zircon U-Pb ages and Hf isotopic data, major and trace elements, and Sr-Nd-Pb isotopic compositions are reported for Nanshanping alkaline rocks from the Zijingshan district in southwestern Fujian Province (the Interior or Western Cathaysia Block) of South China. The Nanshanping alkaline rocks, which consist of porphyritic quartz monzonite, porphyritic syenite, and syenite, revealed a Late Cretaceous age of 100-93 Ma. All of the rocks show high SiO2, K2O + Na2O, and LREE but low CaO, Fe2O3T, MgO, and HFSE (Nb, Ta, P, and Ti) concentrations. These rocks also exhibit uniform initial 87Sr/86Sr ratios of 0.7078 to 0.7087 and εNd(t) values of -4.1 to -7.2, thus falling within the compositional field of Cretaceous basalts and mafic dikes occurring in the Cathaysia Block. Additionally, these rocks display initial Pb isotopic compositions with a 206Pb/204Pbi ratio of 18.25 to 18.45, a 207Pb/204Pbi ratio of 15.63 to 15.67, and a 208Pb/204Pbi ratio of 38.45 to 38.88. Combined with the zircon Hf isotopic compositions (εHf(t) = -11.7 to -3.2), which are different from those of the basement rocks, we suggest that Nanshanping alkaline rocks were primarily derived from a subduction-related enriched mantle source. High Rb/Sr (0.29-0.65) and Zr/Hf (37.5-49.2) but relatively low Ba/Rb (4.4-8.1) ratios suggest that the parental magmas of these rocks were most likely formed via partial melting of a phlogopite-bearing mantle source with carbonate metasomatism. The relatively high SiO2 (62.35-70.79 wt.%) and low Nb/Ta (10.0-15.3) ratios, positive correlation between SiO2 and (87Sr/86Sr)I, and negative correlation between SiO2 and εNd(t) of these rocks suggest that the crustal materials were also involved in formation of the Nanshanping alkaline rocks. Combined with geochemical and isotopic features, we infer magmatic processes similar to AFC (assimilation and fractional crystallization) involving early fractionation of clinopyroxene and olivine and subsequent fractionation of

  18. Devonian granitoids and their hosted mafic enclaves in the Gorny Altai terrane, northwestern Central Asian Orogenic Belt: crust-mantle interaction in a continental arc setting

    NASA Astrophysics Data System (ADS)

    Chen, Ming; Sun, Min

    2016-04-01

    Granitoids are a major component in the upper continental crust and hold key information on how did the continental crust grow and differentiate. This study focuses on the Yaloman intrusive complex from the Gorny Altai terrane, northwestern Central Asian Orogenic Belt (CAOB). The association of granitoids and mafic enclaves can provide important clues on the source nature, petrogenetic processes and geodynamic setting of the Yaloman intrusive complex, which in turn will shed light on the crustal evolution in the northwestern CAOB. Zircon U-Pb dating shows that the granitoids, including quartz diorites and granodiorites, were emplaced in ca. 389-387 Ma. The moderate Na2O + K2O contents and low A/CNK values indicate that these rocks belong to the sub-alkaline series with metaluminous to weakly peraluminous compositions. The granitoids yield two-stage zircon Hf model ages of ca. 0.79-1.07 Ga and whole-rock Nd model ages of ca. 0.90-0.99 Ga, respectively, implying that they were mainly sourced from Neoproterozoic juvenile crustal materials. The mafic enclaves show an almost identical crystallization age of ca. 389 Ma. The identification of coarse-grained xenocrysts and acicular apatites, together with the fine-grained texture, makes us infer that these enclaves are likely to represent magmatic globules commingled with the host magmas. The low SiO2 and high MgO contents of the mafic enclaves further suggest that substantial mantle-derived mafic melts were probably involved in their formation. Importantly, the SiO2 contents of the granitoids and mafic enclaves are well correlated with other major elements and most of the trace elements. Also a broadly negative correlation exists between the SiO2 contents and whole-rock epsilon Nd (390 Ma) values of the granitoids. Given the observation of reversely zoned plagioclases within the granitoids and the common occurrence of igneous mafic enclaves, we propose that magma mixing probably played an important role in the formation

  19. Cretaceous crust-mantle interaction and tectonic evolution of Cathaysia Block in South China: Evidence from pulsed mafic rocks and related magmatism

    NASA Astrophysics Data System (ADS)

    Li, Bin; Jiang, Shao-Yong; Zhang, Qian; Zhao, Hai-Xiang; Zhao, Kui-Dong

    2015-10-01

    Cretaceous tectono-magmatic evolution of the Cathaysia Block in South China is important but their mechanism and geodynamics remain highly disputed. In this study we carried out a detailed geochemical study on the recently found Kuokeng mafic dikes in the western Fujian Province (the Interior Cathaysia Block) to reveal the petrogenesis and geodynamics of the Cretaceous magmatism. Kuokeng mafic dikes were emplaced in three principal episodes: ~ 129 Ma (monzogabbro), ~ 107 Ma (monzodiorite), and ~ 97 Ma (gabbro). Geochemical characteristics indicate that the monzogabbros were derived from the unmodified mantle source, while gabbros were likely derived from metasomatized mantle by subducted slab (fluids and sediments). Sr-Nd isotope compositions indicate that the parental magmas of the monzodiorites were generated by mixing of enriched, mantle-derived, mafic magmas and felsic melts produced by partial melting of crustal materials. Until the Early Cretaceous (~ 123 Ma), the dominant ancient Interior Cathaysia lithospheric mantle exhibited insignificant subduction signature, indicating the melting of asthenospheric mantle and the consequent back-arc extension, producing large-scale partial melting of the crustal materials under the forward subduction regime of the paleo-Pacific plate. The monzodiorites and gabbros appear to be associated with northwestward subduction of Pacific plate under an enhanced lithospheric extensional setting, accompanying with mantle modification, which triggered shallower subduction-related metasomatically enriched lithospheric mantle to melt partially. After ca. 110 Ma, the coastal magmatic belts formed due to a retreat and rollback of the subducting Pacific Plate underneath SE China in the continental margin arc system.

  20. Modeling urban air pollution with optimized hierarchical fuzzy inference system.

    PubMed

    Tashayo, Behnam; Alimohammadi, Abbas

    2016-10-01

    Environmental exposure assessments (EEA) and epidemiological studies require urban air pollution models with appropriate spatial and temporal resolutions. Uncertain available data and inflexible models can limit air pollution modeling techniques, particularly in under developing countries. This paper develops a hierarchical fuzzy inference system (HFIS) to model air pollution under different land use, transportation, and meteorological conditions. To improve performance, the system treats the issue as a large-scale and high-dimensional problem and develops the proposed model using a three-step approach. In the first step, a geospatial information system (GIS) and probabilistic methods are used to preprocess the data. In the second step, a hierarchical structure is generated based on the problem. In the third step, the accuracy and complexity of the model are simultaneously optimized with a multiple objective particle swarm optimization (MOPSO) algorithm. We examine the capabilities of the proposed model for predicting daily and annual mean PM2.5 and NO2 and compare the accuracy of the results with representative models from existing literature. The benefits provided by the model features, including probabilistic preprocessing, multi-objective optimization, and hierarchical structure, are precisely evaluated by comparing five different consecutive models in terms of accuracy and complexity criteria. Fivefold cross validation is used to assess the performance of the generated models. The respective average RMSEs and coefficients of determination (R (2)) for the test datasets using proposed model are as follows: daily PM2.5 = (8.13, 0.78), annual mean PM2.5 = (4.96, 0.80), daily NO2 = (5.63, 0.79), and annual mean NO2 = (2.89, 0.83). The obtained results demonstrate that the developed hierarchical fuzzy inference system can be utilized for modeling air pollution in EEA and epidemiological studies.

  1. Volcanism on Mercury (dikes, lava flows, pyroclastics): Crust/mantle density contrasts, the evolution of compressive stress and the presence of mantle volatiles

    NASA Astrophysics Data System (ADS)

    Wilson, L.; Head, J. W., III

    2008-09-01

    Background. There is great uncertainty about the internal structure of Mercury and the composition of the mantle [e.g., 1, 2]. The high mean density of the body suggests that it may have lost parts of its crust and mantle in a giant impact at some stage after most of its initial accretion was sufficiently complete that at least partial separation of a core had occurred. It is the uncertainty about the timing of the giant impact, and hence the physico-chemical state of proto-Mercury at the time that it occurred, that leads to difficulties in predicting the interior structure and mantle composition. However, it seems reasonable to assume that the Mercury we see today has some combination of a relatively low-density crust and a relatively highdensity mantle; uncertainty remains about the presence and types of volatiles [2]. The second uncertainty is the nature of the surface plains units, specifically, are these lava flows and pyroclastics erupted from the interior, or impact-reworked earlier crust [3-5] (Figs. 1-2)? The detection of candidate pyroclastic deposits [4] has very important implications for mantle volatiles. Furthermore, whatever the surface composition, the presence of planet-wide systems of wrinkle ridges and thrust faults implies that a compressive crustal stress regime became dominant at some stage in the planet's history [3, 6]. If the plains units are indeed lava flows, then the fact that the products of the compressive regime deform many plains units suggests that the development of the compressive stresses may have played a vital role in determining when and if surface eruptions of mantle-derived magmas could occur. This would be analogous to the way in which the change with time from extensional to compressive global stresses in the lithosphere of the Moon influenced the viability of erupting magmas from deep mantle sources [7-9]. Analysis: To investigate the relationship between lithospheric stresses and magma eruption conditions [e.g., 9-11] we

  2. Archean crust-mantle geochemical differentiation

    NASA Technical Reports Server (NTRS)

    Tilton, G. R.

    1983-01-01

    Isotope measurements on carbonatite complexes and komatiites can provide information on the geochemical character and geochemical evolution of the mantle, including the sub-continental mantle. Measurements on young samples establish the validity of the method. These are based on Sr, Nd and Pb data from the Tertiary-Mesozoic Gorgona komatiite and Sr and Pb data from the Cretaceous Oka carbonatite complex. In both cases the data describe a LIL element-depleted source similar to that observed presently in MORB. Carbonatite data have been used to study the mantle beneath the Superior Province of the Canadian Shield one billion years (1 AE) ago. The framework for this investigation was established by Bell et al., who showed that large areas of the province appear to be underlain by LIL element-depleted mantle (Sr-85/Sr-86=0.7028) at 1 AE ago. Additionally Bell et al. found four complexes to have higher initial Sr ratios (Sr-87/Sr-86=0.7038), which they correlated with less depleted (bulk earth?) mantle sources, or possibly crustal contamination. Pb isotope relationships in four of the complexes have been studied by Bell et al.

  3. Seizure prediction using adaptive neuro-fuzzy inference system.

    PubMed

    Rabbi, Ahmed F; Azinfar, Leila; Fazel-Rezai, Reza

    2013-01-01

    In this study, we present a neuro-fuzzy approach of seizure prediction from invasive Electroencephalogram (EEG) by applying adaptive neuro-fuzzy inference system (ANFIS). Three nonlinear seizure predictive features were extracted from a patient's data obtained from the European Epilepsy Database, one of the most comprehensive EEG database for epilepsy research. A total of 36 hours of recordings including 7 seizures was used for analysis. The nonlinear features used in this study were similarity index, phase synchronization, and nonlinear interdependence. We designed an ANFIS classifier constructed based on these features as input. Fuzzy if-then rules were generated by the ANFIS classifier using the complex relationship of feature space provided during training. The membership function optimization was conducted based on a hybrid learning algorithm. The proposed method achieved highest sensitivity of 80% with false prediction rate as low as 0.46 per hour. PMID:24110134

  4. Comments on "Functional equivalence between radial basis function networks and fuzzy inference systems".

    PubMed

    Anderson, H C; Lotfi, A; Westphal, L C; Jang, J R

    1998-01-01

    The above paper claims that under a set of minor restrictions radial basis function networks and fuzzy inference systems are functionally equivalent. The purpose of this letter is to show that this set of restrictions is incomplete and that, when it is completed, the said functional equivalence applies only to a small range of fuzzy inference systems. In addition, a modified set of restrictions is proposed which is applicable for a much wider range of fuzzy inference systems.

  5. Annual Rainfall Forecasting by Using Mamdani Fuzzy Inference System

    NASA Astrophysics Data System (ADS)

    Fallah-Ghalhary, G.-A.; Habibi Nokhandan, M.; Mousavi Baygi, M.

    2009-04-01

    Long-term rainfall prediction is very important to countries thriving on agro-based economy. In general, climate and rainfall are highly non-linear phenomena in nature giving rise to what is known as "butterfly effect". The parameters that are required to predict the rainfall are enormous even for a short period. Soft computing is an innovative approach to construct computationally intelligent systems that are supposed to possess humanlike expertise within a specific domain, adapt themselves and learn to do better in changing environments, and explain how they make decisions. Unlike conventional artificial intelligence techniques the guiding principle of soft computing is to exploit tolerance for imprecision, uncertainty, robustness, partial truth to achieve tractability, and better rapport with reality. In this paper, 33 years of rainfall data analyzed in khorasan state, the northeastern part of Iran situated at latitude-longitude pairs (31°-38°N, 74°- 80°E). this research attempted to train Fuzzy Inference System (FIS) based prediction models with 33 years of rainfall data. For performance evaluation, the model predicted outputs were compared with the actual rainfall data. Simulation results reveal that soft computing techniques are promising and efficient. The test results using by FIS model showed that the RMSE was obtained 52 millimeter.

  6. Streamflow Forecasting Using Nuero-Fuzzy Inference System

    NASA Astrophysics Data System (ADS)

    Nanduri, U. V.; Swain, P. C.

    2005-12-01

    The prediction of flow into a reservoir is fundamental in water resources planning and management. The need for timely and accurate streamflow forecasting is widely recognized and emphasized by many in water resources fraternity. Real-time forecasts of natural inflows to reservoirs are of particular interest for operation and scheduling. The physical system of the river basin that takes the rainfall as an input and produces the runoff is highly nonlinear, complicated and very difficult to fully comprehend. The system is influenced by large number of factors and variables. The large spatial extent of the systems forces the uncertainty into the hydrologic information. A variety of methods have been proposed for forecasting reservoir inflows including conceptual (physical) and empirical (statistical) models (WMO 1994), but none of them can be considered as unique superior model (Shamseldin 1997). Owing to difficulties of formulating reasonable non-linear watershed models, recent attempts have resorted to Neural Network (NN) approach for complex hydrologic modeling. In recent years the use of soft computing in the field of hydrological forecasting is gaining ground. The relatively new soft computing technique of Adaptive Neuro-Fuzzy Inference System (ANFIS), developed by Jang (1993) is able to take care of the non-linearity, uncertainty, and vagueness embedded in the system. It is a judicious combination of the Neural Networks and fuzzy systems. It can learn and generalize highly nonlinear and uncertain phenomena due to the embedded neural network (NN). NN is efficient in learning and generalization, and the fuzzy system mimics the cognitive capability of human brain. Hence, ANFIS can learn the complicated processes involved in the basin and correlate the precipitation to the corresponding discharge. In the present study, one step ahead forecasts are made for ten-daily flows, which are mostly required for short term operational planning of multipurpose reservoirs. A

  7. Perturbation Biology: Inferring Signaling Networks in Cellular Systems

    PubMed Central

    Miller, Martin L.; Gauthier, Nicholas P.; Jing, Xiaohong; Kaushik, Poorvi; He, Qin; Mills, Gordon; Solit, David B.; Pratilas, Christine A.; Weigt, Martin; Braunstein, Alfredo; Pagnani, Andrea; Zecchina, Riccardo; Sander, Chris

    2013-01-01

    We present a powerful experimental-computational technology for inferring network models that predict the response of cells to perturbations, and that may be useful in the design of combinatorial therapy against cancer. The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is quantified in terms of relative changes in the measured levels of proteins, phospho-proteins and cellular phenotypes such as viability. Computational network models are derived de novo, i.e., without prior knowledge of signaling pathways, and are based on simple non-linear differential equations. The prohibitively large solution space of all possible network models is explored efficiently using a probabilistic algorithm, Belief Propagation (BP), which is three orders of magnitude faster than standard Monte Carlo methods. Explicit executable models are derived for a set of perturbation experiments in SKMEL-133 melanoma cell lines, which are resistant to the therapeutically important inhibitor of RAF kinase. The resulting network models reproduce and extend known pathway biology. They empower potential discoveries of new molecular interactions and predict efficacious novel drug perturbations, such as the inhibition of PLK1, which is verified experimentally. This technology is suitable for application to larger systems in diverse areas of molecular biology. PMID:24367245

  8. Overlapping Sr-Nd-Hf-O isotopic compositions in Permian mafic enclaves and host granitoids in Alxa Block, NW China: Evidence for crust-mantle interaction and implications for the generation of silicic igneous provinces

    NASA Astrophysics Data System (ADS)

    Dan, Wei; Wang, Qiang; Wang, Xuan-Ce; Liu, Yu; Wyman, Derek A.; Liu, Yong-Sheng

    2015-08-01

    In general, the mantle provides heat and/or material for the generation of the silicic igneous provinces (SIPs). The rarity of mafic microgranular enclaves (MMEs), however, hampers understanding of the mantle's role in generating SIPs and the process of crust-mantle interaction. The widespread distributed MMEs in the newly reported Alxa SIP provide an opportunity to study these processes. This study integrates in situ zircon U-Pb age and Hf-O isotope analyses, whole-rock geochemistry and Sr-Nd isotope results for the MMEs and host granitoids in the Alxa Block. SIMS zircon U-Pb dating reveals that there are two generations of MMEs and host granitoids. The MMEs in the Bayannuoergong batholith were formed at ca. 278 Ma, similar to the age (280 Ma) of host granitoids, and the MMEs and host granitoids in the Yamaitu pluton were formed at ca. 272-270 Ma. All MMEs have relatively low SiO2 (50.7-61.4 wt.%) and Th (0.8-2.8 ppm), but relatively high MgO (2.6-4.9 wt.%), Cr (23-146 ppm) and Ni (6-38 ppm) contents compared to the host granitoids, with SiO2 (63.6-77.5 wt.%), Th (5.2-41 ppm), MgO (0.23-2.1 wt.%), Cr (10-38 ppm) and Ni (5-14 ppm). All MMEs have whole rock Sr-Nd and zircon Hf-O isotope compositions similar to their corresponding host granitoids. The 280 Ma MMEs have lower whole rock εNd(t) (- 13.5) and higher initial 87Sr/86Sr values (0.7095) and zircon δ18O values (6.3‰) compared to the εNd(t) (- 11.5), initial 87Sr/86Sr values (0.7070) and zircon δ18O values (5.6‰) of the 270 Ma MMEs. The occurrences of quartz xenocrysts, K-feldspar megacrysts, corroded feldspars and acicular apatites indicate that the MMEs are the products of the mixing between mantle- and crust-derived magmas. The striking similarities in the zircon Hf-O isotopic compositions in both MME-host granitoid pairs indicate that the granitoids and MMEs have similar sources. The granitoids are proposed to be mainly sourced from magmas generated by remelting of newly formed mafic rocks, which

  9. A Modular Artificial Intelligence Inference Engine System (MAIS) for support of on orbit experiments

    NASA Technical Reports Server (NTRS)

    Hancock, Thomas M., III

    1994-01-01

    This paper describes a Modular Artificial Intelligence Inference Engine System (MAIS) support tool that would provide health and status monitoring, cognitive replanning, analysis and support of on-orbit Space Station, Spacelab experiments and systems.

  10. DYNAMICAL INFERENCE FROM A KINEMATIC SNAPSHOT: THE FORCE LAW IN THE SOLAR SYSTEM

    SciTech Connect

    Bovy, Jo; Hogg, David W.; Murray, Iain

    2010-03-10

    If a dynamical system is long-lived and non-resonant (that is, if there is a set of tracers that have evolved independently through many orbital times), and if the system is observed at any non-special time, it is possible to infer the dynamical properties of the system (such as the gravitational force or acceleration law) from a snapshot of the positions and velocities of the tracer population at a single moment in time. In this paper, we describe a general inference technique that solves this problem while allowing (1) the unknown distribution function of the tracer population to be simultaneously inferred and marginalized over, and (2) prior information about the gravitational field and distribution function to be taken into account. As an example, we consider the simplest problem of this kind: we infer the force law in the solar system using only an instantaneous kinematic snapshot (valid at 2009 April 1.0) for the eight major planets. We consider purely radial acceleration laws of the form a{sub r} = -A [r/r{sub 0}]{sup -a}lpha, where r is the distance from the Sun. Using a probabilistic inference technique, we infer 1.989 < alpha < 2.052 (95% interval), largely independent of any assumptions about the distribution of energies and eccentricities in the system beyond the assumption that the system is phase-mixed. Generalizations of the methods used here will permit, among other things, inference of Milky Way dynamics from Gaia-like observations.

  11. MORB mantle hosts the missing Eu (Sr, Nb, Ta and Ti) in the continental crust: New perspectives on crustal growth, crust-mantle differentiation and chemical structure of oceanic upper mantle

    NASA Astrophysics Data System (ADS)

    Niu, Yaoling; O'Hara, Michael J.

    2009-09-01

    We have examined the high quality data of 306 mid-ocean ridge basalt (MORB) glass samples from the East Pacific Rise (EPR), near-EPR seamounts, Pacific Antarctic Ridge (PAR), near-PAR seamounts, Mid-Atlantic Ridge (MAR), and near-MAR seamounts. The data show a correlated variation between Eu/Eu* and Sr/Sr*, and both decrease with decreasing MgO, pointing to the effect of plagioclase crystallization. The observation that samples with MgO > 9.5 wt.% (before plagioclase on the liquidus) show Eu/Eu* > 1 and Sr/Sr* > 1 and that none of the major phases (i.e., olivine, orthopyroxene, clinopyroxene, spinel and garnet) in the sub-ridge mantle melting region can effectively fractionate Eu and Sr from otherwise similarly incompatible elements indicates that the depleted MORB mantle (DMM) possesses excess Sr and Eu, i.e., [Sr/Sr*]DMM > 1 and [Eu/Eu*]DMM > 1. Furthermore, the well-established observation that DNb ≈ DTh, DTa ≈ DU and DTi ≈ DSm during MORB mantle melting, yet primitive MORB melts all have [Nb/Th]PMMORB > 1, [Ta/U]PMMORB > 1 and [Ti/Sm]PMMORB > 1 (where PM indicates primitive mantle normalized), also points to the presence of excess Nb, Ta and Ti in the DMM, i.e., [Nb/Th]PMDMM > 1, [Ta/U]PMDMM > 1 and [Ti/Sm]PMDMM > 1. The excesses of Eu, Sr, Nb, Ta and Ti in the DMM complement the well-known deficiencies of these elements in the bulk continental crust (BCC). These new observations, which support the notion that the DMM and BCC are complementary in terms of the overall abundances of incompatible elements, offer new insights into the crust-mantle differentiation. These observations are best explained by partial melting of amphibolite of MORB protolith during continental collision, which produces andesitic melts with a remarkable compositional (major and trace element abundances as well as key elemental ratios) similarity to the BCC, as revealed by andesites in southern Tibet produced during the India-Asia continental collision. An average amphibolite of MORB

  12. An expert system shell for inferring vegetation characteristics: Prototype help system (Task 1)

    NASA Technical Reports Server (NTRS)

    1993-01-01

    The NASA Vegetation Workbench (VEG) is a knowledge based system that infers vegetation characteristics from reflectance data. A prototype of the VEG subgoal HELP.SYSTEM has been completed and the Help System has been added to the VEG system. It is loaded when the user first clicks on the HELP.SYSTEM option in the Tool Box Menu. The Help System provides a user tool to support needed user information. It also provides interactive tools the scientist may use to develop new help messages and to modify existing help messages that are attached to VEG screens. The system automatically manages system and file operations needed to preserve new or modified help messages. The Help System was tested both as a help system development and a help system user tool.

  13. Artificial frame filling using adaptive neural fuzzy inference system for particle image velocimetry dataset

    NASA Astrophysics Data System (ADS)

    Akdemir, Bayram; Doǧan, Sercan; Aksoy, Muharrem H.; Canli, Eyüp; Özgören, Muammer

    2015-03-01

    Liquid behaviors are very important for many areas especially for Mechanical Engineering. Fast camera is a way to observe and search the liquid behaviors. Camera traces the dust or colored markers travelling in the liquid and takes many pictures in a second as possible as. Every image has large data structure due to resolution. For fast liquid velocity, there is not easy to evaluate or make a fluent frame after the taken images. Artificial intelligence has much popularity in science to solve the nonlinear problems. Adaptive neural fuzzy inference system is a common artificial intelligence in literature. Any particle velocity in a liquid has two dimension speed and its derivatives. Adaptive Neural Fuzzy Inference System has been used to create an artificial frame between previous and post frames as offline. Adaptive neural fuzzy inference system uses velocities and vorticities to create a crossing point vector between previous and post points. In this study, Adaptive Neural Fuzzy Inference System has been used to fill virtual frames among the real frames in order to improve image continuity. So this evaluation makes the images much understandable at chaotic or vorticity points. After executed adaptive neural fuzzy inference system, the image dataset increase two times and has a sequence as virtual and real, respectively. The obtained success is evaluated using R2 testing and mean squared error. R2 testing has a statistical importance about similarity and 0.82, 0.81, 0.85 and 0.8 were obtained for velocities and derivatives, respectively.

  14. Parameter and Structure Inference for Nonlinear Dynamical Systems

    NASA Technical Reports Server (NTRS)

    Morris, Robin D.; Smelyanskiy, Vadim N.; Millonas, Mark

    2006-01-01

    A great many systems can be modeled in the non-linear dynamical systems framework, as x = f(x) + xi(t), where f() is the potential function for the system, and xi is the excitation noise. Modeling the potential using a set of basis functions, we derive the posterior for the basis coefficients. A more challenging problem is to determine the set of basis functions that are required to model a particular system. We show that using the Bayesian Information Criteria (BIC) to rank models, and the beam search technique, that we can accurately determine the structure of simple non-linear dynamical system models, and the structure of the coupling between non-linear dynamical systems where the individual systems are known. This last case has important ecological applications.

  15. An evolutionary approach toward dynamic self-generated fuzzy inference systems.

    PubMed

    Zhou, Yi; Er, Meng Joo

    2008-08-01

    An evolutionary approach toward automatic generation of fuzzy inference systems (FISs), termed evolutionary dynamic self-generated fuzzy inference systems (EDSGFISs), is proposed in this paper. The structure and parameters of an FIS are generated through reinforcement learning, whereas an action set for training the consequents of the FIS is evolved via genetic algorithms (GAs). The proposed EDSGFIS algorithm can automatically create, delete, and adjust fuzzy rules according to the performance of the entire system, as well as evaluation of individual fuzzy rules. Simulation studies on a wall-following task by a mobile robot show that the proposed EDSGFIS approach is superior to other related methods.

  16. FINDS: A fault inferring nonlinear detection system. User's guide

    NASA Technical Reports Server (NTRS)

    Lancraft, R. E.; Caglayan, A. K.

    1983-01-01

    The computer program FINDS is written in FORTRAN-77, and is intended for operation on a VAX 11-780 or 11-750 super minicomputer, using the VMS operating system. The program detects, isolates, and compensates for failures in navigation aid instruments and onboard flight control and navigation sensors of a Terminal Configured Vehicle aircraft in a Microwave Landing System environment. In addition, FINDS provides sensor fault tolerant estimates for the aircraft states which are then used by an automatic guidance and control system to land the aircraft along a prescribed path. FINDS monitors for failures by evaluating all sensor outputs simultaneously using the nonlinear analytic relationships between the various sensor outputs arising from the aircraft point mass equations of motion. Hence, FINDS is an integrated sensor failure detection and isolation system.

  17. Earth system sensitivity inferred from Pliocene modelling and data

    USGS Publications Warehouse

    Lunt, D.J.; Haywood, A.M.; Schmidt, G.A.; Salzmann, U.; Valdes, P.J.; Dowsett, H.J.

    2010-01-01

    Quantifying the equilibrium response of global temperatures to an increase in atmospheric carbon dioxide concentrations is one of the cornerstones of climate research. Components of the Earths climate system that vary over long timescales, such as ice sheets and vegetation, could have an important effect on this temperature sensitivity, but have often been neglected. Here we use a coupled atmosphere-ocean general circulation model to simulate the climate of the mid-Pliocene warm period (about three million years ago), and analyse the forcings and feedbacks that contributed to the relatively warm temperatures. Furthermore, we compare our simulation with proxy records of mid-Pliocene sea surface temperature. Taking these lines of evidence together, we estimate that the response of the Earth system to elevated atmospheric carbon dioxide concentrations is 30-50% greater than the response based on those fast-adjusting components of the climate system that are used traditionally to estimate climate sensitivity. We conclude that targets for the long-term stabilization of atmospheric greenhouse-gas concentrations aimed at preventing a dangerous human interference with the climate system should take into account this higher sensitivity of the Earth system. ?? 2010 Macmillan Publishers Limited. All rights reserved.

  18. An expert system shell for inferring vegetation characteristics: The learning system (tasks C and D)

    NASA Technical Reports Server (NTRS)

    Harrison, P. Ann; Harrison, Patrick R.

    1992-01-01

    This report describes the implementation of a learning system that uses a data base of historical cover type reflectance data taken at different solar zenith angles and wavelengths to learn class descriptions of classes of cover types. It has been integrated with the VEG system and requires that the VEG system be loaded to operate. VEG is the NASA VEGetation workbench - an expert system for inferring vegetation characteristics from reflectance data. The learning system provides three basic options. Using option one, the system learns class descriptions of one or more classes. Using option two, the system learns class descriptions of one or more classes and then uses the learned classes to classify an unknown sample. Using option three, the user can test the system's classification performance. The learning system can also be run in an automatic mode. In this mode, options two and three are executed on each sample from an input file. The system was developed using KEE. It is menu driven and contains a sophisticated window and mouse driven interface which guides the user through various computations. Input and output file management and data formatting facilities are also provided.

  19. Rifting process of the Izu-Ogasawara-Mariana arc-backarc system inferred from active source seismic studies

    NASA Astrophysics Data System (ADS)

    Takahashi, N.; Kodaira, S.; Miura, S.; Sato, T.; Yamashita, M.; No, T.; Takizawa, K.; Kaiho, Y.; Kaneda, Y.

    2008-12-01

    The Izu-Ogasawara-Mariana (IBM) arc-backarc system has continued the crustal growth through crustal thickening by magmatic activities and crustal thinning by backarc opening. Tatsumi et al (2008) proposed petrological crustal growth model started from basaltic magmas rising from the slab, and showed the consistency with the seismic velocity model. Although crustal growth by the crustal thickening are modeled, crustal structural change by the backarc opening are not still unknown yet. The Shikoku Basin and Parece Vela Basin were formed by the backarc opening during approximately 15-30 Ma. Since 6 Ma, the Mariana Trough has opened and the stage already moved to spreading process from rifting process. In the northern Izu-Ogasawara arc, the Sumisu rift is in the initial rifting stage. Therefore, understanding of the crustal change by the backarc opening from rifting to spreading is indispensable to know the crustal growth of whole Izu-Ogasawara-Mariana island arc. Japan Agency for Marine-Earth Science and Technology (JAMSTEC) has carried out seismic studies using a multichannel reflection survey system and ocean bottom seismographs (OBSs) around the IBM arc since 2003 (Takahashi et al., 2007; Kodaira et al., 2007; Takahashi et al., 2008; Kodaira et al., 2008). We already obtained eight P-wave velocity models across the IBM arc and these structures record the crustal structural change during the backarc opening process from the rifting stage to the spreading stage. As the results, we identified characteristics of the crustal structural change accompanied with backarc opening as follows. (1) Beneath the initial rifting stage without normal faults, for example, in the northern tip of the Mariana Trough, crustal thickening are identified. (2) Beneath the initial rifting stage with normal faults, for example, in the Sumisu Rift, the crustal thickness is almost similar to that beneath the volcanic front. Although an existence of the crust-mantle transition layer with

  20. Efficient Inference of Parsimonious Phenomenological Models of Cellular Dynamics Using S-Systems and Alternating Regression

    PubMed Central

    Daniels, Bryan C.; Nemenman, Ilya

    2015-01-01

    The nonlinearity of dynamics in systems biology makes it hard to infer them from experimental data. Simple linear models are computationally efficient, but cannot incorporate these important nonlinearities. An adaptive method based on the S-system formalism, which is a sensible representation of nonlinear mass-action kinetics typically found in cellular dynamics, maintains the efficiency of linear regression. We combine this approach with adaptive model selection to obtain efficient and parsimonious representations of cellular dynamics. The approach is tested by inferring the dynamics of yeast glycolysis from simulated data. With little computing time, it produces dynamical models with high predictive power and with structural complexity adapted to the difficulty of the inference problem. PMID:25806510

  1. Inferring superposition and entanglement in evolving systems from measurements in a single basis

    SciTech Connect

    Schelpe, Bella; Kent, Adrian; Munro, William; Spiller, Tim

    2003-05-01

    We discuss what can be inferred from measurements on evolving one- and two-qubit systems using a single measurement basis at various times. We show that, given reasonable physical assumptions, carrying out such measurements at quarter-period intervals is enough to demonstrate coherent oscillations of one or two qubits between the relevant measurement basis states. One can thus infer from such measurements alone that an approximately equal superposition of two measurement basis states has been created during a coherent oscillation experiment. Similarly, one can infer that a near-maximally entangled state of two qubits has been created part way through an experiment involving a putative SWAP gate. These results apply even if the relevant quantum systems are only approximate qubits. We discuss applications to fundamental quantum physics experiments and quantum-information processing investigations.

  2. Hydroclimatic Extremes: Inferences and Prediction from a Dynamical Systems Perspective

    NASA Astrophysics Data System (ADS)

    Lall, U.

    2015-12-01

    Hydroclimatic extremes , such as major floods and droughts, or periods with a high frequency of clustered tornadoes, fires or cyclones, have often been thought of as random, rare events, and much of the literature on these topics has been obsessed with the estimation of the tail probabilities (e.g., the 100 year event) of these processes. It has taken the "acceptance" of the notion of climate change to question whether the machinery developed for such estimation or even the associated questions are reasonable. However, much of the literature that has evolved since has focused on how to detect and model changes in these probabilities using a variety of methods. In this talk, I will argue that while such efforts may be useful in a certain, outdated context, they are not necessarily leading to an improvement in eihter the science of the application of the science to disaster risk mitigation. I develop an argument that hydroclimatic extremes result from an organization of the associated global and local dynamical systems that leads to the systems trajectories locking into a particular region of state space. Such excursions could be considered as rare events, in their ultimate expression, or in their frequency of visitation and persistence in those states. An open question is whether the dynamics of the system under such conditions are marked by high or low predictabilty in the Lyapunov sense. A characterization of the dimension and predictability of hydroclimatic extremes would allow us to better understand the potential implications of climate change, and also of whether or not a regional drought or similar persistent regime is likely to dissipate or grow.

  3. Large-Scale Optimization for Bayesian Inference in Complex Systems

    SciTech Connect

    Willcox, Karen; Marzouk, Youssef

    2013-11-12

    The SAGUARO (Scalable Algorithms for Groundwater Uncertainty Analysis and Robust Optimization) Project focused on the development of scalable numerical algorithms for large-scale Bayesian inversion in complex systems that capitalize on advances in large-scale simulation-based optimization and inversion methods. The project was a collaborative effort among MIT, the University of Texas at Austin, Georgia Institute of Technology, and Sandia National Laboratories. The research was directed in three complementary areas: efficient approximations of the Hessian operator, reductions in complexity of forward simulations via stochastic spectral approximations and model reduction, and employing large-scale optimization concepts to accelerate sampling. The MIT--Sandia component of the SAGUARO Project addressed the intractability of conventional sampling methods for large-scale statistical inverse problems by devising reduced-order models that are faithful to the full-order model over a wide range of parameter values; sampling then employs the reduced model rather than the full model, resulting in very large computational savings. Results indicate little effect on the computed posterior distribution. On the other hand, in the Texas--Georgia Tech component of the project, we retain the full-order model, but exploit inverse problem structure (adjoint-based gradients and partial Hessian information of the parameter-to-observation map) to implicitly extract lower dimensional information on the posterior distribution; this greatly speeds up sampling methods, so that fewer sampling points are needed. We can think of these two approaches as ``reduce then sample'' and ``sample then reduce.'' In fact, these two approaches are complementary, and can be used in conjunction with each other. Moreover, they both exploit deterministic inverse problem structure, in the form of adjoint-based gradient and Hessian information of the underlying parameter-to-observation map, to achieve their

  4. An expert system shell for inferring vegetation characteristics: Implementation of additional techniques (task E)

    NASA Technical Reports Server (NTRS)

    Harrison, P. Ann

    1992-01-01

    The NASA VEGetation Workbench (VEG) is a knowledge based system that infers vegetation characteristics from reflectance data. The VEG subgoal PROPORTION.GROUND.COVER has been completed and a number of additional techniques that infer the proportion ground cover of a sample have been implemented. Some techniques operate on sample data at a single wavelength. The techniques previously incorporated in VEG for other subgoals operated on data at a single wavelength so implementing the additional single wavelength techniques required no changes to the structure of VEG. Two techniques which use data at multiple wavelengths to infer proportion ground cover were also implemented. This work involved modifying the structure of VEG so that multiple wavelength techniques could be incorporated. All the new techniques were tested using both the VEG 'Research Mode' and the 'Automatic Mode.'

  5. Explorations of electric current system in solar active regions. I - Empirical inferences of the current flows

    NASA Technical Reports Server (NTRS)

    Ding, Y. J.; Hong, Q. F.; Hagyard, M. J.; Deloach, A. C.; Liu, X. P.

    1987-01-01

    Techniques to identify sources of electric current systems and their channels of flow in solar active regions are explored. Measured photospheric vector magnetic fields together with high-resolution white-light and H-alpha filtergrams provide the data base to derive the current systems in the photosphere and chromosphere. As an example, the techniques are then applied to infer current systems in AR 2372 in early April 1980.

  6. Review of Medical Image Classification using the Adaptive Neuro-Fuzzy Inference System

    PubMed Central

    Hosseini, Monireh Sheikh; Zekri, Maryam

    2012-01-01

    Image classification is an issue that utilizes image processing, pattern recognition and classification methods. Automatic medical image classification is a progressive area in image classification, and it is expected to be more developed in the future. Because of this fact, automatic diagnosis can assist pathologists by providing second opinions and reducing their workload. This paper reviews the application of the adaptive neuro-fuzzy inference system (ANFIS) as a classifier in medical image classification during the past 16 years. ANFIS is a fuzzy inference system (FIS) implemented in the framework of an adaptive fuzzy neural network. It combines the explicit knowledge representation of an FIS with the learning power of artificial neural networks. The objective of ANFIS is to integrate the best features of fuzzy systems and neural networks. A brief comparison with other classifiers, main advantages and drawbacks of this classifier are investigated. PMID:23493054

  7. Bayesian parameter inference and model selection by population annealing in systems biology.

    PubMed

    Murakami, Yohei

    2014-01-01

    Parameter inference and model selection are very important for mathematical modeling in systems biology. Bayesian statistics can be used to conduct both parameter inference and model selection. Especially, the framework named approximate Bayesian computation is often used for parameter inference and model selection in systems biology. However, Monte Carlo methods needs to be used to compute Bayesian posterior distributions. In addition, the posterior distributions of parameters are sometimes almost uniform or very similar to their prior distributions. In such cases, it is difficult to choose one specific value of parameter with high credibility as the representative value of the distribution. To overcome the problems, we introduced one of the population Monte Carlo algorithms, population annealing. Although population annealing is usually used in statistical mechanics, we showed that population annealing can be used to compute Bayesian posterior distributions in the approximate Bayesian computation framework. To deal with un-identifiability of the representative values of parameters, we proposed to run the simulations with the parameter ensemble sampled from the posterior distribution, named "posterior parameter ensemble". We showed that population annealing is an efficient and convenient algorithm to generate posterior parameter ensemble. We also showed that the simulations with the posterior parameter ensemble can, not only reproduce the data used for parameter inference, but also capture and predict the data which was not used for parameter inference. Lastly, we introduced the marginal likelihood in the approximate Bayesian computation framework for Bayesian model selection. We showed that population annealing enables us to compute the marginal likelihood in the approximate Bayesian computation framework and conduct model selection depending on the Bayes factor.

  8. The relative roles of visuospatial and linguistic working memory systems in generating inferences during visual narrative comprehension.

    PubMed

    Magliano, Joseph P; Larson, Adam M; Higgs, Karyn; Loschky, Lester C

    2016-02-01

    This study investigated the relative roles of visuospatial versus linguistic working memory (WM) systems in the online generation of bridging inferences while viewers comprehend visual narratives. We contrasted these relative roles in the visuospatial primacy hypothesis versus the shared (visuospatial & linguistic) systems hypothesis, and tested them in 3 experiments. Participants viewed picture stories containing multiple target episodes consisting of a beginning state, a bridging event, and an end state, respectively, and the presence of the bridging event was manipulated. When absent, viewers had to infer the bridging-event action to comprehend the end-state image. A pilot study showed that after viewing the end-state image, participants' think-aloud protocols contained more inferred actions when the bridging event was absent than when it was present. Likewise, Experiment 1 found longer viewing times for the end-state image when the bridging-event image was absent, consistent with viewing times revealing online inference generation processes. Experiment 2 showed that both linguistic and visuospatial WM loads attenuated the inference viewing time effect, consistent with the shared systems hypothesis. Importantly, however, Experiment 3 found that articulatory suppression did not attenuate the inference viewing time effect, indicating that (sub)vocalization did not support online inference generation during visual narrative comprehension. Thus, the results support a shared-systems hypothesis in which both visuospatial and linguistic WM systems support inference generation in visual narratives, with the linguistic WM system operating at a deeper level than (sub)vocalization.

  9. Design of a Software Sensor for Feedwater Flow Measurement Using a Fuzzy Inference System

    SciTech Connect

    Na, Man Gyun; Shin, Sun Ho; Jung, Dong Won

    2005-06-15

    Venturi meters are used to measure the feedwater flow rate in most current pressurized water reactors. These meters can decrease the thermal performance of nuclear power plants because the feedwater flow rate can be overmeasured due to their fouling phenomena that make corrosion products caused by long-term operation accumulate in the feedwater flow meters. Therefore, in this paper, a software sensor using a fuzzy inference system is developed in order to increase the thermal efficiency by accurately estimating online the feedwater flow rate. The fuzzy inference system to be used for black-box modeling of the feedwater system is equipped with an automatic design algorithm that automates the selection of the input signals to the fuzzy inference system and its fuzzy rule generation including parameter optimization. The proposed algorithm was verified by using the numerical simulation data of the MARS code for Kori Nuclear Power Plant Unit 1 and also the real plant data of Yonggwang Nuclear Power Plant Unit 3. In the simulations using numerical simulation data and real plant data, the relative 2{sigma} errors and the relative maximum error are small enough. The proposed method can be applied successfully to validate and monitor the existing feedwater flow meters.

  10. Free-energy inference from partial work measurements in small systems.

    PubMed

    Ribezzi-Crivellari, Marco; Ritort, Felix

    2014-08-19

    Fluctuation relations (FRs) are among the few existing general results in nonequilibrium systems. Their verification requires the measurement of the total work performed on a system. Nevertheless in many cases only a partial measurement of the work is possible. Here we consider FRs in dual-trap optical tweezers where two different forces (one per trap) are measured. With this setup we perform pulling experiments on single molecules by moving one trap relative to the other. We demonstrate that work should be measured using the force exerted by the trap that is moved. The force that is measured in the trap at rest fails to provide the full dissipation in the system, leading to a (incorrect) work definition that does not satisfy the FR. The implications to single-molecule experiments and free-energy measurements are discussed. In the case of symmetric setups a second work definition, based on differential force measurements, is introduced. This definition is best suited to measure free energies as it shows faster convergence of estimators. We discuss measurements using the (incorrect) work definition as an example of partial work measurement. We show how to infer the full work distribution from the partial one via the FR. The inference process does also yield quantitative information, e.g., the hydrodynamic drag on the dumbbell. Results are also obtained for asymmetric dual-trap setups. We suggest that this kind of inference could represent a previously unidentified and general application of FRs to extract information about irreversible processes in small systems.

  11. FINDS: A fault inferring nonlinear detection system programmers manual, version 3.0

    NASA Technical Reports Server (NTRS)

    Lancraft, R. E.

    1985-01-01

    Detailed software documentation of the digital computer program FINDS (Fault Inferring Nonlinear Detection System) Version 3.0 is provided. FINDS is a highly modular and extensible computer program designed to monitor and detect sensor failures, while at the same time providing reliable state estimates. In this version of the program the FINDS methodology is used to detect, isolate, and compensate for failures in simulated avionics sensors used by the Advanced Transport Operating Systems (ATOPS) Transport System Research Vehicle (TSRV) in a Microwave Landing System (MLS) environment. It is intended that this report serve as a programmers guide to aid in the maintenance, modification, and revision of the FINDS software.

  12. From free energy measurements to thermodynamic inference in nonequilibrium small systems

    NASA Astrophysics Data System (ADS)

    Alemany, A.; Ribezzi-Crivellari, M.; Ritort, F.

    2015-07-01

    Fluctuation theorems (FTs), such as the Crooks or Jarzynski equalities (JEs), have become an important tool in single-molecule biophysics where they allow experimentalists to exploit thermal fluctuations and measure free-energy differences from non-equilibrium pulling experiments. The rich phenomenology of biomolecular systems has stimulated the development of extensions to the standard FTs, to encompass different experimental situations. Here we discuss an extension of the Crooks fluctuation relation that allows the thermodynamic characterization of kinetic molecular states. This extension can be connected to the generalized JE under feedback. Finally we address the recently introduced concept of thermodynamic inference or how FTs can be used to extract the total entropy production distribution in nonequilibrium systems from partial entropy production measurements. We discuss the significance of the concept of effective temperature in this context and show how thermodynamic inference provides a unifying comprehensive picture in several nonequilibrium problems.

  13. A novel fuzzy logic inference system for decision support in weaning from mechanical ventilation.

    PubMed

    Kilic, Yusuf Alper; Kilic, Ilke

    2010-12-01

    Weaning from mechanical ventilation represents one of the most challenging issues in management of critically ill patients. Currently used weaning predictors ignore many important dimensions of weaning outcome and have not been uniformly successful. A fuzzy logic inference system that uses nine variables, and five rule blocks within two layers, has been designed and implemented over mathematical simulations and random clinical scenarios, to compare its behavior and performance in predicting expert opinion with those for rapid shallow breathing index (RSBI), pressure time index and Jabour' weaning index. RSBI has failed to predict expert opinion in 52% of scenarios. Fuzzy logic inference system has shown the best discriminative power (ROC: 0.9288), and RSBI the worst (ROC: 0.6556) in predicting expert opinion. Fuzzy logic provides an approach which can handle multi-attribute decision making, and is a very powerful tool to overcome the weaknesses of currently used weaning predictors.

  14. Extending the functional equivalence of radial basis function networks and fuzzy inference systems.

    PubMed

    Hunt, K J; Haas, R; Murray-Smith, R

    1996-01-01

    We establish the functional equivalence of a generalized class of Gaussian radial basis function (RBFs) networks and the full Takagi-Sugeno model (1983) of fuzzy inference. This generalizes an existing result which applies to the standard Gaussian RBF network and a restricted form of the Takagi-Sugeno fuzzy system. The more general framework allows the removal of some of the restrictive conditions of the previous result.

  15. Functional equivalence between radial basis function networks and fuzzy inference systems.

    PubMed

    Jang, J R; Sun, C T

    1993-01-01

    It is shown that, under some minor restrictions, the functional behavior of radial basis function networks (RBFNs) and that of fuzzy inference systems are actually equivalent. This functional equivalence makes it possible to apply what has been discovered (learning rule, representational power, etc.) for one of the models to the other, and vice versa. It is of interest to observe that two models stemming from different origins turn out to be functionally equivalent.

  16. Network inference of AP pattern formation system in D.melanogaster by structural equation modeling

    NASA Astrophysics Data System (ADS)

    Aburatani, S.; Toh, H.

    2014-03-01

    Within the field of systems biology, revealing the control systems functioning during embryogenesis is an important task. To clarify the mechanisms controlling sequential events, the relationships between various factors and the expression of specific genes should be determined. In this study, we applied a method based on Structural Equation Modeling (SEM), combined with factor analysis. SEM can include the latent variables within the constructed model and infer the relationships among the latent and observed variables, as a network model. We improved a method for the construction of initial models for the SEM calculation, and applied our approach to estimate the regulatory network for Antero-Posterior (AP) pattern formation in D. melanogaster embryogenesis. In this new approach, we combined cross-correlation and partial correlation to summarize the temporal information and to extract the direct interactions from the gene expression profiles. In the inferred model, 18 transcription factor genes were regulated by not only the expression of other genes, but also the estimated factors. Since each factor regulated the same type of genes, these factors were considered to be involved in maternal effects or spatial morphogen distributions. The interpretation of the inferred network model allowed us to reveal the regulatory mechanism for the patterning along the head to tail axis in D. melanogaster.

  17. Combining tree-based and dynamical systems for the inference of gene regulatory networks

    PubMed Central

    Huynh-Thu, Vân Anh; Sanguinetti, Guido

    2015-01-01

    Motivation: Reconstructing the topology of gene regulatory networks (GRNs) from time series of gene expression data remains an important open problem in computational systems biology. Existing GRN inference algorithms face one of two limitations: model-free methods are scalable but suffer from a lack of interpretability and cannot in general be used for out of sample predictions. On the other hand, model-based methods focus on identifying a dynamical model of the system. These are clearly interpretable and can be used for predictions; however, they rely on strong assumptions and are typically very demanding computationally. Results: Here, we propose a new hybrid approach for GRN inference, called Jump3, exploiting time series of expression data. Jump3 is based on a formal on/off model of gene expression but uses a non-parametric procedure based on decision trees (called ‘jump trees’) to reconstruct the GRN topology, allowing the inference of networks of hundreds of genes. We show the good performance of Jump3 on in silico and synthetic networks and applied the approach to identify regulatory interactions activated in the presence of interferon gamma. Availability and implementation: Our MATLAB implementation of Jump3 is available at http://homepages.inf.ed.ac.uk/vhuynht/software.html. Contact: vhuynht@inf.ed.ac.uk or G.Sanguinetti@ed.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25573916

  18. Path-space variational inference for non-equilibrium coarse-grained systems

    NASA Astrophysics Data System (ADS)

    Harmandaris, Vagelis; Kalligiannaki, Evangelia; Katsoulakis, Markos; Plecháč, Petr

    2016-06-01

    In this paper we discuss information-theoretic tools for obtaining optimized coarse-grained molecular models for both equilibrium and non-equilibrium molecular simulations. The latter are ubiquitous in physicochemical and biological applications, where they are typically associated with coupling mechanisms, multi-physics and/or boundary conditions. In general the non-equilibrium steady states are not known explicitly as they do not necessarily have a Gibbs structure. The presented approach can compare microscopic behavior of molecular systems to parametric and non-parametric coarse-grained models using the relative entropy between distributions on the path space and setting up a corresponding path-space variational inference problem. The methods can become entirely data-driven when the microscopic dynamics are replaced with corresponding correlated data in the form of time series. Furthermore, we present connections and generalizations of force matching methods in coarse-graining with path-space information methods. We demonstrate the enhanced transferability of information-based parameterizations to different observables, at a specific thermodynamic point, due to information inequalities. We discuss methodological connections between information-based coarse-graining of molecular systems and variational inference methods primarily developed in the machine learning community. However, we note that the work presented here addresses variational inference for correlated time series due to the focus on dynamics. The applicability of the proposed methods is demonstrated on high-dimensional stochastic processes given by overdamped and driven Langevin dynamics of interacting particles.

  19. Evaluation of probabilistic and logical inference for a SNP annotation system.

    PubMed

    Shen, Terry H; Tarczy-Hornoch, Peter; Detwiler, Landon T; Cadag, Eithon; Carlson, Christopher S

    2010-06-01

    Genome wide association studies (GWAS) are an important approach to understanding the genetic mechanisms behind human diseases. Single nucleotide polymorphisms (SNPs) are the predominant markers used in genome wide association studies, and the ability to predict which SNPs are likely to be functional is important for both a priori and a posteriori analyses of GWA studies. This article describes the design, implementation and evaluation of a family of systems for the purpose of identifying SNPs that may cause a change in phenotypic outcomes. The methods described in this article characterize the feasibility of combinations of logical and probabilistic inference with federated data integration for both point and regional SNP annotation and analysis. Evaluations of the methods demonstrate the overall strong predictive value of logical, and logical with probabilistic, inference applied to the domain of SNP annotation.

  20. Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems

    PubMed Central

    Toni, Tina; Welch, David; Strelkowa, Natalja; Ipsen, Andreas; Stumpf, Michael P.H.

    2008-01-01

    Approximate Bayesian computation (ABC) methods can be used to evaluate posterior distributions without having to calculate likelihoods. In this paper, we discuss and apply an ABC method based on sequential Monte Carlo (SMC) to estimate parameters of dynamical models. We show that ABC SMC provides information about the inferability of parameters and model sensitivity to changes in parameters, and tends to perform better than other ABC approaches. The algorithm is applied to several well-known biological systems, for which parameters and their credible intervals are inferred. Moreover, we develop ABC SMC as a tool for model selection; given a range of different mathematical descriptions, ABC SMC is able to choose the best model using the standard Bayesian model selection apparatus. PMID:19205079

  1. Video-based cargo fire verification system with fuzzy inference engine for commercial aircraft

    NASA Astrophysics Data System (ADS)

    Sadok, Mokhtar; Zakrzewski, Radek; Zeliff, Bob

    2005-02-01

    Conventional smoke detection systems currently installed onboard aircraft are often subject to high rates of false alarms. Under current procedures, whenever an alarm is issued the pilot is obliged to release fire extinguishers and to divert to the nearest airport. Aircraft diversions are costly and dangerous in some situations. A reliable detection system that minimizes false-alarm rate and allows continuous monitoring of cargo compartments is highly desirable. A video-based system has been recently developed by Goodrich Corporation to address this problem. The Cargo Fire Verification System (CFVS) is a multi camera system designed to provide live stream video to the cockpit crew and to perform hotspot, fire, and smoke detection in aircraft cargo bays. In addition to video frames, the CFVS uses other sensor readings to discriminate between genuine events such as fire or smoke and nuisance alarms such as fog or dust. A Mamdani-type fuzzy inference engine is developed to provide approximate reasoning for decision making. In one implementation, Gaussian membership functions for frame intensity-based features, relative humidity, and temperature are constructed using experimental data to form the system inference engine. The CFVS performed better than conventional aircraft smoke detectors in all standardized tests.

  2. Nested sampling for parameter inference in systems biology: application to an exemplar circadian model

    PubMed Central

    2013-01-01

    Background Model selection and parameter inference are complex problems that have yet to be fully addressed in systems biology. In contrast with parameter optimisation, parameter inference computes both the parameter means and their standard deviations (or full posterior distributions), thus yielding important information on the extent to which the data and the model topology constrain the inferred parameter values. Results We report on the application of nested sampling, a statistical approach to computing the Bayesian evidence Z, to the inference of parameters, and the estimation of log Z in an established model of circadian rhythms. A ten-fold difference in the coefficient of variation between degradation and transcription parameters is demonstrated. We further show that the uncertainty remaining in the parameter values is reduced by the analysis of increasing numbers of circadian cycles of data, up to 4 cycles, but is unaffected by sampling the data more frequently. Novel algorithms for calculating the likelihood of a model, and a characterisation of the performance of the nested sampling algorithm are also reported. The methods we develop considerably improve the computational efficiency of the likelihood calculation, and of the exploratory step within nested sampling. Conclusions We have demonstrated in an exemplar circadian model that the estimates of posterior parameter densities (as summarised by parameter means and standard deviations) are influenced predominately by the length of the time series, becoming more narrowly constrained as the number of circadian cycles considered increases. We have also shown the utility of the coefficient of variation for discriminating between highly-constrained and less-well constrained parameters. PMID:23899119

  3. Systems modeling approaches for microbial community studies: from metagenomics to inference of the community structure

    PubMed Central

    Hanemaaijer, Mark; Röling, Wilfred F. M.; Olivier, Brett G.; Khandelwal, Ruchir A.; Teusink, Bas; Bruggeman, Frank J.

    2015-01-01

    Microbial communities play important roles in health, industrial applications and earth's ecosystems. With current molecular techniques we can characterize these systems in unprecedented detail. However, such methods provide little mechanistic insight into how the genetic properties and the dynamic couplings between individual microorganisms give rise to their dynamic activities. Neither do they give insight into what we call “the community state”, that is the fluxes and concentrations of nutrients within the community. This knowledge is a prerequisite for rational control and intervention in microbial communities. Therefore, the inference of the community structure from experimental data is a major current challenge. We will argue that this inference problem requires mathematical models that can integrate heterogeneous experimental data with existing knowledge. We propose that two types of models are needed. Firstly, mathematical models that integrate existing genomic, physiological, and physicochemical information with metagenomics data so as to maximize information content and predictive power. This can be achieved with the use of constraint-based genome-scale stoichiometric modeling of community metabolism which is ideally suited for this purpose. Next, we propose a simpler coarse-grained model, which is tailored to solve the inference problem from the experimental data. This model unambiguously relate to the more detailed genome-scale stoichiometric models which act as heterogeneous data integrators. The simpler inference models are, in our opinion, key to understanding microbial ecosystems, yet until now, have received remarkably little attention. This has led to the situation where the modeling of microbial communities, using only genome-scale models is currently more a computational, theoretical exercise than a method useful to the experimentalist. PMID:25852671

  4. Welding Penetration Control of Fixed Pipe in TIG Welding Using Fuzzy Inference System

    NASA Astrophysics Data System (ADS)

    Baskoro, Ario Sunar; Kabutomori, Masashi; Suga, Yasuo

    This paper presents a study on welding penetration control of fixed pipe in Tungsten Inert Gas (TIG) welding using fuzzy inference system. The welding penetration control is essential to the production quality welds with a specified geometry. For pipe welding using constant arc current and welding speed, the bead width becomes wider as the circumferential welding of small diameter pipes progresses. Having welded pipe in fixed position, obviously, the excessive arc current yields burn through of metals; in contrary, insufficient arc current produces imperfect welding. In order to avoid these errors and to obtain the uniform weld bead over the entire circumference of the pipe, the welding conditions should be controlled as the welding proceeds. This research studies the intelligent welding process of aluminum alloy pipe 6063S-T5 in fixed position using the AC welding machine. The monitoring system used a charge-coupled device (CCD) camera to monitor backside image of molten pool. The captured image was processed to recognize the edge of molten pool by image processing algorithm. Simulation of welding control using fuzzy inference system was constructed to simulate the welding control process. The simulation result shows that fuzzy controller was suitable for controlling the welding speed and appropriate to be implemented into the welding system. A series of experiments was conducted to evaluate the performance of the fuzzy controller. The experimental results show the effectiveness of the control system that is confirmed by sound welds.

  5. Free-energy inference from partial work measurements in small systems

    PubMed Central

    Ribezzi-Crivellari, Marco; Ritort, Felix

    2014-01-01

    Fluctuation relations (FRs) are among the few existing general results in nonequilibrium systems. Their verification requires the measurement of the total work performed on a system. Nevertheless in many cases only a partial measurement of the work is possible. Here we consider FRs in dual-trap optical tweezers where two different forces (one per trap) are measured. With this setup we perform pulling experiments on single molecules by moving one trap relative to the other. We demonstrate that work should be measured using the force exerted by the trap that is moved. The force that is measured in the trap at rest fails to provide the full dissipation in the system, leading to a (incorrect) work definition that does not satisfy the FR. The implications to single-molecule experiments and free-energy measurements are discussed. In the case of symmetric setups a second work definition, based on differential force measurements, is introduced. This definition is best suited to measure free energies as it shows faster convergence of estimators. We discuss measurements using the (incorrect) work definition as an example of partial work measurement. We show how to infer the full work distribution from the partial one via the FR. The inference process does also yield quantitative information, e.g., the hydrodynamic drag on the dumbbell. Results are also obtained for asymmetric dual-trap setups. We suggest that this kind of inference could represent a previously unidentified and general application of FRs to extract information about irreversible processes in small systems. PMID:25099353

  6. The early solar system abundance of /sup 244/Pu as inferred from the St. Severin chondrite

    SciTech Connect

    Hudson, G.B.; Kennedy, B.M.; Podosek, F.A.; Hohenberg, C.M.

    1987-03-01

    We describe the analysis of Xe released in stepwise heating of neutron-irradiated samples of the St. Severin chondrite. This analysis indicates that at the time of formation of most chondritic meteorites, approximately 4.56 x 10/sup 9/ years ago, the atomic ratio of /sup 244/Pu//sup 238/U was 0.0068 +- 0.0010 in chondritic meteorites. We believe that this value is more reliable than that inferred from earlier analyses of St. Severin. We feel that this value is currently the best available estimate for the early solar system abundance of /sup 244/Pu. 42 refs., 2 tabs.

  7. Determining geophysical properties from well log data using artificial neural networks and fuzzy inference systems

    NASA Astrophysics Data System (ADS)

    Chang, Hsien-Cheng

    Two novel synergistic systems consisting of artificial neural networks and fuzzy inference systems are developed to determine geophysical properties by using well log data. These systems are employed to improve the determination accuracy in carbonate rocks, which are generally more complex than siliciclastic rocks. One system, consisting of a single adaptive resonance theory (ART) neural network and three fuzzy inference systems (FISs), is used to determine the permeability category. The other system, which is composed of three ART neural networks and a single FIS, is employed to determine the lithofacies. The geophysical properties studied in this research, permeability category and lithofacies, are treated as categorical data. The permeability values are transformed into a "permeability category" to account for the effects of scale differences between core analyses and well logs, and heterogeneity in the carbonate rocks. The ART neural networks dynamically cluster the input data sets into different groups. The FIS is used to incorporate geologic experts' knowledge, which is usually in linguistic forms, into systems. These synergistic systems thus provide viable alternative solutions to overcome the effects of heterogeneity, the uncertainties of carbonate rock depositional environments, and the scarcity of well log data. The results obtained in this research show promising improvements over backpropagation neural networks. For the permeability category, the prediction accuracies are 68.4% and 62.8% for the multiple-single ART neural network-FIS and a single backpropagation neural network, respectively. For lithofacies, the prediction accuracies are 87.6%, 79%, and 62.8% for the single-multiple ART neural network-FIS, a single ART neural network, and a single backpropagation neural network, respectively. The sensitivity analysis results show that the multiple-single ART neural networks-FIS and a single ART neural network possess the same matching trends in

  8. Multi-agent based control of large-scale complex systems employing distributed dynamic inference engine

    NASA Astrophysics Data System (ADS)

    Zhang, Daili

    Increasing societal demand for automation has led to considerable efforts to control large-scale complex systems, especially in the area of autonomous intelligent control methods. The control system of a large-scale complex system needs to satisfy four system level requirements: robustness, flexibility, reusability, and scalability. Corresponding to the four system level requirements, there arise four major challenges. First, it is difficult to get accurate and complete information. Second, the system may be physically highly distributed. Third, the system evolves very quickly. Fourth, emergent global behaviors of the system can be caused by small disturbances at the component level. The Multi-Agent Based Control (MABC) method as an implementation of distributed intelligent control has been the focus of research since the 1970s, in an effort to solve the above-mentioned problems in controlling large-scale complex systems. However, to the author's best knowledge, all MABC systems for large-scale complex systems with significant uncertainties are problem-specific and thus difficult to extend to other domains or larger systems. This situation is partly due to the control architecture of multiple agents being determined by agent to agent coupling and interaction mechanisms. Therefore, the research objective of this dissertation is to develop a comprehensive, generalized framework for the control system design of general large-scale complex systems with significant uncertainties, with the focus on distributed control architecture design and distributed inference engine design. A Hybrid Multi-Agent Based Control (HyMABC) architecture is proposed by combining hierarchical control architecture and module control architecture with logical replication rings. First, it decomposes a complex system hierarchically; second, it combines the components in the same level as a module, and then designs common interfaces for all of the components in the same module; third, replications

  9. Travel Time Estimation Using Freeway Point Detector Data Based on Evolving Fuzzy Neural Inference System.

    PubMed

    Tang, Jinjun; Zou, Yajie; Ash, John; Zhang, Shen; Liu, Fang; Wang, Yinhai

    2016-01-01

    Travel time is an important measurement used to evaluate the extent of congestion within road networks. This paper presents a new method to estimate the travel time based on an evolving fuzzy neural inference system. The input variables in the system are traffic flow data (volume, occupancy, and speed) collected from loop detectors located at points both upstream and downstream of a given link, and the output variable is the link travel time. A first order Takagi-Sugeno fuzzy rule set is used to complete the inference. For training the evolving fuzzy neural network (EFNN), two learning processes are proposed: (1) a K-means method is employed to partition input samples into different clusters, and a Gaussian fuzzy membership function is designed for each cluster to measure the membership degree of samples to the cluster centers. As the number of input samples increases, the cluster centers are modified and membership functions are also updated; (2) a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi-Sugeno type fuzzy rules. Testing datasets consisting of actual and simulated data are used to test the proposed method. Three common criteria including mean absolute error (MAE), root mean square error (RMSE), and mean absolute relative error (MARE) are utilized to evaluate the estimation performance. Estimation results demonstrate the accuracy and effectiveness of the EFNN method through comparison with existing methods including: multiple linear regression (MLR), instantaneous model (IM), linear model (LM), neural network (NN), and cumulative plots (CP).

  10. A Context-Aware Interactive Health Care System Based on Ontology and Fuzzy Inference.

    PubMed

    Chiang, Tzu-Chiang; Liang, Wen-Hua

    2015-09-01

    In the present society, most families are double-income families, and as the long-term care is seriously short of manpower, it contributes to the rapid development of tele-homecare equipment, and the smart home care system gradually emerges, which assists the elderly or patients with chronic diseases in daily life. This study aims at interaction between persons under care and the system in various living spaces, as based on motion-sensing interaction, and the context-aware smart home care system is proposed. The system stores the required contexts in knowledge ontology, including the physiological information and environmental information of the person under care, as the database of decision. The motion-sensing device enables the person under care to interact with the system through gestures. By the inference mechanism of fuzzy theory, the system can offer advice and rapidly execute service, thus, implementing the EHA. In addition, the system is integrated with the functions of smart phone, tablet PC, and PC, in order that users can implement remote operation and share information regarding the person under care. The health care system constructed in this study enables the decision making system to probe into the health risk of each person under care; then, from the view of preventive medicine, and through a composing system and simulation experimentation, tracks the physiological trend of the person under care, and provides early warning service, thus, promoting smart home care. PMID:26265236

  11. Dynamics of Pre-3 Ga Crust-Mantle Evolution

    NASA Astrophysics Data System (ADS)

    Patchett, P. J.; Chase, C. G.; Vervoort, J. D.

    2004-05-01

    During 3.0 to 2.7 Ga, the Earth's crust underwent a non-uniformitarian change from a pre-3.0 Ga environment where long-term preservation of cratons was rare and difficult, to post-2.7 Ga conditions where cratons were established and new continental crust generation took place largely at craton margins. Many models view the Earth's surface during pre-3 Ga time as broadly equivalent to the post 2.7 Ga regime. Any such uniformitarian or gradual evolution cannot explain the conundrum that only a tiny amount of pre-3 Ga crust is preserved today coupled with the fact that very little pre-3 Ga crust was incorporated into the large amount of new craton that came into existence during 3.0-2.7 Ga. If large volumes of pre-3 Ga continental crust existed, it disappeared either just prior to 3 Ga, or during 3.0-2.7 Ga. To explain sudden appearance of surviving but dominantly juvenile continental crust in a model where continents were large prior to 3 Ga, it would be necessary either that pre-3 Ga continent was recycled into the mantle at sites systematically different from those where new 3.0-2.7 Ga crust was made, or that widespread continent destruction preceded the 3.0-2.7 Ga crustal genesis. From expected mantle overturn in response to the heat budget, it is likely that most pre-3 Ga crust was both more mafic and shorter-lived than after 3 Ga. Although Nd and Hf ratios for pre-3 Ga rocks are uncertain due to polymetamorphism, it appears that depleted upper mantle was widespread by 2.7 Ga, even pre-3 Ga. Depletion may have been largely achieved by formation, subduction and storage of mafic crust for periods of 200-500 m.y. The rapid change to large surviving continents during 3.0-2.7 Ga was due to declining mantle overturn, and particularly to development of the ability to maintain subduction in one zone of the earth's surface for the time needed to allow evolution to felsic igneous rock compositions. In as much as storage of subducted slabs is probably occurring today, and in view of the apparent Proterozoic Pb age of some present mantle plume sources, we believe sequestration of enriched material in the deep mantle is still underestimated, and that isotopic mass balance calculations involving only continent and upper mantle will overestimate the mean continental age.

  12. Crust-mantle interactions and crustal deformations, some geological observations

    NASA Astrophysics Data System (ADS)

    Jolivet, Laurent; Clerc, Camille; Sternai, Pietro; Bellahsen, Nicolas; Faccenna, Claudio; Ringenbach, Jean-Claude; Gorini, Christian; Leroy, Sylvie; Pik, Raphaël

    2015-04-01

    Crustal deformations at plate boundaries or intracontinental are governed by the relative movements of plates, and most published models consider the lithosphere as the main stress guide in extensional or compressional contexts. The possible contribution of the underlying asthenospheric flow to crustal deformation through viscous coupling is often neglected. Since the early days of plate tectonics, and even earlier, two schools of thought have been developed in parallel whether mantle convection is considered or not. This reflects nowadays in the difficulty of reconciling lithospheric-scale models and global-scale convection models to explain tectonic features observed at the surface. Still, recent studies reemphasized the role of mantle convection in shaping mountain belts or rifts and the consequences of different styles of convection on the geometry and kinematics of mountain belts. We present here a number of geological observations in convergent or divergent contexts that may suggest a strong coupling between asthenospheric flow and crustal deformation. Several of these examples, especially in extensional contexts, show a deformation distributed over wide zones, accommodated by shallow-dipping shear zones and with a constant asymmetry over large distances. This is the case of the Mediterranean back-arc basins, such as the Aegean Sea, the northern Tyrrhenian Sea or the Alboran domain, where extension is taken up by shallow-dipping extensional shear zones and normal faults with a constant asymmetry. A similar image is also observed across the Gulf of Lion passive margin that also belongs to the Mediterranean back-arc basins. Such is also the case of some of the Atlantic passive margins where shallow-dipping normal faults and extensional shear zones control the extraction of the lower crust and the mantle with a constant asymmetry across the entire margin. Finally, the distribution and geometry of normal faults across the Afar region also show a constant asymmetry. We discuss these contexts and search for the main controlling parameters for this asymmetric distributed deformation. These parameters include an original heterogeneity of the crust and lithosphere (tectonic heritage) and a possible contribution of the underlying asthenospheric flow. We discuss the relations between the observed asymmetry and the direction and sense of the mantle flow underneath. Finally, we extend this question to larger-scale processes such as obduction and continental collision.

  13. Design considerations for flight test of a fault inferring nonlinear detection system algorithm for avionics sensors

    NASA Technical Reports Server (NTRS)

    Caglayan, A. K.; Godiwala, P. M.; Morrell, F. R.

    1986-01-01

    The modifications to the design of a fault inferring nonlinear detection system (FINDS) algorithm to accommodate flight computer constraints and the resulting impact on the algorithm performance are summarized. An overview of the flight data-driven FINDS algorithm is presented. This is followed by a brief analysis of the effects of modifications to the algorithm on program size and execution speed. Significant improvements in estimation performance for the aircraft states and normal operating sensor biases, which have resulted from improved noise design parameters and a new steady-state wind model, are documented. The aircraft state and sensor bias estimation performances of the algorithm's extended Kalman filter are presented as a function of update frequency of the piecewise constant filter gains. The results of a new detection system strategy and failure detection performance, as a function of gain update frequency, are also presented.

  14. Inference of Disease-Related Molecular Logic from Systems-Based Microarray Analysis

    PubMed Central

    Varadan, Vinay; Anastassiou, Dimitris

    2006-01-01

    Computational analysis of gene expression data from microarrays has been useful for medical diagnosis and prognosis. The ability to analyze such data at the level of biological modules, rather than individual genes, has been recognized as important for improving our understanding of disease-related pathways. It has proved difficult, however, to infer pathways from microarray data by deriving modules of multiple synergistically interrelated genes, rather than individual genes. Here we propose a systems-based approach called Entropy Minimization and Boolean Parsimony (EMBP) that identifies, directly from gene expression data, modules of genes that are jointly associated with disease. Furthermore, the technique provides insight into the underlying biomolecular logic by inferring a logic function connecting the joint expression levels in a gene module with the outcome of disease. Coupled with biological knowledge, this information can be useful for identifying disease-related pathways, suggesting potential therapeutic approaches for interfering with the functions of such pathways. We present an example providing such gene modules associated with prostate cancer from publicly available gene expression data, and we successfully validate the results on additional independently derived data. Our results indicate a link between prostate cancer and cellular damage from oxidative stress combined with inhibition of apoptotic mechanisms normally triggered by such damage. PMID:16789819

  15. Adaptive neuro-fuzzy inference system to improve the power quality of a split shaft microturbine power generation system

    NASA Astrophysics Data System (ADS)

    Oğuz, Yüksel; Üstün, Seydi Vakkas; Yabanova, İsmail; Yumurtaci, Mehmet; Güney, İrfan

    2012-01-01

    This article presents design of adaptive neuro-fuzzy inference system (ANFIS) for the turbine speed control for purpose of improving the power quality of the power production system of a split shaft microturbine. To improve the operation performance of the microturbine power generation system (MTPGS) and to obtain the electrical output magnitudes in desired quality and value (terminal voltage, operation frequency, power drawn by consumer and production power), a controller depended on adaptive neuro-fuzzy inference system was designed. The MTPGS consists of the microturbine speed controller, a split shaft microturbine, cylindrical pole synchronous generator, excitation circuit and voltage regulator. Modeling of dynamic behavior of synchronous generator driver with a turbine and split shaft turbine was realized by using the Matlab/Simulink and SimPowerSystems in it. It is observed from the simulation results that with the microturbine speed control made with ANFIS, when the MTPGS is operated under various loading situations, the terminal voltage and frequency values of the system can be settled in desired operation values in a very short time without significant oscillation and electrical production power in desired quality can be obtained.

  16. Portable inference engine: An extended CLIPS for real-time production systems

    NASA Technical Reports Server (NTRS)

    Le, Thach; Homeier, Peter

    1988-01-01

    The present C-Language Integrated Production System (CLIPS) architecture has not been optimized to deal with the constraints of real-time production systems. Matching in CLIPS is based on the Rete Net algorithm, whose assumption of working memory stability might fail to be satisfied in a system subject to real-time dataflow. Further, the CLIPS forward-chaining control mechanism with a predefined conflict resultion strategy may not effectively focus the system's attention on situation-dependent current priorties, or appropriately address different kinds of knowledge which might appear in a given application. Portable Inference Engine (PIE) is a production system architecture based on CLIPS which attempts to create a more general tool while addressing the problems of real-time expert systems. Features of the PIE design include a modular knowledge base, a modified Rete Net algorithm, a bi-directional control strategy, and multiple user-defined conflict resolution strategies. Problems associated with real-time applications are analyzed and an explanation is given for how the PIE architecture addresses these problems.

  17. Evaluation of a dual processor implementation for a fault inferring nonlinear detection system

    NASA Technical Reports Server (NTRS)

    Godiwala, P. M.; Caglayan, A. K.; Morrell, F. R.

    1987-01-01

    The design of a modified fault inferring nonlinear detection system (FINDS) algorithm for a dual-processor configured flight computer is described. The algorithm was changed in order to divide it into its translational dynamics and rotational kinematics and to use it for parallel execution on the flight computer. The FINDS consists of: (1) a no-fail filter (NFF), (2) a set of test-of-mean detection tests, (3) a bank of first order filters to estimate failure levels in individual sensors, and (4) a decision function. NFF filter performance using flight recorded sensor data is analyzed using a filter autoinitialization routine. The failure detection and isolation capability of the partitioned algorithm is evaluated. A multirate implementation for the bias-free and bias filter gain and covariance matrices is discussed.

  18. An expert system shell for inferring vegetation characteristics: Atmospheric techniques (Task G)

    NASA Technical Reports Server (NTRS)

    Harrison, P. Ann; Harrison, Patrick R.

    1993-01-01

    The NASA VEGetation Workbench (VEG) is a knowledge based system that infers vegetation characteristics from reflectance data. The VEG Subgoals have been reorganized into categories. A new subgoal category 'Atmospheric Techniques' containing two new subgoals has been implemented. The subgoal Atmospheric Passes allows the scientist to take reflectance data measured at ground level and predict what the reflectance values would be if the data were measured at a different atmospheric height. The subgoal Atmospheric Corrections allows atmospheric corrections to be made to data collected from an aircraft or by a satellite to determine what the equivalent reflectance values would be if the data were measured at ground level. The report describes the implementation and testing of the basic framework and interface for the Atmospheric Techniques Subgoals.

  19. Adaptive neuro-fuzzy inference systems for automatic detection of breast cancer.

    PubMed

    Ubeyli, Elif Derya

    2009-10-01

    This paper intends to an integrated view of implementing adaptive neuro-fuzzy inference system (ANFIS) for breast cancer detection. The Wisconsin breast cancer database contained records of patients with known diagnosis. The ANFIS classifiers learned how to differentiate a new case in the domain by given a training set of such records. The ANFIS classifier was used to detect the breast cancer when nine features defining breast cancer indications were used as inputs. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the impacts of features on the detection of breast cancer were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performances and classification accuracies and the results confirmed that the proposed ANFIS model has potential in detecting the breast cancer. PMID:19827261

  20. Adaptive neuro-fuzzy inference system for analysis of Doppler signals.

    PubMed

    Ubeyli, Elif Derya

    2006-01-01

    In this study, a new approach based on adaptive neuro-fuzzy inference system (ANFIS) was presented for detection of ophthalmic artery stenosis. Decision making was performed in two stages: feature extraction using the wavelet transform (WT) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. The ophthalmic arterial Doppler signals were recorded from 128 subjects that 62 of them had suffered from ophthalmic artery stenosis and the rest of them had been healthy subjects. Some conclusions concerning the impacts of features on the detection of ophthalmic artery stenosis were obtained through analysis of the ANFIS. The performance of the ANFIS classifier was evaluated in terms of training performance and classification accuracies (total classification accuracy was 97.59%) and the results confirmed that the proposed ANFIS classifier has potential in detecting the ophthalmic artery stenosis. PMID:17945697

  1. The early solar system abundance of Pu-244 as inferred from the St. Severin chondrite

    NASA Technical Reports Server (NTRS)

    Hudson, G. B.; Kennedy, B. M.; Podosek, F. A.; Hohenberg, C. M.

    1989-01-01

    The isotopic composition of Xe released in stepwise heating of neutron-irradiated samples of the St. Severin chondrite was measured. This analysis shows that at the time of formation of most chondritic meteorites, approximately 4.56 x 10 to the 9th yr ago, the atomic ratio of Pu-244/U-238 was 0.0068 + or - 0.0010 in chondritic meteorites. This value is believed to be more reliable that inferred from earlier analyses of St. Severin and is the best estimate for the early solar system abundance of Pu-244. The comparison of Xe compositions between irradiated and unirradiated samples shows that the composition of trapped (ambient) Xe in St. Severin has a significantly lower value of Xe-136/Xe-130 than average carbonaceous chondrites.

  2. Travel Time Estimation Using Freeway Point Detector Data Based on Evolving Fuzzy Neural Inference System.

    PubMed

    Tang, Jinjun; Zou, Yajie; Ash, John; Zhang, Shen; Liu, Fang; Wang, Yinhai

    2016-01-01

    Travel time is an important measurement used to evaluate the extent of congestion within road networks. This paper presents a new method to estimate the travel time based on an evolving fuzzy neural inference system. The input variables in the system are traffic flow data (volume, occupancy, and speed) collected from loop detectors located at points both upstream and downstream of a given link, and the output variable is the link travel time. A first order Takagi-Sugeno fuzzy rule set is used to complete the inference. For training the evolving fuzzy neural network (EFNN), two learning processes are proposed: (1) a K-means method is employed to partition input samples into different clusters, and a Gaussian fuzzy membership function is designed for each cluster to measure the membership degree of samples to the cluster centers. As the number of input samples increases, the cluster centers are modified and membership functions are also updated; (2) a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi-Sugeno type fuzzy rules. Testing datasets consisting of actual and simulated data are used to test the proposed method. Three common criteria including mean absolute error (MAE), root mean square error (RMSE), and mean absolute relative error (MARE) are utilized to evaluate the estimation performance. Estimation results demonstrate the accuracy and effectiveness of the EFNN method through comparison with existing methods including: multiple linear regression (MLR), instantaneous model (IM), linear model (LM), neural network (NN), and cumulative plots (CP). PMID:26829639

  3. Travel Time Estimation Using Freeway Point Detector Data Based on Evolving Fuzzy Neural Inference System

    PubMed Central

    Tang, Jinjun; Zou, Yajie; Ash, John; Zhang, Shen; Liu, Fang; Wang, Yinhai

    2016-01-01

    Travel time is an important measurement used to evaluate the extent of congestion within road networks. This paper presents a new method to estimate the travel time based on an evolving fuzzy neural inference system. The input variables in the system are traffic flow data (volume, occupancy, and speed) collected from loop detectors located at points both upstream and downstream of a given link, and the output variable is the link travel time. A first order Takagi-Sugeno fuzzy rule set is used to complete the inference. For training the evolving fuzzy neural network (EFNN), two learning processes are proposed: (1) a K-means method is employed to partition input samples into different clusters, and a Gaussian fuzzy membership function is designed for each cluster to measure the membership degree of samples to the cluster centers. As the number of input samples increases, the cluster centers are modified and membership functions are also updated; (2) a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi-Sugeno type fuzzy rules. Testing datasets consisting of actual and simulated data are used to test the proposed method. Three common criteria including mean absolute error (MAE), root mean square error (RMSE), and mean absolute relative error (MARE) are utilized to evaluate the estimation performance. Estimation results demonstrate the accuracy and effectiveness of the EFNN method through comparison with existing methods including: multiple linear regression (MLR), instantaneous model (IM), linear model (LM), neural network (NN), and cumulative plots (CP). PMID:26829639

  4. Multiple Adaptive Neuro-Fuzzy Inference System with Automatic Features Extraction Algorithm for Cervical Cancer Recognition

    PubMed Central

    Subhi Al-batah, Mohammad; Mat Isa, Nor Ashidi; Klaib, Mohammad Fadel; Al-Betar, Mohammed Azmi

    2014-01-01

    To date, cancer of uterine cervix is still a leading cause of cancer-related deaths in women worldwide. The current methods (i.e., Pap smear and liquid-based cytology (LBC)) to screen for cervical cancer are time-consuming and dependent on the skill of the cytopathologist and thus are rather subjective. Therefore, this paper presents an intelligent computer vision system to assist pathologists in overcoming these problems and, consequently, produce more accurate results. The developed system consists of two stages. In the first stage, the automatic features extraction (AFE) algorithm is performed. In the second stage, a neuro-fuzzy model called multiple adaptive neuro-fuzzy inference system (MANFIS) is proposed for recognition process. The MANFIS contains a set of ANFIS models which are arranged in parallel combination to produce a model with multi-input-multioutput structure. The system is capable of classifying cervical cell image into three groups, namely, normal, low-grade squamous intraepithelial lesion (LSIL) and high-grade squamous intraepithelial lesion (HSIL). The experimental results prove the capability of the AFE algorithm to be as effective as the manual extraction by human experts, while the proposed MANFIS produces a good classification performance with 94.2% accuracy. PMID:24707316

  5. Multiple adaptive neuro-fuzzy inference system with automatic features extraction algorithm for cervical cancer recognition.

    PubMed

    Al-batah, Mohammad Subhi; Isa, Nor Ashidi Mat; Klaib, Mohammad Fadel; Al-Betar, Mohammed Azmi

    2014-01-01

    To date, cancer of uterine cervix is still a leading cause of cancer-related deaths in women worldwide. The current methods (i.e., Pap smear and liquid-based cytology (LBC)) to screen for cervical cancer are time-consuming and dependent on the skill of the cytopathologist and thus are rather subjective. Therefore, this paper presents an intelligent computer vision system to assist pathologists in overcoming these problems and, consequently, produce more accurate results. The developed system consists of two stages. In the first stage, the automatic features extraction (AFE) algorithm is performed. In the second stage, a neuro-fuzzy model called multiple adaptive neuro-fuzzy inference system (MANFIS) is proposed for recognition process. The MANFIS contains a set of ANFIS models which are arranged in parallel combination to produce a model with multi-input-multioutput structure. The system is capable of classifying cervical cell image into three groups, namely, normal, low-grade squamous intraepithelial lesion (LSIL) and high-grade squamous intraepithelial lesion (HSIL). The experimental results prove the capability of the AFE algorithm to be as effective as the manual extraction by human experts, while the proposed MANFIS produces a good classification performance with 94.2% accuracy. PMID:24707316

  6. Spatial distribution of arsenic in the Texas Gulf Coastal Aquifer System and inferences regarding hydrogeochemical controls

    NASA Astrophysics Data System (ADS)

    Gates, J. B.; Nicot, J.; Scanlon, B. R.

    2008-12-01

    Arsenic is a prominent trace element in the Gulf Coastal Aquifer System (GCAS) in Texas, particularly in the southwestern portion where 29% of wells exceed the USEPA maximum contaminant level of 10 μg/L for drinking water. While the dominant source is generally thought to be geogenic rather than anthropogenic, little is known about the hydrologic/geochemical mechanisms affecting occurrence in groundwater. The aim of this study was to assess spatial trends in hydrochemistry on a regional scale to help infer relevant processes. The investigation included geostatistical analysis of water quality results from the Texas Water Development Board groundwater database (n>1000) and chemical/isotopic analysis of a transect (17 wells) in the unconfined portion of the Jasper Aquifer, where some of the highest arsenic concentrations in the GCAS are found. Across the GCAS, arsenic and other oxyanion-forming elements (vanadium, molybdenum etc) are most common in the Miocene-age Jasper Aquifer, and tend to decrease with decreasing aquifer age. Principal Component Analysis suggests that spatial variations in arsenic in the GCAS as a whole are related to both total mineralization (TDS), and a second orthogonal component comprised of several trace elements (most prominently vanadium and silicon). A similar relationship is apparent for the Jasper Aquifer, but without a strong correspondence to TDS. The Jasper Aquifer transect also reflects these patterns. Near-neutral pH and slightly-oxidizing conditions observed in the transect are not likely to promote reductive dissolution or desorption from mineral oxides, and no relationship with pH or Eh is present. Rather, maximum arsenic values in the transect (120 μg/L) coincide with the boundary of the underlying Catahoula Formation which is a known source of saline fluids. Mixing of upward leakage with meteoric recharge is therefore considered to be a likely mechanism controlling arsenic concentrations. This inference is consistent with

  7. The Role of Probability-Based Inference in an Intelligent Tutoring System.

    ERIC Educational Resources Information Center

    Mislevy, Robert J.; Gitomer, Drew H.

    Probability-based inference in complex networks of interdependent variables is an active topic in statistical research, spurred by such diverse applications as forecasting, pedigree analysis, troubleshooting, and medical diagnosis. This paper concerns the role of Bayesian inference networks for updating student models in intelligent tutoring…

  8. Adaptive neuro-fuzzy inference system for real-time monitoring of integrated-constructed wetlands.

    PubMed

    Dzakpasu, Mawuli; Scholz, Miklas; McCarthy, Valerie; Jordan, Siobhán; Sani, Abdulkadir

    2015-01-01

    Monitoring large-scale treatment wetlands is costly and time-consuming, but required by regulators. Some analytical results are available only after 5 days or even longer. Thus, adaptive neuro-fuzzy inference system (ANFIS) models were developed to predict the effluent concentrations of 5-day biochemical oxygen demand (BOD5) and NH4-N from a full-scale integrated constructed wetland (ICW) treating domestic wastewater. The ANFIS models were developed and validated with a 4-year data set from the ICW system. Cost-effective, quicker and easier to measure variables were selected as the possible predictors based on their goodness of correlation with the outputs. A self-organizing neural network was applied to extract the most relevant input variables from all the possible input variables. Fuzzy subtractive clustering was used to identify the architecture of the ANFIS models and to optimize fuzzy rules, overall, improving the network performance. According to the findings, ANFIS could predict the effluent quality variation quite strongly. Effluent BOD5 and NH4-N concentrations were predicted relatively accurately by other effluent water quality parameters, which can be measured within a few hours. The simulated effluent BOD5 and NH4-N concentrations well fitted the measured concentrations, which was also supported by relatively low mean squared error. Thus, ANFIS can be useful for real-time monitoring and control of ICW systems. PMID:25607665

  9. Bayesian Model Comparison and Parameter Inference in Systems Biology Using Nested Sampling

    PubMed Central

    Pullen, Nick; Morris, Richard J.

    2014-01-01

    Inferring parameters for models of biological processes is a current challenge in systems biology, as is the related problem of comparing competing models that explain the data. In this work we apply Skilling's nested sampling to address both of these problems. Nested sampling is a Bayesian method for exploring parameter space that transforms a multi-dimensional integral to a 1D integration over likelihood space. This approach focusses on the computation of the marginal likelihood or evidence. The ratio of evidences of different models leads to the Bayes factor, which can be used for model comparison. We demonstrate how nested sampling can be used to reverse-engineer a system's behaviour whilst accounting for the uncertainty in the results. The effect of missing initial conditions of the variables as well as unknown parameters is investigated. We show how the evidence and the model ranking can change as a function of the available data. Furthermore, the addition of data from extra variables of the system can deliver more information for model comparison than increasing the data from one variable, thus providing a basis for experimental design. PMID:24523891

  10. Bayesian model comparison and parameter inference in systems biology using nested sampling.

    PubMed

    Pullen, Nick; Morris, Richard J

    2014-01-01

    Inferring parameters for models of biological processes is a current challenge in systems biology, as is the related problem of comparing competing models that explain the data. In this work we apply Skilling's nested sampling to address both of these problems. Nested sampling is a Bayesian method for exploring parameter space that transforms a multi-dimensional integral to a 1D integration over likelihood space. This approach focuses on the computation of the marginal likelihood or evidence. The ratio of evidences of different models leads to the Bayes factor, which can be used for model comparison. We demonstrate how nested sampling can be used to reverse-engineer a system's behaviour whilst accounting for the uncertainty in the results. The effect of missing initial conditions of the variables as well as unknown parameters is investigated. We show how the evidence and the model ranking can change as a function of the available data. Furthermore, the addition of data from extra variables of the system can deliver more information for model comparison than increasing the data from one variable, thus providing a basis for experimental design. PMID:24523891

  11. Inference in `poor` languages

    SciTech Connect

    Petrov, S.

    1996-10-01

    Languages with a solvable implication problem but without complete and consistent systems of inference rules (`poor` languages) are considered. The problem of existence of finite complete and consistent inference rule system for a ``poor`` language is stated independently of the language or rules syntax. Several properties of the problem arc proved. An application of results to the language of join dependencies is given.

  12. Application of an adaptive neuro-fuzzy inference system to ground subsidence hazard mapping

    NASA Astrophysics Data System (ADS)

    Park, Inhye; Choi, Jaewon; Jin Lee, Moung; Lee, Saro

    2012-11-01

    We constructed hazard maps of ground subsidence around abandoned underground coal mines (AUCMs) in Samcheok City, Korea, using an adaptive neuro-fuzzy inference system (ANFIS) and a geographical information system (GIS). To evaluate the factors related to ground subsidence, a spatial database was constructed from topographic, geologic, mine tunnel, land use, and ground subsidence maps. An attribute database was also constructed from field investigations and reports on existing ground subsidence areas at the study site. Five major factors causing ground subsidence were extracted: (1) depth of drift; (2) distance from drift; (3) slope gradient; (4) geology; and (5) land use. The adaptive ANFIS model with different types of membership functions (MFs) was then applied for ground subsidence hazard mapping in the study area. Two ground subsidence hazard maps were prepared using the different MFs. Finally, the resulting ground subsidence hazard maps were validated using the ground subsidence test data which were not used for training the ANFIS. The validation results showed 95.12% accuracy using the generalized bell-shaped MF model and 94.94% accuracy using the Sigmoidal2 MF model. These accuracy results show that an ANFIS can be an effective tool in ground subsidence hazard mapping. Analysis of ground subsidence with the ANFIS model suggests that quantitative analysis of ground subsidence near AUCMs is possible.

  13. Crop parameters estimation by fuzzy inference system using X-band scatterometer data

    NASA Astrophysics Data System (ADS)

    Pandey, Abhishek; Prasad, R.; Singh, V. P.; Jha, S. K.; Shukla, K. K.

    2013-03-01

    Learning fuzzy rule based systems with microwave remote sensing can lead to very useful applications in solving several problems in the field of agriculture. Fuzzy logic provides a simple way to arrive at a definite conclusion based upon imprecise, ambiguous, vague, noisy or missing input information. In the present paper, a subtractive based fuzzy inference system is introduced to estimate the potato crop parameters like biomass, leaf area index, plant height and soil moisture. Scattering coefficient for HH- and VV-polarizations were used as an input in the Fuzzy network. The plant height, biomass, and leaf area index of potato crop and soil moisture measured at its various growth stages were used as the target variables during the training and validation of the network. The estimated values of crop/soil parameters by this methodology are much closer to the experimental values. The present work confirms the estimation abilities of fuzzy subtractive clustering in potato crop parameters estimation. This technique may be useful for the other crops cultivated over regional or continental level.

  14. Inference for Stochastic Chemical Kinetics Using Moment Equations and System Size Expansion.

    PubMed

    Fröhlich, Fabian; Thomas, Philipp; Kazeroonian, Atefeh; Theis, Fabian J; Grima, Ramon; Hasenauer, Jan

    2016-07-01

    Quantitative mechanistic models are valuable tools for disentangling biochemical pathways and for achieving a comprehensive understanding of biological systems. However, to be quantitative the parameters of these models have to be estimated from experimental data. In the presence of significant stochastic fluctuations this is a challenging task as stochastic simulations are usually too time-consuming and a macroscopic description using reaction rate equations (RREs) is no longer accurate. In this manuscript, we therefore consider moment-closure approximation (MA) and the system size expansion (SSE), which approximate the statistical moments of stochastic processes and tend to be more precise than macroscopic descriptions. We introduce gradient-based parameter optimization methods and uncertainty analysis methods for MA and SSE. Efficiency and reliability of the methods are assessed using simulation examples as well as by an application to data for Epo-induced JAK/STAT signaling. The application revealed that even if merely population-average data are available, MA and SSE improve parameter identifiability in comparison to RRE. Furthermore, the simulation examples revealed that the resulting estimates are more reliable for an intermediate volume regime. In this regime the estimation error is reduced and we propose methods to determine the regime boundaries. These results illustrate that inference using MA and SSE is feasible and possesses a high sensitivity. PMID:27447730

  15. Adaptive network based on fuzzy inference system for equilibrated urea concentration prediction.

    PubMed

    Azar, Ahmad Taher

    2013-09-01

    Post-dialysis urea rebound (PDUR) has been attributed mostly to redistribution of urea from different compartments, which is determined by variations in regional blood flows and transcellular urea mass transfer coefficients. PDUR occurs after 30-90min of short or standard hemodialysis (HD) sessions and after 60min in long 8-h HD sessions, which is inconvenient. This paper presents adaptive network based on fuzzy inference system (ANFIS) for predicting intradialytic (Cint) and post-dialysis urea concentrations (Cpost) in order to predict the equilibrated (Ceq) urea concentrations without any blood sampling from dialysis patients. The accuracy of the developed system was prospectively compared with other traditional methods for predicting equilibrated urea (Ceq), post dialysis urea rebound (PDUR) and equilibrated dialysis dose (eKt/V). This comparison is done based on root mean squares error (RMSE), normalized mean square error (NRMSE), and mean absolute percentage error (MAPE). The ANFIS predictor for Ceq achieved mean RMSE values of 0.3654 and 0.4920 for training and testing, respectively. The statistical analysis demonstrated that there is no statistically significant difference found between the predicted and the measured values. The percentage of MAE and RMSE for testing phase is 0.63% and 0.96%, respectively. PMID:23806679

  16. Inference for Stochastic Chemical Kinetics Using Moment Equations and System Size Expansion.

    PubMed

    Fröhlich, Fabian; Thomas, Philipp; Kazeroonian, Atefeh; Theis, Fabian J; Grima, Ramon; Hasenauer, Jan

    2016-07-01

    Quantitative mechanistic models are valuable tools for disentangling biochemical pathways and for achieving a comprehensive understanding of biological systems. However, to be quantitative the parameters of these models have to be estimated from experimental data. In the presence of significant stochastic fluctuations this is a challenging task as stochastic simulations are usually too time-consuming and a macroscopic description using reaction rate equations (RREs) is no longer accurate. In this manuscript, we therefore consider moment-closure approximation (MA) and the system size expansion (SSE), which approximate the statistical moments of stochastic processes and tend to be more precise than macroscopic descriptions. We introduce gradient-based parameter optimization methods and uncertainty analysis methods for MA and SSE. Efficiency and reliability of the methods are assessed using simulation examples as well as by an application to data for Epo-induced JAK/STAT signaling. The application revealed that even if merely population-average data are available, MA and SSE improve parameter identifiability in comparison to RRE. Furthermore, the simulation examples revealed that the resulting estimates are more reliable for an intermediate volume regime. In this regime the estimation error is reduced and we propose methods to determine the regime boundaries. These results illustrate that inference using MA and SSE is feasible and possesses a high sensitivity.

  17. Respiratory motion prediction by using the adaptive neuro fuzzy inference system (ANFIS)

    NASA Astrophysics Data System (ADS)

    Kakar, Manish; Nyström, Håkan; Rye Aarup, Lasse; Jakobi Nøttrup, Trine; Rune Olsen, Dag

    2005-10-01

    The quality of radiation therapy delivered for treating cancer patients is related to set-up errors and organ motion. Due to the margins needed to ensure adequate target coverage, many breast cancer patients have been shown to develop late side effects such as pneumonitis and cardiac damage. Breathing-adapted radiation therapy offers the potential for precise radiation dose delivery to a moving target and thereby reduces the side effects substantially. However, the basic requirement for breathing-adapted radiation therapy is to track and predict the target as precisely as possible. Recent studies have addressed the problem of organ motion prediction by using different methods including artificial neural network and model based approaches. In this study, we propose to use a hybrid intelligent system called ANFIS (the adaptive neuro fuzzy inference system) for predicting respiratory motion in breast cancer patients. In ANFIS, we combine both the learning capabilities of a neural network and reasoning capabilities of fuzzy logic in order to give enhanced prediction capabilities, as compared to using a single methodology alone. After training ANFIS and checking for prediction accuracy on 11 breast cancer patients, it was found that the RMSE (root-mean-square error) can be reduced to sub-millimetre accuracy over a period of 20 s provided the patient is assisted with coaching. The average RMSE for the un-coached patients was 35% of the respiratory amplitude and for the coached patients 6% of the respiratory amplitude.

  18. Inference for Stochastic Chemical Kinetics Using Moment Equations and System Size Expansion

    PubMed Central

    Thomas, Philipp; Kazeroonian, Atefeh; Theis, Fabian J.; Grima, Ramon; Hasenauer, Jan

    2016-01-01

    Quantitative mechanistic models are valuable tools for disentangling biochemical pathways and for achieving a comprehensive understanding of biological systems. However, to be quantitative the parameters of these models have to be estimated from experimental data. In the presence of significant stochastic fluctuations this is a challenging task as stochastic simulations are usually too time-consuming and a macroscopic description using reaction rate equations (RREs) is no longer accurate. In this manuscript, we therefore consider moment-closure approximation (MA) and the system size expansion (SSE), which approximate the statistical moments of stochastic processes and tend to be more precise than macroscopic descriptions. We introduce gradient-based parameter optimization methods and uncertainty analysis methods for MA and SSE. Efficiency and reliability of the methods are assessed using simulation examples as well as by an application to data for Epo-induced JAK/STAT signaling. The application revealed that even if merely population-average data are available, MA and SSE improve parameter identifiability in comparison to RRE. Furthermore, the simulation examples revealed that the resulting estimates are more reliable for an intermediate volume regime. In this regime the estimation error is reduced and we propose methods to determine the regime boundaries. These results illustrate that inference using MA and SSE is feasible and possesses a high sensitivity. PMID:27447730

  19. Data mining in forecasting PVT correlations of crude oil systems based on Type1 fuzzy logic inference systems

    NASA Astrophysics Data System (ADS)

    El-Sebakhy, Emad A.

    2009-09-01

    Pressure-volume-temperature properties are very important in the reservoir engineering computations. There are many empirical approaches for predicting various PVT properties based on empirical correlations and statistical regression models. Last decade, researchers utilized neural networks to develop more accurate PVT correlations. These achievements of neural networks open the door to data mining techniques to play a major role in oil and gas industry. Unfortunately, the developed neural networks correlations are often limited, and global correlations are usually less accurate compared to local correlations. Recently, adaptive neuro-fuzzy inference systems have been proposed as a new intelligence framework for both prediction and classification based on fuzzy clustering optimization criterion and ranking. This paper proposes neuro-fuzzy inference systems for estimating PVT properties of crude oil systems. This new framework is an efficient hybrid intelligence machine learning scheme for modeling the kind of uncertainty associated with vagueness and imprecision. We briefly describe the learning steps and the use of the Takagi Sugeno and Kang model and Gustafson-Kessel clustering algorithm with K-detected clusters from the given database. It has featured in a wide range of medical, power control system, and business journals, often with promising results. A comparative study will be carried out to compare their performance of this new framework with the most popular modeling techniques, such as neural networks, nonlinear regression, and the empirical correlations algorithms. The results show that the performance of neuro-fuzzy systems is accurate, reliable, and outperform most of the existing forecasting techniques. Future work can be achieved by using neuro-fuzzy systems for clustering the 3D seismic data, identification of lithofacies types, and other reservoir characterization.

  20. Performance Evaluation of the Machine Learning Algorithms Used in Inference Mechanism of a Medical Decision Support System

    PubMed Central

    Bal, Mert; Amasyali, M. Fatih; Sever, Hayri; Kose, Guven; Demirhan, Ayse

    2014-01-01

    The importance of the decision support systems is increasingly supporting the decision making process in cases of uncertainty and the lack of information and they are widely used in various fields like engineering, finance, medicine, and so forth, Medical decision support systems help the healthcare personnel to select optimal method during the treatment of the patients. Decision support systems are intelligent software systems that support decision makers on their decisions. The design of decision support systems consists of four main subjects called inference mechanism, knowledge-base, explanation module, and active memory. Inference mechanism constitutes the basis of decision support systems. There are various methods that can be used in these mechanisms approaches. Some of these methods are decision trees, artificial neural networks, statistical methods, rule-based methods, and so forth. In decision support systems, those methods can be used separately or a hybrid system, and also combination of those methods. In this study, synthetic data with 10, 100, 1000, and 2000 records have been produced to reflect the probabilities on the ALARM network. The accuracy of 11 machine learning methods for the inference mechanism of medical decision support system is compared on various data sets. PMID:25295291

  1. A Boolean Consistent Fuzzy Inference System for Diagnosing Diseases and Its Application for Determining Peritonitis Likelihood

    PubMed Central

    Dragović, Ivana; Turajlić, Nina; Pilčević, Dejan; Petrović, Bratislav; Radojević, Dragan

    2015-01-01

    Fuzzy inference systems (FIS) enable automated assessment and reasoning in a logically consistent manner akin to the way in which humans reason. However, since no conventional fuzzy set theory is in the Boolean frame, it is proposed that Boolean consistent fuzzy logic should be used in the evaluation of rules. The main distinction of this approach is that it requires the execution of a set of structural transformations before the actual values can be introduced, which can, in certain cases, lead to different results. While a Boolean consistent FIS could be used for establishing the diagnostic criteria for any given disease, in this paper it is applied for determining the likelihood of peritonitis, as the leading complication of peritoneal dialysis (PD). Given that patients could be located far away from healthcare institutions (as peritoneal dialysis is a form of home dialysis) the proposed Boolean consistent FIS would enable patients to easily estimate the likelihood of them having peritonitis (where a high likelihood would suggest that prompt treatment is indicated), when medical experts are not close at hand. PMID:27069500

  2. Prediction of Missing Flow Records Using Multilayer Perceptron and Coactive Neurofuzzy Inference System

    PubMed Central

    Tfwala, Samkele S.; Wang, Yu-Min; Lin, Yu-Chieh

    2013-01-01

    Hydrological data are often missing due to natural disasters, improper operation, limited equipment life, and other factors, which limit hydrological analysis. Therefore, missing data recovery is an essential process in hydrology. This paper investigates the accuracy of artificial neural networks (ANN) in estimating missing flow records. The purpose is to develop and apply neural networks models to estimate missing flow records in a station when data from adjacent stations is available. Multilayer perceptron neural networks model (MLP) and coactive neurofuzzy inference system model (CANFISM) are used to estimate daily flow records for Li-Lin station using daily flow data for the period 1997 to 2009 from three adjacent stations (Nan-Feng, Lao-Nung and San-Lin) in southern Taiwan. The performance of MLP is slightly better than CANFISM, having R2 of 0.98 and 0.97, respectively. We conclude that accurate estimations of missing flow records under the complex hydrological conditions of Taiwan could be attained by intelligent methods such as MLP and CANFISM. PMID:24453876

  3. Prediction of antimicrobial peptides based on the adaptive neuro-fuzzy inference system application.

    PubMed

    Fernandes, Fabiano C; Rigden, Daniel J; Franco, Octavio L

    2012-01-01

    Antimicrobial peptides (AMPs) are widely distributed defense molecules and represent a promising alternative for solving the problem of antibiotic resistance. Nevertheless, the experimental time required to screen putative AMPs makes computational simulations based on peptide sequence analysis and/or molecular modeling extremely attractive. Artificial intelligence methods acting as simulation and prediction tools are of great importance in helping to efficiently discover and design novel AMPs. In the present study, state-of-the-art published outcomes using different prediction methods and databases were compared to an adaptive neuro-fuzzy inference system (ANFIS) model. Data from our study showed that ANFIS obtained an accuracy of 96.7% and a Matthew's Correlation Coefficient (MCC) of0.936, which proved it to be an efficient model for pattern recognition in antimicrobial peptide prediction. Furthermore, a lower number of input parameters were needed for the ANFIS model, improving the speed and ease of prediction. In summary, due to the fuzzy nature ofAMP physicochemical properties, the ANFIS approach presented here can provide an efficient solution for screening putative AMP sequences and for exploration of properties characteristic of AMPs. PMID:23193592

  4. Classifying work rate from heart rate measurements using an adaptive neuro-fuzzy inference system.

    PubMed

    Kolus, Ahmet; Imbeau, Daniel; Dubé, Philippe-Antoine; Dubeau, Denise

    2016-05-01

    In a new approach based on adaptive neuro-fuzzy inference systems (ANFIS), field heart rate (HR) measurements were used to classify work rate into four categories: very light, light, moderate, and heavy. Inter-participant variability (physiological and physical differences) was considered. Twenty-eight participants performed Meyer and Flenghi's step-test and a maximal treadmill test, during which heart rate and oxygen consumption (VO2) were measured. Results indicated that heart rate monitoring (HR, HRmax, and HRrest) and body weight are significant variables for classifying work rate. The ANFIS classifier showed superior sensitivity, specificity, and accuracy compared to current practice using established work rate categories based on percent heart rate reserve (%HRR). The ANFIS classifier showed an overall 29.6% difference in classification accuracy and a good balance between sensitivity (90.7%) and specificity (95.2%) on average. With its ease of implementation and variable measurement, the ANFIS classifier shows potential for widespread use by practitioners for work rate assessment. PMID:26851475

  5. Adaptive neuro-fuzzy inference system for classification of ECG signals using Lyapunov exponents.

    PubMed

    Ubeyli, Elif Derya

    2009-03-01

    This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for classification of electrocardiogram (ECG) signals. Decision making was performed in two stages: feature extraction by computation of Lyapunov exponents and classification by the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, and atrial fibrillation beat) obtained from the PhysioBank database were classified by four ANFIS classifiers. To improve diagnostic accuracy, the fifth ANFIS classifier (combining ANFIS) was trained using the outputs of the four ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the saliency of features on classification of the ECG signals were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in classifying the ECG signals. PMID:19084286

  6. Modelling Dissolved Pollutants in Krishna River Using Adaptive Neuro Fuzzy Inference Systems

    NASA Astrophysics Data System (ADS)

    Matli, C. S.; Umamahesh, N. V.

    2014-01-01

    Water quality models are used to describe the discharge concentration relationships in the river. Number of models exists to simulate the pollutant loads in a river, of which some of them are based on simple cause effect relationships and others on highly sophisticated physical and mathematical approaches that require extensive data inputs. Fuzzy rule based modeling extensively used in other disciplines, is attempted in the present study for modeling water quality with respect of dissolved pollutants in Krishna river flowing in Southern part of India. Adaptive Neuro Fuzzy Inference Systems (ANFIS), a recent development in the area of neuro-computing, based on the concept of fuzzy sets is used to model highly non-linear relationships and are capable of adaptive learning. This paper presents the results of the application of ANFIS for modeling dissolved pollutants in the Krishna River. The application and validation of the models is carried out using water quality and flow data obtained from the monitoring stations on the river. The results indicate that the models are quite successful in simulating the physical processes of the relationships between discharge and concentrations.

  7. Prediction of missing flow records using multilayer perceptron and coactive neurofuzzy inference system.

    PubMed

    Tfwala, Samkele S; Wang, Yu-Min; Lin, Yu-Chieh

    2013-01-01

    Hydrological data are often missing due to natural disasters, improper operation, limited equipment life, and other factors, which limit hydrological analysis. Therefore, missing data recovery is an essential process in hydrology. This paper investigates the accuracy of artificial neural networks (ANN) in estimating missing flow records. The purpose is to develop and apply neural networks models to estimate missing flow records in a station when data from adjacent stations is available. Multilayer perceptron neural networks model (MLP) and coactive neurofuzzy inference system model (CANFISM) are used to estimate daily flow records for Li-Lin station using daily flow data for the period 1997 to 2009 from three adjacent stations (Nan-Feng, Lao-Nung and San-Lin) in southern Taiwan. The performance of MLP is slightly better than CANFISM, having R (2) of 0.98 and 0.97, respectively. We conclude that accurate estimations of missing flow records under the complex hydrological conditions of Taiwan could be attained by intelligent methods such as MLP and CANFISM. PMID:24453876

  8. Prediction of grain size of nanocrystalline nickel coatings using adaptive neuro-fuzzy inference system

    NASA Astrophysics Data System (ADS)

    Hayati, M.; Rashidi, A. M.; Rezaei, A.

    2011-01-01

    This paper presents application of adaptive neuro-fuzzy inference system (ANFIS) for prediction of the grain size of nanocrystalline nickel coatings as a function of current density, saccharin concentration and bath temperature. For developing ANFIS model, the current density, saccharin concentration and bath temperature are taken as input, and the resulting grain size of the nanocrystalline coating as the output of the model. In order to provide a consistent set of experimental data, the nanocrystalline nickel coatings have been deposited from Watts-type bath using direct current electroplating within a large range of process parameters i.e., current density, saccharin concentration and bath temperature. Variation of the grain size because of the electroplating parameters has been modeled using ANFIS, and the experimental results and theoretical approaches have been compared to each other as well. Also, we have compared the proposed ANFIS model with artificial neural network (ANN) approach. The results have shown that the ANFIS model is more accurate and reliable compared to the ANN approach.

  9. Subsethood-product fuzzy neural inference system (SuPFuNIS).

    PubMed

    Paul, S; Kumar, S

    2002-01-01

    A new subsethood-product fuzzy neural inference system (SuPFuNIS) is presented in this paper. It has the flexibility to handle both numeric and linguistic inputs simultaneously. Numeric inputs are fuzzified by input nodes which act as tunable feature fuzzifiers. Rule based knowledge is easily translated directly into a network architecture. Connections in the network are represented by Gaussian fuzzy sets. The novelty of the model lies in a combination of tunable input feature fuzzifiers; fuzzy mutual subsethood-based activation spread in the network; use of the product operator to compute the extent of firing of a rule; and a volume-defuzzification process to produce a numeric output. Supervised gradient descent is employed to train the centers and spreads of individual fuzzy connections. A subsethood-based method for rule generation from the trained network is also suggested. SuPFuNIS can be applied in a variety of application domains. The model has been tested on Mackey-Glass time series prediction, Iris data classification, Hepatitis medical diagnosis, and function approximation benchmark problems. We also use a standard truck backer-upper control problem to demonstrate how expert knowledge can be used to augment the network. The performance of SuPFuNIS compares excellently with other various existing models.

  10. Asymmetric subsethood-product fuzzy neural inference system (ASuPFuNIS).

    PubMed

    Velayutham, C Shunmuga; Kumar, Satish

    2005-01-01

    This paper presents an asymmetric subsethood-product fuzzy neural inference system (ASuPFuNIS) that directly extends the SuPFuNIS model by permitting signal and weight fuzzy sets to be modeled by asymmetric Gaussian membership functions. The asymmetric subsethood-product network admits both numeric as well as linguistic inputs. Input nodes, which act as tunable feature fuzzifiers, fuzzify numeric inputs with asymmetric Gaussian fuzzy sets; and linguistic inputs are presented as is. The antecedent and consequent labels of standard fuzzy if-then rules are represented as asymmetric Gaussian fuzzy connection weights of the network. The model uses mutual subsethood based activation spread and a product aggregation operator that works in conjunction with volume defuzzification in a gradient descent learning framework. Despite the increase in the number of free parameters, the proposed model performs better than SuPFuNIS, on various benchmarking problems, both in terms of the performance accuracy and architectural economy and compares excellently with other various existing models with a performance better than most of them.

  11. Genetic-algorithm-based multiple regression with fuzzy inference system for detection of nocturnal hypoglycemic episodes.

    PubMed

    Ling, Steve S H; Nguyen, Hung T

    2011-03-01

    Hypoglycemia or low blood glucose is dangerous and can result in unconsciousness, seizures, and even death. It is a common and serious side effect of insulin therapy in patients with diabetes. Hypoglycemic monitor is a noninvasive monitor that measures some physiological parameters continuously to provide detection of hypoglycemic episodes in type 1 diabetes mellitus patients (T1DM). Based on heart rate (HR), corrected QT interval of the ECG signal, change of HR, and the change of corrected QT interval, we develop a genetic algorithm (GA)-based multiple regression with fuzzy inference system (FIS) to classify the presence of hypoglycemic episodes. GA is used to find the optimal fuzzy rules and membership functions of FIS and the model parameters of regression method. From a clinical study of 16 children with T1DM, natural occurrence of nocturnal hypoglycemic episodes is associated with HRs and corrected QT intervals. The overall data were organized into a training set (eight patients) and a testing set (another eight patients) randomly selected. The results show that the proposed algorithm performs a good sensitivity with an acceptable specificity. PMID:21349796

  12. Intelligent Modeling Combining Adaptive Neuro Fuzzy Inference System and Genetic Algorithm for Optimizing Welding Process Parameters

    NASA Astrophysics Data System (ADS)

    Gowtham, K. N.; Vasudevan, M.; Maduraimuthu, V.; Jayakumar, T.

    2011-04-01

    Modified 9Cr-1Mo ferritic steel is used as a structural material for steam generator components of power plants. Generally, tungsten inert gas (TIG) welding is preferred for welding of these steels in which the depth of penetration achievable during autogenous welding is limited. Therefore, activated flux TIG (A-TIG) welding, a novel welding technique, has been developed in-house to increase the depth of penetration. In modified 9Cr-1Mo steel joints produced by the A-TIG welding process, weld bead width, depth of penetration, and heat-affected zone (HAZ) width play an important role in determining the mechanical properties as well as the performance of the weld joints during service. To obtain the desired weld bead geometry and HAZ width, it becomes important to set the welding process parameters. In this work, adaptative neuro fuzzy inference system is used to develop independent models correlating the welding process parameters like current, voltage, and torch speed with weld bead shape parameters like depth of penetration, bead width, and HAZ width. Then a genetic algorithm is employed to determine the optimum A-TIG welding process parameters to obtain the desired weld bead shape parameters and HAZ width.

  13. User's guide to the Fault Inferring Nonlinear Detection System (FINDS) computer program

    NASA Technical Reports Server (NTRS)

    Caglayan, A. K.; Godiwala, P. M.; Satz, H. S.

    1988-01-01

    Described are the operation and internal structure of the computer program FINDS (Fault Inferring Nonlinear Detection System). The FINDS algorithm is designed to provide reliable estimates for aircraft position, velocity, attitude, and horizontal winds to be used for guidance and control laws in the presence of possible failures in the avionics sensors. The FINDS algorithm was developed with the use of a digital simulation of a commercial transport aircraft and tested with flight recorded data. The algorithm was then modified to meet the size constraints and real-time execution requirements on a flight computer. For the real-time operation, a multi-rate implementation of the FINDS algorithm has been partitioned to execute on a dual parallel processor configuration: one based on the translational dynamics and the other on the rotational kinematics. The report presents an overview of the FINDS algorithm, the implemented equations, the flow charts for the key subprograms, the input and output files, program variable indexing convention, subprogram descriptions, and the common block descriptions used in the program.

  14. A Boolean Consistent Fuzzy Inference System for Diagnosing Diseases and Its Application for Determining Peritonitis Likelihood.

    PubMed

    Dragović, Ivana; Turajlić, Nina; Pilčević, Dejan; Petrović, Bratislav; Radojević, Dragan

    2015-01-01

    Fuzzy inference systems (FIS) enable automated assessment and reasoning in a logically consistent manner akin to the way in which humans reason. However, since no conventional fuzzy set theory is in the Boolean frame, it is proposed that Boolean consistent fuzzy logic should be used in the evaluation of rules. The main distinction of this approach is that it requires the execution of a set of structural transformations before the actual values can be introduced, which can, in certain cases, lead to different results. While a Boolean consistent FIS could be used for establishing the diagnostic criteria for any given disease, in this paper it is applied for determining the likelihood of peritonitis, as the leading complication of peritoneal dialysis (PD). Given that patients could be located far away from healthcare institutions (as peritoneal dialysis is a form of home dialysis) the proposed Boolean consistent FIS would enable patients to easily estimate the likelihood of them having peritonitis (where a high likelihood would suggest that prompt treatment is indicated), when medical experts are not close at hand.

  15. Prediction of Scour Depth around Bridge Piers using Adaptive Neuro-Fuzzy Inference Systems (ANFIS)

    NASA Astrophysics Data System (ADS)

    Valyrakis, Manousos; Zhang, Hanqing

    2014-05-01

    Earth's surface is continuously shaped due to the action of geophysical flows. Erosion due to the flow of water in river systems has been identified as a key problem in preserving ecological health of river systems but also a threat to our built environment and critical infrastructure, worldwide. As an example, it has been estimated that a major reason for bridge failure is due to scour. Even though the flow past bridge piers has been investigated both experimentally and numerically, and the mechanisms of scouring are relatively understood, there still lacks a tool that can offer fast and reliable predictions. Most of the existing formulas for prediction of bridge pier scour depth are empirical in nature, based on a limited range of data or for piers of specific shape. In this work, the application of a Machine Learning model that has been successfully employed in Water Engineering, namely an Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed to estimate the scour depth around bridge piers. In particular, various complexity architectures are sequentially built, in order to identify the optimal for scour depth predictions, using appropriate training and validation subsets obtained from the USGS database (and pre-processed to remove incomplete records). The model has five variables, namely the effective pier width (b), the approach velocity (v), the approach depth (y), the mean grain diameter (D50) and the skew to flow. Simulations are conducted with data groups (bed material type, pier type and shape) and different number of input variables, to produce reduced complexity and easily interpretable models. Analysis and comparison of the results indicate that the developed ANFIS model has high accuracy and outstanding generalization ability for prediction of scour parameters. The effective pier width (as opposed to skew to flow) is amongst the most relevant input parameters for the estimation.

  16. A Neuro-Fuzzy Inference System Combining Wavelet Denoising, Principal Component Analysis, and Sequential Probability Ratio Test for Sensor Monitoring

    SciTech Connect

    Na, Man Gyun; Oh, Seungrohk

    2002-11-15

    A neuro-fuzzy inference system combined with the wavelet denoising, principal component analysis (PCA), and sequential probability ratio test (SPRT) methods has been developed to monitor the relevant sensor using the information of other sensors. The parameters of the neuro-fuzzy inference system that estimates the relevant sensor signal are optimized by a genetic algorithm and a least-squares algorithm. The wavelet denoising technique was applied to remove noise components in input signals into the neuro-fuzzy system. By reducing the dimension of an input space into the neuro-fuzzy system without losing a significant amount of information, the PCA was used to reduce the time necessary to train the neuro-fuzzy system, simplify the structure of the neuro-fuzzy inference system, and also, make easy the selection of the input signals into the neuro-fuzzy system. By using the residual signals between the estimated signals and the measured signals, the SPRT is applied to detect whether the sensors are degraded or not. The proposed sensor-monitoring algorithm was verified through applications to the pressurizer water level, the pressurizer pressure, and the hot-leg temperature sensors in pressurized water reactors.

  17. Modeling hourly dissolved oxygen concentration (DO) using two different adaptive neuro-fuzzy inference systems (ANFIS): a comparative study.

    PubMed

    Heddam, Salim

    2014-01-01

    This article presents a comparison of two adaptive neuro-fuzzy inference systems (ANFIS)-based neuro-fuzzy models applied for modeling dissolved oxygen (DO) concentration. The two models are developed using experimental data collected from the bottom (USGS station no: 420615121533601) and top (USGS station no: 420615121533600) stations at Klamath River at site KRS12a nr Rock Quarry, Oregon, USA. The input variables used for the ANFIS models are water pH, temperature, specific conductance, and sensor depth. Two ANFIS-based neuro-fuzzy systems are presented. The two neuro-fuzzy systems are: (1) grid partition-based fuzzy inference system, named ANFIS_GRID, and (2) subtractive-clustering-based fuzzy inference system, named ANFIS_SUB. In both models, 60 % of the data set was randomly assigned to the training set, 20 % to the validation set, and 20 % to the test set. The ANFIS results are compared with multiple linear regression models. The system proposed in this paper shows a novelty approach with regard to the usage of ANFIS models for DO concentration modeling.

  18. Modeling hourly dissolved oxygen concentration (DO) using two different adaptive neuro-fuzzy inference systems (ANFIS): a comparative study.

    PubMed

    Heddam, Salim

    2014-01-01

    This article presents a comparison of two adaptive neuro-fuzzy inference systems (ANFIS)-based neuro-fuzzy models applied for modeling dissolved oxygen (DO) concentration. The two models are developed using experimental data collected from the bottom (USGS station no: 420615121533601) and top (USGS station no: 420615121533600) stations at Klamath River at site KRS12a nr Rock Quarry, Oregon, USA. The input variables used for the ANFIS models are water pH, temperature, specific conductance, and sensor depth. Two ANFIS-based neuro-fuzzy systems are presented. The two neuro-fuzzy systems are: (1) grid partition-based fuzzy inference system, named ANFIS_GRID, and (2) subtractive-clustering-based fuzzy inference system, named ANFIS_SUB. In both models, 60 % of the data set was randomly assigned to the training set, 20 % to the validation set, and 20 % to the test set. The ANFIS results are compared with multiple linear regression models. The system proposed in this paper shows a novelty approach with regard to the usage of ANFIS models for DO concentration modeling. PMID:24057665

  19. Adaptive neuro-fuzzy inference system for temperature and humidity profile retrieval from microwave radiometer observations

    NASA Astrophysics Data System (ADS)

    Ramesh, K.; Kesarkar, A. P.; Bhate, J.; Venkat Ratnam, M.; Jayaraman, A.

    2015-01-01

    The retrieval of accurate profiles of temperature and water vapour is important for the study of atmospheric convection. Recent development in computational techniques motivated us to use adaptive techniques in the retrieval algorithms. In this work, we have used an adaptive neuro-fuzzy inference system (ANFIS) to retrieve profiles of temperature and humidity up to 10 km over the tropical station Gadanki (13.5° N, 79.2° E), India. ANFIS is trained by using observations of temperature and humidity measurements by co-located Meisei GPS radiosonde (henceforth referred to as radiosonde) and microwave brightness temperatures observed by radiometrics multichannel microwave radiometer MP3000 (MWR). ANFIS is trained by considering these observations during rainy and non-rainy days (ANFIS(RD + NRD)) and during non-rainy days only (ANFIS(NRD)). The comparison of ANFIS(RD + NRD) and ANFIS(NRD) profiles with independent radiosonde observations and profiles retrieved using multivariate linear regression (MVLR: RD + NRD and NRD) and artificial neural network (ANN) indicated that the errors in the ANFIS(RD + NRD) are less compared to other retrieval methods. The Pearson product movement correlation coefficient (r) between retrieved and observed profiles is more than 92% for temperature profiles for all techniques and more than 99% for the ANFIS(RD + NRD) technique Therefore this new techniques is relatively better for the retrieval of temperature profiles. The comparison of bias, mean absolute error (MAE), RMSE and symmetric mean absolute percentage error (SMAPE) of retrieved temperature and relative humidity (RH) profiles using ANN and ANFIS also indicated that profiles retrieved using ANFIS(RD + NRD) are significantly better compared to the ANN technique. The analysis of profiles concludes that retrieved profiles using ANFIS techniques have improved the temperature retrievals substantially; however, the retrieval of RH by all techniques considered in this paper (ANN, MVLR and

  20. Physical connectivity in the Mesoamerican Barrier Reef System inferred from 9 years of ocean color observations

    NASA Astrophysics Data System (ADS)

    Soto, I.; Andréfouët, S.; Hu, C.; Muller-Karger, F. E.; Wall, C. C.; Sheng, J.; Hatcher, B. G.

    2009-06-01

    Ocean color images acquired from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) from 1998 to 2006 were used to examine the patterns of physical connectivity between land and reefs, and among reefs in the Mesoamerican Barrier Reef System (MBRS) in the northwestern Caribbean Sea. Connectivity was inferred by tracking surface water features in weekly climatologies and a time series of weekly mean chlorophyll- a concentrations derived from satellite imagery. Frequency of spatial connections between 17 pre-defined, geomorphological domains that include the major reefs in the MBRS and river deltas in Honduras and Nicaragua were recorded and tabulated as percentage of connections. The 9-year time series of 466 weekly mean images portrays clearly the seasonal patterns of connectivity, including river plumes and transitions in the aftermath of perturbations such as hurricanes. River plumes extended offshore from the Honduras coast to the Bay Islands (Utila, Cayo Cochinos, Guanaja, and Roatán) in 70% of the weekly mean images. Belizean reefs, especially those in the southern section of the barrier reef and Glovers Atoll, were also affected by riverine discharges in every one of the 9 years. Glovers Atoll was exposed to river plumes originating in Honduras 104/466 times (22%) during this period. Plumes from eastern Honduras went as far as Banco Chinchorro and Cozumel in Mexico. Chinchorro appeared to be more frequently connected to Turneffe Atoll and Honduran rivers than with Glovers and Lighthouse Atolls, despite their geographic proximity. This new satellite data analysis provides long-term, quantitative assessments of the main pathways of connectivity in the region. The percentage of connections can be used to validate predictions made using other approaches such as numerical modeling, and provides valuable information to ecosystem-based management in coral reef provinces.

  1. Error modeling of DEMs from topographic surveys of rivers using fuzzy inference systems

    NASA Astrophysics Data System (ADS)

    Bangen, Sara; Hensleigh, James; McHugh, Peter; Wheaton, Joseph

    2016-02-01

    Digital elevation models (DEMs) have become common place in the earth sciences as a tool to characterize surface topography and set modeling boundary conditions. All DEMs have a degree of inherent error that is propagated to subsequent models and analyses. While previous research has shown that DEM error is spatially variable it is often represented as spatially uniform for analytical simplicity. Fuzzy inference systems (FIS) offer a tractable approach for modeling spatially variable DEM error, including flexibility in the number of inputs and calibration of outputs based on survey technique and modeling environment. We compare three FIS error models for DEMs derived from TS surveys of wadeable streams and test them at 34 sites in the Columbia River basin. The models differ in complexity regarding the number/type of inputs and degree of site-specific parameterization. A 2-input FIS uses inputs derived from the topographic point cloud (slope, point density). A 4-input FIS adds interpolation error and 3-D point quality. The 5-input FIS adds bed-surface roughness estimates. Both the 4 and 5-input FIS model output were parameterized to site-specific values. In the wetted channel we found (i) the 5-input FIS resulted in lower mean δz due to including roughness, and (ii) the 4 and 5-input FIS resulted in a higher standard deviation and maximum δz due to the inclusion of site-specific bank heights. All three FIS gave plausible estimates of DEM error, with the two more complicated models offering an improvement in the ability to detect spatially localized areas of DEM uncertainty.

  2. Application of Bayesian inference to the study of hierarchical organization in self-organized complex adaptive systems

    NASA Astrophysics Data System (ADS)

    Knuth, K. H.

    2001-05-01

    We consider the application of Bayesian inference to the study of self-organized structures in complex adaptive systems. In particular, we examine the distribution of elements, agents, or processes in systems dominated by hierarchical structure. We demonstrate that results obtained by Caianiello [1] on Hierarchical Modular Systems (HMS) can be found by applying Jaynes' Principle of Group Invariance [2] to a few key assumptions about our knowledge of hierarchical organization. Subsequent application of the Principle of Maximum Entropy allows inferences to be made about specific systems. The utility of the Bayesian method is considered by examining both successes and failures of the hierarchical model. We discuss how Caianiello's original statements suffer from the Mind Projection Fallacy [3] and we restate his assumptions thus widening the applicability of the HMS model. The relationship between inference and statistical physics, described by Jaynes [4], is reiterated with the expectation that this realization will aid the field of complex systems research by moving away from often inappropriate direct application of statistical mechanics to a more encompassing inferential methodology.

  3. Information Warfare-Worthy Jamming Attack Detection Mechanism for Wireless Sensor Networks Using a Fuzzy Inference System

    PubMed Central

    Misra, Sudip; Singh, Ranjit; Rohith Mohan, S. V.

    2010-01-01

    The proposed mechanism for jamming attack detection for wireless sensor networks is novel in three respects: firstly, it upgrades the jammer to include versatile military jammers; secondly, it graduates from the existing node-centric detection system to the network-centric system making it robust and economical at the nodes, and thirdly, it tackles the problem through fuzzy inference system, as the decision regarding intensity of jamming is seldom crisp. The system with its high robustness, ability to grade nodes with jamming indices, and its true-detection rate as high as 99.8%, is worthy of consideration for information warfare defense purposes. PMID:22319307

  4. Forward-Chaining Versus A Graph Approach As The Inference Engine In Expert Systems

    NASA Astrophysics Data System (ADS)

    Neapolitan, Richard E.

    1986-03-01

    Rule-based expert systems are those in which a certain number of IF-THEN rules are assumed to be true. Based on the verity of some assertions, the rules deduce as many new conclusions as possible. A standard technique used to make these deductions is forward-chaining. In forward-chaining, the program or 'inference engine' cycles through the rules. At each rule, the premises for the rule are checked against the current true assertions. If all the premises are found, the conclusion is added to the list of true assertions. At that point it is necessary to start over at the first rule, since the new conclusion may be a premise in a rule already checked. Therefore, each time a new conclusion is deduced it is necessary to start the rule checking procedure over. This process continues until no new conclusions are added and the end of the list of rules is reached. The above process, although quite costly in terms of CPU cycles due to the necessity of repeatedly starting the process over, is necessary if the rules contain 'pattern variables'. An example of such a rule is, 'IF X IS A BACTERIA, THEN X CAN BE TREATED WITH ANTIBIOTICS'. Since the rule can lead to conclusions for many values of X, it is necessary to check each premise in the rule against every true assertion producing an association list to be used in the checking of the next premise. However, if the rule does not contain variable data, as is the case in many current expert systems, then a rule can lead to only one conclusion. In this case, the rules can be stored in a graph, and the true assertions in an assertion list. The assertion list is traversed only once; at each assertion a premise is triggered in all the rules which have that assertion as a premise. When all premises for a rule trigger, the rule's conclusion is added to the END of the list of assertions. It must be added at the end so that it will eventually be used to make further deductions. In the current paper, the two methods are described in

  5. Inferring cortical function in the mouse visual system through large-scale systems neuroscience.

    PubMed

    Hawrylycz, Michael; Anastassiou, Costas; Arkhipov, Anton; Berg, Jim; Buice, Michael; Cain, Nicholas; Gouwens, Nathan W; Gratiy, Sergey; Iyer, Ramakrishnan; Lee, Jung Hoon; Mihalas, Stefan; Mitelut, Catalin; Olsen, Shawn; Reid, R Clay; Teeter, Corinne; de Vries, Saskia; Waters, Jack; Zeng, Hongkui; Koch, Christof

    2016-07-01

    The scientific mission of the Project MindScope is to understand neocortex, the part of the mammalian brain that gives rise to perception, memory, intelligence, and consciousness. We seek to quantitatively evaluate the hypothesis that neocortex is a relatively homogeneous tissue, with smaller functional modules that perform a common computational function replicated across regions. We here focus on the mouse as a mammalian model organism with genetics, physiology, and behavior that can be readily studied and manipulated in the laboratory. We seek to describe the operation of cortical circuitry at the computational level by comprehensively cataloging and characterizing its cellular building blocks along with their dynamics and their cell type-specific connectivities. The project is also building large-scale experimental platforms (i.e., brain observatories) to record the activity of large populations of cortical neurons in behaving mice subject to visual stimuli. A primary goal is to understand the series of operations from visual input in the retina to behavior by observing and modeling the physical transformations of signals in the corticothalamic system. We here focus on the contribution that computer modeling and theory make to this long-term effort. PMID:27382147

  6. Inferring cortical function in the mouse visual system through large-scale systems neuroscience.

    PubMed

    Hawrylycz, Michael; Anastassiou, Costas; Arkhipov, Anton; Berg, Jim; Buice, Michael; Cain, Nicholas; Gouwens, Nathan W; Gratiy, Sergey; Iyer, Ramakrishnan; Lee, Jung Hoon; Mihalas, Stefan; Mitelut, Catalin; Olsen, Shawn; Reid, R Clay; Teeter, Corinne; de Vries, Saskia; Waters, Jack; Zeng, Hongkui; Koch, Christof

    2016-07-01

    The scientific mission of the Project MindScope is to understand neocortex, the part of the mammalian brain that gives rise to perception, memory, intelligence, and consciousness. We seek to quantitatively evaluate the hypothesis that neocortex is a relatively homogeneous tissue, with smaller functional modules that perform a common computational function replicated across regions. We here focus on the mouse as a mammalian model organism with genetics, physiology, and behavior that can be readily studied and manipulated in the laboratory. We seek to describe the operation of cortical circuitry at the computational level by comprehensively cataloging and characterizing its cellular building blocks along with their dynamics and their cell type-specific connectivities. The project is also building large-scale experimental platforms (i.e., brain observatories) to record the activity of large populations of cortical neurons in behaving mice subject to visual stimuli. A primary goal is to understand the series of operations from visual input in the retina to behavior by observing and modeling the physical transformations of signals in the corticothalamic system. We here focus on the contribution that computer modeling and theory make to this long-term effort.

  7. Inferring cortical function in the mouse visual system through large-scale systems neuroscience

    PubMed Central

    Hawrylycz, Michael; Anastassiou, Costas; Arkhipov, Anton; Berg, Jim; Buice, Michael; Cain, Nicholas; Gouwens, Nathan W.; Gratiy, Sergey; Iyer, Ramakrishnan; Lee, Jung Hoon; Mihalas, Stefan; Mitelut, Catalin; Olsen, Shawn; Reid, R. Clay; Teeter, Corinne; de Vries, Saskia; Waters, Jack; Zeng, Hongkui; Koch, Christof

    2016-01-01

    The scientific mission of the Project MindScope is to understand neocortex, the part of the mammalian brain that gives rise to perception, memory, intelligence, and consciousness. We seek to quantitatively evaluate the hypothesis that neocortex is a relatively homogeneous tissue, with smaller functional modules that perform a common computational function replicated across regions. We here focus on the mouse as a mammalian model organism with genetics, physiology, and behavior that can be readily studied and manipulated in the laboratory. We seek to describe the operation of cortical circuitry at the computational level by comprehensively cataloging and characterizing its cellular building blocks along with their dynamics and their cell type-specific connectivities. The project is also building large-scale experimental platforms (i.e., brain observatories) to record the activity of large populations of cortical neurons in behaving mice subject to visual stimuli. A primary goal is to understand the series of operations from visual input in the retina to behavior by observing and modeling the physical transformations of signals in the corticothalamic system. We here focus on the contribution that computer modeling and theory make to this long-term effort. PMID:27382147

  8. Prediction analysis and comparison between agriculture and mining stocks in Indonesia by using adaptive neuro-fuzzy inference system (ANFIS)

    NASA Astrophysics Data System (ADS)

    Mahandrio, Irsantyo; Budi, Andriantama; Liong, The Houw; Purqon, Acep

    2015-09-01

    The growing patterns in cultural and mining sectors are interesting particularly in developed country such as in Indonesia. Here, we investigate the local characteristics of stocks between the sectors of agriculture and mining which si representing two leading companies and two common companies in these sectors. We analyze the prediction by using Adaptive Neuro Fuzzy Inference System (ANFIS). The type of Fuzzy Inference System (FIS) is Sugeno type with Generalized Bell membership function (Gbell). Our results show that ANFIS is a proper method to predicting the stock market with the RMSE : 0.14% for AALI and 0.093% for SGRO representing the agriculture sectors, meanwhile, 0.073% for ANTM and 0.1107% for MDCO representing the mining sectors.

  9. The new physician as unwitting quantum mechanic: is adapting Dirac's inference system best practice for personalized medicine, genomics, and proteomics?

    PubMed

    Robson, Barry

    2007-08-01

    What is the Best Practice for automated inference in Medical Decision Support for personalized medicine? A known system already exists as Dirac's inference system from quantum mechanics (QM) using bra-kets and bras where A and B are states, events, or measurements representing, say, clinical and biomedical rules. Dirac's system should theoretically be the universal best practice for all inference, though QM is notorious as sometimes leading to bizarre conclusions that appear not to be applicable to the macroscopic world of everyday world human experience and medical practice. It is here argued that this apparent difficulty vanishes if QM is assigned one new multiplication function @, which conserves conditionality appropriately, making QM applicable to classical inference including a quantitative form of the predicate calculus. An alternative interpretation with the same consequences is if every i = radical-1 in Dirac's QM is replaced by h, an entity distinct from 1 and i and arguably a hidden root of 1 such that h2 = 1. With that exception, this paper is thus primarily a review of the application of Dirac's system, by application of linear algebra in the complex domain to help manipulate information about associations and ontology in complicated data. Any combined bra-ket can be shown to be composed only of the sum of QM-like bra and ket weights c(), times an exponential function of Fano's mutual information measure I(A; B) about the association between A and B, that is, an association rule from data mining. With the weights and Fano measure re-expressed as expectations on finite data using Riemann's Incomplete (i.e., Generalized) Zeta Functions, actual counts of observations for real world sparse data can be readily utilized. Finally, the paper compares identical character, distinguishability of states events or measurements, correlation, mutual information, and orthogonal character, important issues in data mining

  10. The new physician as unwitting quantum mechanic: is adapting Dirac's inference system best practice for personalized medicine, genomics, and proteomics?

    PubMed

    Robson, Barry

    2007-08-01

    What is the Best Practice for automated inference in Medical Decision Support for personalized medicine? A known system already exists as Dirac's inference system from quantum mechanics (QM) using bra-kets and bras where A and B are states, events, or measurements representing, say, clinical and biomedical rules. Dirac's system should theoretically be the universal best practice for all inference, though QM is notorious as sometimes leading to bizarre conclusions that appear not to be applicable to the macroscopic world of everyday world human experience and medical practice. It is here argued that this apparent difficulty vanishes if QM is assigned one new multiplication function @, which conserves conditionality appropriately, making QM applicable to classical inference including a quantitative form of the predicate calculus. An alternative interpretation with the same consequences is if every i = radical-1 in Dirac's QM is replaced by h, an entity distinct from 1 and i and arguably a hidden root of 1 such that h2 = 1. With that exception, this paper is thus primarily a review of the application of Dirac's system, by application of linear algebra in the complex domain to help manipulate information about associations and ontology in complicated data. Any combined bra-ket can be shown to be composed only of the sum of QM-like bra and ket weights c(), times an exponential function of Fano's mutual information measure I(A; B) about the association between A and B, that is, an association rule from data mining. With the weights and Fano measure re-expressed as expectations on finite data using Riemann's Incomplete (i.e., Generalized) Zeta Functions, actual counts of observations for real world sparse data can be readily utilized. Finally, the paper compares identical character, distinguishability of states events or measurements, correlation, mutual information, and orthogonal character, important issues in data mining

  11. A fuzzy inference system for modelling streamflow: Case of Letaba River, South Africa

    NASA Astrophysics Data System (ADS)

    Katambara, Zacharia; Ndiritu, John

    Streamflow modelling of Letaba River in South Africa is complicated by several factors including the existence of dams and other storage structures whose releases are intermittent and based on rules of thumb depending on the irrigation demands and the need to maintain the flow required in the Kruger National park (KNP). The KNP is located about a hundred kilometres downstream of the main storage and water flows through an alluvial aquifer where complex surface-groundwater interactions occur. Farmers abstract water intermittently along the route directly from the river or indirectly from the alluvial aquifer complicating the flow patterns even more. Consequently, the streamflow series in the river shows very little similarity to what would be considered as natural. The actual abstractions are not measured and only monthly estimates of the abstractions currently exist. Like in many other basins in South Africa, streamflow, groundwater level, rainfall and evaporation data in Letaba is sparse and not very reliable. The Takagi-Sugeno fuzzy inference system using subtractive clustering, an approach which are capable of dealing with vague and inadequate information and data has therefore been used to develop a daily streamflow model for Letaba River. In order to take into account the spatial variability and to maximize the use of the available data, the model is applied in a semi-distributed manner consisting of three river reaches. The shuffled complex evolution (SCE-UA) optimizer has been used to calibrate the model. Six years of data from March 2002 to April 2008 has been used for model calibration and verification. To maximize the Nash-Sutcliffe efficiency, the minimum number of clusters required was found to be 10 for 1000 data points in calibration. An analysis of the location of the cluster centers, the coefficients relating the inputs with the simulated streamflow, and the degrees of membership indicates that no single cluster can be associated to the simulation

  12. Single-trial lambda wave identification using a fuzzy inference system and predictive statistical diagnosis

    NASA Astrophysics Data System (ADS)

    Saatchi, R.

    2004-03-01

    The aim of the study was to automate the identification of a saccade-related visual evoked potential (EP) called the lambda wave. The lambda waves were extracted from single trials of electroencephalogram (EEG) waveforms using independent component analysis (ICA). A trial was a set of EEG waveforms recorded from 64 scalp electrode locations while a saccade was performed. Forty saccade-related EEG trials (recorded from four normal subjects) were used in the study. The number of waveforms per trial was reduced from 64 to 22 by pre-processing. The application of ICA to the resulting waveforms produced 880 components (i.e. 4 subjects × 10 trials per subject × 22 components per trial). The components were divided into 373 lambda and 507 nonlambda waves by visual inspection and then they were represented by one spatial and two temporal features. The classification performance of a Bayesian approach called predictive statistical diagnosis (PSD) was compared with that of a fuzzy logic approach called a fuzzy inference system (FIS). The outputs from the two classification approaches were then combined and the resulting discrimination accuracy was evaluated. For each approach, half the data from the lambda and nonlambda wave categories were used to determine the operating parameters of the classification schemes while the rest (i.e. the validation set) were used to evaluate their classification accuracies. The sensitivity and specificity values when the classification approaches were applied to the lambda wave validation data set were as follows: for the PSD 92.51% and 91.73% respectively, for the FIS 95.72% and 89.76% respectively, and for the combined FIS and PSD approach 97.33% and 97.24% respectively (classification threshold was 0.5). The devised signal processing techniques together with the classification approaches provided for an effective extraction and classification of the single-trial lambda waves. However, as only four subjects were included, it will be

  13. Measure of librarian pressure using fuzzy inference system: A case study in Longyan University

    NASA Astrophysics Data System (ADS)

    Huang, Jian-Jing

    2014-10-01

    As the hierarchy of middle managers in college's librarian. They may own much work pressure from their mind. How to adapt psychological problem, control the emotion and keep a good relationship in their work place, it becomes an important issue. Especially, they work in China mainland environment. How estimate the librarians work pressure and improve the quality of service in college libraries. Those are another serious issues. In this article, the authors would like discuss how can we use fuzzy inference to test librarian work pressure.

  14. Testing Mendelian inheritance from field-collected parasites: Revealing duplicated loci enables correct inference of reproductive mode and mating system.

    PubMed

    Detwiler, Jillian T; Criscione, Charles D

    2011-09-01

    Cryptic aspects of parasite population biology, e.g., mating systems, are increasingly being inferred from polymorphic and co-dominant genetic markers such as microsatellite loci. Underlying the use of such co-dominant markers is the assumption of Mendelian inheritance. The failure to meet this assumption can lead to artifactual statistics and erroneous population inferences. Here, we illustrate the importance of testing the Mendelian segregation and assortment of genetic markers and demonstrate how field-collected samples can be utilised for this purpose. To examine the reproductive mode and mating system of hermaphroditic parasites, we developed microsatellites for the cestode, Oochoristica javaensis. Among loci, we found a bimodal distribution of F(IS) (a fixation index that quantifies the deviation from Hardy-Weinberg equilibrium within subpopulations) values where loci were either highly negative (close to -1) or highly positive (∼0.8). By conducting tests of Mendelian segregation from natural crosses, we determined that loci with negative F(IS) values were in fact duplicated loci that were amplified by a single primer pair. Genetic crosses also provided linkage data and indicated that the duplicated loci most likely arose via tandem duplications rather than whole genome/chromosome duplications. By correcting for the duplicated loci, we were able to correctly infer that O. javaensis has sexual reproduction, but the mating system is highly inbred. To assist others in testing Mendelian segregation and independent assortment from natural samples, we discuss the benefits and limitations, and provide guidelines for particular parasite systems amenable to the methods employed here.

  15. Microstructural and Rheological Constraints on the Mantle Strength of Strike-Slip Fault Systems: Evidence from the Bogota Peninsula Shear Zone, New Caledonia

    NASA Astrophysics Data System (ADS)

    Chatzaras, V.; Titus, S.; Tikoff, B.; Drury, M. R.

    2014-12-01

    Crust-mantle coupling along major strike-slip fault zones suggests that these two lithospheric layers act as an integrated system. In such a system, the spatial and temporal evolution of mantle strength across strike-slip shear zones has proven a key component in understanding lithospheric deformation and rheology. The Bogota Peninsula shear zone is exposed in the mantle section of the New Caledonia ophiolite. It contains a unique microstructural and textural record across a 4-km wide mylonitic zone bordered by a wider zone of weaker deformation. The shear zone is interpreted as a paleotransform fault, based on the orientations of fabrics and dikes inside and outside the zone. No ultramylonites or pseudotachylites were observed within the shear zone. Olivine grain size paleopiezometers suggest variation of the shear zone stresses, with the highest values recorded in the center of the shear zone, coincident with increasing olivine CPO strength toward the shear zone center. By estimating the finite strain in the zone, and assuming that all portions of the shear zone were active synchronously, we can correlate the increased stresses to increased strain rates. We compare the mantle strength in the Bogota Peninsula shear zone to other transform faults, such as the San Andreas fault (SAF) system. The differential stresses in the upper mantle of the SAF system, determined from xenoliths, is similar to those observed in the New Caledonia. Further, the width of shearing deformation in Bogota Peninsula shear zone is similar to that inferred for other transform zones, in both the upper crust and lithospheric mantle. These similarities suggest that viscous flow in the lithospheric mantle is in mechanical communication to brittle deformation in the upper crust. We propose a "Lithospheric Feedback" model, in which displacement due to mantle flow loads the crust during interseismic cycles, while the upper crust effectively limits the strength of the lithosphere.

  16. Integration of process planning and production scheduling with particle swarm optimization (PSO) algorithm and fuzzy inference systems

    NASA Astrophysics Data System (ADS)

    Yang, Yahong; Zhao, Fuqing; Hong, Yi; Yu, Dongmei

    2005-12-01

    Integration of process planning with scheduling by considering the manufacturing system's capacity, cost and capacity in its workshop is a critical issue. The concurrency between them can also eliminate the redundant process and optimize the entire production cycle, but most integrated process planning and scheduling methods only consider the time aspects of the alternative machines when constructing schedules. In this paper, a fuzzy inference system (FIS) in choosing alternative machines for integrated process planning and scheduling of a job shop manufacturing system is presented. Instead of choosing alternative machines randomly, machines are being selected based on the machines reliability. The mean time to failure (MTF) values is input in a fuzzy inference mechanism, which outputs the machine reliability. The machine is then being penalized based on the fuzzy output. The most reliable machine will have the higher priority to be chosen. In order to overcome the problem of un-utilization machines, sometimes faced by unreliable machine, the particle swarm optimization (PSO) have been used to balance the load for all the machines. Simulation study shows that the system can be used as an alternative way of choosing machines in integrated process planning and scheduling.

  17. Fuzzy inference systems, ASKE, knowledge value added, and Monte Carlo risk simulation for evaluating intangible human capital investments

    NASA Astrophysics Data System (ADS)

    Mun, Johnathan; de Albuquerque, Nelson R.; Liong, Choong-Yeun; Salleh, Abdul Razak

    2013-04-01

    This paper presents the ASKE-Risk method, coupled with Fuzzy Inference Systems, and Monte Carlo Risk Simulation to measure and prioritize Individual Technical Competence of a value chain to assess changes in the human capital of a company. ASKE is an extension of the method Knowledge Value Added, which proposes the use of a proxy variable for measuring the flow of knowledge used in a key process, creating a relationship between the company's financial results and the resources used in each of the business processes.

  18. Intelligent detection of hypoglycemic episodes in children with type 1 diabetes using adaptive neural-fuzzy inference system.

    PubMed

    San, Phyo Phyo; Ling, Sai Ho; Nguyen, Hung T

    2012-01-01

    Hypoglycemia, or low blood glucose, is the most common complication experienced by Type 1 diabetes mellitus (T1DM) patients. It is dangerous and can result in unconsciousness, seizures and even death. The most common physiological parameter to be effected from hypoglycemic reaction are heart rate (HR) and correct QT interval (QTc) of the electrocardiogram (ECG) signal. Based on physiological parameters, an intelligent diagnostics system, using the hybrid approach of adaptive neural fuzzy inference system (ANFIS), is developed to recognize the presence of hypoglycemia. The proposed ANFIS is characterized by adaptive neural network capabilities and the fuzzy inference system. To optimize the membership functions and adaptive network parameters, a global learning optimization algorithm called hybrid particle swarm optimization with wavelet mutation (HPSOWM) is used. For clinical study, 15 children with Type 1 diabetes volunteered for an overnight study. All the real data sets are collected from the Department of Health, Government of Western Australia. Several experiments were conducted with 5 patients each, for a training set (184 data points), a validation set (192 data points) and a testing set (153 data points), which are randomly selected. The effectiveness of the proposed detection method is found to be satisfactory by giving better sensitivity, 79.09% and acceptable specificity, 51.82%. PMID:23367375

  19. Fuzzy logic and adaptive neuro-fuzzy inference system for characterization of contaminant exposure through selected biomarkers in African catfish.

    PubMed

    Karami, Ali; Keiter, Steffen; Hollert, Henner; Courtenay, Simon C

    2013-03-01

    This study represents a first attempt at applying a fuzzy inference system (FIS) and an adaptive neuro-fuzzy inference system (ANFIS) to the field of aquatic biomonitoring for classification of the dosage and time of benzo[a]pyrene (BaP) injection through selected biomarkers in African catfish (Clarias gariepinus). Fish were injected either intramuscularly (i.m.) or intraperitoneally (i.p.) with BaP. Hepatic glutathione S-transferase (GST) activities, relative visceral fat weights (LSI), and four biliary fluorescent aromatic compounds (FACs) concentrations were used as the inputs in the modeling study. Contradictory rules in FIS and ANFIS models appeared after conversion of bioassay results into human language (rule-based system). A "data trimming" approach was proposed to eliminate the conflicts prior to fuzzification. However, the model produced was relevant only to relatively low exposures to BaP, especially through the i.m. route of exposure. Furthermore, sensitivity analysis was unable to raise the classification rate to an acceptable level. In conclusion, FIS and ANFIS models have limited applications in the field of fish biomarker studies.

  20. Structural Information Inference from Lanthanoid Complexing Systems: Photoluminescence Studies on Isolated Ions

    NASA Astrophysics Data System (ADS)

    Greisch, Jean Francois; Harding, Michael E.; Chmela, Jiri; Klopper, Willem M.; Schooss, Detlef; Kappes, Manfred M.

    2016-06-01

    The application of lanthanoid complexes ranges from photovoltaics and light-emitting diodes to quantum memories and biological assays. Rationalization of their design requires a thorough understanding of intramolecular processes such as energy transfer, charge transfer, and non-radiative decay involving their subunits. Characterization of the excited states of such complexes considerably benefits from mass spectrometric methods since the associated optical transitions and processes are strongly affected by stoichiometry, symmetry, and overall charge state. We report herein spectroscopic measurements on ensembles of ions trapped in the gas phase and soft-landed in neon matrices. Their interpretation is considerably facilitated by direct comparison with computations. The combination of energy- and time-resolved measurements on isolated species with density functional as well as ligand-field and Franck-Condon computations enables us to infer structural as well as dynamical information about the species studied. The approach is first illustrated for sets of model lanthanoid complexes whose structure and electronic properties are systematically varied via the substitution of one component (lanthanoid or alkali,alkali-earth ion): (i) systematic dependence of ligand-centered phosphorescence on the lanthanoid(III) promotion energy and its impact on sensitization, and (ii) structural changes induced by the substitution of alkali or alkali-earth ions in relation with structures inferred using ion mobility spectroscopy. The temperature dependence of sensitization is briefly discussed. The focus is then shifted to measurements involving europium complexes with doxycycline an antibiotic of the tetracycline family. Besides discussing the complexes' structural and electronic features, we report on their use to monitor enzymatic processes involving hydrogen peroxide or biologically relevant molecules such as adenosine triphosphate (ATP).

  1. Inference, simulation, modeling, and analysis of complex networks, with special emphasis on complex networks in systems biology

    NASA Astrophysics Data System (ADS)

    Christensen, Claire Petra

    Across diverse fields ranging from physics to biology, sociology, and economics, the technological advances of the past decade have engendered an unprecedented explosion of data on highly complex systems with thousands, if not millions of interacting components. These systems exist at many scales of size and complexity, and it is becoming ever-more apparent that they are, in fact, universal, arising in every field of study. Moreover, they share fundamental properties---chief among these, that the individual interactions of their constituent parts may be well-understood, but the characteristic behaviour produced by the confluence of these interactions---by these complex networks---is unpredictable; in a nutshell, the whole is more than the sum of its parts. There is, perhaps, no better illustration of this concept than the discoveries being made regarding complex networks in the biological sciences. In particular, though the sequencing of the human genome in 2003 was a remarkable feat, scientists understand that the "cellular-level blueprints" for the human being are cellular-level parts lists, but they say nothing (explicitly) about cellular-level processes. The challenge of modern molecular biology is to understand these processes in terms of the networks of parts---in terms of the interactions among proteins, enzymes, genes, and metabolites---as it is these processes that ultimately differentiate animate from inanimate, giving rise to life! It is the goal of systems biology---an umbrella field encapsulating everything from molecular biology to epidemiology in social systems---to understand processes in terms of fundamental networks of core biological parts, be they proteins or people. By virtue of the fact that there are literally countless complex systems, not to mention tools and techniques used to infer, simulate, analyze, and model these systems, it is impossible to give a truly comprehensive account of the history and study of complex systems. The author

  2. Fuzzy logic inference-based Pavement Friction Management and real-time slippery warning systems: A proof of concept study.

    PubMed

    Najafi, Shahriar; Flintsch, Gerardo W; Khaleghian, Seyedmeysam

    2016-05-01

    Minimizing roadway crashes and fatalities is one of the primary objectives of highway engineers, and can be achieved in part through appropriate maintenance practices. Maintaining an appropriate level of friction is a crucial maintenance practice, due to the effect it has on roadway safety. This paper presents a fuzzy logic inference system that predicts the rate of vehicle crashes based on traffic level, speed limit, and surface friction. Mamdani and Sugeno fuzzy controllers were used to develop the model. The application of the proposed fuzzy control system in a real-time slippery road warning system is demonstrated as a proof of concept. The results of this study provide a decision support model for highway agencies to monitor their network's friction and make appropriate judgments to correct deficiencies based on crash risk. Furthermore, this model can be implemented in the connected vehicle environment to warn drivers of potentially slippery locations. PMID:26914521

  3. Fuzzy logic inference-based Pavement Friction Management and real-time slippery warning systems: A proof of concept study.

    PubMed

    Najafi, Shahriar; Flintsch, Gerardo W; Khaleghian, Seyedmeysam

    2016-05-01

    Minimizing roadway crashes and fatalities is one of the primary objectives of highway engineers, and can be achieved in part through appropriate maintenance practices. Maintaining an appropriate level of friction is a crucial maintenance practice, due to the effect it has on roadway safety. This paper presents a fuzzy logic inference system that predicts the rate of vehicle crashes based on traffic level, speed limit, and surface friction. Mamdani and Sugeno fuzzy controllers were used to develop the model. The application of the proposed fuzzy control system in a real-time slippery road warning system is demonstrated as a proof of concept. The results of this study provide a decision support model for highway agencies to monitor their network's friction and make appropriate judgments to correct deficiencies based on crash risk. Furthermore, this model can be implemented in the connected vehicle environment to warn drivers of potentially slippery locations.

  4. Hydrological connectivity inferred from diatom transport through the riparian-stream system

    NASA Astrophysics Data System (ADS)

    Martínez-Carreras, N.; Wetzel, C. E.; Frentress, J.; Ector, L.; McDonnell, J. J.; Hoffmann, L.; Pfister, L.

    2015-07-01

    Diatoms (Bacillariophyta) are one of the most common and diverse algal groups (ca. 200 000 species, ≈ 10-200 μm, unicellular, eukaryotic). Here we investigate the potential of aerial diatoms (i.e. diatoms nearly exclusively occurring outside water bodies, in wet, moist or temporarily dry places) to infer surface hydrological connectivity between hillslope-riparian-stream (HRS) landscape units during storm runoff events. We present data from the Weierbach catchment (0.45 km2, northwestern Luxembourg) that quantify the relative abundance of aerial diatom species on hillslopes and in riparian zones (i.e. surface soils, litter, bryophytes and vegetation) and within streams (i.e. stream water, epilithon and epipelon). We tested the hypothesis that different diatom species assemblages inhabit specific moisture domains of the catchment (i.e. HRS units) and, consequently, the presence of certain species assemblages in the stream during runoff events offers the potential for recording whether there was hydrological connectivity between these domains or not. We found that a higher percentage of aerial diatom species was present in samples collected from the riparian and hillslope zones than inside the stream. However, diatoms were absent on hillslopes covered by dry litter and the quantities of diatoms (in absolute numbers) were small in the rest of hillslope samples. This limits their use for inferring hillslope-riparian zone connectivity. Our results also showed that aerial diatom abundance in the stream increased systematically during all sampled events (n = 11, 2011-2012) in response to incident precipitation and increasing discharge. This transport of aerial diatoms during events suggested a rapid connectivity between the soil surface and the stream. Diatom transport data were compared to two-component hydrograph separation, and end-member mixing analysis (EMMA) using stream water chemistry and stable isotope data. Hillslope overland flow was insignificant during

  5. RegPredict: an integrated system for regulon inference in prokaryotes by comparative genomics approach

    SciTech Connect

    Novichkov, Pavel S.; Rodionov, Dmitry A.; Stavrovskaya, Elena D.; Novichkova, Elena S.; Kazakov, Alexey E.; Gelfand, Mikhail S.; Arkin, Adam P.; Mironov, Andrey A.; Dubchak, Inna

    2010-05-26

    RegPredict web server is designed to provide comparative genomics tools for reconstruction and analysis of microbial regulons using comparative genomics approach. The server allows the user to rapidly generate reference sets of regulons and regulatory motif profiles in a group of prokaryotic genomes. The new concept of a cluster of co-regulated orthologous operons allows the user to distribute the analysis of large regulons and to perform the comparative analysis of multiple clusters independently. Two major workflows currently implemented in RegPredict are: (i) regulon reconstruction for a known regulatory motif and (ii) ab initio inference of a novel regulon using several scenarios for the generation of starting gene sets. RegPredict provides a comprehensive collection of manually curated positional weight matrices of regulatory motifs. It is based on genomic sequences, ortholog and operon predictions from the MicrobesOnline. An interactive web interface of RegPredict integrates and presents diverse genomic and functional information about the candidate regulon members from several web resources. RegPredict is freely accessible at http://regpredict.lbl.gov.

  6. Adaptive neuro-fuzzy inference system for acoustic analysis of 4-channel phonocardiograms using empirical mode decomposition.

    PubMed

    Becerra, Miguel A; Orrego, Diana A; Delgado-Trejos, Edilson

    2013-01-01

    The heart's mechanical activity can be appraised by auscultation recordings, taken from the 4-Standard Auscultation Areas (4-SAA), one for each cardiac valve, as there are invisible murmurs when a single area is examined. This paper presents an effective approach for cardiac murmur detection based on adaptive neuro-fuzzy inference systems (ANFIS) over acoustic representations derived from Empirical Mode Decomposition (EMD) and Hilbert-Huang Transform (HHT) of 4-channel phonocardiograms (4-PCG). The 4-PCG database belongs to the National University of Colombia. Mel-Frequency Cepstral Coefficients (MFCC) and statistical moments of HHT were estimated on the combination of different intrinsic mode functions (IMFs). A fuzzy-rough feature selection (FRFS) was applied in order to reduce complexity. An ANFIS network was implemented on the feature space, randomly initialized, adjusted using heuristic rules and trained using a hybrid learning algorithm made up by least squares and gradient descent. Global classification for 4-SAA was around 98.9% with satisfactory sensitivity and specificity, using a 50-fold cross-validation procedure (70/30 split). The representation capability of the EMD technique applied to 4-PCG and the neuro-fuzzy inference of acoustic features offered a high performance to detect cardiac murmurs. PMID:24109851

  7. Adaptive neuro-fuzzy inference system for acoustic analysis of 4-channel phonocardiograms using empirical mode decomposition.

    PubMed

    Becerra, Miguel A; Orrego, Diana A; Delgado-Trejos, Edilson

    2013-01-01

    The heart's mechanical activity can be appraised by auscultation recordings, taken from the 4-Standard Auscultation Areas (4-SAA), one for each cardiac valve, as there are invisible murmurs when a single area is examined. This paper presents an effective approach for cardiac murmur detection based on adaptive neuro-fuzzy inference systems (ANFIS) over acoustic representations derived from Empirical Mode Decomposition (EMD) and Hilbert-Huang Transform (HHT) of 4-channel phonocardiograms (4-PCG). The 4-PCG database belongs to the National University of Colombia. Mel-Frequency Cepstral Coefficients (MFCC) and statistical moments of HHT were estimated on the combination of different intrinsic mode functions (IMFs). A fuzzy-rough feature selection (FRFS) was applied in order to reduce complexity. An ANFIS network was implemented on the feature space, randomly initialized, adjusted using heuristic rules and trained using a hybrid learning algorithm made up by least squares and gradient descent. Global classification for 4-SAA was around 98.9% with satisfactory sensitivity and specificity, using a 50-fold cross-validation procedure (70/30 split). The representation capability of the EMD technique applied to 4-PCG and the neuro-fuzzy inference of acoustic features offered a high performance to detect cardiac murmurs.

  8. Risk Mapping of Cutaneous Leishmaniasis via a Fuzzy C Means-based Neuro-Fuzzy Inference System

    NASA Astrophysics Data System (ADS)

    Akhavan, P.; Karimi, M.; Pahlavani, P.

    2014-10-01

    Finding pathogenic factors and how they are spread in the environment has become a global demand, recently. Cutaneous Leishmaniasis (CL) created by Leishmania is a special parasitic disease which can be passed on to human through phlebotomus of vector-born. Studies show that economic situation, cultural issues, as well as environmental and ecological conditions can affect the prevalence of this disease. In this study, Data Mining is utilized in order to predict CL prevalence rate and obtain a risk map. This case is based on effective environmental parameters on CL and a Neuro-Fuzzy system was also used. Learning capacity of Neuro-Fuzzy systems in neural network on one hand and reasoning power of fuzzy systems on the other, make it very efficient to use. In this research, in order to predict CL prevalence rate, an adaptive Neuro-fuzzy inference system with fuzzy inference structure of fuzzy C Means clustering was applied to determine the initial membership functions. Regarding to high incidence of CL in Ilam province, counties of Ilam, Mehran, and Dehloran have been examined and evaluated. The CL prevalence rate was predicted in 2012 by providing effective environmental map and topography properties including temperature, moisture, annual, rainfall, vegetation and elevation. Results indicate that the model precision with fuzzy C Means clustering structure rises acceptable RMSE values of both training and checking data and support our analyses. Using the proposed data mining technology, the pattern of disease spatial distribution and vulnerable areas become identifiable and the map can be used by experts and decision makers of public health as a useful tool in management and optimal decision-making.

  9. Electron Precipitation Parameters and Ionospheric Conductances Inferred from Auroral Images Acquired by the Visible Imaging Systems (VIS) on the Polar Spacecraft

    NASA Technical Reports Server (NTRS)

    Sigwarth, John B.; Bekerat, Hamed A.

    2008-01-01

    The Visible Imaging System (VIS) on the polar spacecraft provided time sequences of auroral images at multiple wavelengths that yield information of auroral dynamics on a global scale with a spatial resolution of - 20 km and temporal resolution of approx. 1 minute. Time sequences of VIS images in which the aurora was highly dynamic are used to infer global maps for the electron precipitation parameters, energy flux and characteristic energies, and ionospheric conductances. The maps are inferred from the corresponding VIS images using an auroral model (Lumerzheim et al., 1987). The temporal and spatial resolution of the VIS inferred patterns are unprecedented. The inferred patterns are highly structured and vary significantly on a time scale of less than 5 minutes. These patterns can be very beneficial for global physics-based numerical models for the high-latitude ionosphere which previously had to rely on statistical models for the electron precipitation and ionospheric conductance.

  10. The Birth Environment of the Solar System Inferred from a "Mixing-Fallback" Supernova Model

    NASA Astrophysics Data System (ADS)

    Miki, J.; Takigawa, A.; Tachibana, S.; Huss, G. R.

    2007-03-01

    The birth environment of the solar system was evaluated from abundances of short-lived radionuclides and a mixing-fallback supernova model. The solar system may have formed within several parsec from a massive star with >20 solar mass.

  11. Analysis prediction of Indonesian banks (BCA, BNI, MANDIRI) using adaptive neuro-fuzzy inference system (ANFIS) and investment strategies

    NASA Astrophysics Data System (ADS)

    Trianto, Andriantama Budi; Hadi, I. M.; Liong, The Houw; Purqon, Acep

    2015-09-01

    Indonesian economical development is growing well. It has effect for their invesment in Banks and the stock market. In this study, we perform prediction for the three blue chips of Indonesian bank i.e. BCA, BNI, and MANDIRI by using the method of Adaptive Neuro-Fuzzy Inference System (ANFIS) with Takagi-Sugeno rules and Generalized bell (Gbell) as the membership function. Our results show that ANFIS perform good prediction with RMSE for BCA of 27, BNI of 5.29, and MANDIRI of 13.41, respectively. Furthermore, we develop an active strategy to gain more benefit. We compare between passive strategy versus active strategy. Our results shows that for the passive strategy gains 13 million rupiah, while for the active strategy gains 47 million rupiah in one year. The active investment strategy significantly shows gaining multiple benefit than the passive one.

  12. Adaptive neuro-fuzzy inference system for classification of background EEG signals from ESES patients and controls.

    PubMed

    Yang, Zhixian; Wang, Yinghua; Ouyang, Gaoxiang

    2014-01-01

    Background electroencephalography (EEG), recorded with scalp electrodes, in children with electrical status epilepticus during slow-wave sleep (ESES) syndrome and control subjects has been analyzed. We considered 10 ESES patients, all right-handed and aged 3-9 years. The 10 control individuals had the same characteristics of the ESES ones but presented a normal EEG. Recordings were undertaken in the awake and relaxed states with their eyes open. The complexity of background EEG was evaluated using the permutation entropy (PE) and sample entropy (SampEn) in combination with the ANOVA test. It can be seen that the entropy measures of EEG are significantly different between the ESES patients and normal control subjects. Then, a classification framework based on entropy measures and adaptive neuro-fuzzy inference system (ANFIS) classifier is proposed to distinguish ESES and normal EEG signals. The results are promising and a classification accuracy of about 89% is achieved. PMID:24790547

  13. Adaptive Neuro-Fuzzy Inference System for Classification of Background EEG Signals from ESES Patients and Controls

    PubMed Central

    Yang, Zhixian; Wang, Yinghua; Ouyang, Gaoxiang

    2014-01-01

    Background electroencephalography (EEG), recorded with scalp electrodes, in children with electrical status epilepticus during slow-wave sleep (ESES) syndrome and control subjects has been analyzed. We considered 10 ESES patients, all right-handed and aged 3–9 years. The 10 control individuals had the same characteristics of the ESES ones but presented a normal EEG. Recordings were undertaken in the awake and relaxed states with their eyes open. The complexity of background EEG was evaluated using the permutation entropy (PE) and sample entropy (SampEn) in combination with the ANOVA test. It can be seen that the entropy measures of EEG are significantly different between the ESES patients and normal control subjects. Then, a classification framework based on entropy measures and adaptive neuro-fuzzy inference system (ANFIS) classifier is proposed to distinguish ESES and normal EEG signals. The results are promising and a classification accuracy of about 89% is achieved. PMID:24790547

  14. [Occurence of salmonellae in laying hens in different housing systems and inferences for control].

    PubMed

    Methner, Ulrich; Diller, Roland; Reiche, Renate; Böhland, Karin

    2006-01-01

    As eggs represent now as ever the most important source for Salmonella infection in human beings and because of the currently occurring shift in housing conditions for laying hens from conventional cages to alternative systems it was studied whether the Salmonella prevalence in layers is influenced by the housing system. Following systems were considered: organic farming with free range management systems, floor management systems with free range, floor management systems without free range, conventional cages. 453 pooled faecal samples as single or double examination per herd from 329 flocks in different housing systems for table egg production from three Federal Lander were examined bacteriologically. The share of layer flocks which were Salmonella positive at least once independently of the housing system amounted to 32.2%. Analysis of the Salmonella findings in the single housing systems revealed that the share of Salmonella positive flocks was higher in conventional cage systems (46.3%) than in alternative housing systems (32.996% in organic farming with free range management systems, 21.9% in floor management systems with free range, 23.4% in floor management systems without free range). The results of the study clearly show that Salmonella Enteritidis (mostly phage type 4, other phage types rarely) presents with a share of 78% the dominant serovar in laying hens. The total number of all other serovars (apart from Salmonella Enteritidis and subspecies I rough) reached a share of ca. 14%, however, no other single serovar was dominant. As Salmonella Enteritidis is the predominant serovar in laying hens it is strongly recommended to use Salmonella Enteritidis vaccines for immunisation programmes of chickens during the rearing period. Because of the high prevalence of Salmonella organisms in the different housing systems, detailed information on the epidemiology of Salmonella in laying hens are needed to introduce effective control measures. Of particular

  15. Developing a Dynamic Inference Expert System to Support Individual Learning at Work

    ERIC Educational Resources Information Center

    Hung, Yu Hsin; Lin, Chun Fu; Chang, Ray I.

    2015-01-01

    In response to the rapid growth of information in recent decades, knowledge-based systems have become an essential tool for organizational learning. The application of electronic performance-support systems in learning activities has attracted considerable attention from researchers. Nevertheless, the vast, ever-increasing amount of information is…

  16. Coseismic and postseismic deformation due to the South Napa earthquake inferred from modeling of Global Positioning System data

    NASA Astrophysics Data System (ADS)

    Murray, J. R.; Svarc, J. L.; Pollitz, F. F.; Floyd, M.; Funning, G.; Johanson, I. A.

    2014-12-01

    Tectonic ground deformation due to the 24 August 2014 M6 South Napa earthquake was recorded by continuous GPS (CGPS) sites of the Plate Boundary Observatory, Bay Area Regional Deformation, and USGS networks. Additionally, survey-mode GPS (SGPS) measurements were carried out following the event to densify the spatial coverage and record postseismic deformation. We compare earthquake offsets estimated using two sets of time series for the same sites, one with position estimates at five minute intervals and the other at one day intervals. On average the offset magnitudes from the five-minute positions are ~70% those estimated from the daily data, demonstrating that substantial postseismic deformation occurred immediately following the coseismic slip. Fitting the daily position time series for sites within ~35 km of the epicenter with a combination of coseismic offset and a logarithmic decay that begins immediately following the event indicates that cumulative displacement from 25 August 2014 to 24 September 2014 is on average ~70% of the estimated displacement on 24 August at these sites. While earthquakes on creeping faults of the San Andreas system have often generated postseismic displacement of similar magnitude to the coseismic, the mapped trace associated with this earthquake was not known to creep. Using the coseismic offsets estimated from the five-minute solutions and a Bayesian inference approach, the most likely planar fault that passes through the epicenter and intersects the Earth's surface is vertical and strikes 155o, in good agreement with seismic moment tensor estimates. The peak GPS-inferred coseismic slip extends ~12 km northwest and up-dip of the hypocenter. Initial postseismic slip models derived from GPS data show shallow afterslip near and to the southeast of the inferred coseismic slip; the afterslip is generally shallower and southeast of the zone of aftershocks. However, the resulting GPS residuals exhibit more complex spatial patterns that

  17. Inference, simulation, modeling, and analysis of complex networks, with special emphasis on complex networks in systems biology

    NASA Astrophysics Data System (ADS)

    Christensen, Claire Petra

    Across diverse fields ranging from physics to biology, sociology, and economics, the technological advances of the past decade have engendered an unprecedented explosion of data on highly complex systems with thousands, if not millions of interacting components. These systems exist at many scales of size and complexity, and it is becoming ever-more apparent that they are, in fact, universal, arising in every field of study. Moreover, they share fundamental properties---chief among these, that the individual interactions of their constituent parts may be well-understood, but the characteristic behaviour produced by the confluence of these interactions---by these complex networks---is unpredictable; in a nutshell, the whole is more than the sum of its parts. There is, perhaps, no better illustration of this concept than the discoveries being made regarding complex networks in the biological sciences. In particular, though the sequencing of the human genome in 2003 was a remarkable feat, scientists understand that the "cellular-level blueprints" for the human being are cellular-level parts lists, but they say nothing (explicitly) about cellular-level processes. The challenge of modern molecular biology is to understand these processes in terms of the networks of parts---in terms of the interactions among proteins, enzymes, genes, and metabolites---as it is these processes that ultimately differentiate animate from inanimate, giving rise to life! It is the goal of systems biology---an umbrella field encapsulating everything from molecular biology to epidemiology in social systems---to understand processes in terms of fundamental networks of core biological parts, be they proteins or people. By virtue of the fact that there are literally countless complex systems, not to mention tools and techniques used to infer, simulate, analyze, and model these systems, it is impossible to give a truly comprehensive account of the history and study of complex systems. The author

  18. Inference in dynamic systems using B-splines and quasilinearized ODE penalties.

    PubMed

    Frasso, Gianluca; Jaeger, Jonathan; Lambert, Philippe

    2016-05-01

    Nonlinear (systems of) ordinary differential equations (ODEs) are common tools in the analysis of complex one-dimensional dynamic systems. We propose a smoothing approach regularized by a quasilinearized ODE-based penalty. Within the quasilinearized spline-based framework, the estimation reduces to a conditionally linear problem for the optimization of the spline coefficients. Furthermore, standard ODE compliance parameter(s) selection criteria are applicable. We evaluate the performances of the proposed strategy through simulated and real data examples. Simulation studies suggest that the proposed procedure ensures more accurate estimates than standard nonlinear least squares approaches when the state (initial and/or boundary) conditions are not known. PMID:26602190

  19. Information theory and signal transduction systems: from molecular information processing to network inference.

    PubMed

    Mc Mahon, Siobhan S; Sim, Aaron; Filippi, Sarah; Johnson, Robert; Liepe, Juliane; Smith, Dominic; Stumpf, Michael P H

    2014-11-01

    Sensing and responding to the environment are two essential functions that all biological organisms need to master for survival and successful reproduction. Developmental processes are marshalled by a diverse set of signalling and control systems, ranging from systems with simple chemical inputs and outputs to complex molecular and cellular networks with non-linear dynamics. Information theory provides a powerful and convenient framework in which such systems can be studied; but it also provides the means to reconstruct the structure and dynamics of molecular interaction networks underlying physiological and developmental processes. Here we supply a brief description of its basic concepts and introduce some useful tools for systems and developmental biologists. Along with a brief but thorough theoretical primer, we demonstrate the wide applicability and biological application-specific nuances by way of different illustrative vignettes. In particular, we focus on the characterisation of biological information processing efficiency, examining cell-fate decision making processes, gene regulatory network reconstruction, and efficient signal transduction experimental design.

  20. Response of hydrothermal system to stress transients at Lassen Volcanic Center, California, inferred from seismic interferometry with ambient noise

    NASA Astrophysics Data System (ADS)

    Taira, Taka'aki; Brenguier, Florent

    2016-10-01

    Time-lapse monitoring of seismic velocity at volcanic areas can provide unique insight into the property of hydrothermal and magmatic fluids and their temporal variability. We established a quasi real-time velocity monitoring system by using seismic interferometry with ambient noise to explore the temporal evolution of velocity in the Lassen Volcanic Center, Northern California. Our monitoring system finds temporal variability of seismic velocity in response to stress changes imparted by an earthquake and by seasonal environmental changes. Dynamic stress changes from a magnitude 5.7 local earthquake induced a 0.1 % velocity reduction at a depth of about 1 km. The seismic velocity susceptibility defined as ratio of seismic velocity change to dynamic stress change is estimated to be about 0.006 MPa-1, which suggests the Lassen hydrothermal system is marked by high-pressurized hydrothermal fluid. By combining geodetic measurements, our observation shows that the long-term seismic velocity fluctuation closely tracks snow-induced vertical deformation without time delay, which is most consistent with an hydrological load model (either elastic or poroelastic response) in which surface loading drives hydrothermal fluid diffusion that leads to an increase of opening of cracks and subsequently reductions of seismic velocity. We infer that heated-hydrothermal fluid in a vapor-dominated zone at a depth of 2-4 km range is responsible for the long-term variation in seismic velocity[Figure not available: see fulltext.

  1. A general framework for modeling tumor-immune system competition and immunotherapy: Mathematical analysis and biomedical inferences

    NASA Astrophysics Data System (ADS)

    d'Onofrio, Alberto

    2005-09-01

    In this work we propose and investigate a family of models, which admits as particular cases some well known mathematical models of tumor-immune system interaction, with the additional assumption that the influx of immune system cells may be a function of the number of cancer cells. Constant, periodic and impulsive therapies (as well as the non-perturbed system) are investigated both analytically for the general family and, by using the model by Kuznetsov et al. [V.A. Kuznetsov, I.A. Makalkin, M.A. Taylor, A.S. Perelson, Nonlinear dynamics of immunogenic tumors: parameter estimation and global bifurcation analysis, Bull. Math. Biol. (1994) 56(2) 295-321), via numerical simulations. Simulations seem to show that the shape of the function modeling the therapy is a crucial factor only for very high values of the therapy period T, whereas for realistic values of T, the eradication of the cancer cells depends on the mean values of the therapy term. Finally, some medical inferences are proposed.

  2. Methane leakage from evolving petroleum systems: Masses, rates and inferences for climate feedback

    NASA Astrophysics Data System (ADS)

    Berbesi, L. A.; di Primio, R.; Anka, Z.; Horsfield, B.; Wilkes, H.

    2014-02-01

    The immense mass of organic carbon contained in sedimentary systems, currently estimated at 1.56×1010 Tg (Des Marais et al., 1992), bears the potential of affecting global climate through the release of thermally or biologically generated methane to the atmosphere. Here we investigate the potential of naturally-occurring gas leakage, controlled by petroleum generation and degradation as a forcing mechanism for climate at geologic time scales. We addressed the potential methane contributions to the atmosphere during the evolution of petroleum systems in two different, petroliferous geological settings: the Western Canada Sedimentary Basin (WCSB) and the Central Graben area of the North Sea. Besides 3D numerical simulation, different types of mass balance and theoretical approaches were applied depending on the data available and the processes taking place in each basin. In the case of the WCSB, we estimate maximum thermogenic methane leakage rates in the order of 10-2-10-3 Tg/yr, and maximum biogenic methane generation rates of 10-2 Tg/yr. In the case of the Central Graben, maximum estimates for thermogenic methane leakage are in the order in 10-3 Tg/yr. Extrapolation of our results to a global scale suggests that, at least as a single process, thermal gas generation in hydrocarbon kitchen areas would not be able to influence climate, although it may contribute to a positive feedback. Conversely, only the sudden release of subsurface methane accumulations, formed over geologic timescales, can possibly allow for petroleum systems to exert an effect on climate.

  3. From Birdsong to Human Speech Recognition: Bayesian Inference on a Hierarchy of Nonlinear Dynamical Systems

    PubMed Central

    Yildiz, Izzet B.; von Kriegstein, Katharina; Kiebel, Stefan J.

    2013-01-01

    Our knowledge about the computational mechanisms underlying human learning and recognition of sound sequences, especially speech, is still very limited. One difficulty in deciphering the exact means by which humans recognize speech is that there are scarce experimental findings at a neuronal, microscopic level. Here, we show that our neuronal-computational understanding of speech learning and recognition may be vastly improved by looking at an animal model, i.e., the songbird, which faces the same challenge as humans: to learn and decode complex auditory input, in an online fashion. Motivated by striking similarities between the human and songbird neural recognition systems at the macroscopic level, we assumed that the human brain uses the same computational principles at a microscopic level and translated a birdsong model into a novel human sound learning and recognition model with an emphasis on speech. We show that the resulting Bayesian model with a hierarchy of nonlinear dynamical systems can learn speech samples such as words rapidly and recognize them robustly, even in adverse conditions. In addition, we show that recognition can be performed even when words are spoken by different speakers and with different accents—an everyday situation in which current state-of-the-art speech recognition models often fail. The model can also be used to qualitatively explain behavioral data on human speech learning and derive predictions for future experiments. PMID:24068902

  4. Bayesian inference-based environmental decision support systems for oil spill response strategy selection.

    PubMed

    Davies, Andrew J; Hope, Max J

    2015-07-15

    Contingency plans are essential in guiding the response to marine oil spills. However, they are written before the pollution event occurs so must contain some degree of assumption and prediction and hence may be unsuitable for a real incident when it occurs. The use of Bayesian networks in ecology, environmental management, oil spill contingency planning and post-incident analysis is reviewed and analysed to establish their suitability for use as real-time environmental decision support systems during an oil spill response. It is demonstrated that Bayesian networks are appropriate for facilitating the re-assessment and re-validation of contingency plans following pollutant release, thus helping ensure that the optimum response strategy is adopted. This can minimise the possibility of sub-optimal response strategies causing additional environmental and socioeconomic damage beyond the original pollution event.

  5. Image haze removal using a hybrid of fuzzy inference system and weighted estimation

    NASA Astrophysics Data System (ADS)

    Wang, Jyun-Guo; Tai, Shen-Chuan; Lin, Cheng-Jian

    2015-05-01

    The attenuation of the light transmitted through air can reduce image quality when taking a photograph outdoors, especially in a hazy environment. Hazy images often lack sufficient information for image recognition systems to operate effectively. In order to solve the aforementioned problems, this study proposes a hybrid method combining fuzzy theory with weighted estimation for the removal of haze from images. A transmission map is first created based on fuzzy theory. According to the transmission map, the proposed method automatically finds the possible atmospheric lights and refines the atmospheric lights by mixing these candidates. Weighted estimation is then employed to generate a refined transmission map, which removes the halo artifact from around the sharp edges. Experimental results demonstrate the superiority of the proposed method over existing methods with regard to contrast, color depth, and the elimination of halo artifacts.

  6. Observations of ionospheric gravity and diamagnetic current systems inferred from CHAMP and Swarm measurements

    NASA Astrophysics Data System (ADS)

    Alken, P.

    2015-12-01

    Large scale currents in the ionosphere are driven by a variety of sources, including neutral winds, gravity, and plasma pressure gradients. While thestronger day-time wind-driven currents have been extensively studied, gravity and diamagnetic currents in the ionosphere have receivedlittle attention, but can have substantial effects even during the night. With the availability of a new generation of magnetic field models basedon high-accuracy satellite magnetic measurements, it becomes increasingly important to account for these smaller current systems. In this study,we use over a decade of high-quality geomagnetic field measurements from the CHAMP and Swarm missions to study the seasonal and longitudinalstructure of these currents. These results allow us to visualize the global structure of these currents and quantify their magneticperturbations both on the ground and at satellite altitude.

  7. Gradient Matching Methods for Computational Inference in Mechanistic Models for Systems Biology: A Review and Comparative Analysis.

    PubMed

    Macdonald, Benn; Husmeier, Dirk

    2015-01-01

    Parameter inference in mathematical models of biological pathways, expressed as coupled ordinary differential equations (ODEs), is a challenging problem in contemporary systems biology. Conventional methods involve repeatedly solving the ODEs by numerical integration, which is computationally onerous and does not scale up to complex systems. Aimed at reducing the computational costs, new concepts based on gradient matching have recently been proposed in the computational statistics and machine learning literature. In a preliminary smoothing step, the time series data are interpolated; then, in a second step, the parameters of the ODEs are optimized, so as to minimize some metric measuring the difference between the slopes of the tangents to the interpolants, and the time derivatives from the ODEs. In this way, the ODEs never have to be solved explicitly. This review provides a concise methodological overview of the current state-of-the-art methods for gradient matching in ODEs, followed by an empirical comparative evaluation based on a set of widely used and representative benchmark data.

  8. An expert system shell for inferring vegetation characteristics: Interface for the addition of techniques (Task H)

    NASA Technical Reports Server (NTRS)

    Harrison, P. Ann

    1993-01-01

    All the NASA VEGetation Workbench (VEG) goals except the Learning System provide the scientist with several different techniques. When VEG is run, rules assist the scientist in selecting the best of the available techniques to apply to the sample of cover type data being studied. The techniques are stored in the VEG knowledge base. The design and implementation of an interface that allows the scientist to add new techniques to VEG without assistance from the developer were completed. A new interface that enables the scientist to add techniques to VEG without assistance from the developer was designed and implemented. This interface does not require the scientist to have a thorough knowledge of Knowledge Engineering Environment (KEE) by Intellicorp or a detailed knowledge of the structure of VEG. The interface prompts the scientist to enter the required information about the new technique. It prompts the scientist to enter the required Common Lisp functions for executing the technique and the left hand side of the rule that causes the technique to be selected. A template for each function and rule and detailed instructions about the arguments of the functions, the values they should return, and the format of the rule are displayed. Checks are made to ensure that the required data were entered, the functions compiled correctly, and the rule parsed correctly before the new technique is stored. The additional techniques are stored separately from the VEG knowledge base. When the VEG knowledge base is loaded, the additional techniques are not normally loaded. The interface allows the scientist the option of adding all the previously defined new techniques before running VEG. When the techniques are added, the required units to store the additional techniques are created automatically in the correct places in the VEG knowledge base. The methods file containing the functions required by the additional techniques is loaded. New rule units are created to store the new rules

  9. Quantifying Modern Recharge to the Nubian Sandstone Aquifer System: Inferences from GRACE and Land Surface Models

    NASA Astrophysics Data System (ADS)

    Mohamed, A.; Sultan, M.; Ahmed, M.; Yan, E.

    2014-12-01

    The Nubian Sandstone Aquifer System (NSAS) is shared by Egypt, Libya, Chad and Sudanand is one of the largest (area: ~ 2 × 106 km2) groundwater systems in the world. Despite its importance to the population of these countries, major hydrological parameters such as modern recharge and extraction rates remain poorly investigated given: (1) the large extent of the NSAS, (2) the absence of comprehensive monitoring networks, (3) the general inaccessibility of many of the NSAS regions, (4) difficulties in collecting background information, largely included in unpublished governmental reports, and (5) limited local funding to support the construction of monitoring networks and/or collection of field and background datasets. Data from monthly Gravity Recovery and Climate Experiment (GRACE) gravity solutions were processed (Gaussian smoothed: 100 km; rescaled) and used to quantify the modern recharge to the NSAS during the period from January 2003 to December 2012. To isolate the groundwater component in GRACE data, the soil moisture and river channel storages were removed using the outputs from the most recent Community Land Model version 4.5 (CLM4.5). GRACE-derived recharge calculations were performed over the southern NSAS outcrops (area: 835 × 103 km2) in Sudan and Chad that receive average annual precipitation of 65 km3 (77.5 mm). GRACE-derived recharge rates were estimated at 2.79 ± 0.98 km3/yr (3.34 ± 1.17 mm/yr). If we take into account the total annual extraction rates (~ 0.4 km3; CEDARE, 2002) from Chad and Sudan the average annual recharge rate for the NSAS could reach up to ~ 3.20 ± 1.18 km3/yr (3.84 ± 1.42 mm/yr). Our recharge rates estimates are similar to those calculated using (1) groundwater flow modelling in the Central Sudan Rift Basins (4-8 mm/yr; Abdalla, 2008), (2) WaterGAP global scale groundwater recharge model (< 5 mm/yr, Döll and Fiedler, 2008), and (3) chloride tracer in Sudan (3.05 mm/yr; Edmunds et al. 1988). Given the available global

  10. Crustal growth of oceanic island arc inferred from seismic structure of Mariana arc-backarc system

    NASA Astrophysics Data System (ADS)

    Takahashi, N.; Kodaira, S.; Ito, A.; Klemperer, S. L.; Kaneda, Y.; Suyehiro, K.

    2004-12-01

    The Izu-Ogasawara-Marina arc (IBM arc) is one of the typical oceanic island arcs and it has developed repeating magmatic arc volcanisms and backarc spreading since Eocene. Because tectonics of the IBM arc is relatively simple and does not include collisions between the arc and a continent, it is one of best targets to research crustal growth. In 2003, wide-angle seismic survey using 106 ocean bottom seismographs had been carried out as a part of Margin program in collaboration between US and Japan in Mariana region. The seismic line runs from a serpentine diaper near the trench to Parece Vela basin through the Mariana arc, the Marina trough and the West Mariana ridge. We present the characteristics of the seismic structure of the Mariana arc-backarc system and discuss the crustal growth process by comparison with a structure of the northern Izu-Ogasawara arc. Main structural characteristics of the Mariana arc-backarc system are (1) variation of the crustal thickness (Mariana arc: 20 km, West Mariana ridge: 17 km, Mariana trough and Parece Vela basin: 6 km), (2) distribution of an andesitic middle crust with about P-wave velocity of 6 km/s, (3) variation of P-wave velocity in the middle crust (4) velocity anomalies of the lower crust in transition area between the arc and the backarc, (5) thickening of the lower crust under the Mariana trough axis and (6) slow mantle velocities under the Mariana arc, Mariana trough axis and the West Mariana ridge. Above characteristics from (1) to (4) are common to the seismic structure of the northern Izu-Ogasawara arc. In particular, the vertical P-wave velocity gradients of the middle crust under the forearc in both regions tend to become large rather than those under the arc. Main differences of seismic structures between both regions are the velocity gradients and an existence of a thin transition layer between the middle and lower crust. These differences and similarities of the velocity gradient might originate the age and

  11. Regional tectonic deformation in Southern California, inferred from terrestrial geodesy and the global positioning system

    NASA Astrophysics Data System (ADS)

    Shen, Zhengkang

    Tectonic deformation in two regions in Southern California, the Southern Coast Ranges and the Los Angeles Basin, was studied. Results show that in the Southern Coast Ranges, regional deformation is predominantly controlled by deep strike slip motion along the San Andreas Fault, at a rate of 32 plus or minus 2 mm/yr. The deep slip along the San Gregorio-Hosgri Fault is about 1-3 mm/yr, assuming a locked fault depth of 20 km. Convergence normal to the San Andreas Fault in the Southern Coast ranges is not significantly different from zero. About 5 mm/yr convergence is detected from the Santa Maria Basin. In the Los Angeles Basin area, this study demonstrates about 10 mm/yr relative motion trending northwest from San Pedro Hill to the San Gabriel Mountains. The direction of motion closely parallels to the trend of the frontal fault system at the southern margin of the San Gabriel Mountains. The basin suffers from north-south convergence and east-west extension, at a rate of about 0.07 mu rad/yr for either components. The convergence rate normal to the San Andreas across the basin is 4 plus or minus 3 mm/yr, implying smaller compression than previous estimates (e.g., Cline et al. 1984).

  12. Inferring social network structure in ecological systems from spatio-temporal data streams

    PubMed Central

    Psorakis, Ioannis; Roberts, Stephen J.; Rezek, Iead; Sheldon, Ben C.

    2012-01-01

    We propose a methodology for extracting social network structure from spatio-temporal datasets that describe timestamped occurrences of individuals. Our approach identifies temporal regions of dense agent activity and links are drawn between individuals based on their co-occurrences across these ‘gathering events’. The statistical significance of these connections is then tested against an appropriate null model. Such a framework allows us to exploit the wealth of analytical and computational tools of network analysis in settings where the underlying connectivity pattern between interacting agents (commonly termed the adjacency matrix) is not given a priori. We perform experiments on two large-scale datasets (greater than 106 points) of great tit Parus major wild bird foraging records and illustrate the use of this approach by examining the temporal dynamics of pairing behaviour, a process that was previously very hard to observe. We show that established pair bonds are maintained continuously, whereas new pair bonds form at variable times before breeding, but are characterized by a rapid development of network proximity. The method proposed here is general, and can be applied to any system with information about the temporal co-occurrence of interacting agents. PMID:22696481

  13. Nyamulagira’s magma plumbing system inferred from 15 years of InSAR

    USGS Publications Warehouse

    Wauthier, Christelle; Cayol, Valerie; Poland, Michael; Kervyn, François; D'Oreye, Nicolas; Hooper, Andrew; Samsonov, Sergei; Tiampo, Kristy; Smets, Benoit; Pyle, D. M.; Mather, T.A.; Biggs, J.

    2013-01-01

    Nyamulagira, located in the east of the Democratic Republic of Congo on the western branch of the East African rift, is Africa’s most active volcano, with an average of one eruption every 3 years since 1938. Owing to the socio-economical context of that region, the volcano lacks ground-based geodetic measurements but has been monitored by interferometric synthetic aperture radar (InSAR) since 1996. A combination of 3D Mixed Boundary Element Method and inverse modelling, taking into account topography and source interactions, is used to interpret InSAR ground displacements associated with eruptive activity in 1996, 2002, 2004, 2006 and 2010. These eruptions can be fitted by models incorporating dyke intrusions, and some (namely the 2006 and 2010 eruptions) require a magma reservoir beneath the summit caldera. We investigate inter-eruptive deformation with a multi-temporal InSAR approach. We propose the following magma plumbing system at Nyamulagira by integrating numerical deformation models with other available data: a deep reservoir (c. 25 km depth) feeds a shallower reservoir (c. 4 km depth); proximal eruptions are fed from the shallow reservoir through dykes while distal eruptions can be fed directly from the deep reservoir. A dyke-like conduit is also present beneath the upper southeastern flank of Nyamulagira.

  14. Estimating oxygen consumption from heart rate using adaptive neuro-fuzzy inference system and analytical approaches.

    PubMed

    Kolus, Ahmet; Dubé, Philippe-Antoine; Imbeau, Daniel; Labib, Richard; Dubeau, Denise

    2014-11-01

    In new approaches based on adaptive neuro-fuzzy systems (ANFIS) and analytical method, heart rate (HR) measurements were used to estimate oxygen consumption (VO2). Thirty-five participants performed Meyer and Flenghi's step-test (eight of which performed regeneration release work), during which heart rate and oxygen consumption were measured. Two individualized models and a General ANFIS model that does not require individual calibration were developed. Results indicated the superior precision achieved with individualized ANFIS modelling (RMSE = 1.0 and 2.8 ml/kg min in laboratory and field, respectively). The analytical model outperformed the traditional linear calibration and Flex-HR methods with field data. The General ANFIS model's estimates of VO2 were not significantly different from actual field VO2 measurements (RMSE = 3.5 ml/kg min). With its ease of use and low implementation cost, the General ANFIS model shows potential to replace any of the traditional individualized methods for VO2 estimation from HR data collected in the field. PMID:24793823

  15. Early accretion of protoplanets inferred from a reduced inner solar system 26Al inventory

    NASA Astrophysics Data System (ADS)

    Schiller, Martin; Connelly, James N.; Glad, Aslaug C.; Mikouchi, Takashi; Bizzarro, Martin

    2015-06-01

    The mechanisms and timescales of accretion of 10-1000 km sized planetesimals, the building blocks of planets, are not yet well understood. With planetesimal melting predominantly driven by the decay of the short-lived radionuclide 26Al (26Al→26Mg; t1/2 = 0.73 Ma), its initial abundance determines the permissible timeframe of planetesimal-scale melting and its subsequent cooling history. Currently, precise knowledge about the initial 26Al abundance [(26Al/27Al)0] exists only for the oldest known solids, calcium aluminum-rich inclusions (CAIs) - the so-called canonical value. We have determined the 26Al/27Al of three angrite meteorites, D'Orbigny, Sahara 99555 and NWA 1670, at their time of crystallization, which corresponds to (3.98 ± 0.15) ×10-7, (3.64 ± 0.18) ×10-7, and (5.92 ± 0.59) ×10-7, respectively. Combined with a newly determined absolute U-corrected Pb-Pb age for NWA 1670 of 4564.39 ± 0.24 Ma and published U-corrected Pb-Pb ages for the other two angrites, this allows us to calculate an initial (26Al/27Al)0 of (1.33-0.18+0.21) ×10-5 for the angrite parent body (APB) precursor material at the time of CAI formation, a value four times lower than the accepted canonical value of 5.25 ×10-5. Based on their similar 54Cr/52Cr ratios, most inner solar system materials likely accreted from material containing a similar 26Al/27Al ratio as the APB precursor at the time of CAI formation. To satisfy the abundant evidence for widespread planetesimal differentiation, the subcanonical 26Al budget requires that differentiated planetesimals, and hence protoplanets, accreted rapidly within 0.25 ± 0.15 Ma of the formation of canonical CAIs.

  16. Final Report: Large-Scale Optimization for Bayesian Inference in Complex Systems

    SciTech Connect

    Ghattas, Omar

    2013-10-15

    The SAGUARO (Scalable Algorithms for Groundwater Uncertainty Analysis and Robust Optimiza- tion) Project focuses on the development of scalable numerical algorithms for large-scale Bayesian inversion in complex systems that capitalize on advances in large-scale simulation-based optimiza- tion and inversion methods. Our research is directed in three complementary areas: efficient approximations of the Hessian operator, reductions in complexity of forward simulations via stochastic spectral approximations and model reduction, and employing large-scale optimization concepts to accelerate sampling. Our efforts are integrated in the context of a challenging testbed problem that considers subsurface reacting flow and transport. The MIT component of the SAGUARO Project addresses the intractability of conventional sampling methods for large-scale statistical inverse problems by devising reduced-order models that are faithful to the full-order model over a wide range of parameter values; sampling then employs the reduced model rather than the full model, resulting in very large computational savings. Results indicate little effect on the computed posterior distribution. On the other hand, in the Texas-Georgia Tech component of the project, we retain the full-order model, but exploit inverse problem structure (adjoint-based gradients and partial Hessian information of the parameter-to- observation map) to implicitly extract lower dimensional information on the posterior distribution; this greatly speeds up sampling methods, so that fewer sampling points are needed. We can think of these two approaches as "reduce then sample" and "sample then reduce." In fact, these two approaches are complementary, and can be used in conjunction with each other. Moreover, they both exploit deterministic inverse problem structure, in the form of adjoint-based gradient and Hessian information of the underlying parameter-to-observation map, to achieve their speedups.

  17. Rule-based fuzzy inference system for estimating the influent COD/N ratio and ammonia load to a sequencing batch reactor.

    PubMed

    Kim, Y J; Bae, H; Ko, J H; Poo, K M; Kim, S; Kim, C W; Woo, H J

    2006-01-01

    A fuzzy inference system using sensor measurements was developed to estimate the influent COD/N ratio and ammonia load. The sensors measured ORP, DO and pH. The sensor profiles had a close relationship with the influent COD/N ratio and ammonia load. To confirm this operational knowledge for constructing a rule set, a correlation analysis was conducted. The results showed that a rule generation method based only on operational knowledge did not generate a sufficiently accurate relationship between sensor measurements and target variables. To compensate for this defect, a decision tree algorithm was used as a standardized method for rule generation. Given a set of inputs, this algorithm was used to determine the output variables. However, the generated rules could not estimate the continuous influent COD/N ratio and ammonia load. Fuzzified rules and the fuzzy inference system were developed to overcome this problem. The fuzzy inference system estimated the influent COD/N ratio and ammonia load quite well. When these results were compared to the results from a predictive polynomial neural network model, the fuzzy inference system was more stable. PMID:16532750

  18. Early accretion of protoplanets inferred from a reduced inner solar system 26Al inventory

    PubMed Central

    Schiller, Martin; Connelly, James N.; Glad, Aslaug C.; Mikouchi, Takashi; Bizzarro, Martin

    2016-01-01

    The mechanisms and timescales of accretion of 10–1000 km sized planetesimals, the building blocks of planets, are not yet well understood. With planetesimal melting predominantly driven by the decay of the short-lived radionuclide 26Al (26Al→26Mg; t1/2 = 0.73 Ma), its initial abundance determines the permissible timeframe of planetesimal-scale melting and its subsequent cooling history. Currently, precise knowledge about the initial 26Al abundance [(26Al/27Al)0] exists only for the oldest known solids, calcium aluminum-rich inclusions (CAIs) – the so-called canonical value. We have determined the 26Al/27Al of three angrite meteorites, D’Orbigny, Sahara 99555 and NWA 1670, at their time of crystallization, which corresponds to (3.98 ± 0.15)×10−7, (3.64 ± 0.18)×10−7, and (5.92 ± 0.59)×10−7, respectively. Combined with a newly determined absolute U-corrected Pb–Pb age for NWA 1670 of 4564.39 ± 0.24 Ma and published U-corrected Pb–Pb ages for the other two angrites, this allows us to calculate an initial (26Al/27Al)0 of (1.33−0.18+0.21)×10−5 for the angrite parent body (APB) precursor material at the time of CAI formation, a value four times lower than the accepted canonical value of 5.25 × 10−5. Based on their similar 54Cr/52Cr ratios, most inner solar system materials likely accreted from material containing a similar 26Al/27Al ratio as the APB precursor at the time of CAI formation. To satisfy the abundant evidence for widespread planetesimal differentiation, the subcanonical 26Al budget requires that differentiated planetesimals, and hence protoplanets, accreted rapidly within 0.25 ± 0.15 Ma of the formation of canonical CAIs. PMID:27429474

  19. Modeling Pb (II) adsorption from aqueous solution by ostrich bone ash using adaptive neural-based fuzzy inference system.

    PubMed

    Amiri, Mohammad J; Abedi-Koupai, Jahangir; Eslamian, Sayed S; Mousavi, Sayed F; Hasheminejad, Hasti

    2013-01-01

    To evaluate the performance of Adaptive Neural-Based Fuzzy Inference System (ANFIS) model in estimating the efficiency of Pb (II) ions removal from aqueous solution by ostrich bone ash, a batch experiment was conducted. Five operational parameters including adsorbent dosage (C(s)), initial concentration of Pb (II) ions (C(o)), initial pH, temperature (T) and contact time (t) were taken as the input data and the adsorption efficiency (AE) of bone ash as the output. Based on the 31 different structures, 5 ANFIS models were tested against the measured adsorption efficiency to assess the accuracy of each model. The results showed that ANFIS5, which used all input parameters, was the most accurate (RMSE = 2.65 and R(2) = 0.95) and ANFIS1, which used only the contact time input, was the worst (RMSE = 14.56 and R(2) = 0.46). In ranking the models, ANFIS4, ANFIS3 and ANFIS2 ranked second, third and fourth, respectively. The sensitivity analysis revealed that the estimated AE is more sensitive to the contact time, followed by pH, initial concentration of Pb (II) ions, adsorbent dosage, and temperature. The results showed that all ANFIS models overestimated the AE. In general, this study confirmed the capabilities of ANFIS model as an effective tool for estimation of AE. PMID:23383640

  20. Prediction of Radical Scavenging Activities of Anthocyanins Applying Adaptive Neuro-Fuzzy Inference System (ANFIS) with Quantum Chemical Descriptors

    PubMed Central

    Jhin, Changho; Hwang, Keum Taek

    2014-01-01

    Radical scavenging activity of anthocyanins is well known, but only a few studies have been conducted by quantum chemical approach. The adaptive neuro-fuzzy inference system (ANFIS) is an effective technique for solving problems with uncertainty. The purpose of this study was to construct and evaluate quantitative structure-activity relationship (QSAR) models for predicting radical scavenging activities of anthocyanins with good prediction efficiency. ANFIS-applied QSAR models were developed by using quantum chemical descriptors of anthocyanins calculated by semi-empirical PM6 and PM7 methods. Electron affinity (A) and electronegativity (χ) of flavylium cation, and ionization potential (I) of quinoidal base were significantly correlated with radical scavenging activities of anthocyanins. These descriptors were used as independent variables for QSAR models. ANFIS models with two triangular-shaped input fuzzy functions for each independent variable were constructed and optimized by 100 learning epochs. The constructed models using descriptors calculated by both PM6 and PM7 had good prediction efficiency with Q-square of 0.82 and 0.86, respectively. PMID:25153627

  1. Adaptive Neuro-Fuzzy Inference System Applied QSAR with Quantum Chemical Descriptors for Predicting Radical Scavenging Activities of Carotenoids.

    PubMed

    Jhin, Changho; Hwang, Keum Taek

    2015-01-01

    One of the physiological characteristics of carotenoids is their radical scavenging activity. In this study, the relationship between radical scavenging activities and quantum chemical descriptors of carotenoids was determined. Adaptive neuro-fuzzy inference system (ANFIS) applied quantitative structure-activity relationship models (QSAR) were also developed for predicting and comparing radical scavenging activities of carotenoids. Semi-empirical PM6 and PM7 quantum chemical calculations were done by MOPAC. Ionisation energies of neutral and monovalent cationic carotenoids and the product of chemical potentials of neutral and monovalent cationic carotenoids were significantly correlated with the radical scavenging activities, and consequently these descriptors were used as independent variables for the QSAR study. The ANFIS applied QSAR models were developed with two triangular-shaped input membership functions made for each of the independent variables and optimised by a backpropagation method. High prediction efficiencies were achieved by the ANFIS applied QSAR. The R-square values of the developed QSAR models with the variables calculated by PM6 and PM7 methods were 0.921 and 0.902, respectively. The results of this study demonstrated reliabilities of the selected quantum chemical descriptors and the significance of QSAR models. PMID:26474167

  2. Prediction of radical scavenging activities of anthocyanins applying adaptive neuro-fuzzy inference system (ANFIS) with quantum chemical descriptors.

    PubMed

    Jhin, Changho; Hwang, Keum Taek

    2014-01-01

    Radical scavenging activity of anthocyanins is well known, but only a few studies have been conducted by quantum chemical approach. The adaptive neuro-fuzzy inference system (ANFIS) is an effective technique for solving problems with uncertainty. The purpose of this study was to construct and evaluate quantitative structure-activity relationship (QSAR) models for predicting radical scavenging activities of anthocyanins with good prediction efficiency. ANFIS-applied QSAR models were developed by using quantum chemical descriptors of anthocyanins calculated by semi-empirical PM6 and PM7 methods. Electron affinity (A) and electronegativity (χ) of flavylium cation, and ionization potential (I) of quinoidal base were significantly correlated with radical scavenging activities of anthocyanins. These descriptors were used as independent variables for QSAR models. ANFIS models with two triangular-shaped input fuzzy functions for each independent variable were constructed and optimized by 100 learning epochs. The constructed models using descriptors calculated by both PM6 and PM7 had good prediction efficiency with Q-square of 0.82 and 0.86, respectively. PMID:25153627

  3. Adaptive neuro-fuzzy inference systems for semi-automatic discrimination between seismic events: a study in Tehran region

    NASA Astrophysics Data System (ADS)

    Vasheghani Farahani, Jamileh; Zare, Mehdi; Lucas, Caro

    2012-04-01

    Thisarticle presents an adaptive neuro-fuzzy inference system (ANFIS) for classification of low magnitude seismic events reported in Iran by the network of Tehran Disaster Mitigation and Management Organization (TDMMO). ANFIS classifiers were used to detect seismic events using six inputs that defined the seismic events. Neuro-fuzzy coding was applied using the six extracted features as ANFIS inputs. Two types of events were defined: weak earthquakes and mining blasts. The data comprised 748 events (6289 signals) ranging from magnitude 1.1 to 4.6 recorded at 13 seismic stations between 2004 and 2009. We surveyed that there are almost 223 earthquakes with M ≤ 2.2 included in this database. Data sets from the south, east, and southeast of the city of Tehran were used to evaluate the best short period seismic discriminants, and features as inputs such as origin time of event, distance (source to station), latitude of epicenter, longitude of epicenter, magnitude, and spectral analysis (fc of the Pg wave) were used, increasing the rate of correct classification and decreasing the confusion rate between weak earthquakes and quarry blasts. The performance of the ANFIS model was evaluated for training and classification accuracy. The results confirmed that the proposed ANFIS model has good potential for determining seismic events.

  4. Application of adaptive neuro-fuzzy inference system and cuckoo optimization algorithm for analyzing electro chemical machining process

    NASA Astrophysics Data System (ADS)

    Teimouri, Reza; Sohrabpoor, Hamed

    2013-12-01

    Electrochemical machining process (ECM) is increasing its importance due to some of the specific advantages which can be exploited during machining operation. The process offers several special privileges such as higher machining rate, better accuracy and control, and wider range of materials that can be machined. Contribution of too many predominate parameters in the process, makes its prediction and selection of optimal values really complex, especially while the process is programmized for machining of hard materials. In the present work in order to investigate effects of electrolyte concentration, electrolyte flow rate, applied voltage and feed rate on material removal rate (MRR) and surface roughness (SR) the adaptive neuro-fuzzy inference systems (ANFIS) have been used for creation predictive models based on experimental observations. Then the ANFIS 3D surfaces have been plotted for analyzing effects of process parameters on MRR and SR. Finally, the cuckoo optimization algorithm (COA) was used for selection solutions in which the process reaches maximum material removal rate and minimum surface roughness simultaneously. Results indicated that the ANFIS technique has superiority in modeling of MRR and SR with high prediction accuracy. Also, results obtained while applying of COA have been compared with those derived from confirmatory experiments which validate the applicability and suitability of the proposed techniques in enhancing the performance of ECM process.

  5. Adaptive Neuro-Fuzzy Inference System Applied QSAR with Quantum Chemical Descriptors for Predicting Radical Scavenging Activities of Carotenoids

    PubMed Central

    Jhin, Changho; Hwang, Keum Taek

    2015-01-01

    One of the physiological characteristics of carotenoids is their radical scavenging activity. In this study, the relationship between radical scavenging activities and quantum chemical descriptors of carotenoids was determined. Adaptive neuro-fuzzy inference system (ANFIS) applied quantitative structure-activity relationship models (QSAR) were also developed for predicting and comparing radical scavenging activities of carotenoids. Semi-empirical PM6 and PM7 quantum chemical calculations were done by MOPAC. Ionisation energies of neutral and monovalent cationic carotenoids and the product of chemical potentials of neutral and monovalent cationic carotenoids were significantly correlated with the radical scavenging activities, and consequently these descriptors were used as independent variables for the QSAR study. The ANFIS applied QSAR models were developed with two triangular-shaped input membership functions made for each of the independent variables and optimised by a backpropagation method. High prediction efficiencies were achieved by the ANFIS applied QSAR. The R-square values of the developed QSAR models with the variables calculated by PM6 and PM7 methods were 0.921 and 0.902, respectively. The results of this study demonstrated reliabilities of the selected quantum chemical descriptors and the significance of QSAR models. PMID:26474167

  6. Prediction of matching condition for a microstrip subsystem using artificial neural network and adaptive neuro-fuzzy inference system

    NASA Astrophysics Data System (ADS)

    Salehi, Mohammad Reza; Noori, Leila; Abiri, Ebrahim

    2016-11-01

    In this paper, a subsystem consisting of a microstrip bandpass filter and a microstrip low noise amplifier (LNA) is designed for WLAN applications. The proposed filter has a small implementation area (49 mm2), small insertion loss (0.08 dB) and wide fractional bandwidth (FBW) (61%). To design the proposed LNA, the compact microstrip cells, an field effect transistor, and only a lumped capacitor are used. It has a low supply voltage and a low return loss (-40 dB) at the operation frequency. The matching condition of the proposed subsystem is predicted using subsystem analysis, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). To design the proposed filter, the transmission matrix of the proposed resonator is obtained and analysed. The performance of the proposed ANN and ANFIS models is tested using the numerical data by four performance measures, namely the correlation coefficient (CC), the mean absolute error (MAE), the average percentage error (APE) and the root mean square error (RMSE). The obtained results show that these models are in good agreement with the numerical data, and a small error between the predicted values and numerical solution is obtained.

  7. Neuro-fuzzy controller of low head hydropower plants using adaptive-network based fuzzy inference system

    SciTech Connect

    Djukanovic, M.B.; Calovic, M.S.; Vesovic, B.V.; Sobajic, D.J.

    1997-12-01

    This paper presents an attempt of nonlinear, multivariable control of low-head hydropower plants, by using adaptive-network based fuzzy inference system (ANFIS). The new design technique enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near optimal manner. The controller has flexibility for accepting more sensory information, with the main goal to improve the generator unit transients, by adjusting the exciter input, the wicket gate and runner blade positions. The developed ANFIS controller whose control signals are adjusted by using incomplete on-line measurements, can offer better damping effects to generator oscillations over a wide range of operating conditions, than conventional controllers. Digital simulations of hydropower plant equipped with low-head Kaplan turbine are performed and the comparisons of conventional excitation-governor control, state-feedback optimal control and ANFIS based output feedback control are presented. To demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired neuro-fuzzy controller, the controller has been implemented on a complex high-order non-linear hydrogenerator model.

  8. 48 CFR 1631.205-81 - Inferred reasonableness.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... 48 Federal Acquisition Regulations System 6 2011-10-01 2011-10-01 false Inferred reasonableness... PRINCIPLES AND PROCEDURES Contracts With Commercial Organizations 1631.205-81 Inferred reasonableness. If the... the subcontract's costs shall be inferred....

  9. 48 CFR 1631.205-81 - Inferred reasonableness.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... 48 Federal Acquisition Regulations System 6 2010-10-01 2010-10-01 true Inferred reasonableness... PRINCIPLES AND PROCEDURES Contracts With Commercial Organizations 1631.205-81 Inferred reasonableness. If the... the subcontract's costs shall be inferred....

  10. Evolutionary processes in a continental island system: molecular phylogeography of the Aegean Nigella arvensis alliance (Ranunculaceae) inferred from chloroplast DNA.

    PubMed

    Bittkau, C; Comes, H P

    2005-11-01

    Continental shelf island systems, created by rising sea levels, provide a premier setting for studying the effects of past fragmentation, dispersal, and genetic drift on taxon diversification. We used phylogeographical (nested clade) and population genetic analyses to elucidate the relative roles of these processes in the evolutionary history of the Aegean Nigella arvensis alliance (= 'coenospecies'). We surveyed chloroplast DNA (cpDNA) variation in 455 individuals from 47 populations (nine taxa) of the alliance throughout its core range in the Aegean Archipelago and surrounding mainland areas of Greece and Turkey. The study revealed the presence of three major lineages, with largely nonoverlapping distributions in the Western, Central, and Eastern Aegean. There is evidence supporting the idea that these major lineages evolved in situ from a widespread (pan-Aegean) ancestral stock as a result of multiple fragmentation events, possibly due to the influence of post-Messinian sea flooding, Pleistocene eustatic changes and corresponding climate fluctuations. Over-sea dispersal and founder events appear to have played a rather insignificant role in the group's history. Rather, all analytical approaches identified the alliance as an organism group with poor seed dispersal capabilities and a susceptibility to genetic drift. In particular, we inferred that the observed level of cpDNA differentiation between Kikladian island populations of Nigella degenii largely reflects population history, (viz. Holocene island fragmentation) and genetic drift in the near absence of seed flow since their time of common ancestry. Overall, our cpDNA data for the N. arvensis alliance in general, and N. degenii in particular, indicate that historical events were important in determining the phylogeographical patterns seen, and that genetic drift has historically been relatively more influential on population structure than has cytoplasmic gene flow.

  11. Comparison of adaptive neuro-fuzzy inference system and artificial neutral networks model to categorize patients in the emergency department.

    PubMed

    Azeez, Dhifaf; Ali, Mohd Alauddin Mohd; Gan, Kok Beng; Saiboon, Ismail

    2013-01-01

    Unexpected disease outbreaks and disasters are becoming primary issues facing our world. The first points of contact either at the disaster scenes or emergency department exposed the frontline workers and medical physicians to the risk of infections. Therefore, there is a persuasive demand for the integration and exploitation of heterogeneous biomedical information to improve clinical practice, medical research and point of care. In this paper, a primary triage model was designed using two different methods: an adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN).When the patient is presented at the triage counter, the system will capture their vital signs and chief complains beside physiology stat and general appearance of the patient. This data will be managed and analyzed in the data server and the patient's emergency status will be reported immediately. The proposed method will help to reduce the queue time at the triage counter and the emergency physician's burden especially duringdisease outbreak and serious disaster. The models have been built with 2223 data set extracted from the Emergency Department of the Universiti Kebangsaan Malaysia Medical Centre to predict the primary triage category. Multilayer feed forward with one hidden layer having 12 neurons has been used for the ANN architecture. Fuzzy subtractive clustering has been used to find the fuzzy rules for the ANFIS model. The results showed that the RMSE, %RME and the accuracy which evaluated by measuring specificity and sensitivity for binary classificationof the training data were 0.14, 5.7 and 99 respectively for the ANN model and 0.85, 32.00 and 96.00 respectively for the ANFIS model. As for unseen data the root mean square error, percentage the root mean square error and the accuracy for ANN is 0.18, 7.16 and 96.7 respectively, 1.30, 49.84 and 94 respectively for ANFIS model. The ANN model was performed better for both training and unseen data than ANFIS model in

  12. Comparison of adaptive neuro-fuzzy inference system and artificial neutral networks model to categorize patients in the emergency department.

    PubMed

    Azeez, Dhifaf; Ali, Mohd Alauddin Mohd; Gan, Kok Beng; Saiboon, Ismail

    2013-01-01

    Unexpected disease outbreaks and disasters are becoming primary issues facing our world. The first points of contact either at the disaster scenes or emergency department exposed the frontline workers and medical physicians to the risk of infections. Therefore, there is a persuasive demand for the integration and exploitation of heterogeneous biomedical information to improve clinical practice, medical research and point of care. In this paper, a primary triage model was designed using two different methods: an adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN).When the patient is presented at the triage counter, the system will capture their vital signs and chief complains beside physiology stat and general appearance of the patient. This data will be managed and analyzed in the data server and the patient's emergency status will be reported immediately. The proposed method will help to reduce the queue time at the triage counter and the emergency physician's burden especially duringdisease outbreak and serious disaster. The models have been built with 2223 data set extracted from the Emergency Department of the Universiti Kebangsaan Malaysia Medical Centre to predict the primary triage category. Multilayer feed forward with one hidden layer having 12 neurons has been used for the ANN architecture. Fuzzy subtractive clustering has been used to find the fuzzy rules for the ANFIS model. The results showed that the RMSE, %RME and the accuracy which evaluated by measuring specificity and sensitivity for binary classificationof the training data were 0.14, 5.7 and 99 respectively for the ANN model and 0.85, 32.00 and 96.00 respectively for the ANFIS model. As for unseen data the root mean square error, percentage the root mean square error and the accuracy for ANN is 0.18, 7.16 and 96.7 respectively, 1.30, 49.84 and 94 respectively for ANFIS model. The ANN model was performed better for both training and unseen data than ANFIS model in

  13. An adaptive neuro fuzzy inference system controlled space cector pulse width modulation based HVDC light transmission system under AC fault conditions

    NASA Astrophysics Data System (ADS)

    Ajay Kumar, M.; Srikanth, N. V.

    2014-03-01

    In HVDC Light transmission systems, converter control is one of the major fields of present day research works. In this paper, fuzzy logic controller is utilized for controlling both the converters of the space vector pulse width modulation (SVPWM) based HVDC Light transmission systems. Due to its complexity in the rule base formation, an intelligent controller known as adaptive neuro fuzzy inference system (ANFIS) controller is also introduced in this paper. The proposed ANFIS controller changes the PI gains automatically for different operating conditions. A hybrid learning method which combines and exploits the best features of both the back propagation algorithm and least square estimation method is used to train the 5-layer ANFIS controller. The performance of the proposed ANFIS controller is compared and validated with the fuzzy logic controller and also with the fixed gain conventional PI controller. The simulations are carried out in the MATLAB/SIMULINK environment. The results reveal that the proposed ANFIS controller is reducing power fluctuations at both the converters. It also improves the dynamic performance of the test power system effectively when tested for various ac fault conditions.

  14. Seasonal rainfall forecasting by adaptive network-based fuzzy inference system (ANFIS) using large scale climate signals

    NASA Astrophysics Data System (ADS)

    Mekanik, F.; Imteaz, M. A.; Talei, A.

    2016-05-01

    Accurate seasonal rainfall forecasting is an important step in the development of reliable runoff forecast models. The large scale climate modes affecting rainfall in Australia have recently been proven useful in rainfall prediction problems. In this study, adaptive network-based fuzzy inference systems (ANFIS) models are developed for the first time for southeast Australia in order to forecast spring rainfall. The models are applied in east, center and west Victoria as case studies. Large scale climate signals comprising El Nino Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and Inter-decadal Pacific Ocean (IPO) are selected as rainfall predictors. Eight models are developed based on single climate modes (ENSO, IOD, and IPO) and combined climate modes (ENSO-IPO and ENSO-IOD). Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Pearson correlation coefficient (r) and root mean square error in probability (RMSEP) skill score are used to evaluate the performance of the proposed models. The predictions demonstrate that ANFIS models based on individual IOD index perform superior in terms of RMSE, MAE and r to the models based on individual ENSO indices. It is further discovered that IPO is not an effective predictor for the region and the combined ENSO-IOD and ENSO-IPO predictors did not improve the predictions. In order to evaluate the effectiveness of the proposed models a comparison is conducted between ANFIS models and the conventional Artificial Neural Network (ANN), the Predictive Ocean Atmosphere Model for Australia (POAMA) and climatology forecasts. POAMA is the official dynamic model used by the Australian Bureau of Meteorology. The ANFIS predictions certify a superior performance for most of the region compared to ANN and climatology forecasts. POAMA performs better in regards to RMSE and MAE in east and part of central Victoria, however, compared to ANFIS it shows weaker results in west Victoria in terms of prediction errors and RMSEP skill

  15. Drought prediction using co-active neuro-fuzzy inference system, validation, and uncertainty analysis (case study: Birjand, Iran)

    NASA Astrophysics Data System (ADS)

    Memarian, Hadi; Pourreza Bilondi, Mohsen; Rezaei, Majid

    2016-08-01

    This work aims to assess the capability of co-active neuro-fuzzy inference system (CANFIS) for drought forecasting of Birjand, Iran through the combination of global climatic signals with rainfall and lagged values of Standardized Precipitation Index (SPI) index. Using stepwise regression and correlation analyses, the signals NINO 1 + 2, NINO 3, Multivariate Enso Index, Tropical Southern Atlantic index, Atlantic Multi-decadal Oscillation index, and NINO 3.4 were recognized as the effective signals on the drought event in Birjand. Based on the results from stepwise regression analysis and regarding the processor limitations, eight models were extracted for further processing by CANFIS. The metrics P-factor and D-factor were utilized for uncertainty analysis, based on the sequential uncertainty fitting algorithm. Sensitivity analysis showed that for all models, NINO indices and rainfall variable had the largest impact on network performance. In model 4 (as the model with the lowest error during training and testing processes), NINO 1 + 2(t-5) with an average sensitivity of 0.7 showed the highest impact on network performance. Next, the variables rainfall, NINO 1 + 2(t), and NINO 3(t-6) with the average sensitivity of 0.59, 0.28, and 0.28, respectively, could have the highest effect on network performance. The findings based on network performance metrics indicated that the global indices with a time lag represented a better correlation with El Niño Southern Oscillation (ENSO). Uncertainty analysis of the model 4 demonstrated that 68 % of the observed data were bracketed by the 95PPU and D-Factor value (0.79) was also within a reasonable range. Therefore, the fourth model with a combination of the input variables NINO 1 + 2 (with 5 months of lag and without any lag), monthly rainfall, and NINO 3 (with 6 months of lag) and correlation coefficient of 0.903 (between observed and simulated SPI) was selected as the most accurate model for drought forecasting using CANFIS

  16. Estimating the DNA strand breakage using a fuzzy inference system and agarose gel electrophoresis, a case study with toothed carp Aphanius sophiae exposed to cypermethrin.

    PubMed

    Poorbagher, Hadi; Moghaddam, Maryam Nasrollahpour; Eagderi, Soheil; Farahmand, Hamid

    2016-07-01

    The DNA breakage has been widely used in ecotoxicological studies to investigate effects of pesticides in fishes. The present study used a fuzzy inference system to quantify the breakage of DNA double strand in Aphanius sophiae exposed to the cypermethrin. The specimens were adapted to different temperatures and salinity for 14 days and then exposed to cypermethrin. DNA of each specimens were extracted, electrophoresed and photographed. A fuzzy system with three input variables and 27 rules were defined. The pixel value curve of DNA on each gel lane was obtained using ImageJ. The DNA breakage was quantified using the pixel value curve and fuzzy system. The defuzzified values were analyzed using a three-way analysis of variance. Cypermethrin had significant effects on DNA breakage. Fuzzy inference systems can be used as a tool to quantify the breakage of double strand DNA. DNA double strand of the gill of A. sophiae is sensitive enough to be used to detect cypermethrin in surface waters in concentrations much lower than those reported in previous studies.

  17. The Lithosphere of The East African Rift System: Insights From Three-Dimensional Density Modelling

    NASA Astrophysics Data System (ADS)

    Woldetinsae, G.; Götze, H. J.

    2004-12-01

    We use the gravity data that cover the large part of the Afro-Arabian rift system, the eastern branch (Ethiopia-Afar and northern Kenya), in order to produce a regional density model. In an earlier work the new and old gravity data were compiled, evaluated and homogenised using a consistent data reduction procedures. Three basic constraints widely spaced over a 1500 km rift length have been generated between 1969 and 2003 by an international consortium with information from isostatic models, global tomography, geological, geochemical evidences, and petrological and experimental results. These are integrated and applied to the model to constrain upper and lower crustal structures underneath the Rift and Plateau areas. New crustal thickness estimations (Dugda et al., 2004 in press) and inferences from recent velocity models along the axis of the Main Ethiopian Rift (Keller et al., 2004) are added to the density model. Thirty parallel planes cutting across the entire plateau region and Rift system (Afar-Ethiopia-Kenya) are interactively modelled using a starting geometry that invoke asthenospheric upwelling. Densities for the upper crust are calculated using Nafe Drake method, averaged from earlier interpretation and measured ones from the Geological Survey of Ethiopia database (e.g. Geothermal project, GSE petrophysical laboratory, pers. communication). Densities for lower crust are estimated using the approach by Sobolov and Babyko (1994). We used also lower crustal densities calculated by (Simyu and Keller, 1997) for the northern part of Kenya rift. The preliminary model offers a possibility to quantify depth, thickness and volumes of different geological interfaces and bodies. As for example, the estimation of the volume of volcanic constructs on the western plateau of Ethiopia is relatively larger than the eastern plateau. The load map derived from the model indicated maximum crustal loads at the crust/mantle interface (ca. 40km) on the eastern and western flanks

  18. Lithium isotope constraints on crust-mantle interactions and surface processes on Mars

    NASA Astrophysics Data System (ADS)

    Magna, Tomáš; Day, James M. D.; Mezger, Klaus; Fehr, Manuela A.; Dohmen, Ralf; Aoudjehane, Hasnaa Chennaoui; Agee, Carl B.

    2015-08-01

    Lithium abundances and isotope compositions are reported for a suite of martian meteorites that span the range of petrological and geochemical types recognized to date for Mars. Samples include twenty-one bulk-rock enriched, intermediate and depleted shergottites, six nakhlites, two chassignites, the orthopyroxenite Allan Hills (ALH) 84001 and the polymict breccia Northwest Africa (NWA) 7034. Shergottites unaffected by terrestrial weathering exhibit a range in δ7Li from 2.1 to 6.2‰, similar to that reported for pristine terrestrial peridotites and unaltered mid-ocean ridge and ocean island basalts. Two chassignites have δ7Li values (4.0‰) intermediate to the shergottite range, and combined, these meteorites provide the most robust current constraints on δ7Li of the martian mantle. The polymict breccia NWA 7034 has the lowest δ7Li (-0.2‰) of all terrestrially unaltered martian meteorites measured to date and may represent an isotopically light surface end-member. The new data for NWA 7034 imply that martian crustal surface materials had both a lighter Li isotope composition and elevated Li abundance compared with their associated mantle. These findings are supported by Li data for olivine-phyric shergotitte NWA 1068, a black glass phase isolated from the Tissint meteorite fall, and some nakhlites, which all show evidence for assimilation of a low-δ7Li crustal component. The range in δ7Li for nakhlites (1.8 to 5.2‰), and co-variations with chlorine abundance, suggests crustal contamination by Cl-rich brines. The differences in Li isotope composition and abundance between the martian mantle and estimated crust are not as large as the fractionations observed for terrestrial continental crust and mantle, suggesting a difference in the styles of alteration and weathering between water-dominated processes on Earth versus possibly Cl-S-rich brines on Mars. Using high-MgO shergottites (>15 wt.% MgO) it is possible to estimate the δ7Li of Bulk Silicate Mars (BSM) to be 4.2 ± 0.9‰ (2σ). This value is at the higher end of estimates for the Bulk Silicate Earth (BSE; 3.5 ± 1.0‰, 2σ), but overlaps within uncertainty.

  19. Subduction in fancy: stripping young slabs as a result of similar crust-mantle rheologies

    NASA Astrophysics Data System (ADS)

    Agard, Philippe; Yamato, Philippe; Soret, Mathieu; Prigent, Cécile; Guillot, Stéphane; Plunder, Alexis; Dubacq, Benoît; Monié, Patrick; Chauvet, Alain

    2016-04-01

    Understanding subduction rheology in both space and time has been a challenge since the advent of plate tectonics. We herein focus on "subduction infancy", which corresponds to the first ~0-2 My immediately following subduction nucleation, when a newly born slab penetrates into the overriding plate mantle and heats up. The only remnants of this critical, yet elusive, geodynamic step are thin metamorphic soles, commonly found beneath pristine, 100-1000 km long portions of oceanic lithosphere emplaced on top of continents (i.e., ophiolites). In this study, we show how, during subduction infancy, transient mechanical properties of both the mantle and crust across the subduction plate interface (during ~100s ky) control and hinder the penetration of tectonic plates into the mantle, and how this results in strong peaks of resistance and even slicing of their surface - leaving behind thin, chopped-off metamorphic slivers (i.e., metamorphic soles). These findings constrain the mechanical behaviour of the subduction plate interface (with implications for coupling processes and earthquake generation) as well as the properties of the crust and mantle. They also highlight the role of fluids in enabling subduction to overcome this early resistance.

  20. Isotopic constraints on anorthosite genesis and implications for crust-mantle evolution

    SciTech Connect

    Ashwal, L.D.

    1985-01-01

    Crystallization ages of anorthosite massifs, determined from whole-rock and internal Sm-Nd and Rb-Sr isochrons range between about 1.1 and 1.6 Ga, arguing against a discrete anorthosite event. Metamorphic ages of some massifs are as much as 200-300 Ma younger, indicating that the Grenville orogeny was not a causative factor in anorthosite genesis. Variable crustal contamination effects are evident in many massifs, particularly in border zones. In some late-stage ferrogabbros, mafic silicates and/or Fe-Ti oxides are not in isotopic equilibrium with plagioclase, suggesting that crystallization took place both before and after contamination. The most isotopically primitive materials are Al-rich opx megacrysts. Isotopic data to date are compatible with a two-stage model involving (1) emplacement of basaltic magma into lower crustal chambers where fractionation and accumulation of olivine and Al-rich opx, and eventually plagioclase took place, and (2) detachment and ascent of buoyant anorthositic mushes to upper crustal emplacement sites. Besides being useful as indicators of Proterozoic mantle evolution, anorthosites can be used as tracers to map our basement types through which they were emplaced.

  1. The bearing of spinel cataclasites on the crust-mantle structure of the moon

    NASA Technical Reports Server (NTRS)

    Herzberg, C. T.

    1978-01-01

    Subsolidus thermodynamic calculations have been made to define the temperature and pressure conditions required to equilibrate lunar spinel cataclasites (olivine + high alumina orthopyroxene + pleonaste spinel + plagioclase + or - cordierite) that occur as clasts in 15445, 73263, and 72435. The results, which are subject to modification by improved thermodynamic data and experiment, indicate that those samples that are cordierite-free and of high Mg/(Mg + Fe) were derived from the lower crust and possibly from a high-velocity zone of the uppermost mantle. However, the cordierite-bearing type in 72435,8 /low Mg/(Mg + Fe)/ resided in the upper levels of the crust prior to excavation by impact. Consideration of the relevant supersolidus phase equilibria indicates that the whole-rock chemistry of all spinel cataclasites can only be explained by pleonaste spinel accumulation. These materials are interpreted to be primordial cumulate rocks formed during the differentiation of the lunar magma ocean.

  2. Equipment fault diagnosis system of sequencing batch reactors using rule-based fuzzy inference and on-line sensing data.

    PubMed

    Kim, Y J; Bae, H; Poo, K M; Ko, J H; Kim, B G; Park, T J; Kim, C W

    2006-01-01

    The importance of a detection technique to prevent process deterioration is increasing. For the fast detection of this disturbance, a diagnostic algorithm was developed to determine types of equipment faults by using on-line ORP and DO profile in sequencing batch reactors (SBRs). To develop the rule base for fault diagnosis, the sensor profiles were obtained from a pilot-scale SBR when blower, influent pump and mixer were broken. The rules were generated based on the calculated error between an abnormal profile and a normal profile, e(ORP)(t) and e(DO)(t). To provide intermediate diagnostic results between "normal" and "fault", a fuzzy inference algorithm was incorporated to the rules. Fuzzified rules could present the diagnosis result "need to be checked". The diagnosis showed good performance in detecting and diagnosing various faults. The developed algorithm showed its applicability to detect faults and make possible fast action to correct them. PMID:16722090

  3. Inferring biotic interactions from proxies.

    PubMed

    Morales-Castilla, Ignacio; Matias, Miguel G; Gravel, Dominique; Araújo, Miguel B

    2015-06-01

    Inferring biotic interactions from functional, phylogenetic and geographical proxies remains one great challenge in ecology. We propose a conceptual framework to infer the backbone of biotic interaction networks within regional species pools. First, interacting groups are identified to order links and remove forbidden interactions between species. Second, additional links are removed by examination of the geographical context in which species co-occur. Third, hypotheses are proposed to establish interaction probabilities between species. We illustrate the framework using published food-webs in terrestrial and marine systems. We conclude that preliminary descriptions of the web of life can be made by careful integration of data with theory.

  4. Groundwater recharge areas of a volcanic aquifer system inferred from hydraulic, hydrogeochemical and stable isotope data: Mount Vulture, southern Italy

    NASA Astrophysics Data System (ADS)

    Parisi, Serena; Paternoster, Michele; Kohfahl, Claus; Pekdeger, Asaf; Meyer, Hanno; Hubberten, Hans Wolfgang; Spilotro, Giuseppe; Mongelli, Giovanni

    2011-02-01

    Environmental isotope techniques, hydrogeochemical analysis and hydraulic data are employed to identify the main recharge areas of the Mt. Vulture hydrogeological basin, one of the most important aquifers of southern Italy. The groundwaters are derived from seepage of rainwater, flowing from the highest to the lowest elevations through the shallow volcanic weathered host-rock fracture zones. Samples of shallow and deep groundwater were collected at 48 locations with elevations ranging from 352 to 1,100 m above sea level (a.s.l.), for stable isotope (δ18O, δD) and major ion analyses. A complete dataset of available hydraulic information has been integrated with measurements carried out in the present study. Inferred recharge elevations, estimated on the basis of the local vertical isotopic gradient of δ18O, range between 550 and 1,200 m a.s.l. The isotope pattern of the Quaternary aquifer reflects the spatial separation of different recharge sources. Knowledge of the local hydrogeological setting was the starting point for a detailed hydrogeochemical and isotopic study to define the recharge and discharge patterns identifying the groundwater flow pathways of the Mt. Vulture basin. The integration of all the data allowed for the tracing of the groundwater flows of the Mt. Vulture basin.

  5. Bayesian inference on proportional elections.

    PubMed

    Brunello, Gabriel Hideki Vatanabe; Nakano, Eduardo Yoshio

    2015-01-01

    Polls for majoritarian voting systems usually show estimates of the percentage of votes for each candidate. However, proportional vote systems do not necessarily guarantee the candidate with the most percentage of votes will be elected. Thus, traditional methods used in majoritarian elections cannot be applied on proportional elections. In this context, the purpose of this paper was to perform a Bayesian inference on proportional elections considering the Brazilian system of seats distribution. More specifically, a methodology to answer the probability that a given party will have representation on the chamber of deputies was developed. Inferences were made on a Bayesian scenario using the Monte Carlo simulation technique, and the developed methodology was applied on data from the Brazilian elections for Members of the Legislative Assembly and Federal Chamber of Deputies in 2010. A performance rate was also presented to evaluate the efficiency of the methodology. Calculations and simulations were carried out using the free R statistical software.

  6. Bayesian Inference on Proportional Elections

    PubMed Central

    Brunello, Gabriel Hideki Vatanabe; Nakano, Eduardo Yoshio

    2015-01-01

    Polls for majoritarian voting systems usually show estimates of the percentage of votes for each candidate. However, proportional vote systems do not necessarily guarantee the candidate with the most percentage of votes will be elected. Thus, traditional methods used in majoritarian elections cannot be applied on proportional elections. In this context, the purpose of this paper was to perform a Bayesian inference on proportional elections considering the Brazilian system of seats distribution. More specifically, a methodology to answer the probability that a given party will have representation on the chamber of deputies was developed. Inferences were made on a Bayesian scenario using the Monte Carlo simulation technique, and the developed methodology was applied on data from the Brazilian elections for Members of the Legislative Assembly and Federal Chamber of Deputies in 2010. A performance rate was also presented to evaluate the efficiency of the methodology. Calculations and simulations were carried out using the free R statistical software. PMID:25786259

  7. Using surface creep rate to infer fraction locked for sections of the San Andreas fault system in northern California from alignment array and GPS data

    USGS Publications Warehouse

    Lienkaemper, James J.; McFarland, Forrest S.; Simpson, Robert W.; Caskey, S. John

    2014-01-01

    Surface creep rate, observed along five branches of the dextral San Andreas fault system in northern California, varies considerably from one section to the next, indicating that so too may the depth at which the faults are locked. We model locking on 29 fault sections using each section’s mean long‐term creep rate and the consensus values of fault width and geologic slip rate. Surface creep rate observations from 111 short‐range alignment and trilateration arrays and 48 near‐fault, Global Positioning System station pairs are used to estimate depth of creep, assuming an elastic half‐space model and adjusting depth of creep iteratively by trial and error to match the creep observations along fault sections. Fault sections are delineated either by geometric discontinuities between them or by distinctly different creeping behaviors. We remove transient rate changes associated with five large (M≥5.5) regional earthquakes. Estimates of fraction locked, the ratio of moment accumulation rate to loading rate, on each section of the fault system provide a uniform means to inform source parameters relevant to seismic‐hazard assessment. From its mean creep rates, we infer the main branch (the San Andreas fault) ranges from only 20%±10% locked on its central creeping section to 99%–100% on the north coast. From mean accumulation rates, we infer that four urban faults appear to have accumulated enough seismic moment to produce major earthquakes: the northern Calaveras (M 6.8), Hayward (M 6.8), Rodgers Creek (M 7.1), and Green Valley (M 7.1). The latter three faults are nearing or past their mean recurrence interval.

  8. Air Quality Analysis by Using Fuzzy Inference System and Fuzzy C-mean Clustering in Tehran, Iran from 2009–2013

    PubMed Central

    HAMEDIAN, Amir Abbas; JAVID, Allahbakhsh; MOTESADDI ZARANDI, Saeed; RASHIDI, Yousef; MAJLESI, Monireh

    2016-01-01

    Background: Since the industrial revolution, the rate of industrialization and urbanization has increased dramatically. Regarding this issue, specific regions mostly located in developing countries have been confronted with serious problems, particularly environmental problems among which air pollution is of high importance. Methods: Eleven parameters, including CO, SO2, PM10, PM2.5, O3, NO2, benzene, toluene, ethyl-benzene, xylene, and 1,3-butadiene, have been accounted over a period of two years (2011–2012) from five monitoring stations located at Tehran, Iran, were assessed by using fuzzy inference system and fuzzy c-mean clustering. Results: These tools showed that the quality of criteria pollutants between the year 2011 and 2012 did not as much effect the public health as the other pollutants did. Conclusion: Using the air EPA AQI, the quality of air, and also the managerial plans required to improve the quality can be misled. PMID:27516999

  9. Inferring Instantaneous, Multivariate and Nonlinear Sensitivities for the Analysis of Feedback Processes in a Dynamical System: Lorenz Model Case Study

    NASA Technical Reports Server (NTRS)

    Aires, Filipe; Rossow, William B.; Hansen, James E. (Technical Monitor)

    2001-01-01

    A new approach is presented for the analysis of feedback processes in a nonlinear dynamical system by observing its variations. The new methodology consists of statistical estimates of the sensitivities between all pairs of variables in the system based on a neural network modeling of the dynamical system. The model can then be used to estimate the instantaneous, multivariate and nonlinear sensitivities, which are shown to be essential for the analysis of the feedbacks processes involved in the dynamical system. The method is described and tested on synthetic data from the low-order Lorenz circulation model where the correct sensitivities can be evaluated analytically.

  10. OFMspert - Inference of operator intentions in supervisory control using a blackboard architecture. [operator function model expert system

    NASA Technical Reports Server (NTRS)

    Jones, Patricia S.; Mitchell, Christine M.; Rubin, Kenneth S.

    1988-01-01

    The authors proposes an architecture for an expert system that can function as an operator's associate in the supervisory control of a complex dynamic system. Called OFMspert (operator function model (OFM) expert system), the architecture uses the operator function modeling methodology as the basis for the design. The authors put emphasis on the understanding capabilities, i.e., the intent referencing property, of an operator's associate. The authors define the generic structure of OFMspert, particularly those features that support intent inferencing. They also describe the implementation and validation of OFMspert in GT-MSOCC (Georgia Tech-Multisatellite Operations Control Center), a laboratory domain designed to support research in human-computer interaction and decision aiding in complex, dynamic systems.

  11. Temporal order of evolution of DNA replication systems inferred by comparison of cellular and viral DNA polymerases

    PubMed Central

    Koonin, Eugene V

    2006-01-01

    Background The core enzymes of the DNA replication systems show striking diversity among cellular life forms and more so among viruses. In particular, and counter-intuitively, given the central role of DNA in all cells and the mechanistic uniformity of replication, the core enzymes of the replication systems of bacteria and archaea (as well as eukaryotes) are unrelated or extremely distantly related. Viruses and plasmids, in addition, possess at least two unique DNA replication systems, namely, the protein-primed and rolling circle modalities of replication. This unexpected diversity makes the origin and evolution of DNA replication systems a particularly challenging and intriguing problem in evolutionary biology. Results I propose a specific succession for the emergence of different DNA replication systems, drawing argument from the differences in their representation among viruses and other selfish replicating elements. In a striking pattern, the DNA replication systems of viruses infecting bacteria and eukaryotes are dominated by the archaeal-type B-family DNA polymerase (PolB) whereas the bacterial replicative DNA polymerase (PolC) is present only in a handful of bacteriophage genomes. There is no apparent mechanistic impediment to the involvement of the bacterial-type replication machinery in viral DNA replication. Therefore, I hypothesize that the observed, markedly unequal distribution of the replicative DNA polymerases among the known cellular and viral replication systems has a historical explanation. I propose that, among the two types of DNA replication machineries that are found in extant life forms, the archaeal-type, PolB-based system evolved first and had already given rise to a variety of diverse viruses and other selfish elements before the advent of the bacterial, PolC-based machinery. Conceivably, at that stage of evolution, the niches for DNA-viral reproduction have been already filled with viruses replicating with the help of the archaeal

  12. FUNCTIONAL OVERLAP OF ROOT SYSTEMS IN AN OLD-GROWTH FOREST INFERRED FROM TRACER 15N UPTAKE

    EPA Science Inventory

    Belowground competition for nutrients and water is considered a key factor affecting spatial organization and productivity of individual stems within forest stands, yet there are few data describing the lateral extent and overlap of competing root systems. We quantified the func...

  13. Volcano deformation in central Main Ethiopian Rift system (Aluto Volcano) inferred from continuous GPS and dynamic gravity observations

    NASA Astrophysics Data System (ADS)

    Birhanu, Yelebe; Biggs, Juliet; Gottsmann, Joachim; Lewi, Elias; Lloyd, Ryan; Bekele, Berhanu

    2016-04-01

    Silicic volcanic centres in the rift systems frequently experience unrest indicating long-term activity in the underlying magmatic system, but it is difficult to distinguish the contributions of hydrothermal fluids, magma or gasses. Aluto volcano which is located in the central MER system is situated between the Lakes Ziway and Langano in the north and south respectively. Continuous GPS installed from April 2013 to October 2015 shows subsidence initially, with the largest subsidence observed in the eastern part of the caldera (2 cm/yr). InSAR observations from TerraSAR-X show a radially-symmetric pattern of long-term subsidence. Dynamic gravity surveys carried out in October 2014 and 2015 showed that there is a net mass loss in the western and central part of the caldera and mass gain in the eastern and southern part of the caldera, with a sharp gradient between the two. This complex spatial pattern of gravity change is significantly different to the simple pattern of deformation indicating multiple sources of pressure and mass change exist within the caldera. We explain the ratio of gravity to height change (dg/dh) throughout the volcano by considering cooling and crystallisation of magma body, draining and precipitation of hydrothermal fluids and changes in the water table and lake levels. Keywords: volcano deformation, dynamic gravity, continental rift

  14. Deterministic inference for stochastic systems using multiple shooting and a linear noise approximation for the transition probabilities.

    PubMed

    Zimmer, Christoph; Sahle, Sven

    2015-10-01

    Estimating model parameters from experimental data is a crucial technique for working with computational models in systems biology. Since stochastic models are increasingly important, parameter estimation methods for stochastic modelling are also of increasing interest. This study presents an extension to the 'multiple shooting for stochastic systems (MSS)' method for parameter estimation. The transition probabilities of the likelihood function are approximated with normal distributions. Means and variances are calculated with a linear noise approximation on the interval between succeeding measurements. The fact that the system is only approximated on intervals which are short in comparison with the total observation horizon allows to deal with effects of the intrinsic stochasticity. The study presents scenarios in which the extension is essential for successfully estimating the parameters and scenarios in which the extension is of modest benefit. Furthermore, it compares the estimation results with reversible jump techniques showing that the approximation does not lead to a loss of accuracy. Since the method is not based on stochastic simulations or approximative sampling of distributions, its computational speed is comparable with conventional least-squares parameter estimation methods.

  15. Probabilistic expert systems for forensic inference from DNA markers in horses: applications to confirm genealogies with lack of genetic data.

    PubMed

    Dobosz, Marina; Bocci, Chiara; Bonuglia, Margherita; Grasso, Cinzia; Merigioli, Sara; Russo, Alessandra; De Iuliis, Paolo

    2010-01-01

    Microsatellites have been used for parentage testing and individual identification in forensic science because they are highly polymorphic and show abundant sequences dispersed throughout most eukaryotic nuclear genomes. At present, genetic testing based on DNA technology is used for most domesticated animals, including horses, to confirm identity, to determine parentage, and to validate registration certificates. But if genetic data of one of the putative parents are missing, verifying a genealogy could be questionable. The aim of this paper is to illustrate a new approach to analyze complex cases of disputed relationship with microsatellites markers. These cases were solved by analyzing the genotypes of the offspring and other horses' genotypes in the pedigrees of the putative dam/sire with probabilistic expert systems (PESs). PES was especially efficient in supplying reliable, error-free Bayesian probabilities in complex cases with missing pedigree data. One of these systems was developed for forensic purposes (FINEX program) and is particularly valuable in human analyses. We applied this program to parentage analysis in horses, and we will illustrate how different cases have been successfully worked out.

  16. Stress Pattern of the Shanxi Rift System, North China, Inferred from the Inversion of New Focal Mechanisms

    NASA Astrophysics Data System (ADS)

    Li, B.; Atakan, K.; Sorensen, M. B.; Havskov, J.

    2014-12-01

    Earthquake focal mechanisms of the Shanxi rift system, North China, are investigated for the time period 1965 - Apr. 2014. A total of 143 focal mechanisms of ML ≥ 3.0 earthquakes were compiled. Among them, 105 solutions are newly determined by combining the P-wave first motions and full waveform inversion, and 38 solutions are from available published data. Stress tensor inversion was then performed based on the new database. The results show that most solutions exhibit normal or strike-slip faulting, and the regional stress field is characterized by a stable, dominating NNW-SSE extension and an ENE-WSW compression. This correlates well with results from GPS data, geological field observations and leveling measurements across the faults. Heterogeneity exists in the regional stress field, as indicated by individual stress tensor inversions conducted for five subzones. While the minimum stress axis (σ3) appears to be consistent and stable, the orientations, especially the plunges, of the maximum and intermediate stresses (σ1 and σ2) vary significantly among the different subzones. Based on our results and combining multidisciplinary observations from geological surveys, GPS and cross-fault monitoring, a kinematic model is proposed, to illustrate the present-day stress field and its correlation with the regional tectonics, as well as the current crustal deformation of the Shanxi rift system. Results obtained in this study, may help to understand the geodynamics, neotectonic activity, active seismicity and potential seismic hazard in this region of North China.

  17. "Groundwater ages" of the Lake Chad multi-layer aquifers system inferred from 14C and 36Cl data

    NASA Astrophysics Data System (ADS)

    Bouchez, Camille; Deschamps, Pierre; Goncalves, Julio; Hamelin, Bruno; Seidel, Jean-Luc; Doumnang, Jean-Claude

    2014-05-01

    Assessment of recharge, paleo-recharge and groundwater residence time of aquifer systems of the Sahel is pivotal for a sustainable management of this vulnerable resource. Due to its stratified aquifer system, the Lake Chad Basin (LCB) offers the opportunity to assess recharge processes over time and to link climate and hydrology in the Sahel. Located in north-central Africa at the fringe between the Sahel and the Sahara, the lake Chad basin (LCB) is an endorheic basin of 2,5.106 km2. With a monsoon climate, the majority of the rainfall occurs in the southern one third of the basin, the Chari/Logone River system transporting about 90% of the runoff generated within the drainage basin. A complex multi-layer aquifer system is located in the central part of the LCB. The Quaternary unconfined aquifer, covering 500 000 km2, is characterized by the occurrence of poorly understood piezometric depressions. Artesian groundwaters are found in the Plio-Pleistocene lacustrine and deltaic sedimentary aquifers (early Pliocene and Continental Terminal). The present-day lake is in hydraulic contact with the Quaternary Aquifer, but during past megalake phases, most of the Quaternary aquifer was submerged and may experience major recharge events. To identify active recharge area and assess groundwater dynamics, one hundred surface and groundwater samples of all layers have been collected over the southern part of the LCB. Major and trace elements have been analyzed. Measurements of 36Cl have been carried out at CEREGE, on the French 5 MV AMS National Facility ASTER and 14C activities have been analyzed for 17 samples on the French AMS ARTEMIS. Additionally, the stable isotopic composition was measured on the artesian aquifer samples. In the Quaternary aquifer, results show a large scatter with waters having very different isotopic and geochemical signature. In its southern part and in the vicinity of the surface waters, groundwaters are predominantly Ca-Mg-HCO3 type waters with very

  18. Tectonic history of the north portion of the San Andreas fault system, California, inferred from gravity and magnetic anomalies

    USGS Publications Warehouse

    Griscom, A.; Jachens, R.C.

    1989-01-01

    Geologic and geophysical data for the San Andreas fault system north of San Francisco suggest that the eastern boundary of the Pacific plate migrated eastward from its presumed original position at the base of the continental slope to its present position along the San Andreas transform fault by means of a series of eastward jumps of the Mendocino triple junction. These eastward jumps total a distance of about 150 km since 29 Ma. Correlation of right-laterally displaced gravity and magnetic anomalies that now have components at San Francisco and on the shelf north of Point Arena indicates that the presently active strand of the San Andreas fault north of the San Francisco peninsula formed recently at about 5 Ma when the triple junction jumped eastward a minimum of 100 km to its present location at the north end of the San Andreas fault. -from Authors

  19. Comparison of adaptive neuro-fuzzy inference system (ANFIS) and Gaussian processes for machine learning (GPML) algorithms for the prediction of skin temperature in lower limb prostheses.

    PubMed

    Mathur, Neha; Glesk, Ivan; Buis, Arjan

    2016-10-01

    Monitoring of the interface temperature at skin level in lower-limb prosthesis is notoriously complicated. This is due to the flexible nature of the interface liners used impeding the required consistent positioning of the temperature sensors during donning and doffing. Predicting the in-socket residual limb temperature by monitoring the temperature between socket and liner rather than skin and liner could be an important step in alleviating complaints on increased temperature and perspiration in prosthetic sockets. In this work, we propose to implement an adaptive neuro fuzzy inference strategy (ANFIS) to predict the in-socket residual limb temperature. ANFIS belongs to the family of fused neuro fuzzy system in which the fuzzy system is incorporated in a framework which is adaptive in nature. The proposed method is compared to our earlier work using Gaussian processes for machine learning. By comparing the predicted and actual data, results indicate that both the modeling techniques have comparable performance metrics and can be efficiently used for non-invasive temperature monitoring. PMID:27452775

  20. Using adaptive neuro-fuzzy inference system technique for crosstalk correction in simultaneous 99mTc/201Tl SPECT imaging: A Monte Carlo simulation study

    NASA Astrophysics Data System (ADS)

    Heidary, Saeed; Setayeshi, Saeed

    2015-01-01

    This work presents a simulation based study by Monte Carlo which uses two adaptive neuro-fuzzy inference systems (ANFIS) for cross talk compensation of simultaneous 99mTc/201Tl dual-radioisotope SPECT imaging. We have compared two neuro-fuzzy systems based on fuzzy c-means (FCM) and subtractive (SUB) clustering. Our approach incorporates eight energy-windows image acquisition from 28 keV to 156 keV and two main photo peaks of 201Tl (77±10% keV) and 99mTc (140±10% keV). The Geant4 application in emission tomography (GATE) is used as a Monte Carlo simulator for three cylindrical and a NURBS Based Cardiac Torso (NCAT) phantom study. Three separate acquisitions including two single-isotopes and one dual isotope were performed in this study. Cross talk and scatter corrected projections are reconstructed by an iterative ordered subsets expectation maximization (OSEM) algorithm which models the non-uniform attenuation in the projection/back-projection. ANFIS-FCM/SUB structures are tuned to create three to sixteen fuzzy rules for modeling the photon cross-talk of the two radioisotopes. Applying seven to nine fuzzy rules leads to a total improvement of the contrast and the bias comparatively. It is found that there is an out performance for the ANFIS-FCM due to its acceleration and accurate results.

  1. Effective viscoplastic behavior of polycrystalline aggregates lacking four independent slip systems inferred from homogenization methods; application to olivine

    NASA Astrophysics Data System (ADS)

    Detrez, F.; Castelnau, O.; Cordier, P.; Merkel, S.; Raterron, P.

    2015-10-01

    Polycrystalline aggregates lacking four independent systems for the glide of dislocations can deform in a purely viscoplastic regime only if additional deformation mechanisms (such as grain boundary sliding and diffusion) are activated. We introduce an implementation of the self-consistent scheme in which this additional physical mechanism, considered as a stress relaxation mechanism, is represented by a nonlinear isotropic viscoplastic potential. Several nonlinear extensions of the self-consistent scheme, including the second-order method of Ponte-Castañeda, are used to provide an estimate of the effective viscoplastic behavior of such polycrystals. The implementation of the method includes an approximation of the isotropic potential to ensure convergence of the attractive fixed-point numerical algorithm. The method is then applied to olivine polycrystals, the main constituent of the Earth's upper mantle. Due to the extreme local anisotropy of the local constitutive behavior and the subsequent intraphase stress and strain-rate field heterogeneities, the second-order method is the only extension providing qualitative and quantitative accurate results. The effective viscosity is strongly dependent on the strength of the relaxation mechanism. For olivine, a linear viscous relaxation (e.g. diffusion) could be relevant; in that case, the polycrystal stress sensitivity is reduced compared to that of dislocation glide, and the most active slip system is not necessarily the one with the smallest reference stress due to stress concentrations. This study reveals the significant importance of the strength and stress sensitivity of the additional relaxation mechanism for the rheology and lattice preferred orientation in such highly anisotropic polycrystalline aggregates.

  2. The Bayes Inference Engine

    SciTech Connect

    Hanson, K.M.; Cunningham, G.S.

    1996-04-01

    The authors are developing a computer application, called the Bayes Inference Engine, to provide the means to make inferences about models of physical reality within a Bayesian framework. The construction of complex nonlinear models is achieved by a fully object-oriented design. The models are represented by a data-flow diagram that may be manipulated by the analyst through a graphical programming environment. Maximum a posteriori solutions are achieved using a general, gradient-based optimization algorithm. The application incorporates a new technique of estimating and visualizing the uncertainties in specific aspects of the model.

  3. Lithospheric Shear Velocity Models Beneath Continental Margins in Antarctica Inferred From Genetic Algorithm Inversion for Teleseismic Receiver Functions

    NASA Astrophysics Data System (ADS)

    Kanao, M.; Shibutani, T.

    2005-12-01

    Seismic shear velocity models of the crust and the uppermost mantle were studied by teleseismic receiver function analyses beneath the permanent stations of the Federation of Digital Seismographic Networks (FDSN) at Antarctic continental margins. In order to eliminate the starting model dependency, a non-linear Genetic Algorithm (GA) was introduced in the time domain inversion of the receiver functions. A plenty of velocity models with an acceptable fit to the receiver function waveforms were generated during the inversion, and a stable model was produced by employing a weighted average of the best 1,000 models encountered in the development of the GA. The shear velocity model beneath the MAW (67.6S, 62.9E) has a sharp Moho boundary at 44 km depth that might have involved in a reworked metamorphic event of adjacent Archaean Napier Complex. A fairly sharp Moho was identified about 28 km depth beneath DRV (66.7S, 140.0E), with a middle grade variation of the crustal velocities that might have been caused by the Early Proterozoic metamorphism. A similar sharp Moho has been found at 40 km beneath SYO (69.0S, 39.6E). Thus Moho depth is consistent with that from refraction / wide-angle reflection surveys around the station. Fairly complicated velocity variations within the crust may have a relationship with lithology of granulite facies metamorphic rocks in the shallow crust associated with Pan-African events. Broadening low velocity zones about 30 km depths with transitional crust-mantle boundary at VNDA (77.5S, 161.9E), might be caused by the rift system besides the Trans Antarctic Mountains. As for the Antarctic Peninsular, very broad Moho was found around 36 km depths around PMSA (64.8S, 64.0W). The evidence of velocity variations within the crust reflects the tectonic histories of each terrain where these permanent stations are located.

  4. Spatial and temporal geochemical trends in the hydrothermal system of Yellowstone National Park: Inferences from river solute fluxes

    USGS Publications Warehouse

    Hurwitz, S.; Lowenstern, J. B.; Heasler, H.

    2007-01-01

    We present and analyze a chemical dataset that includes the concentrations and fluxes of HCO3-, SO42-, Cl-, and F- in the major rivers draining Yellowstone National Park (YNP) for the 2002-2004 water years (1 October 2001 - 30 September 2004). The total (molar) flux in all rivers decreases in the following order, HCO3- > Cl- > SO42- > F-, but each river is characterized by a distinct chemical composition, implying large-scale spatial heterogeneity in the inputs of the various solutes. The data also display non-uniform temporal trends; whereas solute concentrations and fluxes are nearly constant during base-flow conditions, concentrations decrease, solute fluxes increase, and HCO3-/Cl-, and SO42-/Cl- increase during the late-spring high-flow period. HCO3-/SO42- decreases with increasing discharge in the Madison and Falls Rivers, but increases with discharge in the Yellowstone and Snake Rivers. The non-linear relations between solute concentrations and river discharge and the change in anion ratios associated with spring runoff are explained by mixing between two components: (1) a component that is discharged during base-flow conditions and (2) a component associated with snow-melt runoff characterized by higher HCO3-/Cl- and SO42-/Cl-. The fraction of the second component is greater in the Yellowstone and Snake Rivers, which host lakes in their drainage basins and where a large fraction of the solute flux follows thaw of ice cover in the spring months. Although the total river HCO3- flux is larger than the flux of other solutes (HCO3-/Cl- ??? 3), the CO2 equivalent flux is only ??? 1% of the estimated emission of magmatic CO2 soil emissions from Yellowstone. No anomalous solute flux in response to perturbations in the hydrothermal system was observed, possibly because gage locations are too distant from areas of disturbance, or because of the relatively low sampling frequency. In order to detect changes in river hydrothermal solute fluxes, sampling at higher

  5. Inferring the interconnections between surface water bodies, tile-drains and an unconfined aquifer-aquitard system: A case study

    NASA Astrophysics Data System (ADS)

    Colombani, N.; Di Giuseppe, D.; Faccini, B.; Ferretti, G.; Mastrocicco, M.; Coltorti, M.

    2016-06-01

    Shallow lenses in reclaimed coastal areas are precious sources of freshwater for crop development, but their seasonal behaviour is seldom known in tile-drained fields. In this study, field monitoring and numerical modelling provide a robust conceptual model of these complex environments. Crop and meteorological data are used to implement an unsaturated flow model to reconstruct daily recharge. Groundwater fluxes and salinity, water table elevation, tile-drains' discharge and salinity are used to calibrate a 2D density-dependent numerical model to quantify non-reactive solute transport within the aquifer-aquitard system. Results suggest that lateral fluxes in low hydraulic conductivity sediments are limited, while water table fluctuation is significant. The use of depth-integrated monitoring to calibrate the model results in poor efficiency, while multi-level soil profiles are crucial to define the mixing zone between fresh and brackish groundwater. Measured fluxes and chloride concentrations from tile-drains not fully compare with calculated ones due to preferential flow through cracks.

  6. Investigation of the robustness of adaptive neuro-fuzzy inference system for tracking moving tumors in external radiotherapy.

    PubMed

    Torshabi, Ahmad Esmaili

    2014-12-01

    In external radiotherapy of dynamic targets such as lung and breast cancers, accurate correlation models are utilized to extract real time tumor position by means of external surrogates in correlation with the internal motion of tumors. In this study, a correlation method based on the neuro-fuzzy model is proposed to correlate the input external motion data with internal tumor motion estimation in real-time mode, due to its robustness in motion tracking. An initial test of the performance of this model was reported in our previous studies. In this work by implementing some modifications it is resulted that ANFIS is still robust to track tumor motion more reliably by reducing the motion estimation error remarkably. After configuring new version of our ANFIS model, its performance was retrospectively tested over ten patients treated with Synchrony Cyberknife system. In order to assess the performance of our model, the predicted tumor motion as model output was compared with respect to the state of the art model. Final analyzed results show that our adaptive neuro-fuzzy model can reduce tumor tracking errors more significantly, as compared with ground truth database and even tumor tracking methods presented in our previous works. PMID:25412886

  7. Cortical circuits for perceptual inference.

    PubMed

    Friston, Karl; Kiebel, Stefan

    2009-10-01

    This paper assumes that cortical circuits have evolved to enable inference about the causes of sensory input received by the brain. This provides a principled specification of what neural circuits have to achieve. Here, we attempt to address how the brain makes inferences by casting inference as an optimisation problem. We look at how the ensuing recognition dynamics could be supported by directed connections and message-passing among neuronal populations, given our knowledge of intrinsic and extrinsic neuronal connections. We assume that the brain models the world as a dynamic system, which imposes causal structure on the sensorium. Perception is equated with the optimisation or inversion of this internal model, to explain sensory input. Given a model of how sensory data are generated, we use a generic variational approach to model inversion to furnish equations that prescribe recognition; i.e., the dynamics of neuronal activity that represents the causes of sensory input. Here, we focus on a model whose hierarchical and dynamical structure enables simulated brains to recognise and predict sequences of sensory states. We first review these models and their inversion under a variational free-energy formulation. We then show that the brain has the necessary infrastructure to implement this inversion and present stimulations using synthetic birds that generate and recognise birdsongs.

  8. Cortical circuits for perceptual inference.

    PubMed

    Friston, Karl; Kiebel, Stefan

    2009-10-01

    This paper assumes that cortical circuits have evolved to enable inference about the causes of sensory input received by the brain. This provides a principled specification of what neural circuits have to achieve. Here, we attempt to address how the brain makes inferences by casting inference as an optimisation problem. We look at how the ensuing recognition dynamics could be supported by directed connections and message-passing among neuronal populations, given our knowledge of intrinsic and extrinsic neuronal connections. We assume that the brain models the world as a dynamic system, which imposes causal structure on the sensorium. Perception is equated with the optimisation or inversion of this internal model, to explain sensory input. Given a model of how sensory data are generated, we use a generic variational approach to model inversion to furnish equations that prescribe recognition; i.e., the dynamics of neuronal activity that represents the causes of sensory input. Here, we focus on a model whose hierarchical and dynamical structure enables simulated brains to recognise and predict sequences of sensory states. We first review these models and their inversion under a variational free-energy formulation. We then show that the brain has the necessary infrastructure to implement this inversion and present stimulations using synthetic birds that generate and recognise birdsongs. PMID:19635656

  9. Inferring genetic networks from microarray data.

    SciTech Connect

    May, Elebeoba Eni; Davidson, George S.; Martin, Shawn Bryan; Werner-Washburne, Margaret C.; Faulon, Jean-Loup Michel

    2004-06-01

    In theory, it should be possible to infer realistic genetic networks from time series microarray data. In practice, however, network discovery has proved problematic. The three major challenges are: (1) inferring the network; (2) estimating the stability of the inferred network; and (3) making the network visually accessible to the user. Here we describe a method, tested on publicly available time series microarray data, which addresses these concerns. The inference of genetic networks from genome-wide experimental data is an important biological problem which has received much attention. Approaches to this problem have typically included application of clustering algorithms [6]; the use of Boolean networks [12, 1, 10]; the use of Bayesian networks [8, 11]; and the use of continuous models [21, 14, 19]. Overviews of the problem and general approaches to network inference can be found in [4, 3]. Our approach to network inference is similar to earlier methods in that we use both clustering and Boolean network inference. However, we have attempted to extend the process to better serve the end-user, the biologist. In particular, we have incorporated a system to assess the reliability of our network, and we have developed tools which allow interactive visualization of the proposed network.

  10. Linguistic Markers of Inference Generation While Reading.

    PubMed

    Clinton, Virginia; Carlson, Sarah E; Seipel, Ben

    2016-06-01

    Words can be informative linguistic markers of psychological constructs. The purpose of this study is to examine associations between word use and the process of making meaningful connections to a text while reading (i.e., inference generation). To achieve this purpose, think-aloud data from third-fifth grade students ([Formula: see text]) reading narrative texts were hand-coded for inferences. These data were also processed with a computer text analysis tool, Linguistic Inquiry and Word Count, for percentages of word use in the following categories: cognitive mechanism words, nonfluencies, and nine types of function words. Findings indicate that cognitive mechanisms were an independent, positive predictor of connections to background knowledge (i.e., elaborative inference generation) and nonfluencies were an independent, negative predictor of connections within the text (i.e., bridging inference generation). Function words did not provide unique variance towards predicting inference generation. These findings are discussed in the context of a cognitive reflection model and the differences between bridging and elaborative inference generation. In addition, potential practical implications for intelligent tutoring systems and computer-based methods of inference identification are presented.

  11. An expert system shell for inferring vegetation characteristics: Changes to the historical cover type database (Task F)

    NASA Technical Reports Server (NTRS)

    1993-01-01

    All the options in the NASA VEGetation Workbench (VEG) make use of a database of historical cover types. This database contains results from experiments by scientists on a wide variety of different cover types. The learning system uses the database to provide positive and negative training examples of classes that enable it to learn distinguishing features between classes of vegetation. All the other VEG options use the database to estimate the error bounds involved in the results obtained when various analysis techniques are applied to the sample of cover type data that is being studied. In the previous version of VEG, the historical cover type database was stored as part of the VEG knowledge base. This database was removed from the knowledge base. It is now stored as a series of flat files that are external to VEG. An interface between VEG and these files was provided. The interface allows the user to select which files of historical data to use. The files are then read, and the data are stored in Knowledge Engineering Environment (KEE) units using the same organization of units as in the previous version of VEG. The interface also allows the user to delete some or all of the historical database units from VEG and load new historical data from a file. This report summarizes the use of the historical cover type database in VEG. It then describes the new interface to the files containing the historical data. It describes minor changes that were made to VEG to enable the externally stored database to be used. Test runs to test the operation of the new interface and also to test the operation of VEG using historical data loaded from external files are described. Task F was completed. A Sun cartridge tape containing the KEE and Common Lisp code for the new interface and the modified version of the VEG knowledge base was delivered to the NASA GSFC technical representative.

  12. Sulfur Isotopic Inferences of the Controls on Porewater Sulfate Profiles in the Northern Cascadia Margin Gas Hydrate System

    NASA Astrophysics Data System (ADS)

    Bui, T.; Pohlman, J.; Lapham, L.; Riedel, M.; Wing, B. A.

    2010-12-01

    The flux of methane from gas hydrate bearing seeps in the marine environment is partially mitigated by the anaerobic oxidation of methane coupled with sulfate reduction. Sedimentary porewater sulfate profiles above gas hydrate deposits are frequently used to estimate the efficacy of this important microbial biofilter. However, to differentiate how other processes (e.g., sulfate reduction coupled to organic matter oxidation, sulfide re-oxidation and sulfur disproportionation) affect sulfate profiles, a complete accounting of the sulfur cycle is necessary. To this end, we have obtained the first ever measurements of minor sulfur isotopic ratios (33S/32S, 36S/32S), in conjunction with the more commonly measured 34S -32S ratio, from porewater sulfate above a gas hydrate-bearing seep. Characteristic minor isotopic fractionations, even when major isotopic fractionations are similar in magnitude, help to quantify the contributions of different microbial processes to the overall sulfur cycling in the system. Down to sediment depths of 1.5 to 4 meters, the δ34S values of porewater sulfate generally increased in association with a decrease in sulfate concentrations as would be expected for active sulfate reduction. Of greater interest, covariance between the δ34S values and measured minor isotopic fractionation suggests sulfide reoxidation and sulfur disproportionation are important components of the local sulfur cycle. We hypothesize that sulfide reoxidation is coupled to redox processes involving Fe(III) and Mn(IV) reduction and that the reoxidized forms of sulfur are available for additional methane oxidation. Recognizing that sulfate reduction is only one of several microbial processes controlling sulfate profiles challenges current paradigms for interpreting sulfate profiles and may alter our understanding of methane oxidation at gas hydrate-bearing seeps.

  13. Active fault segments as potential earthquake sources: Inferences from integrated geophysical mapping of the Magadi fault system, southern Kenya Rift

    NASA Astrophysics Data System (ADS)

    Kuria, Z. N.; Woldai, T.; van der Meer, F. D.; Barongo, J. O.

    2010-06-01

    uplifted, heavily fractured and deformed basin to the north (highly disturbed magnetic signatures) characteristic of on going active rifting; and a refined architecture of the asymmetry graben to the south with an intrarift horst, whose western graben is 4 km deep and eastern graben is much deeper (9 km), with a zone of significant break in magnetic signatures at that depth, interpreted as source of the hot springs south of Lake Magadi (a location confirmed near surface by ground magnetic and resistivity data sets). The magnetic sources to the north are shallow at 15 km depth compared to 22 km to the south. The loss of magnetism to the north is probably due to increased heat as a result of magmatic intrusion supporting active rifting model. Conclusively, the integrated approach employed in this research confirms that fault system delineated to the north is actively deforming under E-W normal extension and is a potential earthquake source probably related to magmatic intrusion, while the presence of fluids within the south fault zone reduce intensity of faulting activity and explains lack of earthquakes in a continental rift setting.

  14. Long Term Variability of the Canary Current Upwelling System inferred from Fine Scale Analysis of Satellite-derived SST

    NASA Astrophysics Data System (ADS)

    Relvas, P.; Luis, J. M.; Silva, P. L.; Santos, A. M.

    2011-12-01

    Satellite-derived sea surface temperature (SST) trends are built at the pixel scale to investigate long term changes in oceanic patterns. We consider that the SST time-series already available is long enough to attempt the analysis at the decadal scale. The analysis extends from 1982 to 2009 and is applied to the eastern boundary of the North Atlantic, from 10 to 45 N extending until 30 W, covering the Canary Current Upwelling System. Monthly mean SST data from the Advanced Very High Resolution Radiometer (AVHRR) on board NOAA series satellites, with a spatial resolution of 4x4 km, were provided by the NASA Physical Oceanography Distributed Active Archive Center (PO.DAAC) at the Jet Propulsion Laboratory. SST estimates are derived from the Pathfinder Version 5 algorithms. Whenever possible the time series are limited to the night-time passes to avoid any solar heating effect. Only high quality SST values with a flag assignment of 6 and 7 (Kilpatrick et al., 2001) are used. Using only the highest quality values creates a data processing problem that is posed by the voids left on the temperature grids on the node positions corresponding to the rejected values. The case is further complicated by the fact that the voids, originated by unfavourable weather conditions of strong cloud cover and coastal fogs, most intense during strong upwelling events, are located on variable positions on the grids depending on the month the grid refers to. This is particular evident on the winter months where large areas of the ocean did not have any reliable measure, even on a monthly average. We apply several procedures to fill these data gaps that guarantee that annual and seasonal averages are not biased towards summer temperatures. To investigate the spatial variability of the long term SST trend a robust linear fit was applied to each individual pixel, crossing along the time the same 4x4 km pixel in all the processed monthly mean AVHRR SST images from 1982 until 2009. Fields of

  15. Adaptive neuro-fuzzy inference systems with k-fold cross-validation for energy expenditure predictions based on heart rate.

    PubMed

    Kolus, Ahmet; Imbeau, Daniel; Dubé, Philippe-Antoine; Dubeau, Denise

    2015-09-01

    This paper presents a new model based on adaptive neuro-fuzzy inference systems (ANFIS) to predict oxygen consumption (V˙O2) from easily measured variables. The ANFIS prediction model consists of three ANFIS modules for estimating the Flex-HR parameters. Each module was developed based on clustering a training set of data samples relevant to that module and then the ANFIS prediction model was tested against a validation data set. Fifty-eight participants performed the Meyer and Flenghi step-test, during which heart rate (HR) and V˙O2 were measured. Results indicated no significant difference between observed and estimated Flex-HR parameters and between measured and estimated V˙O2 in the overall HR range, and separately in different HR ranges. The ANFIS prediction model (MAE = 3 ml kg(-1) min(-1)) demonstrated better performance than Rennie et al.'s (MAE = 7 ml kg(-1) min(-1)) and Keytel et al.'s (MAE = 6 ml kg(-1) min(-1)) models, and comparable performance with the standard Flex-HR method (MAE = 2.3 ml kg(-1) min(-1)) throughout the HR range. The ANFIS model thus provides practitioners with a practical, cost- and time-efficient method for V˙O2 estimation without the need for individual calibration.

  16. Genetic algorithm-artificial neural network and adaptive neuro-fuzzy inference system modeling of antibacterial activity of annatto dye on Salmonella enteritidis.

    PubMed

    Yolmeh, Mahmoud; Habibi Najafi, Mohammad B; Salehi, Fakhreddin

    2014-01-01

    Annatto is commonly used as a coloring agent in the food industry and has antimicrobial and antioxidant properties. In this study, genetic algorithm-artificial neural network (GA-ANN) and adaptive neuro-fuzzy inference system (ANFIS) models were used to predict the effect of annatto dye on Salmonella enteritidis in mayonnaise. The GA-ANN and ANFIS were fed with 3 inputs of annatto dye concentration (0, 0.1, 0.2 and 0.4%), storage temperature (4 and 25°C) and storage time (1-20 days) for prediction of S. enteritidis population. Both models were trained with experimental data. The results showed that the annatto dye was able to reduce of S. enteritidis and its effect was stronger at 25°C than 4°C. The developed GA-ANN, which included 8 hidden neurons, could predict S. enteritidis population with correlation coefficient of 0.999. The overall agreement between ANFIS predictions and experimental data was also very good (r=0.998). Sensitivity analysis results showed that storage temperature was the most sensitive factor for prediction of S. enteritidis population. PMID:24566279

  17. Use of an adaptive neuro-fuzzy inference system to obtain the correspondence among balance, gait, and depression for Parkinson's disease

    NASA Astrophysics Data System (ADS)

    Woo, Youngkeun; Lee, Juwon; Hwang, Sujin; Hong, Cheol Pyo

    2013-03-01

    The purpose of this study was to investigate the associations between gait performance, postural stability, and depression in patients with Parkinson's disease (PD) by using an adaptive neuro-fuzzy inference system (ANFIS). Twenty-two idiopathic PD patients were assessed during outpatient physical therapy by using three clinical tests: the Berg balance scale (BBS), Dynamic gait index (DGI), and Geriatric depression scale (GDS). Scores were determined from clinical observation and patient interviews, and associations among gait performance, postural stability, and depression in this PD population were evaluated. The DGI showed significant positive correlation with the BBS scores, and negative correlation with the GDS score. We assessed the relationship between the BBS score and the DGI results by using a multiple regression analysis. In this case, the GDS score was not significantly associated with the DGI, but the BBS and DGI results were. Strikingly, the ANFIS-estimated value of the DGI, based on the BBS and the GDS scores, significantly correlated with the walking ability determined by using the DGI in patients with Parkinson's disease. These findings suggest that the ANFIS techniques effectively reflect and explain the multidirectional phenomena or conditions of gait performance in patients with PD.

  18. Adaptive neuro-fuzzy inference systems with k-fold cross-validation for energy expenditure predictions based on heart rate.

    PubMed

    Kolus, Ahmet; Imbeau, Daniel; Dubé, Philippe-Antoine; Dubeau, Denise

    2015-09-01

    This paper presents a new model based on adaptive neuro-fuzzy inference systems (ANFIS) to predict oxygen consumption (V˙O2) from easily measured variables. The ANFIS prediction model consists of three ANFIS modules for estimating the Flex-HR parameters. Each module was developed based on clustering a training set of data samples relevant to that module and then the ANFIS prediction model was tested against a validation data set. Fifty-eight participants performed the Meyer and Flenghi step-test, during which heart rate (HR) and V˙O2 were measured. Results indicated no significant difference between observed and estimated Flex-HR parameters and between measured and estimated V˙O2 in the overall HR range, and separately in different HR ranges. The ANFIS prediction model (MAE = 3 ml kg(-1) min(-1)) demonstrated better performance than Rennie et al.'s (MAE = 7 ml kg(-1) min(-1)) and Keytel et al.'s (MAE = 6 ml kg(-1) min(-1)) models, and comparable performance with the standard Flex-HR method (MAE = 2.3 ml kg(-1) min(-1)) throughout the HR range. The ANFIS model thus provides practitioners with a practical, cost- and time-efficient method for V˙O2 estimation without the need for individual calibration. PMID:25959320

  19. Bee algorithm and adaptive neuro-fuzzy inference system as tools for QSAR study toxicity of substituted benzenes to Tetrahymena pyriformis.

    PubMed

    Zarei, Kobra; Atabati, Morteza; Kor, Kamalodin

    2014-06-01

    A quantitative structure-activity relationship (QSAR) was developed to predict the toxicity of substituted benzenes to Tetrahymena pyriformis. A set of 1,497 zero- to three-dimensional descriptors were used for each molecule in the data set. A major problem of QSAR is the high dimensionality of the descriptor space; therefore, descriptor selection is one of the most important steps. In this paper, bee algorithm was used to select the best descriptors. Three descriptors were selected and used as inputs for adaptive neuro-fuzzy inference system (ANFIS). Then the model was corrected for unstable compounds (the compounds that can be ionized in the aqueous solutions or can easily metabolize under some conditions). Finally squared correlation coefficients were obtained as 0.8769, 0.8649 and 0.8301 for training, test and validation sets, respectively. The results showed bee-ANFIS can be used as a powerful model for prediction of toxicity of substituted benzenes to T. pyriformis. PMID:24638918

  20. An exploratory investigation of an adaptive neuro fuzzy inference system (ANFIS) for estimating hydrometeors from TRMM/TMI in synergy with TRMM/PR

    NASA Astrophysics Data System (ADS)

    Islam, Tanvir; Srivastava, Prashant K.; Rico-Ramirez, Miguel A.; Dai, Qiang; Han, Dawei; Gupta, Manika

    2014-08-01

    The authors have investigated an adaptive neuro fuzzy inference system (ANFIS) for the estimation of hydrometeors from the TRMM microwave imager (TMI). The proposed algorithm, named as Hydro-Rain algorithm, is developed in synergy with the TRMM precipitation radar (PR) observed hydrometeor information. The method retrieves rain rates by exploiting the synergistic relations between the TMI and PR observations in twofold steps. First, the fundamental hydrometeor parameters, liquid water path (LWP) and ice water path (IWP), are estimated from the TMI brightness temperatures. Next, the rain rates are estimated from the retrieved hydrometeor parameters (LWP and IWP). A comparison of the hydrometeor retrievals by the Hydro-Rain algorithm is done with the TRMM PR 2A25 and GPROF 2A12 algorithms. The results reveal that the Hydro-Rain algorithm has good skills in estimating hydrometeor paths LWP and IWP, as well as surface rain rate. An examination of the Hydro-Rain algorithm is also conducted on a super typhoon case, in which the Hydro-Rain has shown very good performance in reproducing the typhoon field. Nevertheless, the passive microwave based estimate of hydrometeors appears to suffer in high rain rate regimes, and as the rain rate increases, the discrepancies with hydrometeor estimates tend to increase as well.

  1. Design of an expert system based on neuro-fuzzy inference analyzer for on-line microstructural characterization using magnetic NDT method

    NASA Astrophysics Data System (ADS)

    Ghanei, S.; Vafaeenezhad, H.; Kashefi, M.; Eivani, A. R.; Mazinani, M.

    2015-04-01

    Tracing microstructural evolution has a significant importance and priority in manufacturing lines of dual-phase steels. In this paper, an artificial intelligence method is presented for on-line microstructural characterization of dual-phase steels. A new method for microstructure characterization based on the theory of magnetic Barkhausen noise nondestructive testing method is introduced using adaptive neuro-fuzzy inference system (ANFIS). In order to predict the accurate martensite volume fraction of dual-phase steels while eliminating the effect and interference of frequency on the magnetic Barkhausen noise outputs, the magnetic responses were fed into the ANFIS structure in terms of position, height and width of the Barkhausen profiles. The results showed that ANFIS approach has the potential to detect and characterize microstructural evolution while the considerable effect of the frequency on magnetic outputs is overlooked. In fact implementing multiple outputs simultaneously enables ANFIS to approach to the accurate results using only height, position and width of the magnetic Barkhausen noise peaks without knowing the value of the used frequency.

  2. On the criticality of inferred models

    NASA Astrophysics Data System (ADS)

    Mastromatteo, Iacopo; Marsili, Matteo

    2011-10-01

    Advanced inference techniques allow one to reconstruct a pattern of interaction from high dimensional data sets, from probing simultaneously thousands of units of extended systems—such as cells, neural tissues and financial markets. We focus here on the statistical properties of inferred models and argue that inference procedures are likely to yield models which are close to singular values of parameters, akin to critical points in physics where phase transitions occur. These are points where the response of physical systems to external perturbations, as measured by the susceptibility, is very large and diverges in the limit of infinite size. We show that the reparameterization invariant metrics in the space of probability distributions of these models (the Fisher information) are directly related to the susceptibility of the inferred model. As a result, distinguishable models tend to accumulate close to critical points, where the susceptibility diverges in infinite systems. This region is the one where the estimate of inferred parameters is most stable. In order to illustrate these points, we discuss inference of interacting point processes with application to financial data and show that sensible choices of observation time scales naturally yield models which are close to criticality.

  3. Effects of parasite pressure on parasite mortality and reproductive output in a rodent-flea system: inferring host defense trade-offs.

    PubMed

    Warburton, Elizabeth M; Kam, Michael; Bar-Shira, Enav; Friedman, Aharon; Khokhlova, Irina S; Koren, Lee; Asfur, Mustafa; Geffen, Eli; Kiefer, Daniel; Krasnov, Boris R; Degen, A Allan

    2016-09-01

    Evaluating host resistance via parasite fitness helps place host-parasite relationships within evolutionary and ecological contexts; however, few studies consider both these processes simultaneously. We investigated how different levels of parasite pressure affect parasite mortality and reproductive success in relationship to host defense efforts, using the rodent Gerbillus nanus and the flea Xenopsylla conformis as a host-parasite system. Fifteen immune-naïve male rodents were infested with 20, 50, or 100 fleas for four weeks. During this time number of new imagoes produced per adult flea (our flea reproductive output metric), flea mortality, and change in circulating anti-flea immunoglobulin G (our measure of adaptive immune defense) were monitored. Three hypotheses guided this work: (1) increasing parasite pressure would heighten host defenses; (2) parasite mortality would increase and parasite reproductive output would decrease with increasing investment in host defense; and (3) hosts under high parasite pressure could invest in behavioral and/or immune responses. We predicted that at high infestation levels (a) parasite mortality would increase; (b) flea reproductive output per individual would decrease; and (c) host circulating anti-flea antibody levels would increase. The hypotheses were partially supported. Flea mortality significantly increased and flea reproductive output significantly decreased as flea pressure increased. Host adaptive immune defense did not significantly change with increasing flea pressure. Therefore, we inferred that investment in host behavioral defense, either alone or in combination with density-dependent effects, may be more efficient at increasing flea mortality and decreasing flea reproductive output than antibody production during initial infestation in this system. PMID:27130319

  4. Effects of parasite pressure on parasite mortality and reproductive output in a rodent-flea system: inferring host defense trade-offs.

    PubMed

    Warburton, Elizabeth M; Kam, Michael; Bar-Shira, Enav; Friedman, Aharon; Khokhlova, Irina S; Koren, Lee; Asfur, Mustafa; Geffen, Eli; Kiefer, Daniel; Krasnov, Boris R; Degen, A Allan

    2016-09-01

    Evaluating host resistance via parasite fitness helps place host-parasite relationships within evolutionary and ecological contexts; however, few studies consider both these processes simultaneously. We investigated how different levels of parasite pressure affect parasite mortality and reproductive success in relationship to host defense efforts, using the rodent Gerbillus nanus and the flea Xenopsylla conformis as a host-parasite system. Fifteen immune-naïve male rodents were infested with 20, 50, or 100 fleas for four weeks. During this time number of new imagoes produced per adult flea (our flea reproductive output metric), flea mortality, and change in circulating anti-flea immunoglobulin G (our measure of adaptive immune defense) were monitored. Three hypotheses guided this work: (1) increasing parasite pressure would heighten host defenses; (2) parasite mortality would increase and parasite reproductive output would decrease with increasing investment in host defense; and (3) hosts under high parasite pressure could invest in behavioral and/or immune responses. We predicted that at high infestation levels (a) parasite mortality would increase; (b) flea reproductive output per individual would decrease; and (c) host circulating anti-flea antibody levels would increase. The hypotheses were partially supported. Flea mortality significantly increased and flea reproductive output significantly decreased as flea pressure increased. Host adaptive immune defense did not significantly change with increasing flea pressure. Therefore, we inferred that investment in host behavioral defense, either alone or in combination with density-dependent effects, may be more efficient at increasing flea mortality and decreasing flea reproductive output than antibody production during initial infestation in this system.

  5. Adaptive neuro-fuzzy inference system model for adsorption of 1,3,4-thiadiazole-2,5-dithiol onto gold nanoparticales-activated carbon

    NASA Astrophysics Data System (ADS)

    Ghaedi, M.; Hosaininia, R.; Ghaedi, A. M.; Vafaei, A.; Taghizadeh, F.

    2014-10-01

    In this research, a novel adsorbent gold nanoparticle loaded on activated carbon (Au-NP-AC) was synthesized by ultrasound energy as a low cost routing protocol. Subsequently, this novel material characterization and identification followed by different techniques such as scanning electron microscope (SEM), Brunauer-Emmett-Teller (BET) and transmission electron microscopy (TEM) analysis. Unique properties such as high BET surface area (>1229.55 m2/g) and low pore size (<22.46 Å) and average particle size lower than 48.8 Å in addition to high reactive atoms and the presence of various functional groups make it possible for efficient removal of 1,3,4-thiadiazole-2,5-dithiol (TDDT). Generally, the influence of variables, including the amount of adsorbent, initial pollutant concentration, contact time on pollutants removal percentage has great effect on the removal percentage that their influence was optimized. The optimum parameters for adsorption of 1,3,4-thiadiazole-2, 5-dithiol onto gold nanoparticales-activated carbon were 0.02 g adsorbent mass, 10 mg L-1 initial 1,3,4-thiadiazole-2,5-dithiol concentration, 30 min contact time and pH 7. The Adaptive neuro-fuzzy inference system (ANFIS), and multiple linear regression (MLR) models, have been applied for prediction of removal of 1,3,4-thiadiazole-2,5-dithiol using gold nanoparticales-activated carbon (Au-NP-AC) in a batch study. The input data are included adsorbent dosage (g), contact time (min) and pollutant concentration (mg/l). The coefficient of determination (R2) and mean squared error (MSE) for the training data set of optimal ANFIS model were achieved to be 0.9951 and 0.00017, respectively. These results show that ANFIS model is capable of predicting adsorption of 1,3,4-thiadiazole-2,5-dithiol using Au-NP-AC with high accuracy in an easy, rapid and cost effective way.

  6. Water quality analysis in rivers with non-parametric probability distributions and fuzzy inference systems: application to the Cauca River, Colombia.

    PubMed

    Ocampo-Duque, William; Osorio, Carolina; Piamba, Christian; Schuhmacher, Marta; Domingo, José L

    2013-02-01

    The integration of water quality monitoring variables is essential in environmental decision making. Nowadays, advanced techniques to manage subjectivity, imprecision, uncertainty, vagueness, and variability are required in such complex evaluation process. We here propose a probabilistic fuzzy hybrid model to assess river water quality. Fuzzy logic reasoning has been used to compute a water quality integrative index. By applying a Monte Carlo technique, based on non-parametric probability distributions, the randomness of model inputs was estimated. Annual histograms of nine water quality variables were built with monitoring data systematically collected in the Colombian Cauca River, and probability density estimations using the kernel smoothing method were applied to fit data. Several years were assessed, and river sectors upstream and downstream the city of Santiago de Cali, a big city with basic wastewater treatment and high industrial activity, were analyzed. The probabilistic fuzzy water quality index was able to explain the reduction in water quality, as the river receives a larger number of agriculture, domestic, and industrial effluents. The results of the hybrid model were compared to traditional water quality indexes. The main advantage of the proposed method is that it considers flexible boundaries between the linguistic qualifiers used to define the water status, being the belongingness of water quality to the diverse output fuzzy sets or classes provided with percentiles and histograms, which allows classify better the real water condition. The results of this study show that fuzzy inference systems integrated to stochastic non-parametric techniques may be used as complementary tools in water quality indexing methodologies.

  7. Adaptive neuro-fuzzy inference system model for adsorption of 1,3,4-thiadiazole-2,5-dithiol onto gold nanoparticales-activated carbon.

    PubMed

    Ghaedi, M; Hosaininia, R; Ghaedi, A M; Vafaei, A; Taghizadeh, F

    2014-10-15

    In this research, a novel adsorbent gold nanoparticle loaded on activated carbon (Au-NP-AC) was synthesized by ultrasound energy as a low cost routing protocol. Subsequently, this novel material characterization and identification followed by different techniques such as scanning electron microscope(SEM), Brunauer-Emmett-Teller(BET) and transmission electron microscopy (TEM) analysis. Unique properties such as high BET surface area (>1229.55m(2)/g) and low pore size (<22.46Å) and average particle size lower than 48.8Å in addition to high reactive atoms and the presence of various functional groups make it possible for efficient removal of 1,3,4-thiadiazole-2,5-dithiol (TDDT). Generally, the influence of variables, including the amount of adsorbent, initial pollutant concentration, contact time on pollutants removal percentage has great effect on the removal percentage that their influence was optimized. The optimum parameters for adsorption of 1,3,4-thiadiazole-2, 5-dithiol onto gold nanoparticales-activated carbon were 0.02g adsorbent mass, 10mgL(-1) initial 1,3,4-thiadiazole-2,5-dithiol concentration, 30min contact time and pH 7. The Adaptive neuro-fuzzy inference system (ANFIS), and multiple linear regression (MLR) models, have been applied for prediction of removal of 1,3,4-thiadiazole-2,5-dithiol using gold nanoparticales-activated carbon (Au-NP-AC) in a batch study. The input data are included adsorbent dosage (g), contact time (min) and pollutant concentration (mg/l). The coefficient of determination (R(2)) and mean squared error (MSE) for the training data set of optimal ANFIS model were achieved to be 0.9951 and 0.00017, respectively. These results show that ANFIS model is capable of predicting adsorption of 1,3,4-thiadiazole-2,5-dithiol using Au-NP-AC with high accuracy in an easy, rapid and cost effective way. PMID:24858196

  8. Estimation of Flow Duration Curve for Ungauged Catchments using Adaptive Neuro-Fuzzy Inference System and Map Correlation Method: A Case Study from Turkey

    NASA Astrophysics Data System (ADS)

    Kentel, E.; Dogulu, N.

    2015-12-01

    In Turkey the experience and data required for a hydrological model setup is limited and very often not available. Moreover there are many ungauged catchments where there are also many planned projects aimed at utilization of water resources including development of existing hydropower potential. This situation makes runoff prediction at locations with lack of data and ungauged locations where small hydropower plants, reservoirs, etc. are planned an increasingly significant challenge and concern in the country. Flow duration curves have many practical applications in hydrology and integrated water resources management. Estimation of flood duration curve (FDC) at ungauged locations is essential, particularly for hydropower feasibility studies and selection of the installed capacities. In this study, we test and compare the performances of two methods for estimating FDCs in the Western Black Sea catchment, Turkey: (i) FDC based on Map Correlation Method (MCM) flow estimates. MCM is a recently proposed method (Archfield and Vogel, 2010) which uses geospatial information to estimate flow. Flow measurements of stream gauging stations nearby the ungauged location are the only data requirement for this method. This fact makes MCM very attractive for flow estimation in Turkey, (ii) Adaptive Neuro-Fuzzy Inference System (ANFIS) is a data-driven method which is used to relate FDC to a number of variables representing catchment and climate characteristics. However, it`s ease of implementation makes it very useful for practical purposes. Both methods use easily collectable data and are computationally efficient. Comparison of the results is realized based on two different measures: the root mean squared error (RMSE) and the Nash-Sutcliffe Efficiency (NSE) value. Ref: Archfield, S. A., and R. M. Vogel (2010), Map correlation method: Selection of a reference streamgage to estimate daily streamflow at ungaged catchments, Water Resour. Res., 46, W10513, doi:10.1029/2009WR008481.

  9. A comparative study of artificial neural network, adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountain region

    NASA Astrophysics Data System (ADS)

    He, Zhibin; Wen, Xiaohu; Liu, Hu; Du, Jun

    2014-02-01

    Data driven models are very useful for river flow forecasting when the underlying physical relationships are not fully understand, but it is not clear whether these data driven models still have a good performance in the small river basin of semiarid mountain regions where have complicated topography. In this study, the potential of three different data driven methods, artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and support vector machine (SVM) were used for forecasting river flow in the semiarid mountain region, northwestern China. The models analyzed different combinations of antecedent river flow values and the appropriate input vector has been selected based on the analysis of residuals. The performance of the ANN, ANFIS and SVM models in training and validation sets are compared with the observed data. The model which consists of three antecedent values of flow has been selected as the best fit model for river flow forecasting. To get more accurate evaluation of the results of ANN, ANFIS and SVM models, the four quantitative standard statistical performance evaluation measures, the coefficient of correlation (R), root mean squared error (RMSE), Nash-Sutcliffe efficiency coefficient (NS) and mean absolute relative error (MARE), were employed to evaluate the performances of various models developed. The results indicate that the performance obtained by ANN, ANFIS and SVM in terms of different evaluation criteria during the training and validation period does not vary substantially; the performance of the ANN, ANFIS and SVM models in river flow forecasting was satisfactory. A detailed comparison of the overall performance indicated that the SVM model performed better than ANN and ANFIS in river flow forecasting for the validation data sets. The results also suggest that ANN, ANFIS and SVM method can be successfully applied to establish river flow with complicated topography forecasting models in the semiarid mountain regions.

  10. ARPOP: an appetitive reward-based pseudo-outer-product neural fuzzy inference system inspired from the operant conditioning of feeding behavior in Aplysia.

    PubMed

    Cheu, Eng Yeow; Quek, Chai; Ng, See Kiong

    2012-02-01

    Appetitive operant conditioning in Aplysia for feeding behavior via the electrical stimulation of the esophageal nerve contingently reinforces each spontaneous bite during the feeding process. This results in the acquisition of operant memory by the contingently reinforced animals. Analysis of the cellular and molecular mechanisms of the feeding motor circuitry revealed that activity-dependent neuronal modulation occurs at the interneurons that mediate feeding behaviors. This provides evidence that interneurons are possible loci of plasticity and constitute another mechanism for memory storage in addition to memory storage attributed to activity-dependent synaptic plasticity. In this paper, an associative ambiguity correction-based neuro-fuzzy network, called appetitive reward-based pseudo-outer-product-compositional rule of inference [ARPOP-CRI(S)], is trained based on an appetitive reward-based learning algorithm which is biologically inspired by the appetitive operant conditioning of the feeding behavior in Aplysia. A variant of the Hebbian learning rule called Hebbian concomitant learning is proposed as the building block in the neuro-fuzzy network learning algorithm. The proposed algorithm possesses the distinguishing features of the sequential learning algorithm. In addition, the proposed ARPOP-CRI(S) neuro-fuzzy system encodes fuzzy knowledge in the form of linguistic rules that satisfies the semantic criteria for low-level fuzzy model interpretability. ARPOP-CRI(S) is evaluated and compared against other modeling techniques using benchmark time-series datasets. Experimental results are encouraging and show that ARPOP-CRI(S) is a viable modeling technique for time-variant problem domains.

  11. Comparative structure-toxicity relationship study of substituted benzenes to Tetrahymena pyriformis using shuffling-adaptive neuro fuzzy inference system and artificial neural networks.

    PubMed

    Jalali-Heravi, Mehdi; Kyani, Anahita

    2008-06-01

    The purpose of this study was to develop the structure-toxicity relationships for a large group of 268 substituted benzene to the ciliate Tetrahymena pyriformis using mechanistically interpretable descriptors. The shuffling-adaptive neuro fuzzy inference system (Shuffling-ANFIS) has been successfully applied to select the important factors affecting the toxicity of substituted benzenes to T. pyriformis. The results of the proposed model were compared with the model of linear-free energy response surface and also the principal component analysis Bayesian-regularized neural network (PCA-BRANN) trained using the same data. The presented model shows a better statistical parameter in comparison with the previous models. The results of the model are promising and descriptive. Five descriptors of octanol-water partition coefficient (logP), bond information content (BIC0), number of R-CX-R (C-026), eigenvalue sum from Z weighted distance matrix (SEigZ) and fragment based polar surface area (PSA) selected by Shuffling-ANFIS reveal the role of hydrophobicity, electronic and steric interactions in the mechanism of toxic action. Sequential zeroing of weights (SZW) as a sensitivity analysis method revealed that the hydrophobicity and electronic interactions play a major role in toxicity of these compounds. Satisfactory results (q(2)=0.828 and RMSE=0.348) in comparison with the previous works indicate that the Shuffling-ANFIS-ANN technique is able to model a diverse chemical class with more than one mechanism of toxicity by using simple and interpretable descriptors. Shuffling-ANFIS can be used as powerful feature selection technique, because its application in prediction of toxicity potency results in good statistical and interpretable physiochemical parameters. PMID:18499226

  12. Adaptive neuro-fuzzy inference system (ANFIS) to predict CI engine parameters fueled with nano-particles additive to diesel fuel

    NASA Astrophysics Data System (ADS)

    Ghanbari, M.; Najafi, G.; Ghobadian, B.; Mamat, R.; Noor, M. M.; Moosavian, A.

    2015-12-01

    This paper studies the use of adaptive neuro-fuzzy inference system (ANFIS) to predict the performance parameters and exhaust emissions of a diesel engine operating on nanodiesel blended fuels. In order to predict the engine parameters, the whole experimental data were randomly divided into training and testing data. For ANFIS modelling, Gaussian curve membership function (gaussmf) and 200 training epochs (iteration) were found to be optimum choices for training process. The results demonstrate that ANFIS is capable of predicting the diesel engine performance and emissions. In the experimental step, Carbon nano tubes (CNT) (40, 80 and 120 ppm) and nano silver particles (40, 80 and 120 ppm) with nanostructure were prepared and added as additive to the diesel fuel. Six cylinders, four-stroke diesel engine was fuelled with these new blended fuels and operated at different engine speeds. Experimental test results indicated the fact that adding nano particles to diesel fuel, increased diesel engine power and torque output. For nano-diesel it was found that the brake specific fuel consumption (bsfc) was decreased compared to the net diesel fuel. The results proved that with increase of nano particles concentrations (from 40 ppm to 120 ppm) in diesel fuel, CO2 emission increased. CO emission in diesel fuel with nano-particles was lower significantly compared to pure diesel fuel. UHC emission with silver nano-diesel blended fuel decreased while with fuels that contains CNT nano particles increased. The trend of NOx emission was inverse compared to the UHC emission. With adding nano particles to the blended fuels, NOx increased compared to the net diesel fuel. The tests revealed that silver & CNT nano particles can be used as additive in diesel fuel to improve combustion of the fuel and reduce the exhaust emissions significantly.

  13. Towards Context Sensitive Information Inference.

    ERIC Educational Resources Information Center

    Song, D.; Bruza, P. D.

    2003-01-01

    Discusses information inference from a psychologistic stance and proposes an information inference mechanism that makes inferences via computations of information flow through an approximation of a conceptual space. Highlights include cognitive economics of information processing; context sensitivity; and query models for information retrieval.…

  14. Multimodel inference and adaptive management

    USGS Publications Warehouse

    Rehme, S.E.; Powell, L.A.; Allen, C.R.

    2011-01-01

    Ecology is an inherently complex science coping with correlated variables, nonlinear interactions and multiple scales of pattern and process, making it difficult for experiments to result in clear, strong inference. Natural resource managers, policy makers, and stakeholders rely on science to provide timely and accurate management recommendations. However, the time necessary to untangle the complexities of interactions within ecosystems is often far greater than the time available to make management decisions. One method of coping with this problem is multimodel inference. Multimodel inference assesses uncertainty by calculating likelihoods among multiple competing hypotheses, but multimodel inference results are often equivocal. Despite this, there may be pressure for ecologists to provide management recommendations regardless of the strength of their study’s inference. We reviewed papers in the Journal of Wildlife Management (JWM) and the journal Conservation Biology (CB) to quantify the prevalence of multimodel inference approaches, the resulting inference (weak versus strong), and how authors dealt with the uncertainty. Thirty-eight percent and 14%, respectively, of articles in the JWM and CB used multimodel inference approaches. Strong inference was rarely observed, with only 7% of JWM and 20% of CB articles resulting in strong inference. We found the majority of weak inference papers in both journals (59%) gave specific management recommendations. Model selection uncertainty was ignored in most recommendations for management. We suggest that adaptive management is an ideal method to resolve uncertainty when research results in weak inference.

  15. Decision generation tools and Bayesian inference

    NASA Astrophysics Data System (ADS)

    Jannson, Tomasz; Wang, Wenjian; Forrester, Thomas; Kostrzewski, Andrew; Veeris, Christian; Nielsen, Thomas

    2014-05-01

    Digital Decision Generation (DDG) tools are important software sub-systems of Command and Control (C2) systems and technologies. In this paper, we present a special type of DDGs based on Bayesian Inference, related to adverse (hostile) networks, including such important applications as terrorism-related networks and organized crime ones.

  16. Modeling of stage-discharge relationship for Gharraf River, southern Iraq using backpropagation artificial neural networks, M5 decision trees, and Takagi-Sugeno inference system technique: a comparative study

    NASA Astrophysics Data System (ADS)

    Al-Abadi, Alaa M.

    2014-12-01

    The potential of using three different data-driven techniques namely, multilayer perceptron with backpropagation artificial neural network (MLP), M5 decision tree model, and Takagi-Sugeno (TS) inference system for mimic stage-discharge relationship at Gharraf River system, southern Iraq has been investigated and discussed in this study. The study used the available stage and discharge data for predicting discharge using different combinations of stage, antecedent stages, and antecedent discharge values. The models' results were compared using root mean squared error (RMSE) and coefficient of determination (R 2) error statistics. The results of the comparison in testing stage reveal that M5 and Takagi-Sugeno techniques have certain advantages for setting up stage-discharge than multilayer perceptron artificial neural network. Although the performance of TS inference system was very close to that for M5 model in terms of R 2, the M5 method has the lowest RMSE (8.10 m3/s). The study implies that both M5 and TS inference systems are promising tool for identifying stage-discharge relationship in the study area.

  17. Validate High Stakes Inferences by Designing Good Experiments, Not Audit Items: A Comment on "Self-Monitoring Assessments Educational Accountability Systems"

    ERIC Educational Resources Information Center

    Briggs, Derek C.

    2010-01-01

    The use of large-scale assessments for making high stakes inferences about students and the schools in which they are situated is premised on the assumption that tests are sensitive to good instruction. An increase in the quality of classroom instruction should cause, on the average, an increase in test scores. In work with a number of colleagues…

  18. Visual Inference Programming

    NASA Technical Reports Server (NTRS)

    Wheeler, Kevin; Timucin, Dogan; Rabbette, Maura; Curry, Charles; Allan, Mark; Lvov, Nikolay; Clanton, Sam; Pilewskie, Peter

    2002-01-01

    The goal of visual inference programming is to develop a software framework data analysis and to provide machine learning algorithms for inter-active data exploration and visualization. The topics include: 1) Intelligent Data Understanding (IDU) framework; 2) Challenge problems; 3) What's new here; 4) Framework features; 5) Wiring diagram; 6) Generated script; 7) Results of script; 8) Initial algorithms; 9) Independent Component Analysis for instrument diagnosis; 10) Output sensory mapping virtual joystick; 11) Output sensory mapping typing; 12) Closed-loop feedback mu-rhythm control; 13) Closed-loop training; 14) Data sources; and 15) Algorithms. This paper is in viewgraph form.

  19. Graphical inference for Infovis.

    PubMed

    Wickham, Hadley; Cook, Dianne; Hofmann, Heike; Buja, Andreas

    2010-01-01

    How do we know if what we see is really there? When visualizing data, how do we avoid falling into the trap of apophenia where we see patterns in random noise? Traditionally, infovis has been concerned with discovering new relationships, and statistics with preventing spurious relationships from being reported. We pull these opposing poles closer with two new techniques for rigorous statistical inference of visual discoveries. The "Rorschach" helps the analyst calibrate their understanding of uncertainty and "line-up" provides a protocol for assessing the significance of visual discoveries, protecting against the discovery of spurious structure.

  20. SYMBOLIC INFERENCE OF XENOBIOTIC METABOLISM

    PubMed Central

    MCSHAN, D.C.; UPDADHAYAYA, M.; SHAH, I.

    2009-01-01

    We present a new symbolic computational approach to elucidate the biochemical networks of living systems de novo and we apply it to an important biomedical problem: xenobiotic metabolism. A crucial issue in analyzing and modeling a living organism is understanding its biochemical network beyond what is already known. Our objective is to use the available metabolic information in a representational framework that enables the inference of novel biochemical knowledge and whose results can be validated experimentally. We describe a symbolic computational approach consisting of two parts. First, biotransformation rules are inferred from the molecular graphs of compounds in enzyme-catalyzed reactions. Second, these rules are recursively applied to different compounds to generate novel metabolic networks, containing new biotransformations and new metabolites. Using data for 456 generic reactions and 825 generic compounds from KEGG we were able to extract 110 biotransformation rules, which generalize a subset of known biocatalytic functions. We tested our approach by applying these rules to ethanol, a common substance of abuse and to furfuryl alcohol, a xenobiotic organic solvent, which is absent in metabolic databases. In both cases our predictions on the fate of ethanol and furfuryl alcohol are consistent with the literature on the metabolism of these compounds. PMID:14992532

  1. Quantum Inference on Bayesian Networks

    NASA Astrophysics Data System (ADS)

    Yoder, Theodore; Low, Guang Hao; Chuang, Isaac

    2014-03-01

    Because quantum physics is naturally probabilistic, it seems reasonable to expect physical systems to describe probabilities and their evolution in a natural fashion. Here, we use quantum computation to speedup sampling from a graphical probability model, the Bayesian network. A specialization of this sampling problem is approximate Bayesian inference, where the distribution on query variables is sampled given the values e of evidence variables. Inference is a key part of modern machine learning and artificial intelligence tasks, but is known to be NP-hard. Classically, a single unbiased sample is obtained from a Bayesian network on n variables with at most m parents per node in time (nmP(e) - 1 / 2) , depending critically on P(e) , the probability the evidence might occur in the first place. However, by implementing a quantum version of rejection sampling, we obtain a square-root speedup, taking (n2m P(e) -1/2) time per sample. The speedup is the result of amplitude amplification, which is proving to be broadly applicable in sampling and machine learning tasks. In particular, we provide an explicit and efficient circuit construction that implements the algorithm without the need for oracle access.

  2. Circular inferences in schizophrenia.

    PubMed

    Jardri, Renaud; Denève, Sophie

    2013-11-01

    A considerable number of recent experimental and computational studies suggest that subtle impairments of excitatory to inhibitory balance or regulation are involved in many neurological and psychiatric conditions. The current paper aims to relate, specifically and quantitatively, excitatory to inhibitory imbalance with psychotic symptoms in schizophrenia. Considering that the brain constructs hierarchical causal models of the external world, we show that the failure to maintain the excitatory to inhibitory balance results in hallucinations as well as in the formation and subsequent consolidation of delusional beliefs. Indeed, the consequence of excitatory to inhibitory imbalance in a hierarchical neural network is equated to a pathological form of causal inference called 'circular belief propagation'. In circular belief propagation, bottom-up sensory information and top-down predictions are reverberated, i.e. prior beliefs are misinterpreted as sensory observations and vice versa. As a result, these predictions are counted multiple times. Circular inference explains the emergence of erroneous percepts, the patient's overconfidence when facing probabilistic choices, the learning of 'unshakable' causal relationships between unrelated events and a paradoxical immunity to perceptual illusions, which are all known to be associated with schizophrenia. PMID:24065721

  3. Inferring horizontal gene transfer.

    PubMed

    Ravenhall, Matt; Škunca, Nives; Lassalle, Florent; Dessimoz, Christophe

    2015-05-01

    Horizontal or Lateral Gene Transfer (HGT or LGT) is the transmission of portions of genomic DNA between organisms through a process decoupled from vertical inheritance. In the presence of HGT events, different fragments of the genome are the result of different evolutionary histories. This can therefore complicate the investigations of evolutionary relatedness of lineages and species. Also, as HGT can bring into genomes radically different genotypes from distant lineages, or even new genes bearing new functions, it is a major source of phenotypic innovation and a mechanism of niche adaptation. For example, of particular relevance to human health is the lateral transfer of antibiotic resistance and pathogenicity determinants, leading to the emergence of pathogenic lineages. Computational identification of HGT events relies upon the investigation of sequence composition or evolutionary history of genes. Sequence composition-based ("parametric") methods search for deviations from the genomic average, whereas evolutionary history-based ("phylogenetic") approaches identify genes whose evolutionary history significantly differs from that of the host species. The evaluation and benchmarking of HGT inference methods typically rely upon simulated genomes, for which the true history is known. On real data, different methods tend to infer different HGT events, and as a result it can be difficult to ascertain all but simple and clear-cut HGT events. PMID:26020646

  4. Moment inference from tomograms

    USGS Publications Warehouse

    Day-Lewis, F. D.; Chen, Y.; Singha, K.

    2007-01-01

    Time-lapse geophysical tomography can provide valuable qualitative insights into hydrologic transport phenomena associated with aquifer dynamics, tracer experiments, and engineered remediation. Increasingly, tomograms are used to infer the spatial and/or temporal moments of solute plumes; these moments provide quantitative information about transport processes (e.g., advection, dispersion, and rate-limited mass transfer) and controlling parameters (e.g., permeability, dispersivity, and rate coefficients). The reliability of moments calculated from tomograms is, however, poorly understood because classic approaches to image appraisal (e.g., the model resolution matrix) are not directly applicable to moment inference. Here, we present a semi-analytical approach to construct a moment resolution matrix based on (1) the classic model resolution matrix and (2) image reconstruction from orthogonal moments. Numerical results for radar and electrical-resistivity imaging of solute plumes demonstrate that moment values calculated from tomograms depend strongly on plume location within the tomogram, survey geometry, regularization criteria, and measurement error. Copyright 2007 by the American Geophysical Union.

  5. Circular inferences in schizophrenia.

    PubMed

    Jardri, Renaud; Denève, Sophie

    2013-11-01

    A considerable number of recent experimental and computational studies suggest that subtle impairments of excitatory to inhibitory balance or regulation are involved in many neurological and psychiatric conditions. The current paper aims to relate, specifically and quantitatively, excitatory to inhibitory imbalance with psychotic symptoms in schizophrenia. Considering that the brain constructs hierarchical causal models of the external world, we show that the failure to maintain the excitatory to inhibitory balance results in hallucinations as well as in the formation and subsequent consolidation of delusional beliefs. Indeed, the consequence of excitatory to inhibitory imbalance in a hierarchical neural network is equated to a pathological form of causal inference called 'circular belief propagation'. In circular belief propagation, bottom-up sensory information and top-down predictions are reverberated, i.e. prior beliefs are misinterpreted as sensory observations and vice versa. As a result, these predictions are counted multiple times. Circular inference explains the emergence of erroneous percepts, the patient's overconfidence when facing probabilistic choices, the learning of 'unshakable' causal relationships between unrelated events and a paradoxical immunity to perceptual illusions, which are all known to be associated with schizophrenia.

  6. Inferring Horizontal Gene Transfer

    PubMed Central

    Lassalle, Florent; Dessimoz, Christophe

    2015-01-01

    Horizontal or Lateral Gene Transfer (HGT or LGT) is the transmission of portions of genomic DNA between organisms through a process decoupled from vertical inheritance. In the presence of HGT events, different fragments of the genome are the result of different evolutionary histories. This can therefore complicate the investigations of evolutionary relatedness of lineages and species. Also, as HGT can bring into genomes radically different genotypes from distant lineages, or even new genes bearing new functions, it is a major source of phenotypic innovation and a mechanism of niche adaptation. For example, of particular relevance to human health is the lateral transfer of antibiotic resistance and pathogenicity determinants, leading to the emergence of pathogenic lineages [1]. Computational identification of HGT events relies upon the investigation of sequence composition or evolutionary history of genes. Sequence composition-based ("parametric") methods search for deviations from the genomic average, whereas evolutionary history-based ("phylogenetic") approaches identify genes whose evolutionary history significantly differs from that of the host species. The evaluation and benchmarking of HGT inference methods typically rely upon simulated genomes, for which the true history is known. On real data, different methods tend to infer different HGT events, and as a result it can be difficult to ascertain all but simple and clear-cut HGT events. PMID:26020646

  7. Reliability of the Granger causality inference

    NASA Astrophysics Data System (ADS)

    Zhou, Douglas; Zhang, Yaoyu; Xiao, Yanyang; Cai, David

    2014-04-01

    How to characterize information flows in physical, biological, and social systems remains a major theoretical challenge. Granger causality (GC) analysis has been widely used to investigate information flow through causal interactions. We address one of the central questions in GC analysis, that is, the reliability of the GC evaluation and its implications for the causal structures extracted by this analysis. Our work reveals that the manner in which a continuous dynamical process is projected or coarse-grained to a discrete process has a profound impact on the reliability of the GC inference, and different sampling may potentially yield completely opposite inferences. This inference hazard is present for both linear and nonlinear processes. We emphasize that there is a hazard of reaching incorrect conclusions about network topologies, even including statistical (such as small-world or scale-free) properties of the networks, when GC analysis is blindly applied to infer the network topology. We demonstrate this using a small-world network for which a drastic loss of small-world attributes occurs in the reconstructed network using the standard GC approach. We further show how to resolve the paradox that the GC analysis seemingly becomes less reliable when more information is incorporated using finer and finer sampling. Finally, we present strategies to overcome these inference artifacts in order to obtain a reliable GC result.

  8. Gene-network inference by message passing

    NASA Astrophysics Data System (ADS)

    Braunstein, A.; Pagnani, A.; Weigt, M.; Zecchina, R.

    2008-01-01

    The inference of gene-regulatory processes from gene-expression data belongs to the major challenges of computational systems biology. Here we address the problem from a statistical-physics perspective and develop a message-passing algorithm which is able to infer sparse, directed and combinatorial regulatory mechanisms. Using the replica technique, the algorithmic performance can be characterized analytically for artificially generated data. The algorithm is applied to genome-wide expression data of baker's yeast under various environmental conditions. We find clear cases of combinatorial control, and enrichment in common functional annotations of regulated genes and their regulators.

  9. Bayesian inference in geomagnetism

    NASA Technical Reports Server (NTRS)

    Backus, George E.

    1988-01-01

    The inverse problem in empirical geomagnetic modeling is investigated, with critical examination of recently published studies. Particular attention is given to the use of Bayesian inference (BI) to select the damping parameter lambda in the uniqueness portion of the inverse problem. The mathematical bases of BI and stochastic inversion are explored, with consideration of bound-softening problems and resolution in linear Gaussian BI. The problem of estimating the radial magnetic field B(r) at the earth core-mantle boundary from surface and satellite measurements is then analyzed in detail, with specific attention to the selection of lambda in the studies of Gubbins (1983) and Gubbins and Bloxham (1985). It is argued that the selection method is inappropriate and leads to lambda values much larger than those that would result if a reasonable bound on the heat flow at the CMB were assumed.

  10. BIE: Bayesian Inference Engine

    NASA Astrophysics Data System (ADS)

    Weinberg, Martin D.

    2013-12-01

    The Bayesian Inference Engine (BIE) is an object-oriented library of tools written in C++ designed explicitly to enable Bayesian update and model comparison for astronomical problems. To facilitate "what if" exploration, BIE provides a command line interface (written with Bison and Flex) to run input scripts. The output of the code is a simulation of the Bayesian posterior distribution from which summary statistics e.g. by taking moments, or determine confidence intervals and so forth, can be determined. All of these quantities are fundamentally integrals and the Markov Chain approach produces variates heta distributed according to P( heta|D) so moments are trivially obtained by summing of the ensemble of variates.

  11. Dynamical inference of hidden biological populations

    NASA Astrophysics Data System (ADS)

    Luchinsky, D. G.; Smelyanskiy, V. N.; Millonas, M.; McClintock, P. V. E.

    2008-10-01

    Population fluctuations in a predator-prey system are analyzed for the case where the number of prey could be determined, subject to measurement noise, but the number of predators was unknown. The problem of how to infer the unmeasured predator dynamics, as well as the model parameters, is addressed. Two solutions are suggested. In the first of these, measurement noise and the dynamical noise in the equation for predator population are neglected; the problem is reduced to a one-dimensional case, and a Bayesian dynamical inference algorithm is employed to reconstruct the model parameters. In the second solution a full-scale Markov Chain Monte Carlo simulation is used to infer both the unknown predator trajectory, and also the model parameters, using the one-dimensional solution as an initial guess.

  12. Structural inference for uncertain networks

    NASA Astrophysics Data System (ADS)

    Martin, Travis; Ball, Brian; Newman, M. E. J.

    2016-01-01

    In the study of networked systems such as biological, technological, and social networks the available data are often uncertain. Rather than knowing the structure of a network exactly, we know the connections between nodes only with a certain probability. In this paper we develop methods for the analysis of such uncertain data, focusing particularly on the problem of community detection. We give a principled maximum-likelihood method for inferring community structure and demonstrate how the results can be used to make improved estimates of the true structure of the network. Using computer-generated benchmark networks we demonstrate that our methods are able to reconstruct known communities more accurately than previous approaches based on data thresholding. We also give an example application to the detection of communities in a protein-protein interaction network.

  13. Bayes factors and multimodel inference

    USGS Publications Warehouse

    Link, W.A.; Barker, R.J.; Thomson, David L.; Cooch, Evan G.; Conroy, Michael J.

    2009-01-01

    Multimodel inference has two main themes: model selection, and model averaging. Model averaging is a means of making inference conditional on a model set, rather than on a selected model, allowing formal recognition of the uncertainty associated with model choice. The Bayesian paradigm provides a natural framework for model averaging, and provides a context for evaluation of the commonly used AIC weights. We review Bayesian multimodel inference, noting the importance of Bayes factors. Noting the sensitivity of Bayes factors to the choice of priors on parameters, we define and propose nonpreferential priors as offering a reasonable standard for objective multimodel inference.

  14. Learning to Observe "and" Infer

    ERIC Educational Resources Information Center

    Hanuscin, Deborah L.; Park Rogers, Meredith A.

    2008-01-01

    Researchers describe the need for students to have multiple opportunities and social interaction to learn about the differences between observation and inference and their role in developing scientific explanations (Harlen 2001; Simpson 2000). Helping children develop their skills of observation and inference in science while emphasizing the…

  15. Feature Inference Learning and Eyetracking

    ERIC Educational Resources Information Center

    Rehder, Bob; Colner, Robert M.; Hoffman, Aaron B.

    2009-01-01

    Besides traditional supervised classification learning, people can learn categories by inferring the missing features of category members. It has been proposed that feature inference learning promotes learning a category's internal structure (e.g., its typical features and interfeature correlations) whereas classification promotes the learning of…

  16. Improving Inferences from Multiple Methods.

    ERIC Educational Resources Information Center

    Shotland, R. Lance; Mark, Melvin M.

    1987-01-01

    Multiple evaluation methods (MEMs) can cause an inferential challenge, although there are strategies to strengthen inferences. Practical and theoretical issues involved in the use by social scientists of MEMs, three potential problems in drawing inferences from MEMs, and short- and long-term strategies for alleviating these problems are outlined.…

  17. Causal Inference in Retrospective Studies.

    ERIC Educational Resources Information Center

    Holland, Paul W.; Rubin, Donald B.

    1988-01-01

    The problem of drawing causal inferences from retrospective case-controlled studies is considered. A model for causal inference in prospective studies is applied to retrospective studies. Limitations of case-controlled studies are formulated concerning relevant parameters that can be estimated in such studies. A coffee-drinking/myocardial…

  18. Causal Inference and Developmental Psychology

    ERIC Educational Resources Information Center

    Foster, E. Michael

    2010-01-01

    Causal inference is of central importance to developmental psychology. Many key questions in the field revolve around improving the lives of children and their families. These include identifying risk factors that if manipulated in some way would foster child development. Such a task inherently involves causal inference: One wants to know whether…

  19. Double jeopardy in inferring cognitive processes.

    PubMed

    Fific, Mario

    2014-01-01

    Inferences we make about underlying cognitive processes can be jeopardized in two ways due to problematic forms of aggregation. First, averaging across individuals is typically considered a very useful tool for removing random variability. The threat is that averaging across subjects leads to averaging across different cognitive strategies, thus harming our inferences. The second threat comes from the construction of inadequate research designs possessing a low diagnostic accuracy of cognitive processes. For that reason we introduced the systems factorial technology (SFT), which has primarily been designed to make inferences about underlying processing order (serial, parallel, coactive), stopping rule (terminating, exhaustive), and process dependency. SFT proposes that the minimal research design complexity to learn about n number of cognitive processes should be equal to 2 (n) . In addition, SFT proposes that (a) each cognitive process should be controlled by a separate experimental factor, and (b) The saliency levels of all factors should be combined in a full factorial design. In the current study, the author cross combined the levels of jeopardies in a 2 × 2 analysis, leading to four different analysis conditions. The results indicate a decline in the diagnostic accuracy of inferences made about cognitive processes due to the presence of each jeopardy in isolation and when combined. The results warrant the development of more individual subject analyses and the utilization of full-factorial (SFT) experimental designs.

  20. Inverse Ising inference with correlated samples

    NASA Astrophysics Data System (ADS)

    Obermayer, Benedikt; Levine, Erel

    2014-12-01

    Correlations between two variables of a high-dimensional system can be indicative of an underlying interaction, but can also result from indirect effects. Inverse Ising inference is a method to distinguish one from the other. Essentially, the parameters of the least constrained statistical model are learned from the observed correlations such that direct interactions can be separated from indirect correlations. Among many other applications, this approach has been helpful for protein structure prediction, because residues which interact in the 3D structure often show correlated substitutions in a multiple sequence alignment. In this context, samples used for inference are not independent but share an evolutionary history on a phylogenetic tree. Here, we discuss the effects of correlations between samples on global inference. Such correlations could arise due to phylogeny but also via other slow dynamical processes. We present a simple analytical model to address the resulting inference biases, and develop an exact method accounting for background correlations in alignment data by combining phylogenetic modeling with an adaptive cluster expansion algorithm. We find that popular reweighting schemes are only marginally effective at removing phylogenetic bias, suggest a rescaling strategy that yields better results, and provide evidence that our conclusions carry over to the frequently used mean-field approach to the inverse Ising problem.

  1. Double jeopardy in inferring cognitive processes

    PubMed Central

    Fific, Mario

    2014-01-01

    Inferences we make about underlying cognitive processes can be jeopardized in two ways due to problematic forms of aggregation. First, averaging across individuals is typically considered a very useful tool for removing random variability. The threat is that averaging across subjects leads to averaging across different cognitive strategies, thus harming our inferences. The second threat comes from the construction of inadequate research designs possessing a low diagnostic accuracy of cognitive processes. For that reason we introduced the systems factorial technology (SFT), which has primarily been designed to make inferences about underlying processing order (serial, parallel, coactive), stopping rule (terminating, exhaustive), and process dependency. SFT proposes that the minimal research design complexity to learn about n number of cognitive processes should be equal to 2n. In addition, SFT proposes that (a) each cognitive process should be controlled by a separate experimental factor, and (b) The saliency levels of all factors should be combined in a full factorial design. In the current study, the author cross combined the levels of jeopardies in a 2 × 2 analysis, leading to four different analysis conditions. The results indicate a decline in the diagnostic accuracy of inferences made about cognitive processes due to the presence of each jeopardy in isolation and when combined. The results warrant the development of more individual subject analyses and the utilization of full-factorial (SFT) experimental designs. PMID:25374545

  2. The empirical accuracy of uncertain inference models

    NASA Technical Reports Server (NTRS)

    Vaughan, David S.; Yadrick, Robert M.; Perrin, Bruce M.; Wise, Ben P.

    1987-01-01

    Uncertainty is a pervasive feature of the domains in which expert systems are designed to function. Research design to test uncertain inference methods for accuracy and robustness, in accordance with standard engineering practice is reviewed. Several studies were conducted to assess how well various methods perform on problems constructed so that correct answers are known, and to find out what underlying features of a problem cause strong or weak performance. For each method studied, situations were identified in which performance deteriorates dramatically. Over a broad range of problems, some well known methods do only about as well as a simple linear regression model, and often much worse than a simple independence probability model. The results indicate that some commercially available expert system shells should be used with caution, because the uncertain inference models that they implement can yield rather inaccurate results.

  3. Quantum-Like Representation of Non-Bayesian Inference

    NASA Astrophysics Data System (ADS)

    Asano, M.; Basieva, I.; Khrennikov, A.; Ohya, M.; Tanaka, Y.

    2013-01-01

    This research is related to the problem of "irrational decision making or inference" that have been discussed in cognitive psychology. There are some experimental studies, and these statistical data cannot be described by classical probability theory. The process of decision making generating these data cannot be reduced to the classical Bayesian inference. For this problem, a number of quantum-like coginitive models of decision making was proposed. Our previous work represented in a natural way the classical Bayesian inference in the frame work of quantum mechanics. By using this representation, in this paper, we try to discuss the non-Bayesian (irrational) inference that is biased by effects like the quantum interference. Further, we describe "psychological factor" disturbing "rationality" as an "environment" correlating with the "main system" of usual Bayesian inference.

  4. Computational statistics using the Bayesian Inference Engine

    NASA Astrophysics Data System (ADS)

    Weinberg, Martin D.

    2013-09-01

    This paper introduces the Bayesian Inference Engine (BIE), a general parallel, optimized software package for parameter inference and model selection. This package is motivated by the analysis needs of modern astronomical surveys and the need to organize and reuse expensive derived data. The BIE is the first platform for computational statistics designed explicitly to enable Bayesian update and model comparison for astronomical problems. Bayesian update is based on the representation of high-dimensional posterior distributions using metric-ball-tree based kernel density estimation. Among its algorithmic offerings, the BIE emphasizes hybrid tempered Markov chain Monte Carlo schemes that robustly sample multimodal posterior distributions in high-dimensional parameter spaces. Moreover, the BIE implements a full persistence or serialization system that stores the full byte-level image of the running inference and previously characterized posterior distributions for later use. Two new algorithms to compute the marginal likelihood from the posterior distribution, developed for and implemented in the BIE, enable model comparison for complex models and data sets. Finally, the BIE was designed to be a collaborative platform for applying Bayesian methodology to astronomy. It includes an extensible object-oriented and easily extended framework that implements every aspect of the Bayesian inference. By providing a variety of statistical algorithms for all phases of the inference problem, a scientist may explore a variety of approaches with a single model and data implementation. Additional technical details and download details are available from http://www.astro.umass.edu/bie. The BIE is distributed under the GNU General Public License.

  5. Bayesian Inference: with ecological applications

    USGS Publications Warehouse

    Link, William A.; Barker, Richard J.

    2010-01-01

    This text provides a mathematically rigorous yet accessible and engaging introduction to Bayesian inference with relevant examples that will be of interest to biologists working in the fields of ecology, wildlife management and environmental studies as well as students in advanced undergraduate statistics.. This text opens the door to Bayesian inference, taking advantage of modern computational efficiencies and easily accessible software to evaluate complex hierarchical models.

  6. Active inference, communication and hermeneutics.

    PubMed

    Friston, Karl J; Frith, Christopher D

    2015-07-01

    Hermeneutics refers to interpretation and translation of text (typically ancient scriptures) but also applies to verbal and non-verbal communication. In a psychological setting it nicely frames the problem of inferring the intended content of a communication. In this paper, we offer a solution to the problem of neural hermeneutics based upon active inference. In active inference, action fulfils predictions about how we will behave (e.g., predicting we will speak). Crucially, these predictions can be used to predict both self and others--during speaking and listening respectively. Active inference mandates the suppression of prediction errors by updating an internal model that generates predictions--both at fast timescales (through perceptual inference) and slower timescales (through perceptual learning). If two agents adopt the same model, then--in principle--they can predict each other and minimise their mutual prediction errors. Heuristically, this ensures they are singing from the same hymn sheet. This paper builds upon recent work on active inference and communication to illustrate perceptual learning using simulated birdsongs. Our focus here is the neural hermeneutics implicit in learning, where communication facilitates long-term changes in generative models that are trying to predict each other. In other words, communication induces perceptual learning and enables others to (literally) change our minds and vice versa.

  7. Active inference, communication and hermeneutics☆

    PubMed Central

    Friston, Karl J.; Frith, Christopher D.

    2015-01-01

    Hermeneutics refers to interpretation and translation of text (typically ancient scriptures) but also applies to verbal and non-verbal communication. In a psychological setting it nicely frames the problem of inferring the intended content of a communication. In this paper, we offer a solution to the problem of neural hermeneutics based upon active inference. In active inference, action fulfils predictions about how we will behave (e.g., predicting we will speak). Crucially, these predictions can be used to predict both self and others – during speaking and listening respectively. Active inference mandates the suppression of prediction errors by updating an internal model that generates predictions – both at fast timescales (through perceptual inference) and slower timescales (through perceptual learning). If two agents adopt the same model, then – in principle – they can predict each other and minimise their mutual prediction errors. Heuristically, this ensures they are singing from the same hymn sheet. This paper builds upon recent work on active inference and communication to illustrate perceptual learning using simulated birdsongs. Our focus here is the neural hermeneutics implicit in learning, where communication facilitates long-term changes in generative models that are trying to predict each other. In other words, communication induces perceptual learning and enables others to (literally) change our minds and vice versa. PMID:25957007

  8. Active inference, communication and hermeneutics.

    PubMed

    Friston, Karl J; Frith, Christopher D

    2015-07-01

    Hermeneutics refers to interpretation and translation of text (typically ancient scriptures) but also applies to verbal and non-verbal communication. In a psychological setting it nicely frames the problem of inferring the intended content of a communication. In this paper, we offer a solution to the problem of neural hermeneutics based upon active inference. In active inference, action fulfils predictions about how we will behave (e.g., predicting we will speak). Crucially, these predictions can be used to predict both self and others--during speaking and listening respectively. Active inference mandates the suppression of prediction errors by updating an internal model that generates predictions--both at fast timescales (through perceptual inference) and slower timescales (through perceptual learning). If two agents adopt the same model, then--in principle--they can predict each other and minimise their mutual prediction errors. Heuristically, this ensures they are singing from the same hymn sheet. This paper builds upon recent work on active inference and communication to illustrate perceptual learning using simulated birdsongs. Our focus here is the neural hermeneutics implicit in learning, where communication facilitates long-term changes in generative models that are trying to predict each other. In other words, communication induces perceptual learning and enables others to (literally) change our minds and vice versa. PMID:25957007

  9. Insights on the injection mechanisms inferred from AMS fabrics of sand injectites in a turbiditic system, the exemple of Bevon area of the SE Basin (France).

    NASA Astrophysics Data System (ADS)

    Robion, Philippe; Mehl, Caroline

    2016-04-01

    We propose to investigate the set up mechanisms of sands injection in the case of dykes injected in host marls of Aptian-Albian age in the Vocontian basin (SE France). Several models have been proposed for a downward injection of the dyke in the Bevons area and we guess that AMS fabric investigations can be used to infer the flow direction. 144 drill cores distributed on 14 sites were sampled, among which 8 sites in the injectites and 6 sites in the host rocks. The studied dykes are generally of a few decimeters thick and are setting up in both in vertical or oblique position with respect to the subhorizontal bedding of the host rocks. There were sampled from one side to the other in order to track the flow direction by identification of imbricated fabric. Magnetic mineralogy, i.e. unblocking temperature inferred from IRM 3 axes demagnetization, indicates that the ferromagnetics s.l. mineralogy is dominated by an assemblage of magnetite (unblocking temperature Tub=580°C) and pyrrhotite (Tub=325°C). Magnetic susceptibility is low, typical for siliciclastic rocks, ranging from 4x10-5 up to 1.7x10-4 SI. Degree of magnetic anisotropy is likely representative of AMS measurements in sedimentary rocks with weak values, below than 5 %. In marly host rocks magnetic mineralogy is dominated by pyrrhotite associated with magnetite and both the magnetic susceptibility and degree of anisotropy are slightly lower than for injectites. Regarding magnetic fabric axes distribution, despite some dispersion, the results show that minimum axes of AMS (K3) are parallel to the dyke plane, and maximum axes (K1) are roughly in horizontal position. In marly host rocks, the magnetic fabric is related to tectonic shortening. We interpret that the host rocks have recorded the regional tectonic imprint while the magnetic fabric of the injectites are related to early sedimentary processes. The mechanism of set up proposed to explain the magnetic fabric in the Bevon injectites is a step

  10. Optimal inference with suboptimal models: Addiction and active Bayesian inference

    PubMed Central

    Schwartenbeck, Philipp; FitzGerald, Thomas H.B.; Mathys, Christoph; Dolan, Ray; Wurst, Friedrich; Kronbichler, Martin; Friston, Karl

    2015-01-01

    When casting behaviour as active (Bayesian) inference, optimal inference is defined with respect to an agent’s beliefs – based on its generative model of the world. This contrasts with normative accounts of choice behaviour, in which optimal actions are considered in relation to the true structure of the environment – as opposed to the agent’s beliefs about worldly states (or the task). This distinction shifts an understanding of suboptimal or pathological behaviour away from aberrant inference as such, to understanding the prior beliefs of a subject that cause them to behave less ‘optimally’ than our prior beliefs suggest they should behave. Put simply, suboptimal or pathological behaviour does not speak against understanding behaviour in terms of (Bayes optimal) inference, but rather calls for a more refined understanding of the subject’s generative model upon which their (optimal) Bayesian inference is based. Here, we discuss this fundamental distinction and its implications for understanding optimality, bounded rationality and pathological (choice) behaviour. We illustrate our argument using addictive choice behaviour in a recently described ‘limited offer’ task. Our simulations of pathological choices and addictive behaviour also generate some clear hypotheses, which we hope to pursue in ongoing empirical work. PMID:25561321

  11. Physics of Inference

    NASA Astrophysics Data System (ADS)

    Toroczkai, Zoltan

    Jaynes's maximum entropy method provides a family of principled models that allow the prediction of a system's properties as constrained by empirical data (observables). However, their use is often hindered by the degeneracy problem characterized by spontaneous symmetry breaking, where predictions fail. Here we show that degeneracy appears when the corresponding density of states function is not log-concave, which is typically the consequence of nonlinear relationships between the constraining observables. We illustrate this phenomenon on several examples, including from complex networks, combinatorics and classical spin systems (e.g., Blume-Emery-Griffiths lattice-spin models). Exploiting these nonlinear relationships we then propose a solution to the degeneracy problem for a large class of systems via transformations that render the density of states function log-concave. The effectiveness of the method is demonstrated on real-world network data. Finally, we discuss the implications of these findings on the relationship between the geometrical properties of the density of states function and phase transitions in spin systems. Supported in part by Grant No. FA9550-12-1-0405 from AFOSR/DARPA and by Grant No. HDTRA 1-09-1-0039 from DTRA.

  12. Functional neuroanatomy of intuitive physical inference.

    PubMed

    Fischer, Jason; Mikhael, John G; Tenenbaum, Joshua B; Kanwisher, Nancy

    2016-08-23

    To engage with the world-to understand the scene in front of us, plan actions, and predict what will happen next-we must have an intuitive grasp of the world's physical structure and dynamics. How do the objects in front of us rest on and support each other, how much force would be required to move them, and how will they behave when they fall, roll, or collide? Despite the centrality of physical inferences in daily life, little is known about the brain mechanisms recruited to interpret the physical structure of a scene and predict how physical events will unfold. Here, in a series of fMRI experiments, we identified a set of cortical regions that are selectively engaged when people watch and predict the unfolding of physical events-a "physics engine" in the brain. These brain regions are selective to physical inferences relative to nonphysical but otherwise highly similar scenes and tasks. However, these regions are not exclusively engaged in physical inferences per se or, indeed, even in scene understanding; they overlap with the domain-general "multiple demand" system, especially the parts of that system involved in action planning and tool use, pointing to a close relationship between the cognitive and neural mechanisms involved in parsing the physical content of a scene and preparing an appropriate action. PMID:27503892

  13. Active inference and robot control: a case study

    PubMed Central

    Nizard, Ange; Friston, Karl; Pezzulo, Giovanni

    2016-01-01

    Active inference is a general framework for perception and action that is gaining prominence in computational and systems neuroscience but is less known outside these fields. Here, we discuss a proof-of-principle implementation of the active inference scheme for the control or the 7-DoF arm of a (simulated) PR2 robot. By manipulating visual and proprioceptive noise levels, we show under which conditions robot control under the active inference scheme is accurate. Besides accurate control, our analysis of the internal system dynamics (e.g. the dynamics of the hidden states that are inferred during the inference) sheds light on key aspects of the framework such as the quintessentially multimodal nature of control and the differential roles of proprioception and vision. In the discussion, we consider the potential importance of being able to implement active inference in robots. In particular, we briefly review the opportunities for modelling psychophysiological phenomena such as sensory attenuation and related failures of gain control, of the sort seen in Parkinson's disease. We also consider the fundamental difference between active inference and optimal control formulations, showing that in the former the heavy lifting shifts from solving a dynamical inverse problem to creating deep forward or generative models with dynamics, whose attracting sets prescribe desired behaviours. PMID:27683002

  14. Children's Category-Based Inferences Affect Classification

    ERIC Educational Resources Information Center

    Ross, Brian H.; Gelman, Susan A.; Rosengren, Karl S.

    2005-01-01

    Children learn many new categories and make inferences about these categories. Much work has examined how children make inferences on the basis of category knowledge. However, inferences may also affect what is learned about a category. Four experiments examine whether category-based inferences during category learning influence category knowledge…

  15. Causal inference from observational data.

    PubMed

    Listl, Stefan; Jürges, Hendrik; Watt, Richard G

    2016-10-01

    Randomized controlled trials have long been considered the 'gold standard' for causal inference in clinical research. In the absence of randomized experiments, identification of reliable intervention points to improve oral health is often perceived as a challenge. But other fields of science, such as social science, have always been challenged by ethical constraints to conducting randomized controlled trials. Methods have been established to make causal inference using observational data, and these methods are becoming increasingly relevant in clinical medicine, health policy and public health research. This study provides an overview of state-of-the-art methods specifically designed for causal inference in observational data, including difference-in-differences (DiD) analyses, instrumental variables (IV), regression discontinuity designs (RDD) and fixed-effects panel data analysis. The described methods may be particularly useful in dental research, not least because of the increasing availability of routinely collected administrative data and electronic health records ('big data'). PMID:27111146

  16. We infer light in space.

    PubMed

    Schirillo, James A

    2013-10-01

    In studies of lightness and color constancy, the terms lightness and brightness refer to the qualia corresponding to perceived surface reflectance and perceived luminance, respectively. However, what has rarely been considered is the fact that the volume of space containing surfaces appears neither empty, void, nor black, but filled with light. Helmholtz (1866/1962) came closest to describing this phenomenon when discussing inferred illumination, but previous theoretical treatments have fallen short by restricting their considerations to the surfaces of objects. The present work is among the first to explore how we infer the light present in empty space. It concludes with several research examples supporting the theory that humans can infer the differential levels and chromaticities of illumination in three-dimensional space. PMID:23435628

  17. Inferring Diversity: Life After Shannon

    NASA Astrophysics Data System (ADS)

    Giffin, Adom

    The diversity of a community that cannot be fully counted must be inferred. The two preeminent inference methods are the MaxEnt method, which uses information in the form of constraints and Bayes' rule which uses information in the form of data. It has been shown that these two methods are special cases of the method of Maximum (relative) Entropy (ME). We demonstrate how this method can be used as a measure of diversity that not only reproduces the features of Shannon's index but exceeds them by allowing more types of information to be included in the inference. A specific example is solved in detail. Additionally, the entropy that is found is the same form as the thermodynamic entropy.

  18. Perception, illusions and Bayesian inference.

    PubMed

    Nour, Matthew M; Nour, Joseph M

    2015-01-01

    Descriptive psychopathology makes a distinction between veridical perception and illusory perception. In both cases a perception is tied to a sensory stimulus, but in illusions the perception is of a false object. This article re-examines this distinction in light of new work in theoretical and computational neurobiology, which views all perception as a form of Bayesian statistical inference that combines sensory signals with prior expectations. Bayesian perceptual inference can solve the 'inverse optics' problem of veridical perception and provides a biologically plausible account of a number of illusory phenomena, suggesting that veridical and illusory perceptions are generated by precisely the same inferential mechanisms.

  19. Inferring Epidemic Network Topology from Surveillance Data

    PubMed Central

    Wan, Xiang; Liu, Jiming; Cheung, William K.; Tong, Tiejun

    2014-01-01

    The transmission of infectious diseases can be affected by many or even hidden factors, making it difficult to accurately predict when and where outbreaks may emerge. One approach at the moment is to develop and deploy surveillance systems in an effort to detect outbreaks as timely as possible. This enables policy makers to modify and implement strategies for the control of the transmission. The accumulated surveillance data including temporal, spatial, clinical, and demographic information, can provide valuable information with which to infer the underlying epidemic networks. Such networks can be quite informative and insightful as they characterize how infectious diseases transmit from one location to another. The aim of this work is to develop a computational model that allows inferences to be made regarding epidemic network topology in heterogeneous populations. We apply our model on the surveillance data from the 2009 H1N1 pandemic in Hong Kong. The inferred epidemic network displays significant effect on the propagation of infectious diseases. PMID:24979215

  20. Causal inference in biology networks with integrated belief propagation.

    PubMed

    Chang, Rui; Karr, Jonathan R; Schadt, Eric E

    2015-01-01

    Inferring causal relationships among molecular and higher order phenotypes is a critical step in elucidating the complexity of living systems. Here we propose a novel method for inferring causality that is no longer constrained by the conditional dependency arguments that limit the ability of statistical causal inference methods to resolve causal relationships within sets of graphical models that are Markov equivalent. Our method utilizes Bayesian belief propagation to infer the responses of perturbation events on molecular traits given a hypothesized graph structure. A distance measure between the inferred response distribution and the observed data is defined to assess the 'fitness' of the hypothesized causal relationships. To test our algorithm, we infer causal relationships within equivalence classes of gene networks in which the form of the functional interactions that are possible are assumed to be nonlinear, given synthetic microarray and RNA sequencing data. We also apply our method to infer causality in real metabolic network with v-structure and feedback loop. We show that our method can recapitulate the causal structure and recover the feedback loop only from steady-state data which conventional method cannot. PMID:25592596

  1. Optimal fuzzy inference for short-term load forecasting

    SciTech Connect

    Mori, Hiroyuki; Kobayashi, Hidenori

    1996-02-01

    This paper proposes an optimal fuzzy inference method for short-term load forecasting. The proposed method constructs an optimal structure of the simplified fuzzy inference that minimizes model errors and the number of the membership functions to grasp nonlinear behavior of power system short-term loads. The model is identified by simulated annealing and the steepest descent method. The proposed method is demonstrated in examples.

  2. Optimal fuzzy inference for short-term load forecasting

    SciTech Connect

    Mori, Hiroyuki; Kobayashi, Hidenori

    1995-12-31

    This paper proposes an optimal fuzzy inference method for short-term load forecasting. The proposed method constructs an optimal structure of the simplified fuzzy inference that minimizes model errors and the number of the membership functions to grasp nonlinear behavior of power system short-term loads. The model is identified by simulated annealing and the steepest descent method. The proposed method is demonstrated in examples.

  3. Algorithm Optimally Orders Forward-Chaining Inference Rules

    NASA Technical Reports Server (NTRS)

    James, Mark

    2008-01-01

    People typically develop knowledge bases in a somewhat ad hoc manner by incrementally adding rules with no specific organization. This often results in a very inefficient execution of those rules since they are so often order sensitive. This is relevant to tasks like Deep Space Network in that it allows the knowledge base to be incrementally developed and have it automatically ordered for efficiency. Although data flow analysis was first developed for use in compilers for producing optimal code sequences, its usefulness is now recognized in many software systems including knowledge-based systems. However, this approach for exhaustively computing data-flow information cannot directly be applied to inference systems because of the ubiquitous execution of the rules. An algorithm is presented that efficiently performs a complete producer/consumer analysis for each antecedent and consequence clause in a knowledge base to optimally order the rules to minimize inference cycles. An algorithm was developed that optimally orders a knowledge base composed of forwarding chaining inference rules such that independent inference cycle executions are minimized, thus, resulting in significantly faster execution. This algorithm was integrated into the JPL tool Spacecraft Health Inference Engine (SHINE) for verification and it resulted in a significant reduction in inference cycles for what was previously considered an ordered knowledge base. For a knowledge base that is completely unordered, then the improvement is much greater.

  4. Perceptual Inference and Autistic Traits

    ERIC Educational Resources Information Center

    Skewes, Joshua C; Jegindø, Else-Marie; Gebauer, Line

    2015-01-01

    Autistic people are better at perceiving details. Major theories explain this in terms of bottom-up sensory mechanisms or in terms of top-down cognitive biases. Recently, it has become possible to link these theories within a common framework. This framework assumes that perception is implicit neural inference, combining sensory evidence with…

  5. Science Shorts: Observation versus Inference

    ERIC Educational Resources Information Center

    Leager, Craig R.

    2008-01-01

    When you observe something, how do you know for sure what you are seeing, feeling, smelling, or hearing? Asking students to think critically about their encounters with the natural world will help to strengthen their understanding and application of the science-process skills of observation and inference. In the following lesson, students make…

  6. Sample Size and Correlational Inference

    ERIC Educational Resources Information Center

    Anderson, Richard B.; Doherty, Michael E.; Friedrich, Jeff C.

    2008-01-01

    In 4 studies, the authors examined the hypothesis that the structure of the informational environment makes small samples more informative than large ones for drawing inferences about population correlations. The specific purpose of the studies was to test predictions arising from the signal detection simulations of R. B. Anderson, M. E. Doherty,…

  7. Improving Explanatory Inferences from Assessments

    ERIC Educational Resources Information Center

    Diakow, Ronli Phyllis

    2013-01-01

    This dissertation comprises three papers that propose, discuss, and illustrate models to make improved inferences about research questions regarding student achievement in education. Addressing the types of questions common in educational research today requires three different "extensions" to traditional educational assessment: (1)…

  8. Degradation monitoring using probabilistic inference

    NASA Astrophysics Data System (ADS)

    Alpay, Bulent

    In order to increase safety and improve economy and performance in a nuclear power plant (NPP), the source and extent of component degradations should be identified before failures and breakdowns occur. It is also crucial for the next generation of NPPs, which are designed to have a long core life and high fuel burnup to have a degradation monitoring system in order to keep the reactor in a safe state, to meet the designed reactor core lifetime and to optimize the scheduled maintenance. Model-based methods are based on determining the inconsistencies between the actual and expected behavior of the plant, and use these inconsistencies for detection and diagnostics of degradations. By defining degradation as a random abrupt change from the nominal to a constant degraded state of a component, we employed nonlinear filtering techniques based on state/parameter estimation. We utilized a Bayesian recursive estimation formulation in the sequential probabilistic inference framework and constructed a hidden Markov model to represent a general physical system. By addressing the problem of a filter's inability to estimate an abrupt change, which is called the oblivious filter problem in nonlinear extensions of Kalman filtering, and the sample impoverishment problem in particle filtering, we developed techniques to modify filtering algorithms by utilizing additional data sources to improve the filter's response to this problem. We utilized a reliability degradation database that can be constructed from plant specific operational experience and test and maintenance reports to generate proposal densities for probable degradation modes. These are used in a multiple hypothesis testing algorithm. We then test samples drawn from these proposal densities with the particle filtering estimates based on the Bayesian recursive estimation formulation with the Metropolis Hastings algorithm, which is a well-known Markov chain Monte Carlo method (MCMC). This multiple hypothesis testing

  9. The effects of liquid composition, temperature, and pressure on the equilibrium dihedral angles of binary solid-liquid systems inferred from a lattice-like model

    NASA Astrophysics Data System (ADS)

    Takei, Yasuko; Shimizu, Ichiko

    2003-10-01

    Dihedral angles of binary eutectic systems, such as silicate+melt systems, silicate+H 2O systems, binary alloys, and binary organic systems, tend to decrease with increasing concentration of the solid component in the liquid phase. This empirical law is useful to estimate dihedral angles in the Earth's interior from phase diagrams of solid-liquid systems. In this paper, we investigate the mechanism underlying this empirical law. By employing a lattice-like model in which the liquid phase is treated as a regular solution, we clarify the liquid composition, temperature, and pressure effects on the solid-liquid interfacial tension. It is shown that the non-ideality in chemical bonding causes a strong compositional dependence of the solid-liquid interfacial tension; due to the non-ideality in chemical bonding, the solid surface preferentially adsorbs the solid component, which results in the decrease of the interfacial tension with increasing concentration of this component in the bulk liquid phase. With this effect, the significant decrease of the dihedral angle with T observed in the SiO 2-H 2O system near the monotectic temperature, and the decrease with P observed in the forsterite-H 2O system, can be explained semi-quantitatively.

  10. Inferring biochemical reaction pathways: the case of the gemcitabine pharmacokinetics

    PubMed Central

    2012-01-01

    Background The representation of a biochemical system as a network is the precursor of any mathematical model of the processes driving the dynamics of that system. Pharmacokinetics uses mathematical models to describe the interactions between drug, and drug metabolites and targets and through the simulation of these models predicts drug levels and/or dynamic behaviors of drug entities in the body. Therefore, the development of computational techniques for inferring the interaction network of the drug entities and its kinetic parameters from observational data is raising great interest in the scientific community of pharmacologists. In fact, the network inference is a set of mathematical procedures deducing the structure of a model from the experimental data associated to the nodes of the network of interactions. In this paper, we deal with the inference of a pharmacokinetic network from the concentrations of the drug and its metabolites observed at discrete time points. Results The method of network inference presented in this paper is inspired by the theory of time-lagged correlation inference with regard to the deduction of the interaction network, and on a maximum likelihood approach with regard to the estimation of the kinetic parameters of the network. Both network inference and parameter estimation have been designed specifically to identify systems of biotransformations, at the biochemical level, from noisy time-resolved experimental data. We use our inference method to deduce the metabolic pathway of the gemcitabine. The inputs to our inference algorithm are the experimental time series of the concentration of gemcitabine and its metabolites. The output is the set of reactions of the metabolic network of the gemcitabine. Conclusions Time-lagged correlation based inference pairs up to a probabilistic model of parameter inference from metabolites time series allows the identification of the microscopic pharmacokinetics and pharmacodynamics of a drug with a

  11. Modelling hourly dissolved oxygen concentration (DO) using dynamic evolving neural-fuzzy inference system (DENFIS)-based approach: case study of Klamath River at Miller Island Boat Ramp, OR, USA.

    PubMed

    Heddam, Salim

    2014-01-01

    In this study, we present application of an artificial intelligence (AI) technique model called dynamic evolving neural-fuzzy inference system (DENFIS) based on an evolving clustering method (ECM), for modelling dissolved oxygen concentration in a river. To demonstrate the forecasting capability of DENFIS, a one year period from 1 January 2009 to 30 December 2009, of hourly experimental water quality data collected by the United States Geological Survey (USGS Station No: 420853121505500) station at Klamath River at Miller Island Boat Ramp, OR, USA, were used for model development. Two DENFIS-based models are presented and compared. The two DENFIS systems are: (1) offline-based system named DENFIS-OF, and (2) online-based system, named DENFIS-ON. The input variables used for the two models are water pH, temperature, specific conductance, and sensor depth. The performances of the models are evaluated using root mean square errors (RMSE), mean absolute error (MAE), Willmott index of agreement (d) and correlation coefficient (CC) statistics. The lowest root mean square error and highest correlation coefficient values were obtained with the DENFIS-ON method. The results obtained with DENFIS models are compared with linear (multiple linear regression, MLR) and nonlinear (multi-layer perceptron neural networks, MLPNN) methods. This study demonstrates that DENFIS-ON investigated herein outperforms all the proposed techniques for DO modelling. PMID:24705953

  12. Nonparametric inference of network structure and dynamics

    NASA Astrophysics Data System (ADS)

    Peixoto, Tiago P.

    The network structure of complex systems determine their function and serve as evidence for the evolutionary mechanisms that lie behind them. Despite considerable effort in recent years, it remains an open challenge to formulate general descriptions of the large-scale structure of network systems, and how to reliably extract such information from data. Although many approaches have been proposed, few methods attempt to gauge the statistical significance of the uncovered structures, and hence the majority cannot reliably separate actual structure from stochastic fluctuations. Due to the sheer size and high-dimensionality of many networks, this represents a major limitation that prevents meaningful interpretations of the results obtained with such nonstatistical methods. In this talk, I will show how these issues can be tackled in a principled and efficient fashion by formulating appropriate generative models of network structure that can have their parameters inferred from data. By employing a Bayesian description of such models, the inference can be performed in a nonparametric fashion, that does not require any a priori knowledge or ad hoc assumptions about the data. I will show how this approach can be used to perform model comparison, and how hierarchical models yield the most appropriate trade-off between model complexity and quality of fit based on the statistical evidence present in the data. I will also show how this general approach can be elegantly extended to networks with edge attributes, that are embedded in latent spaces, and that change in time. The latter is obtained via a fully dynamic generative network model, based on arbitrary-order Markov chains, that can also be inferred in a nonparametric fashion. Throughout the talk I will illustrate the application of the methods with many empirical networks such as the internet at the autonomous systems level, the global airport network, the network of actors and films, social networks, citations among

  13. Identifying inference attacks against healthcare data repositories

    PubMed Central

    Vaidya, Jaideep; Shafiq, Basit; Jiang, Xiaoqian; Ohno-Machado, Lucila

    Health care data repositories play an important role in driving progress in medical research. Finding new pathways to discovery requires having adequate data and relevant analysis. However, it is critical to ensure the privacy and security of the stored data. In this paper, we identify a dangerous inference attack against naive suppression based approaches that are used to protect sensitive information. We base our attack on the querying system provided by the Healthcare Cost and Utilization Project, though it applies in general to any medical database providing a query capability. We also discuss potential solutions to this problem. PMID:24303279

  14. Migration of the Pee Dee River system inferred from ancestral paleochannels underlying the South Carolina Grand Strand and Long Bay inner shelf

    USGS Publications Warehouse

    Baldwin, W.E.; Morton, R.A.; Putney, T.R.; Katuna, M.P.; Harris, M.S.; Gayes, P.T.; Driscoll, N.W.; Denny, J.F.; Schwab, W.C.

    2006-01-01

    Several generations of the ancestral Pee Dee River system have been mapped beneath the South Carolina Grand Strand coastline and adjacent Long Bay inner shelf. Deep boreholes onshore and high-resolution seismic-reflection data offshore allow for reconstruction of these paleochannels, which formed during glacial lowstands, when the Pee Dee River system incised subaerially exposed coastal-plain and continental-shelf strata. Paleochannel groups, representing different generations of the system, decrease in age to the southwest, where the modern Pee Dee River merges with several coastal-plain tributaries at Winyah Bay, the southern terminus of Long Bay. Positions of the successive generational groups record a regional, southwestward migration of the river system that may have initiated during the late Pliocene. The migration was primarily driven by barrier-island deposition, resulting from the interaction of fluvial and shoreline processes during eustatic highstands. Structurally driven, subsurface paleotopography associated with the Mid-Carolina Platform High has also indirectly assisted in forcing this migration. These results provide a better understanding of the evolution of the region and help explain the lack of mobile sediment on the Long Bay inner shelf. Migration of the river system caused a profound change in sediment supply during the late Pleistocene. The abundant fluvial source that once fed sand-rich barrier islands was cut off and replaced with a limited source, supplied by erosion and reworking of former coastal deposits exposed at the shore and on the inner shelf.

  15. Origin of the X1X1X2X2/X1X2Y sex chromosome system of Harttia punctata (Siluriformes, Loricariidae) inferred from chromosome painting and FISH with ribosomal DNA markers.

    PubMed

    Blanco, Daniel Rodrigues; Vicari, Marcelo Ricardo; Lui, Roberto Laridondo; Artoni, Roberto Ferreira; de Almeida, Mara Cristina; Traldi, Josiane Baccarin; Margarido, Vladimir Pavan; Moreira-Filho, Orlando

    2014-04-01

    Harttia is a genus in the subfamily Loricariinae that accommodates fishes popularly known as armored catfishes. They show extensive karyotypic diversity regarding interspecific numerical/structural variation of the karyotypes, with the presence of the XX/XY1Y2 multiple sex chromosome system, as found in H. carvalhoi. In this context, this study aimed to characterize Harttia punctata chromosomally, for the first time, and to infer the rearrangements that originated the X1X1X2X2/X1X2Y multiple sex chromosome system present in this species. The data obtained in this study, with classical (Giemsa, C-banding and AgNORs) and molecular methodologies (fluorescence in situ hybridization) and chromosome microdissection, indicated that a translocation between distinct acrocentric chromosomes bearing rRNA genes, accompanied by deletions in both chromosomes, might have originated the neo-Y chromosome in this species. The data also suggest that the multiple sex chromosome systems present in H. carvalhoi and H. punctata had an independent origin, evidencing the recurrence of chromosome alterations in species from this genus.

  16. Network Plasticity as Bayesian Inference

    PubMed Central

    Legenstein, Robert; Maass, Wolfgang

    2015-01-01

    General results from statistical learning theory suggest to understand not only brain computations, but also brain plasticity as probabilistic inference. But a model for that has been missing. We propose that inherently stochastic features of synaptic plasticity and spine motility enable cortical networks of neurons to carry out probabilistic inference by sampling from a posterior distribution of network configurations. This model provides a viable alternative to existing models that propose convergence of parameters to maximum likelihood values. It explains how priors on weight distributions and connection probabilities can be merged optimally with learned experience, how cortical networks can generalize learned information so well to novel experiences, and how they can compensate continuously for unforeseen disturbances of the network. The resulting new theory of network plasticity explains from a functional perspective a number of experimental data on stochastic aspects of synaptic plasticity that previously appeared to be quite puzzling. PMID:26545099

  17. Statistical learning and selective inference

    PubMed Central

    Taylor, Jonathan; Tibshirani, Robert J.

    2015-01-01

    We describe the problem of “selective inference.” This addresses the following challenge: Having mined a set of data to find potential associations, how do we properly assess the strength of these associations? The fact that we have “cherry-picked”—searched for the strongest associations—means that we must set a higher bar for declaring significant the associations that we see. This challenge becomes more important in the era of big data and complex statistical modeling. The cherry tree (dataset) can be very large and the tools for cherry picking (statistical learning methods) are now very sophisticated. We describe some recent new developments in selective inference and illustrate their use in forward stepwise regression, the lasso, and principal components analysis. PMID:26100887

  18. Causal inference based on counterfactuals

    PubMed Central

    Höfler, M

    2005-01-01

    Background The counterfactual or potential outcome model has become increasingly standard for causal inference in epidemiological and medical studies. Discussion This paper provides an overview on the counterfactual and related approaches. A variety of conceptual as well as practical issues when estimating causal effects are reviewed. These include causal interactions, imperfect experiments, adjustment for confounding, time-varying exposures, competing risks and the probability of causation. It is argued that the counterfactual model of causal effects captures the main aspects of causality in health sciences and relates to many statistical procedures. Summary Counterfactuals are the basis of causal inference in medicine and epidemiology. Nevertheless, the estimation of counterfactual differences pose several difficulties, primarily in observational studies. These problems, however, reflect fundamental barriers only when learning from observations, and this does not invalidate the counterfactual concept. PMID:16159397

  19. Bayesian Estimation and Inference Using Stochastic Electronics

    PubMed Central

    Thakur, Chetan Singh; Afshar, Saeed; Wang, Runchun M.; Hamilton, Tara J.; Tapson, Jonathan; van Schaik, André

    2016-01-01

    In this paper, we present the implementation of two types of Bayesian inference problems to demonstrate the potential of building probabilistic algorithms in hardware using single set of building blocks with the ability to perform these computations in real time. The first implementation, referred to as the BEAST (Bayesian Estimation and Stochastic Tracker), demonstrates a simple problem where an observer uses an underlying Hidden Markov Model (HMM) to track a target in one dimension. In this implementation, sensors make noisy observations of the target position at discrete time steps. The tracker learns the transition model for target movement, and the observation model for the noisy sensors, and uses these to estimate the target position by solving the Bayesian recursive equation online. We show the tracking performance of the system and demonstrate how it can learn the observation model, the transition model, and the external distractor (noise) probability interfering with the observations. In the second implementation, referred to as the Bayesian INference in DAG (BIND), we show how inference can be performed in a Directed Acyclic Graph (DAG) using stochastic circuits. We show how these building blocks can be easily implemented using simple digital logic gates. An advantage of the stochastic electronic implementation is that it is robust to certain types of noise, which may become an issue in integrated circuit (IC) technology with feature sizes in the order of tens of nanometers due to their low noise margin, the effect of high-energy cosmic rays and the low supply voltage. In our framework, the flipping of random individual bits would not affect the system performance because information is encoded in a bit stream. PMID:27047326

  20. Bayesian Estimation and Inference Using Stochastic Electronics.

    PubMed

    Thakur, Chetan Singh; Afshar, Saeed; Wang, Runchun M; Hamilton, Tara J; Tapson, Jonathan; van Schaik, André

    2016-01-01

    In this paper, we present the implementation of two types of Bayesian inference problems to demonstrate the potential of building probabilistic algorithms in hardware using single set of building blocks with the ability to perform these computations in real time. The first implementation, referred to as the BEAST (Bayesian Estimation and Stochastic Tracker), demonstrates a simple problem where an observer uses an underlying Hidden Markov Model (HMM) to track a target in one dimension. In this implementation, sensors make noisy observations of the target position at discrete time steps. The tracker learns the transition model for target movement, and the observation model for the noisy sensors, and uses these to estimate the target position by solving the Bayesian recursive equation online. We show the tracking performance of the system and demonstrate how it can learn the observation model, the transition model, and the external distractor (noise) probability interfering with the observations. In the second implementation, referred to as the Bayesian INference in DAG (BIND), we show how inference can be performed in a Directed Acyclic Graph (DAG) using stochastic circuits. We show how these building blocks can be easily implemented using simple digital logic gates. An advantage of the stochastic electronic implementation is that it is robust to certain types of noise, which may become an issue in integrated circuit (IC) technology with feature sizes in the order of tens of nanometers due to their low noise margin, the effect of high-energy cosmic rays and the low supply voltage. In our framework, the flipping of random individual bits would not affect the system performance because information is encoded in a bit stream.

  1. Bayesian Estimation and Inference Using Stochastic Electronics.

    PubMed

    Thakur, Chetan Singh; Afshar, Saeed; Wang, Runchun M; Hamilton, Tara J; Tapson, Jonathan; van Schaik, André

    2016-01-01

    In this paper, we present the implementation of two types of Bayesian inference problems to demonstrate the potential of building probabilistic algorithms in hardware using single set of building blocks with the ability to perform these computations in real time. The first implementation, referred to as the BEAST (Bayesian Estimation and Stochastic Tracker), demonstrates a simple problem where an observer uses an underlying Hidden Markov Model (HMM) to track a target in one dimension. In this implementation, sensors make noisy observations of the target position at discrete time steps. The tracker learns the transition model for target movement, and the observation model for the noisy sensors, and uses these to estimate the target position by solving the Bayesian recursive equation online. We show the tracking performance of the system and demonstrate how it can learn the observation model, the transition model, and the external distractor (noise) probability interfering with the observations. In the second implementation, referred to as the Bayesian INference in DAG (BIND), we show how inference can be performed in a Directed Acyclic Graph (DAG) using stochastic circuits. We show how these building blocks can be easily implemented using simple digital logic gates. An advantage of the stochastic electronic implementation is that it is robust to certain types of noise, which may become an issue in integrated circuit (IC) technology with feature sizes in the order of tens of nanometers due to their low noise margin, the effect of high-energy cosmic rays and the low supply voltage. In our framework, the flipping of random individual bits would not affect the system performance because information is encoded in a bit stream. PMID:27047326

  2. Crystallization Processes in Mercury's Core Inferred from In-situ High-Pressure Melting Experiments in the Fe-S-Si-C System

    NASA Astrophysics Data System (ADS)

    Martin, A. M.; Van Orman, J. A.; Hauck, S. A., II; Sun, N.; Yu, T.; Wang, Y.

    2014-12-01

    Based upon the high pressure melting temperatures in the Fe-FeS system, an iron "snow" process has been suggested to occur in Mercury's core. However, recent results from the MESSENGER mission indicate very reducing conditions in Mercury, under which a substantial amount of silicon should also dissolve into the core. The presence of Si can significantly modify the chemical and physical properties of Mercury's core (e.g., phase relations, crystallization, density). Moreover, up to 4 wt% C could have been incorporated into the core during the planet formation. In order to test the iron snow hypothesis in a system that is likely to be closer to the actual core composition, we performed in situ high-pressure, high-temperature experiments in the Fe-FeS-Fe2Si-Fe3C system using a multi-anvil press on a synchrotron (Advanced Photon Source, Argonne). To observe low degree eutectic melting, we separated the samples in two parts: (1) an iron rod presaturated with Si and C and (2) a mixture of FeS, Fe2Si and Fe3C. Eutectic melting temperature and phase relations were determined at various pressures between 4.5 and 15.5 GPa using energy dispersive X-ray diffraction and imaging. Temperature was quenched soon after melting in order to preserve the eutectic melt composition. The X-ray images, diffraction spectra and back-scattered electron images of the recovered samples show that eutectic melting occurs in the range of 800 - 900°C in all our experiments. These temperatures are close to the eutectic temperatures in the Fe-FeS-Fe3C system, indicating that Si does not change the eutectic temperatures significantly. Melting therefore occurs at much lower temperature than suggested for the Fe-S-Si system at similar pressures. This difference may be explained by the presence of C and by the higher silicon content in our starting composition. Our experimental setup may also be more suitable for detecting the low degrees of melting in metallic systems. Such low eutectic melting

  3. Inferences on mating and sexual systems of two Pacific Cinetorhynchus shrimps (Decapoda, Rhynchocinetidae) based on sexual dimorphism in body size and cheliped weaponry

    PubMed Central

    Bauer, Raymond T.; Okuno, Junji; Thiel, Martin

    2014-01-01

    Abstract Sexual dimorphism in body size and weaponry was examined in two Cinetorhynchus shrimp species in order to formulate hypotheses on their sexual and mating systems. Collections of Cinetorhynchus sp. A and Cinetorhynchus sp. B were made in March, 2011 on Coconut Island, Hawaii, by hand dipnetting and minnow traps in coral rubble bottom in shallow water. Although there is overlap in male and female size, some males are much larger than females. The major (pereopod 1) chelipeds of males are significantly larger and longer than those of females. In these two Cinetorhynchus species, males and females have third maxillipeds of similar relative size, i.e., those of males are not hypertrophied and probably not used as spear-like weapons as in some other rhynchocinetid (Rhynchocinetes) species. Major chelae of males vary with size, changing from typical female-like chelae tipped with black corneous stout setae to subchelate or prehensile appendages in larger males. Puncture wounds or regenerating major chelipeds were observed in 26.1 % of males examined (N = 38 including both species). We interpret this evidence on sexual dimorphism as an indication of a temporary male mate guarding or “neighborhoods of dominance” mating system, in which larger dominant robustus males defend females and have greater mating success than smaller males. Fecundity of females increased with female size, as in most caridean species (500–800 in Cinetorhynchus sp. A; 300–3800 in Cinetorhynchus sp. B). Based on the sample examined, we conclude that these two species have a gonochoric sexual system (separate sexes) like most but not all other rhynchocinetid species in which the sexual system has been investigated. PMID:25561837

  4. Temporal evolution of a hydrothermal system in Kusatsu-Shirane Volcano, Japan, inferred from the complex frequencies of long-period events

    USGS Publications Warehouse

    Kumagai, H.; Chouet, B.A.; Nakano, M.

    2002-01-01

    We present a detailed description of temporal variations in the complex frequencies of long-period (LP) events observed at Kusatsu-Shirane Volcano. Using the Sompi method, we analyze 35 LP events that occurred during the period from August 1992 through January 1993. The observed temporal variations in the complex frequencies can be divided into three periods. During the first period the dominant frequency rapidly decreases from 5 to 1 Hz, and Q of the dominant spectral peak remains roughly constant with an average value near 100. During the second period the dominant frequency gradually increases up to 3 Hz, and Q gradually decreases from 160 to 30. During the third period the dominant frequency increases more rapidly from 3 to 5 Hz, and Q shows an abrupt increase at the beginning of this period and then remains roughly constant with an average value near 100. Such temporal variations can be consistently explained by the dynamic response of a hydrothermal crack to a magmatic heat pulse. During the first period, crack growth occurs in response to the overall pressure increase in the hydrothermal system caused by the heat pulse. Once crack formation is complete, heat gradually changes the fluid in the crack from a wet misty gas to a dry gas during the second period. As heating of the hydrothermal system gradually subsides, the overall pressure in this system starts to decrease, causing the collapse of the crack during the third period.

  5. Origin of the p-process radionuclides 92Nb and 146Sm in the early solar system and inferences on the birth of the Sun.

    PubMed

    Lugaro, Maria; Pignatari, Marco; Ott, Ulrich; Zuber, Kai; Travaglio, Claudia; Gyürky, György; Fülöp, Zsolt

    2016-01-26

    The abundances of (92)Nb and (146)Sm in the early solar system are determined from meteoritic analysis, and their stellar production is attributed to the p process. We investigate if their origin from thermonuclear supernovae deriving from the explosion of white dwarfs with mass above the Chandrasekhar limit is in agreement with the abundance of (53)Mn, another radionuclide present in the early solar system and produced in the same events. A consistent solution for (92)Nb and (53)Mn cannot be found within the current uncertainties and requires the (92)Nb/(92)Mo ratio in the early solar system to be at least 50% lower than the current nominal value, which is outside its present error bars. A different solution is to invoke another production site for (92)Nb, which we find in the α-rich freezeout during core-collapse supernovae from massive stars. Whichever scenario we consider, we find that a relatively long time interval of at least ∼ 10 My must have elapsed from when the star-forming region where the Sun was born was isolated from the interstellar medium and the birth of the Sun. This is in agreement with results obtained from radionuclides heavier than iron produced by neutron captures and lends further support to the idea that the Sun was born in a massive star-forming region together with many thousands of stellar siblings. PMID:26755600

  6. Origin of the p-process radionuclides 92Nb and 146Sm in the early solar system and inferences on the birth of the Sun

    PubMed Central

    Lugaro, Maria; Pignatari, Marco; Ott, Ulrich; Zuber, Kai; Travaglio, Claudia; Gyürky, György; Fülöp, Zsolt

    2016-01-01

    The abundances of 92Nb and 146Sm in the early solar system are determined from meteoritic analysis, and their stellar production is attributed to the p process. We investigate if their origin from thermonuclear supernovae deriving from the explosion of white dwarfs with mass above the Chandrasekhar limit is in agreement with the abundance of 53Mn, another radionuclide present in the early solar system and produced in the same events. A consistent solution for 92Nb and 53Mn cannot be found within the current uncertainties and requires the 92Nb/92Mo ratio in the early solar system to be at least 50% lower than the current nominal value, which is outside its present error bars. A different solution is to invoke another production site for 92Nb, which we find in the α-rich freezeout during core-collapse supernovae from massive stars. Whichever scenario we consider, we find that a relatively long time interval of at least ∼10 My must have elapsed from when the star-forming region where the Sun was born was isolated from the interstellar medium and the birth of the Sun. This is in agreement with results obtained from radionuclides heavier than iron produced by neutron captures and lends further support to the idea that the Sun was born in a massive star-forming region together with many thousands of stellar siblings. PMID:26755600

  7. Origin of the p-process radionuclides 92Nb and 146Sm in the early solar system and inferences on the birth of the Sun.

    PubMed

    Lugaro, Maria; Pignatari, Marco; Ott, Ulrich; Zuber, Kai; Travaglio, Claudia; Gyürky, György; Fülöp, Zsolt

    2016-01-26

    The abundances of (92)Nb and (146)Sm in the early solar system are determined from meteoritic analysis, and their stellar production is attributed to the p process. We investigate if their origin from thermonuclear supernovae deriving from the explosion of white dwarfs with mass above the Chandrasekhar limit is in agreement with the abundance of (53)Mn, another radionuclide present in the early solar system and produced in the same events. A consistent solution for (92)Nb and (53)Mn cannot be found within the current uncertainties and requires the (92)Nb/(92)Mo ratio in the early solar system to be at least 50% lower than the current nominal value, which is outside its present error bars. A different solution is to invoke another production site for (92)Nb, which we find in the α-rich freezeout during core-collapse supernovae from massive stars. Whichever scenario we consider, we find that a relatively long time interval of at least ∼ 10 My must have elapsed from when the star-forming region where the Sun was born was isolated from the interstellar medium and the birth of the Sun. This is in agreement with results obtained from radionuclides heavier than iron produced by neutron captures and lends further support to the idea that the Sun was born in a massive star-forming region together with many thousands of stellar siblings.

  8. Origin of the p-process radionuclides 92Nb and 146Sm in the early solar system and inferences on the birth of the Sun

    NASA Astrophysics Data System (ADS)

    Lugaro, Maria; Pignatari, Marco; Ott, Ulrich; Zuber, Kai; Travaglio, Claudia; Gyürky, György; Fülöp, Zsolt

    2016-01-01

    The abundances of 92Nb and 146Sm in the early solar system are determined from meteoritic analysis, and their stellar production is attributed to the p process. We investigate if their origin from thermonuclear supernovae deriving from the explosion of white dwarfs with mass above the Chandrasekhar limit is in agreement with the abundance of 53Mn, another radionuclide present in the early solar system and produced in the same events. A consistent solution for 92Nb and 53Mn cannot be found within the current uncertainties and requires the 92Nb/92Mo ratio in the early solar system to be at least 50% lower than the current nominal value, which is outside its present error bars. A different solution is to invoke another production site for 92Nb, which we find in the α-rich freezeout during core-collapse supernovae from massive stars. Whichever scenario we consider, we find that a relatively long time interval of at least ˜10 My must have elapsed from when the star-forming region where the Sun was born was isolated from the interstellar medium and the birth of the Sun. This is in agreement with results obtained from radionuclides heavier than iron produced by neutron captures and lends further support to the idea that the Sun was born in a massive star-forming region together with many thousands of stellar siblings.

  9. Inferring Land Surface Model Parameters for the Assimilation of Satellite-Based L-Band Brightness Temperature Observations into a Soil Moisture Analysis System

    NASA Technical Reports Server (NTRS)

    Reichle, Rolf H.; De Lannoy, Gabrielle J. M.

    2012-01-01

    The Soil Moisture and Ocean Salinity (SMOS) satellite mission provides global measurements of L-band brightness temperatures at horizontal and vertical polarization and a variety of incidence angles that are sensitive to moisture and temperature conditions in the top few centimeters of the soil. These L-band observations can therefore be assimilated into a land surface model to obtain surface and root zone soil moisture estimates. As part of the observation operator, such an assimilation system requires a radiative transfer model (RTM) that converts geophysical fields (including soil moisture and soil temperature) into modeled L-band brightness temperatures. At the global scale, the RTM parameters and the climatological soil moisture conditions are still poorly known. Using look-up tables from the literature to estimate the RTM parameters usually results in modeled L-band brightness temperatures that are strongly biased against the SMOS observations, with biases varying regionally and seasonally. Such biases must be addressed within the land data assimilation system. In this presentation, the estimation of the RTM parameters is discussed for the NASA GEOS-5 land data assimilation system, which is based on the ensemble Kalman filter (EnKF) and the Catchment land surface model. In the GEOS-5 land data assimilation system, soil moisture and brightness temperature biases are addressed in three stages. First, the global soil properties and soil hydraulic parameters that are used in the Catchment model were revised to minimize the bias in the modeled soil moisture, as verified against available in situ soil moisture measurements. Second, key parameters of the "tau-omega" RTM were calibrated prior to data assimilation using an objective function that minimizes the climatological differences between the modeled L-band brightness temperatures and the corresponding SMOS observations. Calibrated parameters include soil roughness parameters, vegetation structure parameters

  10. Thirty years of magmatic fluid release at Campi Flegrei caldera (Italy) inferred by diffuse CO2 emission, fumarole composition and physical simulations of the hydrothermal system.

    NASA Astrophysics Data System (ADS)

    Chiodini, G.; Caliro, S.; Cardellini, C.; De Martino, P.; Petrillo, Z.

    2012-12-01

    The temporal variation of magmatic fluid release at Campi Flegrei caldera is investigated using numerical simulations of the hydrothermal system constrained by diffuse CO2 emission data and by the chemical composition of fumarolic vents. The main aim is to understand the recent dynamics of Campi Flegrei, where hundreds of thousands of people live in an area subjected since the middle of the 20th century to a long term crisis characterized by several episodes of ground uplift and correspondent seismic swarms (bradyseism), the most significant of which occurred in A.D. 1950-1953, 1970-1972, and 1982-1984 (maximum total ground uplift ~4 m). In 1998, the first measurements of diffuse degassing from the Solfatara crater, the most active zone of Campi Flegrei, revealed the very intense release of hydrothermal- magmatic CO2 (~1500 t/d) and of thermal energy (~100 W) highlighting that the expulsion of deep fluids is the main form of energy loss from the entire caldera and suggesting an important role of magma degassing during the crisis. The hydrothermal system of Solfatara recently underwent large changes, including compositional variations of fumarolic effluents, compositional homogenization of the fluid released at different vents, changes in the pattern of diffuse degassing, increases in the pressures of the system, and increases in the temperature and in the flow rate of the fumaroles. Furthermore, after 20 yr of subsidence, an uplift period started in 2005. Comparing long-term series of geochemical signals with ground deformation and seismicity, we show that these changes are caused by repeated injections of magmatic fluid into the hydrothermal system. The frequency of the degassing episodes has increased in the last years, causing the almost continuous increase of the magmatic component of the fumaroles, pulsed uplift episodes and swarms of low magnitude earthquakes. Physical simulations of the process show that total injected fluid masses in each episode of magma

  11. An algebra-based method for inferring gene regulatory networks

    PubMed Central

    2014-01-01

    Background The inference of gene regulatory networks (GRNs) from experimental observations is at the heart of systems biology. This includes the inference of both the network topology and its dynamics. While there are many algorithms available to infer the network topology from experimental data, less emphasis has been placed on methods that infer network dynamics. Furthermore, since the network inference problem is typically underdetermined, it is essential to have the option of incorporating into the inference process, prior knowledge about the network, along with an effective description of the search space of dynamic models. Finally, it is also important to have an understanding of how a given inference method is affected by experimental and other noise in the data used. Results This paper contains a novel inference algorithm using the algebraic framework of Boolean polynomial dynamical systems (BPDS), meeting all these requirements. The algorithm takes as input time series data, including those from network perturbations, such as knock-out mutant strains and RNAi experiments. It allows for the incorporation of prior biological knowledge while being robust to significant levels of noise in the data used for inference. It uses an evolutionary algorithm for local optimization with an encoding of the mathematical models as BPDS. The BPDS framework allows an effective representation of the search space for algebraic dynamic models that improves computational performance. The algorithm is validated with both simulated and experimental microarray expression profile data. Robustness to noise is tested using a published mathematical model of the segment polarity gene network in Drosophila melanogaster. Benchmarking of the algorithm is done by comparison with a spectrum of state-of-the-art network inference methods on data from the synthetic IRMA network to demonstrate that our method has good precision and recall for the network reconstruction task, while also

  12. Variable and Extreme Irradiation Conditions in the Early Solar System Inferred from the Initial Abundance of 10Be in Isheyevo CAIs

    NASA Astrophysics Data System (ADS)

    Gounelle, Matthieu; Chaussidon, Marc; Rollion-Bard, Claire

    2013-02-01

    A search for short-lived 10Be in 21 calcium-aluminum-rich inclusions (CAIs) from Isheyevo, a rare CB/CH chondrite, showed that only 5 CAIs had 10B/11B ratios higher than chondritic correlating with the elemental ratio 9Be/11B, suggestive of in situ decay of this key short-lived radionuclide. The initial (10Be/9Be)0 ratios vary between ~10-3 and ~10-2 for CAI 411. The initial ratio of CAI 411 is one order of magnitude higher than the highest ratio found in CV3 CAIs, suggesting that the more likely origin of CAI 411 10Be is early solar system irradiation. The low (26Al/27Al)0 [<= 8.9 × 10-7] with which CAI 411 formed indicates that it was exposed to gradual flares with a proton fluence of a few 1019 protons cm-2, during the earliest phases of the solar system, possibly the infrared class 0. The irradiation conditions for other CAIs are less well constrained, with calculated fluences ranging between a few 1019 and 1020 protons cm-2. The variable and extreme value of the initial 10Be/9Be ratios in carbonaceous chondrite CAIs is the reflection of the variable and extreme magnetic activity in young stars observed in the X-ray domain.

  13. The Anatomy of a Fumarole inferred from a 3-D High-Resolution Electrical Resistivity Image of Solfatara Hydrothermal System (Phlegrean Fields, Italy)

    NASA Astrophysics Data System (ADS)

    Gresse, M.; Vandemeulebrouck, J.; Chiodini, G.; Byrdina, S.; Lebourg, T.; Johnson, T. C.

    2015-12-01

    Solfatara, the most active crater in the Phlegrean Fields volcanic complex, shows since ten years a remarkable renewal of activity characterized by an increase of CO2 total degassing from 1500 up to 3000 tons/day, associated with a large ground uplift (Chiodini et al., 2015). In order to precisely image the structure of the shallow hydrothermal system, we performed an extended electrical DC resistivity survey at Solfatara, with about 40 2-D profiles of length up to 1 km, as well as soil temperature and CO2 flux measurements over the area. We then realized a 3-D inversion from the ~40 000 resistivity data points, using E4D code (Johnson et al., 2010). At large scale, results clearly delineate two contrasted structures: - A very conductive body (resistivity < 5 Ohm.m) located beneath the Fangaia mud pools, and likely associated to a mineralized liquid rich plume. - An elongated more resistive body (20-30 Ohm.m) connected to the main fumarolic area and interpreted as the gas reservoir feeding the fumaroles. At smaller scale, our resistivity model originally highlights the 3-D anatomy of a fumarole and the interactions between condensate layers and gas chimneys. This high-resolution image of the shallow hydrothermal structure is a new step for the modeling of this system.

  14. The lithosphere and asthenosphere system in Italy as inferred from the Vp and Vs 3D velocity model and moho map

    NASA Astrophysics Data System (ADS)

    ciaccio, M.; Di Stefano, R.

    2013-12-01

    We present an updated high resolution tomographic P- and S-wave velocity model of the lithosphere and asthenosphere system in Italy, obtained by adding the observations from 7.200 earthquakes to the previously inverted dataset (165,000 P-wave arrivals) and by including S-waves readings from 10 years of seismic data recorded at three components seismic stations. The main strength of this research is the use of a method able to model P- and S- seismic phases refracted at the Moho discontinuity. We use a new and original map of the 3D Moho geometry obtained by integrating selected high quality controlled source seismic and teleseismic receiver function data. Resolution strongly benefits also from the fast increase in number and quality of INGV National Seismic Network since year 2003 and from its integration with several permanent regional seismic networks. This study confirms the main structural features in the best resolved parts of the inverted volume and much better image details in some of the previously less resolved areas, due to both the larger number of inverted phases and the more even distribution of seismic stations. Surface basins and relationships between the Adriatic, Tyrrhenian, and Adria plates are better imaged. The investigation of the physical properties of the lithosphere-asthenosphere system if improved by the use of a large dataset of good quality S-readings.

  15. Automated adaptive inference of phenomenological dynamical models

    PubMed Central

    Daniels, Bryan C.; Nemenman, Ilya

    2015-01-01

    Dynamics of complex systems is often driven by large and intricate networks of microscopic interactions, whose sheer size obfuscates understanding. With limited experimental data, many parameters of such dynamics are unknown, and thus detailed, mechanistic models risk overfitting and making faulty predictions. At the other extreme, simple ad hoc models often miss defining features of the underlying systems. Here we develop an approach that instead constructs phenomenological, coarse-grained models of network dynamics that automatically adapt their complexity to the available data. Such adaptive models produce accurate predictions even when microscopic details are unknown. The approach is computationally tractable, even for a relatively large number of dynamical variables. Using simulated data, it correctly infers the phase space structure for planetary motion, avoids overfitting in a biological signalling system and produces accurate predictions for yeast glycolysis with tens of data points and over half of the interacting species unobserved. PMID:26293508

  16. Inferring epigenetic dynamics from kin correlations.

    PubMed

    Hormoz, Sahand; Desprat, Nicolas; Shraiman, Boris I

    2015-05-01

    Populations of isogenic embryonic stem cells or clonal bacteria often exhibit extensive phenotypic heterogeneity that arises from intrinsic stochastic dynamics of cells. The phenotypic state of a cell can be transmitted epigenetically in cell division, leading to correlations in the states of cells related by descent. The extent of these correlations is determined by the rates of transitions between the phenotypic states. Therefore, a snapshot of the phenotypes of a collection of cells with known genealogical structure contains information on phenotypic dynamics. Here, we use a model of phenotypic dynamics on a genealogical tree to define an inference method that allows extraction of an approximate probabilistic description of the dynamics from observed phenotype correlations as a function of the degree of kinship. The approach is tested and validated on the example of Pyoverdine dynamics in Pseudomonas aeruginosa colonies. Interestingly, we find that correlations among pairs and triples of distant relatives have a simple but nontrivial structure indicating that observed phenotypic dynamics on the genealogical tree is approximately conformal--a symmetry characteristic of critical behavior in physical systems. The proposed inference method is sufficiently general to be applied in any system where lineage information is available. PMID:25902540

  17. Carbon Monitoring System Flux Estimation and Attribution: Impact of ACOS-GOSAT X(CO2) Sampling on the Inference of Terrestrial Biospheric Sources and Sinks

    NASA Technical Reports Server (NTRS)

    Liu, Junjie; Bowman, Kevin W.; Lee, Memong; Henze, David K.; Bousserez, Nicolas; Brix, Holger; Collatz, G. James; Menemenlis, Dimitris; Ott, Lesley; Pawson, Steven; Jones, Dylan; Nassar, Ray

    2014-01-01

    Using an Observing System Simulation Experiment (OSSE), we investigate the impact of JAXA Greenhouse gases Observing SATellite 'IBUKI' (GOSAT) sampling on the estimation of terrestrial biospheric flux with the NASA Carbon Monitoring System Flux (CMS-Flux) estimation and attribution strategy. The simulated observations in the OSSE use the actual column carbon dioxide (X(CO2)) b2.9 retrieval sensitivity and quality control for the year 2010 processed through the Atmospheric CO2 Observations from Space algorithm. CMS-Flux is a variational inversion system that uses the GEOS-Chem forward and adjoint model forced by a suite of observationally constrained fluxes from ocean, land and anthropogenic models. We investigate the impact of GOSAT sampling on flux estimation in two aspects: 1) random error uncertainty reduction and 2) the global and regional bias in posterior flux resulted from the spatiotemporally biased GOSAT sampling. Based on Monte Carlo calculations, we find that global average flux uncertainty reduction ranges from 25% in September to 60% in July. When aggregated to the 11 land regions designated by the phase 3 of the Atmospheric Tracer Transport Model Intercomparison Project, the annual mean uncertainty reduction ranges from 10% over North American boreal to 38% over South American temperate, which is driven by observational coverage and the magnitude of prior flux uncertainty. The uncertainty reduction over the South American tropical region is 30%, even with sparse observation coverage. We show that this reduction results from the large prior flux uncertainty and the impact of non-local observations. Given the assumed prior error statistics, the degree of freedom for signal is approx.1132 for 1-yr of the 74 055 GOSAT X(CO2) observations, which indicates that GOSAT provides approx.1132 independent pieces of information about surface fluxes. We quantify the impact of GOSAT's spatiotemporally sampling on the posterior flux, and find that a 0.7 gigatons of

  18. Inferences on the hydrothermal system beneath the resurgent dome in Long Valley Caldera, east-central California, USA, from recent pumping tests and geochemical sampling

    USGS Publications Warehouse

    Farrar, C.D.; Sorey, M.L.; Roeloffs, E.; Galloway, D.L.; Howle, J.F.; Jacobson, R.

    2003-01-01

    Quaternary volcanic unrest has provided heat for episodic hydrothermal circulation in the Long Valley caldera, including the present-day hydrothermal system, which has been active over the past 40 kyr. The most recent period of crustal unrest in this region of east-central California began around 1980 and has included periods of intense seismicity and ground deformation. Uplift totaling more than 0.7 m has been centered on the caldera's resurgent dome, and is best modeled by a near-vertical ellipsoidal source centered at depths of 6-7 km. Modeling of both deformation and microgravity data now suggests that (1) there are two inflation sources beneath the caldera, a shallower source 7-10 km beneath the resurgent dome and a deeper source ???15 km beneath the caldera's south moat and (2) the shallower source may contain components of magmatic brine and gas. The Long Valley Exploration Well (LVEW), completed in 1998 on the resurgent dome, penetrates to a depth of 3 km directly above this shallower source, but bottoms in a zone of 100??C fluid with zero vertical thermal gradient. Although these results preclude extrapolations of temperatures at depths below 3 km, other information obtained from flow tests and fluid sampling at this well indicates the presence of magmatic volatiles and fault-related permeability within the metamorphic basement rocks underlying the volcanic fill. In this paper, we present recently acquired data from LVEW and compare them with information from other drill holes and thermal springs in Long Valley to delineate the likely flow paths and fluid system properties under the resurgent dome. Additional information from mineralogical assemblages in core obtained from fracture zones in LVEW documents a previous period of more vigorous and energetic fluid circulation beneath the resurgent dome. Although this system apparently died off as a result of mineral deposition and cooling (and/or deepening) of magmatic heat sources, flow testing and tidal

  19. Category Representation for Classification and Feature Inference

    ERIC Educational Resources Information Center

    Johansen, Mark K.; Kruschke, John K.

    2005-01-01

    This research's purpose was to contrast the representations resulting from learning of the same categories by either classifying instances or inferring instance features. Prior inference learning research, particularly T. Yamauchi and A. B. Markman (1998), has suggested that feature inference learning fosters prototype representation, whereas…

  20. Enantiomer resolution by pressure increase: inferences from experimental and topological results for the binary enantiomer system (R)- and (S)-mandelic acid.

    PubMed

    Rietveld, Ivo B; Barrio, Maria; Tamarit, Josep-Lluis; Do, Bernard; Céolin, René

    2011-12-15

    In pharmacy, racemic compounds are often problematic, because generally only one of the enantiomers possesses therapeutic activity and it is often difficult to separate them. Even though this problem is likely as old as the pharmaceutical industry, one thermodynamically obvious way of separating racemic crystals has never been studied experimentally, which is by using pressure. Data have been obtained on the equilibria of the (R)- and (S)-mandelic acid system as a function of pressure and temperature. With the use of thermodynamic arguments including the Clapeyron, Schröder, and Prigogine-Defay equations, it has been demonstrated that the conglomerate (crystals of separated enantiomers) becomes more stable than the racemic compound at approximately 0.64 GPa and 460 K. Even though this pressure is still higher than at the bottom of the Mariana Trench, there are no technical obstacles to produce such conditions, making pressure a viable option for separating enantiomers.

  1. Is it possible to infer the equation of state of a mixture of hard discs from that of the one-component system?

    NASA Astrophysics Data System (ADS)

    Santos, Andres

    Based on exact asymptotic properties of the composition-independent virial coefficients of a binary mixture of hard discs in the limits α ≡ σ2/σ1 → 0, α → 1 and α → ∞, R. J. Wheatley (1998, Molec. Phys., 93, 965) has recently proposed an approximate interpolation equation for these coefficients. In this note, the equation of state equivalent to this interpolation is obtained, expressing the compressibility factor of the mixture in terms of that of the pure system. An extension to an arbitrary number of components is also given. The equation of state derived here is compared with another one recently proposed by following a different route (Santos, A., Yuste, S. B., and Lopez de Haro, M., 1999, Molec. Phys., 96, 1) and with Monte Carlo simulation results. It is shown that the latter equation is more accurate than the former one, at least for not too disparate mixtures (0.7 ≤ α < 1).

  2. Exploiting Human Resource Requirements to Infer Human Movement Patterns for Use in Modelling Disease Transmission Systems: An Example from Eastern Province, Zambia.

    PubMed

    Alderton, Simon; Noble, Jason; Schaten, Kathrin; Welburn, Susan C; Atkinson, Peter M

    2015-01-01

    In this research, an agent-based model (ABM) was developed to generate human movement routes between homes and water resources in a rural setting, given commonly available geospatial datasets on population distribution, land cover and landscape resources. ABMs are an object-oriented computational approach to modelling a system, focusing on the interactions of autonomous agents, and aiming to assess the impact of these agents and their interactions on the system as a whole. An A* pathfinding algorithm was implemented to produce walking routes, given data on the terrain in the area. A* is an extension of Dijkstra's algorithm with an enhanced time performance through the use of heuristics. In this example, it was possible to impute daily activity movement patterns to the water resource for all villages in a 75 km long study transect across the Luangwa Valley, Zambia, and the simulated human movements were statistically similar to empirical observations on travel times to the water resource (Chi-squared, 95% confidence interval). This indicates that it is possible to produce realistic data regarding human movements without costly measurement as is commonly achieved, for example, through GPS, or retrospective or real-time diaries. The approach is transferable between different geographical locations, and the product can be useful in providing an insight into human movement patterns, and therefore has use in many human exposure-related applications, specifically epidemiological research in rural areas, where spatial heterogeneity in the disease landscape, and space-time proximity of individuals, can play a crucial role in disease spread.

  3. Investigating the usefulness of satellite-derived fluorescence data in inferring gross primary productivity within the carbon cycle data assimilation system

    NASA Astrophysics Data System (ADS)

    Koffi, E. N.; Rayner, P. J.; Norton, A. J.; Frankenberg, C.; Scholze, M.

    2015-07-01

    Simulations of carbon fluxes with terrestrial biosphere models still exhibit significant uncertainties, in part due to the uncertainty in model parameter values. With the advent of satellite measurements of solar induced chlorophyll fluorescence (SIF), there exists a novel pathway for constraining simulated carbon fluxes and parameter values. We investigate the utility of SIF in constraining gross primary productivity (GPP). As a first test we assess whether SIF simulations are sensitive to important parameters in a biosphere model. SIF measurements at the wavelength of 755 nm are simulated by the Carbon-Cycle Data Assimilation System (CCDAS) which has been augmented by the fluorescence component of the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model. Idealized sensitivity tests of the SCOPE model stand-alone indicate strong sensitivity of GPP to the carboxylation capacity (Vcmax) and of SIF to the chlorophyll AB content (Cab) and incoming short wave radiation. Low sensitivity is found for SIF to Vcmax, however the relationship is subtle, with increased sensitivity under high radiation conditions and lower Vcmax ranges. CCDAS simulates well the patterns of satellite-measured SIF suggesting the combined model is capable of ingesting the data. CCDAS supports the idealized sensitivity tests of SCOPE, with SIF exhibiting sensitivity to Cab and incoming radiation, both of which are treated as perfectly known in previous CCDAS versions. These results demonstrate the need for careful consideration of Cab and incoming radiation when interpreting SIF and the limitations of utilizing SIF to constrain Vcmax in the present set-up in the CCDAS system.

  4. Production of hybrid granitic magma at the advancing front of basaltic underplating: Inferences from the Sesia Magmatic System (south-western Alps, Italy)

    NASA Astrophysics Data System (ADS)

    Sinigoi, Silvano; Quick, James E.; Demarchi, Gabriella; Klötzli, Urs S.

    2016-05-01

    The Permian Sesia Magmatic System of the southwestern Alps displays the plumbing system beneath a Permian caldera, including a deep crustal gabbroic complex, upper crustal granite plutons and a bimodal volcanic field dominated by rhyolitic tuff filling the caldera. Isotopic compositions of the deep crustal gabbro overlap those of coeval andesitic basalts, whereas granites define a distinct, more radiogenic cluster (Sri ≈ 0.708 and 0.710, respectively). AFC computations starting from the best mafic candidate for a starting melt show that Nd and Sr isotopic compositions and trace elements of andesitic basalts may be modeled by reactive bulk assimilation of ≈ 30% of partially depleted crust and ≈ 15%-30% gabbro fractionation. Trace elements of the deep crustal gabbro cumulates require a further ≈ 60% fractionation of the andesitic basalt and loss of ≈ 40% of silica-rich residual melt. The composition of the granite plutons is consistent with a mixture of relatively constant proportions of residual melt delivered from the gabbro and anatectic melt. Chemical and field evidence leads to a conceptual model which links the production of the two granitic components to the evolution of the Mafic Complex. During the growth of the Mafic Complex, progressive incorporation of packages of crustal rocks resulted in a roughly steady state rate of assimilation. Anatectic granite originates in the hot zone of melting crust located above the advancing mafic intrusion. Upward segregation of anatectic melts facilitates the assimilation of the partially depleted restite by stoping. At each cycle of mafic intrusion and incorporation, residual and anatectic melts are produced in roughly constant proportions, because the amount of anatectic melt produced at the roof is a function of volume and latent heat of crystallization of the underplated mafic melt which in turn produces proportional amounts of hybrid gabbro cumulates and residual melt. Such a process can explain the

  5. Exploiting Human Resource Requirements to Infer Human Movement Patterns for Use in Modelling Disease Transmission Systems: An Example from Eastern Province, Zambia.

    PubMed

    Alderton, Simon; Noble, Jason; Schaten, Kathrin; Welburn, Susan C; Atkinson, Peter M

    2015-01-01

    In this research, an agent-based model (ABM) was developed to generate human movement routes between homes and water resources in a rural setting, given commonly available geospatial datasets on population distribution, land cover and landscape resources. ABMs are an object-oriented computational approach to modelling a system, focusing on the interactions of autonomous agents, and aiming to assess the impact of these agents and their interactions on the system as a whole. An A* pathfinding algorithm was implemented to produce walking routes, given data on the terrain in the area. A* is an extension of Dijkstra's algorithm with an enhanced time performance through the use of heuristics. In this example, it was possible to impute daily activity movement patterns to the water resource for all villages in a 75 km long study transect across the Luangwa Valley, Zambia, and the simulated human movements were statistically similar to empirical observations on travel times to the water resource (Chi-squared, 95% confidence interval). This indicates that it is possible to produce realistic data regarding human movements without costly measurement as is commonly achieved, for example, through GPS, or retrospective or real-time diaries. The approach is transferable between different geographical locations, and the product can be useful in providing an insight into human movement patterns, and therefore has use in many human exposure-related applications, specifically epidemiological research in rural areas, where spatial heterogeneity in the disease landscape, and space-time proximity of individuals, can play a crucial role in disease spread. PMID:26421926

  6. Exploiting Human Resource Requirements to Infer Human Movement Patterns for Use in Modelling Disease Transmission Systems: An Example from Eastern Province, Zambia

    PubMed Central

    Alderton, Simon; Noble, Jason; Schaten, Kathrin; Welburn, Susan C.; Atkinson, Peter M.

    2015-01-01

    In this research, an agent-based model (ABM) was developed to generate human movement routes between homes and water resources in a rural setting, given commonly available geospatial datasets on population distribution, land cover and landscape resources. ABMs are an object-oriented computational approach to modelling a system, focusing on the interactions of autonomous agents, and aiming to assess the impact of these agents and their interactions on the system as a whole. An A* pathfinding algorithm was implemented to produce walking routes, given data on the terrain in the area. A* is an extension of Dijkstra’s algorithm with an enhanced time performance through the use of heuristics. In this example, it was possible to impute daily activity movement patterns to the water resource for all villages in a 75 km long study transect across the Luangwa Valley, Zambia, and the simulated human movements were statistically similar to empirical observations on travel times to the water resource (Chi-squared, 95% confidence interval). This indicates that it is possible to produce realistic data regarding human movements without costly measurement as is commonly achieved, for example, through GPS, or retrospective or real-time diaries. The approach is transferable between different geographical locations, and the product can be useful in providing an insight into human movement patterns, and therefore has use in many human exposure-related applications, specifically epidemiological research in rural areas, where spatial heterogeneity in the disease landscape, and space-time proximity of individuals, can play a crucial role in disease spread. PMID:26421926

  7. Inferring processes from spatial patterns: the role of directional and non-directional forces in shaping fish larvae distribution in a freshwater lake system.

    PubMed

    Bertolo, Andrea; Blanchet, F Guillaume; Magnan, Pierre; Brodeur, Philippe; Mingelbier, Marc; Legendre, Pierre

    2012-01-01

    Larval dispersal is a crucial factor for fish recruitment. For fishes with relatively small-bodied larvae, drift has the potential to play a more important role than active habitat selection in determining larval dispersal; therefore, we expect small-bodied fish larvae to be poorly associated with habitat characteristics. To test this hypothesis, we used as model yellow perch (Perca flavescens), whose larvae are among the smallest among freshwater temperate fishes. Thus, we analysed the habitat association of yellow perch larvae at multiple spatial scales in a large shallow fluvial lake by explicitly modelling directional (e.g. due to water currents) and non-directional (e.g. due to aggregation) spatial patterns. This allowed us to indirectly assess the relative roles of drift (directional process) and potential habitat choice on larval dispersal. Our results give weak support to the drift hypothesis, whereas yellow perch show a strong habitat association at unexpectedly small sizes, when compared to other systems. We found consistent non-directional patterns in larvae distributions at both broad and medium spatial scales but only few significant directional components. The environmental variables alone (e.g. vegetation) generally explained a significant and biologically relevant fraction of the variation in fish larvae distribution data. These results suggest that (i) drift plays a minor role in this shallow system, (ii) larvae display spatial patterns that only partially covary with environmental variables, and (iii) larvae are associated to specific habitats. By suggesting that habitat association potentially includes an active choice component for yellow perch larvae, our results shed new light on the ecology of freshwater fish larvae and should help in building more realistic recruitment models.

  8. Inferring heuristic classification hierarchies from natural language input

    NASA Technical Reports Server (NTRS)

    Hull, Richard; Gomez, Fernando

    1993-01-01

    A methodology for inferring hierarchies representing heuristic knowledge about the check out, control, and monitoring sub-system (CCMS) of the space shuttle launch processing system from natural language input is explained. Our method identifies failures explicitly and implicitly described in natural language by domain experts and uses those descriptions to recommend classifications for inclusion in the experts' heuristic hierarchies.

  9. Towards a Better Understanding of the Hydrologic Setting of the Nubian Sandstone Aquifer System: Inferences from Groundwater Flow Models, CL-36 Ages, and GRACE Data

    NASA Astrophysics Data System (ADS)

    Sultan, M.; Mohamed, A.; Yan, E.; Ahmed, E.; Sturchio, N. C.

    2015-12-01

    The Nubian Sandstone Aquifer System (NSAS), one of the largest (area: ~2×106 km2) groundwater systems worldwide, is formed of three major sub-basins: Kufra (Libya, NE Chad and NW Sudan), Dakhla (Egypt), and N. Sudan Platform (Sudan). To determine the mean residence time of water in the aquifer, the connectivity of its sub-basins and the groundwater flow across these sub-basins have to be understood. An integrated approach was adopted to address these issues using: (1) a regional calibrated groundwater flow model that simulates early (>10,000 years) steady-state conditions under wet climatic periods, and later (<10,000 years) transient conditions under arid condition; (2) 36Cl ages, and (3) GRACE solutions. Our findings include: (1) the NSAS was recharged (recharge: plains: 2-7 mm/yr; highlands 10-27 mm/yr) in the previous wet climatic periods on a regional scale, yet its outcrops are still receiving in dry periods appreciable precipitation over the highlands and modest (3.04±1.10 km3/yr) local recharge; (2) a progressive increase in 36Cl groundwater ages were observed along groundwater flow directions and along structures that are sub-parallel to the groundwater flow direction; (3) the NE-SW Pelusium shear zone provides a preferred groundwater flow pathway from the Kufra to the Dakhla sub-basin as evidenced by the relatively high hydraulic conductivities and relatively younger ages of groundwater along the shear zone compared to the groundwater ages in areas surrounding the shear zone; (4) the E-W trending Uweinat-Aswan basement uplift impedes groundwater flow from the N-Sudan Platform sub-basin as evidenced by the difference in groundwater isotopic compositions across the uplift, the depletion in GRACE-derived total water storage north but not south, of the uplift, and groundwater ages that are indicative of autochthonous precipitation and recharge over the Dakhla sub-basin. Our findings provide valuable insights into optimum ways for the utilization of the NSAS

  10. F-OWL: An Inference Engine for Semantic Web

    NASA Technical Reports Server (NTRS)

    Zou, Youyong; Finin, Tim; Chen, Harry

    2004-01-01

    Understanding and using the data and knowledge encoded in semantic web documents requires an inference engine. F-OWL is an inference engine for the semantic web language OWL language based on F-logic, an approach to defining frame-based systems in logic. F-OWL is implemented using XSB and Flora-2 and takes full advantage of their features. We describe how F-OWL computes ontology entailment and compare it with other description logic based approaches. We also describe TAGA, a trading agent environment that we have used as a test bed for F-OWL and to explore how multiagent systems can use semantic web concepts and technology.

  11. Inferring the magmatic plumbing system and melt evolution from olivine-hosted melt inclusions at Cinder Cone, Lassen Volcanic National Park, California

    NASA Astrophysics Data System (ADS)

    Walowski, K. J.; Wallace, P. J.; Cashman, K. V.; Clynne, M. A.

    2011-12-01

    Monogenetic basaltic cinder cones are the most abundant volcanic landform on Earth. While typically short-lived, cinder cone eruptions often display a range of eruption styles, including Strombolian, violent Strombolian, and even sub-Plinian activity. However, the processes driving explosive cinder cone eruptions remain poorly understood. In this study we investigate the volatile (H2O, CO2), major, and trace element chemistry of olivine-hosted melt inclusions from the tephra of 'Cinder Cone,' Lassen Volcanic NP, to better understand basaltic cinder cone eruptions and their underlying plumbing systems. Erupted in 1666 C.E., Cinder Cone is a young, un-vegetated cinder cone with well-preserved lava flows and tephra deposits. We have divided the tephra sequence, previously described by Heiken (1978) as Units 1, 2 and 3, into nine fall samples (LCC-9 through LCC-1). From these nine, we have obtained data from four tephra samples that span the sequence, one each corresponding with Units 1 and 3, and two from Unit 2. Olivine-hosted melt inclusions from the tephra at Cinder Cone trapped some of the most volatile-rich (1.7-3.4 wt% H2O, 530-1375 ppm CO2) and primitive (8.4-9.7 wt% MgO; olivine hosts Fo88-90) melts yet measured in the Cascade Arc (Ruscitto et al., 2010, EPSL). The melt inclusions, however, do not show evidence of the temporal changes in composition seen in whole rock and bulk tephra data that result from crustal contamination. Nearly all of the analyzed melt inclusions have lower SiO2 (50.4 wt.%) and higher TiO2 contents (0.8-0.95 wt%) than the whole-rock compositions (53-60 wt% and 0.5-0.85 wt.%, respectively). The range in H2O and CO2 concentrations likewise remains remarkably constant throughout the tephra deposit, with only the basal-most unit having a few higher H2O values. The differences between the whole-rock and melt inclusion compositions suggest that olivine crystallized from a primitive parental magma as it was rising to the surface, prior to the

  12. Response of a hydrothermal system to magmatic heat inferred from temporal variations in the complex frequencies of long-period events at Kusatsu-Shirane Volcano, Japan

    USGS Publications Warehouse

    Nakano, M.; Kumagai, H.

    2005-01-01

    We investigate temporal variations in the complex frequencies (frequency and quality factor Q) of long-period (LP) events that occurred at Kusatsu-Shirane Volcano, central Japan. We analyze LP waveforms observed at this volcano in the period between 1988 and 1995, which covers a seismically active period between 1989 and 1993. Systematic temporal variations in the complex frequencies are observed in October-November 1989, July-October 1991, and September 1992-January 1993. We use acoustic properties of a crack filled with hydrothermal fluids to interpret the observed temporal variations in the complex frequencies. The temporal variations in October-November 1989 can be divided into two periods, which are explained by a gradual decrease and increase of a gas-volume fraction in a water-steam mixture in a crack, respectively. The temporal variations in July-October 1991 can be also divided into two periods. These variations in the first and second periods are similar to those observed in November 1989 and in September-November 1992, respectively, and are interpreted as drying of a water-steam mixture and misty gas in a crack, respectively. The repeated nature of the temporal variations observed in similar seasons between July and November suggests the existence of seasonality in the occurrence of LP events. This may be caused by a seasonally variable meteoritic water supply to a hydrothermal system, which may have been heated by the flux of volcanic gases from magma beneath this volcano. ?? 2005 Elsevier B.V. All rights reserved.

  13. Patterns of intestinal schistosomiasis among mothers and young children from Lake Albert, Uganda: water contact and social networks inferred from wearable global positioning system dataloggers.

    PubMed

    Seto, Edmund Y W; Sousa-Figueiredo, José C; Betson, Martha; Byalero, Chris; Kabatereine, Narcis B; Stothard, J Russell

    2012-11-01

    The establishment of a national control programme (NCP) in Uganda has led to routine treatment of intestinal schistosomiasis with praziquantel in the communities along Lake Albert. However, because regular water contact remains a way of life for these populations, re-infection continues to mitigate the sustainability of the chemotherapy-based programme. A six-month longitudinal study was conducted in one Lake Albert community with the aim of characterizing water contact exposure and infection among mothers and their young preschool-aged children as the latter are not yet formally included within the NCP. At baseline the cohort of 37 mothers, 36 preschool-aged children had infection prevalences of 62% and 67%, respectively, which diminished to 20% and 29%, respectively, at the 6-month post-treatment follow-up. The subjects wore global positioning system (GPS) datalogging devices over a 3-day period shortly after baseline, allowing for the estimation of time spent at the lakeshore as an exposure metric, which was found to be associated with prevalence at follow-up (OR = 2.1, P = 0.01 for both mothers and young children and odds ratio (OR) = 4.4, P = 0.01 for young children alone). A social network of interpersonal interactions was also derived from the GPS data, and the exposures were positively associated both with the number and duration of peer interaction, suggesting the importance of socio-cultural factors associated with water contact behaviour. The findings illustrate reduction in both prevalence and intensity of infection in this community after treatment as well as remarkably high rates of water contact exposure and re-infection, particularly among younger children. We believe that this should now be formally considered within NCP, which may benefit from more in-depth ethnographic exploration of factors related to water contact as this should provide new opportunities for sustaining control.

  14. Submarine canyon-head morphologies and inferred sediment transport processes in the Almanzora-Alías-Garrucha canyon system (SW Mediterranean)

    NASA Astrophysics Data System (ADS)

    Durán, R.; Puig, P.; Muñoz, A.; Elvira, E.; Guillén, J.

    2015-12-01

    Submarine canyons are morphological incisions into continental margins that act as major conduits of sediment from shallow- to deep-sea regions. Different transport processes and triggering mechanisms involving various time-scales can operate through them. Canyon heads are key areas for understanding the shelf-to-canyon sedimentary dynamics and assessing the predominant hydrodynamic and sedimentary processes shaping their morphology. High-resolution multibeam bathymetries were conducted at the various heads from the Almanzora-Alías-Garrucha canyon system to recognize their specific morphological features. A direct connection from the Almanzora River was evidenced by the coalescence of cyclic steps on the prodelta deposits and their continuation towards various canyon heads. This suggests the occurrence of flood events causing hyperpycnal flows that progress directly into the canyon. A second type of canyon head results from the formation and merging of linear gullies at the southern limit of the prodelta, being interpreted as the morphological expression of the distal off-shelf transport of flood-related hyperycnal flows potentially transformed into wave-supported sediment gravity flows. These two canyon head occur at 80-90 m water depth, incising only the outer shelf. A third canyon head morphological type was found at much shallower water depths (10-20 m), being disconnected from any major river source. They cut into the infralittoral prograding wedge and some tributaries show crescent shaped bedforms (CSB) along their axis. These CSB have been observed until a water depth of 90 m and have been interpreted as the result of storm-induced sediment gravity flows. An instrumented mooring was deployed from October 2014 to April 2015 to monitor the contemporary sediment transport processes through a canyon axis with CSB. The sedimentary dynamics was governed by storms, with several down-canyon transport events, but none of the storms triggered a sediment gravity flow.

  15. Increased mantle heat flow with on-going rifting of the West Antarctic rift system inferred from characterisation of plagioclase peridotite in the shallow Antarctic mantle

    NASA Astrophysics Data System (ADS)

    Martin, A. P.; Cooper, A. F.; Price, R. C.

    2014-03-01

    The lithospheric, and shallow asthenospheric, mantle in Southern Victoria Land are known to record anomalously high heat flow but the cause remains imperfectly understood. To address this issue plagioclase peridotite xenoliths have been collected from Cenozoic alkalic igneous rocks at three localities along a 150 km transect across the western shoulder of the West Antarctic rift system in Southern Victoria Land, Antarctica. There is a geochemical, thermal and chronological progression across this section of the rift shoulder from relatively hot, young and thick lithosphere in the west to cooler, older and thinner lithosphere in the east. Overprinting this progression are relatively more recent mantle refertilising events. Melt depletion and refertilisation was relatively limited in the lithospheric mantle to the west but has been more extensive in the east. Thermometry obtained from orthopyroxene in these plagioclase peridotites indicates that those samples most recently affected by refertilising melts have attained the highest temperatures, above those predicted from idealised dynamic rift or Northern Victoria Land geotherms and higher than those prevailing in the equivalent East Antarctic mantle. Anomalously high heat flow can thus be attributed to entrapment of syn-rift melts in the lithosphere, probably since regional magmatism commenced at least 24 Myr ago. The chemistry and mineralogy of shallow plagioclase peridotite mantle can be explained by up to 8% melt extraction and a series of refertilisation events. These include: (a) up to 8% refertilisation by a N-MORB melt; (b) metasomatism involving up to 1% addition of a subduction-related component; and (c) addition of ~ 1.5% average calcio-carbonatite. A high MgO group of clinopyroxenes can be modelled by the addition of up to 1% alkalic melt. Melt extraction and refertilisation mainly occurred in the spinel stability field prior to decompression and uplift. In this region mantle plagioclase originates by a

  16. Pathway network inference from gene expression data

    PubMed Central

    2014-01-01

    Background The development of high-throughput omics technologies enabled genome-wide measurements of the activity of cellular elements and provides the analytical resources for the progress of the Systems Biology discipline. Analysis and interpretation of gene expression data has evolved from the gene to the pathway and interaction level, i.e. from the detection of differentially expressed genes, to the establishment of gene interaction networks and the identification of enriched functional categories. Still, the understanding of biological systems requires a further level of analysis that addresses the characterization of the interaction between functional modules. Results We present a novel computational methodology to study the functional interconnections among the molecular elements of a biological system. The PANA approach uses high-throughput genomics measurements and a functional annotation scheme to extract an activity profile from each functional block -or pathway- followed by machine-learning methods to infer the relationships between these functional profiles. The result is a global, interconnected network of pathways that represents the functional cross-talk within the molecular system. We have applied this approach to describe the functional transcriptional connections during the yeast cell cycle and to identify pathways that change their connectivity in a disease condition using an Alzheimer example. Conclusions PANA is a useful tool to deepen in our understanding of the functional interdependences that operate within complex biological systems. We show the approach is algorithmically consistent and the inferred network is well supported by the available functional data. The method allows the dissection of the molecular basis of the functional connections and we describe the different regulatory mechanisms that explain the network's topology obtained for the yeast cell cycle data. PMID:25032889

  17. Long-range forecast of all India summer monsoon rainfall using adaptive neuro-fuzzy inference system: skill comparison with CFSv2 model simulation and real-time forecast for the year 2015

    NASA Astrophysics Data System (ADS)

    Chaudhuri, S.; Das, D.; Goswami, S.; Das, S. K.

    2016-02-01

    All India summer monsoon rainfall (AISMR) characteristics play a vital role for the policy planning and national economy of the country. In view of the significant impact of monsoon system on regional as well as global climate systems, accurate prediction of summer monsoon rainfall has become a challenge. The objective of this study is to develop an adaptive neuro-fuzzy inference system (ANFIS) for long range forecast of AISMR. The NCEP/NCAR reanalysis data of temperature, zonal and meridional wind at different pressure levels have been taken to construct the input matrix of ANFIS. The membership of the input parameters for AISMR as high, medium or low is estimated with trapezoidal membership function. The fuzzified standardized input parameters and the de-fuzzified target output are trained with artificial neural network models. The forecast of AISMR with ANFIS is compared with non-hybrid multi-layer perceptron model (MLP), radial basis functions network (RBFN) and multiple linear regression (MLR) models. The forecast error analyses of the models reveal that ANFIS provides the best forecast of AISMR with minimum prediction error of 0.076, whereas the errors with MLP, RBFN and MLR models are 0.22, 0.18 and 0.73 respectively. During validation with observations, ANFIS shows its potency over the said comparative models. Performance of the ANFIS model is verified through different statistical skill scores, which also confirms the aptitude of ANFIS in forecasting AISMR. The forecast skill of ANFIS is also observed to be better than Climate Forecast System version 2. The real-time forecast with ANFIS shows possibility of deficit (65-75 cm) AISMR in the year 2015.

  18. Holocene Paleoceanographic Conditions of Cabo Frio Upwelling System (Rio de Janeiro / Brazil). As Inferred by Bulk and Molecular Geochemical Approach From Sedimentary Organic Matter.

    NASA Astrophysics Data System (ADS)

    Gurgel, M. H.; Sifeddine, A.; Lallier-Vergès, E.; Boussafir, M.; Jacob, J.

    2005-12-01

    The main upwelling areas in Brazil are along its central coast. Among these, the Cabo Frio (23 deg S x 42 deg W) zone has the strongest signal of low sea surface temperature. The Cabo Frio coastal upwelling cell is controlled by three factors: the topography and orientation of the coastline; the position of the Brazilian Current axes, and the wind pattern, the last being the determining factor. Upwelling events in this region are concentrated in the austral spring and summer (September to April). Their occurrence is associated with NE-ENE winds, and they are inhibited when the wind blows from the southern quadrant (atmospheric frontal passages linked to polar advection). This pattern is associated with the seasonal displacements of the South Atlantic Convergence Zone (SACZ) over this region. Better knowledge of the SACZ behaviour over the time is very important to understanding the climatic variability over the South America in the Holocene. A high resolution sampling study is being done on two Kullenberg piston cores collected from the Brazilian continental shelf, 30-35 km southwest from the Cabo Frio island (115 m and 124 m water depth). AMS radiocarbon dating of organic matter from the core bases gives ages of 3,300 BP and 12,600 BP, respectively. We present the results of elemental analysis (TC and TN), Rock-Eval analysis (TOC, HI and OI), sedimentary organic matter petrographic analysis and alkenone sea surface paleo temperatures. Despite coming from an upwelling area, sediments have very low TOC content and show an increase from around 8,000 year with variations modulated by millennial and secular cycles. Sedimentary organic matter is marine type II that, associated with optical analysis, indicates a high-degraded state and a little continental contribution. The range of paleo temperature values are similar to results of other studies carried out in the same system and are coherent with regional Brazilian Current dynamics. These first results identify two

  19. sick: The Spectroscopic Inference Crank

    NASA Astrophysics Data System (ADS)

    Casey, Andrew R.

    2016-03-01

    There exists an inordinate amount of spectral data in both public and private astronomical archives that remain severely under-utilized. The lack of reliable open-source tools for analyzing large volumes of spectra contributes to this situation, which is poised to worsen as large surveys successively release orders of magnitude more spectra. In this article I introduce sick, the spectroscopic inference crank, a flexible and fast Bayesian tool for inferring astrophysical parameters from spectra. sick is agnostic to the wavelength coverage, resolving power, or general data format, allowing any user to easily construct a generative model for their data, regardless of its source. sick can be used to provide a nearest-neighbor estimate of model parameters, a numerically optimized point estimate, or full Markov Chain Monte Carlo sampling of the posterior probability distributions. This generality empowers any astronomer to capitalize on the plethora of published synthetic and observed spectra, and make precise inferences for a host of astrophysical (and nuisance) quantities. Model intensities can be reliably approximated from existing grids of synthetic or observed spectra using linear multi-dimensional interpolation, or a Cannon-based model. Additional phenomena that transform the data (e.g., redshift, rotational broadening, continuum, spectral resolution) are incorporated as free parameters and can be marginalized away. Outlier pixels (e.g., cosmic rays or poorly modeled regimes) can be treated with a Gaussian mixture model, and a noise model is included to account for systematically underestimated variance. Combining these phenomena into a scalar-justified, quantitative model permits precise inferences with credible uncertainties on noisy data. I describe the common model features, the implementation details, and the default behavior, which is balanced to be suitable for most astronomical applications. Using a forward model on low-resolution, high signal

  20. Universum Inference and Corpus Homogeneity

    NASA Astrophysics Data System (ADS)

    Vogel, Carl; Lynch, Gerard; Janssen, Jerom

    Universum Inference is re-interpreted for assessment of corpus homogeneity in computational stylometry. Recent stylometric research quantifies strength of characterization within dramatic works by assessing the homogeneity of corpora associated with dramatic personas. A methodological advance is suggested to mitigate the potential for the assessment of homogeneity to be achieved by chance. Baseline comparison analysis is constructed for contributions to debates by nonfictional participants: the corpus analyzed consists of transcripts of US Presidential and Vice-Presidential debates from the 2000 election cycle. The corpus is also analyzed in translation to Italian, Spanish and Portuguese. Adding randomized categories makes assessments of homogeneity more conservative.

  1. Information Theory, Inference and Learning Algorithms

    NASA Astrophysics Data System (ADS)

    Mackay, David J. C.

    2003-10-01

    Information theory and inference, often taught separately, are here united in one entertaining textbook. These topics lie at the heart of many exciting areas of contemporary science and engineering - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography. This textbook introduces theory in tandem with applications. Information theory is taught alongside practical communication systems, such as arithmetic coding for data compression and sparse-graph codes for error-correction. A toolbox of inference techniques, including message-passing algorithms, Monte Carlo methods, and variational approximations, are developed alongside applications of these tools to clustering, convolutional codes, independent component analysis, and neural networks. The final part of the book describes the state of the art in error-correcting codes, including low-density parity-check codes, turbo codes, and digital fountain codes -- the twenty-first century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, David MacKay's groundbreaking book is ideal for self-learning and for undergraduate or graduate courses. Interludes on crosswords, evolution, and sex provide entertainment along the way. In sum, this is a textbook on information, communication, and coding for a new generation of students, and an unparalleled entry point into these subjects for professionals in areas as diverse as computational biology, financial engineering, and machine learning.

  2. Adaptive neuro-fuzzy inference system multi-objective optimization using the genetic algorithm/singular value decomposition method for modelling the discharge coefficient in rectangular sharp-crested side weirs

    NASA Astrophysics Data System (ADS)

    Khoshbin, Fatemeh; Bonakdari, Hossein; Hamed Ashraf Talesh, Seyed; Ebtehaj, Isa; Zaji, Amir Hossein; Azimi, Hamed

    2016-06-01

    In the present article, the adaptive neuro-fuzzy inference system (ANFIS) is employed to model the discharge coefficient in rectangular sharp-crested side weirs. The genetic algorithm (GA) is used for the optimum selection of membership functions, while the singular value decomposition (SVD) method helps in computing the linear parameters of the ANFIS results section (GA/SVD-ANFIS). The effect of each dimensionless parameter on discharge coefficient prediction is examined in five different models to conduct sensitivity analysis by applying the above-mentioned dimensionless parameters. Two different sets of experimental data are utilized to examine the models and obtain the best model. The study results indicate that the model designed through GA/SVD-ANFIS predicts the discharge coefficient with a good level of accuracy (mean absolute percentage error = 3.362 and root mean square error = 0.027). Moreover, comparing this method with existing equations and the multi-layer perceptron-artificial neural network (MLP-ANN) indicates that the GA/SVD-ANFIS method has superior performance in simulating the discharge coefficient of side weirs.

  3. Comparison of an adaptive neuro-fuzzy inference system and an artificial neural network in the cross-talk correction of simultaneous 99 m Tc / 201Tl SPECT imaging using a GATE Monte-Carlo simulation

    NASA Astrophysics Data System (ADS)

    Heidary, Saeed; Setayeshi, Saeed; Ghannadi-Maragheh, Mohammad

    2014-09-01

    The aim of this study is to compare the adaptive neuro-fuzzy inference system (ANFIS) and the artificial neural network (ANN) to estimate the cross-talk contamination of 99 m Tc / 201 Tl image acquisition in the 201 Tl energy window (77 ± 15% keV). GATE (Geant4 Application in Emission and Tomography) is employed due to its ability to simulate multiple radioactive sources concurrently. Two kinds of phantoms, including two digital and one physical phantom, are used. In the real and the simulation studies, data acquisition is carried out using eight energy windows. The ANN and the ANFIS are prepared in MATLAB, and the GATE results are used as a training data set. Three indications are evaluated and compared. The ANFIS method yields better outcomes for two indications (Spearman's rank correlation coefficient and contrast) and the two phantom results in each category. The maximum image biasing, which is the third indication, is found to be 6% more than that for the ANN.

  4. Bayesian inference for OPC modeling

    NASA Astrophysics Data System (ADS)

    Burbine, Andrew; Sturtevant, John; Fryer, David; Smith, Bruce W.

    2016-03-01

    The use of optical proximity correction (OPC) demands increasingly accurate models of the photolithographic process. Model building and inference techniques in the data science community have seen great strides in the past two decades which make better use of available information. This paper aims to demonstrate the predictive power of Bayesian inference as a method for parameter selection in lithographic models by quantifying the uncertainty associated with model inputs and wafer data. Specifically, the method combines the model builder's prior information about each modelling assumption with the maximization of each observation's likelihood as a Student's t-distributed random variable. Through the use of a Markov chain Monte Carlo (MCMC) algorithm, a model's parameter space is explored to find the most credible parameter values. During parameter exploration, the parameters' posterior distributions are generated by applying Bayes' rule, using a likelihood function and the a priori knowledge supplied. The MCMC algorithm used, an affine invariant ensemble sampler (AIES), is implemented by initializing many walkers which semiindependently explore the space. The convergence of these walkers to global maxima of the likelihood volume determine the parameter values' highest density intervals (HDI) to reveal champion models. We show that this method of parameter selection provides insights into the data that traditional methods do not and outline continued experiments to vet the method.

  5. Dopamine, affordance and active inference.

    PubMed

    Friston, Karl J; Shiner, Tamara; FitzGerald, Thomas; Galea, Joseph M; Adams, Rick; Brown, Harriet; Dolan, Raymond J; Moran, Rosalyn; Stephan, Klaas Enno; Bestmann, Sven

    2012-01-01

    The role of dopamine in behaviour and decision-making is often cast in terms of reinforcement learning and optimal decision theory. Here, we present an alternative view that frames the physiology of dopamine in terms of Bayes-optimal behaviour. In this account, dopamine controls the precision or salience of (external or internal) cues that engender action. In other words, dopamine balances bottom-up sensory information and top-down prior beliefs when making hierarchical inferences (predictions) about cues that have affordance. In this paper, we focus on the consequences of changing tonic levels of dopamine firing using simulations of cued sequential movements. Crucially, the predictions driving movements are based upon a hierarchical generative model that infers the context in which movements are made. This means that we can confuse agents by changing the context (order) in which cues are presented. These simulations provide a (Bayes-optimal) model of contextual uncertainty and set switching that can be quantified in terms of behavioural and electrophysiological responses. Furthermore, one can simulate dopaminergic lesions (by changing the precision of prediction errors) to produce pathological behaviours that are reminiscent of those seen in neurological disorders such as Parkinson's disease. We use these simulations to demonstrate how a single functional role for dopamine at the synaptic level can manifest in different ways at the behavioural level.

  6. Trandimensional Inference in the Geosciences

    NASA Astrophysics Data System (ADS)

    Bodin, Thomas

    2016-04-01

    An inverse problem is the task often occurring in many branches of Earth sciences, where the values of some model parameters describing the Earth must be obtained given noisy observations made at the surface. In all applications of inversion, assumptions are made about the nature of the model parametrisation and data noise characteristics, and results can significantly depend on those assumptions. These quantities are often manually `tuned' by means of subjective trial-and-error procedures, and this prevents to accurately quantify uncertainties in the solution. A Bayesian approach allows these assumptions to be relaxed by incorporating relevant parameters as unknowns in the inference problem. Rather than being forced to make decisions on parametrisation, the level of data noise and the weights between data types in advance, as is often the case in an optimization framework, the choice can be informed by the data themselves. Probabilistic sampling techniques such as transdimensional Markov chain Monte Carlo, allow sampling over complex posterior probability density functions, thus providing information on constraint, trade-offs and uncertainty in the unknowns. This presentation will present a review of transdimensional inference, and its application to different problems, ranging from Geochemistry to Solid Earth Geophysics.

  7. Inference is bliss: using evolutionary relationship to guide categorical inferences.

    PubMed

    Novick, Laura R; Catley, Kefyn M; Funk, Daniel J

    2011-01-01

    Three experiments, adopting an evolutionary biology perspective, investigated subjects' inferences about living things. Subjects were told that different enzymes help regulate cell function in two taxa and asked which enzyme a third taxon most likely uses. Experiment 1 and its follow-up, with college students, used triads involving amphibians, reptiles, and mammals (reptiles and mammals are most closely related evolutionarily) and plants, fungi, and animals (fungi are more closely related to animals than to plants). Experiment 2, with 10th graders, also included triads involving mammals, birds, and snakes/crocodilians (birds and snakes/crocodilians are most closely related). Some subjects received cladograms (hierarchical diagrams) depicting the evolutionary relationships among the taxa. The effect of providing cladograms depended on students' background in biology. The results illuminate students' misconceptions concerning common taxa and constraints on their willingness to override faulty knowledge when given appropriate evolutionary evidence. Implications for introducing tree thinking into biology curricula are discussed. PMID:21463358

  8. Paleoethological Inference: Therapsids as a Model System.

    ERIC Educational Resources Information Center

    Graves, Brent M.; Duvall, David

    1983-01-01

    Paleoethology seeks to study behavior of ancient animals by building inferential cases of hypothesized behavior patterns. Discusses paleoethological methods and reasons for studying therapsids. Also discusses metabolic rate and possible existence/use of vomeronasal organ to gain insight into the behavior/physiology of these ancient herbivors. (JN)

  9. Inferences about Action Engage Action Systems

    ERIC Educational Resources Information Center

    Taylor, Lawrence J.; Lev-Ari, Shiri; Zwaan, Rolf A.

    2008-01-01

    Verbal descriptions of actions activate compatible motor responses [Glenberg, A. M., & Kaschak, M. P. (2002). Grounding language in action. "Psychonomic Bulletin & Review, 9", 558-565]. Previous studies have found that the motor processes for manual rotation are engaged in a direction-specific manner when a verb disambiguates the direction of…

  10. An Adaptive Network-based Fuzzy Inference System for the detection of thermal and TEC anomalies around the time of the Varzeghan, Iran, (Mw = 6.4) earthquake of 11 August 2012

    NASA Astrophysics Data System (ADS)

    Akhoondzadeh, M.

    2013-09-01

    Anomaly detection is extremely important for forecasting the date, location and magnitude of an impending earthquake. In this paper, an Adaptive Network-based Fuzzy Inference System (ANFIS) has been proposed to detect the thermal and Total Electron Content (TEC) anomalies around the time of the Varzeghan, Iran, (Mw = 6.4) earthquake jolted in 11 August 2012 NW Iran. ANFIS is the famous hybrid neuro-fuzzy network for modeling the non-linear complex systems. In this study, also the detected thermal and TEC anomalies using the proposed method are compared to the results dealing with the observed anomalies by applying the classical and intelligent methods including Interquartile, Auto-Regressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN) and Support Vector Machine (SVM) methods. The duration of the dataset which is comprised from Aqua-MODIS Land Surface Temperature (LST) night-time snapshot images and also Global Ionospheric Maps (GIM), is 62 days. It can be shown that, if the difference between the predicted value using the ANFIS method and the observed value, exceeds the pre-defined threshold value, then the observed precursor value in the absence of non seismic effective parameters could be regarded as precursory anomaly. For two precursors of LST and TEC, the ANFIS method shows very good agreement with the other implemented classical and intelligent methods and this indicates that ANFIS is capable of detecting earthquake anomalies. The applied methods detected anomalous occurrences 1 and 2 days before the earthquake. This paper indicates that the detection of the thermal and TEC anomalies derive their credibility from the overall efficiencies and potentialities of the five integrated methods.

  11. Universal Darwinism As a Process of Bayesian Inference.

    PubMed

    Campbell, John O

    2016-01-01

    Many of the mathematical frameworks describing natural selection are equivalent to Bayes' Theorem, also known as Bayesian updating. By definition, a process of Bayesian Inference is one which involves a Bayesian update, so we may conclude that these frameworks describe natural selection as a process of Bayesian inference. Thus, natural selection serves as a counter example to a widely-held interpretation that restricts Bayesian Inference to human mental processes (including the endeavors of statisticians). As Bayesian inference can always be cast in terms of (variational) free energy minimization, natural selection can be viewed as comprising two components: a generative model of an "experiment" in the external world environment, and the results of that "experiment" or the "surprise" entailed by predicted and actual outcomes of the "experiment." Minimization of free energy implies that the implicit measure of "surprise" experienced serves to update the generative model in a Bayesian manner. This description closely accords with the mechanisms of generalized Darwinian process proposed both by Dawkins, in terms of replicators and vehicles, and Campbell, in terms of inferential systems. Bayesian inference is an algorithm for the accumulation of evidence-based knowledge. This algorithm is now seen to operate over a wide range of evolutionary processes, including natural selection, the evolution of mental models and cultural evolutionary processes, notably including science itself. The variational principle of free energy minimization may thus serve as a unifying mathematical framework for universal Darwinism, the study of evolutionary processes operating throughout nature.

  12. Entropic Biological Score: a cell cycle investigation for GRNs inference.

    PubMed

    Lopes, Fabrício M; Ray, Shubhra Sankar; Hashimoto, Ronaldo F; Cesar, Roberto M

    2014-05-15

    Inference of gene regulatory networks (GRNs) is one of the most challenging research problems of Systems Biology. In this investigation, a new GRNs inference methodology, called Entropic Biological Score (EBS), which linearly combines the mean conditional entropy (MCE) from expression levels and a Biological Score (BS), obtained by integrating different biological data sources, is proposed. The EBS is validated with the Cell Cycle related functional annotation information, available from Munich Information Center for Protein Sequences (MIPS), and compared with some existing methods like MRNET, ARACNE, CLR and MCE for GRNs inference. For real networks, the performance of EBS, which uses the concept of integrating different data sources, is found to be superior to the aforementioned inference methods. The best results for EBS are obtained by considering the weights w1=0.2 and w2=0.8 for MCE and BS values, respectively, where approximately 40% of the inferred connections are found to be correct and significantly better than related methods. The results also indicate that expression profile is able to recover some true connections, that are not present in biological annotations, thus leading to the possibility of discovering new relations between its genes. PMID:24631265

  13. Inference for reaction networks using the linear noise approximation.

    PubMed

    Fearnhead, Paul; Giagos, Vasilieos; Sherlock, Chris

    2014-06-01

    We consider inference for the reaction rates in discretely observed networks such as those found in models for systems biology, population ecology, and epidemics. Most such networks are neither slow enough nor small enough for inference via the true state-dependent Markov jump process to be feasible. Typically, inference is conducted by approximating the dynamics through an ordinary differential equation (ODE) or a stochastic differential equation (SDE). The former ignores the stochasticity in the true model and can lead to inaccurate inferences. The latter is more accurate but is harder to implement as the transition density of the SDE model is generally unknown. The linear noise approximation (LNA) arises from a first-order Taylor expansion of the approximating SDE about a deterministic solution and can be viewed as a compromise between the ODE and SDE models. It is a stochastic model, but discrete time transition probabilities for the LNA are available through the solution of a series of ordinary differential equations. We describe how a restarting LNA can be efficiently used to perform inference for a general class of reaction networks; evaluate the accuracy of such an approach; and show how and when this approach is either statistically or computationally more efficient than ODE or SDE methods. We apply the LNA to analyze Google Flu Trends data from the North and South Islands of New Zealand, and are able to obtain more accurate short-term forecasts of new flu cases than another recently proposed method, although at a greater computational cost.

  14. Mechanical equivalent of Bayesian inference from monitoring data

    NASA Astrophysics Data System (ADS)

    Cappello, Carlo; Bolognani, Denise; Zonta, Daniele

    2016-04-01

    Structural health monitoring requires engineers to understand the state of a structure from its observed response. When this information is uncertain, Bayesian probability theory provides a consistent framework for making inference. However, structural engineers are often unenthusiastic about Bayesian logic and prefer to make inference using heuristics. Herein we propose a quantitative method for logical inference based on a formal analogy between linear elastic mechanics and Bayesian inference with Gaussian variables. We start by discussing the estimation of a single parameter under the assumption that all of the uncertain quantities have a Gaussian distribution and that the relationship between the observations and the parameter is linear. With these assumptions, the analogy is stated as follows: the expected value of the considered parameter corresponds to the position of a bar with one degree of freedom and uncertain observations of the parameter are modelled as linear elastic springs placed in series or parallel. If we want to extend the analogy to multiple parameters, we simply have to express the potential energy of the mechanical system associated to the inference problem. The expected value of the parameters is then calculated by minimizing that potential energy. We conclude our contribution by presenting the application of mechanical equivalent to a real-life case study in which we seek the elongation trend of a cable belonging to Adige Bridge, a cable-stayed bridge located North of Trento, Italy.

  15. Universal Darwinism As a Process of Bayesian Inference.

    PubMed

    Campbell, John O

    2016-01-01

    Many of the mathematical frameworks describing natural selection are equivalent to Bayes' Theorem, also known as Bayesian updating. By definition, a process of Bayesian Inference is one which involves a Bayesian update, so we may conclude that these frameworks describe natural selection as a process of Bayesian inference. Thus, natural selection serves as a counter example to a widely-held interpretation that restricts Bayesian Inference to human mental processes (including the endeavors of statisticians). As Bayesian inference can always be cast in terms of (variational) free energy minimization, natural selection can be viewed as comprising two components: a generative model of an "experiment" in the external world environment, and the results of that "experiment" or the "surprise" entailed by predicted and actual outcomes of the "experiment." Minimization of free energy implies that the implicit measure of "surprise" experienced serves to update the generative model in a Bayesian manner. This description closely accords with the mechanisms of generalized Darwinian process proposed both by Dawkins, in terms of replicators and vehicles, and Campbell, in terms of inferential systems. Bayesian inference is an algorithm for the accumulation of evidence-based knowledge. This algorithm is now seen to operate over a wide range of evolutionary processes, including natural selection, the evolution of mental models and cultural evolutionary processes, notably including science itself. The variational principle of free energy minimization may thus serve as a unifying mathematical framework for universal Darwinism, the study of evolutionary processes operating throughout nature. PMID:27375438

  16. Methods for causal inference from gene perturbation experiments and validation

    PubMed Central

    Meinshausen, Nicolai; Hauser, Alain; Mooij, Joris M.; Peters, Jonas; Versteeg, Philip; Bühlmann, Peter

    2016-01-01

    Inferring causal effects from observational and interventional data is a highly desirable but ambitious goal. Many of the computational and statistical methods are plagued by fundamental identifiability issues, instability, and unreliable performance, especially for large-scale systems with many measured variables. We present software and provide some validation of a recently developed methodology based on an invariance principle, called invariant causal prediction (ICP). The ICP method quantifies confidence probabilities for inferring causal structures and thus leads to more reliable and confirmatory statements for causal relations and predictions of external intervention effects. We validate the ICP method and some other procedures using large-scale genome-wide gene perturbation experiments in Saccharomyces cerevisiae. The results suggest that prediction and prioritization of future experimental interventions, such as gene deletions, can be improved by using our statistical inference techniques. PMID:27382150

  17. Bayesian multimodel inference for dose-response studies

    USGS Publications Warehouse

    Link, W.A.; Albers, P.H.

    2007-01-01

    Statistical inference in dose?response studies is model-based: The analyst posits a mathematical model of the relation between exposure and response, estimates parameters of the model, and reports conclusions conditional on the model. Such analyses rarely include any accounting for the uncertainties associated with model selection. The Bayesian inferential system provides a convenient framework for model selection and multimodel inference. In this paper we briefly describe the Bayesian paradigm and Bayesian multimodel inference. We then present a family of models for multinomial dose?response data and apply Bayesian multimodel inferential methods to the analysis of data on the reproductive success of American kestrels (Falco sparveriuss) exposed to various sublethal dietary concentrations of methylmercury.

  18. Methods for causal inference from gene perturbation experiments and validation.

    PubMed

    Meinshausen, Nicolai; Hauser, Alain; Mooij, Joris M; Peters, Jonas; Versteeg, Philip; Bühlmann, Peter

    2016-07-01

    Inferring causal effects from observational and interventional data is a highly desirable but ambitious goal. Many of the computational and statistical methods are plagued by fundamental identifiability issues, instability, and unreliable performance, especially for large-scale systems with many measured variables. We present software and provide some validation of a recently developed methodology based on an invariance principle, called invariant causal prediction (ICP). The ICP method quantifies confidence probabilities for inferring causal structures and thus leads to more reliable and confirmatory statements for causal relations and predictions of external intervention effects. We validate the ICP method and some other procedures using large-scale genome-wide gene perturbation experiments in Saccharomyces cerevisiae The results suggest that prediction and prioritization of future experimental interventions, such as gene deletions, can be improved by using our statistical inference techniques. PMID:27382150

  19. Transdimensional inference in the geosciences.

    PubMed

    Sambridge, M; Bodin, T; Gallagher, K; Tkalcic, H

    2013-02-13

    Seismologists construct images of the Earth's interior structure using observations, derived from seismograms, collected at the surface. A common approach to such inverse problems is to build a single 'best' Earth model, in some sense. This is despite the fact that the observations by themselves often do not require, or even allow, a single best-fit Earth model to exist. Interpretation of optimal models can be fraught with difficulties, particularly when formal uncertainty estimates become heavily dependent on the regularization imposed. Similar issues occur across the physical sciences with model construction in ill-posed problems. An alternative approach is to embrace the non-uniqueness directly and employ an inference process based on parameter space sampling. Instead of seeking a best model within an optimization framework, one seeks an ensemble of solutions and derives properties of that ensemble for inspection. While this idea has itself been employed for more than 30 years, it is now receiving increasing attention in the geosciences. Recently, it has been shown that transdimensional and hierarchical sampling methods have some considerable benefits for problems involving multiple parameter types, uncertain data errors and/or uncertain model parametrizations, as are common in seismology. Rather than being forced to make decisions on parametrization, the level of data noise and the weights between data types in advance, as is often the case in an optimization framework, the choice can be informed by the data themselves. Despite the relatively high computational burden involved, the number of areas where sampling methods are now feasible is growing rapidly. The intention of this article is to introduce concepts of transdimensional inference to a general readership and illustrate with particular seismological examples. A growing body of references provide necessary detail. PMID:23277604

  20. SERIES - Satellite Emission Range Inferred Earth Surveying

    NASA Technical Reports Server (NTRS)

    Macdoran, P. F.; Spitzmesser, D. J.; Buennagel, L. A.

    1983-01-01

    The Satellite Emission Range Inferred Earth Surveying (SERIES) concept is based on the utilization of NAVSTAR Global Positioning System (GPS) radio transmissions without any satellite modifications and in a totally passive mode. The SERIES stations are equipped with lightweight 1.5 m diameter dish antennas mounted on trailers. A series baseline measurement accuracy demonstration is considered, taking into account a 100 meter baseline estimation from approximately one hour of differential Doppler data. It is planned to conduct the next phase of experiments on a 150 m baseline. Attention is given to details regarding future baseline measurement accuracy demonstrations, aspects of ionospheric calibration in connection with subdecimeter baseline accuracy requirements of geodesy, and advantages related to the use of the differential Doppler or pseudoranging mode.

  1. Mechanisms of phonological inference in speech perception.

    PubMed

    Gaskell, M G; Marslen-Wilson, W D

    1998-04-01

    Cross-modal priming experiments have shown that surface variations in speech are perceptually tolerated as long as they occur in phonologically viable contexts. For example, [symbol: see text] (frayp) gains access to the mental representation of freight when in the context of [symbol: see text] (frayp bearer) because the change occurs in normal speech as a process of place assimilation. The locus of these effects in the perceptual system was examined. Sentences containing surface changes were created that either agreed with or violated assimilation rules. The lexical status of the assimilated word also was manipulated, contrasting lexical and nonlexical accounts. Two phoneme monitoring experiments showed strong effects of phonological viability for words, with weaker effects for nonwords. It is argued that the listener's percept of the form of speech is a product of a phonological inference process that recovers the underlying form of speech. This process can operate on both words and nonwords, although it interacts with the retrieval of lexical information.

  2. cosmoabc: Likelihood-free inference for cosmology

    NASA Astrophysics Data System (ADS)

    Ishida, Emille E. O.; Vitenti, Sandro D. P.; Penna-Lima, Mariana; Trindade, Arlindo M.; Cisewski, Jessi; M.; de Souza, Rafael; Cameron, Ewan; Busti, Vinicius C.

    2015-05-01

    Approximate Bayesian Computation (ABC) enables parameter inference for complex physical systems in cases where the true likelihood function is unknown, unavailable, or computationally too expensive. It relies on the forward simulation of mock data and comparison between observed and synthetic catalogs. cosmoabc is a Python Approximate Bayesian Computation (ABC) sampler featuring a Population Monte Carlo variation of the original ABC algorithm, which uses an adaptive importance sampling scheme. The code can be coupled to an external simulator to allow incorporation of arbitrary distance and prior functions. When coupled with the numcosmo library, it has been used to estimate posterior probability distributions over cosmological parameters based on measurements of galaxy clusters number counts without computing the likelihood function.

  3. Bayesian Nonparametric Inference – Why and How

    PubMed Central

    Müller, Peter; Mitra, Riten

    2013-01-01

    We review inference under models with nonparametric Bayesian (BNP) priors. The discussion follows a set of examples for some common inference problems. The examples are chosen to highlight problems that are challenging for standard parametric inference. We discuss inference for density estimation, clustering, regression and for mixed effects models with random effects distributions. While we focus on arguing for the need for the flexibility of BNP models, we also review some of the more commonly used BNP models, thus hopefully answering a bit of both questions, why and how to use BNP. PMID:24368932

  4. Generic Comparison of Protein Inference Engines*

    PubMed Central

    Claassen, Manfred; Reiter, Lukas; Hengartner, Michael O.; Buhmann, Joachim M.; Aebersold, Ruedi

    2012-01-01

    Protein identifications, instead of peptide-spectrum matches, constitute the biologically relevant result of shotgun proteomics studies. How to appropriately infer and report protein identifications has triggered a still ongoing debate. This debate has so far suffered from the lack of appropriate performance measures that allow us to objectively assess protein inference approaches. This study describes an intuitive, generic and yet formal performance measure and demonstrates how it enables experimentalists to select an optimal protein inference strategy for a given collection of fragment ion spectra. We applied the performance measure to systematically explore the benefit of excluding possibly unreliable protein identifications, such as single-hit wonders. Therefore, we defined a family of protein inference engines by extending a simple inference engine by thousands of pruning variants, each excluding a different specified set of possibly unreliable identifications. We benchmarked these protein inference engines on several data sets representing different proteomes and mass spectrometry platforms. Optimally performing inference engines retained all high confidence spectral evidence, without posterior exclusion of any type of protein identifications. Despite the diversity of studied data sets consistently supporting this rule, other data sets might behave differently. In order to ensure maximal reliable proteome coverage for data sets arising in other studies we advocate abstaining from rigid protein inference rules, such as exclusion of single-hit wonders, and instead consider several protein inference approaches and assess these with respect to the presented performance measure in the specific application context. PMID:22057310

  5. Adaptive Neuro-Fuzzy Inference System (ANFIS)-Based Models for Predicting the Weld Bead Width and Depth of Penetration from the Infrared Thermal Image of the Weld Pool

    NASA Astrophysics Data System (ADS)

    Subashini, L.; Vasudevan, M.

    2012-02-01

    Type 316 LN stainless steel is the major structural material used in the construction of nuclear reactors. Activated flux tungsten inert gas (A-TIG) welding has been developed to increase the depth of penetration because the depth of penetration achievable in single-pass TIG welding is limited. Real-time monitoring and control of weld processes is gaining importance because of the requirement of remoter welding process technologies. Hence, it is essential to develop computational methodologies based on an adaptive neuro fuzzy inference system (ANFIS) or artificial neural network (ANN) for predicting and controlling the depth of penetration and weld bead width during A-TIG welding of type 316 LN stainless steel. In the current work, A-TIG welding experiments have been carried out on 6-mm-thick plates of 316 LN stainless steel by varying the welding current. During welding, infrared (IR) thermal images of the weld pool have been acquired in real time, and the features have been extracted from the IR thermal images of the weld pool. The welding current values, along with the extracted features such as length, width of the hot spot, thermal area determined from the Gaussian fit, and thermal bead width computed from the first derivative curve were used as inputs, whereas the measured depth of penetration and weld bead width were used as output of the respective models. Accurate ANFIS models have been developed for predicting the depth of penetration and the weld bead width during TIG welding of 6-mm-thick 316 LN stainless steel plates. A good correlation between the measured and predicted values of weld bead width and depth of penetration were observed in the developed models. The performance of the ANFIS models are compared with that of the ANN models.

  6. Likelihood free inference for Markov processes: a comparison.

    PubMed

    Owen, Jamie; Wilkinson, Darren J; Gillespie, Colin S

    2015-04-01

    Approaches to Bayesian inference for problems with intractable likelihoods have become increasingly important in recent years. Approximate Bayesian computation (ABC) and "likelihood free" Markov chain Monte Carlo techniques are popular methods for tackling inference in these scenarios but such techniques are computationally expensive. In this paper we compare the two approaches to inference, with a particular focus on parameter inference for stochastic kinetic models, widely used in systems biology. Discrete time transition kernels for models of this type are intractable for all but the most trivial systems yet forward simulation is usually straightforward. We discuss the relative merits and drawbacks of each approach whilst considering the computational cost implications and efficiency of these techniques. In order to explore the properties of each approach we examine a range of observation regimes using two example models. We use a Lotka-Volterra predator-prey model to explore the impact of full or partial species observations using various time course observations under the assumption of known and unknown measurement error. Further investigation into the impact of observation error is then made using a Schlögl system, a test case which exhibits bi-modal state stability in some regions of parameter space. PMID:25720092

  7. Network geometry inference using common neighbors

    NASA Astrophysics Data System (ADS)

    Papadopoulos, Fragkiskos; Aldecoa, Rodrigo; Krioukov, Dmitri

    2015-08-01

    We introduce and explore a method for inferring hidden geometric coordinates of nodes in complex networks based on the number of common neighbors between the nodes. We compare this approach to the HyperMap method, which is based only on the connections (and disconnections) between the nodes, i.e., on the links that the nodes have (or do not have). We find that for high degree nodes, the common-neighbors approach yields a more accurate inference than the link-based method, unless heuristic periodic adjustments (or "correction steps") are used in the latter. The common-neighbors approach is computationally intensive, requiring O (t4) running time to map a network of t nodes, versus O (t3) in the link-based method. But we also develop a hybrid method with O (t3) running time, which combines the common-neighbors and link-based approaches, and we explore a heuristic that reduces its running time further to O (t2) , without significant reduction in the mapping accuracy. We apply this method to the autonomous systems (ASs) Internet, and we reveal how soft communities of ASs evolve over time in the similarity space. We further demonstrate the method's predictive power by forecasting future links between ASs. Taken altogether, our results advance our understanding of how to efficiently and accurately map real networks to their latent geometric spaces, which is an important necessary step toward understanding the laws that govern the dynamics of nodes in these spaces, and the fine-grained dynamics of network connections.

  8. Network geometry inference using common neighbors.

    PubMed

    Papadopoulos, Fragkiskos; Aldecoa, Rodrigo; Krioukov, Dmitri

    2015-08-01

    We introduce and explore a method for inferring hidden geometric coordinates of nodes in complex networks based on the number of common neighbors between the nodes. We compare this approach to the HyperMap method, which is based only on the connections (and disconnections) between the nodes, i.e., on the links that the nodes have (or do not have). We find that for high degree nodes, the common-neighbors approach yields a more accurate inference than the link-based method, unless heuristic periodic adjustments (or "correction steps") are used in the latter. The common-neighbors approach is computationally intensive, requiring O(t4) running time to map a network of t nodes, versus O(t3) in the link-based method. But we also develop a hybrid method with O(t3) running time, which combines the common-neighbors and link-based approaches, and we explore a heuristic that reduces its running time further to O(t2), without significant reduction in the mapping accuracy. We apply this method to the autonomous systems (ASs) Internet, and we reveal how soft communities of ASs evolve over time in the similarity space. We further demonstrate the method's predictive power by forecasting future links between ASs. Taken altogether, our results advance our understanding of how to efficiently and accurately map real networks to their latent geometric spaces, which is an important necessary step toward understanding the laws that govern the dynamics of nodes in these spaces, and the fine-grained dynamics of network connections. PMID:26382454

  9. The ventral pallidum and orbitofrontal cortex support food pleasantness inferences

    PubMed Central

    Simmons, W. Kyle; Rapuano, Kristina M.; Ingeholm, John E.; Avery, Jason; Kallman, Seth; Hall, Kevin D.; Martin, Alex

    2013-01-01

    Food advertisements often promote choices that are driven by inferences about the hedonic pleasures of eating a particular food. Given the individual and public health consequences of obesity, it is critical to address unanswered questions about the specific neural systems underlying these hedonic inferences. For example, although regions such as the orbitofrontal cortex (OFC) are frequently observed to respond more to pleasant food images than less hedonically pleasing stimuli, one important hedonic brain region in particular has largely remained conspicuously absent among human studies of hedonic response to food images. Based on rodent research demonstrating that activity in the ventral pallidum underlies the hedonic pleasures experienced upon eating food rewards, one might expect that activity in this important ‘hedonic hotspot’ might also track inferred food pleasantness. To date, however, no human studies have assessed this question. We thus asked human subjects to undergo fMRI and make item-by-item ratings of how pleasant it would be to eat particular visually perceived foods. Activity in the ventral pallidum was strongly modulated with pleasantness inferences. Additionally, activity within a region of the orbitofrontal cortex that tracks the pleasantness of tastes was also modulated with inferred pleasantness. Importantly, the reliability of these findings is demonstrated by their replication when we repeated the experiment at a new site with new subjects. These two experiments demonstrate that the ventral pallidum, in addition to the OFC, plays a central role in the moment-to-moment hedonic inferences that influence food-related decision-making. PMID:23397317

  10. Inference of asynchronous Boolean network from biological pathways.

    PubMed

    Das, Haimabati; Layek, Ritwik Kumar

    2015-01-01

    Gene regulation is a complex process with multiple levels of interactions. In order to describe this complex dynamical system with tractable parameterization, the choice of the dynamical system model is of paramount importance. The right abstraction of the modeling scheme can reduce the complexity in the inference and intervention design, both computationally and experimentally. This article proposes an asynchronous Boolean network framework to capture the transcriptional regulation as well as the protein-protein interactions in a genetic regulatory system. The inference of asynchronous Boolean network from biological pathways information and experimental evidence are explained using an algorithm. The suitability of this paradigm for the variability of several reaction rates is also discussed. This methodology and model selection open up new research challenges in understanding gene-protein interactive system in a coherent way and can be beneficial for designing effective therapeutic intervention strategy.

  11. The Impact of Disablers on Predictive Inference

    ERIC Educational Resources Information Center

    Cummins, Denise Dellarosa

    2014-01-01

    People consider alternative causes when deciding whether a cause is responsible for an effect (diagnostic inference) but appear to neglect them when deciding whether an effect will occur (predictive inference). Five experiments were conducted to test a 2-part explanation of this phenomenon: namely, (a) that people interpret standard predictive…

  12. Local and Global Thinking in Statistical Inference

    ERIC Educational Resources Information Center

    Pratt, Dave; Johnston-Wilder, Peter; Ainley, Janet; Mason, John

    2008-01-01

    In this reflective paper, we explore students' local and global thinking about informal statistical inference through our observations of 10- to 11-year-olds, challenged to infer the unknown configuration of a virtual die, but able to use the die to generate as much data as they felt necessary. We report how they tended to focus on local changes…

  13. Causal inference in economics and marketing.

    PubMed

    Varian, Hal R

    2016-07-01

    This is an elementary introduction to causal inference in economics written for readers familiar with machine learning methods. The critical step in any causal analysis is estimating the counterfactual-a prediction of what would have happened in the absence of the treatment. The powerful techniques used in machine learning may be useful for developing better estimates of the counterfactual, potentially improving causal inference.

  14. Genetic Network Inference Using Hierarchical Structure

    PubMed Central

    Kimura, Shuhei; Tokuhisa, Masato; Okada-Hatakeyama, Mariko

    2016-01-01

    Many methods for inferring genetic networks have been proposed, but the regulations they infer often include false-positives. Several researchers have attempted to reduce these erroneous regulations by proposing the use of a priori knowledge about the properties of genetic networks such as their sparseness, scale-free structure, and so on. This study focuses on another piece of a priori knowledge, namely, that biochemical networks exhibit hierarchical structures. Based on this idea, we propose an inference approach that uses the hierarchical structure in a target genetic network. To obtain a reasonable hierarchical structure, the first step of the proposed approach is to infer multiple genetic networks from the observed gene expression data. We take this step using an existing method that combines a genetic network inference method with a bootstrap method. The next step is to extract a hierarchical structure from the inferred networks that is consistent with most of the networks. Third, we use the hierarchical structure obtained to assign confidence values to all candidate regulations. Numerical experiments are also performed to demonstrate the effectiveness of using the hierarchical structure in the genetic network inference. The improvement accomplished by the use of the hierarchical structure is small. However, the hierarchical structure could be used to improve the performances of many existing inference methods. PMID:26941653

  15. The Reasoning behind Informal Statistical Inference

    ERIC Educational Resources Information Center

    Makar, Katie; Bakker, Arthur; Ben-Zvi, Dani

    2011-01-01

    Informal statistical inference (ISI) has been a frequent focus of recent research in statistics education. Considering the role that context plays in developing ISI calls into question the need to be more explicit about the reasoning that underpins ISI. This paper uses educational literature on informal statistical inference and philosophical…

  16. Reinforcement learning or active inference?

    PubMed

    Friston, Karl J; Daunizeau, Jean; Kiebel, Stefan J

    2009-01-01

    This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a free-energy formulation of perception. In this formulation, agents adjust their internal states and sampling of the environment to minimize their free-energy. Such agents learn causal structure in the environment and sample it in an adaptive and self-supervised fashion. This results in behavioural policies that reproduce those optimised by reinforcement learning and dynamic programming. Critically, we do not need to invoke the notion of reward, value or utility. We illustrate these points by solving a benchmark problem in dynamic programming; namely the mountain-car problem, using active perception or inference under the free-energy principle. The ensuing proof-of-concept may be important because the free-energy formulation furnishes a unified account of both action and perception and may speak to a reappraisal of the role of dopamine in the brain.

  17. Active inference and epistemic value.

    PubMed

    Friston, Karl; Rigoli, Francesco; Ognibene, Dimitri; Mathys, Christoph; Fitzgerald, Thomas; Pezzulo, Giovanni

    2015-01-01

    We offer a formal treatment of choice behavior based on the premise that agents minimize the expected free energy of future outcomes. Crucially, the negative free energy or quality of a policy can be decomposed into extrinsic and epistemic (or intrinsic) value. Minimizing expected free energy is therefore equivalent to maximizing extrinsic value or expected utility (defined in terms of prior preferences or goals), while maximizing information gain or intrinsic value (or reducing uncertainty about the causes of valuable outcomes). The resulting scheme resolves the exploration-exploitation dilemma: Epistemic value is maximized until there is no further information gain, after which exploitation is assured through maximization of extrinsic value. This is formally consistent with the Infomax principle, generalizing formulations of active vision based upon salience (Bayesian surprise) and optimal decisions based on expected utility and risk-sensitive (Kullback-Leibler) control. Furthermore, as with previous active inference formulations of discrete (Markovian) problems, ad hoc softmax parameters become the expected (Bayes-optimal) precision of beliefs about, or confidence in, policies. This article focuses on the basic theory, illustrating the ideas with simulations. A key aspect of these simulations is the similarity between precision updates and dopaminergic discharges observed in conditioning paradigms. PMID:25689102

  18. Causal Inference in Public Health

    PubMed Central

    Glass, Thomas A.; Goodman, Steven N.; Hernán, Miguel A.; Samet, Jonathan M.

    2014-01-01

    Causal inference has a central role in public health; the determination that an association is causal indicates the possibility for intervention. We review and comment on the long-used guidelines for interpreting evidence as supporting a causal association and contrast them with the potential outcomes framework that encourages thinking in terms of causes that are interventions. We argue that in public health this framework is more suitable, providing an estimate of an action’s consequences rather than the less precise notion of a risk factor’s causal effect. A variety of modern statistical methods adopt this approach. When an intervention cannot be specified, causal relations can still exist, but how to intervene to change the outcome will be unclear. In application, the often-complex structure of causal processes needs to be acknowledged and appropriate data collected to study them. These newer approaches need to be brought to bear on the increasingly complex public health challenges of our globalized world. PMID:23297653

  19. How to infer gene networks from expression profiles, revisited.

    PubMed

    Penfold, Christopher A; Wild, David L

    2011-12-01

    Inferring the topology of a gene-regulatory network (GRN) from genome-scale time-series measurements of transcriptional change has proved useful for disentangling complex biological processes. To address the challenges associated with this inference, a number of competing approaches have previously been used, including examples from information theory, Bayesian and dynamic Bayesian networks (DBNs), and ordinary differential equation (ODE) or stochastic differential equation. The performance of these competing approaches have previously been assessed using a variety of in silico and in vivo datasets. Here, we revisit this work by assessing the performance of more recent network inference algorithms, including a novel non-parametric learning approach based upon nonlinear dynamical systems. For larger GRNs, containing hundreds of genes, these non-parametric approaches more accurately infer network structures than do traditional approaches, but at significant computational cost. For smaller systems, DBNs are competitive with the non-parametric approaches with respect to computational time and accuracy, and both of these approaches appear to be more accurate than Granger causality-based methods and those using simple ODEs models.

  20. Inference-based constraint satisfaction supports explanation

    SciTech Connect

    Sqalli, M.H.; Freuder, E.C.

    1996-12-31

    Constraint satisfaction problems are typically solved using search, augmented by general purpose consistency inference methods. This paper proposes a paradigm shift in which inference is used as the primary problem solving method, and attention is focused on special purpose, domain specific inference methods. While we expect this approach to have computational advantages, we emphasize here the advantages of a solution method that is more congenial to human thought processes. Specifically we use inference-based constraint satisfaction to support explanations of the problem solving behavior that are considerably more meaningful than a trace of a search process would be. Logic puzzles are used as a case study. Inference-based constraint satisfaction proves surprisingly powerful and easily extensible in this domain. Problems drawn from commercial logic puzzle booklets are used for evaluation. Explanations are produced that compare well with the explanations provided by these booklets.

  1. Inferring coupling strength from event-related dynamics

    NASA Astrophysics Data System (ADS)

    Łęski, Szymon; Wójcik, Daniel K.

    2008-10-01

    We propose an approach for inferring strength of coupling between two systems from their transient dynamics. This is of vital importance in cases where most information is carried by the transients, for instance, in evoked potentials measured commonly in electrophysiology. We show viability of our approach using nonlinear and linear measures of synchronization on a population model of thalamocortical loop and on a system of two coupled Rössler-type oscillators in nonchaotic regime.

  2. Statistical Physics of High Dimensional Inference

    NASA Astrophysics Data System (ADS)

    Advani, Madhu; Ganguli, Surya

    To model modern large-scale datasets, we need efficient algorithms to infer a set of P unknown model parameters from N noisy measurements. What are fundamental limits on the accuracy of parameter inference, given limited measurements, signal-to-noise ratios, prior information, and computational tractability requirements? How can we combine prior information with measurements to achieve these limits? Classical statistics gives incisive answers to these questions as the measurement density α =N/P --> ∞ . However, modern high-dimensional inference problems, in fields ranging from bio-informatics to economics, occur at finite α. We formulate and analyze high-dimensional inference analytically by applying the replica and cavity methods of statistical physics where data serves as quenched disorder and inferred parameters play the role of thermal degrees of freedom. Our analysis reveals that widely cherished Bayesian inference algorithms such as maximum likelihood and maximum a posteriori are suboptimal in the modern setting, and yields new tractable, optimal algorithms to replace them as well as novel bounds on the achievable accuracy of a large class of high-dimensional inference algorithms. Thanks to Stanford Graduate Fellowship and Mind Brain Computation IGERT grant for support.

  3. On Bayesian Inductive Inference & Predictive Estimation

    NASA Technical Reports Server (NTRS)

    Cheeseman, Peter; Stutz, John; Smelyanskiy, Vadim

    2004-01-01

    We investigate Bayesian inference and the Principle of Maximum Entropy (PME) as methods for doing inference under uncertainty. This investigation is primarily through concrete examples that have been previously investigated in the literature. We find that it is possible to do Bayesian inference and PME inference using the same information, despite claims to the contrary, but that the results are not directly comparable. This is because Bayesian inference yields a probability density function (pdf) over the unknown model parameters, whereas PME yields point estimates. If mean estimates are extracted from the Bayesian pdfs, the resulting parameter estimates can differ radically from the PME values and also from the Maximum Likelihood values. We conclude that these differences are due to the Bayesian inference not assuming anything beyond the given prior probabilities and the data, whereas PME implicitly assumes that the given constraints are the only constraints that are operating. Since this assumption can be wrong, PME values may have to be revised when subsequent data shows evidence for more constraints. The entropy concentration previously "proved" by E. T. Jaynes is shown to be in error. Further, we show that PME is a generalized form of independence assumption, and so can be a very powerful method of inference when the variables being investigated are largely independent of each other.

  4. Efficient Bayesian inference for natural time series using ARFIMA processes

    NASA Astrophysics Data System (ADS)

    Graves, Timothy; Gramacy, Robert; Franzke, Christian; Watkins, Nicholas

    2016-04-01

    Many geophysical quantities, such as atmospheric temperature, water levels in rivers, and wind speeds, have shown evidence of long memory (LM). LM implies that these quantities experience non-trivial temporal memory, which potentially not only enhances their predictability, but also hampers the detection of externally forced trends. Thus, it is important to reliably identify whether or not a system exhibits LM. We present a modern and systematic approach to the inference of LM. We use the flexible autoregressive fractional integrated moving average (ARFIMA) model, which is widely used in time series analysis, and of increasing interest in climate science. Unlike most previous work on the inference of LM, which is frequentist in nature, we provide a systematic treatment of Bayesian inference. In particular, we provide a new approximate likelihood for efficient parameter inference, and show how nuisance parameters (e.g., short-memory effects) can be integrated over in order to focus on long-memory parameters and hypothesis testing more directly. We illustrate our new methodology on the Nile water level data and the central England temperature (CET) time series, with favorable comparison to the standard estimators [1]. In addition we show how the method can be used to perform joint inference of the stability exponent and the memory parameter when ARFIMA is extended to allow for alpha-stable innovations. Such models can be used to study systems where heavy tails and long range memory coexist. [1] Graves et al, Nonlin. Processes Geophys., 22, 679-700, 2015; doi:10.5194/npg-22-679-2015.

  5. Inferring unstable equilibrium configurations from experimental data

    NASA Astrophysics Data System (ADS)

    Virgin, L. N.; Wiebe, R.; Spottswood, S. M.; Beberniss, T.

    2016-09-01

    This research considers the structural behavior of slender, mechanically buckled beams and panels of the type commonly found in aerospace structures. The specimens were deflected and then clamped in a rigid frame in order to exhibit snap-through. That is, the initial equilibrium and the buckled (snapped-through) equilibrium configurations both co-existed for the given clamped conditions. In order to transit between these two stable equilibrium configurations (for example, under the action of an externally applied load), it is necessary for the structural component to pass through an intermediate unstable equilibrium configuration. A sequence of sudden impacts was imparted to the system, of various strengths and at various locations. The goal of this impact force was to induce relatively intermediate-sized transients that effectively slowed-down in the vicinity of the unstable equilibrium configuration. Thus, monitoring the velocity of the motion, and specifically its slowing down, should give an indication of the presence of an equilibrium configuration, even though it is unstable and not amenable to direct experimental observation. A digital image correlation (DIC) system was used in conjunction with an instrumented impact hammer to track trajectories and statistical methods used to infer the presence of unstable equilibria in both a beam and a panel.

  6. Causal inference and the hierarchical structure of experience

    PubMed Central

    Johnson, Samuel G. B.; Keil, Frank C.

    2014-01-01

    Children and adults make rich causal inferences about the physical and social world, even in novel situations where they cannot rely on prior knowledge of causal mechanisms. We propose that this capacity is supported in part by constraints provided by event structure—the cognitive organization of experience into discrete events that are hierarchically organized. These event-structured causal inferences are guided by a level-matching principle, with events conceptualized at one level of an event hierarchy causally matched to other events at that same level, and a boundary-blocking principle, with events causally matched to other events that are parts of the same superordinate event. These principles are used to constrain inferences about plausible causal candidates in unfamiliar situations, both in diagnosing causes (Experiment 1) and predicting effects (Experiment 2). The results could not be explained by construal level (Experiment 3) or similarity-matching (Experiment 4), and were robust across a variety of physical and social causal systems. Taken together, these experiments demonstrate a novel way in which non-causal information we extract from the environment can help to constrain inferences about causal structure. PMID:25347533

  7. Efficient Bayesian inference for natural time series using ARFIMA processes

    NASA Astrophysics Data System (ADS)

    Graves, T.; Gramacy, R. B.; Franzke, C. L. E.; Watkins, N. W.

    2015-11-01

    Many geophysical quantities, such as atmospheric temperature, water levels in rivers, and wind speeds, have shown evidence of long memory (LM). LM implies that these quantities experience non-trivial temporal memory, which potentially not only enhances their predictability, but also hampers the detection of externally forced trends. Thus, it is important to reliably identify whether or not a system exhibits LM. In this paper we present a modern and systematic approach to the inference of LM. We use the flexible autoregressive fractional integrated moving average (ARFIMA) model, which is widely used in time series analysis, and of increasing interest in climate science. Unlike most previous work on the inference of LM, which is frequentist in nature, we provide a systematic treatment of Bayesian inference. In particular, we provide a new approximate likelihood for efficient parameter inference, and show how nuisance parameters (e.g., short-memory effects) can be integrated over in order to focus on long-memory parameters and hypothesis testing more directly. We illustrate our new methodology on the Nile water level data and the central England temperature (CET) time series, with favorable comparison to the standard estimators. For CET we also extend our method to seasonal long memory.

  8. Bayesian Inference of High-Dimensional Dynamical Ocean Models

    NASA Astrophysics Data System (ADS)

    Lin, J.; Lermusiaux, P. F. J.; Lolla, S. V. T.; Gupta, A.; Haley, P. J., Jr.

    2015-12-01

    This presentation addresses a holistic set of challenges in high-dimension ocean Bayesian nonlinear estimation: i) predict the probability distribution functions (pdfs) of large nonlinear dynamical systems using stochastic partial differential equations (PDEs); ii) assimilate data using Bayes' law with these pdfs; iii) predict the future data that optimally reduce uncertainties; and (iv) rank the known and learn the new model formulations themselves. Overall, we allow the joint inference of the state, equations, geometry, boundary conditions and initial conditions of dynamical models. Examples are provided for time-dependent fluid and ocean flows, including cavity, double-gyre and Strait flows with jets and eddies. The Bayesian model inference, based on limited observations, is illustrated first by the estimation of obstacle shapes and positions in fluid flows. Next, the Bayesian inference of biogeochemical reaction equations and of their states and parameters is presented, illustrating how PDE-based machine learning can rigorously guide the selection and discovery of complex ecosystem models. Finally, the inference of multiscale bottom gravity current dynamics is illustrated, motivated in part by classic overflows and dense water formation sites and their relevance to climate monitoring and dynamics. This is joint work with our MSEAS group at MIT.

  9. Bayesian inference for Markov jump processes with informative observations.

    PubMed

    Golightly, Andrew; Wilkinson, Darren J

    2015-04-01

    In this paper we consider the problem of parameter inference for Markov jump process (MJP) representations of stochastic kinetic models. Since transition probabilities are intractable for most processes of interest yet forward simulation is straightforward, Bayesian inference typically proceeds through computationally intensive methods such as (particle) MCMC. Such methods ostensibly require the ability to simulate trajectories from the conditioned jump process. When observations are highly informative, use of the forward simulator is likely to be inefficient and may even preclude an exact (simulation based) analysis. We therefore propose three methods for improving the efficiency of simulating conditioned jump processes. A conditioned hazard is derived based on an approximation to the jump process, and used to generate end-point conditioned trajectories for use inside an importance sampling algorithm. We also adapt a recently proposed sequential Monte Carlo scheme to our problem. Essentially, trajectories are reweighted at a set of intermediate time points, with more weight assigned to trajectories that are consistent with the next observation. We consider two implementations of this approach, based on two continuous approximations of the MJP. We compare these constructs for a simple tractable jump process before using them to perform inference for a Lotka-Volterra system. The best performing construct is used to infer the parameters governing a simple model of motility regulation in Bacillus subtilis. PMID:25720091

  10. Adaptive inference for distinguishing credible from incredible patterns in nature

    USGS Publications Warehouse

    Holling, Crawford S.; Allen, C.R.

    2002-01-01

    Strong inference is a powerful and rapid tool that can be used to identify and explain patterns in molecular biology, cell biology, and physiology. It is effective where causes are single and separable and where discrimination between pairwise alternative hypotheses can be determined experimentally by a simple yes or no answer. But causes in ecological systems are multiple and overlapping and are not entirely separable. Frequently, competing hypotheses cannot be distinguished by a single unambiguous test, but only by a suite of tests of different kinds, that produce a body of evidence to support one line of argument and not others. We call this process "adaptive inference". Instead of pitting each member of a pair of hypotheses against each other, adaptive inference relies on the exuberant invention of multiple, competing hypotheses, after which carefully structured comparative data are used to explore the logical consequences of each. Herein we present an example that demonstrates the attributes of adaptive inference that have developed out of a 30-year study of the resilience of ecosystems.

  11. Inference and the introductory statistics course

    NASA Astrophysics Data System (ADS)

    Pfannkuch, Maxine; Regan, Matt; Wild, Chris; Budgett, Stephanie; Forbes, Sharleen; Harraway, John; Parsonage, Ross

    2011-10-01

    This article sets out some of the rationale and arguments for making major changes to the teaching and learning of statistical inference in introductory courses at our universities by changing from a norm-based, mathematical approach to more conceptually accessible computer-based approaches. The core problem of the inferential argument with its hypothetical probabilistic reasoning process is examined in some depth. We argue that the revolution in the teaching of inference must begin. We also discuss some perplexing issues, problematic areas and some new insights into language conundrums associated with introducing the logic of inference through randomization methods.

  12. Metacognitive inferences from other people's memory performance.

    PubMed

    Smith, Robert W; Schwarz, Norbert

    2016-09-01

    Three studies show that people draw metacognitive inferences about events from how well others remember the event. Given that memory fades over time, detailed accounts of distant events suggest that the event must have been particularly memorable, for example, because it was extreme. Accordingly, participants inferred that a physical assault (Study 1) or a poor restaurant experience (Studies 2-3) were more extreme when they were well remembered one year rather than one week later. These inferences influence behavioral intentions. For example, participants recommended a more severe punishment for a well-remembered distant rather than recent assault (Study 1). These metacognitive inferences are eliminated when people attribute the reporter's good memory to an irrelevant cause (e.g., photographic memory), thus undermining the informational value of memory performance (Study 3). These studies illuminate how people use lay theories of memory to learn from others' memory performance about characteristics of the world. (PsycINFO Database Record

  13. Metacognitive inferences from other people's memory performance.

    PubMed

    Smith, Robert W; Schwarz, Norbert

    2016-09-01

    Three studies show that people draw metacognitive inferences about events from how well others remember the event. Given that memory fades over time, detailed accounts of distant events suggest that the event must have been particularly memorable, for example, because it was extreme. Accordingly, participants inferred that a physical assault (Study 1) or a poor restaurant experience (Studies 2-3) were more extreme when they were well remembered one year rather than one week later. These inferences influence behavioral intentions. For example, participants recommended a more severe punishment for a well-remembered distant rather than recent assault (Study 1). These metacognitive inferences are eliminated when people attribute the reporter's good memory to an irrelevant cause (e.g., photographic memory), thus undermining the informational value of memory performance (Study 3). These studies illuminate how people use lay theories of memory to learn from others' memory performance about characteristics of the world. (PsycINFO Database Record PMID:27414693

  14. Are Evaluations Inferred Directly From Overt Actions?

    ERIC Educational Resources Information Center

    Brown, Donald; And Others

    1975-01-01

    The operation of a covert information processing mechanism was investigated in two experiments of the self-persuasion phenomena; i. e., making an inference about a stimulus on the basis of one's past behavior. (Editor)

  15. Metamodel-Driven Evolution with Grammar Inference

    NASA Astrophysics Data System (ADS)

    Bryant, Barrett R.; Liu, Qichao; Mernik, Marjan

    2010-10-01

    Domain-specific modeling (DSM) has become one of the most popular techniques for incorporating model-driven engineering (MDE) into software engineering. In DSM, domain experts define metamodels to describe the essential problems in a domain. A model conforms to a schema definition represented by a metamodel in a similar manner to a programming language conforms to a grammar. Metamodel-driven evolution is when a metamodel undergoes evolutions to incorporate new concerns in the domain. However, this results in losing the ability to use existing model instances. Grammar inference is the problem of inferring a grammar from sample strings which the grammar should generate. This paper describes our work in solving the problem of metamodel-driven evolution with grammar inference, by inferring the metamodel from model instances.

  16. Investigating noise tolerance in an efficient engine for inferring biological regulatory networks.

    PubMed

    Komori, Asako; Maki, Yukihiro; Ono, Isao; Okamoto, Masahiro

    2015-06-01

    Biological systems are composed of biomolecules such as genes, proteins, metabolites, and signaling components, which interact in complex networks. To understand complex biological systems, it is important to be capable of inferring regulatory networks from experimental time series data. In previous studies, we developed efficient numerical optimization methods for inferring these networks, but we have yet to test the performance of our methods when considering the error (noise) that is inherent in experimental data. In this study, we investigated the noise tolerance of our proposed inferring engine. We prepared the noise data using the Langevin equation, and compared the performance of our method with that of alternative optimization methods. PMID:25790786

  17. How difficult is inference of mammalian causal gene regulatory networks?

    PubMed

    Djordjevic, Djordje; Yang, Andrian; Zadoorian, Armella; Rungrugeecharoen, Kevin; Ho, Joshua W K

    2014-01-01

    Gene regulatory networks (GRNs) play a central role in systems biology, especially in the study of mammalian organ development. One key question remains largely unanswered: Is it possible to infer mammalian causal GRNs using observable gene co-expression patterns alone? We assembled two mouse GRN datasets (embryonic tooth and heart) and matching microarray gene expression profiles to systematically investigate the difficulties of mammalian causal GRN inference. The GRNs were assembled based on > 2,000 pieces of experimental genetic perturbation evidence from manually reading > 150 primary research articles. Each piece of perturbation evidence records the qualitative change of the expression of one gene following knock-down or over-expression of another gene. Our data have thorough annotation of tissue types and embryonic stages, as well as the type of regulation (activation, inhibition and no effect), which uniquely allows us to estimate both sensitivity and specificity of the inference of tissue specific causal GRN edges. Using these unprecedented datasets, we found that gene co-expression does not reliably distinguish true positive from false positive interactions, making inference of GRN in mammalian development very difficult. Nonetheless, if we have expression profiling data from genetic or molecular perturbation experiments, such as gene knock-out or signalling stimulation, it is possible to use the set of differentially expressed genes to recover causal regulatory relationships with good sensitivity and specificity. Our result supports the importance of using perturbation experimental data in causal network reconstruction. Furthermore, we showed that causal gene regulatory relationship can be highly cell type or developmental stage specific, suggesting the importance of employing expression profiles from homogeneous cell populations. This study provides essential datasets and empirical evidence to guide the development of new GRN inference methods for

  18. Universal Darwinism As a Process of Bayesian Inference

    PubMed Central

    Campbell, John O.

    2016-01-01

    Many of the mathematical frameworks describing natural selection are equivalent to Bayes' Theorem, also known as Bayesian updating. By definition, a process of Bayesian Inference is one which involves a Bayesian update, so we may conclude that these frameworks describe natural selection as a process of Bayesian inference. Thus, natural selection serves as a counter example to a widely-held interpretation that restricts Bayesian Inference to human mental processes (including the endeavors of statisticians). As Bayesian inference can always be cast in terms of (variational) free energy minimization, natural selection can be viewed as comprising two components: a generative model of an “experiment” in the external world environment, and the results of that “experiment” or the “surprise” entailed by predicted and actual outcomes of the “experiment.” Minimization of free energy implies that the implicit measure of “surprise” experienced serves to update the generative model in a Bayesian manner. This description closely accords with the mechanisms of generalized Darwinian process proposed both by Dawkins, in terms of replicators and vehicles, and Campbell, in terms of inferential systems. Bayesian inference is an algorithm for the accumulation of evidence-based knowledge. This algorithm is now seen to operate over a wide range of evolutionary processes, including natural selection, the evolution of mental models and cultural evolutionary processes, notably including science itself. The variational principle of free energy minimization may thus serve as a unifying mathematical framework for universal Darwinism, the study of evolutionary processes operating throughout nature. PMID:27375438

  19. Causal inference in economics and marketing

    PubMed Central

    Varian, Hal R.

    2016-01-01

    This is an elementary introduction to causal inference in economics written for readers familiar with machine learning methods. The critical step in any causal analysis is estimating the counterfactual—a prediction of what would have happened in the absence of the treatment. The powerful techniques used in machine learning may be useful for developing better estimates of the counterfactual, potentially improving causal inference. PMID:27382144

  20. Causal inference in economics and marketing.

    PubMed

    Varian, Hal R

    2016-07-01

    This is an elementary introduction to causal inference in economics written for readers familiar with machine learning methods. The critical step in any causal analysis is estimating the counterfactual-a prediction of what would have happened in the absence of the treatment. The powerful techniques used in machine learning may be useful for developing better estimates of the counterfactual, potentially improving causal inference. PMID:27382144

  1. Operation of the Bayes Inference Engine

    SciTech Connect

    Hanson, K.M.; Cunningham, G.S.

    1998-07-27

    The authors have developed a computer application, called the Bayes Inference Engine, to enable one to make inferences about models of a physical object from radiographs taken of it. In the BIE calculational models are represented by a data-flow diagram that can be manipulated by the analyst in a graphical-programming environment. The authors demonstrate the operation of the BIE in terms of examples of two-dimensional tomographic reconstruction including uncertainty estimation.

  2. A Network Inference Workflow Applied to Virulence-Related Processes in Salmonella typhimurium

    SciTech Connect

    Taylor, Ronald C.; Singhal, Mudita; Weller, Jennifer B.; Khoshnevis, Saeed; Shi, Liang; McDermott, Jason E.

    2009-04-20

    Inference of the structure of mRNA transcriptional regulatory networks, protein regulatory or interaction networks, and protein activation/inactivation-based signal transduction networks are critical tasks in systems biology. In this article we discuss a workflow for the reconstruction of parts of the transcriptional regulatory network of the pathogenic bacterium Salmonella typhimurium based on the information contained in sets of microarray gene expression data now available for that organism, and describe our results obtained by following this workflow. The primary tool is one of the network inference algorithms deployed in the Software Environment for BIological Network Inference (SEBINI). Specifically, we selected the algorithm called Context Likelihood of Relatedness (CLR), which uses the mutual information contained in the gene expression data to infer regulatory connections. The associated analysis pipeline automatically stores the inferred edges from the CLR runs within SEBINI and, upon request, transfers the inferred edges into either Cytoscape or the plug-in Collective Analysis of Biological of Biological Interaction Networks (CABIN) tool for further post-analysis of the inferred regulatory edges. The following article presents the outcome of this workflow, as well as the protocols followed for microarray data collection, data cleansing, and network inference. Our analysis revealed several interesting interactions, functional groups, metabolic pathways, and regulons in S. typhimurium.

  3. Inferences of Ice Processes From Properties

    NASA Astrophysics Data System (ADS)

    Alley, R. B.; Wilen, L. A.; Spencer, M. K.; Hansen, D. P.; Fitzpatrick, J. J.

    2001-12-01

    Barclay Kamb's pioneering work on the physics and mineralogy of laboratory and natural ices has guided glaciological research spanning 40 years. Much of that research required extremely tedious use of optical universal stages to study thin sections of ice. Recent advances in digital systems have revolutionized data collection and offer great opportunities to use ice properties to infer processes that operate too slowly for proper laboratory investigation, leading toward a greatly improved understanding of the history of ice and its softness for further deformation (Wilen, 1999; Hansen and Wilen, in review; Wilen et al., this meeting). Patterns of nearest-neighbor c-axis orientations reveal the influence of nucleation-and-growth recrystallization (typically indicative of steady-state deformation) or polygonization. Combining these results with correlations between grain sizes and dust and chemical loadings reveals impurity effects on active processes. The relations between mean grain size and c-axis-fabric strength may show the importance of grain-boundary processes in deformation. Bubble sizes reveal climate conditions during firnification, and bubble shapes can provide information on in situ strain rates. These and many other possibilities should enhance our understanding of ice flow and of the paleoclimatic records archived in ice.

  4. Active Inference and Learning in the Cerebellum.

    PubMed

    Friston, Karl; Herreros, Ivan

    2016-09-01

    This letter offers a computational account of Pavlovian conditioning in the cerebellum based on active inference and predictive coding. Using eyeblink conditioning as a canonical paradigm, we formulate a minimal generative model that can account for spontaneous blinking, startle responses, and (delay or trace) conditioning. We then establish the face validity of the model using simulated responses to unconditioned and conditioned stimuli to reproduce the sorts of behavior that are observed empirically. The scheme's anatomical validity is then addressed by associating variables in the predictive coding scheme with nuclei and neuronal populations to match the (extrinsic and intrinsic) connectivity of the cerebellar (eyeblink conditioning) system. Finally, we try to establish predictive validity by reproducing selective failures of delay conditioning, trace conditioning, and extinction using (simulated and reversible) focal lesions. Although rather metaphorical, the ensuing scheme can account for a remarkable range of anatomical and neurophysiological aspects of cerebellar circuitry-and the specificity of lesion-deficit mappings that have been established experimentally. From a computational perspective, this work shows how conditioning or learning can be formulated in terms of minimizing variational free energy (or maximizing Bayesian model evidence) using exactly the same principles that underlie predictive coding in perception.

  5. The renormalization group via statistical inference

    NASA Astrophysics Data System (ADS)

    Bény, Cédric; Osborne, Tobias J.

    2015-08-01

    In physics, one attempts to infer the rules governing a system given only the results of imperfect measurements. Hence, microscopic theories may be effectively indistinguishable experimentally. We develop an operationally motivated procedure to identify the corresponding equivalence classes of states, and argue that the renormalization group (RG) arises from the inherent ambiguities associated with the classes: one encounters flow parameters as, e.g., a regulator, a scale, or a measure of precision, which specify representatives in a given equivalence class. This provides a unifying framework and reveals the role played by information in renormalization. We validate this idea by showing that it justifies the use of low-momenta n-point functions as statistically relevant observables around a Gaussian hypothesis. These results enable the calculation of distinguishability in quantum field theory. Our methods also provide a way to extend renormalization techniques to effective models which are not based on the usual quantum-field formalism, and elucidates the relationships between various type of RG.

  6. Functional network inference of the suprachiasmatic nucleus.

    PubMed

    Abel, John H; Meeker, Kirsten; Granados-Fuentes, Daniel; St John, Peter C; Wang, Thomas J; Bales, Benjamin B; Doyle, Francis J; Herzog, Erik D; Petzold, Linda R

    2016-04-19

    In the mammalian suprachiasmatic nucleus (SCN), noisy cellular oscillators communicate within a neuronal network to generate precise system-wide circadian rhythms. Although the intracellular genetic oscillator and intercellular biochemical coupling mechanisms have been examined previously, the network topology driving synchronization of the SCN has not been elucidated. This network has been particularly challenging to probe, due to its oscillatory components and slow coupling timescale. In this work, we investigated the SCN network at a single-cell resolution through a chemically induced desynchronization. We then inferred functional connections in the SCN by applying the maximal information coefficient statistic to bioluminescence reporter data from individual neurons while they resynchronized their circadian cycling. Our results demonstrate that the functional network of circadian cells associated with resynchronization has small-world characteristics, with a node degree distribution that is exponential. We show that hubs of this small-world network are preferentially located in the central SCN, with sparsely connected shells surrounding these cores. Finally, we used two computational models of circadian neurons to validate our predictions of network structure.

  7. Functional network inference of the suprachiasmatic nucleus.

    PubMed

    Abel, John H; Meeker, Kirsten; Granados-Fuentes, Daniel; St John, Peter C; Wang, Thomas J; Bales, Benjamin B; Doyle, Francis J; Herzog, Erik D; Petzold, Linda R

    2016-04-19

    In the mammalian suprachiasmatic nucleus (SCN), noisy cellular oscillators communicate within a neuronal network to generate precise system-wide circadian rhythms. Although the intracellular genetic oscillator and intercellular biochemical coupling mechanisms have been examined previously, the network topology driving synchronization of the SCN has not been elucidated. This network has been particularly challenging to probe, due to its oscillatory components and slow coupling timescale. In this work, we investigated the SCN network at a single-cell resolution through a chemically induced desynchronization. We then inferred functional connections in the SCN by applying the maximal information coefficient statistic to bioluminescence reporter data from individual neurons while they resynchronized their circadian cycling. Our results demonstrate that the functional network of circadian cells associated with resynchronization has small-world characteristics, with a node degree distribution that is exponential. We show that hubs of this small-world network are preferentially located in the central SCN, with sparsely connected shells surrounding these cores. Finally, we used two computational models of circadian neurons to validate our predictions of network structure. PMID:27044085

  8. Active Inference and Learning in the Cerebellum.

    PubMed

    Friston, Karl; Herreros, Ivan

    2016-09-01

    This letter offers a computational account of Pavlovian conditioning in the cerebellum based on active inference and predictive coding. Using eyeblink conditioning as a canonical paradigm, we formulate a minimal generative model that can account for spontaneous blinking, startle responses, and (delay or trace) conditioning. We then establish the face validity of the model using simulated responses to unconditioned and conditioned stimuli to reproduce the sorts of behavior that are observed empirically. The scheme's anatomical validity is then addressed by associating variables in the predictive coding scheme with nuclei and neuronal populations to match the (extrinsic and intrinsic) connectivity of the cerebellar (eyeblink conditioning) system. Finally, we try to establish predictive validity by reproducing selective failures of delay conditioning, trace conditioning, and extinction using (simulated and reversible) focal lesions. Although rather metaphorical, the ensuing scheme can account for a remarkable range of anatomical and neurophysiological aspects of cerebellar circuitry-and the specificity of lesion-deficit mappings that have been established experimentally. From a computational perspective, this work shows how conditioning or learning can be formulated in terms of minimizing variational free energy (or maximizing Bayesian model evidence) using exactly the same principles that underlie predictive coding in perception. PMID:27391681

  9. Knowledge-based inference engine for online video dissemination

    NASA Astrophysics Data System (ADS)

    Zhou, Wensheng; Kuo, C.-C. Jay

    2000-10-01

    To facilitate easy access to rich information of multimedia over the Internet, we develop a knowledge-based classification system that supports automatic Indexing and filtering based on semantic concepts for the dissemination of on-line real-time media. Automatic segmentation, annotation and summarization of media for fast information browsing and updating are achieved in the same time. In the proposed system, a real-time scene-change detection proxy performs an initial video structuring process by splitting a video clip into scenes. Motional and visual features are extracted in real time for every detected scene by using online feature extraction proxies. Higher semantics are then derived through a joint use of low-level features along with inference rules in the knowledge base. Inference rules are derived through a supervised learning process based on representative samples. On-line media filtering based on semantic concepts becomes possible by using the proposed video inference engine. Video streams are either blocked or sent to certain channels depending on whether or not the video stream is matched with the user's profile. The proposed system is extensively evaluated by applying the engine to video of basketball games.

  10. Pb, Sr, and Nd isotopic compositions of a suite of Large Archean, igneous rocks, eastern Beartooth Mountains - Implications for crust-mantle evolution

    NASA Technical Reports Server (NTRS)

    Wooden, J. L.; Mueller, P. A.

    1988-01-01

    Compositionally diverse Late Archean rocks (2.74-2.79 Ga old) from the eastern Beartooth Mountains (Montana and Wyoming) were studied and shown to have the same initial Pb, Sr, and Nd isotopic ratios. Lead and Sr initial ratios are higher and Nd initial values lower than predicted for rocks derived from model mantle sources and strongly indicate the involvement of an older crustal reservoir in the genesis of these rocks. A model involving subduction of continental detritus and contamination of the overlying mantle is suggested.

  11. Hf sbnd Nd sbnd Sr isotopes and incompatible element abundances in island arcs: implications for magma origins and crust-mantle evolution

    NASA Astrophysics Data System (ADS)

    White, William M.; Patchett, Jonathan

    1984-02-01

    We present Hf, Nd and Sr isotopic data and abundances of K, Rb, Cs, Ba, Sr, Hf and REE for 32 samples from seven intra-oceanic island arcs. Samples from the Marianas, Izu, Aleutian and New Britain arcs have tightly grouped 176Hf/ 177Hf˜ 0.28320, 143Nd/ 144Nd˜ 0.51303 and 87Sr/ 86Sr˜ 0.7035 close to, but distinct from, mid-ocean ridge basalts (MORB) for 143Nd/ 144Nd and 87Sr/ 86Sr . In contrast, samples from the Sunda, Banda and Lesser Antilles arcs are much more variable towards lower 176Hf/ 177Hf and 143Nd/ 144Nd , and higher 87Sr/ 86Sr . Isotopically, island arcs on the whole are closely similar to ocean islands. Some commonly-occurring features of the trace element geochemistry of island arcs are apparent in our data: alkali and alkaline-earth elements, particularly Cs, have high abundance relative to LREE compared to oceanic basalts; negative Ce anomalies occur in six out of seven arcs. However, Hf does not appear underabundant relative to REE. The isotopic data require a continental component in all island arcs, in addition to probable mantle and oceanic crust contributions, even for the arcs with isotope ratios close to MORB. In the absence of continental crust, we can best explain this component by subducted pelagic sediment in the arc magma source region. The involvement of sediments in all arcs implies that there is an inherent recycling of older continent to island arcs, and potentially to new continent, of at least 1%. Conservative calculations show that the upper subducted slab (basalt + sediment) passes beyond the arc magma genesis zone and enters the deep mantle with a minimum of 500-1000 ppm K, and corresponding amounts of other incompatible elements. If this material is not completely homogenized with the mantle and later becomes part of the source of ocean island magmas, then the ocean island—island arc isotopic similarity is a result of their similar mix of source materials—mantle peridotite with trace element signatures from oceanic crust and sediment.

  12. Pb, Sr, and Nd isotopic compositions of a suite of Late Archean, igneous rocks, eastern Beartooth Mountains: implications for crust-mantle evolution

    USGS Publications Warehouse

    Wooden, J.L.; Mueller, P.A.

    1988-01-01

    A series of compositionally diverse, Late Archean rocks (2.74-2.79 Ga old) from the eastern Beartooth Mountains, Montana and Wyoming, U.S.A., have the same initial Pb, Sr, and Nd isotopic ratios. Lead and Sr initial ratios are higher and Nd initial ratios lower than would be expected for rocks derived from model mantle sources and strongly indicate the involvement of an older crustal reservoir in the genesis of these rocks. Crustal contamination during emplacement can be ruled out for a variety of reasons. Instead a model involving subduction of continental detritus and contamination of the overlying mantle as is often proposed for modern subduction environments is preferred. This contaminated mantle would have all the isotopic characteristics of mantle enriched by internal mantle metasomatism but would require no long-term growth or changes in parent to daughter element ratios. This contaminated mantle would make a good source for some of the Cenozoic mafic volcanics of the Columbia River, Snake River Plain, and Yellowstone volcanic fields that are proposed to come from ancient, enriched lithospheric mantle. The isotopic characteristics of the 2.70 Ga old Stillwater Complex are a perfect match for the proposed contaminated mantle which provides an alternative to crustal contamination during emplacement. The Pb isotopic characteristics of the Late Archean rocks of the eastern Beartooth Mountains are similar to those of other Late Archean rocks of the Wyoming Province and suggest that Early Archean, upper crustal rocks were common in this terrane. The isotopic signatures of Late Archean rocks in the Wyoming Province are distinctive from those of other Archean cratons in North America which are dominated by a MORB-like, Archean mantle source (Superior Province) and/or a long-term depleted crustal source (Greenland). ?? 1988.

  13. Petrogenesis of the early Cretaceous volcanic rocks in the North Huaiyang tectono-magmatic unit of the Dabie Orogen, eastern China: Implications for crust-mantle interaction

    NASA Astrophysics Data System (ADS)

    Gao, Xin-Yu; Zhao, Tai-Ping; Zhao, Jun-Hong

    2016-03-01

    New elemental and isotopic data are presented for the early Cretaceous felsic to mafic volcanic rocks in the North Huaiyang tectono-magmatic unit (NHY) of the Dabie Orogen, in order to investigate their petrogenesis and provide insights into the nature of the late Mesozoic lithosphere mantle beneath the region and its tectonic relationship with neighboring blocks. LA-ICP-MS zircon U-Pb dating reveals that volcanic rocks of the Jingangtai Formation erupted in a quite short interval about 5 Mys during the Early Cretaceous (128-123 Ma). The rocks have wide ranges of SiO2 (48-68 wt.%) and MgO (0.6-5.6 wt.%) contents. They are enriched in large-ion-lithophile-elements (LILE) (e.g. Rb, Ba) and light rare-earth-elements (LREE), and depleted in high field strength elements (e.g. Nb, Ta and Ti) with weak negative Eu anomalies (Eu/Eu∗ = 0.71-0.94). Meanwhile, the rocks show relatively high whole-rock initial 87Sr/86Sr ratios (0.7074-0.7094), strong negative εNd(t) (-19.1 to -15.8) and zircon εHf values (-20.7 to -14.1). Such typical "continental" geochemical characteristics did not result from crustal contamination during magma ascent, but from an enriched mantle source modified by materials from the subducted Yangtze Craton during the Triassic continental collision. We propose that the petrogenesis of the large-scale contemporaneous magmatism of Dabie Orogen including felsic to mafic volcanic rocks in the NHY reflects an intensive lithospheric thinning and extension during the early Cretaceous as a tectonic response to the change of plate motion of westward subducted Pacific Plate beneath the Eurasian continent.

  14. Origin of the late Early Cretaceous granodiorite and associated dioritic dikes in the Hongqilafu pluton, northwestern Tibetan Plateau: A case for crust-mantle interaction

    NASA Astrophysics Data System (ADS)

    Li, Jiyong; Niu, Yaoling; Hu, Yan; Chen, Shuo; Zhang, Yu; Duan, Meng; Sun, Pu

    2016-09-01

    We present a detailed study of geochronology, mineral chemistries, bulk-rock major and trace element abundances, and Sr-Nd-Hf isotope compositions of the granodiorite and associated dioritic dikes in the Hongqilafu pluton at the northwestern margin of the Tibetan Plateau. The granodiorite and dioritic dikes yielded zircon U-Pb ages of ~ 104 Ma and ~ 100 Ma, respectively. The dioritic dikes comprise varying lithologies of gabbroic diorite, diorite porphyry and granodiorite porphyry, exhibiting a compositional spectrum from intermediate to felsic rocks. Their mineral compositions display disequilibrium features such as large major element compositional variations of plagioclase, clinopyroxene and amphibole crystals. These dioritic dikes are enriched in incompatible elements (Ba, Rb, Th, U, K) and Sr-Nd-Hf isotopes (87Sr/86Sri: 0.7066 to 0.7071, εNd(t): - 5.3 to - 7.4, εHf(t): - 3.6 to - 6.2). We suggest that the dioritic dikes were most likely derived from partial melting of mantle wedge metasomatized by the subducted/subducting seafloor with a sediment component, followed by AFC processes with fractional crystallization of clinopyroxene, amphibole and plagioclase and assimilation of lower continental crust. The mantle-wedge derived magma parental to the dioritic dikes underplated and induced the lower continental crust to melt, forming the felsic crustal magma parental to the granodiorite with mantle melt signatures and having more enriched isotope compositions (87Sr/86Sri: 0.7087 to 0.7125, εNd(t): - 9.5 to - 11.6, εHf(t): - 10.3 to - 14.1) than those of the dioritic dikes. The Hongqilafu pluton is thus the product of mantle-crust interaction at an active continental margin subduction setting over the period of several million years. This understanding further indicates that the closure timing of the Shyok back-arc basin and the collision between the Kohistan-Ladakh Arc and the Karakoram Terrane may have taken place later than ~ 100 Ma.

  15. Comparative analysis of Vening-Meinesz Moritz isostatic models using the constant and variable crust-mantle density contrast - a case study of Zealandia

    NASA Astrophysics Data System (ADS)

    Bagherbandi, Mohammad; Tenzer, Robert

    2013-04-01

    We compare three different numerical schemes of treating the Moho density contrast in gravimetric inverse problems for finding the Moho depths. The results are validated using the global crustal model CRUST2.0, which is determined based purely on seismic data. Firstly, the gravimetric recovery of the Moho depths is realized by solving Moritz's generalization of the Vening-Meinesz inverse problem of isostasy while the constant Moho density contrast is adopted. The Pratt-Hayford isostatic model is then facilitated to estimate the variable Moho density contrast. This variable Moho density contrast is subsequently used to determine the Moho depths. Finally, the combined least-squares approach is applied to estimate jointly the Moho depths and density contract based on a priori error model. The EGM2008 global gravity model and the DTM2006.0 global topographic/bathymetric model are used to generate the isostatic gravity anomalies. The comparison of numerical results reveals that the optimal isostatic inverse scheme should take into consideration both the variable depth and density of compensation. This is achieved by applying the combined least-squares approach for a simultaneous estimation of both Moho parameters. We demonstrate that the result obtained using this method has the best agreement with the CRUST2.0 Moho depths. The numerical experiments are conducted at the regional study area of New Zealand's continental shelf.

  16. Question of Ages of Cenozoic Volcanic Centers Inferred Beneath the West Antarctic Ice Sheet (WAIS) in the West Antarctic Rift System (WR) from Coincident Aeromagnetic and Radar Ice Sounding Surveys

    NASA Astrophysics Data System (ADS)

    Behrendt, J. C.; Finn, C. A.; Blankenship, D. D.

    2007-12-01

    The recently acquired radar ice sounding surveys (Holt, et al., 2006) extending the 1990s Central West Antarctica (CWA) aerogeophysical survey to the Amundsen and Bellingshausen sea coasts allows us to revise a thought experiment reported by Behrendt et al., 1991 from very limited bed elevation data. Were the ice of the WAIS flowing through the WR to be compressed to the density of crustal rock, almost all of the area beneath the WAIS would be at or above sea level, much >1 km elevation. There are only about 10-20% of the very deep areas (such as the Bentley subglacial trench and the Byrd Subglacial Basin) filled with 3-4-km thick ice that would be well below sea level. The age of the 5-7-km high rift shoulder bounding the asymmetric WR from northern Victoria Land through the Horlick Mountains (where it diverges from the Transantarctic Mountains) to the Ellsworth Mountains has been reported as old as Cretaceous. Volcanic exposures associated with the West Antarctic rift system in the present WAIS area extend at least to 34 Ma and the West Antarctic ice sheet has flowed through the rift possibly as far back in time as 25 Ma. Active volcanism has been reported for the WR at only a few widely scattered locations, so speculations about present volcanic activity beneath the WAIS are quite uncertain, and it is probably quite rare. The Central West Antarctic aeromagnetic and radar ice sounding survey carried out in the 1990s revealed about 1000 "volcanic centers" characterized by 100-1000 nT shallow source magnetic anomalies, at least 400 of which have associated bed topography. About 80% of these show relief <200 m and have been interpreted as smoothed off as they were erupted (injected) into the moving WAIS. Several kilometer-thick highly magnetic sources are required to fit these anomalies requiring high remanent magnetizations in the present field direction. We interpreted these sources as subvolcanic intrusions which must be younger than about 100 Ma because the

  17. Role of Utility and Inference in the Evolution of Functional Information

    PubMed Central

    Sharov, Alexei A.

    2009-01-01

    Functional information means an encoded network of functions in living organisms from molecular signaling pathways to an organism’s behavior. It is represented by two components: code and an interpretation system, which together form a self-sustaining semantic closure. Semantic closure allows some freedom between components because small variations of the code are still interpretable. The interpretation system consists of inference rules that control the correspondence between the code and the function (phenotype) and determines the shape of the fitness landscape. The utility factor operates at multiple time scales: short-term selection drives evolution towards higher survival and reproduction rate within a given fitness landscape, and long-term selection favors those fitness landscapes that support adaptability and lead to evolutionary expansion of certain lineages. Inference rules make short-term selection possible by shaping the fitness landscape and defining possible directions of evolution, but they are under control of the long-term selection of lineages. Communication normally occurs within a set of agents with compatible interpretation systems, which I call communication system. Functional information cannot be directly transferred between communication systems with incompatible inference rules. Each biological species is a genetic communication system that carries unique functional information together with inference rules that determine evolutionary directions and constraints. This view of the relation between utility and inference can resolve the conflict between realism/positivism and pragmatism. Realism overemphasizes the role of inference in evolution of human knowledge because it assumes that logic is embedded in reality. Pragmatism substitutes usefulness for truth and therefore ignores the advantage of inference. The proposed concept of evolutionary pragmatism rejects the idea that logic is embedded in reality; instead, inference rules are

  18. Uncertainty and inference in deterministic and noisy chaos

    NASA Astrophysics Data System (ADS)

    Strelioff, Christopher Charles

    We study dynamical systems which exhibit chaotic dynamics with a focus on sources of real and apparent randomness including sensitivity to perturbation, dynamical noise, measurement uncertainty and finite data samples for inference. This choice of topics is motivated by a desire to adapt established theoretical tools such as the Perron-Frobenius operator and symbolic dynamics to experimental data. First, we study prediction of chaotic time series when a perfect model is available but the initial condition is measured with uncertainty. A common approach for predicting future data given these circumstances is to apply the model despite the uncertainty. In systems with fold dynamics, we find prediction is improved over this strategy by recognizing this behavior. A systematic study of the logistic map demonstrates prediction can be extended three time steps using an approximation of the relevant Perron-Frobenius operator dynamics. Next, we show how to infer kth order Markov chains from finite data by applying Bayesian methods to both parameter estimation and model-order selection. In this pursuit, we connect inference to statistical mechanics through information-theoretic (type theory) techniques and establish a direct relationship between Bayesian evidence and the partition function. This allows for straightforward calculation of the expectation and variance of the conditional relative entropy and the source entropy rate. Also, we introduce a method that uses finite data-size scaling with model-order comparison to infer the structure of out-of-class processes. Finally, we study a binary partition of time series data from the logistic map with additive noise, inferring optimal, effectively generating partitions and kth order Markov chain models. Here we adapt Bayesian inference, developed above, for applied symbolic dynamics. We show that reconciling Kolmogorov's maximum-entropy partition with the methods of Bayesian model selection requires the use of two separate

  19. Inference of Isoforms from Short Sequence Reads

    NASA Astrophysics Data System (ADS)

    Feng, Jianxing; Li, Wei; Jiang, Tao

    Due to alternative splicing events in eukaryotic species, the identification of mRNA isoforms (or splicing variants) is a difficult problem. Traditional experimental methods for this purpose are time consuming and cost ineffective. The emerging RNA-Seq technology provides a possible effective method to address this problem. Although the advantages of RNA-Seq over traditional methods in transcriptome analysis have been confirmed by many studies, the inference of isoforms from millions of short sequence reads (e.g., Illumina/Solexa reads) has remained computationally challenging. In this work, we propose a method to calculate the expression levels of isoforms and infer isoforms from short RNA-Seq reads using exon-intron boundary, transcription start site (TSS) and poly-A site (PAS) information. We first formulate the relationship among exons, isoforms, and single-end reads as a convex quadratic program, and then use an efficient algorithm (called IsoInfer) to search for isoforms. IsoInfer can calculate the expression levels of isoforms accurately if all the isoforms are known and infer novel isoforms from scratch. Our experimental tests on known mouse isoforms with both simulated expression levels and reads demonstrate that IsoInfer is able to calculate the expression levels of isoforms with an accuracy comparable to the state-of-the-art statistical method and a 60 times faster speed. Moreover, our tests on both simulated and real reads show that it achieves a good precision and sensitivity in inferring isoforms when given accurate exon-intron boundary, TSS and PAS information, especially for isoforms whose expression levels are significantly high.

  20. Logic tree-based GIS inference of geologic structure

    NASA Astrophysics Data System (ADS)

    Ryerson, Charles C.; Anderson, Thomas S.

    2007-10-01

    We describe the concept for a logic-tree based geographic information system (GIS) that can infer subsurface geology and material properties using geoinformatics concepts. A proof-of-concept system was devised and tested integrating the capabilities of traditional terrain- and image-analysis procedures with a GIS to manipulate geospatial data. Structured logic trees were developed to guide an analyst through an interactive, geologic analysis based on querying and mentoring heuristic logic. The hypotheses were that a GIS can be programmed to 1) follow the fundamental logic sequence developed for traditional terrain- and image analysis procedures; 2) augment that sequence with correlative geospatial data from a variety of sources; and 3) integrate the inferences and data to develop "best-guess" estimates. We also developed a method to estimate depth to bedrock, and expanded an existing method to determine water table depth. Blind evaluations indicate that an analyst can infer the correct geologic conditions 70-80% of the time using this method. This geologic analysis technique can be applied wherever an estimate of subsurface geology is needed. We apply the results of our geological analysis to the prediction of local site specific seismic propagation. Comparisons are made with synthetic seismograms computed from a limited set of geological vignettes.

  1. Neuronal integration of dynamic sources: Bayesian learning and Bayesian inference

    NASA Astrophysics Data System (ADS)

    Siegelmann, Hava T.; Holzman, Lars E.

    2010-09-01

    One of the brain's most basic functions is integrating sensory data from diverse sources. This ability causes us to question whether the neural system is computationally capable of intelligently integrating data, not only when sources have known, fixed relative dependencies but also when it must determine such relative weightings based on dynamic conditions, and then use these learned weightings to accurately infer information about the world. We suggest that the brain is, in fact, fully capable of computing this parallel task in a single network and describe a neural inspired circuit with this property. Our implementation suggests the possibility that evidence learning requires a more complex organization of the network than was previously assumed, where neurons have different specialties, whose emergence brings the desired adaptivity seen in human online inference.

  2. Estimating uncertainty of inference for validation

    SciTech Connect

    Booker, Jane M; Langenbrunner, James R; Hemez, Francois M; Ross, Timothy J

    2010-09-30

    We present a validation process based upon the concept that validation is an inference-making activity. This has always been true, but the association has not been as important before as it is now. Previously, theory had been confirmed by more data, and predictions were possible based on data. The process today is to infer from theory to code and from code to prediction, making the role of prediction somewhat automatic, and a machine function. Validation is defined as determining the degree to which a model and code is an accurate representation of experimental test data. Imbedded in validation is the intention to use the computer code to predict. To predict is to accept the conclusion that an observable final state will manifest; therefore, prediction is an inference whose goodness relies on the validity of the code. Quantifying the uncertainty of a prediction amounts to quantifying the uncertainty of validation, and this involves the characterization of uncertainties inherent in theory/models/codes and the corresponding data. An introduction to inference making and its associated uncertainty is provided as a foundation for the validation problem. A mathematical construction for estimating the uncertainty in the validation inference is then presented, including a possibility distribution constructed to represent the inference uncertainty for validation under uncertainty. The estimation of inference uncertainty for validation is illustrated using data and calculations from Inertial Confinement Fusion (ICF). The ICF measurements of neutron yield and ion temperature were obtained for direct-drive inertial fusion capsules at the Omega laser facility. The glass capsules, containing the fusion gas, were systematically selected with the intent of establishing a reproducible baseline of high-yield 10{sup 13}-10{sup 14} neutron output. The deuterium-tritium ratio in these experiments was varied to study its influence upon yield. This paper on validation inference is the

  3. Computationally efficient Bayesian inference for inverse problems.

    SciTech Connect

    Marzouk, Youssef M.; Najm, Habib N.; Rahn, Larry A.

    2007-10-01

    Bayesian statistics provides a foundation for inference from noisy and incomplete data, a natural mechanism for regularization in the form of prior information, and a quantitative assessment of uncertainty in the inferred results. Inverse problems - representing indirect estimation of model parameters, inputs, or structural components - can be fruitfully cast in this framework. Complex and computationally intensive forward models arising in physical applications, however, can render a Bayesian approach prohibitive. This difficulty is compounded by high-dimensional model spaces, as when the unknown is a spatiotemporal field. We present new algorithmic developments for Bayesian inference in this context, showing strong connections with the forward propagation of uncertainty. In particular, we introduce a stochastic spectral formulation that dramatically accelerates the Bayesian solution of inverse problems via rapid evaluation of a surrogate posterior. We also explore dimensionality reduction for the inference of spatiotemporal fields, using truncated spectral representations of Gaussian process priors. These new approaches are demonstrated on scalar transport problems arising in contaminant source inversion and in the inference of inhomogeneous material or transport properties. We also present a Bayesian framework for parameter estimation in stochastic models, where intrinsic stochasticity may be intermingled with observational noise. Evaluation of a likelihood function may not be analytically tractable in these cases, and thus several alternative Markov chain Monte Carlo (MCMC) schemes, operating on the product space of the observations and the parameters, are introduced.

  4. Deep Learning for Population Genetic Inference

    PubMed Central

    Sheehan, Sara; Song, Yun S.

    2016-01-01

    Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumvent this problem, we introduce a novel likelihood-free inference framework by applying deep learning, a powerful modern technique in machine learning. Deep learning makes use of multilayer neural networks to learn a feature-based function from the input (e.g., hundreds of correlated summary statistics of data) to the output (e.g., population genetic parameters of interest). We demonstrate that deep learning can be effectively employed for population genetic inference and learning informative features of data. As a concrete application, we focus on the challenging problem of jointly inferring natural selection and demography (in the form of a population size change history). Our method is able to separate the global nature of demography from the local nature of selection, without sequential steps for these two factors. Studying demography and selection jointly is motivated by Drosophila, where pervasive selection confounds demographic analysis. We apply our method to 197 African Drosophila melanogaster genomes from Zambia to infer both their overall demography, and regions of their genome under selection. We find many regions of the genome that have experienced hard sweeps, and fewer under selection on standing variation (soft sweep) or balancing selection. Interestingly, we find that soft sweeps and balancing selection occur more frequently closer to the centromere of each chromosome. In addition, our demographic inference suggests that previously estimated bottlenecks for African Drosophila melanogaster are too extreme. PMID:27018908

  5. Maximum likelihood inference of reticulate evolutionary histories.

    PubMed

    Yu, Yun; Dong, Jianrong; Liu, Kevin J; Nakhleh, Luay

    2014-11-18

    Hybridization plays an important role in the evolution of certain groups of organisms, adaptation to their environments, and diversification of their genomes. The evolutionary histories of such groups are reticulate, and methods for reconstructing them are still in their infancy and have limited applicability. We present a maximum likelihood method for inferring reticulate evolutionary histories while accounting simultaneously for incomplete lineage sorting. Additionally, we propose methods for assessing confidence in the amount of reticulation and the topology of the inferred evolutionary history. Our method obtains accurate estimates of reticulate evolutionary histories on simulated datasets. Furthermore, our method provides support for a hypothesis of a reticulate evolutionary history inferred from a set of house mouse (Mus musculus) genomes. As evidence of hybridization in eukaryotic groups accumulates, it is essential to have methods that infer reticulate evolutionary histories. The work we present here allows for such inference and provides a significant step toward putting phylogenetic networks on par with phylogenetic trees as a model of capturing evolutionary relationships. PMID:25368173

  6. Scene Construction, Visual Foraging, and Active Inference

    PubMed Central

    Mirza, M. Berk; Adams, Rick A.; Mathys, Christoph D.; Friston, Karl J.

    2016-01-01

    This paper describes an active inference scheme for visual searches and the perceptual synthesis entailed by scene construction. Active inference assumes that perception and action minimize variational free energy, where actions are selected to minimize the free energy expected in the future. This assumption generalizes risk-sensitive control and expected utility theory to include epistemic value; namely, the value (or salience) of information inherent in resolving uncertainty about the causes of ambiguous cues or outcomes. Here, we apply active inference to saccadic searches of a visual scene. We consider the (difficult) problem of categorizing a scene, based on the spatial relationship among visual objects where, crucially, visual cues are sampled myopically through a sequence of saccadic eye movements. This means that evidence for competing hypotheses about the scene has to be accumulated sequentially, calling upon both prediction (planning) and postdiction (memory). Our aim is to highlight some simple but fundamental aspects of the requisite functional anatomy; namely, the link between approximate Bayesian inference under mean field assumptions and functional segregation in the visual cortex. This link rests upon the (neurobiologically plausible) process theory that accompanies the normative formulation of active inference for Markov decision processes. In future work, we hope to use this scheme to model empirical saccadic searches and identify the prior beliefs that underwrite intersubject variability in the way people forage for information in visual scenes (e.g., in schizophrenia). PMID:27378899

  7. Scene Construction, Visual Foraging, and Active Inference.

    PubMed

    Mirza, M Berk; Adams, Rick A; Mathys, Christoph D; Friston, Karl J

    2016-01-01

    This paper describes an active inference scheme for visual searches and the perceptual synthesis entailed by scene construction. Active inference assumes that perception and action minimize variational free energy, where actions are selected to minimize the free energy expected in the future. This assumption generalizes risk-sensitive control and expected utility theory to include epistemic value; namely, the value (or salience) of information inherent in resolving uncertainty about the causes of ambiguous cues or outcomes. Here, we apply active inference to saccadic searches of a visual scene. We consider the (difficult) problem of categorizing a scene, based on the spatial relationship among visual objects where, crucially, visual cues are sampled myopically through a sequence of saccadic eye movements. This means that evidence for competing hypotheses about the scene has to be accumulated sequentially, calling upon both prediction (planning) and postdiction (memory). Our aim is to highlight some simple but fundamental aspects of the requisite functional anatomy; namely, the link between approximate Bayesian inference under mean field assumptions and functional segregation in the visual cortex. This link rests upon the (neurobiologically plausible) process theory that accompanies the normative formulation of active inference for Markov decision processes. In future work, we hope to use this scheme to model empirical saccadic searches and identify the prior beliefs that underwrite intersubject variability in the way people forage for information in visual scenes (e.g., in schizophrenia). PMID:27378899

  8. Hierarchical cosmic shear power spectrum inference

    NASA Astrophysics Data System (ADS)

    Alsing, Justin; Heavens, Alan; Jaffe, Andrew H.; Kiessling, Alina; Wandelt, Benjamin; Hoffmann, Till

    2016-02-01

    We develop a Bayesian hierarchical modelling approach for cosmic shear power spectrum inference, jointly sampling from the posterior distribution of the cosmic shear field and its (tomographic) power spectra. Inference of the shear power spectrum is a powerful intermediate product for a cosmic shear analysis, since it requires very few model assumptions and can be used to perform inference on a wide range of cosmological models a posteriori without loss of information. We show that joint posterior for the shear map and power spectrum can be sampled effectively by Gibbs sampling, iteratively drawing samples from the map and power spectrum, each conditional on the other. This approach neatly circumvents difficulties associated with complicated survey geometry and masks that plague frequentist power spectrum estimators, since the power spectrum inference provides prior information about the field in masked regions at every sampling step. We demonstrate this approach for inference of tomographic shear E-mode, B-mode and EB-cross power spectra from a simulated galaxy shear catalogue with a number of important features; galaxies distributed on the sky and in redshift with photometric redshift uncertainties, realistic random ellipticity noise for every galaxy and a complicated survey mask. The obtained posterior distributions for the tomographic power spectrum coefficients recover the underlying simulated power spectra for both E- and B-modes.